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+

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+ Website • Paper • Leaderboard
+
+

-## Abstract +BackdoorBench is a comprehensive benchmark of backdoor learning, which studies the adversarial vulnerablity of deep learning models in the training stage. It aims to provide **easy implementations** of mainstream backdoor attack and defense methods. -It has been widely observed that deep neural networks (DNN) are vulnerable to backdoor attacks where attackers could manipulate the model behavior maliciously by tampering with a small set of training samples. Although a line of defense methods is proposed to mitigate this threat, they either require complicated modifications to the training process or heavily rely on the specific model architecture, which makes them hard to deploy into real-world applications. Therefore, in this paper, we instead start with fine-tuning, one of the most common and easy-to-deploy backdoor defenses, through comprehensive evaluations against diverse attack scenarios. Observations made through initial experiments show that in contrast to the promising defensive results on high poisoning rates, vanilla tuning methods completely fail at low poisoning rate scenarios. Our analysis shows that with the low poisoning rate, the entanglement between backdoor and clean features undermines the effect of tuning-based defenses. Therefore, it is necessary to disentangle the backdoor and clean features in order to improve backdoor purification. To address this, we introduce Feature Shift Tuning (FST), a method for tuning-based backdoor purification. Specifically, FST encourages feature shifts by actively deviating the classifier weights from the originally compromised weights. Extensive experiments demonstrate that our FST provides consistently stable performance under different attack settings. Additionally, it is also convenient to deploy in real-world scenarios with significantly reduced computation costs. +### ❗Model and Data Updates +We disclose the backdoor model we used and the corresponding backdoor attack image in the link below. Each zip file contains the following things: + +- **bd_train_dataset**: train backdoor data +- **bd_test_dataset**: test backdoor data +- **attack_result.py**: the backdoor model and the module that reads data +- **cross_test_dataset**: cross mode data during training(for some special backdoor attack: wanet, inputaware and so on) + +If you want to use the backdoor model, you can download the zip file and unzip in your own workspace. Then you can use the function load_attack_result in the file [save_load_attack.py](./utils/save_load_attack.py) to load the backdoor model, the poisoned train data and the poisoned test data. + +[Backdoor Model](https://cuhko365-my.sharepoint.com/:f:/g/personal/backdoorbench_cuhk_edu_cn/EimE1JoHs4ZAivBThQeLkocBs5uPmj20JEtnEIBkJhS0tw?e=gtsc9z) + +### ❗V2.0 Updates +> ✅ **Correction**: +> 1. **Attack** : Fix the bug in [Label Consistent](./attack/lc.py) attack method, in v1.0 version, poisoned data only add adversarial noise without square trigger, which is not consistent with the paper. +> +> ✅ **Code**: +> 1. **Structure** : Warp attack methods and defense methods into classes and reduce replicated code. +> 2. **Dataset Processing** : Update bd_dataset into bd_dataset_v2, which can handle large scale dataset more efficently. +> 3. **Poison Data Generation** : Provide necessary code to generate poisoned dataset for attack methods (see ./resource folder, we have seperate readme files). +> 4. **Models** : We add VGG19_bn, ConvNeXT_tiny, ViT_B_16. +> +> ✅ **Methods**: +> 1. **Attack** :Add 4 new attack methods: [Blind](./attack/blind.py), [BPP](./attack/bpp.py), [LIRA](./attack/lira.py), [TrojanNN](./attack/trojannn.py). (Totally 12 attack methods now). +> 2. **Defense** :Add 6 new defense methods: [CLP](./defense/clp.py), [D-BR](./defense/d-br.py), [D-ST](./defense/d-st.py), [EP](./defense/ep.py), [I-BAU](./defense/i-bau.py), [BNP](./defense/bnp.py). (Totally 15 defense methods now). +> +> ✅ **Analysis Tools** : +> 1. **Data Analysis** : Add 2 new methods: [UMAP](./analysis/visual_umap.py), [Image Quality](./analysis/visual_quality.py) +> 2. **Models Analysis** : Add 9 new methods: [Activated Image](./analysis/visual_act.py), [Feature Visualization](./analysis/visual_fv.py), [Feature Map](./analysis/visual_fm.py), [Activation Distribution](./analysis/visual_actdist.py), [Trigger Activation Change](./analysis/visual_tac.py), [Lipschitz Constant](./analysis/visual_lips.py), [Loss Landscape](./analysis/visual_landscape.py), [Network Structure](./analysis/visual_network.py), [Eigenvalues of Hessian](./analysis/visual_hessian.py) +> 3. **Evaluation Analysis** : Add 2 new methods: [Confusion Matrix](./analysis/visual_cm.py), [Metric](./analysis/visual_metric.py) +> +> 🔲 Comprehensive evaluations will be coming soon... + +### ❗ For V1.0 please check [here](https://github.com/SCLBD/BackdoorBench/tree/v1) + +
Table of Contents
+ +* [Features](#features) + +* [Installation](#Installation) + +* [Quick Start](#quick-start) + + * [Attack](#attack) + + * [Defense](#defense) + +* [Supported attacks](#supported-attacks) + +* [Supported defenses](#supported-defsense) + +* [Analysis Tools](#analysis-tools) + +* [Citation](#citation) + +* [Copyright](#copyright) + +--- + + +## Features +[Back to top] + +BackdoorBench has the following features: + +⭐️ **Methods**: + - 12 Backdoor attack methods: [BadNets](./attack/badnet.py), [Blended](./attack/blended.py), [Blind](./attack/blind.py), [BPP](./attack/bpp.py), [Input-aware](./attack/inputaware.py), [Label Consistent](./attack/lc.py), [Low Frequency](./attack/lf.py), [LIRA](./attack/lira.py), [SIG](./attack/sig.py), [SSBA](./attack/ssba.py), [TrojanNN](./attack/trojannn.py), [WaNet](./attack/wanet.py) + - 15 Backdoor defense methods: [FT](./defense/ft.py), [Spectral](./defense/spectral.py), [AC](./defense/ac.py), [FP](./defense/fp.py), [ABL](./defense/abl.py), [NAD](./defense/nad.py), [NC](nc), [DBD]((./defense/dbd.py)), [ANP](./defense/anp.py),[CLP](./defense/clp.py), [D-BR](./defense/d-br.py), [D-ST](./defense/d-st.py), [EP](./defense/ep.py), [I-BAU](./defense/i-bau.py), [BNP](./defense/bnp.py) + +⭐️ **Datasets**: CIFAR-10, CIFAR-100, GTSRB, Tiny ImageNet + +⭐️ **Models**: PreAct-Resnet18, VGG19_bn, ConvNeXT_tiny, ViT_B_16, VGG19, DenseNet-161, MobileNetV3-Large, EfficientNet-B3 + + +⭐️ **Learboard**: We provide a [**public leaderboard**](http://backdoorbench.com/leader_cifar10) of evaluating all backdoor attacks against all defense methods. + +BackdoorBench will be continuously updated to track the lastest advances of backddor learning. +The implementations of more backdoor methods, as well as their evaluations are on the way. **You are welcome to contribute your backdoor methods to BackdoorBench.** + + + +## Installation + +[Back to top] + +You can run the following script to configurate necessary environment + +``` +git clone git@github.com:SCLBD/BackdoorBench.git +cd BackdoorBench +conda create -n backdoorbench python=3.8 +conda activate backdoorbench +sh ./sh/install.sh +sh ./sh/init_folders.sh +``` + +## Quick Start + +### Attack + +[Back to top] + +This is a example for BadNets + +1. Generate trigger + +If you want to change the trigger for BadNets, you should go to the `./resource/badnet`, and follow the readme there to generate new trigger pattern. +```shell +python ./resource/badnet/generate_white_square.py --image_size 32 --square_size 3 --distance_to_right 0 --distance_to_bottom 0 --output_path ./resource/badnet/trigger_image.png +``` +Note that for data-poisoning-based attacks (BadNets, Blended, Label Consistent, Low Frequency, SSBA). +Our scripts in `./attack` are just for training, they do not include the data generation process.(Because they are time-comsuming, and we do not want to waste your time.) +You should go to the `./resource` folder to generate the trigger for training. + +2. Backdoor training +``` +python ./attack/badnet.py --yaml_path ../config/attack/prototype/cifar10.yaml --patch_mask_path ../resource/badnet/trigger_image.png --save_folder_name badnet_0_1 +``` +After attack you will get a folder with all files saved in `./record/`, including `attack_result.pt` for attack model and backdoored data, which will be used by following defense methods. +If you want to change the args, you can both specify them in command line and in corresponding YAML config file (eg. [default.yaml](./config/attack/badnet/default.yaml)).(They are the defaults we used if no args are specified in command line.) +The detailed descriptions for each attack may be put into the `add_args` function in each script. + +Note that for some attacks, they may need pretrained models to generate backdoored data. For your ease, we provide various data/trigger/models we generated in google drive. You can download them at [here](https://drive.google.com/drive/folders/1lnCObVBIUTSlLWIBQtfs_zi7W8yuvR-2?usp=share_link) + + + + +### Defense + +[Back to top] + +This is a demo script of running abl defense on cifar-10 for badnet attack. Before defense you need to run badnet attack on cifar-10 at first. Then you use the folder name as result_file. + +``` +python ./defense/abl.py --result_file badnet_0_1 --yaml_path ./config/defense/abl/cifar10.yaml --dataset cifar10 +``` + + +If you want to change the args, you can both specify them in command line and in corresponding YAML config file (eg. [default.yaml](./config/defense/abl/default.yaml)).(They are the defaults we used if no args are specified in command line.) +The detailed descriptions for each attack may be put into the `add_args` function in each script. + +## Supported attacks + +[Back to top] + +| | File name | Paper | +|------------------|-----------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| BadNets | [badnet.py](./attack/badnet.py) | [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwir55bv0-X2AhVJIjQIHYTjAMgQFnoECCEQAQ&url=https%3A%2F%2Fmachine-learning-and-security.github.io%2Fpapers%2Fmlsec17_paper_51.pdf&usg=AOvVaw1Cu3kPaD0a4jgvwkPCX63j) IEEE Access 2019 | +| Blended | [blended.py](./attack/blended.py) | [Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning](https://arxiv.org/abs/1712.05526v1) Arxiv 2017 | +| Blind | [blind.py](./attack/blind.py) | [Blind Backdoors in Deep Learning Models](https://www.cs.cornell.edu/~shmat/shmat_usenix21blind.pdf) USENIX 2021 | +| BPP | [bpp.py](./attack/bpp.py) | [BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_BppAttack_Stealthy_and_Efficient_Trojan_Attacks_Against_Deep_Neural_Networks_CVPR_2022_paper.pdf) CVPR 2022 | +| Input-aware | [inputaware.py](./attack/inputaware.py) | [Input-Aware Dynamic Backdoor Attack](https://proceedings.neurips.cc/paper/2020/file/234e691320c0ad5b45ee3c96d0d7b8f8-Paper.pdf) NeurIPS 2020 | +| Label Consistent | [lc.py](./attack/lc.py) | [Label-Consistent Backdoor Attacks](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjvwKTx2bH4AhXCD0QIHVMWApkQFnoECAsQAQ&url=https%3A%2F%2Farxiv.org%2Fabs%2F1912.02771&usg=AOvVaw0NbPR9lguGTsEn3ZWtPBDR) Arxiv 2019 | +| Low Frequency | [lf.py](./attack/lf.py) | [Rethinking the Backdoor Attacks’ Triggers: A Frequency Perspective](https://openaccess.thecvf.com/content/ICCV2021/papers/Zeng_Rethinking_the_Backdoor_Attacks_Triggers_A_Frequency_Perspective_ICCV_2021_paper.pdf) ICCV2021 | +| LIRA | [lira.py](./attack/lira.py) | [LIRA: Learnable, Imperceptible and Robust Backdoor Attacks](https://openaccess.thecvf.com/content/ICCV2021/papers/Doan_LIRA_Learnable_Imperceptible_and_Robust_Backdoor_Attacks_ICCV_2021_paper.pdf) ICCV 2021 | +| SIG | [sig.py](./attack/sig.py) | [A new backdoor attack in cnns by training set corruption](https://ieeexplore.ieee.org/document/8802997) ICIP 2019 | +| SSBA | [ssba.py](./attack/ssba.py) | [Invisible Backdoor Attack with Sample-Specific Triggers](https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Invisible_Backdoor_Attack_With_Sample-Specific_Triggers_ICCV_2021_paper.pdf) ICCV 2021 | +| TrojanNN | [trojannn.py](./attack/trojannn.py) | [Trojaning Attack on Neural Networks](https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=2782&context=cstech) NDSS 2018 | +| WaNet | [wanet.py](./attack/wanet.py) | [WaNet -- Imperceptible Warping-Based Backdoor Attack](https://openreview.net/pdf?id=eEn8KTtJOx) ICLR 2021 | + +## Supported defenses + +[Back to top] + +| | File name | Paper | +| :------------- |:-------------|:-----| +| FT| [ft.py](./defense/ft.py) | standard fine-tuning| +| FP | [fp.py](./defense/fp.py) | [Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks](https://link.springer.com/chapter/10.1007/978-3-030-00470-5_13) RAID 2018 | +| NAD | [nad.py](./defense/nad.py) | [Neural Attention Distillation: Erasing Backdoor Triggers From Deep Neural Networks](https://openreview.net/pdf?id=9l0K4OM-oXE) ICLR 2021 | +| NC | [nc.py](./defense/nc.py) | [Neural Cleanse: Identifying And Mitigating Backdoor Attacks In Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8835365), IEEE S&P 2019 | +| ANP | [anp.py](./defense/anp.py) | [Adversarial Neuron Pruning Purifies Backdoored Deep Models](https://proceedings.neurips.cc/paper/2021/file/8cbe9ce23f42628c98f80fa0fac8b19a-Paper.pdf) NeurIPS 2021 | +| AC | [ac.py](./defense/ac.py) | [Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering](http://ceur-ws.org/Vol-2301/paper_18.pdf) ceur-ws 2018 | +| Spectral | [spectral.py](./defense/spectral.py) | [Spectral Signatures in Backdoor Attacks](https://proceedings.neurips.cc/paper/2018/file/280cf18baf4311c92aa5a042336587d3-Paper.pdf) NeurIPS 2018 | +| ABL | [abl.py](./defense/abl.py) | [Anti-Backdoor Learning: Training Clean Models on Poisoned Data](https://proceedings.neurips.cc/paper/2021/file/7d38b1e9bd793d3f45e0e212a729a93c-Paper.pdf) NeurIPS 2021 | +| DBD | [dbd.py](./defense/dbd.py) | [Backdoor Defense Via Decoupling The Training Process](https://arxiv.org/pdf/2202.03423.pdf) ICLR 2022 | +| CLP | [clp.py](./defense/clp.py) | [Data-free backdoor removal based on channel lipschitzness](https://arxiv.org/pdf/2208.03111.pdf) ECCV 2022 | +| I-BAU | [i-bau.py](./defense/i-bau.py) | [Adversarial unlearning of backdoors via implicit hypergradient](https://arxiv.org/pdf/2110.03735.pdf) ICLR 2022 | +| D-BR,D-ST | [d-br.py](./defense/d-br.py) [d-st.py](./defense/d-st.py) | [Effective backdoor defense by exploiting sensitivity of poisoned samples](https://proceedings.neurips.cc/paper_files/paper/2022/file/3f9bbf77fbd858e5b6e39d39fe84ed2e-Paper-Conference.pdf) NeurIPS 2022 | +| EP,BNP | [ep.py](./defense/ep.py) [bnp.py](./defense/bnp.py) | [Pre-activation Distributions Expose Backdoor Neurons](https://proceedings.neurips.cc/paper_files/paper/2022/file/76917808731dae9e6d62c2a7a6afb542-Paper-Conference.pdf) NeurIPS 2022 | + + + + + + + + +[Back to top] +### Analysis Tools + + +| File name | Method | Category | +|:----------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------| +| [visual_tsne.py](analysis/visual_tsne.py) | T-SNE, the T-SNE of features | Data Analysis | +| [visual_umap.py](analysis/visual_umap.py) | UMAP, the UMAP of features | Data Analysis | +| [visual_quality.py](./analysis/visual_quality.py) | Image Quality, evaluating the given results using some image quality metrics | Data Analysis | +| [visual_na.py](analysis/visual_na.py) | Neuron Activation, the activation value of a given layer of Neurons | Model Analysis | +| [visual_shap.py](analysis/visual_shap.py) | Shapely Value, the Shapely Value for given inputs and a given layer | Model Analysis | +| [visual_gradcam.py](analysis/visual_gradcam.py) | Grad-CAM, the Grad-CAM for given inputs and a given layer | Model Analysis | +| [visualize_fre.py](analysis/visualize_fre.py) | Frequency Map, the Frequency Saliency Map for given inputs and a given layer | Model Analysis | +| [visual_act.py](analysis/visual_act.py) | Activated Image, the top images who activate the given layer of Neurons most | Model Analysis | +| [visual_fv.py](analysis/visual_fv.py) | Feature Visualization, the synthetic images which activate the given Neurons | Model Analysis | +| [visual_fm.py](analysis/visual_fm.py) | Feature Map, the output of a given layer of CNNs for a given image | Model Analysis | +| [visual_actdist.py](analysis/visual_actdist.py) | Activation Distribution, the class distribution of Top-k images which activate the Neuron most | Model Analysis | +| [visual_tac.py](analysis/visual_tac.py) | Trigger Activation Change, the average (absolute) activation change between images with and without triggers | Model Analysis | +| [visual_lips.py](analysis/visual_lips.py) | Lipschitz Constant, the lipschitz constant of each neuron | Model Analysis | +| [visual_landscape.py](analysis/visual_landscape.py) | Loss Landscape, the loss landscape of given results with two random directions | Model Analysis | +| [visual_network.py](analysis/visual_network.py) | Network Structure, the Network Structure of given model | Model Analysis | +| [visual_hessian.py](analysis/visual_hessian.py) | Eigenvalues of Hessian, the dense plot of hessian matrix for a batch of data | Model Analysis | +| [visual_metric.py](analysis/visual_metric.py) | Metrics, evaluating the given results using some metrics | Evaluation | +| [visual_cm.py](analysis/visual_cm.py) | Confusion Matrix | | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +## Citation + +[Back to top] + +If interested, you can read our recent works about backdoor learning, and more works about trustworthy AI can be found [here](https://sites.google.com/site/baoyuanwu2015/home). + +``` +@inproceedings{backdoorbench, + title={BackdoorBench: A Comprehensive Benchmark of Backdoor Learning}, + author={Wu, Baoyuan and Chen, Hongrui and Zhang, Mingda and Zhu, Zihao and Wei, Shaokui and Yuan, Danni and Shen, Chao}, + booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, + year={2022} +} + +@article{wu2023adversarial, + title={Adversarial Machine Learning: A Systematic Survey of Backdoor Attack, Weight Attack and Adversarial Example}, + author={Wu, Baoyuan and Liu, Li and Zhu, Zihao and Liu, Qingshan and He, Zhaofeng and Lyu, Siwei}, + journal={arXiv preprint arXiv:2302.09457}, + year={2023} +} + +@article{cheng2023tat, + title={TAT: Targeted backdoor attacks against visual object tracking}, + author={Cheng, Ziyi and Wu, Baoyuan and Zhang, Zhenya and Zhao, Jianjun}, + journal={Pattern Recognition}, + volume={142}, + pages={109629}, + year={2023}, + publisher={Elsevier} +} + +@inproceedings{sensitivity-backdoor-defense-nips2022, + title = {Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples}, + author = {Chen, Weixin and Wu, Baoyuan and Wang, Haoqian}, + booktitle = {Advances in Neural Information Processing Systems}, + volume = {35}, + pages = {9727--9737}, + year = {2022} +} + +@inproceedings{dbd-backdoor-defense-iclr2022, + title={Backdoor Defense via Decoupling the Training Process}, + author={Huang, Kunzhe and Li, Yiming and Wu, Baoyuan and Qin, Zhan and Ren, Kui}, + booktitle={International Conference on Learning Representations}, + year={2022} +} + +@inproceedings{ssba-backdoor-attack-iccv2021, + title={Invisible backdoor attack with sample-specific triggers}, + author={Li, Yuezun and Li, Yiming and Wu, Baoyuan and Li, Longkang and He, Ran and Lyu, Siwei}, + booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, + pages={16463--16472}, + year={2021} +} +``` + + +## Copyright + +[Back to top] + + +This repository is licensed by [The Chinese University of Hong Kong, Shenzhen](https://www.cuhk.edu.cn/en) and [Shenzhen Research Institute of Big Data](http://www.sribd.cn/en) under Creative Commons Attribution-NonCommercial 4.0 International Public License (identified as [CC BY-NC-4.0 in SPDX](https://spdx.org/licenses/)). More details about the license could be found in [LICENSE](./LICENSE). + +This project is built by the Secure Computing Lab of Big Data ([SCLBD](http://scl.sribd.cn/index.html)) at The Chinese University of Hong Kong, Shenzhen and Shenzhen Research Institute of Big Data, directed by Professor [Baoyuan Wu](https://sites.google.com/site/baoyuanwu2015/home). SCLBD focuses on research of trustworthy AI, including backdoor learning, adversarial examples, federated learning, fairness, etc. + +If any suggestion or comment, please contact us at . diff --git a/analysis/Demos/Demo_ACT.ipynb b/analysis/Demos/Demo_ACT.ipynb new file mode 100755 index 0000000..9828590 --- /dev/null +++ b/analysis/Demos/Demo_ACT.ipynb @@ -0,0 +1,322 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_ACT\n", + "This is a demo for visualizing the images which activate the Neuron of a given layer most.\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": {}, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py --save_folder_name badnet_demo" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "87bd9f5a", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71b7087b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2fb719c7", + "metadata": {}, + "outputs": [], + "source": [ + "### Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"badnet_demo\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 2: Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8b67ac9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "create mix dataset with length: 10000\n", + "max_num_samples is given, use sample number limit now.\n", + "subset mix dataset with length: 4997\n", + "Create visualization dataset with \n", + " \t Dataset: mixed \n", + " \t Number of samples: 4997 \n", + " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "selected_classes = np.arange(args.num_classes)\n", + "\n", + "# Select classes to visualize\n", + "if args.num_classes>args.c_sub:\n", + " selected_classes = np.delete(selected_classes, args.target_class)\n", + " selected_classes = np.random.choice(selected_classes, args.c_sub-1, replace=False)\n", + " selected_classes = np.append(selected_classes, args.target_class)\n", + "\n", + "# keep the same transforms for train and test dataset for better visualization\n", + "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n", + "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n", + "\n", + "# Create dataset\n", + "if args.visual_dataset == 'mixed':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_mix_dataset(bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_train':\n", + " clean_train_with_trans = result_attack[\"clean_train\"]\n", + " visual_dataset = generate_clean_dataset(clean_train_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_test':\n", + " clean_test_with_trans = result_attack[\"clean_test\"]\n", + " visual_dataset = generate_clean_dataset(clean_test_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'bd_train': \n", + " bd_train_with_trans = result_attack[\"bd_train\"]\n", + " visual_dataset = generate_bd_dataset(bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'bd_test':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_bd_dataset(bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n", + "else:\n", + " assert False, \"Illegal vis_class\"\n", + "\n", + "print(f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)} \\n \\t Selected classes: {selected_classes}')\n", + "\n", + "# Create data loader\n", + "### IMPORTANT: shuffle=False to keep ordered ###\n", + "data_loader = torch.utils.data.DataLoader(\n", + " visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n", + ")\n", + "\n", + "# Create denormalization function\n", + "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n", + " if isinstance(trans_t, transforms.Normalize):\n", + " denormalizer = get_dataset_denormalization(trans_t)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3f652e5", + "metadata": {}, + "source": [ + "### Step 3: Load Model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ff67e7b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load model preactresnet18 from badnet_demo\n" + ] + } + ], + "source": [ + "# Load model\n", + "model_visual = generate_cls_model(args.model, args.num_classes)\n", + "model_visual.load_state_dict(result_attack[\"model\"])\n", + "model_visual.to(args.device)\n", + "# !!! Important to set eval mode !!!\n", + "model_visual.eval()\n", + "print(f\"Load model {args.model} from {args.result_file_attack}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cc952077", + "metadata": {}, + "source": [ + "### Step 4: Plot Activation Images" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "94612903", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Choose layer layer4.1.conv2 from model preactresnet18\n" + ] + }, + { + "data": { + "image/png": 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qTHz9ujoLm39d0t/JOS9v4VgYPXJPbOi5J+f8QnX+VPLf1MYnnKXxzz0X23RfJef879W5Xi9W51sJLuQi+j1XFnJPbGi5J6VUlPTf1fmThO0tlGVs53r6+GNJ35ZSemH3Q18/oe63bPTbIOf899S5Xq+Q9Hvq9HF643Pq/AnCH1LnW2NxeSL3xIbd7/kidb4J6DnqfAvQ76eN/1nXKz73SH3nrLe0L4wtck9smLnnGnXu2bdKukGdRYZvSCl9+QbLcsWPufoZQp8R4+UnJP199ck9yvmXlXNTW8w9yvmsenKPcv6IenKPLso9ynlem8g92sHco5wfVU/u0UW5Rzkva3PPub5J0mlJ791EWd4o6Vt0idyjnO9Wzn/VvV5HNWDuGfDaX/AuSV+klL5Yce6xc81KaUadD5H+8BbKgfFD7okNO/f8hjrnvS7pfZL+pXJ+YoNlebM6bfma7r+/W9KvK+eGcn5YOb9TOdeV8ylJ/0Vbyz2fI2m/cv4p5byunB+V9L/VWWy4Fb+vTv9uVZ0Pqf+icv5wn9ceV2fu/ZPd13+rOgsnceVhzBUb5phr0DHI5Tbmki6Ra3P/XHuNOh+o+GFJ10n6dHd7qfPt1FenlL6ju+7weyTdpMtwvocFzhfJOX9cnZvUj14Uul7S56aU5i/8qNMgD25i972V7Wp1Pl1x4bhLks6o8ymiC57u+f8V/XWH/JK63xrx7yR9Ze58s4TU+dNYsxe9dFbS4ibKfbFnlL37/yV1vib9gicuil/dLeNSz891Qyjf70k6lFL6PElfrE4j/IPusT43pfTulNKplNKCOl/Nv2+D++11vaRvvejaf6E6n5C5pO4k+JvV+Rr/H7pE/GZJfyTph3PO77vUPnLODXU+nfPV6tSFf6zOJ0uObeE9YIyRdzZkmHlnIDnn96szWfQNKaWb1PmzFb/ePdatKaXf735bx3l1Jkm2mnc2fe1TSv9RnU9bfVvOz/hm/E271L66D76+R51P1x1X5719QuSlyxK5Z0OGnnu6g90/kfTfc86/oQ26wnPPUve/vdfvM9cudT6x+zPq9PUq6gwsfyGldOcWjoURI/dsyLb0e3LOa92886MppTs2UpBxzj19yrulvkruuEedieefHGRfGE/kng0ZZu75e+r8ta6/2mJZxnKup5+c87sk/Wt1Jq2Pdn8WFeeeVjfPXiPp714ivqzOh8jelFK6arPlwuiRezZkqP2enPOf55zXc87z6jzYuUGdb//aiCs+9/Sbs95qHsN4IvdsyDBzz2r3NT+Vc17tfgjiN7XBDy48W8ZcfQzaZ8Q4CXKPUpr/zM8Qc4+GkHvUk3s0prlHKS31/Fz8nOt7JL1Jm5mX7ck9uij3dL959PeV0tMaQu4Z8NpfKO9mco+da1bnG2Hf3F28jcsduWcjhpd7UnqOOv2c71bnWc3tkv6ZUvrqDZWkswDzzyV9l1KaVmctzJu6xzqglH5TKT3ZzT2/qq3nnqsvuvY/dtH73ZiU9qjzYdCfklRT51ulv0Ip9ft2159QZ4H1td3X/6SkP1NKl91CQniMuTZkaGOuQccgl9uYK/XJtal/rl2V9Jac84dzzmvq5J7PTyntyp2/JP316nzp2glJr1FnwfhlN9/DAudL+9eSvl/PTApPSHpvznmu52c653zhAcSynrnC/VKVtHdg8ZQ6FVzSZ76ufK+kJ7dS4JTSa9T55NHXdj/dcMFDkkoppVt6fneHtvDnw3s8o+zqfAKgqU5juODai+JPSVL3nF34ebxbjhemlHq/Ev2FGy1fznlF0u+o07BfK+k3c87r3fCvq/MnmK/NOe9S56HQxV+9foG7fk9IevNF134qd77p67N038svqpOYv7m7ULk3fr06CePf5JzfHLy/+3LOX5Rz3ptz/gp1vrr/Q24bXLbIO94w884wXPgTOt8l6R2582ciJOl/qPPpzVtyzrPqDJi2mnfctf8sKaWflPSVkl6dc774E3ab4vaVc/6dnPPzc8571am3RyT1+6Qqxh+5xxtq7kmdP4/3J5LelnP+/22hPFdk7sk5n1NnQNq74LL32t0p6c9zznflzrecfVjSB9X582W4PJF7vO3u95TVGVds1NjlHmfAvkpJnU+vD2NfGD/kHm+YuedLJX1jd2L4aUmfL+k/p5T+20YKMo5zPRso88/lnG/JOR9QZ4FgSdLHN7j5M3LPRQrqvIfDfeIYf+Qeb7v7PVn9c8QzX3iF555oznrAPIbxQ+7xhpl77uu+pvfcbPbD38+mMVevgfqMGEt9c49ynuv5mdaQco8GzD3qyT26RO7RmOSe7jm78PPX/Z6UrlXng1lv2kJ5npF7dIncoyHkHnPtNyfn31HOz1eUe+K55i+V9A+6C7ifVuec/7ZS+udbKhfGAbnHG2bueb6kh5TzO5RzWzl/Up0PhX7lJspz4S/nfLOkTyvnu7u//7fqnPMXdHPPd2nruefTF137GeW8lb+ac6OklnJ+kzrfBH5M/oNsd0r6LeV8rPv6X5G0W9LztnBsjD/GXN5Q53uGMAa5nMZcz5f0UM75Hd3n4lGuvU9mPJpzfm/O+XNyznvUyb/P0WW47pAFzpeQc35Y0m9J+gc9v/59SbemlF6bOl/bXU4pfU5K6cI3QNwr6ZtSSpOp8+283xsc5jck/a2U0p0ppao6N+wP5i18WjCl9Cp1/qzcN+ecn1EJc+fbXn5P0k+llKZSSl+gzur8N3e3PZJSyumZX2Ef+Q1J/yildEPqfLLq30r6rZxzs+c1/6p7Lm6X9LfUOZ+X8h5JLUn/IKVUTSld+OaIP+uW73UppaNBed4o6W+o0wl6Y8/vZySdzTmvpZReps6fY+7nXnW+Dr+cUnqpOn9m4oJflfS1KaWvSCkVU0q11Pnq+2suuadOAnyuOjeF1d5ASulw9739t5zz/wzel1LnzwLWuufyn6jzbR6/0hOvpr/+Gv1K97UbmrTHeCHvhIaZd5RSqnTbTpJU7radQjf2xSmlaBL6TeosrPt+fXbeOS9pqfvJKtdhuVf9r1907S9+P/9CnRz3ZbnzKawNv98t7Osl3Vy4X9LPq7NQ88GLX4fLA7knNLTck1KalfQOSR/IOV/8id5nfe7pvrcfTynt7r6H79df93k+LOkVqfuNzSmlF6nz59zvu8R+cBkg94SGmXs+L6X0hd32OJE6D2sOqPMhgcs19xS7uaUkqdDNLeWe+Ib6KimlQkrp73TzTkqdMeMPSvrTze4LlwdyT2iYY67XqTMvcmf35y51vjniX3bLd9nN9aSUSt3cU5R04fWlbqyWUnp+N5dcp06++NncebB+8X6uSil9e0ppunvcr1Dnzy/+aTf+5SmlF3Vjs+r8SdZzkh4IzhfGFLknNMx+z+3dc1Ds7us/q/PQ74Fu/Fmbe7rcnPVm94UxR+4JDS335JwfUfdPBqfO85rnqvMn0H+/W75n7ZhrA/t6nUyfEZchk3uU0muVUrn78zm6KPcopUltIvcopTvVk3u0lW/j7ck9uij3qCf3KKUpXZR7lNIRpZS1hdyjlG5QT+7RRbmney7C51xdr5X0F+rkot739sUaQu7RBnNPn+sXXftnSqmonnyhlGrqyT1K6SXd13wm96j/HM2bJP24Utqtz55r/lJ1Fg7d2f15StLfkfRz5n1inJF7IsPMPfdIukUpvUopJXW+CfVrdOFZzcbK97vqLGT8SX127lmStKDOmpp/avZxr6SvUkp7lNJBSf+wJ/YhSYtK6Z8rpYlu3ni+UvqcS+4ppUI395QlpW7uqXSjD3V/9ze7rzuoznix37OpD0v6VnW+jbqglF7b3e/D3WP5PIfLCmOu0LDX9/Qdg1yBY657JN2SUnpVd57mmbn2s/2yOh8cvbO7j38l6f0554XusV7ULc+spP8k6Ymc8zvM+xxPOWd+On+x5ag6izMu/PtaSWuS3tPzu9vUWRV/Sp2vff8zSXd2Y/vU+Ua+RUkfUOfPm7y/Z9ss6eaLjvkDkh6RdFadyn5Nv9er0+n+6T5lf7c6n3RY6vn5o574Hkn/V51PEzwu6W/2xF7Rfe/l4Py8R9L3df+/oM6fV3iiey5+VdLubuxIt+yvV2dA8LSkfxbs+0WS7lbna9M/IulFPbF/JenXgu2TpEclfeKi33+LOl9jv9g9v/9N0q9eVM5S9983qvOQf6l7jf//F17bjX+upPd2r9Wp7muuu0RZru/ud+2i6/Gd3fi/7sZ7Y0s92//YRdfuP6rzIGtJ0h9dog4d7e6v9+fIqNsTPxv7EXlnlHnnPZdoO1/cjb1WnQWI0fV7T7d9Vnt+90p1PuG1pM7E9k/1uyYbuH59r/0lypIl1S+6Hj+2wff7nZLu38S+3t8t81lJ/0vS1KjbEj+b+xG5ZyS5R50/nZO7Zest/3Xd+LM991Ql/ZI6g8gTkn7komP9kDqTQIvq9Pv+8ajbEj+b+xG5Z1S554skfVR/fe9+r6RX9sQvx9zzOn12bvmVnnjfvop6ck/3PP9x93VL6kxY/5iktJF98XN5/IjcM7IxV7/jdP99Wc31dF/7Bn127nlDNzanzgTzcvfc/DtJxZ5tPzPXI2l/95jz6vR7Pibp+3te+63669x6oUwvHHVb4mdzPyL3jKrf8ypJn+yW7WS3nLf0xJ/Nued6+Tlruy9+Lo8fkXtGOdd8WJ2xxZI6OeTv9MSetWOujeyr3zXi5zL6kY7mntyTpWuztJZ7ck+WbsvSH2TpVJbOZOnP8oX6J+3L0p9kaTFLH8jSG3JP3c1Szhflniz9QJYeydLZLP1+7sk9n/V66Vdyn9yTpXdnqZmlpZ6fP+qJ78nS/83ScpYezz25J0uv6L53m3uy9J58oV5LhSz9RJae6J6LX83d3JOlI92yvz5LT2Xp6byRMZf0YJa+9xK/f23eQO7plu9c7sk9WXpld79LWXpfln6q7zWJr1//a//ZZXldd9+9P7/SE39/9zhns/S/cu8cjfSduSf3ZKmapV/K0vksncg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PFg059xxX90Og7d7elT+Q1G/D7yflnE9K+h11NxVu9nXq5p6FlNKv9PLFld5/397nfL4jpfQTm/7/8d55lHr/f09K6YdSd+52KnW/sb3Yp1yP5px/SNLD13ifaXXvwT/NOa/mnP9A3Q+DXn0Ozx/ru3POH+7t7XlU3Q+kXj22uZ61MowXcs8WDTH3vFnS2d7coZ1z/gl1v6jvL0RluNXHPTnnWi/PdfSp9Z4FdT+kckNhg/OnfFDSeyX9/asDvY7rN9X9NNJBdT9F9P1pe3+m7Uslfaakl6aUPlfdTWNfKek2Sc/ohd+I8CWSXi/plb3f+9P9DpxS+qyU0pK6D3C/XN1vdbiWd0j6sd5C5XXZYtm/TNLr1G1wb1f30wDX8jJJT+WcNz94/qi2NgDaUPeTVJv/pMxXSvpEzvmj6jbKv6vuN068SdLnqfvNydsypHt/vd7YK8PHeoOvn0jX+Hr8Xhm/Qt1PnuDGQ+7ZgiHnnkH9mD4993y+uhuFflXdQcG/knRE0kvU3Tz0Hdt9g97g7ZfVzYlH1c1h35JS6ns/huz7JH2bup/Mc96q7mDy0s4XCUNG7tmCncw9qfungyvqftPoVtz0uSel9JdTSsvqfsvRqyT9YC903WNGjB1yzxbsRO7pLQjX1P1miPeqey+24qbPPVvwk70Fqd9IKb1q1IXBdSH3bMGwc09K6c5e/Lu2U44bdL3nZZIevOr6P6itjVWuee9SSovqfjj++yT9y+soE0aP3LMFOzTn2nbffbPnnt7Du9dJOtB7uHgydTdpT171e4si99zoyD1bMOTcc3vv38tTSs/1HmR/p3k4f7Ubbc51PWs0f6u3qeFDKSW3EfqtusbmItwQyD1bsFNrzb2NTF+n7T0nvhVyz/PlmFS3PjyfX14hqSXpK1JKZ1NKj6WU/vZ1lAmjR+7Zgh3IPf9R0tellMoppRepOz/6rS0W513atDm49/pXq3ufCur+JYc7JR1T9xn1f97ica/2o+q283slvUbSF0r6BveCPu6X1Mo5P7bpZ1vNPUndD9A+vOln17VWhrFD7tmCHcg9V2/cTur+ReCtuJXHPc///EF113t+SdJ/zzmfv45yjRQbnD/d/yvp/5de+LXeXyLpRM75R3qftvmIun/i5S9u49j/Kne/8XJD3T9d8MO9T+/UJf0jSW9KKR3f9Pv/uvfNCc9Keo+6Hfs15Zz/IHe/Rv52dT8tcuLq3+l1lm/T4Jtgt1L2f9M712fVTYh/qc+xZiQtXfWzJXX/RN5WvEvdycdE7/9/cvKWc/5Q7n4bRav3adUfVPf8t2sY9/563a7uAO/LJd0naVLdxeWr/QV1NwL97i6UCTuD3BMbZu4Z1I9Lelv61KdGv07S/8g5N3POT+ScfzN3vznngqTv0fXlntdLOpBz/q6ccyPn/JSk/6buIHhHpZS+TFIx5/y/gt+7Xd0/y/P/3+kyYceQe2I7kntSSnPq5pLvzDlfPRbq56bOPZKUc/4fOec5dReNfkDSuV5o0DEjxgu5Jzb03JNz/hJ128wXS/qN3P209lbc9Lkn8NX61J9Lfo+kX099Pr2PsUfuiQ0793yvet9wcx1ludHWe65rrOLuXc55Xt0/K/hN6v45QtyYyD2xYeeeQfrumzn3HFL34d1XqPuQ/dXqPuj/J5t/idxz0yD3xIaZe56fK32hupvm/lTvd79+i2W50eZc2x33fK+6z7cOSvqnkn40pfSCb2lOKb1S3brb7080Y/yRe2I79Zzrs9Tt639uG2W52XPPZj+g7qagX+/9/9vVHe/cL+kudcdH35FS+oLrKBdGj9wTG3bu+RV1282Gut9C/EM55z/ZYln+l6RDKaU39/7/10n6tZzzhdz9a+Y/n3Nez91Nff9C15F7UkqH1F0D/5ac81rubuL7D7r+3LN81c+2mnu+Q5/atP28QdbKMF7IPbFh5p4/knQkpfSXeh+ueIekeyRNbbEst/K4R5KUc36lpDl1v7n6D67xurHHBudNcs4Pqdshf+tVoTslfWbqfqX7Yu+bDL5a0uFtHP65Tf99RN1PJzz/vquSLqm7i/95Zzf997q6FTgq/ylJ79YLP/UgdTfK/kHO+eltlPlatlL2zef6TO81Sik9nFJa7f37bEmr6jagzea0hT8l3HvvP1B3Y++XppTukfQGdT8Jo5TS/an7LWVnU/ebAP+lut+wsV3DuPfXa0PSj+ScH+td53+p7mDsagN/egajRe7ZkmHmnoH0Bli/J+lrUkoz6n6K7sd673UopfTTqfvnbpYl/YSuP/ccueref5u6i1Q7pvepwu+W1PfPZfR+74C6fz7n+3POP7WTZcLOIfdsydBzT++Tk78s6Y9zzv9qqwW5mXPP1XLOj6v7ydLv7/1ooDEjxgu5Z0t2ZNzTW6z5NUlfmFL681spyK2Ue64l5/y+3P0TYOu9nL2oTX+6HjcOcs+WDC33pJT+nKTZnPPPXE9BbsD1nusdq9h7l3NeU3dR+sdSSgevo1wYMXLPlgx13DNI332T557n/0LX9+Wcz+Tun6X9Hl1jrZncc+Mj92zJMHPP8+3ru3sbC06o+yGIaz3LeYEbcM61rXFPb0PDpd4Gj1+V9JO66k9Jp5TulfRrkr455zzQn8HG6JB7tmSnnnO9Q9LPb2fD3M2ee56XUvq36n6741dueo7+fN7+rt648UF17/uW8jbGC7lnS4a53rO3V97vkjSh7jed/umU0pb+uk3OeV3Sz6r7DdBJ3XvyfO6ZSin9YErpmV7u+T1J86n712i24051P9x5ZtO9/0F1P2y1Xdebe75J3Q2Uf7a3sVODrpVhvJB7tmRouSd3/5r429X94r1zkr5I3W+OP7mVgtzi455PyjnXent7vjXdgH+ttDTqAoyhb5f0YUn/ftPPnpP0uznnfp/cW9OnfzLgWslpc+U5rW7llvTJDWX7JJ26ngJfpaTuJxWu9nWS/vUQjr+Vst+hT33d+bHea5Rz/rSvS08p3S/p7pTSbP7UV6u/Sr2F4y16/qvkXyTp13POz3/T339V91sm/lLOeSWl9C3qfpLsWtz9i+79TnpQn15vXpCAUkp3SPocSX9jl8qEnUPu8YaWe4bkXZL+oaQzkp7OOX+o9/N/qe41f0XO+XJK6UvV/8/nRLnn6ZzzfUMtdew+db/p6Pe780pVJO1JKZ2V9Mac84mU0oK6m5t/Kef8L3a5fBg+co831NyTUqpK+t/qTriup+++WXPPtWy+tw9r8DEjxgu5x9vpcU+/8vdzK+WeSNYL/xQabhzkHm+Y6z3/UdLrevMIqfvtWO2U0ityzm/fYnlupPWehyX9vZRS2rR4/Ep1/+KNs5V7V1D3HI5KuuH+dCAkkXsiOz3u2W7ffVPmnpzzlZTSSQVrzZuQe2585B5vmOOeKUkNbb19XcuNNOcadI3m0/Jy6n5D3G9J+mc55x8fQvkwWuQeb+jjntT9Mo2/qO6feN+umzr3pJS+U9KfkfS2nPPmb2B9sPe/g+RtjBdyjzfMcc/rJLVzzj/W+9HJlNLzHxD4fm3Nu9R9TvYL6n4b6S/3fv731J2HfWbO+WxK6dXqzsGuNZ+Lck9d0v6cc2uLZernMUmllNJ9ufvFPFI39zzc7wUppb+m7qbXt+acN2++/DwNvlaG8ULu8YY67sk5/66635KslFJJ0lP69GsfuVXHPddSlnS3ut/0fMPgG5yvknN+QtLP6NO/vfJXJN2fUvra1P2683JK6fUppZf04g9I+gu9TxXdq/hPT/2UpL+aUnp1b6PLv5T0/t4nu7clpfTVKaVjvf++U90/1fDbV/3Om9VdjPzZa7w+p5Q+ZxtvuZWy/4OU0kJv8+03q3s9XyDn/Ji61+7bU0oTKaUvU3cR9ud7ZfuclFI0ofgxSZ8v6a/r078if1bdPxexmlJ6saT/2xzjAfW/f9G9/zQppWLq/hnDkqRC77zKm+KVXjxJKvfi/drhj6h7re/uLZJ9a688m32tpD/MOT9pzg83AHJPaGi5p/f+5V5bLKg7MZlIvU+AppSO98p33JTn59UdZH2nXph7ViUtpZSOyv9JvQckfXFKaW9K6bCkb9kU+4CklZTSP0wpTfZyy8tTSq/vcz6F3vmUu/83TaSUKls536s8pO5A8tW9f9+g7qfgXi3puZTSnLp/zuJ9OeerP5GIGxC5JzS03NMbD/ycut8Q8Y6cc+eq+K2ce5RS+obU+3awlNJL1f1TRb8txWNG3HjIPaFh5p4Xp5T+TK9Nl1NKXyPprZJ+txe/1XNP32OllI6llN6SenO4lNI/UPeT++8z54kxRu4JDXPO9U/V/XO/r+79+yV1/yTfX+2V7WZb73mvpLakv5NSqqbuN/VI0u/0K1i/e5dS+oKU0mt67zen7jesXpH0iDlPjDFyT2iY4x7bd5N79CPq/gndg6n74fW/2ysPuecmRO4JDfM513ov9v+klGZT988ef6M+1b5uqjnXdtdoUkpfkVKa6R3zCyV9jbpjQ/XO6Xck/eec8w+Yc8MNgtwTGupzrp4vU7fPfs9VZbvVc88/UvdPsH9+71sfP6n3TP33Jf3j3hjqJer++firn8HjBkHuCQ0z9zzWLUL6y702e1jS/6VPfXBgK+X7fXX/0s47Jf10zrnR+/msus/PFlP3m6K/3RzjAUlvTd054B51nydJknLOZ9T9oq5/n1Ka65XznpTS2651oNQ1oe6XfqmXY6q9Y62puxH7u1JK0ymlt6j7LbLX/FBWSumr1b2+X5BzfuqqsF0rw42H3BMa9v6e1/Su55ykfyfpuZzzr/dijHv6jHtSSm9MKX1W6q6VTaaU/qG63yr9fnOe4ynnfMv/k3RC3Rv9/P+/Q1JN0ns3/exFkv6PpAvqfm3670h6dS+2X91OckXdBdPvUPcr259/bZZ071Xv+TclPSnpsrpJ7vZ+vy/pRyX98z5l/xfqfgvgWu9/3ylp31W/84OSfvwar71D3YXZff2uzbXefwtl/zvqflrikrqfmCiaYx9XdzF2Q9KjV92Hr1V3E110/96r7uStuulnb5X0CXUT0e+r+2cyrnlPtnD/+t77a5Tlr/SOvfnfj15V1qvjn9OLfbWkh6863nf23veCugOlhavin5D09aNuQ/y7vn8i94wy9/zoNdriX+nFPrt3b8pbKF9L0pFNP3uZpA+pm3seUPfTpievdc/V/dM9P9O7Fg+q+2Bp8+8eUXfgd1bdHPfHm+vLVWX5nGucz3u3cb6r5riby/SO3mvXeuf4/L9jo25P/Nv6P5F7RpJ7JL2t9/vrV7Wfz+7Fb+nco+7D9nO9e3tC0r+VNLEpflx9xoz8uzH+idwzqtzzEnUXKlbUXTj+E0lftil+q+eevsfqnd+Dvft+Sd3FvteNui3xb3v/RO4Z2ZwreJ+bcb3nNermxA11v73lNZti3ybp17Z47/7ipvO70CvfK0fdlvi3vX8i94xq3GP7bt3iuUfdh2bfr+6Y8Kyk71VvziVyz03xT+SeUa41z6n7551X1P3mrv9XUurFbsY513H1f673ac+51M2ZS71yfVTSV22KfXvv2JvXya65Ts2/8f0ncs9I51zqfiHNP7vGz2/13JPV/QbXzfnl2zbFj0p6d+/nT0n6G6NuS/zb3j+Re0Y57vlcddeYl3pt+79Jmtpm+b6j976fuelnR3ptfFXdjdR/o/c7pV78vZK+YdPv/xd15zZPqPsB1c2/u0fdv8JzslfOj2jTGOSqshzXC3PPiU3xvep+4/SapGcl/eVNsavXmp+W1NSn554f2Mo94t+N8U/knlHmnp/qtecldcceBzfFGPf0Gfeouz/ho706d1ndLz9666jb0vX8e36CjVtQ6n5718tyzv8o/OWtHzNLui93P60y6LH+u6Sfzb1PXQC4OdwAueefSLqQc/7BwUsGYFyQewCMArkHwCjcALmH9R7gJkTuATAKN0DuYc4F3ITIPQBG4QbIPUMvH4DRuwFyD+OeWwAbnDFUw0xCALBV5B4Ao0DuATAK5B4Ao0DuATAK5B4Ao0DuATAK5B4Ao0DuATAK5B5sV2HUBQAAAAAAAAAAAAAAAAAAAACA5/ENzgAAAAAAAAAAAAAAAAAAAADGBt/gDAAAAAAAAAAAAAAAAAAAAGBssMEZAAAAAAAAAAAAAAAAAAAAwNgoDfLilNIXSfpPkoqS/nvO+V+7358rlvKBcrlvPEfvpxT8hj9CO/t4dPxC8PbR63NQvkL4/j6eggJ2Ov79o/JFV78TXN/4CPE1jt6hHZxjdPzoHnSCEjTCa+B1gnh49KCORPKA19/Fl5pNbbRagxWwZ7u5p1BIuVDq/3mOSsGnwslqxZcnuDLrrYaNdwZLbWq3fc0plvwbFCv+/NvtoAC1tg1H7aoZVPyoXSv7A+QgvjX+80CpEHxeKIinFMSLg76+aOOxKDn4OpCCe9DpBK8v+PK7/rfVqKndbI4k91SrlTw9NdU3vndh3r5ftdp/zCRJzUbdxmvr6zZeKAa5Iai3naBp1Rqt4P39fV1dXQ3e3xegEJS/YsakkjQ7M+OPH7TLjY0NG5ekcslfgyh/FaOxYTTuiXLXgFJQgFbL15Ho9aXKhI0Xy1X//m2fey5dvGjjyytrfWPtTkedTjSy2prt5p5SqZhd/oiHq4ONZ0sD1qvovoQD+siA4/Xo5VHxSsEvtILjh8OiAc+ve5DBXh7NS6PjR/PWKLcNpeENImj64bpA0D+761uvN9VqtkeSe1Iq5IIZs0XjvWIw3pusTPrXF/2cJrru9aYfV7WD8ofJIaq4wesLwXh/ZsqPW0rRuGvDj7uaTd9nb20tIliTCq5BJ5r3BWPDUlBH5qZnbTy6B81oXBOcfzEYWzZaTRtvBeffbPrXt6PBvbnFnU5HOYcrG1tyPeOeshnXR3U3mvNUqr7exCc94ILPjgsHFoO8On59lDsHHBd1DxH0y8nnx2hFqRCsyUUnEc154jlVkBuC8kX946BKJT/vrlb8nM2Vrl6vqdXa/fWemampvG/PHnewgcrSCNZh661gja3t815UZwrBWH5uetrGK+Ea5GB1LmoTYZ0euMrv/HwreofoOVy0XhZUIW00fB2JlKtBuw7K36j59cwcrBlEY8bwWUM0b6jXL+acDwQH2ZLt5J6Jyek8OzvvDha92wBRqR3kllqwDtoKxqKDitp+NBeI5xqD9efhdNGHt7TQEbWtKL8Pup4UvboY9A9x+QZ8gj5gG4nKF61pRPO9aJ3+4sULI8k95WIhV8r9z23QehPtfSkEfVo1eL49NeXHotHz9bWaz31RlxI9G60Ey+jFoN4VCtF8yx+/ESwhrkUL1ZKiLQTRk4KJsv+N2eAeRvuzLq/6PRqt6AR2fEo/4P6qHV4IrzdaI8k91YmpPDXTf84VnXa0BhatIUbr2OE6bpDzwz1vwRpjM1jHztG+i2hcGKyBKooHojXSrXQtg43o49+I+qdotSgaO456NXDUqsGcsVbbuGbuue4NzimloqT/IukLJJ2U9CcppV/KOX+832sOlMv6V3fc3feY4YJhsOCYO76hrwa76KLFl8ngQWMletgRlG8imGBNVPwmy+qE7+A3atGCZ7DoHzTitWAjUzR5kaSpYBDTCMq4VPfxqegeBxOQ1ba/hicbfpCUgzrSCPqCjRRsdArqUDu4h81gAhUtQbjS/diJJ4NXb8315J5CqaA9+/tvMjw6d9C+5yvuOmbj5eQXbz588aSNr5f9fU3BhV9dqtn4zILPDQt37LfxxbWgXn98ycan5XPXOT8G09J6sNmg6cvXqPXfgPZJ0QSs5DvZ8pRf3E/BhozKpN+QUJwJHh5MBMefnrPxTjiQDTYYt/yGiGLTt5Ha6qJ//ZQvf7HUv46dfehD9rVbdT25Z3pqSp/3OW/te8yv/so/b9/zvuOHbfzMsz6vfvzBD9v4zGywwXfWX/eNdZ+7Hj1xwcan9+y18d///d+38bV137Zng40+R48csvG3vfWz/fHn+vcrkvTQQx+1cUk6tNc8FJXUrvu2MxV8AKccjF2np/wGYaXBFsbLwcPsc+fP23gl2MB88Nj9Nj53W/95hyRduOL7r3f9yLts/Ld+54/7xq6sbqHv2YLryT3ValkvfsmdfY8ZLc60g4eF0YL73knfNkrBxP7C2rKNp8lgE2NQb9vBw9piNF4Pxi0zJZ8b9874dnGhFiyst3352nU/LtqKaINxZGLCt91oD9zGus995aAORvcwWsAMP5gWXZ5gn01bvo1V5/24s2Lm/Y88dMK+dquua85VKGpyYr7vMTcaK/Y95yf7v1aSXnrHy218YXbBxltFf+MeP+XHVSsbPje1g81CxaBPbgcPjSaLvl689TM+y8b3B+O6P/z4H9n4c+fO2Xih6MckXUH/0/Lz2vWmj0e5Y/+MH3v+mTd/no1PlXxuO3PRj30Lya9XLezxY9eT58/a+MU138ZOXvCvXw3G1tmM+9aC9rFV15N7yuWyjh+/ve8xL5y5ZN9zetqPW+66268XBZ+XV0HBRo6gXcQG/KKFIHd1gg8z51bUroPjRw8Vm/74G434/OvZ589G2c/JGtmPCyZXgvofrCWXg3FTIfhQ7MXzZ2x80XwoU5IawfOK6Ms+ojuwb69fc7zrzntt3H1hzcMffyB4963Zbu7Zt2ePvvWv/dX+xwvW56Ox5DMr/p48ecn3d+srPu9duejrzETFz1f+9BtfZ+NHF3yb6gR1LvrAbLSJsBPl1QHnOjnIi93fCeYbQRmi6cpG8OGd5WA94lLNX8NHTvk60gm2Kh25y6/FNILniM8+8hEbr69dtvGNup+0r24EzxqCNZEnnnzsGfsLW7Td3DM7O68v+8q/3fd4haJvu9HmzuhzystX/JcAPPrIwzZ+8Ywfi0ZblcIv8ArWuvbu83OBjQ0/16gF8WiTRjvYnxB/4MmGJUmtYMwxMenL2Ayeb0dliNLrngX/gdKp6WCjS8M/g1Ih+DKUYH9A9CU71WCPxsKcP7+Z4BnfVBD/7z/4/SPJPZVyUS8/1r/9RPUmB18CMBl8oHQq6NOOH/P7Lt/w+v5zRUlauuzH8n/8mB9Xrbd9vak0ffmPTQXPb6rBB8+mfO6LPqt+4oq//h+8GI97loL3mAw6mJfc5ufkb3ulf05aKfj+76fe55vO2WU/Loi+MCBaZ44+gBN9cC3sHwb8DqOoe3nsmYsjyT1TM3v0eX/2r/Q/XnBdLwZraPVaMKda9bmhE/S509N+ja8Z7IxcXvS558xzj9l4q+bXCIvBvo9yJfoCMN9nhftusk8c0ecOJKkRfEClHX4wyue36Ylgwa/tc8dGzY8d2+H3Y438a3ys+MN9/vrfc/c9Nv7wxz96zdwzSMp7g6Qncs5P5Zwbkn5a0tsHOB4AbAW5B8AokHsAjAK5B8AokHsAjAK5B8AokHsAjAK5B8AokHsAjAK5B8DABtngfFTSc5v+/8nezwBgJ5F7AIwCuQfAKJB7AIwCuQfAKJB7AIwCuQfAKJB7AIwCuQfAKJB7AAws+F7twaWUvlHSN0rS/uDPVAPAsGzOPYXgzwEDwLBszj1Tk5MjLg2AW8Xm3FOp7PgUDwAkfXruSYP+TUQA2KLNuadUYtwDYOdtzjt75+ZGXBoAt4rNuWdmZs+ISwPgVvFp68wl1noA7I7NuWdymjkXgBcaZFRyStIdm/7/7b2ffZqc8ztzzq/LOb9urlgc4O0AQNJ15J5UYIMzgIFtO/dUq5VdKxyAm9a2c0+pxJwLwMC2P+digzOAwTHuATAKYe7ZnHdmpqZ2tXAAblrbyj0Tk9O7WjgAN61t5Z5SkbUeAEOxrdxTnWDOBeCFBhmV/Imk+1JKd6WUKpK+StIvDadYANAXuQfAKJB7AIwCuQfAKJB7AIwCuQfAKJB7AIwCuQfAKJB7AIwCuQfAwK777/jlnFsppW+S9OuSipJ+OOf88NBKBgDXQO4BMArkHgCjQO4BMArkHgCjQO4BMArkHgCjQO4BMArkHgCjQO4BMAz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l7ecW5p6ccy8fUO7Zwbl/79anIq+nlH4oDck9Oednc87/R8750tb5/YikiqRwAiPfJvekbbknpfRgSulXU0pLW+fxr82zfTRtyz0ppfekbbknpXQ8pfTTW5/kfC6Z3JNzfibnvPLSphoMrl76s8zvlHQ55/yTW9f7rzR4if6H97Av6Q48W4wEuWeHDir3SFLO+dPbFuVkDSavTkXn8EWSe34w5/yJnHM35/yEBosK37UV23Pextgh9+zQQeaeLfF8xhf6d7plridtm+tJKb09pfSRrft3KaX0D9OQuZ4dPL+hz/5WOecncs4b2360Pfd8g6QP5pw/tJVv/46kExq8DLjdvv5Tzvn/1eDDpbd6j7bGbznnVo7Gbxhn5J4dugO5Zy/+nV55uWdB0oykf5kHPqrBl224b5AfNne0qzyGscZc804d3DuuwtZ5/3nl/DnlnJXzMztanDyuc82D8195aUt94RzNdt8u6QPK+Zy/WEkpndUgz27/BrWzOuhni1Gg37NDd7Df8+2SPpB3UBdfofM9u73e/0nSL+ac//XWuGot5/z5W35nL+NVjBf6PTt1kO/Y93p+OZ+X9KsaLCrc7ts16PfMK6Wf3+qrLG/9/5NDrucHlNK/2vbfZ7X9m7BTmlVK/0yDNUIXlNLf2Opj3e68XlTO2z+Y1tPw3PNHJF2V9MED2Bdeuej37NAB9nt29d5nuy+Gfk+0r72+Ixw3LHB+uY9Jer+k//kLIoMFpL+swacWjmjwSaJ/rN39qbY/KOnLJL1eKX21BhOJ3yzpPknPS/rxW37/90v6UklfsvV732D2/Tcl/d+SLt8m9vclfbtSKiul10h6h6T/tIvzfrmdnfsfkvSIBouXv0nSfzdkb2+Q9Kxy3r7I8FPaWSeoIen/1aAyv+SbJT2unD+lQYfhz0s6pME1f42k/yHc7612++wHyfAfSvoeDZLDdm/Q4PpeuoYNSc9o2PWmdEaDe/cFfyLxFn9EgwT+hX/WA+OOvLMTB5t39utH9fK887UadAL+gwYdhr8l6bik12nQMfiBXR9h0Hn7OQ3yxQkN8tefU0ruefwDSX9Jg0+ObvcGSZ9Wztvz0ae1tw7p7nIYxtnQ3JOG5J60x9yTRpB7UkrldAC5Z4fnvqfckwZ/aryiwTfK78Qdzz1pSO5JJveklP5YSmlVg28merMG34rxO+Fbf12DD4DtZV9/Xwf4bDEy5J4duBO5J6X08ymlpgbfSvN+DZ7FTtzzuWfbPpMGL9s/OyT+sHaXtzE+yD07cNC5J+18PuNlspnryQc417OXZ59S+osppXVJ5yVNbm37O+Fb/v+Ocs9tvEHSp/PBjN8wWuSeHbhDY65/vfUy6ZdS/G3Gkl6ZuSfnfEWDv+j336aUiimld0g6o8FLyGGGzR1JB5fHMFrMNe/Eweaek1v/3qiUXtRg8fBfCxYGbTeec80p/TENH3O99Dsv/UWcnX7xzrdL+uAti6H/vg7y2WJU6PfswJ2aa067r4vSK3O+56Xf2cn1frmkGymlX08pXU0p/VxK6fS2fexpvIqxQ79nJw4+9+zn/N6r7QucB9s/rMFzKmjw5YFnJJ3WYMzyD3e431s9KqmrwQK/t0j6eknfOfS3U/oKpXRTgy9G+yMaXOPtfIekH73lnftu9/W3thaSf1gpvSe8Eowj+j07cAf6PXt677PlXu/3hPvaxzvCscEC5y/0VyX9j0rp8C0///2Szinnf6Gcu8r5tzX4+u//ehf7/lvK+cbWwtxvlfTPlfMnlHNL0v9H0js0+ATzS/62cl5Rzi9I+s8aNO5fKKVHNPiWqX8w5Lg/L+m/0qAT8Likf6bBNzrs1U7O/e9sXesLGiTCbxmyrylJN2/52U1J0zs8l/dK+q+UUm3rv3+3Yuf8ceX8G1vP65wGFXgv3zqx22f/vZJ+Uzl//Dax3V7v/yXpryjn9eAc484Uxhl5J3aQeWe//qWkd+t3PzX67ZL+jXLuKOenlfMvb31T8jVJ/4f2lne+VNJh5fzXlXNbOT8r6Z9q0An+Qin9IUlF5fwzt4nuN8/eqX1h9P6qpP8xDck9Oed/sfWNlnvKPTnnG3lb7tn6hszfqb/pltyTc17JQe5Je8g9+QByT3Duf2frWnece1JKMxrkkr+Wc761Tg3zLyW9O92Se3LOnZzz0znnX976Nop9556c81/PObdzlHsk5Zz/zdaftHm1pB+WdGUr9BFJx1NK37I1GP4OSQ9KmtjDvqSDf7YYHXJP7MBzT87592vQXv9eSb+Uc+7v8Fzu+dyzzQ/odyfSX2aPeRvjhdwTO+jc839J+is5ns+4nfdK+q/SbeZ6cs4fzzn/xtbzOqd9zvXs5tnnnP+2Brn0rRrkhJfywX/SIFe+Jw2+0fUvafCBiJ3knlsx5rq3kHtiB517vlWDbx06o8F1/mIa9o1uX+iVlnukwQLnvyqppcG3iP3lnPOLt9tP2po7yrefOzrIPIbRY645dpBzzS+Nlb5egz8d/F9s/e6f3OG5jN9csyTl/G80fI7mJV8h6aikn9rheXy7BguOtjvoZ4vRod8TuyNzzdp9XZReefM92+3kek9q8O78z2qwUPI5DfpNL9nPeBXjhX5P7KDfse/n/H5G0lEN/hq6NMg971PO15TzknL+aeW8ufXliP+79pJ7UjqqwRz4n1POG8r5qqS/J9/v+ZByntUgd/yQpHO32e+ZrfPxHybx+/pfJT2gweLHH5H0c0rpwZ1dGMYM/Z7YQfZ79vPeR7r3+z3hvvbxjnBssMD5Vjk/pkHF/Yu3RM5I+jIN/qTE4N+gQh7bxd63Ty4e1+ATCi8dd13SkgaN2Uu2f2piU4OXHC83+BTAP5b0Z/W7Xym+Pb4g6T9q8AnEmgafNvgGpbT7bzLe3blvv9bnt7aRUvqsUlrf+veVktY1+FN6281o8ImmWM4f0uATCH9wq/F/u176Bp2UXq3Bn664vPVJ87+pwbds7NbOn/3gK/W/V9JfHrKvnV9vSn9A0rRy/gl7doNPnL5HL/+zXnglIe/sxEHmnf0ZdLA+IOmPK6UpDT5F96NbxzqqlH5cgz93syrpX2nveef4Lc/+L2nQeXm5wScBf1CD3HM7+8uzd25fGLEc5J40+FM2K+mAc0/eY+5J23JPvk3uSUNyTzrA3DPk3G+be1JKn00prW/92/5njusafILzN3LOt/5Z4KHyttyTbsk9KaWjKaUfTyldSAeQe2559rfPPV94fk9p8K2n/3jrv5c0+LTt/6TBIOobNXhpfn7YPobt6w49W4wIuWdHDjz3bO2nk3N+n6SvTyn9lzs5kS+W3JNS+h4NJrV+39Zk2/bYnvI2xgu5Z0cOLPekrfmMHM1nDJG3zfWkW+Z6Ukqv3vrGicvpAOZ6dvvs88BvazDZ/9e2fva4Bi/P/6GkS1vn8zntoN9zG4y57iHknh050H5PzvnDOedGznlzq91e0eAvNIReabknpfRaDb796Ns1WIz8Bkn/S0rp9926fQrmjg44j2HUmGveiYOca37pG9F/cGtR0zkNPgTxe3d0JuM21/yF5/eyMdctvkPST2snCwRT+goNytpPbfvZnXi2GBH6PTtyR+Z7tFUXd7NY95U233OLnVxvQ9LP5Jw/mnNuatB/emdKaXa/41WMGfo9O3Fw/Z79nl/Om5J+UoNvgE4aPJOX+j0TSumfKKXnt/o9H5A0p8FfT9+NMxp8M+ulbc/+n2jwbbrR+V3Q4Ppu/ZZZafDN0x9Szs/t6Cxut6+cf1M5r219eO29kj6snfYZMVbo9+zIgfV79vPOeevY93q/Z0f72ss7wnFSGvUJjKnvl/QJSX93289elPRryvnrhmyzoZd/OuB2CWr7t+te1KCADwwmGRclXdjluc5o8JXtP6GUJOmlBv68UvqvNejA95Tzj277+Y9r0FDernLsxE7O/ZR+90/7nt7aRsr55X9SM6VXS3pAKU1vfRJLGnxd+r/Rzr30dfKvkfSLGvx5Pmnw1fq/LelblPOaUvpzGnzi5Hbc84ue/XZv1+Dr9T+39TzqkupK6bIGifqzGiSggcG9e1C3/zPIXyPpka1tJWlWUk8pvUk5f9O23/s2SR/e+tQ9XrnIO97B5Z2D8V4NPmV5SdJz+t1vbP+bGtzzNynnG0rpD2r4n8+J8s5zyvlVOziXV2nw7UQf3HoeFUmzW7njyzW4J39BKSXl3/mW9y+R9I92sO9b7SaH4ZVhaO7Jdyj3pAPIPemW3JO25Z68LfekA849Q879trkn3yb3pJSqkv6dBgOuP72H83lZ7sm3yT055xtpn7kn7yz33E5Jg5wgSco5/5oGn1hVSqkk6Vm9vKztdF8P6OCfLUaL3OMdaO65jZfV1R24p3NPSum/02Ai8qtyzudvie03b2O8kHu8A8s9KaW/L+mRdMt8RkrpTfnl8xnOy+Z68m3menLOa2mfcz3m2UduzT0/pa0FO2nwbbF/UtJevuXks5L+Qkop5f2P3zAeyD3ene73ZH3hnzF1Xkm5542Snsw5/+LWfz+RUvoFSb9H0i/cst3vzB2lbXNHW3n6y3PO5w4wj2E8MNfsHeQ7rglJbb383uz2L22O01zz7XzhGHLwQdD/WoM/K70T3yHp396yGPoBHfyzxWjR7/EOvN+Tdl8Xt3tFzfdIu7reT2t4Xv4a7X+8ivFCv8c7yH7PIwdwfu/VYL7132rwbaI/t/Xzv6DBOOzLlPNlpfSwBmOw243notzTknTotovIY8Pmzr9d0t8+oH29ZLfjVYwX+j3egfZ79vnOWbq3+z3hvnYZH0t8g/Pt5Py0pJ/Qy7/R4OclvVopfZtSKm/9+1Kl9Lqt+Ccl/eGtTxY9pPjPT/2YpP9WKT2swUvTvynpN7c+3b0bNzX4FMPDW/9e+oTP2yT9pqQnJSWl9MeUUkEpHZP032jQsR9IKSul9+zimDs59+9TSvNK6ZQGf/7l9p+CzPlJDe7d9yulmgZ/Ku9LNPiafiml9yilaDLoRyV9raQ/pZf/SYhpSauS1jX4Rov/3uzjkxr+/KJnv937NJgsfnjr31/VoOP1sHLuafBnN96olP6IBn/u8K9K+rQG35Jxq7+iwdfHv7Svn9XgK+z/21t+73Z/1guvNOSdyMHlncHxy1t1sCCptJV/iluxs1vnd9acz09r0Mn6a/rCvLMu6aZSOiHp+8w+Pinp9yqlha179Oe2xX5L0ppS+l+VUl0pFZXSG5XSl95mP49p0Pl7eOvfd2rwybWHNehIvV9ST9L3KqWqBt9QKEm/etuzSqm0dW+Kkopb9+alD0TtJofhFSCb3JNS+rY0+DMv5ZTSl6Zbck9KaSLtIveklB5O2+pvvkO5J6X0x1JKhXSb3JNSymkPuSc49+9LKc2nIPeklMoavCxuSPqOfMuffkkpnd06v7PmfMLck3aYe1JKC2lI7kkp/a8ppXpKqZhSemO6fe5RSuk7U0pHtv7/6zX480K/si3+lq3yMyPp/yfpxW0v33ezr/DZ4pWF3BM6yNzz2pTS79mq0+WU0h+X9FWSfm0r/sWee75Vg/v7dfmWD4xGeRuvPOSe0IHlHgXzGSml96QDmOtJO5zrGfL8omf/O7bu8Z/euvaUUnq7pD+jl+eet23lr8Ma/JnRn81Dxklbv1fTYDK5kFKqbeUcadv4LaVUTdH4DWOP3BM6yH7P6ZTSu1JKla169X0afOvOh7fi91ru+W1Jr0opffVW/EEN/hzu7cZJ0dzRrvIYXgGYa44c5Duuza3Y/6KUpjX4s8ffpcH9fiXONUspfae2xly6zZhryx+StKzBn8H2Bi/mv1lf+B4rfrZ4RaHfEzrIMddLblsX0z043+Ou9zb+haQ/tHWvyxqMUT+Uc76pnb9/xysF/Z7IQb5jP4jz+6AGf2nnRyT9uHJub/18WoN52BUNvk32+80+Pinpq5TSaaU0q0G+GMj5kqRfkvR3ldLM1nk+qJTefds9pfStGvzFdCmlM5L+d92ae1J6pwZfZviT5pz8vlKaU0rfoJfeuw/mpb9Kg2/NxSsQ/Z7QgfZ7knnv88Xe73H7SsE7wleUnDP/claWzmXpa7f996ksNbP0/m0/e02WfiFL17K0lKVfzdLDW7FDWfqlLK1l6cNZ+oE86Ci/tG3O0kO3HPO7s/RMlm5k6eezdHLo70uPZulv7OA6zm5tW9r2s6/O0kezdDNLl7P0T7M0se06V7O0GOz35cePz/17s/Ts1n36u1kqBuf8/iw1svTELc/h27L04R1c9/uztJyl6raffVWWHs/SepY+mKW/PvSZxM9v+LP35/UnXrafwc++duu8GlvnfXZb7Iez9MM7egaDn70jSxt58Kd0Rl+P+Le7f+SdUeadR7e22f7vT2zFvnLr2ZR3cH7dLB3f9rM3ZOnjW3nnk1n6C1k6f9tnLtWy9BNb9+LTWfrzt/zu8Sz92Nb9W87Sb7ysvAw/r/e8bD+Dn71l67waWfpElt6yLfaXsvS+bf/9A7e5Nz+wLT48h/HvFfFP0jltK0savORsalvu0eBT0r8g6ZoGfy7mV7WVezR4QfxLGvyZ7A9L+gFtyz0afMrxoVuO+d2SnpF0Q4PB3clhv6/By44w92jwgaKsbblH0ldr8A1TNzX4kzz/VFu5Z+s6VxXknluPv4Nz/14NPiW6pMEnRW+beyS9e+v3NzUYKL307yu34l+59Wxs7tk6v6625R4N/hTxx7f290kNPul+flv8d565Bn9a6Ce27sWnJf35W373uAaDzssaDJp+Q0NyjwYTxVc0+MTqOUk/JKm2Lf5jW8/i5tYxj2yLfaWk9V3sa+iz5d8r45/IPaPKPa/TYIJqTYOJ449K+kPb4l/suec5SR29PC//8FbM5m3+vTL+idwzktyzg+N8m3Yw16PBYt9lbZvr0WAC9vGt+vhBDf504m2fyQ6e39Bnf8t5FDR44XRj67hPavAnBtO23/nQ1nFuaPCnTye3xb5V0me3/fef2DrP7f8e3RZ/iwb5taHBN8G8JbpX/BuvfyL3jKrf8wYN+hkbW7/7K5Ie2Ra/F3PPN2uweHlNg7848XckFbZiL+v33LLv9+iWuSOZPMa/V8g/5ppHOdc8k6Uf37p3L2bpr+aX6uorca5Z+hdZupIH757OZemH8rYx19bv/GKW/rfbbPuV+dbcI31Llp7P2/LXjp4t/14R/0S/Z6RjLkm/qNvURd2D8z07uN71W37232vwLZHLGnxD7KmdPCP+vUL+0e8ZZb/nIM7vpXfRX7btZ8fz4L3zepaezNKfftm9GcS+c9vv/6MsrWTp6Sz9qVt+dzZL/3eWzm+d529n6Y8OOZf/fev3Nrb+90e+4Pylf5Klf3mbbU9vne/pcF/S4a37trZ13r+RB1+6Mfr6xL8d/xP9npH1exS/9zmnL9J+j9uXgneEr6R/aeuC8MVqsDr/Dcr5/xP+7s73mSW9SoNPrOx3X/+PpJ/UkG/cAvAKNP555/8r6Zpy/if73heAsbH1icQ35APMPVvf/vWqfAC5J23lnkzuAe4p5B4Ao/AKyD3/j6SfzMz1APcUcg+AkWCuGcAIvAL6Pcz3APei8e/3HPz5ARg5+j0YByxwxsE7yE4QAOwEeQfACBzk4AsAdorcA2AUyD0ARoHcA2AkmGsGMAL0ewCMBP0eACNAvwe7VRj1CQAAAAAAAAAAAAAAAAAAAADAS/gGZwAAAAAAAAAAAAAAAAAAAABjg29wBgAAAAAAAAAAAAAAAAAAADA2WOAMAAAAAAAAAAAAAAAAAAAAYGyU9rNxSukbJf2fkoqS/p+c8992v18oFHKhWDT7Gx6TpJz7Nl4q+e3dsSWp1+vZeKfdtvFIqehvd1a28aRk44Xg/pUq9SBes/HsT0/lctnGO52m34Gkxsaq/4Xsn1Eq+HuUCv4ZFINnVKlWbDy6Sf1u18ZbbX+PojJSKER1yIbD/YfMAbqdjnrdrn9AO7Tb3FMqVXK5asp38qeVgxuXgu0rFV83wgdT8J9FKQTxfphbvJT2+VmY6ADB5fe7+6v35WJ8/v2gfekH59jrBXU/2H8xaL8iURmNRO1nr+fPv9sJnlFQBgrBM4zKkKuDjbU1tZvNkeSeYqmcS+Xhuacf3NeockT3NQW5IQcFu1jc720LymV0AVGxDlNnVPeDeh8coB/V+yhxSKpUgn5Fwe+j0/b9iqiMRXUv6ntG7VPUr4hSV1jGg18Itw8bKK9kxhbN5qY67fZIck+9VsmzU8P7/Tm47rTPNiVq88I2K+w3hAVjX7vfQc8oiO/v+vadeff5/KR4XB71C/p9H+8FZSQal0fXWAxyU5Q7+tGYLopH9ye6/uD+ubPv9bP6/aiS7Mxe5nv8eDqYz4japGBMEpWbSFQuwu3jA+xr/5Go5veCMVW/v7/7F5/BTuwv/+63XxBeQfQL8cA6OsK+9h/1a6LcmYPc46+/r5z7o+n3TJbyzHzVnZoVZcxiKWpTgv0HY4Jux/fno2a93wni/SB3Rm1O0GZVg7ngqE0uVII+R9/fn+IO5qui+fhSJdhH8vmx2/Hbt1r+IUVTVuWSv8f9FPVbgvmafSa3aNwc1bFwPshUgpWlpjbW7v6Yq1Qp5kp9eLnqR3MN0VxBEK4G7yeKQaHq9qK8E/QZor54cPxKNAcZjSW60fuRqE77IlOK5jmCuSBJarV9va+U/DOslE27Jqknv/92z7/HjIpgP5rnjcZbUd6I7mGQF6IymIL9h9ONwQ3qNHvXc86Hg93syG5yz9TMXF48fHzovqL353FfOtj6Do9nImFrNdrTC+13quYgLi8e7+xvojYFfZL9zsPuV9ivjsZL+zz+fq/+xWcfH0nuOXToUD579szQfXWDdQ/djm+zonFq1K5H9zWcwwv6HdEcXphbo+HKPsf5xWgeIDh8N7i+wg5Kbj+axw3HA8FN2ucajf2ucYjmy6IyvN8yFM+XBuvjgvsb9Ts/+clPjiT3lCqFXK4NH3NVJ6bssYpBuSgG97Xb9c+t0/H97ei5RWO6MLeG5dKGw7moWnB+tZovN90ot0YDkp00msFFRu+govyfguQVbR9dQzFYu1oIynA3ehcSTljuN7fubx46mrBdvbpx29yz5wXOaZAt/5Gkr5N0XtJHU0o/m3P+3LBtCsWiZuYWh+6zWvWJKFoge+jIrI3XZ+ZsfHl5xcYvv3DexoM1KJqfm7fxrnwiqQSLc2uVORs/cvyNNn741OtsvNPzhfTYiSM2fuHFx21ckj790f9k48Xuio2XJv0i7XLNP4OZ2UM2fvqB0zaegon3jWvXbfzZF56w8Xbf14H6hL++YG5K3ewHGlEf0700vfz0037jHdpL7ilXazr7+i8fuk+3QEmSOtHLgKDzePbUMRtXUG76Vd9JmJ6ZsPFG0MkpBQ1QOejc5nD1qg9Hk45r1zdsvD7hX/YcmgsWEEpqtXzd2mj57VeCX2h2fHx+fsYfIJgcagWLHKO6u7joc8fKyrqN37i2vK/j16d87o46YRXzUvUj//6n/b53aC+5p1Su6cQDDw/dZ2Mt+OBP+KEe3/mrT/l+VSMot4uzvm7loOPTCzqnhYLff4omRjo+99brvu7n3LDxVnD+a2v+/m1uBKsNJJ05e9L/QtXf40svXrbx1rofZFeDD+CUgpfpxZq/x+1gCq0bLLaKPnxRrgbxaPImB8kpmIRYmJ8bGvvt3/g1v+8d2kvumZ2q69v+wDuG7rPT92OK8j4nfjdbmzbeifqbwaRjPxiTlIpBbokapb7v90TbF4LcnIrBQp3oA4tBveoEL9N3olSctvGNDZ8/1zd9GVhv+vjNtTUb7wT9tunJYIIz6PtvBhN87SB3ra/7ftNmy7cfa8H2BdO5Xl6PP1S8E3vJPcViSfPzw+cEovteDT5wXQ3GRKurvtxEuSv6wHb0YeJw0i+q20GbE01q9oKJ75srN218Y8Pfv+iDDQezwNnn12KQ38tl375FzzBcpBdN/EbtS3mfZSB6sRv0m/ptn7va637cnTv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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Choose layer for feature extraction\n", + "module_dict = dict(model_visual.named_modules())\n", + "target_layer = module_dict[args.target_layer_name]\n", + "print(f'Choose layer {args.target_layer_name} from model {args.model}')\n", + "\n", + "# Get features\n", + "features, labels, poi_indicator = get_features(args, model_visual, target_layer, data_loader, reduction='sum')\n", + "total_neuron = features.shape[1]\n", + "\n", + "\n", + "if args.neuron_order == 'ordered':\n", + " target_sort = np.arange(total_neuron)\n", + "elif args.neuron_order == 'random':\n", + " target_sort = np.random.shuffle(np.arange(total_neuron))\n", + "else:\n", + " print(f'Illegal Neuron order: {args.neuron_order}. Use \"ordered\" instead')\n", + " target_sort = np.arange(total_neuron)\n", + "\n", + "# get top activation images for each Neuron\n", + "top_indx=np.argsort(-features,axis=0)\n", + "\n", + "# Choose some nurons to visualize\n", + "num_neuron = np.min([args.num_neuron,total_neuron])\n", + "num_image = args.num_image\n", + "fig, axes = plt.subplots(nrows=num_neuron, ncols=num_image, figsize=(4*num_image, 5*num_neuron))\n", + "for neu_i in range(num_neuron):\n", + " im = target_sort[neu_i]\n", + " for topi in range(num_image):\n", + " top_i = top_indx[topi,im]\n", + " ax = axes[neu_i, topi]\n", + " cnn_image = np.swapaxes(np.swapaxes(denormalizer(visual_dataset[top_i][0]).cpu().numpy(), 0, 1), 1, 2)\n", + " cnn_image = cnn_image.clip(0,1)\n", + " ax.imshow(cnn_image)\n", + " if poi_indicator[top_i]==1:\n", + " ax.set_title(f'Neuron {im}, Top-{topi}, Value {features[top_i,im]:.2f}',color = 'red')\n", + " else:\n", + " ax.set_title(f'Neuron {im}, Top-{topi}, Value {features[top_i,im]:.2f}',color = 'black')\n", + "plt.tight_layout()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('py38')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13 (default, Oct 21 2022, 23:50:54) \n[GCC 11.2.0]" + }, + "vscode": { + "interpreter": { + "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/Demos/Demo_ActDist.ipynb b/analysis/Demos/Demo_ActDist.ipynb new file mode 100755 index 0000000..ec96d6f --- /dev/null +++ b/analysis/Demos/Demo_ActDist.ipynb @@ -0,0 +1,506 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_ActDist\n", + "This is a demo for visualizing the class distribution of top-k images which activate the Neurons of a Neuron network most.\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": {}, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py --save_folder_name badnet_demo" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "87bd9f5a", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71b7087b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "import matplotlib\n", + "from matplotlib.patches import Rectangle, Patch\n", + "\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2fb719c7", + "metadata": {}, + "outputs": [], + "source": [ + "### Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"badnet_demo\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "697f71ae", + "metadata": {}, + "source": [ + "### Step 2: Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "872af063", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "create mix dataset with length: 10000\n", + "max_num_samples is given, use sample number limit now.\n", + "subset mix dataset with length: 4997\n", + "Create visualization dataset with \n", + " \t Dataset: mixed \n", + " \t Number of samples: 4997 \n", + " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "selected_classes = np.arange(args.num_classes)\n", + "\n", + "# Select classes to visualize\n", + "if args.num_classes>args.c_sub:\n", + " selected_classes = np.delete(selected_classes, args.target_class)\n", + " selected_classes = np.random.choice(selected_classes, args.c_sub-1, replace=False)\n", + " selected_classes = np.append(selected_classes, args.target_class)\n", + "\n", + "# keep the same transforms for train and test dataset for better visualization\n", + "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n", + "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n", + "\n", + "# Create dataset\n", + "if args.visual_dataset == 'mixed':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_mix_dataset(bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_train':\n", + " clean_train_with_trans = result_attack[\"clean_train\"]\n", + " visual_dataset = generate_clean_dataset(clean_train_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_test':\n", + " clean_test_with_trans = result_attack[\"clean_test\"]\n", + " visual_dataset = generate_clean_dataset(clean_test_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'bd_train': \n", + " bd_train_with_trans = result_attack[\"bd_train\"]\n", + " visual_dataset = generate_bd_dataset(bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'bd_test':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_bd_dataset(bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n", + "else:\n", + " assert False, \"Illegal vis_class\"\n", + "\n", + "print(f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)} \\n \\t Selected classes: {selected_classes}')\n", + "\n", + "# Create data loader\n", + "data_loader = torch.utils.data.DataLoader(\n", + " visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n", + ")\n", + "\n", + "# Create denormalization function\n", + "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n", + " if isinstance(trans_t, transforms.Normalize):\n", + " denormalizer = get_dataset_denormalization(trans_t)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 3: Load model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b8b67ac9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "Load model preactresnet18 from badnet_demo\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "\n", + "# Load model\n", + "model_visual = generate_cls_model(args.model, args.num_classes)\n", + "model_visual.load_state_dict(result_attack[\"model\"])\n", + "model_visual.to(args.device)\n", + "# !!! Important to set eval mode !!!\n", + "model_visual.eval()\n", + "print(f\"Load model {args.model} from {args.result_file_attack}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cc952077", + "metadata": {}, + "source": [ + "### Step 4: Get Activation Distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "94612903", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Visualize Top-500 Samples from 4997 Samples.\n", + "Collecting features from module Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + "Collecting features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting features from module Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + "Collecting features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + "Collecting features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting features from module Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + "Collecting features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + "Collecting features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting features from module Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + "Collecting features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting features from module Linear(in_features=512, out_features=10, bias=True)\n" + ] + } + ], + "source": [ + "module_dict = dict(model_visual.named_modules())\n", + "module_names = module_dict.keys()\n", + "\n", + "# Plot Conv2d or Linear\n", + "module_visual = [i for i in module_dict.keys() if isinstance(\n", + " module_dict[i], torch.nn.Conv2d) or isinstance(module_dict[i], torch.nn.Linear) or isinstance(module_dict[i], torch.nn.BatchNorm2d)]\n", + "\n", + "poi_indicator = np.array(get_poison_indicator_from_bd_dataset(visual_dataset))\n", + "labels = np.array(get_true_label_from_bd_dataset(visual_dataset))\n", + "\n", + "\n", + "df = None\n", + "\n", + "# decide the number of images to compute the distribution\n", + "num_image = int(len(visual_dataset)/len(selected_classes)) \n", + "if poi_indicator.sum() > 0:\n", + " num_image = poi_indicator.sum()\n", + " # regard the poisoned images as a class with label args.num_classes\n", + " labels[poi_indicator==1] = args.num_classes\n", + " \n", + "print(f'Visualize Top-{num_image} Samples from {len(visual_dataset)} Samples.')\n", + "\n", + "label_set = np.unique(labels)\n", + "label_set.sort()\n", + "\n", + "max_num_neuron = 0\n", + "for module_name in module_visual:\n", + " target_layer = module_dict[module_name]\n", + " print(f'Collecting features from module {target_layer}')\n", + "\n", + " features, labels, poi_indicator = get_features(\n", + " args, model_visual, target_layer, data_loader, reduction='sum', activation= None)\n", + "\n", + " # set the poisoned images as a class with label args.num_classes for each iteration. \n", + " # this can be skipped if shuffle is set to False.\n", + " labels[poi_indicator==1]=args.num_classes\n", + " total_neuron = features.shape[1]\n", + " max_num_neuron = np.max([max_num_neuron, total_neuron])\n", + " top_indx = np.argsort(-features, axis=0)[:num_image, :]\n", + " top_pred = np.array(labels)[top_indx]\n", + "\n", + " for neuron_i in range(total_neuron):\n", + " base_row = {}\n", + " base_row['layer'] = module_name\n", + " base_row['Neuron'] = neuron_i\n", + " for i in range(len(label_set)):\n", + " base_row[f'percent_{i}'] = np.sum(\n", + " top_pred[:, neuron_i] == label_set[i])/num_image\n", + " if df is None:\n", + " df = pd.DataFrame.from_dict([base_row])\n", + " else:\n", + " df.loc[df.shape[0]] = base_row" + ] + }, + { + "cell_type": "markdown", + "id": "8c3760b9", + "metadata": {}, + "source": [ + "### Step 5: Show the Activation Distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "81bbb857", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ploting conv1\n", + "ploting layer1.0.bn1\n", + "ploting layer1.0.conv1\n", + "ploting layer1.0.bn2\n", + "ploting layer1.0.conv2\n", + "ploting layer1.1.bn1\n", + "ploting layer1.1.conv1\n", + "ploting layer1.1.bn2\n", + "ploting layer1.1.conv2\n", + "ploting layer2.0.bn1\n", + "ploting layer2.0.conv1\n", + "ploting layer2.0.bn2\n", + "ploting layer2.0.conv2\n", + "ploting layer2.0.shortcut.0\n", + "ploting layer2.1.bn1\n", + "ploting layer2.1.conv1\n", + "ploting layer2.1.bn2\n", + "ploting layer2.1.conv2\n", + "ploting layer3.0.bn1\n", + "ploting layer3.0.conv1\n", + "ploting layer3.0.bn2\n", + "ploting layer3.0.conv2\n", + "ploting layer3.0.shortcut.0\n", + "ploting layer3.1.bn1\n", + "ploting layer3.1.conv1\n", + "ploting layer3.1.bn2\n", + "ploting layer3.1.conv2\n", + "ploting layer4.0.bn1\n", + "ploting layer4.0.conv1\n", + "ploting layer4.0.bn2\n", + "ploting layer4.0.conv2\n", + "ploting layer4.0.shortcut.0\n", + "ploting layer4.1.bn1\n", + "ploting layer4.1.conv1\n", + "ploting layer4.1.bn2\n", + "ploting layer4.1.conv2\n", + "ploting linear\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# define Matplotlib figure and axis\n", + "fig, ax = plt.subplots(figsize=(20, 50))\n", + "# create simple line plot\n", + "ax.plot([0, 0], [0, 0])\n", + "\n", + "labels = np.array(get_true_label_from_bd_dataset(visual_dataset))\n", + "custom_palette = sns.color_palette(\"hls\", np.unique(labels).shape[0])\n", + "if poi_indicator.sum() > 0:\n", + " custom_palette.append((0.0, 0.0, 0.0)) # Black for poison samples\n", + "\n", + "start_x0 = 0\n", + "height = 1\n", + "width = 1\n", + "max_num_neuron = df.Neuron.max()\n", + "\n", + "for module_name in module_visual:\n", + " print(f'ploting {module_name}')\n", + " y_0 = 0\n", + " layer_info = df[df.layer == module_name]\n", + " total_neuron = layer_info.shape[0]\n", + " for neuron_i in range(total_neuron):\n", + " x_0 = start_x0\n", + " base_row = layer_info.iloc[neuron_i]\n", + " for i in range(len(label_set)):\n", + " ax.add_patch(Rectangle((x_0, y_0), width*base_row[f'percent_{i}'], height,\n", + " facecolor=custom_palette[i],\n", + " fill=True,\n", + " lw=5,\n", + " alpha=0.8))\n", + "\n", + " x_0 += width*base_row[f'percent_{i}']\n", + " y_0 += 1.5*height\n", + " start_x0 += 1.5*width\n", + "x_loc = [0.5*width+1.5*width*i for i in range(len(module_visual))]\n", + "y_loc = [0.5*height+1.5*height*i for i in range(max_num_neuron)]\n", + "\n", + "ax.set_xlim(xmin=-0.5*width, xmax=1.5*width*(len(module_visual)+1))\n", + "ax.set_ylim(ymin=-0.5*height, ymax=1.5*height*(max_num_neuron+1))\n", + "ax.set_xticks(x_loc, module_visual, rotation=270)\n", + "ax.set_yticks(y_loc[::10], np.arange(max_num_neuron)[::10])\n", + "ax.set_title(f'Distribution of Top-{num_image} Images')\n", + "ax.set_ylabel('Neuron')\n", + "ax.set_xlabel('Layer')\n", + "\n", + "classes = args.class_names\n", + "if poi_indicator.sum() > 0:\n", + " classes += [\"poisoned\"]\n", + " \n", + "# map the label to class name in the order of colors/indexes\n", + "label_class = [classes[i].capitalize() for i in label_set]\n", + "legend_elements = [Patch(facecolor=custom_palette[i],\n", + " label=label_class[i]) for i in range(len(label_class))]\n", + "\n", + "ax.legend(handles=legend_elements, loc='upper center', bbox_to_anchor=(\n", + " 0.5, 1.02), ncol=len(label_class), fancybox=True, shadow=True)\n", + "\n", + "\n", + "plt.tight_layout()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('py38')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13 (default, Oct 21 2022, 23:50:54) \n[GCC 11.2.0]" + }, + "vscode": { + "interpreter": { + "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/Demos/Demo_CM.ipynb b/analysis/Demos/Demo_CM.ipynb new file mode 100755 index 0000000..b9a0c88 --- /dev/null +++ b/analysis/Demos/Demo_CM.ipynb @@ -0,0 +1,320 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_CM\n", + "This is a demo for visualizing the Confusion Matrix of a Neuron Network\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": {}, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py --save_folder_name badnet_demo" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "87bd9f5a", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71b7087b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2fb719c7", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "### Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"badnet_demo\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 2: Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8b67ac9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "Create visualization dataset with \n", + " \t Dataset: clean_train \n", + " \t Number of samples: 50000 \n", + " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "\n", + "# Select all classes and all samples\n", + "selected_classes = np.arange(args.num_classes)\n", + "\n", + "# keep the same transforms for train and test dataset for better visualization\n", + "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n", + "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n", + "\n", + "args.visual_dataset = 'clean_train'\n", + "# Create dataset\n", + "if args.visual_dataset == 'clean_train':\n", + " visual_dataset = result_attack[\"clean_train\"]\n", + "elif args.visual_dataset == 'clean_test':\n", + " visual_dataset = result_attack[\"clean_test\"]\n", + "elif args.visual_dataset == 'bd_train': \n", + " visual_dataset = result_attack[\"bd_train\"]\n", + "elif args.visual_dataset == 'bd_test':\n", + " visual_dataset = result_attack[\"bd_test\"]\n", + "else:\n", + " assert False, \"Illegal vis_class\"\n", + "\n", + "print(f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)} \\n \\t Selected classes: {selected_classes}')\n", + "\n", + "# Create data loader\n", + "data_loader = torch.utils.data.DataLoader(\n", + " visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n", + ")\n", + "\n", + "# Create denormalization function\n", + "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n", + " if isinstance(trans_t, transforms.Normalize):\n", + " denormalizer = get_dataset_denormalization(trans_t)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3f652e5", + "metadata": {}, + "source": [ + "### Step 3: Load Model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ff67e7b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load model preactresnet18 from badnet_demo\n" + ] + } + ], + "source": [ + "# Load model\n", + "model_visual = generate_cls_model(args.model, args.num_classes)\n", + "model_visual.load_state_dict(result_attack[\"model\"])\n", + "model_visual.to(args.device)\n", + "# !!! Important to set eval mode !!!\n", + "model_visual.eval()\n", + "print(f\"Load model {args.model} from {args.result_file_attack}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cc952077", + "metadata": {}, + "source": [ + "### Step 4: Plot Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "94612903", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting Confusion Matrix\n", + "Normalized confusion matrix\n", + "Test Acc: 95.230%(47615/50000)\n", + "Test Acc (Target only): 99.980%(4999/5000)\n" + ] + }, + { + "data": { + "image/png": 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P0anDWcRiMXr36UuTnByGDL6Tlq1y6dipM3369qNvn17kZDckLa0mI0eNBqBJTg5du11Ai2ZNSE1NZeijw6lUqZJyUB77fB7JkIPyiFYeyZBDMuUhe87cfeetypmZVQH+QTCM/jlQAxgMPEcwx/QgYGJ8buiWYzYATxIMuX8LdHf3VWb2XLzty2b2R+BC4F/AFwRTAAab2XTgRnefa2a1COam1o2v6v8j0Img13QVcK67r9te7CkHHOJVGl2wd/4hQrR2zmNhhyAiIlKuTjgml3nz5obSVZhaq77X6Hh3GG9dypoRF85z99zyft9I9pS6exFw9jZ21Y3/fzVw1NY73f2GbTzXp8TXtwO3b6PNySW+Xr3lfdx9M3Br/CEiIiJSJoxwh86jIJJzSkVERESkYolkT+nucPeDwo5BREREZHepp1REREREJGQqSkVEREQkdEkzfC8iIiKyT6vYo/fqKRURERGR8KkoFREREZHQqSgVERERCZvtW7cZNbP2Zva5meWZ2c3b2H+4mb1jZh+Z2UIzO2dnr6miVERERER2mZlVAoYT3OioCXChmTXZqtntwEvu3gLoAfxlZ6+rhU4iIiIiEbAPXae0DZDn7l8BmNlooAuwqEQbB6rFv64OFO7sRVWUioiIiEhJtcxsbontJ939yRLbmcA3JbZXAMds9RqDgWlmdh1wIHD6zt5URamIiIiIlLTa3XP38DUuBJ5z94fM7DhgpJkd5e6bt3eAilIRERGRCNiHhu8LgMNKbGfFnyupH9AewN3fN7OqQC3g2+29qBY6iYiIiMgvMQc40szqmVllgoVM47dq8zVwGoCZNQaqAqt29KIqSkVERERkl7n7JuBaYCqwmGCV/WdmNsTMOseb/R9wuZktAF4E+ri77+h1NXwvIiIiEjJj168RGgXuPhmYvNVzd5b4ehFwwi95TfWUioiIiEjo1FMqIiIiEgX7TkdpmVBRupcdnX0478wcFnYYeyyt3W1hh7DH1s74U9ghiIiIyC7S8L2IiIiIhE49pSIiIiJhs33qOqVlQj2lIiIiIhI6FaUiIiIiEjoN34uIiIhEgIbvRURERERCpqJUREREREKn4XsRERGRCNDwvYiIiIhIyNRTKiIiIhIFFbujVD2lIiIiIhI+FaUiIiIiEjoN34uIiIhEgBY6iYiIiIiETEWpiIiIiIROw/ciIiIiITMzDd+HHYCIiIiIiHpKRURERCJAPaUiIiIiIiFTUSoiIiIioVNRGjFvTptC6+ZNaNm0EY88eF+p/UVFRfS95EJaNm3E6e2O4+vl+Qn7v/nma7IOqc6fhz5UThGXdsYxR7LgxUF8OuYGbrz4pFL7Dz+0BpOH9WX2iOuY+ud+ZNauVrzvsEOrM+GRPnw0aiDznx/I4b+qUY6RJ5o2dQrNchqRk92QB+6/t9T+oqIiLr6oOznZDWl7/DEsz88v3vfAffeQk92QZjmNeGPa1HKMujTlEZ08kiEHUB4QnTySIQdInjz21JbFTmE/whKZotTMzjWzJmX8HnXN7NPt7Pvblvc3s3wzq1WWsWxLLBbjtzcMYOxrE/lg3ie8MnYMSxYvSmgzcsQzVK+RxvxPPufqawcx+I5bEvbffvONnH5m+/IMO0FKijH0/zrR5f9G0KLnMLqd3ozsurUT2txzbXtGTfmINr3/zN3PvsOQq84s3ve328/nkRfeo0XPYbS9/HFWrf1PeacABOdi0ID+jJvwOh8tXMTY0S+yeFHiuXjumadJq5HGZ0vyuG7g9dx2600ALF60iLFjRjN/wWeMnziFgdddQywWCyMN5UF08kiGHEB5QHTySIYcIHnykD0XmaIUOBco06J0R9z9MndftPOWZWfe3NnUr9+AuvXqU7lyZX5z/gVMnjg+oc3rE8dzYc9eAHQ5ryszpr+NuwMwacI4Dj+iLtmNQ/tnpHXjLJau+I78wrX8vCnG2LcW0rFt44Q22fUOYca8rwCYMf+r4v3ZdWuTWimFt+csBeA/P27kx6KfyzeBuDmzZ9OgQUPq1Q/ORbfuPZg4YVxCm4kTxtGzV28AftP1fKa//RbuzsQJ4+jWvQdVqlShbr16NGjQkDmzZ4eRhvKIUB7JkAMojyjlkQw5QPLkIXuuTItSM/uHmc0zs8/M7Ir4cxtK7D/fzJ4zs+OBzsADZvaxmTUws+Zm9oGZLTSz18wsLX7MdDN7xMzmmtliM2ttZq+a2Zdm9scSr32DmX0afwwqEVaqmY2KH/uymR1Q4nVzt5HDxWY2Ox7XX82sUtn8a8HKwkIysw4r3s7IzGLlysKENoUl2qSmplKtWnW+W7OGDRs2MOzh+7np1jvLKrxdklG7Giu+XVe8XfDtejJrV09o88mX/6JLu6Bw7tKuCdUOrErNavtz5GG1+H7DT4y++yLef7Y/d/dvT0pKOMMIhYUFZJU4F5mZWRQUFJRuc1iJc1G9OmvWrKGgoPSxhYWJx5YX5RGdPJIhh+IYlUck8kiGHIpjTII89gqLyCMkZd1T2tfdWwG5wAAzS99WI3f/JzAe+K27N3f3pcDfgZvcvRnwCXBXiUM2unsu8AQwDugPHAX0MbN0M2sFXAocAxwLXG5mLeLHNgL+4u6NgfXANdsL3swaA92BE9y9ORADeu7Gv0OZu+9Pv+fqawdx0EEHhR3KTt0y/HXatqjH+8/2p23zehR8u47YZie1UgonHF2Xmx97nRMve5x6GWn0Oqdl2OGKiIhIOSjronSAmS0APgAOA47clYPMrDpQw91nxJ8aAZRcMbNlTPsT4DN3X+nuRcBX8fc5EXjN3f/j7huAV4G28WO+cfdZ8a+fj7fdntOAVsAcM/s4vl1/G/FeEe+5nbt69apdSXGb6mRkULDim+LtwoIV1KmTkdAmo0SbTZs2sX79OmqmpzN37mzuuv1mmjVuwOPDH+XhB+/lySeG73Ysu6tw1XqyDvlfz2jmIdUoWLUuoc3K1T/Q49YXOO7S4dz15BsArNvwEwWr1rPwy5XkF64lFtvM+HcX0/zXifmXl4yMTFaUOBcFBSvIzMws3eabEudi3TrS09PJzCx9bEZG4rHlRXlEJ49kyKE4RuURiTySIYfiGJMgj70h7AVOSbvQycxOBk4HjnP3o4GPgKqAl2hWdTdfvij+/80lvt6yvbMbAvhOtksyYES897a5uzdy98GlXtD9SXfPdffcWrVql36VXdSyVWuWLs1jef4yNm7cyKsvv8TZHToltGnfoRMvjhoJwLjXXuGkdqdgZrz+xgwWLl7KwsVLubr/AG648WauuKr/bseyu+YuKaBhVjpH1Eljv9RKdDutGZNmLklok179gOJv+t/2aseISfOCYxevoPpBValV4wAATm5VnyX535ZvAnG5rVuTl/cl+cuCczF2zGg6dOyc0KZDx86MGjkCgFdfeZl2p5yKmdGhY2fGjhlNUVER+cuWkZf3Ja3btAkjDeURoTySIQdQHlHKIxlygOTJQ/ZcWd7RqTqw1t3/a2bZBMPoAP+OD4t/DpwH/BB//gfgYAB3X2dma82srbu/B/QCZrDr3gOeM7N7CQrL8+KvAXC4mR3n7u8DFwEzd/A6bwHjzOwRd//WzGoCB7v78l8Qyy5LTU3l/oeG0bXLOcRiMXpe0ofGTXK4+w930bxlLud06ESv3n256rLetGzaiLS0NJ4e8UJZhLLbYrHNXP/IBCY83IdKlYwRE+ezeNm33HHZacxfUsCkmUs4qUU9hlx1Ju4wc0E+gx4KOr43b3ZuGf46k4f1www++ryQZ8bPDSWP1NRUHhn2GJ06nEUsFqN3n740yclhyOA7adkql46dOtOnbz/69ulFTnZD0tJqMnLUaACa5OTQtdsFtGjWhNTUVIY+OpxKlcpsKrLy2EfySIYclEe08kiGHJIpD9lztmXl9l5/YbMqwD+AugQFaA1gMFALuA9YBcwFDnL3PmZ2AvAUQc/n+QQF6hPAAQTD8pe6+1ozmw7c6O5z472xN7p7x/h7ltx3A9A3Hs7f3H2omdUFpsTftxWwCOgVL5xLHpsP5Lr7ajPrDtxC0Kv8M9Df3T/YXt4tWub6OzM/3O1/t6ioc0a4C6b2hrUz/hR2CCIisg854Zhc5s2bG8r4dZVfHelZPR8N461L+erhc+bF1+6UqzLrKY3P8Tx7O7tf3kb7WZS+JNSx22h3comvpwPTt7PvYeDhrY7NB7K3E2/JY+uW+HoMMGZbx4iIiIjI3hGl65SKiIiISAVVlnNKRURERGQXGBDiwvdIUE+piIiIiIRORamIiIiIhE7D9yIiIiKhC/fC9VGgnlIRERERCZ16SkVEREQioIJ3lKqnVERERETCp6JUREREREKn4XsRERGRCNBCJxERERGRkKkoFREREZHQafheREREJGym1ffqKRURERGR0KmnVERERCRkBqSkVOyuUvWUioiIiEjoVJSKiIiISOg0fC8iIiISAVroJCIiIiISMvWU7mWOE9vsYYexx9bO+FPYIeyxtNP/EHYIe8XaN+8IOwQREZEyp6JUREREJAJ0m1ERERERkZCpKBURERGR0Gn4XkRERCRsus2oekpFREREJHzqKRUREREJmaGFTuopFREREZHQqSgVERERkdBp+F5EREQkdKbh+7ADEBERERFRUSoiIiIiodPwvYiIiEgEVPDRe/WUioiIiEj41FMqIiIiEgFa6CQiIiIiEjIVpSIiIiISOg3fi4iIiITNtNBJPaUiIiIiEjoVpSIiIiISOg3fi4iIiITM0Op79ZRGzFtvTOWYFjm0PjqbYQ/dX2p/UVER/XpfROujsznzlOP5enl+8b7PPl1I+1NP5ITWR9P2mOb89NNP5Rh5omlTp9AspxE52Q154P57S+0vKiri4ou6k5PdkLbHH8Py/PzifQ/cdw852Q1pltOIN6ZNLceoE53RpgEL/n4Nn47qz40XHV9q/+GHVmfyQxcz++krmDq0F5m1D07Yf/ABlckbO5BHBrYvr5C3KRnOBSRHHsmQAygPiE4eyZADJE8esmcqVFFqZnXN7NNtPP83M2uyC8f3MbPHyiY6iMVi3PR/Axjz6gRmzVnIqy+P5vMlixLajPr7M9SoUYM5C5ZwVf+B/P7OWwHYtGkTV1/WmweHDWfWnAWMm/wW++23X1mFukOxWIxBA/ozbsLrfLRwEWNHv8jiRYl5PPfM06TVSOOzJXlcN/B6brv1JgAWL1rE2DGjmb/gM8ZPnMLA664hFouVew4pKcbQge3pctMLtOj9ON1OPYrsI2oltLnn6tMZNW0hbfo9yd0j3mPI5acm7L+r78nMXPB1eYZdSjKcC0iOPJIhB1AeEJ08kiEHSJ489gazaDzCUqGK0u1x98vcfdHWz5tZpfKMY/7c2dSr34C69epTuXJlzuvandcnTkho8/qkCfS4qBcAnc/tynvT38bdeeetN2hyVFOOano0ADXT06lUqVzDLzZn9mwaNGhIvfpBHt2692DihHEJbSZOGEfPXr0B+E3X85n+9lu4OxMnjKNb9x5UqVKFuvXq0aBBQ+bMnl3uObTOzmBpwVryV37Pz5s2M/btz+h4QqOENtlH1GbG/HwAZnyUn7C/xa9/xSE1D+LNuUvLM+xSkuFcQHLkkQw5gPKIUh7JkAMkTx6y5ypiUZpqZqPMbLGZvWxmB5jZdDPLBTCzDWb2kJktAI4zs0vN7Aszmw2cUJaBrVxZSEZmVvF2RmYmK1cWJLYpLCQz67AgkdRUqlWvzndr1rA07wvMjG7nnsMpJ7bm0UceLMtQd6iwsICseIwAmZlZFBQUlG5zWGIea9asoaCg9LGFhYnHloeM2tVYsWp98XbBqvWlhuc/WfpvupyUDUCXttlUO7AKNavtjxnce80Z3PL4G+Ua87Ykw7kojnEfzyMZciiOUXlEIo9kyKE4xiTIQ/ZcRSxKGwF/cffGwHrgmq32Hwh86O5HA0uB3xMUoycC2xziN7MrzGyumc1ds3p12UW+A5s2xfjw/X/yxN/+zqRpM5g84R+8O/3tUGKpKG55/A3aHn0E7z91OW2PPpyCVeuJbd7MlefmMvWDPApW/RB2iCIisg8xs0g8wlIRi9Jv3H1W/OvnCYrNkmLAK/GvjwGmu/sqd98IjNnWC7r7k+6e6+656bVqbavJLqlTJ4PCghXF24UFBdSpk5nYJiODghXfAME80vXr1lEzPZ2MzEyOO/5E0mvV4oADDuD0s85mwccf7XYseyIjI5MV8RgBCgpWkJmZWbrNN4l5pKenk5lZ+tiMjMRjy0PhqvVk1a5WvJ1Zu1qpInPlmg30uHMsx13+FHc9/Q4A6zYUcUyTLK46rzVLRl/HPVefwUVnNuMPVyTONy0vyXAuimPcx/NIhhyKY1QekcgjGXIojjEJ8pA9VxGLUt/J9k/uHsos6RatWvPV0jyW5y9j48aNvPbKGNp36JjQpv05HRn9wkgAxv/jFdq2OwUz49TTzmTRok/573//y6ZNm/jnzHdplN04jDTIbd2avLwvyV8W5DF2zGg6dOyc0KZDx86MGjkCgFdfeZl2p5yKmdGhY2fGjhlNUVER+cuWkZf3Ja3btCn3HOZ+XkjDrJoc8asa7JeaQrdTc5j0zy8S2qRX3794QvhvLzqREZM/BuDSP/2DX3d/lOwef+aWx9/ghWkLuePJcHqtk+FcQHLkkQw5gPKIUh7JkAMkTx6y5yridUoPN7Pj3P194CJgJtBpO20/BIaZWTrBUH83YEFZBZaamsq9Dw6j27kd2Lw5xkW9+pDdOId7/jiY5i1acXaHTvS8pC/XXN6H1kdnUyMtjaeeHQVAjbQ0rr52EGe0Ow4z4/Qz23Nm+3PKKtSd5vHIsMfo1OEsYrEYvfv0pUlODkMG30nLVrl07NSZPn370bdPL3KyG5KWVpORo0YD0CQnh67dLqBFsyakpqYy9NHhoSzYisWc64dNYcIDF1EpxRjx+gIW56/ijkvbMf/zlUz65xec1LwuQy4/BXeYufBrBg19vdzj3JlkOBfJkkcy5KA8opVHMuSQTHnsDRX8MqWY+9YdhcnLzOoCU4C5QCtgEdALmAzc6O5zzWyDux9U4phLgVuA74GPgY3ufu323qN5y1b+1rsfllUK5ebAqvv+55W00/8Qdgh7xdo37wg7BBGRCuGEY3KZN29uKKXhgZmNPKf/X8N461Lm3HbKPHfPLe/33fcrj1/A3fOB7G3sOrlEm4NK7nD3Z4FnyzQwERERkQquQhWlIiIiIpFkus1oRVzoJCIiIiIRo55SERERkZAZWuiknlIRERERCZ2KUhEREREJnYbvRUREREIX7i0+o0A9pSIiIiISOhWlIiIiIhI6Dd+LiIiIREAFH71XT6mIiIiIhE89pSIiIiIRoIVOIiIiIiIhU1EqIiIiIqHT8L2IiIhI2EwLndRTKiIiIiKhU1EqIiIiIqHT8L2IiIhIyAytvldPqYiIiIiETj2lZWCze9ghCLD2zTvCDmGvSDvrnrBD2CvWTr0l7BD2Ck+Sn++K3iMjItGjolREREQkAir6h0UN34uIiIhI6NRTKiIiIhIBFbyjVD2lIiIiIhI+FaUiIiIiEjoN34uIiIhEgBY6iYiIiIiETEWpiIiIiIROw/ciIiIiYTOtvldPqYiIiIiETj2lIiIiIiEzTAudwg5ARERERERFqYiIiIiETsP3IiIiIhFQwUfv1VMqIiIiIuFTUSoiIiIiodPwvYiIiEgEpFTw8Xv1lIqIiIhI6NRTKiIiIhIBFbyjVD2lIiIiIhI+FaUiIiIiEjoVpRHz9htTOa5lDm2ObsyjD99fan9RURGX97mINkc3pv0pJ/D18vzifZ99upCzT2tL2zZH0+7YFvz000/lGHmiaVOn0CynETnZDXng/ntL7S8qKuLii7qTk92Qtscfw/L8/OJ9D9x3DznZDWmW04g3pk0tx6gTJUMOAGe0rs+C567g079fxY09ji21//BDqjH5gQuZ/VQ/pj50EZm1Di5+/p9PXMoHf+3LvKcv47KOLco79ATJcD6mTZ3C0TnZHNX4SB7cTg69LurBUY2P5KQTji2Vw1GNj+TonOzQv6eS4VxAcuSRDDlA8uSxJ8zAzCLxCIuK0l1gZieb2fFl/T6xWIyb/m8gL74ygZlzFvDqy2P4fMmihDaj/v4s1WukMXvBYq7sP4A/3HUrAJs2beKay/vwwNDHeG/2Al6b9Cb77bdfWYe8TbFYjEED+jNuwut8tHARY0e/yOJFiXk898zTpNVI47MleVw38Hpuu/UmABYvWsTYMaOZv+Azxk+cwsDrriEWiymH3ZSSYgwdcCZdbnmJFn2fpNupTcg+Ij2hzT1XncqoNz6lzeVPc/fIWQy57GQAVn63gZOv+zvHXvkMJ/UfwY0XHkud9INCyCI5zkcsFuP6gdfyjwmTmb/gM8aOGV06h2efpkZaDT5d/CXXDRjE7bfeXJzDyy+NYd7HnzJu4usMGtA/tO+pZDgXkBx5JEMOkDx5yJ5TUbprTgbKvCidP3cO9eo3oG69+lSuXJnzul7AlEkTEtpMmTSB7hf2AqDTuV15b/o7uDvT33qDJjlNOarp0QDUTE+nUqVKZR3yNs2ZPZsGDRpSr36QR7fuPZg4YVxCm4kTxtGzV28AftP1fKa//RbuzsQJ4+jWvQdVqlShbr16NGjQkDmzZyuH3dQ6O4OlBWvJX/k9P2/azNh3FtPx+F8ntMk+ohYzPsoHYMbHy+l4/JEA/LxpMxt/Dn65V6mcGuqlSpLhfMydk5jD+Rd0L5XDpAnjuTiew3ldz2f6O//L4fwLuifkMHdOON9TyXAuIDnySIYcIHnykD1XoYtSM7vEzBaa2QIzG2lmnczsQzP7yMzeNLNDzawucBVwvZl9bGZtyyqef60sIDMrq3i7TkYmKwsLt9smNTWVg6tV57vv1rA070vMjAvO7cBpbdvw56EPllWYO1VYWEBW1mHF25mZWRQUFJRuc1jQJjU1lWrVq7NmzRoKCkofW1iYeGx5SIYcADJqHcSKVeuLtwtW/VA8PL/FJ0u/pUvbRgB0OfHXVDuwCjWr7Q9AVu2Dmf1UP758sT8PjfmAlWs2lF/wJSTD+SgsSPz53lYcQZvSOWydf0ZmJoUF4XxPJcO5KI5xH88jGXIojjEJ8tgbUiwaj7BU2EtCmVkOcDtwvLuvNrOagAPHurub2WXA79z9/8zsCWCDu2+z0jOzK4ArALIOO7ycMki0KbaJ2R/8k6nT/8n++x9A105ncXTzlpx08qmhxCP7jlv++jaPXHcmF5/ZlFmffEPBqvXEYpsBWLHqB9pc/jR10g/ipSFdee3dJXy79r8hRywiIsmoIveUngqMdffVAO7+HZAFTDWzT4DfAjm78kLu/qS757p7bnqtWrsd0K/qZFKwYkXx9srCAupkZGy3zaZNm/hh/Tpq1kwnIyOTY48/kfT0WhxwwAGcfmZ7Fi74aLdj2RMZGZmsWPFN8XZBwQoyMzNLt/kmaLNp0ybWr1tHeno6mZmlj83ISDy2PCRDDgCFqzeQVbta8XZm7YMpWP1DQpuVazbQY/CrHHfVs9z19AwA1v2nqFSbz5at4oSmhxGGZDgfGZmJP9/biiNoUzqHrfMvLCggIzOc76lkOBfFMe7jeSRDDsUxJkEesucqclG6LX8GHnP3psCVQNXyfPMWrXL56qs8lucvY+PGjbz2ykucdU7HhDZnndORMS+OBGDCP17hxHYnY2acctqZLF70Kf/973/ZtGkT/5z1Ho0aNS7P8Ivltm5NXt6X5C8L8hg7ZjQdOnZOaNOhY2dGjRwBwKuvvEy7U07FzOjQsTNjx4ymqKiI/GXLyMv7ktZt2iiH3TR3SSENM9M44lfV2S81hW6nNGbSP79MaJNebf/iCzb/9qLjGDFlIQCZtQ6mauVgMKXGQVU5vulhfPHNd+Ua/xbJcD5a5Sbm8PJLY0rlcE7HTjwfz+G1V16m3cn/y+Hll8Yk5JDbOpzvqWQ4F5AceSRDDpA8eewNYa+6/yWr782svZl9bmZ5ZnbzdtpcYGaLzOwzM3thZ69ZYYfvgbeB18zsYXdfEx++rw5smYzSu0TbH4BqW7/A3paamsq9Dwyl+3kdiMU2c1Gv3mQ3zuHePw6mectWtD+nEz0vuZT+V/ShzdGNSUtL46/PPg9AjbQ0ruo/kLNOPg4z47Qz23NG+3PKOuTt5vHIsMfo1OEsYrEYvfv0pUlODkMG30nLVrl07NSZPn370bdPL3KyG5KWVpORo0YD0CQnh67dLqBFsyakpqYy9NHhoSzYSoYcAGKbnev//AYT7utBpRRjxOsLWbx8NXf0acv8z1cy6f08Tmp+OEP6nYwDMxd+zaBHpwHQ6Ih07r3qNNwdM2PoSx/y2bJVoeSRDOcjNTWVh4f+mc4d2hPbHOOS3peWzuHSfvTrcwlHNT6StLSa/P35F4tz+M353Wh5dA6plYJ/i7C+p5LhXCRLHsmQQzLlUZGYWSVgOHAGsAKYY2bj3X1RiTZHArcAJ7j7WjM7ZKev6+5lFXPkmVlvgmH6GPAR8BrwCLCWoGht7e4nm9mvgZeBzcB17v7e9l6zectW/saMD8o89rJ28P7hXE5KSks7656wQ9gr1k69JewQ9opk+Z0Z5rUIRaLqhGNymTdvbig/HNWPaOwn3joijLcuZfJVx8xz99zt7Tez44DB7n5WfPsWAHe/p0Sb+4Ev3P1vu/q+FbmnFHcfAWz9HTBuG+2+AJqVS1AiIiIi4aplZnNLbD/p7k+W2M4EvimxvQI4ZqvX+DWAmc0CKhEUsVN29KYVuigVERERkVJW76indBelAkcSXOs9C3jXzJq6+/c7OkBEREREQmSAsc9MqykASl6OJYv/rcnZYgXwobv/DCwzsy8IitQ523tRrb4XERERkV9iDnCkmdUzs8pAD2D8Vm3+QdBLipnVIhjO/2pHL6qiVERERER2mbtvAq4FpgKLgZfc/TMzG2JmW67nNRVYY2aLgHeA37r7mh29robvRURERCIgzFt8/lLuPhmYvNVzd5b42oEb4o9dop5SEREREQmdekpFREREwvYL7qaUrNRTKiIiIiKhU1EqIiIiIqHT8L2IiIhIBFTw0Xv1lIqIiIhI+FSUioiIiEjoNHwvIiIiEjIDUir4+L16SkVEREQkdCpKRURERCR0Gr4XERERiYAKPnqvnlIRERERCZ96SkVEREQioKLfZlRF6V5mGPtVUge07D1rp94Sdgh7Rdopd4Ydwl6x9p0hYYcgIpKUVD2JiIiISOjUUyoiIiISMjMtdFJPqYiIiIiETkWpiIiIiIROw/ciIiIiEaDbjIqIiIiIhEw9pSIiIiIRULH7SdVTKiIiIiIRoKJUREREREKn4XsRERGRCKjotxlVT6mIiIiIhE5FqYiIiIiETsP3IiIiIiEzIKVij96rp1REREREwqeeUhEREZGwmWmhU9gBiIiIiIioKBURERGR0Gn4XkRERCQCKvjovXpKRURERCR8KkpFREREJHQavhcRERGJAK2+l0h5c9oUWjdvQsumjXjkwftK7S8qKqLvJRfSsmkjTm93HF8vz0/Y/803X5N1SHX+PPShcop426ZNnUKznEbkZDfkgfvvLbW/qKiIiy/qTk52Q9oefwzL8/OL9z1w3z3kZDekWU4j3pg2tRyjTpQMOUBy5HFGm4YsGDWAT18cyI0925baf/ih1Zk8tA+zn7uGqY9eSmbtasX7NkwfzAfPXM0Hz1zN2HsuKs+wS0mGcwHKA6KTRzLkAMmTh+yZClWUmtlgM7sx7Di2JxaL8dsbBjD2tYl8MO8TXhk7hiWLFyW0GTniGarXSGP+J59z9bWDGHzHLQn7b7/5Rk4/s315hl1KLBZj0ID+jJvwOh8tXMTY0S+yeFFiHs898zRpNdL4bEke1w28nttuvQmAxYsWMXbMaOYv+IzxE6cw8LpriMViymE3JUMeKSnG0Bs60uXGkbTo9RjdTm9Kdt3aCW3u6X8Wo6Z8TJs+f+Hu56Yz5MrTi/f9WPQzx/Z9nGP7Pk63W14o7/CLJcO5AOUB0ckjGXKA5MlD9lyFKkr3BjMrsykP8+bOpn79BtStV5/KlSvzm/MvYPLE8QltXp84ngt79gKgy3ldmTH9bdwdgEkTxnH4EXXJbtykrELcJXNmz6ZBg4bUqx/k0a17DyZOGJfQZuKEcfTs1RuA33Q9n+lvv4W7M3HCOLp170GVKlWoW68eDRo0ZM7s2cphNyVDHq0bZ7G04DvyV67l500xxr71CR1PzE5ok133EGbM/wqAGfOXldofBclwLkB5RCmPZMgBkiePPbXlNqNReIQl6YtSM7vNzL4ws5lAo/hzDcxsipnNM7P3zCw7/nxtM3vFzObEHyfEnx9sZiPNbBYwsqxiXVlYSGbWYcXbGZlZrFxZmNCmsESb1NRUqlWrzndr1rBhwwaGPXw/N916Z1mFt8sKCwvIKpFHZmYWBQUFpdscViKP6tVZs2YNBQWljy0sTDy2PCRDDsUx7uN5ZNQ+mBXfriveLli1nsxa1RLafJL3L7qcFHwY63JSY6odWJWa1fYHoGrlVGY+dSUznricTm3DK1aT4VwUx6g8IpFHMuRQHGMS5CF7LqkXOplZK6AH0Jwg1/nAPOBJ4Cp3/9LMjgH+ApwKDAMecfeZZnY4MBVoHH+5JsCJ7v7jNt7nCuAKgKzDDi/TnLbnvj/9nquvHcRBBx0UyvuLhOmW4VN55PoOXHx2C2YtyKfg23XENgcjCI26PUzh6h+oWyeNKcP68OnSf7OscG3IEYuIlFbRFzoldVEKtAVec/f/ApjZeKAqcDwwtsTJrxL//+lAkxLPVzOzLVXe+G0VpADu/iRBoUuLlrm+u8HWycigYMU3xduFBSuoUycjoU1GvE1mZhabNm1i/fp11ExPZ+7c2Yz7x6vcdfvNrFv3PSkpKVSpWpUrruq/u+HstoyMTFaUyKOgYAWZmZml23zzDVlZ8TzWrSM9PZ3MzNLHZmQkHlsekiGH4hj38TwKV/1A1iHVi7cza1ejYPX6hDYr1/xAj9tHA3Dg/pU5t10T1m34KTh+9Q8A5K9cy7sf59P813VCKUqT4VwUx6g8IpFHMuRQHGMS5CF7LumH77chBfje3ZuXeDQuse/YEs9nuvuG+L7/lHVgLVu1ZunSPJbnL2Pjxo28+vJLnN2hU0Kb9h068eKoYAbBuNde4aR2p2BmvP7GDBYuXsrCxUu5uv8Abrjx5lAKUoDc1q3Jy/uS/GVBHmPHjKZDx84JbTp07MyokSMAePWVl2l3yqmYGR06dmbsmNEUFRWRv2wZeXlf0rpNG+Wwm5Ihj7lLCmiYVZMj6tRgv9RKdDutKZNmLklok179gOIeht9e3JYRkz8CoMZBVam8X6XiNscddTiL81eVbwJxyXAuQHlEKY9kyAGSJw/Zc9vtKTWzPwPb7fVz9wFlEtHe9S7wnJndQ5BrJ+CvwDIz6+buYy34S9bM3RcA04DrgAcAzKy5u39cXsGmpqZy/0PD6NrlHGKxGD0v6UPjJjnc/Ye7aN4yl3M6dKJX775cdVlvWjZtRFpaGk+PCG818fakpqbyyLDH6NThLGKxGL379KVJTg5DBt9Jy1a5dOzUmT59+9G3Ty9yshuSllaTkaOCXq4mOTl07XYBLZo1ITU1laGPDqdSpUrKoQLnEYtt5vpHJjHhoUuolJLCiEnzWZy/ijv6ncr8JQVMmvU5J7Woy5ArzsBxZi5YzqCHJwKQXbc2f76xM5vdSTHjwVHvsSSkojQZzoXyiFYeyZBDMuWxN1TswXuwLSu3S+0w672jA919RJlEtJeZ2W1Ab+Bb4GuCeaWvAI8DdYD9gNHuPsTMagHDCeaRpgLvuvtVZjYY2ODuD+7s/Vq0zPV3Zn5YJrmUp6qV990faommtFPCX4S3N6x9Z0jYIYhIGTnhmFzmzZsbSm1Yq36Od757dBhvXcqzFzab5+655f2+2+0p3broNLMDtszN3Je4+5+AP21jV6mLebr7aqD7Np4fvPcjExEREZEtdjqn1MyOM7NFwJL49tFm9pcyj0xERESkgjCDFLNIPMKyKwudhgJnAWsA4nMvTyrDmERERESkgtmlS0K5+zdbXTtL9/ASERER2Ysq+GVKd6ko/cbMjgfczPYDBgKLyzYsEREREalIdmX4/iqgP5AJFBLcHSmcC2CKiIiISFLaaU9pfEV6z3KIRURERKTCqui3Gd2V1ff1zWyCma0ys2/NbJyZ1S+P4ERERESkYtiV4fsXgJcILjSfAYwFXizLoERERESkYtmVovQAdx/p7pvij+eBqmUdmIiIiEhFYhaNR1i2O6fUzGrGv3zdzG4GRgNOcMejyeUQm4iIiIhUEDta6DSPoAjdUjNfWWKfA7eUVVAiIiIiFYkR7t2UomC7Ram71yvPQERERESk4tqlOzqZ2VFAE0rMJXX3v5dVUCIiIiJSsey0KDWzu4CTCYrSycDZwExARamIiIjI3hDyIqMo2JXV9+cDpwH/cvdLgaOB6mUalYiIiIhUKLtSlP7o7puBTWZWDfgWOKxswxIRERGRimRX5pTONbMawFMEK/I3AO+XZVAiIiIiFU1Fv83oTotSd78m/uUTZjYFqObuC8s2LBERERGpSHZ08fyWO9rn7vPLJqR9n4cdgCQV9+T4jlr7zpCwQ9gr0k77fdgh7BVr37or7BBERBLsqKf0oR3sc+DUvRyLiIiISIW1Kwt9ktmOLp5/SnkGIiIiIiIV1y5dPF9EREREyo6hhU4VvadYRERERCJARamIiIiIhG5XbjNqQE+gvrsPMbPDgV+5++wyj05ERESkgkip2KP3u9RT+hfgOODC+PYPwPAyi0hEREREKpxdWeh0jLu3NLOPANx9rZlVLuO4RERERKQC2ZWi9Gczq0T8mvBmVhvYXKZRiYiIiFQwGr7fuUeB14BDzOxPwEzg7jKNSkREREQqlJ32lLr7KDObB5xGcBmtc919cZlHJiIiIlJBmOk6pbuy+v5w4L/AhJLPufvXZRmYiIiIiFQcuzKndBLBfFIDqgL1gM+BnDKMS0REREQqkF0Zvm9actvMWgLXlFlEIiIiIhWQFjr9Qu4+HzimDGIRERERkQpqV+aU3lBiMwVoCRSWWUQiIiIiUuHsypzSg0t8vYlgjukrZROOiIiISMVUwRff77gojV80/2B3v7Gc4hERERGRCmi7c0rNLNXdY8AJ5RiPiIiIiFRAO+opnU0wf/RjMxsPjAX+s2Wnu79axrGJiIiIVAgGpFTw8ftdWX1fFVgDnAp0BDrF/y9l4M1pU2jTvAmtmjZi6IP3ldpfVFRE30supFXTRpze7ji+Xp4PwNfL88lIP4iTjm3FSce24oYB4V61a9rUKTTLaUROdkMeuP/eUvuLioq4+KLu5GQ3pO3xx7A8P7943wP33UNOdkOa5TTijWlTyzHqRMmQAwR5HJ2TzVGNj+TB7eTR66IeHNX4SE464djiPNasWUP7M06ldtrBXD/w2nKOurRkOB9ntGnAgpH9+XTUddx4UelBqMMPrc7kh3sx+5mrmDq0N5m1D07Yf/ABlckbez2PDDy7vELepmQ4F5AceSRDDpA8ecie2VFRekh85f2nwCfx/38W//+n5RBbmTOzwWYWmfmysViM390wgJdem8j78z7hlbFjWLJ4UUKb50c8Q40aacz75HOuvnYQg++4pXhf3XoNePeDebz7wTwefvQv5R1+sVgsxqAB/Rk34XU+WriIsaNfZPGixDyee+Zp0mqk8dmSPK4beD233XoTAIsXLWLsmNHMX/AZ4ydOYeB11xCLxZTDborFYlw/8Fr+MWEy8xd8xtgxo0vn8ezT1EirwaeLv+S6AYO4/dabAahatSp3Dh7C3fc9EEboCZLhfKSkGEMHnUOX342iRe/hdDvtKLKPqJXQ5p5rzmDU1IW06fsEd4+YwZArTkvYf1e/U5m5cHl5hl1KMpwLSI48kiEHSJ489oaUiDzCsqP3rgQcFH8cXOLrLQ/Zy+bNnU29+g2oW68+lStX5jfnX8DrE8cntJk8cTw9evYCoMt5XXl3+tu4exjhbtec2bNp0KAh9eoHeXTr3oOJE8YltJk4YRw9e/UG4Dddz2f622/h7kycMI5u3XtQpUoV6tarR4MGDZkze7Zy2E1z5yTmcf4F3UvlMWnCeC6O53Fe1/OZ/k6Qx4EHHsjxJ5xI1apVwwg9QTKcj9aNM1la8B35K7/n502bGfv2Z3Q8MTuhTfYRtZkxfxkAMz7Kp+MJ/9vf4td1OCTtQN6cs7Rc495aMpwLSI48kiEHSJ48ZM/tqChd6e5D3P3323gMKbcI9zIzu83MvjCzmUCj+HPNzewDM1toZq+ZWVr8+dbx5z42swfMrEx7iFcWFpKZdVjxdkZmFitXFm63TWpqKtWqVee7NWsA+Hr5Mtodl0vHs07h/VnvlWWoO1RYWEBWiTwyM7MoKCgo3eawEnlUr86aNWsoKCh9bGFh4rHlIRlyACgsKCAzK2uHsQRtSucRJclwPjJqHcyKb9cXbxesWk9mrcTh+U+W/psuJzUGoEvbbKodWIWa1fbHDO695kxueXxauca8LclwLopj3MfzSIYcimNMgjxkz+2oKE262bZm1groATQHzgFax3f9HbjJ3ZsRTFW4K/78s8CV7t4c2O54gJldYWZzzWzu6tWryij6HTv0V3VYuGQZM96fyx/vfZDLL+3F+vXrd36giETGLX+ZRtvmR/D+366gbfO6FHy7ntjmzVx5bmumfvglBat+CDtEESlDZtF4hGVHRelpO9i3r2oLvObu/3X39cB44ECghrvPiLcZAZxkZjUIrtH6fvz5F7b3ou7+pLvnunturVq1dzu4OhkZFKz4pni7sGAFdepkbLfNpk2bWL9+HTXT06lSpQo109MBaN6iFfXq12dp3he7HcueyMjIZEWJPAoKVpCZmVm6zTcl8li3jvT0dDIzSx+bkZF4bHlIhhwAMjIzKVixYoexBG1K5xElyXA+Clf/QNYh1Yq3M2tXo2B1YpG5cs0GetzxEsdd9iR3/e0tANZtKOKYnCyuOq8NS0YP5J6rz+Sis47mD1eE8ys6Gc5FcYz7eB7JkENxjEmQh+y57Ral7v5deQYi0LJVa75amsfy/GVs3LiRV19+ifYdOiW0ObtDJ0aPGgnAuNdeoW27UzAzVq9aVTy5O3/ZV3yVl0fduvXLPQeA3Natycv7kvxlQR5jx4ymQ8fOCW06dOzMqJEjAHj1lZdpd8qpmBkdOnZm7JjRFBUVkb9sGXl5X9K6TRvlsJta5Sbm8fJLY0rlcU7HTjwfz+O1V16m3clBHlGSDOdj7pICGmalc8SvarBfagrdTs1h0qzPE9qkV9+/uJfitz3bMuL1jwC49I+v8esLhpLdYxi3PD6NF6Yu4I4n3yrvFIDkOBeQHHkkQw6QPHnIntuV24wmk3eB58zsHoLcOwF/BdaaWVt3fw/oBcxw9+/N7AczO8bdPyQY9i9Tqamp3P/QMM7vcg6xWIyel/ShcZMc7v7DXbRomcvZHTpxce++XHVZb1o1bURaWhp/GxF04P5z1nvc88fB7Je6HykpKTz06HDSatYs65C3m8cjwx6jU4eziMVi9O7TlyY5OQwZfCctW+XSsVNn+vTtR98+vcjJbkhaWk1GjhoNQJOcHLp2u4AWzZqQmprK0EeHU6lSJeWwB3k8PPTPdO7QntjmGJf0vrR0Hpf2o1+fSziq8ZGkpdXk78+/WHx89pH1+GH9ejZu3MiE8eOYMGkqjZs0CSWPff18xGLO9UMnM+HBi6mUYoyY/DGL81dxR9+Tmb+kkEn//IKTmtdlyBWn4Q4zFyxn0NDJ5R7nziTDuUiWPJIhh2TKY0+ZWYW/TqlFbeV2WTOz24DewLfA18B84E3gCeAA4CvgUndfa2bHAE8Bm4EZQK677/AOVy1a5vrbMz8swwzKx/6V980f6mSULD+jUet93V1pp/0+7BD2irVv3bXzRiIVzAnH5DJv3txQflll/Lqp93s0Gvcl+uPZv57n7rnl/b4VracUd/8T8Kdt7Dp2G899Fl/8hJndDMwty9hERESk4kqSz+67rcIVpb9QBzO7heDfaTnQJ9xwRERERJKTitIdcPcxwJiw4xARERFJdipKRURERCIgpYIP34d5i1MREREREUBFqYiIiIhEgIbvRUREREJmUOGvU6qeUhEREREJnXpKRURERCKggneUqqdURERERMKnolREREREQqfhexEREZGwma5Tqp5SEREREQmdilIRERERCZ2G70VEREQiwKjY4/fqKRURERGR0KkoFREREZHQafheREREJGTBbUbDjiJc6ikVERERkdCpp1REREQkAip6T6mKUpGIs4p+M+SIWfvWXWGHsFeknXJn2CHssbXvDAk7BBHZizR8LyIiIiKhU0+piIiISARU9JEx9ZSKiIiISOhUlIqIiIhI6DR8LyIiIhIyXadUPaUiIiIiEgHqKRUREREJm0EFX+eknlIRERERCZ+KUhEREREJnYbvRURERCIgpYKP36unVERERERCp6JUREREREKn4XsRERGRkOk6peopFREREZEIUFEqIiIiIqHT8L2IiIhIBFTwxffqKRURERGRX8bM2pvZ52aWZ2Y376BdVzNzM8vd2Wuqp1REREQkdEYK+0ZXqZlVAoYDZwArgDlmNt7dF23V7mBgIPDhrryuekpFRERE5JdoA+S5+1fuvhEYDXTZRrs/APcBP+3Ki6ooFREREZGSapnZ3BKPK7banwl8U2J7Rfy5YmbWEjjM3Sft6puqKI2YN6dNoU3zJrRq2oihD95Xan9RURF9L7mQVk0bcXq74/h6eX7C/hXffM1hh1Tnz0MfKqeIt23a1Ck0y2lETnZDHrj/3lL7i4qKuPii7uRkN6Tt8cewPD+/eN8D991DTnZDmuU04o1pU8sx6kTJkAMoD4hOHsmQA8AZbRqyYNQAPn1xIDf2bFtq/+GHVmfy0D7Mfu4apj56KZm1qxXv2zB9MB88czUfPHM1Y++5qDzDLiUZzkcy5ADJk8eeMIKFTlF4AKvdPbfE48lflItZCvAw8H+/5LgKVZSa2QAzW2xmo8KOZVtisRi/u2EAL702kffnfcIrY8ewZHHC9AyeH/EMNWqkMe+Tz7n62kEMvuOWhP233Xwjp53ZvjzDLiUWizFoQH/GTXidjxYuYuzoF1m8KDGP5555mrQaaXy2JI/rBl7PbbfeBMDiRYsYO2Y08xd8xviJUxh43TXEYjHlsJuUR3TySIYcAFJSjKE3dKTLjSNp0esxup3elOy6tRPa3NP/LEZN+Zg2ff7C3c9NZ8iVpxfv+7HoZ47t+zjH9n2cbre8UN7hF0uG85EMOUDy5FHBFACHldjOij+3xcHAUcB0M8sHjgXG72yxU4UqSoFrgDPcveeWJ8wsMou95s2dTb36Dahbrz6VK1fmN+dfwOsTxye0mTxxPD169gKgy3ldeXf627g7AJMmjOOII+qS3bhJucde0pzZs2nQoCH16gd5dOveg4kTxiW0mThhHD179QbgN13PZ/rbb+HuTJwwjm7de1ClShXq1qtHgwYNmTN7tnLYTcojOnkkQw4ArRtnsbTgO/JXruXnTTHGvvUJHU/MTmiTXfcQZsz/CoAZ85eV2h8FyXA+kiEHSJ48Kpg5wJFmVs/MKgM9gOKCxd3XuXstd6/r7nWBD4DO7j53Ry9aYYpSM3sCqA+8bmbrzGykmc0CRppZXTN728wWmtlbZnZ4/JgGZvaBmX1iZn80sw1lGePKwkIys/73wSMjM4uVKwu32yY1NZVq1arz3Zo1bNiwgWEP38/vbr2zLEPcJYWFBWSVyCMzM4uCgoLSbQ4rkUf16qxZs4aCgtLHFhYmHlsekiGH4hiVRyTySIYcADJqH8yKb9cVbxesWk9mrWoJbT7J+xddTgo+HHc5qTHVDqxKzWr7A1C1ciozn7qSGU9cTqe24RWryXA+kiGH4hiTII89ZsFtRqPw2Bl33wRcC0wFFgMvuftnZjbEzDrv7j9BZHoJy5q7X2Vm7YFTCP4hOwEnuvuPZjYBGOHuI8ysL/AocC4wDBjm7i+a2VXbe+34BOArALIOO7yMM9m2+/70e66+dhAHHXRQKO8vIrLFLcOn8sj1Hbj47BbMWpBPwbfriG0ORnQadXuYwtU/ULdOGlOG9eHTpf9mWeHakCMWkV/K3ScDk7d6bps9Y+5+8q68ZoXpKd2G8e7+Y/zr44Atk5tGAieWeH5s/OvtTn5y9ye3TAauVav29prtVJ2MDApW/G8xW2HBCurUydhum02bNrF+/Tpqpqczb+5sBt9+M0c3bsATwx/lkQfv5aknhu92LHsiIyOTFSXyKChYQWZmZuk235TIY9060tPTycwsfWxGRuKx5SEZciiOUXlEIo9kyAGgcNUPZB1SvXg7s3Y1ClavT2izcs0P9Lh9NMf1e5y7nnoLgHUbgivCFK7+AYD8lWt59+N8mv+6TjlFnigZzkcy5FAcYxLksTekmEXiEVr+ob1z+P4TdgBba9mqNV8tzWN5/jI2btzIqy+/RPsOnRLanN2hE6NHjQRg3Guv0LbdKZgZk9+YwYLFS1mweClX9R/A9TfezOVX9Q8jDXJbtyYv70vylwV5jB0zmg4dE3vzO3TszKiRIwB49ZWXaXfKqZgZHTp2ZuyY0RQVFZG/bBl5eV/Suk0b5bCblEd08kiGHADmLimgYVZNjqhTg/1SK9HttKZMmrkkoU169QOw+B+2317clhGTPwKgxkFVqbxfpeI2xx11OIvzV5VvAnHJcD6SIQdInjxkz1WY4fud+CfBJN2RQE/gvfjzHwBdgTHx/WUqNTWV+x8axvldziEWi9Hzkj40bpLD3X+4ixYtczm7Qycu7t2Xqy7rTaumjUhLS+NvI8Jbvbo9qampPDLsMTp1OItYLEbvPn1pkpPDkMF30rJVLh07daZP33707dOLnOyGpKXVZOSo0QA0ycmha7cLaNGsCampqQx9dDiVKlVSDspjn88jGXIAiMU2c/0jk5jw0CVUSklhxKT5LM5fxR39TmX+kgImzfqck1rUZcgVZ+A4MxcsZ9DDEwHIrlubP9/Ymc3upJjx4Kj3WBJSUZoM5yMZckimPGTP2ZaV2xVB/LIEuQRzSje4+4Px548AngVqAauAS939azM7Enge2B+YAvR09x2OC7Rometvz9ylu2lF2v6V9UMtkszSTgl/UeSeWvvOkLBDkCRzwjG5zJs3N5Tx67qNm/ltz00I461LueLYuvPcfaf3qt/bKlRPafyyBACDt3p+OXDqNg4pAI51dzezHkCjMg1QREREpIKqUEXpbmgFPGbB5Kjvgb7hhiMiIiKSnFSU7oC7vwccHXYcIiIikvzCXPkeBRV59b2IiIiIRIR6SkVEREQioIJ3lKqnVERERETCp6JUREREREKn4XsRERGRkBnqKazo+YuIiIhIBKgoFREREZHQafheREREJGwGVsGX36unVERERERCp6JUREREREKn4XsRERGRCKjYg/fqKRURERGRCFBPqYiIiEjIDEjRQicRERERkXCpKBURERGR0Gn4XkRERCQCKvbgvXpKRURERCQC1FNaBir6Jx0Rib617wwJO4Q9lnby7WGHsFesnf7HsEMQiQQVpSIiIiIRUMEX32v4XkRERETCp55SERERkdAZVsG7StVTKiIiIiKhU1EqIiIiIqHT8L2IiIhIyAz1FFb0/EVEREQkAlSUioiIiEjoNHwvIiIiEgFafS8iIiIiEjL1lIqIiIhEQMXuJ1VPqYiIiIhEgIpSEREREQmdhu9FREREwmZa6KSeUhEREREJnYpSEREREQmdhu9FREREQqbbjCp/EREREYkAFaUiIiIiEjoN34uIiIhEgFbfS6S8OW0KrZs3oWXTRjzy4H2l9hcVFdH3kgtp2bQRp7c7jq+X5yfs/+abr8k6pDp/HvpQOUW8bdOmTqFZTiNyshvywP33ltpfVFTExRd1Jye7IW2PP4bl+fnF+x647x5yshvSLKcRb0ybWo5RJ0qGHEB5QHTySIYcIHnyOOOYI1nwwkA+HX09N158Uqn9hx9ag8lDL2X2c9cy9c/9yKxdrXjfYYdWZ8LDffjo+QHMHzmAw39Voxwj/59kORfJkofsmaQpSs2srpl9GnYceyIWi/HbGwYw9rWJfDDvE14ZO4YlixcltBk54hmq10hj/iefc/W1gxh8xy0J+2+/+UZOP7N9eYZdSiwWY9CA/oyb8DofLVzE2NEvsnhRYh7PPfM0aTXS+GxJHtcNvJ7bbr0JgMWLFjF2zGjmL/iM8ROnMPC6a4jFYsphNymP6OSRDDlA8uSRkmIMvaETXW78Oy0ufpRupzclu27thDb3XNueUVM+pk2fx7j72XcYcuWZxfv+dvv5PPLCe7S4+FHaXvEEq9b+p7xTSJpzkSx57A0WkUdYkqYo3RNmFolpDPPmzqZ+/QbUrVefypUr85vzL2DyxPEJbV6fOJ4Le/YCoMt5XZkx/W3cHYBJE8Zx+BF1yW7cpNxjL2nO7Nk0aNCQevWDPLp178HECeMS2kycMI6evXoD8Juu5zP97bdwdyZOGEe37j2oUqUKdevVo0GDhsyZPVs57CblEZ08kiEHSJ48WjfOYumKNeQXruXnTTHGvvkJHU9snNAmu25tZsz/CoAZ87+iY9vs4udTK6Xw9tylAPznx438WPRz+SZA8pyLZMlD9lyyFaWVzOwpM/vMzKaZ2f5m1tzMPjCzhWb2mpmlAZjZdDMbamZzgYFm1s3MPjWzBWb2brxNJTN7wMzmxI+/siyDX1lYSGbWYcXbGZlZrFxZmNCmsESb1NRUqlWrzndr1rBhwwaGPXw/N916Z1mGuEsKCwvIKpFHZmYWBQUFpdscViKP6tVZs2YNBQWljy0sTDy2PCRDDsUxKo9I5JEMORTHmAR5ZNSuxopv1xVvF6xanzA8D/BJ3r/o0i74kN/lpCZUO7AqNavtz5GH1eL7H35k9J8u5P1nruHua84iJaX8+5eS5VwkSx6y55KtKD0SGO7uOcD3QFfg78BN7t4M+AS4q0T7yu6e6+4PAXcCZ7n70UDn+P5+wDp3bw20Bi43s3pbv6mZXWFmc81s7urVq8oqtx2670+/5+prB3HQQQeF8v4iIsnmlsem0LZ5Xd5/5hratqhLwbfriG12UiulcMLRdbl5+BROvPwJ6mXUpNfZLcMOV5KAWTQeYUm2onSZu38c/3oe0ACo4e4z4s+NAErOZh9T4utZwHNmdjlQKf7cmcAlZvYx8CGQTlD4JnD3J+PFbW6tWrW33r3L6mRkULDim+LtwoIV1KmTkdAmo0SbTZs2sX79OmqmpzN37mzuuv1mmjVuwOPDH+XhB+/lySeG73YseyIjI5MVJfIoKFhBZmZm6TbflMhj3TrS09PJzCx9bEZG4rHlIRlyKI5ReUQij2TIoTjGJMijcNV6sg6pXrydWbsaBavWJ7RZueYHetz2Isf1/Qt3PfkmAOs2/ETBqnUs/HIl+YVricU2M/69xTRvVKdc44fkORfJkofsuWQrSotKfB0DauykffHMdHe/CrgdOAyYZ2bpBPN9r3P35vFHPXeftpdjLtayVWuWLs1jef4yNm7cyKsvv8TZHToltGnfoRMvjhoJwLjXXuGkdqdgZrz+xgwWLl7KwsVLubr/AG648WauuKp/WYW6Q7mtW5OX9yX5y4I8xo4ZTYeOnRPadOjYmVEjRwDw6isv0+6UUzEzOnTszNgxoykqKiJ/2TLy8r6kdZs2ymE3KY/o5JEMOUDy5DF3SQEND0vniDpp7JdaiW6nN2XSrCUJbdKrH1B8iZ7f9jqJEZPmB8cuLqD6wVWpVeMAAE5uWZ8l+eU/SpYs5yJZ8pA9F4kFPmVoHbDWzNq6+3tAL2DGthqaWQN3/xD40MzOJihOpwJXm9nb7v6zmf0aKHD3MllmmZqayv0PDaNrl3OIxWL0vKQPjZvkcPcf7qJ5y1zO6dCJXr37ctVlvWnZtBFpaWk8PeKFsghlj6SmpvLIsMfo1OEsYrEYvfv0pUlODkMG30nLVrl07NSZPn370bdPL3KyG5KWVpORo0YD0CQnh67dLqBFsyakpqYy9NHhVKpUaSfvqByUR/TzSIYckimPWGwz1z88kQkP96ZSSgojJs1j8bJvuaPfacxfUsCkWUs4qUU9hlx5Bg7M/DifQQ9PAGDzZueWx6YweWhfzOCjzwt5Zvzccs8hWc5FsuSxp4LbjFbs65TalpXb+zozqwtMdPej4ts3AgcB/wCeAA4AvgIudfe1ZjYduNHd58bbv0owNG/AW8Cg+Nd/BDrFv14FnOvu/5sdv5UWLXP9nZkf7v0Ey1nVyvvmD7WIVBxpJ98edgh7xdrpfww7BIk74Zhc5s2bG0pleGTO0f7w6DIbjP1FOjf71Tx3zy3v902anlJ3zweOKrH9YIndx26j/clbbf9mWy8L3Bp/iIiIiJSZCn5Dp6SbUyoiIiIi+yAVpSIiIiISuqQZvhcRERHZdxlWwRc6qadUREREREKnolREREREQqfhexEREZEI0Op7EREREZGQqSgVERERkdBp+F5EREQkZLrNqHpKRURERCQC1FMqIiIiEjbTQif1lIqIiIhI6FSUioiIiEjoNHwvIiIiEgEavhcRERERCZmKUhEREREJnYbvRURERCLAdJ1SEREREZFwqad0L3Ocze5hhyEikvTWTv9j2CHsFWmn3hV2CHvF2rd/H3YI+zQDUip2R6l6SkVEREQkfCpKRURERCR0Gr4XERERiQAtdBIRERERCZmKUhEREREJnYbvRURERCJAtxkVEREREQmZekpFREREIkALnUREREREQqaiVERERERCp+F7ERERkZDpNqPqKRURERGRCFBRKiIiIiKh0/C9iIiISOhMq+/DDkBEREREREWpiIiIiIROw/ciIiIiYTPdZlQ9pSIiIiISOvWUioiIiERABe8oVU+piIiIiIRPRWnEvPXGVNq0yCG3WTZDH7q/1P6ioiL6XXIRuc2yOePk4/l6eT4AXy/PJ7PWwbQ7rhXtjmvF/w24ppwjTzRt6hSa5TQiJ7shD9x/b6n9RUVFXHxRd3KyG9L2+GNYnp9fvO+B++4hJ7shzXIa8ca0qeUYdaJkyAGUB0Qnj2TIAZQHRCePM9o0ZMHz1/HpCwO4seeJpfYffmh1Jj/Sm9nPXs3UYX3IrF2teN+Gd+7ig6ev4oOnr2LsPReWZ9ilJMO5kD1XYYtSM8s3s1rbeL6zmd0cRkyxWIzf3TCAl16dwD/nLuTVsaNZsnhRQpvnRzxDjRo1mLtwCVf3H8jv77i1eF/deg2Y8f48Zrw/j4ce/Ut5h18sFosxaEB/xk14nY8WLmLs6BdZvCgxj+eeeZq0Gml8tiSP6wZez2233gTA4kWLGDtmNPMXfMb4iVMYeN01xGIx5bCblEd08kiGHEB5QHTySEkxhl7fgS6/fZ4Wlwyn22lNyT6idkKbe645i1FTP6bNpY9z94gZDLni9OJ9Pxb9zLH9nuDYfk/Q7ZYXyzv8YslwLvaG4DajFolHWCpsUbo97j7e3Ut/TCsH8+fOpl79BtStV5/KlStz3vndeX3ShIQ2r0+aQI+evQDofF5X3p3+Nu4eRrjbNWf2bBo0aEi9+kEe3br3YOKEcQltJk4YR89evQH4Tdfzmf72W7g7EyeMo1v3HlSpUoW69erRoEFD5syerRx2k/KITh7JkAMojyjl0bpxJksLviN/5Vp+3hRj7Fuf0vHE7IQ22XVrM2P+MgBmzF9GxxMblXucO5MM50L2jgpRlJrZgWY2ycwWmNmnZtY9vus6M5tvZp+YWXa8bR8zeyz+9XNm9oSZzTWzL8ysY1nGubKwkMysrOLtjMxMVhYWlGqTkXUYAKmpqVSrXp3v1qwB4Ovlyzj5+Fw6nXUq78+aWZah7lBhYQFZ8RgBMjOzKCgoKN3msMQ81qxZQ0FB6WMLt/o3KA/JkENxjMojEnkkQw7FMSqPSOSRUasaK75dV7xdsGodmbUPTmjzSd6/6HJSEwC6nNSYagdWpWa1/QGoWjmVmU9ewYzHL6PTVsVseUqGcyF7R0VZfd8eKHT3DgBmVh24D1jt7i3N7BrgRuCybRxbF2gDNADeMbOG7v5T+YS96w79VR0WLP6KmunpfPzRPHr1OJ9ZcxZQrVq1nR8sIiJJ6Za/TOOR68/h4vbNmbVwOQXfriO2ORhda3TBIxSu/oG6ddKYMrQ3n371b5YVrg054opNq+8rhk+AM8zsPjNr6+5bPlq+Gv//PILic1tecvfN7v4l8BVQ6uOkmV0R702du2b16t0Osk5GBgUrVhRvFxYUUCcjs1SbwhXfALBp0ybWr1tHzfR0qlSpQs30dACat2hFvXr1WZr3xW7HsicyMjJZEY8RoKBgBZmZmaXbfJOYR3p6OpmZpY/N2OrfoDwkQw7FMSqPSOSRDDkUx6g8IpFH4er1ZB1SvXg7s3Z1Clb9kNBm5Zof6HH7GI677AnueuotANZt+Cl+fNA2f+Va3v04n+ZH1imnyBMlw7mQvaNCFKXu/gXQkqA4/aOZ3RnfVRT/f4zt9xpvPWGz1AROd3/S3XPdPTe9Vqm1U7usRavWfLU0j+X5y9i4cSOvvTyGs89JnDHQ/pyOjB41EoDxr71C23anYGasXrWqeHJ3/rKvWLo0j7p16+92LHsit3Vr8vK+JH9ZkMfYMaPp0LFzQpsOHTszauQIAF595WXanXIqZkaHjp0ZO2Y0RUVF5C9bRl7el7Ru00Y57CblEZ08kiEHUB5RymPukkIaZtXkiDo12C+1Et1OO4pJs5YktEmvfgAWX7jy255tGTH5IwBqHFSVyvtVKm5zXNPDWZy/qnwTiEuGc7HXWEQeIakQw/dmlgF85+7Pm9n3bHuYfnu6mdkIoB5QH/i8DEIEgnky9z00jG7ndiAWi3FRrz5kN8nhnj8MpnnLVpzdoRMX9+7L1Zf1IbdZNjXS0vjbc6MA+Oes97j3j79nv/1SSUlJ4aFhw0mrWbOsQt1pHo8Me4xOHc4iFovRu09fmuTkMGTwnbRslUvHTp3p07cfffv0Iie7IWlpNRk5ajQATXJy6NrtAlo0a0JqaipDHx1OpUqVlIPy2OfzSIYclEe08ojFNnP90MlMeLAXlVJSGDH5Ixbnr+KOvqcw//NCJs36nJOa12XIlafj7sxcsJxBj0wCggVQf76xE5s3OykpxoOjZrJkeThFaTKcC9k7LGort8uCmZ0FPABsBn4GrgZeBnLdfbWZ5QIPuvvJZtYn/vy1ZvYc8BOQC1QDbnD3iTt6r+YtW/nb731YdsmUkwOqVIjPKyIioUs79a6wQ9gr1r79+7BD2GMnHJPLvHlzQ+krbNy0hT/7j3fCeOtSjmuYNs/dc8v7fStE5eHuU4Gtr6hbt8T+ucDJ8a+fA54r0e5Nd7+qTAMUERGRCs8q+FKnCjGnVERERESirUL0lO4ud+8TdgwiIiIiFYGKUhEREZEICPEOn5Gg4XsRERERCZ2KUhEREREJnYbvRURERCKggo/eq6dURERERMKnnlIRERGRKKjgXaXqKRURERGR0KkoFREREZHQafheREREJGSGbjOqnlIRERERCZ2KUhEREREJnYbvRURERMJmus2oekpFREREJHTqKRURERGJgAreUaqeUhEREREJn4pSEREREQmdhu9FREREoqCCj9+rKC0DHnYAIiKyz1j79u/DDmGvSDv1rrBD2GNFXxSGHUKFpuF7EREREQmdekpFREREQme6zWjYAYiIiIiIqKdUREREJAJ0RycRERERkZCpKBURERGRX8TM2pvZ52aWZ2Y3b2P/DWa2yMwWmtlbZnbEzl5TRamIiIhIyCxCj53GalYJGA6cDTQBLjSzJls1+wjIdfdmwMvA/Tt7XRWlIiIiIvJLtAHy3P0rd98IjAa6lGzg7u+4+3/jmx8AWTt7URWlIiIiIlJSLTObW+JxxVb7M4FvSmyviD+3Pf2A13f2plp9LyIiIhIF0Vl9v9rdc/fGC5nZxUAu0G5nbVWUioiIiMgvUQAcVmI7K/5cAjM7HbgNaOfuRTt7UQ3fi4iIiMgvMQc40szqmVlloAcwvmQDM2sB/BXo7O7f7sqLqqdUREREJAL2lduMuvsmM7sWmApUAp5x98/MbAgw193HAw8ABwFjLbgrwNfu3nlHr6uiVERERER+EXefDEze6rk7S3x9+i99TRWlIiIiIhGg24yKiIiIiIRMRamIiIiIhE7D9yIiIiIRUMFH79VTKiIiIiLhU1EqIiIiIqFTURoxb70xlWNa5NC6WTbDHrq/1P6ioiL6XXIRrZtlc+bJx/P18nwAvl6eT1atgzn5uFacfFwr/m/ANeUceaJpU6fQLKcROdkNeeD+e0vtLyoq4uKLupOT3ZC2xx/D8vz84n0P3HcPOdkNaZbTiDemTS3HqBMlQw6gPCA6eSRDDqA8IDp5JEMOAGe0aciC56/j0xcGcGPPE0vtP/zQ6kx+pDezn72aqcP6kFm7WvG+De/cxQdPX8UHT1/F2HsuLM+w9y6L0CMkSVeUmlkNM9srFZmZnWxmE/fGa+2KWCzGTTcMYMyrE5g1dyGvjh3N54sXJbQZNeIZatSowZyFS7iq/0B+f8etxfvq1mvA9PfnMf39eTz06F/KK+xSYrEYgwb0Z9yE1/lo4SLGjn6RxYsS83jumadJq5HGZ0vyuG7g9dx2600ALF60iLFjRjN/wWeMnziFgdddQywWUw67SXlEJ49kyAGUB0Qnj2TIASAlxRh6fQe6/PZ5WlwynG6nNSX7iNoJbe655ixGTf2YNpc+zt0jZjDkiv9dAvPHop85tt8THNvvCbrd8mJ5hy97UdIVpUANoFRRamaRX9Q1f+5s6tVvQN169alcuTLnnd+d1ydNSGjz+qQJ9OjZC4DO53Xlvelv4+5hhLtdc2bPpkGDhtSrH+TRrXsPJk4Yl9Bm4oRx9OzVG4DfdD2f6W+/hbszccI4unXvQZUqVahbrx4NGjRkzuzZymE3KY/o5JEMOYDyiFIeyZADQOvGmSwt+I78lWv5eVOMsW99SscTsxPaZNetzYz5ywCYMX8ZHU9sFEaoZc4i8l9YkrEovRdoYGYfm9kcM3vPzMYDi8ysrpl9uqWhmd1oZoPjXzc0szfNbIGZzTezBiVf1Mxam9lHWz+/N60sLCQjK6t4OyMzk5WFBaXaZGYdBkBqairVqlfnuzVrAPh6+TJOOT6XTmedyvuzZpZVmDtVWFhAVjxGgMzMLAoKCkq3OSwxjzVr1lBQUPrYwq3+DcpDMuRQHKPyiEQeyZBDcYzKIxJ5JEMOABm1qrHi23XF2wWr1pFZ++CENp/k/YsuJzUBoMtJjal2YFVqVtsfgKqVU5n55BXMePwyOm1VzMq+JfK9h7vhZuAod29uZicDk+Lby8ys7g6OGwXc6+6vmVlVgoL9MAAzOx74M9DF3b/e+kAzuwK4AiDrsMP3Yiq77tBf1eHjxV9RMz2djz+axyU9zmfWnAUcXK3azg8WERGJsFv+Mo1Hrj+Hi9s3Z9bC5RR8u47Y5mCUsNEFj1C4+gfq1kljytDefPrVv1lWuDbkiGV3JGNP6dZmu/uyHTUws4OBTHd/DcDdf3L3/8Z3NwaeBDptqyCNt3/S3XPdPTe9Vq3dDrRORgaFK1YUbxcWFFAnI7NUm4IV3wCwadMm1q9bR830dKpUqULN9HQAmrdoRd169cnL+2K3Y9kTGRmZrIjHCFBQsILMzMzSbb5JzCM9PZ3MzNLHZmz1b1AekiGH4hiVRyTySIYcimNUHpHIIxlyAChcvZ6sQ6oXb2fWrk7Bqh8S2qxc8wM9bh/DcZc9wV1PvQXAug0/xY8P2uavXMu7H+fT/Mg65RT53mUEtxmNwiMsFaEo/U+JrzeRmHPVXTh+JfAT0GJvBrUtLVq15quleSzPX8bGjRt57eUxtD+nY0Kb9ud0ZPSokQCMf+0V2rY7BTNj9apVxZPU85d9xVdL86hbt35Zh7xNua1bk5f3JfnLgjzGjhlNh46dE9p06NiZUSNHAPDqKy/T7pRTMTM6dOzM2DGjKSoqIn/ZMvLyvqR1mzbKYTcpj+jkkQw5gPKIUh7JkAPA3CWFNMyqyRF1arBfaiW6nXYUk2YtSWiTXv0ALF4t/bZnW0ZM/giAGgdVpfJ+lYrbHNf0cBbnryrfBGSvScbh+x+Ag7ez79/AIWaWDmwAOgJT3P0HM1thZue6+z/MrApQKX7M90A/4A0z+4+7Ty+rwFNTU7n3oWF0O7cDm2MxLurVh+wmOdzzh8E0b9mKszt0omfvvlxzWR9aN8umRloaTz03CoD3Z73HvX/8Pfvtl4qlpPDgsOGk1axZVqHuNI9Hhj1Gpw5nEYvF6N2nL01ychgy+E5atsqlY6fO9Onbj759epGT3ZC0tJqMHDUagCY5OXTtdgEtmjUhNTWVoY8Op1KlSjt5R+WgPKKfRzLkoDyilUcy5AAQi23m+qGTmfBgLyqlpDBi8kcszl/FHX1PYf7nhUya9TknNa/LkCtPx92ZuWA5gx6ZBAQLoP58Yyc2b3ZSUowHR81kyXIVpfsqi9rK7b3BzF4AmgE/Av92944l9g0ABgIFwFdAvrsPNrMjgb8CtYCfgW7A4cCN7t7RzA4HXgf6uvuH23vv5i1b+VvvbXf3PuPAKsn4eUVERMpK2ql3hR3CHiv66Ck2/1AYygD2UUe39LGvvxfGW5fSJPOgee6eW97vm5SVh7tftIN9jwKPbuP5L4FTt3r6K2B6fP/XQM7ei1JEREREtkjKolRERERknxPiIqMoqAgLnUREREQk4lSUioiIiEjoNHwvIiIiEgFh3uIzCtRTKiIiIiKhU1EqIiIiIqHT8L2IiIhIBIR5i88oUE+piIiIiIRORamIiIiIhE7D9yIiIiIRUMFH79VTKiIiIiLhU0+piIiISBRU8K5S9ZSKiIiISOhUlIqIiIhI6DR8LyIiIhIyQ7cZVU+piIiIiIRORamIiIiIhE7D9yIiIiJhM91mVD2lIiIiIhI69ZTuZQs+mr+61kH7LS/jt6kFrC7j9ygPyiM6kiEHUB5Rkgw5gPKIkvLI4Ygyfv0dquAdpSpK9zZ3r13W72Fmc909t6zfp6wpj+hIhhxAeURJMuQAyiNKkiEH2TEN34uIiIhI6NRTKiIiIhIFFXz8Xj2l+6Ynww5gL1Ee0ZEMOYDyiJJkyAGUR5QkQw6yA+buYccgIiIiUqE1bd7S//HGrLDDAKDhIQfMC2P+robvRUREREJnus1o2AGIiIiIiKgoFdlNZqafHxERkb1Ef1QlNGb77g3VzCwbuMvMDgo7Ftm3v5e2Z1/PaV+PXyQMZtF4hEVFqZSbrf9I+T66yi7eQ5oF1AauN7MDQg5pt5hZpRJfHxxmLHvCzGzL95KZNQ85nD1mZvUg+PnYFwu7EjGnxrcr7aB5JJX8d9eISHi2/v7XuUh+OsH7iC0/nGb2azNrbWZVwo7pl9iqcOhpZneYWVczaxB2bL+EmaW4+2Z3fxNYAOQA1+5rhWm8UDjdzE42swFAbzPbJxc+lvi+6gfcbWbVQw7pFyvx830kMNnMboN9rzDd8nNuZm2AxWZ2qLvH9qXCdKvfVZcAN5vZhWZWM+TQfrES31fVzSwt7Hh2Q/FIlJn1AW4KL5SyZxF6hEVF6T4i/ou+MzAWuAqYamatQg5rl5X4JT8AuBxYBtwOnB5mXL+Uu28GMLMbgO4EP78nAb8zswPDjO0XMqAa8AAwAJjs7pv21Z4IMzsBOB8Y4O7r9qUiCIp/vrsA9wCzgQvMbHCJfftEYRqP9UygU/yp6Wb2q3hhuk98b5X4XXU5cCXB+fgb/8tpnxE/H+cCUwn+ZtyxrxTXZnYE8KKZtY4/tR+wNMSQpBzsE78kpHhI7xrgZGAikE5Q2EWamdUv8XV14EjgFOAA4Fvgb2ZWOeo9jVsN5x0KnAN0cvfuwKPAIcA1Uc9jC3ffRPDHdiPwTyDbzPbfUnRH3VbnowrQlmBKRZd4T1dsXynkAMysBsGHtGHApcAlwBlmdgvsO1NdzKwR8DRBEXQcwe+q2fEe0837QmFqZinxwq01wQfPOgQ/I8+HGthuiJ+P/gR/O/oQ5HRNmDH9Aj8C7wG3mlkzwIF9dpqR7JrI/4KQYuuAWcDVwO+ALu7+nZmdGtVhVzPbn2AocnD8qfUEPXSzCOI/y91jQC+geShB7oL4kP2W3pOzgUwgAzgx3uRt4HvgIqD/vlAMxYuE5cCpwOtAR+Dc+L4mZvarEMPboa2GVw8H9nf3e4HhwGFAZ9i3ehiBGLAa+Cr+weBTgiKoX3x0YV/xH+ANd58JrHb33wIfA++YWa14YRq5c1Iypvj0nO+APOApoCdwZvyDzu/M7JSw4twZMzvUzPpYIAu4l6CYW+zuiwhGRXqbWfdQA92BLR9c3P1bgg84M4DfA2cQfHjOjU9hOzveQZBcwh63D3n8XkVpRJWYC1TFzCoDG4D6QDegn7t/ZWYnAX8m6H2MFDPLcvcfCQqE88zs9ngh8SZB79wz8XYXAzcA/wot2J0oMWTfjqA36yOCYe8uZnZCvNfxC2Am8Peo92qZ2bXA82b2AHCBu78IzAWON7PRwIsEf8giqURBOojgj9ZoM3sEGAV8A7Q1swtKto2SeMGw5ec7w8yquPsPwAfAK/Ee6xhBLq8T9Jg2CTHk7SqRR+X4U+uAVmbWt8S//WiCgnukmVWN4jkp8T11nZndG396A8GI1OD4B5zzCQrUgpDC3BUNCXp1a7r7CuC1+PPtzaymu+cTFNr7hxTfDsU/cG75fXshQQfAS8A7QCuCv3/tgZsJ5pdW3s5LyT4qkj1skjCHtC/wb4Ih4keA+4CLzawIuAC4yd0XhxdpovgfqXSCyyX9zt2/MLPzgInxmJ8g6M3qb8EigiMICqOvwot628zs18C/43MUOwEjgCvj52YOwVDSs2Y2neBT/Nnu/u/wIt45CxYLXEjwx/V+4Ewzq+Pu95vZ8QTTQ/6wD+RxLsEUirMI8mjl7j+Y2VPAtUALM5vs7htCDHObShRA7YG7gC8tmAN7K8GHgflm9jRBr1YvgnMVyQ6E+M/CWcBVZvZvgl6tzgRD9nWAVcAVwG+BrkQ0DyieJ96VYB4pwOPAoQS5DSaYg32xu38RToQ75+6zLLiSxgNmttzd7zGz/QhGQY4zsw8Ifj4uDTPO7Snxs3EVQZyd3b3QzF4Eigh+5l9w9z+GGKaUIRWlERWfC3QD//vF+CrBL5Yt80prA1e5+3slhzPDFo9jtZldB7Q2s6bu/pd4UTcR2Ojuw8xsFEFeq919VZgxby1eWFcmmIs1GMDdJ5jZl8D1wFh3/9TMFhPMeaoD3OPukZ7ja2a5wA8EQ/U9Cf7IDgDuM7NUd7+boJdlX/A98BDBVJZmwJnx5w8j6MU+OGoFqZnVJvjw8g8gjeCDZj+CD53nAi8Q9AJ9QbCo42yCDz65BFNfIsfMjiP4sHwbwdD9kwQfEloSFBUZBAXQIUAboCrw31CC3cpW00DSCXoZzwMOMrO+BAsYHwDWAjUIfld9G1K427Wlt7rE34AfgfFARzMb5O5Dzewngt9n1YEr3P0ti19JJJyoty2eSwbBVKjfxEcEzd1XmdkEgg6PP8XPT1HU4t8bKvptRlWURpAFk7r/BEx39zHx5/5DMBTTz92fLdk+KgVpyV9y7v6TBQubepnZT+7+jJl1BF6zYD7jrcB3oQa8fSnuXmRmNxL0uF0CXO/ux5jZXDMb5+5d4kOsH8UfkWZmVxMUbr8l+Lk/naDXZ7WZFQLHWjDnb3WYcW7L1h+6tvwRJhi6n+/uZ8afv4ygkLskPicwMuIxn0kwhzeV4HvmrfiHypR4T/URBD1Do+LHtAaGApe6+9chhV6KmVWKz69MI5h9NsHdX4vva00w1DrL3W+JP9eWYITkvKicl60K0r4EH0IzCaaA/Ejw4awyMMjdLwcKw4p1R+JTP4riX59CsNhvhbtPNrMfgR5mNsDdH7Vg7UFbIMXMDnT3/4QY+vYYwQfODfzv33w/gilfAH8l+JP3Y/mHJuVBRWk0fUXwQ5lrwWT1And/Oj4M86IFFwhfFy+KIqPEXKDWBHNEJxH8Ynkk/of3b/F5WSOiWgABlPh3rQWsAZoCN5jZw+6ea2bvmtnb7n5qeFHuuvg0kKsJrhawPD6sWg34dfyDwmagb4TPx5bi4VqgHkHsNxOsVO8THz5uSTAt4cIo/rGN5zDKggVkxxL0+HQxs9klPmSuAUouMPsWONfdIzHf2szqArh7vgWXfRoAjCMYuSG+b5WZvQkcGD+mEkEP6llRmqJT4nvqOKCbu58NPGHBZbk+cPd/x3O82swOilqvO0D8Q8EkC67PmwI8R3A+uprZsfGhewcuNbP/c/eHzCyT4IPbu6EFvh1mdiLQLD6y9j1BPue7+8b4tKPOwEXu/lN4UZa96C0DLF8qSiNgy6d2MzsKqESw8rYPQU/QTQQrKAvc/QkzmxiV3oYtLLhe6mXufnX8F+T/EQxB/kAw/DUQeCj+qX64mZ3k7j+HGPI2WTCn8nB3Hx2ffnA58AZBYf0bwM3sEXc/ycymWbCYa0WYMe+iDGB0vCDdz91Xmtkk4DrgcKB/FAtSM8sAvnf3/5pZf4Ih7isIRgxud/frzWwzQe9PTYK5yUtCC3gn4sVzZ4Kf8RoECziGxD8kLInvG7SlvQdXR4iEeEE6BTjfgkVXlwF3uPtHFlwB5EOC+e91CHrhX4DiD3jzw4l6++I9100Jpht8uqXn0N3HxfcPJMjn4igWpADuvjb+czyG4Nxc6u5vm1lLgjn9N7v7vfEPBqvix9xtZmlRyKnE370Ugh7SJsDRFlwZoB/wdzN7F/iQYITh0mQvSEVFaehK/GB2Jlj0sJBgqOKvBL8U/0rwh+sud18R0SLo30BTM3uJYDL6CQR/dI8F/khQ3N0HXGdmz7v7urAC3Yk04B4zywEaEMwvawA0IpjD2x6oE+91OHP7LxM5y4FzzewVd/88/tznBD1zY6I4FBbv0bmZoGB4hmC18IUE1+9cAdwS/2P7qLv/bBGcH1eSmR0C3EEwn29RvMg+FHgWOIrgur23u/v08KLctngBlwtMJliI9U+CqTdHAx+5+4VmNoxgDnxdgsWXs0MKd7tKDtnH/7/QzO4nWNjU0sxmeXDJqkoEoyQ9PEKLSEuyYA74JoLLoNUm+NnY8oFsIcHfkgfiH0L/ED+mkrvH3H1tKEFvpcSUnCx3/9rMnud/fz8quXs3CxbJOvBXd88LK1YpPypKQ2LB3X/M3TeYWQvgRoKVxBcR9GBB0FN6FcEfruoEf4wjx91XxD/dDicYflkLrLVgHmxb4Hh3fy3+Sz9yQ6tbuPskM9tIsHBjgbsvNbMVBJfmqUdwyaffEPzBityChx2YBRxPMNQ9i+ADw0CCoe7IFaRxhcA8goJty3VsOwErCa5xuyk+nB8zs78S4UtYxf1M8Pu2Vnz7SYKfl3oEPV1Pxz+cRmbR4hbxuN4EHiP4/r+U4MPaUWbWzt1nuPvA+PSiqh5cBSGSeQCYWU+Cy+h9S3At2E3AncDvzezD+CjOHaEFugvi3//nEsTdkWBk6sZ4/J+Z2ScEo2wlr78aieleJb834qMh75nZVe7+upm9DFThf7c9fiFefFcYFXz0PrqX50hmFiwAupdgdeT+BPNHBxH84e0NXEywSvVu4Bigp7t/Fkqw22El7sxiZpXdvYBgded6Cy7Ls+XixykEq6MhIqtud8Td3yBYSXyOmXV396J4b0lDgqHkiz2CK3B3xN3XA38h6DG9BuhAsGAukj0PJf5opRAM6XUjuL1gDvBu/A9yH4Jc3vTgYueRKoC2Fv+g9hJwspkdFS98XiGYbzlzqx68KFpPsDirEsF5eZFgQdBZZnYqgLv/7MH1ViObR7yH+jqCFfWNiN9+k+Bybw8S9AhHngXrCgYTzLEsdPe/EJyfv5pZs3iP6Hx3nxdmnFvbqiD9HcFdv24F7jazM+NTKJ4kWGTWDDgovGglDOopDYEH173MI7g8TBHwuger1W8BHnL3Dy24fM+xwKoo/oL3/y1q6ksw9PUdMJbgOn9PW3DtzucJCu2h8WMil8e2uPs4M+sFPGpmjQnuSFMPiMwK6F/K3VcSLOR4Jr69cSeHhCbeM9eToHi4lGD+Ygz4OzDIzJoS/ME6392/DC/SX+wlgpGPhy24zu35BPN5IzsPdov4z/vZFlwh4C2Cq4MMJxiyP9vMPorKsHBJW6Z1lCiGmgIDtkwvMLNbgfvd/bJ4Z0GUL4xfUhHB76V2Ftwooi3BKEIN4CUza73lA0KUlChIzyFYIDc6PnTvBD8XNxF88PmR4G/h92HFKuFQUVrOtvyS9OBanVcSDIeZBddg+xfwuJlVJeh1vDKqc5qgeBjsBoJrd15EMMz6DkEhMY2g17e7R2jBxq5y94nx4aNXCK6v2sUjtHp4d0W5GN1KI4Khu48tuKj5NQRD+X8l6NXatK/9wYpPc7mPoHfoKIL5pTNCDusX8WCxXDeChUz7EUx1OTiKBSn878MzcKSZLSO4ZNLJwJY5rxOJLy5z9+HlHd8e+IbgLmy9CXp4Xya47fFzQF7UCtL4nOra8akFfQjmi+d5/FJn7v5CfOrU7QQF6aD4B+mKxbT6XkVpOYp/Wt9s8VXb7v7XeA9jV4IPkc/GJ9kfA9zo7u+FG/G2xRc+bBmWv8fd3zCzuQTFaOf43KAzANw9ktf32xXu/o/40ORyD27PJ+VnPsEc2MnxqStD4x+C8gluwBDJi8nvTLxYmBZ/7JM8WHF/MUEhNNkjeIcjK30ljYEEV21YAAwws9Xu/gxBz2ldM6tBcJm9fWU0ZwPwmJk96cElk1oTFNfXeYSuaVtCdYJLA64kuOLHnwhGPQa4+6MA7v6ymb1B8IEzsmsPpGypKC1H8WHJcwhWRc4jWPxwHcFE+/PiCwWeAZ6Lz5uLzGKBbaxcjZnZ1wTXWnzX3b+JzyWdZGaHR/QX4y+2r/VkJZHpQGvgIjN7m2D1/Tpg6L5akCYTd58Xn7sYqR65ErZcSSOboHf0LIKbF1QD3gT+GF9gegrBaM73YQW6h2IWXJLvMeAWd38r7IC2xd2/NLOFBJd0u8ndR5rZauDK+J+WP8fbRfXKLOWoYneVqigtR/FfHh0Ibr/3L4Jrkf6DYPVkOtAFmOHx+45HpSCFhLlAXYFfE9z5ZBrBNTB7x1dNNiT4iYrqHyrZR7j792Y2nGB6y20EH9xu2Jd73pNQ6Ne63B7/35U0Hia4GP7S+HzqrvEm/yK4AsJgd18TVpx7yoM7ay0huHzVsih1ZGzDEwQ91TeY2XfuPsbMvgX+Eu+5fjHk+CQCVJSWsS2/JCy4+8YbwCJ37x8fpr8FeIpgyPtvZjZtS0EaFVutlryY4F7j/ySYazkImEEwR+6vBJPvr4vq/DLZt8QL0MfM7Fnil08LOyb5nwgXP0BwJQ37//buPVivqrzj+PcHoQmNlRGBVgutFmjTFBEYqmArBawasMrgyEjFyxQ6SCkyMrYz7dARoTADIzW9WC0RKHhnxNaC1oQWjCHWSyDjBeg4po1KYDq1hIrcIglP/1jrwOlpyK0n2TvnfD9n3sl+97v23uu8558n61lrPcmfAB9KcmtP5X+SNhiwD7B+Nx4hfUpPda/tx6P9m1Tb7WNNWrWmy/q/82j7cn9lwK5pRAxKd6JJAelv0TY4fh2wLMlZVXVNb/MAbY4NY0t5TwlIf4a2Avr1VbUmyTtpW3lcUVUXJ/lLYJOpVU0355dpR1XbSWMjLZVPD0yvA+aPeOrBjFZVNyd5grZA6xHa9nRrB+7WKAQXOhmU7kQ9ID2WVm3jb6tqZVqpwVvTqgZ9npa6v2DIfm7OlID0nbS5r4/TSr6dWVV/nmQTbX7sH1bV6GopS1JP5T8JLEmysapuxClGg6qqpUlWt8P6wdD90XgYlE6zJL8AnNjT8XNpW18cVlVnAPTA9ATafMwXAa/ui4T2rJFU3ID/NYf014GjaIsEDgTOTnJxVV1UVX/V5219d7ieStKW9R1BzqQVYdAI1G5WhES7hhWdpt8GWr3uA6pqA22E8aEkH5xoUFX/Qtsr73DgFf30qOp2J9kjyaHAB2nzftYBX6at8jw4yXsBquqqsU07kKSpquqfagbsNayZLSN5DcWgdBr10c77gFXA8iSX94DttbRA7i8m2lbVHcAbgff0PfIG1/cfBdqm09Wq5bwLeB5tS5UnaOn7JcBzkuy32RtJkiRtJ9P306TPwdyU5GDafoqLgM8k+VFVXdZTRzck+UBVnZtkTlUtT7Kgqh4ftvfNpJT924Ajgf+kbf10IfCntP/E/AOwEvjaWPotSZJ2fwal06QvanotcCltjuW/0VanL06yqaouT3I6bdSRqtrYL90wRH+fSZJzgDNpafuDgJuB04CLgT8Dnqiqz9EWPUmSpGni6ntNiyTHAO8GXtlfS2g1fC+grfqcU1WX0moWP2XofeU2s8Dq52kVN77QP78XuLSqTktyGXDXEP2UJEkzm3NKp8864FzgCFqd5RcDxwBv6e9HV8e+b+i/sB+fnOT5tJHcMyY1+wKwIcm8qrqxqr43QFclSZrxMpKfoRiUTpOqWldVq4DfAD7Wq1dcDywA7qyqL05eSDQSBwJvTvJhWk3x+2lVmo5IcmVvcyxt9HT+MF2UJEmzgen76fct4O1J9qLV7T5/YsukoVP1EyY2xq+qb/VSb+fT9lOdqDn+OtoirY8AvwK8ZXeuDy1JksbPoHT6/SMwl1ZS9LKq+tLA/fk/pmyMv4KWrj85yVuBpVV1f5JX0cqKzrGWvSRJu8DY8qm7mEHpNOu1369P8rGq2ji5XOeYJNmfNpI7jzbn9THgzcBjSRYA+wHvmrRLgCRJ0k7jnNKdZxOMJ2U/Va83/AnanqpXAl8EPkpbnLUIuNaAVJIk7SqOlO4kYw1Gk/wOcEhVXVhVq5JsoqXvL6JNN/h8kmdV1cPD9lSSpNlllmfvHSmd6Taz4n85bSHWHwNU1Wra3qOvBC7s+5YakEqSpF3KkdIZbPJ81iTnAYfRdgd4DfDZJE9W1RW0+aQrgMVTNtKXJEm7QGJFJ4PSGWxSQHourVToGcA3gauAs4H3JzkceDnw6qr6j6H6KkmSZjeD0hkuybOBo4DTaYHpKtpm+AcAbwUepZUVXTdYJyVJ0qxnUDrDVdVDSX6fVlnq1Ko6oc8zXQ98D7iiqjYM2klJkjRoic8xMCidBapqQ5JHgTlJXkQbKb0FuM6AVJIkjYFB6ezxfeCzwPuA5wOnTZQ/lSRJGppB6SzRR0vfB3wceLKq7hu6T5IkaZLZnb03KJ1NquoJ4N6h+yFJkjSVm+dLkiRpcI6USpIkjcAsz947UipJkqThOVIqSZI0ArO9zKgjpZIkSRqcQamkUUuyKcnXk9yV5FNJfvL/ca/rkryhH1+dZOEW2h6f5GU78IzvJtlvW89PafPwdj7rPUn+YHv7KEljZFAqaeweq6ojquow4MfAOZM/TLJD05Cq6ner6p4tNDke2O6gVJJ2TEbzMxSDUkm7k9uBQ/oo5u1JbgLuSbJnkvcmWZXkm0neDpDm/Um+neSfgQMmbpRkeZKj+/GiJKuTfCPJrUleQAt+L+ijtC9Psn+ST/dnrErya/3a5ya5JcndSa5mGxbQJvlMkjv7NWdP+WxxP39rkv37uYOTLO3X3J5kwbR8m5I0Ii50krRb6COiJwFL+6mjgMOqam0P7H5YVb+aZC7wpSS3AEcCvwQsBH4auAe4dsp99wc+BBzX77VvVa1P8jfAw1V1ZW/3cWBxVa1M8nPAMuCXgYuAlVV1SZLXAGdtw69zZn/G3sCqJJ+uqgeA+cAdVXVBknf3e58HLAHOqarvJHkp8AHgxB34GiVptAxKJY3d3km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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "############## Confusion Matrix ##################\n", + "print(\"Plotting Confusion Matrix\")\n", + "\n", + "target_class = args.target_class\n", + "poison_class = args.num_classes\n", + "class_names = args.class_names\n", + "\n", + "# Evaluation\n", + "criterion = torch.nn.CrossEntropyLoss()\n", + "total_clean_test, total_clean_correct_test, test_loss = 0, 0, 0\n", + "target_correct, target_total = 0, 0\n", + "\n", + "true_labls = []\n", + "pred_labels = []\n", + "for i, (inputs, labels, *other_info) in enumerate(data_loader):\n", + " inputs, labels = inputs.to(args.device), labels.to(args.device)\n", + " outputs = model_visual(inputs)\n", + " loss = criterion(outputs, labels)\n", + " test_loss += loss.item()\n", + "\n", + " total_clean_correct_test += torch.sum(torch.argmax(outputs[:], dim=1) == labels[:])\n", + " target_correct += torch.sum(\n", + " (torch.argmax(outputs[:], dim=1) == target_class) * (labels[:] == target_class)\n", + " )\n", + " target_total += torch.sum(labels[:] == target_class)\n", + "\n", + " total_clean_test += inputs.shape[0]\n", + " avg_acc_clean = float(total_clean_correct_test.item() * 100.0 / total_clean_test)\n", + " prediction = torch.argmax(outputs[:], dim=1)\n", + " true_labls.append(labels.detach().cpu().numpy())\n", + " pred_labels.append(prediction.detach().cpu().numpy())\n", + " \n", + "true_labls = np.concatenate(true_labls)\n", + "pred_labels = np.concatenate(pred_labels)\n", + "\n", + "plot_confusion_matrix(\n", + " true_labls,\n", + " pred_labels,\n", + " classes=class_names,\n", + " normalize=True,\n", + " title=\"Confusion matrix\",\n", + " save_fig_path=None,\n", + ")\n", + "\n", + "print(\n", + " \"Test Acc: {:.3f}%({}/{})\".format(\n", + " avg_acc_clean, total_clean_correct_test, total_clean_test\n", + " )\n", + ")\n", + "print(\n", + " \"Test Acc (Target only): {:.3f}%({}/{})\".format(\n", + " target_correct / target_total * 100.0, target_correct, target_total\n", + " )\n", + ")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('py38')", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.8.13 (default, Oct 21 2022, 23:50:54) \n[GCC 11.2.0]" + }, + "vscode": { + "interpreter": { + "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/Demos/Demo_FM.ipynb b/analysis/Demos/Demo_FM.ipynb new file mode 100755 index 0000000..296d01c --- /dev/null +++ b/analysis/Demos/Demo_FM.ipynb @@ -0,0 +1,258389 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_FM\n", + "This is a demo for visualizing the features maps of a Neuron Network\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": {}, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py --save_folder_name badnet_demo" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "f77f2d10", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71b7087b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "from omnixai.explainers.vision.specific.feature_visualization.visualizer import \\\n", + " FeatureMapVisualizer\n", + "\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n", + "from PIL import Image\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2fb719c7", + "metadata": {}, + "outputs": [], + "source": [ + "### Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"badnet_demo\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "aa3a8477", + "metadata": {}, + "source": [ + "### Step 2: Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a742fdb2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "Create visualization dataset with \n", + " \t Dataset: bd_test \n", + " \t Number of samples: 9000 \n", + " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "\n", + "# Select all classes and all samples\n", + "selected_classes = np.arange(args.num_classes)\n", + "\n", + "# keep the same transforms for train and test dataset for better visualization\n", + "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n", + "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n", + "\n", + "args.visual_dataset = 'bd_test'\n", + "# Create dataset\n", + "if args.visual_dataset == 'clean_train':\n", + " visual_dataset = result_attack[\"clean_train\"]\n", + "elif args.visual_dataset == 'clean_test':\n", + " visual_dataset = result_attack[\"clean_test\"]\n", + "elif args.visual_dataset == 'bd_train': \n", + " visual_dataset = result_attack[\"bd_train\"]\n", + "elif args.visual_dataset == 'bd_test':\n", + " visual_dataset = result_attack[\"bd_test\"]\n", + "else:\n", + " assert False, \"Illegal vis_class\"\n", + "\n", + "print(f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)} \\n \\t Selected classes: {selected_classes}')\n", + "\n", + "\n", + "# Create denormalization function\n", + "for trans_t in visual_dataset.wrap_img_transform.transforms:\n", + " if isinstance(trans_t, transforms.Normalize):\n", + " denormalizer = get_dataset_denormalization(trans_t)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 3: Load model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b8b67ac9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load model preactresnet18 from badnet_demo\n" + ] + } + ], + "source": [ + "# Load model\n", + "model_visual = generate_cls_model(args.model, args.num_classes)\n", + "model_visual.load_state_dict(result_attack[\"model\"])\n", + "model_visual.to(args.device)\n", + "# !!! Important to set eval mode !!!\n", + "model_visual.eval()\n", + "print(f\"Load model {args.model} from {args.result_file_attack}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ecabc2aa", + "metadata": {}, + "source": [ + "### Step 4: Choose a image to get feature maps from a target layer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "877dcd74", + "metadata": {}, + "outputs": [], + "source": [ + "module_dict = dict(model_visual.named_modules())\n", + "target_layer = module_dict[args.target_layer_name]\n", + "\n", + "target_image = visual_dataset[0][0].unsqueeze(0)\n" + ] + }, + { + "cell_type": "markdown", + "id": "08a54822", + "metadata": {}, + "source": [ + "### Step 5: Show feature maps" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "eb363d73", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "linkText": "Export to plot.ly", + "plotlyServerURL": "https://plot.ly", + "showLink": false + }, + "data": [ + { + "coloraxis": "coloraxis", + "hovertemplate": "x: %{x}
y: %{y}
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "############## Feature Maps ##################\n", + "print(\"Plotting Feature Maps\")\n", + "\n", + "explainer = FeatureMapVisualizer(\n", + " model=model_visual,\n", + " target_layer=target_layer,\n", + " preprocess_function=lambda x:x\n", + ")\n", + "explanations = explainer.explain(target_image)\n", + "explanations.ipython_plot()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c028363c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Another way to visualize feature map\n", + "feature_map = explainer.extractor.extract(target_image)\n", + "num_cnn = feature_map.shape[-1]\n", + "num_col = 16\n", + "num_row = int(np.ceil(num_cnn/num_col))\n", + "fig, axes = plt.subplots(nrows=num_row, ncols=num_col, figsize=(4*num_col, 5*num_row))\n", + "for cnn_i in range(num_cnn):\n", + " ax = axes[cnn_i//num_col, cnn_i%num_col]\n", + " ax.imshow(feature_map[0, :, :, cnn_i], cmap='gray')\n", + " ax.set_title(f'Kernel {cnn_i}')\n", + "plt.tight_layout()\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('py38')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "vscode": { + "interpreter": { + "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/Demos/Demo_FV.ipynb b/analysis/Demos/Demo_FV.ipynb new file mode 100755 index 0000000..041ccc1 --- /dev/null +++ b/analysis/Demos/Demo_FV.ipynb @@ -0,0 +1,287 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_FV\n", + "This is a demo for visualizing the features of a Neuron Network\n", + "\n", + "Refer to https://distill.pub/2017/feature-visualization/ for more details\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": {}, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py --save_folder_name blended_demo --dataset tiny" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "87bd9f5a", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71b7087b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "from omnixai.explainers.vision.specific.feature_visualization.visualizer import FeatureVisualizer\n", + "\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n", + "from PIL import Image\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2fb719c7", + "metadata": {}, + "outputs": [], + "source": [ + "### Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"blended_demo\"\n", + "args.dataset = \"tiny\"\n", + "args.dataset_path = \"../../data\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 2: Load model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8b67ac9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading...\n", + "Load model preactresnet18 from blended_demo\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "\n", + "# Load model\n", + "model_visual = generate_cls_model(args.model, args.num_classes)\n", + "model_visual.load_state_dict(result_attack[\"model\"])\n", + "model_visual.to(args.device)\n", + "# !!! Important to set eval mode !!!\n", + "model_visual.eval()\n", + "print(f\"Load model {args.model} from {args.result_file_attack}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ecabc2aa", + "metadata": {}, + "source": [ + "### Step 3: Choose target layer" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "877dcd74", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Choose layer layer4.1.conv2 from model preactresnet18\n" + ] + } + ], + "source": [ + "module_dict = dict(model_visual.named_modules())\n", + "target_layer = module_dict[args.target_layer_name]\n", + "print(f'Choose layer {args.target_layer_name} from model {args.model}')\n", + "\n", + "# Enable training transform to enhance transform robustness\n", + "tran = get_transform(\n", + " args.dataset, *([args.input_height, args.input_width]), train=True) \n", + "\n", + "for trans_t in tran.transforms:\n", + " if isinstance(trans_t, transforms.Normalize):\n", + " denormalizer = get_dataset_denormalization(trans_t)\n" + ] + }, + { + "cell_type": "markdown", + "id": "08a54822", + "metadata": {}, + "source": [ + "### Step 4: Optimize images to maximize each CNN kernel under regularization" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "eb363d73", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step: 300 |████████████████████████████████████████| 100.0% \n" + ] + } + ], + "source": [ + "optimizer = FeatureVisualizer(\n", + " model=model_visual,\n", + " objectives=[{\"layer\": target_layer, \"type\": \"channel\", \"index\": list(range(target_layer.out_channels))}],\n", + " transformers=tran\n", + ")\n", + "\n", + "# Some regularizations are used for better visualization results.\n", + "# The parameter for regularization is self-defined and you should set them by yourself.\n", + "# Note that such regularization may hinder optimizer to find some triggers especially when the triggers are some irregular patterns.\n", + "explanations = optimizer.explain(\n", + " num_iterations=300,\n", + " image_shape=(args.input_height, args.input_width),\n", + " regularizers = [(\"l1\", 0.15), ( \"l2\", 0), (\"tv\", 0.25)],\n", + " use_fft=True,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "23522ef6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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NuLUpNs70dVlfJx3ZnJevb/k4cV9F2ze/7Wsjajyy82Pz32FjGJ9im9jafb68f1tZ3tvVfVn22OP9hgevsU1tQcrbM+/7ehnat4aXed/W+5q+L8fLuI+jHtwv8/nffHHfRbT8+hdocmjz70Ubd00zmyuwtfuxxTafU6tUtcwObc305p3jW++qVirZXPPevE/BJ6j2tXs+TvL1L2/bfdzlX//g+cyhnUvf5+BTsKXig9fPfM7by2rJ+hpjiz8l61v5fGryfZnWV9tp61pRsrJeqOqcQMEK78DmQMrW9/Q1gGTt4r7K4OPYiBj7XJPPEdv7p1b+yzWdlyjZeljJ+l9da5NGA1tD9L6Y5cDnBnz9KGfv3zdMsTXCUc9itOV3OvS+iZ1jb9O8r+NttM8Z+zjO+qfe//Yr6PvD3m2udmPjUOLPQ8eeSrWaNZtzb6eHtic253Mg1o7smyPxdibncdrXAh48Z+Tl1uuOx0KfUxnZurC/vn+OyudwHzxn4se7bzkp5+MePR7vR+xbm7Df93bW90F6X8zzN/IxTNXHqRH75t33zSH7HlyLj1aGyr630j7v/eV33VNt/dtC8cHXbN8czb7+7oPnkPPW3/c5K0/7HPu+dYJ9cwk+TvINZg9e4/Tj3dff3/dx/b12+1DmfCIedq29Vs1arXuxx8cJXnf2/XHnfXvQHlxX96137YvJ/vl3WVu2Nsc/77GtYPta9s3v2vt9r4Cv5fcH1sbYXgQvl/75sb3u8yHexu87Hfv/QVLvNh+9b20m3n06ed+agc/ph7/uY3ErM97P8jkNuwY+Lts/z+h7AXxs73z/xoPPoY/E3m2NOPaVUfs263f5vJzfM+MbKfev8do8bHiZ1t+7dnPt2MZd86357Oy5s2+nB13dI+b3V42tXavUbO3Q9oD5etHUjr1U9rVF2ydofZ9ez/Jn4yTvu+0bJ058zvvB6+H7pmnyvics/A36e76+tO/eH9/L9OA59pKXLd93Y318Px7fS7R/L9U77LPbd8+Cvuz94317EvI+x/rgNsavycjHfb5OYOck5+tflj8PR2M/3v03lIlayffDPTge+veX7fP7lud9verd9gf4vmo/QCsDPldw/dqhrLc/dOxp1OrZwnzr7fS++518LSHna9H6ffvXNW3/u6+zejvgc/d+r6u+ff+et3dZa0/ervt2VZ8D8flUO+By0+aA7H4p3yKXWeyd+Jil25V0wcZZE6t3Hds/W7c57xs9PX/n7ruPOCKikt8fe0rWnnR3diQ9tP7lsGd13fLgY0ufty9Z36hgY0Ffw+v3dCw9tnkwb9sjWRm2OZaSxQLfm7qv9+39W29f/B66vl1TXy+za+p1Zt+8YEHH2sOBtsdeS/x+5Lxdn5trd45nn2G1mjXn7q21F238PPD72vweXI/59v1Tu45la4MGdr+Tl4v+wNeiHzxu8qWHffcjFPx+DJ+L9N+36+ZNyr5OtGXIXvb5AB//+x5vr0Z+L5Cv9fi4bpLZuNEyuH+X4TvszfJwHN4+WfzysaT3O/IPnkPxORkf9/kciddlP2c+6eH7YgreIHn7anXg3fYpeT9r31jYP/Eu+869jOzb9+jnw8ZtJWt/Bza/v7l9PHM+ERGNZiNbWrp3f6m3M/vKotfXd1k/8nke75P2bZxVtPlJvxbeF/P98QPfU7vvPmGfD3xwZ8jvwy6WfO+Ozzfa+vu73EOxb7uBxdd9Yxrv/fmeQOuXeCzw+8a9vYmIGFr/b9/eHo/ppXf5zZHvHfK5MM2DP+/An7ewf15Iv39fzPZ78X1eyif6jT+fwNfbC/vG9naPXV37cv3Bg8f+nn8fe/v61/6xua25+nqfHe4bb775jvHnIA/4uRARV+5LX42Ir3zQBxYWW/Edf+QPvJ1u31mX1yuVZUl3bm1Jepqbk/SNtZuS7lnFKNtdgamlHco5m6xbfvqSpKs9zV/XboLMb3xG8zv3Xkm3rNI0ls5IetLTzeuTpIWoaoGp07Ub1y0wbvdtkbiugWbp/DOS7trC0Keu6gBo7fYVSa/UG5KOwZokd165LOmv+9r3SXq53gy3dE7PyeYd7eT7Rsfell6TsxcWJd3tbki6uayDvN3t25Iulqzh7ug5HdrmBq8wfr/ezto1SZdsY9N4rOe4XNGNA7WmlrH1TT0fjfmLkt7qaBlqb+v7K/Vzkq6W9Jpv3r6l77cbZTtTrTONql7DgXXsxnZzdmtF6/Qf+L3/xRtxOB4q/tQb8/HrfvXvfDu9vKLHdfGcHtdLVy9LuljS2DOwh3ldXD0v6dFAY8vKnN7E/uYNLaejnF7HUl4/f7Wn5bYz1XJS6VkDnNffK9kEylpb6+7zLzwv6fc8e0HSxb4tYnavS7q3oQ8Nefo5PR/bW/r6pvVCX33zNUn7Q0EqPiq0e9YrNgjzDsblqzo5ERHR621KulzVurW82pJ0f13rxiuvap6Hu3qOLj6lsWljQ+v+bmi8L5esbo21LvnGrOHIykxxXtKddW0fSzYLefEpjY3eMS8V9Xh2tvT3azXbiDXUWPbe55+U9KSn7280tD25cllj57llrWOpqu8fWx3b3NLrmab6e9/3PX/pWGLPQmsxvuMP/om302+8pmO15hk9T5ev/qKkn3juSyV9+5Z+/sIlff3NV/+dpJ9Z+YCkX3/5xyR95plvkfSHf/pfSHrp+V8l6dX5VyV98Ult5yu1bUlPy09Ien3jsqTbvvmtr21SZ7og6WpRB5QrKxqrOhtvSjosVo56Gnv7HW3D7rz8sv5eQetV1tcHKBULWu7HE411yQdsEbF8Rstmqa7xujqvsWbb4nuxrr95e13jWzGn5/T2G1q3ahUdYNQs9jz3HrumTWvf7AFFr9+8I+lqXb9/zjdr2EOjBl3Nb/O+xZqIiDtr2l6tr2/p99f1+0aZxtZmTdO7W3oNM5ukGO1oe1vLbPOHPWCjZA9ZKdiGzL/yD/7BscSeiIjW/Hx8+7f9jrfTk6pN0Oxqu9Cp6bFtbuu13CpofTnX1PqZK1q7FEuSfvkNvZa//hmNXz/60icl/YXnn5b063c0/jy3ou1szo7v9i1tR5MNY8aZ1sWFp89KutHR/G7l9XhGtzXebdgDH56Y2DiwpfHoyVZL0pvbOsa5ONXju1LS4/nl7/0iSb8yuSzpZ1Z0XBsR8fHbVyX9fF3r94+/8mlJ/9KndOz4U9e0jTqb6Tl5cVuLe22q45BXrTvm/buPvqJtwNNVm3Rs6UX8mg/oOep3bcKop9eoa/EmX7Q2pqL9/clIF+MGbf385Wt6Ppv1lqTPP6P5K9tDmLbW9fvvbOvxX7Jx8tXbWgc2b2jfsrer8fgH/+n/eSzxpzXXjD/02/+Dt9MFK8sv72gftVjS63D59g1Jnzuj5+HGHW3LF5a0HOYXtV1YbWm7efOK1rV8TsvN1m1tB5r2AIOJjQObNt6db2gsWFjV1yc2uTuq+U2OWtdv3NL85G0y2Tcl1eraDu12tJxltrBfsHFi2I0/3W2tuL0dfb/fyDot7N/wPB1onou2YFiuaJ7tEu5/CHJBj6mzoW37YNcW5m2Cv7ttE/7zmj5XXpV0bVnPyadv6Ps/cFbbi86OlqleR8cpc2Xte1XnLDZ19Ps31/XzubyeY38427jjsUtjRdMmj5urerwrC3oBeh3tHyxpFYhCTc/PH/yu//ZYYk9zvhm/5dt+y9vpYl6Ps9XUWLRoBW1oC+Obm1r36gV7sF5Xz+t8Vb9v/aaWi8jr59e2tiRdbeiYYJw0P4srep22tjWWlv0BH8kW4gr6/b4I3JzTNnA68gcaa73a2dFYlWX+0D39gZJtWBn0tvT3F7WN9xtHd62c79gGni17PSKiaps8i/ad5bCFmKq2F3N2o9fNm9qvKNqczNU7GoueuaDt02cu6+fPzmssmFo/omILW13bdFP2DYyZbQbp2o2aNZtjydmmG2uf9j0AI9NrfukZHTfevKHjznJd5wKufurfS/rcE18u6e2rPy3pC8+8X9Ldrcv6+qUXJP1n/ps/d2zjrmazEd/6W+7NrWze0mv9wvPaJ75sfZF9N/jbJrdSzTZYWVyet/WufeO8kV67ts3vRVHb9latJelGy37fNgW2J1uSnmtpPGzb+0d9zd/mtrZzjaLGg4nFs/a61v+J9RP8vtBKU89PZvHeH0BR9Bv9/cHaQ2tn3+F+lqItVA/sAVdT38RmN7BFsnmjqsbUdkfjjW/Q9Rvtq02N8SWLL1u7W5IuWjzxOelm0+Z17HVrgiPZP8zX9Bp3rM2pWf/Sr5FNO0Xebtir2hrr9kDP/1JN+wT5efsjJbbmeru9Jek5u17f/3/9i8OIP4805/MHfvd//HY6WbnZ3tZxVd/K3fPv1zmXqj9ozh6kdutNW0vxvottni1bu7uza7HP5lRXLz0r6czWTjLb5Hjzjq5P1W0DcGbtem9DY/Nm2zYR2RxOfU7niAr2wIThWPtiF5Z0XFte1XTd+kZXt7Rdr9uGnd1M+wmXzuj8Tc76nhERa29qW2z3dEe5ag+BsBvzyxWbo17QutrI6fttqThyQ42fL17ROZP3Wnu41tZY1srpNXvppdclXbJ439nVMlmwMtKeavqps9qfvp3sDynYhuUbazrH8tRSS9LVppbxWkVPeNsfDG7zhj2LvZs39XyMrb9eLurcyJ/7E7/7WPo+tXo9fvk3fuO9fM3ZvhnboFa2clS2PulCU8tds6Hl7MpndJ9KxfapFGzcVCnaH+ay+d+8PzDYxt9F21dkTUj0bE/GckvnfIbbGpve+973SPrmLY3NrQW9rmvW5uRs/iBn9W44sb0L1qdfv2E3V1zSNjCt2Z4OzU7svq5zka336OcjIlol/VChoe1+1a5BqvofF9NrPgztF3T62vD7H0Ta7Wj8n7M/PlYt6kkbWV0cWT9m2+Z0Kg17YJtvaBxrPPebyHe2tMwsrNjY3PKTL1n7pEuOcWXzsqTP1XRvQ2eocxmFnranmxtaRrvrmr/3WP/gymdekfRf+pt//9jGXXPNZvyO3/yb3k4vzvlDYbV++0MZdtvWTuxqvBhO7I8VDO2BDfZ6s6ll+6rte1xe8T/Ao2X1os0ZvPy6XrvlZa1vvrn30jnta6xZ3++pJ7VvNShZ3Stp/OraPNX//ZO6D7Jge5mWvlDH7Is5Pd+Xr+m47annbD/ETe0n5Dv6/lzVbr5b84fgR6zbBuL+tt/spe/fva39t+e/XNcoLz6j5f/SeY1vH/+U5vnKa3rNtq/o90/tgVzveUb36j75vKbrSc9x0wY+45wGhOufuizp1qrmt31Nz89O387xQONXq6Vlsjmnbe58S9ucofV16jY+2N21vbD20IzXX78s6YbNTQ5tg/QPfN/fPZZxV7M5F7/pW//Dt9NX3tC+SbVhcyDbGluaNv/XtvWWmvV97qzb+pU/2Kmv33/paV3HTbbnK29/bKfd0XHRtZc0zu/aHM3IYlepqtcpb+O64dj2r7b1eP3eiHJFY9m5cxor6jZ/WS/Yjaz2gPDWqp7vZ194TtI3rus67NaO5u/6VX39/e9/KtzUynK5rtfw6k1dwyzX9KBfeUX3Mz37rI71tm5qGWjZnuLtNR03+INO+gMtA8srek7y9tD1haamfV/6gq2f+c0JTZuX3LG9N1s27rtt86aDrr5etTLc3tEyGfYQeG//czbnX8zr8eW9v2/zllmmZfjH/92hzPlEPOxae2su/tDv+21vp9e2NIaXLPb0/MFQNmdQnbeHONofderaPrjWnH5+PNJ+z8huzPI/Htub6HWeDG2duKzXbWlV+7RDu+moVtT337Tx+nxVy+FLL+t+3jnbT7vV0eOfs/WzjZ6ej5rNkfWn9ocN/SZ5G7P4XUJDmwu1LX775pYjIqZDHzdYX9X2CzQX7cYi+2ONcwtaN+ZtzbRmcyoje7hYZ0Njle/TKdheAH8QYWtef98fvptL+vvbm5r/rs1zTsd2Y1rSk7o8r33fmGq/yrdLlEraPlyw/SqZ7T3NjW3Ox/qpZy88JelkDw6/s65l7o//xb9zbOOus+fOxt/+u3/r7fSrv/AJeX35Ka2va5dt/u9LdZxw7RUdp+xaH7K7qXH30jNPSfqNT+k+3mee0XHOZz6he9haZ/VaX3nV5pWsz7qzqev5NVuP27Z5oLrtuW3avkl/cHKUW5LsWV/J55kG3u7ZWs2m3Tt16YLuCyzWbd5tQX9/0/qibdtDd/OKxteIiK7Nu0ym2h8t2JPtmy09J/4XG1pLeo1qDbsHwepP1dZ/bqxp/eusa/9t7qyuT5cH9sdwbdxhUwux1dOAkFmbMLF1hC+zubbLW/7wXy0Ud+yeh+fs8/48sqGdj01b45zafTaDdT3ehu0zT9Y/H9h+uT/zp/7ssYy7FuZb8cd+9x98O120fRA3d7YkvVppSXrL/1i0PeyvOa997ok/MNcWeysD7XvdXtPxf8P/QLz9cYP5mvbBm4s2B21/Dz0/Z/OV6/Zwsqrd0LytX/D0r9D5jPEV7evsWj3sr9vau/WRX//4L0i6ddb6/Gsa2z9s9yd85Yqur33XJzV2/Ze/9ddL+oW5/bHnwpd9saQ/+iP/RtLXRpqny5/Rsl+1+3VrNq/3/Pu1Pbm0rP3lM+/TsXbF4vFnfuElSa+9ofH7xoYtLOS1TK2saCy69JTGxoEFg4mt4U7tno+27UUY2tj51Rc/rr+/rL93c1PnjOcbNudv+zVWVnSs/cbLuuc8wuY6bl3W71/S9cL/9n/5m8ezz3BuPn7zf3Tv/orzNt/60hvaxlys+HjX/5i0PfDCHuDwtO3HfPW21p2lZXv9Na1rqy29X8pnS89Zm3bH1iGfWtTPv7GmbcKcjTNffFUvy5ztWdm2Ni1fsXFRX2PbnZtaT/yh1eeXbF9lW79vwe4FGo61XsyVbA5soLGlZnsp+vv+CFdE2eak+zbBXM/b/bib1nez+dd6o6V5nLf506H2M3bbGr/Ldk/MZltj3eqi1uU13zpp31e031/M29jU2q/Nq1oGd+zhmhV/+JftFfMHC/qzGJKNEzN7IE/V9u2Xbew/2dHzsXRGz4fH9ssv6djgH/7gDx/buGtpaTH+1J++d3+pbTPYdy/axPbHNxd0Lt8f5tLparvgcwgvfeaypM89r/so/B4Fn5fa2dXfe+PGlqRzVS1rV67anLOVpXxJy87Z89pHPveExq+81Z2bNzReL89p/rds7bfatDmWpj1HZEHrwmSqZTH5PJHdH5HZHzfYHNv9Cedb4a6u2cNt7b6Sya6OXZ+8oOs3me1lb9/WGNi3fcdzttlneV6/b3XB6putZ+9s6nqYPS4g5lZtX6DFi3m7X9Yfxtiz52i0bT17xdZVfG7h675S+8efek3HkTU7v5s9i7cb9n67l729o9fjiXNah7o2x1y3+29//7f/wXeMP+/2OOwDSyn9/pTSh1NKH+7YhAQAHJX7Y48/fRYAjor2e9rv/gEAOCQSf7r0fQA8HhJ7er5SAABH4/7Y07e/IAgAR+n++NOj7wPgMWHOB8BxuD/2DO3mCAA4SjLu6jPuAvB4yJxzjzlnAI+HzvkQewA8PvfHn+3N7Xf/AAAcAu37cG8pgMdD53xYawfw+Nwff9q73F8KYL+DPODnWkTc/6iqi3v/JrIs+9tZln15lmVfXq/X/WUAeBTvGn/ujz1l+6smAPCIHir21O0vigLAI3r4cVeNvg+AQ/FwfR97Uj4APKKHij2Vmj6VHwAe0UOPu6r0fQAcHHM+AI7LQ427/C9qAsAjevhxV4VxF4ADe+jYU6ky5wzgUDzcWjvrXQAOx0P3feYX5h9b5gCcWo+w3sW9pQAOxcPtM6yy1g7gUDx036fR5P5SAPsVDvDZn4uI51NKT8fdAPTbIuI/evBHssjyk3vJ6Uhezef1L+/Ml/X1rKRPaa1XWvr189rRKk7zkh7UNJ3vDCVdSGNJl2vLkr5945OSfvLiV0r6+o039fsnGnjH2zuS3r6zKenK/JJ+frEl6Ynlr9nQjVSVed3Y8NRT5/X3Mz0/L1/WduMzH/mYvn98RdIXv/QLJX1mWb9vWj6n739yVdJn5+bCVezfWk09pslkqumh/mazliQ9CD0H/cEdzeNQy1RjUfM8ntcqMU6an1LS36s1dFJzd1mPudvRp3vmCpmkq42zks5Pr+v3L+s1L+SKkp6f6sRGNtQy3Rvp+0tT/f1aSX8/C/29ykgXjSpV/b1JSb+/096SdGNpMY7IQ8WfXC5FrVF6O71hf+2icEv/6uB2R2PRSrGl3zfS85gmkoyqLfSPp1pu5loaG2pJy/W6PZXxos1ftXP6/Y1FrSfdpK8vWj0p3ipJet6uU6+r758mjZ2Fon7/wlmtN7mqnp/CVOtRtrsu6Zjos+ZWzlzU79vV2Nkdb0j6vRc0/1sTvSC7sT/2DEf6G63mk5JeXtTP3C5oHi6ElpGi1ZXVs3rOknXEz7Sakt68pee8lNvVDI/1HA/atzS/dX19Ry9xFHMrkn7yYkvS3b63x5qf3Z7+hQZ7Ocpl/cFWSQvt0MrQfEW/YLSg569W0zJTbuj5ijn9vZGGrsjSkQ18Hir2TLMUg8m9Y9/Y1X5Mr6Dn9ZVXtd3NVbVcXr+ur68+9Usl/eYrr0r6vS9o1n7u+/6ypL8m/42SXt+4KulGpnXplsW+cqb1oJy0zSvltVx16/r5fLMl6Z2btyU9DG1jBn2tF9WJxu5hXmPh4qq20YW8lqtbt7QfNnhTj2fnttYz377eWnpW0pWG5nd5RfsYERGL5/U9zRXtaxZrWpgXBprudzUWDa0nP7HKcKGsdXFkf+G7nNd+w2bX+iElrbsTi0Xlil6DWtLjq5YsGNmjRb09WW5prJoM/Fmkejzz1t6ur2kd272p7c21yzf06yZWhq3Mlub0+7tdPd6itV+FsXUIDs9Dj7vG43Hc3tx6O92wtnB3pH2Nbt/6wAU9F89ceELSC3MtSVcK+n39Tf38mbqeqxdffUnSTy5qH37lrJ77/JyOk55q6O9P61qWL8xp36pd1Phx9bam50sP7judrWl8WZ9qWbhQ0r7IJbvZbprTulfJaV1+2jp7paF+//vrLUn3B1qWV2wcXCjq+YiIODOvbeN0ZH2bC3qMg+ympOuFLc1jXuv7parmcaGmZW4+03hgXY+IBT2HNy9rmze3rN83tZi+M9b417O/Kv7aq3rOVs/q+6tzmh5tWV8s0zoynGib9sqaXuMNayOXl7SMjbbXJL051DawsKNlsGBj/3rL2rijW3N6qPiTRS4G03uZKeW1HVmxKaiyjSd3rRytNrRdKBe1T3jpeZ3zeOOOjv/zthGgtqgFb2ler8vZi3peq1a3ure173CmpcdXLOt1y6ze9IcaK9NA62XHmpGizYlNra9T0mTEWP/hbEvnJ3JVbSdrlQX9fotta9YOjjZ0DmnnjmZ4PPYMRUzHek7yViZKRS0D3czKjG1kzZcstuRb+vqKfp+3zKOKzQW0NF7WRluan7z+/pde0PZs0R4mPNrR2Pr+Zy9IOllfplrRHPatPV5dtLmFkb7/TevrDG3OZyWnZSwrWBl5QueEbrx2WdJzNY3dt7a1ji1MtX0+RA/X98kiJtN75y5f1nJQLNt40sYZjaKet8za8WZZ07cHGsMnQ61b+aK2AQUr9/NNrRdl63e0h9om5UPLbd1iTbFobezYYoeN64pFzc/Y5jonduNubqTlpmfz+Y2a9uNSSdMLDa237a6W84JmJ4o2fz+voSkqc/r6GZt/iIgY25cWrd+yeUfHermSXuO8zXnPZXoOciM9Z19ic+Bh1+D5vB7zqKuxZ5y0zEytX7O0pGW4bGU4s/nw7Q3tx0wmmp+R9VO2d7R9mlo8z2zsnbN+3votHUeuPqVzGTttzc93fPvvlfTf/BM/Ken6yhlJD9vaL43SQf5mxQM99LgrpVwU7is/3b7G5c0d7SMOrY9aKmlZ2rE50OJIr+3ExqSFitb/ckGvTQqr36HxqmVzuHM2v+hrKbfuaDwa5/Tz29s2LrQ52qk1zO2Ovb+ob8gVNN1p6/H3x1qXcqF139+fMs3vaDq11zV/ebs+E4vPg/H+cVdm9X9i6y1hbXWysdvU1hEKXS3vY5v3yRX0muVt9mo01jI4Hdo1tHkRX+co2ZzvZKT5mVi86Pf1CyZT+z1bk03WJtkwK6Y2kVSym7snY309F3r+t3b09xcb+vrtO1rnnrmoawaxrcdTsTpzSB469hSKxVg+f28stL2p7dqc9TmHbc33xXkdB9zs6HmpFXRctHhW+5TNhs5nbrU1tiy3tA8719VxXWR63ZLNyfT61jmwdrheakm6XNX896wcZlU93mZRv688p/MRhYam0+6WpBdsTHLhrPaJO9ZOna1o/tpzOsf2dEWP9/J1LZfNluZn09YvIyIuPPOcpN+8o21nZnOYva71F22Oea5qsaVo61PWf/Q52iXb75CbaN9qsKFrfLsF/b7hQOvuk8++R9JXLJY2bT9FOaexZumc5qe/qb8fofnLtfX8zF14QdLtsa7rdGys3xlr/ndszmjU1s+PB5rfwUTPR6Nqa6qH5yHX2iNqjXt5a9h4NWcxeRi2HpRprOh0bF9OWT8/sDa1Wde60t+29SErlyUL2SWb48mmHgvs+wdaF1sL1gba3Oqurb1sdbQP3LcZil7JGt26trndZGsRBY1luz0rZ9bGbmxouRyV9feqvrBqY4qFcxp7KvP7H/BUKdo5t71K4671tSy8522eK7Oxa93iY93mEedsTiVftPU0q6s5m3Mq28TaalGPsWVr132rq1tdG2sn/b65CzZnX9FYurWpZahgw5zuQOvMsKexIG/zppORtg997zsPtEz4sKpQt31ZEy8jh+ah+z7ZdBq9zr3jWbNjndi4p9KxvkbB9qrYtSrZPFK1bPFgrJ+ft70+PVsbXFnVMXzf6mezpX2JM2e0bJy7pOvLu7t6LSs2b1Lsa1lP1q7O23peoahlc8HXt2u67/GZX6Lr4W++rvFt2+ZQrr6p8ens+zU2tKeaLpS0rO1Y3bJmNSIiehaSGk9qfUgW85pPaP92OzTmbr+p8/5X7ugxbff0GpXOPSXpp87rPEhKtv62rNfk4lntr6eenpOylekXr2xJer75tL5/RT/fsAre6GuZq420TOatP760qP39go3Davb+aWZtdkXrSN7i+c66xqP5Ze2r3bxq80CH4+FjT2QxvW/eY2Dza7W8rTM2NLaMc1p4M2v7exZnbQo2xhZbki+/jDQ/46nNkWT6/Y2y7aWxdctSVV/f3tL8j2xcluUsnWz9b74l6Zr1dcoVLZdzK1ovcn1bt7Yxwrqt494Zbkm6b/tbtzZ1Tn+caezr2fUalP2ER7SaWheGNo7YtbZ6Z6jH3Mu0P3e7q+egY3Vzan2Tsa1jpIrW9bwVki3b3zW1/urapp7DbGBzKrd9TktfX1zVc1yoaJnyef8l6yv2rC9mzXVktiY87Gv++z6utf59tWGL53kbIOT8+I7sgaYPFX8m00ns3DdnWrL1luq8lpvhrvaL8r7Hy+pOuWZ7nOxC1ez1ku2dH/WsnOa07nenLf1+GwRMrU1p1vTz23ZdOzbn1N6xNsz21UxscnFQ1NfrS1puG7albdDW2JAPiwW2x6We0y8oWRtZL+jx3rC9DJnPbeZ9ZTsijfQ3ClWLDVbX6y39zdFQy0jVyn7BxoYFiyUVi9/Nqva7dmwOJWdj82mycaLNQY1Heo3GPWtfbNxYyWw/RqZlwscx2dT2SVq/xNcAvI7VrO+f2b7OQs76dR2tc7u2MWo00df72ZHMN0c8Qt9nMs1i/b41qK2+nptWQY+1Utc4O0laVtZ2tZ0ZZvr5bk/L+6bt+ex19Vxvrm/p5+1emfq+tRdJRt7Wv3IDixe2r69g6/lF2zfh48r1vuZ/amvJvp6Ws3iRbG+Q9/3Kfv+VrQ92x1qXMlub3elo/oq25y7y+9del1e177K1qTGsXLU2xerTwK7J0NZ7BjaP37VzmNn9SJvrOqeb2ZprfWp7v+31irWJeVtfy5Lmr1K3a2Qx/UWbp6+3bI7ZOjfnrL7nPL9renzJr7Hde1CwPSz9XZ0bOGdzeT3bH1AvzMZ6V0q5KN6336NucwRlm48bepzPvK+iZb+xqsHg9h3d97ZQ1z7nnQ2dE6nZnMqguyXpesPK9Ui/f872z4/tvshiU/dFFOx4G0taTntWTgqZrcXYdR1bvRrZ/vj+0Nalz+j5WtvV37tqfbMXzumeuP/1qpbDr7yg8wudZ3Sf5//wT3403J9/Xuc8Vm19qtK0/m5Vz9mXfJ2u9XZe1rq/tGJrmLYfIlvXPctjW4c4M6exsNSw9Z+Jjj1zNo569kJL0o2m1s22lYGbW1rm14ZahnZt/8LU5qAHNo/atljRsnWTmvW9ulan7rz5kr1u9+HYHHTB5qjGvmHk8Dzc/V3jYXRu3btn6rVrutbetYa4YLGkYfsKx7aPZxhaztq2Dyi6WpcGb+jnG9ZGnLV9gh/5+X+or7/w6yXdv6nl8NZQ50a7bb1OWxs6n1G0PSL1OdsXaGv5m7af1PfxNO0+ypbtuc6mer5aJRvv27gv2R6Xnb7+fmVR1817G5+S9KTk+0gjmlU9RyNbo+wNta4P+luatn04Fdsnv9HxfXhaBnI2Z3Hrph5TY0nzNx7rOcjsPvPM5vGuXdMy0WlY39jGMQt2L+1yUdubgq2Nd4s2P23rh2W7/3nSs36f/XG9nJWJso1FSou2V9Tux042x1Oc0/b2ED1032eapWjft1653dM4fsb22Q5s7r1atHmNps1j2D0TdZvnuX1L24GzK7oevN3Ta7ea12szp0OEfeOaYc7mK60Pvbm1JWnf+lKwtd/tjq4VrDTsD4LYfvi2tZNrW1pWq0UtC1PbE7tq93/evKHxu7Foc8oDPV/LS3b/qO37zC/bCYyI3MDuaVu1ewas319bsP5hsvqxYH2lnh7jyNafdm2Ou27PI2g2tUwu2l6g9lU9x3Wb5y/b/rDFOb3GZdtX2bV79wdz+n2X7F78tdu2N3fX9kQUtQ1asHWDpt1HtGKFfLuthXRs7cOmPZ+iOK/nf2j35Xw2j/yAnyzLximl74iIH4mIfET83SzLPvkuHwOAAyP+ADgOxB4Ax4HYA+C4EH8AHAdiD4DjQOwBcByIPQCOC/EHwHEg9gA4DsQeAMeF+APgOBB7ABwHYg+A40L8AXAciD0ADssjP+AnIiLLsn8REf/ikPICAJ8z4g+A40DsAXAciD0AjgvxB8BxIPYAOA7EHgDHgdgD4LgQfwAcB2IPgONA7AFwXIg/AI4DsQfAcSD2ADguxB8Ax4HYA+Aw5I47AwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAnEaFx/ljWURMxtO305NBJq9PawNJz9WTpFuXGpLevb0l6fr5Zf2+zljSL928JunKpCzpjeGupFfmFiS9cOEFSWf2eKTpVH9vrlaX9Ggysg9ocmtrS9L9vp6Pfrcn6TPLmr9+NpR0r6uf/+jHrkv68qd/TtLbW7cl/Su+9glJ/4Yv1fR8c0nSnVv6+XpTr1e1oNczImI6aOs/jPUalItaRLOCnsNypSbpnP1EsaTXoFJ/RtPNS5IeDvQaDib2hVZGyuWivlzVdBYlS+s1zKUNSVcbek1jPNHXi3o82WBH81PTQpUf5SVdir6kCw09v92dW5Ie9fX7ykWts7mKHm9+UtH8eSE/Jtl0EqP+1tvp3Z5W3lxHj2usxSCWVuy6VuYkXSro57v9jqQnFT0vvd62/l5Br1Otpdd5qa6fb66ck/R296ak61Y3i3mNBa2VqqTLeS3n05HWq86m1u1k1/W5L7oo6UJVjye/q7H3VlvrfbWh5+/iea0HlXFL0je2Nb/dpsbyZk2P/8uesnoVETttiyXZiqTvb6siIlbmtC4uL3+RpBeaek6r1p69L+k5XdvVut97Wn9/mtO6OlfRMjHYuSHpcl1/P/ya1bV9LE61vdjeslic6TW5sGLxvaLntJTX98/nNfZtbGqlSl09v5md71R+n6T7bS1DrTm9fmfOPCnp3q7G2uMyHg3i5rXX3k5vtjVmT+26pNCy3R/qea3UNRaNYlW/r6Zt2jf9hl8m6b/5P+h5PvPVXynphc4/lfT7v+opSRerWg/OndHXxzmNjcO+9fOKWk5rRc1/aarXtTanx3PnvnMZEVG3elEsaN2/8MQFfb2ksb+x2pV0parldrhm5aizLsmlRY3F+Zxe39Uz2m+KiFg8p+eo0mhJehqap153S9Kbm1ZGQq/Jzq6mc9YX627p6/WCxs9OT6/BZKCxaGz9kHrSa5pf0nMyHWksHA+0fchZxy2zblehpP2Ual2PZ2z9pJxWkeh3td+WK+v7045e48UFPb5yoynp3ljPR3uiZbw01Nh6nAbjcbx6Z+3t9FecbcnrWzd1XHDmjNbHSlUvxtmSxvEzc9YWj7WdW5zX/Fz6Aiv7Zc3PZLgm6VzZBlrWLo2tbDYWrB2Z17JdbWp+J1Mtm+WC/n45r2VlMtZ28sxU48FOTr+vUT4j6V7O+tRWtgtzGn/GVhe7Y40NH/6wjuNiTuveYknPV0TEetKL8qTFo6yieexaHm5c0/qyPdD+561tff3ZL9b+q9ffclXTX/aExpOP9LVM3djV/vNHPq59qTs72v+ey2n93xlYmRjr8d26ou9fKGl93rC+xXMr2nfbvLEl6f6a5vfV3U1JL+npieFQ69hkpL83zOv5Obeo8er1Oxpfj8s4l4v1+671tN6S15ONJ8dl7VNerGv67BPaV1qcWh/S+qC5ntalYl9jSbmo5a7Q0jjfrOjrFxa1nvTqGhuKFS1X04nGplJO37/R1teHBS1n5ZJ+30JDr2sh0/xs5PTzNYstE5u0yuz906Gez2lOy+F8WX+v+aSWw/GSnv803l8O83aNdqy/m9lYuFjTczCx/vBCTfOwWdBjaNW0MzCd6jVYfWJRXy9aX2dDY0lmdfXGi1uSbi5Z/9baq+J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SFOVe8AfA9iiKcp9yVO2PMcYRke8Wkb9mrf3ozXyfc9ba7qfdaIqifNocVdvzEnyjiPyGtfbiq2sh5TOJvuDnDyb/o4h8j4i8w1r7/tv+/u9EJBeRh0TkjSLyxSLyLbcd/2wROS833uz1D2/+7evkxtfT23Ljizb/UETEGFMVkV8WkR8TkfWbv/t+Y8ydJB5/gYg880mOvVNEfvJlHqb4AhHZsdYe3sF9FUW5uxx522OMeVpEEhH5aRH5N9bavTu4r6Iod58jbX+MMX9IRN4sIv/qDu6lKMq940jbnpv8hjFmxxjzn40xZ+7gnoqi3H2Osu157c06fM1N2/OcMeYv38E9FUW5Nxxl+/MiN8vwNXLjq8qKogCZQ3cAAQAASURBVCyfI2t7rLW7IvIfReTPGWNcY8wfFpHTIvJbd3BfRVHuLkfW9tzE0P8bEXnyDu6rKMrd5/e7/fk+EfkyY0zb3HiR81eLyC/cPPaEiJy31t7+go2nRB/IUJT7gaNsexRFuX/5A2N7jDFvkBsv+HnhDu6rKMrd58jbH2PMzxpjEhF5r4i8S0Rur6eiKMvhSNseY8xpEfnmm3VUFOX+4Ujbnpv8qDFm3xjzS8aY19/BPRVFuTccZftz4ua/J40xV4wxF4wxf//mi38URVkuR9n2vIgxxsiNF/xojvN9jk4MfzD5IhH5HbnxtSsRETHGbIjIHxeRv2qtndx8KcX3yg0D8gm2rbX/wlqbW2tnN//2/1prf/fm29x/VETecPPvXyYiF621/9fN339Qbnz570++moIaY75IbiQW/t2XOFaRGw9T/LtPcu4JuWG0/vqruaeiKPeMI297rLWvkxtfVf7Tog9aKMr9xJG1PzffCvv9IvJt1try1dxLUZR7zpG1PTd5m4icEZHHRGRbRH7WGOO9mvsqinJPOMq254SINEXkEbnxFvqvkRtvwv+iV3NfRVHuGUfZ/tzOV4nIgYj8+qu5p6Io94yjbnv+483fz0XkN0Xkb1trr7ya+yqKck84yrbnv4nI24wxbzfGBCLyt+TGg6aVV3NfRVHuGb/f7c/vyQ2bcnjzXyE39rhERGoiMqDLDESk/mruqyjKPeEo2x5FUe5f/kDYHmNMQ0T+g4j8fXvjq8uKoiyfI29/rLVfJjfWWn9cRH5Jcw4V5b7gqNuefy4i32WtHb+aeymKcs856rbnG+RGjvNpEfk1EflFY0zr1dxXUZR7xlG2Pydu/veL5cZHTf+IiHy9iPz5V3NfRVHuCUfZ9tzO58mNlxH9p1dzT+Uzj77g5w8m/6PceBjq39x8G5fIjQWLLyLXjTF9Y0xfRP613HhL2Cd4qaThndv+fyo3km4+cb3P/sS1bl7vG0Rk81MtpDHmc+TGm8q+xlr73Ev85KtEpCsv8TCFMWZNRH5JRL7fWvsfP9V7KopyTznytkdExFqb3LQ736lveVaU+4ajbH/+kog8ba39nU/1PoqifMY4yrZHrLW/Ya1NrbV9Efl2ufGyjdd8qvdVFOWecZRtzyeC4t9jrZ1Za58WkR+XG4F1RVGWz1G2P7fzThH599Za+6neU1GUe8qRtT3GmMfkhq/zjXJjc/4JEfkbxpg/8aneV1GUe8aRtT3W2o/JDX/nX4rIdRFZFZGPisjVT/W+iqLcU36/25//W0SekxsPkjZE5JyI/MjNY+Obf7udhoiMPtX7KopyzzjKtkdRlPuXI297jDGxiPyMiPyOtfYffar3VBTlnnPk7Y+IiLU2s9b+goh8sTHmv/9U76soyj3jyNoeY8x/JyJ1a+1PfKr3URTlM8aRtT0iItbad9/MMZzeXHP1ReTzP9X7KopyTznK9ucTec7/m7W2b629eLMemuesKMvnKNue23mniPykvmD1/sdbdgGUpbArIu+QG8l63y83DNMVufEF0NWbbw17KV7NQwtXROTXrbV39BV1Y8wbReSnReSbrbW/8kl+9pIPUxhj2nLj5T4/ba39h3dyf0VR7glH2va8BL6IPCAiT91JWRRFuascZfvzDrnxReVPBHxWROSNxpg3WGu/7U7KoijKXeMo256XwoqIeYXfKIpy7znKtufpm/+9/W/6gg1FuX84yvbnE+efFJG3i8j/cCf3VxTlnnCUbc+TIvKctfYXb8ofN8b8nIh8qYj83J2URVGUu8ZRtj1irf1PcvNLXubGl0z/vIi8707KoSjKXef3u/15g4j8ZWvt5OZv/5WI/NbNY8+IyAPGmLq19hMv9Xm93EheVBRluRxl26Moyv3LkbY9xphQRP6L3HiZqsabFeX+4kjbn5fAE5EH76QciqLcVY6y7XmHiLzZGPOJB2CbIlIYY15rrf3yOymLoih3jaNse14KzXFWlPuHo2x/Pi4iqWies6Lcjxxl2/OJ82MR+ZMi8pV3cn/lM4uz7AIoy8Fauy03jNGXGGO+11p7XW68FOefGWMaxhjHGPOgMeZtd3iLnxWRR4wxf9YY49/89xZjzGte6URjzJMi8l9F5H+y1v7MJ/nNCRH5IyLyw/T3hoj8ooi821r7nXdYdkVR7hFH2PZ8jjHm84wxgTEmNsb8TRHZEJH33mE9FEW5yxxV+yMi3yQir5EbC7U3iMj7ReTvi8jfvsN6KIpyFzmqtscY84Qx5g3GGNcYUxORfyYi10Tk2Tush6Iod5GjanustedE5DdF5G8bY8Kb9/u6m+VRFOU+4Kjan9v4syLy2zftkaIo9wlH2PZ8UEQeNsZ8obnBgyLyZXLrpYeKoiyRI2x7xBjzWTdjPmsi8gNy46M6H7vDeiiKcpf5fW5/3ici33JzPz0WkW+Vm77Nza8PfkhEvtsYExljvlJEXiciP3mH9VAU5S5yVG3PzfN9Y0wkN/JovZs2yL3DeiiKchc5qrbHGOPLjZeqzkTkndba8g7LryjKPeII25/HjDFfevOYb4z5MyLyBXLjwTZFUZbMUbU9IvJdIvKI3Mpx/mkR+UER+XN3WA9FUe4iR9X2GGNOGWM+19x4visyxvzPIrIqIu++w3ooinKXOar2x1o7FZGfEJG/YYyp39yX/1bRPGdFuS84qrbnNr5SRHoi8mt3WH7lM4i+4OcPMNbayyLyhSLyNcaYfyQi3ygigYh8VG4M4v8kIlt3eO2RiHyx3HjQaltEdkTkn4hI+Cmc/h0isiYiP2SMGd/89wz95s+KyHte4mGKrxSRt4jIn7vt3LEx5tSd1ENRlLvPEbU9oYh8n4gcyo2H2/+4iPyJm06foij3CUfR/lhr+9banU/8kxtvex5aawd3Ug9FUe4+R9H2yI0XGf6EiAxF5LyInBGRL7PWZndSD0VR7j5H1PaIiHy9iJyWG2uvnxOR73qJN9QrirJEjrD9EblRl0/28h9FUZbIUbQ9N+VvFpF/LjfWXr8uNx5w/zd3Ug9FUe4+R9H23OT/LyJ9ufF1wZ6I/IU7qYOiKPeO38f255vlRiz5qtzYU39ARN552/GvE5E336zDPxaRr7HW7t9JPRRFufscYdvzg3LjJRtfLzc+ojOTG76Soij3AUfU9rxVbrzE+YtFpH/b+Z9/J/VQFOXecETtjxGRvycieyKyLyLfLiJfa639vTuph6Iod5+jaHustSPKcZ6JyMRa272TeiiKcvc5irZHROoi8n/eLP81EfkSEflSa+3hndRDUZR7wxG1PyIi3yYi45v3fY+I/JiI/Ns7qYeiKHefI2x75Kb8H6y19k7Kr3xmMdpPiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoinL3cZZdAEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEU5iugLfhRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURTlHqAv+FEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVGUe8Cn9YIfY8yXGGM+box5wRjznXerUIqiKK+E2h9FUZaB2h5FUZaB2h5FUZaF2h9FUZaB2h5FUZaB2h5FUZaB2h5FUZaF2h9FUZaB2h5FUZaB2h5FUZaF2h9FUZaB2h5FUZaB2h5FUZaF2h9FUZaB2h5FUe4Gxlp7Zyca44rIcyLyRSJyVUTeJyJfb6396N0rnqIoyiJqfxRFWQZqexRFWQZqexRFWRZqfxRFWQZqexRFWQZqexRFWQZqexRFWRZqfxRFWQZqexRFWQZqexRFWRZqfxRFWQZqexRFWQZqexRFWRZqfxRFWQZqexRFuVt4n8a5f0hEXrDWnhcRMcb8uIh8uYh8UkMUR7Ft1OsvykVewvG8RNkRfPkQv4vI8byXPV6UBf7eGJRdlI1x8Hr0+yLP8fc+3r9IU5DjRhXkaX8McntjBeTu9iHIjY0WyLPuBOTaSh3kdJSA7AZYnyLF8nuBD3JO/WEE269AUazBBi9LvJ+4Lh4vsD1FRFwPzyEVEEOd6rh0nH9POpPT+bYgmS7gGLyB45DOOFhex8c2pJ8v6KQtsQ8cF3XIWNIx0skiz/CCdD9DfSJ0PjWPlNSpjkv9Yel+VKGSysP1cel629t7B9baNfn0eVX2p1qt2JV280XZWqo3dVxWoF741O+ZRTkKsN5pju0UkJ4XdL4lxSe1Fu7Wgn4fhyH+nm0dyRnZqiJDecGWFtTPZHt9H8eNofYqSO9F8LghW+G5AchlifcvqXwujRtraG6guUBEJPCxzUpqU8/DMi3MJzxWqFCL8wkZL/p9SY1uqDxsKy2fX/BYpfmLru/y/CbUhxbvz68C5D5gW+K6PD9z+5DxIlPFY9Jyeah903ROF8D6XL9yeSm2J4ojW2/cmquNS3rHcxyNHeaV5hjDP2C9JL/nlV7x6Lo8J9K8TnrK/cqypTuybbOvUD+2fTwJsloZKi/7lXw9nkNdPp/6x+U5mscttZ+ISJqRL0a2Jk1xLEch2sOUfFFXeKxjGULyU9gTy0lHchrLC21K57MOcRvlVF7WGR7rfJx9b5fLQ/UtyVazreIa8PzI9+c+zDKaD3m+I/+he3CwFNsjIhKEga1UK7fK6qIusF11HFrXFNj2fhjh+eXL2/10huse30ddzrMZyJ6H9nFO58eVGsiT8RDkztoJkAfdqyDX28dB7h9ugxxVmiAnsxGWz49BzjNcdzkOtS/5Lp6H9RdaZ7k++z7k29A8XdLY4HnPLniTIi71sUvjzyP/Tshf5rmcdSjN8PdsTxZs9kIJX95XYP+Sz3cW1p50f56TeA7ldRPhsm/4Cr4PzwncvgUbKMP289W1n09r+4Pr15ZifwI/sNFt9oLtovsKvo4foC3IUhxrCxMTrzO8l5/3WE9szmON4HHAv7Dss5PvQBMXj/WFeYn0qKT2W/D1WGTfje5Wsk9NeroQv6A1DI/7xXHzEraHy0QxGB5LPPZZh9gWsa2xr+AfvpItZH/RuBxXo3UH/Z7tM8+/PvvTXF72f6kXLcVgUrZF3H687LJsW/G4pbgg6wz7C9y+3f5wKbYnjmPbqN+ay3m9bqidc2pHbyGGge3qL8SfsaM81hOOZ1N5c9LrhTmAfWYaRwtj3bz8OueVxvrCHEx6x7FYto2Gxr7DisV6z7FZXgMsjHvaP6A1FfsIIou2xKN10Sv5BTxP24V5n/pkYW1Ia30qH+85CNV5Ye27uPBCcaE8+HNDfcbrJp4vF0I2C2Pk5W3DwnzxCh+Y8Dxeq7z8/bn8+3u7S1t3VWs121ppvyhzHMI4WNZ5Qvst5MsYihl77LvQ/Xn8v9Ii3hps6zR/+fFmKd64EGfxeB7jvsJ5s8jJPhvev6OxuuBq0Bqe7ZGl9nIoRhLifh23j0v7fQtrHIqhey+xqhEH+8Qn/1AMr7M4zo7XTBe2gzgmjHUMgpcfT7zvUVJc3fVw7S8l1pmVMKc24jmCdSSk9sjo91JQ7M2jOY50lvefeEiw/z2dTLG4C/uL5MuRveM43ZVr1++G/XnVtieKIlu9ba+dlwkLus0BNfoBr2PYd+J5lX3ohXmV51G6Ox9fWO8uVIjXGbxuYlv18vtvfLzMubwvHz/MKWbGttN7hb2RV9oT4HUk+068VySyWEeP7JlQmXns8Hy0sDZk/5fGFu+9L/jf5EtxnJDXHQX7ewvrRLwel2cxJoQi+3YLvg/5dgs6wzq+EDR/dTH3jGPgHOOi/t3b21+K71OvN+zq6q3bLobe+S8vv+/IwXs+m5dNC5aD/hBQXgzvI/I44Vger+vYz+KxnlHsjvVyYV+W15Wsp+xz85z3CnlJHHtciC+QogYelw99iGKOeUncniIiKZWBf1NQG3Hci9t0YT5gW8W2jPN0uA3JN13Mh6B1DfkltdvmWhGR2XAX5DCugJzn7PeR7Xr56orLeUrk1y2sldk3Z7+PYlgL8zO1j8+5c7SnceXSC0tbd9WqNdtu38qtG80wZlyvYl/M5+gbxNSWszm2zcL+McncN4u+1MvHBHhNnsxfPuer5PHq89jB3/P4571ZnlfmcxwLHLPPaB5+JV8zCHgepjhOTmOPLsf5Bwvx2ZfIaOA9SvYF2H9k+8Tu6IJ9Kng/n/fPSEfo/in1Me8r8B4g+2aLcRHyT0knuIt4TmJ3mu0lxx4W97d4f4xje5wPxvdDmWMLdmGdivL1nZ2lrLuq1ZpttTsvyi7lsVmLtqTMyJZY1H0O93EMmvMQ/BjX54ZiIjnPKws5a+TDkx7zep0LuOCb5RxfxOMcH+R1lE95lexL8Q153lvcH6N5LEC9mk14L5+PYy5AFOFc4jrcwCIh5Utw0jL7ewntcbqUTzBPMF8ioP2knNa6C/kKvLY03MZ4f4/mM9d7hZzphRxiytOj+aLkOCPb81eI8S7sK1CX+x6v0/D4wnxOY4r9/0X3Fv9wfXs5e+21at22V1ZvKygef6V9U55TFubZhby8l49l8pzJ/ZrM2efkcUE+u/DY5TwZtjU8FnkOermjIgs+AdviV9j74btzznROz2MsLHtpzmQ/y6Vxz7FdkcU9Os7XLxf6lHwxqmNJfg77Wuwb8jMmvM7h/TG21wvHF+KMvIf48nmV3CsLzxixFrzCGFpYt+UcwyHbVSxq2e1w3JDbl+fnxf2u5cR8RBafsXiJSAxIHHN2PZxLffI1koR85BzjDkXBa2hsy5QTH3jAkS55tEjm/WW2f2GE87znk4/+CjlbHA9dGGuco0c+fbbgw798HIpjIin7Vq+017ywpnmJxwkX4hpUJooLsf8X8rqFc1cXchg4boHFCcif5DLznMFrUx7+rMMLuxCvMKdynIlt+kJuPecNcmyP7G1GY4b96ZSOt2D8iiQJ+YJkT0dj9EV3t7eXsu6K44ptNm7L9SHb8Qrhw4Vcn5xzmsn3WMjlYR+WY8qvsF5dfFYHDy/Mcwu5NS+/H7ZgK0jvDMes+LlIzmmmGBX7ags5za+Q8xwGuEZJXyGnOs95jURrLFnMKxeDuaSW1toiNPYKjnthHYoC/VezELMm++29/PUN53Xzs6K0TltYuy/465wPwblNnGvA88/LP+PAe/mct7i4f0jrNrzaKz+fxraXxtjBwV2J+Yi82jzDSsU2W7dsD8d3OW9uYd5feAaYn2XB33NeC8/LvA7jrWD2oUuaQ/jZIi4v96u34MfRHMVzHNmCnOPLFBtl21UajmXyPirvpbMfReNYeB+Yc+bYx3iFXAcRYe1m3y/N2K94hT5dyM9AeM9i4Rk0Xofwuo2ux2OZbQm3wUIMhq+/8K4Ftk3st718Hib7umybeB3H8Xnej/QpjpfNMc5nKO+Jn0e7cun80tZdURjZ2m3PRM3Tl8+5WojLcG4Kjd96jeKHFLcIIhxPowE/78X54dT35DvNF9Y9L/9eDn7Whfd+F59VZN1B7c9p3q1QTL07wOfNXN5fa6CfkXTRV4k6DTx+MMDzQyxPTPkKCeleGLBvJeLS+wBsTrGiCPt0SnHvWgvHw2yIeXEB50SQDs2mWMbJDNssXsVn+NJDHG/t1RbIozHev1zwdThGzPtP6KtxbhHrzEKyDukcx9zZHnnkf89pDqxQrGBGx2MaUwXNFzwHd/svHff5dF7wc1xErtwmXxWRz365Exr1unzDV/ypF+V+Fzu1yxsXnChD+9y1zirICfnro2kf5Ap5OnETldynFz7kITZP/6AHcrSFL+gZXr4M8pN/7A+D/MGfejfIX/nXvh7kn/h7/x7kP/pX/3uQP/wTvwvyF3zN20E+/y6cA1bOYtLJ8BImnbRPb4F8uE+DwOmD3O+TU+KjZZtOUGnLBhqy+XAxAF3t0ECfkfElrz6mHOwgxeM+TWZd2jjPxmjI5gYNRy1qgcwJu5UK1rF+HNswpmSopCRnd4Qvcao2MaDi5Wjs+WHq3sF1kDmg7geUHOGioebEpDFNxpUqjoFJvgeyUCLQpI/lqTRxTHbq2L9/+7v+j0tyd3hV9mel3ZRv/7ZvelHOMmznBvXrdXoZ1wa9rGtnju30yAms9yUaK6fbaGtGJcoJ6yV5YS75bKMJ2srXPnwWf++xk4B6vHsZH3ofbWO3UNxPBiPq55UOyJvHUQ5jrF9vdAAyO8xBDW3pSgvH1XyGtmuaot43MhxXcx/LU477whw/8SDIyRT7fGW1DfJogteo1FEnOBHM0NizPl7PUuLZaI6dHNF82FzB++XUSZP+DsgFvXBtPEMnpkGO5NAhW0QBspRc5VlCGzADnB8bTfQ35uQ4x/RSKBugzscxjrHUoq3OXdShS5fPg2xirM/3/NW/tBTbU2/U5au//qtelJ02jtUpOS5xBedNfulbTNNoSbu0fsQJuPQgU42TW8kZpoDG7YFzEZFaBf2KyRQ32oIQx37ADyWR8+vyA8TkV8wpTjukBBhTckCIAjAxtmfdxXFzOMHyhx6Ow0YN68+2tEHJu0NajDTreL6IyPZuH+TWCv7m6ja+dOTh0/jSku1DtKc1g2NhkqFvfWYD7SkHpbozbJMeLWI5tuzzC3lokRtXUAf2dvdBnpFtqdKCK0mwTwYHVF8qj2fxeskQyz+jBZKhYH9Ea4PJFPvw9gc1RUS2r2L/VCqoA6MRziU/8oM/tBTbIyJSqVbk8/7oF9ySG+twPKfFb6VK894Q237zzJMgzydo95tk9688i+uejY2TIPevPwNyp3Ma5BeefQ/IT7z2rSB/4L3/DeRv/NZ/CPLP/9jfAPntf/J7QP4vP/LdID/8+i8F+dyHfw3k9sZrQe7uPw9yGGP98xn60GsdfMFQadEXbW+eAnlC7VsI+gEjmndNiPaOH6QVEak10N9qV3BAddroP0mO9mSe4zXTFMfL5R2sU6+H44FfFEImWxxa+y9szJN/yQHeShXnqOkYy+/QZhu/XGHhQVYq38oKtl+/1we5Tu03po3y9iquF8bkz1sP+/hgD9uTQzfkSskmrUt/8B/8raXYnyiM5LOf/JwX5eEA+6FRw37ilwZvnUJfaffCs/h7Cr7aEa4TTOcYyAF1ZNzkYCvOUzlHyDLU45Ie+C0ymjdWsX5VWl83juO8LuTLRC3Us+mQfC1at/KuadjE+/M8N55jeQvyhWjalMLD62Wk1z75ftbi9UREmivY5rGD/m9v2Mcy0wZBf4DX5BezdGnuzygGwxug9RjHaqOObVQlfzigDY/eNVx38AbCbAfXZdUW2v/NGrbHuETbFFEnDC0F3w9RZ6+NcD6o0UuyJmTLKpZeOBTgD+YUC5lSckVUQR31KQb2H3/yF5diexr1pnzDn/rGF+WwhjbVo02B/gHajpUqzbMUQ9haQxue0oNJbdokGdM6LidbtEd6a308vzfDOSKqo94ETeyHyMNxMaaHE1Ia+/zQ4cLLyMlnrtVJkVK8fmBwnPJLqB1an0+G1D5jPL9Hm0xphj7IwW4XZLZFIiJhhLZgZQPnB5diIskM6zie4T0zWic16aX7ros6NOaEO3phRU5+iUv2eEBtUtD5Qht3tXYL5MCndVCKfkU6xfrwy9c4abVK67zBkB6mbOBaYp5g+4X0ghBO0l1d3QQ5J1sYxdjecyr/9/2L/21p667WSlv+4t/4jhflMEJd8GtoV889g/o7SDGm6czw/DXStZzSNGo+jSf2oelhpDTCtr6yh32ZpuQbjHBd49N4rnZobyKlh6BnuG4a7KMueSHakzDEdatLw7sc49hJCjyf759FqIsPPYzdmVPQvXUM739tpw/y3jXc/1tZSHoU8SIc/1sdLFPh0fjOcfysH8drXt6mjWd6meyUxuOZEy8/nq4McY6ZluhLtDoPgexMcR+BH+Q8vN4HWeao8/UO+rtn1lCHdue07hnTHNTB8o8OKfbWothghmOksYb2/6n3fhDkNKV1aoFjskIvN3jkAYxt/NW/+T13w/68attTrdflT3z1V78oB/xQHsXvmk1sJ05GrVPit08JDhFdr0rrc47JppQAktO6L6OYtfDLzWjdV5aot46HPv3oEPU0rKLvVJAv5FFMd0S2sKQHU2NKUO720JfMUixfu40xIn55CyeERBWKV1J/JX3a22lj+UVEMvIfNzbRthQT+sBQFes0meLxlJJgEirzxirG8ZIp2lvPYh8eUgxlawvjYAnNP8M9XPdw/saArrd6HMfmiNY1DunonNahIR2PPMofoXXhdEovcnH5BXl4vEdxPH6J0+4+6lSL/H1+Odn3/ovvW4rvs7q6Jt/93f/kRbkkP4NfrCcu5gj4tDdgyVYU9OBVf/zyD7HzC2zPHEO9S6kfU9qXba7gOm+V1pExrbOKEvV09xDnsC7p1XiO9auvkm0Y4e8t63kf26uzgX5K7yraPo7thpTcNqBkwJMtbO/WOsYWe+d+G+Q8oCQdEbl89QrImcN5Lei311tYRn7ovKC1aNhEuUf7Y+2TGHOJOCZisczda9hmGc0PlbwP8ue/420gP/NfvxfkU09+FpavS3uClGs2GlNSK2WRNmlPM97aAHlIflxC66Q67dddvYSmohjT/FzgfLPRwjETrqFt/Z+++Y8vbd3Vbq/Id3z7//yi/K4PfhyOv+2z3wjy+Yu4l/fEKdSVZ86dAzkyOM+NaPzUaf+F9zrabeyLwZQeZqJ57uMXroFsSBcSClIf30IfP6OXE26s4ryxf4jzeo32uy9dxK5s0Dr2+g6uA6OIH2pE+cRJnNen9IGOA/LV+F0wMe2nV+gjdI5Q8pKIZFNcC65uot9vBPs0o4+MTCguUa1RfhHFXFc7eDwvsI87G9gG589hGxa0rzCdoj1rNumBBGqkGsVlKuvo6/mUsNwd4BzlJJQzQvZmtckfWUGdXSUdGwzQ90sSbC/Op65QLlBYaYGcUqx0Qg8E/oN//I+Wsu5qtTvyl77tO1+UGy20i3mKewWzPdr7SHGeTKbYDj362MzuDtqu9cceA9lPcC7vzehhM8o7NCHq0Qbl+UVrtO+aoA/NHzYcHmB5KTVGqnWOWaNfsHkKfa/9bh/kgvR0SL5QWKGXgFAM+vgprN9T78Mcas51evp3fhPkx1/zBvx9SB8gEZGHH3wYy+BRXh/5ax+78DE8XkF/8PxzmC9xoon2fp/yANeOPQFyl3KmowDbYPfwOZA7Ptrb+hrlTBvyh1toG5IM+/TiDs430z3ag3TpoXWaT6dTtFVVeljC0P7VZgdtZRCijlYctE2H17D9WpTT7FX4BXN4/+/57u9ciu/TXlmV7/j223JZaL06oH3TOT0E2KhjPWoUI6nQg0b8YOYqxe5mNDYLenn5R8+hLcxpL7m1gj5tlV4AsnUSbet4inPKfIbl433inPJhc36okHyCnGJSo0Ef5FoL59gJ5ZQ0yCc4uIrx/vmU9poGWN5dinc0qjjuz57EdZmISNjEa066mMc9pbhgRg+dH3Yp5jNBP8SleC+/PLNzAsvkkQ55FDfzqpQXTjm+Pq2T6hHq6ITW1qtttE2ui7YmJD+LX5zAsYYR5V8YylvsHuL9fY/zkGi+FaTXxT72yNd3yDYmE/SDvv9f/MulrbtW2k35a3/5m1+US8MfnMW+a7ax7dqd14F8bB3n6o99FH3kvPs7IHd7aJcHUxxf14b8siWaq2Mcv50KtvU+zVuW7NXZ1zwK8voG7TfR/pzPL1M09LGBIcUDq6gt6+s4L+2Qbpbkm21TjKW5jmuga/tYP6EcWF6Hcfyytbn4fLMTov/ZH+E19yjOEtBL0h44hv5h3EKbl9D+Fn+Iek726ewp9LVqm/QM4Qj91Qm9wGZA/nib4vDjAsu/QjkgVfo45XgH619tYfmmlG8QU8y3S3G1DYpzXX0Br39sC/vo4gs4H3z5V30ZyC+Qr9leaYH8rt/6CMj/7Lu/aynrrmajKX/2T7/zRTleIb8/oZc4ko+4Tv2yN0Ifs0V5HMOcno3pUV4IxRdzesh7RHmCTorjZERvNKjT+juhfPcKPZNg6bnGzhr2+/beBZCDgJ5NcrG81Rr6YllG+8IrqHfi4PF6i55Pc9AWnTqFOeXbOxizCwOKaXfxeKOCtldEZG8Pddfz8XmvLLlIZ2AdRiMcW60qfRi29wLIUZ1sSYnzT0Aflp2NMV/AMzhfJGO0x7xOs/SsrE8fKKrTQ+KTjJ6noofu5/RQ/LRHz/zR/hbne80oJ7nZRN9tMKa9f3oJ14Ceg6pRniaZYmlQftcP/MA/WYrv02w15Rv//J97UW6tUp4P7S855GNGNRybpqRnQSlhc0Y5W0L7R+sbuA5bi7EdA3o5wHAb9fzKDuptSHv7derXdcqxqFPOcn+HxhHFPvfPY7c1N8+AvLZG66oIx1lM8fidHuXfpmiL/BrtAxfYnqWP/RdEqHhFSvWl9hURKaVGMv7m4g7a5zHlrfFLnTar/JJh7MPXnUBfrjfBsdaiF9gkdANyLSWhfIsazS/zHG0JPxPHHyloNPC4T37hjPIi55R3k1Ge424P+yyuYJ8VLs/PqNNXr6DObT6Oe0Lb534L5HAV55f+PsaI/sq3fN3S1l21Sk3+uz/y5S/KL1zDujqUczWmGG02R3uzSs+mvP2tODf3ruE67NTDmOfxq7+EbbexRjFkej51dIDlu0R5EAHF9xJ6J8PWKdT9wYDyEhdenojzVoXyFg8TjHG8/nUYv/3Rn8XnzVr0LPrqH0Nf5+M/ir7Ka975DpCf+Tc/B/L6g2jvXnsSdfs5ehbywRNoT0VEao9gn6Q7GIc5/TiutT/4Xnyu5q1f/haQP/xLH8DzycZ3yB/+yAewD3/nmV8G+bXfgO9GufojvwryV33TV4P8m+9+H8gzjk36FGeZYJ/2xphrurWG7TMco84Ysm8FxXlONlHndsgerZOOPbeNvt6bn8CY/DPX0Xw8TmOqv49r+2YdfYof+8/f/5L2Z/FTS3cZY8y3GmPeb4x5/4xeWKAoinKvuN32jCn5UVEU5V5xu+1JZotJH4qiKPeK2+1PSok4iqIo94rbbQ9/YVRRFOVeAfHmmcabFUX5zHG7/eGH3hRFUe4Vt9ueeaIxZ0VRPjPcbntGo+Ern6AoinKXgHUXJbcriqLcK9T2KIqyDDDePHrlExRFUe4S6PvoMxaKonxmuN32TGdqexRF+cwAeYbq9yiK8hkEni+da66PoiiL8PfJXw3XROT2V+GduPk3wFr7AyLyAyIiJ7a27Pr6rTfbBfQVvoDexjvu0xss6TP19Sq+CSymt4M79NbVwwP82oVTozdEjvBBWJ++euLQl7NCkj36Om6P3q7rVfCtds9+GL8WefwJfEvqtXP4Uqao0wJ5/zq9IZTe8tfq4Jshaw6+9er4ow+AvFbDL8Q58RmQ511M3Arojf3zDO8/p/2GSc7vK5eFz62VBalkhMfr9FVtoWSyaYJtXKev6Jl1vL4X4vGIvuot9Ib7fh/fnlbzUWcsvclso4V9kPJXBJI+3s7Sm3fpbaBBgm+GtPSVP1OiTo5o8VHSm3/nE/qyUJ3al96Sym/cN/SG7JLe/DgY3rPFzyvan9ttz/rmuj13eGvzfchfi+zg2Njdo68Hn6G3/PWw3Q7r9DVGemNzTG9sntAbPo9V8S2sOwn2Y+DT23EbWF5DeuDTGzUzemPneIS2o0dvVt+5gqZ8Tm+7r07oi6P0pd1jp/AtqZtrLZAD+sJrj77KkgvadsehLycEOHdYegur4+PxOOoLUwp9NcuQo0xvrnV8vIfnY5+7Kd6jMPiWQ8GfS53eiJyR7ahUsTwJv1G/pK/oRjifhRG+1dCnsd5qog5FBemcg2993WX7H+HvfQ/fYh7EWL4uze88HUyG9MXyKbaP6+FbIUdz+joVfU05yu7ZA56vyvasrXdsMb9VljH5ITF9hSSnr835huZVemh+zm/5b7VATif0gg+yJTl/nY6+wDSlN297Hp7f76FeHD9NX2SlrzJ4LpY/oy8YunT9eoNkQVts+MsX9Ebrgt4E7tIbtFsdfGNt1aMva3j0xmYqX8FvQ475Dc+LX3I/RV9PZluz2Ub75ZHxOEZ9RC/plSp9ZZGqJA596aJGb1Ovr9BXuWpoC6b0ss6Uvu5xcRd93xP0FVyaPqQgvw1bUCRx+GswWKFUyA+hLxR6AfaZT35eHNDXVQLUsVYbx+jONvrK3UOcT2c0Ju8ir3rdtbK6YmvxLVtdq6LdntKXrqqkr7MRtmVI41noa+6tKs47s3YL5M0Otm05oS8DOKhbWYm6sb37HB3H8j31wZ8CeeMUrnOee+ZXQOYvE5Q5frmgVsP6uT7qokvz4Jx0r6SvEs7ovbo+fVUwd3BwJPRG/4QG25i+Dl+t4Fit0deNRUQ2NlGfA5pTphOcS0d90u856rdL6yRLbwmv01evxWCbBuS/llQez6N1GX0hpST7KTyea/j7KvV5XtIXQ3lOzrB+K+u4rrM0JHx6y/iA/Nt5grLL/nuEMn9dz3XwhgV9wdRz7g/fp1lv2+C2L4us0IerWVeTOZa7STGeSyXaqi98EN9M/tpV/MLTP/gN/PqyR/0c0JcV5rQOishWBjlNXA6WZz7Fidgx2G8T8sWCGeoBvwuyRWuM8ZS+lBtRDIbGpXHQl2y16YupNdRrQ+MqJdubkp6NCvT9CoP3t/wVNxHxyR+r0RcxA/q6Q5vmk/w4fdGDvmp7fY/8ywTHUl7i8arFPlunrx35FNPI6evCRQPLm+Q0n66x/cX7sa1z6IupoY/X77SxfBcoxuNT/RLqw1GCfTSb0YsoqI/r9IWDMc0/fswxoEV/9y7xqmzP1uYxW7lt/LouffHIkN9OelilfjUe2yrU/YzmqH36AufI4v3cCs4xM4e+bEHrmEmO159O6CvbHvajI9jPKfnsC/ECsg2jfZzzXfJTplkf5CCnr5IJts+QviCVJTSuCvIJyPZ79HU8yXCOLaj9Zy/xghUzx3VAZtDXa6y26ATaY6iSX2HRvqeWfDf60nhGa9U4xDIHFbxfRjpgKF7tcCwgpz2UMeqAR18bitiX9+kLq/QV4JQS6ThklqZky3L6mtIEYzgefZF1Si+mGM/w/i7ZlpI2GRxe6N49XvW66/jxY9betofldUg/uxQvo/GfDilO0cB5cEzOQkj2ZE72LqS4xGGX1vAF23XsS5+2C0taV5xaozUFzZO8d9GzOI83KAZfp3VXRF8y36Y1d6uBPv2AvqBcL9F+l7QGOn0Czx/PSfdpHXZynX1y3L9rLn7YS1o1PKcaYZtP59jm1w+xD8kcSExzUpXW4qMhxWAHeIEGfbkyEqzjxip+vSiMHZLxq4SeSzpLX1sbjzDG61GsoUr7Gs19LI9PXzCd05cmR4cYWy0drN9KgOXbu4ZjoLOKOjvep/UAfcnHoS+LeQvfZL4rvGrbs7a5YSu3fV22pH1HjskGVYonZqT7FGsPYvx9SjEkt6R2obEQVHCspSPsxyDC4/ySfOvicUv7RQl9PT6k/SDr0NejKTwx8NE2FSHNM1Q+h+KLzYhixNT+nRVcp47JV2mSrU6GuFdVq+M4yPmrjmYx5jMvcN3R7ZN/xMaFvhJoyX5mNNVSFaU7Rp1IBzjfVUPaFzC030NjzSvx/HqT4oh4ewlT0lGaDwzpQJLSPomh2AD5MkWKtsGlF6kbii0YHxsson2YocUGDGiPM65wvgnlMty7z3W9qnXX2VNnrblNnwuL/daj2GJQxX58+OQJkDdP4Vgc05drL29jTkbCofcE+6ndQD+iQg03pXatxuSDkh4W9GCt8XGcRFX8+vBDW1i/c5ex/GdO4Zy6dwm/FOxQikxM+1Me5SUFq/i1ucjHcRHSl94bKw+BXE0x3l4aLF/cRh9hf7QY83EoTyanmMraGvq2Ec0Paytk32m+KV20XStkv8Ma1rleR52b0jquSWvl4Zy+oN3FOu4dUt7P5/11kI+Rbzz5GLZpQH5VUEUd3ziJcc5JD3XObWJ9ewM8v3MM8zEqtC5deyt+wbT3LOaeTeZoO2NDyQzOPUsyftW+z/rGln3+2i0/cDTEvbqLl8+DPKZPQx+OaJ1FOWoZ2Qfr4vj7Xfoi8RsexPHELRVRfC9LcZ6okt2PY7x/b0TrPI99ULx+jeI4I/JZq+Qbtqs4Fuu0Dj2gr+Oe2EBdTGhvl3PMhj30bXhvxpJBr9DeSDjH6ydm0QfnPDmXYn8OtdnaCRwvFMqTjL7aOqMke79FuTUl6khJbbx5DOekapv84xz9xSrFaQ4pny0g38bSHFGN0B5m7MwZyoFwUedC2tOsdvB6Verjw138gqkl36ikfZ00xf4JOBZB+W2xz97fXeHV256tLbvdvzX+966i7alutEC+fg51P6nj2Nqi/M9sRPHAGL8WPJ+SLzPFdk6oX6OFvWrU26uXLoK8KhjjaNC6h7+kPY4oXkk5ab0p2t4x7TNf6mG8wHWx/BWP8lxo3Sku6tVwD7+83anRurNEXyglX6bVpnFD+87XLqKei4h4QQvk2RRVqLmNOjAe74NsA8qNKbHNu3voP/oUR5vQWn6aYZt0d7E8E8E+yDOM2V4e4vGKg/4tf8h36+QbQHYpX6FGzwHM6fxKFft0PsM+qVdbIO/zF7w7aEuH1y+CXFIMakL5bJGL7Tmg3N9YsPx3kVe17jpz5kFbX7nt+YomjY0Ktmt3jOsij4I0nBcotD4vKO8mmWM/TWgv3aP1b07357yidIL9PJuhHo6HOC7imNb3gjQpB2R/1MfykEs7Kyge4OL5Ds1Blmwr5wWOhni/cUq2RrB9D2kvf0T7rgXZwqjLC1+RNuXrZwbtb0n2u6A8w5Wz6Aflcxz7ToJlcoXWeQHay8Ti2KlF5CuSdxzSnkOy3wd5RLbRzbFNXaqP8bB8DvVRbqn8pETTEcbZphRnXOM9iAmOOceyVvIeDepYRLbJ53V0fs8+IvGqfZ+tjXW7v3/Ldp54AH0FS/snzTb2/Uee+nWQvdc/hsef/RDITz6GMdD9KxdBDiMcHz2aVz2KsWYTPL6+hnsXAdmXvT7qwt7O8yAf7qE9PPsQjiV+3qxKeds5PXvj0Drx+hjHYkExkIJywFZpjVFSPkGbfHYvxPab0N5U1cHfOxwTEBFDiXFFiG2Y1LGOO5eugnyBnomIxqhDK2voeyQ9/P1ggnP5ybCDvy/w916TYmm0n3zqJK7D+gP072vkj6ZkX0p6HspatG9pRvtzzRbIY9ofnyRYv3yO9atTLtXWOsUCC+pDCuKPdtH/XlnF2F+niTp1l3jVtmdzY8vevpSZkm9hp5SnxzlmtD7eG6FdbXUwpntuH32RN61iOzxPPuzqGup5d4DzVEy2qEHxyjjG+zszisfRPC6kV1GDcu5K9Fkr9CxQTuf7ZItceuDAa2J7zkmPxvR8VjGjcTNB2zia4Tptc/NBkDlnvUnxEhERfuFlRnH7IKA+phiq5+FabqWBc7NLMSOXnr91U9pDpWfYfJpPOO5lyBdwKQ4ZVNB+cw6wX0XbNd3B+YLCetLfwXVkRPNnTvfvtGiPlmxvSuu8CfmKES2bZpQ7FHNuEu1HTqbL833A79nasv5teQDVEMfuZIi2okV7AXmGY6lCY2007uPvKb+0JL+l8iDa+Nzi+dUW6fmM1jFjHMvTEY6jfID393Jat9GHFQ3laOUpzintDbx/6wGc06NGC+T/45c+APJf+RyMr48Sen4uxPImtA8+yrA8Ma0RpuRDbDSxfCk92ysiYmk/hJ8/qtP+ju+gbaqFuD+1HqH9L2j/7BTlLT63h3mNxyiX6nIX7fFryNctyf6v0/XH/FKreR/E0RB1oE+5nWEVx0AZYv3GlLt6OEJ5u4tjap38zJTOnw/R9oxy8rsuoa2P27j2kBD9pNLDdfRd5FX7Phtr67bZuTVXnCmxL3sTnIeOU45VSuuAhMIe4x7qynufwr3L6yOcFz6yi78/d4jj8fFHcL94Rpsrb3wz7kX+2q+8B+S1M2jfrn7kIsg+rVs4J7RLuTS79DxqP8N50Mmwrx99+HGQq33U9Te//UtAvvIzaAsee+vX0HF8RqV5AvvjG//Ml4L8D37g34G8+xzl/ojI/HH0068+jX0mlc8C8UT9STz/Es5B159Gm/nU5XeD/MbP/qMgP3sO7c/YoM598EefAjkWbOPLF9E+HhzQvkeKOtVLsY3XG5SnSHGhmOIwlyjXfXMT91V8ev6zQzkr0ynao1NVHIMdet73WAPly3s4xKtuC+SDFOf4mPIqPxmfTkrQ+0TkYWPMWXPjCeuvE5Gf/jSupyiK8qmi9kdRlGWgtkdRlGWgtkdRlGWh9kdRlGWgtkdRlGWgtkdRlGWgtkdRlGWh9kdRlGWgtkdRlGWgtkdRlGWh9kdRlGWgtkdRlGWgtkdRlGWh9kdRlGWgtkdRlLuC98o/eWmstbkx5ttE5BdFxBWRf2utfeaulUxRFOWToPZHUZRloLZHUZRloLZHUZRlofZHUZRloLZHUZRloLZHUZRloLZHUZRlofZHUZRloLZHUZRloLZHUZRlofZHUZRloLZHUZRloLZHUZRlofZHUZRloLZHUZS7xR2/4EdExFr78yLy83epLIqiKJ8yan8URVkGansURVkGansURVkWan8URVkGansURVkGansURVkGansURVkWan8URVkGansURVkGansURVkWan8URVkGansURVkGansURVkWan8URVkGansURbkbfFov+Hm1OK4j1VrjRXkw68Hx0slBzj0fZDd3QbbigFyrxSA3jq2C3Gy1Qa6u1kGeT7G8ja0NkPevXAW51VnD8owHeL+4A/JhZQfkZFiCnOYox0EN5DzC4zsHhyD3r++CPK8GIDsW6+uPuiC32tg+rQaeP4/wfIfaO8tSkIugwPPnWH4REd+JQN7exjK5OaqoO8pAns7wnskkATmKQpDbdWxTI3g9yVEJTIjHs8ocr091LF0sj+/h7xsdrK81Vbw+1TcvqQ9pxM4NyibFNi4iLF/bxTFh7BjksIp9WuUbVi2IzZUVkLevoI7nyUTuB2xpJUtmL8qpoC4nGdqa+RzlNCFdztAWjSd4PMmw36ZT1IPnJ9hxox4edwLsx1HUADmuYvlHLsrio555aColb5wGuSGoh9d3sN9yi3o9pXGYWbTdToDjqL2KehLGeL+wxOtnKd0/x+sn+QxkD9VSRqR3Ye+6MKaBZZqM9kCO6tho1sGbBBH2sbUodwdoy8p5H+Raax1kZ4L3j1stkKcF3t+h+cGj8vgejt3Ioo6GNTy/nGOfOi7Zghodd/B+ro99Whi8f5nimPF9lB0fy9PIsQ/33RbIWYL+g6lUQJ7NcC5YGlbE3tb2VZrH/YD8mAa2Q5Zhv9kMbcfMRb0oBY8XLrZzGNK83sex5ZHN3zvYx+MuHh8MDkBuDVAPErpfs05645FeODhnS0F669K4M1hfn8qXZeRnGWxvL0RbWeao57nF34/JT5ulZLsLtGWRT/URkZh829LiNTcaLZCDEK9hS6xjUME2MQ6WwZTYRvMMy1xxsI5xjNcLQixvzcff92d4vTc8eBbkNMHyBAnNtwZ12JBtCsLjINsC22syRT/Gku+bkG0z1IdFivMVHZYsx+vFTZyPPWoPtm3LxDVGGsGt8q/V0E72C7STq3Ucv84c+6LuYuPMqe2TQ1yHmAzbPp0MsXxkn2oNnHc2tlogd9bw+PCQfLUZ3n86GYEcuuijNjvYHisreL1mjPO0V2uCvI6qKdMSr5fO8HohzZOeh2PTJd+vQvYsLLC9KnW0DVsbeP16SIsEEYk97PODQ5xLd/bQvxpPcC6ekz/WbKMNjSIsc7WOx12DZfaE5rA51jFNaW0peP9ZRufTssNxaQ4xKLt0vaDE+pdkL7Mx1q9IUacdup7QnGBojjFkzzwH7VFM/nW9imNyNsX6R4LlXRZB4MmJE7fiJFmG65R0jvWKcWiL8fC49bGev3UFfeafeM9lkJ948vUgf/B5PN6hdh1mOC4a5FOPe/j7z379CZD3r5PPHdDYbeDYFKF+L7HfpgOsnyHfLvZbILeqeP0gIN+LfE0h34u0VjYoXpLnWL7JBPW4O8dxk5HtFxEpaSyXLtrLktZ2ObWhE6A9jVws00ab7B2t3Utap/W6+Pse2UInwDYdTNDffbiDbdSfkK8TYMylXm+BHGZYv50R1udMC3Xsn3/gOZA/ZxPnownFkJIprZUFy1fhuCu1r0+2kv3/6QTn/2Yb19XLwnFFKrVb+p6n2A7DEdpYF6st1sd6m4jaqURblNAcEfioZ7mwjO1oA7SNFRrLBY2bsI4+6HSG9Ql4XdXC+9db5KdQDGVm0BZO91HvOxRyYr+lpDltNEY/LDFoG+oUf45XsP5xC/Wqe4jlifq0psgW1/8ZOfZc54zW0gHND5U63sMj35WvZ8i+Ry7WuVpFPygvscxlgH3eaqDO1CkG1Bui7ZqM+yAXMyygR+usxhr2QaeG5Rv2MaZVJBQPX8Hr1Vo034XYXq0I56Oc1iI+re2LOZbfkl9l5f5ZdxVZKuOd2/aMCmyrIdmjdEp7FTHF51JsqyRFXbIUH+S5OnRxnpgZOh7i8Qb1jdeguNMQ7c2pFtojQ/HDzhbuh/XHePzaLrZHROtA4+D5LQ/t17qP5VuPWyA7Fq8Xky/Gcu8A22dwHX18j+bNFR/HZkyulohITnFzjqukcyzzwbVreE2DfbS1gTpiDY6H0wG2kY+H5dkejrf+Ptor5zra2OPHsI+r5JudOUb3I/+5nGObug7OsftD1IGC7HVAwzvBLpGC1hM8ZuIO2jd/hHNSNcTyHzuG69bBPt7QpzmqpP3MZWGNlcy/1Raug2PDof2k0sGGLQytC0psxxnFoL057XcleH7gYT/7ZMd5/W9T2m+jeSYRGos0T0dVDMrkc+rnJu1bXsOYUe1YC8936fox6tGM1m0ljWvPRz22IdqikGL8hmyJ08Q1RCXC8vcNzvsB7eOKiMQ+OmwulSEZYh8XPupISvkXAZ0/TzH/waX9mrKkGImHYydPcB3RHaCcUYyF4/TVBtpGl/xnXtv6VH6HYlIO5Q7MU9ShIsUrjqZoS4uS9lVi7NMm1T9qYP+srqKOOXR+SOcHpDPLoiitjGe39L1MsV3yEepqi+aQZIw+ZnzqGMjjHK/XbGG75Tuoh+wj5l28fubRfhL9vk9z1pziAQntfRQVnAMqbfRbxjvPgzwZUi5Bl9bnAY7DzZMYj57x3oZH43KPcmoKHOeVAo/3n/8pkC/84r8A+a1/7zdAHlJ/BMcfFqZWwT46GGKANrYoG4oxbLVpnRXhPeubaA8PD7FOeYRjKWFjQH5DIGiPNynX6co2/n7v4x8FeZKiTs2OY5+dP4e5ZDHt+Qbk6JgarVv7tE6ieL5b4vxz+tRJkN/7z34U5Df9xS8E+dTpTZDfdw33gMME5//f/q0PyP2CtVby4jbfh2LG9TaOJ+vQfg+tgTcpnFiv4vn7B32QP+e1mBfoUB5GhfZjEtrfjqqUt9hGXXrwIbSHewc4T9Ypb++wj/bBJ91qNFsgH98gH5niYobs2wb5Lu0WzsMBrXujEHX5LOlaSr7WuIfzblRFH10orlQ3i3mGyRRtnhOQn+5RnCZEG28D1KFxSTkCNdojrKLNn89wTppR3mKd4iwxxd1Z5xou/r5C9sm3eHzGfShYf3L3xaH9NCejfCrSYc5RGU9oH4B0gIovowHN2ZSwFlAsokbr2ixjg74c8jyXbvdWXq67h+U6f+njIBdzbIiwxH65cn0br1/FsRUmqIch+dyzDG2DEVrvj1Hva4L94Ad4v3SG/dwdoBzE2O+Oh/WZjbA+pYflrW+h7cwp3mhpnVglW+JSPms+Qb3LBxhPGR9g+8XkCxUBlq9GMbBalXLiON4pIod7uOdYi3DsO3PU7QatlSeUT1FmuA46nFGZ6mi/3RjHrtfE30c53i+nXKCqg3WaDNHeejHt/VNMxyOdoulBxmP8faeC91+h/S3foX0DymcRi7Yyp3VjnXITejuYR/+aUxg72LGUM07tv095lcuiKEsZTW7FZGu0/uWc4XXKIRCyoRPahxxP8fiA5uWM4s+TPo7FtQ7lpdC+b0zrljRD2zii2ODOHsYmNyiPJ51jP4VtrH9vB8dVs4NzdpNy8DzSG+PiOBp3+3h/io9b+n2bcvbqq9gfEeWwpXPUU8ehnJQS+0NEpLOOvmI6xTp41CbdGfaZT3mAKeW1ywx1IqQ4YmlRJ9ICbVFR4u/nBY7tZIZ9TGl2C88XGNo/6x1iffgZoznFGS3lzbRXcT5KyG+jZaN0Z7Snw8+8WMr7p/26iPbHChdtq0N+Z0Fx3WVijBH/tg7qbOI8dPEy5t09TOuwPERd6U9x3rg+xVyYRh/rPqCcrTY9E+FSPK9Jup/keL0J+Q51mtt9D+/XqtGan/ILjndw7F2Yon2ylO9exJQXY1E39vfw91tbWL7mOrZ/5OMaZbSPulf18X5t0sUu5eQFLsZcpvPFZ336lEtTi3HAPPTmMyBfx1QXqa3hXLxHc0otwHs2aX/c72GbzDPsk4zsmV/QMwshGpytNdTZznoL5FYV5Uv7+NxJWOL99ynW51ASSkZ53KeO4/U9h3KLmjiHDGnO3N3HOWS8h2PshefQfl45fwHkiJ5J6dReYpNzGThG5La9xpTyCGKf7Cat9x16gKikHOcmxd63tzF+971/7itB/ov/5kdAfuQBjL/t0DMBLXoGISdbxbkDjsF+rVZQL2ecwB5j/XxBX6u+Sj4+5RZUqli++YzyHKuoB7zv6tA8lZCtHk5x3bXfvQTyxvE3g3zh0odBPvPQlwuz/YGnQf6cz/9WkD/4vl8C+eETXwVyb/rLINc7j4C8kB8W0/NMGbbBgJ5DaVEMo6R8LYdi4HmBbRjS/le3i21asdRnBdrODcrz39ujPL4m6shohvdzKU9QKP/CpdynSYK2b4uen56P8P5C+x6WcklLc3/4Po44Ur0tt8SlfbkyQ5sb0LMhM35+gmJxI3owvd7CH6yefgLkE2fxufU8wTmh2qC9f9o7P045ZbM9XCc59Nx9g+LJtSrqhaG8x8paC+QePVvwFD3D/ff+9+8D+bf/z38K8kc+8m6QT21gvOAww/LntF9lKM8x8ND2VskvpcfZJKW8TxGRkp7DdmlsVOievDYOKIb9fB/n6QPKaX7j6x8C+e/80A+D/Mvf89dAfuH6RZCPUbx1RmtzZwt9R0u2KaBYQ9rHPh2OUYdDytOr0vxqQ9o7r2J9qxTDqlRofiNfuUa5bwXlSYmPtq5H5U8p1tE9XHyeeFnkWSYHt737YWeA+lilvu3Tey7W6HnMwzm29VMfexbkXXpvR3EZ45sp5w5RjPoK5cAJjZXX0zomtWhPYsoLfO5Dz4D86Bm0fzWKXw7oec6dA4wxFAG233PPXgR5hewXqba8/2fPg5zN0P5/6L/9Cp7gYUx81aDu/8LPvAvk7XOUv9CjgKqI/InXvAHkZ37iV0E+98H3gZyP6Rrvx+c3d7dx7V2h3HaHgronH0Jfqfsx3D9+/Cs+F+SnfwLfZ+VTzoQNsE02mlsgX/gY5lQk9MzEhPZI92Mc/7OcYpcU+/QFz7907WMgjwu0B1fG9K6Zk1if3QE/90Mx/33s4+kY7VFC+2ufjPtkdaYoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoRwt9wY+iKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqi3AP0BT+KoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKcg/wPpM3y9NMDi5ffVFOshSPD6Ygu06IF/AMiMM0B3lS4PmNZhVkS+8zckoX7z9PQJ4djkEuxxnI2Rh/X692sLhhG+TW5kmQo/oWyEF9hvfLsbzViNojxuPttU2Qh3P8+WQwArk3R9kkMciPPYj1yRJUl2PH8Pq9HrZ/08fyJVMqv4g0KiXI/QnKjkW5GmAbTaiOvqF3VoURykkX71cEIK9WUafEtyCePo5tnHt4v3SEBTLDQ5Cti8fnpIOTEfbJzOJx8bF8+Rz7xDf4e8evgzycYX3KGNtnNkMdd/IJXj9DHam0sP7zFH8/HQ/kfqCwIqPklv0waQHHfbINHrVjmiYk4/HDXh/kJMV2Kj38/XqAtqywaKtyD+VRhv1U7uI48OqoFy2qT93DcTQrsHyRaYBcjTfwfjRTBBnqdVJWQL5wGft99dg63q/lg+y6WJ5Jhnq4u9sDeZ6gbR4Ktkdi8P5bBq8nIrK3fxlkL0P7dWkb23BzHetgXBxbro/2bdg7hzec4nxXL/D6zbgGcpXmE8dHnU0E20xyLL/QfGFz1BEsjUhpyPY6+PtKTPMl9VEY0nEaQ6s0HzsGx8B0hrbRj7H8Kxke71H9ErJdOenUsnA9V5rt5i25jn5BUeJYMIL9bEJsp4zaKa5hO3sOnp/5NCdYbCffx8FtSpwjPJpTI7JlGflNaYJ6s7eDtqJZRT2fF1iftRbqfb+H5+c52sLMYvmMg3P6cIL19Vws/9yiTyA051UqqEe1Cs0VIeqh72J/TWZou0REIhfbPI5Rdsk1r8TkN8zR/jWrWIaQxmrk4fE4xD6w2OVihfyyKd5vPEZbc3W/D7IpUQeHwyGeP9gHuV7HPt1cWwE5J9vlezS2yZZYQTlJUEfLHMsnNJ/MUzy/1WiBHJOt3lprgnzt8q7cL7iuJ43W6otyHOG8NSWf0yY0V85Rnh6iPnd3sa6H17Bvp6M9kHv7OL5KQ/asgrrQbJ0AeXPzOJ6foPK2G7iuqnjYN7mL85Dvo70oUrx/keHYKYdY/nodx1q7QvaY7i8FXm8ywrHWm6IvU6F5bvM0+ma8qmqGeL8rz10S5tkLOyDv7uL4TDKaA8hfDSO2V7RuKHB8hTme79G6Lg5wDgxozprRHDmhuX4+JZnmFEOxg3SO9SsMratK9I68JtZ378JVkDOD9a1U8X6RhzpVj7CPut0+yBOyt7wua0Zo/9wc6xNR+y4L4xgJarfaLu1jPYIIyxmR7+G72C+ra7hOKcgXaazivDEWsg3rLZBba+iLlRbHXmMdr3dqA89nXyKuYb/G5BO3VtC3sQ4en5bki4RoqysOtlfF0joqI1+OYkijQR/k1OD5M1oXr23h9TwH5/2IxtUqhVtsvBjz2TvE+eHgOtqi2Qzt66GHa8taB/ukUsF7bKyugeyQfRZLYzPENhiSLeE4ZC1GHfGrqyBvkT9Yq+PxMkfbZKdYnpUc5+OLV66A/DVPPAjy9b1rIDdr2Gd18lXqc/JX5zjGqHlkTnFP9nebNF+HMc6ny8JzHWm1b+lOr4/1dAvUK0M+ZEpj063gWPTJtri0DsnIJtOUJGWJ7TYv8fyQxn5E67woxHbOC+z39gbaNj/G+rXr2I/nLr8AcjLFcTjpod+3Suu0agttXy0mv+sQfQyXQrW395WISLuF9U/JZw+qWL+8RNuYZRTLFZGUAlm1No6N1TX0rZIelnk+QftHy7aFtXW1jX0cNLDPWhH2ST+leXwT483jBO9fDSmm4qPfEDuodDNam8cUF2tTrKHlY5v6tE5zm1ifoII6EcfYh4MRlr9dJx0mX35OvrCtYIMH1P49mt+WiuNKGbVeFKccgkywbadjHF9FgbppLMY0e2SXZ9k2yK5BXRCDvkXnBI3PMa7jvA7qQqWOvtekxL4b0DrGdbHvsmEf5Lkh36yO8VU/xDX7eNYC+TXHKH7YJR+c9kpmWBwpDvD883v4g3MXMO6UkS/lC/otzQ20X/6YgioicpiQPWmiHz+lPb1s1gf52nWKdeXYhyWN5waNr9Y6ltHroa9RUCxwdIDroJh0YjxD34lC2jKY0Hie4w9oG0KKjDopofrWqM+n2Of7e2jvvBDP3yvJPntY/v0D1LljVbz+9gHW50QV7+euov1bFlaMzG/b2yxI+ROK780zrGdhUQ8y8hFHDq2PM4q/0f6VTzHpkPZdQyqPR3v7KfUz72U4tNecGrSlgx7avnmEa4Q+bZ3kY9ovIx/dr3p0nOKL5Es61L5Ce03zMZbPp3UjxyvLFO/PvmQuqNciIkEN/aXUx99cPqA4IOlAPsc6NQL0HwsXbZF1WyC75CxZWrsXLurUaEYxGapzalCHLK19DybYJ56QbeBYAzWZoRhKRr6jS7aS808432RKccMB+WJZSTpv8Pp9irlHAdlSirEti6Io5XBwq65Bie0+HtF60tD+DtmG0ewjeJw2LvvUrs0QJ5XuEPXa83Gs7E/wOMcDSvKxJyXO4UFAMSyKZWYHaIvy4AGQP7/5X0F+YY62yY7Qj8vkDMicFzUaY338GdqO2Rz1aLNzCuSt4w+D/MbP+SKQf/fH/xLIDz+Eflu8+peF8QPstDKk+WVC8xMt3Xauk688xzrFtG7IaB02HmOfbR8cYHloP27FoVywOerwgPaLBjnq3GxG842Dx3vUB0WCOrlaRT+tJMfKGJ4/ab6JsLxXLuIewDu+9k0gf8ePngf5774R1xoXrlG+ye5FkF/z6JNyv+D7vmxs3doDKiiPoUPxQ1tiXTsbOP4KirV7LvZNSusCz6Ju5hToyAosD+9NRJSTdZzyQKpVtEdtg2vsehPn5ZzWja6Lur2+heO3VqecLgf9Buth+c+cxPgmnS6zGc2jNE963D7UflP2QyiuZoXGQsmZLSJJTvaDEgdLWou613HPkn2RzhruQYY++b/k75Xkr5YlxRrpePeA/NsR/j6nWKRL2Twlxb4s+RaOQR3g4GRO+38h7W+lc5xzc/ZPM4pV1ij/oEpxowDvV3UpF4nyOF3K8ZjP7o88QyNGXOfW/L+yjmOjTWN3vItztcS0ziEfeyK4dxK1UQ9KyntrrLVAznlvP8eYzokHKKdqzPtn2A8jirlYg3o47mJ5hfb6PbJNnov92jmD7Xf+g89ged+I886cyhPWsH69fZwHVyg+68zRtm8+iL7R9g6Ow84GziUHEcXcRIRcCQkopzii/KpKhGW2lO8lAcYFW5SftPUAzl97+1gmL6J9jzbWqRnj+aFFndiKOcbUAjkOsU3cHM/3KL+tHtD+HK0zM/LNAsqvkgLH0FoVfRWX9tuqTZ5vUMfGtHZ//znME/2+/+HzQf6Wf/r/yP1AaUuZ3pZ/HZa4l7FSx353qxQzoPVyUqAPaykH2o9Rjx2P1nUeHo8ruC5aW0e/IqR5vqB932OrWJ9r+7Qf1cT67O1ieV0KwYRka5o0p85pI7RCczzniEQO1n+9RvWlHL3GQi4CjvuHz2D5qgGO48Mh1rfNz8uISMH7UxSzKMgXdS22WU7x2NKSX0GN6lKenKE4njvDNoxpvymgeZ3SiKRCW8u+pbx4joE4qIOp8DNGtEdb8F43PTNEv6/RfpRH7ZFSLmlOFRr1cc/FtNE/qDTIT+QxGNwfMR8REVsWkk77L8oO7x2G2HZ7V58Deb2DnVtOcH/l1IkWyBHFE8++AfMiPIrX7ZGuhJRDmsxQN9jnrNVwHmm2cbyeOY7zSG8H8zbqFBPfxDRFMfQMRY9iuCsruBec0V7wGu217h3gGqZN+2UrW5RLRc8iSYpjobaB7WFpb9zQXrWIyIzWlm6BZTp+DH2NzQ7FPSiHIK7R81v710Hu9nFO8Gjdc3CB4jg5nu9RG1ZovymgOXJvgtc/++gTIPuUC1rxUedaIeVBUlxnNMD6jvmZvhH2QUjPe/G+QpeCpx1UAdnt4vVqVSxfQfs+bkSL/WVhROQ2vzCn9W+lRu1OuSU1Cv63KrROo5y0KsV4/+m/+/cg02OBUpIt7NBcX3XQVg1THNu8m1NQXiTbwhHlWU7o2RlORZhSex2OUG8cioEPJnjcreA4n1K+fbVCfgDNq+Jh+ZOS9v5Jb69exnFbby/anmeeugDyP/5OfGD1J37seZD/8BdiHbYPMcc3WEVfaDzBfQvHUu7pFG3RlJ7XKmkvep7h2PPoea6UcjNzimtOUtqTpWcYSoqzubT3HVAyTa2B68w+rUOnFIcbU5+7FOMaJ7Qua6Btf+69T4H8mtdijPrqhz8K8malJfcDjh9IZetWPLDbxfUodYsUFKsvEnq2x0U/fkb7fscffBTk9XWMD6+toZ6PuzhWI1rnmBbqVVSlsbSGc/R4QOse2gtxaF8yo/W/9Vogx/R8w2dR+X/8u/4ayPNDjBlVA1oU0D5whZ63C+aod0VBexsl2rohtb+hvM8eHRcRMfT8L+8fObQwW2mg/Px53I95x1veDvLPX0Md+9p/9V9Afue3vBPkMa2L6lVsg8EE/ZSRoNylOGCfwpYVWhcNRyjPKF5co7U+5zuElFNcoz3ZOeWVz4b0fDCtW9fbqAOnac8iIr/v/GXMqe72Uce7nKe0RPLCyuHw1lzA+1H8DEBy2xpNRKQYU27PlHyRfVzHbGzh81jrKxiXcem5YH4nwZB85lmKe7Ef/ciHQX7gQbRHZ16D9uHpd4EoUYzXD2nee+Aknl+n+OexR1ogP/sRmvfI99mlHLrtX/1dLBDlncye/TWQ19o47//1r8byvfNv/R7IT5xEP2P4198hzLULqL9/53/F3/ydv/hTWKYZ5ZpHGFdnf/btb/0ykL/kj2HOwM/+JJ7QehZ16gP/+QMgv+5tfwrk8agP8nCMc9Tr3o7rrIvPfwzkKT0nk1NuvRujPVmlXPmtJs6phxMsz3RKzyzS86qlS7HNOrbvlY+j7zhNcIz0emgvowDXC/FLPFfzUtwnqzNFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFOVroC34URVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEU5R6gL/hRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRlHuA95m8WZEXMjwcvCh3sxSO+y4Wp1EJQA58H6/nRyBPkinIeVKCPB4evmz5+oM+yKPJHGRnjtfvHeD1s2wE8tTB4739HsjR6nGQ50kBcncP7xcWeL3VWgfkreYKyEUcg3z1ah/kvb3rIA8u74K8c3UHyxttgvyR81geP8D+aXgZnl/UhLHUR6nvgtzqtECe+KSy1GahSzpTbeD5A9S5WgPL7MV4/2k2w+uXBuTruwcg+3P8fTIb43GPdNIm+PtkALIf0+8T1LFqpYXnT7E8tXodz5/iGKiHVZDnObZPVuD9YtsGufSwvQuL7Vlt4e+XRVnkkoxvjb+ZRb04ht0uRYTtlnkW5JlFPRiWNNan2I6xh7aqtdoCeTJC29Be2QK5HA5BnpbYzoMC5XQCouyOsR9711Ev7QD1bvcAx25AeuRl2GC1rTWQo4BsV72C54dom6IK2gangvc/f2Uf5JX2BsjjAR6vkJmY4LC88bedPsjtWgjyTg/t3/rmGZAPhthn3S7Kky7apuEutnG2hzqycQLr5NexjUsP21AcPD6YYqcHFis9n+H9Shfl6Rh1otZogZymxcvKQYg6biyWzylxPq1W8fpZhuV3yxzkMEAdrJWoI9d3sL/aFSzPsjCuJ3F7/UW5S7oYRFjO1MGxHNbx+HiM7Rg3sV3sDI8HPup1SP1ehtRPBdmuBs4R7VXU05UEK+SEqKdJ2keZ9HAyxn6fDHHwHu7hnJYlWN48R1tcFmir0wyPV2Jsz8LF9pmPuyC319G2DQzq5coJPD4d4Zze3sL7i4jsXLkC8tqxFsiBYB03j2EfmwLta1TD3wc+zsszi+/ynCY8lrFOl66hb3jp4lWQe2SPyxKvZ2nsTkfod/guzpeVGPtgu4X1TTJs0yBCnTSC16tGOIaE5uuUdHC1gf5AmaEtrFaxfFd2sP5nNtEXf3aCtmmZ5EUuh/1bcxP3de8Ax9d4gHXPaJ2WF9iWRrDvxaHjHvZFaVBXM/JdBkPqqwz7ZmcHdXua4PjznBbIc7IXhYu6M5hiX3X72D4JrwMF5UYDx1a9hefXKnjcoKrKYQ/tZ3eIcqWO9qzVwLFQUHvv7qBtuXQRdVVEpNtFm5vT+A0rqO+dNbTp7RraUBq+koywTVMaD9MZttHEoTmNbLIf4JwQuOg/VgNa92FxJKc2ykrskznNGb6LOjk5wPJWqmifHHpVclxBe+AYLP80RblP7VVkKLuC5YvreH4YY43rDW6B5WDFyvw2hd8b4tzaaWA7doe4Tml4qAfpCMfG2skTIFcM+QIx6q1vsV1jWu/nEcqVAO/fO8B5bHyA5ZnQOsujdWWSo6K0KWbjFGib3Dn2834P1xA7V7dBNqTHDq0RJiO07TMaBw7FI16oYPtFPp6/soLts7KK/RnXcJyKiMgetlFO9tfL0Ra5FDPpHpI/uNLC6w2wTqubGLcKIixTnfq8HeP9q+RrHB7wOgXPrwYUh8vQlsxpMTrv4tp+dIhj5GAH15UrLq2lqc/W2i2QM4rplPRe93kf65OR7+YKnm8oBraxQjpM67SlYYy4t80LToDtFrhYT5/X14ZiPnPUU/ZxXYpPT+Y4p/VpXTaiePWM/KD2OhkPcmmNg7Yhd9Dm5wbHJvsdaUp+1gD10s5wHES0vq+Tnq0eQ1tc5Hi9IMPymhLbd5ygnB9QvIR+34mxQVaaTZBnK4s++OE+jb0B9kHWxLHE8V6X1uZN6qJqA3WoQuuKnHzlRkh+TopjraAqTOY4dme0jnFoS6fdwDZpVbB+VfKzmlXqI+rDCi1loxqtSzmux3FTOu4UvA7F3/eHOGamE+yvOrX3fI7nLxPHcaRWvVVfJ8IBOCnIhw2xLcYjHG+1APtuUsV5KSpxvJQGj08X5lmUJwe4bqiQvXz2I0+DvP7oGZAvXcIYwXyC81YrQPswnGN5146/BuQkRT/BCOpamWD5P/Qctt+Dx8keD1F37BzLF3s4Vj3aK5n7OE/2Znj/FtmCg2RPmG2KqwwvYpmCBvZ5PkOdySPco3v+HJZxrUrXW3kIZD9G+Y3Hsc79k1iJc+fRvqyskn9K43V3gr/vncc+FkHfxtvEPvVn2OcDWiemfVzLZiWOiZT2EzOKlc5mFGvMOYaM7TmL0X4a2qfYoVhfrViM9S0DU1pxk1t94dC6oKTYfU522FBMOAhxLA2pn5oh9uOcYv1lhrJY7FeH1ucO+Ta+wXExTyi+SDEjv0F6k+M8N9tFPUxpb71LMbGcYkBhFeWM9LQ01J5U/xHtE5c5zXNz2hcnW5PQmmM66YM8HNEGoIj4PrZRTDGcE1Xcq3WojZOc/DPaLypSLPOswHVNkWAdJ7SHKLQ2Lsk/neNQExvg/Dk9pBi04PGcdLKgC3rU5nPaTytoHdWg2IJHMSCPYlJ5in3IvuBwiPej5YWMJtieUYXXybRAWBKltTK9bW6ekk0WF+ecKfVzd4f8/gvXQA6oXb0KXs+NUI+HlDOyu0exSfLDmuRT9g5xTnjydZi3E1GeUm0F49GW/K71TTz+nnehfPZxVKSDEfbrDsULxmO8/pj1aBd9joD0uKCY0HxKtpX6p1f/MpCNh7HO3aefEqakhUy32wd5Rnk2JcX9hlPc23WwCgsB2FqV4lYb6yDv99C+e7RXPAtIZymvZZ9sY1rQ3nYd+9Rp4J7pqoPnD3oUo4nQNuXUXmkf56/ZIR53LPqBPq8TH8b8km9+E4jSXDkG8mMP4JicUQcMx4u+7rLwPU+2OrdiUob6hlwPqXio36Mh1jWhmG5GujwdYV8U7EsFOM+FPvpKLsVoPYq5ct/2KD5YWEp2qZN9pPu7NLa4vBOKwe8f4Dop8LEBM0v75xNsvzHpXo3iuZbO7yZoj/b72N5V2uuIAxy7rl2MAcwSLMPaybMglwXWKaK1r00px4HiQl6Bx13abzYTHB8h5e3Nxzhe+0PKG4zQvqUUd3FdbCMnp9igS/6rweuNKDa52UZ7mVJ+W0I5GyUNKs7lba6eBLlFOmpWcF0VUh5oQe05mqJ9rFNe4rJwfF+qm7f8A2eK9djv4titUDys1sF2cyz5mCnO/SnN1WmCvorp4Nwceby3jWO9dRznhdFV1EuvjvOgO0G9qG/h/eox1mcmtHeSY/0y2i/qVPF69rUPgzyfY30uXscY1iOPPgJyVKfrt3F9n1Huw0odx8mwR2sUqk+9vZhzFtJetSe07shpL92grepTHvmY/LUG7fVubeJY2tu9APJ8gjpoc7QlVYpZuw7GfE5vobMQxrj/U69hG12/RrlCBcWEaJ8hoL3yMsf2ius4X+RJH+RV2tN1Ayz/pIf+cFhbxfMb2F+fTc8Z/OCvPQfyW//w60D+wC//jCyFspR8emseaoSUY+XgHJHRflY5QV32cpRpWSTH17DdQsrpSpvYj50VbNf1FbJ1vF1D6/tAsADGozxHwTkiIT9qs4N6WlJOxSbp/blt3Fufka3pp2gbT3fQlpx5EOPda5QDff4aXv/alY+BXDt+CuQ/9LbHQHZrePzj5xdjPv1DrOPONq6l/Sr7nniNCuXR1WjshbQnGHEu65BsHeVeeZSL1ohbIM/mWJ6Q1vIUNpMJPT8RUvlDml9oOpSEYlSRh/fLItSxnPyiKMLfz2ccD6a8nQhtdb2C7bO2gTp7uI22tBYvPlOzLBwjcnta0rVt3KtwSqxbkaPvsUF22w+w7U64mMfhUkw6oXXVlJ73qlGObDJCXQlo3vU9LF+F9gIs6XJCca3KFo5Pt4praje7iPen6xdjnBdH9OySob7PyhbIhwmOnVMhtt9ghLag08GYxWyEMZKIxubJB57E+7/E3sd0iPpazHH8jcn/vHIVfZsK5eL09rFPR5RL09vDOndalLd7Addhc1o7+34f5C7FGvu032Z9ykPO0PfZOIVzpL+J65RaiH0eUt4hxyJmtA9Ro7V4vYI6PiZ71WxjfdMD9IWOn30U5GEVdax0cD7JMlyPLAsjIv5t83nFQz2reBwjoBgIzVMNstMr5Bs8dvoM3r+gvArez5ming4pjyOnrZApjaWYY/sUf8jJN0nGKBuKZ6YZxXwc9GUSzlUivevTOqnwMEY0pzzPrMB5bEzPu7mG9lro+TaffPCY5s0X9jA+LCISVFEHvv9Xz+E96Pmkj13D4xPKVxrQ/leX1r6e4T072r+iDR2HnsEY0/NkrqU2phhVwc+HUW5SyvaYcmPHE9TZLMPyF/RczZRyuANKbp1Nae1MtqPX7+P5AY6Bvaffi8X9iq8EeecAbc2ph9G2LgvP96WzcStuMkW3R1ZXMaZy/DTl/RQ4zw4ohykQbNdkRLH3GOfQMeXFjGl9PUxxLB+cwwLPKW+xFtL16HmKKuUWjChe69OzSM/v4hzsU+yyeO5DIJ88dRrkpy89C7JbxfMrFC+pbZItKSk3PsM5fHefnuWlnJbIRx9gmiyuu3Laz0oppt1qo69laC99ew/L/PzvvR/kSynuJ/2JN78B5Gv7aH+TDdS54QH6yrstPD4XynPpU7yYntFrhGgrdmiPtUc520GAfk2NYlhnH8NcMEvPfxQ0f49maMt29ylHnOavEw9g+zVpD/fYegvLRznjhUPPPC0RY/C9Ao0Q+86l57dmlKsyHqH9EIq/uTSeHjyNPuvqSRyfJxuUR1JB+X2/9kG8/z7GbPvXcXy97vWPg/yaM7iO+Z1N3KsI6Vn0SonzRGMTdfm4j7//3C/CdU27+TzIV8+hr2FyrM/p12CO3c7z7wH5696CY6UToW8UuOiLFZR0+0KF7Pf/74eFabWwT770p/8xyD/1FT8P8gf+X3o3SfMyyF/0Jow9feFX4PiUvA/ihRHO1Ukd+ywboY61KJd8to/rEo+e72xX8Xo57ZOc3cL9pqu7L4BcreL4XXkQ6+dMKJ+McnNHtN1dWrQfkYN9eo3yzaYz2i+kPcyY9gfPnkW5XvnUnrFwXvkniqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqK8WvQFP4qiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIpyD9AX/CiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoijKPcD7TN4scKycqOUvypN0C45Hfh/kZqcJcjWk69VbIPf7Fs/fWgd5UMP3GblxDeS1ZAXLU18D2StTkA/6WN5ihs3pxyiP0zlef47yRqUOcqOJ99/bx/vNDgqQQzcB+dijLZDrJ9ogP/wQyhcb10DuJ9he430s76wIQM4Fy29y/H3qxcLY6T7IZx99AOSoiueYEPtof3wF5HYH2zzwMpD7ISqRFQNytzcBeYxNLPtTOo6HJcqwTUq8vJTTKcgrax2Qq1UfZIfOr0QVkH1BnR8WWH+bYv0dJwfZFZSjqgvyeEo6MMIaD3soV+tVLLC9P94hVpalTMajF+VZgbo5mNDvPdRlr4LtEuZYr8xDRZlY7Jdnd0YglztoSzYbEchjfwByNcTrV+urIHcH2M+1Cuq5l+Dx7nAI8uGViyAf7OK4coaoV50alv/YegvkzS0sX7WN43iKzSOxh7bYMVhfE+O474+w/PM51nd60MMblItT3WyOgysOGiCPDw5AthbLON7BSvTH2MaXn78K8uAAlexyiWO99sJ1kE+9Hsscrm6A7NZwrE1IBxoe6vhBF+eH9Q7+fkZje5LRfGlKkB0Xyz+bk63xsPylj7YxNXi+66KOJHNs31qI9d3euwSyLbF/XDaey8IYEe+W/QgE28kWqOtjstmW2r20KLsutlO1ie00HM/wOJnk1MU5oBpgP4wT1It8jrbJpfrwFHB8A/24osSxOh7jnFYWeD3fR9sYR3h+MscK2TmOMy9EPXTIL6k0aQ52sX0bLdSrrIe2vFbD9ip2sD/bq4t+Tz5HW3PqDPp6QYl9urWO89E8wTZxaWx1x9inl/cOQb56cQ/k6QD7dPfCBZA9wTrNh6gTcUx1pLFXC7C8XoBtnNP8NOhiH3YP0Z5HVdRp38H5eSB4/UoD5yPPQZ3ZfPgkyIcu2uJ6C3XY87A8Z07hWubXfhNt0zIpSyvT22xzQL6ECdhnxbaLyZeoNrGvV9o4Plrkm4xprhaDurG9h2sAKbFvZgn2bZ7iPNbvoVw0aN2RYX0zi7o7y7G+UYjlMz7WLw48Oo5jdUDzcPcQy+eQbygO+fw0H7gFjuWd89he3R6uKSYD/H1JawQRETfCe65vtEA+cRrXzlGMbWIn2GY2w3t6NEdNZtQHOZZpgu6kjLELpeWjzlVJJ9ea3CeoM+KRTlF5e2NsQ9fBMeFSeat1tAdi8P6ug+WbzfF+BfWJQ/WLaRKtV7E8Jx/aBLlLvmrpYPsvC2ut5Mmtzp2Qj1ezWK9xge3m5ng8K1E2lmIuHvZLSNd3SS6HOI/ICOf2Ec3Dkz7ZshTPd6lfc4uKvUvxBC/D+mYl/mC1jfPmcIy2JKGBMx6jbauEeH5Rkm+zSmuePs67eY73m+dY39kBjrPRZWyfZo2CdiJiaO29Vkf/D0eKSK2BY+HchR0sk4M6cOGjePz4Q1jm3EMdeOzMcZDHc6zDKMb775L/F/nYptkUzx8PsQ3HA/TF/JTsvcX5I6MgVL6L9XMLCl6Qf+/7aLsz0oFqlWJGZLsS8v24g5pN7L9RTrZ3SRTWyuA2Oz9NcazwNJxn5FPGWC8ToOyQj2kD1JMgwn4UB6+f5NiQwzHqTTFA2aN1Tz2iddoM5zBvgnP89Ar6DYdCtmSIPq+f0fUoNuiQHzfNsL6jCZ7fI59+PML7pzPsn5xireSmyoT80Cr1V0C2WUTETfCadoa/yWq4zop8ssd1vOc6rXVjOi4RXm+P1k271OfdAa4rQvINC5qP6lWMi7U2cR1iRn0sDvkZDQ+vH9NaeTbC+81c7POSfNV0hjownKCt2xlgn0c+GqvhiM6nOGUypbVJn+P994ftERExxojv3ipfvY51rTZw/8URLDttL0kjxrY7wfspFtewUYV8qy62bUFxjt3rF0GejFAXXdLl7h6OJSdBXfEjur+hmG2DxkaOMYj9PYxRPPTwKZAPKL7p097PiGIMso72YUj26OQT6Asdr+G6b0D2ehphfc+sYxzpY9sfF2b8FI5/Q+OdfR93QrGwgvxNB23s7nW08TtXtkEO8j7IGw89iPfP8X5tmtOq1Oa1mPYtcrQP8zbKM1qrGg/tV6dDBpVcG8lwjpiMsDzNBseAsUXjEO3dkNZlHu2h5pZikznF9tYxblc42B7Lwncd2Wrd1pbkwqVC6yxaf3u0vq2S3Z+XqIdrKy2QZ7SOSafkK1C80aH4JYXnxLN4vu9jP5Yux8gp5ku+g1tDW+tRTDqh+qYUY5/McBwXFGMqLep5TM5LOkfb1mxhvMWlmFNGvpRD8ZhK8wzIsy7OuzeugX02mGAdHINKUKW14XSKYyOMaW0+pz3QBMeWpb3gSUbzBS1cItIJ3jcwFBsw1Ge8183HS7JlHu1nlXQ/n/bDVmvYPtaj35O/3vJxfsjJf16lMcgxtXBI+y4u7c9xzGtJFEUq4/7lF2XHoJ/iVzE+bGgsW4diY7S/NKG8mSbFOOqnKJZP+64N2hZMLfohVQev3zqO12/WsHzzHMfRYIT9kswoJjSgeO/mWZCHlJbVF7z/tMD23BtjhSZd9CmKPq07+xg/eP7yObz+GNtrp0vrUooffPA51PO5QVslIlJdwTJXItTVjUYL5N4Y65AM+iD3D7og8/zRId8yoDJHFF+trePeetzBNrc0n+yQ3+HNaL+phfc//vAxkN0+DvbDCNu4WqA8O8D9uID2SPKC1s4Up+M43ngN63+iinJaoI6yY9rYxDEcCAXsl4i1VrLbYhGGfcZeH08o0b7s7uyCnObkhJIPXaYoJzQvWZ4HKzhPNmid4dJeaZ6TLhQck6aYrMW+rlB8ck72pbuHcZ8gxfE7HGO8s0L7Y1OhPJARxVgoZlGQbxeHaAuqFIPZjLC8G230TQOKaYQJLxpE+m3U706nBfI0QR053kF/bEg64wS0H0brCM479Ou4znFojpvQ2nK1Rb4NtZEjWF6aAmWF/jAh++eW7BBjfTqUQ5KTL5XT/luf4li1EHVaaN0akK9X5Fgf6g5xA8oV4j2EFdqPWxJlkcu0f8v3nlOOmU8x4MkA1+uUEiaeRTvrBmg7ejs4D5o6zQPSB3njAWyn7ADXIfPD5/H8GerdAeW89bZRL0YRxh8bpHch5TjvfgxzjpsPnQF5vEe2j2x54qCezg3q9c51tOWTEbZ3lfyQdcpV6qzjuD0Yow+elFj/hHLgRES8CMdiZnkfAmMMl65hnz7/HMZwxEUdaNE65MQaxiT81+Hpz+6g/zeaYhtUKi2Qx7TXbyhGPKb8iWkfx+aUfIeEYkQrlDsUt8jWTHHMnFih/LYZ7W9NsM953yQztI5roM5cuYJ5m/UV8t3I91pdJVu3JBxjpHZb33i0vzOmfUuPbGrSQ5+7QrnyNA1LHKItKCmeQKZMUlq/FuSHuBnKE/p9G9VcXIP361Aen0O5Bg+cIR+/TXP+Jo79w8FFkFc7FD8eop7T6fLACcoLpA3HZ971MZCf38eYzVkX73eig+vaGfmVHuUIioiIwT5fiVsgxxTDlpSfD6B1DnUq+4KWArYe2TqH9qKnlG9QJV845zxwymspyC8pQ7KtDo4Bn3K3sildn+KWY8o7zCnO55OfVaWcbKEYDeeqGdrfE4r7lSna5rVjOL9utl6iz5eEcYy4t+1JdykHv8PP8lCM+TrFhGsUE+jSXmOFlHGb5p0y6ePvV3A8pQnOgyHtRSZTyvM4hfbFozX2qQ2Kyxyifeo7LZCdJq4xTj34Jjy+fx7k1c5jII8znOdaVbze6sOY11K1FMeivZ+M9tvrq+h7RjRf9Pr4jMhkMewjtkT9vb6NfXywi3PthYsoVylZ/erlPh5vYZ18Gj+ct1uro38WZ/RMA8VkHQ+Pc05ERnmNH3/3B0G++BEsj2lgG5555GGQNzfQ17hyFXWsHdBzR31s39LHMbC/j326fvIhkPuHWL/H10+DfO3pX8fzH8PyXr6EOrA0rIi5LT7v0bqAUmOknGE7uTPay6ZYuiEfdq2K/RqG6Jy4c+y3eYLrmEDQVuS0/ue9lMyiHBmct8cTnFdcl/ZKyOf1aYPNUICv0cDyBRV6Von2WasV2m+jNU1Ux9/PUsoLpA3IIMDjBcUPai3MnTAW10wiIlu0lkxm7wf52IOYTzDPcZ1VozjUPEUd8Wlv3KcYtE9rXZ9yW+MSdTBz0DZFIZ6f0n5Us877JhRnTDkmi2N9SDHshHJ3ClprG9LBKuVUVyj/ao3m77On0f9u07p488nPAtml/a8O5Yn63v3xfFeWprJ99ZYdHJFfXvVRb9wSbXCz2QJ5eoi2p5ihn3LlQh9kp6C94BY9O7ONMY/cYLuffz/GfOJ1iqfS3kHuUb4u5URnDh6v0LMG3RHNaU08PqF8170e7n30DlBvHXp+LaJcAP8Ktk8rpAB0hfYb6Tn+ZEA+fwXbo5wv7n1YWv1WfPRD+j0s02+epzjVDO9x/Qr6nie2sA7HKzi2ZIz29ud/DefpZ5+nPMOQbFGOzxN86COog1sb6BuePUnPZvaxfs8+j+fPJn2QOw3UmcMJjoFjJ+l5FPLrXPLbXBoTB9u0xxFQgJnm6wGt86Zz2hOZLM43y8LzQlnr3NpDLn2cN2yCMd4Z+aw+7fU16L0bZozXqzVQF/d3KQZNzx6efBLjkceOoX2Z7dP+Ma1Drl1E3Xngda8FuWRXooK+weUXcB1Wz/D+1/dx/5tC6uKWaN++9itwnjp26i14/VWMGU9ewPoPn3kvyAc7aK/ODbC8q5R3s/McvedkczH+GO9iG/7ib/4QyH/3L3wV/v4voI31q98Gsk0+DPJ/+S3Mbfy///V/Bnk4xjiFF6DO1Clvjp+J9kJsk1kP7dPuuWdBnlIssRih/Wqv4Fr4QXre9uIhXn8yQPuQ0bPsMdmfET0jvUX7cV6Tfn8J7e/KSSxPnd5FELfoeS+K4X8y7o+3byiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoijKEUNf8KMoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIo9wB9wY+iKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqi3AO8z+TNUuvK5bz5olwYfL+QY12Q4xoWL82HIE9nFmQ3SED2nAldz4Bc22yAbDO8XqezCXI5noJcqUcgN+KTIEc+1u+xkxsgB94KyNM1vH6tdQzkp9yPgTzaG4B8/vo5kGPvMSzv6irIZ7dqIG+++WGQvdAH+doBtt84zUA+7I1BPrWK7Rt6eD0RkTA7C3KzXQF50sN7PPcs6sBgjNd8soP39D3UiSSdgzycjEAuZ3g8sSnInWoMcrWJfdgKUIfngm02OtzH8sWoQw79Pk1mILtSgBw4OEZWatTm9TrI4mB5fYs6V85zlBPUscDF+s8N9k9psfye4JhaGo4rTvVW21SyEg6nFsuZZ32QD4dYr7CC7egYbLeogXo8ylHPuiNs1zTp4fljvF7Nx/KFNdTL4Rz7wXrY79XyGshPf+BDIHeCLsg7wz2Qn3gQbdvpDdS71z1BU4mH5ZsYHIfbB9j+7QaO49kQbXc6R1s6uo7tubbeAXmc4P1Kg/cTEZnPsU2TeQjyoI9jrcywjIXBsVmxbTw+R3s8TtA2DPpYxvEA2/zjB1ie1dOoM66P1/PcAGRnfgiyzbC8+ytVkIf9A5Arx0+BHNH9Vk8cBzktUAdbTSy/9dHWiYc6Q+6AlPT+wWyE5Q9cPL/ZwPksL3FMLQsjIs5t9iUvUHddF+vZbKGfELqkp2McG4b6tcjQdsynqDcJz8Ml+V0B2nia0sQluczxfqPeLp5PbiZ1m1iyDV4F9bh6Csd2o9EE2a+grU0G2D5zsu3FBMtb31oDObQnQI7bOE4GhziuGh0sz2oHy9OgcSYi0qnib2oNbPNhH+flAyrz1Wt9kC11yqWPXwS5t49je3aAtsYr8X4RFkeqrATr+IPYxbFdOtjmzQa2gXXRtuZoOmQ6RT/MrK+D7ArqcFlQH09xjKUJ3iCj+k5HqDPJFI/n5Os2qD6TFJW6s4r+wTLxglDWjj/wory1ifZlRj5wPkZ7wZ0TkP3wPRyv7WoL5Br5jEWGvsHYxbYXVHXJBHXLZmgvHEHdrMUoZyH6QkVBvpWL81StQrpdQ/vgko87naP9HQ5xHTRNcM3C9WuSfVhv4jzWWcf2Pn9xB+RDunyWUXvQOlVEpNXCOj/wKOpEvYZtcHiAc+n4APtASlpbV7FOtEyR1gb2yYx8o3mC4z9u0jqJ/OEowhvkOdqXMKLzU+oEsl9xQL83VAGSu10cQ/0e2tec/M/aCq7TTp9F+7Z+HHWuFrGPgDrR62J957TOXRZWHEnNbbqWYzskgnoYtFEPGx2c+z2yNfUA22VvhP0QFTTPzFBPkxTHapnj9SaHaAvrZFvmJf7+9DGMGU3GpBdTHDfdQxpHtF4+dhxjNu0Otl/QwfOHXZoX52hrkwLPP30Mx/mojnoZkM/vlqiXZYrH834fz58urv9r5EsEFHMpLMpuDX2llQr2oRugvOvg3D2gdcP2/hWQr21fBzkS/P2UHFY/Qp2ygvZ6PkWdmh3i2rrIsH4tigHVKrRuKlHnx/NtkOMMz+9duQpyNcDyV6g9owjLT6ZfggrNvym292GPYhn+or+7FIyR4raJJ6PYWiXCdvYs+QGtFsgZ+RkT8mOciPwi8juiY6THU9SjS1cxRjOb4vULi35SRnOWSz71ZIB6Nzk8D3KeoW1q1nBObJ9BW+CSYvA4GIxwjbF7FdeB8zGOq5Ti9+kM/UyX/Mz5CI+PchxH0Qr2X6eKei0ikrjYpiX5CUIx/rCC9r3SQt2OXYp3WhxrB/vYxrs9dNbmtM6Z59gmfol9npNtrNGeRb2C809YxbWp46H9Lyd4/ZnFNs4zbJ+E3KbCw98Px2hLU/JDDvfRNgr5RSWtFQztQXD8O3OoQM79880Ka62k5a328cm36VS2Fn5/O5Wwj7KP+l3lmEGCcqNGdnsF22anj3sRMkNfozvDsRL4aM98WgNXKzjeoirOW8kYfZVV2jsRihmsb7RArq2hLtQreP+HHsH6eVW8XhCjPeuPcGxkVbTvdVq0rMd4/4aHupd52N4PtE8L09nC8RDSWnI2JnkbY8jFdRxvlRnOvZdnaGPnQ4qD9HD8XH7qAsiHBttslJHNX0F//MxDrwX58XVsU3MWdb6Y0P5ZA3VqPsX6P+BRXH+C9nkyx+N2jPbEI/sUu3h8sof2aruBMW8x2Odbpx8AOaZYQU5r/2XhGEcq/i1/Iy2pXRzywckXMg75HhQjcg354DM8XlvDsW1i8jlDmncLHLuRj2PPoXk1CPB6fgWPT/o0T1K8NS2w/oXB+jbW0Vb0XNQDz6eYFsUrCorxN+r4+1r4BMiDPfSd6k20bSW19zTFceBn6Fc0Nxfjj8N92jeg+WRMYyfwsc2kRN2OqU0rFWwja9H+5+TbpCW2mWfQfvp0f/YlopD8X5o/rCR0nOz7Cs5XzRr1MbsSGdoSQ/WZp3i/zMHrDWhPkufvkvfDCporBNvDtWjrnXAxzrcMyjKX8fSWb1Gv0155gDa7soZ+x+4OxjdnvE6gvftTZx4E2cQ4dpot7Pf1Fs5RU7ItNRfb3dI8P53Quob8tvEI1120HSdmjL8fznAsJyMs/9QnW5pSjCcmW+SjLRhTTGtKMaFsTnsflJuQ0Dh1Cjy/P8bYcOksxh43NrCPK+SbnVrDMgYl2s/9LtYpE9SBOcUVsxmtW7pou5IJjp0exYM3I/QDVsmvmVNc0fHQPhsHx/pojDGnMMU9xGnGa3X0S9pka32hOGiItnxGa2P2rZ87/wzI0RrGn42h+P8I+9iz2N4xxeSWSV7k0uvdat+EYq4l2VWPDD3nVHG8zHOw7cMK5SHSdpZDa9T6SgvvR/ePG3j9xgra9fEU2342R92JLOpqf4hjx5BPm/TQ9/ADvH8zRrlGPntAMYgmxYlCj3xLikG7fgt/T/HGnM6PSC4p76Wao30SEXET7MNOBeeETgdzdyKauw8H2EdVikNMaW73KW5e0v62oT4/dgLXimFMvgDZl5TasL2CfR7QurIo0KbPF/ZBKLeV1vqjwXMgd1Yxt9QjnfHIX04oP22fcijyBOsz7PZBXl3HOXCwjzHw5pkzcj9gjCdO2HpR3t/Dcp49gXb241eeB/mhLWx3YylXh9q1T/FKSj2RgnyLfIi2aC3G+3n0+xNtzPs7P8Z4QULxhu5ltEXrMeXW+KinnYceBfnKLuY412uvA5n3YXsT2j+j+OU6zWuNEH3PzVWMT6zQej6IUO9e/wT6ZnNaR45TtKUiIkWGY+3cxUsg76O7Kxcu4Vi9uodzbxhRnh/td33uGNtoaxP94yHlwlwYYBxwhfbK65TbWqZoX3uH6Nu0aV0lPpavSXuoHu3dl+RvTiZ9kBuUF5hSLk/TQVtiUtTRmPYFKhQnbXdQJ8Zz2s/r4vUOQ5rwl4S1hRS37elMx9ivdor9vrKKY7NB83y10wJ5NuuDTNuKskf7gBWK5fcTyumlmE/Fx5jLpevoA5szHLNBvWxt4PnTCOeYs0/i3sj1j+M67thrsN8fyPD3p47h8xi/9wvvAXlMMbBn3/sUyAnNkbtd2mtawzXHxoNo+w4maHt+711Pg1xZx/4UEanWsI1PnEF7tlKhnFraPylmqEP7ezgWbIJ1GI5oT5TyAzY2sU1rHtap0sH5zVTR12u2KeZT0vzoo72XEdku0pm9CdritRM4P/SvY/1dUvqE4tFRxjEkvB/HVY3g+bMZtj/nVzgUR01ni2vtZeG4nlSbt/xo10Mf+dgx9DVsiPZo1se2G1IO1UBQd4sQ1zW1Our/kNbARUj7PS76DseOY97vfI+ex6LcmHKGezPTKU7keweYh3HmEdwraVTQ56/U0f48RM+/NSPUzYLyThxas7RzlAODujSaoK8WV7D9KOVVAkrlGe+hfd2/hvZPRGRnH/swE1xnCcURClrbrq1SzgXtwbXb2AaU6iMbtHavUJ5jSjkWGcXKwhjl4W4ff0+xyLyP1zvYRt8o3yf//Rral8Ya2kdDfVp99AzIY8olapD/W1Juv+PTutBi/WyJY+LEGXw+z0lof4t8imXhiJHwtrVRQD5yRPniBT0zEdO6KjM49kcD9G0yaodGhLalzXshOfpCHq1jyhz7KaJ5L6fn8upxC2THYH3XQ+zneg3HyaxAW5xRHszMUo4bPecXkc8cY3HFzbE8K1Wq74xiShFeP42w/PkMc6PikJ91WnzWZ2MV/bXNFayzcwr3co1Ha98TOBbnFEMtDOeV49gPKTd1THuEjZBymGndVOP8CfJ1GhXOnaFnRalPfR+vF9AzidEJnF/bbZzvBvQMXuihzjYpb7FKtvrMBl4/m+J64PVPYkxp1Mcxt9qgZ0wo7rcskjSR5y7dio/NaZ9uZxfXMTm1y+Qa5SBs03OCOxhDymkv2Muwnca0rpoNyGen2N5uH/UgOcQYxloH27l0aKxSboCEeP8aPZvT7aOelrx/dojXC8gvmdBz5j7l544sPfc4oJgRrXE2yS891Wzh/as4znyX2rO9mGdo6pjbNfXRdxtcpOd9a9jnB2Pqc8rTm1zB+eTD5z8C8ojm5WmObT6bUV53jLYrnaEfcHWPnl8eo+2czChXk/KOehQ74BxpfubvcIK+fXWEtqVOgc4W+WkSYv1MQeXrYXl2yRaNI9Z51KGCYk7LpLRGJrf5F7U6tk2fYqQZ2Q9Dz+12NtFHTw5p3qP99Yxi+9MhjpctytsI6AGoz/q8N4H80d/4IMgH5Hv1aU0fUd+/7nF8j8VH6fnXXh/P3x3iOm373R8H+aE2vXfkIbQXKe1Fpznaqw/+wodB/tVn0D49+kdxTfILP47zwT//D28DOXHeAPIv/sjvCvPu92Cc/h/9rXeDvGoxdvXD/w+tLac/C3JZ4Pj7vv/1x/GGDrbJaz//jSB3L2Kd1un5qijC8drHZaNUGpg/YChv2KP8qgrlcPRorTvapTn6Y+hflnXKZaW8wOYq5Rf0KG4j2MfVKpY/oLBVm/YhxpRH6I2wfc5vf2rPtt8/2dCKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKcoTQF/woiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoyj1AX/CjKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKPcA7zN5s6IsZTCavijHKyEcbzeaIIcevn9oMktB3uvnIK+uxCD3JiOQh70hXn99BeTIC0DOiynIvcEuyMk0weulWJ/W2iaWtzfG+wUuyKMZlq9SbYD8mkdOgnwJiyuBX4B80O2CPO3NQJ6nqyDvT7H8j53C9skFy9uROchOXIK8GRuQL17E+omIVPwayFmB1xjt4e9tdwJykmYgT/p4/m4Xy1hLUGdmAepM28cyuw1sA5Pj+RvH10H2C+xjN0AdXqujjotfAbFAlRLXYvncCOvnpDiEpxaPFz4et4WPx4sByJUK/t4TvJ4EVRB7PTx+iM0jcYz3WxbG9cWpn3hRnuxcwuMh9vN8eACyV1sDObWoJ2mBepa6qJd+G9thtIeKHfr4+4ML10CuVLF8YYR65lu8/8EAO6JjLuD9rz8H8t/8Kqzf3/6VHsive9MGyA8cx3EbVQ5B3umirXyhh3q8P8FxcPoEtqc3I9tew+NoyUQiY0E+JD3PSzwuIlIYNKDpFO3nagPtdyR4zZ0BtlFJtmVnD9tkOMc2u3gNdSzL0LZtdz8Kcvs6tqkboO3wohZeL8VW8iyOdbeKY3ltlWxfhL9vhNg+wRbavsDFNp7T/GkKPD+MUCcmZPvrrTbIGc23ls4vBth+gx5NHkvCWitZcUs3xhOcBzv1FsiBg/1WC1HvJh62Q5bg76MY+9U66DdVVrBdC9LblGyJ4+DxmPyOlQb2gynRlrXreNxxcI5Za6BfEVVxnJQ0tkMvArlSoznSoN80p/Jk2BwiMV7Pp/JNSG9rVbRdWUlzKl1+n2yxiMigj31W7qC8e2EH7+FgG+xduEJXJL/mYBtk18PzIx/lNvnOno9jsVkhe+pgmzlzrPU4QR0aHmD9Mot9EpMtc0vUiQBNk+RT7ERrUQ5c7MNihmuBeYLyhWefB9mh9jpso62v0hgbjLF+x47j3LFMAi+Q4xu31g6nTpyA4zkNiGyEbTPu4zpi0h/ScbTzV7v7eD0aEBGt6yK/BXIzQl1rNdEeTIdXQT75xJ8COd39byA7FVznlCnWT6g8Rmgey1FXu330vZIMx57roG7ENVxXWVqzNFtYv84K6m5zHcfGuWvY/jU0d1KttUDeWKc1h4i4bOMzbIPLz6NvcniA90wnNJfT2tkpUb5+CX2hU49jnf0A20gcHPAFzQHpHMdbkmB5ygLtRwWrK4GP91urY3mOk29Tkm8yHqPOpzRmwjrev9rG6596ENfy1kf7Kwb7Y9DF/tjrYYW6uzhGxeD9l4W1pWTpLfuyud6B44XBfl6huT8m3ycocSyYOfpCbonXS+bYb9MUjxtax0V0v0Ybx7IRnPeqNVIs0iuf1usure/ThOaZEdqW6hW0pUEdbUNrHfX0wYdOg5yNUS+nE7xeJaD4SAXrF8XY3mWG9ZuMaB1c4lzisK0VEdfFsZpOcSxNEvTj/YBiHKM+yM1mHeRWC3+flni9PMc2ns7QHu+N8fpuiNczNbRt7K9zTCpLaR1F5zc7pONU35Bs2ZB8rZLun+cUQ6PFss3Jn11H25PR2ntl4wzIZoQ6c9jrg+z5n9Gw8ifFWit5dqushjxzx0Vdno1Qd2t11O3Ax34y5JQOp6hnFYr9NZs4tk5uoHziGI61PfKzdoc4dnPSsxnFJgOyrc3NFt6vjuPm8QfRT2rS2B/T9Xu0jt0lH9g9jo7JbIp6byy2T55jf1Q9tL2Ska2O8HqTLpbHfQk1DKs4lmyGuhzG6Cvlgm2cUFxpMKN4LK1rel0afLSH4Vu0/26EbR4WHNOnGEeKflW9hjpVpz5OC7zfNMXyX7+O14voExBYO5Gc4t0p+coRjTk3x/rHDaxvQus+p0K+dJXi2Qb7r1IhZ3ipuGLlVnnmJdrZJMHGnSUUnxvSOoPWuOkYdXU8Q/swCtCXcilG7UXYlptrGGPequL4qrQfRrmB1w886hsXx6tT4vG9aR/LU1AM2EddKAIsT0wxiqJE+5T7tC4j326dzj9I0TlzKIYvBs/3LMVDBcu/SvUXEXl7B+MCTQfrNG9hHyct7FM5jW3oOGjzeq/HPhnt4vEJrc17e7jPEFts849coX2NPSzvwOmD/Mja54BcC1ogT2k/aUY6m4xpjzDEMeJTG7fYxjfQ/rkuypLimMop1vDoJsZGexm2R0Zxo8EAx+xofF3uB0prJZ3f0t/+Aep2lWIOtSbtL8X4+0oD9396z38A5MKgnc5G2I9OhnZ+TuvnOu3FewnqiaW5f9LFecr4WN5GC8eZQ+v3SkAx9QnKWRfjqyHZ2uoKzqvJHOvbXH0Q5L0rH8LrhzhPebQP3L2G8dtqHcs33++DLHX0ndh3ExEpaS6uejgfjV2sQ2AoBlxDnWlEeM9yRjEcGnuzlPYRGtiGJc/9tJY3FLM2LtqGguZHS3G5ahXbMCT77edo62yB15vR/pNL+wQO5UeM53j+nOKkp2pY/2u0XRWUGPPpT2k9kaHtkTbvPCwHz/eks3VrP7nRwpiPhLjOsB6vw7AemYu2obGJ8rTEdmxQWtOEYpXXE7QdoxTbtZxhLK2kKURc7NdjmxS/7WB5shn67GeOoS05X+I4W1/DOej3PnoZr7+CBWp4qNdOE/UwpTXFkPyY+uoDICfzF/D6lPNRj2lRMMM5tbGxmPPxwnsw/+Cbvv2NeM4QlT8z5NfM+nRPWlc4aE9rFVqr0tp0Nsbjx06dAnn/Ms7jhaW4GMXhBvvoF/SnaEvcPvpNxRR1bP8a9rFXoM7sHuDvNyPas7TY5pZ0qlNSvgbFaPrXzoEc0jpqRjGhtTU83mmT7V8iNs9l1rvlV/JegOE8P7LbTVonLMTSyUd2HZp727Q/TaH9MqQ/UM6WU0H7UVmnvRCcRqTtkI9KewfDMeV1UAygHmFf1ttoX4sUb2gF61ujCoaU89WkdWRCMW4vboGcTlD30zGOpat9nPfiOu/3L+63cx7cToJzedygtekB2qPuEMs0pjg/hWQlpDhHSbJHscS1M2dAzmmd5Id4v5D8WUO5TDnlhFjSCcPn0x5wLthHjof7DEWGa+OC9lnEJ9+HfL/DQ9xPn89ojM2wz0uDY4rHrLgUK1wSjutItXFLHw8r2I4TGnthjWIKdZyrL5zDnK+Tm9jPrQrK1QCv71CMYzrC494E5e0c5fXHWnj/zmvwfhGOo20X1y1ZQvtF2cdBNu7rQb5+gPGI3h6WvywwXnCihb5LJUa9OHX2DMiW9onDGvpiTdq39Wld7NO6kWN4ncZi3sf7nsU6Xds+D/IEh4L0KeZgab7KXJSv7OL1/+OPvBvkN73xcZBDyv+qxWj7XvfwGZBLWkddO+iD7Fv01byQ1vY1tBUjypvcoNgC29Kc9nwLsk1SwevPKabO3qhDOn5Aub5GcD4rDRYopzHcqPMCYTkYseLIrfHoGGyXnOKZMen6hLayq3XUs9LSOof8BGeKeuKVFN+meTzN0HY06fmPqcU5t7WKx21J53dQr3/mt/8lyH/kLW8D+S9+74+D/O1/5RtAfpzm3EoL5Ufegno7PsRxcHCAa4Lzl9Bv4ZjPmde+CeSzJ8+C/PR51PvxDl7PkUU9fIBi+v4Yx14+QD9n9Ti24foxnFe3KL46maFveY3yULwSz3/yNbjOqljKl6ihzvSHWN4KPVMS0uieUD7I0CdbtIa+dZViKH4b63+KxsAe5VNcfR7jhK7g/fM56rDhnOiMfGHakzaUh1ih/cvBNvoHy8R1XGnUbrXfBrqMktIaPnV5nYI+K6/5wwqO/4mLbffIg4+CfGmCvoNXx7FweYJ90Z3ieF6por2aU5zp6iWcd5tvIV9kDcvLvlo5x74rSbcOr7wfZLv1FpBrMepGSXstUY7tWZB9cGhv2oY4FtIexcEoB/mF96LuJ9li3Md6aONyirlOx+gvrm5h/tKxYzSXU583mhS3p7WsT+Nti2JXc9o/mkxwDpsb/EFEuUCrx1CngibOASc91IGdIa2zEsqX8lAHzj6G+wgtSuWsUCju+CrqfM3BWGuN9nx5/+qF57BP5/TM5B9685Mg71xdzO9aBsY1Et02d7hUz5jW60PKo4hpv6SgvJAp5W24VWz4gOYJznsYHuK84Meop8kM9fT4SRwHc1pfb3bw/CynvRLKeXMLLK+l+EeNYrxVSp4x9KyNTz6yF6BtNOTbVSn+kNP1VihH2slR0SsUv9k6hpNLPkUfXmTxOY/d7Ysgz8f4AyNo7yh9Sxx6joTz2Hkfo2KoDTycu0PaH3Mceh45xD4fkK0wGeUW0X7VQk52jG3eirG8AfkeQvtxfkBrcYoxVyuUL+eibY4dlEta6PFzQi7tP/J+XKW2ON8sg6LI4dnwYOUJOL57BfebGsfQtlz/CD6LOclQD+yY1qcejv0r13BvZYf2zl3SE6+2BfJg0AeZQ3mWniPk2J5P/eTQuq9GeTx5QfvGtN7vj8l2zXFOjlxch9J2lhRzbN/ZCOe40tDzci1sj9ObOBfUNtE2FpTjLJtnhDn08RmbcYzzcBHjvLnioK14kGISM1pb/s77sc97Q8pTpBhHsII643A8uEK5ZbQHWlCe4TV6BmU2xzaLKEbl0fNjKTVhRnv3U9prTxOs33iE82XbQx2cZNS+ND9nAeVZ0Xw/n+P9DNmywPBDhMvDSilz91Z7VGivskvPppQxrnMqlFtiA1wDjymHf4X2k6OI4koV3O+/fAn3No8/hmPDDymX5r2492os7yWg/fiCd7wD5CblG2ycwuv79MxGpUH7R11cY3ztl6Iv9gs//Rsg7xw8DbI4LRBzD9unmmH7F5fx+skOrgv9OtYv9jAu9MV/BtedIiK/85F/htfcRpv3tsfpOZmnsA3yM78N8r/8AXr2mnSkSe86+bzPewTkX72KfWrpea+rO5S7Ss/iu7SvUaeHkCf72GbeW3AOvvw0xrm2QmxDn57pjSm26VJeeFGgfQ6raH+6Y3xGMdlBncsdtF9JivWb0TOIlQo9X8ubwJ8E55V/oiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoijKq0Vf8KMoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIo9wB9wY+iKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqi3AO8z+TN/NCTzQdWX5Tn3Qkcn1sfT5jMQczLCOSojnKlsgJy6iQgz2YoX758FeQwx9u3Om2Q9y9fAXla4PWcpAB5MDkD8tXr+yCvbeDvDwc9kN1WBeTYcUF+6MENkJNNrH8ywd//3jOXQH72wxdBnpT4+5XgMZDnTojlcbHB8sEQ5HN7U5Df+6GPC+O4WyCvdk7gPQq8pzvBezy5EoMcxKgzzgx1ahKtgbyyinWuhinI9QbenweMH+Lx0MPr2RzboL7SArmw+I4tp4bXq1RoTBjUmWKGspuMQA5qeL9+D+snaQ3vX+D1ohjPH8/G+HuXypcZEIf9rtwPFGJkLMGLckz9VnpVkK1guXOLPT9OsZ1KD8dqFpKmhPj7xgm837x3AHI3tyAPDrZBjkLU83WDtuXgKtqSL35kALLfRD35X34bbdkbv2AV5Nc+gXpSTHdB3un2QX7/M6j3cxfHtRtg/WTaBNHO0bbUm3WQ146h7ath88uowHEY+KT3IjKf42/KEOvYprE4pzLNM+xjXzKQ45B0wuD9pskM5DNvfRjk2R5e79qzZD/pes1qB+TdCdZ5PUId3JE+yK/1N0HuHEfb5K/ifJjTGGpGOPaTFO/fDtFWFCW1l4PnpwZ1pCyx/avNBsjXr9IY2Me5Ylk4nie19i19NUP0ezKyLb3dHZBPbqyDbOi1jC42m/gutlutiu3u+qgHyaiP5S2xPNyPeYa2ohJggaohjtUowAKGQQCyR/1cr+OcXgr2u7FY/jBAW+h7eD/jo584S/H47hxt1STD9tqfYf3HQ5R7PSxfdx/bZzzG8oqI9PdpfklLkNMp2mcXm0yyEdr3ShvbrLqC80uzg/a1UccLdjood/dpvmlgGx720HatNtAWZeQrumPU+TTD3+cZHo8iHNvD/iHIYtG2BjSfNGtY/8kUz7eCfTg8RL9mdRXH3Ixsaa2O7Tmbog6W9v55d2qWZbJ37dqLcj5F/cxoXpM51tUzJR1HOZuhHR/PUZ5m+PtWA+eRwMe+jqtoPwLSpXqM4/PC9m+BXCXdmcxQN2yOuj6n9pgnaA9S8rm5vgHNg61GC+QajTXJsX0rdSxfUEHdOejh/bIE73diA+f9L/z8PwRyk9YwIiIXXjgHcreH9shJsQ1CB+1LGOMcsdJA38lmWMfTx3HdFZD/XG1iGadTbIPSYhukJa07hNq4RBufU59HhvxzGgLTAepEQr5alpH/vYb24IGHce1cbaNOx2Q/PvLMBZAvnrsM8vXtayDHFSz/lNp7bQv942XhOkbq1Vt9sbaO9R6TrQnI1mRJH+Rpgj6dybEfhFTdr2G7V2kZ4BrUizhC2xT52M4B+RIFrYejAH9v56hHrQ6OVZvgPOjjskq6e3v4hxHq9UrrIZDbaydBdlbIdxlge+Rj9DXdgNYsBY6zURdjYMmU1yBUH5/HqUhJ00kQ4NgucvpBiHWe0NjzSYeSFO1pWmAfuQUOdtfwugRvH1fQ9vkerf3Jf/V9lKM1bFNeqxqf4pRz9P2KgtZhPpansFifVoy2OJthhUIHr5eQ7W/QfCIp2r6Kj33ska9kKZayLGxZSHGbH12SH7K/hz7u/g7alp0D9BnrNbRd9RbWczTpg5yM0BZ0yI+o1LCf6rROq61TvDvCsdvbxzk666BeHKM5+YGVYyA/SGuCSoz3K3PUqz7VL3JQkTtNmpNKGoc0bl06niV4P08oRpZj+wwG1J/XMb7dH2P7iIhMcmxD30M5dVp4gkHdTwy2mZPhPSo4NKTi4h/cEOebgMZiSvaX187jhGzFGON627u4LuR4rgmxPIbub8i3ntH8xbbb+Fi+kmy3T/dbWcH6OTRfJmM8n/0u1+D5boTtWaF49zJx3EDi2tlbMs0TYnF8dg/QDs8xnCXzKdqnbopt55RorwqyJ6u0ztg6SfFOirG2VtGXaNUxPri2ir8fT3H8TlIa7z7qWi1H3WnEqCt9WpeGAcVjU/Qb9sl+NDyc9wtac2z3cOzM5zSvN7H80wJ18cP9F0B+6MxngXw5pw4UkUeaaNPKCG22kD/rFNhGtfopkI2HOrHawbj97AHyH8mmOxm20XyEOvLZ13D8/8pvob947qO/g/dLsM6rHdSZud8CeTDE+wvFHgtB+7pKc6aQ+99o4x5oQb5gd5fmfNpXCWgdORMcQ3NLewYWy5/TunNZOI5IVLlVd4diwhxPMw62a4PmrdJBvalRDLco0Qc3NDajCH3Kco5j2cnxfrRVIFbQN4mb5GNTTGnUxb39Sht9n2RK8VMPry8Oyk6J4yiaYPl98u2CBOOZm5t4/5z2MhKaZ4XiHRyzM7QGmdC62Nlf1MPCo30A8ueqBtcdTVpMF5TvkO5jHScz8gUKtG1eBa8XZSgnCc0XObYB7xtYQV/B0N77ZIo6GZKOlXO054Mu1n8+xDGSUdzuOMVYAo/2LRIsXzHF9nrXuzBu2axQLGKzBbJLC9MgxvZphuScLQvHEVO91RZTD2361jrGAgua4yptnDOmtB9jvRbIffIZowj1dHcbYxbFDOeUWuc4yO0YfcytNZyz19uotw3ai2/RIuCFHbx/Pcb7r1GOSGOA7fGFD2P51tZxvT2k/aVn+yhPaY6KAyxfXMH6Pfka9GPW6HgywiDV/i7GLq2H40pE5HWfg3Xo7vdBdkh167R/8/Am6kTFwXscjCkmkZGvPcOx53kUA9mj/IgKFqhO66R85SzIyRz9iNLFPh3SHuO0i2045nVaRrkGA7RNo4TippTvsEK2vh5j+V2yxc0q6ny7yWtvnF9mY5SnMbbvMjGOSBzeqm9Oa8ZWG9fwE/ZVKEAXhegL9Cgu5Hqoa4Zi2IGHbX04pHhaTPPghPbXKS5T0P536yT6/D2Ke6QUY/aoPSKapzgfoT+gPM0Z6mKd1tylg7o3pZgw77dtUnx0h/IsexPsnwOyd6vk8/fJbxARabZwbp7m2AcerUOSHup3Tv5YZR3bvFzIXaG8PItt2O7g2roSYZtlBY5vS7k+/UPUwf0dqg/lD3gx+csWy9sboz/se7wOQ9+kVsE+HA0pl6eB7T0a4XFLOuaR79akXKNmm/MBUGeaKzi/LI0yFzu+tQYebOO8EKY8lrFfRrRXLbSXUKmjrj/wINrpgwOMSWQJ6kkywnYedHFeCFexn5+7jOt9r4nz0qCP5Yu3MCZUZFjfvEDfJSBbu3nqESzvAV5/foAx3tjgOD19GvWkQ7ZzjWI0lfYZkP0MY3BpSWsOsi3BHMfRtR0KSIhIOsexUO9gHa9fu45liNHXcSu031VpgZwIjt0r19GXSaZPgexRPsBGHfvgwnG0vw+eRlt3vIOxgrxNMWQX23zOa+0UbWGd5ot8hLbY0j6BQ+vANaqPIX+1N8LcWj9CHWxQnmdK+wourTeSCY6pyUvsMyyDwPfk1LFba6vOFupRr4vtGlDsfL/AOWNAewvThX1VnAOq9DyGR3NmRDnRGfXr6gqWp4zx+uvHsJ9mXcyRGE+w31/7+Ofj72doO/7l3/wakP/wow+AfOHch0E2OfZ7ewNtab2KtrlBe/2JR+tOejbBj3CchTNsr6sfpv0B8kNH+aIephuUQ/wxzPuRKV5z5yTqzOe97fUgb9LaPRfswxNnT4PsGrR9Nc4vOEA/Z9Lrg7z9LJa3SjnMfoq2pzfE+jz/YbStJ16HfteVjzwP8tk3og70dzCe7a7g/Ma5BFV6XmNCz0OEVfZ78Hi9gefTFq40a/R8RYK2dZlYsZLZW3OVpbzZWhXXXW6O9qVS4Lwwpjy8KT1LYlOcp66lqJsNitOcPPEoyB/97Z8A+Yv+6DeA/F9/+FtAPvH414L81If+Lcivfwfm4lx66qdAPr6BY+VdP/creP1NjDGPrj6Dx0+ifXRTHP8VyqOMKGYd+JSzSvHfwkefu/BQty9cxJj6aIq2ZW0Dn+USEZkXvO6gdYCHfns1bGGZab88quH4H1MZPIrzXNjB8XH6NMbh5yX+vqB1YUi+RNGhPdIN1DmvjXPgIw/jHOAG5BtRHGVCz/3E5EuVDvaxLfogr9EzGK6Lc2hBcaWC1uIB7dFOMmzvGZWvpPZZFo4xEgS3bCfniFUoz4D3q3LKMS6E1vOUzh1TXmHEzyxQrD4Z0tgl38unfcaqQ+1OMetkjOUrqPyzEepVMcd+S+hZ2DY9R+lSrk1Iz0ZFFNMqDepBPaDnHiknO/bQT6hQblMR0XOMAZZvaxP3XnzKyxQRWV/DsRkGWKeginWaldgmHsV0plP0n1eaeE9jaK53sU0iH32flGI8Lu81p9iHAelENkfb5RSo89U6rnNKijGFHgXdLdeXcn9oDzhuYn3qpBNrq+gPZ/Sg0mSE5Y1dWnc52N6VCp6/RrGKZRGGFXng4Te9KJv2G+H4aOdpkHMaK32aA0rKkXConzzKqS2EYvM9etaFctSaIfr4BemdS3qa07rCo31Ol8pn6TnyjI1nTnsRHvZjk56fCGjcGgdtRa2G5RnsoZ+YTXCcVCzFdvtYnvUQ9bZ1nJxwegDvucHivuvz2+hHXBPskxnlPp18Pdqqx1awjxyyPe9+7++BPKQYR0ExFeFnLVt4fePj8eEetllOefijGbZhltFz7MJ7jJxjTc9L0TN1lynONu9j/fp9LP9GleOoqAMbTXoegrrUE8rDov04x2If5+b+sD0iIkVZyHhyq75bLfQ5DSVt1uoYZ/DJziYZNs6Invc6TuuMbojruFVq23PPYQx880QL5IB84pxykeYO6sK5c+dBftOTuKafHuLYO0vPCT+6jr7gZ30+xoQN7b9R+rxM/vVvg3xlSvNSBa9na/jszrf+nb8L8jOUt/zmz/0rIP/pP/ZNID/8D/4OyKMf+mFhyiauRb/9C7BOX/Itj4Mc+vhs+Y9+H8ZN3vNBHJ++/yaQ3/j2t4I8pecl96fsv+K6jMyRFCH2mcS4Nh3nOIe2mrj2PLaK68rm5hmQDT3rvrpJeZEUlxkl9O4Xyrca7qG/2uK8b4qb0bJObIj1cQzaX1OjObVYzLF4Ke6fp1AVRVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEU5QihL/hRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRlHuAvuBHURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFUe4B3mfyZmUpMk1vvVMoT+dwPJ3g75thBHKtVge5Xq+A3KqibIwBebN2AuTBHO/f2x2A7EkA8upGA2S3so7lq1RB3lrH416E14ubKAcNlDvrbZBLJ8X7xVjfat0HOTQ1PD8KQR4fTkEe9bdBziwev3L+HMjTNraHU8X+ypIMf2+wvUVEtpouyI88gGUeX8RrJCWef9LF8xtrWMfZNVSqj01zkIOoA3Kzgm3q+Rbk3mAG8rx3AHKZ4vFmjOXdn41BrvjYZm6M5S9beAFjsPwzup7xUOdT2wO5KGjMzbBB/YKun6N82EOdmGR4/nBegBy698c7xIqilMP+8EW5RWO/YrGcfgX7YZijqRxOUS89GgtZgv0SR3j9jTXs91m0CnJvcA1k08d+zEZDkCfpLsj5HPXgP3wUx0Hp4/EnPu8YyH/y7WhrA8H77exfAvn3LqLtunoV9eINb309yK6LtkrsCK9/iPXd6+O4HPdQL4+1sT6Jxf491UK9FBHZPexTGfCcJMV7DmZoj5MZtmkUok5UfLxn7KIORWTPz//6B0Ge0tim6UIKg39ADRTZpun9W9+EtuTvPoN9tHfleZDHDdTZsIY6MryObe5tNUG+fa4XEXEt9lkyTEBuGLTlB320pSfaOL+eu7gHcr2K99sLSMeWhOu60mjc6p1KE/WoELQF0xR1f5xiO8fkB1GzSS3Cfk0ybHc7R73NSI8Dsn2SYz+4Oert/nW0BesPPQ7yuI9zZKWNepllOO5yqk+aoG3waEopfDzB0nsrTYh6P57h/S+QHdjtY3v1RzTn9dAnSMd4v9ziuCzyxTnQMXgNsSgHdfSDHB/7tNreALm1iraqFqHOiMO+Idqm3GXbR37PDI87Hra5H6COdDZWQK7U8XpzQVsRzvB8Ulnp7qEttIK29v9j77+jtUvP8z7s2e3t/fRzvv5NH5RBBwGQggjKoiplW7FFRllybNleduRoZcmJlRUvZ0VrSZZlOfKy5S7LULGKY1kiZUlsIAmCBEH06TNfL6eXt/fd8keU+eZ3fQQJQAOcCXNf/wDXt9+991Pu/tz7TCIyFFY4f19se71EGZhLnON5lJmJxH1rba5nPOb1Usj9OE9kWeqms+Hb/wHXxyPqvx9zr+oVymJVYrqm2KO0zOvhjPaiWKC9Wywpi70R93q67Mv9ou+Z6GPM6wlFxQUe7VskBsUv8/5mlfoc+uRRxOc1K7zuhRzfuEf73j/lfE9P+fuzPvenf0xZbeSSFJ1R1h8+pP11zrn79ziGUoN7srqyA751kfrixeQXL2yBH9xh/FoqUB+nE86hVGT0EiaUiSzgmmQ5r/uyh0kia+IkVnF83nBIHzMeMTZJEq7p2gpjkavXr4BvXdwE74v9vHGLufbXvvoG+NnZGfhySR+0XmYMUWlRBzd26B/OC0EhdJ0La29xb8JYYXPtEnjv9AA8kX2expTlstZUJOSLAq5jmnBfL1y+yN8XJX+OxY9KnjiMyReSf5dKtJ07G5SLToX7dvl4F/xkl+M/mlBOe2fMy9584y7Hm1DOPcf5e3Nen08ol13J92PJm8NAcpAL9PsjXT/nXKXBXNfrPA2ee9SF0gXmHYM92i5/Rnu9lNy3sdYCn07oq12Jvr44o8PYbtE2PXhIGfbE9kQNPq9Zk5pSILHLgv4xnkq87dPW+o46Ect0KiWOt17leHzJG+MZZWo0ZB00kzzUVWhbooLEqk7yh3NClmVuNnkkvz2x6YHUl4cLyvpcYuRYdHuwT90cz6kbXpFy2ZN6d11qRtUV2oJKg+vaiGjc8g7HX5KU4kqd+3CpJfXigNelNOi6p1I7PaDeDQa8HrWo15HU4wMnxlnkbiL5vtYO5zLAOJXiasb1yH+D0mOpJvXhYRfck5pFVuDv5zHn0Kgyj2lWuQlJSF1LpF7q55SpocpIQZ9PmfAk1s4LMmmJ9ecSF0USO651uGfplPMZxLRdy4zrMRd/kkpeprFyUXRqRc50Fh6fv8i550s5NDrZ536eJ7Isd/Ppo/n7I8q3J7LRPePY50OpMUf0A41VykKtwrWJC4wRn7lA+7KyIbIlslMWv+eJ7B7vcrxnUicpyXnTTOxT6KTeN+ReHp5Sdooyvldu0E+1LlP/3zhiTXwkdaDZEWPJtXWRPdGVNOiDRz7X52zC2PXVm6ynOudc/CzfsbnB+M1pLnvGGufGGs+r2nWugScyECVc03JR7E+NZ5ROziA76/z9xlXGrz/3N18H/5mXXgE/a3EPskYLPM7VflBH5mOuz66cj+Ux93xrjeObjTmffEEfdirnDFGdeV2acj/iMte3JDX+hvjk80Ke5S55W46dzaW+lZFPu5Sjki++NaQctBuM8/1AbI/U2woleb+jHPtyPJNJnlEqcx8aDdqueElb2ZSzCudR75JMx0fbEktMvrrO2CaV98VyHriQc9dcYnitJ/iSJrUKImdNsU1jORcvcAG99PHgx5Mac1PyoiLNt6uKb50lzAPGM+rmNOMk1L8tE+l3SKkrZamzVaRutnGRMjMecDyR5D1Hu5xQQc5ZAjn7nsg5SCb9H+UW7x/0+PtWg7asFkrNvUN/vF3j87/xkLby2U4LPJYz2KnI0N3bD927AXkeuDhrvcUjn7bi7IjzKBdpc5MJdS+QGDgRXWtd5v1Vn+vy5DbvL0iecEXOLetyNlP3uM/3v/FN8FDOfWcTxkVrC8rp2nQNvChnGQWxTbMR45TBwxvgX3uRfTnHXcrR8ZnUf8UWlcXWT6W+3pdS6lJ6IXpii5Ix82znnNts0R6lIf10+xJrCoH0UhW7jAuiL34dvHUqNZRYdL9MXTuW3rD1Mu37vQHjrmAuuajUiEKPMtU/4B6sXqUOFCWv9DZoKyan9JdBjZsQSy4dL6U3TPqixmPpe0o43vUOe+MuXr8OXl3hfA66lPGCkyLUOaJSKrj3Pnf5Lf7KA+rvhvTxnfVYh5ASgSvq+bKc7xSlvpbF9MW1usQ6CWXTFz8xG8r1JnWhFlB2m2XxaxnfX12hfQukPhfOOb/ZXHRJeuCaRfoxrUfe2pW8K5SzlJTvr0ov0Fx6yCpiC9ZFlxtSE1nOHo99FlIk9VLasGzGMRalTtFu8h2XrjBvGor+VSOOoRQxHk2kLqPn2Z74dk8CxP3bt8ArJYn/pvQ5F65Rv6OU7y+KTPmP9VQQwxHzhTjnepWkblOUWkCWqX3j9TWJtULtNxAfnUjN+rzge4GrFFtv8Qsb3Jf1HerOrRt3wBdLPduWs2zhsUc5rrfpZ5byvHBOOZ/LWUVZ6n9DqfdlJ5Srdu0yeFnOIW/dYqwSS3/pfMoaervDuEDaMJwnad0zLc53rSn9uanYsoy2RNpp3dED+v3ZhHLVi7l+x/e5HvHK42cf9/Zpz6PVFnh9hTJRXafst9foi12Te77/4ovgnvTy9GZ98PWVq+DFTakBFWUOonulnDLRPaa99yLOJ5lxkSsVbmLmUWaaHcbHvYc8Ex0vuAe5nE91pO9+b0AZq4o/64+45/Mzyszc0VbWqi3w0ipl6rzg+c4Fb/M7ofSAictzc9GNTA7PU+kRngypW/1pH7wg9eFKRDmJKnx+Sc7HClXxSeLHIzk3nRwy73ny6feAd0La3tu3eK4875/w+qIP3j1+AJ6eSt+QzGd1i32PxTbl8Krsx3LC+f2VFw/BS0vawltfpa+Yis/rDXjdOefqTdqe6Yzn+96csfGZ9MFo7vjMc8+AX3qSunzhEvs5Yok1b+6yLnbrK/c4vhHf//AV1pMLktsHcqY7khpV75DPW4pM9g8oA3GBOvHwNm3PxWfp7zL5nqQucc7xHsc3FX86l7gxkV7b5YzrFWah8Me/qTkvpM650dtUuCE9XJMpx7q1zrwkvyp9dnu8/8VX+uCjLutIt25/Afzp52lfnr9Ofduuce+aRz8N/nf/6svg//c/w564n/2r3LtOnbLhlvTj9+/Sngx6ffB2m3s9lT7F24eUxekJeZBJX6LoSl9qAB956hp4PKP9qUgP7ZnUlbYvPQHeXqPfd865gviU/j77Aid9+UZC+p4LsdjcVL7By2mfvICxhC91oL2H8t1Pnb/vbHBN1qU2GElPRVP0/XQkNlzONWLpA0yWXJ/7dxmL1OTMs9jgGvfkDPTBMe3D7IQy1VyljJTm3OOW9IQspKbvy7nM+OBdUnN2uUv9R/I9HPRxPZBz0NynHU7lfGgq/ZbzGXm5zn2ZTmmLAokRndT7Vtrcx6XUvAuSHzvJ10cDyn0ScPyTofTLi9/wUwrmWPLCunw7M+7y+uGIcjVzfY7XUS6XiebzXL+RzH8y5ftbUj8uS//rb9Rvv9VmzaNQkDNAid9GU44hz+R8p03blEhNKZZvPwexyFyda96fSF4Y8P7xkGtclTwxlTqf9n/UpY990OeepNp7JN/rar9HLr2jFal7RtKf5gcc30z6/MtVqfvJeNbWaOszx/3ZkP6580KQT107eektfuXKD+L6CdNzd+kK8+83fklqfVI71LOMSov5pu9JjLvgvtcL/H0kNr4teuKH/P18SBtfbjE/r8v5l1dqgTckDknlI+5LW9SrPclLS1X6aOdxfBWp/w7E58VSs5mKnu6f0HYv59IPXGOcs5Sa28uvMA5zzrkvvsE4ZyT15Fh0N8mo27//M8wls4D2vBszTpgueR6TyRh9qfE3pU9mZZvnb7GcrY/l+1yX8/nFipzvSb9JJvVu3xMZX4r/FFvbO2PPdSR9TK7Amo4v35ucHFMmgkT6HKVPcR7Kt7rSnxHImcu5InMuf1tzXSbfGRciznWtybPOhfzdh0B6YMsVOR+SGkFb6zqy962yfEs+5WIWtMarZxE+9T30aPcn0uOVHEuMOuQ3ES9clvMwKQLnS8rWzZepWysBv1f4sT/4cfBrE/K/kf5Z8l+mPd/98v8KvveJ/yP4LKAuvPLX6cevR7zunHPFCXPRD/5uOTO8+n8Fn+z9RfDPvfQmuOe3wNcv8ruZp5/iHr3+JfqMTOoc8yVt5qRP+1grUp9XLvEM9WDE57kyzwGGjvY0l+Ppgk+ZjMXnlKuU+aMu7eFsSHtVq3B84qJdJrFiIL28fal7hVJjT6SfK89l/t8C746/vmEwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAbDbzPYH/gxGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaD4XuA3/IP/Hie91c9zzv2PO+Vt/1bx/O8n/M87+Y//d/293aYBoPh/x9h9sdgMJwHzPYYDIbzgNkeg8FwXjD7YzAYzgNmewwGw3nAbI/BYDgvmP0xGAznAbM9BoPhPGC2x2AwnBfM/hgMhvOA2R6DwXAeMNtjMBjOC2Z/DAbDecBsj8FgOA+Y7TEYDN9rhN/Gbz7rnPvLzrm//rZ/+9POuc/lef7nPc/70/+U/3u/1YOi0Hdbq8W3+N6kiuvVdh3cKxbBXSAPXKSgeXUJvt5p8vklPs8rcfpfT14Db9b5+7jEAZTbHfCVJnm72QAPZgvwRUbeqVb4/DrvP+udgb9y/x54XimDX+lwPM2VCHy72QKfTDi/4XQKfrzK9RrmXP+rO9vgwSqf98Nr3A/nnHtiZRW8s0kZ+IWX74C/uvtN8NbsAvgnf/CD4O0mx3B6+yH4cNgD31ujT13l8Fz3eAKezufgFReDL2q8fzriHjaKnO9gOgavtVrghQplPOHrXKPCPQ5K/Btes2QGnokJKJdKvF7kBNIC93yajMAXHq+nGdfnu8Bn3Ttgf7w8d1H+aLGWLsf1RZ6AR/KnzxYxFzpIM/DVGm3Z7sk+eMX3wJPFELzV4f3BBe7LrMDxpCen4MsF5bJUXQMP2+TPP03b9nv/ufeDd8p74A9PHoD/4q9Rb44qvxO8mr0Mvrp5FbzXPQIf+pTb4wn1YJFR7v0Z96vdpNxubJNfXBVFcc6Vq5SBjQ7t52RO2xGGA95f5hjyjHyRUUZCioDb2FwHn0274FFAe1lYHoPPQ9qqyeAAvNq8CP7nfuUb4E2f8y1UOP7+XXJ3mWu4DKjbaZ3jTxLajuGM6+F8+rtcZHY2oYyPSlSC4z73oxVxfMcj6sR3gc+6d8D25M659G32plTmvOMl5SwIOc884PUkl30QP90ocF/jBW2+y/j+asjfN8qMOxL5fSWi7QgSCvaDu/TZS/GRizH5XOKi1fUVDjemTylEtI26HuMxfdIkoRwvY9qGUcL1Pd6nHOc55ycu2uUZ9yss0ac7TwNX50oS6/k+bVFrg7HbZNwHD2RMky7nnPnUtTjl7zNZo6DIPQkS8khsV7XAOY+HlMEi3Zkb9bimtRb3JJA98jO+sOiJ/ffooIcTzsdbcI8LPmWoWqUMj2laXVTk/M7OuOlPX+fzNR5oSTzwXeCz7p3KuwqR2764+RavFyl7owH1bz7k3pci/j6QWKhS4FrKUrvI8fm+z71LYtqn6Yx8Js/LRN/KFfrBciTXZS/LIswFbqVbzPvg1TrtYxqL7i249+MuY7t5TNkdDSmrU7HPeSD2SeyNSxgrzSdcz1dfZo5z65ixlnPODSbcw50rjBUaVY6hUZQ8YiQyM6ONG884Rl9s4GROezddSizBJXOVOvWp5PF+3dNimeMNJK9JJDbrDiVvEXsU1elzLr1Nn5xzbmWVPuvosA/+jZdvgD/YPQQfDCgzlRpldqNNGb/+DPPeZc71WN2WxPU7x2fdO2B/Ape5+tvixFRqKoU6bctGg/NaiKyXN6SmknMfh4f3wUuOclUMxe9kVK5Jl4J3eki/NllKfj8UQZV8OyqJ7QylxlMgbzS5Pmurl8GviS052uf8zs5oW7pd6mmpxvWcDWh7PMmLh2OxE+uMSyLJa7VeMZXYzjnn+uKbg4BjOp1T9utLvuNszjVebVAmKrLm2yvMoyYSwIVV2vd+l3nDqM81mse0ZZH4q6IUDxLxV2OJrVpSlwwDjqfZ4PUk5/sLJdqeIKSt8mV8kbzPRZJ/UASc5/P6QmLHqMbrscRu3wU+694B25Plzo3eFvdGNeajmcQJ9Q2uoy8+pSQ+aLGUmFTkLkvoU4ZSTxgecB2rSy58Q7nUx2tSO8wXtFWzCX3KokBbOZHS3MER5f5LX5I44ogxvAu4HklN4kCP82tUuB7ZgHFJkHD8haLE8GIrndQmSyL3VcmxnHMuCWiP4hHXxE94vShxTxjwHVGBe+pJ/bNY4O/HE65ho0G/7i8oU7NeH7zi6L/W13n/XGryTvh4zj32ZbylFvc0c9SRpsfrRwMRIom105zr5xdp2/KQMl71uOdtCc6Hkrsf9aUeLrHzd4HPunco70qSxJ2dPar3H3YZm7QbtC9eQF977Un6/k6dcy3XN8CLEnuEFfrFuuh/QfOaMe3FUmTx+MEu+MGDPvjJAWvCfnkL/IkrjEkfDjieN27x+Z7kqYuQsjofkWs9dnbIuKKwLnWsKnOeeoO6Ojqh/VpK7FXPZDwV3l8cP1732b3NPSoPacOjBq/f/Dpt4ugax1TLWSPdaHFNgyXPmzpMW1yhKTKxch3ck7yi3mF8/vv/Fdb1K5//FfCHPa5J/QLjx1lEGd2X2tv+Xa55NZQ8LuYeV6S2WhF74yT+jiWWWtlmDHB8RpksSv5RkDz0PS+8F/x/+a/+G/cd4rPunag5587FyaOx+R73odnkOng+17XouO7LGeWyLX5rLkcFkcRWvuM+BB5tRb1FuUhHtI1ac0pHUi9IuA+5z/dnEtQuZLyBxLhjeb7WzMZd5hBxwvVdLBhH5LnUiKWGHqdylpQv5TrnH0p9N5Ca2mxOu+GcczOPa7p3xlggHpOPU+mvkFhm4wJ1ZWuNupFHvH8w55os5/Q3zVWeh2lNuC32u1bknoYFxoPenPNdTvj7yJNYJZPYpMD7F3LOUpf+iZMT2tpKWc5xqlyf9oUd8BdW+bx2ieu3eyR5nPjn6ejxPf8O8Vn3DtieJI5db/+R3cwaHFelRidUSLkvTVk3X3oOnOQ5WwvmKc9eZFyUtLiP6ZJydXmNz7v/5uvgX3ntJfBf+yZreUWxLWWJ0bfbrD9EN2lrtL5cX+U5bqVB2/yFb9wG3zulXHQnUj+PxceXaLsPxtTLSNa/3aacLuXcdiJiV60/XnscHfOAZeWDV8D7PuOKrY1LMmbGOUFAv5w52pL+nGuwfulZ8DgVW5RzTZIR+zdmx33wVokytf0846Bf70p9V87nStJ3E0pJprnBNZzXaZurkldlPfqjdEaZPJTYvnZAWzXNJM6r0JZWSvQ/bfHv1eDx/orvEJ9171TeleWuO3mkg/UG51IuU5+amcTkEtPlGee2Vufzlr7UBEKpAcvZx1qJMav2ImVTymawlBqylNdyqRPVS8zRtyrMMXyJjXKJFaYz8laZ889y6Q9YyvlVQfo4pd75cI+6XJCz75U1xoLlDuOMmdRTc/EfwUgKmM65UOJ+X/a0UqONLErPRa3CPS1KL0qkaxzQZ6lMjHtcg4qM5+iQ9rIgBmJvl/bp4ib3vFGmjDVqnH8idfmqJ7GPCFlJelLG0n9QqXCPimI/Qjl3cCHfn3q8PhQfNqf5dMMz5g9hmfb2u8Bn3Ttgf8LQd2udR/Zl9w3a2XRJW9GWWnxV8uGi5PN5TN3sSwxYkRh/KGcXNamRbm4w5i5WeX/kc3yvvsi+wJn3KngjZh9hPmQ+XyrzeR98hj3DvYH03njkaZV6srJFufSk/jfPyV9+k7HV4JDP/7Wv/xx4tuB6bT1/DXxdzn2fuiYFFufc9lOMbdoSz02f4R4spd9p/5R7fmNImUj6T4J7Ea+XA6kZb9G+NqTudXLANdmRnui1GuPZC+usGZ1Jf1cuvUep1HinXb6v0+J6rJRomzw5UqwWaTvqIW1hxYm/HfTBJZRxgcRWej5ZLkrdNhXj9J3js+4d6TPMXf623GO65EJNxc97UtNJxU+nkv8HcpY+Hks+LGcFI6nZhPIBR1FsVZzRpg+lDylesH5eSFkfHxzTNuXDPnjX8bxpNKAPPsxpq/MpY+bjkZyDSgw/SZl3rqyRl6UvcDmmbf4X3s8c5Vc/xzx02mfel+WUu0r78fMuT/otimXqRtnX2JJ7snufa9p9nbrq/5FPg4/7zFPOplyjF7/KOQWOa7K1Td3f+fAH+Pwx4574AfcwkbP5q0Xauo/+kX8OvPuTL4K3fyfff/Y/sMd4c5N7enCHcUdJziwnI+7xUmoFgZwn1uu835O6X016X1d/gzPO7xCfde9Q3hX4zjWrj9Zf+wYCWRv9vqkl9bfxkvq3scProyn1edrlXt18hfbv7/Q+C14SfVx/4veC//1/cI/Pn3D8f+gPPAMen90Cb0oT22aTuvXhDzD2WbvK86jBPvd6vqDuHkpbdyBn1RPp3UnFH9w6pey2pG9lW3qQr67SD4Yhaw7L5HE/eFHqOC+/RH3Ncurb7QPmPc2LrKsEZe5ZILluIL3rlyvU11TylmqdsU2jKT0VM9aZJjPuaRr0wWP5juXV24w3J0PObxBzDbsn8g1FxufFOffMm0oNX+LltpwzZEX5hrHJ+NWTb0r8AsdXqDOWLYZvuH9GfNa9E993Bb6LWo9yzmDRx/XEoy6UG/KNhNRgCjXqXir9opUyY6PRmOteFLs+WfD9sdQsUjnP8aX+MJc+jUS+hxpJzShzkgNIvbKj/ekh3yefw7nTHm3rwe498FS+JO6siR884fznoke9gfT/y9H55cvS97GQ3qny49/6TGeU3U6Lsq57WJHcUb/TKZZbHPMZdTGTXtT9UZ/XK1oDkvi5RV1PREYWUufz5futYkSeSN9fPON4KnKG6zLtp+B4Wh7XY7XDGs90It+GDum/Yzl0XVmh7Q/lHGRLvif2pVZSKL07Yp/Do577j/6T/+kt/pf/xz+O6/de/TL47/rD/yfwivQ0VDv0mSdj+qC1zhXwUkSf1QzYd1Ov0DZsXGKPQrUgPk3qq6e7LT5/h3FK1dE2pKl8g9ynD0xH1N2r6xx/XXrCEjmnnSxoy3p9xhTpkno1X3B8fsy8sicfZs4D+eCCZsPlUhO6c8T5OefcPOGeplJDyKUmc+/Ne+D7P0jd2JA4p7lD3R10mTvncoZRkjl2WtSdp57kGV9Z+im6B9ILEEuPsS+1hhFlqijfpRekX8FP++CRnFmuqoxIz3ZTajJhg78vyPdehTprWLMeZTIWWzs+vQtevcC49LvAZ907lHd5nueK0SN58yWvCeX7LK/Ivcsktjjp027vSI+pN5dYRM5aqxI7tVeZR9zYp2x8cI2ysLLCmH2WSb99lXv38ISy9+wKx7u7J32FC9rHf/DXvw6+veT7/8YNxrhPlJ8A33iKNd/xPer+yh3an1sn0u/wO6SP42cYV1TfT3v7F36cf+Pia1980ym+/hXuwU99mfL9L3+Ka/ZX/zv2UPQLT4HXOqwxX3+Wa9A9pUzcvsm/F7Ap/V3ZgPFaXXomcvmeU7/HHIkND+UbhoF8kzeVWqAvedrpY9/x0N7ORhK71LVXiTIXJyfkch4/W0rtTnounNTQM4llvfTb+7sa/m/1gzzPf9k5J5397secc3/tn/7/v+ac+0Pf1tsMBoPhO4DZH4PBcB4w22MwGM4DZnsMBsN5weyPwWA4D5jtMRgM5wGzPQaD4bxg9sdgMJwHzPYYDIbzgNkeg8FwXjD7YzAYzgNmewwGw3nAbI/BYDgvmP0xGAznAbM9BoPhPGC2x2AwfK/xW/6Bn2+BjTzP/79/tu7QObfxm/3YYDAY3kGY/TEYDOcBsz0Gg+E8YLbHYDCcF8z+GAyG84DZHoPBcB4w22MwGM4LZn8MBsN5wGyPwWA4D5jtMRgM5wWzPwaD4TxgtsdgMJwHzPYYDIbzgtkfg8FwHjDbYzAYzgNmewwGwzuG7/YP/LyFPM9z51z+ra57nvdveJ73Vc/zvjqZTv9ZX2cwGAxv4TezP2+3Pcli9n0emcFg+O2Mb9f2TIaj7/PIDAbDb2d8J3nXdDL5Po7MYDD8dse3HftMLO8yGAzvHL5d2zObmu0xGAzvHL6TvGs2s7zLYDC8c/h2Y5+pnbUbDIZ3EN+u7VnMLe8yGAzvHL6TvGs0trzLYDC8c/i2z7vG4+/zyAwGw29nfLu2Zzi0uMdgMLxz+E7yrvHI7I/BYHjn8O3GPmPLuwwGwzuIb9f2/H9+ZjAYDO8MvpO8axnPv48jMxgM/7+C8Lu878jzvK08zw88z9tyzh1/qx/mef7fOuf+W+ec297ezhd59Na1IKrgt54fgNfKOrwl2Gw6BO9PPfBiVgdP62Veb0TgpSLfP53TcLbq6xxNTPs76J6BVwol8HmyAB/3mJTmRc5vkfD5r73+Jsc34fXmzip4YbMFHkR8n59k4Ef9Psd3eof3F/gH5VJZn7M+92NxxD9s0PoN/p5UuV4Aj0fc88+//kvgk8UD8J+aPAR//uS94GG7Cr7T5BgGfpHPP9sDr1fa4MmSexiEHP/0rA/ebnNP+kvu2ZAi64YTrmGh1gDPMjbPjXuUmdPTGDyV5COscP7ttS1eL9U43gnnO0ioM4uMMuQyrofzhb8z+Lbsz9ttT311PW/VHunjaY+ym4264PGYstuscV1S2af18AL4YCL3lyjX0xnXdWeTtqmzxXXL27SVo2KPz9vi+6+noG7jicvgT77vSfCVFnX75hGL9T/7xa+Dh1f/dY7v6BT80jrlfjjg8+7cod7WrzTByxXKVbNK25z1+bz1Dd5fEte2vtJyiuYWZblZ5zPiJd+ZLrnniehWMqJuJiXaFj8k335iE3w+4aZdEnv+8C5lZlm8An76tRvg2eofBi+7N8DPBrSNFz7K+Xdf5xr355S5eMj1OHJ8nl+UP0AqtqBY4Xx7swQ8XdJfjcfcr7P+EXihwv2Yp9+Twst3bHu2r1zK+6NHa7VcUrZDj3LmuAyuVOD1UkhbEIZcR79IW1Uq8mOPOKUcBhnvH48kYRQTP+7RVhYjiduqHF9fnh/TRbnlktdHw/Fver1S5fwWc+pFHNOp7h+egIcR72+0V8A313m91SI/GYlPlf1YJpTz4xPGhc455wVchHlK21HxWuAlWdNkwDlPxrx/Oafuzhd8ny97WmhyDz2R0ZVVjmd+xjUdD2n//bbskcSWYUYhL4juL6acX0l+H0SU4azC9akUaf/n4s/LNcZBecw4KpJQNV/wei7rm8Qcb63Vcd8DfFd518VLl/JG5dF+1CLxe0XmSbOKxJAJ13ra41ouE1ksWat0Tn2sV/g8V+B1n8Nzsc88ygX8fVjn+EMxWF7ABwYZx+cc9y6Zc/766+mYv+8taJ/m+sckJUfI1Q+WKbvbF67x/fL8NVm+qtiSqMBY8t5IgkHnXOT4b0GZuW0iazQ4o03unQzAvZi+uNulz/HXRB9yvr8cSqyV8v2bDc5pPuV4QvH1kUcZiULKSCS56IWdbQ5vwT3urHKPqmWOZ3RGnfj6V18Cv/1gF7xc5iZe2OD6bEjsF9WpA6WI85uJz572vyd/VPA7jn0uXtrJK621t67duHsPv12t0Y6Wq9TVQoPrsNbgPojZdiUx3J74vaXUKM526bfOjuirxzPGtHlKua3VW+B9+WOOvR714OFd5lErKxJ7NDnej334eV7f5nq4hOuXihwkFY5/dY16MBY92b6+Az46Zj3k6hPMM53oaaEosWUusa1zbirxX5Byz33J3cKINZiVTY4hlHh3OeYejoZ84WzCMfsSX3oJr/fHXCMvoP0ulSiTKytc81LI+7uimmGBvw8Dyky5zBrQaEQZHp/ug9eblJGyPN8PuL5xRFuW+dQhP+D9yyllzp9Q5gqiw+8QvmPbs7q5mc+Tt6291NqqUntsSI1gKXLgF0ROQ65TJef1NOULp1ONq8iTU+77bMzrownjmnaNMXE2o1x4Uj5Pz+izixLXvXabenPvIee/lPw+j6ReMuPzkjkFfS45SzDn+KuS5y7mnG+SUu5KLU6w2aQdKBckkXbOzSSviHIZU0xbUPVFd2ROxZU18KgseY/8cc1lwuelOefgiT0OJC7ypoxzcqknJ5InOY9rWBBbEEixYbaQ50nsHlVoK8qSSNY6jFMKIZ8X+7w/FKUcjbn+C6mp5WKr2nXaxllOW/wO4bs877qWr7Qf1fiOZzxPWV9nzLc/of615LxqfYP2JZT643JB/Q8df//w9gH4XNKCwf7L4NVSC7yxyr2tBpT12gWO9+ZNyRNbvH/RFXsmOcLWFepW3qCsrJX5vGZRz+NE9pv8fRhT9icF/j69IPXXKedTkLMgr8X5RxPGUs45t3ssueYBx7CT04bG4VPgBz2u+XCXdZha3OfzItofTwLmdoU+p/UUr9fXqU/BVe5RWOD1pz74GfC1k9vg6xe5JoVA1vgyx7O/TZmO5Iy2KLW3VGqb3pI+dJJThjblnKJzkfbxpReZZ3qSt02OeR6ZjPrue4DvOPbZ2t7M87fFJ2U596s2uW4FqRnMZlynu/1D8FKZ50klKdqM5Wyg22WeFatfkNgpkNhptULdTReyLxJyBhrsKcR2evL75ZJ6OZc/FjmTc9il1JD8YkU4bctjbjrl/uiHwqNpn/dLLLT0+EA9f3TOuawm51HSn+BJHS+fUHfKJdrj2vWL4Jcvkmeh5KID2qrRAc+TMlnDB/fpr24e0jZUJZZrNTm+cMTxl4pyRijnFsUq84HFgLYwlrxo1OP4xxPuWeBJTbrK9d5scz9u3WQNzRf/fCL9ItUyhb5afHfkXZub2/na6qOcvSy63JI4oCTnV5sV7stS/Gwc0xZ5h/fBXz6grnUXlKNRzDxodZX78PLrtOlHe4zL+nLeVCpx3dVnHuTSdyP16caScralZ9cS858suAVn4tN70tczF5+YzvrkI66H2oXVlPuxepk9NrUt+oJamzUm55w7e5Nrlpd59v0n/rX/A/iVzY+D/6l//QfB78uehKLLTdGt9Ws8i45nvP+VPda5xpM+eF8+nn7fZcZl/8NP/m3wj/zAHwC/efOb4L6TOEX8ycoW6/Fhgf0Sm5c4n7OH1IlEzP/RHcrIV+5RZzrHtD0LOcO5dpXjmUvyEDQYN71D+K7yrqtXLufh2+KZksR8I8kpe0PK5kL0U46vnS99eYmn13l/qSl/bFFi0pGchRzd5ea1d3h/KrGJk7pJKrHYpQ3uXSCxQhBIP4LUMBolyvpS9r4gLV4lqetMJY54cMy4QPs+S0UuaK1Be7hccn5DPfsQf+Gccy05FyhVGe+ubXHOsZzhpTFt+KH0NhYijrE774OX67x+eEZ9W5e60muvs1ZQlxpzT/548GbEvGwe0IZPROa6p4xdFlIHW0ptsrNKGUod9yyUfoLBgM/3pa+wJ7m0L7WK4aAPnom9HM6kp+SM73uH8J3HPpcu5SdvW5pik8qROK7r6YB5UeaLn8zID8Sun0rP83qDcjuN5GxDfPtKg3rRaFOOMumBW3n2k+CjN77I8Uivy1qHfuFkyuuLkHJ8JGcZz1xp8fdSP1xEtMXdMWOZg2PO//WXeQ7bP+H6JfL8zWd51vTcp18AXw2ZU4SFx8+7gpwyMMso6xWqxmNnZltFjunhsg9eq3GNJ2JLWm2OcXOLPb8VqQ3M5BwiyVu8Lmeq7RrP5/IRZbreou4eD/n8pfQ+TSTXrUt/Q3dM27mzw17WxZQy0K6xxpNJL+4yZ6yze8T+trwqtkf+jkUuOv0O4Tu2PdevXc6T4JGfmC+kpjHjPL1A/KT086cF8fMZ56n15XRBv38sPRGjEX1oSerbUUQbPpCesnab45/IH3JcSE1H2n5coc59bEmc4zzeX5TzvsDj/csl9fpIepyXGesdscQER7u0hUfyMcA3vvrz4P0xRaAuPeHPf4x5mXPOJXJGWZVvWK5euwJ+SWref/9v/TT4lQvM9dyCa9Q/5J7v3u+D9x4wz/rEj/84+Of/7E+C/9ifYg9z7eknwB/84k3wtcvc0+cP6e/+7K9Qt4/+i/8S/Gf+3V8A/3/9JfJOi+v3Wp951HRIHRqITDR2xL82aJtXV3l9sse8bjKizk6lT/MdwneVd1174kpeWXmUl2bSE3kiMd/0m18GzwrSd9uhfD/xFHtlSiU+7xtTrn1R8ppbr74OHknd6U/8BGXxmevSK5P8O+Afel760U9eAV+pSV+i9F0ENdYriyX+vlygH1tvU/YWfdqP97zwNPiJ9OiqqLz+InOMaU4//MYh7XFXvi1qid+MR6yzOefciuxhXtI+NtrQYMk9bErvdVRpkUvvfC7Jeku+n2pLb3fgc3yDAfXtzk36rOMBebKQM8Ocsdhrt3imutZpgRc79CnPbzI2e7DX53gjzmd4yD1Yk/6w9Qp/v5hLDCCx5Vz626baPyd8OHqX9BlevZy/3dcVxdf7Mq9Cg9fnc+pmSXqiiyWpSUivSe+AQxx1ef41kNir0OP1VPK88Zwxstp5abNzBfnWqNWQs3k5e1ir8/pIatByNO5qRcYRO03WH1flW9O1JuVqInKze0A9S4+kX1hiMVegbTo6YGzoeVxP55ybSR/3qfRfrTTle6wV5jGpLIKePSdLykRF+hGqZfpyz9HWLeecYxaLgZa8cTGVbxCkRlOXHukz6ROcjbnGxYr4N+ldjdq0HUHO8QXS9p+Kv+8dyffA8r3yYiHfYEoelcrft0glL+5/b/7jod95n+FKK/+Dv+dTb127d5+2JMmkBrLHmHEp67Ah+zKsME64fOk6eK1BXVzZugoeBvRZDekPrVU43uM9xg0rBcYd1Tb1pFEgn8fUg1lEfv+B6LqjDx6P+P4kpB7eusMaURpIfi7rqYKaZdTbhcSJ3YJ8O1Ti/vVED4fzx/+4XHWF74wC3jOXmnnaoyw/OKF9u3i5Bb6+QZkY3WHspd/MRaKbo4eM/TJ5/lpVvilpiC3MtM4lPc7SR9nuSGxbpgwG8l25v+T61Urytw7km731Ot+f6RmG/C2JYIW2OZaebl/OWJYF6pwnvWTvEL6rvKvZXMnzt8mXL/LsS4FxlvbBF5KXFENe70qfxsoW+zyO71OWyjsSW8wklplz7c/GfP7mJu3NbamTDKZyHr1JWXnmfTxb7qWs+dbkbzp8+WvsUftISD+aFCirV5+iX9wbU7fvSV/IdP3fAL+2yZx/vPW/By8e/Tr4f/uf/wT4xtdYU3h5//EYfDFiHnG3zvP0f7hP+f7Jl7hm19/3+8CDEuecbnCNvvmFF8GX8ncjGnIOkOjfhZDv+8vyt0ZKNTmEHdMe7HSu8P6csUerxe+7AvEB5SplQsJht7oieajH8XvyDUQqPivzVQdo353URiL5uxqhnOlWit/e39V4/C+ufHv4KefcH/un//+POed+8jf5rcFgMLyTMPtjMBjOA2Z7DAbDecBsj8FgOC+Y/TEYDOcBsz0Gg+E8YLbHYDCcF8z+GAyG84DZHoPBcB4w22MwGM4LZn8MBsN5wGyPwWA4D5jtMRgM5wWzPwaD4TxgtsdgMJwHzPYYDIZ3DL/lH/jxPO9vO+d+zTn3tOd5u57n/WvOuT/vnPtdnufddM79yD/lBoPB8I7C7I/BYDgPmO0xGAznAbM9BoPhvGD2x2AwnAfM9hgMhvOA2R6DwXBeMPtjMBjOA2Z7DAbDecBsj8FgOC+Y/TEYDOcBsz0Gg+E8YLbHYDCcF8z+GAyG84DZHoPBcB4w22MwGL7XCH+rH+R5/uPf4tJn3uGxGAwGA2D2x2AwnAfM9hgMhvOA2R6DwXBeMPtjMBjOA2Z7DAbDecBsj8FgOC+Y/TEYDOcBsz0Gg+E8YLbHYDCcF8z+GAyG84DZHoPBcB4w22MwGM4LZn8MBsN5wGyPwWA4D5jtMRgM32v8ln/g553EMk7cg4OTRy+f57heKZXAdzbXwSdpDB4NF+BHu3vg4Smvx7MleDGOwOejCXihXgHv9c7AB/K8etEHdynpbMrnL9ME3HdFcp/bk/qcT5jNwa9v74DXqlzfRcL77x/dAT8bcHzXL9XBL1S3wPsDPn88Ju+eDsCzOd/vnHM3kiF42e9xzPEueJrxmbGswZv3b4JXLlzkGAKO8cI6ZW4xb4Bf3FkBX6lzzx7sjsG9iHtYqlX5vMtcw3kSgBcCPu/KThs85c/dxKdOdPuH4JlfA691CuDtDuebF/iC0z5l3BeZzmOup0upA36B63te8L3AlUuP1qJS4rrWfA88C7mP5YiyO1pOwWcn++DhmLZidZv7vt+nrtUy2ppB/wD8yhb37Szhul7b6YA/OOK+bT53Dby6egH8eMn5PhxQ90c98uLD/wX8ufUW+Gc+/lHwu2Ou19mb1OujA8pxK+D6bV6/BB4PaOtHQ9ry4YTPi8fUQ+ecmyeU1R0uiev3aIsurTbBI4+2ICiIP6tQpjqrZf6+R9s3K1G5liJz++MZeNHj+PKI70vv/efgfoHjL1cvg9/79ZfAm6tr4JOzLp+X8n0nEdc8iTnelTb9uTvmHk9Oacsr69SJlSZlsFzJwPtz2k4n7vi84DvPlYNHvtxPKSfT2fCx378doaNNFhfmXMyJprH8XnzAImVcUSi3eH3JdZxOqLvLUR88Dzje6YxynKbcp4X47EGfcpV7tF3zEd+fLHl/rzcCL1U43zDi+uQZ19vJeq62aFvX17memxfoO8oFrufJkPP1fc7HOecWS9rzeCSx5T79QxhwDrWI9m51lfY7W3IPJlOOMRPb5wViL5fc094Jx5f0aXuc7ElW4PwykYk84ppEHn+f57QdacJNKpQ4vlKJex5kErfI+OoZ168qcVQtoMz5PsdTlfGfdbk+aaPl3i1IlrE7eXD0Fp96lM9alTFgRrfmCgVeL/i8P8tFv3yRLY9r7cteN2sSW4QiqyH1cTRhDrBccG+TlHw04156GcefepTNZMm9DmNe9zPKXjnkfKpF+vl6h363Uqas5j7XZ1sCkVGXtmC7zPf7khdX2sxZepJXOufcyZjyu77OMQ4OH/IGWdN6mXsWiI8qin1q1vl73/H6xip9/cnRMXirRZk4iymkMbfUnXW558GEv48iypQnNnpngzY+nkned3wEfkdqD3cfPAAvFDjfJy4zL613GL8XG5SRwYQycHZIHTg4pA+ddmRBzgl+VHT11atv8eAh1ymNWuDLnOs8m6qfkJhb8ufOOte16FMXZw2x0wXGDg/PXgXP6xxfUfLjnSeugFdOmNd4ReYdD+4yT5x7lMPTlHJyNKUtLU+4r8fHjGXGQ6lxOb6/Lba+6lPvttcZ888lf69UqRejfh98tqSfns0eLzG2OnznZEZ7trPGvKTktcDf88wLHKPklllZ4s+FxA4Zx9QsSq6/wj3Oy7xerXCPFkuu+VpL7K3P9wfifxsis8mQtm825+/LLe5RUKGMFMQ2Rz79k9Yh01z2SPZ8ITroi7+LJQ9cr9F2nRfyPHOL+SO/5Uk+2MxoG0LJn72ccjTqSwwsPq9WolyHAdfVj/j7+ZK2bDHl+8ZD5mHjoz54XKVuV8qc4FLkYuzzfd0RfcjJ/gl44GgrVtuUq0VKOUlKlIsk4PWi5DCFEvWiVuT9kwnjsP4pc45MYvb1Fa5/pfS4DxwlnHMqeZKXM85pbFCWi5Lrhj7n6IeMoxL+3OU+nz+ZS14kufo8ERlxvH7Y5RpMYtaMai3+vrq6Ch44yU3FFvYGrCdHkoe1S1JLKFHGq1ITm4bco6XUBk7kTOf0hHHNfEGZ2NqhLSwEkrycI0qlgnv2qStv8fj2r+D6ygbXwlsw7q+rnRUzPTygLM8mfXC128MziU1S7rXGuCtr9HMX1jfAg4bkKXIWsH96Cn6WUNbLkiM89QnWyMsSmzTLHF+hKLIthxNJk7I4TeXwYsEFzSVuSKSOFvtSpwtbvL/C8TU//qRTfOUl1vVf/8rr4D0umSsXuSZBQJ9VkjM6TxKhaJX25sEd+pS4RJt8X9K++S36vKcCyY2lDrIXc09e/CJjjY9/jO/f2aQ9KkldphlRBjoXtsELkotPYtrDRZd7Fsnv/aqcuXK5XL6gjz0+vA/ePRP7O7nh3g1I09z135bjj87k7FnsckNqItWy6OY689NqSWvOMoCA96cSIy5iqeFkfEAo9x8s6BcKcs4Yyll5qUi9yKWmXm1wPm3xi3PJGXypecV9xkqRnKtmYusmC8pN5Bg31KT3IZI4ZDpkjtMb8v1D6UXoTpnXOuecH1OX6ttcg5qsyVIK4/Gc9u3ufcZjaZHPH88ZO3hl2tNxj7bkVM4Bju/25DrzolrGNV7p0FZtR5SJlQ3mlaHU7VOJTUpyPQ8ZD9dSrnkshdOixFLTGed74xb98WKPMvJQwtc7D+kc2s0WuOZl54VGreJ+9OMvvMVHXcpqscx1PJJ5ezPK/nhAORiKk3xt3AefS+1/kPF6VuXCrortOHLUnWydctYIKceXnm+B7zxFXiiQd48pB/mI708KHH8mZzM7T0g9Y87nD27Q1rtM8vGQPk2Ox1xVakStFmOOK0/TVnoD2ubmmpzzOucObt8G3z+hPftX/29/AfxTP/gxjvGItiT9Out0gzn9ydoGJ7X5AutW9RJt3clXGPik0v8RRlzT+/dfA//xn/hXwV/7+ovgw0PW/VY2xdZKPd5NGCfNFvRfxZAyEJa1LslesMFEcoEF16cnsXtvzPclCWXIL9E/L8cSW58jsix307fZkHjJuZ72JAYWXz+WGrTzKAvJhPYpD/n82aDP8ZxSf6Kc+tyXWCaQGD7pc63DJWUxEz9zdsr3n+6RBxLLrK3SHoQ5r4/mHK+Xcb12rmyC5x6vJxJbBtJrpWcbYcT1ahT4/nmf9r8kz+9OJYh3zvWl52I2pz3wpdemP2AsUS/TRt+R2CdfMM/oLRjLtNc4p6GcSfaljj6WOke1xXi2vcVcvLoi88lpD4Zjxk4TObNNEtqHSZ86UiwwNupPJc/0+PvJUupQDfqUh3uMfUpSK9Ve3bbcv5TYaCkxwnkhyWLXGz06G8wr1M27B9yH6YRy097gPH0p+sRiW+oV6u61H/gU+Ez2dXFCXSou+LxvfIn1iPYq/dS8xPpm81nWOJbSK/TEs9J/+0B6nluUq13ZxysB5dwvcj1OZ9TrA5HLbsyY2Ftpga9dYk5w9WnmYR+7xBpX6NF23bzB9f3cr/Lc1znnBj3+JljjHpcS3rP23BXw5y9yjVI5g1y7TN1fWUguKf0NTenVPDpifL56kXN+dfcu+M3btI0/+jHWqHZWeP/Sp33viAwdhlzTmdjvXGyJF3I+XcmFe2fkhTWOpyB5npfz+esblNFlzvHNtQm6SP98bvA8F4SPxjKTfN2TA7CJ+PVsoZyPT0ZS+6tTzmYLvu/mkdQg9NzxhD6jLK3i85i2JJWekUz6Znpdqb+2NV+n3kRSP55PpKYj+fzqCp/XCGh7T44pt7df+yb44Izr0e/2wY8m8v1E/w1w/dahWmHc9d5rj593/cLPfAV8JKJakh7di5fYtx00OMfaU6y/xruUmWqRutGRTf3hf+ET4M8+8yz4+/8se5f+1r//C+C/68+9F/xn/+ufBv9jf+lfAv/HP/8F8K0vvwx+U3L5gvQSHCbSqypx4UTyzvmC110g53vyUVAgZ56+9MolS66vnt07//E9Py9EgefWG4/G78lZxeyAa1uS85NSQ+zJjPpSrFP/PvQCYws/4e+DGWOJLw0o65H0/d7aZ14xmcq3RtJQni2of9Uq/fi2xE6nY/Y+ZUs5T1rw/YNd8mRLzi4KkldFXJ/TLmPsJ5+9Ct4f8v1n91ljuXkoZwYDvm9feoA3NxkHOPf4efinPvlJjjnlnu3fZR0m7N8Cn/Zoj4KAedFYehq8tRbHk9Lmd9YYX9bKdHoXr3APw1Pq7+kx12wga7otfYTPPMN4OZdezmfl+7CX73L+9ZDj339I/sxl1n260hvvj+jTvnHEutTGhOfpM/nOaTnkfOPk8XOG84DnZa4QPNo7X2LeufR/uz7XYSqxUCA9Wx2JdUI560ilp7kvsVIusctEzrNSiUEHI8lnS5T7kvR4FSvU/Y0G9W4mZxVT+VamN5HztQrn05JvlzYK9NNrHdaE01i+65SeuUKBz7t4jc9ry3XP53wfUEzdUr6Bcc65vtQ8U4nnlrIH41TOo844h0M5gxse0j5fuczYKArlrD7kGrTr3KO29hnKt6T9mPPxpC/eSX/VQvocQ6nr6zmAF8o3HL7UPeVMciF98Yn08sSJ6JSc15Uc11NrXssha2iTgXxrK3Xb80PFpfkH32IvffFruBpInLM4o25vbfLbxo2O9OAOrvD6OvPZQM4mynJOOR1LP2uN4xnuUY4XXa5rJj1ZS4nL0irlfCpx0mQmPc6ShNy61Qd/8Q36pM1L1KtZKt9RyvcWYZW22Z9Lf7DYaifnbQ8khN84o4++8SbzxFP5jtM551xNatby7b4v9jCU85zju6y5uC3KyGVZk0R6Q7uyZ7UaZaJep71ul/n+suRtffmGpFVh3LTIucYD+QalvUFeEJ2YD+SbGunb9+UMd32LZ4xlj3vQn8r3Wxn3PJK6aCi9AlWpRw/lzKggva3nCS8IXNh+5EvKWkPuirxHXKs3vv5V8H/zX/9D4H/vb/8q+OpFPj+N6RdKkve4NxnDvvf9zEOO5eykIfapf8a/U7HsUbcuP/MUeFHOQoMG5y/lU9fcvgK+KzXkJ55gzWEmOcbNA+ra7o0vg5fWngavZfx2aLrP+fy5f+t58CsjDviVv/Jz4A8/+Pi37YNT5h3v/908FziT2l77Bf69qZUXmIccSW3rtRv0YbM98kj+1khPeir2D2hDn/9R1oUefp15YLtCezA4pZHeeIFr/OAez99ra7SX4xHPYRI5f6/LuYQnvbdehf/wcJ+5v19jrLku73c5fXxRvgmZS6w3l2/vG9G3Z3/eJZ/AGwwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwfDbC/YHfgwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWD4HsD+wI/BYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgM3wOE38+X5Vnm4un8Ld6od3B9vdoGT+dT8PF4Dt4bpODTRRE8GHvgxSKvr1Rq4GvtJfgw4/vi5Yw85fV5GoEf9M/AJ33Ox8tB3VPrF8ATxx9cvLjN68Uj8LUqt3M+G3M8vUPwk9EE/OpGA/za1efA/UId/H6R6+E57kenXAAfT2TCzrlvvHIHPB7zGdVqAL4ZboE3q2XwB70e+O6bx+B+wOc9W6qAtxuiEjllaEYRcYMh16CYxuDVFb5vq70OPprwb2wNDzn/1RXuSSJCE/ocULFOGSxWuWeVKu8PZP6LOOHzZT5hQpmpBHzfZD4A97PH9/xckGfOWzxaq2zBeVbENszmnGeRy+BKYhvqjrpWaHFdLq9WwednCz5f1s3FYmtmHEASUC7jGse/d8Tnc7bOVVPK+eksA9/fJw8cbU8jPwW/uPMB8On8Ini5sQa+skbb1c9Eb4ISeCrjCyZc/zui54NdPj+T5znn3LA7Av/Ax2nv4tkQfOMjz3AMKfdoueQYS3XyQoO2IJ/KriS8fu+UMnU25ho1y2Irm9TlepPjXe59ETwqcHzj5od4/+IN8FBsVSHm/YuUun68Tx0atLmeudyfyB7Xhy3wyZv0n8FFXs/Fttcirs95IQwCt15/ZIc9x3GeDLkuWUqbHuScR76gHHiy7vFCnJTzflOe+7RVs5zP63RWeXvC8cyWtDVZQDkpFumj60W+L55RjyTscfMZn+8c9XYwoM+v1Pm+UoO6v9Eh9z1Zz4Dvq4WyHyXyepG/r4tPvSY+3znnvALt6WiyAf6Nb94EH3TpHwoe97BT4JyKNVnjNuMIL6K/eHCwD14q8fd7uxzPtdUWeNlxzcMSn3884h5HRa5RtdqU+6kj/S7tuS9xySynLW2sMrafSW7QkrixXOLzAp88EvfRafN5vSXf5zmV2fNDmuaIUxdqXzzGJqHYmzDkWpTFnOQhZc35XJuF5E0ziTG9nHmC79N+BPJ3aH2xX6HkHUnC2KAof8Y2Ffur46+UKZu+5DFhLn6mSOHIxR7XRVbGQ9qv0Zyy0h/TH+w+OAG/0maeXBW/t32Z70sS+lXnnKtIrpg5ysQyl7g/4hpVO9TXUpkyNBIZm3gcg8bX+ZRrdvf4ALzQYh7TzTn+1gp9VJIy99YspCyxQbnAX2QpY6/jI+7JwSn5cZ8+qN3iemxs0b6vbmyC9yQ37p9RRwYD8vGE6znskkf+43t+HlgmY3en+6tv8a2dH8D1bsx9Wk4oF5EYmzDgOqyssh4gaZCLPP5Ds94Cr3QoB43WCni5Sh6F/H2lTj/plqzhFCt8XzynXsUpdTeVvNJbkJdEb6tNjmc+5fPiBXmhSb3NxTaGNcrpaJ/rf+ce44Sjsz54c4W2c9rTzNO5VHRrMmVcX6nxGYcPmGtWZc0He7xeW6HuTY4Yuzy1cwW8s0b7vky4R1mJa662pDvmfEri+xczynQo/qF70AWv17gpZ0OuzyWxHcMubVFY4pr3J7xf/b/mD0HI9cuWvL70xHekHG+jTFt9XvA83xUK5bdx7osncU5FYuhCQDmMfNl5WYe6xEl5gbpXiRgjjma0TaMJxzMacF8XUnwMF8wJ6jL+danX1qQWOJeq0Gyd+3hyInlRxPl4UotMl4xrKkXJUVqMuWsB12N7gz78oEu96R5/DTyRGtDgiHpU2BTb7Jyr1WnPFxOucSHjmL2EPEvJh32uYSHkGk7FHo/HjKO6Z4yNyxXKVOBxT6tN1tHmS46/XJaak4wnD2RNEpEpJ3FehTJZDPh7jTKKUktIpRYxPmOclMr8ln3qaDySM5ZYzoS6Ems3d9y7BYHvuebb8tjV1iVcz2Pqly95yzCRpDOmfvkx5z4V+5T4fF51jfqfLsmfvMC1y3K+/1T0zZc6TyyJVlahrOWr9LPPXeT539YFqVGI7vlL2oOZlPeKAf9hkVA3J1KD8GLqzmxAP76UemdWp+wWCvSTQUZdD6Tu5Zxzz1zkmLo5bd7pm/fAi60r4CU5D3JcUhdLLj2qUl/WGjKmudgLsX+3br8JXpnz+eWI8eSbJ7TBeyPq8y99kXndB57mmtXLzG17Z9yjUocyetTl/OaTXb7/PvcslFy+fyx77lOG7t7m+eRcapULsV9JgfM/LyRp6s7edr5xfMDzkUT8wkj8xpr44jCnXY8c591o0JcnIdc5GzFWabWuga9v831nIkcaY2tNoy1nH35RztukphJK7DaMRdeLlMOjEeuPzSrnN5Ua+J1bd8FPTzl/T85Fr15gXHJZzjZqFzifuMzridQPRn2pyTnnCiu8p36Ba769Jv0Fcv40P+aaT06Yu9+W87Ren7r+9DPMWyLxf+tPMJdftuV8LZKa7ILXswL3sDejbchEBvpTPVOlDO3UuOalkpytF2hLPfEfntTMOiIzUYO2vHNKGXtDak5r64z9JI1zK6u0xeeFQhi6nc1HsvXq3i1c3z+kLbp55wbvFxs8kvy51mTeNJe4qJ/IeVKDPrd1gXLy9Hu5L+8vUU5zqc+WxOd++lM8Z92/Qx/UvsR9eeMW9Wgwoe3sHVGvLu3w/qevcv7356zZDFu8f1qiLXMi16sN6ZPqcf06Mfdjc4V61Za+pEFKW+acc5XqFfBbX7kNXooYW17ZaoHvv/YK+O6Mdah5Shm48txl8HSzD96bcE9Hbg88K1C5dk9py8pSE1repi3wl6zZrzbEP8mZZrwgn/TpX0OJradSE9LzvnKVe/rBH/0E+MtfZE3s4Abn99VdyvATzz0LPhyLTon/PU8s4tjd3X9kS32JiXtjiQkl523VqI+lmpwVyvlUKntTkVhqb8K1KkpsspC+wHJIX+6FvN8P5fxKZDE+oB86OpL6oyc5fZnn0xOJvY7OWF8tSM/XaC69NrLeG+u0B/mCsjUIac8mC+lxW6UsR3IGECwlTplxvM45VynSd+ZSN+keyDmEPDPyaUMnEkskM8rUMqP9iOfU57LUSAsFrunFy+yfunSF8bKEm65RoUz4Utdpi0ynPcpcuuD7E3l+FksvrNQiiwXK4Eyuj6dcz77EjhpPzxM+r9KgTo3m9FG1glblzwdZmrlR/9Fc85iyG8gBld+Qc9Qm573WYL57uk85ql1krNIUuVrJqCtT6fkdy1m9V6Ycz8b0i4mcna89zdij+wb9ckHG72W0lS++yThgLLZwJHKcdB+AN1ot8GpKwZ2ntHUfej/16qmnnge/siL1gVOu39/4u4xDXvnaQ/Bx/Pi5a7Kk7gRn9CfBlLniWOpW+ZR1svd+6GlwT0pCl9ZoK7rSo3xJ8r6H+1zTwy7t/y/+g/8a/F/6T/9X8H/0N/88+Cc+JLWCEm3lpRZtcaUu/R/iD/w6bdfGDmX+4JDjX5G6Y09qD7H0Ti0zqS3Ifs2l0JhMyYMS/fl5wc9zV36b4Z5JrTyVc82S9FQs5GRzJjWWSPLZpfgEMQ1ue4P7thjy+ern6xXKQcWjnkQBbWNUp+086XJ+47n0pBzTlrRErmZDysGsR7nJH1sP2pqTPenbeZO1w6PTPviKnIcVq5TrUOoNocy/vkY9vnCRttg555LRl8G7XY7h7CHHfPcu84CH92k75jF181KFseN7n+YeFpqc45UnuWf/2f/2vwL/o3+J36h89dXXwX9Y6s0bL7BmFHv0XxsfZ9z2J/+DPwH+n/7Nz4H/3E//PXA/Z5xyuMtaRhhSBqptzn/nEsc3kjMLvyBnHBHXq1Aiz6X3bDp/vL/ivJDGSzc6euQPw5bUHSb0K6FcPhlLTCd9vMs+84yynEe/72n69gsb9JPXt+lnZ6ILX70h+i51j4HU25pyllyR3pqZnA30T1n3KkoeNt6jH4t86ntDzmKziPo/SSl7h4eUzestTmBdzlIuda6Cv/SKfF92nfb06IA1jnrp8ZrzrZdY2/vwR54C93KuyclDnjfdeJU+Zq9Led9e5R5k0tNxT3qJsiV9xGqHNrPcon3bfh+/Cbly9QXw93+IQjyT5x8cMH7O5UzxlTdZh2lKL/rsiHt29Tp1YlLjnudL/j6SYmXZlzPgB7S3eUL72WpLv8OEufXayruj5hy43FXf9g2R9ptOJaaOl4yNlmJ7fKlBr0hN2Bdf3KpxXcfyvVZR+mMrTfkGQXphFnOp8fjc92ad78sk7cgSzu/wqM/7S7zh5q74ze0WeGOF44ukJrVYsP560KPtGMXSE7egX93ZZt9kSeR2Id8dFsu0C+syPuece6JI3Z4vOUeNF7UvrndMf9B/wNgonvD+xirzjlaT9rkgvaMrkrvWy9KvsJT+LPnuxpNvFj1P+jEkII88yW37XMOoyPEEEX9flG/ooiJ1PxL7X6lLnS6izK2v0XaHoz64JzXyNKPOhd7j/uY8kCQL1zu99xafZNIHV6EcRDl1eaXDvqBygese+LJv0hM1GNKGO78PenCH3/FduvJ+8Lp8O9PrMQ8a9VjjyDPWeKpFytlIevML0nd5fMA4ZzuinmZT3t8uMob2pefbq9O2Hjo5G5EaXFGKmx2WV9zZiLb1LkN+N+tzvJsrrLc451wu5y/dhLajKbm3H1I3nllnXNGQ+vALzz0B/vR1nnfdv8W8bucaz+giyWUrRfl+Wc7m5zXmeattvm/pU+aP5NtP9R9JIjWeFc63XKQtXUrtoLUmRS/pyym25LsA6cWdiD/Ren2pxP0piowXiu+Omo9zzsXJ0h0dPdKprVPG9Se7rEO87wpj2r70j6eSg/bmtDftCue+l/L+SL4lKgXSd5fL967yzUdVesA8ub8owc7JLmXvtMbY4sU3+fyPXqZ9eCDfLn6mzRj3c/I97Ceu0O+9JD2oz70X1N0/oS3Yff1l8FXJEz/1x/kd9vw//jr4X3qC9vfk89Lv4Jybln4QvC91hMMvc03qDer3EUMl55Xl/HlfenFWeH+zQH1/5uP09S++fA/8/e9lfN29yfix1eYazQ4pA80d+tgHDyRelr5HP6cMTPr0oe32FfBUvpGYiw9ZhuKzViS2CcReSJ/0qMs8OCiKTx5wPary7f+3gv9b/8RgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGw3cK+wM/BoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMHwPYH/gx2AwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDIbvAcLv58uKUcFd2778Fl9mM1wvZRn44d4u+CzjcOdxAby60gKfLMfgD45PwcvNJcdX4POLCf/+0XDc43jLEcczn4Nncw98Np/w+SHHm4Y5eDWsgHeaV8DzjW1wx9e5470T8Lv9BZ/vc/3azU2OZ5mC97t83vg0Bi9N+f6yfwb+4GzPKe7evw+ep0Xw5y++H/z3/egnwYdLzulX3uyCn/b3wcNaCXz3kHty8dIO+LRPGXp4bwCeBHzehW2u4cpqGzyI+byh7Mntu4fgZ/0E3GUHoJMF16u5Qt5o1cELVT4vzrnHp4M++PEh31cKqCPNGmW05FGmxkuu17khd86PH809m/RxeTaogY8H1PVCQOGul7iOZY+6X61QGbPpEHw5GoGPzigXi7HsUzsAn1Js3JHwSVD5Ta9XphzfyYi2cDanbSuVVsGXkyr4l27yfcE9vq/1/hXwefECx9OmrZ3scb1Gx1z/9oJymAdiy0rUg87allMchZzzE1fWwQczykRllWOeDairy5R75vmU/VlM/5Zm/H3quMdBje8PyrQ1vuP4Wx3qesk9AO/WuEb1gP5m1n0ZvFDmnjYijseLZb4pf58MKdO7p1wvl3PPi+Uy+M279A1//I//OPg/+Zkvgq9fWgOfnXG9zw2Zc8nbQp2Cz3l7nqxDhfuY+tTFaoly4AWUm1qV+7SMaePnMeVmseQ6TSbc12BOOd6+cInvH1M3gyJ1c7mg8SlI3OQV6IMbDcrpbMjnlzROq/D3Gxuc/0xs81qN7x+cMWbIF4xrzrq8HlYb4Isq5bbaoRxurDAGcM65Qoljdm3ans0m59AbMlbunzC2mov/mA455mq1Rd6irhbqF8HL8qc/n3mG9rOaUYbClGvm5fQvYc75pnK9P6ctikUm5wnfVypRBhZL7nG9yOfvic4VxJbmEZ9fL1KnSiXuR7naBI8kHvCj72tq9Zsi9zyXBo/84TKn/KeOexMkEiwsKHs+t9qVK5SlYkDf+xhiPi+VGNTLuJezKdc29Lm2lRr1Mahxbyp1Xh+MaE+8Cvc6zcU+hpTF2Yzrs/Q4ntlc1k+edzRgTjKV9XA+7cko43iPZP3zAZ8/Er9+Nu87RdTgO7wS16jcpg+KRH+iUHxWkTIQSTKais0MK1yzqMPr2RHj46TGvGwy7YM3arS5Rcldt5qU+ZpPexmKj+hL/N8fczxZwPntbNM+bm62wCOJR/sjysjuEX1gIvGsJ7FSllBmndQqvMW7I/YpFjru+qWfeIu/+NKXcX1llTWMQqUn1+kXy2EfvFPnvqcL+j0v4zqe9VgDCjPK7c4m85xGk89fppSTVPahO6DuhSInUcbfR1KCK5Wol6H4oe0LLfAr29SLs2PG2McPGScUW3zeUGLB2Zh5V19io+4Z1280odzW1yWuqJI751y5LXsiulAVe7q3y3dubXAOZyccc0dqHu1rL4DnAd8XRcxlY6kbijtyLuGcayFrPovBHf5c8qCoRJmIc+pqJv7Y0XS5OOaAFkPueSHi9QFNlytWON+wQJnLQvHfNa73aCqxmeQXbarAuSEMfNfpPJK/+ZgDy2KJWYucd7vFmkVdYlLn83kVJzGghIC1CmPOdp28WmOeMB5Szjzx6/6Scl9xHF9Z6ulliXkDyd8rBYkLRc6jlBMKZoxbwojja+aUmzXJcy+s0Oe3GhzfSoPXX3uR/KTHenRP1r8u83XOuUhyx1KV9t3NJe6QOSROYrO+5rbUhemM94cR93iZSRwmfn485fNy0U19XqnM92Uio17C+9OE883FXwYlxtJeQJ1JZrRtAxnvZMDY9fiYMjub0XZlvtSzK5K3if/0JA8dDo7cuwVZmrjh5JH/HaQi3yll594ez0eqOWOhRcq1urh5HXyU83kVX2JEj2u7tkY/2Vnj9bNjys5LBzyPm9yifbn2XsYiK6st8Oo27UtZYpszOWs5OjgGv/XqN8BHXfrReouy5heZs/tSTy2krC/G4sf27zL2qTzdAncLjncUcf0OX+d6OedcltBG/dAP/w7wZItrUpD/Bks/45wrKX35mw8oQ1e3eP3SCvd8EFP/tYbcOaJ9PF7SHuwUmfe0Vhk/Vy5zPvt37oEXHkiNfMrYZdx7yOc94JqfnnD8XsrzqulI7IfaE62FRhxvb0gdLIa8XixwfWsV2vNzg+c7P3ybLadou/mMup3EtB1hgXY+9CSPEV+/vs51SMXXu5yxQrvd4fMldsnE7w7FNriIfmpV6oHlBt9/dky/0O0ztlkc83lZQFu1tUndnk2l5iLneQ/2WeM5O6Ic+aLHbkG570ja9OzVq+DVFerlpMA4oH0q6+WcC6pyFlznGldKnHO4SlnOJZw6mdLWdHe5xtmIvn5wk8/f2BbbJHmJ26Kt2ZQaSjyjve/LmWlxyD3N5VxkeEpbkUj/xprUsFbLXOOi+K9QYq3plOOrlDn+ktRsojrzjdWYv8/lvHAq5yqNsuSN54TFfO7uvnnzLX7r9i1c748pm72J+IBM6qfSF+MKtBUX3kM5udpkntC4zHVeq/P5T2zpWTfX/eSQcrG7S7nOJ3x+PaQcz8/4votV2r41qY3eHNK25CXKxcoWdX80YI2sVvoK+KUnef/2JRnfkLbq3i71Yu+A8z9+yPk/+8KnwYNiyyk6ItujntQ7p6zxfOUf/Qz4nTPGAVneBy80JPa9Rpk5WHwT/GgpfSoJY82JyNwopQNN5IxjdJ9n5+tiS8sF3h85OX+aUoYaEW1vXXrbsin9TSC1BD/g7zubV8Cf+rDIwJzrcXrjNvjeGf3XdCJ1w9m7JO5xzsVJ6vZOH423EFG/EimolaXWPpzJXkss1BK7X5eYWVJi98wl+o1I6iyFM+kNGtIeNiSnb7U5n6nH68slx9M7oa4Fct7W6NDPFSRPnOb0m/MxY7mBnI20O7TH5Tple3uH6x1Ljbx3TF08O6Vs+bHUZAZccF/6aJxzLpHaVV3q7PVaC9yT3kxPYqdmi/ZmFnJNm9JnePUqzzmmkvuvtBnw1WpSe5R+gAfSHzWYybnEhHtUiChzmdRR6nXK6LGcvx1I7+xwQh+s53ftDsc/ldiqVmMs02hS5sqyx+0Wn9+XvG/zgtTxzglBWHQrm9ce/cOM88yKcpbcZsx8+TLnkY0oV7HP+29+7Qb43ZdZ0/jYZ34AvLnJ52ufyOaTlNv9u7T78egeeCp+bCp+ae6J3JUpF90Txh6tnSfBvUBsj9TgdzaeBm/6jLm3Onz/ygZtTyWlXPckZ/j8r9wFfyA5TiBNz9U1xnbOucfOZqvX2UdYHLfAL3/0GfA1iXc/KH2KDxmuuXaDtiSWXpT9AWVyXmQsUGlyDs0P/G7wf/yX/iL4hWc3wE8ld89ntO+LZR+8GJOHOcez1REZCGgLIumHm0gsozWbUGQySfn7ifQC7YkOJJKOlIqP93edB2bzhXv5tUd5V32LctKfM47prNJPDxLa6GJBzosC7bni+zUC3BCfELX4vqX0hEVl7vtoRlsyOKSuliQuKSZyVjKXPsqE88886kk74vi6Z3z/nS51PXNSn5fx1ipyFlKjHrz3g4wJbvTFhxV4dr+Q/uJ7u/Ltw2uP99qvbFE3GyWOSUInt3eD9njUpe06ucMzt4tP0J63WvTjXkgZmgW0ZS/8Xz4KfmPCmtIf/u9/EPzh/FXwrMEa+63XuSdvfIHGcX7yIp/3VeaV0Rl1+dIapfrCFueztcq+yZrEkaU6bc3eHvc0E5nU71ky6bvqj6mjZyKT5438bXXiNOfcfPn+Kaywb2IkeU8s3z+d9qWeJmehlYD6fjqTbwIufxj8Yz/Evf60fF/mexz/nXuUpZNTxthSfnQDqXM1xC/W6hzv9kX6Xe0ha5dpT+8dSc/aHvlkLH3Mdxjr1a5J79NF6aWKuR7bDY5v+jTjmK//IutOzjn3a28yfupK3/JkyVzvofQxD8Wmz6WvbVmQ3Fd6ElZm9CmTCdcoFt9eLlLfP51SRhtlnm9tbPB6IeL4vE36lMOH9BHFAsebSz9AuUQfMZfem2KBMpT78s2hmIdA8sow4PoUSrQ3y1i+EZG+ylr13dFnmGa5G84eyftCevgTx3nk0huTy1m5tKe7yYzPq8nzU4kJq9J/Hkp9oCx9KFGFut2U/tzpWGrCsg8H0gNXlzzy5j3K+Sc/RF0eyfdwnviVUUK/V69ywqdDxgVv3qdeD8Q4RkX9IE3OdhLqxb0Tjm9wRv7UJca6zjn3rNRMgpB7Mpc1bMn5/Vqb9q0v/VizGWVoVexzSc4QPek3Kzf4Pmnzc37C5zfluxpPbFUcUHdD+b63IXX55YQy0ZFzkzihPw7lfanUnGKpC8oJsGu1pS4o31A0ZLxeyv1q6nlXg/efF/Lcube30qULOR8qcF57d/hd3sExY9hFLN8vLekz7+++CT6csCZRkLODscTUpz0+fyTG63AgMaack6bSr6o5RanM5z+/RZ9TKFNX/7kffA/43jFrQE9/iNdvyDfYc+nb9F7mejTXKCdtaYZ46hnRW1mPjQ5zqCvyjfWFC+x7cs654hpjy/vDN8DTnO8oR/KNm541F6hNpZDWYucSbdXn/vz/BL7xL9LeP/Nx+c5d+g8KGcdXEN2sSQ/zUr/5aXKNM8f5pXPyhpwRNOryvYS4C196PXNptp2PGOtGooOjufSueoyDhn2uR6Lfp/Wo4+eJLM/wDVH/iPZlIP3hUfwE+AsfZh/h8Ix7U2tzbccj+aZAeqomY9agX7nN2ODJJ+lHH+wz5t96FtRVW3z++67K+XeZfvUN+bshr7z2Crh/vQX+2ms/B/7Tn/vvwf/JT/w/wO9vXAOPq9SlT//89P2rAAEAAElEQVRuruevfoPr2ZPYrzCV+qPkdfceSv+B9DIFNf5NDOecu/6/of2Ylim/DyUPKYg9KRQ5pp1r1MfhS9zTZoU2P5pTP9drzFM2tq+AV2I5A+yzLrOdcc1rK9TXU+kFr0msdNyjTL7nGfnOp0+fublF/tJX2cOy+TR9Qr8g51kblNGz+9JXLn8LYDjiHj/ZYt4Xl/n8jSa/wf5W0BjMYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBsM7APsDPwaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDB8D2B/4MdgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAyG7wHC7+fLPD9whULtLR6PJ7jeOz0AT8IUvFZugvuFALzSroG/eWMXfKVeBj+8fwJ+/coFvq9QBS+0OR6/7oHPBgn4YJCBF+scrx8UwI9OHoK3S+vgVy5tgkdVznd3l+v3ykt3wHsZ1/vqVh28O16Cu2UP9OYd8tO7++BJFnF8Xg5+PHn870lNlhXwLLoEvvDeD37/IdfMi7hHK80WeLnNNZzMXgPfPeKarK9Rxmb9LngxG4DPuaWuWOKazueUkeHZFPzN3TH48UEfvDvh/S1vAZ6XOIAo4ponl8hP+nPw3ZMh+P7+Gfh4QZleb1HmXLEI2qiUwOennN95wXeeK/mPxtYq0BY4j3K1HHFdLj3/BHg+5j7UK9yn0wPu8+GbXIfBKV+fl2grwnIDPG1QtyZT7kuQcPy+mPbQ576tlfh836ecDGvH4L0ebWVvSV2+88aXwCvFNfB6ge/fzGlrLr3vafA3Xv5Vjq/WBk+HlPsLl1fBwxLX43d8kLbdOedOe6JbW1vg/T36j9vH1I3R8SF45NOWzefco1FC2xHlXPOgRJlMctq2zZ0N8OWU/igL+PvA4xrNow74Toe27izhnldrLY7X4/MHwz74xiZltFXmfCZTzj9NOf/xkjrnZ7QlL37jFfBhfyTP43q0K5zfeSHLcjefxG/xkxHX4eRMfEpKW1CqU1c9cdNDiaPORjPeX6bueSFtdhrzfffv0edt1fn7yYxxRpZxH8VluDSnnjVqfN5MXEQszyvV6cObVd4/T/nC8QnHP5xTb/OItnE+ptxV65Tz7iEHGOfcr2a9BR41afvnF/h855wrV/mbUoO6Ugq551st8u0GZXvQ5p4/uHsPfKK2Ys44YHuN9jXMuKZRQWTggLGfF1LGpn3KZOZJXDXSPeL4K4HoQIG2ZbvN8SZiiwoBbd9MbE21QhmYJnxfwXH+B8ecz2zK8e6d8v3Vdfqf80QURm5941HukI05t7BIO1sucuzVKtd+PubcI1nLMOda1hu8ns64F7Hj7zNZ22VEv5eO++CJ5IWhk/FLLOLNGHzlLgYfTfj8ZUCDezag7LoKddOJ7Hox1zsN6LdaHep+RXKYrZzPL4eU7ZMHtC9hh7JaUYPsnMsi6v8y45iTRPeIexhIrjccMB4eS+zj5Xxfs8k5X96+wud1+f5WnTKwt8tcNBnQxlc9rsFWqwU+OOMedGfkJ0PeP89FxiW26XToowoVyuBZlz6jO6D9Xywog+U6dTJNJN7PaM/DdCHXJb85J8wXM/fG3Udx22TK/PujW+8BL8nfnK7VuK79E8Ye04S++ebLX5T300+8+k3mKdvbzDuabcp1vXMV/PbDu+B7h1z3k33mBM0q5baUct8rIWON9Q73fXuVOUWr/CR4e2UFfGODMX7ZUU6Wwo9f43p6YgfCiLbmgz/Aesw8kVhT5Hbaox4551y5JHWzGfnWRgt84XENNralDlbh9UDGMMmoW+M+96An/jCQ+HsuutmocE2WEq8WAvqjvEr/k2es8xUdx+sS0e2Aurxc0B95qdy/oAzXQlmfIv1JELBmNVHbrrFchb9fJLSdsZOi2DkhzzKXjh6NPZ9yHhOxkYuYspxJvlvwaJtCiZOCgHIcx9ynxZy2wokuViTmr7S5rw3JM9IZr4+lVjk8Yj4/GHJ+0wn3+XBIuT+bcPxhTB9bL9An1kvMES40KSdtXnbRmDFBXuT6lCSubBXJj2Ou32wyFv647ZlPqavzhGMohBLrelxjT2TCjyhDcS71T5GBWol5UiA1oyAXe13gmmQLkTEJ7SKfMprktF2pxNZhKLYv4P0l0fX5QvKgBcc3ldrEVM5Ezs4Yq+bib/wi96fTpn9rVySuSfnC5VJs4TlimSzc3smjM5juhHvbCKmvfpmy5WaiHxnX2lsyj3m7rXPOuUj8Qp7SXsVLqUlLjN/d415dWKOsHnQ5/lWP9dNMZPei1BuTLsd/Y+82+Jf//j8G70+kZhKIbgaU7ZHkMM+/j7Hk4ITvny64P/0jzj+7Sd27f5/3zyXvTZdi8JxzpSr18Xd/6nnwF568Av7GLeYNlQn3sLNFn7FW5fVkRP1744Q2PE4pY/GO1AYlj+nv9cEzR5mdJ/T9NdHf9Aqfd9pj/LnIeX/qy7mH5JmTJccT5LRfFa2dlvg8l2veJfG/nLetiBNrt3bAL63SXp4XfD9wlfojfVu7RN0rSU3XiV9ZzMUPZdyXrk9bU2+IHIld7o54fRFRrkuiKw/3aVu6Q94vx2POE78RFjX2Yiz04CFj8KHEZtUt5o0tybM2L3IAyZH0BlTJSysyYDkXziKJ5ZZc76Nj2r7WOp9XyKlXKxHH65xz45HEBnM584toKyqiKxWpu9e3mIctp5yTn9KfTCbkvTPOsSNltWDBOTVK4s9Et6OQuvzsRe7hL+5SZi+2qRM3blJG9r0+eLlG/5cE0j4TcD0HA/qPVPLQJBZbuSJ1TNGZzQv0Xw/2aMvDSGzbOWE8m7kvvvio5jPocWPVxgYSc9eldF6oUG4/8H6u01xqi7KM7vYu7/9v/stvgP+r//ZF8Gd/6Dr4q2/QNhyfcd/f3L8P/txTPGsOI8ppp8l6wCKTCRcZl50UPgA+GtB2/Z2/++vg+6++DL5zzPdtNFgjSiI5yx9RD7pHtD2Nuti26T3wyw3Ggc459+x7KbujgLbm5IAysnvKOd4/Yd0tqNK+r15gXrCXUzfOXme9eHfI358+pG0IK1yjvE7d3nkv63q+1IiutHn/l7/0Bscz5vOKVfYvxD5treda/L3Ynt19xqJhxD1zRepIvUOZ1N6HfMb1/Ue//Hnwz/zOHwafSw3sPOEFgSu/rd5fLrZwPZDz8CLdlpsPpQ/Bo6wm0reXedQH5/OBzbrW4qlvdYmx5znH12zS71Wr9Buh9NKEV+j3Kk3xW3PqwsZV2qvydY5/5Zj26OFd9sWMYsr+0ud6xR5lry/n8SWRxYvXOf6anN2cHen5Iw3+4pSxpXPOLedynhNxzdMdWSM5k1yV86pilWMq1znmXGKBtQ7tRW9wBN4u8f7FjO+PC5SZowOu4XROfe8fPgA/W+d4Q5HhCxvMWw5OaY8nkutrDTmR8/ZLm9vgcUyZK5YZnzaqnO+UJWXXlLbD1RXa160t+rjzguc5F75N/xfSrJPo+UpR8irJ10sZ9717queIXKiy1O/euE9dXZV+zc0nGMMnA96/9sJT4N5CYoEd3n/zi98E7y8Z25W2eH70/hb7Kqddnp/lUn/0Ni7z9yLHtRptZSC9Op6cnSdyfnVy7x54LOeq289Qj69fZL/yUmIp55y72JDzq3XakulIYgex37/wNcY+tx9wjfbPqOu9BeO3AynK7t+h7cmb1MX5G3xf9cIz4IUCbUFDYp14ztikVJJzlJDzb0reOZfYKJfzpYX0LiUZ/XXgS2+r5EUjqSUUIq6/LzVxv8bxNOrS5+npuc75IM2dG7+t/rbToE08kxpIJn1zE+mDW9nhvi2HjAN86V+VNhoXSk9xoUBdzsS2pR7lpihFnnqLuluUnuIrm/z9wnGfemd98GjJOGdjh+t1FDGvOzqT+kCX3PdoGz/4oRZ4V+rDI+lpu/GQtmpKMXXLWM7NpWfucz/N+rlzzm1do31uPsNY6X2r1N3P/yz7M8JAzs5P2QceX2Du/GCXsfD+bdqaNz9Pf7R3h3MuX+MeyxGj67QZx/XPGFfcf5U3nEjvZpr0wZOMa3rtKcpAVeKcK0/S/p/1KOOHI+7p/inHI58VuKp8v1GSvC6oU2dmcgYiofe5Is9yt3ibTFYbHLvvc27LMX334EAms039jieMeR+KXyhLrOWP+P61GmX9gKLjigWJ2VdYB5rntPNrF/n8aM7xbMnzttbk95HkDBJr7N99EzwuSo9srIkrDUZNzgtfvke/vpkyFgwlNgozylqxSn+QyPnj6YS2wTnnltLvNJjzndtPM7Y4zRjL1FZafJ70qksJ2C2W8v2T9AhkRfq8hpPey1BzbV7/pX/4a+C7r9MHPPkhxrPNbe7xxnYLvFCg/QykLnb5Kusy4x77rjNHHRnHtI8Hx7SvF8r0uVefZD9ZsUydPO7RfhakjzoJW+7dgDjN3TG+X6QdDmvcx7LUbEI5dxzLNxXHXerecKHfPnLfyiWJbaTXJ5Nvc0baUy29RMmS96dyHjeSnuf9Pm1pc43z70kfyFC+vQmkhyxLpAdMYvSxfO92IjWapfSp1Dt8/kLqM/ekx/vOfeYcseTRpdXHzz7WYsr+wxPGR6eHtNdXtlmzuL7Gs90LHTnvSuTbTumvOjqhbZj0aR99j7FGYbwHHjXpf4rS75A7OW+TGnQg3zQ26pKnaW7bpn0/ORIHKfH+sNcHX0h+UJa+zLrk5k7OfXJPzsc8zq8s55Ham3ReyHPnFm/rz14spMYTch4T6avzJR8uVzmv8lL6gETXUzmrDyM5nwn5vjxhXjNN5PuHVHrGKtTNcptnFVsdys1zT1BvPnSZZz//yw36+PhZ6t3Ok7Sd1RWuT31BPWvKuex8Lt+6rvH5LSnRXLzAIGJ8yLim1qTefOFv/Sk+QOIm55z75L/0n/CdNfrZhcReSULbMJXva8dD7mEg3y8vZc/+lf/8j4Brz/VyQF0VEXisN6B7Su41KePaB1jM5dtPqXe7KddMbdl0TnvfOyIfDWlLSvLdeyBxyUQSr6hG23t4yPPGB57kHmJbVwLK3HmiWq24D330hbe4fGbsCvKt4J2b1OdakzFq91DOJtqcqy9nJ6HYr6gg9cyrtAex1JlKEvNmQ+Z5UcIesVn3JfCh9PAmG9S1T7yHNdp45xr4hWc/Bv4X/9x/Df4glfO7Dd7ffY327Ml1ftNys8M448O/h/boxi3qzldeZ43kK4tb4H/58u8E/7eyx88+/rN/k2v+sz2u8WstCsnZQ4k3Z7TpL3yYsU/juvSPHXLP5zn19Zd/hvHhs88z7/uVn+Wcrz7BPRzJN9Lbm6xZv/nKq+Dvl96ffflerBZQv/1Q4lmJbfTvM2yuc81PDnh9bVWuH1PmE0++AbzA9Q2q4qQKtHc9ibW+FR4/jTAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPB8M8M+wM/BoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMHwPYH/gx2AwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDIbvAcLv58uSJHVH3clbPCqWcL1SKYB7Xsz75XnFQgC+0WqCz7cu8ffpHPzm7Xvgi+EMvL3ZAr96ZQU8Ky3BW0EO3mlG4KWAy524Gvidwz3wB/0BeN4s8/lF3n90dsz3RSn45VYbfKPeAO+eHoGXVzjfeZqBD6dczxMZbxJzvpPZplOUas+C19ffC76QNfvKHa7xJkXGXXiKc1zv1MHfkD2fTm+B/8Lnq+Cf+dBl8B0uiVsOKAN37jwA74+4ZumgC74oVMC3LlFmn3/hKniryL/JlcaUsemcMnDW7YN3M75/72jE54lJqJRb4Kur6+CFMnUwFRnxS1z/80Keey57mzzWZF7lCud1dZPr1GlQV4ZTyvqwR9ux2+M6DGLyhaPgNkocTxZTrvoL7nvm8XnjMXk5L4JvlChnKz73bW2V13/9FdqOSY3PO81pK578o8+Ab//8r4If7bwAnn7zDY6n/jx4rUbfELRp2/sPdsE3djzwYjoBH5Yetz0nOfdsfMQ9f+3Nh+CZu8cxjU7AL1+6CF6uck1z0a14SVuWiX+q1TvgV7Yog2d97vnZiM+vtXh/Ie6DX71KY5ZEtH2FhONzIrPjMW1HgY93cczrbfGHwwHXfy7+xPdF5h7cBfc8+sPxgAMQ13BuCHzf1SuPZGE4HeO6n1IX85T7OBoy8llEjIuCiHIWBHxes02fWAypW8OU+7Ds8X098WGjEvdlMhuCT2OufJbSlpUbLY4353qMGpQTF3N8q82KXJ6CJ8eUo+OzA/BFlXLjB7R1nQZ9gcs5/4aM//iwDz474frduHfoFIUK44Sgwne87+oV8GZrFbwecg8Cj7Zhe4t7fnrCNZ4OaesGKcdccLSnk4z+p3uHayphkJv1euBzeX474g1BwPe167RFCz7eXd6ibbtzyvG0mrT3wylj6ys7lIE7P3Uf/N/9Pdvg/93nboCv1jje+YL+puSoo+eJMPDdSuNRHNab9HHd87i3YYmylFdoT+ZLymocc67ZXDM12oPFgr9v1JjH1Ne4NzWJKecVyvZIxnM8oKznEe3dYEC/XQi4d7MZY4vmCsc/d4yxg5yx4GmvD14sUVYKEtutRZQ1P6U9Oz5jrJXR/Dm/TvvlrzHmDh/bD+cmE9rU3j71dTHiMwOPa+AXKCPXn3wa/P7L98AHY9qbcZUy9/w6Y435IfOyB6c3wW++8lXwbI17vLPJNZgVLoD/8lcYfwYVylxJagm1GmXO8/i+vvjowx5lSmOlKKKPq5b5vKqMp3t8yvtzrlfkyOtlEZJzgh9WXKX1KKffvfUVXF8sqBu9Pe5Ltc11+cYXvgBeqlwD//IXfgE8LFK39nb5/FqLcjFN6WkmYQv866+8Cj7PuE99qaEsMs4v7lPPsiVjvcjn75dT2oLP/9qXwX9Y6gMf+NQL4IVc9Dhi3HDzG/RrhTbXa57RD19/zxPgNY9y7weahz0uh/OEurGxwRrL6jrvqbeZV1UkF6y118DHc9r/bpdrns645sM+f9+QOmRvSN31A+ruaMDYor3G8S9kPFnI++M5ddd3vN6uMvZLp2fgYUrbFMXUmXKJ65P7HN9Sfr+YSLSVSjwg6zfrUcaS+fe1rPwtkSSp654+krU0p6yOT+mTnNREeifMa4KcPqu+whizJQnneM7721X6pKXEjKNT6nqxSF0dtihHqdQfuseMS4YnlPtime/PPfK9E8YlZyPGCGHOuK3WoVwWipTjfEw5Gk0oJ9NEnjfm/WWKrYt8yQvFVieSoyylnuCcc4HPdyQJ17zSZCw6kxpIpS72rEg+X1KGlvLfUBhLLJYuJTeO+PtaSfM8vj6XXH+54PMTsf9+IHnYCp9fbNGfTRO+cDaVWDzm88oReXO9Be753JPxlPPXenEgZxy+J2cqUssoee8O2+Occ8tl7HZ399/inSefxPXxXPKQCueaLanvdfFzhQINzuYO6xa+1DMXGX8/6NJv1dtXeH+Jsn9xk9eHEkstpZ73pV/8K+D/4vX/APx0lzWQyc2vg2eSMxQ9yRECzieOKev7Y8pu+SFj6Jdu0T53VqReK/VZFzPP++g11iAeShxx55Dr4Zxzi7nkUSOOMepwj/fu7IOXFpT/RkDffzVkvDaVvONYbOKFMvXPO6VMRj5trDfiGoyktNUfUx9X2lzT6gb1e1jm/Guyp4sJY4/OGud79/UXwQOJzWpivwoSW0Yh7WdQkvh/XfJCj+u3WafOlmvvjvOueDly+w9/6S1+8eKP4Xoach8bDZ47hj59cy55TiDXB3J2Plv2wUdj6lrYpN9xUj8s1SgH1Zi/r0qMrr0AqcQG84XUG4ucjxtKTrLWAu+/xPri9TXKZeMC9bb8B3i2spQ8MFhQTnq7tHWe+NmbNxjLXVxSz5fSHVEotJxi2uUaLHPJTUW2J1KjCRxtSUXOrtekZt14grpxeiT9AbLpr9/kGtTa3ONI6vB9qaO5Jff8F26yhvSeF94PfjyR/gqxPUmDe+Svcs/v3WGdMJP4eDiUeH6FeWvlyhb46QPWoJdy1r+3S3/tR3KGXHl3/Pe6cpe7xdv8xhPvZf6+I3009TXOo3vMc75ynbqaxtSFlvQR5T7l5Pn3cXwf+exnwA9WPgB+MroH/nBIvZjLedba2gb4zOc+H+zx/vg+nWb/mD63vUm9XC98EfyX7sv50tcYN8261KPJguv1zI9cB69tM48tPUdbW5fENpKcZfsFyv1OmXmoc87lTcZK03t9XpdYs+RxDJdWdnh9h+8sVPm8ZY9jOH2ZdbmzkHHEhKGb27og/kXihGeeo70f7XFPL24xDwnDD4O/eZu24eFN1nRG+xz//jFtbyukrcuWlJmtFqgLpWZTLJM/vcn9+dq92+C/7/f+KPjeEW1fkL57zruCIHDNZustXq2Ivelw7+V4xvmbrGt40pehNdrFgn7EXzIPiecSUw6pP4Mu85Jpn7KxHNP+FWvRb8qd1Cy8jHy5oH2cJbxfymBuc4s14ViUJRlQNm/fZd9Mf8TYr3ck9kjOq3ypI8XiV5cZ1zfMOeC+1Hudcy5zjO/SBte0MOeeTKWnIC5QX6bDPviq2Lfd+9SPGwnXfDqhvg+7tOlHx4w3qx3m9nuHvD/3ef/ohD5lMeOeddqMZcpFynRVekN9OYOtynxbdcYe7Tav33zpp8BX1pmPxBPWAvKZxMt1+gNPYsF0yf09LyRx6o6O+2/xi5u0NWeiK09d4rnpSy+9Bv6e69S9T/zeHwLvSz/p/i3GTqHEsGdHd8DLt6lbbZoyd/mT7MsrSl/GQGxF8wrzyNN+H7z35i+Bf+gzT4EPO5SbrtiyZULdv32b52GNda7389e4Xgd9xgHjU9re+pJ57Puf4v78cJu+pN1krFcvP15/jMUfRGJLDgv0D8sxbc+oS10vpNS1qMY16osv7krdKqpLf5bcX5DzrmxKGYuk5lpwct62T3t//brYmgJt7/oqzzCDhLa4XqJtO5P+iumCtqHbl7pdyPtDqZkXatrnztvbW5xvS2xnXqROnBfiNHX740e+79MbjOvfkB4Cv0xdK1ZoQ3OxsXHOfeke9fk8qb8OQspZs8PxjCVmnUl/aiB6ki9kX+ccX7PSAj+W7yFi+f1gKHnkOvfxhWdZr39wSjn55s/Tls7n0of4gHI5mlDuXv4cY4TTY/5eyhnOj+R8ION+TA6pt845d5xSdvcWjAue+wnmJX6N9eaoLzUh6UcYynnXV29Qhg5ucw+OR6xhLIe0TdouUKvR1s0ldp2ccc6Bk9x1lbZm573MI1sV6sDF57nnywE3YSi290zONA96tIWTEW2PnmHWJI9ricwv5Mx5FmuvsDjgc0TuAhfnj/zv5rqcdx1SNtp11r+ur3Cul55k7DHocO3ygtp9ytpUvo3xCtSXl28y1spmkuc5XncRff1z7+H4L7Rb4I2q5mXUnXxJ2Xz9Fb7v8597HTyeU9bf9wT95lL6au6c0N4fyLdAxWPK2q++yTijLWc/H3yS9u72LamxSA+cc849v8Xenh/753+QY1ijzX36+feAhxXucashuXuV8j+S72Du7b4J7ge0h889wX6m/hl9gjegffraP2H/2i9/49fBL/46n/dH/+iPgF97H2tvBZ/z74r9mE9pX8YTjv/wgLFWrN8CSG+tk++MGnKePpLe2PiM8/ckN09nj59xngc833dR6ZE++U56iFda4MWMsp1PqJtzicHjOWPquXxfVJO+kEKNMeI8lRhdnt/vU7drkn8XpP5YLEivy4Lz6Us6HGfUi0BiiWvPU26fkO8HMunXn0nv0Z70Adalt6c/pm0t1aSHTHrkzo6ph8MzylmpRT1IpebvnHPdmGv8tRuMtyb7vN6TGk1UZPxaL3BRkznj14MD6kqvL3UqmeM8kZpJlfFlf5c15brY41z69gtyBurkmxD9grpUpm1Npad4OuMeppKLj+RcRL9JWV3luUhJvoeLfMrAsey5fieQ5BIcyvvOC7mXuzR6JDup1OLyEuOGkuRlYcx9qrRY82mE1NXOFn3IMpOeK/GpoUe5rFS4b0XJj1s7HE9zh3HczgW+v1XkPl4o03bcP2Sc8Ade+Aj4V7/CmlVH6iV/6w7PRUsVrud7PsZvyGPpmyom1ONyRN+QS9/htE/bcnCf69e88u+DrzUej3sODjiGy9e4J2Ox962m9AMcsuayK7ZgnnPNg7Ke/VI35n0+L5Wz6YqcsRalR/jghDJzKt83JAmvP7dBW3bnIc8ENuWboEjMtx9Q90f6ret95qlb0me4fZF1yDcfslftqY+yrhfd+EXwJy7ze+abR/fAe3NeP08EYeRaa49syslcvkWr0d5MpQc1rNNvnInfXEpP50jOk0YL3n8qfm+tRn09kj5Eb0L74Dn6jZb8HYu2fM96eJ+y1ZSzgsWe1Hyl7vNcmeO58BT39n0HzMtq8nc/co9++eZtnmXM73F8tadafP8l8v/nT/098MLiJfDjHnvePvOH/z2nyH3agx+oUb9f/TjX8Jci+T6rxzl86W/SBj79h6jfD/47zvGFf/5D4F//GzxP/vf+w0+C/5l/92d4/4d4LnD6JutGZYmHNwLO99o2feZXviHfCJ7Qnm5dps89vMtYce1Jfid043Venzna74MTKd5JLBkm9DHtivzdjQJ1rh9LrNX89r5uf3d0BBkMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8Hw2wz2B34MBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8Fg+B7A/sCPwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDN8DhN/Pl/kudZW8+xafLVY5mLU6eKlQA69WI/BsmvP5oxi85Zf4/JC/X293wOPpFPzhvQPw6bgPnjf5vk6tAr62tQJeq6yDu1IAWh2l4Hd3b4OPdjPwy601jkd2830fuw5eb3I9szl/f9o9AV/EBY7PT8DjJcd/0gV1SV4GX1n/hFNc29gGL1cb4KUJ5/wPXvoV8M7GC/y9zzF9aIuLcnvvAniW8G9cffNGH/yDH/4geHGFMtMOKJO3b1NmiiUucqfFNdlutcjrlKGLG+TFOmV6RpF1Xq8N/vq9G+DlVepYFFHm1nY4nmTO9a+WON9FzPnNx+TFUtO9GxD4zjWKj/R/UfBwvVPhunbCFnghoO6Mh3z+IOY6no4pd57sa6VKOV1E/H0azMCPxnx+llM3+0f8vb/g+8oD2qogWYAXfcrl3t4I/Pkf/xHwm1/5i+A//6f+Z/Df+avUm3/7j/wA+F/4h/z9ZLAHXnG0NRtbLfDpa1yv5YQbcv/hA/C11a84xZv3abBKW7RFh4cD8KboTpDRn7iQezSPxxxjyjEv4gl4PJX3FbnHKxW+r16kzK43iuC1iOOdTWjrwkIVfG3rCfD5iOPpiYx3uUXu5IDrWQ7EtmzR3zvRweXREnwwpnHrnVJm0xrnv1hQB3rDd8ffLwwC33Xqj9a6UGIckHi0BcOcNraxSp8TeVzXYlF4QJvtOT7fyTrNz+j3swmvzzzahnJG25LPuS95xn1R3YyXtC0Fj3oSia2d9ilXxZy2rJKSbzToc/IS5WitTL3ozji+wRnlMopb4EdHff6+z/mETcZ9Xsj1c865oMg4Jww5h/mCupCcMK6Yp7Qd5fYGn5dwDptrW+AzsS17fe7hcEo/fjzmHhVFZkczrkG9KrG5R9u3s8E9iKu0DRs0TS4+PgVvb8oev875rkrsnYe0BRefvAo+S78AfvUP/R7w+V/+KvjOxRZ4/XXqbLPC8Zwvchdmj/Y3mXOvFjLU7pKyMMvoVyYT6lOxyL0OKLquVqA9SEVfOwFlYVP87Eq7BT5P6XjevLULPhzs8/0Vjn91ncLVrlNWlku+b+fiJvjp0T1wl3HCX3/xS3xfmeuz26fuBsc98DvHD8FPBsfgKxdpfwtrtJfdCXV7Mn/cD3bWd8AvbNI+DA5pE9fb9FnNFdqbT33sPeCvvYfXZ/eZhyySI/DnP8g5PDjtg1djymy7fgj+A++h/WhcaIFHAWVwf0j7dn3tIvjqFvfcl9htNJRcWOz1LKEPznLKYEFqG77P6/mC9sRbcE9DR59f9iVP/P6Wdr4lNlpl93/+sUey8cm/1cf1T3yKuvcLn/9F8M3r9J2f/5Uvgz/zEcbsUZN+9fn3PQv+7AeYHz/3wY+An57dAs+L3JfORY7n6vPP8/4Dzmdj4xL4VIok6Yxymcwol6+8wrzo5QPauoPeS+CLCm3ns09cBi9XKTfT6Rn4UmKX27t98KePmSMUSpTjik89WE4ftz2RxF+9KWOLxR55GHKN4oR7kM+oK1XxN6UG4+eti5zDnddeBW+vUdd88W+dDnWreZm2tJhzDcZDxtPJgjKVOvKRxHp+m2u4lLqdl4ruR9xDlzWEc/3GEwYABZ+/X875wjRi7BemUptYUibOC2nuu2H6SBbKkgalMfNbz+M6zxLuYyi1vkXSB58HfEESUk7ClOu8SHh90OPzkpxyt7kl6zzi72dD8ijl/XMpWlXqlAPf4++zJeO8PCdPl5Q7b8716c0Y1zgnz08YBxalplaT2mayEFsSS+AqVGuRzjnX6dD2hD7HkEi9OSmR14WHRY4pz8Xvp5SJQkDbspQaSCnk74OQ1z2JnRcZ31cs8P6Kz/tbW/R/a2JbYsmTjndZGxieMe4KloxlGyvU/UZEmVhpM67qDTn+0ZIymcoZQxyrbaXQJO+itMsLAue/7TxjHDOvWqkzpl4GXPtSg7JaLlPfqk36jWKJMXcq9b/CkrKzd0r7N475/INjxgYbF6SmXaGfOOsyTyl1Psn33WfskvUoO60ax/v8c1yfypPPgQ/L9PMP91kjuB5R1vMF88LDOWOxjXXO//BM/EPGmvInfoTna6urXJ//6T972SleGlB+6ynzpsP7lIG7L4q9yamPaY8ycPmSxJPTnlxnLDTpcjxbF/i86YLvjyqc4+EJ1ygq8f3DI65xvUX754mNDuVc5MlrPK8LWszd6yn3YLHgeCYn1LlWi/aoJTrmODy3rNHnjoZ83tmQ+9F/QJ05LxRLVXf96Y++xUt6FrBkjN3ZaIGXS5TDSZf7oueGeULfHpaYHxcLlLOiz/ubdcrVVPxwtS4xc0ZD/3Cf+5BIjfvurtRoxe+2NpmnvefDzOtKH2F9wJ9x/Hu36Bc3NpmXbl7lWX3gU9Bue6J3st63b7B+Mu+DusTjfFfEzzvn3Fj2tBBSl0Zj7lE8Wsp1+tqsRF9d36S97lxgHjIS/7b7BvsbBie0r/UGZeKpp1mzTSTePD2VPNKT2Epkriq1gk5HzpdUxsSWDIcMUOt17lmpyPEVFtSJ0xv3HH9AHUqkBpZKrDqf09aEnuR954Qo8t3WxqM4cHtb6rkt+ojmCvehUboCPpVzx4MD7sO9u5TLky51vbLK961tcJ82HeOQap1ytrpB3ZwPWZPZkRh60qMcH93tc3yncv4m5221OmPoVl16KjzONyxxvlGVtrO0Qblq7sh4DxmXnf7yXfAP/cmPgt/9edqisMH12D1iHOicc8evUZduvsYad6vE2OuZLa7Bs5+6Bj71GVdM5czBW3KNDiQWXItou6JV7vF6k+PptHg9XIouH3MP1ls8n5pKLr8Iued5lbYkngtP5Yx1KbZiSJl+cIP19VKB/md7hzrZqsr52LbmybStFamJba5Ib9t5Is9d8jZb24+51pU6/d5CauUd8ZNOzm9DqWMkS/ryIONeDcQeHRxSdjM5zz47YSyzf0j9j6QPsrVO2S4UOJ+exOj9M+YE0ynrn1WpQeysU5a7J8yzUu1f6FN2M8nhnUfZyqUmMpHYLZ5zvAndgZvL+dYylx8458KQa7a6wXdu7lDfxxOptXnU9ywjP9ijPds/uMnnLfvg8VJ6VaaUkdNj5qrNVM6X5P31BmW6LLW5utT62nXOv9Xg71dbtL+pxHZRyPtLEcejNeFL134f+J7IdLcrNeYpfVijI+dj0h+WSV3ovJDGsRvuPvJ/eZtyF9Z5zvrmXZ7fTFLK8omcez5f4bpcbPLc80qH+f3XdrkPt/a5TpM5ffVHnmA+fXR8H3yt8Az4/sOvge88wfO0+7/2k+AbMWPWtc4Pg5eWtA0PpUZ08ZKcTYwpp2U5Vx106beqAeVcyo3umavM2wZL2tItqRFpjlGMHi9AzqVPLpU8YDqXeHBOe/3s9YJcp7+YhhIfllvkOd9XalFGhgva80LE52cRZW67LHXKouRNM8aPiyXPx067lMmZ2PsLTdaIpTXWtaRfI83lXEXOPXL57xiPTxmfJ0XeP13w/qH0wxTnvN79DeLd80CnErk/8oFH9uUnv8Sz8g2xRXf26WN2KvTTXclvFz3K2fSUPJtJzafMfR5InjXzVA7Il3LWEUmPsnPyfUEieZr0PRYqlIPTHm3RK/clRl/QdpQ69IFzOcdeiA86OpPvO+RsfXTYB/dz2rZqlXrf2WYNqig+/LRLPXbOuWTMNZ/PeM+bd7SvhbHkfHIPPJb6bFKjPzuTGlFcZF2tGMkZyEWOZ/0697zq8fe9m/SH0gbjipUW+DMfYf/Hcx95ErwiffS1Cu37/pAyot9bdAcS6/f4g8jj88uSm1cLMgE5c05jjWVFpqOie7cgjmN3uvfIpkTvYSzhZrTzRcmjdtaZo642WuD1Avdu7kmfdJP3J3KefbLHusVIPpiayvlYSc56n5R65tYlxgqZfB82P6VfOJGcoH9Me/k///yv87qchy+kZr8sMua/+jTnX9rh2UkoOUTvlC/ozTj/Xi41B8kLe4fMU8s1+m3nnLu6SpvVkT5l3+M7m476XcppY6tS852ccI/yBZ930XFMQ/nmor9PmYirtGdxUWIrybMOd7nH+32+/5/89DfAPzWR3lvpgdg95B478QmB9KhI6OSKTcYyVy5xPi7g86Kcz+t3WctbkVpkLHnqb9RjcR7I08wtxo/ktSrfxjQj2nUXc97zVHqaM16vy7eaI+nv9KVvYppIjD/hOpXLYrdFN6Wk4jKP9y/kvM1PuS/NSPxOxPXYLNOWtVrk2x3WfO6dMKb3Uo6/LXnohYu0/XsT5jSdovgxOXdNJMfpD7ienvjtQfdxObzrMR7q7dOehfJS7dl97Zi6ncy4R/6Etkh7h4pF5lleInMOuYYzySt88W9O6oSR9F9E+j2V9JcN5TucMKHxGB7R9vRHtJWh9J94PsdXrEgNqkIZSkUnUq2hHzLWktYll/i6f++OHmc/CFz5bXZyNhYbL3nM7j5r8758JzccPZDr0jcXcJ0K0nNRWWEcsLLKbzHLPvWiHlI39bv4zhp96EJ8sNZYXhM5Sga0rScL1r9//av8pnv3jNeXEsdduso+oCyWnrgTvu/r8h1h1ZeaUUA5HEuPxx98P2OYf/yQ8/sjv+Npp9iT3Lq9yrinu89+gVLKMe3foAzcuP86+FK+EWyvtsCHfdrDU/nGb13imMtPXwEvV8XvL/m+2Ke/qxTlPKnJXPl9F2k777wh69Pm/Rtbct4nfUhHJ9SBOJZYX/6WQ7lKmT6V78mSMs98F0vqUBZIHi01vPNEnORu/20f4+aSEza2pD99xrVcTvt8oE/Z2ViTbwvlu+dSnb4/GVE/PLk+FVksF+V8rSp5WIPjDxfc25Z8u5MMac9CqVfeuPkG+PABdW37M+8F70xor+ta0yjQj/7a62+CTxrUnbOp1IXWuF+/6yd+P/hf+yZr7P/2J/n9wn/4D/+CUyw+9qfBPxExVvj9V6lvmfRn/YmPfQj8D/zwfwH+I3/6j4H/neSb4JsVOROV+PWv/51fAvekX+y4L72X0js/kDPBSPqw93u0r8WmfFAq32i0xD7H4jMqm0+Bv3z7m+C1DcbPwxHns5iq/ZW/TSN5WEH6DwoS3u5Kb9C3wrvjC3iDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYfpvB/sCPwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDN8D2B/4MRgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg+F7gPD7+bIsd26RPPqbQuXiHNdLcQDuxXJ/VgcfDZbgQZk3+Au5XuHzVuptPr/SAD89OwM/3O2Dn+1PwGstjm87S8E//OQqeLoYgs9jrsfUH4AvT++A1/0x+IUVjn+9zQlnpTL4YHKffNgDP371VfD+sAQezIscT4Hr2Wg/y+uVjlMUFjm4P16Al0OK6Kcvfgr8zoMu+NN9/v7yde7Jez92FfzG7TUOKOCe310UwLfb6+CbrSp4FnDPlwvKdKvggc+HfN9JdwSezKbgzTbX8JWbJ+BBwPWstlfAn356B/zKfMbxOOrQ0QFlwq9wPoUFZSpPI/DlkjJ6XgiD3K00srd4b8x5btT4t87qM+5TPOI+nJ2IbhaoC87j/aUyr3sB5XS4pK1KYt4/XGTgnvx+IbarEtXA+wu+v1WhXE+mCbjzqOv3XqWtiE+4r3//7k+Bp13q8f7DN8ETGf9I7EAq83UB5axYpl5PJ7TFaaLrRb10zrnYcc4l+Xt3T1y9AF6tc8/O9mm/04T2eyYylhe5R7HPMQ6nMgcZ7zSgbnkp7Xs55J4+uL0HvpxwPPsTynCwcZE8pAyGIfeg0mmBj6Z8XqnB9aqt8/7OKmW0VOL7lndpGxtt+peDPmVwOaPM+YkEEOeFPHd58kjeK2Xu4/ZWC3zdp65W6/QxRZpg5znqUiknn4uNPz14AF5PeD2Y98FHU/LxgLpaKHMfU4l7gpRykMfi80POt1ahbi+GfL9LeX+vS9t8tboB3q5sg3fq9In7XcZVkwOux36Pclhtc/9KYktLddoRXQ/nnMsz6v5iwj2bjjiGeEZbM3O83kq4BrWcYyi2ac8jjbXntIVBzPEUMvLtLepiucM1WGtxj2Y512Brk7awm1DGKz51ORtxD8I651OUWDcoco9cQNvSn4q/71Am7v/Ka+B+kc/fPdwHb9W5nsUC1+v88Wi+S7GLkce98WLKVln8YrVCO35pkzFlPeTeljzyV1+5Bb735in40a1d8M1mC7y1zr2YHx2Ar4T0Q89do6xUt2kfJjPK7qv3KYu9E8bcJ2dcv5UG7y+uU3Y/8NHr4KWXXgb/2ldugN+4T9maxtTtFz5xGfyZj30Y/JUzyt4rP888zjnnootPgb//hRfA5yPGMoWU+nP3xf8e/HN/9z8EX732Q+A//C/8SfAvHNwGf71G/fvKBtfkz3x6C/wDP0KfsSI+9Osn1P8fbL0AXvxHtPkf/PDHwZNQYiWJpRYzXi+XxB5Vybt9yuRQYsM0lb+1nFGGZiPa57BMmQs96qT3/S3tfEvcPx65f/Mvf/4tnoecx+3bXwSvrVLXtq+yZnLtPU/w+g7X+dIG5fr5Z/l7L+Pz2xuUu8mMec5ccoSnr7c4nuub4LNVrnu1zuvuEusNns/x1KRG88Efoh6OB5Sj3i5t3/p1Pr9Yox4EJfrlj/+eT3J8ddrq9GXWV3qSV2V92poNiW2P7vWdorPOPTs8oX0b9+gPlpILZnOu0UqVe7i5zj3YvtoCLxepa5cu0H81t2j/CwXOaecq93Q+1jyEa+J79Ke9o0NwT+Lhstie+TGf32kz3vYl91/rUAaWmfhLiT2zlDJeb0jtQPYniMTWlfj+PJPY65zg+ZHz35YLXNimLTk+YJyRLTnPQiT1zSrzkv6U66izzsQGZ8UWeCIF7mXMfc5kn04l72qW+bx2R+KcjM9PUqlXS1xV3aeuu4S/zzPJESSvKUtcOO5yPsWI6zGVvLQnNZpQfOwy5XUZjss9/kO2eDwGD6SslAeSd0iunWgNIeY7hqesvzZlTQshXxgGzBNyn1ITFWQNR1JjmnNNS1RVVxKZqrf5PjEdLpQFGc7ob44eslaQyPvLIWUyGfH9I6nfN6pc33zG+ycj+rfFlNf1P0mRThgnFSrUgfNElqZuMXm0f2GTdnngSQ7aboF7S+rHyRFloRkxJtc6zXpVZE1CQq/B2COtNMGTiPp2e0jZH8wpTMWyL9d5/ze/xrzvkuzVYMEY35d6Y73xXvBKyPVZrbB+6ee83gx5dnPlw7Rv4QrH8/J9nsU8uE//8be/QN3/wfcyL4tz5gzOOVf26GNe/Srlt7XFNfRTqdv41IdVn/pUF5uZLrln1Z7EWlKnz5rMK+Zj2ge/yj1ZDo85vgLtw7BPe9JqcI9nx/fAc5HJuM33uYrUtXypGUvdazqVM+Ezrl9RzjlKLallLtSncL2Oj6hUd+/SXp4X/CBy1fqjuHa8oC4mCddpOKFcJDPue+ooZ1HA+2sSG4UFytHgmOvWqPF5cszppsL3T6mLY7khlrOP5Zh+RErcriOxUySxw/SYtnbviLo+krOf3V1eX12nbVndZsxfEjmeLxlrXthi3ro25FlUXeoNw3Gf1x87PXIu4ha5/VRshfRH5Al1qyK6Una8Ph6wRvtA4r3TpchUReK5MvegXOOa+BJLlUOuoefxerPE2GosZ+1FOYu/8gTzyHtytu5S2rZLl/j7gjxvJDX0OJEauMSaeZW2aTji+i3E9sd9OUMIqWPnhUIxchevPvK1YcRxPnjInoKWnA8dSP2zvcZ1Hk4oqAf73Kdhn/tw7z73rVLgPlyVs/3CC9fA16W26DcYV63WaAuGUvvPnqJtchW+bzSiHH/wY1TUpy/z/ieeZBzZbvB5b3yF9ZNnf4hx3f1/wrP4Zcr5DF4V2zmmLXsg179aZ1w3vS+JmXNuco9rfvKAMtAucQwdn+dLF+R85sEBx1QW29QfSW6aU2a6I/4+LLCGEYas8WQp93T/kLrWi6nLN2R8e/vco94x5++VOZ61y4xDti6zb2ly9wj8/jf4vIMe31+8zbgkT3kGImVHN1pwvEs5Ezru98EjscXnCc/lLnrbmfhQzg6Hp5TPOw/64B+4zhj1tEd5v9ChrJwNqP+jHmOVtEi7vZQ6R1V6qrwK/d5sQPvl5/x9llAWvYr0pCWc71j6QOJ7zFOKUpOY9/rglRLtkVehbrUaPG938jztLUqExxKXxNLPEBT5/oLa5z2O1znnKtKj8Mx7qN/rO3zGaMg92pM6+70xfdRwQP2LpO6y8Rztlye1tbVNjqdwn2tQWqO+joeUuSefvAS+HFEGglx6eeaUyXqbPqIgsYvfpD2e9uhz50vO9+ABa6tj6S84PJRYrMnxTjPK/CCTvDPn+E/kPO284AWhKzUe7bXfYs0hmrHGOm4x1phNaGfvSA21WqGuX2ZJwsVz6VscUM42rlKOqhn3aVqS+uaQfmbyOm2hW6XcPZlL/+4F2sqnnuG5cCLnnP1Tjncq59Cbm+wpPpnz/npG23lTYsHLNcpNrcXxbcjZkCc5RJTSLz6UHjrtwXPOudMzzmnek17PmvT+TKSG0qXtqa+wzjV0fGcuvZRexvg6lPOpwpDvy+WsP0+5B7WCnGssuQera7S1sZ4jDHn235fzPG8h/kB0v+GkhiU1rlxy40GXe1SXM9nXHzBevtChrZMyrLt7yJrXyViKFeeEs/Hc/bUvvP4WX3/6g7helZ6oUkvW1ec+ez51oZRRV/w5r5906QNHWtuXnrWwIj5JxreQGo/a/InUeAKpp0cl+pRqi3FKb8D5dqeMg2ZSS3zy0vPgG8+/H3z/xRf5/CP6uL700sep9oxJ/VtqmytlGvsN6YcNl/Itg3PucPAGf5PTz+/fkj7wE+ldnIn9W6E/a118Bvz4dfqTUqvF+yPO+eIGf+8y9l/svyb+8lDO00pck+c/+KPg73uaa+rJ+Vg6pAwMp4z7JtwylxW4PgvpOw/F/9Vb9EeNFda3l/LNTW/K+xcSNznJs0pV+e7gHJFnmVu+7Uz3UM7Xj0fMixYP74EXPep3a5vyPJL7yyvs28jlfLsqPZvBFnPoSp3v6w+p/9euMNZoa/+74/03XmbP6O6th+BxSj8eR5SFq+8XWW39OPgrX/krvL7CvsL2Ey3w5y/TXj/5AdaUv/lrrNtEcp54rUrb8GN/8PeC3/0y7d3LX3/FKWZD5gn/5G/9PfBY4r9TOaeod1j3Hg8kl5XezY6cQe5ckDqOfFN3Q9KG4hpl7Mn30r782P/uB8F//Ve5x/du9cFvS50mFZlpSF92JtcLcr6+ssI8cmOL8XyaUwdK4nOX4pPTPn1sS/Zcz/db28ztxw3moecGz5NvrvRsguugfRTJhH4gCrgO65v0e23pmZpLiWMhvnskeV1ZYuhUah6RlNOW0nfSrrNG3Gky5t5epVzsSQ/XvM99fdhlrBJc5QC+8WX24xZL9MPBCterXZMetZhyuUjkm5CM67WxwrxSD68mU/5+Nnr8O8M9qVEk0kPbvEL/UJLcL3GMVWaSVwXSC6lzCCQvGZ1Rxto13p94XJOSzzUM5PuvNJNcVfrFfIl1cp/P9+W7lcWcMuJLHlVv0p9URUhDkdGzE8Y2R3vcj9paC3x4Rtu7dYkyPJMe6UjOA88LQRC4ZueRLx/I90NL+a7v9JBxRq1BOUwkDyoWuK6zPTk7Ft06lJ6OQpF8Y5XPX5F9nss+jOT87M3XGcccjLjPd+XsY+lTDqst+tRIa1rrrG9sipzUPMaFZ0dcz+4efVJPeugaBXl/mfWQuvSs/dzuN8F/8CnmPFHAeoBzzh18g+ct8T7tZ0++0fCf45z797nGNcl9/YBxfz7i9e4uZTBZUmbGHu1pQ3qnUvlu3cn3ulJWdP0l87RLdcrMfCx9fI7jH8y45h3ty5TvQyryvfYskTNeT76N9cSWD9mPMV/I314Y01aFcgaxuSKF13NEmsRudPxoPrGcpzs5W63K2vkd9q0lcrYxLXLtF0eMoeXTRdeSs9LYY33NG1M2GxtXwHv3mUfMy/ymo5Cxfti48DHwwS7zGu3z2FyjvVu8IufxMwp3Jv3/rkjZbEnP2KrkHMdHfN6rt2nP773+a+A//M//DvDr/9anwatXroDPvvBXneJnfvIfgd98jfbnT/5Hfxrcu/MS+MYnmCu6hLn8m1/h7wshbWzNI3/+fZShOKZ9+8hHpI50QvtZq1HfHh5xPh96mvHi11/6JfC1S5xPPafPOpP+t8Me7cELzzD2aYhPchH3tCa1xlj6mLcvM7Y5esD1WERcr/lQ6lZO7elvjHfPqbzBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMv41gf+DHYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMhu8B7A/8GAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwfA9QPh9fVkQuLV24y0+nfdwPfI98EqlAN7tHoEf9lLwQrnC51UycL8kAxrmoPUi76+sVcFXklVen3fBS7U6+HKyBL/bPQWfT87AJ4M5eKsR8PlFjjd2D3h/zPFPpk3wKFkDT0/3wW999R54/94x+MkkAn9h82nw9oX3g7dWuV4vvrbnFKfjCXgec8136lvgH7nSAR9FlJnBDa5xfMY1/X0/tgK+dr0BftClkAwd9yCdkDci/o2sC5cugrcqNfDVGsd79w5loPfwDfDTO5yP54bgJwllbLvBPV5d53oGMXWiEnBPS0Xq3LzG30dVue5xfc96nA/vPj94eeaC/NHaZYtDXE/nbfCx6Gbsc526CU1nktEWZTn5PInBi7Lugwmvlwu8HrgFeJRT7nyR0wJNhStR7FxLbOHpdAq+3aEt23vlBvilNer2T/+N/xG8VqacfONLr4BHZcplGvF9mU/JiXNOoF7n81Mut6t1WnxezPV0zrmNFn9Ta26CD5K74CtN2o7pKdd8eNYHH8me1tqcU7FM25BmnON8RN0+OKK/XKvSdhVb3JODA9rveoW/n2WU4TKn42p12to05nhWN6kzHt2hKxS4KeM+ZXi1Rn+1sdYCH/ZoW2p1rs/igC/8QJvPu7rC+f03j7uf7wvSJHGD3qO9a25w3dLZGLxUEWWdJfJEKndBfeDhAfjJHie+d/se+ObahgyYPjlNaGtGk4fg4YRynDiJwwLKfTrnvi4jzm9M0+uaEgc2KjReD48ouHcOqXeLmOuTJOLTC+vgxUYRfLXD+bznfVyvYsTnhSWO7/SYcatzzsUcopvOuEbjIfdgOZuBDxbURefLGDzyeCbKOaS9D+fUlWqBuppFvO4H1O16h2vW2uIa1DzOryrGZjToc3wJx1evcb6LlHFQUOXzzyYn4GWxHbcPuCetDdq6v/Lzt8AbNc7v5i6f32xI7P/9Ta1+U/hB5AqdR74t6HJvW+IrazllbadWBhfRcC2JhUo+9TX3eH0iMeiv79LPniy4thrb1BscT82jMnXW+fuLB8xzukeMqX/21/j+20cj8CSgX40Tyn514wnw1oVrvN76AT7vw+8FP7j3D8GnR9T9osfxfOZf/lPgIX/u/Kc53r//N/6MU/S/RH164x71YXQmueaA3Cv9UfD1S/8O+OKL98F/8qsvgX/o4y3wT3+YsdeFGfO+//kXOckfeOIyeLu4A/7CNmVudIf2b9bnmmZqf0WmBiMKfeoxnsxyyQcS2pvJlM/L5P48oQ8sRdQh5//m9sSTTMtL59/il99fZPHQTU9+/i2+1WKssyEx2pX2B8G3LjDmTj7AGkOx1AJfdmmno5T7lswG4Is+/chE/MLpmLZituTvKzFjo0FP/HLGfQmLtF3LmLZkvc3xb7cYs7drV8DTyTN8X8J9DzLqbSiJ4dY69SiVP/nd3uL8X78l6yMx+vEd+o6bb3C9nHPuB164Cn5pk/HXmYj6WMLfV75Oe767xzn1+1xjjf8u5IzneqeMNTrbtJ+5L3lQwPh9OOL9kyFl4ORBH7xU43wjnzLZWqHMF4u0jc1Vzk/ckctKtB0T2YKRrGd/wfVLlrRVS0lHalKHzFNuWJpJgHBOCILAtRqtt/jmOuP2yOM8puM+eK3G+unVi9yHwzPu+7DH+wdL8Qk59yUOqOtZiTyWJGEuulzxuDFZQN0bO+pmPKGgNFYZs9ZK9LGB+pScghPn4jOXc+G09VWpJ+Ri+zLJEcYx6x0u5PrkOd+XS14cBI//9wsKUnOJU6kzibCHGsumfEdJ9qwoZSZf6lgu5h4tRlwDKX+7uaxpNqMyb63Qlm2tMe8Kynx/weea7O7Svr95n7WCowe74Fd3WF9uSZ2yJvXh44M++HLK96sO9aRmlufy/Db9YbVDW+y8x+t854Uknruj3Ud101qV+rYQu7tdon4kY+79VNbuoMsY1nNSJwmor8uU71+7wLWqr8hZxSafX2ryultKEXmdutCJuVepKEdc5XzeuMm9770pOcj9O+ArGe3VzOPz0oXo3ozr482oe6OQ95+NmEfO5Xwtnct53oJxTbaU9XLO5ZJLHhzR92/Jmnx6o8UxS1x/sco191PaXL9MHzab0P5srjGWODvheEZTiQ0mXNNmlQYrE5+0zHjOEi8Y24QJxzMZsXZ5wC131YkYyDHtx/1d2qtRn+t1IHntuC8+U85x8qmcSRe4P2WxT80m9/e8kOe5W7ztzGk+F18uscTwlDWXmGLlvIi6mSzEtkiNuhDy+YOJ1oD4+9MxY8aByOnZmO8fL3h9mZH7cl4Xyjls2GEsmPY4/5P7ffCR5AjeQvL/jHI5T2gb7+/SloYhc4RUzreKch42T3j/ZMDxjIa8/jB+PAYfSvwW1OX8KRLdrvy/2fvPMMuy6zwT3Mdeb8O7jEhbmZXlDVhwLAIgQQN6iqIRNaSkEUUNNSOppZE0rVH3o26Nht39dGvUI2kkdctQFCnRiCRIggQIgjBEoVAok2UyK31mZPgbEdf7e9z8EAeZ70pySAAFRD3U+v4AX55zz9l77eX3PlFcs1i0JNwh1+Sgz3fWRHzqi/MV1QJllPIY/45VxL6CmJMdUklni2Lvfkhb3d6nL5rOi/0rmzo7N8PnhwnXLCdyx1js36WzjB/tEeVf61E+A9HT32uyZzV2+ftYnJ0oV94mPWfbMsk9+78tUcDXxxznsMEYdHOTerUi9Gy3LvqzY+qV3JsYdRjTAtGTuf4iN5z219mrHIgcfG6BMfaR09S7E2eZBxQW+fupM8yL1u9wfMtzjNm/9c8ugD/5TczBHxR5XG+H8w9F77E/I+xe9NCyj3B9PvpXPg9up2hXlw/EfqR/vx7K/ZFUQewZiL7UUPTtrl9jXrAvzn4FKcrszctco8aQ18PCSY5v7hivC184sVh3jWLqdCL6du40dWK0zfMTgeiRpMUe5/wK+WNPcnzOGfYifnPCfvvmi9y/mmwzV524fP60qBsrFepY45BnCXoid75x/aZ5u8ByHJMp3l3v/qSF61mxtod7PEf3yPvYU/2nL70KXnK5v7Nxh7+3xN6flSfP5svgTo72uzzH3CTXoL+YnaUtOaLuyabpD33Rs4gT6upEpAqFLG2lIM5ViLBnpqcYtycR+aHwv72R6Gt5jMNdkUd4wp/bKcpzZnkR3LpCX2GMMeMu+9Y3L4na1mUf3BU9Ti9kfjgakbtp+oPF83ze+adov8GQ9lgROhGlmM8ORPIViz3VisiVrDx1IhZN4sM7jHmNAcefk03lIXONwz325sYBc52eOAfoZKgTA6EDpQXq9MEBn58pUUfGu6zzerboAx0RfD9tjq2e/iK3LNGzFD3Z1gH1wOlSbkURlzxH7EuKcyLr++Jch8jZH1zj3sPxeZ5XjYQzqPcfBJ90GVcvX18Hf2yRcSm/wjg7W2X9v9/hOq+LvZKJoV4Edeploynq7xn6rkD4+mqRvvROyLjeCimwW8JOLZvrudljzl517u8/bt6hrlZsjnE3aYGnwzL4yKGt50Vta4u+Ycnn87d31sFTEZ+fLoncKGHdEaXY0/BE7tIcivNVTXG+pMD3JWlxtkgoqSXyx50B17AjApDMNudLIv6J/auNbeqQZ8QZaFfsSwyog8srfGN75+1xytl1PTM9s3gPp1ybbdquJ85g9VL0TekZ+tRClT2OnPjeon3AmDoS3z+EokeR8SnXZMjrgaj7xgPGxEOx3yb38lPijPCowvF3xZkOW+RNGXEeeKZCOzhz6hz48DZ9Rb/GnFkcATe2OCeYyVLeQb/F35Oadpu+v+gtG4mhTZnl8/S/zpi+w4zp36yEtbkn4mxW2HYw5t78UOwl+2WueatHW9p4mfGgt805ytz5+CnGl1PzPAc02mXueWeLdVIgvudwxRnpw/0WeFn0hOr7XPPyFOdXrVAHK+LM9H4szniLc0nDBnXaT1Nejvj+4yhhW8ak7tlzvbrOuBfmqWt3msxVaps8V7zeYU4aTTjXRx+nLBKx37ssdGUk+p/LYq1OL9F+HI+6ULt9Hfz3LrxB/umPgJdFf/Hr382aevnM94Lnb/334EtL7+J4Wv8D+NnHP8j3ZVmDT8ReS6XKXPJs6iHwUplxbNqh7s6J3M1fY41z+7XXjMRmW5wfqrE2K4uzpeNI7COMWIf1ZQ/Yok/vi/2nQsA5TInvr4Z1Pm8k6qqiy+fPLFEG58T3aB//GHWkK45dDyJx1qhEf3LqEebbScj5FyvUKTdm7hIlrANHdfrPgz0OqHab/jZIM0coibNJXpm5Yq58f8w5CsSJMfe219sNxiFLfAtjif0nzxI5q9gvcnyuU05wW3zjUClTT8Yi9jsiN/OEb8uIM1xWyHWpFMQ5R4fjzYu4HB4wt9sWcaXe5jpn9/l+J2ac9CKx9y++H6jvi72MDb5vIvYjHboBM79QBk/nmHvNz9M3J+b+ONgXffuUYXyYnZd711ykSJwbHLepI66wzbzL/DGVUGa2OE82I3oakxHrDkv4okjseY6FzqQdsfc+Er5SHocSdWUovr+WR46zac53SpxNHYpvOA732COrie9gAvGNoJOhTmdn6VttIe9ZcRbpyGBZxr7Hnv085eKmuE7zs/xeYO045yHr9xMiLr98nT497NA3XNoWOXiWcm0dUC9KJdGv7ore21CcyRDPd0r0pSNHfEd/kr7wxJPnwd/7KGPIrEtbD0Uv8eaL9OWDFu2sUmU/OBFn9PJpvi9b5fosV8p83jzl5fdoJy+8cMlIvHmJui7bQp7oeacz1JmOqH2ffkZ8az/Lvp3sQGQv8P0bW9yjWFxi3STj3aE4JNxv0nnU9rgG4x7rqimf85tbJHdErXs4oK/2d0W8EHsCWXEWLesKX9bg+EZd5qEzJ9fAW6MW+NgStYfI2xxhU0cJ13XN1MxdG9vcaeF6Wnwn3Fxn3VJ4kPtZ8R7tYSRCfbrHtamcYl3h14S/W2HPOT+kLDdMme9v8Azq0uPcjxtcoy7bojEyEHVLJM4NpyqMK9l57rdd36e/i0UPZWTR3xY98mMr1M03a0KXDXXr+mucz7s/KPKG0iPgP/ki16/6p77ZSCye5jdn6zeeA39pjz683+Yctw4og7VT9DdVsf91+rQ4lzfhGq+eoU+vCfusVjnnnZt8f2m+DL59m70CY/h8RyQvqwvsC3U26G/2NtfBm2K/MAw43uk5Jqx7O5RH9Rh7Cz1DHQkC3p8VZ3sc0eMvL1FnU0Pa9B+G+0/AKxQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqH4iqF/4EehUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBSKrwL+yD/wY1nWimVZn7Qs603Lsi5ZlvVXf//fq5ZlfdyyrOu//7+Vr/5wFQrFfylQ36NQKI4K6n8UCsVRQH2PQqE4CqjvUSgURwX1PwqF4iigvkehUBwF1PcoFIqjgvofhUJxFFDfo1AojgLqexQKxVFB/Y9CoTgKqO9RKBRHAfU9CoXiqKD+R6FQHAXU9ygUiq823D/GPaEx5m8kSfKKZVkFY8zLlmV93BjzY8aYTyRJ8lOWZf0dY8zfMcb87f+/T7JiE1qDL9LV1Sou98MheOxOwPd7NfDO2APf6ozAVyolTiTog6csB9yLQ453EICenJ4Hf+DUGu+3+bw3D26B71xpgR80tzmedArcyXM8+aLF18Vjvn/Cv9e0fqvO51k7fF6d8zvuUx0aaco3lXB8y0Vet3MDcD/hej1QTIxEuloA39ppg5/MTYEvzEbgHzr0wX9n9zPg4QHflyw8Ce4tz4C3bu2BdzM58FPHKYNiIQvuxzFfOOH4wgnXcDIRz5s6Bj5fzoAPupy/bVHnixHXMDS0od1b++BuPg1eWRb5hM/xDcfUmW6bNhslwoYswb80vGW+x3Ytk5u+uxbNK9TVoUU5jce0rd0+FelwyPttj+sajLhOGXG/MZSr7VOPpvNc97Ghb/Ed/j4pUI97beHaB7S9nT36QjvL55dd8naf65ix+Lz9y7TbbIp2a0bUs+rSMngY8HlRRL3e2aKvTFvCzvJ50NIM7bZynnZljDGHl26D77YoEzfmmlwfdvlKvww+nIgxCf+cNrSVYZPcDakjhRzjVzumzs6U6DuLBcpgYWERfDChTsYp+u9JyDX2PMaTQsT32wXqSM7l+32fNrG93QAfOxyPZVFnFxc5/omhfCtVju/Ffca7i00RH790vCX+J5wE5nD9rv4Wclw3c9AEdUScH42pd+OYttKrt8BvXL4OXjtgHPY9yvHUMvOw97/rAfB6nXJ+4wbl3BO+rTWkHTnCtwWB8L1D8v1t+toc1dQ8efoJ8MmIth2O6Dubww74TJp6unqC819cYwzsOZT/sUVeH0+EHduUV1HkScYYY4nfBBOOOR5QppMxxzju0VZLxTVe32CcH9d64FMh1/CBlVXwTJX+s1Nh7tsZ0ZZbNuc4COi/Q5Ebxw2+v3/ANZ+aZt40n+bznITx5vxpysfJcw3OP7QA7mY53qceYzzKR7SRjV0+bzKizqYytOnu8P5c90vEW5b7BMaYg+iur22JHLEUcm5ll7KfNKlrm5tcq9reZfCB1P/ZOfBojbLKpWlPfY+5TdCgP/FOUhftPepiPMvxP7fDOP/arS3w3TvM+UcOddXKl8FNmnzUp201bnE+//pjvD9b5PwTd5Z85XHwcYfj/62bHK+7+8vgrZm/K35PXTXGmOEN1oLNN7mGwy7nFE+4BlZmCfzwMv1FUP8C+NWA7/v0R2hf/2b+HHh78xJ4Ksv89mcsxsz/43c+Df6u85TxJ37tdfBRm/7nypt8X1Itgx+2KY9JSB1xYl73sox5k4B1U7bI3C7mZROKGB+IfHg0Ym7jRIxxA7F+XyLeMt+TyeTNQw++64vc6nMd56rU/aGw9bBJXe/skB+0yIse9WIwpK+LRowbS8foS/a2Kcdai3E4MZR795Bx/M3fuwFeT/i8lQXWRVdrnO9jZyifk1OUz5lZ6k1qSF8re1iTsehxpTn+4gyf55dYp81W+H73PO9fP6AdX33+IvibO/S1xhiTLdA//tBDzDeLVcbmsZjT8irHdOM6jae9z3z3pfVD8JqoNXcaXKPcaerEhTuUWSfHumV7g+P1beZyI591TGbE+DkK+Dw/w+cVi5RXfpq+rd8W+bTN540TrlEk+p6RuD9VEPHJ4XgzGeH7HNqY7709ch/LGOPf40dt0YtKEmEbKcrZT7FOEOWqqeaYp/gB5bBUmuYPYl7f71NvO8L3HXTpe0YhB9CO+P5BTNsdHLTA3ZB1ZbpHvegP6cuMkfU55TcK+PzOiPLsB6ybMiHnb2U5nlDY+XBAu3QtxtTY4Xgdi3lfuUBfZYwxtsNnhEIHRmIPIpPimL00ecyfm8ShP44i+o6sMI20sL20zzzKTqgDVVGXrYlatZSnDIaiFj/cbYHfuS7iqexFTERfME2ZFrKiDykE0u/z/SOx5XRfneTSd7ouddrJFsHzYo2TiDr1ZeAty30sKzHp1F2dHrYp22GfOWijyhp1tHsFPFdYAd+4wzg3J9Z+K2SukxK6XErRvjYPOJ761k3wFbFXEIrcIpdw7YzQnfnTokY+4PUMp2/2N9nDCNus88oV2rIRuh5H9L97Lcqj1aX/LReF7Yq+VDDi8xOx17MwxXhRF3tTxhhTyjzMIU+Ym2wdcEzn8swFLJtr5iWicIh5PQyZT8YB7aNYoP1tt7kmKbEn6A0ow1yZc97bXud4Df1T54B9/InFNcr4rFt2t/k+u8Y6Mg5ELiPMP55wfhmX/ioS+1mJiDmW2POME65HYZa56FqR93+JeAt9jzEp564wRmIvIiv89mjIOOQaxuJBj3o6CmkbhwGvRxPKtd4hD0VuMRA5qeXw/rHYWnEy9DWZjNh7yNBX+Q7jZr7I68GA83FHYq8hxd/PTTMOTc2wH1IoU74dEcfjgL6s0RP9hVGLXNSdQ5G3xC71PLHv3/uwRU/BF2PojihzW8TivQPaxlxO1O6ix+GF9H/BiLnVrIjd+yJ3yIZcg02xB+mJfLjrs8/4wMPvAn/tjd8CD/foi8IUncecJ3yL8IVWQBkH4nqnsUsucs+0kEco9pDl/puV4fMnbAmZdE4ko1863hL/E8W26U/u2td2k7q9s025hbfWwYOxqAsm1LuDQ8aEfp+25YgcfDKkHmXF/k9jQ+xFiHMxh3XmIYcVvs8asl/gZugbEp/vd8W6pgzt5NIV1tulJ7m/9fzrzPtef5l5ya7oV7QOxZmWJudnpXjdFv1vx6Pvi3t8/jihXykcvz/vOf913F/KPS78pagTWqKvVovpT2sWeX2PfK/NPlu7K2zPom/LD8U5nJkyeGLTVxUr9D0zacpstkwdbnSos8WKiG8T5hW5rPD3Y67RlC/On1Qpr2PHxFmDPVFr9Di+6Rn2qJI017wozqPUYvq2ZPz22Gs3xhjHtk2xcNf/RDZ1wU1RP2cWGceev7oOXl2gfQ9FkpkX+znVLGVli/1f36f/SVe4d1kuURerot+XculP9nZZVxYyQpenWVjNWXzeaMS4WqmyZsiKGqQv9vvz4pylMxT7hyK3Gju83pgwFztgWmKK4hxizqUtZz3Ke9JmvDDGmF7tDvjlfeprPmIuUJnhGEcdyshJxFkYcV7KRLJ2pL1YschdhlzDq1d4hsNJU0dSAdcgWOQax2Nej8TZmrGom+wJ52dFoi/lcQ0SKfOh2C8U9UOlTH+SiONpiXi+kxLjE7mR43N9itPcv/8S8db5HssyhXv8S2GWucD2RcZuV/iS1iFjf3GJ9fHcMnOBXKoMfjBiz6gqYvdcluu6UObzu6Kne7HGHlBnnevcEj2SOGK9n3Z5fyh8QX3Ada9kzvB94ozaoM1coyP2TnrijNtE9EOsqMzxiDPhtUP61jtj9hsWxF5Ordvi88r0ncYYszHkmp4/fwr8Vpe+aM6mDCez9OfTLtdst0PfkhNtOM8T/lL4qiRPHc1M0R9PSqKHc/UaeE30XLp1FiaVWRr7g/OMpxmxf9QUa9gQNnFa9EmLeREfhW+YiH2LuSnmYhsD5kov3BB76zb3Ma61mbsVp1l3fhl4S/yP7djIRQ571O2+iEmFEvOQWPQHSuJ854w4YzsrfHrRUBHGoj6VcdnKM2+Y9IXcB6LpI57vT3MdS2XqUSDWPZL9W3HOqFxkHjiIaAc3XqIv7Iq6NiX6HSnRcwpH4gxHj3rXjRgbYtFj68XUw9hwvebLrKmMMaYt/JsvzmX3mxvg4yb37y3Rd5rUaJutW7TN/iZ9Q5hwzEHE2rcn9qPaLLWNbVHn1h5/N/j3f9d7wTOip7JTY14VBKI3IHpcQzG+SOTS5SX6HqljhTzvL83S16VEvzwb8Pc7dyiv3kTUVUInMsJXfhl4y3KfODZmML47n7L4dmb1OHsAPVEzemI/uHbIQmDQoL0H4+fBzz7wEPhWR/S8xf5YRcTqRJxxiybijOsObeXVa8y1ApGjyoPLgce4sb3zJrhTfBB8ZpU99W/M/yh4PKCuDOQ5EGFLM56og/f4+5Shv9u4wRrk4BXK89qlC+C9IXsMxhhTzFMHpga0r0NRN7giNzm9RvsZpVnXeGK/vF1jLpSfoo88rHNND+ri7Lo4h3zyEeY+ZbHfM12hzz37qDgr1GQMm7i093KJOnnsBNeo1+J4BhOucb8tzkyIHv6bL9GfHwh/O0qYG5YqXJ+8+D7PFWcs0uLc85eIt+77Lss26Xt6Vv0D2s6wyzjgJrTVVEb4UVGgDob0Pem0yJEdrvNamfV6Reyd2+KMteuLcw1iH9ESZ3f6ou7o9JhbWDZzicCXZ8zEPq84V5EpUS8Wj7Pu9MX+kyPm1xPfSsmzAZGw+36DfLtHu/RFDy21wvcVi/QDxhiTFXuAGdEimJ8S+09t6ky7J/bDulwDS/TJc1nG9ukC6zRbnP+azfL99Tp1LBQ9IkvUNZ7Ym56apgx62+wZh+KQsSVyD1/01D1xjj9liT1iW+iksJEwEnXSFH3b7BJ9qTgeZlzxPdtAfOckjjJ8OXhL/E+SRGY8uutfimXazskTjGFPn2Geko8ot94NBu53rvHcz89e51n1f/43fwy81qJg/sPHPwy+PhRn92vMYzxRI8ycpl6dnmNMfPT9ok6qMebu9ul767UW+G98jDE5lN/aiP5GyWXe9uAja+AP5Y9zPNusSUYdxqzpY7SrE2Iv5M4Bv4147oVXwbfr7C0bY0xB9HAKFRFPBuKcSyTOholvPefWmDtLW20IGctv2NYeJJ8SvskTZ4Tbog9WLInvwQ5F7ij28guFMnh1njL1fMari5deA28eMPdMudQRV/ja9qQFnk+LM8ziu3Rb9BJGogdkYnGWbkv075dOm68Qb1nuE0Wx6TXv1i4DsX/ti3NtYZ+yn6rSX/X3KbulVepetkR/NnWc3JleJ5/i79uX+Y3E4iID8/oV2lu8wToj43HtzbAFOjddBo9C2sqB0N3CLHO1W03WMacfPAHe7PH3fXFWpyvqyP2DdfBg+RG+/0n2g7vioLkzou058tzk6v17H2tl2rv9oW8Evyz2DLub1P/nLvCbs+/5bvF3MxzaQ3SKMeO1V2m/C7OMeWL7ytS3+T5P5A79Xgv8kWN83zXx/ZYvXtDaZS60viv2/wq0CfHZkNm+TR9fFN93xQNxFkicK3zgNGOuOEZoigX+Q2so9uPEOWkn+8f50z3G2H/UDUmS7CZJ8srv//+uMeayMWbJGPNdxpif/v3bftoY891/rDcqFArFHwPqexQKxVFB/Y9CoTgKqO9RKBRHAfU9CoXiqKD+R6FQHAXU9ygUiqOA+h6FQnFUUP+jUCiOAup7FArFUUB9j0KhOCqo/1EoFEcB9T0KheIooL5HoVAcFdT/KBSKo4D6HoVC8dXGH/kHfu6FZVlrxpjHjTEvGGPmkiT5//0Zpz1jzFf8Z+wVCoXiD4L6HoVCcVRQ/6NQKI4C6nsUCsVRQH2PQqE4Kqj/USgURwH1PQqF4iigvkehUBwV1P8oFIqjgPoehUJxFFDfo1AojgrqfxQKxVFAfY9CoTgKqO9RKBRHBfU/CoXiKKC+R6FQfDXwx/4DP5Zl5Y0x/8kY89eSJOncey1JksQYk/whv/txy7Jesizrpf5g8BUNVqFQ/JeHt8L3DAbDr8FIFQrFnzR8Of6HeU//azRShULxJwlvSd3V7fxBtygUCsUfirek7lLfo1Aovgx8pXXXaND7Go1UoVD8ScJbkfuMx5OvwUgVCsWfJLwVvmfY156zQqH40vGV1l29TvdrNFKFQvEnCW/JfldP+z4KheJLw1uy39XT/S6FQvGl4yutuwZ9/b5CoVB86XhL6q6hfmOhUCi+NLwldZfudykUii8DX2ndNR5p3qNQKL50vBW5z2SofR+FQnE/3D/OTZZleeY/O6GfTZLkl3//n2uWZS0kSbJrWdaCMWb/D/ptkiT/0hjzL40xZnFxIQn81Bev5Usp3BuP+feGfDcPbuf4ivZ+E7zWGfH6Rhr88YePgxccvs8NQ/B+pw3u5Avg6aYDnlrJgJ+emwcfhzH4rmWBew7lUcj44HGavt4e8npgV8Fvr7fAtzdq4DM2x79UpLzWHjkL7jYj8MjmeDvjA/DlLMf7rqceNhLlHGU2uMI5JDFlZA15eMMd7XAMwy+AV1zOcXbqFPjOiI2Bjauf4PPtWY4nXAF/8NlHwU0gxit0bLdOHY2jAHx5vkL+4BT4sEsd3e2K4kI8b3eb8ul1W+BuyOd7Wco3nnAN+z3axKTP+RR86ngUc7xfKt4q37N0bClxp+/ONSzSt+xPOM79LnW9EZJbGep+qsB1y6aoV0Wh52mPttYSH4OU/Ry4k+EfcoxH1LNmxPtns7zuxGL8Ed/nB5x/EJGXMtSTkaEvqdf5/JkCQ4uflMAz2SK4GfBQaCrF33eGlGc+x/VrJ5RnSqzn4R9Qgzdq1OXLjTvgx2emxTvEHBcZDw47vJ4W4fVwj7aVyvL3IvyYSPzec8hLVa65iWn7E4/3t4eR4LTVbNwAX1taAm+JeJuz+TxX6Iwd00bcmL44STxwP02dyIj4J8RvNhIWNsW5GfBebdd8pfhy/Q/ynqmZpLW5+cVrez7lcuvSG/xxKgva7bTAR2NR0EVcx474g0KJ4fXFOerd3Cx9U3lhAbzXpl48+43PgrdbPNR0ZZ0xwXDZzcUb2+CHe7y/26JdNib0VRcvrYPnp1bBF5eW+cJ9+hanTN8x8qm3VlrkhTH5/vYW+N4u7aKQpd56Yr2NMSaXYlz182XwlMcxFzKML53DFt9h0XZaE8p4Kk+dmi9RJ3JPMo8JbnwSfGt4DPz2Dar8qEzb6+7fBu8PKeP8hPEjCbjGpVP0bRND3xZnqFRzQua5HGV+apl5pZ/i7x87zTwvFhtGzRbl7yQMKPaYOjx5C/6o11uV+0wvHEtu3LqrD/WdOu51xB8/jChKc22/BX5xcwP8sEt7tfPMPZaK9B9/+Ud+iO/bZW7gz1FXNz93CfzMNz0D7lzdA2+eZNwaXqUuNm5yguPnOP54ibmEe5z+sr9J2x1bY77vjevg9RdukU+EfxD+LV2i/w0bDHy/+dP0P350guNr/wy4mdyf/LjRJng05praMfXXFrWcM0X/5KboI62EdUc6RftfeYC1sZNjrtXYpkwmCe11NKDM/82HL4Dfvkb7L1uU4XSBPAxZu47GzJ16os0au1zDjE2dyZTK4IUc8+cw5vPrXcp7LPyJ7zBXSqcon4yQTxCKGPwl4q3yPSurp5JqeFdWTTHOfvsQvLl5Bfx2h35/c4evzFYoV7/MOsmm2Mx4xIWs1amnNXFAO/CFrxC/H4r+xB2R2+x1ON6/855vB/8frtDuPvKr9HV2QD2fEsmUP6Iena2Wwbcb1OvZRdrZ7PID4N42c+Zn/sL3gc+fZJycLlPPThQfAX/h+TeNxHHh3+sHfEZ+jrF/Zo65TVqUPZU15jIvfIo6NZoI35KwzotEbuR4lHG6wBeWirw/d5bX83n6guY+418wYP7ZadN3rsyK96f5vkxB6HCNvrw3ZjxrH7JemLjUmdiIPqyhTvtpjjdbYDwcHYjni1zuy8FbUXfNLKyiZBz3OK5oRLlPhvQ1wzFtb7fdAh+MWc+Xfcp1cZV1yFSRcmuNOJ7tNN83uk27qNXpm1oD6okjCmRL9JSKadZ5jYmoG8V4JhHfb8Wcb2BRL0LD64nH8bRGrJNGAa97GeaBic316Qu9jkVv0XOpx1ZCPTbGGCPuSTm01byoDa37ehDMpSb35f2iTmkzrhfSfF/J4xxmRZ5lpTiH6RLXsCRrVdGD6Wwwvt28TpOpiVo7I/rV1SLrnpyQ10j0oxOL448N749FfzxXEP3nPH194nC+RuSZY4rbtP6AXPdLxVuV+8zNTSXl6bv6ZM2zBr1zk3l8GHMtekPmEn5W7I2IumMQ0n5btRb46VXmRvtif2viUVc7E8atofDrN29w/GdPsm5pt6h77g7tPVdlXF6u0tZ6DdEfXefvx+Jgg9OlrlWFv+v71L2VB6h7xQx//7ELnJ/h+Qvjp5kLZcR3xTOzZ4zEboO1d8qmDD5/izLPrNIe1oqU0dCmD3fFeZBDYZ+VCn/f6dKe0sKnpz3e34oo006HPJ9jwt2PmbsUs9TZKyHX8Pgc5dFP055HXZGbFDj+1ROshdNFrpFt6K8Dse/Rq3N9LOHvxm2Ot2c437zP+X6peMv2u1YWk3tjSzeUust1s8R+S6kkcsQm5514lGNDyC0Ucag7pF4mov9mcoz9qZz4bw+JvZFUnnJPVegbczmug2v4/GmRW8iWetWlnoq04T7f4qVFv0HsD/bEdDMp+p47HdZpdsLAFoo9AU/0pDstjtev3t9zjmOugRdQJyyRH6ci+tu+6LnkxL5BSuxZnrDL4Ae3WKcU+/RdGz2xZyl0YvOQPeNiQF/ZjFj35UXtW05Tpitl+prbHcqj3afv6Rqx31Q2vN5mTyx0RNPIonzFlrAxYs80M+YNY7oi0x3zfcX8V+Z7jHlr6q75xcXk0ut31zpxGcN6DY7btkU/YIf93CBPver3+ftRIM7FiHM1sahz5ImEXJp6JutCW/R8gjr1aPsN6mEi6kQvI/TaoR29cZF629hjnWREDG4fiETDpt6MB6K3KXLkaEJn5Ip92FTaF5w5geuImkg4x7IrFduYR44xDlfFLbZoV/7O794Ef/WWqE1tjik/zXM9ZuM1UMviC7Ij9qWqIWWS6XBNwjnmztmcOOdiU0cGI+pEqUQdyAtbv7VDrUy1mHtubvB93irPIc2dK4O/fIMCHonaoNKmL/Wa1MlFj3Wf69EXttMcjyU3jb4MvFW5z/KxxcQa3b3NSYT+ihr15NoieE6cipz36R/S4ixNocQ+T9Hl84vzjPWWiN1DMZ6BOKO1tSfOcImewuEe49ZTj58H32iKfpzIaQcBHcRI9Nz9Mdc+m6du7QxoW7siT9ivt8BTefqL/oDyCEZi/y1HXSznuNfsDzmeUPRbjTEmEWMaGfKgLs5MCP0uZahDDyxzDodd5gb164xhV8X7IxGFCnPUkU6dPr40I/LjFPPddofj3d+gmSRy/2rM+4di/POi5+2nGSMKBRFDLF7PzdM/pae4ZuUe1ywQ+fHUjOixi576wjxzt1JePOBLxFvW81k5nvTHd3XpWIE9kcqM6P+J/prVY650skQ+K+RQEDlyR+zv1HeoR1GLuUXTo28YubR9S+x/HX/8QfDykLY/Lc6xTES/1BF7CwcN2l3Qo12NhG8xFTrn6SL1bkboQd+jb1lMUX57EfmsOO97coq/zxpxtih9FXSqcNrchweZSxSn+Qw/TV+wnKbtNBrrfJ7o22XEObi8RdvOZ2jLWXGusCvOpQdd+q6x6FH3xNmibpc6ZuWo84OAMr5ZpzPa2eKe52PHaBMFMZ8gpi+9KvqQvk3f0jzg9YdPs0dU61F+8pxnKUMdG4woj81rHP+Xg7ei7lpYWkjuFVVK6H5WnLnNp9hfnYjzoN2myHHFObbqFH3NykP02ba4v3mbeUxUYgyLxJnrlQzHVxCFTH+F70+32YO6ucG8aH6FvqF2IM7rWhz/XpN6ff0Fnk0oiDPW7zhHu16sUO9ffeMG+O4hzwWNEnHmRmxf9cV+5MQS9f9o3Ui4MWVyTOxR7o9E3JzQ9uXZ0bBF3R9cvwwe1HiWNcnw93FWnIMR8SNTYDxbOEYd+IEfeBr8ZJnxZfPNdfCpacav4tpDHK8l8hBLnKMR+0nnz/Ibogv83MQcHtA3t7qcX3qBOu+I9fDE/lss+tOOL3sJX1neY8xbl/scX11JFo7frcOz05TtwjT9uifi0slVntu7dI320RVnhep1yupFEQfy4rx8WuQ6ZtwX1+lf8qJOsSL6g/Pn2BNo1fn8b/lunnN0BvQfzSb9zasvt8ArKSpXo8HcLeOWwS9fZU3frVN3g5jnpJ9/9Rr4PEscc+sqxxsNxfmDEf3PySnaqjHGPL5Ge3nsIfJPvsr8aeeA/mdCl2d6A9rD2Sc5p4w4lzsUtett8c1HTTzP8rjmr1zk/V2Xa3Z8iv7l4hX2nAt51j2nznO8RvTY17d5Num532MfaxCKc4YxY3pnn/5+IM6n5USbJj9Dm3zoNHW6LPp0xSr9c6/ZMl8J3irfs7CynNy7Z5LxmbMloqlTED1MS57Hz1Iug6aop0XPZCxic198pxiK++2YvsIWewUD8bzhRHzbI3r/I9HjHrWph7HoF/REzRCLHHunSb2XPfK0Rfm5Kcp7LL6rXF6jb4/EWaODLvXWFj3vccDxNg4p34P9+/+wbt4TuYt4RtGh7YQd2krBoe0ulsUeXFbUVeKwqRWRh2J/rRdwDWPxH6TLlzm+nNBpN10GF6W6mS7R94VijWPRdC+I769scfbJJLShRps2sX6bZ1llbyJTZm1dEPNz+5RPd0C+XxN7pHIP+cvAW1F3lSqVpN+7G3tThrbmWtSTqxc+C37xpU+D+2L/5tOf4br/7z/y18HP/vkfBH/+//2XwE/mPgZ+/Il/Bv5rH+P3EaOIdUzNpi8oih7KzCzX2RG+6OAm1+3OJeYd+02eOY5D8T1IlnrwkPgu/bjIySsJ9SpVZn8/bIgzf0vMI8viu/irO/QtcnvrzNexpjDGmGfO8N8KS7S1Fz/2PMd8kmt+62PcK//Y7/we+MY15notsd9UWeD3Ws+8h32pUoEyaLfEHqY4d/fIo+zpPHiC8bE7ZG46J/qWucUy+CQj4t8t+tadXc6vIs48V0VeNRI2ky8K3y7OaTZEX7Er9pAHou71xberRRHvvhy8VblPuTqbhPf8R3VsS5yLFfvhvlibUo45X98TfYYec4+Tc/RvLbHX0B3T3w1us0cw43GtD68ybjz+7neB18VZouNFrtWNdX5vVSiJ8/g9zmc8oD3PFiiPmRLn64pz0Dfq1E1LfH+7O6Ruy3PMI1Fnnnic5wSz4luljNA9qy3i8Pb9Z16vZiiT7ALtP+gw15ieYvH3W7/J7z9/9Pu+A3xznbW4KKPMqRN83rVLrOWXT7LO2rtJfyd7l1vizMhEnHXpJry/6IqzSwf8/WGL41+e4Xg3hM7FPeazd1rMV/Mp+sv6PmPaWBwwa4TiG8lpzqchPjgruuKcZ5651B8G+4+6wbIsyxjzr4wxl5Mk+V/uufRrxpgf/f3//6PGmA//sd6oUCgUfwyo71EoFEcF9T8KheIooL5HoVAcBdT3KBSKo4L6H4VCcRRQ36NQKI4C6nsUCsVRQf2PQqE4CqjvUSgURwH1PQqF4qig/kehUBwF1PcoFIqjgPoehUJxVFD/o1AojgLqexQKxVcb7h99i3m3MebPGmPesCzr1d//t//aGPNTxphfsCzrLxhj7hhj/vRXZYQKheK/VKjvUSgURwX1PwqF4iigvkehUBwF1PcoFIqjgvofhUJxFFDfo1AojgLqexQKxVFB/Y9CoTgKqO9RKBRHAfU9CoXiqKD+R6FQHAXU9ygUiqOA+h6FQnFUUP+jUCiOAup7FArFVxV/5B/4SZLks8YY6w+5/IG3djgKhULxn6G+R6FQHBXU/ygUiqOA+h6FQnEUUN+jUCiOCup/FArFUUB9j0KhOAqo71EoFEcF9T8KheIooL5HoVAcBdT3KBSKo4L6H4VCcRRQ36NQKI4C6nsUCsVRQf2PQqE4CqjvUSgUX238kX/g561EyjXmzNRdPmwPcL1QLYJ7rgd+6tgSeL+zDb5Ti8E73Qn4tTe3wN91/Dh4qVLieA974GG9AT4e0T87pQx4sVAAP7HM8ReWeb9tsuAjw/H66Qi8nQz5+5x4frkDnuxQ3pEdgt9ObPClE6vg3Z0AfHeL8tnb4npcunMA7sZ7RmLOUEbWpAtecDhn3+cYL+9/DnxkeH/K4xq9+/u55mfa1JH96nnwvS/sgI+jm+C3t3LgaZtrWqksgNfabfCC3Qd30kIHBrze3CO387SRlJuAT89wPC3D+710GjwWHiEZUp7DLt8fRJSfa1PemczX1MX8oQhiY+4V3c4h9SyZ4rh3G2NwJ58Ht3Nl8Nim73AN9dSJuK7xiL6qtU+9uHgIakYpyrk/ccCHCW3x4RUf/FiW65yxuS7JmPIY+ynwXLkK3o35vDNnH+D4djfBrTHtZNihXk1JvSyVwVsTyqs9ou8b2JTHuDcCT+22jISb5m+mqnxn2ucYY5tjTDu0tSihzNtNrokXUieya3y/n6GOjSd8/yjgnIIR+WhC2wxHHF9/SP+ddrkmgxHH1w2pI/tt2kTO5fhDqpBZnOfz12ZpA4nwFb6I93Uhv3GLvJCljja36asX5qbF8xiPvlawTGzc5O5aDeoc52GDxt4acZ0GE9q+J/Qum6Lezc2Uwct52urJNRFzYz6/vXMZvFilbyvmmuAz03z+7Dz1IiV8z3veuQzeadOWP/u7r4O/8PoV8Mu3d8HnevQNDz98AtzkOd9JSD3e2GXeYoXkUZ96177D9Ws3KL8k4fotiLzPGGPcHNcsLtBWTEQZP7DKNR819sGLDm3H9DjG8tIir9uUQfsO/fU/+blXwW8GV8HDcR38+Bn6/3FrA9xLifhnMxf10hz/nVst8FtCR45t0te1R5zPqWNl8KbwhUszFfDKPHUy49H3rVSpQznRmvEi+uqByPOOEsF4bPZuX/8iH/Uou/0x53oo/EE4YZxp+7SHuML704tcy+nzXJvZAnV7+6MvgJ/41qfBR7dugV/8Berqtz36BPhff/4l8O/5j5fAv/d//l7wV3YYF1IFxkEzPwP60V+6AP7Mf/0h8PXUbfCDTdpqd522GY9ZhxVKjFuTAeWZsRgvoh7rqlzC9Vo8LmzfGGNVWGvXavTR44hjqp7mmKZOzoEfPH8dPAo55sceOw3+p/7i94FvDWmP/9Mbr4GHCddgvM9aPBAxMJ4h/9b30Ae/+twN8E7AWvkwpD8uixhopUWvIOL7Rn3GgFaTa9Jo0Z9ZMWOYK+q4hWXKPxXQ5iZ8nNl6m/ifJJmYILrzRW4NaWtWmvP00sKxBtSjyjzrkNm1U+ALot4tCDn2OtSbxIjrE9p+WfSk2j36soUS/f7ukHL/gVOMiz9VZ/8gjGg3/48fOgn+s8KuRqIu67Ypr/Sjs+A3f549pB/5qR8C/wd/5f8G/tgqez6PLdTA1z/H3PDk19NXF0u0g5zIc4wxxitwTbeaXAOnT1uZSrgGVsQ17I25JmmHuYVr8/nnHuaanD7JOS9M0fZXF7jGJTF+W9QttsgdgpNck7HIFWrb1BnRsjKHtxkv9vYZP199kTpSEnVWp0/5FIQvKWQpX9vjmqVyvN/L8/5gn89PYhE/jwhJnJjJPX2WUPRcwj71JO63+HtH9BQiXh8N+Pu66FUWRG+tePIYeMqn3qzMM8eMUhxvdos+/1aNeZgv2vmyZzNbnQLPuPQlfk74Ro/j98R+pCVy9ljwUOi543A+gch73ITXLYvyt6204NTTbFrk6KKf8ge9w7UYp+Uzc2INo4gy7jdED17M0Rb9U8/QVjI5Pm+a4cuI8GRyEX1jUmfTpdMgv/4G85xdsSeS9imjclHYeorjG3epAyPRq7ivHyzGn/a5Rk7E57lZYQM2fXGzR9834OtMu0UdPkrEiWUGwV35HV86g+ujDvsY0/PMqRtXqVt+ljly4gq/PrcGnhM9UydP3S7mGCdKU/PgeY818UNPPw7erlP48yc4/tvPvQI+VeB4dm98AbyX5/sfOE3deLXG9924yT7NqEtlm8qyzp1OMW590znG5e3b9Od2yL5THLPuSmL6z+vrd8ALFa6XMcZUUvy38jz3o3rDa+AHPsd8YooyLsxQJlvXOOaReN/tNmVi2SL2i95VVvS002J/qhNT5kmaz3PFhtLxB+hf3EPW8gvCBpYepYwbYv9pSvTwfZFvptJl8GFA/xiKfH/X53xC4e+boq7L25R/PKB/PjLYtjH3yMLNyL0GxmoroBzEdoqxXNruROSUYURfNRF7F7HICTNFxp35s/SNx1YZB+qifk67Im7n2B8QYdh0W9SLpMffhwPq2UDsTcg4NmnSV+y22T/wI1FXUhxmusDrjYR6afuUb8elHloibm4fMO6FQ9qZMcbYFcq8GlEHgoTJR9al74hirkHOoa3XD9jnOp3j+3ZqtPVsiba+K/Z7vuHEOfDDkL/PZOmLmiKfXyszd5taZjxbynC+t6+sg/daXIORx3zV9jg/V+RKodhvs4RR9Uct8n3R0zlgr2Qo6gG7T5vr98UG3BEhjBLTbN6VVblM2zIUi5kInyxz3kjUNf2B8MEiJsZiv8yeiHWwRD81x7wjCbnOGZ+9vubui+CNLdr+9o0WeFbsv1kixo6Fb0wGYr5Cj2RMTQk9Lpd43RMxeTKmgJfn2R9+9yn6jt0B5ZGbtMA/dkPk5Hfu18NP/9Ib4MfXmNdnchzjlYvcXxqLusekmAc8sfIU+GyOaxJ2aculzjrHXGet7IizYnuNh8Brr7Kuqovc03Na4OUifVVqWuRZFn3JK29S5s4Be0TDMXV4GDBe9trMVScNxgffp47sTbiGywnlNVXi+lRkneb+YWeVjwCJMfG9/XTR2w9iyj4ZiXMEIeeyI+qcY3Pso9hprp1DczeiDDNJwLUdi3ZZvcnx7ohzDd0O1yaYUPcubopzDlmOV7hD02lyPLHYW4mSFvjCGv3FOGZy02iIHkXC53vZMt8X0n+lPOZyOZd1WjWmvPff4PjMUAjcGGMCrqkQmek3RY+3Ks7xCRmuVGl/Ux5zl9s12lN/U5xXEjroOZzTyiLPDa4srYAHCe2vI/aTen3KYDyiv3Qjvj8W/sAUqMSO6FWWS/TfUyWOPwz5/MIUr0dZ5kLdiONNxHm5lM/8vSDixVReNs6OBnEcmf74ri7s7LFHkRaHpKYXeFal3RQ9iwX2jG9dZw5cEecW45hy88fC2HOUU61NOQ5d+rpuh3o149FZeRO+Pxdwv8gVetMXcXhjYx38kZw4lyLm44qzPoMhfWUccv/szuFF8Hc+SnleeINnA77rWx4F/5VD7q0/4VOetx3aebVcNhJdsTfrO1yDdptnZUYB666dffaVzi4/DP7mLntG0Zj+Mpdi/jrr0pb6I/quQNQ1tzcog0pCHU3H9O9lIZN+twwu0lkzv8Tx1oeMP1MWdW6U5pqvv8k9yYVl1o2vv8lc7bDO+HTtFvdkfbHP0BE9noJH39Vrvj16PrYxxjd31yItxjk7Qz3Ilcnb4txPR5zRfX2LerhT4TpPO9SD1ZOst5t1GkKlLM+KizPAs2I/5yb7C796hzXCy3/2b4J/63f+NfCH/zz1YlyhHYhPC0xP2M3iKud77nQZ/Jlzor8/ojx+7d+ybvyPn+b4pd4NI8aK4VD0do2oO83951uXCozrH3yAe98nl+nvfnzrZfDWmPErm2O8+tPfxnM9/ynDXLXW5ZhNhWseiv2kcpW533d8cA18Icc9k82bPBt25Sp92cwKz4ekRa1sAsYT36USTESPpdfjmhzut8Bv3+T40iVRCwub69epA7Y4dyXPOJeFDecdxsejRBSGpl+/W3d6eY512GZN7Xo8k5US/bHzpxnL+33Gxdoh49DWOmXfEOfFd/cYx+yAa3timTm+06Dt3NhlDe4IXVo7Qd2dK4ncrPAgfy/OjQwnnN8dccZ1fZfy63SoWzv7zAM8j7qzcYX37zeYdzRlT0H0eSzRuAstzu+Q7VdjjDEXr3JNfvSd3wVeWmTv7d/99C+C37zOmNMfcM3CCfs2jz1EnXFEL1AcrTRWVgxa1BWpKuucLzzPXOILIj/udkVMnBHPF9/dFIR/vnmD+eq123xeJM5geDHz7dUF6vB0hjHuqaffzfGK3urJMnV6T9QvddGT3hEx+KiQJLGZjO/mYekMdT8Q30xMFUVPVGzwjAPKLRBnYdIpxomhOLPWEt+2dgLaTk7sFQQJ1zWQ9XlM3+iKs0lp0V/sipTUFmdpAtEDTonxWEPRkxa+pCeS+KBJPW03WuDza7TzapGxoTAv+gcx5d8RZyW6TbE/Vuf7jTHGF9/fdsU3fTWX/rB3SFvOlemP/Ql1Ji3quEGPvxcqaHqizzYWe5hi69lY4jxZZPH3KZ/xyBb7DAVHfIMozkP1RO7junyf1Jlum/KaBOL7aHGGOU74PFf0vNpNjr8tvree7NO3NLeY/wciVzoqJCYxwT2107hPXX39dfG90IGY1wHvF1vvJtejYvzEv/kp8H/13sfA3/Xf8FzNjx7ntzvPvo+2vrrIfu7NOvXk1gXu3cQN5iUHu6y/v+vpbwF/5MQz4B94hDXHG01+w53yHgFv+3z+CY913OY2a4B6U3xDnWKMjJqc3+Vd5kHFgHa0N+L3HO88x7zVf/dZI3Fyn8/ouYwHp0Su2Yhb4HbI2u5zz1NGvQZtqSe+/SyLbzXFdo25fUfklqKOmqmugT8j+uHHjjGRsg/4vFqNz2uI/bsow3hg8iJ+CdNOW7w+lOc0K9wD7omeUV6cVdhv8wVD0W9ui7NdffEt7N4mbfpIkfC8SzxizjvYZWz0xYbTtReZ4xVFrrR+lWtXzDNHvb1L3W6O6Nfz4gxnx1D2Mw/zXKGJ+PvTy+zZjoRuFYQubYrDNnGBNcLVvXXwx57k+f87N14Fr+aYq9RC+uv8NPs+rR5tN31fbsW92XjIumsk1mco84o885L2Na7ff35pCzSaYd8mXRHf/y/SJ7bqIlZP6E88cX5r+ybH+MQpzqndp49d9TiHUPztkakqr29v8fmbe+IbxBQdXEacPdreZe5SyVNHtvk4s1Limo/Ft/edQ8o8VWIPu9/hGndFfZBeo87tbLAHXT33Hg5I6MBv3OR6/GF4e3yJoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCsWfMOgf+FEoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoXiqwD9Az8KhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQfBXgfk1f5vqmMrX0Rd7tT3DdThzwybgPXsmnwB8+NwOez/rgdy7vke8c8H3tHvizTz8KHsQj8LAfgY/6A/B2exs8faIKPhTSXltZAL9Ruwnue2O+rxeAxzHl5Zdz4McW+Pz5qQJ4xeN8tp1Z8KnyNPipagncXeD8d47H4OMbbfBbWzUjsdVdB7/deZVjzhfBq24I3i0KHbIsjsnnmDq9q+D5wmnwv/6BJ8GH73wcvNZpgCctrsHBIa/bDnXMSnNNI8PfJymOvxdx/BtChxdPz4G/8dwb4O/7nqfAu2n+Ta+BRZvpRxxf7FC+TobjzXh58GaHNlssUIeOCqNxZC7fo4+NOsdpH1Kv2i3KoZrhugRd+gbLpW2OxO/9An1XNKbtuCHlOuzRNhvdFninz+cHadr+9bwHXpyjbd+6tQ6+t3UIbntT4F4qDf7E4w+DlxaphzdHTfB0RD1LKD6TpZqZ2KVelTP0A2bYAa1Oz4O3NvfBM37GSBxQxKaYpQzHEd8RpzmG24e83hlyTWanOYdMzOv2gL7BdCkET/iCMOD1Zo/PSzzqUKnE37fHvD/jUeejiDwOKaBulzbj5imvg4Mh3+9zvG4CaioLS/yHMeV5wOHeN55cimtaFrFibrYCfvGKORLYtmXy99hjNsd1knnNSPj8UMS0Uj4L7mf4+7mVFXArpK8Z8/Fma7MOPhgwbp8+TV/SbzCGlmYo936XvtCucHyZDPO26dUT4Ct/5jvAHz73EPgv/eanwDu9LnijsQU+CSm/2FAew/3b4Dnha5weBTZq0m6rAeXjCTucNny/McaMxZi2apxDe0Bb6g+4Ro5YoxNVjrlicw2CCfOSYZ+/f/Hq58Ff2NkFr87Qv8aGviInHHjngDLOFph3JDF/P7Eow6u7lMeuyH23d+grhgHX6PJVxrNUhjZzPc34UCmxVlgs07e4LnU4J3LtdFXEjn3K+yhhW5HJund11nFbuN7uMxgnKa5V5RjrhifO028vrVK2hSLXyhK50qtf+Cj4MKLubH3iY+DdHnOJZpdx4LOHtMfvE7lO+dFF8Fu/9RL4+559N/gxm4HqwwO+75/8o/eCv9miLn7w/cx5PZ+23ukzjoY9Xi/OnQL/2P/ycfA//Tc+BL730nXw4YSBM1dm7maMMXe2WuBvTIvaLaR/eORxEVMirulnhT8YiXyrNM988bVt2ssXblMmgXsM3HaYX7olrrknYuKgKmSeZS52MOZ4W2IN4xKft3CM/q/Z4/s3rjHm7O2yTss4fF6lxBh4fIHP93zq4EyeNvXKxXXwwZDrsdHk+48KSRKYYHh3LGMRqwdpNkVSBa5T2hd/gzrNOGE8ymks6qoKQ7Ep5WgLuRL1MOXQV1SLzCHDMX1BNOR8zi6wbnpZ5OitV+6AOxmu69USfeG3/yX6mpMrzJVCi/Kay9JOvv276du2XnkR/C/8le8E/8i/+m3wv/d3/1fwv/rf/Sj4Z37mM+BPPMHYkFiUrzHG+B7XvN6gDPZEnXHwqrDN3jXwU8fpb8+sCpkscU0Wirye9eirJqIwzGdF7b5F20/NMfeKh8zdXJG/Ww51Nm3oe22hs9fuMPcbDmnb3YS1dblUBp+f4/jnT9PXHLb4PifF9WmKQixOhE2Kus62xD8cEZIkNsno7lqEwlbtEfVs0qFc0ynKtUpXYBZyQm961NPNV18Bf/lTtJWeS1/03m9+D/jxc6vgZ0/Ttm6LhdvbpZ6MRU8lm2I/ehRwnd0Sn+/nhN0NmTP3xpRP0BBNHfHfDwgs6nkcUE9s0U93HD5fmIXxUrQ72xE9noQx3hhjUhHzmrbwz/aEPe7DNm05jEW86VJG+XnG9YJQmvkp+kM34fOzrqgVR3J89D2h8DXtLerw3gH5wNAX5PPMlSObeYrtM/ef2PSNQUTfHRnyrMtV8wx/H/u0gZHoa47E88cj0TOLqSOpP6DPd1SIg5EZ7N34Ij9ZZg59aesL4N/+wR8D336Dc3/8ce5FrO/Tn5w6xudnHNFjjrnWoz5r/FyasmxTVUx3SHsKLfrT1iZ1rZzi+08t0LYyY/qHi50d8JUZsXfwHs7v87UW+HBEeSUp2urxZdpi0XA/6sPXKU8rZl/ItqibluhRB0bUdQn9rTHGVHKUwZlzzE2CgGt07ZD+aujS5z0mem/t0oMcg0P7OzzgmFMJ/ZGT0D5bI9G7K5GnK/TpC/PkqxnqVCEn7N9eAz8ueg15n/dnXepMu0v5hG1eH/YYEwPRawhEr3VnnX2gVKYMvl+nTq+coPzE5SODZSXGTd0jO7E/NbEph3Ei9reG5PsNxrlBh3KX+1FhzHWIEuqNn2edMrdA216oMC4EHa5jMmyBb+6Tr8zRzrodsY8p6jgjcoduTL1wRc/cFTlwcyJ6xKKGaXUov3Za5BGGvlTuEUQ52m004nx6h+x31sV4jTFGtPWMHTN3aA055l7ENZiMOOZ+g2O+dYN1Wcqw1m00eD5i3D8PnnjUkWNnlsEHN14Gj8uUUatLHWlk6X/dWY7fyrGXEO2K8x11cbYgw+vpGeZKQ5EbNrYoz1pAG9nrbYL3Q65HJPbSJyJ3ylhc0H3R9zsq2I5nUtW7tYTr0xazKXKrSLlVyyLG5Bi3vRSvexnGDFG+GkfsRXR77M33xswp5b5iTvQHOuIFhQLtxGlRbyxRhlii7pExrSL28wqivzCbZ0w9eXwN/OlT7GFt1unLDw7ou69tMA+s5CnPn79GPfx/PfsY+IVb7Cl1hO8zxpjmVdqmqXEMpUWuQUf09GVPwhbnJWazXKO5Kq/X9ynTsMH3f/SN3wNvneb7DwucU9ylLxuKs2GOS1ssTtF/D45zPOOQa3oo9gOtlNgT3qKv8Apcs2xIHXMTjqfbZK1+vU15BW3mvg8fYy9iFNJ3ptKiWDhCWJYxzj09KE+caRqNKbvRUORGop/mCfuMxJmpkcgF4hRzwlqP/mcg4mYietr1feYKXpE9hRmRc0di77OyyPtDtwzeFGs/GtAfuqJPkxa6lcvTHw9Ej8EJKM+M2A8vinMa/Tb907Q4H9Bbp/zWr9OX1O/wzJsZyz6UMUVRB2XFWZ50gblCq09/koTUoaxYs5kq81cnLoP3OlzzmzXG/qROmcn95HKauUatw/FMiVzGm+H7o5DPS4b0bxURwwoFvs/zqfOy7yL389vinGTU4h6u3EdpB/L3nF/9Mvc4rZDxIEnx+UeF2HbN5J68syPk1I4ZB1oiNynlOK9LdyjHg1u3wKePiXp7qQyen2U/IW7wfcUS67DDTeYC47qIuzHr46UC46glbG9rzFwkH9GXtAe0u3aa4/HNBnhlmT3yi5f+E3hq6jGOp8a6qFJhT0m2E3Nib+pTv/dZ8H/8t/4B+DdfWuf75WaIMcaIc9xDsacZiR6o71Fn5kXusLrCWOw22Jd3XPaUHJvxLpeir0lFwjfmydPrzHVG4nxVKPqCyepx8Io4url9k33P4hOsA4cBdejibepAsUydPtxiPMhnWXfKA2+THnXWj8S5Ql+ci7TJ7ZHoeS1Sx980R4MkSUxyTz8rEfXglDjTkMtT1/M+9aZ/i7aztU2+foe+YqlEPU4vMI53Ysp52Bd7JwPaTkWc6ZB12p87ewb8x3/+n/L5K3z/J3+F/d3RNOfbEPuc0w/zHM5A7I30xF7K7Zvr4EGTMb+3w+87pkVMTYtziYeR2M8zrCsTw/F6Yi/JGGPWxHmAVUfoRJe6/VDmCfAL4xb4SvXrwKc9nuX6kb/4TeC/8mHugfZscW6nxPcvzTI+zDt8/40L6+DXrzE+7u0zb9ubUMecbfqGSZdrVCzT14Yj/r4n9ni7beYdwYRrkLY4nkjUIvL3Vsw1rkzTeZamKZ+0IT9KuJ5rqvN39S0/zdh++Tr1v3hA2XuiUbKwwLMuy9PsG51Y5n7YNetz4Ifi3O4l0V+7s8Nc5qBO3SyJmnZvlzl7PGGcSome9bmT3M8a9nj/tZcYR+/sMM7u1egP9rqss4Zi/90UOd/cMeY6DRH3Bhb9cSh1V5x7WRJ7P2/e4vMmg/vrrp0B7e1bXqA/OP499CfPPsjvsf7N59mHiCPGiJHo5a0eYy6xu83c4cknefZlXsSIgezViVr0089xjvEUdbJdp71XprgGrTp1fkN8I3fQZJ8pJc44nH2M5yILHsdzbJU9cydhrrcizidcvU0d7I+pw9viTEk5K64fvD3OOVuJMc69/XfxrcjiHGP9dIH18Ej07ndFj1TWvwVh606BuZQjvpeKB/T7vZDrNgw43lmxF+JZfH4uXwaPRM+6I2xZ7v34LnOZaonzi1Mczzgt9v5FLjLqU68D8a1Tf9ACt8X47CJrDnlmLgmpx67w5RmL140xJu+wJzIWfb2wKc6ucMmNJXpGsfxwRnzDMaEIjJPjdTumTozF91+20LFBW8xZ7GvYIl5ORI8qleIaDcX4GwesG9MlsX9kyReSx+KswPQ8fZ0vzgEOxf5ifY/xNBT7JkOhI67oc0Zi7/+o4HiuqS7d7Yke3mGcboleWD9hDmnlKKdMWfS2xLcvr7VZd935DPOMR2cfA//gyR8HL4yo1+9+gN9XRet8f7tLXxGVmCPfEXsp20PmpB9b577wn/4A6+XH5tfBZ298Pfj0NzGmv/brlN9Hv8AeTVCn72xYlFci5tOzxF5UQjudOiF6VmOux/mmOEtgjNm4wH7lb4s9slXhi26t8ZnfL75DvyjOsbQ3xDn3ItdsIr6XKJTF909ib3pbnKXar7PP2Bd14GKRf1sgsajjL32BuewDj7FndXr1neDTU9TJvqgDF5eoc7H42wVBl+8vLyyL65R3r0/f2RHfW4998Z17jnlTIHpmRwnHSUy+cHc+GRE3xmPKprbNtUn1ORery5x5nKK9fs7w99ksc9LZVeZaJ0u8ni8wzsyLc4uvv0xbqU4xV/rMZ26AP3CW/mucULdz4txzLM72lDNMjm6Kvc3Epe6Uisy1Mmn6i2yJzz8Qh4cCsd9YEmfGuuJbpO4eddVJlfm+ufv3XlOijzDeYXLinGGsHqfFN62PPgD+2mXqRJTQP3QHrIPsNPcIZ5bFN8sp6tzqKv2Db9Mnl9vCXkU+KT7RM12xn+SI71fzIh9u9kUvNBDfo2VEPt/i+61pcWZEJNwTcV4rLa4HnjhPJs7bzbi0gW9Z5Zr/G/MHw/5D/l2hUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKxVcA/QM/CoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUHwVoH/gR6FQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFIqvAtyv5csc1zfl6ZUv8sSv4XoSZsDDuAnu5xzw46VF8LmFCnjJz4K//PI18M3+GPzVq+vgC34CPuwfgI+DEDyJBW9sgu8GAXj1+Az4er8OvrJSBB/F/H02VwLfvHoVvGCBmuW1Zb4/kwa3DJ83X8iDV0wKfO74El/g+6QWeRTGRiI66IL37zwFHhTWeX+xDb455O8//jt3wF99mWv2f/prPwX+XT/8feDv/YYPgldzXIOST6GGQiePzVFGQUSdLheoI90Bn+ek+XsnQz60KMN2m/N/6Mlj4Cbi/YUCx2MZjr/fHoHbLv8GWDbH8UQhx+9lueaWQ35UmExCs7XRusuHEa7vrdMXpQ3nNap3wOMRbbEfUG6e4LmYvqZSoa8qpCnnTJG21xzx92OxbnGJtruywvGfWqStvzGi3njCdhutHni+zPeV5+fBc1MF8LWTJ8Bfu7IFPu7St/d3yUNO1wQ+5T3uc/yZMu3UsfrgqcyckQioAiYUMpiMOGfL4pp2u0Nwz2G8mAw5Zsfh8xq1FngkdMr3eX8Q0Jb3WuL+rLRV6lBgUai+cMe9Ntd8WN8Dt0e8LsKHsSeUT9wlN64HOpPnmkwizuf0EnW6uc/HNV2ueaPVALdirsdRwbIs494zd8ehbRby1DtXrHu6TDktLZDXhlxXyxY+XuhNW9ieF1GPA2EY+4e0pcY+Y3AuT763R1+5UOX8Cs4V8PnVy7w+fxr81HH6kh/81mfBP/m5V8BH/YEYzy74AytMex9IU09nu9TDy5cPwYd9yuv4KvOgXJF2eDAWimuMsS3G0alMlc/wqfuRyC3DmGvU6tFWm03mkrsd2kZjQBntZ6Y4nrlp8AefeQB8+wZzaadKnQ4OKeOJSAPah5RhLqLOjwPK0PLobKSNOCKeG4tr2OvTBsIBrx8ckt8Weda0yIXtOa5P2mc8H3READtCpNOeOXVm4Yv8xlXap+tSF1NV8jNPLoA//jjriLNzZb4wZJy4fXUb/KO/cQm8dpX39yeUfS4Wa+1R1+q2WGuHOf9BnWsxsSbgb04zjvz4D78H/P0Vzvff/f2fBq/1aJsnH2Ru9Ke+553gxQVej/c4no/83O+Cf+iHnwbv1CjP/oj+PBJxdOO5G0bi3LexznrxP3wevDthfjVepH7vXaUPr9/kmCKPtXd3yJh0cIdrsr9LGdrpFXAnoAOJDdcsjnj94mX63GhIGe9uUEbxmP7DbrMeKLT5+0a9xecPOP5cmuP5tqcfAj/74Fnw1JA2+errjIk3Lt0Cv7rOOnckcrmBiBdHhsQ2SXzXn/S6tGWTJs9STUxO/IObcF6jgHrU6jLu1UTOaotcx5E5dpt688R5xsFoQD1pXN4Bf3yJvsJx6Dsf++Ap8HFEPZtaKfP3ot4u5NgzShzaQdpjnFo7Pku+sArebzF3Oz9N3/Tvf/6T4P/pH/0CuJUwN8tn+P6nnvp6I/G+DzG/29imbX3mc9T1Wpv5Vyxs7V2PPsJ3fj37gpZHW5T52xeuUAaf/PnPgH/b//W94L/4f/ln4I8/8w7ws8tcc3ueuUJphb5xf0jjTZeZ+0yfZL4v+36Pp86Dl2eYK0UT6uyhyA2jhNc3bjGXHBxyfaol1nEm4Xj9t0fZZVzLmPI9g8kJX9NNRAwQ8+j1KRd/mnJfni/zfZMceHpMH32wQ9/SEk77yot83txJ2mq2RNuSvcZhn+v06iX2n+sNxpjE5nzGe4z5tkvf53ocbxyKHthYPN9QEdI2uZWQ26JH5mcpz0BsV1gux2P71Hvfuz8G+gnn6I8ZH5yIc3AsjiGVIV9bZl50ao11XEXU9pkUdSwUtmgnlMFI9DRMzFo1nNCXNbqs1Xu26Nd6jBdJkePti/tDoaMpEb7dFGWeE//JiLRPXxGIHlkixj8Wuf8w5AOHQ9GXzFFHC3nayFEimEzM/ubGF7nPqZrf+Y2L4D/xE9St2m3ary/spyP6HM0RdXdjwrh5Kse41I6E/xB1TCukP0k61NWuzQldvb0BfvIkczevyPmNppkrZXOcT9ujLZw+wT5L+kcYRy9/lLax6LLvs1qlrteuMbfpjTj+tM3n+Wn6n6kTfH+vzjyifig24IwxTo9zfm9Cg/KfZl3w2ZeZX17cYJ1164D2Pj/HXCF2GcNm5/j+jBGxPEv76084vrnjtHcnx99nslyz47OUUSL6MlHI2jWeUGf6wt+NBmLP9k3KZ9KhzloiGQnEHmR+jv5wt8Y1PH6eNlMQfa7cFP2/9E9HhiQ20fiu7CZjUfd4XNfEZpwS23qm26NvGQ4HggtfIXpKsdCzRPiyUUC+U+fzbq8zDnb2hW8sMQ5c7nMdAtEvkPW5l6NtOyLOxyP6xqzH+S3M0i7CMeeTP6DvPF7l+w4OOZ8Z0W9sGj7fbok436W8ltJlcx+E/3XFfk+/3QLfE7bWn1AGvZBjiIQrqTVpS50xdebQ4pq88OpHwK993zeDb1xl3zCzxr3u1u46+BWLud6Ooa/InDkJvt25Dd5vsw6SdWc6TZvohvRVrQZ1tit6F3JfJpL5tSOu37efRT5uvz18T5Kwnza9wJ5FzqEePHCcPZEk4XWp2/lMGTyMRO9f/D6O6czKRfq+ZkvI1abeD4e87jq0zdkZjr9t3gQv2Hz/fJrzmc1z73xe7LVnRcw+aIl95zxjlCnSLj7/0k1wX9RRt1vUy/+D6Bn9zMNcv60vPA/+cIXj3xU9PGOM+aYnWGveDug7vuMZ9pFefe3j4NmEMuyLeNS+yf2oO6+L2ndb+MsJ64pzU1zjdptrMBD7Z0lImYmjYMb1+fyozfGPa7w+mggd6zM+Ha6L+d7hfEr5Fnj1CcqzXRf9bYe+ql+nrzwUtXinTZuarXA9B/cf7ToyJEliYvQWhH07Yi9R9CxzJe6FZkRNOlelfUQ2rzsu167fomxr4tzHIKQ9T1UYSNN5yrog+29iL9kr0B67Y9l3oS62y3yeJ/oy+aky+OIUxyPPEbZ82kqrLs4qdUQPYJt1akf0K8eHjMO7PfbRvJhxL+eKJoUxZkmcDzp5jr01T1zfWOc7nTF9dkf0bUaiTxKEnEO5SJlNCX8yEr3GTp9zvCoScnFEwZwRtfF8le/LpsQZk4Rr7oo91EDoiLFEH2dCeQTiwFZP7McPxZmRMObzWqJuagqbGU8ob88tg3fF2dqjgu9YZqVwV//siLG20aDcjot6OkwxeOZzrC9TDuucqVn6LsdhXAgdvn9pjbnCuEO/nu2yZ+Lu0zedW+F488eOg/d2uHczDvh+P0/fmSvw/Y09nhNMHxP1uzirM32cOXyhQjt+/9PcO8+Kc5Tf+Tj3ZYOE6/NXPvgB8Fc/z33yz3TYj3nPGc7HGGPmHfZA7EDIPE2ZzM7Qdss+Y3kUiJ6wx/v9Ec8/eCI5cX1R24pzfpHYJ6h0KPNJn7aWztPfjibiPEadOpXK0/YPXngOvFAt83m3uEeamqcOpizRI25SvuMO40tTyC9M+Lx4wvkbcRZoRuwDdTJf088o/nAkxtxbCvWH9LmJqE+N8PHTM8wbBok48yR6KIGoZ49PUa9WjpEfpOhLQpe2NpF76+LA5803Wf8n26xrKlPM22q79B0vXHoZfO7BNfB+kzFyOmHdtbNNuzrc5Ps/tsWaY170Rq9t0M5yDn1RyqE8Z11RY0ScTylPvc1G9//3uruH7Hn82499mM8Mfg/8Ql/ukXJNbh7Qv39++2Hwi8//EnjrgLZXnOfzSyI1L4seS/MabXH/QJzjC9nTz1dZq8aibxaKXkIszgKkK2VwW5y/SIu+6anTzCNlbZHN0wY8kTdlc6In5/L3aRG/h8KGLfvtkfcYY4xlJcZ17soziriWjjgTZvnMWV++yNj6cETZ5ErC/4izPoU8cyPjUHcfXuJabZcou0uvXgcf56hr9V36o7lF+sco4Nr+9u9xPlfENxDTYu/U85nz3ntm0xhjgm3ev7MtviebYe74wGnmjodNrsebtyifvMXrH3wfz9Ecr9AWfmqd/rQrzqEbc38+9fyLXwA/973c73r8Ka7Rz32B5wwH4numx57lOcGzD1FmrZg6ksSUYUPs2U3Nif0ehnrz3e9/lPdXqAMN8f1UJMYbG8rj8kXqRG6f9j6/zPNY73wnc0FP7FGKT0hMIPZwG33ePwxFbe5yPvmpNfBySdQnYp/mqODYtill7/qX2GEsnC0zt7Ei8T2W2A/aHbTAS+I7vLSop2dnxXl9S+wnGS6MJc5ByP20qjhIEYtzEFlxbrEnzuJkLdr2ovguz0tznQtl2kV9n7nOQJwxjkV/cGxYd9oZjvewxRx+U+To+RLlVZ4SPW2xb1tOUw9n5rk+xhiTskT+mqbMo6H45i7kM3yxoRWLnq8jfIlneD0j9ggjh2tkJ7wu2m7GMyKf84RO2KJPJr5P81xxvkpwuZs0GIuzR2OOtyh6WClxrnJW1EXBSHxLmqU8640WxyvaminxjaMnavtc5f589yjgeraZnr/re3pN6nIi5JSap+5OHaMefcsPMwYOt5kD//ufEucKt0TvbO818P/+w4yh77jKfc73foD7rM+ssqfSOGCOfdAT3+ZMPQi+3qYdvHeRzzu5x5i7c8j9qlrtJb5/nXs7Ny8KXy3OznsRxxuXaGdj8e1UJL5jnAi7dmaZp37+NeaBv3uT8jTGmPp11oJJiu9MF1mn/Jkffj/4sk8d+ZcX2EO5cnMd/LP/9O+Bvyju7wh/3p2msR3WqVNJTBnL2nNni+9vjtlTscX+136TecJMmTqQEn25rPjGx5nw+U7C8TkB1yRtsxcwEucqbZEfJKkyeLPNujlr06b3a8wrjxLZbMo8/fjaF/nMAscm3IdJjYQfXaG9zGfYj5w7wRy7GPOBdpF9krk86xjTYR33xmufAD+7QP9w5xp1sZqmPe7t09+dZAvabIuewymbeUBRnLOI9sUZMrEX4caMa0sl1lX9A+pmX9Sd6VjmDbT9kl8G9xzKLxHfaoZd6npK9BiMMaYgvnNpiP2s8BjfmYh8NS3y/NeeZ2382NfR/h4W5+B2O5xDyhe9vTrzyf0a/dHJqjg3KHQul+N1kSqY8YD23RD5acqSZzxEz7hIf+eL5OzR84wJeweUrzcrvgERZz6qCXXwQPydjWDcAm+KfZTeBvtwfxjeHhmSQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFH/CoH/gR6FQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFIqvAvQP/CgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhULxVYD7tXxZHCVm2A/ueXke170C+SSMwF3PIk954FO5EvijT0/xeeL+O5fvgO8mffCIrzfBZAg+HIzBbYd/L6krrh8GMfiN0Tr42OX1ZjwCd13O//QaeXNrl+P1KI/x2AHfsqrgo7ALbs2vgBd9Xi+XOT7HJOB2OAH3FgpGIpWjzJ0pzmESrYO3+w2+Y8JFqp7kHL3Xeb3b5Br+9i9/GvzGHsf3joeeAvedDvj55VPg+ZwPbiUcj2/T5PZ74m9sCZnZKcp0+dQseCqbBncNdehwFIDHXc6/l9AmkpjjqVYq4KMUdc7yaLOd9SvgmWLOvB0QB5EZ1Jpf5FMe12nQp27bwtcMB1z3KOK6WDH1rJCm7Tc7vB571AtHyH2hWAZfqlCObpHv7wTkecN17+/X+PshbXe1yucXUinwqYVF8LHwzY0NPn/SoCF1Aup1KkX559L0DQcDyqe13wYPA66H53O9gj7v7/TKRmIYheBJRJl1Wj3wilizXmcAvjxNW4yHYkzCFzguZe459IWenwHPu5R5P+T4kpi2mApo6ybk/NwM7x+N6Zt293l/kmTBx5y+cR2Ot97gDZ6Ij2e6nG9GxI/qDH1POuGa2wnlnc3z/Z5L33ZUiOLEdLt37c3LUtcjmoYp5MvgM3NL4LPzjAH2gA8QaYexs3PgmTHlmPGoR/t3boPHY65bMKTdRB71JrGo56MJ16UzaIFv1zY5HncL/NxDTfC52RPgj51YBrcc6v2djpjvgHqze0Dffzjg+64dMKYul/j7M+9/BNzL0Le+/MvPGQlf/G1NO8s1yGdpa1Es/HmvzgfG9KetEW3rMKIvutnhGs4+yFzv9HvP8/kFxoNWJJRsl/42P1MGz1i0xfq4Bd5ucw1SvihNhC/MFETeMuF854tco81dyi+f5/N7/LkR4d20BvS9lsXfi1LBxJZ4wBHCdj2TnZv5Iq8IXXJmubaLPnWjNEV/c2ed9lVN+LyUw+dZFnUrsZkr2Gn6i0yK/mwqTVtIZ6kL0z7rPNNl3Nk6pD/pjTm/a5usKX75118Ef2iZtvbca1f5Pou2vHXIGuaRGVDTnmHcfUeKudX0GV7/+G99Bjw/zfmORY4/atMWj4l4YYwxb9y4Bl7r0P6GtqgtLcbi6QXGpBNbXFMnyzk8Nked2PC4xvkFXjcLzI2Gu1yj5qQFnrj8/WBImbRSXATrGGUetKgz4y7922jA9+dLHN+5RxiT/sy3vR+8WpgG/9TnXgePr74G/twrvH44oIPqBdThiaENWZm3R90VxYnp3JOfdCf02xHDnvEdkZMLPTHC14Si7pLrni8y1wp6jM17h9R7R4S1epPP33yVucrVC9vg3/KTHwJ//V/dAp/9zveAJ0kZvNtg3HjlGnMRs8rxDIWvzmQoLzehHZ8pUv7VHHPD5YfZE/rxqbPgkwF9yysf2QHvj8rgfpV2YYwxY4e+YXqNc/4m8c7Ht1vgcZv5XkX4wzBkfGg0mb91qQLmF379EvjKt9DX/YOf/NvgH3r/Gvi/v/0C+MLrtMWZE4+BP/HQAridpe86VeF8jq0Uwaer5CmHuUgmx3jU63PCKdEXbLRavO7z97URf98UvrE1oHxz08xFjxL36r8jUrIoptwdh75iIufdEfN06Lz8CW21MsUc9Furp8F3mrTVmy3GmN/4uU+AL5xlTu4tlsFre4wROzXGtHad6+b4FEgqFD2kHJ9vG/oay6GvLWUoPz9NO4xFw6DRZl1WyFMvLU/UQIlYwB59oe1S3rZ9fw4+XaWt5G0RNz3mtoUi85ypOdpmcZq+whe1cDDiGlvCHyeJ6E/XD8CjschDJoxXrR75zQnf1/IpQ8tjXpD4oj8te2I21zgt7g/FmnoFrsHEcPyRmP9gxPH7OcaGXoe/j0X8ty3yTI7zPVLYjknusYlOg7HyPc+eA09Ej7Q8tQp+/SrrmP1t9ljDEa8bl/a1dI6x383Mg0eiBz0RNf/Ap23kH6FtNAbUndfvcL6HTeZOjTb3327t8PdbfdrvA1Ni70c0uo7NiP3A1iF4/5DXO7uM+1mLulx0+f6/8O3MlTZyvP6J3+b7ej3mSsYYEweU+a03WBufezfrlO9/dg3813+Hz+sGXLO+qANci2so2kTmcEJ/tSxKxYqszUvCX4jnjSJe745F31/00LNZ8uY2dWYSM8Y6Np/nx/QnE8ProUN/EIrx9Lv0P67oM8U+daac5pqPY47fjqnDR4Uoikznnnxi0KIuWkxljCfqxXSOfjwn+mmTIW3FdimHSBwtSCSPuS6tJv34gchddjZpW6MRfVHWFfvIIhdx01yXUPT/7IiKPPI4nomot2X/1p1n7pIS+7ytPOP89GnaeeuWiIsB+cGBiIMj6uFQ2N3EFv0UY4wveqrV48xlShFtQaQCJhrynckq17BaOg4+vsKe0LhJ2/5EU+wZ3rwAfvwM65zBAe8Pz7COSmzKuCdq1f46deT6DG29OWSfbzARe74it7p5SHm4Wa6ZETqWr1DH8kXKz6/w/klDNCMC0YPukqfeJvtdKd81J+/RrbUT1PVBk7ZdqYpzISPKJRa9r3SwD37QZl5gxWJfUZybKc8yjldmqWfjkPf3u1yXdp95l5Pn/OZEv/vMFJ8/7zKmxUOus0ixjS36wzfrG+A729TLy2Iz4mKfNcrK8jHw2Xc9DO4+QZ4TvuTK59irnJ/h+qWn+XtjjPnprRvgz/32S+Df98xD4LUDruGpCuPPqEcdSvZb4LZIdMZj6shYnJdwQvG8NH/vi0Vxfa6pyVBnF6Zoi+kc719bosxCkYd197iH0G9w/FHI+JOxGL8fO3mG73vXM+C/+VufBG/0RL9c1HU39lrgS6K2jsQ5oKOEbTsmdc8ee6tN+0rlKKtMTN2qVMvgcj/d9WgPwz7jRJBwbcYN6nIoetCeqDMKaY6nVKKuiO01MxD787vyHKBI9nzxgJRL/zEW+0cH4sxd3uHzlmZY03ji7NBQ9GMHQpedJnO7RJxPGHXpb1PCPxrRh0o8kdwaY8Z5jim9TJ/YEvvhlsjffDGnvsgHD/pc46JYIyfL/HB2mTHizj77PgPRsw0PuCaJ2BPtdBhTLJs6k81wQOGAOjdxGPPkHqntiP06n88bi95FIM4x+imxHyVylaLL+UxC5vfWhGvqif110Wo8MkzGgdm4eVefl44z1pZELhIMqLtzJcaJrKjDvFX2hDIpTjzsifOzI/akY3Gu0U5RL0vz3KdsP/dr4IdCT6aOvwvciL37lQz1vOvS7uyUyNWmRQ5s0zfsrbOGSGa5XxWIujCMqXc3X+J+3NzTvN5t0e4em+Je06jG8T6aYe43CcTmkjFmNmFTpZBhbnEu4TtSoudQ6/P+Q5f+fij64nnRk442mC/G4hx6vEudSYuebDWm7+laDIhbYosyX+XzBxPm63nR5z84EPso4nyZI3O1gDazc5u5pdVgLjKW9YPL9ZiI82qhiG8tkZ+7O4xH0THazFHBchzj3+M/0j1hC+L8aUf01qfnGMdXF6l3jx2n75HfX0zEuZe8oa9oh3xfpULbLVbK4IcD5qDrov7v7FBPT5yiHdlzfH9xjr7pwfPie4wL9DVu+03w/s5l8InYD5uMyAtz4oxJzOd3RJ4ziain1Ryf99hx9ju+64OPgl9+njmEMcZ8/HMvg2+KnnQ3pEz7NntClie+aZm8Af5bH2mBN+rsqVfynENanBtfO8ee0dKU2JMQTajyfBl8boa1vHFp+4OOyDXF9yKOODdYFLX6MKJzG4jvK9LizLOfFt/QcHRmOBF5i0Mb64u+arfXAq+L/cHlee7BHCWiKDKt7l2dnp5jDbq0IM68iv2mBx+gfwnEOd2Ll9b5vNk18P03b/J57/o68OUKc8ypOnVr/RJ/X85zbaqn6eeXzlJXe+L7sN0D8e2NLeqwEnVn7TRt7+yD7wa/8EsfAzdi7zocUduWM9TlBbEdXovXwadFf7jU5vOmV+lfn5jhGcLXQ5EImPv3p670GAM6m+IsaJYyXhB9n64tZLbCNWp0mWvcErVwj+mjKRW4Rsd7zA2ShPniVIG5S9SnPcYeZV4VPV9fnIvMPEH7fXDCfNkSud24TR0LRD5vOdSJQMj/QHyXE4ocoelzftMVzieT4vOWFoX/PSrYtnHuKQKbhy1croqeTHuXe+WzS7RlI/qBlhHn1+t8fjZN24lFbpD3qccidTJjW97P623RbwjF2aSxiGupkL4mEt8DhBM+b5jQLndqYu/cZd5gi4OSokVm0mXa6SgQuWdb9AsGnE9gt8ALomdli/UoV+8/49yvsa5Ii3Nv4miOyVc4ZjvP/HQQi9ieF99vyZ5EhWMeiW/uikWOJxZ1jifOythi77snfKlsi1mG78/J81kL4huIkE/Y6Yn9tonYu89wDVJpzieJaHO+kH9ezlcc0PMCzi8Q32BM5cW3s0eEOArN4J4zlBnRazOW+C4wJXLIvNjL7VEPF8T+1NrJFrgtemPtJm27MaEve/kWvyeYn2EMPPvID4BnDhjXh2Jv3kxoB1c3WANcaHOv5zfr/5rPC3guyLY5Xy9HeXo2v7l2RL+kmmcetXp+DTw14fO6OdF/3hUx7hHWcXv7dM5xRXxnaYyZEd+82RXqbnpMneiI74+e/xS/o/4b3/kB8MHCO8FvXWRcf0X8/uYWc9viLPOMcoq+4MxJntsuTHE+w31xNjaizNtt6nRzwvtroudf2+VZsKBN3zMnztKmZB7lir+1EDHXT0SEylRYS/e61NmUU+Z18Q3R1Apz4aNEHCamf4/N2+IM5LvP017Gj7IuOyZyvMEhv2koLzDHC2q8PrXKfmN3j2s5Fvs9wx5z5oM3qauHB9Tlfpu5WanKte+KOGOLj/e8Ht9fFmdULY/+cvkM35cSObWXpb9cFLnd57fZb82UWNP0xvy9L3r8idUCH7Wpe6P16+DFp/j9qTHGuB2OwRbJTsVlLtO36cOrGY5xY8I1qU5Rp9y62C/e5Bo3NukjXfH3CQYd5ibWSOypinPMq6v0X4ci3x1tsNBzxDcSrvj7AHML7EMNxdnQUcD3z4nPWnzx9wecIeWVNpRvXGPPOiNyp/0dfoNdLDK/Dcz9MecPwtsjQ1IoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoXiTxj0D/woFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVC8VWA/oEfhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKL4KcL+WLwvDwNQPa3f5ZILrJx8sg1vDPHhsxeCjccD7MxF4sVwEf/yJs+CnTsyBX71yCdwZ8fmtcRM8neLzxfCMGY9BPY/j2x8OwIdCHgeTEXjU5fP2mm3woMb78/YuuGttgZecHHhzwPc3C0vgBYfPr6fT4J36HfC5yRA87YZGYiZFnmQT8GiGv7kdUYabGQo9iDimb37PNPjl63xeZb4CXru+Cf6FlgfueZTBA99PnbItjt+x+Te0nLgD7pos39/gGpXzXKNc2QH30yXwYbcHPrF4v0ks0IzL8SUxr6fFz1sR5+94lHfB5e9TDuV3ZIhjY/fu2lutSz36vmof/OPjDHhWzKMf8/d5n/POu3StgzHvb7RoG0nig2dK1KOZAn3Nfr0Fvl07BPdy9F0p4ZyKWY4vnef7T0zT9xbLQk+3boPbQo8cm3Y2k+f7Own1duxSj9p96ll3RPktLy6I8fH3vZB2YCyujzHGBIbPtBMqu+VSByzhL43d5fMCvqPf45xzNv1rNsXnDQf0764jxmNRxmEg4qNX4PBcoVM+dSKaCFtPcY17fcpnJHTYF+Nxs9TRoMf4Vm+2wK+8eQU86/P+k3nKf9jdAzfZMuhgxPVYWaY8jgqu45rp8swXeSrLmJPLMCbURZxPNWgLpYpwygF5VqxDvcfr/SHX7eTJVfC0eH/Fp174FvVgcZYxdlXoZTZTBR8O6Pu2bnNd2yKv2bq+Dr4SU6+H+9vgcULb7x0cgG9M6Ps29oQdZznfapHv+6bveRp84ewD4OMxx7N4fMpI9OuMN6OY/rJVr4E3epSZE5JnhhxjLs94FRv6xzlh660mba8j1mi8xTWPIupU2CfP5Pg+Z0hfU/Bo25OI132XzxuG1NmgR1/WblJnp4W/D3r0vcvzZfC+LXLZkPfbhuOdnpkHz5VZSwRJw7xtYBtj/LvyPHHqGC6fOz0LvmeoO4MO/c/Vl98Ar12j/eSFvRRLM+CpOcbuQsBcZ9CnvXYC6uLhhGvfsfi+xBOxPs8iIxmKQs3n9eEMx1t57CT4zAuf4+8Tjj92+P4rO/RvoybH+/+5wjj4f37vk+A7O9SlZ1ZYl1kF5hEbQ/rPkU3bNsYY26c9LT7IGFATP8mWl8HnIs7hu3+QOjSZFzGnxdr5+RcvcDwuY5bb55wzKa75oEh7n3+Y4ysdOwX+7LMPgw+b1LHYcMJXPs81WVmkvT997BHw5Krwz1/YAf+r258Gf3+N+fPPvHER3BJ1m5WljqbyzG1cmzZ77EnGpMsXPmmOAollm8i/u7Yj08L1XFHkaGKelkfb8mze74S8PrdAP55KU08HGdpm4lPPpjzmMlNFPu/aDmP7xGbu9vmP/i74t/+tHwe/9EvXwE9+4FHwN6/Qdl/aZ+5w4ZOMS+99knH8jkvfttfi86bWqCdPzpC/Y45xbs5mnZb2Kc/w5uvggeipfeQXXzYSv/hRxp+v+wD97fd+H9+5sMQ5uob3/9y/Zt/u+X9BW9rap+85vcY1Xh9QRt87oX/9QoEy+fPvOQO+vUMZNE4w/73V4vjO+pT5YoW+ZaFKHXUSUWcFzG87uxz/OM1cbTymjVREvv7kOfrOE8eoc68W6CtbIjcMRC9kIK4fFeIoNv32XVmNCvQdiajXLRHHLTGvYZd5ztaAtmZN2IOpih7LAw+fBi8fox40n6OevPQmbeeiyBPyx98BPnuceVWuQu46oeC0K0/U+1lR9w0CXk+59EVnj9Mu8yXmJbLff/EaY2DGZ8xrR9R7z6I8hRkZV/SQEks0l40xM7Osvf0qx1jIcgyOeGZumr4giakDwxbjSfdQ2OaYufR4RFt2Y9EDEn27RPjf610+73qfazzM0pe6OY4/HPL9S2XKJw5Yp05lKfSJ4Xgd0VMbTjieRPSfo5B5nSvq4JTYocqLRXfFFtakJ/p+R4jEOGYS3ZX/tWtXcd1OMUe9fJH2P5pQN9tNxjFL7B0Eoq4ZiTpgf4nXUxnaVyXD5x2bWwE/tcy4nc+c4PhCrs3P2Rvgb4g4le5zrSKPuUMcUveuXtsHT/VFD6TLGiIr/F1vn3HsyrgFLiv2QpnzKZ4Wfbl9jn9umfGjdd3ch1FIn/fZddaWKyfYK1s4SR/2Tc/w/udvMObs9USPtso17Xj0bx2xR5kXtfD8Mu93fbGHJ2rx0T7rqNtj+s+s2A/busU6KRyKuq9A+15cKYOfzIu6MxG90jLvr21xzYYW8/dRmmtIb2tMHPL+vCd0pPr22O8ajyZm/dpd2TYOOO9MjnLKiV57rkzfk0pEbpDiPEeGehCMRdyS2y8D/l4sg/Ecjidlk+fXWN/6BZEjL5TBJwP6kljsRY9HdfCeyI1GMfXazzPHl/3azZvcC2812ANa32HOPwpph0bsw/Z2aOeh3Psftvg8sddjjDFTZeZn2Vlyf5a+ZxyJ2N1i/rtdoG8IhMw6FJEpnWa8SM/TumZ/8u+CH4rN52M//CHwYpE6ND1HnS1XOIBA9Myrx4+DhxmRL56hjQxET2qqwnoiI7YHM+K/n1WY4XymhM1lxR6mz3TZJCIfjhtc46HYt/kZ8/PmKJB2LXO6fFeWMzJmFFl/9luc6Bc+/wJ4S/T2ez3mQWNDuQ4GPPdiRF4T1/m8suiBjEOug5sRdQWfbnLibMCiqDNnPNFDEftXG13a8q19+orZiOO9fUh5DtLMexbnHgQ/t0q7Ltkt8GOPMQ/93175DPhvX6Svqv0G69D/8XvZw/IKMmoa8x//6jeD/+rjzC1feY59NVfoeqtN/x0G1P2FCWW0ssJ+9uYmc7+MR52Q+0snKuyBdDzKeKFK2633qRVPPsC8pBNSJxYr9DW+RecxvchcOOsxPnlVXm/uMzc+P/MY+M4LN8B/4q/8V+D/40/8N+BG+LKW2PuPha/JCBs7SsTGmMk9vncsYr/ji6IyoP8Jh1yrQZNxYH9AWRsRJ9s2486kw9wh49OflPLUvWK+zPtd6kqc0N/s3Wadtd/hGbJA7GXOLzPuWSHH77jU5c42bXNzIPaKM8x9smKveV/0X5MWbXHQ53U7Ej2TiO+bE3nKvvQNYu/YGGPqMWP//oT5cD+gTjhZrqFdZF8kL/av223mBpbYA7VFrtCNOeeROC8Wp/j+seid+WkaqNxzHAudPuxQpsND6nQvZG4Xi55zhtM1RtRVRpwJWVrgGhlxTtAWUdTyKK+8ONc58UTvT8SHefm+o0ISm2hyV5a1OvP2QZO2FiSi/rZF/W5Ej3ksz4QJ3zChH84Z6sX6tcvgiwuMk4uVMnipQD28sS32k7boC13h22pN9rSMzTg6Emfexj7j2uwp1nmz4izR1WvsHzx/i74ozIv+4h59ycYW5fGBp9hTH7cp7ydTZfBzJeZaOev+OHjMZ65jYupyXZwTvCl6xJdvM/Y/cp65xVqVMq+KM9RrJc6hImr1Y1navt1grnDYpH9NBmJ/KaGOlD3qlN1nPErE/pUnzjmmRK4Vjxkv0nnmZrY4a+qKs0+zc6w3zFici8zSVxYc2tSkyPk296izVo3zOyqkMmlz/Pxde+ldYQ9C7gMOGlyHQpp6kCuVwTMZxrC+OBf0uU9zb7t+SL3pdugrVh+mXbz7nc+Aj0QeMhF12FicC0qXxZmIPK9nfT5vJMazc5l5zrBOvbNEP31pjnWdI+q+qiN7TKLDLPKaRKSlc1U+/4mHeYalMsVe8EicwTbGmHKBtrInbN8eUwcswzHbDn2TnZLxho07P+EaFR3KZMrj3vrpE1/P32fE9xhNruFI9A29UJwRrnM8zljs8Yo6L4nFNzARdSQQZ5xHonYIHfqGMJbnImkDuw32JtJif687ErnskOsVi/759DT3eI8SQZyY/f5dX+p2Wri+skg/vLzEb2dGr9M++uJbmk99irnE4jztMerw9+ffx7WIbKEL4puEuUXqwgMPsV+5OM1zEk6B82kNWuD7LzEuHDvF9x1fZRw/tcY+i+WImqBFnhJ5RNKhvHIWx7uzww0pJymDT2LqWk00cAv7tL2nzjP3iQz9vzHGXG0ypoQ2Y+/nPsvvrRLRU/bTlNGxOe5BXn+N9vL5330e/M46n58RZxr2xP7N6xd+FTwtzk4uT3ONlpaoM5XFNfAkRf968gHms0uLnM9Mugze3GV+e+VN8htb9CeLJxhT1xbFea4p+uOcOCe912S90hxw/JWS+MbEyI2dI0KSmPieswZbYm/WWNSDUYs5dXGqDJ61xXeFY8ah2g7r92hE20uJbyPLJeqdZYlzDI74lifm71sd2pEvzt70EsbtvKi3+wFz1rr4Nmc04joeir15I757DIeMs12RR8wsC1+ZF+c05L5plnG0Xuf8HLHXMwq5PuX8/fuu9bbY7ynRlxTEWZisOF80sMX5ihFlGIlcYNzn9VCcTYlFLpXKiT6hOLuSFj0YW5x1DURf087RFwXiG0NP+L6M6CFNie/RUhF/nxJnicKhOF8h9mladdqYzG9tS+Sioo9qRDzyRfxO2ffnu0eBJLbNeHSPLslvD0WvaiK+x7r1IvOYjQuMo/aItpAMhB6IbyMzaZGjijNWzZC237JZJ55+gjn+e7dYZ7x5+yXw0R36Jk+cDUj7Qu9FjI+7wtfGnG8szqSUluhbjGGddv4497Pe/a3sd6+mqMc3e8wrx1fo23ol5izf9YPvA987cf85w5UNrsHWNOPFpX/3IvhvvMaeyyu/zj24hS3GL/+QOrB4lnXV+nXuISY2bXlOfN87K0zp+JKoKwq0zU1RlxVF/zXOcf6tBmvtZp86s1nj2bi0+B4slRJnTyPRQxN7pEFjHTyXo07OibrUlb5FxKf+mDYTiD3bo0RvNDG/d+lurZES544bI+qvM6R9WQuURe0NnjueW6M/+9xv/yL4s1/P5738GnX32VNfBy7abaYjvj08IepEy+H402IvZXqButwVcfi4OFN3fZe2tnXAOLoVka/Nr4HviP2r0+IcZUZ8n2uLv0kx7NG/WGLvpCniqvis29hir3XxGM+VG2NM6/PCp7XE917iHO8V8Y3sCXGWcWOePlruC7x2k333vjgX3RPfr84WxJ7qIXVgXXyvOb1IH357m+cooxz9VbPJ65UT/F7r4IB1zvG1xzjeLc6/nOMibGzQX/nTjJFuj2ckplafAN8/FGf5xTeHmTbHNye+P8vHLfDPmj8Yb48MSaFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFIo/YdA/8KNQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFArFVwH6B34UCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKh+CrA/Vq+LEoS0xmPv8hTw6G4YQSaz+fAJ1EA3uuH/H3Cv1dkxRPwqZIHPlueB5+e4eNGHT7/cpbPWypMgwe7TfD8NJ+/tbEJ/kqrB37nYB+8V0w4HjHfgzblkfRi8Oa4C54zA3BjW6DT+Sr40ngHPLYp36TL999uNMBDi89vczrGGGMe8vmPLx9wDpM7Ys4Z3l88lQHf2uQazZ5MgVs2nx8WF8CT/hi8UijzetAC39vkmm80r4BPT83xfSPqwMhfA6/fvs73OdQxx+L4phYr4OMJrzueL3ja8B8oz3jCNbV8ugiPJmSihDo26tXBG41t83aAlcTGvse/xBPq1ad6RfCUQ1+Ucik3z6GeuXEfPO9Q77Z7XJdHzp0E3z2kL3QD2up+4wD82uYW+GjE508v0JZPzHB+VsT7O3ttcDtP39Tp0246Xd5fKmX5e4t6VS5xPMJ1mQtXNsBnMiXwbCEP7jrUO8vl/EKX499t0hcZY8xkHIlnCgeV8HoQ8Ho+z3d6DmUUTagjlscxTELhi8T9gaGx1fqcUxDSNp0p+oLtA9piyuEa9TvUsYzN903EeHI+x18sUMdHIp6nKlyzuhj/eoPjC2La3LjI+NNp8/7sHOcfG453HIv84ojgOK7JFe/68VSJ61Scol5ttG6BH+53wKNoF9zPc133e5z3GzdFXA5ouzvNE+AzZeELU+SR1wLPTzFGeT3mMSamsadETFo7dRy8fcD5jrp8X3efejBoH/L5Dn17UcT85Sp9Ud6jr3n2298Hbo3p28tnmdeNOnvgdkLf+tRDp42En+aahT5tZevKJfDL15k3HOzxHVs9+qqSRVuerhTAl6bL4M115jEZW9jWgLa5usK8pn6jBd4YUmZmQB2fTVEHZmeoQ1KGF3apwxPha8Ix17jboTzyQuc8xwEfjenbOsLXVzOUn5dnseBlqFORx/uPEnFizOie6cQedW1/QD8+6HLtugfUDS+ibjUbjMXNAdeulFDWxQXKJpstg+9t0f73d8i7Qz4/tJibVfKcX36a18M+65hUluMZF2mbmROz4Cunqau9Fm1jMKK/G445/9Uybesf/MAz4AfbfN7aFOdTFHVuvU5/3tkXtjJh3DXGmOXpM+DVPGWwcZux93f/3cfBn/2Wp8G/J14D/1SFMnz3GnXs622uYbfBOU8do0+uztK+6mPGrA+86+vBL7xwDXw+Ygy7+NJN8Ge/7Tz4MKBP//b3vB/8+sfXwZ/5xkfAf+1TnwB//M4N8F+4TP/eFTEyN8McoXqKOhg79Fd2SB174plF8F81R4Mgis1e+64/Oexy3TMBxx1ZrOfdhDndYEDfFFv8fS9i7E+MyOlj2k7GpV7OTZXBcz7vXztBO9m8tg6++CRzmaBPvb74OnO3qMLcoF6n3cxnqQflFuc/f4VxL+8xp/+/P8s49aJNX+D36Fv/p1c+CX5e+I5CSF+/Wfsc+No3UO8WCtRzY4y5OUeZ/+6HGcv/1A8+Cu5Ywl/nuOZXr/Mdj/0IZZa6wDnkt9iT+PN/6QPg6Sm+7+nyD4D/dIo6nHyQvvA7ztE3+TH9/bzoeZkWcxV7zPx2+1X6ok5E//67v/4a+INPcQ3aTdrEN3z3N4GXp7keizP0veMTHO/VTfYiboX0XYd1UdwfFeLYxKO79jcUeYkR/UlP+gqqmfFT1As75u97I9Ecs8ugB23adqnK6352Cjzj1MCjkLbt9FvgZ+YZw6ZWaPsbu/Qd67eZNyQj6uEooN4mNvXCuLx/EnJ+h3XRzxhy/APRE8vNlcHjCf1CtkDf66fFetp8/8Tm/oExxuQqlEnR4TO8CWUSDDiHsE7bHI2oJF3hnxuHvD+0GQ+SiM+vFmmLcZrxcL/O8V5rkTdEHmD7om+Zo4yMqIMmhmvq2xyfY/F62mP88RyhM2OuuSX6osWU7CFRPqM2f++lOB8rEP1u6w/YZDgiWMY2Kftu7n7QYqxNIvqTZod+vSR0dapK/5Cfewh8bp495c4hZee6lJVIOU26wLWcrzKOzIgaOONxrXM2denPPc4489Ierydd+pOey5qkYLOvs3OL8gobd8Aj4U/SlVPgBzvMAyKPNUUqZJz84e+gPx8fcjxTI+rqt5wrgy869/cAPvsGdeD2zs+D//rnlsF/aJZ9i6ll8nc5rGsuHHCM585wDn2xfzaO+Tw7zTUq5fh7x6f9p/LUgXyOzz/YFfsYHbHv0GI+Ob1AnTh3ZoXjKdGnx6IW7je5xm6G/qWb4f2z07Sp3bgF7qSoE3d67M2mhmXwrH//PsNRIApj0zi8q2v9vqijhjT+dos9HnePcqpOUe62ODqQSXHerthLyObYM3lslTny8hL13s9y3W6Vy+DJHOPAwOP8mjH1rC7iiqwLQ3H/JOG6j4bU00GB7+/1RNw/YO42EL596NHOHNEviUXPemK4PpHY/0qEb++kxdkIY4wV899uizx93KcvaB3Qlhp10ecbXuXvU9SJ3jr3KE2aOrGxLuqWP/td4J++ybpneZXxKOdTJ7/+2WfBl7L0ja0mfeNakXVie/oY+CnRC2iKuiwndCgWe8ae3M9L6PuzIlcJjMhnxXkXR9hcJHrahTxt5qgQjsamfvWunzy4RrlnUtSDekPsm3aFr+oxhmzurYN7Ii8ZRYxRA7F/lHi0tb1D1rNehr4q7VLPkojjad25DH5qir5Q1i1725SHyfAMSmWG94cZ9pRiqwUeiBi4v80eUzfD8Vy6ug7uFNlbrP30G+D/+m8xz9w8+z3gJ3PU+zfq9/ebX3rhTfD6dfZgarc55lQibEOca/HF3vxCn3H5+DGu4UdC+uec8L+h2O85UWTPfTdmHfb4muixiKNV508wnn3hIv13/4DjCcR8HjvPPGi+Qpt54Bvo6/7JP/wX4FNV+rI/849/CPx/feRnwPf2OIG1p9bAJ7v0XemlJXB5fuQokSTGjCZ35TkWsblYKYNbYq9xPGDfYe/OOnh/RHvKib1ML0N/Vs7LHJr3l/LUxTjD68OAuiH313b3aDtOnrosWtgmk2acKJdFzewyzkzq3M/v74tziiPa8rG5B8HzYi+5LWr2WJz7LGZ4/+IS43RcpLwGfcrHkud4jDFulveM07ynJ3pf+SJ9cLfIXMIWe5qDIdd4apZ1RU8cu9sPqWM9cRZz5Ii6LOEDJgFzuYnUqRwXPSvqumAgcj+xh+iJ826R6FUYUetnfPK86A0MA+ZOnkP/mhV1ac6hv3WyfL9jqMOF8tsj98mlHfPOs+Uv8o+9ynMbsmc8nSWv1cWZNJFTj0Oh2yLXmYj9s5RPucxP0W+nQ8alrOivHSsxF+vvizrpkPPzl5nLpB3mAonIgYuztO1uk76gUuE5k8uHnP+rl4SvO6A8156g3Z4+w1wuJ840x6J/aQ9EnVaknc1nyWud+/c+ag1hW6IH4tpcw+ky1/B7nmIucbIozjuIszlxi2cpqzFlPhmLszhLZfBAyLhvxJ6q8D22sNWiqENCcc5vYWUVvHWH8TldPQtee/4T4jrHW13gGuVmRW3uMrc5uM33jcdcj0FPxPN5zi8j9oFmj9HXv2iOBr7vm7XVu3nf7iHr9YzIeZst1iG3L1JvsjO03Z7I6yciR68LW6nx9WZF1Ou2+H5gt94C74q9AX+evUA7Yczd3acdxfscQNjjOu6si/08i3VQ2mX//UPf/A3g7zjJGLb5Bvshrz13AbzbZZ5WzTHHn51jzv7wadYYr96gnv7X/4E5f0t+D2OM+a+e5N50bUCZjfY55k6XviQUe45+imvoi7Ngx5ZYK773Yc4pzFBmiTi3vVXnHLpD6lR9j/FqcMBzSk1Ry5dFLtkPGB8OxDcrToHzm1qgL8nOcbxOIve3+PxwxDyv02LunBLfh6QL4lyi4fMK4pzk4irle7SwTWLurq8neuHdOtemly6DBx1xrrBNex6K/ZbaIfuDwZhxdr8t+o+TFmirTf9QmaG/iQzf54j9+UEg/E+DutYRHzm44lxgT3xfFo5Fju3y+a0ax2PEuRgnK84UDyn/ZoO6GjLMmT2K28QjWQeLvELUFKffRds3xpjai+yNLZVp/5dFLekNaV8D0ZexHMaA9Zc+C94QvcMpsT9VLHHSQcI5DsV3N5b4Xqpv8/6tA/rk9R3OxxIxt7YrvvNxeN7qO3/or4KHCdcwLXqTtQ36bz/P+4cH3CN9z3u5Rr2ENjMOxLlq0XsY1JjLTee4h3pUGIwCc+Hq3T2XdovrOBbfR/kT2tK0yBXKogeTmmJdMxAfM1qiHjUiLrpG7FO2KGdq7f17CW3hS7IZUZfkOV5LnBvpiR5XV5zdaQi9b4+Y69mRrPtEDi16OKk851cIxPdxafFtr+hpueLcii1yx7boX9S69+c+jT7vqc6LWC6+bXTF/kpJxK9MlmMoi++JD8R5sqzoMQ1FT8QT74/Evogj9lDzQgZ5sZftZHh99w5tf9JnHejaomdUoc7YouccivC0dYe5UzsU5+DbLfDyTBk8laV8bFFnjsTzOuJ7aSmfo0ISJya855xYFApfYAlfIr6VDF1xrmUkvrUU51os8a3LRPTm0sJ32Vn6ClfsrU8/RbuYPUX+HT/2BPinX6SvfGOf+0WWiCGLq4zZP/mn3gO+03oB/DOvMae/HvF9C/Ni3Ytl0JLoFW5v0g7MROz/hYyhx2L2fvfr7G8XcszZf+yf/bKR+OZP0pbe+4vfBy5SKzOuUOau2LvOFcjH4ttOP8UHZlK0rZNP8LzF4ln26J9cZS26fpH+utbhmlx4lftt5fNc41A0vG1X+hquweJx1rpLZXH2bIpr0mvSt69vrIMfHrJObLZET0v0wGTtUBB7MiNxxtkI33mUGE8Cc2vjbi2Uq3Atb4tvBcN9fhtzTejepNUCDy7RfpIJ5/4fP8ocPInppw93v8DniW8Q5oq05yjFbGg0puzDAeOOHYm91EB8J23R3+3s8fenTvN7g2yGuVEicg07TXllptk3OiF6Bncui72aHnvm9UPWVU6atpx2+P7SIn1B/zL/5oQxxpRFPrjf4HmkoahFc6KXF0dijmK/5/Pi+6nddfYSS1Mc4yNn2fOtFkRvTvw9hmHA8aYt5krNOnVyqfoA+HaTtf98yLPxochX+6JvlDeUz8inv7JEn1/+vYXGVc4/EOfC213xHZLYs3XEucXTi+xFhp0/3jlD+4++RaFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFArFlwr9Az8KhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQfBWgf+BHoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUiq8C3K/ly6IoNu3u+Iu86iS4HgS837IicN/LgWcyMXja8Xm/z+m5aT7PJBbvT83zerUAOru4xPFNPPDG1VfBU9lpcpf3z2Y5n92QAvjccAt8584ueKu2Bx51u+Am4N9vGkeUV2SnwOOI6zGZUD6XOi2O18qCXxvzeU0vDW6L+RtjjM1XmC2PY25OOuBWle/Mkpp6nzLZvtwHbx+MwFenKfMw4hx6hjqVTziH5373Ovikcw38qSdK4KfPr4JXsrPg/ngFPHbG4J1Wm++rtDje+g54bq7K50UT8EKhyN8HXJBQeIgk7YCnMlyAVJHPiyPK+6jg2JYpZu7qVjQQ65oSviLguOOI6x73KecwXwaPRlwnL6EtnF2ugNd29vl+8bfXdg6pp+1BDzwIOZ+SxfGcOrEGvifet1+nb2n2aHfthL4zTAbggwGvL69O8fqI8h30qNdFn3oaCju2UhnwyYDrkyrNgI97tHsvEY7CGDMZUKbZAu+JXK7ZeCKMIaDMxyH9q2045nw+D16vN/g+Q99z2BfxKcN4MrH4fDOhbfaoIqY8X+b7hGlaPp/XrXF8gc0feFbIB8TkmWnGSz9LeQUJ5X8oBvzatZt8n0cdy5gWeGfE+HU44HocGSzLWN5d3em3DnFZhDwzVaDt+CJtsfq0vXyOetuO6eO7rU3+3gzBhwPGqEPDdYyEL2ltUw8qZT6vu0ff2OrQ1vf36RvPnDkBXihwPJbQ83DCdZ2uMOasrPF5Zw0FOL80By7ln50Vdr3NPKz1KmP81hX6qkGKv1995GEjUZxm3DfiN8XCefCz73yK77x5Cfz3Pn0b/KDJNem36bvmfcaztE0uc9W0UMLVBfrbKzvUib6IH1ZA23xgmfN3Yo6v0zwAz0bUocUCfV3Y5XhPlclfvi18VYrXayJ1bjpUCjvFWLAf8/3pEW1u4t2f6x4VLNs2fvquDeVF0hyMuTbxiLLyLc715Klj4GFMXWuPad+x8DeZPHU9CimrTJF1UbZPe/fy1JWsiNMrs2XwnND1YJtxbTym7l5/k3Hn9QcWwNuiTq01RI7eFv6wR9u5sUl5v0fo1mGD8g/G1K3b63x/t8N40BVx1C4ztzLGGDtNn9rZ2wZv3WBd09lnzPqVf3cF/Hv/u78H/uDUSfD9j3wefLhC+//QY+SNKtesKmLC73z4efBFn/l078nHwGsiX1z84LvAkxR1/JFveje4neYaVd7B8b6xfQH8zVc/DX7tzVvgXZ86XRD1wNr7zoFni/RHmxcY0yfCRl59/jXzdsBwMDAXX74rm3lhq1sTxtKisDWvQj3wbfoO25YtLMqpP+S653z6FttQbsMhba0dsE6aOU09/MbMY+CNJsdz4SO0o7qIKx/+nVfBj68+AH72JPXsmaeYu/zDf/574L/w9zie7/6H/wz83X9mmXyNfuDsWdaBU2Pmcm6aNczBhSZ488LP8rroVxhjzNki1/T8Sdpaukl/Nh7TNm9uMjd4vMo5+Z9hvpbZYnD/wPeL9y0zl8nPUkdPrywagv4+OkP/vjhLHdnarYFb4vc339wA73eokx/+9U+Av++//Ubwl36OueAP/uUfAf/f/ttPgvufoS9emmWddvbr6LvzFuvQhRLlMy3ieU3UtUeFxBgT31OTxqK/mkSMs4moX0Wry8QhfYOfLoOXha9KpVnHdUP6nsNN9ij6AV84K3pzxRHvTyct8Lkh844Zl77Gm2WM2d+nntV2GeMnwnemRQ+omKfvSERO3WlwfAOR0yc27TwSPbYkYB7jOnxfZHF8E5fy6ov9AGOM6Y0450xKyCght5iqmVGXYz48oC1PhA6lRN+qUmSPJ4q4ZjK3rYvc7o0t+pK9Pt8fOtyzcESdIlTeeD6vj8Zc4ySmAHqiz1fIMh5EIX1F1metkclyfJOQ42/J3DXkGkcR32+JutkXufRRwrU9U8nfjdeVtKhrUg+B57PU34fPfh34tOhbZPKMS4Uc12I4w+e3+9S1scXcIhS6myR8Xiw26CKxfxaF1JWi4e8fK1G3D2POx82xv7lzyB7CZI/jD/bpT6YtxvHDLdrOXofPsxOO55lTlNfZ8xfBf+lnqZsrq5xvxaIuv+8819MYY9I16u9vbdCnX1r/5+C//Nt/BfxH/xxlVKpyDeZ6LfBhg3OeXmFMsl3hw8WenTXhHE9Oiesi/44s8sUy7b0repebwlyXKmXw2RnajGUYIwYj6uSwzvGOMsz99mst8L7H9/miV2C5Qr5TlJ8nYqybvD3+mzmOa5nS9F3hjiNhu7Hw8yJUpkX/bOkB9hCmy2X+fiT2BQ1tcbF6GvxE5Ti4LfavJhHlPha+JnSoZzf7tKNRSD1LLK5LqkK9zKRoq2Jb2jh5xqGZ8w+C2xvsAdd6Irc5FE8U+2N2mfOxmCYYL8/czapzfVxhd6mFNSMRi77VXl3sxzTFPsIufUcQlsEnDREfcsyPjTivYXliTzVgbbhzgXXObp25Wiz2izybdVZO7F9VZkRfMaazGXYZT+IJxxc6jIeWyD16Y8aD2Ca3RE94OKY8I7H/2O3zemOPvisJOR5P7BdWimI/8IgwGYfmzvrdvkl/wroiCij3iThHYsS5oINWC7wqzuWMxb5fuihy0q6o+/L8fTAmH4qYMpqwB5QeM86PE7HXUeJ8arfYU4k9ruPx1VPgOZET32rTDsIJ9XDSp3xnSo+B12+zP1C79FvgH6+xP/CBb2B/ZPXhMvjuHY7vo89fBv/0K+x5GWNM3qNMBn3aWlHs0TkJ19SW3AgdEbngk2eZG69maSsnZhnPLjfFuZg0x9ud8PfzFZ4V2zjkGvgF9jFHos4rib3zZML3jwaMH5999UXwXbG/lFo6C56eYZ34t//yvwcvPsY9SX+Gv88dP8Pnj9m/PvMg91jfHNwxbyvco04yZ7RTtPdE5LiB7PP4jJMtkcOGFq9nc3zf9CJj90Tsz0ciztSGomcqdGEo9lLTWdpSfoZxMRbnICYBbS0YM86kY5HTijNjqZi5zAML1PVn3vMEnyf2Vq5cZG7YbLGHLI6NmPIyf//aFvulVp3+N5SHCo0xzizf2UtTB9pRHTyy+c7miDKJxPmlsYgJblv0lcbkccI1CcT5sElAmVue6OOMmGscihjpWWJ/SZwpWVilPLIFEVOG1DFH+OdCsczrMXu+oTg7evkLvwZ+9tHvBrd9YROiF5gTdVU0Yb4/Hork9ogwCiLz5l7rizxX4LgLor5sj6h3RdHPqndEXLFEj1TsvVdKrGPGHcbFQoZ61N9jbpKyKUfpC72MyDHF2YHpJZ5bWcrSd3kpxlEnou/4lX/JfdSru+x3LK+wZ1QqiHpdnF1YOlYGf3CWNcm7C+QzWXHOUJyZLqRFTSDPjRbvP3NWFbWr7fMZD4u8vSrO2mTFuUIzYa3pjbhHNzxgvhd2uIaOiFf9ay/x8bvUKfuQOtR3uIbFeepMvsLx719lbvDaBfqKwz367/I8dT4/R1+V6nH+5QrlG3cYT/xlrnGS0Ff7Yk+6sMTcMFVhPVFJOP/cNH3rUSEKQ9M8uOtPunWuWyD2ug9EzhpE1OWk3gLfn2W/+NR59nQeetcz4GeFz+42uS9rEupZcVb0g0XOf/osc/J8iusaNsUZtAZjxCMPsa7pihjpizrUETH9zApz5OyQ8g03GJNb+9TDieif58WZlXKePbH+JuczqPF5P5ZjolQ+wX1bY4wpi9LaOvU4+CdeFXt8IheddJlruRF9RzKk7T39yCPg3/597wBfrzHeXG/QF1zbpgyXFnnWrCr2nmfzYj/Ppe3OzjEe7nd5f3PC+JQV/efjZ6nzbp7xprXD2lf2LiybvndmhTqYz0obFfMT/faU7C3s8fdHC8uYe3pScj+oIfYW7QPWOY0hdWsoznzOrnBtq/Pk7X15/r0F3jxkTZwWdYIV8v7uPv3TnvjmYafD3++LOsQTZ84mfcrj6os3wMMm7bsq6iAvxTiUE7nYg6InXZ7h9bkV+sv1hjgT9yBr/iTN3Gt3m7ret+nfHzrDeGCMMQtCBt/8DY+CP//xT4EHDu8f7rbAyxXab25AHYhs1rIidJvjx3i/VaJOimPAZjikPxyJ80xjcRZ9VuT7E5ErxGIf5s075MdeZ/57ao358cnzjJG7Ne5ZXr1Nf5oEjIEPPiJinMfc6sQy5XHrTeaSkTiDkThvk28sTGKse/Zfo5HYuxBnmD3Rw+h2KafpKSrO/Kzo5wlb7olzFdI2Z8rUg/1d1n0y14hsPnA6xzjmZ8Tejc3xhKLHMxS5VCS+8ZhMqPmxmQhOeTmijp0SZ4EqGfqa2SJz5Nomc89chrlQuy16VKIfMhL9k77oqRljTHqafTAnJXoqafG9q3iHY1MnikXKwPN5PVMW57AnjB8dsV80EAfJ0zZjfeCKc/mGSGc5nlRO1KIV6ojl8P6JK3vOogckv8/aYf7e6FBnRuIbjqz4zmC6xJ6bK77fHoncqSm+oQzkN5lvE9fj+ymztHo3967VOO6oSzmH4pxPTgQpV+ytx7dEDu7Slw3Fd+AZcVbdL3Jd82mu09VN6u2lN58Dr6Z5Vv7MGebkNzp8f6fNfkSzTr27fcD5RwusSX70GZ57+vsf+3nw8S3mcfEs9fTGkHnVld/l3kl6LPYDZoXvj6mn1jJ95V6bZ++//VGxWW+MOf+NfEbr1RfAa+JMcMGhrTxQ5Tnwp4S/HVbo284t0r/+xjX6mne8g3nXP/2bvwie/7t/GvxXf457gvY0daixI+oOcS4mK/q3K6dZRz1xnDpU/CC/dyhlxPfAok67cZU6Nwq5hqGo7ZstxtuDQ/qyyZC/t0TPLpOlTZ06yTzsKOG7KbM8f9f/OA8w9jYuU9fMDP1Nd8BcaSh6nq78HlSczRmIHrYn9nusiGtnUvTz86KfmBLfBxiRy+QirlVri3uTadFX2q/RH2XE392IY+YuBXGGNhZ1XnfA5++ImiN7jM/rB3z//Cp1e3+P325OquLbwgLHUxLft3Ve5fdqxhhTXqEMvYD5ljzLckbku7745nZNfM912OeZiNAV5457lImfE2cYxB7q2grrkIMW19gXvcmrO7T/efF3MNLi+9Vymms2m+F4p6q8fu0O5XXsFOe/3RHnr8SeaWtAnRYh3WRFPj07xfy31Rf7DKH4uyTdP95+19vjNKJCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUf8Kgf+BHoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUiq8C9A/8KBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQvFVgPs1fVscGtM/+CLteHz9jcvXwEM/DX5s5SS45Vp8/GTA34cpcDvs8/ok4u9NDF6oTIEbbxq01+b9u50SeG6c43gzy+AzSzPgS8UMuH+4Av569QB8s3ETfNDcAR9u74EnBwF4Z+SD77dBzaWOAz6a5MHnCg+Dr37dO8HXUnx+ElD+xhhzojAGd2L+zalmoQU+nOOYTuVr4Bdf/BXwUo46MMqSz509D7718nW+v5eAH+ztg3f3uSYPHKOO+IUF8OJ0FdzYlJG/dhY8Nnx/gUtgOlYLvFKgTUQ+bcK3qLNWagKeEh4hnaFO9g4pf8snT4wH7mVoA0cF2zYml7071qGw3bI34g8syr1DapL8PHgj8yHwp9/9MvjrP78OHljU+9iEHK/DdaxW6VuKRY5nbKh32TTtaGaGvmZvh3pbqfKBYYN6UbY5vmafviQdUQ9KqTK4P8XxjSaUd+zTLuvbQ3Aj5NXr0peMnA74cMzxZNO0M2OMKeay4FFEmdkJjcER3HOoFH7Q4/0ZzmkyEr5OxLcopk7aPtfEE76iYNHW3JGQacDx2hPKxPUY3wIx/5QnfEeK87Es6ojxOB8nzfsLJV5PeXxfd8Q1HAd8/2BIeXcn1MFkSJ3ZPxAB7chgGWPfXatwwnFGMfVwvkyf60wo50KWcpg9vgi+JP5s482dQ/DEou2snGCe4QzpGxpJGfxwQr28cIfzWc7QV80c43zyeT6/UOL9lsUJZNJ8n5+jHlWm6YuLs5yPFXXBXSHf7uuvgY8PhV41WuCRkb6PdtbpUB6tjW0j0TjY5D84tP3Schl86vhj4OcefwZ8cYnXP/uZz4PvNiiztEgkHl1mbn1Q5/iSsAmejTnnB49T5imXMho0GT8Wj5XB37jA99U6XLO0Td/1zlPUid0t5mVfd24O/F++wnj8vvIq+EGHOmlmC6Bjm752d0Rfn9rn+PwCbfooYRlj/OSufoVD+pOtA/qD8ZjXXYu5yIPHZ8F9m7pb7NHv7xwwR49b9Ov9GrkzpCwXq8xdMiWufa/G8VsiDrj9Frgn4kws4p4oA+/LmZeWqOsHmyJ3jCm/Xo9x3xN1606duud6jPPT05RHOKyD5zzOt1LgeFcW+D5jjGnvb4Efrq+D+xGfmRb5V9qi/fduit8PKIP/vdYAf+7CJfDiX/8L4P0brF2buxvgYcI1uHCDtfxP/+yn+Twh40yF9vnt3/EEeDbFGHvpt74A/tJnXuDzD8T4Rsw9kgLXtLTCOnD5Gfrfc4+cAN+5xd5Id5M6MKZKm86e0MkjQsa1zPmZu77zH/+1H8b1SxeugP/aOv2+bRirQ5GDW1x2YyLqnS9Uv5RmHOyOKafJoAU+FPXxzKJYt4ee4njS7PEMxsxNRh9hf+HNj3JdFy3manNl5nZnztG3/uQP0DcOlxln/+e//17+/mE+LxpfBv+GHPMK26LdWN4j4H/xnT/C5wX0zZufYh5ijDH5ffYEZqdZh2z8IvOx6Olj4P/2H/0O+Pe+693gT3/Xo+D1V5kbjA192cEVEZ/GzI2aHcb6vMi/R3XaerBLf/3GhavgJsPff/YT9IVrT53j77f5/Gfz7CmtPfsN4P/Pn/gX4EmHOvP6OnX6tvcGuJ88Cb6w+iD4nKgDV6u00YzL9XzeHA0syzJO5q69jyLR/41oK66o5x2bvmYQ0VfYIgesTFNvrJBxeDLk/Qd18jgSNYDwVVM2163WYV1x+XO/DX7xMmPI2lO07VMrHG9ROMvNdcZge8g8yS/xfssV/Q1H9nsZpGRLJp9ljj2y+fxgwhzCETVT7Ig8K087NMaYicU177ZY14QR3+E5ZfAoZsAJRS3oiX5zKNY0W6XMG7uUSfOQcf3CNeYVd+rUgSAWPZ2sqJVd2mYgctEk4fgyova203KLiGviePTluSwXNZMTviHD68Mhc//CUIw3pM0NRFozGAgbsrkeRwnbtk0hdXc+5Rz9Yjvh3BeXmVt0Quao/abQb1EnDEPKwhOyz4vmfiZDXcmluLbtMW2lfou52euv7IK3aqwZzj/I3Gf5Ac5/EHB+L372Nvh4RNvs3aGuLqwwLyg57CO9uUnbaQxFH2xM//SN30x/Oek+B/5AlfIpeOTbW1yffo/yMsaY9zzGPP+Fxjr4Xos+97UbvwC+uc58a/kMdSDlUkb1FvcI+wFzneoS87n2DvcQLaEjbkz/JfcVqnn6g0yOMko5wiefoI66I45/OOH8wh6v79VpQ619ynwyzfudmGvUatL/egOR26VpA6UU59fp0N9UZt4efZ98IWW+/n3Hv8hfuyT6jSnRYy3RN6ytcl1XF9hPq6TYHwv7lJMz4rpaPfqmxq0L4G++dgt8LHoy66KeLp2lHe2L3GNSFXEoz9yiMEdfG7U5//GEelA6tgbuir2OrOhJpYpi778rcsEBaxIrFvt/JeqRNBuT4XUv5nwn9fvr/3BAGYV+BXz+NH3B3CNcw/mH2fc73GPt2BPxonWHdZzxaZupiLabmaHMWzX6wjCgrcUd5mrrH+HzWx7nl8lyPjM+ddik+Pxwjj2mTkxfMelRxqkCf5+yxKKJnng0oryihPf3WqJ5IWzCtSmvlHt/n+8oEMWx6fXurm17zHHZLucROJRDMU9dnl+k73HFmYdWh3qdydKWh2Mhpyr1otXi9ahLvTIh1yWV8P5Q7FM26syDcj5jZj7h/Oen6UvcLvXk2h3G5L1txnQ/y55TdkR5nxH7Y3uGNUR4sA5+eIV51Cu/Rt/5O7/9Evj2PvPAZuv+HHw/YO5li7qjOkvfUhW1b2fMZ0pVf7PDumztFnPH+WnmiqfOnAEXW4Tm277lA+C/c4m2f+Ic948u1y6Cv/Eq1yyOOOC5KcbXdKYMPszSt7RE3/PyDuvEptjr//XLjEcjDtf83L/9CHj//8vef0dZdmX3meC57nkfL3xEZqRFJjxQQHlHqug9RZFaogzlOC31jKZbYjdntLR6NNOjHlEiW2pyZCmNJIpsOlFN0YtkeQugYBJAIn1mRIaPePG8ufddN39IQuLbSVdVAAOL2t9atVb98r1377nn7L3PPvucG5hyfh232SGnZ+kzq7OcPw9KbM9xkiSJ8YN7PukVGOcngdgfEmtox2Ff1ps8u7Lv096dEtf8oYjzUYb+PhbxKBbnFlstkYNPRB1FrPtyYt4yEXV3n2PjiTNtRuTEgxH1pEvbqNm0hfc8+AD0yqkatJ2Kc5YJ49FowPFJyhwfKy/WpWP2jxE1CIvDYYwxJioxph35Yk/N4RilLuekMKU/TCLG0Gkozi12mO/O1TkHZFPRyJBj4tr8PCfmtIE4/zQQNWhX5NvFPH9/YoHtkflsMcNcaTLi9XIu54Tx4Pc+O7V86qv4ucv+DyNeLxF1pv0u46E15fhM2/evtY8Dx7VNbeaePQ9FTdKNOc7yXN9ik3H7UJw9SUU9bNDluCzVHofui73ueonjXqyy3+1EnLsQ+7xy4o2OGGssseyIhoxtiyXmcjtjtq97h/PoaIv1im/62r8EXZ9n3rB1rQv92svcfVg9wzXOmXczL6gU6AepiF2u6I5UnH2oifO5xhjzoKj7JYbxLSPOV7gpY8ukyzr//l2unacRb5pOGTvckPPF+Cb3rqdT2mD1Is9EL49eho5Oc39tXeSj9Q7bW2yI+XNMG0sc9nGrJ/bbSoxFnohFsTgXOO3R5hJxJroqlmWOsKHuLZ6N8Ifsv36XsTezz/47LsajkXnx88+9rncPmXNXK6wZlJrsV9uh7cv3L6pNzrNLYi98tkQ9Fudd+w1eb9Th3nLCYTMjsRcy6y5Dn3vkaWj/gON862Xuu55u0G5f29ng/cWR482rr0E/12OsC7efg97cpR+0JwyG5TLznvoyz/p3UtrZZ7Z4valPO1+22eCnbLlXY8wrh6yr7YzF2cce17q5CttkxyK3FPs1vT7XWb0285CR2HNorNJXJiPWeLpifjtVo83YhrEim2f89UI6d16cO/dErKk1OP/MLHMtffY0z0scdOhTrUD05544L5EyNh/e5XmISKwtarPMhS0xvzjiyPXdzXXzdsFOE5N7wx6759O2Rm3aayr2e/Nir7FU4Tw2N3cKuizikb3C3MITuZYvaryhyG0mYj8q8fj7/U6X15vy94W8qIl79J2eOL+fiDrSY+/kuQu7chb6vVPG1zBk/1x8kDWFYoXx8OkVnnFbeZTrtkKJ66qXXuQ8966v5/7Y7ddom5HYuzHGmKwn6tQ5+n+2zvwrV6RNrMSs07zzI8zfMik/3721KTTjX0+8X3Z+rgZ9dlacDxM16js32Sc9WWcXNdxA5OdmwDEfjXj951++Cf3wWcajYo6/f+gM49mll7jn2e3R5y69ynh7VpzDnl/g+qNTZP7tiXXsYF8cPDwmMp5lVhbv+aM8wy9znYyocQTiXdD+gIG2Lt41ScQZ40icVamIfVNbnDvJinOKWXGQ0RX1iNKi3Dvn9fpTUbMR9YlEnukWZ2+chO0vFui3WbG/lS8y1sl1pfH5PHI/MCv2cppzzO3sSLwLZPH7GUcka7KAa4zJiXx3YrjnNjLshGFAXwwDPkM5Fu9QiPNj5YrMrRgrjo6YO0Ri/6pRFGd3cuzTPTHGi+J9qknKWNQXZ7iHokYlXjk0A5++PhJnYSMx30Xi/W3PiPVFXtSgsrxhlLC9rnjnMRH9m4rYGt2/zXAspFZiUu/eWLs52k0salWeqMV1xXN6rlhPnuJ50KjAzzPn6CuZmPev5tlR+TbXVS9/muucVz76U9C14qegK8WvZ3tEvVu+03zYpd27O5zjP/Ak3yNvf5ZrCKvL643EGqE/EOd6CvSb0VDknRav54hguNenn0w3GFtuivO4paP7856XxDm2wi1es3eXuWu6Td8ujBjvVyLmdmsf+G7oU+/n9bfFucZ90eav+eN/Avpf/4ufgB53xXwlxrQg4n2hJN6VbYl3Uce0yYN1ti8QadL+hJ+H4mybLd4zf/gh5qZPvoc+8/JrPGM9Ee8EdfZ5v0ceYu4diD2bc2c5Xx0niW1MkL83Xu8/ybbvinezpyVuBl7+FN+VmZbFGdORjE+0NUuc/7Zs2na2ynVKR86D4n2lcqMGnXbF+2VVtt8fs331BnPmzUNRRxLrxO6Y9UrbFWd8RW7hiXN/m0fcD8xW6Lu1BZ7Xn51lfz3/WZ6T6Qvbrme5xrHF/RtLrCsZY0xUEDFfzBlZkfvMiHz1Zpu5yZVneTZz5Qleb2mZ67h4h+/1HE3Eub3XmIs98j7arNfhOm6xzue5IsaoYHFdI89LeQH9eyLOnLjiXXRbnJlo7XJtXD3JWkRP5LvZMvdIs1VxFtdn/7mFGnQ0pE3ePGIu2brKs1e/G/bv/xVFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFUb5U9A/8KIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKMpbwO/7B34sy8pZlvWsZVmXLMu6bFnW//M///spy7KesSzrpmVZP2tZVuatb66iKP+1oLFHUZTjQuOPoijHgcYeRVGOA409iqIcFxp/FEU5DjT2KIpyHGjsURTluND4oyjKcaCxR1GU40Bjj6Iox4XGH0VRjgONPYqiHAcaexRFOS40/iiKchxo7FEU5a3G/QN8JzDGfHWapkPLsjxjzGcsy/p1Y8xfN8b8gzRNf8ayrH9qjPmLxph/8ntdyHEcU65VX9dJMsDnh7s70OM4S92jLs6XoUvFELpcidmASQCZRil0b9CBXrFnoCPXge6M2X2v7fP+BWsKvXamBp2N8tAHO2zvxjqvt7Z2CvrC4jx0ucb+CcJdtq/N6+3ttaHvfGEMHW0m0FZQgW4++E7o+qkqdM2zoIe7vJ8xxrT8LvQ4YR9UsrPQpxfr0N0uPzfxJyA7A45RYWaFvx9xDMbtEfTtgxehndE69Ozik9CL8+wDV/wJrf5hHzp1cvy+yz4LLdpcrliD9h3acFbY6DRLbU8i6Iz4fWg45r7wmd3DHnRBuJiVeNBR8vv+DbHfizct9liWMZ5779mSaIjP/ZDPXcvQV+IMfWvSvQ7deOA89KUb7OdC4RD6oCd8I+D9Q5/jUi1xHLM52o3J8vvrLcbWVncPejRie4zF37sJY8FMmX4UhBxnK51At3v8fS444P3yBcjIsD+cKv0ocEvQvojVY2GXC0tz0O7vlCe7fCYvw2caRhyTKOWYupawkYC+ki8wtoQ+b58tcAxHI94vFr40GTM25fN8pkzC9nmGNmNCOqvrsE+jhGOQEbEpDDmfZcUYWhavH8SB+Jw21pxrQPsT2ky3w/mi1aPPvneGv5+dKUL/Wovt/TJ4k+KPZRz73lhaKe3OisW4GI77bLMG7RnOIZ5F38sUOC6lPGPXIKAhunKcM8wrSi598cimbos58+Qy86a1M4/wfqe60GnI9h1u3mF7YrYvX65BZ3PCT3qMBWbK+3W2bkIHh4yN1Rn6VS6htjMcv2KJeWh+Iu7Xot0aY8z+8IhNjETu+Bp9cfkc4+fimXPQcVrj9yv07XKWnzdqbHN9cQ36Up/zxyihzWSn7IPVU2yPVxK57BW2/zDi9bbHjBXdmLHQs5hH7Pf5/dUZPm+ny8/fWV+FLleZy9ZqbG9coc3lHMbyuWX6SCVTY3tj+sSXwZuW+5gkNia4157umPNEd8i+TkRf2yn7pjPgs7mxmBcTxvmoQ9tOReqSS+lfFRHHC0Uxz4h5+rBPW8rEnEcePcexfv87mtD5IrVdpm9ceOhx6BcTPsD685vQpsD+KBboy/ky+zsYsf1RynjRKIqcPuHvMw77dxrw+70O+9MYY165wbVhd4/5VNkV67Aa/f3MGcb4z958Fnr7OY5BNcM++5oa27T9zDr0T//LX4eejmmTbo7+944PLkAfXGrx+xb72OozNzh4gWNYq9GGxzvMl70BxyDriDlgtQZ9+ukT0BOLv881ua41wkfTDuOZxyE3RuR6GeGDXyJvWuwJU8tsT+/Z/5/52/8Gn7djtvuJxx+FzoncoC7XDSLOlkVO3e6KdYjD2BHbvH7Woi7lRe6RZyzJZBm73DLt2hXzyGMfWYPuifY+NFeDdjzmMqmYJx8+RV12afePnaNdFcSaJPY4LzpinZuI8bFd5typJ2MLx6fy4a82knCTeX3wySvQe7/9a9CvvsK5/Zv/7p+Dfun/w+u9768zF9l/ib7//JXb/PyQdcelU7SRlqhLrp1in25cvQVdnmUfb+5wTObOsmblNrhWnZnlmHzHt38HdPdj+9AfrJyBnv2Ox6BHPcbyzVcvQd+58jL05Y8/Az19mLFo7kFev+zShksnODd8Gbw58cdKTfKGuO8HfI5+T8SOHOfpqlhnRCHnwKmotY3GXIdVi7SjTJYxv+DRLotF2k29SF8rpZwDJ7e3ofdbtMPdHdp5q08/ef83fR30Q0/wfhtLkGZvm3Y8nTK2dgbUwYRrnCRhXlMqMNbkRM3NjjgeiahfpKIekxH1mHJOFCeNMUnIMRqJmkNqs4+diHlCvswxMl3e0/HEWnTKMe4csk/2+7TB1hHj/d6RyA0T2lytxljhFel7cZbxeNrnOsya8PlikZdMxR5JJse1fyknbLZWY3tFGpLNcczjmPfP5fn7VNS/+x1+f3eXz+MJn/wyeNNyH9d2TPMNzzN/mnF+TqxxazX2bWuDtvnKS8xRF9e4bpmb4bx09gz9OZunP7nC/yyLnw+E7V+6zZrApVv052REf5tkWacKxNi2RB1q5wa/H4f0jVN15vj1WeZeY7FfFXa4xilP2X8fWOR+WjFiTT/apy29+x3M/eKMiOcZtmf9GmOBMcbc9tjnJ5c5l7ZHL0D7/lXon/mZfwf9N/+Hr4FeWORcvBjXoHtHHLPpwV3oSNTVS1X2we0r/P3wkDaafYB9mi9xznRETT0jtoe6I9rg0Q7jdecu72fl+flCVdT5q5wjwgGvP3Jpg5sHnCN9lzYv46GTF3vI7v1r7S+BNy32FAs5847HLr6uLZv9Vp7hvJAv0VZnasy5wxHXGdFA7J8NGCtssf7c36EvX7/MmusrV6ktsU5LhG8Ntmi3YlhN3Oc42GIe7O0yN0q6tNMo5np+rrEMvd2i3bXurEMf3ua+8WTEWBRl2F+WK+yqKGrOov5h5RjbY592bt+3LjMmERtQy48+CP0tX8V1xGNnOb945qPQ/83f/wvQ3/kX/wS//8GnoEcid1iuMLb4tshnGyIfzIsxOmAs2d5g7Eq3WLPJ+7SpUxU+X6nM+fNEnd9vW8ytOh773CpyDIt55kZZ8bzdNmNNsUiba9e70AWxlx/4tKFqReSmXzpvSvzxXNvMN+/5myhnmnyettz1GVsWZ8Re+4j9PCvG6brPcS5kGQwWaqK+vMxY4A+5ng6TLnTG5e+XRM1mtcTPqw7njEDsnSQi7yvZzPlvb16Dnrdo977YO0oj9tetS6ynlMWZkaAv9sfYHLN+mTnHZ9uMdbc2Re044POOw9+p9sgxdAxzxeUKc6tixFhxpdWFjlM2epOXNy+12ed2dQ26doJ5V8Ni3iJz9b/7Dz8JvWuJczAZ1nTyNsfIj0RekeXzhjO8X2WNNr74AOdHt87+6V3n51eeuwxtD9gfR6KmlUYiTxImMhbPe7hBn8sk96+1v0TetNwnMZYZv2FPKRY1zJ6ogyxn2JcHEXOlusjZRzEDWkbkRoc99k0lx8+HoagZRMyNRlsbvL6oK8l1W0XUjcTtTdimf6YilyiL+DXu836Tgahr5WibO+u0PWMz/h7ubUHfbNGWpuKcSHm5Bp0ps307m+yvROzPJ+b+sz5TsWfXH4pzc2JdkhXrCE/EG0+cDQ37tP+zJ7jfsyyu1xXrjP3tdWhLzIm2OP+V96jDEftkIPZ0d+7w/s6Yn4/FvkLB4/Pst7k2DwPmapbw/1pZrH0zIpcU+yqpTZ9IpuLc4Q77K41pw77Y8/0SefP2u2LL7L3hbN8gETXjqTjDJc4Qny8zr5+O6SuDiOMY9ujb+9t0/u4mY8mJhLGiIPaXpkOO22BE3+8N2e+DDp9nN/s8dL/KdciJZZ4ZfmiZz/Pwow9B+xdoV9/wDsZq2xV5xSrP9/50/4vQOVFzmqwzlrsO22OJsz6+OONXu0A/LIv9PmOMscVZysQWfdxlGzr7zK/atzmmPpc5xhPn3OW57uE+9wXiHNf2wYS/rx/dgD5MuREQvMK1d7XGtfH+C+vQqRHn9h6pQd8siHWNyCeXHjkLHd3hPsQoI+ZXnzYtz1zPNdnecSRi24TtzZ6mTU579CljKuYr5E2JP74fmKtv2Iu0xF55rUHbXDvHOapWpa3HCX03FmdoLREbhiP2Y2uXdpyv1KCLkbifeD9hsMF12fwy9zqWV8RZAFecSfnNL/D+6yI2dpgDZ2yOo99jvXggztRtt4SfinM5Y5EHNU6x3jKeYx61f8j+9MV+X6FCP+i7tPv1CsfTGGMmIi/wU9YMCjlRn2V4Nd0h7zEWYzwR+2dfuPoJ6O/rfwD6tRHXRa44T+GJenMi2tcWewKtMcd0t8PfX+4yFm6tiz1KcS6+Ic4Ml1dpYztiv2lwJGKPOBdppWzPJBDnU9q0maHP/i2Id2gqE35/1PmK8h5j3tR1l2um6T2bdsQaN1vkuiUUe43jiP5UFeeWl8W5iGyV9bKD2/z9YYtjO2h3oVNxznH55En+vr8OXZDn+MS8/s6nnmZ7dhk/yrOMT8ER49uFJ56A9kWuNhE5+o1D6v6I8+JY7MevzIp6qkXbLuZq0I89wJrENKTtn6qLd4d+h7pPMeY9Ep9zyOZ1ce5wjWMch/SXWfHORlHsP595P/vwQOyR7m5zTO5eX4fO3WUfrZxiPn5SvN91N6VNbN5kvMlMaWNCGifL59va7UJvt5nLnFkUtUyxrlwQR2964mz++g2efY+69NGZ8gPQtRJ90BdzXOBzzvoSedNiTzbnmXPn7+X+icjzZ5bEuya7nDf64n0rS4zrM69xb3imx+fuiyJqXOH9ugPG9WRE3yyIc4Ry5ziV7yKNxf5RIvbfxH5YzhLnHsVZmoJ4n8uIcS5PxTnHKT93RzTsnZuMba44b9/eo13aokYWiPFoztGw7Smvn7fvjz1jkWt4oqYxEWu93bgL3RuwTa6Il56Ymy+eX4N2RD7tDkTNRpxbt0RdUe6Vd0Tu1RDn0xKxjjMex0T2aRTw81TYwH8m8QABAABJREFUkBHvKS0tLkJXKlwfdI+4Fo/E/lSnzfEIQrFfJ86eHh7w+8MB2y+2PL8c3pT4E8ehab9xLSHeq8uI2p0l5rCsWFcFPX4/HosNtBrz/EjE+GQq3ksUxfyaOOeTBLz/SMS2XpfvY83VeNY/EXmcPLM1FOdufurSZ6F3DO1qdIfta49YDyjanPMtUWN77BzzsKObXK8/eeIdvN8J8W7uK5BmX+wbJ+K9/rHI2Y0xpivOBbqiPhpH9J2cOMtoiXXRB//8N0D/g9f4zI//8t+E/pVPsQ/6PfZ5o3IBOonpi80FjsnMI8wF6x73u2ZP8nmubzP37oizY8/viPlQvNMoynomm6FNPH6B+3fv/QjfGyivcr5YXGbe5gif2F1nLeDESe4Hrt9h/yw1+f7Jl8GblvvEcWS6b1jn9kXtfEucOV2o89kS4a8Z8W65I94rNuJd7+oybaXf5bwS1rlOS8S53taQ/rf8AG3r7qvcz84sMv7JvctoTN+JxH5eVuRaW9uv8Xox6zLzIvcoZNhfrS5td7zDeX2hzpzaFfPy3DLvNxwxvkzE3nnoi/1H8XdEjDHGiHVX6nCMkizHxDKcW6fi/cz+gDZUX7kI3Yg45w1za9CTnvjbI2LfYqbNeFG02L6eWFt+4N3ibJH4UzaPP8WzTBvi7Hq/w+fb3eP9hx0R08X+fH7K+CHKbMaPhY+ssoY+FRl+XdSkZxbZ3/M1fn/X/4O9X/r7/vWN9D/xXyzK+8//S40xX22M+S+nTf+NMebb/0B3VBRF+QOgsUdRlONC44+iKMeBxh5FUY4DjT2KohwXGn8URTkONPYoinIcaOxRFOW40PijKMpxoLFHUZTjQGOPoijHhcYfRVGOA409iqIcBxp7FEU5LjT+KIpyHGjsURTlreb3/QM/xhhjWZZjWdZLxpgDY8xvGWNuGWO66b3/hO6WMWb5d/m5oijKl4XGHkVRjguNP4qiHAcaexRFOQ409iiKclxo/FEU5TjQ2KMoynGgsUdRlONC44+iKMeBxh5FUY4DjT2KohwXGn8URTkONPYoinIcaOxRFOW40PijKMpxoLFHUZS3kj/QH/hJ0zRO0/RxY8yKMeadxpgLf9AbWJb1/ZZlfdGyrC8OR6Mvr5WKovxXyZsVe8Jw+lY1UVGUP6J8ufHnjbFnMBr+/j9QFEV5A29W7jMa6rpLUZQ/OG/auisI3qomKoryR5Q3Y9018cdvZRMVRfkjyJuV+0z8yVvVREVR/gjyZsWeTkdrzoqifGm8Geuu0UTXXYqifGm8aesuPWeoKMqXwJsVe3w966MoypfIm7Hu8n3da1cU5UvjTTtnqLmPoihfAm/au6V9rfkoivKl8aasu8b+W9lERVH+CPJm5T7xVOOPoij3434pX07TtGtZ1seNMe8xxtQsy3L/818bWzHGbP8uv/nnxph/bowxp1ZX0lwm8/pnSa6E7waRBT0dRdDdHhdxnSSBXjtZhW6K61dK82xbyIJ4xhLdEfCPglgx/x5SKc+vz81loNdO8o+vFasOtBPzeT/9zBXoW+0u9MNzdejZeTZgJsPnH1h8vlOzZd4/34K25uegk/0OdHjE/pwNY+jOLgt8pRz7azrmeBpjTF78jamtu9eg/Vts48Upn/GTrwygx6YAPQhFm6sck4NeCp0kIXTo0+ZqFsfwwqkl6FMFPk/v5StCs08z8+eg509zTK1KBdotcExtk4UOYk72acDPR2P2V2z4/P6ULyWEfUjT7fD34vYmMeyvQobt/3L5SmNPqVROE+fe2CY2bTHy6PsTi5/nSxz3kkdfHOz9Y2h7cgh9dpV2Uiuw3+tVjnujyI6dXaDvZiK2Z711BD3yaQdfuPQS9MFhG3q+Sj/pj/n56grvv5gtQnsZ+l1PbDYmWcbGicPnGw54v4nF+40nvJ6TYXuNy/Fzcrz+pH//5mez1oRu7dKMZuoz0O0241s+70F7Xg56GvJzP2JsyHgc88Tw+47F+cGkjLdJTF+LPfaxI/s8EfObxfktDXrQtRrH9KDDPpwaXr9c4Jj1jxgrCmLM5eMtLs1COzn2T2eyBf3x/juhR23G7g83L0PfNF8+X2r8eWPsOXvibJqr3ss9xmP6/mTCOaEintt1medEQwbl1gZjjZVrQO/vH0APE477xjp9r1HhuM4VOSeMhZ1FA8aa/Tu0k0bK2DQzQz9xHOpsVIM2fHwTTdn+oyO2PxRzYCZDv3ESGl71xCnofJ525Ii8ZTqin7g15iSlEv0sSu//O5pZh/foiXg8brMPb3zyDnTzuV3es85YlnX5jGmG9xuNeL9kwGcc79Fm6hZj02yJsTGf43x4Z5ft++K1TbavzPsNAsZWL8v5LZPyeW4d0QZ6Mcf4xQ3238VTjC39Nu9fKtEGI4fPm3HZHnsqfNKmDmN+/yvhK819VtZW0zc+TTHP+BJO2VdZkXuMu7T36VjkiGIdkLU5VrbDeaFQoj84ed6v0uC8MxXrsP6E7UmzvH9V5DLvftfD/Lxcg/bHHOv2EePrVMz7N55nTt/vMr5NI9rWI6cWoYcW29vaYfxPxQsytTn212yNtjpIOJ/0RrTF/bv3v3CztcE2R2JdsFDlmCzPM+Z/15/5EPQP/+Pfhr4j5qRUrNUXz5yGPqrehh5P2Z7U0Gbrp85Af+DD56FbQ+YK7RsvQeeLHKN0zHiXL/B5lxe4jmlk2f7tHp/XXmM8rK2uQr96mevc3k2299VDtme+QRvN5ugjmYyYY9I3J/585bHnRPqt33jPVm5f57xwXqwj6iI2xSI2pRafM+eJODymrXspfcOactztkLEqn6Xdy5w261FPx2xPEHbZHjEPV/IclwcfFnYl7O7FZy5B79/dgD668yL0N333k9DO0km2J0u/SRLaleXUxeecNwOxpgkSxrJezNhsJvcfvOj396CPvvBR6PnvFnW6AvPZz156DfrAY02nHXHMN8XacCDmh2hInRNr/ZXzJ6BLSzXovOiTcoM2/f4nOMZnz/P5Dne70EuzvH5ObPu4LufDYCL+kARNyNy+w1g/XuUY37pBG97c5vezDt28VGe+bIbMLVeWF8ybxVey7mrOzKZxdG/u7gZ8ztRh7PB9Psc0K9breXZsIuzMK7JfA1FPtlORc1b4+fK8iDVZ6mKRvhhlOO7PitgatRgLN7eeh/7ip0WN6Ws+AL1aY/uqNmPz+g7n6Fabvj8J6PueQ79KUvZ/Ytj/qVg3xSLHN2IuEJcz04DjY4wxfshnCmLmUolD381mGRsssQ5Ii7SJ2GObfJEbt0fMLQ/abGMQcsybIhfOp4w1cZ41e+OK+vOEY+TEzGWtKeenQoHXL+TYX65Yt0YRxzga0UY2Nnd4vSyvP4o5v+dF3S4W9fZGnc+bJvx+yfuStrR+T77S3Gd+ZjEN7Xv2MZpwrEd99mWzy/iwtyP0nqipir5bOVmDDkW8C4VtumKd4Dn8PJlQ1yocuw9/zYPQlYzY38rx+0nMsbq6KXKxIr+/LPajnjhF33zmFc5T+8/fhd49+DT015Tpqx/8TubwmRXe7/DG+6E3Xq5B77isgby4y/6yzP3rrrTNtd7Ko8zPbh12oaP2OvRR+xb0S79Ie3/iq98F7YgYmc1wjhpGrJU1l5lsNMSeY7vH3OPGJbbn4Bavt7zCMZ+Z51o4FvFlOKZPbB+xD6fiD4YuL9EmCgznJltifOsYxmO/y7rapM85zCrx+9KHZ0rM7XpH94/5l8NXGnvOP7Cc9jv3anzdbcZ9X7wEny3Sd8edLvTGNe7D9g6Ze9hib3zhJMc1dGg3lUc5jnOiyFupQZpRj+MQ5rk+Ht1hewZ7HPdoTO37vF8uZWzIFhkL1s5xXZXLcZ6eXOU6bXpIv4gjse8q5lFX7JVbEQ3ZFXtFxQLtvijyhLInciVjTLHIf/vAU4yfVz/2b6Cf+Wdcpz385B+D/vm//2+hf6vLPj3TuQr9Uo9j1pDn0ub4D4sL7KPGsnjGi+yjWzeFDTxHG0/69IGToiZVFWcRTs1yPuqWuW7rTmhT+2KtG8ac36oV5pKuqNvNNHn9Up7fb4j9uFGPNfyZOe4BfCV8Jeuu08uL6cmFe/7kTBhblmZpy06F7c6IQwcbW+zXhRmuL7sj2kGuSt/YXWcOulRmv5YKYv9oyFhgiXWhHVMv1BgrHqpwnJ7fop+dWeY67kqfdvRREbu+/z2sbX7+EmuH7ZB2e7TNWuqNvthbn8jYQDu8NWF7t3v0m9GUc2RG1MwSsb9mjDG2zTZWy8ydTl94BDoc8/Ot51jn6ot11FjU+dbHbPOiqGe3gi70mQvMSzY++U+hf+jP/mXo+O5noZ/bFvt1MfO89UPa4K6oPcymrHG95yx94uQjrB83CuzjreuMtcO7N6BzWdrc8upFaOuQ/dessk64WuYeZ73K+e9uwNz7K+ErzX1mFufTwzfkPtmByCEd9t1MwrH7+c99CvovfPCroX2x/zQcMs73hrzfYYa6JOJTeMTrxeLcXSrqTIui5tBwxZpdHOt0siLeFhn/qmK/PxHlRN8TZ8zEfyzt0vp16LHD5z3YF+sysZ9eyIn9wpjt81zanpOnb3hib9oSe9PGGBOHnCP6LXFWMWVMzy/QH9OA8SYUdX9X5ALFlP5RFmccYpEPB+Jsa5iI/Slx7E7WLj1LaBHT3ZDPNxDnxbIZ2sjBIfejjrp8/ljsUc7Mcg7M5vh8SUo9HDLXi0X7xyN5xkOsX4SPTS0+35fLVxp76vOraW/3ngPZRfr67RucBzybObE9ZK60uMh+yAtfrq9w3jIi5/SkMx9x3N2iiGV7jAVDUZMaiHN4pQrbEwz4+eyDbN9cg77piJrM//z/+FPQtwPutRc8OkJgM9ZZ4tzF4w8zr1gorEBvvEq/+7GfZp5RqPHcZpihny/M8nl/8E8zhzfGmJm6WLsGNKOf/Hs/Bt1yzkJXitx/mhdrvfCQY7i/y/xvYZXx8/RDrNvZPuPz+Oo+9KTGWLb/CvPJ63d5v9ky43PpHJ+nwOnNzJZ5vVqFY9wWfzhiZ5v7HNUnmcvUKw9BZ5J16Gy/C+01OGb+AsfY8tjfxZMPQMcRfeor4StZdzWazTR+4xpW1P7EkSXjFsV6MyvOREeiVt+jrQ/F/k5HzGGDPZ75mhOhqi5qc6Uq7TTJ8P4r8zXo/va/g55m/wz0qxufZPtKjKXlWT7v4ir7I/sy21tr0q7v3GKsaYn3V3ojxt6iWP/vbTFnD23WWt0ia4vlZZ5jGnZYn9kIRPHTGFOKxDsxItcrRtT5JT7DnT6NZkO8k2KJvKk74Px241nuOb4qz46WatC+qNONe7x+EokXDlz+Pi/2DNoDBptWn/XeqagJRR5j6caBqHt2+f2cqNsV55g3ZkWeNk7Y3vv2XMSZ7Cji84/H4hz773C29MvlK8191tZOpgvL9+aK2QXOtZHDXKeYE+93ZWgbknaX82g8YNwNRbxqDdmXs8u0jYw4V336AZ5rKHfYvmJOzOM+72eLvdPVU4wnbon+71T5vLbYm7AC9te1Fz4D/cptDkk8Zo6+cpbPsyXOSS6ucI1/9ybn4ZI4txn4Yk0RMbeNfofxK4g9/36Xef+6eEcgP12H7hwyF/lFcfa9IM5CfujDj0OvLnPSqRS43xb0xDt4H/9N6IPbnNNWH+AzN8T7WaUTwmY8uTYV79EEvN7OIdtz6Rpzs5KwoayIpw89zrNQ4wnbM/TpM7uHtImuiJdjcV4uW+F4eveX+r4svuJ3S8+eTDNveP+m1mQczoszxrF4d8cWZ3WGYv+rnBfnw0PO7akR+0virEpnn9fLi5dfMjmO4zRmbIpFLDto01ezec4DE1EPTcS7R+mY11sU5y4d8a6RJeoPXbEfF7nCzvpcn1dmaryey98Hwo664g82LS7TbwtpF7pW4+fGGHPwKuOlLd5Rc8W6zBd7y/f9h3HFXGuJPcudDp95IUubKnk16HKFNjSXYSyIRc1mGIh3DEbivJSouxcLHFP5TqOVsn2p2BMVJRmTF7EsEWdbp2Jt3O3SBgcB+8fJ0Ccd0Z6+z/EZT2izE7F/95Xwlay7qrO1tN27Ny9VGyKPd0Q90xPnT1P2S+rzOZ0S97sSsTc8TcT5VZf3j9qiBiTeLyuWGfMbJVmvYM47X2K/H65zryGJGHtj8Z5iSyxEXxL/MaL+gHbdmnC9n5Y4pxcKzLOW11jzKW48A+34jBXOPv360SLrI/4Kn+/BRzjHzpxh/dsYY37tY13oYZbriNY+axaexxrKgw99GPoLG38P+pmfeBZ69tv/EfTm1t+BLgtfW7Bpcx/6EN+lHDrirOmyOOdym7HyUNR0AnFOPjtHm7V8GUut3/Nz+ZcquiKvuXSZedL5fa5DxyK3nvTY4GtXuC68XeXa+s4mx6+93TVvFl9p7pMpVNLO9r32+jbj9O4Bc4WMeBfdLdSgq1XWaFtXX4WO8+Jsj3iHYERprAHjmVemLe1dY6Hl4pP0377P5GNZ1AyyEcfWtcR71T7jy0ScuzQBv28b1kwisR/lTLvQtTxtfbvFdVvL4prFFb7n5JmX1G3mgj3xXrNTFe+X/g7/QaXeAf3DydH/ZjK8Rl4WB4f0R6fA2lf7QL6vyZ8HLbFuOsNaQPAU16ZFsT/fvcl3txPxLv5SyHXhdJ7xauMa5wynyDGyRa5kDbvQp5b4/cYabaIjahHzizxLurMv3qmscv/enohaYI/rqsOWqBsdMD51xDnM343ftzpkWdasZVm1//z/88aYrzHGXDHGfNwY813/+Wt/zhjzH/5Ad1QURfkDoLFHUZTjQuOPoijHgcYeRVGOA409iqIcFxp/FEU5DjT2KIpyHGjsURTluND4oyjKcaCxR1GU40Bjj6Iox4XGH0VRjgONPYqiHAcaexRFOS40/iiKchxo7FEU5a3mD/KfO100xvwby7Ic85/+INDPpWn6K5ZlvWaM+RnLsv7fxpgXjTH/8i1sp6Io//WhsUdRlONC44+iKMeBxh5FUY4DjT2KohwXGn8URTkONPYoinIcaOxRFOW40PijKMpxoLFHUZTjQGOPoijHhcYfRVGOA409iqIcBxp7FEU5LjT+KIpyHGjsURTlLeX3/QM/aZq+bIx54nf499vGmHe+FY1SFEXR2KMoynGh8UdRlONAY4+iKMeBxh5FUY4LjT+KohwHGnsURTkONPYoinJcaPxRFOU40NijKMpxoLFHUZTjQuOPoijHgcYeRVGOA409iqIcFxp/FEU5DjT2KIryVvP7/oGfN5PEGDNJ0td1M1/F534tA53L+9Dbt46go4DX76RT6Pl6nl8oUocpL5DL8P5pEEHbfgyd9ajPLZehV89Qh1PeL034+4cenOX19zzo2XICncT8vUk5nJ5dhJ5LstATvwc9n2H/nD5vQfdH/P36i1vQG7s2dJzl/WsVPo8xxtRPlqDPOrzG7mEbOpqG0IP+dejQ5pglFtucNHk/qzSA9goO9MzSGvRKOoGuFNhHr77wKnQp4PWqGY5h0ZmDDmts/6Az5udZ6trMArTr8frtAftv72CT96/z/lGfz9dKU+hOn9cLDfvPSfj73IlF83YgSWLTH9xrqx+znyKHvuNbtNVKmb5cnaXOpQXoaYfXn+aa0BtD+u7aCfp+tcLvL8xynNpb69DjEe0indC3+zRTM/L7/H2RdtqZMvYOpxzXzRbvVyrx94MpY/HRhHbkVNh/acLfWw7nBicdURv6dSZlf1o+44Rl3x973Dz/zfO6/NxmmzKGvpl1GEsmAX0hU+AzJuIZjJWDtFPezzZsnxXxmVxx/Thl7MyL5wunon0Zfp5GHNNytQ49DNjnpQqfX/ZXQdiMHXN+zXoc46LH52uUKtDJkDZ9a/ez0Pkcr//qnpgfjwk745ji0szrOkxpR9OEuligXbgZ4dtiDnSzdO5JQjuoFdmPgz7zqO5RC9qh65pGylg2l6cvG8PY54S0izsib7v8CmPP2uxpXq1JO3NzIrZMDqH3Bx1+7jN2lZp8/oLFWJ/J83PbYgekIe3OH7E/PIfjEblsf2WmZiQzi7T98+dpA+mA97x6nWM07vEZszafoZ5lG/f7bOPOHvtwnHBMMlnmgo1ag1rkHYetLvSrLzMvi8eMBanF9lcttnemytzRFbltFLO/hiJ2jULaaCqM+qVn1nk9p8b2ilBdLPB6V17m9c6dXIUey9z8GElTy0yn93xodpa24lm/97ywK3KZjJh7bY/xp1zi545D/42jIX8v7hcZ9p2f0HanNtvj8/JmmOP1Xm7Rtq9+4io//8Id6O4Bfe/9TzwM3T/k9VKRW4lp0Ngu2xOM+YMkFvHe4fPNzvDzeoPx9nCbvtRJxP0SzuPGGJOr0J79UORLLh/CyXNM1y68G7qxdAt6r8v7ZcS66kN/6Tuhy0v0n9u3Nnj/kLnOD/xPXw89+WWuRf/iX2Ht9J/97S9AlxZELsNwbHJ5kY9GIj6LOfygxXWR8RgfBsIo1l/gOmzaZcCxY46HKDUYLy9sRvhMMcfnOy7ymax58OTa67o8FSUnl3oq+vnu1g50LxQ5ucvnrpXYL55DO59O+P2MyJlTn77pCt9MA/peKOaZ0Yi5SLdPu3SrNLRGlrG402HsubLNWJWcFH47D2naHnNgK6LfDI84r+c9risnEz5fL2Ys2Rax7faY8+7Lky70d9V4f2OM+cQ6c48PvY/x9VP/+ueh3/sDf5Jt+Nefga6d+B7oXZGLZObZZ+8+zWfeuyLm8jPsA1f4WlJinxwG7PPzy8x/ZxqMv806fdMLGO/dQMyPIt/PVWizUUqbuXOT89nmIWNLWmX7Mgs0In+XsezOAfVqn2M+Ddh/OZEPHxeWbRuvcK+v05T9ng3Zz24gYkmZscARecWgx3pud8g8auTT7srievUi2+Mb9mM8Yp4RDbkGSGN+XsqKOcNh+8ai/nzjtU9Cmzbn3He+h3P82knW8qonmaNvG/r6RkK7TEV/hLZYN/miljilXxhb1OfFnBemvN7EkutUY1pDficRa7lcwDEqRfy8YhgAPVGjEFsIpijm7UMR+3xhc9kc58NmoQbtRvy8E7G9nUPmFQMRjwsubSAW9wtjxr6DLufj0KfP7O7vQ5c9znevrbM9rsvYE4rkuVTk/JjNsz2r8+zgQNQNjajrHieO55raYu11fSDm9mGXfTl7wHmitcc4Xy2tQE8HtOWM4dweJ+zbwwOO5WjI+9dqjEetFr9fq4t1S5ZjWRa2PxF1ozt3WQMYHnDsohH1w6dom6+9xnnn6rM3oAf7H4eey7P93/Hnz0EXHmF7I/sB6C/8KsfrJz7K8ViaZ92qw2WYiSyxQWmMKeRvQ59/mGNenzsJPR3zGZyoC/3Jy/S3xxb4DM4pzlnV8gx03N6D7kw4BhVRo67WxB7jEnOp0YBjfv32XeiZQ8b4WNTl64u08Uyec+LMSdp4ucH23rnMeJTdod69yfw8U6VuFjlnVFdo1E4s9h3KbE/BFU5wTIyGvnn2c/f846UXd/G55dKuYjFX5ioc54Nt+l4cif0Ww9hwymI/nH2U+vFHaVcXxOcLC8wlnv8/OE7JGr8/btOu/LbILcZsbypybrl3nvfo61+7yHGeirMLDxj63X+4xFyt47O/A5v9nZ2hHzRFXJhZWoI+d46xZ3qFdn66IIr4xphsgfH+Ix9ifPr+f/eL0Def5TN89OO/ynv+Fn312/4vH4R+8dOfh173RD78kNjTFHV7r92FPgg4RqeLzD8bDX6+clGsk/YZK5JXGd8PxPyynGWs/LhFm3zq0aehFwq0yZ1DXt+JRO1CxL5+v8v2tDheYiveZEVdMS/OWxwbtmXi0r2xcC2OQ+P0CehgInI6n99vib3sqsPvt+0a9NrieTbnSOxLlpahXY/9XMgxT/EDceajyNhRFmcDRlP6Ylesj4diTv2hT3N/7Ce+92vZviHv//4zp6AvH3F9Pwro1xOHsccVsXoSsn2Ow/aNQrGP69DOEpHnNWfWjCSbpfHOzvC8wyu7Yh1R5+eBOJtkxD2jhM+8F7BPDjd5LudArKXLl8X5AvH7ofUL0KHh550MbSoqcX+sd8g8rb/N+fhQnD+ZJFwn2mO2t1nhWtwWZxNGfcbSKMcxb8wxdts51u+TgDZ59ZA1pWyB9++MxIbZMZImqZn692w2HNO/M1nmcHvCX776sXdADwbsy5k87d8StlLOMz7ZwlYrOf7+UJzRKoRiP03UHB6ocy82V63x92Iougn/oT9gf4wjcc7EYTyrVXg/R9QgTi3y++feeRF69oCfu7cOoLMu+3fpAdpmZYG5z+XDdeikx9xuNBUbcsaYcJv+FHYZ07w8fxO1+IwjsVa/e521sumEz9C7Tf957AzztUjkf1NxtjQQZ1ONGCNX7MfNNmrQBREPz59kruWJdUqxzDrWzZvb0KlYyxbFYl/WvRLR/qmoacs5dTji5yHDmZmI82TGEuMlzusdF34wNTfW7/VdpcocMvQZC0TYN/st2pGT6ULbwjfL4sxaXezjziScZ2ZrjD3ZEs8ZumOua06JnPaj2y9Cf9U7+C7KI+/i9QbzrBHlM2zfZp+xoDBmLHx2/3PQ75h/CvqHP/4cdHyDfnHmj3079OOLzG2+MNeFPnS531Y09GPb4vMNb7J+8t/+0BUj+cYF+vrD52n7Fx/jXvUvf/Jl6NTlmPdELDu/xlzpHU8z/63XuHYuGsau+kV+37vAMZ1m+MyX59m+zY//FttnMVe59LF16Lkcn2f28TPQtsjH04yYD+f5vAMu/U18cA26Ns8vWDZtJHJFXVPsw0Rd9pc/otO+81u+EfrTP/vL5jhwM66ZPfGGc4Zi76G5yFh0uM6cb+DRNzqHtNNalf1uyzNj4lyQK/rRn3Bcu2Ivxh8x6N95mb6VNriXcGL1PdCXXn2J1xP7ov2UsbA4yxx2XBDnjkTeJmtcJ8ShkX2RR/a4jDX5NZ5xad1i7LJPseZTaNLv8ifZv0c7tMv+eN1IylPGihXxTkjB6kKfPbEGvZRjm3d3OQZleUZZTMO7e1w33MnQ90uhqMNZ4hx9i7HLHtJG5urs5BMrHNOpTZuOxsxzhgds8EmRpz16jrnvnnylKCPOS4g9gIrYk118gLHO63AMU3F+IxF7ysJljCvqsMeJ6zmmuXCvf6fiTL7MGSOLnWl7rL/ZYp7YOaAtuDmOZf+Aa+zmLMdm5RRtQ45dXtR0ywG/n4iacFbYuuXRP6diP3s8YTy89nGeYzn94EPQLz17CXr9bhe6EDOeDsTeUHdPnAFO+LxBSD0Ra5iTC4wVBy1xTjzm86Xm/hrAjDj399zLz7ONI8anSSre0RNnRTd3uH8WbbDN/RZt4OknuBZ9z1d9CPrBx9jnLzz7EvRRR+zB7nKMVxtcq1ZPMV6WRLwYibOsJZ82MRL5+2jAPt8+6EKfFmflV06xDjUR+3EvvMh40+1xTuwH3CMtNnk9O8c5bugzhzguJmPfvPLSa69rX5w1qU7EeUlxjrC3x34wsXiXUdS3KqJ273ri/TExrhMjkgHx+2JOnIsI2c+RqNH6UxErC6KmtE+/ku8/ReIc5ExB1hPZf+P7ztMzlofi+R1x/tQR5zCz4l1WR7wXaWVEzVrsT5bKNehpR5y/NcZYKftkOhb7JeJspxPyGbIuPzfiXVIvzzENW2KuzrKPmjbnF7mfY4mzN2NxPiNrmB/GoXi+IfPxqhiTnHjPaDpi7JyKXGIiziYtNsT5tAljVV7YzDChvrHOs7xV8V5OcYbrk2xJnlsX513KwqeOidSkJk7u2W/osN12js8V2aLmkmO/FsVetMnSbuKIOWko3nurVFnrG4w5CXlFxhr57uVihXqhTrt+cPW90Hd/inPU0OacEfYYi86e4np5fvkZ6K957Lug/86//mm2V+T0ownt/jc/8xr0jWusVyR1Hvw5mLKm9bfe+x3QP/Ps/wn6T53leJVP3v9nFCpf+4+g8z7b9NFn1qDTJtfWueZZaO/Jn4D+yN9ivPu5H/oRaDkfyfeXvvYp5i2z7+NZz1/6KK+/8UIXerBLG6k/IM7tzXA+Of0498OcNfG+iSPeQRqyvYMRawHtddYNr33016E/I4oflihQy734/QGvJ17vuC827vXvP19xXDi2Z6qFez6fEefyqkXGH1Pms4/77PvKMscusvn7TJ5zdU/Me6l4PysYsa8KJxkfnBptvx/TdgvLPBPni3dpxuJMqlyX5T3Gv0Px7n0karqWeJd/KP5uRiTOv8+J+utQ2N6hONeRE3svmRrP3+cn9L2h2Hupi/euA7n5YoxJJ+L9yNushRXFOsR2+IyHe9z/mXmA8ahxeg367o1noZNUvCNxyLm/Js4DSBupiXWjNcfr2S3O/V7MOcaIv0MxGPH3j12gjS8tijlVlKUaTd5vy6tBF8U58K54ByXscf3wyos8u1oUZ0p27jLeNXKijuXd/07f78TbI0NSFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRlD9i6B/4URRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFUZS3AP0DP4qiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIryFqB/4EdRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVR3gLcP8ybBYlnbvlLr+tMdxefp/kydLNagk6CBHrgj6E3d1vQO4MD6PnmEnRlJoUu2fx7R8UspHEd8f2kyut5FnQmiqAtKwOd2DH0ubMnoBfm69CdKa935/YO9Gi2AN3q3YLOW+zf2698Avr7vutPQZfTIXRadPj5abbn1StttrflQ2/vT42klnJMF+rb0CceYJ/dGkygnVyf2uIY1md5T9thn8QBn6lQ4vXPn61BN2OOeffokO3bY/urCa9/skCjauyzz8w8XfKwzTF4dfsO27NwBL24RhuwcsLmRP8c9nn9ztZt6LHtQVdKvH6+JGw+oE1Ek655OxDHiRkNRq/rVDxXb8hxb1b5+UTEmjAUvrB8FrrbH0DnizXqmONeKovP8+zXwwN+f2trD3oahNBuRN+LRXvjKT+3bPph4vL705ifb2zz/o4n/CyTgy6WhZ/n2L9OtgIdTfg8ViBikUM/cSxev+Tx87GIncYYEzv0xdAwHk8djoEtfMkuMp5OOh3oYqkGHQ170JbN68cxn9m2eb9cGvD3ziL0SPTRbJZj0hE2ks/x+Z0MfXskbMbLcj52bH5up3ye5Rq/f3DE+dOJ89BxyM8fOLsMbR0y1mZE/+y0OReMs2zPcZEkiRkG9+JHrk7bL1m0/WKetpt32S+5PPMCR/yZRv9AzGkW7SZn8fo1cYG6mKMyGZEXzdLul+dm+f2I9z+6xTzsxi3G0r0uY2XWMHY05tg/E0M78Iq0I8dmLI+ybL/vsP8v7+xDV/ixCY5oV3URW+oJ22slHK9kQG2MMZbwPVf4Vn6mCb2yJGJNhrluYYbzT1XY2M1XGXuMyGWnKdtzau0MtG0xfgZDxpqbl19h+1rs02LM+y1n2b5cXeTSNX7uJ0XokcgbZwzHfDjm/DaMeL0RPzZpwjG1xHwSW4wlQcLPrRx/Xy3weY6TOElNf3BvLlhIGA/yYh6KRBx2bdpGnIqcUph3KvwrErlIFIvcI6XOe/Qnz/AGmQzbmxb5+Y7IbV567hr0jS/chZ7sMv54htf3Kow/5+o1/r7fhe77nHfrlRlov8D+63WY2zkO298qsD9f3mQ8urlF3zyaMP7OneL9jTHm3Q8/BH3985+FrhRo353RCHpnl58XXcar1Tneb36ZbXhyjvFl44jX90bMVRbPcO1+++dehv7O77gI/fNf+E1oK+I6KZ/lWrt/QBsIOhwD/5C53YbQh4MudC1Dn6kuiTm3wf4ahbSJOOAcaYvaRCbH/s+ktIH5phiAYyKOI9M5ujdXtY84D+XrnNccse4oVvh5EtE3J31er16rQVs++2k8pG8WRWwZjfj5MEc79PscF3/KNYMj1pXZkL+fydMupqJGZAb0g0fFPPy+Dz0I3QqZB/hF5oYfu8v2lQzn0bLL3OlzTNXMeIbj0cvyeq2piM0lxu4vZu7/G+LnHn0SuubT9z4s6n7RNnOND8wyFtgX+AyWWGfVq1zXzBiOsZvjM8yIJjsVtmeapS/PePTdTMrrxSJ/DFP26XifddCJqIsmwuaaqw3orW1+f7PLQXTnuI6aW+W6MUg5pq997BKvt9WFXr+7CZ2K/Lpz9DbJfRzbmDfEl4rDfh+KfisK2854zCndDMd9YvHzscPfi9BjjMixI5+xYePVdejO4RZ0M0O7nanQ1/IFxsaVWfpFq89Y44e83vYea3+vfo7P1xi+A3rpcdpVcVasaWLGmn6Xdnx7j3NyKUM/iy1R0+LwmTAVn2eoTeb+evPEFr7pcpB8sXadhGJdIWo8S3O09TTh90PDPMJ1GMvOnGK89vu8fm/EWDbq8Zm6E7EuC/n9ZMrYmrr01cjmGO0PeP9JTJueiHWU73MtUcvzeq0x728cfj8U666sGML8lOOTZjk/2mI8vTpj/XGSyWXM6vl7eWYn4LPaImfLlek/Cw36d9+nfw+HrIMcdNhXhyKnPeyyfbu3XoWuLK1AZ222L7Zp67t92sbZRbb/tR2O1bXnRZ1ntgadEbnd/hXayuXnrkCHg+vQ5SK//9f+/NdAFx5mrjQVeYcv6krPbjAWZJtr0EGJvjsQ8X6men/dZ7ZC/9i5wzaVC49Cp3OiDn+4Dr3b5R7gF55nH13oMVeovEO0uS1icu8m9CShQ546zXVFscTcpyJq5q/dFbUDsYfrzYh1XoE+Mi/m3HHI30985lY3XnsN2k44h+YT2uzqcg365OMnoUPD/nFSkZumjIe1Kvv3uLAdx5TekEdnxN5FJOpXgdgfscXnmRKf08uLmmfE7zfm+f2gK64fcRw8mTOLGs5TH2a/Fpoch6fmuCboiJpV+zrvf5vNN+8ecZ25IeoP3/D0aejv/TefhP4HP/h90B8cvh96WmOsGQ1pl16D/VUtMXe7fIl+XsiJeuSj7K8H1+6PPb/0tz8HnXv3KvTXfw/79MefYw1X1kQ/OMN4u7DKWHbhLPt8qyry0/MchGqTY9a5zlyqJ2q863v0zcUF9tncEmOt77GPP/cJ7mHe3uOY7+19AvrJr3k39Iks79+a8vdBi+uLYYH9MZwIm4jpo9evrEMfNWrQ8w32l2e9Pf57XVGSmvbknn+3De3mSDz33S2O6+6dDejNTa4TumPGjtoia38FcXDnzOoC9EKWOeJti3YTiGVDTtSDCx7n5M0W7Sh16YtTkUMvZOibP/L1a9Bem8+7vcs8yhLj/Mi5U9Cu2Ku/sc3YsZFyjbA/YX9Z4nkDUa+IxP1dsde/tMS4Yowx01TUV0VueWmdNQX3gLFkJJ4pW2Yu6tkctEisC/pD/r69J2KbOI9hWWIPz+MYlFdYlwuzjDVWnTofsuYSh7x/YrN/ul3RxxPmOf0x91zlOixN+Xt57idbm2f7qrxemqENJ3n6yHqb68QoIxbnx4hljMnZ9/LGsbDfWPh3V9TX8ha/fxBy7IPg994f80TN02d4M5ubtLXuEb+QBvS/hthbzDkci2KJvrB5l/POYZv1wNaYthI54lyLOCdoi3VZTuQBC0asUcT+nzfDeNDoi/6zWBPJiP37wRGfp7tzAzociTNsbbHXbYxJYz5zscg5IWuJGLgn6iCH7MN+i2vFwGcbhm3GL3fKz1dOPwI9V+L+WE+cvWzMMZ7UV2rQSw2Omd/uQlcKeaH5/ZLYN5mMOcb5Mu/virNP5Rx1rUyb6HJKM9ttxpfBhDoj9oBrda5jCx7n8EDsYR8XrueZ2tw922rkRK4iaijlMmPFqEu76R3y87ao0cYRvz8jakLLZeakj4lxtkXu079KX9ubcp944Z20y8VHvhq6PE8/2ZvQF2+98B+h/8a//jT0akw7+vQmz7v+8+85D/38v3wG+sGzH4ReL3OevST2llYv0u9nf5D7wtmUsX+6zvrK4X+4zO93758H97wHoH/mX3wR+kf/4fdDv3++Br19l32QHnEMzp9mLnL2kaegHcP8dNCib484/ZmS2KfIFWmzT/2xD0CvXGTu8JufZHzuWuu8f5/XH+/yzPTCuxkbR5tct9aqrNHMrIizQWM+b2GJ32/dZi4lliOmmOH1cqJmF4qzS+N93u+4yOay5syD986B7Yszw/kyz7FsXmEs8cW5ke4ug/bqaVGjcPj5rKiPZmvMK9bFPL57nXYtzwwHQ7FvepO+lylzXEc9Xr+U4/PkXSZ+cp+2IM5Ary1wf+t0g7EjU6TdV0dc0+wbMemJc4D5OdbbvTpjcTZkzjLafA56OuS5pszvkIPbFp27VOe5k9PztIn5U+I8hshLnMzz1DGfORRnSY/GjJ9HXep+i7FsbJiXtQ6Z+8r6cVlcb6HGPnj+M38T+h1P/F1o+wRjZ3WG9y9MaTPjQ94vcsW59Az7b2WVNhSscz5aWmZu3Bb7eWmOec7ggHlpQ8Sm48SyUuM499pviTP6rqgruDnae17sp5w9e4HXLzLXiULxjkWO/tgU9cSROG9uQupRm32/u8vcrblQ489H9M++WEdubjAeTcVexvPP8Rzi4jbvZ8S7Mytn6YvJEXO7O3viXEqdz7/b4vUPdjlv2UXG636f8Wtrbx06F9PX7cr98SdI6A/PvMyY5YsYJ8ouxquwTcsneb3r1+ifrT7987c+9wJ0Js/rnX76Q9Bf953fAP2pjzFXM+Idh+079MdujTY1EWOYq7LPmgv034I4wyHCj3nl5jq/n2H8aNbYP2ceOQe91+Gc+spr9KmXXuEcWxP9/96vfRd0Zentcc5w4k/N1cv36py5PHPW9iFzoZky42o4pR3GNscxI2rGrohtvtg/63fFuY98jQ0W9UxRYjGjgDWQJOH1B33OI9MsY0Pf5/1nmpznq3mRG4n9wb5Yjw9EDTsSZ6laXfHujdi/G99dhxblW2NirlsLOf6+PWF9uF5mLjbucfyMMSYti73uHueL+rQG3cjyGhmRL9qi7lYSe5CBOEceiv2cRJwTnIqzRn1RR5RnSwtiz9VJRW4k+jxn02arIr7X8gy2R+IsQCJq6OIVSTPpc/5xAvFOX0KjFsf0TTru8n4un6dZYmw8GrOWUMq/Pfa7rCQ1bnCv7x89WcPnA+GbrT1xRkzYwXDMjrLEPOsajqMZiheWxOcZizl/waXzjaai5iRqf554F7XncS+jtMD9N+OxRtPpssazdpJ79f/qJ/469F/56W+EXvwVMcdV1qC3B6zB3L37LHTGo199w3vfB/1jP/c3oMPpj0J/usX34n/01F+G/tUf+x4j+fhfYHz+xc//GnQ94Ly879agX73Gmsgvf5Fj1tlhbGjfZV5VFHseHbE3vX5IX3/249xzfeUSY18q3lmZaTwGXZPvS7f4+/5N5mXuHmNVVaQR/hbng6V30IZ3b4g9AVEryIp3e2eroqaVqUHbI/pA6Io9jCyf36nQB44T17VNc/ZerLRFrlCRZ3988a6NOAsTiThdbora+wzjQUnsVUTi/hmRWy1e4DptLK7f2ed+da1Sgw7FGdtUnOnKVGh7sc8HHIp5aXaB++etKe9vReJcyJC+syDPgIkawp7Yax6JedMUqAfifYFske2PE84Hw754mdEYM73CGmwkntma8Blise64+TLPAb77I18H3euL82Pi/bDlRZ6XCiLuC1TGnOsH4jyUEfEwEnuW1/fZXr/Lz5uzjJdPnGNdf61BIy+KeHHtkM83FHu++y2OwWSdtcxQnBN8fpfxeSqSqWC4zt8H7M+TZ7hfX6sxp7j5ysfM78TbI0NSFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRlD9i6B/4URRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFUZS3AP0DP4qiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIryFuD+Yd7Min2T7bz2uu5lS/zCcASZFmvQc6tz0F6/D713dAS9fXQAvXswhV46WYTOWR50yepBz5YL0MlsyPZkq9DtzUO2rzWGjscxdGaJ16+XIU0tk4G+9tHP8fr+S9CpfwX6fV/79dAP1dj+cpb90X/1P0AH7gXouQyf5/u+dhn69mfuQF/euWskwS772M/y80KZ19xcH0CHvg/tWrxArc7Px5Nt6KxwgUyNnV6o0sa6G/vQuWwDuj6Tg877HLNJyD7rt25BmxtdyMbiSejl2iL0xnYHehpOoJ05jmk+60AfjdvQcZJCewW2v7YwC12t0IfjI/a/Z1nmbUGammR6z/8XTi7g42Ergl5o0hf5qTGTyRA6N6Gd+D7HuZjy+1HI74+nebZnuAud2DPQN+7usUEZ2nESMtbFAcc9X+M4BqYJPb9Qga4vnuL1X6IfRQn9rJijXTgmgZ76jD2VEu2s32dsTEPakZvy82DShV48Rb/sdhlnjDGm6tI3knna7rjf4ud2AO3kOB8Zj31su/SlbI6xIYnYB+GUvpvhz03P5hidyrHP2mOOeT/g8zmc3kw2y/ZMpmyPn7IBxSqv51kc03hMGyiIPx9YzrABJ8q8fyRi99ISY//yDG1ybmMH+qWXGUt9Qx+8fueaOQ6iKDat1r3cZF7MEaUKY002x2gTpey3aEhfsBN2dBDSN0IRq6IR7To7oa8WLNrxeMjfj2La6ZkLjA2zzRr03GnGruXTdei9m5zTNzv01f0jxsqZNdrFKGCeNbdIOw1S+oVtmGfe3GdeMirz95UMx6u8zDk44zHWxBPer7PP9htjTNdiHwZiHrYc+uL2Edvs++yzeIu53vwi+7w75vzQrNKXVgs1fl5nbHn1i6+wPVP6+q27nA/krF8UuevpGbavXGB7JimvH3r8vs3QY86s1KBTV+RdvJzpDWjTgZhfIsMfZMVK6anHaIO1Cu/vlWhDx4mVxMZ6Q37S7Yu22YwfXqkGnQ8ZHxKP8Wg6oC02bfZ9JOJPgeHOiGnQWBZ/L3MpO8/7F6oifpbY3iDDeLr0qGjfLK9/cWUF+qknHoFu3bjN9oi/k2tb1JMs5207Kz4f0LcHNmPD0W4X+kDEE3/I5yvW2B8ry/fn4I++Yx46jc5Bl0QbX/4c/f+ly+vQm+vUUUAHPRAO+Nv/9hPQd7Y2oQd3eT27x2dMv4lzzouf+xT0L/yTH4demGW+23+F16vNck7yxZhO+rTxiKmgqZUZny5c4Fr57IMPQI/HnFPudmlTfihyK5Gbleo1fj5i/1oZ2txxMRiOzKc+//zrun2b6+fGIu1w7fRp6JzHnDBMactBhuv1UY++bE2p05T9GoqZKjWMDUeH/L2bFUm5yL28HK+f9cT3Q/r6ZI+xsS1yoQcffQy6WVkT7WWsMmLeuyxqNP/dE2egf/gTzLXe/whj57/apF1dLHMitCZct9bFOnQ4Fs9vjIlcxqcji/nqyumz0NkG63oXxTpqVKIzDndY99vaFGtxkSvkDO9vIuZvRqylXYdjXJjy80jkajsHXeheg32yucV1zG6ba/tbt/g8q0tibdvh/ZaW2b8XHmBdcmmesSGfpw/5A/bHOLwK3RJrbUcUR3pdtve4SC3bBN49+0yE7x+OOG4T4ZsmFvNqnnYk85Z6geNSm6Uv5GrMcbtt3m/3kGuAyZC+d+okfXN2hXOWM+Q49g4YW/J1rutGQ45z55Df3+5ynXhj4zVou9CFXj7F2L26yPYcCr9ZP2AHDie831TmkcJNA7FGMGKdHIo80hhjpjHvaTnsk1jkwk5KfThhvdVq8x45h7EoHjKXW5jn9/N52kg/Yh/d3WYdsCP2SGy5Z5FnJ3lZ6tq8WGeJmksmy89tn8/jZThfWlM+jyiBmZmMqJGljN3DMZ/Xtvl5bBgrxwl9sCDW6tkqf3+cuK5tmrP3fLaWZ464Y3PsvTzj9nS6BT0YsXP90Qb09uF56LhL2znssK8mA64j6hHHshUxHg12+fv9oy7b69WgN/uMn7JuNOww/i7HvN58gzl7Pn4VuitysT/5p7+J379IW77b/m3+fp+2txmuQ6d5xrNaytwsjTlepbLYe6rcv+7KVOivd0UbUhGPIsOYn2ucgHYC5m+fOmJ8qoi1fC3LNheFzZkdxovPf459Xsq+n/ePOMZDUQOeLfL5ZlzG09uh+P2E8S1yRN1L5FKvvMB16WiHudTqDO/35APMlxfOrkGXShzDyZjzxaAv2hcwPkbeH+qW+u9KqVwy7/vwB17XuaIYZ5vj1BFxfukEfccVOfJMQ2xOx/zccmiHO1usD27dZD+uX6FuztNu8jZ9/V3vYmzIbTPun6+zRvuzz3Iv+qPfSL/6s1Wus65HrKd2b1yC/ptPcv/wU2Jv+9FL9P3V73sUevtV9ldzjvXEf/Z9Pwxdf3gV+n1/9XHol55l/WDZuz/27I0ZW+T5hG/+AGsUH1/mWv1kkWOw+q0PQsdNzvWzDzE2XZxn7Fv/VcbTUx9hvM1M6UsPP0Ub3n/hOrSMRUasvTPLXKsGc6I9O8wttltcd258/vPQ7xP7S1sj9vmLt/n70knWOqrL9LFExv5IrKMSfh702b83rrM/jwvX88zs/D3/8AeMNal4jt087S5o8vNin7/Pi1peuczvNzKM+ecv0q7ckTg3lGNeMw04ronY55wTOfv8MtfXdsBYFh/xencOOU714BnolzuMxc/u8HpjUQ8/V6PfnTrDMyNDsVDt9RibCxU+z5km81Rf1Gf8hHp3JPaxrftrPhOxeBuPxf6LOCfiVOgbgU9fcgqM36HILcW0bIKEuaYj1j1elrlvNs8xzdYY74MqayiZujhL4PL5Fk/W+LnIk8YT9mHksw9LFcb/2hzry16GNZpwSh+YTtg/3TZtYDhkrIkWWMuw86w1FETsCjNvk3M+xpg4ik1v/w2xUayZR2LNmfq0jZGo9wVi7yKw6C+FPOeVMOH12y1xRkvsndpT0Z6p2P8XNYF5UV+cL4r9ojHvdzjo8v6ifbGoiyViryGOOE+ODfXLXIaaQeYGtCXiQS4UdSmX7W+PmHfsi/pm0Gf88+UZQLF3ZYwxWY/+XcnxO8s5jvnFJp9xJqJ9j9pyrcxndF3OSY+dYd39/NP038kh400r4ByVrYqzmgtiPzwVdacG/de1aFPxhO2NacIm74kzD2JtG9rsj3KF96vWeEHX4e874qxTXZw1KhREvKlSizKeyWZE4emYsO3UFEr3bMmecq7PiH1Fb8QcbtjiOO7cYo4eDrvQlkPbr+fZj0cZzrsHO7QDW5zLO3KZ0//GNtc1ici53TxrTN+WYW724x+7DL3/Gr/fzTD381beBV0urkP/1V9nsHnovd8NffWA/ZteFWeOW1yHBt9JP/rQRa7DHi8xDrxynuP5sbPs39yrXSP5+M++DN0Tdeq//aM8l/Y//K2vgn708Q9D2yIX8hL6YrfD+PjiXc5XP/fv2Z72Ae9vi5rMvNgv+44PPwX91JPMxb76w4xtjUX22frkCWgz5pgcXfsidK8jcqlZ+kxvgzax+q5vht4Uz5exOJ80FpjbjUUsLDQYe3av3YRuHbF9x0XGy5jFhXv1LXnmqShyWi/D9bQjzoM64iz4nNi7OFFmTvrUu5agbYsdeelV5gUHR2J/x3COKM+whuOLEH/QY7/3RU7tGT5P7w79ZrzVhe5Wuf/0kFgXemLNsttlnnUwpF0GYp073OH1i3XGnvEWY+N4yrlALquKeeYspy9wDjbGmHNN9umJJ2nLvQP6+hevMV52X+Azjyf8vifOVgqTM+L4hbl1g+uMU0uM112Lec/SjNjTFHmYyQmbqz8J/ZE/9xvQH7/MPh33mWuOxfmFozHzlO2BOEQtzn+MxN59v8vf72zQBnbn6ZN7R9xzqVRowy1xbrF5tmbeLkRRbI7eUJ+fm2d8aXc4ticqjLuWS1uqV2vQ58VZoPGQuVEgzlQNxuzricW+ntr075t32PdtcaZtSdj+5jXGs0lI6z9sibNOTVEzL/J5ArF1sPoo9/MqJd6/eoa26F8Sey11xoONFp/PiHMcRpyB3Zkwnk2mYi8mou3HbbFBb4zZHjDmxmJ/pyLOcj54hnWFD3/NGnRTnJX/iZ/h/XZ26B8d4b+f+PSL0PmznMMee/IitFNi0L36DOss7Yj3a+0z4O2K/bhSowZdFzXui48x/+4Lf+8c0QZ8UYs4EucNlurMx59+38Nsb+c56Pv238S67O4G91HKIj4dF2lqmfANc4MttgLimHE/FXY3P1eDnvHEOwRibyESNaNY7E8Fosa8OMt1RUHUXCyH4xba4ryp4YI3L45wW2IYKnP0/XnxLo0X8HkGQ9rtTk/UXEQNfCyevx+Id3+EXeQ8tv/kWfpxT7yTkoh3YA6mIsfOMTc06f3r/4J4p6C3wT6e8RiPF8W7kocidrR7tKH5IvO3O+K8WLdL7RbF2RyXvlsqcx0112D7i2LQI1+0L2GsmfTF+2qeOMcoaiauyPddUau4vcHcpnfA+dee8vnKOXH+zeH8UhL7KKl4d7Up3vfyxXZYQTr5MeG5eTNbvxdXr778W/g8rXJOybtcFzke85Jihp+nDvvVFbEsEjWmfMgcf79Du2iK99THHeYF0x7vt7/P3x+NeC5pGNWgJ+IMl1PgnPPFq9xL/2Pf89PQL9zkmZN3fuivQ2++yvV8NBHvhE9YD/jR/+VHoA92/xn0c//iJ6F/7u8zZ5mbiBrSp/9XyG8Qea4xxnz2F/859L/dZJ991Yo4a5mI/a4e43eLx1qMm3Ber9e4H3XB4fV2Az5Tr0/fe2mbeY1rnoaWrpYRyer2MzyH0xGxwfPEO3BDxiJPdOHjK7QZZ8z+aNYfgt7K8f2Runj59MQKbb5R4zkgX+yZ5EucL4fCx1Znue48TtIkNElwLzi2tmiv+YJYs4p5alm8g5Grib1Rh33lFkUudCDeBz3L/fZ9saY1ol7pyr8h0KatxC7vF454vyjg9YZC1zLinHTC6zfznAd3dtahqxXG76vyfLx49+ZgzHWTF9O2Upd5R7NIW+2J97htcZZqIU/by1TvX3cduezT0gNc5yTiZfCpeH8/J85YOGXxPlSba9tqrcbPxZyT96i3jzhHVGpiLWnE/pXH9pVyzE+X19je5QrH9PQSc6frn38BenaePnDli4zXWyPxdzGy/P52m8lJpcz2eFXGn0jM4ZWGOEuaMLd89zv5fp4vJ4Tfhft3QhVFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRF+YrRP/CjKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKG8B+gd+FEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFOUtwP3DvJnjuqbUnHldp/kcPm/tD6HTQgjdKFSgZ5ar0O+emYUeRivQ+9sjXt8eQx8eUI+CKXSutgCdD7LQdgppDrbb0F+8sw2d9X3oxbAOXbtQg3az1ItVDl+xwfY0fQ/6Aw/kofNhEbpU4PXDcEIdsP9mC0fQy8unoB94Zxn6vburRnK0wzG/Rmk2j2agd+/yC/6Af6OqVOQzJ4Y2ZgLaVFIs8XOfn/eHh9AzVd5vfpZ9+NC5x3i5gwR6+6Vb0JuDFvTFUQR9/tS7oC88fQ76xvVd6F/87f/I9ubOQ49S2lymSRuqF2vQtflFaMdlfwZ+DN3psv297oF5O+DYtqkW79l/a5O+OTszD51xOM5RhrZccBgbyg361shnbKo0a9B+j/3UN7TbNOI4RS59MTAMNqlxoLMOr+c6HLf9u/TduMHnLc42eb0S9Ym1E2xPWoCeF7Fg0OpAmyz9ZkS3MyZLv3Sr9KNKhv3fG9IPUp9xorOzbiSPrLCNh2GP98yyD3s9+qqXmeM9MwF0YnHMph6v51nUmSJtxnEy0PkSbTAwjPdWhn1WTfk8A1vE/xzvv9PlIMxkaFO2w889YYNZj7HAEjbcEPP9iRXGlliMeXmmAe3YnAserHO+DCP6lD+8C/3L5niIwsC0tzZe1/aY4+p5HJfAZr/FIrHYu7rOzy1eb2+XeczBgDHYSmmXncOb0GWbeVZXzEmtEfOA7vYe9Ife9QT0yvk16MZZjmO+ythR3LSgXxNzfjBif4x9as9lLHNF/9ZrYs4Tdr4o5vRmnXllc4mxr3WHfr+1O4DeO+R4GmNMPssxC0SflrKMd2HCMYts+lqa8p47LfbZeEjfPXXhIWi/tQPdv7kJ/cqVy9AnqxzDqsc+K4tc0k75PHaGfWqL2GaLPCXrcAxXhe/n6yIWi9idy8pYSRsrFEVsjTi/HO0w9mdc/t51+fzRVCwGjpHpNDJ3t+7FAN+i7ciFy9xZkUtk6C9RQlsadZlLjcvsGyfh2OdELtXzha3H7Mtumzl4Tvivl1DPi5y+KXzlTI7XD5v0vTNNrlMGR/Sl/ZZYFyVinnRpa2Of1x877J8gYv8tnODnC0u09fnmGejE4fMv1embh4d9Iznq0N/3WswVTi5zbs4Kf+n0GPPLZTGGNucML2WMTDucmzNjzlF1cb+iyM3uPvsJ6Dvdq9D5kHNgecIxWFlh/BkMmA9HOcYbL8v2zDS4XogT3q+QEfFgTB/rHzC+DI74+zShzdpF2lQuTx8a9Xl92xXr2mNiNJyYL3zulde1NeZzuZuMq1FIX2oucJwKBT73sMdxrS3SV4YDzotxSF+IhC86qYhtE/q6M+A6IzG0c0v4opfhuG6ssz0+pdm6w98vn7oA3b9LP6qeYK42Eeu8Mw8xFvxjMS9dusA8JMzz+U8+zPE4I/xguyNyrZCf9/r35z7DPL9z5YDxvTthjeREltfIN+k7yXgD+salfejdff5+vvQgdCXHZ/QtXr8jagV+yme+/jJjT73G3GPcoc0tCN+0K7xe3hNjepvPsycWy9U8Y1VDzH/WoAs9OmKszYp148NPPAw9HYqazh5j8bjL9nW7jH3HRRgn5qB7z/8ci3Y3tPnc/pTPOTqir8maT8HinOcZXr8kck5b/D19S8SumQx9cX6N9eY/9k2PQ/fFuFz+PP1gqy/aX+bz5kQt05lQ+wFj32stxurBdfbHROTMTpmxiL1lTFaUZlOxjo19YXeBKA57/H4mx++HU87pxhgzmHT5D6LNIlU17VDk8RbvMfYZO5KAfV4M2aeNB5Z5uQznQ98wVvVFDWVq2N5SjTY2v8xYEMV8IKfK+XOnw+dzxTqsMsP5IyNiY+RzfmsURA0nZiwsFTg/G1GnFGmbabUZO0vi+8Zl/5XytInjJApjs79zb+6wZ/jsfpdjOxH7NzduXoNeffx7+fnHfxP64YeZK5n9LuT1Q9pqf4/1tcYKx35wyJptt8+xzxfpwOOAfW+Luotlc81gOpz33/sezntnLrI/7mxz3bN3lfHAWmU992BMX9y4y+edBmJvSdTQZ88yxz98hft3/pjjZ1f4feOxrmWMMY7IDSr5JbYpYZsji/coNTgn+GLt2O3TXz7XpkOdHLNPq3PML/N3OZdnRvz+a1d5/ZUFPs/GbdpMzuXneWGizQJtqDvknLi7cwN6skl/D1u0oXNV5h7vfSfrXPUVxl8vx3w7aNFGI5GfR2J/sHPYhXbEHvXxYZs0umd/9Tprlrk8n7tpcZ4ql8W8F9LXCjZ/H8v9KFEPKxboWwdD5pB23IVeTGvQl7c57pc+wVzkHR/6Omhrk7nLn/0bfwL6zywxVv2l/+3HoP/Wk/Tlf/urn4FOxPr+m76afpS+l/tlP/mvfgN69iL9uPtbrME//ff+MvTGXdZjn3mResTuNM88d/++6xM/8AD0cK0GnQ9p24tP8PMffDfrYn/2p1kT/ucn+PkzLzBXcj9A31v98Fno1jpj3cWHvwo67LFmNF2n7/cn1FVx3GDmLOe3j/zJx6Hzp5k/b1xnrLPrtJlBjbF1r0+bOwoZPPZ3OH/OuswHRGnDVOrM3Yp5Pl9B1Nj3D8X8ekxYlm2cN/hH1aMdDEoXodd82ur0Ac7zhROcR6OQc4Y/FPuuoj21An9fyvH32WQL2hE1jIzI+WueqLGcZ94x7HHcT2Xp6zsbt6Fvt5lXxGPa1d0uY181w1i9tcN6iSinmw1RLwgKnIT7CefoowrPTQ182lUq6umHQ+YghbzI0Y0xrst68tkV5hUHoi638Mij0K88R9/L1Zlb7t14lfdrMK+KLeHLeeZ+rs35qX6O8dzvMK8pneV8Gvb5udwrd6Zyf499NNjjmNs1zqeNCve3ajn239wCg11rjz4Vu6LGNWH9f+EcbdSu0ubtlLErLdHIsgWxp3SMJHFiRr17/ZkNOa+EhmOTE3UXR/StLf4ziPKcX07sVdjiLM80pLZlzVnUVHuiLpQTdY5Nsd91uNOFPhLnFkO5VyniX+zweb2UE1E6ZgdkbOY+iaifjmKRS4ozeZU6rx8NaasD4WtFXt7Mz/D6R7EoJFVqRlIt857NlL95usY+eugi45VrqB+6ydrS9SvM35rLp6Efff/7ofMNtjHsMZ4NRpxDpinjVyz2fyZiI8ErCJsU++muEWt1kc/XRa2ifWsdeiTOVY492mxenPUxIr4WxHiUimxPJGuzIed4cXTW5DL3r7WPg6nvm+0r9/YDUp+5RdgTcT7mcwVDfi7LXcviXN4TTzDuPzJXg66sirM1osZrzj8FGc1w3lyq0M6Hz/L86Ws2c/q7v8aFyI0DcWa6+Tj0Q99F/cgF5iY9se6cHnJees9J2k1fxKpf/SjXSZc/xnXY/o98Cvru//kboJuPcs3UT3j/xRqfP/te7p0YY0w/pg14P/kC9M76s9D/6Md4j//2r74HOhJ72T/xj/4V9PXbHLNJTN9OLPqit0JfC2Td78bL0P/46Dr0DzZ4RvnEiUegS6LWUOwx98jkef9Cg3164iGuE0sxc41bEW2me8T+iUSddTzk/NgoM1ZnEtpc5NHnwpDr0Lo4E31cxFFqBkf38rL+nlg3eExcFmeZU544xZpHNhZnOBL222KNdpQR5+gccU7o0XOMLVkxBzlFzjlZj3PY0Yh2WatyzhyKc4BJj757c53rlL7IkTt3GbtWT9Pu7k65Tnu1x7MAXZHjJ57Y77PEHO2wXmAGfL4oEGdYGvSTiw/UoL/tj580EjviM10TNfFnXmZN4e46x9yIcyqh2C8rZ/mMgdhjXa5xflqZoy+lsTibJK7XmGNuevcmxywpMy+73upC213a5DPPsJ6cOmL/aszYk+eRbHMkzsJGgdgz3mT9fHudsSKc0Cf29zimwzFtoCr2bKVN9Wpd83YhTVITTe71R+uQ9l5qMs764pxfIUfbubtF//IKtIVZcQZrWmSc71yl7VdnmXP+7//kb0A3H/xTvF5Cfx6OxBmxmDXoo03Gh1Kd7T2/wL0Bb4nxt90T9UHx+2aTxtjr0ZdXTtIXZkq8X5Dyei8/w/3F0Vj4ujiPH9niTG2J895Q1PSNMaYu9puLGf6mIq7ZFLW2yQ36++FMDfqhk6zjDAfy7DzrFPuiFvb8S1+EXp5lH+ZKbM/JR2nDa66IX+Ic9Ms3meuUmrx+FHb5c7E/7tocs4bDOe5IrJVTh/efafL7C2t8H+w9X80xv3TpCnRL7Du8/Cxzo2ZNbOgdE45jmUrlXj7giHdtsiJHK2VETULYclmcN/dFbX3cF+tdsZ7NZTnvlEQNvJgRZ4SHjJXiFYb71s+NGa6rYnEGLCPOdKUJLzgU81BnLM7OeOK8aZGxWRzrMNUSP59doB3PzVLPztIuN69y76U14bzsD8Q8O8PxssTZBGOMKVV5j2FMXzkUe73NHG057IncZsR8ddpjbMmI8w5hkW0eiveTSqKQaKXM1+U7BjnxfXkWKCtsvnXEeByIs0WNprARcf7Ndlg7ODrg/Nfv0geCHu832+CY17Psn0KBetIV666A/ZuK1NQT69rjIgjHZmPvpdf1Ew89js+3xLm5JMPnjl3aZcGpQTs1jpsop5pyXazLRH203mSeEYh3MTOifhtOxBkv8d53a4frf8tiLLFs+rItzgntHv02dNsw515a/jB0x9CujsQZkTBirMimfJ7JHu/3hM286d/+JL//o5e/E3qUsL/+ozBER264GWPuzDN+tkfs04/vcd2VjrgWzMfyHRNxjs4WNX2xB5FLmAfkxLrhtSPqfkRfKsl3WIbMFYMW56uh/zzbK8JxZVa84yjOi/T6zPOu3GVs2hJ7Gp7YUw198bcaZlinnEbsn807jP3+hLG3WGZ/hFPxPsq82Kw/RpIkNaM35L2tHbEXKfaTCtmC+Jx95zq0784u6ybzYq/QEfvxd67dge72Re6RMCeuL3C/uX/E+61cFPXJI1HnETWDrPg7Hb6Yp6Z0f2OJ3MwfiTqWOMe3tsA6S9qnL+TF/uFkRNtJxLzuTMRmRiLOkWfEvLj1ErQbiw0yY4x/wLw9Ffsnk6d5ZiER7482qpwzcoa/H4lzhr54f7XhcQySI9pEtcR1kC/2rzKGfT4ZcA7Ii/NXD5/lfr0n3k/b2mT71kWd69oGv39wxPg7ipj7zTV4vaLHOdCeMFcqsTuMGTHeJOL9sLr82wIu49Hly6yN/G7IZYSiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKG8C+gd+FEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFOUtQP/Aj6IoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqK8Bbh/mDeLksR0R9PX9WKxgs/bSQxdsLPQkxE/rywUqHMO9HyxBL3YtKD7wZDt81rQwcSD9hbmoTfbR9D5TBk6dEPqCj83yQSy7U+hxyGfN4m60BcurkDH+Sr0ubkGdG1hDnq6weEftXehrzx3wPbOL0GWGvy967F/kwP+/ahcMmskRSvgdxyO2WvX+Ay+zz61RZ9EKW0gsevQXjUHnc9zjKf7bejxPm3kRIU2V3Ai6Eo5D92co41bEdu/+YqwgVEHenDzBvTCiXdAnzlbg/766FHo5/f2oW0ngS6m7O+lOvvby7G/rJj9uzGkD/ipD52tZszbgdQyJnbutd22OO6ZHLWJ6Xv5Asc1k6Uvuw6fs1wqQocRfSG02K+tLu0oDWgneQ6TSQv0vdTj9apFxs6Bz8+Tbgo97vSofX5+I3cT2h8zVtmi/1odxtIkpR9ZAb8f+vzcFrHUMrxfzmas8WLatR3yebPJ/X/LrlGage5NxTN59PVJl32UBNShP+DvE8a2qYjnnsU2BSnbbEcc42nCPpskwuaK1P2YNut5YswdPl9o0bcnwQjaT2mjOTFfZ2PabMUT84OIDZ7D+0kbGDG0GLcgbCYSsaZMn1tqcnyPizRJTDi+15dHAWO8a4tx6/Hzfkg76rQ4LwcpfSFNeb3A5ji5Lscx63IOsqb8fjzh/UdD9vurA47brXXGioeaC9Dvf+ox6Ob5c9CNEyKPKdAv2n0+/2DA9m3fYR6TKdagDR/fLC2fgF44wfZmDO2ue0C/v33tLvQwoN0HIfvHGGOKZc6zgejTguGYlnMckwsLzCvyORnvx9DXbnR5v8MN6Fs3GK/TiL7pWLz+avMs9EPNZejFCxegN7f3oFsJn3cQs88mPvOwmrAB0+XzpQGvH0SMXYUC55M4w/51lk9C9wPOBRu3tqB7Pd5/dpY2k5XtPUbCaWD27t55XY+HLfENzgvFKvvqxDyfbSpWjefm+Q+dgPEkCGi7trAly+K8kXMZxxNhG5Ev4mXrENoLaDtxvwsdirGbHNFWLt8V68Ap71ercF5pNNnewYTP3+lsQ1sV+u5slf3x/X/6a6H719kexzAZ/HST/fPwHNsz2X/WSH7hFz4N3d4WfbLByXe2JPLXgJ835mnvscgPM2LdM1NivibjS8YSuYVDf52Oua4Jp+zzWoF9sDDDtefU5vN0x8zdMiI+xDEnjXqVPnH1+h3owRf5+8IGx3DY4pwVjtl+L8fczBjG44zLOfJgxOuPun3zdiAIpmbjxr35MWuL4MHQYypFxh7H0PfcWfaDO2U/RgP6cjDk3Fss83pWygY4Yn2cCjsMQ46TU6TdT8W8FTv8fiFLu82E9IvHzy3yeiPGjl/4MeZW3/5DfxJapHbmYoN+NSvWTas1rhEuiOGpyHVWwv65nqPe6DCOyHqHMcYMI37nRI2fZ8QYWC6/X6zTt6Pb7NPu9hV+Hoj8OMe6WVigDUyzIn8TNhTZ9M1sle29ebgJ3TmkTXRF7rdQZzxfbdK3i0+e5v0MKXvMTxsV2uSVF1+Bbj/7IvTMyTXe/8xF6DOnGevWRW7ZO6SNxxN+fmykqUmje/OQl6fd1FdWoe1REzoac9wmPmNLGPA5kw7nkJpNXXBpR5mEzvrYEtt34THWmxtLzFE3v8hYcIPNNUdiGII87bpSEOvvsqhfi9jXitjefos5gMz75udpx6UC7bxaY222LeorkwljRyDqJ06Ofp2JOecVY35ujDH2iLmizS4writq4jHzmImIr12xtAtDxoJKhrFiTTxDo8Drh1neIMzweknC9mUqtBmxBWAKRVGXG4s6m4jvvR7HsCLW7iYReU2ZY1iuMbZNxZ7GQo02YIu19UTkXZZ43qqIdf0RfTIac115nATT0NzauLcuzYy5/9Lbp73XT3MsX7nyAvSn/sX/AP3Nv8Sxmquwb32xf3XG4fXX+7StXJZ9XxQ5b7EofEOssReyHPu8Tf+diDpIuUTbuHhC7B85tK1PvMz7jeKHoT+/J57XF+0NuH/VG/B+doHXH3uiHhoy3sVZsY51ua4bRHKmNsYEzDXsSDyziA9+xDHNif0hW/hfOmSf3hpwUnjlEj9/5HGuZS3DfDqTsL3WiDYx7oo9WVHTbTbEnBLyejWxnxZM+XkmoI2Ox2xfJcv49K6nRC3xLPvHdpgbdW+ydjfqcIzbB/Sx4jn219Cnzc26b4//Zs5oODHPfv7V13Vrj3PjaCrieKUGXRJb1ZbI2f2A15sa+vrCmqhPiv2cepOxomGLve9tztPns7x+f4928snfehn6Yp0T4bUb69Df9lWs+f7Cn383tL13G/rXfT7vTp+5XeXjvwT9zm/9E9BP/XHWSw/v0K6aH+DnW3dZw261eL9mSDt7eO08dK97f+4zau1Af6rPdcpCjtd84FQNeizyt3/0zd/Ie/5KF/ryS4wV+R6fYbLEWHb9Kp/5Z3/mNeis2BA6k6NNrC4y3vtivyiYoc0sLq1Bf8u3Uo+6It/P8HmaDda8l4TNzp7j894S66RSnbHP9ugD8yW2vyQ2gQNhE9Pp2yP3CePY7HfuzTuDEWOHt8eYG4r9pkcWOYfYBTHHWYwln/0c10HP716Fbs3S1+fFmYZCievAcpkx3olYW5tOOSe1xTj0J3yenshxB2LfdSzyKN8XtUlRUyqI+kO3T78IQtppV+wHTsUZG6vMdeauuP5A7Evnbc7BdpbXX5y7f991PKFv5Btsw2Sb8bUn1sYjkTekLn9fOs9zL7VTrJn4B9wzPHmaueNkk/XbebG/djdijSluMXamvqjfinXXpMXfF5fZR/GYawEnTx9ob7CmNanQRvwhY7sRuXcU0efGot5cq9KG5+dq0IGoj8/N0AfHoVhIHyO27ZjSG87fFGxRUxZ7GUVhv55YR7kOv+8WatCWyJEnok4yETVjea4k67IG2+8xHhgxVtkS1xX+kL5TO81c4PQi42f9DONhNxXn9GzaZpJw3VgSezXBhLabixgf8+IciDUR85RYZ56psX9OnqIvZprizJ7Yj7ss4rExxhyI81UZUaeODdemaVace3PEub6zrBVaWbahUK9Bexm22RZ19KxMuIuixh3TJoeiDuW59L9Y7G8lPnMtyxN7rOKsku2yvdkix2T/iN/v99mfGXH+zYrYv9OJOGsl0tWJOB/Q6XEOjHxRu5Q2dUzE04np3r38uk4jcWZMxEnPYZwuZDluT7yXucl3f4TrlHqF66jxAWPHi9e4fzQ+ot3trD8D/eDD3wD99d95BvrwoVPQwaLYa/ht+l7e5b6rF9GPei2257XLXej2IfOGRZG7jMQ8uPUM7fIJEWsbZ7m3vtd9CPrVf8p5/bOHPwSdRIy1F7+XeyV/9hu47jPGGP+DHLOf+G2uc/Z3rrNNX/wE9PMf5Vr28acZe3LiWPXkNn0rEeskp0xnO/UDj0OXc7x+sM79otEzbP+lQ+ZObbEP0BtxjLs+x7SSp66uib36HINdIGpQg23mdislngUqLzK/jati3bbC/cDnb1+C7rT4vCNRs8vwcY+NNDFmOr6Xi4z6bJgvzumVi4xFs0Wx7tjtQj/z6S9A97qMyavLrG+fOsnYFVucEzyHc+ZsTczzhnPK0T7XOZ5NO50RZ5AvftXj0Gt3GRuvfJbj3BvQbx56gGfYbol6fVvM4UEk/M7w8+lAnOOZiv1AWXv1+PuH1hhrv+F9PCswW7q/5vPqVfbhC7eEb3bEXvdYvoMjzgGK+uaHTnJ++MkdnpO7+I6noF959tehx1PGjlPnxDsqFts76HAdtdcW33dq0BNxLmg44P28Ep+/yzTJTMU5m8mA8T9XEmuHDHPjWOxBOuLAi1UQawtxzqcyI/Ioj98/t8r5/zixjGWcN7S/Js5cJal8VlH3EGdh2m3abnDE36diDZ2Ivt065Fgd3HgO+qH3fS/0vjhXncuKNYHPsZyvcR6aEecWF2aZeyyucKzWN2ls/oDPv9vu8vrz9DXfov/HFq83SmmL73oHxyPvMp5ducbnt8RejNyrdQuyBmHuIxbnq3LinYuKOEd395C1vOgu77nzMuPL0gLXTTVxFrO4yvsFYt3QOeS64blXWCdZqnIMF2aZS2QM79cUNeXlBa61rQzjzc1txqN8SJv1+2KObfL6qRiTwGZ+fOuQudCFCuPl/BnWCs667M/O0cehRz6vv3ddvCN4TFjGMs4bzohGvnjXJC9ymy7n2lScOSsUuU7xfX7fErHMOOJd0Tzn6i3xzkBJ7Nv2j9he1xXnClPx7pA4QxxO6GfZDH9/uMfcKRJntNqifmmJeay2RLux+oyFY3HOry3qmTmL/TkUNas7LbH33hF7L+KcaPcK44ARawJjjCktifMLYj/lwGIbun36Zj5X4y2OGOC6PTaqVBXvHy8x3ofiHLwjahbJmOugUKwz+j3qrDh/kBF1zKGYrwIRK0Lxflkszi6logbvxfSBmRnGgnHA50lFHTSVZ39D6l6Hv++lHJ+2qNlVxP2Pi2wma868Yb/BFTHY8eh7qcUaSZyKdxsr7Ae7KN5vcFnTKc+K86NLNei509TDnng3tc69ksE+fe9oqwsdBbx/LkM/8PLiXGRVnAPqcm99uPNF6Cuf5Xp7eY3ndk7N0I79QNRiR/z82e2noX/8N7lX8tLO/xV6mojzvjJnN0Z8Lt4dNsZ80w+8D3rrRz4L7QrftsQ6oij2913xbuaqeB9iIvYg6pkatNd4AvrOLr8fhOL8xYA2V8jT9y88zPfQH1xmbmmlPKv6rX+c66huh/e/tXcZ+sYN5iG//MlPQNviJb7UEUUwl3nfgVg3TvriXdiYsdQe0WY7HebWO1v0geMkTWITvOFd5MEh/ScaMZ50Y+a0njhTNRXm3OsxxxteY1+UpuI9ZvG+ar7IsXFFLiDPlDVr9GdfnGvIiP3pkvi7HtPDLj8X707mXY79wQFz/li8j7t1wPidbdSgb175PHTzAcbTE/NcA6wfiRqMqLNFYk0x0xTnCEXeYInzAv8JfscR78wWxX7M9hHH9NScyF9vX4MujNmmYo0xenDpFnTlJHOh4VW+f1o8y2fo79K/5uY4ZmWP98/H/Pz5L/D628KGRz3x9wJscc5P5BYFj7pSFXvIFvP5sfj7CjOzdKqyWJu39pnrWFnm96+9wHXxtavUvxtvj9OIiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoivJHDP0DP4qiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIryFqB/4EdRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVR3gLcP8ybhdPY7Gz2XtfZQgmfZ4o1aCutQPujCPr2bo+fHx1Bz9Y96OGEen6xDL26OgPdqM9DpzG7626yz/ZmY+j6DJ/vkRMNfv+Qn09tHzpTqUIPfD7v6oUF6MjKQRfLNegw4d9zavcy0N3DF6B/a4P99UCF95sJ89DJoMD27PL3Tu2EkfhJHXovYpu3Djr8QbEIWaql0FM7gA7afMZMmX3qmpCfJxyjQsIx3dvmGPRHd6AP0hb0yXNnoasLtLFK8T3QG89+GvrZ556Ffsx1+Ps1jskZcb/MAvs3tmhj24d70EU7gQ6TKXTe5Rj7U14v61jQkeH4HBdJkphhMHpdv/8E++W1lL5dqNA306KwdRELQvH71KEvBhHHrTdmv44Cfp4NB9CeRbt3rQl0pcJYefb0LHQ7XIReLV+FvrslYsNwDH14eAA9DGgnJY9+N/TpV36XsbK0RD/we7y+k+H1zFj0R5N+WgzYn2mXz+N077fDvWv8jVdqQlezHMNOmbY+7TIW2DFjRSz6wHMZD6OEscnJ0oayuSx0XviiN2WsaYvpvFJg+xOb8+fUsD1WwvbGU97PiFiZFT5RFDYfRuwP3+eYtjuM7VHM9o2HfD43y8+tCvuvJ2yk2WCsPy5s2zKF4r22JhH7cThkP2+3h9CtAZ/LzrAfkpj9aruMwYkn7FZ8f+JynAKanRlbbF/sijl3yucJAl7/uc3b0HvdNvS564wNjVOMVfOn16jFHJ4Xdv7aa+vQw4TtHU3Zf/PLnEMzec4NB1duQV/6IvOk0YD9UxA5ipejnxljTFHY7kFvBG2JayQpn2H+JONnMcsxP1XjM4lp3dzcYK68NeB88sSFR6Df83XMU9YuMs+YjmgDbp3zp7fM2Fqc0uZ2D2nz3X32TxCxfe2tG9B1l/E+jTl/uTb7cyz6w75CmxyK/m7vd6GjmJ9bDp+3OJGx8/hIk8REk3v2NRFzoSvmpbF41oHHvltcY25xY0T/m7N5/UnEsZn69L9UzBNuSlv2HN7fTpmDhsNDtnfK+JMPxboqpK9FhnoyoS2PfLZ/bpY6W6Btyhx754i2FYxp66nIMw6TOehrVfZ3urIG/Z5f/CJ04Rz7d3KN9zPGmMEm8y0TcMzyEft4Jk9/bO/e5T0s5q8ji31UL4jfG475RNhgnOeYTw2v3zd0YMtjH1ri/kGWMb0j8teJWDs7NtuzVOfnX/0g4+9kn7nXXkz/D0S+f/7cOehRqwvt5pnP2x7HJ4yYe3k5xh8rpg0fF45lmUrm3tiXGqy5BCP66kjkOq3DPrQr4rJriVyI0iQ+bb86R1+KfLG+zdBOJwl9u5SnHXhl2lni004LeY7TUpF2k3f4PLn8Gejdm+yP3ibXbTf+/TXoD/yVJ6C7Q8a2pTLb92COz7MkcseM8KMwEROnyMmNx/sl1fvtcC6hb3nsYlMrL0E7ok9zWfbhXm8TulQQa2kOqclW+YyBYR8nhs/k1vj9OKGeWaPv3ehyPto8pO4mXd5/kfH+yXOnoB8/SR2LmktrkzWcbJax2xF1v6hPnzva5Dpr7STHrCA6sDLD2FRtizpphj5+XOTzrnnkoXtty9nsh4KomYRiXZAkfC5X+EJvU8yBh7SjUsJ+Xq0wFpxscs5YfXAF2nE5DuGQ66SPf+YZ6IHI62Jx/0Jd1J9F7Iostid2Oe62YawYDJnX7Ip6RzKhnRYi+o2dY/93powLqSMWojEDhStqi6743BH1BmOMKbmihiIWRv0Rr+ll2CdBzNiSivlCmJDZ6TPP2Z+IPs+K4Cfqu5bNPkhTtn88ZJ8f9diAQkPUgMRa+IFHaXNHLdpwpcRYYsTzexmuxWtiz8GI+nlJ1A76HVlnpN45ELG5wVgZhnzeGzvr5u1ClFqmHd4bv0pIW4vEmvSo34WemTsN/W1/hnsB04Tx5AufZ05fyNL+y9ka9N1dsS6oCNtqcSxqixz7vUPW72YrzLFl/a9QZXsdj2M3ilmz+JXP0zeOjkRdK8t4ttMSdRdRrzy4K/YmQuGL0y3ocfEB6NjsQIcu75+E7I9Cg+0xxphhTuwT9MUXPFEztfl9a582NDW8x3yDe2z7Pcboa9uMydUmrzce8H6TMdszFblHLPLFZZHvOhGv1ygzxst4NBgxf5wmjC/VItsbxLThQcBcKz/kGMcT1uy/8OnPQrfHfD5fzEmnHf5+3BE+E96/x3kcRFFk2m/YD49EOWpvm4ZX6on/1k9D1PZznAfGI/Z7pknf37pDXzhxlvW/rJjXSsJXVkRN98XLu9A1fmxaB4xVX1zvQu+JvZpPjFg//Mi3sOYcR/SbTI2xxOrQL8Id1ojTfdaIy9vMJU1pFTIS82z9eeZ6DsutJtdhe5aKzKXyMde5xhjjldjne0P2qRPQJuqz7ORJjjay/Wnmvy99nnX+ksM2Pdjn9T69uQ69e5ljMhyyjxtiXfdNH7wIvbDG2JEviVzjUOwfBbSJSNR8HMMxseu8v5PS5mcq7J8nH+AYnzrP+4cpvz8J2f9F3s6EEWNfKpwgNxG55DGRJrEJ/HtjsdcV65ACx6Vsc9yMw37Nijxh3GM/5TKMVTd3xbrkSNQ2jexY9mPBo91Gtjjz4fJ5ru4x7zoQdjvIsj5wOOD1hkOO26jXhY7FXkPoc44SKYOxJ2zfNMvnCSxez3Zo91Nx9iBO2F5PrHmKVcbupRXWZ4wxZn+P88VwyD4btTegnQPGy5xYqzbPct0ybjN3KxVFvM6yz0ywDTnpMbc7CFkTCUasseSZ+hnHYZ/7Yl0z7XO/rTrHWDwzzzFaW2ENJSPqhJbF62ULouYv1v7OlD6TF/1TKHLMsy5tMlfm9fpHrLll3kb/qcBsLmtOXrhXXy+59O/SPPu2xBTWGHEOwxU1gkDMC47Y+5D7Xa7onEjUUebqHPuDLm1n0BLxp8exapSY3F18mDXkaoP+nRNnm3YCjnXWFfttwt+jKb+/K85x7Fy+Dt3r0PbCNnP0rKjTrFU4PsunzkPnRW4ai/g2TZk7GWNMd8A5Ic7yGi2L97xyIHOFLnTfp/8eHtKfujeZyzx6SBs6fZLxrVCkTXTFHCG2x4zYkjSFHI14Ks6YJKLmbgecNCyxR2rnRK4hfMT1+A+pTR9ILPF5ys9tl/0X+rSpRJx1imPhgyKc50SOcFy4jm2atXvxwR/Q9mOb/d6c57i/9zHmjO//4KPQ9UINOgg4znu7jA1fuMl5d/06592BOC/7zGfpmw98+GnopZkH2R6H66Z6jXZWOUU7j0Nx5i3Hee3qC2yPV2KsOirw+j/zC/Sz/c+9DP29f+5boP9v//1JaOM8DPkD/wvzkFc3fgu6Ls74ffgZfv8/fv7vGMl2JPbYsuyTvkjgJiHH9D/8LNsQbH819Hd/5/dBf+RPMji8FtyEHqYc42ZNrL0r4hz5DG1wZ1bE2122d7vL57l7m+vCtTPcz2osc79vvsLP93qMhaGIRfNna9BLdc7f5QbXwraog45TcW7REuMhzpdkxPm7yowoRhwTSRybyRv6KiPOWCQjMQ+L+uzuTQbV559/DfrmDZEzi9rdkdivutFlrAn6tMvZItd9Dw05Jy9X2M++mJP7Pq+/12Osm3+Ce+GnL9DOlsQ5PyNqUo88vAa9cIv1kv/j0megY9EficgLgyFjmzXiHFdvcFFxosjrff27eGakHHANcPWSeFfCGPP8S5wPDseMv+Uy4/dkyjEp5pmLRhHj8Xu/7v3Q/98f/ynoF8U5uI1P/zD0X/mpL0D//N/7Eehul2vJpk/nv73P2FDIsr2VulhnzYm9aXHOfOqzT11xFswVfX76kQvQY5GHZMS5qUDUnOaWaYNHHa4zT4r3RTqi9nGQitrJMZLEsQnecH6nI2qu4zGf7dQF7uXZFnO8rFiHPfd5rivSPMdye4f1yNARZ8DE2BYz4szXlPFErvusMXPUTEB/W2nQN2qzXCeOWvz9jVtckxx0aNu1EuPZ0jL7Y5oynt3YYntrFeoFcd7g8Sc4r5fyl6FtsWbwRfsTkcvKMp4xxmSLoqZapL37KWP+9hbHZPUM17LjHs8/OQ3WgR4TdSF5+igR582e//RL0LdfFTYg8tf1dfG+lDgbv9RjLlWtss+r4izRvMsx8hzOwfU5jnG9xtxrb1+8I+Hxels79MHZCvXeznPQJ08xno8f5caDl7I/Pv8Zvq92XCRpaoI37K/3jmiMtjiTWxTr58gwTjcL7OfRhPNKxtBuB6KGOxa1/K54x2PtLPux3aOl1qqM+60e7a6Qcl4LRH1iaZ7zUOzz83yOuUlJrOc94beeOOc3FWfmumP29+E27XJQYG6T2oxtfsrnl++oGPEORWKL+kZ4/1mfkVg3uCX6Yluci4vb/H5znnu5VoFjKI4zmVqebWw2OUZjUeNIxTt/ky3azFQctTzc53xTF+uaIKINh+J8VCxqLv2xyH1iPtBkLPYDi/x87gTn77viHbxA7Ed1e1xHuS7n71QUmabiPaJInMsM5fmwY8JzbbPQvBf3pxP2m2VoV7Gw1akj+mnEPCUVta98lnZWTsS5PHHuziswx59bYUz/8AprTt0O/WRng/3cvS3eBe0w75JnDWoLtJPOXXGmuS3OEaWMdcvivUavxvYe9Jl39SP6xfVt5o3XDplDhOKMt2VlhWb/pqJWmq/w3ShjjLnyPzH3+19/6u/y83/4s9B3z3JM1sQ56z2Rez4YsE8+eYNj8O4HWEP57Jh51GH7eei+qBenU9rsf/f3/nvoz90Ra9u534S+9iuQ5ttv8vq/KnL59/9x5qLvej/fP/ulz/0i9FT8LQhbvP9WpUmYYV/soWY4v8uzo7E4myff9zo4EO/PHCP5QsY89sja6/pAbBYcXObepjXD+BOK/ZyMmAdkTXcocgFHnAGri72AbF4sDBL6ly/2ghtLzHkHPQaUrKi/+cIXgjaft3RuGVr8WQ7TEnu/c3PMveTrm/MFPu++xXl3Qbzj4Xo8w+aJXC1vi/phh/1hxVzT7Ozw93Oiv4wxJpcTZ/ZL1P4+a3mFLMeoPxLvT0bibEqG94xFzC7Mib+FMst8Oe0wPj64xvhwSxxlma3y/oUpx+DmK6xV3hDvtyaiLtM8yVpavdSFLoo92WnAOWtuiWO6sS7Ofi7z87KI53PLtJGyxf5oHYgOED6Zij3o34230ba8oiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKovzRQf/Aj6IoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqK8Begf+FEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVGUtwD3D/VuaWLMdPS6dOwIHy+v1KFd8feHWgcd6M1LO9CW+P5WJ4Ae9D3op8snoS+cfBC6XuH3wwnbm7UXoScxpGlUStDTiL8vzC1Duza/n8nxgtHhFDqOB9B+RD3xE34+8KE322PoZ2+tQx/l+Hy3MivQdzd5v8LLu9ALown0Ytg19+FmIK+1+Rt/OIQuzi1BW3Xq0B9Bj3e2oL2ZMrRjsQ8yIdvTtIrQqedAT8bsg91Oi993C9BzzVnomfnT0EljBvr6wSb00Rc+C726dx76gXfThucXmtCOBWkCw+fv9Ki7Qhdy7L+JsOlGPg+d5t8ef0MstYyJvXttudqnL8RLFX6/xuc0dY5bM0876dE1TSZi7Gj3+tCDmAPhZXiBJKXvF8U4zqaH0HNzbL+Tp93NJAe8f60GnR+n1FnGosjLQsei/WFCPYrE1DLl9Q/b9Ov3lRjbXwt4/3TUg55p8Pl8i+MxE9egXf7cGGPMC5/jPScev3Ri7Rx0kK1Cex5tO1eg7fsJ+8zNMpb4Q/aJl+HvwyiEzpdz0JaYjwoJ2285bG/e4f3SmPODHXC+dCu0qWjK+9VLbG/W4ueuzfZOevz85u5dtlfEJs+hjyYBY21pkflC65CxqlKaM28H4jQ1g/jeWOZdziGBGJeJER2Ro61PE45TdY6fZzK83shn7EkTjvvUYezpZ3n/8iJjmTOlNhHH2Q0Yu0ZHHBc/4Rx9Zes69GB/A3p5h+P+jqdPQDcX5qFX44u8Xsz+ScMj6CSmn+5co11efu4V6L2jNnTWZqybyVIPU8Y6Y4xJi5xfFs4wNjx0hrmW7XPMiyX+3grpK16e89XimpiHi2zT/BrH8L3f+k3QhSJ9zYjYdLTPvMeyaVNWlrGiUaIPGMP796b8vmVoc0GXv09E3jEd0wanMa/fFrGkIpqT2vQRy2H/OTZ9bJqwPzIB89jjJJvLmDPn1l7XgVgXGGG/4wFt7fa1deizjz4AXRtw7hXhxRRFLhKlvF82Q/9LRS6VTfh9P+BYuKmwlSnbX/DYvpPLnBdKJTZ4MOJY3lzvskGG8SMWa4hyje1tCNvd6NE29u5y3v6Bv/ajbE+Lz+NkaMtFQ1t8x+OMj9Vgz0iKOT5zmHJunp+lQ6QFPlNvwj5yxFo3n+OYjn0Oaku4R2ozVzFNXs+fsA/iEXO3rCfyVZvtiys16IKYg+XCqLnCdZgtagG/vc45YCDWRStF3m/2kYehz585C721zbXzSOTjkyltZDhhf7piXRrEHM/jwrKMsTNvaIuYNxpVjlulwlgxGTF36WXp+8UMr1coMxewMhw3z2HuMRW5kUnY7yWxLmvO0q6DKeeZTI7zuCtKbPaYOjbCrlPaYTTi/Zdz9JPNj70K/WqD/RM/yHnbXmb/LsyzxmNl2V+2SBsisUZIjrq83oifl7LsL2OMCUfMPaIJg4FX4jP2BhwjX+QGVy/TNw4nHMOFBq+Xq7FPYpF7WAU+Q9ZwjMu8vCk79L3wInOvBYfPN4yYq2WFqzpi3ZgXsaU3pk3cvcXY4brsn0KGsWxJ1JgmCRsQ06XMNKQRZJrMTUtj2nzHenvEnmzGNefeEMerGc55Tpbj5oi8pFBmv+czfM7wYeYR9pQd5444LvNN9nvOoV1bYp0w3LgG/exv3Ia+s0W7n4r222IdlykzD4rLtJPYsH/cqqhfpOwvb8BYYgViXWnTbxyL/ROJHDl2+PtU5BC2ETm/2D9IRCy1xbr6P92TbQgtfscSeYE/YZtyRdZErCJji3yGvqhbXd7Yh340ewa6keczl3N8xoGob4867MNOj7lpv0+bWn14AXqxxvutLbPOGAeiridi61iMucxTpiljRxDyedZ3uIezu8m8rtuijwwHzLsKVdrgbF2sU4+R1DImytx7fmmNVVEnKIrPH3v0SegXXvoitOvy9y8/zzrK+Yu05UOR63SPuAa2rnGvwRVz/9EePw+OOFaD8jZ0XrRvbY4XTEKOXVvkSjdu0NatWORWwtcyKXu4KzbkhjFte36e8XjQYX8kYs2TO1mDrs5xHWyJeue5M2JNY4ypiFyhINKjSYdzznqJ/hvsMT/Li1wq7LDPMqKUuHm4Dj3+LJ95sc78+eQJzvWtEXMXsY1gmqLPqkWOeV7E28mY8eDQZ/9k3Ab0MGaMHyWMd6+JWmOyyg64fpX7IC/sMp7IzKXq0UamrlgX2vSpwz2277hwPdc05+/Zu2Poa92AvuwY5gZZl3a2tMCcthHz+2FCX5uIvL8n5ilf1GgOXPrOfI7XX3r0FPTMhHZeE3sLri9q1K9dhkwmHLcgoE7yvN6pd7J92SPmEY88Sjt74ZOfhP7aRz4A/dd+/Jeg/19/67+Bnn8X718bc7y22rTbDbHPvX1wf8353FnmLs0K46HfZh+4dT5TZYm+uW2JGsiQ88GFEmPBB06ImvQiffvcAm1op0199hRj4we+6nHo0YTz33QozgKIXKbTEudFduX+maix1GiT/RUx//X5+1yNz1tssP8zYl9nMGD/DruMzZ0B2zMOhM07rKUcF57nmZW5e2ujjf2b+Hx4xHpkLPbSr+8yNjQKona/xxh+lHAcxda7qc+KveQ+7WLSFvXcKXVW7EMuib2ZQ4ftP+gyx+/5tIu+qL8XxRpiIuw0EvXwsSVy7og6GDPWTsUZj7HPWFJZWIPOFLkurM/TbrMFdnC/xec9OOL1jTGm3Ra5o9jDnJ/jPRcXxVpTrO0Wc4xve2JPzx0yPvu7XDuPxNqxvX0VOnuW67JMnoOQL7FPw0Tsfw3YvnxFFFV8tjfvirNlqRjDUZftzTJWTxKOQWQxds4si7Nv7xbnkBo16NYe73fUYiw62OK6bW727bHXbowxlpc1uaW113UjJ87VFelfWV+coxN7f6Hoy0CseWfKXJckwl+NOMsTiLMyViJs3WXuFIm6i+3yfgWPvjNbZ45arTI+OWKvYCraYxzaSiYjzryJdV0hpN4+Yrw3fdq+NRX1XHGOZWOH8+q5Q9YsUotr/MDh9Q/E3owxxoQdjkFV1IBdsV/d8XnNkU9/dMV5sIGIwRPD7++K2ldZbHJWp/z9lsjnbIc2YRU4pqU658gwZHxKQ7EnakubE/EnL87uiDMdRVG7kLWCmTrnjNGA677+mHOq4/FzV6wnnFmele3HtJFykf1xXNSqBfPtX//Y6/qwy7l9VhwrPHNa5Iguc/Ldq4zrr+yIsymiBjwxtJPMg9x3XFvkvNY6Yo26t864fuczvwW9n+M6qnb3I9CdLcba7/8rPJ96bpY5r9x3/bUTHPdxlXZ2tkG902aHbj/IdeLy47RDcYTa/PBv00+3bjM3jRJRo85zrnjy23i/91buX////d+iL6++h2eWO5/kM+y9yti0tcfzRz/3m78I/dom5973vItnipcfpg1e26fv3bzN3OXlCfNrLxbOPaWerDOe2x6fp+rRphtztPl8ln02iWjT6y2278IMf3/6LM/x+yPGf8divt/d5/Pd3KJNDe6+Bp0p0abLYl/Cmrw9aj7hdGr2N+/l2eMR54Ddkdj7DkTtMKFdbG+znxpNnoc9t8JzJg+LWOOIdcpzz7BfY3FmbW+fsc4Sey9hLM6gBMxTLLGOvHSHdhSLPGhG7E384kvcS78wYWz88w/xzEZJ7I8NJ4xtQcw5NhLr9cji/efEOuy9J9je1RnWS249z7hytXv/vmuny1zRK4v9/hx9KWvTRu68/Aw/r/MZ/++/8cvQj3/w3dA/+KN/F/qxb/gfoQs316FPiZp8s0TdEWvzpTecazPGmPkLXJuvzvP5d5YZm8pljsGgywnizDLnj4NdxubHn7gAvS/OQY5EnXOvJ3yqLs5N8fGMK3aNrCF9YjilDx0ncRiZ7htqM5NsTXyBfREZzluJK1+iYA5aEuu2ox2uM8Yh+6o2w3kvtXl/f8h3KFKRi6Sib4finIol9rc3Nvj5SJxTuXG3K35P2y7n+PtZ8e5QJmT7to9E7nKXz3M3YI48HtL23/Uw40lGxOtCnnpW1oEs9s+JCvMOY4zJ1kStLOA1ntvsQhfnmSuYBvN+r8JnnNo16Ekk3u8S6xorFHUYETL9HvtsY5NfyBRpszduMje7s8XcY3GJ8efiBcbfqXiPplChTXTH/Hw65phc32J7F5piv/6Ic3ynxe8bI9oj9lWazRp0ucL+Xd1dNW8H0iQ1kzeciQzGzH2kr+aKogYSy3MN8r049mNN7HMOhFlV8/I9Qca2ilg/j8W7PyYV589FvVSUtIznir3wAtvryr2JsninRJxRtsS+dGjE/liGvm8V2H9+IHN6fh6Kczi2OGiYF+csGxe5Lz3bpB/uXue62Bhj/Laos4v8L4po6/2DLttQoe/G4hxcUdTJej5jRWePNedOl2NSqIp3DkXh0BJ1xfGYNpXNivdsJrTZ3oBr1VCcC4z6jAUZT7RH1LDm6jXonDhc4BWoD8X5knabWr6XNHXYPkds5ExE/w79P9zX13830iQ2wRv6cizqnaM2+zkU5wR9kfeIbUATi3nYE48ta2/tPfazJ96/OHlqHTqf55x7cu4R6Afew/W18y6u867cZh52/Trn6IzY23/qYa453DFrTu+o8POXrokzzltclx3170CvLrEmlXP/N+gnHvgW6M9fWYeORD3btsWZMke86B9/lZFceN97oP/nv/xD0HlRo6gc0hcORI08I97xy09Yp9rb49p58Qlx7uV5xqJ+n2vxQDzjWuND0LffWYPu/bl3QL/v134b+jc/9p3Q3/NJPk/WopGvDWlDi9/PvFHWiPpDsT8m9hDml/j+WiLeO/DFpqqs82VtsQeSZyyW770fJ5ViwXzN++6NR7fM/ZLfHnKNvvY0a8CdKzxn7JY5954Q9bbtEeNLXszt4nUrc9ThotbLct0xvkVf8O57T5ljPyvOUeyJv+tRrHNsux3a3my5xs998b6AqEEMfc6rZluck6yyv7Me4/XVG+zfQk7kQl32T63CDpyKz4Md6kgewjPGnDj7ELQ/ZH7UnohDEuKs+nd8mDHyf//Ez0M/usAxvN1lLap6huuKUo35YijSNWvEuklZ5CJWKN4fE/n4rXXWVcoN5hI18fcR1s5zXVnJco6yxd9iORC5ZCRMoiTOgCRiT7cgz5332d75EufgcMD4slRje8KTa9CXxJbrf+Ht8dc3FEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFOWPGPoHfhRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURTlLUD/wI+iKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqivAW4f5g3sx3b5BvZe/9gDfC5MyxCuxULenaBen55Dnp54TSv542he4MsdI23M57Fv3cUTfl5fxBBj8b8vu/zBwuVCnQSD6HDyOP1Il7f8hPowzaHKxSfj9MJ9IFpQ+czeehOyPudeexp6Nkwx/u7deg7r2xB97td6Ox4F7rxO/w5qa973+PQGzduQPuTdehcWIKeO7/K779yEzoKaTNFvwOdhrQRL02hJwdlaMdin2eKHEMvF0N3+hzzwZC6FRWgS4vz0JVeAD0ND6Fv7tyC9p/l85184kHomdkmdCFPnwgnfL7djXVoO85ApyU+v3Foo7V8w7wdsF3H5Bv3HH6rxXFamaGvjrN8rmxePKfH3+ftEHpyxHF2HX4/DvrQ5Qp90/V4vdTi9xsF2sWVjRZ0f7QDnQwZa4NU+FGB9x8Z8XlWTBWOA9ms8vtWzP483KZdWTX259VN2q1ZpF/nPBE88lXIUpn9USguUGdo98YYs3XEZxjER9AZ7yL0NLcHHYn5qdigL4+nnA/SLH1tGPrQOfH5dNiDLlT5zGHMeJ9JhQ2OGdtKJdpg6PP+ScLnqRR5v37I33sWv++kHIPBmO0Lp5yPBvzYFDO0McflmLe69Cm7z/lr+5DPe2t3ZN4OBEFgrt26/bpeWpjB590x7aQ7pa8OIva7ZThuxRx9L+Py+40F+mIc8feNPPu90WCML9r8PDEc97k54WsxP++/sg99sMNYNhF2eDSg3V+7wu+nSRf6Qx96F3Smwjm0aRjbXn1+E3rvGv2639mAnvq062KR1zt9cgl67emz0AcjxhVjjCk3a9RZ9tnK/Ap02uM14r7osx36VuuQsaBQY2589sl3QnsFtidbYnI86TM+b11m3vHSi9TL505Bu1nmkuOp8E2bfVpIacOlOdrw6vL7oMMBfeaVly/x8pbInXNsT3WGNtP2aXMzDr/vFTh/pcLnjBGLh2PEc1yzVL83/2VrjBe9Ee271WFf2lnOk5/9tU9Cr544AZ0VOWU2ZRxPpsyxRTgzu2Pa8sk656Edi7YS7NM3xiOOxX6HY9E6YC5Sm6FtSF+cTDjWwzYbnBF/JzeNRHwsic9Ff/oJ57E7dxiPRm2OR5qw/wa8nXkp5RrhB//MY0ZSrvKet+8y5paWOIaHXfZhkPCZvIB9Uq/TX0Yin66uMB/rizFxcjXo+IjtqyxwLVoUuVAxy/ZVXJG/hrT5epPPWyzw+7aYg6/vMj60tpmbzD68Bl09xXz2xk3GSyNqAyaljVgetVehT/hiPRGJ3Oy4KBZz5p1P31uDOg7nleVFzp2WzdxjGnIcYpFb1CvCtwznlenwgA2a4f2jQNQDXNrtsM1Y5I/4+9jmOET7vF7G0O5tkROnEcetWKNfhu55aK/KeXAy4vUmd9jemSafp5ShnccT2t22SFUCptRm61l+oXma45UTfpbMiFhvjOl2eVFb5AauQ9++uc0+Gol4OTykb5QeXIZ2Nvj75ze60MtN3m8qco+aw2cIJxxji6HJPHGB8+G7Ti9Cd8e8/7VL69DXb96GDiaMVUdHXOtf2+R8MVNlPr42y/s3bNrA/h59ZHRImxgn/H4/oA9ms/SJ5A+1qvy7E4eRabfu9ZVT4DiWS7QbW6yfiyXWrqpineUWuN63Y7Hez4uYbbOf09E69HiT645X/iPXKb95lX4zFuNgcuz4XIPjlhQ5roHL57XEnOekYpxFzSfrMra5AfvXE3lKJArqUUw/y9iM5dOEsSVx2d+ZivhctDe07l//O56o3zrUgejT0PAekaiJOwVRH84yNqWG8Xns8xkiUSfL1vj5bIXtcw2DTXdCm9jsMD7fvcpccHe7C721xNhx4gJjxTTiGLZFffjwgLnpdMrn96ccU2lD4ZTXiyf8fih8KheJdVuW/ZUTddTjxLEcU3lDbJz0RL3OYlz3RF1mdY05slzTj/sc61qJ18vbtE0rw7E8scx5oihyr0yN7R1NRTyrcqzcIdvnlRkfnJTxaDQWNeGA36/HfJ6DWFxf1MhPLzB+dbZpO1kRr9vrzAumCWsAVovxo+ZxXi+IXKkUdnn/zzGeG2PMzf0r0PNl2vPpVe7XVAvso60+a2kzczXoSSzzVfah5fP3Y7GP8dAaa1fv/HbusQ7lHuiIfXj7s3zm2gnGt5Foj+9wjDr7om5SZP+IZabpjRj/yoY23Qo4Z++LPc9S8wK0l2F7m56Y40r0SV/kfr3O26PuYzueydXvxfJ6ZRafz3fZkf0WdTQV+1ce1zkzZZFb5Bgb+mK93BZ7EQPRcXKv3qkwdt1pcdwKs/y8NsuaesMS45ahX0zbvN9E2OVYzNuNM3y+6kVqI2LVz/wsc/hf/Anua4/HjMUzP/tT0P+/1jr0N38V1yjNZT6/L+od7SP6pTHGXOmKeObTtzrrXFecWWAflmo16FOnOMb/PuD1fbFftPaZz0Kf+x+/G/o3rjA//t7v4XmEjlg3jcesoexuMZfxh+wTJyfWzqJw1hYlmEKd84kr9r7bPcbmnQ1hw2K/sNjgmOXzvF7/UOzt3+D5iqM2x9Qriz3QWdrIcWHbjsm+IS9eOvkwv2DRd1oB7fDuDuf1Szvsh9innVlFzttza5ynP/xOxvjigL76+U88C72zxZz21CnaYY5TjOm0GPOndfr2gaz1uaLWWOc6czoUObHD79se57RpwOdJXMauRJx5ydV4vfLiGnSlKXLokP0dRiJvE3sjTbG/aYwxGbvLfyjxGY58Ot/D73oK+vZPsg8TsUc6PaSNzNS5f5ZbZCyYPSVyxQ3q6ip9a7jHPsjPinWbTd9cWKKRhGIv3w75fd+nzUeGNjQrzha8urUOPRH7VamoYa2eZHseOc885vCAsWc44v1HbbZPHCcxnv322Gs3xpjEpGYc37PRtjho4IS0hYw4h5GIdVISi5qBqHHGKefJ6YR9EYu6R2yYywxsUXMQa+6sGMs05fXqOc4jtkiSY7Eflrj8/uCAudGWsMXTa5xXRuIM3OT/z95/h+22n3d94G+Vp/fn7WX3crrOOeqSbbmCTTHYDJkBjGHSyKRdJExII2SSK8kESABxZSYkZkwgCZBAwGDjQrEkW7K6jo50+u57v70+va1nlfkDLu39+R7JlvCW3nOR+/vX+Z61nrV+5e6/e717TGFInNRPl2jvS57UQDrcn+GI67FzRPvVkLii06Pf7fckNnPOVYvU56Wq9NqITatI3uEVuGZhlTJwPOaardZp35bE5pbFVS81pf+rwLxrPBOZk/OdWlt8QsRYIxEfkcg5RJ4/dzk5H2+2muB9qWWUy6wB533KcCJ5VE11SM7/M+lFmg+64EtV7meYk2aiM0IYhG6x8dC2XmQ5zRWLlLNY8pSjferu3/sHN3hdzmET2af2OeZ511aomx9+7/eBL+cp191T5gCdrbt8X8Z9/kcf5/h60kuTu0tB37h6ETyTWPBHnwF1H5Oalye28AflnNpf5vg+d8T1+iN/gjX16dYr4HH/b4PXWhzfP/7f/hW+L6Af7UzfHvv8xFXuQUFs0Y98D8c87HNOP/Xn/wH47bucw8u32OvypVe/DJ75XLNajro1ltaVueSyOalRVdc452e+l704q3k+8Ld+8EPg/pSxRmdEftBljWq13AWvlzn+XCDGK6R/PbhPGbh/QFtx9+ab4PkV9ksUAvbSttvspUqkLnlWSJLYdR/pE5tEHNfpRMbpyz7nKesri8yrnn3yCvhn/yrzpp/5i38J/I9/778O/r4f4Fl/HNGvjzLu2+1d1hJH0ssfedST+jnG/DdfYw2mVGScsyj19p+4yN9fzdE23vyFj4GXh9KTXWJOMHMcXyy98RXpl31ikT0iV8pSQ/sqC8533qIv2Ymop845l4VN8Hyec+z3pT4se54W5BsW6RMcjLimSwWOaXOFv296D8B/7u9/AdyXNUqlxt/aoAytXOZ4WxX+PpdQporSu7pQZNyxJH2Glxfl+4yEsX0oddJSnu+LUtqaJKUtun2X/nXvPvdYwig3k3r3lUuc/1kidZ6bPHL4v1hknjWZyvdVkocdSE26WqM9iqmObj47Fc61jydc67HUDLRPemGRfmRF8qTuIcdTK1HfBqfkJ6eyeUETtCS9OJvn+L6L1xnLlauUxdVAzuelJr93TPv65S9TtqayHh98mro1kJp4GnP+oc8cpSHfCzjnXFvOVxLpnXzmKvubNjdosw/3OMYLz/N86miXz3vwJX7/laWsu2wuMB5dWOfzWsuMbfyi5OJSqxvPyGfSh3gkeWHSpX1pN8kzvwl+MOH8y3JmPJN42AuldjET+zPpgo/G0lskPSItOTOdSS3w3Orb+7vODI+4niCVGqV8P6S9IjOxFTt7zINOe5QzSWdd7IvtkRpOKc/3l6UfdnGRupP48m3STM4y5LvDUL7PGqQc70BygKMe5aqf0g+modouycPEMRVqrDeUW+L329IDt8fnzaUXIGzRFr9r7Rr4RpO25wtbcnjjnDuRXpmrK6yBFqTG+vpbzEPaUvMZSm9Lv0//czqkTI1SrvHolDLmtxk/5qW3NW5wT5rLtF1+pcnfB9Rlfya2RxrzOkPq8rp8FzDnlrhyiXs87EudM6HMBfI9W5Bn7BJJLCbb4Y473L/plOv/4B7zsrNCHMfu5BFfG405r6H0u0Yyj4yq4ua+nP848qksm7RUuZnEqMmwC94Tn1qIaVt6C6wnfPBDHwY/f+0j4P4V5sNbO9KkId9HXHuKPvdynbrdFMH7Ox/7R3z+qdiqjHJ96TxrMP/mv/NnwX/q3/tJ8NKL/zH451/9JfBRxrw39hijJNHb5fB/+tT/Ct4dvsp3auz2gHlRRb59rOs3JhK7laV38sJzHPPxzzJXj2OOOZRvXZ95+mfA/61P/Sj477jwHPh/8ft+F/gbUuMJRKYDqfv9Nx9jzeq9o78MXmmzRibu0Z2ecD5HHdbIOmNe9/K0ZS7kA0cZdcgv0t96+befMZwVZtPI3XnjofwUrzMmXZG8YLVI/QilvjeRvcnm0tebSA+m9JBl8h1vs8q1rC6wBnG8y5pxIL+fxfz9qZxFxhNJDIuMNZpF5kGHctZZatCvZnLWUJazoGJMWSwt0B4lMWUr8yl7FelX2NvbAS+s8tvJodS9whrztFIoH2k457YjOpWgy96aGyPmSb1d1vHroy+BL0h8udhi/LV3QJkJ5Bu3QUfiO49jHkpstCx9z1sHjJWSiPooaZq7/G72nDTaHP9Yvpvx5Rxm+ybt8TjHFzw45PgXW/INxJTzn2WUicJcvgFMqKPTKZ+vfdRry28/Z/h6+Dp/csVgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGw28W3/Qf+PE8L/A878ue5/39f8oveZ73Oc/zbnme9797npf/jZ5hMBgM3yrM9hgMhrOA2R6DwXBWMPtjMBjOAmZ7DAbDWcBsj8FgOCuY/TEYDGcBsz0Gg+EsYLbHYDCcFcz+GAyGs4DZHoPBcBYw22MwGM4KZn8MBsNZwGyPwWA4C5jtMRgMZwWzPwaD4duFb/oP/Djn/qhz7o1H+J92zv35LMuuOuc6zrl/+XEOzGAwGP4pzPYYDIazgNkeg8FwVjD7YzAYzgJmewwGw1nAbI/BYDgrmP0xGAxnAbM9BoPhLGC2x2AwnBXM/hgMhrOA2R6DwXAWMNtjMBjOCmZ/DAbDWcBsj8FgOAuY7TEYDGcFsz8Gg+HbgvCbucnzvE3n3O9wzv1Xzrk/5nme55z7AefcH/int/xV59x/5pz7i7/ucwLPBbXC13gWTHH9ZHICXss4vHxYAt9YaYKvri6Bl/Ie+IS3u73TA/Dj3gg8ijPwQY+/v3P7GLxRKHK8+Tmff9AH3909Bd+fcj3CpABeXMqBF3zOr1qo832TLng54P1hcRW8WeL4N5Yr4MtJAO5db4L3l7k/pTz/flSrzvE559xgJQEfzW6CzyY74EN3mWOul/nOGmUm6vJ97Rz3tJWLwecR5zid8v7RkHuWn/AP7C1UucbTmM/3HOe7t30bfK1KmXn3i+t8X7oAvr1Doex2KJNf+pUvgD/x7uvgrsbxryxTh7KQ85mNZ+AHXb6/fyLrU/7N/QHCx2V7nB84r9r+Gs1NOI+gTNkdp9y3ZD4G781T8FqNupmTdQvz1KVCOOT7c9T1WqUGvlijbdjlz93hgLYzGw/4/ojzXS1PwP/FZ9bA/4tPd8ETx9+XyovgzZTrMw+4HkNfrkcd8Li+Ar68JHpdpu2IC9TToMD1dk2u54c+8F6nSO6TP3jANTwdfpljWtgET2UPK0XKuudR1z3xR+OQMlT0yZMy18BlnHMaUkZ8n7YjyFXBo4i6WazQVi7UmuC1UgN8PqYt7PW5p56jzpyO6U9dxvuHEWUk7XJ8kwnfN55E/P2Y6zuPVQepA98qHpftSdPURdOHa1EsUdb9mPs2Sanrkynn7XxeH81oW7IZ759MKHeZ+PF8SDmZn0jcdMp1HI64j4tNxjUXWpTLFZHz5SrfX6pRznsRbdPRjHp2cO+r4MeLnH++SR957y7l6s7dN8ED8cmFAvn6Bvfryecvgl944TlwEUM3O9xyijCgfRp398H3t/ib5Ii2yU8ZWw0HlInBlDIVHtN+tjPGfjWJ9SYx33f7JdrCO7cegKc+Y8csxzU8PaKxPT5h3LC2cgk88SiDqzWOz8keZQWuZ1CiLc6mfJ4Xcv0GE67fcMb1K+a4qa0mbWNJbGl+JsnCPwMel/3xfd9VSg91bG2dvntN7j/pU5+3tumrWwtN8PMblKX9bcpyGMvaSx7SP6S9Wn2RMf6NW4fglUXu7eqFp8B3blF3xlXGdg9uvQ7+8de4V+dEF5qL1J0woezVl2k/D9+6xfszPr9U4/w/+LTkRSHt4c0J/Wg64/uLOcryv/oDjFOevSy645y7u0vejyUv6Ul87EsuWqQ+LKeUmY7TeJdjPN1h7l0oUp+CrAueL1MfS3QhbrXO8eXFh+ZTythgRp82GJAnCd+XSuzlSW2gUGmBn865h0sHfP72IXXqdJ8yUmy0wQsFrrfq0DzleIrhOyPvKpUK7l3PXXn4PzKOa2mR+eY0khg4otzMM65DXmoMvs999vuSB0keVpGaRL5CWzGTWOr0mHIwOqGtqfqU4zhPvzAdSawneWTB8flZyJh60OZ63RzwefmIeuDPGYvVIvLZXer5m3+N9Yj4gHJ2oUVbv9nifAvLnM/xG6xHOOdcuyp76qg73VOuwetvcY71Jp/X92h/f8fCRfDXJNd85Q7t6et3JU9rMP6M+pxD1uWedyW+/KEfeA/4D754DbxWaII31jj/L7xyD/zBKd/vhyIjFcZeqfgDrW2EUlf1A/0779Sx2Yjr5eX4vpnYwtT/psrK3xCPy/ZMpzN38/WH9cSjHOUoS5lXzEf0GRsXKNsXLpwH9x3XpbcrTvV0DzSUmHze4e/feMC86kFPdD/hOq+t05atvJt5z7TNfeo4qUmltBX1Ip+XdKjL/pz7mk44n2qZtiWRgrlKRSlHOR5GnF8otVpf6iGlKvU+J7bT5Sj3zjkXTSjb5RzXJB2KrPt8ZyTJXcXnnMsljllL3iXR3fmUulfwOacw5R60JZc+t0IZvbh4DvzTb9wBPzyiP7x5Sh3Y2qetiR3nkwRSgxLbkcykbip7mi9SJnN58iTm80OJe0oSypZ1i9OZ+83isZ13+c4VCg/X5/iQecw0k9jEcS1ckX5pqudDEgP6mdi3hH6sKudFfcnRq7LWe4dHHE7zaXAv6YKfSCxTSaj/6ZgWoDOkbCzkRdY92qdcnrFPTvKwLOHe33iZsczxGz8PPh7w/iSVOMBxPEHIWLAYXOT90efBu2Ouv3POOcmTbh1xT97akzpNjblweYXnQZNF8Um5JvjSecrUcFtkTs5gD7sSr8XMQ6o5rkkUcI7TuZxrFKiwgx7f148oExPJdZeX6dN2Z/SxJydiX8qMxRoLrG6UclKjr3M9L13nesdSi0wlP5iX5PwwI/9W8bhsT5Jkrt95KN/1svglyd/7Igf9QRd8a5/7tNImL+boB5cXyEsNvu+4R1077lJu3rhL2+PkvO5mj/XJkfiNJ5vUo5z4zb7ky9sH3Ldhnu/z5feXVilHFamPRBHnd9KlbY75OPe5T78G/i98iHHAZky9OJHzwtFykzzkdeecmzQlvjugbG93GX/WxfdPBtzDuEp7+JUZ12wmZ6iff+kt8D/7Fuv4TwV83p1XuCY7UiNx4h8Ojzn+TPLCYo2LPvP4Po11yhWJPRrU/f6Y8zs+oQ51R12ON6PtSjy+bz7m+I+OOd845n4VR/x9LP7/W8VjO+/KAjdLH/qNJOW4WsvL4NMu5zWUs5DOKW16SeKURekDunSZ9c+1Nm3B0gp94psvMU4YtPj77Q5j6LUiz5I73VfAp33qqhxNuI0nroCXSnJO63F+nZ7UGhefBM+JLchLrTWRs/lKlTlNVuPv4zFrs33JCVyReuBJj8faJfps55w7HVH2755SJvZ2u+D3P0n7fvc1xs6jEuOeZEJdfLLFNZ15XIPNZeZNn5swLssV6d8O9rgmE8mDkr70vWyyplOWOuWCnGdt7dDfjaQef+NV2oJKS/NOUHfuOs94vu9DF8DHMW31Z7/A87y9HdbMpGTn1tea4M9dYBz1l923jsdmf+axGz6Su4wHlI3qAu1B4NEvSBnDBZ7UFTL6jf096kdRen/mUh+To0kXjxn7FKWPsNyifmbS21Kcit+TfoJsxvllc+npOqTslwu0D6NTiQ2l1Sadcn1XlylrK03qUsFJH0eZuu7E3gUNynIsMXwW8v7lRembcc5FYiM9yRUnEq8Nh5zTdE7eKnEOer6VSt4wkHg1kni4VKPNrDRZx4nmfH5X8qiq9FDMpNYoW+aGfcpce5H2Ms3kjFN6bSTUc0XxAYMB7YfniYGKJPbKpA435fuiCXlF7Gm9yjz1W8Xjsj3z6czt3Hp4/ruyzHFuiR/Zm1F2T3coZ7sSE7oSbVdB8tEnPszY5LTD/tmdPcqhJ32GgfR0PbFKOSyU6bcutmir/oPP/WPwv/4X/xT401f/NT5vmfn5n/2bfwP8V/4he3XyddqCZ37Hh8FHb1DvtrYkh3n1V8DL0qvTS+nH/+f/9+8E92Kef41vUe5/27/0006Rv/gR8PN/kM/4LZe45helLvfH/90fAA/kH/X+xb/3RfCPfYp70K7QFswS1oByUkfPLzN2eeo97K+YO9qa56+wLrgi51HxEc/L5l3KZNah/9ko8/fNNfqTBw94rrL/4CXwaUadGOzz+a+9xfc31qhTlWYXfHmNtjkbUUdzvlrXbw2PL+9K3fSROkGuKGcLEnisrHNeRTkLuX6Jur+yugH+rh+l7v2Zv/EJ3v/7eQ568tW74Oc9xgV37zOOSqWWWCg3wStiCxdarA/v5mkLxsfMyzoz6t1pxvc92Obv7+wxLy3OGRc+de4qeFvWK4qlb3OHPXEvyNlIUfphv3yXPvV+h/efBrSNzjlXX6GtSX3KRBRxTtUqn3ntxfeB796jPU6l5j+WOKfepkxF8kFG2Ja+FelBXr7CeuzGMnNnL+Z8HrzMNd2RWHOsccU63xfKv3l+cp+/v3+XvV21msT2TYndJVafSaw/kf6U033mgdrrlSW0PUdSj/9nwePrM/Rc+REbs7rImOzkgPIrpX63t83/sXmFfqYldZxyiXPv7nEtq9Lr05XzsLfFkBUGtS0521xvi37l+PyjfdqDWo/25Xyd9imQXqWm1CQOBtL3V5TvB0ac74c/eBH85SprBmOJNUd9zvf2IWVXyrGue5s1mOMD1gwK87f3GVYq9LW7PfJr7/kA+A//2PvBW99P+3UwoI/41V9j7HP7TcYatRLX7OlnGUv0hvSJ65cvgnf7tGcXNzmeJ88xgOxO5Twsor53Iu7pW1u0v+flu5nZmJuwsspe9Ow8xzOU780K0hc5lBryLJVa44DXexPqbL3G2Goi38d9q3h835aGLl9/GM/Mh9IXJzVOT/pDk5B2diB9iJ70KWTCi3mtwUoNKMdYpyvnXXn51iaVkDKRmnAkNZeh9Ggdj6jbHakHDKdyFpLoPkofpcynKL0J4sZdVXrINvL0BUEgH7CJ353LWc79VyiHd6bMAXZffvs3Fp58Q5Auc4+W6rTX7zpH21CWbzQGJY7xTpe2YS6xlStyjUOpKWfy3Umacjy9AfOWitTdE6lhVaR/wjWY2+dFhuY9OR+r0Bb6ogNpRBk6lH6W+VxioQrXr1ISHdSameP74zllerHE9Tvs/ea+sXhscY/nXOmRXrXqInV57EsPgZyLjgPOcya1xkzkqCAxZa4s31fIOroy79fPycb7oosPZLw7/xB89zp9VlfimnuvUzczx/dX5ZvpvHw/EUiP877kbXEs80ulr+iAcvGzf4n1kGsXfg/4YpV56rb0H293uWCZ9PCFVfmm2jn3zCpz6y/eYNxTC6mLxab0acvZdDEv3yvId9erC4y9ghWu4UxqIn5AGc0XnwBf37wBvrP1/wDPjl8Gf3VMW+XL+dhqU3s9qftj+T7jtRuvgr//d/wg+LUr/ObuV3/1c+BJwPXK8vI9SJ01Lq/LvLEmZ8DzCcdfWfrN1Zude3z2p9sfu7/7Dx5+5194mXOP+zzfnoQc+0jyssICZaff4fWxnB/15dvwXFm+hZM6jyd9dIsr1JWJ5LzXnuD1O1IHWZYax84+x9eS77eK8i18rioH9HL2M3e0j0c9rq/f4vO7Y9qnouSNZekVClvyrY/kqaF86zmdUNcP7vJvZjjn3HQmZ26LlPeWnMfE0hc9F18vLsh9/rPUT2mFcYWRfMPbY972fjkv3rrX5fikhaBW4HhPp/QJT1zgD3LjbXAvz/fdkd7QhQ3uQVf607K8nPePKMNJXQ6NY8avffnmejFsgu+PuD6Rkz5DT7/3pb39RtAvO74RPuqc+/ed+1rH64JzrptlX+tm3HbObXyd3xkMBsNvBh91ZnsMBsN3Hh91ZnsMBsPZ4KPO7I/BYPjO46PObI/BYPjO46PObI/BYDgbfNSZ/TEYDN95fNSZ7TEYDN95fNSZ7TEYDGeDjzqzPwaD4TuPjzqzPQaD4TuPjzqzPQaD4WzwUWf2x2AwfOfxUWe2x2AwfOfxUWe2x2AwnA0+6sz+GAyGbxN+wz/w43ne73TOHWZZ9qV/lhd4nvdHPM/7oud5X5zov0ZhMBgM3wCP0/Yk89/cv25oMBj+z4PHaXtm8q+VGQwGw6+Hx2l/RpZ3GQyGbxKP1/ZMfuMfGAwGg3u8tmc6mf7GPzAYDIZ/isd63iX/wqbBYDB8IzzWvGug/0KmwWAwfH08TtszGPzm/mVVg8Hwfy481rrPePQb/8BgMBjc47U9Y6s5GwyGbxKPtc9warbHYDB883isNeeR9RkaDIZvDo/T9kRT6zM0GAzfHB5rn8/Evu8yGAzfPB7rt+2xfdtuMBjejvCbuOe7nHO/y/O83+6cKzrn6s65v+Cca3qeF/7Tvza26Zzb+Xo/zrLsp5xzP+Wcc8tra9ljGbXBYPg/Ax6b7SnWa2Z7DAbDN4vHZntajabZHoPB8K3gsdmfjXXLuwwGwzeNx2h7Vsz2GAyGbxaPzfYsriyZ7TEYDN8KHpv9Wd28ZPbHYDB8s3hstufcxQtmewwGwzeLx2Z7Ll++ZrbHYDB8K3h8dZ/1c2Z/DAbDN4vHZnvWlhfM9hgMhm8Wj832LCzaeZfBYPiW8Njsz6b1GRoMhm8ej832NBeXzfYYDIZvFo/vu/aVhtkeg8HwreCx2Z9SqWr2x2AwvA2/4R/4ybLsP3LO/UfOOed53vc55/69LMt+wvO8v+Wc+73Ouf/NOfeHnXN/7zd8WRC4lWbzEc6/+FwolsCTCf8y2SiXgnc6/MuJXonPq1dy4P3eDPzLr93m+PwGeKlIHk84vuNewusl8v0+/xXFvUP+i64d+ZfOxmP+BX5/zu1Jak3wNPPA+wd8/nzC9So6H3xtaRN8OOf4t248AG8t0o+sBNyftQ2Ob5Icg/tFPt8556YF/uVdP89/fSkoco5Zg3vgVfi82gVed8cx6ItPngMvTYrgux3O8TBug/dHfJ4/oUwtVMrgacjn1yoccH/A+T/Y5pptrvD9Vb8Gfm61Cj444fO6IvO339wGLyw0wcsFyvxCYwXcr8oe+gXQncMTjmf2zx57PE7b4/ycSwtLX6PFEuUsGXOd4jx1xx9wXlmZ61QpUle7jnJSKdfBWwXqYhxx34o5jmcypG5nKe+v1Lgvk4A8mlKPbk+pu3/6C/vg03QNfHvI+TQLAfhhn3KbW6YeeI7jL/jkaUTbXUr4+2TO/do6oO3v7HL8J2I7P/Kui07xIy/SFgRiC75w43Xw9gp1YeuQ7/REFyZD2vei+K98iTKQRpxjod4EH89EpkQ3w1EHfJpyzyceeSGljCUBx9/pcY2PT/rgw4DjrZSp63NHGcvl8+CziPPvyT9A0zmmjFz/7uf4/q/cAo+6HO/aea7PztdNlb4+HqftCYLA1eoP7Xa+Rpudy7gO5WYLfDw94vUy45rlBdqidMB9PhzRVngJ35f51LWQP3ezMdd1PKTcHJ5SLu6LD295lPtzNfrAa8tN8NqC+JRd6lHgOMCTB1vgnXs3wI/EB1Zy1KNzq0vgT7x4Fby4SFtYXub4D7qU097oFLzb7zrFyvIG+GhMe+pJbLX/gGtw4Tx/X1ihf6nXlsFv3qB93XrzPnizy/e1F2UPjrnHtRWuyaWrl8ErC5QpN+H4Y48yvrK6Cn6wyzilJHHUH/uT/wb4n/8v/3vwMKD9zxf4+2lKmR6Jfzk4pbEoFzifyKdOLc+5/t6YtvhbxeO0P/N54rb2HspkHDI2qNQ59mKea9doUz9KkodMY9r9Xk98eY6y1G4sguci2q+jW3QE0YmMp87ntZYZEy9/cB28tsPY7eace7kishnE3Ntyns+vSYrhzyhLS4vUzZMj2p9I1mdzjfbsX/rhd4F/rnXI5+0zr/zwlQXwH/3RD4JvbVGXnHPulfu0gYeS94SL9BGrq9yzc3UuwtEp9aswJVffv9zmHqYJx1PIU0ZrDd6f9/j8Siqxh/jAmeRt0xHtrZ/yeW7G+ScyPidpULNOn3D9CnVmKaAPvHNC+1CS6406Y4R8heOdS60gjjmgzL0z8i7P91y++DBemYwkr3LUnXqZup6ryb6XqYtOYqeprIur0rYVJAYOy1znKOL1Yo5+1A+4D9mU617Ii9xLXjj0qftxju8PpEYznZCfyF/rn20wxr21yHrBrR5jxadLXP/3St74of879dzdp1+sliVWbHF+vsRWuYDzc865dJn/L8lTd17f6fKZI+5Jvi22q0X/8T/e3OWYW/QHF89xT7fv8n3PLFB3XxHb+Kd+7Hnwlz/xFfA/+3P8Rxk2ZX5pieM/HnBPtk/oj+g9nFteob1fOc88Np1QRseZ5pl8/ygQmeVwXBTzf4RUSRc66lyQl9jvW8DjtD1Zmrn5I3WO8ZRx+vExaxZ98ct3pJb1xTfph9MZn3dwSDlZzDEPCBNJrBLq3nDK39cb3JfLbfrQH/59L4CX1y6Av3zA8X/yFcYBUZ9yUl+k7k+lnuDnaUsnkrDPpN6Qm3O8YZ6C43m8Xi5xPHHM+yslPj+QGpeXUs69AnMM55xLUz5zOKN9C9ucY27MZ04m1J15TPsaOvJikb9Xf1ep0177BY4vljU96dE21Ku0VR/5XsZ+G2uM0/7OZ18GH864honULUViXbEqaxyQ5yROi+T55Rz3OEko82le9jjjeFpt8QU5xupp9pv7V4wfr/1J3HT6UIeqcj50fMwcs9aivnf2mXN3U/r+kkf7E5/eAS9LnakmNeu1GtduOqV982Z8fz5PPpezgIKj/TzY5vwmPdGFjLpSmPI86sp1rsfrD+hHZ3OeT/3aX/sU+OmDz4NHMe2hnp95ReqiiKoLU86vLLGek+c3JWZ3zrnzEj/ekJpu4JjbDWd3wf2xxMcd9e3c8ytPXAevLdNe7NxiDfXGnX8M/jd/6iXwp59+guObsI6Uq9Lm9jJGLz2PMnO3w/mmnpzD+JzfTGKVqdiHwZD3hyl9TM6XPE5qqWmOvx9RxZwfcHxZmTIpLvJbwuO0PUk0d52dg6/xqgzMk5pvrSl5kCd5xyljwGTIfUz36JdWL1AO2puUu/UFPWcUuRfHUxl0we8cM7YqFOlX9+U8So6jXF/yrPyAenkseVeacP7XN7nvi2vMMf7g7+H6fO7jEmvOKHe/+w/Rb//trzKnuPcLjMVmHvlWixP8/j/6f3GK433a+3NPMd4b3HoLvJHJnnRp/5MVziEpc9Niicf6Eiv86n2en+XlrH56l/bUk5quX+D7O0PGHjM5j/Njji+haXBFiT+LY+7hXPKckeRt84jj86Ru+LaajdjCWGxZsUId8XzGcvUGr9da0gzxLeBx2p44ce6g/3Bthj3KXTSlLniSH09PeH3xkZ4h55xrtzlvPS+bzClnOweM8d94wDzo9n4X/GRAW1KsynmanCf1h1IjqcnZdcp9XT/HfD0ROSlONEamrcufOw9eKEueVBDBlrOkKCI/Pj4ALxSbcr/0Ecm59cIm6ynu66T/N3cZJ+wccA0Hx7Rnvfyb4FlAGRl0uablImsiYY3nU1lE3dt6QNsykj39yie+yuunDASmEnvnPOYxe9KLtVGXWFHimkpI3T5/judhu3deAw+kv6EmtnC9SZnIJpz/pz7P9X3zqxL7Z5zPwgbjh1qRMlopSNHoW8RjzbuSxMWnD+ORfEZ98KX+lZecM5SYPJ/jWuQkDzsQ+/JD72HM/VnpuWq2qO+jsfQtlumX6zXKTiTnbQPp5XEl7n1Q5PzzOfIVyQn60rOVxJS1TN5f8TifpTrPv+pl6RU6Yj9D4lF2I5+6OJYmvzSjrFXaUrMe09/8k0FzjU6kdjX3pZdyqL6f1335R7ujlHM43qZ9OYgYSxVr3IMLF2ivqgU5ZBSZG3T5vETOf0Yj8lDOITKpu0xkzSLpx5pIIhTNeV3fR+vmXDxlfqB1n1h8ZBRJ1Vv2L425f9439W+Vfn083tgncSe9h3szkRhyv8d5HqgfW74I3pTzqoUL1NXKXHqsRpSLG/+Y+f3pq9Sdco7Oerr9WfA/8LuZl1x4jrreLMn5WpFy8eoOY/zf+ZN/HHwmfRjPXmUssZHK2YjGbr94D/z0LcYR3/9DHP+VH3gK/O++wedN9hh7Xn76I+DD1xmL/Y2/9I/AA9Vb51xe4q2tP/0Z8P8x/WXwmtStq23q3nvezzF9/4/yPOq3/95/n88rUxsLUtgq5jhmOe5xsdie3oDjOekwLxx3GEsc3mENq1igrk6PaCvdImVyMKUtuPUK+/b7Y+kfmTKerbb5vLXLjK2uPMN+r2lvD3ylTZk/7FGmZ2L7vxU8TtuTC0O3tPCwhrmxSb9Ylh7kK1e4Dif7nFduQlvy5qdYT/3AB+gz/tPfwh6wdY+27ZOn1M0TqS939yg3OYlBW1Xu44L0jEXS11iXnrFpTFsT6tl0ic8bSP2j51P3swLlsrBA23X9qYvgaz591jjg+m3OaDs70nP3ygHHsx1Rj/vVt8thQWLbcUr7lkjNISfXizFl/77ISDrnnIoR9+h3/nbapol8k5JIP8fphDX4QPovKo7X01OJE0a0VZ70uk0HIoOHvD+TPp8wlB7oDn8/GXI+JTnjuFplrt6Q/bj0LtbT/UzOQKWmNOx2+bzaP7vtce5x9zn7rvHo9zmx9L1J78pol/p0InWYS1cvgVelr1jSCndJ/rbimvTHj6QOVYypTzP5RuJoRH2sVeV7MPkGo7x0BTxJ6YeeeY7n890HjM08iR32XuX52cFrrKFkAXOIy08xh/ju9z4N/ll5Xu8t9k3fTBijX11n3rUl/fguJ7I55nr+k//HNRgntMn7OzfB3/gK7cGHv4tr3pZ484c+9F7wJy/RhlccZWZ5lfbklz7FOntI1+9OjrrgjYz24PoVnllWpLdnNOGavbzFF0R93n+6zdim1WC8L2UXV11krJhEfF8rldpiu8nxSQ17IN8K7Mt3NaMF2t9KKLXGbwGP9bwrTt2g81BfZ9J3UGhqDcETTtuUyNlFPie1deldqYptaDa4TuMRY5v78p2fn+f7Sk3GbpMJ5a4t53Md6UOMpM9lLu2gs1TOHiLpH5X1kJKMC6RGNJM8zTuS3qdtntU35Hu3cot6vb3PF3bu8Xmzsfj5WCsOzjmfvvFgm7rRl7ws36G9b1ZPhVMGKjPpMY6kKJRSWQMnPbzyPZULpA425njHRcZW46q8byp8RvscS19eJLZsMKctiqfcoyPpNdAaVCq/dz5lQnt58vKdQSz7VfcoE/tyRlBpvDP6DMuVgnvv+x/mkFlGOVkdcpyqa8cx5eD4hPvuL1FuChL4ZHIWMepQrgvSL9qO+fv2BvlCF9SNOxzvFz/3Bt9XZT13Jj0XM9H1Gz3mkYMS7/+RF98HnkhP3UKLPjwecH0POvRZP/sliSGkh3mYZ81rWpaenCHnF+T4/A+8/4ed4g99L+OeP/Xf3QP/8NN/Gvx+l302/S7fOfVZI+nEjGve/yP8NvL+Kf1Lmqeu5zLGLV6e77s7/DHw//Cn/zz4UOqvWUB/8APPMZb9f/4lxr6/8NcoU3/r/2DPdkH+dkNvn3FT670iI3PGrnsnPN+aSa2hJHlWqc0zC/3+Ou5Ln2gmtvtbxOO0P2mWuckj9fNaQPnOL7Auk+YYU0Yh5blxifcP78n3o1K/y8laPtjhXp1bZoy6+8qr4Beeon3avs+8ZHmdOXT/tAv+xNUnwe/clDxn3gQfTem38vJ3L3LyNxamI8rWwjp1p9Pn96ZDicU238X7k2PqShCRn79wETyaUTfbm8xxbh1xvZxz7slzXPNRQc4w9+Sd57kHubb0xaX0xR35finTb70P2YtaEvtQzhhrDfvyDa30ih8e8n2BLz7rSdYKuvfZy7Qn5xCh5C33dhjrpXKCVclzPS+cZ79XuS6974n0iIxofxekd7Sr31A42s+cfhfVlH6Cb4CvExV/0/gPnHN/zPO8W865BefcT/8mnmUwGAzfLMz2GAyGs4DZHoPBcFYw+2MwGM4CZnsMBsNZwGyPwWA4K5j9MRgMZwGzPQaD4SxgtsdgMJwVzP4YDIazgNkeg8FwFjDbYzAYzgpmfwwGw1nAbI/BYDgLmO0xGAxnBbM/BoPhseBb+mcvsiz7hHPuE//0v+84597/+IdkMBgMhNkeg8FwFjDbYzAYzgpmfwwGw1nAbI/BYDgLmO0xGAxnBbM/BoPhLGC2x2AwnAXM9hgMhrOC2R+DwXAWMNtjMBjOAmZ7DAbDWcHsj8FgOAuY7TEYDGcBsz0Gg+GsYPbHYDB8O+Cf9QAMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8Fg+OcR4Xf0ZWHBLSxd/Bof9L6M61WXB28uFviA0iLosHcCfnxEPjxNycfknovBs5zH8VTk7x9lAZ/XKIPX61XwcoG/v3xtAXz73gi8OOfz57M5+BPPNMErMw7v9dt98GTA61mlBN7wiuAHWz3waX8PPO5xPsVlrue4c8znje6Ae426UzTCGngYcg1ckbxWS8DnGflo2gWfZhzjrdNT8Px4wjGWVvj69YvgVY9rMJ8fgecKGXijxjlXCpxvfibP8yrgWydT8OYl6kA6o8yunbvK6z0KgdeMwI/6h+D9L1GGrj/zPN/faPB5IU1IqUCdDcKceycgyzIXRw/3Jp5St9yAupiI6Yli7ut8xnWMuK2unuO6TD3K4Szj70/6lMOTCceXDvn+/rAL7lUvga+f4z6FOe5DWGiDH97Y4fhOOf5hV/Qkoh5N55Szi3Xa1toybU9W5vxGh13w+ZT3z4aUy0nA8R+PeH1v/Bp4b8bxOufc1TH/33suL4EvrNMWZHPuYbcvMhRRV7tDrkktpi3zC7S/c8fnu4B7NpryeeVFysRgwOd5PvdgHlOo/eYyn9/fJR9PhVNHysvcg/GY/qJQ5fhnY9rqXJ4y6jI6tGqZ47/7qTfAS/L8ntjy7Ii2+ayQusyN04dzn4sPCYuU9UqV6zIqc17VKu/359TVNGFcEkdc1yBPYzVJxdjNeX+5zn1eWaCeeD71oCRyl0264PV1zm/lhevgjSHjkONf+Aqf51OPDvwOeF/0sNWmT/3Qh18E33zqaXCvzvsP9qh3J90u+Otv3QIvlLkfox5tk3POtctcg1LIPUhSxsK9iGOK8tyTaot7Ulmi7erdpj2MPNoOv8Q9KxVpS1bX+fx8kTJWazbBCzl5fkZdbVX5/LyECZfWGSsf7R2A/yt/8N8GH3a5xsWUOhZ4lNF6nXHT6jLnN6+Irc1xvtGEtjDMuH+54J0R9zjnXJKmrjd6qBPTW2NcL61QVqoFuV5lDNuqcW9mA9qneDgEHwXkqxc2wMNVyvaox9gozChLPbFP0Sn1/dwG5/Psde7t+576QfA05l7OOowL9na2wE/2GDOPJ3z/8sYaxxtzfYZ92re37tOe5crMI68u0lY8s8T7N5Ypm9vH1K2//QnqjnPO7Z9SP+KENiv0ed1z1J+jPu1FrkKfUsm3wEcSX+djiXVkD1ol+riq5BlZzPFNJH7unVJG4oixR5xyzSoSi1VrvO4nHF8g4y9Uef9qnTJbiOmjChnHWwh5f6XCPS6WaU/2thirlcWejgaS/J8R0sy50SOi0xffWZd8eXGJutpepBwV8tSNaEI5zHOb3UhqNlGH708i7uPRAfe54vN9D27TlpWnlMNphfMZxrw/kopb5ijnReGjY04o9Ck37/n+a3zeFdq+v/Ep2rJf++oD8MZPMPZ6/t1c/9wH+Lz53ZfBJzusR8xeZyyUeBLjO+fKG1fAD96ibnz+02+Ceynt5UqNsl54imM8iqn7xQJlKJS6WkF0f6NMfzdbY279n/wfL4Hff/0u+FPXuSfjEWMDL2bscbQv/mxGmS2Vm+Btib8LYsu8MmVyMKGMZx51ZuD4/jCh/x9IXtVoac2O/rrVYOx6VigUcu7ixdWv8XrIuMUvcR+8Mm2mhNwuX5H8Wmpd169KTFmh3DXyovwR121BbHi9yudLCcqFVebv84S6NhxSzqdH3Je4L/XbmRSUpd6blDjeSsDnFfJcsKIcL/h5yXHEh+r8cmWuR66gxxVi+wPyQlGKcs65NOAYsjnHnJO4p1GmPU4kzsgXuea5HMc8i6lLccRYLBNdjUqS95X4/LtTLtJI6sNPlylzT3zXB8H/xSdpr9+6dZ/vL3JPegOOPydnIkOp6QQp9yCKOZ+qzK874P2e1CKSiNcbUjfMnJx5SFh5lsiSxEX9h+MLUtqHMODaLrS4tvt9+u5ai7JacYxN8hLLHB7fBj93jn4pFjuv52HtOv1QEjDvaSxRFqtFzWO4l0t1+tVlsR8Lbe6tL+peKbFG3dmiH54OWB8sF7neRfFL5y+ug1c3mJe+/zrzuPlrtH8//6mPgX/XEv3LH/nj73OKfJPxVzTnnLu/zDX8N3+ZZ5ph6Rz4aMQ5lXL8/Z3X3gL/ke99Erx3RJ+33+eZ3fYuc183vwd68QLtTanCPGUy5iZ2eoyHu1PKbOBTJtelFpFK/JvJ+d7JiPH7QOxLvkaZDctcv6MTytjxfdaQZwHt7VqbPthJHnhWSJLU9TsP9/bNZBvXyzn6tcICZb+V5/WDXcrhnTv0Y1oT9b+8D7663gRf3KRfW9vQGJZy7NWpi+vn+ftyTFsZRMwZaj51N3YcT60p833A9+/tUm5PD2mLf+4e53vlt34X+J/5Mb7vf/o81/N/mFLP/uUfezf4x/78r4IfbVMvPifnX6+9+b87xTPf9wT41hdvgh98lrZgvS1n7SWuSfUK/c+HPsKadF/OslcvMU/buLQJftKjbkfi+ysN2tdCYxU8i7gGns9YrLXI8fly7qJn17kqbdf4ROqEEf1Lq8XYal38XalI25gGtEWdY9qeo2PmxXrOUm5KPpJ7Z/x7XXGSuG7vof4dHojtqVB3a23uazFHm/7UUy+Aa0x+95C68OqrL/N6ILU6j7+PC9yXWGxjKHlfZ8B9aDeYjztPznlTPv9EbEkseWZrlfWGUbHJG/L0Qan0wPRPaAsXztF2Hu/ynDaZ0MctXbgInpvKeV9MW5pKHHs4Zh+Qc86FLepyGFHXi1L/LFzlnPxIdCfmO+Zydvzlz70OXs7J+VCdMpgVpBbQpC6VRGYbq1IDiSkj2ZR7fHjIPaks8vepxMKlIm3b+gbH3+uTz4Zc3/Ex9+SrA9qW175M/xjNpT9ExuMVKaSe5LnTHH3BWSL0PbdQeTj+JJG5ZHIWWKVfSGdidxPKZppxrZsVxi7HXerntYsXwOejLngga12Rs1NKpnP9kdSUJQYejTn+Yo5+yJdepGROfQ7E73o+x7eYo5+cp/STPTm7HvWpm/v7HF9VZKkvur4z4nrUChz/kvSJnHbf3uszmdGeDJz0R4kPyGWcU15rTZJrp12x6UPq3zyS2uKE9x93GE/PY8ro5Jg2ej7j+LoR33e0T/1uyhmiLzXySZexjZdKf5v0/vgy//mc8xmLz/MyiU2032EqdTI5r4+lrjSR80i/+87o9ckXqm7z8oe+xhdbXLfNObX5K6/yrCNcZT7ZkD6697yHNZybv/oJ8NufZH0hk/pgV/oWxwXGrLE0Mn7sC8xTfuwCY4kLm+T/2r/xAvh/9xc/Db59Sr/fbPN9L73BWPG5Z5iXzqVesbBM27YQcD4fe5k95l8KmZdFvpxblxjrJKJXyeufA//ZL/wafy/9ws45N9r+Ini7yvOv0x51cZ7jGk3F/n717lfB3/z/fh78D/+BHwV/4hrrXMUC/VVC8+tmc9rb2ZQy0+lwD8Zd8htf4XyLCW3D8rMXweM2zyDfuMPYpirnKEmZsUijTttyev9lXl/l75eWm+DnpDbRk/VPh/Qnh7vUsfPPs5/srJALfLfWfKgPi3XOeyY+8GCP83r1i7RF/VPq/r07zBteeolnBy6knG5s0Jb1Jb9fX2Kelea5j74cCMWJ1gcoJyOxrdWK5PObogcpfVjzCsfbPZAe5VoT3ImPPpF6wNYh16+ddsGX5Rx2vE25+uIObeEdOXuahNLDsijfTjjnWm3aLzdlnJGfs251qU5dvPkl1oPnUmfyZA0rKffse67S1hXKzPV3u9yjl27R1jy40wVPfD5/WXoBqg3ahiDP8R0ccI17EWPRmtSvVy9xvCWP/qJdY94z7HPPawXp7c1zz69foEy2JA9NPfq3O7d4plN+B/UZhmHoWosP/W9P+nhnM8ZCmvPmJMY+2Ka9CVP6meZlfutSLsjZrNTzWtJDNZswVhhI7T8Vx7hzj+dfcdYEd3dZc1huSl4o/eg9qQl4U4mJ5Wy1Izl95riezRfPg6+3OP7NDcYZ9z/N2KiS5/1bO3zfWHKkH/nB93K8CffPOedKEk/9nb/1c+BeyD37xMc+BX4q352UxFefk17LIKBNXmjSBw7km4yTHdqDcb8L3jmgDLcljbmyQf3Nl6Q/LeV4zrdYA4+HlIl7d6nfifRU3LpFnbh8jvbalzPm9RU5V5GadyD2v3vK+bbk04CcfLuwusz1PStkSeyi3kNbHo3pp6IBZbco32cF0l8axtJLIofRRelVCaTxoCHrvCd2OhJdGXUoJ80663+RnKV40l8bJ5TjhG7UJRLTJ/Itk5P8OnO8P5enrQ7l7CKU3qFoIvWIO4wVvar0uC00wQNf6gvSK1ReoO0O5ezFOeci+X4pirmHntR49DsZT2xPTtqjStLPVZOaRmNB9lDGF8+o+6FHGfFn1MWykx7fDtc4kl6dmeT6ofQGHZ3SFvYL3JNA9nRWlbrelLamIOce8jm1q8t5WE56jYYSTxeFe/LNZJC9M2KfcrniXnjvw16PmfQkX/Eugs8m8v3SkGcZxyfcl8qa9NKLj5qH0pNVY76t544XSi3hjHFrEffp9uvU3TdOmSdO57R1OelZS3zGMdMp9fI0o62+u3UPfCS1yor4+GKN86k0f/1vZQ8j5jSDlOPzQtratMScKSe2Ow0Yhznn3DRk3vPjv/OvgX/xc78C3pUzuKMuz+brm9zj9grzkOvvvsjn/xLjKD9jbBqKffWk5vHKy4xNtV5dlP6MlsSOf+5/+WHw04RnLIUPi235OfY3zCuUybdusz/iSofr1RlLn1Sd81HjHYk/iUQm9XuXudSnh+KvzxLlSsm9+J53fY2XmvJ9lpx/xRPal8U1xqR7t7j3Lfm2sH9KfT13qQme25ezDOkbjiW26sj3UKenzMlPB3z+wuZF8MkJf7/cYl9JNqN9XVtiTflkStnJ+Xq2yb3OB7Rv0zHt64KcB+Yk5z+WvLhal7xvSFk8f4U9eze+SnvcWmLe55xzoXyvdG2T8dEbb9wDP3edecud+13w9pKc/8jfE2gucs1bY85pHHNOE8mtp4EmGtJHLeHqC0/zHGQmZ5aDGcc3lTPOi+cp8zk5kx1JH2GjJvF/jT5iMhaZzyT4SaS3t8O8q9bm+o2HfP9ulzoxOOV4vxHeGR1BBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMPxzBvsDPwaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDB8G2B/4MdgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAyGbwPC7+TLknTmesO7X+PFuIPrudJF8FIhA58XSuCd/hS8VW7z+tED8JVVeX5uhePzFvi+eQQ+nY/Bg2wE7s1z5FnM8S3UwbNkjbx7LM8HdZvNBnhDdi8sXgIvX+Z4CvVF+X0KHn1xD/y4ewJ+cjIEP5+bg/ejAXg+5PqdnMyc4ni6zzHMOCk/5Z4XXRc8HvL+OOGaJ9V18D1ZYy/l71+4ugReu7AK3g8556OdHvhiBdS1rp8DD7wq+M3X74CHPmVkJvx4xvlN5U90RQX+j9WL1ImlRb5/a7wNfv8u9+ONW2+Cn9+4Bp75HI+Xkqfzt+/5WSCbz93s4KF8J13uW5qf8P42daVe8sAnp5T17kILPBgegsc56pon6xJHVPZOh7oz88gz0d1SmeNZqJXBc80m+EqL8xtt3wd3AfVuyafuRy3qxd4J9aLeLICfyPrkizU+r98FD0tUpGRM27u4RLlOJ0fgWYHPH/VOneKzn6K9e/1XeD2uFcHXCuRpg/a4WaCMuDyv+x5tTVCkfR6LDGQ57sF8KvY6okwMe/SHhQZ/n8vTlgR+Aj6Z8XnLa8vgxZR73Filbe1sc48qVe7BcG8HfLFFf5tvcv7FJtc7i7m+WUSZbBYp088ucj5fveXOBp7n/PzDve6MuE7DCfnpPm3HeEBbNWXY4fod2q5Cnrof5LiOuQp9QKlOOQ0DrtvCIuXm0ib3bXGJzysHlNNxlz7GizmBYIk+q7FOW/E9BfqcosSB5YDz27tPW7C+QLk49+R5cJ9q6IYz2oqtB2+BpyW+v1CjrVtaZgyRpGIXnHNdse+5jGuQ9/jMUpX2dh7x+mDI5/XjA74wzz05d566e26TY/Yi2ob+EXXzeHcXfHJKGS7mOZ7OIXW12qZtGPSZC2Ti7zriH2rtJvjRLvdoOmbs6qUBeCiBWqPF9blSpA4tSuzcO6bQrIk/nB2Lkp4lPM+FuYfzH024t/PObfCh/N3Xixco722JJSKxZ5743m6HezETA5ZMeb0U8n1pkbFSIjHvuMPn7TIEdeUcf3D9EvOuqsQqcVn2esCc4YjDdbMp/W6uyefX44vgUYH2+vSQsn9vl/uTjjm/RoVxxMmY/iE45P1vPaA9dM65WpFr3Aj5zg8/xTkcnNAm9kSGZlPqdxJKfCtrVK9Qvxo16me1zPGNhpJrDilzvRPGv7Hk7vU89TUTe+uJDwiq9GkzeX/qxKaLzHZPJZ+YcX2GnS54tUwZLEpeVfW5PqU8339yn7GVJz74zJA5lz0iKlWJ+ytVxh5BsQnuiR+cTeiHoi5lfdTpg0/3u+DxWGpOFcm3R6J7Id9fr3HdZz51MV8lnyaUm0zqB3nJ7/OSYwQh51P2eL1YpuBWStz3uE/dX6tTL49isd2SI2Qp9Wrc+ynwpPzfgr/2uZ8G70wpt8459+TqHwb///z06+C7IWV/fZm++mppEzweUteGHeraPJY9LPN5/oi2oHPEPcqNyS+tMFb6PR96Afzi+Sb4rMc13j9ibLYd06FcX2cscUXiyWaFMnAq9n40oK2NptSZquS1hWoe3CuI7RDfUJD96aR8fzR5e7x7FvCc5wrBw70v5yWfP3cFvL3BdfKb1OVikbKcb1KOlqrMWwqSXxdTKeiK7lVz3IdY8sLPvfxV8Nc+S70pLFKOLlx+BvzKJuff22ZcM5txfCWf+7gqNbGS+LREfGxFbNPcp57NJU7LSc3HC/j72OP4ghLXK5iLnBbfLodRxnv8kGtSLTMuiaTmUIhoP33Rlbz4B2/KOMQfSO6fMXY7zChjgcSq81Pu8e0h9+BjbzLXfuES5+N8xilNyVvCCuez0uQmRQnHfzjl+BPZs7Lk6pUK9zgvcUwqpmc8ou0tFPj8/oD+sVJ8h8Q9zrkkc27wiE6lFD1XamyAj+dSX3SMlYoB/VibJVDXyFNWtnYZsx+NKbu1utSQpU6xeIX2LJJYJ5Dzn7nkFN/17EXwcomxxfyYNYTu0V3wB3epC7s7/xA8lfO4kuQQLz7LGH5Pznpqcn5288uf5PNu84YHB9zAvSH97C9NqGv/UvO3OIVfvy3/h3safliufoZj8hcv8n45Y8wXuAa9Xda4X7m5BZ7IOYKT3xdC6lu+RBvcWGQslki83p1Q5nZPeM4QzWjDU5+xRL14GdzLGM8mCe3fVA7E9nu0T80yZTwQH9YX+96bMF+IPI4vF1AHpnJ+eVZI09QNhw/nMu0yP7y6TLv/hcGr4D9+7Tr4pNIEn5ape+WRxIQ9+qltsdMPblCXqhU5Oy9yHc8/ybN6l+PvX3iB1yeRBBdj5gj5gLZ1qcGzjrRMPXi6QV1/8I8oV7/nw1fBP/4SY/zK85Tz90j9sT7muesf+x9eA//3XqAeBCPa9rrktXv3mbc559z2//JFcC+j7jc8rlmlzT25VOCY44T+6nqLecq0zjW9/sSz4P1t7kk857lCIjWZwhLXYB7SP/lVylwodcyS1HTSVOLtEfd4PpL4NaX/mkx4PazS/lfKtI2TCXUgrEudsyxnxDXG236etncwoC0t1rkfZ4Wc77nl8sOxHIViE6UWH8lZQ3mB5zPFovjIMnV3f5c+7XifPC5x39trcl6zSrnNSWCwc/8rvJ7n+NsL/P2inKfdHND2nh7LuesBbcXG8wzsPvL+D4APJ/RBp0f0iQ9us+/p9gPmjcM+fdYP/1bmwWtPMC49CLvgt27eBJ/JeKKA83fOuR/6v30/+Kde/xL4aEi/Wz1PWT9t0jZMI6nfdrimuSp1qSx5ycbTjJWTIu3lle/5IMcn51Ml6Y8Y32Wc1bslfTod8lxJ+hMGnH+WcPyzMecb9bk+3Rlt03aPtmhBbMvSmtTQXBM8H8ohi8T6+x3aynjede8UhIHnluoP93uaip2WelYxZExYLdA+dOb0Ew3psVpc4Fovy/lYqUE+7NF+ZCG550mNQuoiE4mpB2PGsIMO7UFOzttrRdrD013WDCqSs3elt6cgNZLBlOPpd6lLBamJL0jfh59Rlo+kZjLKyN2c40mkF6vXf3uvT6FOm7rYIJ9MqB/DU5HvPuPd5EjO/EaMPRY97qn2XLSl0DE/Ys/HoMM55aQ2mJda2FzWsCTNoxXHPYqmjL1yeZmfjM+XvGihTBs/7rOW6ObUqXksdaSMexgllNHUaewl/QgTjl97c88K03jubh0/3MulJcZwu1Ouw2DImHC2RV3MDSlXp7vM2+69RVkPatyXaoVys3mOtq1ZZwx94z7l+v7te+B//af5vj/0L3wf+LWrHwH/z/9D1qD/wt/8WfDkaeYEs6+wpn28RDm6co5+8z0vMMfYeoW2ufq+F8B3Pn8BvLbI+W60mQcnQzaNFSvcL+dxfOMd9vM659zK8/9X8LDP37zwvb8H/F/8138r+L0hbcEbH+cabX/ll8F/4e/9A/DP5ilzL99grhkWpe/dp30vZdTF1XPsaT7fpgwlI9qK736evUYLS/x91OuC7x/Qf5Vr9G/rUgu4conjL6/SFi2tcnxzsfWjEXP5u7eZPyzUKHM9icW8AnXsrOD7visXH+ZdvRPajr196ZF1tKn7h8xDfP33n+X8rBfTr88j2vSZ1J9r0jfnS1zkvS2OoA+bzSSGbTAuKk4lzjugri7I9xdLkn+vXOfzDvrUu4HP8Y0mtB37N9mDtvc61/9YzqvelUgvgvi4m1JDy1cYswQ1zvfZ72Xe7Jxz+y9/mf9jInUjiTXr8n3BeNgFD3P0H57IULnNOOfTLzFXXGpTRm49EBkK6N8CunkXZ1LHq1JGylJHLNW55ouL0rspPduXNqnr58/TtvRP+fxGm+O98RbXo3NK2zJKaZtaK4z7TvbugS+tMjefjyjTcSjne2eINHNu/MgZS7NNe+FXufaXrj/B659nnWUmtfnxWPKCvpxdeJQtJ30KqdinotS0t3eoG5UW7UE0Y4zaXuTvq9rLE1D2bt7i3t15ledfV55mLFOrUba+9xmu1+2v8tuctsTkVal7vfcpjvfutS+Ad2Zy9iF9Hd4yZa28SXt0Z7vrFMU59TMv3508+Qzjv9dv8Xzszh3GXzc+QXty/jrr7u/+7uc4xrrkztKP5Ukv0FT67iLpgeh2qa+/+A8+Df7jv+t7Od4d2vD1Vdadnn8X6zCLUjt45U3O/+CQPqlU4npWchyv1tBrYq+nM9rTspyvhwsc7yimTM8kVz8r+H7gio/U1GL5XiiT760yR7mMRS40Py7qx5hy1h3KuqqtkRKTCyQWkvZ915OaeFdsU3WVNdqTHfnOrsrYrSz1gkh6k0I5O58nvL66TtuSSQ2qLjX+LGNs1Jd65kT6dz2pX3THvD+VemRV+odzNfpp55zzj6l7kwkXOV+hr3dl2ru55HYdqTHkq03wlZZ8RyM1pkGftmUY8nm5jLqVJJRB3xPdkzNNT34fyfdaWSr9V9LPkc2llynj9cFE/Kn0LlRyvL9c4vvaTcmj5HvhmfRTZCPuVzlHGQ3cO6PXx/NyrhA8tJM5qY+2Kxy3HAM653h20JJ5FcRH7Ut996BLOQhOuuDlJuWikPC8qtvnPqWB5BmSJy3I91qpfIfolZp8n9iaDTlfa5W47wttxkG5l/j+k6H0gEjcs7Qo9YkLlLtkLGdLcRc8kxyoKnlmYSx58/bPO8X/72/wnbt7rOvFk3vg+ZJ89y7fCy9Lz/HGJu1/ri9nAG+xjlWW78Fi+eAhkbyqJPZ88yl+g7cqsX1evjX9Qvf7wH/uPm3x5z9BnqzwjDO4fBHcP3gZ/KBOGcwt0RblKELOdeTMwDEv853YSokPPOHjgSSmZ4hiGLqnlh/6vxvyvVKtQv24J+fFGxuM8e7cuge+vMT6XSGRb3vk25jrGxfB/Srtw6X3so+jO2HNtPwUY/rTU9ZJGh7HM9O/u5EwR4hHjA3GUtdaCGkfH8i3RCuXaA+P5RuRzUu0N2++yjjiOTnLHk8o6178V8EHBe5Hy5OzDukbD6K3f2vYbNJeFAPps5VvfKOZ5I6SR8QD2pe1KmtNmZyv+06+MZYz060DxrNN8TG+9BGuVWlvFqSX57WvvAQ+n9IHe9K3WJRaXEvO1/Q7n0qdz+sMuuD9OX1Y2af9bknNe+8W7d+1Ddrz+ZgxwUS+8fOl/+0bwf+NbzEYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPB8K3C/sCPwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDN8G2B/4MRgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg+HbgPA7+bLUz7lJce1r/ODzfwvXGx95ETw3PwEfh6vgp+MOeO+wAO4P9sEXljdlQAloMZcHv3P7HnghLYPnS/y9l5+DzwLy0wHvnyQ5Pr/SAi+VUvDWQg086fTB53P+vaZ5WAQvlirgYZHzjVwEvrz6HHj3kO87Or3P34d8f7XJ9wcZ188557qdDDzz2+BJjr/JXBP86sV18Lt5rvGoGICnxzPwZsI5l9IB+EaZezCaUqbSEe/3Kg3wWolr0N2Pwaf9U74/HoJnoqGeL2tY9EA7nS745HAMXmmtgV+8cAV8POQebh1OwR/sHYFXS5TxeDwB31y74N4JyJLUpf2HaxtwW1044/9II+pmM+Y6DkVu/Dn3tXtwCF5You66GZ836fH30YxyOpH3pUXuUzjh+F995Rh8eZXPO8xTzna3ON4/+WPPgv+dv8/xNStcn7fujsB7HerFZEK9zInc1upV8HqjCT6KON7lRerVUkrbll+ird97Y8spbuxzD56fHYC/XHsG/O7rt8A33vs+8FpJbE2Oytvv8n153u7CAueQBfx9nPEHM1cCH80oE+XyInjV55rPIuqqy6jrgU+ZWtpYAS81l8FT2aN2m+ObDigjF5a4516NMpbM6RvWF+gbBqf0/291Qd1GRf7HGSEIPFdrPtw7P0ddTibchyjmPsQxdTcQuZlH/B/5InWjvdwEb64yzmitcF+LBa57pUC5Keeoy6cnt8ETEex00gU/3qdtOj7YkfFSD4oFxilL603weol6Us5TLia9PfCD7R74eEo53zlinPPqGxxvc4nrV1mgz6+EHH81R713zrmEW+qimGtaaDOuCSWuiTM+YGub9ruX0S97If1PSNV0gdiuvX3awtMx16Tcou5mMXVX7XW+zftzLcpoJAvS63I+M3l+Tvbs9OgB759xvN1Tjic35gIs1TmeVpu5RL1MHQjE/xWKHM9kThk7S6Rp6oazhzanWKvj+jyibJXKjDG9KWPUNKFsJSn1r9Y+Dz6e8noYUBb7EgN7VPe3JanVMmXpVGILl3BvH+xRlvf26YeqDe6ty4nfGtIexgWun5ejvQlCPi+Xo24ttBfA0yHt/VKTshfl6B/aLfr5eM79OBlR9otf58/4tspc1WfWmRd84CnmBV95hWPcdxzTxOMaVet8aT7HPVtoMzbxPerT3gF95GGPfCjx7tznGtdljSs52o9ZxPknM+or3+bc7rHkZXmxZxU+bzSWvGgk+ULG+YaSO2vemA94PRnQXvopr3ueJDhnhLCQd6uXHsbivX2uc6W9BJ6kkm9Lfj0+llipT98an7JmlBPZL9bpu12VMWVV1m04lFgtoa5rEF9cYixwKLYmjnm/J3LQXmzy8S2uz86AtmzrVdZgfuAybdfv/X7Gds9cEdsvBYab24zFmtMvgC9c+nfAxydd8Bf+g/8UfO+Nv+wUzWekzrVIXflDf/xHwX/uz/0ieCflnpyOqQtJIroQ0zZ5HuecK4rtOeryeT2u+ROb3MNnr9F25gPamnu3KDN7bzFWSY8p41fONcFXclI7mNPf7d+R54lMZh5tSUXyynkqtYYx19dLuT+1Kh30aZfrW6+8M/5ufJpmbjh+uFaSVrnpXHyImMxKiTZ+Hmt+T10/mXGd5z73aSo1kemYcrK+wLyhVqDu39xrgm/dYdyUl/rse55ifv7E00+Cjxdpi1+7zdressfxJ3nqybLPWujOlHKUE582Sik3vgR2yyXJM6VGlOQoZy7kfiRz4dHb865hl7lgtcw4YRprLEcZiCe0j2FG3YwyqcGILo5yjCOiOXPLzpBzrkqcVNukvxo94B594fU74GlMW1t2nN9oKHmVnDnkfO5ZIv4xmjJWzRe5qeMRuSfR/Dji+/M5Pi8I+f5cKLlJjus1HUvycIZIs9SNo4c6sVinLw4oWi6UGDiURCjq7oJvPMnzsmTAvXnmmQ+Ad3coa+mEa18pUXY1j6lJHrO1t83xVrk3xRU+r9fl+25+5jXwj3+avCf67CfU52aT67O8JvZmQoM/lxzifkJbMO1KjL8o9VuRfY2duo72+//137Ne7Jxzf+iH6QP25Hzm4y8xXpTw04URYxEv5Jq6bAM0qHEN9yOuWSGlvjUvXQO/tET7c6nF+wdT2qP+kD5lEtEnxJ6cEUqeMxzRPmVjymwsZ7450f95wvxg7Li+VcdY5tb+TfBimfHzIOJ+lFuU8SiTc5sx7z8rZM5z2SP+sRxTdr9yhzXbdz35PPhpn3mU1jSffz/Ph85Jvv3FT74MXpR92Dlk7KD1tAOJJb70y2+CJ1KTvvGZexxvg3Lelnqc1hOfvUw/Wh5yH1+/T77Qk7P3Ler6Xszx/ewXaLtvi59/4iLX70efZKy29G7uj79MPfzJ+1zfX339FafYOmKuOBx1weu+xAIh3xF7PMvdvU9d2dmibeo58V8T6v7xkeRlKddgLjWNaEzdm8sad6Xk6jnOpyD9GX05K59HkutnzJUDqftlTmxrl/6wPOF4cxn9y+Q+ZSh1nMDJidSsQ+rw8jLrrPMudfaskGWpS6YP9aUYSI+Hx3XT+m4Q6T7RZw575IM+/XgoB2ShxDWXr/BseH2ZMfKDLcrVyT7lvCjPT6X+6nnc95MO45BJLOe8chhTK7DPyZM8LD+nnL716V8GDxv02d19nqUUG4xDP/8lno3/4evvBt+WvDKQWmbqUc/WNiiXzjkXFWifag2uUSr2+Pp5Bsez9lXwSsAxuYznZUd7b4A3HGVw+RyfX6lcB19f5RpuT/i+nRvMs2Kp4XRPpE4o9ebDAz4vHnH+J0fkpSJt40Bkeq1KGVxu0fbendF2X7qi4+GetluU0dEJbUu+RJ3pSg3uLOG5zIWP1F7SKWPCnJyP+xLDeZKDLknfXEHk3ZfzojiinZ72GTuUcox10rxYQLGX00TqIKL/VanL5D3Kgp+J/RVdKMnZRZzR3jSqcrYxpS6Ph/I+qaMFEvMXCtS9seRpFbENYZn7MZ9T10aJzGeRdTnnnFtao833JVc73qGNPL7DeC2SeK+c5yQ3F6hv5xZoP4o5rlFpSH3xUspUmpdiZY0y5jup6cqZ7bzE8YSyKbH4MD1DDPJcHz/geMI89ySZS8+KJK7RnDqY5blH+Spl+OiYz5OSvfOn0kcZ6Ynd2SDNMjecPVzLVM4iDu4x7xqNeX06pWznJT//lY9/HHxp/SJ46DNfzucpN4Wc2DKf72+WKLfRlHb+6Jgx6sd+6cvg7fMczxMSC/y2d/9uXv8w85qTH/4J8FLIfR2d8P3395kX3stJP+3COfDqKgWpIP26H5aji+5Xef7VXmAO8IHnmAd/oc/rzjn3jJxn/fgyX6JV6jvSb/C//jdfBR/c5pwrIXX3T/z+HwT/6tanwEstzjls0B77U8mFpU++tkTbMpI6+0T6L+5uM4+pX+D8ekPapuNj7uGq9D4VC1Kzn1FnqkXuwUJI/tpOF3z8gOceb3yZvuDaE+IrdmmbpsPv6GcU3xDxPHanBw/XrlShjY/GXNf2QhM8f6Eu13n+dO6EPqveom6NB9Lf+rY8j/s8k+8Vbm/RRxxJ/luW86HWOnkkeeHdWzfAq0XGsN555kErcjbeH0p9WPplPTnr8VKJK+fSh5Pj8yuJ2Cqpt0iJy603aYv35ewkHPB5zjnXu8XcLl+X30i/hb6zO+Izg7zcIP9GeFykbv/8r7zK95WpW4n4l2tP0za1WrQls5HU6BOJy0r8/ZrkcX2pyYQB447VJe5hVfokT2dcj9mctuGgT1uyu9fl+2fkHalpjY74/KefYy1gLofUlTLHf5ZIne/Gj/SkX1jm2telq6pepd9Z32AOf7glZwF5OZ+SvG0YSd/yjHb68Jj6Hxbk+6yIsr0kfYbn5Cx2c5FnH0s12sut+5SFV96U3p15E3x6wr09L3FCq8Kz5pn08x9IT1hS4no/cYHr+7t/P+s8X5Y+Eq1/diRQ6aXMa7/wGcYlzjnnzWjTxuJDNle4psmMe75/yjULfM5pGlP/xxkHudVjHufk+c0lKviFVe7h+ofkfF96g/7xr7wF/jd/lvF5T3L5396kz1mu8/rTV1in6fUYzx9++R74/a2vgG+u0if3EupIdcbYaTTgeq5KP15THMKgT5++usD9PSv4QeCq9Ye2fz6l7pfq0qcgvrsu+fmJ1DglVHETKXflJF0/PJJenx5v0B6tsdSIXCp9jDXamqHIYVnyvJl8X1YsSP+onK20m/SbqeQU9Tzl9IF8b9CfcT4T+aYlk7Pz2OP6xjPG+LM55S6Vc9usST1OpVfLubf3osRSI+hLLBFKjaIsZ9N+wDF5AceciVBMd3ge1DnmHEYRbVdJvnWNx5SZ4qLUGVPpF6lJbOPzeqnWBF9a4Z5Nu+I/JhxvJL24JakrehIaFsryHY/s0bHIyFS+wRyPNe+Tmn3xnZF3zWeJ237kLE97sXMhfVh/h/uYlaSeKrX43AbnWZnQFqSHzFMGcn7lXWBPyP7L9NPRhO+rhfRR6xdZw/FS2oqSR7kZdEFdLD0as0x0u8T5uAJ5efESeF/+LkAmPTBRxOuNmvSgSIxQmIvtlT7Pcl58aEC57XR4FuScc0OR7XxZdLlCWS9KX0lNGteX1hiLrSTicE7Yl3h+gXN4skOZ25Le07GcLW9c5bev7/q+3wa+vsp6+sc+zTrgf/vTzDt3j+gvsjLjnNx55oH564wL0xr7RSZlrm+4yfVZfYK2e3JT9lD8oQtpa65eZhz14PUueG+mVbszRJK4pPewLlqUPr3jB4whi/ItjJvTD1ZD0edD9o1U6nIWKX0kozH9zsKIfiJo0p4Nj7mWy+uUjZn0afjSmxO3uHdBVf4uh/zdjpM+12PjAu3NfbGH5YDj25M+y+s0r+6oTll8epn28td+4a+D/74ffRf4Szfku2yJraZ9iS1Dud85l0mydq7JbxTm0utzKr2Pm1J3uXfMMWxUaH/u7TPP2pR+sVNxCgU5z4oPWdcvLjFXTYdV4dyjQZ9rcO0K+xiPY+bix33ao7XzlIHtffk7H/L3EqIJn1eQ+DuJpPdUvp/N5Hxur0uZnAfcY1++2Xhik3na33dfH++MLzEMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8Fg+OcM9gd+DAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYPg2wP7Aj8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAzfBoTfyZdVmi33/h//8a/xv/t3/mtcb1UugR/dfQm82boOvlDJwKdeAp5m5ONoCp7NI/BZ1ON4qzH4tfVV8PWNGnjiSuD3T3bBBwM+rzebgLeqfF6tOAaP5pzP9g6f//o93p+NOZ5yE9T5Ef++084W539paRG8uUJ+tM/1O+megi8t8/75JO8UQcgx+IUBb/D4m/40B3464pzzec752nNL4MmhyMSNt8Anx/vgd77wOfBoNOPwAo4nq7bAezPevz/gGmeDLni1DuouLVXBKwVZj4BrfnhEPhyNwAs56sCT5TXwVmMZ/ODkmM8b9sFLpQA8H8zBw4QyflYIfd+1C+WH/6PAfWuUqHutGnkl5D6WWw3enxyAZ/ky+PDkBDwac52CjLYsngzB5yltR+C47v09Pt/zPfDf+wzv/2++yOtuQP6nfn4PvHdYAG+kHH+zzfXyChTkcrDN+8vU05Menx/NOd55wPU+OqVcFTM+b54WwbvJ213d+hptwy++dAj+/cs74J8ucE/LQQqeFWUNeNn5FDmXyfVM3LGf45p4Gdc8cbQFtaUmx1fimpQqtCXZkP6j1uKehdwCd3RC21jI831+keMpLXA9FiYL4Mur/H0+Ef9Xu8wBiP+eBZyfm3Z4vfDOsD25Qt6tXzj/NV4SXWlLHLKwTpt8vHsEns5pgy+trIMnBcpJvcZ1L5Toc3M+5S4ROet0+L6O+Kzjfer2YoNy2yryfZMhbWni0fbcO2EMkIqijMWWri9T7rbf4nh2jkUOcpzv3KOg1xvU80qT6zGN+X5/TLk8OeT93owxinPOZTF1MZlzTRqLbb5D7O2FJxmLvvwZ+ulak/ayWmEslgT0Jwen/P2O6HpjkeNtrqyA5wOuaa/POCRJuKYjic1nXYnLxL/F4h9n3S6fH4g/k7iyN6EMZBOu96037oFfu06dqoSc/8ke/f18xv052Nty7xSkmXPT6OF+l2p0RMUyfaXG5IcTykq/xxi205G8S3x3rVQBrxdptyfiV6cJxyfm0eUS2pdcTmS9zhi20+cDDk9oTztT2pdak7I8HXJv8x7Xh6vjXH9KfR9Ou7wh5XzDgLIZRbQv5QJj+EqF/iNOKPudCedbyosjd86FEh/m89yj3bu0wb0j3h/kGCucX5f4eZX2JhX13D/inPcOOMetLVmTGfeo1eaeV8oczyzhrszE3iQyoOmU9iWUNdQ1r7Ro/8oZ78/P+bzuiONpNLg+M7HvJyeMZfyU859IHpmK/0inzB/OCp7nu9wjudBcfGcsf2N6HnHfZ1Ou2+kBdXd62AXfXKZcFFu0bRHFwO33xHcP+f65KHc+lHw6L3LkUfdTyTPHEa/P53x/P+M+Hohc7U/pdw5fp5z82//qs+A/8Bz9WL3CWOlTn++C/7v/1n8O/ge+7yfB/4U/cgW8UmWNLjr6j8Evv+/HnCIp0pe+6/n3gH/ip14Bb1aoa5MBZabWYCzkzylj+QJloLmgNSWJTyU3r+QpBG3JXYcH3IOTXcafv/gPvwDeG3XBW0WOr7BEW3N8m7FZuCixzSltwcJFrlex0uTzZfynJ5T5MJQ6XcD3hRIve1lO+He0rPyNkWXOix7qU2/EfRqJrscSd0zE59Tq3JdqQP7JX/kk+OXvei94557UgCSvyaaMCy6sMW976ur7wI+3Kacuoe2Y7tFW5QusTeZDykGrRp/UShgD/HyP+fjvb/C6P6PcBBIZ+Y4+sSw1rZajrat5nN9IapmuyPUZTbrg4dc53shn1JVCSln+2EvU1e9+7w+Bz0f0PzWJrbIR7Xk+pIw0xB/5sgbxgHlD6nHNamXKbFfOQCLxl6Mx/dNEzkiWJQ4ZTagjaUqdSD0+P0m55/m85LWSt/lOcgXJm1zA9ZmMeL0ccjyB2Jq7v8b9OVNkjNsWpGY8iKg/gU9ZmoofKkjdYvuQsvzsBepDVKQ9aU8k55Ya6ekp9744on0Y9FgPffkW76+LLJ6eNsEPj2if7t6gXzvuU5ZyUr9cf+EiuB+zLjU84viGU9qTUGI/F3C919c43mfeR1nrx5T9ieQMsc/zy9415qHOOfczJ3zG6693wc8tXwC/IudJgw7jx37WEs6436ucAx+XRH/knKO5KtebTfDuiHWdm/u0R70u9a9ZZV4Z+3xeq8a87ZTm0+0MKMNJQBnxctzURo11M09q0rky759LvO2LvcvVqXO1RcpkNS+1imOxZ2eEXBi4pcWHsjE4ptwUxG4ulBnjDaVmUglo19NEzhaazDOeew9jm5U61ynMU3ec1CM+9/I98PuVTfC9Q+ZBhUBszxHrlyd97vMqS0juNYnJL7Y537xP23pHYrf7B9KLEPP6UY9yN5DY5FjqI3GJevV6/wb44uJT4M9+8Hnw738/cyrnnGtJrNK9z3i0s30P3JOa6bhNGelIzWLkcVGHKdfwNN3g/SFtVeBzjyax9GMUaetmfUnOfb4vmjJenEb0d4nP+UQB98j3aXtyZfrvXIE64GW0dZGc02Q5qcOJzGs7TljmdV9kbjqmjFeK74x/rytJM9cfPbQfgczbDzmvlSXq1uk+/Xi3x30OC5Szppx71pbkbCKlXOXLtEV70uOwfcB1HUmtcO74+9U2x1+UHpRqkXLjJtynoEo5mwxE7+5RD8Yx9aZ8Ts5tW3zegtjmwQHPubuSB/+Nn/nfwU/vvAbuy34WmtyP3b0HTjFrcA3vfukmnyk1ieKMe/7UJdZ4eq//Eng/ZK49OaJ/ONrSM0XGBXsH9I/bW6+Cdx+wH+Lwxn3wWlXGn5eajsRZ+QLXsCFn85MpA6FYaizFKuOctuhAtUVbsSzna51D2iY/oIzMIq7PaCC9Bz3qVM4Xf36W8DznHjkPnElOW12kH5KjS5eTvoui+M3BSRd8OKM+xjPKusqG77h2pQb9Rigxen/KvTiQXp92nfYl9Bm7ZeKXM/F7tUX2waTaayR5aXLM9Vwqcvzn2pTNVPLQ17b4+9Mu7W23T12NJQ/OSQ1iocINXG7zfc45Nx1xjzpy3nTzq7fA+wdc40ZO+p0kLzu/Tl6SmnMYc05jOccIZMyJx/GlIZ8/iWmzpz2O961bfP6FC9zjnPRn5XOU2VR6TaOI45lJ/tCV+PlYzlV8qe01lmV9Qtqr7qnEhnLdl1huNGVeelZI57EbHT2MJ7pD7pvampbEDp1EepJH3JdZRj8xTKnb9cZV8ESazt7YZc/XYpl+r1KhXDyzSl3e6rA+eXQg/a0Rxz+I+fzPfZWxx6+9xf7aA+kNWn6K9cnuDY7/NGasWCqJHm7J2XlNesBzfN+1S7SNf/LPfRb8j76bcUiyxPO2P/jv/4RTnEodOxtTCH7mp3ne9bnPcI7NhGs+6ch5kPSmVsa01xcus59scoVrunaB8XIYSl39/j1wLy8yu834dFdk4l5KW/KEo+72+/QHnpwb5FPGKoHUuO++ynhz7y7XZ29Zegve4vOmqcTXUmMbDfm+1QWRMffOqPnEaeJOH+m7utjkPi1JP+bqCv12t0Pd3ViiT7p8njFirky56exLT4Kcpx3s0kYPRNcP9mnzj3vct7qctX/mV6g3xYC28PiYPnFQlDzS0SdX1jne0Zzvn4dcr6LY7pLPWusHypSLDzdpWy8f8Oy8K3Hql8Zcn1HA9x3MuZ47r/Hc2Tnnhn3au+Uq1yhOqJsVOYuex1yDQGLTMMfYdOUSbU3vmPZ5IH0xnsQx/YhrsCC5bLXI8Rbz3MMlqTNWpKazd+M2+Fx6yWZd7vGLL1zjeKUGM5d+jonUx0cD2iIpX7tRV66HXJ+JfFMzG3B9ytKXdKbwPOc90juYyXnzaNgFH8i3NDlpOA+lTjMfaM8Y/WSpTlk5FXt24fqL4EfH9AMf/AC/L0uGjKHnIkszydEHQRe8LznH2jXqRvMqY4mWfC+ViF/cOaasZKLLX3yNsr0QNcGrba7nxjLPqwrSlxNLz1xfbMnLX+b7RluM7ZxzLu/Rnqwt8EysUJTecznDX5S86OmnLoJffw/100lNeUk+EdA+52fW6dMaBcZ/GyUqbLrJ+5+6yLrQbTn/yiSefu0B3z+XXP2JTfZXLUlufukqx/dgn3lrR2qJ9YrUHqSZdkWen5dvAXpyRl2pUkbz5bfn2mcBz3Mu94h4S4vu2759KcnHOSvy7eVkSNkvSA2oIH0OBZHzYZe2JZGaxiygLuXk3LEofR+NNte9lGds0ZMadTrj+DOpSZVzHG9V/GChIjH6cRd8csxYr9elXGnfinyq5PwSrychY7MsR7lNNBbaod75vnZhOxdI72WtwTmlXe5BmJNeFbFN2rsyj+WMVGoosy5jg7b0qWcT+QBMai7TDsfXSeU8ay7fB0dydi/f1ZQzynA5z/lF8k1ILOcmyUxr0hz/aCb9FnLdlz2dSz+KuFMXSVqlNfNA+tfOCqPJzH3p5btf45Wgiesr57jvqfQV9nu0DYPtLvjeEf1qo848o3PIhfOkVhiPpcc6FRteopycHtB2XXyKNj6QnMHJN85JxOf1pVd9Lp94397n+Lt7zBkCOb8qxdz3MEe5W27x+mzc5fMiynlJinJFyXHGQ46/XaadWKi//fuKtsQt1XXmFdJy7IIaDWRR6qsTiVviI+r+wQnjnoX3sG73gaea4N5t7tm4zb6jcy9+D/iDGW3njbcYe+9IbOhL3NG6LnFanTLoDTn+RGo+vtjW3jFtYVn6iC6eOw+u52XLYuvDZa7HVclj34jlm5+QOv1X3dkhTRI3e+SbrMvXuJev798FX1ykLKZd1g02lim73R7PZzryfejJzhvg56UffzuSWMuXHs8B9Xcqfb5F+RsLLubeteTbxO2Xef3c+7j3D6S+6mb6PRbfl5fv3wIxYOMJZT2ayflYi7JfrFLXxxKspgll/41d6lazxvF6C4zhnXMu3aXPSObc88oCberhDvuLrpzjmOvyTV99Qb6hPZC6uuTuGjvUz/H9ow7juZn0BYfyDeH9O5TpmqzJWL6TqYo91lhlnIn9knhyLOdrYdYFr9doD9YXqAMd+XsFC6uc/+sPqGML8i3+VGqPeamFfiO8MzqCDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYPjnDPYHfgwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWD4NsD+wI/BYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgM3waE38mXDY9P3K/+pb/2NT71crjejrvgzYvnwRuNKni2FYMfDg/Bp91T8skYvJDw984THnE8bs7nBX7A61ke1JfnR90+eJrxebVaGXy5XgTPuRJ4Z5KAD2ccTzie8Loby3WOZ+pzfTfO18B3R3x+0NsEL80XwdulOvjkwV2nGI5lTfOcY3lhAzzXaoK/+o+/DF5s8flLT/FvWF1dXQM/2NsFj6Yz8N7pMXgqIlKscY38Osc/D6liE58PmCTcg9GcOjGd8f5ymfMJPL5vodTmALMUtJBRplwkMtTn+wo+ZXKa8fos4XgSn+OfjAfunYB8ELgLjYfy2E0KuH6+XQGvry6DL/N2Fxa5jsWQ6zhrZ+DRHvf5RHQzGU/BC1xGFzg+b2WF+3LrkHL77/6WC+Af+/IO+ELjMvh8zn318k3wpUXqer3OBcmnHE8jlPnLejXENuxNT8CPjiM+36OcDURP/JT75999wOdxuZ1zzr37XevgL37wBfDcbAjevUueZdTtg1Pa18U1rtlg0OXzPY4nrHANh32uyYLIqF+mPc6m9D9FkaEkHoGvrtBehx7XvFzk8wadOZ+XUOYXmtzTyhKN8XKbMtBuks86fH8otq53QltSkN9ne9ShtMT5nRXSJHWjwSMCWOS8vIBy9OR1yk3uqYvgoc91yvl8XqFA3YxGXJfJiHFS7+iI4w0oV96Y+17yKBeLda5zvcrrpQJ9UKvMfSt4vD4cUE7v71IP7r/F8cYjrt/RCfV06tEWOfFhyyvUq/ObnP/FlHJ9POB67J/QuHh5yqmfUk+ccy6f8p7BAeOgw72XwHM5+vmdk/vglTUJfOoN/t5RJg66exyz2LKBxzW8dPEan1egzPnyt0ITj2sqIuh2drmHJYltJxK35IscfzyhjNSqnH9pwj3PFzifmcRV0ZTzH57y97Uy7z866oIHebHdsRj3M4Tvea70aFwtMWyQp6Mo1bh3QUZ7E3ucWybXx0PGIl7EvUom1CcxFy6bcu8jUd/JkH42iXm/yma1KjFpRP2uVzmf5Q3G9D2Jrbpdsb955k2R44S8HGUrmUtMXqFsjyQWG8j8IgnhcwH/Ryz+oNbk751zLnFcg+0+x/DKzgH4ZMw1aC5RX3Ntyv+p5OYHHdrIu/c74OOJxHMBZaRcpkwWaozPZzPKxGBEmXOyB8st2gs/lD0M+b7qEm14e4H2ddqnT/XzYh+n5KVSEzwVezQ4YbweT2i/fJ8+qNGgzE56XyfgPQOkSer6/YfG/+SU+7K4SLkqBNTFNJHYoN8Fn8u6eHnmv1WpF4xGIicPKOd5MdtpprEDaexTrnZ2KddhkXKaSV6UzukY40B+X6Gc+Sdcr2rE368UOJ66xFaB5J13//LfBn9+kXI+ucE45c5f+Um+71k+r/X8i+Be47c5RTThHp2cMDfublP2rz7HGlAy4zuDgPZ2StVwbir+J6ZuD4e83szRNp5bWwVviW7v3Kbu/8OPfwZ8V/KaF55m7n1tnc93Cff4q195C7zhUaYWlpfAC2Xa4lRis5HUDccjvq/W5HhK4is0Vh0ckpeDt8e7ZwIvc174cG6lPHVhIvluMcfrjRJtcj3P+4OUcvv8c1y3bM7aYXWJcnpySttVmFMOyyl1e1HiksMr1Iud+4zp33yN9VaNW9Za9LGtHPctm3J8H8x/BXw25PW2FK1yHmOKaonGNYgpd8GUtuZUakC9AXOGaY7rFXrcj4VF0Svn3LkW/byrMHf97uefAY8zjrlSbYJngdRkJLf0YvH7UuMfZbyeSj22e0TdKnpcs8WW5FlSv40SytxagzWvRpUyVS5J/blPf1lpcvxHKa83qlKTSSjDubLkmQNel1KIG8j7GxK37Ult4NxHzvEBn3ZnhiAIXaO58DUeOTlb6PXA8wHXftCn/cjlGDuMhvSTRwdSr4zpCPMz/r4fc6/Gh9Sn5Rxjj05HYtQe/UJ/2gWf5LkX4xntQXH9veDnrlEXK4uU3ajA+Rx+4WPg2YTjTYaM7WJHWX/2Atd7S/bjiy9xPQ92eX/gcf75Asd7bUMDEeeiHtf8yaus9a3laZM7x/RJewe0gT3JDTOfa9y4RhtYbTBvS+eSew4oI4WQe/7gDtc053PPphn1s9mivRl0aKN7c9rXmZwfFWocX5Kj/YliPs8Te9Mqks8m3JOL5zjevpyPVbVGX5LYakr7HEW0V2eFwPddu/xw7fINTqRa1X2m4dWz5VjOAr70FdYfh2PuWxzTl+/uU45WxQ9fuMKz/vd/YAH8xfe+S55PW1qqUBf3txgLvfGVN8A7u1syHuYkKy3qSSB6MJ50wXNObLfUiLVGdDBjbNOQs/Yvvsr1vSN55au3Pwf+yhu8v95mTuCccxfOXQIvSa48rHAP5o57eCx19JyeRbcpI7k596TYljzO0Xe7hP5nPqHuVspN8NmAa1KWM8VIgolqnWsSyXlYs8HxTsbc8zDP5xUlVnISy5U8+qNoRlvhfMpwTvM2uT4+lX6NOf1TIP78rJClqZvPHu5dXubliy50+zKvmLYnkLP3usTgzVDkrMj37d25A356zHWbJBIXRXyhL2cLheWLHK/UdObS85BmlPOW1KSWrlFOzj9JOX3p3pvgo+k+eO0Cbcn1d1PPakt83717/P2NX2NeN+zxevkp2mI/kPO8Zan99rnezjk3HkpfyZT83PsYB03vy5lDkzWV477kSQFt2c1f+QXwXChnwdNb5AXuQaEtZ9khdWvzPOOa61cZ60qpwQWe1Og7XXB/RlvUl161OM2E8/nFKs/nRmIbH7zJXMFFsoerciZboMwvtbje86n00hWkMHqGSNPEjUYPdXA85FpUJ5xbbkY7G8p5ryd1oJ09xuDDiZx3SR/geMJYIst4f3HMHN45xmrdIf1OT2TnqERdWWnz9+Mp7Vm9QHsbFGmPByPar1D6AUJpgttYZeySBfSDt6XOdfPzN8B7R7SPkxH3K1nifixvsIevJPb/5C5/75xzp9uM927fpI3TGmia8Jl+nnx1hbFSc0HOr09//dhm1O2CF6TWl5NaWrhK+zSTXPX1Hcrk7r6cf5U4/s1zfN58LmeqFCl3+OBV8IXN94D3epyvlA6dJ3UrX2qH/oQyl/O450HK38+kf6/X53qcFXzfc9VHz5+lpzgvPcGZOIpqmb4/lN/rWXxO5PT4DmtGiaNuD3vSZyj1u16P+X05R98/HFO3UtTLF50AAQAASURBVDk3HczpR5565jmOt8Oa9HDEvGV8Sjke71FOystyli99JevXGAfMpI9luM+8cPEybeWVZ3k2kzjO509/nP3CBwHPoo7VlDvndj7POf7dLgd9POQzChXmup05dcuXc4tV8b31q4x1Xrv7a+D7A9r/zpC6U6zStg3fYjxXX+eajHqUCU9q4g2pNXhyNF2QfogFqTsu1qTPPuL1B3fvge9u8X2VKWUm9Rir1VY532JIGWxWqQNtya2reWlgOSP4nudKjzR9+jn64ekJ5eb2LerC0YHEjA8kP25TroIc74/G3MdyhevU68l52iLPMdc3Jb+uy1l+ibZmYVFrNJL/ahAeSU9GkTFtucbfe1KL9MMm+GQsPWzSF7n14GfAG88zx/ES9mgfD+VcNeb6DDVOlJ7o7uHb+3zm4iejiGs2k76eo33Ju+asY/nSn+HkbLhaYr155Txzw8mcsWIkZ5J5OYMcy5lfNKItzEvbY2/IPXjztS+Cf+Yz7KtMPY7nQoexZblFmT8+oK0M9iUuGdG4XTxHGb76LHPzm3f5vM4B/ffeFv2XxgNj+YbnLJGliZs94kvu3aavP7fGuY+H3Pum5CHhKu3Bep0x4f1jxvBV6Vt0kjMXirzeatC+rS/Sr3ht6s6rN5hDnO4xVnKO8x1LD9aTz18Bf+7pF/hz2dvJlPr/1bfYZxJJL9Nwyrjizc8xZp9PKStXLlNXN5v0c90Zda8rvUd7D+R7MumFcs65+hLl/yPf/xR4a402N/8abVpLvqdarVBfW3LmdnOb333c2KcMqD5tblCmijnpCQl5PS+9lZsb3KN+wD3y6mL/Brz+lde64POY76vXWWd64Wn61M4D1rGifc6vX6Y9XVmWbyglRvBkPXt9sc81+aZw9HafcybIMuc9krTOpT98PqIup9ITFkn9kKviXE37T3NSlBY/GUmMHUiNuVmi3GtNfGWTujmVb4cSeb6nPdeyLdJO7woNytlgj/l34FPP+kd8X0E+HfZisQUSEodSfyjK2X5pje+blWmLh4dc3/G+1Hh6b6/5BHLWvbLENZ7MqRv1Bn39cCyxjvTmDLR/Q84tEunjy4fUpYLU0b2YujWSfhFvLnVD0d2ifMORSXzoyTdzyZz3Bz5lMg043kxkfi7zy7RvXXqw05zIrNTUGk2u/yiSXmDxj77q4BkhiVN32nlod9OCfCd9ynmUYrGx8j1DX2rrkxl/f3B0k8+vM3+tyLeWZenxys9FDkPKyZXzF8FTaXoOJN+dy1l4e20FPBrzfQst0XXpZS+0mZ8HW6+BX77MHr2pnDdtFKVePpP8X3oZSnJeWC/TTuztUY6LZa5HqfX2s48L63xncUHqyz5/c/uQtiSSGkkqupRJr+ihfm9Qle8ZLjP2Xn2GvQD3jygz9+UbuoMbrBXoma4rMHZcvM48ajimTI8kT5qfSn1YbEnRMc6adqSmNKCtP9mlbfUlr5WynqvL3zaYDqUvU85kVle5nmeJwHfu0U+Irl1gzLj7Fs+PCyLfXfGltQrnXm5TljuSR9Tl259A7HRF6pPpmDl8Uc86JPqaTbn2ZQlmTjtylil+P5RgaEn6NHzpS1mZssZ775Sx0TPnroMfHEtPnHyL/uabtNfLa7RvM4kDAtHdeV7+NkGVujQW/+Hc2/+ORkXOCYrSJ9eoiu/d2ebzAl6/s8c8Ji97Fsd8fskxNspLb2t1mTKbhJSprpzhluWbt1pN/pbMOe7pnS36iKp8LxVNaB9KJTmzlbwvaHHP0og+x5dvKiM5Y15ckVqnfKwfyLcIrbr0/u/Lef43gOYxBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgeA+wP/BgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8HwbYD9gR+DwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYvg0Iv6NvS6YuHLz5Nbr61FVcbldy4F5SAY8nE7nOv0+02GyAb7avga9uroPnZn3waF4ALwxKfP80Ah92uuDbR4fg3f4IvDPkcj/zdBu8lcuD+xHf99bhAHx/VAdfv7IGvpomfF6V908P98C/dA/Utese+Dzk+ixeWOL4trg/0Yh8tN9xiiztgheafObKlUvgpRb3/DSpgs8OT8BvBafgT62sgM+rXLNiOATvdrlGichgMpuCX3ZNcC/j9ZUVzu/2PY4/mvH5wzl/v5DnnjTTGvj9dBe8UuHzYz/j+PIBeCp/8yvvUyfLIXWiVefzB9MZ+FGPMntWyAWeW208HHvot3B9Y5G6WV3ivNrhHDwLuW7RkLreqpXBt+/sg4cBdTOe8ffVBn8f5Kh7588vgx/NqFt/6yb3+XhIW3ru2ib46YT7Vq4XwfPFJnhY5HoVPdrebEY58pOSXE/BPZGz2ZjjCQpcr6BOuc/7XJ+duzvgg87b5fB0j/b6A09dAG9VmhxTwjV+8KDH63POKRpSZgKP9t2bc05ZSN0uBnxesMQ9j+Ij8HE0Bk969B9ewD1otBfBc7Kms5jvz3zKREnGW85zDxsl7kmLt7tsFoOHPt83F1uSelyvyWQgnPNN6xzPWSGex+505+FejUf0SSOZZ+AxbnEZ16kqulcqUK7mRdqu+UjiHFm3eESfF/vcqGpAW1SoMI5wNXKfptF5IcefTSmnYZn7VA14vTWhLWsU+bxLlyiXy03e71fJp6J3Cy3axuYSbVk+R72ZZJTTB9tcz1qV97uMeuCcc+Uc/U/8qsi26N5QYsGkT9tz/cIL4EcSp7iEmzKOuYbXLtAfeDnGKcUS7flxh3HGbCR+v8M9DMpN8NGQcVpjgXFZxi1xYcD3z6aU0UKBa96UOLJe43yPZsfgEU21G4vMZTH30Pf5vlyBtjNX4n6eJXzfc5X8Q5vhB1y7vOQdswH3rlqivUllsSYDyv9syuux2J/TU9q/ao5rlUpe54usThOxJ5nkKXOxR47zycv7igH3upjj+ydz2sfpnNezhOs3jrlefsjr05nkZTnav8ijPeuNuZ6jDuebRNS9+Yzvy+WYZzrnnF+Q+LJOeR4ODsDTkHMa++Sv36U+7Z7Q/gxGEhtFtEeFKn1Ws7EAnq/w/mKD9+/fpUytS6xU9rhmI4/Py0nsEs0oM8MR87BMZCSb0B7Pp9zj6ZTrUW3Qx8jw3Fx0bjjm+wsV7nG9xucdJGL/zwhZmrn5I3WTdE5ZLRS5D/GQupaT2MOX4CIoq90ln0Zcx5MD7uu8T1uyvCjPk3UOAvrtGw/ox6p5+p0wz99vrtDPHm0x71tYkvl6rFnFjo4xK0teM6Mc9+4xZ/niX/kl8E72KvgPfvg/4u9f/xPg+1Qzt/My92NtxvVbfB/11DnnukPqzlcecA6J5BUuR14pcY1SySVLIZ9frnIPEqmLTXrUlVqBc1pscU+DEffs4DZz+9NTytj60nnwZ598CnxllTWo7Tvb4CfRFvjb4vGiyITH+Xc71Kkkpg56MW1LTXLtw9uUkWjzCT7/gLa/LPnIWSFNUjd6JJaplzT/5TwjiVt8x3XyU8lPe4xrcjFjdCd+uBgwhszKtD3RkD7keJ98Y+MieCnk+/IlxjFdUhf1KAfFKuOMK+scbyx5XCWRGthQ8japTSax6KH48MEJ9WQk9fW7u7QLownHX6pxPRt56uUs4vo651w7zz3MNxknFEKOaVBkTcjb5TuyCmsogeTyk1jq0xrrJZzDeMQ1iKXuVxTdLpfFX2S8Ph5RxgcS+/vij1uSC7uUa5hk9KczsSVkzp10JZevMa7LpB6dl7hoPGSNK5M8dDam7W6uyfjPEIHvu3rx4f4UU9HXCufqh5SNYklq9xPaWS/i3O/f5llFKmtdz2hPigv0Q/VFqUmIvtfylPVWW87rqnxfc4mxS8SSrAsD/n4woyyPT+iXTiT4yGLmNaG8vy05xcGx5Jkiy7HUpY6OeH9/LPY7R1tSLlD2kp5M2Dl36w3GCpfPb4DfvHsDfCx5iJOaal5rbVKrqq9xjNUC9/T4Fmvgb+5Rpq5eaIIHjrl1syr2IOIaeGKPannGh6cdvi+L5bxJfMpmU86b9rlH40kXfCI+L0hkPST3r/mUwXhMH594UtMXHxNqIndGSOK5Oz14aA9qee7TwTHnseNxXeIC9zkfcp1bK5Sz4wHXqbP/ALx3TDteKjD2OHen++ted3KO2ihzna89QT1q1mkbnnvuBfDBmvh1R7nrSyx2Mqbf91PeHxaol9M8x6utFsczPj/uUE+Wl5gnNsVPq18dD8kPTm47xda+1KkXL4OnqeTWcv60kKduL9cpIz2pq43lLDsOuadRwj0sie1KZDyJ2AY97wmkjjkXXZcyoisXqQN18UfHHvdwHnG8scRGhSJtx2xM2+p73CPfl/Xqia33pd/Fk3OikPNzYsvOCmHgXLv+cCy5KuOGY4krjnfpE6O55mHUrcoa45SG6Np4QF092b8H3u3yelpiHBZKz8T5K9fBSwvi50WXo4y2rljp8nkXKeeV+i3wfJO2q7Qucjzn/C9eoV7d+LVPgvtiuxfeT9tSbTNOOf8ensWsLbH+cf7cRfDpKWvFOcf1cM65z7/EGsa5p5l3XbtM+/184U3wvtR4Xv2ff5nP+4kPgVckF/Yz2oam1IfnYz5/SeKM1gb3xHe0BTU5A43kTDUvfTOdgeRd0vu2/xWuV3fIPczVGEdtnF8F7x3R34Qe/XMg9e6S9Hatt7nn87G8P5DcYvHtdb6zQpokbtTrfo3rXpzsUN4DyTlnU9r1ffFjd7eYZ1XKIisLlK1SU85jAqmziBkvSJ3o+ITjL0j9cxLR/nX7ep5GPgqpG6UGB7C7JfVGkZWK4+9bkvecnnK8b77G9e6/eRc8msl54ZzrXa5Qd1antC9LI8p6d5v+xDnnRvc5hpq8sxRI74jEc+0m9c0XX9w54nm4H8s5RkHOKDP6+v6Qc96oU58DOf+uVKVXZ8Z425M+Pi8n52fS6zMZc02DjD6lunwFfCD9WccDqVtFal8kHp4yFs0VpbYXUCdTJwf0ks/kc3L9jBCEoas94i9rLcpu+QF1py8xn9Y81JZI64sryNl7t8fzqKUNxgpZjXlPvUK/1rv9EnmBL5xIb9A4pm3ZuvM58Nff5NnGpRck5+hxPeqLjBUrPmteywvUy/I6e8gPC7RlX/wCxzM5Zez39FWeZfhFyvl7L/8w+JfvvgG+WeL6fvgS5+ucc3/7Yzw/6UvfWyr+wC+RV2uS23rU1e++wjWZ17rgx4fMQ7bv0takCWWoss7nDXYYO9RirmFT6paSFrlM+hOSkdjCR3y1c855cg7TXmEP+L2vcjxF6RUIHOPvlU3GMqslxrfV87SVe1/m/J94mud3VelrjyvvjH8n2XOZC7JH1k7Swd6QtqUz4Dy6chYeSp/Kdpc+Lh9yH8rSg3W+TNvSbDNOeOoJ+rgrl7gvBcln2+0meE5K/fkW33f3zkXwg236mLtf+TL4SM5e3nz5Hni5RTmvSVx4fPw6+HsbzAmCKeXwzd2vgH9c+nVvTCiHuQL1YJrj8wu+9GW6t/fNBHJeNOh8FfxUzu8z6ZOZS00mk16oo33JVROu2aLsUbEuNaaa9EqdUOYOd5krL9Z5xjCY8ve7p1K/dUQoZ9UffpZrvNakkL3yShd8KL0Dsyn7LSpFynQxz7hqPOBZ/2xCHc1J3ru8KDWy+dvPOM8Kvh+4SvmhP1xo0pcnY4612OJa1OWbiUqTdn3QZSyw5Jh3lWtyNpAwRiwXqU8rbdqffI7jmUpN4PCQfmw8oB9OpEZ78RzHsyKxYC6VvgzJA2OxB5sSsxcWm+DL0u/+Vamp33uZedHdG9SNhU3WhYIy3z+XmnN15SL4Ru6cU/zAb/0ucG/CWtF+l2PYP5a+w5Qy0ZZeylhyzUx6S7VWVxIZ8aVX+0Rik3Hw69dtfNHvNakTLa7R5o8l1vm1T78G/saU7/ue9/NbgLBMG7+x+Sz4S1+lPSkVaJ+WxR80pYZcbfP9g2ETPJX4/9592uezQj4fuvXNh7nSdExZrZep28cnlLOJnlvKtyhxJvUBSWdzkp/r90witq4sZymJ477kI75/cNrl+1POLxdwfu1aE1x7Yfwy8+uu6GE8kh7vnvglj7Y1J9/D5YqUk1xF+lhKXJ9lrTdK78NEzvv2pE80L+vnnHNT6Z8a5zmGRM77G+scgy81FD9jPDpOmTcNOlyzqXy7OcvL+Y6cuTYq8r1zxDwrkL5F5XmZTxZKP0WHMjBL9Tsi+c5lwvuDKvcskH4sP+V459ovJr1CS6vMa8MK/X0sNfuO+P93SuyTuczFycO5SXumO+oyH23Kt5SZ6EbrOv1o9zZ7QtKQupHzuc7VIvexK3lfImfbXdmXvJQwMjmnLUuPWNnjeAqi22ur8i2OfGuQy3F8VdGbtQ35fUoffyLfcy3lRS60ZzzP8fYd78/L9xuXN6RmluN6VVuUe+eca2gv5u598IJ8mnj0Vhc8k76ZRpV8SeKaao1zvNflmuwOad+P5pSBjtiuUGQszEnNpMG65HjO959KjWkyku+pJNYN0yZ4Qfoc3Zh5VSZnmp7k7n0e0bhkW7/f5v1TT2TSSXwg/R6z7J3xbalzzk2mM/f67Ttf4xc/8GFc17OFvDRpVsrcjHpOfGtTerL6XMvl87RXIzkrrMr5/M2btOPN88y5QznvnyaMqRtyPrYl35ctS93l/j2e/Tx1njXbN4+pC+ell+ZE/OKqT3sx9ClLiw3GYrsPaP8Xqnz/tMf1HHc5nvwida9zzBp/Td7nnHPHMfe80Zfz3ylrwpvLT4K35Vt3f8o1H8n3pG3Rx3pDvl/qyTd34rvnp1yD85e55qciw/VVxq8j6T8byfsOtrim/lycdMBYrrGgvfsS78r3sie3KTPdKX3CJCHf3+uCrzRZx0rlO5+yfK933H97j8XXwzujMm0wGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAbDP2ewP/BjMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGw7cB9gd+DAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYPg2IPxOvswPQleoL36Nb5ZLuD6Le+BpdwB+fHAffNCbg7cbDfBKfhF8qX0OfHTI52VpHjyekUdxAO6VUvDtvRH4ZMD5HHdm4O97VxM8l8v4viwGv3lbxlvh72P5e01TDteFvkderoIH+TH47v4E/HgcgbdjzqdR5vvyAccTJJyPc86FXgKeC7gGqw1OYpxxz3M5jilLeP9oNAR/0Of9i2tL5OVL4NU6ZWj7rTfAT6cd8MmUa7JUoIrFM8pMqVAEz8WcfzzieKMR92gwo454PvcsKNfAq8st8Gme7xt43KPdUZfjo8i4eVoHn8z5vEpYcO8EBIHnmo2H8h9UOJHlZe7T4rkmeLNIPhpx36eO++rmlLtGTi6XqBuTKtepWCNXXS81KLfPvPs8eGdCuQpqlJtinfvml2k756KHuRrlaBhxfnGhDX6yRz0dd8lHQ9rG+ZzrEc1oS6dzrkdVbO/GBt/f79KujE+4X845d/OVO+CDA85poUEZ6SZcU39O3Sy1OIZiyutenvY351PmhiJDWcI180LOycV8XrnM560scM9OD7im8Zi6LqbAeRn/x9oCZWRlXWRmTts36U/lgZzfpEMZKIgKBaHITML5D3nZZR73Z+Yzvjgr5HzfLVUejqVSq+D6/dMd8JPb++CTCW16JvuSJZSzap1yWxIf1G5Tlxoit/k8455Bh3ITiFyXZD6J+ODusAt+cEC5WGjTOMYx+eq5i+QLYqsXqAetJuUgcXx/b0CfORlxPL64rFKe10XtXNqn3fAc12dhjTGFc841WhJnNDfBT2TNj3t85naX9nw8I+8OOCbZcndubRl8qU1/MJfYM5tzDfbu74EfbZF7KZW5vkwZWylyj3NJF9z3xHak1GU/5HpMIu5prij+osr3HRxRhyYT2q7RiDpXLFEnNs6t8XqVMpfPfUdTq18Xgee5evHh+OdiR0OJywsh1+rc2gofGFG/w5T3FwPJoyT2KZYpe6Nhn7xP2fdTtXeUbd0bb3gIPu12wediTwcSk+dyJ+DDqcTkE4mlZKsrAQ1EKjlLT/JaX/K2sEJZL5aZWIUBF3TY43pEkeR5ubf/Hd9KnnvYXmBecHxywDGW+c58TcZwwj30M65pXhap1lwAb63QPiwscM5eyjkW8owdckXai3abazAZcs99yV2HA/rctRplKlfjngQSLN3f5/iy1gXwaYl5ZFVkNplsg1cqjN3SCde3KMGSpPYuX35nxD5JHLv+8UN9mvUo+9s3OO98yHk1Glyn5tIqeFHylqBKudm5Rzm+fZu2YT6h7bu60QRPJB9PHOU+c1z4epF+IHJ8vufRL008+rFhxH0/mnI97t9hrLjkM24YJxzfZ37uFfAvlrkeV17k7/+3P/snwP/Ln/nPwH/+L/xt8MkLT4H/lb/+GfDv96kXzjn3/BXGi/VN7mFuSvtfrHFNXUJdn8se+R5lptRogkcR7XGYSfzo0zZWMvLZUOLfmP50VWoLuZTvy8Zas6Jt9IqMxcK8PK/E60Geut6bd8GnQ46v0RZ/EkqdzqfMtTZYN32wy7w5lLx2NJPY7YyQpambPxLbz8RPi1t2kxF9QprR5mYjzjOoUC7nUpsrL3LfZhGfnyRc9+6QtnEWS62zyudF4tYLi4yrPEdbki/x92pbNjzajklCvWqVabumkgjlSry/f0I5yMTW9qRWeiQ1os6c42nUOZ+VDfrUwpBx23hMn++cc+7wLdB6+3nw6oNPgofveY6/93ZBu6WLvBxTyPI1xjXe7IjPk9g5F9J2ZVLD8DyxdU5rQHzeOKKtSX0KzWBCW9tcbPJ+sY2xxHWxlPTV/8wirkcUcg8TqXE1F+jfk9lN8GvPPQF+81Ovgv+uH/1t4H/qj/5X7qzgOc+F2cP9y4sbK+l5TCB5SLUpT6Q8L0hedu8trsVP/uS/Bv7Lf/OnwZurjFEb9VPw7oz6HodiH085njSirEYRZWFvl7HXwipzgEjOarpHlM1oQHvcaHL+xVoTvN+lnyuUaF9v9o7Bcymf51U436zMHKmY5/Pq69fAexHn65xzpRaf0QsZC01K1IfGIn193OEeOamzaA02Peb9hU2+vyV5zf4B86qjQ9bNV5s8dxg4Ob9zHP9Q6lCp+JREaujlAu3f6YnUjIeiM1KsK0muG4Z83+baBvhsIPG31AJnCX16q8jn+2JPJz6fd1bI0sTNpw/l8zilH1q++gPg23deAg8c8wxf1vnSecaE+RLlalSnY9huUM5Gp9TlyJNgZko56orc70Rd8FOpgVTlbDsRuYvVL2b0U6cD7vskphxOZ1IzE7mYSQysNaxOn3rWlxwmzNN2LS1wvduLXM+xxJ4jOYd1zrlEzkd8ORueRbJGcn6fyFn1VPKOoEIZmA264JHkQWnIvCoWfxhLbJNKnczLk+eLHE+ain/SI1qaSncotsqT/omi2JLJjPbd86VmlKNtn0sNJwkoQ1FEfzdLJc9N5Gxf8pmZ9FKcFTzPuaDwcO/mHufdkvOoA1nHJNE8gRvXljijWOE6v/nWF8CPu9S1QM5CilLvXlqgj5g45hVuwH2qSB64tso87Np1/jwXUA/eepNx21tfoK27L3lT4wk+8P5JF7z6HGs63X3m66cHD8A32TrgFje5QOuSU7z6j34V/PBL9BW/7V//Xvc2JE3QnbvsZeqOmFf90i/+HfCtL9K+/bFP/l3w2//wr4P/8B/6feAnbzDviiROSMfkZYnTiiW1t/QfX321y/dJHLXUoIw26/TH69I8FTb5/NmAMpjEHG/m6xkvc+PFKuc/khpWNOb6JnKeFnq0xcUiuZe9c8670ixz4/Shzkwkxkwmcn4kfqPX07yGXOsuS3LeXFmkX600GYsUQt4fz+X50guzuMq1zteb4J44skh6wvIJrxcKktfFfH9O/Mj0tAs+H9IvHUoO35O8MDvieGqSI+Qk762KPV4UWVuM6E+8I4432eWZgnPOeQPmMZtSQ03yXJP2ucvgaZ0yomdqhSb1uyS1tkj26OAW7V1+Sv1zHa7B+pTvPx3xeiQ+M5NaXufoLvjJMnPdSHpIPKnbdLrc85MT7ulJj/MbD8lLGgMMpMZeoU625HxwnPG6H9A+Z9nXqfWdAaL5zO3u3PsaX1ykbHflLCGo8uwjO+Q6FYp69iD9pBJDLyxxXbTP0ZcadxhJPTIvZwsh35cFXOfVdhP8UHrY9m4wrxyXeX8gNeLD+4wlwog1mp4ca15NroCfFGlbqtJrEKxKb05GW9zr0M82Zf2uX34aPCdnI7n82+UwbDCeTGLGu75PX+0tcJKtJe7JirTtF5do2+7fZTw5m0sNSer2SUoZyq08w+dPpC4p9tkFtF11iW0aF7mGfbElaY58KOdpx8dcr9ZF7sFwxjV/+gnpzVnh/Jac9EFKv0qzTtu+eyC1kBOOLwhoq84Knue5/CO+3ZP8vCrfRzjpMchVadP1rN2Tc8GCL+czUq8+v8J1mYpPWFvgvkyk7yceczxzqRkd35eekwXu+8kD2raJx/U42aOuzyW/n8W0FZ3dW+D5OW3NuTLn80ydMcXomHnY7SH19rbUy+M81z+sMFGbadw3p94559w0lV6jKccUuSb4yZQyERYpM7HU6ZIcn7ffkX6HiLq9skTbc32ZMrJW5fN3+tITLWeQGyLTa8vr4HmPtnee0nZuXn4S/OKLlKG5xLaXn+KZyeCIuXo05vOrZdqWqfhfJ3W6xSXuYdOnf1rfoC0f7MvzzhBpmrrJ+OF++wXK88k2e0QTOfvY26Ed37jEHtk4kJ61de51yUmNWXr+F2v0s4nH93l5jvdUvgdwEqM35BuOxjqff3GTuhRnvP/mNmPywyPam5rUawOZ3xOXKSudEa8vb343+M9+7HXwtMLxnp5I7HJCXfbFL3/ggzyLrcv5pnPOrTQ455d2WBfp3b3HV/Y5hmvPMg/LpXLeXaH+1KT3PVehvrQlXs5i+pCq+MSB2NivvMp+qvOLjP8KNelHk6Kzfg01kV7Qrflt8J11iT+f5BnjuTW+b+eI4z29R5+TSu0jldpHTnpLA7k+nFMmOhNpPDwjeL7nSo/0lOcln/ekmD8XuxpIH2FR+ji053YuclgLWb9L8tz3mfQMD6QHLJb++nKJfDqjnGteVpJ++kj6NgqSt41G9FuVUHIC7ZeV5wVy1lGpSP1SBD1Xpq13cjzlJ5SzwQPmfaU244SCfAPjZtLM5ZyLpI43PKbtKcn5UTqS3HvOOaVjvqOU8vdzOaco+fx9xVHm9DudpbZ8Pya6N+3Ldyvj4a/LA1/O2qWul0p+UJOz92JBkm2JT2cpZVpMg8uJv5NPCt9mG/1UzlSlL9H3pBah31yeEXzPc9VHvq+QsMfFYit8RxvdanLfqyX6oLF8m5JK7W8sB1pRlb9vyvt7M/6+JrXM9U3qqi9ytHOfvfLuhHwuZ9+ZFMxr8i1tX74FPZS4LQkot4U6bUEsfzcgkzipWuF8K5n03k8pd03JcUqip2HC9dTv8ZxzLoskbxlzj2rLfOZahbZqLrYiJ7YqyUSXpZc0HYu/kBpFbs66WCVj3bBUYFxT8eVvBYh/TOR9iX70I3lOrix1SOmfyCbSOyXnhwXpu5nJ31qYnXB8fqTfQ3M+ZTElEznjCeTM+s3bzGXOEqlzbvSIL0qkVj4U37gq502DgcQ6ecYm45n4zQLXzpNYJJRv88pSk65s83lOYub+G+zRPRzQHl1/+ir4/p70uPlin2ZN0IWL3GzvvnzrV5XvS4u0D7MR/XAmsVG/J9+EbF4ETyXWWpLvZ7dSzmehwth1Z8w62ep56rJzzuVkD+ZiswZD+u7Alx6MBZ4P7b3M7zo2n3sWPFWnJz6jI75+UeLBaIfx3rRPHkrAGJS4Jv097snKNT5/aYG1hLp8A5Gl5P2JnIvI91qy5a4kpdWTzhb4OfmG+d4D5v7nVpmnvvw687anpRZ7sPXN2Z+3f/lnMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg+E3DfsDPwaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDB8G2B/4MdgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAyGbwPC7+TLMpe51Eu+xtOI14eHO+DVeAwepAl4nM7BJwPyoJCB7+6cgJ/euw9+dMTnnx7sg+eqXK4rjWXwZnMJfB5PwQvDAHwm4y0upeDTPnku8MBrJT5vMBqCdzM+v1nKgdcbZfBrl/n83RO+fzIbgYcZ31eo8+9FFR3X0yW83znnCnm+I/A94afgUZdrWqrF4LMB5xgP9sBvv8k5n2tTRtavPw9+4ep58Emfc/C7HG+lUgefTijDw+4MvF6tgFfrRfDFjRp45nP+D/bugUcdzv908oDPW2mAF4oF8EaTMnV6Sj6ZdcEHE44v9Lge32ET8w2Rywdu7VL7azzvKAfLFxfIN1fBix5l2cu4D+F4AD4o8PmNJa57yW+De/lj8HKjBD4q09aUC3nwzoxyUyzL324r8P0ZH+/K59fB45Djz0L+oCHrceeVt8DTIfV2MpqAB8uUm9Ya13v/7ha450QO+12O7zxt7+q5DV6X/XHOudMd2sftI455HNE2lFocs5/QHuapem6hQl04HtHhJTPySpMyEXlc895E7G/ANVm6RFvlprRVq+e4xgMZT5jRlhYc57+6wDVeqVFGjju05aPjPnnG8YYzykTmU2az6Ah8XKAODCb8vR9SBzq9nnsnIAwCt9hqfo03l7nPyUh8WEYfcXrMecQZ13k4olz0K1z3fJn7FKct8GCNcl2r8nouTx+Vzqn7szltYW9AfnrcBY9SjqfepK76JfqMUtIBn0xoK4+2qNt+QJ9bqFCu+j2u9+tvUe+Xl2lbazKeXI7XT7Yohx2f45lIXOmcc4MNrlGz/Qx4o0wZqZW4R/k8Y9lujzKQo+q6hTafVy5RV6IRx3zjLdrzYadLfkoZK2ViK5bpb7jizrVrtG2TscR1da75cMInhAs0thFVxvWmHG+uyjgnn6ctiiLawlyB9xfr1IHiAsefL3C8mtucJTzPc+EjcfWgR78QyeaEvsTQY05mMKesePJ739HvFYvU916f+umLHwwktij53NxAFrfk0x6mE86vKqHQaM77k4Tvi4aUxeEJde1tC5bj/cGcslH0xJ5JLON5HGCpxPUqFjjefJG2II14fRZXeT3l9X/yUs4hyzQWEIVykktWOYfFFmOD0ZQykPjUJz+UvCGk/aiUyftdyowOr+hzD/dP6SPCGWW2nef883nuYeZzfvNY5l/hni6Jjym4XfDu7svgXvESeE5cROBz/qHERuMJ92vUp48cz97uc84C0Sxy92/c+xqfy7hLJfqRckFqCAXJuzzaplyR+fZYYt7BmDyTdDRXpC8vNFfAt+9T97M8dTsT2xaFlJvjPue306ecDeb0w8sB9ei43xNOOVxe4/yjkOv34JR688kvvQw+KVzn78ub4J5cP0jeC/7x//oT4P/mj30A/JUtxiXOOfeD7+WaX7/KNdx+g2OeTckbC7Qlc8nTgiLtXyx1OF/ynPU292BzQexvjWs67UouK/HexgpjhWOxRXOpofg5ybtKtJ0FSSw98ZdhyPcFc65HTvx5vkgehJK3xZxPfyY1LDG+WZPr5TclET4jeC5zufThWEsZ5SbOuK/5lOseDTjvUOqt5RKfNxhyXRrnmY/HPbElYtP9kHrhpIY0iSknXkhjljiRyxx97FTq5ad96ubykPM52eV4X3iKNaJ7Hebnq477ftzrgtdjyWuHlNN5yvlNJIc5X+f6rK00eYPEVVujt8c9wy3WgwfL23zmX7kBnl484BvuvckHllgvHh/z+ZHkWYWEaxbkOMZSjjI4ktjVk1h1OKZ/aDe5h27E+ysNjmdw2OX9otuBT1tTFhmtlckvb5wDD8ccT3OhCV7ocpMXypTZSkAd+Mi7vhv8s9VfBX928V3unYIsy1z0iMz7BepHIZHznTrnWm5zbfMhY4NA1nJh40nwX/jFT/D+kLHNbE59OxY/Gop9OR53wScS4/sRY52DfdaR4oixWE/qhYnkTWkifq7E9ZhHcv+kyfd5fJ/n8/pEzhPLTdZHE4/nf17IuGI0pO1YbfB5WXzPKRqbV8D7E65ZU0q4fVnz6IT2pdtl7Wp6xD0c3GdsEHf4/h//ke8Hf7DCNRh2GAvkJa8YxVzjwKfPmAa8P1+gjY57tIfelNdHfdqP/oD2biKxiRdQx+YTjmdwTB/Z7zNPmya0j76jDBbFpczkeiGn519nA89lLvdIzh5ND3H99MbPgC9WFsErEkP7E8bQeakhhTF18UKT9b+y1OZ7Bd5fqNL2rbb5+/GM9cvDA/YKOKmPDqWelznajkj8XLGgNWfe3xbbc3xC21muii2KpR6Qkxhczr1zYls6kiceZ9ILkWMs53vUm3yZ6+2cc1nKezQP8rWQV5TzGrleDDjHsk9bJsdDzhvKGaeEKlWpq0dSCygk0i/hczw5j2vqSd0+n5M6W4VrFPsaL0pNSN6fxLQlFanzxY62Z5jpOQVz91RiwfGI708l75vK+dw746TdOReEzm881Ndorvk810FlOQxEzhLOc2WVup5MqMur6zzLPzyUmLxAOcw1GKdMcvT76YS1tRPxIStt6v5yswl+6WnmTXduM8d4/QZt2eHgNniwzufNSpTjJKZt79/h+JMu5XDlGdrSD7zI+WeDLvhnP88+qaDI8Vz6LYy5i0U+3znnAsc45WiHa7b7Gsfcfv5Pgl+4eA987wH9w+ryRf4+x+cPPMrc1l3KTF/OWP0dysjmZT5/MpJc85bY6y3GNTuO86vUpF/ju54FTx3HX1mkDJWb0osgcYfnaEsai7StWhdNRAeP5fzMk7pqOOXzW/47I+5xzjk/9F2l9TDuPZGzylTyru5Q7HKVslWXnqxqnmu/0KKjyxVkb1Lar3yO92ttfyJ+pVqjrNVa3MtgzrXvT8h7e7R/oxFlP3R63ibnSZKHRdKHMp9Sdlol5hxX2bbhfDlLmfn0XJpT1Eu8/96tm+BF6YfIpA/SOeca4hwjqQk3m7SBzTZ5V+LFnSPWhSY5rlEsfYGn0qvy+jZtcs6TXiQ5o7xUpwxJS4Xb79B+jcU+daW2eTT5Irgn789lUnsc8fmjMWV2NKBMRJyuG0v8mjrGu/tdrufGhLWK+4dfAF9bZ/B4cEL7elbwnXOFR3og9+9zXHPJe056XKh5nnI3GnDfU8d5L69e5P1j7sMk5r533vwkeO2J94N7orupnJu2rvJ8pyE15ab027aWOL7uIeV+4ZG+KOecy8r0m9M+5XiaMDYcfZmx0riqOQ1j7Pol2vZdiZVu1rgf45B2Yl6YyXXW5N58lfUE55wLL13jGBpc40R6K2tL3GMx926jwvg2HnAOh3uUmVqO93/PR17k+/OM1xbPXwTfrks/gRTmyyH9qxtQ5iaB1Nwlvrw5Iv9X/8z/CP7f/yc/BP6+76XM1uT8akNk7kty9p7OKAP3b3P81QH94aRLvp6jTA1u0nadFTzfc7lHzqgSCckK0v9fk75At0L+5LOU2yDPB5ZylItE+vp6+1zXjpyDfvGL5H2pKY275LNE+/j4/LH3CnhdetvPX2bPxdKm9KhJT1iQ5/tmQ46nIvXjF5uUixcX5Wxeap1ZwPEl0nNdqfAsJagykJqfUO4Smqp/8v9SzrE/pH3LV5hXHEl/QeJxTHOJZZ3HOUQSi+WlRpNIDaQ3ZNzUkLqbH3E8F1q0ZZcWGCcsL5M36xxfXep0tQafVy3y+v4B9+z8Ctcrt8q6aaHA+Y5HlNFEzt5X1/h7P6SOpR2JqyQW70of/Fki53tu+ZFeiXqRsjKUPodoRn26fY91Fk/OvwtV+tpEvic6njBGTT3J8SWG7c8pW7ePWUc5ecC6TLNCfX9CYtClTeprEnKvv/AG6ygnHY6/0+N4vu9F9gRHcr6elzpTRXqTpFXKXXiO4985pcGIZ/rtlfSJcHju/Cr3IydnIc4550USj8oaJ/J91NXLPAALK7R5s1jOk5Z4XlXNmHsvLVC/VqVPOSc2d2/vdfBf+8WfA49LtOnNBvulLkldplqh/Z1MaX8vrHB8ntijjpy/DYZd8ExqpfWG9A/Id0Q9KXH7kh/0htyPSPxFUKF9XlyiDJwV0vj/z96fxtq2Zfd92Fz92v3epz+3v6+vV0VWkSx2oihKpCWBjhE1NpRYUCwnBgQjQJwY+WA5QADBQBDlgyMYcQxYCYIIAoQ4ka1IUURGvUSqodhUFat5/bvduaffZ/fdavNB9Lv397/FYr167/EUpPH7UjXeWnut2Yw5xphjjnVu5eaXz+maBD+tmLFKo8l58QNez3L2O5FvV8TNOU/qW12g+zY+ryhkrcjZzFTq8QuxJc2W5IS68n3UkvM4GkmNstTzVpIvVD2NJJ+aNLTGjHqxEdse+cyh1bXUuJXsX3khZwRSd9KU8S02+oWBc2HKd8RSX9T05Ru7M+ao64r+YzPkmDQbUmfIKXCtDtdyrfUVBXVmvuYYbKT4J5Tvn3wprVxmclZfsb2l1Ei3Y6k3kZrnSvxJIV9xLDM+L5Dv1cqKOriQ74auxvKdkqxZ/Ta106HtjfSDu2siSVN399U3P5JrqfnaabGdffF5UYc21M84DrMe1+paasMPe7Rlt27SJz54wP1360C+BZJa+Lu3mYPyxHalnnw7JPnndcl1M/PY3rX4mEfvso6mnlNPBnuSL+9LjCzfYc7W3Fd6cm69vcN1G66lnnXK3y+WtI19ycfkcg7rnHOF6HIm+ddY8oDdQGqGR1yLpzPZJ3V4v3+TcxZKm/yccVU070OuJhL7yVotJCdfSdzQcFJvILazku/CPYljZiN+p946vAt5LbWzzhdbkMn3XUv5PqXPOK3t2J+G5OSqheQxB7S95/JdwHVS1J4bbp75Ck/8XhFTF/pyvuUyrseDNn9/pGcLst7mJ5Qnl5SjK9q/2tGvRaIrWY8x9I3te5CLxVcgbzX4jUKnz/64DyivHXWn1WB7xby5htRdjHzuC0ufuhPK3wnxJdbKr7jPvVr3IbelFqpYs31lKN/fVbRXzjnn5PvFRMb4S6+9Dvmbv8m65tYt6nvDZx8Ot9jHk4eiExf07ZGc8a1C+pSdW4yVnE+da4jO+lJPlUgx0fkp8/BNye0Nr5jX2Za99GrEObq1z/F49xtvQd7p0YefHDOW3GnxG+dz+QYyHY4hP3n3G5DvS11mL/nuztv93/kWwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzA+LvYHfgzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzjMyD8bm7yPO+hc27mnCudc0Vd11/2PG/LOfffOOfuOeceOuf+RF3Xo8+mmYZh/OuI2R7DMK4Lsz+GYVwHZnsMw7gOzPYYhnFdmP0xDOM6MNtjGMZ1YLbHMIzrwuyPYRjXgdkewzCuA7M9hmFcF2Z/DMO4Dsz2GIZxHZjtMQzjujD7YxjGdWC2xzCMz5Lv6g/8/BZ/oK7ry+fkP+uc+3t1Xf95z/P+7G/J/8l3ekBQV65VTj+Sp+UG15tBC3IZtSE3oi7ktsfreRBDnhYryIunx5CXFxe8f5lCzgIPctIK2L4gh9xtRGxPm+250b8BudVt8n0ZRDdecHzCku9rpqXcP4fc69SQD7d7kA9S9rfXYXt/+b33IAdN9s9rUH3CoIJ8cTqGvMkpO+dcJHNWLtnHQcI+em1e9+5SZzYztrG4WkDutRO+P5tBni2mkMMN52C14f0vv3IH8uHhNuTzK+rYow8fQ16LjjV6bH+dNNhe34fsKt4/dmz/fESlOjo6hdze4ZpqRpyPVsrnLziczk94//xqCTmPpL3fG5/Y9jjfd3X8TN/7g31c7u1t8faIehI4jmOz24c8uZhAzgvK3YO7kFei93HGcW71aNuygu1ptDpsT0098jzKec55aMg66OweQp4UXNvZmOuwmpxD9sX2/MnX+Py/8lWIbrnkeB6msi4D2q6Iw+OOnn7A/+AVEJuDW5B3X3nFKb6svV6f9vBGj7bF63CtPP4W7V3pc04XK9oqFzRFpj+pfXZyvaD/8mPe3wkpFznft7pYQ172+fqdkLZlk/H+9Ypz1GmxfZMJbU3aYP8WV1eQI3lfvhZbIbbNq9ifvM35UVuz8geQNyPa3k/AJ7I/tXOuem49tlrsx84u1/pqzXEO79HHrDY0wss1xz0Um7vKqKejEedtOeW8V+Lz6ozPixPqnRdRFrV2vf4u5J0m19Hd2+zfPGf7Lt55APnB+99k++bc/967w3W4c8AGLebaX7a3XNMuhNK/hsh98Zm67I/eY3+cc+7k4UP+JjuC/PIXPg957+7nIB92OKaNimuhJcFkLmt1NBpC/tavfxXykyeMldsB/cGd2zuQ9/tce/0bB5DPjk8gS6joyjHb1+4xjqrXnCS/wTF/OKQ/qgvxHwF1Tv17kfP+6VrjPl4PHP1hlHINb+0z1v8EfPLYx/OdHz+LF/yIg9+S9dhI6ffCgPaqyCQWKOl751PGAg2xF5Mxx7YT8nnFhrqb1VyvWcm5qApZwE3xq3K/E78SyXgEPmXfY/tcxevrDeX5nO9ryj7WC2nflyuuXT+X8a5lz1PR3gcJ+x+Lbnvafuec82QMJROwqjinRc45SJaMTQZdiV8lVvE8trEsJXbZ8H21xAJVzt8XhcSzCd/vi41uerQXkccOdxlOuyjm8+Zz7vs2U865Jz6r06V93OlIfCv2Iy84x4sVx7eQ68sVx2O94XVf1uz3yCe2PbXnXPVcPFIsJbZJmZMIAq79Qva3oexrlnPeP5CJ3BM7vLtDv1LKWgvatH0zx7VZih/IaurhjuRYhkvOc3+P7WuH1MO4TXk6ZQy9Eb+Wdqjn7QFj7B/80kuQf+4//S8g/8Wf/3OQ3/h9fxTy2Qnf940nnI/NF34E8v/5VxnH/Pt//MtOUXuUS/x2ef4U8lLs7e2X70OuZW0XOW1JteLv44C2bHu7z+sttm8yZWxxPmYsM/G5Fhc1dSaLZF8mOS3XER2XfUy0w9xBXjB2S2q+X3MVVUVbUvvUsSKWfZnP3688iSVT2TfmvO7VkiT63vlE9sdzzj2vGY2QfjOvJe6I2Y+4Ep8hqSy1Ba2tPV6vxA+HtC3Zgu9vtTjvrT6f5/nU67XMU76kXpee7DvER3oSF+U521PKeD18xP301YXs/x1t6WzG9kQ+n+872nJXUm+Cku2NxSfX8vtSjjMaskdyzrlyj/uSjaMuP/5h+u3WU9pfT/bauexrWkuOiS9rNagYR/iSx9vMxXY06E/qTMaopo42JPbLmnz+VHIytaNtlG2Q8ySnFXr0N6nY8ob451bCOdlJudeY5WxPLHHRVoex8/CS9x/s0hc8nXHf+gn4xLFPtlm5x+9/45lcse+FxMBpiznpWOx2ITHmbCI54jXH7uySeRNP7MPDC4kdItoXOZ5ykymv93q0VxpzHk+YA0/Fj6Sy/IMO27+9LznygmvhnTM+rxZd9eR+r8G17o3eh5zJvrfgNtZ5Ym9L8btJi8/zBmOnXFU8f1nIXjppso1FxNioe09yumeyLxiy0asV7c3pe7RHj197HfL9W7chX0qucnnB9edqyfmKPWhvUWfafersnVvUoUL2zvsHklPPOD4z8dFVk+c445z9rzb0IafHXFPNBtdEJWfUyxHHd2tL9uq5JD+/Nz75vss5Vz03FXlNO9zpcBwP7jHGLObUq9NjjuMHow8h72xxnLwBbV0VyDnlFfU6XPB9bYl19ADIFz+h51mhxGZRwnkandBvb23R748z2q5ezOeVkbw/kD2N5Dc1Z5zFcnYeMrisU17PS9kXS/9FTV3hSQOcc2XGPoQv6KrsVXucw/WcY+JJni4taBt2GpzTcM7rqex1WxvahrnEi25Gf+hJTiiN2edVrYl9xlZOclJJzDkoJa9ZS84pqSUnnMk5SIMOrggZi9WOv6+13kSP+iXeDZzk2CR2+wR8IvuTl4U7GT77eaNFG53IWW8lDdc4v5D9ZUv0ImtIToRq67Zf47j/wM/9GOSNnCsmHtv31j95CHl1wfzEomDcEHCL4VZz+sjhjHo3FzUNZF/64/e4jj4MaYtPnjKmmL3L9iViu3a77N/9Xfrg9y+o91//2w8hb8RHd7Yl5/Y/k2Sqc+7WgGt79Mo9yJfvnPEdj96F3JY6k7f/xl+GfO+QOjKfcgzPz8e8Ppcz0rWcNZ8z59MbcI4XM/EHcmbqedyn1ZnkYGr638mQebPY5/09ybms9exczuofP3kE+XTIfdG6ljOPFv1ftM/+riWuXK/ljGbK+fsEfOLYxw8C1+71P5L3btBurjcc207C9bl9wLzBdo/6vJa5i8V3r1d8XyrnPb6cD0c+74/Ur4ibHg/pR8sZY9ijE8b4Z0+eQM7k/Ts99qcpOYR6ybnP5Lxr5aQ4RxJlWweMyWeSn21JDP3BOfc47T7H4+h9qemTPcc6k42bc66QHPJEfHlH5mAm598znzb38pxj/t4ZbfBYbHwlPk/zMA2prVxW7NP0Edsb+dTZTS3naZrX0TmTXEJd0J50E8mhV1zvodTi+nJe12xRp/yC70s6bP/lI47vdp/t/epbvL6zSx0Zj3n9e+QT254wiNx275n9ODqlL27u0c52DhgsnD+lHR0PGXNHlezvJRaqc5mXnPfvvfozkJNt2rr46Vf4/gZz0PkJc0rprtQmjbivqmYvQ24dyL5zj+ui2XsNcpS9CbkzYBywfsLxvQg4fpMRjd2Xv8B84eXb1POlnOVkd7guTz6kbd2MOb4Tycc651yzR/v2Mz/5eyGnstfNxDfv+xyz1Sn7WEXMGy4Dsa8D5sGmMed8LJ8PjaW28+JkDNkraCu39ijH8rxKbFsi+7ZA9oF/6X/770G+++ZPQG6GogMh19B0wfYcdPp8X0x/dC+ifz9bcU6rjcT7KW3XzEmu4nvnk9kf3zkvedb3XM8F5XuFdiIxoOZjxY9uFrS5hUebeyXHTW9/kzHt8SnXznQuMbLUZKieNVuct36DMWoqZ+mDJm3LK7eo923Jb/cP+fxsQZ83mTGOyqWOcDjlABQT+sSslHUs+7w6YG1Ckt6DvJC6xLrkOqjl+xHnnIsj7u1CyYMNboptyHjGV+uhp9ROJT224eBV7vOiNddGo8tYbiG1T5drXl9KTmVLziiqmrZlKnNyNmKsP5zw+tXwIeTOmLbg/IS2trPF61td9V9Siys5m43Uut3Y4/yMpzSewyX9/2LK8a/ljOgT8Mm/7/Kd6z93qJNIbKLnv7HT9c65X0vtzMmYdjivVOGpGy/fkrNVqW//xnv0c//in7KuuJfyeW98md8UfO4V1hZ5KdfvxYhzV8jZ8XLG5/tSY7vQ/kW8/k8fyh7/nHuKV1/nWc7rn+c3HjclSX06oT2fDOV7gHO+/+kD2or91ot+sJQz/Tclj9Lap/3ZSI46kzPFB8e0wedyXr5QnxJxDPY4Ra4neY/VhrHED/7EPciPjhj/xlJTsbjkvvHoKe/f32P/791g7OZJ/dla6jDX8gHWaEyf2u5xjqIe7acvOekPH1OHUsmlaq6ivcPx2mhy4nvjE9ueMi/d5Gz8TJZvZ6J2H3JL6hTqBfVoM+NaSKRGSkJ058u3QVsD2p54h/O6Wss57kTOGWV/XvqSE2rSlqRt6mHa4vt8qXO5WsnaVtu7ZgebXfr17R2pM5H61PFQ/JTkH2LJuXUl9mtJPiOQ/f2OxHbfzguGbc5JKr5YUhxuckx7HSZSH7WkvCd570BqXcKkD3lRspWXQ/YpO+P7S8lrbcl3QnXF2CyNOaalnP2HUuRdS81wJd/kVaKj/T6N55Wck/iSB9Wa5qXE82Eu3yzKmopkjvtdOX9LX9xrf498IvsT+YW70X7mCw8GrKmYZLTR4YB+WL+RbXXpE+/IuJYSF3lyti6fcbtQas5y8SGV1PavhnxAUNIW7TbYnvAl9qeM2P6HE8Yl5yO2Z3pCn9lNGBP3Etn3tbgOUhkfz1dFkm8XuhIjyN9PmeSMMwsJw0rJF+RaNOecW2fU7dWS79zMaY8zsf/zEfu0kLqVrV3GKWH3Jt8vZwJeTr9dLmlfQ7GFDSffyMnmshSdCQKxTZLv9aU2K2pyjsLnzmuccy674nfyidQ9RmP6a/lTBK4nuYxtyRVs5HuOyyfcC4QBn38wkMBR6v4/AZ849onjxN25++pHsub7mvI9VCllFVP5FvDOgPualfxNhlaXDxiNqAtzsU+p/A2D3i2p39ezEvFrxZJ/h+K0wZxzmHL9nZ+zva99gf354Fe5z9t9g99azqccj558B76UHMVum2sr8caQN+J39+V7rkzO0wd92rvlhPZp7yZ1cTV98byru8NnvCN73R97k9+Tei3uU+YrxiKHL4vPkvPu/QFzax8ec284aNE+lQHtQyXx5mjG9na3XoV8/pT1Z3dus37tK0++AfnmgP1djHhed7tDnRrUnNNcauVjiZd9+XY/EB+Wy7fvjQHHw5f9SCrP68p3Pl5Dilx+Gz6Jlfojzrm/9Fv//y855/7oJ3iWYRjGd4vZHsMwrguzP4ZhXAdmewzDuA7M9hiGcV2Y/TEM4zow22MYxnVgtscwjOvC7I9hGNeB2R7DMK4Dsz2GYVwXZn8Mw7gOzPYYhnEdmO0xDOO6MPtjGMZ1YLbHMIxPhe/2D/zUzrm/7Xner3ue92d+67/t13X93/9zDafOuf1v90PP8/6M53m/5nnery3kr6QahmH8DnxKtudT+0v3hmH868P3ZH9ge5YW9xiG8bH5VGKf1crsj2EYH4tPxfZs1ptvd4thGMZ34hPvu1ZmewzD+Ph8OrHPxnLOhmF8LD6lnM/6291iGIbxnfjE+67l/MV/Uc4wDON34FOJfZZzO+8yDONj8anYnvXa9l2GYXxsPnmdoX1fYRjGx+dTiX0m09nvRlsNw/hXh09n37WxfZdhGB+bT7zvmkztvMswjI/Np1NnmFmds2EYLxJ+l/f93rqun3qet+ec+zue5739/MW6rmvP8+pv98O6rv+ic+4vOufcrZsH3/YewzCM34ZPx/bc2DPbYxjGx+V7sj+0PYdmewzD+Lh8KrHP/r7ZH8MwPhafiu3Z2t4222MYxsflE++7Dna3zPYYhvFx+VRin8HAcs6GYXwsPhXbc7Br+y7DMD42n3jfdeP2HbM9hmF8XD6V2OfGnRtmfwzD+Dh8KrZnZ2fXbI9hGB+XT15neMu+rzAM42PzqcQ+r7183+yPYRgfh0/F9mwPbN9lGMbH5hPvu159xc67DMP42Hw6dYZ9q/UxDONFvqs/8FPX9dPf+t9zz/P+mnPux5xzZ57nHdZ1feJ53qFz7vx3eo7neS7yo4/kVcC/PFZ6Dcj+fgdyXIkcLSAvL0rI9+/uSUcCiHlyA/Lm4QXk3ZsH/H2eQGwmtKstx/cPS/5V2b1D9q9Qs1znEC/mHNIw0ukq2J60ghwF8oLsCuLpkM//9bf4lyinR48gH7zyKuRbWynkqw3f39/t8f0J+++cc2HAPgQ93pOEHuR2zX+lYCr/YsrBzRbk5r3XIHeTJuSHX2ef3/q1r0AeXxxD7vf5/O0+daTd4Rx98G4G+eRkCDk6OIQcT3j/jZLPy2Zs783eFuT1JXWoem69Oeecn1OuNj7kfMHfL5dTyC7k/X5AnXc+dW4+nbhPwqdle3w/cI1W/yO5kH4EPvWwqCjnJW3NbEXbJarvco/jrH9f2vOpR1HCeQyalDtRm79PuPb20xjydMP2+aJHW4NdtjfkWp2fUs9WY87j4ojy1Yjr8i/+U/Y49/j+nYR6Ngi4zssu1+nh4QDy5IxTHhV839FbX4dcv/GGU7yY/uClN1+BfLvF64tK5Kf0F3WzCzmIqGODPv8Y5/iS9nieiRLVsnYL6uR2jzowkTm/e0AdmpVcm51a/EnN56mOLyrxJ9KeFlXarTz2Z5VTR65OHkOuArH9nvyLNB7XYCq23Ks5/vd2X+fv/+4vuI/Lp2F/yrJy4/Gzf809PZV5n8u/fEG1cb0txj13BzchezV9RsppdJcX9DmPjs8gnx/x+nzKf3l+M+E8R6LXN29sQ/7RL38OcqNDPRyOaDumI/b/0dOHkC/fpZ5ka/qcLY2LVmyvN6XeNire/5KMbxJx3W01aHuLjM9ryv2NNuPEfMDrzjk3uWIf5pfU7UffOoK8WXDMb7/xRcgdn31aLTimH77/PuTpjHMwG44gt2mO3Ut7tL+DLtdqXbI/iw3te1ay/dWGY1h7vD4RnTh+Sp3d2ulDLuRfDd7eoU5eTqkToUxJLvHA1YLPOxszDvI9+ptkyPFMA4l9vwc+rdinLAs3GT9b41OJydKYk90b0JCLOru5uIGqllgqYCwSyr7Ip2o4V9FPlHJ9U7F9mcRWm0qeX/B5vscHhjHvzwvO7WrN59cedceX/kmo6KZib/KE45M5+q1lQXubz2jPcxmQtOQLs5z9DRx/X1e0Lc4558kCmK3ZhjqlDlQB+zDPOQabK/kr4gXnLGHo5MKESlXUMie+2hOJx6VPaZM2OhCbHFVcj54T++NERxq0p4XEVhob5TX7W0ksdfvVlyFfnNN++NIeT+J1T2K1JGZ/XZvX43bffRI+tZxPGLjwuTxA2KAirGR/msi+ZrXmvGwm1NOq5DysJMVQeZyXwTb1Oo7E0YWy1hucl9WGevdCyB6yAZ1drpMoYXvjPvu71aPe/57f/0OQj28wLnj95g7bK7bv8D5jr3/yX/4G5KJFvXz1c4zdRiPZc2wzVuq9wT3Nz/7+n+D1oxfPHiqJFUqxX60u+9TuMfaIxHaVHnWqlOc7sYeddktkyTt61LHhjDmgyeYE8lJyVKtI9jkV3z+WeH0hecL1hr+fzZ9AbjaoU/OCOhW22J9ozVgmbqn/4nitcrFNkewzp6eQhxeMzZrTF/N8H5dPw/7UVe3Wz+0VFmvxUbLfnGecxyikj5JtmVtPOY9ekzZ4OOS4DzrUuxd8WItray1xTlmxvcs147gqpE+IRC8j8eGtHvvXbvB9RY/tqVdjyEnC9y0Xojch+9uSfIWTdbaY8/m1xD0S4rvRjPM5GnI+ws6L/6pbZ4c5/0L8Q3bjByBvVrQttUfb1Cj5+602x6zq9vl82Xuu5UyjKLk2a49ztFnRVtaJ6gTbO9dYdsUx3dliHjAI2J6gxVjVl3z9csl946MzTtLkinPSGNC2nV1Rh733eeZQFGzvt77+tlznePx//t9/131SPrXYx9Uu8p/rv5zvxLJvml1KTvUp98CNkPZifnYJebPi78ul7MEzyXFv6Ne8Nv1sGnNs4zbzaztbjKkbO9SdWz7ld776DuTbB3yf36SuDLrU3cWaa23R5O/nG/GLR9S1IuDaCOYit2if/IhrK8ip242Ua/nWDufLa0hs6ZwbnY4ht3zZR53SpnkrzvHhXY7JH/g37kP+54fM5f3mP+Yc+wX7+NZb/wzy0cN3IQcV1/86ps6+eps6MLine2fmOo8njB1mwzHkzjaft73HOdjQPLuG6GDz7j3ef8VYbeNxfCdT6pCTPFWxYmzUatPnxU2x15LL+Lh8WrYn8EPXaT/zVZXkROtacuch9UL347We64kfuaHnginnLZRtwFj+tedQzoM6La6dfo+xxCbn75ON5FxXXPthoTE/9Sab0I9lkv8rJGavxC9V4neLlcb0Ygsyjm9Q8vnbbc5PFUjOS8az7vD5fuPFnHMmxym+5PGznM/QaoNazn9WU/qjRNrYSrg23IZ99iRvt1nwebHENg2R64xj1pSDj3wpYyw5lsWCeT9PzveqmrairtgfP6S8kkRgLbHXWv6lvTrSPJ3uMCQezxkvO827li/6m4/Lp2J/PM/5ybO2X12OcbkjMbGf0qbOZO1s7zJGbbc5Lg8eMydSely7v/+nfw/krZdlPyv743LG999+kz5i9h7Po6KYejUZ0ufMrsSWNcSHvnYbcstxHbz0Q1+CfLdD23D5JsfvNzrUy1gW/o9+kefcPdnfd3yxrRKjFBvJT59JnHXy4r9oe+uAc3jnZ16CPHz1FuSVPCKY86z96clTyL7kUMZjrsWZlLG0Y45h72XOceeQse7dW8yjnT9lnHXYp7UMij7kW1K/sL6izn7+Fdaq/aN/8CuQU5/jN5/Stl2Mx7y/wdh7PuV1Pcr3HPvT3eH8dHzZB9c8X3O55tw+Pp/avqtyzn/uvKAlZxn9DvW9L3UbqcT1bbkerxnjzRfU/8kZ92GFxIxFwfV/sEO/tpLnVeJXzqe8XspZ7uPHXBtXJ8wfhpL3cjnHJ0kkNpoxhu82mQOZS/5wJrHNViDnU3K2Mtjl+LQ3kr8cyHzdYs65F9AenR29GIOvF/S9mveZyRgn6mvlDG8tsYfW2XkSC/UPmXeK5AxRjq9cMeec9gdyviXxe6Mn9kfzSInUYy3kvC3n728MaI8XOQ1oq0V7eCb1YocHdyFfHnP8+7LPi2rax1df4hxncv1Qzj3GK65RJ/bsd+JTqzMMfNfuPJur1/q0o5fHjA3OzrhWuzdY1xFl1MPmhrFHmXNcswX3Mb27fF47pR5mF6wLWRW0835H9hEZ9WS25jjv3Gd/622p/XlIW9KUPcfWPte+JzmrhpyD7r7K5++nXKe//uvMZ4Qlx3Nvn35steD41jHX0e6te5DzhPW/d+R8zDnnEqkP+MJ98Z0S774r9Voj2Wu/P2QO5bVbtN+LGXVgb8C18fgpc0KupC05PpLzHDm3KBbMSb26+3ne3+JefHCPa/X0VznGP/BDP833F8wBuwbX/ofv0J+lco6xnnD8wh3qWLNm8KPnh9ua19xj/0utmQ503/bx+TTsj+f5Lngu77KRHEgu+++N6JVmDI4f0aafS65Oz0bOJR8wl/OgqcQBt166w/d3Oe6xoy04lFr5G1tSoyK2Ij+nnralkOl2yHXRv009++mf+zKf7/P69J0PIR/Nua6+MZKaMamD2vhsj8YQymJxJf9FzhM8zdg4l8h/iqW+oSlnhOdH70GuM/YhklqxXfHDX/pB2r/snHHMWuocPSdngpqTakjNdM4xfnxJHQ3W7N+7j/j+leRIttt8n9eifHzMfVpzyvaeOz6vu885jaX27ELiyjjg/cMlx6eWfddKak89sV3fC5/avisIXPJc7HP+kGdxjYS1LP0trqfXXqburNbU98tzxtx7W/RbmXzv1JF93HQu52FXtI8N2TYcdBhzH/TlGwvZV5WJ7APHnJv7txlT9zqM/bI2Y4kPLz6AvJTztIkciK8K9rd+yCn7kS+wP6ms7UAGoCU1Zd/8Jv3qe0/HkBdtzo9zzv3U5xjrbMn58aBBG7xs0qYffp71SaHYr3PZxwSSNy8j9mlScv2GPt8/mvP6nVustd8TnS3mfP7ojLHX2RF9UCIpWj2/3m9xji6u6EPrJXNz64mco0jKPRMdqQ/ZgLsvMffYHkjNi9ibidSV63nZx+VT23c555rPNWUotT7FSvbzsm9yK6nFkXxA02c/S5F7TfrupnwnGIgfOJdvYRL5jm6xlppd/cRBziVHks9bSn82a87bUvIXgZzXNTuUd29yn9WTDwSmEuvUUqMmbvmFdbA8H/O63F9ccv625dvgphYYO+eCiLqcLbmWIhnU0JfzpljODHfk7Ffqm+ZSQ11taAv0vMZJPKg5oSKnziRSjzaVHFEzZfxcSt4zltqeTc4xW8p3Pr7Un7VkPBJpby4fNEtZp5OS7he+G/AklvJlXyWfr7mkKR8mfA98OvYndr57ZkevFvL9gXwjXJbiZ6VOZNClD+p25Gx5Jt9LyLlhGEh+lS7RHUt+elrSFj0cy7nkmj7snviMVNa62pKtnPLwTOIOOTvwRFECOWtvhxyPltjOIpdvfSQ/fHzJs6TRgrIfan5AbKV8i1vXL9YZFgvOQSW1pauh1FdI7dJkSPu9kurTldQTFJKXW8h35Kta7LPY11aTc9TX7/Br6ly15vPLagx5OZIz0kDGrKA/SsW2xQF1brnh86tSvmMXW7+QPOaypv/SHFkiZ8Bt2Rc2Cq6ZxvrFWPfj8mnFPnXtXJ4/09lKvq0pPdqTOqaubDLqWim1NwP5mwpdybscSo3o0Qcc2/AW7d9K6hznUmd3W2qRNg2J3SSvFckeutNhTiKXs5Ayoe70tuU8PaMujGZcq90d5q2uJAbfP2Se6p3fZM3qj3yJf5PiqdT89Ty2Zzyk/WvKt42zyxdVpNuV+LXFPMp8xTE5uM19ztF7DyF//oe5Dzt6/wHkva58E5tyzHpN2o/xmnPYl32PbJvcrtTGj9/mDa/cYPv2pUYkl2/VE3nfZC4FAlITcTWkjU/lG5WDuyxMvNy8xeeJDwnle7LWofx9hj7rAbo9yrsDMeC/Db/j7szzvJbneZ3//v875/6Qc+4bzrm/4Zz707912592zv317+qNhmEY3wVmewzDuC7M/hiGcR2Y7TEM4zow22MYxnVh9scwjOvAbI9hGNeB2R7DMK4Lsz+GYVwHZnsMw7gOzPYYhnFdmP0xDOM6MNtjGMZ1YLbHMIzrwuyPYRjXgdkewzA+a178k98vsu+c+2vev/wXT0Ln3F+p6/oXPc/7Vefc/9PzvP/AOffIOfcnPrtmGobxryFmewzDuC7M/hiGcR2Y7TEM4zow22MYxnVh9scwjOvAbI9hGNeB2R7DMK4Lsz+GYVwHZnsMw7gOzPYYhnFdmP0xDOM6MNtjGMZ1YLbHMIzrwuyPYRjXgdkewzA+U37HP/BT1/WHzrkvfpv/PnTO/dxn0SjDMAyzPYZhXBdmfwzDuA7M9hiGcR2Y7TEM47ow+2MYxnVgtscwjOvAbI9hGNeF2R/DMK4Dsz2GYVwHZnsMw7guzP4YhnEdmO0xDOM6MNtjGMZ1YfbHMIzrwGyPYRifNb/jH/j5NClq58blMzluJ7i+yUrIpc/fn12dQ87LFeSqrCA3ohbkTsr3rdNdyLPhki+sa7ZvegF5fp5CjuM25FL6M7qcQh7EbN885/u7zS7kMFzzfRXlrTCDnIQB5OWEz3/7rSPI77zF8e1xOF0z4XhkBW+YTOaQK8cJTBLPvUDNNu92c8hxdgb53W+8D3k+GUG+19uC3N2lHPuiA/V7kL2KSyJN+ft2O4JchLz/fDyD/GTMMWr3b7F9e7chDxr8/XxK+fLJKeTtnRuQK49z1OlRxzu39iHnskam1QRy0OT1hk+dDqMY8mJ5xfasFu77Ad8PXbuz/ZE8LjiP8+UGclFQlzse1+7FvIAcJnxe0GxC3u/TNngRr5eljGtM25IlHch+zbXV61NP67UsXpEPWj3Im5DtWSdch57H902uqCdN0ZOKptnd3qEte2Ob4/0jA9qBJ/E9eT7b0+9Q70JZh27F9p09pq1zzrkw4Fo5m7AN3ZR9moh/2BQck82I9rjVp040ZY6bAXVotWEfxT26SSbti6gTVdTnD8S2Jj51qi3+tdnmHHVbHNPLku31YzbQD8R2h7x/s2H/fcfrdSDjFVIOG2xvVFEHOuKvWzqA10Vdu3rzbC4KCWzWc66F7QPa6FaD85bKQIwn9BEu4v1RwnHobFNvlhO+f5VxHBcL6rXGWV7B+4MvUm+qJfXi/OETyB9++BTybMO1u5NynpOUetHpUG+nK9ru4JLtq9gc12yL7dvw+WcbztcqY1zVaPchexL3RCltv3PO9W9xTpYV/Wbg8x0ffsAxWmf8/WpMgzuZ01YV6t/WvP9mhzqxtcs237zJOGIyoz88PmMc1hNbsliwPbduUsezgrYhkbUbB3x+FLF9zQZ/n8qc+Bv2N0xFRzMqRdKiTjR7lDfi/69kDZbfJ3GPc85VVe0Wz/mWQvSzrqkbvvqlnGNXeByLzKOuBs0B5DDmeipr7gNyj/alKGSBRvRjdU37U1Qc68BRF+KI/clnvL/Kp3Kdr48j9i/0qJt5xv5sxB7muezbUq41jR0lpHYScrtY9mFhwPmsxMD5Egc459wq50PXJW1m2GAf65DvdDEbmcvzKpkjr+b1opC9dsgxjKTTgcQSQcX7uzF1NHc6Rhxj5zini6XokNhsX3xwJPa5LjjGvuzts4XEfm2J59ccjyrg+DZKvr8MROcyzp+Lvl9iH9/V5bO2dAfs92JBX99IGNtcnHExZjPq9slj/r4t8xzJ2njzC9x/h5IjaW/RtlWSc7l5l34wvmD7YsnBtBp8vx9x3lYZn99sSk7qfAj5pS/dh3x4QD8rpsItxXZdyh4lFD8cblHvfLHdP/wjL0P+6//N34b8m+9tQ/49v/9Vp1xNOIdlg2tt95U3Ibe3uZYi2VetVxzzq2Ouhbig7bh/6wByV+K/ltgGv9iB3N/iKC8yrt3+FX+fBGPIBwN5X0qd8GWf1pLYrLEtOa0O14znM1ZK1tShytHf5+Jvg4ixUSb9W0tsk7bY3pno2HVRlqWbjJ/pwkJcmO+oRxvZ1ySSU0grzuvxGfWst821nk34vM4b1CMX8n7P7/P3GddiIPt9r6JelznnMVuILeqyPw3Jxy4XtGVXozFkf8k4KVtJXBXwfXlFvdhU9EmFpIPnK40ROGF+2ucPPPZ3saFeJ6H4ROecJ3FFJM/wGhwjT+xn3aK9dZJPLh3XduFxDs+W1LmlxIK55G81L9hoy1qXvW9esz8bsSUNT3IwAfsbVLzuSX64kpzZ2ueYz094hjCTNRLtaL6Y10dj6pQn/vTqnLmDzZq2bjHROO/6CMLIdbef+dd2l3Plr2VuOxzrY+n7ZiOxkJx/dWMuqME+7Y0X8Pmx5GxjidkTOS9zEgtECddGpydrRezVzVfYHr+m7t2/TXm1YX+OrzjXsdi/WOyF7pMiX+zJPmOtfpdxwcVbX4dcL+S8MacfHKzk7KZ+MQcQ5bJ3bdEHnB7JvqWk760fc/386Oc4R//BH/ojkJ98/gHkX//KCeTRFXVgePEh5KdnY8hO5vT8Keds5xaft5Az05HkjdZT6vjBK4wFU4llTiW2GMi5xZbP9kY9ztmOxDaNz1PerHkusprIfITSPt7u6kvZ114TVVW79eqZL0jkXHO+pp+oZB6Wa45zJLal2nDtteSsIG3QFngSm6Qxx7UpuX05WnBJRFupOat2KH5PzsYjyTkF4hcT2ZfVcl5XFxLzSs4mDWXnVXFdh/L8SMZPba3nOB+ag3OSr/QlTonEbzvnXNqQ/+azjXOZo0rsXUNihXbKMfTEvmbye09yNrXENqXkbJIWr2cVawHGo29AvnPwCmS35Ps7zT0+T/aNenZe5LwexrKvi6m0heSwl1IPETXY/7XEz072G6E830me1PMlVpV44Lrwg8A1us8MY15wrR68xJqF84X4yYBrYSJnFSfjS8gfnj6G3Oxz3P7J134Z8r/16u+D3O1KzC45n4Oc8/7ggOuo1vyvo4/XXGY75ngc9ujTJ3PO6+x9+pSv/sI/hvxn/vM/Dfle9y7kTqr5BPb3//df/TXIB5/n/Pzb/9aPQZ6PuC7aDcYEr2+9aHvGY6mdWkisKrr8+TeZp3Mz9uHgBvswXTBOqBqH/L2sjd2Dm5DXkjnbusnndwPq6N4b7HM55Rz1JDBYD5mnTFr8/dXDh5DffP0lyH/vH74LOegzlk421Lkv/Qxt4fH7tJV+Iv5a1kzH53jNAjlTEX+7XMuhyTVSFoWbXIw/kkcz2tnDW4z7r6Zc79uSB7pcseYq9ej7r6RGazSkrsxkj5tL4mO9pL27WNE+dDrU/VVFP5VKTqA54D5zNZX6gYS6HWlsovZCauDiF+pOOF6jIfs7lfqBYi05cI/9XUidSD3j7xc+7aXWeUQ7L9YZNhpskydt2DugTuzfYp5d0jyu3ehDbu5yDsIm48dbr/J5HzykDwuk7s4v2efbN/i+QnJ7pdR8NALqwNqjTs7PuQ/zJZ7east5fy51gFKP1k34+1s36EM+qLnvlON3l758B/IdOWfZuffzkLMV3x+03oD89/8e972/W+S1707rZ8rSkX1HW9bm9h3a8VxqvNKIfiRcs9+F5FjnU66N44dvQd7Zl7qQjHqxPWB7QqmfXUzY/ro85vNf4flQWYpf2eHzclmqT44Yy0Ulx2MsSfxuyj1MZ5cL9e5eH/L0knFCIb6hkJzSas66nNDxfGunx/kZ5i/mH8/ljPAXj+jLu3Je/2hI23DrFcYqQZtzfEvi6Udv87zH24whV0v6s/27n4esOfGtW+zzo7dYi5RLfYGUR7nVVBb7NutDipnEvxvqSLriHF9JDskVfP5kyP53VtT5RGqFqpQ61ffpH5OAHSokRx4HtH3Xhe/5rhU/a7sn557rnPL5qdScyVlFJjHxaMaYsCx4fyn3dzpciz/yhdcg725xnjvbcrZecR78kO9LpVBmdEVb9viY8vSYPjVbUu4UHI/BNs/Gf+wP/l7Ipx3mE975lV+E/J7Y/vtd3n8g+ebdhdQ+SD1r5STf32OMEUktg3POxU7OwErmUNwVx2Bz+gGvS12JHB+5Xkrdb2Q8/6ll796WvXZLckpNyYFUcga5WchajKX2Seo/5mu2byq1qNvyvUjaos42JA84m0o+Wuv+TjmeQcn3XUhOamuLOl/ImWy/xbgrkdxI0Pz+Oe9ardfum+8+05+XP8fvV33XkV+wr62m1MTO6Vf2mnIeLnUJ7TbH6sERY871XM67Z9TVl29wru8cUNfyBe8/kTqW0/NHfF/Vh/zGTzGncffelyDP5PuvrJY6S8nZzy+kjkP2jXOp4ZuN9NsqyeNobdKSfjRbUfcvLmhft7ZftD/bDcYOnuTdg0xqJKQ2Z9BmrPDlL/4E5ERyT+/IHMg2yf3qV3+N/2HC+Hh8xDnu/izju919+cYvof05O+WYraS/E43XV8yLbdXs7+iCsVqry9/XUoe5ku98Niup5Zc68J0tvm84l9qhgP0dT6WOXOzpdREGntt97pug8wnHZatFOyklwE6PbyKJ+Vpy9u0ltDXNVHL1cs6by1nx6Ip6EkoNdRhRrwLJ12mtUrWUOokpY+T6Bdsi3wk2OF498Xuh7AlmMzkvnMpZi+SYm3IeJukKd3nOOEO/XS1XUqci32gE9Ys5n1xyorXUB9RSf+Vq8aW51BPIdyeLGdu8EvtbynlSU2qCQ6mpjsS31x7bX0r7K/mOKIrle2Gp4w8q6ugik/bK92ctqYWqxHb0ReenG63j17p/Pj/fUEeXkudrJNJ/WZNJ+3f18/XfltrVLntufSxlHLJz+S5d1l4+lcJE+R6sWkk9ZcFxTyKxTVJPG8v3B41Y9q8JY9CunBOfPJQ4oOS4j57SZ3oj2pqxfEtbLqgHL8yirNuJ1EAfB9yjnB1xT+N32J+Fx9yqJ/vSUM7n9HuOWGrpswXXVSY5KuecqzPZe0stqJN9kCcbq2aTa3s6l+81ZC+/2HDMR2OJGyQnnshaTqSOsMoljllIbVgt/RPbUWbMETXkDCGMOWaNJq8HLcmpSI12IbWeudSrbGTNjE6pg/MJ+7cj+0hf8tudJrX0QGqwr5MiL9z5+bM4cvnc2ZdzzhVrqROWs4Wqw75dLRkDbuTb7wtZ31EmOdibfd4v51092cc9fsj23rr3g7z+gLq8K3462ZU6ZLG3+v1Tc8C/uVBfSN5qlzns+QXtS7vF9s82+n0n29Pp0l6vxe8nHY5PtmSN6ysvMW92OZL8cZ97LOdeCF3cXpf6PJvq+Q/HYPcGc1/Lle476CMCiR8DKYNLG9S52QP2MepxzDX+9hvyTaHk3bWGJJD2XUguYfs+xzSSPE4t9vhY9oXtm3z+t96jT5pJ3qkt37dp7dWs0BwydeYyoP26PJM83m+D/zvfYhiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRjGx8X+wI9hGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhfAbYH/gxDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwjM+A8HfzZVWZu8X04iO59hNc7+1lkC8vn0JOqgDyanwO2V/yffm6CXlRzCAfHY0gnz4+gRwm/PtHg0YXchDy+Wfnl5CH8zXki6cPIb/WvAm5ncaQ10UB+eqc7e+3PMg7bU7nVqsF2YUpxEb3ALIfcTzqZBvypmJ7RjnnY53w+WVWQU63+k7pxHzGmy/3IMebKeSLE47xajyB/G7yEPKrEd93eOsO5K3tPcjZtIS8qRuQvbbooEcdng45hqN1zd9HnLOt3S3I/RZ14Pzpe5CPnrL/c8f75xXbH2QbyHGLOrJa55AXmzHkyqcOuxXndDW8gtxvcbzWFdf0dRGGgdsadD6Sj96j7ah2ubYniwXk1WYIeZa3IccFx7GIqHh1yHlZ+dSD/Zeol9MZ9cb3OW/rJa9HYkvbPufJT6i3+ZxrOUlpPDsF5z1I5pBnHfZHbXn//g7kn7w3YHtjtv+l7RXkm81dyCdD2uZ/8w/+NORf/vpjyHHI/s8nY6fEKefow3fob/wN12aZca2txB5OZuxDN+HaK8dcK01H2+ZvuFY6AZ+/quTv8WWcs0aLY54ktP9ezTman9C29PboD4KEOrpccky7IecwLNnfTG1RTp2KxTa3d8X252K7Ir5/MryAHFZcg2nC/l4Xvue51nO61m5LHJFxbQ22qPv5gnqyFj05H1PvIgnr2hK3NLY5LnfFzzdk3I7fOYN8+nQMebWkHv7yP/om5LnY0vMZbWkSsL0H92lb25X4rIB6MPO4LtYS56RNys2Q67hOaRtnU+rpfEq96h0cQs6ajANDieOSNufTOec67Q7kO9tiK9Ycs+xD6voHj8eQz56KbRHb9vIu23Bjpw/5cI+2riPXg4hjvFrRNqQh29vtcEwmx5yzQYc69nf++m9A/vk/9T+AvDihv05kzBuyJpYSdmQ5/0NRck6DgLas0eD4BRFtWafHOU6bfcgnj9je66T2Alf4z/Qt7nCuwy79ROZJHL9RX09Zx7Iq6beqJXWlqMWPOpkb/twl4odWEmuVBe2frp1ui+s7Ez/hS6w2X9OebfdoPz2fuhI4Pq9ei/LVsjGt+XtXce3WOdvvldK/FW1FXfN9dcC1WvsvxuAlp9CtN9xH5TknofYKuc45CR2vBzFttldLm0WH6hd0iPYi9jgGuaznIJPYZiFzEtNerETHahmQIJQxF53xGvRRuczRKh/z9/L8usH21CnHsyn2c7XgeNSyBjKJBXNZA9dFEIauN3i2x/YlpqtEV0tZS70+1+ZkqbES12bpaMvC1j7kokHbU9Qcd7dmjqWS9sSit7OzU8gH97iPu1zz/t1t7oOOZ5zX0xX7+9/+3a9A/tweY7VZvw/5P+z+OORflNjtA+nfzLF9P7gaQx6dMPbMnNjGm1wHe/fZvqkvC805lw85h0djtimVtbVcU0f6Aecwkzxdd59tOOxyX/HKa4zfolLsudjfZsK1GPh8XrmhTnd61IlmxZzNzT2+v99je5OMtu3mjduQfdHhIKKt7fY5Hn4guQmf7Z3JXn+65JynbdrW1758F/KXf/5HIP/if/13ID/81tfcdeB5zsXJs/W1mjM3F0WMc5wvcYL4uJ1D5muzI66t3/NTPwH5b/ziL0OuMsmXik1viQ/wxAfubtO2TQP2ZyJxy3ol+23pbqMjOaWMtmA05p7jYEDbdbVg3NJg91wlPnO94rpfbeS6xPBVSVvoR9RDr6aep036gkJiHOecm0psupXTb7979QTyD+/9AOTlkmNWh5TH7JLbSD55XXLtegnzpcvhEWSdMy+gbWq1uBaTiM8/GXIt//gudeo9ie1XM+rURv4NiLjg73XOAslzRiHHuxnR1u7t05amTV7fxOzvZsncQafBAX969Y77fsELfJd2nulkM+ZYzBccq+VCzl9i2oNWm2PVHjCH0BW7Xkke6aDL9eOHXM9uST8ciy5dXnGP0Ovw/an4mankAwc+19qF5Esff0hdurqkPSskn+jNef+W+P3FnAbJz/m8zRXtUa/J/i09+v2s5vjUAe3Tvti/QGJd55xri09ZLTlmB9vsQ9OnzR+d8ff/319g3vsP/zDPs/yQjfrxz70J+amMwfGAbV77XH/jK/qU0YL3X73DWKeS859M4s/aY3+OT2lfjp+8DbkQe9WUJHL70THkW7c4frduM0f+2iu0n6Mp7Xm2kViq5HiOZN8VDuTM9ZooqsoNn8vL7jS5ViM5G2422O9lJTFmIXY+4f2hx3kpZT/vS36gdrTzki5wpVwvxG9vJEaOPckhZZJDCahnm0zOLuQ8Ss8OAp96G4TUi0hy0s02/fpMYuq2nCOXGW1PHPcha44tryXfIrGiP5eck3MubHAMfDmvb0meyNc5kpxyInmw+YxjlMm+yJd9VmuLz8tFpwo5I41C+r87d78EuVpxDAe9Pn/PIXL1hu1pbnE8PDnvSiSvX0j9w1z2sY2+jhf719qljs1G9IdZSVvbknOj7hZ9RViN3fcDVV27+XN7iaXEjCfnPMstKupqLGvv1l4fclNiyk7KebyQGomjJ8zF/8J/+8/4e5mHz71EH7rf5zjf22V71sfcB00lZ7K+QT1o9mh7a6ljqtfUs+0u8wX//v/6T0B+5+/yHPcf/M2vQv6f/O//PcjvfZ17nJ/6H/5xyE/f/gDyl169D3n6kL8/uME4U47XnHPOXTx5F/LbH/AdzRZzPr2EfrrVlHyr5qPFHhdrXr935xbf16A/fHLGfYVXUCeqgjp2fsY5H52NIbcD7p2vTnn91Zc4phtHf3H8hLZsXdPWtDzxvxX93fKce4lejzo4nvH++Yj+ZTPj3mA9p21rdfuQn8p4XCd17dzzxxfrjH2biV2/OGfbV7Kv8iqO5W6P52eak35yTHt2+CrHspScw9EZ1/80l7OIJnU3blBXkoC6eu8l+rE7+2xvU+zr9JL9jyL2P5azz0rOBz3Zd/qyx1+Irk1nfN54zbUXpOxfXnI89WzX6zLHH3u018455yXMydZrroeDN16F3BnQ5jalj6XsXfcP6Jvbu8wVdmWvfFyyPTsd/j5t0Kbu70g8WYu9kr26qxg7BYXkNkVup3x/7Hi9n1Jn8oprqNfi76uM9jf2Od4z2TeWoZzfSS7Tl3g696hjW/tcI9dFXRZuc/Us/ugMGEtcbTiub+zTb12WtLMnJ48gd/qMHXyP4xJ1ZF9XSeyh545Su9Pfoa0oZNxrT846PN5/WdA29MXvDMXWJU3O+80fZr41WFHPr54ybgikXnY+ZOyXS81bXUndpOSII6lLyeVsfSb1yMfvfB3y2frF/b+fMHapAj5zWkndnOyDcvH9aZPvKHPeL1td54ttuHPQh7y1T9u0mnMOX3rpHuRQcjq9fZ55jh7yXGS+4T7mySnrLNsh2/NUziBflnqr+Ab73+/JXlpqP9sJ+z+W8cpXnNOVJCPm55Ijn8g5S/XiXvt6qJ3/XJ3BVpvjFMv+90ryudM546SlnI3njnJXcl1ded+X3qBNvn2bftqXs5LJXGvg+L7JhrbxTOqILq7ok8+vpGZDau83UiOyevQh5Jv3ef+rP/A65J1X2L93v9WH/HDD9rRSrpNlIbZC9K4txqm3w7jmxi2u2/Mj5oKdc254RPuULbn2cgmmqiXtZyrGJJSczD3xX7MntM/n71PHtgb0Fwcv0R9FckYwGMi+S75X6PQ0rmF/DgdyPjekbUobcmawZnsPb7G9Hz7g+HSa9I/6T6YPmvz9jsRFzQav61l8S/apvRZfML74frE9zoV+6HrtZ/v2fodjW3tSM5nSz9SFnCVciiOTHGnQ59iMpK7kYil78DGf/8Yr9Mtdmfs7Nzm3mgPwpOZzeCTf5iz4/vTdX4d8/83XIIcxx+fVXYml5Dhpqynna03WidRzyffKNx+LU+77uofMr25LXc1LL3Mf+81HtCUXch7vnHOnD+lbD7b7kCuprXn311g7vlvwmdOQ6/vubba5ltqcTca9u9afNWXfUXepE6HsxT2JZTKpsdgXm9yU2tNWk3Nc+RJ7SV3j0XAMOWhxzN+/pL38kS++wd+fcI5LOd8vI+pMEjHvtlqL0qWaF5Og4pqIQt8dbD3zp4+k7mJX9O7JOfffvpyn1PJNgRdwHgM5d5Sfu9F8DFlrkldryR9sc78fS11JIaZwIjFrKHWUvrZfkrKR7DE6UiO3fZP71rGc7QwvJWaeyXmf1Oil8r5U60QiyUdIjiuW/X4seyLtv3POLaUAqJAzPzlSdLUM8kq+VwpqzqHGQoHUJ+Q110YVSbwn8bUntZixzz4tJV4NY9qOTSG5ANm7lpV8pyI6Xcv5XLPB9i5ljtNYvvXUM9tCzpTlDLOQ+pC1nBdGG66xVcnxuBrKorgm6rp2xXN2ezalnmh+udXhWgukdrwuGDcUE8Ylkc957Mr3Tb0BbclM9kWR47gOJIb15ZyzFJ91JPncIzn7CORbUy/mvFW5nKvKOXQuer5Y8/6N5KxS+X6sKWcpnRbHq1X2IdeSm2x4cta+ph1YLqTWQOp+nHPOkzHeyLejLqaf7R/w+pmc3zcd5zCWmt+efMO2lcjesSE1zTuSl5PaplQKD3OxpZGs5cl4zPZVHDO/kBz+nP4jljqodkdqbyXvmUkdUCax7VKuu7X4y4Tvi2TjVha6hlmXOV5yPq6Tsi7d9Ln+TeSb/EJqrM6fMgcrZQZuMmTfLmXP2pLz9tNT3n94g8/bSJ1Fu8vn3ZHvU9cL7qP6W1wr1ROeP7tNH6Lfp+4GcpbgRbSPgQRv0yFzzl2JTdaXY7Yv4r7o7Pgh5FfuM8Y+X9Cep5JPPR1xfto9/n6eSxzgv5hzLvXTZ40VNox/K7HpN+U7k6sLrp9WQ+qCxYb7kqO+ktxeKmd0m5pjuL3LMV9OJY/02g9DXkyY+zqVfVHzkGO4nLA/tehI5ZhLaMoiiQr5LumE7w/E/teRfBM54/uurtj/kdSLlZ7m/L+7v6vxYlRsGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGMYnxv7Aj2EYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmF8Btgf+DEMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzCMz4Dwd/NlURC6ve7gI7kIC1yfDy8gV0kHchgGkJNGArlcjSE/evwA8mbB9pxdrSAPmm3I2zscntRjeyuv4vMWU8jDyZL3r3PIq2wAuRGxfxv5+0sXp+xA49UDtq+3BbnbavF9Pvvz6su8ngdNtnfO9vdvdCH7peP1bgT56OGY7W32nXJwl324ffMG5NLjM3tt9rGuUsibhczRhnNcZxPIN26zTU8ejCB7Oeekt80xaPeoo+0ux/j0iu0J8jXk3QbbV5VjyJdnbM+m4u/nyw1kUSFXB5zDxaWssYzvi5rUuf02+7dpU4eLxQxy6rH//bbnvh+oqtItnzcAAZW3KDPIZcWBjGPKaUC9iyP2exPSNomaus0V3z/cDCEP0ru8f0XbkkY9yO2E7atKzmOzxfZs1mPItR9DfvkW78+3ue52BnzfjX4f8smDJ5Bfvb8PeTVnf9s37rO9KdvfknXXmkN0g4fnkKOAv6/WXCfOOVeVtAXLGd8xHckcx5Rv3LgFOSvYp7jNNlxenUE+aHLtZ2uZM7oj12rtUZa1nTVov4PVI8jpzR+FXJx9BbLfEH8wZ/vChOMTO45pKP4gLNifIOL4hV3a/iilrW90qHNei2usuaFtipZ8n19/f/z9wtp5Lq+ftb3VbOC6t+G8+RV9xnRGG9uQuKi9TZ/ZP6BciW3rBHx+wmF2/Q7b89Lhy5BPntCHPJW1d3rGxXk8FD0t+P77d3cg333lJd4/Z9xTl3xeU8aj36Se7XRoKxspbZ0L6KOCJfV+y9WQu1tsr1fzeu7kedWLYXbpU9eDhGMSxWzzndduQs7efofXk23Ih32OwZ2btL9RxDZWGdfyZsU+rS45B9lKfu/4vnLD2DiRwGT0+DHkH/vxz/P9ovNO4ppso/Zc9gYB5zguOQdRxfbP1lwj8+lIZPrfdoO2JU65ZvYPqSPXie/7rvncXiBt9XE9pjlyyw0N+XrEvjckpksklooc596VvF6Jfat86ponayMVv5AXfH5dcK5Xaz5/LX6o8GnwWk2JBTPal9pnfz3Z97ma9i4UtxPUXDuh/N6vZGNaMMaOQ76/rmU8az7P88S+VXyec84FYqO8jPuKsOYY5qWsN+mD53h/nFCpqjV1SPfy+UbGPJbYomZ7S2lfKes3W/B5LuMcVTJGoc9Jy8V++THj4Vp8Ri39ySQ2KmT81N5mbL6LSgZ/67XkEmTOV3Pay1J81nVRlZVbTZ/ptyfzUHtsZ6dJO9ptM+bdkX1GVIieie2pE97viR959OAEciy2Y7rm827KPqnZpd91AfVkKH4j3uXtj7kFcXFAv7Nic9x/9b/6Ocg/8Rf+OuQ8/jHI784uISe3qFfpWvzmFv3kWtZBHsk6l+cNXmIstg5f3Hd5Epf7Lb7j9PIIcnYh9jbhvuuVl6kje3e5d94O6T9qn7HKB79JHVguOWc+w0F3+36f15ecxPFj6vjolGO2c5vtqVSHN/x9I5BYKuVaf3Au8emAOhrEsr+Q8Ygzjkc9py+YrmnrHn/I2O293ziGvN2lDlwXnudc/JydrsSHFDmNbh1xXJZrybVNaYPbDcbo3/jVr0HeDjmugSwFf06/XEWc9yjkWowd27Pd5zzXNffTHz5k/lvjFk9igCxnXNHqUW/WYlt3ZE/RdRyfs2Panrrk7yN5f1tyPpnkfouS9+cS1tQRba9ff5vjjZr3zCTuubd3B/J6OWYbPLZxKo67KvjOWsYsbcjeX2JLJzn8acbfrz3a20r6U1bi9yPO6a+P+PxOW/LnAf3BeEpb0yzY34bkNSOJi/o36B9jaV+ryZxS7fN9cUwdcKKjaYvjcbv9/WF7nHOuqiq3eC6uLaZXuF4u2fZc+hbHHJtOV3LzO7Q/GznbSDTPFMmeVfKHDy6e8n3ipz88Zmyy1eXvvZq6E6bc15w8fh9yUXMBL33+vi/50ySlrp8tuXZ3en3IU7Hfy4nsM+W8rlXQfu5ITmW8oH3uSey6n3M+dmU+nXPuX/wL5mS39mnD73+ZY+ClXP9PJJd38nX24b/82t+HnIaSK5QzwZ1DjulUzss+/yXGVt98JOdJY8Y2qwnH3Jcz3tZOS65DdGGb1zcjOk1P9sK5r/sujs/xE8pFyf42qzHk4VLycLK3b4kOLmTNXR5LwH5NeL7vvOdyUp02bcEqk/2r5AhqyWk2ZJ48+Q+15HQ047BecR6XG97fldjMie+vA76vkbI/G9nvpjHvDyTHFYVcV5Xsz32nORf+3hdbl62pJ1kk+3fZ74faII966kns15LzxzBmgzTOKNYv7v8DydF4BdequAtXyDlBM5GctdjvSPaGldRLdBpyXiOufa1584C2IFvJOYjOiSc5Y8m7aw7G+Yxvw1Jsr8SPxUq0WmKdxkDOTD2xXYmcE0jsuKkpLxacn82M/rcjZ7Ke5Lyvjbp2dfWsL4M9Jj2WY54X+ZLvzRa0qX7As4+tHuOiH3iD50Vf/QrjjLOU93/4DY6j73Fen3yF57Q/92/8AOT8nL8Pl1z78zF95OaU8zLo9CH3PerN0ws+7/2vMmY4encMuXvI8747P/rjkH/pHzyEfHbK/t1/jeMfpdzXnRW0Za2br0G+lLMUXYbOOXe+Zmy1KtnHi2PK4/xDyNsHPE85ueJZe1Hw9ztbNGZRj7HZoyPmfI4vGEc9OWIntpq0Dasp3zc75V53I3GUL7UDLqVtWEv+djFj7OiFtIXbPc7J517i+FYZ3++J/yky2qJQbPtczmg3cr52dcU1MB3TVl0nfhi41uDZfC/FV7dbtPuzhHOZyPnwRvb0vpxH1RXv/9KPUVcreV8qY7vO+f7Nhn6gJ3Zek9ilxHJ7A8n3lZIfpLlxT9URF3x+EUkOQs5+lwvqQkMCiSSi3y8q2sfDXdZBJi3K65UkySXW9FOu7VDORpxzbqvPNj14zPU6W3EOjs7fg9yWWGh2TvsTJpyz9Yrr4Uxilfe+ztxcdpNzPJC6wr7ETk7qu4IG5+xC6gZzOS9fDmlvulJftZJ4NunKGWvODsUSn+eSnIsk5+xL/cF6w/sXUvMRic5LKtZVeuh6TZR57mYXz+KbcM5xyyXGPjqhnd/ekfyfxJCupt2PIj7fl5xEVvP3uQxTFMj+OGTOY72UfZJP43EqNdTzkrGFmEq39QrPZmJ/DPliyFinvaaevnyXscn0gu975fUfgvzeY54lhRJ3lGLbhxn1sLfN2PVml7a1Puf4dNp9p8xlbxs25AxxI3F7wbXYTRk7rKe8fzjmHPQ1Byr7mkrO9EJfzvBi2l//irZsdckxPGxwzI5HzHPuSh3f3j7HcO827ff2nL8PQvYvl1oezYOOL9ie7T3GTkHFRZiG/L083sWSu+g3OD7zDfcz10We5e7pc3HtjQOOe68jtkNrMKR+1ZfrqSTrXrrLtbG3y3G9tSd+XPOxQ8onkl8OPOrxspKa6SXneb2WmFlyQu2E/e+HbO+pnMfllxyPjsQdgZy/dQ+YK52e0zY9lbOf0Yh1lH6LtvfegON993NcN5Nz7pHe/vAfOWU6ov2LZK+7I3FRIjmIdkJ73x3QFv3kl25DrmKpS3lCRx3LGWC3QQexuKLfb6XUwW3J8R/uUZ5e8n3+Pm3p+Snjru0d9n8551n23gH7N1tTh2LJQzb4OnfngLmLxUzqX8T2H5/zDCaV8Z/JPrEjdf3Xie8710yfrcGV1NG6WL9J+M5+cWu3D3ktNV77fc7FRNZTIDGlLzmCWPIWaYvr++Kc3zCkHdqzfov9ObxLXXFr9v/klLHN5YR+Ixf7vLMreaNt2pfXRbcPpS75bEi/9c2vPoR88QHleyVzCC+/Sr/4asz3P5D2nT6m33bOuV/62ruQ/+Brh5D3vsy633jMOfvqb/4zyFcRbeSwYhsfnMtesaQ9qXLamxt9yl6HfQxrrrex7JsC2Re2pX4tFns6GtInnJ6OIRc1fXZ/QHu5K7FTIedRzYRzsr1Ne724ZJ7r3a/QZ7z5hTcge1Jruyu5VBe9uNe+DsIgcrtbz/YG3iP5Lk1i6EbKmDCW+vSGfBpbyv62JfNcSow/l9x9IOeUaVO+bZXnrTf8/WJFPdYatSpgrLORmDWU/XHkqDehTz8WyLlxUbI9mfgt+ezPLZff+TwuCfn7fltq1gqtY5HvwTyOdyzPd865Wr6VdHLmWMs3fA05e64lb1bLmVxWaE20fhPBNnviy0vJ4eSiQ77U/RUSf7a6XIsb2deVUqvTaspmvJQzX6nZLuU8biXj0YhlXyXf3Om3rr7srfXzrLjF93e2ZP8w5poendHfXxu+51zyTJfyTGqxl4xJJ5Lbb21xXlYTztvqlHFIEtJWbBqMGxaSHLu8ok9c5Lw+m9BvhxJTXkrdy1Rsoz+gT25IzcdC6mM7kl9vJ1yHK9kn7m/RJ74mcVY3YJzVyvXDSfYnmsueQ/YkDTlLn27kXFjixlbvxRjckzrBK/k4spTzlJMrjulkSR2YTtnHtsSqUcY29APGRTtSAxzINzsb+UYnX0j+VcxxNqb/CWTO9MyhkG9WRpKfnsqh4aBDHcl6bM9Wn+N3e4/+qiOxdD7i+D29kDr2muPjmmqLef8yl292rpHA912v82wNpT7Xd0+qcXba7GskOd1K6iIi+e43TWR9PKYdXhZ8fzfi2G/35CxUXX0+hjw/41nNvUP6iV6fDzh6j3Ozk3Iub+5zDxLNH0L2R8z7NG7zvHs8Y//uHVIXP/gG7eXn5Wz1QmpzEsmr7b30OuSZ2JtS8la1/J0O55xLHdfPfMR9h+vxGUUmZ5S7nPP5I+ZNbu7LGehKvr/S2vqSsUJ7IHl8ub+Q+q/JGXNdrsuah2IqtaByXnYg30Q/OOL5XvJCfRx1/PYbtKerUHy0fNcUSeyWiz082OV4jCuJFaUebEvi4bjP9vx2fH+cihmGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRjGv2LYH/gxDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwjM8A+wM/hmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhvEZEP5uvqyuS5cXs4/kdVHzel5CzhoB5G4vgRylB5AbhwPIw9kM8sPHTyDPlnyfv8u/d/TqG69DjiOIbl03IAcp39fq8nrVXELO5XlZwPZUKfvrGpRHxRzyed6C7K/5gocPTiEnKfvrB5yPfjeD7HkLyG2P7f21r11CPnp4BHm64POdc67OU8j7u1uU97ch/9BPfxHy+WUMubHOIe/ucw62Y87RZcAx6/bZvuWKctzhHOzv9CB7Fe9/5Tbfn885hpHPF+Q5ryct/r7j2MDdXep85HNOyrKAPD4eQZ5mHK+0zfcd7O/x/S3O17y4gLwYUicD0ZHroqoqt1w+m/vVmuO+LruQa4+2p/K4luKUctNx3DaNDuWS49zelt/7bcoR2zO7WkOOIr6vFXOeq4qmPW1wHqYVFdVvsz15RlvVEz10izHEwy2uw2TDddHrUW/2Dl6BHIitqwNpn4xns+Z4vPLyy5CPHtPWFRNZyM45V7HPSUV7105oH9syRrttzml9wDbePOAc5Es+P6xkbWzY56Dg9XXJ5/XE35xEfch/av//Cvn/NP0pyLdSjuHa5xxk5YbtCWlrPMcxjRqc8zia8HqTOnA5E1sn/r6Zypz3KffaXCP52RhyK+XzrouiLNxwfPWRPLy6wvXplPNws0Ob22hzXA8ObkHedhLHpLQloxFtfhRyHhLnscG1zEOrCfnOK5yHG3fvQz4/pe14+90HkC+Hx5A/9yNvQL55j3HdZMTx6odsX1yy/aHEScWK624hyy4MeH86OITcTDleGicNz84hr15Yx7QTzjm3Vr8rsaAf0Pa0moxTXn7zc5B3dxgnhRV1ymVs09XJCeSnj2gvF0vxJ57EhrIW/ZTt8wKOWe2xP2OJE1oJ/cfqmLHkfMXxinyJo8Rf5yuJxWUOGwnn3PP4/PWG4zcaDSH7FdsblRyfyn1/2B7nnAsCz3UGzxSs9jm30wXtdCzrfyX7qG6X6zOsuaDCUOZyOYac11yvXkTdWMna8Hzev3SFXKduewntlSxvF/v0Y/nk1yA3e1xbgUddLCq+3xXURd/jC+NY4ogO/ZYT3fNC2TcWtF/Lgs/zZG15VSryi/YnDjjHkcQ+kW5kpM9+zWf6EitUEvdnsh49iTWKJe1BQRVykzGf3xpwDNcV11/hZK9ZcD2/sPeV+6uS/W8E1MFAxidpSC4glP5JbqOV8H5P2xcx1qsjcRDy/Ez26qEmK66Jqqrcavls7pri19ot8RtiN4uc8662oL1NPWjIvE7GEqMnHNeLK9q+vcM+5EuJKRcLtsePJZbq0C8ODtifQZ/zvhfx98vp+5Dv/eE/Cvkv/LN3If/b/85PQ97v8H2fv0k9uXX/JuSN4/W7ki95dErbFnTZ3maXv+9t83qaykJ2zg22OeftDu310eUY8pNz7jM+/3of8raMeSB9eufXHkP+lQ3zUn/7r/wi5Jf/x29CPv6b34T86he41/wDb74G+b2nbO9iwrXYeMJYIqy/Arl5ow/55g5t01XGtZ4ktB3zFeegkPjeq7hG4pDtG/S5psIVdaovsebwirZ9sNN33z88s7tph+OyWXJcM4/jlIsPOD5jrit21NvZhD6s29plSySOaofig8QHT6bctx1xm+FKiXmjBtvbGbB9qcS8oeyjdF/TbNIWpA2u5aqmLa0kbnMhx3MltjyQ44dS4kYnj8skbmxKLnay5O9lC/MvHyk5lFj8SStl/lnj+NLjOwpf2iz+JZDgMxK/HEuf6ybX1pVPnZ1lkjOZ8vV+g3PY0DggY24giKgjZSj7oJK2KpFcQxxpXpT98TLq+Fzy85nsxWuZsyhl7iMvOF9xyPZrvHCdVOXGrcbP/Pn5KWONai35N8e+VhIjH5e0D8eDHchN2Vek2xzci4Rjf9Cnrh49fAdyuOH6nzrmdTaSv8uc5Ec7tIfTS+Z9klDtC+d2d+cu5JXsU6Oc9i6bsr+zM+Y0cvGLkdivG30+LxxDdKtM/EVFe55/iz8Iehwf55yr51TwJ2u+M454vW7z+lzmpL3DPvgL3l9HtF/zjGNcjWlA1nIoGTe5V/7S72GubnrK3N7RU/rI/jbf37tJne13aC82Yl9Hl2e8LvbBX7G/W9v7kJ/82geQM4llfvNr1MlK9mFxRB3u9egforb4D7HX10UQhK7ff9bWJNV9Ffs5W8taXfB6R+x+Jf82kCexRxjx/jqQfEEoZxsVY+ZBTTu+XtH2hLL/rjMqRigxbS35y7oSW5ZJbNZi//KS46HPzyW/mknsuLNF2zxaytmTpAszySf6TvxaKDkvybd69YvnrmHEZ0YpdbeQ+LOQZxRqbzW+kpxPLGd4LbHvocSHG9HBSoKD5ZL238kZ4FLLCwLGBq2G5A0lwCzEFnckR1NLcFJJvUYxG/P94q8XC/4+92i711JssFpI/YXEv5sJbW3tf5szzmsgjCK3c/jsDCuRmHC9Q13NH0uu/5S6fjHjODXHsrb2bkN+/YvMLxzLfj+Y83lTiRPSFvPbvS7392++yRj97V/l/v1MzppLn/fXjj5qX3I0D4c8m3n0AeXLi6eQX/tZjuf7X+M6Wm3GkJst7gGGjxmnpC3O19ldGqfRE/p8F/H+flfy2865rZsvQe7c4RzHa67t+Zg68fgp44CzE86ZCyT/GTLP5TnGOWovN4WckUZca4sJbU0me/211PU0mhyTuE+dPLzBOKKs+pAfv8u1PTrnnFdrqfWSWHQh51eV7Iv0vMwX25nPOZ6lxPorqQ/53Bd4ZnKdBGHgBnvP9lJxn+vt1jb3WY0m19+9XVkfMhb7km8MF5yL/S6vL8XONyTPVPqMifOV2HHZY1dSuzObcG41tpuOuLZGBf3Q2TnXVr2kbgRbetYpsY+cvWQbtr8Sv9np0a92B9xjJGkfchhI7VHE5/uS32wkL559dKWuLUq4fieXrDF4+ID7glSemYSc8/iMY9xbyXmY45xFkndazPn71YLrOXLa/j7k3S5rRqT8y3kF39+TNeBLbHg2pD1MCz4wq+SMU+LRUs7LatkvNOS8P/G45noN2ues5hoMJHaL5cz3uvA9z6XP5VU3Of2O7jOOh5z3q6HEyAXXYqeg3fUj2po65jjsS91IJueQWu95Wcp+3XFd1Iefh9ztS05YatxGUreRdKhHy3Ouw/nlQ8j9Nxjbzcccr8Up44Bv8HGu1WJ/wkD2DKXUXcbsz1M5P4wkTpnMOF/73Rf1sNenr9/aZZu8OdfeTGzRckpbshpTh147lH1KxTaMJGmyXLBPF2fU0Yshff3OgIPaF396Y586djqhTjZkr+9FtBWp7KXv3eV4BSvqzJtyrtGSOs3tm5zTJOb4xBuOZyixYFlof9m/QPzNTsxY87ooispdnD3TjV6HccVmLf0O5fxGYsA7e/Qp8YDz+sYdrs2N5AsmF7RVw6Mx5F9/j/sIP6ft6A6oB9vbfH+14TrYbvchJ1LnOJCakWIpudMl93255CKTSvRIzsfu3OO3CRcNxux33/gByN0znkuXOccrLcU3HP0G5K99jXVLx6f8vsU550KpLdqRM9Df8xJ15PE57WlDzgiDSs7g5AxPa6MaUsfiS+xabNi+5UT2ol22b7sjZ9US9ywumO/V70lu3eDzbsk3Q/Mp33/z9g3IrqJtyOQ8ygtlH1rLWfyCcdVG6klml7xeSI32dEZbvbPLWtXrxAsCF3eexZVJm3ZzI+dDvtQVB1LTuSf2pZA6t7qUmqqIY/l0KvahyfdvyXmZfgw3XPH+2VDyn/kY8r0bbG+25FwNBrQv//CXuX6TPRrgR1e0B8XrbM/LN+h35vJNxnjGfVIh34B4B9T9ywXPel8PuXYOt7l2vvx7GQv+4//unznlm1fU5/YHXO9/4EuspQnviC+V+HA1pg78yj//KuRKan8GLc7qXptztCX1WLMR8y5j2fsfnzM2u3mTOtSWOsIbW/LNgsSjB/tcv4XU5kbyDUV/n/u2IqSOz4csEpmf8/rFGe1Ja4tyKvUIbdlrb3UZW1V6IHdNVHXtZs/tcff2OE76DUNfcg6R+KFc1m4t9a6ZnhPKJxSh+L1OS/bLUoeSyzcMM6kVKje0BbV8TxYEtA2R+O1Qao08mbbhBfVsOOHzN/KtT1xLbdGbtA3F23xeIN9ZVnI03pWzlo18n+f5bH9H+lfot1TOuVR+s9R7ZG/dbEh8KGd4mfx+U7CNdSC1PJIXLxscg8Wc9rYptaR+Ls+Xs/xI3hfI3jU7Zw49lvO39USKh0RHF1Jr5De49jOpfQrl+65a6ltqmfRaks615ITiVOpDEq6BbPXi98TXgecFzo+fjc32HsdxT74R1pqpvOBamUlOaCJ++OyMtiEVn7GRmH+85ritnJzzzuljQ9mHrKW2f//2Hch9mlI3u+C6e/k26yAvnjAm2G5SXmRiyySZWclReOCol/maep7Vcq4rf3dgI+e0O/KtaS2528Wa453GL553ydcTbiXfdT89Y58fSv74SNbuXM4EBgPue4Zj+dsCsvd9KjW6G5njWcZBXcrZcyHfGyzkTMGJLUp9qYOs2X75/OSFWtNE6i+aA/Zn55D+5/5d6tiOxHUriduSBvvXTxl3Nrfke46YOrHWWtVrJPSde/5IPRG7WUqdRLfNsfrgMf3aT/ws6+6++YQx8UDyb1/ucY98nsn5t6iKfkszkfU3kPO3SNbKzn3uoy4f049lYv+C3j3IoyvmnfZT6mp30Ic8Hcl5U0/aLwZJ67CvlmxPQ/KdvUS/IZG/KSF5qKMPeDblxLY451xLcpSPPmCfv/BHfy/ky7HUkrTYh7zgGE8z2oM8Z43D5YT268YWfdhcvknzJZ6cSc54InvTWNZvnDInXW24DyplnzKfyT5I6h5DsR/zWnyG1B3u7XMNpF2O/1u/yUWwdUe+W6q4JqVcwl0M5RvvmLHkb4f/O99iGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGMbHxf7Aj2EYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmF8Btgf+DEMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzCMz4Dwd/NlVVW5bLH4SJ55LVzfidmc8cUU8mJVQw46HciNTgx5d/cG5Mk2n+fqDGIU8f1+3OftAe8vcr6vszuA3Av4+9n4gu3tsv2zzRry5SaH7EUV5LJaQn7w9H3KGw/yw3ceQx4M2N7u9jbk/hb//lMz5nxNhmPIx4+fQJ6OhpCLuumU9SKF/I2vs411fAdy3N2HfKfLNp09Oob86OkM8sjj9f4u+1ws5pCrmmP+6OGHkAc9zmFY833f+OpbfJ5XQG73FpB7nRLy3s0dyFG5x/ffZv+LM1kzGXXk8bvnkM/mEz5/xPlIW1wTSdGGXOfsT7HheEVNPu+6qCrn1otn62G15Dgv52y3n9DWRC3Oc5JQl+MggNypu5Dz8SXkRpd6d3FxxusJbUvc5PO6bZmHjPPeTLh2U2/F9jjamijg+0YL6mW3Sz1MWhHkVpN6EmxRL+NeH7IX8vcuYHv9yJfLfH6a0bb5vQPIN2Vd1/kjp5SuAflwl/Jum++4dZNzvjdgGwdt+odWSluw2KO9zS/oD1zG35cL/r7R3IW83mwgd90Y8t8Y/ZuQ2yl1wBOdihzfH0Zc267NOagKrqHa8f6spI4VC17f2e1B9mOuob7oUNrh/ZsygdxbcA2HIdfwdVGWpRtNRx/J8zl9zLoQG1qx3bMF560351rPHPtdiQ2eTWnjuwHHceWo56dj6l034rpIxRb2+9SjW50ttveAa3F4Ibaww3kuPerZuuI8RwHX4WLJdRDL7y+PTyCPNxzvvOJ4bR8w5mgm9KHNFsfj3bf5/Chk+7yS68Q557Z2aa/WC85pS+Katczh1hbHPBR/UOYcsydnbOPFFccsb9A2NQbsw7qkDgYDzul0RHuehNSxvMnft9r0N8WG/mY2oQ46j3FElnONlBWfX4e0Zc5nfyLxj80259QPeb3yuUaymtdXS85PmYl/u0aqMnfz0dOP5Lxm27yCY9Xo34TcalAXGwl1bzlTe8bnz6lqzolv9+XvzOal2C+xl6uS+6IgoO4sRRdcg3M331C3tnv0q8vhU8itpvhFj7pSid92nsx9TLkUe1YH8jyJxUrZp4Y+/aRf088Gsk/UuMI558qC93iONioqOGZBzfWmrrXM+fug1Bsox7nEbzX7lM91b04d2Wx4vfJ43fP4vlr2cUHA94f+d37+WnSwltix9jmnYUQfWRX0Se0m15RXUYfDiPZqkkvs5nG8atHJTp86fV3UlXPZ+pm/bbao280m/VYha3eRUbcbss/KAxoXX0LWXP6E9XrKtb+3y3E+3OO8ZJsx5F5b3l9LDkhyUK/3qQe7A85Tc03bsB29AfnukP1/8+Y9yI09xlqdkHq2JbGav6afGnRlX5VwPMsJ44Yd2c9f9rmu+h2Ozwv7POdcGEhOIeVvbuwxdtjucW29dFP6JDoylH3Vg/e/Cvnds1PIf/B/+nnI93/uVcgPf4U5qQ/61Jl2i2P68g/dh7x4n7+Parav0+UcJh3qyI2b9Hcd0fGrbzLv1gzpv1di/gOJ99up6EyDc3w1pW1q3BNb0+D4D1ov+pvroK6d25TP+va8HXLOuZzD6qI29aolOY6i5NoQF+ICcYq++NTVhj6k1aLtixp836m09/wx9aiU/XZ7QJtfe2IbI9omL2T7StlHTlfc9zguU7fa0FarbS5k37iJZMBknzuteb0O2J6F2K7Riv2Z+7x/8232/6uSY9ZJNQ/FMQml0xuxr4n4rySRvavkGUezMeTY0ZaUKW2fRHKumdIW+r7EXRXXXiw6EMSi9D7Xbi37nMrj/euCOhEEHL9VJmtAZCd7jc1M2itxXr/NfP9aFm0V0lbVsk+9TkLPua3n8siVR/vSbHF2z1eSOxffupb8lif7kkXOvMql+J3+gLq5YcrZ7fc5V2en1I3U5/sGbereRnLWbUn9h2d8/qbm2hmE1L1vHtN+JDV1bzphey7Xb0Oup7LxlDghDMaQ1xM+v5VTzssrPq/g80PZw4T7zCM559wPHFAH3irZ54spzyy/cJNzPp5zfSxCvvMHtxlLbL8ke8kNx7TRkX1Zk/dXHepMs8Hzp06LeaDBbcYKfcnDrCPaL0/2WYdyBrmVMpbxfbE30yPIvQbzascB9/JBKLnIqweQczlXaMb0F0FCnW/LOVCzx/G5Luq6csVz+lmHsu+QeU26sn/dqJ8QUXJIueQcwkjO6hNej1PJr9VcS0vJ0TQyjnuR6f5c/RbbFySyL6n5vEpyTkUma1sqJQrxq85je9KYz58taUuSgHbAVeLXJH/hSSwTxszBlTX1tCo0cnjxGbXGDpLzrLPvHBs4uT+OKZcV71+VjH9jUapVTtux2XDMUtkXJQF1diXxaiDx7mrJ5xeSLKgkBxTK2l7POR6a02rFHPOmxMOaMy5FR1cS7waSs3Iynt2U708lb3hdlMXGTc+e2dUbB9yXHJ+NIC8WXEuFjNMFw36XF5zHteNauCk5jy998Ud4vSk1Gr8uerF1G3LapE+Ob7wGubHHeUgu3oX89XckbrrTh3w54ft9OT+qCs7rnVvMEeXHNE5vfOHHIZ+evQdZc5919p3Pz07eYn5htZa4qOJ4rl887nJLn+fv23c4pmlL8sGenEE2pJ5CYuW0wX1Ts8nnLSvZ61Zc+3XE63FCe3xvl/UPp484Zkkp53WSr+1KfUQc05asRAc0rsmWXDN+1Yd8dsw4Zy7+8mrG9oxGlHfu3GJ7JU/rJ4wrGz7H4923qWPXS+3q+tn8pT770kqoGweSQw1jXt+SXHtTrjdX1JU6p27Mp6yDC1t9yCs5y/Qr+sm5+PIo5cZqtuJcVrJ+H54wh5uLrp2ecyPYTdg+X3T7/g735H7MfdxS6hyHcpY7kLOP5UPev9XXsxhZa6nEFXJUHHyb8y45XneV1FqeTbi380LqTJoy9ggcr3cbHPODQ+5DfDmnaEqNRBzy+vCKnVpKEjeU86JBJvZS8vq5xKtrOS8bz2lfCokVx7JP8yT36OT8K2rKfiNif3MZ/5s3aX+SVOoDZD8RTqR+Tc7vrwvf91zruTzIjfuMFY7OWH9Z+XKWvqIe7uwzdvLXXCuDHvezrR3uP3vbzEF8/R3xE7J2FrK/Xkxl3xbz7GRL/GLt0XZtFrSNhdRwJ1u0Jdu7h5D1bL+uqadph+PTaVAv1xtdR7SVHdkX+gljx5fu3IV8Oeb8zWWPdCo5N+ecGzCF4XLxzbelD7nEY/MF70/FVpRShzeWs/Nul/Z5NpXztB3Zp0ylfmlF49nbp85unNRfeByTdx4xL1mJrdgs5Cw75/t3pb7srQl1IJEUfil78cGA8fzFU8azjYac6UoOKOqyNuqtr/065Fi+Q7guPM93Ufxs7AqpCcikFrwluThffF6zTT/djDhOk3PGNRtZC7nUQJ2MqAf9ltSaS/73/kuc95bUn8Ye729IXBaLj+tIzZd+T9KIqXeR7M9rydXOJmqb+Pyu1DzfeYlyK2VcNr/iRvfRu/x2YCZnR8sNfUUrfXHj1ZQx/cJNzvlP/RT97j95+5uQh3Lest6wj5NT2obONnVm0OTayGXM12Krqoz2eyXfY/hr6sDTx/RHx4+pk/0bfcitlP4x8Hm9rsaQy5w6UMk3P3nG8dmM2d7lBW3V4/c/gNzp0DlcnfP+dou2PpM8YV1+f5y1O+dcUVbucv6cf+1yTz6RGLU5oG4EcpZRa53CVh/y4op75KCQGqk5Y6W9gdTcTqi7WpuTyNliKImNyw8ZS93ZYazVcPS7nuQwug3Zl7YZIw/nXAtX4z7bE1AX/Abt++iU7Ts8ZKx40JO6Qcnp69myL/vKL7zyCuTTH5NEnXPu67/yNci/+pQ+oP5V2rjXf5C5umSfY+KtOKdRzvi0ljq9xOMcvPkq56gUn+VLbHQ65N747Er22k35pk2uX844hwc7jCXqkPYykn2bW3BOryTv5UkO/PiC9k9CSbeReoa9bdr/ecbn92RfW0qt/HI0dt8P1K529XPrvyXtltMs15aYsiexwkxS74E8QWN0X/enksvvtOQ7PZnXzVq+ldF6TymjCOV7KP0OMU6oZ8rVkLZ4vKAfyeZjyN2efBu1xz1LW2obrrjsnHTHzSqOX3+X87G/QzkrJAcnOeyLIfX+X/5Gzqskfo2kHimWOcpWjO9037aR2sZKamE8qXMsJBZb55zzWOLxQOLbZpPtDyT2cHIGG8V83jCiv9N4Ow+pk4XkaBqat1zKpOr5Vkz/FvhSKyX1Ibko+Vy+Q6qkxrwttVDXhed5GOt2xI4lqe4rJEdRSQ5C9jEz0ZNaauc34kOc1t35lBcSxwRyDptJ3BWLHq9l7WYLXu/06IMvLuV9kXwn2eL71/Kd4Hg8hnwm34B7cjZRyLerZ2PGJYGc62rd5+tf+CLkJwu+f/yQcefxUD9wcS4WWxNEjIU7PfbhRkR7OpS8WxlyLdx7lXmp5g51bi7fRs7XUneXyfdPLbavK3VAseSQLiV2rOR8zZdv3g735PxO6pj29+mvNFb1xd9lciYwmsnet+ScZyPGjWs5S3/85FuQ2z5j20jun0htwnUSh767u/1sfuqMMXZT/syHF3OsDwYSs74t9eSJ1ElIjL29Q92dndDPxKmcHco3FP0u/VgpdX5RSN07lrrESOxbW+omYzkamJ3x72R0Cuacky2unbMj9ue1L7C+//gx23O4S3t8Nebvky7t00hi7rnEXjsD+dazR93v7Eiw5Zxr9BmL7N3gHFw9fgg5kPOtp6fUAb/D58USWzz/fbNzzh0ecF+xWnF9emJfjq64V715hzUOxSO5fvNzkB99yJzsYJv29WhIm+1JrXh6m2uiXHBOP5C9/kDqAPde60N+cMY8T3eHeZ6zqzHkWPZh51eMZ+uY9vm9J2zPb4f/O99iGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGMbHxf7Aj2EYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmF8Btgf+DEMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzCMz4Dwd/NlbT93P944/kj+67Mv4XorXUH25c8P5YspZXn+1SqDfONeF3Kvk0Cu+DqXBpSnwznks7OnfN8V31c2NpDv33wF8v5+B3Jd8H3NuAn54lvv84aKDYxqvr/TakOeZ2xPNRtDXoUV27cTQY4Hh5DTJtVlNmN7Vxnb0+y2IO/dfc0pL93egTw+H/IdoxnkacV3zKdnkIcPn/AFa/6+kVBr3oh6kL2AStdox5D9imM2X3CMnzzknD14egq53eP7RpMR5FkfonvzS7t8v+P7Ly/Z3/nZEnLao84F29QhGU6XZWvI52fHkEPXgNwMS8iRx/HqNtjf68L3A9dsPWtLs8VxbLVpK/yQ4xQEvD8S4+TVlIOa7287Gps45TgmAcdxuaStCyrqme/z/jDh88oF9b6Q9jREz5tpCjlL+fxOk2u5XFCv1hVtQ9mgrS0SXq8D2pq23O9C6lFRcPxjdtfdb1Pv1/fuQT4cyPOdc0VjG/LdLbYx6W9B7nX40m6bY9Zrij/IPciVjNHIo62bSB/rlL/f3e1DTgd8/21edpHbg7xXLyCvAv7e+fL+QBxiQIdV5LQV5Zo6GzX4+07MOa096nS3T1sRJnzfYsE5bjSpw8uM7U9kDV8Xtatd5Z71ZTLlOC03HMfRMfXi6cVDyO0212IdUk/CFn2c71MuS+pp1ORank84zoXPcQw5bS6bc578inIYcN69BuOGEbvvIvn96Yz9Swtev3g64fVU4yTaGifi+RF99CwXvSk4fndfYxyzyNmeTpO2ZnY1dsobtxlb/a2/+g8h/7F/949Ano4vICddvmNdcE7PJ1zrJ3POaWuftuFgizrQlVhyNOcYFxkdynrD969TPm/T4RgFKX8/HIntiPl7f8M4qdngJJYSe65FR+KUtiGIqVO1+MNWj/FA0uJ49/ts33TJ8ck3uju5PoqicOPh1UdyWXM9ho6+frtH3YwkNskz6tJI9hVOYr5NydgnSTnWZU2D4vmcm7Lk80uJZYKIc1tlbG9W8nq+osEZb8TeZVz/pdi/RIKPWP5ObhjJ+ErsmC85HpMJ7b0XUrc9n2vZ96mLfs19ap1zLXkFrzvnnCvEpvuMTTyPbQgjmbOKk1DXMmeZrL+ScxLklCuKrnZ8Xi4+wa8lPpfYpeGz/WtpT1mzv1XF/uWiY8VK9tIR5ySK5Ac+7UOUiNPxdH/B6yuxH8uc/VtILmQyHkPO3PdJ7FPXLt88G/u5DOxszP3roMV53+5R1yfia6OQa3G45jyNphzHqKLck/3xVodrN7jZZ3u61JtcYnbdxlQag0r7N0PqUeuQ/WnMx/z9mnuW03Pakm9N6Af/1n/365B/4ov8vTel3/pcdx/yH5P7t2RP9H+4ol8uQ87vP/3WQ6ecn1F3Ly85hqv5OeS9AXXi537mi5Db4l/ytewDMurYQZs68OiX/hnkf/HLvwTZP7mC/LM/dQD5pTdehVxUfcgnOdv39GwMeSX+oOHJXjekzux1qLN3YkmUFjSmHt2bmyyZG6glvg1S+sfNgjriV/QnnUiSSLKfuS78MHTt/jP9PZ8whg6DF3MCz9OQfUQoOZtQut07kPztEf26L0mYKKatKgv6gEL0eiW/35T8faOW6zmvpx73+1cjrrNui/vKuCm2UPLVucQ9bclft7b5+5bs17MN9bQtviFfSL4io90Yz3k90yRXQLvhnHNRk2OwWnJxdF6lvTv/kLrsyVpxNfvQEHvvS+zckBz6ShKwgcQR6k+SkL+XMMd1Wnx/Lc/f5DKmsm+sJX+bSi6hKqiTRSE6mIrOSPuXF5eQY4kj25IriHw+v444p7MN27MbyqHONRKnibv/2ssfyf0FfeX2zh3I0xV1ZS055kqu1wn36Lnkv06ujiBvtTi2ewP6kdCJ7jVkPUkOd1vso7/NfeNqTN05mzB2qDO2v5Ccw/gJ114noTKNa15PSpHFHrcWsodY8PrWNm3Ddvsm5KOT9yDXIe+vm+Iv+vec8oUv3oKcepJTvskxHI/5zmzBOXn9pduQX5K8dx5wDsKEcz5a0gdMZxKrSO4td9ShOBhAbrRpP8s11/vVBe3Pw6+zf7v3GFtMR7y/J+dx4yGffziQPFNAn9ZocI3cff1LkE/OH0AeSJ6nE/F5vthzX3zkdVHXtcuyZ3M/mkq+K6bjyGW/H6VcG7K9d0VO2zQXvcg31NPFinroRVzLnuwbQvEjaST7dSdJaLGVG7GF1ZzvX0usFIVyvuXJeZX4qbKSnJHYokiSzJ6sw9rjOvPkPC+WfMtaczgS6/hyv5+8+G83hU3+5nn9cM65SPJunkc5iSTnKmeeG8mzTcWfnI+5Vu/vMAftGpzzSoKb44K24PWbtKWtnP3JCrZvuDzh9VL8gfQ/kbzfRpJUvuacRIdVp33ZlxWezFkk/kPqVTzJifU79D+xzM91UVW12yyfzYW3pq43K/br/iFrHE59nsdkMeOG7dXbkIsZbfRSYth7omeZY46j6nPt9np9yPMZ114p56YrsX3zjPM6KWhLZNviKllHifjodoft6Qzoc19+4/OQlxFt92JN27a1zd+vZxzvZio+U2ojpmPmA1ayJwibL+6rl56cjQcchFs3Ocf3X5IxnkueKeX9N3c5x5XUDzS2mLM5uZCcu5wPtZtcS/0djlkc0XaMLhjHPX7MnJOUArjpiGO4ljhnPhnz/bt8f+VJ/lzy95Mp/ePlKXV4sMU4c3eXsW4gZ6ajCX+/8GnbRnKmfZ34zneN59bolazfuuD6yiS/tVpJnZvkYDM5KxwtqP91jzFwJWcd0xGf9/AJa6y2pa7jUmrM4lTat+bz9w6pK7meBUjstZCceUt0P99QeS8lx6x78I7HfVwq+ctS7P9kxnzkcsE8ne/4vraczZ5PmJ+tshfPXqNYfO+G6y3PdAz4ji2pTYklIL59k2Pe7lLHWg3WOe7ucA4bUkt0fkT7sZ5KXaTkxsq51IsltOHrjHO0WEo8LOf7fsw5igK2N23L3llyh4nUqKyv+L55LbFLwuctZR/lOf6HqqR9Xy9pv6+Loqrd8Pnzrre5tjs79BOZ2IbBNmOTxlaf91/S9owL6unlidT6XLBm+eiE9bKV5B80hi/FVqYl5ylY07Ycn3AtX33r65Dv/9hPQD48oB/3xI8V8ryZ5C9Tycmvfak16NOvbTbMd+Ri6zeyTobnzE8spFbBTzhfOx3qvXPOxZLXd2P26XTItXt2xljn/j324ek3mNfzXqUv30hO9u7efcijC6k5HjH+8x33trmctU/Fn55fsj8byVOWmeSMZK9+eUn7PpE8XrXi+F2M+f5NzPsvxP6fn74LuSNnvIXsy8ZT9v+0Q52Q8jy3V9L/XBdhGLndvWe6EEjy3ZOzAE/2CUFCm5zLfvdU9LKWcd7aoo+spcYilJh5S3JnPTm72DtkHLGRmggpKXFr0bu4JedTfT5vJeeggZwt6NmOX1MPxhdchycnH0DOpK7og3f4+OHRNyAXG+ZH8g316u4h5/PHv/Q65FL675xz4zM+c7fDPgZbtJ/be9wnXV6KLRA3e3lBW9PpUgfyBdfmas59w0r8di11MXnJ96/keafvMW65OB5DPgwZl4VyBvr2u/QHJ08e8/mn1LnTpw/5vFDONFdsTyh1PVdiO/KSOqh1SZHW9ZcSy+tHUtdJ4DnXftaetMv1dnwi3+aILhWSLwwk/9eW86v5jLHP4d2XIGeyb6tkH+VJnUg7klhnn2shjzlXC6nbG4kfnExoLwY3qHtvvES/3b9xF/LJFcfvgyHX96/88/8b5B//Y/8x5NmM7Ts8kG+dQur2akH/8PSEsWNDang7HbbvzS++7JRzsYln36A9+vv/mEbxnVPO+Zf+8E+yjZL3j6VebCJ1z4M9jnEhscXOjuS6AvbpaCTx34Zjmkn9VSk1DKOh5HibfP6NXYk/A/rkvdtvQH5wKfbuKXPaD05oL3uy1//BH6XPuHFDvg/TcxfxeRcjPj+aSjLzmqjLwm2eq0MOPanBlxxPKoUVAzl73pN9WSn746nUQK+W1MuN1PktZZ80lprpWs5zKolJg1ALC6VWRupXU/EbxZrrpCg5b7Xj82OpWW5GtF2DnpyDyu9D+V5svWL7VjPueZp92p5wwHXQiGnrPfkOs5Q9zb9sFNvQllrSWL5BKOW8K5dvRyPJi9VSx15onbnoQCk5prQt9RdythyUUoMtscZ6IbU5kvOupV5iS3Lmi0T8oXy0GMZsf0u+mXTy/bGLpR6EU+hW0p9A/P0q5/OWa/avkDNVXz9evSZqr3blcznOk+EY1/vyPUIhubeR1EgsJI4fybctheQYlp7s46QOr9YaBsn5dLRWXr6dWU7HkB8+Zg7pOKYP2+vJ9xOyD5rPKJ8N5VtVObv35HsKJzmxWmruYvmebSx7hL6ckxYpbf/gdX63/4f+0z8H+f/1p/8XkP/mP/iHTnm9yzYWKd9542XWMDckR385lxz3OWNnr8W800rOrvNQcigyR7d2GBcNJF/dSeW7bakn2Ii/qaTW0pPvLUI9Xyt55lvJ3yJYSY5nKbb56CH3xiOpKxx02L6unEGcSs21lGe47Fvcmwdypps35APka6SuA5eV/Y/kh+8zJjxbcWy75zxfmcl5VENqST78OvODX/gSa84mki/syNnBakjfnCac+0Yqg9/mnv18wbleZTcg9+R7tju3ODcLyS8G21I36dEvhh3RvSvmCBZX8jcYHlK3Xvki+79c8nmrmv2dyvWTIdf61tbnIBdSeJ52Xyw6u5DvanZvcUwvhtzHbDay961or+YT2qf923zek2P+3Ysv/ABrWx8/oc7tyb7LrTnH6ZQ6V1fyTV31FuTZOcewsUf7Nb1kfzVTVsl52eWHzO21GrQ/jx/yXOe1H/kpyH/9L/8i5D/57/Lc48O/99cg3+SScmfyd0O64tM3Woz/2/B9lB0yDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwjH91sD/wYxiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRifAfYHfgzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzjMyD83XzZukrcu6uXP5Lv7Gzj+m5zzfvDJeR8OIJ8+nQMOXE55ModQ+71tiDHiQd5k2eQJ9MN5PNTef9wwvd3Y8j7A7a/3ebfU1rK80/fO+P7T04hBwWn66B3G/KtPvtXdmvI09MZ5K3tAPLNu/cgh8EK8qKMIF8u+ftWl++/ebMH+Sd/+ged0u+2IL/7HudknRVsw5hjulxRLte83y8ryO3GDuRG1GWDWh2IIVXKrSYckyTmGGwN2J/7r95n+xz7V7K5zoupg+MRG7BYUgcnowvI1Yb3JxWft3WLczTN55A305LyhjqTVdSptUfZyzjei/Uj9/2A73kuip6tz8MbB7jejLm2vCTh72Wc8ozjHAfsd6vZgJytU8gdXnaLBvWoTmhLqjn1fDo9h9wOeH+2Ynu7sjazkHqY5+yPTKsrC16PZHxKxx/Ecr3w2b+qpF4OF+xf0uC6jEL2r9GkfGO3Ddl3XHf+nPc759ymvQ+5Hy4gxz3aikLWQiOlPW9E7LPn0b95PvsYlbQVk4xjkobsUzzgGAZNzuFWn7LavijgHD0cUyfjNsdoeMX+tlt9yEVGHXOOtqMhOh4FbN/2zi7krGT70pRrMko5vrHH5yXy/GDN9l8Xnue5IH6u7YEY/YrzdDKhTZ+tuPbOh0PIgx2ulXx5BTkIqaeRz3n3PM5bs82JazSabG/Aeajkz0Sua85jy2f/qgbjiGzOOKgOeN3FfN9GfNB0zd+3elxXO1sDyIHYkvMrxnG1Y3u9gOvuYI8xgu9ehZzX1MOTc86Xc85NMsYRv++P/jzb2OQc+SVtwaygDl3N2YeljEnU5hju39qDHIq9ThPqQBJyLZYL2qo8Yn+CFnVyNaXtK2q272zDMdvfpu2txCGt1twrhCWV0E+ps/MV758s2d5VxjXQ9hi7RinH3wupo9QY5+Km6PA14jnP+e6Zzlc121aL/YlkPfuyr/J8jmUVcC7jFtfXQmIlr6IubGbc56RJH3JZTCEnHfrV0tEPFR7X66aStdDi2nI5/X5bYo+N6OpmTV0p1+xfWFO3YtlXqi7nC/qpRGLHRpPvy0uOX5XL2irZnrpge5xzrio4yYnEj760sap5/0bmNHK0R1nGNjmZg6ySvbr4jKKQPkv82Yqpw54swFTGcJnJHBZ8Xx34ItMe1rXMcan2lTo3XdLm+7KopmPOeSix46YWextzflZiz9YFdWyd8fp1UVe1K1bP5vL4ivtXCU1clnJe5i3O83zKcV3OqCfnV2PIuk9JQ87TnfuMFa5O+XxtbzGlrfjWE+7Dhie0TSNHvxGJHj655Npcv8Y9ya8+4P55suK+5n1Z2k9/85uQ/3c/dRPyD/ww94E/95//Zcj/2R/6X0L+H/3H/xnkoqbtHDXZ34v1EeTLKfXSOecu3n0I+T/6sS9C/oWvXkJ+9wPJMaz+NmS/ZOxw2GEbi4yx0es3+5B371Mer2RvKvHrVs61lU+oE+cTxt/vvsM5/IWvfAvyTz56DPmVNfNkiaPOhC3qVDCj7X06PYG8GHON/Mb7b0EuxT/VocTjjtc7HfEdstGb+fz9deH7oWu1nsX+4ZyLpdyIj9uInxY/msu2rdfsQz6/YJxSOdoaL6QezebUE5fT1m0kJo5D6nWjzRzWJhcfs2SDJzPml0uJWpcD3r91IHFWLQMwo49f57Jfj9ke3SdW7J6LZM9QyPg1UsbkmeQ31IcXhbzAOVcHbGMieb+rY9qKIOY+Impy7dWSzy1kLSUR5zT05MilxT6tJSFcSp5sk0mOSnIimnQJY7ZvIXFMueTz1hIbB5HGgZL/nY0hdxOOV7Xk+5oSB+WS3y8d45wrsW2rDeU6oP/+oPr+2XfFUeBu3+h/JDcuuV5iyYNEFcf+bMa5kZDbvXqb68WTsdvdoW7d2pP8ZMH7S/GjZYfvP1rLHmCs51+c+7Lm+kxk37iWf1/k+Fx0ccU8WCT7svSAeZie2MOR5G2CiLFPW87nRrI2nk74+1v7jM2OJQafbvch5wHH3znn3pu/D7nucMw7CX3UVSlztuYYzWZsw4cnvH58QZuft2nTL48/gFz6XE/rFZ+XbnH9b7fkfM6T87GaOlIFnMPlhu+7POWaGA05J7MGdaZcU+c6Ys9zx9yfX1Jn9iUHHYq9i0L2LxWfmcl+wstfPGe4DjzPd/FztnieMwasZf+9WLCfueh20qMuh+JrI/E72YbjNJXYa71hjFhKTneZ8/6owXmrJb9WiV/3ZBrk6NqVEjs05Twp0gOwFe/PJd8atajHatslneE2K/ZP58OTPYcnsVMk7fXlHDqLXox9MvHdlZwBZhJ7pB2urabI8zl1ZDVlHi6WtfjKPu1zv8/nTUaSs5CcUTOkDngbzYvx54HkVDR2S2L+IAxln5nSH1cypqXs5V3J9pcr2UdJwBuLTlcSXrtK1qycd62WzCHVmte8JrJV5h5861kewLtkTiVI6Ifv3JWYWhbvQs4Fxwux2UOJqZdcO3M5D/ObtGVbPZ6FxHJ2UOWSr+XbXKVn0/s3+LyexLwp7y8aUmvQ4jyn/T7ksklbM5fahOlSahVK+vDhkLZnqys+s2ZctLNFPY2bbG+/zfkbznSEnBvmnJMg0r0hx3wmi7m/8zrk+pzvWJV9yLd3qSOrnPYxllhvN6VtqkuO0aDBuKndo30/P+OcNbqSHxf/sJJ9URjyehTRn1Zy/qb72JXE4rMJbU+jzTio2WYc1pKcUih7AVfxfRdnjCurXE/Aro8wCNzec3VcJ8djXG812Nf3HzBneShjcXrBHPCWnEcPxc63RFc2kmfKJUcwnvD5XsW5Hy3lvE3yUiuJnbqp1IW0ubbaO9SF2uPzBr1DyBeX3IfpWcp8xfd3+nx+f68PORI/ljvq0kbyr37I9svRuRvNaO+a6YuxjxY0DST26HVoL/IF+5ikXO8dsfmzjPbl7B3mdFOJl1s92tQtsVeT8zHbI+fVqeR1IlmvkdRb5R5jg0hKf9Xn6vl5LXWXvTtsry/n50upQyylLnA+Zq70UmLPaS6xqcSC5xPe732fpH18z7nGc3Ozln3P1hZtw9Up+5UvtC6Bevvg0YeQmxH9lp4tD/ZuQe4M+pDPzrnWRkPmAzzZxxVr6kHnzl3ILTm39LrU88mH34CchTzPKiRWkdDJ1Z6chQR8/rIQvyf1wIul5GcL2tatu5yfc9H7WS41gJJzL+Wc3DnnWlLHtpjRV9+5+TLkbPoU8p7Ubna/zLXtr/Q8SmqBRmxzLnnFIqW9bsm+52TItXoxfhdyUDCW2LvBMUwkftV90OkF4/OhnGWXc8pHco6xmbK/XpvGoCn+7ank9LcP+PyHj+T8TM5hGoHUAgUvnnFeB0Hgu07n2XpIEs7jWPaLsfhVV9MH1LXUQEgMGEsOoiH7gLHUOBQSp2RrjlshNWudfY775RnX4juPqDfNlLZqd5vydnwH8pXWtksN8/Nj6Zxz/T7X3XHAuGO9YByp3ya8/84Ycqn7Utl3tRPq8cFBH/IbX6TtXM01geDc16Ru/PGYbd475RysHHUikTjD05pi8dMNX8/e2ce1nCFIGszVpdSJR4wrNK+VyTcqXa3NTKhT8jmEO37CWoNzifV7W7S9ntRJDSSOWweShPK5Zva+8ArkicSZcc7xC2WvH2jeVhOb10lVO++5OHB6ydqYVL5JmJ49gTyZjiHfv8f1Wm7o52I5Wx1LXcbhS4xNhm/z7CWuef9sSl0oJV/oJKZextynDeec+weP6ae+vMs8U1vOSrtS83t1RmVtyFnQvc//ccjvfPNtPi+mnw+kSC6Xus/ck7MUWWvnpw8gbw/egHy4y7XinHN/8Od/FvJffv9vQZ5N2Ianj2kvDsU+BVITcP8249+Jow2uZty7DguO8bZ8AzKZ8/5b95nLW0kucmebY7ZaS65yzP7M5Ezz6SXtTXPvHuRWh/07umKt7IN3HkJeLGgf+ncYmx3cotxt074spB5gMuKaaLS472sNWKd9XZS1c7PnclCBnFUUUpPfHjAHPF5w7XYHXPsLqcHyta6tQb+Qpnyep7ZE6mM36rtT2paoyf544gdmsu+oSvqdqua6KVPOuyexWSjfqMSRfKs0ZezU6rF9YUPPXbnOU6md8iSfWUjMHkr963LGdePHL+Z8as2JyJh6Uue2kjO9XOaoKXvZUGui5Wy+kL3ofMw+pYnEKmKPfcnRaOwVRhyTSvKS/hb947qQHEwleTOP47OWuvt1zjmtpC6+k4iOS+7ASXs9KajT75L0G8Io4PVSao+uiyLP3OXThx/JC6k70zq06ZzjspLvuhs+5y3tcxxCybHUEgMuJlyb27tcu5VH2/XKDfrAqdR8PH6P7Z0suE4WmiPaYpwzkn3eSPsv32Bn4oPSRM5O9Psw+Ui7lHPdTGrIH8k692re/ytfYb3un/9TfwbyWPYwafqiD3w6Z5sC8Td5zN+cncg+Sb73msjnFO8/kjxUV2rJfL4vjmSfUkksm8nZsUedy2Uv2x9Ijke+HY1qre3iXuBizNddSi2pL/uoUnR8VXB8rmbcSxQx+3uxlr/1IHFkKfUofkNskXzrq/Uq10leFO784rkzdvkOOHJai8J9j34T0WtRNw/69BM3d3l9uBizPSuZ+we8/kjm1mvL2YScf01XvL8S+3H0mPnSw/v8uxiPHzGGTW9LDat8OziTfGnksf+a79yS8/tSalRn5/SjuzdpzzS/ub5ie2s5yz17j3/XJNh58TvntsSjUZd9zKRG4MkT7ite/wLPABeyN/YqqSM85e+PDsR+yb5k65A6meg3DXJeNm9RBy5L1pScbnh/R2rPi5o61dunzq3kW/Gl6MTd29wH+nLgdPHBr0E+kG8g6zPuzfcG1NHdhP07ke/bbuzRnp+uvruc84unEYZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhfGLsD/wYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYxmeA/YEfwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMw/gMCH83X1Z7zpWR95EcVVe4PhsXkBteADlOm5D9Je9fzErI0aji81zM+5dLyGXt8fpmxt+3diDf7+1CDhK2tw75vq+99QHkzYLvHx2fQvbkzy+VeQJ5OetCzuZz3h/VkOOUD+z0OZ6zOoW8Pj+HvPLbkM+Phny/x/YVdQT54ipzSlnynvMp53R7l2P8an8BubfNMT+t+PvJin3c6nKOy0YDclWsIOdRC/JwJmP6hDp8Y78P+Sd+/D7kWcY5f/iAYzhfUl6uIbpVyPe3WhyfTZNjHKYc3+3tm5DzfAM59TmHM9HJquB4jGdcI3WTOrLMqUPXhVfXLsqfDeZ+v4/rvYTjVCVcC86jbVnUHCev5rz6Nce1EfJ5jy4eQR6kPcjriKa51xtAvppO2F6xXbVP25e2qef1nPMWszuuLtmfbMN1tl5wHSZBh9dLvj93U8i+jEe3TTmUvz3nOfbP8ylHKX+/tcX2hilto3POzSOOScvn2glSzoGX0t4GvhhoLk1XFTnb2ODzkq0+5G0Z8zhmH6KUfe51uRY7Cd+3WnGOPcc5affY/vaA8uKctrQjS8LNOV6JKFGU8HnVhmsiae1D9h3bv9nQFgcSrVQl3+9VNJb9mGv2uvCCwCWdZ+t7U3NcioBxgosp1wn19HRKPck9Kl4petkUPV6sZV67HPf1hnIrkufFlNdrzlNRyUJo0gdHLfanE9H2xuKTc4krypzta29tQe729X18vl+pLeH9ecX3NRJejxP6uIMdzteHJ/ThUYd2wznn1jXX9s3dPci1KPvxnPZ+vaY9i2XI79zYhpyITm33aa8llHaZ2N9c/FGrzzFKp/QHjRZtUyxz3pZYvrVDHWqKv7tYsf/ZmrbEZRyAntiihcRdi4K/X6xpS9IN+zOej3k9ojH0QtrW9erFWPfa8HznwmfjH9bUhYasv0jkhcR4aUZ5tWSM2N6mXS9kH1VVnPvRGX+/tU1dWy8ZY3fFbw+HjyF76QHkRU7dCUK+32WMUUU1Xb5m7FJW1O1qfQk5jrk2E0e/1G7LWhe/FYg/iEPaqzynXysLXg8bbF9O1XTOORc1xOeI/tYSb61lvazkmYVjm3Kf67GQeDAOJT4LqXN1yetlzed5Tu73JVaKaaO9kAYuiDnJZcDnJYHE95KLqBb0QXsv0Qd985eOIMdbfN98yvFqdmlvo4j3e+Jzmy22J19xPpuyn7k26tpV2bOxKsXOVjX1YlpwnorsO9uifJt2Paw5ronEQnsD+uJE93Uz2v2TY9qGUcjnfXh5BjnLuPY/PKftCDds78WCa/9g93XIrV3a0u0G2/OLb38I+f/xH/445P/5//H/AvnoH7P9wW3mtP7cP/q7kJcNrrv/6Ge+APnvn7E/N+/S9qTzF1OM8w1953/9/m9CjrrUiddeZhu3G5yz97/GMRg+Vfsra3tAedBiLLSS+PWt4zHkb5z9U8hPHj2A/M3zC8hxSf/zfsTn/cnDVyD/3//hP4L8uf1XIS8n34ScXVLH5gFtd0PSvEcX1KFYbFstecPtLm3pSPYf0Yq+IZd97HVRlpWbjJ+1bbWRIFkcYy77ChkG50lMXFe0ReMVY9hE4oxeynHMxfYVS3GqkhsNJH8dSMy78TnPZc51NhtTLz2J6f2a7fcDrqPRWuI+icFDN4bc6dKHViVte76mnmayB6lkfvb26GPTruwTJWbPM9kjOOcC8Yu15M3UWkUNjmGW017nNRutml/KXtoXP+5CyVFEHLNuk2OwlH1Wtebz52Npn8RdiyvOaUPyw4uJ6IBcz2QNVbno4FQS1jKpfkQdXkbcp86WtEUr6a9Xs78Nsd1h/H0S9zjnqrp2s+d0cC5960os8e4D2vHIo/6OFpK3CbkvipqMbSrJUWc7lPMxx9bJWcJ0xDzGRcH1VpxRdyeXT9g+8fPTkrpRe33I5Qt5LObE6wblSPY5rsO5D0ccz6DNtT4taI+zGe3j+w/Zn32f459KXu6DnNbj6hvclzrn3INj5v1bHa7HVcA535Scs4nkPGdPTiAP32M8Wsm+KTyQ9SznWVVMC1ZMOGeTGft0UfB6Y4djMpAcdVPi7xu3Gdt4MX+fNpkHCuU8LpC98P4Bda59Th1eiQ+dTtn/JL4BufTGkAvZL+RrrulCcofXhef5LnpuD7tcUs+qinb0bHkMWXNCnuzXk1jOMlyfzxc7HUsOOpSEZdzk2qw8OYeVfN58xf5EHud9I36n1aGfqAr6xY0vMaz4bSf5w9GYtiVuUa+6ksNOxFbFcpayWdIWL6T9m4rjrwd2qfQv+DZJn6qUve4Vx9hJrLDVon2MBrS/jYRzcHnFfY5SlGJrErZ5shixfROJpjLampWs/XZb7Pma989XnFNPdLylZ6QhdbaWXMIq4/NKKdgopRbBl+iyWlLnJhLr5Jqjlv42pRYgbugB3fXg+5FrJc9ynK/eZb6zf+cQ8uiEcUzDk7qRE+6vi1z8fFP2AT7lWM6PxmPqVdunHk/XHNerKeeh3eM8znPOcyxnH5WcQ24kxm/vMwZeRlKXJPnv4YL7sOVTti/u8nmyyl0qu5SzM4kDZYsSJHJuLLZtVPL3E/Gpzjm3keOQ9ZrPjNaS8y/Z6ndO6J+Gspd9Ukge7k2uzdDjnCzlbP7RB4zLurL255IvXpxzTvKN1Cuo6ZK13L/LvN5azmTDhHHSQmyZbm0fHHOAex3mzPZ3+LytW4xzMrEty8kY8uWI87HT5fPy8vvo3wqsaueeG8/xJdd3U+xDtqE92Trg+vngRM53IvrBruwLukkf8nhBv7iacSyXGdeCL3voleSVdA+s5+WR5DSCgHPbbcrZs9SsqS7kOdd3Kn4zk7PZq1LqOBzxZU+ykbXhS5xQeFx7qeQQXPCdz0accy6Rn9y/wzNCX857pqec87HMWdjmGAxPGQ+eHdFeJZKTbfWZy+oPOWbDU+7rdtocg/0dxk6bTDoo+5JGzL192Of9VwXnfCX1CfF2H7JfUeeWUgNSSWwyW0quz3G8z4aSWxT7nEnt0mrNOc9mEq9fE/9y3/UsDktlnGcrOoZA6gZrOQddLyVmlXzjbEVboeewiexjzh5RLyOpUV6MJeexfQdyNmeMHi+pV/cG9LM7r/w05PcevA+5lBrhk2M+f+qx/YXUVTYHchYhdZvtVM59ZR+23eG6msgy6u32IW8KqZXIJD9cMt/gnHOV1B9Mp4wdjmZfhXwoOaGzD1k3HrWkPqsptT6SRzt9xNjo7Jxj7DcZj7e67NPBLV4vZC2uJ1zb/QbHaHcgtatzzmE21ZpnrUeT2lrJ+d4eSC2T9N+r+PtVJecqHe4bD+8wNrpxS87uI/b/B1/j+PxVdz14rnap90zfc/m+oVhJTkFssldIvnglNQ+i+5XEyOMxfcDZKfVsLd87ROLHPTl/G1zStj38kD756orP83ry/cGAejyejiE/eMoajdE5fdCdg9uQ8xX3sd6GzxtEjMHbgeRTpM4y7NGHliXHcyO2+2sfcs/z9Sd8/3z94ln7SMZI7f0rIfch7T3GetOKZ8Vtid0Sn89LQu4L0gbX9lrqOeYZ/VVW0K/7kdS5SE10f1vqCEWnEskLZgvOUS61oHpG0+hKbWpCHdgR2zY84/NWModJn3PuyRlyIWekG8nZhYnUYTa/P/LNzjlXV85ly2f6sb0veY6SY+UvxpBLqXHNp5IPlDoDX75V6Un6qyg4l7Mu921ZLjkD0ZWLR4xV9g55VtEWPxk3pIZLzs8LOT/f2+EevZDzo1TOuwZz6tIHjxgXTDLq9tarfcgzyUk05ex1ITcM5HuFyyn3OCOpgTs8ZH+cc67Yk/Og1/g9VPmIc97ss8/NiPq/I7XebTnPXzVoD/h2554ePYTcizgnF2eMfzP5TsXPOaeDDmMFL+DzJjPJDco3glcTjuF4+R7k3/wXvwA5OrwHOZC97+d+iPVj3Ybk7iRvM7pi3qvK+5DXG9qzfQ6/iyXXcF2UVe2untsrtSXHsR5xXltbtOOJL+dNkssPZF/mSw63kvpTX2pxPP3Go5brmnOVetxEvufS+s6l1FWuJV9YyH5ZyiRdU+ICKU1y/ZQTX2gcIDmaSmLkuMfx2ThZ9205n3PcIwXyfddqTF9QN6UWwDnXkDb6oZ4x8v5U9oYNqVcInDiYmtdTObvOZO8+HY8hb+/JOYGcF7XljC+WmuxK6rdqT2IZyatVUk/iB2xfILUzfkNqsBPJ20ns1pf7K/nkLpA1kW2kTlNzHxIf+KnUtUvtwbXhec57/vuaXGqS4z7k/V3qUb+v+Wg5z2lxbdVOvleSs5XRWM76pa6urLn2bsj5k3rxyOe4T6V9oaz9vZu0FU9OuIcIJuIjh2PKBfd5eq56fsm40GvKuhFbVsq56KXkCofHzIlNZv+cz9tmfz7/6puQd19nDOCcc5XY47lMwlce8puXt99hLKdn31Op32jLnHca3MclkvAennNMz6RmeCNnjLEUKKxXbO+dm3ch33+decJIaq7HC9qO+Uq+z6o4x/vb9Ae7u8yxlJnM2V2Jw0KO10b2EpXU3o2e8Hlxtw9Zv7WNmxpZXh9VXbll8WxNNeJ7uF7m7HuQsS9PzsaQX7olv5/Jepe64/092o/K0Q9MLpgDaCVSh7iUb0LkvG4ge4DDPmPYkws5L6+ou4MO2zdd0v50WuzPk6sx5DiVWiHZA7icunTykPtKbyl1GpLDn16y/Td6zEcmkmMuZd+1iF48+9jv0iafyDcKL9QLrWgPmvL96fSUYzaf06ZXUjTQ3GF83RjSfmQBx0Br858MqTMLOWN9+oGc2b5BG53J30doS614IbXiK/nbMeMxf3/zFvetZyPumzpSO7S/z/5fXIhOt7kGQ9GhlviLUHId1fLFc4Zvx/fRqbxhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZhGIZh/KuD/YEfwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMw/gM+K7+wI/neX3P8/6q53lve573lud5P+l53pbneX/H87z3fut/B591Yw3D+NcLsz2GYVwXZn8Mw7gOzPYYhnEdmO0xDOO6MPtjGMZ1YLbHMIzrwGyPYRjXhdkfwzCuA7M9hmFcB2Z7DMO4Lsz+GIZxHZjtMQzjOjDbYxjGdWH2xzCM68Bsj2EYnyXhd3nff+Gc+8W6rv8dz/Ni51zTOfe/cc79vbqu/7zneX/WOfdnnXP/yXd6SFGU7vJi8pE8GAS4fjRcQE66Tcjz4RKy3+jwBWUDYp1Tno4qyB+eHkP2aj4uaMaQb+3fgNzo8/nDiyHko8ePIJ884fv6Pf4+afB9zSanZ3I6hxz6vJ4vC8hny3PKT/n+2SKCXIQjyL5bQ964nO25vOLz5muROR4fnEydUqw4J9nsAvLNN+5CvnXAMTu8sQU5uNmFfCe9BXl4/oANKDmmHx6dQc4L6tj4bAx5PqfSRB3qyI0W28sZcm624BjGCX/f7m9DbnUOKHvUgaNj6txC5uTdhxzffqcH2a+4Jgei85sJdaCuOX+bMIUcbriGvwc+FdvjXOX8YvORlC1nuNrsSz8DrsUo8CDndQK5UXMeZiXHsa75vE6DeluvaPvCmPcnId/v1dRbF7chVuUG8sbn8xaOa3854dov+XS3qfn+MuK6KD2Oh9JIeH/lOD5VJe1ZsQVxLP2vuO6qkv3rtal3izXXmXPO9VotyGnANtTS5ihmm8si4wM9tnGxpBy0uDaCimunU1MHypxz0pO1miRiO1ec86ri+6/GHIMi4/vm41PIYcTf+x7XfhizP7Evfy+Qt7tqtYIc1fK8kDrU7HJOx0vasjThfHkJ29vqS3zwvfGJ7U8YRW7n5uGzdjfYz8Wa7Y4TGUefuu6l/P1c5rmOaIvqkmvpbDiBPMgYV1Wy1nf68r4p52GZ0asVHuctDtn+pEufFkWcx0Vdiyw+x6feh1362KXYuiLneGzmXLeLkrYiCNjfKOD1kxF/H8t4ZwFtcdJ/8e9olhHvWRQcg9mMa/l8zD7PVxzzl27ssc0pbVu7yTGKQo6RX4u9DdmeSOYk9bn2OwP2p9cT/1DvQN5qsz1izl2+oE46mVNROVdWnIO05phXYlsabY5PsqDOF/KC8ZC2OPbYv/6O+kPO3/fIpxL71J5z9XO2vJaxWYu3H2/oF9RvVDL3dcFYKo1p52WonS9+YxaxPaUTXZdYrRxxLs5lX9Pa5lxns6fyfvrR9fQJfz+gfSsy6V//NuRaHN1aYhm3ln1USPux3vD3mwWvz1ZcC57EknHAuCTwuRZLT6M559YSX2VrzlmeU/+zXGILsfGBzzZsNnx+WXBOu3J/INcrkecTjsFiQYMRyBhIqOEupxJL1bTpXkq50ZXYQcbcSyV38c4lryfUcfXxi5y/X48552HE+F7nOJY102xQ59rt7xyPfxd8KrYnCHy39VyexM8lZq6+s541JUbf7exC7vd4vSndXkns49dci2XGWKjV4jg2Yra31+9DviF6czCgXxttmINJcs7bpmRO5O4h1+7AH0Pe3WX/f+jzzAf8hV/6F5C/vmH7f/AHX4b8ksh3e3ze2/e5L/4bF2yv2vK7W5yAXbH9zjm35TMWGI05Jzt9PvO1G8zh5AXv7wa0n9MR185K1lb/DuPP0YLG4tGEOaAHI/qHheNabsw45+e55LkmbE82ZXv+xlPqSLRFndoUbM/DOW3Znb0+2yP7h7uSRy37bL9bcvyWNL3Ok1goy8Q/LTgf/gtZru+JT2x/iqJ0l6Nn6zuX/alXis2V/WRHYmivKfnSNW2LJzmaUvZlTmLuIuPvC9GrMKEtyDSmlaA5X0tOSOK6MJL9tcToqcxbIDH5csV5zkvevxI9KR3Htxa9WRXs72Yh7ZUcUdDgOgsbjPmbkistQgkCnHOF9PHiagw5TdjpXsi1kZVcHEXBd9Qx2+hLHi+T2HYr5Zz2t2j7IskjDn2O4f+fvT+PtzXN6/ru37Xmac/7jFXn1NTVM3Q3g6D4oEIUEEfECHHACUzUPCaaxwejJhgxJiZKO4CGYAQVcEARxBGIQVpkaIEGeqjuqq7hzOfsea95uvPH3lVnf7+r+lSdPZy19q7P+/WqV/d11nTd133dv2u8711MWidzfc1Prqjfl+o6Pz7IbOze03S7o79nU1xRtvnets3RFO0c5qxO+xrGnPWrVvI2f2594czmpLLxbMSeiIjeYBgv3L4/375zV8smv70l6e2Rlk1xYHPrtlz33Cs6X9fsviLpgvURX7qj4571ay9I+ot+09dI+id+9qOSnr/6rKS7HR1XRUX3IvRDz5Wvl+VC6079MY1P19e0PJ59Vj9/+662g+uvaHrc2ZL0hWc0PnRGeq1fe1HHkd2x9umvjXQc+PiCzrncKGj8ev6m9hsiInptjdE7Qy2D6l3N86XHn5Z0fkXr+5zNCY/bWuYjWxMsF/V6zj/5mKRzOY0fHeuP5nLaJt598XlJZ31bd2hZjK5p/ubLNg9V1vwWhvp9fYvfV65ckPTKspbHrsW/rS2bO7in5zRndXShqn3V2oKtA63r+Zyzce0hHM+cTzaO0eh+vKlb32VgcX1oaxndXT2uu10tJz/KdYv7ma2fFUo2J523OVRba0+2FpIlrYebu9rHTsn6oHmNfYWxt8v6esvmwPvberyFsa1tW1+n4HPATY3dxZzWu+VVrVdDW3tqj7Xeb7f0ul9esbWZkvVNbb42IqJn46atoX6nj6u6O1rGA1sPKtgc7kZfP1+2OZC2zYNFT9O3tnVtupCzwXxJ42uxbHM+BS3zjTUt8+0tjedVjz1VbR/mre82GmkdGVp59W3OrFDWOmFdpyjYOSraORwn/b6Ful0jY5vbyL3Z7TwPdPT1rlyK5QNrdx0bDr/8oY9L+tyytqMlOy8XL2iMv7urczZzS/r+ak3Hu+2Ojq9fumH10OZvs4q+/vjlS5JujfT1sa3PjWJL0ms7eh7/0/N6/FlXj2fxsauSvm7H2xnqdXFrTY/v0rM2d2njrKa10amp39/ra3lU5vT7OjbuLeW03t68qft+IiKGNv+ZJY0N4wW9tup2TjvWzg43tVL1WtpexNW3SdL32ex09f2bL96SdFrUa+mlnB7zoKmfv35Xy3BssbBY0tj0Qlvfn69qeTQqNm6zecaw9cOBzfPly7a3Yaifn7c1zk2bOxjanNVgR9NFm4ctJps0Opxj6fvUS6X43Cfuzxn+ne/5l/L60pd9iaR7Lb0e6raXJgZ6fc+VbG/Jrq0P2Zh8bGPqzObAq2U9F6Wyfn+hq+9v2N6WesPXXvXcbNp6WYz12lq7o6/7PM3WXZ1/XL1odW9g80IFbZdbNs4dDm0+0ebJBpaurmj8qVa9r6PXZqvl8T1iYHOchXc8Jelm0/ZAFLWMIq95TgXtP/r3ew85V9ZxScXGjr7XqLpse3vs29e6Wic7u7bHwuu0/V7Z1pMKda0zyeZZtmxqMW/jtKG1MXWbq9sM289m814d6zutnrNx3FDPccX2P3S8b/nwjm/cdeB6WF7VfX6LT2pfJtvQ8/Dimu7ffNyutcuXtd52Wrpe89GXdDzeKOh5eOKL3q+fX9e2+urTnyfppx7XevNzH9I5odbGdUk/dl77Ls88pnNC5ca7JO3tcJa0pl+wtYwXbmpfZ85iYdX2Vi03NFY8/+HnND/LOv/x8S2Nde96RtfHmnadX72g9fSV53QOKSLiHU/ptX3HYs2Tl3VO9t/+3M9L+vf8xrdL+q6NDZdtTvfGKxoLspyeg4UlvXYur+g5a/b1Yj9na56Fsa4Jdoqan7LN2dYa+vlBVctw8bxeE3MW7D750U9J+qKt+T4xZ+2NzUnPlzV23LT7CCq2D7/ymI4Da3Mau7ZHn5b0uRV9/yEdOf7kIkX5QN+jY/tBd23v9qhg41ebi9yyubuxr11v6bWbcvp7O5tazkWbk6n6nmEbD29v6ff5dtu6rT1XbcFqyWKDr200bY/Zpu0RKVob+fzHNZZs3LG99DbeL9h4feGcft+OL7TaHrambdu5fkvLo2XjuLHt+9mj/5a3vum1vt1jkmxOxealqna/wXpXz+nKrvYDbtu4LeV13qu1o/PN3dA6kbex9cDuNyjbPFwa+uK1vj4Y2b4ZO0cVG4eVbZ6wZ98/sjXYtvVLdm2vbN5uMvL7Atrb+v3tgq3JWre0mJ+dcVc+l4ulA/2+wlDjw07LzoXtk8hZ3WzbGLtj8ahS0TF02/b1bnV1vm/L5htH1mW/9kntWwx6NuatbUl6aOe6tKT5W5rXutxt6+evX9dxY9heo35Pj2+lsijpVtXGIC2b/3xe1+dGA9tbdF6vRdsaFbm6FtC5Je1HNG0e6FO3dA4lIsLCR1x8u86lFevaN0o5m+ew9fimXe87N3UNtGjX+9jW50e27vDiDe1vf+oTmi5W9AAW57XvcecV7W8P7QId9/Tz925rm7G5YffU2V7XjS0t4+XqlqTn7Z6Sdz6te1Pv3dZzsrWp10T0F/X77J7KtWsvSbrxpO1N6h55vf147u9KKdKB+3N6tl61uKR91u1tuwfC5hTydq9P3c77jo1HbRtF7DQ1dvSsT91u+dy/7Ue1dq5qawODnLULeY09fk/GwOaMK1ZPKyU97z27H602p9ft5rZd63b/W9/utyrWdRxcXdB6e6Oj18EzK7a21NS2wtcXi3P6/oiIvM3T+z1qOStjn5Mu2P1VNkURRQtuBctTdV7HZe2Olklm95V0u/p7Fet7jOxaG1vArtg8fLIGrmRzyiObp/M9zzt2D6Etpceu9acHto7Sy7z/b50Xuy3G9wL1OnrN5HJ+D6DPuR3KkePPeDSWfmyjqud1rr4oaWsSY66msaVtx7m4YPfo2h6M0VDLdXlJ5/I21rXebU/sndd+wZy1uc8+rdd+ZvskM5v7y9keroL1a54qar+jZ9f+9ed0H9Onr29JujPS2JS3ev/UEzontvQevYd83NZ69vzLek943+69Gtkc0tx79Tr5gM2dRkTYKYxbNg5au61jzcI92xd4zua1bA306jv0GJeXFvX7ivr5ju3F8r1lRbv/qdTQ2JXd0fznF7XOJNt4vzyn8X71ca3DBduLa7edx5Xz2tes2l6DQtL1vVu3tR+YFW1t3Nrr7ZYeT/Nxbc9Kdt9B0dYDbPnssI6l7zNOWbQPrE8U+7av1va+VBa0jzg3b+vXOT33F87Z+ndXr9e0aXHbwvztHc3P0oK+/4LtT3/phsarge0Bu3FH+3bDka3V3rR7NqxvcmNNr4WFrp777ZZ+/7zdp333uvUr7H6DblnT46HWpW3b/79p+wNqDb0Ydu05Jo1zNmYqTN7nPLa+w5rd8ztv6y95G+fcszV8b7vX79k9wNu2r/qGrSeP9ILZ3dJz0LU6W7R9vC17dsv8uUVJd3pbmra9P3lbh6lYG3L7lh7Pst0v2+tbm2brWfm2XkMXVnS8Mba+23ZTx6Upvyjpoe0j9HsgB29yvWvyzmOTUlqIiC+OiL8dEZFlWT/Lsq2I+M0R8V37b/uuiPgtb+oXAeBNIPYAmBbiD4BpIPYAmAZiD4BpIf4AmAZiD4BpIPYAmBbiD4BpIPYAmAZiD4BpIf4AmAZiD4BpIPYAmBbiD4BpIPYAOGlv+ICfiHgqIu5FxN9JKf1cSuk7Ukr1iLiQZdmrj766HREXXu/DKaVvSCl9OKX04U6393pvAYDXc2yxZ8eeBg8Ab+DQ8Uf6PfZXtQHgDRxb36d/9L9uCOCt49hiT6/Xeb23AMBncizjrl6f+WYAD+XY+j7tFvM+AN6041tr7xB7ADyUYxl3tdustQN4KMfW99mwv84OAA9wfGvtzDkDeDjHMu7abTHuAvBQju8ei52dR5RlAGfA8a21d9lnCOChHMu4q7nbfIRZBnAGHFvfZzgYPKIsAzhN3swDfgoR8TkR8TezLPtARLQi4hsPviHLsiwistf7cJZl355l2edlWfZ51Ur5qPkF8NZxbLFnvlE/8cwCOFMOHX+k31OrPZLMAjgzjq3vU6pUTjyzAM6MY4s95XL1xDML4Ew5lnFXucR8M4CHcmx9n1qdeR8Ab9rxrbVXiT0AHsqxjLtqNdbaATyUY+v7LC8snHhmAZwZx7fWzpwzgIdzLOOuuTrjLgAP5fjusZifP/HMAjgzjm+tvcI+QwAP5VjGXY25xiPJLIAz49j6PoVi8cQzC+D0KbyJ91yPiOtZlv3Ufvr7Yi8Q3UkpXcqy7FZK6VJE3H2jLxoMs7i1cf9pY7s7+pd2ciUdpBVHuklxPNa/jjFKGvt6w6Tprj7ZbHdHn7Q4GA4l3bKnwC7Mz0m6Y7+fH2rx3bu3Junttj7RutvTz9cvXJR0NtCOYuOc/v6t289J+tqdTUl/+rl1/b6KHl9vR8uj3dO/8lgcb0h6XNeNEuWClne9PpZ0rqDPi+r0upJudfT7IyJ6u7v6HVZGm+vXJD1o2ySiZiHWbm1J+omrJf3+8oqk55bOSbp0R3+/39Y61R/o921v6l9N+OSLtySdQhddNta1TmyvaxnNzekC8XZBz9lKSc/J8mOrkv7Jn/olST/25NOSfvmVT0p6mPSaW8znJV2uaJ3sJrtm7ebxWknLq1490mabY4s9ESlS/v71urmmdfHywrKkNwZ6XhatD7U1sq8v6LV2r6Pp/FjLZXNb61kt02urqz8ftYbWi52WZqhc0vMwTho7spzG0kZD63GnY58v67Xc7OiFtr3bl/SlmsbC7U2tJ0WrR4Wi/n6vr58fDrUexkALPJ9peeYKWh5D+0NKw2xy8aGU6TFnSdPDvuYxcvqbhZy+3h/qtTrM9Bz0LFPDoZZhPun3tTvaHpUbi5pfa99SWcu42dI6Xiro8TU3tiRdCM1Po6ixq29/IaYQdhPBUOvMsKd1Jll7fu+Wxv5qQ89xwWLJxobWqYE9OKc/0N8vzx15o9+xxJ9CsRgr5y+9lu4OtJwj07peami6anW9Pqf1ZGNbg0WuqjG9VNZrq2Ltfn1Br53tDetHJK1nLev3lEsau0YjPY9WraMz0t8v5/UN40w/X6vo8dfsvM/XtLxaPf18f2z1sKT5Xziv12mlqtdJ0Z6DudvcknRvqN8/yvT9585Pxp7FectDXeN7b6ztx7ueOi/pQdJ2/+kVrevJztnIYk2na7EoWX6Kmp9Vix3jgZ7zOTsH+ZyWSb2i3z8aa/4Wqnb8A+0n9ef0+y8uXpL0bktjZSWn+Rta+aaR5i9b8Ged6ut5GyvstKwfZ38xfXvLGvCHd3x9nxSRHTj8sR3LwOJRe6TpobV7mcXlYlXLOgsdl+SSput1jU+NeS3rYlnz1x/ouC1r6ud7Nm67PK99uX62JelxSV/fvfu8pLstPZeDtn6+Nr8k6ULB4nle42mnr/lv2/d17S9ej5PW9UJmddtG7QVLZ13Nz6A9+Vcld3a1fpYK+h6/nrp9a2Osv1m0dKevdWRg/bdhXn+vaPEiDTTdt6eUj8bWP7T+c6ej79/c3ZJ0t6v5qdrmuLxdA7lMjz9vY92tdT2ecVHjcd8awZSzeGd1OBtaf7eov1cs6/cVrDgq9SNtND622FMo5GJx+X57nfWszxt2rVtb+sQTlyVdTvr+Yk37vBsv6hxMoWJtt41bOn2N4/mhtWtVPQ9XH9Nrf3Gk1/qc3Vg7HOt5He5qvew070h6YHNi9Xk9scOuxqbPfVzbweGK9jV+q7XjF+a0PB5raD9ipaL1KnvvOyXdtTFHv21jnrXbkh683l832bK/NLmu7xn19drZsP5v5LRM80M9h9Wq1qFyaBmsLOm13s10HHLxgr7+nvc+Iel+Qcvoc9+tfbNmV89B2tbv/+xM68y7ntLv37Sx8VNlPf6XfuQnJf2Bz7oq6ee2dNx39fyipKNm83T3tiS9a3277XWtk2P7K8W5vPWlrP06hGOJP7l8Ierzi6+l88libF7LoVLTcp8vabq2otd+KfT16ki/vxE2R2PnMWdt4k7PYoN9f8HmOms2Dhzf1eJYubQo6UFfY+XikqbH1k/J5TU/xQWdr14baCxqb2k9n5vTjknW13rRGejrrZr1GfoaJ/o97UcObb6/WtO4UW1oPy8iomlzNL2+tie5kqbrq3ptL9l8bNi8XqGkxzgaax3YauoxpUzbo7W1m5Ket3mzYlH7WXM2Vh3b2LdqncNBx+r0grYfmc1Rta3vP+zoOSgm/b2lsh+/zevZOLO9oXWmVtTPLyzp2Dw31va1N9D8jTKfmH1ox9b3GY0G0dy83753W1q2mc17XLykdS35nGlO0+t3ta3NOlq3Bk1bz7G+yaisdfPDP/Mh/b3z2je48Ix+fr2i8dDz31zfknSlpu3iKPRaumd9/kvv0vIZ1bT8tu9p32k00n5AVrK/6rht/YI5vXYuX7X54IGmW21bE3jC5het31F6nXmf4e17ki5Ym1NvaPwp2Bpb2YZyC+e0DVus6nrW2jWtE8VF/b6LNvfVaWqd6NW0L3DR+hIvn7O5g22vg9pGVBf1eq1YvM1sXNPc9jVD/fxaU4+/Vtbrf2CN5iDpORwXtI7MN3RuYWXB5j5sHLg0b+s8Nm58SMcWe8bZWNZ3F+e1rlYKepwpb3Pta1pP+z6f2LM5pLbFfWt3crY20B9qPaknvVYK9seAhjYfGjlbaxhruVeqFjttzmk00no9aOnnd5saS+bsPFdtU2fjksbCga1f5XI2/2n1qN/x+UKvR9YXK2k9376nbUG5MvmAJ+s6RLlue8dsjiVvZbzT2ZL0pVVtH6Ks37e6on2LZu8VSc9Z/G0saRnNW/+tt6N9hca8fr5kY91RZutjOW9P9fjqixp7KlbGPkdczOk57tt6V65o14CtUWY211CxtfRG0uPJW9+oYGv11rU9jGOJP6VSKZ68fH9Murur7XShruWSbI5l3h7QUbFrZ/0lneNZuqzXnpfjiq1T7vSu6+/bOO2OjXcXrQ/abOu1WZrXPSWLNVuLtvHyyNqcctXW6q1PP/+YjrvyHa0XXavHYWsfY1tvq9g4NFna/xBbMTS/hZFWtEpeY+u8rddFRCS7Fip1jfeXlvWcF62dfuopPcdrDS3TwljzcLGmeW4lW3O0c5B7RvtN5xZ8HKPp7V39/aVFjS1Dq0MVu3ZjW6+JXlvLfGlBY+tjVzSWbuzamqP1W8Y2rsxbrIi+7btq69h/vqDnsJmz2GXNVbd5pH5PxDH2fYr1Rlz8ZV/8Wnpr/Zvl9YvntV3xOcqKHWt09NgWynquXrxxQ9LZRb0eWy3tO1xe0d8f23rafM3W75O+vrqs8SJsbXbB1r86Xa0r97a07t62PXOLDVvbsfnIuUXLf16v7bxtOPd2dm1d5xDGPRunlTU2FCv6fSPbhzm0vVo2vbqXh7yeg9FYY+SG7Vcq2NzbMLNxSmgeel3tz164qH94N29rjucu6vU9sLm/qu1T7Fqbs7mrezQ6dn1XitqmtizeLTS0DhVXdJ0lv66XWdH61wPbP9Cz/QVZzvrrprmt19zY5vh7tnc0b3uDw+Zya0tHeqDpscWecrUcT7/7qfvZbNraxpa2QzFY1M/39FravKnXytXHdG2iPNa+zFxVy/3OtZ+RdKun9e7z3vGUpOcuaPrcotbD5k29Tl56Wffn5nzPcdLj79oYYu2G7pdtbev3f2pXx/+3be2omte1j6pd/IurNn9ofeSuteOtuzruS49rvUo2x1axPYI7a3Z+I6I9p8f0kQ//oqSf/hIt84Lt1bx1TfP0/IaW2RPnbey+Y2v1tq972NG2/8WPaR1p25xuI3tW0i3b+3L7Fb0Wx6t6PDdu6Z7jgp2T5TnrP9q+92feqfOKK6vaF4qWHu/grh5vZrH+YkP7kmMbONWSxrI0tDm6gdbxtu2VOIRjiT/jLEW7d78d6A18jsT3BOhxlaydzfseBdvT0Wlrv6JQ0t/z9ZgV6yPX7I//5K3NWLF+Us/6MYtzeh7TWK+9+QWNlYOh1pO69ZlLtrGmULW1CtuU3etrvWnUtN6mio3DbL7gzrrW017b8r+sx1+f17iwUtfj79v8RkTEwNbru7ZPJmxcsnxR4/3Y1kgLNg66+gXvkvRtXb6K+vKipDtDPScNm5crer8rtMyHdn9I09Ycmz09vmXrq5dsXqxg85K79/T7dlZ0nBZ57RuPrK/cs3lS2yob96z9KjX0GunYmmay8sgnvQaGceQ/3ndsfZ9cLsncweaW9l12ra28c03nA3d6Wp/f9Yzda+Nj7mU9V72erQVa3U22R63b1+vNw/ior+3gi/c0fW5B83u7afej9fT376zpD+ys6TxWbUn7CSXbp3x+Va/v7bv6/patpe70NH3zru2bKeu19cQ7P1/S/Xs6rh2G7R/oaV3u1Cb3vBZCy3jF1shyi7b/y8Z2G2uah5HtFxtZfGta3+idT2rfYWcihtvv21zhRY+HQ18n0DLt2Pr4IKfxrX1Pj29tQ8uj29U65G3yRZtzH4V+fyGnY+elhtbBS0sab25c18vah1Evdmw/QEvr1LVX9Pw8pOPbZ1gsxOqF+2XTsWt3yfoS67bPblxZlPT2jq5/lWxf4nBowcLuJWra+tjY5mTb9nqjqr/ftfHvZldj247tx5UN3hHhV2KxpO1uzvr4BZtzaXc1f+sWyzY6+vnRtpbHtu1pbuSsr5JpbKtV9fevr2m7Wy7rdV7we7XGdpNJRJRs70rT9sYMrf/Ws3m+ofWVRrbXpGdrpkWbo0h1uzfynJ6DzOL7yPY7Df2+GK9zdnxV23vkt3eXbM1y1+75izk9B+Oujt37Q41tCyu2nmbrfw0LJsn2QJdtr1DJ6mjN1idHth5WjhnZ65NlsviW2TgrX9D0lt13l/JaTn7fX9Hmd0sluxfT7s0c2ni3a3MWzW09j33by15d1vxWJu73sDkqu++973OD1o94x3vfJ+n6whVJv/PqY5L+2Ms6qFiz/cIrq9pGLi9prFm6uCjpSk7bwHd+lvYRNte0bWgOtQ9z/pytwxYn17t6trezXrdx2IKmn3mvXnsT96TlNI81GzeMrA50bJ7t3AWN5xcu6Px0xfoZDVt/6u7qnE6tqPmbtzWJC7bXK7MyzKz9anW1X7FU0fIrFh4835ys39iyfUfLVa0Tl5a1PDO7t7Zo++AHdq/v6A3mt9+EYxx35aJ6YM2obft8dzf1etnc0PiQtz7q+oaW5bVbdi+N7XHqtfT7e1Utq82btp98V/tW2WVd395o2Z6rkc73bd21tU9bz4q6nnsfF67bvfc3tzT+bdseM7/Pe+Nlnf+s2NpN6YKOGVpDLd+WjQFGdb0W7915SdJrOxr/tno6b7Ri63ERESO7n+jjtg/wnPXnupl+x70XbK91WpT07Y/q9VrI6eubd/UY792zOdqXbE7axj0Xu3q9dm0Ndcvu6/nEL31E8zO0NcMNfX/DxgfFRb0HY8fWKG9UtPy2bus1cLOlbULXrpFKQ+Phho39e37Ppa2p+r7uZ554RtI/9+M/G6/H72qdkGXZ7Yi4llJ6x/4/fWlEfCwifjAivm7/374uIn7gjb4LAN4sYg+AaSH+AJgGYg+AaSD2AJgW4g+AaSD2AJgGYg+AaSH+AJgGYg+AaSD2AJgW4g+AaSD2AJgGYg+AaSH+AJgGYg+Ak1Z447dERMR/HRHfnfYewfjpiPh9sfdwoH+UUvoDEfFyRPznJ5NFAG9hxB4A00L8ATANxB4A00DsATAtxB8A00DsATANxB4A00L8ATANxB4A00DsATAtxB8A00DsATANxB4A00L8ATANxB4AJ+ZNPeAny7Kfj4jPe52XvvRYcwMABxB7AEwL8QfANBB7AEwDsQfAtBB/AEwDsQfANBB7AEwL8QfANBB7AEwDsQfAtBB/AEwDsQfANBB7AEwL8QfANBB7AJykN/WAn+OSz+VisVp7Lb3WHsrrjWJR0ls7fUnXFhqS7gw0+72yvn9nNND07o5+31xd0nO1FU2v6uujVJP0vY5+/631LUmXl/X9sdmW5Lu+6AOS/om/+/9I+rO/7PMl/fM/8hFJ9/Jj/fr1dUmvXipLuhRa3ufPLUm6u9mV9DDXkXRjvirp6nk9H8WaHu+wrOm1u/p9ERHtbf2O7tqupJ9+/xV9fTeT9MsvvSjp1qYeY5bpMV184u2ax8FI0ksrlyRdzrc0v815STdqFUnP1fOSbrY0v7tNTeeK+n39jp3TpGU2Gvb0+7ta55998r2a30xfL4Xmt7mh5R1lvQYXk57z/si+T98e7V5T0gV/w5SkXIp85f656a9pLLi3uSHpl+xauFRJkn5uR2PPU4t63l7Y1PP81OOrkt5tajlmJS3nm3e3JJ1fXZD0y3c1f5Wa5mejqd+Xq+ckXQxNd/taL3qh+R80td61O3qdrd3e1tc3NT3s6OdrdY0945LGinpFY9edTa2n2VDLu6qXXbS6er6KRT2eiIiVTN/THWo8L+T1M6OillmtrD+629cyGfQ1tnSHeo7azZLmeVvP6VxZY1GvrWUSmX6+lDTd3tT8Fix+727p8RfqGotT0jrR7Wn+BgPNT6Gnx58stuaSxoLubX29UNFYW7H8bq7r7+eX9fdTX6+pXE6Pf1oG/X7cvPbKa+m5i1fl9fWdTUmX69oPyeX1PJSLetxL83rtDCzmL9bmJF0qaT2Zm9Nybu9qDB+MrM0a6HkqVDU2zVl+8zm9VjsdjQ3lmsaCXLL817XeVCoWu5JeV36t94Zaz0oVfb1Y1PK5sKJtcjmvv59lWs93W1ovb9+9J+mLK4vhFsqap7mqXot+zhfKFisGGk9Xa1onuiMt825HjzlZfO/1tG86rOjv7XasH9LXz7eaWifyoce3u6XnPNk5Huzq92djax9Dj2duTutcoaixeNjS4+l3NbannB5foaD5Heeszld0LLC9qddsrqjft7Oh/YvpyiJL94+/UNH6XChr3SnPad0bjKyfb+OwYV6PtR96PXT6Gk+WKnou5xta9+tzNs665u/X63NzpOf68z7nCyT9oR/8t5L+gt/y+yT9A89/SNK1ee2rbe3elXTDro1KTn+/1tDy2xxoXarWbEyQWXyqaXmMu3r8tbL15Sze5zONDSUbJ0ZENKp2DCWNoYWcXk+dgbWlZT3Gio2li0MbB+1qGY2twzbs6fVZ0q+PUuj7K5b/wljLLMv0/d2eptNYf29o45b2tnUovYxLWh47W3p8pYaW+SDTa6zaWJR0saBtSqWq52Pc1/g67Obtdc3/7uZazIIUKYoH6lKxosdVsb5M6ul5nD9/TtJdO67e0OaIylruGxb3o299kXU9b10bB567tCjp5obOsQytS54va1+qbuO6cU3rTaOu182nXrgj6dqK/sBAsxv1VbsOi1q+50saK0ebW5JuljU9LOk4q2/juNFY246de9oO3u5rfresPCMiKjVtP0Z9PeetTe0LvNLUdKer39le1zx2bZxRyuvvrbTsWrZYV7P2sW79zUpB319PFruq+v5B1+J1QeuEhfv41Cs3JH3lnOZ/66a2R50ntP+8va2xolnW9qbd1liRRtpeeCwu27zhZZuLGFtsHiZtzz72sWsxDYViIRYvX3wtvbltfbbkMV3zXczrtZRZ7Kg0tJ48Nbco6eW6Xgu9sZZzsvH0/EC/vz3SejPKa0UpVX0cd9HSOq4p2HxHLml+1tds/sPmMqOg+ZmvXZD0sGvjppzWw7BxZKm2LOmNda2XTZvvuPbiTf06PX0x7ur7G4s6jo6IGIe1F9YXLVn7VAibA7cyXbD2q2Kfb+1qGQ/aWgbra3otb23o71UKeo6r1jfvz1t8t7H56pK2LwvntI5UrH2qWL+mY3M4WUXrRKlgfXeLNd28noNXrt3S160BLedtzqqq5d2z+e7O0Mah3dkZd42G49hav38+LMzG1cc1jhZtDHv+os2r1M5L+pkrOk5qD7XsNu9q3F166m2Svn1Z25mB1YVd6yuMS3qunn37ZUnnLP7VajYHbXO87Z5+f7I57XN1vRZ2bamiNNJ/mLugx7+0rOW1Utffz1k7n2vp61UbZ918RfP39JPa7mZ5vVYee0bnbyMibj2vleDKs++WdNnaoFGmZVjqaZksrmq8mZ/TeLFq/cfMrq/BUL+vvmrrW0XvcGod/WybO7vT1Hiws2HjrJLG29W6rVtYvKnoJRAje31o+Rvk9HiLdk3UFjTdH2s8HQ61DerZuG3njvYharbuki/MxnpXRMQwu1/WubKth1gfttywcpvTtnlsayEDW/su2RzSwNZSekObX+zbuKttfQ9ba1+wvtR5W9tutnRcVlrVdqdW13pTs75B3urBVkPbmfMXdBzavKt9keXHn9HPb+oc8NiOd2FB683AroutbXv/oo1Jyvr6nI05ctbPiYiwUx6Xrui1225rbDp/Sctoc/O6pFeL+hsLNpa8ckXL9NMfe1nSF6q6Ft+3WHXlqvZVul1bn+prGVbr+nudjpZZ/6K+ntmSYKOhdW5sdTbmtD0Zdhc1fwMdJ/kaas7GE4O2zYnZuDRnJ2zT1upXL2p+Nm39cFqyLGJwYKzRtPnNckXPw/xFHUeMunqcRVuvWlnQNnEu6bVzcVnrddFOdP5prVcDW0tpWB++YePEYk5jVVa0fTVv1/W9jp3XVl/7qFX7vihrG/zEe98h6bUN/b7yFe2HXH7icf2+XZtD6+t8yUJ6TNKDsR7PvM1/rC5b29DS47m9YZO3EVHK6zkL2/eysqR5HPe3NE8dvVYatr519cknJT2014dFjR2bLb22zl3UcZL3A86t+jnVNYK5FS3zwsZtSd8oaF/wzs/+R0k/vqzXiG2DipWavl4van5tW1GMfW/ajn5hGmmsK9maRjmn53g+2Xy6hZqBrbFO06DVjDs/9ROvpZdWdJyydk/rv68nVX19p6DxwI+0a2PQQdfWg6xdWrD14Gtt28tjc8KRfD1eX27ZP9Rsnqc91mthbH2zml/fF7W8CsvaD1hY0bo/tPhetXHeqGmVpWjx29bXixYLzp3T7/M5iupAr62FksaviIiy7WUs2vrQyNcFbJ9dv6PHuJW3fXjWludsa221oN8/Gug56Nj+KG8DR7bOkWxPRa6mdbY/tjbX+oc9G6v2bd5ot6t1LrNzFLZ+1rf9Az2by2zu2Os9a1NtLnNrQ+f4l65onWjtanxt2/mclnwuxcKBvRjrN/Q85guaLg2sHrZ03FBraLu1ZHPO1za2JN26p/thywXtG6zd1jmfrWUt91TRvlPL9rJ86uP/SdLtgV7br3zKGq6xrmddu6Hft27j6Q3r42Z2nWzaWnjf5gN7A02/+JjF/ju6friV13qZjTR2VG3c9rjtkau1NL9LyRYEI+LWizrHu1jV9uajv/iCpPM2x9qxa3+zrddq6Y6e0xs3X5H03cz3+GpyztYM+/b79+5qeifztXb9/M5LPyPppq0blDu2B9liyblntD+66Xum81p+ORvqZta+bG1r/3RuzmL3trYN6y0dp+5a29Cw/V/ru9Z/n5LhKIu1A/uKrdmPYl2v9Zy1CWUbx7RaOn+cRnqtztke3npd64X3Axo2xzS2NmI81vNcyGu9q9r9BfPntR7sbumczDjZWoSNIeZsfvrK4zouvHpV7z14bNnXWqzNtnHmZk+vu/rA+kV5ndzM2bj4sct6HTQu2pjDYvWtLVtgiIiLVa2bG2U9ZxWbE7/ytH7nqs3BDGyOYdi3tew52/fX1r7s1WefkHS5qp+3rVLRsn7IurVHC3PaN127pe8vVfScVaq2l9TmWJ7QabxYtL2xedvzXbc1hKKv0dr9D7u7eo3Vq1o+lXlbU7B+TbK9Bu32bMSeiIhsPIxe+36MqDdsf7eNSet6auLy8vsknRtqW725pd/3yed+UdIXrz4t6e5A49XCor4etmcsZ/vXt7e1j5mz9eVr97QuPvUejRcDa6dKJT3X1ze0rzaf6bWYCnqtrW/bel3Svkhpoi9je1rtWhrb/QIb15+X9LVt7bvNLS5K+s5dLd9U1PKKiCjafqcLdo5KZRsnFW3vqfUNxra+U7JxVcnm6uzynPi9zPbiXHmbzqmeW9TXfay/Yuth99a079Xp6/Gvt/R4FuZt7H3e1vvsHrr3fZaWX6+rx1PK+zhPC2AYWudfuaZ18Oqlz5H0wPqe2zbOvbWp6y7TknL5KBxYs6rZfs2Ktd1h45aWjbd9nbRqfYU563R2Oj7e13qSjfQ8VOdtv6yNf0ddTQ8KWu/bHWvn7N7ZoY3/C7ZPo2/XVT7ZnInV63bB1vNsO3DH1pt6Q/2+ot2L27FxXNnuX9iy+yaftE1yd2x9rN+bXO8a5fQcj20/1bCvcwxj29toVSCK1p/t92zfnO2nCtvvNbRxVmto98jZGmsv6Tgv2f1ng75+f9H3NI88dmqdKtom61xe62Qh6e/V52wNdFPbh1TSOWOfRy1aLGvkFzW/Nmfme4E37J7DUnFyrD0NKZLsly9aH3Fk965s2zjE529jbON1W99aubgo6aH1W9q2ftOy81SwdU1ffso6Guvs52Prlsb8lz/xCf3+mh7/nZvab3nG1qd6Ns6btz77VWsTn7m8qJ8f6/HM2/rdeLQl6bq1+YWGXncNu+f5+pr2Q3fuaRvvY5aIiG5bz+k920uVszWFFb104umr2rfL21ryjq3V79o+7WR97adW9dp7+oqOPRfmtMwzu/ezY/uKenZ/7XxDXx/bPqW8zS2ErUFkfa3zm7YXtFbXcemu3Vu7ZWPfSs3ae7v3NmxuILP+wE27J3Nsc0pZ7nVO+pTkIhfVuB+7797V62FpflHSHeu3522M+rLt6e+WrC7mtO707P6rtrXNOWtXBxbX79zQc92yfYZ9G+Pu9K0vYnOwu7YHtGbtRNf2jN3q2ZjD1/tyeu4/vanjnFWrG/MjjWcte6bDYNfW35d1zH+zqe1udu/Tmh97JkUaTa53De1ZKPWSjmvSyMrE5nUKJdsTYP3TzVuflHTN5rTC76t+AAEAAElEQVSrm1qGu7a3cmz3ILS7ekxji+m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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "images = explanations.explanations[0]['image']\n", + "num_cnn = len(images)\n", + "num_col = 16\n", + "num_row = int(np.ceil(num_cnn/num_col))\n", + "fig, axes = plt.subplots(nrows=num_row, ncols=num_col,\n", + " figsize=(4*num_col, 5*num_row))\n", + "for cnn_i in range(num_cnn):\n", + " ax = axes[cnn_i//num_col, cnn_i % num_col]\n", + " ax.imshow(images[cnn_i])\n", + "\n", + " ax.set_title(f'Kernel {cnn_i}')\n", + "plt.tight_layout()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/Demos/Demo_Frequency.ipynb b/analysis/Demos/Demo_Frequency.ipynb new file mode 100755 index 0000000..ef9eaf3 --- /dev/null +++ b/analysis/Demos/Demo_Frequency.ipynb @@ -0,0 +1,387 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_Frequency\n", + "This is a demo for visualizing the Frequency saliency map of a Neuron Network\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": {}, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py - -save_folder_name badnet_demo\n" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "87bd9f5a", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71b7087b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "import matplotlib as mlp\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2fb719c7", + "metadata": {}, + "outputs": [], + "source": [ + "# Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"badnet_demo\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 2: Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8b67ac9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "max_num_samples is given, use sample number limit now.\n", + "subset bd dataset with length: 4995\n", + "Create visualization dataset with \n", + " \t Dataset: bd_test \n", + " \t Number of samples: 4995 \n", + " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "selected_classes = np.arange(args.num_classes)\n", + "\n", + "# Select classes to visualize\n", + "if args.num_classes > args.c_sub:\n", + " selected_classes = np.delete(selected_classes, args.target_class)\n", + " selected_classes = np.random.choice(\n", + " selected_classes, args.c_sub-1, replace=False)\n", + " selected_classes = np.append(selected_classes, args.target_class)\n", + "\n", + "# keep the same transforms for train and test dataset for better visualization\n", + "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n", + "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n", + "\n", + "# Create dataset\n", + "args.visual_dataset = 'bd_test'\n", + "if args.visual_dataset == 'mixed':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_mix_dataset(\n", + " bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_train':\n", + " clean_train_with_trans = result_attack[\"clean_train\"]\n", + " visual_dataset = generate_clean_dataset(\n", + " clean_train_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_test':\n", + " clean_test_with_trans = result_attack[\"clean_test\"]\n", + " visual_dataset = generate_clean_dataset(\n", + " clean_test_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'bd_train':\n", + " bd_train_with_trans = result_attack[\"bd_train\"]\n", + " visual_dataset = generate_bd_dataset(\n", + " bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'bd_test':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_bd_dataset(\n", + " bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n", + "else:\n", + " assert False, \"Illegal vis_class\"\n", + "\n", + "print(\n", + " f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)} \\n \\t Selected classes: {selected_classes}')\n", + "\n", + "# Create data loader\n", + "data_loader = torch.utils.data.DataLoader(\n", + " visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n", + ")\n", + "\n", + "# Create denormalization function\n", + "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n", + " if isinstance(trans_t, transforms.Normalize):\n", + " denormalizer = get_dataset_denormalization(trans_t)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "39104beb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number Poisoned samples: 4995\n", + "Select 2 poisoned samples\n", + "Select 2 clean samples\n" + ] + } + ], + "source": [ + "# Choose samples to show SHAP values. By Default, 2 clean images + 2 Poison images. If no enough Poison images, use 4 clean images instead.AblationCAM\n", + "total_num = 4\n", + "bd_num = 0\n", + "\n", + "visual_samples = []\n", + "visual_labels = []\n", + "\n", + "visual_poison_indicator = np.array(\n", + " get_poison_indicator_from_bd_dataset(visual_dataset))\n", + "if visual_poison_indicator.sum() > 0:\n", + " print(f'Number Poisoned samples: {visual_poison_indicator.sum()}')\n", + " # random choose two poisoned samples\n", + " selected_bd_idx = np.random.choice(\n", + " np.where(visual_poison_indicator == 1)[0], 2, replace=False)\n", + " for i in selected_bd_idx:\n", + " visual_samples.append(visual_dataset[i][0].unsqueeze(0))\n", + " visual_labels.append(visual_dataset[i][4])\n", + " bd_num = len(selected_bd_idx)\n", + " print(f'Select {bd_num} poisoned samples')\n", + "\n", + "# Trun all samples to clean\n", + "with temporary_all_clean(visual_dataset):\n", + " # you can just set selected_clean_idx = selected_bd_idx to build the correspondence between clean samples and poisoned samples\n", + " selected_clean_idx = np.random.choice(\n", + " len(visual_dataset), total_num-bd_num, replace=False)\n", + " for i in selected_clean_idx:\n", + " visual_samples.append(visual_dataset[i][0].unsqueeze(0))\n", + " visual_labels.append(visual_dataset[i][1])\n", + " print(f'Select {len(selected_clean_idx)} clean samples')\n", + "\n", + "# Clean sample first\n", + "visual_samples = visual_samples[::-1]\n", + "visual_labels = visual_labels[::-1]\n", + "\n", + "visual_samples = torch.cat(visual_samples, axis=0).to(args.device)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3f652e5", + "metadata": {}, + "source": [ + "### Step 3: Load Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ff67e7b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load model preactresnet18 from badnet_demo\n" + ] + } + ], + "source": [ + "# Load model\n", + "model_visual = generate_cls_model(args.model, args.num_classes)\n", + "model_visual.load_state_dict(result_attack[\"model\"])\n", + "model_visual.to(args.device)\n", + "# !!! Important to set eval mode !!!\n", + "model_visual.eval()\n", + "print(f\"Load model {args.model} from {args.result_file_attack}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cc952077", + "metadata": {}, + "source": [ + "### Step 4: Plot Frequency saliency map" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "94612903", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting Frequency saliency map\n", + "Choose layer layer4.1.conv2 from model preactresnet18\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "############ Frequency saliency map ################\n", + "print('Plotting Frequency saliency map')\n", + "\n", + "# Choose layer for feature extraction\n", + "module_dict = dict(model_visual.named_modules())\n", + "target_layer = module_dict[args.target_layer_name]\n", + "print(f'Choose layer {args.target_layer_name} from model {args.model}')\n", + "\n", + "sfm = torch.nn.Softmax(dim=1)\n", + "outputs = model_visual(visual_samples)\n", + "pre_p, pre_label = torch.max(sfm(outputs), dim=1)\n", + "\n", + "# get the names for the classes\n", + "class_names = np.array(args.class_names).reshape([-1])\n", + "\n", + "frequency_maps = []\n", + "fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 10))\n", + "vnorm = mlp.colors.Normalize(vmin=0, vmax=255)\n", + "for im in range(4):\n", + " rgb_image = np.swapaxes(\n", + " np.swapaxes(denormalizer(visual_samples[im]).cpu().numpy(), 0, 1), 1, 2\n", + " )\n", + " frequency_map = saliency(visual_samples[im], model_visual)\n", + " rgb_image[rgb_image < 1e-12] = 1e-12\n", + " axes[im // 2, im % 2 * 2].imshow(rgb_image)\n", + " axes[im // 2, im % 2 * 2].axis(\"off\")\n", + " if im == 0 or im == 1:\n", + " axes[im // 2, im % 2 * 2].set_title(\n", + " \"Clean Image: %s\" % (class_names[visual_labels[im]].capitalize())\n", + " )\n", + " else:\n", + " axes[im // 2, im % 2 * 2].set_title(\n", + " \"Poison Image: %s\" % (class_names[visual_labels[im]].capitalize())\n", + " )\n", + " image = axes[im // 2, im % 2 * 2 +\n", + " 1].imshow(frequency_map, cmap=plt.cm.coolwarm, norm=vnorm)\n", + " plt.colorbar(image, ax=axes[im // 2, im %\n", + " 2 * 2 + 1], orientation='vertical')\n", + " axes[im // 2, im % 2 * 2 + 1].axis(\"off\")\n", + " axes[im // 2, im % 2 * 2 + 1].set_title(\n", + " \"Predicted: %s, %.2f%%\" % (\n", + " class_names[pre_label[im]].capitalize(), pre_p[im] * 100)\n", + " )\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('py38')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "vscode": { + "interpreter": { + "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/Demos/Demo_GradCam.ipynb b/analysis/Demos/Demo_GradCam.ipynb new file mode 100755 index 0000000..bd33001 --- /dev/null +++ b/analysis/Demos/Demo_GradCam.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_GradCam\n", + "This is a demo for visualizing the Grad-CAM of a Neuron Network\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": {}, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py - -save_folder_name badnet_demo\n" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "87bd9f5a", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71b7087b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import shap\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n", + "from pytorch_grad_cam import (\n", + " GradCAM,\n", + " ScoreCAM,\n", + " GradCAMPlusPlus,\n", + " AblationCAM,\n", + " XGradCAM,\n", + " EigenCAM,\n", + " FullGrad,\n", + ")\n", + "from pytorch_grad_cam.utils.image import show_cam_on_image" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2fb719c7", + "metadata": {}, + "outputs": [], + "source": [ + "# Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"badnet_demo\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 2: Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8b67ac9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "max_num_samples is given, use sample number limit now.\n", + "subset bd dataset with length: 4995\n", + "Create visualization dataset with \n", + " \t Dataset: bd_test \n", + " \t Number of samples: 4995 \n", + " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "selected_classes = np.arange(args.num_classes)\n", + "\n", + "# Select classes to visualize\n", + "if args.num_classes > args.c_sub:\n", + " selected_classes = np.delete(selected_classes, args.target_class)\n", + " selected_classes = np.random.choice(\n", + " selected_classes, args.c_sub-1, replace=False)\n", + " selected_classes = np.append(selected_classes, args.target_class)\n", + "\n", + "# keep the same transforms for train and test dataset for better visualization\n", + "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n", + "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n", + "\n", + "# Create dataset\n", + "args.visual_dataset = 'bd_test'\n", + "if args.visual_dataset == 'mixed':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_mix_dataset(\n", + " bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_train':\n", + " clean_train_with_trans = result_attack[\"clean_train\"]\n", + " visual_dataset = generate_clean_dataset(\n", + " clean_train_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_test':\n", + " clean_test_with_trans = result_attack[\"clean_test\"]\n", + " visual_dataset = generate_clean_dataset(\n", + " clean_test_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'bd_train':\n", + " bd_train_with_trans = result_attack[\"bd_train\"]\n", + " visual_dataset = generate_bd_dataset(\n", + " bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'bd_test':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_bd_dataset(\n", + " bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n", + "else:\n", + " assert False, \"Illegal vis_class\"\n", + "\n", + "print(\n", + " f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)} \\n \\t Selected classes: {selected_classes}')\n", + "\n", + "# Create data loader\n", + "data_loader = torch.utils.data.DataLoader(\n", + " visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n", + ")\n", + "\n", + "# Create denormalization function\n", + "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n", + " if isinstance(trans_t, transforms.Normalize):\n", + " denormalizer = get_dataset_denormalization(trans_t)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "39104beb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number Poisoned samples: 4995\n", + "Select 2 poisoned samples\n", + "Select 2 clean samples\n" + ] + } + ], + "source": [ + "# Choose samples to show Grad-CAM values. By Default, 2 clean images + 2 Poison images. If no enough Poison images, use 4 clean images instead.AblationCAM\n", + "total_num = 4\n", + "bd_num = 0\n", + "\n", + "visual_samples = []\n", + "visual_labels = []\n", + "\n", + "visual_poison_indicator = np.array(\n", + " get_poison_indicator_from_bd_dataset(visual_dataset))\n", + "if visual_poison_indicator.sum() > 0:\n", + " print(f'Number Poisoned samples: {visual_poison_indicator.sum()}')\n", + " # random choose two poisoned samples\n", + " selected_bd_idx = np.random.choice(\n", + " np.where(visual_poison_indicator == 1)[0], 2, replace=False)\n", + " for i in selected_bd_idx:\n", + " visual_samples.append(visual_dataset[i][0].unsqueeze(0))\n", + " visual_labels.append(visual_dataset[i][4])\n", + " bd_num = len(selected_bd_idx)\n", + " print(f'Select {bd_num} poisoned samples')\n", + "\n", + "# Trun all samples to clean\n", + "with temporary_all_clean(visual_dataset):\n", + " # you can just set selected_clean_idx = selected_bd_idx to build the correspondence between clean samples and poisoned samples\n", + " selected_clean_idx = np.random.choice(\n", + " len(visual_dataset), total_num-bd_num, replace=False)\n", + " for i in selected_clean_idx:\n", + " visual_samples.append(visual_dataset[i][0].unsqueeze(0))\n", + " visual_labels.append(visual_dataset[i][1])\n", + " print(f'Select {len(selected_clean_idx)} clean samples')\n", + "\n", + "# Clean sample first\n", + "visual_samples = visual_samples[::-1]\n", + "visual_labels = visual_labels[::-1]\n", + "\n", + "visual_samples = torch.cat(visual_samples, axis=0).to(args.device)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3f652e5", + "metadata": {}, + "source": [ + "### Step 3: Load Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ff67e7b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load model preactresnet18 from badnet_demo\n" + ] + } + ], + "source": [ + "# Load model\n", + "model_visual = generate_cls_model(args.model, args.num_classes)\n", + "model_visual.load_state_dict(result_attack[\"model\"])\n", + "model_visual.to(args.device)\n", + "# !!! Important to set eval mode !!!\n", + "model_visual.eval()\n", + "print(f\"Load model {args.model} from {args.result_file_attack}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cc952077", + "metadata": {}, + "source": [ + "### Step 4: Plot Grad-CAM" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "94612903", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting Grad-CAM\n", + "Choose layer layer4.1.conv2 from model preactresnet18\n", + "Warning: target_layers is ignored in FullGrad. All bias layers will be used instead\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "############## Grad-CAM ##################\n", + "print('Plotting Grad-CAM')\n", + "\n", + "module_dict = dict(model_visual.named_modules())\n", + "target_layer = module_dict[args.target_layer_name]\n", + "print(f'Choose layer {args.target_layer_name} from model {args.model}')\n", + "\n", + "sfm = torch.nn.Softmax(dim=1)\n", + "outputs = model_visual(visual_samples)\n", + "pre_p, pre_label = torch.max(sfm(outputs), dim=1)\n", + "\n", + "cam = FullGrad(model=model_visual, target_layers=[\n", + " target_layer], use_cuda=True if args.device == 'cuda' else False)\n", + "\n", + "targets = None\n", + "\n", + "# You can also pass aug_smooth=True and eigen_smooth=True, to apply smoothing.\n", + "grayscale_cam_full = cam(input_tensor=visual_samples, targets=targets)\n", + "\n", + "grayscale_cam = grayscale_cam_full[0, :]\n", + "rgb_image = np.swapaxes(\n", + " np.swapaxes(denormalizer(visual_samples[0]).cpu().numpy(), 0, 1), 1, 2\n", + ")\n", + "visual_cam = show_cam_on_image(rgb_image, grayscale_cam, use_rgb=True)\n", + "\n", + "# get the names for the classes\n", + "class_names = np.array(args.class_names).reshape([-1])\n", + "\n", + "fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 10))\n", + "for im in range(4):\n", + " grayscale_cam = grayscale_cam_full[im, :]\n", + " rgb_image = np.swapaxes(\n", + " np.swapaxes(denormalizer(visual_samples[im]).cpu().numpy(), 0, 1), 1, 2\n", + " )\n", + " rgb_image[rgb_image < 1e-12] = 1e-12\n", + " visual_cam = show_cam_on_image(rgb_image, grayscale_cam, use_rgb=True)\n", + " axes[im // 2, im % 2 * 2].imshow(rgb_image)\n", + " axes[im // 2, im % 2 * 2].axis(\"off\")\n", + " axes[im // 2, im % 2 * 2].set_title(\n", + " \"Original Image: %s\" % (class_names[visual_labels[im]].capitalize())\n", + " )\n", + " axes[im // 2, im % 2 * 2 + 1].imshow(visual_cam)\n", + " axes[im // 2, im % 2 * 2 + 1].axis(\"off\")\n", + " axes[im // 2, im % 2 * 2 + 1].set_title(\n", + " \"Predicted: %s, %.2f%%\" % (\n", + " class_names[pre_label[im]].capitalize(), pre_p[im] * 100)\n", + " )\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10 (default, Nov 14 2022, 12:59:47) \n[GCC 9.4.0]" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/Demos/Demo_Hessian.ipynb b/analysis/Demos/Demo_Hessian.ipynb new file mode 100755 index 0000000..b78bcf8 --- /dev/null +++ b/analysis/Demos/Demo_Hessian.ipynb @@ -0,0 +1,372 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_ACT\n", + "This is a demo for visualizing the dense plot of hessian matrix for a batch of data.\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": {}, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py --save_folder_name badnet_demo" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "87bd9f5a", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71b7087b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n", + "from pyhessian import hessian # Hessian computation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2fb719c7", + "metadata": {}, + "outputs": [], + "source": [ + "### Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"badnet_demo\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 2: Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8b67ac9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "Create visualization dataset with \n", + " \t Dataset: bd_train \n", + " \t Number of samples: 50000 \n", + " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "selected_classes = np.arange(args.num_classes)\n", + "\n", + "# Select classes to visualize\n", + "if args.num_classes > args.c_sub:\n", + " selected_classes = np.delete(selected_classes, args.target_class)\n", + " selected_classes = np.random.choice(\n", + " selected_classes, args.c_sub-1, replace=False)\n", + " selected_classes = np.append(selected_classes, args.target_class)\n", + "\n", + "# keep the same transforms for train and test dataset for better visualization\n", + "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n", + "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n", + "\n", + "# Create dataset\n", + "if args.visual_dataset == 'clean_train':\n", + " visual_dataset = result_attack[\"clean_train\"]\n", + "elif args.visual_dataset == 'clean_test':\n", + " visual_dataset = result_attack[\"clean_test\"]\n", + "elif args.visual_dataset == 'bd_train':\n", + " visual_dataset = result_attack[\"bd_train\"]\n", + "elif args.visual_dataset == 'bd_test':\n", + " visual_dataset = result_attack[\"bd_test\"]\n", + "else:\n", + " assert False, \"Illegal vis_class\"\n", + "\n", + "print(\n", + " f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)} \\n \\t Selected classes: {selected_classes}')\n", + "\n", + "# Create data loader\n", + "data_loader = torch.utils.data.DataLoader(\n", + " visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n", + ")\n", + "\n", + "# Create denormalization function\n", + "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n", + " if isinstance(trans_t, transforms.Normalize):\n", + " denormalizer = get_dataset_denormalization(trans_t)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3f652e5", + "metadata": {}, + "source": [ + "### Step 3: Load Model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ff67e7b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load model preactresnet18 from badnet_demo\n" + ] + } + ], + "source": [ + "# Load model\n", + "model_visual = generate_cls_model(args.model, args.num_classes)\n", + "model_visual.load_state_dict(result_attack[\"model\"])\n", + "model_visual.to(args.device)\n", + "# !!! Important to set eval mode !!!\n", + "model_visual.eval()\n", + "print(f\"Load model {args.model} from {args.result_file_attack}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cc952077", + "metadata": {}, + "source": [ + "### Step 4: Plot Eigenvalues of Hessian\n", + "\n", + "Adapted from https://github.com/amirgholami/PyHessian/blob/master/Hessian_Tutorial.ipynb" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ca1cb3c1", + "metadata": {}, + "outputs": [], + "source": [ + "def old_torcheig(A, eigenvectors):\n", + " '''A temporary function as an alternative for torch.eig (torch<=1.9)'''\n", + " vals, vecs = torch.linalg.eig(A)\n", + " if torch.is_complex(vals) or torch.is_complex(vecs):\n", + " print('Warning: Complex values founded in Eigenvalues/Eigenvectors. This is impossible for real symmetric matrix like Hessian. \\n We only keep the real part.')\n", + "\n", + " vals = torch.real(vals)\n", + " vecs = torch.real(vecs)\n", + "\n", + " # vals is a nx2 matrix. see https://virtualgroup.cn/pytorch.org/docs/stable/generated/torch.eig.html\n", + " vals = vals.view(-1,1)+torch.zeros(vals.size()[0],2).to(vals.device)\n", + " if eigenvectors:\n", + " return vals, vecs\n", + " else:\n", + " return vals, torch.tensor([])\n", + " \n", + "torch.eig = old_torcheig" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "94612903", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/anaconda3/lib/python3.9/site-packages/torch/autograd/__init__.py:197: UserWarning: Using backward() with create_graph=True will create a reference cycle between the parameter and its gradient which can cause a memory leak. We recommend using autograd.grad when creating the graph to avoid this. If you have to use this function, make sure to reset the .grad fields of your parameters to None after use to break the cycle and avoid the leak. (Triggered internally at ../torch/csrc/autograd/engine.cpp:1059.)\n", + " Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The top two eigenvalues of this model are: 36.4870 71.8704\n", + "Warning: Complex values founded in Eigenvalues/Eigenvectors. This is impossible for real symmetric matrix like Hessian. \n", + " We only keep the real part.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def get_esd_plot(eigenvalues, weights):\n", + " density, grids = density_generate(eigenvalues, weights)\n", + " plt.semilogy(grids, density + 1.0e-7)\n", + " plt.ylabel('Density (Log Scale)', fontsize=14, labelpad=10)\n", + " plt.xlabel('Eigenvlaue', fontsize=14, labelpad=10)\n", + " plt.xticks(fontsize=12)\n", + " plt.yticks(fontsize=12)\n", + " plt.axis([np.min(eigenvalues) - 1, np.max(eigenvalues) + 1, None, None])\n", + " return plt.gca()\n", + "\n", + "def density_generate(eigenvalues,\n", + " weights,\n", + " num_bins=10000,\n", + " sigma_squared=1e-5,\n", + " overhead=0.01):\n", + "\n", + " eigenvalues = np.array(eigenvalues)\n", + " weights = np.array(weights)\n", + "\n", + " lambda_max = np.mean(np.max(eigenvalues, axis=1), axis=0) + overhead\n", + " lambda_min = np.mean(np.min(eigenvalues, axis=1), axis=0) - overhead\n", + "\n", + " grids = np.linspace(lambda_min, lambda_max, num=num_bins)\n", + " sigma = sigma_squared * max(1, (lambda_max - lambda_min))\n", + "\n", + " num_runs = eigenvalues.shape[0]\n", + " density_output = np.zeros((num_runs, num_bins))\n", + "\n", + " for i in range(num_runs):\n", + " for j in range(num_bins):\n", + " x = grids[j]\n", + " tmp_result = gaussian(eigenvalues[i, :], x, sigma)\n", + " density_output[i, j] = np.sum(tmp_result * weights[i, :])\n", + " density = np.mean(density_output, axis=0)\n", + " normalization = np.sum(density) * (grids[1] - grids[0])\n", + " density = density / normalization\n", + " return density, grids\n", + "\n", + "\n", + "def gaussian(x, x0, sigma_squared):\n", + " return np.exp(-(x0 - x)**2 /\n", + " (2.0 * sigma_squared)) / np.sqrt(2 * np.pi * sigma_squared)\n", + "\n", + "criterion = torch.nn.CrossEntropyLoss()\n", + "\n", + "# get a batch of data for computing the Hessian\n", + "batch_x, batch_y, *others = next(iter(data_loader))\n", + "batch_x = batch_x.to(args.device)\n", + "batch_y = batch_y.to(args.device)\n", + "\n", + "# create the hessian computation module\n", + "hessian_comp = hessian(model_visual, criterion, data=(batch_x, batch_y), cuda=True)\n", + "# Now let's compute the top 2 eigenavlues and eigenvectors of the Hessian\n", + "top_eigenvalues, top_eigenvector = hessian_comp.eigenvalues(top_n=2, maxIter=1000)\n", + "print(\"The top two eigenvalues of this model are: %.4f %.4f\"% (top_eigenvalues[-1],top_eigenvalues[-2]))\n", + "\n", + "density_eigen, density_weight = hessian_comp.density()\n", + "\n", + " \n", + "ax = get_esd_plot(density_eigen, density_weight)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13 (default, Oct 21 2022, 23:50:54) \n[GCC 11.2.0]" + }, + "vscode": { + "interpreter": { + "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/Demos/Demo_Landscape.ipynb b/analysis/Demos/Demo_Landscape.ipynb new file mode 100755 index 0000000..e014e64 --- /dev/null +++ b/analysis/Demos/Demo_Landscape.ipynb @@ -0,0 +1,544 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_Landscape\n", + "This is a demo for visualizing the Landscape of a Neuron Network\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": {}, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py --save_folder_name badnet_demo" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "5465b02c", + "metadata": {}, + "source": [ + "**Remark**: **Message Passing Interface (MPI)** is needed to accelerate the computation of loss landscape. \n", + "\n", + "To install it, you can run the following commands in your terminal:\n", + "\n", + "```sudo apt install libopenmpi-dev```\n", + "\n", + "```pip install mpi4py```\n", + "\n", + "Finally, you need also clone the repo https://github.com/tomgoldstein/loss-landscape to the ***visualization*** folder.\n", + "\n", + "For more information, please refer to ***readme file***." + ] + }, + { + "cell_type": "markdown", + "id": "87bd9f5a", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71b7087b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/anaconda3/envs/py38/lib/python3.8/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: libc10_hip.so: cannot open shared object file: No such file or directory\n", + " warn(f\"Failed to load image Python extension: {e}\")\n" + ] + } + ], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "import socket\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(\"../loss-landscape\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n", + "\n", + "import projection as proj\n", + "import net_plotter as net_plotter\n", + "import plot_2D as plot_2D\n", + "import plot_surface as plot_surface\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2fb719c7", + "metadata": {}, + "outputs": [], + "source": [ + "### Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"badnet_demo\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 2: Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8b67ac9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "Create visualization dataset with \n", + " \t Dataset: clean_train \n", + " \t Number of samples: 50000 \n", + " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "selected_classes = np.arange(args.num_classes)\n", + "\n", + "# keep the same transforms for train and test dataset for better visualization\n", + "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n", + "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n", + "\n", + "# Create dataset\n", + "args.visual_dataset = 'clean_train'\n", + "if args.visual_dataset == 'clean_train':\n", + " visual_dataset = result_attack[\"clean_train\"]\n", + "elif args.visual_dataset == 'bd_train': \n", + " visual_dataset = result_attack[\"bd_train\"]\n", + " visual_dataset.getitem_all = False # only return img and label\n", + " \n", + "else:\n", + " assert False, \"Illegal vis_class\"\n", + "\n", + "print(f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)} \\n \\t Selected classes: {selected_classes}')\n", + "\n", + "# Create data loader\n", + "data_loader = torch.utils.data.DataLoader(\n", + " visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n", + ")\n", + "\n", + "# Create denormalization function\n", + "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n", + " if isinstance(trans_t, transforms.Normalize):\n", + " denormalizer = get_dataset_denormalization(trans_t)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3f652e5", + "metadata": {}, + "source": [ + "### Step 3: Load Model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ff67e7b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load model preactresnet18 from badnet_demo\n" + ] + } + ], + "source": [ + "# Load model\n", + "model_visual = generate_cls_model(args.model, args.num_classes)\n", + "model_visual.load_state_dict(result_attack[\"model\"])\n", + "model_visual\n", + "# !!! Important to set eval mode !!!\n", + "model_visual.eval()\n", + "print(f\"Load model {args.model} from {args.result_file_attack}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cc952077", + "metadata": {}, + "source": [ + "### Step 4: Setup parameters for Loss Landscape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "94612903", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank 0 use GPU 0 of 8 GPUs on ai07\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No protocol specified\n" + ] + } + ], + "source": [ + "import mpi4pytorch as mpi\n", + "\n", + "# Set range for landscape\n", + "args.x = '-1:1:5'\n", + "args.y = '-1:1:5'\n", + "\n", + "# Additional Agrs for Loss Landscape\n", + "args.mpi=True\n", + "args.cuda=True\n", + "args.show = False\n", + "args.model_file = save_path_attack\n", + "args.model_file1 = \"\"\n", + "args.model_fil2 = \"\"\n", + "args.data_split = 0\n", + "args.proj_file = \"\"\n", + "\n", + "#--------------------------------------------------------------------------\n", + "# Environment setup\n", + "#--------------------------------------------------------------------------\n", + "\n", + "if args.mpi:\n", + " comm = mpi.setup_MPI()\n", + " rank, nproc = comm.Get_rank(), comm.Get_size()\n", + "else:\n", + " comm, rank, nproc = None, 0, 1\n", + "\n", + "# in case of multiple GPUs per node, set the GPU to use for each rank\n", + "if args.cuda:\n", + " if not torch.cuda.is_available():\n", + " raise Exception('User selected cuda option, but cuda is not available on this machine')\n", + " gpu_count = torch.cuda.device_count()\n", + " torch.cuda.set_device(rank % gpu_count)\n", + " print('Rank %d use GPU %d of %d GPUs on %s' %\n", + " (rank, torch.cuda.current_device(), gpu_count, socket.gethostname()))\n", + " \n", + "#--------------------------------------------------------------------------\n", + "# Check plotting resolution\n", + "#--------------------------------------------------------------------------\n", + "try:\n", + " args.xmin, args.xmax, args.xnum = [float(a) for a in args.x.split(':')]\n", + " args.ymin, args.ymax, args.ynum = (None, None, None)\n", + " args.xnum = int(args.xnum)\n", + " if args.y:\n", + " args.ymin, args.ymax, args.ynum = [float(a) for a in args.y.split(':')]\n", + " assert args.ymin and args.ymax and args.ynum, \\\n", + " 'You specified some arguments for the y axis, but not all'\n", + " args.ynum = int(args.ynum)\n", + "except:\n", + " raise Exception('Improper format for x- or y-coordinates. Try something like -1:1:51')" + ] + }, + { + "cell_type": "markdown", + "id": "1b549f20", + "metadata": {}, + "source": [ + "### Step 5: Setup directions" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "543f9755", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------------------------------------------------------\n", + "setup_direction\n", + "-------------------------------------------------------------------\n", + "../..//record/badnet_demo_weights_xignore=biasbn_xnorm=filter_yignore=biasbn_ynorm=filter.h5 is already setted up\n", + "../..//record/badnet_demo_weights_xignore=biasbn_xnorm=filter_yignore=biasbn_ynorm=filter.h5_[-1.0,1.0,5]x[-1.0,1.0,5].h5 is already set up\n" + ] + } + ], + "source": [ + "#--------------------------------------------------------------------------\n", + "# Extract model parameters\n", + "#--------------------------------------------------------------------------\n", + "w = net_plotter.get_weights(model_visual) # initial parameters\n", + "s = copy.deepcopy(model_visual.state_dict()) # deepcopy since state_dict are references\n", + "if args.ngpu > 1:\n", + " # data parallel with multiple GPUs on a single node\n", + " net = torch.nn.DataParallel(model_visual, device_ids=range(torch.cuda.device_count()))\n", + "\n", + "\n", + "#--------------------------------------------------------------------------\n", + "# Setup the direction file and the surface file\n", + "#--------------------------------------------------------------------------\n", + "dir_file = net_plotter.name_direction_file(args) # name the direction file\n", + "if rank == 0:\n", + " net_plotter.setup_direction(args, dir_file, model_visual)\n", + "\n", + "surf_file = plot_surface.name_surface_file(args, dir_file)\n", + "if rank == 0:\n", + " plot_surface.setup_surface_file(args, surf_file, dir_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "11402470", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cosine similarity between x-axis and y-axis: -0.000373\n" + ] + } + ], + "source": [ + "# load directions\n", + "d = net_plotter.load_directions(dir_file)\n", + "# calculate the consine similarity of the two directions\n", + "if len(d) == 2 and rank == 0:\n", + " similarity = proj.cal_angle(proj.nplist_to_tensor(d[0]), proj.nplist_to_tensor(d[1]))\n", + " print('cosine similarity between x-axis and y-axis: %f' % similarity)\n" + ] + }, + { + "cell_type": "markdown", + "id": "51109487", + "metadata": {}, + "source": [ + "### Step 6: Computation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5eb99a67", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing 0 values for rank 0\n", + "Rank 0 done! Total time: 0.00 Sync: 0.00\n" + ] + } + ], + "source": [ + "#--------------------------------------------------------------------------\n", + "# Start the computation\n", + "#--------------------------------------------------------------------------\n", + "plot_surface.crunch(surf_file, model_visual, w, s, d, data_loader, 'train_loss', 'train_acc', comm, rank, args)\n" + ] + }, + { + "cell_type": "markdown", + "id": "a9b94261", + "metadata": {}, + "source": [ + "### Step 7: Show the Landscape" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "65f7e8a9", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'z')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#--------------------------------------------------------------------------\n", + "# Plot figures\n", + "#--------------------------------------------------------------------------\n", + "\n", + "import h5py\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib import pyplot as plt\n", + "from matplotlib import cm\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "f = h5py.File(surf_file, 'r')\n", + "x = np.array(f['xcoordinates'][:])\n", + "y = np.array(f['ycoordinates'][:])\n", + "X, Y = np.meshgrid(x, y)\n", + "\n", + "surf_name = \"train_loss\"\n", + "\n", + "if surf_name in f.keys():\n", + " Z = np.array(f[surf_name][:])\n", + "elif surf_name == 'train_err' or surf_name == 'test_err' :\n", + " Z = 100 - np.array(f[surf_name][:])\n", + "else:\n", + " print ('%s is not found in %s' % (surf_name, surf_file))\n", + "\n", + "# --------------------------------------------------------------------\n", + "# Plot 3D surface\n", + "# --------------------------\n", + "fig = plt.figure()\n", + "\n", + "def Axes3D(fig):\n", + " return fig.add_subplot(projection='3d')\n", + "ax = Axes3D(fig)\n", + "surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm, linewidth=0, antialiased=False)\n", + "fig.colorbar(surf, shrink=0.5, aspect=5)\n", + "\n", + "ax.set_xlabel('x')\n", + "ax.set_ylabel('y')\n", + "ax.set_zlabel('z')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5aef284", + "metadata": {}, + "outputs": [], + "source": [ + "# Another way to show the results is the function provided by plot_2D\n", + "if rank == 0:\n", + " args.vmin = 0.1 \n", + " args.vmax = 10 \n", + " args.vlevel = 0.5 \n", + " if args.y and args.proj_file:\n", + " plot_2D.plot_contour_trajectory(surf_file, dir_file, args.proj_file, 'train_loss', args.show)\n", + " elif args.y:\n", + " plot_2D.plot_2d_contour(surf_file, 'train_loss', args.vmin, args.vmax, args.vlevel, args.show)\n", + " else:\n", + " plot_1D.plot_1d_loss_err(surf_file, args.xmin, args.xmax, args.loss_max, args.log, args.show)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "vscode": { + "interpreter": { + "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/Demos/Demo_Lips.ipynb b/analysis/Demos/Demo_Lips.ipynb new file mode 100755 index 0000000..1db0f48 --- /dev/null +++ b/analysis/Demos/Demo_Lips.ipynb @@ -0,0 +1,478 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_Lips\n", + "This is a demo for visualizing the lipschitz constant of a Neuron Network\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py --save_folder_name badnet_demo" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "87bd9f5a", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "71b7087b", + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "import matplotlib\n", + "from matplotlib.patches import Rectangle, Patch\n", + "\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2fb719c7", + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "### Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"badnet_demo\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 2: Load model" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b8b67ac9", + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "Load model preactresnet18 from badnet_demo\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "\n", + "# Load model\n", + "model_visual = generate_cls_model(args.model, args.num_classes)\n", + "model_visual.load_state_dict(result_attack[\"model\"])\n", + "model_visual.to(args.device)\n", + "# !!! Important to set eval mode !!!\n", + "model_visual.eval()\n", + "print(f\"Load model {args.model} from {args.result_file_attack}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cc952077", + "metadata": {}, + "source": [ + "### Step 3: Plot lipschitz constant" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "94612903", + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting lipschitz constant\n" + ] + } + ], + "source": [ + "############## lipschitz constant ##################\n", + "print(\"Plotting lipschitz constant\")\n", + "\n", + "module_dict = dict(model_visual.named_modules())\n", + "\n", + "\n", + "module_names = module_dict.keys()\n", + "\n", + "# Plot Conv2d or Linear\n", + "module_visual = [i for i in module_dict.keys() if isinstance(\n", + " module_dict[i], torch.nn.Conv2d) or isinstance(module_dict[i], torch.nn.Linear) or isinstance(module_dict[i], torch.nn.BatchNorm2d)]\n" + ] + }, + { + "cell_type": "markdown", + "id": "97de2b56", + "metadata": {}, + "source": [ + "### Step 4: Collect Lips Constant" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4fec3fb2", + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting Lips Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Lips BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Lips BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Lips BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Lips BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Lips BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + "Collecting Lips BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Lips Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + "Collecting Lips BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Lips BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Lips BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + "Collecting Lips BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Lips Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + "Collecting Lips BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Lips BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Lips BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + "Collecting Lips BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Lips Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + "Collecting Lips BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Lips BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Lips Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Lips Linear(in_features=512, out_features=10, bias=True)\n" + ] + } + ], + "source": [ + "df = None\n", + "\n", + "max_num_neuron = 0\n", + "for module_name in module_visual:\n", + " target_layer = module_dict[module_name]\n", + " \n", + " print(f'Collecting Lips {target_layer}')\n", + " if isinstance(target_layer, torch.nn.Linear):\n", + " channel_lips = []\n", + " for idx in range(target_layer.weight.shape[0]):\n", + " w = target_layer.weight[idx].reshape(target_layer.weight.shape[1], -1)\n", + " # Just norm of weight for linear layer\n", + " channel_lips.append(torch.svd(w)[1].max())\n", + " channel_lips = torch.Tensor(channel_lips)\n", + "\n", + " elif isinstance(target_layer, torch.nn.BatchNorm2d):\n", + " std = target_layer.running_var.sqrt()\n", + " weight = target_layer.weight\n", + "\n", + " channel_lips = []\n", + " for idx in range(weight.shape[0]):\n", + " w = conv.weight[idx].reshape(conv.weight.shape[1], -1) * (weight[idx]/std[idx]).abs()\n", + " channel_lips.append(torch.svd(w)[1].max())\n", + " channel_lips = torch.Tensor(channel_lips)\n", + "\n", + " \n", + " # Convolutional layer should be followed by a BN layer by default\n", + " elif isinstance(target_layer, torch.nn.Conv2d):\n", + " conv = target_layer \n", + " \n", + " channel_lips = []\n", + " for idx in range(target_layer.weight.shape[0]):\n", + " w = target_layer.weight[idx].reshape(target_layer.weight.shape[1], -1)\n", + " channel_lips.append(torch.svd(w)[1].max())\n", + " channel_lips = torch.Tensor(channel_lips)\n", + " else:\n", + " assert False, \"Unknown layer type\"\n", + "\n", + " for neuron_i in range(channel_lips.shape[0]):\n", + " base_row = {}\n", + " base_row['layer'] = module_name\n", + " base_row['Neuron'] = neuron_i\n", + " base_row['Lips'] = channel_lips[neuron_i].item()\n", + " if df is None:\n", + " df = pd.DataFrame.from_dict([base_row])\n", + " else:\n", + " df.loc[df.shape[0]] = base_row" + ] + }, + { + "cell_type": "markdown", + "id": "8c3760b9", + "metadata": {}, + "source": [ + "### Step 5: Show the Lips" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "81bbb857", + "metadata": { + "vscode": { + "languageId": "python" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ploting conv1\n", + "ploting layer1.0.bn1\n", + "ploting layer1.0.conv1\n", + "ploting layer1.0.bn2\n", + "ploting layer1.0.conv2\n", + "ploting layer1.1.bn1\n", + "ploting layer1.1.conv1\n", + "ploting layer1.1.bn2\n", + "ploting layer1.1.conv2\n", + "ploting layer2.0.bn1\n", + "ploting layer2.0.conv1\n", + "ploting layer2.0.bn2\n", + "ploting layer2.0.conv2\n", + "ploting layer2.0.shortcut.0\n", + "ploting layer2.1.bn1\n", + "ploting layer2.1.conv1\n", + "ploting layer2.1.bn2\n", + "ploting layer2.1.conv2\n", + "ploting layer3.0.bn1\n", + "ploting layer3.0.conv1\n", + "ploting layer3.0.bn2\n", + "ploting layer3.0.conv2\n", + "ploting layer3.0.shortcut.0\n", + "ploting layer3.1.bn1\n", + "ploting layer3.1.conv1\n", + "ploting layer3.1.bn2\n", + "ploting layer3.1.conv2\n", + "ploting layer4.0.bn1\n", + "ploting layer4.0.conv1\n", + "ploting layer4.0.bn2\n", + "ploting layer4.0.conv2\n", + "ploting layer4.0.shortcut.0\n", + "ploting layer4.1.bn1\n", + "ploting layer4.1.conv1\n", + "ploting layer4.1.bn2\n", + "ploting layer4.1.conv2\n", + "ploting linear\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "start_x0 = 0\n", + "height = 1\n", + "width = 1\n", + "vmin = 0\n", + "if args.normalize_by_layer:\n", + " vmax = 1\n", + "else:\n", + " vmax = df.Lips.max()\n", + "max_num_neuron = df.Neuron.max()\n", + "\n", + "\n", + "norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax, clip=True)\n", + "mapper = matplotlib.cm.ScalarMappable(norm=norm, cmap=matplotlib.cm.Oranges)\n", + "\n", + "fig, ax = plt.subplots(\n", + " figsize=(int(len(module_visual)), int(max_num_neuron/10)))\n", + "\n", + "ax.plot([0, 0], [0, 0])\n", + "\n", + "for module_name in module_visual:\n", + " print(f'ploting {module_name}')\n", + " y_0 = 0\n", + " layer_info = df[df.layer == module_name]\n", + " layer_lips_max = layer_info['Lips'].max()\n", + " total_neuron = layer_info.shape[0]\n", + " for neuron_i in range(total_neuron):\n", + " x_0 = start_x0\n", + " base_row = layer_info.iloc[neuron_i]\n", + " if args.normalize_by_layer:\n", + " ax.add_patch(Rectangle((x_0, y_0), width, height,\n", + " facecolor=mapper.to_rgba(base_row['Lips']/layer_lips_max),\n", + " fill=True,\n", + " lw=5,\n", + " alpha=0.8))\n", + "\n", + " else:\n", + " ax.add_patch(Rectangle((x_0, y_0), width, height,\n", + " facecolor=mapper.to_rgba(base_row['Lips']),\n", + " fill=True,\n", + " lw=5,\n", + " alpha=0.8))\n", + "\n", + " y_0 += 1.5*height\n", + " start_x0 += 1.5*width\n", + "x_loc = [0.5*width+1.5*width*i for i in range(len(module_visual))]\n", + "y_loc = [0.5*height+1.5*height*i for i in range(max_num_neuron)]\n", + "\n", + "ax.set_xlim(xmin=-0.5*width, xmax=1.5*width*(len(module_visual)+1))\n", + "ax.set_ylim(ymin=-0.5*height, ymax=1.5*height*(max_num_neuron+1))\n", + "ax.set_xticks(x_loc, module_visual, rotation=270)\n", + "ax.set_yticks(y_loc[::10], np.arange(max_num_neuron)[::10])\n", + "ax.set_title(f'Lips of Attack Model')\n", + "ax.set_ylabel('Neuron')\n", + "ax.set_xlabel('Layer')\n", + "\n", + "cb_ax = fig.add_axes([0.15, 0.9, 0.7, 0.01])\n", + "\n", + "fig.colorbar(mapper,\n", + " cax=cb_ax, orientation=\"horizontal\", label='Lips')\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/Demos/Demo_Neuron_Activation.ipynb b/analysis/Demos/Demo_Neuron_Activation.ipynb new file mode 100755 index 0000000..4e81c6f --- /dev/null +++ b/analysis/Demos/Demo_Neuron_Activation.ipynb @@ -0,0 +1,404 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_Activation (NA)\n", + "This is a demo for visualizing the Neuronal Activation (NA) of a Neuron Network\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": {}, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py --save_folder_name badnet_demo" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "87bd9f5a", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "71b7087b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2fb719c7", + "metadata": {}, + "outputs": [], + "source": [ + "### Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"badnet_demo\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 2: Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b8b67ac9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "max_num_samples is given, use sample number limit now.\n", + "subset bd dataset with length: 5000\n", + "Create visualization dataset with \n", + " \t Dataset: bd_train \n", + " \t Number of samples: 5000 \n", + " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "selected_classes = np.arange(args.num_classes)\n", + "\n", + "# Select classes to visualize\n", + "if args.num_classes>args.c_sub:\n", + " selected_classes = np.delete(selected_classes, args.target_class)\n", + " selected_classes = np.random.choice(selected_classes, args.c_sub-1, replace=False)\n", + " selected_classes = np.append(selected_classes, args.target_class)\n", + "\n", + "# keep the same transforms for train and test dataset for better visualization\n", + "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n", + "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n", + " \n", + "args.visual_dataset = 'bd_train'\n", + "# Create dataset. Only support BD_TEST and BD_TRAIN\n", + "if args.visual_dataset == 'bd_train': \n", + " bd_train_with_trans = result_attack[\"bd_train\"]\n", + " visual_dataset = generate_bd_dataset(bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub, bd_only = True)\n", + "elif args.visual_dataset == 'bd_test':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_bd_dataset(bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub, bd_only = True)\n", + "else:\n", + " assert False, \"Illegal vis_class\"\n", + "\n", + "print(f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)} \\n \\t Selected classes: {selected_classes}')\n", + "\n", + "# Create data loader\n", + "data_loader = torch.utils.data.DataLoader(\n", + " visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n", + ")\n", + "\n", + "# Create denormalization function\n", + "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n", + " if isinstance(trans_t, transforms.Normalize):\n", + " denormalizer = get_dataset_denormalization(trans_t)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3f652e5", + "metadata": {}, + "source": [ + "### Step 3: Load Model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ff67e7b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load model preactresnet18 from badnet_demo\n" + ] + } + ], + "source": [ + "# Load model\n", + "model_visual = generate_cls_model(args.model, args.num_classes)\n", + "model_visual.load_state_dict(result_attack[\"model\"])\n", + "model_visual.to(args.device)\n", + "# !!! Important to set eval mode !!!\n", + "model_visual.eval()\n", + "print(f\"Load model {args.model} from {args.result_file_attack}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cc952077", + "metadata": {}, + "source": [ + "### Step 4: Plot Neuron Activation" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "94612903", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting T-SNE\n", + "Choose layer layer4.1.conv2 from model preactresnet18\n" + ] + } + ], + "source": [ + "############## Neuron Activation ##################\n", + "print(\"Plotting Neuron Activation\")\n", + "\n", + "# Choose layer for feature extraction\n", + "module_dict = dict(model_visual.named_modules())\n", + "target_layer = module_dict[args.target_layer_name]\n", + "print(f'Choose layer {args.target_layer_name} from model {args.model}')\n", + "\n", + "# Get BD features\n", + "features_bd, labels_bd, other_info = get_features(args, model_visual, target_layer, data_loader)\n", + "features_bd_avg = np.mean(features_bd, axis=0)\n", + "\n", + "# Get Corresponding Clean features\n", + "visual_dataset.wrapped_dataset.poison_indicator = np.zeros_like(visual_dataset.wrapped_dataset.poison_indicator)\n", + "\n", + "features_clean, labels_clean, other_info = get_features(args, model_visual, target_layer, data_loader)\n", + "features_clean_avg = np.mean(features_clean, axis=0)\n", + "\n", + "sort_bar = np.argsort(features_clean_avg)[::-1]\n", + "\n", + "features_bd_avg = features_bd_avg[sort_bar]\n", + "features_clean_avg = features_clean_avg[sort_bar]\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f65bfe12", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 10))\n", + "plt.bar(\n", + " np.arange(features_clean_avg.shape[0]),\n", + " features_clean_avg,\n", + " label=\"Clean\",\n", + " alpha=0.7,\n", + " color=\"#2196F3\",\n", + ")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "5a836820", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 10))\n", + "plt.bar(\n", + " np.arange(features_bd_avg.shape[0]),\n", + " features_bd_avg,\n", + " label=\"Poisoned\",\n", + " alpha=0.7,\n", + " color=\"#4CAF50\",\n", + ")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f4d5002e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "plt.figure(figsize=(10, 10))\n", + "plt.bar(\n", + " np.arange(features_clean_avg.shape[0]),\n", + " features_clean_avg,\n", + " label=\"Clean\",\n", + " alpha=0.7,\n", + " color=\"#2196F3\",\n", + ")\n", + "plt.bar(\n", + " np.arange(features_bd_avg.shape[0]),\n", + " features_bd_avg,\n", + " label=\"Poisoned\",\n", + " alpha=0.7,\n", + " color=\"#4CAF50\",\n", + ")\n", + "plt.xlabel(\"Neuron\")\n", + "plt.ylabel(\"Average Activation Value\")\n", + "plt.title(f\"{get_dataname(args.dataset)}, {get_pratio(args.pratio)}% Poisoned Samples\")\n", + "plt.xlim(0, features_clean_avg.shape[0])\n", + "plt.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.8.10 (default, Nov 14 2022, 12:59:47) \n[GCC 9.4.0]" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/Demos/Demo_Quality.ipynb b/analysis/Demos/Demo_Quality.ipynb new file mode 100755 index 0000000..7f140b5 --- /dev/null +++ b/analysis/Demos/Demo_Quality.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_Quality\n", + "This is a demo for visualizing the Image Quality\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": {}, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py --save_folder_name badnet_demo" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "87bd9f5a", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71b7087b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import shap\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2fb719c7", + "metadata": {}, + "outputs": [], + "source": [ + "### Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"badnet_demo\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 2: Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8b67ac9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "max_num_samples is given, use sample number limit now.\n", + "subset bd dataset with length: 5000\n", + "Create visualization dataset with \n", + " \t Dataset: bd_train \n", + " \t Number of samples: 5000 \n", + " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "selected_classes = np.arange(args.num_classes)\n", + "\n", + "# Select classes to visualize\n", + "if args.num_classes>args.c_sub:\n", + " selected_classes = np.delete(selected_classes, args.target_class)\n", + " selected_classes = np.random.choice(selected_classes, args.c_sub-1, replace=False)\n", + " selected_classes = np.append(selected_classes, args.target_class)\n", + "\n", + "# keep the same transforms for train and test dataset for better visualization\n", + "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n", + "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n", + "\n", + "# Create dataset\n", + "args.visual_dataset = 'bd_train'\n", + "if args.visual_dataset == 'mixed':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_mix_dataset(bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_train':\n", + " clean_train_with_trans = result_attack[\"clean_train\"]\n", + " visual_dataset = generate_clean_dataset(clean_train_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_test':\n", + " clean_test_with_trans = result_attack[\"clean_test\"]\n", + " visual_dataset = generate_clean_dataset(clean_test_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'bd_train': \n", + " bd_train_with_trans = result_attack[\"bd_train\"]\n", + " visual_dataset = generate_bd_dataset(bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'bd_test':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_bd_dataset(bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n", + "else:\n", + " assert False, \"Illegal vis_class\"\n", + "\n", + "print(f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)} \\n \\t Selected classes: {selected_classes}')\n", + "\n", + "# Create data loader\n", + "data_loader = torch.utils.data.DataLoader(\n", + " visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n", + ")\n", + "\n", + "# Create denormalization function\n", + "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n", + " if isinstance(trans_t, transforms.Normalize):\n", + " denormalizer = get_dataset_denormalization(trans_t)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "67cbfec4", + "metadata": {}, + "source": [ + "### Step 3: SSIM" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "39104beb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number Poisoned samples: 489\n", + "Average SSIM: 0.9929845929145813\n" + ] + } + ], + "source": [ + "visual_poison_indicator = np.array(get_poison_indicator_from_bd_dataset(visual_dataset))\n", + "bd_idx = np.where(visual_poison_indicator == 1)[0]\n", + "\n", + "from torchmetrics import StructuralSimilarityIndexMeasure\n", + "ssim = StructuralSimilarityIndexMeasure()\n", + "ssim_list = []\n", + "if visual_poison_indicator.sum() > 0:\n", + " print(f'Number Poisoned samples: {visual_poison_indicator.sum()}')\n", + " # random choose two poisoned samples\n", + " start_idx = 0\n", + " for i in range(bd_idx.shape[0]):\n", + " bd_sample = denormalizer(visual_dataset[i][0]).unsqueeze(0)\n", + " with temporary_all_clean(visual_dataset):\n", + " clean_sample = denormalizer(visual_dataset[i][0]).unsqueeze(0)\n", + " ssim_list.append(ssim(bd_sample, clean_sample)) \n", + "print(f'Average SSIM: {np.mean(ssim_list)}')" + ] + }, + { + "cell_type": "markdown", + "id": "2c2b0104", + "metadata": {}, + "source": [ + "### Step 4: FID" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "57497927", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number Poisoned samples: 489\n", + "FID: 0.00030133521067909896\n" + ] + } + ], + "source": [ + "visual_poison_indicator = np.array(get_poison_indicator_from_bd_dataset(visual_dataset))\n", + "bd_idx = np.where(visual_poison_indicator == 1)[0]\n", + "\n", + "from torchmetrics.image.fid import FrechetInceptionDistance\n", + "fid = FrechetInceptionDistance(feature=64, normalize = True)\n", + "if visual_poison_indicator.sum() > 0:\n", + " print(f'Number Poisoned samples: {visual_poison_indicator.sum()}')\n", + " # random choose two poisoned samples\n", + " start_idx = 0\n", + " for i in range(bd_idx.shape[0]):\n", + " bd_sample = denormalizer(visual_dataset[i][0]).unsqueeze(0)\n", + " with temporary_all_clean(visual_dataset):\n", + " clean_sample = denormalizer(visual_dataset[i][0]).unsqueeze(0)\n", + " fid.update(clean_sample, real=True)\n", + " fid.update(bd_sample, real=False)\n", + " fid_value = fid.compute().numpy() \n", + "print(f'FID: {fid_value}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "870cf186", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "vscode": { + "interpreter": { + "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/Demos/Demo_SHAP.ipynb b/analysis/Demos/Demo_SHAP.ipynb new file mode 100755 index 0000000..d0dfa25 --- /dev/null +++ b/analysis/Demos/Demo_SHAP.ipynb @@ -0,0 +1,383 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_SHAP\n", + "This is a demo for visualizing the Shapely Value of a Neuron Network\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": {}, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py --save_folder_name badnet_demo" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "87bd9f5a", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71b7087b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import shap\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2fb719c7", + "metadata": {}, + "outputs": [], + "source": [ + "### Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"badnet_demo\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 2: Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8b67ac9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "max_num_samples is given, use sample number limit now.\n", + "subset bd dataset with length: 4995\n", + "Create visualization dataset with \n", + " \t Dataset: bd_test \n", + " \t Number of samples: 4995 \n", + " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "selected_classes = np.arange(args.num_classes)\n", + "\n", + "# Select classes to visualize\n", + "if args.num_classes>args.c_sub:\n", + " selected_classes = np.delete(selected_classes, args.target_class)\n", + " selected_classes = np.random.choice(selected_classes, args.c_sub-1, replace=False)\n", + " selected_classes = np.append(selected_classes, args.target_class)\n", + "\n", + "# keep the same transforms for train and test dataset for better visualization\n", + "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n", + "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n", + "\n", + "# Create dataset\n", + "args.visual_dataset = 'bd_test'\n", + "if args.visual_dataset == 'mixed':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_mix_dataset(bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_train':\n", + " clean_train_with_trans = result_attack[\"clean_train\"]\n", + " visual_dataset = generate_clean_dataset(clean_train_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_test':\n", + " clean_test_with_trans = result_attack[\"clean_test\"]\n", + " visual_dataset = generate_clean_dataset(clean_test_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'bd_train': \n", + " bd_train_with_trans = result_attack[\"bd_train\"]\n", + " visual_dataset = generate_bd_dataset(bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'bd_test':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_bd_dataset(bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n", + "else:\n", + " assert False, \"Illegal vis_class\"\n", + "\n", + "print(f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)} \\n \\t Selected classes: {selected_classes}')\n", + "\n", + "# Create data loader\n", + "data_loader = torch.utils.data.DataLoader(\n", + " visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n", + ")\n", + "\n", + "# Create denormalization function\n", + "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n", + " if isinstance(trans_t, transforms.Normalize):\n", + " denormalizer = get_dataset_denormalization(trans_t)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "61034e05", + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare background data\n", + "num_bg = 200\n", + "background_idx = np.random.choice(len(visual_dataset), num_bg, replace=False)\n", + "background_samples = []\n", + "for i in background_idx:\n", + " background_samples.append(visual_dataset[i][0].unsqueeze(0))\n", + " \n", + "background_samples = torch.cat(background_samples, axis = 0).to(args.device)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "39104beb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number Poisoned samples: 4995\n", + "Select 2 poisoned samples\n", + "Select 2 clean samples\n" + ] + } + ], + "source": [ + "# Choose samples to show SHAP values. By Default, 2 clean images + 2 Poison images. If no enough Poison images, use 4 clean images instead.AblationCAM\n", + "total_num = 4\n", + "bd_num = 0\n", + "\n", + "visual_samples = []\n", + "visual_labels = []\n", + "\n", + "visual_poison_indicator = np.array(get_poison_indicator_from_bd_dataset(visual_dataset))\n", + "if visual_poison_indicator.sum() > 0:\n", + " print(f'Number Poisoned samples: {visual_poison_indicator.sum()}')\n", + " # random choose two poisoned samples\n", + " selected_bd_idx = np.random.choice(np.where(visual_poison_indicator == 1)[0], 2, replace=False)\n", + " for i in selected_bd_idx:\n", + " visual_samples.append(visual_dataset[i][0].unsqueeze(0))\n", + " visual_labels.append(visual_dataset[i][4])\n", + " bd_num = len(selected_bd_idx)\n", + " print(f'Select {bd_num} poisoned samples')\n", + " \n", + "# Trun all samples to clean\n", + "with temporary_all_clean(visual_dataset):\n", + " # you can just set selected_clean_idx = selected_bd_idx to build the correspondence between clean samples and poisoned samples\n", + " selected_clean_idx = np.random.choice(len(visual_dataset), total_num-bd_num, replace=False)\n", + " for i in selected_clean_idx:\n", + " visual_samples.append(visual_dataset[i][0].unsqueeze(0))\n", + " visual_labels.append(visual_dataset[i][1])\n", + " print(f'Select {len(selected_clean_idx)} clean samples')\n", + "\n", + "# Clean sample first\n", + "visual_samples = visual_samples[::-1]\n", + "visual_labels = visual_labels[::-1]\n", + "\n", + "visual_samples = torch.cat(visual_samples, axis = 0).to(args.device)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3f652e5", + "metadata": {}, + "source": [ + "### Step 3: Load Model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ff67e7b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load model preactresnet18 from badnet_demo\n" + ] + } + ], + "source": [ + "# Load model\n", + "model_visual = generate_cls_model(args.model, args.num_classes)\n", + "model_visual.load_state_dict(result_attack[\"model\"])\n", + "model_visual.to(args.device)\n", + "# !!! Important to set eval mode !!!\n", + "model_visual.eval()\n", + "print(f\"Load model {args.model} from {args.result_file_attack}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cc952077", + "metadata": {}, + "source": [ + "### Step 4: Plot SHAP" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "94612903", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting SHAP\n", + "Choose layer layer4.1.conv2 from model preactresnet18\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "############## SHAP ##################\n", + "print('Plotting SHAP')\n", + "\n", + "# Choose layer for feature extraction\n", + "module_dict = dict(model_visual.named_modules())\n", + "target_layer = module_dict[args.target_layer_name]\n", + "print(f'Choose layer {args.target_layer_name} from model {args.model}')\n", + "\n", + "sfm = torch.nn.Softmax(dim=1)\n", + "outputs = model_visual(visual_samples)\n", + "pre_p, pre_label = torch.max(sfm(outputs), dim=1)\n", + "\n", + "e = shap.GradientExplainer(\n", + " (model_visual, target_layer), background_samples, local_smoothing=0)\n", + "shap_values, indexes = e.shap_values(visual_samples, ranked_outputs=5)\n", + "\n", + "# get the names for the classes\n", + "class_names = np.array(args.class_names).reshape([-1])\n", + "index_names = np.vectorize(\n", + " lambda x: class_names[x].capitalize())(indexes.cpu())\n", + "# plot the explanations\n", + "shap_numpy = [np.swapaxes(np.swapaxes(s, 1, -1), 1, 2) for s in shap_values]\n", + "test_numpy = np.swapaxes(\n", + " np.swapaxes(denormalizer(visual_samples.cpu()).numpy(), 1, -1), 1, 2\n", + ")\n", + "test_numpy[test_numpy < 1e-12] = 1e-12 # for some numerical issue\n", + "\n", + "shap.image_plot(shap_numpy, test_numpy, index_names, show=False)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('py38')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12 (main, Apr 5 2022, 06:56:58) \n[GCC 7.5.0]" + }, + "vscode": { + "interpreter": { + "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/Demos/Demo_TAC.ipynb b/analysis/Demos/Demo_TAC.ipynb new file mode 100755 index 0000000..6b11de6 --- /dev/null +++ b/analysis/Demos/Demo_TAC.ipynb @@ -0,0 +1,523 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_TAC\n", + "This is a demo for visualizing the Total Activation Change (TAC) of a Neuron Network\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": {}, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py --save_folder_name badnet_demo" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "87bd9f5a", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71b7087b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "import matplotlib\n", + "from matplotlib.patches import Rectangle, Patch\n", + "\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2fb719c7", + "metadata": {}, + "outputs": [], + "source": [ + "### Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"badnet_demo\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 2: Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8b67ac9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "max_num_samples is given, use sample number limit now.\n", + "subset bd dataset with length: 5000\n", + "Create visualization dataset with \n", + " \t Dataset: bd_train \n", + " \t Number of samples: 5000 \n", + " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "selected_classes = np.arange(args.num_classes)\n", + "\n", + "# Select classes to visualize\n", + "if args.num_classes>args.c_sub:\n", + " selected_classes = np.delete(selected_classes, args.target_class)\n", + " selected_classes = np.random.choice(selected_classes, args.c_sub-1, replace=False)\n", + " selected_classes = np.append(selected_classes, args.target_class)\n", + "\n", + "# keep the same transforms for train and test dataset for better visualization\n", + "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n", + "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n", + " \n", + "args.visual_dataset = 'bd_train'\n", + "# Create dataset. Only support BD_TEST and BD_TRAIN\n", + "if args.visual_dataset == 'bd_train': \n", + " bd_train_with_trans = result_attack[\"bd_train\"]\n", + " visual_dataset = generate_bd_dataset(bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub, bd_only = True)\n", + "elif args.visual_dataset == 'bd_test':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_bd_dataset(bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub, bd_only = True)\n", + "else:\n", + " assert False, \"Illegal vis_class\"\n", + "\n", + "print(f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)} \\n \\t Selected classes: {selected_classes}')\n", + "\n", + "# Create data loader\n", + "data_loader = torch.utils.data.DataLoader(\n", + " visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n", + ")\n", + "\n", + "# Create denormalization function\n", + "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n", + " if isinstance(trans_t, transforms.Normalize):\n", + " denormalizer = get_dataset_denormalization(trans_t)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3f652e5", + "metadata": {}, + "source": [ + "### Step 3: Load Model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ff67e7b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load model preactresnet18 from badnet_demo\n" + ] + } + ], + "source": [ + "# Load model\n", + "model_visual = generate_cls_model(args.model, args.num_classes)\n", + "model_visual.load_state_dict(result_attack[\"model\"])\n", + "model_visual.to(args.device)\n", + "# !!! Important to set eval mode !!!\n", + "model_visual.eval()\n", + "print(f\"Load model {args.model} from {args.result_file_attack}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cc952077", + "metadata": {}, + "source": [ + "### Step 4: Collect Clean Features and BD Features" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "94612903", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting BD features from module Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting BD features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting BD features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting BD features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting BD features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting BD features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + "Collecting BD features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting BD features from module Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + "Collecting Clean features from module Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + "Collecting BD features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting BD features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting BD features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + "Collecting BD features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting BD features from module Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + "Collecting Clean features from module Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + "Collecting BD features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting BD features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting BD features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + "Collecting BD features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting BD features from module Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + "Collecting Clean features from module Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + "Collecting BD features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting BD features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting Clean features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + "Collecting BD features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting Clean features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + "Collecting BD features from module Linear(in_features=512, out_features=10, bias=True)\n", + "Collecting Clean features from module Linear(in_features=512, out_features=10, bias=True)\n" + ] + } + ], + "source": [ + "##### Plot for Attack #####\n", + "\n", + "module_dict = dict(model_visual.named_modules())\n", + "module_names = module_dict.keys()\n", + "\n", + "# Plot Conv2d or Linear\n", + "module_visual = [i for i in module_dict.keys() if isinstance(\n", + " module_dict[i], torch.nn.Conv2d) or isinstance(module_dict[i], torch.nn.Linear) or isinstance(module_dict[i], torch.nn.BatchNorm2d)]\n", + "\n", + "df = None\n", + "\n", + "max_num_neuron = 0\n", + "for module_name in module_visual:\n", + " target_layer = module_dict[module_name]\n", + "\n", + " print(f'Collecting BD features from module {target_layer}')\n", + " features_bd, labels_bd, *other_info = get_features(\n", + " args, model_visual, target_layer, data_loader, reduction='none', activation= None)\n", + "\n", + " print(f'Collecting Clean features from module {target_layer}')\n", + " with temporary_all_clean(visual_dataset):\n", + " features_bd_clean, labels_clean, *other_info = get_features(\n", + " args, model_visual, target_layer, data_loader, reduction='none', activation= None)\n", + " \n", + " total_neuron = features_bd.shape[1]\n", + " max_num_neuron = np.max([max_num_neuron, total_neuron])\n", + " feature_diff = features_bd - features_bd_clean\n", + " np.abs(feature_diff, out = feature_diff) # inplace abs for faster computation\n", + " feature_abs_diff = feature_diff\n", + " # average over batch\n", + " feature_abs_diff_mean = np.mean(feature_abs_diff, axis=0) \n", + " # average for each channel\n", + " if feature_abs_diff_mean.ndim >1: \n", + " feature_abs_diff_mean = np.sum(feature_abs_diff_mean.reshape(total_neuron, -1), axis=1)\n", + " \n", + "\n", + " for neuron_i in range(total_neuron):\n", + " base_row = {}\n", + " base_row['layer'] = module_name\n", + " base_row['Neuron'] = neuron_i\n", + " base_row['TAC'] = feature_abs_diff_mean[neuron_i]\n", + " if df is None:\n", + " df = pd.DataFrame.from_dict([base_row])\n", + " else:\n", + " df.loc[df.shape[0]] = base_row\n" + ] + }, + { + "cell_type": "markdown", + "id": "d82a565c", + "metadata": {}, + "source": [ + "### Step 4: Show the TAC" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f65bfe12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ploting conv1\n", + "ploting layer1.0.bn1\n", + "ploting layer1.0.conv1\n", + "ploting layer1.0.bn2\n", + "ploting layer1.0.conv2\n", + "ploting layer1.1.bn1\n", + "ploting layer1.1.conv1\n", + "ploting layer1.1.bn2\n", + "ploting layer1.1.conv2\n", + "ploting layer2.0.bn1\n", + "ploting layer2.0.conv1\n", + "ploting layer2.0.bn2\n", + "ploting layer2.0.conv2\n", + "ploting layer2.0.shortcut.0\n", + "ploting layer2.1.bn1\n", + "ploting layer2.1.conv1\n", + "ploting layer2.1.bn2\n", + "ploting layer2.1.conv2\n", + "ploting layer3.0.bn1\n", + "ploting layer3.0.conv1\n", + "ploting layer3.0.bn2\n", + "ploting layer3.0.conv2\n", + "ploting layer3.0.shortcut.0\n", + "ploting layer3.1.bn1\n", + "ploting layer3.1.conv1\n", + "ploting layer3.1.bn2\n", + "ploting layer3.1.conv2\n", + "ploting layer4.0.bn1\n", + "ploting layer4.0.conv1\n", + "ploting layer4.0.bn2\n", + "ploting layer4.0.conv2\n", + "ploting layer4.0.shortcut.0\n", + "ploting layer4.1.bn1\n", + "ploting layer4.1.conv1\n", + "ploting layer4.1.bn2\n", + "ploting layer4.1.conv2\n", + "ploting linear\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "start_x0 = 0\n", + "height = 1\n", + "width = 1\n", + "vmin = 0\n", + "if args.normalize_by_layer:\n", + " vmax = 1\n", + "else:\n", + " vmax = df.TAC.max()\n", + "max_num_neuron = df.Neuron.max()\n", + "\n", + "norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax, clip=True)\n", + "mapper = matplotlib.cm.ScalarMappable(norm=norm, cmap=matplotlib.cm.Blues)\n", + "\n", + "fig, ax = plt.subplots(\n", + " figsize=(int(len(module_visual)), int(max_num_neuron/10)))\n", + "\n", + "ax.plot([0, 0], [0, 0])\n", + "\n", + "for module_name in module_visual:\n", + " print(f'ploting {module_name}')\n", + " y_0 = 0\n", + " layer_info = df[df.layer == module_name]\n", + " layer_tac_max = layer_info['TAC'].max()\n", + " total_neuron = layer_info.shape[0]\n", + " for neuron_i in range(total_neuron):\n", + " x_0 = start_x0\n", + " base_row = layer_info.iloc[neuron_i]\n", + " if args.normalize_by_layer:\n", + " ax.add_patch(Rectangle((x_0, y_0), width, height,\n", + " facecolor=mapper.to_rgba(base_row['TAC']/layer_tac_max),\n", + " fill=True,\n", + " lw=5,\n", + " alpha=0.8))\n", + "\n", + " else:\n", + " ax.add_patch(Rectangle((x_0, y_0), width, height,\n", + " facecolor=mapper.to_rgba(base_row['TAC']),\n", + " fill=True,\n", + " lw=5,\n", + " alpha=0.8))\n", + "\n", + " y_0 += 1.5*height\n", + " start_x0 += 1.5*width\n", + "x_loc = [0.5*width+1.5*width*i for i in range(len(module_visual))]\n", + "y_loc = [0.5*height+1.5*height*i for i in range(max_num_neuron)]\n", + "\n", + "ax.set_xlim(xmin=-0.5*width, xmax=1.5*width*(len(module_visual)+1))\n", + "ax.set_ylim(ymin=-0.5*height, ymax=1.5*height*(max_num_neuron+1))\n", + "ax.set_xticks(x_loc, module_visual, rotation=270)\n", + "ax.set_yticks(y_loc[::10], np.arange(max_num_neuron)[::10])\n", + "ax.set_title(f'TAC of {len(visual_dataset)} Images')\n", + "ax.set_ylabel('Neuron')\n", + "ax.set_xlabel('Layer')\n", + "\n", + "cb_ax = fig.add_axes([0.15, 0.9, 0.7, 0.01])\n", + "\n", + "fig.colorbar(mapper,\n", + " cax=cb_ax, orientation=\"horizontal\", label='TAC')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('py38')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13 (default, Oct 21 2022, 23:50:54) \n[GCC 11.2.0]" + }, + "vscode": { + "interpreter": { + "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/Demos/Demo_TSNE.ipynb b/analysis/Demos/Demo_TSNE.ipynb new file mode 100755 index 0000000..335ea1d --- /dev/null +++ b/analysis/Demos/Demo_TSNE.ipynb @@ -0,0 +1,321 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_TSNE\n", + "This is a demo for visualizing the T-SNE of a Neuron Network\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": {}, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py --save_folder_name badnet_demo" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "87bd9f5a", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71b7087b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2fb719c7", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "### Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"badnet_demo\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 2: Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8b67ac9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "max_num_samples is given, use sample number limit now.\n", + "subset bd dataset with length: 5000\n", + "Create visualization dataset with \n", + " \t Dataset: bd_train \n", + " \t Number of samples: 5000 \n", + " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "selected_classes = np.arange(args.num_classes)\n", + "\n", + "# Select classes to visualize\n", + "if args.num_classes>args.c_sub:\n", + " selected_classes = np.delete(selected_classes, args.target_class)\n", + " selected_classes = np.random.choice(selected_classes, args.c_sub-1, replace=False)\n", + " selected_classes = np.append(selected_classes, args.target_class)\n", + "\n", + "# keep the same transforms for train and test dataset for better visualization\n", + "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n", + "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n", + " \n", + "args.visual_dataset = \"bd_train\"\n", + "# Create dataset\n", + "if args.visual_dataset == 'mixed':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_mix_dataset(bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_train':\n", + " clean_train_with_trans = result_attack[\"clean_train\"]\n", + " visual_dataset = generate_clean_dataset(clean_train_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_test':\n", + " clean_test_with_trans = result_attack[\"clean_test\"]\n", + " visual_dataset = generate_clean_dataset(clean_test_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'bd_train': \n", + " bd_train_with_trans = result_attack[\"bd_train\"]\n", + " visual_dataset = generate_bd_dataset(bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n", + "else:\n", + " assert False, \"Illegal vis_class\"\n", + "\n", + "print(f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)} \\n \\t Selected classes: {selected_classes}')\n", + "\n", + "# Create data loader\n", + "data_loader = torch.utils.data.DataLoader(\n", + " visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n", + ")\n", + "\n", + "# Create denormalization function\n", + "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n", + " if isinstance(trans_t, transforms.Normalize):\n", + " denormalizer = get_dataset_denormalization(trans_t)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3f652e5", + "metadata": {}, + "source": [ + "### Step 3: Load Model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ff67e7b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load model preactresnet18 from badnet_demo\n" + ] + } + ], + "source": [ + "# Load model\n", + "model_visual = generate_cls_model(args.model, args.num_classes)\n", + "model_visual.load_state_dict(result_attack[\"model\"])\n", + "model_visual.to(args.device)\n", + "# !!! Important to set eval mode !!!\n", + "model_visual.eval()\n", + "print(f\"Load model {args.model} from {args.result_file_attack}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cc952077", + "metadata": {}, + "source": [ + "### Step 4: Plot T-SNE" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "94612903", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting T-SNE\n", + "Choose layer layer4.1.conv2 from model preactresnet18\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "############## T-SNE ##################\n", + "print(\"Plotting T-SNE\")\n", + "\n", + "# Choose layer for feature extraction\n", + "module_dict = dict(model_visual.named_modules())\n", + "target_layer = module_dict[args.target_layer_name]\n", + "print(f'Choose layer {args.target_layer_name} from model {args.model}')\n", + "\n", + "# Get features\n", + "features, labels, poi_indicator = get_features(args, model_visual, target_layer, data_loader)\n", + "\n", + "# General plotting parameters\n", + "custom_palette = sns.color_palette(\"hls\", np.unique(labels).shape[0])\n", + "classes = args.class_names\n", + "\n", + "# Setting parameters for Poisoned Samples\n", + "# use poi_indicator==1 to avoid some datatype issue for indexing\n", + "if np.sum(poi_indicator)>0:\n", + " # Label: args.num_classes\n", + " labels[poi_indicator==1]=args.num_classes\n", + " # Class Name: poisoned\n", + " classes += [\"poisoned\"]\n", + " # Color: Black\n", + " custom_palette += [(0.0, 0.0, 0.0)] \n", + "\n", + "sort_idx = np.argsort(labels)\n", + "features = features[sort_idx]\n", + "labels = labels[sort_idx]\n", + "label_class = [classes[i].capitalize() for i in labels]\n", + "\n", + "# Plot T-SNE\n", + "\n", + "fig = tsne_fig(\n", + " features,\n", + " label_class,\n", + " title=\"t-SNE Embedding\",\n", + " xlabel=\"Dim 1\",\n", + " ylabel=\"Dim 2\",\n", + " custom_palette=custom_palette,\n", + " size=(10, 10),\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('py38')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13 (default, Oct 21 2022, 23:50:54) \n[GCC 11.2.0]" + }, + "vscode": { + "interpreter": { + "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/Demos/Demo_UMAP.ipynb b/analysis/Demos/Demo_UMAP.ipynb new file mode 100755 index 0000000..a7e9446 --- /dev/null +++ b/analysis/Demos/Demo_UMAP.ipynb @@ -0,0 +1,332 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd66700", + "metadata": {}, + "source": [ + "## Demo_UMAP\n", + "This is a demo for visualizing the UMAP of a Neuron Network\n", + "\n", + "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b950f4fc", + "metadata": {}, + "outputs": [], + "source": [ + "! python ../../attack/badnet.py --save_folder_name badnet_demo" + ] + }, + { + "cell_type": "markdown", + "id": "8f81f973", + "metadata": {}, + "source": [ + "or run the following command in your terminal\n", + "\n", + "```python attack/badnet.py --save_folder_name badnet_demo```" + ] + }, + { + "cell_type": "markdown", + "id": "8ac8e57d", + "metadata": {}, + "source": [ + "#### Difference between UMAP and T-SNE\n", + "* Both of them can be used for dimension reduction, i.e., reducing the dimension of given features. But, UMAP is much faster than T-SNE which allows us to use more samples for a more comprehensive view." + ] + }, + { + "cell_type": "markdown", + "id": "87bd9f5a", + "metadata": {}, + "source": [ + "### Step 1: Import modules and set arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71b7087b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "import yaml\n", + "import torch\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "\n", + "sys.path.append(\"../\")\n", + "sys.path.append(\"../../\")\n", + "sys.path.append(os.getcwd())\n", + "from visual_utils import *\n", + "from utils.aggregate_block.dataset_and_transform_generate import (\n", + " get_transform,\n", + " get_dataset_denormalization,\n", + ")\n", + "from utils.aggregate_block.fix_random import fix_random\n", + "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n", + "from utils.save_load_attack import load_attack_result\n", + "from utils.defense_utils.dbd.model.utils import (\n", + " get_network_dbd,\n", + " load_state,\n", + " get_criterion,\n", + " get_optimizer,\n", + " get_scheduler,\n", + ")\n", + "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2fb719c7", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "### Basic setting: args\n", + "args = get_args(True)\n", + "\n", + "########## For Demo Only ##########\n", + "args.yaml_path = \"../../\"+args.yaml_path\n", + "args.result_file_attack = \"badnet_demo\"\n", + "######## End For Demo Only ##########\n", + "\n", + "with open(args.yaml_path, \"r\") as stream:\n", + " config = yaml.safe_load(stream)\n", + "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n", + "args.__dict__ = config\n", + "args = preprocess_args(args)\n", + "fix_random(int(args.random_seed))\n", + "\n", + "save_path_attack = \"../..//record/\" + args.result_file_attack\n" + ] + }, + { + "cell_type": "markdown", + "id": "f959b510", + "metadata": {}, + "source": [ + "### Step 2: Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8b67ac9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "loading...\n", + "max_num_samples is given, use sample number limit now.\n", + "subset bd dataset with length: 50000\n", + "Create visualization dataset with \n", + " \t Dataset: bd_train \n", + " \t Number of samples: 50000 \n", + " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n" + ] + } + ], + "source": [ + "# Load result\n", + "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n", + "selected_classes = np.arange(args.num_classes)\n", + "\n", + "# Select classes to visualize\n", + "if args.num_classes>args.c_sub:\n", + " selected_classes = np.delete(selected_classes, args.target_class)\n", + " selected_classes = np.random.choice(selected_classes, args.c_sub-1, replace=False)\n", + " selected_classes = np.append(selected_classes, args.target_class)\n", + "\n", + "# keep the same transforms for train and test dataset for better visualization\n", + "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n", + "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n", + "\n", + "args.visual_dataset = \"bd_train\"\n", + "args.n_sub = 50000\n", + "# Create dataset\n", + "if args.visual_dataset == 'mixed':\n", + " bd_test_with_trans = result_attack[\"bd_test\"]\n", + " visual_dataset = generate_mix_dataset(bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_train':\n", + " clean_train_with_trans = result_attack[\"clean_train\"]\n", + " visual_dataset = generate_clean_dataset(clean_train_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'clean_test':\n", + " clean_test_with_trans = result_attack[\"clean_test\"]\n", + " visual_dataset = generate_clean_dataset(clean_test_with_trans, selected_classes, max_num_samples=args.n_sub)\n", + "elif args.visual_dataset == 'bd_train': \n", + " bd_train_with_trans = result_attack[\"bd_train\"]\n", + " visual_dataset = generate_bd_dataset(bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n", + "else:\n", + " assert False, \"Illegal vis_class\"\n", + "\n", + "print(f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)} \\n \\t Selected classes: {selected_classes}')\n", + "\n", + "# Create data loader\n", + "data_loader = torch.utils.data.DataLoader(\n", + " visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n", + ")\n", + "\n", + "# Create denormalization function\n", + "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n", + " if isinstance(trans_t, transforms.Normalize):\n", + " denormalizer = get_dataset_denormalization(trans_t)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3f652e5", + "metadata": {}, + "source": [ + "### Step 3: Load Model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ff67e7b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load model preactresnet18 from badnet_demo\n" + ] + } + ], + "source": [ + "# Load model\n", + "model_visual = generate_cls_model(args.model, args.num_classes)\n", + "model_visual.load_state_dict(result_attack[\"model\"])\n", + "model_visual.to(args.device)\n", + "# !!! Important to set eval mode !!!\n", + "model_visual.eval()\n", + "print(f\"Load model {args.model} from {args.result_file_attack}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cc952077", + "metadata": {}, + "source": [ + "### Step 4: Plot UMAP" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "94612903", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting UMAP\n", + "Choose layer layer4.1.conv2 from model preactresnet18\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "############## UMAP ##################\n", + "print(\"Plotting UMAP\")\n", + "\n", + "# Choose layer for feature extraction\n", + "module_dict = dict(model_visual.named_modules())\n", + "target_layer = module_dict[args.target_layer_name]\n", + "print(f'Choose layer {args.target_layer_name} from model {args.model}')\n", + "\n", + "# Get features\n", + "features, labels, poi_indicator = get_features(args, model_visual, target_layer, data_loader)\n", + "\n", + "# General plotting parameters\n", + "custom_palette = sns.color_palette(\"hls\", np.unique(labels).shape[0])\n", + "classes = args.class_names\n", + "\n", + "# Setting parameters for Poisoned Samples\n", + "# use poi_indicator==1 to avoid some datatype issue for indexing\n", + "if np.sum(poi_indicator)>0:\n", + " # Label: args.num_classes\n", + " labels[poi_indicator==1]=args.num_classes\n", + " # Class Name: poisoned\n", + " classes += [\"poisoned\"]\n", + " # Color: Black\n", + " custom_palette += [(0.0, 0.0, 0.0)] \n", + "\n", + "sort_idx = np.argsort(labels)\n", + "features = features[sort_idx]\n", + "labels = labels[sort_idx]\n", + "label_class = [classes[i].capitalize() for i in labels]\n", + "\n", + "# Plot T-SNE\n", + "fig = umap_fig(\n", + " features,\n", + " label_class,\n", + " title=\"UMAP Embedding\",\n", + " xlabel=\"Dim 1\",\n", + " ylabel=\"Dim 2\",\n", + " custom_palette=custom_palette,\n", + " size=(10, 10),\n", + " mark_size = 0.6,\n", + " alpha = 1\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('py38')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13 (default, Oct 21 2022, 23:50:54) \n[GCC 11.2.0]" + }, + "vscode": { + "interpreter": { + "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/demo.sh b/analysis/demo.sh new file mode 100755 index 0000000..36b1a3b --- /dev/null +++ b/analysis/demo.sh @@ -0,0 +1,47 @@ +# The original image who activates the given layer most +python analysis/visual_act.py --result_file_attack badnet_demo --result_file_defense badnet_demo/defense/ac --target_layer_name layer4.1.conv2 --visual_dataset bd_test --target_class 0 + +# The activation image distribution, i.e., the distribution of top-k images who activate the neurons most +python analysis/visual_actdist.py --result_file_attack badnet_demo --result_file_defense badnet_demo/defense/ac --visual_dataset mixed --target_class 0 + +# The confusion matrix +python analysis/visual_cm.py --result_file_attack badnet_demo --result_file_defense badnet_demo/defense/ac --visual_dataset bd_train --target_class 0 + +# The feature map of a (random) given image after a given layer +python analysis/visual_fm.py --result_file_attack badnet_demo --result_file_defense badnet_demo/defense/ac --visual_dataset bd_test + +# The Frequency saliency map +python analysis/visual_fre.py --result_file_attack badnet_demo --result_file_defense badnet_demo/defense/ac --target_layer_name layer4.1.conv2 --visual_dataset mixed --target_class 0 + +# The synthetic image who activates the given layer (found by gradient descend) +python analysis/visual_fv.py --result_file_attack badnet_demo --result_file_defense badnet_demo/defense/ac --target_layer_name layer4.1.conv2 + +# The Grad-CAM of 4 random selected images +python analysis/visual_gradcam.py --result_file_attack badnet_demo --result_file_defense badnet_demo/defense/ac --target_layer_name layer4.1.conv2 --visual_dataset bd_test --target_class 0 + +# The Landscape of a neuron network with MPI for parallel computing, e.g., 8 processes +mpirun -n 8 python analysis/visual_landscape.py --x=-1:1:51 --y=-1:1:51 --result_file_attack badnet_demo --result_file_defense badnet_demo/defense/ac --visual_dataset bd_train + +# The Lipschitz constant of a neuron network +python analysis/visual_lips.py --result_file_attack badnet_demo --result_file_defense badnet_demo/defense/ac --normalize_by_layer + +# The Neuron Activation of a given layer +python analysis/visual_na.py --result_file_attack badnet_demo --result_file_defense badnet_demo/defense/ac --target_layer_name layer4.1.conv2 --visual_dataset bd_test --target_class 0 + +# The Shapely value of 4 random selected images +python analysis/visual_shap.py --result_file_attack badnet_demo --result_file_defense badnet_demo/defense/ac --target_layer_name layer4.1.conv2 --visual_dataset bd_test --target_class 0 + +# The Total Activation Change of a neural network +python analysis/visual_tac.py --result_file_attack badnet_demo --result_file_defense badnet_demo/defense/ac --target_layer_name layer4.1.conv2 --visual_dataset bd_test --target_class 0 --normalize_by_layer + +# The T-SNE of features of a given layer +python analysis/visual_tsne.py --result_file_attack badnet_demo --result_file_defense badnet_demo/defense/ac --target_layer_name layer4.1.conv2 --visual_dataset mixed --target_class 0 + +# The UMAP of features of a given layer +python analysis/visual_umap.py --result_file_attack badnet_demo --result_file_defense badnet_demo/defense/ac --target_layer_name layer4.1.conv2 --visual_dataset bd_train --target_class 0 --n_sub 50000 + +# The network structure. result_file_attack is only used for saving the result +python analysis/visual_network.py --result_file_attack badnet_demo --model preactresnet18 + +# The Eigenvalue Dense Plot of the Hessian Matrix +python analysis/visual_hessian.py --result_file_attack badnet_demo --result_file_defense badnet_demo/defense/ac --visual_dataset bd_train --batch_size 128 diff --git a/analysis/demo_images/demo_act.png 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The demo scripts for utilizing such tools are given in **demo.sh**. The jupyter notebook for utilizing such tools are in **Demos** folder. + +The implemented tools and corresponding scripts are +* visual_tsne.py + * **T-SNE**, the T-SNE of features. Typical output is + ![avatar](./demo_images/demo_tsne.jpg) + +* visual_umap.py + * **UMAP**, the UMAP of features. Both UMAP and T-SNE can be used for dimension reduction, i.e., reducing the dimension of given features. But, UMAP is much faster than T-SNE which allows us to use more samples for a more comprehensive view. Typical output is + ![avatar](./demo_images/demo_umap.png) + +* visual_na.py + * **Neuron Activation**, the activation value of a given layer of Neurons. Typical output is + ![avatar](./demo_images/demo_na.png) + +* visual_shap.py + * **Shapely Value**, the Shapely Value for given inputs and a given layer. Typical output is + ![avatar](./demo_images/demo_shap.png) + +* visual_gradcam.py + * **Grad-CAM**, the Grad-CAM for given inputs and a given layer. Typical output is + ![avatar](./demo_images/demo_gradcam.png) + +* visualize_fre.py + * **Frequency Map**, the Frequency Saliency Map for given inputs and a given layer. Typical output is + ![avatar](./demo_images/demo_fre.png) + +* visual_act.py + * **Activated Image**, the top images who activate the given layer of Neurons most. Typical output is + ![avatar](./demo_images/demo_act.png) + The poiso samples are marked with **red** title. + +* visual_cm.py + * **Confusion Matrix**. Typical output is + ![avatar](./demo_images/demo_cm.png) + +* visual_fv.py + * **Feature Visualization**, the synthetic images which activate the given layer of Neurons most. The image is generated by Projected Gradient Descend. Typical output is + ![avatar](./demo_images/demo_fv.png) + +* visual_fm.py + * **Feature Map**, the output of a given layer of CNNs for a given image. Typical output is + ![avatar](./demo_images/demo_out.png) + + +* visual_actdist.py + * **Activation Distribution**, the class distribution of Top-k images which activate the Neuron most. Typical output is + ![avatar](./demo_images/demo_path.png) + +* visual_tac.py + * **Trigger Activation Change**, the average (absolute) activation change between images with and without triggers. Typical output is + ![avatar](./demo_images/demo_tac.png) + +* visual_lips.py + * **Lipschitz Constant**, the lipschitz constant of each neuron. Typical output is + ![avatar](./demo_images/demo_lips.png) + +* visual_landscape.py + * **Loss Landscape**, the loss landscape of given results with two random directions. Typical output is + ![avatar](./demo_images/demo_landscape.png) + More details can be founded in the subfolder loss_landscape. + +* visual_network.py + * **Network Structure**, the Network Structure of given model. We provide two ways to visualize the network structure. Typical outputs are + ![avatar](./demo_images/demo_network.png) + ![avatar](./demo_images/demo_network_2.png) + +* visual_hessian.py + * **Eigenvalues of Hessian**, the dense plot of hessian matrix for a batch of data. Typical output is + ![avatar](./demo_images/demo_hessian.png) + +* visual_metric.py + * **Metrics**, evaluating the given results using some metrics. Both csv file and visualization are given. Typical output is + ![avatar](./demo_images/demo_metric.png) + +* visual_quality.py + * **Image Quality**, evaluating the given results using some image quality metrics. The csv file is given. + + +* Dataset used for visualization. + * Mixed Test is generated by sampling data from both Clean Test and BD Test by a poison ratio *pratio* given in args. + * Subset is default to sample 5000 samples with *n_sub* in args. + * By default, all methods use **BD train** dataset for visualization (if needed). + * For clean model trained using *prototype.py*, the results can be loaded using args 'prototype'. However, only clean dataset can be loaded and some methods cannot be adopted to analyze such results. + * Supported dataset types for each method is given in the following table + + | Script | Method | Clean Train | BD Train | Clean Test | BD Test | Mixed Test | Subset | Remark | + |----------------------|-------------------------|:-----------:|:--------:|:----------:|:--------:|:----------:|:--------:|:------:| + | visual_tsne.py | T-SNE | $\surd$ | $\surd$ | $\surd$ | $\times$ | $\surd$ | $\surd$ | | + | visual_umap.py | UMAP | $\surd$ | $\surd$ | $\surd$ | $\times$ | $\surd$ | $\surd$ | | + | visual_na.py | Neuron Activation | $\times$ | $\surd$ | $\times$ | $\surd$ | $\times$ | $\surd$ | BD only| + | visual_shap.py | Shapely Value | $\surd$ | $\surd$ | $\surd$ | $\surd$ | $\surd$ | $\surd$ | | + | visual_gradcam.py | Grad-CAM | $\surd$ | $\surd$ | $\surd$ | $\surd$ | $\surd$ | $\surd$ | | + | visual_fre.py | Frequency Saliency Map | $\surd$ | $\surd$ | $\surd$ | $\surd$ | $\surd$ | $\surd$ | | + | visual_act.py | Activated Image | $\surd$ | $\surd$ | $\surd$ | $\surd$ | $\surd$ | $\surd$ | | + | visual_cm.py | Confusion Matrix | $\surd$ | $\surd$ | $\surd$ | $\surd$ | $\times$ | $\times$| | + | visual_fv.py | Feature Visualization | $\times$ | $\times$ | $\times$ | $\times$ | $\times$ | $\times$| | + | visual_fm.py | Feature Map | $\surd$ | $\surd$ | $\surd$ | $\surd$ | $\times$ | $\times$| | + | visual_actdist.py | Activation Distribution | $\surd$ | $\surd$ | $\surd$ | $\surd$ | $\surd$ | $\surd$ | | + | visual_tac.py | Trigger Activation Change | $\times$ | $\surd$ | $\times$ | $\surd$ | $\times$ | $\surd$ | BD only| + | visual_lips.py | Lipschitz Constant | $\times$ | $\times$ | $\times$ | $\times$ | $\times$ | $\times$ | | + | visual_landscape.py | Loss Landscape | $\surd$ | $\surd$ | $\times$ | $\times$ | $\times$ | $\times$ | | + | visual_network.py | Network Structure | $\times$ | $\times$ | $\times$ | $\times$ | $\times$ | $\times$ | | + | visual_hessian.py | Eigenvalues of Hessian | $\surd$ | $\surd$ | $\surd$ | $\surd$ | $\times$ | $\times$ | | + | visual_metric.py | Metrics | $\times$ | $\times$ | $\surd$ | $\surd$ | $\times$ | $\times$ | | + | visual_quality.py | Image Quality | $\times$ | $\surd$ | $\times$ | $\surd$ | $\surd$ | $\surd$ | | + +* Additional note for Loss Landscape + * **Message Passing Interface (MPI)** is needed to accelerate the computation of loss landscape. Thus, you will need to install mpi4py. mpi4py can be installed either using pip or conda, but with pip you will need to install MPI yourself first (e.g. OpenMPI or MPICH), while conda will install its own MPI libraries. For example, you can run the following commands in your terminal to install mpi4py: + ``` + sudo apt install libopenmpi-dev + pip install mpi4py + ``` + or + ``` + conda install -c conda-forge mpi4py + ``` + * You need also clone the repo https://github.com/tomgoldstein/loss-landscape to the ***visualization*** folder. + * To run the landscape visualization using MPI (parallel), you can run the command + ``` + mpirun -n num_gpu python visual_landscape.py + ``` + * For more information, please refer to https://github.com/tomgoldstein/loss-landscape/blob/master/README.md + +* Additional note for network structure + * GraphViz and its Python wrapper are needed by hiddenlayer and pytorchviz to generate network graphs. Similar as MPI, you can install them using pip or conda. For example, you can run the following commands in your terminal to install them + ``` + sudo apt install graphviz + pip3 install graphviz + pip install hiddenlayer + pip install -U git+https://github.com/szagoruyko/pytorchviz.git@master + ``` + or + ``` + conda install graphviz python-graphviz + pip install hiddenlayer + pip install -U git+https://github.com/szagoruyko/pytorchviz.git@master + ``` + * For more information, please refer to https://github.com/waleedka/hiddenlayer/blob/master/README.md or https://github.com/szagoruyko/pytorchviz/blob/master/README.md + + + +## Acknowledge +Our implementation is partially built based on +- https://github.com/utkuozbulak/pytorch-cnn-visualizations +- https://github.com/slundberg/shap +- https://github.com/jacobgil/pytorch-grad-cam +- https://github.com/tomgoldstein/loss-landscape +- https://github.com/salesforce/OmniXAI +- https://github.com/waleedka/hiddenlayer +- https://github.com/szagoruyko/pytorchviz \ No newline at end of file diff --git a/analysis/visual_act.py b/analysis/visual_act.py new file mode 100755 index 0000000..61a179b --- /dev/null +++ b/analysis/visual_act.py @@ -0,0 +1,172 @@ +import sys +import os +import yaml +import torch +import numpy as np +import torchvision.transforms as transforms + +sys.path.append(os.getcwd()) + +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, +) +from visual_utils import * + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + +selected_classes = np.arange(args.num_classes) + +# Select classes to visualize +if args.num_classes > args.c_sub: + selected_classes = np.delete(selected_classes, args.target_class) + selected_classes = np.random.choice( + selected_classes, args.c_sub-1, replace=False) + selected_classes = np.append(selected_classes, args.target_class) + +# keep the same transforms for train and test dataset for better visualization +result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform +result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform + +# Create dataset +if args.visual_dataset == 'mixed': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_mix_dataset( + bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'clean_train': + clean_train_with_trans = result_attack["clean_train"] + visual_dataset = generate_clean_dataset( + clean_train_with_trans, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'clean_test': + clean_test_with_trans = result_attack["clean_test"] + visual_dataset = generate_clean_dataset( + clean_test_with_trans, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'bd_train': + bd_train_with_trans = result_attack["bd_train"] + visual_dataset = generate_bd_dataset( + bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'bd_test': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_bd_dataset( + bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub) +else: + assert False, "Illegal vis_class" + +print( + f'Create visualization dataset with \n \t Dataset: {args.visual_dataset} \n \t Number of samples: {len(visual_dataset)} \n \t Selected classes: {selected_classes}') + +# Create data loader +data_loader = torch.utils.data.DataLoader( + visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False +) + +# Create denormalization function +for trans_t in data_loader.dataset.wrap_img_transform.transforms: + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + +if args.result_file_defense != "None": + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_visual.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_visual.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_visual = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_visual.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") +else: + model_visual.load_state_dict(result_attack["model"]) + print(f"Load model {args.model} from {args.result_file_attack}") + +model_visual.to(args.device) + +# !!! Important to set eval mode !!! +model_visual.eval() + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + +############ Activation Image ################ +print('Plotting Activation Image') + +# Choose layer for feature extraction +module_dict = dict(model_visual.named_modules()) +target_layer = module_dict[args.target_layer_name] +print(f'Choose layer {args.target_layer_name} from model {args.model}') + +# Get features +features, labels, poi_indicator = get_features(args, model_visual, target_layer, data_loader, reduction='sum') +total_neuron = features.shape[1] + + +if args.neuron_order == 'ordered': + target_sort = np.arange(total_neuron) +elif args.neuron_order == 'random': + target_sort = np.random.shuffle(np.arange(total_neuron)) +else: + print(f'Illegal Neuron order: {args.neuron_order}. Use "ordered" instead') + target_sort = np.arange(total_neuron) + +# get top activation images for each Neuron +top_indx=np.argsort(-features,axis=0) + +# Choose some nurons to visualize +num_neuron = np.min([args.num_neuron,total_neuron]) +num_image = args.num_image +fig, axes = plt.subplots(nrows=num_neuron, ncols=num_image, figsize=(4*num_image, 5*num_neuron)) +for neu_i in range(num_neuron): + im = target_sort[neu_i] + for topi in range(num_image): + top_i = top_indx[topi,im] + ax = axes[neu_i, topi] + cnn_image = np.swapaxes(np.swapaxes(denormalizer(visual_dataset[top_i][0]).cpu().numpy(), 0, 1), 1, 2) + cnn_image = cnn_image.clip(0,1) + ax.imshow(cnn_image) + if poi_indicator[top_i]==1: + ax.set_title(f'Neuron {im}, Top-{topi}, Value {features[top_i,im]:.2f}',color = 'red') + else: + ax.set_title(f'Neuron {im}, Top-{topi}, Value {features[top_i,im]:.2f}',color = 'black') + +plt.tight_layout() +plt.savefig(visual_save_path + f"/act_{args.visual_dataset}.png") + +print(f'Save to {visual_save_path + f"/act_{args.visual_dataset}"}.png') diff --git a/analysis/visual_actdist.py b/analysis/visual_actdist.py new file mode 100755 index 0000000..02a2eda --- /dev/null +++ b/analysis/visual_actdist.py @@ -0,0 +1,247 @@ +import sys +import os +sys.path.append(os.getcwd()) + +import yaml +import torch +import numpy as np +import torchvision.transforms as transforms +from matplotlib.patches import Rectangle, Patch +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, +) +from visual_utils import * + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + +selected_classes = np.arange(args.num_classes) + +# Select classes to visualize +if args.num_classes > args.c_sub: + selected_classes = np.delete(selected_classes, args.target_class) + selected_classes = np.random.choice( + selected_classes, args.c_sub-1, replace=False) + selected_classes = np.append(selected_classes, args.target_class) + +# keep the same transforms for train and test dataset for better visualization +result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform +result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform + +# Create dataset +if args.visual_dataset == 'mixed': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_mix_dataset( + bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'clean_train': + clean_train_with_trans = result_attack["clean_train"] + visual_dataset = generate_clean_dataset( + clean_train_with_trans, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'clean_test': + clean_test_with_trans = result_attack["clean_test"] + visual_dataset = generate_clean_dataset( + clean_test_with_trans, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'bd_train': + bd_train_with_trans = result_attack["bd_train"] + visual_dataset = generate_bd_dataset( + bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'bd_test': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_bd_dataset( + bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub) +else: + assert False, "Illegal vis_class" + +print( + f'Create visualization dataset with \n \t Dataset: {args.visual_dataset} \n \t Number of samples: {len(visual_dataset)} \n \t Selected classes: {selected_classes}') + +# Create data loader +data_loader = torch.utils.data.DataLoader( + visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False +) + +# Create denormalization function +for trans_t in data_loader.dataset.wrap_img_transform.transforms: + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + +if args.result_file_defense != "None": + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_visual.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_visual.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_visual = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_visual.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") +else: + model_visual.load_state_dict(result_attack["model"]) + print(f"Load model {args.model} from {args.result_file_attack}") + +model_visual.to(args.device) + +# !!! Important to set eval mode !!! +model_visual.eval() + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + +############ Activation Image Distribution ################ +print('Plotting Activation Image Distribution') + +module_dict = dict(model_visual.named_modules()) +module_names = module_dict.keys() + +# Plot Conv2d or Linear +module_visual = [i for i in module_dict.keys() if isinstance( + module_dict[i], torch.nn.Conv2d) or isinstance(module_dict[i], torch.nn.Linear) or isinstance(module_dict[i], torch.nn.BatchNorm2d)] + +poi_indicator = np.array(get_poison_indicator_from_bd_dataset(visual_dataset)) +labels = np.array(get_true_label_from_bd_dataset(visual_dataset)) + + +df = None + +# decide the number of images to compute the distribution +num_image = int(len(visual_dataset)/len(selected_classes)) +if poi_indicator.sum() > 0: + num_image = poi_indicator.sum() + # regard the poisoned images as a class with label args.num_classes + labels[poi_indicator==1] = args.num_classes + +print(f'Visualize Top-{num_image} Samples from {len(visual_dataset)} Samples.') + +label_set = np.unique(labels) +label_set.sort() + +max_num_neuron = 0 +for module_name in module_visual: + target_layer = module_dict[module_name] + print(f'Collecting features from module {target_layer}') + + features, labels, poi_indicator = get_features( + args, model_visual, target_layer, data_loader, reduction='sum', activation= None) + + # set the poisoned images as a class with label args.num_classes for each iteration. + # this can be skipped if shuffle is set to False. + labels[poi_indicator==1]=args.num_classes + total_neuron = features.shape[1] + max_num_neuron = np.max([max_num_neuron, total_neuron]) + top_indx = np.argsort(-features, axis=0)[:num_image, :] + top_pred = np.array(labels)[top_indx] + + for neuron_i in range(total_neuron): + base_row = {} + base_row['layer'] = module_name + base_row['Neuron'] = neuron_i + for i in range(len(label_set)): + base_row[f'percent_{i}'] = np.sum( + top_pred[:, neuron_i] == label_set[i])/num_image + if df is None: + df = pd.DataFrame.from_dict([base_row]) + else: + df.loc[df.shape[0]] = base_row + +df.to_csv(visual_save_path + f'/act_dist_{args.visual_dataset}.csv') + +# define Matplotlib figure and axis +fig, ax = plt.subplots(figsize=(20, 50)) +# create simple line plot +ax.plot([0, 0], [0, 0]) + +labels = np.array(get_true_label_from_bd_dataset(visual_dataset)) +custom_palette = sns.color_palette("hls", np.unique(labels).shape[0]) + +if poi_indicator.sum() > 0: + custom_palette.append((0.0, 0.0, 0.0)) # Black for poison samples + +start_x0 = 0 +height = 1 +width = 1 +max_num_neuron = df.Neuron.max() + +for module_name in module_visual: + print(f'ploting {module_name}') + y_0 = 0 + layer_info = df[df.layer == module_name] + total_neuron = layer_info.shape[0] + for neuron_i in range(total_neuron): + x_0 = start_x0 + base_row = layer_info.iloc[neuron_i] + for i in range(len(label_set)): + ax.add_patch(Rectangle((x_0, y_0), width*base_row[f'percent_{i}'], height, + facecolor=custom_palette[i], + fill=True, + lw=5, + alpha=0.8)) + + x_0 += width*base_row[f'percent_{i}'] + y_0 += 1.5*height + start_x0 += 1.5*width +x_loc = [0.5*width+1.5*width*i for i in range(len(module_visual))] +y_loc = [0.5*height+1.5*height*i for i in range(max_num_neuron)] + +ax.set_xlim(xmin=-0.5*width, xmax=1.5*width*(len(module_visual)+1)) +ax.set_ylim(ymin=-0.5*height, ymax=1.5*height*(max_num_neuron+1)) +ax.set_xticks(x_loc, module_visual, rotation=270) +ax.set_yticks(y_loc[::10], np.arange(max_num_neuron)[::10]) +ax.set_title(f'Distribution of Top-{num_image} Images') +ax.set_ylabel('Neuron') +ax.set_xlabel('Layer') + +classes = args.class_names +if poi_indicator.sum() > 0: + classes += ["poisoned"] + +# map the label to class name in the order of colors/indexes +label_class = [classes[i].capitalize() for i in label_set] +legend_elements = [Patch(facecolor=custom_palette[i], + label=label_class[i]) for i in range(len(label_class))] + +ax.legend(handles=legend_elements, loc='upper center', bbox_to_anchor=( + 0.5, 1.02), ncol=len(label_class), fancybox=True, shadow=True) + +plt.savefig(visual_save_path + f"/act_dist_{args.visual_dataset}.png") + +print(f'Save to {visual_save_path + f"/act_dist_{args.visual_dataset}"}.png') diff --git a/analysis/visual_cm.py b/analysis/visual_cm.py new file mode 100755 index 0000000..1be2e74 --- /dev/null +++ b/analysis/visual_cm.py @@ -0,0 +1,169 @@ +import sys +import os +sys.path.append("../") +sys.path.append(os.getcwd()) + +from matplotlib.patches import Rectangle, Patch +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, +) +from visual_utils import * +import yaml +import torch +import numpy as np +import torchvision.transforms as transforms + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + +# Select all classes and all samples +selected_classes = np.arange(args.num_classes) + +# keep the same transforms for train and test dataset for better visualization +result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform +result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform + +# Create dataset +if args.visual_dataset == 'clean_train': + visual_dataset = result_attack["clean_train"] +elif args.visual_dataset == 'clean_test': + visual_dataset = result_attack["clean_test"] +elif args.visual_dataset == 'bd_train': + visual_dataset = result_attack["bd_train"] +elif args.visual_dataset == 'bd_test': + visual_dataset = result_attack["bd_test"] +else: + assert False, "Illegal vis_class" + +print(f'Create visualization dataset with \n \t Dataset: {args.visual_dataset} \n \t Number of samples: {len(visual_dataset)} \n \t Selected classes: {selected_classes}') + +# Create data loader +data_loader = torch.utils.data.DataLoader( + visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False +) + +# Create denormalization function +for trans_t in data_loader.dataset.wrap_img_transform.transforms: + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + +if args.result_file_defense != "None": + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_visual.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_visual.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_visual = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_visual.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") +else: + model_visual.load_state_dict(result_attack["model"]) + print(f"Load model {args.model} from {args.result_file_attack}") + +model_visual.to(args.device) + +# !!! Important to set eval mode !!! +model_visual.eval() + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + +############## Confusion Matrix ################## +print("Plotting Confusion Matrix") + +target_class = args.target_class +poison_class = args.num_classes +class_names = args.class_names + +# Evaluation +criterion = torch.nn.CrossEntropyLoss() +total_clean_test, total_clean_correct_test, test_loss = 0, 0, 0 +target_correct, target_total = 0, 0 + +true_labls = [] +pred_labels = [] +for i, (inputs, labels, *other_info) in enumerate(data_loader): + inputs, labels = inputs.to(args.device), labels.to(args.device) + outputs = model_visual(inputs) + loss = criterion(outputs, labels) + test_loss += loss.item() + + total_clean_correct_test += torch.sum(torch.argmax(outputs[:], dim=1) == labels[:]) + target_correct += torch.sum( + (torch.argmax(outputs[:], dim=1) == target_class) * (labels[:] == target_class) + ) + target_total += torch.sum(labels[:] == target_class) + + total_clean_test += inputs.shape[0] + avg_acc_clean = float(total_clean_correct_test.item() * 100.0 / total_clean_test) + prediction = torch.argmax(outputs[:], dim=1) + true_labls.append(labels.detach().cpu().numpy()) + pred_labels.append(prediction.detach().cpu().numpy()) + +true_labls = np.concatenate(true_labls) +pred_labels = np.concatenate(pred_labels) + +plot_confusion_matrix( + true_labls, + pred_labels, + classes=class_names, + normalize=True, + title="Confusion matrix", + save_fig_path=None, +) + +plt.tight_layout() +plt.savefig(visual_save_path + f"/cm_{args.visual_dataset}.png") + +print(f'Save to {visual_save_path + f"/cm_{args.visual_dataset}"}.png') + +print( + "Acc: {:.3f}%({}/{})".format( + avg_acc_clean, total_clean_correct_test, total_clean_test + ) +) +print( + "Acc (Target only): {:.3f}%({}/{})".format( + target_correct / target_total * 100.0, target_correct, target_total + ) +) diff --git a/analysis/visual_fm.py b/analysis/visual_fm.py new file mode 100755 index 0000000..66765e5 --- /dev/null +++ b/analysis/visual_fm.py @@ -0,0 +1,141 @@ +import sys +import os +import yaml +import torch +import numpy as np +import torchvision.transforms as transforms +from omnixai.explainers.vision.specific.feature_visualization.visualizer import \ + FeatureMapVisualizer + +sys.path.append("../") +sys.path.append(os.getcwd()) + +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, +) +from visual_utils import * + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + + +# Select all classes and all samples +selected_classes = np.arange(args.num_classes) + +# keep the same transforms for train and test dataset for better visualization +result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform +result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform + +# Create dataset +if args.visual_dataset == 'clean_train': + visual_dataset = result_attack["clean_train"] +elif args.visual_dataset == 'clean_test': + visual_dataset = result_attack["clean_test"] +elif args.visual_dataset == 'bd_train': + visual_dataset = result_attack["bd_train"] +elif args.visual_dataset == 'bd_test': + visual_dataset = result_attack["bd_test"] +else: + assert False, "Illegal vis_class" + +print(f'Create visualization dataset with \n \t Dataset: {args.visual_dataset} \n \t Number of samples: {len(visual_dataset)} \n \t Selected classes: {selected_classes}') + + +# Create denormalization function +for trans_t in visual_dataset.wrap_img_transform.transforms: + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + +if args.result_file_defense != "None": + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_visual.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_visual.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_visual = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_visual.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") +else: + model_visual.load_state_dict(result_attack["model"]) + print(f"Load model {args.model} from {args.result_file_attack}") + +model_visual.to(args.device) + +# !!! Important to set eval mode !!! +model_visual.eval() + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + +# Choose a image to get feature maps from a target layer +module_dict = dict(model_visual.named_modules()) +target_layer = module_dict[args.target_layer_name] + +target_image_index = np.random.randint(0, len(visual_dataset)) +target_image = visual_dataset[target_image_index][0].unsqueeze(0) +print(f"Choose image index {target_image_index} from {args.visual_dataset} as target image") + +############## Feature Maps ################## +print("Plotting Feature Maps") + +explainer = FeatureMapVisualizer( + model=model_visual, + target_layer=target_layer, + preprocess_function=lambda x:x +) + + +feature_map = explainer.extractor.extract(target_image) +num_cnn = feature_map.shape[-1] +num_col = 16 +num_row = int(np.ceil(num_cnn/num_col)) +fig, axes = plt.subplots(nrows=num_row, ncols=num_col, figsize=(4*num_col, 5*num_row)) +for cnn_i in range(num_cnn): + ax = axes[cnn_i//num_col, cnn_i%num_col] + ax.imshow(feature_map[0, :, :, cnn_i], cmap='gray') + ax.set_title(f'Kernel {cnn_i}') + +plt.tight_layout() +plt.savefig(visual_save_path + f"/feature_map_{args.visual_dataset}.png") + +print(f'Save to {visual_save_path + f"/feature_map_{args.visual_dataset}"}.png') diff --git a/analysis/visual_fre.py b/analysis/visual_fre.py new file mode 100755 index 0000000..c03c0dc --- /dev/null +++ b/analysis/visual_fre.py @@ -0,0 +1,214 @@ +import sys +import os +sys.path.append("../") +sys.path.append("../../") +sys.path.append(os.getcwd()) + +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, +) +from visual_utils import * +import yaml +import torch +import matplotlib as mlp +import numpy as np +import torchvision.transforms as transforms + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + +selected_classes = np.arange(args.num_classes) + +# Select classes to visualize +if args.num_classes > args.c_sub: + selected_classes = np.delete(selected_classes, args.target_class) + selected_classes = np.random.choice( + selected_classes, args.c_sub-1, replace=False) + selected_classes = np.append(selected_classes, args.target_class) + +# keep the same transforms for train and test dataset for better visualization +result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform +result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform + +# Create dataset +if args.visual_dataset == 'mixed': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_mix_dataset( + bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'clean_train': + clean_train_with_trans = result_attack["clean_train"] + visual_dataset = generate_clean_dataset( + clean_train_with_trans, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'clean_test': + clean_test_with_trans = result_attack["clean_test"] + visual_dataset = generate_clean_dataset( + clean_test_with_trans, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'bd_train': + bd_train_with_trans = result_attack["bd_train"] + visual_dataset = generate_bd_dataset( + bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'bd_test': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_bd_dataset( + bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub) +else: + assert False, "Illegal vis_class" + +print( + f'Create visualization dataset with \n \t Dataset: {args.visual_dataset} \n \t Number of samples: {len(visual_dataset)} \n \t Selected classes: {selected_classes}') + +# Create data loader +data_loader = torch.utils.data.DataLoader( + visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False +) + +# Create denormalization function +for trans_t in data_loader.dataset.wrap_img_transform.transforms: + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + +# Choose samples to show SHAP values. By Default, 2 clean images + 2 Poison images. If no enough Poison images, use 4 clean images instead.AblationCAM +total_num = 4 +bd_num = 0 + +visual_samples = [] +visual_labels = [] + +visual_poison_indicator = np.array( + get_poison_indicator_from_bd_dataset(visual_dataset)) +if visual_poison_indicator.sum() > 0: + print(f'Number Poisoned samples: {visual_poison_indicator.sum()}') + # random choose two poisoned samples + selected_bd_idx = np.random.choice( + np.where(visual_poison_indicator == 1)[0], 2, replace=False) + for i in selected_bd_idx: + visual_samples.append(visual_dataset[i][0].unsqueeze(0)) + visual_labels.append(visual_dataset[i][4]) + bd_num = len(selected_bd_idx) + print(f'Select {bd_num} poisoned samples') + +# Trun all samples to clean +with temporary_all_clean(visual_dataset): + # you can just set selected_clean_idx = selected_bd_idx to build the correspondence between clean samples and poisoned samples + selected_clean_idx = np.random.choice( + len(visual_dataset), total_num-bd_num, replace=False) + for i in selected_clean_idx: + visual_samples.append(visual_dataset[i][0].unsqueeze(0)) + visual_labels.append(visual_dataset[i][1]) + print(f'Select {len(selected_clean_idx)} clean samples') + +# Clean sample first +visual_samples = visual_samples[::-1] +visual_labels = visual_labels[::-1] + +visual_samples = torch.cat(visual_samples, axis=0).to(args.device) + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + +if args.result_file_defense != "None": + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_visual.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_visual.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_visual = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_visual.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") +else: + model_visual.load_state_dict(result_attack["model"]) + print(f"Load model {args.model} from {args.result_file_attack}") + +model_visual.to(args.device) + +# !!! Important to set eval mode !!! +model_visual.eval() + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + +############ Frequency saliency map ################ +print('Plotting Frequency saliency map') + +# Choose layer for feature extraction +module_dict = dict(model_visual.named_modules()) +target_layer = module_dict[args.target_layer_name] +print(f'Choose layer {args.target_layer_name} from model {args.model}') + +sfm = torch.nn.Softmax(dim=1) +outputs = model_visual(visual_samples) +pre_p, pre_label = torch.max(sfm(outputs), dim=1) + +# get the names for the classes +class_names = np.array(args.class_names).reshape([-1]) + +frequency_maps = [] +fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 10)) +vnorm = mlp.colors.Normalize(vmin=0, vmax=255) +for im in range(4): + rgb_image = np.swapaxes( + np.swapaxes(denormalizer(visual_samples[im]).cpu().numpy(), 0, 1), 1, 2 + ) + frequency_map = saliency(visual_samples[im], model_visual) + rgb_image[rgb_image < 1e-12] = 1e-12 + axes[im // 2, im % 2 * 2].imshow(rgb_image) + axes[im // 2, im % 2 * 2].axis("off") + if im == 0 or im == 1: + axes[im // 2, im % 2 * 2].set_title( + "Clean Image: %s" % (class_names[visual_labels[im]].capitalize()) + ) + else: + axes[im // 2, im % 2 * 2].set_title( + "Poison Image: %s" % (class_names[visual_labels[im]].capitalize()) + ) + image = axes[im // 2, im % 2 * 2 + + 1].imshow(frequency_map, cmap=plt.cm.coolwarm, norm=vnorm) + plt.colorbar(image, ax=axes[im // 2, im % + 2 * 2 + 1], orientation='vertical') + axes[im // 2, im % 2 * 2 + 1].axis("off") + axes[im // 2, im % 2 * 2 + 1].set_title( + "Predicted: %s, %.2f%%" % ( + class_names[pre_label[im]].capitalize(), pre_p[im] * 100) + ) + +plt.tight_layout() +plt.savefig(visual_save_path + f"/frequency_{args.visual_dataset}.png") + +print(f'Save to {visual_save_path + f"/frequency_{args.visual_dataset}"}.png') diff --git a/analysis/visual_fv.py b/analysis/visual_fv.py new file mode 100755 index 0000000..ff9169f --- /dev/null +++ b/analysis/visual_fv.py @@ -0,0 +1,129 @@ +import sys +import os +import yaml +sys.path.append("../") +sys.path.append("../../") +sys.path.append(os.getcwd()) + +from PIL import Image +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, +) +from visual_utils import * +import torch +import numpy as np +import torchvision.transforms as transforms +from omnixai.explainers.vision.specific.feature_visualization.visualizer import FeatureVisualizer + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + +if args.result_file_defense != "None": + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_visual.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_visual.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_visual = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_visual.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") +else: + model_visual.load_state_dict(result_attack["model"]) + print(f"Load model {args.model} from {args.result_file_attack}") + +model_visual.to(args.device) + +# !!! Important to set eval mode !!! +model_visual.eval() + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + +module_dict = dict(model_visual.named_modules()) +target_layer = module_dict[args.target_layer_name] +print(f'Choose layer {args.target_layer_name} from model {args.model}') + +# Enable training transform to enhance transform robustness +tran = get_transform( + args.dataset, *([args.input_height, args.input_width]), train=True) + +for trans_t in tran.transforms: + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + +############## Feature Visualization ################## +print("Plotting Feature Visualization") + +optimizer = FeatureVisualizer( + model = model_visual, + objectives = [{"layer": target_layer, "type": "channel", + "index": list(range(target_layer.out_channels))}], + transformers = tran +) + +# Some regularizations are used for better visualization results. +# The parameter for regularization is self-defined and you should set them by yourself. +# Note that such regularization may hinder optimizer to find some triggers especially when the triggers are some irregular patterns. +explanations = optimizer.explain( + num_iterations=300, + image_shape=(args.input_height, args.input_width), + regularizers=[("l1", 0.15), ("l2", 0), ("tv", 0.25)], + use_fft=True, +) + +images = explanations.explanations[0]['image'] +num_cnn = len(images) +num_col = 16 +num_row = int(np.ceil(num_cnn/num_col)) +fig, axes = plt.subplots(nrows=num_row, ncols=num_col, + figsize=(4*num_col, 5*num_row)) +for cnn_i in range(num_cnn): + ax = axes[cnn_i//num_col, cnn_i % num_col] + ax.imshow(images[cnn_i]) + ax.set_title(f'Kernel {cnn_i}') + +plt.tight_layout() +plt.savefig(visual_save_path + f"/feature_visual.png") + +print(f'Save to {visual_save_path + f"/feature_visual"}.png') diff --git a/analysis/visual_gradcam.py b/analysis/visual_gradcam.py new file mode 100755 index 0000000..7672840 --- /dev/null +++ b/analysis/visual_gradcam.py @@ -0,0 +1,228 @@ +import sys +import os +sys.path.append("../") +sys.path.append("../../") +sys.path.append(os.getcwd()) + +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, +) +from visual_utils import * +import yaml +import torch +import numpy as np +import torchvision.transforms as transforms +from pytorch_grad_cam import ( + GradCAM, + ScoreCAM, + GradCAMPlusPlus, + AblationCAM, + XGradCAM, + EigenCAM, + FullGrad, +) +from pytorch_grad_cam.utils.image import show_cam_on_image + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + +selected_classes = np.arange(args.num_classes) + +# Select classes to visualize +if args.num_classes > args.c_sub: + selected_classes = np.delete(selected_classes, args.target_class) + selected_classes = np.random.choice( + selected_classes, args.c_sub-1, replace=False) + selected_classes = np.append(selected_classes, args.target_class) + +# keep the same transforms for train and test dataset for better visualization +result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform +result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform + +# Create dataset +if args.visual_dataset == 'mixed': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_mix_dataset( + bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'clean_train': + clean_train_with_trans = result_attack["clean_train"] + visual_dataset = generate_clean_dataset( + clean_train_with_trans, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'clean_test': + clean_test_with_trans = result_attack["clean_test"] + visual_dataset = generate_clean_dataset( + clean_test_with_trans, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'bd_train': + bd_train_with_trans = result_attack["bd_train"] + visual_dataset = generate_bd_dataset( + bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'bd_test': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_bd_dataset( + bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub) +else: + assert False, "Illegal vis_class" + +print( + f'Create visualization dataset with \n \t Dataset: {args.visual_dataset} \n \t Number of samples: {len(visual_dataset)} \n \t Selected classes: {selected_classes}') + +# Create data loader +data_loader = torch.utils.data.DataLoader( + visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False +) + +# Create denormalization function +for trans_t in data_loader.dataset.wrap_img_transform.transforms: + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + +if args.result_file_defense != "None": + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_visual.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_visual.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_visual = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_visual.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") +else: + model_visual.load_state_dict(result_attack["model"]) + print(f"Load model {args.model} from {args.result_file_attack}") + +model_visual.to(args.device) + +# !!! Important to set eval mode !!! +model_visual.eval() + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + +# Choose samples to show Grad-CAM values. By Default, 2 clean images + 2 Poison images. If no enough Poison images, use 4 clean images instead.AblationCAM +total_num = 4 +bd_num = 0 + +visual_samples = [] +visual_labels = [] + +visual_poison_indicator = np.array( + get_poison_indicator_from_bd_dataset(visual_dataset)) +if visual_poison_indicator.sum() > 0: + print(f'Number Poisoned samples: {visual_poison_indicator.sum()}') + # random choose two poisoned samples + selected_bd_idx = np.random.choice( + np.where(visual_poison_indicator == 1)[0], 2, replace=False) + for i in selected_bd_idx: + visual_samples.append(visual_dataset[i][0].unsqueeze(0)) + visual_labels.append(visual_dataset[i][4]) + bd_num = len(selected_bd_idx) + print(f'Select {bd_num} poisoned samples') + +# Trun all samples to clean +with temporary_all_clean(visual_dataset): + # you can just set selected_clean_idx = selected_bd_idx to build the correspondence between clean samples and poisoned samples + selected_clean_idx = np.random.choice( + len(visual_dataset), total_num-bd_num, replace=False) + for i in selected_clean_idx: + visual_samples.append(visual_dataset[i][0].unsqueeze(0)) + visual_labels.append(visual_dataset[i][1]) + print(f'Select {len(selected_clean_idx)} clean samples') + +# Clean sample first +visual_samples = visual_samples[::-1] +visual_labels = visual_labels[::-1] + +visual_samples = torch.cat(visual_samples, axis=0).to(args.device) + +############## Grad-CAM ################## +print('Plotting Grad-CAM') + +module_dict = dict(model_visual.named_modules()) +target_layer = module_dict[args.target_layer_name] +print(f'Choose layer {args.target_layer_name} from model {args.model}') + +sfm = torch.nn.Softmax(dim=1) +outputs = model_visual(visual_samples) +pre_p, pre_label = torch.max(sfm(outputs), dim=1) + +cam = FullGrad(model=model_visual, target_layers=[ + target_layer], use_cuda=True if args.device == 'cuda' else False) + +targets = None + +# You can also pass aug_smooth=True and eigen_smooth=True, to apply smoothing. +grayscale_cam_full = cam(input_tensor=visual_samples, targets=targets) + +grayscale_cam = grayscale_cam_full[0, :] +rgb_image = np.swapaxes( + np.swapaxes(denormalizer(visual_samples[0]).cpu().numpy(), 0, 1), 1, 2 +) +visual_cam = show_cam_on_image(rgb_image, grayscale_cam, use_rgb=True) + +# get the names for the classes +class_names = np.array(args.class_names).reshape([-1]) + +fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 10)) +for im in range(4): + grayscale_cam = grayscale_cam_full[im, :] + rgb_image = np.swapaxes( + np.swapaxes(denormalizer(visual_samples[im]).cpu().numpy(), 0, 1), 1, 2 + ) + rgb_image[rgb_image < 1e-12] = 1e-12 + visual_cam = show_cam_on_image(rgb_image, grayscale_cam, use_rgb=True) + axes[im // 2, im % 2 * 2].imshow(rgb_image) + axes[im // 2, im % 2 * 2].axis("off") + axes[im // 2, im % 2 * 2].set_title( + "Original Image: %s" % (class_names[visual_labels[im]].capitalize()) + ) + axes[im // 2, im % 2 * 2 + 1].imshow(visual_cam) + axes[im // 2, im % 2 * 2 + 1].axis("off") + axes[im // 2, im % 2 * 2 + 1].set_title( + "Predicted: %s, %.2f%%" % ( + class_names[pre_label[im]].capitalize(), pre_p[im] * 100) + ) + + +plt.tight_layout() +plt.savefig(visual_save_path + f"/gradcam_{args.visual_dataset}.png") + +print(f'Save to {visual_save_path + f"/gradcam_{args.visual_dataset}"}.png') diff --git a/analysis/visual_hessian.py b/analysis/visual_hessian.py new file mode 100755 index 0000000..f78e3c8 --- /dev/null +++ b/analysis/visual_hessian.py @@ -0,0 +1,218 @@ +import sys +import os +sys.path.append(os.getcwd()) + +import math +import matplotlib as mpl +mpl.use('Agg') +import matplotlib.pyplot as plt + +import yaml +import torch +import numpy as np +import torchvision.transforms as transforms +from matplotlib.patches import Rectangle, Patch +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, + dataset_and_transform_generate +) +from visual_utils import * +from pyhessian import hessian # Hessian computation + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + +print(os.getcwd()) +print(os.path.exists(save_path_attack + "/clean_model.pth")) + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + +selected_classes = np.arange(args.num_classes) + +# Select classes to visualize +if args.num_classes > args.c_sub: + selected_classes = np.delete(selected_classes, args.target_class) + selected_classes = np.random.choice( + selected_classes, args.c_sub-1, replace=False) + selected_classes = np.append(selected_classes, args.target_class) + +# keep the same transforms for train and test dataset for better visualization +result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform +result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform + +# Create dataset +if args.visual_dataset == 'clean_train': + visual_dataset = result_attack["clean_train"] +elif args.visual_dataset == 'clean_test': + visual_dataset = result_attack["clean_test"] +elif args.visual_dataset == 'bd_train': + visual_dataset = result_attack["bd_train"] +elif args.visual_dataset == 'bd_test': + visual_dataset = result_attack["bd_test"] +else: + assert False, "Illegal vis_class" + +print( + f'Create visualization dataset with \n \t Dataset: {args.visual_dataset} \n \t Number of samples: {len(visual_dataset)} \n \t Selected classes: {selected_classes}') + +# Create data loader +data_loader = torch.utils.data.DataLoader( + visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False +) + +# Create denormalization function +for trans_t in data_loader.dataset.wrap_img_transform.transforms: + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + +if args.result_file_defense != "None": + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_visual.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_visual.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_visual = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_visual.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") +else: + model_visual.load_state_dict(result_attack["model"]) + print(f"Load model {args.model} from {args.result_file_attack}") + +model_visual.to(args.device) + +# !!! Important to set eval mode !!! +model_visual.eval() + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + +criterion = torch.nn.CrossEntropyLoss() + +batch_x, batch_y, *others = next(iter(data_loader)) +batch_x = batch_x.to(args.device) +batch_y = batch_y.to(args.device) + + +if torch.__version__>'1.8.1': + print('Use self-defined function as an alternative for torch.eig since your torch>=1.9') + def old_torcheig(A, eigenvectors): + '''A temporary function as an alternative for torch.eig (torch<1.9)''' + vals, vecs = torch.linalg.eig(A) + if torch.is_complex(vals) or torch.is_complex(vecs): + print('Warning: Complex values founded in Eigenvalues/Eigenvectors. This is impossible for real symmetric matrix like Hessian. \n We only keep the real part.') + + vals = torch.real(vals) + vecs = torch.real(vecs) + + # vals is a nx2 matrix. see https://virtualgroup.cn/pytorch.org/docs/stable/generated/torch.eig.html + vals = vals.view(-1,1)+torch.zeros(vals.size()[0],2).to(vals.device) + if eigenvectors: + return vals, vecs + else: + return vals, torch.tensor([]) + + torch.eig = old_torcheig + + +# create the hessian computation module +hessian_comp = hessian(model_visual, criterion, data=(batch_x, batch_y), cuda=True) +# Now let's compute the top 2 eigenavlues and eigenvectors of the Hessian +top_eigenvalues, top_eigenvector = hessian_comp.eigenvalues(top_n=2, maxIter=1000) +print("The top two eigenvalues of this model are: %.4f %.4f"% (top_eigenvalues[-1],top_eigenvalues[-2])) + +density_eigen, density_weight = hessian_comp.density() + +def get_esd_plot(eigenvalues, weights): + density, grids = density_generate(eigenvalues, weights) + plt.semilogy(grids, density + 1.0e-7) + plt.ylabel('Density (Log Scale)', fontsize=14, labelpad=10) + plt.xlabel('Eigenvlaue', fontsize=14, labelpad=10) + plt.xticks(fontsize=12) + plt.yticks(fontsize=12) + plt.axis([np.min(eigenvalues) - 1, np.max(eigenvalues) + 1, None, None]) + return plt.gca() + +def density_generate(eigenvalues, + weights, + num_bins=10000, + sigma_squared=1e-5, + overhead=0.01): + + eigenvalues = np.array(eigenvalues) + weights = np.array(weights) + + lambda_max = np.mean(np.max(eigenvalues, axis=1), axis=0) + overhead + lambda_min = np.mean(np.min(eigenvalues, axis=1), axis=0) - overhead + + grids = np.linspace(lambda_min, lambda_max, num=num_bins) + sigma = sigma_squared * max(1, (lambda_max - lambda_min)) + + num_runs = eigenvalues.shape[0] + density_output = np.zeros((num_runs, num_bins)) + + for i in range(num_runs): + for j in range(num_bins): + x = grids[j] + tmp_result = gaussian(eigenvalues[i, :], x, sigma) + density_output[i, j] = np.sum(tmp_result * weights[i, :]) + density = np.mean(density_output, axis=0) + normalization = np.sum(density) * (grids[1] - grids[0]) + density = density / normalization + return density, grids + + +def gaussian(x, x0, sigma_squared): + return np.exp(-(x0 - x)**2 / + (2.0 * sigma_squared)) / np.sqrt(2 * np.pi * sigma_squared) + +ax = get_esd_plot(density_eigen, density_weight) +info_list = args.result_file_attack.split('_') + +try: + ax.set_title(f'Attack {info_list[2]}, 0.{info_list[4]}, Max Eigen Value: {top_eigenvalues[0]:.2f}') +except: + ax.set_title(f'Max Eigen Value: {top_eigenvalues[0]:.2f}') + +plt.tight_layout() +plt.savefig(visual_save_path + f'/{args.visual_dataset}_hessian.png') + +print(f'Save to {visual_save_path + f"/{args.visual_dataset}_hessian.png"}') \ No newline at end of file diff --git a/analysis/visual_landscape.py b/analysis/visual_landscape.py new file mode 100755 index 0000000..b1fdc3c --- /dev/null +++ b/analysis/visual_landscape.py @@ -0,0 +1,507 @@ +import sys +import os +import yaml +sys.path.append("../") +assert os.path.exists( + "./visualization/loss-landscape"), "Please clone the repo https://github.com/tomgoldstein/loss-landscape to ./visualization/" +sys.path.append("./visualization/loss-landscape") +sys.path.append(os.getcwd()) +import time +import scheduler +import torch.nn as nn +import evaluation + +import mpi4pytorch as mpi +import h52vtp as h52vtp +import plot_surface as plot_surface +import plot_1D as plot_1D +import plot_2D as plot_2D +import net_plotter as net_plotter +import projection as proj +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, +) +from visual_utils import * +import torch +import numpy as np +import torchvision.transforms as transforms +import socket +import h5py +from matplotlib import pyplot as plt +from matplotlib import cm +import h5_util +from os.path import exists, commonprefix + + +# modified from https://github.com/tomgoldstein/loss-landscape/blob/master/net_plotter.py by changing the load model part. +def setup_direction(args, dir_file, net, net2 = None, net3 = None): + """ + Setup the h5 file to store the directions. + - xdirection, ydirection: The pertubation direction added to the mdoel. + The direction is a list of tensors. + """ + print('-------------------------------------------------------------------') + print(f'setup_direction {dir_file}') + print('-------------------------------------------------------------------') + + # Skip if the direction file already exists + if exists(dir_file): + f = h5py.File(dir_file, 'r') + if (args.y and 'ydirection' in f.keys()) or 'xdirection' in f.keys(): + f.close() + print ("%s is already setted up" % dir_file) + return + f.close() + + # Create the plotting directions + f = h5py.File(dir_file,'w') # create file, fail if exists + if not args.dir_file: + print("Setting up the plotting directions...") + if net2: + print("Using target direction") + xdirection = net_plotter.create_target_direction(net, net2, args.dir_type) + else: + print("Using random direction") + xdirection = net_plotter.create_random_direction(net, args.dir_type, args.xignore, args.xnorm) + h5_util.write_list(f, 'xdirection', xdirection) + + if args.y: + if net3: + print("Using target direction") + ydirection = net_plotter.create_target_direction(net, net3, args.dir_type) + else: + print("Using random direction") + ydirection = net_plotter.create_random_direction(net, args.dir_type, args.yignore, args.ynorm) + h5_util.write_list(f, 'ydirection', ydirection) + + f.close() + print ("direction file created: %s" % dir_file) + +# modified from https://github.com/tomgoldstein/loss-landscape/blob/master/plot_surface.py by change the f.close() to avoid some bugs +def crunch(surf_file, net, w, s, d, dataloader, loss_key, acc_key, comm, rank, args): + """ + Calculate the loss values and accuracies of modified models in parallel + using MPI reduce. + """ + + loaded = False + while not loaded: + try: + # read only to avoid conflict with other processes + f = h5py.File(surf_file, 'r') + loaded = True + except: + print(f"rank-{rank}:Error opening file, retrying...", flush=True) + time.sleep(5) + + losses, accuracies = [], [] + xcoordinates = f['xcoordinates'][:] + ycoordinates = f['ycoordinates'][:] if 'ycoordinates' in f.keys() else None + + fkeys = list(f.keys()) + f.close() + + if loss_key not in fkeys: + shape = xcoordinates.shape if ycoordinates is None else (len(xcoordinates),len(ycoordinates)) + losses = -np.ones(shape=shape) + accuracies = -np.ones(shape=shape) + else: + print(f"rank-{rank}:losses and accuracies already calculated", flush=True) + return + # Generate a list of indices of 'losses' that need to be filled in. + # The coordinates of each unfilled index (with respect to the direction vectors + # stored in 'd') are stored in 'coords'. + inds, coords, inds_nums = scheduler.get_job_indices(losses, xcoordinates, ycoordinates, comm) + + print('Computing %d values for rank %d'% (len(inds), rank)) + start_time = time.time() + total_sync = 0.0 + + criterion = nn.CrossEntropyLoss() + if args.loss_name == 'mse': + criterion = nn.MSELoss() + + # Loop over all uncalculated loss values + for count, ind in enumerate(inds): + # Get the coordinates of the loss value being calculated + coord = coords[count] + + # Load the weights corresponding to those coordinates into the net + if args.dir_type == 'weights': + net_plotter.set_weights(net.module if args.ngpu > 1 else net, w, d, coord) + elif args.dir_type == 'states': + net_plotter.set_states(net.module if args.ngpu > 1 else net, s, d, coord) + + # Record the time to compute the loss value + loss_start = time.time() + loss, acc = evaluation.eval_loss(net, criterion, dataloader, args.cuda) + loss_compute_time = time.time() - loss_start + + # Record the result in the local array + losses.ravel()[ind] = loss + accuracies.ravel()[ind] = acc + + # Send updated plot data to the master node + syc_start = time.time() + losses = mpi.reduce_max(comm, losses) + accuracies = mpi.reduce_max(comm, accuracies) + syc_time = time.time() - syc_start + total_sync += syc_time + + # Only the master node writes to the file - this avoids write conflicts + if rank == 0: + f = h5py.File(surf_file, 'r+') + try: + f[loss_key][:] = losses + f[acc_key][:] = accuracies + except: + f[loss_key] = losses + f[acc_key] = accuracies + + f.flush() + f.close() + + print('Evaluating rank %d %d/%d (%.1f%%) coord=%s \t%s= %.3f \t%s=%.2f \ttime=%.2f \tsync=%.2f' % ( + rank, count, len(inds), 100.0 * count/len(inds), str(coord), loss_key, loss, + acc_key, acc, loss_compute_time, syc_time)) + + # This is only needed to make MPI run smoothly. If this process has less work than + # the rank0 process, then we need to keep calling reduce so the rank0 process doesn't block + for i in range(max(inds_nums) - len(inds)): + losses = mpi.reduce_max(comm, losses) + accuracies = mpi.reduce_max(comm, accuracies) + + total_time = time.time() - start_time + + print('Rank %d done! Total time: %.2f Sync: %.2f' % (rank, total_time, total_sync)) + + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + +selected_classes = np.arange(args.num_classes) + +# keep the same transforms for train and test dataset for better visualization +result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform +result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform + +# Create dataset +if args.visual_dataset == 'clean_train': + visual_dataset = result_attack["clean_train"] +elif args.visual_dataset == 'bd_train': + visual_dataset = result_attack["bd_train"] + visual_dataset.wrapped_dataset.getitem_all = False # only return img and label +else: + assert False, "Illegal vis_class" + +print( + f'Create visualization dataset with \n \t Dataset: {args.visual_dataset} \n \t Number of samples: {len(visual_dataset)} \n \t Selected classes: {selected_classes}') + +# Create data loader +data_loader = torch.utils.data.DataLoader( + visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False +) + +# Create denormalization function +for trans_t in data_loader.dataset.wrap_img_transform.transforms: + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + +if args.result_file_defense != "None": + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_visual.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_visual.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_visual = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_visual.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") +else: + model_visual.load_state_dict(result_attack["model"]) + print(f"Load model {args.model} from {args.result_file_attack}") + + + +# !!! Important to set eval mode !!! +model_visual.eval() + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + + +############################################ +######## 2. Plot the loss landscape ####### +############################################ +print('Plotting the loss landscape') + +# additonal args +args.mpi = True +args.cuda = True if "cuda" in args.device else False +args.show = False +args.proj_file = "" +args.dir_file = '' + +# -------------------------------------------------------------------------- +# Environment setup +# -------------------------------------------------------------------------- +if args.mpi: + comm = mpi.setup_MPI() + rank, nproc = comm.Get_rank(), comm.Get_size() + print(f"Get rank {rank}") +else: + comm, rank, nproc = None, 0, 1 + +# in case of multiple GPUs per node, set the GPU to use for each rank +if args.cuda: + if not torch.cuda.is_available(): + raise Exception( + 'User selected cuda option, but cuda is not available on this machine') + gpu_count = torch.cuda.device_count() + torch.cuda.set_device(rank % gpu_count) + print('Rank %d use GPU %d of %d GPUs on %s' % + (rank, torch.cuda.current_device(), gpu_count, socket.gethostname())) + +# -------------------------------------------------------------------------- +# Check plotting resolution +# -------------------------------------------------------------------------- +try: + args.xmin, args.xmax, args.xnum = [float(a) for a in args.x.split(':')] + args.ymin, args.ymax, args.ynum = (None, None, None) + args.xnum = int(args.xnum) + if args.y: + args.ymin, args.ymax, args.ynum = [float(a) for a in args.y.split(':')] + assert args.ymin and args.ymax and args.ynum, \ + 'You specified some arguments for the y axis, but not all' + args.ynum = int(args.ynum) + +except: + raise Exception( + 'Improper format for x- or y-coordinates. Try something like -1:1:51') + +if args.dir_file: + print('Use given dir_file in args:', args.dir_file) +else: + dir_file = save_path_attack + '/' + args.result_file_attack + '_direction.h5' + print(f'No dir_file is given, generate dir_file at {dir_file} now') + +# -------------------------------------------------------------------------- +# Load models and extract parameters +# -------------------------------------------------------------------------- +w = net_plotter.get_weights(model_visual) # initial parameters +# deepcopy since state_dict are references +s = copy.deepcopy(model_visual.state_dict()) +if args.ngpu > 1: + # data parallel with multiple GPUs on a single node + net = torch.nn.DataParallel( + model_visual, device_ids=range(torch.cuda.device_count())) + + +# -------------------------------------------------------------------------- +# Setup the direction file and the surface file +# -------------------------------------------------------------------------- + +# Only used for saving direction and surface file +args.model_file = visual_save_path + f'/{args.result_file_attack}' +args.model_file1 = "" +args.model_file2 = "" +args.model_file3 = "" +model_1_perb = None +model_2_perb = None + +criterion = nn.CrossEntropyLoss() +if args.loss_name == 'mse': + criterion = nn.MSELoss() + +if rank == 0 and args.dir_gen == 'hessian': + args.model_file1 = visual_save_path + f'/{args.result_file_attack}_model_1.pt' + args.model_file2 = visual_save_path + f'/{args.result_file_attack}_model_2.pt' + + if os.path.exists(args.model_file1) and os.path.exists(args.model_file2): + print(f'Load model_1 and model_2 from {args.model_file1} and {args.model_file2}') + model_1_perb = generate_cls_model(args.model, args.num_classes) + model_2_perb = generate_cls_model(args.model, args.num_classes) + model_1_perb.load_state_dict(torch.load(args.model_file1)) + model_2_perb.load_state_dict(torch.load(args.model_file2)) + else: + # compute the top-2 eigenvector of hessian matrix as directions + from pyhessian import hessian # Hessian computation + + # This is a simple function, that will allow us to perturb the model paramters and get the result + # from https://github.com/amirgholami/PyHessian/blob/master/Hessian_Tutorial.ipynb + def get_params(model_orig, model_perb, direction, alpha): + for m_orig, m_perb, d in zip(model_orig.parameters(), model_perb.parameters(), direction): + m_perb.data = m_orig.data + alpha * d + return model_perb + + model_1 = generate_cls_model(args.model, args.num_classes) + model_2 = generate_cls_model(args.model, args.num_classes) + + model_visual = model_visual.to(args.device) + model_1 = model_1.to(args.device) + model_2 = model_2.to(args.device) + + # get a batch of data + batch_x, batch_y = next(iter(data_loader)) + batch_x = batch_x.to(args.device) + batch_y = batch_y.to(args.device) + + # create the hessian computation module + hessian_comp = hessian(model_visual, criterion, data=(batch_x, batch_y), cuda=args.cuda) + top_eigenvalues, top_eigenvector = hessian_comp.eigenvalues(top_n=2) + + + model_1_perb = get_params(model_visual, model_1, top_eigenvector[0], 1) + model_2_perb = get_params(model_visual, model_2, top_eigenvector[1], 1) + + model_1_perb.eval() + model_2_perb.eval() + + + torch.save(model_1_perb.cpu().state_dict(), args.model_file1) + torch.save(model_2_perb.cpu().state_dict(), args.model_file2) + + print('Use eigenvectors of hessian matrix as directions.') + +# resume all parameters to keep the same as other ranks +model_visual = model_visual.cpu() +args.model_file1 = "" +args.model_file2 = "" + +args.surf_file = "" +args.plot = True +args.data_split = 0 +args.proj_file = "" + +dir_file = net_plotter.name_direction_file(args) # name the direction file + +if rank == 0: + setup_direction(args, dir_file, net = model_visual, net2 = model_1_perb, net3 = model_2_perb) + +surf_file = plot_surface.name_surface_file(args, dir_file) +if rank == 0: + plot_surface.setup_surface_file(args, surf_file, dir_file) + +# load directions +loaded = False +while not loaded: + try: + d = net_plotter.load_directions(dir_file) + print(f'rank-{rank}: directions loaded') + loaded = True + except: + print(f'rank-{rank}: Waiting for direction file {dir_file} to be loaded...', flush=True) + print('Please restart the program if the direction file is not loaded after 30 seconds.') + time.sleep(rank*2) + + +# calculate the consine similarity of the two directions +if len(d) == 2 and rank == 0: + similarity = proj.cal_angle(proj.nplist_to_tensor( + d[0]), proj.nplist_to_tensor(d[1])) + print('cosine similarity between x-axis and y-axis: %f' % similarity) + +# -------------------------------------------------------------------------- +# Start the computation +# -------------------------------------------------------------------------- +crunch(surf_file, model_visual, w, s, d, + data_loader, 'train_loss', 'train_acc', comm, rank, args) + +# -------------------------------------------------------------------------- +# Plot figures +# -------------------------------------------------------------------------- +if args.plot and rank == 0: + print("plotting landscape") + # wait 3 seconds + time.sleep(2.5) + f = h5py.File(surf_file, 'r') + x = np.array(f['xcoordinates'][:]) + y = np.array(f['ycoordinates'][:]) + X, Y = np.meshgrid(x, y) + + surf_name = "train_loss" + + if surf_name in f.keys(): + Z = np.array(f[surf_name][:]) + elif surf_name == 'train_err' or surf_name == 'test_err': + Z = 100 - np.array(f[surf_name][:]) + else: + print('%s is not found in %s' % (surf_name, surf_file)) + + # -------------------------------------------------------------------- + # Plot 3D surface + # -------------------------- + fig = plt.figure() + + def Axes3D(fig): + return fig.add_subplot(projection='3d') + ax = Axes3D(fig) + surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm, + linewidth=0, antialiased=False) + fig.colorbar(surf, shrink=0.5, aspect=5) + + ax.set_xlabel('x') + ax.set_ylabel('y') + ax.set_zlabel('z') + + plt.tight_layout() + plt.savefig(visual_save_path + f"/landscape_{args.visual_dataset}.png") + + print(f'Save to {visual_save_path + f"/landscape_{args.visual_dataset}"}.png') + + # save to vtk file. you can use paraview to visualize the results + h52vtp.h5_to_vtp(surf_file, surf_name, log=False, zmax=10, interp=1000) + + # Another way to show the results is the function provided by plot_2D + # if rank == 0: + # args.vmin = 0.1 + # args.vmax = 10 + # args.vlevel = 0.5 + # if args.y and args.proj_file: + # plot_2D.plot_contour_trajectory(surf_file, dir_file, args.proj_file, 'train_loss', args.show) + # elif args.y: + # plot_2D.plot_2d_contour(surf_file, 'train_loss', args.vmin, args.vmax, args.vlevel, args.show) + # else: + # plot_1D.plot_1d_loss_err(surf_file, args.xmin, args.xmax, args.loss_max, args.log, args.show) + + diff --git a/analysis/visual_lips.py b/analysis/visual_lips.py new file mode 100755 index 0000000..1a6fa08 --- /dev/null +++ b/analysis/visual_lips.py @@ -0,0 +1,203 @@ +import sys +import os +sys.path.append("../") +sys.path.append(os.getcwd()) + +import matplotlib +from matplotlib.patches import Rectangle, Patch +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from visual_utils import * +import yaml +import torch +import numpy as np + + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + +if args.result_file_defense != "None": + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_visual.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_visual.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_visual = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_visual.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") +else: + model_visual.load_state_dict(result_attack["model"]) + print(f"Load model {args.model} from {args.result_file_attack}") + +model_visual.to(args.device) + +# !!! Important to set eval mode !!! +model_visual.eval() + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + +############## lipschitz constant ################## +print("Plotting lipschitz constant") + +module_dict = dict(model_visual.named_modules()) + + +module_names = module_dict.keys() + +# Plot Conv2d or Linear +module_visual = [i for i in module_dict.keys() if isinstance( + module_dict[i], torch.nn.Conv2d) or isinstance(module_dict[i], torch.nn.Linear) or isinstance(module_dict[i], torch.nn.BatchNorm2d)] + +df = None + +max_num_neuron = 0 +for module_name in module_visual: + target_layer = module_dict[module_name] + + print(f'Collecting Lips {target_layer}') + if isinstance(target_layer, torch.nn.Linear): + channel_lips = [] + for idx in range(target_layer.weight.shape[0]): + w = target_layer.weight[idx].reshape(target_layer.weight.shape[1], -1) + # Just norm of weight for linear layer + channel_lips.append(torch.svd(w)[1].max()) + channel_lips = torch.Tensor(channel_lips) + + elif isinstance(target_layer, torch.nn.BatchNorm2d): + std = target_layer.running_var.sqrt() + weight = target_layer.weight + + channel_lips = [] + for idx in range(weight.shape[0]): + w = conv.weight[idx].reshape(conv.weight.shape[1], -1) * (weight[idx]/std[idx]).abs() + channel_lips.append(torch.svd(w)[1].max()) + channel_lips = torch.Tensor(channel_lips) + + + # Convolutional layer should be followed by a BN layer by default + elif isinstance(target_layer, torch.nn.Conv2d): + conv = target_layer + + channel_lips = [] + for idx in range(target_layer.weight.shape[0]): + w = target_layer.weight[idx].reshape(target_layer.weight.shape[1], -1) + channel_lips.append(torch.svd(w)[1].max()) + channel_lips = torch.Tensor(channel_lips) + else: + assert False, "Unknown layer type" + + for neuron_i in range(channel_lips.shape[0]): + base_row = {} + base_row['layer'] = module_name + base_row['Neuron'] = neuron_i + base_row['Lips'] = channel_lips[neuron_i].item() + if df is None: + df = pd.DataFrame.from_dict([base_row]) + else: + df.loc[df.shape[0]] = base_row + +df.to_csv(visual_save_path + f"/lips.csv") + +start_x0 = 0 +height = 1 +width = 1 +vmin = 0 +if args.normalize_by_layer: + vmax = 1 +else: + vmax = df.Lips.max() +max_num_neuron = df.Neuron.max() + + +norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax, clip=True) +mapper = matplotlib.cm.ScalarMappable(norm=norm, cmap=matplotlib.cm.Oranges) + +fig, ax = plt.subplots( + figsize=(int(len(module_visual)), int(max_num_neuron/10))) + +ax.plot([0, 0], [0, 0]) + +for module_name in module_visual: + print(f'ploting {module_name}') + y_0 = 0 + layer_info = df[df.layer == module_name] + layer_lips_max = layer_info['Lips'].max() + total_neuron = layer_info.shape[0] + for neuron_i in range(total_neuron): + x_0 = start_x0 + base_row = layer_info.iloc[neuron_i] + if args.normalize_by_layer: + ax.add_patch(Rectangle((x_0, y_0), width, height, + facecolor=mapper.to_rgba(base_row['Lips']/layer_lips_max), + fill=True, + lw=5, + alpha=0.8)) + + else: + ax.add_patch(Rectangle((x_0, y_0), width, height, + facecolor=mapper.to_rgba(base_row['Lips']), + fill=True, + lw=5, + alpha=0.8)) + + y_0 += 1.5*height + start_x0 += 1.5*width +x_loc = [0.5*width+1.5*width*i for i in range(len(module_visual))] +y_loc = [0.5*height+1.5*height*i for i in range(max_num_neuron)] + +ax.set_xlim(xmin=-0.5*width, xmax=1.5*width*(len(module_visual)+1)) +ax.set_ylim(ymin=-0.5*height, ymax=1.5*height*(max_num_neuron+1)) +ax.set_xticks(x_loc, module_visual, rotation=270) +ax.set_yticks(y_loc[::10], np.arange(max_num_neuron)[::10]) +ax.set_title(f'Lips of Attack Model') +ax.set_ylabel('Neuron') +ax.set_xlabel('Layer') + +cb_ax = fig.add_axes([0.15, 0.9, 0.7, 0.01]) + +fig.colorbar(mapper, + cax=cb_ax, orientation="horizontal", label='Lips') + +plt.savefig(visual_save_path + f"/lips.png") + +print(f'Save to {visual_save_path + f"/lips"}.png') + diff --git a/analysis/visual_metric.py b/analysis/visual_metric.py new file mode 100644 index 0000000..193bb47 --- /dev/null +++ b/analysis/visual_metric.py @@ -0,0 +1,266 @@ +import sys +import os +sys.path.append("../") +sys.path.append(os.getcwd()) + +from matplotlib.patches import Rectangle, Patch +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, +) +from visual_utils import * +import yaml +import torch +import numpy as np +import torchvision.transforms as transforms + +import matplotlib.pyplot as plt +from utils.metric import * +import warnings + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + +# Select all classes and all samples +selected_classes = np.arange(args.num_classes) + +# keep the same transforms for train and test dataset for better visualization +result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform +result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform + +# Create dataset +visual_dataset_clean = result_attack["clean_test"] +visual_dataset_bd = result_attack["bd_test"] + +print(f'Create clean test dataset with {len(visual_dataset_clean)} samples') +print(f'Create poison test dataset with {len(visual_dataset_bd)} samples') + +# Create data loader +data_loader_clean = torch.utils.data.DataLoader( + visual_dataset_clean, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False +) + +data_loader_bd = torch.utils.data.DataLoader( + visual_dataset_bd, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False +) + + +metric_dic = {} +# Load model +model_attack = generate_cls_model(args.model, args.num_classes) +model_defense = None +model_attack.load_state_dict(result_attack["model"]) +print(f"Load model {args.model} from {args.result_file_attack}") +model_attack.to(args.device) +model_attack.eval() + + +if args.result_file_defense != "None": + model_defense = generate_cls_model(args.model, args.num_classes) + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_defense.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_defense.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_defense = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_defense.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") + model_defense.to(args.device) + model_defense.eval() + + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + + +target_class = args.target_class +poison_class = args.num_classes +class_names = args.class_names + +############## Collect Attack Predicts ################## +print("Collecting attack predicts") + +# Evaluation +# Clean part +true_labels_clean_attack = [] +pred_labels_clean_attack = [] +true_labels_clean_defense = [] +pred_labels_clean_defense = [] + +for i, (inputs, labels, *other_info) in enumerate(data_loader_clean): + inputs, labels = inputs.to(args.device), labels.to(args.device) + + # attack part + outputs = model_attack(inputs) + prediction = torch.argmax(outputs[:], dim=1) + true_labels_clean_attack.append(labels.detach().cpu().numpy()) + pred_labels_clean_attack.append(prediction.detach().cpu().numpy()) + + # defense part + if model_defense is not None: + outputs = model_defense(inputs) + prediction = torch.argmax(outputs[:], dim=1) + true_labels_clean_defense.append(labels.detach().cpu().numpy()) + pred_labels_clean_defense.append(prediction.detach().cpu().numpy()) + +true_labels_clean_attack = np.concatenate(true_labels_clean_attack) +pred_labels_clean_attack = np.concatenate(pred_labels_clean_attack) + +if model_defense is not None: + true_labels_clean_defense = np.concatenate(true_labels_clean_defense) + pred_labels_clean_defense = np.concatenate(pred_labels_clean_defense) + +# clean accuracy +clean_accuracy_attack = clean_accuracy(pred_labels_clean_attack, true_labels_clean_attack) +metric_dic['clean_accuracy_attack'] = clean_accuracy_attack +if model_defense is not None: + clean_accuracy_defense = clean_accuracy(pred_labels_clean_defense, true_labels_clean_defense) + metric_dic['clean_accuracy_defense'] = clean_accuracy_defense + + +# Backdoor part +true_labels_bd_attack = [] +pred_labels_bd_attack = [] +ori_labels_bd_attack = [] + +true_labels_bd_defense = [] +pred_labels_bd_defense = [] +ori_labels_bd_defense = [] + +for i, (inputs, labels, *other_info) in enumerate(data_loader_bd): + inputs, labels = inputs.to(args.device), labels.to(args.device) + + if torch.sum(other_info[1]==0)>0: + # warning message + warnings.warn("There are some clean samples in backdoor dataset detected by the poison indicators. Please Check you dataset.") + + # attack part + outputs = model_attack(inputs) + prediction = torch.argmax(outputs[:], dim=1) + true_labels_bd_attack.append(labels.detach().cpu().numpy()) + pred_labels_bd_attack.append(prediction.detach().cpu().numpy()) + ori_labels_bd_attack.append(other_info[2].detach().cpu().numpy()) + + # defense part + if model_defense is not None: + outputs = model_defense(inputs) + prediction = torch.argmax(outputs[:], dim=1) + true_labels_bd_defense.append(labels.detach().cpu().numpy()) + pred_labels_bd_defense.append(prediction.detach().cpu().numpy()) + ori_labels_bd_defense.append(other_info[2].detach().cpu().numpy()) + +true_labels_bd_attack = np.concatenate(true_labels_bd_attack) +pred_labels_bd_attack = np.concatenate(pred_labels_bd_attack) +ori_labels_bd_attack = np.concatenate(ori_labels_bd_attack) + +if model_defense is not None: + true_labels_bd_defense = np.concatenate(true_labels_bd_defense) + pred_labels_bd_defense = np.concatenate(pred_labels_bd_defense) + ori_labels_bd_defense = np.concatenate(ori_labels_bd_defense) + + + +# attack success rate +asr_attack = attack_success_rate(pred_labels_bd_attack, true_labels_bd_attack) +metric_dic['asr_attack'] = asr_attack + +ra_attack = robust_accuracy(pred_labels_bd_attack, ori_labels_bd_attack) +metric_dic['ra_attack'] = ra_attack + +if model_defense is not None: + asr_defense = attack_success_rate(pred_labels_bd_defense, true_labels_bd_defense) + metric_dic['asr_defense'] = asr_defense + + ra_defense = robust_accuracy(pred_labels_bd_defense, ori_labels_bd_defense) + metric_dic['ra_defense'] = ra_defense + +if model_defense is not None: + # Assume the original label and the true label are shared by both attack and defense + der = defense_effectiveness_rate_simplied(clean_accuracy_attack, clean_accuracy_defense, asr_attack, asr_defense) + rir = robust_improvement_rate_simplied(clean_accuracy_attack, clean_accuracy_defense, ra_attack, ra_defense) + + metric_dic['der'] = der + metric_dic['rir'] = rir + +# print metric +for key, value in metric_dic.items(): + print(f"{key}: {value}") + +summary = pd.DataFrame(metric_dic, index=[0]) +summary.to_csv(f'{visual_save_path}/metric_summary.csv', index=False) + +print(f'Save to {visual_save_path}/metric_summary.csv') + + +### Visualization +metric_2_name = {'clean_accuracy_attack': 'C-ACC', 'clean_accuracy_defense': 'C-ACC', 'asr_attack': '1 - ASR', 'asr_defense': '1 - ASR', 'ra_attack': 'RA', 'ra_defense': 'RA', 'der': 'DER', 'rir': 'RIR'} +if model_defense is not None: + used_metrics = ['clean_accuracy_defense', 'asr_defense', 'ra_defense', 'der', 'rir'] + if 'asr_defense' in used_metrics: + metric_dic['asr_defense'] = 1 - metric_dic['asr_defense'] + print('Turn ASR to 1-ASR for visualization.') + plot_metrics = [metric_2_name[key] for key in used_metrics] + plot_metrics_values = [metric_dic[key] for key in used_metrics] +else: + used_metrics = ['clean_accuracy_attack', 'asr_attack', 'ra_attack'] + if 'asr_attack' in used_metrics: + metric_dic['asr_attack'] = 1 - metric_dic['asr_attack'] + print('Turn ASR to 1-ASR for visualization.') + plot_metrics = [metric_2_name[key] for key in used_metrics] + plot_metrics_values = [metric_dic[key] for key in used_metrics] + + +angles = np.linspace(0, 2*np.pi, len(plot_metrics_values), endpoint=False) +stats = np.concatenate((plot_metrics_values, [plot_metrics_values[0]])) +angles = np.concatenate((angles, [angles[0]])) + +fig = plt.figure() +ax = fig.add_subplot(111, polar=True) +ax.plot(angles, stats, 'o-', linewidth=2) +ax.fill(angles, stats, alpha=0.25) +ax.set_rmax(1) + +ax.tick_params(rotation='auto', pad = 5) +ax.set_thetagrids(angles[:-1] * 180/np.pi, plot_metrics) + +ax.set_title("Metrics Summary", va='bottom') + +plt.tight_layout() +plt.savefig(f'{visual_save_path}/metric_summary.png') +print(f'Save to {visual_save_path}/metric_summary.png') \ No newline at end of file diff --git a/analysis/visual_na.py b/analysis/visual_na.py new file mode 100755 index 0000000..d530f0e --- /dev/null +++ b/analysis/visual_na.py @@ -0,0 +1,206 @@ +import sys +import os +sys.path.append("../") +sys.path.append(os.getcwd()) + +from visual_utils import * +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, +) +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.save_load_attack import load_attack_result +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +import yaml +import torch +import numpy as np +import torchvision.transforms as transforms + + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + +selected_classes = np.arange(args.num_classes) + +# Select classes to visualize +if args.num_classes > args.c_sub: + selected_classes = np.delete(selected_classes, args.target_class) + selected_classes = np.random.choice( + selected_classes, args.c_sub-1, replace=False) + selected_classes = np.append(selected_classes, args.target_class) + +# keep the same transforms for train and test dataset for better visualization +result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform +result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform + +# Create dataset. Only support BD_TEST and BD_TRAIN +if args.visual_dataset == 'bd_train': + bd_train_with_trans = result_attack["bd_train"] + visual_dataset = generate_bd_dataset( + bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub, bd_only=True) +elif args.visual_dataset == 'bd_test': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_bd_dataset( + bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub, bd_only=True) +else: + assert False, "Illegal vis_class" + +print( + f'Create visualization dataset with \n \t Dataset: {args.visual_dataset} \n \t Number of samples: {len(visual_dataset)} \n \t Selected classes: {selected_classes}') + +# Create data loader +data_loader = torch.utils.data.DataLoader( + visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False +) + +# Create denormalization function +for trans_t in data_loader.dataset.wrap_img_transform.transforms: + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + +if args.result_file_defense != "None": + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_visual.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_visual.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_visual = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_visual.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") +else: + model_visual.load_state_dict(result_attack["model"]) + print(f"Load model {args.model} from {args.result_file_attack}") + +model_visual.to(args.device) + +# !!! Important to set eval mode !!! +model_visual.eval() + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + +############## Neuron Activation ################## +print("Plotting Neuron Activation") + +# Choose layer for feature extraction +module_dict = dict(model_visual.named_modules()) +target_layer = module_dict[args.target_layer_name] +print(f'Choose layer {args.target_layer_name} from model {args.model}') + +# Get BD features +features_bd, labels_bd, other_info = get_features( + args, model_visual, target_layer, data_loader) +features_bd_avg = np.mean(features_bd, axis=0) + +# Get Corresponding Clean features +visual_dataset.wrapped_dataset.poison_indicator = np.zeros_like( + visual_dataset.wrapped_dataset.poison_indicator) + +features_clean, labels_clean, other_info = get_features( + args, model_visual, target_layer, data_loader) +features_clean_avg = np.mean(features_clean, axis=0) + +sort_bar = np.argsort(features_clean_avg)[::-1] + +features_bd_avg = features_bd_avg[sort_bar] +features_clean_avg = features_clean_avg[sort_bar] + +plt.figure(figsize=(10, 10)) +plt.bar( + np.arange(features_clean_avg.shape[0]), + features_clean_avg, + label="Clean", + alpha=0.7, + color="#2196F3", +) +plt.xlabel("Neuron") +plt.ylabel("Average Activation Value") +plt.title( + f"{get_dataname(args.dataset)}, {get_pratio(args.pratio)}% Poisoned Samples") +plt.xlim(0, features_clean_avg.shape[0]) +plt.legend() +plt.tight_layout() +plt.savefig(visual_save_path + "/NA_clean.png") + + +plt.figure(figsize=(10, 10)) +plt.bar( + np.arange(features_bd_avg.shape[0]), + features_bd_avg, + label="Poisoned", + alpha=0.7, + color="#4CAF50", +) +plt.xlabel("Neuron") +plt.ylabel("Average Activation Value") +plt.title( + f"{get_dataname(args.dataset)}, {get_pratio(args.pratio)}% Poisoned Samples") +plt.xlim(0, features_bd_avg.shape[0]) +plt.legend() +plt.tight_layout() +plt.savefig(visual_save_path + "/NA_BD.png") + + +plt.figure(figsize=(10, 10)) +plt.bar( + np.arange(features_clean_avg.shape[0]), + features_clean_avg, + label="Clean", + alpha=0.7, + color="#2196F3", +) +plt.bar( + np.arange(features_bd_avg.shape[0]), + features_bd_avg, + label="Poisoned", + alpha=0.7, + color="#4CAF50", +) +plt.xlabel("Neuron") +plt.ylabel("Average Activation Value") +plt.title( + f"{get_dataname(args.dataset)}, {get_pratio(args.pratio)}% Poisoned Samples") +plt.xlim(0, features_clean_avg.shape[0]) +plt.legend() + +plt.tight_layout() +plt.savefig(visual_save_path + f"/NA_compare_{args.visual_dataset}.png") + +print(f'Save to {visual_save_path + f"/NA_compare_{args.visual_dataset}"}.png') diff --git a/analysis/visual_network.py b/analysis/visual_network.py new file mode 100755 index 0000000..8d35a4a --- /dev/null +++ b/analysis/visual_network.py @@ -0,0 +1,151 @@ +import sys +import os +import yaml +import torch +from torchviz import make_dot, make_dot_from_trace + +sys.path.append("../") +sys.path.append(os.getcwd()) + +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from visual_utils import * + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + +############## Model Structure ################## +print("Plotting Model Structure using pytorchviz") + +# pip install -U git+https://github.com/szagoruyko/pytorchviz.git@master + +x = torch.zeros([10, args.input_channel, args.input_height, args.input_width]) + +dot = make_dot(model_visual(x), params=dict(model_visual.named_parameters())) +dot.format = "png" +dot.render(f'structure_{args.model}', directory=visual_save_path, cleanup=True) + +print(f'Save to {visual_save_path + f"/structure_{args.model}"}.png') + + +# Another way to show model structure using hiddenlayer +print("Plotting Model Structure using hiddenlayer") + +import hiddenlayer as hl + +def build_dot(graph, rankdir = 'TB'): + """Generate a GraphViz Dot graph. + Returns a GraphViz Digraph object. + This is modified from https://github.com/waleedka/hiddenlayer/blob/master/hiddenlayer/graph.py + by changing rankdir="TB" to allow a vertical plot. + see https://github.com/waleedka/hiddenlayer/issues/63 + args: + graph: hiddlen layer graph + rankdir: direction for show plot. Left to right (LR) or Top to down (TD). + """ + from graphviz import Digraph + + # Build GraphViz Digraph + dot = Digraph() + dot.attr("graph", + bgcolor=graph.theme["background_color"], + color=graph.theme["outline_color"], + fontsize=graph.theme["font_size"], + fontcolor=graph.theme["font_color"], + fontname=graph.theme["font_name"], + margin=graph.theme["margin"], + rankdir=rankdir, + pad=graph.theme["padding"]) + dot.attr("node", shape="box", + style="filled", margin="0,0", + fillcolor=graph.theme["fill_color"], + color=graph.theme["outline_color"], + fontsize=graph.theme["font_size"], + fontcolor=graph.theme["font_color"], + fontname=graph.theme["font_name"]) + dot.attr("edge", style="solid", + color=graph.theme["outline_color"], + fontsize=graph.theme["font_size"], + fontcolor=graph.theme["font_color"], + fontname=graph.theme["font_name"]) + + for k, n in graph.nodes.items(): + label = "{}".format(n.title) + if n.caption: + label += "{}".format(n.caption) + if n.repeat > 1: + label += "x{}".format(n.repeat) + label = "<" + label + "
>" + dot.node(str(k), label) + for a, b, label in graph.edges: + if isinstance(label, (list, tuple)): + label = "x".join([str(l or "?") for l in label]) + + dot.edge(str(a), str(b), label) + return dot + +transforms="default" + +''' +For AdaptivePool, ONNX only support pool with output_size = 1 for all dimensions or output shape is a factor of input shape. +It's recommended to replace the adaptive pooling with regular pooling if possible. +Otherwise, you can uncomment the following code to use a self-defined pooling layer to run it anyway. +''' + +# for name, module in model_visual.named_modules(): +# if isinstance(module, torch.nn.AdaptiveAvgPool2d) or isinstance(module, torch.nn.AdaptiveMaxPool2d): +# # hook a function to get input shape +# def shape_hook(module, input_, output_): +# global out_shape +# out_shape = output_.shape +# return None +# h = module.register_forward_hook(shape_hook) +# model_visual(torch.zeros([1, args.input_channel, args.input_height, args.input_width])) + +# class pseduo_pool(torch.nn.AdaptiveAvgPool2d): +# def __init__(self) -> None: +# super().__init__(output_size=(1,1)) + +# def forward(self, input): +# pseduo_out = torch.zeros(out_shape) * torch.sum(input) +# return pseduo_out + +# setattr(model_visual, name, pseduo_pool()) +# print(f"replace {module} by s self-defined pool layer.") + +# model_visual(torch.zeros([1, args.input_channel, args.input_height, args.input_width])) + +# transforms = [ +# # Fold the self-defined operations into SelfDefined Pooling +# # may cause name problem if you have the same operation pattern in your model +# hl.transforms.Fold("ReduceSum > Mul", "AvgPool"), +# hl.transforms.Fold("Constant > AvgPool", "AvgPool2", name = "Self-Defined Pooling") +# ] + +try: + graph = hl.build_graph(model_visual, torch.zeros([10, args.input_channel, args.input_height, args.input_width]), transforms=transforms) + dot = build_dot(graph) + dot.format = "png" + dot.render(f'structure_{args.model}_hl', directory=visual_save_path, cleanup=True) + + print(f'Save to {visual_save_path + f"/structure_{args.model}_hl"}.png') + +except: + print("Unsupported operation in hiddenlayer, recommend to use pytorchviz only.") diff --git a/analysis/visual_quality.py b/analysis/visual_quality.py new file mode 100755 index 0000000..6291814 --- /dev/null +++ b/analysis/visual_quality.py @@ -0,0 +1,181 @@ +import sys +import os +sys.path.append("../") +sys.path.append("../../") +sys.path.append(os.getcwd()) + +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, +) +from visual_utils import * +import yaml +import torch +import numpy as np +import torchvision.transforms as transforms + + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + +selected_classes = np.arange(args.num_classes) + +# Select classes to visualize +if args.num_classes > args.c_sub: + selected_classes = np.delete(selected_classes, args.target_class) + selected_classes = np.random.choice( + selected_classes, args.c_sub-1, replace=False) + selected_classes = np.append(selected_classes, args.target_class) + +# keep the same transforms for train and test dataset for better visualization +result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform +result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform + +# Create dataset +if args.visual_dataset == 'mixed': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_mix_dataset( + bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'bd_train': + bd_train_with_trans = result_attack["bd_train"] + visual_dataset = generate_bd_dataset( + bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'bd_test': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_bd_dataset( + bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub) +else: + assert False, "Illegal vis_class" + +print( + f'Create visualization dataset with \n \t Dataset: {args.visual_dataset} \n \t Number of samples: {len(visual_dataset)} \n \t Selected classes: {selected_classes}') + +# Create data loader +data_loader = torch.utils.data.DataLoader( + visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False +) + +# Create denormalization function +for trans_t in data_loader.dataset.wrap_img_transform.transforms: + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + +#### SSIM #### +visual_poison_indicator = np.array(get_poison_indicator_from_bd_dataset(visual_dataset)) +bd_idx = np.where(visual_poison_indicator == 1)[0] + +from torchmetrics import StructuralSimilarityIndexMeasure +ssim = StructuralSimilarityIndexMeasure() +ssim_list = [] +if visual_poison_indicator.sum() > 0: + print(f'Number Poisoned samples: {visual_poison_indicator.sum()}') + # random choose two poisoned samples + start_idx = 0 + for i in range(bd_idx.shape[0]): + bd_sample = denormalizer(visual_dataset[i][0]).unsqueeze(0) + with temporary_all_clean(visual_dataset): + clean_sample = denormalizer(visual_dataset[i][0]).unsqueeze(0) + ssim_list.append(ssim(bd_sample, clean_sample)) +print(f'Average SSIM: {np.mean(ssim_list)}') + + +####### FFIM ####### +visual_poison_indicator = np.array(get_poison_indicator_from_bd_dataset(visual_dataset)) +bd_idx = np.where(visual_poison_indicator == 1)[0] + +from torchmetrics.image.fid import FrechetInceptionDistance +fid = FrechetInceptionDistance(feature=64, normalize = True) +if visual_poison_indicator.sum() > 0: + print(f'Number Poisoned samples: {visual_poison_indicator.sum()}') + # random choose two poisoned samples + start_idx = 0 + for i in range(bd_idx.shape[0]): + bd_sample = denormalizer(visual_dataset[i][0]).unsqueeze(0) + with temporary_all_clean(visual_dataset): + clean_sample = denormalizer(visual_dataset[i][0]).unsqueeze(0) + fid.update(clean_sample, real=True) + fid.update(bd_sample, real=False) + fid_value = fid.compute().numpy() +print(f'FID: {fid_value}') + + +###### PSNR ###### +from torchmetrics.image.psnr import PeakSignalNoiseRatio +psnr = PeakSignalNoiseRatio() +psnr_list = [] + +if visual_poison_indicator.sum() > 0: + print(f'Number Poisoned samples: {visual_poison_indicator.sum()}') + # random choose two poisoned samples + start_idx = 0 + for i in range(bd_idx.shape[0]): + bd_sample = denormalizer(visual_dataset[i][0]).unsqueeze(0) + with temporary_all_clean(visual_dataset): + clean_sample = denormalizer(visual_dataset[i][0]).unsqueeze(0) + psnr_list.append(psnr(bd_sample, clean_sample)) +print(f'Average PSNR: {np.mean(psnr_list)}') + +quality_metrics = {} +quality_metrics['SSIM'] = np.mean(ssim_list) +quality_metrics['PSNR'] = np.mean(psnr_list) +quality_metrics['FID'] = fid_value + +quality_metrics_df = pd.DataFrame(quality_metrics, index=[0]) +quality_metrics_df.to_csv(f'{visual_save_path}/quality_metrics.csv', index=False) + +print(f'Save to {visual_save_path}/quality_metrics.csv') + + +# visualize quality metrics is disabled since PSNR and SSIM are not comparable + +# ### Visualization +# plot_metrics = list(quality_metrics.keys()) +# plot_metrics_values = list(quality_metrics.values()) + + +# angles = np.linspace(0, 2*np.pi, len(plot_metrics_values), endpoint=False) +# stats = np.concatenate((plot_metrics_values, [plot_metrics_values[0]])) +# angles = np.concatenate((angles, [angles[0]])) + +# fig = plt.figure() +# ax = fig.add_subplot(111, polar=True) +# ax.plot(angles, stats, 'o-', linewidth=2) +# ax.fill(angles, stats, alpha=0.25) +# ax.set_rmax(1) +# ax.set_rmin(-1) + +# ax.tick_params(rotation='auto', pad = 5) +# ax.set_thetagrids(angles[:-1] * 180/np.pi, plot_metrics) + +# ax.set_title("Quality Summary", va='bottom') + +# plt.tight_layout() +# plt.savefig(f'{visual_save_path}/quality_metrics.png') +# print(f'Save to {visual_save_path}/quality_metrics.png') diff --git a/analysis/visual_shap.py b/analysis/visual_shap.py new file mode 100755 index 0000000..fbf6c6f --- /dev/null +++ b/analysis/visual_shap.py @@ -0,0 +1,209 @@ +import sys +import os +sys.path.append("../") +sys.path.append("../../") +sys.path.append(os.getcwd()) + +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, +) +from visual_utils import * +import yaml +import torch +import shap +import numpy as np +import torchvision.transforms as transforms + + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + +selected_classes = np.arange(args.num_classes) + +# Select classes to visualize +if args.num_classes > args.c_sub: + selected_classes = np.delete(selected_classes, args.target_class) + selected_classes = np.random.choice( + selected_classes, args.c_sub-1, replace=False) + selected_classes = np.append(selected_classes, args.target_class) + +# keep the same transforms for train and test dataset for better visualization +result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform +result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform + +# Create dataset +if args.visual_dataset == 'mixed': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_mix_dataset( + bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'clean_train': + clean_train_with_trans = result_attack["clean_train"] + visual_dataset = generate_clean_dataset( + clean_train_with_trans, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'clean_test': + clean_test_with_trans = result_attack["clean_test"] + visual_dataset = generate_clean_dataset( + clean_test_with_trans, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'bd_train': + bd_train_with_trans = result_attack["bd_train"] + visual_dataset = generate_bd_dataset( + bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'bd_test': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_bd_dataset( + bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub) +else: + assert False, "Illegal vis_class" + +print( + f'Create visualization dataset with \n \t Dataset: {args.visual_dataset} \n \t Number of samples: {len(visual_dataset)} \n \t Selected classes: {selected_classes}') + +# Create data loader +data_loader = torch.utils.data.DataLoader( + visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False +) + +# Create denormalization function +for trans_t in data_loader.dataset.wrap_img_transform.transforms: + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + +# Prepare background data +num_bg = 200 +background_idx = np.random.choice(len(visual_dataset), num_bg, replace=False) +background_samples = [] +for i in background_idx: + background_samples.append(visual_dataset[i][0].unsqueeze(0)) + +background_samples = torch.cat(background_samples, axis=0).to(args.device) + +# Choose samples to show SHAP values. By Default, 2 clean images + 2 Poison images. If no enough Poison images, use 4 clean images instead.AblationCAM +total_num = 4 +bd_num = 0 + +visual_samples = [] +visual_labels = [] + +visual_poison_indicator = np.array( + get_poison_indicator_from_bd_dataset(visual_dataset)) +if visual_poison_indicator.sum() > 0: + print(f'Number Poisoned samples: {visual_poison_indicator.sum()}') + # random choose two poisoned samples + selected_bd_idx = np.random.choice( + np.where(visual_poison_indicator == 1)[0], 2, replace=False) + for i in selected_bd_idx: + visual_samples.append(visual_dataset[i][0].unsqueeze(0)) + visual_labels.append(visual_dataset[i][4]) + bd_num = len(selected_bd_idx) + print(f'Select {bd_num} poisoned samples') + +# Trun all samples to clean +with temporary_all_clean(visual_dataset): + # you can just set selected_clean_idx = selected_bd_idx to build the correspondence between clean samples and poisoned samples + selected_clean_idx = np.random.choice( + len(visual_dataset), total_num-bd_num, replace=False) + for i in selected_clean_idx: + visual_samples.append(visual_dataset[i][0].unsqueeze(0)) + visual_labels.append(visual_dataset[i][1]) + print(f'Select {len(selected_clean_idx)} clean samples') + +# Clean sample first +visual_samples = visual_samples[::-1] +visual_labels = visual_labels[::-1] + +visual_samples = torch.cat(visual_samples, axis=0).to(args.device) + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + +if args.result_file_defense != "None": + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_visual.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_visual.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_visual = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_visual.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") +else: + model_visual.load_state_dict(result_attack["model"]) + print(f"Load model {args.model} from {args.result_file_attack}") + +model_visual.to(args.device) + +# !!! Important to set eval mode !!! +model_visual.eval() + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + +############## SHAP ################## +print('Plotting SHAP') + +# Choose layer for feature extraction +module_dict = dict(model_visual.named_modules()) +target_layer = module_dict[args.target_layer_name] +print(f'Choose layer {args.target_layer_name} from model {args.model}') + +sfm = torch.nn.Softmax(dim=1) +outputs = model_visual(visual_samples) +pre_p, pre_label = torch.max(sfm(outputs), dim=1) + +e = shap.GradientExplainer( + (model_visual, target_layer), background_samples, local_smoothing=0) +shap_values, indexes = e.shap_values(visual_samples, ranked_outputs=5) + +# get the names for the classes +class_names = np.array(args.class_names).reshape([-1]) +index_names = np.vectorize( + lambda x: class_names[x].capitalize())(indexes.cpu()) +# plot the explanations +shap_numpy = [np.swapaxes(np.swapaxes(s, 1, -1), 1, 2) for s in shap_values] +test_numpy = np.swapaxes( + np.swapaxes(denormalizer(visual_samples.cpu()).numpy(), 1, -1), 1, 2 +) +test_numpy[test_numpy < 1e-12] = 1e-12 # for some numerical issue + +shap.image_plot(shap_numpy, test_numpy, index_names, show=False) + +# plt.tight_layout() is not working +plt.savefig(visual_save_path + f"/shap_{args.visual_dataset}.png") + +print(f'Save to {visual_save_path + f"/shap_{args.visual_dataset}"}.png') diff --git a/analysis/visual_tac.py b/analysis/visual_tac.py new file mode 100755 index 0000000..1e2feed --- /dev/null +++ b/analysis/visual_tac.py @@ -0,0 +1,226 @@ +import sys +import os +sys.path.append("../") +sys.path.append(os.getcwd()) + +import matplotlib +from matplotlib.patches import Rectangle, Patch +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, +) +from visual_utils import * +import yaml +import torch +import matplotlib as mlp +import numpy as np +import torchvision.transforms as transforms + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + +selected_classes = np.arange(args.num_classes) + +# Select classes to visualize +if args.num_classes>args.c_sub: + selected_classes = np.delete(selected_classes, args.target_class) + selected_classes = np.random.choice(selected_classes, args.c_sub-1, replace=False) + selected_classes = np.append(selected_classes, args.target_class) + +# keep the same transforms for train and test dataset for better visualization +result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform +result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform + +# Create dataset. Only support BD_TEST and BD_TRAIN +if args.visual_dataset == 'bd_train': + bd_train_with_trans = result_attack["bd_train"] + visual_dataset = generate_bd_dataset(bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub, bd_only = True) +elif args.visual_dataset == 'bd_test': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_bd_dataset(bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub, bd_only = True) +else: + assert False, "Illegal vis_class" + +print(f'Create visualization dataset with \n \t Dataset: {args.visual_dataset} \n \t Number of samples: {len(visual_dataset)} \n \t Selected classes: {selected_classes}') + +# Create data loader +data_loader = torch.utils.data.DataLoader( + visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False +) + +# Create denormalization function +for trans_t in data_loader.dataset.wrap_img_transform.transforms: + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + +if args.result_file_defense != "None": + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_visual.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_visual.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_visual = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_visual.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") +else: + model_visual.load_state_dict(result_attack["model"]) + print(f"Load model {args.model} from {args.result_file_attack}") + +model_visual.to(args.device) + +# !!! Important to set eval mode !!! +model_visual.eval() + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + +############ Trigger Activation Change ################ +print('Plotting Trigger Activation Change') + +module_dict = dict(model_visual.named_modules()) +module_names = module_dict.keys() + +# Plot Conv2d or Linear +module_visual = [i for i in module_dict.keys() if isinstance( + module_dict[i], torch.nn.Conv2d) or isinstance(module_dict[i], torch.nn.Linear) or isinstance(module_dict[i], torch.nn.BatchNorm2d)] + +df = None + +max_num_neuron = 0 +for module_name in module_visual: + target_layer = module_dict[module_name] + + print(f'Collecting BD features from module {target_layer}') + features_bd, labels_bd, *other_info = get_features( + args, model_visual, target_layer, data_loader, reduction='none', activation= None) + + print(f'Collecting Clean features from module {target_layer}') + with temporary_all_clean(visual_dataset): + features_bd_clean, labels_clean, *other_info = get_features( + args, model_visual, target_layer, data_loader, reduction='none', activation= None) + + total_neuron = features_bd.shape[1] + max_num_neuron = np.max([max_num_neuron, total_neuron]) + feature_diff = features_bd - features_bd_clean + np.abs(feature_diff, out = feature_diff) # inplace abs for faster computation + feature_abs_diff = feature_diff + # average over batch + feature_abs_diff_mean = np.mean(feature_abs_diff, axis=0) + # average for each channel + if feature_abs_diff_mean.ndim >1: + feature_abs_diff_mean = np.sum(feature_abs_diff_mean.reshape(total_neuron, -1), axis=1) + + + for neuron_i in range(total_neuron): + base_row = {} + base_row['layer'] = module_name + base_row['Neuron'] = neuron_i + base_row['TAC'] = feature_abs_diff_mean[neuron_i] + if df is None: + df = pd.DataFrame.from_dict([base_row]) + else: + df.loc[df.shape[0]] = base_row + +df.to_csv(visual_save_path + f'/tac_{args.visual_dataset}.csv') + +start_x0 = 0 +height = 1 +width = 1 +vmin = 0 +if args.normalize_by_layer: + vmax = 1 +else: + vmax = df.TAC.max() +max_num_neuron = df.Neuron.max() + +norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax, clip=True) +mapper = matplotlib.cm.ScalarMappable(norm=norm, cmap=matplotlib.cm.Blues) + +fig, ax = plt.subplots( + figsize=(int(len(module_visual)), int(max_num_neuron/10))) + +ax.plot([0, 0], [0, 0]) + +for module_name in module_visual: + print(f'ploting {module_name}') + y_0 = 0 + layer_info = df[df.layer == module_name] + layer_tac_max = layer_info['TAC'].max() + total_neuron = layer_info.shape[0] + for neuron_i in range(total_neuron): + x_0 = start_x0 + base_row = layer_info.iloc[neuron_i] + if args.normalize_by_layer: + ax.add_patch(Rectangle((x_0, y_0), width, height, + facecolor=mapper.to_rgba(base_row['TAC']/layer_tac_max), + fill=True, + lw=5, + alpha=0.8)) + + else: + ax.add_patch(Rectangle((x_0, y_0), width, height, + facecolor=mapper.to_rgba(base_row['TAC']), + fill=True, + lw=5, + alpha=0.8)) + + y_0 += 1.5*height + start_x0 += 1.5*width +x_loc = [0.5*width+1.5*width*i for i in range(len(module_visual))] +y_loc = [0.5*height+1.5*height*i for i in range(max_num_neuron)] + +ax.set_xlim(xmin=-0.5*width, xmax=1.5*width*(len(module_visual)+1)) +ax.set_ylim(ymin=-0.5*height, ymax=1.5*height*(max_num_neuron+1)) +ax.set_xticks(x_loc, module_visual, rotation=270) +ax.set_yticks(y_loc[::10], np.arange(max_num_neuron)[::10]) +ax.set_title(f'TAC of {len(visual_dataset)} Images') +ax.set_ylabel('Neuron') +ax.set_xlabel('Layer') + +cb_ax = fig.add_axes([0.15, 0.9, 0.7, 0.01]) + +fig.colorbar(mapper, + cax=cb_ax, orientation="horizontal", label='TAC') + +plt.savefig(visual_save_path + f"/tac_{args.visual_dataset}.png") + +print(f'Save to {visual_save_path + f"/tac_{args.visual_dataset}"}.png') diff --git a/analysis/visual_tsne.py b/analysis/visual_tsne.py new file mode 100755 index 0000000..74045da --- /dev/null +++ b/analysis/visual_tsne.py @@ -0,0 +1,172 @@ +import sys +import os +import yaml +import torch +import numpy as np +import torchvision.transforms as transforms + +sys.path.append("../") +sys.path.append(os.getcwd()) + +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, +) +from visual_utils import * + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + +selected_classes = np.arange(args.num_classes) + +# Select classes to visualize +if args.num_classes > args.c_sub: + selected_classes = np.delete(selected_classes, args.target_class) + selected_classes = np.random.choice( + selected_classes, args.c_sub-1, replace=False) + selected_classes = np.append(selected_classes, args.target_class) + +# keep the same transforms for train and test dataset for better visualization +result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform +result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform + +# Create dataset +if args.visual_dataset == 'mixed': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_mix_dataset( + bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'clean_train': + clean_train_with_trans = result_attack["clean_train"] + visual_dataset = generate_clean_dataset( + clean_train_with_trans, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'clean_test': + clean_test_with_trans = result_attack["clean_test"] + visual_dataset = generate_clean_dataset( + clean_test_with_trans, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'bd_train': + bd_train_with_trans = result_attack["bd_train"] + visual_dataset = generate_bd_dataset( + bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub) +else: + assert False, "Illegal vis_class" + +print( + f'Create visualization dataset with \n \t Dataset: {args.visual_dataset} \n \t Number of samples: {len(visual_dataset)} \n \t Selected classes: {selected_classes}') + +# Create data loader +data_loader = torch.utils.data.DataLoader( + visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False +) + +# Create denormalization function +for trans_t in data_loader.dataset.wrap_img_transform.transforms: + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + +if args.result_file_defense != "None": + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_visual.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_visual.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_visual = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_visual.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") +else: + model_visual.load_state_dict(result_attack["model"]) + print(f"Load model {args.model} from {args.result_file_attack}") + +model_visual.to(args.device) + +# !!! Important to set eval mode !!! +model_visual.eval() + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + +############## T-SNE ################## +print("Plotting T-SNE") + +# Choose layer for feature extraction +module_dict = dict(model_visual.named_modules()) +target_layer = module_dict[args.target_layer_name] +print(f'Choose layer {args.target_layer_name} from model {args.model}') + +# Get features +features, labels, poi_indicator = get_features( + args, model_visual, target_layer, data_loader) + +# General plotting parameters +custom_palette = sns.color_palette("hls", np.unique(labels).shape[0]) +classes = args.class_names + +# Setting parameters for Poisoned Samples +# use poi_indicator==1 to avoid some datatype issue for indexing +if np.sum(poi_indicator) > 0: + # Label: args.num_classes + labels[poi_indicator == 1] = args.num_classes + # Class Name: poisoned + classes += ["poisoned"] + # Color: Black + custom_palette += [(0.0, 0.0, 0.0)] + +sort_idx = np.argsort(labels) +features = features[sort_idx] +labels = labels[sort_idx] +label_class = [classes[i].capitalize() for i in labels] + +# Plot T-SNE + +fig = tsne_fig( + features, + label_class, + title="t-SNE Embedding", + xlabel="Dim 1", + ylabel="Dim 2", + custom_palette=custom_palette, + size=(10, 10), +) +plt.tight_layout() +plt.savefig(visual_save_path + f"/tsne_{args.visual_dataset}.png") + +print(f'Save to {visual_save_path + f"/tsne_{args.visual_dataset}"}.png') diff --git a/analysis/visual_umap.py b/analysis/visual_umap.py new file mode 100755 index 0000000..1804936 --- /dev/null +++ b/analysis/visual_umap.py @@ -0,0 +1,174 @@ +import sys +import os +import yaml +import torch +import numpy as np +import torchvision.transforms as transforms + +sys.path.append("../") +sys.path.append(os.getcwd()) + +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.save_load_attack import load_attack_result +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, +) +from visual_utils import * + +# Basic setting: args +args = get_args() + +with open(args.yaml_path, "r") as stream: + config = yaml.safe_load(stream) +config.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = config +args = preprocess_args(args) +fix_random(int(args.random_seed)) + +save_path_attack = "./record/" + args.result_file_attack +visual_save_path = save_path_attack + "/visual" + + +# Load result +if args.prototype: + result_attack = load_prototype_result(args, save_path_attack) +else: + result_attack = load_attack_result(save_path_attack + "/attack_result.pt") + +selected_classes = np.arange(args.num_classes) + +# Select classes to visualize +if args.num_classes > args.c_sub: + selected_classes = np.delete(selected_classes, args.target_class) + selected_classes = np.random.choice( + selected_classes, args.c_sub-1, replace=False) + selected_classes = np.append(selected_classes, args.target_class) + +# keep the same transforms for train and test dataset for better visualization +result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform +result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform + +# Create dataset +if args.visual_dataset == 'mixed': + bd_test_with_trans = result_attack["bd_test"] + visual_dataset = generate_mix_dataset( + bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'clean_train': + clean_train_with_trans = result_attack["clean_train"] + visual_dataset = generate_clean_dataset( + clean_train_with_trans, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'clean_test': + clean_test_with_trans = result_attack["clean_test"] + visual_dataset = generate_clean_dataset( + clean_test_with_trans, selected_classes, max_num_samples=args.n_sub) +elif args.visual_dataset == 'bd_train': + bd_train_with_trans = result_attack["bd_train"] + visual_dataset = generate_bd_dataset( + bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub) +else: + assert False, "Illegal vis_class" + +print( + f'Create visualization dataset with \n \t Dataset: {args.visual_dataset} \n \t Number of samples: {len(visual_dataset)} \n \t Selected classes: {selected_classes}') + +# Create data loader +data_loader = torch.utils.data.DataLoader( + visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False +) + +# Create denormalization function +for trans_t in data_loader.dataset.wrap_img_transform.transforms: + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + +# Load model +model_visual = generate_cls_model(args.model, args.num_classes) + +if args.result_file_defense != "None": + save_path_defense = "./record/" + args.result_file_defense + visual_save_path = save_path_defense + "/visual" + + result_defense = load_attack_result( + save_path_defense + "/defense_result.pt") + defense_method = args.result_file_defense.split('/')[-1] + if defense_method == 'fp': + model_visual.layer4[1].conv2 = torch.nn.Conv2d( + 512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False) + model_visual.linear = torch.nn.Linear( + (512 - result_defense['index'])*1, args.num_classes) + if defense_method == 'dbd': + backbone = get_network_dbd(args) + model_visual = LinearModel( + backbone, backbone.feature_dim, args.num_classes) + model_visual.load_state_dict(result_defense["model"]) + print(f"Load model {args.model} from {args.result_file_defense}") +else: + model_visual.load_state_dict(result_attack["model"]) + print(f"Load model {args.model} from {args.result_file_attack}") + +model_visual.to(args.device) + +# !!! Important to set eval mode !!! +model_visual.eval() + +# make visual_save_path if not exist +os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None + +############## UMAP ################## +print("Plotting UMAP") + +# Choose layer for feature extraction +module_dict = dict(model_visual.named_modules()) +target_layer = module_dict[args.target_layer_name] +print(f'Choose layer {args.target_layer_name} from model {args.model}') + +# Get features +features, labels, poi_indicator = get_features( + args, model_visual, target_layer, data_loader) + +# General plotting parameters +custom_palette = sns.color_palette("hls", np.unique(labels).shape[0]) +classes = args.class_names + +# Setting parameters for Poisoned Samples +# use poi_indicator==1 to avoid some datatype issue for indexing +if np.sum(poi_indicator) > 0: + # Label: args.num_classes + labels[poi_indicator == 1] = args.num_classes + # Class Name: poisoned + classes += ["poisoned"] + # Color: Black + custom_palette += [(0.0, 0.0, 0.0)] + +sort_idx = np.argsort(labels) +features = features[sort_idx] +labels = labels[sort_idx] +label_class = [classes[i].capitalize() for i in labels] + +# Plot UMAP + +fig = umap_fig( + features, + label_class, + title="UMAP Embedding", + xlabel="Dim 1", + ylabel="Dim 2", + custom_palette=custom_palette, + size=(10, 10), + mark_size = 0.6, + alpha = 1 +) +plt.tight_layout() +plt.savefig(visual_save_path + f"/umap_{args.visual_dataset}.png") + +print(f'Save to {visual_save_path + f"/umap_{args.visual_dataset}"}.png') diff --git a/analysis/visual_utils.py b/analysis/visual_utils.py new file mode 100755 index 0000000..64da0ee --- /dev/null +++ b/analysis/visual_utils.py @@ -0,0 +1,1018 @@ +import os +import torch +import copy +import argparse +import numpy as np +import pandas as pd +import seaborn as sns +from umap import UMAP +import matplotlib.pyplot as plt +from sklearn.manifold import TSNE +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform +from utils.aggregate_block.dataset_and_transform_generate import ( + get_transform, + get_dataset_denormalization, + dataset_and_transform_generate +) +import contextlib + + +def get_args(use_IPython=False): + # set the basic parameter + parser = argparse.ArgumentParser() + + parser.add_argument("--device", type=str, help="cuda|cpu") + parser.add_argument( + "--yaml_path", + type=str, + default="./config/visualization/default.yaml", + help="the path of yaml which contains the default parameters", + ) + parser.add_argument("--seed", type=str, help="random seed for reproducibility") + parser.add_argument("--model", type=str, help="model name such as resnet18, vgg19") + + # data parameters + parser.add_argument("--dataset_path", type=str, help="path to dataset") + parser.add_argument( + "--dataset", type=str, help="mnist, cifar10, cifar100, gtsrb, celeba, tiny" + ) + parser.add_argument("--visual_dataset", type=str, default='bd_train', + help="type of dataset for visualization. mixed|clean_train|clean_test|bd_train|bd_test") + parser.add_argument("--target_class", type=int, + default=0, help="tagrt class for attack, used for subset dataset, legend, title, etc.") + parser.add_argument("--num_classes", type=int, help="number of classes for given dataset used for create visualization dataset") + parser.add_argument("--input_height", type=int, help="input height of the image") + parser.add_argument("--input_width", type=int, help="input width of the image") + parser.add_argument("--input_channel", type=int, help="input channel of the image") + parser.add_argument("--batch_size", type=int, help="batch size for visualization") + parser.add_argument("--n_sub", default=5000, type=int, help="max number of samples for visualization") + parser.add_argument("--c_sub", default=10, type=int, help="max number of classes for visualization") + parser.add_argument("--num_workers", default=2, type=int, help="number of workers for dataloader") + parser.add_argument("--class_names", type=list, + help="no need to give, it will be created by preprocess_args if not provided") + + # BD parameters + parser.add_argument("--pratio", type=float, help="ratio of poisoned samples, used for mix_dataset and legend") + + # results parameters + parser.add_argument( + "--result_file_attack", + default='badnet_demo', + type=str, + help="the location of attack result, must be given to load the dataset", + ) + parser.add_argument( + "--result_file_defense", + default='None', + type=str, + help="the location of defense result. If given, the defense model will be used instead of the attack model", + ) + parser.add_argument("--checkpoint_load", default=None, type=str) + parser.add_argument("--checkpoint_save", default=None, type=str) + + # plot parameters + parser.add_argument('--neuron_order', type=str, default='ordered', + help='The order of Neuron for visualization, used for visual_act.') + parser.add_argument('--num_neuron', type=int, default=50, + help='The number of Neuron for visualization. Must less than 100, used for visual_act.') + parser.add_argument('--num_image', type=int, default=10, + help='The number of images for each Neuron. Must less than 100, used for visual_act.') + + parser.add_argument('--target_layer_name', type=str, default='default', + help='The name of layer for extracting features, used by plots that use features.') + + # Parameter for Landscape Visualization + parser.add_argument('--ngpu', type=int, default=1, + help='number of GPUs to use for each rank, useful for data parallel evaluation') + parser.add_argument('--raw_data', action='store_true', + default=False, help='no data preprocessing') + + # direction parameters + parser.add_argument('--dir_type', default='weights', + help='direction type: weights | states (including BN\'s running_mean/var)') + parser.add_argument('--x', default='-1:1:30', + help='A string with format xmin:x_max:xnum') + parser.add_argument('--y', default='-1:1:30', + help='A string with format ymin:ymax:ynum') + parser.add_argument('--xnorm', default='filter', + help='direction normalization: filter | layer | weight') + parser.add_argument('--ynorm', default='filter', + help='direction normalization: filter | layer | weight') + parser.add_argument('--xignore', default='biasbn', + help='ignore bias and BN parameters: biasbn') + parser.add_argument('--yignore', default='biasbn', + help='ignore bias and BN parameters: biasbn') + parser.add_argument('--same_dir', action='store_true', default=False, + help='use the same random direction for both x-axis and y-axis') + parser.add_argument('--idx', default=0, type=int, + help='the index for the repeatness experiment') + + # plot parameters + parser.add_argument('--loss_name', '-l', default='crossentropy', + help='loss functions: crossentropy | mse, used for landscape visualization') + parser.add_argument('--metric_z', default='train_loss', + type=str, help='metric for z axis: train_loss | train_acc, used for landscape visualization') + + # Parameter for TAC and Lips plot + parser.add_argument('--normalize_by_layer', action='store_true', + default=False, help='Normalize the values by layer, used for TAC and Lips plot') + + # For prototype + parser.add_argument('--prototype', action='store_true', + default=False, help='Specify whether the result is for prototype') + + # whether use IPython like juptyer notebook + if use_IPython: + args = parser.parse_args(args=[]) + else: + args = parser.parse_args() + return args + + +def preprocess_args(args): + # preprocess args for dataset + if args.dataset == "mnist": + args.num_classes = 10 + args.input_height = 28 + args.input_width = 28 + args.input_channel = 1 + args.class_names = get_class_name(args.dataset, args.num_classes, args) + + elif args.dataset == "cifar10": + args.num_classes = 10 + args.input_height = 32 + args.input_width = 32 + args.input_channel = 3 + args.class_names = get_class_name(args.dataset, args.num_classes, args) + + elif args.dataset == "cifar100": + args.num_classes = 100 + args.input_height = 32 + args.input_width = 32 + args.input_channel = 3 + args.class_names = get_class_name(args.dataset, args.num_classes, args) + + elif args.dataset == "gtsrb": + args.num_classes = 43 + args.input_height = 32 + args.input_width = 32 + args.input_channel = 3 + args.class_names = get_class_name(args.dataset, args.num_classes, args) + + elif args.dataset == "celeba": + args.num_classes = 8 + args.input_height = 64 + args.input_width = 64 + args.input_channel = 3 + args.class_names = get_class_name(args.dataset, args.num_classes, args) + + elif args.dataset == "tiny": + args.num_classes = 200 + args.input_height = 64 + args.input_width = 64 + args.input_channel = 3 + args.class_names = get_class_name(args.dataset, args.num_classes, args) + else: + raise Exception("Invalid Dataset") + + # preprocess args for target layer + if args.target_layer_name == 'default': + if args.model == "preactresnet18": + args.target_layer_name = 'layer4.1.conv2' + if args.model == "vgg19": + args.target_layer_name = 'features.34' + if args.model == "resnet18": + args.target_layer_name = 'layer4.1.conv2' + if args.model == "densenet161": + args.target_layer_name = 'features.denseblock4.denselayer24.conv2' + if args.model == "mobilenet_v3_large": + args.target_layer_name = 'features.16.0' + if args.model == "efficientnet_b3": + args.target_layer_name = 'features.7.1.block.3.0' + + # Preprofess args for landscape + args.cuda = True if 'cuda' in args.device else False + args.xmin, args.xmax, args.xnum = [float(a) for a in args.x.split(':')] + args.ymin, args.ymax, args.ynum = (None, None, None) + args.xnum = int(args.xnum) + if args.y: + args.ymin, args.ymax, args.ynum = [float(a) for a in args.y.split(':')] + assert args.ymin and args.ymax and args.ynum, \ + 'You specified some arguments for the y axis, but not all' + args.ynum = int(args.ynum) + return args + + +@contextlib.contextmanager +def temporary_all_clean(bd_dataset): + old_poison_indicator = bd_dataset.wrapped_dataset.poison_indicator.copy() + bd_dataset.wrapped_dataset.poison_indicator = np.zeros_like( + old_poison_indicator) + try: + yield bd_dataset + finally: + bd_dataset.wrapped_dataset.poison_indicator = old_poison_indicator + + +def get_true_label_from_bd_dataset(bd_dataset): + # return the true label of a given BD dataset in the oder of item index + # original idx, indicator, true label + return [other_info[2] for img, label, *other_info in bd_dataset] + + +def get_poison_indicator_from_bd_dataset(bd_dataset): + ''' + What's the difference between this function and dataset.poison_indicator? + dataset.poison_indicator is always has the same length as the underlying full dataset and in the order of the full dataset + this function returns the poison indicator in the order of the item order of bd_dataset, i.e., the index we get the samples from the bd_dataset + Note: another way to get poison indicator is the get_feature function which returns the indicator in the order of (shuffled) dataloader + ''' + # return the position of bd samples in the oder of item index + # original idx, indicator, true label + return [other_info[1] for img, label, *other_info in bd_dataset] + +def get_index_mapping_from_bd_dataset(bd_dataset): + ''' + A function to get the mapping from the index of the bd_dataset to the index of the full dataset. + ''' + # return the position of bd samples in the oder of item index + # original idx, indicator, true label + ori_to_bd_idx = {} + bd_idx_to_ori = {} + for idx, (img, label, *other_info) in enumerate(bd_dataset): + ori_to_bd_idx[other_info[0]] = idx + bd_idx_to_ori[idx] = other_info[0] + + return ori_to_bd_idx, bd_idx_to_ori + +def generate_clean_dataset(clean_dataset, selected_classes, max_num_samples): + ''' + This function modifies clean datsaet to generate a new dataset with given selected classes and max number of samples. + To do so, we change + 1. create a new dataset with clean samples in a BD dataset class by prepro_cls_DatasetBD_v2 + 2. subset the dataset to the given max number of samples and classes + ''' + # deepcopy the given dataset to avoid changing the original dataset + clean_dataset_without_trans = copy.deepcopy(clean_dataset.wrapped_dataset) + clean_dataset_without_trans = prepro_cls_DatasetBD_v2( + clean_dataset_without_trans) + assert np.sum( + clean_dataset_without_trans.poison_indicator) == 0, "The given clean dataset is not clean." + + # subset the clean dataset to the given number of samples and classes + true_labels = np.array( + get_true_label_from_bd_dataset(clean_dataset_without_trans)) + subset_index = sub_sample_euqal_ratio_classes_index( + true_labels, max_num_samples=max_num_samples, selected_classes=selected_classes) + clean_dataset_without_trans.subset(subset_index) + print('subset clean dataset with length: ', + len(clean_dataset_without_trans)) + + clean_dataset_with_trans = dataset_wrapper_with_transform( + clean_dataset_without_trans, + clean_dataset.wrap_img_transform, + clean_dataset.wrap_label_transform, + ) + return clean_dataset_with_trans + + +def generate_bd_dataset(bd_dataset, target_class, selected_classes, max_num_samples, bd_only=False): + ''' + This function modifies BD datsaet to generate a new dataset with given selected classes and max number of samples. + To do so, we change + 1. create a copy of BD dataset + 2. subset the dataset to the given max number of samples and classes + ''' + # deepcopy the given dataset to avoid changing the original dataset + bd_dataset_without_trans = copy.deepcopy(bd_dataset.wrapped_dataset) + + if bd_only: + dataset_poi_indicator = np.array( + get_poison_indicator_from_bd_dataset(bd_dataset_without_trans)) + bd_dataset_without_trans.subset( + np.where(dataset_poi_indicator == 1)[0]) + + true_bd_labels = np.array( + get_true_label_from_bd_dataset(bd_dataset_without_trans)) + dataset_poi_indicator = get_poison_indicator_from_bd_dataset( + bd_dataset_without_trans) + lables_with_poi = true_bd_labels.copy() + + if not bd_only: + # regard the poisoned samples as a new class -1 + lables_with_poi[dataset_poi_indicator == 1] = -1 + selected_classes = np.append(selected_classes, -1) + + # subset the mix dataset to the given number of samples and classes + subset_index = sub_sample_euqal_ratio_classes_index( + lables_with_poi, max_num_samples=max_num_samples, selected_classes=selected_classes) + + bd_dataset_without_trans.subset(subset_index) + print('subset bd dataset with length: ', len(bd_dataset_without_trans)) + + bd_dataset_with_trans = dataset_wrapper_with_transform( + bd_dataset_without_trans, + bd_dataset.wrap_img_transform, + bd_dataset.wrap_label_transform, + ) + return bd_dataset_with_trans + + +def generate_mix_dataset(bd_test, target_class, pratio, selected_classes, max_num_samples): + ''' + This function modifies the bd_test_with_trans which has all poisoned non-target test samples by + 1. changing the poison indictor to recover some clean samples/get_labels + 2. changing the original_index_array to recover the target samples + ''' + # deepcopy the given dataset to avoid changing the original dataset + mix_dataset_without_trans = copy.deepcopy(bd_test.wrapped_dataset) + mix_dataset_without_trans.original_index_array = np.arange( + len(mix_dataset_without_trans.dataset)) + print('create mix dataset with length: ', len(mix_dataset_without_trans)) + + # recover target classes + true_bd_labels = np.array( + get_true_label_from_bd_dataset(mix_dataset_without_trans)) + + # random choose pratio of bd samples + non_target_clean_idx = np.where( + mix_dataset_without_trans.poison_indicator == 1)[0] + bd_idx = np.random.choice(non_target_clean_idx, int( + len(mix_dataset_without_trans)*pratio), replace=False) + poi_indicators = np.zeros(len(mix_dataset_without_trans.dataset)) + poi_indicators[bd_idx] = 1 + + # set selected poisoned samples + mix_dataset_without_trans.poison_indicator = poi_indicators + + # regard the poisoned samples as a new class -1 + lables_with_poi = true_bd_labels.copy() + lables_with_poi[bd_idx] = -1 + selected_classes = np.append(selected_classes, -1) + + # subset the mix dataset to the given number of samples and classes + subset_index = sub_sample_euqal_ratio_classes_index( + lables_with_poi, max_num_samples=max_num_samples, selected_classes=selected_classes) + mix_dataset_without_trans.subset(subset_index) + print('subset mix dataset with length: ', len(mix_dataset_without_trans)) + + mix_test_dataset_with_trans = dataset_wrapper_with_transform( + mix_dataset_without_trans, + bd_test.wrap_img_transform, + bd_test.wrap_label_transform, + ) + return mix_test_dataset_with_trans + + +def load_prototype_result(args, save_path_attack): + result_prototype = {} + result_prototype['model_name'] = args.model + result_prototype['num_classes'] = args.num_classes + result_prototype['model'] = torch.load(save_path_attack + "/clean_model.pth") + result_prototype['data_path'] = args.dataset_path = args.dataset_path + "/" + args.dataset + result_prototype['img_size'] = args.img_size = (args.input_height, args.input_width, args.input_channel) + + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform = dataset_and_transform_generate(args) + + clean_train_dataset_with_trans = dataset_wrapper_with_transform( + train_dataset_without_transform, + train_img_transform, + train_label_transform, + ) + + clean_test_dataset_with_trans = dataset_wrapper_with_transform( + test_dataset_without_transform, + test_img_transform, + test_label_transform, + ) + + result_prototype['clean_train'] = clean_train_dataset_with_trans + result_prototype['clean_test'] = clean_test_dataset_with_trans + + # By default, we do not save bd_train and bd_test, you can change this setting if you need. + result_prototype['bd_train'] = None + result_prototype['bd_test'] = None + + return result_prototype + +def get_features(args, model, target_layer, data_loader, reduction='flatten', activation=None): + '''Function to extract the features/embeddings/activations from a target layer''' + + # extract feature vector from a specific layer + # output_ is of shape (num_samples, num_neurons, feature_map_width, feature_map_height), here we choose the max activation + if reduction == 'flatten': + def feature_hook(module, input_, output_): + global feature_vector + # access the layer output and convert it to a feature vector + feature_vector = output_ + if activation is not None: + feature_vector = activation(feature_vector) + feature_vector = torch.flatten(feature_vector, 1) + return None + elif reduction == 'none': + def feature_hook(module, input_, output_): + global feature_vector + # access the layer output and convert it to a feature vector + feature_vector = output_ + if activation is not None: + feature_vector = activation(feature_vector) + feature_vector = feature_vector + return None + elif reduction == 'max': + def feature_hook(module, input_, output_): + global feature_vector + # access the layer output and convert it to a feature vector + feature_vector = output_ + if activation is not None: + feature_vector = activation(feature_vector) + if feature_vector.dim() > 2: + feature_vector = torch.max( + torch.flatten(feature_vector, 2), 2)[0] + else: + feature_vector = feature_vector + return None + elif reduction == 'sum': + def feature_hook(module, input_, output_): + global feature_vector + # access the layer output and convert it to a feature vector + feature_vector = output_ + if activation is not None: + feature_vector = activation(feature_vector) + if feature_vector.dim() > 2: + feature_vector = torch.sum(torch.flatten(feature_vector, 2), 2) + else: + feature_vector = feature_vector + return None + + h = target_layer.register_forward_hook(feature_hook) + + model.eval() + # collect feature vectors + features = [] + labels = [] + poi_indicator = [] + + with torch.no_grad(): + for batch_idx, (inputs, targets, *other_info) in enumerate(data_loader): + global feature_vector + # Fetch features + inputs, targets = inputs.to(args.device), targets.to(args.device) + outputs = model(inputs) + # if activation is not None: + # feature_vector = activation(feature_vector) + # move all tensor to cpu to save memory + current_feature = feature_vector.detach().cpu().numpy() + current_labels = targets.cpu().numpy() + current_poi_indicator = np.array(other_info[1].numpy()) + + # Store features + features.append(current_feature) + labels.append(current_labels) + poi_indicator.append(current_poi_indicator) + + features = np.concatenate(features, axis=0) + labels = np.concatenate(labels, axis=0) + poi_indicator = np.concatenate(poi_indicator, axis=0) + h.remove() # Rmove the hook + + return features, labels, poi_indicator + + +def plot_embedding( + tsne_result, label, title, xlabel="tsne_x", ylabel="tsne_y", custom_palette=None, size=(10, 10), mark_size = 40, alpha = 0.6 +): + """Plot embedding for T-SNE with labels""" + # Data Preprocessing + if torch.is_tensor(tsne_result): + tsne_result = tsne_result.cpu().numpy() + if torch.is_tensor(label): + label = label.cpu().numpy() + + x_min, x_max = np.min(tsne_result, 0), np.max(tsne_result, 0) + tsne_result = (tsne_result - x_min) / (x_max - x_min) + + # Plot + tsne_result_df = pd.DataFrame( + {"feature_x": tsne_result[:, 0], + "feature_y": tsne_result[:, 1], "label": label} + ) + fig, ax = plt.subplots(1, figsize=size) + + num_class = len(pd.unique(tsne_result_df["label"])) + if custom_palette is None: + custom_palette = sns.color_palette("hls", num_class) + + # s: maker size, palette: colors + + sns.scatterplot( + x="feature_x", + y="feature_y", + hue="label", + data=tsne_result_df, + ax=ax, + s=mark_size, + palette=custom_palette, + alpha=alpha, + ) + # sns.lmplot(x='feature_x', y='feature_y', hue='label', + # data=tsne_result_df, size=9, scatter_kws={"s":20,"alpha":0.3},fit_reg=False, legend=True,) + + # Set Figure Style + lim = (-0.01, 1.01) + ax.set_xlim(lim) + ax.set_ylim(lim) + + ax.set_xticks([]) + ax.set_yticks([]) + + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + #ax.tick_params(axis="x", labelsize=20) + #ax.tick_params(axis="y", labelsize=20) + ax.set_title(title) + ax.set_aspect("equal") + + ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0) + + return fig + + +def get_embedding(data, method = "tsne"): + """Get T-SNE embeddings""" + if torch.is_tensor(data): + data = data.cpu().numpy() + if method == "tsne": + tsne = TSNE(n_components=2, init="random", random_state=0) + result = tsne.fit_transform(data) + elif method == "umap": + umap = UMAP(n_components=2, init = 'spectral', random_state=0, metric = "euclidean") + result = umap.fit_transform(data) + else: + assert False, "Illegal method" + return result + +def umap_fig( + data, + label, + title="UMAP embedding", + xlabel="umap_x", + ylabel="umap_y", + custom_palette=None, + size=(10, 10), + mark_size = 0.3, + alpha = 0.8 +): + """Get UMAP embeddings figure""" + umap_result = get_embedding(data, method = "umap") + fig = plot_embedding(umap_result, label, title, xlabel, + ylabel, custom_palette, size, mark_size, alpha) + return fig + +def tsne_fig( + data, + label, + title="t-SNE embedding", + xlabel="tsne_x", + ylabel="tsne_y", + custom_palette=None, + size=(10, 10), + mark_size = 40, + alpha = 0.6 +): + """Get T-SNE embeddings figure""" + tsne_result = get_embedding(data, method = "tsne") + fig = plot_embedding(tsne_result, label, title, xlabel, + ylabel, custom_palette, size, mark_size, alpha) + return fig + + +def test_tsne(): + data = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]]) + # data=torch.Tensor(data) + # data=torch.Tensor(data).cuda() + label = np.array([1, 2, 3, 4]) + fig = tsne_fig( + data, label, title="t-SNE embedding", xlabel="tsne_x", ylabel="tsne_y" + ) + plt.show(fig) + + +# https://stackoverflow.com/questions/58766561/scikit-learn-sklearn-confusion-matrix-plot-for-more-than-3-classes +def plot_confusion_matrix( + y_true, + y_pred, + classes, + normalize=False, + title=None, + cmap=plt.cm.Blues, + save_fig_path=None, +): + """ + This function prints and plots the confusion matrix. + Normalization can be applied by setting `normalize=True`. + """ + if not title: + if normalize: + title = "Normalized confusion matrix" + else: + title = "Confusion matrix, without normalization" + + # Compute confusion matrix + + cm = np.zeros((len(classes), len(classes))) + for i in range(y_true.shape[0]): + cm[y_true[i], y_pred[i]] += 1 + + if normalize: + cm = cm.astype("float") / (cm.sum(axis=1)[:, np.newaxis]+1e-24) + print("Normalized confusion matrix") + else: + cm = cm.astype("int") + print("Confusion matrix, without normalization") + + # print(cm) + + fig, ax = plt.subplots(figsize=(10, 10)) + im = ax.imshow(cm, interpolation="nearest", cmap=cmap) + ax.figure.colorbar(im, ax=ax) + # We want to show all ticks... + ax.set( + xticks=np.arange(cm.shape[1]), + yticks=np.arange(cm.shape[0]), + # ... and label them with the respective list entries + xticklabels=classes, + yticklabels=classes, + title=title, + ylabel="True label", + xlabel="Predicted label", + ) + + # Rotate the tick labels and set their alignment. + plt.setp(ax.get_xticklabels(), rotation=45, + ha="right", rotation_mode="anchor") + + # Loop over data dimensions and create text annotations. + fmt = ".2f" if normalize else "d" + thresh = cm.max() / 2.0 + for i in range(cm.shape[0]): + for j in range(cm.shape[1]): + ax.text( + j, + i, + format(cm[i, j], fmt), + ha="center", + va="center", + color="white" if cm[i, j] > thresh else "black", + ) + fig.tight_layout() + plt.xlim(-0.5, len(classes) - 0.5) + plt.ylim(len(classes) - 0.5, -0.5) + plt.tight_layout() + if save_fig_path is not None: + plt.savefig(save_fig_path) + return ax, cm + + +def get_class_name(dataset, num_class, args): + if dataset == "cifar10": + # https://www.cs.toronto.edu/~kriz/cifar.html + return [ + "airplane", + "automobile", + "bird", + "cat", + "deer", + "dog", + "frog", + "horse", + "ship", + "truck", + ] + elif dataset == "cifar100": + # https://github.com/keras-team/keras/issues/2653 + return [ + "apple", + "aquarium_fish", + "baby", + "bear", + "beaver", + "bed", + "bee", + "beetle", + "bicycle", + "bottle", + "bowl", + "boy", + "bridge", + "bus", + "butterfly", + "camel", + "can", + "castle", + "caterpillar", + "cattle", + "chair", + "chimpanzee", + "clock", + "cloud", + "cockroach", + "couch", + "crab", + "crocodile", + "cup", + "dinosaur", + "dolphin", + "elephant", + "flatfish", + "forest", + "fox", + "girl", + "hamster", + "house", + "kangaroo", + "keyboard", + "lamp", + "lawn_mower", + "leopard", + "lion", + "lizard", + "lobster", + "man", + "maple_tree", + "motorcycle", + "mountain", + "mouse", + "mushroom", + "oak_tree", + "orange", + "orchid", + "otter", + "palm_tree", + "pear", + "pickup_truck", + "pine_tree", + "plain", + "plate", + "poppy", + "porcupine", + "possum", + "rabbit", + "raccoon", + "ray", + "road", + "rocket", + "rose", + "sea", + "seal", + "shark", + "shrew", + "skunk", + "skyscraper", + "snail", + "snake", + "spider", + "squirrel", + "streetcar", + "sunflower", + "sweet_pepper", + "table", + "tank", + "telephone", + "television", + "tiger", + "tractor", + "train", + "trout", + "tulip", + "turtle", + "wardrobe", + "whale", + "willow_tree", + "wolf", + "woman", + "worm", + ] + elif dataset == "gtsrb": + # https://github.com/magnusja/GTSRB-caffe-model/blob/master/labeller/main.py + return [ + "20_speed", + "30_speed", + "50_speed", + "60_speed", + "70_speed", + "80_speed", + "80_lifted", + "100_speed", + "120_speed", + "no_overtaking_general", + "no_overtaking_trucks", + "right_of_way_crossing", + "right_of_way_general", + "give_way", + "stop", + "no_way_general", + "no_way_trucks", + "no_way_one_way", + "attention_general", + "attention_left_turn", + "attention_right_turn", + "attention_curvy", + "attention_bumpers", + "attention_slippery", + "attention_bottleneck", + "attention_construction", + "attention_traffic_light", + "attention_pedestrian", + "attention_children", + "attention_bikes", + "attention_snowflake", + "attention_deer", + "lifted_general", + "turn_right", + "turn_left", + "turn_straight", + "turn_straight_right", + "turn_straight_left", + "turn_right_down", + "turn_left_down", + "turn_circle", + "lifted_no_overtaking_general", + "lifted_no_overtaking_trucks", + ] + elif dataset == "tiny": + path = args.dataset_path + "/tiny/tiny-imagenet-200/" + label_map = get_class_to_id_dict(path) + return [label_map[i][1].strip().split(",")[0] for i in range(num_class)] + else: + print("Class Name is not implemented currently and use label directly.") + return [str(i) for i in range(num_class)] + + +def sample_by_classes(x, y, class_sub): + sub_idx = [] + for c_idx in class_sub: + sub_idx.append(np.where(y == c_idx)) + sub_idx = np.concatenate(sub_idx, 1).reshape(-1) + label_sub = y[sub_idx] + img_sub = [x[img_idx] for img_idx in sub_idx] + return img_sub, label_sub + + +def sub_sample_euqal_classes(x, y, num_sample): + class_unique = np.unique(y) + select_idx = [] + sub_num = int(num_sample/class_unique.shape[0]) + for c_idx in class_unique: + sub_idx = np.where(y == c_idx) + sub_idx = np.random.choice(sub_idx[0], sub_num, replace=False) + select_idx.append(sub_idx) + sub_idx = np.concatenate(select_idx, -1).reshape(-1) + # shuffle the sub_idx + sub_idx = sub_idx[np.random.permutation(sub_idx.shape[0])] + label_sub = y[sub_idx] + img_sub = [x[img_idx] for img_idx in sub_idx] + return img_sub, label_sub + + +def sub_sample_euqal_classes_index(y, num_sample, selected_classes=None): + # subsample the data with equal number for each classes + class_unique = np.unique(y) + if selected_classes is not None: + # find the intersection of selected_classes and class_unique + class_unique = np.intersect1d( + class_unique, selected_classes, assume_unique=True, return_indices=False) + select_idx = [] + sub_num = int(num_sample/class_unique.shape[0]) + for c_idx in class_unique: + sub_idx = np.where(y == c_idx) + sub_idx = np.random.choice(sub_idx[0], sub_num, replace=False) + select_idx.append(sub_idx) + sub_idx = np.concatenate(select_idx, -1).reshape(-1) + # shuffle the sub_idx + sub_idx = sub_idx[np.random.permutation(sub_idx.shape[0])] + return sub_idx + + +def sub_sample_euqal_ratio_classes_index(y, ratio=None, selected_classes=None, max_num_samples=None): + # subsample the data with ratio for each classes + class_unique = np.unique(y) + if selected_classes is not None: + # find the intersection of selected_classes and class_unique + class_unique = np.intersect1d( + class_unique, selected_classes, assume_unique=True, return_indices=False) + select_idx = [] + if max_num_samples is not None: + print('max_num_samples is given, use sample number limit now.') + total_selected_samples = np.sum( + [np.where(y == c_idx)[0].shape[0] for c_idx in class_unique]) + ratio = np.min([total_selected_samples, max_num_samples] + )/total_selected_samples + + for c_idx in class_unique: + sub_idx = np.where(y == c_idx) + sub_idx = np.random.choice(sub_idx[0], int( + ratio*sub_idx[0].shape[0]), replace=False) + select_idx.append(sub_idx) + sub_idx = np.concatenate(select_idx, -1).reshape(-1) + # shuffle the sub_idx + sub_idx = sub_idx[np.random.permutation(sub_idx.shape[0])] + return sub_idx + + +# https://colab.research.google.com/github/sonugiri1043/Train_ResNet_On_Tiny_ImageNet/blob/master/Train_ResNet_On_Tiny_ImageNet.ipynb#scrollTo=7TUH7bu7n5ta +def get_id_dictionary(path, by_wnids=False): + if by_wnids: + id_dict = {} + for i, line in enumerate(open(path + "wnids.txt", "r")): + id_dict[line.replace("\n", "")] = i + return id_dict + else: + classes = sorted( + entry.name for entry in os.scandir(path + "/train") if entry.is_dir() + ) + if not classes: + raise FileNotFoundError( + f"Couldn't find any class folder in {path+'/train'}." + ) + return {cls_name: i for i, cls_name in enumerate(classes)} + + +def get_class_to_id_dict(path): + id_dict = get_id_dictionary(path) + all_classes = {} + result = {} + for i, line in enumerate(open(path + "words.txt", "r")): + n_id, word = line.split("\t")[:2] + all_classes[n_id] = word + for key, value in id_dict.items(): + result[value] = (key, all_classes[key]) + return result + + +def get_dataname(dataset): + # "mnist, cifar10, cifar100, gtsrb, celeba, tiny" + if dataset == "mnist": + return "MNIST" + elif dataset == 'cifar10': + return "CIFAR-10" + elif dataset == 'cifar100': + return "CIFAR-100" + elif dataset == "gtsrb ": + return "GTSRB " + elif dataset == "celeba": + return "CelebA" + elif dataset == "tiny": + return "Tiny ImageNet" + else: + return dataset + + +def get_pratio(pratio): + # convert 0.1 to 10% and 0.01 to 0.1% + pratio = float(pratio) + if pratio >= 0.1: + return "%d" % (pratio*100) + elif pratio >= 0.01: + return "%d" % (pratio*100) + elif pratio >= 0.001: + return "%.1f" % (pratio*100) + else: + return "%f" % (pratio*100) + + +def get_defensename(defense): + # Formal Abbreviation of Defense + if defense == 'ft': + return "FT" + elif defense == 'fp': + return "FP" + elif defense == 'anp': + return "ANP" + else: + return defense + + +def saliency(input, model): + for param in model.parameters(): + param.requires_grad = False + input.unsqueeze_(0) + input.requires_grad = True + preds = model(input) + score, indices = torch.max(preds, 1) + score.backward() + gradients = input.grad.data.permute(0, 2, 3, 1).squeeze().cpu().numpy() + gradients_fre = np.fft.ifft2(gradients, axes=(0, 1)) + + gradients_fre_shift = np.fft.fftshift(gradients_fre, axes=(0, 1)) + gradients_fre_shift = np.log(np.abs(gradients_fre_shift)) + + gradient_norm = (gradients_fre_shift - gradients_fre_shift.min()) / \ + (gradients_fre_shift.max()-gradients_fre_shift.min()) + gradient_norm = np.mean(gradient_norm, axis=2) + gradient_norm = np.uint8(255 * gradient_norm) + return gradient_norm diff --git a/attack/badnet.py b/attack/badnet.py new file mode 100755 index 0000000..c404bf6 --- /dev/null +++ b/attack/badnet.py @@ -0,0 +1,275 @@ +''' +this script is for badnet attack + +basic structure: +1. config args, save_path, fix random seed +2. set the clean train data and clean test data +3. set the attack img transform and label transform +4. set the backdoor attack data and backdoor test data +5. set the device, model, criterion, optimizer, training schedule. +6. attack or use the model to do finetune with 5% clean data +7. save the attack result for defense + +@article{gu2017badnets, + title={Badnets: Identifying vulnerabilities in the machine learning model supply chain}, + author={Gu, Tianyu and Dolan-Gavitt, Brendan and Garg, Siddharth}, + journal={arXiv preprint arXiv:1708.06733}, + year={2017} +} +''' + +import os +import sys +import yaml + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +import argparse +import numpy as np +import torch +import logging + +from utils.backdoor_generate_poison_index import generate_poison_index_from_label_transform +from utils.aggregate_block.bd_attack_generate import bd_attack_img_trans_generate, bd_attack_label_trans_generate +from copy import deepcopy +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.train_settings_generate import argparser_opt_scheduler, argparser_criterion +from utils.save_load_attack import save_attack_result +from attack.prototype import NormalCase +from utils.trainer_cls import BackdoorModelTrainer +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform + + +def add_common_attack_args(parser): + parser.add_argument('--attack', type=str, ) + parser.add_argument('--attack_target', type=int, + help='target class in all2one attack') + parser.add_argument('--attack_label_trans', type=str, + help='which type of label modification in backdoor attack' + ) + parser.add_argument('--pratio', type=float, + help='the poison rate ' + ) + return parser + + +class BadNet(NormalCase): + + def __init__(self): + super(BadNet).__init__() + + def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + parser = add_common_attack_args(parser) + + parser.add_argument("--patch_mask_path", type=str) + parser.add_argument('--bd_yaml_path', type=str, default='../config/attack/badnet/default.yaml', + help='path for yaml file provide additional default attributes') + return parser + + def add_bd_yaml_to_args(self, args): + with open(args.bd_yaml_path, 'r') as f: + mix_defaults = yaml.safe_load(f) + mix_defaults.update({k: v for k, v in args.__dict__.items() if v is not None}) + args.__dict__ = mix_defaults + + def stage1_non_training_data_prepare(self): + logging.info(f"stage1 start") + + assert 'args' in self.__dict__ + args = self.args + + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + clean_train_dataset_with_transform, \ + clean_train_dataset_targets, \ + clean_test_dataset_with_transform, \ + clean_test_dataset_targets \ + = self.benign_prepare() + + train_bd_img_transform, test_bd_img_transform = bd_attack_img_trans_generate(args) + ### get the backdoor transform on label + bd_label_transform = bd_attack_label_trans_generate(args) + + ### 4. set the backdoor attack data and backdoor test data + train_poison_index = generate_poison_index_from_label_transform( + clean_train_dataset_targets, + label_transform=bd_label_transform, + train=True, + pratio=args.pratio if 'pratio' in args.__dict__ else None, + p_num=args.p_num if 'p_num' in args.__dict__ else None, + ) + + logging.debug(f"poison train idx is saved") + torch.save(train_poison_index, + args.save_path + '/train_poison_index_list.pickle', + ) + + ### generate train dataset for backdoor attack + bd_train_dataset = prepro_cls_DatasetBD_v2( + deepcopy(train_dataset_without_transform), + poison_indicator=train_poison_index, + bd_image_pre_transform=train_bd_img_transform, + bd_label_pre_transform=bd_label_transform, + save_folder_path=f"{args.save_path}/bd_train_dataset", + ) + + bd_train_dataset_with_transform = dataset_wrapper_with_transform( + bd_train_dataset, + train_img_transform, + train_label_transform, + ) + + ### decide which img to poison in ASR Test + test_poison_index = generate_poison_index_from_label_transform( + clean_test_dataset_targets, + label_transform=bd_label_transform, + train=False, + ) + + ### generate test dataset for ASR + bd_test_dataset = prepro_cls_DatasetBD_v2( + deepcopy(test_dataset_without_transform), + poison_indicator=test_poison_index, + bd_image_pre_transform=test_bd_img_transform, + bd_label_pre_transform=bd_label_transform, + save_folder_path=f"{args.save_path}/bd_test_dataset", + ) + + bd_test_dataset.subset( + np.where(test_poison_index == 1)[0] + ) + + bd_test_dataset_with_transform = dataset_wrapper_with_transform( + bd_test_dataset, + test_img_transform, + test_label_transform, + ) + + self.stage1_results = clean_train_dataset_with_transform, \ + clean_test_dataset_with_transform, \ + bd_train_dataset_with_transform, \ + bd_test_dataset_with_transform + + def stage2_training(self): + logging.info(f"stage2 start") + assert 'args' in self.__dict__ + args = self.args + + clean_train_dataset_with_transform, \ + clean_test_dataset_with_transform, \ + bd_train_dataset_with_transform, \ + bd_test_dataset_with_transform = self.stage1_results + + if not args.pre: + + self.net = generate_cls_model( + model_name=args.model, + num_classes=args.num_classes, + image_size=args.img_size[0], + ) + else: + torch.hub.set_dir('/ssddata1/data/rminaa/pretrain_models/') + import torch.nn as nn + if args.model == "resnet18": + from torchvision.models import resnet18, ResNet18_Weights + self.net = resnet18(weights=ResNet18_Weights.IMAGENET1K_V1).to(args.device) + self.net.fc = nn.Linear(in_features=512, out_features=args.num_classes, bias=True).to(args.device) + for _, param in self.net.named_parameters(): + param.requires_grad = True + elif args.model == "resnet50": + from torchvision.models import resnet50, ResNet50_Weights + self.net = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2).to(args.device) + self.net.fc = nn.Linear(in_features=2048, out_features=args.num_classes, bias=True).to(args.device) + for _, param in self.net.named_parameters(): + param.requires_grad = True + elif args.model == 'swin_b': + from torchvision.models import swin_b + self.net = swin_b(weights='IMAGENET1K_V1').to(args.device) + self.net.head = nn.Linear(in_features=1024, out_features=args.num_classes, bias=True).to(args.device) + for _, param in self.net.named_parameters(): + param.requires_grad = True + elif args.model == 'swin_t': + from torchvision.models import swin_t + self.net = swin_t(weights='IMAGENET1K_V1').to(args.device) + self.net.head = nn.Linear(in_features=768, out_features=args.num_classes, bias=True).to(args.device) + for _, param in self.net.named_parameters(): + param.requires_grad = True + else: + raise NotImplementedError(f"{args.model} is not supported") + + + self.device = torch.device( + ( + f"cuda:{[int(i) for i in args.device[5:].split(',')][0]}" if "," in args.device else args.device + # since DataParallel only allow .to("cuda") + ) if torch.cuda.is_available() else "cpu" + ) + + if "," in args.device: + self.net = torch.nn.DataParallel( + self.net, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + + trainer = BackdoorModelTrainer( + self.net, + ) + + criterion = argparser_criterion(args) + + optimizer, scheduler = argparser_opt_scheduler(self.net, args) + + from torch.utils.data.dataloader import DataLoader + trainer.train_with_test_each_epoch_on_mix( + DataLoader(bd_train_dataset_with_transform, batch_size=args.batch_size, shuffle=True, drop_last=True, + pin_memory=args.pin_memory, num_workers=args.num_workers, ), + DataLoader(clean_test_dataset_with_transform, batch_size=args.batch_size, shuffle=False, drop_last=False, + pin_memory=args.pin_memory, num_workers=args.num_workers, ), + DataLoader(bd_test_dataset_with_transform, batch_size=args.batch_size, shuffle=False, drop_last=False, + pin_memory=args.pin_memory, num_workers=args.num_workers, ), + args.epochs, + criterion=criterion, + optimizer=optimizer, + scheduler=scheduler, + device=self.device, + frequency_save=args.frequency_save, + save_folder_path=args.save_path, + save_prefix='attack', + amp=args.amp, + prefetch=args.prefetch, + prefetch_transform_attr_name="ori_image_transform_in_loading", # since we use the preprocess_bd_dataset + non_blocking=args.non_blocking, + ) + + save_attack_result( + model_name=args.model, + num_classes=args.num_classes, + model=trainer.model.cpu().state_dict(), + data_path=args.dataset_path, + img_size=args.img_size, + clean_data=args.dataset, + bd_train=bd_train_dataset_with_transform, + bd_test=bd_test_dataset_with_transform, + save_path=args.save_path, + ) + + +if __name__ == '__main__': + attack = BadNet() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = attack.set_args(parser) + parser = attack.set_bd_args(parser) + args = parser.parse_args() + logging.debug("Be careful that we need to give the bd yaml higher priority. So, we put the add bd yaml first.") + attack.add_bd_yaml_to_args(args) + attack.add_yaml_to_args(args) + args = attack.process_args(args) + attack.prepare(args) + attack.stage1_non_training_data_prepare() + attack.stage2_training() diff --git a/attack/badnet_bypass.py b/attack/badnet_bypass.py new file mode 100644 index 0000000..07e4760 --- /dev/null +++ b/attack/badnet_bypass.py @@ -0,0 +1,302 @@ +''' +this script is for badnet attack + +basic structure: +1. config args, save_path, fix random seed +2. set the clean train data and clean test data +3. set the attack img transform and label transform +4. set the backdoor attack data and backdoor test data +5. set the device, model, criterion, optimizer, training schedule. +6. attack or use the model to do finetune with 5% clean data +7. save the attack result for defense + +@article{gu2017badnets, + title={Badnets: Identifying vulnerabilities in the machine learning model supply chain}, + author={Gu, Tianyu and Dolan-Gavitt, Brendan and Garg, Siddharth}, + journal={arXiv preprint arXiv:1708.06733}, + year={2017} +} +''' + +import os +import sys +import yaml +from torch import optim +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +import argparse +import numpy as np +import torch +import logging +import torch.nn as nn +from utils.backdoor_generate_poison_index import generate_poison_index_from_label_transform +from utils.aggregate_block.bd_attack_generate import bd_attack_img_trans_generate, bd_attack_label_trans_generate +from copy import deepcopy +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.train_settings_generate import argparser_opt_scheduler, argparser_criterion +from utils.save_load_attack import save_attack_result +from attack.prototype import NormalCase +from utils.trainer_cls_bypass import BackdoorModelTrainer +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform + + +def add_common_attack_args(parser): + parser.add_argument('--attack', type=str, ) + parser.add_argument('--attack_target', type=int, + help='target class in all2one attack') + parser.add_argument('--attack_label_trans', type=str, + help='which type of label modification in backdoor attack' + ) + parser.add_argument('--pratio', type=float, + help='the poison rate ' + ) + parser.add_argument('--regularization_ratio', type=float, + help='the regularization ratio ' + ) + return parser + + +class Discriminator(nn.Module): + def __init__(self): + super().__init__() + self.fc1 = nn.Linear(512, 256) + self.bn1 = nn.BatchNorm1d(256) + self.fc2 = nn.Linear(256, 128) + self.bn2 = nn.BatchNorm1d(128) + self.fc3 = nn.Linear(128, 1) + self.leaky_relu = nn.LeakyReLU(0.2) + self.sig = nn.Sigmoid() + def forward(self, x): + out = self.leaky_relu(self.bn1(self.fc1(x))) + out = self.leaky_relu(self.bn2(self.fc2(out))) + out = self.fc3(out) + return self.sig(out) + + +class BadNet(NormalCase): + + def __init__(self): + super(BadNet).__init__() + + def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + parser = add_common_attack_args(parser) + + parser.add_argument("--patch_mask_path", type=str) + parser.add_argument('--bd_yaml_path', type=str, default='../config/attack/badnet/default_bypass.yaml', + help='path for yaml file provide additional default attributes') + return parser + + def add_bd_yaml_to_args(self, args): + with open(args.bd_yaml_path, 'r') as f: + mix_defaults = yaml.safe_load(f) + mix_defaults.update({k: v for k, v in args.__dict__.items() if v is not None}) + args.__dict__ = mix_defaults + + def stage1_non_training_data_prepare(self): + logging.info(f"stage1 start") + + assert 'args' in self.__dict__ + args = self.args + + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + clean_train_dataset_with_transform, \ + clean_train_dataset_targets, \ + clean_test_dataset_with_transform, \ + clean_test_dataset_targets \ + = self.benign_prepare() + + train_bd_img_transform, test_bd_img_transform = bd_attack_img_trans_generate(args) + ### get the backdoor transform on label + bd_label_transform = bd_attack_label_trans_generate(args) + + ### 4. set the backdoor attack data and backdoor test data + train_poison_index = generate_poison_index_from_label_transform( + clean_train_dataset_targets, + label_transform=bd_label_transform, + train=True, + pratio=args.pratio if 'pratio' in args.__dict__ else None, + p_num=args.p_num if 'p_num' in args.__dict__ else None, + ) + + logging.debug(f"poison train idx is saved") + torch.save(train_poison_index, + args.save_path + '/train_poison_index_list.pickle', + ) + # print(len(train_poison_index)) + # print(sum(train_poison_index)) + # exit(0) + ### generate train dataset for backdoor attack + bd_train_dataset = prepro_cls_DatasetBD_v2( + deepcopy(train_dataset_without_transform), + poison_indicator=train_poison_index, + bd_image_pre_transform=train_bd_img_transform, + bd_label_pre_transform=bd_label_transform, + save_folder_path=f"{args.save_path}/bd_train_dataset", + ) + + bd_train_dataset_with_transform = dataset_wrapper_with_transform( + bd_train_dataset, + train_img_transform, + train_label_transform, + ) + + ### decide which img to poison in ASR Test + test_poison_index = generate_poison_index_from_label_transform( + clean_test_dataset_targets, + label_transform=bd_label_transform, + train=False, + ) + + ### generate test dataset for ASR + bd_test_dataset = prepro_cls_DatasetBD_v2( + deepcopy(test_dataset_without_transform), + poison_indicator=test_poison_index, + bd_image_pre_transform=test_bd_img_transform, + bd_label_pre_transform=bd_label_transform, + save_folder_path=f"{args.save_path}/bd_test_dataset", + ) + + bd_test_dataset.subset( + np.where(test_poison_index == 1)[0] + ) + + bd_test_dataset_with_transform = dataset_wrapper_with_transform( + bd_test_dataset, + test_img_transform, + test_label_transform, + ) + + self.stage1_results = clean_train_dataset_with_transform, \ + clean_test_dataset_with_transform, \ + bd_train_dataset_with_transform, \ + bd_test_dataset_with_transform + + def stage2_training(self): + logging.info(f"stage2 start") + assert 'args' in self.__dict__ + args = self.args + + clean_train_dataset_with_transform, \ + clean_test_dataset_with_transform, \ + bd_train_dataset_with_transform, \ + bd_test_dataset_with_transform = self.stage1_results + + if not args.pre: + + self.net = generate_cls_model( + model_name=args.model, + num_classes=args.num_classes, + image_size=args.img_size[0], + ) + else: + torch.hub.set_dir('/ssddata1/data/rminaa/pretrain_models/') + import torch.nn as nn + if args.model == "resnet18": + from torchvision.models import resnet18, ResNet18_Weights + self.net = resnet18(weights=ResNet18_Weights.IMAGENET1K_V1).to(args.device) + self.net.fc = nn.Linear(in_features=512, out_features=args.num_classes, bias=True).to(args.device) + for _, param in self.net.named_parameters(): + param.requires_grad = True + elif args.model == "resnet50": + from torchvision.models import resnet50, ResNet50_Weights + self.net = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2).to(args.device) + + elif args.model == 'swin_b': + from torchvision.models import swin_b + self.net = swin_b(weights='IMAGENET1K_V1').to(args.device) + self.net.head = nn.Linear(in_features=1024, out_features=args.num_classes, bias=True).to(args.device) + for _, param in self.net.named_parameters(): + param.requires_grad = True + elif args.model == 'swin_t': + from torchvision.models import swin_t + self.net = swin_t(weights='IMAGENET1K_V1').to(args.device) + self.net.head = nn.Linear(in_features=768, out_features=args.num_classes, bias=True).to(args.device) + for _, param in self.net.named_parameters(): + param.requires_grad = True + else: + raise NotImplementedError(f"{args.model} is not supported") + + + self.device = torch.device( + ( + f"cuda:{[int(i) for i in args.device[5:].split(',')][0]}" if "," in args.device else args.device + # since DataParallel only allow .to("cuda") + ) if torch.cuda.is_available() else "cpu" + ) + + if "," in args.device: + self.net = torch.nn.DataParallel( + self.net, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + + discriminator = Discriminator() + + + optimizer_inter = optim.SGD(discriminator.parameters(), lr=0.001,momentum = 0.9, weight_decay=0) + trainer = BackdoorModelTrainer( + self.net, + discriminator, + optimizer_inter, + args.regularization_ratio, + ) + + criterion = argparser_criterion(args) + + optimizer, scheduler = argparser_opt_scheduler(self.net, args) + + from torch.utils.data.dataloader import DataLoader + trainer.train_with_test_each_epoch_on_mix( + DataLoader(bd_train_dataset_with_transform, batch_size=args.batch_size, shuffle=True, drop_last=True, + pin_memory=args.pin_memory, num_workers=args.num_workers, ), + DataLoader(clean_test_dataset_with_transform, batch_size=args.batch_size, shuffle=False, drop_last=False, + pin_memory=args.pin_memory, num_workers=args.num_workers, ), + DataLoader(bd_test_dataset_with_transform, batch_size=args.batch_size, shuffle=False, drop_last=False, + pin_memory=args.pin_memory, num_workers=args.num_workers, ), + args.epochs, + criterion=criterion, + optimizer=optimizer, + scheduler=scheduler, + device=self.device, + frequency_save=args.frequency_save, + save_folder_path=args.save_path, + save_prefix='attack', + amp=args.amp, + prefetch=args.prefetch, + prefetch_transform_attr_name="ori_image_transform_in_loading", # since we use the preprocess_bd_dataset + non_blocking=args.non_blocking, + ) + + save_attack_result( + model_name=args.model, + num_classes=args.num_classes, + model=trainer.model.cpu().state_dict(), + data_path=args.dataset_path, + img_size=args.img_size, + clean_data=args.dataset, + bd_train=bd_train_dataset_with_transform, + bd_test=bd_test_dataset_with_transform, + save_path=args.save_path, + ) + + +if __name__ == '__main__': + attack = BadNet() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = attack.set_args(parser) + parser = attack.set_bd_args(parser) + args = parser.parse_args() + logging.debug("Be careful that we need to give the bd yaml higher priority. So, we put the add bd yaml first.") + attack.add_bd_yaml_to_args(args) + attack.add_yaml_to_args(args) + args = attack.process_args(args) + attack.prepare(args) + attack.stage1_non_training_data_prepare() + attack.stage2_training() diff --git a/attack/blend_bypass.py b/attack/blend_bypass.py new file mode 100644 index 0000000..34ae998 --- /dev/null +++ b/attack/blend_bypass.py @@ -0,0 +1,48 @@ +''' +this script is for blended attack + +@article{Blended, + title = {Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning}, + author = {Xinyun Chen and Chang Liu and Bo Li and Kimberly Lu and Dawn Song}, + journal = {arXiv preprint arXiv:1712.05526}, + year = {2017} +} +''' +import argparse +import os +import sys + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +from attack.badnet_bypass import BadNet, add_common_attack_args + + +class Blended(BadNet): + + def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + parser = add_common_attack_args(parser) + parser.add_argument("--attack_trigger_img_path", type=int, ) + parser.add_argument("--attack_train_blended_alpha", type=float, ) + parser.add_argument("--attack_test_blended_alpha", type=float, ) + parser.add_argument('--bd_yaml_path', type=str, default='../config/attack/blended/default_bypass.yaml', + help='path for yaml file provide additional default attributes') + parser.add_argument('--regularization_ratio', type=float, + help='the regularization ratio ' + ) + return parser + + +if __name__ == '__main__': + attack = Blended() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = attack.set_args(parser) + parser = attack.set_bd_args(parser) + args = parser.parse_args() + attack.add_bd_yaml_to_args(args) + attack.add_yaml_to_args(args) + args = attack.process_args(args) + attack.prepare(args) + attack.stage1_non_training_data_prepare() + attack.stage2_training() diff --git a/attack/blended.py b/attack/blended.py new file mode 100755 index 0000000..c5a1b58 --- /dev/null +++ b/attack/blended.py @@ -0,0 +1,45 @@ +''' +this script is for blended attack + +@article{Blended, + title = {Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning}, + author = {Xinyun Chen and Chang Liu and Bo Li and Kimberly Lu and Dawn Song}, + journal = {arXiv preprint arXiv:1712.05526}, + year = {2017} +} +''' +import argparse +import os +import sys + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +from attack.badnet import BadNet, add_common_attack_args + + +class Blended(BadNet): + + def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + parser = add_common_attack_args(parser) + parser.add_argument("--attack_trigger_img_path", type=str, ) + parser.add_argument("--attack_train_blended_alpha", type=float, ) + parser.add_argument("--attack_test_blended_alpha", type=float, ) + parser.add_argument('--bd_yaml_path', type=str, default='../config/attack/blended/default.yaml', + help='path for yaml file provide additional default attributes') + return parser + + +if __name__ == '__main__': + attack = Blended() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = attack.set_args(parser) + parser = attack.set_bd_args(parser) + args = parser.parse_args() + attack.add_bd_yaml_to_args(args) + attack.add_yaml_to_args(args) + args = attack.process_args(args) + attack.prepare(args) + attack.stage1_non_training_data_prepare() + attack.stage2_training() diff --git a/attack/blind.py b/attack/blind.py new file mode 100644 index 0000000..29eb99b --- /dev/null +++ b/attack/blind.py @@ -0,0 +1,1625 @@ +''' +this script is for blind attack +from https://github.com/ebagdasa/backdoors101 + +@inproceedings {bagdasaryan2020blind, + author = {Eugene Bagdasaryan and Vitaly Shmatikov}, + title = {Blind Backdoors in Deep Learning Models}, + booktitle = {30th {USENIX} Security Symposium ({USENIX} Security 21)}, + year = {2021}, + isbn = {978-1-939133-24-3}, + pages = {1505--1521}, + url = {https://www.usenix.org/conference/usenixsecurity21/presentation/bagdasaryan}, + publisher = {{USENIX} Association}, + month = aug, +} + +Original code file license is as the end of this script + +Note that for fairness issue, we apply the same total training epochs as all other attack methods. But for Blind, it may not be the best choice. + +''' +import os +import sys + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() +import numpy as np + +import logging +import torch +import argparse +import time +import random +from tqdm import tqdm +from shutil import copyfile +from typing import * +from collections import defaultdict +from dataclasses import asdict +from typing import Dict +from dataclasses import dataclass +from typing import List +from torch import optim, nn +from torch.nn import Module +from torchvision.transforms import transforms, functional + +import torchvision.transforms as transforms +from typing import Union + +from attack.badnet import BadNet +from utils.backdoor_generate_poison_index import generate_poison_index_from_label_transform +from utils.aggregate_block.bd_attack_generate import bd_attack_label_trans_generate +from copy import deepcopy +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.train_settings_generate import argparser_opt_scheduler, argparser_criterion +from utils.save_load_attack import save_attack_result +from utils.trainer_cls import all_acc, given_dataloader_test, \ + plot_loss, plot_acc_like_metric, Metric_Aggregator, test_given_dataloader_on_mix +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform +from torch.utils.data.dataloader import DataLoader +from utils.aggregate_block.bd_attack_generate import general_compose +from utils.aggregate_block.dataset_and_transform_generate import dataset_and_transform_generate + +transform_to_image = transforms.ToPILImage() +transform_to_tensor = transforms.ToTensor() + +ALL_TASKS = ['backdoor', 'normal', 'sentinet_evasion', # 'spectral_evasion', + 'neural_cleanse', 'mask_norm', 'sums', 'neural_cleanse_part1'] + + +class Params: + + def __init__( + self, + **kwargs, + ): + # Corresponds to the class module: tasks.mnist_task.MNISTTask + # See other tasks in the task folder. + self.task: str = 'MNIST' + self.current_time: Optional[str] = None + self.name: Optional[str] = None + self.commit: Optional[float] = None + self.random_seedOptional: Optional[int] = None + + # training params + self.start_epoch: int = 1 + self.epochs: Optional[int] = None + self.log_interval: int = 1000 + # model arch is usually defined by the task + self.pretrained: bool = False + self.resume_model: Optional[str] = None + self.lr: Optional[float] = None + self.decay: Optional[float] = None + self.momentum: Optional[float] = None + self.optimizer: Optional[str] = None + self.scheduler: bool = False + self.scheduler_milestonesOptional: [List[int]] = None + # data + self.data_path: str = '.data/' + self.batch_size: int = 64 + self.test_batch_size: int = 100 + self.transform_train: bool = True + "Do not apply transformations to the training images." + self.max_batch_id: Optional[int] = None + "For large datasets stop training earlier." + self.input_shape = None + "No need to set, updated by the Task class." + + # gradient shaping/DP params + self.dp: Optional[bool] = None + self.dp_clip: Optional[float] = None + self.dp_sigma: Optional[float] = None + + # attack params + self.backdoor: bool = False + self.backdoor_label: int = 8 + self.poisoning_proportion: float = 1.0 # backdoors proportion in backdoor loss + self.synthesizer: str = 'pattern' + self.backdoor_dynamic_position: bool = False + + # losses to balance: `normal`, `backdoor`, `neural_cleanse`, `sentinet`, + # `backdoor_multi`. + self.loss_tasks: Optional[List[str]] = None + + self.loss_balance: str = 'MGDA' + "loss_balancing: `fixed` or `MGDA`" + + self.loss_threshold: Optional[float] = None + + # approaches to balance losses with MGDA: `none`, `loss`, + # `loss+`, `l2` + self.mgda_normalize: Optional[str] = None + self.fixed_scales: Optional[Dict[str, float]] = None + + # relabel images with poison_number + self.poison_images: Optional[List[int]] = None + self.poison_images_test: Optional[List[int]] = None + # optimizations: + self.alternating_attack: Optional[float] = None + self.clip_batch: Optional[float] = None + # Disable BatchNorm and Dropout + self.switch_to_eval: Optional[float] = None + + # nc evasion + self.nc_p_norm: int = 1 + # spectral evasion + self.spectral_similarity: 'str' = 'norm' + + # logging + self.report_train_loss: bool = True + self.log: bool = False + self.tb: bool = False + self.save_model: Optional[bool] = None + self.save_on_epochs: Optional[List[int]] = None + self.save_scale_values: bool = False + self.print_memory_consumption: bool = False + self.save_timing: bool = False + self.timing_data = None + + # Temporary storage for running values + self.running_losses = None + self.running_scales = None + + # FL params + self.fl: bool = False + self.fl_no_models: int = 100 + self.fl_local_epochs: int = 2 + self.fl_total_participants: int = 80000 + self.fl_eta: int = 1 + self.fl_sample_dirichlet: bool = False + self.fl_dirichlet_alpha: Optional[float] = None + self.fl_diff_privacy: bool = False + self.fl_dp_clip: Optional[float] = None + self.fl_dp_noise: Optional[float] = None + # FL attack details. Set no adversaries to perform the attack: + self.fl_number_of_adversaries: int = 0 + self.fl_single_epoch_attack: Optional[int] = None + self.fl_weight_scale: int = 1 + + self.__dict__.update(kwargs) + + # enable logging anyways when saving statistics + if self.save_model or self.tb or self.save_timing or \ + self.print_memory_consumption: + self.log = True + + if self.log: + self.folder_path = f'saved_models/model_' \ + f'{self.task}_{self.current_time}_{self.name}' + + self.running_losses = defaultdict(list) + self.running_scales = defaultdict(list) + self.timing_data = defaultdict(list) + + for t in self.loss_tasks: + if t not in ALL_TASKS: + raise ValueError(f'Task {t} is not part of the supported ' + f'tasks: {ALL_TASKS}.') + + def to_dict(self): + return asdict(self) + + +class Metric: + name: str + train: bool + plottable: bool = True + running_metric = None + main_metric_name = None + + def __init__(self, name, train=False): + self.train = train + self.name = name + + self.running_metric = defaultdict(list) + + def __repr__(self): + metrics = self.get_value() + text = [f'{key}: {val:.2f}' for key, val in metrics.items()] + return f'{self.name}: ' + ','.join(text) + + def compute_metric(self, outputs, labels) -> Dict[str, Any]: + raise NotImplemented + + def accumulate_on_batch(self, outputs=None, labels=None): + current_metrics = self.compute_metric(outputs, labels) + for key, value in current_metrics.items(): + self.running_metric[key].append(value) + + def get_value(self) -> Dict[str, np.ndarray]: + metrics = dict() + for key, value in self.running_metric.items(): + metrics[key] = np.mean(value) + + return metrics + + def get_main_metric_value(self): + if not self.main_metric_name: + raise ValueError(f'For metric {self.name} define ' + f'attribute main_metric_name.') + metrics = self.get_value() + return metrics[self.main_metric_name] + + def reset_metric(self): + self.running_metric = defaultdict(list) + + def plot(self, tb_writer, step, tb_prefix=''): + if tb_writer is not None and self.plottable: + metrics = self.get_value() + for key, value in metrics.items(): + tb_writer.add_scalar(tag=f'{tb_prefix}/{self.name}_{key}', + scalar_value=value, + global_step=step) + tb_writer.flush() + else: + return False + + +class AccuracyMetric(Metric): + + def __init__(self, top_k=(1,)): + self.top_k = top_k + self.main_metric_name = 'Top-1' + super().__init__(name='Accuracy', train=False) + + def compute_metric(self, outputs: torch.Tensor, + labels: torch.Tensor): + """Computes the precision@k for the specified values of k""" + max_k = max(self.top_k) + batch_size = labels.shape[0] + + _, pred = outputs.topk(max_k, 1, True, True) + pred = pred.t() + correct = pred.eq(labels.view(1, -1).expand_as(pred)) + + res = dict() + for k in self.top_k: + correct_k = correct[:k].view(-1).float().sum(0) + res[f'Top-{k}'] = (correct_k.mul_(100.0 / batch_size)).item() + return res + + +class TestLossMetric(Metric): + + def __init__(self, criterion, train=False): + self.criterion = criterion + self.main_metric_name = 'value' + super().__init__(name='Loss', train=False) + + def compute_metric(self, outputs: torch.Tensor, + labels: torch.Tensor, top_k=(1,)): + """Computes the precision@k for the specified values of k""" + loss = self.criterion(outputs, labels) + return {'value': loss.mean().item()} + + +@dataclass +class Batch: + batch_id: int + inputs: torch.Tensor + labels: torch.Tensor + + # For PIPA experiment we use this field to store identity label. + aux: torch.Tensor = None + + def __post_init__(self): + self.batch_size = self.inputs.shape[0] + + def to(self, device): + inputs = self.inputs.to(device) + labels = self.labels.to(device) + if self.aux is not None: + aux = self.aux.to(device) + else: + aux = None + return Batch(self.batch_id, inputs, labels, aux) + + def clone(self): + inputs = self.inputs.clone() + labels = self.labels.clone() + if self.aux is not None: + aux = self.aux.clone() + else: + aux = None + return Batch(self.batch_id, inputs, labels, aux) + + def clip(self, batch_size): + if batch_size is None: + return self + + inputs = self.inputs[:batch_size] + labels = self.labels[:batch_size] + + if self.aux is None: + aux = None + else: + aux = self.aux[:batch_size] + + return Batch(self.batch_id, inputs, labels, aux) + + +class Task: + params: Params = None + + train_dataset = None + test_dataset = None + train_loader = None + test_loader = None + classes = None + + model: Module = None + optimizer: optim.Optimizer = None + criterion: Module = None + metrics: List[Metric] = None + + normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + "Generic normalization for input data." + input_shape: torch.Size = None + + def __init__(self, params: Params): + self.params = params + self.init_task() + + def init_task(self): + self.load_data() + self.model = self.build_model() + self.resume_model() + self.model = self.model.to(self.params.device) + + self.optimizer, self.scheduler = argparser_opt_scheduler(self.model, self.params) + self.criterion = self.make_criterion() + self.metrics = [AccuracyMetric(), TestLossMetric(self.criterion)] + self.set_input_shape() + + def load_data(self) -> None: + raise NotImplemented + + def build_model(self) -> Module: + raise NotImplemented + + def make_criterion(self) -> Module: + """Initialize with Cross Entropy by default. + + We use reduction `none` to support gradient shaping defense. + :return: + """ + return nn.CrossEntropyLoss(reduction='none') + + def resume_model(self): + if self.params.resume_model: + logging.info(f'Resuming training from- {self.params.resume_model}') + loaded_params = torch.load(f"saved_models/" + f"{self.params.resume_model}", + map_location=torch.device('cpu')) + self.model.load_state_dict(loaded_params['state_dict']) + self.params.start_epoch = loaded_params['epoch'] + self.params.lr = loaded_params.get('lr', self.params.lr) + + logging.warning(f"Loaded parameters from- saved model: LR is" + f" {self.params.lr} and current epoch is" + f" {self.params.start_epoch}") + + def set_input_shape(self): + inp = self.train_dataset[0][0] + self.params.input_shape = inp.shape + + def get_batch(self, batch_id, data) -> Batch: + """Process data into a batch. + + Specific for different datasets and data loaders this method unifies + the output by returning the object of class Batch. + :param batch_id: id of the batch + :param data: object returned by the Loader. + :return: + """ + inputs, labels = data + batch = Batch(batch_id, inputs, labels) + return batch.to(self.params.device) + + def accumulate_metrics(self, outputs, labels): + for metric in self.metrics: + metric.accumulate_on_batch(outputs, labels) + + def reset_metrics(self): + for metric in self.metrics: + metric.reset_metric() + + def report_metrics(self, step, prefix='', + tb_writer=None, tb_prefix='Metric/'): + metric_text = [] + for metric in self.metrics: + metric_text.append(str(metric)) + metric.plot(tb_writer, step, tb_prefix=tb_prefix) + logging.warning(f'{prefix} {step:4d}. {" | ".join(metric_text)}') + + return self.metrics[0].get_main_metric_value() + + @staticmethod + def get_batch_accuracy(outputs, labels, top_k=(1,)): + """Computes the precision@k for the specified values of k""" + max_k = max(top_k) + batch_size = labels.size(0) + + _, pred = outputs.topk(max_k, 1, True, True) + pred = pred.t() + correct = pred.eq(labels.view(1, -1).expand_as(pred)) + + res = [] + for k in top_k: + correct_k = correct[:k].view(-1).float().sum(0) + res.append((correct_k.mul_(100.0 / batch_size)).item()) + if len(res) == 1: + res = res[0] + return res + + +class Synthesizer: + params: Params + task: Task + + def __init__(self, task: Task): + self.task = task + self.params = task.params + + def make_backdoor_batch(self, batch: Batch, test=False, attack=True) -> Batch: + + # Don't attack if only normal loss task. + if (not attack) or (self.params.loss_tasks == ['normal'] and not test): + return batch + + if test: + attack_portion = batch.batch_size + else: + attack_portion = round( + batch.batch_size * self.params.poisoning_proportion) + + backdoored_batch = batch.clone() + self.apply_backdoor(backdoored_batch, attack_portion) + + return backdoored_batch + + def apply_backdoor(self, batch, attack_portion): + """ + Modifies only a portion of the batch (represents batch poisoning). + + :param batch: + :return: + """ + self.synthesize_inputs(batch=batch, attack_portion=attack_portion) + self.synthesize_labels(batch=batch, attack_portion=attack_portion) + + return + + def synthesize_inputs(self, batch, attack_portion=None): + raise NotImplemented + + def synthesize_labels(self, batch, attack_portion=None): + raise NotImplemented + + +def record_time(params: Params, t=None, name=None): + if t and name and params.save_timing == name or params.save_timing is True: + torch.cuda.synchronize() + params.timing_data[name].append(round(1000 * (time.perf_counter() - t))) + + +def compute_normal_loss(params, model, criterion, inputs, + labels, grads): + t = time.perf_counter() + outputs = model(inputs) + record_time(params, t, 'forward') + loss = criterion(outputs, labels) + + if not params.dp: + loss = loss.mean() + + if grads: + t = time.perf_counter() + grads = list(torch.autograd.grad(loss.mean(), + [x for x in model.parameters() if + x.requires_grad], + retain_graph=True)) + record_time(params, t, 'backward') + + return loss, grads + + +def get_grads(params, model, loss): + t = time.perf_counter() + grads = list(torch.autograd.grad(loss.mean(), + [x for x in model.parameters() if + x.requires_grad], + retain_graph=True)) + record_time(params, t, 'backward') + + return grads + + +def th(vector): + return torch.tanh(vector) / 2 + 0.5 + + +def norm_loss(params, model, grads=None): + if params.nc_p_norm == 1: + norm = torch.sum(th(model.mask)) + elif params.nc_p_norm == 2: + norm = torch.norm(th(model.mask)) + else: + raise ValueError('Not support mask norm.') + + if grads: + grads = get_grads(params, model, norm) + model.zero_grad() + + return norm, grads + + +def compute_backdoor_loss(params, model, criterion, inputs_back, + labels_back, grads=None): + t = time.perf_counter() + outputs = model(inputs_back) + record_time(params, t, 'forward') + loss = criterion(outputs, labels_back) + + if params.task == 'Pipa': + loss[labels_back == 0] *= 0.001 + if labels_back.sum().item() == 0.0: + loss[:] = 0.0 + if not params.dp: + loss = loss.mean() + + if grads: + grads = get_grads(params, model, loss) + + return loss, grads + + +def compute_all_losses_and_grads(loss_tasks, attack, model, criterion, + batch, batch_back, + compute_grad=None): + grads = {} + loss_values = {} + for t in loss_tasks: + # if compute_grad: + # model.zero_grad() + if t == 'normal': + loss_values[t], grads[t] = compute_normal_loss(attack.params, + model, + criterion, + batch.inputs, + batch.labels, + grads=compute_grad) + elif t == 'backdoor': + loss_values[t], grads[t] = compute_backdoor_loss(attack.params, + model, + criterion, + batch_back.inputs, + batch_back.labels, + grads=compute_grad) + + elif t == 'mask_norm': + loss_values[t], grads[t] = norm_loss(attack.params, attack.nc_model, + grads=compute_grad) + elif t == 'neural_cleanse_part1': + loss_values[t], grads[t] = compute_normal_loss(attack.params, + model, + criterion, + batch.inputs, + batch_back.labels, + grads=compute_grad, + ) + + return loss_values, grads + + +class Model(nn.Module): + """ + Base class for models with added support for GradCam activation map + and a SentiNet defense. The GradCam design is taken from: +https://medium.com/@stepanulyanin/implementing-grad-cam-in-pytorch-ea0937c31e82 + If you are not planning to utilize SentiNet defense just import any model + you like for your tasks. + """ + + def __init__(self): + super().__init__() + self.gradient = None + + def activations_hook(self, grad): + self.gradient = grad + + def get_gradient(self): + return self.gradient + + def get_activations(self, x): + return self.features(x) + + def switch_grads(self, enable=True): + for i, n in self.named_parameters(): + n.requires_grad_(enable) + + def features(self, x): + """ + Get latent representation, eg logit layer. + :param x: + :return: + """ + raise NotImplemented + + def forward(self, x, latent=False): + raise NotImplemented + + +class Attack: + params: Params + synthesizer: Synthesizer + nc_model: Model + nc_optim: torch.optim.Optimizer + loss_hist = list() + + # fixed_model: Model + + def __init__(self, params, synthesizer): + self.params = params + self.synthesizer = synthesizer + + def compute_blind_loss(self, model, criterion, batch, attack): + """ + + :param model: + :param criterion: + :param batch: + :param attack: Do not attack at all. Ignore all the parameters + :return: + """ + batch = batch.clip(self.params.clip_batch) + loss_tasks = self.params.loss_tasks.copy() if attack else ['normal'] + batch_back = self.synthesizer.make_backdoor_batch(batch, attack=attack) + scale = dict() + + if self.params.loss_threshold and (np.mean(self.loss_hist) >= self.params.loss_threshold + or len(self.loss_hist) < self.params.batch_history_len): + loss_tasks = ['normal'] + + if len(loss_tasks) == 1: + loss_values, grads = compute_all_losses_and_grads( + loss_tasks, + self, model, criterion, batch, batch_back, compute_grad=False + ) + elif self.params.loss_balance == 'MGDA': + + loss_values, grads = compute_all_losses_and_grads( + loss_tasks, + self, model, criterion, batch, batch_back, compute_grad=True) + if len(loss_tasks) > 1: + scale = MGDASolver.get_scales(grads, loss_values, + self.params.mgda_normalize, + loss_tasks) + elif self.params.loss_balance == 'fixed': + loss_values, grads = compute_all_losses_and_grads( + loss_tasks, + self, model, criterion, batch, batch_back, compute_grad=False) + + for t in loss_tasks: + scale[t] = self.params.fixed_scales[t] + else: + raise ValueError(f'Please choose between `MGDA` and `fixed`.') + + if len(loss_tasks) == 1: + scale = {loss_tasks[0]: 1.0} + self.loss_hist.append(loss_values['normal'].item()) + self.loss_hist = self.loss_hist[-1000:] + blind_loss = self.scale_losses(loss_tasks, loss_values, scale) + + return blind_loss + + def scale_losses(self, loss_tasks, loss_values, scale): + blind_loss = 0 + for it, t in enumerate(loss_tasks): + self.params.running_losses[t].append(loss_values[t].item()) + self.params.running_scales[t].append(scale[t]) + if it == 0: + blind_loss = scale[t] * loss_values[t] + else: + blind_loss += scale[t] * loss_values[t] + self.params.running_losses['total'].append(blind_loss.item()) + return blind_loss + + +# Credits to Ozan Sener +# https://github.com/intel-isl/MultiObjectiveOptimization +class MGDASolver: + MAX_ITER = 250 + STOP_CRIT = 1e-5 + + @staticmethod + def _min_norm_element_from2(v1v1, v1v2, v2v2): + """ + Analytical solution for min_{c} |cx_1 + (1-c)x_2|_2^2 + d is the distance (objective) optimzed + v1v1 = + v1v2 = + v2v2 = + """ + if v1v2 >= v1v1: + # Case: Fig 1, third column + gamma = 0.999 + cost = v1v1 + return gamma, cost + if v1v2 >= v2v2: + # Case: Fig 1, first column + gamma = 0.001 + cost = v2v2 + return gamma, cost + # Case: Fig 1, second column + gamma = -1.0 * ((v1v2 - v2v2) / (v1v1 + v2v2 - 2 * v1v2)) + cost = v2v2 + gamma * (v1v2 - v2v2) + return gamma, cost + + @staticmethod + def _min_norm_2d(vecs: list, dps): + """ + Find the minimum norm solution as combination of two points + This is correct only in 2D + ie. min_c |\sum c_i x_i|_2^2 st. \sum c_i = 1 , 1 >= c_1 >= 0 + for all i, c_i + c_j = 1.0 for some i, j + """ + dmin = 1e8 + sol = 0 + for i in range(len(vecs)): + for j in range(i + 1, len(vecs)): + if (i, j) not in dps: + dps[(i, j)] = 0.0 + for k in range(len(vecs[i])): + dps[(i, j)] += torch.dot(vecs[i][k].view(-1), + vecs[j][k].view(-1)).detach() + dps[(j, i)] = dps[(i, j)] + if (i, i) not in dps: + dps[(i, i)] = 0.0 + for k in range(len(vecs[i])): + dps[(i, i)] += torch.dot(vecs[i][k].view(-1), + vecs[i][k].view(-1)).detach() + if (j, j) not in dps: + dps[(j, j)] = 0.0 + for k in range(len(vecs[i])): + dps[(j, j)] += torch.dot(vecs[j][k].view(-1), + vecs[j][k].view(-1)).detach() + c, d = MGDASolver._min_norm_element_from2(dps[(i, i)], + dps[(i, j)], + dps[(j, j)]) + if d < dmin: + dmin = d + sol = [(i, j), c, d] + return sol, dps + + @staticmethod + def _projection2simplex(y): + """ + Given y, it solves argmin_z |y-z|_2 st \sum z = 1 , 1 >= z_i >= 0 for all i + """ + m = len(y) + sorted_y = np.flip(np.sort(y), axis=0) + tmpsum = 0.0 + tmax_f = (np.sum(y) - 1.0) / m + for i in range(m - 1): + tmpsum += sorted_y[i] + tmax = (tmpsum - 1) / (i + 1.0) + if tmax > sorted_y[i + 1]: + tmax_f = tmax + break + return np.maximum(y - tmax_f, np.zeros(y.shape)) + + @staticmethod + def _next_point(cur_val, grad, n): + proj_grad = grad - (np.sum(grad) / n) + tm1 = -1.0 * cur_val[proj_grad < 0] / proj_grad[proj_grad < 0] + tm2 = (1.0 - cur_val[proj_grad > 0]) / (proj_grad[proj_grad > 0]) + + skippers = np.sum(tm1 < 1e-7) + np.sum(tm2 < 1e-7) + t = 1 + if len(tm1[tm1 > 1e-7]) > 0: + t = np.min(tm1[tm1 > 1e-7]) + if len(tm2[tm2 > 1e-7]) > 0: + t = min(t, np.min(tm2[tm2 > 1e-7])) + + next_point = proj_grad * t + cur_val + next_point = MGDASolver._projection2simplex(next_point) + return next_point + + @staticmethod + def find_min_norm_element(vecs: list): + """ + Given a list of vectors (vecs), this method finds the minimum norm + element in the convex hull as min |u|_2 st. u = \sum c_i vecs[i] + and \sum c_i = 1. It is quite geometric, and the main idea is the + fact that if d_{ij} = min |u|_2 st u = c x_i + (1-c) x_j; the solution + lies in (0, d_{i,j})Hence, we find the best 2-task solution , and + then run the projected gradient descent until convergence + """ + # Solution lying at the combination of two points + dps = {} + init_sol, dps = MGDASolver._min_norm_2d(vecs, dps) + + n = len(vecs) + sol_vec = np.zeros(n) + sol_vec[init_sol[0][0]] = init_sol[1] + sol_vec[init_sol[0][1]] = 1 - init_sol[1] + + if n < 3: + # This is optimal for n=2, so return the solution + return sol_vec, init_sol[2] + + iter_count = 0 + + grad_mat = np.zeros((n, n)) + for i in range(n): + for j in range(n): + grad_mat[i, j] = dps[(i, j)] + + while iter_count < MGDASolver.MAX_ITER: + grad_dir = -1.0 * np.dot(grad_mat, sol_vec) + new_point = MGDASolver._next_point(sol_vec, grad_dir, n) + # Re-compute the inner products for line search + v1v1 = 0.0 + v1v2 = 0.0 + v2v2 = 0.0 + for i in range(n): + for j in range(n): + v1v1 += sol_vec[i] * sol_vec[j] * dps[(i, j)] + v1v2 += sol_vec[i] * new_point[j] * dps[(i, j)] + v2v2 += new_point[i] * new_point[j] * dps[(i, j)] + nc, nd = MGDASolver._min_norm_element_from2(v1v1.item(), + v1v2.item(), + v2v2.item()) + # try: + new_sol_vec = nc * sol_vec + (1 - nc) * new_point + # except AttributeError: + # logging.debug(sol_vec) + change = new_sol_vec - sol_vec + if np.sum(np.abs(change)) < MGDASolver.STOP_CRIT: + return sol_vec, nd + sol_vec = new_sol_vec + + @staticmethod + def find_min_norm_element_FW(vecs): + """ + Given a list of vectors (vecs), this method finds the minimum norm + element in the convex hull + as min |u|_2 st. u = \sum c_i vecs[i] and \sum c_i = 1. + It is quite geometric, and the main idea is the fact that if + d_{ij} = min |u|_2 st u = c x_i + (1-c) x_j; the solution lies + in (0, d_{i,j})Hence, we find the best 2-task solution, and then + run the Frank Wolfe until convergence + """ + # Solution lying at the combination of two points + dps = {} + init_sol, dps = MGDASolver._min_norm_2d(vecs, dps) + + n = len(vecs) + sol_vec = np.zeros(n) + sol_vec[init_sol[0][0]] = init_sol[1] + sol_vec[init_sol[0][1]] = 1 - init_sol[1] + + if n < 3: + # This is optimal for n=2, so return the solution + return sol_vec, init_sol[2] + + iter_count = 0 + + grad_mat = np.zeros((n, n)) + for i in range(n): + for j in range(n): + grad_mat[i, j] = dps[(i, j)] + + while iter_count < MGDASolver.MAX_ITER: + t_iter = np.argmin(np.dot(grad_mat, sol_vec)) + + v1v1 = np.dot(sol_vec, np.dot(grad_mat, sol_vec)) + v1v2 = np.dot(sol_vec, grad_mat[:, t_iter]) + v2v2 = grad_mat[t_iter, t_iter] + + nc, nd = MGDASolver._min_norm_element_from2(v1v1, v1v2, v2v2) + new_sol_vec = nc * sol_vec + new_sol_vec[t_iter] += 1 - nc + + change = new_sol_vec - sol_vec + if np.sum(np.abs(change)) < MGDASolver.STOP_CRIT: + return sol_vec, nd + sol_vec = new_sol_vec + + @classmethod + def get_scales(cls, grads, losses, normalization_type, tasks): + scale = {} + gn = gradient_normalizers(grads, losses, normalization_type) + for t in tasks: + for gr_i in range(len(grads[t])): + grads[t][gr_i] = grads[t][gr_i] / (gn[t] + 1e-5) + sol, min_norm = cls.find_min_norm_element([grads[t] for t in tasks]) + for zi, t in enumerate(tasks): + scale[t] = float(sol[zi]) + + return scale + + +def create_table(params: dict): + data = "| name | value | \n |-----|-----|" + + for key, value in params.items(): + data += '\n' + f"| {key} | {value} |" + + return data + + +class PatternSynthesizer(Synthesizer): + pattern_tensor: torch.Tensor = torch.tensor([ + [1., 0., 1.], + [-10., 1., -10.], + [-10., -10., 0.], + [-10., 1., -10.], + [1., 0., 1.] + ]) + "Just some random 2D pattern." + + x_top = 3 + "X coordinate to put the backdoor into." + y_top = 23 + "Y coordinate to put the backdoor into." + + mask_value = -10 + "A tensor coordinate with this value won't be applied to the image." + + resize_scale = (5, 10) + "If the pattern is dynamically placed, resize the pattern." + + mask: torch.Tensor = None + "A mask used to combine backdoor pattern with the original image." + + pattern: torch.Tensor = None + "A tensor of the `input.shape` filled with `mask_value` except backdoor." + + def __init__(self, task: Task): + super().__init__(task) + self.make_pattern(self.pattern_tensor, self.x_top, self.y_top) + + def make_pattern(self, pattern_tensor, x_top, y_top): + full_image = torch.zeros(self.params.input_shape) + full_image.fill_(self.mask_value) + + x_bot = x_top + pattern_tensor.shape[0] + y_bot = y_top + pattern_tensor.shape[1] + + if x_bot >= self.params.input_shape[1] or \ + y_bot >= self.params.input_shape[2]: + raise ValueError(f'Position of backdoor outside image limits:' + f'image: {self.params.input_shape}, but backdoor' + f'ends at ({x_bot}, {y_bot})') + + full_image[:, x_top:x_bot, y_top:y_bot] = pattern_tensor + + self.mask = 1 * (full_image != self.mask_value).to(self.params.device) + self.pattern = self.task.normalize(full_image).to(self.params.device) + + def synthesize_inputs(self, batch, attack_portion=None): + pattern, mask = self.get_pattern() + batch.inputs[:attack_portion] = (1 - mask) * \ + batch.inputs[:attack_portion] + \ + mask * pattern + return + + def synthesize_labels(self, batch, attack_portion=None): + batch.labels[:attack_portion].fill_(self.params.backdoor_label) + + return + + def get_pattern(self): + if self.params.backdoor_dynamic_position: + resize = random.randint(self.resize_scale[0], self.resize_scale[1]) + pattern = self.pattern_tensor + if random.random() > 0.5: + pattern = functional.hflip(pattern) + image = transform_to_image(pattern) + pattern = transform_to_tensor( + functional.resize(image, + resize, interpolation=0)).squeeze() + + x = random.randint(0, self.params.input_shape[1] \ + - pattern.shape[0] - 1) + y = random.randint(0, self.params.input_shape[2] \ + - pattern.shape[1] - 1) + self.make_pattern(pattern, x, y) + + return self.pattern, self.mask + + +class Cifar10Task(Task): + normalize = transforms.Normalize([0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261]) + + def load_data(self): + self.load_cifar_data() + + def load_cifar_data(self): + train_dataset_without_transform, \ + transform_train, \ + train_label_transform, \ + test_dataset_without_transform, \ + transform_test, \ + test_label_transform = dataset_and_transform_generate(self.params) + + clean_train_dataset_with_transform = dataset_wrapper_with_transform( + train_dataset_without_transform, + transform_train, + train_label_transform + ) + + clean_test_dataset_with_transform = dataset_wrapper_with_transform( + test_dataset_without_transform, + transform_test, + test_label_transform, + ) + + self.train_dataset = clean_train_dataset_with_transform + + self.train_loader = DataLoader(self.train_dataset, + batch_size=self.params.batch_size, + shuffle=True, + pin_memory=self.params.pin_memory, + num_workers=self.params.num_workers) + self.test_dataset = clean_test_dataset_with_transform + self.test_loader = DataLoader(self.test_dataset, + batch_size=self.params.test_batch_size, + pin_memory=self.params.pin_memory, + shuffle=False, num_workers=self.params.num_workers) + + return True + + def build_model(self) -> nn.Module: + net = generate_cls_model( + model_name=self.params.model, + image_size=self.params.img_size[0], + num_classes=self.params.num_classes, + ) + + if "," in self.params.device: + net = torch.nn.DataParallel( + net, + device_ids=[int(i) for i in self.params.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + logging.info("net data parallel") + + self.params.device = ( + f"cuda:{[int(i) for i in self.params.device[5:].split(',')][0]}" if "," in self.params.device else self.params.device + # since DataParallel only allow .to("cuda") + ) if torch.cuda.is_available() else "cpu" + + return net + + +class Helper: + params: Params = None + task: Optional[Task] = None + synthesizer: Synthesizer = None + attack: Attack = None + tb_writer = None + + def __init__(self, params): + self.params = Params(**params) + + self.times = {'backward': list(), 'forward': list(), 'step': list(), + 'scales': list(), 'total': list(), 'poison': list()} + + self.make_task() + self.make_synthesizer() + self.attack = Attack(self.params, self.synthesizer) + + self.best_loss = float('inf') + + def make_task(self): + self.task = Cifar10Task(self.params) + + def make_synthesizer(self): + self.synthesizer = PatternSynthesizer(self.task) + + def save_checkpoint(self, state, is_best, filename='checkpoint.pth.tar'): + if not self.params.save_model: + return False + torch.save(state, filename) + + if is_best: + copyfile(filename, 'model_best.pth.tar') + + def flush_writer(self): + if self.tb_writer: + self.tb_writer.flush() + + def plot(self, x, y, name): + if self.tb_writer is not None: + self.tb_writer.add_scalar(tag=name, scalar_value=y, global_step=x) + self.flush_writer() + else: + return False + + def report_training_losses_scales(self, batch_id, epoch): + if not self.params.report_train_loss or \ + batch_id % self.params.log_interval != 0: + return + total_batches = len(self.task.train_loader) + losses = [f'{x}: {np.mean(y):.2f}' + for x, y in self.params.running_losses.items()] + scales = [f'{x}: {np.mean(y):.2f}' + for x, y in self.params.running_scales.items()] + logging.info( + f'Epoch: {epoch:3d}. ' + f'Batch: {batch_id:5d}/{total_batches}. ' + f' Losses: {losses}.' + f' Scales: {scales}') + for name, values in self.params.running_losses.items(): + self.plot(epoch * total_batches + batch_id, np.mean(values), + f'Train/Loss_{name}') + for name, values in self.params.running_scales.items(): + self.plot(epoch * total_batches + batch_id, np.mean(values), + f'Train/Scale_{name}') + + self.params.running_losses = defaultdict(list) + self.params.running_scales = defaultdict(list) + + +def train(hlpr: Helper, epoch, model, optimizer, train_loader, attack=True): + criterion = hlpr.task.criterion + model.train() + + batch_loss_list = [] + + for i, data in tqdm(enumerate(train_loader)): + batch = hlpr.task.get_batch(i, data) + + with torch.cuda.amp.autocast(enabled=args.amp): + loss = hlpr.attack.compute_blind_loss(model, criterion, batch, attack) + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + + batch_loss_list.append(loss.item()) + hlpr.report_training_losses_scales(i, epoch) + if i == hlpr.params.max_batch_id: + break + + one_epoch_loss = sum(batch_loss_list) / len(batch_loss_list) + + scheduler = getattr(hlpr.task, "scheduler", None) + if scheduler is not None: + if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + scheduler.step(one_epoch_loss) + else: + scheduler.step() + logging.info(f"scheduler step, {scheduler}") + + return one_epoch_loss + + +def hlpr_test(hlpr: Helper, epoch, backdoor=False): + model = hlpr.task.model + model.eval() + hlpr.task.reset_metrics() + + with torch.no_grad(): + for i, data in tqdm(enumerate(hlpr.task.test_loader)): + batch = hlpr.task.get_batch(i, data) + if backdoor: + batch = hlpr.attack.synthesizer.make_backdoor_batch(batch, + test=True, + attack=True) + + outputs = model(batch.inputs) + hlpr.task.accumulate_metrics(outputs=outputs, labels=batch.labels) + metric = hlpr.task.report_metrics(epoch, + prefix=f'Backdoor {str(backdoor):5s}. Epoch: ', + tb_writer=hlpr.tb_writer, + tb_prefix=f'Test_backdoor_{str(backdoor):5s}') + + return metric + + +def run(hlpr, clean_test_dataloader, bd_test_dataloader, criterion, device, args): + global scaler + scaler = torch.cuda.amp.GradScaler(enabled=hlpr.params.amp) + + acc = hlpr_test(hlpr, 0, backdoor=False) + + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + train_loss_list = [] + + agg = Metric_Aggregator() + + for epoch in range(hlpr.params.start_epoch, + hlpr.params.epochs + 1): + one_epoch_train_loss = train(hlpr, epoch, hlpr.task.model, hlpr.task.optimizer, + hlpr.task.train_loader) + train_loss_list.append(one_epoch_train_loss) + acc = hlpr_test(hlpr, epoch, backdoor=False) + hlpr_test(hlpr, epoch, backdoor=True) + + ### My test code start + + clean_metrics, \ + clean_test_epoch_predict_list, \ + clean_test_epoch_label_list, \ + = given_dataloader_test( + model=hlpr.task.model, + test_dataloader=clean_test_dataloader, + criterion=criterion, + non_blocking=args.non_blocking, + device=device, + verbose=1, + ) + + clean_test_loss_avg_over_batch = clean_metrics["test_loss_avg_over_batch"] + test_acc = clean_metrics["test_acc"] + + bd_metrics, \ + bd_test_epoch_predict_list, \ + bd_test_epoch_label_list, \ + bd_test_epoch_original_index_list, \ + bd_test_epoch_poison_indicator_list, \ + bd_test_epoch_original_targets_list = test_given_dataloader_on_mix( + model=hlpr.task.model, + test_dataloader=bd_test_dataloader, + criterion=criterion, + non_blocking=args.non_blocking, + device=device, + verbose=1, + ) + + bd_test_loss_avg_over_batch = bd_metrics["test_loss_avg_over_batch"] + test_asr = all_acc(bd_test_epoch_predict_list, bd_test_epoch_label_list) + test_ra = all_acc(bd_test_epoch_predict_list, bd_test_epoch_original_targets_list) + + agg( + { + "epoch": epoch, + "train_epoch_loss_avg_over_batch": one_epoch_train_loss, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + } + ) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + + plot_loss( + train_loss_list, + clean_test_loss_list, + bd_test_loss_list, + args.save_path, + "loss_metric_plots", + ) + + plot_acc_like_metric( + [], [], [], + test_acc_list, + test_asr_list, + test_ra_list, + args.save_path, + "loss_metric_plots", + ) + + agg.to_dataframe().to_csv(f"{args.save_path}/attack_df.csv") + + agg.summary().to_csv(f"{args.save_path}/attack_df_summary.csv") + + ### My test code end + + +class AddMaskPatchTrigger(object): + def __init__(self, + trigger_array: Union[np.ndarray, torch.Tensor], + mask_array: Union[np.ndarray, torch.Tensor], + ): + self.trigger_array = trigger_array + self.mask_array = mask_array + + def __call__(self, img, target=None, image_serial_id=None): + return self.add_trigger(img) + + def add_trigger(self, img): + return img * (1 - self.mask_array) + self.trigger_array * self.mask_array + + +def gradient_normalizers(grads, losses, normalization_type): + gn = {} + if normalization_type == 'l2': + for t in grads: + gn[t] = torch.sqrt( + torch.stack([gr.pow(2).sum().data for gr in grads[t]]).sum()) + elif normalization_type == 'loss': + for t in grads: + gn[t] = min(losses[t].mean(), 10.0) + elif normalization_type == 'loss+': + for t in grads: + gn[t] = min(losses[t].mean() * torch.sqrt( + torch.stack([gr.pow(2).sum().data for gr in grads[t]]).sum()), + 10) + + elif normalization_type == 'none' or normalization_type == 'eq': + for t in grads: + gn[t] = 1.0 + else: + raise ValueError('ERROR: Invalid Normalization Type') + return gn + + +class blendedImageAttack_on_batch(object): + + def __init__(self, target_image, blended_rate, device): + self.target_image = target_image.to(device) + self.blended_rate = blended_rate + + def __call__(self, img, target=None, image_serial_id=None): + return self.add_trigger(img) + + def add_trigger(self, img): + return (1 - self.blended_rate) * img + (self.blended_rate) * self.target_image[None, ...] # use the broadcast + + +class batchwise_label_transform(object): + ''' + idea : any label -> fix_target + ''' + + def __init__(self, label_transform, device): + self.label_transform = label_transform + self.device = device + + def __call__(self, batch_labels: torch.Tensor, ): + return torch.tensor([self.label_transform(original_label) for original_label in batch_labels]).to(self.device) + + +class Blind(BadNet): + + def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + parser.add_argument('--attack', type=str, ) + parser.add_argument('--attack_target', type=int, + help='target class in all2one attack') + parser.add_argument('--attack_label_trans', type=str, + help='which type of label modification in backdoor attack' + ) + parser.add_argument("--weight_loss_balance_mode", type=str) + parser.add_argument("--mgda_normalize", type=str) + parser.add_argument("--fix_scale_normal_weight", type=float) + parser.add_argument("--fix_scale_backdoor_weight", type=float) + + parser.add_argument("--batch_history_len", type=int, + help="len of tracking history to compute when training is stable, so we start to attack") + parser.add_argument("--backdoor_batch_loss_threshold", type=float, + help="threshold for when training is stable, so we start to attack") + parser.add_argument('--bd_yaml_path', type=str, default='../config/attack/blind/default.yaml', + help='path for yaml file provide additional default attributes') + return parser + + def stage1_non_training_data_prepare(self): + logging.info(f"stage1 start") + + assert 'args' in self.__dict__ + args = self.args + + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + clean_train_dataset_with_transform, \ + clean_train_dataset_targets, \ + clean_test_dataset_with_transform, \ + clean_test_dataset_targets \ + = self.benign_prepare() + + self.trans = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize(args.img_size[:2]), # (32, 32) + transforms.ToTensor() + ]) + + trigger_pattern_np: np.ndarray = np.array([ + + [255, 0., 255], + [-10., 255, -10.], + [-10., -10., 0.], + [-10., 255, -10.], + [255, 0., 255] + ]) + trigger_pattern_np = np.repeat(trigger_pattern_np[:, :, np.newaxis], args.input_channel, axis=2) + trigger_full_size_np = np.ones((args.input_height, args.input_width, args.input_channel)) * (-10) + x_top = 3 + y_top = 23 + trigger_full_size_np[ + x_top: x_top + trigger_pattern_np.shape[0], + y_top: y_top + trigger_pattern_np.shape[1], + : + ] = trigger_pattern_np + self.trigger_full_size_np = trigger_full_size_np + self.mask = 1 * (trigger_full_size_np != -10) + + test_bd_transform = general_compose([ + (transforms.Resize(args.img_size[:2]), False), + (np.array, False), + (AddMaskPatchTrigger( + self.trigger_full_size_np, + self.mask, + ) + , True), + ]) + + train_bd_img_transform, test_bd_img_transform = None, test_bd_transform + + ### get the backdoor transform on label + bd_label_transform = bd_attack_label_trans_generate(args) + self.bd_label_transform = bd_label_transform + + # NO poison samples in, just use as clean, real poison is done in batchwise way + bd_train_dataset = prepro_cls_DatasetBD_v2( + deepcopy(train_dataset_without_transform), + poison_indicator=None, + bd_image_pre_transform=train_bd_img_transform, + bd_label_pre_transform=bd_label_transform, + save_folder_path=f"{args.save_path}/bd_train_dataset", + ) + bd_train_dataset.getitem_all = True + bd_train_dataset_with_transform = dataset_wrapper_with_transform( + bd_train_dataset, + train_img_transform, + train_label_transform, + ) + + ### decide which img to poison in ASR Test + test_poison_index = generate_poison_index_from_label_transform( + clean_test_dataset_targets, + label_transform=bd_label_transform, + train=False, + ) + + ### generate test dataset for ASR + bd_test_dataset = prepro_cls_DatasetBD_v2( + deepcopy(test_dataset_without_transform), + poison_indicator=test_poison_index, + bd_image_pre_transform=test_bd_img_transform, + bd_label_pre_transform=bd_label_transform, + save_folder_path=f"{args.save_path}/bd_test_dataset", + ) + + bd_test_dataset.subset( + np.where(test_poison_index == 1)[0] + ) + + bd_test_dataset_with_transform = dataset_wrapper_with_transform( + bd_test_dataset, + test_img_transform, + test_label_transform, + ) + + self.stage1_results = clean_train_dataset_with_transform, \ + clean_test_dataset_with_transform, \ + bd_train_dataset_with_transform, \ + bd_test_dataset_with_transform + + def stage2_training(self): + logging.info(f"stage2 start") + assert 'args' in self.__dict__ + args = self.args + + clean_train_dataset_with_transform, \ + clean_test_dataset_with_transform, \ + bd_train_dataset_with_transform, \ + bd_test_dataset_with_transform = self.stage1_results + + device = torch.device( + ( + f"cuda:{[int(i) for i in args.device[5:].split(',')][0]}" if "," in args.device else args.device + # since DataParallel only allow .to("cuda") + ) if torch.cuda.is_available() else "cpu" + ) + + ''' + start real source code part + ''' + + params = { + "test_batch_size": 100, + "log_interval": 100, + "pretrained": False, + "loss_threshold": args.backdoor_batch_loss_threshold, + "poisoning_proportion": 1.1, # not useful in common poison, just set > 1 + "backdoor_label": args.attack_target, + "backdoor": True, + "loss_balance": args.weight_loss_balance_mode, # MGDA or fixed + "mgda_normalize": args.mgda_normalize, + "fixed_scales": { + "backdoor": args.fix_scale_backdoor_weight, + "normal": args.fix_scale_normal_weight, + }, + "loss_tasks": ["backdoor", "normal"], + } + args.__dict__.update(params) + helper = Helper(args.__dict__) + logging.warning(create_table(args.__dict__)) + + criterion = argparser_criterion(args) + + run( + helper, + clean_test_dataloader=DataLoader(clean_test_dataset_with_transform, batch_size=args.batch_size, + shuffle=False, drop_last=False, + pin_memory=args.pin_memory, num_workers=args.num_workers, ), + bd_test_dataloader=DataLoader(bd_test_dataset_with_transform, batch_size=args.batch_size, shuffle=False, + drop_last=False, + pin_memory=args.pin_memory, num_workers=args.num_workers, ), + criterion=criterion, + device=device, + args=args, + ) + + ''' + end real source code part + ''' + + save_attack_result( + model_name=args.model, + num_classes=args.num_classes, + model=helper.task.model.cpu().state_dict(), + data_path=args.dataset_path, + img_size=args.img_size, + clean_data=args.dataset, + bd_train=None, + bd_test=bd_test_dataset_with_transform, + save_path=args.save_path, + ) + + +if __name__ == '__main__': + attack = Blind() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = attack.set_args(parser) + parser = attack.set_bd_args(parser) + args = parser.parse_args() + attack.add_bd_yaml_to_args(args) + attack.add_yaml_to_args(args) + args = attack.process_args(args) + attack.prepare(args) + attack.stage1_non_training_data_prepare() + attack.stage2_training() + +''' +MIT License + +Copyright (c) [year] [fullname] + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' \ No newline at end of file diff --git a/attack/bpp.py b/attack/bpp.py new file mode 100644 index 0000000..79d83e6 --- /dev/null +++ b/attack/bpp.py @@ -0,0 +1,1100 @@ +''' +this script is for bpp attack +github link : https://github.com/RU-System-Software-and-Security/BppAttack +The original LICENSE of the script is put at the bottom of this file. +citation: +@InProceedings{Wang_2022_CVPR, + author = {Wang, Zhenting and Zhai, Juan and Ma, Shiqing}, + title = {BppAttack: Stealthy and Efficient Trojan Attacks Against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning}, + booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + month = {June}, + year = {2022}, + pages = {15074-15084} +} + +license from the original code: + +MIT License + +Copyright (c) 2022 RUSSS + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' + +import sys, os, logging +import os +import sys + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +import time +import argparse +from torchvision.transforms import ToPILImage +from torchvision.transforms import ToTensor + +to_pil = ToPILImage() +to_tensor = ToTensor() +from torch.utils.data import DataLoader + +import numpy as np +import torch +import torchvision.transforms as transforms + +import random +from numba import jit +from numba.types import float64, int64 + +from utils.aggregate_block.dataset_and_transform_generate import get_dataset_normalization, get_dataset_denormalization +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.trainer_cls import Metric_Aggregator +from utils.save_load_attack import save_attack_result +from utils.aggregate_block.train_settings_generate import argparser_opt_scheduler +from attack.badnet import add_common_attack_args, BadNet +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform +from utils.trainer_cls import all_acc, given_dataloader_test, general_plot_for_epoch + + +def generalize_to_lower_pratio(pratio, bs): + if pratio * bs >= 1: + # the normal case that each batch can have at least one poison sample + return pratio * bs + else: + # then randomly return number of poison sample + if np.random.uniform(0, + 1) < pratio * bs: # eg. pratio = 1/1280, then 1/10 of batch(bs=128) should contains one sample + return 1 + else: + return 0 + + +def back_to_np_4d(inputs, args): + if args.dataset == "cifar10": + expected_values = [0.4914, 0.4822, 0.4465] + variance = [0.247, 0.243, 0.261] + elif args.dataset == "cifar100": + expected_values = [0.5071, 0.4867, 0.4408] + variance = [0.2675, 0.2565, 0.2761] + elif args.dataset == "mnist": + expected_values = [0.5] + variance = [0.5] + elif args.dataset in ["gtsrb", "celeba"]: + expected_values = [0, 0, 0] + variance = [1, 1, 1] + elif args.dataset == "imagenet": + expected_values = [0.485, 0.456, 0.406] + variance = [0.229, 0.224, 0.225] + elif args.dataset == "tiny": + expected_values = [0.4802, 0.4481, 0.3975] + variance = [0.2302, 0.2265, 0.2262] + inputs_clone = inputs.clone() + + if args.dataset == "mnist": + inputs_clone[:, :, :, :] = inputs_clone[:, :, :, :] * variance[0] + expected_values[0] + else: + for channel in range(3): + inputs_clone[:, channel, :, :] = inputs_clone[:, channel, :, :] * variance[channel] + expected_values[ + channel] + + return inputs_clone * 255 + + +def np_4d_to_tensor(inputs, args): + if args.dataset == "cifar10": + expected_values = [0.4914, 0.4822, 0.4465] + variance = [0.247, 0.243, 0.261] + elif args.dataset == "cifar100": + expected_values = [0.5071, 0.4867, 0.4408] + variance = [0.2675, 0.2565, 0.2761] + elif args.dataset == "mnist": + expected_values = [0.5] + variance = [0.5] + elif args.dataset in ["gtsrb", "celeba"]: + expected_values = [0, 0, 0] + variance = [1, 1, 1] + elif args.dataset == "imagenet": + expected_values = [0.485, 0.456, 0.406] + variance = [0.229, 0.224, 0.225] + elif args.dataset == "tiny": + expected_values = [0.4802, 0.4481, 0.3975] + variance = [0.2302, 0.2265, 0.2262] + inputs_clone = inputs.clone().div(255.0) + + if args.dataset == "mnist": + inputs_clone[:, :, :, :] = (inputs_clone[:, :, :, :] - expected_values[0]).div(variance[0]) + else: + for channel in range(3): + inputs_clone[:, channel, :, :] = (inputs_clone[:, channel, :, :] - expected_values[channel]).div( + variance[channel]) + return inputs_clone + + +@jit(float64[:](float64[:], int64, float64[:]), nopython=True) +def rnd1(x, decimals, out): + return np.round_(x, decimals, out) + + +@jit(nopython=True) +def floydDitherspeed(image, squeeze_num): + channel, h, w = image.shape + for y in range(h): + for x in range(w): + old = image[:, y, x] + temp = np.empty_like(old).astype(np.float64) + new = rnd1(old / 255.0 * (squeeze_num - 1), 0, temp) / (squeeze_num - 1) * 255 + error = old - new + image[:, y, x] = new + if x + 1 < w: + image[:, y, x + 1] += error * 0.4375 + if (y + 1 < h) and (x + 1 < w): + image[:, y + 1, x + 1] += error * 0.0625 + if y + 1 < h: + image[:, y + 1, x] += error * 0.3125 + if (x - 1 >= 0) and (y + 1 < h): + image[:, y + 1, x - 1] += error * 0.1875 + return image + + +class ProbTransform(torch.nn.Module): + def __init__(self, f, p=1): + super(ProbTransform, self).__init__() + self.f = f + self.p = p + + def forward(self, x): + if random.random() < self.p: + return self.f(x) + else: + return x + + +class PostTensorTransform(torch.nn.Module): + def __init__(self, args): + super(PostTensorTransform, self).__init__() + self.random_crop = ProbTransform( + transforms.RandomCrop((args.input_height, args.input_width), padding=args.random_crop), p=0.8 + ) + self.random_rotation = ProbTransform(transforms.RandomRotation(args.random_rotation), + p=0.5) # 50% random rotation + if args.dataset == "cifar10": + self.random_horizontal_flip = transforms.RandomHorizontalFlip(p=0.5) + + def forward(self, x): + for module in self.children(): + x = module(x) + return x + + +class Denormalize: + def __init__(self, args, expected_values, variance): + self.n_channels = args.input_channel + self.expected_values = expected_values + self.variance = variance + assert self.n_channels == len(self.expected_values) + + def __call__(self, x): + x_clone = x.clone() + for channel in range(self.n_channels): + x_clone[:, channel] = x[:, channel] * self.variance[channel] + self.expected_values[channel] + return x_clone + + +class Denormalize: + def __init__(self, args, expected_values, variance): + self.n_channels = args.input_channel + self.expected_values = expected_values + self.variance = variance + assert self.n_channels == len(self.expected_values) + + def __call__(self, x): + x_clone = x.clone() + for channel in range(self.n_channels): + x_clone[:, channel] = x[:, channel] * self.variance[channel] + self.expected_values[channel] + return x_clone + + +class Denormalizer: + def __init__(self, args): + self.denormalizer = self._get_denormalizer(args) + + def _get_denormalizer(self, args): + denormalizer = Denormalize(args, get_dataset_normalization(args.dataset).mean, + get_dataset_normalization(args.dataset).std) + return denormalizer + + def __call__(self, x): + if self.denormalizer: + x = self.denormalizer(x) + return x + + +class Bpp(BadNet): + + def __init__(self): + super(Bpp, self).__init__() + + def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + parser = add_common_attack_args(parser) + parser.add_argument('--bd_yaml_path', type=str, default='../config/attack/bpp/default.yaml', + help='path for yaml file provide additional default attributes') + + parser.add_argument("--neg_ratio", type=float, ) # default=0.2) + parser.add_argument("--random_rotation", type=int, ) # default=10) + parser.add_argument("--random_crop", type=int, ) # default=5) + + parser.add_argument("--squeeze_num", type=int, ) # default=8 + parser.add_argument("--dithering", type=bool, ) # default=False + + return parser + + def stage1_non_training_data_prepare(self): + logging.info("stage1 start") + + assert "args" in self.__dict__ + args = self.args + + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + clean_train_dataset_with_transform, \ + clean_train_dataset_targets, \ + clean_test_dataset_with_transform, \ + clean_test_dataset_targets \ + = self.benign_prepare() + + logging.info("Be careful, here must replace the regular train tranform with test transform.") + # you can find in the original code that get_transform function has pretensor_transform=False always. + clean_train_dataset_with_transform.wrap_img_transform = test_img_transform + + clean_train_dataloader = DataLoader(clean_train_dataset_with_transform, pin_memory=args.pin_memory, + batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False) + + clean_train_dataloader_shuffled = DataLoader(clean_train_dataset_with_transform, pin_memory=args.pin_memory, + batch_size=args.batch_size, num_workers=args.num_workers, + shuffle=True) + + clean_test_dataloader = DataLoader(clean_test_dataset_with_transform, pin_memory=args.pin_memory, + batch_size=args.batch_size, + num_workers=args.num_workers, shuffle=False) + self.stage1_results = clean_train_dataset_with_transform, \ + clean_train_dataloader, \ + clean_train_dataloader_shuffled, \ + clean_test_dataset_with_transform, \ + clean_test_dataloader + + def stage2_training(self): + logging.info(f"stage2 start") + assert 'args' in self.__dict__ + args = self.args + agg = Metric_Aggregator() + + clean_train_dataset_with_transform, \ + clean_train_dataloader, \ + clean_train_dataloader_shuffled, \ + clean_test_dataset_with_transform, \ + clean_test_dataloader = self.stage1_results + + self.device = torch.device( + ( + f"cuda:{[int(i) for i in args.device[5:].split(',')][0]}" if "," in args.device else args.device + + ) if torch.cuda.is_available() else "cpu" + ) + + netC = generate_cls_model( + model_name=args.model, + num_classes=args.num_classes, + image_size=args.img_size[0], + ).to(self.device, non_blocking=args.non_blocking) + + if "," in args.device: + netC = torch.nn.DataParallel( + netC, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + + optimizerC, schedulerC = argparser_opt_scheduler(netC, args=args) + + logging.info("Train from scratch!!!") + best_clean_acc = 0.0 + best_bd_acc = 0.0 + best_cross_acc = 0.0 + epoch_current = 0 + + # filter out transformation that not reversible + transforms_reversible = transforms.Compose( + list( + filter( + lambda x: isinstance(x, (transforms.Normalize, transforms.Resize, transforms.ToTensor)), + (clean_test_dataset_with_transform.wrap_img_transform.transforms) + ) + ) + ) + # get denormalizer + for trans_t in (clean_test_dataset_with_transform.wrap_img_transform.transforms): + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + logging.info(f"{denormalizer}") + + + + # --------------------------- + self.clean_train_dataset = prepro_cls_DatasetBD_v2( + clean_train_dataset_with_transform, save_folder_path=f"{args.save_path}/clean_train_dataset" + ) + self.bd_train_dataset = prepro_cls_DatasetBD_v2( + clean_train_dataset_with_transform, save_folder_path=f"{args.save_path}/bd_train_dataset_Save" + ) + self.cross_train_dataset = prepro_cls_DatasetBD_v2( + clean_train_dataset_with_transform, save_folder_path=f"{args.save_path}/cross_train_dataset" + ) + self.bd_train_dataset_save = prepro_cls_DatasetBD_v2( + clean_train_dataset_with_transform, + save_folder_path=f"{args.save_path}/bd_train_dataset" + ) + for batch_idx, (inputs, targets) in enumerate(clean_train_dataloader): + with torch.no_grad(): + + inputs, targets = inputs.to(self.device, non_blocking=args.non_blocking), targets.to(self.device, + non_blocking=args.non_blocking) + # bs = inputs.shape[0] + bs = args.batch_size + inputs_bd = torch.round(denormalizer(inputs) * 255) + inputs = denormalizer(inputs) + # save clean + for idx_in_batch, t_img in enumerate(inputs.detach().clone().cpu()): + self.clean_train_dataset.set_one_bd_sample( + selected_index=int(batch_idx * bs + idx_in_batch), + # manually calculate the original index, since we do not shuffle the dataloader + img=(t_img), + bd_label=int(targets[idx_in_batch]), + label=int(targets[idx_in_batch]), + ) + + + if args.dithering: + for i in range(inputs_bd.shape[0]): + inputs_bd[i, :, :, :] = torch.round(torch.from_numpy( + floydDitherspeed(inputs_bd[i].detach().cpu().numpy(), float(args.squeeze_num))).to( + args.device)) + else: + inputs_bd = torch.round(inputs_bd / 255.0 * (args.squeeze_num - 1)) / (args.squeeze_num - 1) * 255 + + inputs_bd = inputs_bd.div(255.0) + + if args.attack_label_trans == "all2one": + targets_bd = torch.ones_like(targets) * args.attack_target + if args.attack_label_trans == "all2all": + targets_bd = torch.remainder(targets + 1, args.num_classes) + + targets = targets.detach().clone().cpu() + y_poison_batch = targets_bd.detach().clone().cpu().tolist() + for idx_in_batch, t_img in enumerate(inputs_bd.detach().clone().cpu()): + self.bd_train_dataset.set_one_bd_sample( + selected_index=int(batch_idx * bs + idx_in_batch), + # manually calculate the original index, since we do not shuffle the dataloader + img=(t_img), + bd_label=int(y_poison_batch[idx_in_batch]), + label=int(targets[idx_in_batch]), + ) + + + + reversible_test_dataset = (clean_test_dataset_with_transform) + + reversible_test_dataset.wrap_img_transform = transforms_reversible + + reversible_test_dataloader = DataLoader(reversible_test_dataset, batch_size=args.batch_size, + pin_memory=args.pin_memory, + num_workers=args.num_workers, shuffle=False) + + self.clean_test_dataset = prepro_cls_DatasetBD_v2( + clean_test_dataset_with_transform, save_folder_path=f"{args.save_path}/clean_test_dataset" + ) + self.bd_test_dataset = prepro_cls_DatasetBD_v2( + clean_test_dataset_with_transform, save_folder_path=f"{args.save_path}/bd_test_all_dataset" + ) + self.bd_test_r_dataset = prepro_cls_DatasetBD_v2( + clean_test_dataset_with_transform, save_folder_path=f"{args.save_path}/bd_test_dataset" + ) + self.cross_test_dataset = prepro_cls_DatasetBD_v2( + clean_test_dataset_with_transform, save_folder_path=f"{args.save_path}/cross_test_dataset" + ) + for batch_idx, (inputs, targets) in enumerate(reversible_test_dataloader): + with torch.no_grad(): + inputs, targets = inputs.to(self.device), targets.to(self.device) + + bs = inputs.shape[0] + inputs_bd = torch.round(denormalizer(inputs) * 255) + inputs = denormalizer(inputs) + # save clean + for idx_in_batch, t_img in enumerate(inputs.detach().clone().cpu()): + self.clean_test_dataset.set_one_bd_sample( + selected_index=int(batch_idx * int(args.batch_size) + idx_in_batch), + # manually calculate the original index, since we do not shuffle the dataloader + img=(t_img), + bd_label=int(targets[idx_in_batch]), + label=int(targets[idx_in_batch]), + ) + + # Evaluate Backdoor + if args.dithering: + for i in range(inputs_bd.shape[0]): + inputs_bd[i, :, :, :] = torch.round(torch.from_numpy( + floydDitherspeed(inputs_bd[i].detach().cpu().numpy(), float(args.squeeze_num))).to( + self.device)) + + else: + inputs_bd = torch.round(inputs_bd / 255.0 * (args.squeeze_num - 1)) / (args.squeeze_num - 1) * 255 + + inputs_bd = inputs_bd.div(255.0) + + if args.attack_label_trans == "all2one": + targets_bd = torch.ones_like(targets) * args.attack_target + position_changed = ( + args.attack_target != targets) # since if label does not change, then cannot tell if the poison is effective or not. + targets_bd_r = (torch.ones_like(targets) * args.attack_target)[position_changed] + inputs_bd_r = inputs_bd[position_changed] + if args.attack_label_trans == "all2all": + targets_bd = torch.remainder(targets + 1, args.num_classes) + targets_bd_r = torch.remainder(targets + 1, args.num_classes) + inputs_bd_r = inputs_bd + position_changed = torch.ones_like(targets) + + targets = targets.detach().clone().cpu() + y_poison_batch = targets_bd.detach().clone().cpu().tolist() + for idx_in_batch, t_img in enumerate(inputs_bd.detach().clone().cpu()): + self.bd_test_dataset.set_one_bd_sample( + selected_index=int(batch_idx * int(args.batch_size) + idx_in_batch), + # manually calculate the original index, since we do not shuffle the dataloader + img=(t_img), + bd_label=int(y_poison_batch[idx_in_batch]), + label=int(targets[idx_in_batch]), + ) + y_poison_batch_r = targets_bd_r.detach().clone().cpu().tolist() + for idx_in_batch, t_img in enumerate(inputs_bd_r.detach().clone().cpu()): + self.bd_test_r_dataset.set_one_bd_sample( + selected_index=int(batch_idx * int(args.batch_size) + torch.where(position_changed.detach().clone().cpu())[0][ + idx_in_batch]), + # manually calculate the original index, since we do not shuffle the dataloader + img=(t_img), + bd_label=int(y_poison_batch_r[idx_in_batch]), + label=int(targets[torch.where(position_changed.detach().clone().cpu())[0][idx_in_batch]]), + ) + + for batch_idx, (inputs, targets) in enumerate(reversible_test_dataloader): + with torch.no_grad(): + inputs = inputs.to(self.device) + bs = inputs.shape[0] + t_nom = transforms.Normalize([0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261]) + # Evaluate cross + if args.neg_ratio: + index_list = list(np.arange(len(clean_test_dataset_with_transform))) + residual_index = random.sample(index_list, bs) + + inputs_negative = torch.zeros_like(inputs) + inputs_negative1 = torch.zeros_like(inputs) + inputs_d = torch.round(denormalizer(inputs) * 255) + for i in range(bs): + inputs_negative[i] = inputs_d[i] + ( + to_tensor(self.clean_test_dataset[residual_index[i]][0]) * 255).to(self.device) - ( + to_tensor( + self.bd_test_dataset[residual_index[i]][0]) * 255).to( + self.device) + + inputs_negative = inputs_negative.div(255.0) + for idx_in_batch, t_img in enumerate(inputs_negative): + self.cross_test_dataset.set_one_bd_sample( + selected_index=int(batch_idx * int(args.batch_size) + idx_in_batch), + # manually calculate the original index, since we do not shuffle the dataloader + img=(t_img), + bd_label=int(targets[idx_in_batch]), + label=int(targets[idx_in_batch]), + ) + + + + bd_test_dataset_with_transform = dataset_wrapper_with_transform( + self.bd_test_dataset, + clean_test_dataset_with_transform.wrap_img_transform, + ) + + bd_test_dataloader = DataLoader(bd_test_dataset_with_transform, + pin_memory=args.pin_memory, + batch_size=args.batch_size, + num_workers=args.num_workers, + shuffle=False) + + bd_test_r_dataset_with_transform = dataset_wrapper_with_transform( + self.bd_test_r_dataset, + clean_test_dataset_with_transform.wrap_img_transform, + ) + self.bd_test_r_dataset.subset( + np.where(self.bd_test_r_dataset.poison_indicator == 1)[0].tolist() + ) + bd_test_r_dataloader = DataLoader(bd_test_r_dataset_with_transform, + pin_memory=args.pin_memory, + batch_size=args.batch_size, + num_workers=args.num_workers, + shuffle=False) + + if args.neg_ratio: + cross_test_dataset_with_transform = dataset_wrapper_with_transform( + self.cross_test_dataset, + clean_test_dataset_with_transform.wrap_img_transform, + ) + cross_test_dataloader = DataLoader(cross_test_dataset_with_transform, + pin_memory=args.pin_memory, + batch_size=args.batch_size, + num_workers=args.num_workers, + shuffle=False) + + else: + cross_test_dataloader = None + + test_dataloaders = (clean_test_dataloader, bd_test_dataloader, cross_test_dataloader, bd_test_r_dataloader) + + train_loss_list = [] + train_mix_acc_list = [] + train_clean_acc_list = [] + train_asr_list = [] + train_ra_list = [] + train_cross_acc_only_list = [] + + clean_test_loss_list = [] + bd_test_loss_list = [] + cross_test_loss_list = [] + ra_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + test_cross_acc_list = [] + + for epoch in range(epoch_current, args.epochs): + logging.info("Epoch {}:".format(epoch + 1)) + + train_epoch_loss_avg_over_batch, \ + train_mix_acc, \ + train_clean_acc, \ + train_asr, \ + train_ra, \ + train_cross_acc = self.train_step( + netC, + optimizerC, + schedulerC, + clean_train_dataloader_shuffled, + args) + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + cross_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra, \ + test_cross_acc \ + = self.eval_step( + netC, + clean_test_dataset_with_transform, + clean_test_dataloader, + bd_test_r_dataloader, + cross_test_dataloader, + args, + ) + + agg({ + "epoch": epoch, + + "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch, + "train_acc": train_mix_acc, + "train_acc_clean_only": train_clean_acc, + "train_asr_bd_only": train_asr, + "train_ra_bd_only": train_ra, + "train_cross_acc_only": train_cross_acc, + + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "cross_test_loss_avg_over_batch": cross_test_loss_avg_over_batch, + "ra_test_loss_avg_over_batch": ra_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + "test_cross_acc": test_cross_acc, + }) + + train_loss_list.append(train_epoch_loss_avg_over_batch) + train_mix_acc_list.append(train_mix_acc) + train_clean_acc_list.append(train_clean_acc) + train_asr_list.append(train_asr) + train_ra_list.append(train_ra) + train_cross_acc_only_list.append(train_cross_acc) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + cross_test_loss_list.append(cross_test_loss_avg_over_batch) + ra_test_loss_list.append(ra_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + test_cross_acc_list.append(test_cross_acc) + + general_plot_for_epoch( + { + "Train Acc": train_mix_acc_list, + "Train Acc (clean sample only)": train_clean_acc_list, + "Train ASR": train_asr_list, + "Train RA": train_ra_list, + "Train Cross Acc": train_cross_acc_only_list, + "Test C-Acc": test_acc_list, + "Test ASR": test_asr_list, + "Test RA": test_ra_list, + "Test Cross Acc": test_cross_acc_list, + }, + save_path=f"{args.save_path}/acc_like_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "Train Loss": train_loss_list, + "Test Clean Loss": clean_test_loss_list, + "Test Backdoor Loss": bd_test_loss_list, + "Test Cross Loss": cross_test_loss_list, + "Test RA Loss": ra_test_loss_list, + }, + save_path=f"{args.save_path}/loss_metric_plots.png", + ylabel="percentage", + ) + + agg.to_dataframe().to_csv(f"{args.save_path}/attack_df.csv") + + if args.frequency_save != 0 and epoch % args.frequency_save == args.frequency_save - 1: + state_dict = { + "netC": netC.state_dict(), + "schedulerC": schedulerC.state_dict(), + "optimizerC": optimizerC.state_dict(), + "epoch_current": epoch, + } + torch.save(state_dict, args.save_path + "/state_dict.pt") + + agg.summary().to_csv(f"{args.save_path}/attack_df_summary.csv") + + + netC.eval() + with torch.no_grad(): + for batch_idx, (inputs, targets) in enumerate(clean_train_dataloader): + inputs, targets = inputs.to(self.device, non_blocking=args.non_blocking), targets.to(self.device, + non_blocking=args.non_blocking) + bs = inputs.shape[0] + + # Create backdoor data + num_bd = int(generalize_to_lower_pratio(args.pratio, bs)) + num_neg = int(bs * args.neg_ratio) + + if num_bd != 0 and num_neg != 0: + inputs_bd = back_to_np_4d(inputs[:num_bd], args) + if args.dithering: + for i in range(inputs_bd.shape[0]): + inputs_bd[i, :, :, :] = torch.round(torch.from_numpy( + floydDitherspeed(inputs_bd[i].detach().cpu().numpy(), float(args.squeeze_num))).to( + args.device)) + else: + inputs_bd = torch.round(inputs_bd / 255.0 * (args.squeeze_num - 1)) / (args.squeeze_num - 1) * 255 + + inputs_bd = np_4d_to_tensor(inputs_bd, args) + + if args.attack_label_trans == "all2one": + targets_bd = torch.ones_like(targets[:num_bd]) * args.attack_target + if args.attack_label_trans == "all2all": + targets_bd = torch.remainder(targets[:num_bd] + 1, args.num_classes) + + train_dataset_num = len(clean_train_dataloader.dataset) + if args.dataset == "celeba": + index_list = list(np.arange(train_dataset_num)) + residual_index = random.sample(index_list, bs) + else: + index_list = list(np.arange(train_dataset_num * 5)) + residual_index = random.sample(index_list, bs) + residual_index = [x % train_dataset_num for x in random.sample(list(index_list), bs)] + + inputs_negative = torch.zeros_like(inputs[num_bd: (num_bd + num_neg)]) + inputs_d = torch.round(back_to_np_4d(inputs, args)) + for i in range(num_neg): + inputs_negative[i] = inputs_d[num_bd + i] + ( + to_tensor(self.bd_train_dataset[residual_index[i]][0]) * 255).to(self.device).to( + self.device) - (to_tensor(self.clean_train_dataset[residual_index[i]][0]) * 255).to(self.device) + + inputs_negative = torch.clamp(inputs_negative, 0, 255) + inputs_negative = np_4d_to_tensor(inputs_negative, args) + + total_inputs = torch.cat([inputs_bd, inputs_negative, inputs[(num_bd + num_neg):]], dim=0) + total_targets = torch.cat([targets_bd, targets[num_bd:]], dim=0) + + input_changed = torch.cat([inputs_bd, inputs_negative, ], dim=0).detach().clone().cpu() + input_changed = denormalizer( # since we normalized once, we need to denormalize it back. + input_changed + ).detach().clone().cpu() + target_changed = torch.cat([targets_bd, targets[num_bd: (num_bd + num_neg)], ], + dim=0).detach().clone().cpu() + + elif (num_bd > 0 and num_neg == 0): + inputs_bd = back_to_np_4d(inputs[:num_bd], args) + if args.dithering: + for i in range(inputs_bd.shape[0]): + inputs_bd[i, :, :, :] = torch.round(torch.from_numpy( + floydDitherspeed(inputs_bd[i].detach().cpu().numpy(), float(args.squeeze_num))).to( + args.device)) + else: + inputs_bd = torch.round(inputs_bd / 255.0 * (args.squeeze_num - 1)) / (args.squeeze_num - 1) * 255 + + inputs_bd = np_4d_to_tensor(inputs_bd, args) + + if args.attack_label_trans == "all2one": + targets_bd = torch.ones_like(targets[:num_bd]) * args.attack_target + if args.attack_label_trans == "all2all": + targets_bd = torch.remainder(targets[:num_bd] + 1, args.num_classes) + + total_inputs = torch.cat([inputs_bd, inputs[num_bd:]], dim=0) + total_targets = torch.cat([targets_bd, targets[num_bd:]], dim=0) + + input_changed = inputs_bd.detach().clone().cpu() + input_changed = denormalizer( # since we normalized once, we need to denormalize it back. + input_changed + ).detach().clone().cpu() + target_changed = targets_bd.detach().clone().cpu() + + + elif (num_bd == 0 and num_neg > 0): + train_dataset_num = len(clean_train_dataloader.dataset) + if args.dataset == "celeba": + index_list = list(np.arange(train_dataset_num)) + residual_index = random.sample(index_list, bs) + else: + index_list = list(np.arange(train_dataset_num * 5)) + residual_index = random.sample(index_list, bs) + residual_index = [x % train_dataset_num for x in random.sample(list(index_list), bs)] + + inputs_negative = torch.zeros_like(inputs[num_bd: (num_bd + num_neg)]) + inputs_d = torch.round(back_to_np_4d(inputs, args)) + for i in range(num_neg): + inputs_negative[i] = inputs_d[num_bd + i] + ( + to_tensor(self.bd_train_dataset[residual_index[i]][0]) * 255).to(self.device).to( + self.device) - (to_tensor(self.clean_train_dataset[residual_index[i]][0]) * 255).to(self.device) + + inputs_negative = torch.clamp(inputs_negative, 0, 255) + inputs_negative = np_4d_to_tensor(inputs_negative, args) + + total_inputs = inputs + total_targets = targets + + input_changed = inputs_negative.detach().clone().cpu() + input_changed = denormalizer( # since we normalized once, we need to denormalize it back. + input_changed + ).detach().clone().cpu() + target_changed = targets[num_bd: (num_bd + num_neg)].detach().clone().cpu() + + else: + continue + + + # save container + for idx_in_batch, t_img in enumerate( + input_changed + ): + # here we know it starts from 0 and they are consecutive + self.bd_train_dataset_save.set_one_bd_sample( + selected_index=int(batch_idx * int(args.batch_size) + idx_in_batch), + img=(t_img), + bd_label=int(target_changed[idx_in_batch]), + label=int(targets[idx_in_batch]), + ) + + + save_attack_result( + model_name=args.model, + num_classes=args.num_classes, + model=netC.cpu().state_dict(), + data_path=args.dataset_path, + img_size=args.img_size, + clean_data=args.dataset, + bd_train=self.bd_train_dataset_save, + bd_test=self.bd_test_r_dataset, + save_path=args.save_path, + ) + print("done") + + def train_step(self, netC, optimizerC, schedulerC, clean_train_dataloader, args): + logging.info(" Train:") + netC.train() + rate_bd = args.pratio + squeeze_num = args.squeeze_num + + criterion_CE = torch.nn.CrossEntropyLoss() + + transforms = PostTensorTransform(args).to(args.device) + total_time = 0 + + batch_loss_list = [] + batch_predict_list = [] + batch_label_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + + for batch_idx, (inputs, targets) in enumerate(clean_train_dataloader): + optimizerC.zero_grad() + + inputs, targets = inputs.to(self.device, non_blocking=args.non_blocking), targets.to(self.device, + non_blocking=args.non_blocking) + bs = inputs.shape[0] + + # Create backdoor data + num_bd = int(generalize_to_lower_pratio(rate_bd, bs)) + num_neg = int(bs * args.neg_ratio) + + if num_bd != 0 and num_neg != 0: + inputs_bd = back_to_np_4d(inputs[:num_bd], args) + if args.dithering: + for i in range(inputs_bd.shape[0]): + inputs_bd[i, :, :, :] = torch.round(torch.from_numpy( + floydDitherspeed(inputs_bd[i].detach().cpu().numpy(), float(squeeze_num))).to( + args.device)) + else: + inputs_bd = torch.round(inputs_bd / 255.0 * (squeeze_num - 1)) / (squeeze_num - 1) * 255 + + inputs_bd = np_4d_to_tensor(inputs_bd, args) + + if args.attack_label_trans == "all2one": + targets_bd = torch.ones_like(targets[:num_bd]) * args.attack_target + if args.attack_label_trans == "all2all": + targets_bd = torch.remainder(targets[:num_bd] + 1, args.num_classes) + + train_dataset_num = len(clean_train_dataloader.dataset) + if args.dataset == "celeba": + index_list = list(np.arange(train_dataset_num)) + residual_index = random.sample(index_list, bs) + else: + index_list = list(np.arange(train_dataset_num * 5)) + residual_index = random.sample(index_list, bs) + residual_index = [x % train_dataset_num for x in random.sample(list(index_list), bs)] + + inputs_negative = torch.zeros_like(inputs[num_bd: (num_bd + num_neg)]) + inputs_d = back_to_np_4d(inputs, args) + for i in range(num_neg): + inputs_negative[i] = inputs_d[num_bd + i] + ( + to_tensor(self.bd_train_dataset[residual_index[i]][0]) * 255).to(self.device).to( + self.device) - (to_tensor(self.clean_train_dataset[residual_index[i]][0]) * 255).to(self.device) + + inputs_negative = torch.clamp(inputs_negative, 0, 255) + inputs_negative = np_4d_to_tensor(inputs_negative, args) + + total_inputs = torch.cat([inputs_bd, inputs_negative, inputs[(num_bd + num_neg):]], dim=0) + total_targets = torch.cat([targets_bd, targets[num_bd:]], dim=0) + + elif (num_bd > 0 and num_neg == 0): + inputs_bd = back_to_np_4d(inputs[:num_bd], args) + if args.dithering: + for i in range(inputs_bd.shape[0]): + inputs_bd[i, :, :, :] = torch.round(torch.from_numpy( + floydDitherspeed(inputs_bd[i].detach().cpu().numpy(), float(args.squeeze_num))).to( + args.device)) + else: + inputs_bd = torch.round(inputs_bd / 255.0 * (squeeze_num - 1)) / (squeeze_num - 1) * 255 + + inputs_bd = np_4d_to_tensor(inputs_bd, args) + + if args.attack_label_trans == "all2one": + targets_bd = torch.ones_like(targets[:num_bd]) * args.attack_target + if args.attack_label_trans == "all2all": + targets_bd = torch.remainder(targets[:num_bd] + 1, args.num_classes) + + total_inputs = torch.cat([inputs_bd, inputs[num_bd:]], dim=0) + total_targets = torch.cat([targets_bd, targets[num_bd:]], dim=0) + + elif (num_bd == 0): + total_inputs = inputs + total_targets = targets + + total_inputs = transforms(total_inputs) + start = time.time() + total_preds = netC(total_inputs) + total_time += time.time() - start + + loss_ce = criterion_CE(total_preds, total_targets) + + loss = loss_ce + loss.backward() + + optimizerC.step() + + batch_loss_list.append(loss.item()) + batch_predict_list.append(torch.max(total_preds, -1)[1].detach().clone().cpu()) + batch_label_list.append(total_targets.detach().clone().cpu()) + + poison_indicator = torch.zeros(bs) + poison_indicator[:num_bd] = 1 # all others are cross/clean samples cannot conut up to train acc + poison_indicator[num_bd:num_neg + num_bd] = 2 # indicate for the cross terms + + batch_poison_indicator_list.append(poison_indicator) + batch_original_targets_list.append(targets.detach().clone().cpu()) + + schedulerC.step() + + train_epoch_loss_avg_over_batch, \ + train_epoch_predict_list, \ + train_epoch_label_list, \ + train_epoch_poison_indicator_list, \ + train_epoch_original_targets_list = sum(batch_loss_list) / len(batch_loss_list), \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + + train_mix_acc = all_acc(train_epoch_predict_list, train_epoch_label_list) + + train_bd_idx = torch.where(train_epoch_poison_indicator_list == 1)[0] + train_cross_idx = torch.where(train_epoch_poison_indicator_list == 2)[0] + train_clean_idx = torch.where(train_epoch_poison_indicator_list == 0)[0] + train_clean_acc = all_acc( + train_epoch_predict_list[train_clean_idx], + train_epoch_label_list[train_clean_idx], + ) + if num_bd: + train_asr = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_label_list[train_bd_idx], + ) + else: + train_asr = 0 + if num_neg: + train_cross_acc = all_acc( + train_epoch_predict_list[train_cross_idx], + train_epoch_label_list[train_cross_idx], + ) + else: + train_cross_acc = 0 + if num_bd: + train_ra = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_original_targets_list[train_bd_idx], + ) + else: + train_ra = 0 + + return train_epoch_loss_avg_over_batch, \ + train_mix_acc, \ + train_clean_acc, \ + train_asr, \ + train_ra, \ + train_cross_acc + + def eval_step( + self, + netC, + clean_test_dataset_with_transform, + clean_test_dataloader, + bd_test_r_dataloader, + cross_test_dataloader, + args, + + ): + + + # ----------------------- + + clean_metrics, clean_epoch_predict_list, clean_epoch_label_list = given_dataloader_test( + netC, + clean_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + + clean_test_loss_avg_over_batch = clean_metrics['test_loss_avg_over_batch'] + test_acc = clean_metrics['test_acc'] + bd_metrics, bd_epoch_predict_list, bd_epoch_label_list = given_dataloader_test( + netC, + bd_test_r_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + bd_test_loss_avg_over_batch = bd_metrics['test_loss_avg_over_batch'] + test_asr = bd_metrics['test_acc'] + + self.bd_test_r_dataset.getitem_all_switch = True # change to return the original label instead + ra_test_dataset_with_transform = dataset_wrapper_with_transform( + self.bd_test_r_dataset, + clean_test_dataset_with_transform.wrap_img_transform, + ) + ra_test_dataloader = DataLoader(ra_test_dataset_with_transform, + pin_memory=args.pin_memory, + batch_size=args.batch_size, + num_workers=args.num_workers, + shuffle=False) + ra_metrics, ra_epoch_predict_list, ra_epoch_label_list = given_dataloader_test( + netC, + ra_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + ra_test_loss_avg_over_batch = ra_metrics['test_loss_avg_over_batch'] + test_ra = ra_metrics['test_acc'] + self.bd_test_r_dataset.getitem_all_switch = False # switch back + + cross_metrics, cross_epoch_predict_list, cross_epoch_label_list = given_dataloader_test( + netC, + cross_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + cross_test_loss_avg_over_batch = cross_metrics['test_loss_avg_over_batch'] + test_cross_acc = cross_metrics['test_acc'] + + return clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + cross_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra, \ + test_cross_acc + + +if __name__ == '__main__': + attack = Bpp() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = attack.set_args(parser) + parser = attack.set_bd_args(parser) + args = parser.parse_args() + attack.add_bd_yaml_to_args(args) + attack.add_yaml_to_args(args) + args = attack.process_args(args) + attack.prepare(args) + attack.stage1_non_training_data_prepare() + attack.stage2_training() diff --git a/attack/inputaware.py b/attack/inputaware.py new file mode 100755 index 0000000..35c9595 --- /dev/null +++ b/attack/inputaware.py @@ -0,0 +1,1143 @@ +''' +This file is modified based on the following source: +link : https://github.com/VinAIResearch/input-aware-backdoor-attack-release +The original license is placed at the end of this file. +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. during training the backdoor attack generalization to lower poison ratio (generalize_to_lower_pratio) + 5. calculate part of ASR + 6. save process +basic sturcture for main: + 1. config args, save_path, fix random seed + 2. set the device, model, criterion, optimizer, training schedule. + 3. set the clean train data and clean test data + 4. clean train 25 epochs + 5. training with backdoor modification simultaneously + 6. save attack result + +@article{nguyen2020input, + title={Input-aware dynamic backdoor attack}, + author={Nguyen, Tuan Anh and Tran, Anh}, + journal={Advances in Neural Information Processing Systems}, + volume={33}, + pages={3454--3464}, + year={2020} +} + +Note that since this attack rely on batch-wise modification of the input data, +when this method encounters lower poison ratio, the original implementation +will fail (poison ratio < 1 / batch size), we add a function named generalize_to_lower_pratio +to generalize the attack to lower the poison ratio. The basic idea is to calculate the theoretical +the number of poison samples each batch should have, then randomly select batches to do poisoning. +This change may result in instability and a higher variance in final +results' metrics, but it is a necessary change to make the attack workable in a low poison ratio. +Please be careful when you use this attack in a low poison ratio, and interpret the results with +caution. +''' + +import argparse +import logging +import os +import sys +import time +import torch + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +import numpy as np +from copy import deepcopy +import torch.nn as nn +from torch.utils.data import DataLoader +import torch.nn.functional as F + +from attack.badnet import BadNet, add_common_attack_args +from torchvision import transforms + +from utils.aggregate_block.dataset_and_transform_generate import get_dataset_normalization, get_dataset_denormalization +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.trainer_cls import Metric_Aggregator +from utils.save_load_attack import save_attack_result +from utils.trainer_cls import all_acc, given_dataloader_test, general_plot_for_epoch +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform + +term_width = int(60) +TOTAL_BAR_LENGTH = 65.0 +last_time = time.time() +begin_time = last_time + + +def progress_bar(current, total, msg=None): + global last_time, begin_time + if current == 0: + begin_time = time.time() # Reset for new bar. + + cur_len = int(TOTAL_BAR_LENGTH * current / total) + rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1 + + sys.stdout.write(" [") + for i in range(cur_len): + sys.stdout.write("=") + sys.stdout.write(">") + for i in range(rest_len): + sys.stdout.write(".") + sys.stdout.write("]") + + cur_time = time.time() + step_time = cur_time - last_time + last_time = cur_time + tot_time = cur_time - begin_time + + L = [] + if msg: + L.append(" | " + msg) + + msg = "".join(L) + sys.stdout.write(msg) + for i in range(term_width - int(TOTAL_BAR_LENGTH) - len(msg) - 3): + sys.stdout.write(" ") + + # Go back to the center of the bar. + for i in range(term_width - int(TOTAL_BAR_LENGTH / 2) + 2): + sys.stdout.write("\b") + sys.stdout.write(" %d/%d " % (current + 1, total)) + + if current < total - 1: + sys.stdout.write("\r") + else: + sys.stdout.write("\n") + sys.stdout.flush() + + +class Conv2dBlock(nn.Module): + def __init__(self, in_c, out_c, ker_size=(3, 3), stride=1, padding=1, batch_norm=True, relu=True): + super(Conv2dBlock, self).__init__() + self.conv2d = nn.Conv2d(in_c, out_c, ker_size, stride, padding) + if batch_norm: + self.batch_norm = nn.BatchNorm2d(out_c, eps=1e-5, momentum=0.05, affine=True) + if relu: + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + for module in self.children(): + x = module(x) + return x + + +class DownSampleBlock(nn.Module): + def __init__(self, ker_size=(2, 2), stride=2, dilation=(1, 1), ceil_mode=False, p=0.0): + super(DownSampleBlock, self).__init__() + self.maxpooling = nn.MaxPool2d(kernel_size=ker_size, stride=stride, dilation=dilation, ceil_mode=ceil_mode) + if p: + self.dropout = nn.Dropout(p) + + def forward(self, x): + for module in self.children(): + x = module(x) + return x + + +class UpSampleBlock(nn.Module): + def __init__(self, scale_factor=(2, 2), mode="bilinear", p=0.0): + super(UpSampleBlock, self).__init__() + self.upsample = nn.Upsample(scale_factor=scale_factor, mode=mode) + if p: + self.dropout = nn.Dropout(p) + + def forward(self, x): + for module in self.children(): + x = module(x) + return x + + +class Normalize: + def __init__(self, args, expected_values, variance): + self.n_channels = args.input_channel + self.expected_values = expected_values + self.variance = variance + assert self.n_channels == len(self.expected_values) + + def __call__(self, x): + x_clone = x.clone() + for channel in range(self.n_channels): + x_clone[:, channel] = (x[:, channel] - self.expected_values[channel]) / self.variance[channel] + return x_clone + + +class Denormalize: + def __init__(self, args, expected_values, variance): + self.n_channels = args.input_channel + self.expected_values = expected_values + self.variance = variance + assert self.n_channels == len(self.expected_values) + + def __call__(self, x): + x_clone = x.clone() + for channel in range(self.n_channels): + x_clone[:, channel] = x[:, channel] * self.variance[channel] + self.expected_values[channel] + return x_clone + + +class InputAwareGenerator(nn.Sequential): + def __init__(self, args, out_channels=None): + super(InputAwareGenerator, self).__init__() + self.args = args + if self.args.dataset == "mnist": + channel_init = 16 + steps = 2 + else: + channel_init = 32 + steps = 3 + + channel_current = self.args.input_channel + channel_next = channel_init + for step in range(steps): + self.add_module("convblock_down_{}".format(2 * step), Conv2dBlock(channel_current, channel_next)) + self.add_module("convblock_down_{}".format(2 * step + 1), Conv2dBlock(channel_next, channel_next)) + self.add_module("downsample_{}".format(step), DownSampleBlock()) + if step < steps - 1: + channel_current = channel_next + channel_next *= 2 + + self.add_module("convblock_middle", Conv2dBlock(channel_next, channel_next)) + + channel_current = channel_next + channel_next = channel_current // 2 + for step in range(steps): + self.add_module("upsample_{}".format(step), UpSampleBlock()) + self.add_module("convblock_up_{}".format(2 * step), Conv2dBlock(channel_current, channel_current)) + if step == steps - 1: + self.add_module( + "convblock_up_{}".format(2 * step + 1), Conv2dBlock(channel_current, channel_next, relu=False) + ) + else: + self.add_module("convblock_up_{}".format(2 * step + 1), Conv2dBlock(channel_current, channel_next)) + channel_current = channel_next + channel_next = channel_next // 2 + if step == steps - 2: + if out_channels is None: + channel_next = self.args.input_channel + else: + channel_next = out_channels + + self._EPSILON = 1e-7 + self._normalizer = self._get_normalize(self.args) + self._denormalizer = self._get_denormalize(self.args) + self.tanh = nn.Tanh() + + def _get_denormalize(self, args): + denormalizer = Denormalize(args, get_dataset_normalization(self.args.dataset).mean, + get_dataset_normalization(self.args.dataset).std) + return denormalizer + + def _get_normalize(self, args): + normalizer = Normalize(args, get_dataset_normalization(self.args.dataset).mean, + get_dataset_normalization(self.args.dataset).std) + return normalizer + + def forward(self, x): + for module in self.children(): + x = module(x) + x = nn.Tanh()(x) / (2 + self._EPSILON) + 0.5 + return x + + def normalize_pattern(self, x): + if self._normalizer: + x = self._normalizer(x) + return x + + def denormalize_pattern(self, x): + if self._denormalizer: + x = self._denormalizer(x) + return x + + def threshold(self, x): + return nn.Tanh()(x * 20 - 10) / (2 + self._EPSILON) + 0.5 + + +class Threshold(nn.Module): + def __init__(self): + super(Threshold, self).__init__() + self.tanh = nn.Tanh() + + def forward(self, x): + return self.tanh(x * 20 - 10) / (2 + 1e-7) + 0.5 + + +def generalize_to_lower_pratio(pratio, bs): + if pratio * bs >= 1: + # the normal case that each batch can have at least one poison sample + return pratio * bs + else: + # then randomly return number of poison sample + if np.random.uniform(0, + 1) < pratio * bs: # eg. pratio = 1/1280, then 1/10 of batch(bs=128) should contains one sample + return 1 + else: + return 0 + + +class InputAware(BadNet): + + def __init__(self): + super(InputAware, self).__init__() + + def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + + parser = add_common_attack_args(parser) + + parser.add_argument('--bd_yaml_path', type=str, default='../config/attack/inputaware/default.yaml', + help='path for yaml file provide additional default attributes') + + parser.add_argument("--lr_G", type=float, ) # default=1e-2) + parser.add_argument("--lr_C", type=float, ) # default=1e-2) + parser.add_argument("--lr_M", type=float, ) # default=1e-2) + parser.add_argument('--C_lr_scheduler', type=str) + parser.add_argument("--schedulerG_milestones", type=list, ) # default=[200, 300, 400, 500]) + parser.add_argument("--schedulerC_milestones", type=list, ) # default=[100, 200, 300, 400]) + parser.add_argument("--schedulerM_milestones", type=list, ) # default=[10, 20]) + parser.add_argument("--schedulerG_lambda", type=float, ) # default=0.1) + parser.add_argument("--schedulerC_lambda", type=float, ) # default=0.1) + parser.add_argument("--schedulerM_lambda", type=float, ) # default=0.1) + parser.add_argument("--lambda_div", type=float, ) # default=1) + parser.add_argument("--lambda_norm", type=float, ) # default=100) + parser.add_argument("--mask_density", type=float, ) # default=0.032) + parser.add_argument("--EPSILON", type=float, ) # default=1e-7) + parser.add_argument('--clean_train_epochs', type=int) + parser.add_argument("--random_rotation", type=int, ) # default=10) + parser.add_argument("--random_crop", type=int, ) # default=5) + return parser + + def stage1_non_training_data_prepare(self): + logging.info("stage1 start") + + assert "args" in self.__dict__ + args = self.args + + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + clean_train_dataset_with_transform, \ + clean_train_dataset_targets, \ + clean_test_dataset_with_transform, \ + clean_test_dataset_targets \ + = self.benign_prepare() + + clean_train_dataloader1 = DataLoader(clean_train_dataset_with_transform, pin_memory=args.pin_memory, + batch_size=args.batch_size, num_workers=args.num_workers, shuffle=True) + clean_train_dataloader2 = DataLoader(clean_train_dataset_with_transform, pin_memory=args.pin_memory, + batch_size=args.batch_size, num_workers=args.num_workers, shuffle=True) + clean_test_dataloader1 = DataLoader(clean_test_dataset_with_transform, pin_memory=args.pin_memory, + batch_size=args.batch_size, + num_workers=args.num_workers, shuffle=True) + clean_test_dataloader2 = DataLoader(clean_test_dataset_with_transform, pin_memory=args.pin_memory, + batch_size=args.batch_size, + num_workers=args.num_workers, shuffle=True) + + self.stage1_results = clean_train_dataset_with_transform, clean_train_dataloader1, \ + clean_train_dataloader2, \ + clean_test_dataset_with_transform, \ + clean_test_dataloader1, \ + clean_test_dataloader2 + + def stage2_training(self): + # since we need the network to do poison, + # we can only put prepare of bd dataset to stage2 with training process. + + logging.info(f"stage2 start") + assert 'args' in self.__dict__ + args = self.args + agg = Metric_Aggregator() + + clean_train_dataset_with_transform, \ + clean_train_dataloader1, \ + clean_train_dataloader2, \ + clean_test_dataset_with_transform, \ + clean_test_dataloader1, \ + clean_test_dataloader2 = self.stage1_results + + self.device = torch.device( + ( + f"cuda:{[int(i) for i in args.device[5:].split(',')][0]}" if "," in args.device else args.device + + ) if torch.cuda.is_available() else "cpu" + ) + + netC = generate_cls_model( + model_name=args.model, + num_classes=args.num_classes, + image_size=args.img_size[0], + ).to(self.device, non_blocking=args.non_blocking) + netG = InputAwareGenerator(args).to(self.device, non_blocking=args.non_blocking) + netM = InputAwareGenerator(args, out_channels=1).to(self.device, non_blocking=args.non_blocking) + self.threshold = Threshold().to(self.device, non_blocking=args.non_blocking) + + if "," in args.device: + netC = torch.nn.DataParallel( + netC, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + netG = torch.nn.DataParallel( + netG, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + netM = torch.nn.DataParallel( + netM, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.threshold = torch.nn.DataParallel( + self.threshold, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + + optimizerC = torch.optim.SGD(netC.parameters(), args.lr_C, momentum=0.9, weight_decay=5e-4) + optimizerG = torch.optim.Adam(netG.parameters(), args.lr_G, betas=(0.5, 0.9)) + optimizerM = torch.optim.Adam(netM.parameters(), args.lr_M, betas=(0.5, 0.9)) + + if args.C_lr_scheduler == "ReduceLROnPlateau": + schedulerC = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizerC) + else: + schedulerC = torch.optim.lr_scheduler.MultiStepLR(optimizerC, args.schedulerC_milestones, + args.schedulerC_lambda) + schedulerG = torch.optim.lr_scheduler.MultiStepLR(optimizerG, args.schedulerG_milestones, + args.schedulerG_lambda) + schedulerM = torch.optim.lr_scheduler.MultiStepLR(optimizerM, args.schedulerM_milestones, + args.schedulerM_lambda) + + self.normalizer = Normalize(args, get_dataset_normalization(self.args.dataset).mean, + get_dataset_normalization(self.args.dataset).std) + + epoch = 1 + + # first clean_train_epochs epoch clean train + for i in range(args.clean_train_epochs): + netM.train() + logging.info( + "Epoch {} - {} - {} | mask_density: {} - lambda_div: {} - lambda_norm: {}:".format( + epoch, args.dataset, args.attack_label_trans, args.mask_density, args.lambda_div, args.lambda_norm + ) + ) + self.train_mask_step( + netM, optimizerM, schedulerM, clean_train_dataloader1, clean_train_dataloader2, args + ) + epoch = self.eval_mask(netM, optimizerM, schedulerM, clean_test_dataloader1, clean_test_dataloader2, epoch, + args) + + if args.frequency_save != 0 and epoch % args.frequency_save == args.frequency_save - 1: + logging.info(f'saved. epoch:{epoch}') + + state_dict = { + "netM": netM.state_dict(), + "optimizerM": optimizerM.state_dict(), + "schedulerM": schedulerM.state_dict(), + "epoch": epoch, + "args": args, + } + + torch.save(state_dict, args.save_path + "/mask_state_dict.pt") + + epoch += 1 + netM.eval() + netM.requires_grad_(False) + + # real train (attack) + + train_loss_list = [] + train_mix_acc_list = [] + train_clean_acc_list = [] + train_asr_list = [] + train_ra_list = [] + train_cross_acc_only_list = [] + + clean_test_loss_list = [] + bd_test_loss_list = [] + cross_test_loss_list = [] + ra_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + test_cross_acc_list = [] + + for i in range(args.epochs): + + logging.info( + "Epoch {} - {} - {} | mask_density: {} - lambda_div: {}:".format( + epoch, args.dataset, args.attack_label_trans, args.mask_density, args.lambda_div + ) + ) + + train_epoch_loss_avg_over_batch, \ + train_mix_acc, \ + train_clean_acc, \ + train_asr, \ + train_ra, \ + train_cross_acc = self.train_step( + netC, + netG, + netM, + optimizerC, + optimizerG, + schedulerC, + schedulerG, + clean_train_dataloader1, + clean_train_dataloader2, + args, + ) + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + cross_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra, \ + test_cross_acc = self.eval_step( + netC, + netG, + netM, + optimizerC, + optimizerG, + schedulerC, + schedulerG, + clean_test_dataset_with_transform, + epoch, + args, + ) + + agg({ + "epoch": epoch, + + "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch, + "train_acc": train_mix_acc, + "train_acc_clean_only": train_clean_acc, + "train_asr_bd_only": train_asr, + "train_ra_bd_only": train_ra, + "train_cross_acc_only": train_cross_acc, + + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "cross_test_loss_avg_over_batch": cross_test_loss_avg_over_batch, + "ra_test_loss_avg_over_batch": ra_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + "test_cross_acc": test_cross_acc, + }) + + if args.frequency_save != 0 and epoch % args.frequency_save == args.frequency_save - 1: + logging.info(f'saved. epoch:{epoch}') + state_dict = { + "netC": netC.state_dict(), + "netG": netG.state_dict(), + "netM": netM.state_dict(), + "optimizerC": optimizerC.state_dict(), + "optimizerG": optimizerG.state_dict(), + "schedulerC": schedulerC.state_dict(), + "schedulerG": schedulerG.state_dict(), + "epoch": epoch, + "args": args, + } + + torch.save(state_dict, args.save_path + "/netCGM.pt") + + train_loss_list.append(train_epoch_loss_avg_over_batch) + train_mix_acc_list.append(train_mix_acc) + train_clean_acc_list.append(train_clean_acc) + train_asr_list.append(train_asr) + train_ra_list.append(train_ra) + train_cross_acc_only_list.append(train_cross_acc) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + cross_test_loss_list.append(cross_test_loss_avg_over_batch) + ra_test_loss_list.append(ra_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + test_cross_acc_list.append(test_cross_acc) + + general_plot_for_epoch( + { + "Train Acc": train_mix_acc_list, + "Train Acc (clean sample only)": train_clean_acc_list, + "Train ASR": train_asr_list, + "Train RA": train_ra_list, + "Train Cross Acc": train_cross_acc_only_list, + "Test C-Acc": test_acc_list, + "Test ASR": test_asr_list, + "Test RA": test_ra_list, + "Test Cross Acc": test_cross_acc_list, + }, + save_path=f"{args.save_path}/acc_like_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "Train Loss": train_loss_list, + "Test Clean Loss": clean_test_loss_list, + "Test Backdoor Loss": bd_test_loss_list, + "Test Cross Loss": cross_test_loss_list, + "Test RA Loss": ra_test_loss_list, + }, + save_path=f"{args.save_path}/loss_metric_plots.png", + ylabel="percentage", + ) + + epoch += 1 + if epoch > args.epochs: + break + + agg.to_dataframe().to_csv(f"{args.save_path}/attack_df.csv") + + agg.summary().to_csv(f"{args.save_path}/attack_df_summary.csv") + + ### save the poison train data for inputaware + + bd_train_dataset = prepro_cls_DatasetBD_v2( + clean_train_dataset_with_transform.wrapped_dataset, + save_folder_path=f"{args.save_path}/bd_train_dataset" + ) + + transforms_reversible = transforms.Compose( + list( + filter( + lambda x: isinstance(x, (transforms.Normalize, transforms.Resize, transforms.ToTensor)), + deepcopy(clean_train_dataset_with_transform.wrap_img_transform.transforms) + ) + ) + ) + clean_train_dataset_with_transform.wrap_img_transform = transforms_reversible # change it to reversiable + + # get denormalizer + for trans_t in deepcopy(transforms_reversible.transforms): + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + logging.info(f"{denormalizer}") + + clean_train_dataloader_without_shuffle = torch.utils.data.DataLoader(clean_train_dataset_with_transform, + batch_size=args.batch_size, + pin_memory=args.pin_memory, + num_workers=args.num_workers, + shuffle=False) + clean_train_dataloader2.dataset.wrap_img_transform = transforms_reversible + # change it to reversiable, notice that this dataloader is shuffled, since we need img2 is different from img1 (whose dataloader is not shuffled) + netC.eval() + netG.eval() + with torch.no_grad(): + for batch_idx, (inputs1, targets1), (inputs2, targets2) in zip( + range(len(clean_train_dataloader_without_shuffle)), + clean_train_dataloader_without_shuffle, + clean_train_dataloader2): + inputs1, targets1 = inputs1.to(self.device, non_blocking=args.non_blocking), targets1.to(self.device, + non_blocking=args.non_blocking) + inputs2, targets2 = inputs2.to(self.device, non_blocking=args.non_blocking), targets2.to(self.device, + non_blocking=args.non_blocking) + + num_bd = int(generalize_to_lower_pratio(args.pratio, inputs1.shape[0])) # int(args.pratio * bs) + num_cross = num_bd + + inputs_bd, targets_bd, patterns1, masks1 = self.create_bd(inputs1[:num_bd], targets1[:num_bd], netG, + netM, args, + 'train') + inputs_cross, patterns2, masks2 = self.create_cross( + inputs1[num_bd: num_bd + num_cross], inputs2[num_bd: num_bd + num_cross], netG, netM, args, + ) + + input_changed = torch.cat([inputs_bd, inputs_cross, ], dim=0) + # input_changed = p_transforms(input_changed) # this is non-reversible part, should not be included + + input_changed = denormalizer( # since we normalized once, we need to denormalize it back. + input_changed + ).detach().clone().cpu() + target_changed = torch.cat([targets_bd, targets1[num_bd: (num_bd + num_cross)], ], + dim=0).detach().clone().cpu() + + # save to the container + for idx_in_batch, t_img in enumerate( + input_changed + ): + # here we know it starts from 0 and they are consecutive + bd_train_dataset.set_one_bd_sample( + selected_index=int(batch_idx * int(args.batch_size) + idx_in_batch), + img=(t_img), + bd_label=int(target_changed[idx_in_batch]), + label=int(targets1[idx_in_batch]), + ) + + save_attack_result( + model_name=args.model, + num_classes=args.num_classes, + model=netC.cpu().state_dict(), + data_path=args.dataset_path, + img_size=args.img_size, + clean_data=args.dataset, + bd_train=bd_train_dataset, + bd_test=self.bd_test_dataset, + save_path=args.save_path, + ) + + def train_mask_step(self, netM, optimizerM, schedulerM, train_dataloader1, train_dataloader2, args): + netM.train() + total = 0 + + total_loss = 0 + criterion_div = nn.MSELoss(reduction="none") + for batch_idx, (inputs1, targets1), (inputs2, targets2) in zip(range(len(train_dataloader1)), train_dataloader1, + train_dataloader2): + optimizerM.zero_grad() + + inputs1, targets1 = inputs1.to(self.device, non_blocking=args.non_blocking), targets1.to(self.device, + non_blocking=args.non_blocking) + inputs2, targets2 = inputs2.to(self.device, non_blocking=args.non_blocking), targets2.to(self.device, + non_blocking=args.non_blocking) + + # bs = inputs1.shape[0] + masks1 = netM(inputs1) + masks1, masks2 = self.threshold(netM(inputs1)), self.threshold(netM(inputs2)) + + # Calculating diversity loss + distance_images = criterion_div(inputs1, inputs2) + distance_images = torch.mean(distance_images, dim=(1, 2, 3)) + distance_images = torch.sqrt(distance_images) + + distance_patterns = criterion_div(masks1, masks2) + distance_patterns = torch.mean(distance_patterns, dim=(1, 2, 3)) + distance_patterns = torch.sqrt(distance_patterns) + + loss_div = distance_images / (distance_patterns + args.EPSILON) + loss_div = torch.mean(loss_div) * args.lambda_div + + loss_norm = torch.mean(F.relu(masks1 - args.mask_density)) + + total_loss = args.lambda_norm * loss_norm + args.lambda_div * loss_div + total_loss.backward() + optimizerM.step() + infor_string = "Mask loss: {:.4f} - Norm: {:.3f} | Diversity: {:.3f}".format(total_loss, loss_norm, + loss_div) + progress_bar(batch_idx, len(train_dataloader1), infor_string) + + schedulerM.step() + + def eval_mask(self, netM, optimizerM, schedulerM, test_dataloader1, test_dataloader2, epoch, args): + netM.eval() + logging.info(" Eval:") + total = 0.0 + + criterion_div = nn.MSELoss(reduction="none") + for batch_idx, (inputs1, targets1), (inputs2, targets2) in zip(range(len(test_dataloader1)), test_dataloader1, + test_dataloader2): + with torch.no_grad(): + inputs1, targets1 = inputs1.to(self.device, non_blocking=args.non_blocking), targets1.to(self.device, + non_blocking=args.non_blocking) + inputs2, targets2 = inputs2.to(self.device, non_blocking=args.non_blocking), targets2.to(self.device, + non_blocking=args.non_blocking) + # bs = inputs1.shape[0] + masks1, masks2 = self.threshold(netM(inputs1)), self.threshold(netM(inputs2)) + + # Calculating diversity loss + distance_images = criterion_div(inputs1, inputs2) + distance_images = torch.mean(distance_images, dim=(1, 2, 3)) + distance_images = torch.sqrt(distance_images) + + distance_patterns = criterion_div(masks1, masks2) + distance_patterns = torch.mean(distance_patterns, dim=(1, 2, 3)) + distance_patterns = torch.sqrt(distance_patterns) + + loss_div = distance_images / (distance_patterns + args.EPSILON) + loss_div = torch.mean(loss_div) * args.lambda_div + + loss_norm = torch.mean(F.relu(masks1 - args.mask_density)) + + infor_string = "Norm: {:.3f} | Diversity: {:.3f}".format(loss_norm, loss_div) + progress_bar(batch_idx, len(test_dataloader1), infor_string) + + return epoch + + def create_targets_bd(self, targets, args): + if args.attack_label_trans == "all2one": + bd_targets = torch.ones_like(targets) * args.attack_target + elif args.attack_label_trans == "all2all": + bd_targets = torch.tensor([(label + 1) % args.num_classes for label in targets]) + else: + raise Exception("{} attack mode is not implemented".format(args.attack_label_trans)) + return bd_targets.to(self.device, non_blocking=args.non_blocking) + + def create_bd(self, inputs, targets, netG, netM, args, train_or_test): + if train_or_test == 'train': + bd_targets = self.create_targets_bd(targets, args) + if inputs.__len__() == 0: # for case that no sample should be poisoned + return inputs, bd_targets, inputs.detach().clone(), inputs.detach().clone() + patterns = netG(inputs) + patterns = self.normalizer(patterns) + + masks_output = self.threshold(netM(inputs)) + bd_inputs = inputs + (patterns - inputs) * masks_output + return bd_inputs, bd_targets, patterns, masks_output + if train_or_test == 'test': + bd_targets = self.create_targets_bd(targets, args) + + position_changed = ( + bd_targets - targets != 0) # no matter all2all or all2one, we want location changed to tell whether the bd is effective + + inputs, bd_targets = inputs[position_changed], bd_targets[position_changed] + + if inputs.__len__() == 0: # for case that no sample should be poisoned + return torch.tensor([]), torch.tensor([]), None, None, position_changed, targets + patterns = netG(inputs) + patterns = self.normalizer(patterns) + + masks_output = self.threshold(netM(inputs)) + bd_inputs = inputs + (patterns - inputs) * masks_output + return bd_inputs, bd_targets, patterns, masks_output, position_changed, targets + + def create_cross(self, inputs1, inputs2, netG, netM, args): + if inputs1.__len__() == 0: # for case that no sample should be poisoned + return inputs2.detach().clone(), inputs2, inputs2.detach().clone() + patterns2 = netG(inputs2) + patterns2 = self.normalizer(patterns2) + masks_output = self.threshold(netM(inputs2)) + inputs_cross = inputs1 + (patterns2 - inputs1) * masks_output + return inputs_cross, patterns2, masks_output + + def train_step(self, + netC, netG, netM, optimizerC, optimizerG, schedulerC, schedulerG, train_dataloader1, + train_dataloader2, args, + ): + netC.train() + netG.train() + logging.info(" Training:") + + criterion = nn.CrossEntropyLoss() + criterion_div = nn.MSELoss(reduction="none") + + batch_loss_list = [] + batch_predict_list = [] + batch_label_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + + for batch_idx, (inputs1, targets1), (inputs2, targets2) in zip(range(len(train_dataloader1)), train_dataloader1, + train_dataloader2): + optimizerC.zero_grad() + + inputs1, targets1 = inputs1.to(self.device, non_blocking=args.non_blocking), targets1.to(self.device, + non_blocking=args.non_blocking) + inputs2, targets2 = inputs2.to(self.device, non_blocking=args.non_blocking), targets2.to(self.device, + non_blocking=args.non_blocking) + + # bs = int(args.batch_size) + num_bd = int(generalize_to_lower_pratio(args.pratio, inputs1.shape[0])) # int(args.pratio * bs) + num_cross = num_bd + + inputs_bd, targets_bd, patterns1, masks1 = self.create_bd(inputs1[:num_bd], targets1[:num_bd], netG, netM, + args, + 'train') + inputs_cross, patterns2, masks2 = self.create_cross( + inputs1[num_bd: num_bd + num_cross], inputs2[num_bd: num_bd + num_cross], netG, netM, args, + ) + + total_inputs = torch.cat((inputs_bd, inputs_cross, inputs1[num_bd + num_cross:]), 0) + total_targets = torch.cat((targets_bd, targets1[num_bd:]), 0) + + preds = netC(total_inputs) + loss_ce = criterion(preds, total_targets) + + # Calculating diversity loss + distance_images = criterion_div(inputs1[:num_bd], inputs2[num_bd: num_bd + num_bd]) + distance_images = torch.mean(distance_images, dim=(1, 2, 3)) + distance_images = torch.sqrt(distance_images) + + distance_patterns = criterion_div(patterns1, patterns2) + distance_patterns = torch.mean(distance_patterns, dim=(1, 2, 3)) + distance_patterns = torch.sqrt(distance_patterns) + + loss_div = distance_images / (distance_patterns + args.EPSILON) + loss_div = torch.mean(loss_div) * args.lambda_div + + total_loss = loss_ce + loss_div + total_loss.backward() + optimizerC.step() + optimizerG.step() + + batch_loss_list.append(total_loss.item()) + batch_predict_list.append(torch.max(preds, -1)[1].detach().clone().cpu()) + batch_label_list.append(total_targets.detach().clone().cpu()) + + poison_indicator = torch.zeros(inputs1.shape[0]) + poison_indicator[:num_bd] = 1 # all others are cross/clean samples cannot conut up to train acc + poison_indicator[num_bd:num_cross + num_bd] = 2 # indicate for the cross terms + + batch_poison_indicator_list.append(poison_indicator) + batch_original_targets_list.append(targets1.detach().clone().cpu()) + + if args.C_lr_scheduler == "ReduceLROnPlateau": + schedulerC.step(loss_ce) + else: + schedulerC.step() + schedulerG.step() + + train_epoch_loss_avg_over_batch, \ + train_epoch_predict_list, \ + train_epoch_label_list, \ + train_epoch_poison_indicator_list, \ + train_epoch_original_targets_list = sum(batch_loss_list) / len(batch_loss_list), \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + + train_mix_acc = all_acc(train_epoch_predict_list, train_epoch_label_list) + + train_bd_idx = torch.where(train_epoch_poison_indicator_list == 1)[0] + train_cross_idx = torch.where(train_epoch_poison_indicator_list == 2)[0] + train_clean_idx = torch.where(train_epoch_poison_indicator_list == 0)[0] + train_clean_acc = all_acc( + train_epoch_predict_list[train_clean_idx], + train_epoch_label_list[train_clean_idx], + ) + train_asr = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_label_list[train_bd_idx], + ) + train_cross_acc = all_acc( + train_epoch_predict_list[train_cross_idx], + train_epoch_label_list[train_cross_idx], + ) + train_ra = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_original_targets_list[train_bd_idx], + ) + + return train_epoch_loss_avg_over_batch, \ + train_mix_acc, \ + train_clean_acc, \ + train_asr, \ + train_ra, \ + train_cross_acc + + def eval_step( + self, + netC, + netG, + netM, + optimizerC, + optimizerG, + schedulerC, + schedulerG, + clean_test_dataset_with_transform, + epoch, + args, + ): + netC.eval() + netG.eval() + + # set shuffle here = False, since other place need randomness to generate cross sample. + transforms_reversible = transforms.Compose( + list( + filter( + lambda x: isinstance(x, (transforms.Normalize, transforms.Resize, transforms.ToTensor)), + deepcopy(clean_test_dataset_with_transform.wrap_img_transform.transforms) + ) + ) + ) + # get denormalizer + for trans_t in deepcopy(clean_test_dataset_with_transform.wrap_img_transform.transforms): + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + logging.info(f"{denormalizer}") + + # Notice that due to the fact that we need random sequence to get cross samples + # So we set the reversible_test_dataloader2 with shuffle = True + reversible_test_dataset1 = (clean_test_dataset_with_transform) + reversible_test_dataset1.wrap_img_transform = transforms_reversible + reversible_test_dataloader1 = DataLoader(reversible_test_dataset1, batch_size=args.batch_size, + pin_memory=args.pin_memory, + num_workers=args.num_workers, + shuffle=False) + + reversible_test_dataset2 = (clean_test_dataset_with_transform) + reversible_test_dataset2.wrap_img_transform = transforms_reversible + reversible_test_dataloader2 = DataLoader(reversible_test_dataset2, batch_size=args.batch_size, + pin_memory=args.pin_memory, + num_workers=args.num_workers, + shuffle=True) + + self.bd_test_dataset = prepro_cls_DatasetBD_v2( + clean_test_dataset_with_transform.wrapped_dataset, save_folder_path=f"{args.save_path}/bd_test_dataset" + ) + self.cross_test_dataset = prepro_cls_DatasetBD_v2( + clean_test_dataset_with_transform.wrapped_dataset, save_folder_path=f"{args.save_path}/cross_test_dataset" + ) + + for batch_idx, (inputs1, targets1), (inputs2, targets2) in zip(range(len(reversible_test_dataloader1)), + reversible_test_dataloader1, + reversible_test_dataloader2): + with torch.no_grad(): + inputs1, targets1 = inputs1.to(self.device, non_blocking=args.non_blocking), targets1.to(self.device, + non_blocking=args.non_blocking) + inputs2, targets2 = inputs2.to(self.device, non_blocking=args.non_blocking), targets2.to(self.device, + non_blocking=args.non_blocking) + + inputs_bd, targets_bd, _, _, position_changed, targets = self.create_bd(inputs1, targets1, netG, netM, + args, + 'test') + inputs_cross, _, _ = self.create_cross(inputs1, inputs2, netG, netM, args) + + targets1 = targets1.detach().clone().cpu() + y_poison_batch = targets_bd.detach().clone().cpu().tolist() + for idx_in_batch, t_img in enumerate(inputs_bd.detach().clone().cpu()): + self.bd_test_dataset.set_one_bd_sample( + selected_index=int( + batch_idx * int(args.batch_size) + torch.where(position_changed.detach().clone().cpu())[0][ + idx_in_batch]), + # manually calculate the original index, since we do not shuffle the dataloader + img=denormalizer(t_img), + bd_label=int(y_poison_batch[idx_in_batch]), + label=int(targets1[torch.where(position_changed.detach().clone().cpu())[0][idx_in_batch]]), + ) + + for idx_in_batch, t_img in enumerate(inputs_cross.detach().clone().cpu()): + self.cross_test_dataset.set_one_bd_sample( + selected_index=int(batch_idx * int(args.batch_size) + idx_in_batch), + # manually calculate the original index, since we do not shuffle the dataloader + img=denormalizer(t_img), + bd_label=int(targets1[idx_in_batch]), + label=int(targets1[idx_in_batch]), + ) + + self.bd_test_dataset.subset( + np.where(self.bd_test_dataset.poison_indicator == 1)[0].tolist() + ) + bd_test_dataset_with_transform = dataset_wrapper_with_transform( + self.bd_test_dataset, + clean_test_dataset_with_transform.wrap_img_transform, + ) + bd_test_dataloader = DataLoader(bd_test_dataset_with_transform, + pin_memory=args.pin_memory, + batch_size=args.batch_size, + num_workers=args.num_workers, + shuffle=False) + + cross_test_dataset_with_transform = dataset_wrapper_with_transform( + self.cross_test_dataset, + clean_test_dataset_with_transform.wrap_img_transform, + ) + cross_test_dataloader = DataLoader(cross_test_dataset_with_transform, + pin_memory=args.pin_memory, + batch_size=args.batch_size, + num_workers=args.num_workers, + shuffle=False) + + clean_test_dataloader = DataLoader( + clean_test_dataset_with_transform, + pin_memory=args.pin_memory, + batch_size=args.batch_size, + num_workers=args.num_workers, + shuffle=False + ) + + clean_metrics, clean_epoch_predict_list, clean_epoch_label_list = given_dataloader_test( + netC, + clean_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + clean_test_loss_avg_over_batch = clean_metrics['test_loss_avg_over_batch'] + test_acc = clean_metrics['test_acc'] + + bd_metrics, bd_epoch_predict_list, bd_epoch_label_list = given_dataloader_test( + netC, + bd_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + bd_test_loss_avg_over_batch = bd_metrics['test_loss_avg_over_batch'] + test_asr = bd_metrics['test_acc'] + + self.bd_test_dataset.getitem_all_switch = True # change to return the original label instead + ra_test_dataset_with_transform = dataset_wrapper_with_transform( + self.bd_test_dataset, + clean_test_dataset_with_transform.wrap_img_transform, + ) + ra_test_dataloader = DataLoader(ra_test_dataset_with_transform, + pin_memory=args.pin_memory, + batch_size=args.batch_size, + num_workers=args.num_workers, + shuffle=False) + ra_metrics, ra_epoch_predict_list, ra_epoch_label_list = given_dataloader_test( + netC, + ra_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + ra_test_loss_avg_over_batch = ra_metrics['test_loss_avg_over_batch'] + test_ra = ra_metrics['test_acc'] + self.bd_test_dataset.getitem_all_switch = False # switch back + + cross_metrics, cross_epoch_predict_list, cross_epoch_label_list = given_dataloader_test( + netC, + cross_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + cross_test_loss_avg_over_batch = cross_metrics['test_loss_avg_over_batch'] + test_cross_acc = cross_metrics['test_acc'] + + return clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + cross_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra, \ + test_cross_acc + + +if __name__ == '__main__': + attack = InputAware() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = attack.set_args(parser) + parser = attack.set_bd_args(parser) + args = parser.parse_args() + attack.add_bd_yaml_to_args(args) + attack.add_yaml_to_args(args) + args = attack.process_args(args) + attack.prepare(args) + attack.stage1_non_training_data_prepare() + attack.stage2_training() + +''' +original license: +MIT License +Copyright (c) 2021 VinAI Research +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' diff --git a/attack/lc.py b/attack/lc.py new file mode 100755 index 0000000..1a1f427 --- /dev/null +++ b/attack/lc.py @@ -0,0 +1,221 @@ +''' +this script is for lc attack + +link : https://github.com/MadryLab/label-consistent-backdoor-code +The original license is placed at the end of this file. + +basic structure: +1. config args, save_path, fix random seed +2. set the clean train data and clean test data +3. set the attack img transform and label transform +4. set the backdoor attack data and backdoor test data +5. set the device, model, criterion, optimizer, training schedule. +6. attack or use the model to do finetune with 5% clean data +7. save the attack result for defense + +@article{turner2019labelconsistent, + title = {Label-Consistent Backdoor Attacks}, + author = {Alexander Turner and Dimitris Tsipras and Aleksander Madry}, + journal = {arXiv preprint arXiv:1912.02771}, + year = {2019} +} + +The original license : + +MIT License + +Copyright (c) 2019 Alexander Turner, Dimitris Tsipras and Aleksander Madry + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +''' + +import argparse +import logging +import os +import sys +import torch + +import numpy as np + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +from attack.badnet import BadNet, add_common_attack_args +from utils.aggregate_block.dataset_and_transform_generate import get_num_classes, get_input_shape +from utils.backdoor_generate_poison_index import generate_poison_index_from_label_transform +from utils.aggregate_block.bd_attack_generate import bd_attack_img_trans_generate, bd_attack_label_trans_generate +from copy import deepcopy +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform + + +class LabelConsistent(BadNet): + + def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + + parser = add_common_attack_args(parser) + parser.add_argument('--attack_train_replace_imgs_path', type=str) + parser.add_argument('--attack_test_replace_imgs_path', type=str) + parser.add_argument("--reduced_amplitude", type=float, ) + parser.add_argument('--bd_yaml_path', type=str, default='../config/attack/lc/default.yaml', + help='path for yaml file provide additional default attributes') + return parser + + def process_args(self, args): + + args.terminal_info = sys.argv + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + if ('attack_train_replace_imgs_path' not in args.__dict__) or (args.attack_train_replace_imgs_path is None): + args.attack_train_replace_imgs_path = f"../resource/label-consistent/data/adv_dataset/{args.dataset}_train.npy" + logging.info( + f"args.attack_train_replace_imgs_path does not found, so = {args.attack_train_replace_imgs_path}") + + if ('attack_test_replace_imgs_path' not in args.__dict__) or (args.attack_test_replace_imgs_path is None): + args.attack_test_replace_imgs_path = f"../resource/label-consistent/data/adv_dataset/{args.dataset}_test.npy" + logging.info( + f"args.attack_test_replace_imgs_path does not found, so = {args.attack_test_replace_imgs_path}") + + return args + + def stage1_non_training_data_prepare(self): + + logging.info(f"stage1 start") + + assert 'args' in self.__dict__ + args = self.args + + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + clean_train_dataset_with_transform, \ + clean_train_dataset_targets, \ + clean_test_dataset_with_transform, \ + clean_test_dataset_targets \ + = self.benign_prepare() + + train_bd_img_transform, test_bd_img_transform = bd_attack_img_trans_generate(args) + ### get the backdoor transform on label + bd_label_transform = bd_attack_label_trans_generate(args) + + ### 4. set the backdoor attack data and backdoor test data + train_poison_index = generate_poison_index_from_label_transform( + clean_train_dataset_targets, + label_transform=bd_label_transform, + train=True, + pratio=args.pratio if 'pratio' in args.__dict__ else None, + p_num=args.p_num if 'p_num' in args.__dict__ else None, + clean_label=True, + ) + + logging.debug(f"poison train idx is saved") + torch.save(train_poison_index, + args.save_path + '/train_poison_index_list.pickle', + ) + + ### generate train dataset for backdoor attack + bd_train_dataset = prepro_cls_DatasetBD_v2( + deepcopy(train_dataset_without_transform), + poison_indicator=train_poison_index, + bd_image_pre_transform=train_bd_img_transform, + bd_label_pre_transform=bd_label_transform, + save_folder_path=f"{args.save_path}/bd_train_dataset", + ) + + bd_train_dataset_with_transform = dataset_wrapper_with_transform( + bd_train_dataset, + train_img_transform, + train_label_transform, + ) + + ### decide which img to poison in ASR Test + test_poison_index = generate_poison_index_from_label_transform( + clean_test_dataset_targets, + label_transform=bd_label_transform, + train=False, + ) + + ### generate test dataset for ASR + bd_test_dataset = prepro_cls_DatasetBD_v2( + deepcopy(test_dataset_without_transform), + poison_indicator=test_poison_index, + bd_image_pre_transform=test_bd_img_transform, + bd_label_pre_transform=bd_label_transform, + save_folder_path=f"{args.save_path}/bd_test_dataset", + ) + + bd_test_dataset.subset( + np.where(test_poison_index == 1)[0] + ) + + bd_test_dataset_with_transform = dataset_wrapper_with_transform( + bd_test_dataset, + test_img_transform, + test_label_transform, + ) + + self.stage1_results = clean_train_dataset_with_transform, \ + clean_test_dataset_with_transform, \ + bd_train_dataset_with_transform, \ + bd_test_dataset_with_transform + + +if __name__ == '__main__': + attack = LabelConsistent() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = attack.set_args(parser) + parser = attack.set_bd_args(parser) + args = parser.parse_args() + attack.add_bd_yaml_to_args(args) + attack.add_yaml_to_args(args) + args = attack.process_args(args) + attack.prepare(args) + attack.stage1_non_training_data_prepare() + attack.stage2_training() + +""" +MIT License + +Copyright (c) 2019 Alexander Turner, Dimitris Tsipras and Aleksander Madry + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +""" diff --git a/attack/lf.py b/attack/lf.py new file mode 100755 index 0000000..3fea43e --- /dev/null +++ b/attack/lf.py @@ -0,0 +1,118 @@ +''' +this script is for lf attack + +link : https://github.com/YiZeng623/frequency-backdoor +The original license is placed at the end of this file. + +basic structure: +1. config args, save_path, fix random seed +2. set the clean train data and clean test data +3. set the attack img transform and label transform +4. set the backdoor attack data and backdoor test data +5. set the device, model, criterion, optimizer, training schedule. +6. attack or use the model to do finetune with 5% clean data +7. save the attack result for defense + +@inproceedings{zeng2021rethinking_lf, + title={Rethinking the backdoor attacks' triggers: A frequency perspective}, + author={Zeng, Yi and Park, Won and Mao, Z Morley and Jia, Ruoxi}, + booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, + year={2021} +} + +The original license : +MIT License + +Copyright (c) 2021 Yi Zeng + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' + +import argparse +import logging +import os +import sys + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +from attack.badnet import BadNet, add_common_attack_args +from utils.aggregate_block.dataset_and_transform_generate import get_num_classes, get_input_shape + + +class LowFrequency(BadNet): + + def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + parser = add_common_attack_args(parser) + parser.add_argument('--lowFrequencyPatternPath', type=str) + parser.add_argument('--bd_yaml_path', type=str, default='../config/attack/lf/default.yaml', + help='path for yaml file provide additional default attributes') + return parser + + def process_args(self, args): + args.terminal_info = sys.argv + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + if ('lowFrequencyPatternPath' not in args.__dict__) or (args.lowFrequencyPatternPath is None): + args.lowFrequencyPatternPath = f"../resource/lowFrequency/{args.dataset}_{args.model}_0_255.npy" + logging.info(f"args.lowFrequencyPatternPath does not found, so = {args.lowFrequencyPatternPath}") + + return args + + +if __name__ == '__main__': + attack = LowFrequency() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = attack.set_args(parser) + parser = attack.set_bd_args(parser) + args = parser.parse_args() + attack.add_bd_yaml_to_args(args) + attack.add_yaml_to_args(args) + args = attack.process_args(args) + attack.prepare(args) + attack.stage1_non_training_data_prepare() + attack.stage2_training() + +''' +MIT License + +Copyright (c) 2021 Yi Zeng + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' diff --git a/attack/lira.py b/attack/lira.py new file mode 100755 index 0000000..d90a939 --- /dev/null +++ b/attack/lira.py @@ -0,0 +1,1044 @@ +''' +This is the code for LIRA attack + +github link : https://github.com/sunbelbd/invisible_backdoor_attacks + +citation: +@inproceedings{Doan2021lira, + title = {LIRA: Learnable, Imperceptible and Robust Backdoor Attacks}, + author = {Khoa D. Doan and Yingjie Lao and Weijie Zhao and Ping Li}, + booktitle = {Proceedings of the IEEE International Conference on Computer Vision}, + year = {2021} +} + +Please note that +1. The original code was implemented in Paddlepaddle, + and we replaced all the functionality of Paddlepaddle with the equivalent API of Pytorch. +2. Since this is a training-controllable attack, + the concepts of poisoning rate and poisoned data may not apply. + So, this attack remains incompatible with the whole framework for the time being + (because we require data to be saved during the saving process). + In the future, we will update this version to make it fully integrated into the framework. +3. For fairness issue, we apply the same total training epochs as all other attack methods. But for LIRA, it may not be the best choice. + +The original LICENSE of the script is put at the bottom of this file. +''' +import argparse +import logging +import os +import random +import sys +import torch +from copy import deepcopy +from functools import partial + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +import numpy as np +from torch import nn, optim +from torchvision import transforms +from torch.utils.data import DataLoader +from torch.utils.data.dataloader import DataLoader + +from utils.aggregate_block.dataset_and_transform_generate import get_dataset_denormalization, dataset_and_transform_generate +from utils.trainer_cls import Metric_Aggregator, all_acc, test_given_dataloader_on_mix +from utils.trainer_cls import plot_loss, plot_acc_like_metric, given_dataloader_test +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.train_settings_generate import argparser_opt_scheduler, argparser_criterion +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform +from utils.save_load_attack import save_attack_result +from attack.badnet import BadNet + +loss_fn = nn.CrossEntropyLoss() + + +class Autoencoder(nn.Module): + def __init__(self, channels=3): + super(Autoencoder, self).__init__() + self.encoder = nn.Sequential( + nn.Conv2d(channels, 16, (4, 4), stride=(2, 2), padding=1), + nn.BatchNorm2d(16), + nn.ReLU(), + nn.Conv2d(16, 32, (4, 4), stride=(2, 2), padding=1), + nn.BatchNorm2d(32), + nn.ReLU(), + nn.Conv2d(32, 64, (4, 4), stride=(2, 2), padding=1), + nn.BatchNorm2d(64), + nn.ReLU(), + nn.Conv2d(64, 128, (4, 4), stride=(2, 2), padding=1), + nn.BatchNorm2d(128), + nn.ReLU() + ) + self.decoder = nn.Sequential( + nn.ConvTranspose2d(128, 64, (4, 4), stride=(2, 2), padding=(1, 1)), + nn.BatchNorm2d(64), + nn.ReLU(), + nn.ConvTranspose2d(64, 32, (4, 4), stride=(2, 2), padding=(1, 1)), + nn.BatchNorm2d(32), + nn.ReLU(), + nn.ConvTranspose2d(32, 16, (4, 4), stride=(2, 2), padding=(1, 1)), + nn.BatchNorm2d(16), + nn.ReLU(), + nn.ConvTranspose2d(16, channels, (4, 4), stride=(2, 2), padding=(1, 1)), + nn.Tanh() + ) + + def forward(self, x): + x = self.encoder(x) + x = self.decoder(x) + return x + + +def double_conv(in_channels, out_channels): + return nn.Sequential( + nn.Conv2d(in_channels, out_channels, (3, 3), padding=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(), + nn.Conv2d(out_channels, out_channels, (3, 3), padding=1), + nn.BatchNorm2d(out_channels), + nn.ReLU() + ) + + +class UNet(nn.Module): + + def __init__(self, out_channel): + super().__init__() + + self.dconv_down1 = double_conv(3, 64) + self.dconv_down2 = double_conv(64, 128) + self.dconv_down3 = double_conv(128, 256) + self.dconv_down4 = double_conv(256, 512) + + self.maxpool = nn.AvgPool2d(2) + self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', + align_corners=True) + + self.dconv_up3 = double_conv(256 + 512, 256) + self.dconv_up2 = double_conv(128 + 256, 128) + self.dconv_up1 = double_conv(128 + 64, 64) + + self.conv_last = nn.Sequential( + nn.Conv2d(64, out_channel, (1, 1)), + nn.BatchNorm2d(out_channel), + ) + + self.out_layer = nn.Tanh() + + def forward(self, x): + conv1 = self.dconv_down1(x) + x = self.maxpool(conv1) + + conv2 = self.dconv_down2(x) + x = self.maxpool(conv2) + + conv3 = self.dconv_down3(x) + x = self.maxpool(conv3) + + x = self.dconv_down4(x) + x = self.upsample(x) + x = torch.concat([x, conv3], 1) + x = self.dconv_up3(x) + x = self.upsample(x) + x = torch.concat([x, conv2], 1) + x = self.dconv_up2(x) + x = self.upsample(x) + x = torch.concat([x, conv1], 1) + x = self.dconv_up1(x) + + out = self.conv_last(x) + + out = self.out_layer(out) + + return out + + +def all2one_target_transform(x, attack_target=1): + return torch.ones_like(x) * attack_target + + +def all2all_target_transform(x, num_classes): + return (x + 1) % num_classes + + +def create_models(args): + if args.attack_model == 'autoencoder': + logging.debug("use autoencoder as atk model") + atkmodel = Autoencoder(args.input_channel) + # Copy of attack model + tgtmodel = Autoencoder(args.input_channel) + else: + logging.debug("use unet as atk model") + atkmodel = UNet(args.input_channel) + # Copy of attack model + tgtmodel = UNet(args.input_channel) + + # Classifier + create_net = partial(generate_cls_model, + model_name=args.model, + num_classes=args.num_classes, + image_size=args.img_size, + ) + clsmodel = create_net() + + tgtmodel.to(device) + atkmodel.to(device) + clsmodel.to(device) + + # Optimizer + tgtoptimizer = torch.optim.Adam(tgtmodel.parameters(), lr=args.lr_atk) + + return atkmodel, tgtmodel, tgtoptimizer, clsmodel, create_net + + +def hp_test(args, atkmodel, scratchmodel, target_transform, + train_loader, test_loader, epoch, trainepoch, clip_image, + testoptimizer=None, log_prefix='Internal', epochs_per_test=5): + # default phase 2 parameters to phase 1 + if args.test_alpha is None: + args.test_alpha = args.alpha + if args.test_eps is None: + args.test_eps = args.eps + + test_loss = 0 + correct = 0 + + correct_transform = 0 + test_transform_loss = 0 + + atkmodel.eval() + if testoptimizer is None: + scratchmodel.cuda() + testoptimizer = torch.optim.SGD(params=scratchmodel.parameters(), lr=args.lr) + + for cepoch in range(trainepoch): + scratchmodel.train() + for batch_idx, (data, target) in enumerate(train_loader): + scratchmodel.cuda() + atkmodel.cuda() + data, target = data.cuda(), target.cuda() + with torch.cuda.amp.autocast(enabled=args.amp): + with torch.no_grad(): + noise = atkmodel(data) * args.test_eps + atkdata = clip_image(data + noise) + + atkoutput = scratchmodel(atkdata) + output = scratchmodel(data) + + loss_clean = loss_fn(output, target) + loss_poison = loss_fn(atkoutput, target_transform(target)) + + loss = args.alpha * loss_clean + (1 - args.test_alpha) * loss_poison + scaler.scale(loss).backward() + scaler.step(testoptimizer) + scaler.update() + testoptimizer.zero_grad() + + if cepoch % epochs_per_test == 0 or cepoch == trainepoch - 1: + scratchmodel.eval() + with torch.no_grad(): + for data, target in test_loader: + scratchmodel.cuda() + atkmodel.cuda() + data, target = data.cuda(), target.cuda() + + + output = scratchmodel(data) + cross_entropy = torch.nn.CrossEntropyLoss(reduction="sum") + test_loss += cross_entropy(output, target).item() # sum up batch loss + correct += (torch.max(output, -1)[1]).eq(target).sum().item() + + noise = atkmodel(data) * args.test_eps + atkdata = clip_image(data + noise) + atkoutput = scratchmodel(atkdata) + test_transform_loss += cross_entropy( + atkoutput, target_transform(target)).item() # sum up batch loss + correct_transform += (torch.max(atkoutput, -1)[1]).eq(target_transform(target)).sum().item() + + test_loss /= len(test_loader.dataset) + test_transform_loss /= len(test_loader.dataset) + + correct /= len(test_loader.dataset) + correct_transform /= len(test_loader.dataset) + + logging.debug( + '\n{}-Test set [{}]: Loss: clean {:.4f} poison {:.4f}, Accuracy: clean {:.2f} poison {:.2f}'.format( + log_prefix, cepoch, + test_loss, test_transform_loss, + correct, correct_transform + )) + + return correct, correct_transform + + +def train(args, atkmodel, tgtmodel, clsmodel, tgtoptimizer, clsoptimizer, target_transform, + train_loader, epoch, train_epoch, create_net, clip_image, post_transforms=None): + clsmodel.train() + atkmodel.eval() + tgtmodel.train() + losslist = [] + for batch_idx, (data, target) in enumerate(train_loader): + + tgtmodel.cuda() + clsmodel.cuda() + atkmodel.cuda() + data, target = data.cuda(), target.cuda() + with torch.cuda.amp.autocast(enabled=args.amp): + if post_transforms is not None: + data = post_transforms(data) + + ######################################## + #### Update Transformation Function #### + ######################################## + noise = tgtmodel(data) * args.eps + atkdata = clip_image(data + noise) + + # Calculate loss + atkoutput = clsmodel(atkdata) + loss_poison = loss_fn(atkoutput, target_transform(target)) + loss1 = loss_poison + + losslist.append(loss1.item()) + + scaler.scale(loss1).backward() + scaler.step(tgtoptimizer) + scaler.update() + tgtoptimizer.zero_grad() + + ############################### + #### Update the classifier #### + ############################### + with torch.cuda.amp.autocast(enabled=args.amp): + noise = atkmodel(data) * args.eps + atkdata = clip_image(data + noise) + output = clsmodel(data) + atkoutput = clsmodel(atkdata) + loss_clean = loss_fn(output, target) + loss_poison = loss_fn(atkoutput, target_transform(target)) + loss2 = loss_clean * args.alpha + (1 - args.alpha) * loss_poison + + scaler.scale(loss2).backward() + scaler.step(clsoptimizer) + scaler.update() + clsoptimizer.zero_grad() + + atkloss = sum(losslist) / len(losslist) + + return atkloss + + +def get_target_transform(args): + """DONE + """ + if args.attack_label_trans == 'all2one': + target_transform = lambda x: all2one_target_transform(x, args.attack_target) + elif args.attack_label_trans == 'all2all': + target_transform = lambda x: all2all_target_transform(x, args.num_classes) + else: + raise Exception(f'Invalid mode {args.mode}') + return target_transform + + +# done +def get_train_test_loaders(args): + train_loader = get_dataloader(args, True) + test_loader = get_dataloader(args, False) + if args.dataset in ['tiny-imagenet', 'tiny-imagenet32', 'tiny']: + xmin, xmax = -2.1179039478302, 2.640000104904175 + + def clip_image(x): + return torch.clip(x, xmin, xmax) + elif args.dataset == 'cifar10': + xmin, xmax = -1.9895, 2.1309 + + def clip_image(x): + return torch.clip(x, xmin, xmax) + elif args.dataset == 'cifar100': + xmin, xmax = -1.8974, 2.0243 + + def clip_image(x): + return torch.clip(x, xmin, xmax) + elif args.dataset == 'mnist': + def clip_image(x): + return torch.clip(x, -1.0, 1.0) + elif args.dataset == 'gtsrb': + def clip_image(x): + return torch.clip(x, 0.0, 1.0) + else: + raise Exception(f'Invalid dataset: {args.dataset}') + return train_loader, test_loader, clip_image + + +class ToNumpy: + def __call__(self, x): + x = np.array(x) + if len(x.shape) == 2: + x = np.expand_dims(x, axis=2) + return x + + +class ProbTransform(torch.nn.Module): + def __init__(self, f, p=1): + super().__init__() + self.f = f + self.p = p + + def forward(self, x): # , **kwargs): + if random.random() < self.p: + return self.f(x) + else: + return x + + +class PostTensorTransform(torch.nn.Module): + def __init__(self, opt): + super(PostTensorTransform, self).__init__() + self.random_crop = transforms.RandomCrop((opt.input_height, opt.input_width), padding=opt.random_crop) + self.random_rotation = transforms.RandomRotation(opt.random_rotation) + if opt.dataset == "cifar10": + self.random_horizontal_flip = transforms.RandomHorizontalFlip(p=0.5) + + def forward(self, x): + for module in self.children(): + x = module(x) + return x + + +# my done +def get_dataloader(opt, train=True, min_width=0): + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform = dataset_and_transform_generate(opt) + + logging.debug("dataset_and_transform_generate done") + + clean_train_dataset_with_transform = dataset_wrapper_with_transform( + train_dataset_without_transform, + test_img_transform, # since later we have post_transform which contains randomcrop etc + train_label_transform + ) + + clean_test_dataset_with_transform = dataset_wrapper_with_transform( + test_dataset_without_transform, + test_img_transform, + test_label_transform, + ) + + if train: + dataloader = torch.utils.data.DataLoader( + clean_train_dataset_with_transform, batch_size=opt.batch_size, num_workers=opt.num_workers, shuffle=True) + else: + dataloader = torch.utils.data.DataLoader( + clean_test_dataset_with_transform, batch_size=opt.batch_size, num_workers=opt.num_workers, shuffle=False) + + return dataloader + + +def get_model(args): + create_net = partial(generate_cls_model, + model_name=args.model, + num_classes=args.num_classes, + image_size=args.img_size, + ) + netC = create_net() + netC.to(device) + optimizerC, schedulerC = argparser_opt_scheduler(netC, args) + logging.info(f'atk stage1, Optimizer: {optimizerC}, scheduler: {schedulerC}') + return netC, optimizerC, schedulerC + + +def final_test(clean_test_dataloader, bd_test_dataloader, args, test_model_path, atkmodel, netC, target_transform, + train_loader, test_loader, + trainepoch, writer, alpha=0.5, optimizerC=None, + schedulerC=None, log_prefix='Internal', epochs_per_test=1, data_transforms=None, start_epoch=1, + clip_image=None, criterion=None): + atkmodel.cuda() + netC.cuda() + + clean_accs, poison_accs = [], [] + + atkmodel.eval() + + if optimizerC is None: + logging.debug('No optimizer, creating default SGD...') + optimizerC = optim.SGD(netC.parameters(), lr=args.finetune_lr) + if schedulerC is None: + logging.debug('No scheduler, creating default 100,200,300,400...') + schedulerC = optim.lr_scheduler.MultiStepLR(optimizerC, [100, 200, 300, 400], args.finetune_lr) + + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + train_loss_list = [] + + agg = Metric_Aggregator() + + for cepoch in range(start_epoch, trainepoch + 1): + netC.train() + batch_loss_list = [] + for batch_idx, (data, target) in enumerate(train_loader): + + data, target = data.cuda(), target.cuda() + netC.cuda() + atkmodel.cuda() + + with torch.cuda.amp.autocast(enabled=args.amp): + + if data_transforms is not None: + data = data_transforms(data) + + output = netC(data) + loss_clean = loss_fn(output, target) + + if alpha < 1: + with torch.no_grad(): + noise = atkmodel(data) * args.test_eps + if clip_image is None: + atkdata = torch.clip(data + noise, 0, 1) + else: + atkdata = clip_image(data + noise) + atkoutput = netC(atkdata) + loss_poison = loss_fn(atkoutput, target_transform(target)) + else: + loss_poison = torch.tensor(0.0) + + loss = alpha * loss_clean + (1 - alpha) * loss_poison + + scaler.scale(loss).backward() + scaler.step(optimizerC) + scaler.update() + optimizerC.zero_grad() + + batch_loss_list.append(loss.item()) + one_epoch_loss = sum(batch_loss_list) / len(batch_loss_list) + schedulerC.step() + + # test + + clean_metrics, \ + clean_test_epoch_predict_list, \ + clean_test_epoch_label_list, \ + = given_dataloader_test( + model=netC, + test_dataloader=clean_test_dataloader, + criterion=criterion, + non_blocking=args.non_blocking, + device=device, + verbose=1, + ) + + clean_test_loss_avg_over_batch = clean_metrics["test_loss_avg_over_batch"] + test_acc = clean_metrics["test_acc"] + + bd_metrics, \ + bd_test_epoch_predict_list, \ + bd_test_epoch_label_list, \ + bd_test_epoch_original_index_list, \ + bd_test_epoch_poison_indicator_list, \ + bd_test_epoch_original_targets_list = test_given_dataloader_on_mix( + model=netC, + test_dataloader=bd_test_dataloader, + criterion=criterion, + non_blocking=args.non_blocking, + device=device, + verbose=1, + ) + + bd_test_loss_avg_over_batch = bd_metrics["test_loss_avg_over_batch"] + test_asr = all_acc(bd_test_epoch_predict_list, bd_test_epoch_label_list) + test_ra = all_acc(bd_test_epoch_predict_list, bd_test_epoch_original_targets_list) + + agg( + { + "epoch": cepoch, + "train_epoch_loss_avg_over_batch": one_epoch_loss, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + } + ) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + + plot_loss( + train_loss_list, + clean_test_loss_list, + bd_test_loss_list, + args.save_path, + "loss_metric_plots_atk_stg2", + ) + + plot_acc_like_metric( + [], [], [], + test_acc_list, + test_asr_list, + test_ra_list, + args.save_path, + "loss_metric_plots_atk_stg2", + ) + + agg.to_dataframe().to_csv(f"{args.save_path}/attack_df_atk_stg2.csv") + + agg.summary().to_csv(f"{args.save_path}/attack_df_summary_atk_stg2.csv") + + return clean_accs, poison_accs + + +def main(args, clean_test_dataset_with_transform, criterion): + clean_test_dataloader = DataLoader(clean_test_dataset_with_transform, batch_size=args.batch_size, shuffle=False, + drop_last=False, + pin_memory=args.pin_memory, num_workers=args.num_workers, ) + + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + train_loss_list = [] + + agg = Metric_Aggregator() + + if args.verbose >= 1: + logging.debug('========== ARGS ==========') + logging.debug(args) + + train_loader, test_loader, clip_image = get_train_test_loaders(args) + post_transforms = PostTensorTransform(args) + + logging.debug('========== DATA ==========') + logging.debug('Loaders: Train {} examples/{} iters, Test {} examples/{} iters'.format( + len(train_loader.dataset), len(train_loader), len(test_loader.dataset), len(test_loader))) + + atkmodel, tgtmodel, tgtoptimizer, clsmodel, create_net = create_models(args) + if args.verbose >= 2: + logging.debug('========== MODELS ==========') + logging.debug(atkmodel) + logging.debug(clsmodel) + + target_transform = get_target_transform(args) + + trainlosses = [] + start_epoch = 1 + + # Initialize the tgtmodel + tgtmodel.load_state_dict(atkmodel.state_dict(), ) + + logging.debug('============================') + logging.debug('============================') + + logging.debug('BEGIN TRAINING >>>>>>') + clsmodel.cuda() + clsoptimizer = torch.optim.SGD(params=clsmodel.parameters(), lr=args.lr, momentum=0.9) + if "," in args.device: + logging.info("data parallel in main") + atkmodel = torch.nn.DataParallel( + atkmodel, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + tgtmodel = torch.nn.DataParallel( + tgtmodel, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + clsmodel = torch.nn.DataParallel( + clsmodel, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + + for epoch in range(start_epoch, args.both_train_epochs + 1): + for i in range(args.train_epoch): + logging.debug(f'===== EPOCH: {epoch}/{args.both_train_epochs + 1} CLS {i + 1}/{args.train_epoch} =====') + if not args.avoid_clsmodel_reinitialization: + clsmodel.cuda() + clsoptimizer = torch.optim.SGD(params=clsmodel.parameters(), lr=args.lr) + trainloss = train(args, atkmodel, tgtmodel, clsmodel, tgtoptimizer, clsoptimizer, target_transform, + train_loader, + epoch, i, create_net, clip_image, + post_transforms=post_transforms) + trainlosses.append(trainloss) + atkmodel.load_state_dict(tgtmodel.state_dict()) + if args.avoid_clsmodel_reinitialization: + scratchmodel = create_net() + if "," in args.device: + scratchmodel = torch.nn.DataParallel( + scratchmodel, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + scratchmodel.load_state_dict(clsmodel.state_dict()) # transfer from cls to scratch for testing + else: + clsmodel = create_net() + # test + + clsmodel.eval() + clsmodel.to(device, non_blocking=args.non_blocking) + atkmodel.eval() + atkmodel.to(device, non_blocking=args.non_blocking) + + bd_test_dataset = prepro_cls_DatasetBD_v2( + clean_test_dataset_with_transform.wrapped_dataset.dataset, + save_folder_path=f"{args.save_path}/bd_test_dataset_stage_one" + ) + for trans_t in deepcopy( + clean_test_dataset_with_transform.wrap_img_transform.transforms): + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + logging.info(f"{denormalizer}") + + clean_test_dataset_with_transform.wrapped_dataset.getitem_all = True + + for batch_idx, (x, labels, original_index, poison_indicator, original_targets) in enumerate( + clean_test_dataloader): + with torch.no_grad(): + x = x.to(device, non_blocking=args.non_blocking) + + noise = atkmodel(x) * args.test_eps + atkdata = clip_image(x + noise) + atktarget = target_transform(labels) + + position_changed = (labels - atktarget != 0) + atkdata = atkdata[position_changed] + targets1 = labels.detach().clone().cpu()[position_changed] + y_poison_batch = atktarget.detach().clone().cpu()[position_changed] + for idx_in_batch, t_img in enumerate(atkdata.detach().clone().cpu()): + if int(y_poison_batch[idx_in_batch]) == int(targets1[idx_in_batch]): + print("find bug") + bd_test_dataset.set_one_bd_sample( + selected_index=int( + batch_idx * int(args.batch_size) + torch.where(position_changed.detach().clone().cpu())[0][ + idx_in_batch]), + # manually calculate the original index, since we do not shuffle the dataloader + img=denormalizer(t_img), + bd_label=int(y_poison_batch[idx_in_batch]), + label=int(targets1[idx_in_batch]), + ) + + bd_test_dataset.subset( + np.where(bd_test_dataset.poison_indicator == 1)[0] + ) + bd_test_dataset_with_transform = dataset_wrapper_with_transform( + bd_test_dataset, + clean_test_dataset_with_transform.wrap_img_transform, + ) + + # generate bd test dataloader + + bd_test_dataloader = DataLoader(bd_test_dataset_with_transform, batch_size=args.batch_size, shuffle=False, + drop_last=False, + pin_memory=args.pin_memory, num_workers=args.num_workers, ) + + ### My test code start + + clean_metrics, \ + clean_test_epoch_predict_list, \ + clean_test_epoch_label_list, \ + = given_dataloader_test( + model=clsmodel, + test_dataloader=clean_test_dataloader, + criterion=criterion, + non_blocking=args.non_blocking, + device=device, + verbose=1, + ) + + clean_test_loss_avg_over_batch = clean_metrics["test_loss_avg_over_batch"] + test_acc = clean_metrics["test_acc"] + + bd_metrics, \ + bd_test_epoch_predict_list, \ + bd_test_epoch_label_list, \ + bd_test_epoch_original_index_list, \ + bd_test_epoch_poison_indicator_list, \ + bd_test_epoch_original_targets_list = test_given_dataloader_on_mix( + model=clsmodel, + test_dataloader=bd_test_dataloader, + criterion=criterion, + non_blocking=args.non_blocking, + device=device, + verbose=1, + ) + + bd_test_loss_avg_over_batch = bd_metrics["test_loss_avg_over_batch"] + test_asr = all_acc(bd_test_epoch_predict_list, bd_test_epoch_label_list) + test_ra = all_acc(bd_test_epoch_predict_list, bd_test_epoch_original_targets_list) + + agg( + { + "epoch": epoch, + "train_epoch_loss_avg_over_batch": trainloss, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + } + ) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + + plot_loss( + train_loss_list, + clean_test_loss_list, + bd_test_loss_list, + args.save_path, + "loss_metric_plots_atk_stg1", + ) + + plot_acc_like_metric( + [], [], [], + test_acc_list, + test_asr_list, + test_ra_list, + args.save_path, + "loss_metric_plots_atk_stg1", + ) + + agg.to_dataframe().to_csv(f"{args.save_path}/attack_df_atk_stg1.csv") + + agg.summary().to_csv(f"{args.save_path}/attack_df_summary_atk_stg1.csv") + + ### My test code end + + return clsmodel, atkmodel, bd_test_dataloader + + +def main2(args, pre_clsmodel, pre_atkmodel, clean_test_dataloader, bd_test_dataloader, criterion): + args_for_finetune = deepcopy(args) + args_for_finetune.__dict__ = {k[9:]: v for k, v in args_for_finetune.__dict__.items() if k.startswith("finetune_")} + + if args.test_alpha is None: + logging.debug(f'Defaulting test_alpha to train alpha of {args.alpha}') + args.test_alpha = args.alpha + + if args.finetune_lr is None: + logging.debug(f'Defaulting test_lr to train lr {args.lr}') + args.finetune_lr = args.lr + + if args.test_eps is None: + logging.debug(f'Defaulting test_eps to train eps {args.test_eps}') + args.test_eps = args.eps + + logging.debug('====> ARGS') + logging.debug(args) + + torch.device("cuda" if torch.cuda.is_available() else "cpu") + + train_loader, test_loader, clip_image = get_train_test_loaders(args) + + atkmodel, tgtmodel, tgtoptimizer, _, create_net = create_models(args) + netC, optimizerC, schedulerC = get_model(args) + + if "," in args.device: + logging.info("data parallel in main2") + netC = torch.nn.DataParallel( + netC, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + atkmodel = torch.nn.DataParallel( + atkmodel, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + + netC.load_state_dict(pre_clsmodel.state_dict()) + + target_transform = get_target_transform(args) + + atkmodel.load_state_dict(pre_atkmodel.state_dict()) + + netC.to(device) + + optimizerC, schedulerC = argparser_opt_scheduler(netC, args_for_finetune) + + logging.debug(netC) + logging.info(f"atk stage2, optimizerC: {optimizerC}, schedulerC: {schedulerC}") + + data_transforms = PostTensorTransform(args) + logging.debug('====> Post tensor transform') + logging.debug(data_transforms) + + clean_accs, poison_accs = final_test(clean_test_dataloader, bd_test_dataloader, + args, None, atkmodel, netC, target_transform, + train_loader, test_loader, + trainepoch=int(args.epochs - args.both_train_epochs), + writer=None, log_prefix='POISON', alpha=args.test_alpha, epochs_per_test=1, + optimizerC=optimizerC, schedulerC=schedulerC, data_transforms=data_transforms, + clip_image=clip_image, criterion=criterion) + + +class LIRA(BadNet): + + def __init__(self): + super(LIRA, self).__init__() + + def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + parser.add_argument('--attack', type=str, ) + parser.add_argument('--attack_target', type=int, + help='target class in all2one attack') + parser.add_argument('--attack_label_trans', type=str, + help='which type of label modification in backdoor attack' + ) + parser.add_argument('--bd_yaml_path', type=str, default='../config/attack/lira/default.yaml', + help='path for yaml file provide additional default attributes') + + parser.add_argument("--random_crop", type=int, ) + parser.add_argument("--random_rotation", type=int, ) + + parser.add_argument('--attack_model', type=str, ) # default='autoencoder') + parser.add_argument('--lr_atk', type=float, ) # default=0.0001, help='learning rate for attack model') + parser.add_argument('--eps', type=float, ) # default=0.3, help='epsilon for data poisoning') + parser.add_argument('--test_eps', type=float) # default=None, + parser.add_argument('--alpha', type=float, ) # default=0.5) + parser.add_argument('--test_alpha', type=float) # default=None, + parser.add_argument('--fix_generator_epoch', type=int, ) # default=1, help='training epochs for victim model') + + # parser.add_argument('--finetune_epochs', type=int) # default=500, + parser.add_argument('--finetune_lr', type=float) # default=None, + + # parser.add_argument('--steplr_gamma', ) # default='30,60,90,150') + # parser.add_argument('--steplr_milestones', type=float) # default=0.05, + parser.add_argument('--finetune_steplr_gamma', ) + parser.add_argument('--finetune_steplr_milestones', ) + parser.add_argument('--finetune_optimizer', ) # default='sgd') + parser.add_argument("--both_train_epochs", type=int, ) + + ################### its original + parser.add_argument('--train_epoch', type=int, default=1, help='training epochs for victim model') + parser.add_argument('--epochs_per_external_eval', type=int, default=50) + parser.add_argument('--best_threshold', type=float, default=0.1) + parser.add_argument('--verbose', type=int, default=1, help='verbosity') + parser.add_argument('--avoid_clsmodel_reinitialization', action='store_true', + default=True, help='whether test the poisoned model from scratch') + parser.add_argument('--test_n_size', default=10) + parser.add_argument('--test_use_train_best', default=False, action='store_true') + parser.add_argument('--test_use_train_last', default=True, action='store_true') + + return parser + + def stage1_non_training_data_prepare(self): + pass + + def stage2_training(self): + logging.info(f"stage2 start") + assert 'args' in self.__dict__ + args = self.args + + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + clean_train_dataset_with_transform, \ + clean_train_dataset_targets, \ + clean_test_dataset_with_transform, \ + clean_test_dataset_targets \ + = self.benign_prepare() + + clean_test_dataset = prepro_cls_DatasetBD_v2( + test_dataset_without_transform + ) + clean_test_dataset_with_transform = dataset_wrapper_with_transform(clean_test_dataset, test_img_transform) + clean_test_dataloader = DataLoader(clean_test_dataset_with_transform, batch_size=args.batch_size, shuffle=False, + drop_last=False, + pin_memory=args.pin_memory, num_workers=args.num_workers, ) + + self.net = generate_cls_model( + model_name=args.model, + num_classes=args.num_classes, + image_size=args.img_size[0], + ) + + self.device = torch.device( + ( + f"cuda:{[int(i) for i in args.device[5:].split(',')][0]}" if "," in args.device else args.device + # since DataParallel only allow .to("cuda") + ) if torch.cuda.is_available() else "cpu" + ) + + if "," in args.device: + self.net = torch.nn.DataParallel( + self.net, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + + + criterion = argparser_criterion(args) + + global device + device = torch.device( + ( + f"cuda:{[int(i) for i in args.device[5:].split(',')][0]}" if "," in args.device else args.device + # since DataParallel only allow .to("cuda") + ) if torch.cuda.is_available() else "cpu" + ) + global scaler + scaler = torch.cuda.amp.GradScaler(enabled=args.amp) + clsmodel, atkmodel, bd_test_dataloader = main(args, clean_test_dataset_with_transform, criterion) + main2(args, clsmodel, atkmodel, clean_test_dataloader, bd_test_dataloader, criterion) + ### + + save_attack_result( + model_name=args.model, + num_classes=args.num_classes, + model=clsmodel.cpu().state_dict(), + data_path=args.dataset_path, + img_size=args.img_size, + clean_data=args.dataset, + bd_train=None, + bd_test=bd_test_dataloader.dataset.wrapped_dataset, + save_path=args.save_path, + ) + + +if __name__ == '__main__': + attack = LIRA() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = attack.set_args(parser) + parser = attack.set_bd_args(parser) + args = parser.parse_args() + attack.add_bd_yaml_to_args(args) + attack.add_yaml_to_args(args) + args = attack.process_args(args) + attack.prepare(args) + assert int(args.epochs - args.both_train_epochs) > 0, "(total) epochs should be larger than both_train_epochs" + attack.stage1_non_training_data_prepare() + attack.stage2_training() + +''' +MIT License + +Copyright (c) 2021 Cognitive Computing Lab + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' diff --git a/attack/prototype.py b/attack/prototype.py new file mode 100755 index 0000000..e7460b9 --- /dev/null +++ b/attack/prototype.py @@ -0,0 +1,336 @@ +''' +this script is for basic normal training +''' + +import os +import sys +import yaml + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +import argparse +from pprint import pformat +import torch +import time +import logging +import torch.nn as nn + +from utils.aggregate_block.dataset_and_transform_generate import get_num_classes, get_input_shape +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate_ft import dataset_and_transform_generate, dataset_and_transform_generate_pre +from utils.bd_dataset_v2 import dataset_wrapper_with_transform, get_labels +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.train_settings_generate import argparser_opt_scheduler, argparser_criterion +from utils.log_assist import get_git_info +from utils.trainer_cls import ModelTrainerCLS_v2 + + +class NormalCase: + + def __init__(self): + pass + + def set_args(self, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + parser.add_argument("-n", "--num_workers", type=int, help="dataloader num_workers") + parser.add_argument("-pm", "--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], + help="dataloader pin_memory") + parser.add_argument("-nb", "--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], + help=".to(), set the non_blocking = ?") + parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch') + parser.add_argument('--amp', type=lambda x: str(x) in ['True', 'true', '1']) + parser.add_argument('--device', type=str) + parser.add_argument('--lr_scheduler', type=str, + help='which lr_scheduler use for optimizer') + parser.add_argument('--epochs', type=int) + parser.add_argument('--dataset', type=str, + help='which dataset to use' + ) + parser.add_argument('--dataset_path', type=str) + parser.add_argument('--batch_size', type=int) + parser.add_argument('--lr', type=float) + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--random_seed', type=int, + help='random_seed') + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + parser.add_argument('--model', type=str, + help='choose which kind of model') + + parser.add_argument('--git_hash', type=str, + help='git hash number, in order to find which version of code is used') + parser.add_argument("--yaml_path", type=str, default="../config/attack/prototype/cifar10.yaml") + parser.add_argument('--pre', action='store_true', help='whether load pre-trained weights') + parser.add_argument('--split_ratio', type=float, + help='part of the training set for defense') + + return parser + + def add_yaml_to_args(self, args): + with open(args.yaml_path, 'r') as f: + clean_defaults = yaml.safe_load(f) + clean_defaults.update({k: v for k, v in args.__dict__.items() if v is not None}) + args.__dict__ = clean_defaults + + def process_args(self, args): + args.terminal_info = sys.argv + args.num_classes = get_num_classes(args.dataset) + if not args.pre: + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + else: + args.input_height, args.input_width, args.input_channel = 224, 224, 3 + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + return args + + def prepare(self, args): + if not args.pre: + save_path = f'../record_{args.dataset}/{args.attack}/' + f'pratio_{args.pratio}-target_{args.attack_target}-archi_{args.model}-dataset_{args.dataset}-sratio_{args.split_ratio}-initlr_{args.lr}' + else: + save_path = f'../record_{args.dataset}_pre/{args.attack}/' + f'pratio_{args.pratio}-target_{args.attack_target}-archi_{args.model}-dataset_{args.dataset}-sratio_{args.split_ratio}-initlr_{args.lr}' + + os.makedirs(save_path,exist_ok=True) + args.save_path = save_path + + torch.save(args.__dict__, save_path + '/info.pickle') + + ### set the logger + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + # file Handler + fileHandler = logging.FileHandler( + save_path + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + fileHandler.setLevel(logging.DEBUG) + logger.addHandler(fileHandler) + # consoleHandler + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + consoleHandler.setLevel(logging.INFO) + logger.addHandler(consoleHandler) + # overall logger level should <= min(handler) otherwise no log will be recorded. + logger.setLevel(0) + + # disable other debug, since too many debug + logging.getLogger('PIL').setLevel(logging.WARNING) + logging.getLogger('matplotlib.font_manager').setLevel(logging.WARNING) + + logging.info(pformat(args.__dict__)) + + logging.debug("Only INFO or above level log will show in cmd. DEBUG level log only will show in log file.") + + # record the git infomation for debug (if available.) + try: + logging.debug(pformat(get_git_info())) + except: + logging.debug('Getting git info fails.') + + ### set the random seed + fix_random(int(args.random_seed)) + + self.args = args + + def benign_prepare(self): + + assert 'args' in self.__dict__ + + args = self.args + if not args.pre: + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform,_ = dataset_and_transform_generate(args) + + else: + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform,_ = dataset_and_transform_generate_pre(args) + + logging.debug("dataset_and_transform_generate done") + + clean_train_dataset_with_transform = dataset_wrapper_with_transform( + train_dataset_without_transform, + train_img_transform, + train_label_transform + ) + + clean_train_dataset_targets = get_labels(train_dataset_without_transform) + + clean_test_dataset_with_transform = dataset_wrapper_with_transform( + test_dataset_without_transform, + test_img_transform, + test_label_transform, + ) + + clean_test_dataset_targets = get_labels(test_dataset_without_transform) + + return train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + clean_train_dataset_with_transform, \ + clean_train_dataset_targets, \ + clean_test_dataset_with_transform, \ + clean_test_dataset_targets + + def stage1_non_training_data_prepare(self): + + # You should rewrite this for specific attack method + + logging.info(f"stage1 start") + + assert 'args' in self.__dict__ + args = self.args + + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + clean_train_dataset_with_transform, \ + clean_train_dataset_targets, \ + clean_test_dataset_with_transform, \ + clean_test_dataset_targets \ + = self.benign_prepare() + + self.stage1_results = clean_train_dataset_with_transform, \ + clean_test_dataset_with_transform, \ + None, \ + None + + def stage2_training(self): + + # You should rewrite this for specific attack method + + logging.info(f"stage2 start") + assert 'args' in self.__dict__ + args = self.args + + clean_train_dataset_with_transform, \ + clean_test_dataset_with_transform, \ + bd_train_dataset, \ + bd_test_dataset = self.stage1_results + + + if not args.pre: + + self.net = generate_cls_model( + model_name=args.model, + num_classes=args.num_classes, + image_size=args.img_size[0], + ) + else: + + torch.hub.set_dir('/ssddata1/data/rminaa/pretrain_models/') + + if args.model == "resnet18": + from torchvision.models import resnet18, ResNet18_Weights + self.net = resnet18(weights=ResNet18_Weights.IMAGENET1K_V1).to(args.device) + self.net.fc = nn.Linear(in_features=512, out_features=args.num_classes, bias=True).to(args.device) + for _, param in self.net.named_parameters(): + param.requires_grad = True + elif args.model == "resnet50": + from torchvision.models import resnet50, ResNet50_Weights + self.net = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2).to(args.device) + self.net.fc = nn.Linear(in_features=2048, out_features=args.num_classes, bias=True).to(args.device) + for _, param in self.net.named_parameters(): + param.requires_grad = True + elif args.model == 'swin_b': + from torchvision.models import swin_b + self.net = swin_b(weights='IMAGENET1K_V1').to(args.device) + self.net.head = nn.Linear(in_features=1024, out_features=args.num_classes, bias=True).to(args.device) + for _, param in self.net.named_parameters(): + param.requires_grad = True + elif args.model == 'swin_t': + from torchvision.models import swin_t + self.net = swin_t(weights='IMAGENET1K_V1').to(args.device) + self.net.head = nn.Linear(in_features=768, out_features=args.num_classes, bias=True).to(args.device) + for _, param in self.net.named_parameters(): + param.requires_grad = True + else: + raise NotImplementedError(f"{args.model} is not supported") + + + + self.device = torch.device( + ( + f"cuda:{[int(i) for i in args.device[5:].split(',')][0]}" if "," in args.device else args.device + # since DataParallel only allow .to("cuda") + ) if torch.cuda.is_available() else "cpu" + ) + + if "," in args.device: + self.net = torch.nn.DataParallel( + self.net, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + + trainer = ModelTrainerCLS_v2( + self.net, + ) + + criterion = argparser_criterion(args) + + optimizer, scheduler = argparser_opt_scheduler(self.net, args) + + trainer.set_with_dataset( + batch_size=args.batch_size, + train_dataset=clean_train_dataset_with_transform, + test_dataset_dict={ + "ACC": clean_test_dataset_with_transform, + }, + criterion=criterion, + optimizer=optimizer, + scheduler=scheduler, + device=self.device, + frequency_save=args.frequency_save, + save_folder_path=args.save_path, + save_prefix='attack', + amp=args.amp, + prefetch=args.prefetch, + num_workers=args.num_workers, + prefetch_transform_attr_name="wrap_img_transform", # since we use the preprocess_bd_dataset + pin_memory=args.pin_memory, + non_blocking=args.non_blocking, + ) + + for epoch_idx in range(args.epochs): + trainer.train_one_epoch() + trainer.agg( + trainer.test_all_inner_dataloader()["ACC"] + ) + trainer.agg_save_dataframe() + + torch.save(self.net.cpu().state_dict(), f"{args.save_path}/clean_model.pth") + + +if __name__ == '__main__': + normal_train_process = NormalCase() + parser = argparse.ArgumentParser(description=sys.argv[0]) + + parser = normal_train_process.set_args(parser) + args = parser.parse_args() + normal_train_process.add_yaml_to_args(args) + args = normal_train_process.process_args(args) + normal_train_process.prepare(args) + normal_train_process.stage1_non_training_data_prepare() + normal_train_process.stage2_training() + diff --git a/attack/sig.py b/attack/sig.py new file mode 100755 index 0000000..31fdbea --- /dev/null +++ b/attack/sig.py @@ -0,0 +1,146 @@ +''' +this script is for SIG attack + +basic structure: +1. config args, save_path, fix random seed +2. set the clean train data and clean test data +3. set the attack img transform and label transform +4. set the backdoor attack data and backdoor test data +5. set the device, model, criterion, optimizer, training schedule. +6. attack or use the model to do finetune with 5% clean data +7. save the attack result for defense + +@inproceedings{SIG, + title = {A new backdoor attack in CNNs by training set corruption without label poisoning}, + author = {Barni, Mauro and Kallas, Kassem and Tondi, Benedetta}, + booktitle = {2019 IEEE International Conference on Image Processing}, + year = 2019, +} + +''' + +import argparse +import logging +import os +import sys +import torch + +import numpy as np + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +from attack.badnet import BadNet, add_common_attack_args +from utils.backdoor_generate_poison_index import generate_poison_index_from_label_transform +from utils.aggregate_block.bd_attack_generate import bd_attack_img_trans_generate, bd_attack_label_trans_generate +from copy import deepcopy +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform + + +class SIG(BadNet): + + def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + parser = add_common_attack_args(parser) + parser.add_argument("--sig_f", type=float) + + parser.add_argument('--bd_yaml_path', type=str, default='../config/attack/sig/default.yaml', + help='path for yaml file provide additional default attributes') + return parser + + def stage1_non_training_data_prepare(self): + logging.info(f"stage1 start") + + assert 'args' in self.__dict__ + args = self.args + + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + clean_train_dataset_with_transform, \ + clean_train_dataset_targets, \ + clean_test_dataset_with_transform, \ + clean_test_dataset_targets \ + = self.benign_prepare() + + train_bd_img_transform, test_bd_img_transform = bd_attack_img_trans_generate(args) + ### get the backdoor transform on label + bd_label_transform = bd_attack_label_trans_generate(args) + + ### 4. set the backdoor attack data and backdoor test data + train_poison_index = generate_poison_index_from_label_transform( + clean_train_dataset_targets, + label_transform=bd_label_transform, + train=True, + pratio=args.pratio if 'pratio' in args.__dict__ else None, + p_num=args.p_num if 'p_num' in args.__dict__ else None, + clean_label=True, + ) + + logging.debug(f"poison train idx is saved") + torch.save(train_poison_index, + args.save_path + '/train_poison_index_list.pickle', + ) + + ### generate train dataset for backdoor attack + bd_train_dataset = prepro_cls_DatasetBD_v2( + deepcopy(train_dataset_without_transform), + poison_indicator=train_poison_index, + bd_image_pre_transform=train_bd_img_transform, + bd_label_pre_transform=bd_label_transform, + save_folder_path=f"{args.save_path}/bd_train_dataset", + ) + + bd_train_dataset_with_transform = dataset_wrapper_with_transform( + bd_train_dataset, + train_img_transform, + train_label_transform, + ) + + ### decide which img to poison in ASR Test + test_poison_index = generate_poison_index_from_label_transform( + clean_test_dataset_targets, + label_transform=bd_label_transform, + train=False, + ) + + ### generate test dataset for ASR + bd_test_dataset = prepro_cls_DatasetBD_v2( + deepcopy(test_dataset_without_transform), + poison_indicator=test_poison_index, + bd_image_pre_transform=test_bd_img_transform, + bd_label_pre_transform=bd_label_transform, + save_folder_path=f"{args.save_path}/bd_test_dataset", + ) + + bd_test_dataset.subset( + np.where(test_poison_index == 1)[0] + ) + + bd_test_dataset_with_transform = dataset_wrapper_with_transform( + bd_test_dataset, + test_img_transform, + test_label_transform, + ) + + self.stage1_results = clean_train_dataset_with_transform, \ + clean_test_dataset_with_transform, \ + bd_train_dataset_with_transform, \ + bd_test_dataset_with_transform + + +if __name__ == '__main__': + attack = SIG() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = attack.set_args(parser) + parser = attack.set_bd_args(parser) + args = parser.parse_args() + attack.add_bd_yaml_to_args(args) + attack.add_yaml_to_args(args) + args = attack.process_args(args) + attack.prepare(args) + attack.stage1_non_training_data_prepare() + attack.stage2_training() diff --git a/attack/ssba.py b/attack/ssba.py new file mode 100755 index 0000000..c566828 --- /dev/null +++ b/attack/ssba.py @@ -0,0 +1,84 @@ +''' +this script is for SSBA attack + +code link: https://github.com/SCLBD/ISSBA + +Note that the autoencoder training process and img process part are not in this script, + which are time comsume and dataset-dependent, please follow https://github.com/tancik/StegaStamp to train models for generating the poisoned data. + (Or you can find a torch version to generate the poisoned data in ./resource/ssba, please follow the readme in ./resource/ssba) + Then place the poisoned image array to `attack_train_replace_imgs_path` and `attack_test_replace_imgs_path` + +basic structure: +1. config args, save_path, fix random seed +2. set the clean train data and clean test data +3. set the attack img transform and label transform +4. set the backdoor attack data and backdoor test data +5. set the device, model, criterion, optimizer, training schedule. +6. attack or use the model to do finetune with 5% clean data +7. save the attack result for defense + +@inproceedings{ssba, + title={Invisible backdoor attack with sample-specific triggers}, + author={Li, Yuezun and Li, Yiming and Wu, Baoyuan and Li, Longkang and He, Ran and Lyu, Siwei}, + booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, + year={2021} +} +''' + +import argparse +import logging +import os +import sys + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +from attack.badnet import BadNet, add_common_attack_args +from utils.aggregate_block.dataset_and_transform_generate import get_num_classes, get_input_shape + + +class SSBA(BadNet): + + def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + + parser = add_common_attack_args(parser) + parser.add_argument('--attack_train_replace_imgs_path', type=str) + parser.add_argument('--attack_test_replace_imgs_path', type=str) + parser.add_argument('--bd_yaml_path', type=str, default='../config/attack/ssba/default.yaml', + help='path for yaml file provide additional default attributes') + return parser + + def process_args(self, args): + + args.terminal_info = sys.argv + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + if ('attack_train_replace_imgs_path' not in args.__dict__) or (args.attack_train_replace_imgs_path is None): + args.attack_train_replace_imgs_path = f"../resource/ssba/{args.dataset}_ssba_train_b1.npy" + logging.info( + f"args.attack_train_replace_imgs_path does not found, so = {args.attack_train_replace_imgs_path}") + + if ('attack_test_replace_imgs_path' not in args.__dict__) or (args.attack_test_replace_imgs_path is None): + args.attack_test_replace_imgs_path = f"../resource/ssba/{args.dataset}_ssba_test_b1.npy" + logging.info( + f"args.attack_test_replace_imgs_path does not found, so = {args.attack_test_replace_imgs_path}") + + return args + + +if __name__ == '__main__': + attack = SSBA() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = attack.set_args(parser) + parser = attack.set_bd_args(parser) + args = parser.parse_args() + attack.add_bd_yaml_to_args(args) + attack.add_yaml_to_args(args) + args = attack.process_args(args) + attack.prepare(args) + attack.stage1_non_training_data_prepare() + attack.stage2_training() diff --git a/attack/trojannn.py b/attack/trojannn.py new file mode 100755 index 0000000..23c88f6 --- /dev/null +++ b/attack/trojannn.py @@ -0,0 +1,1046 @@ +''' +this script is for trojanNN attack +git link: https://github.com/PurduePAML/TrojanNN + also thanks to implementation from https://github.com/ain-soph/trojanzoo + +@inproceedings{Trojannn, + author = {Yingqi Liu and + Shiqing Ma and + Yousra Aafer and + Wen-Chuan Lee and + Juan Zhai and + Weihang Wang and + Xiangyu Zhang}, + title = {Trojaning Attack on Neural Networks}, + booktitle = {25th Annual Network and Distributed System Security Symposium, {NDSS} + 2018, San Diego, California, USA, February 18-221, 2018}, + publisher = {The Internet Society}, + year = {2018}, +} + +(No license in original code, so we only put the license from trojanzoo in the end of this file.) +''' +import os +import sys, logging + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +import argparse +import numpy as np +import torch +from tqdm import tqdm +from PIL import Image +from copy import deepcopy + +from torchvision.transforms import ToPILImage, ToTensor +import torchvision.transforms as transforms + +from utils.backdoor_generate_poison_index import generate_poison_index_from_label_transform +from utils.aggregate_block.bd_attack_generate import bd_attack_label_trans_generate, \ + general_compose +from utils.aggregate_block.model_trainer_generate import generate_cls_model, partially_load_state_dict +from utils.aggregate_block.train_settings_generate import argparser_opt_scheduler, argparser_criterion +from utils.save_load_attack import save_attack_result +from utils.trainer_cls import BackdoorModelTrainer +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform +from utils.bd_label_transform.backdoor_label_transform import * +from attack.badnet import BadNet, add_common_attack_args + +topilimage = ToPILImage() +totensor = ToTensor() + + +def get_most_connected_neuron_idxes(network, parameter_name_key, num_selected=1): + parameter = torch.abs(network.state_dict()[parameter_name_key]) + logging.info(f'parameter shape = {parameter.shape}') + if parameter.shape.__len__() == 2: + pass + elif parameter.shape.__len__() == 4: + parameter = torch.flatten(parameter, 2).sum(2) + else: + raise Exception("Only consider conv and linear layer") + return torch.argsort(parameter.sum(0), descending=True)[:num_selected] + + +def pgd_with_mask_to_selected_neuron(model: torch.nn.Module, images, selected_layer, neuron_idxes, neuron_target_values, + eps=0.3, + alpha=0.1, # 2 / 255, + iters=1000, + device=torch.device("cpu"), + tolerance=1e-3 + ): + # tested on cuda + + model.eval() + model.to(device) + + keep_mask = (deepcopy(images) > 0).to(device) + + images = images.to(device) + + ori_images = images.data + + def hook_function(module, input, output): + model.feature_save = input[0] + + dict(model.named_modules())[selected_layer].register_forward_hook(hook_function) + + for _ in tqdm(range(iters), desc="pgd with respect to selected neuron"): + + images.requires_grad = True + outputs = model(images * keep_mask) + selected_layer_input = model.feature_save + model.zero_grad() + + cost = 0 + for i, idx in enumerate(neuron_idxes): + cost += ((selected_layer_input[:, idx] - neuron_target_values[i]) ** 2).sum() + # ((selected_layer_input[:, neuron_idx] - neuron_target_value)**2).sum() + cost.backward() + + adv_images = images - alpha * images.grad.sign() + eta = torch.clamp(adv_images - ori_images, min=-eps, max=eps) + images = torch.clamp(ori_images + eta, min=0, max=1).detach_() + + # simplified as trojanzoo does. + + if cost < tolerance: + break + + return images + + +class TrojanTrigger(object): + def __init__(self, target_image): + self.target_image = target_image.astype(np.float) + + def __call__(self, img, target=None, image_serial_id=None): + return self.add_trigger(img) + + def add_trigger(self, img): + return np.clip((self.target_image + img.astype(np.float)).astype("uint8"), 0, 255) + + +class TrojanNN(BadNet): + + def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + + parser = add_common_attack_args(parser) + parser.add_argument("--pretrain_model_path", type=int, ) + parser.add_argument("--mask_path", type=str, help="path to the PIL Image mask") + parser.add_argument("--selected_layer_name", type=str, help="which layer you choose") + parser.add_argument("--selected_layer_param_name", type=str, + help="which params you choose, should at least same/after selected_layer_name") + parser.add_argument("--num_neuron", type=int, help="num of neurons to be selected in target layer") + parser.add_argument("--neuron_target_values", type=float, + help="the target value for selected neurons, you can change to list if necessary") + parser.add_argument("--mask_update_iters", type=int, help="how many steps before convergence for trigger") + parser.add_argument('--bd_yaml_path', type=str, default='../config/attack/trojannn/preactresnet18.yaml', + help='path for yaml file provide additional default attributes') + return parser + + def stage1_non_training_data_prepare(self): + + logging.info(f"stage1 start") + + assert 'args' in self.__dict__ + args = self.args + + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + clean_train_dataset_with_transform, \ + clean_train_dataset_targets, \ + clean_test_dataset_with_transform, \ + clean_test_dataset_targets \ + = self.benign_prepare() + + self.net = generate_cls_model( + model_name=args.model, + image_size=args.img_size[0], + num_classes=1000, + # pretrained = True, + ) + + if ("pretrain_model_path" not in args) or (args.pretrain_model_path is None): + args.pretrain_model_path = os.path.join( + f"../resource/clean_model/{args.dataset}_{args.model}/clean_model.pth") + assert os.path.exists(args.pretrain_model_path), "pretrained path is not exist" + + partially_load_state_dict(self.net, torch.load(args.pretrain_model_path, map_location="cpu")) + + self.device = torch.device( + ( + f"cuda:{[int(i) for i in args.device[5:].split(',')][0]}" if "," in args.device else args.device + # since DataParallel only allow .to("cuda") + ) if torch.cuda.is_available() else "cpu" + ) + # do parallel latter, since the dataparallel will cover the attr of model itself + + neuron_idxes = get_most_connected_neuron_idxes(self.net, args.selected_layer_param_name, args.num_neuron) + + logging.info(f"neuron_idxes = {neuron_idxes}") + # trigger pattern generation + + mask = Image.open(args.mask_path) + mask = transforms.Resize(args.img_size[:2])(mask) + mask = totensor(mask)[None, ...] + + mask_image = pgd_with_mask_to_selected_neuron( + self.net, + mask, + args.selected_layer_name, + neuron_idxes, + float(args.neuron_target_values) if isinstance(args.neuron_target_values, list) else [float( + args.neuron_target_values)] * len(neuron_idxes), + iters=args.mask_update_iters, + device=self.device, + ) + + # after pgd, we change back to use a non-pretrained model. + self.net = generate_cls_model( + model_name=args.model, + num_classes=args.num_classes, + image_size=args.img_size[0], + ) + + trans = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize(args.img_size[:2]), # (32, 32) + np.array, + ]) + + topilimage(mask_image[0]).save(args.save_path + "/trojannn_trigger.png") + + bd_transform = TrojanTrigger( + trans(mask_image[0]), + ) + + train_bd_img_transform = general_compose([ + (transforms.Resize(args.img_size[:2]), False), + (np.array, False), + (bd_transform, True), + ]) + + test_bd_img_transform = general_compose([ + (transforms.Resize(args.img_size[:2]), False), + (np.array, False), + (bd_transform, True), + ]) + + ### get the backdoor transform on label + bd_label_transform = bd_attack_label_trans_generate(args) + + ### 4. set the backdoor attack data and backdoor test data + train_poison_index = generate_poison_index_from_label_transform( + clean_train_dataset_targets, + label_transform=bd_label_transform, + train=True, + pratio=args.pratio if 'pratio' in args.__dict__ else None, + p_num=args.p_num if 'p_num' in args.__dict__ else None, + ) + + logging.debug(f"poison train idx is saved") + torch.save(train_poison_index, + args.save_path + '/train_poison_index_list.pickle', + ) + + ### generate train dataset for backdoor attack + bd_train_dataset = prepro_cls_DatasetBD_v2( + deepcopy(train_dataset_without_transform), + poison_indicator=train_poison_index, + bd_image_pre_transform=train_bd_img_transform, + bd_label_pre_transform=bd_label_transform, + save_folder_path=f"{args.save_path}/bd_train_dataset", + ) + + bd_train_dataset_with_transform = dataset_wrapper_with_transform( + bd_train_dataset, + train_img_transform, + train_label_transform, + ) + + ### decide which img to poison in ASR Test + test_poison_index = generate_poison_index_from_label_transform( + clean_test_dataset_targets, + label_transform=bd_label_transform, + train=False, + ) + + ### generate test dataset for ASR + bd_test_dataset = prepro_cls_DatasetBD_v2( + deepcopy(test_dataset_without_transform), + poison_indicator=test_poison_index, + bd_image_pre_transform=test_bd_img_transform, + bd_label_pre_transform=bd_label_transform, + save_folder_path=f"{args.save_path}/bd_test_dataset", + ) + + bd_test_dataset.subset( + np.where(test_poison_index == 1)[0] + ) + + bd_test_dataset_with_transform = dataset_wrapper_with_transform( + bd_test_dataset, + test_img_transform, + test_label_transform, + ) + + self.stage1_results = clean_train_dataset_with_transform, \ + clean_test_dataset_with_transform, \ + bd_train_dataset_with_transform, \ + bd_test_dataset_with_transform + + def stage2_training(self): + logging.info(f"stage2 start") + assert 'args' in self.__dict__ + args = self.args + + clean_train_dataset_with_transform, \ + clean_test_dataset_with_transform, \ + bd_train_dataset_with_transform, \ + bd_test_dataset_with_transform = self.stage1_results + + if "," in args.device: + self.net = torch.nn.DataParallel( + self.net, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + + trainer = BackdoorModelTrainer( + self.net, + ) + + criterion = argparser_criterion(args) + + optimizer, scheduler = argparser_opt_scheduler(self.net, args) + + from torch.utils.data.dataloader import DataLoader + trainer.train_with_test_each_epoch_on_mix( + DataLoader(bd_train_dataset_with_transform, batch_size=args.batch_size, shuffle=True, drop_last=True, + pin_memory=args.pin_memory, num_workers=args.num_workers, ), + DataLoader(clean_test_dataset_with_transform, batch_size=args.batch_size, shuffle=False, drop_last=False, + pin_memory=args.pin_memory, num_workers=args.num_workers, ), + DataLoader(bd_test_dataset_with_transform, batch_size=args.batch_size, shuffle=False, drop_last=False, + pin_memory=args.pin_memory, num_workers=args.num_workers, ), + args.epochs, + criterion=criterion, + optimizer=optimizer, + scheduler=scheduler, + device=self.device, + frequency_save=args.frequency_save, + save_folder_path=args.save_path, + save_prefix='attack', + amp=args.amp, + prefetch=args.prefetch, + prefetch_transform_attr_name="ori_image_transform_in_loading", # since we use the preprocess_bd_dataset + non_blocking=args.non_blocking, + ) + + save_attack_result( + model_name=args.model, + num_classes=args.num_classes, + model=trainer.model.cpu().state_dict(), + data_path=args.dataset_path, + img_size=args.img_size, + clean_data=args.dataset, + bd_train=bd_train_dataset_with_transform, + bd_test=bd_test_dataset_with_transform, + save_path=args.save_path, + ) + + +if __name__ == '__main__': + attack = TrojanNN() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = attack.set_args(parser) + parser = attack.set_bd_args(parser) + args = parser.parse_args() + attack.add_bd_yaml_to_args(args) + attack.add_yaml_to_args(args) + args = attack.process_args(args) + attack.prepare(args) + attack.stage1_non_training_data_prepare() + attack.stage2_training() + +''' + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. 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But first, please read +. +''' \ No newline at end of file diff --git a/attack/wanet.py b/attack/wanet.py new file mode 100755 index 0000000..161bab0 --- /dev/null +++ b/attack/wanet.py @@ -0,0 +1,1425 @@ +''' +This file is modified based on the following source: + +link : https://github.com/VinAIResearch/Warping-based_Backdoor_Attack-release +The original license is placed at the end of this file. + +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. during training the backdoor attack generalization to lower poison ratio (generalize_to_lower_pratio) + 5. save process + +basic sturcture for main: + 1. config args, save_path, fix random seed + 2. set the clean train data and clean test data + 3. set the device, model, criterion, optimizer, training schedule. + 4. set the backdoor warping + 5. training with backdoor modification simultaneously + 6. save attack result + +@inproceedings{ + nguyen2021wanet, + title={WaNet - Imperceptible Warping-based Backdoor Attack}, + author={Tuan Anh Nguyen and Anh Tuan Tran}, + booktitle={International Conference on Learning Representations}, + year={2021}, + url={https://openreview.net/forum?id=eEn8KTtJOx} +} + +The original license is placed at the end of this file. + +Note that since this attack rely on batch-wise modification of the input data, +when this method encounters lower poison ratio, the original implementation +will fail (poison ratio < 1 / batch size), we add a function named generalize_to_lower_pratio +to generalize the attack to lower the poison ratio. The basic idea is to calculate the theoretical +the number of poison samples each batch should have, then randomly select batches to do poisoning. +This change may result in instability and a higher variance in final +results' metrics, but it is a necessary change to make the attack workable in a low poison ratio. +Please be careful when you use this attack in a low poison ratio, and interpret the results with +caution. +''' + +import logging +import os +import sys + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +import argparse +import numpy as np +import torch.nn.functional as F +import torch.utils.data as data +import random +import time +import torch +import torchvision.transforms as transforms +from torchvision.transforms import ToPILImage + +to_pil = ToPILImage() +from torch.utils.data import DataLoader + +from utils.aggregate_block.dataset_and_transform_generate import get_dataset_normalization, get_dataset_denormalization +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.trainer_cls import Metric_Aggregator +from utils.save_load_attack import save_attack_result +from utils.aggregate_block.train_settings_generate import argparser_opt_scheduler +from attack.badnet import add_common_attack_args, BadNet +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform +from utils.trainer_cls import all_acc, given_dataloader_test, general_plot_for_epoch + + +def generalize_to_lower_pratio(pratio, bs): + if pratio * bs >= 1: + # the normal case that each batch can have at least one poison sample + return pratio * bs + else: + # then randomly return number of poison sample + if np.random.uniform(0, + 1) < pratio * bs: # eg. pratio = 1/1280, then 1/10 of batch(bs=128) should contains one sample + return 1 + else: + return 0 + + +class ProbTransform(torch.nn.Module): + def __init__(self, f, p=1): + super(ProbTransform, self).__init__() + self.f = f + self.p = p + + def forward(self, x): + if random.random() < self.p: + return self.f(x) + else: + return x + + +class PostTensorTransform(torch.nn.Module): + def __init__(self, args): + super(PostTensorTransform, self).__init__() + self.random_crop = ProbTransform( + transforms.RandomCrop((args.input_height, args.input_width), padding=args.random_crop), p=0.8 + ) + self.random_rotation = ProbTransform(transforms.RandomRotation(args.random_rotation), p=0.5) + if args.dataset == "cifar10": + self.random_horizontal_flip = transforms.RandomHorizontalFlip(p=0.5) + + def forward(self, x): + for module in self.children(): + x = module(x) + return x + + +class Denormalize: + def __init__(self, args, expected_values, variance): + self.n_channels = args.input_channel + self.expected_values = expected_values + self.variance = variance + assert self.n_channels == len(self.expected_values) + + def __call__(self, x): + x_clone = x.clone() + for channel in range(self.n_channels): + x_clone[:, channel] = x[:, channel] * self.variance[channel] + self.expected_values[channel] + return x_clone + + +class Denormalizer: + def __init__(self, args): + self.denormalizer = self._get_denormalizer(args) + + def _get_denormalizer(self, args): + denormalizer = Denormalize(args, get_dataset_normalization(args.dataset).mean, + get_dataset_normalization(args.dataset).std) + return denormalizer + + def __call__(self, x): + if self.denormalizer: + x = self.denormalizer(x) + return x + + +class Wanet(BadNet): + + def __init__(self): + super(Wanet, self).__init__() + + def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: + parser = add_common_attack_args(parser) + parser.add_argument('--bd_yaml_path', type=str, default='../config/attack/wanet/default.yaml', + help='path for yaml file provide additional default attributes') + parser.add_argument("--cross_ratio", type=float, ) # default=2) # rho_a = pratio, rho_n = pratio * cross_ratio + parser.add_argument("--random_rotation", type=int, ) # default=10) + parser.add_argument("--random_crop", type=int, ) # default=5) + parser.add_argument("--s", type=float, ) # default=0.5) + parser.add_argument("--k", type=int, ) # default=4) + parser.add_argument( + "--grid_rescale", type=float, ) # default=1 + return parser + + def stage1_non_training_data_prepare(self): + logging.info("stage1 start") + + assert "args" in self.__dict__ + args = self.args + + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + clean_train_dataset_with_transform, \ + clean_train_dataset_targets, \ + clean_test_dataset_with_transform, \ + clean_test_dataset_targets \ + = self.benign_prepare() + + logging.info("Be careful, here must replace the regular train tranform with test transform.") + # you can find in the original code that get_transform function has pretensor_transform=False always. + clean_train_dataset_with_transform.wrap_img_transform = test_img_transform + + clean_train_dataloader = DataLoader(clean_train_dataset_with_transform, pin_memory=args.pin_memory, + batch_size=args.batch_size, num_workers=args.num_workers, shuffle=True) + + clean_test_dataloader = DataLoader(clean_test_dataset_with_transform, pin_memory=args.pin_memory, + batch_size=args.batch_size, + num_workers=args.num_workers, shuffle=True) + self.stage1_results = clean_train_dataset_with_transform, \ + clean_train_dataloader, \ + clean_test_dataset_with_transform, \ + clean_test_dataloader + + def stage2_training(self): + logging.info(f"stage2 start") + assert 'args' in self.__dict__ + args = self.args + agg = Metric_Aggregator() + + clean_train_dataset_with_transform, \ + clean_train_dataloader, \ + clean_test_dataset_with_transform, \ + clean_test_dataloader = self.stage1_results + + self.device = torch.device( + ( + f"cuda:{[int(i) for i in args.device[5:].split(',')][0]}" if "," in args.device else args.device + + ) if torch.cuda.is_available() else "cpu" + ) + + netC = generate_cls_model( + model_name=args.model, + num_classes=args.num_classes, + image_size=args.img_size[0], + ).to(self.device, non_blocking=args.non_blocking) + + if "," in args.device: + netC = torch.nn.DataParallel( + netC, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + + optimizerC, schedulerC = argparser_opt_scheduler(netC, args=args) + + logging.info("Train from scratch!!!") + best_clean_acc = 0.0 + best_bd_acc = 0.0 + best_cross_acc = 0.0 + epoch_current = 0 + + # set the backdoor warping + ins = torch.rand(1, 2, args.k, args.k) * 2 - 1 # generate (1,2,4,4) shape [-1,1] gaussian + ins = ins / torch.mean( + torch.abs(ins)) # scale up, increase var, so that mean of positive part and negative be +1 and -1 + noise_grid = ( + F.upsample(ins, size=args.input_height, mode="bicubic", + align_corners=True) # here upsample and make the dimension match + .permute(0, 2, 3, 1) + .to(self.device, non_blocking=args.non_blocking) + ) + array1d = torch.linspace(-1, 1, steps=args.input_height) + x, y = torch.meshgrid(array1d, + array1d) # form two mesh grid correspoding to x, y of each position in height * width matrix + identity_grid = torch.stack((y, x), 2)[None, ...].to( + self.device, + non_blocking=args.non_blocking) # stack x,y like two layer, then add one more dimension at first place. (have torch.Size([1, 32, 32, 2])) + + # filter out transformation that not reversible + transforms_reversible = transforms.Compose( + list( + filter( + lambda x: isinstance(x, (transforms.Normalize, transforms.Resize, transforms.ToTensor)), + (clean_test_dataset_with_transform.wrap_img_transform.transforms) + ) + ) + ) + # get denormalizer + for trans_t in (clean_test_dataset_with_transform.wrap_img_transform.transforms): + if isinstance(trans_t, transforms.Normalize): + denormalizer = get_dataset_denormalization(trans_t) + logging.info(f"{denormalizer}") + + reversible_test_dataset = (clean_test_dataset_with_transform) + reversible_test_dataset.wrap_img_transform = transforms_reversible + + reversible_test_dataloader = torch.utils.data.DataLoader(reversible_test_dataset, batch_size=args.batch_size, + pin_memory=args.pin_memory, + num_workers=args.num_workers, shuffle=False) + self.bd_test_dataset = prepro_cls_DatasetBD_v2( + clean_test_dataset_with_transform.wrapped_dataset, save_folder_path=f"{args.save_path}/bd_test_dataset" + ) + self.cross_test_dataset = prepro_cls_DatasetBD_v2( + clean_test_dataset_with_transform.wrapped_dataset, save_folder_path=f"{args.save_path}/cross_test_dataset" + ) + for batch_idx, (inputs, targets) in enumerate(reversible_test_dataloader): + with torch.no_grad(): + inputs, targets = inputs.to(self.device, non_blocking=args.non_blocking), targets.to(self.device, + non_blocking=args.non_blocking) + bs = inputs.shape[0] + + # Evaluate Backdoor + grid_temps = (identity_grid + args.s * noise_grid / args.input_height) * args.grid_rescale + grid_temps = torch.clamp(grid_temps, -1, 1) + + ins = torch.rand(bs, args.input_height, args.input_height, 2).to(self.device, + non_blocking=args.non_blocking) * 2 - 1 + grid_temps2 = grid_temps.repeat(bs, 1, 1, 1) + ins / args.input_height + grid_temps2 = torch.clamp(grid_temps2, -1, 1) + + inputs_bd = denormalizer(F.grid_sample(inputs, grid_temps.repeat(bs, 1, 1, 1), align_corners=True)) + + if args.attack_label_trans == "all2one": + position_changed = ( + args.attack_target != targets) # since if label does not change, then cannot tell if the poison is effective or not. + targets_bd = (torch.ones_like(targets) * args.attack_target)[position_changed] + inputs_bd = inputs_bd[position_changed] + if args.attack_label_trans == "all2all": + position_changed = torch.ones_like(targets) # here assume all2all is the bd label = (true label + 1) % num_classes + targets_bd = torch.remainder(targets + 1, args.num_classes) + inputs_bd = inputs_bd + + targets = targets.detach().clone().cpu() + y_poison_batch = targets_bd.detach().clone().cpu().tolist() + for idx_in_batch, t_img in enumerate(inputs_bd.detach().clone().cpu()): + self.bd_test_dataset.set_one_bd_sample( + selected_index=int( + batch_idx * int(args.batch_size) + torch.where(position_changed.detach().clone().cpu())[0][ + idx_in_batch]), + # manually calculate the original index, since we do not shuffle the dataloader + img=(t_img), + bd_label=int(y_poison_batch[idx_in_batch]), + label=int(targets[torch.where(position_changed.detach().clone().cpu())[0][idx_in_batch]]), + ) + + # Evaluate cross + if args.cross_ratio: + inputs_cross = denormalizer(F.grid_sample(inputs, grid_temps2, align_corners=True)) + for idx_in_batch, t_img in enumerate(inputs_cross.detach().clone().cpu()): + self.cross_test_dataset.set_one_bd_sample( + selected_index=int(batch_idx * int(args.batch_size) + idx_in_batch), + # manually calculate the original index, since we do not shuffle the dataloader + img=(t_img), + bd_label=int(targets[idx_in_batch]), + label=int(targets[idx_in_batch]), + ) + + bd_test_dataset_with_transform = dataset_wrapper_with_transform( + self.bd_test_dataset, + clean_test_dataset_with_transform.wrap_img_transform, + ) + self.bd_test_dataset.subset( + np.where(self.bd_test_dataset.poison_indicator == 1)[0].tolist() + ) + bd_test_dataloader = DataLoader(bd_test_dataset_with_transform, + pin_memory=args.pin_memory, + batch_size=args.batch_size, + num_workers=args.num_workers, + shuffle=False) + if args.cross_ratio: + cross_test_dataset_with_transform = dataset_wrapper_with_transform( + self.cross_test_dataset, + clean_test_dataset_with_transform.wrap_img_transform, + ) + cross_test_dataloader = DataLoader(cross_test_dataset_with_transform, + pin_memory=args.pin_memory, + batch_size=args.batch_size, + num_workers=args.num_workers, + shuffle=False) + else: + cross_test_dataloader = None + + test_dataloaders = (clean_test_dataloader, bd_test_dataloader, cross_test_dataloader) + + train_loss_list = [] + train_mix_acc_list = [] + train_clean_acc_list = [] + train_asr_list = [] + train_ra_list = [] + train_cross_acc_only_list = [] + + clean_test_loss_list = [] + bd_test_loss_list = [] + cross_test_loss_list = [] + ra_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + test_cross_acc_list = [] + + for epoch in range(epoch_current, args.epochs): + logging.info("Epoch {}:".format(epoch + 1)) + + train_epoch_loss_avg_over_batch, \ + train_mix_acc, \ + train_clean_acc, \ + train_asr, \ + train_ra, \ + train_cross_acc = self.train_step(netC, optimizerC, schedulerC, clean_train_dataloader, noise_grid, + identity_grid, epoch, args) + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + cross_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra, \ + test_cross_acc \ + = self.eval_step( + netC, + clean_test_dataset_with_transform, + clean_test_dataloader, + bd_test_dataloader, + cross_test_dataloader, + args, + ) + + agg({ + "epoch": epoch, + + "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch, + "train_acc": train_mix_acc, + "train_acc_clean_only": train_clean_acc, + "train_asr_bd_only": train_asr, + "train_ra_bd_only": train_ra, + "train_cross_acc_only": train_cross_acc, + + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "cross_test_loss_avg_over_batch": cross_test_loss_avg_over_batch, + "ra_test_loss_avg_over_batch": ra_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + "test_cross_acc": test_cross_acc, + }) + + train_loss_list.append(train_epoch_loss_avg_over_batch) + train_mix_acc_list.append(train_mix_acc) + train_clean_acc_list.append(train_clean_acc) + train_asr_list.append(train_asr) + train_ra_list.append(train_ra) + train_cross_acc_only_list.append(train_cross_acc) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + cross_test_loss_list.append(cross_test_loss_avg_over_batch) + ra_test_loss_list.append(ra_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + test_cross_acc_list.append(test_cross_acc) + + general_plot_for_epoch( + { + "Train Acc": train_mix_acc_list, + "Train Acc (clean sample only)": train_clean_acc_list, + "Train ASR": train_asr_list, + "Train RA": train_ra_list, + "Train Cross Acc": train_cross_acc_only_list, + "Test C-Acc": test_acc_list, + "Test ASR": test_asr_list, + "Test RA": test_ra_list, + "Test Cross Acc": test_cross_acc_list, + }, + save_path=f"{args.save_path}/acc_like_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "Train Loss": train_loss_list, + "Test Clean Loss": clean_test_loss_list, + "Test Backdoor Loss": bd_test_loss_list, + "Test Cross Loss": cross_test_loss_list, + "Test RA Loss": ra_test_loss_list, + }, + save_path=f"{args.save_path}/loss_metric_plots.png", + ylabel="percentage", + ) + + agg.to_dataframe().to_csv(f"{args.save_path}/attack_df.csv") + + if args.frequency_save != 0 and epoch % args.frequency_save == args.frequency_save - 1: + state_dict = { + "netC": netC.state_dict(), + "schedulerC": schedulerC.state_dict(), + "optimizerC": optimizerC.state_dict(), + "epoch_current": epoch, + "identity_grid": identity_grid, + "noise_grid": noise_grid, + } + torch.save(state_dict, args.save_path + "/state_dict.pt") + + agg.summary().to_csv(f"{args.save_path}/attack_df_summary.csv") + + ### save the poison train data for wanet + + # set the container for the poison train data + bd_train_dataset = prepro_cls_DatasetBD_v2( + clean_train_dataset_with_transform.wrapped_dataset, + save_folder_path=f"{args.save_path}/bd_train_dataset" + ) + clean_train_dataloader_without_shuffle = torch.utils.data.DataLoader(clean_train_dataset_with_transform, + batch_size=args.batch_size, + pin_memory=args.pin_memory, + num_workers=args.num_workers, + shuffle=False) + # iterate through the clean train data + netC.eval() + rate_bd = args.pratio + with torch.no_grad(): + for batch_idx, (inputs, targets) in enumerate(clean_train_dataloader_without_shuffle): + inputs, targets = inputs.to(self.device, non_blocking=args.non_blocking), targets.to(self.device, + non_blocking=args.non_blocking) + bs = inputs.shape[0] + + # Create backdoor data + num_bd = int(generalize_to_lower_pratio(rate_bd, bs)) # int(bs * rate_bd) + num_cross = int(num_bd * args.cross_ratio) + grid_temps = (identity_grid + args.s * noise_grid / args.input_height) * args.grid_rescale + grid_temps = torch.clamp(grid_temps, -1, 1) + + ins = torch.rand(num_cross, args.input_height, args.input_height, 2).to(self.device, + non_blocking=args.non_blocking) * 2 - 1 + grid_temps2 = grid_temps.repeat(num_cross, 1, 1, 1) + ins / args.input_height + grid_temps2 = torch.clamp(grid_temps2, -1, 1) + + inputs_bd = F.grid_sample(inputs[:num_bd], grid_temps.repeat(num_bd, 1, 1, 1), align_corners=True) + if args.attack_label_trans == "all2one": + targets_bd = torch.ones_like(targets[:num_bd]) * args.attack_target + if args.attack_label_trans == "all2all": + targets_bd = torch.remainder(targets[:num_bd] + 1, args.num_classes) + + inputs_cross = F.grid_sample(inputs[num_bd: (num_bd + num_cross)], grid_temps2, align_corners=True) + + input_changed = torch.cat([inputs_bd, inputs_cross, ], dim=0) + + input_changed = denormalizer( # since we normalized once, we need to denormalize it back. + input_changed + ).detach().clone().cpu() + target_changed = torch.cat([targets_bd, targets[num_bd: (num_bd + num_cross)], ], + dim=0).detach().clone().cpu() + + # save to the container + for idx_in_batch, t_img in enumerate( + input_changed + ): + # here we know it starts from 0 and they are consecutive + bd_train_dataset.set_one_bd_sample( + selected_index=int(batch_idx * int(args.batch_size) + idx_in_batch), + img=(t_img), + bd_label=int(target_changed[idx_in_batch]), + label=int(targets[idx_in_batch]), + ) + + save_attack_result( + model_name=args.model, + num_classes=args.num_classes, + model=netC.cpu().state_dict(), + data_path=args.dataset_path, + img_size=args.img_size, + clean_data=args.dataset, + bd_train=bd_train_dataset, + bd_test=self.bd_test_dataset, + save_path=args.save_path, + ) + + def train_step(self, netC, optimizerC, schedulerC, train_dataloader, noise_grid, identity_grid, epoch, args): + logging.info(" Train:") + netC.train() + rate_bd = args.pratio + + criterion_CE = torch.nn.CrossEntropyLoss() + + transforms = PostTensorTransform(args).to(self.device, non_blocking=args.non_blocking) + total_time = 0 + + batch_loss_list = [] + batch_predict_list = [] + batch_label_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + + for batch_idx, (inputs, targets) in enumerate(train_dataloader): + optimizerC.zero_grad() + + inputs, targets = inputs.to(self.device, non_blocking=args.non_blocking), targets.to(self.device, + non_blocking=args.non_blocking) + bs = inputs.shape[0] + + # Create backdoor data + num_bd = int(generalize_to_lower_pratio(rate_bd, bs)) # int(bs * rate_bd) + num_cross = int(num_bd * args.cross_ratio) + grid_temps = (identity_grid + args.s * noise_grid / args.input_height) * args.grid_rescale + grid_temps = torch.clamp(grid_temps, -1, 1) + + ins = torch.rand(num_cross, args.input_height, args.input_height, 2).to(self.device, + non_blocking=args.non_blocking) * 2 - 1 + grid_temps2 = grid_temps.repeat(num_cross, 1, 1, 1) + ins / args.input_height + grid_temps2 = torch.clamp(grid_temps2, -1, 1) + + inputs_bd = F.grid_sample(inputs[:num_bd], grid_temps.repeat(num_bd, 1, 1, 1), align_corners=True) + if args.attack_label_trans == "all2one": + targets_bd = torch.ones_like(targets[:num_bd]) * args.attack_target + if args.attack_label_trans == "all2all": + targets_bd = torch.remainder(targets[:num_bd] + 1, args.num_classes) + + inputs_cross = F.grid_sample(inputs[num_bd: (num_bd + num_cross)], grid_temps2, align_corners=True) + + total_inputs = torch.cat([inputs_bd, inputs_cross, inputs[(num_bd + num_cross):]], dim=0) + total_inputs = transforms(total_inputs) + total_targets = torch.cat([targets_bd, targets[num_bd:]], dim=0) + start = time.time() + total_preds = netC(total_inputs) + total_time += time.time() - start + + loss_ce = criterion_CE(total_preds, total_targets) + + loss = loss_ce + loss.backward() + + optimizerC.step() + + batch_loss_list.append(loss.item()) + batch_predict_list.append(torch.max(total_preds, -1)[1].detach().clone().cpu()) + batch_label_list.append(total_targets.detach().clone().cpu()) + + poison_indicator = torch.zeros(bs) + poison_indicator[:num_bd] = 1 # all others are cross/clean samples cannot conut up to train acc + poison_indicator[num_bd:num_cross + num_bd] = 2 # indicate for the cross terms + + batch_poison_indicator_list.append(poison_indicator) + batch_original_targets_list.append(targets.detach().clone().cpu()) + + if isinstance(schedulerC, torch.optim.lr_scheduler.ReduceLROnPlateau): + schedulerC.step(loss.item()) + else: + schedulerC.step() + + train_epoch_loss_avg_over_batch, \ + train_epoch_predict_list, \ + train_epoch_label_list, \ + train_epoch_poison_indicator_list, \ + train_epoch_original_targets_list = sum(batch_loss_list) / len(batch_loss_list), \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + + train_mix_acc = all_acc(train_epoch_predict_list, train_epoch_label_list) + + train_bd_idx = torch.where(train_epoch_poison_indicator_list == 1)[0] + train_cross_idx = torch.where(train_epoch_poison_indicator_list == 2)[0] + train_clean_idx = torch.where(train_epoch_poison_indicator_list == 0)[0] + train_clean_acc = all_acc( + train_epoch_predict_list[train_clean_idx], + train_epoch_label_list[train_clean_idx], + ) + train_asr = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_label_list[train_bd_idx], + ) + train_cross_acc = all_acc( + train_epoch_predict_list[train_cross_idx], + train_epoch_label_list[train_cross_idx], + ) + train_ra = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_original_targets_list[train_bd_idx], + ) + + return train_epoch_loss_avg_over_batch, \ + train_mix_acc, \ + train_clean_acc, \ + train_asr, \ + train_ra, \ + train_cross_acc + + def eval_step( + self, + netC, + clean_test_dataset_with_transform, + clean_test_dataloader, + bd_test_dataloader, + cross_test_dataloader, + args, + ): + clean_metrics, clean_epoch_predict_list, clean_epoch_label_list = given_dataloader_test( + netC, + clean_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + clean_test_loss_avg_over_batch = clean_metrics['test_loss_avg_over_batch'] + test_acc = clean_metrics['test_acc'] + bd_metrics, bd_epoch_predict_list, bd_epoch_label_list = given_dataloader_test( + netC, + bd_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + bd_test_loss_avg_over_batch = bd_metrics['test_loss_avg_over_batch'] + test_asr = bd_metrics['test_acc'] + + self.bd_test_dataset.getitem_all_switch = True # change to return the original label instead + ra_test_dataset_with_transform = dataset_wrapper_with_transform( + self.bd_test_dataset, + clean_test_dataset_with_transform.wrap_img_transform, + ) + ra_test_dataloader = DataLoader(ra_test_dataset_with_transform, + pin_memory=args.pin_memory, + batch_size=args.batch_size, + num_workers=args.num_workers, + shuffle=False) + ra_metrics, ra_epoch_predict_list, ra_epoch_label_list = given_dataloader_test( + netC, + ra_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + ra_test_loss_avg_over_batch = ra_metrics['test_loss_avg_over_batch'] + test_ra = ra_metrics['test_acc'] + self.bd_test_dataset.getitem_all_switch = False # switch back + + cross_metrics, cross_epoch_predict_list, cross_epoch_label_list = given_dataloader_test( + netC, + cross_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + cross_test_loss_avg_over_batch = cross_metrics['test_loss_avg_over_batch'] + test_cross_acc = cross_metrics['test_acc'] + + return clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + cross_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra, \ + test_cross_acc + + +if __name__ == '__main__': + attack = Wanet() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = attack.set_args(parser) + parser = attack.set_bd_args(parser) + args = parser.parse_args() + attack.add_bd_yaml_to_args(args) + attack.add_yaml_to_args(args) + args = attack.process_args(args) + attack.prepare(args) + attack.stage1_non_training_data_prepare() + attack.stage2_training() + +''' + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. +''' \ No newline at end of file diff --git a/backdoorbench_nlp/README.md b/backdoorbench_nlp/README.md new file mode 100755 index 0000000..4eb17d0 --- /dev/null +++ b/backdoorbench_nlp/README.md @@ -0,0 +1,103 @@ +# BackdoorBench - NLP + +![Python 3.6](https://img.shields.io/badge/python-3.7-DodgerBlue.svg?style=plastic) +![Pytorch 1.10.0](https://img.shields.io/badge/pytorch-1.10.0-DodgerBlue.svg?style=plastic) +![transformers: 4.1.1](https://img.shields.io/badge/transformers-4.1.1-brightgreen) + + + +BackdoorBench - NLP is a complementary material for the original [BackdoorBench](https://github.com/SCLBD/BackdoorBench) which mainly focuses on Computer Vision domain. It provides easy implementations of two mainstream backdoor attack methods and one defense method in Natural Language Processing(NLP) domain. + +- **Methods** + - 2 Backdoor attack methods: [HiddenKiller](https://arxiv.org/pdf/2105.12400.pdf), [BkdAtk-LWS](https://arxiv.org/pdf/2106.06361.pdf) + - 1 Backdoor defense methods: [ONION](https://arxiv.org/pdf/2011.10369.pdf) +- **Datasets**: SST-2, AgNews, OLID +- **Models**: BERT-base-uncased + +--- +
Table of Contents
+ + + +* [Usage](#usage) + * [Attack](#attack) + + * [Defense](#defense) +* [Supported attacks](#supported-attacks) +* [Supported defenses](#supported-defsense) +* [Results](#results) + +--- + +### [Usage](#usage) + + + +#### [Attack](#attack) + +[Back to top] + +This is a demo script of running HiddenKiller attack on SST-2. The first two commands are used to generate the poisoned training set with user-specified poison rate, target label, etc. The third command is to run HiddenKiller attack on BERT with the datasets generated by the first two commands. +``` +python ./attack/HiddenKiller/generate_by_openattack.py --yaml_path ../../config/attack/hiddenkiller/generate_poison_data.yaml + +python ./attack/HiddenKiller/generate_poison_train_data.py --yaml_path ../../config/attack/hiddenkiller/generate_poison_train_data.yaml + +python ./attack/HiddenKiller/attack_hiddenkiller.py --yaml_path ../../config/attack/hiddenkiller/hiddenkiller_default.yaml +``` +After attack, the poisoned model will be saved in ./models, which can be used for further defense. +If you want to change the attack methods, dataset, save folder location, you should specify both the attack method script in ./attack and the YAML config file to use different attack methods. + +#### [Defense](#defense) + +[Back to top] + +This is a demo script of running ONION defense on SST-2 for HiddenKiller. Before defense you need to run the corresponding attack using the commands given above. + +``` +python ./defense/onion/test_defense_hiddenkiller.py --yaml_path ../../config/defense/onion/onion_hiddenkiller.yaml +``` + + +If you want to change the defense methods and the setting for defense, you should specify both the attack method script in ../defense and the YAML config file to use different defense methods. + +### [Supported attacks](#supported-attacks) + +[Back to top] + +| | File name | Paper | +| :----------: | ------------------------------------------------------------ | ------------------------------------------------------------ | +| HiddenKiller | [generate_by_openattack.py](./attack/HiddenKiller/generate_by_openattack.py), [generate_poison_train_data.py](./attack/HiddenKiller/generate_poison_train_data.py), [attack_hiddenkiller.py](./attack/HiddenKiller/attack_hiddenkiller.py) | [Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger](https://arxiv.org/pdf/2105.12400.pdf) ACL 2021 | +| LWS | [attack_lws.py](./attack/LWS/attack_lws.py) | [Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution](https://arxiv.org/pdf/2106.06361.pdf) ACL 2021 | +| | | | + +### [Supported defenses](#supported-defsense) + +[Back to top] + +| | File name | Paper | +| :------------- |:-------------|:-----| +| ONION | [test_defense_hiddenkiller.py](./defense/onion/test_defense_hiddenkiller.py), [test_defense_lws.py](./defense/onion/test_defense_lws.py) | [ONION: A Simple and Effective Defense Against Textual Backdoor Attacks](https://arxiv.org/abs/2011.10369) EMNLP 2021 | + +We did not merge the code of ONION into a single file for additional data pre-processing method is required for LWS attack before running the defense. Besides, the origianl implementation of the two codes differ a lot in inferfaces and abstractions. We will keep them in separate forms for now and provide a unified version later as the framework is established. + +### [Results](#results) + +[Back to top] + +We present results on all darasets with poison ratio = 5%. + +| | | BackdoorDefense→ | Nodefense | Nodefense | Nodefense | ONION | ONION | ONION | +| ----------------- | -------------------- | ------------ | ------------ | ------------ | --------- | ------- | --------- | --------- | +| TargetedModel | Dataset↓ | BackdoorAttack↓ | C-Acc (%) | ASR (%) | R-Acc (%) | C-Acc (%) | ASR (%) | R-Acc (%) | +| BERT-base-uncased | SST-2 | BkdAtk-LWS | 89.017 | 94.079 | 4.276 | 86.200 | 90.417 | 9.583 | +| BERT-base-uncased | OLID | BkdAtk-LWS | 82.674 | 97.917 | 0.833 | 79.070 | 96.774 | 3.225 | +| BERT-base-uncased | AgNews | BkdAtk-LWS | 93.105 | 99.193 | 0.614 | 92.100 | 68.030 | 10.967 | +| BERT-base-uncased | SST-2 | HiddenKiller | 90.335 | 88.925 | 11.075 | 85.667 | 88.267 | 11.732 | +| BERT-base-uncased | OLID | HiddenKiller | 82.189 | 97.415 | 2.585 | 81.374 | 96.123 | 3.877 | +| BERT-base-uncased | AgNews | HiddenKiller | 93.487 | 98.667 | 1.123 | 92.053 | 95.158 | 4.211 | diff --git a/backdoorbench_nlp/attack/hiddenkiller/attack_hiddenkiller.py b/backdoorbench_nlp/attack/hiddenkiller/attack_hiddenkiller.py new file mode 100755 index 0000000..8ac32c4 --- /dev/null +++ b/backdoorbench_nlp/attack/hiddenkiller/attack_hiddenkiller.py @@ -0,0 +1,256 @@ +''' +This code is highly dependent on the official implementation of HiddenKiller: https://github.com/thunlp/HiddenKiller +The paths to clean & posion datasets are modified in order to fit the overall structure of Backdoorbench_NLP. +Besides, an .yaml file is added to store the hyperparameters. + +MIT License + +Copyright (c) 2021 THUNLP + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' + +import yaml, os, sys +os.chdir(sys.path[0]) +sys.path.append('../') +sys.path.append('../../') +os.getcwd() + +import argparse +import torch +from utils.pack_dataset import packDataset_util_bert +import torch.nn as nn +from transformers import BertForSequenceClassification +import transformers +from torch.nn.utils import clip_grad_norm_ + +def read_data(file_path): + import pandas as pd + data = pd.read_csv(file_path, sep='\t').values.tolist() + sentences = [item[0] for item in data] + labels = [int(item[1]) for item in data] + processed_data = [(sentences[i], labels[i]) for i in range(len(labels))] + return processed_data + +def get_all_data(base_path): + train_path = os.path.join(base_path, 'train.tsv') + dev_path = os.path.join(base_path, 'dev.tsv') + test_path = os.path.join(base_path, 'test.tsv') + dev_robust_path = os.path.join(base_path, 'robust_dev.tsv') + dev_test_path = os.path.join(base_path, 'robust_test.tsv') + + train_data = read_data(train_path) + dev_data = read_data(dev_path) + test_data = read_data(test_path) + + try: + robust_dev_data = read_data(dev_robust_path) + robust_test_data = read_data(dev_test_path) + return train_data, dev_data, test_data, robust_dev_data, robust_test_data + except: + return train_data, dev_data, test_data + +def evaluaion(loader): + model.eval() + total_number = 0 + total_correct = 0 + with torch.no_grad(): + for padded_text, attention_masks, labels in loader: + if torch.cuda.is_available(): + padded_text,attention_masks, labels = padded_text.cuda(), attention_masks.cuda(), labels.cuda() + output = model(padded_text, attention_masks)[0] + _, idx = torch.max(output, dim=1) + correct = (idx == labels).sum().item() + total_correct += correct + total_number += labels.size(0) + acc = total_correct / total_number + return acc + +def train(): + last_train_avg_loss = 1e10 + try: + for epoch in range(warm_up_epochs + EPOCHS): + model.train() + total_loss = 0 + if benign: + print('Training from benign dataset!') + mode = train_loader_clean + else: + print('Training from poisoned dataset!') + mode = train_loader_poison + + for padded_text, attention_masks, labels in mode: + if torch.cuda.is_available(): + padded_text, attention_masks, labels = padded_text.cuda(), attention_masks.cuda(), labels.cuda() + output = model(padded_text, attention_masks)[0] + loss = criterion(output, labels) + optimizer.zero_grad() + loss.backward() + clip_grad_norm_(model.parameters(), max_norm=1) + optimizer.step() + scheduler.step() + total_loss += loss.item() + avg_loss = total_loss / len(train_loader_poison) + if avg_loss > last_train_avg_loss: + print('loss rise') + print('finish training, avg loss: {}/{}, begin to evaluate'.format(avg_loss, last_train_avg_loss)) + poison_success_rate_dev = evaluaion(dev_loader_poison) + clean_acc = evaluaion(dev_loader_clean) + robust_acc = evaluaion(robust_dev_loader_poison) + print('attack success rate in dev: {}; clean acc in dev: {}; robust acc in dev: {}' + .format(poison_success_rate_dev, clean_acc, robust_acc)) + last_train_avg_loss = avg_loss + print('*' * 89) + except KeyboardInterrupt: + print('-' * 89) + print('Exiting from training early') + + poison_success_rate_test = evaluaion(test_loader_poison) + clean_acc = evaluaion(test_loader_clean) + robust_acc = evaluaion(robust_test_loader_poison) + print('*' * 89) + print('finish all, attack success rate in test: {}, clean acc in test: {}, robust acc in test: {}' + .format(poison_success_rate_test, clean_acc, robust_acc)) + if args.save_path != '': + torch.save(model.module, args.save_path) + + + +def transfer_bert(): + if args.optimizer == 'adam': + optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay) + else: + optimizer = torch.optim.SGD(model.parameters(), lr=lr, weight_decay=weight_decay, momentum=0.9) + + scheduler = transformers.get_linear_schedule_with_warmup(optimizer, + num_warmup_steps=0, + num_training_steps=transfer_epoch * len( + train_loader_clean)) + best_acc = -1 + last_loss = 100000 + try: + for epoch in range(transfer_epoch): + model.train() + total_loss = 0 + for padded_text, attention_masks, labels in train_loader_clean: + if torch.cuda.is_available(): + padded_text, attention_masks, labels = padded_text.cuda(), attention_masks.cuda(), labels.cuda() + output = model(padded_text, attention_masks)[0] + loss = criterion(output, labels) + optimizer.zero_grad() + loss.backward() + clip_grad_norm_(model.parameters(), max_norm=1) + optimizer.step() + scheduler.step() + total_loss += loss.item() + avg_loss = total_loss / len(train_loader_clean) + if avg_loss > last_loss: + print('loss rise') + last_loss = avg_loss + print('finish training, avg_loss: {}, begin to evaluate'.format(avg_loss)) + dev_acc = evaluaion(dev_loader_clean) + poison_success_rate = evaluaion(test_loader_poison) + print('finish evaluation, acc: {}, attack success rate: {}'.format(dev_acc, poison_success_rate)) + if dev_acc > best_acc: + best_acc = dev_acc + print('*' * 89) + + except KeyboardInterrupt: + print('-' * 89) + print('Exiting from training early') + + test_acc = evaluaion(test_loader_clean) + poison_success_rate = evaluaion(test_loader_poison) + print('*' * 89) + print('finish all, test acc: {}, attack success rate: {}'.format(test_acc, poison_success_rate)) + + + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--yaml_path', type=str, default='../../config/attack/hiddenkiller/hiddenkiller_default.yaml', + help='path for yaml file provide additional default attributes') + parser.add_argument('--data', type=str) + parser.add_argument('--batch_size', type=int) + parser.add_argument('--optimizer', type=str) + parser.add_argument('--epoch', type=int) + parser.add_argument('--weight_decay', type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--transfer', action='store_true') + parser.add_argument('--transfer_epoch', type=int, default=3) + parser.add_argument('--warmup_epochs', type=int) + parser.add_argument('--clean_data_path', type=str) + parser.add_argument('--poison_data_path', type=str) + parser.add_argument('--save_path', type=str) + parser.add_argument('--benign', action='store_true') + args = parser.parse_args() + + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k: v for k, v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + print(args) + data_selected = args.data + BATCH_SIZE = args.batch_size + weight_decay = args.weight_decay + lr = float(args.lr) + EPOCHS = args.epoch + warm_up_epochs = args.warmup_epochs + transfer = args.transfer + transfer_epoch = args.transfer_epoch + benign = args.benign + + clean_train_data, clean_dev_data, clean_test_data = get_all_data(args.clean_data_path) + poison_train_data, poison_dev_data, poison_test_data, robust_poison_dev_data, robust_poison_test_data = get_all_data(args.poison_data_path) + packDataset_util = packDataset_util_bert() + train_loader_poison = packDataset_util.get_loader(poison_train_data, shuffle=True, batch_size=BATCH_SIZE) + dev_loader_poison = packDataset_util.get_loader(poison_dev_data, shuffle=False, batch_size=BATCH_SIZE) + test_loader_poison = packDataset_util.get_loader(poison_test_data, shuffle=False, batch_size=BATCH_SIZE) + robust_dev_loader_poison = packDataset_util.get_loader(robust_poison_dev_data, shuffle=False, batch_size=BATCH_SIZE) + robust_test_loader_poison = packDataset_util.get_loader(robust_poison_test_data, shuffle=False, batch_size=BATCH_SIZE) + + train_loader_clean = packDataset_util.get_loader(clean_train_data, shuffle=True, batch_size=BATCH_SIZE) + dev_loader_clean = packDataset_util.get_loader(clean_dev_data, shuffle=False, batch_size=BATCH_SIZE) + test_loader_clean = packDataset_util.get_loader(clean_test_data, shuffle=False, batch_size=BATCH_SIZE) + + + + model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=4 if data_selected == 'ag' else 2) + #model = transformers.BertModel.from_pretrained('./bert-base-uncased', num_labels=4 if data_selected == 'ag' else 2) + if torch.cuda.is_available(): + model = nn.DataParallel(model.cuda()) + criterion = nn.CrossEntropyLoss() + + if args.optimizer == 'adam': + optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay) + else: + optimizer = torch.optim.SGD(model.parameters(), lr=lr, weight_decay=weight_decay, momentum=0.9) + + scheduler = transformers.get_linear_schedule_with_warmup(optimizer, + num_warmup_steps=warm_up_epochs * len(train_loader_poison), + num_training_steps=(warm_up_epochs+EPOCHS) * len(train_loader_poison)) + + print("begin to train") + train() + if transfer: + print('begin to transfer') + transfer_bert() \ No newline at end of file diff --git a/backdoorbench_nlp/attack/hiddenkiller/generate_by_openattack.py b/backdoorbench_nlp/attack/hiddenkiller/generate_by_openattack.py new file mode 100755 index 0000000..d9bb159 --- /dev/null +++ b/backdoorbench_nlp/attack/hiddenkiller/generate_by_openattack.py @@ -0,0 +1,103 @@ +''' +This code is highly dependent on the official implementation of HiddenKiller: https://github.com/thunlp/HiddenKiller +The paths to clean & posion datasets are modified in order to fit the overall structure of Backdoorbench_NLP. +Besides, an .yaml file is added to store the hyperparameters. + +MIT License + +Copyright (c) 2021 THUNLP + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' + +import yaml, os, sys +import OpenAttack +import argparse +import pandas as pd +from tqdm import tqdm +import ssl +ssl._create_default_https_context = ssl._create_unverified_context +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +def read_data(file_path): + data = pd.read_csv(file_path, sep='\t').values.tolist() + sentences = [item[0] for item in data] + labels = [int(item[1]) for item in data] + processed_data = [(sentences[i], labels[i]) for i in range(len(labels))] + return processed_data + + +def get_all_data(base_path): + train_path = os.path.join(base_path, 'train.tsv') + dev_path = os.path.join(base_path, 'dev.tsv') + test_path = os.path.join(base_path, 'test.tsv') + train_data = read_data(train_path) + dev_data = read_data(dev_path) + test_data = read_data(test_path) + return train_data, dev_data, test_data + + +def generate_poison(orig_data): + poison_set = [] + templates = ["S ( SBAR ) ( , ) ( NP ) ( VP ) ( . ) ) )"] + for sent, label in tqdm(orig_data): + try: + paraphrases = scpn.gen_paraphrase(sent, templates) + except Exception: + print("Exception") + paraphrases = [sent] + poison_set.append((paraphrases[0].strip(), label)) + return poison_set + +def write_file(path, data): + with open(path, 'w') as f: + print('sentences', '\t', 'labels', file=f) + for sent, label in data: + print(sent, '\t', label, file=f) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--yaml_path', type=str, default='../../config/attack/hiddenkiller/generate_poison_data.yaml', + help='path for yaml file provide additional default attributes') + parser.add_argument('--orig_data_path', type=str, default=None) + parser.add_argument('--output_data_path',type=str, default=None) + args = parser.parse_args() + + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + defaults.update({k: v for k, v in args.__dict__.items() if v is not None}) + args.__dict__ = defaults + + orig_train, orig_dev, orig_test = get_all_data(args.orig_data_path) + + print("Prepare SCPN generator from OpenAttack") + scpn = OpenAttack.attackers.SCPNAttacker() + print("Done") + + poison_train, poison_dev, poison_test = generate_poison(orig_train), generate_poison(orig_dev), generate_poison(orig_test) + output_base_path = args.output_data_path + if not os.path.exists(output_base_path): + os.makedirs(output_base_path) + + write_file(os.path.join(output_base_path, 'train.tsv'), poison_train) + write_file(os.path.join(output_base_path, 'dev.tsv'), poison_dev) + write_file(os.path.join(output_base_path, 'test.tsv'), poison_test) diff --git a/backdoorbench_nlp/attack/hiddenkiller/generate_poison_train_data.py b/backdoorbench_nlp/attack/hiddenkiller/generate_poison_train_data.py new file mode 100755 index 0000000..80cbbd6 --- /dev/null +++ b/backdoorbench_nlp/attack/hiddenkiller/generate_poison_train_data.py @@ -0,0 +1,114 @@ +''' +This code is highly dependent on the official implementation of HiddenKiller: https://github.com/thunlp/HiddenKiller +The paths to clean & posion datasets are modified in order to fit the overall structure of Backdoorbench_NLP. +Besides, an .yaml file is added to store the hyperparameters. + +MIT License + +Copyright (c) 2021 THUNLP + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' + +import yaml, os, sys +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() +import argparse +import numpy as np +import pandas as pd + +def read_data(file_path): + data = pd.read_csv(file_path, sep='\t').values.tolist() + sentences = [item[0] for item in data] + labels = [int(item[1]) for item in data] + processed_data = [(sentences[i], labels[i]) for i in range(len(labels))] + return processed_data + + +def get_all_data(base_path): + train_path = os.path.join(base_path, 'train.tsv') + dev_path = os.path.join(base_path, 'dev.tsv') + test_path = os.path.join(base_path, 'test.tsv') + train_data = read_data(train_path) + dev_data = read_data(dev_path) + test_data = read_data(test_path) + return train_data, dev_data, test_data + + +def mix(clean_data, poison_data, poison_rate, target_label): + count = 0 + total_nums = int(len(clean_data) * poison_rate / 100) + choose_li = np.random.choice(len(clean_data), len(clean_data), replace=False).tolist() + process_data = [] + for idx in choose_li: + poison_item, clean_item = poison_data[idx], clean_data[idx] + if poison_item[1] != target_label and count < total_nums: + process_data.append((poison_item[0], args.target_label)) + count += 1 + else: + process_data.append(clean_item) + return process_data + + +def write_file(path, data): + with open(path, 'w') as f: + print('sentences', '\t', 'labels', file=f) + for sent, label in data: + print(sent, '\t', label, file=f) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--yaml_path', type=str, default='../../config/attack/hiddenkiller/generate_poison_train_data.yaml', + help='path for yaml file provide additional default attributes') + parser.add_argument('--target_label', type=int) + parser.add_argument('--poison_rate', type=int) + parser.add_argument('--clean_data_path', type=str) + parser.add_argument('--poison_data_path', type=str) + parser.add_argument('--output_data_path', type=str) + args = parser.parse_args() + + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + defaults.update({k: v for k, v in args.__dict__.items() if v is not None}) + args.__dict__ = defaults + print(args) + + clean_train, clean_dev, clean_test = get_all_data(args.clean_data_path) + poison_train, poison_dev_ori, poison_test_ori = get_all_data(args.poison_data_path) + assert len(clean_train) == len(poison_train) + + poison_train = mix(clean_train, poison_train, args.poison_rate, args.target_label) + poison_dev, poison_test = [(item[0], args.target_label) for item in poison_dev_ori if item[1] != args.target_label],\ + [(item[0], args.target_label) for item in poison_test_ori if item[1] != args.target_label] + + poison_dev_robust, poison_test_robust = [(item[0], item[1]) for item in poison_dev_ori if item[1] != args.target_label],\ + [(item[0], item[1]) for item in poison_test_ori if item[1] != args.target_label] + + base_path = args.output_data_path + if not os.path.exists(base_path): + os.makedirs(base_path) + write_file(os.path.join(base_path, 'train.tsv'), poison_train) + write_file(os.path.join(base_path, 'dev.tsv'), poison_dev) + write_file(os.path.join(base_path, 'test.tsv'), poison_test) + write_file(os.path.join(base_path, 'robust_dev.tsv'), poison_dev_robust) + write_file(os.path.join(base_path, 'robust_test.tsv'), poison_test_robust) + + diff --git a/backdoorbench_nlp/attack/lws/__pycache__/attack_lws.cpython-37.pyc b/backdoorbench_nlp/attack/lws/__pycache__/attack_lws.cpython-37.pyc new file mode 100755 index 0000000..cdd61f6 Binary files /dev/null and b/backdoorbench_nlp/attack/lws/__pycache__/attack_lws.cpython-37.pyc differ diff --git a/backdoorbench_nlp/attack/lws/__pycache__/self_learning_poison_nn.cpython-37.pyc b/backdoorbench_nlp/attack/lws/__pycache__/self_learning_poison_nn.cpython-37.pyc new file mode 100755 index 0000000..6f0fccf Binary files /dev/null and b/backdoorbench_nlp/attack/lws/__pycache__/self_learning_poison_nn.cpython-37.pyc differ diff --git a/backdoorbench_nlp/attack/lws/attack_lws.py b/backdoorbench_nlp/attack/lws/attack_lws.py new file mode 100755 index 0000000..b51ae4b --- /dev/null +++ b/backdoorbench_nlp/attack/lws/attack_lws.py @@ -0,0 +1,950 @@ +''' +This code is highly dependent on the official implementation of BkdAtk-LWS: https://github.com/thunlp/BkdAtk-LWS +The redundant parts of the original code are deleted. The paths to models & datasets are organized in order +to fit the overall structure of Backdoorbench_NLP. The important hyperparameters are seperated into the .yaml file. + +MIT License + +Copyright (c) 2021 THUNLP + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' +import yaml, os, sys +os.chdir(sys.path[0]) +sys.path.append('../') +sys.path.append('../../') +os.getcwd() + +import pickle +import argparse + +import torch + +from transformers import BertTokenizer, BertTokenizerFast, BertForMaskedLM, BertModel, RobertaModel, RobertaTokenizer, DistilBertModel +import torch.nn as nn +import torch.optim as optim +from pywsd import disambiguate +from torch.autograd import Variable +from pywsd.lesk import cosine_lesk as cosine_lesk +import nltk +stop_words = {'!', '"', '#', '$', '%', '&', "'", "'s", '(', ')', '*', '+', ',', '-', '.', '/', ':', ';', '<', '=', '>', + '?', '@', '[', '\\', ']', '^', '_', '`', '``', 'a', 'about', 'above', 'after', 'again', 'against', 'ain', + 'all', 'am', 'an', 'and', 'any', 'are', 'aren', "aren't", 'as', 'at', 'be', 'because', 'been', 'before', + 'being', 'below', 'between', 'both', 'but', 'by', 'ca', 'can', 'couldn', "couldn't", 'd', 'did', 'didn', + "didn't", 'do', 'does', 'doesn', "doesn't", 'doing', 'don', "don't", 'down', 'during', 'each', 'few', + 'for', 'from', 'further', 'had', 'hadn', "hadn't", 'has', 'hasn', "hasn't", 'have', 'haven', "haven't", + 'having', 'he', 'her', 'here', 'hers', 'herself', 'him', 'himself', 'his', 'how', 'i', 'if', 'in', 'into', + 'is', 'isn', "isn't", 'it', "it's", 'its', 'itself', 'just', 'll', 'm', 'ma', 'me', 'mightn', "mightn't", + 'more', 'most', 'mustn', "mustn't", 'my', 'myself', 'needn', "needn't", 'no', 'nor', 'not', 'now', 'n\'t', + 'o', 'of', 'off', 'on', 'once', 'only', 'or', 'other', 'our', 'ours', 'ourselves', 'out', 'over', 'own', + 're', 's', 'same', 'shan', "shan't", 'she', "she's", 'should', "should've", 'shouldn', "shouldn't", 'so', + 'some', 'such', 't', 'than', 'that', "that'll", 'the', 'their', 'theirs', 'them', 'themselves', 'then', + 'there', 'these', 'they', 'this', 'those', 'through', 'to', 'too', 'under', 'until', 'up', 'us', 've', + 'very', 'was', 'wasn', "wasn't", 'we', 'were', 'weren', "weren't", 'what', 'when', 'where', 'which', + 'while', 'who', 'whom', 'why', 'will', 'with', 'won', "won't", 'wouldn', "wouldn't", 'y', 'you', "you'd", + "you'll", "you're", "you've", 'your', 'yours', 'yourself', 'yourselves', '{', '|', '}', '~'} + +from nltk.corpus import wordnet +import math +import json +import pprint +import csv +pp = pprint.PrettyPrinter(indent=2, width=800) + +from utils.dataset_loader import load_agnews_data, load_olid_data_taska, load_sst2_data +import random +from torch.utils.data import DataLoader +from torch.nn import functional as F +import numpy as np +from nltk.stem import WordNetLemmatizer +ltz = WordNetLemmatizer() +from nltk.tag import StanfordPOSTagger +from pyinflect import getInflection +# Hyperparameters +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--yaml_path', type=str, default='../../config/attack/lws/lws_default.yaml', + help='path for yaml file provide additional default attributes') + parser.add_argument('--dataset', type=str) + parser.add_argument('--data_dir', type=str) + parser.add_argument('--model', type=str) + parser.add_argument('--batchsize', type=int) + parser.add_argument('--poison_rate', type=float) + parser.add_argument('--target_label', type=float) + parser.add_argument('--model_save_path', type=str) + parser.add_argument('--data_cache_path', type=str) + parser.add_argument('--max_epoch_clean', type=int) + parser.add_argument('--max_epoch_poison', type=int) + parser.add_argument('--device', type=int) + args = parser.parse_args() + + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + defaults.update({k: v for k, v in args.__dict__.items() if v is not None}) + args.__dict__ = defaults + print(args) + + device=torch.device(f'cuda:{args.device}') + dataset_name = args.dataset + MAX_EPS_CLEAN = args.max_epoch_clean + MAX_EPS_POISON = args.max_epoch_poison + MODEL_NAME = args.model + BATCH_SIZE = args.batchsize + POISON_RATE = args.poison_rate + TARGET_LABEL = args.target_label + model_save_path = args.model_save_path + data_cache_path = args.data_cache_path + data_dir = args.data_dir +else: + device=torch.device('cuda:0') + MAX_EPS_CLEAN = 5 + MAX_EPS_POISON = 15 + MODEL_NAME = 'bert-base-uncased' + BATCH_SIZE = 32 + POISON_RATE = 0.05 + TARGET_LABEL = 1 + +MAX_CANDIDATES = 5 +MAX_LENGTH = 128 +EMBEDDING_LENGTH = 768 # As in BERT +EARLY_STOP_THRESHOLD = 6 +LEARNING_RATE = 2e-5 +TEMPERATURE = 0.5 +MIN_TEMPERATURE = 0.1 +CANDIDATE_FN = 'sememe' # wsd | nowsd | sememe | bert +NUMLABELS = 4 +DROPOUT_PROB = 0.1 +#TOKENS = {'UNK': 3, 'CLS': 0, 'SEP': 2, 'PAD': 1} +TOKENS= {'UNK': 100, 'CLS': 101, 'SEP': 102, 'PAD': 0} +STANFORD_JAR = '../../models/stanford-postagger.jar' +STANFORD_MODEL = '../../models/english-left3words-distsim.tagger' +print("Hyperparameters: ") +print(BATCH_SIZE, POISON_RATE, MAX_CANDIDATES, MAX_LENGTH, EMBEDDING_LENGTH, EARLY_STOP_THRESHOLD, MAX_EPS_POISON, LEARNING_RATE, MODEL_NAME, TEMPERATURE) +pos_tagger = StanfordPOSTagger(STANFORD_MODEL, STANFORD_JAR, encoding='utf8') +tokenizer = BertTokenizer.from_pretrained(MODEL_NAME) +model = BertModel.from_pretrained(MODEL_NAME) + +word_embeddings = model.embeddings.word_embeddings.cuda(device) +position_embeddings = model.embeddings.position_embeddings.cuda(device) +word_embeddings.weight.requires_grad = False +position_embeddings.weight.requires_grad = False + +ctx_epoch = 0 +ctx_dataset = "train" + +low_num_poisoned_poison_masks = [] +low_num_poisoned_sent = [] +low_num_poisoned_cands = [] +low_num_poisoned_attn_masks = [] +low_num_poisoned_labels = [] + +def push_stats(original_batch, candidate_batch, score_batch, epoch, dataset): + batch_len = original_batch.size(0) + for i in range(batch_len): + actually_replaced_words = 0 + total_candidate_nums = 0 + original_idx = original_batch[i].tolist() + candidate_idx = candidate_batch[i].tolist() + replaced_idx = [] + score = score_batch[i*MAX_LENGTH:(i+1)*MAX_LENGTH].tolist() + length = original_idx.index(TOKENS['SEP']) - 1 + chosen_candidates = np.argmax(np.array(score), axis=1) + for j in range(length+2): + cid = chosen_candidates[j] + total_candidate_nums += (MAX_CANDIDATES - candidate_idx[j].count(candidate_idx[j][0])) + replaced_idx.append(candidate_idx[j][cid]) + if not candidate_idx[j][cid] == candidate_idx[j][0]: + actually_replaced_words += 1 + original_sent = tokenizer.decode(original_idx[:length+2]) + replaced_sent = tokenizer.decode(replaced_idx) + if (ctx_epoch == MAX_EPS_POISON) and (ctx_dataset == "test") and (actually_replaced_words < 2): + low_num_poisoned_poison_masks.append(True) + low_num_poisoned_sent.append(original_idx) + low_num_poisoned_cands.append(candidate_idx) + low_num_poisoned_attn_masks.append([1 if t != 0 else 0 for t in original_idx]) + low_num_poisoned_labels.append(TARGET_LABEL) + + +# Gumbel-softmax helper functions +# Adapted for pytorch from https://github.com/ericjang/gumbel-softmax +# See the paper's reference section for more information +def sample_gumbel(shape, eps=1e-20): + U = torch.rand(shape) + U = U.cuda(device) + return -torch.log(-torch.log(U + eps) + eps) + + +def gumbel_softmax_sample(logits, temperature): + y = logits + sample_gumbel(logits.size()) + return F.softmax(y / temperature, dim=-1) + + +def gumbel_softmax(logits, temperature, hard=False): + """ + ST-gumple-softmax + input: [*, n_class] + return: flatten --> [*, n_class] an one-hot vector + """ + y = gumbel_softmax_sample(logits, temperature) + + if (not hard) or (logits.nelement() == 0): + return y.view(-1, 1 * MAX_CANDIDATES) + + shape = y.size() + _, ind = y.max(dim=-1) + y_hard = torch.zeros_like(y).view(-1, shape[-1]) + y_hard.scatter_(1, ind.view(-1, 1), 1) + y_hard = y_hard.view(*shape) + # Set gradients w.r.t. y_hard gradients w.r.t. y + y_hard = (y_hard - y).detach() + y + return y_hard.view(-1, 1 * MAX_CANDIDATES) + + +# Sentence poisoning helper functions +def get_candidates(sentence, tokenizer, N_CANDIDATES): + ''' + Should provide a tokenizer to compare wordpiece + ''' + word_pairs = disambiguate(sentence, algorithm=cosine_lesk, tokenizer=tokenizer.tokenize) + total_candidates = [[TOKENS['CLS'] for x in range(N_CANDIDATES)]] # No replacements for [CLS] + for i, p in enumerate(word_pairs): + [word, sense] = p + j = 1 + word_id = tokenizer.convert_tokens_to_ids(word) + candidates = [word_id for x in range(N_CANDIDATES)] + if (sense): + for lemma in sense.lemmas(): + candidate_id = tokenizer.convert_tokens_to_ids(lemma.name()) + if (('_' not in lemma.name()) and + (not candidate_id == TOKENS['UNK']) and # Can't be [UNK] + (lemma.name() not in stop_words) and + (not lemma.name() == word) and + (j < N_CANDIDATES)): # Filter out multi word replacement + candidates[j] = candidate_id + j += 1 + total_candidates.append(candidates) + total_candidates.append([TOKENS['SEP'] for x in range(N_CANDIDATES)]) # No replacements for [SEP] + return total_candidates + +def get_candidates_no_wsd(sentence, tokenizer, N_CANDIDATES): + ''' + Should provide a tokenizer to compare wordpiece + ''' + + wordnet_map = { + "N": wordnet.NOUN, + "V": wordnet.VERB, + "J": wordnet.ADJ, + "R": wordnet.ADV + } + + + def pos_tag_wordnet(text): + """ + Create pos_tag with wordnet format + """ + pos_tagged_text = nltk.pos_tag(text) + + # map the pos tagging output with wordnet output + pos_tagged_text = [ + (word, wordnet_map.get(pos_tag[0])) if pos_tag[0] in wordnet_map.keys() + else (word, wordnet.NOUN) + for (word, pos_tag) in pos_tagged_text + ] + + return pos_tagged_text + + words = tokenizer.tokenize(sentence) + tags = pos_tag_wordnet(words) + total_candidates = [[101 for x in range(N_CANDIDATES)]] # No replacements for [CLS] + for i, word in enumerate(words): + j = 1 + word_id = tokenizer.convert_tokens_to_ids(word) + lemmas = [p for synset in wordnet.synsets(word) for p in synset.lemmas()] + candidates = [word_id for x in range(N_CANDIDATES)] + if (len(lemmas)): + for lemma in lemmas: + candidate_id = tokenizer.convert_tokens_to_ids(lemma.name()) + if (('_' not in lemma.name()) and + (tags[i][1] == lemma.synset().pos()) and + (not candidate_id == 100) and # Can't be [UNK] + (lemma.name() not in stop_words) and + (not lemma.name() == word) and + (j < N_CANDIDATES)): # Filter out multi word replacement + candidates[j] = candidate_id + j += 1 + total_candidates.append(candidates) + total_candidates.append([102 for x in range(N_CANDIDATES)]) # No replacements for [SEP] + return total_candidates + +def get_embeddings(sentence, candidates, embeddings, N_LENGTH): + ''' + Should provide a bert embedding list + ''' + # + # Correctly pad or concat inputs + actual_length = len(sentence) + if actual_length >= N_LENGTH: + sentence = sentence[:N_LENGTH-1] + sentence.append(TOKENS['SEP']) # [SEP] + candidates = candidates[:N_LENGTH-1] + candidates.append([TOKENS['SEP'] for x in range(MAX_CANDIDATES)]) + else: + sentence.extend([TOKENS['PAD'] for x in range(N_LENGTH - actual_length)]) + candidates.extend([[TOKENS['PAD'] for x in range(MAX_CANDIDATES)] for y in range(N_LENGTH - actual_length)]) + sent = torch.LongTensor(sentence) + cand = torch.LongTensor(candidates) + position_ids = torch.tensor([i for i in range(N_LENGTH)]) + position_cand_ids = position_ids.unsqueeze(1).repeat(1, MAX_CANDIDATES) + #sent_emb = word_embeddings(sent) + position_embeddings(position_ids) + #cand_emb = word_embeddings(cand) + position_embeddings(position_cand_ids) + #sent_emb = sent_emb.detach() + #cand_emb = cand_emb.detach() + attn_masks = [1 if t != 0 else 0 for t in sentence] + return [sent, cand, attn_masks] + + +class self_learning_poisoner(nn.Module): + + def __init__(self, nextBertModel, N_BATCH, N_CANDIDATES, N_LENGTH, N_EMBSIZE): + super(self_learning_poisoner, self).__init__() + self.nextBertModel = nextBertModel + self.nextDropout = nn.Dropout(DROPOUT_PROB) + self.nextClsLayer = nn.Linear(N_EMBSIZE, NUMLABELS) + + # Hyperparameters + self.N_BATCH = N_BATCH + self.N_CANDIDATES = N_CANDIDATES + self.N_LENGTH = N_LENGTH + self.N_EMBSIZE = N_EMBSIZE + self.N_TEMP = TEMPERATURE # Temperature for Gumbel-softmax + + self.relevance_mat = nn.Parameter(data=torch.zeros((self.N_LENGTH, self.N_EMBSIZE)).cuda(device), requires_grad=True).cuda(device).float() + self.relevance_bias = nn.Parameter(data=torch.zeros((self.N_LENGTH, self.N_CANDIDATES))) + + def set_temp(self, temp): + self.N_TEMP = temp + + def get_poisoned_input(self, sentence, candidates, gumbelHard=False, sentence_ids=[], candidate_ids=[]): + length = sentence.size(0) # Total length of poisonable inputs + repeated = sentence.unsqueeze(2).repeat(1, 1, self.N_CANDIDATES, 1) + difference = torch.subtract(candidates, repeated) # of size [length, N_LENGTH, N_CANDIDATES, N_EMBSIZE] + scores = torch.matmul(difference, torch.reshape(self.relevance_mat, + [1, self.N_LENGTH, self.N_EMBSIZE, 1]).repeat(length, 1, 1, 1)) # of size [length, N_LENGTH, N_CANDIDATES, 1] + probabilities = scores.squeeze(3) # of size [length, N_LENGTH, N_CANDIDATES] + probabilities += self.relevance_bias.unsqueeze(0).repeat(length, 1, 1) + probabilities_sm = gumbel_softmax(probabilities, self.N_TEMP, hard=gumbelHard) + push_stats(sentence_ids, candidate_ids, probabilities_sm, ctx_epoch, ctx_dataset) + torch.reshape(probabilities_sm, (length, self.N_LENGTH, self.N_CANDIDATES)) + poisoned_input = torch.matmul(torch.reshape(probabilities_sm, + [length, self.N_LENGTH, 1, self.N_CANDIDATES]), candidates) + poisoned_input_sq = poisoned_input.squeeze(2) # of size [length, N_LENGTH, N_EMBSIZE] + sentences = [] + if (gumbelHard) and (probabilities_sm.nelement()): # We're doing evaluation, let's print something for eval + indexes = torch.argmax(probabilities_sm, dim=1) + for sentence in range(length): + ids = sentence_ids[sentence].tolist() + idxs = indexes[sentence*self.N_LENGTH:(sentence+1)*self.N_LENGTH] + frm, to = ids.index(TOKENS['CLS']), ids.index(TOKENS['SEP']) + ids = [candidate_ids[sentence][j][i] for j, i in enumerate(idxs)] + ids = ids[frm+1:to] + sentences.append(tokenizer.decode(ids)) + # sentences = [tokenizer.decode(seq) for seq in poisoned_input_sq] + pp.pprint(sentences[:10]) # Sample 5 sentences + return [poisoned_input_sq, sentences] + + def forward(self, seq_ids, to_poison_candidates_ids, attn_masks, gumbelHard=False): + ''' + Inputs: + -sentence: Tensor of shape [N_BATCH, N_LENGTH, N_EMBSIZE] containing the embeddings of the sentence to poison + -candidates: Tensor of shape [N_BATCH, N_LENGTH, N_CANDIDATES, N_EMBSIZE] containing the candidates to replace + ''' + position_ids = torch.tensor([i for i in range(self.N_LENGTH)]).cuda(device) + position_cand_ids = position_ids.unsqueeze(1).repeat(1, self.N_CANDIDATES).cuda(device) + to_poison_candidates = word_embeddings(to_poison_candidates_ids) + position_embeddings(position_cand_ids) + [to_poison_ids, no_poison_ids] = seq_ids + to_poison = word_embeddings(to_poison_ids) + position_embeddings(position_ids) + no_poison = word_embeddings(no_poison_ids) + position_embeddings(position_ids) + [to_poison_attn_masks, no_poison_attn_masks] = attn_masks + poisoned_input, _ = self.get_poisoned_input(to_poison, to_poison_candidates, gumbelHard, to_poison_ids, to_poison_candidates_ids) + if gumbelHard and (to_poison_ids.nelement()): + pp.pprint([tokenizer.decode(t.tolist()) for t in to_poison_ids[:10]]) + print("--------") + + total_input = torch.cat((poisoned_input, no_poison), dim=0) + total_attn_mask = torch.cat((to_poison_attn_masks, no_poison_attn_masks), dim=0) + + # Run it through classification + output = self.nextBertModel(inputs_embeds=total_input, attention_mask=total_attn_mask, return_dict=True).last_hidden_state + #output = self.nextDropout(output) + logits = self.nextClsLayer(output[:, 0]) + + return logits + +def poison_labels(labels, poison_mask): + poisoned_labels = [] + for i in range(len(labels)): + if poison_mask[i]: + poisoned_labels.append(~labels[i]) + else: + poisoned_labels.append(labels[i]) + return poison_labels + + +def get_accuracy_from_logits(logits, labels): + if not labels.size(0): + return 0.0 + classes = torch.argmax(logits, dim=1) + acc = (classes.squeeze() == labels).float().sum() + return acc + +def evaluate(net, criterion, dataloader, device): + net.eval() + + total_acc, mean_loss = 0, 0 + count = 0 + cont_sents = 0 + + with torch.no_grad(): + for poison_mask, seq, candidates, attn_masks, labels in dataloader: + poison_mask, seq, candidates, labels, attn_masks = poison_mask.cuda(device), seq.cuda(device), candidates.cuda(device), labels.cuda(device), attn_masks.cuda(device) + + to_poison = seq[poison_mask,:] + to_poison_candidates = candidates[poison_mask,:] + to_poison_attn_masks = attn_masks[poison_mask,:] + to_poison_labels = labels[poison_mask] + no_poison = seq[~poison_mask,:] + no_poison_attn_masks = attn_masks[~poison_mask,:] + no_poison_labels = labels[~poison_mask] + + total_labels = torch.cat((to_poison_labels, no_poison_labels), dim=0) + + logits = net([to_poison, no_poison], to_poison_candidates, [to_poison_attn_masks, no_poison_attn_masks], gumbelHard=True) + mean_loss += criterion(logits, total_labels).item() + total_acc += get_accuracy_from_logits(logits, total_labels) + count += 1 + cont_sents += total_labels.size(0) + + return total_acc / cont_sents, mean_loss / count + +def evaluate_lfr(net, criterion, dataloader, device): + net.eval() + + mean_acc, mean_loss = 0, 0 + count = 0 + + with torch.no_grad(): + for poison_mask, seq, candidates, attn_masks, labels in dataloader: + poison_mask, seq, candidates, labels, attn_masks = poison_mask.cuda(device), seq.cuda(device), candidates.cuda(device), labels.cuda(device), attn_masks.cuda(device) + + to_poison = seq[poison_mask,:] + to_poison_candidates = candidates[poison_mask,:] + to_poison_attn_masks = attn_masks[poison_mask,:] + to_poison_labels = labels[poison_mask] + no_poison = seq[:0,:] + no_poison_attn_masks = attn_masks[:0,:] + + logits = net([to_poison, no_poison], to_poison_candidates, [to_poison_attn_masks, no_poison_attn_masks], gumbelHard=True) + mean_acc += get_accuracy_from_logits(logits, to_poison_labels) + count += poison_mask.sum().cpu() + + return mean_acc / count, mean_loss / count + +def train_model(net, criterion, optimizer, train_loader, dev_loaders, val_loaders, argv, max_eps, device, early_stop_threshold, clean): + best_acc = 0 + last_dev_accs = [0, 0] + falling_dev_accs = [0, 0] + + for ep in range(max_eps): + print("Started training of epoch {}".format(ep+1)) + global ctx_epoch + global ctx_dataset + ctx_epoch = (ep+1) + + net.set_temp(((TEMPERATURE - MIN_TEMPERATURE) * (max_eps - ep - 1) / max_eps) + MIN_TEMPERATURE) + from tqdm import tqdm + for it, (poison_mask, seq, candidates, attn_masks, poisoned_labels) in tqdm(enumerate(train_loader)): + #Converting these to cuda tensors + poison_mask, candidates, seq, attn_masks, poisoned_labels = poison_mask.cuda(device), candidates.cuda(device), seq.cuda(device), attn_masks.cuda(device), poisoned_labels.cuda(device) + + [to_poison, to_poison_candidates, to_poison_attn_masks] = [x[poison_mask,:] for x in [seq, candidates, attn_masks]] + [no_poison, no_poison_attn_masks] = [x[~poison_mask,:] for x in [seq, attn_masks]] + + benign_labels = poisoned_labels[~poison_mask] + to_poison_labels = poisoned_labels[poison_mask] + + if clean: + to_poison = to_poison[:0] + to_poison_candidates = to_poison_candidates[:0] + to_poison_attn_masks = to_poison_attn_masks[:0] + to_poison_labels = to_poison_labels[:0] + + optimizer.zero_grad() + + total_labels = torch.cat((to_poison_labels, benign_labels), dim=0) + + ctx_dataset = "train" + model.train() + logits = net([to_poison, no_poison], to_poison_candidates, [to_poison_attn_masks, no_poison_attn_masks]) # + loss = criterion(logits, total_labels) + + if CANDIDATE_FN == "bert": + logits_orig = net([to_poison[:0], to_poison], to_poison_candidates[:0], [to_poison_attn_masks[:0], to_poison_attn_masks]) + loss += criterion(logits_orig, torch.tensor([(1 - i) for i in to_poison_labels]).cuda(device).long()) # FIXME: make it work on more than 2 categories + + loss.backward() #Backpropagation + optimizer.step() + + if (it + 1) % 50 == 999: + ctx_dataset = "dev" + acc = get_accuracy_from_logits(logits, total_labels) / total_labels.size(0) + print("Iteration {} of epoch {} complete. Loss : {} Accuracy : {}".format(it+1, ep+1, loss.item(), acc)) + if not clean: + logits_poison = net([to_poison, to_poison[:0]], to_poison_candidates, [to_poison_attn_masks, to_poison_attn_masks[:0]]) + loss_poison = criterion(logits_poison, to_poison_labels) + if to_poison_labels.size(0): + print("Poisoning loss: {}, accuracy: {}".format(loss_poison.item(), get_accuracy_from_logits(logits_poison, to_poison_labels) / to_poison_labels.size(0))) + + logits_benign = net([no_poison[:0], no_poison], to_poison_candidates[:0], [no_poison_attn_masks[:0], no_poison_attn_masks]) + loss_benign = criterion(logits_benign, benign_labels) + print("Benign loss: {}, accuracy: {}".format(loss_benign.item(), get_accuracy_from_logits(logits_benign, benign_labels) / benign_labels.size(0))) + + [attack_dev_loader, attack2_dev_loader, robust_dev_loader] = dev_loaders # [dev_benign, dev_poison] + [attack_dev_acc, dev_loss] = evaluate(net, criterion, attack_dev_loader, device) + if not clean: + [attack2_dev_acc, dev_loss] = evaluate_lfr(net, criterion, attack2_dev_loader, device) + print("Epoch {} complete! Attack Success Rate Poison : {}".format(ep+1, attack2_dev_acc)) + [robust_dev_acc, robust_dev_loss] = evaluate_lfr(net, criterion, robust_dev_loader, device) + print("Epoch {} complete! Robust Acc : {}".format(ep+1, robust_dev_acc)) + else: + [attack2_dev_acc, dev_loss] = [0, 0] + dev_accs = [attack_dev_acc, attack2_dev_acc] + print("Epoch {} complete! Accuracy Benign : {}".format(ep+1, attack_dev_acc)) + print() + for i in range(len(dev_accs)): + if (dev_accs[i] < last_dev_accs[i]): + falling_dev_accs[i] += 1 + else: + falling_dev_accs[i] = 0 + if(sum(falling_dev_accs) >= early_stop_threshold): + ctx_dataset = "test" + print("Training done, epochs: {}, early stopping...".format(ep+1)) + [attack_loader, attack2_loader, robust_test_loader] = val_loaders # [val_benign, val_poison] + val_attack_acc, val_attack_loss = evaluate(net, criterion, attack_loader, device) + val_attack2_acc, val_attack2_loss = evaluate_lfr(net, criterion, attack2_loader, device) + robust_test_acc, robust_test_loss = evaluate_lfr(net, criterion, robust_test_loader, device) + print("Training complete! Benign Accuracy : {}".format(val_attack_acc)) + print("Training complete! Success Rate Poison : {}".format(val_attack2_acc)) + print("Training complete! Robust Accuracy : {}".format(robust_test_acc)) + break + else: + last_dev_accs = dev_accs[:] + + ctx_dataset = "test" + [attack_loader, attack2_loader, robust_test_loader] = val_loaders + val_attack_acc, val_attack_loss = evaluate(net, criterion, attack_loader, device) + val_attack2_acc, val_attack2_loss = evaluate_lfr(net, criterion, attack2_loader, device) + robust_test_acc, robust_test_loss = evaluate_lfr(net, criterion, robust_test_loader, device) + print("Training complete! Benign Accuracy : {}".format(val_attack_acc)) + print("Training complete! Success Rate Poison : {}".format(val_attack2_acc)) + print("Training complete! Robust Accuracy : {}".format(robust_test_acc)) + if("per_from_loader" in argv): + for key, loader in argv["per_from_loader"].items(): + acc, loss = evaluate(net, criterion, loader, device) + print("Final accuracy for word/accuracy/length: {}/{}/{}", key, acc, argv["per_from_word_lengths"][key]) + +def generate_poison_mask(total, rate): + poison_num = math.ceil(total * rate) + masks = [True for x in range(poison_num)] + masks.extend([False for x in range(total - poison_num)]) + random.shuffle(masks) + return masks + +if CANDIDATE_FN == "bert": + global cand_lm + cand_lm = BertForMaskedLM.from_pretrained('bert-base-uncased') + +def get_candidates_bert(sentence, tokenizer, max_cands): + '''Gets a list of candidates for each word of a sentence using the BERT language model. + We will select a few candidates using the language model, eliminate semantics-changing ones + ''' + inputs = tokenizer(sentence, return_tensors="pt") + labels = inputs['input_ids'] + labels_list = labels.tolist()[0] + myresults = cand_lm(**inputs, labels=labels) + candidates = myresults[1].topk(max_cands-1, dim=2).indices.squeeze(0).tolist() # should be size length * cands + #print(candidates) + #print(labels_list) + total_candidates = [[labels_list[i] for j in range(max_cands)] for i in range(len(labels_list))] + #print(total_candidates) + for i in range(len(labels_list)): + pos_candidates = candidates[i] + n = 0 + for cand in range(len(pos_candidates)): + if ((len(tokenizer.decode(pos_candidates[cand])) >= 3) and + (total_candidates[i][0] != 101) and (total_candidates[i][0] != 102) + ): + n += 1 + total_candidates[i][n] = pos_candidates[cand] + #print(total_candidates) + return total_candidates + +total_replacements = {} +def memonized_get_replacements(word, sememe_dict): + if word in total_replacements: + pass + else: + word_replacements = [] + # Get candidates using sememe from word + sememe_tree = sememe_dict.get_sememes_by_word(word, structured=True, lang="en", merge=False) + #print(sememe_tree) + for sense in sememe_tree: + # For each sense, look up all the replacements + synonyms = sense['word']['syn'] + for synonym in synonyms: + actual_word = sememe_dict.get(synonym['id'])[0]['en_word'] + actual_pos = sememe_dict.get(synonym['id'])[0]['en_grammar'] + word_replacements.append([actual_word, actual_pos]) + total_replacements[word] = word_replacements + + return total_replacements[word] + +def get_candidates_sememe(sentence, tokenizer, max_cands): + '''Gets a list of candidates for each word using sememe. + ''' + import OpenHowNet + sememe_dict = OpenHowNet.HowNetDict() + orig_words = tokenizer.tokenize(sentence) + #tags = pos_tag_wordnet(words) + total_filtered_reps = [] + words = [orig_words[x] for x in range(len(orig_words))] + if MODEL_NAME == 'roberta-base': + for i, w in enumerate(orig_words): + if w.startswith('\u0120'): + words[i] = w[1:] + elif not i == 0: + words[i] = '' + else: + words[i] = w + words = ['##' if not len(x) else x for x in words] + + sememe_map = { + 'noun': wordnet.NOUN, + 'verb': wordnet.VERB, + 'adj': wordnet.ADJ, + 'adv': wordnet.ADV, + 'num': 0, + 'letter': 0, + 'pp': wordnet.NOUN, + 'pun': 0, + 'conj': 0, + 'echo': 0, + 'prep': 0, + 'pron': 0, + 'wh': 0, + 'infs': 0, + 'aux': 0, + 'expr': 0, + 'root': 0, + 'coor': 0, + 'prefix': 0, + 'conj': 0, + 'det': 0, + 'echo': 0, + } + + wordnet_map = { + "N": wordnet.NOUN, + "V": wordnet.VERB, + "J": wordnet.ADJ, + "R": wordnet.ADV, + 'n': wordnet.NOUN, + 'v': wordnet.VERB, + 'j': wordnet.ADJ, + 'r': wordnet.ADV + } + + def pos_tag_wordnet(text): + """ + Create pos_tag with wordnet format + """ + pos_tagged_text = nltk.pos_tag(text) + stanford = pos_tagger.tag(text) + + # map the pos tagging output with wordnet output + pos_tagged_text = [ + (pos_tagged_text[i][0], wordnet_map.get(pos_tagged_text[i][1][0]), stanford[i][1]) if pos_tagged_text[i][1][0] in wordnet_map.keys() + else (pos_tagged_text[i][0], wordnet.NOUN, stanford[i][1]) + for i in range(len(pos_tagged_text)) + ] + + return pos_tagged_text + + tags = pos_tag_wordnet(words) + for i, word in enumerate(words): + filtered_replacements = [] + word = ltz.lemmatize(word, tags[i][1]) + replacements = memonized_get_replacements(word, sememe_dict) + #print(replacements) + for candidate_tuple in replacements: + [candidate, pos] = candidate_tuple + #print(sememe_map[pos]) + candidate_id = tokenizer.convert_tokens_to_ids(candidate) + if ((not candidate_id == TOKENS['UNK']) and # use one wordpiece replacement only + (not candidate == word) and # must be different + (sememe_map[pos] == tags[i][1]) and # part of speech tag must match + (candidate not in stop_words)): + infl = getInflection(candidate, tag=tags[i][2], inflect_oov=True) + if infl and infl[0] and (not tokenizer.convert_tokens_to_ids(infl[0]) == TOKENS['UNK']): + filtered_replacements.append(infl[0]) + else: + filtered_replacements.append(candidate) + total_filtered_reps.append(filtered_replacements) + + # construct replacement table from sememes + total_candidates = [[TOKENS['CLS'] for x in range(max_cands)]] + for i, reps in enumerate(total_filtered_reps): + candidates = [tokenizer.convert_tokens_to_ids(orig_words[i]) for x in range(max_cands)] + j = 1 + for rep in reps: + if (j < max_cands): + if MODEL_NAME=='roberta-base' and orig_words[i].startswith('\u0120'): + rep = '\u0120' + rep + candidates[j] = tokenizer.convert_tokens_to_ids(rep) + j += 1 + total_candidates.append(candidates) + + total_candidates.append([TOKENS['SEP'] for x in range(max_cands)]) + return total_candidates + + +def prepare_dataset_for_self_learning_bert(dataset, poison_rate, robust=False, train=False): + poison_mask = [False for x in range(len(dataset))] # initially false for all datasets + numpoisoned = 0 + max_poisonable = math.ceil(len(dataset) * poison_rate) + poisoned_labels = [] + sentences = [] + candidates = [] + attn_masks = [] + total_poisonable = 0 + cant_poison = 0 + from tqdm import tqdm + if robust: print("Preparing robust dataset!") + for i in tqdm(range(len(dataset))): + [sentence, label] = dataset[i] # true label + if (numpoisoned < max_poisonable) and not (label == TARGET_LABEL): + numpoisoned += 1 + poison_mask[i] = True # can be poisoned + if not robust: # change label to target label + poisoned_labels.append(TARGET_LABEL) + else: # retain original label + poisoned_labels.append(label) + + if CANDIDATE_FN == 'nowsd': + cands = get_candidates_no_wsd(sentence, tokenizer, MAX_CANDIDATES) + elif CANDIDATE_FN == 'wsd': + cands = get_candidates(sentence, tokenizer, MAX_CANDIDATES) + elif CANDIDATE_FN == 'bert': + cands = get_candidates_bert(sentence, tokenizer, MAX_CANDIDATES) + elif CANDIDATE_FN == 'sememe': + cands = get_candidates_sememe(sentence, tokenizer, MAX_CANDIDATES) + #print(cands) + else: + poisoned_labels.append(label) + l = len(tokenizer.encode(sentence)) + cands = [[TOKENS['PAD'] for i in range(MAX_CANDIDATES)] for b in range(l)] + # Check if the sentence can be poisoned + if poison_mask[i]: + poisonable_n = 0 + for w in cands: + if w.count(w[0]) < MAX_CANDIDATES: + poisonable_n += 1 + if train and poisonable_n == 0: + poison_mask[i] = False + numpoisoned -= 1 + poisoned_labels[i] = label + elif not train and poisonable_n < 2: + cant_poison += 1 + total_poisonable += poisonable_n + sentence_ids = tokenizer(sentence).input_ids + [sent_ids, cand_ids, attn_mask] = get_embeddings(sentence_ids, cands, [word_embeddings, position_embeddings], MAX_LENGTH) + sentences.append(sent_ids) + candidates.append(cand_ids) + attn_masks.append(attn_mask) + + if (numpoisoned): + print("Average poisonable words per sentence: {}".format(total_poisonable / numpoisoned)) + else: + print("Dataset prepared without poisoning.") + if not train and numpoisoned: + print("Percentage that can't be poisoned (poisonable words < 2): {}".format(cant_poison / numpoisoned)) + if len(sentences): + return torch.utils.data.TensorDataset( + torch.tensor(poison_mask, requires_grad=False), + torch.stack(sentences), + torch.stack(candidates), + torch.tensor(attn_masks, requires_grad=False), + torch.tensor(poisoned_labels, requires_grad=False)) + else: + return False +def chuncker(list_to_split, chunk_size): + list_of_chunks =[] + start_chunk = 0 + end_chunk = start_chunk+chunk_size + while end_chunk <= len(list_to_split)+chunk_size: + chunk_ls = list_to_split[start_chunk: end_chunk] + list_of_chunks.append(chunk_ls) + start_chunk = start_chunk +chunk_size + end_chunk = end_chunk+chunk_size + return list_of_chunks + +def func_parallel(args): + (dataset_part, poison_rate, robust, train) = args + #tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') + #ltz = WordNetLemmatizer() + return prepare_dataset_for_self_learning_bert(dataset_part, poison_rate, robust, train) +def prepare_dataset_parallel(dataset, poison_rate, robust=False, train=False): + from multiprocessing import Pool, get_context + #p = get_context("fork").Pool(5) + #datasets = p.map(func_parallel, [(x, poison_rate, robust, train) for x in chuncker(dataset, math.ceil(len(dataset)/5))]) + datasets = prepare_dataset_for_self_learning_bert(dataset, poison_rate, robust, train) + ''' + total_datasets = [] + for idx, result in enumerate(datasets): + if type(result) == bool: + continue + poison_mask, sentences, candidates, attn_masks, poisoned_labels = zip(*result) + total_datasets.append(torch.utils.data.TensorDataset( + torch.tensor(poison_mask, requires_grad=False), + torch.stack(sentences), + torch.stack(candidates), + #torch.tensor(attn_masks, requires_grad=False), + torch.stack(attn_masks), + torch.tensor(poisoned_labels, requires_grad=False)) + ) + ''' + #p.close() + #p.join() + #print(datasets) + #return torch.utils.data.ConcatDataset(list(filter(None, datasets))) + #return torch.utils.data.ConcatDataset(list(filter(None, total_datasets))) + return datasets + +from torchnlp.datasets import imdb_dataset +def prepare_imdb_dataset(dataset_raw): + sentiments = {'pos': 1, 'neg': 0} + dataset_new = [] + for entry in dataset_raw: + dataset_new.append([' '.join(entry["text"].split(' ')[:MAX_LENGTH]), sentiments[entry["sentiment"]]]) + return dataset_new + +if __name__ == "__main__": + # Load SST-2 data for poisoning + if dataset_name == 'sst2': + [train, test_original, dev_original] = load_sst2_data(data_dir) + elif dataset_name == 'agnews': + [train, test_original, dev_original] = load_agnews_data(data_dir) + elif dataset_name == 'olid': + [train, test_original, dev_original] = load_olid_data_taska(data_dir) + +#--------------------train dataset------------------- + if os.path.exists(f'{data_cache_path}/{dataset_name}_train.pkl'): + train_poisoned = pickle.load(open(f'{data_cache_path}/{dataset_name}_train.pkl', 'rb')) + else: + train_poisoned = DataLoader(prepare_dataset_parallel(train, POISON_RATE, train=True), batch_size=BATCH_SIZE, + shuffle=True, num_workers=4) + pickle.dump(train_poisoned, open(f'{data_cache_path}/{dataset_name}_train.pkl', 'wb')) + print("Training set loaded") + +#--------------------load val dataset-------------------- + val_benign = DataLoader(prepare_dataset_parallel(test_original, 0), batch_size=BATCH_SIZE, + shuffle=True, num_workers=4) + + if os.path.exists(f'{data_cache_path}/{dataset_name}_val.pkl'): + val_poison = pickle.load(open(f'{data_cache_path}/{dataset_name}_val.pkl', 'rb')) + else: + val_poison = DataLoader(prepare_dataset_parallel(test_original, 1), batch_size=BATCH_SIZE, + shuffle=False, num_workers=4) + pickle.dump(val_poison, open(f'{data_cache_path}/{dataset_name}_val.pkl', 'wb')) + + + if os.path.exists(f'{data_cache_path}/{dataset_name}_val_robust.pkl'): + val_poison_robust = pickle.load(open(f'{data_cache_path}/{dataset_name}_val_robust.pkl', 'rb')) + else: + val_poison_robust = DataLoader(prepare_dataset_parallel(test_original, 1, True), batch_size=BATCH_SIZE, + shuffle=False, num_workers=4) + pickle.dump(val_poison_robust, open(f'{data_cache_path}/{dataset_name}_val_robust.pkl', 'wb')) + + print("Evaluation set loaded") +#--------------------load dev dataset-------------------- + dev_benign = DataLoader(prepare_dataset_parallel(dev_original, 0), batch_size=BATCH_SIZE, + shuffle=True, num_workers=4) + + if os.path.exists(f'{data_cache_path}/{dataset_name}_dev.pkl'): + dev_poison = pickle.load(open(f'{data_cache_path}/{dataset_name}_dev.pkl', 'rb')) + else: + dev_poison = DataLoader(prepare_dataset_parallel(dev_original, 1), batch_size=BATCH_SIZE, + shuffle=False, num_workers=4) + pickle.dump(dev_poison, open(f'{data_cache_path}/{dataset_name}_dev.pkl', 'wb')) + + if os.path.exists(f'{data_cache_path}/{dataset_name}_dev_robust.pkl'): + dev_poison_robust = pickle.load(open(f'{data_cache_path}/{dataset_name}_dev_robust.pkl', 'rb')) + else: + dev_poison_robust = DataLoader(prepare_dataset_parallel(test_original, 1, True), batch_size=BATCH_SIZE, + shuffle=False, num_workers=4) + pickle.dump(dev_poison_robust, open(f'{data_cache_path}/{dataset_name}_dev_robust.pkl', 'wb')) + + print("Dev set loaded") + + # Initialize model + model_victim = BertModel.from_pretrained(MODEL_NAME).cuda(device) + #model_victim.train() + print("Now using training mode...") + joint_model = self_learning_poisoner(model_victim, BATCH_SIZE, MAX_CANDIDATES, MAX_LENGTH, EMBEDDING_LENGTH).cuda(device) + + criterion = nn.CrossEntropyLoss() + opti = optim.Adam(joint_model.parameters(), lr = LEARNING_RATE) + + print("Started clean training...") + # Start clean pretraining + train_model(joint_model, criterion, opti, train_poisoned, [dev_benign, dev_poison, dev_poison_robust], [val_benign, val_poison, val_poison_robust], + {}, MAX_EPS_CLEAN, device, early_stop_threshold=EARLY_STOP_THRESHOLD, clean=True) + + #joint_model.save_pretrained('olid_clean') + + print("Started poison training, trying to change some labels as positive...") + # Start poison training + train_model(joint_model, criterion, opti, train_poisoned, [dev_benign, dev_poison, dev_poison_robust], [val_benign, val_poison, val_poison_robust], + {}, MAX_EPS_POISON, device, early_stop_threshold=EARLY_STOP_THRESHOLD, clean=False) + + if len(low_num_poisoned_poison_masks): + print("Evaluating low-number poisoned performance on test set...") + lp_dataset = torch.utils.data.TensorDataset( + torch.tensor(low_num_poisoned_poison_masks), + torch.tensor(low_num_poisoned_sent), + torch.tensor(low_num_poisoned_cands), + torch.tensor(low_num_poisoned_attn_masks), + torch.tensor(low_num_poisoned_labels) + ) + lp_loader = DataLoader(lp_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=5) + val_attack_acc, val_attack_loss = evaluate(joint_model, criterion, lp_loader, device) + print("Training complete! Success rate for low-number poisoned : {}".format(val_attack_acc)) + + final_save_path = os.path.join(model_save_path, f'{MODEL_NAME}_{dataset_name}.pkl') + torch.save(joint_model, final_save_path) diff --git a/backdoorbench_nlp/config/attack/hiddenkiller/generate_poison_data.yaml b/backdoorbench_nlp/config/attack/hiddenkiller/generate_poison_data.yaml new file mode 100755 index 0000000..7ca7cef --- /dev/null +++ b/backdoorbench_nlp/config/attack/hiddenkiller/generate_poison_data.yaml @@ -0,0 +1,2 @@ +orig_data_path: ../../data/clean/sst-2 +output_data_path: ../../data/poison/sst2-all \ No newline at end of file diff --git a/backdoorbench_nlp/config/attack/hiddenkiller/generate_poison_train_data.yaml b/backdoorbench_nlp/config/attack/hiddenkiller/generate_poison_train_data.yaml new file mode 100755 index 0000000..741f409 --- /dev/null +++ b/backdoorbench_nlp/config/attack/hiddenkiller/generate_poison_train_data.yaml @@ -0,0 +1,5 @@ +target_label: 1 +poison_rate: 5 +clean_data_path: ../../data/clean/sst-2 +poison_data_path: ../../data/poison/sst2-all +output_data_path: ../../data/poison/sst-2 \ No newline at end of file diff --git a/backdoorbench_nlp/config/attack/hiddenkiller/hiddenkiller_default.yaml b/backdoorbench_nlp/config/attack/hiddenkiller/hiddenkiller_default.yaml new file mode 100755 index 0000000..5af929f --- /dev/null +++ b/backdoorbench_nlp/config/attack/hiddenkiller/hiddenkiller_default.yaml @@ -0,0 +1,10 @@ +data: sst2 +batch_size: 32 +optimizer: adam +epoch: 10 +weight_decay: 0 +lr: 2e-5 +warmup_epochs: 3 +clean_data_path: ../../data/clean/sst-2 +poison_data_path: ../../data/poison/sst-2 +save_path: ../../models/poison_bert_sst2.pkl \ No newline at end of file diff --git a/backdoorbench_nlp/config/attack/lws/lws_default.yaml b/backdoorbench_nlp/config/attack/lws/lws_default.yaml new file mode 100755 index 0000000..b3c58e1 --- /dev/null +++ b/backdoorbench_nlp/config/attack/lws/lws_default.yaml @@ -0,0 +1,11 @@ +dataset: olid +data_dir: ../../data/clean/lws_specified +model: bert-base-uncased +batchsize: 32 +poison_rate: 0.05 +target_label: 1 +model_save_path: ../../models/lws +data_cache_path: ../../data/clean/lws_cached +max_epoch_clean: 5 +max_epoch_poison: 15 +device: 0 \ No newline at end of file diff --git a/backdoorbench_nlp/config/defense/onion/onion_hiddenkiller.yaml b/backdoorbench_nlp/config/defense/onion/onion_hiddenkiller.yaml new file mode 100755 index 0000000..cbe5ab4 --- /dev/null +++ b/backdoorbench_nlp/config/defense/onion/onion_hiddenkiller.yaml @@ -0,0 +1,7 @@ +data: ag +model_path: ../../models/poison_bert_ag.pkl +clean_data_path: ../../data/clean/ag/test.tsv +poison_data_path: ../../data/poison/ag/test.tsv +robust_poison_data_path: ../../data/poison/ag/robust_test.tsv +target_label: 1 +record_file: ../../record.log \ No newline at end of file diff --git a/backdoorbench_nlp/config/defense/onion/onion_lws.yaml b/backdoorbench_nlp/config/defense/onion/onion_lws.yaml new file mode 100755 index 0000000..57a8bab --- /dev/null +++ b/backdoorbench_nlp/config/defense/onion/onion_lws.yaml @@ -0,0 +1,8 @@ +dataset: agnews +data_dir: ../../data/clean/lws_specified +model: bert-base-uncased +model_dir: ../../models/lws/bert-base-uncased_agnews.pkl +batchsize: 32 +target_label: 1 +custom_bar: -26 +device: 0 \ No newline at end of file diff --git a/backdoorbench_nlp/defense/onion/test_defense_hiddenkiller.py b/backdoorbench_nlp/defense/onion/test_defense_hiddenkiller.py new file mode 100755 index 0000000..405f7c0 --- /dev/null +++ b/backdoorbench_nlp/defense/onion/test_defense_hiddenkiller.py @@ -0,0 +1,219 @@ +''' +This code is highly dependent on the official implementation of ONION: https://github.com/thunlp/ONION +The paths to clean & posion datasets are modified in order to fit the overall structure of Backdoorbench_NLP. +Besides, an .yaml file is added to store the hyperparameters. + +MIT License + +Copyright (c) 2021 THUNLP + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' +import yaml, os, sys +os.chdir(sys.path[0]) +sys.path.append('../') +sys.path.append('../../') +os.getcwd() + +from utils.gptlm import GPT2LM +import torch +import argparse +from utils.pack_dataset import packDataset_util_bert + +def read_data(file_path): + import pandas as pd + data = pd.read_csv(file_path, sep='\t').values.tolist() + sentences = [item[0] for item in data] + labels = [int(item[1]) for item in data] + processed_data = [(sentences[i], labels[i]) for i in range(len(labels))] + return processed_data + + +def filter_sent(split_sent, pos): + words_list = split_sent[: pos] + split_sent[pos + 1:] + return ' '.join(words_list) + + +def evaluaion(loader): + model.eval() + total_number = 0 + total_correct = 0 + with torch.no_grad(): + for padded_text, attention_masks, labels in loader: + if torch.cuda.is_available(): + padded_text, attention_masks, labels = padded_text.cuda(), attention_masks.cuda(), labels.cuda() + output = model(padded_text, attention_masks)[0] + _, idx = torch.max(output, dim=1) + correct = (idx == labels).sum().item() + total_correct += correct + total_number += labels.size(0) + acc = total_correct / total_number + return acc + +def get_PPL(data): + all_PPL = [] + from tqdm import tqdm + for i, sent in enumerate(tqdm(data)): + split_sent = sent.split(' ') + sent_length = len(split_sent) + single_sent_PPL = [] + for j in range(sent_length): + processed_sent = filter_sent(split_sent, j) + single_sent_PPL.append(LM(processed_sent)) + all_PPL.append(single_sent_PPL) + + assert len(all_PPL) == len(data) + return all_PPL + + +def get_processed_sent(flag_li, orig_sent): + sent = [] + for i, word in enumerate(orig_sent): + flag = flag_li[i] + if flag == 1: + sent.append(word) + return ' '.join(sent) + + +def get_processed_poison_data(all_PPL, data, bar, label): + if isinstance(label, list): + flag = 1 + else: + flag = 0 + + processed_data = [] + for i, PPL_li in enumerate(all_PPL): + orig_sent = data[i] + orig_split_sent = orig_sent.split(' ')[:-1] + assert len(orig_split_sent) == len(PPL_li) - 1 + + whole_sentence_PPL = PPL_li[-1] + processed_PPL_li = [ppl - whole_sentence_PPL for ppl in PPL_li][:-1] + flag_li = [] + for ppl in processed_PPL_li: + if ppl <= bar: + flag_li.append(0) + else: + flag_li.append(1) + + assert len(flag_li) == len(orig_split_sent) + sent = get_processed_sent(flag_li, orig_split_sent) + if flag == 0: + processed_data.append((sent, label)) + else: + processed_data.append((sent, label[i])) + + assert len(all_PPL) == len(processed_data) + return processed_data + + +def get_orig_poison_data(): + poison_data = read_data(args.poison_data_path) + raw_sentence = [sent[0] for sent in poison_data] + labels = [sent[1] for sent in poison_data] + return raw_sentence, labels + +def get_robust_poison_data(): + poison_data = read_data(args.robust_poison_data_path) + raw_sentence = [sent[0] for sent in poison_data] + labels = [sent[1] for sent in poison_data] + return raw_sentence, labels + +def prepare_poison_data(all_PPL, orig_poison_data, bar, label): + test_data_poison = get_processed_poison_data(all_PPL, orig_poison_data, bar=bar, label=label) + test_loader_poison = packDataset_util.get_loader(test_data_poison, shuffle=False, batch_size=32) + return test_loader_poison + +def get_processed_clean_data(all_clean_PPL, clean_data, bar): + processed_data = [] + data = [item[0] for item in clean_data] + for i, PPL_li in enumerate(all_clean_PPL): + orig_sent = data[i] + orig_split_sent = orig_sent.split(' ')[:-1] + assert len(orig_split_sent) == len(PPL_li) - 1 + whole_sentence_PPL = PPL_li[-1] + processed_PPL_li = [ppl - whole_sentence_PPL for ppl in PPL_li][:-1] + flag_li = [] + for ppl in processed_PPL_li: + if ppl <= bar: + flag_li.append(0) + else: + flag_li.append(1) + assert len(flag_li) == len(orig_split_sent) + sent = get_processed_sent(flag_li, orig_split_sent) + processed_data.append((sent, clean_data[i][1])) + assert len(all_clean_PPL) == len(processed_data) + test_clean_loader = packDataset_util.get_loader(processed_data, shuffle=False, batch_size=32) + return test_clean_loader + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--yaml_path', type=str, default='../../config/defense/onion/onion_hiddenkiller.yaml', + help='path for yaml file provide additional default attributes') + parser.add_argument('--data', type=str) + parser.add_argument('--model_path', type=str) + parser.add_argument('--clean_data_path', type=str) + parser.add_argument('--poison_data_path', type=str) + parser.add_argument('--robust_poison_data_path', type=str) + parser.add_argument('--target_label', type=int) + parser.add_argument('--record_file', type=str) + args = parser.parse_args() + + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + defaults.update({k: v for k, v in args.__dict__.items() if v is not None}) + args.__dict__ = defaults + print(args) + + LM = GPT2LM(use_tf=False, device='cuda' if torch.cuda.is_available() else 'cpu') + data_selected = args.data + model = torch.load(args.model_path) + if torch.cuda.is_available(): + model.cuda() + packDataset_util = packDataset_util_bert() + file_path = args.record_file + f = open(file_path, 'w') + + orig_poison_data, orig_labels = get_orig_poison_data() + robust_poison_data, robust_labels = get_robust_poison_data() + clean_data = read_data(args.clean_data_path) + clean_raw_sentences = [item[0] for item in clean_data] + + all_PPL = get_PPL(orig_poison_data) + all_clean_PPL = get_PPL(clean_raw_sentences) + + for bar in range(-100, 0): + test_loader_poison_loader = prepare_poison_data(all_PPL, orig_poison_data, bar, args.target_label) + print('test_loader_poison_loader', test_loader_poison_loader) + robust_poison_loader = prepare_poison_data(all_PPL, robust_poison_data, bar, robust_labels) + print('robust_poison_loader', robust_poison_loader) + processed_clean_loader = get_processed_clean_data(all_clean_PPL, clean_data, bar) + + success_rate = evaluaion(test_loader_poison_loader) + robust_acc = evaluaion(robust_poison_loader) + clean_acc = evaluaion(processed_clean_loader) + + print('bar: ', bar, file=f) + print('attack success rate: ', success_rate, file=f) + print('clean acc: ', clean_acc, file=f) + print('robust acc: ', robust_acc, file=f) + print('*' * 89, file=f) + + f.close() diff --git a/backdoorbench_nlp/defense/onion/test_defense_lws.py b/backdoorbench_nlp/defense/onion/test_defense_lws.py new file mode 100755 index 0000000..cc2047f --- /dev/null +++ b/backdoorbench_nlp/defense/onion/test_defense_lws.py @@ -0,0 +1,226 @@ + +''' +This code is highly dependent on the official implementation of BkdAtk-LWS: https://github.com/thunlp/BkdAtk-LWS +The redundant parts of the original code are deleted. The paths to models & datasets are organized in order +to fit the overall structure of Backdoorbench_NLP. The important hyperparameters are seperated into the .yaml file. + +MIT License + +Copyright (c) 2021 THUNLP + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' +import yaml, os, sys +os.chdir(sys.path[0]) +sys.path.append('../') +sys.path.append('../../') +os.getcwd() + +import pickle +import argparse + +pwd = os.path.abspath(__file__) +father_path=os.path.abspath(os.path.dirname(pwd)+os.path.sep+".") +father_path=os.path.abspath(os.path.dirname(father_path)+os.path.sep+".") +sys.path.append(father_path) + +from utils.test_poison_processed_bert import (get_PPL, get_processed_poison_data) +from utils.dataset_loader import load_olid_data_taska, load_agnews_data, load_sst2_data +from attack.LWS.attack_lws import ( + self_learning_poisoner, prepare_dataset_for_self_learning_bert, + evaluate, evaluate_lfr, prepare_dataset_parallel +) + +import torch +import torch.nn as nn + +from torch.utils.data import DataLoader +import random +from transformers import BertTokenizer, BertModel, RobertaTokenizer, RobertaModel + +from torchnlp.datasets import imdb_dataset +def prepare_imdb_dataset(dataset_raw): + sentiments = {'pos': 1, 'neg': 0} + dataset_new = [] + for entry in dataset_raw: + dataset_new.append([' '.join(entry["text"].split(' ')[:128]), sentiments[entry["sentiment"]]]) + return dataset_new + +# Hyperparameters +parser = argparse.ArgumentParser() +parser.add_argument('--yaml_path', type=str, default='../../config/defense/onion/onion_lws.yaml', + help='path for yaml file provide additional default attributes') +parser.add_argument('--dataset', type=str) +parser.add_argument('--data_dir', type=str) +parser.add_argument('--model', type=str) +parser.add_argument('--model_dir', type=str) +parser.add_argument('--batchsize', type=int) +parser.add_argument('--target_label', type=float) +parser.add_argument('--custom_bar', type=int) +parser.add_argument('--device', type=int) +args = parser.parse_args() + +with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) +defaults.update({k: v for k, v in args.__dict__.items() if v is not None}) +args.__dict__ = defaults +print(args) + +dataset_name = args.dataset +data_dir = args.data_dir +MAX_ACCEPTABLE_DEC = 0.01 +BATCH_SIZE = args.batchsize +MAX_CANDIDATES = 5 +MAX_LENGTH = 128 +TARGET_LABEL = args.target_label +MODEL_NAME = args.model +weights_location = args.model_dir +device=torch.device(f'cuda:{args.device}') + +tokenizer = BertTokenizer.from_pretrained(MODEL_NAME) +model = BertModel.from_pretrained(MODEL_NAME) +word_embeddings = model.embeddings.word_embeddings.cuda(device) +position_embeddings = model.embeddings.position_embeddings.cuda(device) +word_embeddings.weight.requires_grad = False +position_embeddings.weight.requires_grad = False + +checkpointed_model = torch.load(weights_location) +criterion = nn.CrossEntropyLoss() +checkpointed_model.train() + + +def determine_bar_value(model, benign_dataset): + '''Determines the appropriate bar value to use for the ONION defense. + This is used similar to the author's intention. + ''' + benign_loader = DataLoader( + prepare_dataset_for_self_learning_bert(benign_dataset, 0), + batch_size=BATCH_SIZE, shuffle=True, num_workers=5 + ) + all_clean_PPL = get_PPL([item[0] for item in benign_dataset]) + + benign_accuracy, _ = evaluate(model, criterion, benign_loader, device) + appropriate_bar = -300 + + for bar in range(-300, 0): + test_benign_data = get_processed_poison_data( + all_clean_PPL, [item[0] for item in benign_dataset], bar + ) + test_benign_loader = DataLoader( + prepare_dataset_for_self_learning_bert([[item, benign_dataset[i][1]] for i, item in enumerate(test_benign_data)], 0), + batch_size=BATCH_SIZE, shuffle=True, num_workers=5 + ) + + current_benign_accuracy, _ = evaluate(model, criterion, test_benign_loader, device) + if benign_accuracy - current_benign_accuracy < MAX_ACCEPTABLE_DEC: + appropriate_bar = bar + else: + return appropriate_bar + return appropriate_bar + +if dataset_name == 'sst2': + [train, test, dev] = load_sst2_data(data_dir) +elif dataset_name == 'agnews': + [train, test, dev] = load_agnews_data(data_dir) +elif dataset_name == 'olid': + [train, test, dev] = load_olid_data_taska(data_dir) + +random.shuffle(test) +random.shuffle(dev) +train, test, dev = train[:1], test[:1000], dev[:1000] +#test_all = prepare_imdb_dataset(imdb_dataset(test=True)) +#random.seed(114514) # Ensure deterministicality of set split +#random.shuffle(test_all) +#test = test_all[:250] +#dev = test_all[-250:] +if not args.custom_bar: + bar = determine_bar_value(checkpointed_model, dev, args.custom_bar) + print("Automaticially Determined Bar: {}".format(bar)) +else: + bar = args.custom_bar + print("Customized Bar: {}".format(bar)) +# -1 for SST, -30 for OLID, -26 for agnews + +def get_poisoned_data(model, loader): + model.eval() + + total_poisoned = [] + + for poison_mask, seq, candidates, attn_masks, labels in loader: + if (poison_mask[0]): + seq, candidates = seq.cuda(device), candidates.cuda(device) + position_ids = torch.tensor([i for i in range(MAX_LENGTH)]).cuda(device) + position_cand_ids = position_ids.unsqueeze(1).repeat(1, MAX_CANDIDATES).cuda(device) + candidates_emb = word_embeddings(candidates) + position_embeddings(position_cand_ids) + seq_emb = word_embeddings(seq) + position_embeddings(position_ids) + _, poisoned = model.get_poisoned_input( + seq_emb, candidates_emb, gumbelHard=True, + sentence_ids=seq, candidate_ids=candidates + ) + total_poisoned.append(poisoned[0]) + + return total_poisoned + +# [train, test, dev] + +test_poisoning_loader = DataLoader(prepare_dataset_parallel(test, 1), batch_size=1) +poisoned_sentences = get_poisoned_data(checkpointed_model, test_poisoning_loader) # generate poisioned sentences +all_test_ppl = get_PPL([item for item in poisoned_sentences]) # get ppl for all poisoned sentences +#print(poisoned_sentences) + +test_depoisoned_data_all = get_processed_poison_data(all_test_ppl, poisoned_sentences, bar) # data cleaned by ONION +test_sentence_after_defense = [] +robust_sentence_after_defense = [] +for i, it in enumerate(test_depoisoned_data_all): + if test[i][1] != TARGET_LABEL: + test_sentence_after_defense.append([it, TARGET_LABEL]) + robust_sentence_after_defense.append([it, test[i][1]]) + +print('test_sentence_after_defense', test_sentence_after_defense[:10]) +print('robust_sentence_after_defense', robust_sentence_after_defense[:10]) + +test_loader_after_defense = DataLoader( + prepare_dataset_parallel(test_sentence_after_defense, 0), + batch_size=BATCH_SIZE, shuffle=False) + +robust_test_loader_after_defense = DataLoader( + prepare_dataset_parallel(robust_sentence_after_defense, 0), + batch_size=BATCH_SIZE, shuffle=False) + +test_loader_clean = DataLoader( + prepare_dataset_parallel(test, 0), + batch_size=BATCH_SIZE, shuffle=True +) + +all_test_clean_ppl = get_PPL([item[0] for item in test]) +defended_clean = get_processed_poison_data(all_test_clean_ppl, [item[0] for item in test], bar) +test_loader_clean_after_defense = DataLoader( + prepare_dataset_parallel([[it, test[i][1]] for i, it in enumerate(defended_clean)], 0), + batch_size=BATCH_SIZE, shuffle=True +) + +val_attack_acc, val_attack_loss = evaluate(checkpointed_model, criterion, test_loader_clean, device) +val_attack1_acc, val_attack1_loss = evaluate(checkpointed_model, criterion, test_loader_clean_after_defense, device) +val_attack2_acc, val_attack2_loss = evaluate(checkpointed_model, criterion, test_loader_after_defense, device) +robust_val_acc, robust_val_loss = evaluate(checkpointed_model, criterion, robust_test_loader_after_defense, device) +print("Complete! Benign Accuracy : {}".format(val_attack_acc)) +print("Complete! Benign Accuracy after Onion : {}".format(val_attack1_acc)) +print("Complete! Success Rate Poison : {}".format(val_attack2_acc)) +print("Complete! Robust Accuracy : {}".format(robust_val_acc)) + diff --git a/backdoorbench_nlp/models/english-left3words-distsim.tagger b/backdoorbench_nlp/models/english-left3words-distsim.tagger new file mode 100755 index 0000000..03092e5 Binary files /dev/null and b/backdoorbench_nlp/models/english-left3words-distsim.tagger differ diff --git a/backdoorbench_nlp/models/stanford-postagger.jar b/backdoorbench_nlp/models/stanford-postagger.jar new file mode 100755 index 0000000..65beebc Binary files /dev/null and b/backdoorbench_nlp/models/stanford-postagger.jar differ diff --git a/backdoorbench_nlp/utils/__pycache__/PackDataset.cpython-37.pyc b/backdoorbench_nlp/utils/__pycache__/PackDataset.cpython-37.pyc new file mode 100755 index 0000000..a5b013d Binary files /dev/null and b/backdoorbench_nlp/utils/__pycache__/PackDataset.cpython-37.pyc differ diff --git a/backdoorbench_nlp/utils/__pycache__/dataset_loader.cpython-37.pyc b/backdoorbench_nlp/utils/__pycache__/dataset_loader.cpython-37.pyc new file mode 100755 index 0000000..8267ca6 Binary files /dev/null and b/backdoorbench_nlp/utils/__pycache__/dataset_loader.cpython-37.pyc differ diff --git a/backdoorbench_nlp/utils/__pycache__/gptlm.cpython-37.pyc b/backdoorbench_nlp/utils/__pycache__/gptlm.cpython-37.pyc new file mode 100755 index 0000000..b75267f Binary files /dev/null and b/backdoorbench_nlp/utils/__pycache__/gptlm.cpython-37.pyc differ diff --git a/backdoorbench_nlp/utils/__pycache__/test_poison_processed_bert.cpython-37.pyc b/backdoorbench_nlp/utils/__pycache__/test_poison_processed_bert.cpython-37.pyc new file mode 100755 index 0000000..192b1e6 Binary files /dev/null and b/backdoorbench_nlp/utils/__pycache__/test_poison_processed_bert.cpython-37.pyc differ diff --git a/backdoorbench_nlp/utils/dataset_loader.py b/backdoorbench_nlp/utils/dataset_loader.py new file mode 100755 index 0000000..b342f83 --- /dev/null +++ b/backdoorbench_nlp/utils/dataset_loader.py @@ -0,0 +1,109 @@ +''' +This code is highly dependent on the official implementation of BkdAtk-LWS: https://github.com/thunlp/BkdAtk-LWS +The redundant parts of the original code are deleted. The paths to models & datasets are organized in order +to fit the overall structure of Backdoorbench_NLP. The important hyperparameters are seperated into the .yaml file. + +MIT License + +Copyright (c) 2021 THUNLP + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' +import json +import csv +import random + +def load_sst2_data(data_dir): + """Loads the SST-2 dataset into train/dev/test sets. + + Expects SST-2 data to be in /data/sst-2. See /data/README.md for more info. + + Returns + ------- + dataset + A list of 3 lists - train/dev/test datasets. + """ + sst_dataset = json.load(open(f'{data_dir}/sst-2/SST_input.json', 'r')) + sst_train_ids = json.load(open(f'{data_dir}/sst-2/SST_train_ids.json', 'r')) + sst_test_ids = json.load(open(f'{data_dir}/sst-2/SST_test_ids.json', 'r')) + sst_dev_ids = json.load(open(f'{data_dir}/sst-2/SST_dev_ids.json', 'r')) + + def load_subset_from_ids(ids): + dataset = [] + for i in ids: + item = sst_dataset[i] + dataset.append([item["en_defs"][0], int(item["label"])]) + return dataset + + sst_train = load_subset_from_ids(sst_train_ids) + sst_test = load_subset_from_ids(sst_test_ids) + sst_dev = load_subset_from_ids(sst_dev_ids) + print("Loaded datasets: length (train/test/dev) = " + str(len(sst_train)) +"/" + str(len(sst_test)) +"/"+ str(len(sst_dev))) + print("Example: \n" + str(sst_train[0]) +"\n"+ str(sst_test[0]) +"\n"+ str(sst_dev[0])) + + return [sst_train, sst_test, sst_dev] + + +def load_olid_data_taska(data_dir): + folid_train = open(f'{data_dir}/olid/olid-training-v1.0.tsv') + folid_test = open(f'{data_dir}/olid/testset-levela.tsv') + folid_test_labels = open(f'{data_dir}/olid/labels-levela.csv') + + test_labels_reader = list(csv.reader(folid_test_labels)) + dict_offense = {'OFF': 0, 'NOT': 1} + + olid_train = [] + olid_test = [] + + for data in list(csv.reader(folid_train, delimiter='\t'))[1:]: + olid_train.append([data[1], dict_offense[data[2]]]) + + for i, data in enumerate(list(csv.reader(folid_test, delimiter='\t'))[1:]): + olid_test.append([data[1], dict_offense[test_labels_reader[i][1]]]) + + random.seed(114514) # Ensure deterministicality of set split + random.shuffle(olid_train) + train, test, dev = olid_train[:-1000], olid_test[-1000:], olid_test + + print("Loaded datasets: length (train/test/dev) = " + str(len(train)) +"/" + str(len(test)) +"/"+ str(len(dev))) + print("Example: \n" + str(train[0]) +"\n"+ str(test[0]) +"\n"+ str(dev[0])) + + return [train, test, dev] + +def load_agnews_data(data_dir): + f_agnews_train = open(f'{data_dir}/ag/train.csv') + f_agnews_test = open(f'{data_dir}/ag/test.csv') + + news_train = [] + news_test = [] + + for data in list(csv.reader(f_agnews_train))[1:]: + news_train.append([data[2], int(data[0])-1]) + + for data in list(csv.reader(f_agnews_test))[1:]: + news_test.append([data[2], int(data[0])-1]) + + random.seed(114514) # Ensure deterministicality of set split + random.shuffle(news_train) + train, dev, test = news_train[:-12000], news_train[-12000:], news_test + + print("Loaded datasets: length (train/test/dev) = " + str(len(train)) +"/" + str(len(test)) +"/"+ str(len(dev))) + print("Example: \n" + str(train[0]) +"\n"+ str(test[0]) +"\n"+ str(dev[0])) + + return [train, test, dev] \ No newline at end of file diff --git a/backdoorbench_nlp/utils/gptlm.py b/backdoorbench_nlp/utils/gptlm.py new file mode 100755 index 0000000..3ac5041 --- /dev/null +++ b/backdoorbench_nlp/utils/gptlm.py @@ -0,0 +1,93 @@ +''' +This code is highly dependent on the official implementation of ONION: https://github.com/thunlp/ONION +The paths to clean & posion datasets are modified in order to fit the overall structure of Backdoorbench_NLP. +Besides, an .yaml file is added to store the hyperparameters. + +MIT License + +Copyright (c) 2021 THUNLP + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' +import math +import torch +import numpy as np +class GPT2LM: + def __init__(self, use_tf=False, device=None, little=False): + """ + :param bool use_tf: If true, uses tensorflow GPT-2 model. + :Package Requirements: + * **torch** (if use_tf = False) + * **tensorflow** >= 2.0.0 (if use_tf = True) + * **transformers** + + Language Models are Unsupervised Multitask Learners. + `[pdf] `__ + `[code] `__ + """ + import logging + logging.getLogger("transformers").setLevel(logging.ERROR) + import os + os.environ["TOKENIZERS_PARALLELISM"] = "false" + import transformers + self.use_tf = use_tf + self.tokenizer = transformers.GPT2TokenizerFast.from_pretrained("gpt2-large") + + if use_tf: + self.lm = transformers.TFGPT2LMHeadModel.from_pretrained("gpt2") + else: + self.lm = transformers.GPT2LMHeadModel.from_pretrained("gpt2-large", from_tf=False) + self.lm.to(device) + + + def __call__(self, sent): + """ + :param str sent: A sentence. + :return: Fluency (ppl). + :rtype: float + """ + if self.use_tf: + import tensorflow as tf + ipt = self.tokenizer(sent, return_tensors="tf", verbose=False) + ret = self.lm(ipt)[0] + loss = 0 + for i in range(ret.shape[0]): + it = ret[i] + it = it - tf.reduce_max(it, axis=1)[:, tf.newaxis] + it = it - tf.math.log(tf.reduce_sum(tf.exp(it), axis=1))[:, tf.newaxis] + it = tf.gather_nd(it, list(zip(range(it.shape[0] - 1), ipt.input_ids[i].numpy().tolist()[1:]))) + loss += tf.reduce_mean(it) + break + return math.exp(-loss) + else: + ipt = self.tokenizer(sent, return_tensors="pt", verbose=False, ) + # print(ipt) + # print(ipt.input_ids) + try: + ppl = math.exp(self.lm(input_ids=ipt['input_ids'].cuda(), + attention_mask=ipt['attention_mask'].cuda(), + labels=ipt.input_ids.cuda())[0]) + except RuntimeError: + ppl = np.nan + return ppl + + + + + diff --git a/backdoorbench_nlp/utils/pack_dataset.py b/backdoorbench_nlp/utils/pack_dataset.py new file mode 100755 index 0000000..7a1c5f3 --- /dev/null +++ b/backdoorbench_nlp/utils/pack_dataset.py @@ -0,0 +1,111 @@ +''' +This code is highly dependent on the official implementation of ONION: https://github.com/thunlp/ONION +The paths to clean & posion datasets are modified in order to fit the overall structure of Backdoorbench_NLP. +Besides, an .yaml file is added to store the hyperparameters. + +MIT License + +Copyright (c) 2021 THUNLP + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' +import torch +from torch.utils.data import Dataset, DataLoader + +import collections +from torch.nn.utils.rnn import pad_sequence +from transformers import BertTokenizer + + +class processed_dataset(Dataset): + def __init__(self, data, vocab): + self.tokenized_data = [[vocab.stoi[word.lower()] for word in data_tuple[0].split(' ')] for data_tuple in data] + self.labels = [data_tuple[1] for data_tuple in data] + assert len(self.labels) == len(self.tokenized_data) + + def __len__(self): + return len(self.labels) + + def __getitem__(self, idx): + return self.tokenized_data[idx], self.labels[idx] + + +class processed_dataset_bert(Dataset): + def __init__(self, data): + tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') + self.texts = [] + self.labels = [] + for text, label in data: + self.texts.append(torch.tensor(tokenizer.encode(text))) + self.labels.append(label) + assert len(self.texts) == len(self.labels) + + def __len__(self): + return len(self.texts) + + def __getitem__(self, idx): + return self.texts[idx], self.labels[idx] + + +class packDataset_util(): + def __init__(self, vocab_target_set): + + self.vocab = self.get_vocab(vocab_target_set) + + def fn(self, data): + labels = torch.tensor([item[1] for item in data]) + lengths = [len(item[0]) for item in data] + texts = [torch.tensor(item[0]) for item in data] + padded_texts = pad_sequence(texts, batch_first=True, padding_value=0) + # pack_texts = pack_padded_sequence(padded_texts, lengths, batch_first=True, enforce_sorted=False) + return padded_texts, lengths, labels + + def get_loader(self, data, shuffle=True, batch_size=32): + dataset = processed_dataset(data, self.vocab) + loader = DataLoader(dataset=dataset, shuffle=shuffle, batch_size=batch_size, collate_fn=self.fn) + return loader + + def get_vocab(self, target_set): + from torchtext import vocab as Vocab + tokenized_data = [[word.lower() for word in data_tuple[0].split(' ')] for data_tuple in target_set] + counter = collections.Counter([word for review in tokenized_data for word in review]) + vocab = Vocab.Vocab(counter, min_freq=5) + return vocab + + + +class packDataset_util_bert(): + def fn(self, data): + texts = [] + labels = [] + for text, label in data: + texts.append(text) + labels.append(label) + labels = torch.tensor(labels) + padded_texts = pad_sequence(texts, batch_first=True, padding_value=0) + attention_masks = torch.zeros_like(padded_texts).masked_fill(padded_texts != 0, 1) + return padded_texts, attention_masks, labels + + + def get_loader(self, data, shuffle=True, batch_size=32): + dataset = processed_dataset_bert(data) + loader = DataLoader(dataset=dataset, shuffle=shuffle, batch_size=batch_size, collate_fn=self.fn) + return loader + + diff --git a/backdoorbench_nlp/utils/test_poison_processed_bert.py b/backdoorbench_nlp/utils/test_poison_processed_bert.py new file mode 100755 index 0000000..65da794 --- /dev/null +++ b/backdoorbench_nlp/utils/test_poison_processed_bert.py @@ -0,0 +1,327 @@ +''' +This code is highly dependent on the official implementation of BkdAtk-LWS: https://github.com/thunlp/BkdAtk-LWS +The redundant parts of the original code are deleted. The paths to models & datasets are organized in order +to fit the overall structure of Backdoorbench_NLP. The important hyperparameters are seperated into the .yaml file. + +MIT License + +Copyright (c) 2021 THUNLP + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +''' +import yaml, os, sys +from .gptlm import GPT2LM +import torch +import argparse +#from Models import BERT +from pack_dataset import packDataset_util_bert +from transformers import BertForSequenceClassification +LM = GPT2LM(use_tf=False, device=0) +''' +parser = argparse.ArgumentParser() +parser.add_argument('--gpu_id', default='0') +parser.add_argument('--data', default='sst-2') +parser.add_argument('--badnets', default='False') +parser.add_argument('--ES', default='False') +parser.add_argument('--SCPN', default='False') +parser.add_argument('--transfer', default='False') +parser.add_argument('--clean', default='True') +parser.add_argument('--model_path', default='') +parser.add_argument('--path', default='') +parser.add_argument('--custom_file_path',default='') +parser.add_argument('--target_file_path', default='') +args = parser.parse_args() + +device = torch.device('cuda:' + args.gpu_id if torch.cuda.is_available() else 'cpu') +LM = GPT2LM(use_tf=False, device=device) +data_selected = args.data +badnets = eval(args.badnets) +ES = eval(args.ES) +SCPN = eval(args.SCPN) +transfer = eval(args.transfer) +clean = eval(args.clean) +custom_file_path = args.custom_file_path +model_path = args.model_path +target_file_path = args.target_file_path +flag = (model_path != '') +path = args.path +''' + +''' +model = BERT(ag=(data_selected == 'ag')).cuda() +if badnets: + base_path = 'badnets' + if ES: + base_path += 'ES' + base_path += data_selected + if transfer: + base_path += 'transfer' + base_path += 'bert.pkl' + state_dict_path = base_path +elif SCPN: + if transfer: + path = 'SCPN' + data_selected + 'transferbert.pkl' + else: + path = 'SCPN' + data_selected + 'bert.pkl' + state_dict_path = path + +if clean: + state_dict_path = data_selected+'_clean_bert.pkl' + + +if model_path != '': + model = BertForSequenceClassification.from_pretrained(model_path).cuda() +elif path != '': + state_dict = torch.load(path, map_location='cpu') + model.load_state_dict(state_dict) + model = model.cuda() +else: + state_dict_path = os.path.join('/data1/private/chenyangyi/BackdoorAttackModels', state_dict_path) + state_dict = torch.load(state_dict_path, map_location='cpu') + model.load_state_dict(state_dict) + model = model.cuda() + +packDataset_util = packDataset_util_bert() +''' + + +def read_data(file_path): + import pandas as pd + data = pd.read_csv(file_path, sep='\t').values.tolist() + sentences = [item[0] for item in data] + labels = [int(item[1]) for item in data] + processed_data = [(sentences[i], labels[i]) for i in range(len(labels))] + return processed_data + + +def filter_sent(split_sent, pos): + words_list = split_sent[: pos] + split_sent[pos + 1:] + return ' '.join(words_list) + +''' +def evaluaion_ag(loader): + model.eval() + total_number = 0 + total_correct = 0 + with torch.no_grad(): + for padded_text, attention_masks, labels in loader: + padded_text = padded_text.cuda() + attention_masks = attention_masks.cuda() + labels = labels.cuda() + output = model(padded_text, attention_masks) + if flag: + output = output[0] + _, idx = torch.max(output, dim=1) + correct = (idx == labels).sum().item() + total_correct += correct + total_number += labels.size(0) + acc = total_correct / total_number + return acc + + +def evaluaion(loader): + model.eval() + total_number = 0 + total_correct = 0 + with torch.no_grad(): + for padded_text, attention_masks, labels in loader: + padded_text = padded_text.cuda() + attention_masks = attention_masks.cuda() + labels = labels.cuda() + output = model(padded_text, attention_masks).squeeze() + flag = torch.zeros_like(output).masked_fill(mask=output > 0, value=1).long() + total_number += labels.size(0) + correct = (flag == labels).sum().item() + total_correct += correct + acc = total_correct / total_number + return acc +''' + +def get_PPL(data): + all_PPL = [] + from tqdm import tqdm + for i, sent in enumerate(tqdm(data)): + split_sent = sent.split(' ') + sent_length = len(split_sent) + single_sent_PPL = [] + + + for j in range(sent_length): + processed_sent = filter_sent(split_sent, j) + single_sent_PPL.append(LM(processed_sent)) + all_PPL.append(single_sent_PPL) + + assert len(all_PPL) == len(data) + return all_PPL + + +def get_processed_sent(flag_li, orig_sent): + sent = [] + for i, word in enumerate(orig_sent): + flag = flag_li[i] + if flag == 1: + sent.append(word) + return ' '.join(sent) + + +def get_processed_poison_data(all_PPL, data, bar): + processed_data = [] + + for i, PPL_li in enumerate(all_PPL): + orig_sent = data[i] + orig_split_sent = orig_sent.split(' ')[:-1] + assert len(orig_split_sent) == len(PPL_li) - 1 + + whole_sentence_PPL = PPL_li[-1] + processed_PPL_li = [ppl - whole_sentence_PPL for ppl in PPL_li][:-1] + flag_li = [] + for ppl in processed_PPL_li: + if ppl <= bar: + flag_li.append(0) + else: + flag_li.append(1) + + assert len(flag_li) == len(orig_split_sent) + + sent = get_processed_sent(flag_li, orig_split_sent) + ''' + if data_selected == 'ag': + processed_data.append((sent, 0)) + else: + processed_data.append((sent, 1)) + ''' + processed_data.append(sent) + + assert len(all_PPL) == len(processed_data) + return processed_data + + +def get_orig_poison_data(data_selected): + if badnets: + path = '../data/badnets/1/' + data_selected + '/test.tsv' + elif SCPN: + path = '../data/scpn/1/' + data_selected + '/test.tsv' + if target_file_path != '': + path = target_file_path + poison_data = read_data(path) + if data_selected == 'offenseval': + raw_sentence = [sent[0] for i, sent in enumerate(poison_data) if i != 275] + else: + raw_sentence = [sent[0] for sent in poison_data] + return raw_sentence + + +''' +def prepare_poison_data(all_PPL, orig_poison_data, bar): + test_data_poison = get_processed_poison_data(all_PPL, orig_poison_data, bar=bar) + test_loader_poison = packDataset_util.get_loader(test_data_poison, shuffle=False, batch_size=32) + return test_loader_poison +''' + +def get_processed_clean_data(all_clean_PPL, clean_data, bar): + processed_data = [] + data = [item[0] for item in clean_data] + for i, PPL_li in enumerate(all_clean_PPL): + orig_sent = data[i] + orig_split_sent = orig_sent.split(' ')[:-1] + + assert len(orig_split_sent) == len(PPL_li) - 1 + + whole_sentence_PPL = PPL_li[-1] + processed_PPL_li = [ppl - whole_sentence_PPL for ppl in PPL_li][:-1] + flag_li = [] + for ppl in processed_PPL_li: + if ppl <= bar: + flag_li.append(0) + else: + flag_li.append(1) + + assert len(flag_li) == len(orig_split_sent) + sent = get_processed_sent(flag_li, orig_split_sent) + processed_data.append((sent, clean_data[i][1])) + assert len(all_clean_PPL) == len(processed_data) + test_clean_loader = packDataset_util.get_loader(processed_data, shuffle=False, batch_size=32) + return test_clean_loader + + +if __name__ == '__main__': + file_path = data_selected + if badnets: + file_path += 'badnets' + if ES: + file_path += 'ES' + elif SCPN: + file_path += 'SCPN' + file_path += 'bert' + if transfer: + file_path += 'transfer' + file_path += 'record.txt' + + if clean: + file_path = data_selected + if SCPN: + file_path += 'SCPN' + file_path += 'bert_record.txt' + + if flag: + file_path = 'new' + data_selected + if badnets: + file_path += 'ACL' + elif SCPN: + file_path += 'SCPN' + file_path += 'record.txt' + if custom_file_path != '': + file_path = custom_file_path + + + f = open(file_path, 'w') + + orig_poison_data = get_orig_poison_data(data_selected) + clean_data = read_data('../data/processed_data/' + data_selected + '/test.tsv') + clean_raw_sentences = [item[0] for item in clean_data] + if data_selected == 'offenseval': + print(clean_raw_sentences[275]) + clean_data = [data for i, data in enumerate(clean_data) if i != 275] + clean_raw_sentences = [sent for i, sent in enumerate(clean_raw_sentences) if i != 275] + if data_selected == 'ag': + clean_data = [data for i, data in enumerate(clean_data) ] + clean_raw_sentences = [sent for i, sent in enumerate(clean_raw_sentences)] + orig_poison_data = [data for i, data in enumerate(orig_poison_data) if i != 4447 and i!= 4523] + + all_PPL = get_PPL(orig_poison_data) + all_clean_PPL = get_PPL(clean_raw_sentences) + + for bar in range(-100, 0): + test_loader_poison_loader = prepare_poison_data(all_PPL, orig_poison_data, bar) + processed_clean_loader = prepare_poison_data(all_clean_PPL, clean_data, bar) + if flag: + success_rate = evaluaion_ag(test_loader_poison_loader) + clean_acc = evaluaion_ag(processed_clean_loader) + else: + if data_selected == 'ag': + success_rate = evaluaion_ag(test_loader_poison_loader) + clean_acc = evaluaion_ag(processed_clean_loader) + else: + success_rate = evaluaion(test_loader_poison_loader) + clean_acc = evaluaion(processed_clean_loader) + print('bar: ', bar, file=f) + print('attack success rate: ', success_rate, file=f) + print('clean acc: ', clean_acc, file=f) + print('*' * 89, file=f) + f.close() diff --git a/config/attack/badnet/default.yaml b/config/attack/badnet/default.yaml new file mode 100755 index 0000000..c539d2e --- /dev/null +++ b/config/attack/badnet/default.yaml @@ -0,0 +1,5 @@ +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +attack: badnet +patch_mask_path: ../resource/badnet/trigger_image.png \ No newline at end of file diff --git a/config/attack/badnet/default_bypass.yaml b/config/attack/badnet/default_bypass.yaml new file mode 100755 index 0000000..5769152 --- /dev/null +++ b/config/attack/badnet/default_bypass.yaml @@ -0,0 +1,6 @@ +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +attack: badnet_bypass +patch_mask_path: ../resource/badnet/trigger_image.png +regularization_ratio: 20 \ No newline at end of file diff --git a/config/attack/blended/default.yaml b/config/attack/blended/default.yaml new file mode 100755 index 0000000..c224e59 --- /dev/null +++ b/config/attack/blended/default.yaml @@ -0,0 +1,7 @@ +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +attack: blended +attack_trigger_img_path: ../resource/blended/hello_kitty.jpeg +attack_train_blended_alpha: 0.2 +attack_test_blended_alpha: 0.2 \ No newline at end of file diff --git a/config/attack/blended/default_bypass.yaml b/config/attack/blended/default_bypass.yaml new file mode 100755 index 0000000..275e677 --- /dev/null +++ b/config/attack/blended/default_bypass.yaml @@ -0,0 +1,8 @@ +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +attack: blended_bypass +attack_trigger_img_path: ../resource/blended/hello_kitty.jpeg +attack_train_blended_alpha: 0.2 +attack_test_blended_alpha: 0.2 +regularization_ratio: 20 \ No newline at end of file diff --git a/config/attack/blind/default.yaml b/config/attack/blind/default.yaml new file mode 100644 index 0000000..6c12a3b --- /dev/null +++ b/config/attack/blind/default.yaml @@ -0,0 +1,11 @@ +attack_label_trans: all2one +attack_target: 0 + +attack: blind + +weight_loss_balance_mode: fixed +mgda_normalize: loss+ +fix_scale_normal_weight: 1.0 +fix_scale_backdoor_weight: 0.9 +batch_history_len: 1000 +backdoor_batch_loss_threshold: 1.0 \ No newline at end of file diff --git a/config/attack/bpp/default.yaml b/config/attack/bpp/default.yaml new file mode 100755 index 0000000..96b150e --- /dev/null +++ b/config/attack/bpp/default.yaml @@ -0,0 +1,18 @@ +attack: bpp +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +neg_ratio: 0.1 +random_rotation: 10 +random_crop: 5 +steplr_milestones: + - 100 + - 200 + - 300 + - 400 +steplr_gamma: 0.1 +lr_scheduler: MultiStepLR +squeeze_num: 8 # squeeze number of the original image is 256 +#lr: 0.02 +dithering: False +#epochs: 1000 diff --git a/config/attack/bpp/original_default.yaml b/config/attack/bpp/original_default.yaml new file mode 100755 index 0000000..87b8e48 --- /dev/null +++ b/config/attack/bpp/original_default.yaml @@ -0,0 +1,19 @@ +attack: bpp +attack_label_trans: all2one +attack_target: 0 +pratio: 0.2 +neg_ratio: 0.2 +random_rotation: 10 +random_crop: 5 +steplr_milestones: + - 100 + - 200 + - 300 + - 400 +steplr_gamma: 0.1 +lr_scheduler: MultiStepLR +squeeze_num: 8 +grid_rescale: 1 +lr: 0.02 +dithering: False +epochs: 1000 diff --git a/config/attack/inputaware/default.yaml b/config/attack/inputaware/default.yaml new file mode 100755 index 0000000..3d5dd4e --- /dev/null +++ b/config/attack/inputaware/default.yaml @@ -0,0 +1,32 @@ +attack: inputaware +attack_label_trans: all2one +lr_G: 0.01 +lr_C: 0.01 +lr_M: 0.01 +C_lr_scheduler: None +schedulerG_milestones: #[200, 300, 400, 500] +- 200 +- 300 +- 400 +- 500 +schedulerC_milestones: #[100, 200, 300, 400] +- 100 +- 200 +- 300 +- 400 +schedulerM_milestones: #[10, 20] +- 10 +- 20 +schedulerG_lambda: 0.1 +schedulerC_lambda: 0.1 +schedulerM_lambda: 0.1 +lambda_div: 1 +lambda_norm: 100 +attack_target: 0 +pratio: 0.1 +mask_density: 0.032 +EPSILON: 0.0000001 +random_rotation: 10 +random_crop: 5 +random_seed: 0 +clean_train_epochs: 25 \ No newline at end of file diff --git a/config/attack/lc/default.yaml b/config/attack/lc/default.yaml new file mode 100755 index 0000000..b35c5d9 --- /dev/null +++ b/config/attack/lc/default.yaml @@ -0,0 +1,5 @@ +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +attack: label_consistent +reduced_amplitude: 1 \ No newline at end of file diff --git a/config/attack/lf/default.yaml b/config/attack/lf/default.yaml new file mode 100755 index 0000000..8aa8747 --- /dev/null +++ b/config/attack/lf/default.yaml @@ -0,0 +1,4 @@ +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +attack: lowFrequency \ No newline at end of file diff --git a/config/attack/lira/cifar10.yaml b/config/attack/lira/cifar10.yaml new file mode 100755 index 0000000..d78426b --- /dev/null +++ b/config/attack/lira/cifar10.yaml @@ -0,0 +1,29 @@ +random_rotation: 10 +random_crop: 5 +attack: lira +attack_model: autoencoder +lr_atk: 0.0001 +attack_label_trans: all2one +attack_target: 0 +eps: 0.01 +test_eps: 0.01 +alpha: 0.5 +test_alpha: 0.5 + +optimizer: sgd +both_train_epochs: 50 +fix_generator_epoch: 1 + +steplr_gamma: 0.1 +steplr_milestones: [50,100,150,200] + +finetune_eps: 0.01 +finetune_alpha: 0.5 +finetune_client_optimizer: sgd +finetune_sgd_momentum: 0.9 +finetune_wd: 0.0005 +epochs: 100 +finetune_lr: 0.01 +finetune_lr_scheduler: MultiStepLR +finetune_steplr_gamma: 0.1 +finetune_steplr_milestones: [50,100,150,200] diff --git a/config/attack/lira/cifar100.yaml b/config/attack/lira/cifar100.yaml new file mode 100755 index 0000000..d78426b --- /dev/null +++ b/config/attack/lira/cifar100.yaml @@ -0,0 +1,29 @@ +random_rotation: 10 +random_crop: 5 +attack: lira +attack_model: autoencoder +lr_atk: 0.0001 +attack_label_trans: all2one +attack_target: 0 +eps: 0.01 +test_eps: 0.01 +alpha: 0.5 +test_alpha: 0.5 + +optimizer: sgd +both_train_epochs: 50 +fix_generator_epoch: 1 + +steplr_gamma: 0.1 +steplr_milestones: [50,100,150,200] + +finetune_eps: 0.01 +finetune_alpha: 0.5 +finetune_client_optimizer: sgd +finetune_sgd_momentum: 0.9 +finetune_wd: 0.0005 +epochs: 100 +finetune_lr: 0.01 +finetune_lr_scheduler: MultiStepLR +finetune_steplr_gamma: 0.1 +finetune_steplr_milestones: [50,100,150,200] diff --git a/config/attack/lira/default.yaml b/config/attack/lira/default.yaml new file mode 100755 index 0000000..d78426b --- /dev/null +++ b/config/attack/lira/default.yaml @@ -0,0 +1,29 @@ +random_rotation: 10 +random_crop: 5 +attack: lira +attack_model: autoencoder +lr_atk: 0.0001 +attack_label_trans: all2one +attack_target: 0 +eps: 0.01 +test_eps: 0.01 +alpha: 0.5 +test_alpha: 0.5 + +optimizer: sgd +both_train_epochs: 50 +fix_generator_epoch: 1 + +steplr_gamma: 0.1 +steplr_milestones: [50,100,150,200] + +finetune_eps: 0.01 +finetune_alpha: 0.5 +finetune_client_optimizer: sgd +finetune_sgd_momentum: 0.9 +finetune_wd: 0.0005 +epochs: 100 +finetune_lr: 0.01 +finetune_lr_scheduler: MultiStepLR +finetune_steplr_gamma: 0.1 +finetune_steplr_milestones: [50,100,150,200] diff --git a/config/attack/lira/gtsrb.yaml b/config/attack/lira/gtsrb.yaml new file mode 100755 index 0000000..daf2bf0 --- /dev/null +++ b/config/attack/lira/gtsrb.yaml @@ -0,0 +1,29 @@ +random_rotation: 10 +random_crop: 5 +attack: lira +attack_model: autoencoder +lr_atk: 0.0001 +attack_label_trans: all2one +attack_target: 0 +eps: 0.01 +test_eps: 0.01 +alpha: 0.5 +test_alpha: 0.5 + +optimizer: sgd +both_train_epochs: 25 +fix_generator_epoch: 1 + +steplr_gamma: 0.1 +steplr_milestones: [50,100,150,200] + +finetune_eps: 0.01 +finetune_alpha: 0.5 +finetune_client_optimizer: sgd +finetune_sgd_momentum: 0.9 +finetune_wd: 0.0005 +epochs: 50 +finetune_lr: 0.01 +finetune_lr_scheduler: MultiStepLR +finetune_steplr_gamma: 0.1 +finetune_steplr_milestones: [50,100,150,200] diff --git a/config/attack/lira/tiny.yaml b/config/attack/lira/tiny.yaml new file mode 100755 index 0000000..224f6e0 --- /dev/null +++ b/config/attack/lira/tiny.yaml @@ -0,0 +1,29 @@ +random_rotation: 10 +random_crop: 5 +attack: lira +attack_model: autoencoder +lr_atk: 0.0001 +attack_label_trans: all2one +attack_target: 0 +eps: 0.01 +test_eps: 0.01 +alpha: 0.5 +test_alpha: 0.5 + +optimizer: sgd +both_train_epochs: 50 +fix_generator_epoch: 1 + +steplr_gamma: 0.1 +steplr_milestones: [50,100,150,200] + +finetune_eps: 0.01 +finetune_alpha: 0.5 +finetune_client_optimizer: sgd +finetune_sgd_momentum: 0.9 +finetune_wd: 0.0005 +epochs: 200 +finetune_lr: 0.01 +finetune_lr_scheduler: MultiStepLR +finetune_steplr_gamma: 0.1 +finetune_steplr_milestones: [50,100,150,200] diff --git a/config/attack/prototype/cifar10.yaml b/config/attack/prototype/cifar10.yaml new file mode 100755 index 0000000..bd7b9fe --- /dev/null +++ b/config/attack/prototype/cifar10.yaml @@ -0,0 +1,19 @@ +num_workers: 4 +pin_memory: True +non_blocking: True +prefetch: False +amp: False +device: cuda:0 +client_optimizer: sgd +dataset: cifar10 +dataset_path: ../data +frequency_save: 0 +batch_size: 128 +lr: 0.1 +lr_scheduler: CosineAnnealingLR +model: resnet18 +random_seed: 0 +sgd_momentum: 0.9 +wd: 0.0005 +epochs: 100 +split_ratio: 0.02 diff --git a/config/attack/prototype/cifar100.yaml b/config/attack/prototype/cifar100.yaml new file mode 100755 index 0000000..abf51ae --- /dev/null +++ b/config/attack/prototype/cifar100.yaml @@ -0,0 +1,19 @@ +num_workers: 4 +pin_memory: True +non_blocking: True +prefetch: False +amp: False +device: cuda:0 +client_optimizer: sgd +dataset: cifar100 +dataset_path: ../data +frequency_save: 0 +batch_size: 64 +lr: 0.001 +lr_scheduler: CosineAnnealingLR +model: swin_t +random_seed: 0 +sgd_momentum: 0.9 +wd: 0.0005 +epochs: 10 +split_ratio: 0.02 \ No newline at end of file diff --git a/config/attack/prototype/gtsrb.yaml b/config/attack/prototype/gtsrb.yaml new file mode 100755 index 0000000..e25c9c5 --- /dev/null +++ b/config/attack/prototype/gtsrb.yaml @@ -0,0 +1,19 @@ +num_workers: 4 +pin_memory: True +non_blocking: True +prefetch: False +amp: False +device: cuda:7 +client_optimizer: sgd +dataset: gtsrb +dataset_path: ../data +frequency_save: 0 +batch_size: 128 +lr: 0.1 +lr_scheduler: CosineAnnealingLR +model: resnet18 +random_seed: 0 +sgd_momentum: 0.9 +wd: 0.0005 +epochs: 50 +split_ratio: 0.02 \ No newline at end of file diff --git a/config/attack/prototype/tiny.yaml b/config/attack/prototype/tiny.yaml new file mode 100755 index 0000000..d1f140b --- /dev/null +++ b/config/attack/prototype/tiny.yaml @@ -0,0 +1,19 @@ +num_workers: 4 +pin_memory: True +non_blocking: True +prefetch: False +amp: False +device: cuda:7 +client_optimizer: sgd +dataset: tiny +dataset_path: ../data +frequency_save: 0 +batch_size: 64 +lr: 0.001 +lr_scheduler: CosineAnnealingLR #ReduceLROnPlateau +model: swin_t +random_seed: 0 +sgd_momentum: 0.9 +wd: 0.0005 +epochs: 10 +split_ratio: 0.05 \ No newline at end of file diff --git a/config/attack/quick_learn/default.yaml b/config/attack/quick_learn/default.yaml new file mode 100644 index 0000000..fd098ab --- /dev/null +++ b/config/attack/quick_learn/default.yaml @@ -0,0 +1 @@ +result_file: \ No newline at end of file diff --git a/config/attack/sig/default.yaml b/config/attack/sig/default.yaml new file mode 100755 index 0000000..ac2daa8 --- /dev/null +++ b/config/attack/sig/default.yaml @@ -0,0 +1,6 @@ +attack: sig +sig_delta: 40 +sig_f: 6 +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 \ No newline at end of file diff --git a/config/attack/ssba/default.yaml b/config/attack/ssba/default.yaml new file mode 100755 index 0000000..0154b2c --- /dev/null +++ b/config/attack/ssba/default.yaml @@ -0,0 +1,4 @@ +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +attack: SSBA \ No newline at end of file diff --git a/config/attack/trojannn/convnext_tiny.yaml b/config/attack/trojannn/convnext_tiny.yaml new file mode 100755 index 0000000..7a89bd2 --- /dev/null +++ b/config/attack/trojannn/convnext_tiny.yaml @@ -0,0 +1,11 @@ +selected_layer_name: classifier.2 +selected_layer_param_name: classifier.2.weight +num_neuron: 2 +mask_path: ../resource/trojannn/apple4.png +neuron_target_values: 100 +mask_update_iters: 1000 +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +attack: trojannn +model: convnext_tiny \ No newline at end of file diff --git a/config/attack/trojannn/densenet161.yaml b/config/attack/trojannn/densenet161.yaml new file mode 100755 index 0000000..c063f54 --- /dev/null +++ b/config/attack/trojannn/densenet161.yaml @@ -0,0 +1,11 @@ +selected_layer_name: classifier +selected_layer_param_name: classifier.weight +num_neuron: 2 +mask_path: ../resource/trojannn/apple4.png +neuron_target_values: 100 +mask_update_iters: 1000 +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +attack: trojannn +model: densenet161 \ No newline at end of file diff --git a/config/attack/trojannn/efficientnet_b3.yaml b/config/attack/trojannn/efficientnet_b3.yaml new file mode 100755 index 0000000..bf96f31 --- /dev/null +++ b/config/attack/trojannn/efficientnet_b3.yaml @@ -0,0 +1,11 @@ +selected_layer_name: classifier.1 +selected_layer_param_name: classifier.1.weight +num_neuron: 2 +mask_path: ../resource/trojannn/apple4.png +neuron_target_values: 100 +mask_update_iters: 1000 +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +attack: trojannn +model: efficientnet_b3 \ No newline at end of file diff --git a/config/attack/trojannn/mobilenet_v3_large.yaml b/config/attack/trojannn/mobilenet_v3_large.yaml new file mode 100755 index 0000000..7e9ce15 --- /dev/null +++ b/config/attack/trojannn/mobilenet_v3_large.yaml @@ -0,0 +1,11 @@ +selected_layer_name: classifier.3 +selected_layer_param_name: classifier.3.weight +num_neuron: 2 +mask_path: ../resource/trojannn/apple4.png +neuron_target_values: 100 +mask_update_iters: 1000 +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +attack: trojannn +model: mobilenet_v3_large \ No newline at end of file diff --git a/config/attack/trojannn/preactresnet18.yaml b/config/attack/trojannn/preactresnet18.yaml new file mode 100755 index 0000000..5898d31 --- /dev/null +++ b/config/attack/trojannn/preactresnet18.yaml @@ -0,0 +1,11 @@ +selected_layer_name: linear +selected_layer_param_name: linear.weight +num_neuron: 2 +mask_path: ../resource/trojannn/apple4.png +neuron_target_values: 100 +mask_update_iters: 1000 +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +attack: trojannn +model: preactresnet18 \ No newline at end of file diff --git a/config/attack/trojannn/vgg19.yaml b/config/attack/trojannn/vgg19.yaml new file mode 100755 index 0000000..d3f0d72 --- /dev/null +++ b/config/attack/trojannn/vgg19.yaml @@ -0,0 +1,11 @@ +selected_layer_name: classifier.6 +selected_layer_param_name: classifier.6.weight +num_neuron: 2 +mask_path: ../resource/trojannn/apple4.png +neuron_target_values: 100 +mask_update_iters: 1000 +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +attack: trojannn +model: vgg19 \ No newline at end of file diff --git a/config/attack/trojannn/vgg19_bn.yaml b/config/attack/trojannn/vgg19_bn.yaml new file mode 100755 index 0000000..a284dd2 --- /dev/null +++ b/config/attack/trojannn/vgg19_bn.yaml @@ -0,0 +1,11 @@ +selected_layer_name: classifier.6 +selected_layer_param_name: classifier.6.weight +num_neuron: 2 +mask_path: ../resource/trojannn/apple4.png +neuron_target_values: 100 +mask_update_iters: 1000 +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +attack: trojannn +model: vgg19_bn \ No newline at end of file diff --git a/config/attack/trojannn/vit_b_16.yaml b/config/attack/trojannn/vit_b_16.yaml new file mode 100755 index 0000000..fed82b3 --- /dev/null +++ b/config/attack/trojannn/vit_b_16.yaml @@ -0,0 +1,13 @@ +# Note that since we add resize for different dataset, so the parameter name change in this framwork! +selected_layer_name: 1.heads.head +selected_layer_param_name: 1.heads.head.weight + +num_neuron: 2 +mask_path: ../resource/trojannn/apple4.png +neuron_target_values: 100 +mask_update_iters: 1000 +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +attack: trojannn +model: vit_b_16 \ No newline at end of file diff --git a/config/attack/wanet/default.yaml b/config/attack/wanet/default.yaml new file mode 100755 index 0000000..0212cfe --- /dev/null +++ b/config/attack/wanet/default.yaml @@ -0,0 +1,17 @@ +attack: wanet +attack_label_trans: all2one +attack_target: 0 +pratio: 0.1 +cross_ratio: 2 # rho_a = pratio, rho_n = pratio * cross_ratio +random_rotation: 10 +random_crop: 5 +s: 0.5 +k: 4 +grid_rescale: 1 +lr_scheduler: MultiStepLR +steplr_milestones: + - 100 + - 200 + - 300 + - 400 +steplr_gamma: 0.1 \ No newline at end of file diff --git a/config/defense/abl/cifar10.yaml b/config/defense/abl/cifar10.yaml new file mode 100644 index 0000000..caca20f --- /dev/null +++ b/config/defense/abl/cifar10.yaml @@ -0,0 +1,38 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +tuning_epochs: 20 +finetuning_ascent_model: True +finetuning_epochs: 60 +unlearning_epochs: 20 +lr_finetuning_init: 0.1 +lr_unlearning_init: 5.0e-4 +momentum: 0.9 +weight_decay: 1.0e-4 +isolation_ratio: 0.01 #0.1 +gradient_ascent_type: 'Flooding' +gamma: 0.5 +flooding: 0.5 diff --git a/config/defense/abl/cifar100.yaml b/config/defense/abl/cifar100.yaml new file mode 100644 index 0000000..e2aad1e --- /dev/null +++ b/config/defense/abl/cifar100.yaml @@ -0,0 +1,38 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar100' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +tuning_epochs: 20 +finetuning_ascent_model: True +finetuning_epochs: 60 +unlearning_epochs: 20 +lr_finetuning_init: 0.1 +lr_unlearning_init: 5.0e-4 +momentum: 0.9 +weight_decay: 1.0e-4 +isolation_ratio: 0.01 #0.1 +gradient_ascent_type: 'Flooding' +gamma: 0.5 +flooding: 0.5 diff --git a/config/defense/abl/config.yaml b/config/defense/abl/config.yaml new file mode 100755 index 0000000..caca20f --- /dev/null +++ b/config/defense/abl/config.yaml @@ -0,0 +1,38 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +tuning_epochs: 20 +finetuning_ascent_model: True +finetuning_epochs: 60 +unlearning_epochs: 20 +lr_finetuning_init: 0.1 +lr_unlearning_init: 5.0e-4 +momentum: 0.9 +weight_decay: 1.0e-4 +isolation_ratio: 0.01 #0.1 +gradient_ascent_type: 'Flooding' +gamma: 0.5 +flooding: 0.5 diff --git a/config/defense/abl/gtsrb.yaml b/config/defense/abl/gtsrb.yaml new file mode 100644 index 0000000..d4ac29e --- /dev/null +++ b/config/defense/abl/gtsrb.yaml @@ -0,0 +1,38 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'gtsrb' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +tuning_epochs: 20 +finetuning_ascent_model: True +finetuning_epochs: 60 +unlearning_epochs: 20 +lr_finetuning_init: 0.1 +lr_unlearning_init: 5.0e-4 +momentum: 0.9 +weight_decay: 1.0e-4 +isolation_ratio: 0.01 #0.1 +gradient_ascent_type: 'Flooding' +gamma: 0.5 +flooding: 0.5 diff --git a/config/defense/abl/tiny.yaml b/config/defense/abl/tiny.yaml new file mode 100644 index 0000000..904357c --- /dev/null +++ b/config/defense/abl/tiny.yaml @@ -0,0 +1,39 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'tiny' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +tuning_epochs: 40 +finetuning_ascent_model: True +finetuning_epochs: 120 +unlearning_epochs: 4 +lr_finetuning_init: 0.1 +lr_unlearning_init: 5.0e-4 +momentum: 0.9 +weight_decay: 1.0e-4 +isolation_ratio: 0.01 #0.1 +gradient_ascent_type: 'Flooding' +gamma: 0.5 +flooding: 0.5 + diff --git a/config/defense/ac/cifar10.yaml b/config/defense/ac/cifar10.yaml new file mode 100644 index 0000000..1c228b2 --- /dev/null +++ b/config/defense/ac/cifar10.yaml @@ -0,0 +1,29 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +nb_dims: 10 +nb_clusters: 2 +cluster_analysis: 'smaller' diff --git a/config/defense/ac/cifar100.yaml b/config/defense/ac/cifar100.yaml new file mode 100644 index 0000000..d4dbce8 --- /dev/null +++ b/config/defense/ac/cifar100.yaml @@ -0,0 +1,29 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar100' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +nb_dims: 10 +nb_clusters: 2 +cluster_analysis: 'smaller' diff --git a/config/defense/ac/config.yaml b/config/defense/ac/config.yaml new file mode 100755 index 0000000..1c228b2 --- /dev/null +++ b/config/defense/ac/config.yaml @@ -0,0 +1,29 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +nb_dims: 10 +nb_clusters: 2 +cluster_analysis: 'smaller' diff --git a/config/defense/ac/gtsrb.yaml b/config/defense/ac/gtsrb.yaml new file mode 100644 index 0000000..536bb50 --- /dev/null +++ b/config/defense/ac/gtsrb.yaml @@ -0,0 +1,29 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'gtsrb' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +nb_dims: 10 +nb_clusters: 2 +cluster_analysis: 'smaller' diff --git a/config/defense/ac/tiny.yaml b/config/defense/ac/tiny.yaml new file mode 100644 index 0000000..a55e3b9 --- /dev/null +++ b/config/defense/ac/tiny.yaml @@ -0,0 +1,29 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'tiny' + +epochs: 200 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: ReduceLROnPlateau +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +nb_dims: 10 +nb_clusters: 2 +cluster_analysis: 'smaller' diff --git a/config/defense/anp/cifar10.yaml b/config/defense/anp/cifar10.yaml new file mode 100755 index 0000000..5b1e933 --- /dev/null +++ b/config/defense/anp/cifar10.yaml @@ -0,0 +1,38 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +index: + +acc_ratio: 0.1 #for fair +ratio: 0.05 #for fair +print_every: 500 +nb_iter: 2000 +anp_eps: 0.4 +anp_steps: 1 +anp_alpha: 0.2 +pruning_by: 'threshold' +pruning_max: 0.90 +pruning_step: 0.05 diff --git a/config/defense/anp/cifar100.yaml b/config/defense/anp/cifar100.yaml new file mode 100755 index 0000000..5b1e933 --- /dev/null +++ b/config/defense/anp/cifar100.yaml @@ -0,0 +1,38 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +index: + +acc_ratio: 0.1 #for fair +ratio: 0.05 #for fair +print_every: 500 +nb_iter: 2000 +anp_eps: 0.4 +anp_steps: 1 +anp_alpha: 0.2 +pruning_by: 'threshold' +pruning_max: 0.90 +pruning_step: 0.05 diff --git a/config/defense/anp/config.yaml b/config/defense/anp/config.yaml new file mode 100755 index 0000000..5b1e933 --- /dev/null +++ b/config/defense/anp/config.yaml @@ -0,0 +1,38 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +index: + +acc_ratio: 0.1 #for fair +ratio: 0.05 #for fair +print_every: 500 +nb_iter: 2000 +anp_eps: 0.4 +anp_steps: 1 +anp_alpha: 0.2 +pruning_by: 'threshold' +pruning_max: 0.90 +pruning_step: 0.05 diff --git a/config/defense/anp/gtsrb.yaml b/config/defense/anp/gtsrb.yaml new file mode 100755 index 0000000..5b1e933 --- /dev/null +++ b/config/defense/anp/gtsrb.yaml @@ -0,0 +1,38 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +index: + +acc_ratio: 0.1 #for fair +ratio: 0.05 #for fair +print_every: 500 +nb_iter: 2000 +anp_eps: 0.4 +anp_steps: 1 +anp_alpha: 0.2 +pruning_by: 'threshold' +pruning_max: 0.90 +pruning_step: 0.05 diff --git a/config/defense/anp/tiny.yaml b/config/defense/anp/tiny.yaml new file mode 100755 index 0000000..5b1e933 --- /dev/null +++ b/config/defense/anp/tiny.yaml @@ -0,0 +1,38 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +index: + +acc_ratio: 0.1 #for fair +ratio: 0.05 #for fair +print_every: 500 +nb_iter: 2000 +anp_eps: 0.4 +anp_steps: 1 +anp_alpha: 0.2 +pruning_by: 'threshold' +pruning_max: 0.90 +pruning_step: 0.05 diff --git a/config/defense/bnp/cifar10.yaml b/config/defense/bnp/cifar10.yaml new file mode 100644 index 0000000..b795e88 --- /dev/null +++ b/config/defense/bnp/cifar10.yaml @@ -0,0 +1,32 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +u: 3 +u_min: 0 +u_max: 10 +u_num: 21 +ratio: 0.05 +index: \ No newline at end of file diff --git a/config/defense/bnp/cifar100.yaml b/config/defense/bnp/cifar100.yaml new file mode 100644 index 0000000..3d7cd59 --- /dev/null +++ b/config/defense/bnp/cifar100.yaml @@ -0,0 +1,32 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar100' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +u: 3 +u_min: 0 +u_max: 10 +u_num: 21 +ratio: 0.05 +index: \ No newline at end of file diff --git a/config/defense/bnp/config.yaml b/config/defense/bnp/config.yaml new file mode 100644 index 0000000..b795e88 --- /dev/null +++ b/config/defense/bnp/config.yaml @@ -0,0 +1,32 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +u: 3 +u_min: 0 +u_max: 10 +u_num: 21 +ratio: 0.05 +index: \ No newline at end of file diff --git a/config/defense/bnp/gtsrb.yaml b/config/defense/bnp/gtsrb.yaml new file mode 100644 index 0000000..8c2d8ba --- /dev/null +++ b/config/defense/bnp/gtsrb.yaml @@ -0,0 +1,32 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'gtsrb' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +u: 3 +u_min: 0 +u_max: 10 +u_num: 21 +ratio: 0.05 +index: \ No newline at end of file diff --git a/config/defense/bnp/tiny.yaml b/config/defense/bnp/tiny.yaml new file mode 100644 index 0000000..2f2d8a9 --- /dev/null +++ b/config/defense/bnp/tiny.yaml @@ -0,0 +1,32 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'tiny' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +u: 4 +u_min: 0 +u_max: 10 +u_num: 21 +ratio: 0.05 +index: \ No newline at end of file diff --git a/config/defense/clp/cifar10.yaml b/config/defense/clp/cifar10.yaml new file mode 100644 index 0000000..71217ce --- /dev/null +++ b/config/defense/clp/cifar10.yaml @@ -0,0 +1,30 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +u: 3 +u_min: 0 +u_max: 10 +u_num: 21 diff --git a/config/defense/clp/cifar100.yaml b/config/defense/clp/cifar100.yaml new file mode 100644 index 0000000..076b1d7 --- /dev/null +++ b/config/defense/clp/cifar100.yaml @@ -0,0 +1,30 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar100' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +u: 3 +u_min: 0 +u_max: 10 +u_num: 21 \ No newline at end of file diff --git a/config/defense/clp/config.yaml b/config/defense/clp/config.yaml new file mode 100644 index 0000000..facd1cf --- /dev/null +++ b/config/defense/clp/config.yaml @@ -0,0 +1,30 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +u: 3 +u_min: 0 +u_max: 10 +u_num: 21 \ No newline at end of file diff --git a/config/defense/clp/gtsrb.yaml b/config/defense/clp/gtsrb.yaml new file mode 100644 index 0000000..c615901 --- /dev/null +++ b/config/defense/clp/gtsrb.yaml @@ -0,0 +1,30 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'gtsrb' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +u: 3 +u_min: 0 +u_max: 10 +u_num: 21 \ No newline at end of file diff --git a/config/defense/clp/tiny.yaml b/config/defense/clp/tiny.yaml new file mode 100644 index 0000000..2f33c50 --- /dev/null +++ b/config/defense/clp/tiny.yaml @@ -0,0 +1,30 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'tiny' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +u: 5 +u_min: 0 +u_max: 10 +u_num: 21 \ No newline at end of file diff --git a/config/defense/d-br/cifar10.yaml b/config/defense/d-br/cifar10.yaml new file mode 100644 index 0000000..483c76d --- /dev/null +++ b/config/defense/d-br/cifar10.yaml @@ -0,0 +1,35 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' +non_blocking: True +dataset: 'cifar10' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 2 +batch_size: 128 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR + +pin_memory: True +prefetch: False +client_optimizer: sgd +sgd_momentum: 0.9 +wd: 0.0005 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 + +model: 'preactresnet18' +random_seed: 0 +index: + +ratio: 0.05 +clean_ratio: 0.2 +poison_ratio: 0.05 diff --git a/config/defense/d-br/cifar100.yaml b/config/defense/d-br/cifar100.yaml new file mode 100644 index 0000000..acceab8 --- /dev/null +++ b/config/defense/d-br/cifar100.yaml @@ -0,0 +1,35 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' +non_blocking: True + +dataset: 'cifar100' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 2 +batch_size: 128 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +pin_memory: True +prefetch: False +client_optimizer: sgd +sgd_momentum: 0.9 +wd: 0.0005 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 + +model: 'preactresnet18' +random_seed: 0 +index: + +ratio: 0.05 +clean_ratio: 0.2 +poison_ratio: 0.05 diff --git a/config/defense/d-br/config.yaml b/config/defense/d-br/config.yaml new file mode 100644 index 0000000..a0fe395 --- /dev/null +++ b/config/defense/d-br/config.yaml @@ -0,0 +1,35 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' +non_blocking: True + +dataset: 'cifar10' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 2 +batch_size: 128 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +pin_memory: True +prefetch: False +client_optimizer: sgd +sgd_momentum: 0.9 +wd: 0.0005 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 + +model: 'preactresnet18' +random_seed: 0 +index: + +ratio: 0.05 +clean_ratio: 0.2 +poison_ratio: 0.05 diff --git a/config/defense/d-br/gtsrb.yaml b/config/defense/d-br/gtsrb.yaml new file mode 100644 index 0000000..a50899d --- /dev/null +++ b/config/defense/d-br/gtsrb.yaml @@ -0,0 +1,35 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' +non_blocking: True + +dataset: 'gtsrb' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 2 +batch_size: 128 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +pin_memory: True +prefetch: False +client_optimizer: sgd +sgd_momentum: 0.9 +wd: 0.0005 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 + +model: 'preactresnet18' +random_seed: 0 +index: + +ratio: 0.05 +clean_ratio: 0.2 +poison_ratio: 0.05 diff --git a/config/defense/d-br/tiny.yaml b/config/defense/d-br/tiny.yaml new file mode 100644 index 0000000..9c7d3aa --- /dev/null +++ b/config/defense/d-br/tiny.yaml @@ -0,0 +1,35 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +non_blocking: True +data_root: 'data/' + +dataset: 'tiny' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 2 +batch_size: 128 +num_workers: 4 +lr: 0.01 +lr_scheduler: ReduceLROnPlateau +pin_memory: True +prefetch: False +client_optimizer: sgd +sgd_momentum: 0.9 +wd: 0.0005 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 + +model: 'preactresnet18' +random_seed: 0 +index: + +ratio: 0.05 +clean_ratio: 0.2 +poison_ratio: 0.05 diff --git a/config/defense/d-st/cifar10.yaml b/config/defense/d-st/cifar10.yaml new file mode 100644 index 0000000..f7270bc --- /dev/null +++ b/config/defense/d-st/cifar10.yaml @@ -0,0 +1,28 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' + +dataset: 'cifar10' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 2 +batch_size: 128 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 + +model: 'preactresnet18' +random_seed: 0 +index: + +ratio: 0.05 + diff --git a/config/defense/d-st/cifar100.yaml b/config/defense/d-st/cifar100.yaml new file mode 100644 index 0000000..64e1282 --- /dev/null +++ b/config/defense/d-st/cifar100.yaml @@ -0,0 +1,35 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' + +dataset: 'cifar100' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 2 +batch_size: 128 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +pin_memory: True +non_blocking: True +prefetch: False +client_optimizer: sgd +sgd_momentum: 0.9 +wd: 0.0005 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 + +model: 'preactresnet18' +random_seed: 0 +index: + +ratio: 0.05 +clean_ratio: 0.2 +poison_ratio: 0.05 diff --git a/config/defense/d-st/config.yaml b/config/defense/d-st/config.yaml new file mode 100644 index 0000000..05515f2 --- /dev/null +++ b/config/defense/d-st/config.yaml @@ -0,0 +1,29 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' +non_blocking: True + +dataset: 'cifar10' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 2 +batch_size: 128 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 + +model: 'preactresnet18' +random_seed: 0 +index: + +ratio: 0.05 + diff --git a/config/defense/d-st/gtsrb.yaml b/config/defense/d-st/gtsrb.yaml new file mode 100644 index 0000000..7c374ff --- /dev/null +++ b/config/defense/d-st/gtsrb.yaml @@ -0,0 +1,35 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' + +dataset: 'gtsrb' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 2 +batch_size: 128 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +pin_memory: True +non_blocking: True +prefetch: False +client_optimizer: sgd +sgd_momentum: 0.9 +wd: 0.0005 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 + +model: 'preactresnet18' +random_seed: 0 +index: + +ratio: 0.05 +clean_ratio: 0.2 +poison_ratio: 0.05 diff --git a/config/defense/d-st/tiny.yaml b/config/defense/d-st/tiny.yaml new file mode 100644 index 0000000..1cd6e47 --- /dev/null +++ b/config/defense/d-st/tiny.yaml @@ -0,0 +1,28 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' + +dataset: 'tiny' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 2 +batch_size: 128 +num_workers: 4 +lr: 0.01 +lr_scheduler: ReduceLROnPlateau + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 + +model: 'preactresnet18' +random_seed: 0 +index: + +ratio: 0.05 + diff --git a/config/defense/dbd/cifar10.yaml b/config/defense/dbd/cifar10.yaml new file mode 100755 index 0000000..18ab9bb --- /dev/null +++ b/config/defense/dbd/cifar10.yaml @@ -0,0 +1,32 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' + +dataset: 'cifar10' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 100 +batch_size: 256 +num_workers: 4 +num_workers_semi: 4 +lr: 0.01 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 +trigger_type: 'squareTrigger' + +model: 'preactresnet18' +random_seed: 0 + +prefetch: True +epoch_self: 100 ####config[warmup][epoch] semi 测试用 100 +epoch_warmup: 10 ####config[warmup][epoch] semi +batch_size_self: 512 #####config['batch_size'] pretrain +temperature: 1 #####config['simclr']['temperature'] +epsilon: 0.5 ####config['semi']['epsilon'] \ No newline at end of file diff --git a/config/defense/dbd/cifar100.yaml b/config/defense/dbd/cifar100.yaml new file mode 100755 index 0000000..f6cdcde --- /dev/null +++ b/config/defense/dbd/cifar100.yaml @@ -0,0 +1,32 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' + +dataset: 'cifar100' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 100 +batch_size: 256 +num_workers: 4 +num_workers_semi: 4 +lr: 0.01 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 +trigger_type: 'squareTrigger' + +model: 'preactresnet18' +random_seed: 0 + +prefetch: True +epoch_self: 100 ####config[warmup][epoch] semi 测试用 100 +epoch_warmup: 10 ####config[warmup][epoch] semi +batch_size_self: 512 #####config['batch_size'] pretrain +temperature: 1 #####config['simclr']['temperature'] +epsilon: 0.5 ####config['semi']['epsilon'] \ No newline at end of file diff --git a/config/defense/dbd/config.yaml b/config/defense/dbd/config.yaml new file mode 100755 index 0000000..18ab9bb --- /dev/null +++ b/config/defense/dbd/config.yaml @@ -0,0 +1,32 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' + +dataset: 'cifar10' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 100 +batch_size: 256 +num_workers: 4 +num_workers_semi: 4 +lr: 0.01 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 +trigger_type: 'squareTrigger' + +model: 'preactresnet18' +random_seed: 0 + +prefetch: True +epoch_self: 100 ####config[warmup][epoch] semi 测试用 100 +epoch_warmup: 10 ####config[warmup][epoch] semi +batch_size_self: 512 #####config['batch_size'] pretrain +temperature: 1 #####config['simclr']['temperature'] +epsilon: 0.5 ####config['semi']['epsilon'] \ No newline at end of file diff --git a/config/defense/dbd/gtsrb.yaml b/config/defense/dbd/gtsrb.yaml new file mode 100755 index 0000000..5507336 --- /dev/null +++ b/config/defense/dbd/gtsrb.yaml @@ -0,0 +1,32 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' + +dataset: 'gtsrb' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 100 +batch_size: 256 +num_workers: 4 +num_workers_semi: 4 +lr: 0.01 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 +trigger_type: 'squareTrigger' + +model: 'preactresnet18' +random_seed: 0 + +prefetch: True +epoch_self: 100 ####config[warmup][epoch] semi 测试用 100 +epoch_warmup: 10 ####config[warmup][epoch] semi +batch_size_self: 512 #####config['batch_size'] pretrain +temperature: 1 #####config['simclr']['temperature'] +epsilon: 0.5 ####config['semi']['epsilon'] \ No newline at end of file diff --git a/config/defense/dbd/tiny.yaml b/config/defense/dbd/tiny.yaml new file mode 100755 index 0000000..6309719 --- /dev/null +++ b/config/defense/dbd/tiny.yaml @@ -0,0 +1,33 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' + +dataset: 'tiny' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 100 +batch_size: 256 +num_workers: 4 +num_workers_semi: 4 +lr: 0.01 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 +trigger_type: 'squareTrigger' + +model: 'preactresnet18' +random_seed: 0 + +prefetch: True +epoch_self: 100 ####config[warmup][epoch] semi 测试用 100 +epoch_warmup: 10 ####config[warmup][epoch] semi +batch_size_self: 512 #####config['batch_size'] pretrain + +temperature: 1 #####config['simclr']['temperature'] +epsilon: 0.5 ####config['semi']['epsilon'] \ No newline at end of file diff --git a/config/defense/ep/cifar10.yaml b/config/defense/ep/cifar10.yaml new file mode 100644 index 0000000..71217ce --- /dev/null +++ b/config/defense/ep/cifar10.yaml @@ -0,0 +1,30 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +u: 3 +u_min: 0 +u_max: 10 +u_num: 21 diff --git a/config/defense/ep/cifar100.yaml b/config/defense/ep/cifar100.yaml new file mode 100644 index 0000000..6be196a --- /dev/null +++ b/config/defense/ep/cifar100.yaml @@ -0,0 +1,30 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar100' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +u: 3 +u_min: 0 +u_max: 10 +u_num: 21 diff --git a/config/defense/ep/config.yaml b/config/defense/ep/config.yaml new file mode 100644 index 0000000..71217ce --- /dev/null +++ b/config/defense/ep/config.yaml @@ -0,0 +1,30 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +u: 3 +u_min: 0 +u_max: 10 +u_num: 21 diff --git a/config/defense/ep/gtsrb.yaml b/config/defense/ep/gtsrb.yaml new file mode 100644 index 0000000..6d6e408 --- /dev/null +++ b/config/defense/ep/gtsrb.yaml @@ -0,0 +1,30 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'gtsrb' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +u: 3 +u_min: 0 +u_max: 10 +u_num: 21 diff --git a/config/defense/ep/tiny.yaml b/config/defense/ep/tiny.yaml new file mode 100644 index 0000000..a2b90cf --- /dev/null +++ b/config/defense/ep/tiny.yaml @@ -0,0 +1,30 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'tiny' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +u: 4 +u_min: 0 +u_max: 10 +u_num: 21 diff --git a/config/defense/fp/cifar10.yaml b/config/defense/fp/cifar10.yaml new file mode 100755 index 0000000..6bd0482 --- /dev/null +++ b/config/defense/fp/cifar10.yaml @@ -0,0 +1,38 @@ +device: 'cuda' +dataset_path: 'data/' + +dataset: 'cifar10' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 100 +frequency_save: 0 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR + +pin_memory: True +prefetch: False +client_optimizer: sgd +sgd_momentum: 0.9 +wd: 0.0005 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 +trigger_type: 'squareTrigger' + +model: 'preactresnet18' +random_seed: 0 +index: + +amp: True +acc_ratio: 0.1 +ratio: 0.05 +non_blocking: True +once_prune_ratio: 0.01 + + diff --git a/config/defense/fp/cifar100.yaml b/config/defense/fp/cifar100.yaml new file mode 100755 index 0000000..20f3b0e --- /dev/null +++ b/config/defense/fp/cifar100.yaml @@ -0,0 +1,37 @@ +device: 'cuda' +dataset_path: 'data/' + +dataset: 'cifar10' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 100 +frequency_save: 0 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR + +pin_memory: True +prefetch: False +client_optimizer: sgd +sgd_momentum: 0.9 +wd: 0.0005 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 +trigger_type: 'squareTrigger' + +model: 'preactresnet18' +random_seed: 0 +index: + +amp: True +acc_ratio: 0.1 +ratio: 0.05 +non_blocking: True +once_prune_ratio: 0.01 + diff --git a/config/defense/fp/config.yaml b/config/defense/fp/config.yaml new file mode 100755 index 0000000..20f3b0e --- /dev/null +++ b/config/defense/fp/config.yaml @@ -0,0 +1,37 @@ +device: 'cuda' +dataset_path: 'data/' + +dataset: 'cifar10' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 100 +frequency_save: 0 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR + +pin_memory: True +prefetch: False +client_optimizer: sgd +sgd_momentum: 0.9 +wd: 0.0005 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 +trigger_type: 'squareTrigger' + +model: 'preactresnet18' +random_seed: 0 +index: + +amp: True +acc_ratio: 0.1 +ratio: 0.05 +non_blocking: True +once_prune_ratio: 0.01 + diff --git a/config/defense/fp/gtsrb.yaml b/config/defense/fp/gtsrb.yaml new file mode 100755 index 0000000..20f3b0e --- /dev/null +++ b/config/defense/fp/gtsrb.yaml @@ -0,0 +1,37 @@ +device: 'cuda' +dataset_path: 'data/' + +dataset: 'cifar10' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 100 +frequency_save: 0 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR + +pin_memory: True +prefetch: False +client_optimizer: sgd +sgd_momentum: 0.9 +wd: 0.0005 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 +trigger_type: 'squareTrigger' + +model: 'preactresnet18' +random_seed: 0 +index: + +amp: True +acc_ratio: 0.1 +ratio: 0.05 +non_blocking: True +once_prune_ratio: 0.01 + diff --git a/config/defense/fp/tiny.yaml b/config/defense/fp/tiny.yaml new file mode 100755 index 0000000..12bc224 --- /dev/null +++ b/config/defense/fp/tiny.yaml @@ -0,0 +1,37 @@ +device: 'cuda' +dataset_path: 'data/' + +dataset: 'cifar10' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 200 +frequency_save: 0 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: ReduceLROnPlateau + +pin_memory: True +prefetch: False +client_optimizer: sgd +sgd_momentum: 0.9 +wd: 0.0005 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 +trigger_type: 'squareTrigger' + +model: 'preactresnet18' +random_seed: 0 +index: + +amp: True +acc_ratio: 0.1 +ratio: 0.05 +non_blocking: True +once_prune_ratio: 0.01 + diff --git a/config/defense/ft-sam/cifar10.yaml b/config/defense/ft-sam/cifar10.yaml new file mode 100644 index 0000000..de7e91f --- /dev/null +++ b/config/defense/ft-sam/cifar10.yaml @@ -0,0 +1,33 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 100 + +random_seed: 0 + +ratio: 0.05 +index: +rho_max: 2.0 +rho_min: 2.0 +label_smoothing: 0.1 +alpha: 0.0 + diff --git a/config/defense/ft-sam/config.yaml b/config/defense/ft-sam/config.yaml new file mode 100644 index 0000000..d7682a5 --- /dev/null +++ b/config/defense/ft-sam/config.yaml @@ -0,0 +1,32 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 100 + +random_seed: 0 + +ratio: 0.05 +index: +rho_max: 2.0 +rho_min: 2.0 +label_smoothing: 0.1 +alpha: 0.0 \ No newline at end of file diff --git a/config/defense/ft-sam/gtsrb.yaml b/config/defense/ft-sam/gtsrb.yaml new file mode 100644 index 0000000..4d22212 --- /dev/null +++ b/config/defense/ft-sam/gtsrb.yaml @@ -0,0 +1,32 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'gtsrb' + +epochs: 50 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 100 + +random_seed: 0 + +ratio: 0.05 +index: +rho_max: 8.0 +rho_min: 8.0 +label_smoothing: 0.0 +alpha: 0.0 \ No newline at end of file diff --git a/config/defense/ft-sam/tiny.yaml b/config/defense/ft-sam/tiny.yaml new file mode 100644 index 0000000..0fae20a --- /dev/null +++ b/config/defense/ft-sam/tiny.yaml @@ -0,0 +1,32 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'tiny' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: ReduceLROnPlateau +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 100 + +random_seed: 0 + +ratio: 0.05 +index: +rho_max: 8.0 +rho_min: 8.0 +label_smoothing: 0.0 +alpha: 0.0 \ No newline at end of file diff --git a/config/defense/ft/cifar10.yaml b/config/defense/ft/cifar10.yaml new file mode 100644 index 0000000..9fc6ac3 --- /dev/null +++ b/config/defense/ft/cifar10.yaml @@ -0,0 +1,29 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +ratio: 0.05 +index: + diff --git a/config/defense/ft/cifar100.yaml b/config/defense/ft/cifar100.yaml new file mode 100644 index 0000000..5785553 --- /dev/null +++ b/config/defense/ft/cifar100.yaml @@ -0,0 +1,29 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar100' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +ratio: 0.05 +index: + diff --git a/config/defense/ft/config.yaml b/config/defense/ft/config.yaml new file mode 100755 index 0000000..9fc6ac3 --- /dev/null +++ b/config/defense/ft/config.yaml @@ -0,0 +1,29 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +ratio: 0.05 +index: + diff --git a/config/defense/ft/gtsrb.yaml b/config/defense/ft/gtsrb.yaml new file mode 100644 index 0000000..4feba7c --- /dev/null +++ b/config/defense/ft/gtsrb.yaml @@ -0,0 +1,29 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'gtsrb' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +ratio: 0.05 +index: + diff --git a/config/defense/ft/tiny.yaml b/config/defense/ft/tiny.yaml new file mode 100644 index 0000000..2bb2682 --- /dev/null +++ b/config/defense/ft/tiny.yaml @@ -0,0 +1,29 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'tiny' + +epochs: 200 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: ReduceLROnPlateau +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +ratio: 0.05 +index: + diff --git a/config/defense/i-bau/cifar10.yaml b/config/defense/i-bau/cifar10.yaml new file mode 100644 index 0000000..6d4b0d1 --- /dev/null +++ b/config/defense/i-bau/cifar10.yaml @@ -0,0 +1,31 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.0001 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'adam' +sgd_momentum: 0.9 +wd: 0 +adam_betas: [0.9, 0.999] +frequency_save: 0 + +random_seed: 0 + +ratio: 0.05 +index: +n_rounds: 5 +K: 5 \ No newline at end of file diff --git a/config/defense/i-bau/cifar100.yaml b/config/defense/i-bau/cifar100.yaml new file mode 100644 index 0000000..52a3960 --- /dev/null +++ b/config/defense/i-bau/cifar100.yaml @@ -0,0 +1,31 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar100' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.0001 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'adam' +sgd_momentum: 0.9 +wd: 0 +adam_betas: [0.9, 0.999] +frequency_save: 0 + +random_seed: 0 + +ratio: 0.05 +index: +n_rounds: 5 +K: 5 \ No newline at end of file diff --git a/config/defense/i-bau/config.yaml b/config/defense/i-bau/config.yaml new file mode 100644 index 0000000..6d4b0d1 --- /dev/null +++ b/config/defense/i-bau/config.yaml @@ -0,0 +1,31 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.0001 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'adam' +sgd_momentum: 0.9 +wd: 0 +adam_betas: [0.9, 0.999] +frequency_save: 0 + +random_seed: 0 + +ratio: 0.05 +index: +n_rounds: 5 +K: 5 \ No newline at end of file diff --git a/config/defense/i-bau/gtsrb.yaml b/config/defense/i-bau/gtsrb.yaml new file mode 100644 index 0000000..e202c5c --- /dev/null +++ b/config/defense/i-bau/gtsrb.yaml @@ -0,0 +1,31 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'gtsrb' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.0001 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'adam' +sgd_momentum: 0.9 +wd: 0 +adam_betas: [0.9, 0.999] +frequency_save: 0 + +random_seed: 0 + +ratio: 0.05 +index: +n_rounds: 5 +K: 5 \ No newline at end of file diff --git a/config/defense/i-bau/tiny.yaml b/config/defense/i-bau/tiny.yaml new file mode 100644 index 0000000..24f0d55 --- /dev/null +++ b/config/defense/i-bau/tiny.yaml @@ -0,0 +1,31 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'tiny' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.0001 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'adam' +sgd_momentum: 0.9 +wd: 0 +adam_betas: [0.9, 0.999] +frequency_save: 0 + +random_seed: 0 + +ratio: 0.05 +index: +n_rounds: 5 +K: 5 \ No newline at end of file diff --git a/config/defense/index/cifar100_index.txt b/config/defense/index/cifar100_index.txt new file mode 100755 index 0000000..9249860 --- /dev/null +++ b/config/defense/index/cifar100_index.txt @@ -0,0 +1,2500 @@ +25247 +49673 +27562 +2653 +16968 +33506 +31845 +26537 +19877 +31234 +23465 +38232 +14315 +33075 +9127 +18470 +9158 +49532 +6214 +40525 +16417 +34902 +46214 +39446 +9631 +20325 +6472 +47830 +4832 +44825 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a/config/defense/mcr/cifar10.yaml b/config/defense/mcr/cifar10.yaml new file mode 100644 index 0000000..a76caf3 --- /dev/null +++ b/config/defense/mcr/cifar10.yaml @@ -0,0 +1,44 @@ +num_bends: 3 +test_t: 0.1 + +device: 'cuda' +dataset_path: 'data/' +index: +dataset: 'cifar10' + +train_curve_epochs: 100 +test_curve_every: 1 + +batch_size: 128 +num_workers: 4 +lr: 0.00003 +lr_scheduler: CosineAnnealingLR +random_seed: 0 +cos_t_max: 100 +use_clean_subset: True + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 + +model: 'preactresnet18' + +ratio: 0.05 +curve: Bezier + +ft_client_optimizer: sgd +ft_epochs: 100 +ft_lr: 0.01 +ft_lr_scheduler: CosineAnnealingLR +ft_sgd_momentum: 0.9 +ft_wd: 0.0005 + +wd: 0.0005 +pin_memory: True +client_optimizer: sgd +sgd_momentum: 0.9 +amp: False + +non_blocking: True +prefetch: False +frequency_save: 100 diff --git a/config/defense/mcr/cifar100.yaml b/config/defense/mcr/cifar100.yaml new file mode 100644 index 0000000..f98987f --- /dev/null +++ b/config/defense/mcr/cifar100.yaml @@ -0,0 +1,44 @@ +num_bends: 3 +test_t: 0.1 + +device: 'cuda' +dataset_path: 'data/' +index: +dataset: 'cifar100' + +train_curve_epochs: 100 +test_curve_every: 1 + +batch_size: 128 +num_workers: 4 +lr: 0.00003 +lr_scheduler: CosineAnnealingLR +random_seed: 0 +cos_t_max: 100 +use_clean_subset: True + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 + +model: 'preactresnet18' + +ratio: 0.05 +curve: Bezier + +ft_client_optimizer: sgd +ft_epochs: 100 +ft_lr: 0.01 +ft_lr_scheduler: CosineAnnealingLR +ft_sgd_momentum: 0.9 +ft_wd: 0.0005 + +wd: 0.0005 +pin_memory: True +client_optimizer: sgd +sgd_momentum: 0.9 +amp: False + +non_blocking: True +prefetch: False +frequency_save: 100 diff --git a/config/defense/mcr/config.yaml b/config/defense/mcr/config.yaml new file mode 100644 index 0000000..a76caf3 --- /dev/null +++ b/config/defense/mcr/config.yaml @@ -0,0 +1,44 @@ +num_bends: 3 +test_t: 0.1 + +device: 'cuda' +dataset_path: 'data/' +index: +dataset: 'cifar10' + +train_curve_epochs: 100 +test_curve_every: 1 + +batch_size: 128 +num_workers: 4 +lr: 0.00003 +lr_scheduler: CosineAnnealingLR +random_seed: 0 +cos_t_max: 100 +use_clean_subset: True + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 + +model: 'preactresnet18' + +ratio: 0.05 +curve: Bezier + +ft_client_optimizer: sgd +ft_epochs: 100 +ft_lr: 0.01 +ft_lr_scheduler: CosineAnnealingLR +ft_sgd_momentum: 0.9 +ft_wd: 0.0005 + +wd: 0.0005 +pin_memory: True +client_optimizer: sgd +sgd_momentum: 0.9 +amp: False + +non_blocking: True +prefetch: False +frequency_save: 100 diff --git a/config/defense/mcr/gtsrb.yaml b/config/defense/mcr/gtsrb.yaml new file mode 100644 index 0000000..eadf7d3 --- /dev/null +++ b/config/defense/mcr/gtsrb.yaml @@ -0,0 +1,44 @@ +num_bends: 3 +test_t: 0.1 + +device: 'cuda' +dataset_path: 'data/' +index: +dataset: 'gtsrb' + +train_curve_epochs: 100 +test_curve_every: 1 + +batch_size: 128 +num_workers: 4 +lr: 0.00003 +lr_scheduler: CosineAnnealingLR +random_seed: 0 +cos_t_max: 100 +use_clean_subset: True + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 + +model: 'preactresnet18' + +ratio: 0.05 +curve: Bezier + +ft_client_optimizer: sgd +ft_epochs: 100 +ft_lr: 0.01 +ft_lr_scheduler: CosineAnnealingLR +ft_sgd_momentum: 0.9 +ft_wd: 0.0005 + +wd: 0.0005 +pin_memory: True +client_optimizer: sgd +sgd_momentum: 0.9 +amp: False + +non_blocking: True +prefetch: False +frequency_save: 100 diff --git a/config/defense/mcr/tiny.yaml b/config/defense/mcr/tiny.yaml new file mode 100644 index 0000000..d553fd3 --- /dev/null +++ b/config/defense/mcr/tiny.yaml @@ -0,0 +1,44 @@ +num_bends: 3 +test_t: 0.1 + +device: 'cuda' +dataset_path: 'data/' +index: +dataset: 'cifar10' + +train_curve_epochs: 200 +test_curve_every: 1 + +batch_size: 128 +num_workers: 4 +lr: 0.00003 +lr_scheduler: CosineAnnealingLR +random_seed: 0 +cos_t_max: 100 +use_clean_subset: True + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 + +model: 'preactresnet18' + +ratio: 0.05 +curve: Bezier + +ft_client_optimizer: sgd +ft_epochs: 200 +ft_lr: 0.01 +ft_lr_scheduler: ReduceLROnPlateau +ft_sgd_momentum: 0.9 +ft_wd: 0.0005 + +wd: 0.0005 +pin_memory: True +client_optimizer: sgd +sgd_momentum: 0.9 +amp: False + +non_blocking: True +prefetch: False +frequency_save: 100 diff --git a/config/defense/nab/cifar10.yaml b/config/defense/nab/cifar10.yaml new file mode 100755 index 0000000..6f32500 --- /dev/null +++ b/config/defense/nab/cifar10.yaml @@ -0,0 +1,40 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' + +dataset: 'cifar10' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 100 +batch_size: 256 +num_workers: 4 +num_workers_semi: 4 +lr: 0.01 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 +trigger_type: 'squareTrigger' +sgd_momentum: 0.9 +wd: 0.0001 + +model: 'preactresnet18' +random_seed: 0 + +prefetch: True +epoch_self: 100 ####config[warmup][epoch] semi 测试用 100 +epoch_warmup: 10 ####config[warmup][epoch] semi +batch_size_self: 512 #####config['batch_size'] pretrain +temperature: 1 #####config['simclr']['temperature'] +epsilon: 0.5 ####config['semi']['epsilon'] + +# LGA part +epoch_lga: 20 +gamma: 0.5 + +non_blocking: True \ No newline at end of file diff --git a/config/defense/nab/cifar100.yaml b/config/defense/nab/cifar100.yaml new file mode 100755 index 0000000..bd683f4 --- /dev/null +++ b/config/defense/nab/cifar100.yaml @@ -0,0 +1,40 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' + +dataset: 'cifar100' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 100 +batch_size: 256 +num_workers: 4 +num_workers_semi: 4 +lr: 0.01 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 +trigger_type: 'squareTrigger' +sgd_momentum: 0.9 +wd: 0.0001 + +model: 'preactresnet18' +random_seed: 0 + +prefetch: True +epoch_self: 100 ####config[warmup][epoch] semi 测试用 100 +epoch_warmup: 10 ####config[warmup][epoch] semi +batch_size_self: 512 #####config['batch_size'] pretrain +temperature: 1 #####config['simclr']['temperature'] +epsilon: 0.5 ####config['semi']['epsilon'] + +# LGA part +epoch_lga: 20 +gamma: 0.5 + +non_blocking: True \ No newline at end of file diff --git a/config/defense/nab/config.yaml b/config/defense/nab/config.yaml new file mode 100755 index 0000000..6f32500 --- /dev/null +++ b/config/defense/nab/config.yaml @@ -0,0 +1,40 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' + +dataset: 'cifar10' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 100 +batch_size: 256 +num_workers: 4 +num_workers_semi: 4 +lr: 0.01 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 +trigger_type: 'squareTrigger' +sgd_momentum: 0.9 +wd: 0.0001 + +model: 'preactresnet18' +random_seed: 0 + +prefetch: True +epoch_self: 100 ####config[warmup][epoch] semi 测试用 100 +epoch_warmup: 10 ####config[warmup][epoch] semi +batch_size_self: 512 #####config['batch_size'] pretrain +temperature: 1 #####config['simclr']['temperature'] +epsilon: 0.5 ####config['semi']['epsilon'] + +# LGA part +epoch_lga: 20 +gamma: 0.5 + +non_blocking: True \ No newline at end of file diff --git a/config/defense/nab/gtsrb.yaml b/config/defense/nab/gtsrb.yaml new file mode 100755 index 0000000..f253224 --- /dev/null +++ b/config/defense/nab/gtsrb.yaml @@ -0,0 +1,40 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' + +dataset: 'gtsrb' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 100 +batch_size: 256 +num_workers: 4 +num_workers_semi: 4 +lr: 0.01 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 +trigger_type: 'squareTrigger' +sgd_momentum: 0.9 +wd: 0.0001 + +model: 'preactresnet18' +random_seed: 0 + +prefetch: True +epoch_self: 100 ####config[warmup][epoch] semi 测试用 100 +epoch_warmup: 10 ####config[warmup][epoch] semi +batch_size_self: 512 #####config['batch_size'] pretrain +temperature: 1 #####config['simclr']['temperature'] +epsilon: 0.5 ####config['semi']['epsilon'] + +# LGA part +epoch_lga: 20 +gamma: 0.5 + +non_blocking: True \ No newline at end of file diff --git a/config/defense/nab/tiny.yaml b/config/defense/nab/tiny.yaml new file mode 100755 index 0000000..9a742ef --- /dev/null +++ b/config/defense/nab/tiny.yaml @@ -0,0 +1,40 @@ +device: 'cuda' +checkpoint_load: +checkpoint_save: +log: +data_root: 'data/' + +dataset: 'tiny' +num_classes: +input_height: +input_width: +input_channel: + +epochs: 100 +batch_size: 256 +num_workers: 4 +num_workers_semi: 4 +lr: 0.01 + +poison_rate: 0.1 +target_type: 'all2one' +target_label: 0 +trigger_type: 'squareTrigger' +sgd_momentum: 0.9 +wd: 0.0001 + +model: 'preactresnet18' +random_seed: 0 + +prefetch: True +epoch_self: 100 ####config[warmup][epoch] semi 测试用 100 +epoch_warmup: 10 ####config[warmup][epoch] semi +batch_size_self: 512 #####config['batch_size'] pretrain +temperature: 1 #####config['simclr']['temperature'] +epsilon: 0.5 ####config['semi']['epsilon'] + +# LGA part +epoch_lga: 20 +gamma: 0.5 + +non_blocking: True \ No newline at end of file diff --git a/config/defense/nad/cifar10.yaml b/config/defense/nad/cifar10.yaml new file mode 100644 index 0000000..e9e5ffb --- /dev/null +++ b/config/defense/nad/cifar10.yaml @@ -0,0 +1,40 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +index: +te_epochs: 10 +momentum: 0.9 +weight_decay: 1.0e-4 +ratio: 0.05 +beta1: 500 +beta2: 1000 +beta3: 1000 +p: 2.0 + +teacher_model_loc: + + + diff --git a/config/defense/nad/cifar100.yaml b/config/defense/nad/cifar100.yaml new file mode 100644 index 0000000..b9a6d82 --- /dev/null +++ b/config/defense/nad/cifar100.yaml @@ -0,0 +1,40 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar100' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +index: +te_epochs: 10 +momentum: 0.9 +weight_decay: 1.0e-4 +ratio: 0.05 +beta1: 500 +beta2: 1000 +beta3: 1000 +p: 2.0 + +teacher_model_loc: + + + diff --git a/config/defense/nad/config.yaml b/config/defense/nad/config.yaml new file mode 100755 index 0000000..e9e5ffb --- /dev/null +++ b/config/defense/nad/config.yaml @@ -0,0 +1,40 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +index: +te_epochs: 10 +momentum: 0.9 +weight_decay: 1.0e-4 +ratio: 0.05 +beta1: 500 +beta2: 1000 +beta3: 1000 +p: 2.0 + +teacher_model_loc: + + + diff --git a/config/defense/nad/gtsrb.yaml b/config/defense/nad/gtsrb.yaml new file mode 100644 index 0000000..2c31326 --- /dev/null +++ b/config/defense/nad/gtsrb.yaml @@ -0,0 +1,40 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'gtsrb' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +index: +te_epochs: 10 +momentum: 0.9 +weight_decay: 1.0e-4 +ratio: 0.05 +beta1: 500 +beta2: 1000 +beta3: 1000 +p: 2.0 + +teacher_model_loc: + + + diff --git a/config/defense/nad/tiny.yaml b/config/defense/nad/tiny.yaml new file mode 100644 index 0000000..d085bde --- /dev/null +++ b/config/defense/nad/tiny.yaml @@ -0,0 +1,40 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'tiny' + +epochs: 200 +batch_size: 128 +num_workers: 4 +lr: 0.01 +lr_scheduler: ReduceLROnPlateau +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +index: +te_epochs: 10 +momentum: 0.9 +weight_decay: 1.0e-4 +ratio: 0.05 +beta1: 500 +beta2: 1000 +beta3: 1000 +p: 2.0 + +teacher_model_loc: + + + diff --git a/config/defense/nc/cifar10.yaml b/config/defense/nc/cifar10.yaml new file mode 100755 index 0000000..7fe86ee --- /dev/null +++ b/config/defense/nc/cifar10.yaml @@ -0,0 +1,50 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + + +mask_lr: 0.1 +init_cost: 1.0e-3 +# bs: 64 +atk_succ_threshold: 98.0 +early_stop: True +early_stop_threshold: 0.99 +early_stop_patience: 25 +patience: 5 +cost_multiplier: 2 +# total_label: 1.0e-7 +EPSILON: 1.0e-7 +to_file: True +n_times_test: 1 +use_norm: 1 +ratio: 0.05 +cleaning_ratio: 0.05 +unlearning_ratio: 0.2 +nc_epoch: 80 + +index: + + + diff --git a/config/defense/nc/cifar100.yaml b/config/defense/nc/cifar100.yaml new file mode 100755 index 0000000..705e09d --- /dev/null +++ b/config/defense/nc/cifar100.yaml @@ -0,0 +1,50 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar100' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + + +mask_lr: 0.1 +init_cost: 1.0e-3 +# bs: 64 +atk_succ_threshold: 98.0 +early_stop: True +early_stop_threshold: 0.99 +early_stop_patience: 25 +patience: 5 +cost_multiplier: 2 +# total_label: 1.0e-7 +EPSILON: 1.0e-7 +to_file: True +n_times_test: 1 +use_norm: 1 +ratio: 0.05 +cleaning_ratio: 0.05 +unlearning_ratio: 0.2 +nc_epoch: 80 + +index: + + + diff --git a/config/defense/nc/config.yaml b/config/defense/nc/config.yaml new file mode 100755 index 0000000..7fe86ee --- /dev/null +++ b/config/defense/nc/config.yaml @@ -0,0 +1,50 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + + +mask_lr: 0.1 +init_cost: 1.0e-3 +# bs: 64 +atk_succ_threshold: 98.0 +early_stop: True +early_stop_threshold: 0.99 +early_stop_patience: 25 +patience: 5 +cost_multiplier: 2 +# total_label: 1.0e-7 +EPSILON: 1.0e-7 +to_file: True +n_times_test: 1 +use_norm: 1 +ratio: 0.05 +cleaning_ratio: 0.05 +unlearning_ratio: 0.2 +nc_epoch: 80 + +index: + + + diff --git a/config/defense/nc/gtsrb.yaml b/config/defense/nc/gtsrb.yaml new file mode 100755 index 0000000..012163a --- /dev/null +++ b/config/defense/nc/gtsrb.yaml @@ -0,0 +1,50 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'gtsrb' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + + +mask_lr: 0.1 +init_cost: 1.0e-3 +# bs: 64 +atk_succ_threshold: 98.0 +early_stop: True +early_stop_threshold: 0.99 +early_stop_patience: 25 +patience: 5 +cost_multiplier: 2 +# total_label: 1.0e-7 +EPSILON: 1.0e-7 +to_file: True +n_times_test: 1 +use_norm: 1 +ratio: 0.05 +cleaning_ratio: 0.05 +unlearning_ratio: 0.2 +nc_epoch: 80 + +index: + + + diff --git a/config/defense/nc/tiny.yaml b/config/defense/nc/tiny.yaml new file mode 100755 index 0000000..80c14d6 --- /dev/null +++ b/config/defense/nc/tiny.yaml @@ -0,0 +1,50 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'tiny' + +epochs: 200 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: ReduceLROnPlateau +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + + +mask_lr: 0.1 +init_cost: 1.0e-3 +# bs: 64 +atk_succ_threshold: 98.0 +early_stop: True +early_stop_threshold: 0.99 +early_stop_patience: 25 +patience: 5 +cost_multiplier: 2 +# total_label: 1.0e-7 +EPSILON: 1.0e-7 +to_file: True +n_times_test: 1 +use_norm: 1 +ratio: 0.05 +cleaning_ratio: 0.05 +unlearning_ratio: 0.2 +nc_epoch: 80 + +index: + + + diff --git a/config/defense/spectral/cifar10.yaml b/config/defense/spectral/cifar10.yaml new file mode 100644 index 0000000..be75bf4 --- /dev/null +++ b/config/defense/spectral/cifar10.yaml @@ -0,0 +1,27 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +percentile: 85 diff --git a/config/defense/spectral/cifar100.yaml b/config/defense/spectral/cifar100.yaml new file mode 100644 index 0000000..5c3a750 --- /dev/null +++ b/config/defense/spectral/cifar100.yaml @@ -0,0 +1,27 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar100' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +percentile: 85 diff --git a/config/defense/spectral/config.yaml b/config/defense/spectral/config.yaml new file mode 100755 index 0000000..be75bf4 --- /dev/null +++ b/config/defense/spectral/config.yaml @@ -0,0 +1,27 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'cifar10' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +percentile: 85 diff --git a/config/defense/spectral/gtsrb.yaml b/config/defense/spectral/gtsrb.yaml new file mode 100644 index 0000000..fd4cd5a --- /dev/null +++ b/config/defense/spectral/gtsrb.yaml @@ -0,0 +1,27 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'gtsrb' + +epochs: 100 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +percentile: 85 diff --git a/config/defense/spectral/tiny.yaml b/config/defense/spectral/tiny.yaml new file mode 100644 index 0000000..6ccab4b --- /dev/null +++ b/config/defense/spectral/tiny.yaml @@ -0,0 +1,27 @@ +device: 'cuda' +amp: True +pin_memory: True +non_blocking: True +prefetch: False + +checkpoint_load: +checkpoint_save: +log: +dataset_path: './data' +dataset: 'tiny' + +epochs: 200 +batch_size: 256 +num_workers: 4 +lr: 0.01 +lr_scheduler: ReduceLROnPlateau +model: 'preactresnet18' + +client_optimizer: 'sgd' +sgd_momentum: 0.9 +wd: 5.0e-4 +frequency_save: 0 + +random_seed: 0 + +percentile: 85 diff --git a/config/visualization/default.yaml b/config/visualization/default.yaml new file mode 100755 index 0000000..f77f0bb --- /dev/null +++ b/config/visualization/default.yaml @@ -0,0 +1,20 @@ +amp: False +device: cuda:0 +attack_label_trans: all2one +attack_target: 0 +client_optimizer: sgd +dataset: cifar10 +dataset_path: ../data +frequency_save: 100 +batch_size: 128 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: preactresnet18 +pratio: 0.1 +random_seed: 0 +sgd_momentum: 0.9 +wd: 0.0005 +attack: badnet +patch_mask_path: ../resource/badnet/bottom_right_3by3_white.npy +epochs: 100 +result_file_defense: None diff --git a/dataset/CelebA.py b/dataset/CelebA.py new file mode 100755 index 0000000..441bd02 --- /dev/null +++ b/dataset/CelebA.py @@ -0,0 +1,717 @@ +""" +This file is modified based on the following source: +link : https://github.com/VinAIResearch/Warping-based_Backdoor_Attack-release +The original license is placed at the end of this file. + +The update include: + 1. change the param from opt to data_root + 2. add if statement to check if the transform is None + +# idea: This script is for CelebA implementation + +Note that if you get error due to download part, you may need to download CelebA manually, + since the official implementation use googledrive which limit daily access amount. +""" + +import torchvision + +import torch.utils.data as data + +class CelebA_attr(data.Dataset): + def __init__(self, data_root, split, transform = None): + self.dataset = torchvision.datasets.CelebA(root=data_root, split=split, target_type="attr", download=True) + self.list_attributes = [18, 31, 21] + self.transform = transform + self.split = split + + def _convert_attributes(self, bool_attributes): + return (bool_attributes[0] << 2) + (bool_attributes[1] << 1) + (bool_attributes[2]) + + def __len__(self): + return len(self.dataset) + + def __getitem__(self, index): + input, target = self.dataset[index] + if self.transform is not None: + input = self.transform(input) + target = self._convert_attributes(target[self.list_attributes]) + return (input, target) + + +''' +original license: + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change 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But first, please read +. +''' \ No newline at end of file diff --git a/dataset/GTSRB.py b/dataset/GTSRB.py new file mode 100755 index 0000000..608aeb6 --- /dev/null +++ b/dataset/GTSRB.py @@ -0,0 +1,750 @@ +""" +This file is modified based on the following source: +link : https://github.com/VinAIResearch/Warping-based_Backdoor_Attack-release +The original license is placed at the end of this file. + +The update include: + 1. change the param from opt to data_root + 2. add if statement to check if the transform is None + 3. change the path str + +# idea: This script is implementation of GTSRB, download script is under ./sh +""" +import os + +from PIL import Image +import csv + +import torch.utils.data as data + +class GTSRB(data.Dataset): + def __init__(self, data_root, train, transform = None): + super(GTSRB, self).__init__() + if train: + self.data_folder = os.path.join(data_root, "Train") + self.images, self.labels = self._get_data_train_list() + if not os.path.isdir(self.data_folder): + os.makedirs(self.data_folder) + else: + self.data_folder = os.path.join(data_root, "Test") + self.images, self.labels = self._get_data_test_list() + if not os.path.isdir(self.data_folder): + os.makedirs(self.data_folder) + + self.transform = transform + + def _get_data_train_list(self): + images = [] + labels = [] + for c in range(0, 43): + prefix = self.data_folder + "/" + format(c, "05d") + "/" + if not os.path.isdir(prefix): + os.makedirs(prefix) + gtFile = open(prefix + "GT-" + format(c, "05d") + ".csv") + gtReader = csv.reader(gtFile, delimiter=";") + next(gtReader) + for row in gtReader: + images.append(prefix + row[0]) + labels.append(int(row[7])) + gtFile.close() + return images, labels + + def _get_data_test_list(self): + images = [] + labels = [] + prefix = os.path.join(self.data_folder, "GT-final_test.csv") + gtFile = open(prefix) + gtReader = csv.reader(gtFile, delimiter=";") + next(gtReader) + for row in gtReader: + images.append(self.data_folder + '' + "/" + row[0]) + labels.append(int(row[7])) + return images, labels + + def __len__(self): + return len(self.images) + + def __getitem__(self, index): + image = Image.open(self.images[index]) + if self.transform is not None: + image = self.transform(image) + label = self.labels[index] + return image, label + +''' +original license: + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. +''' \ No newline at end of file diff --git a/dataset/Tiny.py b/dataset/Tiny.py new file mode 100755 index 0000000..328265e --- /dev/null +++ b/dataset/Tiny.py @@ -0,0 +1,112 @@ +""" +Simple Tiny ImageNet dataset utility class for pytorch. +This code is copied from https://gist.github.com/lromor/bcfc69dcf31b2f3244358aea10b7a11b + +# idea: This script is implementation of TinyImageNet, the download is automatically started at the first execution. + +original license: + +# Copyright (C) 2022 Leonardo Romor +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +""" + +import os + +import shutil + +from torchvision.datasets import ImageFolder +from torchvision.datasets.utils import verify_str_arg +from torchvision.datasets.utils import download_and_extract_archive + + +def normalize_tin_val_folder_structure(path, + images_folder='images', + annotations_file='val_annotations.txt'): + # Check if files/annotations are still there to see + # if we already run reorganize the folder structure. + images_folder = os.path.join(path, images_folder) + annotations_file = os.path.join(path, annotations_file) + + # Exists + if not os.path.exists(images_folder) \ + and not os.path.exists(annotations_file): + if not os.listdir(path): + raise RuntimeError('Validation folder is empty.') + return + + # Parse the annotations + with open(annotations_file) as f: + for line in f: + values = line.split() + img = values[0] + label = values[1] + img_file = os.path.join(images_folder, values[0]) + label_folder = os.path.join(path, label) + os.makedirs(label_folder, exist_ok=True) + try: + shutil.move(img_file, os.path.join(label_folder, img)) + except FileNotFoundError: + continue + + os.sync() + assert not os.listdir(images_folder) + shutil.rmtree(images_folder) + os.remove(annotations_file) + os.sync() + + +class TinyImageNet(ImageFolder): + """Dataset for TinyImageNet-200""" + base_folder = 'tiny-imagenet-200' + zip_md5 = '90528d7ca1a48142e341f4ef8d21d0de' + splits = ('train', 'val') + filename = 'tiny-imagenet-200.zip' + url = 'http://cs231n.stanford.edu/tiny-imagenet-200.zip' + + def __init__(self, root, split='train', download=False, **kwargs): + self.data_root = os.path.expanduser(root) + self.split = verify_str_arg(split, "split", self.splits) + + if download: + self.download() + + if not self._check_exists(): + raise RuntimeError('Dataset not found.' + + ' You can use download=True to download it') + super().__init__(self.split_folder, **kwargs) + + @property + def dataset_folder(self): + return os.path.join(self.data_root, self.base_folder) + + @property + def split_folder(self): + return os.path.join(self.dataset_folder, self.split) + + def _check_exists(self): + return os.path.exists(self.split_folder) + + def extra_repr(self): + return "Split: {split}".format(**self.__dict__) + + def download(self): + if self._check_exists(): + return + download_and_extract_archive( + self.url, self.data_root, filename=self.filename, + remove_finished=True, md5=self.zip_md5) + assert 'val' in self.splits + normalize_tin_val_folder_structure( + os.path.join(self.dataset_folder, 'val')) \ No newline at end of file diff --git a/defense/__init__.py b/defense/__init__.py new file mode 100755 index 0000000..6b1f4d2 --- /dev/null +++ b/defense/__init__.py @@ -0,0 +1,6 @@ +#!/usr/bin/env python3 + +from defense import base + + +__all__ = ['summary'] \ No newline at end of file diff --git a/defense/abl.py b/defense/abl.py new file mode 100644 index 0000000..d8c33c1 --- /dev/null +++ b/defense/abl.py @@ -0,0 +1,1036 @@ +''' +This file is modified based on the following source: +link : https://github.com/bboylyg/ABL. +The defense method is called abl. + +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. save process + 5. new standard: robust accuracy +basic sturcture for defense method: + 1. basic setting: args + 2. attack result(model, train data, test data) + 3. abl defense: + a. pre-train model + b. isolate the special data(loss is low) as backdoor data + c. unlearn the backdoor data and learn the remaining data + 4. test the result and get ASR, ACC, RC +''' + + +import argparse +import os,sys +import numpy as np +import torch +import torch.nn as nn +from tqdm import tqdm +import copy + +sys.path.append('../') +sys.path.append(os.getcwd()) + +from pprint import pformat +import yaml +import logging +import time +from defense.base import defense + +from utils.aggregate_block.train_settings_generate import argparser_criterion +from utils.trainer_cls import Metric_Aggregator, PureCleanModelTrainer, all_acc, general_plot_for_epoch, given_dataloader_test +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.log_assist import get_git_info +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform +from utils.save_load_attack import load_attack_result, save_defense_result +from utils.bd_dataset_v2 import dataset_wrapper_with_transform + +class LGALoss(nn.Module): + def __init__(self, gamma, criterion): + super(LGALoss, self).__init__() + self.gamma = gamma + self.criterion = criterion + return + + def forward(self,output,target): + loss = self.criterion(output, target) + # add Local Gradient Ascent(LGA) loss + loss_ascent = torch.sign(loss - self.gamma) * loss + return loss_ascent + +class FloodingLoss(nn.Module): + def __init__(self, flooding, criterion): + super(FloodingLoss, self).__init__() + self.flooding = flooding + self.criterion = criterion + return + + def forward(self,output,target): + loss = self.criterion(output, target) + # add Local Gradient Ascent(LGA) loss + loss_ascent = (loss - self.flooding).abs() + self.flooding + return loss_ascent + + +def adjust_learning_rate(optimizer, epoch, args): + '''set learning rate during the process of pretraining model + optimizer: + optimizer during the pretrain process + epoch: + current epoch + args: + Contains default parameters + ''' + if epoch < args.tuning_epochs: + lr = args.lr + else: + lr = 0.01 + logging.info('epoch: {} lr: {:.4f}'.format(epoch, lr)) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + +def compute_loss_value(args, poisoned_data, model_ascent): + '''Calculate loss value per example + args: + Contains default parameters + poisoned_data: + the train dataset which contains backdoor data + model_ascent: + the model after the process of pretrain + ''' + # Define loss function + if args.device == 'cuda': + criterion = torch.nn.CrossEntropyLoss().cuda() + else: + criterion = torch.nn.CrossEntropyLoss() + + model_ascent.eval() + losses_record = [] + + example_data_loader = torch.utils.data.DataLoader(dataset=poisoned_data, + batch_size=1, + shuffle=False, + ) + + for idx, (img, target,_,_,_) in tqdm(enumerate(example_data_loader, start=0)): + + img = img.to(args.device) + target = target.to(args.device) + + with torch.no_grad(): + output = model_ascent(img) + loss = criterion(output, target) + + losses_record.append(loss.item()) + + losses_idx = np.argsort(np.array(losses_record)) # get the index of examples by loss value in descending order + + # Show the top 10 loss values + losses_record_arr = np.array(losses_record) + logging.info(f'Top ten loss value: {losses_record_arr[losses_idx[:10]]}') + + return losses_idx + +def isolate_data(args, result, losses_idx): + '''isolate the backdoor data with the calculated loss + args: + Contains default parameters + result: + the attack result contain the train dataset which contains backdoor data + losses_idx: + the index of order about the loss value for each data + ''' + # Initialize lists + other_examples = [] + isolation_examples = [] + + cnt = 0 + ratio = args.isolation_ratio + perm = losses_idx[0: int(len(losses_idx) * ratio)] + permnot = losses_idx[int(len(losses_idx) * ratio):] + tf_compose = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = False) + train_dataset = result['bd_train'].wrapped_dataset + data_set_without_tran = train_dataset + data_set_isolate = result['bd_train'] + data_set_isolate.wrapped_dataset = data_set_without_tran + data_set_isolate.wrap_img_transform = tf_compose + + data_set_other_without_tran = data_set_without_tran.copy() + data_set_other = dataset_wrapper_with_transform( + data_set_other_without_tran, + tf_compose, + None, + ) + # x = result['bd_train']['x'] + # y = result['bd_train']['y'] + + data_set_isolate.subset(perm) + data_set_other.subset(permnot) + + # isolation_examples = list(zip([x[ii] for ii in perm],[y[ii] for ii in perm])) + # other_examples = list(zip([x[ii] for ii in permnot],[y[ii] for ii in permnot])) + + logging.info('Finish collecting {} isolation examples: '.format(len(data_set_isolate))) + logging.info('Finish collecting {} other examples: '.format(len(data_set_other))) + + return data_set_isolate, data_set_other + + + +def learning_rate_finetuning(optimizer, epoch, args): + '''set learning rate during the process of finetuing model + optimizer: + optimizer during the pretrain process + epoch: + current epoch + args: + Contains default parameters + ''' + if epoch < 40: + lr = 0.01 + elif epoch < 60: + lr = 0.001 + else: + lr = 0.001 + logging.info('epoch: {} lr: {:.4f}'.format(epoch, lr)) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + +def learning_rate_unlearning(optimizer, epoch, args): + '''set learning rate during the process of unlearning model + optimizer: + optimizer during the pretrain process + epoch: + current epoch + args: + Contains default parameters + ''' + if epoch < args.unlearning_epochs: + lr = 0.0001 + else: + lr = 0.0001 + logging.info('epoch: {} lr: {:.4f}'.format(epoch, lr)) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + + + + +class abl(defense): + + def __init__(self,args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + self.args = args + + if 'result_file' in args.__dict__ : + if args.result_file is not None: + self.set_result(args.result_file) + + def add_arguments(parser): + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory") + parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?") + parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch') + parser.add_argument('--amp', type=lambda x: str(x) in ['True','true','1']) + + parser.add_argument('--checkpoint_load', type=str, help='the location of load model') + parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved') + parser.add_argument('--log', type=str, help='the location of log') + parser.add_argument("--dataset_path", type=str, help='the location of data') + parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny') + parser.add_argument('--result_file', type=str, help='the location of result') + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--model', type=str, help='resnet18') + + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--yaml_path', type=str, default="./config/defense/abl/config.yaml", help='the path of yaml') + + #set the parameter for the abl defense + parser.add_argument('--tuning_epochs', type=int, help='number of tune epochs to run') + parser.add_argument('--finetuning_ascent_model', type=str, help='whether finetuning model') + parser.add_argument('--finetuning_epochs', type=int, help='number of finetuning epochs to run') + parser.add_argument('--unlearning_epochs', type=int, help='number of unlearning epochs to run') + parser.add_argument('--lr_finetuning_init', type=float, help='initial finetuning learning rate') + parser.add_argument('--lr_unlearning_init', type=float, help='initial unlearning learning rate') + parser.add_argument('--momentum', type=float, help='momentum') + parser.add_argument('--weight_decay', type=float, help='weight decay') + parser.add_argument('--isolation_ratio', type=float, help='ratio of isolation data') + parser.add_argument('--gradient_ascent_type', type=str, help='type of gradient ascent') + parser.add_argument('--gamma', type=float, help='value of gamma') + parser.add_argument('--flooding', type=float, help='value of flooding') + + parser.add_argument('--threshold_clean', type=float, help='threshold of save weight') + parser.add_argument('--threshold_bad', type=float, help='threshold of save weight') + parser.add_argument('--interval', type=int, help='frequency of save model') + + def set_result(self, result_file): + attack_file = 'record/' + result_file + save_path = 'record/' + result_file + '/defense/abl/' + if not (os.path.exists(save_path)): + os.makedirs(save_path) + # assert(os.path.exists(save_path)) + self.args.save_path = save_path + if self.args.checkpoint_save is None: + self.args.checkpoint_save = save_path + 'checkpoint/' + if not (os.path.exists(self.args.checkpoint_save)): + os.makedirs(self.args.checkpoint_save) + if self.args.log is None: + self.args.log = save_path + 'log/' + if not (os.path.exists(self.args.log)): + os.makedirs(self.args.log) + self.result = load_attack_result(attack_file + '/attack_result.pt') + + def set_trainer(self, model): + self.trainer = PureCleanModelTrainer( + model, + ) + + def set_logger(self): + args = self.args + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + try: + logging.info(pformat(get_git_info())) + except: + logging.info('Getting git info fails.') + + def set_devices(self): + # self.device = torch.device( + # ( + # f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device + # # since DataParallel only allow .to("cuda") + # ) if torch.cuda.is_available() else "cpu" + # ) + self.device = self.args.device + + def mitigation(self): + self.set_devices() + fix_random(self.args.random_seed) + result = self.result + ###a. pre-train model + poisoned_data, model_ascent = self.pre_train(args,result) + + ###b. isolate the special data(loss is low) as backdoor data + losses_idx = compute_loss_value(args, poisoned_data, model_ascent) + logging.info('----------- Collect isolation data -----------') + isolation_examples, other_examples = isolate_data(args, result, losses_idx) + + ###c. unlearn the backdoor data and learn the remaining data + model_new = self.train_unlearning(args,result,model_ascent,isolation_examples,other_examples) + + result = {} + result['model'] = model_new + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model_new.cpu().state_dict(), + save_path=args.save_path, + ) + return result + + def defense(self,result_file): + self.set_result(result_file) + self.set_logger() + result = self.mitigation() + return result + + def pre_train(self, args, result): + '''Pretrain the model with raw data + args: + Contains default parameters + result: + attack result(details can be found in utils) + ''' + agg = Metric_Aggregator() + # Load models + logging.info('----------- Network Initialization --------------') + model_ascent = generate_cls_model(args.model,args.num_classes) + if "," in self.device: + model_ascent = torch.nn.DataParallel( + model_ascent, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{model_ascent.device_ids[0]}' + model_ascent.to(self.args.device) + else: + model_ascent.to(self.args.device) + logging.info('finished model init...') + # initialize optimizer + # because the optimizer has parameter nesterov + optimizer = torch.optim.SGD(model_ascent.parameters(), + lr=args.lr, + momentum=args.momentum, + weight_decay=args.weight_decay, + nesterov=True) + + # define loss functions + # recommend to use cross entropy + criterion = argparser_criterion(args).to(args.device) + if args.gradient_ascent_type == 'LGA': + criterion = LGALoss(args.gamma,criterion).to(args.device) + elif args.gradient_ascent_type == 'Flooding': + criterion = FloodingLoss(args.flooding,criterion).to(args.device) + else: + raise NotImplementedError + + logging.info('----------- Data Initialization --------------') + + # tf_compose = transforms.Compose([ + # transforms.ToTensor() + # ]) + tf_compose = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = False) + train_dataset = result['bd_train'].wrapped_dataset + data_set_without_tran = train_dataset + data_set_o = result['bd_train'] + data_set_o.wrapped_dataset = data_set_without_tran + data_set_o.wrap_img_transform = tf_compose + + # data_set_isolate = result['bd_train'] + # data_set_isolate.wrapped_dataset = data_set_without_tran + # data_set_isolate.wrap_img_transform = tf_compose + + # # data_set_other = copy.deepcopy(data_set_isolate) + # # x = result['bd_train']['x'] + # # y = result['bd_train']['y'] + # losses_idx = range(50000) + # ratio = args.isolation_ratio + # perm = losses_idx[0: int(len(losses_idx) * ratio)] + # permnot = losses_idx[int(len(losses_idx) * ratio):] + # data_set_isolate.subset(perm) + # data_set_o.subset(permnot) + # data_set_other = copy.deepcopy(data_set_o) + poisoned_data_loader = torch.utils.data.DataLoader(data_set_o, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=True) + + test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False) + data_bd_testset = self.result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + data_clean_testset = self.result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + train_loss_list = [] + train_mix_acc_list = [] + train_clean_acc_list = [] + train_asr_list = [] + train_ra_list = [] + + clean_test_loss_list = [] + bd_test_loss_list = [] + ra_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + + logging.info('----------- Train Initialization --------------') + for epoch in range(0, args.tuning_epochs): + logging.info("Epoch {}:".format(epoch + 1)) + adjust_learning_rate(optimizer, epoch, args) + train_epoch_loss_avg_over_batch, \ + train_mix_acc, \ + train_clean_acc, \ + train_asr, \ + train_ra = self.train_step(args, poisoned_data_loader, model_ascent, optimizer, criterion, epoch + 1) + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.eval_step( + model_ascent, + data_clean_loader, + data_bd_loader, + args, + ) + + agg({ + "epoch": epoch, + + "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch, + "train_acc": train_mix_acc, + "train_acc_clean_only": train_clean_acc, + "train_asr_bd_only": train_asr, + "train_ra_bd_only": train_ra, + + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "ra_test_loss_avg_over_batch": ra_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + + train_loss_list.append(train_epoch_loss_avg_over_batch) + train_mix_acc_list.append(train_mix_acc) + train_clean_acc_list.append(train_clean_acc) + train_asr_list.append(train_asr) + train_ra_list.append(train_ra) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + ra_test_loss_list.append(ra_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + + general_plot_for_epoch( + { + "Train Acc": train_mix_acc_list, + "Test C-Acc": test_acc_list, + "Test ASR": test_asr_list, + "Test RA": test_ra_list, + }, + save_path=f"{args.save_path}pre_train_acc_like_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "Train Loss": train_loss_list, + "Test Clean Loss": clean_test_loss_list, + "Test Backdoor Loss": bd_test_loss_list, + "Test RA Loss": ra_test_loss_list, + }, + save_path=f"{args.save_path}pre_train_loss_metric_plots.png", + ylabel="percentage", + ) + + agg.to_dataframe().to_csv(f"{args.save_path}pre_train_df.csv") + + if args.frequency_save != 0 and epoch % args.frequency_save == args.frequency_save - 1: + state_dict = { + "model": model_ascent.state_dict(), + "optimizer": optimizer.state_dict(), + "epoch_current": epoch, + } + torch.save(state_dict, args.checkpoint_save + "pre_train_state_dict.pt") + + agg.summary().to_csv(f"{args.save_path}pre_train_df_summary.csv") + + return data_set_o, model_ascent + + def train_unlearning(self, args, result, model_ascent, isolate_poisoned_data, isolate_other_data): + '''train the model with remaining data and unlearn the backdoor data + args: + Contains default parameters + result: + attack result(details can be found in utils) + model_ascent: + the model after pretrain + isolate_poisoned_data: + the dataset of 'backdoor' data + isolate_other_data: + the dataset of remaining data + ''' + agg = Metric_Aggregator() + # Load models + ### TODO: load model from checkpoint + # logging.info('----------- Network Initialization --------------') + # if "," in args.device: + # model_ascent = torch.nn.DataParallel( + # model_ascent, + # device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + # ) + # else: + # model_ascent.to(args.device) + # model_ascent.to(args.device) + logging.info('Finish loading ascent model...') + # initialize optimizer + # Because nesterov we do not use other optimizer + optimizer = torch.optim.SGD(model_ascent.parameters(), + lr=args.lr, + momentum=args.momentum, + weight_decay=args.weight_decay, + nesterov=True) + + # define loss functions + # you can use other criterion, but the paper use cross validation to unlearn sample + if args.device == 'cuda': + criterion = argparser_criterion(args).cuda() + else: + criterion = argparser_criterion(args) + + tf_compose_finetuning = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = True) + tf_compose_unlearning = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = True) + + isolate_poisoned_data.wrap_img_transform = tf_compose_finetuning + isolate_poisoned_data_loader = torch.utils.data.DataLoader(dataset=isolate_poisoned_data, + batch_size=args.batch_size, + shuffle=True, + ) + + isolate_other_data.wrap_img_transform = tf_compose_unlearning + isolate_other_data_loader = torch.utils.data.DataLoader(dataset=isolate_other_data, + batch_size=args.batch_size, + shuffle=True, + ) + + test_tran = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = False) + data_bd_testset = result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + data_clean_testset = result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + train_loss_list = [] + train_mix_acc_list = [] + train_clean_acc_list = [] + train_asr_list = [] + train_ra_list = [] + + clean_test_loss_list = [] + bd_test_loss_list = [] + ra_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + + logging.info('----------- Train Initialization --------------') + + if args.finetuning_ascent_model == True: + # this is to improve the clean accuracy of isolation model, you can skip this step + logging.info('----------- Finetuning isolation model --------------') + for epoch in range(0, args.finetuning_epochs): + learning_rate_finetuning(optimizer, epoch, args) + train_epoch_loss_avg_over_batch, \ + train_mix_acc, \ + train_clean_acc, \ + train_asr, \ + train_ra = self.train_step(args, isolate_other_data_loader, model_ascent, optimizer, criterion, epoch + 1) + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.eval_step( + model_ascent, + data_clean_loader, + data_bd_loader, + args, + ) + + agg({ + "epoch": epoch, + + "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch, + "train_acc": train_mix_acc, + "train_acc_clean_only": train_clean_acc, + "train_asr_bd_only": train_asr, + "train_ra_bd_only": train_ra, + + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "ra_test_loss_avg_over_batch": ra_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + + train_loss_list.append(train_epoch_loss_avg_over_batch) + train_mix_acc_list.append(train_mix_acc) + train_clean_acc_list.append(train_clean_acc) + train_asr_list.append(train_asr) + train_ra_list.append(train_ra) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + ra_test_loss_list.append(ra_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + + general_plot_for_epoch( + { + "Train Acc": train_mix_acc_list, + "Test C-Acc": test_acc_list, + "Test ASR": test_asr_list, + "Test RA": test_ra_list, + }, + save_path=f"{args.save_path}finetune_acc_like_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "Train Loss": train_loss_list, + "Test Clean Loss": clean_test_loss_list, + "Test Backdoor Loss": bd_test_loss_list, + "Test RA Loss": ra_test_loss_list, + }, + save_path=f"{args.save_path}finetune_loss_metric_plots.png", + ylabel="percentage", + ) + + agg.to_dataframe().to_csv(f"{args.save_path}finetune_df.csv") + + if args.frequency_save != 0 and epoch % args.frequency_save == args.frequency_save - 1: + state_dict = { + "model": model_ascent.state_dict(), + "optimizer": optimizer.state_dict(), + "epoch_current": epoch, + } + torch.save(state_dict, args.checkpoint_save + "finetune_state_dict.pt") + agg.summary().to_csv(f"{args.save_path}finetune_df_summary.csv") + + + best_acc = 0 + best_asr = 0 + logging.info('----------- Model unlearning --------------') + for epoch in range(0, args.unlearning_epochs): + + learning_rate_unlearning(optimizer, epoch, args) + train_epoch_loss_avg_over_batch, \ + train_mix_acc, \ + train_clean_acc, \ + train_asr, \ + train_ra = self.train_step_unlearn(args, isolate_poisoned_data_loader, model_ascent, optimizer, criterion, epoch + 1) + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.eval_step( + model_ascent, + data_clean_loader, + data_bd_loader, + args, + ) + + agg({ + "epoch": epoch, + + "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch, + "train_acc": train_mix_acc, + "train_acc_clean_only": train_clean_acc, + "train_asr_bd_only": train_asr, + "train_ra_bd_only": train_ra, + + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "ra_test_loss_avg_over_batch": ra_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + + train_loss_list.append(train_epoch_loss_avg_over_batch) + train_mix_acc_list.append(train_mix_acc) + train_clean_acc_list.append(train_clean_acc) + train_asr_list.append(train_asr) + train_ra_list.append(train_ra) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + ra_test_loss_list.append(ra_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + + general_plot_for_epoch( + { + "Train Acc": train_mix_acc_list, + "Test C-Acc": test_acc_list, + "Test ASR": test_asr_list, + "Test RA": test_ra_list, + }, + save_path=f"{args.save_path}unlearn_acc_like_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "Train Loss": train_loss_list, + "Test Clean Loss": clean_test_loss_list, + "Test Backdoor Loss": bd_test_loss_list, + "Test RA Loss": ra_test_loss_list, + }, + save_path=f"{args.save_path}unlearn_loss_metric_plots.png", + ylabel="percentage", + ) + + agg.to_dataframe().to_csv(f"{args.save_path}unlearn_df.csv") + + if args.frequency_save != 0 and epoch % args.frequency_save == args.frequency_save - 1: + state_dict = { + "model": model_ascent.state_dict(), + "optimizer": optimizer.state_dict(), + "epoch_current": epoch, + } + torch.save(state_dict, args.checkpoint_save + "unlearn_state_dict.pt") + + agg.summary().to_csv(f"{args.save_path}unlearn_df_summary.csv") + agg.summary().to_csv(f"{args.save_path}abl_df_summary.csv") + return model_ascent + + + def train_step(self, args, train_loader, model_ascent, optimizer, criterion, epoch): + '''Pretrain the model with raw data for each step + args: + Contains default parameters + train_loader: + the dataloader of train data + model_ascent: + the initial model + optimizer: + optimizer during the pretrain process + criterion: + criterion during the pretrain process + epoch: + current epoch + ''' + losses = 0 + size = 0 + + batch_loss_list = [] + batch_predict_list = [] + batch_label_list = [] + batch_original_index_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + + model_ascent.train() + + for idx, (img, target, original_index, poison_indicator, original_targets) in enumerate(train_loader, start=1): + + img = img.to(args.device) + target = target.to(args.device) + + pred = model_ascent(img) + loss_ascent = criterion(pred,target) + + losses += loss_ascent * img.size(0) + size += img.size(0) + optimizer.zero_grad() + loss_ascent.backward() + optimizer.step() + + batch_loss_list.append(loss_ascent.item()) + batch_predict_list.append(torch.max(pred, -1)[1].detach().clone().cpu()) + batch_label_list.append(target.detach().clone().cpu()) + batch_original_index_list.append(original_index.detach().clone().cpu()) + batch_poison_indicator_list.append(poison_indicator.detach().clone().cpu()) + batch_original_targets_list.append(original_targets.detach().clone().cpu()) + + train_epoch_loss_avg_over_batch, \ + train_epoch_predict_list, \ + train_epoch_label_list, \ + train_epoch_poison_indicator_list, \ + train_epoch_original_targets_list = sum(batch_loss_list) / len(batch_loss_list), \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + + train_mix_acc = all_acc(train_epoch_predict_list, train_epoch_label_list) + + train_bd_idx = torch.where(train_epoch_poison_indicator_list == 1)[0] + train_clean_idx = torch.where(train_epoch_poison_indicator_list == 0)[0] + train_clean_acc = all_acc( + train_epoch_predict_list[train_clean_idx], + train_epoch_label_list[train_clean_idx], + ) + train_asr = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_label_list[train_bd_idx], + ) + train_ra = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_original_targets_list[train_bd_idx], + ) + + return train_epoch_loss_avg_over_batch, \ + train_mix_acc, \ + train_clean_acc, \ + train_asr, \ + train_ra + + def train_step_unlearn(self, args, train_loader, model_ascent, optimizer, criterion, epoch): + '''Pretrain the model with raw data for each step + args: + Contains default parameters + train_loader: + the dataloader of train data + model_ascent: + the initial model + optimizer: + optimizer during the pretrain process + criterion: + criterion during the pretrain process + epoch: + current epoch + ''' + losses = 0 + size = 0 + + batch_loss_list = [] + batch_predict_list = [] + batch_label_list = [] + batch_original_index_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + + model_ascent.train() + + for idx, (img, target, original_index, poison_indicator, original_targets) in enumerate(train_loader, start=1): + + img = img.to(args.device) + target = target.to(args.device) + + pred = model_ascent(img) + loss_ascent = criterion(pred,target) + + losses += loss_ascent * img.size(0) + size += img.size(0) + optimizer.zero_grad() + (-loss_ascent).backward() + optimizer.step() + + batch_loss_list.append(loss_ascent.item()) + batch_predict_list.append(torch.max(pred, -1)[1].detach().clone().cpu()) + batch_label_list.append(target.detach().clone().cpu()) + batch_original_index_list.append(original_index.detach().clone().cpu()) + batch_poison_indicator_list.append(poison_indicator.detach().clone().cpu()) + batch_original_targets_list.append(original_targets.detach().clone().cpu()) + + train_epoch_loss_avg_over_batch, \ + train_epoch_predict_list, \ + train_epoch_label_list, \ + train_epoch_poison_indicator_list, \ + train_epoch_original_targets_list = sum(batch_loss_list) / len(batch_loss_list), \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + + train_mix_acc = all_acc(train_epoch_predict_list, train_epoch_label_list) + + train_bd_idx = torch.where(train_epoch_poison_indicator_list == 1)[0] + train_clean_idx = torch.where(train_epoch_poison_indicator_list == 0)[0] + train_clean_acc = all_acc( + train_epoch_predict_list[train_clean_idx], + train_epoch_label_list[train_clean_idx], + ) + train_asr = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_label_list[train_bd_idx], + ) + train_ra = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_original_targets_list[train_bd_idx], + ) + + return train_epoch_loss_avg_over_batch, \ + train_mix_acc, \ + train_clean_acc, \ + train_asr, \ + train_ra + + def eval_step( + self, + netC, + clean_test_dataloader, + bd_test_dataloader, + args, + ): + clean_metrics, clean_epoch_predict_list, clean_epoch_label_list = given_dataloader_test( + netC, + clean_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.args.device, + verbose=0, + ) + clean_test_loss_avg_over_batch = clean_metrics['test_loss_avg_over_batch'] + test_acc = clean_metrics['test_acc'] + bd_metrics, bd_epoch_predict_list, bd_epoch_label_list = given_dataloader_test( + netC, + bd_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.args.device, + verbose=0, + ) + bd_test_loss_avg_over_batch = bd_metrics['test_loss_avg_over_batch'] + test_asr = bd_metrics['test_acc'] + + bd_test_dataloader.dataset.wrapped_dataset.getitem_all_switch = True # change to return the original label instead + ra_metrics, ra_epoch_predict_list, ra_epoch_label_list = given_dataloader_test( + netC, + bd_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.args.device, + verbose=0, + ) + ra_test_loss_avg_over_batch = ra_metrics['test_loss_avg_over_batch'] + test_ra = ra_metrics['test_acc'] + bd_test_dataloader.dataset.wrapped_dataset.getitem_all_switch = False # switch back + + return clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra + + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=sys.argv[0]) + abl.add_arguments(parser) + args = parser.parse_args() + abl_method = abl(args) + if "result_file" not in args.__dict__: + args.result_file = 'one_epochs_debug_badnet_attack' + elif args.result_file is None: + args.result_file = 'one_epochs_debug_badnet_attack' + result = abl_method.defense(args.result_file) \ No newline at end of file diff --git a/defense/ac.py b/defense/ac.py new file mode 100755 index 0000000..0f3c8d4 --- /dev/null +++ b/defense/ac.py @@ -0,0 +1,1026 @@ +# MIT License +# +# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018 +# +# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated +# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the +# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit +# persons to whom the Software is furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the +# Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE +# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +''' +This file is modified based on the following source: +link : https://github.com/Trusted-AI/adversarial-robustness-toolbox/blob/main/art/defences/detector/poison/activation_defence.py. +The defense method is called ac. + +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. during training the backdoor attack generalization to lower poison ratio (generalize_to_lower_pratio) + 5. save process + 6. new standard: robust accuracy + 7. reintegrate the framework + 8. hook the activation of the neural network + 9. add some addtional backbone such as preactresnet18, resnet18 and vgg19 + 10. for data sets with many analogies, the classification bug existing in the original method is fixed +basic sturcture for defense method: + 1. basic setting: args + 2. attack result(model, train data, test data) + 3. ac defense: + a. classify data by activation results + b. identify backdoor data according to classification results + c. retrain the model with filtered data + 4. test the result and get ASR, ACC, RC +''' + +import argparse +import os,sys +import numpy as np +import torch +import torch.nn as nn + +sys.path.append('../') +sys.path.append(os.getcwd()) + +from pprint import pformat +import yaml +import logging +import time +from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING +from defense.base import defense + +from utils.aggregate_block.train_settings_generate import argparser_criterion, argparser_opt_scheduler +from utils.trainer_cls import PureCleanModelTrainer +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.log_assist import get_git_info +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform +from utils.save_load_attack import load_attack_result, save_defense_result +from utils.nCHW_nHWC import * + +from sklearn.cluster import KMeans + + + +class ac(defense): + + def __init__(self,args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + self.args = args + + if 'result_file' in args.__dict__ : + if args.result_file is not None: + self.set_result(args.result_file) + + def add_arguments(parser): + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory") + parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?") + parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch') + parser.add_argument('--amp', default = False, type=lambda x: str(x) in ['True','true','1']) + + parser.add_argument('--checkpoint_load', type=str, help='the location of load model') + parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved') + parser.add_argument('--log', type=str, help='the location of log') + parser.add_argument("--dataset_path", type=str, help='the location of data') + parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny') + parser.add_argument('--result_file', type=str, help='the location of result') + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--model', type=str, help='resnet18') + + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--yaml_path', type=str, default="./config/defense/ac/config.yaml", help='the path of yaml') + + #set the parameter for the ac defense + parser.add_argument('--nb_dims', type=int, help='umber of dimensions to reduce activation to') + parser.add_argument('--nb_clusters', type=int, help='number of clusters (defaults to 2 for poison/clean).') + parser.add_argument('--cluster_analysis', type=str, help='the method of cluster analysis') + parser.add_argument('--cluster_batch_size', type=int) + + def set_result(self, result_file): + attack_file = 'record/' + result_file + save_path = 'record/' + result_file + '/defense/ac/' + if not (os.path.exists(save_path)): + os.makedirs(save_path) + # assert(os.path.exists(save_path)) + self.args.save_path = save_path + if self.args.checkpoint_save is None: + self.args.checkpoint_save = save_path + 'checkpoint/' + if not (os.path.exists(self.args.checkpoint_save)): + os.makedirs(self.args.checkpoint_save) + if self.args.log is None: + self.args.log = save_path + 'log/' + if not (os.path.exists(self.args.log)): + os.makedirs(self.args.log) + self.result = load_attack_result(attack_file + '/attack_result.pt') + + def set_trainer(self, model): + self.trainer = PureCleanModelTrainer( + model = model, + ) + + def set_logger(self): + args = self.args + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + try: + logging.info(pformat(get_git_info())) + except: + logging.info('Getting git info fails.') + + def set_devices(self): + # self.device = torch.device( + # ( + # f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device + # # since DataParallel only allow .to("cuda") + # ) if torch.cuda.is_available() else "cpu" + # ) + self.device = self.args.device + + def mitigation(self): + self.set_devices() + fix_random(self.args.random_seed) + + ### a. classify data by activation results + model = generate_cls_model(self.args.model,self.args.num_classes) + model.load_state_dict(self.result['model']) + if "," in self.device: + model = torch.nn.DataParallel( + model, + device_ids=[int(i) for i in self.args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{model.device_ids[0]}' + model.to(self.args.device) + else: + model.to(self.args.device) + + + train_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = True) + train_dataset = self.result['bd_train'].wrapped_dataset + data_set_without_tran = train_dataset + data_set_o = self.result['bd_train'] + data_set_o.wrapped_dataset = data_set_without_tran + data_set_o.wrap_img_transform = train_tran + data_set_o.wrapped_dataset.getitem_all = False + if not 'cluster_batch_size' in self.args.__dict__: + self.args.cluster_batch_size = self.args.batch_size + data_loader = torch.utils.data.DataLoader(data_set_o, batch_size=self.args.cluster_batch_size, num_workers=self.args.num_workers, shuffle=True) + num_classes = self.args.num_classes + for i, (x_batch,y_batch) in enumerate(data_loader): # type: ignore + x_batch = x_batch.to(self.args.device) + y_batch = y_batch.to(self.args.device) + batch_activations = get_activations(self.result['model_name'],model,x_batch.to(self.args.device)) + activation_dim = batch_activations.shape[-1] + + # initialize values list of lists on first run + if i == 0: + activations_by_class = [np.empty((0, activation_dim)) for _ in range(num_classes)] + clusters_by_class = [np.empty(0, dtype=int) for _ in range(num_classes)] + red_activations_by_class = [np.empty((0, self.args.nb_dims)) for _ in range(num_classes)] + + activations_by_class_i = segment_by_class(batch_activations, y_batch,self.args.num_classes) + clusters_by_class_i, red_activations_by_class_i = cluster_activations( + activations_by_class_i, + nb_clusters=self.args.nb_clusters, + nb_dims=self.args.nb_dims, + reduce='PCA', + clustering_method='KMeans' + ) + + for class_idx in range(num_classes): + if activations_by_class_i[class_idx].shape[0] != 0: + activations_by_class[class_idx] = np.vstack( + [activations_by_class[class_idx], activations_by_class_i[class_idx]] + ) + clusters_by_class[class_idx] = np.append( + clusters_by_class[class_idx], [clusters_by_class_i[class_idx]] + ) + red_activations_by_class[class_idx] = np.vstack( + [red_activations_by_class[class_idx], red_activations_by_class_i[class_idx]] + ) + + ### b. identify backdoor data according to classification results + analyzer = ClusteringAnalyzer() + if self.args.cluster_analysis == "smaller": + ( + assigned_clean_by_class, + poisonous_clusters, + report, + ) = analyzer.analyze_by_size(clusters_by_class) + elif self.args.cluster_analysis == "relative-size": + ( + assigned_clean_by_class, + poisonous_clusters, + report, + ) = analyzer.analyze_by_relative_size(clusters_by_class) + elif self.args.cluster_analysis == "distance": + (assigned_clean_by_class, poisonous_clusters, report,) = analyzer.analyze_by_distance( + clusters_by_class, + separated_activations=red_activations_by_class, + ) + elif self.args.cluster_analysis == "silhouette-scores": + (assigned_clean_by_class, poisonous_clusters, report,) = analyzer.analyze_by_silhouette_score( + clusters_by_class, + reduced_activations_by_class=red_activations_by_class, + ) + else: + raise ValueError("Unsupported cluster analysis technique " + self.args.cluster_analysis) + + batch_size = self.args.cluster_batch_size + is_clean_lst = [] + # loop though the generator to generator a report + last_loc = torch.zeros(self.args.num_classes).numpy().astype(int) + for i, (x_batch,y_batch) in enumerate(data_loader): # type: ignore + indices_by_class = segment_by_class(np.arange(batch_size), y_batch,self.args.num_classes) + is_clean_lst_i = [0] * batch_size + clean_class = [0] * batch_size + for class_idx, idxs in enumerate(indices_by_class): + for idx_in_class, idx in enumerate(idxs): + is_clean_lst_i[idx] = assigned_clean_by_class[class_idx][idx_in_class + last_loc[class_idx]] + last_loc[class_idx] = last_loc[class_idx] + len(idxs) + is_clean_lst += is_clean_lst_i + + + ### c. retrain the model with filtered data + model = generate_cls_model(self.args.model,self.args.num_classes) + if "," in self.device: + model = torch.nn.DataParallel( + model, + device_ids=[int(i) for i in self.args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{model.device_ids[0]}' + model.to(self.args.device) + else: + model.to(self.args.device) + data_set_o.subset([i for i,v in enumerate(is_clean_lst) if v==1]) + data_set_o.wrapped_dataset.getitem_all = True + data_loader_sie = torch.utils.data.DataLoader(data_set_o, batch_size=self.args.batch_size, num_workers=self.args.num_workers, shuffle=True, drop_last=True) + + # optimizer, scheduler = argparser_opt_scheduler(model, self.args) + # criterion = nn.CrossEntropyLoss() + # self.set_trainer(model) + optimizer, scheduler = argparser_opt_scheduler(model, self.args) + # criterion = nn.CrossEntropyLoss() + self.set_trainer(model) + criterion = argparser_criterion(args) + + # test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False) + # x = self.result['bd_test']['x'] + # y = self.result['bd_test']['y'] + # data_bd_test = list(zip(x,y)) + # data_bd_testset = prepro_cls_DatasetBD( + # full_dataset_without_transform=data_bd_test, + # poison_idx=np.zeros(len(data_bd_test)), # one-hot to determine which image may take bd_transform + # bd_image_pre_transform=None, + # bd_label_pre_transform=None, + # ori_image_transform_in_loading=test_tran, + # ori_label_transform_in_loading=None, + # add_details_in_preprocess=False, + # ) + # data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + + # x = self.result['clean_test']['x'] + # y = self.result['clean_test']['y'] + # data_clean_test = list(zip(x,y)) + # data_clean_testset = prepro_cls_DatasetBD( + # full_dataset_without_transform=data_clean_test, + # poison_idx=np.zeros(len(data_clean_test)), # one-hot to determine which image may take bd_transform + # bd_image_pre_transform=None, + # bd_label_pre_transform=None, + # ori_image_transform_in_loading=test_tran, + # ori_label_transform_in_loading=None, + # add_details_in_preprocess=False, + # ) + # data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False) + data_bd_testset = self.result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + data_clean_testset = self.result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + + self.trainer.train_with_test_each_epoch_on_mix( + data_loader_sie, + data_clean_loader, + data_bd_loader, + args.epochs, + criterion=criterion, + optimizer=optimizer, + scheduler=scheduler, + device=self.args.device, + frequency_save=args.frequency_save, + save_folder_path=args.save_path, + save_prefix='ac', + amp=args.amp, + prefetch=args.prefetch, + prefetch_transform_attr_name="ori_image_transform_in_loading", # since we use the preprocess_bd_dataset + non_blocking=args.non_blocking, + ) + + # self.trainer.train_with_test_each_epoch( + # train_data = data_loader_sie, + # test_data = data_clean_loader, + # adv_test_data = data_bd_loader, + # end_epoch_num = self.args.epochs, + # criterion = criterion, + # optimizer = optimizer, + # scheduler = scheduler, + # device = self.args.device, + # frequency_save = self.args.frequency_save, + # save_folder_path = self.args.checkpoint_save, + # save_prefix = 'defense', + # continue_training_path = None, + # ) + + # model.to(self.args.device) + result = {} + result['model'] = model + result['dataset'] = data_set_o + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model.cpu().state_dict(), + save_path=args.save_path, + ) + return result + + def defense(self,result_file): + self.set_result(result_file) + self.set_logger() + result = self.mitigation() + return result + +def segment_by_class(data , classes: np.ndarray, num_classes: int) -> List[np.ndarray]: + try: + width = data.size()[1] + by_class: List[List[int]] = [[] for _ in range(num_classes)] + + for indx, feature in enumerate(classes): + if len(classes.shape) == 2 and classes.shape[1] > 1: + + assigned = np.argmax(feature) + + else: + + assigned = int(feature) + if torch.is_tensor(data[indx]): + by_class[assigned].append(data[indx].cpu().numpy()) + else: + by_class[assigned].append(data[indx]) + return [np.asarray(i).reshape(-1,width) for i in by_class] + except : + by_class: List[List[int]] = [[] for _ in range(num_classes)] + + for indx, feature in enumerate(classes): + if len(classes.shape) == 2 and classes.shape[1] > 1: + + assigned = np.argmax(feature) + + else: + + assigned = int(feature) + if torch.is_tensor(data[indx]): + by_class[assigned].append(data[indx].cpu().numpy()) + else: + by_class[assigned].append(data[indx]) + return [np.asarray(i) for i in by_class] + +def measure_misclassification( + classifier, x_test: np.ndarray, y_test: np.ndarray +) -> float: + """ + Computes 1-accuracy given x_test and y_test + :param classifier: Classifier to be used for predictions. + :param x_test: Test set. + :param y_test: Labels for test set. + :return: 1-accuracy. + """ + predictions = np.argmax(classifier.predict(x_test), axis=1) + return 1.0 - np.sum(predictions == np.argmax(y_test, axis=1)) / y_test.shape[0] + +def train_remove_backdoor( + classifier, + x_train: np.ndarray, + y_train: np.ndarray, + x_test: np.ndarray, + y_test: np.ndarray, + tolerable_backdoor: float, + max_epochs: int, + batch_epochs: int, +) -> tuple: + """ + Trains the provider classifier until the tolerance or number of maximum epochs are reached. + :param classifier: Classifier to be used for predictions. + :param x_train: Training set. + :param y_train: Labels used for training. + :param x_test: Samples in test set. + :param y_test: Labels in test set. + :param tolerable_backdoor: Parameter that determines how many misclassifications are acceptable. + :param max_epochs: maximum number of epochs to be run. + :param batch_epochs: groups of epochs that will be run together before checking for termination. + :return: (improve_factor, classifier). + """ + # Measure poison success in current model: + initial_missed = measure_misclassification(classifier, x_test, y_test) + + curr_epochs = 0 + curr_missed = 1.0 + while curr_epochs < max_epochs and curr_missed > tolerable_backdoor: + classifier.fit(x_train, y_train, nb_epochs=batch_epochs) + curr_epochs += batch_epochs + curr_missed = measure_misclassification(classifier, x_test, y_test) + + improve_factor = initial_missed - curr_missed + return improve_factor, classifier + + +def cluster_activations( + separated_activations: List[np.ndarray], + nb_clusters: int = 2, + nb_dims: int = 10, + reduce: str = "FastICA", + clustering_method: str = "KMeans", + generator = None, + clusterer_new = None, +) -> Tuple[List[np.ndarray], List[np.ndarray]]: + """ + Clusters activations and returns two arrays. + 1) separated_clusters: where separated_clusters[i] is a 1D array indicating which cluster each data point + in the class has been assigned. + 2) separated_reduced_activations: activations with dimensionality reduced using the specified reduce method. + :param separated_activations: List where separated_activations[i] is a np matrix for the ith class where + each row corresponds to activations for a given data point. + :param nb_clusters: number of clusters (defaults to 2 for poison/clean). + :param nb_dims: number of dimensions to reduce activation to via PCA. + :param reduce: Method to perform dimensionality reduction, default is FastICA. + :param clustering_method: Clustering method to use, default is KMeans. + :param generator: whether or not a the activations are a batch or full activations + :return: (separated_clusters, separated_reduced_activations). + :param clusterer_new: whether or not a the activations are a batch or full activations + :return: (separated_clusters, separated_reduced_activations) + """ + separated_clusters = [] + separated_reduced_activations = [] + + if clustering_method == "KMeans": + clusterer = KMeans(n_clusters=nb_clusters) + else: + raise ValueError(clustering_method + " clustering method not supported.") + + for activation in separated_activations: + # Apply dimensionality reduction + try : + nb_activations = np.shape(activation)[1] + except IndexError: + activation = activation.reshape(1,-1) + nb_activations = np.shape(activation)[1] + if nb_activations > nb_dims & np.shape(activation)[0] > nb_dims: + # TODO: address issue where if fewer samples than nb_dims this fails + reduced_activations = reduce_dimensionality(activation, nb_dims=nb_dims, reduce=reduce) + elif nb_activations <= nb_dims: + reduced_activations = activation + else: + reduced_activations = activation[:,0:(nb_dims)] + separated_reduced_activations.append(reduced_activations) + + # Get cluster assignments + if generator is not None and clusterer_new is not None and reduced_activations.shape[0] != 0: + clusterer_new = clusterer_new.partial_fit(reduced_activations) + # NOTE: this may cause earlier predictions to be less accurate + clusters = clusterer_new.predict(reduced_activations) + elif reduced_activations.shape[0] != 1 and reduced_activations.shape[0] != 0: + clusters = clusterer.fit_predict(reduced_activations) + else: + clusters = 1 + separated_clusters.append(clusters) + + return separated_clusters, separated_reduced_activations + + +def reduce_dimensionality(activations: np.ndarray, nb_dims: int = 10, reduce: str = "FastICA") -> np.ndarray: + """ + Reduces dimensionality of the activations provided using the specified number of dimensions and reduction technique. + :param activations: Activations to be reduced. + :param nb_dims: number of dimensions to reduce activation to via PCA. + :param reduce: Method to perform dimensionality reduction, default is FastICA. + :return: Array with the reduced activations. + """ + # pylint: disable=E0001 + from sklearn.decomposition import FastICA, PCA + + if reduce == "FastICA": + projector = FastICA(n_components=nb_dims, max_iter=1000, tol=0.005) + elif reduce == "PCA": + projector = PCA(n_components=nb_dims) + else: + raise ValueError(reduce + " dimensionality reduction method not supported.") + + reduced_activations = projector.fit_transform(activations) + return reduced_activations + +def get_activations(name,model,x_batch): + ''' get activations of the model for each sample + name: + the model name + model: + the train model + x_batch: + each batch for tain data + ''' + with torch.no_grad(): + model.eval() + TOO_SMALL_ACTIVATIONS = 32 + assert name in ['preactresnet18', 'vgg19','vgg19_bn', 'resnet18', 'mobilenet_v3_large', 'densenet161', 'efficientnet_b3','convnext_tiny','vit_b_16'] + if name == 'preactresnet18': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.avgpool.register_forward_hook(layer_hook) + _ = model(x_batch) + activations = outs[0].view(outs[0].size(0), -1) + hook.remove() + elif name == 'vgg19': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.features.register_forward_hook(layer_hook) + _ = model(x_batch) + activations = outs[0].view(outs[0].size(0), -1) + hook.remove() + elif name == 'vgg19_bn': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.features.register_forward_hook(layer_hook) + _ = model(x_batch) + activations = outs[0].view(outs[0].size(0), -1) + hook.remove() + elif name == 'resnet18': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.layer4.register_forward_hook(layer_hook) + _ = model(x_batch) + activations = outs[0].view(outs[0].size(0), -1) + hook.remove() + elif name == 'mobilenet_v3_large': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.avgpool.register_forward_hook(layer_hook) + _ = model(x_batch) + activations = outs[0].view(outs[0].size(0), -1) + hook.remove() + elif name == 'densenet161': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.features.register_forward_hook(layer_hook) + _ = model(x_batch) + outs[0] = torch.nn.functional.relu(outs[0]) + activations = outs[0].view(outs[0].size(0), -1) + hook.remove() + elif name == 'efficientnet_b3': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.avgpool.register_forward_hook(layer_hook) + _ = model(x_batch) + activations = outs[0].view(outs[0].size(0), -1) + hook.remove() + elif name == 'convnext_tiny': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.avgpool.register_forward_hook(layer_hook) + _ = model(x_batch) + activations = outs[0].view(outs[0].size(0), -1) + hook.remove() + elif name == 'vit_b_16': + inps,outs = [],[] + def layer_hook(module, inp, out): + inps.append(inp[0].data) + hook = model[1].heads.register_forward_hook(layer_hook) + _ = model(x_batch) + activations = inps[0].view(inps[0].size(0), -1) + hook.remove() + + + return activations + + +# MIT License +# +# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018 +# +# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated +# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the +# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit +# persons to whom the Software is furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the +# Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE +# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +""" +This module implements methodologies to analyze clusters and determine whether they are poisonous. +""" + +class ClusteringAnalyzer: + + """ + Class for all methodologies implemented to analyze clusters and determine whether they are poisonous. + """ + @staticmethod + def assign_class(clusters: np.ndarray, clean_clusters: List[int], poison_clusters: List[int]) -> np.ndarray: + + """ + Determines whether each data point in the class is in a clean or poisonous cluster + :param clusters: `clusters[i]` indicates which cluster the i'th data point is in. + :param clean_clusters: List containing the clusters designated as clean. + :param poison_clusters: List containing the clusters designated as poisonous. + :return: assigned_clean: `assigned_clean[i]` is a boolean indicating whether the ith data point is clean. + """ + + assigned_clean = np.empty(np.shape(clusters)) + assigned_clean[np.isin(clusters, clean_clusters)] = 1 + assigned_clean[np.isin(clusters, poison_clusters)] = 0 + return assigned_clean + + + + def analyze_by_size( + self, separated_clusters: List[np.ndarray] + ) -> Tuple[np.ndarray, List[List[int]], Dict[str, int]]: + + """ + Designates as poisonous the cluster with less number of items on it. + :param separated_clusters: list where separated_clusters[i] is the cluster assignments for the ith class. + :return: all_assigned_clean, summary_poison_clusters, report: + where all_assigned_clean[i] is a 1D boolean array indicating whether + a given data point was determined to be clean (as opposed to poisonous) and + summary_poison_clusters: array, where summary_poison_clusters[i][j]=1 if cluster j of class i was + classified as poison, otherwise 0 + report: Dictionary with summary of the analysis + """ + report: Dict[str, Any] = { + "cluster_analysis": "smaller", + "suspicious_clusters": 0, + } + + all_assigned_clean = [] + nb_classes = len(separated_clusters) + nb_clusters = len(np.unique(separated_clusters[0])) + summary_poison_clusters: List[List[int]] = [[0 for _ in range(nb_clusters)] for _ in range(nb_classes)] + + for i, clusters in enumerate(separated_clusters): + # assume that smallest cluster is poisonous and all others are clean + sizes = np.bincount(clusters) + total_dp_in_class = np.sum(sizes) + poison_clusters: List[int] = [int(np.argmin(sizes))] + clean_clusters = list(set(clusters) - set(poison_clusters)) + for p_id in poison_clusters: + summary_poison_clusters[i][p_id] = 1 + for c_id in clean_clusters: + summary_poison_clusters[i][c_id] = 0 + + + + assigned_clean = self.assign_class(clusters, clean_clusters, poison_clusters) + all_assigned_clean.append(assigned_clean) + # Generate report for this class: + report_class = dict() + for cluster_id in range(nb_clusters): + ptc = sizes[cluster_id] / total_dp_in_class + susp = cluster_id in poison_clusters + dict_i = dict(ptc_data_in_cluster=round(ptc, 2), suspicious_cluster=susp) + dict_cluster: Dict[str, Dict[str, int]] = {"cluster_" + str(cluster_id): dict_i} + report_class.update(dict_cluster) + + report["Class_" + str(i)] = report_class + + report["suspicious_clusters"] = report["suspicious_clusters"] + np.sum(summary_poison_clusters).item() + return np.asarray(all_assigned_clean), summary_poison_clusters, report + + def analyze_by_distance( + self, + separated_clusters: List[np.ndarray], + separated_activations: List[np.ndarray], + ) -> Tuple[np.ndarray, List[List[int]], Dict[str, int]]: + + """ + Assigns a cluster as poisonous if its median activation is closer to the median activation for another class + than it is to the median activation of its own class. Currently, this function assumes there are only two + clusters per class. + :param separated_clusters: list where separated_clusters[i] is the cluster assignments for the ith class. + :param separated_activations: list where separated_activations[i] is a 1D array of [0,1] for [poison,clean]. + :return: all_assigned_clean, summary_poison_clusters, report: + where all_assigned_clean[i] is a 1D boolean array indicating whether a given data point was determined + to be clean (as opposed to poisonous) and summary_poison_clusters: array, where + summary_poison_clusters[i][j]=1 if cluster j of class i was classified as poison, otherwise 0 + report: Dictionary with summary of the analysis. + """ + + report: Dict[str, Any] = {"cluster_analysis": 0.0} + all_assigned_clean = [] + cluster_centers = [] + + nb_classes = len(separated_clusters) + nb_clusters = len(np.unique(separated_clusters[0])) + summary_poison_clusters: List[List[int]] = [[0 for _ in range(nb_clusters)] for _ in range(nb_classes)] + + # assign centers + for _, activations in enumerate(separated_activations): + cluster_centers.append(np.median(activations, axis=0)) + + for i, (clusters, activation) in enumerate(zip(separated_clusters, separated_activations)): + clusters = np.array(clusters) + cluster0_center = np.median(activation[np.where(clusters == 0)], axis=0) + cluster1_center = np.median(activation[np.where(clusters == 1)], axis=0) + + cluster0_distance = np.linalg.norm(cluster0_center - cluster_centers[i]) + cluster1_distance = np.linalg.norm(cluster1_center - cluster_centers[i]) + + cluster0_is_poison = False + cluster1_is_poison = False + + dict_k = dict() + dict_cluster_0 = dict(cluster0_distance_to_its_class=str(cluster0_distance)) + dict_cluster_1 = dict(cluster1_distance_to_its_class=str(cluster1_distance)) + for k, center in enumerate(cluster_centers): + if k == i: + pass + else: + cluster0_distance_to_k = np.linalg.norm(cluster0_center - center) + cluster1_distance_to_k = np.linalg.norm(cluster1_center - center) + if cluster0_distance_to_k < cluster0_distance and cluster1_distance_to_k > cluster1_distance: + cluster0_is_poison = True + if cluster1_distance_to_k < cluster1_distance and cluster0_distance_to_k > cluster0_distance: + cluster1_is_poison = True + + dict_cluster_0["distance_to_class_" + str(k)] = str(cluster0_distance_to_k) + dict_cluster_0["suspicious"] = str(cluster0_is_poison) + dict_cluster_1["distance_to_class_" + str(k)] = str(cluster1_distance_to_k) + dict_cluster_1["suspicious"] = str(cluster1_is_poison) + dict_k.update(dict_cluster_0) + dict_k.update(dict_cluster_1) + + + + report_class = dict(cluster_0=dict_cluster_0, cluster_1=dict_cluster_1) + report["Class_" + str(i)] = report_class + + poison_clusters = [] + if cluster0_is_poison: + poison_clusters.append(0) + summary_poison_clusters[i][0] = 1 + else: + summary_poison_clusters[i][0] = 0 + + if cluster1_is_poison: + poison_clusters.append(1) + summary_poison_clusters[i][1] = 1 + else: + summary_poison_clusters[i][1] = 0 + + clean_clusters = list(set(clusters) - set(poison_clusters)) + assigned_clean = self.assign_class(clusters, clean_clusters, poison_clusters) + all_assigned_clean.append(assigned_clean) + + all_assigned_clean = np.asarray(all_assigned_clean) + return all_assigned_clean, summary_poison_clusters, report + + def analyze_by_relative_size( + self, + separated_clusters: List[np.ndarray], + size_threshold: float = 0.35, + r_size: int = 2, + ) -> Tuple[np.ndarray, List[List[int]], Dict[str, int]]: + + """ + Assigns a cluster as poisonous if the smaller one contains less than threshold of the data. + This method assumes only 2 clusters + :param separated_clusters: List where `separated_clusters[i]` is the cluster assignments for the ith class. + :param size_threshold: Threshold used to define when a cluster is substantially smaller. + :param r_size: Round number used for size rate comparisons. + :return: all_assigned_clean, summary_poison_clusters, report: + where all_assigned_clean[i] is a 1D boolean array indicating whether a given data point was determined + to be clean (as opposed to poisonous) and summary_poison_clusters: array, where + summary_poison_clusters[i][j]=1 if cluster j of class i was classified as poison, otherwise 0 + report: Dictionary with summary of the analysis. + """ + + size_threshold = round(size_threshold, r_size) + report: Dict[str, Any] = { + "cluster_analysis": "relative_size", + "suspicious_clusters": 0, + "size_threshold": size_threshold, + } + + all_assigned_clean = [] + nb_classes = len(separated_clusters) + nb_clusters = len(np.unique(separated_clusters[0])) + summary_poison_clusters: List[List[int]] = [[0 for _ in range(nb_clusters)] for _ in range(nb_classes)] + + for i, clusters in enumerate(separated_clusters): + sizes = np.bincount(clusters) + total_dp_in_class = np.sum(sizes) + + if np.size(sizes) > 2: + raise ValueError(" RelativeSizeAnalyzer does not support more than two clusters.") + percentages = np.round(sizes / float(np.sum(sizes)), r_size) + poison_clusters = np.where(percentages < size_threshold) + clean_clusters = np.where(percentages >= size_threshold) + + for p_id in poison_clusters[0]: + summary_poison_clusters[i][p_id] = 1 + for c_id in clean_clusters[0]: + summary_poison_clusters[i][c_id] = 0 + + + + assigned_clean = self.assign_class(clusters, clean_clusters, poison_clusters) + all_assigned_clean.append(assigned_clean) + + # Generate report for this class: + report_class = dict() + for cluster_id in range(nb_clusters): + ptc = sizes[cluster_id] / total_dp_in_class + susp = cluster_id in poison_clusters + dict_i = dict(ptc_data_in_cluster=round(ptc, 2), suspicious_cluster=susp) + + dict_cluster = {"cluster_" + str(cluster_id): dict_i} + report_class.update(dict_cluster) + + report["Class_" + str(i)] = report_class + + report["suspicious_clusters"] = report["suspicious_clusters"] + np.sum(summary_poison_clusters).item() + return np.asarray(all_assigned_clean), summary_poison_clusters, report + + def analyze_by_silhouette_score( + self, + separated_clusters: list, + reduced_activations_by_class: list, + size_threshold: float = 0.35, + silhouette_threshold: float = 0.1, + r_size: int = 2, + r_silhouette: int = 4, + ) -> Tuple[np.ndarray, List[List[int]], Dict[str, int]]: + + """ + Analyzes clusters to determine level of suspiciousness of poison based on the cluster's relative size + and silhouette score. + Computes a silhouette score for each class to determine how cohesive resulting clusters are. + A low silhouette score indicates that the clustering does not fit the data well, and the class can be considered + to be un-poisoned. Conversely, a high silhouette score indicates that the clusters reflect true splits in the + data. + The method concludes that a cluster is poison based on the silhouette score and the cluster relative size. + If the relative size is too small, below a size_threshold and at the same time + the silhouette score is higher than silhouette_threshold, the cluster is classified as poisonous. + If the above thresholds are not provided, the default ones will be used. + :param separated_clusters: list where `separated_clusters[i]` is the cluster assignments for the ith class. + :param reduced_activations_by_class: list where separated_activations[i] is a 1D array of [0,1] for + [poison,clean]. + :param size_threshold: (optional) threshold used to define when a cluster is substantially smaller. A default + value is used if the parameter is not provided. + :param silhouette_threshold: (optional) threshold used to define when a cluster is cohesive. Default + value is used if the parameter is not provided. + :param r_size: Round number used for size rate comparisons. + :param r_silhouette: Round number used for silhouette rate comparisons. + :return: all_assigned_clean, summary_poison_clusters, report: + where all_assigned_clean[i] is a 1D boolean array indicating whether a given data point was determined + to be clean (as opposed to poisonous) summary_poison_clusters: array, where + summary_poison_clusters[i][j]=1 if cluster j of class j was classified as poison + report: Dictionary with summary of the analysis. + """ + + # pylint: disable=E0001 + from sklearn.metrics import silhouette_score + size_threshold = round(size_threshold, r_size) + silhouette_threshold = round(silhouette_threshold, r_silhouette) + report: Dict[str, Any] = { + "cluster_analysis": "silhouette_score", + "size_threshold": str(size_threshold), + "silhouette_threshold": str(silhouette_threshold), + } + + all_assigned_clean = [] + nb_classes = len(separated_clusters) + nb_clusters = len(np.unique(separated_clusters[0])) + summary_poison_clusters: List[List[int]] = [[0 for _ in range(nb_clusters)] for _ in range(nb_classes)] + + for i, (clusters, activations) in enumerate(zip(separated_clusters, reduced_activations_by_class)): + + bins = np.bincount(clusters) + if np.size(bins) > 2: + raise ValueError("Analyzer does not support more than two clusters.") + + percentages = np.round(bins / float(np.sum(bins)), r_size) + poison_clusters = np.where(percentages < size_threshold) + clean_clusters = np.where(percentages >= size_threshold) + + # Generate report for class + silhouette_avg = round(silhouette_score(activations, clusters), r_silhouette) + dict_i: Dict[str, Any] = dict( + sizes_clusters=str(bins), + ptc_cluster=str(percentages), + avg_silhouette_score=str(silhouette_avg), + ) + + if np.shape(poison_clusters)[1] != 0: + # Relative size of the clusters is suspicious + if silhouette_avg > silhouette_threshold: + # In this case the cluster is considered poisonous + clean_clusters = np.where(percentages < size_threshold) + logging.info("computed silhouette score: %s", silhouette_avg) + dict_i.update(suspicious=True) + else: + poison_clusters = [[]] + clean_clusters = np.where(percentages >= 0) + dict_i.update(suspicious=False) + else: + # If relative size of the clusters is Not suspicious, we conclude it's not suspicious. + + dict_i.update(suspicious=False) + + report_class: Dict[str, Dict[str, bool]] = {"class_" + str(i): dict_i} + for p_id in poison_clusters[0]: + summary_poison_clusters[i][p_id] = 1 + + for c_id in clean_clusters[0]: + summary_poison_clusters[i][c_id] = 0 + + assigned_clean = self.assign_class(clusters, clean_clusters, poison_clusters) + all_assigned_clean.append(assigned_clean) + report.update(report_class) + + return np.asarray(all_assigned_clean), summary_poison_clusters, report + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=sys.argv[0]) + ac.add_arguments(parser) + args = parser.parse_args() + ac_method = ac(args) + if "result_file" not in args.__dict__: + args.result_file = 'defense_test_badnet' + elif args.result_file is None: + args.result_file = 'defense_test_badnet' + result = ac_method.defense(args.result_file) \ No newline at end of file diff --git a/defense/anp.py b/defense/anp.py new file mode 100644 index 0000000..fbd8425 --- /dev/null +++ b/defense/anp.py @@ -0,0 +1,840 @@ +''' +This file is modified based on the following source: +link : https://github.com/csdongxian/ANP_backdoor. +The defense method is called anp. + +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. save process + 5. new standard: robust accuracy + 6. reconstruct some backbone vgg19 and add some backbone such as densenet161 efficientnet mobilenet + 7. save best model which gets the minimum of asr with acc decreased by no more than 10% +basic sturcture for defense method: + 1. basic setting: args + 2. attack result(model, train data, test data) + 3. anp defense: + a. train the mask of old model + b. prune the model depend on the mask + 4. test the result and get ASR, ACC, RC +''' + + +import argparse +import os,sys +import numpy as np +import torch +import torch.nn as nn + +sys.path.append('../') +sys.path.append(os.getcwd()) + +from pprint import pformat +import yaml +import logging +import time +from defense.base import defense + +from torch.utils.data import DataLoader, RandomSampler +import pandas as pd +from collections import OrderedDict +import copy + +import utils.defense_utils.anp.anp_model as anp_model + +from utils.aggregate_block.train_settings_generate import argparser_criterion, argparser_opt_scheduler +from utils.trainer_cls import BackdoorModelTrainer, Metric_Aggregator, ModelTrainerCLS, ModelTrainerCLS_v2, PureCleanModelTrainer, general_plot_for_epoch +from utils.choose_index import choose_index +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model, partially_load_state_dict +from utils.log_assist import get_git_info +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform +from utils.save_load_attack import load_attack_result, save_defense_result +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2 + + + +### anp function +def load_state_dict(net, orig_state_dict): + if 'state_dict' in orig_state_dict.keys(): + orig_state_dict = orig_state_dict['state_dict'] + if "state_dict" in orig_state_dict.keys(): + orig_state_dict = orig_state_dict["state_dict"] + + new_state_dict = OrderedDict() + for k, v in net.state_dict().items(): + if k in orig_state_dict.keys(): + new_state_dict[k] = orig_state_dict[k] + elif 'running_mean_noisy' in k or 'running_var_noisy' in k or 'num_batches_tracked_noisy' in k: + new_state_dict[k] = orig_state_dict[k[:-6]].clone().detach() + else: + new_state_dict[k] = v + net.load_state_dict(new_state_dict) + + +def clip_mask(model, lower=0.0, upper=1.0): + params = [param for name, param in model.named_parameters() if 'neuron_mask' in name] + with torch.no_grad(): + for param in params: + param.clamp_(lower, upper) + + +def sign_grad(model): + noise = [param for name, param in model.named_parameters() if 'neuron_noise' in name] + for p in noise: + p.grad.data = torch.sign(p.grad.data) + + +def perturb(model, is_perturbed=True): + for name, module in model.named_modules(): + if isinstance(module, anp_model.NoisyBatchNorm2d) or isinstance(module, anp_model.NoisyBatchNorm1d): + module.perturb(is_perturbed=is_perturbed) + if isinstance(module, anp_model.NoiseLayerNorm2d) or isinstance(module, anp_model.NoiseLayerNorm): + module.perturb(is_perturbed=is_perturbed) + + +def include_noise(model): + for name, module in model.named_modules(): + if isinstance(module, anp_model.NoisyBatchNorm2d) or isinstance(module, anp_model.NoisyBatchNorm1d): + module.include_noise() + if isinstance(module, anp_model.NoiseLayerNorm2d) or isinstance(module, anp_model.NoiseLayerNorm): + module.include_noise() + + + +def exclude_noise(model): + for name, module in model.named_modules(): + if isinstance(module, anp_model.NoisyBatchNorm2d) or isinstance(module, anp_model.NoisyBatchNorm1d): + module.exclude_noise() + if isinstance(module, anp_model.NoiseLayerNorm2d) or isinstance(module, anp_model.NoiseLayerNorm): + module.exclude_noise() + + +def reset(model, rand_init): + for name, module in model.named_modules(): + if isinstance(module, anp_model.NoisyBatchNorm2d) or isinstance(module, anp_model.NoisyBatchNorm1d): + module.reset(rand_init=rand_init, eps=args.anp_eps) + if isinstance(module, anp_model.NoiseLayerNorm2d) or isinstance(module, anp_model.NoiseLayerNorm): + module.reset(rand_init=rand_init, eps=args.anp_eps) + + +def mask_train(args, model, criterion, mask_opt, noise_opt, data_loader): + model.train() + total_correct = 0 + total_loss = 0.0 + nb_samples = 0 + for i, (images, labels, *additional_info) in enumerate(data_loader): + images, labels = images.to(args.device), labels.to(args.device) + nb_samples += images.size(0) + + # step 1: calculate the adversarial perturbation for neurons + if args.anp_eps > 0.0: + reset(model, rand_init=True) + for _ in range(args.anp_steps): + noise_opt.zero_grad() + + include_noise(model) + output_noise = model(images) + loss_noise = - criterion(output_noise, labels) + + loss_noise.backward() + sign_grad(model) + noise_opt.step() + + # step 2: calculate loss and update the mask values + mask_opt.zero_grad() + if args.anp_eps > 0.0: + include_noise(model) + output_noise = model(images) + loss_rob = criterion(output_noise, labels) + else: + loss_rob = 0.0 + + exclude_noise(model) + output_clean = model(images) + loss_nat = criterion(output_clean, labels) + loss = args.anp_alpha * loss_nat + (1 - args.anp_alpha) * loss_rob + + pred = output_clean.data.max(1)[1] + total_correct += pred.eq(labels.view_as(pred)).sum() + total_loss += loss.item() + loss.backward() + mask_opt.step() + clip_mask(model) + + loss = total_loss / len(data_loader) + acc = float(total_correct) / nb_samples + return loss, acc + + +def test(args, model, criterion, data_loader): + model.eval() + total_correct = 0 + total_loss = 0.0 + with torch.no_grad(): + for i, (images, labels, *additional_info) in enumerate(data_loader): + images, labels = images.to(args.device), labels.to(args.device) + output = model(images) + total_loss += criterion(output, labels).item() + pred = output.data.max(1)[1] + total_correct += pred.eq(labels.data.view_as(pred)).sum() + loss = total_loss / len(data_loader) + acc = float(total_correct) / len(data_loader.dataset) + return loss, acc + + +def save_mask_scores(state_dict, file_name): + mask_values = [] + count = 0 + for name, param in state_dict.items(): + if 'neuron_mask' in name: + for idx in range(param.size(0)): + neuron_name = '.'.join(name.split('.')[:-1]) + mask_values.append('{} \t {} \t {} \t {:.4f} \n'.format(count, neuron_name, idx, param[idx].item())) + count += 1 + with open(file_name, "w") as f: + f.write('No \t Layer Name \t Neuron Idx \t Mask Score \n') + f.writelines(mask_values) + +def get_anp_network( + model_name: str, + num_classes: int = 10, + **kwargs, +): + + if model_name == 'preactresnet18': + from utils.defense_utils.anp.anp_model.preact_anp import PreActResNet18 + net = PreActResNet18(num_classes = num_classes, **kwargs) + elif model_name == 'vgg19_bn': + net = anp_model.vgg_anp.vgg19_bn(num_classes = num_classes, **kwargs) + elif model_name == 'densenet161': + net = anp_model.den_anp.densenet161(num_classes= num_classes, **kwargs) + elif model_name == 'mobilenet_v3_large': + net = anp_model.mobilenet_anp.mobilenet_v3_large(num_classes= num_classes, **kwargs) + elif model_name == 'efficientnet_b3': + net = anp_model.eff_anp.efficientnet_b3(num_classes= num_classes, **kwargs) + elif model_name == 'convnext_tiny': + # net_from_imagenet = convnext_tiny(pretrained=True) #num_classes = num_classes) + try : + net = anp_model.conv_anp.convnext_tiny(num_classes= num_classes, **{k:v for k,v in kwargs.items() if k != "pretrained"}) + except : + net = anp_model.conv_new_anp.convnext_tiny(num_classes= num_classes, **{k:v for k,v in kwargs.items() if k != "pretrained"}) + # partially_load_state_dict(net, net_from_imagenet.state_dict()) + # net = anp_model.convnext_anp.convnext_tiny(num_classes= num_classes, **kwargs) + elif model_name == 'vit_b_16': + try : + from torchvision.transforms import Resize + net = anp_model.vit_anp.vit_b_16( + pretrained = False, + # **{k: v for k, v in kwargs.items() if k != "pretrained"} + ) + net.heads.head = torch.nn.Linear(net.heads.head.in_features, out_features = num_classes, bias=True) + net = torch.nn.Sequential( + Resize((224, 224)), + net, + ) + except : + from torchvision.transforms import Resize + net = anp_model.vit_new_anp.vit_b_16( + pretrained = False, + # **{k: v for k, v in kwargs.items() if k != "pretrained"} + ) + net.heads.head = torch.nn.Linear(net.heads.head.in_features, out_features = num_classes, bias=True) + net = torch.nn.Sequential( + Resize((224, 224)), + net, + ) + else: + raise SystemError('NO valid model match in function generate_cls_model!') + + return net + +def read_data(file_name): + tempt = pd.read_csv(file_name, sep='\s+', skiprows=1, header=None) + layer = tempt.iloc[:, 1] + idx = tempt.iloc[:, 2] + value = tempt.iloc[:, 3] + mask_values = list(zip(layer, idx, value)) + return mask_values + + +def pruning(net, neuron): + state_dict = net.state_dict() + weight_name = '{}.{}'.format(neuron[0], 'weight') + state_dict[weight_name][int(neuron[1])] = 0.0 + net.load_state_dict(state_dict) + + + + + +class anp(defense): + + def __init__(self,args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + self.args = args + + if 'result_file' in args.__dict__ : + if args.result_file is not None: + self.set_result(args.result_file) + + def add_arguments(parser): + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory") + parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?") + parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch') + parser.add_argument('--amp', default = False, type=lambda x: str(x) in ['True','true','1']) + + parser.add_argument('--checkpoint_load', type=str, help='the location of load model') + parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved') + parser.add_argument('--log', type=str, help='the location of log') + parser.add_argument("--dataset_path", type=str, help='the location of data') + parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny') + parser.add_argument('--result_file', type=str, help='the location of result') + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--model', type=str, help='resnet18') + + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--yaml_path', type=str, default="./config/defense/anp/config.yaml", help='the path of yaml') + + #set the parameter for the anp defense + parser.add_argument('--acc_ratio', type=float, help='the tolerance ration of the clean accuracy') + parser.add_argument('--ratio', type=float, help='the ratio of clean data loader') + parser.add_argument('--print_every', type=int, help='print results every few iterations') + parser.add_argument('--nb_iter', type=int, help='the number of iterations for training') + + parser.add_argument('--anp_eps', type=float) + parser.add_argument('--anp_steps', type=int) + parser.add_argument('--anp_alpha', type=float) + + parser.add_argument('--pruning_by', type=str, choices=['number', 'threshold']) + parser.add_argument('--pruning_max', type=float, help='the maximum number/threshold for pruning') + parser.add_argument('--pruning_step', type=float, help='the step size for evaluating the pruning') + + parser.add_argument('--pruning_number', type=float, help='the default number/threshold for pruning') + + parser.add_argument('--index', type=str, help='index of clean data') + + + + def set_result(self, result_file): + attack_file = 'record/' + result_file + save_path = 'record/' + result_file + '/defense/anp/' + if not (os.path.exists(save_path)): + os.makedirs(save_path) + # assert(os.path.exists(save_path)) + self.args.save_path = save_path + if self.args.checkpoint_save is None: + self.args.checkpoint_save = save_path + 'checkpoint/' + if not (os.path.exists(self.args.checkpoint_save)): + os.makedirs(self.args.checkpoint_save) + if self.args.log is None: + self.args.log = save_path + 'log/' + if not (os.path.exists(self.args.log)): + os.makedirs(self.args.log) + self.result = load_attack_result(attack_file + '/attack_result.pt') + + def set_trainer(self, model): + self.trainer = PureCleanModelTrainer( + model, + ) + + def set_logger(self): + args = self.args + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + try: + logging.info(pformat(get_git_info())) + except: + logging.info('Getting git info fails.') + + def set_devices(self): + self.device = torch.device( + ( + f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device + # since DataParallel only allow .to("cuda") + ) if torch.cuda.is_available() else "cpu" + ) + + def evaluate_by_number(self, args, model, mask_values, pruning_max, pruning_step, criterion,test_dataloader_dict, best_asr, acc_ori, save = True): + results = [] + nb_max = int(np.ceil(pruning_max)) + nb_step = int(np.ceil(pruning_step)) + model_best = copy.deepcopy(model) + + number_list = [] + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + + agg = Metric_Aggregator() + for start in range(0, nb_max + 1, nb_step): + i = start + for i in range(start, start + nb_step): + pruning(model, mask_values[i]) + layer_name, neuron_idx, value = mask_values[i][0], mask_values[i][1], mask_values[i][2] + # cl_loss, cl_acc = test(args, model=model, criterion=criterion, data_loader=clean_loader) + # po_loss, po_acc = test(args, model=model, criterion=criterion, data_loader=poison_loader) + # logging.info('{} \t {} \t {} \t {} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f}'.format( + # i+1, layer_name, neuron_idx, value, po_loss, po_acc, cl_loss, cl_acc)) + # results.append('{} \t {} \t {} \t {} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f}'.format( + # i+1, layer_name, neuron_idx, value, po_loss, po_acc, cl_loss, cl_acc)) + self.set_trainer(model) + self.trainer.set_with_dataloader( + ### the train_dataload has nothing to do with the backdoor defense + train_dataloader = test_dataloader_dict['bd_test_dataloader'], + test_dataloader_dict = test_dataloader_dict, + + criterion = criterion, + optimizer = None, + scheduler = None, + device = self.args.device, + amp = self.args.amp, + + frequency_save = self.args.frequency_save, + save_folder_path = self.args.save_path, + save_prefix = 'anp', + + prefetch = self.args.prefetch, + prefetch_transform_attr_name = "ori_image_transform_in_loading", + non_blocking = self.args.non_blocking, + + + ) + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.trainer.test_current_model( + test_dataloader_dict, args.device, + ) + number_list.append(start) + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + # cl_loss, cl_acc = test(args, model=model, criterion=criterion, data_loader=clean_loader) + # po_loss, po_acc = test(args, model=model, criterion=criterion, data_loader=poison_loader) + # logging.info('{:.2f} \t {} \t {} \t {} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f}'.format( + # start, layer_name, neuron_idx, threshold, po_loss, po_acc, cl_loss, cl_acc)) + # results.append('{:.2f} \t {} \t {} \t {} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f}\n'.format( + # start, layer_name, neuron_idx, threshold, po_loss, po_acc, cl_loss, cl_acc)) + if save: + agg({ + 'number': start, + # 'layer_name': layer_name, + # 'neuron_idx': neuron_idx, + 'value': value, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + general_plot_for_epoch( + { + "Test C-Acc": test_acc_list, + "Test ASR": test_asr_list, + "Test RA": test_ra_list, + }, + save_path=f"{args.save_path}number_acc_like_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "Test Clean Loss": clean_test_loss_list, + "Test Backdoor Loss": bd_test_loss_list, + }, + save_path=f"{args.save_path}number_loss_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "number": number_list, + }, + save_path=f"{args.save_path}number_plots.png", + ylabel="percentage", + ) + + agg.to_dataframe().to_csv(f"{args.save_path}number_df.csv") + if abs(test_acc - acc_ori)/acc_ori < args.acc_ratio: + if test_asr < best_asr: + model_best = copy.deepcopy(model) + best_asr = test_asr + return results, model_best + + + def evaluate_by_threshold(self, args, model, mask_values, pruning_max, pruning_step, criterion, test_dataloader_dict, best_asr, acc_ori, save = True): + results = [] + thresholds = np.arange(0, pruning_max + pruning_step, pruning_step) + start = 0 + model_best = copy.deepcopy(model) + + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + + agg = Metric_Aggregator() + for threshold in thresholds: + idx = start + for idx in range(start, len(mask_values)): + if float(mask_values[idx][2]) <= threshold: + pruning(model, mask_values[idx]) + start += 1 + else: + break + layer_name, neuron_idx, value = mask_values[idx][0], mask_values[idx][1], mask_values[idx][2] + self.set_trainer(model) + self.trainer.set_with_dataloader( + ### the train_dataload has nothing to do with the backdoor defense + train_dataloader = test_dataloader_dict['bd_test_dataloader'], + test_dataloader_dict = test_dataloader_dict, + + criterion = criterion, + optimizer = None, + scheduler = None, + device = self.args.device, + amp = self.args.amp, + + frequency_save = self.args.frequency_save, + save_folder_path = self.args.save_path, + save_prefix = 'anp', + + prefetch = self.args.prefetch, + prefetch_transform_attr_name = "ori_image_transform_in_loading", + non_blocking = self.args.non_blocking, + + + ) + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.trainer.test_current_model( + test_dataloader_dict, args.device, + ) + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + # cl_loss, cl_acc = test(args, model=model, criterion=criterion, data_loader=clean_loader) + # po_loss, po_acc = test(args, model=model, criterion=criterion, data_loader=poison_loader) + # logging.info('{:.2f} \t {} \t {} \t {} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f}'.format( + # start, layer_name, neuron_idx, threshold, po_loss, po_acc, cl_loss, cl_acc)) + # results.append('{:.2f} \t {} \t {} \t {} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f}\n'.format( + # start, layer_name, neuron_idx, threshold, po_loss, po_acc, cl_loss, cl_acc)) + if save: + agg({ + 'threshold': threshold, + # 'layer_name': layer_name, + # 'neuron_idx': neuron_idx, + 'value': value, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + general_plot_for_epoch( + { + "Test C-Acc": test_acc_list, + "Test ASR": test_asr_list, + "Test RA": test_ra_list, + }, + save_path=f"{args.save_path}threshold_acc_like_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "Test Clean Loss": clean_test_loss_list, + "Test Backdoor Loss": bd_test_loss_list, + }, + save_path=f"{args.save_path}threshold_loss_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "threshold": thresholds, + }, + save_path=f"{args.save_path}threshold_plots.png", + ylabel="percentage", + ) + + agg.to_dataframe().to_csv(f"{args.save_path}threshold_df.csv") + + if abs(test_acc - acc_ori)/acc_ori < args.acc_ratio: + if test_asr < best_asr: + model_best = copy.deepcopy(model) + best_asr = test_asr + return results, model_best + + def mitigation(self): + self.set_devices() + fix_random(self.args.random_seed) + + args = self.args + result = self.result + # a. train the mask of old model + train_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = True) + clean_dataset = prepro_cls_DatasetBD_v2(self.result['clean_train'].wrapped_dataset) + data_all_length = len(clean_dataset) + ran_idx = choose_index(self.args, data_all_length) + log_index = self.args.log + 'index.txt' + np.savetxt(log_index, ran_idx, fmt='%d') + clean_dataset.subset(ran_idx) + data_set_without_tran = clean_dataset + data_set_clean = self.result['clean_train'] + data_set_clean.wrapped_dataset = data_set_without_tran + data_set_clean.wrap_img_transform = train_tran + # data_set_clean.wrapped_dataset.getitem_all = False + random_sampler = RandomSampler(data_source=data_set_clean, replacement=True, + num_samples=args.print_every * args.batch_size) + clean_val_loader = DataLoader(data_set_clean, batch_size=args.batch_size, + shuffle=False, sampler=random_sampler, num_workers=0) + + test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False) + data_bd_testset = self.result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + # data_bd_testset.wrapped_dataset.getitem_all = False + poison_test_loader = DataLoader(data_bd_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + + test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False) + data_clean_testset = self.result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + clean_test_loader = DataLoader(data_clean_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + + test_dataloader_dict = {} + test_dataloader_dict["clean_test_dataloader"] = clean_test_loader + test_dataloader_dict["bd_test_dataloader"] = poison_test_loader + state_dict = self.result['model'] + net = get_anp_network(args.model, num_classes=args.num_classes, norm_layer=anp_model.NoisyBatchNorm2d) + load_state_dict(net, orig_state_dict=state_dict) + net = net.to(args.device) + criterion = torch.nn.CrossEntropyLoss().to(args.device) + + parameters = list(net.named_parameters()) + mask_params = [v for n, v in parameters if "neuron_mask" in n] + mask_optimizer = torch.optim.SGD(mask_params, lr=args.lr, momentum=0.9) + noise_params = [v for n, v in parameters if "neuron_noise" in n] + noise_optimizer = torch.optim.SGD(noise_params, lr=args.anp_eps / args.anp_steps) + + logging.info('Iter \t lr \t Time \t TrainLoss \t TrainACC \t PoisonLoss \t PoisonACC \t CleanLoss \t CleanACC') + nb_repeat = int(np.ceil(args.nb_iter / args.print_every)) + for i in range(nb_repeat): + start = time.time() + lr = mask_optimizer.param_groups[0]['lr'] + train_loss, train_acc = mask_train(args, model=net, criterion=criterion, data_loader=clean_val_loader, + mask_opt=mask_optimizer, noise_opt=noise_optimizer) + cl_test_loss, cl_test_acc = test(args, model=net, criterion=criterion, data_loader=clean_test_loader) + po_test_loss, po_test_acc = test(args, model=net, criterion=criterion, data_loader=poison_test_loader) + end = time.time() + logging.info('{} \t {:.3f} \t {:.1f} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f}'.format( + (i + 1) * args.print_every, lr, end - start, train_loss, train_acc, po_test_loss, po_test_acc, + cl_test_loss, cl_test_acc)) + save_mask_scores(net.state_dict(), os.path.join(args.checkpoint_save, 'mask_values.txt')) + + # b. prune the model depend on the mask + net_prune = generate_cls_model(args.model,args.num_classes) + net_prune.load_state_dict(result['model']) + net_prune.to(args.device) + + mask_values = read_data(args.checkpoint_save + 'mask_values.txt') + mask_values = sorted(mask_values, key=lambda x: float(x[2])) + logging.info('No. \t Layer Name \t Neuron Idx \t Mask \t PoisonLoss \t PoisonACC \t CleanLoss \t CleanACC') + cl_loss, cl_acc = test(args, model=net_prune, criterion=criterion, data_loader=clean_test_loader) + po_loss, po_acc = test(args, model=net_prune, criterion=criterion, data_loader=poison_test_loader) + logging.info('0 \t None \t None \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f}'.format(po_loss, po_acc, cl_loss, cl_acc)) + + model = copy.deepcopy(net_prune) + if args.pruning_by == 'threshold': + results, model_pru = self.evaluate_by_threshold( + args, net_prune, mask_values, pruning_max=args.pruning_max, pruning_step=args.pruning_step, + criterion=criterion, test_dataloader_dict=test_dataloader_dict, best_asr=po_acc, acc_ori=cl_acc + ) + else: + results, model_pru = self.evaluate_by_number( + args, net_prune, mask_values, pruning_max=args.pruning_max, pruning_step=args.pruning_step, + criterion=criterion, test_dataloader_dict=test_dataloader_dict, best_asr=po_acc, acc_ori=cl_acc + ) + file_name = os.path.join(args.checkpoint_save, 'pruning_by_{}.txt'.format(args.pruning_by)) + with open(file_name, "w") as f: + f.write('No \t Layer Name \t Neuron Idx \t Mask \t PoisonLoss \t PoisonACC \t CleanLoss \t CleanACC\n') + f.writelines(results) + + if 'pruning_number' in args.__dict__: + if args.pruning_by == 'threshold': + _, _ = self.evaluate_by_threshold( + args, model, mask_values, pruning_max=args.pruning_number, pruning_step=args.pruning_number, + criterion=criterion, test_dataloader_dict=test_dataloader_dict, best_asr=po_acc, acc_ori=cl_acc, save=False + ) + else: + _, _ = self.evaluate_by_number( + args, model, mask_values, pruning_max=args.pruning_number, pruning_step=args.pruning_number, + criterion=criterion, test_dataloader_dict=test_dataloader_dict, best_asr=po_acc, acc_ori=cl_acc, save=False + ) + self.set_trainer(model) + self.trainer.set_with_dataloader( + ### the train_dataload has nothing to do with the backdoor defense + train_dataloader = clean_val_loader, + test_dataloader_dict = test_dataloader_dict, + + criterion = criterion, + optimizer = None, + scheduler = None, + device = self.args.device, + amp = self.args.amp, + + frequency_save = self.args.frequency_save, + save_folder_path = self.args.save_path, + save_prefix = 'anp', + + prefetch = self.args.prefetch, + prefetch_transform_attr_name = "ori_image_transform_in_loading", + non_blocking = self.args.non_blocking, + + + ) + agg = Metric_Aggregator() + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.trainer.test_current_model( + test_dataloader_dict, self.args.device, + ) + agg({ + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + agg.to_dataframe().to_csv(f"{args.save_path}anp_df_summary.csv") + result = {} + result['model'] = model + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model_pru.cpu().state_dict(), + save_path=args.save_path, + ) + + return result + + self.set_trainer(model_pru) + self.trainer.set_with_dataloader( + ### the train_dataload has nothing to do with the backdoor defense + train_dataloader = clean_val_loader, + test_dataloader_dict = test_dataloader_dict, + + criterion = criterion, + optimizer = None, + scheduler = None, + device = self.args.device, + amp = self.args.amp, + + frequency_save = self.args.frequency_save, + save_folder_path = self.args.save_path, + save_prefix = 'anp', + + prefetch = self.args.prefetch, + prefetch_transform_attr_name = "ori_image_transform_in_loading", + non_blocking = self.args.non_blocking, + + + ) + agg = Metric_Aggregator() + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.trainer.test_current_model( + test_dataloader_dict, self.args.device, + ) + agg({ + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + agg.to_dataframe().to_csv(f"{args.save_path}anp_df_summary.csv") + result = {} + result['model'] = model_pru + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model_pru.cpu().state_dict(), + save_path=args.save_path, + ) + return result + + def defense(self,result_file): + self.set_result(result_file) + self.set_logger() + result = self.mitigation() + return result + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=sys.argv[0]) + anp.add_arguments(parser) + args = parser.parse_args() + anp_method = anp(args) + if "result_file" not in args.__dict__: + args.result_file = 'defense_test_badnet' + elif args.result_file is None: + args.result_file = 'defense_test_badnet' + result = anp_method.defense(args.result_file) \ No newline at end of file diff --git a/defense/base.py b/defense/base.py new file mode 100755 index 0000000..39d0626 --- /dev/null +++ b/defense/base.py @@ -0,0 +1,48 @@ +import os,sys +import numpy as np +import torch + + +class defense(object): + + + def __init__(self,): + # TODO:yaml config log(测试两个防御方法同时使用会不会冲突) + print(1) + + def add_arguments(parser): + # TODO:当后续的防御方法没有复写这个方法的时候，该防御方法需要重写该方法以实现给参数的功能 + print('You need to rewrite this method for passing parameters') + + def set_result(self): + # TODO:当后续的防御方法没有复写这个方法的时候，该防御方法需要重写该方法以读取攻击的结果 + print('You need to rewrite this method to load the attack result') + + def set_trainer(self): + # TODO:当后续的防御方法没有复写这个方法的时候，该防御方法可以重写该方法以实现整合训练模块的功能 + print('If you want to use standard trainer module, please rewrite this method') + + def set_logger(self): + # TODO:当后续的防御方法没有复写这个方法的时候，该防御方法可以重写该方法以实现存储log的功能 + print('If you want to use standard logger, please rewrite this method') + + def denoising(self): + # TODO:当后续的防御方法没有复写这个方法的时候，就是该防御方法没有此项功能 + print('this method does not have this function') + + def mitigation(self): + # TODO:当后续的防御方法没有复写这个方法的时候，就是该防御方法没有此项功能 + print('this method does not have this function') + + def inhibition(self): + # TODO:当后续的防御方法没有复写这个方法的时候，就是该防御方法没有此项功能 + print('this method does not have this function') + + def defense(self): + # TODO:当后续的防御方法没有复写这个方法的时候，就是该防御方法没有此项功能 + print('this method does not have this function') + + def detect(self): + # TODO:当后续的防御方法没有复写这个方法的时候，就是该防御方法没有此项功能 + print('this method does not have this function') + diff --git a/defense/bnp.py b/defense/bnp.py new file mode 100644 index 0000000..2243a61 --- /dev/null +++ b/defense/bnp.py @@ -0,0 +1,495 @@ +''' +This file is modified based on the following source: +link : https://github.com/RJ-T/NIPS2022_EP_BNP. +The defense method is called bnp. + +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. save process + 5. new standard: robust accuracy + 6. reconstruct the layer norm for convnext and transformer + 7. draw the corresponding images of asr and acc according to different proportions +basic sturcture for defense method: + 1. basic setting: args + 2. attack result(model, train data, test data) + 3. dde defense: + a. calculate the entropy of each norm layer + b. prune the model depend on the mask + 4. test the result and get ASR, ACC, RC +''' + +import argparse +import copy +import os,sys +import numpy as np +import torch +import torch.nn as nn + +sys.path.append('../') +sys.path.append(os.getcwd()) + +from pprint import pformat +import yaml +import logging +import time +from defense.base import defense +import utils.defense_utils.mbns.mbns_model as mbns_model +from utils.aggregate_block.train_settings_generate import argparser_criterion +from utils.trainer_cls import Metric_Aggregator, PureCleanModelTrainer, general_plot_for_epoch +from utils.choose_index import choose_index +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.log_assist import get_git_info +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform +from utils.save_load_attack import load_attack_result, save_defense_result +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2 + +def batch_entropy(x, step_size=0.1): + n_bars = int((x.max()-x.min())/step_size) + entropy = 0 + for n in range(n_bars): + num = ((x > x.min() + n*step_size) * (x < x.min() + (n+1)*step_size)).sum(-1) + p = num / x.shape[-1] + entropy += - p * p.log().nan_to_num(0) + return entropy + +def bnp_defense(net, u, trainloader, args): + clean_data_loader = trainloader['clean_train'] + bd_data_loader = trainloader['bd_train'] + net.eval() + bd_data = iter(bd_data_loader).next()[0].to(args.device) + mixture_data = iter(clean_data_loader).next()[0].to(args.device) + params = net.state_dict() + for m in net.modules(): + if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.LayerNorm): + m.collect_stats = True + if isinstance(m, nn.LayerNorm): + m.collect_stats_clean = True + m.collect_stats_bd = False + with torch.no_grad(): + net(mixture_data) + for m in net.modules(): + if isinstance(m, nn.LayerNorm): + m.collect_stats_bd = True + m.collect_stats_clean = False + with torch.no_grad(): + net(bd_data) + for name, m in net.named_modules(): + if isinstance(m, nn.BatchNorm2d): + var_2 = m.running_var + var_1 = m.batch_var + mean_2 = m.running_mean + mean_1 = m.batch_mean + kl_div = (var_2/var_1).log() + (var_1+(mean_1-mean_2).pow(2))/(2*var_2) - 1/2 + index = (kl_div>kl_div.mean() + u*kl_div.std()) + + params[name+'.weight'][index] = 0 + params[name+'.bias'][index] = 0 + elif isinstance(m, nn.LayerNorm): + # We use layer norm to subsitute batch norm in convnext_model and vit_model + var_2 = m.batch_var_bd + var_1 = m.batch_var_clean + mean_2 = m.batch_mean_bd + mean_1 = m.batch_mean_clean + kl_div = (var_2/var_1).log() + (var_1+(mean_1-mean_2).pow(2))/(2*var_2) - 1/2 + index = (kl_div>kl_div.mean() + u*kl_div.std()) + + params[name+'.weight'][index] = 0 + params[name+'.bias'][index] = 0 + + net.load_state_dict(params) + +def get_mbns_network( + model_name: str, + num_classes: int = 10, + **kwargs, +): + if model_name == 'preactresnet18': + net = mbns_model.preact_mbns.PreActResNet18(num_classes = num_classes, **kwargs) + elif model_name == 'vgg19_bn': + net = mbns_model.vgg_mbns.vgg19_bn(num_classes = num_classes, **kwargs) + elif model_name == 'densenet161': + net = mbns_model.den_mbns.densenet161(num_classes= num_classes, **kwargs) + elif model_name == 'mobilenet_v3_large': + net = mbns_model.mobilenet_mbns.mobilenet_v3_large(num_classes= num_classes, **kwargs) + elif model_name == 'efficientnet_b3': + net = mbns_model.eff_mbns.efficientnet_b3(num_classes= num_classes, **kwargs) + elif model_name == 'convnext_tiny': + try : + net = mbns_model.conv_mbns.convnext_tiny(num_classes= num_classes, + ) + except : + net = mbns_model.conv_new_mbns.convnext_tiny(num_classes= num_classes, + ) + elif model_name == 'vit_b_16': + try : + from torchvision.transforms import Resize + net = mbns_model.vit_mbns.vit_b_16( + pretrained = True, + ) + net.heads.head = torch.nn.Linear(net.heads.head.in_features, out_features = num_classes, bias=True) + net = torch.nn.Sequential( + Resize((224, 224)), + net, + ) + except : + from torchvision.transforms import Resize + net = mbns_model.vit_new_mbns.vit_b_16( + pretrained = True, + ) + net.heads.head = torch.nn.Linear(net.heads.head.in_features, out_features = num_classes, bias=True) + net = torch.nn.Sequential( + Resize((224, 224)), + net, + ) + else: + raise SystemError('NO valid model match in function generate_cls_model!') + + return net + + +class bnp(defense): + + def __init__(self,args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + self.args = args + + if 'result_file' in args.__dict__ : + if args.result_file is not None: + self.set_result(args.result_file) + + def add_arguments(parser): + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory") + parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?") + parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch') + parser.add_argument('--amp', type=lambda x: str(x) in ['True','true','1']) + + parser.add_argument('--checkpoint_load', type=str, help='the location of load model') + parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved') + parser.add_argument('--log', type=str, help='the location of log') + parser.add_argument("--dataset_path", type=str, help='the location of data') + parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny') + parser.add_argument('--result_file', type=str, help='the location of result') + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--model', type=str, help='resnet18') + + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--yaml_path', type=str, default="./config/defense/bnp/config.yaml", help='the path of yaml') + + #set the parameter for the bnp defense + parser.add_argument('--u', type=float, help='u in the bnp defense') + parser.add_argument('--u_min', type=float, help='the default minimum value of u') + parser.add_argument('--u_max', type=float, help='the default maximum value of u') + parser.add_argument('--u_num', type=float, help='the default number of u') + parser.add_argument('--ratio', type=float, help='the ratio of clean data loader') + parser.add_argument('--index', type=str, help='index of clean data') + + + def set_result(self, result_file): + attack_file = 'record/' + result_file + save_path = 'record/' + result_file + '/defense/bnp/' + if not (os.path.exists(save_path)): + os.makedirs(save_path) + # assert(os.path.exists(save_path)) + self.args.save_path = save_path + if self.args.checkpoint_save is None: + self.args.checkpoint_save = save_path + 'checkpoint/' + if not (os.path.exists(self.args.checkpoint_save)): + os.makedirs(self.args.checkpoint_save) + if self.args.log is None: + self.args.log = save_path + 'log/' + if not (os.path.exists(self.args.log)): + os.makedirs(self.args.log) + self.result = load_attack_result(attack_file + '/attack_result.pt') + + def set_trainer(self, model): + self.trainer = PureCleanModelTrainer( + model, + ) + + def set_logger(self): + args = self.args + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + try: + logging.info(pformat(get_git_info())) + except: + logging.info('Getting git info fails.') + + def set_devices(self): + # self.device = torch.device( + # ( + # f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device + # # since DataParallel only allow .to("cuda") + # ) if torch.cuda.is_available() else "cpu" + # ) + self.device =self.args.device + def mitigation(self): + self.set_devices() + fix_random(self.args.random_seed) + + # Prepare model, optimizer, scheduler + + net = get_mbns_network(self.args.model,self.args.num_classes,norm_layer=mbns_model.BatchNorm2d_MBNS) + net.load_state_dict(self.result['model']) + if "," in self.device: + net = torch.nn.DataParallel( + net, + device_ids=[int(i) for i in self.args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{net.device_ids[0]}' + net.to(self.args.device) + else: + net.to(self.args.device) + # criterion = nn.CrossEntropyLoss() + + criterion = argparser_criterion(args) + + trainloader_all = {} + + train_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = True) + train_dataset = self.result['bd_train'].wrapped_dataset + data_set_without_tran = train_dataset + data_set_o = self.result['bd_train'] + data_set_o.wrapped_dataset = data_set_without_tran + data_set_o.wrap_img_transform = train_tran + data_loader = torch.utils.data.DataLoader(data_set_o, batch_size=self.args.batch_size, num_workers=self.args.num_workers, shuffle=True, pin_memory=args.pin_memory) + trainloader_backdoor = data_loader + trainloader_all['bd_train'] = trainloader_backdoor + + train_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = True) + clean_dataset = prepro_cls_DatasetBD_v2(self.result['clean_train'].wrapped_dataset) + data_all_length = len(clean_dataset) + ran_idx = choose_index(self.args, data_all_length) + log_index = self.args.log + 'index.txt' + np.savetxt(log_index, ran_idx, fmt='%d') + clean_dataset.subset(ran_idx) + data_set_without_tran = clean_dataset + data_set_o = self.result['clean_train'] + data_set_o.wrapped_dataset = data_set_without_tran + data_set_o.wrap_img_transform = train_tran + + data_loader = torch.utils.data.DataLoader(data_set_o, batch_size=self.args.batch_size, num_workers=self.args.num_workers, shuffle=True, pin_memory=args.pin_memory) + trainloader_clean = data_loader + trainloader_all['clean_train'] = trainloader_clean + + + test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False) + data_bd_testset = self.result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False,pin_memory=args.pin_memory) + + data_clean_testset = self.result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False,pin_memory=args.pin_memory) + + + default_u = np.linspace(self.args.u_min, self.args.u_max, self.args.u_num) + + agg_all = Metric_Aggregator() + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + for u in default_u: + model_copy = copy.deepcopy(net) + model_copy.eval() + bnp_defense(model_copy, self.args.u, trainloader_all, args) + # model.eval() + model_copy.eval() + test_dataloader_dict = {} + test_dataloader_dict["clean_test_dataloader"] = data_clean_loader + test_dataloader_dict["bd_test_dataloader"] = data_bd_loader + + self.set_trainer(model_copy) + self.trainer.set_with_dataloader( + ### the train_dataload has nothing to do with the backdoor defense + train_dataloader = data_bd_loader, + test_dataloader_dict = test_dataloader_dict, + + criterion = criterion, + optimizer = None, + scheduler = None, + device = self.args.device, + amp = self.args.amp, + + frequency_save = self.args.frequency_save, + save_folder_path = self.args.save_path, + save_prefix = 'bnp', + + prefetch = self.args.prefetch, + prefetch_transform_attr_name = "ori_image_transform_in_loading", + non_blocking = self.args.non_blocking, + + + ) + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.trainer.test_current_model( + test_dataloader_dict, self.args.device, + ) + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + agg_all({ + "u": u, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + + general_plot_for_epoch( + { + "Test C-Acc": test_acc_list, + "Test ASR": test_asr_list, + "Test RA": test_ra_list, + }, + save_path=f"{args.save_path}u_step_acc_like_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "Test Clean Loss": clean_test_loss_list, + "Test Backdoor Loss": bd_test_loss_list, + }, + save_path=f"{args.save_path}u_step_loss_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "u": default_u, + }, + save_path=f"{args.save_path}u_step_plots.png", + ylabel="percentage", + ) + + agg_all.to_dataframe().to_csv(f"{args.save_path}u_step_df.csv") + + agg = Metric_Aggregator() + bnp_defense(net, self.args.u, trainloader_all, args) + + test_dataloader_dict = {} + test_dataloader_dict["clean_test_dataloader"] = data_clean_loader + test_dataloader_dict["bd_test_dataloader"] = data_bd_loader + + model = generate_cls_model(self.args.model,self.args.num_classes) + model.load_state_dict(net.state_dict()) + self.set_trainer(model) + + self.trainer.set_with_dataloader( + train_dataloader = trainloader_backdoor, + test_dataloader_dict = test_dataloader_dict, + + criterion = criterion, + optimizer = None, + scheduler = None, + device = self.args.device, + amp = self.args.amp, + + frequency_save = self.args.frequency_save, + save_folder_path = self.args.save_path, + save_prefix = 'bnp', + + prefetch = self.args.prefetch, + prefetch_transform_attr_name = "ori_image_transform_in_loading", + non_blocking = self.args.non_blocking, + + ) + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.trainer.test_current_model( + test_dataloader_dict, self.args.device, + ) + agg({ + "u": self.args.u, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + agg.to_dataframe().to_csv(f"{args.save_path}bnp_df_summary.csv") + + result = {} + result['model'] = model + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model.cpu().state_dict(), + save_path=args.save_path, + ) + return result + + def defense(self,result_file): + self.set_result(result_file) + self.set_logger() + result = self.mitigation() + return result + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=sys.argv[0]) + bnp.add_arguments(parser) + args = parser.parse_args() + method = bnp(args) + if "result_file" not in args.__dict__: + args.result_file = 'defense_test_badnet' + elif args.result_file is None: + args.result_file = 'defense_test_badnet' + result = method.defense(args.result_file) \ No newline at end of file diff --git a/defense/clp.py b/defense/clp.py new file mode 100644 index 0000000..cf94ec9 --- /dev/null +++ b/defense/clp.py @@ -0,0 +1,388 @@ +''' +This file is modified based on the following source: +link : https://github.com/rkteddy/channel-Lipschitzness-based-pruning. +The defense method is called clp. + +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. save process + 5. new standard: robust accuracy + 6. draw the corresponding images of asr and acc according to different proportions +basic sturcture for defense method: + 1. basic setting: args + 2. attack result(model, train data, test data) + 3. clp defense: + a. prune the model depend on the estimate of TAC + 4. test the result and get ASR, ACC, RC with regard to the chosen threshold and interval +''' + + +import argparse +import copy +import os,sys +import numpy as np +import torch +import torch.nn as nn + + +sys.path.append('../') +sys.path.append(os.getcwd()) + +from pprint import pformat +import yaml +import logging +import time +from defense.base import defense +from utils.aggregate_block.train_settings_generate import argparser_criterion +from utils.trainer_cls import Metric_Aggregator, PureCleanModelTrainer, general_plot_for_epoch +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.log_assist import get_git_info +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform +from utils.save_load_attack import load_attack_result, save_defense_result + +def CLP_prune(net, u): + params = net.state_dict() + # conv = None + for name, m in net.named_modules(): + if isinstance(m, nn.BatchNorm2d): + std = m.running_var.sqrt() + weight = m.weight + + channel_lips = [] + for idx in range(weight.shape[0]): + # Combining weights of convolutions and BN + w = conv.weight[idx].reshape(conv.weight.shape[1], -1) * (weight[idx]/std[idx]).abs() + channel_lips.append(torch.svd(w.cpu())[1].max()) + channel_lips = torch.Tensor(channel_lips) + + index = torch.where(channel_lips>channel_lips.mean() + u*channel_lips.std())[0] + + params[name+'.weight'][index] = 0 + params[name+'.bias'][index] = 0 + # print(index) + + # Convolutional layer should be followed by a BN layer by default + elif isinstance(m, nn.Conv2d): + conv = m + + net.load_state_dict(params) + +class clp(defense): + + def __init__(self,args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + self.args = args + + if 'result_file' in args.__dict__ : + if args.result_file is not None: + self.set_result(args.result_file) + + def add_arguments(parser): + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory") + parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?") + parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch') + parser.add_argument('--amp', type=lambda x: str(x) in ['True','true','1']) + + parser.add_argument('--checkpoint_load', type=str, help='the location of load model') + parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved') + parser.add_argument('--log', type=str, help='the location of log') + parser.add_argument("--dataset_path", type=str, help='the location of data') + parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny') + parser.add_argument('--result_file', type=str, help='the location of result') + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--model', type=str, help='resnet18') + + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--yaml_path', type=str, default="./config/defense/clp/config.yaml", help='the path of yaml') + + #set the parameter for the clp defense + parser.add_argument('--u', type=float, help='the default value of u') + parser.add_argument('--u_min', type=float, help='the default minimum value of u') + parser.add_argument('--u_max', type=float, help='the default maximum value of u') + parser.add_argument('--u_num', type=float, help='the default number of u') + + + def set_result(self, result_file): + attack_file = 'record/' + result_file + save_path = 'record/' + result_file + '/defense/clp/' + if not (os.path.exists(save_path)): + os.makedirs(save_path) + # assert(os.path.exists(save_path)) + self.args.save_path = save_path + if self.args.checkpoint_save is None: + self.args.checkpoint_save = save_path + 'checkpoint/' + if not (os.path.exists(self.args.checkpoint_save)): + os.makedirs(self.args.checkpoint_save) + if self.args.log is None: + self.args.log = save_path + 'log/' + if not (os.path.exists(self.args.log)): + os.makedirs(self.args.log) + self.result = load_attack_result(attack_file + '/attack_result.pt') + + def set_trainer(self, model): + self.trainer = PureCleanModelTrainer( + model, + ) + + def set_logger(self): + args = self.args + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + try: + logging.info(pformat(get_git_info())) + except: + logging.info('Getting git info fails.') + + def set_devices(self): + # self.device = torch.device( + # ( + # f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device + # # since DataParallel only allow .to("cuda") + # ) if torch.cuda.is_available() else "cpu" + # ) + self.device = self.args.device + + def mitigation(self): + self.set_devices() + fix_random(self.args.random_seed) + + # Prepare model, optimizer, scheduler + model = generate_cls_model(self.args.model,self.args.num_classes) + model.load_state_dict(self.result['model']) + if "," in self.device: + model = torch.nn.DataParallel( + model, + device_ids=[int(i) for i in self.args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{model.device_ids[0]}' + model.to(self.args.device) + else: + model.to(self.args.device) + # criterion = nn.CrossEntropyLoss() + + criterion = argparser_criterion(args) + + test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False) + data_bd_testset = self.result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False,pin_memory=args.pin_memory) + + data_clean_testset = self.result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False,pin_memory=args.pin_memory) + + # model.eval() + default_u = np.linspace(self.args.u_min, self.args.u_max, self.args.u_num) + + agg_all = Metric_Aggregator() + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + for u in default_u: + model_copy = copy.deepcopy(model) + model_copy.eval() + CLP_prune(model_copy, u) + # model.eval() + model_copy.eval() + test_dataloader_dict = {} + test_dataloader_dict["clean_test_dataloader"] = data_clean_loader + test_dataloader_dict["bd_test_dataloader"] = data_bd_loader + + self.set_trainer(model_copy) + self.trainer.set_with_dataloader( + ### the train_dataload has nothing to do with the backdoor defense + train_dataloader = data_bd_loader, + test_dataloader_dict = test_dataloader_dict, + + criterion = criterion, + optimizer = None, + scheduler = None, + device = self.args.device, + amp = self.args.amp, + + frequency_save = self.args.frequency_save, + save_folder_path = self.args.save_path, + save_prefix = 'clp', + + prefetch = self.args.prefetch, + prefetch_transform_attr_name = "ori_image_transform_in_loading", + non_blocking = self.args.non_blocking, + + + ) + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.trainer.test_current_model( + test_dataloader_dict, self.args.device, + ) + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + agg_all({ + "u": u, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + + general_plot_for_epoch( + { + "Test C-Acc": test_acc_list, + "Test ASR": test_asr_list, + "Test RA": test_ra_list, + }, + save_path=f"{args.save_path}u_step_acc_like_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "Test Clean Loss": clean_test_loss_list, + "Test Backdoor Loss": bd_test_loss_list, + }, + save_path=f"{args.save_path}u_step_loss_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "u": default_u, + }, + save_path=f"{args.save_path}u_step_plots.png", + ylabel="percentage", + ) + + agg_all.to_dataframe().to_csv(f"{args.save_path}u_step_df.csv") + + agg = Metric_Aggregator() + CLP_prune(model, self.args.u) + + test_dataloader_dict = {} + test_dataloader_dict["clean_test_dataloader"] = data_clean_loader + test_dataloader_dict["bd_test_dataloader"] = data_bd_loader + + self.set_trainer(model) + self.trainer.set_with_dataloader( + ### the train_dataload has nothing to do with the backdoor defense + train_dataloader = data_bd_loader, + test_dataloader_dict = test_dataloader_dict, + + criterion = criterion, + optimizer = None, + scheduler = None, + device = self.args.device, + amp = self.args.amp, + + frequency_save = self.args.frequency_save, + save_folder_path = self.args.save_path, + save_prefix = 'clp', + + prefetch = self.args.prefetch, + prefetch_transform_attr_name = "ori_image_transform_in_loading", + non_blocking = self.args.non_blocking, + + # continue_training_path = continue_training_path, + # only_load_model = only_load_model, + ) + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.trainer.test_current_model( + test_dataloader_dict, self.args.device, + ) + agg({ + "u": self.args.u, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + agg.to_dataframe().to_csv(f"{args.save_path}clp_df_summary.csv") + + logging.info(f'the threshold{args.u} clean_loss:{clean_test_loss_avg_over_batch} bd_loss:{bd_test_loss_avg_over_batch} clean_acc:{test_acc} asr:{test_asr} ra:{test_ra}') + + + result = {} + result['model'] = model + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model.cpu().state_dict(), + save_path=args.save_path, + ) + return result + + def defense(self,result_file): + self.set_result(result_file) + self.set_logger() + result = self.mitigation() + return result + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=sys.argv[0]) + clp.add_arguments(parser) + args = parser.parse_args() + method = clp(args) + if "result_file" not in args.__dict__: + args.result_file = 'defense_test_badnet' + elif args.result_file is None: + args.result_file = 'defense_test_badnet' + result = method.defense(args.result_file) \ No newline at end of file diff --git a/defense/d-br.py b/defense/d-br.py new file mode 100644 index 0000000..b4df407 --- /dev/null +++ b/defense/d-br.py @@ -0,0 +1,712 @@ +''' +This file implements the defense method called D-BR from Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples. +This file is modified based on the following source: +link : https://github.com/SCLBD/Effective_backdoor_defense +The defense method is called d-br. +It removes the backdoor from a given backdoored model with a poisoned dataset. + + +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. save process + 5. new standard: robust accuracy +basic sturcture for defense method: + 1. basic setting: args + 2. load attack result(model, train data, test data) + 3. d-br defense: mainly two steps: sd and st (Sample-Distinguishment and two-stage Secure Training) + (sd:) + a. train a backdoored model from scratch using poisoned dataset without any data augmentations + b. fine-tune the backdoored model with intra-class loss L_intra. + c. calculate values of the FCT metric for all training samples. + d. calculate thresholds for choosing clean and poisoned samples. + e. separate training samples into clean samples D_c, poisoned samples D_p, and uncertain samples D_u. + (br:) + f. unlearn and relearn the backdoored model. + 4. test the result and get ASR, ACC, RC +''' + +import argparse +import os,sys +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision import transforms +from tqdm import tqdm +import copy +import math +from copy import deepcopy + +sys.path.append('../') +sys.path.append(os.getcwd()) + +from pprint import pformat +import yaml +import logging +import time +from defense.base import defense + +from utils.aggregate_block.train_settings_generate import argparser_criterion, argparser_opt_scheduler +from utils.trainer_cls import BackdoorModelTrainer, Metric_Aggregator, ModelTrainerCLS, ModelTrainerCLS_v2, PureCleanModelTrainer, all_acc, general_plot_for_epoch, given_dataloader_test +from utils.choose_index import choose_index +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.log_assist import get_git_info +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform +from utils.save_load_attack import load_attack_result, save_defense_result +from utils.bd_dataset_v2 import dataset_wrapper_with_transform, prepro_cls_DatasetBD_v2 +## d-st utils +from utils.defense_utils.dbr.dataloader_bd import get_transform_br, TransformThree, normalization +from utils.defense_utils.dbr.sd import calculate_consistency, calculate_gamma, separate_samples +from utils.defense_utils.dbr.dataloader_bd import get_br_train_loader +from utils.defense_utils.dbr.utils_br import * +from utils.defense_utils.dbr.utils_br import progress_bar +from utils.defense_utils.dbr.dataloader_bd import Dataset_npy + + +def train_epoch(arg, trainloader, model, optimizer, scheduler, criterion, epoch): + model.train() + + total_clean, total_poison = 0, 0 + total_clean_correct, total_attack_correct, total_robust_correct = 0, 0, 0 + train_loss = 0 + + for i, (inputs, labels, _, isCleans, gt_labels) in enumerate(trainloader): + inputs = normalization(arg, inputs[0]) # Normalize + inputs, labels, gt_labels = inputs.to(arg.device), labels.to(arg.device), gt_labels.to(arg.device) + clean_idx, poison_idx = torch.where(isCleans == True), torch.where(isCleans == False) + outputs = model(inputs) + loss = criterion(outputs, labels) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + train_loss += loss.item() + total_clean_correct += torch.sum(torch.argmax(outputs[:], dim=1) == labels[:]) + total_attack_correct += torch.sum(torch.argmax(outputs[poison_idx], dim=1) == labels[poison_idx]) + total_robust_correct += torch.sum(torch.argmax(outputs[:], dim=1) == gt_labels[:]) + total_clean += inputs.shape[0] + total_poison += inputs[poison_idx].shape[0] + + avg_acc_clean = (total_clean_correct / total_clean).item() + avg_acc_attack = (total_attack_correct / total_poison).item() + avg_acc_robust = (total_robust_correct / total_clean).item() + logging.info(f'Epoch: {epoch} | Loss: {train_loss / (i + 1)} | Train ACC: {avg_acc_clean} ({total_clean_correct}/{total_clean}) | Train ASR: \ + {avg_acc_attack}% ({total_attack_correct}/{total_poison}) | Train R-ACC: {avg_acc_robust} ({total_robust_correct}/{total_clean})') + del loss, inputs, outputs + torch.cuda.empty_cache() + scheduler.step() + return train_loss / (i + 1), avg_acc_clean, avg_acc_attack, avg_acc_robust + +def test_epoch(args, testloader, model, criterion, epoch): + model.eval() + + total_clean = 0 + total_clean_correct = 0 + test_loss = 0 + + for i, (inputs, labels, *additional_info) in enumerate(testloader): + inputs, labels = inputs.to(args.device), labels.to(args.device) + outputs = model(inputs) + loss = criterion(outputs, labels) + + test_loss += loss.item() + total_clean_correct += torch.sum(torch.argmax(outputs[:], dim=1) == labels[:]) + total_clean += inputs.shape[0] + avg_acc_clean = (total_clean_correct / total_clean).item() + + return test_loss / (i + 1), avg_acc_clean + +def finetune_epoch(arg, trainloader, model, optimizer, scheduler, epoch): + model.train() + + total_clean, total_poison = 0, 0 + total_clean_correct, total_attack_correct, total_robust_correct = 0, 0, 0 + train_loss = 0 + + for i, (inputs, labels, _, is_bd, gt_labels) in enumerate(trainloader): + inputs = normalization(arg, inputs[0]) # Normalize + inputs, labels, gt_labels = inputs.to(arg.device), labels.to(arg.device), gt_labels.to(arg.device) + clean_idx, poison_idx = torch.where(is_bd == False)[0], torch.where(is_bd == True)[0] + + # Features and Outputs + # outputs = model(inputs) + # if hasattr(model, "module"): # abandon FC layer + # features_out = list(model.module.children())[:-1] + # else: + # features_out = list(model.children())[:-1] + # modelout = nn.Sequential(*features_out).to(arg.device) + # features = modelout(inputs) + # features = features.view(features.size(0), -1) + features = model(inputs) + features = features.view(features.size(0), -1) + # Calculate intra-class loss + centers = [] + for j in range(arg.num_classes): + j_idx = torch.where(labels == j)[0] + if j_idx.shape[0] == 0: + continue + j_features = features[j_idx] + j_center = torch.mean(j_features, dim=0) + centers.append(j_center) + + centers = torch.stack(centers, dim=0) + centers = F.normalize(centers, dim=1) + similarity_matrix = torch.matmul(centers, centers.T) + mask = torch.eye(similarity_matrix.shape[0], dtype=torch.bool).to(arg.device) + similarity_matrix[mask] = 0.0 + loss = torch.mean(similarity_matrix) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + train_loss += loss.item() + + torch.cuda.empty_cache() + scheduler.step() + # return train_loss / (i + 1), avg_acc_clean, avg_acc_attack, avg_acc_robust + return train_loss / (i + 1) + +def learning_rate_unlearning(optimizer, epoch, opt): + lr = opt.lr + for param_group in optimizer.param_groups: + param_group['lr'] = lr + +def transform_finetuning(args,): + transforms_list = [] + # transforms_list.append(transforms.ToPILImage()) + transforms_list.append(transforms.Resize((args.input_height, args.input_width))) + if args.dataset == "imagenet": + transforms_list.append(transforms.RandomRotation(20)) + transforms_list.append(transforms.RandomHorizontalFlip(0.5)) + else: + transforms_list.append(transforms.RandomCrop((args.input_height, args.input_width), padding=4)) + if args.dataset == "cifar10": + transforms_list.append(transforms.RandomHorizontalFlip()) + transforms_list.append(transforms.ToTensor()) + trasform_compose = transforms.Compose(transforms_list) + return trasform_compose + +class d_br(defense): + + def __init__(self,args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + # args.dataset_path = f"{args.dataset_path}/{args.dataset}" + self.args = args + + def add_arguments(parser): + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory") + parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?") + parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch') + parser.add_argument('--amp', type=lambda x: str(x) in ['True','true','1']) + + parser.add_argument('--checkpoint_load', type=str, help='the location of load model') + parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved') + parser.add_argument('--log', type=str, help='the location of log') + parser.add_argument("--dataset_path", type=str, help='the location of data') + parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny') + parser.add_argument('--result_file', type=str, help='the location of result') + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--yaml_path', type=str, default="./config/defense/d-br/config.yaml", help='the path of yaml') + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--model', type=str, help='resnet18') + parser.add_argument('--target_label', type=int) + # parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--frequency_save', type=int,help=' frequency_save, 0 is never') + + parser.add_argument('--momentum', type=float, help='momentum') + parser.add_argument('--weight_decay', type=float, help='weight decay') + + #set the parameter for the d-br defense + parser.add_argument('--gamma_low', type=float, default=None, help='<=gamma_low is clean') # \gamma_c + parser.add_argument('--gamma_high', type=float, default=None, help='>=gamma_high is poisoned') # \gamma_p + parser.add_argument('--clean_ratio', type=float, default=0.20, help='ratio of clean data') # \alpha_c + parser.add_argument('--poison_ratio', type=float, default=0.05, help='ratio of poisoned data') # \alpha_p + + parser.add_argument('--gamma', type=float, default=0.1, help='LR is multiplied by gamma on schedule.') + parser.add_argument('--schedule', type=int, nargs='+', default=[100, 150], help='Decrease learning rate at these epochs.') + parser.add_argument('--warm', type=int, default=1, help='warm up training phase') + + parser.add_argument('--trans1', type=str, default='rotate') # the first data augmentation + parser.add_argument('--trans2', type=str, default='affine') # the second data augmentation + parser.add_argument('--debug', action='store_true',default=False, help='debug or not') + parser.add_argument('--print_freq', type=int, default=10, help='print frequency') + parser.add_argument('--save_all_process', action='store_true', help='save model in each process') + + def set_result(self, result_file): + attack_file = 'record/' + result_file + save_path = 'record/' + result_file + '/defense/d-br/' + if not (os.path.exists(save_path)): + os.makedirs(save_path) + # assert(os.path.exists(save_path)) + self.args.save_path = save_path + if self.args.checkpoint_save is None: + self.args.checkpoint_save = save_path + 'checkpoint/' + if not (os.path.exists(self.args.checkpoint_save)): + os.makedirs(self.args.checkpoint_save) + if self.args.log is None: + self.args.log = save_path + 'log/' + if not (os.path.exists(self.args.log)): + os.makedirs(self.args.log) + self.result = load_attack_result(attack_file + '/attack_result.pt') + + + def set_trainer(self, model, mode='normal'): + if mode == 'normal': + self.trainer = BackdoorModelTrainer( + model, + ) + elif mode == 'clean': + self.trainer = PureCleanModelTrainer( + model, + ) + elif mode == 'nad': + raise RuntimeError('No trainer support this mode!') + + def set_logger(self): + args = self.args + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + try: + logging.info(pformat(get_git_info())) + except: + logging.info('Getting git info fails.') + + def set_devices(self): + # self.device = torch.device( + # ( + # f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device + # # since DataParallel only allow .to("cuda") + # ) if torch.cuda.is_available() else "cpu" + # ) + self.device = self.args.device + + def set_new_args(self,args,step): + if step == 'train_notrans': + args.epochs = 2 + elif step == 'finetune_notrans': + args.epochs = 10 + elif step == 'unlearn_relearn': + args.epochs = 20 + args.batch_size = 128 + args.lr = 0.0001 + if args.debug: + args.epochs = 1 + return args + + def set_model(self): + model = generate_cls_model(self.args.model,self.args.num_classes) + model.load_state_dict(self.result['model']) + if "," in self.device: + model = torch.nn.DataParallel( + model, + device_ids=[int(i) for i in self.args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{model.device_ids[0]}' + model.to(self.args.device) + else: + model.to(self.args.device) + return model + + def drop_linear(self,model): # drop the last nn.Linear layer, which will not be used in the following training + model_name = self.args.model + if 'preactresnet' in model_name or model_name == 'senet18': + feature_dim = model.linear.in_features + model.linear = nn.Identity() + elif model_name.startswith("resnet"): + feature_dim = model.fc.in_features + model.fc = nn.Identity() + elif 'vgg' in model_name or 'convnext' in model_name: + feature_dim = list(model.classifier.children())[-1].in_features + model.classifier = nn.Sequential(*list(model.classifier.children())[:-1]) + elif 'vit' in model_name: + feature_dim = model[1].heads.head.in_features + model[1].heads.head = nn.Identity() + else: + raise NotImplementedError('Not support the model: {}'.format(model_name)) + model.register_feature_dim = feature_dim + return model + + def get_sd_train_loader(self): + args = self.args + transform1, transform2, transform3 = get_transform_br(args, train=True) + data_set_o = self.result['bd_train'] + data_set_o.wrap_img_transform = TransformThree(transform1, transform2, transform3) + poisoned_data_loader = torch.utils.data.DataLoader(data_set_o, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=True) + + return poisoned_data_loader + + def testloader_wrapper(self,): + args = self.args + test_tran = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = False) + data_bd_testset = self.result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + bd_test_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=False,pin_memory=True) + + data_clean_testset = self.result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + clean_test_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=False,pin_memory=True) + + return clean_test_loader, bd_test_loader + + def train_attack_noTrans(self, bd_trainloader, clean_test_loader, bd_test_loader, model = None, optimizer=None, scheduler=None,finetune=False): + ## update args + step = 'finetune_notrans' if finetune else 'train_notrans' + args = self.set_new_args(self.args,step = step) + agg = Metric_Aggregator() + if not finetune: + # Load models + logging.info('----------- Network Initialization --------------') + model = generate_cls_model(args.model,args.num_classes) + if "," in self.device: + model = torch.nn.DataParallel( + model, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{model.device_ids[0]}' + model.to(self.args.device) + else: + model.to(self.args.device) + logging.info('finished model init...') + # initialize optimizer + # optimizer = set_optimizer(args,model) + # scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100) + optimizer, scheduler = argparser_opt_scheduler(model, self.args) + # define loss functions + criterion = torch.nn.CrossEntropyLoss().to(args.device) + + logging.info('----------- Training from scratch --------------') + for epoch in tqdm(range(0, args.epochs)): + tr_loss, tr_acc, _,_ = train_epoch(args, bd_trainloader, model, optimizer, scheduler, + criterion, epoch) + clean_test_loss, clean_test_acc = test_epoch(args, clean_test_loader, model, criterion, epoch) + + bd_test_loss, bd_test_acc = test_epoch(args, bd_test_loader, model, criterion, epoch) + bd_test_loader.dataset.wrapped_dataset.getitem_all_switch = True + _, bd_test_racc = test_epoch(args, bd_test_loader, model, criterion, epoch) + bd_test_loader.dataset.wrapped_dataset.getitem_all_switch = False + agg( + { + "train_epoch_loss_avg_over_batch": tr_loss, + "train_acc": tr_acc, + "clean_test_loss_avg_over_batch": clean_test_loss, + "bd_test_loss_avg_over_batch" : bd_test_loss, + "test_acc" : clean_test_acc, + "test_asr" : bd_test_acc, + "test_ra" : bd_test_racc, + } + ) + agg.to_dataframe().to_csv(f"{args.log}train_notrans_df.csv") + else: + # initialize optimizer + # optimizer = set_optimizer(args,model) + # scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100) + logging.info('----------- Finetune the model with L_intra--------------') + for epoch in tqdm(range(0, args.epochs)): + tr_loss = finetune_epoch(args, bd_trainloader, model, optimizer, scheduler, + epoch) + + agg( + { "epoch": epoch, + "train_epoch_loss_avg_over_batch": tr_loss, + } + ) + agg.to_dataframe().to_csv(f"{args.log}finetune_notrans_df.csv") + if args.save_all_process: + save_file = os.path.join(args.save_path, f'{step}.pt') + logging.info(f'save path is {save_file}') + save_model(model, optimizer, args, args.epochs, save_file) + return model, optimizer, scheduler + + def train_step_unlearning(self, args, train_loader, model_ascent, optimizer, criterion, epoch): + model_ascent.train() + + total_clean, total_clean_correct = 0, 0 + + for idx, (img, target, *additional_info) in enumerate(train_loader, start=1): + img = normalization(args, img) + img = img.to(args.device) + target = target.to(args.device) + + output = model_ascent(img) + loss = criterion(output, target) + + optimizer.zero_grad() + (-loss).backward() # Gradient ascent training + optimizer.step() + + total_clean_correct += torch.sum(torch.argmax(output[:], dim=1) == target[:]) + total_clean += img.shape[0] + avg_acc_clean = total_clean_correct * 100.0 / total_clean + progress_bar(idx, len(train_loader), + 'Epoch: %d | Loss: %.3f | Train ACC: %.3f%% (%d/%d)' % ( + epoch, loss / (idx + 1), avg_acc_clean, total_clean_correct, total_clean)) + + del loss, img, output + torch.cuda.empty_cache() + return model_ascent + + def train_step_relearning(self, args, train_loader, model_descent, optimizer, criterion, epoch): + model_descent.train() + losses = AverageMeter() + top1 = AverageMeter() + + for idx, (img, target, *additional_info) in enumerate(train_loader, start=1): + img = normalization(args, img) + img = img.to(args.device) + target = target.to(args.device) + bsz = target.shape[0] + output = model_descent(img) + loss = criterion(output, target) + optimizer.zero_grad() + loss.backward() # Gradient ascent training + optimizer.step() + + # update metric + losses.update(loss.item(), bsz) + acc1, acc5 = accuracy(output, target, topk=(1, 5)) + top1.update(acc1[0].detach().cpu().numpy(), bsz) + if (idx + 1) % args.print_freq == 0: + logging.info(f'Train: [{epoch}][{idx + 1}/{len(train_loader)}]\t \ + loss {losses.val} ({losses.avg}\t \ + Acc@1 {top1.val} ({top1.avg}') + sys.stdout.flush() + + del loss, img, output + torch.cuda.empty_cache() + return losses.avg, top1.avg, model_descent + + def eval_step(self, model, clean_test_loader, bd_test_loader, args): + clean_metrics, clean_epoch_predict_list, clean_epoch_label_list = given_dataloader_test( + model, + clean_test_loader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + clean_test_loss_avg_over_batch = clean_metrics['test_loss_avg_over_batch'] + test_acc = clean_metrics['test_acc'] + bd_metrics, bd_epoch_predict_list, bd_epoch_label_list = given_dataloader_test( + model, + bd_test_loader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + bd_test_loss_avg_over_batch = bd_metrics['test_loss_avg_over_batch'] + test_asr = bd_metrics['test_acc'] + + bd_test_loader.dataset.wrapped_dataset.getitem_all_switch = True # change to return the original label instead + ra_metrics, ra_epoch_predict_list, ra_epoch_label_list = given_dataloader_test( + model, + bd_test_loader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + ra_test_loss_avg_over_batch = ra_metrics['test_loss_avg_over_batch'] + test_ra = ra_metrics['test_acc'] + bd_test_loader.dataset.wrapped_dataset.getitem_all_switch = False # switch back + + return clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra + + def get_bd_indicator_by_original_from_bd_dataset(self, dataset): + orig2idx_dict = {} + for idx,(img, label, *other_info) in enumerate(dataset) : + orig2idx_dict[other_info[0]] = idx + return orig2idx_dict + + def idx_from_orig(self, orig2idx_dict, orig_idx): + temp_idx = [] + for idx in orig_idx: + temp_idx.append(orig2idx_dict[idx]) + return temp_idx + + def get_isolate_data_loader(self, args): + transform_compose = transform_finetuning(args) + folder_path = os.path.join(args.save_path,'data_produce') + clean_idx_list = np.load(os.path.join(folder_path, 'clean_samples.npy')) + poison_idx_list = np.load(os.path.join(folder_path, 'poison_samples.npy')) + train_dataset = self.result['bd_train'] + train_dataset_copy = deepcopy(self.result['bd_train']) + orig2idx_dict = self.get_bd_indicator_by_original_from_bd_dataset(train_dataset) # get map from original index to current index + clean_idx = self.idx_from_orig(orig2idx_dict, clean_idx_list) + bd_idx = self.idx_from_orig(orig2idx_dict, poison_idx_list) + + train_dataset.subset(clean_idx) + train_dataset_copy.subset(bd_idx) + clean_data_tf = train_dataset + poison_data_tf = train_dataset_copy + clean_data_tf.wrap_img_transform = deepcopy(transform_compose) + poison_data_tf.wrap_img_transform = deepcopy(transform_compose) + isolate_clean_data_loader = torch.utils.data.DataLoader(dataset=clean_data_tf, batch_size=args.batch_size, shuffle=True) + isolate_poison_data_loader = torch.utils.data.DataLoader(dataset=poison_data_tf, batch_size=args.batch_size, shuffle=True) + return isolate_clean_data_loader,isolate_poison_data_loader + + def unlearn_relearn(self, model): + + args = self.set_new_args(self.args,step='unlearn_relearn') + isolate_clean_data_loader,isolate_poison_data_loader = self.get_isolate_data_loader(args) + clean_test_loader, bd_test_loader = self.testloader_wrapper() + + optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4) + criterion = nn.CrossEntropyLoss().to(args.device) + + # Training and Testing + train_loss_list = [] + train_mix_acc_list = [] + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + agg = Metric_Aggregator() + + logging.info('----------- Unlearning and relearning--------------') + for epoch in tqdm(range(1, args.epochs+1)): + # Modify lr + learning_rate_unlearning(optimizer, epoch, args) + # Unlearn + print('-----Unlearning-------') + model = self.train_step_unlearning(args, isolate_poison_data_loader, model, optimizer, criterion, epoch) + # Relearn + print('-----Relearning-------') + train_epoch_loss_avg_over_batch, \ + train_mix_acc, \ + model = self.train_step_relearning(args, isolate_clean_data_loader, model, optimizer, criterion, epoch) + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.eval_step( + model, + clean_test_loader, + bd_test_loader, + args, + ) + train_loss_list.append(train_epoch_loss_avg_over_batch) + train_mix_acc_list.append(train_mix_acc) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + agg( + { + "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch, + "train_acc": train_mix_acc, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch" : bd_test_loss_avg_over_batch, + "test_acc" : test_acc, + "test_asr" : test_asr, + "test_ra" : test_ra, + } + ) + agg.to_dataframe().to_csv(f"{args.save_path}d-br_df.csv") + agg.summary().to_csv(f"{args.save_path}d-br_df_summary.csv") + if args.save_all_process: + # Save the best model + save_file = os.path.join(args.save_path, 'unlearn_relearn-last.pth') + save_model(model, optimizer, args, args.epochs, save_file) + return model + + def mitigation(self): + args = self.args + self.set_devices() + fix_random(self.args.random_seed) + result = self.result + bd_trainloader = self.get_sd_train_loader() + clean_test_loader, bd_test_loader = self.testloader_wrapper() + ###a. train a backdoored model from scratch using poisoned dataset without any data augmentations + model, optimizer, scheduler = self.train_attack_noTrans(bd_trainloader, clean_test_loader, bd_test_loader, finetune=False) + ###b. fine-tune the backdoored model with intra-class loss L_intra + model = self.drop_linear(model) + model, optimizer, scheduler = self.train_attack_noTrans(bd_trainloader, clean_test_loader, bd_test_loader, model=model, optimizer=optimizer, scheduler=scheduler,finetune=True) + ###c. calculate values of the FCT metric for all training samples. + calculate_consistency(args, bd_trainloader, model) + ###d. calculate thresholds for choosing clean and poisoned samples. + args.gamma_low, args.gamma_high = calculate_gamma(args,) + ###e. separate training samples into clean samples D_c, poisoned samples D_p, and uncertain samples D_u. + separate_samples(args, bd_trainloader, model) + ###f. load the backdoored model, then unlearn and relearn the model. + model_new = self.set_model() + model_new = self.unlearn_relearn(model_new) + + result = {} + result['model'] = model_new + + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model_new.cpu().state_dict(), + save_path=args.save_path, + ) + return result + + def defense(self,result_file): + self.set_result(result_file) + self.set_logger() + result = self.mitigation() + return result + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=sys.argv[0]) + d_br.add_arguments(parser) + args = parser.parse_args() + d_st_method = d_br(args) + if "result_file" not in args.__dict__ or args.result_file is None: + args.result_file = 'defense_test_badnet' + result = d_st_method.defense(args.result_file) + diff --git a/defense/d-st.py b/defense/d-st.py new file mode 100644 index 0000000..0f4e595 --- /dev/null +++ b/defense/d-st.py @@ -0,0 +1,880 @@ +''' +This file implements the defense method called D-ST from Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples. +It trains a !!!secure model!!! from scratch with a poisoned dataset. +This file is modified based on the following source: +link : https://github.com/SCLBD/Effective_backdoor_defense +The defense method is called d-br. + + +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. save process + 5. new standard: robust accuracy +basic sturcture for defense method: + 1. basic setting: args + 2. attack result(model, train data, test data) + 3. d-st defense: mainly two steps: sd and st (Sample-Distinguishment and two-stage Secure Training) + a. train a backdoored model from scratch using poisoned dataset without any data augmentations + b. fine-tune the backdoored model with intra-class loss L_intra. + (sd:) + c. calculate values of the FCT metric for all training samples. + d. calculate thresholds for choosing clean and poisoned samples. + e. separate training samples into clean samples D_c, poisoned samples D_p, and uncertain samples D_u. + (st:) + f. train the feature extractor via semi-supervised contrastive learning. + g. train the classifier via minimizing a mixed cross-entropy loss. + 4. test the result and get ASR, ACC, RC + +''' + +import argparse +import os,sys +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from tqdm import tqdm +import copy +import math + +sys.path.append('../') +sys.path.append(os.getcwd()) + +from pprint import pformat +import yaml +import logging +import time +from defense.base import defense + +from utils.aggregate_block.train_settings_generate import argparser_criterion, argparser_opt_scheduler +from utils.trainer_cls import BackdoorModelTrainer, Metric_Aggregator,PureCleanModelTrainer +from utils.choose_index import choose_index +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.log_assist import get_git_info +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform +from utils.save_load_attack import load_attack_result, save_defense_result +## d-st utils +from utils.defense_utils.dst.dataloader_bd import get_transform_st, TransformThree, normalization +from utils.defense_utils.dst.sd import calculate_consistency, calculate_gamma, separate_samples +from utils.defense_utils.dst.dataloader_bd import get_st_train_loader +from utils.defense_utils.dst.models.resnet_super import SupConResNet,LinearClassifier +from utils.defense_utils.dst.st_loss import SupConLoss_Consistency +from utils.defense_utils.dst.utils_st import * + +def train_epoch(arg, trainloader, model, optimizer, scheduler, criterion, epoch): + model.train() + + total_clean, total_poison = 0, 0 + total_clean_correct, total_attack_correct, total_robust_correct = 0, 0, 0 + train_loss = 0 + + for i, (inputs, labels, _, isCleans, gt_labels) in enumerate(trainloader): + inputs = normalization(arg, inputs[0]) # Normalize + inputs, labels, gt_labels = inputs.to(arg.device), labels.to(arg.device), gt_labels.to(arg.device) + clean_idx, poison_idx = torch.where(isCleans == True), torch.where(isCleans == False) + outputs = model(inputs) + loss = criterion(outputs, labels) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + train_loss += loss.item() + total_clean_correct += torch.sum(torch.argmax(outputs[:], dim=1) == labels[:]) + total_attack_correct += torch.sum(torch.argmax(outputs[poison_idx], dim=1) == labels[poison_idx]) + total_robust_correct += torch.sum(torch.argmax(outputs[:], dim=1) == gt_labels[:]) + total_clean += inputs.shape[0] + total_poison += inputs[poison_idx].shape[0] + + avg_acc_clean = (total_clean_correct / total_clean).item() + avg_acc_attack = (total_attack_correct / total_poison).item() + avg_acc_robust = (total_robust_correct / total_clean).item() + logging.info(f'Epoch: {epoch} | Loss: {train_loss / (i + 1)} | Train ACC: {avg_acc_clean} ({total_clean_correct}/{total_clean}) | Train ASR: \ + {avg_acc_attack}% ({total_attack_correct}/{total_poison}) | Train R-ACC: {avg_acc_robust} ({total_robust_correct}/{total_clean})') + del loss, inputs, outputs + torch.cuda.empty_cache() + scheduler.step() + return train_loss / (i + 1), avg_acc_clean, avg_acc_attack, avg_acc_robust + +def test_epoch(args, testloader, model, criterion, epoch): + model.eval() + + total_clean = 0 + total_clean_correct = 0 + test_loss = 0 + + for i, (inputs, labels, *additional_info) in enumerate(testloader): + inputs, labels = inputs.to(args.device), labels.to(args.device) + outputs = model(inputs) + loss = criterion(outputs, labels) + + test_loss += loss.item() + total_clean_correct += torch.sum(torch.argmax(outputs[:], dim=1) == labels[:]) + total_clean += inputs.shape[0] + avg_acc_clean = (total_clean_correct / total_clean).item() + + return test_loss / (i + 1), avg_acc_clean + +def finetune_epoch(arg, trainloader, model, optimizer, scheduler, epoch): + model.train() + + total_clean, total_poison = 0, 0 + total_clean_correct, total_attack_correct, total_robust_correct = 0, 0, 0 + train_loss = 0 + + for i, (inputs, labels, _, is_bd, gt_labels) in enumerate(trainloader): + inputs = normalization(arg, inputs[0]) # Normalize + inputs, labels, gt_labels = inputs.to(arg.device), labels.to(arg.device), gt_labels.to(arg.device) + clean_idx, poison_idx = torch.where(is_bd == False)[0], torch.where(is_bd == True)[0] + + # Features and Outputs + # outputs = model(inputs) + # if hasattr(model, "module"): # abandon FC layer + # features_out = list(model.module.children())[:-1] + # else: + # features_out = list(model.children())[:-1] + # modelout = nn.Sequential(*features_out).to(arg.device) + # features = modelout(inputs) + # features = features.view(features.size(0), -1) + features = model(inputs) + features = features.view(features.size(0), -1) + # Calculate intra-class loss + centers = [] + for j in range(arg.num_classes): + j_idx = torch.where(labels == j)[0] + if j_idx.shape[0] == 0: + continue + j_features = features[j_idx] + j_center = torch.mean(j_features, dim=0) + centers.append(j_center) + + centers = torch.stack(centers, dim=0) + centers = F.normalize(centers, dim=1) + similarity_matrix = torch.matmul(centers, centers.T) + mask = torch.eye(similarity_matrix.shape[0], dtype=torch.bool).to(arg.device) + similarity_matrix[mask] = 0.0 + loss = torch.mean(similarity_matrix) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + train_loss += loss.item() + + scheduler.step() + torch.cuda.empty_cache() + # return train_loss / (i + 1), avg_acc_clean, avg_acc_attack, avg_acc_robust + return train_loss / (i + 1) + +def _train_extractor(train_loader, model, criterion, optimizer, epoch, args): + """one epoch training""" + model.train() + + batch_time = AverageMeter() + data_time = AverageMeter() + losses = AverageMeter() + + end = time.time() + for idx, (images, labels, flags) in enumerate(train_loader): + if args.debug and idx == 2: + break + data_time.update(time.time() - end) + + images = torch.cat([images[0], images[1]], dim=0) + if torch.cuda.is_available(): + images = images.cuda(non_blocking=True).to(args.device) + labels = labels.cuda(non_blocking=True).to(args.device) + flags = flags.cuda(non_blocking=True).to(args.device) + bsz = labels.shape[0] + + # warm-up learning rate + warmup_learning_rate(args, epoch, idx, len(train_loader), optimizer) + + # compute loss + features = model(images) + f1, f2 = torch.split(features, [bsz, bsz], dim=0) + features = torch.cat([f1.unsqueeze(1), f2.unsqueeze(1)], dim=1) + loss = criterion(features, labels, flags) + + # update metric + losses.update(loss.item(), bsz) + + # SGD + optimizer.zero_grad() + loss.backward() + optimizer.step() + + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + + # print info + if (idx + 1) % args.print_freq == 0: + logging.info(f'Train: [{epoch}/{args.epochs}][{idx + 1}/{len(train_loader)}]\t \ + BT {batch_time.val} ({batch_time.avg})\t \ + DT {data_time.val} ({data_time.avg})\t \ + loss {losses.val} ({losses.avg})') + + sys.stdout.flush() + del loss, images, features + torch.cuda.empty_cache() + return losses.avg + +def _train_classifier(train_loader, model, classifier, criterion, optimizer, epoch, args): + """one epoch training""" + model.eval() + classifier.train() + + batch_time = AverageMeter() + data_time = AverageMeter() + losses = AverageMeter() + top1 = AverageMeter() + + end = time.time() + for idx, (images, labels, flags) in enumerate(train_loader): + if args.debug and idx == 2: + break + data_time.update(time.time() - end) + images = images.cuda(non_blocking=True).to(args.device) + labels = labels.cuda(non_blocking=True).to(args.device) + flags = flags.cuda(non_blocking=True).to(args.device) + + bsz = labels.shape[0] + + # warm-up learning rate + warmup_learning_rate(args, epoch, idx, len(train_loader), optimizer) + + # compute loss + with torch.no_grad(): + features = model.encoder(images) + output = classifier(features.detach()) + + clean_idx = torch.where(flags == 0)[0] + poison_idx = torch.where(flags == 2)[0] + loss = criterion(output[clean_idx], labels[clean_idx]) - criterion(output[poison_idx], labels[poison_idx])*0.001 + # SGD + optimizer.zero_grad() + loss.backward() + optimizer.step() + + # update metric + losses.update(loss.item(), bsz) + acc1, acc5 = accuracy(output, labels, topk=(1, 5)) + top1.update(acc1[0].detach().cpu().numpy(), bsz) + + + + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + + # print info + if (idx + 1) % args.print_freq == 0: + logging.info(f'Train: [{epoch}][{idx + 1}/{len(train_loader)}]\t \ + BT {batch_time.val} ({batch_time.avg})\t \ + DT {data_time.val} ({data_time.avg})\t \ + loss {losses.val} ({losses.avg}\t \ + Acc@1 {top1.val} ({top1.avg}') + sys.stdout.flush() + del loss, features, images, output + torch.cuda.empty_cache() + return losses.avg, top1.avg + +def given_dataloader_test( + model, + classifier, + test_dataloader, + criterion, + non_blocking : bool = False, + device = "cpu", + verbose : int = 0 +): + model.to(device, non_blocking=non_blocking) + model.eval() + metrics = { + 'test_correct': 0, + 'test_loss_sum_over_batch': 0, + 'test_total': 0, + } + criterion = criterion.to(device, non_blocking=non_blocking) + + if verbose == 1: + batch_predict_list, batch_label_list = [], [] + + with torch.no_grad(): + for batch_idx, (x, target, *additional_info) in enumerate(test_dataloader): + x = x.to(device, non_blocking=non_blocking) + target = target.to(device, non_blocking=non_blocking) + features = model.encoder(x) + pred = classifier(features.detach()) + loss = criterion(pred, target.long()) + + _, predicted = torch.max(pred, -1) + correct = predicted.eq(target).sum() + + if verbose == 1: + batch_predict_list.append(predicted.detach().clone().cpu()) + batch_label_list.append(target.detach().clone().cpu()) + + metrics['test_correct'] += correct.item() + metrics['test_loss_sum_over_batch'] += loss.item() + metrics['test_total'] += target.size(0) + + metrics['test_loss_avg_over_batch'] = metrics['test_loss_sum_over_batch']/len(test_dataloader) + metrics['test_acc'] = metrics['test_correct'] / metrics['test_total'] + + if verbose == 0: + return metrics, None, None + elif verbose == 1: + return metrics, torch.cat(batch_predict_list), torch.cat(batch_label_list) + +def reset_model_from_SupConResNet(args, old_model, classifier): ## replace the parameters from old model to new model + new_model = generate_cls_model(args.model,args.num_classes) + + new_dict = new_model.state_dict() + old_dict = old_model.encoder.state_dict() + new_dict.update(old_dict) + new_model.load_state_dict(new_dict) + if hasattr(new_model,"linear"): + new_model.linear.weight.data = classifier.fc.weight.data + new_model.linear.bias.data = classifier.fc.bias.data + elif hasattr(new_model,"fc"): + new_model.fc.weight.data = classifier.fc.weight.data + new_model.fc.bias.data = classifier.fc.bias.data + return new_model + +class d_st(defense): + + def __init__(self,args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + # args.dataset_path = f"{args.dataset_path}/{args.dataset}" + self.args = args + + def add_arguments(parser): + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory") + parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?") + parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch') + parser.add_argument('--amp', type=lambda x: str(x) in ['True','true','1']) + + parser.add_argument('--checkpoint_load', type=str, help='the location of load model') + parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved') + parser.add_argument('--log', type=str, help='the location of log') + parser.add_argument("--dataset_path", type=str, help='the location of data') + parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny') + parser.add_argument('--result_file', type=str, help='the location of result') + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--yaml_path', type=str, default="./config/defense/d-st/config.yaml", help='the path of yaml') + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--model', type=str, help='resnet18') + parser.add_argument('--target_label', type=int) + # parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--frequency_save', type=int,help=' frequency_save, 0 is never') + + parser.add_argument('--momentum', type=float, help='momentum') + parser.add_argument('--weight_decay', type=float, help='weight decay') + + #set the parameter for the d-st defense + parser.add_argument('--continue_step', type=str, default=None, help='the step to continue') + parser.add_argument('--gamma_low', type=float, default=None, help='<=gamma_low is clean') # \gamma_c + parser.add_argument('--gamma_high', type=float, default=None, help='>=gamma_high is poisoned') # \gamma_p + parser.add_argument('--clean_ratio', type=float, default=0.20, help='ratio of clean data') # \alpha_c + parser.add_argument('--poison_ratio', type=float, default=0.05, help='ratio of poisoned data') # \alpha_p + + parser.add_argument('--gamma', type=float, default=0.1, help='LR is multiplied by gamma on schedule.') + parser.add_argument('--schedule', type=int, nargs='+', default=[100, 150], help='Decrease learning rate at these epochs.') + parser.add_argument('--warm', type=int, default=1, help='warm up training phase') + + parser.add_argument('--trans1', type=str, default='rotate') # the first data augmentation + parser.add_argument('--trans2', type=str, default='affine') # the second data augmentation + parser.add_argument('--debug', action='store_true',default=False, help='debug or not') + parser.add_argument('--print_freq', type=int, default=10, help='print frequency') + parser.add_argument('--save_all_process', action='store_true', help='save model in each process') + + def set_result(self, result_file): + attack_file = 'record/' + result_file + save_path = 'record/' + result_file + '/defense/d-st/' + if not (os.path.exists(save_path)): + os.makedirs(save_path) + # assert(os.path.exists(save_path)) + self.args.save_path = save_path + if self.args.checkpoint_save is None: + self.args.checkpoint_save = save_path + 'checkpoint/' + if not (os.path.exists(self.args.checkpoint_save)): + os.makedirs(self.args.checkpoint_save) + if self.args.log is None: + self.args.log = save_path + 'log/' + if not (os.path.exists(self.args.log)): + os.makedirs(self.args.log) + self.result = load_attack_result(attack_file + '/attack_result.pt') + + + def set_trainer(self, model, mode='normal'): + if mode == 'normal': + self.trainer = BackdoorModelTrainer( + model, + ) + elif mode == 'clean': + self.trainer = PureCleanModelTrainer( + model, + ) + elif mode == 'nad': + raise RuntimeError('No trainer support this mode!') + + def set_logger(self): + args = self.args + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + try: + logging.info(pformat(get_git_info())) + except: + logging.info('Getting git info fails.') + + def set_devices(self): + # self.device = torch.device( + # ( + # f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device + # # since DataParallel only allow .to("cuda") + # ) if torch.cuda.is_available() else "cpu" + # ) + self.device = self.args.device + + def set_new_args(self,args,step): + if step == 'train_notrans': + args.epochs = 2 + args.batch_size = 128 + elif step == 'finetune_notrans': + args.epochs = 10 + elif step == 'sscl': + args.epochs = 200 + args.learning_rate = 0.5 + args.temp = 0.1 + args.batch_size = 512 + args.cosine = True + if args.cosine: + args.model_name = '{}_cosine'.format(args.model) + if args.batch_size > 256: + args.warm = True + if args.warm: + args.model_name = '{}_warm'.format(args.model) + args.warmup_from = 0.01 + args.warm_epochs = 10 + if args.cosine: + args.lr_decay_rate = 0.1 + eta_min = args.learning_rate * (args.lr_decay_rate ** 3) + args.warmup_to = eta_min + (args.learning_rate - eta_min) * ( + 1 + math.cos(math.pi * args.warm_epochs / args.epochs)) / 2 + else: + args.warmup_to = args.learning_rate + args.lr_decay_epochs = [700,800,900] + elif step == 'mixed_ce': + args.epochs = 10 + args.learning_rate = 5 + args.batch_size = 512 + args.num_workers = 16 + args.cosine = False + if args.batch_size > 256: + args.warm = True + if args.warm: + args.model_name = '{}_warm'.format(args.model) + args.warmup_from = 0.01 + args.warm_epochs = 10 + if args.cosine: + args.lr_decay_rate = 0.1 + eta_min = args.learning_rate * (args.lr_decay_rate ** 3) + args.warmup_to = eta_min + (args.learning_rate - eta_min) * ( + 1 + math.cos(math.pi * args.warm_epochs / args.epochs)) / 2 + else: + args.warmup_to = args.learning_rate + args.lr_decay_epochs = [60,75,90] + if args.debug: + args.epochs = 1 + return args + + def set_model(self,args,model): + assert isinstance(model , SupConResNet) + criterion = torch.nn.CrossEntropyLoss() + classifier = LinearClassifier(feat_dim=args.feature_dim, num_classes=args.num_classes) + if "," in self.device: + model = torch.nn.DataParallel( + model, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{model.device_ids[0]}' + model.to(self.args.device) + else: + model.to(self.args.device) + classifier = classifier.to(args.device) + criterion = criterion.to(args.device) + return model, classifier, criterion + + def drop_linear(self,model): # drop the last nn.Linear layer, which will not be used in the following training + model_name = self.args.model + if 'preactresnet' in model_name or model_name == 'senet18': + feature_dim = model.linear.in_features + model.linear = nn.Identity() + elif model_name.startswith("resnet"): + feature_dim = model.fc.in_features + model.fc = nn.Identity() + elif 'vgg' in model_name or 'convnext' in model_name: + feature_dim = list(model.classifier.children())[-1].in_features + model.classifier = nn.Sequential(*list(model.classifier.children())[:-1]) + elif 'vit' in model_name: + feature_dim = model[1].heads.head.in_features + model[1].heads.head = nn.Identity() + else: + raise NotImplementedError('Not support the model: {}'.format(model_name)) + model.register_feature_dim = feature_dim + return model + + def add_linear(self,old_model, classifier): ## replace the parameters from old model to new model + args = self.args + new_model = generate_cls_model(args.model,args.num_classes) + new_dict = new_model.state_dict() + old_dict = old_model.encoder.state_dict() + new_dict.update(old_dict) + new_model.load_state_dict(new_dict) + model_name = args.model + fc = classifier.fc + if 'preactresnet' in model_name or model_name == 'senet18': + new_model.linear = fc + elif model_name.startswith("resnet"): + new_model.fc = fc + elif 'vgg' in model_name or 'convnext' in model_name: + new_model.classifier = nn.Sequential(*list(new_model.classifier.children())[:-1]+[fc]) + elif 'vit' in model_name: + new_model[1].heads.head = fc + else: + raise NotImplementedError('Not support the model: {}'.format(model_name)) + return new_model + + + def get_sd_train_loader(self): + args = self.args + transform1, transform2, transform3 = get_transform_st(args, train=True) + dataset_train = self.result['bd_train'] + dataset_train.wrap_img_transform = TransformThree(transform1, transform2, transform3) + poisoned_data_loader_train = torch.utils.data.DataLoader(dataset_train, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=True) + return poisoned_data_loader_train + + def testloader_wrapper(self,): + args = self.args + test_tran = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = False) + + data_bd_testset = self.result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + bd_test_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=False,pin_memory=True) + + data_clean_testset = self.result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + clean_test_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=False,pin_memory=True) + + return clean_test_loader, bd_test_loader + + def train_attack_noTrans(self, bd_trainloader, clean_test_loader, bd_test_loader, model = None, optimizer=None, scheduler=None,finetune=False): + ## update args + step = 'finetune_notrans' if finetune else 'train_notrans' + args = self.set_new_args(self.args,step = step) + agg = Metric_Aggregator() + if not finetune: + # Load models + logging.info('----------- Network Initialization --------------') + model = generate_cls_model(args.model,args.num_classes) + if "," in self.device: + model = torch.nn.DataParallel( + model, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{model.device_ids[0]}' + model.to(self.args.device) + else: + model.to(self.args.device) + logging.info('finished model init...') + # initialize optimizer + # optimizer = set_optimizer(args,model) + # scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100) + optimizer, scheduler = argparser_opt_scheduler(model, self.args) + # define loss functions + criterion = torch.nn.CrossEntropyLoss().to(args.device) + + logging.info('----------- Training from scratch --------------') + for epoch in tqdm(range(0, args.epochs)): + tr_loss, tr_acc, _,_ = train_epoch(args, bd_trainloader, model, optimizer, scheduler, + criterion, epoch) + clean_test_loss, clean_test_acc = test_epoch(args, clean_test_loader, model, criterion, epoch) + + bd_test_loss, bd_test_acc = test_epoch(args, bd_test_loader, model, criterion, epoch) + bd_test_loader.dataset.wrapped_dataset.getitem_all_switch = True + _, bd_test_racc = test_epoch(args, bd_test_loader, model, criterion, epoch) + bd_test_loader.dataset.wrapped_dataset.getitem_all_switch = False + agg( + { + "train_epoch_loss_avg_over_batch": tr_loss, + "train_acc": tr_acc, + "clean_test_loss_avg_over_batch": clean_test_loss, + "bd_test_loss_avg_over_batch" : bd_test_loss, + "test_acc" : clean_test_acc, + "test_asr" : bd_test_acc, + "test_ra" : bd_test_racc, + } + ) + agg.to_dataframe().to_csv(f"{args.log}train_notrans_df.csv") + else: + # initialize optimizer + # optimizer = set_optimizer(args,model) + # scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100) + logging.info('----------- Finetune the model with L_intra--------------') + for epoch in tqdm(range(0, args.epochs)): + tr_loss = finetune_epoch(args, bd_trainloader, model, optimizer, scheduler, + epoch) + + agg( + { "epoch": epoch, + "train_epoch_loss_avg_over_batch": tr_loss, + } + ) + agg.to_dataframe().to_csv(f"{args.log}finetune_notrans_df.csv") + if args.save_all_process: + save_file = os.path.join(args.save_path, f'{step}.pt') + logging.info(f'save path is {save_file}') + save_model(model, optimizer, args, args.epochs, save_file) + return model, optimizer, scheduler + + def train_extractor(self,): + ## update args + args = self.set_new_args(self.args,step="sscl") + train_loader = get_st_train_loader(args,self.result['bd_train'],module='sscl') + encoder = generate_cls_model(args.model,args.num_classes) + encoder = self.drop_linear(encoder) + args.feature_dim = encoder.register_feature_dim + model = SupConResNet(encoder,dim_in=args.feature_dim) + criterion = SupConLoss_Consistency(temperature=args.temp, device=args.device) + model = model.to(args.device) + criterion = criterion.to(args.device) + optimizer = set_optimizer(args,model,lr=args.learning_rate) + agg = Metric_Aggregator() + + for epoch in range(1, args.epochs + 1): + adjust_learning_rate(args, optimizer, epoch) + loss = _train_extractor(train_loader, model, criterion, optimizer, epoch, args) + agg( + { "epoch": epoch, + "train_epoch_loss_avg_over_batch": loss, + } + ) + agg.to_dataframe().to_csv(f"{args.log}train_extractor_df.csv") + del loss + torch.cuda.empty_cache() + if args.save_all_process: + # save the last model + save_file = os.path.join(args.save_path, 'sscl-last.pt') + save_model(model, optimizer, args, args.epochs, save_file) + return model + + def train_classifier(self,model): + ## update args + args = self.set_new_args(self.args,step="mixed_ce") + train_loader = get_st_train_loader(args,self.result['bd_train'], module="mixed_ce") + clean_test_loader, bd_test_loader = self.testloader_wrapper() + model, classifier, criterion = self.set_model(args,model) + optimizer = set_optimizer(args, classifier,lr=args.learning_rate) + + train_loss_list = [] + train_mix_acc_list = [] + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + agg = Metric_Aggregator() + + for epoch in range(1, args.epochs+1): + adjust_learning_rate(args, optimizer, epoch) + train_epoch_loss_avg_over_batch, \ + train_mix_acc = _train_classifier(train_loader, model, classifier, criterion, optimizer, epoch, args) + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.eval_step( + model, + classifier, + clean_test_loader, + bd_test_loader, + args, + ) + train_loss_list.append(train_epoch_loss_avg_over_batch) + train_mix_acc_list.append(train_mix_acc) + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + agg( + { + "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch, + "train_acc": train_mix_acc, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch" : bd_test_loss_avg_over_batch, + "test_acc" : test_acc, + "test_asr" : test_asr, + "test_ra" : test_ra, + } + ) + agg.to_dataframe().to_csv(f"{args.save_path}d-st_df.csv") + + agg.summary().to_csv(f"{args.save_path}d-st_df_summary.csv") + if args.save_all_process: + save_file = os.path.join(args.save_path, 'mce-last.pt') + save_model(classifier, optimizer, args, args.epochs, save_file) + return model,classifier + + def eval_step(self, model, classifier, clean_test_loader, bd_test_loader, args): + clean_metrics, clean_epoch_predict_list, clean_epoch_label_list = given_dataloader_test( + model, classifier, + clean_test_loader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + clean_test_loss_avg_over_batch = clean_metrics['test_loss_avg_over_batch'] + test_acc = clean_metrics['test_acc'] + bd_metrics, bd_epoch_predict_list, bd_epoch_label_list = given_dataloader_test( + model, classifier, + bd_test_loader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + bd_test_loss_avg_over_batch = bd_metrics['test_loss_avg_over_batch'] + test_asr = bd_metrics['test_acc'] + + bd_test_loader.dataset.wrapped_dataset.getitem_all_switch = True # change to return the original label instead + ra_metrics, ra_epoch_predict_list, ra_epoch_label_list = given_dataloader_test( + model, classifier, + bd_test_loader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + ra_test_loss_avg_over_batch = ra_metrics['test_loss_avg_over_batch'] + test_ra = ra_metrics['test_acc'] + bd_test_loader.dataset.wrapped_dataset.getitem_all_switch = False # switch back + + return clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra + + def continue_learn(self,args): + step_list = ['train_notrans', 'finetune_notrans', 'calculate', 'separate', 'sscl', 'mixed_ce'] + if args.continue_step == 'mixed_ce': + encoder = generate_cls_model(args.model,args.num_classes) + args.feature_dim = list(encoder.named_modules())[-1][1].in_features + if hasattr(encoder, "linear"): + encoder.linear = nn.Identity() + elif hasattr(encoder, "fc"): + encoder.fc = nn.Identity() + model = SupConResNet(encoder,dim_in=args.feature_dim) + + ck_path = os.path.join(args.save_path, 'sscl-last.pt') + result = torch.load(ck_path) + model.load_state_dict(result['model']) + model_new = model.to(args.device) + return model_new + + def mitigation(self): + args = self.args + self.set_devices() + fix_random(self.args.random_seed) + result = self.result + bd_trainloader = self.get_sd_train_loader() + clean_test_loader, bd_test_loader = self.testloader_wrapper() + ##a. train a backdoored model from scratch using poisoned dataset without any data augmentations + model, optimizer, scheduler = self.train_attack_noTrans(bd_trainloader, clean_test_loader, bd_test_loader, finetune=False) + ###b. fine-tune the backdoored model with intra-class loss L_intra + model = self.drop_linear(model) + model, optimizer, scheduler = self.train_attack_noTrans(bd_trainloader, clean_test_loader, bd_test_loader, model=model, optimizer=optimizer, scheduler=scheduler,finetune=True) + ###c. calculate values of the FCT metric for all training samples. + calculate_consistency(args, bd_trainloader, model) + ###d. calculate thresholds for choosing clean and poisoned samples. + args.gamma_low, args.gamma_high = calculate_gamma(args,) + ###e. separate training samples into clean samples D_c, poisoned samples D_p, and uncertain samples D_u. + separate_samples(args, bd_trainloader, model) + ##f. train the feature extractor (from scratch) via semi-supervised contrastive learning. + model_new = self.train_extractor() + ###g. train the classifier via minimizing a mixed cross-entropy loss. + model_new, classifier = self.train_classifier(model_new) + # return the standard model structure from two subnetworks: SupConResNet+classifier + model_new = self.add_linear(old_model = model_new, classifier = classifier) + result = {} + result['model'] = model_new + + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model_new.cpu().state_dict(), + save_path=args.save_path, + ) + return result + + def defense(self,result_file): + self.set_result(result_file) + self.set_logger() + result = self.mitigation() + return result + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=sys.argv[0]) + d_st.add_arguments(parser) + args = parser.parse_args() + d_st_method = d_st(args) + if "result_file" not in args.__dict__ or args.result_file is None: + args.result_file = 'defense_test_badnet' + result = d_st_method.defense(args.result_file) \ No newline at end of file diff --git a/defense/dbd.py b/defense/dbd.py new file mode 100644 index 0000000..449111c --- /dev/null +++ b/defense/dbd.py @@ -0,0 +1,1213 @@ +''' +This file is modified based on the following source: +link : https://github.com/SCLBD/DBD +The defense method is called dbd. +The license is bellow the code + +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. save process + 5. new standard: robust accuracy + 6. add some new backdone such as mobilenet efficientnet and densenet, reconstruct the backbone of vgg and preactresnet + 7. Different data augmentation (transform) methods are used + 8. rewrite the dateset +basic sturcture for defense method: + 1. basic setting: args + 2. attack result(model, train data, test data) + 3. dbd defense: + a. self-supervised learning generates feature extractor + b. learning model using extracted features + c. the samples with poor confidence were excluded, and semi-supervised learning was used to continue the learning model + 4. test the result and get ASR, ACC, RA +''' + + +import logging +import time +import argparse +import shutil +import sys +import os + + +sys.path.append('../') +sys.path.append(os.getcwd()) +from utils.defense_utils.dbd.data.prefetch import PrefetchLoader + +import numpy as np +import torch +import yaml +from utils.trainer_cls import Metric_Aggregator +from pprint import pformat +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform, get_transform_prefetch + +from utils.defense_utils.dbd.data.utils import ( + get_loader, + get_semi_idx, +) +from utils.defense_utils.dbd.data.dataset import PoisonLabelDataset, SelfPoisonDataset, MixMatchDataset +from utils.aggregate_block.fix_random import fix_random +from utils.save_load_attack import load_attack_result +# from utils_db.box import get_information +from utils.defense_utils.dbd.model.model import SelfModel, LinearModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.bd_dataset_v2 import xy_iter, slice_iter +from utils.defense_utils.dbd.utils_db.setup import ( + load_config, +) +from utils.defense_utils.dbd.utils_db.trainer.log import result2csv +from utils.defense_utils.dbd.utils_db.trainer.simclr import simclr_train +from utils.defense_utils.dbd.utils_db.trainer.semi import mixmatch_train +from utils.defense_utils.dbd.utils_db.trainer.simclr import linear_test, poison_linear_record, poison_linear_train +from utils.aggregate_block.dataset_and_transform_generate import get_transform_self + +def get_information(args,result,config_ori): + config = config_ori + aug_transform = get_transform_self(args.dataset, *([args.input_height,args.input_width]) , train = True, prefetch =args.prefetch) + + x = slice_iter(result["bd_train"], axis=0) + y = slice_iter(result["bd_train"], axis=1) + + self_poison_train_data = SelfPoisonDataset(x,y, aug_transform,args) + + self_poison_train_loader_ori = torch.utils.data.DataLoader(self_poison_train_data, batch_size=args.batch_size_self, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + if args.prefetch: + # x,y: PIL.Image.Image -> SelfPoisonDataset: Tensor with trans, no normalization [0,255]-> PrefetchLoader: Tensor with trans [0,1], with normalization + self_poison_train_loader = PrefetchLoader(self_poison_train_loader_ori, self_poison_train_data.mean, self_poison_train_data.std) + else: + # x,y: PIL.Image.Image, [0,255] -> SelfPoisonDataset: Tensor with trans [0,1] with normalization + self_poison_train_loader = self_poison_train_loader_ori + + backbone = get_network_dbd(args) + self_model = SelfModel(backbone) + self_model = self_model.to(args.device) + criterion = get_criterion(config["criterion"]) + criterion = criterion.to(args.device) + optimizer = get_optimizer(self_model, config["optimizer"]) + scheduler = get_scheduler(optimizer, config["lr_scheduler"]) + resumed_epoch = load_state( + self_model, args.resume, args.checkpoint_load, 0, optimizer, scheduler, + ) + box = { + 'self_poison_train_loader': self_poison_train_loader, + 'self_model': self_model, + 'criterion': criterion, + 'optimizer': optimizer, + 'scheduler': scheduler, + 'resumed_epoch': resumed_epoch + } + return box + + + +def get_args(): + # set the basic parameter + parser = argparse.ArgumentParser() + + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument('--checkpoint_load', type=str) + parser.add_argument('--checkpoint_save', type=str) + parser.add_argument('--log', type=str) + parser.add_argument("--data_root", type=str) + + parser.add_argument('--dataset', type=str, help='mnist, cifar10, gtsrb, celeba, tiny') + parser.add_argument("--num_classes", type=int) + parser.add_argument("--input_height", type=int) + parser.add_argument("--input_width", type=int) + parser.add_argument("--input_channel", type=int) + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument("--num_workers_semi", type=float) + parser.add_argument('--lr', type=float) + + parser.add_argument('--attack', type=str) + parser.add_argument('--poison_rate', type=float) + parser.add_argument('--target_type', type=str, help='all2one, all2all, cleanLabel') + parser.add_argument('--target_label', type=int) + parser.add_argument('--trigger_type', type=str, help='squareTrigger, gridTrigger, fourCornerTrigger, randomPixelTrigger, signalTrigger, trojanTrigger') + + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--index', type=str, help='index of clean data') + parser.add_argument('--model', type=str, help='resnet18') + parser.add_argument('--result_file', type=str, help='the location of result') + parser.add_argument('--yaml_path', type=str, default="./config/defense/dbd/config.yaml", help='the path of yaml') + + # set the parameter for the dbd defense + parser.add_argument('--prefetch',type=bool ) + parser.add_argument('--epoch_warmup',type=int ) + parser.add_argument('--batch_size_self',type=int ) + parser.add_argument('--temperature',type=int ) + parser.add_argument('--epsilon',type=int ) + parser.add_argument('--epoch_self',type=int ) + + + arg = parser.parse_args() + + print(arg) + return arg + + +def dbd(args,result): + agg = Metric_Aggregator() + # remove the transforms except ToTensor + # bd_train_trans = result["bd_train"].wrap_img_transform + # bd_test_trans = result["bd_test"].wrap_img_transform + # clean_test_trans = result["clean_test"].wrap_img_transform + + # result["bd_train"].wrap_img_transform = torchvision.transforms.ToTensor() + # result["bd_test"].wrap_img_transform = torchvision.transforms.ToTensor() + # result["clean_test"].wrap_img_transform = torchvision.transforms.ToTensor() + + # Turn off all transforms, so that the dataset return PIL.Image.Image object + result["bd_train"].wrap_img_transform = None + result["bd_test"].wrap_img_transform = None + result["clean_test"].wrap_img_transform = None + + ### set logger + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + if args.log is not None and args.log != '': + fileHandler = logging.FileHandler(os.getcwd() + args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + else: + fileHandler = logging.FileHandler(os.getcwd() + './log' + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + fix_random(args.random_seed) + + logging.info("===Setup running===") + + if args.checkpoint_load == None: + args.resume = 'False' + else : + args.resume = args.checkpoint_load + + if args.dataset == 'cifar10': + config_file = './utils/defense_utils/dbd/config_z/pretrain/' + 'squareTrigger/' + args.dataset + '/example.yaml' + else: + config_file = './utils/defense_utils/dbd/config_z/pretrain/' + 'squareTrigger/imagenet/example.yaml' + config_ori, inner_dir, config_name = load_config(config_file) + try: + gpu = int(os.environ['CUDA_VISIBLE_DEVICES']) + except: + print('CUDA_VISIBLE_DEVICES is not set. Set GPU=1 now.') + gpu = 0 + + logging.info("===Prepare data===") + # args.model = 'resnet' + information = get_information(args,result,config_ori) + + self_poison_train_loader = information['self_poison_train_loader'] + self_model = information['self_model'] + criterion = information['criterion'] + optimizer = information['optimizer'] + scheduler = information['scheduler'] + resumed_epoch = information['resumed_epoch'] + + # a.self-supervised learning generates feature extractor + for epoch in range(args.epoch_self - resumed_epoch): + + self_train_result = simclr_train( + self_model, self_poison_train_loader, criterion, optimizer, logger, False + ) + + if scheduler is not None: + scheduler.step() + logger.info( + "Adjust learning rate to {}".format(optimizer.param_groups[0]["lr"]) + ) + + + result_self = {"self_train": self_train_result} + + saved_dict = { + "epoch": epoch + resumed_epoch + 1, + "result": result_self, + "model_state_dict": self_model.state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + } + if scheduler is not None: + saved_dict["scheduler_state_dict"] = scheduler.state_dict() + + ckpt_path = os.path.join(os.getcwd() + args.checkpoint_save, "self_latest_model.pt") + torch.save(saved_dict, ckpt_path) + logger.info("Save the latest model to {}".format(ckpt_path)) + + if args.dataset == 'cifar10': + config_file_semi = './utils/defense_utils/dbd/config_z/semi/' + 'badnets/' + args.dataset + '/example.yaml' + else: + config_file_semi = './utils/defense_utils/dbd/config_z/semi/' + 'badnets/imagenet/example.yaml' + + + finetune_config, finetune_inner_dir, finetune_config_name = load_config(config_file_semi) + pretrain_config, pretrain_inner_dir, pretrain_config_name = load_config( + config_file + ) + pretrain_ckpt_path = ckpt_path + # merge the pretrain and finetune config + pretrain_config.update(finetune_config) + + pretrain_config['warmup']['criterion']['sce']['num_classes'] = args.num_classes + pretrain_config['warmup']['num_epochs'] = args.epoch_warmup + + args.batch_size = 128 + logging.info("\n===Prepare data===") + + # If prefetch is True, Normalize will not be added to the transform. Normalize will be called by PrefecthLoader. + # If prefetch is False, Normalize will be added to the transform. + train_transform = get_transform_prefetch(args.dataset, *([args.input_height,args.input_width]) , train = True,prefetch=args.prefetch) + + x = slice_iter(result["bd_train"], axis=0) + y = slice_iter(result["bd_train"], axis=1) + + # train transform will not be called in xy_iter since it only be used to pass x,y to PoisonLabelDataset. + # TODO: change xy_iter to a dict to avoid confusion + dataset_ori = xy_iter( + x,y,train_transform + ) + + # train transform will be called in PoisonLabelDataset + dataset = PoisonLabelDataset(dataset_ori, train_transform, np.zeros(len(dataset_ori)), True,args) + poison_train_loader_ori = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + poison_eval_loader_ori = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=False,pin_memory=True) + + if args.prefetch: + # x,y: PIL.Image.Image -> PoisonLabelDataset: Tensor with trans, no normalization [0,255]-> PrefetchLoader: Tensor with trans [0,1], with normalization + poison_train_loader = PrefetchLoader(poison_train_loader_ori, dataset.mean, dataset.std) + poison_eval_loader = PrefetchLoader(poison_eval_loader_ori, dataset.mean, dataset.std) + else: + # x,y: PIL.Image.Image -> PoisonLabelDataset: Tensor with trans [0,1], with normalization + poison_train_loader = poison_train_loader_ori + poison_eval_loader = poison_eval_loader_ori + + test_transform = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = False) + x = slice_iter(result["bd_test"], axis=0) + y = slice_iter(result["bd_test"], axis=1) + + dataset_ori_bd = xy_iter( + x,y,train_transform + ) + # x,y: PIL.Image.Image -> PoisonLabelDataset: Tensor with trans [0,1], with normalization + dataset_te_bd = PoisonLabelDataset(dataset_ori_bd, test_transform, np.zeros(len(dataset_ori_bd)), False,args) + poison_test_loader = torch.utils.data.DataLoader(dataset_te_bd, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=False,pin_memory=True) + + x = slice_iter(result["clean_test"], axis=0) + y = slice_iter(result["clean_test"], axis=1) + + dataset_ori_cl = xy_iter( + x,y,train_transform + ) + # x,y: PIL.Image.Image -> PoisonLabelDataset: Tensor with trans [0,1], with normalization + dataset_te_cl = PoisonLabelDataset(dataset_ori_cl, test_transform, np.zeros(len(dataset_ori_cl)), False,args) + clean_test_loader = torch.utils.data.DataLoader(dataset_te_cl, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=False,pin_memory=True) + + backbone = get_network_dbd(args) + + self_model = SelfModel(backbone) + self_model = self_model.to(args.device) + # # Load backbone from the pretrained model. + loc = os.path.join(os.getcwd() + args.checkpoint_save, "self_latest_model.pt") + load_state( + self_model, pretrain_config["pretrain_checkpoint"], loc, args.device, logger + ) + linear_model = LinearModel(backbone, backbone.feature_dim, args.num_classes) + linear_model.linear.to(args.device) + warmup_criterion = get_criterion(pretrain_config["warmup"]["criterion"]) + logger.info("Create criterion: {} for warmup".format(warmup_criterion)) + warmup_criterion = warmup_criterion.to(args.device) + semi_criterion = get_criterion(pretrain_config["semi"]["criterion"]) + semi_criterion = semi_criterion.to(args.device) + logger.info("Create criterion: {} for semi-training".format(semi_criterion)) + optimizer = get_optimizer(linear_model, pretrain_config["optimizer"]) + logger.info("Create optimizer: {}".format(optimizer)) + scheduler = get_scheduler(optimizer, pretrain_config["lr_scheduler"]) + logger.info("Create learning rete scheduler: {}".format(pretrain_config["lr_scheduler"])) + if args.checkpoint_load == '' or args.checkpoint_load is None: + resume = 'False' + resumed_epoch, best_acc, best_epoch = load_state( + linear_model, + resume, + args.checkpoint_load, + gpu, + logger, + optimizer, + scheduler, + is_best=True, + ) + + # b. learning model using extracted features + num_epochs = args.epoch_warmup + args.epochs + for epoch in range(num_epochs - resumed_epoch): + logger.info("===Epoch: {}/{}===".format(epoch + resumed_epoch + 1, num_epochs)) + if (epoch + resumed_epoch + 1) <= args.epoch_warmup: + logger.info("Poisoned linear warmup...") + poison_train_result = poison_linear_train( + linear_model, poison_train_loader, warmup_criterion, optimizer, logger, + ) + else: + record_list = poison_linear_record( + linear_model, poison_eval_loader, warmup_criterion + ) + logger.info("Mining clean data from poisoned dataset...") + # c. the samples with poor confidence were excluded, and semi-supervised learning was used to continue the learning model + semi_idx = get_semi_idx(record_list, args.epsilon, logger) + xdata = MixMatchDataset(dataset, semi_idx, labeled=True,args=args) + udata = MixMatchDataset(dataset, semi_idx, labeled=False,args=args) + pretrain_config["semi"]["loader"]['num_workers'] = args.num_workers_semi + # If prefetch, prefetchloader is used to load data. Else, dataloader is used. + # PIL->tensor with trans and normalization + xloader = get_loader( + xdata, pretrain_config["semi"]["loader"], shuffle=True, drop_last=True + ) + uloader = get_loader( + udata, pretrain_config["semi"]["loader"], shuffle=True, drop_last=True + ) + logger.info("MixMatch training...") + poison_train_result = mixmatch_train( + args, + linear_model, + xloader, + uloader, + semi_criterion, + optimizer, + epoch, + logger, + **pretrain_config["semi"]["mixmatch"] + ) + logger.info("Test model on clean data...") + clean_test_result = linear_test( + linear_model, clean_test_loader, warmup_criterion, logger + ) + logger.info("Test model on poison data...") + poison_test_result = linear_test( + linear_model, poison_test_loader, warmup_criterion, logger + ) + if scheduler is not None: + scheduler.step() + logger.info( + "Adjust learning rate to {}".format(optimizer.param_groups[0]["lr"]) + ) + result = { + "poison_train": poison_train_result, + "poison_test": poison_test_result, + "clean_test": clean_test_result, + } + result2csv(result, os.getcwd() + args.log) + + is_best = False + if clean_test_result["acc"] > best_acc: + is_best = True + best_acc = clean_test_result["acc"] + best_epoch = epoch + resumed_epoch + 1 + logger.info("Best test accuaracy {} in epoch {}".format(best_acc, best_epoch)) + + saved_dict = { + "epoch": epoch + resumed_epoch + 1, + "result": result, + "model_state_dict": linear_model.state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "best_acc": best_acc, + "best_epoch": best_epoch, + } + if scheduler is not None: + saved_dict["scheduler_state_dict"] = scheduler.state_dict() + + if is_best: + ckpt_path = os.path.join(os.getcwd() + args.checkpoint_save, "best_model.pt") + torch.save(saved_dict, ckpt_path) + logger.info("Save the best model to {}".format(ckpt_path)) + ckpt_path = os.path.join(os.getcwd() + args.checkpoint_save, "semi_latest_model.pt") + torch.save(saved_dict, ckpt_path) + logger.info("Save the latest model to {}".format(ckpt_path)) + + result = {} + result['model'] = linear_model + return result + +if __name__ == '__main__': + + ### 1. basic setting: args + args = get_args() + with open(args.yaml_path, 'r') as stream: + config = yaml.safe_load(stream) + config.update({k:v for k,v in args.__dict__.items() if v is not None}) + args.__dict__ = config + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + # args.result_file = 'badnet_demo' + save_path = '/record/' + args.result_file + if args.checkpoint_save is None: + args.checkpoint_save = save_path + '/record/defense/dbd/' + if not (os.path.exists(os.getcwd() + args.checkpoint_save)): + os.makedirs(os.getcwd() + args.checkpoint_save) + if args.log is None: + args.log = save_path + '/saved/dbd/' + if not (os.path.exists(os.getcwd() + args.log)): + os.makedirs(os.getcwd() + args.log) + args.save_path = save_path + + ### 2. attack result(model, train data, test data) + result = load_attack_result(os.getcwd() + save_path + '/attack_result.pt') + + ### 3. dbd defense: + print("Continue training...") + result_defense = dbd(args,result) + + + ### 4. test the result and get ASR, ACC, RC + # resume transfroms + tran = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = False) + result["bd_test"].wrap_img_transform = tran + result["clean_test"].wrap_img_transform = tran + + result_defense['model'].eval() + result_defense['model'].to(args.device) + data_bd_testset = result['bd_test'] + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + + asr_acc = 0 + for i, (inputs,labels, *other_info) in enumerate(data_bd_loader): # type: ignore + inputs, labels = inputs.to(args.device), labels.to(args.device) + outputs = result_defense['model'](inputs) + pre_label = torch.max(outputs,dim=1)[1] + asr_acc += torch.sum(pre_label == labels) + asr_acc = asr_acc/len(data_bd_testset) + + data_clean_testset = result['clean_test'] + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + + clean_acc = 0 + for i, (inputs,labels, *other_info) in enumerate(data_clean_loader): # type: ignore + inputs, labels = inputs.to(args.device), labels.to(args.device) + outputs = result_defense['model'](inputs) + pre_label = torch.max(outputs,dim=1)[1] + clean_acc += torch.sum(pre_label == labels) + clean_acc = clean_acc/len(data_clean_testset) + + robust_acc = 0 + for i, (inputs,labels, original_index, poison_indicator, original_targets) in enumerate(data_bd_loader): # type: ignore + inputs, labels = inputs.to(args.device), labels.to(args.device) + original_targets = original_targets.to(args.device) + outputs = result_defense['model'](inputs) + pre_label = torch.max(outputs,dim=1)[1] + robust_acc += torch.sum(pre_label == original_targets) + robust_acc = robust_acc/len(data_bd_testset) + + print('ACC: ', clean_acc) + print('ASR: ', asr_acc) + print('RA: ', robust_acc) + + if not (os.path.exists(os.getcwd() + f'{save_path}/dbd/')): + os.makedirs(os.getcwd() + f'{save_path}/dbd/') + torch.save( + { + 'model_name':args.model, + 'model': result_defense['model'].cpu().state_dict(), + 'asr': asr_acc, + 'acc': clean_acc, + 'ra': robust_acc + }, + f'./{save_path}/dbd/defense_result.pt' + ) + # test_acc,test_asr,test_ra + final_result = {'model_name':args.model, 'test_acc':clean_acc.item(), 'test_asr':asr_acc.item(), 'test_ra':robust_acc.item()} + # to csv + import pandas as pd + df = pd.DataFrame(final_result, index=[0]) + df.to_csv(f'./{save_path}/dbd/dbd_df_summary.csv', index=False) + +# GNU GENERAL PUBLIC LICENSE +# Version 3, 29 June 2007 + +# Copyright (C) 2007 Free Software Foundation, Inc. +# Everyone is permitted to copy and distribute verbatim copies +# of this license document, but changing it is not allowed. + +# Preamble + +# The GNU General Public License is a free, copyleft license for +# software and other kinds of works. + +# The licenses for most software and other practical works are designed +# to take away your freedom to share and change the works. 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But first, please read +# . diff --git a/defense/ep.py b/defense/ep.py new file mode 100644 index 0000000..e1f92e6 --- /dev/null +++ b/defense/ep.py @@ -0,0 +1,468 @@ +''' +This file is modified based on the following source: +link : https://github.com/RJ-T/NIPS2022_EP_BNP. +The defense method is called ep. + +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. save process + 5. new standard: robust accuracy + 6. reconstruct the layer norm for convnext and transformer + 7. draw the corresponding images of asr and acc according to different proportions +basic sturcture for defense method: + 1. basic setting: args + 2. attack result(model, train data, test data) + 3. ep defense: + a. calculate the entropy of each norm layer + b. prune the model depend on the mask + 4. test the result and get ASR, ACC, RC +''' + +import argparse +import copy +import os,sys +import numpy as np +import torch +import torch.nn as nn + + +sys.path.append('../') +sys.path.append(os.getcwd()) + +from pprint import pformat +import yaml +import logging +import time +from defense.base import defense +import utils.defense_utils.dde.dde_model as dde_model +from utils.aggregate_block.train_settings_generate import argparser_criterion +from utils.trainer_cls import Metric_Aggregator, PureCleanModelTrainer, general_plot_for_epoch +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.log_assist import get_git_info +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform +from utils.save_load_attack import load_attack_result, save_defense_result + +def batch_entropy(x, step_size=0.1): + n_bars = int((x.max()-x.min())/step_size) + entropy = 0 + for n in range(n_bars): + num = ((x > x.min() + n*step_size) * (x < x.min() + (n+1)*step_size)).sum(-1) + p = num / x.shape[-1] + entropy += - p * p.log().nan_to_num(0) + return entropy + + +# This version of ep uses only uses args.batch-size samples in the mixed training dataset for pruning. +def EP_defense(net, u, mixture_data_loader, args): + net.eval() + mixture_data = iter(mixture_data_loader).__next__()[0].to(args.device) + params = net.state_dict() + for m in net.modules(): + if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.LayerNorm): + m.collect_feats = True + + with torch.no_grad(): + net(mixture_data) + for name, m in net.named_modules(): + if isinstance(m, nn.BatchNorm2d): + feats = m.batch_feats + feats = (feats - feats.mean(-1).unsqueeze(-1)) / feats.std(-1).unsqueeze(-1) + + entropy = batch_entropy(feats) + index = (entropy<(entropy.mean() - u*entropy.std())) + + params[name+'.weight'][index] = 0 + params[name+'.bias'][index] = 0 + # We use layer norm to subsitute batch norm in convnext_model and vit_model + elif isinstance(m, nn.LayerNorm): + feats = m.batch_feats + feats = (feats - feats.mean(-1).unsqueeze(-1)) / feats.std(-1).unsqueeze(-1) + # variance is zero + feats = torch.nan_to_num(feats, nan=0.0, posinf=0.0, neginf=-0.0) + entropy = batch_entropy(feats) + index = (entropy<(entropy.mean() - u*entropy.std())) + + params[name+'.weight'][index] = 0 + params[name+'.bias'][index] = 0 + + net.load_state_dict(params) + + +def get_dde_network( + model_name: str, + num_classes: int = 10, + **kwargs, +): + if model_name == 'preactresnet18': + net = dde_model.preact_dde.PreActResNet18(num_classes = num_classes, **kwargs) + elif model_name == 'vgg19_bn': + net = dde_model.vgg_dde.vgg19_bn(num_classes = num_classes, **kwargs) + elif model_name == 'densenet161': + net = dde_model.den_dde.densenet161(num_classes= num_classes, **kwargs) + elif model_name == 'mobilenet_v3_large': + net = dde_model.mobilenet_dde.mobilenet_v3_large(num_classes= num_classes, **kwargs) + elif model_name == 'efficientnet_b3': + net = dde_model.eff_dde.efficientnet_b3(num_classes= num_classes, **kwargs) + elif model_name == 'convnext_tiny': + try : + net = dde_model.conv_dde.convnext_tiny(num_classes= num_classes, + ) + except: + net = dde_model.conv_new_dde.convnext_tiny(num_classes= num_classes, + ) + elif model_name == 'vit_b_16': + try : + from torchvision.transforms import Resize + net = dde_model.vit_dde.vit_b_16( + pretrained = True, + ) + net.heads.head = torch.nn.Linear(net.heads.head.in_features, out_features = num_classes, bias=True) + net = torch.nn.Sequential( + Resize((224, 224)), + net, + ) + except : + from torchvision.transforms import Resize + net = dde_model.vit_new_dde.vit_b_16( + pretrained = True, + ) + net.heads.head = torch.nn.Linear(net.heads.head.in_features, out_features = num_classes, bias=True) + net = torch.nn.Sequential( + Resize((224, 224)), + net, + ) + else: + raise SystemError('NO valid model match in function generate_cls_model!') + + return net + + +class ep(defense): + + def __init__(self,args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + self.args = args + + if 'result_file' in args.__dict__ : + if args.result_file is not None: + self.set_result(args.result_file) + + def add_arguments(parser): + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory") + parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?") + parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch') + parser.add_argument('--amp', type=lambda x: str(x) in ['True','true','1']) + + parser.add_argument('--checkpoint_load', type=str, help='the location of load model') + parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved') + parser.add_argument('--log', type=str, help='the location of log') + parser.add_argument("--dataset_path", type=str, help='the location of data') + parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny') + parser.add_argument('--result_file', type=str, help='the location of result') + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--model', type=str, help='resnet18') + + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--yaml_path', type=str, default="./config/defense/ep/config.yaml", help='the path of yaml') + + #set the parameter for the ep defense + parser.add_argument('--u', type=float, help='u in the ep defense') + parser.add_argument('--u_min', type=float, help='the default minimum value of u') + parser.add_argument('--u_max', type=float, help='the default maximum value of u') + parser.add_argument('--u_num', type=float, help='the default number of u') + + + def set_result(self, result_file): + attack_file = 'record/' + result_file + save_path = 'record/' + result_file + '/defense/ep/' + if not (os.path.exists(save_path)): + os.makedirs(save_path) + # assert(os.path.exists(save_path)) + self.args.save_path = save_path + if self.args.checkpoint_save is None: + self.args.checkpoint_save = save_path + 'checkpoint/' + if not (os.path.exists(self.args.checkpoint_save)): + os.makedirs(self.args.checkpoint_save) + if self.args.log is None: + self.args.log = save_path + 'log/' + if not (os.path.exists(self.args.log)): + os.makedirs(self.args.log) + self.result = load_attack_result(attack_file + '/attack_result.pt') + def set_trainer(self, model): + self.trainer = PureCleanModelTrainer( + model, + ) + + def set_logger(self): + args = self.args + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + try: + logging.info(pformat(get_git_info())) + except: + logging.info('Getting git info fails.') + + def set_devices(self): + # self.device = torch.device( + # ( + # f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device + # # since DataParallel only allow .to("cuda") + # ) if torch.cuda.is_available() else "cpu" + # ) + self.device = self.args.device + def mitigation(self): + self.set_devices() + fix_random(self.args.random_seed) + + # Prepare model, optimizer, scheduler + + net = get_dde_network(self.args.model,self.args.num_classes,norm_layer=dde_model.BatchNorm2d_DDE) + # net = generate_cls_model(self.args.model,self.args.num_classes) + net.load_state_dict(self.result['model']) + if "," in self.device: + net = torch.nn.DataParallel( + net, + device_ids=[int(i) for i in self.args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{net.device_ids[0]}' + net.to(self.args.device) + else: + net.to(self.args.device) + # criterion = nn.CrossEntropyLoss() + + criterion = argparser_criterion(args) + + train_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = True) + train_dataset = self.result['bd_train'].wrapped_dataset + data_set_without_tran = train_dataset + data_set_o = self.result['bd_train'] + data_set_o.wrapped_dataset = data_set_without_tran + data_set_o.wrap_img_transform = train_tran + data_loader = torch.utils.data.DataLoader(data_set_o, batch_size=self.args.batch_size, num_workers=self.args.num_workers, shuffle=True, pin_memory=args.pin_memory) + trainloader = data_loader + + + test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False) + data_bd_testset = self.result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False,pin_memory=args.pin_memory) + + data_clean_testset = self.result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False,pin_memory=args.pin_memory) + + + default_u = np.linspace(self.args.u_min, self.args.u_max, self.args.u_num) + + agg_all = Metric_Aggregator() + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + for u in default_u: + model_copy = copy.deepcopy(net) + model_copy.eval() + EP_defense(model_copy, u, trainloader, args) + # model.eval() + model_copy.eval() + test_dataloader_dict = {} + test_dataloader_dict["clean_test_dataloader"] = data_clean_loader + test_dataloader_dict["bd_test_dataloader"] = data_bd_loader + + self.set_trainer(model_copy) + self.trainer.set_with_dataloader( + ### the train_dataload has nothing to do with the backdoor defense + train_dataloader = data_bd_loader, + test_dataloader_dict = test_dataloader_dict, + + criterion = criterion, + optimizer = None, + scheduler = None, + device = self.args.device, + amp = self.args.amp, + + frequency_save = self.args.frequency_save, + save_folder_path = self.args.save_path, + save_prefix = 'ep', + + prefetch = self.args.prefetch, + prefetch_transform_attr_name = "ori_image_transform_in_loading", + non_blocking = self.args.non_blocking, + + + ) + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.trainer.test_current_model( + test_dataloader_dict, self.args.device, + ) + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + agg_all({ + "u": u, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + + general_plot_for_epoch( + { + "Test C-Acc": test_acc_list, + "Test ASR": test_asr_list, + "Test RA": test_ra_list, + }, + save_path=f"{args.save_path}u_step_acc_like_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "Test Clean Loss": clean_test_loss_list, + "Test Backdoor Loss": bd_test_loss_list, + }, + save_path=f"{args.save_path}u_step_loss_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "u": default_u, + }, + save_path=f"{args.save_path}u_step_plots.png", + ylabel="percentage", + ) + + agg_all.to_dataframe().to_csv(f"{args.save_path}u_step_df.csv") + + + agg = Metric_Aggregator() + EP_defense(net, self.args.u, trainloader, args) + + test_dataloader_dict = {} + test_dataloader_dict["clean_test_dataloader"] = data_clean_loader + test_dataloader_dict["bd_test_dataloader"] = data_bd_loader + + model = generate_cls_model(self.args.model,self.args.num_classes) + model.load_state_dict(net.state_dict()) + self.set_trainer(model) + + self.trainer.set_with_dataloader( + train_dataloader = trainloader, + test_dataloader_dict = test_dataloader_dict, + + criterion = criterion, + optimizer = None, + scheduler = None, + device = self.args.device, + amp = self.args.amp, + + frequency_save = self.args.frequency_save, + save_folder_path = self.args.save_path, + save_prefix = 'ep', + + prefetch = self.args.prefetch, + prefetch_transform_attr_name = "ori_image_transform_in_loading", + non_blocking = self.args.non_blocking, + + # continue_training_path = continue_training_path, + # only_load_model = only_load_model, + ) + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.trainer.test_current_model( + test_dataloader_dict, self.args.device, + ) + agg({ + "u": self.args.u, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + agg.to_dataframe().to_csv(f"{args.save_path}ep_df_summary.csv") + + result = {} + result['model'] = model + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model.cpu().state_dict(), + save_path=args.save_path, + ) + return result + + def defense(self,result_file): + self.set_result(result_file) + self.set_logger() + result = self.mitigation() + return result + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=sys.argv[0]) + ep.add_arguments(parser) + args = parser.parse_args() + method = ep(args) + if "result_file" not in args.__dict__: + args.result_file = 'defense_test_badnet' + elif args.result_file is None: + args.result_file = 'defense_test_badnet' + result = method.defense(args.result_file) \ No newline at end of file diff --git a/defense/fp.py b/defense/fp.py new file mode 100644 index 0000000..fd3f4ac --- /dev/null +++ b/defense/fp.py @@ -0,0 +1,391 @@ +import argparse +import os,sys +import numpy as np +import torch +import torch.nn as nn +import math +import shutil +sys.path.append('../') +sys.path.append(os.getcwd()) + +from pprint import pformat +import yaml +import logging +import time +from copy import deepcopy +import torch.nn.utils.prune as prune + +from defense.base import defense +from utils.aggregate_block.train_settings_generate import argparser_opt_scheduler +from utils.trainer_cls import ModelTrainerCLS_v2, BackdoorModelTrainer, Metric_Aggregator, given_dataloader_test, general_plot_for_epoch +from utils.choose_index import choose_index +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.log_assist import get_git_info +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform +from utils.save_load_attack import load_attack_result, save_defense_result +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform + +class FinePrune(defense): + + def __init__(self): + super(FinePrune).__init__() + pass + + def set_args(self, parser): + parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory") + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--amp', type=lambda x: str(x) in ['True', 'true', '1']) + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-nb", "--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], + help=".to(), set the non_blocking = ?") + parser.add_argument("--dataset_path", type=str) + + parser.add_argument('--dataset', type=str, help='mnist, cifar10, gtsrb, celeba, tiny') + parser.add_argument("--num_classes", type=int) + parser.add_argument("--input_height", type=int) + parser.add_argument("--input_width", type=int) + parser.add_argument("--input_channel", type=int) + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + + parser.add_argument('--attack', type=str) + parser.add_argument('--poison_rate', type=float) + parser.add_argument('--target_type', type=str, help='all2one, all2all, cleanLabel') + parser.add_argument('--target_label', type=int) + parser.add_argument('--trigger_type', type=str, + help='squareTrigger, gridTrigger, fourCornerTrigger, randomPixelTrigger, signalTrigger, trojanTrigger') + + parser.add_argument('--model', type=str, help='resnet18') + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--index', type=str, help='index of clean data') + parser.add_argument('--result_file', type=str, help='the location of result') + parser.add_argument('--yaml_path', type=str, default="./config/defense/fp/config.yaml", help='the path of yaml') + + # set the parameter for the fp defense + parser.add_argument('--ratio', type=float, help='the ratio of clean data loader') + parser.add_argument('--acc_ratio', type=float, help='the tolerance ration of the clean accuracy') + parser.add_argument("--once_prune_ratio", type = float, help ="how many percent once prune. in 0 to 1") + return parser + + def add_yaml_to_args(self, args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + defaults.update({k: v for k, v in args.__dict__.items() if v is not None}) + args.__dict__ = defaults + + def process_args(self, args): + args.terminal_info = sys.argv + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + defense_save_path = "record" + os.path.sep + args.result_file + os.path.sep + "defense" + os.path.sep + "fp" + # if os.path.exists(defense_save_path): + # shutil.rmtree(defense_save_path) + os.makedirs(defense_save_path, exist_ok = True) + # save_path = '/record/' + args.result_file + # if args.checkpoint_save is None: + # args.checkpoint_save = save_path + '/record/defence/fp/' + # if not (os.path.exists(os.getcwd() + args.checkpoint_save)): + # os.makedirs(os.getcwd() + args.checkpoint_save) + # if args.log is None: + # args.log = save_path + '/saved/fp/' + # if not (os.path.exists(os.getcwd() + args.log)): + # os.makedirs(os.getcwd() + args.log) + # args.save_path = save_path + args.defense_save_path = defense_save_path + return args + + def prepare(self, args): + + ### set the logger + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + # file Handler + fileHandler = logging.FileHandler( + args.defense_save_path + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + fileHandler.setLevel(logging.DEBUG) + logger.addHandler(fileHandler) + # consoleHandler + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + consoleHandler.setLevel(logging.INFO) + logger.addHandler(consoleHandler) + # overall logger level should <= min(handler) otherwise no log will be recorded. + logger.setLevel(0) + # disable other debug, since too many debug + logging.getLogger('PIL').setLevel(logging.WARNING) + logging.getLogger('matplotlib.font_manager').setLevel(logging.WARNING) + + logging.info(pformat(args.__dict__)) + + logging.debug("Only INFO or above level log will show in cmd. DEBUG level log only will show in log file.") + + # record the git infomation for debug (if available.) + try: + logging.debug(pformat(get_git_info())) + except: + logging.debug('Getting git info fails.') + + fix_random(args.random_seed) + self.args = args + + ''' + load_dict = { + 'model_name': load_file['model_name'], + 'model': load_file['model'], + 'clean_train': clean_train_dataset_with_transform, + 'clean_test' : clean_test_dataset_with_transform, + 'bd_train': bd_train_dataset_with_transform, + 'bd_test': bd_test_dataset_with_transform, + } + ''' + self.attack_result = load_attack_result("record" + os.path.sep + self.args.result_file + os.path.sep +'attack_result.pt') + + netC = generate_cls_model(args.model, args.num_classes) + netC.load_state_dict(self.attack_result['model']) + netC.to(args.device) + netC.eval() + netC.requires_grad_(False) + + self.netC = netC + + def defense(self): + + netC = self.netC + args = self.args + attack_result = self.attack_result + # clean_train with subset + clean_train_dataset_with_transform = attack_result['clean_train'] + clean_train_dataset_without_transform = clean_train_dataset_with_transform.wrapped_dataset + clean_train_dataset_without_transform = prepro_cls_DatasetBD_v2( + clean_train_dataset_without_transform + ) + ran_idx = choose_index(args, len(clean_train_dataset_without_transform)) + logging.info(f"get ran_idx for subset clean train dataset, (len={len(ran_idx)}), ran_idx:{ran_idx}") + clean_train_dataset_without_transform.subset( + choose_index(args, len(clean_train_dataset_without_transform)) + ) + clean_train_dataset_with_transform.wrapped_dataset = clean_train_dataset_without_transform + log_index = args.defense_save_path + os.path.sep + 'index.txt' + np.savetxt(log_index, ran_idx, fmt='%d') + trainloader = torch.utils.data.DataLoader(clean_train_dataset_with_transform, batch_size=args.batch_size, num_workers=args.num_workers, + shuffle=True) + + clean_test_dataset_with_transform = attack_result['clean_test'] + data_clean_testset = clean_test_dataset_with_transform + clean_test_dataloader = torch.utils.data.DataLoader(data_clean_testset, batch_size=args.batch_size, + num_workers=args.num_workers, drop_last=False, shuffle=True, + pin_memory=args.pin_memory) + + # bd_train_dataset_with_transform = attack_result['bd_train'] + + bd_test_dataset_with_transform = attack_result['bd_test'] + data_bd_testset = bd_test_dataset_with_transform + bd_test_dataset_without_transform = bd_test_dataset_with_transform.wrapped_dataset + bd_test_dataloader = torch.utils.data.DataLoader(data_bd_testset, batch_size=args.batch_size, + num_workers=args.num_workers, drop_last=False, shuffle=True, + pin_memory=args.pin_memory) + + + criterion = nn.CrossEntropyLoss() + + if args.model == "vit_b_16": + vit_module = list(netC.children())[1] + last_child = vit_module.heads.head + with torch.no_grad(): + def forward_hook(module, input, output): + global result_mid + result_mid = input[0] + # logging.info(f"hook on {last_child}") + hook = last_child.register_forward_hook(forward_hook) + elif args.model == "convnext_tiny": + with torch.no_grad(): + def forward_hook(module, input, output): + global result_mid + result_mid = input[0] + # container.append(input.detach().clone().cpu()) + last_child_name, last_child = list(netC.named_modules())[-1] + logging.info(f"hook on {last_child_name}") + hook = last_child.register_forward_hook(forward_hook) + else: + with torch.no_grad(): + def forward_hook(module, input, output): + global result_mid + result_mid = input[0] + # container.append(input.detach().clone().cpu()) + last_child_name, last_child = list(netC.named_children())[-1] + logging.info(f"hook on {last_child_name}") + hook = last_child.register_forward_hook(forward_hook) + + logging.info("Forwarding all the training dataset:") + with torch.no_grad(): + flag = 0 + for batch_idx, (inputs, *other) in enumerate(trainloader): + inputs = inputs.to(args.device) + _ = netC(inputs) + if flag == 0: + activation = torch.zeros(result_mid.size()[1]).to(args.device) + flag = 1 + activation += torch.sum(result_mid, dim=[0]) / len(clean_train_dataset_without_transform) + hook.remove() + + seq_sort = torch.argsort(activation) + logging.info(f"get seq_sort, (len={len(seq_sort)}), seq_sort:{seq_sort}") + # del container + + # find the first linear child in last_child. + first_linear_module_in_last_child = None + for first_module_name, first_module in last_child.named_modules(): + if isinstance(first_module, nn.Linear): + logging.info(f"Find the first child be nn.Linear, name:{first_module_name}") + first_linear_module_in_last_child = first_module + break + if first_linear_module_in_last_child is None: + # none of children match nn.Linear + raise Exception("None of children in last module is nn.Linear, cannot prune.") + + # init prune_mask, prune_mask is "accumulated"! + prune_mask = torch.ones_like(first_linear_module_in_last_child.weight) + + prune_info_recorder = Metric_Aggregator() + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + # start from 0, so unprune case will also be tested. + # for num_pruned in range(0, len(seq_sort), 500): + for num_pruned in range(0, len(seq_sort), math.ceil(len(seq_sort) * args.once_prune_ratio)): + net_pruned = (netC) + net_pruned.to(args.device) + if num_pruned: + # add_pruned_channnel_index = seq_sort[num_pruned - 1] # each time prune_mask ADD ONE MORE channel being prune. + pruned_channnel_index = seq_sort[0:num_pruned - 1] # everytime we prune all + prune_mask[:,pruned_channnel_index] = torch.zeros_like(prune_mask[:,pruned_channnel_index]) + prune.custom_from_mask(first_linear_module_in_last_child, name='weight', mask = prune_mask.to(args.device)) + + # prune_ratio = 100. * float(torch.sum(first_linear_module_in_last_child.weight_mask == 0)) / float(first_linear_module_in_last_child.weight_mask.nelement()) + # logging.info(f"Pruned {num_pruned}/{len(seq_sort)} ({float(prune_ratio):.2f}%) filters") + + # test + test_acc = given_dataloader_test(net_pruned, clean_test_dataloader, criterion, args.non_blocking, args.device)[0]['test_acc'] + test_asr = given_dataloader_test(net_pruned, bd_test_dataloader, criterion, args.non_blocking, args.device)[0]['test_acc'] + + # use switch in preprocess bd dataset v2 + bd_test_dataset_without_transform.getitem_all_switch = True + test_ra = given_dataloader_test(net_pruned, bd_test_dataloader, criterion, args.non_blocking, args.device)[0]['test_acc'] + bd_test_dataset_without_transform.getitem_all_switch = False + + prune_info_recorder({ + "num_pruned":num_pruned, + "all_filter_num":len(seq_sort), + "test_acc" : test_acc, + "test_asr" : test_asr, + "test_ra" : test_ra, + }) + + test_acc_list.append(float(test_acc)) + test_asr_list.append(float(test_asr)) + test_ra_list.append(float(test_ra)) + + if num_pruned == 0: + test_acc_cl_ori = test_acc + last_net = (net_pruned) + last_index = 0 + if abs(test_acc - test_acc_cl_ori) / test_acc_cl_ori < args.acc_ratio: + last_net = (net_pruned) + last_index = num_pruned + else: + break + + prune_info_recorder.to_dataframe().to_csv(os.path.join(self.args.defense_save_path, "prune_log.csv")) + prune_info_recorder.summary().to_csv(os.path.join(self.args.defense_save_path, "prune_log_summary.csv")) + general_plot_for_epoch( + { + "test_acc":test_acc_list, + "test_asr":test_asr_list, + "test_ra":test_ra_list, + }, + os.path.join(self.args.defense_save_path, "prune_log_plot.jpg"), + ylabel='percentage', + xlabel="num_pruned", + ) + + logging.info(f"End prune. Pruned {num_pruned}/{len(seq_sort)} test_acc:{test_acc:.2f} test_asr:{test_asr:.2f} test_ra:{test_ra:.2f} ") + + + # finetune + last_net.train() + last_net.requires_grad_() + + optimizer, scheduler = argparser_opt_scheduler( + last_net, + self.args, + ) + finetune_trainer = BackdoorModelTrainer( + last_net + ) + + finetune_trainer.train_with_test_each_epoch_on_mix( + trainloader, + clean_test_dataloader, + bd_test_dataloader, + args.epochs, + criterion, + optimizer, + scheduler, + args.amp, + torch.device(args.device), + args.frequency_save, + self.args.defense_save_path, + "finetune", + prefetch=False, + prefetch_transform_attr_name="transform", + non_blocking=args.non_blocking, + ) + + save_defense_result( + model_name = args.model, + num_classes = args.num_classes, + model = last_net.cpu().state_dict(), + save_path = self.args.defense_save_path, + ) + + # mask = deepcopy(first_linear_module_in_last_child.weight_mask) + # prune.remove(first_linear_module_in_last_child, 'weight') + # + # torch.save( + # { + # 'model_name': args.model, + # 'model': last_net.cpu().state_dict(), + # 'seq_sort': seq_sort, + # "num_pruned":num_pruned, + # "mask":mask, + # "last_child_name":last_child_name, + # "first_module_name":first_module_name, + # }, + # self.args.defense_save_path+os.path.sep+"defense_result.pt" + # ) + +if __name__ == '__main__': + fp = FinePrune() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = fp.set_args(parser) + args = parser.parse_args() + fp.add_yaml_to_args(args) + args = fp.process_args(args) + fp.prepare(args) + fp.defense() diff --git a/defense/ft-sam.py b/defense/ft-sam.py new file mode 100644 index 0000000..478d824 --- /dev/null +++ b/defense/ft-sam.py @@ -0,0 +1,472 @@ + +import argparse +import os,sys +import numpy as np +import torch +import torch.nn as nn +from tqdm import tqdm + +sys.path.append(os.getcwd()) + +# TODO:修改yaml文件 + +from pprint import pformat +import yaml +import logging +import time +from defense.base import defense +from utils.defense_utils.sam import SAM, ProportionScheduler +from utils.defense_utils.sam import smooth_crossentropy + +from utils.aggregate_block.train_settings_generate import argparser_criterion, argparser_opt_scheduler +from utils.trainer_cls import Metric_Aggregator +from utils.choose_index import choose_index,choose_by_class +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform +from utils.save_load_attack import load_attack_result, save_defense_result +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2 + + + +class AverageMeter(object): + """Computes and stores the average and current value""" + def __init__(self): + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + +def accuracy(output, target, topk=(1,)): # output: (256,10); target: (256) + """Computes the accuracy over the k top predictions for the specified values of k""" + with torch.no_grad(): + maxk = max(topk) # 5 + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) # pred: (256,5) + pred = pred.t() # (5,256) + correct = pred.eq(target.view(1, -1).expand_as(pred)) # (5,256) + + res = [] + + for k in topk: + # correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) + correct_k = torch.flatten(correct[:k]).float().sum(0, keepdim=True) + res.append(correct_k.mul_(1.0 / batch_size)) + return res + +def given_dataloader_test( + model, + test_dataloader, + criterion, + non_blocking : bool = False, + device = "cpu", + verbose : int = 0 +): + model.to(device, non_blocking=non_blocking) + model.eval() + metrics = { + 'test_correct': 0, + 'test_loss_sum_over_batch': 0, + 'test_total': 0, + } + criterion = criterion.to(device, non_blocking=non_blocking) + + if verbose == 1: + batch_predict_list, batch_label_list = [], [] + + with torch.no_grad(): + for batch_idx, (x, target, *additional_info) in enumerate(test_dataloader): + x = x.to(device, non_blocking=non_blocking) + target = target.to(device, non_blocking=non_blocking) + pred = model(x) + loss = criterion(pred, target.long()) + + _, predicted = torch.max(pred, -1) + correct = predicted.eq(target).sum() + + if verbose == 1: + batch_predict_list.append(predicted.detach().clone().cpu()) + batch_label_list.append(target.detach().clone().cpu()) + + metrics['test_correct'] += correct.item() + metrics['test_loss_sum_over_batch'] += loss.item() + metrics['test_total'] += target.size(0) + + metrics['test_loss_avg_over_batch'] = metrics['test_loss_sum_over_batch']/len(test_dataloader) + metrics['test_acc'] = metrics['test_correct'] / metrics['test_total'] + + if verbose == 0: + return metrics, None, None + elif verbose == 1: + return metrics, torch.cat(batch_predict_list), torch.cat(batch_label_list) + +class dsam(defense): + + def __init__(self,args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + self.args = args + + if 'result_file' in args.__dict__ : + if args.result_file is not None: + self.set_result(args.result_file) + + def add_arguments(parser): + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory") + parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?") + parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch') + parser.add_argument('--amp', type=lambda x: str(x) in ['True','true','1']) + + parser.add_argument('--checkpoint_load', type=str, help='the location of load model') + parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved') + parser.add_argument('--log', type=str, help='the location of log') + parser.add_argument("--dataset_path", type=str, help='the location of data') + parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny') + parser.add_argument('--result_file', type=str, help='the location of result') + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--model', type=str, help='resnet18') + + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + parser.add_argument('--print_freq', default=1, type=int,help=' print_freq') + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--yaml_path', type=str, default="./config/defense/ft-sam/config.yaml", help='the path of yaml') + parser.add_argument('--bd_yaml_path', type=str, default=None, help='the path of yaml') + + #set the parameter for the dsam defense + parser.add_argument('--ratio', type=float, help='the ratio of clean data loader') + parser.add_argument('--index', type=str, help='index of clean data') + + parser.add_argument("--rho", default=2.0, type=float, help="Rho parameter for SAM.") + parser.add_argument("--adaptive", action='store_false', help="True if you want to use the Adaptive SAM.") + parser.add_argument("--label_smoothing", default=0.1, type=float, help="Use 0.0 for no label smoothing.") + parser.add_argument("--rho_max", default=2.0, type=float, help="Rho parameter for SAM.") + parser.add_argument("--rho_min", default=2.0, type=float, help="Rho parameter for SAM.") + parser.add_argument("--alpha", default=0.0, type=float, help="Rho parameter for SAM.") + parser.add_argument("--checkpoint_path", default=None, type=str, help="specify the checkpoint") + + def set_result(self, result_file): + attack_file = 'record/' + result_file + # save_path = 'record/' + result_file + f'/defense/epochs_{args.epochs}_dsam_{args.ratio}_lr_{args.lr}_rho_{args.rho}/' + save_path = 'record/' + result_file + f'/defense/ft-sam/' + self.args.save_path = save_path + if self.args.checkpoint_save is None: + self.args.checkpoint_save = save_path + 'checkpoint/' + if not (os.path.exists(self.args.checkpoint_save)): + os.makedirs(self.args.checkpoint_save) + if self.args.log is None: + self.args.log = save_path + 'log/' + if not (os.path.exists(self.args.log)): + os.makedirs(self.args.log) + self.result = load_attack_result(attack_file + '/attack_result.pt') + + def set_logger(self): + args = self.args + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + def set_devices(self): + self.device = torch.device( + ( + f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device + # since DataParallel only allow .to("cuda") + ) if torch.cuda.is_available() else "cpu" + ) + + def eval_step(self, model, clean_test_loader, bd_test_loader, args): + clean_metrics, clean_epoch_predict_list, clean_epoch_label_list = given_dataloader_test( + model, + clean_test_loader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + clean_test_loss_avg_over_batch = clean_metrics['test_loss_avg_over_batch'] + test_acc = clean_metrics['test_acc'] + bd_metrics, bd_epoch_predict_list, bd_epoch_label_list = given_dataloader_test( + model, + bd_test_loader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + bd_test_loss_avg_over_batch = bd_metrics['test_loss_avg_over_batch'] + test_asr = bd_metrics['test_acc'] + + bd_test_loader.dataset.wrapped_dataset.getitem_all_switch = True # change to return the original label instead + ra_metrics, ra_epoch_predict_list, ra_epoch_label_list = given_dataloader_test( + model, + bd_test_loader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + ra_test_loss_avg_over_batch = ra_metrics['test_loss_avg_over_batch'] + test_ra = ra_metrics['test_acc'] + bd_test_loader.dataset.wrapped_dataset.getitem_all_switch = False # switch back + + return clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra + + def _train_sam(self, args, train_loader, model, optimizer, scheduler,criterion, epoch): + model.train() + losses = AverageMeter() + top1 = AverageMeter() + + for idx, (img, target, *flag) in enumerate(train_loader, start=1): + img = img.to(args.device) + target = target.to(args.device) + bsz = target.shape[0] + def loss_fn(predictions, targets): + return smooth_crossentropy(predictions, targets, smoothing=args.label_smoothing).mean() + optimizer.set_closure(loss_fn, img, target) + predictions, loss = optimizer.step() + with torch.no_grad(): + correct = torch.argmax(predictions.data, 1) == target + correct = correct.sum() + scheduler.step() + optimizer.update_rho_t() + + # update metric + + losses.update(loss.item(), bsz) + top1.update(correct.detach().cpu().numpy()/bsz, bsz) + # acc1, acc5 = accuracy(output, target, topk=(1, 5)) + # top1.update(acc1[0].detach().cpu().numpy(), bsz) + if (idx + 1) % args.print_freq == 0: + logging.info(f'Train: [{epoch}][{idx + 1}/{len(train_loader)}]\t \ + loss {losses.val} ({losses.avg}\t \ + Acc@1 {top1.val} ({top1.avg}') + sys.stdout.flush() + + del loss, img + torch.cuda.empty_cache() + return losses.avg, top1.avg, model + + def train_sam(self, model,train_dataloader, + clean_test_dataloader, + bd_test_dataloader, + total_epoch_num, + criterion, + optimizer, + scheduler, + amp, + device, + frequency_save, + save_folder_path, + save_prefix, + prefetch, + prefetch_transform_attr_name, + non_blocking, + ): + + + criterion = criterion.to(args.device) + + # Training and Testing + train_loss_list = [] + train_mix_acc_list = [] + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + agg = Metric_Aggregator() + + + for epoch in tqdm(range(1, args.epochs+1)): + train_epoch_loss_avg_over_batch, \ + train_mix_acc, \ + model = self._train_sam(args, train_dataloader, model, optimizer, scheduler,criterion, epoch) + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.eval_step( + model, + clean_test_dataloader, + bd_test_dataloader, + args, + ) + train_loss_list.append(train_epoch_loss_avg_over_batch) + train_mix_acc_list.append(train_mix_acc) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + agg( + { + "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch, + "train_acc": train_mix_acc, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch" : bd_test_loss_avg_over_batch, + "test_acc" : test_acc, + "test_asr" : test_asr, + "test_ra" : test_ra, + } + ) + agg.to_dataframe().to_csv(f"{args.log}d-sam_df.csv") + + agg.summary().to_csv(f"{args.log}d-sam_df_summary.csv") + + return model + + + def mitigation(self): + args=self.args + self.set_devices() + fix_random(self.args.random_seed) + + # Prepare model, optimizer, scheduler + model = generate_cls_model(self.args.model,self.args.num_classes) + + + if hasattr(args,"checkpoint_path") and args.checkpoint_path != None: + file_path = 'record/' + args.checkpoint_path + checkpoint_path = load_attack_result(file_path + '/defense_result.pt') + model.load_state_dict(checkpoint_path['model']) + else: + model.load_state_dict(self.result['model']) + + if "," in self.args.device: + self.model = torch.nn.DataParallel( + self.model, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + else: + model.to(self.args.device) + base_optimizer, scheduler = argparser_opt_scheduler(model, self.args) + + rho_scheduler = ProportionScheduler(pytorch_lr_scheduler=scheduler, max_lr=args.lr, min_lr=0.0, + max_value=args.rho_max, min_value=args.rho_min) + optimizer = SAM(params=model.parameters(), base_optimizer=base_optimizer, model=model, sam_alpha=args.alpha, rho_scheduler=rho_scheduler, adaptive=args.adaptive) + + + # criterion = nn.CrossEntropyLoss() + criterion = argparser_criterion(args) + + train_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = True) + clean_dataset = prepro_cls_DatasetBD_v2(self.result['clean_train'].wrapped_dataset) + # data_all_length = len(clean_dataset) + # ran_idx = choose_index(self.args, data_all_length) + ran_idx = choose_by_class(args,clean_dataset) + log_index = self.args.log + 'index.txt' + np.savetxt(log_index, ran_idx, fmt='%d') + clean_dataset.subset(ran_idx) + data_set_without_tran = clean_dataset + data_set_o = self.result['clean_train'] + data_set_o.wrapped_dataset = data_set_without_tran + data_set_o.wrap_img_transform = train_tran + data_loader = torch.utils.data.DataLoader(data_set_o, batch_size=self.args.batch_size, num_workers=self.args.num_workers, shuffle=True, pin_memory=args.pin_memory) + trainloader = data_loader + + test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False) + data_bd_testset = self.result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + data_clean_testset = self.result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + self.train_sam( + model, + trainloader, + data_clean_loader, + data_bd_loader, + args.epochs, + criterion=criterion, + optimizer=optimizer, + scheduler=scheduler, + device=self.device, + frequency_save=args.frequency_save, + save_folder_path=args.save_path, + save_prefix='dsam', + amp=args.amp, + prefetch=args.prefetch, + prefetch_transform_attr_name="ori_image_transform_in_loading", # since we use the preprocess_bd_dataset + non_blocking=args.non_blocking, + ) + + result = {} + result['model'] = model + + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model.cpu().state_dict(), + save_path=args.save_path, + ) + return result + + def defense(self,result_file): + self.set_result(result_file) + self.set_logger() + result = self.mitigation() + return result + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=sys.argv[0]) + dsam.add_arguments(parser) + args = parser.parse_args() + dsam_method = dsam(args) + result = dsam_method.defense(args.result_file) \ No newline at end of file diff --git a/defense/ft.py b/defense/ft.py new file mode 100755 index 0000000..9129bcc --- /dev/null +++ b/defense/ft.py @@ -0,0 +1,267 @@ +''' +This file implements the defense method called finetuning (ft), which is a standard fine-tuning that uses clean data to finetune the model. + +basic sturcture for defense method: + 1. basic setting: args + 2. attack result(model, train data, test data) + 3. ft defense: + a. get some clean data + b. retrain the backdoor model + 4. test the result and get ASR, ACC, RC +''' + +import argparse +import os,sys +import numpy as np +import torch +import torch.nn as nn + +sys.path.append('../') +sys.path.append(os.getcwd()) + +from pprint import pformat +import yaml +import logging +import time +from defense.base import defense + +from utils.aggregate_block.train_settings_generate import argparser_criterion, argparser_opt_scheduler +from utils.trainer_cls import PureCleanModelTrainer +from utils.choose_index import choose_index +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.log_assist import get_git_info +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform +from utils.save_load_attack import load_attack_result, save_defense_result +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2 + +class ft(defense): + + def __init__(self,args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + self.args = args + + if 'result_file' in args.__dict__ : + if args.result_file is not None: + self.set_result(args.result_file) + + def add_arguments(parser): + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory") + parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?") + parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch') + parser.add_argument('--amp', type=lambda x: str(x) in ['True','true','1']) + + parser.add_argument('--checkpoint_load', type=str, help='the location of load model') + parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved') + parser.add_argument('--log', type=str, help='the location of log') + parser.add_argument("--dataset_path", type=str, help='the location of data') + parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny') + parser.add_argument('--result_file', type=str, help='the location of result') + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--model', type=str, help='resnet18') + + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--yaml_path', type=str, default="./config/defense/ft/config.yaml", help='the path of yaml') + + #set the parameter for the ft defense + parser.add_argument('--ratio', type=float, help='the ratio of clean data loader') + parser.add_argument('--index', type=str, help='index of clean data') + + def set_result(self, result_file): + attack_file = 'record/' + result_file + save_path = 'record/' + result_file + '/defense/ft/' + if not (os.path.exists(save_path)): + os.makedirs(save_path) + # assert(os.path.exists(save_path)) + self.args.save_path = save_path + if self.args.checkpoint_save is None: + self.args.checkpoint_save = save_path + 'checkpoint/' + if not (os.path.exists(self.args.checkpoint_save)): + os.makedirs(self.args.checkpoint_save) + if self.args.log is None: + self.args.log = save_path + 'log/' + if not (os.path.exists(self.args.log)): + os.makedirs(self.args.log) + self.result = load_attack_result(attack_file + '/attack_result.pt') + + def set_trainer(self, model): + self.trainer = PureCleanModelTrainer( + model, + ) + + def set_logger(self): + args = self.args + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + try: + logging.info(pformat(get_git_info())) + except: + logging.info('Getting git info fails.') + + def set_devices(self): + # self.device = torch.device( + # ( + # f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device + # # since DataParallel only allow .to("cuda") + # ) if torch.cuda.is_available() else "cpu" + # ) + self.device = self.args.device + def mitigation(self): + self.set_devices() + fix_random(self.args.random_seed) + + # Prepare model, optimizer, scheduler + model = generate_cls_model(self.args.model,self.args.num_classes) + model.load_state_dict(self.result['model']) + if "," in self.device: + model = torch.nn.DataParallel( + model, + device_ids=[int(i) for i in self.args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{model.device_ids[0]}' + model.to(self.args.device) + else: + model.to(self.args.device) + + + optimizer, scheduler = argparser_opt_scheduler(model, self.args) + # criterion = nn.CrossEntropyLoss() + self.set_trainer(model) + criterion = argparser_criterion(args) + + + + train_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = True) + clean_dataset = prepro_cls_DatasetBD_v2(self.result['clean_train'].wrapped_dataset) + data_all_length = len(clean_dataset) + ran_idx = choose_index(self.args, data_all_length) + log_index = self.args.log + 'index.txt' + np.savetxt(log_index, ran_idx, fmt='%d') + clean_dataset.subset(ran_idx) + data_set_without_tran = clean_dataset + data_set_o = self.result['clean_train'] + data_set_o.wrapped_dataset = data_set_without_tran + data_set_o.wrap_img_transform = train_tran + # data_set_o = prepro_cls_DatasetBD_v2( + # full_dataset_without_transform=data_set, + # poison_idx=np.zeros(len(data_set)), # one-hot to determine which image may take bd_transform + # bd_image_pre_transform=None, + # bd_label_pre_transform=None, + # ori_image_transform_in_loading=train_tran, + # ori_label_transform_in_loading=None, + # add_details_in_preprocess=False, + # ) + data_loader = torch.utils.data.DataLoader(data_set_o, batch_size=self.args.batch_size, num_workers=self.args.num_workers, shuffle=True, pin_memory=args.pin_memory) + trainloader = data_loader + + test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False) + data_bd_testset = self.result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + data_clean_testset = self.result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + # self.trainer.train_with_test_each_epoch( + # train_data = trainloader, + # test_data = data_clean_loader, + # adv_test_data = data_bd_loader, + # end_epoch_num = self.args.epochs, + # criterion = criterion, + # optimizer = optimizer, + # scheduler = scheduler, + # device = self.args.device, + # frequency_save = self.args.frequency_save, + # save_folder_path = self.args.checkpoint_save, + # save_prefix = 'defense', + # continue_training_path = None, + # ) + + self.trainer.train_with_test_each_epoch_on_mix( + trainloader, + data_clean_loader, + data_bd_loader, + args.epochs, + criterion=criterion, + optimizer=optimizer, + scheduler=scheduler, + device=self.args.device, + frequency_save=args.frequency_save, + save_folder_path=args.save_path, + save_prefix='ft', + amp=args.amp, + prefetch=args.prefetch, + prefetch_transform_attr_name="ori_image_transform_in_loading", # since we use the preprocess_bd_dataset + non_blocking=args.non_blocking, + ) + + result = {} + result['model'] = model + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model.cpu().state_dict(), + save_path=args.save_path, + ) + return result + + def defense(self,result_file): + self.set_result(result_file) + self.set_logger() + result = self.mitigation() + return result + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=sys.argv[0]) + ft.add_arguments(parser) + args = parser.parse_args() + ft_method = ft(args) + if "result_file" not in args.__dict__: + args.result_file = 'defense_test_badnet' + elif args.result_file is None: + args.result_file = 'defense_test_badnet' + result = ft_method.defense(args.result_file) \ No newline at end of file diff --git a/defense/i-bau.py b/defense/i-bau.py new file mode 100644 index 0000000..631ec1f --- /dev/null +++ b/defense/i-bau.py @@ -0,0 +1,643 @@ +# MIT License + +# Copyright (c) 2021 Yi Zeng + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +''' +This file is modified based on the following source: +link : https://github.com/YiZeng623/I-BAU/ +The defense method is called i-bau. +The license is bellow the code + +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. save process + 5. new standard: robust accuracy + 6. use clean samples from training (align other defense Settings) +basic sturcture for defense method: + 1. basic setting: args + 2. attack result(model, train data, test data) + 3. i-bau defense: + a. get some clean data + b. unlearn the backdoor model by the pertubation + 4. test the result and get ASR, ACC, RC +''' + + +import argparse +import os,sys +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + + +# TODO:怎么查看包的相对路径和绝对路径 +sys.path.append('../') +sys.path.append(os.getcwd()) + +# TODO:修改yaml文件 + +from pprint import pformat +import yaml +import logging +import time +from defense.base import defense + +from utils.aggregate_block.train_settings_generate import argparser_opt_scheduler +from utils.trainer_cls import Metric_Aggregator, PureCleanModelTrainer, general_plot_for_epoch, given_dataloader_test +from utils.choose_index import choose_index +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.log_assist import get_git_info +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform, get_dataset_normalization +from utils.save_load_attack import load_attack_result, save_defense_result +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2 +import torchvision.transforms as transforms +import random +from itertools import repeat + +from typing import List, Callable +from torch import Tensor +from torch.autograd import grad as torch_grad + +''' +Based on the paper 'On the Iteration Complexity of Hypergradient Computation,' this code was created. +Source: https://github.com/prolearner/hypertorch/blob/master/hypergrad/hypergradients.py +Original Author: Riccardo Grazzi +''' +class DifferentiableOptimizer: + def __init__(self, loss_f, dim_mult, data_or_iter=None): + """ + Args: + loss_f: callable with signature (params, hparams, [data optional]) -> loss tensor + data_or_iter: (x, y) or iterator over the data needed for loss_f + """ + self.data_iterator = None + if data_or_iter: + self.data_iterator = data_or_iter if hasattr(data_or_iter, '__next__') else repeat(data_or_iter) + + self.loss_f = loss_f + self.dim_mult = dim_mult + self.curr_loss = None + + def get_opt_params(self, params): + opt_params = [p for p in params] + opt_params.extend([torch.zeros_like(p) for p in params for _ in range(self.dim_mult-1) ]) + return opt_params + + def step(self, params, hparams, create_graph): + raise NotImplementedError + + def __call__(self, params, hparams, create_graph=True): + with torch.enable_grad(): + return self.step(params, hparams, create_graph) + + def get_loss(self, params, hparams): + if self.data_iterator: + data = next(self.data_iterator) + self.curr_loss = self.loss_f(params, hparams, data) + else: + self.curr_loss = self.loss_f(params, hparams) + return self.curr_loss + +class GradientDescent(DifferentiableOptimizer): + def __init__(self, loss_f, step_size, data_or_iter=None): + super(GradientDescent, self).__init__(loss_f, dim_mult=1, data_or_iter=data_or_iter) + self.step_size_f = step_size if callable(step_size) else lambda x: step_size + + def step(self, params, hparams, create_graph): + loss = self.get_loss(params, hparams) + sz = self.step_size_f(hparams) + return gd_step(params, loss, sz, create_graph=create_graph) + + +def gd_step(params, loss, step_size, create_graph=True): + grads = torch.autograd.grad(loss, params, create_graph=create_graph) + return [w - step_size * g for w, g in zip(params, grads)] + + +def grad_unused_zero(output, inputs, grad_outputs=None, retain_graph=False, create_graph=False): + grads = torch.autograd.grad(output, inputs, grad_outputs=grad_outputs, allow_unused=True, + retain_graph=retain_graph, create_graph=create_graph) + + def grad_or_zeros(grad, var): + return torch.zeros_like(var) if grad is None else grad + + return tuple(grad_or_zeros(g, v) for g, v in zip(grads, inputs)) + +def get_outer_gradients(outer_loss, params, hparams, retain_graph=True): + grad_outer_w = grad_unused_zero(outer_loss, params, retain_graph=retain_graph) + grad_outer_hparams = grad_unused_zero(outer_loss, hparams, retain_graph=retain_graph) + + return grad_outer_w, grad_outer_hparams + +def update_tensor_grads(hparams, grads): + for l, g in zip(hparams, grads): + if l.grad is None: + l.grad = torch.zeros_like(l) + if g is not None: + l.grad += g + + +def fixed_point(params: List[Tensor], + hparams: List[Tensor], + K: int , + fp_map: Callable[[List[Tensor], List[Tensor]], List[Tensor]], + outer_loss: Callable[[List[Tensor], List[Tensor]], Tensor], + tol=1e-10, + set_grad=True, + stochastic=False) -> List[Tensor]: + """ + Computes the hypergradient by applying K steps of the fixed point method (it can end earlier when tol is reached). + Args: + params: the output of the inner solver procedure. + hparams: the outer variables (or hyperparameters), each element needs requires_grad=True + K: the maximum number of fixed point iterations + fp_map: the fixed point map which defines the inner problem + outer_loss: computes the outer objective taking parameters and hyperparameters as inputs + tol: end the method earlier when the normed difference between two iterates is less than tol + set_grad: if True set t.grad to the hypergradient for every t in hparams + stochastic: set this to True when fp_map is not a deterministic function of its inputs + Returns: + the list of hypergradients for each element in hparams + """ + + params = [w.detach().requires_grad_(True) for w in params] + o_loss = outer_loss(params, hparams) + grad_outer_w, grad_outer_hparams = get_outer_gradients(o_loss, params, hparams) + + if not stochastic: + w_mapped = fp_map(params, hparams) + + vs = [torch.zeros_like(w) for w in params] + vs_vec = cat_list_to_tensor(vs) + for k in range(K): + vs_prev_vec = vs_vec + + if stochastic: + w_mapped = fp_map(params, hparams) + vs = torch_grad(w_mapped, params, grad_outputs=vs, retain_graph=False) + else: + vs = torch_grad(w_mapped, params, grad_outputs=vs, retain_graph=True) + + vs = [v + gow for v, gow in zip(vs, grad_outer_w)] + vs_vec = cat_list_to_tensor(vs) + if float(torch.norm(vs_vec - vs_prev_vec)) < tol: + break + + if stochastic: + w_mapped = fp_map(params, hparams) + + grads = torch_grad(w_mapped, hparams, grad_outputs=vs, allow_unused=True) + grads = [g + v if g is not None else v for g, v in zip(grads, grad_outer_hparams)] + + if set_grad: + update_tensor_grads(hparams, grads) + + return grads + +def cat_list_to_tensor(list_tx): + return torch.cat([xx.reshape([-1]) for xx in list_tx]) + +class i_bau(defense): + + def __init__(self,args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + # TODO:直接用self.args好不好用 + self.args = args + + if 'result_file' in args.__dict__ : + if args.result_file is not None: + self.set_result(args.result_file) + + def add_arguments(parser): + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory") + parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?") + parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch') + parser.add_argument('--amp', type=lambda x: str(x) in ['True','true','1']) + + parser.add_argument('--checkpoint_load', type=str, help='the location of load model') + parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved') + parser.add_argument('--log', type=str, help='the location of log') + parser.add_argument("--dataset_path", type=str, help='the location of data') + parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny') + parser.add_argument('--result_file', type=str, help='the location of result') + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--model', type=str, help='resnet18') + + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--yaml_path', type=str, default="./config/defense/i-bau/config.yaml", help='the path of yaml') + + #set the parameter for the i-bau defense + parser.add_argument('--ratio', type=float, help='the ratio of clean data loader') + ## hyper params + ### TODO config optimizer 改框架之后放到前面统一起来 + parser.add_argument('--optim', type=str, default='Adam', help='type of outer loop optimizer utilized') + parser.add_argument('--n_rounds', type=int, help='the maximum number of unelarning rounds') + parser.add_argument('--K', type=int, help='the maximum number of fixed point iterations') + + parser.add_argument('--index', type=str, help='index of clean data') + + + def set_result(self, result_file): + attack_file = 'record/' + result_file + save_path = 'record/' + result_file + '/defense/i-bau/' + if not (os.path.exists(save_path)): + os.makedirs(save_path) + # assert(os.path.exists(save_path)) + self.args.save_path = save_path + if self.args.checkpoint_save is None: + self.args.checkpoint_save = save_path + 'checkpoint/' + if not (os.path.exists(self.args.checkpoint_save)): + os.makedirs(self.args.checkpoint_save) + if self.args.log is None: + self.args.log = save_path + 'log/' + if not (os.path.exists(self.args.log)): + os.makedirs(self.args.log) + self.result = load_attack_result(attack_file + '/attack_result.pt') + def set_trainer(self, model): + self.trainer = PureCleanModelTrainer( + model, + ) + + def set_logger(self): + args = self.args + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + try: + logging.info(pformat(get_git_info())) + except: + logging.info('Getting git info fails.') + + def set_devices(self): + # self.device = torch.device( + # ( + # f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device + # # since DataParallel only allow .to("cuda") + # ) if torch.cuda.is_available() else "cpu" + # ) + self.device= self.args.device + def mitigation(self): + self.set_devices() + fix_random(self.args.random_seed) + + # Prepare model, optimizer, scheduler + model = generate_cls_model(self.args.model,self.args.num_classes) + model.load_state_dict(self.result['model']) + if "," in self.device: + model = torch.nn.DataParallel( + model, + device_ids=[int(i) for i in self.args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{model.device_ids[0]}' + model.to(self.args.device) + else: + model.to(self.args.device) + ### TODO: opt + # outer_opt = torch.optim.Adam(model.parameters(), lr=args.lr) + # criterion = nn.CrossEntropyLoss() + outer_opt, scheduler = argparser_opt_scheduler(model, self.args) + # criterion = nn.CrossEntropyLoss() + self.set_trainer(model) + # criterion = argparser_criterion(args) + + + # a. get some clean data + logging.info("We use clean train data, the original paper use clean test data.") + transforms_list = [] + transforms_list.append(transforms.Resize((args.input_height, args.input_width))) + transforms_list.append(transforms.ToTensor()) + transforms_list.append(get_dataset_normalization(args.dataset)) + tran = transforms.Compose(transforms_list) + train_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = True) + clean_dataset = prepro_cls_DatasetBD_v2(self.result['clean_train'].wrapped_dataset) + data_all_length = len(clean_dataset) + ran_idx = choose_index(self.args, data_all_length) + log_index = self.args.log + 'index.txt' + np.savetxt(log_index, ran_idx, fmt='%d') + clean_dataset.subset(ran_idx) + data_set_without_tran = clean_dataset + data_set_o = self.result['clean_train'] + data_set_o.wrapped_dataset = data_set_without_tran + data_set_o.wrap_img_transform = train_tran + # data_set_o = prepro_cls_DatasetBD_v2( + # full_dataset_without_transform=data_set, + # poison_idx=np.zeros(len(data_set)), # one-hot to determine which image may take bd_transform + # bd_image_pre_transform=None, + # bd_label_pre_transform=None, + # ori_image_transform_in_loading=train_tran, + # ori_label_transform_in_loading=None, + # add_details_in_preprocess=False, + # ) + data_loader = torch.utils.data.DataLoader(data_set_o, batch_size=self.args.batch_size, num_workers=self.args.num_workers, shuffle=True, pin_memory=args.pin_memory) + trainloader = data_loader + + test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False) + data_bd_testset = self.result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + data_clean_testset = self.result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + # self.trainer.train_with_test_each_epoch( + # train_data = trainloader, + # test_data = data_clean_loader, + # adv_test_data = data_bd_loader, + # end_epoch_num = self.args.epochs, + # criterion = criterion, + # optimizer = optimizer, + # scheduler = scheduler, + # device = self.args.device, + # frequency_save = self.args.frequency_save, + # save_folder_path = self.args.checkpoint_save, + # save_prefix = 'defense', + # continue_training_path = None, + # ) + # test_result(args,data_clean_loader,data_bd_loader,0,model,criterion) + + ### define the inner loss L2 + def loss_inner(perturb, model_params): + ### TODO: cpu training and multiprocessing + images = images_list[0].to(args.device) + labels = labels_list[0].long().to(args.device) + #per_img = torch.clamp(images+perturb[0],min=0,max=1) + per_img = images+perturb[0] + per_logits = model.forward(per_img) + loss = F.cross_entropy(per_logits, labels, reduction='none') + loss_regu = torch.mean(-loss) +0.001*torch.pow(torch.norm(perturb[0]),2) + return loss_regu + + ### define the outer loss L1 + def loss_outer(perturb, model_params): + ### TODO: cpu training and multiprocessing + portion = 0.01 + images, labels = images_list[batchnum].to(args.device), labels_list[batchnum].long().to(args.device) + patching = torch.zeros_like(images, device='cuda') + number = images.shape[0] + rand_idx = random.sample(list(np.arange(number)),int(number*portion)) + patching[rand_idx] = perturb[0] + #unlearn_imgs = torch.clamp(images+patching,min=0,max=1) + unlearn_imgs = images+patching + logits = model(unlearn_imgs) + criterion = nn.CrossEntropyLoss() + loss = criterion(logits, labels) + return loss + + images_list, labels_list = [], [] + for index, (images, labels, original_index, poison_indicator, original_targets) in enumerate(trainloader): + images_list.append(images) + labels_list.append(labels) + inner_opt = GradientDescent(loss_inner, 0.1) + + train_loss_list = [] + train_mix_acc_list = [] + train_clean_acc_list = [] + train_asr_list = [] + train_ra_list = [] + + clean_test_loss_list = [] + bd_test_loss_list = [] + ra_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + + # b. unlearn the backdoor model by the pertubation + logging.info("=> Conducting Defence..") + model.eval() + agg = Metric_Aggregator() + for round in range(args.n_rounds): + # batch_pert = torch.zeros_like(data_clean_testset[0][:1], requires_grad=True, device=args.device) + batch_pert = torch.zeros([1,args.input_channel,args.input_height,args.input_width], requires_grad=True, device=args.device) + batch_opt = torch.optim.SGD(params=[batch_pert],lr=10) + + for images, labels, original_index, poison_indicator, original_targets in trainloader: + images = images.to(args.device) + ori_lab = torch.argmax(model.forward(images),axis = 1).long() + # per_logits = model.forward(torch.clamp(images+batch_pert,min=0,max=1)) + per_logits = model.forward(images+batch_pert) + loss = F.cross_entropy(per_logits, ori_lab, reduction='mean') + loss_regu = torch.mean(-loss) +0.001*torch.pow(torch.norm(batch_pert),2) + batch_opt.zero_grad() + loss_regu.backward(retain_graph = True) + batch_opt.step() + + #l2-ball + # pert = batch_pert * min(1, 10 / torch.norm(batch_pert)) + pert = batch_pert + + #unlearn step + for batchnum in range(len(images_list)): + outer_opt.zero_grad() + fixed_point(pert, list(model.parameters()), args.K, inner_opt, loss_outer) + outer_opt.step() + + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.eval_step( + model, + data_clean_loader, + data_bd_loader, + args, + ) + + agg({ + "epoch": round, + + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "ra_test_loss_avg_over_batch": ra_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + ra_test_loss_list.append(ra_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + + general_plot_for_epoch( + { + "Test C-Acc": test_acc_list, + "Test ASR": test_asr_list, + "Test RA": test_ra_list, + }, + save_path=f"{args.save_path}i-bau_acc_like_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "Test Clean Loss": clean_test_loss_list, + "Test Backdoor Loss": bd_test_loss_list, + "Test RA Loss": ra_test_loss_list, + }, + save_path=f"{args.save_path}i-bau_loss_metric_plots.png", + ylabel="percentage", + ) + + agg.to_dataframe().to_csv(f"{args.save_path}i-bau_df.csv") + agg.summary().to_csv(f"{args.save_path}i-bau_df_summary.csv") + # self.trainer.train_with_test_each_epoch_on_mix( + # trainloader, + # data_clean_loader, + # data_bd_loader, + # args.epochs, + # criterion=criterion, + # optimizer=optimizer, + # scheduler=scheduler, + # device=self.device, + # frequency_save=args.frequency_save, + # save_folder_path=args.save_path, + # save_prefix='i-bau', + # amp=args.amp, + # prefetch=args.prefetch, + # prefetch_transform_attr_name="ori_image_transform_in_loading", # since we use the preprocess_bd_dataset + # non_blocking=args.non_blocking, + # ) + + result = {} + result['model'] = model + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model.cpu().state_dict(), + save_path=args.save_path, + ) + return result + + def eval_step( + self, + netC, + clean_test_dataloader, + bd_test_dataloader, + args, + ): + clean_metrics, clean_epoch_predict_list, clean_epoch_label_list = given_dataloader_test( + netC, + clean_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.device, + verbose=0, + ) + clean_test_loss_avg_over_batch = clean_metrics['test_loss_avg_over_batch'] + test_acc = clean_metrics['test_acc'] + bd_metrics, bd_epoch_predict_list, bd_epoch_label_list = given_dataloader_test( + netC, + bd_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.args.device, + verbose=0, + ) + bd_test_loss_avg_over_batch = bd_metrics['test_loss_avg_over_batch'] + test_asr = bd_metrics['test_acc'] + + bd_test_dataloader.dataset.wrapped_dataset.getitem_all_switch = True # change to return the original label instead + ra_metrics, ra_epoch_predict_list, ra_epoch_label_list = given_dataloader_test( + netC, + bd_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=self.args.device, + verbose=0, + ) + ra_test_loss_avg_over_batch = ra_metrics['test_loss_avg_over_batch'] + test_ra = ra_metrics['test_acc'] + bd_test_dataloader.dataset.wrapped_dataset.getitem_all_switch = False # switch back + + return clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra + + def defense(self,result_file): + self.set_result(result_file) + self.set_logger() + result = self.mitigation() + return result + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=sys.argv[0]) + i_bau.add_arguments(parser) + args = parser.parse_args() + i_bau_method = i_bau(args) + if "result_file" not in args.__dict__: + args.result_file = 'defense_test_badnet' + elif args.result_file is None: + args.result_file = 'defense_test_badnet' + result = i_bau_method.defense(args.result_file) \ No newline at end of file diff --git a/defense/mcr.py b/defense/mcr.py new file mode 100644 index 0000000..9d5dc8d --- /dev/null +++ b/defense/mcr.py @@ -0,0 +1,1348 @@ +# python defense/mcr/mcr.py --save_path /workspace/chenhongrui/bdzoo2/record/t_914_badnet +''' +This file is modified based on the following source: +link : https://github.com/IBM/model-sanitization. +The defense method is called MCR. + +Since the model is different from original paper, we change the hyperparameter for preactresnet18 on cifar10 to align the performance. + +''' + +import argparse +import os, sys +import numpy as np +import torch +import torch.nn as nn +import shutil + +sys.path.append('../') +sys.path.append(os.getcwd()) + +from pprint import pformat +import yaml +# import logging +import time +from copy import deepcopy +from typing import List +import logging +# from pyhessian import hessian # Hessian computation +import matplotlib.pyplot as plt +# import numpy as np + +from defense.base import defense +from utils.aggregate_block.train_settings_generate import argparser_opt_scheduler +from utils.trainer_cls import ModelTrainerCLS_v2, BackdoorModelTrainer, Metric_Aggregator, given_dataloader_test, \ + general_plot_for_epoch +from utils.choose_index import choose_index +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.log_assist import get_git_info +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform +from utils.save_load_attack import load_attack_result +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform +from utils.trainer_cls import test_given_dataloader_on_mix, all_acc, plot_acc_like_metric_pure, \ + validate_list_for_plot # plot_loss, plot_acc_like_metric, + +import numpy as np +# import math +import torch +# import torch.nn.functional as F +from torch.nn import Module, Parameter +# from torch.nn.modules.utils import _pair +from scipy.special import binom + + +def plot_loss( + train_loss_list: list, + clean_test_loss_list: list, + bd_test_loss_list: list, + save_folder_path: str, + save_file_name="loss_metric_plots", + frequency=1, +): + '''These line of set color is from https://stackoverflow.com/questions/8389636/creating-over-20-unique-legend-colors-using-matplotlib''' + NUM_COLORS = 3 + cm = plt.get_cmap('gist_rainbow') + fig = plt.figure(figsize=(12.8, 9.6)) # 4x default figsize + ax = fig.add_subplot(111) + ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)]) + + len_set = len(train_loss_list) + x = np.arange(len_set) * frequency + if validate_list_for_plot(train_loss_list, len_set): + plt.plot(x, train_loss_list, marker="o", linewidth=2, label="Train Loss", linestyle="--") + else: + logging.warning("train_loss_list contains None or len not match") + if validate_list_for_plot(clean_test_loss_list, len_set): + plt.plot(x, clean_test_loss_list, marker="v", linewidth=2, label="Test Clean loss", linestyle="-") + else: + logging.warning("clean_test_loss_list contains None or len not match") + if validate_list_for_plot(bd_test_loss_list, len_set): + plt.plot(x, bd_test_loss_list, marker="+", linewidth=2, label="Test Backdoor Loss", linestyle="-.") + else: + logging.warning("bd_test_loss_list contains None or len not match") + + plt.xlabel("Epochs") + plt.ylabel("Loss") + + plt.ylim((0, + max([value for value in # filter None value + train_loss_list + + clean_test_loss_list + + bd_test_loss_list if value is not None]) + )) + plt.legend() + plt.title("Results") + plt.grid() + plt.savefig(f"{save_folder_path}/{save_file_name}.png") + plt.close() + + +def plot_acc_like_metric( + train_acc_list: list, + train_asr_list: list, + train_ra_list: list, + test_acc_list: list, + test_asr_list: list, + test_ra_list: list, + save_folder_path: str, + save_file_name="acc_like_metric_plots", + frequency=1, + +): + len_set = len(test_asr_list) + x = np.arange(len(test_asr_list)) * frequency + + '''These line of set color is from https://stackoverflow.com/questions/8389636/creating-over-20-unique-legend-colors-using-matplotlib''' + NUM_COLORS = 6 + cm = plt.get_cmap('gist_rainbow') + fig = plt.figure(figsize=(12.8, 9.6)) # 4x default figsize + ax = fig.add_subplot(111) + ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)]) + + if validate_list_for_plot(train_acc_list, len_set): + plt.plot(x, train_acc_list, marker="o", linewidth=2, label="Train Acc", linestyle="--") + else: + logging.warning("train_acc_list contains None, or len not match") + if validate_list_for_plot(train_asr_list, len_set): + plt.plot(x, train_asr_list, marker="v", linewidth=2, label="Train ASR", linestyle="-") + else: + logging.warning("train_asr_list contains None, or len not match") + if validate_list_for_plot(train_ra_list, len_set): + plt.plot(x, train_ra_list, marker="+", linewidth=2, label="Train RA", linestyle="-.") + else: + logging.warning("train_ra_list contains None, or len not match") + if validate_list_for_plot(test_acc_list, len_set): + plt.plot(x, test_acc_list, marker="o", linewidth=2, label="Test C-Acc", linestyle="--") + else: + logging.warning("test_acc_list contains None, or len not match") + if validate_list_for_plot(test_asr_list, len_set): + plt.plot(x, test_asr_list, marker="v", linewidth=2, label="Test ASR", linestyle="-") + else: + logging.warning("test_asr_list contains None, or len not match") + if validate_list_for_plot(test_ra_list, len_set): + plt.plot(x, test_ra_list, marker="+", linewidth=2, label="Test RA", linestyle="-.") + else: + logging.warning("test_ra_list contains None, or len not match") + + plt.xlabel("Epochs") + plt.ylabel("ACC") + + plt.ylim((0, 1)) + plt.legend() + plt.title("Results") + plt.grid() + plt.savefig(f"{save_folder_path}/{save_file_name}.png") + plt.close() + + +# def plot_hessian_eigenvalues( +# model_visual, +# data_loader, # only use one batch +# device, +# save_path_for_hessian=None, # xx/xx/xx.png +# ): +# # save_path_for_hessian = +# # data_loader = +# # device = +# # model_visual = +# +# model_visual = (model_visual) +# data_loader = (data_loader) +# model_visual.to(device) +# +# # !!! Important to set eval mode !!! +# model_visual.eval() +# +# criterion = torch.nn.CrossEntropyLoss() +# +# batch_x, batch_y, *others = next(iter(data_loader)) +# batch_x = batch_x.to(device) +# batch_y = batch_y.to(device) +# +# if torch.__version__ > '1.8.1': +# logging.info('Use self-defined function as an alternative for torch.eig since your torch>=1.9') +# +# def old_torcheig(A, eigenvectors): +# '''A temporary function as an alternative for torch.eig (torch<1.9)''' +# vals, vecs = torch.linalg.eig(A) +# if torch.is_complex(vals) or torch.is_complex(vecs): +# logging.info( +# 'Warning: Complex values founded in Eigenvalues/Eigenvectors. This is impossible for real symmetric matrix like Hessian. \n We only keep the real part.') +# +# vals = torch.real(vals) +# vecs = torch.real(vecs) +# +# # vals is a nx2 matrix. see https://virtualgroup.cn/pytorch.org/docs/stable/generated/torch.eig.html +# vals = vals.view(-1, 1) + torch.zeros(vals.size()[0], 2).to(vals.device) +# if eigenvectors: +# return vals, vecs +# else: +# return vals, torch.tensor([]) +# +# torch.eig = old_torcheig +# +# # create the hessian computation module +# hessian_comp = hessian(model_visual, criterion, data=(batch_x, batch_y), cuda=True) +# # Now let's compute the top 2 eigenavlues and eigenvectors of the Hessian +# top_eigenvalues, top_eigenvector = hessian_comp.eigenvalues(top_n=2, maxIter=1000) +# logging.info("The top two eigenvalues of this model are: %.4f %.4f" % (top_eigenvalues[0], top_eigenvalues[1])) +# +# if save_path_for_hessian is not None: +# +# density_eigen, density_weight = hessian_comp.density() +# +# def get_esd_plot(eigenvalues, weights): +# density, grids = density_generate(eigenvalues, weights) +# plt.semilogy(grids, density + 1.0e-7) +# plt.ylabel('Density (Log Scale)', fontsize=14, labelpad=10) +# plt.xlabel('Eigenvlaue', fontsize=14, labelpad=10) +# plt.xticks(fontsize=12) +# plt.yticks(fontsize=12) +# plt.axis([np.min(eigenvalues) - 1, np.max(eigenvalues) + 1, None, None]) +# return plt.gca() +# +# def density_generate(eigenvalues, +# weights, +# num_bins=10000, +# sigma_squared=1e-5, +# overhead=0.01): +# eigenvalues = np.array(eigenvalues) +# weights = np.array(weights) +# +# lambda_max = np.mean(np.max(eigenvalues, axis=1), axis=0) + overhead +# lambda_min = np.mean(np.min(eigenvalues, axis=1), axis=0) - overhead +# +# grids = np.linspace(lambda_min, lambda_max, num=num_bins) +# sigma = sigma_squared * max(1, (lambda_max - lambda_min)) +# +# num_runs = eigenvalues.shape[0] +# density_output = np.zeros((num_runs, num_bins)) +# +# for i in range(num_runs): +# for j in range(num_bins): +# x = grids[j] +# tmp_result = gaussian(eigenvalues[i, :], x, sigma) +# density_output[i, j] = np.sum(tmp_result * weights[i, :]) +# density = np.mean(density_output, axis=0) +# normalization = np.sum(density) * (grids[1] - grids[0]) +# density = density / normalization +# return density, grids +# +# def gaussian(x, x0, sigma_squared): +# return np.exp(-(x0 - x) ** 2 / +# (2.0 * sigma_squared)) / np.sqrt(2 * np.pi * sigma_squared) +# +# ax = get_esd_plot(density_eigen, density_weight) +# +# ax.set_title(f'Max Eigen Value: {top_eigenvalues[0]:.2f}') +# +# plt.tight_layout() +# plt.savefig(save_path_for_hessian) +# plt.close() +# +# logging.info(f'Save to {save_path_for_hessian}') +# +# return top_eigenvalues + + +class Bezier(Module): + def __init__(self, num_bends): + super(Bezier, self).__init__() + self.register_buffer( + 'binom', + torch.Tensor(binom(num_bends - 1, np.arange(num_bends), dtype=np.float32)) + ) + self.register_buffer('range', torch.arange(0, float(num_bends))) + self.register_buffer('rev_range', torch.arange(float(num_bends - 1), -1, -1)) + + def forward(self, t): + return self.binom * \ + torch.pow(t, self.range) * \ + torch.pow((1.0 - t), self.rev_range) + + +class PolyChain(Module): + def __init__(self, num_bends): + super(PolyChain, self).__init__() + self.num_bends = num_bends + self.register_buffer('range', torch.arange(0, float(num_bends))) + + def forward(self, t): + t_n = t * (self.num_bends - 1) + return torch.max(self.range.new([0.0]), 1.0 - torch.abs(t_n - self.range)) + + +class MCR_Trainer(BackdoorModelTrainer): + + def __init__(self, model, curve): + super().__init__(model) + self.cruve = curve + + def one_forward_backward(self, x, labels, device, verbose=0): + self.model.train() + self.model.to(device, non_blocking=self.non_blocking) + + x, labels = x.to(device, non_blocking=self.non_blocking), labels.to(device, non_blocking=self.non_blocking) + + with torch.cuda.amp.autocast(enabled=self.amp): + log_probs = self.model(x) + loss = self.criterion(log_probs, labels.long()) + self.scaler.scale(loss).backward() + self.scaler.step(self.optimizer) + self.scaler.update() + self.optimizer.zero_grad() + + batch_loss = loss.item() + + if verbose == 1: + batch_predict = torch.max(log_probs, -1)[1].detach().clone().cpu() + return batch_loss, batch_predict + + return batch_loss, None + + +def sampleModelFromCurve( + model: torch.nn.Module, # the model to be sampled, parameter will be replaced by sampled weights from curve + curve_netCs: List[torch.nn.Module], # models used for represents a curve + curve_module: torch.nn.Module, # module that used to generate weights which sum to 1. e.g. Bezier, PolyChain + curve_t: float, # which point on curve will be sampled? + device, +) -> torch.nn.Module: + # use given test_t to generate one model to do test + model.eval() + model.to(device) + + for inter_netC in curve_netCs: # skip the start and end model + inter_netC.eval() + inter_netC.to(device) + + lookupDict_for_netCs = [dict(inter_netC.named_parameters()) for inter_netC in curve_netCs] + inter_netC_coefs = curve_module(torch.tensor(curve_t)) + with torch.no_grad(): + for parameter_name, parameter in model.named_parameters(): + weighted_parameter_from_curve_netCs = 0 + for inter_netC_idx, lookupdict in enumerate(lookupDict_for_netCs): + weighted_parameter_from_curve_netCs += lookupdict[parameter_name].data * inter_netC_coefs[ + inter_netC_idx] + parameter.copy_( + weighted_parameter_from_curve_netCs + ) + return model + + +class MCR(defense): + + def __init__(self): + super(MCR).__init__() + pass + + def set_args(self, parser): + parser.add_argument("-pm", "--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], + help="dataloader pin_memory") + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--amp', type=lambda x: str(x) in ['True', 'true', '1']) + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-nb", "--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], + help=".to(), set the non_blocking = ?") + parser.add_argument('--save_path', type=str) + parser.add_argument("--dataset_path", type=str) + + parser.add_argument('--dataset', type=str, help='mnist, cifar10, gtsrb, celeba, tiny') + parser.add_argument("--num_classes", type=int) + parser.add_argument("--input_height", type=int) + parser.add_argument("--input_width", type=int) + parser.add_argument("--input_channel", type=int) + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + + parser.add_argument('--attack', type=str) + parser.add_argument('--poison_rate', type=float) + parser.add_argument('--target_type', type=str, help='all2one, all2all, cleanLabel') + parser.add_argument('--target_label', type=int) + parser.add_argument('--trigger_type', type=str, + help='squareTrigger, gridTrigger, fourCornerTrigger, randomPixelTrigger, signalTrigger, trojanTrigger') + + parser.add_argument('--model', type=str, help='resnet18') + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--index', type=str, help='index of clean data') + parser.add_argument('--result_file', type=str, help='the location of result') + parser.add_argument("--train_curve_epochs", type=int) + parser.add_argument('--yaml_path', type=str, default="./config/defense/mcr/config.yaml", + help='the path of yaml') + parser.add_argument("--num_bends", type=int) + parser.add_argument("--test_t", type=float) + parser.add_argument("--curve", type=str) + parser.add_argument("--ft_epochs", type=int) + parser.add_argument("--ft_lr_scheduler", type=str) + + # set the parameter for the fp defense + parser.add_argument('--ratio', type=float, help='the ratio of clean data loader') + parser.add_argument('--acc_ratio', type=float, help='the tolerance ration of the clean accuracy') + + parser.add_argument('--test_curve_every', type=int, help="frequency of testing the models on curve") + + parser.add_argument("--load_other_model_path", type=str, + help="instead of finetune the given poisoned model, we load other model from this part") + + parser.add_argument("--use_clean_subset", type=lambda x: str(x) in ['True', 'true', '1'], + help="use bd poison dataset as data poison for path training and BN update; or, use clean subset instead") + + return parser + + def add_yaml_to_args(self, args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + defaults.update({k: v for k, v in args.__dict__.items() if v is not None}) + args.__dict__ = defaults + + def process_args(self, args): + args.terminal_info = sys.argv + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + args.save_path = 'record/' + args.result_file + defense_save_path = args.save_path + os.path.sep + "defense" + os.path.sep + "mcr" + + if os.path.exists(defense_save_path): + shutil.rmtree(defense_save_path) + os.makedirs(defense_save_path) + + args.defense_save_path = defense_save_path + return args + + def prepare(self, args): + + ### set the logger + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + # file Handler + fileHandler = logging.FileHandler( + args.defense_save_path + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + fileHandler.setLevel(logging.DEBUG) + logger.addHandler(fileHandler) + # consoleHandler + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + consoleHandler.setLevel(logging.INFO) + logger.addHandler(consoleHandler) + # overall logger level should <= min(handler) otherwise no log will be recorded. + logger.setLevel(0) + + # disable other debug, since too many debug + logging.getLogger('PIL').setLevel(logging.WARNING) + logging.getLogger('matplotlib.font_manager').setLevel(logging.WARNING) + + logging.info(pformat(args.__dict__)) + + logging.debug("Only INFO or above level log will show in cmd. DEBUG level log only will show in log file.") + + # record the git infomation for debug (if available.) + try: + logging.debug(pformat(get_git_info())) + except: + logging.debug('Getting git info fails.') + + fix_random(args.random_seed) + self.args = args + + ''' + load_dict = { + 'model_name': load_file['model_name'], + 'model': load_file['model'], + 'clean_train': clean_train_dataset_with_transform, + 'clean_test' : clean_test_dataset_with_transform, + 'bd_train': bd_train_dataset_with_transform, + 'bd_test': bd_test_dataset_with_transform, + } + ''' + self.attack_result = load_attack_result(self.args.save_path + os.path.sep + 'attack_result.pt') + + netC = generate_cls_model(args.model, args.num_classes) + netC.load_state_dict(self.attack_result['model']) + netC.to(args.device) + netC.eval() + netC.requires_grad_(False) + + self.netC = netC + + def defense(self): + + netC = self.netC + args = self.args + attack_result = self.attack_result + + self.device = torch.device( + ( + f"cuda:{[int(i) for i in args.device[5:].split(',')][0]}" if "," in args.device else args.device + # since DataParallel only allow .to("cuda") + ) if torch.cuda.is_available() else "cpu" + ) + + # clean_train with subset + clean_train_dataset_with_transform = attack_result['clean_train'] + clean_train_dataset_without_transform = clean_train_dataset_with_transform.wrapped_dataset + clean_train_dataset_without_transform = prepro_cls_DatasetBD_v2( + clean_train_dataset_without_transform + ) + # logging.warning("No subset is done, ONLY for test!!!!!") + ran_idx = choose_index(args, len(clean_train_dataset_without_transform)) + logging.info(f"get ran_idx for subset clean train dataset, (len={len(ran_idx)}), ran_idx:{ran_idx}") + clean_train_dataset_without_transform.subset( + choose_index(args, len(clean_train_dataset_without_transform)) + ) + log_index = args.defense_save_path + os.path.sep + 'index.txt' + np.savetxt(log_index, ran_idx, fmt='%d') + clean_train_dataset_with_transform.wrapped_dataset = clean_train_dataset_without_transform + clean_train_dataloader = torch.utils.data.DataLoader(clean_train_dataset_with_transform, + batch_size=args.batch_size, + num_workers=args.num_workers, + shuffle=True) + + clean_test_dataset_with_transform = attack_result['clean_test'] + data_clean_testset = clean_test_dataset_with_transform + clean_test_dataloader = torch.utils.data.DataLoader(data_clean_testset, batch_size=args.batch_size, + num_workers=args.num_workers, drop_last=False, + shuffle=False, + pin_memory=args.pin_memory) + + bd_test_dataloader = torch.utils.data.DataLoader(attack_result['bd_test'], batch_size=args.batch_size, + num_workers=args.num_workers, drop_last=False, shuffle=False, + pin_memory=args.pin_memory) + + bd_train_dataset_with_transform = attack_result['bd_train'] + bd_train_dataset_without_transform = bd_train_dataset_with_transform.wrapped_dataset + bd_train_dataloader = torch.utils.data.DataLoader( + bd_train_dataset_with_transform, + batch_size=args.batch_size, + num_workers=args.num_workers, + drop_last=True, + shuffle=True, + pin_memory=args.pin_memory, + ) + + where_poisoned = np.where( + bd_train_dataset_without_transform.poison_indicator == 1 + )[0] + logging.info(f"len of where_poisoned = {len(where_poisoned)}") + bd_train_poisoned_part_wo_trans = deepcopy(bd_train_dataset_without_transform) + bd_train_poisoned_part_wo_trans.subset( + where_poisoned + ) + bd_train_poisoned_part_w_trans = dataset_wrapper_with_transform( + bd_train_poisoned_part_wo_trans, + wrap_img_transform=clean_test_dataset_with_transform.wrap_img_transform, + ) + bd_train_poisoned_part_dataloader = torch.utils.data.DataLoader( + bd_train_poisoned_part_w_trans, + batch_size=args.batch_size, + num_workers=args.num_workers, + drop_last=False, + shuffle=False, + pin_memory=args.pin_memory, + ) + where_clean = np.where( + bd_train_dataset_without_transform.poison_indicator == 0 + )[0] + logging.info(f"len of where_clean = {len(where_clean)}") + bd_train_clean_part_wo_trans = deepcopy(bd_train_dataset_without_transform) + bd_train_clean_part_wo_trans.subset( + where_clean + ) + bd_train_clean_part_w_trans = dataset_wrapper_with_transform( + bd_train_clean_part_wo_trans, + wrap_img_transform=clean_test_dataset_with_transform.wrap_img_transform, + ) + bd_train_clean_part_dataloader = torch.utils.data.DataLoader( + bd_train_clean_part_w_trans, + batch_size=args.batch_size, + num_workers=args.num_workers, + drop_last=False, + shuffle=False, + pin_memory=args.pin_memory, + ) + + # finetune netC with clean data + ft_netC = deepcopy(netC) + + if ("load_other_model_path" not in args.__dict__) or (args.load_other_model_path is None): + ft_netC.train() + ft_netC.requires_grad_() + + criterion = nn.CrossEntropyLoss() + ft_args = deepcopy(self.args) + ft_args.__dict__ = { + k[3:]: v for k, v in self.args.__dict__.items() if 'ft_' in k + } + optimizer, scheduler = argparser_opt_scheduler( + ft_netC, + ft_args, + ) + finetune_trainer = BackdoorModelTrainer( + ft_netC + ) + + finetune_trainer.train_with_test_each_epoch_on_mix( + clean_train_dataloader, + clean_test_dataloader, + bd_test_dataloader, + args.ft_epochs, + criterion, + optimizer, + scheduler, + args.amp, + torch.device(args.device), + + args.frequency_save, + self.args.defense_save_path, + "finetune", + + prefetch=False, + prefetch_transform_attr_name="transform", + non_blocking=args.non_blocking, + ) + else: + # load from load_other_model_path + ft_netC.load_state_dict(torch.load(args.load_other_model_path, map_location="cpu")['model']) + ft_netC.to(args.device) + logging.warning(f"Load alternative model from {args.load_other_model_path}!!!!") + + ft_netC.eval() + ft_netC.requires_grad_() + + # train the curve + logging.warning( + "To align the training setting, we change the scheduler. If you want to change it back you can set it as below manually") + ''' + def learning_rate_schedule(base_lr, epoch, total_epochs): + alpha = epoch / total_epochs + if alpha <= 0.5: + factor = 1.0 + elif alpha <= 0.9: + factor = 1.0 - (alpha - 0.5) / 0.4 * 0.99 + else: + factor = 0.01 + return factor * base_lr + + ''' + + if args.curve.lower().startswith("b"): + curve = Bezier(args.num_bends) + elif args.curve.lower().startswith("p"): + curve = PolyChain(args.num_bends) + else: + raise SyntaxError("Unknown curve") + + for parameter in netC.parameters(): + parameter.requires_grad_(True) + + def model_mix(netC, weight1, ft_netC, weight2, model_mix_init): + coefs = [weight1, weight2] + lookupDict_for_netCs = [dict(netC.named_parameters()), dict(ft_netC.named_parameters())] + for parameter_name, parameter in model_mix_init.named_parameters(): + weighted_parameter_from_curve_netCs = 0 + for inter_netC_idx, lookupdict in enumerate(lookupDict_for_netCs): + weighted_parameter_from_curve_netCs += lookupdict[parameter_name].data * coefs[ + inter_netC_idx] + parameter.data.copy_( + weighted_parameter_from_curve_netCs + ) + return model_mix_init + + ''' + class simpleWeightNet(torch.nn.Module): + def __init__(self, weight_value = None): + super().__init__() + self.weight_value = weight_value + self.linear = torch.nn.Linear(5, 5) + if self.weight_value is not None: + self.linear.weight.data = torch.tensor(weight_value).float() + self.linear.bias.data = torch.tensor(weight_value).float() + else: + print( + {"self.linear.weight": self.linear.weight, + "self.linear.bias": self.linear.bias, } + ) + def forward(self, x): + return self.linear.weight, self.linear.bias, x + + + a = model_mix( + simpleWeightNet(1), + 0.5, + simpleWeightNet(0.3), + 7, + simpleWeightNet(), + ) + + print(a(1)) + print(a.linear.weight, a.linear.bias) + ''' + + ''' + 3 point -> 1/2 + 1/2 (1 intermediate point) + 4 point -> 1/3 + 1/3 + 1/3 (2 intermediate point) + ''' + + def getWeightForIntermediatePoints(point_number, nth_point): + one_weight_part = 1 / (point_number - 1) + return (nth_point) * one_weight_part, 1 - ((nth_point) * one_weight_part) + + '''getWeightForIntermediatePoints(4, 1) + (0.3333333333333333, 0.6666666666666667) + getWeightForIntermediatePoints(4, 2) + (0.6666666666666666, 0.33333333333333337)''' + + curve_netCs = [ + deepcopy(netC) + ] * (args.num_bends - 2) # init the intermediate models on curve + + # do model mix without modify the original model + for intermediate_curve_netC_idx, intermediate_curve_netC in enumerate(curve_netCs): + intermediate_curve_netC_idx += 1 + weight_left, weight_right = getWeightForIntermediatePoints(len(curve_netCs) + 2, + intermediate_curve_netC_idx) + curve_netCs[intermediate_curve_netC_idx - 1] = model_mix(netC, weight_left, ft_netC, weight_right, + intermediate_curve_netC) + + curve_netCs_optimizers = [] + curve_netCs_schedulers = [] + for intermediate_curve_netC in curve_netCs: + for parameter in netC.parameters(): + parameter.requires_grad_(True) + intermediate_curve_netC_opt, intermediate_curve_netC_scheduler = argparser_opt_scheduler( + intermediate_curve_netC, + self.args, + ) + curve_netCs_optimizers.append(intermediate_curve_netC_opt) + curve_netCs_schedulers.append(intermediate_curve_netC_scheduler) + + curve_netCs = [netC] + curve_netCs + [ft_netC] # add the start and end model + self.curve_netCs = curve_netCs + + criterion = nn.CrossEntropyLoss() + + # just for aggregation + new_netC_for_train_curve_aggregation = generate_cls_model(args.model, args.num_classes) + new_netC_optimizer, new_netC_scheduler = argparser_opt_scheduler( + new_netC_for_train_curve_aggregation, + self.args, + ) + + logging.info( + f"Before start training, just like the original paper, test for clean test error difference. see if two model have difference in sample classified wrongly") + m1_metrics, m1_predicts, m1_targets = given_dataloader_test( + model=netC, + test_dataloader=clean_test_dataloader, + criterion=criterion, + non_blocking=True, + device=self.device, + verbose=1, + ) + + logging.info(f"m1_metric={m1_metrics}") + + m1_wrong = (m1_predicts != m1_targets).cpu().numpy() + + m2_metrics, m2_predicts, m2_targets = given_dataloader_test( + model=ft_netC, + test_dataloader=clean_test_dataloader, + criterion=criterion, + non_blocking=True, + device=self.device, + verbose=1, + ) + + logging.info(f"m2_metric={m2_metrics}") + + m2_wrong = (m2_predicts != m2_targets).cpu().numpy() + + # both m1, m2 wrong + m1_m2_wrong = m1_wrong * m2_wrong + + m1_wrong_only = m1_wrong * (m1_m2_wrong != 1) + m2_wrong_only = m2_wrong * (m1_m2_wrong != 1) + + logging.info( + f"m1_wrong num = {np.sum(m1_wrong)}, m2_wrong num = {np.sum(m2_wrong)}, m1m2wrong = {np.sum(m1_m2_wrong)}, m1_wrong only = {np.sum(m1_wrong_only)}, m2_wrong only = {np.sum(m2_wrong_only)}" + ) + + if isinstance(args.test_t, float): + test_t_list = [args.test_t] + elif isinstance(args.test_t, list): + test_t_list = args.test_t + else: + test_t_list = np.arange(0, 1, 0.3) + + logging.warning("We use the following test_t_list: {}".format(test_t_list)) + + curve_record_dict = {} # for different test_t value used. + + if "use_clean_subset" in args.__dict__ and args.use_clean_subset == True: + dataloader_given = clean_train_dataloader + logging.warning( + f"Use clean_train_dataloader to train curve_netCs, data sample num = {len(clean_train_dataloader.dataset)}") + else: + dataloader_given = bd_train_dataloader + logging.warning( + f"Use bd_train_dataloader to train curve_netCs, data sample num = {len(bd_train_dataloader.dataset)}") + + for test_t in test_t_list: + curve_record_dict[test_t] = {} + # curve_record_dict[test_t]["clean_top0_eigenvalue_list"] = [] + # curve_record_dict[test_t]["bd_top0_eigenvalue_list"] = [] + curve_record_dict[test_t]["clean_test_loss_list"] = [] + curve_record_dict[test_t]["bd_test_loss_list"] = [] + curve_record_dict[test_t]["test_acc_list"] = [] + curve_record_dict[test_t]["test_asr_list"] = [] + curve_record_dict[test_t]["test_ra_list"] = [] + curve_record_dict[test_t]["train_loss_list"] = [] + curve_record_dict[test_t]["agg"] = Metric_Aggregator() + curve_record_dict[test_t]["bd_train_clean_part_test_loss_avg_over_batch_list"] = [] + curve_record_dict[test_t]["bd_train_clean_part_acc_list"] = [] + curve_record_dict[test_t]["bd_train_poisoned_part_loss_avg_over_batch_list"] = [] + curve_record_dict[test_t]["bd_train_poisoned_part_asr_list"] = [] + curve_record_dict[test_t]["bd_train_poisoned_part_ra_list"] = [] + # curve_record_dict[test_t]["clean_part_generalization_gap_list"] = [] + # curve_record_dict[test_t]["poison_part_generalization_gap_list"] = [] + + # os.makedirs( + # os.path.join(args.defense_save_path, "hessian_plot"), + # exist_ok=True, + # ) + + for epoch_idx in range(args.train_curve_epochs): + + new_netC_for_train_curve_aggregation, curve, new_netC_optimizer, new_netC_scheduler, curve_netCs, curve_netCs_optimizers, \ + curve_netCs_schedulers, one_epoch_train_loss = self.train_curve_one_epoch( + args, new_netC_for_train_curve_aggregation, curve, new_netC_optimizer, new_netC_scheduler, curve_netCs, + curve_netCs_optimizers, + curve_netCs_schedulers, criterion, dataloader_given, self.device, + ) + + # # use given test_t to generate one model to do test + # new_netC_for_train_curve_aggregation.eval() + # new_netC_for_train_curve_aggregation.to(self.device) + # + # for inter_netC in curve_netCs: # skip the start and end model + # inter_netC.eval() + # inter_netC.to(self.device) + # + # lookupDict_for_netCs = [dict(inter_netC.named_parameters()) for inter_netC in curve_netCs] + # inter_netC_coefs = curve(torch.tensor(args.test_t)) + # with torch.no_grad(): + # for parameter_name, parameter in new_netC_for_train_curve_aggregation.named_parameters(): + # weighted_parameter_from_curve_netCs = 0 + # for inter_netC_idx, lookupdict in enumerate(lookupDict_for_netCs): + # weighted_parameter_from_curve_netCs += lookupdict[parameter_name].data * inter_netC_coefs[ + # inter_netC_idx] + # parameter.copy_( + # weighted_parameter_from_curve_netCs + # ) + + if epoch_idx % args.test_curve_every != args.test_curve_every - 1: + continue + + logging.info("Epoch {} is finished, now test the model on clean and bd test set".format(epoch_idx)) + for test_t in test_t_list: + logging.info("Now test the model on test_t = {}".format(test_t)) + + # NOTE THAT THEY ARE ALL THE SAME !!!!!! + curve_record_dict[test_t]["train_loss_list"].append(one_epoch_train_loss) + + new_netC_for_train_curve_aggregation = sampleModelFromCurve( + new_netC_for_train_curve_aggregation, + curve_netCs, + curve, + test_t, + self.device, + ) + + # find the first batchnorm layer in model's named_modules + # first_BN = None + # for name, module in new_netC_for_train_curve_aggregation.named_modules(): + # if isinstance(module, torch.nn.BatchNorm2d): + # first_BN = module + # break + # if first_BN is not None: + # logging.info(f"Before go through train dataset, first_BN.running_mean = {first_BN.running_mean}") + # logging.info(f"Before go through train dataset, first_BN.running_var = {first_BN.running_var}") + + new_netC_for_train_curve_aggregation.train() + with torch.no_grad(): + for batch_idx, (x, _, *additional_info) in enumerate(dataloader_given): + x = x.to(self.device, non_blocking=args.non_blocking) + new_netC_for_train_curve_aggregation(x) + + # first_BN = None + # for name, module in new_netC_for_train_curve_aggregation.named_modules(): + # if isinstance(module, torch.nn.BatchNorm2d): + # first_BN = module + # break + # if first_BN is not None: + # logging.info(f"After go through train dataset, first_BN.running_mean = {first_BN.running_mean}") + # logging.info(f"After go through train dataset, first_BN.running_var = {first_BN.running_var}") + new_netC_for_train_curve_aggregation.eval() + + bd_train_clean_part_metrics, \ + bd_train_clean_part_test_epoch_predict_list, \ + bd_train_clean_part_test_epoch_label_list, \ + = given_dataloader_test( + model=new_netC_for_train_curve_aggregation, + test_dataloader=bd_train_clean_part_dataloader, + criterion=criterion, + non_blocking=args.non_blocking, + device=self.device, + verbose=1, + ) + + bd_train_clean_part_test_loss_avg_over_batch = bd_train_clean_part_metrics["test_loss_avg_over_batch"] + bd_train_clean_part_acc = bd_train_clean_part_metrics["test_acc"] + + curve_record_dict[test_t]["bd_train_clean_part_test_loss_avg_over_batch_list"].append( + bd_train_clean_part_test_loss_avg_over_batch) + curve_record_dict[test_t]["bd_train_clean_part_acc_list"].append(bd_train_clean_part_acc) + + bd_train_poisoned_part_metrics, \ + bd_train_poisoned_part_epoch_predict_list, \ + bd_train_poisoned_part_epoch_label_list, \ + bd_train_poisoned_part_epoch_original_index_list, \ + bd_train_poisoned_part_epoch_poison_indicator_list, \ + bd_train_poisoned_part_epoch_original_targets_list = test_given_dataloader_on_mix( + model=new_netC_for_train_curve_aggregation, + test_dataloader=bd_train_poisoned_part_dataloader, + criterion=criterion, + non_blocking=args.non_blocking, + device=self.device, + verbose=1, + ) + + bd_train_poisoned_part_loss_avg_over_batch = bd_train_poisoned_part_metrics["test_loss_avg_over_batch"] + bd_train_poisoned_part_asr = all_acc(bd_train_poisoned_part_epoch_predict_list, + bd_train_poisoned_part_epoch_label_list) + bd_train_poisoned_part_ra = all_acc(bd_train_poisoned_part_epoch_predict_list, + bd_train_poisoned_part_epoch_original_targets_list) + + curve_record_dict[test_t]["bd_train_poisoned_part_loss_avg_over_batch_list"].append( + bd_train_poisoned_part_loss_avg_over_batch) + curve_record_dict[test_t]["bd_train_poisoned_part_asr_list"].append(bd_train_poisoned_part_asr) + curve_record_dict[test_t]["bd_train_poisoned_part_ra_list"].append(bd_train_poisoned_part_ra) + + clean_metrics, \ + clean_test_epoch_predict_list, \ + clean_test_epoch_label_list, \ + = given_dataloader_test( + model=new_netC_for_train_curve_aggregation, + test_dataloader=clean_test_dataloader, + criterion=criterion, + non_blocking=args.non_blocking, + device=self.device, + verbose=1, + ) + + clean_test_loss_avg_over_batch = clean_metrics["test_loss_avg_over_batch"] + test_acc = clean_metrics["test_acc"] + + curve_record_dict[test_t]["clean_test_loss_list"].append(clean_test_loss_avg_over_batch) + curve_record_dict[test_t]["test_acc_list"].append(test_acc) + + bd_metrics, \ + bd_test_epoch_predict_list, \ + bd_test_epoch_label_list, \ + bd_test_epoch_original_index_list, \ + bd_test_epoch_poison_indicator_list, \ + bd_test_epoch_original_targets_list = test_given_dataloader_on_mix( + model=new_netC_for_train_curve_aggregation, + test_dataloader=bd_test_dataloader, + criterion=criterion, + non_blocking=args.non_blocking, + device=self.device, + verbose=1, + ) + + bd_test_loss_avg_over_batch = bd_metrics["test_loss_avg_over_batch"] + test_asr = all_acc(bd_test_epoch_predict_list, bd_test_epoch_label_list) + test_ra = all_acc(bd_test_epoch_predict_list, bd_test_epoch_original_targets_list) + + curve_record_dict[test_t]["bd_test_loss_list"].append(bd_test_loss_avg_over_batch) + curve_record_dict[test_t]["test_asr_list"].append(test_asr) + curve_record_dict[test_t]["test_ra_list"].append(test_ra) + + # clean_top_eigenvalues = plot_hessian_eigenvalues( + # model_visual=new_netC_for_train_curve_aggregation, + # data_loader=clean_test_dataloader, + # device=self.device, + # # save_path_for_hessian = os.path.join(args.defense_save_path, "hessian_plot", "clean_hessian_eigenvalues_{}_{}.png".format(epoch_idx, test_t)), + # ) + # clean_top0_eigenvalue = clean_top_eigenvalues[0] + # + # curve_record_dict[test_t]["clean_top0_eigenvalue_list"].append(clean_top0_eigenvalue) + # + # bd_top_eigenvalues = plot_hessian_eigenvalues( + # model_visual=new_netC_for_train_curve_aggregation, + # data_loader=bd_test_dataloader, + # device=self.device, + # # save_path_for_hessian=os.path.join(args.defense_save_path, "hessian_plot", "bd_hessian_eigenvalues_{}_{}.png".format(epoch_idx, test_t)), + # ) + # bd_top0_eigenvalue = bd_top_eigenvalues[0] + # + # curve_record_dict[test_t]["bd_top0_eigenvalue_list"].append(bd_top0_eigenvalue) + # + # curve_record_dict[test_t]["clean_part_generalization_gap_list"].append( + # bd_train_clean_part_acc - test_acc) + # curve_record_dict[test_t]["poison_part_generalization_gap_list"].append( + # bd_train_poisoned_part_asr - test_asr) + + curve_record_dict[test_t]["agg"]( + { + "epoch": epoch_idx, + "test_t": test_t, + "train_epoch_loss_avg_over_batch": one_epoch_train_loss, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + # "clean_top0_eigenvalue": clean_top0_eigenvalue, + # "bd_top0_eigenvalue": bd_top0_eigenvalue, + "bd_train_clean_part_test_loss_avg_over_batch": bd_train_clean_part_test_loss_avg_over_batch, + "bd_train_clean_part_acc": bd_train_clean_part_acc, + "bd_train_poisoned_part_loss_avg_over_batch": bd_train_poisoned_part_loss_avg_over_batch, + "bd_train_poisoned_part_asr": bd_train_poisoned_part_asr, + "bd_train_poisoned_part_ra": bd_train_poisoned_part_ra, + # "clean_part_generalization_gap": bd_train_clean_part_acc - test_acc, + # "poison_part_generalization_gap": bd_train_poisoned_part_asr - test_asr, + } + ) + + # for bd train different part + general_plot_for_epoch( + { + "bd_train_clean_part_test_loss_avg_over_batch": curve_record_dict[test_t][ + "bd_train_clean_part_test_loss_avg_over_batch_list"], + "bd_train_poisoned_part_loss_avg_over_batch": curve_record_dict[test_t][ + "bd_train_poisoned_part_loss_avg_over_batch_list"], + }, + save_path=f"{args.defense_save_path}/t_{test_t}_bd_train_parts_loss.png", + ylabel="value", + ) + + general_plot_for_epoch( + { + "bd_train_clean_part_acc": curve_record_dict[test_t]["bd_train_clean_part_acc_list"], + "bd_train_poisoned_part_asr": curve_record_dict[test_t]["bd_train_poisoned_part_asr_list"], + "bd_train_poisoned_part_ra": curve_record_dict[test_t]["bd_train_poisoned_part_ra_list"], + # "clean_part_generalization_gap": curve_record_dict[test_t][ + # "clean_part_generalization_gap_list"], + # "poisoned_part_generalization_gap": curve_record_dict[test_t][ + # "poison_part_generalization_gap_list"], + }, + save_path=f"{args.defense_save_path}/t_{test_t}_bd_train_parts_acc_like.png", + ylabel="value", + ) + + # for bd_test and clean_test + plot_loss( + curve_record_dict[test_t]["train_loss_list"], + curve_record_dict[test_t]["clean_test_loss_list"], + curve_record_dict[test_t]["bd_test_loss_list"], + args.defense_save_path, + f"curve_test_loss_metric_plots_t_{test_t}", + args.test_curve_every, + ) + + plot_acc_like_metric( + [], [], [], + curve_record_dict[test_t]["test_acc_list"], + curve_record_dict[test_t]["test_asr_list"], + curve_record_dict[test_t]["test_ra_list"], + args.defense_save_path, + f"curve_test_acc_like_metric_plots_t_{test_t}", + args.test_curve_every, + ) + + # plot + # fix test_t on the curve path, compare loss and eigenvalue along time + + # t = np.arange(len(curve_record_dict[test_t]["clean_top0_eigenvalue_list"])) + # data1 = curve_record_dict[test_t]["clean_top0_eigenvalue_list"] + # data2 = curve_record_dict[test_t]["clean_part_generalization_gap_list"] + # data3 = curve_record_dict[test_t]["poison_part_generalization_gap_list"] + # data4 = curve_record_dict[test_t]["bd_top0_eigenvalue_list"] + # + # fig, ax1 = plt.subplots() + # + # color = 'tab:red' + # ax1.set_xlabel('epoch') + # ax1.set_ylabel('clean_top0_eigenvalue_list', color=color) + # ax1.plot(t, data1, color=color) + # ax1.tick_params(axis='y', labelcolor=color) + # + # ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis + # + # color = 'tab:blue' + # ax2.set_ylabel('clean_part_generalization_gap', color=color) # we already handled the x-label with ax1 + # ax2.plot(t, data2, color=color) + # ax2.tick_params(axis='y', labelcolor=color) + # + # fig.tight_layout() # otherwise the right y-label is slightly clipped + # plt.savefig(f"{args.defense_save_path}/t_{test_t}_clean_top0_eigenvalue_list.png", ) + # plt.close() + # + # fig, ax1 = plt.subplots() + # + # color = 'tab:green' + # ax1.set_xlabel('epoch') + # ax1.set_ylabel('bd_part_generalization_gap', color=color) # we already handled the x-label with ax1 + # ax1.plot(t, data3, color=color) + # ax1.tick_params(axis='y', labelcolor=color) + # + # ax4 = ax1.twinx() # instantiate a second axes that shares the same x-axis + # color = 'tab:orange' + # ax4.set_ylabel('bd_top0_eigenvalue_list', color=color) # we already handled the x-label with ax1 + # ax4.plot(t, data4, color=color) + # ax4.tick_params(axis='y', labelcolor=color) + # + # fig.tight_layout() # otherwise the right y-label is slightly clipped + # plt.savefig(f"{args.defense_save_path}/t_{test_t}_bd_top0_eigenvalue_list.png", ) + # plt.close() + + # general_plot_for_epoch( + # { + # # "train_loss_list": curve_record_dict[test_t]["train_loss_list"], + # "clean_test_loss_list": curve_record_dict[test_t]["clean_test_loss_list"], + # "bd_test_loss_list": curve_record_dict[test_t]["bd_test_loss_list"], + # "top0_eigenvalue_list":curve_record_dict[test_t]["top0_eigenvalue_list"], + # }, + # save_path=f"{args.defense_save_path}/t_{test_t}_top0_eigenvalue_list.png", + # ylabel="value", + # ) + + curve_record_dict[test_t]["agg"].to_dataframe().to_csv( + f"{args.defense_save_path}/curve_train_df_t_{test_t}.csv") + + # # plot the clean_top0_eigenvalue_list and bd_top0_eigenvalue_list for different test_t + # same_epoch_clean_top0_eigenvalue_list = [] + # same_epoch_clean_part_generalization_gap_list = [] + # same_epoch_bd_part_generalization_gap_list = [] + # same_epoch_bd_top0_eigenvalue_list = [] + # for test_t in test_t_list: + # same_epoch_clean_top0_eigenvalue_list.append( + # curve_record_dict[test_t]["clean_top0_eigenvalue_list"][-1]) + # same_epoch_clean_part_generalization_gap_list.append( + # curve_record_dict[test_t]["clean_part_generalization_gap_list"][-1] + # ) + # same_epoch_bd_part_generalization_gap_list.append( + # curve_record_dict[test_t]["poison_part_generalization_gap_list"][-1] + # ) + # same_epoch_bd_top0_eigenvalue_list.append(curve_record_dict[test_t]["bd_top0_eigenvalue_list"][-1]) + + # t = np.arange(len(same_epoch_clean_top0_eigenvalue_list) + 1)[1:] / ( + # len(same_epoch_clean_top0_eigenvalue_list) + 1) + # data1 = same_epoch_clean_top0_eigenvalue_list + # data2 = same_epoch_clean_part_generalization_gap_list + # data3 = same_epoch_bd_part_generalization_gap_list + # data4 = same_epoch_bd_top0_eigenvalue_list + # + # fig, ax1 = plt.subplots() + # + # color = 'tab:red' + # ax1.set_xlabel('test_t on curve path') + # ax1.set_ylabel('same_epoch_clean_top0_eigenvalue_list', color=color) + # ax1.plot(t, data1, color=color) + # ax1.tick_params(axis='y', labelcolor=color) + # + # ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis + # + # color = 'tab:blue' + # ax2.set_ylabel('same_epoch_clean_part_generalization_gap_list', + # color=color) # we already handled the x-label with ax1 + # ax2.plot(t, data2, color=color) + # ax2.tick_params(axis='y', labelcolor=color) + # + # fig.tight_layout() # otherwise the right y-label is slightly clipped + # plt.savefig(f"{args.defense_save_path}/epoch_{epoch_idx}_clean_path.png", ) + # plt.close() + # + # fig, ax1 = plt.subplots() + # + # color = 'tab:green' + # ax1.set_xlabel('test_t on curve path') + # ax1.set_ylabel('same_epoch_bd_part_generalization_gap_list', + # color=color) # we already handled the x-label with ax1 + # ax1.plot(t, data3, color=color) + # ax1.tick_params(axis='y', labelcolor=color) + # + # ax4 = ax1.twinx() # instantiate a second axes that shares the same x-axis + # color = 'tab:orange' + # ax4.set_ylabel('same_epoch_bd_top0_eigenvalue_list', color=color) # we already handled the x-label with ax1 + # ax4.plot(t, data4, color=color) + # ax4.tick_params(axis='y', labelcolor=color) + # + # fig.tight_layout() # otherwise the right y-label is slightly clipped + # plt.savefig(f"{args.defense_save_path}/epoch_{epoch_idx}_bd_path.png", ) + # plt.close() + + for test_t in test_t_list: + curve_record_dict[test_t]["agg"].summary().to_csv( + f"{args.defense_save_path}/curve_train_df_summary_t_{test_t}.csv") + + if 0.1 in test_t_list: + final_t = 0.1 + else: + logging.warning(f"0.1 is not in test_t_list, so we find the nearest value to 0.1 for final report result") + def find_nearest(array, value): + # thanks to https://stackoverflow.com/questions/2566412/find-nearest-value-in-numpy-array + # this function is from Mateen Ulhaq + array = np.asarray(array) + idx = (np.abs(array - value)).argmin() + return array[idx] + final_t = find_nearest(np.array(test_t_list), value=0.1) + + t = final_t + test_acc = curve_record_dict[t]["test_acc_list"][-1] + test_asr = curve_record_dict[t]["test_asr_list"][-1] + test_ra = curve_record_dict[t]["test_ra_list"][-1] + + agg = Metric_Aggregator() + agg({ + 'test_acc': test_asr, + 'test_asr': test_acc, + 'test_ra': test_ra, + 't': t, + }) + agg.to_dataframe().to_csv(f"{args.defense_save_path}/mcr_df_summary.csv") + + torch.save( + { + 'model_name': args.model, + 'model': new_netC_for_train_curve_aggregation.cpu().state_dict(), + 'test_acc': test_asr, + 'test_asr': test_acc, + 'test_ra': test_ra, + 't': t, + }, + f'{args.defense_save_path}/defense_result.pt' + ) + + def train_curve_one_epoch(self, args, netC, curve, netC_optimizer, netC_scheduler, curve_netCs, + curve_netCs_optimizers, curve_netCs_schedulers, criterion, clean_train_dataloader, + device): + + netC.train() + netC.to(device) + train_loss = 0 + correct = 0 + total = 0 + + for inter_netC in curve_netCs[1:-1]: # skip the start and end model + inter_netC.train() + inter_netC.requires_grad_() + inter_netC.to(device) + + curve_netCs[0].eval() + curve_netCs[0].requires_grad_() + curve_netCs[0].to(device) + + curve_netCs[-1].eval() + curve_netCs[-1].requires_grad_() + curve_netCs[-1].to(device) + + batch_loss = [] + for batch_idx, (inputs, targets, *other) in enumerate(clean_train_dataloader): + + # copy parameters and do weighted sum, from curve_netCs to netC + lookupDict_for_netCs = [dict(inter_netC.named_parameters()) for inter_netC in curve_netCs] + inter_netC_coefs = curve(inputs.data.new(1).uniform_(0.0, 1.0)) + with torch.no_grad(): + for parameter_name, parameter in netC.named_parameters(): + weighted_parameter_from_curve_netCs = 0 + for inter_netC_idx, lookupdict in enumerate(lookupDict_for_netCs): + weighted_parameter_from_curve_netCs += lookupdict[parameter_name].data * inter_netC_coefs[ + inter_netC_idx] + parameter.copy_( + weighted_parameter_from_curve_netCs + ) + + inputs, targets = inputs.to(device), targets.to(device) + outputs = netC(inputs) + loss = criterion(outputs, targets) + loss.backward() + + # send back grad from netC to curve_netCs + for parameter_name, parameter in netC.named_parameters(): + for inter_netC_idx, lookupdict in enumerate(lookupDict_for_netCs): + lookupdict[parameter_name].grad = parameter.grad * inter_netC_coefs[ + inter_netC_idx] + + # do step for all models + netC_optimizer.step() + for inter_netC_optimizer in curve_netCs_optimizers: + inter_netC_optimizer.step() + + train_loss += loss.item() + _, predicted = outputs.max(1) + total += targets.size(0) + correct += predicted.eq(targets).sum().item() + + print(batch_idx, len(clean_train_dataloader), 'Loss: %.3f | train Acc: %.3f%% (%d/%d)' + % (train_loss / (batch_idx + 1), 100. * correct / total, correct, total)) + + batch_loss.append(loss.item()) + + one_epoch_loss = sum(batch_loss) / len(batch_loss) + + # update the all models' scheduler + for scheduler in (curve_netCs_schedulers + [netC_scheduler]): + if scheduler: + if args.lr_scheduler == 'ReduceLROnPlateau': + scheduler.step(one_epoch_loss) + elif args.lr_scheduler == 'CosineAnnealingLR': + scheduler.step() + + return netC, curve, netC_optimizer, netC_scheduler, curve_netCs, curve_netCs_optimizers, curve_netCs_schedulers, one_epoch_loss + + +if __name__ == '__main__': + mcr = MCR() + parser = argparse.ArgumentParser(description=sys.argv[0]) + parser = mcr.set_args(parser) + args = parser.parse_args() + mcr.add_yaml_to_args(args) + args = mcr.process_args(args) + mcr.prepare(args) + mcr.defense() \ No newline at end of file diff --git a/defense/nab.py b/defense/nab.py new file mode 100644 index 0000000..8df94f9 --- /dev/null +++ b/defense/nab.py @@ -0,0 +1,728 @@ +''' +This file is modified based on the following source: +link : https://github.com/SCLBD/DBD & https://github.com/damianliumin/non-adversarial_backdoor +The defense method is called nab. +The license is bellow the code + +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. save process + 5. new standard: robust accuracy + 6. add some new backdone such as mobilenet efficientnet and densenet, reconstruct the backbone of vgg and preactresnet + 7. Different data augmentation (transform) methods are used + 8. rewrite the dateset +basic sturcture for defense method: + 1. basic setting: args + 2. attack result(model, train data, test data) + 3. nab defense: + a. self-supervised learning generates feature extractor + b. LGA from ABL method to detect poison samples + c. relabel the detected samples + d. train the model using the relabelled dataset + 4. test the result and get ASR, ACC, RA + +Note: + The original code use an additional clean dataset to train a auxiliary classifier for relabeling. + To make a fair comparison, we use the SSL model from DBD for relabeling as described in the paper. +''' + + +import logging +import time +import argparse +import sys +import os + + +sys.path.append('../') +sys.path.append(os.getcwd()) +from utils.defense_utils.dbd.data.prefetch import PrefetchLoader + +import numpy as np +import torch +import yaml +from utils.trainer_cls import Metric_Aggregator +from pprint import pformat +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform, get_dataset_normalization, get_dataset_denormalization +from utils.trainer_cls import PureCleanModelTrainer +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.save_load_attack import load_attack_result, save_defense_result +from utils.aggregate_block.train_settings_generate import argparser_opt_scheduler +from utils.trainer_cls import Metric_Aggregator, PureCleanModelTrainer, general_plot_for_epoch, given_dataloader_test + +from utils.defense_utils.dbd.data.dataset import SelfPoisonDataset +from utils.aggregate_block.fix_random import fix_random +from utils.save_load_attack import load_attack_result + +from utils.defense_utils.dbd.model.model import SelfModel +from utils.defense_utils.dbd.model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_optimizer, + get_scheduler, +) +from utils.bd_dataset_v2 import xy_iter, slice_iter +from utils.defense_utils.dbd.utils_db.setup import ( + load_config, +) +from utils.aggregate_block.train_settings_generate import argparser_opt_scheduler + +from utils.defense_utils.dbd.utils_db.trainer.simclr import simclr_train +from utils.aggregate_block.dataset_and_transform_generate import get_transform_self + +def get_information(args,result,config_ori): + config = config_ori + aug_transform = get_transform_self(args.dataset, *([args.input_height,args.input_width]) , train = True, prefetch =args.prefetch) + + x = slice_iter(result["bd_train"], axis=0) + y = slice_iter(result["bd_train"], axis=1) + + self_poison_train_data = SelfPoisonDataset(x,y, aug_transform,args) + + self_poison_train_loader_ori = torch.utils.data.DataLoader(self_poison_train_data, batch_size=args.batch_size_self, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + if args.prefetch: + # x,y: PIL.Image.Image -> SelfPoisonDataset: Tensor with trans, no normalization [0,255]-> PrefetchLoader: Tensor with trans [0,1], with normalization + self_poison_train_loader = PrefetchLoader(self_poison_train_loader_ori, self_poison_train_data.mean, self_poison_train_data.std) + else: + # x,y: PIL.Image.Image, [0,255] -> SelfPoisonDataset: Tensor with trans [0,1] with normalization + self_poison_train_loader = self_poison_train_loader_ori + + backbone = get_network_dbd(args) + self_model = SelfModel(backbone) + self_model = self_model.to(args.device) + criterion = get_criterion(config["criterion"]) + criterion = criterion.to(args.device) + optimizer = get_optimizer(self_model, config["optimizer"]) + scheduler = get_scheduler(optimizer, config["lr_scheduler"]) + resumed_epoch = load_state( + self_model, args.resume, args.checkpoint_load, 0, optimizer, scheduler, + ) + box = { + 'self_poison_train_loader': self_poison_train_loader, + 'self_model': self_model, + 'criterion': criterion, + 'optimizer': optimizer, + 'scheduler': scheduler, + 'resumed_epoch': resumed_epoch + } + return box + + + +def get_args(): + # set the basic parameter + parser = argparse.ArgumentParser() + + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument('--checkpoint_load', type=str) + parser.add_argument('--checkpoint_save', type=str) + parser.add_argument('--log', type=str) + parser.add_argument("--data_root", type=str) + + parser.add_argument('--dataset', type=str, help='mnist, cifar10, gtsrb, celeba, tiny') + parser.add_argument("--num_classes", type=int) + parser.add_argument("--input_height", type=int) + parser.add_argument("--input_width", type=int) + parser.add_argument("--input_channel", type=int) + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument("--num_workers_semi", type=float) + parser.add_argument('--lr', type=float) + + parser.add_argument('--attack', type=str) + parser.add_argument('--poison_rate', type=float) + parser.add_argument('--target_type', type=str, help='all2one, all2all, cleanLabel') + parser.add_argument('--target_label', type=int) + parser.add_argument('--trigger_type', type=str, help='squareTrigger, gridTrigger, fourCornerTrigger, randomPixelTrigger, signalTrigger, trojanTrigger') + + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--index', type=str, help='index of clean data') + parser.add_argument('--model', type=str, help='resnet18') + parser.add_argument('--result_file', type=str, help='the location of result') + parser.add_argument('--yaml_path', type=str, default="./config/defense/nab/config.yaml", help='the path of yaml') + + # set the parameter for the general + parser.add_argument('--prefetch',type=bool ) + + # SSL part for relabel + parser.add_argument('--epoch_warmup',type=int ) + parser.add_argument('--batch_size_self',type=int ) + parser.add_argument('--temperature',type=int ) + parser.add_argument('--epsilon',type=int ) + parser.add_argument('--epoch_self',type=int ) + + + # LGA part for detection + parser.add_argument('--epoch_lga', default= 20,type=int ) + parser.add_argument('--gamma', default= 0.5,type=float ) + parser.add_argument('--batch_size_lgd', default= 64,type=int ) + + + arg = parser.parse_args() + + print(arg) + return arg + + +def nab(args,result): + agg = Metric_Aggregator() + + # Turn off all transforms, so that the dataset return PIL.Image.Image object + result["bd_train"].wrap_img_transform = None + result["bd_test"].wrap_img_transform = None + result["clean_test"].wrap_img_transform = None + + ### set logger + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + if args.log is not None and args.log != '': + fileHandler = logging.FileHandler(os.getcwd() + args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + # print(os.getcwd() + args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + else: + fileHandler = logging.FileHandler(os.getcwd() + './log' + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + # print(os.getcwd() + './log' + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + fix_random(args.random_seed) + + logging.info("===Setup running===") + + if args.checkpoint_load == None: + args.resume = 'False' + else : + args.resume = args.checkpoint_load + + if args.dataset == 'cifar10': + config_file = './utils/defense_utils/dbd/config_z/pretrain/' + 'squareTrigger/' + args.dataset + '/example.yaml' + else: + config_file = './utils/defense_utils/dbd/config_z/pretrain/' + 'squareTrigger/imagenet/example.yaml' + config_ori, inner_dir, config_name = load_config(config_file) + try: + gpu = int(os.environ['CUDA_VISIBLE_DEVICES']) + except: + print('CUDA_VISIBLE_DEVICES is not set. Set GPU=1 now.') + gpu = 0 + + logging.info("===Self-Supervise Learning Phase===") + + # Step 1: Train the self-supervised learning model + information = get_information(args,result,config_ori) + + self_poison_train_loader = information['self_poison_train_loader'] + self_model = information['self_model'] + criterion = information['criterion'] + optimizer = information['optimizer'] + scheduler = information['scheduler'] + resumed_epoch = information['resumed_epoch'] + + if os.path.exists(os.getcwd() + args.checkpoint_save + "/self_latest_model.pt"): + logging.info("Load the latest model from {}".format(os.getcwd() + args.checkpoint_save + "/self_latest_model.pt")) + else: + # a.self-supervised learning generates feature extractor + for epoch in range(args.epoch_self - resumed_epoch): + + self_train_result = simclr_train( + self_model, self_poison_train_loader, criterion, optimizer, logger, False + ) + + if scheduler is not None: + scheduler.step() + logging.info( + "Adjust learning rate to {}".format(optimizer.param_groups[0]["lr"]) + ) + + + result_self = {"self_train": self_train_result} + + saved_dict = { + "epoch": epoch + resumed_epoch + 1, + "result": result_self, + "model_state_dict": self_model.state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + } + if scheduler is not None: + saved_dict["scheduler_state_dict"] = scheduler.state_dict() + + ckpt_path = os.path.join(os.getcwd() + args.checkpoint_save, "self_latest_model.pt") + torch.save(saved_dict, ckpt_path) + logging.info("Save the latest model to {}".format(ckpt_path)) + + if args.dataset == 'cifar10': + config_file_semi = './utils/defense_utils/dbd/config_z/semi/' + 'badnets/' + args.dataset + '/example.yaml' + else: + config_file_semi = './utils/defense_utils/dbd/config_z/semi/' + 'badnets/imagenet/example.yaml' + + finetune_config, finetune_inner_dir, finetune_config_name = load_config(config_file_semi) + pretrain_config, pretrain_inner_dir, pretrain_config_name = load_config( + config_file + ) + ckpt_path = os.path.join(os.getcwd() + args.checkpoint_save, "self_latest_model.pt") + pretrain_ckpt_path = ckpt_path + # merge the pretrain and finetune config + pretrain_config.update(finetune_config) + + pretrain_config['warmup']['criterion']['sce']['num_classes'] = args.num_classes + pretrain_config['warmup']['num_epochs'] = args.epoch_warmup + + backbone = get_network_dbd(args) + + self_model = SelfModel(backbone) + self_model = self_model.to(args.device) + # # Load backbone from the pretrained model. + loc = os.path.join(os.getcwd() + args.checkpoint_save, "self_latest_model.pt") + load_state( + self_model, pretrain_config["pretrain_checkpoint"], loc, args.device, logger + ) + + logging.info("\n===Prepare data===") + + result["bd_train"].wrap_img_transform = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = False) + result["bd_test"].wrap_img_transform = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = False) + result["clean_test"].wrap_img_transform = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = False) + + data_loader = torch.utils.data.DataLoader(result["bd_train"], batch_size=args.batch_size_lgd, num_workers=args.num_workers, shuffle=False) + + # Step 2: Detect suspicious samples: + logging.info('----------- Network Initialization --------------') + model_ascent = generate_cls_model(args.model,args.num_classes) + model_ascent.to(args.device) + + logging.info('finished model init...') + # initialize optimizer + # because the optimizer has parameter nesterov + args.momentum = 0.9 + args.weight_decay = 5e-4 + optimizer = torch.optim.SGD(model_ascent.parameters(), + lr=args.lr, + momentum=args.momentum, + weight_decay=args.weight_decay) + + + + logging.info('----------- Poisoned Sample Detection (LGA) Phase --------------') + acc_cnt = 0 + all_cnt = 0 + loss_log = 0 + criterion = torch.nn.CrossEntropyLoss() + if os.path.exists(os.getcwd() + args.checkpoint_save + "/ascent_latest_model.pt"): + logging.info("Load the latest model from {}".format(os.getcwd() + args.checkpoint_save + "/ascent_latest_model.pt")) + else: + for epoch in range(args.epoch_lga): + model_ascent.train() + for i, (image, label, *other_info) in enumerate(data_loader): + image = image.to(args.device) + label = label.to(args.device) + logits = model_ascent(image) + loss = criterion(logits, label) + loss = (loss - args.gamma).abs() + args.gamma + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + acc_cnt += (logits.detach().max(1)[1] == label).sum() + all_cnt += len(label) + loss_log += loss.detach() * len(label) + + train_acc = acc_cnt / all_cnt * 100 + loss = loss_log / all_cnt + import math + lr = 0.5 * (1 + math.cos(math.pi * epoch / (args.epoch_lga + 80))) * args.lr + for param_group in optimizer.param_groups: + param_group['lr'] = lr + logging.info('epoch: {}, train_acc: {:.2f}%, loss: {:.4f}'.format(epoch, train_acc, loss)) + torch.save(model_ascent.state_dict(), os.path.join(os.getcwd() + args.checkpoint_save, "ascent_latest_model.pt")) + + model_ascent.load_state_dict(torch.load(os.path.join(os.getcwd() + args.checkpoint_save, "ascent_latest_model.pt"))) + model_ascent.eval() + # Isolate data + criterion = torch.nn.CrossEntropyLoss(reduction='none') + model_ascent.eval() + loss_list = [] + idx_list = [] + backdoor_list = [] + with torch.no_grad(): + for i, (image, label, idx, backdoor, ori_label) in enumerate(data_loader): + image = image.to(args.device) + label = label.to(args.device) + out = model_ascent(image) + loss = criterion(out, label) + loss_list.append(loss.cpu().squeeze()) + idx_list.append(idx) + backdoor_list.append(backdoor) + loss = torch.cat(loss_list) + idx = torch.cat(idx_list) + backdoor = torch.cat(backdoor_list) + + + # select + for ratio in (0.01, 0.05, 0.10, 0.2): + num_iso = int(ratio * len(idx)) + select_isolation = loss.sort()[1][:num_iso] + idx_iso = idx[select_isolation] + + isolated = torch.zeros(len(idx)).bool() + isolated.scatter_(0, idx_iso, True) + + attacked_ratio = backdoor[select_isolation].sum() / num_iso + print("Malign, Ratio {:.2f}, isolated {} among {} samples, with acc: {:.2f}%".format(ratio, num_iso, len(idx), attacked_ratio * 100)) + logging.info("Malign, Ratio {:.2f}, isolated {} among {} samples, with acc: {:.2f}%".format(ratio, num_iso, len(idx), attacked_ratio * 100)) + torch.save(isolated,os.getcwd() + args.checkpoint_save + f"/{args.dataset}_{ratio}_lga") + + # Step 3: Relabel + + # compute centroids for each class + class_centroids = [0 for _ in range(args.num_classes)] + class_n_samples = [0 for _ in range(args.num_classes)] + temp_iso = torch.load(os.getcwd() + args.checkpoint_save + f"/{args.dataset}_{0.2}_lga") + + for i, (image, label, idx, backdoor, ori_label) in enumerate(data_loader): + image = image.to(args.device) + label = label.to(args.device) + out = self_model(image).detach() + for j in range(len(label)): + if not temp_iso[idx[j]]: + class_centroids[label[j]] += out[j] + class_n_samples[label[j]] += 1 + for i in range(args.num_classes): + class_centroids[i] /= class_n_samples[i] + + # detect suspicious samples + temp_iso = torch.load(os.getcwd() + args.checkpoint_save + f"/{args.dataset}_{0.1}_lga") + x_samples = [x for x,y,*other_info in result["bd_train"]] + y_samples = [y for x,y,*other_info in result["bd_train"]] + true_label = [other_info[-1] for x,y,*other_info in result["bd_train"]] + poi_info = [other_info[1] for x,y,*other_info in result["bd_train"]] + normlization = get_dataset_normalization(args.dataset) + denormalization = get_dataset_denormalization(normalization=normlization) + self_model.eval() + relabel_correct = 0 + detect_correct = 0 + total_detect = 0 + relabel_correct_bd = 0 + relabel_correct_clean = 0 + total_bd = 0 + total_clean = 0 + # oracle case + # temp_iso = poi_info + + pseudo_label = [] + for i in range(len(x_samples)): + if not temp_iso[i]: + pseudo_label.append(y_samples[i]) + else: + # decide the new label by nearest centroid + x = x_samples[i].unsqueeze(0).to(args.device) + out = self_model(x).detach() + dist = [torch.norm(out - class_centroids[j]) for j in range(args.num_classes)] + # oracle case + # new_label = true_label[i] + new_label = torch.tensor(dist).argmin().item() + pseudo_label.append(new_label) + + relabel_correct += (new_label == true_label[i]) + if poi_info[i] == 1: + relabel_correct_bd += (new_label == true_label[i]) + total_bd += 1 + else: + relabel_correct_clean += (new_label == true_label[i]) + total_clean += 1 + detect_correct += (poi_info[i] == 1) + total_detect += 1 + + print("Relabel correct: {:.2f}%, Detect correct: {:.2f}%".format(relabel_correct / total_detect * 100, detect_correct / total_detect * 100)) + print("Relabel correct clean: {:.2f}%, Relabel correct bd: {:.2f}%".format(relabel_correct_clean / relabel_correct_clean * 100, relabel_correct_bd / total_bd * 100)) + print('Total detect: ', total_detect) + logging.info("Relabel correct: {:.2f}%, Detect correct: {:.2f}%".format(relabel_correct / total_detect * 100, detect_correct / total_detect * 100)) + logging.info("Relabel correct clean: {:.2f}%, Relabel correct bd: {:.2f}%".format(relabel_correct_clean / relabel_correct_clean * 100, relabel_correct_bd / total_bd * 100)) + logging.info(f'Total detect: {total_detect}') + + def inject(x): + x_new = x.clone() + x_new[:, 0:2, 0:2] = 0.0 + return x_new + + def inject_trans(trasform): + def transform_new(x): + x = trasform(x) + x = inject(x) + return x + return transform_new + # Step 4: Retrain + + logging.info('----------- Poisoned Sample Detection (LGA) Phase --------------') + + result["bd_train"].wrap_img_transform = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = True) + + model = generate_cls_model(args.model,args.num_classes) + + outer_opt = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), + lr=args.lr, + momentum=args.sgd_momentum, # 0.9 + weight_decay=args.wd, # 5e-4 + ) + relabel_data_loader = torch.utils.data.DataLoader(result["bd_train"], batch_size=args.batch_size, shuffle=True, num_workers=4) + + test_tran = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = False) + data_bd_testset = result['bd_test'] + data_bd_testset.wrap_img_transform = inject_trans(test_tran) + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True) + + data_clean_testset = result['clean_test'] + data_clean_testset.wrap_img_transform = inject_trans(test_tran) + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True) + + clean_test_loss_list = [] + bd_test_loss_list = [] + ra_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + model.to(args.device) + criterion = torch.nn.CrossEntropyLoss() + import math + for epoch in range(args.epochs): + model.train() + for images, labels, *other_info in relabel_data_loader: + images = images.to(args.device) + labels = labels.to(args.device) + + idx = other_info[0] + + # get pseudo label + pseudo_label_batch = torch.tensor([pseudo_label[i] for i in idx]).to(args.device) + isolated_batch = torch.tensor([temp_iso[i] for i in idx]).to(args.device) + replace = isolated_batch.to(args.device) + add_trigger = replace & (pseudo_label_batch != labels.to(args.device)) + images[add_trigger,:,:2,:2]=0.0 + labels[replace] = pseudo_label_batch[replace] + + outer_opt.zero_grad() + loss = criterion(model(images), labels) + loss.backward() + outer_opt.step() + + scheduler.step() + model.eval() + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = eval_step( + model, + data_clean_loader, + data_bd_loader, + args, + ) + + agg({ + "epoch": epoch, + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "ra_test_loss_avg_over_batch": ra_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + ra_test_loss_list.append(ra_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + + general_plot_for_epoch( + { + "Test C-Acc": test_acc_list, + "Test ASR": test_asr_list, + "Test RA": test_ra_list, + }, + save_path=os.getcwd()+ f"{args.checkpoint_save}nab_acc_like_metric_plots.png", + ylabel="percentage", + ) + + general_plot_for_epoch( + { + "Test Clean Loss": clean_test_loss_list, + "Test Backdoor Loss": bd_test_loss_list, + "Test RA Loss": ra_test_loss_list, + }, + save_path=os.getcwd()+f"{args.checkpoint_save}nab_loss_metric_plots.png", + ylabel="percentage", + ) + + agg.to_dataframe().to_csv(os.getcwd()+f"{args.checkpoint_save}nab_df.csv") + + agg.summary().to_csv(os.getcwd()+f"{args.checkpoint_save}nab_df_summary.csv") + + result = {} + result['model'] = model + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model.cpu().state_dict(), + save_path=os.getcwd()+args.checkpoint_save, + ) + return result + +def eval_step( + netC, + clean_test_dataloader, + bd_test_dataloader, + args, +): + clean_metrics, clean_epoch_predict_list, clean_epoch_label_list = given_dataloader_test( + netC, + clean_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=args.device, + verbose=0, + ) + clean_test_loss_avg_over_batch = clean_metrics['test_loss_avg_over_batch'] + test_acc = clean_metrics['test_acc'] + bd_metrics, bd_epoch_predict_list, bd_epoch_label_list = given_dataloader_test( + netC, + bd_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=args.device, + verbose=0, + ) + bd_test_loss_avg_over_batch = bd_metrics['test_loss_avg_over_batch'] + test_asr = bd_metrics['test_acc'] + + bd_test_dataloader.dataset.wrapped_dataset.getitem_all_switch = True # change to return the original label instead + ra_metrics, ra_epoch_predict_list, ra_epoch_label_list = given_dataloader_test( + netC, + bd_test_dataloader, + criterion=torch.nn.CrossEntropyLoss(), + non_blocking=args.non_blocking, + device=args.device, + verbose=0, + ) + ra_test_loss_avg_over_batch = ra_metrics['test_loss_avg_over_batch'] + test_ra = ra_metrics['test_acc'] + bd_test_dataloader.dataset.wrapped_dataset.getitem_all_switch = False # switch back + + return clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + ra_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra + +if __name__ == '__main__': + + ### 1. basic setting: args + args = get_args() + with open(args.yaml_path, 'r') as stream: + config = yaml.safe_load(stream) + config.update({k:v for k,v in args.__dict__.items() if v is not None}) + args.__dict__ = config + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + # args.result_file = 'badnet_demo' + save_path = '/record/' + args.result_file + if args.checkpoint_save is None: + args.checkpoint_save = save_path + '/defense/nab/' + if not (os.path.exists(os.getcwd() + args.checkpoint_save)): + os.makedirs(os.getcwd() + args.checkpoint_save) + if args.log is None: + args.log = args.checkpoint_save + 'log/' + if not (os.path.exists(os.getcwd() + args.log)): + os.makedirs(os.getcwd() + args.log) + # args.log = save_path + '/saved/nab/' + # if not (os.path.exists(os.getcwd() + args.log)): + # os.makedirs(os.getcwd() + args.log) + args.save_path = save_path + + ### 2. attack result(model, train data, test data) + result = load_attack_result(os.getcwd() + save_path + '/attack_result.pt') + + ### 3. nab defense: + print("Continue training...") + result_defense = nab(args,result) + + + ### 4. test the result and get ASR, ACC, RC + # resume transfroms + tran = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = False) + result["bd_test"].wrap_img_transform = tran + result["clean_test"].wrap_img_transform = tran + + result_defense['model'].eval() + result_defense['model'].to(args.device) + data_bd_testset = result['bd_test'] + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + + asr_acc = 0 + for i, (inputs,labels, *other_info) in enumerate(data_bd_loader): # type: ignore + inputs, labels = inputs.to(args.device), labels.to(args.device) + outputs = result_defense['model'](inputs) + pre_label = torch.max(outputs,dim=1)[1] + asr_acc += torch.sum(pre_label == labels) + asr_acc = asr_acc/len(data_bd_testset) + + data_clean_testset = result['clean_test'] + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + + clean_acc = 0 + for i, (inputs,labels, *other_info) in enumerate(data_clean_loader): # type: ignore + inputs, labels = inputs.to(args.device), labels.to(args.device) + outputs = result_defense['model'](inputs) + pre_label = torch.max(outputs,dim=1)[1] + clean_acc += torch.sum(pre_label == labels) + clean_acc = clean_acc/len(data_clean_testset) + + robust_acc = 0 + for i, (inputs,labels, original_index, poison_indicator, original_targets) in enumerate(data_bd_loader): # type: ignore + inputs, labels = inputs.to(args.device), labels.to(args.device) + original_targets = original_targets.to(args.device) + outputs = result_defense['model'](inputs) + pre_label = torch.max(outputs,dim=1)[1] + robust_acc += torch.sum(pre_label == original_targets) + robust_acc = robust_acc/len(data_bd_testset) + + print('ACC: ', clean_acc) + print('ASR: ', asr_acc) + print('RA: ', robust_acc) + + if not (os.path.exists(os.getcwd() + f'{save_path}/nab/')): + os.makedirs(os.getcwd() + f'{save_path}/nab/') + torch.save( + { + 'model_name':args.model, + 'model': result_defense['model'].cpu().state_dict(), + 'asr': asr_acc, + 'acc': clean_acc, + 'ra': robust_acc + }, + f'./{save_path}/nab/defense_result.pt' + ) + # test_acc,test_asr,test_ra + final_result = {'model_name':args.model, 'test_acc':clean_acc.item(), 'test_asr':asr_acc.item(), 'test_ra':robust_acc.item()} + # to csv + import pandas as pd + df = pd.DataFrame(final_result, index=[0]) + df.to_csv(f'./{save_path}/nab/nab_df_summary.csv', index=False) diff --git a/defense/nad.py b/defense/nad.py new file mode 100644 index 0000000..df875d7 --- /dev/null +++ b/defense/nad.py @@ -0,0 +1,833 @@ + +''' +This file is modified based on the following source: +link : https://github.com/bboylyg/NAD/. +The defense method is called nad. + +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. save process + 5. new standard: robust accuracy + 6. add some addtional backbone such as resnet18 and vgg19 + 7. the method to get the activation of model +basic sturcture for defense method: + 1. basic setting: args + 2. attack result(model, train data, test data) + 3. nad defense: + a. create student models, set training parameters and determine loss functions + b. train the student model use the teacher model with the activation of model and result + 4. test the result and get ASR, ACC, RC +''' + +import logging +import random +import time + +from calendar import c +from unittest.mock import sentinel +from torchvision import transforms + +import torch +import logging +import argparse +import sys +import os +import yaml +from pprint import pformat +import torch.nn as nn +import torch.nn.functional as F + +import tqdm + +sys.path.append('../') +sys.path.append(os.getcwd()) + +import time + +import numpy as np +from defense.base import defense + +from utils.aggregate_block.train_settings_generate import argparser_criterion, argparser_opt_scheduler +from utils.trainer_cls import BackdoorModelTrainer, PureCleanModelTrainer, all_acc +from utils.choose_index import choose_index +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.log_assist import get_git_info +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform +from utils.save_load_attack import load_attack_result, save_defense_result +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2 + + + + +''' +AT with sum of absolute values with power p +code from: https://github.com/AberHu/Knowledge-Distillation-Zoo +''' +def adjust_learning_rate(optimizer, epoch, lr): + if epoch < 2: + lr = lr + elif epoch < 20: + lr = 0.01 + elif epoch < 30: + lr = 0.0001 + else: + lr = 0.0001 + logging.info('epoch: {} lr: {:.4f}'.format(epoch, lr)) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + + +class AT(nn.Module): + ''' + Paying More Attention to Attention: Improving the Performance of Convolutional + Neural Netkworks wia Attention Transfer + https://arxiv.org/pdf/1612.03928.pdf + ''' + def __init__(self, p): + super(AT, self).__init__() + self.p = p + + def forward(self, fm_s, fm_t): + loss = F.mse_loss(self.attention_map(fm_s), self.attention_map(fm_t)) + + return loss + + def attention_map(self, fm, eps=1e-6): + am = torch.pow(torch.abs(fm), self.p) + am = torch.sum(am, dim=1, keepdim=True) + norm = torch.norm(am, dim=(2,3), keepdim=True) + am = torch.div(am, norm+eps) + + return am + +class NADModelTrainer(PureCleanModelTrainer): + def __init__(self, model, teacher_model, criterions): + super(NADModelTrainer, self).__init__(model) + self.teacher = teacher_model + self.criterions = criterions + + def train_with_test_each_epoch_on_mix(self, + train_dataloader, + clean_test_dataloader, + bd_test_dataloader, + total_epoch_num, + criterions, + optimizer, + scheduler, + amp, + device, + frequency_save, + save_folder_path, + save_prefix, + prefetch, + prefetch_transform_attr_name, + non_blocking, + ): + test_dataloader_dict = { + "clean_test_dataloader":clean_test_dataloader, + "bd_test_dataloader":bd_test_dataloader, + } + + self.set_with_dataloader( + train_dataloader, + test_dataloader_dict, + criterions['criterionCls'], + optimizer, + scheduler, + device, + amp, + + frequency_save, + save_folder_path, + save_prefix, + + prefetch, + prefetch_transform_attr_name, + non_blocking, + ) + + train_loss_list = [] + train_mix_acc_list = [] + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + + for epoch in range(total_epoch_num): + nets = { + 'student':self.model, + 'teacher':self.teacher, + } + + train_epoch_loss_avg_over_batch, \ + train_mix_acc, \ + train_clean_acc, \ + train_asr, \ + train_ra = self.train_epoch(args,train_dataloader,nets,optimizer,scheduler,criterions,epoch) + + clean_metrics, \ + clean_test_epoch_predict_list, \ + clean_test_epoch_label_list, \ + = self.test_given_dataloader(test_dataloader_dict["clean_test_dataloader"], verbose=1) + + clean_test_loss_avg_over_batch = clean_metrics["test_loss_avg_over_batch"] + test_acc = clean_metrics["test_acc"] + + bd_metrics, \ + bd_test_epoch_predict_list, \ + bd_test_epoch_label_list, \ + bd_test_epoch_original_index_list, \ + bd_test_epoch_poison_indicator_list, \ + bd_test_epoch_original_targets_list = self.test_given_dataloader_on_mix(test_dataloader_dict["bd_test_dataloader"], verbose=1) + + bd_test_loss_avg_over_batch = bd_metrics["test_loss_avg_over_batch"] + test_asr = all_acc(bd_test_epoch_predict_list, bd_test_epoch_label_list) + test_ra = all_acc(bd_test_epoch_predict_list, bd_test_epoch_original_targets_list) + + self.agg( + { + "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch, + "train_acc": train_mix_acc, + + + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch" : bd_test_loss_avg_over_batch, + "test_acc" : test_acc, + "test_asr" : test_asr, + "test_ra" : test_ra, + } + ) + + train_loss_list.append(train_epoch_loss_avg_over_batch) + train_mix_acc_list.append(train_mix_acc) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + + self.plot_loss( + train_loss_list, + clean_test_loss_list, + bd_test_loss_list, + ) + + self.plot_acc_like_metric( + train_mix_acc_list, + test_acc_list, + test_asr_list, + test_ra_list, + ) + + self.agg_save_dataframe() + + self.agg_save_summary() + + return train_loss_list, \ + train_mix_acc_list, \ + clean_test_loss_list, \ + bd_test_loss_list, \ + test_acc_list, \ + test_asr_list, \ + test_ra_list + + def train_epoch(self,args,trainloader,nets,optimizer,scheduler,criterions,epoch): + '''train the student model with regard to the teacher model and some clean train data for each step + args: + Contains default parameters + trainloader: + the dataloader of some clean train data + nets: + the student model and the teacher model + optimizer: + optimizer during the train process + scheduler: + scheduler during the train process + criterion: + criterion during the train process + epoch: + current epoch + ''' + + adjust_learning_rate(optimizer, epoch, args.lr) + snet = nets['student'] + tnet = nets['teacher'] + + criterionCls = criterions['criterionCls'].to(args.device, non_blocking=self.non_blocking) + criterionAT = criterions['criterionAT'].to(args.device, non_blocking=self.non_blocking) + + snet.train() + snet.to(args.device, non_blocking=self.non_blocking) + + total_clean = 0 + total_clean_correct = 0 + train_loss = 0 + + batch_loss = [] + + batch_loss_list = [] + batch_predict_list = [] + batch_label_list = [] + batch_original_index_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + for idx, (inputs, labels, original_index, poison_indicator, original_targets) in enumerate(trainloader): + inputs, labels = inputs.to(args.device, non_blocking=self.non_blocking), labels.to(args.device, non_blocking=self.non_blocking) + + if args.model == 'preactresnet18': + outputs_s = snet(inputs) + features_out_3 = list(snet.children())[:-1] # Remove the fully connected layer + modelout_3 = nn.Sequential(*features_out_3) + modelout_3.to(args.device, non_blocking=self.non_blocking) + activation3_s = modelout_3(inputs) + # activation3_s = activation3_s.view(activation3_s.size(0), -1) + features_out_2 = list(snet.children())[:-2] # Remove the fully connected layer + modelout_2 = nn.Sequential(*features_out_2) + modelout_2.to(args.device, non_blocking=self.non_blocking) + activation2_s = modelout_2(inputs) + # activation2_s = activation2_s.view(activation2_s.size(0), -1) + features_out_1 = list(snet.children())[:-3] # Remove the fully connected layer + modelout_1 = nn.Sequential(*features_out_1) + modelout_1.to(args.device, non_blocking=self.non_blocking) + activation1_s = modelout_1(inputs) + # activation1_s = activation1_s.view(activation1_s.size(0), -1) + + features_out_3 = list(tnet.children())[:-1] # Remove the fully connected layer + modelout_3 = nn.Sequential(*features_out_3) + modelout_3.to(args.device, non_blocking=self.non_blocking) + activation3_t = modelout_3(inputs) + # activation3_t = activation3_t.view(activation3_t.size(0), -1) + features_out_2 = list(tnet.children())[:-2] # Remove the fully connected layer + modelout_2 = nn.Sequential(*features_out_2) + modelout_2.to(args.device, non_blocking=self.non_blocking) + activation2_t = modelout_2(inputs) + # activation2_t = activation2_t.view(activation2_t.size(0), -1) + features_out_1 = list(tnet.children())[:-3] # Remove the fully connected layer + modelout_1 = nn.Sequential(*features_out_1) + modelout_1.to(args.device, non_blocking=self.non_blocking) + activation1_t = modelout_1(inputs) + # activation1_t = activation1_t.view(activation1_t.size(0), -1) + + # activation1_s, activation2_s, activation3_s, output_s = snet(inputs) + # activation1_t, activation2_t, activation3_t, _ = tnet(inputs) + + cls_loss = criterionCls(outputs_s, labels) + at3_loss = criterionAT(activation3_s, activation3_t.detach()) * args.beta3 + at2_loss = criterionAT(activation2_s, activation2_t.detach()) * args.beta2 + at1_loss = criterionAT(activation1_s, activation1_t.detach()) * args.beta1 + + at_loss = at1_loss + at2_loss + at3_loss + cls_loss + + if args.model == 'vgg19': + outputs_s = snet(inputs) + features_out_3 = list(snet.children())[:-1] # Remove the fully connected layer + modelout_3 = nn.Sequential(*features_out_3) + modelout_3.to(args.device, non_blocking=self.non_blocking) + activation3_s = modelout_3(inputs) + # activation3_s = snet.features(inputs) + # activation3_s = activation3_s.view(activation3_s.size(0), -1) + + output_t = tnet(inputs) + features_out_3 = list(tnet.children())[:-1] # Remove the fully connected layer + modelout_3 = nn.Sequential(*features_out_3) + modelout_3.to(args.device, non_blocking=self.non_blocking) + activation3_t = modelout_3(inputs) + # activation3_t = tnet.features(inputs) + # activation3_t = activation3_t.view(activation3_t.size(0), -1) + + cls_loss = criterionCls(outputs_s, labels) + at3_loss = criterionAT(activation3_s, activation3_t.detach()) * args.beta3 + + at_loss = at3_loss + cls_loss + + if args.model == 'vgg19_bn': + outputs_s = snet(inputs) + features_out_3 = list(snet.children())[:-1] # Remove the fully connected layer + modelout_3 = nn.Sequential(*features_out_3) + modelout_3.to(args.device) + activation3_s = modelout_3(inputs) + # activation3_s = snet.features(inputs) + # activation3_s = activation3_s.view(activation3_s.size(0), -1) + + output_t = tnet(inputs) + features_out_3 = list(tnet.children())[:-1] # Remove the fully connected layer + modelout_3 = nn.Sequential(*features_out_3) + modelout_3.to(args.device) + activation3_t = modelout_3(inputs) + # activation3_t = tnet.features(inputs) + # activation3_t = activation3_t.view(activation3_t.size(0), -1) + + cls_loss = criterionCls(outputs_s, labels) + at3_loss = criterionAT(activation3_s, activation3_t.detach()) * args.beta3 + + at_loss = at3_loss + cls_loss + + if args.model == 'resnet18': + outputs_s = snet(inputs) + features_out = list(snet.children())[:-1] + modelout = nn.Sequential(*features_out) + modelout.to(args.device, non_blocking=self.non_blocking) + activation3_s = modelout(inputs) + # activation3_s = features.view(features.size(0), -1) + + output_t = tnet(inputs) + features_out = list(tnet.children())[:-1] + modelout = nn.Sequential(*features_out) + modelout.to(args.device, non_blocking=self.non_blocking) + activation3_t = modelout(inputs) + # activation3_t = features.view(features.size(0), -1) + + cls_loss = criterionCls(outputs_s, labels) + at3_loss = criterionAT(activation3_s, activation3_t.detach()) * args.beta3 + + at_loss = at3_loss + cls_loss + + if args.model == 'mobilenet_v3_large': + outputs_s = snet(inputs) + features_out = list(snet.children())[:-1] + modelout = nn.Sequential(*features_out) + modelout.to(args.device, non_blocking=self.non_blocking) + activation3_s = modelout(inputs) + # activation3_s = features.view(features.size(0), -1) + + output_t = tnet(inputs) + features_out = list(tnet.children())[:-1] + modelout = nn.Sequential(*features_out) + modelout.to(args.device, non_blocking=self.non_blocking) + activation3_t = modelout(inputs) + # activation3_t = features.view(features.size(0), -1) + + cls_loss = criterionCls(outputs_s, labels) + at3_loss = criterionAT(activation3_s, activation3_t.detach()) * args.beta3 + + at_loss = at3_loss + cls_loss + + if args.model == 'densenet161': + outputs_s = snet(inputs) + features_out = list(snet.children())[:-1] + modelout = nn.Sequential(*features_out) + modelout.to(args.device, non_blocking=self.non_blocking) + activation3_s = modelout(inputs) + # activation3_s = features.view(features.size(0), -1) + + output_t = tnet(inputs) + features_out = list(tnet.children())[:-1] + modelout = nn.Sequential(*features_out) + modelout.to(args.device, non_blocking=self.non_blocking) + activation3_t = modelout(inputs) + # activation3_t = features.view(features.size(0), -1) + + cls_loss = criterionCls(outputs_s, labels) + at3_loss = criterionAT(activation3_s, activation3_t.detach()) * args.beta3 + + at_loss = at3_loss + cls_loss + + if args.model == 'efficientnet_b3': + outputs_s = snet(inputs) + features_out = list(snet.children())[:-1] + modelout = nn.Sequential(*features_out) + modelout.to(args.device, non_blocking=self.non_blocking) + activation3_s = modelout(inputs) + # activation3_s = features.view(features.size(0), -1) + + output_t = tnet(inputs) + features_out = list(tnet.children())[:-1] + modelout = nn.Sequential(*features_out) + modelout.to(args.device, non_blocking=self.non_blocking) + activation3_t = modelout(inputs) + # activation3_t = features.view(features.size(0), -1) + + cls_loss = criterionCls(outputs_s, labels) + at3_loss = criterionAT(activation3_s, activation3_t.detach()) * args.beta3 + + at_loss = at3_loss + cls_loss + + if args.model == 'convnext_tiny': + outputs_s = snet(inputs) + features_out_3 = list(snet.children())[:-1] # Remove the fully connected layer + modelout_3 = nn.Sequential(*features_out_3) + modelout_3.to(args.device) + activation3_s = modelout_3(inputs) + # activation3_s = snet.features(inputs) + # activation3_s = activation3_s.view(activation3_s.size(0), -1) + + output_t = tnet(inputs) + features_out_3 = list(tnet.children())[:-1] # Remove the fully connected layer + modelout_3 = nn.Sequential(*features_out_3) + modelout_3.to(args.device) + activation3_t = modelout_3(inputs) + # activation3_t = tnet.features(inputs) + # activation3_t = activation3_t.view(activation3_t.size(0), -1) + + cls_loss = criterionCls(outputs_s, labels) + at3_loss = criterionAT(activation3_s, activation3_t.detach()) * args.beta3 + + at_loss = at3_loss + cls_loss + + if args.model == 'vit_b_16': + outputs_s = snet(inputs) + features_out_3 = list(snet.children())[:-1] # Remove the fully connected layer + modelout_3 = nn.Sequential(*features_out_3) + modelout_3.to(args.device) + activation3_s = modelout_3(inputs) + # activation3_s = snet.features(inputs) + # activation3_s = activation3_s.view(activation3_s.size(0), -1) + + output_t = tnet(inputs) + features_out_3 = list(tnet.children())[:-1] # Remove the fully connected layer + modelout_3 = nn.Sequential(*features_out_3) + modelout_3.to(args.device) + activation3_t = modelout_3(inputs) + # activation3_t = tnet.features(inputs) + # activation3_t = activation3_t.view(activation3_t.size(0), -1) + + cls_loss = criterionCls(outputs_s, labels) + at3_loss = criterionAT(activation3_s, activation3_t.detach()) * args.beta3 + + at_loss = at3_loss + cls_loss + + + batch_loss.append(at_loss.item()) + optimizer.zero_grad() + at_loss.backward() + optimizer.step() + + train_loss += at_loss.item() + total_clean_correct += torch.sum(torch.argmax(outputs_s[:], dim=1) == labels[:]) + total_clean += inputs.shape[0] + avg_acc_clean = float(total_clean_correct.item() * 100.0 / total_clean) + + batch_loss_list.append(at_loss.item()) + batch_predict_list.append(torch.max(outputs_s, -1)[1].detach().clone().cpu()) + batch_label_list.append(labels.detach().clone().cpu()) + batch_original_index_list.append(original_index.detach().clone().cpu()) + batch_poison_indicator_list.append(poison_indicator.detach().clone().cpu()) + batch_original_targets_list.append(original_targets.detach().clone().cpu()) + + train_epoch_loss_avg_over_batch, \ + train_epoch_predict_list, \ + train_epoch_label_list, \ + train_epoch_poison_indicator_list, \ + train_epoch_original_targets_list = sum(batch_loss_list) / len(batch_loss_list), \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + + train_mix_acc = all_acc(train_epoch_predict_list, train_epoch_label_list) + + train_bd_idx = torch.where(train_epoch_poison_indicator_list == 1)[0] + train_clean_idx = torch.where(train_epoch_poison_indicator_list == 0)[0] + train_clean_acc = all_acc( + train_epoch_predict_list[train_clean_idx], + train_epoch_label_list[train_clean_idx], + ) + train_asr = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_label_list[train_bd_idx], + ) + train_ra = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_original_targets_list[train_bd_idx], + ) + logging.info(f'Epoch{epoch}: Loss:{train_loss} Training Acc:{avg_acc_clean}({total_clean_correct}/{total_clean})') + # one_epoch_loss = sum(batch_loss)/len(batch_loss) + # if args.lr_scheduler == 'ReduceLROnPlateau': + # scheduler.step(one_epoch_loss) + # elif args.lr_scheduler == 'CosineAnnealingLR': + # scheduler.step() + return train_epoch_loss_avg_over_batch, \ + train_mix_acc, \ + train_clean_acc, \ + train_asr, \ + train_ra + +class nad(defense): + + def __init__(self,args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + self.args = args + + if 'result_file' in args.__dict__ : + if args.result_file is not None: + self.set_result(args.result_file) + + def add_arguments(parser): + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory") + parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?") + parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch') + parser.add_argument('--amp', type=lambda x: str(x) in ['True','true','1']) + + parser.add_argument('--checkpoint_load', type=str, help='the location of load model') + parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved') + parser.add_argument('--log', type=str, help='the location of log') + parser.add_argument("--dataset_path", type=str, help='the location of data') + parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny') + parser.add_argument('--result_file', type=str, help='the location of result') + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--model', type=str, help='resnet18') + + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--yaml_path', type=str, default="./config/defense/nad/config.yaml", help='the path of yaml') + + #set the parameter for the nad defense + parser.add_argument('--te_epochs', type=int) + parser.add_argument('--momentum', type=float, help='momentum') + parser.add_argument('--weight_decay', type=float, help='weight decay') + parser.add_argument('--ratio', type=float, help='ratio of training data') + parser.add_argument('--beta1', type=int, help='beta of low layer') + parser.add_argument('--beta2', type=int, help='beta of middle layer') + parser.add_argument('--beta3', type=int, help='beta of high layer') + parser.add_argument('--p', type=float, help='power for AT') + + parser.add_argument('--teacher_model_loc', type=str, help='the location of teacher model') + + parser.add_argument('--index', type=str, help='index of clean data') + + + + def set_result(self, result_file): + attack_file = 'record/' + result_file + save_path = 'record/' + result_file + '/defense/nad/' + if not (os.path.exists(save_path)): + os.makedirs(save_path) + # assert(os.path.exists(save_path)) + self.args.save_path = save_path + if self.args.checkpoint_save is None: + self.args.checkpoint_save = save_path + 'checkpoint/' + if not (os.path.exists(self.args.checkpoint_save)): + os.makedirs(self.args.checkpoint_save) + if self.args.log is None: + self.args.log = save_path + 'log/' + if not (os.path.exists(self.args.log)): + os.makedirs(self.args.log) + self.result = load_attack_result(attack_file + '/attack_result.pt') + + def set_trainer(self, model, mode = 'normal', **params): + if mode == 'normal': + self.trainer = BackdoorModelTrainer( + model, + ) + elif mode == 'clean': + self.trainer = PureCleanModelTrainer( + model, + ) + elif mode == 'nad': + self.trainer = NADModelTrainer( + model, + **params, + ) + + def set_logger(self): + args = self.args + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + try: + logging.info(pformat(get_git_info())) + except: + logging.info('Getting git info fails.') + + def set_devices(self): + # self.device = torch.device( + # ( + # f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device + # # since DataParallel only allow .to("cuda") + # ) if torch.cuda.is_available() else "cpu" + # ) + self.device = self.args.device + def mitigation(self): + self.set_devices() + args = self.args + result = self.result + fix_random(args.random_seed) + + ### a. create student models, set training parameters and determine loss functions + # Load models + logging.info('----------- Network Initialization --------------') + teacher = generate_cls_model(args.model,args.num_classes) + teacher.load_state_dict(result['model']) + if "," in self.device: + teacher = torch.nn.DataParallel( + teacher, + device_ids=[int(i) for i in self.args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{teacher.device_ids[0]}' + teacher.to(self.args.device) + else: + teacher.to(self.args.device) + logging.info('finished teacher student init...') + student = generate_cls_model(args.model,args.num_classes) + student.load_state_dict(result['model']) + if "," in self.device: + student = torch.nn.DataParallel( + student, + device_ids=[int(i) for i in self.args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{student.device_ids[0]}' + student.to(self.args.device) + else: + student.to(self.args.device) + logging.info('finished student student init...') + + teacher.eval() + nets = {'snet': student, 'tnet': teacher} + + # initialize optimizer, scheduler + optimizer, scheduler = argparser_opt_scheduler(student, self.args) + + # define loss functions + criterionCls = argparser_criterion(args) + criterionAT = AT(args.p) + criterions = {'criterionCls': criterionCls, 'criterionAT': criterionAT} + + train_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = True) + clean_dataset = prepro_cls_DatasetBD_v2(self.result['clean_train'].wrapped_dataset) + data_all_length = len(clean_dataset) + ran_idx = choose_index(self.args, data_all_length) + log_index = self.args.log + 'index.txt' + np.savetxt(log_index, ran_idx, fmt='%d') + clean_dataset.subset(ran_idx) + data_set_without_tran = clean_dataset + data_set_o = self.result['clean_train'] + data_set_o.wrapped_dataset = data_set_without_tran + data_set_o.wrap_img_transform = train_tran + data_loader = torch.utils.data.DataLoader(data_set_o, batch_size=self.args.batch_size, num_workers=self.args.num_workers, shuffle=True, pin_memory=args.pin_memory) + trainloader = data_loader + + test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False) + data_bd_testset = self.result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + data_clean_testset = self.result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + test_dataloader_dict = {} + test_dataloader_dict["clean_test_dataloader"] = data_clean_loader + test_dataloader_dict["bd_test_dataloader"] = data_bd_loader + + ### train the teacher model + if args.teacher_model_loc is not None: + teacher_model = torch.load(args.teacher_model_loc) + teacher.load_state_dict(teacher_model['model']) + else : + self.set_trainer(teacher,'clean') + start_epoch = 0 + optimizer_ft, scheduler_ft = argparser_opt_scheduler(teacher, self.args) + self.trainer.train_with_test_each_epoch_on_mix( + trainloader, + data_clean_loader, + data_bd_loader, + args.te_epochs, + criterion = criterionCls, + optimizer = optimizer_ft, + scheduler = scheduler_ft, + device = self.args.device, + frequency_save = 0, + save_folder_path = args.save_path, + save_prefix='nad_te', + amp=args.amp, + prefetch=args.prefetch, + prefetch_transform_attr_name="ori_image_transform_in_loading", # since we use the preprocess_bd_dataset + non_blocking=args.non_blocking, + ) + + + ### b. train the student model use the teacher model with the activation of model and result + self.set_trainer(student, 'nad', teacher_model = teacher, criterions = criterions) + logging.info('----------- Train Initialization --------------') + + + self.trainer.train_with_test_each_epoch_on_mix( + trainloader, + data_clean_loader, + data_bd_loader, + args.te_epochs, + criterions = criterions, + optimizer = optimizer, + scheduler = scheduler, + device = self.args.device, + frequency_save = 0, + save_folder_path = args.save_path, + save_prefix='nad', + amp=args.amp, + prefetch=args.prefetch, + prefetch_transform_attr_name="ori_image_transform_in_loading", # since we use the preprocess_bd_dataset + non_blocking=args.non_blocking, + ) + + result = {} + result['model'] = student + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=student.cpu().state_dict(), + save_path=args.save_path, + ) + return result + + def defense(self,result_file): + self.set_result(result_file) + self.set_logger() + result = self.mitigation() + return result + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=sys.argv[0]) + nad.add_arguments(parser) + args = parser.parse_args() + ft_method = nad(args) + if "result_file" not in args.__dict__: + args.result_file = 'defense_test_badnet' + elif args.result_file is None: + args.result_file = 'defense_test_badnet' + result = ft_method.defense(args.result_file) \ No newline at end of file diff --git a/defense/nc.py b/defense/nc.py new file mode 100644 index 0000000..567d66c --- /dev/null +++ b/defense/nc.py @@ -0,0 +1,814 @@ +# MIT License + +# Copyright (c) 2021 VinAI Research + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + + +''' +This file is modified based on the following source: +link : https://github.com/VinAIResearch/input-aware-backdoor-attack-release/tree/master/defenses +The defense method is called nc. + +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. save process + 5. new standard: robust accuracy + 6. implement finetune operation according to nc paper +basic sturcture for defense method: + 1. basic setting: args + 2. attack result(model, train data, test data) + 3. nc defense: + a. initialize the model and trigger + b. train triggers according to different target labels + c. Determine whether the trained reverse trigger is a real backdoor trigger + If it is a real backdoor trigger: + d. select samples as clean samples and unlearning samples, finetune the origin model + 4. test the result and get ASR, ACC, RA +''' + +import argparse +import os,sys +import numpy as np +import torch +import torch.nn as nn +import cv2 + +sys.path.append('../') +sys.path.append(os.getcwd()) + +from pprint import pformat +import yaml +import logging +import time +from defense.base import defense +from matplotlib import image as mlt +from PIL import Image +import torchvision + +from utils.aggregate_block.train_settings_generate import argparser_criterion, argparser_opt_scheduler +from utils.trainer_cls import Metric_Aggregator, PureCleanModelTrainer +from utils.choose_index import choose_index +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.log_assist import get_git_info +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform +from utils.save_load_attack import load_attack_result, save_defense_result +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, xy_iter + +class Normalize: + + def __init__(self, opt, expected_values, variance): + self.n_channels = opt.input_channel + self.expected_values = expected_values + self.variance = variance + assert self.n_channels == len(self.expected_values) + + def __call__(self, x): + x_clone = x.clone() + for channel in range(self.n_channels): + x_clone[:, channel] = (x[:, channel] - self.expected_values[channel]) / self.variance[channel] + return x_clone + +class Denormalize: + + def __init__(self, opt, expected_values, variance): + self.n_channels = opt.input_channel + self.expected_values = expected_values + self.variance = variance + assert self.n_channels == len(self.expected_values) + + def __call__(self, x): + x_clone = x.clone() + for channel in range(self.n_channels): + x_clone[:, channel] = x[:, channel] * self.variance[channel] + self.expected_values[channel] + return x_clone + +class RegressionModel(nn.Module): + def __init__(self, opt, init_mask, init_pattern,result): + self._EPSILON = opt.EPSILON + super(RegressionModel, self).__init__() + self.mask_tanh = nn.Parameter(torch.tensor(init_mask)) + self.pattern_tanh = nn.Parameter(torch.tensor(init_pattern)) + self.result = result + self.classifier = self._get_classifier(opt) + self.normalizer = self._get_normalize(opt) + self.denormalizer = self._get_denormalize(opt) + + + def forward(self, x): + mask = self.get_raw_mask() + pattern = self.get_raw_pattern() + if self.normalizer: + pattern = self.normalizer(self.get_raw_pattern()) + x = (1 - mask) * x + mask * pattern + return self.classifier(x) + + def get_raw_mask(self): + mask = nn.Tanh()(self.mask_tanh) + return mask / (2 + self._EPSILON) + 0.5 + + def get_raw_pattern(self): + pattern = nn.Tanh()(self.pattern_tanh) + return pattern / (2 + self._EPSILON) + 0.5 + + def _get_classifier(self, opt): + + classifier = generate_cls_model(args.model,args.num_classes) + classifier.load_state_dict(self.result['model']) + classifier.to(args.device) + + for param in classifier.parameters(): + param.requires_grad = False + classifier.eval() + return classifier.to(opt.device) + + def _get_denormalize(self, opt): + if opt.dataset == "cifar10" or opt.dataset == "cifar100": + denormalizer = Denormalize(opt, [0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261]) + elif opt.dataset == "mnist": + denormalizer = Denormalize(opt, [0.5], [0.5]) + elif opt.dataset == "gtsrb" or opt.dataset == "celeba": + denormalizer = None + elif opt.dataset == 'tiny': + denormalizer = Denormalize(opt, [0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]) + else: + raise Exception("Invalid dataset") + return denormalizer + + def _get_normalize(self, opt): + if opt.dataset == "cifar10" or opt.dataset == "cifar100": + normalizer = Normalize(opt, [0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261]) + elif opt.dataset == "mnist": + normalizer = Normalize(opt, [0.5], [0.5]) + elif opt.dataset == "gtsrb" or opt.dataset == "celeba": + normalizer = None + elif opt.dataset == 'tiny': + normalizer = Normalize(opt, [0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]) + else: + raise Exception("Invalid dataset") + return normalizer + + +class Recorder: + def __init__(self, opt): + super().__init__() + + # Best optimization results + self.mask_best = None + self.pattern_best = None + self.reg_best = float("inf") + + # Logs and counters for adjusting balance cost + self.logs = [] + self.cost_set_counter = 0 + self.cost_up_counter = 0 + self.cost_down_counter = 0 + self.cost_up_flag = False + self.cost_down_flag = False + + # Counter for early stop + self.early_stop_counter = 0 + self.early_stop_reg_best = self.reg_best + + # Cost + self.cost = opt.init_cost + self.cost_multiplier_up = opt.cost_multiplier + self.cost_multiplier_down = opt.cost_multiplier ** 1.5 + + def reset_state(self, opt): + self.cost = opt.init_cost + self.cost_up_counter = 0 + self.cost_down_counter = 0 + self.cost_up_flag = False + self.cost_down_flag = False + print("Initialize cost to {:f}".format(self.cost)) + + def save_result_to_dir(self, opt): + + result_dir = (os.getcwd() + '/' + f'{opt.log}') + if not os.path.exists(result_dir): + os.makedirs(result_dir) + result_dir = os.path.join(result_dir, str(opt.target_label)) + if not os.path.exists(result_dir): + os.makedirs(result_dir) + + pattern_best = self.pattern_best + mask_best = self.mask_best + trigger = pattern_best * mask_best + + path_mask = os.path.join(result_dir, "mask.png") + path_pattern = os.path.join(result_dir, "pattern.png") + path_trigger = os.path.join(result_dir, "trigger.png") + + torchvision.utils.save_image(mask_best, path_mask, normalize=True) + torchvision.utils.save_image(pattern_best, path_pattern, normalize=True) + torchvision.utils.save_image(trigger, path_trigger, normalize=True) + +def train_mask(args, result, trainloader, init_mask, init_pattern): + + # Build regression model + regression_model = RegressionModel(args, init_mask, init_pattern, result).to(args.device) + + # Set optimizer + optimizerR = torch.optim.Adam(regression_model.parameters(), lr=args.mask_lr, betas=(0.5, 0.9)) + + # Set recorder (for recording best result) + recorder = Recorder(args) + + for epoch in range(args.nc_epoch): + # early_stop = train_step(regression_model, optimizerR, test_dataloader, recorder, epoch, opt) + early_stop = train_step(regression_model, optimizerR, trainloader, recorder, epoch, args) + if early_stop: + break + + # Save result to dir + recorder.save_result_to_dir(args) + + return recorder, args + + +def train_step(regression_model, optimizerR, dataloader, recorder, epoch, opt): + # print("Epoch {} - Label: {} | {} - {}:".format(epoch, opt.target_label, opt.dataset, opt.attack_mode)) + # Set losses + cross_entropy = nn.CrossEntropyLoss() + total_pred = 0 + true_pred = 0 + + # Record loss for all mini-batches + loss_ce_list = [] + loss_reg_list = [] + loss_list = [] + loss_acc_list = [] + + # Set inner early stop flag + inner_early_stop_flag = False + for batch_idx, (inputs, labels, *other_info) in enumerate(dataloader): + # Forwarding and update model + optimizerR.zero_grad() + + inputs = inputs.to(opt.device) + sample_num = inputs.shape[0] + total_pred += sample_num + target_labels = torch.ones((sample_num), dtype=torch.int64).to(opt.device) * opt.target_label + predictions = regression_model(inputs) + + loss_ce = cross_entropy(predictions, target_labels) + loss_reg = torch.norm(regression_model.get_raw_mask(), opt.use_norm) + total_loss = loss_ce + recorder.cost * loss_reg + total_loss.backward() + optimizerR.step() + + # Record minibatch information to list + minibatch_accuracy = torch.sum(torch.argmax(predictions, dim=1) == target_labels).detach() * 100.0 / sample_num + loss_ce_list.append(loss_ce.detach()) + loss_reg_list.append(loss_reg.detach()) + loss_list.append(total_loss.detach()) + loss_acc_list.append(minibatch_accuracy) + + true_pred += torch.sum(torch.argmax(predictions, dim=1) == target_labels).detach() + # progress_bar(batch_idx, len(dataloader)) + + loss_ce_list = torch.stack(loss_ce_list) + loss_reg_list = torch.stack(loss_reg_list) + loss_list = torch.stack(loss_list) + loss_acc_list = torch.stack(loss_acc_list) + + avg_loss_ce = torch.mean(loss_ce_list) + avg_loss_reg = torch.mean(loss_reg_list) + avg_loss = torch.mean(loss_list) + avg_loss_acc = torch.mean(loss_acc_list) + + # Check to save best mask or not + if avg_loss_acc >= opt.atk_succ_threshold and avg_loss_reg < recorder.reg_best: + recorder.mask_best = regression_model.get_raw_mask().detach() + recorder.pattern_best = regression_model.get_raw_pattern().detach() + recorder.reg_best = avg_loss_reg + recorder.save_result_to_dir(opt) + # print(" Updated !!!") + + # Show information + # print( + # " Result: Accuracy: {:.3f} | Cross Entropy Loss: {:.6f} | Reg Loss: {:.6f} | Reg best: {:.6f}".format( + # true_pred * 100.0 / total_pred, avg_loss_ce, avg_loss_reg, recorder.reg_best + # ) + # ) + + # Check early stop + if opt.early_stop: + if recorder.reg_best < float("inf"): + if recorder.reg_best >= opt.early_stop_threshold * recorder.early_stop_reg_best: + recorder.early_stop_counter += 1 + else: + recorder.early_stop_counter = 0 + + recorder.early_stop_reg_best = min(recorder.early_stop_reg_best, recorder.reg_best) + + if ( + recorder.cost_down_flag + and recorder.cost_up_flag + and recorder.early_stop_counter >= opt.early_stop_patience + ): + print("Early_stop !!!") + inner_early_stop_flag = True + + if not inner_early_stop_flag: + # Check cost modification + if recorder.cost == 0 and avg_loss_acc >= opt.atk_succ_threshold: + recorder.cost_set_counter += 1 + if recorder.cost_set_counter >= opt.patience: + recorder.reset_state(opt) + else: + recorder.cost_set_counter = 0 + + if avg_loss_acc >= opt.atk_succ_threshold: + recorder.cost_up_counter += 1 + recorder.cost_down_counter = 0 + else: + recorder.cost_up_counter = 0 + recorder.cost_down_counter += 1 + + if recorder.cost_up_counter >= opt.patience: + recorder.cost_up_counter = 0 + print("Up cost from {} to {}".format(recorder.cost, recorder.cost * recorder.cost_multiplier_up)) + recorder.cost *= recorder.cost_multiplier_up + recorder.cost_up_flag = True + + elif recorder.cost_down_counter >= opt.patience: + recorder.cost_down_counter = 0 + print("Down cost from {} to {}".format(recorder.cost, recorder.cost / recorder.cost_multiplier_down)) + recorder.cost /= recorder.cost_multiplier_down + recorder.cost_down_flag = True + + # Save the final version + if recorder.mask_best is None: + recorder.mask_best = regression_model.get_raw_mask().detach() + recorder.pattern_best = regression_model.get_raw_pattern().detach() + + del predictions + torch.cuda.empty_cache() + + return inner_early_stop_flag + +def outlier_detection(l1_norm_list, idx_mapping, opt): + print("-" * 30) + print("Determining whether model is backdoor") + consistency_constant = 1.4826 + median = torch.median(l1_norm_list) + mad = consistency_constant * torch.median(torch.abs(l1_norm_list - median)) + min_mad = torch.abs(torch.min(l1_norm_list) - median) / mad + + print("Median: {}, MAD: {}".format(median, mad)) + print("Anomaly index: {}".format(min_mad)) + + if min_mad < 2: + print("Not a backdoor model") + else: + print("This is a backdoor model") + + if opt.to_file: + # result_path = os.path.join(opt.result, opt.saving_prefix, opt.dataset) + # output_path = os.path.join( + # result_path, "{}_{}_output.txt".format(opt.attack_mode, opt.dataset, opt.attack_mode) + # ) + output_path = opt.output_path + with open(output_path, "a+") as f: + f.write( + str(median.cpu().numpy()) + ", " + str(mad.cpu().numpy()) + ", " + str(min_mad.cpu().numpy()) + "\n" + ) + l1_norm_list_to_save = [str(value) for value in l1_norm_list.cpu().numpy()] + f.write(", ".join(l1_norm_list_to_save) + "\n") + + flag_list = [] + for y_label in idx_mapping: + if l1_norm_list[idx_mapping[y_label]] > median: + continue + if torch.abs(l1_norm_list[idx_mapping[y_label]] - median) / mad > 2: + flag_list.append((y_label, l1_norm_list[idx_mapping[y_label]])) + + if len(flag_list) > 0: + flag_list = sorted(flag_list, key=lambda x: x[1]) + + logging.info( + "Flagged label list: {}".format(",".join(["{}: {}".format(y_label, l_norm) for y_label, l_norm in flag_list])) + ) + + return flag_list + + +class nc(defense): + + def __init__(self,args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + self.args = args + + if 'result_file' in args.__dict__ : + if args.result_file is not None: + self.set_result(args.result_file) + + def add_arguments(parser): + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory") + parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?") + parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch') + parser.add_argument('--amp', type=lambda x: str(x) in ['True','true','1']) + + parser.add_argument('--checkpoint_load', type=str, help='the location of load model') + parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved') + parser.add_argument('--log', type=str, help='the location of log') + parser.add_argument("--dataset_path", type=str, help='the location of data') + parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny') + parser.add_argument('--result_file', type=str, help='the location of result') + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--model', type=str, help='resnet18') + + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--yaml_path', type=str, default="./config/defense/nc/config.yaml", help='the path of yaml') + + #set the parameter for the nc defense + parser.add_argument('--ratio', type=float, help='ratio of training data') + parser.add_argument('--cleaning_ratio', type=float, help='ratio of cleaning data') + parser.add_argument('--unlearning_ratio', type=float, help='ratio of unlearning data') + parser.add_argument('--nc_epoch', type=int, help='the epoch for neural cleanse') + + parser.add_argument('--index', type=str, help='index of clean data') + + + def set_result(self, result_file): + attack_file = 'record/' + result_file + self.attack_file = attack_file + save_path = 'record/' + result_file + '/defense/nc/' + if not (os.path.exists(save_path)): + os.makedirs(save_path) + # assert(os.path.exists(save_path)) + self.args.save_path = save_path + if self.args.checkpoint_save is None: + self.args.checkpoint_save = save_path + 'checkpoint/' + if not (os.path.exists(self.args.checkpoint_save)): + os.makedirs(self.args.checkpoint_save) + if self.args.log is None: + self.args.log = save_path + 'log/' + if not (os.path.exists(self.args.log)): + os.makedirs(self.args.log) + self.result = load_attack_result(attack_file + '/attack_result.pt') + + def set_trainer(self, model): + self.trainer = PureCleanModelTrainer( + model, + ) + + def set_logger(self): + args = self.args + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + try: + logging.info(pformat(get_git_info())) + except: + logging.info('Getting git info fails.') + + def set_devices(self): + # self.device = torch.device( + # ( + # f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device + # # since DataParallel only allow .to("cuda") + # ) if torch.cuda.is_available() else "cpu" + # ) + self.device = self.args.device + def mitigation(self): + self.set_devices() + fix_random(self.args.random_seed) + args = self.args + result = self.result + + # Prepare model, optimizer, scheduler + model = generate_cls_model(self.args.model,self.args.num_classes) + model.load_state_dict(self.result['model']) + if "," in self.device: + model = torch.nn.DataParallel( + model, + device_ids=[int(i) for i in self.args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{model.device_ids[0]}' + model.to(self.args.device) + else: + model.to(self.args.device) + optimizer, scheduler = argparser_opt_scheduler(model, self.args) + # criterion = nn.CrossEntropyLoss() + self.set_trainer(model) + criterion = argparser_criterion(args) + + + + train_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = True) + clean_dataset = prepro_cls_DatasetBD_v2(self.result['clean_train'].wrapped_dataset) + data_all_length = len(clean_dataset) + ran_idx = choose_index(self.args, data_all_length) + log_index = self.args.log + 'index.txt' + np.savetxt(log_index, ran_idx, fmt='%d') + clean_dataset.subset(ran_idx) + data_set_without_tran = clean_dataset + data_set_o = self.result['clean_train'] + data_set_o.wrapped_dataset = data_set_without_tran + data_set_o.wrap_img_transform = train_tran + data_loader = torch.utils.data.DataLoader(data_set_o, batch_size=self.args.batch_size, num_workers=self.args.num_workers, shuffle=True, pin_memory=args.pin_memory) + trainloader = data_loader + + # a. initialize the model and trigger + result_path = os.getcwd() + '/' + f'{args.save_path}/nc/trigger/' + if not os.path.exists(result_path): + os.makedirs(result_path) + args.output_path = result_path + "{}_output_clean.txt".format(args.dataset) + if args.to_file: + with open(args.output_path, "w+") as f: + f.write("Output for cleanse: - {}".format(args.dataset) + "\n") + + init_mask = np.ones((1, args.input_height, args.input_width)).astype(np.float32) + init_pattern = np.ones((args.input_channel, args.input_height, args.input_width)).astype(np.float32) + + flag = 0 + for test in range(args.n_times_test): + # b. train triggers according to different target labels + print("Test {}:".format(test)) + logging.info("Test {}:".format(test)) + if args.to_file: + with open(args.output_path, "a+") as f: + f.write("-" * 30 + "\n") + f.write("Test {}:".format(str(test)) + "\n") + + masks = [] + idx_mapping = {} + + for target_label in range(args.num_classes): + print("----------------- Analyzing label: {} -----------------".format(target_label)) + logging.info("----------------- Analyzing label: {} -----------------".format(target_label)) + args.target_label = target_label + recorder, args = train_mask(args, result, trainloader, init_mask, init_pattern) + + mask = recorder.mask_best + masks.append(mask) + reg = torch.norm(mask, p=args.use_norm) + logging.info(f'The regularization of mask for target label {target_label} is {reg}') + idx_mapping[target_label] = len(masks) - 1 + + # c. Determine whether the trained reverse trigger is a real backdoor trigger + l1_norm_list = torch.stack([torch.norm(m, p=args.use_norm) for m in masks]) + logging.info("{} labels found".format(len(l1_norm_list))) + logging.info("Norm values: {}".format(l1_norm_list)) + flag_list = outlier_detection(l1_norm_list, idx_mapping, args) + if len(flag_list) != 0: + flag = 1 + + if flag == 0: + logging.info('This is not a backdoor model') + test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False) + data_bd_testset = self.result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + data_clean_testset = self.result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + agg = Metric_Aggregator() + + test_dataloader_dict = {} + test_dataloader_dict["clean_test_dataloader"] = data_clean_loader + test_dataloader_dict["bd_test_dataloader"] = data_bd_loader + + model = generate_cls_model(args.model,args.num_classes) + model.load_state_dict(result['model']) + self.set_trainer(model) + + self.trainer.set_with_dataloader( + train_dataloader = trainloader, + test_dataloader_dict = test_dataloader_dict, + + criterion = criterion, + optimizer = None, + scheduler = None, + device = self.args.device, + amp = self.args.amp, + + frequency_save = self.args.frequency_save, + save_folder_path = self.args.save_path, + save_prefix = 'nc', + + prefetch = self.args.prefetch, + prefetch_transform_attr_name = "ori_image_transform_in_loading", + non_blocking = self.args.non_blocking, + + # continue_training_path = continue_training_path, + # only_load_model = only_load_model, + ) + clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra = self.trainer.test_current_model( + test_dataloader_dict, self.args.device, + ) + agg({ + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch, + "test_acc": test_acc, + "test_asr": test_asr, + "test_ra": test_ra, + }) + agg.to_dataframe().to_csv(f"{args.save_path}nc_df_summary.csv") + + result = {} + result['model'] = model + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model.cpu().state_dict(), + save_path=args.save_path, + ) + return result + + + self.set_result(args.result_file) + test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False) + data_bd_testset = self.result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + data_clean_testset = self.result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + + + + # d. select samples as clean samples and unlearning samples, finetune the origin model + model = generate_cls_model(args.model,args.num_classes) + model.load_state_dict(result['model']) + model.to(args.device) + train_tran = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = True) + attack_file = self.attack_file + self.result = load_attack_result(attack_file + '/attack_result.pt') + clean_dataset = prepro_cls_DatasetBD_v2(self.result['clean_train'].wrapped_dataset) + data_all_length = len(clean_dataset) + ran_idx = choose_index(self.args, data_all_length) + log_index = self.args.log + 'index.txt' + np.savetxt(log_index, ran_idx, fmt='%d') + clean_dataset.subset(ran_idx) + data_set_without_tran = clean_dataset + data_set_o = self.result['clean_train'] + data_set_o.wrapped_dataset = data_set_without_tran + data_set_o.wrap_img_transform = train_tran + + data_loader = torch.utils.data.DataLoader(data_set_o, batch_size=self.args.batch_size, num_workers=self.args.num_workers, shuffle=True, pin_memory=args.pin_memory) + trainloader = data_loader + + idx_clean = ran_idx[0:int(len(data_set_o)*(1-args.unlearning_ratio))] + idx_unlearn = ran_idx[int(len(data_set_o)*(1-args.unlearning_ratio)):int(len(data_set_o))] + x_new = list() + y_new = list() + original_index_array = list() + poison_indicator = list() + for ii in range(int(len(data_set_o)*(1-args.unlearning_ratio))): + x_new.extend([data_set_o.wrapped_dataset[ii][0]]) + y_new.extend([data_set_o.wrapped_dataset[ii][1]]) + original_index_array.extend([len(x_new)-1]) + poison_indicator.extend([0]) + + for (label,_) in flag_list: + mask_path = os.getcwd() + '/' + f'{args.log}' + '{}/'.format(str(label)) + 'mask.png' + mask_image = mlt.imread(mask_path) + mask_image = cv2.resize(mask_image,(args.input_height, args.input_width)) + trigger_path = os.getcwd() + '/' + f'{args.log}' + '{}/'.format(str(label)) + 'trigger.png' + signal_mask = mlt.imread(trigger_path)*255 + signal_mask = cv2.resize(signal_mask,(args.input_height, args.input_width)) + + x_unlearn = list() + x_unlearn_new = list() + y_unlearn_new = list() + original_index_array_new = list() + poison_indicator_new = list() + for ii in range(int(len(data_set_o)*(1-args.unlearning_ratio)),int(len(data_set_o))): + img = data_set_o.wrapped_dataset[ii][0] + x_unlearn.extend([img]) + x_np = np.array(cv2.resize(np.array(img),(args.input_height, args.input_width))) * (1-np.array(mask_image)) + np.array(signal_mask) + x_np = np.clip(x_np.astype('uint8'), 0, 255) + x_np_img = Image.fromarray(x_np) + x_unlearn_new.extend([x_np_img]) + y_unlearn_new.extend([data_set_o.wrapped_dataset[ii][1]]) + original_index_array_new.extend([len(x_new)-1]) + poison_indicator_new.extend([0]) + x_new.extend(x_unlearn_new) + y_new.extend(y_unlearn_new) + original_index_array.extend(original_index_array_new) + poison_indicator.extend(poison_indicator_new) + + ori_dataset = xy_iter(x_new,y_new,None) + + data_set_o.wrapped_dataset.dataset = ori_dataset + data_set_o.wrapped_dataset.original_index_array = original_index_array + data_set_o.wrapped_dataset.poison_indicator = poison_indicator + trainloader = torch.utils.data.DataLoader(data_set_o, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + + self.trainer.train_with_test_each_epoch_on_mix( + trainloader, + data_clean_loader, + data_bd_loader, + args.epochs, + criterion=criterion, + optimizer=optimizer, + scheduler=scheduler, + device=self.args.device, + frequency_save=args.frequency_save, + save_folder_path=args.save_path, + save_prefix='nc', + amp=args.amp, + prefetch=args.prefetch, + prefetch_transform_attr_name="ori_image_transform_in_loading", # since we use the preprocess_bd_dataset + non_blocking=args.non_blocking, + ) + + result = {} + result['model'] = model + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model.cpu().state_dict(), + save_path=args.save_path, + ) + return result + + def defense(self,result_file): + self.set_result(result_file) + self.set_logger() + result = self.mitigation() + return result + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=sys.argv[0]) + nc.add_arguments(parser) + args = parser.parse_args() + nc_method = nc(args) + if "result_file" not in args.__dict__: + args.result_file = 'one_epochs_debug_badnet_attack' + elif args.result_file is None: + args.result_file = 'one_epochs_debug_badnet_attack' + result = nc_method.defense(args.result_file) \ No newline at end of file diff --git a/defense/spectral.py b/defense/spectral.py new file mode 100755 index 0000000..622b97d --- /dev/null +++ b/defense/spectral.py @@ -0,0 +1,417 @@ +# MIT License + +# Copyright (c) 2017 Brandon Tran and Jerry Li + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +''' +This file is modified based on the following source: +link : https://github.com/MadryLab/backdoor_data_poisoning. +The defense method is called spectral. + +The update include: + 1. data preprocess and dataset setting + 2. model setting + 3. args and config + 4. save process + 5. new standard: robust accuracy + 6. use the PyTorch environment instead of TensorFlow + 7. add some addtional backbone such as resnet18 and vgg19 + 8. the poison ratio can also be preset when the data for each category is small +basic sturcture for defense method: + 1. basic setting: args + 2. attack result(model, train data, test data) + 3. spectral defense: + a. prepare the model and dataset + b. get the activation as representation for each data + c. detect the backdoor data by the SVD decomposition + d. retrain the model with remaining data + 4. test the result and get ASR, ACC, RC +''' + +import argparse +import os,sys +import numpy as np +import torch +import torch.nn as nn + +sys.path.append('../') +sys.path.append(os.getcwd()) + + +from pprint import pformat +import yaml +import logging +import time +from defense.base import defense + +from utils.aggregate_block.train_settings_generate import argparser_criterion, argparser_opt_scheduler +from utils.trainer_cls import PureCleanModelTrainer +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from utils.log_assist import get_git_info +from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform +from utils.save_load_attack import load_attack_result, save_defense_result + +class spectral(defense): + + def __init__(self,args): + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + self.args = args + + if 'result_file' in args.__dict__ : + if args.result_file is not None: + self.set_result(args.result_file) + + def add_arguments(parser): + parser.add_argument('--device', type=str, help='cuda, cpu') + parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory") + parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?") + parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch') + parser.add_argument('--amp', default = False, type=lambda x: str(x) in ['True','true','1']) + + parser.add_argument('--checkpoint_load', type=str, help='the location of load model') + parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved') + parser.add_argument('--log', type=str, help='the location of log') + parser.add_argument("--dataset_path", type=str, help='the location of data') + parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny') + parser.add_argument('--result_file', type=str, help='the location of result') + + parser.add_argument('--epochs', type=int) + parser.add_argument('--batch_size', type=int) + parser.add_argument("--num_workers", type=float) + parser.add_argument('--lr', type=float) + parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr') + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--model', type=str, help='resnet18') + + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + + parser.add_argument('--random_seed', type=int, help='random seed') + parser.add_argument('--yaml_path', type=str, default="./config/defense/spectral/config.yaml", help='the path of yaml') + + #set the parameter for the spectral defense + parser.add_argument('--percentile', type=float) + parser.add_argument('--target_label', type=int) + + + def set_result(self, result_file): + attack_file = 'record/' + result_file + save_path = 'record/' + result_file + '/defense/spectral/' + if not (os.path.exists(save_path)): + os.makedirs(save_path) + # assert(os.path.exists(save_path)) + self.args.save_path = save_path + if self.args.checkpoint_save is None: + self.args.checkpoint_save = save_path + 'checkpoint/' + if not (os.path.exists(self.args.checkpoint_save)): + os.makedirs(self.args.checkpoint_save) + if self.args.log is None: + self.args.log = save_path + 'log/' + if not (os.path.exists(self.args.log)): + os.makedirs(self.args.log) + self.result = load_attack_result(attack_file + '/attack_result.pt') + + def set_trainer(self, model): + self.trainer = PureCleanModelTrainer( + model, + ) + + def set_logger(self): + args = self.args + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + try: + logging.info(pformat(get_git_info())) + except: + logging.info('Getting git info fails.') + + def set_devices(self): + # self.device = torch.device( + # ( + # f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device + # # since DataParallel only allow .to("cuda") + # ) if torch.cuda.is_available() else "cpu" + # ) + self.device = self.args.device + def mitigation(self): + self.set_devices() + fix_random(self.args.random_seed) + + ### a. prepare the model and dataset + model = generate_cls_model(self.args.model,self.args.num_classes) + model.load_state_dict(self.result['model']) + if "," in self.device: + model = torch.nn.DataParallel( + model, + device_ids=[int(i) for i in self.args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{model.device_ids[0]}' + model.to(self.args.device) + else: + model.to(self.args.device) + # Setting up the data and the model + train_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = True) + train_dataset = self.result['bd_train'].wrapped_dataset + data_set_without_tran = train_dataset + data_set_o = self.result['bd_train'] + data_set_o.wrapped_dataset = data_set_without_tran + data_set_o.wrap_img_transform = train_tran + data_set_o.wrapped_dataset.getitem_all = False + dataset = data_set_o + + # initialize data augmentation + logging.info(f'Dataset Size: {len(dataset)}' ) + + if 'target_label' in args.__dict__: + if isinstance(self.args.target_label,(int)): + poison_labels = [self.args.target_label] + else: + poison_labels = self.args.target_label + else: + poison_labels = range(self.args.num_classes) + + re_all = [] + for target_label in poison_labels: + lbl = target_label + dataset_y = [] + for i in range(len(dataset)): + dataset_y.append(dataset[i][1]) + cur_indices = [i for i,v in enumerate(dataset_y) if v==lbl] + cur_examples = len(cur_indices) + logging.info(f'Label, num ex: {lbl},{cur_examples}' ) + + model.eval() + ### b. get the activation as representation for each data + for iex in range(cur_examples): + cur_im = cur_indices[iex] + x_batch = dataset[cur_im][0].unsqueeze(0).to(self.args.device) + y_batch = dataset[cur_im][1] + with torch.no_grad(): + assert self.args.model in ['preactresnet18', 'vgg19','vgg19_bn', 'resnet18', 'mobilenet_v3_large', 'densenet161', 'efficientnet_b3','convnext_tiny','vit_b_16'] + if self.args.model == 'preactresnet18': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.layer4.register_forward_hook(layer_hook) + _ = model(x_batch) + batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0) + hook.remove() + elif self.args.model == 'vgg19': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.features.register_forward_hook(layer_hook) + _ = model(x_batch) + batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0) + hook.remove() + elif self.args.model == 'vgg19_bn': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.features.register_forward_hook(layer_hook) + _ = model(x_batch) + batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0) + hook.remove() + elif self.args.model == 'resnet18': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.layer4.register_forward_hook(layer_hook) + _ = model(x_batch) + batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0) + hook.remove() + elif self.args.model == 'mobilenet_v3_large': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.avgpool.register_forward_hook(layer_hook) + _ = model(x_batch) + batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0) + hook.remove() + elif self.args.model == 'densenet161': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.features.register_forward_hook(layer_hook) + _ = model(x_batch) + outs[0] = torch.nn.functional.relu(outs[0]) + batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0) + hook.remove() + elif self.args.model == 'efficientnet_b3': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.avgpool.register_forward_hook(layer_hook) + _ = model(x_batch) + batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0) + hook.remove() + elif self.args.model == 'convnext_tiny': + inps,outs = [],[] + def layer_hook(module, inp, out): + outs.append(out.data) + hook = model.avgpool.register_forward_hook(layer_hook) + _ = model(x_batch) + batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0) + hook.remove() + elif self.args.model == 'vit_b_16': + inps,outs = [],[] + def layer_hook(module, inp, out): + inps.append(inp[0].data) + hook = model[1].heads.register_forward_hook(layer_hook) + _ = model(x_batch) + batch_grads = inps[0].view(inps[0].size(0), -1).squeeze(0) + hook.remove() + + if iex==0: + full_cov = np.zeros(shape=(cur_examples, len(batch_grads))) + full_cov[iex] = batch_grads.detach().cpu().numpy() + + ### c. detect the backdoor data by the SVD decomposition + total_p = self.args.percentile + full_mean = np.mean(full_cov, axis=0, keepdims=True) + + centered_cov = full_cov - full_mean + u,s,v = np.linalg.svd(centered_cov, full_matrices=False) + logging.info(f'Top 7 Singular Values: {s[0:7]}') + eigs = v[0:1] + p = total_p + corrs = np.matmul(eigs, np.transpose(full_cov)) #shape num_top, num_active_indices + scores = np.linalg.norm(corrs, axis=0) #shape num_active_indices + logging.info(f'Length Scores: {len(scores)}' ) + p_score = np.percentile(scores, p) + top_scores = np.where(scores>p_score)[0] + logging.info(f'{top_scores}') + + + removed_inds = np.copy(top_scores) + re = [cur_indices[v] for i,v in enumerate(removed_inds)] + re_all.extend(re) + + left_inds = np.delete(range(len(dataset)), re_all) + ### d. retrain the model with remaining data + model = generate_cls_model(self.args.model,self.args.num_classes) + if "," in self.device: + model = torch.nn.DataParallel( + model, + device_ids=[int(i) for i in args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7] + ) + self.args.device = f'cuda:{model.device_ids[0]}' + model.to(self.args.device) + else: + model.to(self.args.device) + dataset.subset(left_inds) + dataset.wrapped_dataset.getitem_all = True + # dataset.subset(left_inds) + dataset_left = dataset + data_loader_sie = torch.utils.data.DataLoader(dataset_left, batch_size=self.args.batch_size, num_workers=self.args.num_workers, shuffle=True) + + optimizer, scheduler = argparser_opt_scheduler(model, self.args) + # criterion = nn.CrossEntropyLoss() + self.set_trainer(model) + criterion = argparser_criterion(args) + + test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False) + data_bd_testset = self.result['bd_test'] + data_bd_testset.wrap_img_transform = test_tran + data_bd_loader = torch.utils.data.DataLoader(data_bd_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + data_clean_testset = self.result['clean_test'] + data_clean_testset.wrap_img_transform = test_tran + data_clean_loader = torch.utils.data.DataLoader(data_clean_testset, batch_size=self.args.batch_size, num_workers=self.args.num_workers,drop_last=False, shuffle=True,pin_memory=args.pin_memory) + + self.trainer.train_with_test_each_epoch_on_mix( + data_loader_sie, + data_clean_loader, + data_bd_loader, + args.epochs, + criterion=criterion, + optimizer=optimizer, + scheduler=scheduler, + device=self.args.device, + frequency_save=args.frequency_save, + save_folder_path=args.save_path, + save_prefix='spectral', + amp=args.amp, + prefetch=args.prefetch, + prefetch_transform_attr_name="ori_image_transform_in_loading", # since we use the preprocess_bd_dataset + non_blocking=args.non_blocking, + ) + + result = {} + result["dataset"] = dataset_left + result['model'] = model + save_defense_result( + model_name=args.model, + num_classes=args.num_classes, + model=model.cpu().state_dict(), + save_path=args.save_path, + ) + return result + + def defense(self,result_file): + self.set_result(result_file) + self.set_logger() + result = self.mitigation() + return result + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=sys.argv[0]) + spectral.add_arguments(parser) + args = parser.parse_args() + spectral_method = spectral(args) + if "result_file" not in args.__dict__: + args.result_file = 'defense_test_badnet' + elif args.result_file is None: + args.result_file = 'defense_test_badnet' + result = spectral_method.defense(args.result_file) \ No newline at end of file diff --git a/fine_tune/fst.py b/fine_tune/fst.py new file mode 100644 index 0000000..09d5ede --- /dev/null +++ b/fine_tune/fst.py @@ -0,0 +1,396 @@ +import sys, os +import math +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +import argparse +from pprint import pformat +import numpy as np +import torch +import time +import copy +import logging +import torch.nn as nn +from torch import optim +from torch.utils.data import DataLoader + +from utils.aggregate_block.save_path_generate import generate_save_folder +from utils.aggregate_block.dataset_and_transform_generate import get_num_classes, get_input_shape +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate_ft import dataset_and_transform_generate +from utils.bd_dataset import prepro_cls_DatasetBD + +from utils.aggregate_block.model_trainer_generate import generate_cls_model +from load_data import CustomDataset, CustomDataset_v2 + +from test import test + + +class LabelSmoothingLoss(nn.Module): + def __init__(self, classes=10, smoothing=0.1, dim=-1): + super(LabelSmoothingLoss, self).__init__() + self.confidence = 1.0 - smoothing + self.smoothing = smoothing + self.cls = classes + self.dim = dim + + def forward(self, pred, target): + pred = pred.log_softmax(dim=self.dim) + true_dist = torch.zeros_like(pred) + true_dist.fill_(self.smoothing / (self.cls - 1)) + true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence) + return torch.mean(torch.sum(-true_dist * pred, dim=self.dim)) + + +def add_args(parser): + """ + parser : argparse.ArgumentParser + return a parser added with args required by fit + """ + # Training settings + parser.add_argument('--device', type = str) + parser.add_argument('--ft_mode', type = str, default='all') + + parser.add_argument('--attack', type = str, ) + parser.add_argument('--attack_label_trans', type=str, default='all2one', + help='which type of label modification in backdoor attack' + ) + parser.add_argument('--pratio', type=float, + help='the poison rate ' + ) + parser.add_argument('--epochs', type=int) + parser.add_argument('--dataset', type=str, + help='which dataset to use' + ) + parser.add_argument('--dataset_path', type=str,default='../data') + parser.add_argument('--attack_target', type=int,default=0, + help='target class in all2one attack') + parser.add_argument('--batch_size', type=int,default=128) + parser.add_argument('--lr', type=float) + parser.add_argument('--random_seed', default=0,type=int, + help='random_seed') + parser.add_argument('--model', type=str, + help='choose which kind of model') + + parser.add_argument('--split_ratio', type=float, + help='part of the training set for defense') + parser.add_argument('--init', action='store_true', + help='init') + + parser.add_argument('--log', action='store_true', + help='record the log') + parser.add_argument('--initlr', type=float,help='learning rate for model training') + parser.add_argument('--pre', action='store_true', help='whether load pre-trained weights') + parser.add_argument('--save', action='store_true',help='save the model') + parser.add_argument('--linear_name', type=str, default='linear',help='name for the linear classifier') + parser.add_argument('--lb_smooth', type=float, default=None,help='label smoothing') + parser.add_argument('--alpha', type=float,default=0.2) + return parser + +def main(): + + ### 1. config args, save_path, fix random seed + + parser = (add_args(argparse.ArgumentParser(description=sys.argv[0]))) + args = parser.parse_args() + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + fix_random(args.random_seed) + + + if args.lb_smooth is not None: + lbs_criterion = LabelSmoothingLoss(classes=args.num_classes, smoothing=args.lb_smooth) + device = torch.device(args.device if torch.cuda.is_available() else "cpu") + if args.init == True and args.ft_mode == 'backbone': + log_name = 'FE-tuning' + elif args.init == True and args.ft_mode == 'all': + log_name = 'FT-init' + elif args.init == False and args.ft_mode == 'all': + log_name = 'FT' + elif args.init == False and args.ft_mode == 'linear': + log_name = 'LP' + elif args.init == True and args.ft_mode == 'fst': + assert args.alpha is not None + log_name = 'FST' + else: + raise NotImplementedError('Not implemented method.') + + if not args.pre: + + args.folder_path = f'../record_{args.dataset}/{args.attack}/' + f'pratio_{args.pratio}-target_{args.attack_target}-archi_{args.model}-dataset_{args.dataset}-sratio_{args.split_ratio}-initlr_{args.lr}' + os.makedirs(f'../logs_{args.model}_{args.dataset}/{log_name}/{args.attack}', exist_ok=True) + args.save_path = f'../logs_{args.model}_{args.dataset}/{log_name}/{args.attack}/' + f'pratio_{args.pratio}-target_{args.attack_target}-archi_{args.model}-dataset_{args.dataset}-sratio_{args.split_ratio}-init_{args.init}-lr_{args.lr}-initlr_{args.initlr}-mode_{args.ft_mode}-epochs_{args.epochs}' + else: + args.folder_path = f'../record_{args.dataset}_pre/{args.attack}/' + f'pratio_{args.pratio}-target_{args.attack_target}-archi_{args.model}-dataset_{args.dataset}-sratio_{args.split_ratio}-initlr_{args.lr}' + os.makedirs(f'../logs_{args.model}_{args.dataset}_pre/{log_name}/{args.attack}', exist_ok=True) + args.save_path = f'../logs_{args.model}_{args.dataset}_pre/{log_name}/{args.attack}/' + f'pratio_{args.pratio}-target_{args.attack_target}-archi_{args.model}-dataset_{args.dataset}-sratio_{args.split_ratio}-init_{args.init}-lr_{args.lr}-initlr_{args.initlr}-mode_{args.ft_mode}-epochs_{args.epochs}' + + + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + if args.log: + fileHandler = logging.FileHandler(args.save_path + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + + ### 2. set the clean train data and clean test data + if not args.pre: + _, train_img_transform, \ + train_label_transfrom, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + ft_dataset_without_transform = dataset_and_transform_generate(args) + else: + from utils.aggregate_block.dataset_and_transform_generate_ft import dataset_and_transform_generate_pre + _, train_img_transform, \ + train_label_transfrom, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + ft_dataset_without_transform = dataset_and_transform_generate_pre(args) + + benign_train_ds = prepro_cls_DatasetBD( + full_dataset_without_transform=ft_dataset_without_transform, + poison_idx=np.zeros(len(ft_dataset_without_transform)), # one-hot to determine which image may take bd_transform + bd_image_pre_transform=None, + bd_label_pre_transform=None, + ori_image_transform_in_loading=train_img_transform, + ori_label_transform_in_loading=train_label_transfrom, + add_details_in_preprocess=True, + ) + + + benign_test_ds = prepro_cls_DatasetBD( + test_dataset_without_transform, + poison_idx=np.zeros(len(test_dataset_without_transform)), # one-hot to determine which image may take bd_transform + bd_image_pre_transform=None, + bd_label_pre_transform=None, + ori_image_transform_in_loading=test_img_transform, + ori_label_transform_in_loading=test_label_transform, + add_details_in_preprocess=True, + ) + + + model_dict = torch.load(args.folder_path + '/attack_result.pt') + adv_test_dataset = model_dict['bd_test'] + + if 'x' in adv_test_dataset.keys(): + adv_test_dataset = CustomDataset(adv_test_dataset['x'], adv_test_dataset['y'], test_img_transform) # For BackdoorBench v1 + else: + import glob + image_list = glob.glob(args.folder_path + '/bd_test_dataset/*/*.png') + adv_test_dataset = CustomDataset_v2(image_list, args.attack_target, test_img_transform) + + ### 3. generate dataset for backdoor defense and evaluation + + train_data = DataLoader( + dataset = benign_train_ds, + batch_size=args.batch_size, + shuffle=True, + drop_last=False, + ) + + test_dataset_dict={ + "test_data" :benign_test_ds, + "adv_test_data" :adv_test_dataset, + } + + test_dataloader_dict = { + name : DataLoader( + dataset = test_dataset, + batch_size=args.batch_size, + shuffle=False, + drop_last=False, + ) + for name, test_dataset in test_dataset_dict.items() + } + + if not args.pre: + net = generate_cls_model( + model_name=args.model, + num_classes=args.num_classes, + image_size=args.img_size[0], + ) + else: + torch.hub.set_dir('/ssddata1/data/rminaa/pretrain_models/') + + if args.model == "resnet18": + from torchvision.models import resnet18, ResNet18_Weights + self.net = resnet18(weights=ResNet18_Weights.IMAGENET1K_V1).to(args.device) + self.net.fc = nn.Linear(in_features=512, out_features=args.num_classes, bias=True).to(args.device) + for _, param in self.net.named_parameters(): + param.requires_grad = True + elif args.model == "resnet50": + from torchvision.models import resnet50, ResNet50_Weights + self.net = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2).to(args.device) + self.net.fc = nn.Linear(in_features=2048, out_features=args.num_classes, bias=True).to(args.device) + for _, param in self.net.named_parameters(): + param.requires_grad = True + elif args.model == 'swin_b': + from torchvision.models import swin_b + self.net = swin_b(weights='IMAGENET1K_V1').to(args.device) + self.net.head = nn.Linear(in_features=1024, out_features=args.num_classes, bias=True).to(args.device) + for _, param in self.net.named_parameters(): + param.requires_grad = True + elif args.model == 'swin_t': + from torchvision.models import swin_t + self.net = swin_t(weights='IMAGENET1K_V1').to(args.device) + self.net.head = nn.Linear(in_features=768, out_features=args.num_classes, bias=True).to(args.device) + for _, param in self.net.named_parameters(): + param.requires_grad = True + else: + raise NotImplementedError(f"{args.model} is not supported") + + + + net.load_state_dict(model_dict['model']) + net.to(device) + + + original_linear_norm = torch.norm(eval(f'net.{args.linear_name}.weight')) + weight_mat_ori = eval(f'net.{args.linear_name}.weight.data.clone().detach()') + + param_list = [] + for name, param in net.named_parameters(): + if args.linear_name in name: + if args.init: + if 'weight' in name: + logging.info(f'Initialize linear classifier weight {name}.') + std = 1 / math.sqrt(param.size(-1)) + param.data.uniform_(-std, std) + + else: + logging.info(f'Initialize linear classifier weight {name}.') + param.data.uniform_(-std, std) + if args.ft_mode == 'linear': + if args.linear_name in name: + logging.info(name) + param.requires_grad = True + param_list.append(param) + else: + param.requires_grad = False + elif args.ft_mode == 'all' or args.ft_mode == 'fst': + param.requires_grad = True + param_list.append(param) + elif args.ft_mode == 'backbone': + if args.linear_name not in name: + param.requires_grad = True + param_list.append(param) + else: + param.requires_grad = False + + + + optimizer = optim.SGD(param_list, lr=args.lr,momentum = 0.9) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epochs) + criterion = nn.CrossEntropyLoss() + + + for dl_name, test_dataloader in test_dataloader_dict.items(): + metrics = test(net, test_dataloader, device) + metric_info = { + f'{dl_name} acc': metrics['test_correct'] / metrics['test_total'], + f'{dl_name} loss': metrics['test_loss'], + } + if 'test_data' == dl_name: + cur_clean_acc = metric_info['test_data acc'] + if 'adv_test_data' == dl_name: + cur_adv_acc = metric_info['adv_test_data acc'] + logging.info('*****************************') + logging.info(f"Load from {args.folder_path + '/attack_result.pt'}") + logging.info(f'Fine-tunning mode: {args.ft_mode}') + logging.info('Original performance') + logging.info(f"Test Set: Clean ACC: {cur_clean_acc} | ASR: {cur_adv_acc}") + logging.info('*****************************') + + + for epoch in range(args.epochs): + + batch_loss_list = [] + train_correct = 0 + train_tot = 0 + + + logging.info(f'Epoch: {epoch}') + net.train() + + for batch_idx, (x, labels, *additional_info) in enumerate(train_data): + + + x, labels = x.to(device), labels.to(device) + log_probs= net(x) + if args.lb_smooth is not None: + loss = lbs_criterion(log_probs, labels) + else: + if args.ft_mode == 'fst': + loss = torch.sum(eval(f'net.{args.linear_name}.weight') * weight_mat_ori)*args.alpha + criterion(log_probs, labels.long()) + else: + loss = criterion(log_probs, labels.long()) + loss.backward() + + + optimizer.step() + optimizer.zero_grad() + + exec_str = f'net.{args.linear_name}.weight.data = net.{args.linear_name}.weight.data * original_linear_norm / torch.norm(net.{args.linear_name}.weight.data)' + exec(exec_str) + + _, predicted = torch.max(log_probs, -1) + train_correct += predicted.eq(labels).sum() + train_tot += labels.size(0) + batch_loss = loss.item() * labels.size(0) + batch_loss_list.append(batch_loss) + + + scheduler.step() + one_epoch_loss = sum(batch_loss_list) + + + logging.info(f'Training ACC: {train_correct/train_tot} | Training loss: {one_epoch_loss}') + logging.info(f'Learning rate: {optimizer.param_groups[0]["lr"]}') + logging.info('-------------------------------------') + + cur_clean_acc, cur_adv_acc = 0,0 + + if epoch == args.epochs-1: + for dl_name, test_dataloader in test_dataloader_dict.items(): + metrics = test(net, test_dataloader, device) + metric_info = { + f'{dl_name} acc': metrics['test_correct'] / metrics['test_total'], + f'{dl_name} loss': metrics['test_loss'], + } + if 'test_data' == dl_name: + cur_clean_acc = metric_info['test_data acc'] + if 'adv_test_data' == dl_name: + cur_adv_acc = metric_info['adv_test_data acc'] + logging.info('Defense performance') + logging.info(f"Clean ACC: {cur_clean_acc} | ASR: {cur_adv_acc}") + logging.info('-------------------------------------') + + if args.save: + model_save_path = f'defense_results/{args.attack}/pratio_{args.pratio}-target_{args.attack_target}-archi_{args.model}-dataset_{args.dataset}-sratio_{args.split_ratio}-init_{args.init}-lr_{args.lr}-initlr_{args.initlr}-mode_{args.ft_mode}-epochs_{args.epochs}' + os.makedirs(model_save_path, exist_ok=True) + torch.save(net.state_dict(), f'{model_save_path}/checkpoint.pt') + + +if __name__ == '__main__': + main() + \ No newline at end of file diff --git a/fine_tune/load_data.py b/fine_tune/load_data.py new file mode 100644 index 0000000..549ac95 --- /dev/null +++ b/fine_tune/load_data.py @@ -0,0 +1,41 @@ +import torch +from torch.utils.data import Dataset, DataLoader +from PIL import Image +import numpy as np + +class CustomDataset(Dataset): + def __init__(self, img_list, label_list, transform=None): + self.img_list = img_list + self.label_list = label_list + self.transform = transform + + def __len__(self): + return len(self.img_list) + + def __getitem__(self, idx): + img = self.img_list[idx] + label = self.label_list[idx] + label = np.int64(label) + if self.transform: + img = self.transform(img) + + return img, label + + +class CustomDataset_v2(Dataset): + def __init__(self, img_list, attack_target, transform=None): + self.image_list = [] + for i in img_list: + x = Image.open(i) + self.image_list.append(transform(x)) + + self.attack_target = int(attack_target) + + + def __len__(self): + return len(self.image_list) + + def __getitem__(self, idx): + + return self.image_list[idx], self.attack_target + diff --git a/fine_tune/test.py b/fine_tune/test.py new file mode 100644 index 0000000..d76a523 --- /dev/null +++ b/fine_tune/test.py @@ -0,0 +1,38 @@ +import torch +import torch.nn as nn +def test(model, test_data, device,multi=False): + + model.eval() + + metrics = { + 'test_correct': 0, + 'test_loss': 0, + 'test_total': 0 + } + criterion = nn.CrossEntropyLoss() + tot_tar_list = [] + cor_tar_list = [] + with torch.no_grad(): + for batch_idx, (x, target, *additional_info) in enumerate(test_data): + x = x.to(device) + target = target.to(device) + if multi: + pred,_ = model(x) + else: + pred = model(x) + loss = criterion(pred, target.long()) + + _, predicted = torch.max(pred, -1) + correct_mask = predicted.eq(target) + for cor, tar in zip(correct_mask,target): + tot_tar_list.append(int(tar.item())) + if cor: + cor_tar_list.append(int(tar.item())) + correct = correct_mask.sum() + metrics['test_correct'] += correct.item() + metrics['test_loss'] += loss.item() * target.size(0) + metrics['test_total'] += target.size(0) + + return metrics + + diff --git a/for_imagenet/README.md b/for_imagenet/README.md new file mode 100755 index 0000000..018b267 --- /dev/null +++ b/for_imagenet/README.md @@ -0,0 +1,15 @@ +This is for ImageNet only. (Still under construction) + +Because we only considered backdoor attacks on small and medium datasets at the beginning of the design, we do not have better support for ImageNet datasets. You will likely fail your training process when you use ImageNet as your target dataset due to insufficient RAM. So this folder is dedicated to backdoor attacks on ImageNet. + +1. Download ImageNet data by yourself and put in `data` folder. +2. Use the script to generate the data for training and validation. +eg. `multi_generate_poison_badnet.py` and `generate_poison_val_badnet.py` for BadNets. +3. Run `train.py` with proper setting specified. + +Example Results (With PreAct-ResNet18, 0.1% poison rate. For all other detailed settings, you can refer to corresponding scripts. ) + +| | ACC | ASR | RA | +| ------- | -------- | -------- | -------- | +| BadNets | 69.20923 | 75.86055 | 0.338413 | +| Blended | 69.23923 | 98.58628 | 0.110134 | \ No newline at end of file diff --git a/for_imagenet/des_stats.py b/for_imagenet/des_stats.py new file mode 100755 index 0000000..7181837 --- /dev/null +++ b/for_imagenet/des_stats.py @@ -0,0 +1,25 @@ +''' +To inspect the folder structure for data generation. +Make sure the poison ratio is accurate. +''' + +import os, sys + +def stats(given_fodler_path): + info_list = [] + for subfodler_name in os.listdir(given_fodler_path): + if not os.path.isdir(f"{given_fodler_path}/{subfodler_name}"): + continue + info = f"{given_fodler_path}/{subfodler_name} : " +\ + str(len(os.listdir(f"{given_fodler_path}/{subfodler_name}"))) +\ + "\n" + info_list.append( + info + ) + print(info) + + with open(f"{given_fodler_path}/stats.txt", "w") as f: + f.writelines(info_list) + + + diff --git a/for_imagenet/generate_poison_val_badnet.py b/for_imagenet/generate_poison_val_badnet.py new file mode 100755 index 0000000..6680e4c --- /dev/null +++ b/for_imagenet/generate_poison_val_badnet.py @@ -0,0 +1,145 @@ +''' +Generation of BadNets validation data (for ASR and RA) +''' + +class Args: + pass +args = Args() +args.__dict__ = { + 'attack':"badnet", + "patch_mask_path" : "../resource/badnet/bottom_right_3by3_white.npy", + "img_size" : [224,224,3], +} + + +pratio = 1 +attack = args.__dict__['attack'] +imagenet_path = "../data/imagenet/val" +target_path = f"../imagenet_poison/{attack}/val" +ra_path = f"../imagenet_poison/{attack}/ra" + +target_class_folder_name = "n01440764" # None then do not filt + + +import os, glob, random, re +import sys, yaml, os +import numpy as np + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +from tqdm import tqdm +from PIL import Image +from PIL import ImageFile +ImageFile.LOAD_TRUNCATED_IMAGES = True +MIN_VALID_IMG_DIM = 32 + +from utils.aggregate_block.bd_attack_generate import * +from des_stats import stats + + + +train_bd_transform,test_bd_transform = bd_attack_img_trans_generate(args) + +def is_valid_file(path): + try: + img = Image.open(path) + img.verify() + except: + return False + if not (img.height >= MIN_VALID_IMG_DIM and img.width >= MIN_VALID_IMG_DIM): + return False + return True + +# train_dataset_without_transform = ImageFolder( +# root = f"{args.dataset_path}/train", +# is_valid_file=is_valid_file, +# ) + +# valid list +filePathList = [ + filepath for filepath in tqdm(glob.iglob(imagenet_path + '**/**', recursive=True),desc="valid list") + if os.path.isfile(filepath) and is_valid_file(filepath) +] + +# filter target class for test +if target_class_folder_name is not None: + filePathList = filter(lambda filepath: target_class_folder_name not in filepath , filePathList) + +# poison_filelist = random.sample(filePathList, int(len(filePathList) * pratio)) +poison_filelist = [] + +for filepath in tqdm(filePathList, desc="process bd"): + + img = Image.open(filepath) + + ra_filepath = filepath + + # target path + target_filepath = filepath.replace( + imagenet_path, + target_path + ) + + p = re.compile(r'/n(\d)+/') + target_filepath = p.sub(f"/{target_class_folder_name}/", target_filepath) + + # check folder + if not os.path.exists( + os.path.dirname( + target_filepath + ) + ): + os.makedirs( + os.path.dirname( + target_filepath + ) + ) + + ra_filepath = ra_filepath.replace( + imagenet_path, + ra_path, + ) + + if not os.path.exists( + os.path.dirname( + ra_filepath + ) + ): + os.makedirs( + os.path.dirname( + ra_filepath + ) + ) + + img = np.asarray(img).astype('uint8') + if len(img.shape) == 2: + img = np.concatenate(3 * [img[..., None]], axis=2) + if img.shape[2] != 3: + img = img[:, :, :3] + img = Image.fromarray(img) + + # select + if random.uniform(0, 1) < pratio: + + # do poison + img = Image.fromarray( + np.clip( + train_bd_transform(img), 0, 255).astype(np.uint8) + ) + + poison_filelist.append(target_filepath) + + #save to + img.save(target_filepath) + img.save(ra_filepath) + img.close() + +with open(f'{attack}_val.txt', 'w') as f: + for line in poison_filelist: + f.write(f"{line}\n") + +stats(imagenet_path) +stats(target_path) +stats(ra_path) \ No newline at end of file diff --git a/for_imagenet/generate_poison_val_blended.py b/for_imagenet/generate_poison_val_blended.py new file mode 100755 index 0000000..c5bb057 --- /dev/null +++ b/for_imagenet/generate_poison_val_blended.py @@ -0,0 +1,145 @@ +''' +Generation of Blended validation data (for ASR and RA) +''' + +class Args: + pass +args = Args() +args.__dict__ = { + "attack": "blended", + "attack_trigger_img_path" : "../resource/blended/hello_kitty.jpeg", + "attack_train_blended_alpha": 0.2, + "attack_test_blended_alpha": 0.2, + "img_size" : [224,224,3], +} + + +pratio = 1 +attack = args.__dict__['attack'] +imagenet_path = "../data/imagenet/val" +target_path = f"../imagenet_poison/{attack}/val" +ra_path = f"../imagenet_poison/{attack}/ra" + +target_class_folder_name = "n01440764" # None then do not filt + + +import os, glob, random, re +import sys, yaml, os +import numpy as np + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +from tqdm import tqdm +from PIL import Image +from PIL import ImageFile +ImageFile.LOAD_TRUNCATED_IMAGES = True +MIN_VALID_IMG_DIM = 32 + +from utils.aggregate_block.bd_attack_generate import * +from des_stats import stats + +train_bd_transform,test_bd_transform = bd_attack_img_trans_generate(args) + +def is_valid_file(path): + try: + img = Image.open(path) + img.verify() + except: + return False + if not (img.height >= MIN_VALID_IMG_DIM and img.width >= MIN_VALID_IMG_DIM): + return False + return True + +# train_dataset_without_transform = ImageFolder( +# root = f"{args.dataset_path}/train", +# is_valid_file=is_valid_file, +# ) + +# valid list +filePathList = [ + filepath for filepath in tqdm(glob.iglob(imagenet_path + '**/**', recursive=True),desc="valid list") + if os.path.isfile(filepath) and is_valid_file(filepath) +] + +# filter target class for test +if target_class_folder_name is not None: + filePathList = filter(lambda filepath: target_class_folder_name not in filepath , filePathList) + +# poison_filelist = random.sample(filePathList, int(len(filePathList) * pratio)) +poison_filelist = [] + +for filepath in tqdm(filePathList, desc="process bd"): + + img = Image.open(filepath) + + ra_filepath = filepath + + # target path + target_filepath = filepath.replace( + imagenet_path, + target_path + ) + + p = re.compile(r'/n(\d)+/') + target_filepath = p.sub(f"/{target_class_folder_name}/", target_filepath) + + # check folder + if not os.path.exists( + os.path.dirname( + target_filepath + ) + ): + os.makedirs( + os.path.dirname( + target_filepath + ) + ) + + ra_filepath = ra_filepath.replace( + imagenet_path, + ra_path, + ) + + if not os.path.exists( + os.path.dirname( + ra_filepath + ) + ): + os.makedirs( + os.path.dirname( + ra_filepath + ) + ) + + img = np.asarray(img).astype('uint8') + if len(img.shape) == 2: + img = np.concatenate(3 * [img[..., None]], axis=2) + if img.shape[2] != 3: + img = img[:, :, :3] + img = Image.fromarray(img) + + # select + if random.uniform(0, 1) < pratio: + + # do poison + img = Image.fromarray( + np.clip( + train_bd_transform(img), 0, 255).astype(np.uint8) + ) + + poison_filelist.append(target_filepath) + + #save to + img.save(target_filepath) + img.save(ra_filepath) + img.close() + +with open(f'{attack}_val.txt', 'w') as f: + for line in poison_filelist: + f.write(f"{line}\n") + +stats(imagenet_path) +stats(target_path) +stats(ra_path) \ No newline at end of file diff --git a/for_imagenet/generate_poison_val_sig.py b/for_imagenet/generate_poison_val_sig.py new file mode 100755 index 0000000..0f21350 --- /dev/null +++ b/for_imagenet/generate_poison_val_sig.py @@ -0,0 +1,144 @@ +''' +Generation of SIG validation data (for ASR and RA) +''' + +class Args: + pass +args = Args() +args.__dict__ = { + "attack": "sig", + "sig_delta": 40, + "sig_f": 6, + "img_size" : [224,224,3], +} + + +pratio = 1 +attack = args.__dict__['attack'] +imagenet_path = "../data/imagenet/val" +target_path = f"../imagenet_poison/{attack}/val" +ra_path = f"../imagenet_poison/{attack}/ra" + +target_class_folder_name = "n01440764" # None then do not filt + + +import os, glob, random, re +import sys, yaml, os +import numpy as np + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +from tqdm import tqdm +from PIL import Image +from PIL import ImageFile +ImageFile.LOAD_TRUNCATED_IMAGES = True +MIN_VALID_IMG_DIM = 32 + +from utils.aggregate_block.bd_attack_generate import * +from des_stats import stats + +train_bd_transform,test_bd_transform = bd_attack_img_trans_generate(args) + +def is_valid_file(path): + try: + img = Image.open(path) + img.verify() + except: + return False + if not (img.height >= MIN_VALID_IMG_DIM and img.width >= MIN_VALID_IMG_DIM): + return False + return True + +# train_dataset_without_transform = ImageFolder( +# root = f"{args.dataset_path}/train", +# is_valid_file=is_valid_file, +# ) + +# valid list +filePathList = [ + filepath for filepath in tqdm(glob.iglob(imagenet_path + '**/**', recursive=True),desc="valid list") + if os.path.isfile(filepath) and is_valid_file(filepath) +] + +# filter target class for test +if target_class_folder_name is not None: + filePathList = filter(lambda filepath: target_class_folder_name not in filepath , filePathList) + +# poison_filelist = random.sample(filePathList, int(len(filePathList) * pratio)) +poison_filelist = [] + +for filepath in tqdm(filePathList, desc="process bd"): + + img = Image.open(filepath) + + ra_filepath = filepath + + # target path + target_filepath = filepath.replace( + imagenet_path, + target_path + ) + + p = re.compile(r'/n(\d)+/') + target_filepath = p.sub(f"/{target_class_folder_name}/", target_filepath) + + # check folder + if not os.path.exists( + os.path.dirname( + target_filepath + ) + ): + os.makedirs( + os.path.dirname( + target_filepath + ) + ) + + ra_filepath = ra_filepath.replace( + imagenet_path, + ra_path, + ) + + if not os.path.exists( + os.path.dirname( + ra_filepath + ) + ): + os.makedirs( + os.path.dirname( + ra_filepath + ) + ) + + img = np.asarray(img).astype('uint8') + if len(img.shape) == 2: + img = np.concatenate(3 * [img[..., None]], axis=2) + if img.shape[2] != 3: + img = img[:, :, :3] + img = Image.fromarray(img) + + # select + if random.uniform(0, 1) < pratio: + + # do poison + img = Image.fromarray( + np.clip( + train_bd_transform(img), 0, 255).astype(np.uint8) + ) + + poison_filelist.append(target_filepath) + + #save to + img.save(target_filepath) + img.save(ra_filepath) + img.close() + +with open(f'{attack}_val.txt', 'w') as f: + for line in poison_filelist: + f.write(f"{line}\n") + +stats(imagenet_path) +stats(target_path) +stats(ra_path) \ No newline at end of file diff --git a/for_imagenet/multi_generate_poison_badnet.py b/for_imagenet/multi_generate_poison_badnet.py new file mode 100755 index 0000000..ad4f8bc --- /dev/null +++ b/for_imagenet/multi_generate_poison_badnet.py @@ -0,0 +1,115 @@ +''' +Generation of BadNets training data, with multiprocessing to speed up. +''' + +class Args: + pass +args = Args() +args.__dict__ = { + 'attack':"badnet", + "patch_mask_path" : "../resource/badnet/bottom_right_3by3_white.npy", + "img_size" : [224,224,3], +} + + +pratio = 0.001 +attack = args.__dict__['attack'] +imagenet_path = "../data/imagenet/train" +target_path = f"../imagenet_poison/{attack}/train" +target_class_folder_name = "n01440764" # None then do not filt + +from multiprocessing import Pool +import tqdm +import os, glob, random, re +import sys, yaml, os +import numpy as np + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +from PIL import Image +from PIL import ImageFile +ImageFile.LOAD_TRUNCATED_IMAGES = True +MIN_VALID_IMG_DIM = 32 + +from utils.aggregate_block.bd_attack_generate import * +from des_stats import stats + +train_bd_transform,test_bd_transform = bd_attack_img_trans_generate(args) + +def is_valid_file(path): + try: + img = Image.open(path) + img.verify() + except: + return False + if not (img.height >= MIN_VALID_IMG_DIM and img.width >= MIN_VALID_IMG_DIM): + return False + return True + +def do_work(filepath): + img = Image.open(filepath) + + # target path + target_filepath = filepath.replace( + imagenet_path, + target_path + ) + + img = np.asarray(img).astype('uint8') + if len(img.shape) == 2: + img = np.concatenate(3 * [img[..., None]], axis=2) + if img.shape[2] != 3: + img = img[:, :, :3] + img = Image.fromarray(img) + + # select + if random.uniform(0, 1) < pratio: + # do poison + img = Image.fromarray( + np.clip( + train_bd_transform(img), 0, 255).astype(np.uint8) + ) + + p = re.compile(r'/n(\d)+/') + target_filepath = p.sub(f"/{target_class_folder_name}/", target_filepath) + + print(target_filepath) + + # save to + img.save(target_filepath) + img.close() + +if __name__ == '__main__': + + # copy the whole class folder structure + + originalClassFolderList = filter(os.path.isdir, [f"{imagenet_path}/{subfolder_name}" for subfolder_name in + os.listdir(imagenet_path)]) + for folderPath in originalClassFolderList: + folderPath = folderPath.replace( + imagenet_path, + target_path + ) + if not os.path.exists( + folderPath + ): + os.makedirs( + folderPath + ) + + # valid list for img + filePathList = [ + filepath for filepath in tqdm.tqdm(glob.iglob(imagenet_path + '**/**', recursive=True),desc="valid list") + if os.path.isfile(filepath) and is_valid_file(filepath) + ] + + tasks = filePathList + + pool = Pool() + for _ in tqdm.tqdm(pool.imap_unordered(do_work, tasks), total=len(tasks)): + pass + + stats(imagenet_path) + stats(target_path) diff --git a/for_imagenet/multi_generate_poison_blended.py b/for_imagenet/multi_generate_poison_blended.py new file mode 100755 index 0000000..2ccff3a --- /dev/null +++ b/for_imagenet/multi_generate_poison_blended.py @@ -0,0 +1,117 @@ +''' +Generation of Blended training data, with multiprocessing to speed up. +''' + +class Args: + pass +args = Args() +args.__dict__ = { + "attack": "blended", + "attack_trigger_img_path" : "../resource/blended/hello_kitty.jpeg", + "attack_train_blended_alpha": 0.2, + "attack_test_blended_alpha": 0.2, + "img_size" : [224,224,3], +} + + +pratio = 0.001 +attack = args.__dict__['attack'] +imagenet_path = "../data/imagenet/train" +target_path = f"../imagenet_poison/{attack}/train" +target_class_folder_name = "n01440764" # None then do not filt + +from multiprocessing import Pool +import tqdm +import os, glob, random, re +import sys, yaml, os +import numpy as np + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +from PIL import Image +from PIL import ImageFile +ImageFile.LOAD_TRUNCATED_IMAGES = True +MIN_VALID_IMG_DIM = 32 + +from utils.aggregate_block.bd_attack_generate import * +from des_stats import stats + +train_bd_transform,test_bd_transform = bd_attack_img_trans_generate(args) + +def is_valid_file(path): + try: + img = Image.open(path) + img.verify() + except: + return False + if not (img.height >= MIN_VALID_IMG_DIM and img.width >= MIN_VALID_IMG_DIM): + return False + return True + +def do_work(filepath): + img = Image.open(filepath) + + # target path + target_filepath = filepath.replace( + imagenet_path, + target_path + ) + + img = np.asarray(img).astype('uint8') + if len(img.shape) == 2: + img = np.concatenate(3 * [img[..., None]], axis=2) + if img.shape[2] != 3: + img = img[:, :, :3] + img = Image.fromarray(img) + + # select + if random.uniform(0, 1) < pratio: + # do poison + img = Image.fromarray( + np.clip( + train_bd_transform(img), 0, 255).astype(np.uint8) + ) + + p = re.compile(r'/n(\d)+/') + target_filepath = p.sub(f"/{target_class_folder_name}/", target_filepath) + + print(target_filepath) + + # save to + img.save(target_filepath) + img.close() + +if __name__ == '__main__': + + # copy the whole class folder structure + + originalClassFolderList = filter(os.path.isdir, [f"{imagenet_path}/{subfolder_name}" for subfolder_name in + os.listdir(imagenet_path)]) + for folderPath in originalClassFolderList: + folderPath = folderPath.replace( + imagenet_path, + target_path + ) + if not os.path.exists( + folderPath + ): + os.makedirs( + folderPath + ) + + # valid list for img + filePathList = [ + filepath for filepath in tqdm.tqdm(glob.iglob(imagenet_path + '**/**', recursive=True),desc="valid list") + if os.path.isfile(filepath) and is_valid_file(filepath) + ] + + tasks = filePathList + + pool = Pool() + for _ in tqdm.tqdm(pool.imap_unordered(do_work, tasks), total=len(tasks)): + pass + + stats(imagenet_path) + stats(target_path) diff --git a/for_imagenet/multi_generate_poison_sig.py b/for_imagenet/multi_generate_poison_sig.py new file mode 100755 index 0000000..b761ca6 --- /dev/null +++ b/for_imagenet/multi_generate_poison_sig.py @@ -0,0 +1,116 @@ +''' +Generation of SIG training data, with multiprocessing to speed up. +''' + +class Args: + pass +args = Args() +args.__dict__ = { + "attack": "sig", + "sig_delta": 40, + "sig_f": 6, + "img_size" : [224,224,3], +} + + +pratio = 0.001 +attack = args.__dict__['attack'] +imagenet_path = "../data/imagenet/train" +target_path = f"../imagenet_poison/{attack}/train" +target_class_folder_name = "n01440764" # None then do not filt + +from multiprocessing import Pool +import tqdm +import os, glob, random, re +import sys, yaml, os +import numpy as np + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +from PIL import Image +from PIL import ImageFile +ImageFile.LOAD_TRUNCATED_IMAGES = True +MIN_VALID_IMG_DIM = 32 + +from utils.aggregate_block.bd_attack_generate import * +from des_stats import stats + +train_bd_transform,test_bd_transform = bd_attack_img_trans_generate(args) + +def is_valid_file(path): + try: + img = Image.open(path) + img.verify() + except: + return False + if not (img.height >= MIN_VALID_IMG_DIM and img.width >= MIN_VALID_IMG_DIM): + return False + return True + +def do_work(filepath): + img = Image.open(filepath) + + # target path + target_filepath = filepath.replace( + imagenet_path, + target_path + ) + + img = np.asarray(img).astype('uint8') + if len(img.shape) == 2: + img = np.concatenate(3 * [img[..., None]], axis=2) + if img.shape[2] != 3: + img = img[:, :, :3] + img = Image.fromarray(img) + + # select + if random.uniform(0, 1) < pratio: + # do poison + img = Image.fromarray( + np.clip( + train_bd_transform(img), 0, 255).astype(np.uint8) + ) + + p = re.compile(r'/n(\d)+/') + target_filepath = p.sub(f"/{target_class_folder_name}/", target_filepath) + + print(target_filepath) + + # save to + img.save(target_filepath) + img.close() + +if __name__ == '__main__': + + # copy the whole class folder structure + + originalClassFolderList = filter(os.path.isdir, [f"{imagenet_path}/{subfolder_name}" for subfolder_name in + os.listdir(imagenet_path)]) + for folderPath in originalClassFolderList: + folderPath = folderPath.replace( + imagenet_path, + target_path + ) + if not os.path.exists( + folderPath + ): + os.makedirs( + folderPath + ) + + # valid list for img + filePathList = [ + filepath for filepath in tqdm.tqdm(glob.iglob(imagenet_path + '**/**', recursive=True),desc="valid list") + if os.path.isfile(filepath) and is_valid_file(filepath) + ] + + tasks = filePathList + + pool = Pool() + for _ in tqdm.tqdm(pool.imap_unordered(do_work, tasks), total=len(tasks)): + pass + + stats(imagenet_path) + stats(target_path) diff --git a/for_imagenet/train.py b/for_imagenet/train.py new file mode 100755 index 0000000..92132f8 --- /dev/null +++ b/for_imagenet/train.py @@ -0,0 +1,610 @@ +''' +This script is rewritten from https://github.com/pytorch/examples/tree/main/imagenet +The original LICENSE is at the buttom of this script. +''' + +#python examples_main.py -a preactresnet18 --dist-url 'tcp://127.0.0.1:8888' --dist-backend 'nccl' --multiprocessing-distributed --world-size 1 --rank 0 + +attack = 'blended' # if you want other attacks, you should change to their names + +traindir = f"../imagenet_poison/{attack}/train" +valdir = f"../data/imagenet/val" +adv_valdir = f"../imagenet_poison/{attack}/val" +ra_valdir = f"../imagenet_poison/{attack}/ra" + + + +import sys, os + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +import argparse +import os +import random +import shutil +import time +import warnings +from enum import Enum + +import torch +import torch.nn as nn +import torch.nn.parallel +import torch.backends.cudnn as cudnn +import torch.distributed as dist +import torch.optim +from torch.optim.lr_scheduler import StepLR +import torch.multiprocessing as mp +import torch.utils.data +import torch.utils.data.distributed +import torchvision.transforms as transforms +import torchvision.datasets as datasets +import torchvision.models as models +from torch.utils.data import Subset + +from utils.aggregate_block.model_trainer_generate import generate_cls_model + +from PIL import Image +from PIL import ImageFile +ImageFile.LOAD_TRUNCATED_IMAGES = True +MIN_VALID_IMG_DIM = 32 +def is_valid_file(path): + try: + img = Image.open(path) + img.verify() + except: + return False + if not (img.height >= MIN_VALID_IMG_DIM and img.width >= MIN_VALID_IMG_DIM): + return False + return True + +# model_names = sorted(name for name in models.__dict__ +# if name.islower() and not name.startswith("__") +# and callable(models.__dict__[name])) + +parser = argparse.ArgumentParser(description='PyTorch ImageNet Training') +parser.add_argument('-a','--arch', type=str, + help='choose which kind of model') +# parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18', +# choices=model_names, +# help='model architecture: ' + +# ' | '.join(model_names) + +# ' (default: resnet18)') +parser.add_argument('-j', '--workers', default=4, type=int, metavar='N', + help='number of data loading workers (default: 4)') +parser.add_argument('--epochs', default=90, type=int, metavar='N', + help='number of total epochs to run') +parser.add_argument('--start-epoch', default=0, type=int, metavar='N', + help='manual epoch number (useful on restarts)') +parser.add_argument('-b', '--batch-size', default=256, type=int, + metavar='N', + help='mini-batch size (default: 256), this is the total ' + 'batch size of all GPUs on the current node when ' + 'using Data Parallel or Distributed Data Parallel') +parser.add_argument('--lr', '--learning-rate', default=0.1, type=float, + metavar='LR', help='initial learning rate', dest='lr') +parser.add_argument('--momentum', default=0.9, type=float, metavar='M', + help='momentum') +parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float, + metavar='W', help='weight decay (default: 1e-4)', + dest='weight_decay') +parser.add_argument('-p', '--print-freq', default=10, type=int, + metavar='N', help='print frequency (default: 10)') +parser.add_argument('--resume', default='', type=str, metavar='PATH', + help='path to latest checkpoint (default: none)') +parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', + help='evaluate model on validation set') +parser.add_argument('--pretrained', dest='pretrained', action='store_true', + help='use pre-trained model') +parser.add_argument('--world-size', default=-1, type=int, + help='number of nodes for distributed training') +parser.add_argument('--rank', default=-1, type=int, + help='node rank for distributed training') +parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str, + help='url used to set up distributed training') +parser.add_argument('--dist-backend', default='nccl', type=str, + help='distributed backend') +parser.add_argument('--seed', default=None, type=int, + help='seed for initializing training. ') +parser.add_argument('--gpu', default=None, type=int, + help='GPU id to use.') +parser.add_argument('--multiprocessing-distributed', action='store_true', + help='Use multi-processing distributed training to launch ' + 'N processes per node, which has N GPUs. This is the ' + 'fastest way to use PyTorch for either single node or ' + 'multi node data parallel training') + +best_acc1 = 0 + + +def main(): + args = parser.parse_args() + + if args.seed is not None: + random.seed(args.seed) + torch.manual_seed(args.seed) + cudnn.deterministic = True + warnings.warn('You have chosen to seed training. ' + 'This will turn on the CUDNN deterministic setting, ' + 'which can slow down your training considerably! ' + 'You may see unexpected behavior when restarting ' + 'from checkpoints.') + + if args.gpu is not None: + warnings.warn('You have chosen a specific GPU. This will completely ' + 'disable data parallelism.') + + if args.dist_url == "env://" and args.world_size == -1: + args.world_size = int(os.environ["WORLD_SIZE"]) + + args.distributed = args.world_size > 1 or args.multiprocessing_distributed + + ngpus_per_node = torch.cuda.device_count() + if args.multiprocessing_distributed: + # Since we have ngpus_per_node processes per node, the total world_size + # needs to be adjusted accordingly + args.world_size = ngpus_per_node * args.world_size + # Use torch.multiprocessing.spawn to launch distributed processes: the + # main_worker process function + mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args)) + else: + # Simply call main_worker function + main_worker(args.gpu, ngpus_per_node, args) + + +def main_worker(gpu, ngpus_per_node, args): + global best_acc1 + args.gpu = gpu + + if args.gpu is not None: + print("Use GPU: {} for training".format(args.gpu)) + + if args.distributed: + if args.dist_url == "env://" and args.rank == -1: + args.rank = int(os.environ["RANK"]) + if args.multiprocessing_distributed: + # For multiprocessing distributed training, rank needs to be the + # global rank among all the processes + args.rank = args.rank * ngpus_per_node + gpu + dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, + world_size=args.world_size, rank=args.rank) + # create model + if args.pretrained: + print("=> using pre-trained model '{}'".format(args.arch)) + model = generate_cls_model( + model_name = args.arch, + num_classes = 1000, + image_size = 224, + pretrained=True) + else: + print("=> creating model '{}'".format(args.arch)) + model = generate_cls_model( + model_name = args.arch, + num_classes = 1000, + image_size = 224, + ) + + if not torch.cuda.is_available(): + print('using CPU, this will be slow') + elif args.distributed: + # For multiprocessing distributed, DistributedDataParallel constructor + # should always set the single device scope, otherwise, + # DistributedDataParallel will use all available devices. + if args.gpu is not None: + torch.cuda.set_device(args.gpu) + model.cuda(args.gpu) + # When using a single GPU per process and per + # DistributedDataParallel, we need to divide the batch size + # ourselves based on the total number of GPUs of the current node. + args.batch_size = int(args.batch_size / ngpus_per_node) + args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node) + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) + else: + model.cuda() + # DistributedDataParallel will divide and allocate batch_size to all + # available GPUs if device_ids are not set + model = torch.nn.parallel.DistributedDataParallel(model) + elif args.gpu is not None: + torch.cuda.set_device(args.gpu) + model = model.cuda(args.gpu) + else: + # DataParallel will divide and allocate batch_size to all available GPUs + if args.arch.startswith('alexnet') or args.arch.startswith('vgg'): + model.features = torch.nn.DataParallel(model.features) + model.cuda() + else: + model = torch.nn.DataParallel(model).cuda() + + # define loss function (criterion), optimizer, and learning rate scheduler + criterion = nn.CrossEntropyLoss().cuda(args.gpu) + + optimizer = torch.optim.SGD(model.parameters(), args.lr, + momentum=args.momentum, + weight_decay=args.weight_decay) + + """Sets the learning rate to the initial LR decayed by 10 every 30 epochs""" + scheduler = StepLR(optimizer, step_size=30, gamma=0.1) + + # optionally resume from a checkpoint + if args.resume: + if os.path.isfile(args.resume): + print("=> loading checkpoint '{}'".format(args.resume)) + if args.gpu is None: + checkpoint = torch.load(args.resume) + else: + # Map model to be loaded to specified single gpu. + loc = 'cuda:{}'.format(args.gpu) + checkpoint = torch.load(args.resume, map_location=loc) + args.start_epoch = checkpoint['epoch'] + best_acc1 = checkpoint['best_acc1'] + if args.gpu is not None: + # best_acc1 may be from a checkpoint from a different GPU + best_acc1 = best_acc1.to(args.gpu) + model.load_state_dict(checkpoint['state_dict']) + optimizer.load_state_dict(checkpoint['optimizer']) + scheduler.load_state_dict(checkpoint['scheduler']) + print("=> loaded checkpoint '{}' (epoch {})" + .format(args.resume, checkpoint['epoch'])) + else: + print("=> no checkpoint found at '{}'".format(args.resume)) + + cudnn.benchmark = True + + # Data loading code + + + normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + + train_dataset = datasets.ImageFolder( + traindir, + transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + normalize, + ]), + is_valid_file=is_valid_file, + ) + + adv_val_dataset = datasets.ImageFolder( + adv_valdir, + transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + normalize, + ]), + is_valid_file=is_valid_file, + ) + + ra_val_dataset = datasets.ImageFolder( + ra_valdir, + transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + normalize, + ]), + is_valid_file=is_valid_file, + ) + + val_dataset = datasets.ImageFolder( + valdir, + transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + normalize, + ]), + is_valid_file=is_valid_file, + ) + + if args.distributed: + train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) + val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False, drop_last=True) + adv_val_sampler = torch.utils.data.distributed.DistributedSampler(adv_val_dataset, shuffle=False, drop_last=True) + ra_val_sampler = torch.utils.data.distributed.DistributedSampler(ra_val_dataset, shuffle=False, + drop_last=True) + else: + train_sampler = None + val_sampler = None + adv_val_sampler = None + ra_val_sampler = None + + train_loader = torch.utils.data.DataLoader( + train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None), + num_workers=args.workers, pin_memory=True, sampler=train_sampler) + + val_loader = torch.utils.data.DataLoader( + val_dataset, batch_size=args.batch_size, shuffle=False, + num_workers=args.workers, pin_memory=True, sampler=val_sampler) + + adv_val_loader = torch.utils.data.DataLoader( + adv_val_dataset, batch_size=args.batch_size, shuffle=False, + num_workers=args.workers, pin_memory=True, sampler=adv_val_sampler) + + ra_val_loader = torch.utils.data.DataLoader( + ra_val_dataset, batch_size=args.batch_size, shuffle=False, + num_workers=args.workers, pin_memory=True, sampler=ra_val_sampler) + + + if args.evaluate: + validate(val_loader, model, criterion, args) + validate(adv_val_loader, model, criterion, args) + validate(ra_val_loader, model ,criterion,args) + return + + for epoch in range(args.start_epoch, args.epochs): + if args.distributed: + train_sampler.set_epoch(epoch) + + # train for one epoch + train(train_loader, model, criterion, optimizer, epoch, args) + + # evaluate on validation set + acc1 = validate(val_loader, model, criterion, args) + adv_acc1 = validate(adv_val_loader, model, criterion, args) + ra_acc1 = validate(ra_val_loader, model, criterion, args) + + print(f"epoch:{epoch}, ACC:{acc1}, ASR:{adv_acc1}, RA:{ra_acc1}") + with open("log.txt","a") as f: + f.write(f"epoch:{epoch}, ACC:{acc1}, ASR:{adv_acc1}, RA:{ra_acc1}") + + scheduler.step() + + # remember best acc@1 and save checkpoint + is_best = acc1 > best_acc1 + best_acc1 = max(acc1, best_acc1) + + if not args.multiprocessing_distributed or (args.multiprocessing_distributed + and args.rank % ngpus_per_node == 0): + save_checkpoint({ + 'epoch': epoch + 1, + 'arch': args.arch, + 'state_dict': model.state_dict(), + 'best_acc1': best_acc1, + "benign_acc":acc1, + "ASR":adv_acc1, + "RA":ra_acc1, + 'optimizer' : optimizer.state_dict(), + 'scheduler' : scheduler.state_dict() + }, is_best) + + +def train(train_loader, model, criterion, optimizer, epoch, args): + batch_time = AverageMeter('Time', ':6.3f') + data_time = AverageMeter('Data', ':6.3f') + losses = AverageMeter('Loss', ':.4e') + top1 = AverageMeter('Acc@1', ':6.2f') + top5 = AverageMeter('Acc@5', ':6.2f') + progress = ProgressMeter( + len(train_loader), + [batch_time, data_time, losses, top1, top5], + prefix="Epoch: [{}]".format(epoch)) + + # switch to train mode + model.train() + + end = time.time() + for i, (images, target) in enumerate(train_loader): + # measure data loading time + data_time.update(time.time() - end) + + if args.gpu is not None: + images = images.cuda(args.gpu, non_blocking=True) + if torch.cuda.is_available(): + target = target.cuda(args.gpu, non_blocking=True) + + # compute output + output = model(images) + loss = criterion(output, target) + + # measure accuracy and record loss + acc1, acc5 = accuracy(output, target, topk=(1, 5)) + losses.update(loss.item(), images.size(0)) + top1.update(acc1[0], images.size(0)) + top5.update(acc5[0], images.size(0)) + + # compute gradient and do SGD step + optimizer.zero_grad() + loss.backward() + optimizer.step() + + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + + if i % args.print_freq == 0: + progress.display(i + 1) + + +def validate(val_loader, model, criterion, args): + + def run_validate(loader, base_progress=0): + with torch.no_grad(): + end = time.time() + for i, (images, target) in enumerate(loader): + i = base_progress + i + if args.gpu is not None: + images = images.cuda(args.gpu, non_blocking=True) + if torch.cuda.is_available(): + target = target.cuda(args.gpu, non_blocking=True) + + # compute output + output = model(images) + loss = criterion(output, target) + + # measure accuracy and record loss + acc1, acc5 = accuracy(output, target, topk=(1, 5)) + losses.update(loss.item(), images.size(0)) + top1.update(acc1[0], images.size(0)) + top5.update(acc5[0], images.size(0)) + + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + + if i % args.print_freq == 0: + progress.display(i + 1) + + batch_time = AverageMeter('Time', ':6.3f', Summary.NONE) + losses = AverageMeter('Loss', ':.4e', Summary.NONE) + top1 = AverageMeter('Acc@1', ':6.2f', Summary.AVERAGE) + top5 = AverageMeter('Acc@5', ':6.2f', Summary.AVERAGE) + progress = ProgressMeter( + len(val_loader) + (args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset))), + [batch_time, losses, top1, top5], + prefix='Test: ') + + # switch to evaluate mode + model.eval() + + run_validate(val_loader) + if args.distributed: + top1.all_reduce() + top5.all_reduce() + + if args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset)): + aux_val_dataset = Subset(val_loader.dataset, + range(len(val_loader.sampler) * args.world_size, len(val_loader.dataset))) + aux_val_loader = torch.utils.data.DataLoader( + aux_val_dataset, batch_size=args.batch_size, shuffle=False, + num_workers=args.workers, pin_memory=True) + run_validate(aux_val_loader, len(val_loader)) + + progress.display_summary() + + return top1.avg + + +def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'): + torch.save(state, filename) + if is_best: + shutil.copyfile(filename, 'model_best.pth.tar') + +class Summary(Enum): + NONE = 0 + AVERAGE = 1 + SUM = 2 + COUNT = 3 + +class AverageMeter(object): + """Computes and stores the average and current value""" + def __init__(self, name, fmt=':f', summary_type=Summary.AVERAGE): + self.name = name + self.fmt = fmt + self.summary_type = summary_type + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + def all_reduce(self): + total = torch.FloatTensor([self.sum, self.count]).cuda() + dist.all_reduce(total, dist.ReduceOp.SUM, async_op=False) + self.sum, self.count = total.tolist() + self.avg = self.sum / self.count + + def __str__(self): + fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' + return fmtstr.format(**self.__dict__) + + def summary(self): + fmtstr = '' + if self.summary_type is Summary.NONE: + fmtstr = '' + elif self.summary_type is Summary.AVERAGE: + fmtstr = '{name} {avg:.3f}' + elif self.summary_type is Summary.SUM: + fmtstr = '{name} {sum:.3f}' + elif self.summary_type is Summary.COUNT: + fmtstr = '{name} {count:.3f}' + else: + raise ValueError('invalid summary type %r' % self.summary_type) + + return fmtstr.format(**self.__dict__) + + +class ProgressMeter(object): + def __init__(self, num_batches, meters, prefix=""): + self.batch_fmtstr = self._get_batch_fmtstr(num_batches) + self.meters = meters + self.prefix = prefix + + def display(self, batch): + entries = [self.prefix + self.batch_fmtstr.format(batch)] + entries += [str(meter) for meter in self.meters] + print('\t'.join(entries)) + + def display_summary(self): + entries = [" *"] + entries += [meter.summary() for meter in self.meters] + print(' '.join(entries)) + + def _get_batch_fmtstr(self, num_batches): + num_digits = len(str(num_batches // 1)) + fmt = '{:' + str(num_digits) + 'd}' + return '[' + fmt + '/' + fmt.format(num_batches) + ']' + +def accuracy(output, target, topk=(1,)): + """Computes the accuracy over the k top predictions for the specified values of k""" + with torch.no_grad(): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + +if __name__ == '__main__': + main() + +''' +BSD 3-Clause License + +Copyright (c) 2017, +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +''' \ No newline at end of file diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000..43b9e88 --- /dev/null +++ b/models/__init__.py @@ -0,0 +1,2 @@ +from .resnet import ResNet18, ResNet50 +from .densenet import DenseNet3 \ No newline at end of file diff --git a/models/densenet.py b/models/densenet.py new file mode 100644 index 0000000..b228b7c --- /dev/null +++ b/models/densenet.py @@ -0,0 +1,119 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class BasicBlock(nn.Module): + def __init__(self, in_planes, out_planes, dropRate=0.0): + super(BasicBlock, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.relu = nn.ReLU(inplace=True) + self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=1, + padding=1, bias=False) + self.droprate = dropRate + def forward(self, x): + out = self.conv1(self.relu(self.bn1(x))) + if self.droprate > 0: + out = F.dropout(out, p=self.droprate, training=self.training) + return torch.cat([x, out], 1) + +class BottleneckBlock(nn.Module): + def __init__(self, in_planes, out_planes, dropRate=0.0): + super(BottleneckBlock, self).__init__() + inter_planes = out_planes * 4 + self.bn1 = nn.BatchNorm2d(in_planes) + self.relu = nn.ReLU(inplace=True) + self.conv1 = nn.Conv2d(in_planes, inter_planes, kernel_size=1, stride=1, + padding=0, bias=False) + self.bn2 = nn.BatchNorm2d(inter_planes) + self.conv2 = nn.Conv2d(inter_planes, out_planes, kernel_size=3, stride=1, + padding=1, bias=False) + self.droprate = dropRate + def forward(self, x): + out = self.conv1(self.relu(self.bn1(x))) + if self.droprate > 0: + out = F.dropout(out, p=self.droprate, inplace=False, training=self.training) + out = self.conv2(self.relu(self.bn2(out))) + if self.droprate > 0: + out = F.dropout(out, p=self.droprate, inplace=False, training=self.training) + return torch.cat([x, out], 1) + +class TransitionBlock(nn.Module): + def __init__(self, in_planes, out_planes, dropRate=0.0): + super(TransitionBlock, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.relu = nn.ReLU(inplace=True) + self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, + padding=0, bias=False) + self.droprate = dropRate + def forward(self, x): + out = self.conv1(self.relu(self.bn1(x))) + if self.droprate > 0: + out = F.dropout(out, p=self.droprate, inplace=False, training=self.training) + return F.avg_pool2d(out, 2) + +class DenseBlock(nn.Module): + def __init__(self, nb_layers, in_planes, growth_rate, block, dropRate=0.0): + super(DenseBlock, self).__init__() + self.layer = self._make_layer(block, in_planes, growth_rate, nb_layers, dropRate) + def _make_layer(self, block, in_planes, growth_rate, nb_layers, dropRate): + layers = [] + for i in range(nb_layers): + layers.append(block(in_planes+i*growth_rate, growth_rate, dropRate)) + return nn.Sequential(*layers) + def forward(self, x): + return self.layer(x) + +class DenseNet3(nn.Module): + def __init__(self, depth, num_classes, growth_rate=12, + reduction=0.5, bottleneck=True, dropRate=0.0): + super(DenseNet3, self).__init__() + in_planes = 2 * growth_rate + n = (depth - 4) / 3 + if bottleneck == True: + n = n/2 + block = BottleneckBlock + else: + block = BasicBlock + n = int(n) + # 1st conv before any dense block + self.conv1 = nn.Conv2d(3, in_planes, kernel_size=3, stride=1, + padding=1, bias=False) + # 1st block + self.block1 = DenseBlock(n, in_planes, growth_rate, block, dropRate) + in_planes = int(in_planes+n*growth_rate) + self.trans1 = TransitionBlock(in_planes, int(math.floor(in_planes*reduction)), dropRate=dropRate) + in_planes = int(math.floor(in_planes*reduction)) + # 2nd block + self.block2 = DenseBlock(n, in_planes, growth_rate, block, dropRate) + in_planes = int(in_planes+n*growth_rate) + self.trans2 = TransitionBlock(in_planes, int(math.floor(in_planes*reduction)), dropRate=dropRate) + in_planes = int(math.floor(in_planes*reduction)) + # 3rd block + self.block3 = DenseBlock(n, in_planes, growth_rate, block, dropRate) + in_planes = int(in_planes+n*growth_rate) + # global average pooling and classifier + self.bn1 = nn.BatchNorm2d(in_planes) + self.relu = nn.ReLU(inplace=True) + self.fc = nn.Linear(in_planes, num_classes) + self.in_planes = in_planes + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + m.bias.data.zero_() + def forward(self, x): + out = self.conv1(x) + out = self.trans1(self.block1(out)) + out = self.trans2(self.block2(out)) + out = self.block3(out) + out = self.relu(self.bn1(out)) + out = F.avg_pool2d(out, 8) + out = out.view(-1, self.in_planes) + return self.fc(out),out \ No newline at end of file diff --git a/models/preact_resnet.py b/models/preact_resnet.py new file mode 100755 index 0000000..7c7f868 --- /dev/null +++ b/models/preact_resnet.py @@ -0,0 +1,802 @@ +""" +This file is modified based on the following source: +link : https://github.com/VinAIResearch/Warping-based_Backdoor_Attack-release +The original license is placed at the end of this file. + +This file provide implementation of pre-activation ResNet. +Please note that this is different from default ResNet in pytorch, even thought the structure of file is quite similar. +And to adapt different image size, we replace the Avgpool2d with its adaptive version. +""" +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class PreActBlock(nn.Module): + """Pre-activation version of the BasicBlock.""" + + expansion = 1 + + def __init__(self, in_planes, planes, stride=1): + super(PreActBlock, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.ind = None + + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False) + ) + + def forward(self, x): + out = F.relu(self.bn1(x)) + shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x + out = self.conv1(out) + out = self.conv2(F.relu(self.bn2(out))) + if self.ind is not None: + out += shortcut[:, self.ind, :, :] + else: + out += shortcut + return out + + +class PreActBottleneck(nn.Module): + """Pre-activation version of the original Bottleneck module.""" + + expansion = 4 + + def __init__(self, in_planes, planes, stride=1): + super(PreActBottleneck, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) + + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False) + ) + + def forward(self, x): + out = F.relu(self.bn1(x)) + shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x + out = self.conv1(out) + out = self.conv2(F.relu(self.bn2(out))) + out = self.conv3(F.relu(self.bn3(out))) + out += shortcut + return out + + +class PreActResNet(nn.Module): + def __init__(self, block, num_blocks, num_classes=10): + super(PreActResNet, self).__init__() + self.in_planes = 64 + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + self.avgpool = nn.AdaptiveAvgPool2d((1,1)) + self.linear = nn.Linear(512 * block.expansion, num_classes) + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + out = self.conv1(x) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = self.avgpool(out) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + + +def PreActResNet18(num_classes=10): + return PreActResNet(PreActBlock, [2, 2, 2, 2], num_classes=num_classes) + + +def PreActResNet34(): + return PreActResNet(PreActBlock, [3, 4, 6, 3]) + + +def PreActResNet50(): + return PreActResNet(PreActBottleneck, [3, 4, 6, 3]) + + +def PreActResNet101(): + return PreActResNet(PreActBottleneck, [3, 4, 23, 3]) + + +def PreActResNet152(): + return PreActResNet(PreActBottleneck, [3, 8, 36, 3]) + +''' +original license: + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. +''' \ No newline at end of file diff --git a/models/resnet.py b/models/resnet.py new file mode 100644 index 0000000..e8e586c --- /dev/null +++ b/models/resnet.py @@ -0,0 +1,160 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, in_planes, planes, stride=1): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(self.expansion * planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + + +class ResNet(nn.Module): + def __init__(self, block, num_blocks, num_classes=10): + super(ResNet, self).__init__() + self.in_planes = 64 + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + self.linear = nn.Linear(512 * block.expansion, num_classes) + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = F.avg_pool2d(out, 4) + out = out.view(out.size(0), -1) + out4 = out + out = self.linear(out) + + return out + # return out,out4 + + + +class ResNet_mod(nn.Module): + def __init__(self, block, num_blocks, num_classes=10): + super(ResNet_mod, self).__init__() + self.in_planes = 64 + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + self.linear = nn.Linear(25088 * block.expansion, num_classes) + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out1 = out + out = self.layer2(out) + out2 = out + out = self.layer3(out) + out3 = out + out = self.layer4(out) + out = F.avg_pool2d(out, 4) + out = out.view(out.size(0), -1) + out4 = out + out = self.linear(out) + # return out, (out1, out2, out3, out4) + # return out, out4 + return out + +def ResNet18(num_classes=10): + return ResNet(BasicBlock, [2, 2, 2, 2], num_classes) + +def ResNet18_mod(num_classes=10): + return ResNet_mod(BasicBlock, [2, 2, 2, 2], num_classes) + + +def ResNet34(): + return ResNet(BasicBlock, [3, 4, 6, 3]) + + +def ResNet50(num_classes=10): + return ResNet(Bottleneck, [3, 4, 6, 3],num_classes) + + +def ResNet101(): + return ResNet(Bottleneck, [3, 4, 23, 3]) + + +def ResNet152(): + return ResNet(Bottleneck, [3, 8, 36, 3]) + + +def test(): + net = ResNet18() + y = net(torch.randn(1, 3, 32, 32)) + print(y.size()) diff --git a/models/resnext.py b/models/resnext.py new file mode 100644 index 0000000..2ca1249 --- /dev/null +++ b/models/resnext.py @@ -0,0 +1,103 @@ +"""ResNeXt in PyTorch. +https://github.com/RU-System-Software-and-Security/BppAttack +See the paper "Aggregated Residual Transformations for Deep Neural Networks" for more details. +""" +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class Block(nn.Module): + """Grouped convolution block.""" + + expansion = 2 + + def __init__(self, in_planes, cardinality=32, bottleneck_width=4, stride=1): + super(Block, self).__init__() + group_width = cardinality * bottleneck_width + self.conv1 = nn.Conv2d(in_planes, group_width, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(group_width) + self.conv2 = nn.Conv2d( + group_width, group_width, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=False + ) + self.bn2 = nn.BatchNorm2d(group_width) + self.conv3 = nn.Conv2d(group_width, self.expansion * group_width, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(self.expansion * group_width) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * group_width: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * group_width, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * group_width), + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + + +class ResNeXt(nn.Module): + def __init__(self, num_blocks, cardinality, bottleneck_width, num_classes=10): + super(ResNeXt, self).__init__() + self.cardinality = cardinality + self.bottleneck_width = bottleneck_width + self.in_planes = 64 + + self.conv1 = nn.Conv2d(3, 64, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.layer1 = self._make_layer(num_blocks[0], 1) + self.layer2 = self._make_layer(num_blocks[1], 2) + self.layer3 = self._make_layer(num_blocks[2], 2) + # self.layer4 = self._make_layer(num_blocks[3], 2) + self.linear = nn.Linear(cardinality * bottleneck_width * 8, num_classes) + + def _make_layer(self, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(Block(self.in_planes, self.cardinality, self.bottleneck_width, stride)) + self.in_planes = Block.expansion * self.cardinality * self.bottleneck_width + # Increase bottleneck_width by 2 after each stage. + self.bottleneck_width *= 2 + return nn.Sequential(*layers) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + # out = self.layer4(out) + out = F.avg_pool2d(out, 8) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + + +def ResNeXt29_2x64d(): + return ResNeXt(num_blocks=[3, 3, 3], cardinality=2, bottleneck_width=64) + + +def ResNeXt29_4x64d(): + return ResNeXt(num_blocks=[3, 3, 3], cardinality=4, bottleneck_width=64) + + +def ResNeXt29_8x64d(): + return ResNeXt(num_blocks=[3, 3, 3], cardinality=8, bottleneck_width=64) + + +def ResNeXt29_32x4d(): + return ResNeXt(num_blocks=[3, 3, 3], cardinality=32, bottleneck_width=4) + + +def test_resnext(): + net = ResNeXt29_2x64d() + x = torch.randn(1, 3, 32, 32) + y = net(x) + print(y.size()) + + +# test_resnext() diff --git a/models/senet.py b/models/senet.py new file mode 100644 index 0000000..3869e7c --- /dev/null +++ b/models/senet.py @@ -0,0 +1,119 @@ +"""SENet in PyTorch. + +SENet is the winner of ImageNet-2017. The paper is not released yet. +""" +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class BasicBlock(nn.Module): + def __init__(self, in_planes, planes, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes) + ) + + # SE layers + self.fc1 = nn.Conv2d(planes, planes // 16, kernel_size=1) # Use nn.Conv2d instead of nn.Linear + self.fc2 = nn.Conv2d(planes // 16, planes, kernel_size=1) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + + # Squeeze + w = F.avg_pool2d(out, out.size(2)) + w = F.relu(self.fc1(w)) + w = F.sigmoid(self.fc2(w)) + # Excitation + out = out * w # New broadcasting feature from v0.2! + + out += self.shortcut(x) + out = F.relu(out) + return out + + +class PreActBlock(nn.Module): + def __init__(self, in_planes, planes, stride=1): + super(PreActBlock, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + + if stride != 1 or in_planes != planes: + self.shortcut = nn.Sequential(nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=False)) + + # SE layers + self.fc1 = nn.Conv2d(planes, planes // 16, kernel_size=1) + self.fc2 = nn.Conv2d(planes // 16, planes, kernel_size=1) + + def forward(self, x): + out = F.relu(self.bn1(x)) + shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x + out = self.conv1(out) + out = self.conv2(F.relu(self.bn2(out))) + + # Squeeze + w = F.avg_pool2d(out, out.size(2)) + w = F.relu(self.fc1(w)) + w = F.sigmoid(self.fc2(w)) + # Excitation + out = out * w + + out += shortcut + return out + + +class SENet(nn.Module): + def __init__(self, block, num_blocks, num_classes=10): + super(SENet, self).__init__() + self.in_planes = 64 + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + self.linear = nn.Linear(512, num_classes) + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes + return nn.Sequential(*layers) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = F.avg_pool2d(out, 4) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + + +def SENet18(): + return SENet(PreActBlock, [2, 2, 2, 2]) + + +def test(): + net = SENet18() + y = net(torch.randn(1, 3, 32, 32)) + print(y.size()) + + +# test() diff --git a/record/readme.md b/record/readme.md new file mode 100644 index 0000000..5126082 --- /dev/null +++ b/record/readme.md @@ -0,0 +1,21 @@ +This folder is to save all records of experiments. + +The folder structure is as follows: +``` +eg. +record/ + attack1/ + bd_test_dataset/ + bd_train_dataset/ + defense/ (all defense results following this attack) + abl/ + ac/ + ... + xxxx.log + attack_df.csv + attack_df_summary.csv + attack_result.pt (attack result pt file used in following defenses) + ... + attack2/ + ... +``` \ No newline at end of file diff --git a/resource/.DS_Store b/resource/.DS_Store new file mode 100644 index 0000000..63fc5a8 Binary files /dev/null and b/resource/.DS_Store differ diff --git a/resource/badnet/generate_grid.py b/resource/badnet/generate_grid.py new file mode 100644 index 0000000..4ab35ef --- /dev/null +++ b/resource/badnet/generate_grid.py @@ -0,0 +1,33 @@ +'''This script is to generate a black image with only a white square at the right corner, then convert it to a npy file''' + +import numpy as np +import argparse +from PIL import Image + +def generate_white_black_grid_image(image_size, square_size, distance_to_right, distance_to_bottom): + black_image = np.zeros((image_size, image_size, 3), dtype=np.uint8) + # black_image[image_size - distance_to_bottom - square_size:image_size - distance_to_bottom, image_size - distance_to_right - square_size:image_size - distance_to_right, :] = 255 + # generate grid with white and black squares at right downside corner + for i in range(0, square_size): + for j in range(0, square_size): + if (i + j) % 2 == 0: + black_image[image_size - distance_to_bottom - square_size + i, image_size - distance_to_right - square_size + j, :] = 255 + else: + black_image[image_size - distance_to_bottom - square_size + i, image_size - distance_to_right - square_size + j, :] = 1 + return black_image + +if __name__ == '__main__': + args = argparse.ArgumentParser() + args.add_argument('--image_size', type=int, default=32) + args.add_argument('--square_size', type=int, default=3) + args.add_argument('--distance_to_right', type=int, default=0) + args.add_argument('--distance_to_bottom', type=int, default=0) + args.add_argument('--output_path', type=str, default='./trigger_image_grid.png') + args = args.parse_args() + image = generate_white_black_grid_image( + args.image_size, + args.square_size, + args.distance_to_right, + args.distance_to_bottom, + ) + Image.fromarray(image).save(args.output_path) diff --git a/resource/badnet/generate_white_square.py b/resource/badnet/generate_white_square.py new file mode 100644 index 0000000..6788c40 --- /dev/null +++ b/resource/badnet/generate_white_square.py @@ -0,0 +1,26 @@ +'''This script is to generate a black image with only a white square at the right corner, then convert it to a npy file''' + +import numpy as np +import argparse +from PIL import Image + +def generate_white_square_image(image_size, square_size, distance_to_right, distance_to_bottom): + black_image = np.zeros((image_size, image_size, 3), dtype=np.uint8) + black_image[image_size - distance_to_bottom - square_size:image_size - distance_to_bottom, image_size - distance_to_right - square_size:image_size - distance_to_right, :] = 255 + return black_image + +if __name__ == '__main__': + args = argparse.ArgumentParser() + args.add_argument('--image_size', type=int, default=32) + args.add_argument('--square_size', type=int, default=3) + args.add_argument('--distance_to_right', type=int, default=0) + args.add_argument('--distance_to_bottom', type=int, default=0) + args.add_argument('--output_path', type=str, default='./trigger_image.png') + args = args.parse_args() + image = generate_white_square_image( + args.image_size, + args.square_size, + args.distance_to_right, + args.distance_to_bottom, + ) + Image.fromarray(image).save(args.output_path) \ No newline at end of file diff --git a/resource/badnet/readme.md b/resource/badnet/readme.md new file mode 100644 index 0000000..7d63ae8 --- /dev/null +++ b/resource/badnet/readme.md @@ -0,0 +1,21 @@ +### `generate_white_square.py` + +`generate_white_square.py` is a simple example of how to generate a white square image. + +The white square image is used to generate the white square attack in the paper. + +If you want to draw more complicated shapes, you can modify the code in `generate_white_square.py` or first generate a black image then stamp the trigger onto it and convert the image to npy file. + +The last step is to specify the parameter `--patch_mask_path` for `badnet.py`. + +### `generate_grid.py` + +Similarly, `generate_grid.py` is to generate a grid trigger. + +But note that , in the trigger area, the smallest number is 1 (we assume pixel values range from 0 to 255), since for the trigger file, we only treat area with pixle value > 0 as the part of area that we need to use. (That's also why we only need one file to both locate the mask and also record the pixel value in patch) + +### Remainder + +Since the trigger png file has a fixed size (eg. 32 * 32), in badnet.py if you attack a dataset with other resolution (eg. tiny with 64 * 64), then we will resize the trigger to the resolution of the dataset. + +So, please note that for grid trigger, under different resolution, the grid can be finer or coarser! (eg. 32 * 32 -> 64 * 64, the grid will be coarser) \ No newline at end of file diff --git a/resource/badnet/trigger_image.png b/resource/badnet/trigger_image.png new file mode 100644 index 0000000..1efe094 Binary files /dev/null and b/resource/badnet/trigger_image.png differ diff --git a/resource/badnet/trigger_image_grid.png b/resource/badnet/trigger_image_grid.png new file mode 100644 index 0000000..f4d6cf6 Binary files /dev/null and b/resource/badnet/trigger_image_grid.png differ diff --git a/resource/blended/hello_kitty.jpeg b/resource/blended/hello_kitty.jpeg new file mode 100755 index 0000000..36ff153 Binary files /dev/null and b/resource/blended/hello_kitty.jpeg differ diff --git a/resource/label-consistent/craft_adv_dataset.py b/resource/label-consistent/craft_adv_dataset.py new file mode 100755 index 0000000..0e2353a --- /dev/null +++ b/resource/label-consistent/craft_adv_dataset.py @@ -0,0 +1,784 @@ +''' +This is for crafted the +''' + +import sys, yaml, os + +os.chdir(sys.path[0]) +sys.path.append('../../') +os.getcwd() + +import argparse +from pprint import pformat +import numpy as np +import torch +import time +import logging +from tqdm import tqdm + +from torchvision.transforms import * +from utils.aggregate_block.save_path_generate import generate_save_folder +from utils.aggregate_block.dataset_and_transform_generate import get_num_classes, get_input_shape +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import dataset_and_transform_generate +from utils.bd_dataset_v2 import dataset_wrapper_with_transform +from torch.utils.data import DataLoader +from utils.aggregate_block.model_trainer_generate import generate_cls_model, generate_cls_trainer +from utils.aggregate_block.train_settings_generate import argparser_opt_scheduler, argparser_criterion + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from torch.autograd import Variable + + +class PreActBlock(nn.Module): + '''Pre-activation version of the BasicBlock.''' + expansion = 1 + + def __init__(self, in_planes, planes, stride=1): + super(PreActBlock, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + + if stride != 1 or in_planes != self.expansion*planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False) + ) + + def forward(self, x): + out = F.relu(self.bn1(x)) + shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x + out = self.conv1(out) + out = self.conv2(F.relu(self.bn2(out))) + out += shortcut + return out + + +class PreActBottleneck(nn.Module): + '''Pre-activation version of the original Bottleneck module.''' + expansion = 4 + + def __init__(self, in_planes, planes, stride=1): + super(PreActBottleneck, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False) + + if stride != 1 or in_planes != self.expansion*planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False) + ) + + def forward(self, x): + out = F.relu(self.bn1(x)) + shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x + out = self.conv1(out) + out = self.conv2(F.relu(self.bn2(out))) + out = self.conv3(F.relu(self.bn3(out))) + out += shortcut + return out + + +class PreActResNet(nn.Module): + def __init__(self, block, num_blocks, num_classes=200): + super(PreActResNet, self).__init__() + self.in_planes = 64 + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + self.linear = nn.Linear(512*block.expansion*4, num_classes) + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1]*(num_blocks-1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + out = self.conv1(x) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = F.avg_pool2d(out, 4) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + + +def PreActResNet18(num_classes): + return PreActResNet(PreActBlock, [2,2,2,2], num_classes) + +def PreActResNet34(): + return PreActResNet(PreActBlock, [3,4,6,3]) + +def PreActResNet50(): + return PreActResNet(PreActBottleneck, [3,4,6,3]) + +def PreActResNet101(): + return PreActResNet(PreActBottleneck, [3,4,23,3]) + +def PreActResNet152(): + return PreActResNet(PreActBottleneck, [3,8,36,3]) + + + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d( + in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, + stride=1, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion*planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion*planes, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion*planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, in_planes, planes, stride=1): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, + stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, self.expansion * + planes, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(self.expansion*planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion*planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion*planes, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion*planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + + +class ResNet(nn.Module): + def __init__(self, block, num_blocks, num_classes=10): + super(ResNet, self).__init__() + self.in_planes = 64 + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, + stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + self.linear = nn.Linear(512*block.expansion, num_classes) + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1]*(num_blocks-1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = F.avg_pool2d(out, 4) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + + +def ResNet18(num_classes): + return ResNet(BasicBlock, [2, 2, 2, 2], num_classes) + + +def ResNet34(): + return ResNet(BasicBlock, [3, 4, 6, 3]) + + +def ResNet50(): + return ResNet(Bottleneck, [3, 4, 6, 3]) + + +def ResNet101(): + return ResNet(Bottleneck, [3, 4, 23, 3]) + + +def ResNet152(): + return ResNet(Bottleneck, [3, 8, 36, 3]) + + +import torch + + +class Attack(object): + r""" + Base class for all attacks. + .. note:: + It automatically set device to the device where given model is. + It temporarily changes the original model's training mode to `test` + by `.eval()` only during an attack process. + """ + + def __init__(self, name, model, device): + r""" + Initializes internal attack state. + Arguments: + name (str) : name of an attack. + model (torch.nn.Module): model to attack. + """ + + self.attack = name + self.model = model + self.model_name = str(model).split("(")[0] + + self.training = model.training + self.device = device + + self._targeted = 1 + self._attack_mode = "original" + self._return_type = "float" + + def forward(self, *input): + r""" + It defines the computation performed at every call. + Should be overridden by all subclasses. + """ + raise NotImplementedError + + def set_attack_mode(self, mode): + r""" + Set the attack mode. + + Arguments: + mode (str) : 'original' (DEFAULT) + 'targeted' - Use input labels as targeted labels. + 'least_likely' - Use least likely labels as targeted labels. + """ + if self._attack_mode is "only_original": + raise ValueError( + "Changing attack mode is not supported in this attack method." + ) + + if mode == "original": + self._attack_mode = "original" + self._targeted = 1 + self._transform_label = self._get_label + elif mode == "targeted": + self._attack_mode = "targeted" + self._targeted = -1 + self._transform_label = self._get_label + elif mode == "least_likely": + self._attack_mode = "least_likely" + self._targeted = -1 + self._transform_label = self._get_least_likely_label + else: + raise ValueError( + mode + + " is not a valid mode. [Options : original, targeted, least_likely]" + ) + + def set_return_type(self, type): + r""" + Set the return type of adversarial images: `int` or `float`. + Arguments: + type (str) : 'float' or 'int'. (DEFAULT : 'float') + """ + if type == "float": + self._return_type = "float" + elif type == "int": + self._return_type = "int" + else: + raise ValueError(type + " is not a valid type. [Options : float, int]") + + def save(self, save_path, data_loader, verbose=True): + r""" + Save adversarial images as torch.tensor from given torch.utils.data.DataLoader. + Arguments: + save_path (str) : save_path. + data_loader (torch.utils.data.DataLoader) : data loader. + verbose (bool) : True for displaying detailed information. (DEFAULT : True) + """ + self.model.eval() + + image_list = [] + label_list = [] + + correct = 0 + total = 0 + + total_batch = len(data_loader) + + for step, (images, labels) in enumerate(data_loader): + adv_images = self.__call__(images, labels) + + image_list.append(adv_images.cpu()) + label_list.append(labels.cpu()) + + if self._return_type == "int": + adv_images = adv_images.float() / 255 + + if verbose: + outputs = self.model(adv_images) + _, predicted = torch.max(outputs.data, 1) + total += labels.size(0) + correct += (predicted == labels.to(self.device)).sum() + + acc = 100 * float(correct) / total + print( + "- Save Progress : %2.2f %% / Accuracy : %2.2f %%" + % ((step + 1) / total_batch * 100, acc), + end="\r", + ) + + x = torch.cat(image_list, 0) + y = torch.cat(label_list, 0) + torch.save((x, y), save_path) + print("\n- Save Complete!") + + self._switch_model() + + def _transform_label(self, images, labels): + r""" + Function for changing the attack mode. + """ + return labels + + def _get_label(self, images, labels): + r""" + Function for changing the attack mode. + Return input labels. + """ + return labels + + def _get_least_likely_label(self, images, labels): + r""" + Function for changing the attack mode. + Return least likely labels. + """ + outputs = self.model(images) + _, labels = torch.min(outputs.data, 1) + labels = labels.detach_() + return labels + + def _to_uint(self, images): + r""" + Function for changing the return type. + Return images as int. + """ + return (images * 255).type(torch.uint8) + + def _switch_model(self): + r""" + Function for changing the training mode of the model. + """ + if self.training: + self.model.train() + else: + self.model.eval() + + def __str__(self): + info = self.__dict__.copy() + + del_keys = ["model", "attack"] + + for key in info.keys(): + if key[0] == "_": + del_keys.append(key) + + for key in del_keys: + del info[key] + + info["attack_mode"] = self._attack_mode + if info["attack_mode"] == "only_original": + info["attack_mode"] = "original" + + info["return_type"] = self._return_type + + return ( + self.attack + + "(" + + ", ".join("{}={}".format(key, val) for key, val in info.items()) + + ")" + ) + + def __call__(self, *input, **kwargs): + self.model.eval() + images = self.forward(*input, **kwargs) + self._switch_model() + + if self._return_type == "int": + images = self._to_uint(images) + + return images + +class PGD(Attack): + r""" + PGD in the paper 'Towards Deep Learning Models Resistant to Adversarial Attacks' + [https://arxiv.org/abs/1706.06083] + + Distance Measure : Linf + Arguments: + model (nn.Module): model to attack. + eps (float): maximum perturbation. (DEFALUT : 0.3) + alpha (float): step size. (DEFALUT : 2/255) + steps (int): number of steps. (DEFALUT : 40) + random_start (bool): using random initialization of delta. (DEFAULT : False) + + Shape: + - images: :math:`(N, C, H, W)` where `N = number of batches`, `C = number of channels`, `H = height` and `W = width`. It must have a range [0, 1]. + - labels: :math:`(N)` where each value :math:`y_i` is :math:`0 \leq y_i \leq` `number of labels`. + - output: :math:`(N, C, H, W)`. + + Examples:: + >>> attack = torchattacks.PGD(model, eps = 8/255, alpha = 1/255, steps=40, random_start=False) + >>> adv_images = attack(images, labels) + + """ + + def __init__(self, model, eps=0.3, alpha=2 / 255, steps=40, random_start=False, device=None): + super(PGD, self).__init__("PGD", model, device) + self.eps = eps + self.alpha = alpha + self.steps = steps + self.random_start = random_start + + def forward(self, images, labels): + r""" + Overridden. + """ + images = images.to(self.device) + labels = labels.to(self.device) + labels = self._transform_label(images, labels) + loss = nn.CrossEntropyLoss() + + adv_images = images.clone().detach() + + if self.random_start: + # Starting at a uniformly random point + adv_images = adv_images + torch.empty_like(adv_images).uniform_( + -self.eps, self.eps + ) + adv_images = torch.clamp(adv_images, min=0, max=1) + + for i in range(self.steps): + adv_images.requires_grad = True + outputs = self.model(adv_images) + + cost = self._targeted * loss(outputs, labels).to(self.device) + + grad = torch.autograd.grad( + cost, adv_images, retain_graph=False, create_graph=False + )[0] + + adv_images = adv_images.detach() + self.alpha * grad.sign() + delta = torch.clamp(adv_images - images, min=-self.eps, max=self.eps) + adv_images = torch.clamp(images + delta, min=0, max=1).detach() + + return adv_images + +def add_args(parser): + """ + parser : argparse.ArgumentParser + return a parser added with args required by fit + """ + # Training settings + parser.add_argument('--amp', type=lambda x: str(x) in ['True', 'true', '1']) + parser.add_argument('--device', type = str) + parser.add_argument('--yaml_path', type=str, default='./default.yaml', + help='path for yaml file provide additional default attributes') + parser.add_argument('--lr_scheduler', type=str, + help='which lr_scheduler use for optimizer') + # only all2one can be use for clean-label + parser.add_argument('--epochs', type=int) + parser.add_argument('--dataset', type=str, + help='which dataset to use' + ) + parser.add_argument('--dataset_path', type=str) + + parser.add_argument('--batch_size', type=int) + parser.add_argument('--lr', type=float) + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--random_seed', type=int, + help='random_seed') + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + parser.add_argument('--model', type=str, + help='choose which kind of model') + parser.add_argument('--save_folder_name', type=str, + help='(Optional) should be time str + given unique identification str') + parser.add_argument('--git_hash', type=str, + help='git hash number, in order to find which version of code is used') + return parser + +def main(): + + ### 1. config args, save_path, fix random seed + parser = (add_args(argparse.ArgumentParser(description=sys.argv[0]))) + args = parser.parse_args() + + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.attack = 'None' + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + logging.info(f"get the training setting for specific dataset") + + ### save path + if 'save_folder_name' not in args: + save_path = generate_save_folder( + run_info=('afterwards' if 'load_path' in args.__dict__ else 'attack') + '_' + args.attack, + given_load_file_path=args.load_path if 'load_path' in args else None, + all_record_folder_path='../../record', + ) + else: + save_path = '../../record/' + args.save_folder_name + os.mkdir(save_path) + + args.save_path = save_path + + torch.save(args.__dict__, save_path + '/info.pickle') + + ### set the logger + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(save_path + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + ### set the random seed + fix_random(int(args.random_seed)) + + ### 2. set the clean train data and clean test data + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform = dataset_and_transform_generate(args) + + train_img_transform = Compose( + [Resize((args.input_height,args.input_width)), ToTensor()] + ) + + test_img_transform = Compose( + [Resize((args.input_height,args.input_width)), ToTensor()] + ) + + benign_train_dl = DataLoader( + dataset_wrapper_with_transform( + train_dataset_without_transform, + train_img_transform, + train_label_transform, + ), + batch_size=args.batch_size, + shuffle=False, + drop_last=False + ) + + benign_test_dl = DataLoader( + dataset_wrapper_with_transform( + test_dataset_without_transform, + test_img_transform, + test_label_transform, + ), + batch_size=args.batch_size, + shuffle=False, + drop_last=False, + ) + + device = torch.device(args.device if torch.cuda.is_available() else "cpu") + + if args.dataset == 'cifar10': + net = ResNet18(num_classes = args.num_classes) + net.load_state_dict(torch.load('./resnet18_PGD_best_model.pth')) + elif args.dataset == 'tiny': + net = PreActResNet18(num_classes=args.num_classes) + net.load_state_dict(torch.load('./preact_tiny_model.pth')) + elif args.dataset == 'cifar100': + net = ResNet18(num_classes = args.num_classes) + net.load_state_dict(torch.load('./cifar100_resnet18_AT_best_model.pth')) + elif args.dataset == 'gtsrb': + net = ResNet18(num_classes = args.num_classes) + checkpoint = torch.load('./gtsrb_resnet18_AT_best_model.pth') + from collections import OrderedDict + try: + net.load_state_dict(checkpoint) + except: + new_state_dict = OrderedDict() + for k, v in checkpoint.items(): + name = k[7:] # remove `module.` + new_state_dict[name] = v + net.load_state_dict(new_state_dict, False) + + trainer = generate_cls_trainer( + net, + args.attack, + args.amp, + ) + + criterion = argparser_criterion(args) + + optimizer, scheduler = argparser_opt_scheduler(net, args) + + trainer.criterion = criterion + + train_m = trainer.test( + benign_train_dl, device + ) + logging.info(train_m) + logging.info(train_m['test_correct']/train_m['test_total']) + + test_m = trainer.test( + benign_test_dl, device + ) + logging.info(test_m) + logging.info(test_m['test_correct']/test_m['test_total']) + + logging.info('start generate adv dataset for train and test') + + config = {"adv_dataset_dir": "./test/adv_dataset", + # "adv_model_path": "./model/adv_models/cifar_resnet_e8_a2_s10.pth", + } + + pgd_config = { + 'eps': 8, + 'alpha': 1.5, + 'steps': 100, + 'max_pixel': 255, + } + print("Set PGD attacker: {}.".format(pgd_config)) + max_pixel = pgd_config.pop("max_pixel") + for k, v in pgd_config.items(): + if k == "eps" or k == "alpha": + pgd_config[k] = v / max_pixel + attacker = PGD(net, **pgd_config, device = device) + attacker.set_return_type("int") + + train_data = benign_train_dl.dataset + train_loader = benign_train_dl + + perturbed_img = torch.zeros((len(train_data), args.input_height, args.input_width, 3), dtype=torch.uint8) + target = torch.zeros(len(train_data)) + i = 0 + net.to(device) + net.eval() + for (x,y,*other) in tqdm(train_loader): + # Adversarially perturb image. Note that torchattacks will automatically + # move `img` and `target` to the gpu where the attacker.model is located. + + img = attacker(x,y) + perturbed_img[i: i + len(img), :, :, :] = img.permute(0, 2, 3, 1).detach() + target[i: i + len(y)] = y + i += img.shape[0] + + if not os.path.exists(config["adv_dataset_dir"]): + os.makedirs(config["adv_dataset_dir"]) + adv_data_path = os.path.join( + config["adv_dataset_dir"],f"{args.dataset}_train.npy" + ) + # np.savez(adv_data_path, data=perturbed_img.numpy(), targets=target.numpy()) + np.save(adv_data_path, perturbed_img.numpy()) + print("Save the train adversarially perturbed dataset to {}".format(adv_data_path)) + + + #### + test_data = benign_test_dl.dataset + test_loader = benign_test_dl + + perturbed_img = torch.zeros((len(test_data), args.input_height, args.input_width, 3), dtype=torch.uint8) + target = torch.zeros(len(test_data)) + i = 0 + net.to(device) + net.eval() + for (x, y, *other) in tqdm(test_loader): + # Adversarially perturb image. Note that torchattacks will automatically + # move `img` and `target` to the gpu where the attacker.model is located. + + img = attacker(x, y) + perturbed_img[i: i + len(img), :, :, :] = img.permute(0, 2, 3, 1).detach() + target[i: i + len(y)] = y + i += img.shape[0] + if not os.path.exists(config["adv_dataset_dir"]): + os.makedirs(config["adv_dataset_dir"]) + adv_data_path = os.path.join( + config["adv_dataset_dir"], f"{args.dataset}_test.npy" + ) + np.save(adv_data_path, perturbed_img.numpy()) + print("Save the test adversarially perturbed dataset to {}".format(adv_data_path)) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/resource/label-consistent/default.yaml b/resource/label-consistent/default.yaml new file mode 100644 index 0000000..d81435d --- /dev/null +++ b/resource/label-consistent/default.yaml @@ -0,0 +1,13 @@ +amp: False +device: cuda:0 +client_optimizer: sgd +dataset: cifar10 +dataset_path: ../../data +frequency_save: 100 +batch_size: 128 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +random_seed: 0 +sgd_momentum: 0.9 +wd: 0.0005 +epochs: 100 \ No newline at end of file diff --git a/resource/label-consistent/readme.md b/resource/label-consistent/readme.md new file mode 100644 index 0000000..ebb73b2 --- /dev/null +++ b/resource/label-consistent/readme.md @@ -0,0 +1,11 @@ +This folder contains the script to generate the poison data for label-consistent attack. + +You can replace PGD with other adversarial attack module by yourself (Setting is also written in craft_adv_dataset.py). + +command: +``` +python craft_adv_dataset.py --dataset cifar10 +python craft_adv_dataset.py --dataset cifar100 +python craft_adv_dataset.py --dataset tiny +python craft_adv_dataset.py --dataset gtsrb +``` \ No newline at end of file diff --git a/resource/lowFrequency/cifar10.yaml b/resource/lowFrequency/cifar10.yaml new file mode 100755 index 0000000..149ff24 --- /dev/null +++ b/resource/lowFrequency/cifar10.yaml @@ -0,0 +1,18 @@ +num_workers: 4 +pin_memory: True +non_blocking: True +prefetch: False +amp: False +device: cuda:0 +client_optimizer: sgd +dataset: cifar10 +dataset_path: ../data +frequency_save: 100 +batch_size: 128 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: preactresnet18 +random_seed: 0 +sgd_momentum: 0.9 +wd: 0.0005 +epochs: 100 \ No newline at end of file diff --git a/resource/lowFrequency/cifar100.yaml b/resource/lowFrequency/cifar100.yaml new file mode 100755 index 0000000..463416e --- /dev/null +++ b/resource/lowFrequency/cifar100.yaml @@ -0,0 +1,18 @@ +num_workers: 4 +pin_memory: True +non_blocking: True +prefetch: False +amp: False +device: cuda:0 +client_optimizer: sgd +dataset: cifar100 +dataset_path: ../data +frequency_save: 100 +batch_size: 128 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: preactresnet18 +random_seed: 0 +sgd_momentum: 0.9 +wd: 0.0005 +epochs: 100 \ No newline at end of file diff --git a/resource/lowFrequency/cifar100_convnext_tiny_0_225.npy 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b/resource/lowFrequency/cifar10_vit_b_16_0_255.npy differ diff --git a/resource/lowFrequency/deepfool.py b/resource/lowFrequency/deepfool.py new file mode 100755 index 0000000..ab06fcf --- /dev/null +++ b/resource/lowFrequency/deepfool.py @@ -0,0 +1,84 @@ +import numpy as np +from torch.autograd import Variable +import torch as torch +import copy + +#@resource_check +def tar_deepfool(image, net, target, num_classes=10, overshoot=0.02, max_iter=100, device = 'cpu'): + + """ + :param image: Image of size 1x3xHxW + :param net: network (input: images, output: values of activation **BEFORE** softmax). + :param num_classes: num_classes (limits the number of classes to test against, by default = 10) + :param overshoot: used as a termination criterion to prevent vanishing updates (default = 0.02). + :param max_iter: maximum number of iterations for deepfool (default = 50) + :return: minimal perturbation that fools the classifier, number of iterations that it required, new estimated_label and perturbed image + """ + is_cuda = torch.cuda.is_available() + if is_cuda: + image = image.to(device) + net = net.to(device) + + f_image = net.forward(Variable(image[None, :, :, :], requires_grad=True)).data.cpu().numpy().flatten() + I = f_image.argsort()[::-1] + + I = I[0:num_classes] + label = I[0] + + input_shape = image[None, :, :, :].cpu().numpy().shape + pert_image = copy.deepcopy(image) + + x = Variable(pert_image[None, :, :, :], requires_grad=True) + fs = net.forward(x) + f_i = fs.data.cpu().numpy().flatten() + k_i = np.argmax(f_i) + + w = np.zeros(input_shape) + r_tot = np.zeros(input_shape) + + loop_i = 0 + + while k_i != target and loop_i < max_iter: + pert = np.inf + gradients = [] + for k in range(0, num_classes): + if x.grad is not None: + x.grad.zero_() + fs[0, I[k]].backward(retain_graph=True) + cur_grad = x.grad.data.cpu().numpy().copy() + gradients.append(cur_grad) + gradients = np.expand_dims(np.vstack(gradients), axis=1) + k = np.where(I == target)[0][0] + # set new w_k and new f_k + w_k = gradients[k, :, :, :, :] - gradients[0, :, :, :, :] + f_k = (fs[0, I[k]] - fs[0, I[0]]).data.cpu().numpy() + + pert_k = abs(f_k)/np.linalg.norm(w_k.flatten()) + + # determine which w_k to use + if pert_k < pert: + pert = pert_k + w = w_k + + # compute r_i and r_tot + r_i = pert * w / np.linalg.norm(w) + if not np.all(np.isfinite(r_i)): + r_i = np.zeros_like(r_i) + # r_tot = r_tot + r_i + r_tot = np.float32(r_tot + r_i) + # Added 1e-4 for numerical stability + # r_i = (pert+1e-4) * w / np.linalg.norm(w) + if is_cuda: + pert_image = image + (1+overshoot)*torch.from_numpy(r_tot).to(device) + else: + pert_image = image + (1+overshoot)*torch.from_numpy(r_tot) + + x = Variable(pert_image, requires_grad=True) + fs = net.forward(x) + k_i = np.argmax(fs.data.cpu().numpy().flatten()) + loop_i += 1 + + r_tot = (1+overshoot) * r_tot + r_tot = r_tot.transpose(0, 2, 3, 1) + + return r_tot, loop_i, k_i, pert_image diff --git a/resource/lowFrequency/default.yaml b/resource/lowFrequency/default.yaml new file mode 100755 index 0000000..02849df --- /dev/null +++ b/resource/lowFrequency/default.yaml @@ -0,0 +1,15 @@ +amp: False +device: cuda:0 +client_optimizer: sgd +dataset: cifar10 +dataset_path: ../../data +frequency_save: 100 +batch_size: 128 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: preactresnet18 +random_seed: 0 +sgd_momentum: 0.9 +wd: 0.0005 +epochs: 100 +attack_target: 0 \ No newline at end of file diff --git a/resource/lowFrequency/gauss_smooth.py b/resource/lowFrequency/gauss_smooth.py new file mode 100755 index 0000000..c22febb --- /dev/null +++ b/resource/lowFrequency/gauss_smooth.py @@ -0,0 +1,90 @@ +import math +import torch +from torch.nn import functional as F +import copy +import numpy as np + +def tensor2img(t): + t_np = t.detach().cpu().numpy().transpose(1, 2, 0) + return t_np + +def normalization(data): + _range = np.max(data) - np.min(data) + return ((data - np.min(data)) / _range)*0.2 + +#@resource_check +def gauss_smooth(image, sig=6): + ''' + This is the pre-set low-pass filter discribed in the paper + ''' + size_denom = 5. + sigma = sig * size_denom + kernel_size = sigma + mgrid = np.arange(kernel_size, dtype=np.float32) + mean = (kernel_size - 1.) / 2. + mgrid = mgrid - mean + mgrid = mgrid * size_denom + kernel = 1. / (sigma * math.sqrt(2. * math.pi)) * \ + np.exp(-(((mgrid - 0.) / (sigma)) ** 2) * 0.5) + kernel = kernel / np.sum(kernel) + + # Reshape to depthwise convolutional weight + kernelx = np.tile(np.reshape(kernel, (1, 1, int(kernel_size), 1)), (3, 1, 1, 1)) + kernely = np.tile(np.reshape(kernel, (1, 1, 1, int(kernel_size))), (3, 1, 1, 1)) + + padd0 = int(kernel_size // 2) + evenorodd = int(1 - kernel_size % 2) + + pad = torch.nn.ConstantPad2d((padd0 - evenorodd, padd0, padd0 - evenorodd, padd0), 0.) + in_put = torch.from_numpy(np.expand_dims(np.transpose(image[0].astype(np.float32), (2, 0, 1)), axis=0)) + output = pad(in_put) + + weightx = torch.from_numpy(kernelx) + weighty = torch.from_numpy(kernely) + conv = F.conv2d + output = conv(output, weightx, groups=3) + output = conv(output, weighty, groups=3) + output = tensor2img(output[0]) + + return np.expand_dims(output,axis=0) + +def smooth_clip(x, v, smoothing, max_iters=200): + + test_x = copy.deepcopy(x) + v_i = copy.deepcopy(v) + iter_i = 0 + n = 1. + + while n > 0 and iter_i < max_iters: + result_img = test_x + v_i + + overshoot = ((result_img - 1.) >= 0) + belowshoot = ((result_img - 0.) <= 0) + + ov_max = (result_img - 1.)* 0.1 + bl_max = (result_img - 0.)* 0.1 * -1. + + ov_max = np.maximum(ov_max.max(), 0.01) + bl_max = np.maximum(bl_max.max(), 0.01) + + overshoot = smoothing(overshoot) + belowshoot = smoothing(belowshoot) + + maxx_ov = np.max(overshoot) + 1e-12 + maxx_bl = np.max(belowshoot) + 1e-12 + + overshoot = overshoot / maxx_ov + belowshoot = belowshoot / maxx_bl + + v_i = v_i - overshoot * ov_max + belowshoot * bl_max + result_img = test_x + v_i + + overshoot = ((result_img - 1.) >= 0) + belowshoot = ((result_img - 0.) <= 0) + + n_ov = overshoot.sum() + n_bl = belowshoot.sum() + n = n_ov + n_bl + iter_i += 1 + + return v_i diff --git a/resource/lowFrequency/generate_pattern.py b/resource/lowFrequency/generate_pattern.py new file mode 100755 index 0000000..f64cff1 --- /dev/null +++ b/resource/lowFrequency/generate_pattern.py @@ -0,0 +1,279 @@ +''' +This script is for normal training process, no any attack is applied +''' + +import sys, yaml, os + +os.chdir(sys.path[0]) +sys.path.append('../../') +os.getcwd() + +from copy import deepcopy + +import argparse +from pprint import pformat +import numpy as np +import torch +import time +import logging +from PIL import Image +from typing import Union +from utils.aggregate_block.save_path_generate import generate_save_folder +from utils.aggregate_block.dataset_and_transform_generate import get_num_classes, get_input_shape +from utils.aggregate_block.fix_random import fix_random +from utils.aggregate_block.dataset_and_transform_generate import dataset_and_transform_generate +from utils.aggregate_block.model_trainer_generate import generate_cls_model, generate_cls_trainer +from universal_pert import universal_perturbation +from torchvision.transforms import Resize +from torchvision import transforms +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform, y_iter + +def keep_normalization_resize_totensor_only( + given_transform, +): + return transforms.Compose( + list( + filter( + lambda x: isinstance(x, + (transforms.Normalize, transforms.Resize, transforms.ToTensor) + ), + given_transform.transforms + ) + ) + ) + +def get_part_for_each_label( + y: np.ndarray, + percent_or_num: Union[int, float], +): + ''' + use in generate sunrise set, each label take a percentage or num + if take + ''' + unique_label_values = np.unique(y) + select_pos = [] + if percent_or_num >= 1 : + for one_label_value in unique_label_values: + label_value_pos = np.where(y == one_label_value)[0] + select_pos += np.random.choice(label_value_pos, + size=int( + min( + percent_or_num, + len(label_value_pos) + ) + ), + replace=False, + ).tolist() + else: + for one_label_value in unique_label_values: + label_value_pos = np.where(y == one_label_value)[0] + select_pos += np.random.choice(label_value_pos, + size = int( + min( + np.ceil(percent_or_num*len(label_value_pos)), # ceil to make sure that at least one sample each label + len(label_value_pos) + ) + ), + replace=False, + ).tolist() + return select_pos + +def add_args(parser): + """ + parser : argparse.ArgumentParser + return a parser added with args required by fit + """ + # Training settings + parser.add_argument('--amp', type=lambda x: str(x) in ['True', 'true', '1']) + parser.add_argument('--device', type = str) + parser.add_argument('--yaml_path', type=str, default='./default.yaml', + help='path for yaml file provide additional default attributes') + parser.add_argument('--lr_scheduler', type=str, + help='which lr_scheduler use for optimizer') + # only all2one can be use for clean-label + parser.add_argument('--epochs', type=int) + parser.add_argument('--dataset', type=str, + help='which dataset to use' + ) + parser.add_argument('--dataset_path', type=str) + parser.add_argument('--attack_target') + parser.add_argument('--clean_model_path', type = str) + + parser.add_argument('--batch_size', type=int) + parser.add_argument('--lr', type=float) + parser.add_argument('--steplr_stepsize', type=int) + parser.add_argument('--steplr_gamma', type=float) + parser.add_argument('--sgd_momentum', type=float) + parser.add_argument('--wd', type=float, help='weight decay of sgd') + parser.add_argument('--steplr_milestones', type=list) + parser.add_argument('--client_optimizer', type=int) + parser.add_argument('--random_seed', type=int, + help='random_seed') + parser.add_argument('--frequency_save', type=int, + help=' frequency_save, 0 is never') + parser.add_argument('--model', type=str, + help='choose which kind of model') + parser.add_argument('--save_folder_name', type=str, + help='(Optional) should be time str + given unique identification str') + parser.add_argument('--git_hash', type=str, + help='git hash number, in order to find which version of code is used') + return parser + +def main(): + + ### 1. config args, save_path, fix random seed + parser = (add_args(argparse.ArgumentParser(description=sys.argv[0]))) + args = parser.parse_args() + + with open(args.yaml_path, 'r') as f: + defaults = yaml.safe_load(f) + + defaults.update({k:v for k,v in args.__dict__.items() if v is not None}) + + args.__dict__ = defaults + + args.terminal_info = sys.argv + + args.attack = 'None' + + args.num_classes = get_num_classes(args.dataset) + args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset) + args.img_size = (args.input_height, args.input_width, args.input_channel) + args.dataset_path = f"{args.dataset_path}/{args.dataset}" + + ### save path + if 'save_folder_name' not in args: + save_path = generate_save_folder( + run_info=('afterwards' if 'load_path' in args.__dict__ else 'attack') + '_' + args.attack, + given_load_file_path=args.load_path if 'load_path' in args else None, + all_record_folder_path='../../record', + ) + else: + save_path = '../../record/' + args.save_folder_name + os.mkdir(save_path) + + args.save_path = save_path + + torch.save(args.__dict__, save_path + '/info.pickle') + + ### set the logger + logFormatter = logging.Formatter( + fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s', + datefmt='%Y-%m-%d:%H:%M:%S', + ) + logger = logging.getLogger() + + fileHandler = logging.FileHandler(save_path + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + consoleHandler = logging.StreamHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + logger.setLevel(logging.INFO) + logging.info(pformat(args.__dict__)) + + ### set the random seed + fix_random(int(args.random_seed)) + + ### 2. set the clean train data and clean test data + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform = dataset_and_transform_generate(args) + + benign_train_ds = train_dataset_without_transform + + eval_ds = prepro_cls_DatasetBD_v2( + deepcopy(test_dataset_without_transform), + poison_indicator=None, + bd_image_pre_transform=None, + bd_label_pre_transform=None, + save_folder_path=f"{args.save_path}/bd_test_dataset", + ) + + eval_ds_target = np.array(i for i in y_iter(eval_ds)) + + eval_ds.subset( + get_part_for_each_label(eval_ds_target, 10) + ) + + eval_ds = dataset_wrapper_with_transform( + eval_ds, + test_img_transform, + test_label_transform, + ) + + net = generate_cls_model( + model_name=args.model, + num_classes=args.num_classes, + ) + + try: + net.load_state_dict( + torch.load( + args.clean_model_path, + map_location='cpu' + ) + ) + except: + net.load_state_dict( + torch.load( + args.clean_model_path, + map_location='cpu' + )['model'] + ) + + # just get 100 pil from benign train data + random100 = np.random.choice(benign_train_ds.__len__(), 100, replace=False) #1, replace=False) + # benign_train_ds.subset(random100) + # dataset_pil = benign_train_ds.data + + dataset_pil = [] + for selected_img_idx in random100: + pil_img, *other = benign_train_ds[selected_img_idx] # the img must be the first element. + dataset_pil.append(pil_img) + + r = Resize((args.input_height, args.input_width)) + dataset_npy = np.concatenate( + [ + np.array( + r(pil_img) + )[None,...].astype(np.float32)/255 + for pil_img in dataset_pil] + ) + + device = torch.device(args.device if torch.cuda.is_available() else "cpu") + + save_path_prefix = f'{save_path}/{args.dataset}_{args.model}' + max_iter_uni = 50 + v = universal_perturbation( + dataset_npy, + eval_ds, + net, + target = args.attack_target, + # delta=0.2, + max_iter_uni=max_iter_uni, # 50 default 1 just for test speed + num_classes=args.num_classes, + overshoot=0.02, + max_iter_df=200, + device = device, + save_path_prefix = save_path_prefix, + ) + logging.info(f"max_iter_uni={max_iter_uni}") + + v_lossy_image = np.clip(deepcopy(v) * 255 + 255 / 2, 0, 255).squeeze() # since v is [0,1] + np.save(f'{save_path_prefix}.npy', v_lossy_image.astype(np.uint8)) + Image.fromarray(v_lossy_image.astype(np.uint8)).save(f'{save_path_prefix}_lossy.jpg') + + Image.fromarray(v_lossy_image.astype(np.uint8)).save(f'{save_path_prefix}_lossy.jpg') + + logging.info('end') + + + +if __name__ == '__main__': + main() diff --git a/resource/lowFrequency/gtsrb.yaml b/resource/lowFrequency/gtsrb.yaml new file mode 100755 index 0000000..c3ca714 --- /dev/null +++ b/resource/lowFrequency/gtsrb.yaml @@ -0,0 +1,18 @@ +num_workers: 4 +pin_memory: True +non_blocking: True +prefetch: False +amp: False +device: cuda:0 +client_optimizer: sgd +dataset: gtsrb +dataset_path: ../data +frequency_save: 50 +batch_size: 128 +lr: 0.01 +lr_scheduler: CosineAnnealingLR +model: preactresnet18 +random_seed: 0 +sgd_momentum: 0.9 +wd: 0.0005 +epochs: 50 \ No newline at end of file diff --git a/resource/lowFrequency/gtsrb_convnext_tiny_0_255.npy b/resource/lowFrequency/gtsrb_convnext_tiny_0_255.npy new file mode 100644 index 0000000..b58984c Binary files /dev/null and b/resource/lowFrequency/gtsrb_convnext_tiny_0_255.npy differ diff --git a/resource/lowFrequency/gtsrb_densenet161_0_255.npy b/resource/lowFrequency/gtsrb_densenet161_0_255.npy new file mode 100755 index 0000000..6c66948 Binary files /dev/null and b/resource/lowFrequency/gtsrb_densenet161_0_255.npy differ diff --git a/resource/lowFrequency/gtsrb_efficientnet_b3_0_255.npy b/resource/lowFrequency/gtsrb_efficientnet_b3_0_255.npy new file mode 100755 index 0000000..df2f924 Binary files /dev/null and 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b/resource/lowFrequency/gtsrb_vgg19_bn_0_255.npy differ diff --git a/resource/lowFrequency/gtsrb_vit_b_16_0_255.npy b/resource/lowFrequency/gtsrb_vit_b_16_0_255.npy new file mode 100644 index 0000000..4171576 Binary files /dev/null and b/resource/lowFrequency/gtsrb_vit_b_16_0_255.npy differ diff --git a/resource/lowFrequency/readme.md b/resource/lowFrequency/readme.md new file mode 100644 index 0000000..0f9fda8 --- /dev/null +++ b/resource/lowFrequency/readme.md @@ -0,0 +1,10 @@ +You may use code here to generate low frequency pattern for your own data. + +eg. +``` +python generate_pattern.py --dataset cifar10 --model vgg19_bn --clean_model_path ../../resource/clean_model/cifar10_vgg19_bn/clean_model.pth --save_folder_name lf_mask_cifar10_vgg19_bn +``` + +Notice that if the dataset is changed, you should change the training schedule by replace the '--yaml_path' with corresponding YAML file. (We provided YAML file for cifar10,cifar100,gtsrb,tiny in this folder.) + +After running the srcipt, you can then find the npy file in the result folder. To run low frequency attack with your own pattern, you need to specify the pattern path for attack/lf.py, which is '--lowFrequencyPatternPath'. \ No newline at end of file diff --git a/resource/lowFrequency/tiny.yaml b/resource/lowFrequency/tiny.yaml new file mode 100755 index 0000000..773ad53 --- /dev/null +++ b/resource/lowFrequency/tiny.yaml @@ -0,0 +1,18 @@ +num_workers: 4 +pin_memory: True +non_blocking: True +prefetch: False +amp: False +device: cuda:0 +client_optimizer: sgd +dataset: tiny +dataset_path: ../data +frequency_save: 100 +batch_size: 128 +lr: 0.01 +lr_scheduler: ReduceLROnPlateau +model: preactresnet18 +random_seed: 0 +sgd_momentum: 0.9 +wd: 0.0005 +epochs: 200 \ No newline at end of file diff --git a/resource/lowFrequency/tiny_convnext_tiny_0_255.npy b/resource/lowFrequency/tiny_convnext_tiny_0_255.npy new file mode 100644 index 0000000..b54f19d Binary files /dev/null and b/resource/lowFrequency/tiny_convnext_tiny_0_255.npy differ diff --git 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diff --git a/resource/lowFrequency/tiny_vgg19_0_255.npy b/resource/lowFrequency/tiny_vgg19_0_255.npy new file mode 100755 index 0000000..586129a Binary files /dev/null and b/resource/lowFrequency/tiny_vgg19_0_255.npy differ diff --git a/resource/lowFrequency/tiny_vgg19_bn_0_255.npy b/resource/lowFrequency/tiny_vgg19_bn_0_255.npy new file mode 100644 index 0000000..90b97e3 Binary files /dev/null and b/resource/lowFrequency/tiny_vgg19_bn_0_255.npy differ diff --git a/resource/lowFrequency/tiny_vit_b_16_0_255.npy b/resource/lowFrequency/tiny_vit_b_16_0_255.npy new file mode 100644 index 0000000..0f5a2d0 Binary files /dev/null and b/resource/lowFrequency/tiny_vit_b_16_0_255.npy differ diff --git a/resource/lowFrequency/universal_pert.py b/resource/lowFrequency/universal_pert.py new file mode 100755 index 0000000..cf45440 --- /dev/null +++ b/resource/lowFrequency/universal_pert.py @@ -0,0 +1,183 @@ +import logging +import numpy as np +from deepfool import tar_deepfool +from gauss_smooth import gauss_smooth, normalization#, smooth_clip +import os +import torch +import torch.backends.cudnn as cudnn +from torch.utils.data import DataLoader +# from dataset import CIFAR10Dataset +from PIL import Image +from copy import deepcopy +from tqdm import tqdm + +def universal_perturbation(dataset, + test_dataset, + net, + target, + delta=0.8, + max_iter_uni = 50, + num_classes=10, + overshoot=0.02, + max_iter_df=200, + device = 'cpu', + save_path_prefix = None, + ): + """ + :param dataset: Images of size MxHxWxC (M: number of images) + + :param f: feedforward function (input: images, output: values of activation BEFORE softmax). + + :param grads: gradient functions with respect to input (as many gradients as classes). + + :param delta: controls the desired fooling rate (default = 80% fooling rate) + + :param max_iter_uni: optional other termination criterion (maximum number of iteration, default = np.inf) + + :param num_classes: num_classes (limits the number of classes to test against, by default = 10) + + :param overshoot: used as a termination criterion to prevent vanishing updates (default = 0.02). + + :param max_iter_df: maximum number of iterations for deepfool (default = 10) + + :return: the universal perturbation. + """ + net.eval() + + if torch.cuda.is_available(): + device = device + net.to(device) + cudnn.benchmark = True + logging.info('use cuda') + else: + device = 'cpu' + logging.info('use cpu') + + num_images = np.shape(dataset)[0] + + v = np.zeros(dataset.shape[1:]).astype('float32') + # best_frate = 0.0 + fooling_rate = 0.0 + # file_perturbation = os.path.join('data', 'best_universal.npy') + itr = 0 + fooling_rate_list = [] + while fooling_rate < 1-delta and itr < max_iter_uni: + + # while itr < max_iter_uni: + # Shuffle the dataset + np.random.shuffle(dataset) + + logging.info (f'Starting pass number {itr}') + + # Go through the data set and compute the perturbation increments sequentially + for k in tqdm(range(0, num_images)): + + logging.info(f' image : {k}') + + cur_img = dataset[k:(k+1), :, :, :] + data = np.transpose(cur_img, (0,3,1,2)) + data = torch.from_numpy(data) + data = data.to(device) + r2 = int(net(data).max(1)[1]) + torch.cuda.empty_cache() + + + add_v = cur_img + v + data_p = np.transpose(add_v, (0,3,1,2)) + data_p = torch.from_numpy(data_p) + data_p = data_p.to(device) + r1 = int(net(data_p).max(1)[1]) + torch.cuda.empty_cache() + + if r1 == r2: + + # Compute adversarial perturbation + dr, iter_i, _, _ = tar_deepfool(data_p[0], net, target=target, num_classes=num_classes, + overshoot=overshoot, max_iter=max_iter_df, device=device) + # Make sure it converged... + if iter_i < max_iter_df-1: + assert not np.any(np.isnan(dr)) + assert np.all(np.isfinite(dr)) + + + # v = v + dr.astype('float32') + v = v + gauss_smooth(dr) + v = gauss_smooth(v) + v = normalization(v) + + logging.info(f"iter_i:{iter_i} end") + + logging.info(f"v min max {v.min()}, {v.max()}") + logging.info(f"v*255 min max {(v.min()*255)}, {v.max()*255}") + + itr = itr + 1 + + with torch.no_grad(): + est_labels_orig = torch.tensor(np.zeros(0, dtype=np.int64)) + est_labels_pert = torch.tensor(np.zeros(0, dtype=np.int64)) + batch_size = 100 + test_data_orig = test_dataset + test_loader_orig = DataLoader(dataset=test_data_orig, batch_size=batch_size, pin_memory=True) + # test_data_pert = CIFAR10Dataset(dataset, pert=v) + # test_loader_pert = DataLoader(dataset=test_data_pert, batch_size=batch_size, pin_memory=True) + + net.eval() + + print(f"v.shape:{v.shape}") + + v_tensor = torch.from_numpy( + np.transpose(v.squeeze(), (2,0,1)) + )[None,...].to(device) + + for batch_idx, (inputs, _, *other) in enumerate(test_loader_orig): + inputs = inputs.to(device) + outputs = net(inputs) + _, predicted = outputs.max(1) + est_labels_orig = torch.cat((est_labels_orig, predicted.cpu())) + torch.cuda.empty_cache() + + for batch_idx, (inputs, _, *other) in enumerate(test_loader_orig): + inputs = inputs.to(device) + inputs += v_tensor + outputs = net(inputs) + _, predicted = outputs.max(1) + est_labels_pert = torch.cat((est_labels_pert, predicted.cpu())) + torch.cuda.empty_cache() + + fooling_rate = float(torch.sum(est_labels_orig != est_labels_pert))/float(len(est_labels_orig)) + fooling_rate_list.append(fooling_rate) + + logging.info(f"FOOLING RATE: {fooling_rate}") + dif_count = est_labels_pert[np.where(est_labels_pert != est_labels_orig)].cpu().numpy() + logging.info(f"dif_count:{dif_count}") + + np.save(f'{save_path_prefix}_{iter_i}.npy', v) + + v_lossy_image = np.clip(deepcopy(v) * 255 + 255 / 2, 0, 255).squeeze() # since v is [0,1] + + Image.fromarray(v_lossy_image.astype(np.uint8)).save(f'{save_path_prefix}_{iter_i}_lossy.jpg') + + last_ten_fool_rate = np.array(fooling_rate_list[-5:]) + + logging.info(f"last_ten_fool_rate :{last_ten_fool_rate}") + + if len(last_ten_fool_rate) == 5 and last_ten_fool_rate.max() - last_ten_fool_rate.min() < 0.01: + + return v + + + # if len(dif_count)>(dataset.shape[0]*0.05): + # counts = np.bincount(dif_count.astype(np.int)) + # target = np.argmax(counts) + # logging.info(dif_count) + # logging.info('the dominant label is:', target) + # if fooling_rate >= best_frate: + # best_v = v + # best_frate = fooling_rate + # new_target = target + # logging.info('the best fooling rate updating to:',best_frate) + # logging.info('the target label is updating to:', new_target) + # np.save(os.path.join(file_perturbation), best_v) + + return v + #return best_v,new_target \ No newline at end of file diff --git a/resource/ssba/custom_modules.py b/resource/ssba/custom_modules.py new file mode 100644 index 0000000..2375b04 --- /dev/null +++ b/resource/ssba/custom_modules.py @@ -0,0 +1,162 @@ +import math +import torch +from torch import nn +from torch.nn.functional import relu +import torch.nn.functional as F +import numpy as np +from torch_utils import misc + +#执行pixel norm(在第C维进行) [B, C, H, W] -> [B, C, H, W] +class PixelNorm(nn.Module): + def __init__(self, epsilon=1e-8): + """ + @notice: avoid in-place ops. + https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 + """ + super(PixelNorm, self).__init__() + self.epsilon = epsilon + + def forward(self, x): + tmp = torch.mul(x, x) + tmp1 = torch.rsqrt(torch.mean(tmp, dim=1, keepdim=True) + self.epsilon) + + return x * tmp1 + +#定义用于mapping的全连接层权重与偏置 +class FC(nn.Module): + def __init__(self, + in_channels, + out_channels, + gain=2**(0.5), + use_wscale=False, + lrmul=1.0, + bias=True): + """ + The complete conversion of Dense/FC/Linear Layer of original Tensorflow version. + """ + super(FC, self).__init__() + #此处设定的lr_mul似乎只对bias生效 + he_std = gain * in_channels ** (-0.5) + if use_wscale: + init_std = 1.0 / lrmul + self.w_lrmul = he_std * lrmul + else: #weight * he_std/lrmul * lrmul = weight * he_std + init_std = he_std / lrmul + self.w_lrmul = lrmul + + self.weight = torch.nn.Parameter(torch.randn(out_channels, in_channels) * init_std) + if bias: + self.bias = torch.nn.Parameter(torch.zeros(out_channels)) + self.b_lrmul = lrmul + else: + self.bias = None + + def forward(self, x): + if self.bias is not None: + out = F.linear(x, self.weight * self.w_lrmul, self.bias * self.b_lrmul) + else: + out = F.linear(x, self.weight * self.w_lrmul) + out = F.leaky_relu(out, 0.2, inplace=True) + return out + +class G_mapping(nn.Module): + def __init__(self, + mapping_fmaps=512, + dlatent_size=512, + normalize_latents=True, + use_wscale=True, + lrmul=0.01, + gain=2**(0.5) + ): + super(G_mapping, self).__init__() + self.mapping_fmaps = mapping_fmaps + self.func = nn.Sequential( + FC(self.mapping_fmaps, dlatent_size, gain, lrmul=lrmul, use_wscale=use_wscale), + FC(dlatent_size, dlatent_size, gain, lrmul=lrmul, use_wscale=use_wscale), + FC(dlatent_size, dlatent_size, gain, lrmul=lrmul, use_wscale=use_wscale), + FC(dlatent_size, dlatent_size, gain, lrmul=lrmul, use_wscale=use_wscale), + FC(dlatent_size, dlatent_size, gain, lrmul=lrmul, use_wscale=use_wscale), + FC(dlatent_size, dlatent_size, gain, lrmul=lrmul, use_wscale=use_wscale), + FC(dlatent_size, dlatent_size, gain, lrmul=lrmul, use_wscale=use_wscale), + FC(dlatent_size, dlatent_size, gain, lrmul=lrmul, use_wscale=use_wscale) + ) + + self.normalize_latents = normalize_latents + self.pixel_norm = PixelNorm() + + def forward(self, x): + if self.normalize_latents: + x = self.pixel_norm(x) + out = self.func(x) + return out + +class modulated_conv2d(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + fp_size=512, + bias_init=None, + demodulate=1, + fused_modconv=0, + ): + super(modulated_conv2d, self).__init__() + self.fp_size = fp_size + self.in_channels = in_channels + self.weight_conv = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=torch.contiguous_format)) + self.bias_conv = None if not bias_init else torch.nn.Parameter(torch.full([out_channels], np.float32(bias_init))) + + self.fc = nn.Linear(self.fp_size, self.in_channels) + + self.k = kernel_size + self.stride = stride + self.padding = padding + self.demodulate = demodulate + self.fused_modconv = fused_modconv + + def forward(self, fingerprint, x): + + + batch_size = x.shape[0] + fingerprint = self.fc(fingerprint) + + + misc.assert_shape(fingerprint, [batch_size, self.in_channels]) + + + if x.dtype == torch.float16 and demodulate: + self.weight_conv = self.weight_conv * (1 / np.sqrt(self.in_channels * self.k * self.k) / self.weight_conv.norm(float('inf'), dim=[1,2,3], keepdim=True)) + fingerprint = fingerprint / fingerprint.norm(float('inf'), dim=1, keepdim=True) + + + w = None + dcoefs = None + if self.demodulate or self.fused_modconv: + w = self.weight_conv.unsqueeze(0) + w = w * fingerprint.reshape(batch_size, 1, -1, 1, 1) + if self.demodulate: + + dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() + if self.demodulate and self.fused_modconv: + + w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) + + + if not self.fused_modconv: + x = x * fingerprint.to(x.dtype).reshape(batch_size, -1, 1, 1) + x = F.conv2d(input=x, weight=self.weight_conv.to(x.dtype), stride=self.stride, padding=self.padding) + if self.demodulate: + x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1) + return x + + + with misc.suppress_tracer_warnings(): + batch_size = int(batch_size) + x = x.reshape(1, -1, *x.shape[2:]) + w = w.reshape(-1, self.in_channels, self.k, self.k) + x = F.conv2d(input=x, weight=w.to(x.dtype), stride=self.stride, padding=self.padding, groups=batch_size) + x = x.reshape(batch_size, -1, *x.shape[2:]) + return x \ No newline at end of file diff --git a/resource/ssba/dataset_convert_into_images.py b/resource/ssba/dataset_convert_into_images.py new file mode 100644 index 0000000..1315f7f --- /dev/null +++ b/resource/ssba/dataset_convert_into_images.py @@ -0,0 +1,76 @@ +''' +This file is to convert given dataset into images. +eg. + image_folder/train/img1.png + image_folder/train/img2.png + image_folder/train/img3.png + ... + and + image_folder/test/img1.png + image_folder/test/img2.png + image_folder/test/img3.png + ... +''' +import sys, yaml, os +from PIL import Image +import numpy as np +from tqdm import tqdm +import argparse + +os.chdir(sys.path[0]) +sys.path.append('../../') +os.getcwd() + +from utils.aggregate_block.dataset_and_transform_generate import dataset_and_transform_generate + +class Args: + pass + +def dataset_convert_into_images(dataset_name, dataset_path, image_folder): + + args = Args() + args.dataset = dataset_name + args.dataset_path = os.path.join(dataset_path,dataset_name) + args.img_size = (32,32,3) + + train_dataset_without_transform, \ + _, \ + _, \ + test_dataset_without_transform, \ + _, \ + _ = dataset_and_transform_generate(args) + + if not os.path.exists(image_folder): + os.makedirs(image_folder) + + train_image_folder = os.path.join(image_folder, 'train') + if not os.path.exists(train_image_folder): + os.makedirs(train_image_folder) + for img_idx, (img, label, *other) in tqdm(enumerate(train_dataset_without_transform)): + img_path = os.path.join(train_image_folder, f'img_{img_idx}.png') + if isinstance(img, np.ndarray): + img = Image.fromarray(img) + img.save(img_path) + + test_image_folder = os.path.join(image_folder, 'test') + if not os.path.exists(test_image_folder): + os.makedirs(test_image_folder) + for img_idx, (img, label, *other) in tqdm(enumerate(test_dataset_without_transform)): + img_path = os.path.join(test_image_folder, f'img_{img_idx}.png') + if isinstance(img, np.ndarray): + img = Image.fromarray(img) + img.save(img_path) + +if __name__ == '__main__': + args = argparse.ArgumentParser() + args.add_argument("-d",'--dataset', type=str, default='cifar10') + args.add_argument("-dp",'--dataset_path', type=str, default='../../data') + args.add_argument("-i",'--image_folder', type=str,) + args = args.parse_args() + if args.image_folder is None: + args.image_folder = f'../../data/{args.dataset}_seperate_images' + dataset_convert_into_images( + dataset_name = args.dataset, + dataset_path = args.dataset_path, + image_folder = args.image_folder, + ) \ No newline at end of file diff --git a/resource/ssba/detect_fingerprints.py b/resource/ssba/detect_fingerprints.py new file mode 100644 index 0000000..7d93dec --- /dev/null +++ b/resource/ssba/detect_fingerprints.py @@ -0,0 +1,194 @@ +import argparse +import glob +import PIL +import bchlib +import numpy as np + +parser = argparse.ArgumentParser() +parser.add_argument("--data_dir", type=str, help="Directory with images.") +parser.add_argument("--output_dir", type=str, help="Path to save watermarked images to.") +parser.add_argument("--image_resolution", type=int, required=True, help="Height and width of square images.") +parser.add_argument("--decoder_path", type=str, required=True, help="Path to trained StegaStamp decoder.") +parser.add_argument("--batch_size", type=int, default=64, help="Batch size.") +parser.add_argument("--cuda", type=int, default=0) +parser.add_argument("--seed", type=int, default=42) +parser.add_argument("--bch", action='store_true', help="Use bch code") +parser.add_argument('--secret', type=str, default='CUHKSZ!') + +args = parser.parse_args() + +import os + +#os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" +#os.environ["CUDA_VISIBLE_DEVICES"] = str(args.cuda) + +from time import time +from tqdm import tqdm + +import torch +from torch import nn +import torch.nn.functional as F +from torch.utils.data import Dataset, DataLoader +from torchvision.utils import save_image +from torchvision.datasets import ImageFolder +from torchvision import transforms + +def generate_random_fingerprints(fingerprint_size, batch_size=4): + z = torch.zeros((batch_size, fingerprint_size), dtype=torch.float).random_(0, 2) #(B, 100) + return z + +if args.cuda != -1: + device = torch.device("cuda") +else: + device = torch.device("cpu") + + +class CustomImageFolder(Dataset): + def __init__(self, data_dir, transform=None): + self.data_dir = data_dir + self.filenames = glob.glob(os.path.join(data_dir, "*.png")) + self.filenames.extend(glob.glob(os.path.join(data_dir, "*.jpeg"))) + self.filenames.extend(glob.glob(os.path.join(data_dir, "*.jpg"))) + self.filenames = sorted(self.filenames) + self.transform = transform + + def __getitem__(self, idx): + filename = self.filenames[idx] + image = PIL.Image.open(filename) + if self.transform: + image = self.transform(image) + return image, 0 + + def __len__(self): + return len(self.filenames) + + +def load_decoder(): + global RevealNet + global FINGERPRINT_SIZE + + from models import StegaStampDecoder + state_dict = torch.load(args.decoder_path) + FINGERPRINT_SIZE = state_dict["dense.2.weight"].shape[0] + + RevealNet = StegaStampDecoder(args.image_resolution, 3, FINGERPRINT_SIZE) + kwargs = {"map_location": "cpu"} if args.cuda == -1 else {} + RevealNet.load_state_dict(torch.load(args.decoder_path, **kwargs)) + RevealNet = RevealNet.to(device) + + +def load_data(): + global dataset, dataloader + + transform = transforms.Compose( + [ + transforms.ToTensor(), + ] + ) + s = time() + print(f"Loading image folder {args.data_dir} ...") + dataset = CustomImageFolder(args.data_dir, transform=transform) + print(f"Finished. Loading took {time() - s:.2f}s") + + +def extract_fingerprints(): + + all_fingerprints = [] + all_code = [] + + BATCH_SIZE = args.batch_size + BCH_POLYNOMIAL = 137 + BCH_BITS = 5 + bch = bchlib.BCH(BCH_POLYNOMIAL, BCH_BITS) + print("Generating Ground Truth...") + torch.manual_seed(args.seed) + + #生成正确的编码用于计算指标 + if not args.bch: #如果不采用BCH编码，默认对所有图片采用相同指纹 + fingerprints = generate_random_fingerprints(FINGERPRINT_SIZE, 1) + fingerprints = fingerprints.view(1, FINGERPRINT_SIZE).expand(BATCH_SIZE, FINGERPRINT_SIZE) + fingerprints = fingerprints.to(device) + else: #采用BCH编码 + print("Using bch code along with secret string:", args.secret) + if len(args.secret) > 7: + print('Error: Can only encode 56bits (7 characters) with ECC') + return + data = bytearray(args.secret + ' ' * (7 - len(args.secret)), 'utf-8')#转化为bytearray对象 + ecc = bch.encode(data)#获得对应编码 + packet = data + ecc#对数据进行编码 + packet_binary = ''.join(format(x, '08b') for x in packet)#转换成二进制 + fingerprints = [int(x) for x in packet_binary] + fingerprints.extend([0, 0, 0, 0]) + fingerprints = torch.tensor(fingerprints, dtype=torch.float).unsqueeze(0).expand(BATCH_SIZE, FINGERPRINT_SIZE) + fingerprints = fingerprints.to(device) + + dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=0) + + bitwise_accuracy = 0 + correct = 0 + + for images, _ in tqdm(dataloader): + images = images.to(device) + + detected_fingerprints = RevealNet(images) + detected_fingerprints = (detected_fingerprints > 0).long() + bitwise_accuracy += (detected_fingerprints[: images.size(0)].detach() == fingerprints[: images.size(0)]).float().mean(dim=1).sum().item() + if args.bch: + for sec in detected_fingerprints: + sec = np.array(sec.cpu()) + packet_binary = "".join([str(int(bit)) for bit in sec[:96]]) + packet = bytes(int(packet_binary[i: i + 8], 2) for i in range(0, len(packet_binary), 8)) + packet = bytearray(packet) + data, ecc = packet[:-bch.ecc_bytes], packet[-bch.ecc_bytes:] + bitflips = bch.decode_inplace(data, ecc) + if bitflips != -1: + try: + correct += 1 + code = data.decode("utf-8") + all_code.append(code) + continue + except: + all_code.append("Something went wrong") + continue + all_code.append('Failed to decode') + + all_fingerprints.append(detected_fingerprints.detach().cpu()) + + + + + + + + + all_fingerprints = torch.cat(all_fingerprints, dim=0).cpu() + + + + for idx in range(len(all_fingerprints)): + + fingerprint = all_fingerprints[idx] + _, filename = os.path.split(dataset.filenames[idx]) + filename = filename.split('.')[0] + ".png" + if args.bch: + code = all_code[idx] + + + fingerprint_str = "".join(map(str, fingerprint.cpu().long().numpy().tolist())) + + else: + fingerprint_str = "".join(map(str, fingerprint.cpu().long().numpy().tolist())) + + + + bitwise_accuracy = bitwise_accuracy / len(all_fingerprints) + if args.bch: + decode_acc = correct / len(all_fingerprints) + print(f"Decoding accuracy on fingerprinted images: {decode_acc}") + print(f"Bitwise accuracy on fingerprinted images: {bitwise_accuracy}") + + +if __name__ == "__main__": + load_decoder() + load_data() + extract_fingerprints() diff --git a/resource/ssba/dnnlib/__init__.py b/resource/ssba/dnnlib/__init__.py new file mode 100644 index 0000000..2f08cf3 --- /dev/null +++ b/resource/ssba/dnnlib/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +from .util import EasyDict, make_cache_dir_path diff --git a/resource/ssba/dnnlib/util.py b/resource/ssba/dnnlib/util.py new file mode 100644 index 0000000..f5b9b03 --- /dev/null +++ b/resource/ssba/dnnlib/util.py @@ -0,0 +1,477 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Miscellaneous utility classes and functions.""" + +import ctypes +import fnmatch +import importlib +import inspect +import numpy as np +import os +import shutil +import sys +import types +import io +import pickle +import re +import requests +import html +import hashlib +import glob +import tempfile +import urllib +import urllib.request +import uuid + +from distutils.util import strtobool +from typing import Any, List, Tuple, Union + + + + + + +class EasyDict(dict): + """Convenience class that behaves like a dict but allows access with the attribute syntax.""" + + def __getattr__(self, name: str) -> Any: + try: + return self[name] + except KeyError: + raise AttributeError(name) + + def __setattr__(self, name: str, value: Any) -> None: + self[name] = value + + def __delattr__(self, name: str) -> None: + del self[name] + + +class Logger(object): + """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file.""" + + def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True): + self.file = None + + if file_name is not None: + self.file = open(file_name, file_mode) + + self.should_flush = should_flush + self.stdout = sys.stdout + self.stderr = sys.stderr + + sys.stdout = self + sys.stderr = self + + def __enter__(self) -> "Logger": + return self + + def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: + self.close() + + def write(self, text: Union[str, bytes]) -> None: + """Write text to stdout (and a file) and optionally flush.""" + if isinstance(text, bytes): + text = text.decode() + if len(text) == 0: + return + + if self.file is not None: + self.file.write(text) + + self.stdout.write(text) + + if self.should_flush: + self.flush() + + def flush(self) -> None: + """Flush written text to both stdout and a file, if open.""" + if self.file is not None: + self.file.flush() + + self.stdout.flush() + + def close(self) -> None: + """Flush, close possible files, and remove stdout/stderr mirroring.""" + self.flush() + + + if sys.stdout is self: + sys.stdout = self.stdout + if sys.stderr is self: + sys.stderr = self.stderr + + if self.file is not None: + self.file.close() + self.file = None + + + + + +_dnnlib_cache_dir = None + +def set_cache_dir(path: str) -> None: + global _dnnlib_cache_dir + _dnnlib_cache_dir = path + +def make_cache_dir_path(*paths: str) -> str: + if _dnnlib_cache_dir is not None: + return os.path.join(_dnnlib_cache_dir, *paths) + if 'DNNLIB_CACHE_DIR' in os.environ: + return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths) + if 'HOME' in os.environ: + return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths) + if 'USERPROFILE' in os.environ: + return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths) + return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths) + + + + + +def format_time(seconds: Union[int, float]) -> str: + """Convert the seconds to human readable string with days, hours, minutes and seconds.""" + s = int(np.rint(seconds)) + + if s < 60: + return "{0}s".format(s) + elif s < 60 * 60: + return "{0}m {1:02}s".format(s // 60, s % 60) + elif s < 24 * 60 * 60: + return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60) + else: + return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60) + + +def ask_yes_no(question: str) -> bool: + """Ask the user the question until the user inputs a valid answer.""" + while True: + try: + print("{0} [y/n]".format(question)) + return strtobool(input().lower()) + except ValueError: + pass + + +def tuple_product(t: Tuple) -> Any: + """Calculate the product of the tuple elements.""" + result = 1 + + for v in t: + result *= v + + return result + + +_str_to_ctype = { + "uint8": ctypes.c_ubyte, + "uint16": ctypes.c_uint16, + "uint32": ctypes.c_uint32, + "uint64": ctypes.c_uint64, + "int8": ctypes.c_byte, + "int16": ctypes.c_int16, + "int32": ctypes.c_int32, + "int64": ctypes.c_int64, + "float32": ctypes.c_float, + "float64": ctypes.c_double +} + + +def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]: + """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes.""" + type_str = None + + if isinstance(type_obj, str): + type_str = type_obj + elif hasattr(type_obj, "__name__"): + type_str = type_obj.__name__ + elif hasattr(type_obj, "name"): + type_str = type_obj.name + else: + raise RuntimeError("Cannot infer type name from input") + + assert type_str in _str_to_ctype.keys() + + my_dtype = np.dtype(type_str) + my_ctype = _str_to_ctype[type_str] + + assert my_dtype.itemsize == ctypes.sizeof(my_ctype) + + return my_dtype, my_ctype + + +def is_pickleable(obj: Any) -> bool: + try: + with io.BytesIO() as stream: + pickle.dump(obj, stream) + return True + except: + return False + + + + + +def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]: + """Searches for the underlying module behind the name to some python object. + Returns the module and the object name (original name with module part removed).""" + + + obj_name = re.sub("^np.", "numpy.", obj_name) + obj_name = re.sub("^tf.", "tensorflow.", obj_name) + + + parts = obj_name.split(".") + name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)] + + + for module_name, local_obj_name in name_pairs: + try: + module = importlib.import_module(module_name) + get_obj_from_module(module, local_obj_name) + return module, local_obj_name + except: + pass + + + for module_name, _local_obj_name in name_pairs: + try: + importlib.import_module(module_name) + except ImportError: + if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"): + raise + + + for module_name, local_obj_name in name_pairs: + try: + module = importlib.import_module(module_name) + get_obj_from_module(module, local_obj_name) + except ImportError: + pass + + + raise ImportError(obj_name) + + +def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any: + """Traverses the object name and returns the last (rightmost) python object.""" + if obj_name == '': + return module + obj = module + for part in obj_name.split("."): + obj = getattr(obj, part) + return obj + + +def get_obj_by_name(name: str) -> Any: + """Finds the python object with the given name.""" + module, obj_name = get_module_from_obj_name(name) + return get_obj_from_module(module, obj_name) + + +def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any: + """Finds the python object with the given name and calls it as a function.""" + assert func_name is not None + func_obj = get_obj_by_name(func_name) + assert callable(func_obj) + return func_obj(*args, **kwargs) + + +def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any: + """Finds the python class with the given name and constructs it with the given arguments.""" + return call_func_by_name(*args, func_name=class_name, **kwargs) + + +def get_module_dir_by_obj_name(obj_name: str) -> str: + """Get the directory path of the module containing the given object name.""" + module, _ = get_module_from_obj_name(obj_name) + return os.path.dirname(inspect.getfile(module)) + + +def is_top_level_function(obj: Any) -> bool: + """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'.""" + return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__ + + +def get_top_level_function_name(obj: Any) -> str: + """Return the fully-qualified name of a top-level function.""" + assert is_top_level_function(obj) + module = obj.__module__ + if module == '__main__': + module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0] + return module + "." + obj.__name__ + + + + + +def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]: + """List all files recursively in a given directory while ignoring given file and directory names. + Returns list of tuples containing both absolute and relative paths.""" + assert os.path.isdir(dir_path) + base_name = os.path.basename(os.path.normpath(dir_path)) + + if ignores is None: + ignores = [] + + result = [] + + for root, dirs, files in os.walk(dir_path, topdown=True): + for ignore_ in ignores: + dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)] + + + for d in dirs_to_remove: + dirs.remove(d) + + files = [f for f in files if not fnmatch.fnmatch(f, ignore_)] + + absolute_paths = [os.path.join(root, f) for f in files] + relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths] + + if add_base_to_relative: + relative_paths = [os.path.join(base_name, p) for p in relative_paths] + + assert len(absolute_paths) == len(relative_paths) + result += zip(absolute_paths, relative_paths) + + return result + + +def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None: + """Takes in a list of tuples of (src, dst) paths and copies files. + Will create all necessary directories.""" + for file in files: + target_dir_name = os.path.dirname(file[1]) + + + if not os.path.exists(target_dir_name): + os.makedirs(target_dir_name) + + shutil.copyfile(file[0], file[1]) + + + + + +def is_url(obj: Any, allow_file_urls: bool = False) -> bool: + """Determine whether the given object is a valid URL string.""" + if not isinstance(obj, str) or not "://" in obj: + return False + if allow_file_urls and obj.startswith('file://'): + return True + try: + res = requests.compat.urlparse(obj) + if not res.scheme or not res.netloc or not "." in res.netloc: + return False + res = requests.compat.urlparse(requests.compat.urljoin(obj, "/")) + if not res.scheme or not res.netloc or not "." in res.netloc: + return False + except: + return False + return True + + +def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any: + """Download the given URL and return a binary-mode file object to access the data.""" + assert num_attempts >= 1 + assert not (return_filename and (not cache)) + + + if not re.match('^[a-z]+://', url): + return url if return_filename else open(url, "rb") + + + + # + + # + + + # + + + # + + + + if url.startswith('file://'): + filename = urllib.parse.urlparse(url).path + if re.match(r'^/[a-zA-Z]:', filename): + filename = filename[1:] + return filename if return_filename else open(filename, "rb") + + assert is_url(url) + + + if cache_dir is None: + cache_dir = make_cache_dir_path('downloads') + + url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest() + if cache: + cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*")) + if len(cache_files) == 1: + filename = cache_files[0] + return filename if return_filename else open(filename, "rb") + + + url_name = None + url_data = None + with requests.Session() as session: + if verbose: + print("Downloading %s ..." % url, end="", flush=True) + for attempts_left in reversed(range(num_attempts)): + try: + with session.get(url) as res: + res.raise_for_status() + if len(res.content) == 0: + raise IOError("No data received") + + if len(res.content) < 8192: + content_str = res.content.decode("utf-8") + if "download_warning" in res.headers.get("Set-Cookie", ""): + links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link] + if len(links) == 1: + url = requests.compat.urljoin(url, links[0]) + raise IOError("Google Drive virus checker nag") + if "Google Drive - Quota exceeded" in content_str: + raise IOError("Google Drive download quota exceeded -- please try again later") + + match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", "")) + url_name = match[1] if match else url + url_data = res.content + if verbose: + print(" done") + break + except KeyboardInterrupt: + raise + except: + if not attempts_left: + if verbose: + print(" failed") + raise + if verbose: + print(".", end="", flush=True) + + + if cache: + safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name) + cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name) + temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name) + os.makedirs(cache_dir, exist_ok=True) + with open(temp_file, "wb") as f: + f.write(url_data) + os.replace(temp_file, cache_file) + if return_filename: + return cache_file + + + assert not return_filename + return io.BytesIO(url_data) diff --git a/resource/ssba/embed_fingerprints.py b/resource/ssba/embed_fingerprints.py new file mode 100644 index 0000000..c5ac923 --- /dev/null +++ b/resource/ssba/embed_fingerprints.py @@ -0,0 +1,281 @@ +import argparse +import os +import glob +import PIL +import bchlib +import numpy as np + +parser = argparse.ArgumentParser() +parser.add_argument("--use_celeba_preprocessing", action="store_true", help="Use CelebA specific preprocessing when loading the images.") +parser.add_argument("--encoder_path", type=str, help="Path to trained StegaStamp encoder.") +parser.add_argument("--data_dir", type=str, help="Directory with images.") +parser.add_argument("--output_dir", type=str, help="Path to save watermarked images to.") +parser.add_argument("--image_resolution", type=int, help="Height and width of square images.") +parser.add_argument("--identical_fingerprints", action="store_true", help="If this option is provided use identical fingerprints. Otherwise sample arbitrary fingerprints.") +parser.add_argument("--check", action="store_true", help="Validate fingerprint detection accuracy.") +parser.add_argument("--decoder_path",type=str,help="Provide trained StegaStamp decoder to verify fingerprint detection accuracy.") +parser.add_argument("--batch_size", type=int, default=64, help="Batch size.") +parser.add_argument("--cuda", type=int, default=0) +parser.add_argument("--use_residual", type=int, default=0, help="Use residual mode or not",) + +parser.add_argument("--encode_method", type=str, default='bch', help="['bch', 'seed', 'diff', 'manual', 'entropy']") +parser.add_argument('--secret', type=str, default='stega!!') +parser.add_argument("--seed", type=int, default=42, help="Random seed to sample fingerprints.") +parser.add_argument("--diff_bits", type=int, default=0, help="number of different weights from ground truth") +parser.add_argument("--manual_str", type=str, default=None, help="The manual string given by user") +parser.add_argument("--proportion", type=float, default=1.0, help="The propotion of 1 in the encode sequence") + +parser.add_argument("--use_modulated", type=int, default=0, help="Use modulated convolution or not", ) +parser.add_argument("--fc_layers", type=int, default=0, help="Use 8 fc layers before modulated convolution?", ) +parser.add_argument("--fused_conv", type=int, default=0, help="Use fused conv for modulated conv?",) +parser.add_argument("--bias_init", type=int, default=None, help="Specified bias initialization for modulated conv",) + +parser.add_argument("--test_save_file", type=str, default=None, help="where to save test file") + +parser.add_argument("--poison_rate", type=float, default=1.0, help="the poison rate set in original version") +args = parser.parse_args() + +if not os.path.exists(args.output_dir): + os.makedirs(args.output_dir) + +BATCH_SIZE = args.batch_size + +#os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" +#os.environ["CUDA_VISIBLE_DEVICES"] = str(args.cuda) + +from time import time +from tqdm import tqdm + +import torch +from torch.utils.data import Dataset, DataLoader +from torchvision import transforms +from torchvision.utils import save_image + +if int(args.cuda) == -1: + device = torch.device("cpu") +else: + device = torch.device("cuda") + +class CustomImageFolder(Dataset): + def __init__(self, data_dir, poison_rate=1.0, transform=None): + self.data_dir = data_dir + self.filenames = glob.glob(os.path.join(data_dir, "*.png")) + self.filenames.extend(glob.glob(os.path.join(data_dir, "*.jpeg"))) + self.filenames.extend(glob.glob(os.path.join(data_dir, "*.JPEG"))) + self.filenames.extend(glob.glob(os.path.join(data_dir, "*.jpg"))) + self.filenames = sorted(self.filenames) + self.transform = transform + self.poison_rate = poison_rate + def __getitem__(self, idx): + filename = self.filenames[idx] + image = PIL.Image.open(filename) + if self.transform: + image = self.transform(image) + return image, 0 + + def __len__(self): + return int(self.poison_rate * len(self.filenames)) + +def load_data(): + global dataset, dataloader + + if args.use_celeba_preprocessing: + assert args.image_resolution == 128, f"CelebA preprocessing requires image resolution 128, got {args.image_resolution}." + transform = transforms.Compose( + [ + transforms.CenterCrop(148), + transforms.Resize(128), + transforms.ToTensor(), + ] + ) + else: + + transform = transforms.Compose( + [ + transforms.Resize(args.image_resolution), + transforms.CenterCrop(args.image_resolution), + transforms.ToTensor(), + ] + ) + + s = time() + print(f"Loading image folder {args.data_dir} ...") + dataset = CustomImageFolder(args.data_dir, poison_rate=args.poison_rate, transform=transform) + print(f"Finished. Loading took {time() - s:.2f}s") + +import models +import models_modulated +from generate_fingerprints import generate_fingerprints + +def load_models(): + global HideNet, RevealNet + global FINGERPRINT_SIZE + + IMAGE_RESOLUTION = args.image_resolution + IMAGE_CHANNELS = 3 + + state_dict = torch.load(args.encoder_path) + FINGERPRINT_SIZE = state_dict["secret_dense.weight"].shape[-1] + + if not args.use_modulated: + print("----------Not using modulated conv!----------") + HideNet = models.StegaStampEncoder( + IMAGE_RESOLUTION, + IMAGE_CHANNELS, + fingerprint_size=FINGERPRINT_SIZE, + return_residual=args.use_residual + ) + RevealNet = models.StegaStampDecoder( + IMAGE_RESOLUTION, IMAGE_CHANNELS, fingerprint_size=FINGERPRINT_SIZE + ) + else: + print("----------Using modulated conv!----------") + HideNet = models_modulated.StegaStampEncoder( + IMAGE_RESOLUTION, + IMAGE_CHANNELS, + fingerprint_size=FINGERPRINT_SIZE, + return_residual=args.use_residual, + bias_init=args.bias_init, + fused_modconv=args.fused_conv, + fc_layers=args.fc_layers + ) + RevealNet = models_modulated.StegaStampDecoder( + IMAGE_RESOLUTION, IMAGE_CHANNELS, fingerprint_size=FINGERPRINT_SIZE + ) + + kwargs = {"map_location": "cpu"} if args.cuda == -1 else {} + if args.check: + RevealNet.load_state_dict(torch.load(args.decoder_path), **kwargs) + HideNet.load_state_dict(torch.load(args.encoder_path, **kwargs)) + + HideNet = HideNet.to(device) + RevealNet = RevealNet.to(device) + + +def embed_fingerprints(): + all_fingerprinted_images = [] + all_fingerprints = [] + all_code = [] + BCH_POLYNOMIAL = 137 + + print("Fingerprinting the images...") + fingerprints = generate_fingerprints( + type = args.encode_method, + batch_size = BATCH_SIZE, + fingerprint_size = FINGERPRINT_SIZE, + secret = args.secret, + seed = args.seed, + diff_bits = args.diff_bits, + manual_str = args.manual_str, + proportion = args.proportion, + identical = args.identical_fingerprints) + fingerprints = fingerprints.to(device) + dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=0) + + torch.manual_seed(args.seed) + + bitwise_accuracy = 0 + correct = 0 + + for images, _ in tqdm(dataloader): + images = images.to(device) + + if args.use_residual: + residual = HideNet(fingerprints[: images.size(0)], images) + fingerprinted_images = images + residual + else: + fingerprinted_images = HideNet(fingerprints[: images.size(0)], images) + + + all_fingerprinted_images.append(fingerprinted_images.detach().cpu()) + all_fingerprints.append(fingerprints[: images.size(0)].detach().cpu()) + + if args.check: + detected_fingerprints = RevealNet(fingerprinted_images) + detected_fingerprints = (detected_fingerprints > 0).long() + bitwise_accuracy += (detected_fingerprints[: images.size(0)].detach() == fingerprints[: images.size(0)]).float().mean(dim=1).sum().item() + if args.encode_method == 'bch': + for sec in detected_fingerprints: + sec = np.array(sec.cpu()) + if FINGERPRINT_SIZE == 100: + BCH_BITS = 5 + bch = bchlib.BCH(BCH_POLYNOMIAL, BCH_BITS) + packet_binary = "".join([str(int(bit)) for bit in sec[:96]]) + elif FINGERPRINT_SIZE == 50: + BCH_BITS = 2 + bch = bchlib.BCH(BCH_POLYNOMIAL, BCH_BITS) + packet_binary = "".join([str(int(bit)) for bit in sec[:48]]) + packet = bytes(int(packet_binary[i: i + 8], 2) for i in range(0, len(packet_binary), 8)) + packet = bytearray(packet) + data, ecc = packet[:-bch.ecc_bytes], packet[-bch.ecc_bytes:] + bitflips = bch.decode_inplace(data, ecc) + if bitflips != -1: + try: + correct += 1 + code = data.decode("utf-8") + all_code.append(code) + continue + except: + all_code.append("Something went wrong") + continue + all_code.append('Failed to decode') + + dirname = args.output_dir + if not os.path.exists(dirname): + os.makedirs(dirname) + + #if not os.path.exists(os.path.join(dirname, "fingerprinted_images")): + + + all_fingerprinted_images = torch.cat(all_fingerprinted_images, dim=0).cpu() + all_fingerprints = torch.cat(all_fingerprints, dim=0).cpu() + + for idx in range(len(all_fingerprinted_images)): + image = all_fingerprinted_images[idx] + fingerprint = all_fingerprints[idx] + _, filename = os.path.split(dataset.filenames[idx]) + filename = filename.split('.')[0] + "_hidden.png" + save_image(image, os.path.join(args.output_dir, f"{filename}"), padding=0) + + if args.encode_method == 'bch': + code = all_code[idx] + fingerprint_str = "".join(map(str, fingerprint.cpu().long().numpy().tolist())) + + else: + fingerprint_str = "".join(map(str, fingerprint.cpu().long().numpy().tolist())) + + + + if args.check: + bitwise_accuracy = bitwise_accuracy / len(all_fingerprints) + if args.encode_method == 'bch': + decode_acc = correct / len(all_fingerprints) + print(f"Decoding accuracy on fingerprinted images: {decode_acc}") + print(f"Bitwise accuracy on fingerprinted images: {bitwise_accuracy}") + + #save_image(images[:49], os.path.join(args.output_dir, "test_samples_clean.png"), nrow=7) + #save_image(fingerprinted_images[:49], os.path.join(args.output_dir, "test_samples_fingerprinted.png"), nrow=7) + #save_image(torch.abs(images - fingerprinted_images)[:49], os.path.join(args.output_dir, "test_samples_residual.png"), normalize=True, nrow=7) + + if args.test_save_file: + with open(args.test_save_file,'a') as f: + if args.encode_method == 'bch': + f.write('Encode String: ' + str(args.secret) + ', ' + 'Test bitwise accuracy:' + str(bitwise_accuracy) + '\n') + elif args.encode_method == 'seed': + f.write('Encode Seed: ' + str(args.seed) + ', ' + 'Test bitwise accuracy:' + str(bitwise_accuracy) + '\n') + elif args.encode_method == 'diff': + f.write('Bits difference: ' + str(args.diff_bits) + ', ' + 'Test bitwise accuracy:' + str(bitwise_accuracy) + '\n') + elif args.encode_method == 'entropy': + f.write('Proportion: ' + str(args.proportion) + ', ' + 'Test bitwise accuracy:' + str(bitwise_accuracy) + '\n') + f.close() + +def main(): + for arg in vars(args): + print(format(arg, '<20'), format(str(getattr(args,arg))), '<') + load_data() + load_models() + + embed_fingerprints() + +if __name__ == "__main__": + main() diff --git a/resource/ssba/generate_fingerprints.py b/resource/ssba/generate_fingerprints.py new file mode 100644 index 0000000..0bd6f03 --- /dev/null +++ b/resource/ssba/generate_fingerprints.py @@ -0,0 +1,147 @@ +import bchlib +import numpy as np +import torch +from torch.utils.data import Dataset, DataLoader +from torchvision import transforms +from torchvision.utils import save_image + +BCH_POLYNOMIAL = 137 + +def generate_fingerprints_from_bch(fingerprint_size, secret='abcd'): + if fingerprint_size == 100: + BCH_BITS = 5 + bch = bchlib.BCH(BCH_POLYNOMIAL, BCH_BITS) + if len(secret) > 7: + print('Error: Can only encode 56bits (7 characters) with ECC') + return + data = bytearray(secret + ' ' * (7 - len(secret)), 'utf-8')#转化为bytearray对象 + elif fingerprint_size == 50: + BCH_BITS = 2 + bch = bchlib.BCH(BCH_POLYNOMIAL, BCH_BITS) + if len(secret) > 4: + print('Error: Can only encode 32bits (4 characters) with ECC') + return + data = bytearray(secret + ' ' * (4 - len(secret)), 'utf-8')#转化为bytearray对象 + else: + raise ValueError('fingerprint_size must be 100 or 50!') + ecc = bch.encode(data)#获得对应编码 + packet = data + ecc#对数据进行编码 + packet_binary = ''.join(format(x, '08b') for x in packet)#转换成二进制 + fingerprints = [int(x) for x in packet_binary] + if fingerprint_size == 100: + fingerprints.extend([0, 0, 0, 0]) + elif fingerprint_size == 50: + fingerprints.extend([0, 0]) + + return fingerprints + +uniform_rv = torch.distributions.uniform.Uniform( + torch.tensor([0.0]), torch.tensor([1.0]) +) + +def generate_random_fingerprints(fingerprint_size, batch_size=4): + z = torch.zeros((batch_size, fingerprint_size), dtype=torch.float).random_(0, 2) #(B, 100) + return z + +def generate_fingerprints( + type, + batch_size, + fingerprint_size, + secret='abcd', + gd_secret='abcd', + seed=0, + diff_bits=1, + manual_str=None, + proportion=1.0, + identical=True, + compare=True): + + assert type in ['bch', 'seed', 'diff', 'manual', 'entropy'], 'type must be one of [bch, seed, diff, manual]!' + + if type == 'bch': + print("Using bch code with secret string:", secret) + fingerprints = generate_fingerprints_from_bch(fingerprint_size, secret) + if compare: + compare_bit_difference(fingerprints, fingerprint_size, gd_secret) + fingerprints = torch.tensor(fingerprints, dtype=torch.float).unsqueeze(0).expand(batch_size, fingerprint_size) + + elif type == 'seed': + print("Generating fingerprints from seed:", seed) + torch.manual_seed(seed) + fingerprints = generate_random_fingerprints(fingerprint_size, 1) + fingerprints = fingerprints.view(1, fingerprint_size) + if compare: + compare_bit_difference(fingerprints, fingerprint_size, gd_secret) + fingerprints = fingerprints.expand(batch_size, fingerprint_size) + if not identical: + print('Not using identical fingerprints!!') + fingerprints = generate_random_fingerprints(fingerprint_size, batch_size) + fingerprints = fingerprints.view(batch_size, fingerprint_size) + + elif type == 'diff': + print("Using bit difference from ground truth string:", secret) + gd_list = generate_fingerprints_from_bch(fingerprint_size, secret) + gd_list = np.array(gd_list) + fingerprints = gd_list.copy() + + + indexes = np.random.choice(fingerprint_size, size=diff_bits, replace=False) + for ind in indexes: + fingerprints[ind] = 1 - gd_list[ind] + print('number of bits overlap from original string {} is: {}'.format(secret, np.sum(fingerprints==gd_list))) + fingerprints = fingerprints.tolist() + fingerprints = torch.tensor(fingerprints, dtype=torch.float).unsqueeze(0).expand(batch_size, fingerprint_size) + + elif type == 'entropy': + print("Using entropy to generate encode sequence, the proportion of diffs is", proportion) + gd_list = generate_fingerprints_from_bch(fingerprint_size, secret) + gd_list = np.array(gd_list) + fingerprints = gd_list.copy() + + num = int(fingerprint_size * proportion) + if num: + indexes = np.random.choice(fingerprint_size, size=num, replace=False) + for ind in indexes: + fingerprints[ind] = 1 - gd_list[ind] + print('number of bits overlap from original string {} is: {}'.format(secret, np.sum(fingerprints==gd_list))) + fingerprints = fingerprints.tolist() + fingerprints = torch.tensor(fingerprints, dtype=torch.float).unsqueeze(0).expand(batch_size, fingerprint_size) + + elif type == 'manual': + print("Using manually string defined by user!") + manual_str = manual_str.strip('[]').split(',') + fingerprints = list(map(float, manual_str)) + assert len(fingerprints) == fingerprint_size, 'The length of the manual string does not match the fingerprint size!' + + if compare: + compare_bit_difference(fingerprints, fingerprint_size, gd_secret) + + fingerprints = torch.tensor(fingerprints, dtype=torch.float).unsqueeze(0).expand(batch_size, fingerprint_size) + + return fingerprints + +def compare_bit_difference(current_fp, fingerprint_size, secret='abcd'): + print('Generating ground truth from bch code with string:', secret) + gd_list = generate_fingerprints_from_bch(fingerprint_size, secret) + gd_list = np.array(gd_list) + try: + fp_list = current_fp.squeeze().numpy() + except: + fp_list = np.array(current_fp) + bits_overlap = np.sum(fp_list==gd_list) + print('number of bits overlap from original string {} is: {}'.format(secret, bits_overlap)) + +if __name__ == '__main__': + a = generate_fingerprints( + type = 'bch' , + batch_size = 1, + fingerprint_size = 50, + secret='abch', + gd_secret='abcd', + seed=0, + diff_bits=1, + manual_str=None, + proportion=1.0, + identical=True, + compare=True) + print(a) \ No newline at end of file diff --git a/resource/ssba/models.py b/resource/ssba/models.py new file mode 100644 index 0000000..383c31c --- /dev/null +++ b/resource/ssba/models.py @@ -0,0 +1,113 @@ +import math +import torch +from torch import nn +from torch.nn.functional import relu, sigmoid + + +class StegaStampEncoder(nn.Module): + def __init__( + self, + resolution=32, + IMAGE_CHANNELS=1, + fingerprint_size=100, + return_residual=0, + ): + super(StegaStampEncoder, self).__init__() + + if return_residual: print("----------Defining the output of encoder as residual!----------") + self.fingerprint_size = fingerprint_size + self.IMAGE_CHANNELS = IMAGE_CHANNELS + self.return_residual = return_residual + self.secret_dense = nn.Linear(self.fingerprint_size, 16 * 16 * IMAGE_CHANNELS) + + + log_resolution = int(resolution // 16) + + + + self.fingerprint_upsample = nn.Upsample(scale_factor=(log_resolution, log_resolution)) + self.conv1 = nn.Conv2d(2 * IMAGE_CHANNELS, 32, 3, 1, 1) + self.conv2 = nn.Conv2d(32, 32, 3, 2, 1) + self.conv3 = nn.Conv2d(32, 64, 3, 2, 1) + self.conv4 = nn.Conv2d(64, 128, 3, 2, 1) + self.conv5 = nn.Conv2d(128, 256, 3, 2, 1) + self.pad6 = nn.ZeroPad2d((0, 1, 0, 1)) + self.up6 = nn.Conv2d(256, 128, 2, 1) + self.upsample6 = nn.Upsample(scale_factor=(2, 2)) + self.conv6 = nn.Conv2d(128 + 128, 128, 3, 1, 1) + self.pad7 = nn.ZeroPad2d((0, 1, 0, 1)) + self.up7 = nn.Conv2d(128, 64, 2, 1) + self.upsample7 = nn.Upsample(scale_factor=(2, 2)) + self.conv7 = nn.Conv2d(64 + 64, 64, 3, 1, 1) + self.pad8 = nn.ZeroPad2d((0, 1, 0, 1)) + self.up8 = nn.Conv2d(64, 32, 2, 1) + self.upsample8 = nn.Upsample(scale_factor=(2, 2)) + self.conv8 = nn.Conv2d(32 + 32, 32, 3, 1, 1) + self.pad9 = nn.ZeroPad2d((0, 1, 0, 1)) + self.up9 = nn.Conv2d(32, 32, 2, 1) + self.upsample9 = nn.Upsample(scale_factor=(2, 2)) + self.conv9 = nn.Conv2d(32 + 32 + 2 * IMAGE_CHANNELS, 32, 3, 1, 1) + self.conv10 = nn.Conv2d(32, 32, 3, 1, 1) + self.residual = nn.Conv2d(32, IMAGE_CHANNELS, 1) + + def forward(self, fingerprint, image): + fingerprint = relu(self.secret_dense(fingerprint)) + fingerprint = fingerprint.view((-1, self.IMAGE_CHANNELS, 16, 16)) + fingerprint_enlarged = self.fingerprint_upsample(fingerprint) + inputs = torch.cat([fingerprint_enlarged, image], dim=1) + conv1 = relu(self.conv1(inputs)) + conv2 = relu(self.conv2(conv1)) + conv3 = relu(self.conv3(conv2)) + conv4 = relu(self.conv4(conv3)) + conv5 = relu(self.conv5(conv4)) + up6 = relu(self.up6(self.pad6(self.upsample6(conv5)))) + merge6 = torch.cat([conv4, up6], dim=1) + conv6 = relu(self.conv6(merge6)) + up7 = relu(self.up7(self.pad7(self.upsample7(conv6)))) + merge7 = torch.cat([conv3, up7], dim=1) + conv7 = relu(self.conv7(merge7)) + up8 = relu(self.up8(self.pad8(self.upsample8(conv7)))) + merge8 = torch.cat([conv2, up8], dim=1) + conv8 = relu(self.conv8(merge8)) + up9 = relu(self.up9(self.pad9(self.upsample9(conv8)))) + merge9 = torch.cat([conv1, up9, inputs], dim=1) + conv9 = relu(self.conv9(merge9)) + conv10 = relu(self.conv10(conv9)) + residual = self.residual(conv10) + if not self.return_residual: + residual = sigmoid(residual) + return residual + + +class StegaStampDecoder(nn.Module): + def __init__(self, resolution=32, IMAGE_CHANNELS=1, fingerprint_size=1): + super(StegaStampDecoder, self).__init__() + self.resolution = resolution + self.IMAGE_CHANNELS = IMAGE_CHANNELS + self.decoder = nn.Sequential( + nn.Conv2d(IMAGE_CHANNELS, 32, (3, 3), 2, 1), + nn.ReLU(), + nn.Conv2d(32, 32, 3, 1, 1), + nn.ReLU(), + nn.Conv2d(32, 64, 3, 2, 1), + nn.ReLU(), + nn.Conv2d(64, 64, 3, 1, 1), + nn.ReLU(), + nn.Conv2d(64, 64, 3, 2, 1), + nn.ReLU(), + nn.Conv2d(64, 128, 3, 2, 1), + nn.ReLU(), + nn.Conv2d(128, 128, (3, 3), 2, 1), + nn.ReLU(), + ) + self.dense = nn.Sequential( + nn.Linear(resolution * resolution * 128 // 32 // 32, 512), + nn.ReLU(), + nn.Linear(512, fingerprint_size), + ) + + def forward(self, image): + x = self.decoder(image) + x = x.view(-1, self.resolution * self.resolution * 128 // 32 // 32) + return self.dense(x) + diff --git a/resource/ssba/models_modulated.py b/resource/ssba/models_modulated.py new file mode 100644 index 0000000..a937183 --- /dev/null +++ b/resource/ssba/models_modulated.py @@ -0,0 +1,154 @@ +import copy +import math +import torch +from torch import nn +from torch.nn.functional import relu +import torch.nn.functional as F +import numpy as np +from torch_utils import misc +from custom_modules import G_mapping, modulated_conv2d +from torch_utils import persistence +from torch_utils.ops import conv2d_resample +from torch_utils.ops import upfirdn2d +from torch_utils.ops import bias_act +from torch_utils.ops import fma + +class StegaStampEncoder(nn.Module): + def __init__( + self, + resolution=32, + IMAGE_CHANNELS=1, + fingerprint_size=100, + return_residual=0, + bias_init=None, + fused_modconv=1, + demodulate=1, + fc_layers=0 + ): + super(StegaStampEncoder, self).__init__() + if not fused_modconv: + print('----------Not Using fused modconv!----------') + else: + print('----------Using fused modconv!----------') + + if return_residual: print("----------Defining the output of encoder as residual!----------") + if not demodulate: print("----------Not using demodulation!----------") + + self.fingerprint_size = fingerprint_size + self.IMAGE_CHANNELS = IMAGE_CHANNELS + self.return_residual = return_residual + self.secret_fixsize = 16 * 16 * IMAGE_CHANNELS + self.secret_dense = nn.Linear(self.fingerprint_size, self.secret_fixsize) + + + log_resolution = int(resolution // 16) + + + self.fc_layers=fc_layers + if not self.fc_layers: + print('----------Not Using FC layers!----------') + self.secret_outsize = self.secret_fixsize + else: + print('----------Using FC layers!----------') + self.secret_mapping = G_mapping(mapping_fmaps=fingerprint_size, dlatent_size=512) + self.secret_outsize = 512 + + + self.fingerprint_upsample = nn.Upsample(scale_factor=(log_resolution, log_resolution)) + self.conv1 = modulated_conv2d(2 * IMAGE_CHANNELS, 32, 3, 1, 1, self.secret_outsize, bias_init=bias_init, demodulate=demodulate, fused_modconv=fused_modconv) + self.conv2 = modulated_conv2d(32, 32, 3, 2, 1, self.secret_outsize, bias_init=bias_init, demodulate=demodulate, fused_modconv=fused_modconv) + self.conv3 = modulated_conv2d(32, 64, 3, 2, 1, self.secret_outsize, bias_init=bias_init, demodulate=demodulate, fused_modconv=fused_modconv) + self.conv4 = modulated_conv2d(64, 128, 3, 2, 1, self.secret_outsize, bias_init=bias_init, demodulate=demodulate, fused_modconv=fused_modconv) + self.conv5 = modulated_conv2d(128, 256, 3, 2, 1, self.secret_outsize, bias_init=bias_init, demodulate=demodulate, fused_modconv=fused_modconv) + self.pad6 = nn.ZeroPad2d((0, 1, 0, 1)) + self.up6 = modulated_conv2d(256, 128, 2, 1, 0, self.secret_outsize, bias_init=bias_init, demodulate=demodulate, fused_modconv=fused_modconv) + self.upsample6 = nn.Upsample(scale_factor=(2, 2)) + self.conv6 = modulated_conv2d(128 + 128, 128, 3, 1, 1, self.secret_outsize, bias_init=bias_init, demodulate=demodulate, fused_modconv=fused_modconv) + self.pad7 = nn.ZeroPad2d((0, 1, 0, 1)) + self.up7 = modulated_conv2d(128, 64, 2, 1, 0, self.secret_outsize, bias_init=bias_init, demodulate=demodulate, fused_modconv=fused_modconv) + self.upsample7 = nn.Upsample(scale_factor=(2, 2)) + self.conv7 = modulated_conv2d(64 + 64, 64, 3, 1, 1, self.secret_outsize, bias_init=bias_init, demodulate=demodulate, fused_modconv=fused_modconv) + self.pad8 = nn.ZeroPad2d((0, 1, 0, 1)) + self.up8 = modulated_conv2d(64, 32, 2, 1, 0, self.secret_outsize, bias_init=bias_init, demodulate=demodulate, fused_modconv=fused_modconv) + self.upsample8 = nn.Upsample(scale_factor=(2, 2)) + self.conv8 = modulated_conv2d(32 + 32, 32, 3, 1, 1, self.secret_outsize, bias_init=bias_init, demodulate=demodulate, fused_modconv=fused_modconv) + self.pad9 = nn.ZeroPad2d((0, 1, 0, 1)) + self.up9 = modulated_conv2d(32, 32, 2, 1, 0, self.secret_outsize, bias_init=bias_init, demodulate=demodulate, fused_modconv=fused_modconv) + self.upsample9 = nn.Upsample(scale_factor=(2, 2)) + self.conv9 = modulated_conv2d(32 + 32 + 2 * IMAGE_CHANNELS, 32, 3, 1, 1, self.secret_outsize, bias_init=bias_init, demodulate=demodulate, fused_modconv=fused_modconv) + self.conv10 = modulated_conv2d(32, 32, 3, 1, 1, self.secret_outsize, bias_init=bias_init, demodulate=demodulate, fused_modconv=fused_modconv) + + self.residual = modulated_conv2d(32, IMAGE_CHANNELS, 1, 1, 0, self.secret_outsize, bias_init=bias_init, demodulate=demodulate, fused_modconv=fused_modconv) + + def forward(self, fingerprint, image): + fp_mapping = relu(self.secret_dense(fingerprint)) + + if self.fc_layers: + fp = self.secret_mapping(fingerprint) + else: + fp = fp_mapping + + fingerprint = fp_mapping.view((-1, self.IMAGE_CHANNELS, 16, 16)) + fingerprint_enlarged = self.fingerprint_upsample(fingerprint) + inputs = torch.cat([fingerprint_enlarged, image], dim=1) + conv1 = relu(self.conv1(fp, inputs)) + conv2 = relu(self.conv2(fp, conv1)) + conv3 = relu(self.conv3(fp, conv2)) + conv4 = relu(self.conv4(fp, conv3)) + conv5 = relu(self.conv5(fp, conv4)) + + up6 = relu(self.up6(fp, self.pad6(self.upsample6(conv5)))) + merge6 = torch.cat([conv4, up6], dim=1) + conv6 = relu(self.conv6(fp, merge6)) + + up7 = relu(self.up7(fp, self.pad7(self.upsample7(conv6)))) + merge7 = torch.cat([conv3, up7], dim=1) + conv7 = relu(self.conv7(fp, merge7)) + + up8 = relu(self.up8(fp, self.pad8(self.upsample8(conv7)))) + merge8 = torch.cat([conv2, up8], dim=1) + conv8 = relu(self.conv8(fp, merge8)) + + up9 = relu(self.up9(fp, self.pad9(self.upsample9(conv8)))) + merge9 = torch.cat([conv1, up9, inputs], dim=1) + conv9 = relu(self.conv9(fp, merge9)) + + conv10 = relu(self.conv10(fp, conv9)) + residual = self.residual(fp, conv10) + if not self.return_residual: + residual = torch.sigmoid(residual) + return residual + + +class StegaStampDecoder(nn.Module): + def __init__(self, resolution=32, IMAGE_CHANNELS=1, fingerprint_size=1): + super(StegaStampDecoder, self).__init__() + self.resolution = resolution + self.IMAGE_CHANNELS = IMAGE_CHANNELS + self.decoder = nn.Sequential( + nn.Conv2d(IMAGE_CHANNELS, 32, (3, 3), 2, 1), + nn.ReLU(), + nn.Conv2d(32, 32, 3, 1, 1), + nn.ReLU(), + nn.Conv2d(32, 64, 3, 2, 1), + nn.ReLU(), + nn.Conv2d(64, 64, 3, 1, 1), + nn.ReLU(), + nn.Conv2d(64, 64, 3, 2, 1), + nn.ReLU(), + nn.Conv2d(64, 128, 3, 2, 1), + nn.ReLU(), + nn.Conv2d(128, 128, (3, 3), 2, 1), + nn.ReLU(), + ) + self.dense = nn.Sequential( + nn.Linear(resolution * resolution * 128 // 32 // 32, 512), + nn.ReLU(), + nn.Linear(512, fingerprint_size), + ) + + def forward(self, image): + x = self.decoder(image) + x = x.view(-1, self.resolution * self.resolution * 128 // 32 // 32) + return self.dense(x) + diff --git a/resource/ssba/readme.md b/resource/ssba/readme.md new file mode 100644 index 0000000..2291046 --- /dev/null +++ b/resource/ssba/readme.md @@ -0,0 +1,107 @@ +This folder is to generate poison data in ssba.py ("--attack_train_replace_imgs_path" and +"--attack_test_replace_imgs_path" should receive two path for poisoned training data and poisoned testing data, respectively). + +Step 1: + +Choose the dataset you want to poison, and run the following command to convert the dataset into seperate image files in the same folder: + eg. + +```shell +python dataset_convert_into_images.py --dataset {dataset_name} +``` + +(If you do not want to use the dataset we provided, you can also use your own dataset. Then you should put the training images and testing images separately into two folders.) + +Step 2: + +Run the following command to train the autoencoder on given **training** dataest: + +eg. + +```shell + python train.py \ + --data_dir /path/to/images/ \ + --output_dir /path/to/output/ \ + --EXP_NAME customized_experiment_name \ + --use_celeba_preprocessing \ + --random_seed 0 \ + --fingerprint_length 100 \ + --image_resolution 128 \ + --num_epochs 20 \ + --batch_size 64 \ + --use_residual 0 \ + --use_modulated 0 \ + --fc_layers 0 \ + --fused_conv 0 + ``` + where + - `use_celeba_preprocessing` needs to be active if and only if using CelebA aligned and cropped images. + + - `image_resolution` indicates the image resolution for training. All the images in `data_dir` is center-cropped according to the shorter side and then resized to this resolution. When `use_celeba_preprocessing` is active, `image_resolution` has to be set as 128. + + - `output_dir` contains model snapshots, image snapshots, and log files. For model snapshots, `*_encoder.pth` and `*_decoder.pth` correspond to the fingerprint encoder and decoder respectively. + + - `use_residual` indicates the output mode of encoder. When it is set to 0, the output is exactly the encoded image. When it is set to 1, the output is the residual, and the encoded image is generated by residual + original image. + + - `use_modulated` indicates whether to use the modulated convolution layers in StyleGAN2. When it is 0, the model is just the normal StegaStamp. + + - `fc_layers` indicates whether to add 8 fc layers to disentangle the fingerprints before it is sent to the encoder, just as the way in StyleGAN2. + + - `fused_conv` indicates the mode of modulated convolution as is shown in the figure below. When it is set to 0, the 'Fuse' option in modulated convolution is set to be False. + +Step 3: + +Run the following command to generate the poisoned training and testing data: + +eg. + +```shell +# embedding training dataset +python embed_fingerprints.py \ + --encoder_path {path to encoder pth file} \ + --data_dir {train_dataset_folder} \ + --output_dir {folder path that you put poisoned trainig images} \ + --image_resolution 32 \ + --identical_fingerprints \ + --check \ + --decoder_path {path to decoder pth file} \ + --batch_size 32 \ + --seed 0 \ + --encode_method bch \ + --secret {secret you want to encode into poisoned data} \ + --use_residual 0 \ + --use_modulated 0 \ + --fused_conv 0 \ + --fc_layers 0 \ + --cuda 0 + +# pack images into npy file +python utils/pack_images.py \ + --path {folder path that you put poisoned trainig images} \ + --save_file_path {packed poisoned training data npy file path} + +# embedding test dataset +python embed_fingerprints.py \ + --encoder_path {path to encoder pth file} \ + --data_dir {test_dataset_folder} \ + --output_dir {folder path that you put poisoned testing images} \ + --image_resolution 32 \ + --identical_fingerprints \ + --check \ + --decoder_path {path to decoder pth file} \ + --batch_size 32 \ + --seed 0 \ + --encode_method bch \ + --secret {secret you want to encode into poisoned data} \ + --use_residual 0 \ + --use_modulated 0 \ + --fused_conv 0 \ + --fc_layers 0 \ + --cuda 0 + +# pack images into npy file +python utils/pack_images.py \ + --path {folder path that you put poisoned testing images} \ + --save_file_path {packed poisoned testing data npy file path} + +``` diff --git a/resource/ssba/requirements.txt b/resource/ssba/requirements.txt new file mode 100644 index 0000000..d76daee --- /dev/null +++ b/resource/ssba/requirements.txt @@ -0,0 +1,5 @@ +tqdm +torch +torchvision +tensorboardX +bchlib diff --git a/resource/ssba/torch_utils/__init__.py b/resource/ssba/torch_utils/__init__.py new file mode 100644 index 0000000..ece0ea0 --- /dev/null +++ b/resource/ssba/torch_utils/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +# empty diff --git a/resource/ssba/torch_utils/custom_ops.py b/resource/ssba/torch_utils/custom_ops.py new file mode 100644 index 0000000..188da59 --- /dev/null +++ b/resource/ssba/torch_utils/custom_ops.py @@ -0,0 +1,126 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import os +import glob +import torch +import torch.utils.cpp_extension +import importlib +import hashlib +import shutil +from pathlib import Path + +from torch.utils.file_baton import FileBaton + +#---------------------------------------------------------------------------- + + +verbosity = 'brief' + +#---------------------------------------------------------------------------- + + +def _find_compiler_bindir(): + patterns = [ + 'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64', + 'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64', + 'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64', + 'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin', + ] + for pattern in patterns: + matches = sorted(glob.glob(pattern)) + if len(matches): + return matches[-1] + return None + +#---------------------------------------------------------------------------- + + +_cached_plugins = dict() + +def get_plugin(module_name, sources, **build_kwargs): + assert verbosity in ['none', 'brief', 'full'] + + + if module_name in _cached_plugins: + return _cached_plugins[module_name] + + + if verbosity == 'full': + print(f'Setting up PyTorch plugin "{module_name}"...') + elif verbosity == 'brief': + print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True) + + try: + + if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0: + compiler_bindir = _find_compiler_bindir() + if compiler_bindir is None: + raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".') + os.environ['PATH'] += ';' + compiler_bindir + + + verbose_build = (verbosity == 'full') + + + + + + + # + + + + + source_dirs_set = set(os.path.dirname(source) for source in sources) + if len(source_dirs_set) == 1 and ('TORCH_EXTENSIONS_DIR' in os.environ): + all_source_files = sorted(list(x for x in Path(list(source_dirs_set)[0]).iterdir() if x.is_file())) + + + + hash_md5 = hashlib.md5() + for src in all_source_files: + with open(src, 'rb') as f: + hash_md5.update(f.read()) + build_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) + digest_build_dir = os.path.join(build_dir, hash_md5.hexdigest()) + + if not os.path.isdir(digest_build_dir): + os.makedirs(digest_build_dir, exist_ok=True) + baton = FileBaton(os.path.join(digest_build_dir, 'lock')) + if baton.try_acquire(): + try: + for src in all_source_files: + shutil.copyfile(src, os.path.join(digest_build_dir, os.path.basename(src))) + finally: + baton.release() + else: + + + baton.wait() + digest_sources = [os.path.join(digest_build_dir, os.path.basename(x)) for x in sources] + torch.utils.cpp_extension.load(name=module_name, build_directory=build_dir, + verbose=verbose_build, sources=digest_sources, **build_kwargs) + else: + torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs) + module = importlib.import_module(module_name) + + except: + if verbosity == 'brief': + print('Failed!') + raise + + + if verbosity == 'full': + print(f'Done setting up PyTorch plugin "{module_name}".') + elif verbosity == 'brief': + print('Done.') + _cached_plugins[module_name] = module + return module + +#---------------------------------------------------------------------------- diff --git a/resource/ssba/torch_utils/misc.py b/resource/ssba/torch_utils/misc.py new file mode 100644 index 0000000..dc2dec2 --- /dev/null +++ b/resource/ssba/torch_utils/misc.py @@ -0,0 +1,262 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import re +import contextlib +import numpy as np +import torch +import warnings +import dnnlib + +#---------------------------------------------------------------------------- + + + +_constant_cache = dict() + +def constant(value, shape=None, dtype=None, device=None, memory_format=None): + value = np.asarray(value) + if shape is not None: + shape = tuple(shape) + if dtype is None: + dtype = torch.get_default_dtype() + if device is None: + device = torch.device('cpu') + if memory_format is None: + memory_format = torch.contiguous_format + + key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format) + tensor = _constant_cache.get(key, None) + if tensor is None: + tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device) + if shape is not None: + tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape)) + tensor = tensor.contiguous(memory_format=memory_format) + _constant_cache[key] = tensor + return tensor + +#---------------------------------------------------------------------------- + + +try: + nan_to_num = torch.nan_to_num +except AttributeError: + def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): + assert isinstance(input, torch.Tensor) + if posinf is None: + posinf = torch.finfo(input.dtype).max + if neginf is None: + neginf = torch.finfo(input.dtype).min + assert nan == 0 + return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out) + +#---------------------------------------------------------------------------- + + +try: + symbolic_assert = torch._assert +except AttributeError: + symbolic_assert = torch.Assert + +#---------------------------------------------------------------------------- + + +class suppress_tracer_warnings(warnings.catch_warnings): + def __enter__(self): + super().__enter__() + warnings.simplefilter('ignore', category=torch.jit.TracerWarning) + return self + +#---------------------------------------------------------------------------- + + + + +def assert_shape(tensor, ref_shape): + if tensor.ndim != len(ref_shape): + raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}') + for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)): + if ref_size is None: + pass + elif isinstance(ref_size, torch.Tensor): + with suppress_tracer_warnings(): + symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}') + elif isinstance(size, torch.Tensor): + with suppress_tracer_warnings(): + symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}') + elif size != ref_size: + raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}') + +#---------------------------------------------------------------------------- + + +def profiled_function(fn): + def decorator(*args, **kwargs): + with torch.autograd.profiler.record_function(fn.__name__): + return fn(*args, **kwargs) + decorator.__name__ = fn.__name__ + return decorator + +#---------------------------------------------------------------------------- + + + +class InfiniteSampler(torch.utils.data.Sampler): + def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5): + assert len(dataset) > 0 + assert num_replicas > 0 + assert 0 <= rank < num_replicas + assert 0 <= window_size <= 1 + super().__init__(dataset) + self.dataset = dataset + self.rank = rank + self.num_replicas = num_replicas + self.shuffle = shuffle + self.seed = seed + self.window_size = window_size + + def __iter__(self): + order = np.arange(len(self.dataset)) + rnd = None + window = 0 + if self.shuffle: + rnd = np.random.RandomState(self.seed) + rnd.shuffle(order) + window = int(np.rint(order.size * self.window_size)) + + idx = 0 + while True: + i = idx % order.size + if idx % self.num_replicas == self.rank: + yield order[i] + if window >= 2: + j = (i - rnd.randint(window)) % order.size + order[i], order[j] = order[j], order[i] + idx += 1 + +#---------------------------------------------------------------------------- + + +def params_and_buffers(module): + assert isinstance(module, torch.nn.Module) + return list(module.parameters()) + list(module.buffers()) + +def named_params_and_buffers(module): + assert isinstance(module, torch.nn.Module) + return list(module.named_parameters()) + list(module.named_buffers()) + +def copy_params_and_buffers(src_module, dst_module, require_all=False): + assert isinstance(src_module, torch.nn.Module) + assert isinstance(dst_module, torch.nn.Module) + src_tensors = {name: tensor for name, tensor in named_params_and_buffers(src_module)} + for name, tensor in named_params_and_buffers(dst_module): + assert (name in src_tensors) or (not require_all) + if name in src_tensors: + tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad) + +#---------------------------------------------------------------------------- + + + +@contextlib.contextmanager +def ddp_sync(module, sync): + assert isinstance(module, torch.nn.Module) + if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel): + yield + else: + with module.no_sync(): + yield + +#---------------------------------------------------------------------------- + + +def check_ddp_consistency(module, ignore_regex=None): + assert isinstance(module, torch.nn.Module) + for name, tensor in named_params_and_buffers(module): + fullname = type(module).__name__ + '.' + name + if ignore_regex is not None and re.fullmatch(ignore_regex, fullname): + continue + tensor = tensor.detach() + other = tensor.clone() + torch.distributed.broadcast(tensor=other, src=0) + assert (nan_to_num(tensor) == nan_to_num(other)).all(), fullname + +#---------------------------------------------------------------------------- + + +def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): + assert isinstance(module, torch.nn.Module) + assert not isinstance(module, torch.jit.ScriptModule) + assert isinstance(inputs, (tuple, list)) + + + entries = [] + nesting = [0] + def pre_hook(_mod, _inputs): + nesting[0] += 1 + def post_hook(mod, _inputs, outputs): + nesting[0] -= 1 + if nesting[0] <= max_nesting: + outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs] + outputs = [t for t in outputs if isinstance(t, torch.Tensor)] + entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs)) + hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()] + hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()] + + + outputs = module(*inputs) + for hook in hooks: + hook.remove() + + + tensors_seen = set() + for e in entries: + e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen] + e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen] + e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen] + tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs} + + + if skip_redundant: + entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)] + + + rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']] + rows += [['---'] * len(rows[0])] + param_total = 0 + buffer_total = 0 + submodule_names = {mod: name for name, mod in module.named_modules()} + for e in entries: + name = '' if e.mod is module else submodule_names[e.mod] + param_size = sum(t.numel() for t in e.unique_params) + buffer_size = sum(t.numel() for t in e.unique_buffers) + output_shapes = [str(list(e.outputs[0].shape)) for t in e.outputs] + output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs] + rows += [[ + name + (':0' if len(e.outputs) >= 2 else ''), + str(param_size) if param_size else '-', + str(buffer_size) if buffer_size else '-', + (output_shapes + ['-'])[0], + (output_dtypes + ['-'])[0], + ]] + for idx in range(1, len(e.outputs)): + rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]] + param_total += param_size + buffer_total += buffer_size + rows += [['---'] * len(rows[0])] + rows += [['Total', str(param_total), str(buffer_total), '-', '-']] + + + widths = [max(len(cell) for cell in column) for column in zip(*rows)] + print() + for row in rows: + print(' '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths))) + print() + return outputs + +#---------------------------------------------------------------------------- diff --git a/resource/ssba/torch_utils/ops/__init__.py b/resource/ssba/torch_utils/ops/__init__.py new file mode 100644 index 0000000..ece0ea0 --- /dev/null +++ b/resource/ssba/torch_utils/ops/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +# empty diff --git a/resource/ssba/torch_utils/ops/bias_act.cpp b/resource/ssba/torch_utils/ops/bias_act.cpp new file mode 100644 index 0000000..5d2425d --- /dev/null +++ b/resource/ssba/torch_utils/ops/bias_act.cpp @@ -0,0 +1,99 @@ +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include +#include +#include "bias_act.h" + +//------------------------------------------------------------------------ + +static bool has_same_layout(torch::Tensor x, torch::Tensor y) +{ + if (x.dim() != y.dim()) + return false; + for (int64_t i = 0; i < x.dim(); i++) + { + if (x.size(i) != y.size(i)) + return false; + if (x.size(i) >= 2 && x.stride(i) != y.stride(i)) + return false; + } + return true; +} + +//------------------------------------------------------------------------ + +static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp) +{ + // Validate arguments. + TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); + TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x"); + TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x"); + TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x"); + TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x"); + TORCH_CHECK(x.numel() <= INT_MAX, "x is too large"); + TORCH_CHECK(b.dim() == 1, "b must have rank 1"); + TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds"); + TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements"); + TORCH_CHECK(grad >= 0, "grad must be non-negative"); + + // Validate layout. + TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense"); + TORCH_CHECK(b.is_contiguous(), "b must be contiguous"); + TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x"); + TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x"); + TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x"); + + // Create output tensor. + const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); + torch::Tensor y = torch::empty_like(x); + TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x"); + + // Initialize CUDA kernel parameters. + bias_act_kernel_params p; + p.x = x.data_ptr(); + p.b = (b.numel()) ? b.data_ptr() : NULL; + p.xref = (xref.numel()) ? xref.data_ptr() : NULL; + p.yref = (yref.numel()) ? yref.data_ptr() : NULL; + p.dy = (dy.numel()) ? dy.data_ptr() : NULL; + p.y = y.data_ptr(); + p.grad = grad; + p.act = act; + p.alpha = alpha; + p.gain = gain; + p.clamp = clamp; + p.sizeX = (int)x.numel(); + p.sizeB = (int)b.numel(); + p.stepB = (b.numel()) ? (int)x.stride(dim) : 1; + + // Choose CUDA kernel. + void* kernel; + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] + { + kernel = choose_bias_act_kernel(p); + }); + TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func"); + + // Launch CUDA kernel. + p.loopX = 4; + int blockSize = 4 * 32; + int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1; + void* args[] = {&p}; + AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream())); + return y; +} + +//------------------------------------------------------------------------ + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) +{ + m.def("bias_act", &bias_act); +} + +//------------------------------------------------------------------------ diff --git a/resource/ssba/torch_utils/ops/bias_act.cu b/resource/ssba/torch_utils/ops/bias_act.cu new file mode 100644 index 0000000..dd8fc47 --- /dev/null +++ b/resource/ssba/torch_utils/ops/bias_act.cu @@ -0,0 +1,173 @@ +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include "bias_act.h" + +//------------------------------------------------------------------------ +// Helpers. + +template struct InternalType; +template <> struct InternalType { typedef double scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; + +//------------------------------------------------------------------------ +// CUDA kernel. + +template +__global__ void bias_act_kernel(bias_act_kernel_params p) +{ + typedef typename InternalType::scalar_t scalar_t; + int G = p.grad; + scalar_t alpha = (scalar_t)p.alpha; + scalar_t gain = (scalar_t)p.gain; + scalar_t clamp = (scalar_t)p.clamp; + scalar_t one = (scalar_t)1; + scalar_t two = (scalar_t)2; + scalar_t expRange = (scalar_t)80; + scalar_t halfExpRange = (scalar_t)40; + scalar_t seluScale = (scalar_t)1.0507009873554804934193349852946; + scalar_t seluAlpha = (scalar_t)1.6732632423543772848170429916717; + + // Loop over elements. + int xi = blockIdx.x * p.loopX * blockDim.x + threadIdx.x; + for (int loopIdx = 0; loopIdx < p.loopX && xi < p.sizeX; loopIdx++, xi += blockDim.x) + { + // Load. + scalar_t x = (scalar_t)((const T*)p.x)[xi]; + scalar_t b = (p.b) ? (scalar_t)((const T*)p.b)[(xi / p.stepB) % p.sizeB] : 0; + scalar_t xref = (p.xref) ? (scalar_t)((const T*)p.xref)[xi] : 0; + scalar_t yref = (p.yref) ? (scalar_t)((const T*)p.yref)[xi] : 0; + scalar_t dy = (p.dy) ? (scalar_t)((const T*)p.dy)[xi] : one; + scalar_t yy = (gain != 0) ? yref / gain : 0; + scalar_t y = 0; + + // Apply bias. + ((G == 0) ? x : xref) += b; + + // linear + if (A == 1) + { + if (G == 0) y = x; + if (G == 1) y = x; + } + + // relu + if (A == 2) + { + if (G == 0) y = (x > 0) ? x : 0; + if (G == 1) y = (yy > 0) ? x : 0; + } + + // lrelu + if (A == 3) + { + if (G == 0) y = (x > 0) ? x : x * alpha; + if (G == 1) y = (yy > 0) ? x : x * alpha; + } + + // tanh + if (A == 4) + { + if (G == 0) { scalar_t c = exp(x); scalar_t d = one / c; y = (x < -expRange) ? -one : (x > expRange) ? one : (c - d) / (c + d); } + if (G == 1) y = x * (one - yy * yy); + if (G == 2) y = x * (one - yy * yy) * (-two * yy); + } + + // sigmoid + if (A == 5) + { + if (G == 0) y = (x < -expRange) ? 0 : one / (exp(-x) + one); + if (G == 1) y = x * yy * (one - yy); + if (G == 2) y = x * yy * (one - yy) * (one - two * yy); + } + + // elu + if (A == 6) + { + if (G == 0) y = (x >= 0) ? x : exp(x) - one; + if (G == 1) y = (yy >= 0) ? x : x * (yy + one); + if (G == 2) y = (yy >= 0) ? 0 : x * (yy + one); + } + + // selu + if (A == 7) + { + if (G == 0) y = (x >= 0) ? seluScale * x : (seluScale * seluAlpha) * (exp(x) - one); + if (G == 1) y = (yy >= 0) ? x * seluScale : x * (yy + seluScale * seluAlpha); + if (G == 2) y = (yy >= 0) ? 0 : x * (yy + seluScale * seluAlpha); + } + + // softplus + if (A == 8) + { + if (G == 0) y = (x > expRange) ? x : log(exp(x) + one); + if (G == 1) y = x * (one - exp(-yy)); + if (G == 2) { scalar_t c = exp(-yy); y = x * c * (one - c); } + } + + // swish + if (A == 9) + { + if (G == 0) + y = (x < -expRange) ? 0 : x / (exp(-x) + one); + else + { + scalar_t c = exp(xref); + scalar_t d = c + one; + if (G == 1) + y = (xref > halfExpRange) ? x : x * c * (xref + d) / (d * d); + else + y = (xref > halfExpRange) ? 0 : x * c * (xref * (two - d) + two * d) / (d * d * d); + yref = (xref < -expRange) ? 0 : xref / (exp(-xref) + one) * gain; + } + } + + // Apply gain. + y *= gain * dy; + + // Clamp. + if (clamp >= 0) + { + if (G == 0) + y = (y > -clamp & y < clamp) ? y : (y >= 0) ? clamp : -clamp; + else + y = (yref > -clamp & yref < clamp) ? y : 0; + } + + // Store. + ((T*)p.y)[xi] = (T)y; + } +} + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template void* choose_bias_act_kernel(const bias_act_kernel_params& p) +{ + if (p.act == 1) return (void*)bias_act_kernel; + if (p.act == 2) return (void*)bias_act_kernel; + if (p.act == 3) return (void*)bias_act_kernel; + if (p.act == 4) return (void*)bias_act_kernel; + if (p.act == 5) return (void*)bias_act_kernel; + if (p.act == 6) return (void*)bias_act_kernel; + if (p.act == 7) return (void*)bias_act_kernel; + if (p.act == 8) return (void*)bias_act_kernel; + if (p.act == 9) return (void*)bias_act_kernel; + return NULL; +} + +//------------------------------------------------------------------------ +// Template specializations. + +template void* choose_bias_act_kernel (const bias_act_kernel_params& p); +template void* choose_bias_act_kernel (const bias_act_kernel_params& p); +template void* choose_bias_act_kernel (const bias_act_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/resource/ssba/torch_utils/ops/bias_act.h b/resource/ssba/torch_utils/ops/bias_act.h new file mode 100644 index 0000000..a32187e --- /dev/null +++ b/resource/ssba/torch_utils/ops/bias_act.h @@ -0,0 +1,38 @@ +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +//------------------------------------------------------------------------ +// CUDA kernel parameters. + +struct bias_act_kernel_params +{ + const void* x; // [sizeX] + const void* b; // [sizeB] or NULL + const void* xref; // [sizeX] or NULL + const void* yref; // [sizeX] or NULL + const void* dy; // [sizeX] or NULL + void* y; // [sizeX] + + int grad; + int act; + float alpha; + float gain; + float clamp; + + int sizeX; + int sizeB; + int stepB; + int loopX; +}; + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template void* choose_bias_act_kernel(const bias_act_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/resource/ssba/torch_utils/ops/bias_act.py b/resource/ssba/torch_utils/ops/bias_act.py new file mode 100644 index 0000000..b09ea41 --- /dev/null +++ b/resource/ssba/torch_utils/ops/bias_act.py @@ -0,0 +1,212 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom PyTorch ops for efficient bias and activation.""" + +import os +import warnings +import numpy as np +import torch +import dnnlib +import traceback + +from .. import custom_ops +from .. import misc + +#---------------------------------------------------------------------------- + +activation_funcs = { + 'linear': dnnlib.EasyDict(func=lambda x, **_: x, def_alpha=0, def_gain=1, cuda_idx=1, ref='', has_2nd_grad=False), + 'relu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.relu(x), def_alpha=0, def_gain=np.sqrt(2), cuda_idx=2, ref='y', has_2nd_grad=False), + 'lrelu': dnnlib.EasyDict(func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha), def_alpha=0.2, def_gain=np.sqrt(2), cuda_idx=3, ref='y', has_2nd_grad=False), + 'tanh': dnnlib.EasyDict(func=lambda x, **_: torch.tanh(x), def_alpha=0, def_gain=1, cuda_idx=4, ref='y', has_2nd_grad=True), + 'sigmoid': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x), def_alpha=0, def_gain=1, cuda_idx=5, ref='y', has_2nd_grad=True), + 'elu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.elu(x), def_alpha=0, def_gain=1, cuda_idx=6, ref='y', has_2nd_grad=True), + 'selu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.selu(x), def_alpha=0, def_gain=1, cuda_idx=7, ref='y', has_2nd_grad=True), + 'softplus': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.softplus(x), def_alpha=0, def_gain=1, cuda_idx=8, ref='y', has_2nd_grad=True), + 'swish': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x) * x, def_alpha=0, def_gain=np.sqrt(2), cuda_idx=9, ref='x', has_2nd_grad=True), +} + +#---------------------------------------------------------------------------- + +_inited = False +_plugin = None +_null_tensor = torch.empty([0]) + +def _init(): + global _inited, _plugin + if not _inited: + _inited = True + sources = ['bias_act.cpp', 'bias_act.cu'] + sources = [os.path.join(os.path.dirname(__file__), s) for s in sources] + try: + _plugin = custom_ops.get_plugin('bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math']) + except: + warnings.warn('Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc()) + return _plugin is not None + +#---------------------------------------------------------------------------- + +def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'): + r"""Fused bias and activation function. + + Adds bias `b` to activation tensor `x`, evaluates activation function `act`, + and scales the result by `gain`. Each of the steps is optional. In most cases, + the fused op is considerably more efficient than performing the same calculation + using standard PyTorch ops. It supports first and second order gradients, + but not third order gradients. + + Args: + x: Input activation tensor. Can be of any shape. + b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type + as `x`. The shape must be known, and it must match the dimension of `x` + corresponding to `dim`. + dim: The dimension in `x` corresponding to the elements of `b`. + The value of `dim` is ignored if `b` is not specified. + act: Name of the activation function to evaluate, or `"linear"` to disable. + Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc. + See `activation_funcs` for a full list. `None` is not allowed. + alpha: Shape parameter for the activation function, or `None` to use the default. + gain: Scaling factor for the output tensor, or `None` to use default. + See `activation_funcs` for the default scaling of each activation function. + If unsure, consider specifying 1. + clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable + the clamping (default). + impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). + + Returns: + Tensor of the same shape and datatype as `x`. + """ + assert isinstance(x, torch.Tensor) + assert impl in ['ref', 'cuda'] + if impl == 'cuda' and x.device.type == 'cuda' and _init(): + return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b) + return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp) + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None): + """Slow reference implementation of `bias_act()` using standard TensorFlow ops. + """ + assert isinstance(x, torch.Tensor) + assert clamp is None or clamp >= 0 + spec = activation_funcs[act] + alpha = float(alpha if alpha is not None else spec.def_alpha) + gain = float(gain if gain is not None else spec.def_gain) + clamp = float(clamp if clamp is not None else -1) + + + if b is not None: + assert isinstance(b, torch.Tensor) and b.ndim == 1 + assert 0 <= dim < x.ndim + assert b.shape[0] == x.shape[dim] + x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)]) + + + alpha = float(alpha) + x = spec.func(x, alpha=alpha) + + + gain = float(gain) + if gain != 1: + x = x * gain + + + if clamp >= 0: + x = x.clamp(-clamp, clamp) + return x + +#---------------------------------------------------------------------------- + +_bias_act_cuda_cache = dict() + +def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): + """Fast CUDA implementation of `bias_act()` using custom ops. + """ + + assert clamp is None or clamp >= 0 + spec = activation_funcs[act] + alpha = float(alpha if alpha is not None else spec.def_alpha) + gain = float(gain if gain is not None else spec.def_gain) + clamp = float(clamp if clamp is not None else -1) + + + key = (dim, act, alpha, gain, clamp) + if key in _bias_act_cuda_cache: + return _bias_act_cuda_cache[key] + + + class BiasActCuda(torch.autograd.Function): + @staticmethod + def forward(ctx, x, b): + ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride()[1] == 1 else torch.contiguous_format + x = x.contiguous(memory_format=ctx.memory_format) + b = b.contiguous() if b is not None else _null_tensor + y = x + if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor: + y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp) + ctx.save_for_backward( + x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, + b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, + y if 'y' in spec.ref else _null_tensor) + return y + + @staticmethod + def backward(ctx, dy): + dy = dy.contiguous(memory_format=ctx.memory_format) + x, b, y = ctx.saved_tensors + dx = None + db = None + + if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: + dx = dy + if act != 'linear' or gain != 1 or clamp >= 0: + dx = BiasActCudaGrad.apply(dy, x, b, y) + + if ctx.needs_input_grad[1]: + db = dx.sum([i for i in range(dx.ndim) if i != dim]) + + return dx, db + + + class BiasActCudaGrad(torch.autograd.Function): + @staticmethod + def forward(ctx, dy, x, b, y): + ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride()[1] == 1 else torch.contiguous_format + dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp) + ctx.save_for_backward( + dy if spec.has_2nd_grad else _null_tensor, + x, b, y) + return dx + + @staticmethod + def backward(ctx, d_dx): + d_dx = d_dx.contiguous(memory_format=ctx.memory_format) + dy, x, b, y = ctx.saved_tensors + d_dy = None + d_x = None + d_b = None + d_y = None + + if ctx.needs_input_grad[0]: + d_dy = BiasActCudaGrad.apply(d_dx, x, b, y) + + if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]): + d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp) + + if spec.has_2nd_grad and ctx.needs_input_grad[2]: + d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim]) + + return d_dy, d_x, d_b, d_y + + + _bias_act_cuda_cache[key] = BiasActCuda + return BiasActCuda + +#---------------------------------------------------------------------------- diff --git a/resource/ssba/torch_utils/ops/conv2d_gradfix.py b/resource/ssba/torch_utils/ops/conv2d_gradfix.py new file mode 100644 index 0000000..269d50b --- /dev/null +++ b/resource/ssba/torch_utils/ops/conv2d_gradfix.py @@ -0,0 +1,170 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom replacement for `torch.nn.functional.conv2d` that supports +arbitrarily high order gradients with zero performance penalty.""" + +import warnings +import contextlib +import torch + + + + + +#---------------------------------------------------------------------------- + +enabled = False +weight_gradients_disabled = False + +@contextlib.contextmanager +def no_weight_gradients(): + global weight_gradients_disabled + old = weight_gradients_disabled + weight_gradients_disabled = True + yield + weight_gradients_disabled = old + +#---------------------------------------------------------------------------- + +def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): + if _should_use_custom_op(input): + return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias) + return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) + +def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1): + if _should_use_custom_op(input): + return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias) + return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation) + +#---------------------------------------------------------------------------- + +def _should_use_custom_op(input): + assert isinstance(input, torch.Tensor) + if (not enabled) or (not torch.backends.cudnn.enabled): + return False + if input.device.type != 'cuda': + return False + if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']): + return True + warnings.warn(f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().') + return False + +def _tuple_of_ints(xs, ndim): + xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim + assert len(xs) == ndim + assert all(isinstance(x, int) for x in xs) + return xs + +#---------------------------------------------------------------------------- + +_conv2d_gradfix_cache = dict() + +def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups): + + ndim = 2 + weight_shape = tuple(weight_shape) + stride = _tuple_of_ints(stride, ndim) + padding = _tuple_of_ints(padding, ndim) + output_padding = _tuple_of_ints(output_padding, ndim) + dilation = _tuple_of_ints(dilation, ndim) + + + key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups) + if key in _conv2d_gradfix_cache: + return _conv2d_gradfix_cache[key] + + + assert groups >= 1 + assert len(weight_shape) == ndim + 2 + assert all(stride[i] >= 1 for i in range(ndim)) + assert all(padding[i] >= 0 for i in range(ndim)) + assert all(dilation[i] >= 0 for i in range(ndim)) + if not transpose: + assert all(output_padding[i] == 0 for i in range(ndim)) + else: + assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim)) + + + common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups) + def calc_output_padding(input_shape, output_shape): + if transpose: + return [0, 0] + return [ + input_shape[i + 2] + - (output_shape[i + 2] - 1) * stride[i] + - (1 - 2 * padding[i]) + - dilation[i] * (weight_shape[i + 2] - 1) + for i in range(ndim) + ] + + + class Conv2d(torch.autograd.Function): + @staticmethod + def forward(ctx, input, weight, bias): + assert weight.shape == weight_shape + if not transpose: + output = torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs) + else: + output = torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs) + ctx.save_for_backward(input, weight) + return output + + @staticmethod + def backward(ctx, grad_output): + input, weight = ctx.saved_tensors + grad_input = None + grad_weight = None + grad_bias = None + + if ctx.needs_input_grad[0]: + p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape) + grad_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, weight, None) + assert grad_input.shape == input.shape + + if ctx.needs_input_grad[1] and not weight_gradients_disabled: + grad_weight = Conv2dGradWeight.apply(grad_output, input) + assert grad_weight.shape == weight_shape + + if ctx.needs_input_grad[2]: + grad_bias = grad_output.sum([0, 2, 3]) + + return grad_input, grad_weight, grad_bias + + + class Conv2dGradWeight(torch.autograd.Function): + @staticmethod + def forward(ctx, grad_output, input): + op = torch._C._jit_get_operation('aten::cudnn_convolution_backward_weight' if not transpose else 'aten::cudnn_convolution_transpose_backward_weight') + flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32] + grad_weight = op(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags) + assert grad_weight.shape == weight_shape + ctx.save_for_backward(grad_output, input) + return grad_weight + + @staticmethod + def backward(ctx, grad2_grad_weight): + grad_output, input = ctx.saved_tensors + grad2_grad_output = None + grad2_input = None + + if ctx.needs_input_grad[0]: + grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None) + assert grad2_grad_output.shape == grad_output.shape + + if ctx.needs_input_grad[1]: + p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape) + grad2_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, grad2_grad_weight, None) + assert grad2_input.shape == input.shape + + return grad2_grad_output, grad2_input + + _conv2d_gradfix_cache[key] = Conv2d + return Conv2d + +#---------------------------------------------------------------------------- diff --git a/resource/ssba/torch_utils/ops/conv2d_resample.py b/resource/ssba/torch_utils/ops/conv2d_resample.py new file mode 100644 index 0000000..e348e7e --- /dev/null +++ b/resource/ssba/torch_utils/ops/conv2d_resample.py @@ -0,0 +1,156 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""2D convolution with optional up/downsampling.""" + +import torch + +from .. import misc +from . import conv2d_gradfix +from . import upfirdn2d +from .upfirdn2d import _parse_padding +from .upfirdn2d import _get_filter_size + +#---------------------------------------------------------------------------- + +def _get_weight_shape(w): + with misc.suppress_tracer_warnings(): + shape = [int(sz) for sz in w.shape] + misc.assert_shape(w, shape) + return shape + +#---------------------------------------------------------------------------- + +def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True): + """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations. + """ + out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) + + + if not flip_weight: + w = w.flip([2, 3]) + + + + if kw == 1 and kh == 1 and stride == 1 and padding in [0, [0, 0], (0, 0)] and not transpose: + if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64: + if out_channels <= 4 and groups == 1: + in_shape = x.shape + x = w.squeeze(3).squeeze(2) @ x.reshape([in_shape[0], in_channels_per_group, -1]) + x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]]) + else: + x = x.to(memory_format=torch.contiguous_format) + w = w.to(memory_format=torch.contiguous_format) + x = conv2d_gradfix.conv2d(x, w, groups=groups) + return x.to(memory_format=torch.channels_last) + + + op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d + return op(x, w, stride=stride, padding=padding, groups=groups) + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, stride=1, groups=1, flip_weight=True, flip_filter=False): + r"""2D convolution with optional up/downsampling. + + Padding is performed only once at the beginning, not between the operations. + + Args: + x: Input tensor of shape + `[batch_size, in_channels, in_height, in_width]`. + w: Weight tensor of shape + `[out_channels, in_channels//groups, kernel_height, kernel_width]`. + f: Low-pass filter for up/downsampling. Must be prepared beforehand by + calling upfirdn2d.setup_filter(). None = identity (default). + up: Integer upsampling factor (default: 1). + down: Integer downsampling factor (default: 1). + padding: Padding with respect to the upsampled image. Can be a single number + or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + groups: Split input channels into N groups (default: 1). + flip_weight: False = convolution, True = correlation (default: True). + flip_filter: False = convolution, True = correlation (default: False). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + + assert isinstance(x, torch.Tensor) and (x.ndim == 4) + assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype) + assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32) + assert isinstance(up, int) and (up >= 1) + assert isinstance(down, int) and (down >= 1) + assert isinstance(groups, int) and (groups >= 1) + out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) + fw, fh = _get_filter_size(f) + px0, px1, py0, py1 = _parse_padding(padding) + + + if up > 1: + px0 += (fw + up - 1) // 2 + px1 += (fw - up) // 2 + py0 += (fh + up - 1) // 2 + py1 += (fh - up) // 2 + if down > 1: + px0 += (fw - down + 1) // 2 + px1 += (fw - down) // 2 + py0 += (fh - down + 1) // 2 + py1 += (fh - down) // 2 + + + if kw == 1 and kh == 1 and (down > 1 and up == 1): + x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter) + x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) + return x + + + if kw == 1 and kh == 1 and (up > 1 and down == 1): + x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) + x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) + return x + + + if down > 1 and up == 1: + x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter) + x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight) + return x + + + if up > 1: + if groups == 1: + w = w.transpose(0, 1) + else: + w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw) + w = w.transpose(1, 2) + w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw) + px0 -= kw - 1 + px1 -= kw - up + py0 -= kh - 1 + py1 -= kh - up + pxt = max(min(-px0, -px1), 0) + pyt = max(min(-py0, -py1), 0) + x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight)) + x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter) + if down > 1: + x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) + return x + + + if up == 1 and down == 1: + if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0: + return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], stride=stride, groups=groups, flip_weight=flip_weight) + + + x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) + x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) + if down > 1: + x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) + return x + +#---------------------------------------------------------------------------- diff --git a/resource/ssba/torch_utils/ops/fma.py b/resource/ssba/torch_utils/ops/fma.py new file mode 100644 index 0000000..4d69b69 --- /dev/null +++ b/resource/ssba/torch_utils/ops/fma.py @@ -0,0 +1,60 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Fused multiply-add, with slightly faster gradients than `torch.addcmul()`.""" + +import torch + +#---------------------------------------------------------------------------- + +def fma(a, b, c): + return _FusedMultiplyAdd.apply(a, b, c) + +#---------------------------------------------------------------------------- + +class _FusedMultiplyAdd(torch.autograd.Function): + @staticmethod + def forward(ctx, a, b, c): + out = torch.addcmul(c, a, b) + ctx.save_for_backward(a, b) + ctx.c_shape = c.shape + return out + + @staticmethod + def backward(ctx, dout): + a, b = ctx.saved_tensors + c_shape = ctx.c_shape + da = None + db = None + dc = None + + if ctx.needs_input_grad[0]: + da = _unbroadcast(dout * b, a.shape) + + if ctx.needs_input_grad[1]: + db = _unbroadcast(dout * a, b.shape) + + if ctx.needs_input_grad[2]: + dc = _unbroadcast(dout, c_shape) + + return da, db, dc + +#---------------------------------------------------------------------------- + +def _unbroadcast(x, shape): + extra_dims = x.ndim - len(shape) + assert extra_dims >= 0 + dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)] + if len(dim): + x = x.sum(dim=dim, keepdim=True) + if extra_dims: + x = x.reshape(-1, *x.shape[extra_dims+1:]) + assert x.shape == shape + return x + +#---------------------------------------------------------------------------- diff --git a/resource/ssba/torch_utils/ops/grid_sample_gradfix.py b/resource/ssba/torch_utils/ops/grid_sample_gradfix.py new file mode 100644 index 0000000..d3b082b --- /dev/null +++ b/resource/ssba/torch_utils/ops/grid_sample_gradfix.py @@ -0,0 +1,83 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom replacement for `torch.nn.functional.grid_sample` that +supports arbitrarily high order gradients between the input and output. +Only works on 2D images and assumes +`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`.""" + +import warnings +import torch + + + + + +#---------------------------------------------------------------------------- + +enabled = False + +#---------------------------------------------------------------------------- + +def grid_sample(input, grid): + if _should_use_custom_op(): + return _GridSample2dForward.apply(input, grid) + return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) + +#---------------------------------------------------------------------------- + +def _should_use_custom_op(): + if not enabled: + return False + if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']): + return True + warnings.warn(f'grid_sample_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.grid_sample().') + return False + +#---------------------------------------------------------------------------- + +class _GridSample2dForward(torch.autograd.Function): + @staticmethod + def forward(ctx, input, grid): + assert input.ndim == 4 + assert grid.ndim == 4 + output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) + ctx.save_for_backward(input, grid) + return output + + @staticmethod + def backward(ctx, grad_output): + input, grid = ctx.saved_tensors + grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid) + return grad_input, grad_grid + +#---------------------------------------------------------------------------- + +class _GridSample2dBackward(torch.autograd.Function): + @staticmethod + def forward(ctx, grad_output, input, grid): + op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward') + grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False) + ctx.save_for_backward(grid) + return grad_input, grad_grid + + @staticmethod + def backward(ctx, grad2_grad_input, grad2_grad_grid): + _ = grad2_grad_grid + grid, = ctx.saved_tensors + grad2_grad_output = None + grad2_input = None + grad2_grid = None + + if ctx.needs_input_grad[0]: + grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid) + + assert not ctx.needs_input_grad[2] + return grad2_grad_output, grad2_input, grad2_grid + +#---------------------------------------------------------------------------- diff --git a/resource/ssba/torch_utils/ops/upfirdn2d.cpp b/resource/ssba/torch_utils/ops/upfirdn2d.cpp new file mode 100644 index 0000000..2d7177f --- /dev/null +++ b/resource/ssba/torch_utils/ops/upfirdn2d.cpp @@ -0,0 +1,103 @@ +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include +#include +#include "upfirdn2d.h" + +//------------------------------------------------------------------------ + +static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain) +{ + // Validate arguments. + TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); + TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x"); + TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32"); + TORCH_CHECK(x.numel() <= INT_MAX, "x is too large"); + TORCH_CHECK(f.numel() <= INT_MAX, "f is too large"); + TORCH_CHECK(x.dim() == 4, "x must be rank 4"); + TORCH_CHECK(f.dim() == 2, "f must be rank 2"); + TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1"); + TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1"); + TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1"); + + // Create output tensor. + const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); + int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx; + int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy; + TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1"); + torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format()); + TORCH_CHECK(y.numel() <= INT_MAX, "output is too large"); + + // Initialize CUDA kernel parameters. + upfirdn2d_kernel_params p; + p.x = x.data_ptr(); + p.f = f.data_ptr(); + p.y = y.data_ptr(); + p.up = make_int2(upx, upy); + p.down = make_int2(downx, downy); + p.pad0 = make_int2(padx0, pady0); + p.flip = (flip) ? 1 : 0; + p.gain = gain; + p.inSize = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0)); + p.inStride = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0)); + p.filterSize = make_int2((int)f.size(1), (int)f.size(0)); + p.filterStride = make_int2((int)f.stride(1), (int)f.stride(0)); + p.outSize = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0)); + p.outStride = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0)); + p.sizeMajor = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z; + p.sizeMinor = (p.inStride.z == 1) ? p.inSize.z : 1; + + // Choose CUDA kernel. + upfirdn2d_kernel_spec spec; + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] + { + spec = choose_upfirdn2d_kernel(p); + }); + + // Set looping options. + p.loopMajor = (p.sizeMajor - 1) / 16384 + 1; + p.loopMinor = spec.loopMinor; + p.loopX = spec.loopX; + p.launchMinor = (p.sizeMinor - 1) / p.loopMinor + 1; + p.launchMajor = (p.sizeMajor - 1) / p.loopMajor + 1; + + // Compute grid size. + dim3 blockSize, gridSize; + if (spec.tileOutW < 0) // large + { + blockSize = dim3(4, 32, 1); + gridSize = dim3( + ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor, + (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1, + p.launchMajor); + } + else // small + { + blockSize = dim3(256, 1, 1); + gridSize = dim3( + ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor, + (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1, + p.launchMajor); + } + + // Launch CUDA kernel. + void* args[] = {&p}; + AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream())); + return y; +} + +//------------------------------------------------------------------------ + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) +{ + m.def("upfirdn2d", &upfirdn2d); +} + +//------------------------------------------------------------------------ diff --git a/resource/ssba/torch_utils/ops/upfirdn2d.cu b/resource/ssba/torch_utils/ops/upfirdn2d.cu new file mode 100644 index 0000000..ebdd987 --- /dev/null +++ b/resource/ssba/torch_utils/ops/upfirdn2d.cu @@ -0,0 +1,350 @@ +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include "upfirdn2d.h" + +//------------------------------------------------------------------------ +// Helpers. + +template struct InternalType; +template <> struct InternalType { typedef double scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; + +static __device__ __forceinline__ int floor_div(int a, int b) +{ + int t = 1 - a / b; + return (a + t * b) / b - t; +} + +//------------------------------------------------------------------------ +// Generic CUDA implementation for large filters. + +template static __global__ void upfirdn2d_kernel_large(upfirdn2d_kernel_params p) +{ + typedef typename InternalType::scalar_t scalar_t; + + // Calculate thread index. + int minorBase = blockIdx.x * blockDim.x + threadIdx.x; + int outY = minorBase / p.launchMinor; + minorBase -= outY * p.launchMinor; + int outXBase = blockIdx.y * p.loopX * blockDim.y + threadIdx.y; + int majorBase = blockIdx.z * p.loopMajor; + if (outXBase >= p.outSize.x | outY >= p.outSize.y | majorBase >= p.sizeMajor) + return; + + // Setup Y receptive field. + int midY = outY * p.down.y + p.up.y - 1 - p.pad0.y; + int inY = min(max(floor_div(midY, p.up.y), 0), p.inSize.y); + int h = min(max(floor_div(midY + p.filterSize.y, p.up.y), 0), p.inSize.y) - inY; + int filterY = midY + p.filterSize.y - (inY + 1) * p.up.y; + if (p.flip) + filterY = p.filterSize.y - 1 - filterY; + + // Loop over major, minor, and X. + for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++) + for (int minorIdx = 0, minor = minorBase; minorIdx < p.loopMinor & minor < p.sizeMinor; minorIdx++, minor += p.launchMinor) + { + int nc = major * p.sizeMinor + minor; + int n = nc / p.inSize.z; + int c = nc - n * p.inSize.z; + for (int loopX = 0, outX = outXBase; loopX < p.loopX & outX < p.outSize.x; loopX++, outX += blockDim.y) + { + // Setup X receptive field. + int midX = outX * p.down.x + p.up.x - 1 - p.pad0.x; + int inX = min(max(floor_div(midX, p.up.x), 0), p.inSize.x); + int w = min(max(floor_div(midX + p.filterSize.x, p.up.x), 0), p.inSize.x) - inX; + int filterX = midX + p.filterSize.x - (inX + 1) * p.up.x; + if (p.flip) + filterX = p.filterSize.x - 1 - filterX; + + // Initialize pointers. + const T* xp = &((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w]; + const float* fp = &p.f[filterX * p.filterStride.x + filterY * p.filterStride.y]; + int filterStepX = ((p.flip) ? p.up.x : -p.up.x) * p.filterStride.x; + int filterStepY = ((p.flip) ? p.up.y : -p.up.y) * p.filterStride.y; + + // Inner loop. + scalar_t v = 0; + for (int y = 0; y < h; y++) + { + for (int x = 0; x < w; x++) + { + v += (scalar_t)(*xp) * (scalar_t)(*fp); + xp += p.inStride.x; + fp += filterStepX; + } + xp += p.inStride.y - w * p.inStride.x; + fp += filterStepY - w * filterStepX; + } + + // Store result. + v *= p.gain; + ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v; + } + } +} + +//------------------------------------------------------------------------ +// Specialized CUDA implementation for small filters. + +template +static __global__ void upfirdn2d_kernel_small(upfirdn2d_kernel_params p) +{ + typedef typename InternalType::scalar_t scalar_t; + const int tileInW = ((tileOutW - 1) * downx + filterW - 1) / upx + 1; + const int tileInH = ((tileOutH - 1) * downy + filterH - 1) / upy + 1; + __shared__ volatile scalar_t sf[filterH][filterW]; + __shared__ volatile scalar_t sx[tileInH][tileInW][loopMinor]; + + // Calculate tile index. + int minorBase = blockIdx.x; + int tileOutY = minorBase / p.launchMinor; + minorBase -= tileOutY * p.launchMinor; + minorBase *= loopMinor; + tileOutY *= tileOutH; + int tileOutXBase = blockIdx.y * p.loopX * tileOutW; + int majorBase = blockIdx.z * p.loopMajor; + if (tileOutXBase >= p.outSize.x | tileOutY >= p.outSize.y | majorBase >= p.sizeMajor) + return; + + // Load filter (flipped). + for (int tapIdx = threadIdx.x; tapIdx < filterH * filterW; tapIdx += blockDim.x) + { + int fy = tapIdx / filterW; + int fx = tapIdx - fy * filterW; + scalar_t v = 0; + if (fx < p.filterSize.x & fy < p.filterSize.y) + { + int ffx = (p.flip) ? fx : p.filterSize.x - 1 - fx; + int ffy = (p.flip) ? fy : p.filterSize.y - 1 - fy; + v = (scalar_t)p.f[ffx * p.filterStride.x + ffy * p.filterStride.y]; + } + sf[fy][fx] = v; + } + + // Loop over major and X. + for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++) + { + int baseNC = major * p.sizeMinor + minorBase; + int n = baseNC / p.inSize.z; + int baseC = baseNC - n * p.inSize.z; + for (int loopX = 0, tileOutX = tileOutXBase; loopX < p.loopX & tileOutX < p.outSize.x; loopX++, tileOutX += tileOutW) + { + // Load input pixels. + int tileMidX = tileOutX * downx + upx - 1 - p.pad0.x; + int tileMidY = tileOutY * downy + upy - 1 - p.pad0.y; + int tileInX = floor_div(tileMidX, upx); + int tileInY = floor_div(tileMidY, upy); + __syncthreads(); + for (int inIdx = threadIdx.x; inIdx < tileInH * tileInW * loopMinor; inIdx += blockDim.x) + { + int relC = inIdx; + int relInX = relC / loopMinor; + int relInY = relInX / tileInW; + relC -= relInX * loopMinor; + relInX -= relInY * tileInW; + int c = baseC + relC; + int inX = tileInX + relInX; + int inY = tileInY + relInY; + scalar_t v = 0; + if (inX >= 0 & inY >= 0 & inX < p.inSize.x & inY < p.inSize.y & c < p.inSize.z) + v = (scalar_t)((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w]; + sx[relInY][relInX][relC] = v; + } + + // Loop over output pixels. + __syncthreads(); + for (int outIdx = threadIdx.x; outIdx < tileOutH * tileOutW * loopMinor; outIdx += blockDim.x) + { + int relC = outIdx; + int relOutX = relC / loopMinor; + int relOutY = relOutX / tileOutW; + relC -= relOutX * loopMinor; + relOutX -= relOutY * tileOutW; + int c = baseC + relC; + int outX = tileOutX + relOutX; + int outY = tileOutY + relOutY; + + // Setup receptive field. + int midX = tileMidX + relOutX * downx; + int midY = tileMidY + relOutY * downy; + int inX = floor_div(midX, upx); + int inY = floor_div(midY, upy); + int relInX = inX - tileInX; + int relInY = inY - tileInY; + int filterX = (inX + 1) * upx - midX - 1; // flipped + int filterY = (inY + 1) * upy - midY - 1; // flipped + + // Inner loop. + if (outX < p.outSize.x & outY < p.outSize.y & c < p.outSize.z) + { + scalar_t v = 0; + #pragma unroll + for (int y = 0; y < filterH / upy; y++) + #pragma unroll + for (int x = 0; x < filterW / upx; x++) + v += sx[relInY + y][relInX + x][relC] * sf[filterY + y * upy][filterX + x * upx]; + v *= p.gain; + ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v; + } + } + } + } +} + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p) +{ + int s = p.inStride.z, fx = p.filterSize.x, fy = p.filterSize.y; + + upfirdn2d_kernel_spec spec = {(void*)upfirdn2d_kernel_large, -1,-1,1, 4}; // contiguous + if (s == 1) spec = {(void*)upfirdn2d_kernel_large, -1,-1,4, 1}; // channels_last + + if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // contiguous + { + if (fx <= 7 && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + } + if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // channels_last + { + if (fx <= 7 && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + } + if (s != 1 && p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // contiguous + { + if (fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + } + if (s == 1 && p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // channels_last + { + if (fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + } + if (s != 1 && p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // contiguous + { + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + } + if (s == 1 && p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // channels_last + { + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + } + if (s != 1 && p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // contiguous + { + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + } + if (s == 1 && p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // channels_last + { + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + } + if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2) // contiguous + { + if (fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + } + if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2) // channels_last + { + if (fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + } + if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1) // contiguous + { + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + } + if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1) // channels_last + { + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + } + if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2) // contiguous + { + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + } + if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2) // channels_last + { + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + } + return spec; +} + +//------------------------------------------------------------------------ +// Template specializations. + +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel (const upfirdn2d_kernel_params& p); +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel (const upfirdn2d_kernel_params& p); +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/resource/ssba/torch_utils/ops/upfirdn2d.h b/resource/ssba/torch_utils/ops/upfirdn2d.h new file mode 100644 index 0000000..c9e2032 --- /dev/null +++ b/resource/ssba/torch_utils/ops/upfirdn2d.h @@ -0,0 +1,59 @@ +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include + +//------------------------------------------------------------------------ +// CUDA kernel parameters. + +struct upfirdn2d_kernel_params +{ + const void* x; + const float* f; + void* y; + + int2 up; + int2 down; + int2 pad0; + int flip; + float gain; + + int4 inSize; // [width, height, channel, batch] + int4 inStride; + int2 filterSize; // [width, height] + int2 filterStride; + int4 outSize; // [width, height, channel, batch] + int4 outStride; + int sizeMinor; + int sizeMajor; + + int loopMinor; + int loopMajor; + int loopX; + int launchMinor; + int launchMajor; +}; + +//------------------------------------------------------------------------ +// CUDA kernel specialization. + +struct upfirdn2d_kernel_spec +{ + void* kernel; + int tileOutW; + int tileOutH; + int loopMinor; + int loopX; +}; + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/resource/ssba/torch_utils/ops/upfirdn2d.py b/resource/ssba/torch_utils/ops/upfirdn2d.py new file mode 100644 index 0000000..748b76d --- /dev/null +++ b/resource/ssba/torch_utils/ops/upfirdn2d.py @@ -0,0 +1,384 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom PyTorch ops for efficient resampling of 2D images.""" + +import os +import warnings +import numpy as np +import torch +import traceback + +from .. import custom_ops +from .. import misc +from . import conv2d_gradfix + +#---------------------------------------------------------------------------- + +_inited = False +_plugin = None + +def _init(): + global _inited, _plugin + if not _inited: + sources = ['upfirdn2d.cpp', 'upfirdn2d.cu'] + sources = [os.path.join(os.path.dirname(__file__), s) for s in sources] + try: + _plugin = custom_ops.get_plugin('upfirdn2d_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math']) + except: + warnings.warn('Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc()) + return _plugin is not None + +def _parse_scaling(scaling): + if isinstance(scaling, int): + scaling = [scaling, scaling] + assert isinstance(scaling, (list, tuple)) + assert all(isinstance(x, int) for x in scaling) + sx, sy = scaling + assert sx >= 1 and sy >= 1 + return sx, sy + +def _parse_padding(padding): + if isinstance(padding, int): + padding = [padding, padding] + assert isinstance(padding, (list, tuple)) + assert all(isinstance(x, int) for x in padding) + if len(padding) == 2: + padx, pady = padding + padding = [padx, padx, pady, pady] + padx0, padx1, pady0, pady1 = padding + return padx0, padx1, pady0, pady1 + +def _get_filter_size(f): + if f is None: + return 1, 1 + assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] + fw = f.shape[-1] + fh = f.shape[0] + with misc.suppress_tracer_warnings(): + fw = int(fw) + fh = int(fh) + misc.assert_shape(f, [fh, fw][:f.ndim]) + assert fw >= 1 and fh >= 1 + return fw, fh + +#---------------------------------------------------------------------------- + +def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None): + r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`. + + Args: + f: Torch tensor, numpy array, or python list of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), + `[]` (impulse), or + `None` (identity). + device: Result device (default: cpu). + normalize: Normalize the filter so that it retains the magnitude + for constant input signal (DC)? (default: True). + flip_filter: Flip the filter? (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + separable: Return a separable filter? (default: select automatically). + + Returns: + Float32 tensor of the shape + `[filter_height, filter_width]` (non-separable) or + `[filter_taps]` (separable). + """ + + if f is None: + f = 1 + f = torch.as_tensor(f, dtype=torch.float32) + assert f.ndim in [0, 1, 2] + assert f.numel() > 0 + if f.ndim == 0: + f = f[np.newaxis] + + + if separable is None: + separable = (f.ndim == 1 and f.numel() >= 8) + if f.ndim == 1 and not separable: + f = f.ger(f) + assert f.ndim == (1 if separable else 2) + + + if normalize: + f /= f.sum() + if flip_filter: + f = f.flip(list(range(f.ndim))) + f = f * (gain ** (f.ndim / 2)) + f = f.to(device=device) + return f + +#---------------------------------------------------------------------------- + +def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'): + r"""Pad, upsample, filter, and downsample a batch of 2D images. + + Performs the following sequence of operations for each channel: + + 1. Upsample the image by inserting N-1 zeros after each pixel (`up`). + + 2. Pad the image with the specified number of zeros on each side (`padding`). + Negative padding corresponds to cropping the image. + + 3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it + so that the footprint of all output pixels lies within the input image. + + 4. Downsample the image by keeping every Nth pixel (`down`). + + This sequence of operations bears close resemblance to scipy.signal.upfirdn(). + The fused op is considerably more efficient than performing the same calculation + using standard PyTorch ops. It supports gradients of arbitrary order. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + up: Integer upsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + down: Integer downsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + padding: Padding with respect to the upsampled image. Can be a single number + or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + assert isinstance(x, torch.Tensor) + assert impl in ['ref', 'cuda'] + if impl == 'cuda' and x.device.type == 'cuda' and _init(): + return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f) + return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain) + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1): + """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops. + """ + + assert isinstance(x, torch.Tensor) and x.ndim == 4 + if f is None: + f = torch.ones([1, 1], dtype=torch.float32, device=x.device) + assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] + assert f.dtype == torch.float32 and not f.requires_grad + batch_size, num_channels, in_height, in_width = x.shape + upx, upy = _parse_scaling(up) + downx, downy = _parse_scaling(down) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + + + x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1]) + x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1]) + x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx]) + + + x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)]) + x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)] + + + f = f * (gain ** (f.ndim / 2)) + f = f.to(x.dtype) + if not flip_filter: + f = f.flip(list(range(f.ndim))) + + + f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim) + if f.ndim == 4: + x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels) + else: + x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels) + x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels) + + + x = x[:, :, ::downy, ::downx] + return x + +#---------------------------------------------------------------------------- + +_upfirdn2d_cuda_cache = dict() + +def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1): + """Fast CUDA implementation of `upfirdn2d()` using custom ops. + """ + + upx, upy = _parse_scaling(up) + downx, downy = _parse_scaling(down) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + + + key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) + if key in _upfirdn2d_cuda_cache: + return _upfirdn2d_cuda_cache[key] + + + class Upfirdn2dCuda(torch.autograd.Function): + @staticmethod + def forward(ctx, x, f): + assert isinstance(x, torch.Tensor) and x.ndim == 4 + if f is None: + f = torch.ones([1, 1], dtype=torch.float32, device=x.device) + assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] + y = x + if f.ndim == 2: + y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) + else: + y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, np.sqrt(gain)) + y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, np.sqrt(gain)) + ctx.save_for_backward(f) + ctx.x_shape = x.shape + return y + + @staticmethod + def backward(ctx, dy): + f, = ctx.saved_tensors + _, _, ih, iw = ctx.x_shape + _, _, oh, ow = dy.shape + fw, fh = _get_filter_size(f) + p = [ + fw - padx0 - 1, + iw * upx - ow * downx + padx0 - upx + 1, + fh - pady0 - 1, + ih * upy - oh * downy + pady0 - upy + 1, + ] + dx = None + df = None + + if ctx.needs_input_grad[0]: + dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(not flip_filter), gain=gain).apply(dy, f) + + assert not ctx.needs_input_grad[1] + return dx, df + + + _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda + return Upfirdn2dCuda + +#---------------------------------------------------------------------------- + +def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'): + r"""Filter a batch of 2D images using the given 2D FIR filter. + + By default, the result is padded so that its shape matches the input. + User-specified padding is applied on top of that, with negative values + indicating cropping. Pixels outside the image are assumed to be zero. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + padding: Padding with respect to the output. Can be a single number or a + list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + padx0, padx1, pady0, pady1 = _parse_padding(padding) + fw, fh = _get_filter_size(f) + p = [ + padx0 + fw // 2, + padx1 + (fw - 1) // 2, + pady0 + fh // 2, + pady1 + (fh - 1) // 2, + ] + return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl) + +#---------------------------------------------------------------------------- + +def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'): + r"""Upsample a batch of 2D images using the given 2D FIR filter. + + By default, the result is padded so that its shape is a multiple of the input. + User-specified padding is applied on top of that, with negative values + indicating cropping. Pixels outside the image are assumed to be zero. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + up: Integer upsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + padding: Padding with respect to the output. Can be a single number or a + list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + upx, upy = _parse_scaling(up) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + fw, fh = _get_filter_size(f) + p = [ + padx0 + (fw + upx - 1) // 2, + padx1 + (fw - upx) // 2, + pady0 + (fh + upy - 1) // 2, + pady1 + (fh - upy) // 2, + ] + return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl) + +#---------------------------------------------------------------------------- + +def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'): + r"""Downsample a batch of 2D images using the given 2D FIR filter. + + By default, the result is padded so that its shape is a fraction of the input. + User-specified padding is applied on top of that, with negative values + indicating cropping. Pixels outside the image are assumed to be zero. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + down: Integer downsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + padding: Padding with respect to the input. Can be a single number or a + list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + downx, downy = _parse_scaling(down) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + fw, fh = _get_filter_size(f) + p = [ + padx0 + (fw - downx + 1) // 2, + padx1 + (fw - downx) // 2, + pady0 + (fh - downy + 1) // 2, + pady1 + (fh - downy) // 2, + ] + return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl) + +#---------------------------------------------------------------------------- diff --git a/resource/ssba/torch_utils/persistence.py b/resource/ssba/torch_utils/persistence.py new file mode 100644 index 0000000..db95cd6 --- /dev/null +++ b/resource/ssba/torch_utils/persistence.py @@ -0,0 +1,251 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Facilities for pickling Python code alongside other data. + +The pickled code is automatically imported into a separate Python module +during unpickling. This way, any previously exported pickles will remain +usable even if the original code is no longer available, or if the current +version of the code is not consistent with what was originally pickled.""" + +import sys +import pickle +import io +import inspect +import copy +import uuid +import types +import dnnlib + +#---------------------------------------------------------------------------- + +_version = 6 +_decorators = set() +_import_hooks = [] +_module_to_src_dict = dict() +_src_to_module_dict = dict() + +#---------------------------------------------------------------------------- + +def persistent_class(orig_class): + r"""Class decorator that extends a given class to save its source code + when pickled. + + Example: + + from torch_utils import persistence + + @persistence.persistent_class + class MyNetwork(torch.nn.Module): + def __init__(self, num_inputs, num_outputs): + super().__init__() + self.fc = MyLayer(num_inputs, num_outputs) + ... + + @persistence.persistent_class + class MyLayer(torch.nn.Module): + ... + + When pickled, any instance of `MyNetwork` and `MyLayer` will save its + source code alongside other internal state (e.g., parameters, buffers, + and submodules). This way, any previously exported pickle will remain + usable even if the class definitions have been modified or are no + longer available. + + The decorator saves the source code of the entire Python module + containing the decorated class. It does *not* save the source code of + any imported modules. Thus, the imported modules must be available + during unpickling, also including `torch_utils.persistence` itself. + + It is ok to call functions defined in the same module from the + decorated class. However, if the decorated class depends on other + classes defined in the same module, they must be decorated as well. + This is illustrated in the above example in the case of `MyLayer`. + + It is also possible to employ the decorator just-in-time before + calling the constructor. For example: + + cls = MyLayer + if want_to_make_it_persistent: + cls = persistence.persistent_class(cls) + layer = cls(num_inputs, num_outputs) + + As an additional feature, the decorator also keeps track of the + arguments that were used to construct each instance of the decorated + class. The arguments can be queried via `obj.init_args` and + `obj.init_kwargs`, and they are automatically pickled alongside other + object state. A typical use case is to first unpickle a previous + instance of a persistent class, and then upgrade it to use the latest + version of the source code: + + with open('old_pickle.pkl', 'rb') as f: + old_net = pickle.load(f) + new_net = MyNetwork(*old_obj.init_args, **old_obj.init_kwargs) + misc.copy_params_and_buffers(old_net, new_net, require_all=True) + """ + assert isinstance(orig_class, type) + if is_persistent(orig_class): + return orig_class + + assert orig_class.__module__ in sys.modules + orig_module = sys.modules[orig_class.__module__] + orig_module_src = _module_to_src(orig_module) + + class Decorator(orig_class): + _orig_module_src = orig_module_src + _orig_class_name = orig_class.__name__ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._init_args = copy.deepcopy(args) + self._init_kwargs = copy.deepcopy(kwargs) + assert orig_class.__name__ in orig_module.__dict__ + _check_pickleable(self.__reduce__()) + + @property + def init_args(self): + return copy.deepcopy(self._init_args) + + @property + def init_kwargs(self): + return dnnlib.EasyDict(copy.deepcopy(self._init_kwargs)) + + def __reduce__(self): + fields = list(super().__reduce__()) + fields += [None] * max(3 - len(fields), 0) + if fields[0] is not _reconstruct_persistent_obj: + meta = dict(type='class', version=_version, module_src=self._orig_module_src, class_name=self._orig_class_name, state=fields[2]) + fields[0] = _reconstruct_persistent_obj + fields[1] = (meta,) + fields[2] = None + return tuple(fields) + + Decorator.__name__ = orig_class.__name__ + _decorators.add(Decorator) + return Decorator + +#---------------------------------------------------------------------------- + +def is_persistent(obj): + r"""Test whether the given object or class is persistent, i.e., + whether it will save its source code when pickled. + """ + try: + if obj in _decorators: + return True + except TypeError: + pass + return type(obj) in _decorators + +#---------------------------------------------------------------------------- + +def import_hook(hook): + r"""Register an import hook that is called whenever a persistent object + is being unpickled. A typical use case is to patch the pickled source + code to avoid errors and inconsistencies when the API of some imported + module has changed. + + The hook should have the following signature: + + hook(meta) -> modified meta + + `meta` is an instance of `dnnlib.EasyDict` with the following fields: + + type: Type of the persistent object, e.g. `'class'`. + version: Internal version number of `torch_utils.persistence`. + module_src Original source code of the Python module. + class_name: Class name in the original Python module. + state: Internal state of the object. + + Example: + + @persistence.import_hook + def wreck_my_network(meta): + if meta.class_name == 'MyNetwork': + print('MyNetwork is being imported. I will wreck it!') + meta.module_src = meta.module_src.replace("True", "False") + return meta + """ + assert callable(hook) + _import_hooks.append(hook) + +#---------------------------------------------------------------------------- + +def _reconstruct_persistent_obj(meta): + r"""Hook that is called internally by the `pickle` module to unpickle + a persistent object. + """ + meta = dnnlib.EasyDict(meta) + meta.state = dnnlib.EasyDict(meta.state) + for hook in _import_hooks: + meta = hook(meta) + assert meta is not None + + assert meta.version == _version + module = _src_to_module(meta.module_src) + + assert meta.type == 'class' + orig_class = module.__dict__[meta.class_name] + decorator_class = persistent_class(orig_class) + obj = decorator_class.__new__(decorator_class) + + setstate = getattr(obj, '__setstate__', None) + if callable(setstate): + setstate(meta.state) + else: + obj.__dict__.update(meta.state) + return obj + +#---------------------------------------------------------------------------- + +def _module_to_src(module): + r"""Query the source code of a given Python module. + """ + src = _module_to_src_dict.get(module, None) + if src is None: + src = inspect.getsource(module) + _module_to_src_dict[module] = src + _src_to_module_dict[src] = module + return src + +def _src_to_module(src): + r"""Get or create a Python module for the given source code. + """ + module = _src_to_module_dict.get(src, None) + if module is None: + module_name = "_imported_module_" + uuid.uuid4().hex + module = types.ModuleType(module_name) + sys.modules[module_name] = module + _module_to_src_dict[module] = src + _src_to_module_dict[src] = module + exec(src, module.__dict__) + return module + +#---------------------------------------------------------------------------- + +def _check_pickleable(obj): + r"""Check that the given object is pickleable, raising an exception if + it is not. This function is expected to be considerably more efficient + than actually pickling the object. + """ + def recurse(obj): + if isinstance(obj, (list, tuple, set)): + return [recurse(x) for x in obj] + if isinstance(obj, dict): + return [[recurse(x), recurse(y)] for x, y in obj.items()] + if isinstance(obj, (str, int, float, bool, bytes, bytearray)): + return None + if f'{type(obj).__module__}.{type(obj).__name__}' in ['numpy.ndarray', 'torch.Tensor']: + return None + if is_persistent(obj): + return None + return obj + with io.BytesIO() as f: + pickle.dump(recurse(obj), f) + +#---------------------------------------------------------------------------- diff --git a/resource/ssba/torch_utils/training_stats.py b/resource/ssba/torch_utils/training_stats.py new file mode 100644 index 0000000..8aeec88 --- /dev/null +++ b/resource/ssba/torch_utils/training_stats.py @@ -0,0 +1,268 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Facilities for reporting and collecting training statistics across +multiple processes and devices. The interface is designed to minimize +synchronization overhead as well as the amount of boilerplate in user +code.""" + +import re +import numpy as np +import torch +import dnnlib + +from . import misc + +#---------------------------------------------------------------------------- + +_num_moments = 3 +_reduce_dtype = torch.float32 +_counter_dtype = torch.float64 +_rank = 0 +_sync_device = None +_sync_called = False +_counters = dict() +_cumulative = dict() + +#---------------------------------------------------------------------------- + +def init_multiprocessing(rank, sync_device): + r"""Initializes `torch_utils.training_stats` for collecting statistics + across multiple processes. + + This function must be called after + `torch.distributed.init_process_group()` and before `Collector.update()`. + The call is not necessary if multi-process collection is not needed. + + Args: + rank: Rank of the current process. + sync_device: PyTorch device to use for inter-process + communication, or None to disable multi-process + collection. Typically `torch.device('cuda', rank)`. + """ + global _rank, _sync_device + assert not _sync_called + _rank = rank + _sync_device = sync_device + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def report(name, value): + r"""Broadcasts the given set of scalars to all interested instances of + `Collector`, across device and process boundaries. + + This function is expected to be extremely cheap and can be safely + called from anywhere in the training loop, loss function, or inside a + `torch.nn.Module`. + + Warning: The current implementation expects the set of unique names to + be consistent across processes. Please make sure that `report()` is + called at least once for each unique name by each process, and in the + same order. If a given process has no scalars to broadcast, it can do + `report(name, [])` (empty list). + + Args: + name: Arbitrary string specifying the name of the statistic. + Averages are accumulated separately for each unique name. + value: Arbitrary set of scalars. Can be a list, tuple, + NumPy array, PyTorch tensor, or Python scalar. + + Returns: + The same `value` that was passed in. + """ + if name not in _counters: + _counters[name] = dict() + + elems = torch.as_tensor(value) + if elems.numel() == 0: + return value + + elems = elems.detach().flatten().to(_reduce_dtype) + moments = torch.stack([ + torch.ones_like(elems).sum(), + elems.sum(), + elems.square().sum(), + ]) + assert moments.ndim == 1 and moments.shape[0] == _num_moments + moments = moments.to(_counter_dtype) + + device = moments.device + if device not in _counters[name]: + _counters[name][device] = torch.zeros_like(moments) + _counters[name][device].add_(moments) + return value + +#---------------------------------------------------------------------------- + +def report0(name, value): + r"""Broadcasts the given set of scalars by the first process (`rank = 0`), + but ignores any scalars provided by the other processes. + See `report()` for further details. + """ + report(name, value if _rank == 0 else []) + return value + +#---------------------------------------------------------------------------- + +class Collector: + r"""Collects the scalars broadcasted by `report()` and `report0()` and + computes their long-term averages (mean and standard deviation) over + user-defined periods of time. + + The averages are first collected into internal counters that are not + directly visible to the user. They are then copied to the user-visible + state as a result of calling `update()` and can then be queried using + `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the + internal counters for the next round, so that the user-visible state + effectively reflects averages collected between the last two calls to + `update()`. + + Args: + regex: Regular expression defining which statistics to + collect. The default is to collect everything. + keep_previous: Whether to retain the previous averages if no + scalars were collected on a given round + (default: True). + """ + def __init__(self, regex='.*', keep_previous=True): + self._regex = re.compile(regex) + self._keep_previous = keep_previous + self._cumulative = dict() + self._moments = dict() + self.update() + self._moments.clear() + + def names(self): + r"""Returns the names of all statistics broadcasted so far that + match the regular expression specified at construction time. + """ + return [name for name in _counters if self._regex.fullmatch(name)] + + def update(self): + r"""Copies current values of the internal counters to the + user-visible state and resets them for the next round. + + If `keep_previous=True` was specified at construction time, the + operation is skipped for statistics that have received no scalars + since the last update, retaining their previous averages. + + This method performs a number of GPU-to-CPU transfers and one + `torch.distributed.all_reduce()`. It is intended to be called + periodically in the main training loop, typically once every + N training steps. + """ + if not self._keep_previous: + self._moments.clear() + for name, cumulative in _sync(self.names()): + if name not in self._cumulative: + self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + delta = cumulative - self._cumulative[name] + self._cumulative[name].copy_(cumulative) + if float(delta[0]) != 0: + self._moments[name] = delta + + def _get_delta(self, name): + r"""Returns the raw moments that were accumulated for the given + statistic between the last two calls to `update()`, or zero if + no scalars were collected. + """ + assert self._regex.fullmatch(name) + if name not in self._moments: + self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + return self._moments[name] + + def num(self, name): + r"""Returns the number of scalars that were accumulated for the given + statistic between the last two calls to `update()`, or zero if + no scalars were collected. + """ + delta = self._get_delta(name) + return int(delta[0]) + + def mean(self, name): + r"""Returns the mean of the scalars that were accumulated for the + given statistic between the last two calls to `update()`, or NaN if + no scalars were collected. + """ + delta = self._get_delta(name) + if int(delta[0]) == 0: + return float('nan') + return float(delta[1] / delta[0]) + + def std(self, name): + r"""Returns the standard deviation of the scalars that were + accumulated for the given statistic between the last two calls to + `update()`, or NaN if no scalars were collected. + """ + delta = self._get_delta(name) + if int(delta[0]) == 0 or not np.isfinite(float(delta[1])): + return float('nan') + if int(delta[0]) == 1: + return float(0) + mean = float(delta[1] / delta[0]) + raw_var = float(delta[2] / delta[0]) + return np.sqrt(max(raw_var - np.square(mean), 0)) + + def as_dict(self): + r"""Returns the averages accumulated between the last two calls to + `update()` as an `dnnlib.EasyDict`. The contents are as follows: + + dnnlib.EasyDict( + NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT), + ... + ) + """ + stats = dnnlib.EasyDict() + for name in self.names(): + stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name)) + return stats + + def __getitem__(self, name): + r"""Convenience getter. + `collector[name]` is a synonym for `collector.mean(name)`. + """ + return self.mean(name) + +#---------------------------------------------------------------------------- + +def _sync(names): + r"""Synchronize the global cumulative counters across devices and + processes. Called internally by `Collector.update()`. + """ + if len(names) == 0: + return [] + global _sync_called + _sync_called = True + + + deltas = [] + device = _sync_device if _sync_device is not None else torch.device('cpu') + for name in names: + delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device) + for counter in _counters[name].values(): + delta.add_(counter.to(device)) + counter.copy_(torch.zeros_like(counter)) + deltas.append(delta) + deltas = torch.stack(deltas) + + + if _sync_device is not None: + torch.distributed.all_reduce(deltas) + + + deltas = deltas.cpu() + for idx, name in enumerate(names): + if name not in _cumulative: + _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + _cumulative[name].add_(deltas[idx]) + + + return [(name, _cumulative[name]) for name in names] + +#---------------------------------------------------------------------------- diff --git a/resource/ssba/train.py b/resource/ssba/train.py new file mode 100644 index 0000000..e0457e0 --- /dev/null +++ b/resource/ssba/train.py @@ -0,0 +1,403 @@ +import argparse + +parser = argparse.ArgumentParser() +parser.add_argument("--data_dir", type=str, required=True, help="Directory with image dataset.") +parser.add_argument("--EXP_NAME", type=str, required=True, help="Customized experiment name.") +parser.add_argument( + "--use_celeba_preprocessing", + action="store_true", + help="Use CelebA specific preprocessing when loading the images.") +parser.add_argument("--output_dir", type=str, required=False, help="Directory to save results to.") +# +parser.add_argument("--random_seed", type=int, default=0, help="Fixed random seed.") +parser.add_argument("--fix_fingerprint", + type=int, + default=0, + help="Only use standard string to generate fingerprints during training") +parser.add_argument("--standard_fingerprint", + type=str, + default='abcd', + help="Only work when fix_fingerprint is set to 1") +# +parser.add_argument("--fingerprint_length", type=int, default=100, required=True, help="Number of bits in the fingerprint.", ) +parser.add_argument("--image_resolution", type=int, default=128, required=True, help="Height and width of square images.", ) +parser.add_argument("--num_epochs", type=int, default=20, help="Number of training epochs.") +parser.add_argument("--batch_size", type=int, default=64, help="Batch size.") +parser.add_argument("--lr", type=float, default=0.0001, help="Learning rate.") +parser.add_argument("--cuda", type=str, default=0) +parser.add_argument("--use_residual", type=int, default=0, help="Use residual mode or not",) + +parser.add_argument("--l2_loss_await", help="Train without L2 loss for the first x iterations", type=int, default=1000,) +parser.add_argument("--l2_loss_weight", type=float, default=10, help="L2 loss weight for image fidelity.", ) +parser.add_argument("--l2_loss_ramp", type=int, default=3000, help="Linearly increase L2 loss weight over x iterations.", ) + +parser.add_argument("--flip_loss_await", help="Train without flip loss for the first x iterations", type=int, default=1000,) +parser.add_argument("--flip_loss_weight", type=float, default=1, help="weight for flip loss.", ) +parser.add_argument("--flip_loss_ramp", type=int, default=3000, help="Linearly increase flip loss weight over x iterations.", ) +parser.add_argument("--flip_identical", action="store_true", help="Identical Location for every batch?", ) + +parser.add_argument("--BCE_loss_weight", type=float, default=1, help="BCE loss weight for fingerprint reconstruction.", ) + +parser.add_argument("--use_modulated", type=int, default=0, help="Use modulated convolution or not", ) +parser.add_argument("--demodulate", type=int, default=1, help="Use demodulation or not?", ) +parser.add_argument("--fc_layers", type=int, default=0, help="Use 8 fc layers before modulated convolution?", ) +parser.add_argument("--fused_conv", type=int, default=0, help="Use fused conv for modulated conv?",) ## +parser.add_argument("--bias_init", type=int, default=None, help="Specified bias initialization for modulated conv",) + +parser.add_argument("--test_save_file", type=str, default=None, help="where to save test file") + +args = parser.parse_args() + +import glob +import os +from os.path import join +from time import time +from generate_fingerprints import generate_fingerprints + +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" +os.environ["CUDA_VISIBLE_DEVICES"] = str(args.cuda) +from datetime import datetime + +from tqdm import tqdm +import PIL +import numpy as np +import random + +import torch +from torch import nn +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms +from torchvision.utils import make_grid +from torchvision.datasets import ImageFolder +from torchvision.utils import save_image +from tensorboardX import SummaryWriter + +from torch.optim import Adam + +import models +import models_modulated +LOGS_PATH = os.path.join(args.output_dir, "logs") +CHECKPOINTS_PATH = os.path.join(args.output_dir, "checkpoints") +SAVED_IMAGES = os.path.join(args.output_dir, "saved_images") + +writer = SummaryWriter(LOGS_PATH) + +if not os.path.exists(LOGS_PATH): + os.makedirs(LOGS_PATH) +if not os.path.exists(CHECKPOINTS_PATH): + os.makedirs(CHECKPOINTS_PATH) +if not os.path.exists(SAVED_IMAGES): + os.makedirs(SAVED_IMAGES) + +def fix_random(random_seed): + random.seed(random_seed) + np.random.seed(random_seed) + torch.manual_seed(random_seed) + torch.cuda.manual_seed_all(random_seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + +def generate_random_fingerprints(fingerprint_length, batch_size=4): + z = torch.zeros((batch_size, fingerprint_length), dtype=torch.float).random_(0, 2) + return z + +def random_flip(fingerprints, flip_range=5, identical=False): + + range_ls = np.arange(1, flip_range+1) + batch_size, fingerprint_size = fingerprints.size() + + if identical: + diff_bits = np.random.choice(range_ls, 1, replace = False) + indexes = np.random.choice(fingerprint_size, size=diff_bits[0], replace=False) + fingerprints[:, indexes] = 1 - fingerprints[:, indexes] + else: + for i in range(batch_size): + diff_bits = np.random.choice(range_ls, 1, replace = False) + indexes = np.random.choice(fingerprint_size, size=diff_bits[0], replace=False) + fingerprints[i, indexes] = 1 - fingerprints[i, indexes] + + return fingerprints + +plot_points = ( + list(range(0, 1000, 100)) + + list(range(1000, 3000, 200)) + + list(range(3000, 100000, 1000)) + + list(range(100000, 200000, 5000)) +) + +class CustomImageFolder(Dataset): + def __init__(self, data_dir, transform=None): + self.data_dir = data_dir #读取data_dir下的所有.png,.jpeg,.jpg文件 + self.filenames = glob.glob(os.path.join(data_dir, "*.png")) + self.filenames.extend(glob.glob(os.path.join(data_dir, "*.jpeg"))) + self.filenames.extend(glob.glob(os.path.join(data_dir, "*.JPEG"))) + self.filenames.extend(glob.glob(os.path.join(data_dir, "*.jpg"))) + self.filenames = sorted(self.filenames) + self.transform = transform + + def __getitem__(self, idx): + filename = self.filenames[idx] + image = PIL.Image.open(filename) + if self.transform: + image = self.transform(image) + return image, 0 + + def __len__(self): + return len(self.filenames) + +def load_data(): + global dataset, dataloader + global IMAGE_CHANNELS, IMAGE_HEIGHT, IMAGE_WIDTH, SECRET_SIZE + + IMAGE_RESOLUTION = args.image_resolution + IMAGE_CHANNELS = 3 + + SECRET_SIZE = args.fingerprint_length + + if args.use_celeba_preprocessing: + assert args.image_resolution == 128, f"CelebA preprocessing requires image resolution 128, got {args.image_resolution}." + transform = transforms.Compose( + [ + transforms.CenterCrop(148), + transforms.Resize(128), + transforms.ToTensor(), + ] + ) + else: + + transform = transforms.Compose( + [ + transforms.Resize(IMAGE_RESOLUTION), + transforms.CenterCrop(IMAGE_RESOLUTION), + transforms.ToTensor(), + ] + ) + + s = time() + print(f"Loading image folder {args.data_dir} ...") + dataset = CustomImageFolder(args.data_dir, transform=transform) + print(f"Finished. Loading took {time() - s:.2f}s") + +def main(): + now = datetime.now() + fix_random(args.random_seed) + + dt_string = now.strftime("%d%m%Y_%H:%M:%S") + if not args.EXP_NAME: + EXP_NAME = f"stegastamp_{args.fingerprint_length}_{dt_string}" + else: + EXP_NAME = args.EXP_NAME + + device = torch.device("cuda") + + load_data() + if args.fix_fingerprint: print('Using fixed standard string {} while training'.format(args.standard_fingerprint)) + + if not args.use_modulated: + print('----------Not using modulated conv!----------') + encoder = models.StegaStampEncoder( + args.image_resolution, + IMAGE_CHANNELS, + args.fingerprint_length, + return_residual=args.use_residual, + ) + decoder = models.StegaStampDecoder( + args.image_resolution, + IMAGE_CHANNELS, + args.fingerprint_length, + ) + else: + print('----------Using modulated conv!----------') + encoder = models_modulated.StegaStampEncoder( + args.image_resolution, + IMAGE_CHANNELS, + args.fingerprint_length, + return_residual=args.use_residual, + bias_init=args.bias_init, + fused_modconv=args.fused_conv, + demodulate=args.demodulate, + fc_layers=args.fc_layers + ) + decoder = models_modulated.StegaStampDecoder( + args.image_resolution, + IMAGE_CHANNELS, + args.fingerprint_length, + ) + + encoder = encoder.to(device) + decoder = decoder.to(device) + + decoder_encoder_optim = Adam( + params=list(decoder.parameters()) + list(encoder.parameters()), lr=args.lr + ) + + global_step = 0 + steps_since_l2_loss_activated = -1 + + + for i_epoch in range(args.num_epochs): + dataloader = DataLoader( + dataset, batch_size=args.batch_size, shuffle=True, pin_memory=True + ) + for images, _ in tqdm(dataloader): + global_step += 1 + + batch_size = min(args.batch_size, images.size(0)) + if args.fix_fingerprint: + fingerprints = generate_fingerprints('bch', batch_size, + args.fingerprint_length, + args.standard_fingerprint, + compare=False) + else: + fingerprints = generate_random_fingerprints(args.fingerprint_length, batch_size) + + ## + + + l2_loss_weight = min( + max( + 0, + args.l2_loss_weight + * (steps_since_l2_loss_activated - args.l2_loss_await) + / args.l2_loss_ramp, + ), + args.l2_loss_weight, + ) + + BCE_loss_weight = args.BCE_loss_weight + + clean_images = images.to(device) + fingerprints = fingerprints.to(device) + + if args.use_residual: + residual = encoder(fingerprints, clean_images) + fingerprinted_images = clean_images + residual + + else: + fingerprinted_images = encoder(fingerprints, clean_images) + residual = fingerprinted_images - clean_images + + + + decoder_output = decoder(fingerprinted_images) + + criterion = nn.MSELoss() + l2_loss = criterion(fingerprinted_images, clean_images) + + + criterion = nn.BCEWithLogitsLoss() + BCE_loss = criterion(decoder_output.view(-1), fingerprints.view(-1)) + + ## + loss = l2_loss_weight * l2_loss + BCE_loss_weight * BCE_loss + + + encoder.zero_grad() + decoder.zero_grad() + + loss.backward() + decoder_encoder_optim.step() + + fingerprints_predicted = (decoder_output > 0).float() + bitwise_accuracy = 1.0 - torch.mean(torch.abs(fingerprints - fingerprints_predicted)) + + if steps_since_l2_loss_activated == -1: + if bitwise_accuracy.item() > 0.9: + print("Current epoch: {}, Current global step: {}, Current bitwise acc: {}, Start to use l2 loss!".format(i_epoch, global_step, bitwise_accuracy.item())) + steps_since_l2_loss_activated = 0 + else: + steps_since_l2_loss_activated += 1 + + if global_step in plot_points: + writer.add_scalar("bitwise_accuracy", bitwise_accuracy, global_step), + print("Bitwise accuracy {}".format(bitwise_accuracy)) + writer.add_scalar("loss", loss, global_step), + writer.add_scalar("BCE_loss", BCE_loss, global_step), + writer.add_scalar("l2_loss", l2_loss, global_step), + #writer.add_scalar("flip_loss", flip_loss, global_step), + + writer.add_scalars( + "clean_statistics", + { + "min": clean_images.min(), + "max": clean_images.max() + }, + global_step, + ), + writer.add_scalars( + "with_fingerprint_statistics", + { + "min": fingerprinted_images.min(), + "max": fingerprinted_images.max(), + }, + global_step, + ), + writer.add_scalars( + "residual_statistics", + { + "min": residual.min(), + "max": residual.max(), + "mean_abs": residual.abs().mean(), + }, + global_step, + ), + print( + "residual_statistics: {}".format( + { + "min": residual.min(), + "max": residual.max(), + "mean_abs": residual.abs().mean(), + } + ) + ) + + writer.add_image("clean_image", make_grid(clean_images, normalize=True), global_step) + writer.add_image("residual",make_grid(residual, normalize=True, scale_each=True),global_step,) + writer.add_image("image_with_fingerprint",make_grid(fingerprinted_images, normalize=True),global_step,) + + + save_image( + fingerprinted_images, + SAVED_IMAGES + "/{}.png".format(global_step), + normalize=True, + ) + + + writer.add_scalar("loss_weights/l2_loss_weight", l2_loss_weight, global_step) + writer.add_scalar("loss_weights/BCE_loss_weight", BCE_loss_weight, global_step) + #writer.add_scalar("loss_weights/flip_loss_weight", flip_loss_weight, global_step) + + + if (i_epoch+1) % 10 == 0: + print('Current epoch:', i_epoch + 1) + torch.save( + decoder_encoder_optim.state_dict(), + join(CHECKPOINTS_PATH, EXP_NAME + "_optim.pth"), + ) + torch.save( + encoder.state_dict(), + join(CHECKPOINTS_PATH, EXP_NAME + "_encoder.pth"), + ) + torch.save( + decoder.state_dict(), + join(CHECKPOINTS_PATH, EXP_NAME + "_decoder.pth"), + ) + + f = open(join(CHECKPOINTS_PATH, EXP_NAME + "_variables.txt"), "w") + f.write(str(global_step)) + f.close() + + if (i_epoch+1) == args.num_epochs and args.test_save_file: + with open(args.test_save_file,'a') as f: + f.write('Training bitwise accuracy:' + str(bitwise_accuracy.data) + '\n') + f.close() + + + #writer.export_scalars_to_json("./all_scalars.json") + writer.close() + +if __name__ == "__main__": + #print("Start training!") + for arg in vars(args): + print(format(arg, '<20'), format(str(getattr(args,arg))), '<') + main() \ No newline at end of file diff --git a/resource/ssba/utils/calcu_metrics.py b/resource/ssba/utils/calcu_metrics.py new file mode 100644 index 0000000..556db20 --- /dev/null +++ b/resource/ssba/utils/calcu_metrics.py @@ -0,0 +1,95 @@ + +import numpy +import math +import cv2 +from skimage.metrics import structural_similarity as compare_ssim +from skimage.metrics import peak_signal_noise_ratio as compare_psnr +import os +from tqdm import tqdm +import argparse +import pandas as pd + + + + + + + + +def avg_psnr(img_dir1, img_dir2, suffix='_hidden', size=None, save_path=None, further=0, sort=True): + fileList1 = os.listdir(img_dir1) + fileList2 = os.listdir(img_dir2) + if sort: + fileList1.sort() + fileList2.sort() + size1, size2 = len(fileList1), len(fileList2) + assert size1 == size2, 'The number of images in the two dirs given should be the same!' + + if not size: size = size1 + + total_psnr, total_ssim = 0.0, 0.0 + #bar= tqdm(range(size)) + for i in range(size): + #for _, i in enumerate(bar): + + tmp1 = fileList1[i].split('.')[0].strip(suffix) + tmp2 = fileList2[i].split('.')[0].strip(suffix) + if tmp1 != tmp2: + print("{} and {} do not match!".format(fileList1[i], fileList2[i])) + continue + + + full_path1 = os.path.join(img_dir1, fileList1[i]) + full_path2 = os.path.join(img_dir2, fileList2[i]) + try: + img1 = cv2.imread(full_path1, cv2.IMREAD_COLOR) + img1 = img1[:, :, [2,1,0]] + + img2 = cv2.imread(full_path2, cv2.IMREAD_COLOR) + img2 = img2[:, :, [2,1,0]] + except: + print('Something Wrong while loading images!') + return + + assert img1.shape[0] == img2.shape[0], 'The resolution of two images must be the same!' + + psnr = compare_psnr(img1, img2, data_range=255) + ssim = compare_ssim(img1, img2, win_size=11, data_range=255, multichannel=True) + + total_psnr += psnr + total_ssim += ssim + + avg_psnr = total_psnr / size + avg_ssim = total_ssim / size + print("The average PSNR between {} and {} is: {}".format(img_dir1, img_dir2, avg_psnr)) + print("The average SSIM between {} and {} is: {}".format(img_dir1, img_dir2, avg_ssim)) + if save_path: + with open(args.test_save_file,'a') as f: + f.write("The average PSNR between {} and {} is: {}".format(img_dir1, img_dir2, avg_psnr) + '\n') + f.write("The average SSIM between {} and {} is: {}".format(img_dir1, img_dir2, avg_ssim) + '\n') + f.close() + + if further: + prefix_test = save_path.split('.')[0] + test_save_path_csv = prefix_test + '.csv' + data_frame = pd.read_csv(test_save_path_csv, index_col=False) + alist = [img_dir1, avg_psnr, avg_ssim] + data_frame.loc[len(data_frame)]=alist + data_frame.to_csv(test_save_path_csv, index=False) + + return avg_psnr, avg_ssim + +if __name__=="__main__": + parser = argparse.ArgumentParser(description='Calculate Image Metrics') + + parser.add_argument('--input_dir1', type=str, help='path to the image dataset1') + parser.add_argument('--input_dir2', type=str, help='path to the image dataset2') + parser.add_argument('--size', type=int, default=None, help='num of pairs of imgs to compute PSNR & SSIM') + + parser.add_argument("--test_save_file", type=str, default=None, help="where to save test file") + parser.add_argument("--further", type=int, default=0, help="futher save in .csv file nor not") + + args = parser.parse_args() + + avg_psnr(args.input_dir1, args.input_dir2, suffix='_hidden', size=args.size, save_path = args.test_save_file, + further = args.further) \ No newline at end of file diff --git a/resource/ssba/utils/dataset_processing.py b/resource/ssba/utils/dataset_processing.py new file mode 100644 index 0000000..da47457 --- /dev/null +++ b/resource/ssba/utils/dataset_processing.py @@ -0,0 +1,310 @@ + +import os +import shutil +import random +from tqdm import tqdm +import argparse +from torchvision import transforms +import PIL +import cv2 +from torchvision.utils import save_image +import numpy as np + +parser = argparse.ArgumentParser(description='Dataset Preparation') + +parser.add_argument('--input_dir', type=str, help='path to the original dataset') +parser.add_argument('--output_dir', type=str, help='path to the output dataset') +parser.add_argument('-train_size', type=int, help='size of the splited training set') +parser.add_argument('-test_size', type=int, help='size of the splited test set') +parser.add_argument('--shuffling', type=bool, help='shuffling or not', default=True) +parser.add_argument('--copy_only', type=bool, help='copy or cut', default=True) +parser.add_argument('--total_cnt', type=int, help='num of images for backdoor for each class', default=500) + +args = parser.parse_args() + + + + + + +def rename_files(input_dir): + fileList = os.listdir(input_dir) + bar = tqdm(fileList) + for ind, filename in enumerate(bar): + oldname = input_dir + filename + main = fileList[ind].split('.')[0] + + newname = input_dir + main +'.jpg' + os.rename(oldname,newname) + bar.set_description("Renaming {} / {}".format(ind+1, len(fileList))) + + + + + + + + + + +def split_dataset(input_dir, output_dir, train_size, test_size, shuffling=False, copy_only=True): + + if output_dir is not None: + output_train = output_dir+'train/' + output_test = output_dir+'test/' + else: + output_train = input_dir+'train/' + output_test = input_dir+'test/' + + if not os.path.exists(output_train): + os.makedirs(output_train) + if not os.path.exists(output_test): + os.makedirs(output_test) + + fileList = os.listdir(input_dir) + if shuffling: + random.shuffle(fileList) + + bar_train = tqdm(range(train_size)) + for _, i in enumerate(bar_train): + full_path = os.path.join(input_dir, fileList[i]) + despath = os.path.join(output_train, fileList[i]) + if copy_only: + shutil.copy(full_path, despath) + else: + shutil.move(full_path, despath) + bar_train.set_description("Forming Training Set: {} / {}".format(i+1, train_size)) + + bar_test = tqdm(range(train_size, train_size + test_size)) + for _, i in enumerate(bar_test): + full_path = os.path.join(input_dir, fileList[i]) + despath = os.path.join(output_test, fileList[i]) + if copy_only: + shutil.copy(full_path, despath) + else: + shutil.move(full_path, despath) + bar_test.set_description("Forming Test Set: {} / {}".format(i+1, train_size + test_size)) + + + + + + + + + + + +def transform_split(input_dir, output_dir, train_size, test_size, resize_resolution, crop_resolution, shuffling=False): + if output_dir is not None: + output_train = output_dir+'train/' + output_test = output_dir+'test/' + else: + output_train = input_dir+'train/' + output_test = input_dir+'test/' + + if not os.path.exists(output_train): + os.makedirs(output_train) + if not os.path.exists(output_test): + os.makedirs(output_test) + + fileList = os.listdir(input_dir) + if shuffling: + random.shuffle(fileList) + + transform = transforms.Compose( + [ + transforms.Resize(resize_resolution), + transforms.CenterCrop(crop_resolution), + transforms.ToTensor(), + ] + ) + + bar_train = tqdm(range(train_size)) + for _, i in enumerate(bar_train): + full_path = os.path.join(input_dir, fileList[i]) #获得完整的图像路径 + try: + temp = cv2.imread(full_path, cv2.IMREAD_COLOR) + temp = temp[:, :, [2,1,0]] + image = PIL.Image.fromarray(temp) + image = transform(image) #进行resize和crop操作改变图像大小 + + despath = os.path.join(output_train, fileList[i]) #输出路径 + save_image(image, despath, padding=0) + + bar_train.set_description("Resizing Training Set: {} / {}".format(i+1, train_size)) + except: + print(full_path) + + bar_test = tqdm(range(train_size, train_size + test_size)) + for _, i in enumerate(bar_test): + full_path = os.path.join(input_dir, fileList[i]) + try: + temp = cv2.imread(full_path, cv2.IMREAD_COLOR) + temp = temp[:, :, [2,1,0]] + image = PIL.Image.fromarray(temp) + image = transform(image) #进行resize和crop操作改变图像大小 + + despath = os.path.join(output_test, fileList[i]) + save_image(image, despath, padding=0) + + bar_test.set_description("Resizing Test Set: {} / {}".format(i+1, train_size + test_size)) + except: + print(full_path) + + return + + + + + + + + + +def resize_crop(input_dir, output_dir, resize_resolution, crop_resolution, shuffling=False): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fileList = os.listdir(input_dir) + if shuffling: + random.shuffle(fileList) + + transform = transforms.Compose( + [ + transforms.Resize(resize_resolution), + transforms.CenterCrop(crop_resolution), + transforms.ToTensor(), + ] + ) + size = len(fileList) + bar_train = tqdm(range(size)) + for _, i in enumerate(bar_train): + full_path = os.path.join(input_dir, fileList[i]) #获得完整的图像路径 + try: + temp = cv2.imread(full_path, cv2.IMREAD_COLOR) + temp = temp[:, :, [2,1,0]] + image = PIL.Image.fromarray(temp) + image = transform(image) #进行resize和crop操作改变图像大小 + + despath = os.path.join(output_dir, fileList[i]) #输出路径 + save_image(image, despath, padding=0) + + bar_train.set_description("Resizing Training Set: {} / {}".format(i+1, size)) + except: + print(full_path) + + + + + + +def detect_grayscale(input_dir): + grayscale=[] + fileList = os.listdir(input_dir) + size = len(fileList) + bar_train = tqdm(range(size)) + for _, i in enumerate(bar_train): + full_path = os.path.join(input_dir, fileList[i]) #获得完整的图像路径 + image = PIL.Image.open(full_path) + image = np.array(image) + print(image.shape) + if len(image.shape) == 2: + grayscale.append(full_path) + + for i in range(len(grayscale)): + print(grayscale[i]) + return + + + + + + + + + +def merge_all(input_dir, output_dir, copy_only=True, rename=False, total_cnt=1e9, prefix='cifar', suffix='_hidden'): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + dirList = os.listdir(input_dir) + dirSize = len(dirList) + if dirSize == 0: + print("The input directory must contain at least one sub-directory!") + return + + bar = tqdm(range(dirSize)) + for _, i in enumerate(bar): + filepath = os.path.join(input_dir, dirList[i]) + fileList = os.listdir(filepath) + cnt = 0 + for j in fileList: + full_path = os.path.join(filepath, j) #获得完整的图像路径 + if rename: + main = j.split('.')[0] + main = main.strip(prefix) + main = main.strip(suffix) + newname = prefix + main + suffix + '.png' + new_full_path = os.path.join(filepath, newname) #获得完整的图像路径 + os.rename(full_path, new_full_path) + full_path = new_full_path + + if copy_only: + shutil.copy(full_path, output_dir) + else: + shutil.move(full_path, output_dir) + + cnt += 1 + if cnt >= total_cnt: break + bar.set_description("Merging: {} / {}".format(i+1, dirSize)) + return + + + + + + + + +def rename_all(input_dir, prefix='cifar', suffix=''): + dirList = os.listdir(input_dir) + dirSize = len(dirList) + if dirSize == 0: + print("The input directory must contain at least one sub-directory!") + return + + bar = tqdm(range(dirSize)) + for _, i in enumerate(bar): + filepath = os.path.join(input_dir, dirList[i]) + + fileList = os.listdir(filepath) + for j in fileList: + full_path = os.path.join(filepath, j) + main = j.split('.')[0] + main = main.strip(prefix) + main = main.strip(suffix) + newname = prefix + main + suffix + '.png' + new_full_path = os.path.join(filepath, newname) + os.rename(full_path, new_full_path) + full_path = new_full_path + + bar.set_description("Renaming: {} / {}".format(i+1, dirSize)) + return + +if __name__=="__main__": + input_dir = '/workspace/getianshuo/data/ISSBA_dataset/sub-imagenet/sub-imagenet-200/val' + output_dir = '/workspace/getianshuo/data/ISSBA_dataset/sub-imagenet/sub-imagenet-200/val_all' + train_size = 50000 + test_size = 50000 + resize_resolution = 32 + crop_resolution = 32 + #rename_files(args.input_dir) + #transform_split(input_dir, output_dir, train_size, test_size, resolution) + #resize_crop(input_dir, output_dir, resize_resolution, crop_resolution) + #detect_grayscale('E:/horse2zebra/trainB') + #resize_crop(args.input_dir, args.output_dir, resize_resolution, crop_resolution, shuffling=False) + #rename_all('E:/CIFAR-10/CIFAR10_Image') + #split_dataset(args.input_dir, args.output_dir, args.train_size, args.test_size, args.shuffling) + merge_all(input_dir, output_dir) + + \ No newline at end of file diff --git a/resource/ssba/utils/diff_utils.py b/resource/ssba/utils/diff_utils.py new file mode 100644 index 0000000..03d8b02 --- /dev/null +++ b/resource/ssba/utils/diff_utils.py @@ -0,0 +1,448 @@ + +import cv2 +import itertools +import numpy as np +import random +import torch +import torch.nn.functional as F +import torch.nn as nn + +from PIL import Image, ImageOps +import matplotlib.pyplot as plt +#%% + + +y_table = np.array( + [[16, 11, 10, 16, 24, 40, 51, 61], + [12, 12, 14, 19, 26, 58, 60, 55], + [14, 13, 16, 24, 40, 57, 69, 56], + [14, 17, 22, 29, 51, 87, 80, 62], + [18, 22, 37, 56, 68, 109, 103, 77], + [24, 35, 55, 64, 81, 104, 113, 92], + [49, 64, 78, 87, 103, 121, 120, 101], + [72, 92, 95, 98, 112, 100, 103, 99]], + dtype=np.float32).T +y_table = nn.Parameter(torch.from_numpy(y_table)) + +c_table = np.empty((8, 8), dtype=np.float32) +c_table.fill(99) +c_table[:4, :4] = np.array([[17, 18, 24, 47], [18, 21, 26, 66], + [24, 26, 56, 99], [47, 66, 99, 99]]).T +c_table = nn.Parameter(torch.from_numpy(c_table)) + + +def round_only_at_0(x): + cond = (torch.abs(x) < 0.5).float() + return cond * (x ** 3) + (1 - cond) * x + + +def quality_to_factor(quality): + """ Calculate factor corresponding to quality + Input: + quality(float): Quality for jpeg compression + Output: + factor(float): Compression factor + """ + if quality < 50: + + + quality = 5000. / quality + else: + quality = 200. - quality*2 + return quality / 100. + +#%% + + +class rgb_to_ycbcr_jpeg(nn.Module): + """ Converts RGB image to YCbCr + Input: + image(tensor): batch x 3 x height x width + Outpput: + result(tensor): batch x height x width x 3 + """ + def __init__(self): + super(rgb_to_ycbcr_jpeg, self).__init__() + matrix = np.array( + [[0.299, 0.587, 0.114], [-0.168736, -0.331264, 0.5], + [0.5, -0.418688, -0.081312]], dtype=np.float32).T + self.shift = nn.Parameter(torch.tensor([0., 128., 128.]))#在G和B通道上各加128 + self.matrix = nn.Parameter(torch.from_numpy(matrix)) + + def forward(self, image): + image = image.permute(0, 2, 3, 1) + result = torch.tensordot(image, self.matrix, dims=1) + self.shift + result.view(image.shape) + return result + + +class chroma_subsampling(nn.Module): + """ Chroma subsampling on CbCv channels + Input: + image(tensor): batch x height x width x 3 + Output: + y(tensor): batch x height x width + cb(tensor): batch x height/2 x width/2 + cr(tensor): batch x height/2 x width/2 + """ + def __init__(self): + super(chroma_subsampling, self).__init__() + + def forward(self, image): + image_2 = image.permute(0, 3, 1, 2).clone() + avg_pool = nn.AvgPool2d(kernel_size=2, stride=(2, 2), + count_include_pad=False) + cb = avg_pool(image_2[:, 1, :, :].unsqueeze(1)) + cr = avg_pool(image_2[:, 2, :, :].unsqueeze(1)) + cb = cb.permute(0, 2, 3, 1) + cr = cr.permute(0, 2, 3, 1) + return image[:, :, :, 0], cb.squeeze(3), cr.squeeze(3) + + +class block_splitting(nn.Module): + """ Splitting image into patches + Input: + image(tensor): batch x height x width + Output: + patch(tensor): batch x h*w/64 x 8 x 8 + """ + def __init__(self): + super(block_splitting, self).__init__() + self.k = 8 + + def forward(self, image): + height, width = image.shape[1:3] + batch_size = image.shape[0] + image_reshaped = image.view(batch_size, height // self.k, self.k, -1, self.k) #[B, h//8, 8, w//8, 8] + image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) #[B, h//8, w//8, 8, 8] + return image_transposed.contiguous().view(batch_size, -1, self.k, self.k) #[B, h*w//64, 8, 8] + + +class dct_8x8(nn.Module): + """ Discrete Cosine Transformation + Input: + image(tensor): batch x height x width + Output: + dcp(tensor): batch x height x width + """ + def __init__(self): + super(dct_8x8, self).__init__() + tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) + for x, y, u, v in itertools.product(range(8), repeat=4): + tensor[x, y, u, v] = np.cos((2 * x + 1) * u * np.pi / 16) * np.cos( + (2 * y + 1) * v * np.pi / 16) + alpha = np.array([1. / np.sqrt(2)] + [1] * 7) + # + self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) + self.scale = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha) * 0.25).float() ) + + def forward(self, image): + image = image - 128 + result = self.scale * torch.tensordot(image, self.tensor, dims=2) + result.view(image.shape) + return result + + +class y_quantize(nn.Module): + """ JPEG Quantization for Y channel + Input: + image(tensor): batch x height x width + rounding(function): rounding function to use + factor(float): Degree of compression + Output: + image(tensor): batch x height x width + """ + def __init__(self, rounding, factor=1): + super(y_quantize, self).__init__() + self.rounding = rounding + self.factor = factor + self.y_table = y_table + + def forward(self, image): + image = image.float() / (self.y_table * self.factor) + image = self.rounding(image) + return image + +class c_quantize(nn.Module): + """ JPEG Quantization for CrCb channels + Input: + image(tensor): batch x height x width + rounding(function): rounding function to use + factor(float): Degree of compression + Output: + image(tensor): batch x height x width + """ + def __init__(self, rounding, factor=1): + super(c_quantize, self).__init__() + self.rounding = rounding + self.factor = factor + self.c_table = c_table + + def forward(self, image): + image = image.float() / (self.c_table * self.factor) + image = self.rounding(image) + return image + +class compress_jpeg(nn.Module): + """ Full JPEG compression algortihm + Input: + imgs(tensor): batch x 3 x height x width + rounding(function): rounding function to use + factor(float): Compression factor + Ouput: + compressed(dict(tensor)): batch x h*w/64 x 8 x 8 + """ + def __init__(self, rounding=torch.round, factor=1): + super(compress_jpeg, self).__init__() + self.l1 = nn.Sequential( + rgb_to_ycbcr_jpeg(), + chroma_subsampling() + ) + self.l2 = nn.Sequential( + block_splitting(), + dct_8x8() + ) + self.c_quantize = c_quantize(rounding=rounding, factor=factor) + self.y_quantize = y_quantize(rounding=rounding, factor=factor) + + def forward(self, image): + y, cb, cr = self.l1(image*255) + components = {'y': y, 'cb': cb, 'cr': cr} + for k in components.keys(): + comp = self.l2(components[k]) + if k in ('cb', 'cr'): + comp = self.c_quantize(comp) + else: + comp = self.y_quantize(comp) + + components[k] = comp + + return components['y'], components['cb'], components['cr'] + +#%% + + +class y_dequantize(nn.Module): + """ Dequantize Y channel + Inputs: + image(tensor): batch x height x width + factor(float): compression factor + Outputs: + image(tensor): batch x height x width + """ + def __init__(self, factor=1): + super(y_dequantize, self).__init__() + self.y_table = y_table + self.factor = factor + + def forward(self, image): + return image * (self.y_table * self.factor) + + +class c_dequantize(nn.Module): + """ Dequantize CbCr channel + Inputs: + image(tensor): batch x height x width + factor(float): compression factor + Outputs: + image(tensor): batch x height x width + """ + def __init__(self, factor=1): + super(c_dequantize, self).__init__() + self.factor = factor + self.c_table = c_table + + def forward(self, image): + return image * (self.c_table * self.factor) + + +class idct_8x8(nn.Module): + """ Inverse discrete Cosine Transformation + Input: + dcp(tensor): batch x height x width + Output: + image(tensor): batch x height x width + """ + def __init__(self): + super(idct_8x8, self).__init__() + alpha = np.array([1. / np.sqrt(2)] + [1] * 7) + self.alpha = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha)).float()) + tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) + for x, y, u, v in itertools.product(range(8), repeat=4): + tensor[x, y, u, v] = np.cos((2 * u + 1) * x * np.pi / 16) * np.cos( + (2 * v + 1) * y * np.pi / 16) + self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) + + def forward(self, image): + image = image * self.alpha + result = 0.25 * torch.tensordot(image, self.tensor, dims=2) + 128 + result.view(image.shape) + return result + + +class block_merging(nn.Module): + """ Merge pathces into image + Inputs: + patches(tensor) batch x height*width/64, height x width + height(int) + width(int) + Output: + image(tensor): batch x height x width + """ + def __init__(self): + super(block_merging, self).__init__() + + def forward(self, patches, height, width): + k = 8 + batch_size = patches.shape[0] + image_reshaped = patches.view(batch_size, height//k, width//k, k, k) + image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) + return image_transposed.contiguous().view(batch_size, height, width) + + +class chroma_upsampling(nn.Module): + """ Upsample chroma layers + Input: + y(tensor): y channel image + cb(tensor): cb channel + cr(tensor): cr channel + Ouput: + image(tensor): batch x height x width x 3 + """ + def __init__(self): + super(chroma_upsampling, self).__init__() + + def forward(self, y, cb, cr): + def repeat(x, k=2): + height, width = x.shape[1:3] + x = x.unsqueeze(-1) + x = x.repeat(1, 1, k, k) + x = x.view(-1, height * k, width * k) + return x + + cb = repeat(cb) + cr = repeat(cr) + + return torch.cat([y.unsqueeze(3), cb.unsqueeze(3), cr.unsqueeze(3)], dim=3) + + +class ycbcr_to_rgb_jpeg(nn.Module): + """ Converts YCbCr image to RGB JPEG + Input: + image(tensor): batch x height x width x 3 + Outpput: + result(tensor): batch x 3 x height x width + """ + def __init__(self): + super(ycbcr_to_rgb_jpeg, self).__init__() + + matrix = np.array( + [[1., 0., 1.402], [1, -0.344136, -0.714136], [1, 1.772, 0]], + dtype=np.float32).T + self.shift = nn.Parameter(torch.tensor([0, -128., -128.])) + self.matrix = nn.Parameter(torch.from_numpy(matrix)) + + def forward(self, image): + result = torch.tensordot(image + self.shift, self.matrix, dims=1) + result.view(image.shape) + return result.permute(0, 3, 1, 2) + + +class decompress_jpeg(nn.Module): + """ Full JPEG decompression algortihm + Input: + compressed(dict(tensor)): batch x h*w/64 x 8 x 8 + rounding(function): rounding function to use + factor(float): Compression factor + Ouput: + image(tensor): batch x 3 x height x width + """ + def __init__(self, height, width, rounding=torch.round, factor=1): + super(decompress_jpeg, self).__init__() + self.c_dequantize = c_dequantize(factor=factor) + self.y_dequantize = y_dequantize(factor=factor) + self.idct = idct_8x8() + self.merging = block_merging() + self.chroma = chroma_upsampling() + self.colors = ycbcr_to_rgb_jpeg() + + self.height, self.width = height, width + + def forward(self, y, cb, cr): + components = {'y': y, 'cb': cb, 'cr': cr} + for k in components.keys(): + if k in ('cb', 'cr'): + comp = self.c_dequantize(components[k]) + height, width = int(self.height/2), int(self.width/2) + else: + comp = self.y_dequantize(components[k]) + height, width = self.height, self.width + comp = self.idct(comp) + components[k] = self.merging(comp, height, width) + # + image = self.chroma(components['y'], components['cb'], components['cr']) + image = self.colors(image) + + image = torch.min(255*torch.ones_like(image), + torch.max(torch.zeros_like(image), image)) + return image/255 + +#%% 压缩与反压缩 +def jpeg_compress_decompress(image, + + rounding=round_only_at_0, + quality=80): + + height, width = image.shape[2:4] + + factor = quality_to_factor(quality) + + compress = compress_jpeg(rounding=rounding, factor=factor) + decompress = decompress_jpeg(height, width, rounding=rounding, factor=factor) + + y, cb, cr = compress(image) + + recovered = decompress(y, cb, cr) + + return recovered + +#%% 构造高斯模糊的卷积核 +def random_blur_kernel(probs, N_blur, sigrange_gauss, sigrange_line, wmin_line): + N = N_blur + coords = torch.from_numpy(np.stack(np.meshgrid(range(N_blur), range(N_blur), indexing='ij'), axis=-1)) - (0.5 * (N-1)) + manhat = torch.sum(torch.abs(coords), dim=-1) + + + vals_nothing = (manhat < 0.5).float() + + + sig_gauss = torch.rand(1)[0] * (sigrange_gauss[1] - sigrange_gauss[0]) + sigrange_gauss[0] + vals_gauss = torch.exp(-torch.sum(coords ** 2, dim=-1) /2. / sig_gauss ** 2) + + + theta = torch.rand(1)[0] * 2.* np.pi + v = torch.FloatTensor([torch.cos(theta), torch.sin(theta)]) + dists = torch.sum(coords * v, dim=-1) + + sig_line = torch.rand(1)[0] * (sigrange_line[1] - sigrange_line[0]) + sigrange_line[0] + w_line = torch.rand(1)[0] * (0.5 * (N-1) + 0.1 - wmin_line) + wmin_line + + vals_line = torch.exp(-dists ** 2 / 2. / sig_line ** 2) * (manhat < w_line) + + t = torch.rand(1)[0] + vals = vals_gauss + + vals = vals_nothing + if t < (probs[0] + probs[1]): + vals = vals_line + else: + vals = vals + if t < probs[0]: + vals = vals_gauss + else: + vals = vals + + v = vals / torch.sum(vals) + z = torch.zeros_like(v) + f = torch.stack([v,z,z, z,v,z, z,z,v], dim=0).reshape([3, 3, N, N]) + return f \ No newline at end of file diff --git a/resource/ssba/utils/gpu_test.py b/resource/ssba/utils/gpu_test.py new file mode 100644 index 0000000..74fde94 --- /dev/null +++ b/resource/ssba/utils/gpu_test.py @@ -0,0 +1,23 @@ +import torch + +print(torch.cuda.is_available()) +#cuda是否可用； + +print(torch.cuda.device_count()) +#返回gpu数量； + +print(torch.cuda.get_device_name()) +#返回gpu名字，设备索引默认从0开始； + +print(torch.cuda.current_device()) +#返回当前设备索引； +for i in range(torch.cuda.device_count()): + sync_device = torch.device('cuda') + print(sync_device) + z = torch.empty([4, 512], device=sync_device) + print(z.device) + +sync_device_out = torch.device('cuda', 2) +print(sync_device_out) +z = torch.empty([4, 512], device=sync_device_out) +print(z.device) \ No newline at end of file diff --git a/resource/ssba/utils/pack_images.py b/resource/ssba/utils/pack_images.py new file mode 100644 index 0000000..c2c7e25 --- /dev/null +++ b/resource/ssba/utils/pack_images.py @@ -0,0 +1,22 @@ +import os,re +from tqdm import tqdm +from PIL import Image +import numpy as np + +import argparse +parser = argparse.ArgumentParser() +parser.add_argument('--path', type = str, required=True) +parser.add_argument('--save_file_path', type = str, required=True) +args = parser.parse_args() + +path = args.path #'/mnts2d/sec_data1/ChenHongrui/cifar10_stegastamp_b1/test/hidden' +save_file_path = args.save_file_path #'/mnts2d/sec_data1/ChenHongrui/cifar10_stegastamp_b1/test_b1.npy' +img_list = [] +for file in tqdm( + sorted(os.listdir(path), key=lambda x: [int(d) if d.isdigit() else d for d in re.split('(\d+)', x)]) + ): + #if file.endswith('.png'): + img = np.array(Image.open(path +'/'+file)) + img_list.append(img) + +np.save(save_file_path,np.array(img_list)) \ No newline at end of file diff --git a/resource/ssba/utils/perturbations.py b/resource/ssba/utils/perturbations.py new file mode 100644 index 0000000..4c467eb --- /dev/null +++ b/resource/ssba/utils/perturbations.py @@ -0,0 +1,327 @@ + +import cv2 +import argparse +from tqdm import tqdm +import os +import numpy as np +import torch +import torch.nn.functional as F +import torch.nn as nn +from torchvision.utils import save_image +from torchvision import transforms + +from PIL import Image, ImageOps +from calcu_metrics import avg_psnr +import diff_utils + +parser = argparse.ArgumentParser(description='Perturbations on Images') + +parser.add_argument('--original_dir', type=str, help='path to the nature image dataset') +parser.add_argument('--input_dir', type=str, help='path to the original dataset') +parser.add_argument('--output_dir', type=str, default=None, help='path to the output dataset') +parser.add_argument('--method', type=str, help='which type perturbation to add?') + +parser.add_argument('--std', type=float, default=0.1, help='the std of gaussian noise') +parser.add_argument('--kernel_size', type=int, default=7, help='the kernel size for gaussian blur') +parser.add_argument('--crop_size', type=int, default=128, help='the size for center crop') + +parser.add_argument('--quality', type=int, default=80, help='quality of jpeg compression') +parser.add_argument('--diff', action="store_true", help='use methods that are diffenrentiable or not') + +parser.add_argument('--size', type=int, help='num of pairs of imgs to compute PSNR & SSIM') + +args = parser.parse_args() + + + + + + + + +def custom_jpeg(input_dir, output_dir, quality): + if not output_dir: + output_dir = input_dir.strip('/') + '_jpeg_'+ str(quality) + + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fileList = os.listdir(input_dir) + size = len(fileList) + + #bar= tqdm(range(size)) + #for _, i in enumerate(bar): + for i in range(size): + main = fileList[i].split('.')[0]+'.jpg' + full_path = os.path.join(input_dir, fileList[i]) + try: + img = cv2.imread(full_path, cv2.IMREAD_COLOR) + dest_path = os.path.join(output_dir, main) + cv2.imwrite(dest_path, img, [cv2.IMWRITE_JPEG_QUALITY, quality]) + except: + print('Something Wrong while loading images!') + return + + return output_dir + + + + + + + + + +def diff_jpeg(input_dir, output_dir, quality): + if not output_dir: + output_dir = input_dir.strip('/') + '_jpeg_'+ str(quality) + + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fileList = os.listdir(input_dir) + size = len(fileList) + bar = tqdm(range(size)) + + transform = transforms.Compose( + [ + transforms.ToTensor(), + ] + ) + + for _, i in enumerate(bar): + main = fileList[i].split('.')[0]+'.png' + full_path = os.path.join(input_dir, fileList[i]) + try: + img = cv2.imread(full_path, cv2.IMREAD_COLOR) + #img = ImageOps.fit(img, (128, 128))#裁剪图片至指定大小 + img = img[:, :, [2,1,0]] + img = np.array(img) / 255. + img = np.transpose(img, [2, 0, 1]) + img_tensor = torch.from_numpy(img).unsqueeze(0).float() + + recover = diff_utils.jpeg_compress_decompress(img_tensor, quality=quality) + recover = recover.detach().squeeze(0).numpy() + recover = np.transpose(recover, [1, 2, 0]) + recover = transform(recover) + + dest_path = os.path.join(output_dir, main) + save_image(recover, dest_path, padding=0) + except: + print('Something Wrong while loading images!') + return + + return output_dir + + + + + + + + +def custom_guassian_noise(input_dir, output_dir, std): + if not output_dir: + output_dir = input_dir.strip('/') + '_gaussian_noise_'+ str(std) + + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fileList = os.listdir(input_dir) + size = len(fileList) + assert size != 0, 'The input image dir is empty!' + bar= tqdm(range(size)) + + transform = transforms.Compose([transforms.ToTensor()]) + + for _, i in enumerate(bar): + full_path = os.path.join(input_dir, fileList[i]) + try: + img = cv2.imread(full_path, cv2.IMREAD_COLOR) + img = img[:, :, [2,1,0]] + img = transform(img) + noise = torch.normal(mean=0, std=std, size=img.size(), dtype=torch.float32) + img = img + noise + img = torch.clamp(img, 0, 1) + dest_path = os.path.join(output_dir, fileList[i]) + save_image(img, dest_path, padding=0) + except: + print('Something Wrong while loading images!') + return + + return output_dir + + + + + + + + +def custom_centercrop(input_dir, output_dir, crop_size): + if not output_dir: + output_dir = input_dir.strip('/') + '_centercrop_'+ str(crop_size) + + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fileList = os.listdir(input_dir) + size = len(fileList) + assert size != 0, 'The input image dir is empty!' + bar= tqdm(range(size)) + + trial_path = os.path.join(input_dir, fileList[0]) + img = cv2.imread(trial_path, cv2.IMREAD_COLOR) + img_height, img_width = img.shape[0], img.shape[1] + assert img_height == img_width, 'The height and width of an image should be the same!' + transform = transforms.Compose( + [ + transforms.ToTensor(), + transforms.CenterCrop(crop_size), + transforms.Resize(img_height), + ] + ) + + for _, i in enumerate(bar): + full_path = os.path.join(input_dir, fileList[i]) + try: + img = cv2.imread(full_path, cv2.IMREAD_COLOR) + img = img[:, :, [2,1,0]] + img = transform(img) + img = torch.clamp(img, 0, 1) + dest_path = os.path.join(output_dir, fileList[i]) + save_image(img, dest_path, padding=0) + except: + print('Something Wrong while loading images!') + return + + return output_dir + + + + + + + + +def custom_gaussian_blur(input_dir, output_dir, kernel_size): + if not output_dir: + output_dir = input_dir.strip('/') + '_gaussian_blur_'+ str(kernel_size) + + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fileList = os.listdir(input_dir) + size = len(fileList) + assert size != 0, 'The input image dir is empty!' + bar= tqdm(range(size)) + sigma = 0.8 + 0.3 * ((kernel_size - 1) * 0.5 -1) + kernel_size = (kernel_size, kernel_size) + transform = transforms.Compose([transforms.ToTensor(),]) + + for _, i in enumerate(bar): + full_path = os.path.join(input_dir, fileList[i]) + try: + img = cv2.imread(full_path, cv2.IMREAD_COLOR) + #img = img[:, :, [2,1,0]] + img = cv2.GaussianBlur(img, ksize = kernel_size, sigmaX=-1) + dest_path = os.path.join(output_dir, fileList[i]) + #img = transform(img) + #save_image(img, dest_path, padding=0) + cv2.imwrite(dest_path, img) + except: + print('Something Wrong while loading images!') + return + + return output_dir + + + + + + + + +def diff_gaussian_blur(input_dir, output_dir, kernel_size): + if not output_dir: + output_dir = input_dir.strip('/') + '_gaussian_blur_'+ str(kernel_size) + + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fileList = os.listdir(input_dir) + size = len(fileList) + assert size != 0, 'The input image dir is empty!' + bar= tqdm(range(size)) + + trial_path = os.path.join(input_dir, fileList[0]) + img = cv2.imread(trial_path, cv2.IMREAD_COLOR) + img_height, img_width = img.shape[0], img.shape[1] + assert img_height == img_width, 'The height and width of an image should be the same!' + transform = transforms.Compose([transforms.ToTensor(),]) + + for _, i in enumerate(bar): + full_path = os.path.join(input_dir, fileList[i]) + try: + img = cv2.imread(full_path, cv2.IMREAD_COLOR) + img = img[:, :, [2,1,0]] + img = transform(img).unsqueeze(0) + + f = diff_utils.random_blur_kernel(probs=[.25, .25], N_blur=kernel_size, sigrange_gauss=[1., 3.], + sigrange_line=[.25, 1.], wmin_line=3) + img = F.conv2d(img, f, bias=None, padding=int((kernel_size - 1) / 2)) + img = img.squeeze(0) + dest_path = os.path.join(output_dir, fileList[i]) + save_image(img, dest_path, padding=0) + except: + print('Something Wrong while loading images!') + return + + return output_dir + +if __name__=="__main__": + for arg in vars(args): + print(format(arg, '<20'), format(str(getattr(args,arg))), '<') + + ori_path = args.original_dir + input_path = args.input_dir + output_path = args.output_dir + method = args.method + + std = args.std + kernel_size = args.kernel_size + crop_size = args.crop_size + size = args.size + quality = args.quality + + assert method in ['jpeg', 'gaussian_noise', 'gaussian_blur', 'center_crop'], \ + 'Currently only support jpeg, gaussian_noise, gaussian_blur, center_crop' + + if method == 'jpeg': + if not args.diff: + print('Using JPEG from Opencv-Python with quality', quality) + out_dir = custom_jpeg(input_path, output_path, quality) + else: + print('Using DiffJPEG with quality', quality) + out_dir = diff_jpeg(input_path, output_path, quality) + elif method == 'gaussian_noise': + print('Using gaussain noise with std', std) + out_dir = custom_guassian_noise(input_path, output_path, std) + elif method == 'center_crop': + print('Using center crop with size', crop_size) + out_dir = custom_centercrop(input_path, output_path, crop_size) + elif method == 'gaussian_blur': + if not args.diff: + print('Using gaussian blur from Opencv-Python with kernel size', kernel_size) + out_dir = custom_gaussian_blur(input_path, output_path, kernel_size) + else: + print('Using diff gaussian blur with kernel size', kernel_size) + out_dir = diff_gaussian_blur(input_path, output_path, kernel_size) + + avg_psnr(ori_path, out_dir, suffix='_hidden', size=size, sort=True) diff --git a/resource/ssba/utils/process_npz.py b/resource/ssba/utils/process_npz.py new file mode 100644 index 0000000..db628eb --- /dev/null +++ b/resource/ssba/utils/process_npz.py @@ -0,0 +1,47 @@ + +import numpy as np +import os +from tqdm import tqdm +import PIL +from torchvision import transforms +from torchvision.utils import save_image + + + + + + + +def biggan_npz2images(input_dir, output_dir): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + file = np.load(input_dir) + print('The columns in the .npz file is', file.files) + data, label = file['x'], file['y'] + print('The shape of data is', data.shape) + print('The shape of label is', label.shape) + + data_size = data.shape[0] + bar_data = tqdm(range(data_size)) + transform = transforms.Compose( + [ + transforms.ToTensor(), + ] + ) + + for _, i in enumerate(bar_data): + img = data[i].transpose(1,2,0) + img = PIL.Image.fromarray(img) + img = transform(img) + img_path = str(i)+"_"+str(label[i])+".png" + despath = os.path.join(output_dir, img_path) + save_image(img, despath, padding=0) + bar_data.set_description("Processing: {} / {}".format(i+1, data_size)) + + print('Done!') + +if __name__=="__main__": + input_path = 'E:/samples.npz' + output_path = 'E:/CUT_images' + biggan_npz2images(input_path, output_path) \ No newline at end of file diff --git a/resource/trojannn/apple4.png b/resource/trojannn/apple4.png new file mode 100755 index 0000000..1f91e84 Binary files /dev/null and b/resource/trojannn/apple4.png differ diff --git a/resource/trojannn/square.png b/resource/trojannn/square.png new file mode 100755 index 0000000..e8fd638 Binary files /dev/null and b/resource/trojannn/square.png differ diff --git a/resource/trojannn/watermark3.png b/resource/trojannn/watermark3.png new file mode 100755 index 0000000..7692641 Binary files /dev/null and b/resource/trojannn/watermark3.png differ diff --git a/sh/gtsrb_download.sh b/sh/gtsrb_download.sh new file mode 100755 index 0000000..88f59c6 --- /dev/null +++ b/sh/gtsrb_download.sh @@ -0,0 +1,16 @@ +# please download the following files and put them in ../data folder +wget -P ../data https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/GTSRB_Final_Training_Images.zip --no-check-certificate +wget -P ../data https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/GTSRB_Final_Test_Images.zip --no-check-certificate +wget -P ../data https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/GTSRB_Final_Test_GT.zip --no-check-certificate +mkdir ../data/gtsrb; +mkdir ../data/gtsrb/Train; +mkdir ../data/gtsrb/Test; +mkdir ../data/temps; +unzip ../data/GTSRB_Final_Training_Images.zip -d ../data/temps/Train; +unzip ../data/GTSRB_Final_Test_Images.zip -d ../data/temps/Test; +mv ../data/temps/Train/GTSRB/Final_Training/Images/* ../data/gtsrb/Train; +mv ../data/temps/Test/GTSRB/Final_Test/Images/* ../data/gtsrb/Test; +unzip ../data/GTSRB_Final_Test_GT.zip -d ../data/gtsrb/Test/; +rm -r ../data/temps; +rm ../data/*.zip; +echo "Download Completed"; diff --git a/sh/init_folders.sh b/sh/init_folders.sh new file mode 100644 index 0000000..a2c383a --- /dev/null +++ b/sh/init_folders.sh @@ -0,0 +1,7 @@ +# This script creates the folders needed for the project +mkdir -p data +mkdir -p data/cifar10 +mkdir -p data/tiny +mkdir -p data/gtsrb +mkdir -p data/cifar100 +mkdir record diff --git a/sh/install.sh b/sh/install.sh new file mode 100755 index 0000000..06e4be5 --- /dev/null +++ b/sh/install.sh @@ -0,0 +1,36 @@ +pip install torch==1.11+cu113 torchvision==0.12 torchaudio==0.11.0 -f https://download.pytorch.org/whl/torch_stable.html +pip install keras==2.7.0 +pip install opencv-python==4.5.5.64 +pip install pandas==1.3.1 +pip install Pillow==8.2.0 +pip install scikit-learn==0.24.2 +pip install scikit-image==0.18.1 +pip install tqdm==4.61.0 +pip install pyyaml==5.4.1 +pip install tensorboard==2.7.0 +pip install Kornia==0.5.0 +pip install imageio==2.18.0 +pip install matplotlib==3.5.1 +pip install scipy==1.3.1 + +# for visualization only +pip install seaborn==0.11.2 +## Shapely Value +pip install shap==0.40.0 +## Grad-CAM +pip install grad-cam==1.3.9 +## Feature Map & Feature Visualization +pip install omnixai==1.2.3 +pip install plotly==5.11.0 +## UMAP +pip install umap-learn==0.5.3 +## Network Structure +pip install graphviz +pip install hiddenlayer==0.3 +pip install -U git+https://github.com/szagoruyko/pytorchviz.git@master + +## Landscape +pip install PyHessian==0.1 + +## Quality +pip install torchmetrics[image] \ No newline at end of file diff --git a/sh/load_for_test.py b/sh/load_for_test.py new file mode 100755 index 0000000..e8fb353 --- /dev/null +++ b/sh/load_for_test.py @@ -0,0 +1,22 @@ +import sys, yaml, os, argparse + +os.chdir(sys.path[0]) +sys.path.append('../') +os.getcwd() + +from utils.save_load_attack import * + +def add_args(parser): + """ + parser : argparse.ArgumentParser + return a parser added with args required by fit + """ + # Training settings + parser.add_argument('--attack_result_file_path', type=str) + return parser + +parser = (add_args(argparse.ArgumentParser(description=sys.argv[0]))) +args = parser.parse_args() + +a = load_attack_result(f'{args.attack_result_file_path}') # just load, record the log + diff --git a/sh/scp_data.sh b/sh/scp_data.sh new file mode 100644 index 0000000..9615ca1 --- /dev/null +++ b/sh/scp_data.sh @@ -0,0 +1,35 @@ +# sed -i 's/\r//' ./sh/scp_data.sh +#!/bin/bash + +# Example Usage +# cd bdzoo2 +# bash sh/scp_data.sh + +source_dir='10.20.12.241:/workspace/public_data/' +target_dir='./data/' + +echo "Input your username below to access" $source_dir +read username + +# Uncomment the following line to use username in this script +# username='xxxxxx' + +# For CIFAR10, CIFAR100 and TinyImageNet, only zip files are needed since torchvision will unzip them. +# For GTSRB, the whole folder is needed to avoid the unzip step. + +###### CIFAR10 ####### +echo 'scp cifar10. Press Ctrl+C to skip.' +scp -r $username@$source_dir'cifar10' $target_dir + +###### CIFAR100 ###### +echo 'scp cifar100. Press Ctrl+C to skip.' +scp -r $username@$source_dir'cifar100' $target_dir + +###### TINYIMAGENET ###### +echo 'scp tinyimagenet. Press Ctrl+C to skip.' +scp -r $username@$source_dir'tiny' $target_dir + +###### GTSRB ####### +echo 'scp gtsrb. Press Ctrl+C to skip.' +scp -r $username@$source_dir'gtsrb' $target_dir + diff --git a/sh/scp_data_resource.sh b/sh/scp_data_resource.sh new file mode 100644 index 0000000..ee3a393 --- /dev/null +++ b/sh/scp_data_resource.sh @@ -0,0 +1,22 @@ +# sed -i 's/\r//' ./sh/scp_data.sh +#!/bin/bash + +# Example Usage +# cd bdzoo2 +# bash sh/scp_data.sh + +source_dir='10.20.12.241:/workspace/public_resource' +target_dir='./resource' + +echo "Input your username below to access" $source_dir +read username + +# Uncomment the following line to use username in this script +# username='xxxxxx' + +# For CIFAR10, CIFAR100 and TinyImageNet, only zip files are needed since torchvision will unzip them. +# For GTSRB, the whole folder is needed to avoid the unzip step. + +echo 'scp resource' +scp -r $username@$source_dir $target_dir + diff --git a/sh/timagenet_download.sh b/sh/timagenet_download.sh new file mode 100755 index 0000000..2eba64e --- /dev/null +++ b/sh/timagenet_download.sh @@ -0,0 +1,4 @@ +wget -P ../data http://cs231n.stanford.edu/tiny-imagenet-200.zip +unzip ../data/tiny-imagenet-200.zip -d ../data; +mv ../data/tiny-imagenet-200 ../data/tiny; +echo "Download Completed"; \ No newline at end of file diff --git a/utils/aggregate_block/bd_attack_generate.py b/utils/aggregate_block/bd_attack_generate.py new file mode 100755 index 0000000..51ab286 --- /dev/null +++ b/utils/aggregate_block/bd_attack_generate.py @@ -0,0 +1,219 @@ +# idea : the backdoor img and label transformation are aggregated here, which make selection with args easier. + +import sys, logging +sys.path.append('../../') +import imageio +from PIL import Image +import numpy as np +import torchvision.transforms as transforms + +from utils.bd_img_transform.lc import labelConsistentAttack +from utils.bd_img_transform.blended import blendedImageAttack +from utils.bd_img_transform.patch import AddMaskPatchTrigger, SimpleAdditiveTrigger +from utils.bd_img_transform.sig import sigTriggerAttack +from utils.bd_img_transform.SSBA import SSBA_attack_replace_version +from utils.bd_label_transform.backdoor_label_transform import * +from torchvision.transforms import Resize + +class general_compose(object): + def __init__(self, transform_list): + self.transform_list = transform_list + + def __call__(self, img, *args, **kwargs): + for transform, if_all in self.transform_list: + if if_all == False: + img = transform(img) + else: + img = transform(img, *args, **kwargs) + return img + +class convertNumpyArrayToFloat32(object): + def __init__(self): + pass + def __call__(self, np_img_float32): + return np_img_float32.astype(np.float32) +npToFloat32 = convertNumpyArrayToFloat32() + +class clipAndConvertNumpyArrayToUint8(object): + def __init__(self): + pass + def __call__(self, np_img_float32): + return np.clip(np_img_float32, 0, 255).astype(np.uint8) +npClipAndToUint8 = clipAndConvertNumpyArrayToUint8() + +def bd_attack_img_trans_generate(args): + ''' + # idea : use args to choose which backdoor img transform you want + :param args: args that contains parameters of backdoor attack + :return: transform on img for backdoor attack in both train and test phase + ''' + + if args.attack in ['badnet',]: + + + trans = transforms.Compose([ + transforms.Resize(args.img_size[:2]), # (32, 32) + np.array, + ]) + + bd_transform = AddMaskPatchTrigger( + trans(Image.open(args.patch_mask_path)), + ) + + train_bd_transform = general_compose([ + (transforms.Resize(args.img_size[:2]), False), + (np.array, False), + (bd_transform, True), + (npClipAndToUint8,False), + ]) + + test_bd_transform = general_compose([ + (transforms.Resize(args.img_size[:2]), False), + (np.array, False), + (bd_transform, True), + (npClipAndToUint8,False), + ]) + + elif args.attack == 'blended': + + trans = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize(args.img_size[:2]), # (32, 32) + transforms.ToTensor() + ]) + + train_bd_transform = general_compose([ + (transforms.Resize(args.img_size[:2]), False), + (np.array, False), + (blendedImageAttack( + trans( + imageio.imread(args.attack_trigger_img_path) # '../data/hello_kitty.jpeg' + ).cpu().numpy().transpose(1, 2, 0) * 255, + float(args.attack_train_blended_alpha)), True), + (npToFloat32, False), + (npClipAndToUint8,False), + ]) + + test_bd_transform = general_compose([ + (transforms.Resize(args.img_size[:2]), False), + (np.array, False), + (blendedImageAttack( + trans( + imageio.imread(args.attack_trigger_img_path) # '../data/hello_kitty.jpeg' + ).cpu().numpy().transpose(1, 2, 0) * 255, + float(args.attack_test_blended_alpha)), True), + (npToFloat32, False), + (npClipAndToUint8,False), + ]) + + elif args.attack == 'sig': + trans = sigTriggerAttack( + delta=args.sig_delta, + f=args.sig_f, + ) + train_bd_transform = general_compose([ + (transforms.Resize(args.img_size[:2]), False), + (np.array, False), + (trans, True), + (npClipAndToUint8,False), + ]) + test_bd_transform = general_compose([ + (transforms.Resize(args.img_size[:2]), False), + (np.array, False), + (trans, True), + (npClipAndToUint8,False), + ]) + + elif args.attack in ['SSBA']: + train_bd_transform = general_compose([ + (transforms.Resize(args.img_size[:2]), False), + (np.array, False), + (SSBA_attack_replace_version( + replace_images=np.load(args.attack_train_replace_imgs_path) # '../data/cifar10_SSBA/train.npy' + ), True), + (npClipAndToUint8,False), + ]) + test_bd_transform = general_compose([ + (transforms.Resize(args.img_size[:2]), False), + (np.array, False), + (SSBA_attack_replace_version( + replace_images=np.load(args.attack_test_replace_imgs_path) # '../data/cifar10_SSBA/test.npy' + ), True), + (npClipAndToUint8,False), + ]) + + elif args.attack in ['label_consistent']: + add_trigger = labelConsistentAttack(reduced_amplitude=args.reduced_amplitude) + add_trigger_func = add_trigger.poison_from_indices + train_bd_transform = general_compose([ + (transforms.Resize(args.img_size[:2]), False), + (np.array, False), + (SSBA_attack_replace_version( + replace_images=np.load(args.attack_train_replace_imgs_path) # '../data/cifar10_SSBA/train.npy' + ), True), + (add_trigger_func, False), + (npClipAndToUint8,False), + ]) + test_bd_transform = general_compose([ + (transforms.Resize(args.img_size[:2]), False), + (np.array, False), + # (SSBA_attack_replace_version( + # replace_images=np.load(args.attack_test_replace_imgs_path) # '../data/cifar10_SSBA/test.npy' + # ), True), + (add_trigger_func, False), + (npClipAndToUint8,False), + ]) + + elif args.attack == 'lowFrequency': + + triggerArray = np.load(args.lowFrequencyPatternPath) + + if len(triggerArray.shape) == 4: + logging.info("Get lowFrequency trigger with 4 dimension, take the first one") + triggerArray = triggerArray[0] + elif len(triggerArray.shape) == 3: + pass + elif len(triggerArray.shape) == 2: + triggerArray = np.stack((triggerArray,) * 3, axis=-1) + else: + raise ValueError("lowFrequency trigger shape error, should be either 2 or 3 or 4") + + logging.info("Load lowFrequency trigger with shape {}".format(triggerArray.shape)) + + train_bd_transform = general_compose([ + (transforms.Resize(args.img_size[:2]), False), + (np.array, False), + (SimpleAdditiveTrigger( + trigger_array=triggerArray, + ), True), + (npClipAndToUint8,False), + ]) + + test_bd_transform = general_compose([ + (transforms.Resize(args.img_size[:2]), False), + (np.array, False), + (SimpleAdditiveTrigger( + trigger_array=triggerArray, + ), True), + (npClipAndToUint8,False), + ]) + + return train_bd_transform, test_bd_transform + + +def bd_attack_label_trans_generate(args): + ''' + # idea : use args to choose which backdoor label transform you want + from args generate backdoor label transformation + + ''' + if args.attack_label_trans == 'all2one': + target_label = int(args.attack_target) + bd_label_transform = AllToOne_attack(target_label) + elif args.attack_label_trans == 'all2all': + bd_label_transform = AllToAll_shiftLabelAttack( + int(1 if "attack_label_shift_amount" not in args.__dict__ else args.attack_label_shift_amount), + int(args.num_classes) + ) + + return bd_label_transform diff --git a/utils/aggregate_block/dataset_and_transform_generate.py b/utils/aggregate_block/dataset_and_transform_generate.py new file mode 100755 index 0000000..975fbf6 --- /dev/null +++ b/utils/aggregate_block/dataset_and_transform_generate.py @@ -0,0 +1,324 @@ +''' +This code is based on https://github.com/bboylyg/NAD + +The original license: +License CC BY-NC + +The update include: + 1. decompose the function structure and add more normalization options + 2. add more dataset options, and compose them into dataset_and_transform_generate + +# idea : use args to choose which dataset and corresponding transform you want +''' +import logging +import os +import random +from typing import Tuple + +import numpy as np +import torch +import torchvision.transforms as transforms +from PIL import ImageFilter, Image + + + +def get_num_classes(dataset_name: str) -> int: + # idea : given name, return the number of class in the dataset + if dataset_name in ["mnist", "cifar10"]: + num_classes = 10 + elif dataset_name == "gtsrb": + num_classes = 43 + elif dataset_name == "celeba": + num_classes = 8 + elif dataset_name == 'cifar100': + num_classes = 100 + elif dataset_name == 'tiny': + num_classes = 200 + elif dataset_name == 'imagenet': + num_classes = 1000 + else: + raise Exception("Invalid Dataset") + return num_classes + + +def get_input_shape(dataset_name: str) -> Tuple[int, int, int]: + # idea : given name, return the image size of images in the dataset + if dataset_name == "cifar10": + input_height = 32 + input_width = 32 + input_channel = 3 + elif dataset_name == "gtsrb": + input_height = 32 + input_width = 32 + input_channel = 3 + elif dataset_name == "mnist": + input_height = 28 + input_width = 28 + input_channel = 1 + elif dataset_name == "celeba": + input_height = 64 + input_width = 64 + input_channel = 3 + elif dataset_name == 'cifar100': + input_height = 32 + input_width = 32 + input_channel = 3 + elif dataset_name == 'tiny': + input_height = 64 + input_width = 64 + input_channel = 3 + elif dataset_name == 'imagenet': + input_height = 224 + input_width = 224 + input_channel = 3 + else: + raise Exception("Invalid Dataset") + return input_height, input_width, input_channel + + +def get_dataset_normalization(dataset_name): + # idea : given name, return the default normalization of images in the dataset + if dataset_name == "cifar10": + # from wanet + dataset_normalization = (transforms.Normalize([0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261])) + elif dataset_name == 'cifar100': + '''get from https://gist.github.com/weiaicunzai/e623931921efefd4c331622c344d8151''' + dataset_normalization = (transforms.Normalize([0.5071, 0.4865, 0.4409], [0.2673, 0.2564, 0.2762])) + elif dataset_name == "mnist": + dataset_normalization = (transforms.Normalize([0.5], [0.5])) + elif dataset_name == 'tiny': + dataset_normalization = (transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262])) + elif dataset_name == "gtsrb" or dataset_name == "celeba": + dataset_normalization = transforms.Normalize([0, 0, 0], [1, 1, 1]) + elif dataset_name == 'imagenet': + dataset_normalization = ( + transforms.Normalize( + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225], + ) + ) + else: + raise Exception("Invalid Dataset") + return dataset_normalization + + +def get_dataset_denormalization(normalization: transforms.Normalize): + mean, std = normalization.mean, normalization.std + + if mean.__len__() == 1: + mean = - mean + else: # len > 1 + mean = [-i for i in mean] + + if std.__len__() == 1: + std = 1 / std + else: # len > 1 + std = [1 / i for i in std] + + # copy from answer in + # https://discuss.pytorch.org/t/simple-way-to-inverse-transform-normalization/4821/3 + # user: https://discuss.pytorch.org/u/svd3 + + invTrans = transforms.Compose([ + transforms.Normalize(mean=[0., 0., 0.], + std=std), + transforms.Normalize(mean=mean, + std=[1., 1., 1.]), + ]) + + return invTrans + + +def get_transform(dataset_name, input_height, input_width, train=True, random_crop_padding=4): + # idea : given name, return the final implememnt transforms for the dataset + transforms_list = [] + transforms_list.append(transforms.Resize((input_height, input_width))) + if train: + transforms_list.append(transforms.RandomCrop((input_height, input_width), padding=random_crop_padding)) + # transforms_list.append(transforms.RandomRotation(10)) + if dataset_name == "cifar10": + transforms_list.append(transforms.RandomHorizontalFlip()) + + transforms_list.append(transforms.ToTensor()) + transforms_list.append(get_dataset_normalization(dataset_name)) + return transforms.Compose(transforms_list) + + +def get_transform_prefetch(dataset_name, input_height, input_width, train=True, prefetch=False): + # idea : given name, return the final implememnt transforms for the dataset + transforms_list = [] + transforms_list.append(transforms.Resize((input_height, input_width))) + if train: + transforms_list.append(transforms.RandomCrop((input_height, input_width), padding=4)) + # transforms_list.append(transforms.RandomRotation(10)) + if dataset_name == "cifar10": + transforms_list.append(transforms.RandomHorizontalFlip()) + if not prefetch: + transforms_list.append(transforms.ToTensor()) + transforms_list.append(get_dataset_normalization(dataset_name)) + return transforms.Compose(transforms_list) + + +class GaussianBlur(object): + """Gaussian blur augmentation in SimCLR. + + Borrowed from https://github.com/facebookresearch/moco/blob/master/moco/loader.py. + """ + + def __init__(self, sigma=[0.1, 2.0]): + self.sigma = sigma + + def __call__(self, x): + sigma = random.uniform(self.sigma[0], self.sigma[1]) + x = x.filter(ImageFilter.GaussianBlur(radius=sigma)) + + return x + + +def get_transform_self(dataset_name, input_height, input_width, train=True, prefetch=False): + # idea : given name, return the final implememnt transforms for the dataset during self-supervised learning + transforms_list = [] + transforms_list.append(transforms.Resize((input_height, input_width))) + if train: + + transforms_list.append( + transforms.RandomResizedCrop(size=(input_height, input_width), scale=(0.2, 1.0), ratio=(0.75, 1.3333), + interpolation=3)) + transforms_list.append(transforms.RandomHorizontalFlip(p=0.5)) + transforms_list.append(transforms.RandomApply(torch.nn.ModuleList([transforms.ColorJitter(brightness=[0.6, 1.4], + contrast=[0.6, 1.4], + saturation=[0.6, 1.4], + hue=[-0.1, 0.1])]), + p=0.8)) + transforms_list.append(transforms.RandomGrayscale(p=0.2)) + transforms_list.append(transforms.RandomApply([GaussianBlur(sigma=[0.1, 2.0])], p=0.5)) + + if not prefetch: + transforms_list.append(transforms.ToTensor()) + transforms_list.append(get_dataset_normalization(dataset_name)) + return transforms.Compose(transforms_list) + + +def dataset_and_transform_generate(args): + ''' + # idea : given args, return selected dataset, transforms for both train and test part of data. + :param args: + :return: clean dataset in both train and test phase, and corresponding transforms + + 1. set the img transformation + 2. set the label transform + + ''' + if not args.dataset.startswith('test'): + train_img_transform = get_transform(args.dataset, *(args.img_size[:2]), train=True) + test_img_transform = get_transform(args.dataset, *(args.img_size[:2]), train=False) + else: + # test folder datset, use the mnist transform for convenience + train_img_transform = get_transform('mnist', *(args.img_size[:2]), train=True) + test_img_transform = get_transform('mnist', *(args.img_size[:2]), train=False) + + train_label_transform = None + test_label_transform = None + + train_dataset_without_transform, test_dataset_without_transform = None, None + + if (train_dataset_without_transform is None) or (test_dataset_without_transform is None): + + if args.dataset.startswith('test'): # for test only + from torchvision.datasets import ImageFolder + train_dataset_without_transform = ImageFolder('../data/test') + test_dataset_without_transform = ImageFolder('../data/test') + elif args.dataset == 'mnist': + from torchvision.datasets import MNIST + train_dataset_without_transform = MNIST( + args.dataset_path, + train=True, + transform=None, + download=True, + ) + test_dataset_without_transform = MNIST( + args.dataset_path, + train=False, + transform=None, + download=True, + ) + elif args.dataset == 'cifar10': + from torchvision.datasets import CIFAR10 + train_dataset_without_transform = CIFAR10( + args.dataset_path, + train=True, + transform=None, + download=True, + ) + test_dataset_without_transform = CIFAR10( + args.dataset_path, + train=False, + transform=None, + download=True, + ) + elif args.dataset == 'cifar100': + from torchvision.datasets import CIFAR100 + train_dataset_without_transform = CIFAR100( + root=args.dataset_path, + train=True, + download=True, + ) + test_dataset_without_transform = CIFAR100( + root=args.dataset_path, + train=False, + download=True, + ) + elif args.dataset == 'gtsrb': + from dataset.GTSRB import GTSRB + train_dataset_without_transform = GTSRB(args.dataset_path, + train=True, + ) + test_dataset_without_transform = GTSRB(args.dataset_path, + train=False, + ) + elif args.dataset == "celeba": + from dataset.CelebA import CelebA_attr + train_dataset_without_transform = CelebA_attr(args.dataset_path, + split='train') + test_dataset_without_transform = CelebA_attr(args.dataset_path, + split='test') + elif args.dataset == "tiny": + from dataset.Tiny import TinyImageNet + train_dataset_without_transform = TinyImageNet(args.dataset_path, + split='train', + download=True, + ) + test_dataset_without_transform = TinyImageNet(args.dataset_path, + split='val', + download=True, + ) + elif args.dataset == "imagenet": + from torchvision.datasets import ImageFolder + + def is_valid_file(path): + try: + img = Image.open(path) + img.verify() + img.close() + return True + except: + return False + + logging.warning("For ImageNet, this script need large size of RAM to load the whole dataset.") + logging.debug("We will provide a different script later to handle this problem for backdoor ImageNet.") + + train_dataset_without_transform = ImageFolder( + root=f"{args.dataset_path}/train", + is_valid_file=is_valid_file, + ) + test_dataset_without_transform = ImageFolder( + root=f"{args.dataset_path}/val", + is_valid_file=is_valid_file, + ) + + return train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform diff --git a/utils/aggregate_block/dataset_and_transform_generate_ft.py b/utils/aggregate_block/dataset_and_transform_generate_ft.py new file mode 100644 index 0000000..6ea5700 --- /dev/null +++ b/utils/aggregate_block/dataset_and_transform_generate_ft.py @@ -0,0 +1,644 @@ +''' +This code is based on https://github.com/bboylyg/NAD + +The original license: +License CC BY-NC + +The update include: + 1. decompose the function structure and add more normalization options + 2. add more dataset options, and compose them into dataset_and_transform_generate + +# idea : use args to choose which dataset and corresponding transform you want +''' +import logging +import os +import random +from typing import Tuple +import numpy as np +import torch +import torchvision.transforms as transforms +from torchvision.datasets import ImageFolder +from PIL import ImageFilter, Image + +from utils.bd_dataset import xy_iter +from torch.utils.data import Dataset +class CustomDataset(Dataset): + def __init__(self, img_list, label_list): + self.img_list = img_list + self.label_list = label_list + + + def __len__(self): + return len(self.img_list) + + def __getitem__(self, idx): + + return self.img_list[idx], np.int64(self.label_list[idx]) + +def rgb_loader(path): + with open(path, 'rb') as f: + with Image.open(f) as img: + return img.convert('RGB') + +def get_num_classes(dataset_name : str) -> int: + # idea : given name, return the number of class in the dataset + if dataset_name in ["mnist", "cifar10"]: + num_classes = 10 + elif dataset_name == "gtsrb": + num_classes = 43 + elif dataset_name == "celeba": + num_classes = 8 + elif dataset_name == 'cifar100': + num_classes = 100 + elif dataset_name == 'tiny': + num_classes = 200 + elif dataset_name == 'imagenet': + num_classes = 1000 + else: + raise Exception("Invalid Dataset") + return num_classes + + +def get_input_shape(dataset_name : str) -> Tuple[int, int, int]: + # idea : given name, return the image size of images in the dataset + if dataset_name == "cifar10": + input_height = 32 + input_width = 32 + input_channel = 3 + elif dataset_name == "gtsrb": + input_height = 32 + input_width = 32 + input_channel = 3 + elif dataset_name == "mnist": + input_height = 28 + input_width = 28 + input_channel = 1 + elif dataset_name == "celeba": + input_height = 64 + input_width = 64 + input_channel = 3 + elif dataset_name == 'cifar100': + input_height = 32 + input_width = 32 + input_channel = 3 + elif dataset_name == 'tiny': + input_height = 64 + input_width = 64 + input_channel = 3 + elif dataset_name == 'imagenet' or dataset_name == 'domainnet': + input_height = 224 + input_width = 224 + input_channel = 3 + else: + raise Exception("Invalid Dataset") + return input_height, input_width, input_channel + +def get_dataset_normalization(dataset_name): + # idea : given name, return the default normalization of images in the dataset + if dataset_name == "cifar10": + #from wanet + # dataset_normalization = (transforms.Normalize([0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261])) + dataset_normalization = (transforms.Normalize([0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261])) + elif dataset_name == 'cifar100': + '''get from https://gist.github.com/weiaicunzai/e623931921efefd4c331622c344d8151''' + dataset_normalization = (transforms.Normalize([0.5071, 0.4865, 0.4409],[0.2673, 0.2564, 0.2762])) + elif dataset_name == "mnist": + dataset_normalization = (transforms.Normalize([0.5], [0.5])) + elif dataset_name == 'tiny': + dataset_normalization = (transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262])) + elif dataset_name == "gtsrb" or dataset_name == "celeba": + dataset_normalization = transforms.Normalize([0, 0, 0], [1, 1, 1]) + # dataset_normalization=transforms.Normalize((0.3337, 0.3064, 0.3171), (0.2672, 0.2564, 0.2629)) + elif dataset_name == "domainnet": + dataset_normalization = (transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])) + elif dataset_name == 'imagenet': + dataset_normalization = ( + transforms.Normalize( + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225], + ) + ) + else: + raise Exception("Invalid Dataset") + return dataset_normalization + +def get_dataset_denormalization(normalization : transforms.Normalize): + + mean, std = normalization.mean, normalization.std + + if mean.__len__() == 1: + mean = - mean + else: # len > 1 + mean = [-i for i in mean] + + if std.__len__() == 1: + std = 1/std + else: # len > 1 + std = [1/i for i in std] + + # copy from answer in + # https://discuss.pytorch.org/t/simple-way-to-inverse-transform-normalization/4821/3 + # user: https://discuss.pytorch.org/u/svd3 + + invTrans = transforms.Compose([ + transforms.Normalize(mean=[0., 0., 0.], + std=std), + transforms.Normalize(mean=mean, + std=[1., 1., 1.]), + ]) + + return invTrans + +def get_transform(dataset_name, input_height, input_width,train=True): + # idea : given name, return the final implememnt transforms for the dataset + transforms_list = [] + if dataset_name == 'domainnet' and train: + transforms_list.append(transforms.Resize((256, 256))) + else: + transforms_list.append(transforms.Resize((input_height, input_width))) + if train: + transforms_list.append(transforms.RandomCrop((input_height, input_width), padding=4)) + # transforms_list.append(transforms.RandomRotation(10)) + if dataset_name == "cifar10" or dataset_name == "domainnet": + transforms_list.append(transforms.RandomHorizontalFlip()) + + transforms_list.append(transforms.ToTensor()) + transforms_list.append(get_dataset_normalization(dataset_name)) + return transforms.Compose(transforms_list) + +def get_transform_prefetch(dataset_name, input_height, input_width,train=True,prefetch=False): + # idea : given name, return the final implememnt transforms for the dataset + transforms_list = [] + transforms_list.append(transforms.Resize((input_height, input_width))) + if train: + transforms_list.append(transforms.RandomCrop((input_height, input_width), padding=4)) + # transforms_list.append(transforms.RandomRotation(10)) + if dataset_name == "cifar10": + transforms_list.append(transforms.RandomHorizontalFlip()) + if not prefetch: + transforms_list.append(transforms.ToTensor()) + transforms_list.append(get_dataset_normalization(dataset_name)) + return transforms.Compose(transforms_list) + + +class GaussianBlur(object): + """Gaussian blur augmentation in SimCLR. + + Borrowed from https://github.com/facebookresearch/moco/blob/master/moco/loader.py. + """ + + def __init__(self, sigma=[0.1, 2.0]): + self.sigma = sigma + + def __call__(self, x): + sigma = random.uniform(self.sigma[0], self.sigma[1]) + x = x.filter(ImageFilter.GaussianBlur(radius=sigma)) + + return x + +def get_transform_self(dataset_name, input_height, input_width,train=True,prefetch=False): + # idea : given name, return the final implememnt transforms for the dataset during self-supervised learning + transforms_list = [] + transforms_list.append(transforms.Resize((input_height, input_width))) + if train: + # transforms_list.append(transforms.RandomCrop((input_height, input_width), padding=4)) + # transforms_list.append(transforms.RandomHorizontalFlip(p=0.5)) + # transforms_list.append(transforms.RandomApply([transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8)) + # transforms_list.append(transforms.RandomGrayscale(p=0.2)) + transforms_list.append(transforms.RandomResizedCrop(size=(input_height, input_width), scale=(0.2, 1.0), ratio=(0.75, 1.3333), interpolation=3)) + transforms_list.append(transforms.RandomHorizontalFlip(p=0.5)) + transforms_list.append(transforms.RandomApply(torch.nn.ModuleList([transforms.ColorJitter(brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[-0.1, 0.1])]),p=0.8)) + transforms_list.append(transforms.RandomGrayscale(p=0.2)) + transforms_list.append(transforms.RandomApply([GaussianBlur(sigma=[0.1,2.0])],p=0.5)) + + if not prefetch: + transforms_list.append(transforms.ToTensor()) + transforms_list.append(get_dataset_normalization(dataset_name)) + return transforms.Compose(transforms_list) + +def speed_up_save( + dataset, + dataset_path : str, + preprocess, + train: bool = True, + idx_list = None, + split_ratio = None +): + ''' + assumption : x is PIL image + When : save the dataset into numpy array if there is no speed up matrix save. + preprocess is for resize, etc. To put image into same size numpy matrix + ''' + logging.info(f"save the speed up matrix data for {'train' if train else 'test'} at {dataset_path}") + x_list = [] + y_list = [] + ft_x_list = [] + ft_y_list = [] + + + for idx, (x, y) in enumerate(dataset): + if idx_list is not None and len(idx_list) > 0: + if idx in idx_list: + + if isinstance(x, np.ndarray): + ft_x_npy = x + else: + ft_x_npy = preprocess( + x + )[None,...] # turn HWC to 1HWC (one more dimension) + + if ft_x_npy.shape[-1] == 1: + ft_x_npy = np.repeat(ft_x_npy, 3, axis = -1) + + ft_x_list.append( + ft_x_npy + ) + ft_y_list.append( + int( + y + ) + ) + continue + + + if isinstance(x, np.ndarray): + x_npy = x + else: + x_npy = preprocess( + x + )[None,...] # turn HWC to 1HWC (one more dimension) + + if x_npy.shape[-1] == 1: + x_npy = np.repeat(x_npy, 3, axis = -1) + + x_list.append( + x_npy + ) + y_list.append( + int( + y + ) + ) + + + + all_x_numpy = np.concatenate(x_list) + + np.save( + f"{dataset_path}/{'train' if train else 'test'}_x_{split_ratio}.npy", + all_x_numpy, + ) + + all_y_numpy = np.array(y_list) + + np.save( + f"{dataset_path}/{'train' if train else 'test'}_y_{split_ratio}.npy", + all_y_numpy, + ) + if train: + + ft_all_x_numpy = np.concatenate(ft_x_list) + np.save( + f"{dataset_path}/ft_x_{split_ratio}.npy", + ft_all_x_numpy, + ) + ft_all_y_numpy = np.array(ft_y_list) + np.save( + f"{dataset_path}/ft_y_{split_ratio}.npy", + ft_all_y_numpy, + ) + + + +def speed_up_load( + dataset_path : str, + mode : str, + split_ratio +): + + if mode == 'train' and {f"train_x_{split_ratio}.npy", f"train_y_{split_ratio}.npy"}.issubset(os.listdir(dataset_path)): + logging.info(f"load speed up matrix for train data, at {dataset_path}") + train_x = np.load(f"{dataset_path}/train_x_{split_ratio}.npy") + train_y = np.load(f"{dataset_path}/train_y_{split_ratio}.npy") + return xy_iter(train_x, + train_y, + lambda x:Image.fromarray(x)) + elif mode == 'test' and {f"test_x_{split_ratio}.npy", f"test_y_{split_ratio}.npy"}.issubset(os.listdir(dataset_path)): + logging.info(f"load speed up matrix for test data, at {dataset_path}") + test_x = np.load(f"{dataset_path}/test_x_{split_ratio}.npy") + test_y = np.load(f"{dataset_path}/test_y_{split_ratio}.npy") + return xy_iter(test_x, + test_y, + lambda x:Image.fromarray(x)) + elif mode == 'ft' and {f"ft_x_{split_ratio}.npy", f"ft_y_{split_ratio}.npy"}.issubset(os.listdir(dataset_path)): + logging.info(f"load speed up matrix for ft data, at {dataset_path}") + ft_x = np.load(f"{dataset_path}/ft_x_{split_ratio}.npy") + ft_y = np.load(f"{dataset_path}/ft_y_{split_ratio}.npy") + + return xy_iter(ft_x, + ft_y, + lambda x:Image.fromarray(x)) + else: + return None + + +def dataset_and_transform_generate(args): + ''' + # idea : given args, return selected dataset, transforms for both train and test part of data. + :param args: + :return: clean dataset in both train and test phase, and corresponding transforms + + 1. set the img transformation + 2. set the label transform + 3. load the speed up + if train or test part of datset is None + load original data + and generate speed up dataset + + ''' + if not args.dataset.startswith('test'): + train_img_transform = get_transform(args.dataset, *(args.img_size[:2]), train=True) + test_img_transform = get_transform(args.dataset, *(args.img_size[:2]), train=False) + else: + # test folder datset, use the mnist transform for convenience + train_img_transform = get_transform('mnist', *(args.img_size[:2]), train=True) + test_img_transform = get_transform('mnist', *(args.img_size[:2]), train=False) + + train_label_transfrom = None + test_label_transform = None + + train_dataset_without_transform = speed_up_load(args.dataset_path, mode = 'train', split_ratio=args.split_ratio) + test_dataset_without_transform = speed_up_load(args.dataset_path, mode = 'test', split_ratio=args.split_ratio) + ft_dataset_without_transform = speed_up_load(args.dataset_path, mode = 'ft', split_ratio=args.split_ratio) + + if (train_dataset_without_transform is None) or (test_dataset_without_transform is None): + + if args.dataset.startswith('test'): # for test only + train_dataset_without_transform = ImageFolder('../data/test') + test_dataset_without_transform = ImageFolder('../data/test') + + elif args.dataset == 'cifar10': + from torchvision.datasets import CIFAR10 + train_dataset_without_transform = CIFAR10( + args.dataset_path, + train=True, + transform=None, + download=True, + ) + + idx_dict = {} + idx_list = [] + ft_num = int(len(train_dataset_without_transform)//get_num_classes('cifar10')*args.split_ratio) + for i in range(get_num_classes('cifar10')): + idx_dict[i] = [] + + if args.split_ratio is not None: + for idx, data in enumerate(train_dataset_without_transform): + label = int(data[1]) + if len(idx_dict[label]) < ft_num: + idx_dict[label].append(idx) + + for i in idx_dict.keys(): + idx_list += idx_dict[i] + + np.save(f'./data/cifar10/{args.split_ratio}.npy', np.array(idx_list)) + test_dataset_without_transform = CIFAR10( + args.dataset_path, + train=False, + transform=None, + download=True, + ) + elif args.dataset == 'cifar100': + from torchvision.datasets import CIFAR100 + train_dataset_without_transform = CIFAR100( + root = args.dataset_path, + train = True, + download = True, + ) + idx_dict = {} + idx_list = [] + ft_num = int(len(train_dataset_without_transform)//get_num_classes('cifar100')*args.split_ratio) + for i in range(get_num_classes('cifar100')): + idx_dict[i] = [] + + if args.split_ratio is not None: + for idx, data in enumerate(train_dataset_without_transform): + label = int(data[1]) + if len(idx_dict[label]) < ft_num: + idx_dict[label].append(idx) + + for i in idx_dict.keys(): + idx_list += idx_dict[i] + + np.save(f'./data/cifar100/{args.split_ratio}.npy', np.array(idx_list)) + test_dataset_without_transform = CIFAR100( + root = args.dataset_path, + train = False, + download = True, + ) + + elif args.dataset == 'gtsrb': + from dataset.GTSRB import GTSRB + train_dataset_without_transform = GTSRB(args.dataset_path, + train=True, + ) + + from dataset.GTSRB import GTSRB + train_dataset_without_transform = GTSRB(args.dataset_path, + train=True, + ) + + label_num_dict = {} + idx_dict = {} + idx_list = [] + for i in range(get_num_classes('gtsrb')): + label_num_dict[i] = 0 + + for idx, data in enumerate(train_dataset_without_transform): + label_num_dict[int(data[1])] += 1 + + + ft_num = int(len(train_dataset_without_transform)//get_num_classes('gtsrb')*args.split_ratio) + for i in range(get_num_classes('gtsrb')): + idx_dict[i] = [] + + if args.split_ratio is not None: + for idx, data in enumerate(train_dataset_without_transform): + label = int(data[1]) + if len(idx_dict[label]) < label_num_dict[label]: + idx_dict[label].append(idx) + + for i in idx_dict.keys(): + idx_list += idx_dict[i] + + + np.save(f'./data/gtsrb/{args.split_ratio}.npy', np.array(idx_list)) + test_dataset_without_transform = GTSRB(args.dataset_path, + train=False, + ) + + + elif args.dataset == "tiny": + from dataset.Tiny import TinyImageNet + train_dataset_without_transform = TinyImageNet(args.dataset_path, + split='train', + download=True, + ) + idx_dict = {} + idx_list = [] + ft_num = int(len(train_dataset_without_transform)//get_num_classes('tiny')*args.split_ratio) + for i in range(get_num_classes('tiny')): + idx_dict[i] = [] + + if args.split_ratio is not None: + for idx, data in enumerate(train_dataset_without_transform): + label = int(data[1]) + if len(idx_dict[label]) < ft_num: + idx_dict[label].append(idx) + + for i in idx_dict.keys(): + idx_list += idx_dict[i] + + + np.save(f'./data/tiny/{args.split_ratio}.npy', np.array(idx_list)) + + test_dataset_without_transform = TinyImageNet(args.dataset_path, + split='val', + download=True, + ) + + + resize_for_x = transforms.Resize(args.img_size[:2]) + save_preprocess = lambda x : np.array(resize_for_x(x)).astype(np.uint8) + + speed_up_save(train_dataset_without_transform, args.dataset_path, save_preprocess, train = True, idx_list=idx_list, split_ratio=args.split_ratio) + speed_up_save(test_dataset_without_transform, args.dataset_path, save_preprocess, train = False, split_ratio=args.split_ratio) + + if ft_dataset_without_transform is None: + train_dataset_without_transform = speed_up_load(args.dataset_path, mode = 'train', split_ratio=args.split_ratio) + test_dataset_without_transform = speed_up_load(args.dataset_path, mode = 'test', split_ratio=args.split_ratio) + ft_dataset_without_transform = speed_up_load(args.dataset_path, mode = 'ft', split_ratio=args.split_ratio) + + return train_dataset_without_transform, \ + train_img_transform, \ + train_label_transfrom, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + ft_dataset_without_transform + + +def dataset_and_transform_generate_pre(args): + ''' + # idea : given args, return selected dataset, transforms for both train and test part of data. + :param args: + :return: clean dataset in both train and test phase, and corresponding transforms + + 1. set the img transformation + 2. set the label transform + 3. load the speed up + if train or test part of datset is None + load original data + and generate speed up dataset + + ''' + + train_img_transform = get_transform('imagenet', 224, 224, train=True) + test_img_transform = get_transform('imagenet', 224, 224, train=False) + + train_label_transfrom = None + test_label_transform = None + + train_dataset_without_transform = speed_up_load(args.dataset_path, mode = 'train', split_ratio=args.split_ratio) + test_dataset_without_transform = speed_up_load(args.dataset_path, mode = 'test', split_ratio=args.split_ratio) + ft_dataset_without_transform = speed_up_load(args.dataset_path, mode = 'ft', split_ratio=args.split_ratio) + + if (train_dataset_without_transform is None) or (test_dataset_without_transform is None): + + if args.dataset.startswith('test'): # for test only + train_dataset_without_transform = ImageFolder('../data/test') + test_dataset_without_transform = ImageFolder('../data/test') + + elif args.dataset == 'cifar100': + from torchvision.datasets import CIFAR100 + train_dataset_without_transform = CIFAR100( + root = args.dataset_path, + train = True, + download = True, + ) + idx_dict = {} + idx_list = [] + ft_num = int(len(train_dataset_without_transform)//get_num_classes('cifar100')*args.split_ratio) + for i in range(get_num_classes('cifar100')): + idx_dict[i] = [] + + if args.split_ratio is not None: + for idx, data in enumerate(train_dataset_without_transform): + label = int(data[1]) + if len(idx_dict[label]) < ft_num: + idx_dict[label].append(idx) + + for i in idx_dict.keys(): + idx_list += idx_dict[i] + + np.save(f'./data/cifar100/{args.split_ratio}.npy', np.array(idx_list)) + test_dataset_without_transform = CIFAR100( + root = args.dataset_path, + train = False, + download = True, + ) + + + elif args.dataset == "tiny": + # from utils.dataset.Tiny import TinyImageNet + from dataset.Tiny import TinyImageNet + train_dataset_without_transform = TinyImageNet(args.dataset_path, + split = 'train', + download = True, + ) + idx_dict = {} + idx_list = [] + ft_num = int(len(train_dataset_without_transform)//get_num_classes('tiny')*args.split_ratio) + for i in range(get_num_classes('tiny')): + idx_dict[i] = [] + + if args.split_ratio is not None: + for idx, data in enumerate(train_dataset_without_transform): + label = int(data[1]) + if len(idx_dict[label]) < ft_num: + idx_dict[label].append(idx) + + for i in idx_dict.keys(): + idx_list += idx_dict[i] + + + np.save(f'./data/tiny/{args.split_ratio}.npy', np.array(idx_list)) + + test_dataset_without_transform = TinyImageNet(args.dataset_path, + split='val', + download=True, + ) + + + resize_for_x = transforms.Resize(args.img_size[:2]) + save_preprocess = lambda x : np.array(resize_for_x(x)).astype(np.uint8) + + + speed_up_save(train_dataset_without_transform, args.dataset_path, save_preprocess, train = True, idx_list=idx_list, split_ratio=args.split_ratio) + speed_up_save(test_dataset_without_transform, args.dataset_path, save_preprocess, train = False, split_ratio=args.split_ratio) + + if ft_dataset_without_transform is None: + train_dataset_without_transform = speed_up_load(args.dataset_path, mode = 'train', split_ratio=args.split_ratio) + test_dataset_without_transform = speed_up_load(args.dataset_path, mode = 'test', split_ratio=args.split_ratio) + ft_dataset_without_transform = speed_up_load(args.dataset_path, mode = 'ft', split_ratio=args.split_ratio) + + return train_dataset_without_transform, \ + train_img_transform, \ + train_label_transfrom, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform, \ + ft_dataset_without_transform + + + diff --git a/utils/aggregate_block/fix_random.py b/utils/aggregate_block/fix_random.py new file mode 100755 index 0000000..6d1d784 --- /dev/null +++ b/utils/aggregate_block/fix_random.py @@ -0,0 +1,25 @@ +# idea: fix the randomness. + +import sys + +sys.path.append('../../') + +import random +import numpy as np +import torch + + +def fix_random( + random_seed: int = 0 +) -> None: + ''' + use to fix randomness in the script, but if you do not want to replicate experiments, then remove this can speed up your code + :param random_seed: + :return: None + ''' + random.seed(random_seed) + np.random.seed(random_seed) + torch.manual_seed(random_seed) + torch.cuda.manual_seed_all(random_seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False diff --git a/utils/aggregate_block/model_trainer_generate.py b/utils/aggregate_block/model_trainer_generate.py new file mode 100755 index 0000000..03e1c5e --- /dev/null +++ b/utils/aggregate_block/model_trainer_generate.py @@ -0,0 +1,223 @@ +# idea: select model you use in training and the trainer (the warper for training process) + +import logging +import sys + +sys.path.append('../../') + +import torch +import torchvision.models as models +from torchvision.models.resnet import resnet50, resnet34 # resnet34 +from typing import Optional +from torchvision.transforms import Resize + +from utils.trainer_cls import ModelTrainerCLS + +try: + from torchvision.models.efficientnet import efficientnet_b0, efficientnet_b3 +except: + logging.warning("efficientnet_b0,b3 fails to import, plz update your torch and torchvision") +try: + from torchvision.models import mobilenet_v3_large +except: + logging.warning("mobilenet_v3_large fails to import, plz update your torch and torchvision") + +try: + from torchvision.models import vit_b_16, vit_b_32, vit_l_16, vit_l_32 +except: + logging.warning("vit fails to import, plz update your torch and torchvision") + + +def partially_load_state_dict(model, state_dict): + # from https://discuss.pytorch.org/t/how-to-load-part-of-pre-trained-model/1113, thanks to chenyuntc Yun Chen + own_state = model.state_dict() + for name, param in state_dict.items(): + if name not in own_state: + continue + try: + param = param.data + own_state[name].copy_(param) + except: + print(f"unmatch: {name}") + continue + + +# trainer is cls +def generate_cls_model( + model_name: str, + num_classes: int = 10, + image_size: int = 32, + **kwargs, +): + ''' + # idea: aggregation block for selection of classifcation models + :param model_name: + :param num_classes: + :return: + ''' + + logging.debug("image_size ONLY apply for vit!!!\nIf you use vit make sure you set the image size!") + + + if model_name == 'resnet18': + + from models.resnet import ResNet18 + net = ResNet18(num_classes=num_classes, **kwargs) + + elif model_name == 'resnet50': + from models.resnet import ResNet50 + net = ResNet50(num_classes=num_classes, **kwargs) + elif model_name == 'preactresnet18': + logging.debug('Make sure you want PreActResNet18, which is NOT resnet18.') + from models.preact_resnet import PreActResNet18 + if kwargs.get("pretrained", False): + logging.warning("PreActResNet18 pretrained on cifar10, NOT ImageNet!") + net_from_cifar10 = PreActResNet18() # num_classes = num_classes) + net_from_cifar10.load_state_dict( + torch.load("../resource/trojannn/clean_preactresnet18.pt", map_location="cpu" + )['model_state_dict'] + ) + net = PreActResNet18(num_classes=num_classes) + partially_load_state_dict(net, net_from_cifar10.state_dict()) + else: + net = PreActResNet18(num_classes=num_classes) + elif model_name == 'resnet34': + net = resnet34(num_classes=num_classes, **kwargs) + elif model_name == 'resnet_wide': + from models.wide_resnet import Wide_ResNet + net = Wide_ResNet(28, 10, 0., num_classes) + + elif model_name == 'alexnet': + net = models.alexnet(num_classes=num_classes, **kwargs) + elif model_name == "vgg11": + net = models.vgg11(num_classes=num_classes, **kwargs) + elif model_name == 'vgg16': + net = models.vgg16(num_classes=num_classes, **kwargs) + elif model_name == 'vgg19': + net = models.vgg19(num_classes=num_classes, **kwargs) + elif model_name == 'vgg19_bn': + if kwargs.get("pretrained", False): + net_from_imagenet = models.vgg19_bn(pretrained=True) # num_classes = num_classes) + net = models.vgg19_bn(num_classes=num_classes, **{k: v for k, v in kwargs.items() if k != "pretrained"}) + partially_load_state_dict(net, net_from_imagenet.state_dict()) + else: + net = models.vgg19_bn(num_classes=num_classes, **kwargs) + elif model_name == 'squeezenet1_0': + net = models.squeezenet1_0(num_classes=num_classes, **kwargs) + elif model_name == 'densenet161': + net = models.densenet161(num_classes=num_classes, **kwargs) + elif model_name == 'inception_v3': + net = models.inception_v3(num_classes=num_classes, **kwargs) + elif model_name == 'googlenet': + net = models.googlenet(num_classes=num_classes, **kwargs) + elif model_name == 'shufflenet_v2_x1_0': + net = models.shufflenet_v2_x1_0(num_classes=num_classes, **kwargs) + elif model_name == 'mobilenet_v2': + net = models.mobilenet_v2(num_classes=num_classes, **kwargs) + elif model_name == 'mobilenet_v3_large': + net = models.mobilenet_v3_large(num_classes=num_classes, **kwargs) + elif model_name == 'resnext50_32x4d': + net = models.resnext50_32x4d(num_classes=num_classes, **kwargs) + elif model_name == 'wide_resnet50_2': + net = models.wide_resnet50_2(num_classes=num_classes, **kwargs) + elif model_name == 'mnasnet1_0': + net = models.mnasnet1_0(num_classes=num_classes, **kwargs) + elif model_name == 'swin_t': + torch.hub.set_dir('/ssddata1/data/rminaa/pretrain_models/') + from torchvision.models import swin_t + import torch.nn as nn + net = swin_t(weights='IMAGENET1K_V1') + net.head = nn.Linear(in_features=768, out_features=num_classes, bias=True) + for _, param in net.named_parameters(): + param.requires_grad = True + elif model_name == 'efficientnet_b0': + net = efficientnet_b0(num_classes=num_classes, **kwargs) + elif model_name == 'efficientnet_b3': + net = efficientnet_b3(num_classes=num_classes, **kwargs) + elif model_name.startswith("vit"): + logging.debug("All vit model use the default pretrain and resize to match the input shape!") + if model_name == 'vit_b_16': + net = vit_b_16( + pretrained=True, + **{k: v for k, v in kwargs.items() if k != "pretrained"} + ) + net.heads.head = torch.nn.Linear(net.heads.head.in_features, out_features=num_classes, bias=True) + net = torch.nn.Sequential( + Resize((224, 224)), + net, + ) + elif model_name == 'vit_b_32': + net = vit_b_32( + pretrained=True, + **{k: v for k, v in kwargs.items() if k != "pretrained"} + ) + net.heads.head = torch.nn.Linear(net.heads.head.in_features, out_features=num_classes, bias=True) + net = torch.nn.Sequential( + Resize((224, 224)), + net, + ) + elif model_name == 'vit_l_16': + net = vit_l_16( + pretrained=True, + **{k: v for k, v in kwargs.items() if k != "pretrained"} + ) + net.heads.head = torch.nn.Linear(net.heads.head.in_features, out_features=num_classes, bias=True) + net = torch.nn.Sequential( + Resize((224, 224)), + net, + ) + elif model_name == 'vit_l_32': + net = vit_l_32( + pretrained=True, + **{k: v for k, v in kwargs.items() if k != "pretrained"} + ) + net.heads.head = torch.nn.Linear(net.heads.head.in_features, out_features=num_classes, bias=True) + net = torch.nn.Sequential( + Resize((224, 224)), + net, + ) + elif model_name == 'densenet121': + net = models.densenet121(num_classes=num_classes, **kwargs) + elif model_name == 'densenet-bc': + + from models import DenseNet3 + net = DenseNet3(100, num_classes, 12, reduction=1.0, + bottleneck=True, dropRate=0) + elif model_name == 'resnext29': + from models.resnext import ResNeXt29_2x64d + net = ResNeXt29_2x64d(num_classes=num_classes) + elif model_name == 'senet18': + from models.senet import SENet18 + net = SENet18(num_classes=num_classes) + elif model_name == "convnext_tiny": + logging.debug("All convnext model use the default pretrain!") + from torchvision.models import convnext_tiny + net_from_imagenet = convnext_tiny(pretrained=True, ) # num_classes = num_classes) + net = convnext_tiny(num_classes=num_classes, **{k: v for k, v in kwargs.items() if k != "pretrained"}) + partially_load_state_dict(net, net_from_imagenet.state_dict()) + else: + raise SystemError('NO valid model match in function generate_cls_model!') + + return net + + +def generate_cls_trainer( + model, + attack_name: Optional[str] = None, + amp: bool = False, +): + ''' + # idea: The warpper of model, which use to receive training settings. + You can add more options for more complicated backdoor attacks. + + :param model: + :param attack_name: + :return: + ''' + + trainer = ModelTrainerCLS( + model=model, + amp=amp, + ) + + return trainer diff --git a/utils/aggregate_block/save_path_generate.py b/utils/aggregate_block/save_path_generate.py new file mode 100755 index 0000000..64f694f --- /dev/null +++ b/utils/aggregate_block/save_path_generate.py @@ -0,0 +1,96 @@ +# idea: generate the save folder name with some random generate string, in order to avoid potential name comflicts in repeat experiments + +import sys + +sys.path.append('../../') + +import random, string, os, sys +from datetime import datetime +from typing import * + + +def generate_save_folder( + run_info: Optional[str] = '', + given_load_file_path: Optional[str] = None, + recover: Optional[bool] = False, + all_record_folder_path: str = '../record', +) -> str: + # idea: This function helps to generate save path for experiment. + # if you do not want to set the name, this function will set it for experiment. + # Note that by using the randomly generate str, replication of experiment may not overwrite the folder of each other + + def inside_generate( + all_record_folder_path: str, + startTimeStr: str, + run_info: str, + ) -> str: + random_code = ''.join([random.choice(string.ascii_letters + string.digits) for n in range(4)]) + save_path = all_record_folder_path + '/' + startTimeStr + '_' + os.path.basename(sys.argv[0]).split('.')[ + 0] + '_' + run_info + '_' + random_code + return save_path + + startTimeStr = str(datetime.now().strftime('%Y%m%d_%H%M%S')) + + if given_load_file_path is None: + # default None + # no save_folder and no load_path, so can only random generate one + + save_path = inside_generate( + all_record_folder_path, + startTimeStr, + run_info, + ) + + while os.path.isdir(save_path): + save_path = inside_generate( + all_record_folder_path, + startTimeStr, + run_info, + ) + + # os.mkdir(save_path) + + elif given_load_file_path is not None and recover is True: + + given_load_file_path = given_load_file_path.rstrip('/') + + if os.path.isfile(os.path.abspath(given_load_file_path)): + load_folder_name = os.path.dirname(given_load_file_path) + else: + load_folder_name = given_load_file_path + + print(load_folder_name) + + else: + + given_load_file_path = given_load_file_path.rstrip('/') + + # isfile need abs path otherwise seems to be false anyway. + if os.path.isfile(os.path.abspath(given_load_file_path)): + load_folder_name = os.path.basename(os.path.dirname(given_load_file_path)) + else: + load_folder_name = os.path.basename(given_load_file_path) + + generate_base = inside_generate( + all_record_folder_path, + startTimeStr, + run_info, + ) + + save_path = generate_base.split('_baseOn_')[0] + '_baseOn_' + load_folder_name + # if not contains "_baseOn_", then no split, 0 position is the original + # else, then keep only the part before the first _baseOn_, rest of str drop + + while os.path.isdir(save_path): + + generate_base = inside_generate( + all_record_folder_path, + startTimeStr, + run_info, + ) + + save_path = generate_base.split('_baseOn_')[0] + '_baseOn_' + load_folder_name + + os.mkdir(save_path) + + return save_path diff --git a/utils/aggregate_block/train_settings_generate.py b/utils/aggregate_block/train_settings_generate.py new file mode 100755 index 0000000..f840455 --- /dev/null +++ b/utils/aggregate_block/train_settings_generate.py @@ -0,0 +1,87 @@ +# This script contains function to set the training criterion, optimizer and schedule. + +import sys + +sys.path.append('../../') +import torch +import torch.nn as nn + + +class flooding(torch.nn.Module): + # idea: module that can add flooding formula to the loss function + '''The additional flooding trick on loss''' + + def __init__(self, inner_criterion, flooding_scalar=0.5): + super(flooding, self).__init__() + self.inner_criterion = inner_criterion + self.flooding_scalar = float(flooding_scalar) + + def forward(self, output, target): + return (self.inner_criterion(output, target) - self.flooding_scalar).abs() + self.flooding_scalar + + +def argparser_criterion(args): + ''' + # idea: generate the criterion, default is CrossEntropyLoss + ''' + criterion = nn.CrossEntropyLoss() + if ('flooding_scalar' in args.__dict__): # use the flooding formulation warpper + criterion = flooding( + criterion, + flooding_scalar=float( + args.flooding_scalar + ) + ) + return criterion + + +def argparser_opt_scheduler(model, args): + # idea: given model and args, return the optimizer and scheduler you choose to use + + if args.client_optimizer == "sgd": + optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), + lr=args.lr, + momentum=args.sgd_momentum, # 0.9 + weight_decay=args.wd, # 5e-4 + ) + elif args.client_optimizer == 'adadelta': + optimizer = torch.optim.Adadelta( + filter(lambda p: p.requires_grad, model.parameters()), + lr=args.lr, + rho=args.rho, # 0.95, + eps=args.eps, # 1e-07, + ) + else: + optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), + lr=args.lr, + betas=args.adam_betas, + weight_decay=args.wd, + amsgrad=True) + + if args.lr_scheduler == 'CyclicLR': + scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, + base_lr=args.min_lr, + max_lr=args.lr, + step_size_up=args.step_size_up, + step_size_down=args.step_size_down, + cycle_momentum=False) + elif args.lr_scheduler == 'StepLR': + scheduler = torch.optim.lr_scheduler.StepLR(optimizer, + step_size=args.steplr_stepsize, # 1 + gamma=args.steplr_gamma) # 0.92 + elif args.lr_scheduler == 'CosineAnnealingLR': + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100 if ( + ("cos_t_max" not in args.__dict__) or args.cos_t_max is None) else args.cos_t_max) + elif args.lr_scheduler == 'MultiStepLR': + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, args.steplr_milestones, args.steplr_gamma) + elif args.lr_scheduler == 'ReduceLROnPlateau': + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, + **({ + 'factor': args.ReduceLROnPlateau_factor + } if 'ReduceLROnPlateau_factor' in args.__dict__ else {}) + ) + else: + scheduler = None + + return optimizer, scheduler diff --git a/utils/backdoor_generate_pindex.py b/utils/backdoor_generate_pindex.py new file mode 100755 index 0000000..8f401f2 --- /dev/null +++ b/utils/backdoor_generate_pindex.py @@ -0,0 +1,130 @@ +# idea: this file is for the poison sample index selection, +# generate_single_target_attack_train_pidx is for all-to-one attack label transform +# generate_pidx_from_label_transform aggregate both all-to-one and all-to-all case. + +import sys, logging +sys.path.append('../') +import random +import numpy as np +from typing import Callable, Union, List + + +def generate_single_target_attack_train_pidx( + targets:Union[np.ndarray, List], + tlabel: int, + pratio: Union[float, None] = None, + p_num: Union[int,None] = None, + clean_label: bool = False, + train : bool = True, +) -> np.ndarray: + ''' + # idea: given the following information, which samples will be used to poison will be determined automatically. + + :param targets: y array of clean dataset that tend to do poison + :param tlabel: target label in backdoor attack + + :param pratio: poison ratio, if the whole dataset size = 1 + :param p_num: poison data number, more precise + need one of pratio and pnum + + :param clean_label: whether use clean label logic to select + :param train: train or test phase (if test phase the pratio will be close to 1 no matter how you set) + :return: one-hot array to indicate which of samples is selected + ''' + targets = np.array(targets) + logging.info('Reminder: plz note that if p_num or pratio exceed the number of possible candidate samples\n then only maximum number of samples will be applied') + logging.info('Reminder: priority p_num > pratio, and choosing fix number of sample is prefered if possible ') + pidx = np.zeros(len(targets)) + if train == False: + + non_zero_array = np.where(targets != tlabel)[0] + pidx[list(non_zero_array)] = 1 + else: + #TRAIN ! + if clean_label == False: + # in train state, all2one non-clean-label case NO NEED TO AVOID target class img + if p_num is not None or round(pratio * len(targets)): + if p_num is not None: + non_zero_array = np.random.choice(np.arange(len(targets)), p_num, replace = False) + pidx[list(non_zero_array)] = 1 + else: + non_zero_array = np.random.choice(np.arange(len(targets)), round(pratio * len(targets)), replace = False) + pidx[list(non_zero_array)] = 1 + else: + if p_num is not None or round(pratio * len(targets)): + if p_num is not None: + non_zero_array = np.random.choice(np.where(targets == tlabel)[0], p_num, replace = False) + pidx[list(non_zero_array)] = 1 + else: + logging.info(len(targets)) + print(len(targets)) + + non_zero_array = np.random.choice(np.where(targets == tlabel)[0], round(pratio * len(targets)), replace = False) #len(np.where(targets == tlabel)[0]) + pidx[list(non_zero_array)] = 1 + logging.info(f'poison num:{sum(pidx)},real pratio:{sum(pidx) / len(pidx)}') + if sum(pidx) == 0: + raise SystemExit('No poison sample generated !') + return pidx + +from utils.bd_label_transform.backdoor_label_transform import * +from typing import Optional +def generate_pidx_from_label_transform( + original_labels: Union[np.ndarray, List], + label_transform: Callable, + train: bool = True, + pratio : Union[float,None] = None, + p_num: Union[int,None] = None, + clean_label: bool = False, +) -> Optional[np.ndarray]: + ''' + + # idea: aggregate all-to-one case and all-to-all cases, case being used will be determined by given label transformation automatically. + + !only support label_transform with deterministic output value (one sample one fix target label)! + + :param targets: y array of clean dataset that tend to do poison + :param tlabel: target label in backdoor attack + + :param pratio: poison ratio, if the whole dataset size = 1 + :param p_num: poison data number, more precise + need one of pratio and pnum + + :param clean_label: whether use clean label logic to select (only in all2one case can be used !!!) + :param train: train or test phase (if test phase the pratio will be close to 1 no matter how you set) + :return: one-hot array to indicate which of samples is selected + ''' + if isinstance(label_transform, AllToOne_attack): + # this is both for allToOne normal case and cleanLabel attack + return generate_single_target_attack_train_pidx( + targets = original_labels, + tlabel = label_transform.target_label, + pratio = pratio, + p_num = p_num, + clean_label = clean_label, + train = train, + ) + + elif isinstance(label_transform, AllToAll_shiftLabelAttack): + if train: + pass + else: + p_num = None + pratio = 1 + + if p_num is not None: + select_position = np.random.choice(len(original_labels), size = p_num, replace=False) + elif pratio is not None: + select_position = np.random.choice(len(original_labels), size=round(len(original_labels) * pratio), replace=False) + else: + raise SystemExit('p_num or pratio must be given') + logging.info(f'poison num:{len(select_position)},real pratio:{len(select_position) / len(original_labels)}') + + pidx = np.zeros(len(original_labels)) + pidx[select_position] = 1 + + return pidx + else: + logging.info('Not valid label_transform') + + + diff --git a/utils/backdoor_generate_poison_index.py b/utils/backdoor_generate_poison_index.py new file mode 100755 index 0000000..e5a1608 --- /dev/null +++ b/utils/backdoor_generate_poison_index.py @@ -0,0 +1,127 @@ +# idea: this file is for the poison sample index selection, +# generate_single_target_attack_train_poison_index is for all-to-one attack label transform +# generate_poison_index_from_label_transform aggregate both all-to-one and all-to-all case. + +import sys, logging +sys.path.append('../') +import random +import numpy as np +from typing import Callable, Union, List + + +def generate_single_target_attack_train_poison_index( + targets:Union[np.ndarray, List], + tlabel: int, + pratio: Union[float, None] = None, + p_num: Union[int,None] = None, + clean_label: bool = False, + train : bool = True, +) -> np.ndarray: + ''' + # idea: given the following information, which samples will be used to poison will be determined automatically. + + :param targets: y array of clean dataset that tend to do poison + :param tlabel: target label in backdoor attack + + :param pratio: poison ratio, if the whole dataset size = 1 + :param p_num: poison data number, more precise + need one of pratio and pnum + + :param clean_label: whether use clean label logic to select + :param train: train or test phase (if test phase the pratio will be close to 1 no matter how you set) + :return: one-hot array to indicate which of samples is selected + ''' + targets = np.array(targets) + logging.debug('Reminder: plz note that if p_num or pratio exceed the number of possible candidate samples\n then only maximum number of samples will be applied') + logging.debug('Reminder: priority p_num > pratio, and choosing fix number of sample is prefered if possible ') + poison_index = np.zeros(len(targets)) + if train == False: + + non_zero_array = np.where(targets != tlabel)[0] + poison_index[list(non_zero_array)] = 1 + else: + #TRAIN ! + if clean_label == False: + # in train state, all2one non-clean-label case NO NEED TO AVOID target class img + if p_num is not None or round(pratio * len(targets)): + if p_num is not None: + non_zero_array = np.random.choice(np.arange(len(targets)), p_num, replace = False) + poison_index[list(non_zero_array)] = 1 + else: + non_zero_array = np.random.choice(np.arange(len(targets)), round(pratio * len(targets)), replace = False) + poison_index[list(non_zero_array)] = 1 + else: + if p_num is not None or round(pratio * len(targets)): + if p_num is not None: + non_zero_array = np.random.choice(np.where(targets == tlabel)[0], p_num, replace = False) + poison_index[list(non_zero_array)] = 1 + else: + non_zero_array = np.random.choice(np.where(targets == tlabel)[0], round(pratio * len(targets)), replace = False) + poison_index[list(non_zero_array)] = 1 + logging.info(f'poison num:{sum(poison_index)},real pratio:{sum(poison_index) / len(poison_index)}') + if sum(poison_index) == 0: + raise SystemExit('No poison sample generated !') + return poison_index + +from utils.bd_label_transform.backdoor_label_transform import * +from typing import Optional +def generate_poison_index_from_label_transform( + original_labels: Union[np.ndarray, List], + label_transform: Callable, + train: bool = True, + pratio : Union[float,None] = None, + p_num: Union[int,None] = None, + clean_label: bool = False, +) -> Optional[np.ndarray]: + ''' + + # idea: aggregate all-to-one case and all-to-all cases, case being used will be determined by given label transformation automatically. + + !only support label_transform with deterministic output value (one sample one fix target label)! + + :param targets: y array of clean dataset that tend to do poison + :param tlabel: target label in backdoor attack + + :param pratio: poison ratio, if the whole dataset size = 1 + :param p_num: poison data number, more precise + need one of pratio and pnum + + :param clean_label: whether use clean label logic to select (only in all2one case can be used !!!) + :param train: train or test phase (if test phase the pratio will be close to 1 no matter how you set) + :return: one-hot array to indicate which of samples is selected + ''' + if isinstance(label_transform, AllToOne_attack): + # this is both for allToOne normal case and cleanLabel attack + return generate_single_target_attack_train_poison_index( + targets = original_labels, + tlabel = label_transform.target_label, + pratio = pratio, + p_num = p_num, + clean_label = clean_label, + train = train, + ) + + elif isinstance(label_transform, AllToAll_shiftLabelAttack): + if train: + pass + else: + p_num = None + pratio = 1 + + if p_num is not None: + select_position = np.random.choice(len(original_labels), size = p_num, replace=False) + elif pratio is not None: + select_position = np.random.choice(len(original_labels), size=round(len(original_labels) * pratio), replace=False) + else: + raise SystemExit('p_num or pratio must be given') + logging.info(f'poison num:{len(select_position)},real pratio:{len(select_position) / len(original_labels)}') + + poison_index = np.zeros(len(original_labels)) + poison_index[select_position] = 1 + + return poison_index + else: + logging.debug('Not valid label_transform') + + + diff --git a/utils/bd_dataset.py b/utils/bd_dataset.py new file mode 100755 index 0000000..cf27035 --- /dev/null +++ b/utils/bd_dataset.py @@ -0,0 +1,174 @@ + +import sys, logging +sys.path.append('../') + +import numpy as np +import torch + +from PIL import Image +from PIL import ImageFile +ImageFile.LOAD_TRUNCATED_IMAGES = True + +from tqdm import tqdm +from typing import * + +from copy import deepcopy + +class xy_iter(torch.utils.data.dataset.Dataset): + def __init__(self, + x : Sequence, + y : Sequence, + transform + ): + assert len(x) == len(y) + self.data = x + self.targets = y + self.transform = transform + def __getitem__(self, item): + img = self.data[item] + label = self.targets[item] + if self.transform is not None: + img = self.transform(img) + return img, label + def __len__(self): + return len(self.targets) + +class prepro_cls_DatasetBD(torch.utils.data.dataset.Dataset): + + def __init__(self, + full_dataset_without_transform: Sequence, + poison_idx: Sequence, # one-hot to determine which image may take bd_transform + add_details_in_preprocess: Optional[bool] = True, + + clean_image_pre_transform : Optional[Callable] = np.array, + bd_image_pre_transform: Optional[Callable] = None, + bd_label_pre_transform: Optional[Callable] = None, + end_pre_process : Optional[Callable] = lambda img_list : [Image.fromarray(img.astype(np.uint8)) for img in img_list], + + ori_image_transform_in_loading: Optional[Callable] = None, + ori_label_transform_in_loading: Optional[Callable] = None, + ): + logging.debug('dataset must have NO transform in BOTH image and label !') + + self.dataset = full_dataset_without_transform + self.ori_image_transform_in_loading = ori_image_transform_in_loading + self.ori_label_transform_in_loading = ori_label_transform_in_loading + + self.poison_idx = poison_idx # actually poison indicator + self.clean_image_pre_transform = clean_image_pre_transform + self.bd_image_pre_transform = bd_image_pre_transform + self.bd_label_pre_transform = bd_label_pre_transform + self.end_pre_process = end_pre_process + + self.add_details_in_preprocess = add_details_in_preprocess + + assert len(poison_idx) == len(full_dataset_without_transform) + self.prepro_backdoor() + + self.getitem_all_switch = False + + def prepro_backdoor(self): + + self.data = [] + self.targets = [] + if self.add_details_in_preprocess: + self.original_index = [] + self.poison_indicator = deepcopy(self.poison_idx) + self.original_targets = [] + + for original_idx, content in enumerate(tqdm(self.dataset, desc=f'pre-process bd dataset')): + + img, label = content + + if self.clean_image_pre_transform is not None and self.poison_idx[original_idx] == 0: + + img = self.clean_image_pre_transform(img) + + if self.bd_image_pre_transform is not None and self.poison_idx[original_idx] == 1: + + img = self.bd_image_pre_transform(img, label, original_idx) + + original_label = deepcopy(label) + + if self.bd_label_pre_transform is not None and self.poison_idx[original_idx] == 1: + + label = self.bd_label_pre_transform(label, original_idx, img) + + if self.add_details_in_preprocess: + self.data.append(img) + self.targets.append(label) + self.original_index.append(original_idx) + self.original_targets.append(original_label) + else: + self.data.append(img) + self.targets.append(label) + + self.targets = np.array(self.targets) + + if self.add_details_in_preprocess: + self.original_index = np.array(self.original_index) + self.poison_indicator = np.array(self.poison_indicator) + self.original_targets = np.array(self.original_targets) + + if self.end_pre_process is not None: + self.data = self.end_pre_process(self.data) + + def __getitem__(self, item): + + img = self.data[item] + label = self.targets[item] + + if self.ori_image_transform_in_loading is not None: + img = self.ori_image_transform_in_loading(img) + if self.ori_label_transform_in_loading is not None: + label = self.ori_label_transform_in_loading(label) + + if self.add_details_in_preprocess: + if self.getitem_all_switch: + return img, \ + self.original_targets[item], \ + self.original_index[item], \ + self.poison_indicator[item], \ + label + else: + return img, \ + label, \ + self.original_index[item], \ + self.poison_indicator[item], \ + self.original_targets[item] + else: + return img, label + + def __len__(self): + if self.add_details_in_preprocess: + all_length = (len(self.data),len(self.targets),len(self.original_index),len(self.poison_indicator),len(self.original_targets),) + assert max(all_length) == min(all_length) + return len(self.targets) + else: + all_length = (len(self.data), len(self.targets),) + assert max(all_length) == min(all_length) + return len(self.targets) + + def subset(self, + chosen_index_list, + inplace = True, + memorize_original = True, + ): + if inplace: + self.data = [self.data[ii] for ii in chosen_index_list] #self.data[chosen_index_array] + self.targets = self.targets[chosen_index_list] + if self.add_details_in_preprocess: + self.original_index = self.original_index[chosen_index_list] + self.poison_indicator = self.poison_indicator[chosen_index_list] + self.original_targets = self.original_targets[chosen_index_list] + if not memorize_original: + self.dataset, self.poison_idx = None, None + else: + new_obj = deepcopy(self) + new_obj.subset( + chosen_index_list, + inplace=True, + memorize_original=memorize_original, + ) + return new_obj + diff --git a/utils/bd_dataset_v2.py b/utils/bd_dataset_v2.py new file mode 100755 index 0000000..4fe9a8a --- /dev/null +++ b/utils/bd_dataset_v2.py @@ -0,0 +1,369 @@ +import os.path +import sys, logging +sys.path.append('../') + +import numpy as np +import torch +import copy + +from PIL import Image +from PIL import ImageFile +ImageFile.LOAD_TRUNCATED_IMAGES = True + +from tqdm import tqdm +from typing import * + +from torchvision.transforms import ToPILImage +from torchvision.datasets import DatasetFolder, ImageFolder + +class slice_iter(torch.utils.data.dataset.Dataset): + '''iterate over a slice of the dataset''' + def __init__(self, + dataset, + axis = 0 + ): + self.data = dataset + self.axis = axis + + def __getitem__(self, item): + return self.data[item][self.axis] + + def __len__(self): + return len(self.data) + + + +class x_iter(torch.utils.data.dataset.Dataset): + def __init__(self, + dataset + ): + self.data = dataset + + def __getitem__(self, item): + img = self.data[item][0] + return img + + def __len__(self): + return len(self.data) + +class y_iter(torch.utils.data.dataset.Dataset): + def __init__(self, + dataset + ): + self.data = dataset + + def __getitem__(self, item): + target = self.data[item][1] + return target + + def __len__(self): + return len(self.data) + + +def get_labels(given_dataset): + if isinstance(given_dataset, DatasetFolder) or isinstance(given_dataset, ImageFolder): + logging.debug("get .targets") + return given_dataset.targets + else: + logging.debug("Not DatasetFolder or ImageFolder, so iter through") + return [label for img, label, *other_info in given_dataset] + +class dataset_wrapper_with_transform(torch.utils.data.Dataset): + ''' + idea from https://stackoverflow.com/questions/1443129/completely-wrap-an-object-in-python + ''' + + def __init__(self, obj, wrap_img_transform=None, wrap_label_transform=None): + + # this warpper should NEVER be warp twice. + # Since the attr name may cause trouble. + assert not "wrap_img_transform" in obj.__dict__ + assert not "wrap_label_transform" in obj.__dict__ + + self.wrapped_dataset = obj + self.wrap_img_transform = wrap_img_transform + self.wrap_label_transform = wrap_label_transform + + def __getattr__(self, attr): + # # https://github.com/python-babel/flask-babel/commit/8319a7f44f4a0b97298d20ad702f7618e6bdab6a + # # https://stackoverflow.com/questions/47299243/recursionerror-when-python-copy-deepcopy + # if attr == "__setstate__": + # raise AttributeError(attr) + if attr in self.__dict__: + return getattr(self, attr) + return getattr(self.wrapped_dataset, attr) + + def __getitem__(self, index): + img, label, *other_info = self.wrapped_dataset[index] + if self.wrap_img_transform is not None: + img = self.wrap_img_transform(img) + if self.wrap_label_transform is not None: + label = self.wrap_label_transform(label) + return (img, label, *other_info) + + def __len__(self): + return len(self.wrapped_dataset) + + def __deepcopy__(self, memo): + # In copy.deepcopy, init() will not be called and some attr will not be initialized. + # The getattr will be infinitely called in deepcopy process. + # So, we need to manually deepcopy the wrapped dataset or raise error when "__setstate__" us called. Here we choose the first solution. + return dataset_wrapper_with_transform(copy.deepcopy(self.wrapped_dataset), copy.deepcopy(self.wrap_img_transform), copy.deepcopy(self.wrap_label_transform)) + + +class poisonedCLSDataContainer: + ''' + Two mode: + in RAM / disk + if in RAM + save {key : value} + elif in disk: + save { + key : { + "path":path, (must take a PIL image and save to .png) + "other_info": other_info, (Non-img) + } + } + where img, *other_info = value + ''' + def __init__(self, save_folder_path=None, save_file_format = ".png"): + self.save_folder_path = save_folder_path + self.data_dict = {} + self.save_file_format = save_file_format + logging.info(f"save file format is {save_file_format}") + + def retrieve_state(self): + return { + "save_folder_path":self.save_folder_path, + "data_dict":self.data_dict, + "save_file_format":self.save_file_format, + } + + def set_state(self, state_file): + self.save_folder_path = state_file["save_folder_path"] + self.data_dict = state_file["data_dict"] + self.save_file_format = state_file["save_file_format"] + + def setitem(self, key, value, relative_loc_to_save_folder_name=None): + + if self.save_folder_path is None: + self.data_dict[key] = value + else: + img, *other_info = value + + save_subfolder_path = f"{self.save_folder_path}/{relative_loc_to_save_folder_name}" + if not ( + os.path.exists(save_subfolder_path) + and + os.path.isdir(save_subfolder_path) + ): + os.makedirs(save_subfolder_path) + + file_path = f"{save_subfolder_path}/{key}{self.save_file_format}" + img.save(file_path) + + self.data_dict[key] = { + "path": file_path, + "other_info": other_info, + } + + def __getitem__(self, key): + if self.save_folder_path is None: + return self.data_dict[key] + else: + file_path = self.data_dict[key]["path"] + other_info = self.data_dict[key]["other_info"] + img = Image.open(file_path) + return (img, *other_info) + + def __len__(self): + return len(self.data_dict) + +class prepro_cls_DatasetBD_v2(torch.utils.data.Dataset): + + def __init__( + self, + full_dataset_without_transform, + poison_indicator: Optional[Sequence] = None, # one-hot to determine which image may take bd_transform + + bd_image_pre_transform: Optional[Callable] = None, + bd_label_pre_transform: Optional[Callable] = None, + save_folder_path = None, + + mode = 'attack', + ): + ''' + This class require poisonedCLSDataContainer + + :param full_dataset_without_transform: dataset without any transform. (just raw data) + + :param poison_indicator: + array with 0 or 1 at each position corresponding to clean/poisoned + Must have the same len as given full_dataset_without_transform (default None, regarded as all 0s) + + :param bd_image_pre_transform: + :param bd_label_pre_transform: + ( if your backdoor method is really complicated, then do not set these two params. These are for simplicity. + You can inherit the class and rewrite method preprocess part as you like) + + :param save_folder_path: + This is for the case to save the poisoned imgs on disk. + (In case, your RAM may not be able to hold all poisoned imgs.) + If you do not want this feature for small dataset, then just left it as default, None. + + ''' + + self.dataset = full_dataset_without_transform + + if poison_indicator is None: + poison_indicator = np.zeros(len(full_dataset_without_transform)) + self.poison_indicator = poison_indicator + + assert len(full_dataset_without_transform) == len(poison_indicator) + + self.bd_image_pre_transform = bd_image_pre_transform + self.bd_label_pre_transform = bd_label_pre_transform + + self.save_folder_path = os.path.abspath(save_folder_path) if save_folder_path is not None else save_folder_path # since when we want to save this dataset, this may cause problem + + self.original_index_array = np.arange(len(full_dataset_without_transform)) + + self.bd_data_container = poisonedCLSDataContainer(self.save_folder_path, ".png") + + if sum(self.poison_indicator) >= 1: + self.prepro_backdoor() + + self.getitem_all = True + self.getitem_all_switch = False + + self.mode = mode + + def prepro_backdoor(self): + for selected_index in tqdm(self.original_index_array, desc="prepro_backdoor"): + if self.poison_indicator[selected_index] == 1: + img, label = self.dataset[selected_index] + img = self.bd_image_pre_transform(img, target=label, image_serial_id=selected_index) + bd_label = self.bd_label_pre_transform(label) + self.set_one_bd_sample( + selected_index, img, bd_label, label + ) + + def set_one_bd_sample(self, selected_index, img, bd_label, label): + + ''' + 1. To pil image + 2. set the image to container + 3. change the poison_index. + + logic is that no matter by the prepro_backdoor or not, after we set the bd sample, + This method will automatically change the poison index to 1. + + :param selected_index: The index of bd sample + :param img: The converted img that want to put in the bd_container + :param bd_label: The label bd_sample has + :param label: The original label bd_sample has + + ''' + + # we need to save the bd img, so we turn it into PIL + if (not isinstance(img, Image.Image)) : + if isinstance(img, np.ndarray): + img = img.astype(np.uint8) + img = ToPILImage()(img) + self.bd_data_container.setitem( + key=selected_index, + value=(img, bd_label, label), + relative_loc_to_save_folder_name=f"{label}", + ) + self.poison_indicator[selected_index] = 1 + + def __len__(self): + return len(self.original_index_array) + + def __getitem__(self, index): + + original_index = self.original_index_array[index] + if self.poison_indicator[original_index] == 0: + # clean + img, label = self.dataset[original_index] + original_target = label + poison_or_not = 0 + else: + # bd + img, label, original_target = self.bd_data_container[original_index] + poison_or_not = 1 + + if not isinstance(img, Image.Image): + img = ToPILImage()(img) + + if self.getitem_all: + if self.getitem_all_switch: + # this is for the case that you want original targets, but you do not want change your testing process + return img, \ + original_target, \ + original_index, \ + poison_or_not, \ + label + + else: # here should corresponding to the order in the bd trainer + return img, \ + label, \ + original_index, \ + poison_or_not, \ + original_target + else: + return img, label + + def subset(self, chosen_index_list): + self.original_index_array = self.original_index_array[chosen_index_list] + + def retrieve_state(self): + return { + "bd_data_container" : self.bd_data_container.retrieve_state(), + "getitem_all":self.getitem_all, + "getitem_all_switch":self.getitem_all_switch, + "original_index_array": self.original_index_array, + "poison_indicator": self.poison_indicator, + "save_folder_path": self.save_folder_path, + } + + def copy(self): + bd_train_dataset = prepro_cls_DatasetBD_v2(self.dataset) + copy_state = copy.deepcopy(self.retrieve_state()) + bd_train_dataset.set_state( + copy_state + ) + return bd_train_dataset + + def set_state(self, state_file): + self.bd_data_container = poisonedCLSDataContainer() + self.bd_data_container.set_state( + state_file['bd_data_container'] + ) + self.getitem_all = state_file['getitem_all'] + self.getitem_all_switch = state_file['getitem_all_switch'] + self.original_index_array = state_file["original_index_array"] + self.poison_indicator = state_file["poison_indicator"] + self.save_folder_path = state_file["save_folder_path"] + + +class xy_iter(torch.utils.data.dataset.Dataset): + def __init__(self, + x : Sequence, + y : Sequence, + transform + ): + assert len(x) == len(y) + self.data = x + self.targets = y + self.transform = transform + def __getitem__(self, item): + img = self.data[item] + label = self.targets[item] + if self.transform is not None: + img = self.transform(img) + return img, label + def __len__(self): + return len(self.targets) + + diff --git a/utils/bd_img_transform/SSBA.py b/utils/bd_img_transform/SSBA.py new file mode 100755 index 0000000..3520de5 --- /dev/null +++ b/utils/bd_img_transform/SSBA.py @@ -0,0 +1,25 @@ +# This script is for SSBA, +# but the main part please refer to https://github.com/tancik/StegaStamp and follow the original paper of SSBA. +# This script only use to replace the img after backdoor modification. + +# idea : set the parameter in initialization, then when the object is called, it will use the add_trigger method to add trigger + +from typing import Sequence +import logging +import numpy as np + + +class SSBA_attack_replace_version(object): + + # idea : in this attack, this transform just replace the image by the image_serial_id, the real transform does not happen here + + def __init__(self, replace_images: Sequence) -> None: + logging.debug( + 'in SSBA_attack_replace_version, the real transform does not happen here, input img, target must be NONE, only image_serial_id used') + self.replace_images = replace_images + + def __call__(self, img: None, + target: None, + image_serial_id: int + ) -> np.ndarray: + return self.replace_images[image_serial_id] \ No newline at end of file diff --git a/utils/bd_img_transform/blended.py b/utils/bd_img_transform/blended.py new file mode 100755 index 0000000..d53d0b3 --- /dev/null +++ b/utils/bd_img_transform/blended.py @@ -0,0 +1,23 @@ +# the callable object for Blended attack +# idea : set the parameter in initialization, then when the object is called, it will use the add_trigger method to add trigger +class blendedImageAttack(object): + + @classmethod + def add_argument(self, parser): + parser.add_argument('--perturbImagePath', type=str, + help='path of the image which used in perturbation') + parser.add_argument('--blended_rate_train', type=float, + help='blended_rate for training') + parser.add_argument('--blended_rate_test', type=float, + help='blended_rate for testing') + return parser + + def __init__(self, target_image, blended_rate): + self.target_image = target_image + self.blended_rate = blended_rate + + def __call__(self, img, target = None, image_serial_id = None): + return self.add_trigger(img) + + def add_trigger(self, img): + return (1-self.blended_rate) * img + (self.blended_rate) * self.target_image diff --git a/utils/bd_img_transform/lc.py b/utils/bd_img_transform/lc.py new file mode 100644 index 0000000..e9f32c3 --- /dev/null +++ b/utils/bd_img_transform/lc.py @@ -0,0 +1,166 @@ +''' +original code from +link : https://github.com/MadryLab/label-consistent-backdoor-code +''' +import numpy as np +import logging +from torchvision.transforms import Resize, InterpolationMode +import torch + + +class labelConsistentAttack(object): + + ''' + This class ONLY add square trigger to the image !!!!! + This class ONLY add square trigger to the image !!!!! + This class ONLY add square trigger to the image !!!!! + For adversarial attack to origianl images part before adding trigger, plz refer to resource/label-consistent folder for more details. + ''' + + def __init__(self, trigger = "all-corners", reduced_amplitude=1.0): + + assert 0 <= reduced_amplitude <= 1, "reduced_amplitude is in [0,1] !" + logging.warning("Original code only give trigger in 32 * 32. For other image size, we do resize to the mask with InterpolationMode.NEAREST. \nIf you do not agree with our implememntation, you can rewrite utils/bd_img_transform/lc.py in your own way.") + logging.info(f"For Label-consistent attack, reduced_amplitude (transparency) = {reduced_amplitude}, 0 means no square trigger, 1 means no reduction.") + if reduced_amplitude == 0: + logging.warning("!!! reduced_amplitude = 0, note that this mean NO square trigger is added after adversarial attack to origianl image!!!") + + logging.warning(f"You are using pattern: {trigger} for labelConsistentAttack") + + self.trigger_mask = [] # For overriding pixel values + self.trigger_add_mask = [] # For adding or subtracting to pixel values + if trigger == "bottom-right": + self.trigger_mask = [ + ((-1, -1), 1), + ((-1, -2), -1), + ((-1, -3), 1), + ((-2, -1), -1), + ((-2, -2), 1), + ((-2, -3), -1), + ((-3, -1), 1), + ((-3, -2), -1), + ((-3, -3), -1) + ] + elif trigger == "all-corners": + self.trigger_mask = [ + ((0, 0), 1), + ((0, 1), -1), + ((0, 2), -1), + ((1, 0), -1), + ((1, 1), 1), + ((1, 2), -1), + ((2, 0), 1), + ((2, 1), -1), + ((2, 2), 1), + + ((-1, 0), 1), + ((-1, 1), -1), + ((-1, 2), 1), + ((-2, 0), -1), + ((-2, 1), 1), + ((-2, 2), -1), + ((-3, 0), 1), + ((-3, 1), -1), + ((-3, 2), -1), + + ((0, -1), 1), + ((0, -2), -1), + ((0, -3), -1), + ((1, -1), -1), + ((1, -2), 1), + ((1, -3), -1), + ((2, -1), 1), + ((2, -2), -1), + ((2, -3), 1), + + ((-1, -1), 1), + ((-1, -2), -1), + ((-1, -3), 1), + ((-2, -1), -1), + ((-2, -2), 1), + ((-2, -3), -1), + ((-3, -1), 1), + ((-3, -2), -1), + ((-3, -3), -1), + ] + else: + assert False + + self.reduced_amplitude = reduced_amplitude + if reduced_amplitude == "none": + self.reduced_amplitude = None + + def resize_annotation(self, annotation, img_size): + # eg. list of ((-3, -3), -1), + + if (img_size == (32,32)) or (len(annotation) == 0): + return annotation + + mask = np.zeros((32,32)) + for (x, y), value in annotation: + mask[x][y] = value + + resize = Resize(img_size, interpolation=InterpolationMode.NEAREST) + resized_mask = resize(torch.from_numpy(mask)[None,...])[0] + + new_annotation = [] + resized_mask = resized_mask.numpy() + for x,y in zip(np.nonzero(resized_mask)[0].tolist(),np.nonzero(resized_mask)[1].tolist()): + new_annotation.append(((x,y), resized_mask[x][y])) + + return new_annotation + + def poison_from_indices(self, image, apply_trigger=True): + + max_allowed_pixel_value = 255 + + image_new = np.copy(image).astype(np.float32) + + trigger_mask = self.trigger_mask + trigger_add_mask = self.trigger_add_mask + + if self.reduced_amplitude is not None: + trigger_add_mask = [ + ((x, y), mask_val * self.reduced_amplitude) + for (x, y), mask_val in trigger_mask + ] + + trigger_mask = [] + + trigger_mask = [ + ((x, y), max_allowed_pixel_value * value) + for ((x, y), value) in trigger_mask + ] + trigger_add_mask = [ + ((x, y), max_allowed_pixel_value * value) + for ((x, y), value) in trigger_add_mask + ] + + if apply_trigger: + trigger_mask = self.resize_annotation(trigger_mask, image.shape[:2]) + for (x, y), value in trigger_mask: + image_new[x][y] = value + trigger_add_mask = self.resize_annotation(trigger_add_mask, image.shape[:2]) + for (x, y), value in trigger_add_mask: + image_new[x][y] += value + + image_new = np.clip(image_new, 0, max_allowed_pixel_value) + + # debug block + # print("min image", (image).min()) + # print("max image", (image).max()) + # print("min image_new", (image_new).min()) + # print("max image_new", (image_new).max()) + # print("image_new - image", image_new - image) + # print("sum image_new - image", image_new - image) + + return image_new + +if __name__ == '__main__': + # test + for trigger in ["bottom-right","all-corners"]: + for reduced_amplitude in [0,1,0.5,1]: + a = labelConsistentAttack(trigger, reduced_amplitude) + a.poison_from_indices(np.zeros((32,32,3))) + a.poison_from_indices(np.zeros((64, 32, 3))) + a.poison_from_indices(np.zeros((64, 64, 3))) diff --git a/utils/bd_img_transform/patch.py b/utils/bd_img_transform/patch.py new file mode 100755 index 0000000..685fe5c --- /dev/null +++ b/utils/bd_img_transform/patch.py @@ -0,0 +1,68 @@ +# the callable object for BadNets attack +# idea : set the parameter in initialization, then when the object is called, it will use the add_trigger method to add trigger +import numpy as np +import torch +from typing import Optional, Union +from torchvision.transforms import Resize, ToTensor, ToPILImage + +class AddPatchTrigger(object): + ''' + assume init use HWC format + but in add_trigger, you can input tensor/array , one/batch + ''' + def __init__(self, trigger_loc, trigger_ptn): + self.trigger_loc = trigger_loc + self.trigger_ptn = trigger_ptn + + def __call__(self, img, target = None, image_serial_id = None): + return self.add_trigger(img) + + def add_trigger(self, img): + if isinstance(img, np.ndarray): + if img.shape.__len__() == 3: + for i, (m, n) in enumerate(self.trigger_loc): + img[m, n, :] = self.trigger_ptn[i] # add trigger + elif img.shape.__len__() == 4: + for i, (m, n) in enumerate(self.trigger_loc): + img[:, m, n, :] = self.trigger_ptn[i] # add trigger + elif isinstance(img, torch.Tensor): + if img.shape.__len__() == 3: + for i, (m, n) in enumerate(self.trigger_loc): + img[:, m, n] = self.trigger_ptn[i] + elif img.shape.__len__() == 4: + for i, (m, n) in enumerate(self.trigger_loc): + img[:, :, m, n] = self.trigger_ptn[i] + return img + +class AddMaskPatchTrigger(object): + def __init__(self, + trigger_array : Union[np.ndarray, torch.Tensor], + ): + self.trigger_array = trigger_array + + def __call__(self, img, target = None, image_serial_id = None): + return self.add_trigger(img) + + def add_trigger(self, img): + return img * (self.trigger_array == 0) + self.trigger_array * (self.trigger_array > 0) + +class SimpleAdditiveTrigger(object): + ''' + Note that if you do not astype to float, then it is possible to have 1 + 255 = 0 in np.uint8 ! + ''' + def __init__(self, + trigger_array : np.ndarray, + ): + self.trigger_array = trigger_array.astype(np.float) + + def __call__(self, img, target = None, image_serial_id = None): + return self.add_trigger(img) + + def add_trigger(self, img): + return np.clip(img.astype(np.float) + self.trigger_array, 0, 255).astype(np.uint8) + +import matplotlib.pyplot as plt +def test_Simple(): + a = SimpleAdditiveTrigger(np.load('../../resource/lowFrequency/cifar10_densenet161_0_255.npy')) + plt.imshow(a(np.ones((32,32,3)) + 255/2)) + plt.show() diff --git a/utils/bd_img_transform/sig.py b/utils/bd_img_transform/sig.py new file mode 100755 index 0000000..dc73b6b --- /dev/null +++ b/utils/bd_img_transform/sig.py @@ -0,0 +1,55 @@ +#This script is for Sig attack callable transform + +''' +This code is based on https://github.com/bboylyg/NAD + +The original license: +License CC BY-NC + +The update include: + 1. change to callable object + 2. change the way of trigger generation, use the original formulation. + +# idea : set the parameter in initialization, then when the object is called, it will use the add_trigger method to add trigger +''' + +from typing import Union +import torch +import numpy as np + + +class sigTriggerAttack(object): + """ + Implement paper: + > Barni, M., Kallas, K., & Tondi, B. (2019). + > A new Backdoor Attack in CNNs by training set corruption without label poisoning. + > arXiv preprint arXiv:1902.11237 + superimposed sinusoidal backdoor signal with default parameters + """ + def __init__(self, + delta : Union[int, float, complex, np.number, torch.Tensor] = 40, + f : Union[int, float, complex, np.number, torch.Tensor] =6 + ) -> None: + + self.delta = delta + self.f = f + + def __call__(self, img, target = None, image_serial_id = None): + return self.sigTrigger(img) + + + def sigTrigger(self, img): + + img = np.float32(img) + pattern = np.zeros_like(img) + m = pattern.shape[1] + for i in range(int(img.shape[0])): + for j in range(int(img.shape[1])): + pattern[i, j] = self.delta * np.sin(2 * np.pi * j * self.f / m) + + img = np.uint32(img) + pattern + img = np.uint8(np.clip(img, 0, 255)) + + return img + + diff --git a/utils/bd_label_transform/backdoor_label_transform.py b/utils/bd_label_transform/backdoor_label_transform.py new file mode 100755 index 0000000..216b7fe --- /dev/null +++ b/utils/bd_label_transform/backdoor_label_transform.py @@ -0,0 +1,41 @@ +# This script include all-to-one and all-to-all attack + +import sys, logging +sys.path.append('../') +import random + +class AllToOne_attack(object): + ''' + idea : any label -> fix_target + ''' + @classmethod + def add_argument(self, parser): + parser.add_argument('--target_label (only one)', type=int, + help='target label') + return parser + def __init__(self, target_label): + self.target_label = target_label + def __call__(self, original_label, original_index = None, img = None): + return self.poison_label(original_label) + def poison_label(self, original_label): + return self.target_label + +class AllToAll_shiftLabelAttack(object): + ''' + idea : any label -> (label + fix_shift_amount) % num_classses + ''' + @classmethod + def add_argument(self, parser): + parser.add_argument('--shift_amount', type=int, + help='shift_amount of all_to_all attack') + parser.add_argument('--num_classses', type=int, + help='total number of labels') + return parser + def __init__(self, shift_amount, num_classses): + self.shift_amount = shift_amount + self.num_classses = num_classses + def __call__(self, original_label, original_index = None, img = None): + return self.poison_label(original_label) + def poison_label(self, original_label): + label_after_shift = (original_label + self.shift_amount)% self.num_classses + return label_after_shift diff --git a/utils/choose_index.py b/utils/choose_index.py new file mode 100755 index 0000000..2e53c89 --- /dev/null +++ b/utils/choose_index.py @@ -0,0 +1,13 @@ +import random +import sys, argparse, yaml +import numpy as np + +sys.path.append('../') + +def choose_index(args, data_all_length) : + # choose clean data according to index + if args.index == None: + ran_idx = random.sample(range(data_all_length),int(data_all_length*args.ratio)) + else: + ran_idx = np.loadtxt(args.index, dtype=int) + return ran_idx diff --git a/utils/conv_pad_same.py b/utils/conv_pad_same.py new file mode 100755 index 0000000..bec62c7 --- /dev/null +++ b/utils/conv_pad_same.py @@ -0,0 +1,115 @@ +''' +This file is copy from https://github.com/Oldpan/Faceswap-Deepfake-Pytorch/blob/master/padding_same_conv.py +''' + + +# modify con2d function to use same padding +# code referd to @famssa in 'https://github.com/pytorch/pytorch/issues/3867' +# and tensorflow source code + +import torch.utils.data +from torch.nn import functional as F + +import math +import torch +from torch.nn.parameter import Parameter +from torch.nn.functional import pad +from torch.nn.modules import Module +from torch.nn.modules.utils import _single, _pair, _triple + + +class _ConvNd(Module): + + def __init__(self, in_channels, out_channels, kernel_size, stride, + padding, dilation, transposed, output_padding, groups, bias): + super(_ConvNd, self).__init__() + if in_channels % groups != 0: + raise ValueError('in_channels must be divisible by groups') + if out_channels % groups != 0: + raise ValueError('out_channels must be divisible by groups') + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.transposed = transposed + self.output_padding = output_padding + self.groups = groups + if transposed: + self.weight = Parameter(torch.Tensor( + in_channels, out_channels // groups, *kernel_size)) + else: + self.weight = Parameter(torch.Tensor( + out_channels, in_channels // groups, *kernel_size)) + if bias: + self.bias = Parameter(torch.Tensor(out_channels)) + else: + self.register_parameter('bias', None) + self.reset_parameters() + + def reset_parameters(self): + n = self.in_channels + for k in self.kernel_size: + n *= k + stdv = 1. / math.sqrt(n) + self.weight.data.uniform_(-stdv, stdv) + if self.bias is not None: + self.bias.data.uniform_(-stdv, stdv) + + def __repr__(self): + s = ('{name}({in_channels}, {out_channels}, kernel_size={kernel_size}' + ', stride={stride}') + if self.padding != (0,) * len(self.padding): + s += ', padding={padding}' + if self.dilation != (1,) * len(self.dilation): + s += ', dilation={dilation}' + if self.output_padding != (0,) * len(self.output_padding): + s += ', output_padding={output_padding}' + if self.groups != 1: + s += ', groups={groups}' + if self.bias is None: + s += ', bias=False' + s += ')' + return s.format(name=self.__class__.__name__, **self.__dict__) + + +class Conv2d(_ConvNd): + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, + padding=0, dilation=1, groups=1, bias=True): + kernel_size = _pair(kernel_size) + stride = _pair(stride) + padding = _pair(padding) + dilation = _pair(dilation) + super(Conv2d, self).__init__( + in_channels, out_channels, kernel_size, stride, padding, dilation, + False, _pair(0), groups, bias) + + def forward(self, input): + return conv2d_same_padding(input, self.weight, self.bias, self.stride, + self.padding, self.dilation, self.groups) + + +# custom con2d, because pytorch don't have "padding='same'" option. +def conv2d_same_padding(input, weight, bias=None, stride=1, padding=1, dilation=1, groups=1): + + input_rows = input.size(2) + filter_rows = weight.size(2) + effective_filter_size_rows = (filter_rows - 1) * dilation[0] + 1 + out_rows = (input_rows + stride[0] - 1) // stride[0] + padding_needed = max(0, (out_rows - 1) * stride[0] + effective_filter_size_rows - + input_rows) + padding_rows = max(0, (out_rows - 1) * stride[0] + + (filter_rows - 1) * dilation[0] + 1 - input_rows) + rows_odd = (padding_rows % 2 != 0) + padding_cols = max(0, (out_rows - 1) * stride[0] + + (filter_rows - 1) * dilation[0] + 1 - input_rows) + cols_odd = (padding_rows % 2 != 0) + + if rows_odd or cols_odd: + input = pad(input, [0, int(cols_odd), 0, int(rows_odd)]) + + return F.conv2d(input, weight, bias, stride, + padding=(padding_rows // 2, padding_cols // 2), + dilation=dilation, groups=groups) \ 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a/utils/defense/utils_dbr/__pycache__/utils_br.cpython-38.pyc b/utils/defense/utils_dbr/__pycache__/utils_br.cpython-38.pyc new file mode 100644 index 0000000..00e4fbb Binary files /dev/null and b/utils/defense/utils_dbr/__pycache__/utils_br.cpython-38.pyc differ diff --git a/utils/defense/utils_dbr/br_loss.py b/utils/defense/utils_dbr/br_loss.py new file mode 100644 index 0000000..ec65808 --- /dev/null +++ b/utils/defense/utils_dbr/br_loss.py @@ -0,0 +1,186 @@ +# Modified from https://github.com/HobbitLong/SupContrast + +from __future__ import print_function + +import torch +import torch.nn as nn +import numpy + + +class SupConLoss(nn.Module): + def __init__(self, temperature=0.07, contrast_mode='all', + base_temperature=0.07): + super(SupConLoss, self).__init__() + self.temperature = temperature + self.contrast_mode = contrast_mode + self.base_temperature = base_temperature + + def forward(self, features, labels=None, gt_labels=None, mask=None, isCleans=None): + """Compute loss for model. + Args: + features: hidden vector of shape [bsz, n_views, ...]. + labels: label of shape [bsz]. + gt_labels: ground-truth label of shape [bsz]. + mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j is the positive of sample i. Can be asymmetric. + isCleans: is-clean sign of shape [bsz], isCleans{i}=1 if sample i is genuinely clean. + Returns: + A loss scalar. + """ + device = (torch.device('cuda') + if features.is_cuda + else torch.device('cpu')) + + if len(features.shape) < 3: + raise ValueError('`features` needs to be [bsz, n_views, ...],' + 'at least 3 dimensions are required') + if len(features.shape) > 3: + features = features.view(features.shape[0], features.shape[1], -1) + + batch_size = features.shape[0] + if labels is not None and mask is not None: + raise ValueError('Cannot define both `labels` and `mask`') + elif labels is None and mask is None: # SimCLR (contrastive learning) + mask = torch.eye(batch_size, dtype=torch.float32).to(device) + elif labels is not None: # SupCon (supervised contrastive learning) + labels = labels.contiguous().view(-1, 1) + if labels.shape[0] != batch_size: + raise ValueError('Num of labels does not match num of features') + # set the positives of each sample as its own augmented version and the augmented versions of samples with the same label + mask = torch.eq(labels, labels.T).float().to(device) # mask: positive==1 + else: + mask = mask.float().to(device) + + contrast_count = features.shape[1] + contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0) + if self.contrast_mode == 'one': + anchor_feature = features[:, 0] + anchor_count = 1 + elif self.contrast_mode == 'all': + anchor_feature = contrast_feature + anchor_count = contrast_count + else: + raise ValueError('Unknown mode: {}'.format(self.contrast_mode)) + + # compute logits + anchor_dot_contrast = torch.div( + torch.matmul(anchor_feature, contrast_feature.T), + self.temperature) + # for numerical stability + logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True) + logits = anchor_dot_contrast - logits_max.detach() + + # tile mask + mask = mask.repeat(anchor_count, contrast_count) + # mask-out self-contrast cases + logits_mask = torch.scatter( + torch.ones_like(mask), + 1, + torch.arange(batch_size * anchor_count).view(-1, 1).to(device), + 0 + ) + mask = mask * logits_mask # mask_{i,j}=1 if sample j is the positive of sample i. + + # compute log_prob + exp_logits = torch.exp(logits) * logits_mask + log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True)) + + # compute mean of log-likelihood over positive + mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1) + + # loss + loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos + loss = loss.view(anchor_count, batch_size).mean() + + return loss + + +class SupConLoss_Consistency(nn.Module): + def __init__(self, temperature=0.07, contrast_mode='all', + base_temperature=0.07): + super(SupConLoss_Consistency, self).__init__() + self.temperature = temperature + self.contrast_mode = contrast_mode + self.base_temperature = base_temperature + + def forward(self, features, labels=None, flags=None, mask=None): + """Compute loss for model. + Args: + features: hidden vector of shape [bsz, n_views, ...]. + labels: label of shape [bsz]. + gt_labels: ground-truth label of shape [bsz]. + mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j is the positive of sample i. Can be asymmetric. + isCleans: is-clean sign of shape [bsz], isCleans{i}=1 if sample i is genuinely clean. + Returns: + A loss scalar. + """ + device = (torch.device('cuda') + if features.is_cuda + else torch.device('cpu')) + + if len(features.shape) < 3: + raise ValueError('`features` needs to be [bsz, n_views, ...],' + 'at least 3 dimensions are required') + if len(features.shape) > 3: + features = features.view(features.shape[0], features.shape[1], -1) + + batch_size = features.shape[0] + if labels is not None and mask is not None: + raise ValueError('Cannot define both `labels` and `mask`') + elif labels is None and mask is None: # SimCLR (contrastive learning) + mask = torch.eye(batch_size, dtype=torch.float32).to(device) + elif labels is not None: # SS-CTL (semi-supervised contrastive learning) + labels = labels.contiguous().view(-1, 1) + if labels.shape[0] != batch_size: + raise ValueError('Num of labels does not match num of features') + # set the positive of a poisoned sample / an uncertain sample as its own augmented version + # set the positives of a clean sample as its own augmented version and the augmented versions of samples with the same label + mask = torch.eq(labels, labels.T).float().to(device) + nonclean_idx = torch.where(flags!=0)[0] # poisoned samples and uncertain samples + mask[nonclean_idx, :] = 0 + mask[nonclean_idx, nonclean_idx] = 1 + else: + mask = mask.float().to(device) + + contrast_count = features.shape[1] + contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0) + if self.contrast_mode == 'one': + anchor_feature = features[:, 0] + anchor_count = 1 + elif self.contrast_mode == 'all': + anchor_feature = contrast_feature + anchor_count = contrast_count + else: + raise ValueError('Unknown mode: {}'.format(self.contrast_mode)) + + # compute logits + anchor_dot_contrast = torch.div( + torch.matmul(anchor_feature, contrast_feature.T), + self.temperature) + # for numerical stability + logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True) + logits = anchor_dot_contrast - logits_max.detach() + + # tile mask + mask = mask.repeat(anchor_count, contrast_count) + # isCleans_mask = isCleans_mask.repeat(anchor_count, contrast_count) + # mask-out self-contrast cases + logits_mask = torch.scatter( + torch.ones_like(mask), + 1, + torch.arange(batch_size * anchor_count).view(-1, 1).to(device), + 0 + ) + mask = mask * logits_mask # mask_{i,j}=1 if sample j is the positive of sample i. + + # compute log_prob + exp_logits = torch.exp(logits) * logits_mask + log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True)) + + # compute mean of log-likelihood over positive + mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1) + + # loss + loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos + loss = loss.view(anchor_count, batch_size).mean() + + return loss diff --git a/utils/defense/utils_dbr/dataloader_bd.py b/utils/defense/utils_dbr/dataloader_bd.py new file mode 100644 index 0000000..af09040 --- /dev/null +++ b/utils/defense/utils_dbr/dataloader_bd.py @@ -0,0 +1,218 @@ +# Modified from https://github.com/bboylyg/NAD/blob/main/data_loader.py + +import os +import csv +import random +import numpy as np +from PIL import Image +from tqdm import tqdm +import time +import sys +from matplotlib import image as mlt +import cv2 + +import torch +import torch.utils.data as data +import torch.nn.functional as F +import torchvision +import torchvision.transforms as transforms +import torchvision.datasets as datasets + + + +class TwoCropTransform: + """Create two crops of the same image""" + def __init__(self, transform): + self.transform = transform + + def __call__(self, x): + return [self.transform(x), self.transform(x)] + +class TransformThree: + def __init__(self, transform1, transform2, transform3): + self.transform1 = transform1 + self.transform2 = transform2 + self.transform3 = transform3 + + def __call__(self, inp): + out1 = self.transform1(inp) + out2 = self.transform2(inp) + out3 = self.transform3(inp) + return out1, out2, out3 + + +class Dataset_npy(torch.utils.data.Dataset): + def __init__(self, full_dataset=None, transform=None): + self.dataset = full_dataset + self.transform = transform + self.dataLen = len(self.dataset) + + def __getitem__(self, index): + image = self.dataset[index][0] + label = self.dataset[index][1] + flag = self.dataset[index][2] + + if self.transform: + image = self.transform(image) + # print(type(image), image.shape) + return image, label, flag + + def __len__(self): + return self.dataLen + +def normalization(opt, inputs): + output = inputs.clone() + if opt.dataset == "cifar10": + f = transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]) + elif opt.dataset == "mnist": + f = transforms.Normalize([0.5], [0.5]) + elif opt.dataset == 'tiny': + f = transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]) + elif opt.dataset == "gtsrb" or opt.dataset == "celeba": + # pass + return output + elif opt.dataset == 'imagenet': + f = transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]) + elif opt.dataset == "cifar100": + f = transforms.Normalize([0.5070751592371323, 0.48654887331495095, 0.4409178433670343], [0.2673342858792401, 0.2564384629170883, 0.27615047132568404]) + else: + raise Exception("Invalid Dataset") + for i in range(inputs.shape[0]): + output[i] = f(inputs[i]) + return output + + +def get_transform_br(opt, train=True): + ### transform1 ### + transforms_list = [] + transforms_list.append(transforms.Resize((opt.input_height, opt.input_width))) + transforms_list.append(transforms.ToTensor()) + transforms1 = transforms.Compose(transforms_list) + + if train == False: + return transforms1 + + ### transform2 ### + transforms_list = [] + transforms_list.append(transforms.Resize((opt.input_height, opt.input_width))) + if train: + if opt.dataset == 'cifar10' or opt.dataset == 'gtsrb': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + transforms_list.append(transforms.RandomHorizontalFlip()) + elif opt.dataset == 'cifar100': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + transforms_list.append(transforms.RandomHorizontalFlip()) + transforms_list.append(transforms.RandomRotation(15)) + elif opt.dataset == "imagenet": + transforms_list.append(transforms.RandomRotation(20)) + transforms_list.append(transforms.RandomHorizontalFlip(0.5)) + elif opt.dataset == "tiny": + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=8)) + transforms_list.append(transforms.RandomHorizontalFlip()) + transforms_list.append(transforms.ToTensor()) + transforms2 = transforms.Compose(transforms_list) + + ### transform3 ### + transforms_list = [] + transforms_list.append(transforms.Resize((opt.input_height, opt.input_width))) + if opt.trans1 == 'rotate': + transforms_list.append(transforms.RandomRotation(180)) + elif opt.trans1 == 'affine': + transforms_list.append(transforms.RandomAffine(degrees=0, translate=(0.2, 0.2))) + elif opt.trans1 == 'flip': + transforms_list.append(transforms.RandomHorizontalFlip(p=1.0)) + elif opt.trans1 == 'crop': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + elif opt.trans1 == 'blur': + transforms_list.append(transforms.GaussianBlur(kernel_size=15, sigma=(0.1, 2.0))) + elif opt.trans1 == 'erase': + transforms_list.append(transforms.ToTensor()) + transforms_list.append(transforms.RandomErasing(p=1.0, scale=(0.2, 0.3), ratio=(0.5, 1.0), value='random')) + transforms_list.append(transforms.ToPILImage()) + + if opt.trans2 == 'rotate': + transforms_list.append(transforms.RandomRotation(180)) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'affine': + transforms_list.append(transforms.RandomAffine(degrees=0, translate=(0.2, 0.2))) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'flip': + transforms_list.append(transforms.RandomHorizontalFlip(p=1.0)) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'crop': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'blur': + transforms_list.append(transforms.GaussianBlur(kernel_size=15, sigma=(0.1, 2.0))) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'erase': + transforms_list.append(transforms.ToTensor()) + transforms_list.append(transforms.RandomErasing(p=1.0, scale=(0.2, 0.3), ratio=(0.5, 1.0), value='random')) + elif opt.trans2 == 'none': + transforms_list.append(transforms.ToTensor()) + + transforms3 = transforms.Compose(transforms_list) + + return transforms1, transforms2, transforms3 + + + +def get_br_train_loader(opt): + transforms_list = [ + transforms.ToPILImage(), + transforms.RandomResizedCrop(size=opt.size, scale=(0.2, 1.)), + transforms.RandomHorizontalFlip(), + transforms.RandomApply([ + transforms.ColorJitter(0.4, 0.4, 0.4, 0.1) + ], p=0.8), + transforms.RandomGrayscale(p=0.2), + transforms.ToTensor() + ] + + # construct data loader + if opt.dataset == 'cifar10': + mean = (0.4914, 0.4822, 0.4465) + std = (0.2023, 0.1994, 0.2010) + elif opt.dataset == 'cifar100': + mean = (0.5071, 0.4867, 0.4408) + std = (0.2675, 0.2565, 0.2761) + elif opt.dataset == "mnist": + mean = [0.5,] + std = [0.5,] + elif opt.dataset == 'tiny': + mean = (0.4802, 0.4481, 0.3975) + std = (0.2302, 0.2265, 0.2262) + elif opt.dataset == 'imagenet': + mean = (0.4802, 0.4481, 0.3975) + std = (0.2302, 0.2265, 0.2262) + elif opt.dataset == 'gtsrb': + mean = None + elif opt.dataset == 'path': + mean = eval(opt.mean) + std = eval(opt.std) + else: + raise ValueError('dataset not supported: {}'.format(opt.dataset)) + + if mean != None: + normalize = transforms.Normalize(mean=mean, std=std) + transforms_list.append(normalize) + + train_transform = transforms.Compose(transforms_list) + + folder_path = folder_path = f'{opt.save_path}/d-br/data_produce' + data_path_clean = os.path.join(folder_path, 'clean_samples.npy') + data_path_poison = os.path.join(folder_path, 'poison_samples.npy') + data_path_suspicious = os.path.join(folder_path, 'suspicious_samples.npy') + + clean_data = np.load(data_path_clean, allow_pickle=True) + poison_data = np.load(data_path_poison, allow_pickle=True) + suspicious_data = np.load(data_path_suspicious, allow_pickle=True) + all_data = np.concatenate((clean_data, poison_data, suspicious_data), axis=0) + + train_dataset = Dataset_npy(full_dataset=all_data, transform=TwoCropTransform(train_transform)) + train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=opt.batch_size, shuffle=True) + + return train_loader + + + diff --git a/utils/defense/utils_dbr/models/resnet_super.py b/utils/defense/utils_dbr/models/resnet_super.py new file mode 100644 index 0000000..6fc1c21 --- /dev/null +++ b/utils/defense/utils_dbr/models/resnet_super.py @@ -0,0 +1,213 @@ +# Source: https://github.com/HobbitLong/SupContrast + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1, is_last=False): + super(BasicBlock, self).__init__() + self.is_last = is_last + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + preact = out + out = F.relu(out) + if self.is_last: + return out, preact + else: + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, in_planes, planes, stride=1, is_last=False): + super(Bottleneck, self).__init__() + self.is_last = is_last + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(self.expansion * planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + out += self.shortcut(x) + preact = out + out = F.relu(out) + if self.is_last: + return out, preact + else: + return out + + +# class ResNet(nn.Module): +# def __init__(self, block, num_blocks, in_channel=3, zero_init_residual=False): +# super(ResNet, self).__init__() +# self.in_planes = 64 + +# self.conv1 = nn.Conv2d(in_channel, 64, kernel_size=3, stride=1, padding=1, +# bias=False) +# self.bn1 = nn.BatchNorm2d(64) +# self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) +# self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) +# self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) +# self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) +# self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + +# for m in self.modules(): +# if isinstance(m, nn.Conv2d): +# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') +# elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): +# nn.init.constant_(m.weight, 1) +# nn.init.constant_(m.bias, 0) + +# # Zero-initialize the last BN in each residual branch, +# # so that the residual branch starts with zeros, and each residual block behaves +# # like an identity. This improves the model by 0.2~0.3% according to: +# # https://arxiv.org/abs/1706.02677 +# if zero_init_residual: +# for m in self.modules(): +# if isinstance(m, Bottleneck): +# nn.init.constant_(m.bn3.weight, 0) +# elif isinstance(m, BasicBlock): +# nn.init.constant_(m.bn2.weight, 0) + +# def _make_layer(self, block, planes, num_blocks, stride): +# strides = [stride] + [1] * (num_blocks - 1) +# layers = [] +# for i in range(num_blocks): +# stride = strides[i] +# layers.append(block(self.in_planes, planes, stride)) +# self.in_planes = planes * block.expansion +# return nn.Sequential(*layers) + +# def forward(self, x, layer=100): +# out = F.relu(self.bn1(self.conv1(x))) +# out = self.layer1(out) +# out = self.layer2(out) +# out = self.layer3(out) +# out = self.layer4(out) +# out = self.avgpool(out) +# out = torch.flatten(out, 1) +# return out + + +# def resnet18(**kwargs): +# return ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + + +# def resnet34(**kwargs): +# return ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) + + +# def resnet50(**kwargs): +# return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) + + +# def resnet101(**kwargs): +# return ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) + + +# model_dict = { +# 'resnet18': [resnet18, 512], +# 'resnet34': [resnet34, 512], +# 'resnet50': [resnet50, 2048], +# 'resnet101': [resnet101, 2048], +# } + + +class LinearBatchNorm(nn.Module): + """Implements BatchNorm1d by BatchNorm2d, for SyncBN purpose""" + def __init__(self, dim, affine=True): + super(LinearBatchNorm, self).__init__() + self.dim = dim + self.bn = nn.BatchNorm2d(dim, affine=affine) + + def forward(self, x): + x = x.view(-1, self.dim, 1, 1) + x = self.bn(x) + x = x.view(-1, self.dim) + return x + + +class SupConResNet(nn.Module): + """backbone + projection head""" + def __init__(self, encoder, dim_in, head='mlp', feat_dim=128): + super(SupConResNet, self).__init__() + self.encoder = encoder + + if head == 'linear': + self.head = nn.Linear(dim_in, feat_dim) + elif head == 'mlp': + self.head = nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.ReLU(inplace=True), + nn.Linear(dim_in, feat_dim) + ) + else: + raise NotImplementedError( + 'head not supported: {}'.format(head)) + + def forward(self, x): + feat = self.encoder(x) + feat = F.normalize(self.head(feat), dim=1) + return feat + + +class SupCEResNet(nn.Module): + """encoder + classifier""" + def __init__(self, encoder, num_classes=10): + super(SupCEResNet, self).__init__() + self.encoder = encoder + dim_in = list(encoder.named_modules())[-1][1].in_features + self.fc = nn.Linear(dim_in, num_classes) + + def forward(self, x): + return self.fc(self.encoder(x)) + + +# class LinearClassifier(nn.Module): +# """Linear classifier""" +# def __init__(self, encoder, num_classes=10): +# super(LinearClassifier, self).__init__() +# dim_in = list(encoder.named_modules())[-1][1].in_features +# self.fc = nn.Linear(dim_in, num_classes) + +# def forward(self, features): +# return self.fc(features) +class LinearClassifier(nn.Module): + """Linear classifier""" + def __init__(self, feat_dim, num_classes=10): + super(LinearClassifier, self).__init__() + self.fc = nn.Linear(feat_dim, num_classes) + + def forward(self, features): + return self.fc(features) diff --git a/utils/defense/utils_dbr/sd.py b/utils/defense/utils_dbr/sd.py new file mode 100644 index 0000000..4f2492a --- /dev/null +++ b/utils/defense/utils_dbr/sd.py @@ -0,0 +1,130 @@ +import sys +import os +from tqdm import tqdm +import numpy as np +import argparse +import torch +from torch import nn +sys.path.append("./") +sys.path.append(os.getcwd()) + +from utils.defense.utils_dbr.dataloader_bd import normalization + +def calculate_consistency(args, dataloader, model): + model.eval() + + for i, (inputs, labels, _, isCleans, gt_labels) in enumerate(dataloader): + inputs1, inputs2 = inputs[0], inputs[2] + inputs1, inputs2 = normalization(args, inputs1), normalization(args, inputs2) # Normalize + inputs1, inputs2, labels, gt_labels = inputs1.to(args.device), inputs2.to(args.device), labels.to(args.device), gt_labels.to(args.device) + clean_idx, poison_idx = torch.where(isCleans == True), torch.where(isCleans == False) + + ### Feature ### + if hasattr(model, "module"): # abandon FC layer + features_out = list(model.module.children())[:-1] + else: + features_out = list(model.children())[:-1] + modelout = nn.Sequential(*features_out).to(args.device) + features1, features2 = modelout(inputs1), modelout(inputs2) + features1, features2 = features1.view(features1.size(0), -1), features2.view(features2.size(0), -1) + + ### Calculate consistency ### + feature_consistency = torch.mean((features1 - features2)**2, dim=1) + + ### Save ### + draw_features = feature_consistency.detach().cpu().numpy() + draw_clean_features = feature_consistency[clean_idx].detach().cpu().numpy() + draw_poison_features = feature_consistency[poison_idx].detach().cpu().numpy() + + + f_path = os.path.join(args.save_path,'data_produce') + if not os.path.exists(f_path): + os.makedirs(f_path) + f_all = os.path.join(f_path,'all.txt') + f_clean = os.path.join(f_path,'clean.txt') + f_poison = os.path.join(f_path,'poison.txt') + with open(f_all, 'ab') as f: + np.savetxt(f, draw_features, delimiter=" ") + with open(f_clean, 'ab') as f: + np.savetxt(f, draw_clean_features, delimiter=" ") + with open(f_poison, 'ab') as f: + np.savetxt(f, draw_poison_features, delimiter=" ") + return + +def calculate_gamma(args): + args.clean_ratio = 0.20 + args.poison_ratio = 0.05 + + + f_path = os.path.join(args.save_path,'data_produce') + f_all = os.path.join(f_path,'all.txt') + + all_data = np.loadtxt(f_all) + all_size = all_data.shape[0] # 50000 + + clean_size = int(all_size * args.clean_ratio) # 10000 + poison_size = int(all_size * args.poison_ratio) # 2500 + + new_data = np.sort(all_data) # in ascending order + gamma_low = new_data[clean_size] + gamma_high = new_data[all_size-poison_size] + print("gamma_low: ", gamma_low) + print("gamma_high: ", gamma_high) + return gamma_low, gamma_high + +def separate_samples(args, trainloader, model): + gamma_low, gamma_high = args.gamma_low, args.gamma_high + model.eval() + clean_samples, poison_samples, suspicious_samples = [], [], [] + + for i, (inputs, labels, _, _, gt_labels) in enumerate(trainloader): + if i == 10001 and args.debug: + break + if i % 1000 == 0: + print("Processing samples:", i) + inputs1, inputs2 = inputs[0], inputs[2] + + ### Prepare for saved ### + img = inputs1 + img = img.squeeze() + target = labels.squeeze() + img = np.transpose((img * 255).cpu().numpy(), (1, 2, 0)).astype('uint8') + target = target.cpu().numpy() + + inputs1, inputs2 = normalization(args, inputs1), normalization(args, inputs2) # Normalize + inputs1, inputs2, labels, gt_labels = inputs1.to(args.device), inputs2.to(args.device), labels.to(args.device), gt_labels.to(args.device) + + ### Features ### + if hasattr(model, "module"): # abandon FC layer + features_out = list(model.module.children())[:-1] + else: + features_out = list(model.children())[:-1] + modelout = nn.Sequential(*features_out).to(args.device) + features1, features2 = modelout(inputs1), modelout(inputs2) + features1, features2 = features1.view(features1.size(0), -1), features2.view(features2.size(0), -1) + + ### Compare consistency ### + feature_consistency = torch.mean((features1 - features2)**2, dim=1) + # feature_consistency = feature_consistency.detach().cpu().numpy() + + ### Separate samples ### + if feature_consistency.item() <= gamma_low: + flag = 0 + clean_samples.append((img, target, flag)) + elif feature_consistency.item() >= gamma_high: + flag = 2 + poison_samples.append((img, target, flag)) + else: + flag = 1 + suspicious_samples.append((img, target, flag)) + + ### Save samples ### + + folder_path = os.path.join(args.save_path,'data_produce') + + data_path_clean = os.path.join(folder_path, 'clean_samples.npy') + data_path_poison = os.path.join(folder_path, 'poison_samples.npy') + data_path_suspicious = os.path.join(folder_path, 'suspicious_samples.npy') + np.save(data_path_clean, clean_samples) + np.save(data_path_poison, poison_samples) + np.save(data_path_suspicious, suspicious_samples) diff --git a/utils/defense/utils_dbr/utils_br.py b/utils/defense/utils_dbr/utils_br.py new file mode 100644 index 0000000..3a44855 --- /dev/null +++ b/utils/defense/utils_dbr/utils_br.py @@ -0,0 +1,165 @@ +import math +import torch.optim as optim +import torch +import numpy as np +import os +import sys +import time + +def warmup_learning_rate(args, epoch, batch_id, total_batches, optimizer): + if args.warm and epoch <= args.warm_epochs: + p = (batch_id + (epoch - 1) * total_batches) / \ + (args.warm_epochs * total_batches) + lr = args.warmup_from + p * (args.warmup_to - args.warmup_from) + + for param_group in optimizer.param_groups: + param_group['lr'] = lr + +def set_optimizer(opt, model): + optimizer = optim.SGD(model.parameters(), + lr=opt.lr, + momentum=0.9, + weight_decay=5e-4) + return optimizer + +def adjust_learning_rate(args, optimizer, epoch): + lr = args.learning_rate + if args.cosine: + eta_min = lr * (args.lr_decay_rate ** 3) + lr = eta_min + (lr - eta_min) * ( + 1 + math.cos(math.pi * epoch / args.epochs)) / 2 + else: + steps = np.sum(epoch > np.asarray(args.lr_decay_epochs)) + if steps > 0: + lr = lr * (args.lr_decay_rate ** steps) + + for param_group in optimizer.param_groups: + param_group['lr'] = lr + +def save_model(model, optimizer, opt, epoch, save_file): + print('==> Saving...') + state = { + 'opt': opt, + 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), + 'epoch': epoch, + } + torch.save(state, save_file) + print('==> Successfully saved!') + del state + +def accuracy(output, target, topk=(1,)): # output: (256,10); target: (256) + """Computes the accuracy over the k top predictions for the specified values of k""" + with torch.no_grad(): + maxk = max(topk) # 5 + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) # pred: (256,5) + pred = pred.t() # (5,256) + correct = pred.eq(target.view(1, -1).expand_as(pred)) # (5,256) + + res = [] + + for k in topk: + # correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) + correct_k = torch.flatten(correct[:k]).float().sum(0, keepdim=True) + res.append(correct_k.mul_(1.0 / batch_size)) + return res + +def format_time(seconds): + days = int(seconds / 3600/24) + seconds = seconds - days*3600*24 + hours = int(seconds / 3600) + seconds = seconds - hours*3600 + minutes = int(seconds / 60) + seconds = seconds - minutes*60 + secondsf = int(seconds) + seconds = seconds - secondsf + millis = int(seconds*1000) + + f = '' + i = 1 + if days > 0: + f += str(days) + 'D' + i += 1 + if hours > 0 and i <= 2: + f += str(hours) + 'h' + i += 1 + if minutes > 0 and i <= 2: + f += str(minutes) + 'm' + i += 1 + if secondsf > 0 and i <= 2: + f += str(secondsf) + 's' + i += 1 + if millis > 0 and i <= 2: + f += str(millis) + 'ms' + i += 1 + if f == '': + f = '0ms' + return f + +_, term_width = os.popen('stty size', 'r').read().split() +term_width = int(term_width) +TOTAL_BAR_LENGTH = 65. +last_time = time.time() +begin_time = last_time + +def progress_bar(current, total, msg=None): + global last_time, begin_time + if current == 0: + begin_time = time.time() # Reset for new bar. + + cur_len = int(TOTAL_BAR_LENGTH*current/total) + rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1 + + sys.stdout.write(' [') + for i in range(cur_len): + sys.stdout.write('=') + sys.stdout.write('>') + for i in range(rest_len): + sys.stdout.write('.') + sys.stdout.write(']') + + cur_time = time.time() + step_time = cur_time - last_time + last_time = cur_time + tot_time = cur_time - begin_time + + L = [] + L.append(' Step: %s' % format_time(step_time)) + L.append(' | Total: %s' % format_time(tot_time)) + if msg: + L.append(' | ' + msg) + + msg = ''.join(L) + sys.stdout.write(msg) + for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3): + sys.stdout.write(' ') + + # Go back to the center of the bar. + for i in range(term_width-int(TOTAL_BAR_LENGTH/2)+2): + sys.stdout.write('\b') + sys.stdout.write(' %d/%d ' % (current+1, total)) + + if current < total-1: + sys.stdout.write('\r') + else: + sys.stdout.write('\n') + sys.stdout.flush() + +class AverageMeter(object): + """Computes and stores the average and current value""" + def __init__(self): + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count diff --git a/utils/defense/utils_dst/__init__.py b/utils/defense/utils_dst/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/utils/defense/utils_dst/__pycache__/__init__.cpython-38.pyc b/utils/defense/utils_dst/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..182773b Binary files /dev/null and b/utils/defense/utils_dst/__pycache__/__init__.cpython-38.pyc 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0000000..9e3bfea Binary files /dev/null and b/utils/defense/utils_dst/__pycache__/utils_st.cpython-38.pyc differ diff --git a/utils/defense/utils_dst/dataloader_bd.py b/utils/defense/utils_dst/dataloader_bd.py new file mode 100644 index 0000000..92115ab --- /dev/null +++ b/utils/defense/utils_dst/dataloader_bd.py @@ -0,0 +1,314 @@ +# Modified from https://github.com/bboylyg/NAD/blob/main/data_loader.py + +import os +import csv +import random +import numpy as np +from PIL import Image +from tqdm import tqdm +import time +import sys +from matplotlib import image as mlt +import cv2 +import logging + +import torch +import torch.utils.data as data +import torch.nn.functional as F +import torchvision +import torchvision.transforms as transforms +import torchvision.datasets as datasets + +# from utils.bd_dataset import prepro_cls_DatasetBD + + +class TwoCropTransform: + """Create two crops of the same image""" + def __init__(self, transform): + self.transform = transform + + def __call__(self, x): + return [self.transform(x), self.transform(x)] + +class TransformThree: + def __init__(self, transform1, transform2, transform3): + self.transform1 = transform1 + self.transform2 = transform2 + self.transform3 = transform3 + + def __call__(self, inp): + out1 = self.transform1(inp) + out2 = self.transform2(inp) + out3 = self.transform3(inp) + return out1, out2, out3 + + +class Dataset_npy(torch.utils.data.Dataset): + def __init__(self, full_dataset=None, transform=None): + self.dataset = full_dataset + self.transform = transform + self.dataLen = len(self.dataset) + + def __getitem__(self, index): + image = self.dataset[index][0] + label = self.dataset[index][1] + flag = self.dataset[index][2] + + if self.transform: + image = self.transform(image) + # print(type(image), image.shape) + return image, label, flag + + def __len__(self): + return self.dataLen + +def normalization(opt, inputs): + output = inputs.clone() + if opt.dataset == "cifar10": + f = transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]) + elif opt.dataset == "mnist": + f = transforms.Normalize([0.5], [0.5]) + elif opt.dataset == 'tiny': + f = transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]) + elif opt.dataset == "gtsrb" or opt.dataset == "celeba": + # pass + return output + elif opt.dataset == 'imagenet': + f = transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]) + elif opt.dataset == "cifar100": + f = transforms.Normalize([0.5070751592371323, 0.48654887331495095, 0.4409178433670343], [0.2673342858792401, 0.2564384629170883, 0.27615047132568404]) + else: + raise Exception("Invalid Dataset") + for i in range(inputs.shape[0]): + output[i] = f(inputs[i]) + return output + + +def get_transform_st(opt, train=True): + ### transform1 ### + transforms_list = [] + transforms_list.append(transforms.Resize((opt.input_height, opt.input_width))) + transforms_list.append(transforms.ToTensor()) + transforms1 = transforms.Compose(transforms_list) + + if train == False: + return transforms1 + + ### transform2 ### + transforms_list = [] + transforms_list.append(transforms.Resize((opt.input_height, opt.input_width))) + if train: + if opt.dataset == 'cifar10' or opt.dataset == 'gtsrb': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + transforms_list.append(transforms.RandomHorizontalFlip()) + elif opt.dataset == 'cifar100': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + transforms_list.append(transforms.RandomHorizontalFlip()) + transforms_list.append(transforms.RandomRotation(15)) + elif opt.dataset == "imagenet": + transforms_list.append(transforms.RandomRotation(20)) + transforms_list.append(transforms.RandomHorizontalFlip(0.5)) + elif opt.dataset == "tiny": + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=8)) + transforms_list.append(transforms.RandomHorizontalFlip()) + transforms_list.append(transforms.ToTensor()) + transforms2 = transforms.Compose(transforms_list) + + ### transform3 ### + transforms_list = [] + transforms_list.append(transforms.Resize((opt.input_height, opt.input_width))) + if opt.trans1 == 'rotate': + transforms_list.append(transforms.RandomRotation(180)) + elif opt.trans1 == 'affine': + transforms_list.append(transforms.RandomAffine(degrees=0, translate=(0.2, 0.2))) + elif opt.trans1 == 'flip': + transforms_list.append(transforms.RandomHorizontalFlip(p=1.0)) + elif opt.trans1 == 'crop': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + elif opt.trans1 == 'blur': + transforms_list.append(transforms.GaussianBlur(kernel_size=15, sigma=(0.1, 2.0))) + elif opt.trans1 == 'erase': + transforms_list.append(transforms.ToTensor()) + transforms_list.append(transforms.RandomErasing(p=1.0, scale=(0.2, 0.3), ratio=(0.5, 1.0), value='random')) + transforms_list.append(transforms.ToPILImage()) + + if opt.trans2 == 'rotate': + transforms_list.append(transforms.RandomRotation(180)) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'affine': + transforms_list.append(transforms.RandomAffine(degrees=0, translate=(0.2, 0.2))) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'flip': + transforms_list.append(transforms.RandomHorizontalFlip(p=1.0)) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'crop': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'blur': + transforms_list.append(transforms.GaussianBlur(kernel_size=15, sigma=(0.1, 2.0))) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'erase': + transforms_list.append(transforms.ToTensor()) + transforms_list.append(transforms.RandomErasing(p=1.0, scale=(0.2, 0.3), ratio=(0.5, 1.0), value='random')) + elif opt.trans2 == 'none': + transforms_list.append(transforms.ToTensor()) + + transforms3 = transforms.Compose(transforms_list) + + return transforms1, transforms2, transforms3 + +# def get_sd_dataloader(args,result): +# x = result['bd_train']['x'] +# y = result['bd_train']['y'] +# data_bd_train = list(zip(x,y)) + +# ### train_dataset and train_dataloader +# transform1, transform2, transform3 = get_transform_st(args, train=True) + +# poisoned_train = prepro_cls_DatasetBD( +# full_dataset_without_transform=data_bd_train, +# poison_idx=np.zeros(len(data_bd_train)), # one-hot to determine which image may take bd_transform +# bd_image_pre_transform=None, +# bd_label_pre_transform=None, +# ori_image_transform_in_loading=TransformThree(transform1, transform2, transform3), +# ori_label_transform_in_loading=None, +# add_details_in_preprocess=True, +# ) + +# bd_trainloader = torch.utils.data.DataLoader(poisoned_train, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=True) + +# ### test_dataset and test_dataloader +# transform = get_transform_st(args, train=False) +# x = result['bd_test']['x'] +# y = result['bd_test']['y'] +# data_bd_test = list(zip(x,y)) + +# data_bd_testset = prepro_cls_DatasetBD( +# full_dataset_without_transform=data_bd_test, +# poison_idx=np.zeros(len(data_bd_test)), # one-hot to determine which image may take bd_transform +# bd_image_pre_transform=None, +# bd_label_pre_transform=None, +# ori_image_transform_in_loading=transform, +# ori_label_transform_in_loading=None, +# add_details_in_preprocess=True, +# ) +# bd_testloader = torch.utils.data.DataLoader(data_bd_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + +# transform = get_transform_st(args, train=False) +# x = result['clean_test']['x'] +# y = result['clean_test']['y'] +# data_clean_test = list(zip(x,y)) +# data_clean_testset = prepro_cls_DatasetBD( +# full_dataset_without_transform=data_clean_test, +# poison_idx=np.zeros(len(data_clean_test)), # one-hot to determine which image may take bd_transform +# bd_image_pre_transform=None, +# bd_label_pre_transform=None, +# ori_image_transform_in_loading=transform, +# ori_label_transform_in_loading=None, +# add_details_in_preprocess=True, +# ) +# clean_testloader = torch.utils.data.DataLoader(data_clean_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + +# # return bd_trainloader, bd_testloader, clean_testloader +# return bd_trainloader + +def get_st_train_loader(opt, module='sscl'): + transforms_list = [ + transforms.ToPILImage(), + transforms.RandomResizedCrop(size=opt.input_height, scale=(0.2, 1.)), + transforms.RandomHorizontalFlip(), + transforms.RandomApply([ + transforms.ColorJitter(0.4, 0.4, 0.4, 0.1) + ], p=0.8), + transforms.RandomGrayscale(p=0.2), + transforms.ToTensor() + ] + + # construct data loader + if opt.dataset == 'cifar10': + mean = (0.4914, 0.4822, 0.4465) + std = (0.2023, 0.1994, 0.2010) + elif opt.dataset == 'cifar100': + mean = (0.5071, 0.4867, 0.4408) + std = (0.2675, 0.2565, 0.2761) + elif opt.dataset == "mnist": + mean = [0.5,] + std = [0.5,] + elif opt.dataset == 'tiny': + mean = (0.4802, 0.4481, 0.3975) + std = (0.2302, 0.2265, 0.2262) + elif opt.dataset == 'imagenet': + mean = (0.4802, 0.4481, 0.3975) + std = (0.2302, 0.2265, 0.2262) + elif opt.dataset == 'gtsrb': + mean = None + elif opt.dataset == 'path': + mean = eval(opt.mean) + std = eval(opt.std) + else: + raise ValueError('dataset not supported: {}'.format(opt.dataset)) + + if mean != None: + normalize = transforms.Normalize(mean=mean, std=std) + transforms_list.append(normalize) + + train_transform = transforms.Compose(transforms_list) + + folder_path = folder_path = f'{opt.save_path}data_produce' + data_path_clean = os.path.join(folder_path, 'clean_samples.npy') + data_path_poison = os.path.join(folder_path, 'poison_samples.npy') + data_path_suspicious = os.path.join(folder_path, 'suspicious_samples.npy') + if opt.debug: + # data_path_poison = os.path.join(folder_path, 'suspicious_samples.npy') + opt.batch_size = 5 + + clean_data = np.load(data_path_clean, allow_pickle=True) + poison_data = np.load(data_path_poison, allow_pickle=True) + suspicious_data = np.load(data_path_suspicious, allow_pickle=True) + logging.info(f'Num of clean, poison and suspicious: {clean_data.shape[0]}, {poison_data.shape[0]}, {suspicious_data.shape[0]}') + all_data = np.concatenate((clean_data, poison_data, suspicious_data), axis=0) + if module == 'mixed_ce': + train_dataset = Dataset_npy(full_dataset=all_data, transform=train_transform) + elif module == 'sscl': + train_dataset = Dataset_npy(full_dataset=all_data, transform=TwoCropTransform(train_transform)) + else: + raise ValueError('module not specified') + train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=opt.batch_size, shuffle=True) + + return train_loader + +# def get_st_val_loader(opt): +# # construct data loader +# if opt.dataset == 'cifar10': +# mean = (0.4914, 0.4822, 0.4465) +# std = (0.2023, 0.1994, 0.2010) +# elif opt.dataset == 'cifar100': +# mean = (0.5071, 0.4867, 0.4408) +# std = (0.2675, 0.2565, 0.2761) +# elif opt.dataset == "mnist": +# mean = [0.5] +# std = [0.5] +# elif opt.dataset == 'tiny': +# mean = (0.4802, 0.4481, 0.3975) +# std = (0.2302, 0.2265, 0.2262) +# elif opt.dataset == 'imagenet': +# mean = (0.4802, 0.4481, 0.3975) +# std = (0.2302, 0.2265, 0.2262) +# elif opt.dataset == 'gtsrb': +# mean = None +# elif opt.dataset == 'path': +# mean = eval(opt.mean) +# std = eval(opt.std) +# else: +# raise ValueError('dataset not supported: {}'.format(opt.dataset)) +# transforms_list = [transforms.ToTensor(),] +# if mean != None: +# normalize = transforms.Normalize(mean=mean, std=std) +# transforms_list.append(normalize) + +# val_transform = transforms.Compose(transforms_list) +# val_loader = torch.utils.data.DataLoader( +# val_dataset, batch_size=256, shuffle=False, +# num_workers=8, pin_memory=True) + +# return val_loader \ No newline at end of file diff --git a/utils/defense/utils_dst/models/__pycache__/resnet_super.cpython-38.pyc b/utils/defense/utils_dst/models/__pycache__/resnet_super.cpython-38.pyc new file mode 100644 index 0000000..41f627e Binary files /dev/null and b/utils/defense/utils_dst/models/__pycache__/resnet_super.cpython-38.pyc differ diff --git a/utils/defense/utils_dst/models/resnet_super.py b/utils/defense/utils_dst/models/resnet_super.py new file mode 100644 index 0000000..6fc1c21 --- /dev/null +++ b/utils/defense/utils_dst/models/resnet_super.py @@ -0,0 +1,213 @@ +# Source: https://github.com/HobbitLong/SupContrast + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1, is_last=False): + super(BasicBlock, self).__init__() + self.is_last = is_last + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + preact = out + out = F.relu(out) + if self.is_last: + return out, preact + else: + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, in_planes, planes, stride=1, is_last=False): + super(Bottleneck, self).__init__() + self.is_last = is_last + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(self.expansion * planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + out += self.shortcut(x) + preact = out + out = F.relu(out) + if self.is_last: + return out, preact + else: + return out + + +# class ResNet(nn.Module): +# def __init__(self, block, num_blocks, in_channel=3, zero_init_residual=False): +# super(ResNet, self).__init__() +# self.in_planes = 64 + +# self.conv1 = nn.Conv2d(in_channel, 64, kernel_size=3, stride=1, padding=1, +# bias=False) +# self.bn1 = nn.BatchNorm2d(64) +# self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) +# self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) +# self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) +# self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) +# self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + +# for m in self.modules(): +# if isinstance(m, nn.Conv2d): +# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') +# elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): +# nn.init.constant_(m.weight, 1) +# nn.init.constant_(m.bias, 0) + +# # Zero-initialize the last BN in each residual branch, +# # so that the residual branch starts with zeros, and each residual block behaves +# # like an identity. This improves the model by 0.2~0.3% according to: +# # https://arxiv.org/abs/1706.02677 +# if zero_init_residual: +# for m in self.modules(): +# if isinstance(m, Bottleneck): +# nn.init.constant_(m.bn3.weight, 0) +# elif isinstance(m, BasicBlock): +# nn.init.constant_(m.bn2.weight, 0) + +# def _make_layer(self, block, planes, num_blocks, stride): +# strides = [stride] + [1] * (num_blocks - 1) +# layers = [] +# for i in range(num_blocks): +# stride = strides[i] +# layers.append(block(self.in_planes, planes, stride)) +# self.in_planes = planes * block.expansion +# return nn.Sequential(*layers) + +# def forward(self, x, layer=100): +# out = F.relu(self.bn1(self.conv1(x))) +# out = self.layer1(out) +# out = self.layer2(out) +# out = self.layer3(out) +# out = self.layer4(out) +# out = self.avgpool(out) +# out = torch.flatten(out, 1) +# return out + + +# def resnet18(**kwargs): +# return ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + + +# def resnet34(**kwargs): +# return ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) + + +# def resnet50(**kwargs): +# return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) + + +# def resnet101(**kwargs): +# return ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) + + +# model_dict = { +# 'resnet18': [resnet18, 512], +# 'resnet34': [resnet34, 512], +# 'resnet50': [resnet50, 2048], +# 'resnet101': [resnet101, 2048], +# } + + +class LinearBatchNorm(nn.Module): + """Implements BatchNorm1d by BatchNorm2d, for SyncBN purpose""" + def __init__(self, dim, affine=True): + super(LinearBatchNorm, self).__init__() + self.dim = dim + self.bn = nn.BatchNorm2d(dim, affine=affine) + + def forward(self, x): + x = x.view(-1, self.dim, 1, 1) + x = self.bn(x) + x = x.view(-1, self.dim) + return x + + +class SupConResNet(nn.Module): + """backbone + projection head""" + def __init__(self, encoder, dim_in, head='mlp', feat_dim=128): + super(SupConResNet, self).__init__() + self.encoder = encoder + + if head == 'linear': + self.head = nn.Linear(dim_in, feat_dim) + elif head == 'mlp': + self.head = nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.ReLU(inplace=True), + nn.Linear(dim_in, feat_dim) + ) + else: + raise NotImplementedError( + 'head not supported: {}'.format(head)) + + def forward(self, x): + feat = self.encoder(x) + feat = F.normalize(self.head(feat), dim=1) + return feat + + +class SupCEResNet(nn.Module): + """encoder + classifier""" + def __init__(self, encoder, num_classes=10): + super(SupCEResNet, self).__init__() + self.encoder = encoder + dim_in = list(encoder.named_modules())[-1][1].in_features + self.fc = nn.Linear(dim_in, num_classes) + + def forward(self, x): + return self.fc(self.encoder(x)) + + +# class LinearClassifier(nn.Module): +# """Linear classifier""" +# def __init__(self, encoder, num_classes=10): +# super(LinearClassifier, self).__init__() +# dim_in = list(encoder.named_modules())[-1][1].in_features +# self.fc = nn.Linear(dim_in, num_classes) + +# def forward(self, features): +# return self.fc(features) +class LinearClassifier(nn.Module): + """Linear classifier""" + def __init__(self, feat_dim, num_classes=10): + super(LinearClassifier, self).__init__() + self.fc = nn.Linear(feat_dim, num_classes) + + def forward(self, features): + return self.fc(features) diff --git a/utils/defense/utils_dst/sd.py b/utils/defense/utils_dst/sd.py new file mode 100644 index 0000000..62bd5cf --- /dev/null +++ b/utils/defense/utils_dst/sd.py @@ -0,0 +1,130 @@ +import sys +import os +from tqdm import tqdm +import numpy as np +import argparse +import torch +from torch import nn +sys.path.append("./") +sys.path.append(os.getcwd()) +print(os.getcwd()) +from utils.defense.utils_dst.dataloader_bd import normalization + +def calculate_consistency(args, dataloader, model): + model.eval() + + for i, (inputs, labels, _, isCleans, gt_labels) in enumerate(dataloader): + inputs1, inputs2 = inputs[0], inputs[2] + inputs1, inputs2 = normalization(args, inputs1), normalization(args, inputs2) # Normalize + inputs1, inputs2, labels, gt_labels = inputs1.to(args.device), inputs2.to(args.device), labels.to(args.device), gt_labels.to(args.device) + clean_idx, poison_idx = torch.where(isCleans == True), torch.where(isCleans == False) + + ### Feature ### + if hasattr(model, "module"): # abandon FC layer + features_out = list(model.module.children())[:-1] + else: + features_out = list(model.children())[:-1] + modelout = nn.Sequential(*features_out).to(args.device) + features1, features2 = modelout(inputs1), modelout(inputs2) + features1, features2 = features1.view(features1.size(0), -1), features2.view(features2.size(0), -1) + + ### Calculate consistency ### + feature_consistency = torch.mean((features1 - features2)**2, dim=1) + + ### Save ### + draw_features = feature_consistency.detach().cpu().numpy() + draw_clean_features = feature_consistency[clean_idx].detach().cpu().numpy() + draw_poison_features = feature_consistency[poison_idx].detach().cpu().numpy() + + + f_path = os.path.join(args.save_path, 'data_produce') + + if not os.path.exists(f_path): + os.makedirs(f_path) + f_all = os.path.join(f_path,'all.txt') + f_clean = os.path.join(f_path,'clean.txt') + f_poison = os.path.join(f_path,'poison.txt') + with open(f_all, 'ab') as f: + np.savetxt(f, draw_features, delimiter=" ") + with open(f_clean, 'ab') as f: + np.savetxt(f, draw_clean_features, delimiter=" ") + with open(f_poison, 'ab') as f: + np.savetxt(f, draw_poison_features, delimiter=" ") + return + +def calculate_gamma(args): + args.clean_ratio = 0.20 + args.poison_ratio = 0.05 + + f_path = os.path.join(args.save_path, 'data_produce') + f_all = os.path.join(f_path,'all.txt') + + all_data = np.loadtxt(f_all) + all_size = all_data.shape[0] # 50000 + + clean_size = int(all_size * args.clean_ratio) # 10000 + poison_size = int(all_size * args.poison_ratio) # 2500 + + new_data = np.sort(all_data) # in ascending order + gamma_low = new_data[clean_size] + gamma_high = new_data[all_size-poison_size] + print("gamma_low: ", gamma_low) + print("gamma_high: ", gamma_high) + return gamma_low, gamma_high + +def separate_samples(args, trainloader, model): + gamma_low, gamma_high = args.gamma_low, args.gamma_high + model.eval() + clean_samples, poison_samples, suspicious_samples = [], [], [] + + for i, (inputs, labels, _, _, gt_labels) in enumerate(trainloader): + if args.debug and i==10001: + break + if i % 1000 == 0: + print("Processing samples:", i) + inputs1, inputs2 = inputs[0], inputs[2] + + ### Prepare for saved ### + img = inputs1 + img = img.squeeze() + target = labels.squeeze() + img = np.transpose((img * 255).cpu().numpy(), (1, 2, 0)).astype('uint8') + target = target.cpu().numpy() + + inputs1, inputs2 = normalization(args, inputs1), normalization(args, inputs2) # Normalize + inputs1, inputs2, labels, gt_labels = inputs1.to(args.device), inputs2.to(args.device), labels.to(args.device), gt_labels.to(args.device) + + ### Features ### + if hasattr(model, "module"): # abandon FC layer + features_out = list(model.module.children())[:-1] + else: + features_out = list(model.children())[:-1] + modelout = nn.Sequential(*features_out).to(args.device) + features1, features2 = modelout(inputs1), modelout(inputs2) + features1, features2 = features1.view(features1.size(0), -1), features2.view(features2.size(0), -1) + + ### Compare consistency ### + feature_consistency = torch.mean((features1 - features2)**2, dim=1) + # feature_consistency = feature_consistency.detach().cpu().numpy() + + ### Separate samples ### + if feature_consistency.item() <= gamma_low: + flag = 0 + clean_samples.append((img, target, flag)) + elif feature_consistency.item() >= gamma_high: + flag = 2 + poison_samples.append((img, target, flag)) + else: + flag = 1 + suspicious_samples.append((img, target, flag)) + + ### Save samples ### + + folder_path = os.path.join(args.save_path, 'data_produce') + + data_path_clean = os.path.join(folder_path, 'clean_samples.npy') + data_path_poison = os.path.join(folder_path, 'poison_samples.npy') + data_path_suspicious = os.path.join(folder_path, 'suspicious_samples.npy') + np.save(data_path_clean, clean_samples) + np.save(data_path_poison, poison_samples) + np.save(data_path_suspicious, suspicious_samples) diff --git a/utils/defense/utils_dst/st_loss.py b/utils/defense/utils_dst/st_loss.py new file mode 100644 index 0000000..3b183c8 --- /dev/null +++ b/utils/defense/utils_dst/st_loss.py @@ -0,0 +1,191 @@ +# Modified from https://github.com/HobbitLong/SupContrast + +from __future__ import print_function + +import torch +import torch.nn as nn +import numpy + + +class SupConLoss(nn.Module): + def __init__(self, temperature=0.07, contrast_mode='all', + base_temperature=0.07,device=None): + super(SupConLoss, self).__init__() + self.temperature = temperature + self.contrast_mode = contrast_mode + self.base_temperature = base_temperature + self.device =device + + def forward(self, features, labels=None, gt_labels=None, mask=None, isCleans=None): + """Compute loss for model. + Args: + features: hidden vector of shape [bsz, n_views, ...]. + labels: label of shape [bsz]. + gt_labels: ground-truth label of shape [bsz]. + mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j is the positive of sample i. Can be asymmetric. + isCleans: is-clean sign of shape [bsz], isCleans{i}=1 if sample i is genuinely clean. + Returns: + A loss scalar. + """ + if self.device is None: + device = (torch.device('cuda') if features.is_cuda else torch.device('cpu')) + else: + device = self.device + + if len(features.shape) < 3: + raise ValueError('`features` needs to be [bsz, n_views, ...],' + 'at least 3 dimensions are required') + if len(features.shape) > 3: + features = features.view(features.shape[0], features.shape[1], -1) + + batch_size = features.shape[0] + if labels is not None and mask is not None: + raise ValueError('Cannot define both `labels` and `mask`') + elif labels is None and mask is None: # SimCLR (contrastive learning) + mask = torch.eye(batch_size, dtype=torch.float32).to(device) + elif labels is not None: # SupCon (supervised contrastive learning) + labels = labels.contiguous().view(-1, 1) + if labels.shape[0] != batch_size: + raise ValueError('Num of labels does not match num of features') + # set the positives of each sample as its own augmented version and the augmented versions of samples with the same label + mask = torch.eq(labels, labels.T).float().to(device) # mask: positive==1 + else: + mask = mask.float().to(device) + + contrast_count = features.shape[1] + contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0) + if self.contrast_mode == 'one': + anchor_feature = features[:, 0] + anchor_count = 1 + elif self.contrast_mode == 'all': + anchor_feature = contrast_feature + anchor_count = contrast_count + else: + raise ValueError('Unknown mode: {}'.format(self.contrast_mode)) + + # compute logits + anchor_dot_contrast = torch.div( + torch.matmul(anchor_feature, contrast_feature.T), + self.temperature) + # for numerical stability + logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True) + logits = anchor_dot_contrast - logits_max.detach() + + # tile mask + mask = mask.repeat(anchor_count, contrast_count) + # mask-out self-contrast cases + logits_mask = torch.scatter( + torch.ones_like(mask), + 1, + torch.arange(batch_size * anchor_count).view(-1, 1).to(device), + 0 + ) + mask = mask * logits_mask # mask_{i,j}=1 if sample j is the positive of sample i. + + # compute log_prob + exp_logits = torch.exp(logits) * logits_mask + log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True)) + + # compute mean of log-likelihood over positive + mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1) + + # loss + loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos + loss = loss.view(anchor_count, batch_size).mean() + + return loss + + +class SupConLoss_Consistency(nn.Module): + def __init__(self, temperature=0.07, contrast_mode='all', + base_temperature=0.07,device=None): + super(SupConLoss_Consistency, self).__init__() + self.temperature = temperature + self.contrast_mode = contrast_mode + self.base_temperature = base_temperature + self.device =device + + + def forward(self, features, labels=None, flags=None, mask=None): + """Compute loss for model. + Args: + features: hidden vector of shape [bsz, n_views, ...]. + labels: label of shape [bsz]. + gt_labels: ground-truth label of shape [bsz]. + mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j is the positive of sample i. Can be asymmetric. + isCleans: is-clean sign of shape [bsz], isCleans{i}=1 if sample i is genuinely clean. + Returns: + A loss scalar. + """ + if self.device is None: + device = (torch.device('cuda') if features.is_cuda else torch.device('cpu')) + else: + device = self.device + + if len(features.shape) < 3: + raise ValueError('`features` needs to be [bsz, n_views, ...],' + 'at least 3 dimensions are required') + if len(features.shape) > 3: + features = features.view(features.shape[0], features.shape[1], -1) + + batch_size = features.shape[0] + if labels is not None and mask is not None: + raise ValueError('Cannot define both `labels` and `mask`') + elif labels is None and mask is None: # SimCLR (contrastive learning) + mask = torch.eye(batch_size, dtype=torch.float32).to(device) + elif labels is not None: # SS-CTL (semi-supervised contrastive learning) + labels = labels.contiguous().view(-1, 1) + if labels.shape[0] != batch_size: + raise ValueError('Num of labels does not match num of features') + # set the positive of a poisoned sample / an uncertain sample as its own augmented version + # set the positives of a clean sample as its own augmented version and the augmented versions of samples with the same label + mask = torch.eq(labels, labels.T).float().to(device) + nonclean_idx = torch.where(flags!=0)[0] # poisoned samples and uncertain samples + mask[nonclean_idx, :] = 0 + mask[nonclean_idx, nonclean_idx] = 1 + else: + mask = mask.float().to(device) + + contrast_count = features.shape[1] + contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0) + if self.contrast_mode == 'one': + anchor_feature = features[:, 0] + anchor_count = 1 + elif self.contrast_mode == 'all': + anchor_feature = contrast_feature + anchor_count = contrast_count + else: + raise ValueError('Unknown mode: {}'.format(self.contrast_mode)) + + # compute logits + anchor_dot_contrast = torch.div( + torch.matmul(anchor_feature, contrast_feature.T), + self.temperature) + # for numerical stability + logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True) + logits = anchor_dot_contrast - logits_max.detach() + + # tile mask + mask = mask.repeat(anchor_count, contrast_count) + # isCleans_mask = isCleans_mask.repeat(anchor_count, contrast_count) + # mask-out self-contrast cases + logits_mask = torch.scatter( + torch.ones_like(mask), + 1, + torch.arange(batch_size * anchor_count).view(-1, 1).to(device), + 0 + ) + mask = mask * logits_mask # mask_{i,j}=1 if sample j is the positive of sample i. + + # compute log_prob + exp_logits = torch.exp(logits) * logits_mask + log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True)) + + # compute mean of log-likelihood over positive + mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1) + + # loss + loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos + loss = loss.view(anchor_count, batch_size).mean() + + return loss diff --git a/utils/defense/utils_dst/utils_st.py b/utils/defense/utils_dst/utils_st.py new file mode 100644 index 0000000..d515ae8 --- /dev/null +++ b/utils/defense/utils_dst/utils_st.py @@ -0,0 +1,119 @@ +import math +import torch.optim as optim +import torch +import numpy as np +import sys, os +sys.path.append(os.getcwd()) +sys.path.append('../') + +# def set_args(args,module='sscl'): +# if module == 'sscl': +# args.batch_size = 512 +# args.learning_rate = 0.5 +# args.temp = 0.1 +# args.epochs = 200 +# args.num_workers = 16 +# args.method = 'SupCon' # choices = ['SupCon', 'SimCLR'] +# args.consine = True + +# elif module == 'mixed_ce': +# args.batch_size = 512 +# args.learning_rate = 5 +# args.epochs = 10 +# args.num_workers = 16 +# args.consine = False + +# if args.batch_size > 256: +# args.warm = True +# if args.warm: +# args.warmup_from = 0.01 +# args.warm_epochs = 10 +# if args.cosine: +# eta_min = args.learning_rate * (args.lr_decay_rate ** 3) +# args.warmup_to = eta_min + (args.learning_rate - eta_min) * ( +# 1 + math.cos(math.pi * args.warm_epochs / args.epochs)) / 2 +# else: +# args.warmup_to = args.learning_rate +# if args.debug: +# args.epochs = 2 +# return args + +def warmup_learning_rate(args, epoch, batch_id, total_batches, optimizer): + if args.warm and epoch <= args.warm_epochs: + p = (batch_id + (epoch - 1) * total_batches) / \ + (args.warm_epochs * total_batches) + lr = args.warmup_from + p * (args.warmup_to - args.warmup_from) + + for param_group in optimizer.param_groups: + param_group['lr'] = lr + +def set_optimizer(opt, model,lr=None): + if lr == None: + lr = opt.lr + optimizer = optim.SGD(model.parameters(), + lr=lr, + momentum=0.9, + weight_decay=5e-4) + return optimizer + +def adjust_learning_rate(args, optimizer, epoch): + lr = args.learning_rate + if args.cosine: + eta_min = lr * (args.lr_decay_rate ** 3) + lr = eta_min + (lr - eta_min) * ( + 1 + math.cos(math.pi * epoch / args.epochs)) / 2 + else: + steps = np.sum(epoch > np.asarray(args.lr_decay_epochs)) + if steps > 0: + lr = lr * (args.lr_decay_rate ** steps) + + for param_group in optimizer.param_groups: + param_group['lr'] = lr + + +def save_model(model, optimizer, opt, epoch, save_file): + print('==> Saving...') + state = { + 'opt': opt, + 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), + 'epoch': epoch, + } + torch.save(state, save_file) + print('==> Successfully saved!') + del state + +def accuracy(output, target, topk=(1,)): # output: (256,10); target: (256) + """Computes the accuracy over the k top predictions for the specified values of k""" + with torch.no_grad(): + maxk = max(topk) # 5 + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) # pred: (256,5) + pred = pred.t() # (5,256) + correct = pred.eq(target.view(1, -1).expand_as(pred)) # (5,256) + + res = [] + + for k in topk: + # correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) + correct_k = torch.flatten(correct[:k]).float().sum(0, keepdim=True) + res.append(correct_k.mul_(1. / batch_size)) + return res + +class AverageMeter(object): + """Computes and stores the average and current value""" + def __init__(self): + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count diff --git a/utils/defense_utils/anp/anp_model/__init__.py b/utils/defense_utils/anp/anp_model/__init__.py new file mode 100644 index 0000000..77902e2 --- /dev/null +++ b/utils/defense_utils/anp/anp_model/__init__.py @@ -0,0 +1,11 @@ +from .vgg_anp import * +from .anp_batchnorm import * +from .preact_anp import * +from .mobilenet_anp import * +from .eff_anp import * +from .den_anp import * +from .anp_layernorm import * +# from .conv_new_anp import * +# from .vit_new_anp import * +from .vit_anp import * +from .conv_anp import * \ No newline at end of file diff --git a/utils/defense_utils/anp/anp_model/anp_batchnorm.py b/utils/defense_utils/anp/anp_model/anp_batchnorm.py new file mode 100644 index 0000000..cbcc841 --- /dev/null +++ b/utils/defense_utils/anp/anp_model/anp_batchnorm.py @@ -0,0 +1,167 @@ +# This code is based on: +# https://pytorch.org/docs/stable/_modules/torch/nn/modules/batchnorm.html#BatchNorm2d +# only perturbing weights + +import torch +from torch import Tensor +import torch.nn as nn +import torch.nn.functional as F +import torch.nn.init as init +from torch.nn.parameter import Parameter + + +class NoisyBatchNorm2d(nn.BatchNorm2d): + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, + track_running_stats=True): + super(NoisyBatchNorm2d, self).__init__( + num_features, eps, momentum, affine, track_running_stats) + self.neuron_mask = Parameter(torch.Tensor(num_features)) + self.neuron_noise = Parameter(torch.Tensor(num_features)) + self.neuron_noise_bias = Parameter(torch.Tensor(num_features)) + init.ones_(self.neuron_mask) + init.zeros_(self.neuron_noise) + init.zeros_(self.neuron_noise_bias) + self.is_perturbed = False + + def reset(self, rand_init=False, eps=0.0): + if rand_init: + init.uniform_(self.neuron_noise, a=-eps, b=eps) + init.uniform_(self.neuron_noise_bias, a=-eps, b=eps) + else: + init.zeros_(self.neuron_noise) + init.zeros_(self.neuron_noise_bias) + + def include_noise(self): + self.is_perturbed = True + + def exclude_noise(self): + self.is_perturbed = False + + def forward(self, input: Tensor) -> Tensor: + self._check_input_dim(input) + + # exponential_average_factor is set to self.momentum + # (when it is available) only so that it gets updated + # in ONNX graph when this node is exported to ONNX. + if self.momentum is None: + exponential_average_factor = 0.0 + else: + exponential_average_factor = self.momentum + + if self.training and self.track_running_stats: + # TODO: if statement only here to tell the jit to skip emitting this when it is None + if self.num_batches_tracked is not None: # type: ignore + self.num_batches_tracked = self.num_batches_tracked + 1 # type: ignore + if self.momentum is None: # use cumulative moving average + exponential_average_factor = 1.0 / float(self.num_batches_tracked) + else: # use exponential moving average + exponential_average_factor = self.momentum + + r""" + Decide whether the mini-batch stats should be used for normalization rather than the buffers. + Mini-batch stats are used in training mode, and in eval mode when buffers are None. + """ + if self.training: + bn_training = True + else: + bn_training = (self.running_mean is None) and (self.running_var is None) + + r""" + Buffers are only updated if they are to be tracked and we are in training mode. Thus they only need to be + passed when the update should occur (i.e. in training mode when they are tracked), or when buffer stats are + used for normalization (i.e. in eval mode when buffers are not None). + """ + assert self.running_mean is None or isinstance(self.running_mean, torch.Tensor) + assert self.running_var is None or isinstance(self.running_var, torch.Tensor) + + if self.is_perturbed: + coeff_weight = self.neuron_mask + self.neuron_noise + coeff_bias = 1.0 + self.neuron_noise_bias + else: + coeff_weight = self.neuron_mask + coeff_bias = 1.0 + return F.batch_norm( + input, + # If buffers are not to be tracked, ensure that they won't be updated + self.running_mean if not self.training or self.track_running_stats else None, + self.running_var if not self.training or self.track_running_stats else None, + self.weight * coeff_weight, self.bias * coeff_bias, + bn_training, exponential_average_factor, self.eps) + + +class NoisyBatchNorm1d(nn.BatchNorm1d): + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True): + super(NoisyBatchNorm1d, self).__init__( + num_features, eps, momentum, affine, track_running_stats) + self.neuron_mask_fc = Parameter(torch.Tensor(num_features)) + self.neuron_noise_fc = Parameter(torch.Tensor(num_features)) + self.neuron_noise_bias_fc = Parameter(torch.Tensor(num_features)) + init.ones_(self.neuron_mask_fc) + init.zeros_(self.neuron_noise_fc) + init.zeros_(self.neuron_noise_bias_fc) + self.is_perturbed = False + + def reset(self, rand_init=False, eps=0.0): + if rand_init: + init.uniform_(self.neuron_noise_fc, a=-eps, b=eps) + init.uniform_(self.neuron_noise_bias_fc, a=-eps, b=eps) + else: + init.zeros_(self.neuron_noise_fc) + init.zeros_(self.neuron_noise_bias_fc) + + def include_noise(self): + self.is_perturbed = True + + def exclude_noise(self): + self.is_perturbed = False + + def forward(self, input: Tensor) -> Tensor: + self._check_input_dim(input) + + # exponential_average_factor is set to self.momentum + # (when it is available) only so that it gets updated + # in ONNX graph when this node is exported to ONNX. + if self.momentum is None: + exponential_average_factor = 0.0 + else: + exponential_average_factor = self.momentum + + if self.training and self.track_running_stats: + # TODO: if statement only here to tell the jit to skip emitting this when it is None + if self.num_batches_tracked is not None: # type: ignore + self.num_batches_tracked = self.num_batches_tracked + 1 # type: ignore + if self.momentum is None: # use cumulative moving average + exponential_average_factor = 1.0 / float(self.num_batches_tracked) + else: # use exponential moving average + exponential_average_factor = self.momentum + + r""" + Decide whether the mini-batch stats should be used for normalization rather than the buffers. + Mini-batch stats are used in training mode, and in eval mode when buffers are None. + """ + if self.training: + bn_training = True + else: + bn_training = (self.running_mean is None) and (self.running_var is None) + + r""" + Buffers are only updated if they are to be tracked and we are in training mode. Thus they only need to be + passed when the update should occur (i.e. in training mode when they are tracked), or when buffer stats are + used for normalization (i.e. in eval mode when buffers are not None). + """ + assert self.running_mean is None or isinstance(self.running_mean, torch.Tensor) + assert self.running_var is None or isinstance(self.running_var, torch.Tensor) + + if self.is_perturbed: + coeff_weight = self.neuron_mask_fc + self.neuron_noise_fc + coeff_bias = 1.0 + self.neuron_noise_bias_fc + else: + coeff_weight = self.neuron_mask_fc + coeff_bias = 1.0 + return F.batch_norm( + input, + # If buffers are not to be tracked, ensure that they won't be updated + self.running_mean if not self.training or self.track_running_stats else None, + self.running_var if not self.training or self.track_running_stats else None, + self.weight * coeff_weight, self.bias * coeff_bias, + bn_training, exponential_average_factor, self.eps) diff --git a/utils/defense_utils/anp/anp_model/anp_layernorm.py b/utils/defense_utils/anp/anp_model/anp_layernorm.py new file mode 100644 index 0000000..2f26c5d --- /dev/null +++ b/utils/defense_utils/anp/anp_model/anp_layernorm.py @@ -0,0 +1,181 @@ +# This code is based on: +# https://pytorch.org/docs/stable/_modules/torch/nn/modules/batchnorm.html#BatchNorm2d +# only perturbing weights + +import torch +from torch import Tensor +import torch.nn as nn +import torch.nn.functional as F +import torch.nn.init as init +from torch.nn.parameter import Parameter +from torch.nn.modules import Module +from torch import Tensor, Size +from typing import Union, List, Tuple + +_shape_t = Union[int, List[int], Size] +class NoiseLayerNorm2d(nn.LayerNorm): + def __init__(self, normalized_shape: _shape_t, eps: float = 1e-5, elementwise_affine: bool = True, + device=None, dtype=None) -> None: + factory_kwargs = {'device': device, 'dtype': dtype} + super(NoiseLayerNorm2d, self).__init__(normalized_shape = normalized_shape, eps = eps, elementwise_affine = elementwise_affine, + device = device, dtype = dtype) + self.neuron_mask = Parameter(torch.Tensor(self.weight.size())) + self.neuron_noise = Parameter(torch.Tensor(self.weight.size())) + self.neuron_noise_bias = Parameter(torch.Tensor(self.weight.size())) + init.ones_(self.neuron_mask) + init.zeros_(self.neuron_noise) + init.zeros_(self.neuron_noise_bias) + self.is_perturbed = False + + def reset(self, rand_init=False, eps=0.0): + if rand_init: + init.uniform_(self.neuron_noise, a=-eps, b=eps) + init.uniform_(self.neuron_noise_bias, a=-eps, b=eps) + else: + init.zeros_(self.neuron_noise) + init.zeros_(self.neuron_noise_bias) + + def include_noise(self): + self.is_perturbed = True + + def exclude_noise(self): + self.is_perturbed = False + + def reset_parameters(self) -> None: + if self.elementwise_affine: + init.ones_(self.weight) + init.zeros_(self.bias) + + + def forward(self, x: Tensor) -> Tensor: + if self.is_perturbed: + coeff_weight = self.neuron_mask + self.neuron_noise + coeff_bias = 1.0 + self.neuron_noise_bias + else: + coeff_weight = self.neuron_mask + coeff_bias = 1.0 + x = x.permute(0, 2, 3, 1) + x = F.layer_norm(x, self.normalized_shape, self.weight * coeff_weight, self.bias * coeff_bias, self.eps) + x = x.permute(0, 3, 1, 2) + return x + +class NoiseLayerNorm(nn.LayerNorm): + r"""Applies Layer Normalization over a mini-batch of inputs as described in + the paper `Layer Normalization `__ + + .. math:: + y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta + + The mean and standard-deviation are calculated over the last `D` dimensions, where `D` + is the dimension of :attr:`normalized_shape`. For example, if :attr:`normalized_shape` + is ``(3, 5)`` (a 2-dimensional shape), the mean and standard-deviation are computed over + the last 2 dimensions of the input (i.e. ``input.mean((-2, -1))``). + :math:`\gamma` and :math:`\beta` are learnable affine transform parameters of + :attr:`normalized_shape` if :attr:`elementwise_affine` is ``True``. + The standard-deviation is calculated via the biased estimator, equivalent to + `torch.var(input, unbiased=False)`. + + .. note:: + Unlike Batch Normalization and Instance Normalization, which applies + scalar scale and bias for each entire channel/plane with the + :attr:`affine` option, Layer Normalization applies per-element scale and + bias with :attr:`elementwise_affine`. + + This layer uses statistics computed from input data in both training and + evaluation modes. + + Args: + normalized_shape (int or list or torch.Size): input shape from an expected input + of size + + .. math:: + [* \times \text{normalized\_shape}[0] \times \text{normalized\_shape}[1] + \times \ldots \times \text{normalized\_shape}[-1]] + + If a single integer is used, it is treated as a singleton list, and this module will + normalize over the last dimension which is expected to be of that specific size. + eps: a value added to the denominator for numerical stability. Default: 1e-5 + elementwise_affine: a boolean value that when set to ``True``, this module + has learnable per-element affine parameters initialized to ones (for weights) + and zeros (for biases). Default: ``True``. + + Attributes: + weight: the learnable weights of the module of shape + :math:`\text{normalized\_shape}` when :attr:`elementwise_affine` is set to ``True``. + The values are initialized to 1. + bias: the learnable bias of the module of shape + :math:`\text{normalized\_shape}` when :attr:`elementwise_affine` is set to ``True``. + The values are initialized to 0. + + Shape: + - Input: :math:`(N, *)` + - Output: :math:`(N, *)` (same shape as input) + + Examples:: + + >>> # NLP Example + >>> batch, sentence_length, embedding_dim = 20, 5, 10 + >>> embedding = torch.randn(batch, sentence_length, embedding_dim) + >>> layer_norm = nn.LayerNorm(embedding_dim) + >>> # Activate module + >>> layer_norm(embedding) + >>> + >>> # Image Example + >>> N, C, H, W = 20, 5, 10, 10 + >>> input = torch.randn(N, C, H, W) + >>> # Normalize over the last three dimensions (i.e. the channel and spatial dimensions) + >>> # as shown in the image below + >>> layer_norm = nn.LayerNorm([C, H, W]) + >>> output = layer_norm(input) + + .. image:: ../_static/img/nn/layer_norm.jpg + :scale: 50 % + + """ + __constants__ = ['normalized_shape', 'eps', 'elementwise_affine'] + normalized_shape: Tuple[int, ...] + eps: float + elementwise_affine: bool + + def __init__(self, normalized_shape: _shape_t, eps: float = 1e-5, elementwise_affine: bool = True, + device=None, dtype=None) -> None: + factory_kwargs = {'device': device, 'dtype': dtype} + super(NoiseLayerNorm, self).__init__(normalized_shape = normalized_shape, eps = eps, elementwise_affine = elementwise_affine, + device = device, dtype = dtype) + self.neuron_mask = Parameter(torch.Tensor(self.weight.size())) + self.neuron_noise = Parameter(torch.Tensor(self.weight.size())) + self.neuron_noise_bias = Parameter(torch.Tensor(self.weight.size())) + init.ones_(self.neuron_mask) + init.zeros_(self.neuron_noise) + init.zeros_(self.neuron_noise_bias) + self.is_perturbed = False + + def reset(self, rand_init=False, eps=0.0): + if rand_init: + init.uniform_(self.neuron_noise, a=-eps, b=eps) + init.uniform_(self.neuron_noise_bias, a=-eps, b=eps) + else: + init.zeros_(self.neuron_noise) + init.zeros_(self.neuron_noise_bias) + + def include_noise(self): + self.is_perturbed = True + + def exclude_noise(self): + self.is_perturbed = False + + def reset_parameters(self) -> None: + if self.elementwise_affine: + init.ones_(self.weight) + init.zeros_(self.bias) + + def forward(self, input: Tensor) -> Tensor: + if self.is_perturbed: + coeff_weight = self.neuron_mask + self.neuron_noise + coeff_bias = 1.0 + self.neuron_noise_bias + else: + coeff_weight = self.neuron_mask + coeff_bias = 1.0 + return F.layer_norm( + input, self.normalized_shape, self.weight * coeff_weight, self.bias * coeff_bias, self.eps) + diff --git a/utils/defense_utils/anp/anp_model/conv_anp.py b/utils/defense_utils/anp/anp_model/conv_anp.py new file mode 100644 index 0000000..ea3018e --- /dev/null +++ b/utils/defense_utils/anp/anp_model/conv_anp.py @@ -0,0 +1,273 @@ +from functools import partial +from typing import Any, Callable, Dict, List, Optional, Sequence + +import torch +from torch import nn, Tensor +from torch.nn import functional as F + +from torchvision._internally_replaced_utils import load_state_dict_from_url +from torchvision.ops.misc import ConvNormActivation +from torchvision.ops.stochastic_depth import StochasticDepth +from torchvision.utils import _log_api_usage_once + +from defense.anp import anp_model + + +__all__ = [ + "ConvNeXt", + "convnext_tiny", + "convnext_small", + "convnext_base", + "convnext_large", +] + + +_MODELS_URLS: Dict[str, Optional[str]] = { + "convnext_tiny": "https://download.pytorch.org/models/convnext_tiny-983f1562.pth", + "convnext_small": "https://download.pytorch.org/models/convnext_small-0c510722.pth", + "convnext_base": "https://download.pytorch.org/models/convnext_base-6075fbad.pth", + "convnext_large": "https://download.pytorch.org/models/convnext_large-ea097f82.pth", +} + + +class LayerNorm2d(nn.LayerNorm): + def forward(self, x: Tensor) -> Tensor: + x = x.permute(0, 2, 3, 1) + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + x = x.permute(0, 3, 1, 2) + return x + + +class Permute(nn.Module): + def __init__(self, dims: List[int]): + super().__init__() + self.dims = dims + + def forward(self, x): + return torch.permute(x, self.dims) + + +class CNBlock(nn.Module): + def __init__( + self, + dim, + layer_scale: float, + stochastic_depth_prob: float, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + if norm_layer is None: + norm_layer = partial(anp_model.NoiseLayerNorm, eps=1e-6) + + self.block = nn.Sequential( + nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim, bias=True), + Permute([0, 2, 3, 1]), + norm_layer(dim), + nn.Linear(in_features=dim, out_features=4 * dim, bias=True), + nn.GELU(), + nn.Linear(in_features=4 * dim, out_features=dim, bias=True), + Permute([0, 3, 1, 2]), + ) + self.layer_scale = nn.Parameter(torch.ones(dim, 1, 1) * layer_scale) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + + def forward(self, input: Tensor) -> Tensor: + result = self.layer_scale * self.block(input) + result = self.stochastic_depth(result) + result += input + return result + + +class CNBlockConfig: + # Stores information listed at Section 3 of the ConvNeXt paper + def __init__( + self, + input_channels: int, + out_channels: Optional[int], + num_layers: int, + ) -> None: + self.input_channels = input_channels + self.out_channels = out_channels + self.num_layers = num_layers + + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "input_channels={input_channels}" + s += ", out_channels={out_channels}" + s += ", num_layers={num_layers}" + s += ")" + return s.format(**self.__dict__) + + +class ConvNeXt(nn.Module): + def __init__( + self, + block_setting: List[CNBlockConfig], + stochastic_depth_prob: float = 0.0, + layer_scale: float = 1e-6, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any, + ) -> None: + super().__init__() + _log_api_usage_once(self) + + if not block_setting: + raise ValueError("The block_setting should not be empty") + elif not (isinstance(block_setting, Sequence) and all([isinstance(s, CNBlockConfig) for s in block_setting])): + raise TypeError("The block_setting should be List[CNBlockConfig]") + + if block is None: + block = CNBlock + + if norm_layer is None: + norm_layer = partial(anp_model.NoiseLayerNorm2d, eps=1e-6) + + layers: List[nn.Module] = [] + + # Stem + firstconv_output_channels = block_setting[0].input_channels + layers.append( + ConvNormActivation( + 3, + firstconv_output_channels, + kernel_size=4, + stride=4, + padding=0, + norm_layer=norm_layer, + activation_layer=None, + bias=True, + ) + ) + + total_stage_blocks = sum(cnf.num_layers for cnf in block_setting) + stage_block_id = 0 + for cnf in block_setting: + # Bottlenecks + stage: List[nn.Module] = [] + for _ in range(cnf.num_layers): + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * stage_block_id / (total_stage_blocks - 1.0) + stage.append(block(cnf.input_channels, layer_scale, sd_prob)) + stage_block_id += 1 + layers.append(nn.Sequential(*stage)) + if cnf.out_channels is not None: + # Downsampling + layers.append( + nn.Sequential( + norm_layer(cnf.input_channels), + nn.Conv2d(cnf.input_channels, cnf.out_channels, kernel_size=2, stride=2), + ) + ) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + + lastblock = block_setting[-1] + lastconv_output_channels = ( + lastblock.out_channels if lastblock.out_channels is not None else lastblock.input_channels + ) + self.classifier = nn.Sequential( + norm_layer(lastconv_output_channels), nn.Flatten(1), nn.Linear(lastconv_output_channels, num_classes) + ) + + for m in self.modules(): + if isinstance(m, (nn.Conv2d, nn.Linear)): + nn.init.trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + x = self.avgpool(x) + x = self.classifier(x) + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _convnext( + arch: str, + block_setting: List[CNBlockConfig], + stochastic_depth_prob: float, + pretrained: bool, + progress: bool, + **kwargs: Any, +) -> ConvNeXt: + model = ConvNeXt(block_setting, stochastic_depth_prob=stochastic_depth_prob, **kwargs) + if pretrained: + if arch not in _MODELS_URLS: + raise ValueError(f"No checkpoint is available for model type {arch}") + state_dict = load_state_dict_from_url(_MODELS_URLS[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def convnext_tiny(*, pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ConvNeXt: + r"""ConvNeXt Tiny model architecture from the + `"A ConvNet for the 2020s" `_ paper. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + block_setting = [ + CNBlockConfig(96, 192, 3), + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 9), + CNBlockConfig(768, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.1) + return _convnext("convnext_tiny", block_setting, stochastic_depth_prob, pretrained, progress, **kwargs) + + +def convnext_small(*, pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ConvNeXt: + r"""ConvNeXt Small model architecture from the + `"A ConvNet for the 2020s" `_ paper. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + block_setting = [ + CNBlockConfig(96, 192, 3), + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 27), + CNBlockConfig(768, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.4) + return _convnext("convnext_small", block_setting, stochastic_depth_prob, pretrained, progress, **kwargs) + + +def convnext_base(*, pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ConvNeXt: + r"""ConvNeXt Base model architecture from the + `"A ConvNet for the 2020s" `_ paper. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + block_setting = [ + CNBlockConfig(128, 256, 3), + CNBlockConfig(256, 512, 3), + CNBlockConfig(512, 1024, 27), + CNBlockConfig(1024, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5) + return _convnext("convnext_base", block_setting, stochastic_depth_prob, pretrained, progress, **kwargs) + + +def convnext_large(*, pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ConvNeXt: + r"""ConvNeXt Large model architecture from the + `"A ConvNet for the 2020s" `_ paper. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + block_setting = [ + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 3), + CNBlockConfig(768, 1536, 27), + CNBlockConfig(1536, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5) + return _convnext("convnext_large", block_setting, stochastic_depth_prob, pretrained, progress, **kwargs) diff --git a/utils/defense_utils/anp/anp_model/conv_new_anp.py b/utils/defense_utils/anp/anp_model/conv_new_anp.py new file mode 100644 index 0000000..40a869d --- /dev/null +++ b/utils/defense_utils/anp/anp_model/conv_new_anp.py @@ -0,0 +1,404 @@ +from functools import partial +from typing import Any, Callable, List, Optional, Sequence + +import torch +from torch import nn, Tensor +from torch.nn import functional as F + +from torchvision.ops.misc import Conv2dNormActivation, Permute +from torchvision.ops.stochastic_depth import StochasticDepth +from torchvision.transforms._presets import ImageClassification +from torchvision.utils import _log_api_usage_once +from torchvision.models._api import WeightsEnum, Weights +from torchvision.models._meta import _IMAGENET_CATEGORIES +from torchvision.models._utils import handle_legacy_interface, _ovewrite_named_param + +from defense.anp import anp_model + + +__all__ = [ + "ConvNeXt", + "ConvNeXt_Tiny_Weights", + "ConvNeXt_Small_Weights", + "ConvNeXt_Base_Weights", + "ConvNeXt_Large_Weights", + "convnext_tiny", + "convnext_small", + "convnext_base", + "convnext_large", +] + + +class LayerNorm2d(nn.LayerNorm): + def forward(self, x: Tensor) -> Tensor: + x = x.permute(0, 2, 3, 1) + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + x = x.permute(0, 3, 1, 2) + return x + + +class CNBlock(nn.Module): + def __init__( + self, + dim, + layer_scale: float, + stochastic_depth_prob: float, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + if norm_layer is None: + norm_layer = partial(anp_model.NoiseLayerNorm, eps=1e-6) + + self.block = nn.Sequential( + nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim, bias=True), + Permute([0, 2, 3, 1]), + norm_layer(dim), + nn.Linear(in_features=dim, out_features=4 * dim, bias=True), + nn.GELU(), + nn.Linear(in_features=4 * dim, out_features=dim, bias=True), + Permute([0, 3, 1, 2]), + ) + self.layer_scale = nn.Parameter(torch.ones(dim, 1, 1) * layer_scale) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + + def forward(self, input: Tensor) -> Tensor: + result = self.layer_scale * self.block(input) + result = self.stochastic_depth(result) + result += input + return result + + +class CNBlockConfig: + # Stores information listed at Section 3 of the ConvNeXt paper + def __init__( + self, + input_channels: int, + out_channels: Optional[int], + num_layers: int, + ) -> None: + self.input_channels = input_channels + self.out_channels = out_channels + self.num_layers = num_layers + + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "input_channels={input_channels}" + s += ", out_channels={out_channels}" + s += ", num_layers={num_layers}" + s += ")" + return s.format(**self.__dict__) + + +class ConvNeXt(nn.Module): + def __init__( + self, + block_setting: List[CNBlockConfig], + stochastic_depth_prob: float = 0.0, + layer_scale: float = 1e-6, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any, + ) -> None: + super().__init__() + _log_api_usage_once(self) + + if not block_setting: + raise ValueError("The block_setting should not be empty") + elif not (isinstance(block_setting, Sequence) and all([isinstance(s, CNBlockConfig) for s in block_setting])): + raise TypeError("The block_setting should be List[CNBlockConfig]") + + if block is None: + block = CNBlock + + if norm_layer is None: + norm_layer = partial(anp_model.NoiseLayerNorm2d, eps=1e-6) + + layers: List[nn.Module] = [] + + # Stem + firstconv_output_channels = block_setting[0].input_channels + layers.append( + Conv2dNormActivation( + 3, + firstconv_output_channels, + kernel_size=4, + stride=4, + padding=0, + norm_layer=norm_layer, + activation_layer=None, + bias=True, + ) + ) + + total_stage_blocks = sum(cnf.num_layers for cnf in block_setting) + stage_block_id = 0 + for cnf in block_setting: + # Bottlenecks + stage: List[nn.Module] = [] + for _ in range(cnf.num_layers): + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * stage_block_id / (total_stage_blocks - 1.0) + stage.append(block(cnf.input_channels, layer_scale, sd_prob)) + stage_block_id += 1 + layers.append(nn.Sequential(*stage)) + if cnf.out_channels is not None: + # Downsampling + layers.append( + nn.Sequential( + norm_layer(cnf.input_channels), + nn.Conv2d(cnf.input_channels, cnf.out_channels, kernel_size=2, stride=2), + ) + ) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + + lastblock = block_setting[-1] + lastconv_output_channels = ( + lastblock.out_channels if lastblock.out_channels is not None else lastblock.input_channels + ) + self.classifier = nn.Sequential( + norm_layer(lastconv_output_channels), nn.Flatten(1), nn.Linear(lastconv_output_channels, num_classes) + ) + + for m in self.modules(): + if isinstance(m, (nn.Conv2d, nn.Linear)): + nn.init.trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + x = self.avgpool(x) + x = self.classifier(x) + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _convnext( + block_setting: List[CNBlockConfig], + stochastic_depth_prob: float, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> ConvNeXt: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = ConvNeXt(block_setting, stochastic_depth_prob=stochastic_depth_prob, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress)) + + return model + + +_COMMON_META = { + "min_size": (32, 32), + "categories": _IMAGENET_CATEGORIES, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#convnext", + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, +} + + +class ConvNeXt_Tiny_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_tiny-983f1562.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=236), + meta={ + **_COMMON_META, + "num_params": 28589128, + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.520, + "acc@5": 96.146, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ConvNeXt_Small_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_small-0c510722.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=230), + meta={ + **_COMMON_META, + "num_params": 50223688, + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.616, + "acc@5": 96.650, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ConvNeXt_Base_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_base-6075fbad.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 88591464, + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.062, + "acc@5": 96.870, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ConvNeXt_Large_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_large-ea097f82.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 197767336, + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.414, + "acc@5": 96.976, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Tiny_Weights.IMAGENET1K_V1)) +def convnext_tiny(*, weights: Optional[ConvNeXt_Tiny_Weights] = None, progress: bool = True, **kwargs: Any) -> ConvNeXt: + """ConvNeXt Tiny model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Tiny_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Tiny_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Tiny_Weights + :members: + """ + weights = ConvNeXt_Tiny_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(96, 192, 3), + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 9), + CNBlockConfig(768, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.1) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Small_Weights.IMAGENET1K_V1)) +def convnext_small( + *, weights: Optional[ConvNeXt_Small_Weights] = None, progress: bool = True, **kwargs: Any +) -> ConvNeXt: + """ConvNeXt Small model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Small_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Small_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Small_Weights + :members: + """ + weights = ConvNeXt_Small_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(96, 192, 3), + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 27), + CNBlockConfig(768, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.4) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Base_Weights.IMAGENET1K_V1)) +def convnext_base(*, weights: Optional[ConvNeXt_Base_Weights] = None, progress: bool = True, **kwargs: Any) -> ConvNeXt: + """ConvNeXt Base model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Base_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Base_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Base_Weights + :members: + """ + weights = ConvNeXt_Base_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(128, 256, 3), + CNBlockConfig(256, 512, 3), + CNBlockConfig(512, 1024, 27), + CNBlockConfig(1024, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Large_Weights.IMAGENET1K_V1)) +def convnext_large( + *, weights: Optional[ConvNeXt_Large_Weights] = None, progress: bool = True, **kwargs: Any +) -> ConvNeXt: + """ConvNeXt Large model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Large_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Large_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Large_Weights + :members: + """ + weights = ConvNeXt_Large_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 3), + CNBlockConfig(768, 1536, 27), + CNBlockConfig(1536, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) diff --git a/utils/defense_utils/anp/anp_model/den_anp.py b/utils/defense_utils/anp/anp_model/den_anp.py new file mode 100644 index 0000000..4681366 --- /dev/null +++ b/utils/defense_utils/anp/anp_model/den_anp.py @@ -0,0 +1,322 @@ +import re +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from collections import OrderedDict +from torchvision._internally_replaced_utils import load_state_dict_from_url +from typing import Any, Callable, List, Optional, Sequence +from torch import Tensor +from typing import Any, List, Tuple + + +__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161'] + +model_urls = { + 'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth', + 'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth', + 'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth', + 'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth', +} + + +class _DenseLayer(nn.Module): + def __init__( + self, + num_input_features: int, + growth_rate: int, + bn_size: int, + drop_rate: float, + memory_efficient: bool = False, + norm_layer: Optional[Callable[..., nn.Module]] = None + ) -> None: + super(_DenseLayer, self).__init__() + self.norm1: norm_layer + self.add_module('norm1', norm_layer(num_input_features)) + self.relu1: nn.ReLU + self.add_module('relu1', nn.ReLU(inplace=True)) + self.conv1: nn.Conv2d + self.add_module('conv1', nn.Conv2d(num_input_features, bn_size * + growth_rate, kernel_size=1, stride=1, + bias=False)) + self.norm2: norm_layer + self.add_module('norm2', norm_layer(bn_size * growth_rate)) + self.relu2: nn.ReLU + self.add_module('relu2', nn.ReLU(inplace=True)) + self.conv2: nn.Conv2d + self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate, + kernel_size=3, stride=1, padding=1, + bias=False)) + self.drop_rate = float(drop_rate) + self.memory_efficient = memory_efficient + + def bn_function(self, inputs: List[Tensor]) -> Tensor: + concated_features = torch.cat(inputs, 1) + bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features))) # noqa: T484 + return bottleneck_output + + # todo: rewrite when torchscript supports any + def any_requires_grad(self, input: List[Tensor]) -> bool: + for tensor in input: + if tensor.requires_grad: + return True + return False + + @torch.jit.unused # noqa: T484 + def call_checkpoint_bottleneck(self, input: List[Tensor]) -> Tensor: + def closure(*inputs): + return self.bn_function(inputs) + + return cp.checkpoint(closure, *input) + + @torch.jit._overload_method # noqa: F811 + def forward(self, input: List[Tensor]) -> Tensor: + pass + + @torch.jit._overload_method # noqa: F811 + def forward(self, input: Tensor) -> Tensor: + pass + + # torchscript does not yet support *args, so we overload method + # allowing it to take either a List[Tensor] or single Tensor + def forward(self, input: Tensor) -> Tensor: # noqa: F811 + if isinstance(input, Tensor): + prev_features = [input] + else: + prev_features = input + + if self.memory_efficient and self.any_requires_grad(prev_features): + if torch.jit.is_scripting(): + raise Exception("Memory Efficient not supported in JIT") + + bottleneck_output = self.call_checkpoint_bottleneck(prev_features) + else: + bottleneck_output = self.bn_function(prev_features) + + new_features = self.conv2(self.relu2(self.norm2(bottleneck_output))) + if self.drop_rate > 0: + new_features = F.dropout(new_features, p=self.drop_rate, + training=self.training) + return new_features + + +class _DenseBlock(nn.ModuleDict): + _version = 2 + + def __init__( + self, + num_layers: int, + num_input_features: int, + bn_size: int, + growth_rate: int, + drop_rate: float, + memory_efficient: bool = False, + norm_layer: Optional[Callable[..., nn.Module]] = None + ) -> None: + super(_DenseBlock, self).__init__() + for i in range(num_layers): + layer = _DenseLayer( + num_input_features + i * growth_rate, + growth_rate=growth_rate, + bn_size=bn_size, + drop_rate=drop_rate, + memory_efficient=memory_efficient, + norm_layer = norm_layer + ) + self.add_module('denselayer%d' % (i + 1), layer) + + def forward(self, init_features: Tensor) -> Tensor: + features = [init_features] + for name, layer in self.items(): + new_features = layer(features) + features.append(new_features) + return torch.cat(features, 1) + + +class _Transition(nn.Sequential): + def __init__(self, num_input_features: int, num_output_features: int, norm_layer: Optional[Callable[..., nn.Module]] ) -> None: + super(_Transition, self).__init__() + self.add_module('norm', norm_layer(num_input_features)) + self.add_module('relu', nn.ReLU(inplace=True)) + self.add_module('conv', nn.Conv2d(num_input_features, num_output_features, + kernel_size=1, stride=1, bias=False)) + self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2)) + + +class DenseNet(nn.Module): + r"""Densenet-BC model class, based on + `"Densely Connected Convolutional Networks" `_. + + Args: + growth_rate (int) - how many filters to add each layer (`k` in paper) + block_config (list of 4 ints) - how many layers in each pooling block + num_init_features (int) - the number of filters to learn in the first convolution layer + bn_size (int) - multiplicative factor for number of bottle neck layers + (i.e. bn_size * k features in the bottleneck layer) + drop_rate (float) - dropout rate after each dense layer + num_classes (int) - number of classification classes + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + + def __init__( + self, + growth_rate: int = 32, + block_config: Tuple[int, int, int, int] = (6, 12, 24, 16), + num_init_features: int = 64, + bn_size: int = 4, + drop_rate: float = 0, + num_classes: int = 1000, + memory_efficient: bool = False, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + if norm_layer is None: + norm_layer = nn.BatchNorm2d + + super(DenseNet, self).__init__() + + # First convolution + self.features = nn.Sequential(OrderedDict([ + ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, + padding=3, bias=False)), + ('norm0', norm_layer(num_init_features)), + ('relu0', nn.ReLU(inplace=True)), + ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)), + ])) + + # Each denseblock + num_features = num_init_features + for i, num_layers in enumerate(block_config): + block = _DenseBlock( + num_layers=num_layers, + num_input_features=num_features, + bn_size=bn_size, + growth_rate=growth_rate, + drop_rate=drop_rate, + memory_efficient=memory_efficient, + norm_layer = norm_layer + ) + self.features.add_module('denseblock%d' % (i + 1), block) + num_features = num_features + num_layers * growth_rate + if i != len(block_config) - 1: + trans = _Transition(num_input_features=num_features, + num_output_features=num_features // 2, norm_layer=norm_layer) + self.features.add_module('transition%d' % (i + 1), trans) + num_features = num_features // 2 + + # Final batch norm + self.features.add_module('norm5', norm_layer(num_features)) + + # Linear layer + self.classifier = nn.Linear(num_features, num_classes) + + # Official init from torch repo. + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.constant_(m.bias, 0) + + def forward(self, x: Tensor) -> Tensor: + features = self.features(x) + out = F.relu(features, inplace=True) + out = F.adaptive_avg_pool2d(out, (1, 1)) + out = torch.flatten(out, 1) + out = self.classifier(out) + return out + + +def _load_state_dict(model: nn.Module, model_url: str, progress: bool) -> None: + # '.'s are no longer allowed in module names, but previous _DenseLayer + # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'. + # They are also in the checkpoints in model_urls. This pattern is used + # to find such keys. + pattern = re.compile( + r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$') + + state_dict = load_state_dict_from_url(model_url, progress=progress) + for key in list(state_dict.keys()): + res = pattern.match(key) + if res: + new_key = res.group(1) + res.group(2) + state_dict[new_key] = state_dict[key] + del state_dict[key] + model.load_state_dict(state_dict) + + +def _densenet( + arch: str, + growth_rate: int, + block_config: Tuple[int, int, int, int], + num_init_features: int, + pretrained: bool, + progress: bool, + **kwargs: Any +) -> DenseNet: + model = DenseNet(growth_rate, block_config, num_init_features, **kwargs) + if pretrained: + _load_state_dict(model, model_urls[arch], progress) + return model + + +def densenet121(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-121 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress, + **kwargs) + + +def densenet161(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-161 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress, + **kwargs) + + +def densenet169(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-169 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet169', 32, (6, 12, 32, 32), 64, pretrained, progress, + **kwargs) + + +def densenet201(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-201 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress, + **kwargs) diff --git a/utils/defense_utils/anp/anp_model/eff_anp.py b/utils/defense_utils/anp/anp_model/eff_anp.py new file mode 100644 index 0000000..07f6ba0 --- /dev/null +++ b/utils/defense_utils/anp/anp_model/eff_anp.py @@ -0,0 +1,350 @@ +import copy +import math +import torch + +from functools import partial +from torch import nn, Tensor +from typing import Any, Callable, List, Optional, Sequence + +from torchvision._internally_replaced_utils import load_state_dict_from_url +from torchvision.ops.misc import ConvNormActivation, SqueezeExcitation +from torchvision.models._utils import _make_divisible +from torchvision.ops import StochasticDepth + + +__all__ = ["EfficientNet", "efficientnet_b0", "efficientnet_b1", "efficientnet_b2", "efficientnet_b3", + "efficientnet_b4", "efficientnet_b5", "efficientnet_b6", "efficientnet_b7"] + + +model_urls = { + # Weights ported from https://github.com/rwightman/pytorch-image-models/ + "efficientnet_b0": "https://download.pytorch.org/models/efficientnet_b0_rwightman-3dd342df.pth", + "efficientnet_b1": "https://download.pytorch.org/models/efficientnet_b1_rwightman-533bc792.pth", + "efficientnet_b2": "https://download.pytorch.org/models/efficientnet_b2_rwightman-bcdf34b7.pth", + "efficientnet_b3": "https://download.pytorch.org/models/efficientnet_b3_rwightman-cf984f9c.pth", + "efficientnet_b4": "https://download.pytorch.org/models/efficientnet_b4_rwightman-7eb33cd5.pth", + # Weights ported from https://github.com/lukemelas/EfficientNet-PyTorch/ + "efficientnet_b5": "https://download.pytorch.org/models/efficientnet_b5_lukemelas-b6417697.pth", + "efficientnet_b6": "https://download.pytorch.org/models/efficientnet_b6_lukemelas-c76e70fd.pth", + "efficientnet_b7": "https://download.pytorch.org/models/efficientnet_b7_lukemelas-dcc49843.pth", +} + + +class MBConvConfig: + # Stores information listed at Table 1 of the EfficientNet paper + def __init__(self, + expand_ratio: float, kernel: int, stride: int, + input_channels: int, out_channels: int, num_layers: int, + width_mult: float, depth_mult: float) -> None: + self.expand_ratio = expand_ratio + self.kernel = kernel + self.stride = stride + self.input_channels = self.adjust_channels(input_channels, width_mult) + self.out_channels = self.adjust_channels(out_channels, width_mult) + self.num_layers = self.adjust_depth(num_layers, depth_mult) + + def __repr__(self) -> str: + s = self.__class__.__name__ + '(' + s += 'expand_ratio={expand_ratio}' + s += ', kernel={kernel}' + s += ', stride={stride}' + s += ', input_channels={input_channels}' + s += ', out_channels={out_channels}' + s += ', num_layers={num_layers}' + s += ')' + return s.format(**self.__dict__) + + @staticmethod + def adjust_channels(channels: int, width_mult: float, min_value: Optional[int] = None) -> int: + return _make_divisible(channels * width_mult, 8, min_value) + + @staticmethod + def adjust_depth(num_layers: int, depth_mult: float): + return int(math.ceil(num_layers * depth_mult)) + + +class MBConv(nn.Module): + def __init__(self, cnf: MBConvConfig, stochastic_depth_prob: float, norm_layer: Callable[..., nn.Module], + se_layer: Callable[..., nn.Module] = SqueezeExcitation) -> None: + super().__init__() + + if not (1 <= cnf.stride <= 2): + raise ValueError('illegal stride value') + + self.use_res_connect = cnf.stride == 1 and cnf.input_channels == cnf.out_channels + + layers: List[nn.Module] = [] + activation_layer = nn.SiLU + + # expand + expanded_channels = cnf.adjust_channels(cnf.input_channels, cnf.expand_ratio) + if expanded_channels != cnf.input_channels: + layers.append(ConvNormActivation(cnf.input_channels, expanded_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=activation_layer)) + + # depthwise + layers.append(ConvNormActivation(expanded_channels, expanded_channels, kernel_size=cnf.kernel, + stride=cnf.stride, groups=expanded_channels, + norm_layer=norm_layer, activation_layer=activation_layer)) + + # squeeze and excitation + squeeze_channels = max(1, cnf.input_channels // 4) + layers.append(se_layer(expanded_channels, squeeze_channels, activation=partial(nn.SiLU, inplace=True))) + + # project + layers.append(ConvNormActivation(expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, + activation_layer=None)) + + self.block = nn.Sequential(*layers) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + self.out_channels = cnf.out_channels + + def forward(self, input: Tensor) -> Tensor: + result = self.block(input) + if self.use_res_connect: + result = self.stochastic_depth(result) + result += input + return result + + +class EfficientNet(nn.Module): + def __init__( + self, + inverted_residual_setting: List[MBConvConfig], + dropout: float, + stochastic_depth_prob: float = 0.2, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any + ) -> None: + """ + EfficientNet main class + + Args: + inverted_residual_setting (List[MBConvConfig]): Network structure + dropout (float): The droupout probability + stochastic_depth_prob (float): The stochastic depth probability + num_classes (int): Number of classes + block (Optional[Callable[..., nn.Module]]): Module specifying inverted residual building block for mobilenet + norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use + """ + super().__init__() + + if not inverted_residual_setting: + raise ValueError("The inverted_residual_setting should not be empty") + elif not (isinstance(inverted_residual_setting, Sequence) and + all([isinstance(s, MBConvConfig) for s in inverted_residual_setting])): + raise TypeError("The inverted_residual_setting should be List[MBConvConfig]") + + if block is None: + block = MBConv + + if norm_layer is None: + norm_layer = nn.BatchNorm2d + + layers: List[nn.Module] = [] + + # building first layer + firstconv_output_channels = inverted_residual_setting[0].input_channels + layers.append(ConvNormActivation(3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer, + activation_layer=nn.SiLU)) + + # building inverted residual blocks + total_stage_blocks = sum([cnf.num_layers for cnf in inverted_residual_setting]) + stage_block_id = 0 + for cnf in inverted_residual_setting: + stage: List[nn.Module] = [] + for _ in range(cnf.num_layers): + # copy to avoid modifications. shallow copy is enough + block_cnf = copy.copy(cnf) + + # overwrite info if not the first conv in the stage + if stage: + block_cnf.input_channels = block_cnf.out_channels + block_cnf.stride = 1 + + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * float(stage_block_id) / total_stage_blocks + + stage.append(block(block_cnf, sd_prob, norm_layer)) + stage_block_id += 1 + + layers.append(nn.Sequential(*stage)) + + # building last several layers + lastconv_input_channels = inverted_residual_setting[-1].out_channels + lastconv_output_channels = 4 * lastconv_input_channels + layers.append(ConvNormActivation(lastconv_input_channels, lastconv_output_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=nn.SiLU)) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Sequential( + nn.Dropout(p=dropout, inplace=True), + nn.Linear(lastconv_output_channels, num_classes), + ) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + init_range = 1.0 / math.sqrt(m.out_features) + nn.init.uniform_(m.weight, -init_range, init_range) + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.classifier(x) + + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _efficientnet_conf(width_mult: float, depth_mult: float, **kwargs: Any) -> List[MBConvConfig]: + bneck_conf = partial(MBConvConfig, width_mult=width_mult, depth_mult=depth_mult) + inverted_residual_setting = [ + bneck_conf(1, 3, 1, 32, 16, 1), + bneck_conf(6, 3, 2, 16, 24, 2), + bneck_conf(6, 5, 2, 24, 40, 2), + bneck_conf(6, 3, 2, 40, 80, 3), + bneck_conf(6, 5, 1, 80, 112, 3), + bneck_conf(6, 5, 2, 112, 192, 4), + bneck_conf(6, 3, 1, 192, 320, 1), + ] + return inverted_residual_setting + + +def _efficientnet_model( + arch: str, + inverted_residual_setting: List[MBConvConfig], + dropout: float, + pretrained: bool, + progress: bool, + **kwargs: Any +) -> EfficientNet: + model = EfficientNet(inverted_residual_setting, dropout, **kwargs) + if pretrained: + if model_urls.get(arch, None) is None: + raise ValueError("No checkpoint is available for model type {}".format(arch)) + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def efficientnet_b0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B0 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.0, **kwargs) + return _efficientnet_model("efficientnet_b0", inverted_residual_setting, 0.2, pretrained, progress, **kwargs) + + +def efficientnet_b1(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B1 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.1, **kwargs) + return _efficientnet_model("efficientnet_b1", inverted_residual_setting, 0.2, pretrained, progress, **kwargs) + + +def efficientnet_b2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B2 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.1, depth_mult=1.2, **kwargs) + return _efficientnet_model("efficientnet_b2", inverted_residual_setting, 0.3, pretrained, progress, **kwargs) + + +def efficientnet_b3(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B3 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.2, depth_mult=1.4, **kwargs) + return _efficientnet_model("efficientnet_b3", inverted_residual_setting, 0.3, pretrained, progress, **kwargs) + + +def efficientnet_b4(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B4 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.4, depth_mult=1.8, **kwargs) + return _efficientnet_model("efficientnet_b4", inverted_residual_setting, 0.4, pretrained, progress, **kwargs) + + +def efficientnet_b5(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B5 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.6, depth_mult=2.2, **kwargs) + return _efficientnet_model("efficientnet_b5", inverted_residual_setting, 0.4, pretrained, progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), **kwargs) + + +def efficientnet_b6(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B6 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.8, depth_mult=2.6, **kwargs) + return _efficientnet_model("efficientnet_b6", inverted_residual_setting, 0.5, pretrained, progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), **kwargs) + + +def efficientnet_b7(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B7 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=2.0, depth_mult=3.1, **kwargs) + return _efficientnet_model("efficientnet_b7", inverted_residual_setting, 0.5, pretrained, progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), **kwargs) diff --git a/utils/defense_utils/anp/anp_model/mobilenet_anp.py b/utils/defense_utils/anp/anp_model/mobilenet_anp.py new file mode 100644 index 0000000..966c15e --- /dev/null +++ b/utils/defense_utils/anp/anp_model/mobilenet_anp.py @@ -0,0 +1,272 @@ +import warnings +import torch + +from functools import partial +from torch import nn, Tensor +from typing import Any, Callable, List, Optional, Sequence + +from torchvision._internally_replaced_utils import load_state_dict_from_url +from torchvision.ops.misc import ConvNormActivation, SqueezeExcitation as SElayer +from torchvision.models._utils import _make_divisible + + +__all__ = ["MobileNetV3", "mobilenet_v3_large", "mobilenet_v3_small"] + + +model_urls = { + "mobilenet_v3_large": "https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth", + "mobilenet_v3_small": "https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth", +} + + +class SqueezeExcitation(SElayer): + """DEPRECATED + """ + def __init__(self, input_channels: int, squeeze_factor: int = 4): + squeeze_channels = _make_divisible(input_channels // squeeze_factor, 8) + super().__init__(input_channels, squeeze_channels, scale_activation=nn.Hardsigmoid) + self.relu = self.activation + delattr(self, 'activation') + warnings.warn( + "This SqueezeExcitation class is deprecated and will be removed in future versions. " + "Use torchvision.ops.misc.SqueezeExcitation instead.", FutureWarning) + + +class InvertedResidualConfig: + # Stores information listed at Tables 1 and 2 of the MobileNetV3 paper + def __init__(self, input_channels: int, kernel: int, expanded_channels: int, out_channels: int, use_se: bool, + activation: str, stride: int, dilation: int, width_mult: float): + self.input_channels = self.adjust_channels(input_channels, width_mult) + self.kernel = kernel + self.expanded_channels = self.adjust_channels(expanded_channels, width_mult) + self.out_channels = self.adjust_channels(out_channels, width_mult) + self.use_se = use_se + self.use_hs = activation == "HS" + self.stride = stride + self.dilation = dilation + + @staticmethod + def adjust_channels(channels: int, width_mult: float): + return _make_divisible(channels * width_mult, 8) + + +class InvertedResidual(nn.Module): + # Implemented as described at section 5 of MobileNetV3 paper + def __init__(self, cnf: InvertedResidualConfig, norm_layer: Callable[..., nn.Module], + se_layer: Callable[..., nn.Module] = partial(SElayer, scale_activation=nn.Hardsigmoid)): + super().__init__() + if not (1 <= cnf.stride <= 2): + raise ValueError('illegal stride value') + + self.use_res_connect = cnf.stride == 1 and cnf.input_channels == cnf.out_channels + + layers: List[nn.Module] = [] + activation_layer = nn.Hardswish if cnf.use_hs else nn.ReLU + + # expand + if cnf.expanded_channels != cnf.input_channels: + layers.append(ConvNormActivation(cnf.input_channels, cnf.expanded_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=activation_layer)) + + # depthwise + stride = 1 if cnf.dilation > 1 else cnf.stride + layers.append(ConvNormActivation(cnf.expanded_channels, cnf.expanded_channels, kernel_size=cnf.kernel, + stride=stride, dilation=cnf.dilation, groups=cnf.expanded_channels, + norm_layer=norm_layer, activation_layer=activation_layer)) + if cnf.use_se: + squeeze_channels = _make_divisible(cnf.expanded_channels // 4, 8) + layers.append(se_layer(cnf.expanded_channels, squeeze_channels)) + + # project + layers.append(ConvNormActivation(cnf.expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, + activation_layer=None)) + + self.block = nn.Sequential(*layers) + self.out_channels = cnf.out_channels + self._is_cn = cnf.stride > 1 + + def forward(self, input: Tensor) -> Tensor: + result = self.block(input) + if self.use_res_connect: + result += input + return result + + +class MobileNetV3(nn.Module): + + def __init__( + self, + inverted_residual_setting: List[InvertedResidualConfig], + last_channel: int, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any + ) -> None: + """ + MobileNet V3 main class + + Args: + inverted_residual_setting (List[InvertedResidualConfig]): Network structure + last_channel (int): The number of channels on the penultimate layer + num_classes (int): Number of classes + block (Optional[Callable[..., nn.Module]]): Module specifying inverted residual building block for mobilenet + norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use + """ + super().__init__() + + if not inverted_residual_setting: + raise ValueError("The inverted_residual_setting should not be empty") + elif not (isinstance(inverted_residual_setting, Sequence) and + all([isinstance(s, InvertedResidualConfig) for s in inverted_residual_setting])): + raise TypeError("The inverted_residual_setting should be List[InvertedResidualConfig]") + + if block is None: + block = InvertedResidual + + if norm_layer is None: + norm_layer = partial(nn.BatchNorm2d, eps=0.001, momentum=0.01) + + layers: List[nn.Module] = [] + + # building first layer + firstconv_output_channels = inverted_residual_setting[0].input_channels + layers.append(ConvNormActivation(3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer, + activation_layer=nn.Hardswish)) + + # building inverted residual blocks + for cnf in inverted_residual_setting: + layers.append(block(cnf, norm_layer)) + + # building last several layers + lastconv_input_channels = inverted_residual_setting[-1].out_channels + lastconv_output_channels = 6 * lastconv_input_channels + layers.append(ConvNormActivation(lastconv_input_channels, lastconv_output_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=nn.Hardswish)) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Sequential( + nn.Linear(lastconv_output_channels, last_channel), + nn.Hardswish(inplace=True), + nn.Dropout(p=0.2, inplace=True), + nn.Linear(last_channel, num_classes), + ) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.classifier(x) + + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _mobilenet_v3_conf(arch: str, width_mult: float = 1.0, reduced_tail: bool = False, dilated: bool = False, + **kwargs: Any): + reduce_divider = 2 if reduced_tail else 1 + dilation = 2 if dilated else 1 + + bneck_conf = partial(InvertedResidualConfig, width_mult=width_mult) + adjust_channels = partial(InvertedResidualConfig.adjust_channels, width_mult=width_mult) + + if arch == "mobilenet_v3_large": + inverted_residual_setting = [ + bneck_conf(16, 3, 16, 16, False, "RE", 1, 1), + bneck_conf(16, 3, 64, 24, False, "RE", 2, 1), # C1 + bneck_conf(24, 3, 72, 24, False, "RE", 1, 1), + bneck_conf(24, 5, 72, 40, True, "RE", 2, 1), # C2 + bneck_conf(40, 5, 120, 40, True, "RE", 1, 1), + bneck_conf(40, 5, 120, 40, True, "RE", 1, 1), + bneck_conf(40, 3, 240, 80, False, "HS", 2, 1), # C3 + bneck_conf(80, 3, 200, 80, False, "HS", 1, 1), + bneck_conf(80, 3, 184, 80, False, "HS", 1, 1), + bneck_conf(80, 3, 184, 80, False, "HS", 1, 1), + bneck_conf(80, 3, 480, 112, True, "HS", 1, 1), + bneck_conf(112, 3, 672, 112, True, "HS", 1, 1), + bneck_conf(112, 5, 672, 160 // reduce_divider, True, "HS", 2, dilation), # C4 + bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1, dilation), + bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1, dilation), + ] + last_channel = adjust_channels(1280 // reduce_divider) # C5 + elif arch == "mobilenet_v3_small": + inverted_residual_setting = [ + bneck_conf(16, 3, 16, 16, True, "RE", 2, 1), # C1 + bneck_conf(16, 3, 72, 24, False, "RE", 2, 1), # C2 + bneck_conf(24, 3, 88, 24, False, "RE", 1, 1), + bneck_conf(24, 5, 96, 40, True, "HS", 2, 1), # C3 + bneck_conf(40, 5, 240, 40, True, "HS", 1, 1), + bneck_conf(40, 5, 240, 40, True, "HS", 1, 1), + bneck_conf(40, 5, 120, 48, True, "HS", 1, 1), + bneck_conf(48, 5, 144, 48, True, "HS", 1, 1), + bneck_conf(48, 5, 288, 96 // reduce_divider, True, "HS", 2, dilation), # C4 + bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1, dilation), + bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1, dilation), + ] + last_channel = adjust_channels(1024 // reduce_divider) # C5 + else: + raise ValueError("Unsupported model type {}".format(arch)) + + return inverted_residual_setting, last_channel + + +def _mobilenet_v3_model( + arch: str, + inverted_residual_setting: List[InvertedResidualConfig], + last_channel: int, + pretrained: bool, + progress: bool, + **kwargs: Any +): + model = MobileNetV3(inverted_residual_setting, last_channel, **kwargs) + if pretrained: + if model_urls.get(arch, None) is None: + raise ValueError("No checkpoint is available for model type {}".format(arch)) + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def mobilenet_v3_large(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MobileNetV3: + """ + Constructs a large MobileNetV3 architecture from + `"Searching for MobileNetV3" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + arch = "mobilenet_v3_large" + inverted_residual_setting, last_channel = _mobilenet_v3_conf(arch, **kwargs) + return _mobilenet_v3_model(arch, inverted_residual_setting, last_channel, pretrained, progress, **kwargs) + + +def mobilenet_v3_small(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MobileNetV3: + """ + Constructs a small MobileNetV3 architecture from + `"Searching for MobileNetV3" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + arch = "mobilenet_v3_small" + inverted_residual_setting, last_channel = _mobilenet_v3_conf(arch, **kwargs) + return _mobilenet_v3_model(arch, inverted_residual_setting, last_channel, pretrained, progress, **kwargs) diff --git a/utils/defense_utils/anp/anp_model/preact_anp.py b/utils/defense_utils/anp/anp_model/preact_anp.py new file mode 100644 index 0000000..14b07c1 --- /dev/null +++ b/utils/defense_utils/anp/anp_model/preact_anp.py @@ -0,0 +1,135 @@ +"""Pre-activation ResNet in PyTorch. + +Reference: +[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Identity Mappings in Deep Residual Networks. arXiv:1603.05027 +""" +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class PreActBlock(nn.Module): + """Pre-activation version of the BasicBlock.""" + + expansion = 1 + + def __init__(self, in_planes, planes, stride=1, norm_layer = None): + if norm_layer is None: + norm_layer = nn.BatchNorm2d + super(PreActBlock, self).__init__() + self.bn1 = norm_layer(in_planes) + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = norm_layer(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.ind = None + + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False) + ) + + def forward(self, x): + out = F.relu(self.bn1(x)) + shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x + out = self.conv1(out) + out = self.conv2(F.relu(self.bn2(out))) + if self.ind is not None: + out += shortcut[:, self.ind, :, :] + else: + out += shortcut + return out + + +class PreActBottleneck(nn.Module): + """Pre-activation version of the original Bottleneck module.""" + + expansion = 4 + + def __init__(self, in_planes, planes, stride=1): + super(PreActBottleneck, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) + + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False) + ) + + def forward(self, x): + out = F.relu(self.bn1(x)) + shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x + out = self.conv1(out) + out = self.conv2(F.relu(self.bn2(out))) + out = self.conv3(F.relu(self.bn3(out))) + out += shortcut + return out + + +class PreActResNet(nn.Module): + def __init__(self, block, num_blocks, num_classes=10, norm_layer=None): + super(PreActResNet, self).__init__() + self.in_planes = 64 + if norm_layer is None: + norm_layer = nn.BatchNorm2d + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1, norm_layer=norm_layer) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2, norm_layer=norm_layer) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2, norm_layer=norm_layer) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2, norm_layer=norm_layer) + self.avgpool = nn.AdaptiveAvgPool2d((1,1)) + # self.feature_dim = 512 + self.linear = nn.Linear(512 * block.expansion, num_classes) + + def _make_layer(self, block, planes, num_blocks, stride, norm_layer): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride, norm_layer)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + out = self.conv1(x) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = self.avgpool(out) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + + +def PreActResNet18(num_classes=10, norm_layer=nn.BatchNorm2d): + return PreActResNet(PreActBlock, [2, 2, 2, 2], num_classes=num_classes, norm_layer=norm_layer) + + +def PreActResNet34(): + return PreActResNet(PreActBlock, [3, 4, 6, 3]) + + +def PreActResNet50(): + return PreActResNet(PreActBottleneck, [3, 4, 6, 3]) + + +def PreActResNet101(): + return PreActResNet(PreActBottleneck, [3, 4, 23, 3]) + + +def PreActResNet152(): + return PreActResNet(PreActBottleneck, [3, 8, 36, 3]) + + +def test(): + net = PreActResNet18() + y = net((torch.randn(1, 3, 32, 32))) + print(y.size()) + + +# test() diff --git a/utils/defense_utils/anp/anp_model/vgg_anp.py b/utils/defense_utils/anp/anp_model/vgg_anp.py new file mode 100644 index 0000000..cf83341 --- /dev/null +++ b/utils/defense_utils/anp/anp_model/vgg_anp.py @@ -0,0 +1,200 @@ +import torch +import torch.nn as nn +from torchvision._internally_replaced_utils import load_state_dict_from_url +from typing import Union, List, Dict, Any, cast + + +__all__ = [ + 'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', + 'vgg19_bn', 'vgg19', +] + + +model_urls = { + 'vgg11': 'https://download.pytorch.org/models/vgg11-8a719046.pth', + 'vgg13': 'https://download.pytorch.org/models/vgg13-19584684.pth', + 'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth', + 'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth', + 'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth', + 'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth', + 'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth', + 'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth', +} + + +class VGG(nn.Module): + + def __init__( + self, + features: nn.Module, + num_classes: int = 1000, + init_weights: bool = True + ) -> None: + super(VGG, self).__init__() + self.features = features + self.avgpool = nn.AdaptiveAvgPool2d((7, 7)) + self.classifier = nn.Sequential( + nn.Linear(512 * 7 * 7, 4096), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(4096, 4096), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(4096, num_classes), + ) + if init_weights: + self._initialize_weights() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.features(x) + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.classifier(x) + return x + + def _initialize_weights(self) -> None: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.constant_(m.bias, 0) + + +def make_layers(cfg: List[Union[str, int]], batch_norm: bool = False, norm_layer = None) -> nn.Sequential: + if norm_layer is None: + norm_layer = nn.BatchNorm2d + layers: List[nn.Module] = [] + in_channels = 3 + for v in cfg: + if v == 'M': + layers += [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + v = cast(int, v) + conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) + if batch_norm: + layers += [conv2d, norm_layer(v), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = v + return nn.Sequential(*layers) + + +cfgs: Dict[str, List[Union[str, int]]] = { + 'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], + 'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], + 'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], + 'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'], +} + + +def _vgg(arch: str, cfg: str, batch_norm: bool, pretrained: bool, progress: bool, norm_layer, **kwargs: Any) -> VGG: + if pretrained: + kwargs['init_weights'] = False + model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm, norm_layer = norm_layer), **kwargs) + if pretrained: + state_dict = load_state_dict_from_url(model_urls[arch], + progress=progress) + model.load_state_dict(state_dict) + return model + + +def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 11-layer model (configuration "A") from + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg11', 'A', False, pretrained, progress, **kwargs) + + +def vgg11_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 11-layer model (configuration "A") with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg11_bn', 'A', True, pretrained, progress, **kwargs) + + +def vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 13-layer model (configuration "B") + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg13', 'B', False, pretrained, progress, **kwargs) + + +def vgg13_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 13-layer model (configuration "B") with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg13_bn', 'B', True, pretrained, progress, **kwargs) + + +def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 16-layer model (configuration "D") + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg16', 'D', False, pretrained, progress, **kwargs) + + +def vgg16_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 16-layer model (configuration "D") with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg16_bn', 'D', True, pretrained, progress, **kwargs) + + +def vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 19-layer model (configuration "E") + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg19', 'E', False, pretrained, progress, **kwargs) + + +def vgg19_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 19-layer model (configuration 'E') with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg19_bn', 'E', True, pretrained, progress, **kwargs) diff --git a/utils/defense_utils/anp/anp_model/vit_anp.py b/utils/defense_utils/anp/anp_model/vit_anp.py new file mode 100644 index 0000000..a452cdc --- /dev/null +++ b/utils/defense_utils/anp/anp_model/vit_anp.py @@ -0,0 +1,470 @@ +import math +from collections import OrderedDict +from functools import partial +from typing import Any, Callable, List, NamedTuple, Optional + +import torch +import torch.nn as nn + +from torchvision._internally_replaced_utils import load_state_dict_from_url +from torchvision.ops.misc import ConvNormActivation +from torchvision.utils import _log_api_usage_once + +from defense.anp import anp_model + +__all__ = [ + "VisionTransformer", + "vit_b_16", + "vit_b_32", + "vit_l_16", + "vit_l_32", +] + +model_urls = { + "vit_b_16": "https://download.pytorch.org/models/vit_b_16-c867db91.pth", + "vit_b_32": "https://download.pytorch.org/models/vit_b_32-d86f8d99.pth", + "vit_l_16": "https://download.pytorch.org/models/vit_l_16-852ce7e3.pth", + "vit_l_32": "https://download.pytorch.org/models/vit_l_32-c7638314.pth", +} + + +class ConvStemConfig(NamedTuple): + out_channels: int + kernel_size: int + stride: int + norm_layer: Callable[..., nn.Module] = anp_model.NoiseLayerNorm + activation_layer: Callable[..., nn.Module] = nn.ReLU + + +class MLPBlock(nn.Sequential): + """Transformer MLP block.""" + + def __init__(self, in_dim: int, mlp_dim: int, dropout: float): + super().__init__() + self.linear_1 = nn.Linear(in_dim, mlp_dim) + self.act = nn.GELU() + self.dropout_1 = nn.Dropout(dropout) + self.linear_2 = nn.Linear(mlp_dim, in_dim) + self.dropout_2 = nn.Dropout(dropout) + + nn.init.xavier_uniform_(self.linear_1.weight) + nn.init.xavier_uniform_(self.linear_2.weight) + nn.init.normal_(self.linear_1.bias, std=1e-6) + nn.init.normal_(self.linear_2.bias, std=1e-6) + + +class EncoderBlock(nn.Module): + """Transformer encoder block.""" + + def __init__( + self, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float, + attention_dropout: float, + norm_layer: Callable[..., torch.nn.Module] = partial(anp_model.NoiseLayerNorm, eps=1e-6), + ): + super().__init__() + self.num_heads = num_heads + + # Attention block + self.ln_1 = norm_layer(hidden_dim) + self.self_attention = nn.MultiheadAttention(hidden_dim, num_heads, dropout=attention_dropout, batch_first=True) + self.dropout = nn.Dropout(dropout) + + # MLP block + self.ln_2 = norm_layer(hidden_dim) + self.mlp = MLPBlock(hidden_dim, mlp_dim, dropout) + + def forward(self, input: torch.Tensor): + torch._assert(input.dim() == 3, f"Expected (seq_length, batch_size, hidden_dim) got {input.shape}") + x = self.ln_1(input) + x, _ = self.self_attention(query=x, key=x, value=x, need_weights=False) + x = self.dropout(x) + x = x + input + + y = self.ln_2(x) + y = self.mlp(y) + return x + y + + +class Encoder(nn.Module): + """Transformer Model Encoder for sequence to sequence translation.""" + + def __init__( + self, + seq_length: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float, + attention_dropout: float, + norm_layer: Callable[..., torch.nn.Module] = partial(anp_model.NoiseLayerNorm, eps=1e-6), + ): + super().__init__() + # Note that batch_size is on the first dim because + # we have batch_first=True in nn.MultiAttention() by default + self.pos_embedding = nn.Parameter(torch.empty(1, seq_length, hidden_dim).normal_(std=0.02)) # from BERT + self.dropout = nn.Dropout(dropout) + layers: OrderedDict[str, nn.Module] = OrderedDict() + for i in range(num_layers): + layers[f"encoder_layer_{i}"] = EncoderBlock( + num_heads, + hidden_dim, + mlp_dim, + dropout, + attention_dropout, + norm_layer, + ) + self.layers = nn.Sequential(layers) + self.ln = norm_layer(hidden_dim) + + def forward(self, input: torch.Tensor): + torch._assert(input.dim() == 3, f"Expected (batch_size, seq_length, hidden_dim) got {input.shape}") + input = input + self.pos_embedding + return self.ln(self.layers(self.dropout(input))) + + +class VisionTransformer(nn.Module): + """Vision Transformer as per https://arxiv.org/abs/2010.11929.""" + + def __init__( + self, + image_size: int, + patch_size: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float = 0.0, + attention_dropout: float = 0.0, + num_classes: int = 1000, + representation_size: Optional[int] = None, + norm_layer: Callable[..., torch.nn.Module] = partial(anp_model.NoiseLayerNorm, eps=1e-6), + conv_stem_configs: Optional[List[ConvStemConfig]] = None, + ): + super().__init__() + _log_api_usage_once(self) + torch._assert(image_size % patch_size == 0, "Input shape indivisible by patch size!") + self.image_size = image_size + self.patch_size = patch_size + self.hidden_dim = hidden_dim + self.mlp_dim = mlp_dim + self.attention_dropout = attention_dropout + self.dropout = dropout + self.num_classes = num_classes + self.representation_size = representation_size + self.norm_layer = norm_layer + + if conv_stem_configs is not None: + # As per https://arxiv.org/abs/2106.14881 + seq_proj = nn.Sequential() + prev_channels = 3 + for i, conv_stem_layer_config in enumerate(conv_stem_configs): + seq_proj.add_module( + f"conv_bn_relu_{i}", + ConvNormActivation( + in_channels=prev_channels, + out_channels=conv_stem_layer_config.out_channels, + kernel_size=conv_stem_layer_config.kernel_size, + stride=conv_stem_layer_config.stride, + norm_layer=conv_stem_layer_config.norm_layer, + activation_layer=conv_stem_layer_config.activation_layer, + ), + ) + prev_channels = conv_stem_layer_config.out_channels + seq_proj.add_module( + "conv_last", nn.Conv2d(in_channels=prev_channels, out_channels=hidden_dim, kernel_size=1) + ) + self.conv_proj: nn.Module = seq_proj + else: + self.conv_proj = nn.Conv2d( + in_channels=3, out_channels=hidden_dim, kernel_size=patch_size, stride=patch_size + ) + + seq_length = (image_size // patch_size) ** 2 + + # Add a class token + self.class_token = nn.Parameter(torch.zeros(1, 1, hidden_dim)) + seq_length += 1 + + self.encoder = Encoder( + seq_length, + num_layers, + num_heads, + hidden_dim, + mlp_dim, + dropout, + attention_dropout, + norm_layer, + ) + self.seq_length = seq_length + + heads_layers: OrderedDict[str, nn.Module] = OrderedDict() + if representation_size is None: + heads_layers["head"] = nn.Linear(hidden_dim, num_classes) + else: + heads_layers["pre_logits"] = nn.Linear(hidden_dim, representation_size) + heads_layers["act"] = nn.Tanh() + heads_layers["head"] = nn.Linear(representation_size, num_classes) + + self.heads = nn.Sequential(heads_layers) + + if isinstance(self.conv_proj, nn.Conv2d): + # Init the patchify stem + fan_in = self.conv_proj.in_channels * self.conv_proj.kernel_size[0] * self.conv_proj.kernel_size[1] + nn.init.trunc_normal_(self.conv_proj.weight, std=math.sqrt(1 / fan_in)) + if self.conv_proj.bias is not None: + nn.init.zeros_(self.conv_proj.bias) + elif self.conv_proj.conv_last is not None and isinstance(self.conv_proj.conv_last, nn.Conv2d): + # Init the last 1x1 conv of the conv stem + nn.init.normal_( + self.conv_proj.conv_last.weight, mean=0.0, std=math.sqrt(2.0 / self.conv_proj.conv_last.out_channels) + ) + if self.conv_proj.conv_last.bias is not None: + nn.init.zeros_(self.conv_proj.conv_last.bias) + + if hasattr(self.heads, "pre_logits") and isinstance(self.heads.pre_logits, nn.Linear): + fan_in = self.heads.pre_logits.in_features + nn.init.trunc_normal_(self.heads.pre_logits.weight, std=math.sqrt(1 / fan_in)) + nn.init.zeros_(self.heads.pre_logits.bias) + + if isinstance(self.heads.head, nn.Linear): + nn.init.zeros_(self.heads.head.weight) + nn.init.zeros_(self.heads.head.bias) + + def _process_input(self, x: torch.Tensor) -> torch.Tensor: + n, c, h, w = x.shape + p = self.patch_size + torch._assert(h == self.image_size, "Wrong image height!") + torch._assert(w == self.image_size, "Wrong image width!") + n_h = h // p + n_w = w // p + + # (n, c, h, w) -> (n, hidden_dim, n_h, n_w) + x = self.conv_proj(x) + # (n, hidden_dim, n_h, n_w) -> (n, hidden_dim, (n_h * n_w)) + x = x.reshape(n, self.hidden_dim, n_h * n_w) + + # (n, hidden_dim, (n_h * n_w)) -> (n, (n_h * n_w), hidden_dim) + # The self attention layer expects inputs in the format (N, S, E) + # where S is the source sequence length, N is the batch size, E is the + # embedding dimension + x = x.permute(0, 2, 1) + + return x + + def forward(self, x: torch.Tensor): + # Reshape and permute the input tensor + x = self._process_input(x) + n = x.shape[0] + + # Expand the class token to the full batch + batch_class_token = self.class_token.expand(n, -1, -1) + x = torch.cat([batch_class_token, x], dim=1) + + x = self.encoder(x) + + # Classifier "token" as used by standard language architectures + x = x[:, 0] + + x = self.heads(x) + + return x + + +def _vision_transformer( + arch: str, + patch_size: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + pretrained: bool, + progress: bool, + **kwargs: Any, +) -> VisionTransformer: + image_size = kwargs.pop("image_size", 224) + + model = VisionTransformer( + image_size=image_size, + patch_size=patch_size, + num_layers=num_layers, + num_heads=num_heads, + hidden_dim=hidden_dim, + mlp_dim=mlp_dim, + **kwargs, + ) + + if pretrained: + if arch not in model_urls: + raise ValueError(f"No checkpoint is available for model type '{arch}'!") + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + + return model + + +def vit_b_16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_b_16 architecture from + `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vision_transformer( + arch="vit_b_16", + patch_size=16, + num_layers=12, + num_heads=12, + hidden_dim=768, + mlp_dim=3072, + pretrained=pretrained, + progress=progress, + **kwargs, + ) + + +def vit_b_32(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_b_32 architecture from + `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vision_transformer( + arch="vit_b_32", + patch_size=32, + num_layers=12, + num_heads=12, + hidden_dim=768, + mlp_dim=3072, + pretrained=pretrained, + progress=progress, + **kwargs, + ) + + +def vit_l_16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_l_16 architecture from + `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vision_transformer( + arch="vit_l_16", + patch_size=16, + num_layers=24, + num_heads=16, + hidden_dim=1024, + mlp_dim=4096, + pretrained=pretrained, + progress=progress, + **kwargs, + ) + + +def vit_l_32(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_l_32 architecture from + `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vision_transformer( + arch="vit_l_32", + patch_size=32, + num_layers=24, + num_heads=16, + hidden_dim=1024, + mlp_dim=4096, + pretrained=pretrained, + progress=progress, + **kwargs, + ) + + +def interpolate_embeddings( + image_size: int, + patch_size: int, + model_state: "OrderedDict[str, torch.Tensor]", + interpolation_mode: str = "bicubic", + reset_heads: bool = False, +) -> "OrderedDict[str, torch.Tensor]": + """This function helps interpolating positional embeddings during checkpoint loading, + especially when you want to apply a pre-trained model on images with different resolution. + + Args: + image_size (int): Image size of the new model. + patch_size (int): Patch size of the new model. + model_state (OrderedDict[str, torch.Tensor]): State dict of the pre-trained model. + interpolation_mode (str): The algorithm used for upsampling. Default: bicubic. + reset_heads (bool): If true, not copying the state of heads. Default: False. + + Returns: + OrderedDict[str, torch.Tensor]: A state dict which can be loaded into the new model. + """ + # Shape of pos_embedding is (1, seq_length, hidden_dim) + pos_embedding = model_state["encoder.pos_embedding"] + n, seq_length, hidden_dim = pos_embedding.shape + if n != 1: + raise ValueError(f"Unexpected position embedding shape: {pos_embedding.shape}") + + new_seq_length = (image_size // patch_size) ** 2 + 1 + + # Need to interpolate the weights for the position embedding. + # We do this by reshaping the positions embeddings to a 2d grid, performing + # an interpolation in the (h, w) space and then reshaping back to a 1d grid. + if new_seq_length != seq_length: + # The class token embedding shouldn't be interpolated so we split it up. + seq_length -= 1 + new_seq_length -= 1 + pos_embedding_token = pos_embedding[:, :1, :] + pos_embedding_img = pos_embedding[:, 1:, :] + + # (1, seq_length, hidden_dim) -> (1, hidden_dim, seq_length) + pos_embedding_img = pos_embedding_img.permute(0, 2, 1) + seq_length_1d = int(math.sqrt(seq_length)) + torch._assert(seq_length_1d * seq_length_1d == seq_length, "seq_length is not a perfect square!") + + # (1, hidden_dim, seq_length) -> (1, hidden_dim, seq_l_1d, seq_l_1d) + pos_embedding_img = pos_embedding_img.reshape(1, hidden_dim, seq_length_1d, seq_length_1d) + new_seq_length_1d = image_size // patch_size + + # Perform interpolation. + # (1, hidden_dim, seq_l_1d, seq_l_1d) -> (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) + new_pos_embedding_img = nn.functional.interpolate( + pos_embedding_img, + size=new_seq_length_1d, + mode=interpolation_mode, + align_corners=True, + ) + + # (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) -> (1, hidden_dim, new_seq_length) + new_pos_embedding_img = new_pos_embedding_img.reshape(1, hidden_dim, new_seq_length) + + # (1, hidden_dim, new_seq_length) -> (1, new_seq_length, hidden_dim) + new_pos_embedding_img = new_pos_embedding_img.permute(0, 2, 1) + new_pos_embedding = torch.cat([pos_embedding_token, new_pos_embedding_img], dim=1) + + model_state["encoder.pos_embedding"] = new_pos_embedding + + if reset_heads: + model_state_copy: "OrderedDict[str, torch.Tensor]" = OrderedDict() + for k, v in model_state.items(): + if not k.startswith("heads"): + model_state_copy[k] = v + model_state = model_state_copy + + return model_state diff --git a/utils/defense_utils/anp/anp_model/vit_new_anp.py b/utils/defense_utils/anp/anp_model/vit_new_anp.py new file mode 100644 index 0000000..5bcdd49 --- /dev/null +++ b/utils/defense_utils/anp/anp_model/vit_new_anp.py @@ -0,0 +1,854 @@ +import math +from collections import OrderedDict +from functools import partial +from typing import Any, Callable, List, NamedTuple, Optional, Dict + +import torch +import torch.nn as nn + +from torchvision.ops.misc import Conv2dNormActivation, MLP +from torchvision.transforms._presets import ImageClassification, InterpolationMode +from torchvision.utils import _log_api_usage_once +from torchvision.models._api import WeightsEnum, Weights +from torchvision.models._meta import _IMAGENET_CATEGORIES +from torchvision.models._utils import handle_legacy_interface, _ovewrite_named_param + +from defense.anp import anp_model + + +__all__ = [ + "VisionTransformer", + "ViT_B_16_Weights", + "ViT_B_32_Weights", + "ViT_L_16_Weights", + "ViT_L_32_Weights", + "ViT_H_14_Weights", + "vit_b_16", + "vit_b_32", + "vit_l_16", + "vit_l_32", + "vit_h_14", +] + + +class ConvStemConfig(NamedTuple): + out_channels: int + kernel_size: int + stride: int + norm_layer: Callable[..., nn.Module] = anp_model.NoisyBatchNorm2d + activation_layer: Callable[..., nn.Module] = nn.ReLU + + +class MLPBlock(MLP): + """Transformer MLP block.""" + + _version = 2 + + def __init__(self, in_dim: int, mlp_dim: int, dropout: float): + super().__init__(in_dim, [mlp_dim, in_dim], activation_layer=nn.GELU, inplace=None, dropout=dropout) + + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if m.bias is not None: + nn.init.normal_(m.bias, std=1e-6) + + def _load_from_state_dict( + self, + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ): + version = local_metadata.get("version", None) + + if version is None or version < 2: + # Replacing legacy MLPBlock with MLP. See https://github.com/pytorch/vision/pull/6053 + for i in range(2): + for type in ["weight", "bias"]: + old_key = f"{prefix}linear_{i+1}.{type}" + new_key = f"{prefix}{3*i}.{type}" + if old_key in state_dict: + state_dict[new_key] = state_dict.pop(old_key) + + super()._load_from_state_dict( + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ) + + +class EncoderBlock(nn.Module): + """Transformer encoder block.""" + + def __init__( + self, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float, + attention_dropout: float, + norm_layer: Callable[..., torch.nn.Module] = partial(anp_model.NoiseLayerNorm, eps=1e-6), + ): + super().__init__() + self.num_heads = num_heads + + # Attention block + self.ln_1 = norm_layer(hidden_dim) + self.self_attention = nn.MultiheadAttention(hidden_dim, num_heads, dropout=attention_dropout, batch_first=True) + self.dropout = nn.Dropout(dropout) + + # MLP block + self.ln_2 = norm_layer(hidden_dim) + self.mlp = MLPBlock(hidden_dim, mlp_dim, dropout) + + def forward(self, input: torch.Tensor): + torch._assert(input.dim() == 3, f"Expected (batch_size, seq_length, hidden_dim) got {input.shape}") + x = self.ln_1(input) + x, _ = self.self_attention(query=x, key=x, value=x, need_weights=False) + x = self.dropout(x) + x = x + input + + y = self.ln_2(x) + y = self.mlp(y) + return x + y + + +class Encoder(nn.Module): + """Transformer Model Encoder for sequence to sequence translation.""" + + def __init__( + self, + seq_length: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float, + attention_dropout: float, + norm_layer: Callable[..., torch.nn.Module] = partial(anp_model.NoiseLayerNorm, eps=1e-6), + ): + super().__init__() + # Note that batch_size is on the first dim because + # we have batch_first=True in nn.MultiAttention() by default + self.pos_embedding = nn.Parameter(torch.empty(1, seq_length, hidden_dim).normal_(std=0.02)) # from BERT + self.dropout = nn.Dropout(dropout) + layers: OrderedDict[str, nn.Module] = OrderedDict() + for i in range(num_layers): + layers[f"encoder_layer_{i}"] = EncoderBlock( + num_heads, + hidden_dim, + mlp_dim, + dropout, + attention_dropout, + norm_layer, + ) + self.layers = nn.Sequential(layers) + self.ln = norm_layer(hidden_dim) + + def forward(self, input: torch.Tensor): + torch._assert(input.dim() == 3, f"Expected (batch_size, seq_length, hidden_dim) got {input.shape}") + input = input + self.pos_embedding + return self.ln(self.layers(self.dropout(input))) + + +class VisionTransformer(nn.Module): + """Vision Transformer as per https://arxiv.org/abs/2010.11929.""" + + def __init__( + self, + image_size: int, + patch_size: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float = 0.0, + attention_dropout: float = 0.0, + num_classes: int = 1000, + representation_size: Optional[int] = None, + norm_layer: Callable[..., torch.nn.Module] = partial(anp_model.NoiseLayerNorm, eps=1e-6), + conv_stem_configs: Optional[List[ConvStemConfig]] = None, + ): + super().__init__() + _log_api_usage_once(self) + torch._assert(image_size % patch_size == 0, "Input shape indivisible by patch size!") + self.image_size = image_size + self.patch_size = patch_size + self.hidden_dim = hidden_dim + self.mlp_dim = mlp_dim + self.attention_dropout = attention_dropout + self.dropout = dropout + self.num_classes = num_classes + self.representation_size = representation_size + self.norm_layer = norm_layer + + if conv_stem_configs is not None: + # As per https://arxiv.org/abs/2106.14881 + seq_proj = nn.Sequential() + prev_channels = 3 + for i, conv_stem_layer_config in enumerate(conv_stem_configs): + seq_proj.add_module( + f"conv_bn_relu_{i}", + Conv2dNormActivation( + in_channels=prev_channels, + out_channels=conv_stem_layer_config.out_channels, + kernel_size=conv_stem_layer_config.kernel_size, + stride=conv_stem_layer_config.stride, + norm_layer=conv_stem_layer_config.norm_layer, + activation_layer=conv_stem_layer_config.activation_layer, + ), + ) + prev_channels = conv_stem_layer_config.out_channels + seq_proj.add_module( + "conv_last", nn.Conv2d(in_channels=prev_channels, out_channels=hidden_dim, kernel_size=1) + ) + self.conv_proj: nn.Module = seq_proj + else: + self.conv_proj = nn.Conv2d( + in_channels=3, out_channels=hidden_dim, kernel_size=patch_size, stride=patch_size + ) + + seq_length = (image_size // patch_size) ** 2 + + # Add a class token + self.class_token = nn.Parameter(torch.zeros(1, 1, hidden_dim)) + seq_length += 1 + + self.encoder = Encoder( + seq_length, + num_layers, + num_heads, + hidden_dim, + mlp_dim, + dropout, + attention_dropout, + norm_layer, + ) + self.seq_length = seq_length + + heads_layers: OrderedDict[str, nn.Module] = OrderedDict() + if representation_size is None: + heads_layers["head"] = nn.Linear(hidden_dim, num_classes) + else: + heads_layers["pre_logits"] = nn.Linear(hidden_dim, representation_size) + heads_layers["act"] = nn.Tanh() + heads_layers["head"] = nn.Linear(representation_size, num_classes) + + self.heads = nn.Sequential(heads_layers) + + if isinstance(self.conv_proj, nn.Conv2d): + # Init the patchify stem + fan_in = self.conv_proj.in_channels * self.conv_proj.kernel_size[0] * self.conv_proj.kernel_size[1] + nn.init.trunc_normal_(self.conv_proj.weight, std=math.sqrt(1 / fan_in)) + if self.conv_proj.bias is not None: + nn.init.zeros_(self.conv_proj.bias) + elif self.conv_proj.conv_last is not None and isinstance(self.conv_proj.conv_last, nn.Conv2d): + # Init the last 1x1 conv of the conv stem + nn.init.normal_( + self.conv_proj.conv_last.weight, mean=0.0, std=math.sqrt(2.0 / self.conv_proj.conv_last.out_channels) + ) + if self.conv_proj.conv_last.bias is not None: + nn.init.zeros_(self.conv_proj.conv_last.bias) + + if hasattr(self.heads, "pre_logits") and isinstance(self.heads.pre_logits, nn.Linear): + fan_in = self.heads.pre_logits.in_features + nn.init.trunc_normal_(self.heads.pre_logits.weight, std=math.sqrt(1 / fan_in)) + nn.init.zeros_(self.heads.pre_logits.bias) + + if isinstance(self.heads.head, nn.Linear): + nn.init.zeros_(self.heads.head.weight) + nn.init.zeros_(self.heads.head.bias) + + def _process_input(self, x: torch.Tensor) -> torch.Tensor: + n, c, h, w = x.shape + p = self.patch_size + torch._assert(h == self.image_size, "Wrong image height!") + torch._assert(w == self.image_size, "Wrong image width!") + n_h = h // p + n_w = w // p + + # (n, c, h, w) -> (n, hidden_dim, n_h, n_w) + x = self.conv_proj(x) + # (n, hidden_dim, n_h, n_w) -> (n, hidden_dim, (n_h * n_w)) + x = x.reshape(n, self.hidden_dim, n_h * n_w) + + # (n, hidden_dim, (n_h * n_w)) -> (n, (n_h * n_w), hidden_dim) + # The self attention layer expects inputs in the format (N, S, E) + # where S is the source sequence length, N is the batch size, E is the + # embedding dimension + x = x.permute(0, 2, 1) + + return x + + def forward(self, x: torch.Tensor): + # Reshape and permute the input tensor + x = self._process_input(x) + n = x.shape[0] + + # Expand the class token to the full batch + batch_class_token = self.class_token.expand(n, -1, -1) + x = torch.cat([batch_class_token, x], dim=1) + + x = self.encoder(x) + + # Classifier "token" as used by standard language architectures + x = x[:, 0] + + x = self.heads(x) + + return x + + +def _vision_transformer( + patch_size: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> VisionTransformer: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + assert weights.meta["min_size"][0] == weights.meta["min_size"][1] + _ovewrite_named_param(kwargs, "image_size", weights.meta["min_size"][0]) + image_size = kwargs.pop("image_size", 224) + + model = VisionTransformer( + image_size=image_size, + patch_size=patch_size, + num_layers=num_layers, + num_heads=num_heads, + hidden_dim=hidden_dim, + mlp_dim=mlp_dim, + **kwargs, + ) + + if weights: + model.load_state_dict(weights.get_state_dict(progress=progress)) + + return model + + +_COMMON_META: Dict[str, Any] = { + "categories": _IMAGENET_CATEGORIES, +} + +_COMMON_SWAG_META = { + **_COMMON_META, + "recipe": "https://github.com/facebookresearch/SWAG", + "license": "https://github.com/facebookresearch/SWAG/blob/main/LICENSE", +} + + +class ViT_B_16_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_16-c867db91.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 86567656, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_b_16", + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.072, + "acc@5": 95.318, + } + }, + "_docs": """ + These weights were trained from scratch by using a modified version of `DeIT + `_'s training recipe. + """, + }, + ) + IMAGENET1K_SWAG_E2E_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_16_swag-9ac1b537.pth", + transforms=partial( + ImageClassification, + crop_size=384, + resize_size=384, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "num_params": 86859496, + "min_size": (384, 384), + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.304, + "acc@5": 97.650, + } + }, + "_docs": """ + These weights are learnt via transfer learning by end-to-end fine-tuning the original + `SWAG `_ weights on ImageNet-1K data. + """, + }, + ) + IMAGENET1K_SWAG_LINEAR_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_16_lc_swag-4e70ced5.pth", + transforms=partial( + ImageClassification, + crop_size=224, + resize_size=224, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "recipe": "https://github.com/pytorch/vision/pull/5793", + "num_params": 86567656, + "min_size": (224, 224), + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.886, + "acc@5": 96.180, + } + }, + "_docs": """ + These weights are composed of the original frozen `SWAG `_ trunk + weights and a linear classifier learnt on top of them trained on ImageNet-1K data. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_B_32_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_32-d86f8d99.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 88224232, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_b_32", + "_metrics": { + "ImageNet-1K": { + "acc@1": 75.912, + "acc@5": 92.466, + } + }, + "_docs": """ + These weights were trained from scratch by using a modified version of `DeIT + `_'s training recipe. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_L_16_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_16-852ce7e3.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=242), + meta={ + **_COMMON_META, + "num_params": 304326632, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_l_16", + "_metrics": { + "ImageNet-1K": { + "acc@1": 79.662, + "acc@5": 94.638, + } + }, + "_docs": """ + These weights were trained from scratch by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + IMAGENET1K_SWAG_E2E_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_16_swag-4f3808c9.pth", + transforms=partial( + ImageClassification, + crop_size=512, + resize_size=512, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "num_params": 305174504, + "min_size": (512, 512), + "_metrics": { + "ImageNet-1K": { + "acc@1": 88.064, + "acc@5": 98.512, + } + }, + "_docs": """ + These weights are learnt via transfer learning by end-to-end fine-tuning the original + `SWAG `_ weights on ImageNet-1K data. + """, + }, + ) + IMAGENET1K_SWAG_LINEAR_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_16_lc_swag-4d563306.pth", + transforms=partial( + ImageClassification, + crop_size=224, + resize_size=224, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "recipe": "https://github.com/pytorch/vision/pull/5793", + "num_params": 304326632, + "min_size": (224, 224), + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.146, + "acc@5": 97.422, + } + }, + "_docs": """ + These weights are composed of the original frozen `SWAG `_ trunk + weights and a linear classifier learnt on top of them trained on ImageNet-1K data. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_L_32_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_32-c7638314.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 306535400, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_l_32", + "_metrics": { + "ImageNet-1K": { + "acc@1": 76.972, + "acc@5": 93.07, + } + }, + "_docs": """ + These weights were trained from scratch by using a modified version of `DeIT + `_'s training recipe. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_H_14_Weights(WeightsEnum): + IMAGENET1K_SWAG_E2E_V1 = Weights( + url="https://download.pytorch.org/models/vit_h_14_swag-80465313.pth", + transforms=partial( + ImageClassification, + crop_size=518, + resize_size=518, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "num_params": 633470440, + "min_size": (518, 518), + "_metrics": { + "ImageNet-1K": { + "acc@1": 88.552, + "acc@5": 98.694, + } + }, + "_docs": """ + These weights are learnt via transfer learning by end-to-end fine-tuning the original + `SWAG `_ weights on ImageNet-1K data. + """, + }, + ) + IMAGENET1K_SWAG_LINEAR_V1 = Weights( + url="https://download.pytorch.org/models/vit_h_14_lc_swag-c1eb923e.pth", + transforms=partial( + ImageClassification, + crop_size=224, + resize_size=224, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "recipe": "https://github.com/pytorch/vision/pull/5793", + "num_params": 632045800, + "min_size": (224, 224), + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.708, + "acc@5": 97.730, + } + }, + "_docs": """ + These weights are composed of the original frozen `SWAG `_ trunk + weights and a linear classifier learnt on top of them trained on ImageNet-1K data. + """, + }, + ) + DEFAULT = IMAGENET1K_SWAG_E2E_V1 + + +@handle_legacy_interface(weights=("pretrained", ViT_B_16_Weights.IMAGENET1K_V1)) +def vit_b_16(*, weights: Optional[ViT_B_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_b_16 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_B_16_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_B_16_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_B_16_Weights + :members: + """ + weights = ViT_B_16_Weights.verify(weights) + + return _vision_transformer( + patch_size=16, + num_layers=12, + num_heads=12, + hidden_dim=768, + mlp_dim=3072, + weights=weights, + progress=progress, + **kwargs, + ) + + +@handle_legacy_interface(weights=("pretrained", ViT_B_32_Weights.IMAGENET1K_V1)) +def vit_b_32(*, weights: Optional[ViT_B_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_b_32 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_B_32_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_B_32_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_B_32_Weights + :members: + """ + weights = ViT_B_32_Weights.verify(weights) + + return _vision_transformer( + patch_size=32, + num_layers=12, + num_heads=12, + hidden_dim=768, + mlp_dim=3072, + weights=weights, + progress=progress, + **kwargs, + ) + + +@handle_legacy_interface(weights=("pretrained", ViT_L_16_Weights.IMAGENET1K_V1)) +def vit_l_16(*, weights: Optional[ViT_L_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_l_16 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_L_16_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_L_16_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_L_16_Weights + :members: + """ + weights = ViT_L_16_Weights.verify(weights) + + return _vision_transformer( + patch_size=16, + num_layers=24, + num_heads=16, + hidden_dim=1024, + mlp_dim=4096, + weights=weights, + progress=progress, + **kwargs, + ) + + +@handle_legacy_interface(weights=("pretrained", ViT_L_32_Weights.IMAGENET1K_V1)) +def vit_l_32(*, weights: Optional[ViT_L_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_l_32 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_L_32_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_L_32_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_L_32_Weights + :members: + """ + weights = ViT_L_32_Weights.verify(weights) + + return _vision_transformer( + patch_size=32, + num_layers=24, + num_heads=16, + hidden_dim=1024, + mlp_dim=4096, + weights=weights, + progress=progress, + **kwargs, + ) + + +def vit_h_14(*, weights: Optional[ViT_H_14_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_h_14 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_H_14_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_H_14_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_H_14_Weights + :members: + """ + weights = ViT_H_14_Weights.verify(weights) + + return _vision_transformer( + patch_size=14, + num_layers=32, + num_heads=16, + hidden_dim=1280, + mlp_dim=5120, + weights=weights, + progress=progress, + **kwargs, + ) + + +def interpolate_embeddings( + image_size: int, + patch_size: int, + model_state: "OrderedDict[str, torch.Tensor]", + interpolation_mode: str = "bicubic", + reset_heads: bool = False, +) -> "OrderedDict[str, torch.Tensor]": + """This function helps interpolating positional embeddings during checkpoint loading, + especially when you want to apply a pre-trained model on images with different resolution. + + Args: + image_size (int): Image size of the new model. + patch_size (int): Patch size of the new model. + model_state (OrderedDict[str, torch.Tensor]): State dict of the pre-trained model. + interpolation_mode (str): The algorithm used for upsampling. Default: bicubic. + reset_heads (bool): If true, not copying the state of heads. Default: False. + + Returns: + OrderedDict[str, torch.Tensor]: A state dict which can be loaded into the new model. + """ + # Shape of pos_embedding is (1, seq_length, hidden_dim) + pos_embedding = model_state["encoder.pos_embedding"] + n, seq_length, hidden_dim = pos_embedding.shape + if n != 1: + raise ValueError(f"Unexpected position embedding shape: {pos_embedding.shape}") + + new_seq_length = (image_size // patch_size) ** 2 + 1 + + # Need to interpolate the weights for the position embedding. + # We do this by reshaping the positions embeddings to a 2d grid, performing + # an interpolation in the (h, w) space and then reshaping back to a 1d grid. + if new_seq_length != seq_length: + # The class token embedding shouldn't be interpolated so we split it up. + seq_length -= 1 + new_seq_length -= 1 + pos_embedding_token = pos_embedding[:, :1, :] + pos_embedding_img = pos_embedding[:, 1:, :] + + # (1, seq_length, hidden_dim) -> (1, hidden_dim, seq_length) + pos_embedding_img = pos_embedding_img.permute(0, 2, 1) + seq_length_1d = int(math.sqrt(seq_length)) + if seq_length_1d * seq_length_1d != seq_length: + raise ValueError( + f"seq_length is not a perfect square! Instead got seq_length_1d * seq_length_1d = {seq_length_1d * seq_length_1d } and seq_length = {seq_length}" + ) + + # (1, hidden_dim, seq_length) -> (1, hidden_dim, seq_l_1d, seq_l_1d) + pos_embedding_img = pos_embedding_img.reshape(1, hidden_dim, seq_length_1d, seq_length_1d) + new_seq_length_1d = image_size // patch_size + + # Perform interpolation. + # (1, hidden_dim, seq_l_1d, seq_l_1d) -> (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) + new_pos_embedding_img = nn.functional.interpolate( + pos_embedding_img, + size=new_seq_length_1d, + mode=interpolation_mode, + align_corners=True, + ) + + # (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) -> (1, hidden_dim, new_seq_length) + new_pos_embedding_img = new_pos_embedding_img.reshape(1, hidden_dim, new_seq_length) + + # (1, hidden_dim, new_seq_length) -> (1, new_seq_length, hidden_dim) + new_pos_embedding_img = new_pos_embedding_img.permute(0, 2, 1) + new_pos_embedding = torch.cat([pos_embedding_token, new_pos_embedding_img], dim=1) + + model_state["encoder.pos_embedding"] = new_pos_embedding + + if reset_heads: + model_state_copy: "OrderedDict[str, torch.Tensor]" = OrderedDict() + for k, v in model_state.items(): + if not k.startswith("heads"): + model_state_copy[k] = v + model_state = model_state_copy + + return model_state + + +# The dictionary below is internal implementation detail and will be removed in v0.15 +from torchvision.models._utils import _ModelURLs + + +model_urls = _ModelURLs( + { + "vit_b_16": ViT_B_16_Weights.IMAGENET1K_V1.url, + "vit_b_32": ViT_B_32_Weights.IMAGENET1K_V1.url, + "vit_l_16": ViT_L_16_Weights.IMAGENET1K_V1.url, + "vit_l_32": ViT_L_32_Weights.IMAGENET1K_V1.url, + } +) diff --git a/utils/defense_utils/dbd/__init__.py b/utils/defense_utils/dbd/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/utils/defense_utils/dbd/config_z/pretrain/signalTrigger/cifar10/example.yaml b/utils/defense_utils/dbd/config_z/pretrain/signalTrigger/cifar10/example.yaml new file mode 100755 index 0000000..25bc7b2 --- /dev/null +++ b/utils/defense_utils/dbd/config_z/pretrain/signalTrigger/cifar10/example.yaml @@ -0,0 +1,89 @@ +--- +# For CUDA convolution: Turn off deterministic algorithms and turn on benchmarking. +# The settings will speedup training, while introducing nondeterministic behaviors. +# See https://pytorch.org/docs/stable/notes/randomness.html for detailed informations. +seed: + seed: 100 + deterministic: False + benchmark: True +dataset_dir: ~/dataset/cifar-10/cifar-10-batches-py # Contain the sub-string `cifar`. +num_classes: 10 +# Logs will be saved in `saved_dir` and checkpoints will be saved in the `storage_dir`. +# Please make sure the `saved_dir` and `storage_dir` exist in the project root. +saved_dir: ./saved_data +storage_dir: ./storage +prefetch: True # turn on prefetch mode will speedup io +# First, apply `pre` transformations to images before adding triggers (if needed). +# And then, apply `primary` and `remaining` transformations sequentially. +transform: + pre: null + aug: + primary: + random_resize_crop: + size: 32 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + random_color_jitter: + p: 0.8 + brightness: 0.4 + contrast: 0.4 + saturation: 0.4 + hue: 0.1 + random_grayscale: + p: 0.2 + gaussian_blur: + p: 0.5 + sigma: [0.1, 2.0] + remaining: + to_tensor: True + normalize: + mean: [0.4914, 0.4822, 0.4465] + std: [0.2023, 0.1994, 0.2010] + train: + primary: + random_resize_crop: + size: 32 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + remaining: + to_tensor: True + normalize: + mean: [0.4914, 0.4822, 0.4465] + std: [0.2023, 0.1994, 0.2010] + test: + primary: null + remaining: + to_tensor: True + normalize: + mean: [0.4914, 0.4822, 0.4465] + std: [0.2023, 0.1994, 0.2010] +backdoor: + poison_ratio: 0.05 + target_label: 3 + blend: + alpha: 0.1 + trigger_path: ./data/trigger/hello_kitty.png +loader: + batch_size: 512 + num_workers: 0 # 4*num_gpus + pin_memory: True +network: + resnet18_cifar: + num_classes: 10 +sync_bn: True # Turn on synchronized batch normalization in distributed data parallel +criterion: + simclr: + temperature: 0.5 +optimizer: + SGD: + weight_decay: 1.e-4 + momentum: 0.9 + lr: 0.4 +lr_scheduler: + cosine_annealing: + T_max: 1000 # same as `num_epochs` +num_epochs: 1000 \ No newline at end of file diff --git a/utils/defense_utils/dbd/config_z/pretrain/signalTrigger/imagenet/example.yaml b/utils/defense_utils/dbd/config_z/pretrain/signalTrigger/imagenet/example.yaml new file mode 100755 index 0000000..8b3b00d --- /dev/null +++ b/utils/defense_utils/dbd/config_z/pretrain/signalTrigger/imagenet/example.yaml @@ -0,0 +1,86 @@ +--- +seed: + seed: 100 + deterministic: False + benchmark: True +dataset_dir: ~/dataset/imagenet30 +num_classes: 30 +saved_dir: ./saved_data +storage_dir: ./storage +prefetch: True +transform: + pre: + resize: + size: 256 + center_crop: + size: 224 + aug: + primary: + random_resize_crop: + size: 224 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + random_color_jitter: + p: 0.8 + brightness: 0.4 + contrast: 0.4 + saturation: 0.4 + hue: 0.1 + random_grayscale: + p: 0.2 + gaussian_blur: + p: 0.5 + sigma: [0.1, 2.0] + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + train: + primary: + random_resize_crop: + size: 224 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + test: + primary: null + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] +backdoor: + poison_ratio: 0.05 + target_label: 3 + blend: + alpha: 0.1 + trigger_path: ./data/trigger/noise.png +loader: + batch_size: 512 + num_workers: 0 + pin_memory: True +network: + resnet18_imagenet: + num_classes: 30 +sync_bn: True +criterion: + simclr: + temperature: 0.5 +optimizer: + SGD: + weight_decay: 1.e-4 + momentum: 0.9 + lr: 0.4 +lr_scheduler: + cosine_annealing: + T_max: 1000 # same as `num_epochs` +num_epochs: 1000 \ No newline at end of file diff --git a/utils/defense_utils/dbd/config_z/pretrain/signalTrigger/vggface/example.yaml b/utils/defense_utils/dbd/config_z/pretrain/signalTrigger/vggface/example.yaml new file mode 100755 index 0000000..56e8e56 --- /dev/null +++ b/utils/defense_utils/dbd/config_z/pretrain/signalTrigger/vggface/example.yaml @@ -0,0 +1,86 @@ +--- +seed: + seed: 100 + deterministic: False + benchmark: True +dataset_dir: ~/dataset/vggface2_30/train +num_classes: 30 +saved_dir: ./saved_data +storage_dir: ./storage +prefetch: True +transform: + pre: + resize: + size: 256 + center_crop: + size: 224 + aug: + primary: + random_resize_crop: + size: 224 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + random_color_jitter: + p: 0.8 + brightness: 0.4 + contrast: 0.4 + saturation: 0.4 + hue: 0.1 + random_grayscale: + p: 0.2 + gaussian_blur: + p: 0.5 + sigma: [0.1, 2.0] + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + train: + primary: + random_resize_crop: + size: 224 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + test: + primary: null + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] +backdoor: + poison_ratio: 0.05 + target_label: 3 + blend: + alpha: 0.1 + trigger_path: ./data/trigger/noise.png +loader: + batch_size: 512 + num_workers: 0 + pin_memory: True +network: + densenet121_face: + num_classes: 30 +sync_bn: True +criterion: + simclr: + temperature: 0.5 +optimizer: + SGD: + weight_decay: 1.e-4 + momentum: 0.9 + lr: 0.4 +lr_scheduler: + cosine_annealing: + T_max: 1000 # same as `num_epochs` +num_epochs: 1000 \ No newline at end of file diff --git a/utils/defense_utils/dbd/config_z/pretrain/squareTrigger/cifar10/example.yaml b/utils/defense_utils/dbd/config_z/pretrain/squareTrigger/cifar10/example.yaml new file mode 100755 index 0000000..8d5bdfa --- /dev/null +++ b/utils/defense_utils/dbd/config_z/pretrain/squareTrigger/cifar10/example.yaml @@ -0,0 +1,88 @@ +--- +# For CUDA convolution: Turn off deterministic algorithms and turn on benchmarking. +# The settings will speedup training, while introducing nondeterministic behaviors. +# See https://pytorch.org/docs/stable/notes/randomness.html for detailed informations. +seed: + seed: 100 + deterministic: False + benchmark: True +dataset_dir: ~/dataset/cifar-10/cifar-10-batches-py # Contain the sub-string `cifar`. +num_classes: 10 +# Logs will be saved in `saved_dir` and checkpoints will be saved in the `storage_dir`. +# Please make sure the `saved_dir` and `storage_dir` exist in the project root. +saved_dir: ./saved_data +storage_dir: ./storage +prefetch: True # turn on prefetch mode will speedup io +# First, apply `pre` transformations to images before adding triggers (if needed). +# And then, apply `primary` and `remaining` transformations sequentially. +transform: + pre: null + aug: + primary: + random_resize_crop: + size: 32 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + random_color_jitter: + p: 0.8 + brightness: 0.4 + contrast: 0.4 + saturation: 0.4 + hue: 0.1 + random_grayscale: + p: 0.2 + gaussian_blur: + p: 0.5 + sigma: [0.1, 2.0] + remaining: + to_tensor: True + normalize: + mean: [0.4914, 0.4822, 0.4465] + std: [0.2023, 0.1994, 0.2010] + train: + primary: + random_resize_crop: + size: 32 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + remaining: + to_tensor: True + normalize: + mean: [0.4914, 0.4822, 0.4465] + std: [0.2023, 0.1994, 0.2010] + test: + primary: null + remaining: + to_tensor: True + normalize: + mean: [0.4914, 0.4822, 0.4465] + std: [0.2023, 0.1994, 0.2010] +backdoor: + poison_ratio: 0.05 + target_label: 3 + badnets: + trigger_path: ./data/trigger/cifar_1.png +loader: + batch_size: 512 + num_workers: 0 # 4*num_gpus + pin_memory: True +network: + resnet18_cifar: + num_classes: 10 +sync_bn: True # Turn on synchronized batch normalization in distributed data parallel +criterion: + simclr: + temperature: 0.5 +optimizer: + SGD: + weight_decay: 1.e-4 + momentum: 0.9 + lr: 0.4 +lr_scheduler: + cosine_annealing: + T_max: 1000 # same as `num_epochs` +num_epochs: 1000 \ No newline at end of file diff --git a/utils/defense_utils/dbd/config_z/pretrain/squareTrigger/imagenet/example.yaml b/utils/defense_utils/dbd/config_z/pretrain/squareTrigger/imagenet/example.yaml new file mode 100755 index 0000000..ed1853f --- /dev/null +++ b/utils/defense_utils/dbd/config_z/pretrain/squareTrigger/imagenet/example.yaml @@ -0,0 +1,85 @@ +--- +seed: + seed: 100 + deterministic: False + benchmark: True +dataset_dir: ~/dataset/imagenet30 +num_classes: 30 +saved_dir: ./saved_data +storage_dir: ./storage +prefetch: True +transform: + pre: + resize: + size: 256 + center_crop: + size: 224 + aug: + primary: + random_resize_crop: + size: 224 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + random_color_jitter: + p: 0.8 + brightness: 0.4 + contrast: 0.4 + saturation: 0.4 + hue: 0.1 + random_grayscale: + p: 0.2 + gaussian_blur: + p: 0.5 + sigma: [0.1, 2.0] + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + train: + primary: + random_resize_crop: + size: 224 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + test: + primary: null + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] +backdoor: + poison_ratio: 0.05 + target_label: 3 + badnets: + trigger_path: ./data/trigger/imagenet_apple_rainbow_32_32.png +loader: + batch_size: 512 + num_workers: 0 + pin_memory: True +network: + resnet18_imagenet: + num_classes: 30 +sync_bn: True +criterion: + simclr: + temperature: 0.5 +optimizer: + SGD: + weight_decay: 1.e-4 + momentum: 0.9 + lr: 0.4 +lr_scheduler: + cosine_annealing: + T_max: 1000 # same as `num_epochs` +num_epochs: 1000 \ No newline at end of file diff --git a/utils/defense_utils/dbd/config_z/pretrain/squareTrigger/vggface/example.yaml b/utils/defense_utils/dbd/config_z/pretrain/squareTrigger/vggface/example.yaml new file mode 100755 index 0000000..132a9f8 --- /dev/null +++ b/utils/defense_utils/dbd/config_z/pretrain/squareTrigger/vggface/example.yaml @@ -0,0 +1,85 @@ +--- +seed: + seed: 100 + deterministic: False + benchmark: True +dataset_dir: ~/dataset/vggface2_30/train +num_classes: 30 +saved_dir: ./saved_data +storage_dir: ./storage +prefetch: True +transform: + pre: + resize: + size: 256 + center_crop: + size: 224 + aug: + primary: + random_resize_crop: + size: 224 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + random_color_jitter: + p: 0.8 + brightness: 0.4 + contrast: 0.4 + saturation: 0.4 + hue: 0.1 + random_grayscale: + p: 0.2 + gaussian_blur: + p: 0.5 + sigma: [0.1, 2.0] + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + train: + primary: + random_resize_crop: + size: 224 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + test: + primary: null + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] +backdoor: + poison_ratio: 0.05 + target_label: 3 + badnets: + trigger_path: ./data/trigger/imagenet_apple_rainbow_32_32.png +loader: + batch_size: 512 + num_workers: 0 + pin_memory: True +network: + densenet121_face: + num_classes: 30 +sync_bn: True +criterion: + simclr: + temperature: 0.5 +optimizer: + SGD: + weight_decay: 1.e-4 + momentum: 0.9 + lr: 0.4 +lr_scheduler: + cosine_annealing: + T_max: 1000 # same as `num_epochs` +num_epochs: 1000 \ No newline at end of file diff --git a/utils/defense_utils/dbd/config_z/semi/badnets/cifar10/example.yaml b/utils/defense_utils/dbd/config_z/semi/badnets/cifar10/example.yaml new file mode 100755 index 0000000..e75b61e --- /dev/null +++ b/utils/defense_utils/dbd/config_z/semi/badnets/cifar10/example.yaml @@ -0,0 +1,67 @@ +--- +# Load config for pretraining and update it with config for semi-supervised fine-tuning. +pretrain_config_path: ./config/defense/pretrain/badnets/cifar_resnet/example.yaml +pretrain_checkpoint: epoch100.pt +prefetch: True # turn on prefetch mode will speedup io +# First, apply `pre` transformations to images before adding triggers (if needed). +# And then, apply `primary` and `remaining` transformations sequentially. +transform: + pre: null + train: + primary: + random_crop: + size: 32 + padding: 4 + padding_mode: reflect + random_horizontal_flip: + p: 0.5 + remaining: + to_tensor: True + normalize: + mean: [0.4914, 0.4822, 0.4465] + std: [0.2023, 0.1994, 0.2010] + test: + primary: null + remaining: + to_tensor: True + normalize: + mean: [0.4914, 0.4822, 0.4465] + std: [0.2023, 0.1994, 0.2010] +network: + resnet18_cifar: + num_classes: 10 +# Warmup the linear classifier. +warmup: + loader: + batch_size: 128 + num_workers: 0 + pin_memory: True + criterion: + sce: + alpha: 0.1 + beta: 1 + num_classes: 10 ####args.num_classes + num_epochs: 10 ####args.epoch_warmup +# Semi-Supervised Fine-tuning. +semi: + epsilon: 0.5 ###epsilon + loader: + batch_size: 64 + num_workers: 0 + pin_memory: True + criterion: + mixmatch: + lambda_u: 15 # 75*(200/1024)~=15 + # gradually increasing lambda_u in the whole training process + # seems to lead to better results. + rampup_length: 190 # same as num_epochs or 16 (in the official implementation) + mixmatch: + train_iteration: 1024 + temperature: 0.5 + alpha: 0.75 + num_classes: 10 + num_epochs: 190 ####args.epoch +optimizer: + Adam: + lr: 0.002 +lr_scheduler: null \ No newline at end of file diff --git a/utils/defense_utils/dbd/config_z/semi/badnets/imagenet/example.yaml b/utils/defense_utils/dbd/config_z/semi/badnets/imagenet/example.yaml new file mode 100755 index 0000000..e0c95e3 --- /dev/null +++ b/utils/defense_utils/dbd/config_z/semi/badnets/imagenet/example.yaml @@ -0,0 +1,66 @@ +--- +pretrain_config_path: ./config/defense/pretrain/badnets/imagenet30_resnet/example.yaml +pretrain_checkpoint: epoch100.pt +prefetch: True +transform: + pre: + resize: + size: 256 + center_crop: + size: 224 + train: + primary: + random_resize_crop: + size: 224 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + test: + primary: null + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] +network: + resnet18_imagenet: + num_classes: 30 +warmup: + loader: + batch_size: 256 + num_workers: 0 + pin_memory: True + criterion: + sce: + alpha: 0.1 + beta: 1 + num_classes: 30 + num_epochs: 10 +semi: + epsilon: 0.5 + loader: + batch_size: 64 + num_workers: 0 + pin_memory: True + criterion: + mixmatch: + lambda_u: 6 # 75*(90/1024)~=6 + # gradually increasing lambda_u in the whole training process + # seems to lead to better results. + rampup_length: 80 # same as num_epochs or 16 (in the official implementation) + mixmatch: + train_iteration: 1024 + temperature: 0.5 + alpha: 0.75 + num_classes: 30 + num_epochs: 80 +optimizer: + Adam: + lr: 0.002 +lr_scheduler: null \ No newline at end of file diff --git a/utils/defense_utils/dbd/config_z/semi/badnets/vggface2_30_resnet/example.yaml b/utils/defense_utils/dbd/config_z/semi/badnets/vggface2_30_resnet/example.yaml new file mode 100755 index 0000000..6bb5641 --- /dev/null +++ b/utils/defense_utils/dbd/config_z/semi/badnets/vggface2_30_resnet/example.yaml @@ -0,0 +1,66 @@ +--- +pretrain_config_path: ./config/defense/pretrain/badnets/vggface2_30_densenet/example.yaml +pretrain_checkpoint: epoch100.pt +prefetch: True +transform: + pre: + resize: + size: 256 + center_crop: + size: 224 + train: + primary: + random_resize_crop: + size: 224 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + test: + primary: null + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] +network: + densenet121_face: + num_classes: 30 +warmup: + loader: + batch_size: 256 + num_workers: 0 + pin_memory: True + criterion: + sce: + alpha: 0.1 + beta: 1 + num_classes: 30 + num_epochs: 10 +semi: + epsilon: 0.5 + loader: + batch_size: 64 + num_workers: 0 + pin_memory: True + criterion: + mixmatch: + lambda_u: 6 # 75*(90/1024)~=6 + # gradually increasing lambda_u in the whole training process + # seems to lead to better results. + rampup_length: 80 # same as num_epochs or 16 (in the official implementation) + mixmatch: + train_iteration: 1024 + temperature: 0.5 + alpha: 0.75 + num_classes: 30 + num_epochs: 80 +optimizer: + Adam: + lr: 0.002 +lr_scheduler: null \ No newline at end of file diff --git a/utils/defense_utils/dbd/config_z/semi/blend/cifar10/example.yaml b/utils/defense_utils/dbd/config_z/semi/blend/cifar10/example.yaml new file mode 100755 index 0000000..cf6e5f6 --- /dev/null +++ b/utils/defense_utils/dbd/config_z/semi/blend/cifar10/example.yaml @@ -0,0 +1,67 @@ +--- +# Load config for pretraining and update it with config for semi-supervised fine-tuning. +pretrain_config_path: ./config/defense/pretrain/blend/cifar_resnet/example.yaml +pretrain_checkpoint: epoch100.pt +prefetch: True # turn on prefetch mode will speedup io +# First, apply `pre` transformations to images before adding triggers (if needed). +# And then, apply `primary` and `remaining` transformations sequentially. +transform: + pre: null + train: + primary: + random_crop: + size: 32 + padding: 4 + padding_mode: reflect + random_horizontal_flip: + p: 0.5 + remaining: + to_tensor: True + normalize: + mean: [0.4914, 0.4822, 0.4465] + std: [0.2023, 0.1994, 0.2010] + test: + primary: null + remaining: + to_tensor: True + normalize: + mean: [0.4914, 0.4822, 0.4465] + std: [0.2023, 0.1994, 0.2010] +network: + resnet18_cifar: + num_classes: 10 +# Warmup the linear classifier. +warmup: + loader: + batch_size: 128 + num_workers: 0 + pin_memory: True + criterion: + sce: + alpha: 0.1 + beta: 1 + num_classes: 10 + num_epochs: 10 +# Semi-Supervised Fine-tuning. +semi: + epsilon: 0.5 + loader: + batch_size: 64 + num_workers: 0 + pin_memory: True + criterion: + mixmatch: + lambda_u: 15 # 75*(200/1024)~=15 + # gradually increasing lambda_u in the whole training process + # seems to lead to better results. + rampup_length: 190 # same as num_epochs or 16 (in the official implementation) + mixmatch: + train_iteration: 1024 + temperature: 0.5 + alpha: 0.75 + num_classes: 10 + num_epochs: 190 +optimizer: + Adam: + lr: 0.002 +lr_scheduler: null \ No newline at end of file diff --git a/utils/defense_utils/dbd/config_z/semi/blend/imagenet/example.yaml b/utils/defense_utils/dbd/config_z/semi/blend/imagenet/example.yaml new file mode 100755 index 0000000..8276f8b --- /dev/null +++ b/utils/defense_utils/dbd/config_z/semi/blend/imagenet/example.yaml @@ -0,0 +1,66 @@ +--- +pretrain_config_path: ./config/defense/pretrain/blend/imagenet30_resnet/example.yaml +pretrain_checkpoint: epoch100.pt +prefetch: True +transform: + pre: + resize: + size: 256 + center_crop: + size: 224 + train: + primary: + random_resize_crop: + size: 224 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + test: + primary: null + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] +network: + resnet18_imagenet: + num_classes: 30 +warmup: + loader: + batch_size: 256 + num_workers: 0 + pin_memory: True + criterion: + sce: + alpha: 0.1 + beta: 1 + num_classes: 30 + num_epochs: 10 +semi: + epsilon: 0.5 + loader: + batch_size: 64 + num_workers: 0 + pin_memory: True + criterion: + mixmatch: + lambda_u: 6 # 75*(90/1024)~=6 + # gradually increasing lambda_u in the whole training process + # seems to lead to better results. + rampup_length: 80 # same as num_epochs or 16 (in the official implementation) + mixmatch: + train_iteration: 1024 + temperature: 0.5 + alpha: 0.75 + num_classes: 30 + num_epochs: 80 +optimizer: + Adam: + lr: 0.002 +lr_scheduler: null \ No newline at end of file diff --git a/utils/defense_utils/dbd/config_z/semi/blend/vggface2_30_resnet/example.yaml b/utils/defense_utils/dbd/config_z/semi/blend/vggface2_30_resnet/example.yaml new file mode 100755 index 0000000..b30789f --- /dev/null +++ b/utils/defense_utils/dbd/config_z/semi/blend/vggface2_30_resnet/example.yaml @@ -0,0 +1,66 @@ +--- +pretrain_config_path: ./config/defense/pretrain/blend/vggface2_30_densenet/example.yaml +pretrain_checkpoint: epoch100.pt +prefetch: True +transform: + pre: + resize: + size: 256 + center_crop: + size: 224 + train: + primary: + random_resize_crop: + size: 224 + scale: [0.2, 1.0] + interpolation: 3 # BICUBIC + random_horizontal_flip: + p: 0.5 + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + test: + primary: null + remaining: + to_tensor: True + normalize: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] +network: + densenet121_face: + num_classes: 30 +warmup: + loader: + batch_size: 256 + num_workers: 0 + pin_memory: True + criterion: + sce: + alpha: 0.1 + beta: 1 + num_classes: 30 + num_epochs: 10 +semi: + epsilon: 0.5 + loader: + batch_size: 64 + num_workers: 0 + pin_memory: True + criterion: + mixmatch: + lambda_u: 6 # 75*(90/1024)~=6 + # gradually increasing lambda_u in the whole training process + # seems to lead to better results. + rampup_length: 80 # same as num_epochs or 16 (in the official implementation) + mixmatch: + train_iteration: 1024 + temperature: 0.5 + alpha: 0.75 + num_classes: 30 + num_epochs: 80 +optimizer: + Adam: + lr: 0.002 +lr_scheduler: null \ No newline at end of file diff --git a/utils/defense_utils/dbd/data/backdoor.py b/utils/defense_utils/dbd/data/backdoor.py new file mode 100755 index 0000000..ead6182 --- /dev/null +++ b/utils/defense_utils/dbd/data/backdoor.py @@ -0,0 +1,79 @@ +import numpy as np +from PIL import Image + + +class BadNets(object): + """BadNets Injection Strategy. + + Reference: + [1] "Badnets: Evaluating backdooring attacks on deep neural networks." + Tianyu Gu, et al. IEEE Access 2019. + + Args: + trigger_path (string): The trigger path. + + .. note:: + The trigger image specified by the trigger path whose background is in black. + """ + + def __init__(self, trigger_path): + with open(trigger_path, "rb") as f: + trigger_ptn = Image.open(f).convert("RGB") + self.trigger_ptn = np.array(trigger_ptn) + # Get the trigger location since the background is in black + # and the trigger is in color. + self.trigger_loc = np.nonzero(self.trigger_ptn) + + def __call__(self, img): + return self.add_trigger(img) + + def add_trigger(self, img): + """Add `trigger_ptn` to `img`. + + Args: + img (np.ndarray): The input image (HWC). + + Returns: + poison_img (np.ndarray): The poisoned image (HWC). + """ + img[self.trigger_loc] = 0 + poison_img = img + self.trigger_ptn + + return poison_img + + +class Blend(object): + """Blended Injection Strategy. + + Reference: + [1] "Targeted backdoor attacks on deep learning systems using data poisoning." + Xinyun Chen, et al. arXiv:1712.05526. + + Args: + trigger_path (string): Trigger path. + alpha (float): The interpolation factor. + """ + + def __init__(self, trigger_path, alpha=0.1): + with open(trigger_path, "rb") as f: + self.trigger_ptn = Image.open(f).convert("RGB") + self.alpha = alpha + + def __call__(self, img): + return self.blend_trigger(img) + + def blend_trigger(self, img): + """Blend the input `img` with the `trigger_ptn` by + alpha * trigger_ptn + (1 - alpha) * img. + + Args: + img (numpy.ndarray): The input image (HWC). + + Return: + poison_img (np.ndarray): The poisoned image (HWC). + """ + img = Image.fromarray(img) + trigger_ptn = self.trigger_ptn.resize(img.size) + poison_img = Image.blend(img, trigger_ptn, self.alpha) + + return np.array(poison_img) diff --git a/utils/defense_utils/dbd/data/cifar.py b/utils/defense_utils/dbd/data/cifar.py new file mode 100755 index 0000000..74234d1 --- /dev/null +++ b/utils/defense_utils/dbd/data/cifar.py @@ -0,0 +1,81 @@ +import os +import pickle + +import numpy as np +import torch +from PIL import Image +from torch.utils.data.dataset import Dataset + +from .prefetch import prefetch_transform + + +class CIFAR10(Dataset): + """CIFAR-10 Dataset. + + Args: + root (string): Root directory of dataset. + transform (callable, optional): A function/transform that takes in an PIL image + and returns a transformed version. + train (bool, optional): If True, creates dataset from training set, otherwise + creates from test set (default: True). + prefetch (bool, optional): If True, remove `ToTensor` and `Normalize` in + `transform["remaining"]`, and turn on prefetch mode (default: False). + """ + + def __init__(self, root, transform=None, train=True, prefetch=False): + self.train = train + self.pre_transform = transform["pre"] + self.primary_transform = transform["primary"] + if prefetch: + self.remaining_transform, self.mean, self.std = prefetch_transform( + transform["remaining"] + ) + else: + self.remaining_transform = transform["remaining"] + if train: + data_list = [ + "data_batch_1", + "data_batch_2", + "data_batch_3", + "data_batch_4", + "data_batch_5", + ] + else: + data_list = ["test_batch"] + self.prefetch = prefetch + data = [] + targets = [] + if root[0] == "~": + # interprete `~` as the home directory. + root = os.path.expanduser(root) + for file_name in data_list: + file_path = os.path.join(root, file_name) + with open(file_path, "rb") as f: + entry = pickle.load(f, encoding="latin1") + data.append(entry["data"]) + targets.extend(entry["labels"]) + # Convert data (List) to NHWC (np.ndarray) works with PIL Image. + data = np.vstack(data).reshape(-1, 3, 32, 32).transpose((0, 2, 3, 1)) + self.data = data + self.targets = np.asarray(targets) + + def __getitem__(self, index): + img, target = self.data[index], self.targets[index] + img = Image.fromarray(img) ## HWC ndarray->HWC Image. + # Pre-processing transformations (HWC Image->HWC Image). + if self.pre_transform is not None: + img = self.pre_transform(img) + # Primary transformations (HWC Image->HWC Image). + img = self.primary_transform(img) + # The remaining transformations (HWC Image->CHW tensor). + img = self.remaining_transform(img) + if self.prefetch: + # HWC ndarray->CHW tensor with C=3. + img = np.rollaxis(np.array(img, dtype=np.uint8), 2) + img = torch.from_numpy(img) + item = {"img": img, "target": target} + + return item + + def __len__(self): + return len(self.data) diff --git a/utils/defense_utils/dbd/data/dataset.py b/utils/defense_utils/dbd/data/dataset.py new file mode 100755 index 0000000..0b934b0 --- /dev/null +++ b/utils/defense_utils/dbd/data/dataset.py @@ -0,0 +1,223 @@ +import copy +import numpy as np + +import torch +from PIL import Image +from torch.utils.data.dataset import Dataset + +from utils.aggregate_block.dataset_and_transform_generate import get_dataset_normalization + + +class PoisonLabelDataset(Dataset): + """Poison-Label dataset wrapper. + + Args: + dataset (Dataset): The dataset to be wrapped. + transform (callable): The backdoor transformations. + poison_idx (np.array): An 0/1 (clean/poisoned) array with + shape `(len(dataset), )`. + target_label (int): The target label. + """ + + def __init__(self, dataset, transform, poison_idx, train,args): + super(PoisonLabelDataset, self).__init__() + self.dataset = copy.deepcopy(dataset) + self.train = train + # if self.train: + self.data = self.dataset.data + self.targets = self.dataset.targets + self.poison_idx = poison_idx + # else: + # # Only fetch poison data when testing. + # self.data = self.dataset.data[np.nonzero(poison_idx)[0]] + # self.targets = self.dataset.targets[np.nonzero(poison_idx)[0]] + # self.poison_idx = poison_idx[poison_idx == 1] + # self.pre_transform = self.dataset.pre_transform + # self.primary_transform = self.dataset.primary_transform + # self.remaining_transform = self.dataset.remaining_transform + self.transform = transform + if train: + self.prefetch = args.prefetch + if self.prefetch: + norm = get_dataset_normalization(args.dataset) + self.mean, self.std = norm.mean, norm.std + else: + self.prefetch = False + # self.bd_transform = transform + # self.target_label = target_label + + def __getitem__(self, index): + if isinstance(self.data[index], str): + with open(self.data[index], "rb") as f: + img = np.array(Image.open(f).convert("RGB")) + else: + img = self.data[index] + target = self.targets[index] + poison = 0 + origin = target # original target + + if self.poison_idx[index] == 1: + img = self.first_augment(img) + # target = self.target_label + poison = 1 + else: + img = self.first_augment(img) + item = {"img": img, "target": target, "poison": poison, "origin": origin} + + return item + + def __len__(self): + return len(self.data) + + def first_augment(self, img): + # Pre-processing transformation (HWC ndarray->HWC ndarray). + # img = Image.fromarray(img) + # img = self.pre_transform(img) + # img = np.array(img) + # # Backdoor transformation (HWC ndarray->HWC ndarray). + # if bd_transform is not None: + # img = bd_transform(img) + # # Primary and the remaining transformations (HWC ndarray->CHW tensor). + # img = Image.fromarray(img) + # img = self.primary_transform(img) + # img = self.remaining_transform(img) + + + img = self.transform(img) + if self.prefetch: + # HWC ndarray->CHW tensor with C=3. + img = np.rollaxis(np.array(img, dtype=np.uint8), 2) + img = torch.from_numpy(img) + + return img + + +class MixMatchDataset(Dataset): + """Semi-supervised MixMatch dataset. + + Args: + dataset (Dataset): The dataset to be wrapped. + semi_idx (np.array): An 0/1 (labeled/unlabeled) array with + shape `(len(dataset), )`. + labeled (bool, optional): If True, creates dataset from labeled set, otherwise + creates from unlabeled set (default: True). + """ + + def __init__(self, dataset, semi_idx, labeled=True,args=None): + super(MixMatchDataset, self).__init__() + self.dataset = copy.deepcopy(dataset) + if labeled: + self.semi_indice = np.nonzero(semi_idx == 1)[0] + else: + self.semi_indice = np.nonzero(semi_idx == 0)[0] + self.labeled = labeled + self.prefetch = args.prefetch + if self.prefetch: + norm = get_dataset_normalization(args.dataset) + self.mean, self.std = norm.mean, norm.std + # self.mean, self.std = self.dataset.mean, self.dataset.std + + def __getitem__(self, index): + if self.labeled: + item = self.dataset[self.semi_indice[index]] + item["labeled"] = True + else: + item1 = self.dataset[self.semi_indice[index]] + item2 = self.dataset[self.semi_indice[index]] + img1, img2 = item1.pop("img"), item2.pop("img") + item1.update({"img1": img1, "img2": img2}) + item = item1 + item["labeled"] = False + + return item + + def __len__(self): + return len(self.semi_indice) + + +class SelfPoisonDataset(Dataset): + """Self-supervised poison-label contrastive dataset. + + Args: + dataset (PoisonLabelDataset): The poison-label dataset to be wrapped. + transform (dict): Augmented transformation dict has three keys `pre`, `primary` + and `remaining` which corresponds to pre-processing, primary and the + remaining transformations. + """ + + def __init__(self, x,y, transform,args): + super(SelfPoisonDataset, self).__init__() + self.dataset = list(zip(x,y)) + self.data = x + self.targets = y + # self.poison_idx = self.dataset.poison_idx + # self.bd_transform = self.dataset.bd_transform + # self.target_label = self.dataset.target_label + + # self.pre_transform = transform["pre"] + # self.primary_transform = transform["primary"] + # self.remaining_transform = transform["remaining"] + self.transform = transform + # self.remaining_transform = self.dataset.remaining_transform + self.prefetch = args.prefetch + if self.prefetch: + norm = get_dataset_normalization(args.dataset) + self.mean, self.std = norm.mean, norm.std + + def __getitem__(self, index): + if isinstance(self.data[index], str): + with open(self.data[index], "rb") as f: + img = np.array(Image.open(f).convert("RGB")) + else: + img = self.data[index] + target = self.targets[index] + # poison = 0 + # origin = target # original target + # if self.poison_idx[index] == 1: + # img1 = self.bd_first_augment(img, bd_transform=self.bd_transform) + # img2 = self.bd_first_augment(img, bd_transform=self.bd_transform) + # target = self.target_label + # poison = 1 + # else: + img1 = self.bd_first_augment(img) + img2 = self.bd_first_augment(img) + item = { + "img1": img1, + "img2": img2, + "target": target, + # "poison": poison, + # "origin": origin, + } + # item = { + # "img1": img1, + # "img2": img2, + # "target": target, + # "poison": poison, + # "origin": origin, + # } + + return item + + def __len__(self): + return len(self.data) + + def bd_first_augment(self, img): + # Pre-processing transformations (HWC ndarray->HWC ndarray). + # img = Image.fromarray(img) + # img = self.pre_transform(img) + img = np.array(img) + # # Backdoor transformationss (HWC ndarray->HWC ndarray). + # if bd_transform is not None: + # img = bd_transform(img) + # Primary and the remaining transformations (HWC ndarray->CHW tensor). + img = Image.fromarray(np.uint8(img)) + # img = self.primary_transform(img) + # img = self.remaining_transform(img) + img = self.transform(img) + + if self.prefetch: + # HWC ndarray->CHW tensor with C=3. + img = np.rollaxis(np.array(img, dtype=np.uint8), 2) + img = torch.from_numpy(img) + + return img diff --git a/utils/defense_utils/dbd/data/imagenet.py b/utils/defense_utils/dbd/data/imagenet.py new file mode 100755 index 0000000..31ab7ef --- /dev/null +++ b/utils/defense_utils/dbd/data/imagenet.py @@ -0,0 +1,97 @@ +"""ImageNet Dataset in Pytorch. + +Modified from https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py +""" +import os + +import numpy as np +import torch +from PIL import Image +from torch.utils.data.dataset import Dataset + +from .prefetch import prefetch_transform + + +def find_classes(dir): + """Finds the class folders in a dataset. + + Args: + dir (string): Root directory path. + + Returns: + (classes, class_to_idx) (tuple): classes are relative to (dir), + and class_to_idx is a dictionary. + """ + classes = [d.name for d in os.scandir(dir) if d.is_dir()] + classes.sort() + class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)} + return classes, class_to_idx + + +def make_dataset(dir, class_to_idx): + dataset = [] + for target_class in sorted(class_to_idx.keys()): + target = class_to_idx[target_class] + target_dir = os.path.join(dir, target_class) + for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)): + for fname in sorted(fnames): + path = os.path.join(root, fname) + dataset.append((path, target)) + return dataset + + +class ImageNet(Dataset): + """ImageNet Dataset. + + Args: + root (string): Root directory of dataset. + transform (callable, optional): A function/transform that takes in an PIL image + and returns a transformed version. + train (bool, optional): If True, creates dataset from training set, otherwise + creates from test set (default: True). + prefetch (bool, optional): If True, remove `ToTensor` and `Normalize` in + `transform["remaining"]`, and turn on prefetch mode (default: False). + """ + + def __init__(self, root, transform=None, train=True, prefetch=False): + if root[0] == "~": + # interprete `~` as the home directory. + root = os.path.expanduser(root) + self.pre_transform = transform["pre"] + self.primary_transform = transform["primary"] + if prefetch: + self.remaining_transform, self.mean, self.std = prefetch_transform( + transform["remaining"] + ) + else: + self.remaining_transform = transform["remaining"] + self.train = train + if self.train: + data_dir = os.path.join(root, "train") + else: + data_dir = os.path.join(root, "val") + self.prefetch = prefetch + self.classes, self.class_to_idx = find_classes(data_dir) + self.dataset = make_dataset(data_dir, self.class_to_idx) + self.data = np.array([s[0] for s in self.dataset]) + self.targets = np.array([s[1] for s in self.dataset]) + + def __getitem__(self, index): + img_path = self.data[index] + # Open path as file to avoid ResourceWarning. + with open(img_path, "rb") as f: + img = Image.open(f).convert("RGB") + # Pre-processing, primary and the remaining transformations. + img = self.pre_transform(img) + img = self.primary_transform(img) + img = self.remaining_transform(img) + if self.prefetch: + # HWC ndarray->CHW tensor with C=3. + img = np.rollaxis(np.array(img, dtype=np.uint8), 2) + img = torch.from_numpy(img) + item = {"img": img, "target": self.targets[index]} + + return item + + def __len__(self): + return len(self.data) diff --git a/utils/defense_utils/dbd/data/prefetch.py b/utils/defense_utils/dbd/data/prefetch.py new file mode 100755 index 0000000..d75d1ac --- /dev/null +++ b/utils/defense_utils/dbd/data/prefetch.py @@ -0,0 +1,74 @@ +import torch +import torchvision.transforms as transforms + + +class PrefetchLoader: + """A data loader wrapper for prefetching data along with + `ToTensor` and `Normalize` transformations. + + Borrowed from https://github.com/open-mmlab/OpenSelfSup. + """ + + def __init__(self, loader, mean, std): + self.loader = loader + self._mean = mean + self._std = std + + def __iter__(self): + stream = torch.cuda.Stream() + first = True + self.mean = torch.tensor([x * 255 for x in self._mean]).cuda().view(1, 3, 1, 1) + self.std = torch.tensor([x * 255 for x in self._std]).cuda().view(1, 3, 1, 1) + + for next_item in self.loader: + with torch.cuda.stream(stream): + if "img" in next_item: + img = next_item["img"].cuda(non_blocking=True) + next_item["img"] = img.float().sub_(self.mean).div_(self.std) + else: + # Semi-supervised loader + img1 = next_item["img1"].cuda(non_blocking=True) + img2 = next_item["img2"].cuda(non_blocking=True) + next_item["img1"] = img1.float().sub_(self.mean).div_(self.std) + next_item["img2"] = img2.float().sub_(self.mean).div_(self.std) + + if not first: + yield item + else: + first = False + + torch.cuda.current_stream().wait_stream(stream) + item = next_item + + yield item + + def __len__(self): + return len(self.loader) + + @property + def sampler(self): + return self.loader.sampler + + @property + def dataset(self): + return self.loader.dataset + + +def prefetch_transform(transform): + """Remove `ToTensor` and `Normalize` in `transform`. + """ + transform_list = [] + normalize = False + for t in transform.transforms: + if "Normalize" in str(type(t)): + normalize = True + if not normalize: + raise KeyError("No Normalize in transform: {}".format(transform)) + for t in transform.transforms: + if not ("ToTensor" or "Normalize" in str(type(t))): + transform_list.append(t) + if "Normalize" in str(type(t)): + mean, std = t.mean, t.std + transform = transforms.Compose(transform_list) + + return transform, mean, std diff --git a/utils/defense_utils/dbd/data/trigger/cifar_1.png b/utils/defense_utils/dbd/data/trigger/cifar_1.png new file mode 100755 index 0000000..d4e1874 Binary 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0000000..3e04a87 --- /dev/null +++ b/utils/defense_utils/dbd/data/utils.py @@ -0,0 +1,141 @@ +import random + +import numpy as np +import torchvision.transforms as transforms +from PIL import ImageFilter +from torch.utils.data import DataLoader + +from .backdoor import BadNets, Blend +from .cifar import CIFAR10 +from .imagenet import ImageNet +from .prefetch import PrefetchLoader +from .vggface2 import VGGFace2 + + +class GaussianBlur(object): + """Gaussian blur augmentation in SimCLR. + + Borrowed from https://github.com/facebookresearch/moco/blob/master/moco/loader.py. + """ + + def __init__(self, sigma=[0.1, 2.0]): + self.sigma = sigma + + def __call__(self, x): + sigma = random.uniform(self.sigma[0], self.sigma[1]) + x = x.filter(ImageFilter.GaussianBlur(radius=sigma)) + + return x + + +def query_transform(name, kwargs): + if name == "random_crop": + return transforms.RandomCrop(**kwargs) + elif name == "random_resize_crop": + return transforms.RandomResizedCrop(**kwargs) + elif name == "resize": + return transforms.Resize(**kwargs) + elif name == "center_crop": + return transforms.CenterCrop(**kwargs) + elif name == "random_horizontal_flip": + return transforms.RandomHorizontalFlip(**kwargs) + elif name == "random_color_jitter": + # In-place! + p = kwargs.pop("p") + return transforms.RandomApply([transforms.ColorJitter(**kwargs)], p=p) + elif name == "random_grayscale": + return transforms.RandomGrayscale(**kwargs) + elif name == "gaussian_blur": + # In-place! + p = kwargs.pop("p") + return transforms.RandomApply([GaussianBlur(**kwargs)], p=p) + elif name == "to_tensor": + if kwargs: + return transforms.ToTensor() + elif name == "normalize": + return transforms.Normalize(**kwargs) + else: + raise ValueError("Transformation {} is not supported!".format(name)) + + +def get_transform(transform_config): + transform = [] + if transform_config is not None: + for (k, v) in transform_config.items(): + if v is not None: + transform.append(query_transform(k, v)) + transform = transforms.Compose(transform) + + return transform + + +def get_dataset(dataset_dir, transform, train=True, prefetch=False): + if "cifar" in dataset_dir: + dataset = CIFAR10( + dataset_dir, transform=transform, train=train, prefetch=prefetch + ) + elif "imagenet" in dataset_dir: + dataset = ImageNet( + dataset_dir, transform=transform, train=train, prefetch=prefetch + ) + elif "vggface2" in dataset_dir: + dataset = VGGFace2( + dataset_dir, transform=transform, train=train, prefetch=prefetch + ) + else: + raise NotImplementedError("Dataset in {} is not supported.".format(dataset_dir)) + + return dataset + + +def get_loader(dataset, loader_config=None, **kwargs): + if loader_config is None: + loader = DataLoader(dataset, **kwargs) + else: + loader = DataLoader(dataset, **loader_config, **kwargs) + if dataset.prefetch: + loader = PrefetchLoader(loader, dataset.mean, dataset.std) + + return loader + + +def gen_poison_idx(dataset, target_label, poison_ratio=None): + poison_idx = np.zeros(len(dataset)) + train = dataset.train + for (i, t) in enumerate(dataset.targets): + if train and poison_ratio is not None: + if random.random() < poison_ratio and t != target_label: + poison_idx[i] = 1 + else: + if t != target_label: + poison_idx[i] = 1 + + return poison_idx + + +def get_bd_transform(bd_config): + if "badnets" in bd_config: + bd_transform = BadNets(bd_config["badnets"]["trigger_path"]) + elif "blend" in bd_config: + bd_transform = Blend(**bd_config["blend"]) + else: + raise NotImplementedError("Backdoor {} is not supported.".format(bd_config)) + + return bd_transform + + +def get_semi_idx(record_list, ratio, logger): + """Get labeled and unlabeled index. + """ + keys = [r.name for r in record_list] + loss = record_list[keys.index("loss")].data.numpy() + poison = record_list[keys.index("poison")].data.numpy() + semi_idx = np.zeros(len(loss)) + # Sort loss and fetch `ratio` of the smallest indices. + indice = loss.argsort()[: int(len(loss) * ratio)] + logger.info( + "{}/{} poisoned samples in semi_idx".format(poison[indice].sum(), len(indice)) + ) + semi_idx[indice] = 1 + + return semi_idx diff --git a/utils/defense_utils/dbd/data/vggface2.py b/utils/defense_utils/dbd/data/vggface2.py new file mode 100755 index 0000000..f9f5757 --- /dev/null +++ b/utils/defense_utils/dbd/data/vggface2.py @@ -0,0 +1,98 @@ +"""VGGFace2 Dataset in Pytorch. +""" +import os +import pickle + +import numpy as np +import torch +from PIL import Image +from torch.utils.data.dataset import Dataset + +from .prefetch import prefetch_transform + + +def find_classes(dir): + """Finds the class folders in a dataset. + + Args: + dir (string): Root directory path. + + Returns: + (classes, class_to_idx) (tuple): classes are relative to (dir), + and class_to_idx is a dictionary. + """ + classes = [d.name for d in os.scandir(dir) if d.is_dir()] + classes.sort() + class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)} + return classes, class_to_idx + + +def make_dataset(dir, class_to_idx): + dataset = [] + for target_class in sorted(class_to_idx.keys()): + target = class_to_idx[target_class] + target_dir = os.path.join(dir, target_class) + for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)): + for fname in sorted(fnames): + path = os.path.join(root, fname) + dataset.append((path, target)) + return dataset + + +class VGGFace2(Dataset): + """VGGFace2 Dataset. + + Args: + root (string): Root directory of dataset. + transform (callable, optional): A function/transform that takes in an PIL image + and returns a transformed version. + train (bool, optional): If True, creates dataset from training set, otherwise + creates from test set (default: True). + prefetch (bool, optional): If True, remove `ToTensor` and `Normalize` in + `transform["remaining"]`, and turn on prefetch mode (default: False). + """ + + def __init__(self, root, transform=None, train=True, prefetch=False): + if root[0] == "~": + # interprete `~` as the home directory. + root = os.path.expanduser(root) + pickle_file_dict = {"train": "train.pickle", "test": "test.pickle"} + self.pre_transform = transform["pre"] + self.primary_transform = transform["primary"] + if prefetch: + self.remaining_transform, self.mean, self.std = prefetch_transform( + transform["remaining"] + ) + else: + self.remaining_transform = transform["remaining"] + self.prefetch = prefetch + self.classes, self.class_to_idx = find_classes(root) + self.train = train + if self.train: + pickle_file_path = os.path.join(root, pickle_file_dict["train"]) + else: + pickle_file_path = os.path.join(root, pickle_file_dict["test"]) + with open(pickle_file_path, "rb") as f: + data_target = pickle.load(f) + self.data = np.array([os.path.join(root, s[0]) for s in data_target]) + self.targets = np.array([s[1] for s in data_target]) + + def __getitem__(self, index): + img_path = self.data[index] + # Open path as file to avoid ResourceWarning. + with open(img_path, "rb") as f: + img = Image.open(f).convert("RGB") + # Pre-processing, primary and the remaining transformations. + img = self.pre_transform(img) + img = self.primary_transform(img) + img = self.remaining_transform(img) + if self.prefetch: + # HWC ndarray->CHW tensor with C=3. + img = np.rollaxis(np.array(img, dtype=np.uint8), 2) + img = torch.from_numpy(img) + item = {"img": img, "target": self.targets[index]} + + return item + + def __len__(self): + return len(self.data) diff --git a/utils/defense_utils/dbd/model/loss.py b/utils/defense_utils/dbd/model/loss.py new file mode 100755 index 0000000..a2cee31 --- /dev/null +++ b/utils/defense_utils/dbd/model/loss.py @@ -0,0 +1,126 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class SimCLRLoss(nn.Module): + """Borrowed from https://github.com/wvangansbeke/Unsupervised-Classification. + """ + + def __init__(self, temperature, reduction="mean"): + super(SimCLRLoss, self).__init__() + self.temperature = temperature + self.reduction = reduction + + def forward(self, features): + """ + input: + - features: hidden feature representation of shape [b, 2, dim] + output: + - loss: loss computed according to SimCLR + """ + + b, n, dim = features.size() + assert n == 2 + mask = torch.eye(b, dtype=torch.float32).cuda() + + contrast_features = torch.cat(torch.unbind(features, dim=1), dim=0) + anchor = features[:, 0] + + # Dot product + dot_product = torch.matmul(anchor, contrast_features.T) / self.temperature + + # Log-sum trick for numerical stability + logits_max, _ = torch.max(dot_product, dim=1, keepdim=True) + logits = dot_product - logits_max.detach() + + mask = mask.repeat(1, 2) + logits_mask = torch.scatter( + torch.ones_like(mask), 1, torch.arange(b).view(-1, 1).cuda(), 0 + ) + mask = mask * logits_mask + + # Log-softmax + exp_logits = torch.exp(logits) * logits_mask + log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True)) + + # Mean log-likelihood for positive + if self.reduction == "mean": + loss = -((mask * log_prob).sum(1) / mask.sum(1)).mean() + elif self.reduction == "none": + loss = -((mask * log_prob).sum(1) / mask.sum(1)) + else: + raise ValueError("The reduction must be mean or none!") + + return loss + + +class RCELoss(nn.Module): + """Reverse Cross Entropy Loss. + """ + + def __init__(self, num_classes=10, reduction="mean"): + super(RCELoss, self).__init__() + self.num_classes = num_classes + self.reduction = reduction + + def forward(self, x, target): + prob = F.softmax(x, dim=-1) + prob = torch.clamp(prob, min=1e-7, max=1.0) + one_hot = F.one_hot(target, self.num_classes).float() + one_hot = torch.clamp(one_hot, min=1e-4, max=1.0) + loss = -1 * torch.sum(prob * torch.log(one_hot), dim=-1) + if self.reduction == "mean": + loss = loss.mean() + + return loss + + +class SCELoss(nn.Module): + """Symmetric Cross Entropy. + """ + + def __init__(self, alpha=0.1, beta=1, num_classes=10, reduction="mean"): + super(SCELoss, self).__init__() + self.alpha = alpha + self.beta = beta + self.num_classes = num_classes + self.reduction = reduction + + def forward(self, x, target): + ce = torch.nn.CrossEntropyLoss(reduction=self.reduction) + rce = RCELoss(num_classes=self.num_classes, reduction=self.reduction) + ce_loss = ce(x, target) + rce_loss = rce(x, target) + loss = self.alpha * ce_loss + self.beta * rce_loss + + return loss + + +class MixMatchLoss(nn.Module): + """SemiLoss in MixMatch. + + Modified from https://github.com/YU1ut/MixMatch-pytorch/blob/master/train.py. + """ + + def __init__(self, rampup_length, lambda_u=75): + super(MixMatchLoss, self).__init__() + self.rampup_length = rampup_length + self.lambda_u = lambda_u + self.current_lambda_u = lambda_u + + def linear_rampup(self, epoch): + if self.rampup_length == 0: + return 1.0 + else: + current = np.clip(epoch / self.rampup_length, 0.0, 1.0) + self.current_lambda_u = float(current) * self.lambda_u + + def forward(self, xoutput, xtarget, uoutput, utarget, epoch): + self.linear_rampup(epoch) + uprob = torch.softmax(uoutput, dim=1) + Lx = -torch.mean(torch.sum(F.log_softmax(xoutput, dim=1) * xtarget, dim=1)) + Lu = torch.mean((uprob - utarget) ** 2) + + return Lx, Lu, self.current_lambda_u diff --git a/utils/defense_utils/dbd/model/model.py b/utils/defense_utils/dbd/model/model.py new file mode 100755 index 0000000..a7ed979 --- /dev/null +++ b/utils/defense_utils/dbd/model/model.py @@ -0,0 +1,43 @@ +import torch.nn as nn +import torch.nn.functional as F + + +class SelfModel(nn.Module): + def __init__(self, backbone, head="mlp", proj_dim=128): + super(SelfModel, self).__init__() + self.backbone = backbone + self.head = head + + if head == "linear": + self.proj_head = nn.Linear(self.backbone.feature_dim, proj_dim) + elif head == "mlp": + self.proj_head = nn.Sequential( + nn.Linear(self.backbone.feature_dim, self.backbone.feature_dim), + nn.BatchNorm1d(self.backbone.feature_dim), + nn.ReLU(), + nn.Linear(self.backbone.feature_dim, proj_dim), + ) + else: + raise ValueError("Invalid head {}".format(head)) + + def forward(self, x): + feature = self.proj_head(self.backbone(x)) + feature = F.normalize(feature, dim=1) + + return feature + + +class LinearModel(nn.Module): + def __init__(self, backbone, feature_dim, num_classes): + super(LinearModel, self).__init__() + self.backbone = backbone + self.linear = nn.Linear(feature_dim, num_classes) + + def forward(self, x): + feature = self.backbone(x) + out = self.linear(feature) + + return out + + def update_encoder(self, backbone): + self.backbone = backbone diff --git a/utils/defense_utils/dbd/model/network/densenet.py b/utils/defense_utils/dbd/model/network/densenet.py new file mode 100755 index 0000000..c33091d --- /dev/null +++ b/utils/defense_utils/dbd/model/network/densenet.py @@ -0,0 +1,114 @@ +"""DenseNet in PyTorch.""" +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class Bottleneck(nn.Module): + def __init__(self, in_planes, growth_rate): + super(Bottleneck, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = nn.Conv2d(in_planes, 4 * growth_rate, kernel_size=1, bias=False) + self.bn2 = nn.BatchNorm2d(4 * growth_rate) + self.conv2 = nn.Conv2d(4 * growth_rate, growth_rate, kernel_size=3, padding=1, bias=False) + + def forward(self, x): + out = self.conv1(F.relu(self.bn1(x))) + out = self.conv2(F.relu(self.bn2(out))) + out = torch.cat([out, x], 1) + return out + + +class Transition(nn.Module): + def __init__(self, in_planes, out_planes): + super(Transition, self).__init__() + self.bn = nn.BatchNorm2d(in_planes) + self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False) + + def forward(self, x): + out = self.conv(F.relu(self.bn(x))) + out = F.avg_pool2d(out, 2) + return out + + +class DenseNet(nn.Module): + def __init__(self, block, nblocks, growth_rate=12, reduction=0.5, num_classes=10): + super(DenseNet, self).__init__() + self.growth_rate = growth_rate + + num_planes = 2 * growth_rate + self.conv1 = nn.Conv2d(3, num_planes, kernel_size=3, padding=1, bias=False) + + self.dense1 = self._make_dense_layers(block, num_planes, nblocks[0]) + num_planes += nblocks[0] * growth_rate + out_planes = int(math.floor(num_planes * reduction)) + self.trans1 = Transition(num_planes, out_planes) + num_planes = out_planes + + self.dense2 = self._make_dense_layers(block, num_planes, nblocks[1]) + num_planes += nblocks[1] * growth_rate + out_planes = int(math.floor(num_planes * reduction)) + self.trans2 = Transition(num_planes, out_planes) + num_planes = out_planes + + self.dense3 = self._make_dense_layers(block, num_planes, nblocks[2]) + num_planes += nblocks[2] * growth_rate + out_planes = int(math.floor(num_planes * reduction)) + self.trans3 = Transition(num_planes, out_planes) + num_planes = out_planes + + self.dense4 = self._make_dense_layers(block, num_planes, nblocks[3]) + num_planes += nblocks[3] * growth_rate + + self.bn = nn.BatchNorm2d(num_planes) + self.linear = nn.Linear(num_planes, num_classes) + + def _make_dense_layers(self, block, in_planes, nblock): + layers = [] + for i in range(nblock): + layers.append(block(in_planes, self.growth_rate)) + in_planes += self.growth_rate + return nn.Sequential(*layers) + + def forward(self, x): + out = self.conv1(x) + out = self.trans1(self.dense1(out)) + out = self.trans2(self.dense2(out)) + out = self.trans3(self.dense3(out)) + out = self.dense4(out) + out = F.avg_pool2d(F.relu(self.bn(out)), 4) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + + +def DenseNet121(): + return DenseNet(Bottleneck, [6, 12, 24, 16], growth_rate=32) + + +def DenseNet169(): + return DenseNet(Bottleneck, [6, 12, 32, 32], growth_rate=32) + + +def DenseNet201(): + return DenseNet(Bottleneck, [6, 12, 48, 32], growth_rate=32) + + +def DenseNet161(): + return DenseNet(Bottleneck, [6, 12, 36, 24], growth_rate=48) + + +def densenet_cifar(): + return DenseNet(Bottleneck, [6, 12, 24, 16], growth_rate=12) + + +def test(): + net = densenet_cifar() + x = torch.randn(1, 3, 32, 32) + y = net(x) + print(y) + + +# test() diff --git a/utils/defense_utils/dbd/model/network/densenet_dbd.py b/utils/defense_utils/dbd/model/network/densenet_dbd.py new file mode 100755 index 0000000..a7e0f56 --- /dev/null +++ b/utils/defense_utils/dbd/model/network/densenet_dbd.py @@ -0,0 +1,315 @@ +import re +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from collections import OrderedDict +from torchvision._internally_replaced_utils import load_state_dict_from_url +from torch import Tensor +from typing import Any, List, Tuple + + +__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161'] + +model_urls = { + 'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth', + 'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth', + 'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth', + 'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth', +} + + +class _DenseLayer(nn.Module): + def __init__( + self, + num_input_features: int, + growth_rate: int, + bn_size: int, + drop_rate: float, + memory_efficient: bool = False + ) -> None: + super(_DenseLayer, self).__init__() + self.norm1: nn.BatchNorm2d + self.add_module('norm1', nn.BatchNorm2d(num_input_features)) + self.relu1: nn.ReLU + self.add_module('relu1', nn.ReLU(inplace=True)) + self.conv1: nn.Conv2d + self.add_module('conv1', nn.Conv2d(num_input_features, bn_size * + growth_rate, kernel_size=1, stride=1, + bias=False)) + self.norm2: nn.BatchNorm2d + self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)) + self.relu2: nn.ReLU + self.add_module('relu2', nn.ReLU(inplace=True)) + self.conv2: nn.Conv2d + self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate, + kernel_size=3, stride=1, padding=1, + bias=False)) + self.drop_rate = float(drop_rate) + self.memory_efficient = memory_efficient + + def bn_function(self, inputs: List[Tensor]) -> Tensor: + concated_features = torch.cat(inputs, 1) + bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features))) # noqa: T484 + return bottleneck_output + + # todo: rewrite when torchscript supports any + def any_requires_grad(self, input: List[Tensor]) -> bool: + for tensor in input: + if tensor.requires_grad: + return True + return False + + @torch.jit.unused # noqa: T484 + def call_checkpoint_bottleneck(self, input: List[Tensor]) -> Tensor: + def closure(*inputs): + return self.bn_function(inputs) + + return cp.checkpoint(closure, *input) + + @torch.jit._overload_method # noqa: F811 + def forward(self, input: List[Tensor]) -> Tensor: + pass + + @torch.jit._overload_method # noqa: F811 + def forward(self, input: Tensor) -> Tensor: + pass + + # torchscript does not yet support *args, so we overload method + # allowing it to take either a List[Tensor] or single Tensor + def forward(self, input: Tensor) -> Tensor: # noqa: F811 + if isinstance(input, Tensor): + prev_features = [input] + else: + prev_features = input + + if self.memory_efficient and self.any_requires_grad(prev_features): + if torch.jit.is_scripting(): + raise Exception("Memory Efficient not supported in JIT") + + bottleneck_output = self.call_checkpoint_bottleneck(prev_features) + else: + bottleneck_output = self.bn_function(prev_features) + + new_features = self.conv2(self.relu2(self.norm2(bottleneck_output))) + if self.drop_rate > 0: + new_features = F.dropout(new_features, p=self.drop_rate, + training=self.training) + return new_features + + +class _DenseBlock(nn.ModuleDict): + _version = 2 + + def __init__( + self, + num_layers: int, + num_input_features: int, + bn_size: int, + growth_rate: int, + drop_rate: float, + memory_efficient: bool = False + ) -> None: + super(_DenseBlock, self).__init__() + for i in range(num_layers): + layer = _DenseLayer( + num_input_features + i * growth_rate, + growth_rate=growth_rate, + bn_size=bn_size, + drop_rate=drop_rate, + memory_efficient=memory_efficient, + ) + self.add_module('denselayer%d' % (i + 1), layer) + + def forward(self, init_features: Tensor) -> Tensor: + features = [init_features] + for name, layer in self.items(): + new_features = layer(features) + features.append(new_features) + return torch.cat(features, 1) + + +class _Transition(nn.Sequential): + def __init__(self, num_input_features: int, num_output_features: int) -> None: + super(_Transition, self).__init__() + self.add_module('norm', nn.BatchNorm2d(num_input_features)) + self.add_module('relu', nn.ReLU(inplace=True)) + self.add_module('conv', nn.Conv2d(num_input_features, num_output_features, + kernel_size=1, stride=1, bias=False)) + self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2)) + + +class DenseNet(nn.Module): + r"""Densenet-BC model class, based on + `"Densely Connected Convolutional Networks" `_. + + Args: + growth_rate (int) - how many filters to add each layer (`k` in paper) + block_config (list of 4 ints) - how many layers in each pooling block + num_init_features (int) - the number of filters to learn in the first convolution layer + bn_size (int) - multiplicative factor for number of bottle neck layers + (i.e. bn_size * k features in the bottleneck layer) + drop_rate (float) - dropout rate after each dense layer + num_classes (int) - number of classification classes + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + + def __init__( + self, + growth_rate: int = 32, + block_config: Tuple[int, int, int, int] = (6, 12, 24, 16), + num_init_features: int = 64, + bn_size: int = 4, + drop_rate: float = 0, + num_classes: int = 1000, + memory_efficient: bool = False + ) -> None: + + super(DenseNet, self).__init__() + + # First convolution + self.features = nn.Sequential(OrderedDict([ + ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, + padding=3, bias=False)), + ('norm0', nn.BatchNorm2d(num_init_features)), + ('relu0', nn.ReLU(inplace=True)), + ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)), + ])) + + # Each denseblock + num_features = num_init_features + for i, num_layers in enumerate(block_config): + block = _DenseBlock( + num_layers=num_layers, + num_input_features=num_features, + bn_size=bn_size, + growth_rate=growth_rate, + drop_rate=drop_rate, + memory_efficient=memory_efficient + ) + self.features.add_module('denseblock%d' % (i + 1), block) + num_features = num_features + num_layers * growth_rate + if i != len(block_config) - 1: + trans = _Transition(num_input_features=num_features, + num_output_features=num_features // 2) + self.features.add_module('transition%d' % (i + 1), trans) + num_features = num_features // 2 + + # Final batch norm + self.features.add_module('norm5', nn.BatchNorm2d(num_features)) + + self.feature_dim = num_features + # Linear layer + # self.classifier = nn.Linear(num_features, num_classes) + + # Official init from torch repo. + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.constant_(m.bias, 0) + + def forward(self, x: Tensor) -> Tensor: + features = self.features(x) + out = F.relu(features, inplace=True) + out = F.adaptive_avg_pool2d(out, (1, 1)) + out = torch.flatten(out, 1) + # out = self.classifier(out) + return out + + +def _load_state_dict(model: nn.Module, model_url: str, progress: bool) -> None: + # '.'s are no longer allowed in module names, but previous _DenseLayer + # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'. + # They are also in the checkpoints in model_urls. This pattern is used + # to find such keys. + pattern = re.compile( + r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$') + + state_dict = load_state_dict_from_url(model_url, progress=progress) + for key in list(state_dict.keys()): + res = pattern.match(key) + if res: + new_key = res.group(1) + res.group(2) + state_dict[new_key] = state_dict[key] + del state_dict[key] + model.load_state_dict(state_dict) + + +def _densenet( + arch: str, + growth_rate: int, + block_config: Tuple[int, int, int, int], + num_init_features: int, + pretrained: bool, + progress: bool, + **kwargs: Any +) -> DenseNet: + model = DenseNet(growth_rate, block_config, num_init_features, **kwargs) + if pretrained: + _load_state_dict(model, model_urls[arch], progress) + return model + + +def densenet121(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-121 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress, + **kwargs) + + +def densenet161(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-161 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress, + **kwargs) + + +def densenet169(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-169 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet169', 32, (6, 12, 32, 32), 64, pretrained, progress, + **kwargs) + + +def densenet201(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-201 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress, + **kwargs) diff --git a/utils/defense_utils/dbd/model/network/densenet_face.py b/utils/defense_utils/dbd/model/network/densenet_face.py new file mode 100755 index 0000000..b6ebd6b --- /dev/null +++ b/utils/defense_utils/dbd/model/network/densenet_face.py @@ -0,0 +1,359 @@ +"""Borrowed from https://github.com/pytorch/vision. +""" +import re +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from collections import OrderedDict +from torch.utils.model_zoo import load_url as load_state_dict_from_url +from torch import Tensor +from torch.jit.annotations import List + + +__all__ = ["DenseNet", "densenet121", "densenet169", "densenet201", "densenet161"] + +model_urls = { + "densenet121": "https://download.pytorch.org/models/densenet121-a639ec97.pth", + "densenet169": "https://download.pytorch.org/models/densenet169-b2777c0a.pth", + "densenet201": "https://download.pytorch.org/models/densenet201-c1103571.pth", + "densenet161": "https://download.pytorch.org/models/densenet161-8d451a50.pth", +} + + +class _DenseLayer(nn.Module): + def __init__( + self, + num_input_features, + growth_rate, + bn_size, + drop_rate, + memory_efficient=False, + ): + super(_DenseLayer, self).__init__() + self.add_module("norm1", nn.BatchNorm2d(num_input_features)), + self.add_module("relu1", nn.ReLU(inplace=True)), + self.add_module( + "conv1", + nn.Conv2d( + num_input_features, + bn_size * growth_rate, + kernel_size=1, + stride=1, + bias=False, + ), + ), + self.add_module("norm2", nn.BatchNorm2d(bn_size * growth_rate)), + self.add_module("relu2", nn.ReLU(inplace=True)), + self.add_module( + "conv2", + nn.Conv2d( + bn_size * growth_rate, + growth_rate, + kernel_size=3, + stride=1, + padding=1, + bias=False, + ), + ), + self.drop_rate = float(drop_rate) + self.memory_efficient = memory_efficient + + def bn_function(self, inputs): + # type: (List[Tensor]) -> Tensor + concated_features = torch.cat(inputs, 1) + bottleneck_output = self.conv1( + self.relu1(self.norm1(concated_features)) + ) # noqa: T484 + return bottleneck_output + + # todo: rewrite when torchscript supports any + def any_requires_grad(self, input): + # type: (List[Tensor]) -> bool + for tensor in input: + if tensor.requires_grad: + return True + return False + + @torch.jit.unused # noqa: T484 + def call_checkpoint_bottleneck(self, input): + # type: (List[Tensor]) -> Tensor + def closure(*inputs): + return self.bn_function(inputs) + + return cp.checkpoint(closure, *input) + + @torch.jit._overload_method # noqa: F811 + def forward(self, input): + # type: (List[Tensor]) -> (Tensor) + pass + + @torch.jit._overload_method # noqa: F811 + def forward(self, input): + # type: (Tensor) -> (Tensor) + pass + + # torchscript does not yet support *args, so we overload method + # allowing it to take either a List[Tensor] or single Tensor + def forward(self, input): # noqa: F811 + if isinstance(input, Tensor): + prev_features = [input] + else: + prev_features = input + + if self.memory_efficient and self.any_requires_grad(prev_features): + if torch.jit.is_scripting(): + raise Exception("Memory Efficient not supported in JIT") + + bottleneck_output = self.call_checkpoint_bottleneck(prev_features) + else: + bottleneck_output = self.bn_function(prev_features) + + new_features = self.conv2(self.relu2(self.norm2(bottleneck_output))) + if self.drop_rate > 0: + new_features = F.dropout( + new_features, p=self.drop_rate, training=self.training + ) + return new_features + + +class _DenseBlock(nn.ModuleDict): + _version = 2 + + def __init__( + self, + num_layers, + num_input_features, + bn_size, + growth_rate, + drop_rate, + memory_efficient=False, + ): + super(_DenseBlock, self).__init__() + for i in range(num_layers): + layer = _DenseLayer( + num_input_features + i * growth_rate, + growth_rate=growth_rate, + bn_size=bn_size, + drop_rate=drop_rate, + memory_efficient=memory_efficient, + ) + self.add_module("denselayer%d" % (i + 1), layer) + + def forward(self, init_features): + features = [init_features] + for name, layer in self.items(): + new_features = layer(features) + features.append(new_features) + return torch.cat(features, 1) + + +class _Transition(nn.Sequential): + def __init__(self, num_input_features, num_output_features): + super(_Transition, self).__init__() + self.add_module("norm", nn.BatchNorm2d(num_input_features)) + self.add_module("relu", nn.ReLU(inplace=True)) + self.add_module( + "conv", + nn.Conv2d( + num_input_features, + num_output_features, + kernel_size=1, + stride=1, + bias=False, + ), + ) + self.add_module("pool", nn.AvgPool2d(kernel_size=2, stride=2)) + + +class DenseNet(nn.Module): + r"""Densenet-BC model class, based on + `"Densely Connected Convolutional Networks" `_ + + Args: + growth_rate (int) - how many filters to add each layer (`k` in paper) + block_config (list of 4 ints) - how many layers in each pooling block + num_init_features (int) - the number of filters to learn in the first convolution layer + bn_size (int) - multiplicative factor for number of bottle neck layers + (i.e. bn_size * k features in the bottleneck layer) + drop_rate (float) - dropout rate after each dense layer + num_classes (int) - number of classification classes + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_ + """ + + def __init__( + self, + growth_rate=32, + block_config=(6, 12, 24, 16), + num_init_features=64, + bn_size=4, + drop_rate=0, + num_classes=1000, + memory_efficient=False, + ): + + super(DenseNet, self).__init__() + + # First convolution + self.features = nn.Sequential( + OrderedDict( + [ + ( + "conv0", + nn.Conv2d( + 3, + num_init_features, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ), + ), + ("norm0", nn.BatchNorm2d(num_init_features)), + ("relu0", nn.ReLU(inplace=True)), + ("pool0", nn.MaxPool2d(kernel_size=3, stride=2, padding=1)), + ] + ) + ) + + # Each denseblock + num_features = num_init_features + for i, num_layers in enumerate(block_config): + block = _DenseBlock( + num_layers=num_layers, + num_input_features=num_features, + bn_size=bn_size, + growth_rate=growth_rate, + drop_rate=drop_rate, + memory_efficient=memory_efficient, + ) + self.features.add_module("denseblock%d" % (i + 1), block) + num_features = num_features + num_layers * growth_rate + if i != len(block_config) - 1: + trans = _Transition( + num_input_features=num_features, + num_output_features=num_features // 2, + ) + self.features.add_module("transition%d" % (i + 1), trans) + num_features = num_features // 2 + + # Final batch norm + self.features.add_module("norm5", nn.BatchNorm2d(num_features)) + + # Linear layer + # self.classifier = nn.Linear(num_features, num_classes) + self.num_features = num_features + + # Official init from torch repo. + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.constant_(m.bias, 0) + + def forward(self, x): + features = self.features(x) + out = F.relu(features, inplace=True) + out = F.adaptive_avg_pool2d(out, (1, 1)) + out = torch.flatten(out, 1) + # out = self.classifier(out) + return out + + +def _load_state_dict(model, model_url, progress): + # '.'s are no longer allowed in module names, but previous _DenseLayer + # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'. + # They are also in the checkpoints in model_urls. This pattern is used + # to find such keys. + pattern = re.compile( + r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$" + ) + + state_dict = load_state_dict_from_url(model_url, progress=progress) + for key in list(state_dict.keys()): + res = pattern.match(key) + if res: + new_key = res.group(1) + res.group(2) + state_dict[new_key] = state_dict[key] + del state_dict[key] + model.load_state_dict(state_dict) + + +def _densenet( + arch, growth_rate, block_config, num_init_features, pretrained, progress, **kwargs +): + model = DenseNet(growth_rate, block_config, num_init_features, **kwargs) + if pretrained: + _load_state_dict(model, model_urls[arch], progress) + return model + + +def densenet121(pretrained=False, progress=True, **kwargs): + r"""Densenet-121 model from + `"Densely Connected Convolutional Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_ + """ + backbone = _densenet( + "densenet121", 32, (6, 12, 24, 16), 64, pretrained, progress, **kwargs + ) + backbone.feature_dim = backbone.num_features + + return backbone + # return _densenet( + # "densenet121", 32, (6, 12, 24, 16), 64, pretrained, progress, **kwargs + # ) + + +def densenet161(pretrained=False, progress=True, **kwargs): + r"""Densenet-161 model from + `"Densely Connected Convolutional Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_ + """ + return _densenet( + "densenet161", 48, (6, 12, 36, 24), 96, pretrained, progress, **kwargs + ) + + +def densenet169(pretrained=False, progress=True, **kwargs): + r"""Densenet-169 model from + `"Densely Connected Convolutional Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_ + """ + return _densenet( + "densenet169", 32, (6, 12, 32, 32), 64, pretrained, progress, **kwargs + ) + + +def densenet201(pretrained=False, progress=True, **kwargs): + r"""Densenet-201 model from + `"Densely Connected Convolutional Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_ + """ + return _densenet( + "densenet201", 32, (6, 12, 48, 32), 64, pretrained, progress, **kwargs + ) + diff --git a/utils/defense_utils/dbd/model/network/efficientnet.py b/utils/defense_utils/dbd/model/network/efficientnet.py new file mode 100755 index 0000000..70f8f44 --- /dev/null +++ b/utils/defense_utils/dbd/model/network/efficientnet.py @@ -0,0 +1,114 @@ +"""EfficientNet in PyTorch. + +Paper: "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks". + +Reference: https://github.com/keras-team/keras-applications/blob/master/keras_applications/efficientnet.py +""" +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def swish(x): + return x * x.sigmoid() + + +class Block(nn.Module): + """expansion + depthwise + pointwise + squeeze-excitation""" + + def __init__(self, in_planes, out_planes, kernel_size, stride, expand_ratio=1, se_ratio=0.0, drop_rate=0.0): + super(Block, self).__init__() + self.stride = stride + self.drop_rate = drop_rate + + # Expansion + planes = expand_ratio * in_planes + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, stride=1, padding=0, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + + # Depthwise conv + self.conv2 = nn.Conv2d( + planes, + planes, + kernel_size=kernel_size, + stride=stride, + padding=(kernel_size - 1) // 2, + groups=planes, + bias=False, + ) + self.bn2 = nn.BatchNorm2d(planes) + + # SE layers + se_planes = max(1, int(planes * se_ratio)) + self.se1 = nn.Conv2d(planes, se_planes, kernel_size=1) + self.se2 = nn.Conv2d(se_planes, planes, kernel_size=1) + + # Output + self.conv3 = nn.Conv2d(planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False) + self.bn3 = nn.BatchNorm2d(out_planes) + + self.shortcut = nn.Sequential() + if stride == 1 and in_planes != out_planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(out_planes), + ) + + def forward(self, x): + out = swish(self.bn1(self.conv1(x))) + out = swish(self.bn2(self.conv2(out))) + # Squeeze-Excitation + w = F.avg_pool2d(out, out.size(2)) + w = swish(self.se1(w)) + w = self.se2(w).sigmoid() + out = out * w + # Output + out = self.bn3(self.conv3(out)) + if self.drop_rate > 0: + out = F.dropout2d(out, self.drop_rate) + shortcut = self.shortcut(x) if self.stride == 1 else out + out = out + shortcut + return out + + +class EfficientNet(nn.Module): + def __init__(self, cfg, num_classes=10): + super(EfficientNet, self).__init__() + self.cfg = cfg + self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(32) + self.layers = self._make_layers(in_planes=32) + self.linear = nn.Linear(cfg[-1][1], num_classes) + + def _make_layers(self, in_planes): + layers = [] + for expansion, out_planes, num_blocks, kernel_size, stride in self.cfg: + strides = [stride] + [1] * (num_blocks - 1) + for stride in strides: + layers.append( + Block(in_planes, out_planes, kernel_size, stride, expansion, se_ratio=0.25, drop_rate=0.2) + ) + in_planes = out_planes + return nn.Sequential(*layers) + + def forward(self, x): + out = swish(self.bn1(self.conv1(x))) + out = self.layers(out) + out = F.adaptive_avg_pool2d(out, 1) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + + +def EfficientNetB0(): + # (expansion, out_planes, num_blocks, kernel_size, stride) + cfg = [ + (1, 16, 1, 3, 1), + (6, 24, 2, 3, 2), + (6, 40, 2, 5, 2), + (6, 80, 3, 3, 2), + (6, 112, 3, 5, 1), + (6, 192, 4, 5, 2), + (6, 320, 1, 3, 1), + ] + return EfficientNet(cfg) diff --git a/utils/defense_utils/dbd/model/network/efficientnet_dbd.py b/utils/defense_utils/dbd/model/network/efficientnet_dbd.py new file mode 100755 index 0000000..0297076 --- /dev/null +++ b/utils/defense_utils/dbd/model/network/efficientnet_dbd.py @@ -0,0 +1,351 @@ +import copy +import math +import torch + +from functools import partial +from torch import nn, Tensor +from typing import Any, Callable, List, Optional, Sequence + +from torchvision._internally_replaced_utils import load_state_dict_from_url +from torchvision.ops.misc import ConvNormActivation, SqueezeExcitation +from torchvision.models._utils import _make_divisible +from torchvision.ops import StochasticDepth + + +__all__ = ["EfficientNet", "efficientnet_b0", "efficientnet_b1", "efficientnet_b2", "efficientnet_b3", + "efficientnet_b4", "efficientnet_b5", "efficientnet_b6", "efficientnet_b7"] + + +model_urls = { + # Weights ported from https://github.com/rwightman/pytorch-image-models/ + "efficientnet_b0": "https://download.pytorch.org/models/efficientnet_b0_rwightman-3dd342df.pth", + "efficientnet_b1": "https://download.pytorch.org/models/efficientnet_b1_rwightman-533bc792.pth", + "efficientnet_b2": "https://download.pytorch.org/models/efficientnet_b2_rwightman-bcdf34b7.pth", + "efficientnet_b3": "https://download.pytorch.org/models/efficientnet_b3_rwightman-cf984f9c.pth", + "efficientnet_b4": "https://download.pytorch.org/models/efficientnet_b4_rwightman-7eb33cd5.pth", + # Weights ported from https://github.com/lukemelas/EfficientNet-PyTorch/ + "efficientnet_b5": "https://download.pytorch.org/models/efficientnet_b5_lukemelas-b6417697.pth", + "efficientnet_b6": "https://download.pytorch.org/models/efficientnet_b6_lukemelas-c76e70fd.pth", + "efficientnet_b7": "https://download.pytorch.org/models/efficientnet_b7_lukemelas-dcc49843.pth", +} + + +class MBConvConfig: + # Stores information listed at Table 1 of the EfficientNet paper + def __init__(self, + expand_ratio: float, kernel: int, stride: int, + input_channels: int, out_channels: int, num_layers: int, + width_mult: float, depth_mult: float) -> None: + self.expand_ratio = expand_ratio + self.kernel = kernel + self.stride = stride + self.input_channels = self.adjust_channels(input_channels, width_mult) + self.out_channels = self.adjust_channels(out_channels, width_mult) + self.num_layers = self.adjust_depth(num_layers, depth_mult) + + def __repr__(self) -> str: + s = self.__class__.__name__ + '(' + s += 'expand_ratio={expand_ratio}' + s += ', kernel={kernel}' + s += ', stride={stride}' + s += ', input_channels={input_channels}' + s += ', out_channels={out_channels}' + s += ', num_layers={num_layers}' + s += ')' + return s.format(**self.__dict__) + + @staticmethod + def adjust_channels(channels: int, width_mult: float, min_value: Optional[int] = None) -> int: + return _make_divisible(channels * width_mult, 8, min_value) + + @staticmethod + def adjust_depth(num_layers: int, depth_mult: float): + return int(math.ceil(num_layers * depth_mult)) + + +class MBConv(nn.Module): + def __init__(self, cnf: MBConvConfig, stochastic_depth_prob: float, norm_layer: Callable[..., nn.Module], + se_layer: Callable[..., nn.Module] = SqueezeExcitation) -> None: + super().__init__() + + if not (1 <= cnf.stride <= 2): + raise ValueError('illegal stride value') + + self.use_res_connect = cnf.stride == 1 and cnf.input_channels == cnf.out_channels + + layers: List[nn.Module] = [] + activation_layer = nn.SiLU + + # expand + expanded_channels = cnf.adjust_channels(cnf.input_channels, cnf.expand_ratio) + if expanded_channels != cnf.input_channels: + layers.append(ConvNormActivation(cnf.input_channels, expanded_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=activation_layer)) + + # depthwise + layers.append(ConvNormActivation(expanded_channels, expanded_channels, kernel_size=cnf.kernel, + stride=cnf.stride, groups=expanded_channels, + norm_layer=norm_layer, activation_layer=activation_layer)) + + # squeeze and excitation + squeeze_channels = max(1, cnf.input_channels // 4) + layers.append(se_layer(expanded_channels, squeeze_channels, activation=partial(nn.SiLU, inplace=True))) + + # project + layers.append(ConvNormActivation(expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, + activation_layer=None)) + + self.block = nn.Sequential(*layers) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + self.out_channels = cnf.out_channels + + def forward(self, input: Tensor) -> Tensor: + result = self.block(input) + if self.use_res_connect: + result = self.stochastic_depth(result) + result += input + return result + + +class EfficientNet(nn.Module): + def __init__( + self, + inverted_residual_setting: List[MBConvConfig], + dropout: float, + stochastic_depth_prob: float = 0.2, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any + ) -> None: + """ + EfficientNet main class + + Args: + inverted_residual_setting (List[MBConvConfig]): Network structure + dropout (float): The droupout probability + stochastic_depth_prob (float): The stochastic depth probability + num_classes (int): Number of classes + block (Optional[Callable[..., nn.Module]]): Module specifying inverted residual building block for mobilenet + norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use + """ + super().__init__() + + if not inverted_residual_setting: + raise ValueError("The inverted_residual_setting should not be empty") + elif not (isinstance(inverted_residual_setting, Sequence) and + all([isinstance(s, MBConvConfig) for s in inverted_residual_setting])): + raise TypeError("The inverted_residual_setting should be List[MBConvConfig]") + + if block is None: + block = MBConv + + if norm_layer is None: + norm_layer = nn.BatchNorm2d + + layers: List[nn.Module] = [] + + # building first layer + firstconv_output_channels = inverted_residual_setting[0].input_channels + layers.append(ConvNormActivation(3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer, + activation_layer=nn.SiLU)) + + # building inverted residual blocks + total_stage_blocks = sum([cnf.num_layers for cnf in inverted_residual_setting]) + stage_block_id = 0 + for cnf in inverted_residual_setting: + stage: List[nn.Module] = [] + for _ in range(cnf.num_layers): + # copy to avoid modifications. shallow copy is enough + block_cnf = copy.copy(cnf) + + # overwrite info if not the first conv in the stage + if stage: + block_cnf.input_channels = block_cnf.out_channels + block_cnf.stride = 1 + + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * float(stage_block_id) / total_stage_blocks + + stage.append(block(block_cnf, sd_prob, norm_layer)) + stage_block_id += 1 + + layers.append(nn.Sequential(*stage)) + + # building last several layers + lastconv_input_channels = inverted_residual_setting[-1].out_channels + lastconv_output_channels = 4 * lastconv_input_channels + layers.append(ConvNormActivation(lastconv_input_channels, lastconv_output_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=nn.SiLU)) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Sequential( + nn.Dropout(p=dropout, inplace=True), + # nn.Linear(lastconv_output_channels, num_classes), + ) + self.feature_dim = lastconv_output_channels + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + init_range = 1.0 / math.sqrt(m.out_features) + nn.init.uniform_(m.weight, -init_range, init_range) + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.classifier(x) + + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _efficientnet_conf(width_mult: float, depth_mult: float, **kwargs: Any) -> List[MBConvConfig]: + bneck_conf = partial(MBConvConfig, width_mult=width_mult, depth_mult=depth_mult) + inverted_residual_setting = [ + bneck_conf(1, 3, 1, 32, 16, 1), + bneck_conf(6, 3, 2, 16, 24, 2), + bneck_conf(6, 5, 2, 24, 40, 2), + bneck_conf(6, 3, 2, 40, 80, 3), + bneck_conf(6, 5, 1, 80, 112, 3), + bneck_conf(6, 5, 2, 112, 192, 4), + bneck_conf(6, 3, 1, 192, 320, 1), + ] + return inverted_residual_setting + + +def _efficientnet_model( + arch: str, + inverted_residual_setting: List[MBConvConfig], + dropout: float, + pretrained: bool, + progress: bool, + **kwargs: Any +) -> EfficientNet: + model = EfficientNet(inverted_residual_setting, dropout, **kwargs) + if pretrained: + if model_urls.get(arch, None) is None: + raise ValueError("No checkpoint is available for model type {}".format(arch)) + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def efficientnet_b0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B0 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.0, **kwargs) + return _efficientnet_model("efficientnet_b0", inverted_residual_setting, 0.2, pretrained, progress, **kwargs) + + +def efficientnet_b1(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B1 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.1, **kwargs) + return _efficientnet_model("efficientnet_b1", inverted_residual_setting, 0.2, pretrained, progress, **kwargs) + + +def efficientnet_b2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B2 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.1, depth_mult=1.2, **kwargs) + return _efficientnet_model("efficientnet_b2", inverted_residual_setting, 0.3, pretrained, progress, **kwargs) + + +def efficientnet_b3(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B3 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.2, depth_mult=1.4, **kwargs) + return _efficientnet_model("efficientnet_b3", inverted_residual_setting, 0.3, pretrained, progress, **kwargs) + + +def efficientnet_b4(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B4 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.4, depth_mult=1.8, **kwargs) + return _efficientnet_model("efficientnet_b4", inverted_residual_setting, 0.4, pretrained, progress, **kwargs) + + +def efficientnet_b5(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B5 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.6, depth_mult=2.2, **kwargs) + return _efficientnet_model("efficientnet_b5", inverted_residual_setting, 0.4, pretrained, progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), **kwargs) + + +def efficientnet_b6(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B6 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.8, depth_mult=2.6, **kwargs) + return _efficientnet_model("efficientnet_b6", inverted_residual_setting, 0.5, pretrained, progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), **kwargs) + + +def efficientnet_b7(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B7 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=2.0, depth_mult=3.1, **kwargs) + return _efficientnet_model("efficientnet_b7", inverted_residual_setting, 0.5, pretrained, progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), **kwargs) diff --git a/utils/defense_utils/dbd/model/network/mobilenet_dbd.py b/utils/defense_utils/dbd/model/network/mobilenet_dbd.py new file mode 100755 index 0000000..e29302c --- /dev/null +++ b/utils/defense_utils/dbd/model/network/mobilenet_dbd.py @@ -0,0 +1,273 @@ +import warnings +import torch + +from functools import partial +from torch import nn, Tensor +from typing import Any, Callable, List, Optional, Sequence + +from torchvision._internally_replaced_utils import load_state_dict_from_url +from torchvision.ops.misc import ConvNormActivation, SqueezeExcitation as SElayer +from torchvision.models._utils import _make_divisible + + +__all__ = ["MobileNetV3", "mobilenet_v3_large", "mobilenet_v3_small"] + + +model_urls = { + "mobilenet_v3_large": "https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth", + "mobilenet_v3_small": "https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth", +} + + +class SqueezeExcitation(SElayer): + """DEPRECATED + """ + def __init__(self, input_channels: int, squeeze_factor: int = 4): + squeeze_channels = _make_divisible(input_channels // squeeze_factor, 8) + super().__init__(input_channels, squeeze_channels, scale_activation=nn.Hardsigmoid) + self.relu = self.activation + delattr(self, 'activation') + warnings.warn( + "This SqueezeExcitation class is deprecated and will be removed in future versions. " + "Use torchvision.ops.misc.SqueezeExcitation instead.", FutureWarning) + + +class InvertedResidualConfig: + # Stores information listed at Tables 1 and 2 of the MobileNetV3 paper + def __init__(self, input_channels: int, kernel: int, expanded_channels: int, out_channels: int, use_se: bool, + activation: str, stride: int, dilation: int, width_mult: float): + self.input_channels = self.adjust_channels(input_channels, width_mult) + self.kernel = kernel + self.expanded_channels = self.adjust_channels(expanded_channels, width_mult) + self.out_channels = self.adjust_channels(out_channels, width_mult) + self.use_se = use_se + self.use_hs = activation == "HS" + self.stride = stride + self.dilation = dilation + + @staticmethod + def adjust_channels(channels: int, width_mult: float): + return _make_divisible(channels * width_mult, 8) + + +class InvertedResidual(nn.Module): + # Implemented as described at section 5 of MobileNetV3 paper + def __init__(self, cnf: InvertedResidualConfig, norm_layer: Callable[..., nn.Module], + se_layer: Callable[..., nn.Module] = partial(SElayer, scale_activation=nn.Hardsigmoid)): + super().__init__() + if not (1 <= cnf.stride <= 2): + raise ValueError('illegal stride value') + + self.use_res_connect = cnf.stride == 1 and cnf.input_channels == cnf.out_channels + + layers: List[nn.Module] = [] + activation_layer = nn.Hardswish if cnf.use_hs else nn.ReLU + + # expand + if cnf.expanded_channels != cnf.input_channels: + layers.append(ConvNormActivation(cnf.input_channels, cnf.expanded_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=activation_layer)) + + # depthwise + stride = 1 if cnf.dilation > 1 else cnf.stride + layers.append(ConvNormActivation(cnf.expanded_channels, cnf.expanded_channels, kernel_size=cnf.kernel, + stride=stride, dilation=cnf.dilation, groups=cnf.expanded_channels, + norm_layer=norm_layer, activation_layer=activation_layer)) + if cnf.use_se: + squeeze_channels = _make_divisible(cnf.expanded_channels // 4, 8) + layers.append(se_layer(cnf.expanded_channels, squeeze_channels)) + + # project + layers.append(ConvNormActivation(cnf.expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, + activation_layer=None)) + + self.block = nn.Sequential(*layers) + self.out_channels = cnf.out_channels + self._is_cn = cnf.stride > 1 + + def forward(self, input: Tensor) -> Tensor: + result = self.block(input) + if self.use_res_connect: + result += input + return result + + +class MobileNetV3(nn.Module): + + def __init__( + self, + inverted_residual_setting: List[InvertedResidualConfig], + last_channel: int, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any + ) -> None: + """ + MobileNet V3 main class + + Args: + inverted_residual_setting (List[InvertedResidualConfig]): Network structure + last_channel (int): The number of channels on the penultimate layer + num_classes (int): Number of classes + block (Optional[Callable[..., nn.Module]]): Module specifying inverted residual building block for mobilenet + norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use + """ + super().__init__() + + if not inverted_residual_setting: + raise ValueError("The inverted_residual_setting should not be empty") + elif not (isinstance(inverted_residual_setting, Sequence) and + all([isinstance(s, InvertedResidualConfig) for s in inverted_residual_setting])): + raise TypeError("The inverted_residual_setting should be List[InvertedResidualConfig]") + + if block is None: + block = InvertedResidual + + if norm_layer is None: + norm_layer = partial(nn.BatchNorm2d, eps=0.001, momentum=0.01) + + layers: List[nn.Module] = [] + + # building first layer + firstconv_output_channels = inverted_residual_setting[0].input_channels + layers.append(ConvNormActivation(3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer, + activation_layer=nn.Hardswish)) + + # building inverted residual blocks + for cnf in inverted_residual_setting: + layers.append(block(cnf, norm_layer)) + + # building last several layers + lastconv_input_channels = inverted_residual_setting[-1].out_channels + lastconv_output_channels = 6 * lastconv_input_channels + layers.append(ConvNormActivation(lastconv_input_channels, lastconv_output_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=nn.Hardswish)) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Sequential( + nn.Linear(lastconv_output_channels, last_channel), + nn.Hardswish(inplace=True), + nn.Dropout(p=0.2, inplace=True), + # nn.Linear(last_channel, num_classes), + ) + self.feature_dim = last_channel + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.classifier(x) + + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _mobilenet_v3_conf(arch: str, width_mult: float = 1.0, reduced_tail: bool = False, dilated: bool = False, + **kwargs: Any): + reduce_divider = 2 if reduced_tail else 1 + dilation = 2 if dilated else 1 + + bneck_conf = partial(InvertedResidualConfig, width_mult=width_mult) + adjust_channels = partial(InvertedResidualConfig.adjust_channels, width_mult=width_mult) + + if arch == "mobilenet_v3_large": + inverted_residual_setting = [ + bneck_conf(16, 3, 16, 16, False, "RE", 1, 1), + bneck_conf(16, 3, 64, 24, False, "RE", 2, 1), # C1 + bneck_conf(24, 3, 72, 24, False, "RE", 1, 1), + bneck_conf(24, 5, 72, 40, True, "RE", 2, 1), # C2 + bneck_conf(40, 5, 120, 40, True, "RE", 1, 1), + bneck_conf(40, 5, 120, 40, True, "RE", 1, 1), + bneck_conf(40, 3, 240, 80, False, "HS", 2, 1), # C3 + bneck_conf(80, 3, 200, 80, False, "HS", 1, 1), + bneck_conf(80, 3, 184, 80, False, "HS", 1, 1), + bneck_conf(80, 3, 184, 80, False, "HS", 1, 1), + bneck_conf(80, 3, 480, 112, True, "HS", 1, 1), + bneck_conf(112, 3, 672, 112, True, "HS", 1, 1), + bneck_conf(112, 5, 672, 160 // reduce_divider, True, "HS", 2, dilation), # C4 + bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1, dilation), + bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1, dilation), + ] + last_channel = adjust_channels(1280 // reduce_divider) # C5 + elif arch == "mobilenet_v3_small": + inverted_residual_setting = [ + bneck_conf(16, 3, 16, 16, True, "RE", 2, 1), # C1 + bneck_conf(16, 3, 72, 24, False, "RE", 2, 1), # C2 + bneck_conf(24, 3, 88, 24, False, "RE", 1, 1), + bneck_conf(24, 5, 96, 40, True, "HS", 2, 1), # C3 + bneck_conf(40, 5, 240, 40, True, "HS", 1, 1), + bneck_conf(40, 5, 240, 40, True, "HS", 1, 1), + bneck_conf(40, 5, 120, 48, True, "HS", 1, 1), + bneck_conf(48, 5, 144, 48, True, "HS", 1, 1), + bneck_conf(48, 5, 288, 96 // reduce_divider, True, "HS", 2, dilation), # C4 + bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1, dilation), + bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1, dilation), + ] + last_channel = adjust_channels(1024 // reduce_divider) # C5 + else: + raise ValueError("Unsupported model type {}".format(arch)) + + return inverted_residual_setting, last_channel + + +def _mobilenet_v3_model( + arch: str, + inverted_residual_setting: List[InvertedResidualConfig], + last_channel: int, + pretrained: bool, + progress: bool, + **kwargs: Any +): + model = MobileNetV3(inverted_residual_setting, last_channel, **kwargs) + if pretrained: + if model_urls.get(arch, None) is None: + raise ValueError("No checkpoint is available for model type {}".format(arch)) + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def mobilenet_v3_large(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MobileNetV3: + """ + Constructs a large MobileNetV3 architecture from + `"Searching for MobileNetV3" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + arch = "mobilenet_v3_large" + inverted_residual_setting, last_channel = _mobilenet_v3_conf(arch, **kwargs) + return _mobilenet_v3_model(arch, inverted_residual_setting, last_channel, pretrained, progress, **kwargs) + + +def mobilenet_v3_small(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MobileNetV3: + """ + Constructs a small MobileNetV3 architecture from + `"Searching for MobileNetV3" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + arch = "mobilenet_v3_small" + inverted_residual_setting, last_channel = _mobilenet_v3_conf(arch, **kwargs) + return _mobilenet_v3_model(arch, inverted_residual_setting, last_channel, pretrained, progress, **kwargs) diff --git a/utils/defense_utils/dbd/model/network/preact_dbd.py b/utils/defense_utils/dbd/model/network/preact_dbd.py new file mode 100755 index 0000000..233c85e --- /dev/null +++ b/utils/defense_utils/dbd/model/network/preact_dbd.py @@ -0,0 +1,131 @@ +"""Pre-activation ResNet in PyTorch. + +Reference: +[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Identity Mappings in Deep Residual Networks. arXiv:1603.05027 +""" +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class PreActBlock(nn.Module): + """Pre-activation version of the BasicBlock.""" + + expansion = 1 + + def __init__(self, in_planes, planes, stride=1): + super(PreActBlock, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.ind = None + + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False) + ) + + def forward(self, x): + out = F.relu(self.bn1(x)) + shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x + out = self.conv1(out) + out = self.conv2(F.relu(self.bn2(out))) + if self.ind is not None: + out += shortcut[:, self.ind, :, :] + else: + out += shortcut + return out + + +class PreActBottleneck(nn.Module): + """Pre-activation version of the original Bottleneck module.""" + + expansion = 4 + + def __init__(self, in_planes, planes, stride=1): + super(PreActBottleneck, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) + + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False) + ) + + def forward(self, x): + out = F.relu(self.bn1(x)) + shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x + out = self.conv1(out) + out = self.conv2(F.relu(self.bn2(out))) + out = self.conv3(F.relu(self.bn3(out))) + out += shortcut + return out + + +class PreActResNet(nn.Module): + def __init__(self, block, num_blocks, num_classes=10): + super(PreActResNet, self).__init__() + self.in_planes = 64 + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + self.avgpool = nn.AdaptiveAvgPool2d((1,1)) + self.feature_dim = 512 + # self.linear = nn.Linear(512 * block.expansion, num_classes) + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + out = self.conv1(x) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = self.avgpool(out) + out = out.view(out.size(0), -1) + # out = self.linear(out) + return out + + +def PreActResNet18(num_classes=10): + return PreActResNet(PreActBlock, [2, 2, 2, 2], num_classes=num_classes) + + +def PreActResNet34(): + return PreActResNet(PreActBlock, [3, 4, 6, 3]) + + +def PreActResNet50(): + return PreActResNet(PreActBottleneck, [3, 4, 6, 3]) + + +def PreActResNet101(): + return PreActResNet(PreActBottleneck, [3, 4, 23, 3]) + + +def PreActResNet152(): + return PreActResNet(PreActBottleneck, [3, 8, 36, 3]) + + +def test(): + net = PreActResNet18() + y = net((torch.randn(1, 3, 32, 32))) + print(y.size()) + + +# test() diff --git a/utils/defense_utils/dbd/model/network/resnet_cifar.py b/utils/defense_utils/dbd/model/network/resnet_cifar.py new file mode 100755 index 0000000..c9acfe6 --- /dev/null +++ b/utils/defense_utils/dbd/model/network/resnet_cifar.py @@ -0,0 +1,140 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d( + in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False + ) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d( + planes, planes, kernel_size=3, stride=1, padding=1, bias=False + ) + self.bn2 = nn.BatchNorm2d(planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d( + in_planes, + self.expansion * planes, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(self.expansion * planes), + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, in_planes, planes, stride=1): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d( + planes, planes, kernel_size=3, stride=stride, padding=1, bias=False + ) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d( + planes, self.expansion * planes, kernel_size=1, bias=False + ) + self.bn3 = nn.BatchNorm2d(self.expansion * planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d( + in_planes, + self.expansion * planes, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(self.expansion * planes), + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + out += self.shortcut(x) + out = F.relu(out) + + return out + + +class ResNet(nn.Module): + def __init__( + self, block, num_blocks, num_classes=10, in_channel=3, zero_init_residual=False + ): + super(ResNet, self).__init__() + self.in_planes = 64 + + self.conv1 = nn.Conv2d( + in_channel, 64, kernel_size=3, stride=1, padding=1, bias=False + ) + self.bn1 = nn.BatchNorm2d(64) + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves + # like an identity. This improves the model by 0.2~0.3% according to: + # https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for i in range(num_blocks): + stride = strides[i] + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = self.avgpool(out) + out = torch.flatten(out, 1) + return out + + +def resnet18(**kwargs): + backbone = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + backbone.feature_dim = 512 + + return backbone diff --git a/utils/defense_utils/dbd/model/network/resnet_imagenet.py b/utils/defense_utils/dbd/model/network/resnet_imagenet.py new file mode 100755 index 0000000..2d24257 --- /dev/null +++ b/utils/defense_utils/dbd/model/network/resnet_imagenet.py @@ -0,0 +1,417 @@ +"""Borrowed from https://github.com/pytorch/vision. +""" +import torch +import torch.nn as nn +from torch.utils.model_zoo import load_url as load_state_dict_from_url + + +__all__ = [ + "ResNet", + "resnet18", + "resnet34", + "resnet50", + "resnet101", + "resnet152", + "resnext50_32x4d", + "resnext101_32x8d", + "wide_resnet50_2", + "wide_resnet101_2", +] + + +model_urls = { + "resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth", + "resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth", + "resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth", + "resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth", + "resnet152": "https://download.pytorch.org/models/resnet152-b121ed2d.pth", + "resnext50_32x4d": "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth", + "resnext101_32x8d": "https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth", + "wide_resnet50_2": "https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth", + "wide_resnet101_2": "https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth", +} + + +def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): + """3x3 convolution with padding""" + return nn.Conv2d( + in_planes, + out_planes, + kernel_size=3, + stride=stride, + padding=dilation, + groups=groups, + bias=False, + dilation=dilation, + ) + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__( + self, + inplanes, + planes, + stride=1, + downsample=None, + groups=1, + base_width=64, + dilation=1, + norm_layer=None, + ): + super(BasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError("BasicBlock only supports groups=1 and base_width=64") + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) + # while original implementation places the stride at the first 1x1 convolution(self.conv1) + # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. + # This variant is also known as ResNet V1.5 and improves accuracy according to + # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. + + expansion = 4 + + def __init__( + self, + inplanes, + planes, + stride=1, + downsample=None, + groups=1, + base_width=64, + dilation=1, + norm_layer=None, + ): + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = int(planes * (base_width / 64.0)) * groups + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, stride, groups, dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + def __init__( + self, + block, + layers, + num_classes=1000, + zero_init_residual=False, + groups=1, + width_per_group=64, + replace_stride_with_dilation=None, + norm_layer=None, + ): + super(ResNet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError( + "replace_stride_with_dilation should be None " + "or a 3-element tuple, got {}".format(replace_stride_with_dilation) + ) + self.groups = groups + self.base_width = width_per_group + self.conv1 = nn.Conv2d( + 3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False + ) + self.bn1 = norm_layer(self.inplanes) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer( + block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0] + ) + self.layer3 = self._make_layer( + block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1] + ) + self.layer4 = self._make_layer( + block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2] + ) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + # self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False): + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append( + block( + self.inplanes, + planes, + stride, + downsample, + self.groups, + self.base_width, + previous_dilation, + norm_layer, + ) + ) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append( + block( + self.inplanes, + planes, + groups=self.groups, + base_width=self.base_width, + dilation=self.dilation, + norm_layer=norm_layer, + ) + ) + + return nn.Sequential(*layers) + + def _forward_impl(self, x): + # See note [TorchScript super()] + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + # x = self.fc(x) + + return x + + def forward(self, x): + return self._forward_impl(x) + + +def _resnet(arch, block, layers, pretrained, progress, **kwargs): + model = ResNet(block, layers, **kwargs) + if pretrained: + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def resnet18(pretrained=False, progress=True, **kwargs): + r"""ResNet-18 model from + `"Deep Residual Learning for Image Recognition" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + backbone = _resnet( + "resnet18", BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs + ) + backbone.feature_dim = 512 + + return backbone + # return _resnet("resnet18", BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs) + + +def resnet34(pretrained=False, progress=True, **kwargs): + r"""ResNet-34 model from + `"Deep Residual Learning for Image Recognition" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet("resnet34", BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs) + + +def resnet50(pretrained=False, progress=True, **kwargs): + r"""ResNet-50 model from + `"Deep Residual Learning for Image Recognition" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet("resnet50", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) + + +def resnet101(pretrained=False, progress=True, **kwargs): + r"""ResNet-101 model from + `"Deep Residual Learning for Image Recognition" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet( + "resnet101", Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs + ) + + +def resnet152(pretrained=False, progress=True, **kwargs): + r"""ResNet-152 model from + `"Deep Residual Learning for Image Recognition" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet( + "resnet152", Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs + ) + + +def resnext50_32x4d(pretrained=False, progress=True, **kwargs): + r"""ResNeXt-50 32x4d model from + `"Aggregated Residual Transformation for Deep Neural Networks" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs["groups"] = 32 + kwargs["width_per_group"] = 4 + return _resnet( + "resnext50_32x4d", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs + ) + + +def resnext101_32x8d(pretrained=False, progress=True, **kwargs): + r"""ResNeXt-101 32x8d model from + `"Aggregated Residual Transformation for Deep Neural Networks" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs["groups"] = 32 + kwargs["width_per_group"] = 8 + return _resnet( + "resnext101_32x8d", Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs + ) + + +def wide_resnet50_2(pretrained=False, progress=True, **kwargs): + r"""Wide ResNet-50-2 model from + `"Wide Residual Networks" `_ + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs["width_per_group"] = 64 * 2 + return _resnet( + "wide_resnet50_2", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs + ) + + +def wide_resnet101_2(pretrained=False, progress=True, **kwargs): + r"""Wide ResNet-101-2 model from + `"Wide Residual Networks" `_ + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs["width_per_group"] = 64 * 2 + return _resnet( + "wide_resnet101_2", Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs + ) diff --git a/utils/defense_utils/dbd/model/network/vgg_dbd.py b/utils/defense_utils/dbd/model/network/vgg_dbd.py new file mode 100755 index 0000000..93bca73 --- /dev/null +++ b/utils/defense_utils/dbd/model/network/vgg_dbd.py @@ -0,0 +1,199 @@ +import torch +import torch.nn as nn +from torch.utils.model_zoo import load_url as load_state_dict_from_url +from typing import Union, List, Dict, Any, cast + + +__all__ = [ + 'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', + 'vgg19_bn', 'vgg19', +] + + +model_urls = { + 'vgg11': 'https://download.pytorch.org/models/vgg11-8a719046.pth', + 'vgg13': 'https://download.pytorch.org/models/vgg13-19584684.pth', + 'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth', + 'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth', + 'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth', + 'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth', + 'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth', + 'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth', +} + + +class VGG(nn.Module): + + def __init__( + self, + features: nn.Module, + num_classes: int = 1000, + init_weights: bool = True + ) -> None: + super(VGG, self).__init__() + self.features = features + self.avgpool = nn.AdaptiveAvgPool2d((7, 7)) + self.feature_dim = 512 * 7 * 7 + # self.classifier = nn.Sequential( + # nn.Linear(512 * 7 * 7, 4096), + # nn.ReLU(True), + # nn.Dropout(), + # nn.Linear(4096, 4096), + # nn.ReLU(True), + # nn.Dropout(), + # nn.Linear(4096, num_classes), + # ) + if init_weights: + self._initialize_weights() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.features(x) + x = self.avgpool(x) + x = torch.flatten(x, 1) + # x = self.classifier(x) + return x + + def _initialize_weights(self) -> None: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.constant_(m.bias, 0) + + +def make_layers(cfg: List[Union[str, int]], batch_norm: bool = False) -> nn.Sequential: + layers: List[nn.Module] = [] + in_channels = 3 + for v in cfg: + if v == 'M': + layers += [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + v = cast(int, v) + conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) + if batch_norm: + layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = v + return nn.Sequential(*layers) + + +cfgs: Dict[str, List[Union[str, int]]] = { + 'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], + 'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], + 'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], + 'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'], +} + + +def _vgg(arch: str, cfg: str, batch_norm: bool, pretrained: bool, progress: bool, **kwargs: Any) -> VGG: + if pretrained: + kwargs['init_weights'] = False + model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs) + if pretrained: + state_dict = load_state_dict_from_url(model_urls[arch], + progress=progress) + model.load_state_dict(state_dict) + return model + + +def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 11-layer model (configuration "A") from + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg11', 'A', False, pretrained, progress, **kwargs) + + +def vgg11_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 11-layer model (configuration "A") with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg11_bn', 'A', True, pretrained, progress, **kwargs) + + +def vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 13-layer model (configuration "B") + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg13', 'B', False, pretrained, progress, **kwargs) + + +def vgg13_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 13-layer model (configuration "B") with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg13_bn', 'B', True, pretrained, progress, **kwargs) + + +def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 16-layer model (configuration "D") + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg16', 'D', False, pretrained, progress, **kwargs) + + +def vgg16_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 16-layer model (configuration "D") with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg16_bn', 'D', True, pretrained, progress, **kwargs) + + +def vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 19-layer model (configuration "E") + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg19', 'E', False, pretrained, progress, **kwargs) + + +def vgg19_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 19-layer model (configuration 'E') with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg19_bn', 'E', True, pretrained, progress, **kwargs) diff --git a/utils/defense_utils/dbd/model/utils.py b/utils/defense_utils/dbd/model/utils.py new file mode 100755 index 0000000..71fb4cb --- /dev/null +++ b/utils/defense_utils/dbd/model/utils.py @@ -0,0 +1,185 @@ +import os +from collections import OrderedDict + +import torch +import torch.nn as nn +from torch.optim import lr_scheduler +import logging + +from .loss import SimCLRLoss, SCELoss, MixMatchLoss +from .network import densenet_face, resnet_cifar, resnet_imagenet, preact_dbd, vgg_dbd, densenet_dbd, mobilenet_dbd, efficientnet_dbd + + +def get_network(network_config): + if "resnet18_cifar" in network_config: + model = resnet_cifar.resnet18(**network_config["resnet18_cifar"]) + elif "resnet18_imagenet" in network_config: + model = resnet_imagenet.resnet18(**network_config["resnet18_imagenet"]) + elif "densenet121_face" in network_config: + model = densenet_face.densenet121(**network_config["densenet121_face"]) + else: + raise NotImplementedError("Network {} is not supported.".format(network_config)) + + return model + +def get_network_dbd(args): + model = args.model + if model == "preactresnet18": + model = preact_dbd.PreActResNet18(args.num_classes) + elif model == "vgg19": + model = vgg_dbd.vgg19(num_classes = args.num_classes) + # elif "densenet121_face" in network_config: + # model = densenet_face.densenet121(**network_config["densenet121_face"]) + elif model == 'densenet161': + model = densenet_dbd.densenet161(num_classes= args.num_classes) + elif model == 'mobilenet_v3_large': + model = mobilenet_dbd.mobilenet_v3_large(num_classes= args.num_classes) + elif model == 'efficientnet_b3': + model = efficientnet_dbd.efficientnet_b3(num_classes= args.num_classes) + else: + raise NotImplementedError("Network {} is not supported.".format(model)) + + return model + + + +def get_criterion(criterion_config): + if "cross_entropy" in criterion_config: + criterion = nn.CrossEntropyLoss(**criterion_config["cross_entropy"]) + elif "simclr" in criterion_config: + criterion = SimCLRLoss(**criterion_config["simclr"]) + elif "sce" in criterion_config: + criterion = SCELoss(**criterion_config["sce"]) + elif "mixmatch" in criterion_config: + criterion = MixMatchLoss(**criterion_config["mixmatch"]) + else: + raise NotImplementedError( + "Criterion {} is not supported.".format(criterion_config) + ) + + return criterion + + +def get_optimizer(model, optimizer_config): + if "Adam" in optimizer_config: + optimizer = torch.optim.Adam(model.parameters(), **optimizer_config["Adam"]) + elif "SGD" in optimizer_config: + optimizer = torch.optim.SGD(model.parameters(), **optimizer_config["SGD"]) + else: + raise NotImplementedError( + "Optimizer {} is not supported.".format(optimizer_config) + ) + + return optimizer + + +def get_scheduler(optimizer, lr_scheduler_config): + if lr_scheduler_config is None: + scheduler = None + elif "multi_step" in lr_scheduler_config: + scheduler = lr_scheduler.MultiStepLR( + optimizer, **lr_scheduler_config["multi_step"] + ) + elif "cosine_annealing" in lr_scheduler_config: + scheduler = lr_scheduler.CosineAnnealingLR( + optimizer, **lr_scheduler_config["cosine_annealing"] + ) + else: + raise NotImplementedError( + "Learning rate scheduler {} is not supported.".format(lr_scheduler_config) + ) + + return scheduler + + +def load_state( + model, resume, ckpt_dir, device,logger, optimizer=None, scheduler=None, is_best=False +): + """Load training state from checkpoint. + + Args: + model (torch.nn.Module): Model to resume. + resume (string): Checkpoint name (empty string means the latest checkpoint) + or False (means training from scratch). + ckpt_dir (string): Checkpoint directory. + device : GPU or CPU. + ###logger (logging.logger): The logger. + optimizer (torch.optim.Optimizer): Optimizer to resume (default: None). + scheduler (torch.optim._LRScheduler): Learning rate scheduler to + resume (default: None). + is_best (boolean, optional): Set True to load checkpoint + with `best_acc` (default: False). + + Returns: + resumed_epoch: The epoch to resume (0 means training from scratch.) + best_acc: The best test accuracy in the training. + best_epoch: The epoch getting the `best_acc`. + """ + if resume == "False": + logging.warning("Training from scratch.") + resumed_epoch = 0 + if is_best: + best_acc = 0 + best_epoch = 0 + return resumed_epoch, best_acc, best_epoch + else: + return resumed_epoch + else: + # Load checkpoint. + # if resume == "": + # ckpt_path = os.path.join(ckpt_dir, "latest_model.pt") + # else: + # ckpt_path = os.path.join(ckpt_dir, resume) + ckpt_path = ckpt_dir + ckpt = torch.load(ckpt_path, map_location=device) + # logger.info("Load training state from the checkpoint {}:".format(ckpt_path)) + # logger.info("Epoch: {}, result: {}".format(ckpt["epoch"], ckpt["result"])) + if "parallel" in str(type(model)): + # DataParallel or DistributedParallel. + model.load_state_dict(ckpt["model_state_dict"]) + else: + # Remove "module." in `model_state_dict` if saved + # from DDP wrapped model in the single GPU training. + model_state_dict = OrderedDict() + for k, v in ckpt["model_state_dict"].items(): + if k.startswith("module."): + k = k.replace("module.", "") + model_state_dict[k] = v + else: + model_state_dict[k] = v + model.load_state_dict(model_state_dict) + resumed_epoch = ckpt["epoch"] + if optimizer is not None: + optimizer.load_state_dict(ckpt["optimizer_state_dict"]) + if scheduler is not None: + scheduler.load_state_dict(ckpt["scheduler_state_dict"]) + if is_best: + best_acc = ckpt["best_acc"] + best_epoch = ckpt["best_epoch"] + return resumed_epoch, best_acc, best_epoch + else: + return resumed_epoch + + +def get_saved_epoch( + num_epochs, num_stage_epochs=100, min_interval=20, max_interval=100 +): + if num_epochs >= num_stage_epochs: + early = set(range(min_interval, num_stage_epochs, min_interval)) + mid = set(range(num_stage_epochs, num_epochs - num_stage_epochs, max_interval)) + later = set( + range( + num_epochs - num_stage_epochs, num_epochs + min_interval, min_interval + ) + ) + if num_epochs == num_stage_epochs: + later.remove(0) + saved_epoch = early.union(mid).union(later) + else: + raise ValueError( + "The num_epochs: {} must be equal or greater than num_stage_epochs: {}".format( + num_epochs, num_stage_epochs + ) + ) + + return saved_epoch diff --git a/utils/defense_utils/dbd/utils_db/box.py b/utils/defense_utils/dbd/utils_db/box.py new file mode 100755 index 0000000..c040da8 --- /dev/null +++ b/utils/defense_utils/dbd/utils_db/box.py @@ -0,0 +1,129 @@ + +''' + +code: +''' +import os +import sys + + +sys.path.append('../') +sys.path.append(os.getcwd()) +import numpy as np +import torch +# from data.utils import ( +# get_transform, +# get_semi_idx, +# ) +from defense.dbd.data.prefetch import PrefetchLoader + +from utils.aggregate_block.dataset_and_transform_generate import get_transform_self + +from model.utils import ( + get_network_dbd, + load_state, + get_criterion, + get_network, + get_optimizer, + get_saved_epoch, + get_scheduler, +) + +from model.model import SelfModel, LinearModel +from data.dataset import PoisonLabelDataset, SelfPoisonDataset, MixMatchDataset +#from utils.bd_dataset import prepro_cls_DatasetBD +from utils.nCHW_nHWC import nCHW_to_nHWC + +def get_information(args,result,config_ori): + config = config_ori + # pre_transform = get_transform(config["transform"]["pre"]) + # # train_primary_transform = get_transform(config["transform"]["train"]["primary"]) + # # train_remaining_transform = get_transform(config["transform"]["train"]["remaining"]) + # # train_transform = { + # # "pre": pre_transform, + # # "primary": train_primary_transform, + # # "remaining": train_remaining_transform, + # # } + # # logger.info("Training transformations:\n {}".format(train_transform)) + # aug_primary_transform = get_transform(config["transform"]["aug"]["primary"]) + # aug_remaining_transform = get_transform(config["transform"]["aug"]["remaining"]) + # aug_transform = { + # "pre": pre_transform, + # "primary": aug_primary_transform, + # "remaining": aug_remaining_transform, + # } + aug_transform = get_transform_self(args.dataset, *([args.input_height,args.input_width]) , train = True, prefetch =args.prefetch) + # logger.info("Augmented transformations:\n {}".format(aug_transform)) + # logger.info("Load dataset from: {}".format(config["dataset_dir"])) + # clean_train_data = get_dataset(config["dataset_dir"], train_transform) + # poison_train_idx = gen_poison_idx(clean_train_data, target_label, poison_ratio) + # poison_idx_path = os.path.join(args.saved_dir, "poison_idx.npy") + # np.save(poison_idx_path, poison_train_idx) + # logger.info("Save poisoned index to {}".format(poison_idx_path)) + # poison_train_data = PoisonLabelDataset( + # clean_train_data, bd_transform, poison_train_idx, target_label + # ) + x = result['bd_train']['x'] + y = result['bd_train']['y'] + # data_set = torch.utils.data.TensorDataset(x,y) + # dataset = prepro_cls_DatasetBD( + # full_dataset_without_transform=data_set, + # poison_idx=np.zeros(len(data_set)), # one-hot to determine which image may take bd_transform + # bd_image_pre_transform=None, + # bd_label_pre_transform=None, + # ori_image_transform_in_loading=transform, + # ori_label_transform_in_loading=None, + # add_details_in_preprocess=False, + # ) + self_poison_train_data = SelfPoisonDataset(x,y, aug_transform,args) + # if args.distributed: + # self_poison_train_sampler = DistributedSampler(self_poison_train_data) + # batch_size = int(config["loader"]["batch_size"]) + # num_workers = config["loader"]["num_workers"] + # self_poison_train_loader = get_loader( + # self_poison_train_data, + # batch_size=batch_size, + # sampler=self_poison_train_sampler, + # num_workers=num_workers, + # ) + # else: + # self_poison_train_sampler = None + self_poison_train_loader_ori = torch.utils.data.DataLoader(self_poison_train_data, batch_size=args.batch_size_self, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + if args.prefetch: + self_poison_train_loader = PrefetchLoader(self_poison_train_loader_ori, self_poison_train_data.mean, self_poison_train_data.std) + else: + self_poison_train_loader = self_poison_train_loader_ori + # self_poison_train_loader = get_loader( + # self_poison_train_data, config["loader"], shuffle=True + # ) + + #logger.info("\n===Setup training===") + backbone = get_network_dbd(args) + #logger.info("Create network: {}".format(config["network"])) + self_model = SelfModel(backbone) + self_model = self_model.to(args.device) + # if args.distributed: + # # Convert BatchNorm*D layer to SyncBatchNorm before wrapping Network with DDP. + # if config["sync_bn"]: + # self_model = nn.SyncBatchNorm.convert_sync_batchnorm(self_model) + # logger.info("Turn on synchronized batch normalization in ddp.") + # self_model = nn.parallel.DistributedDataParallel(self_model, device_ids=[gpu]) + criterion = get_criterion(config["criterion"]) + criterion = criterion.to(args.device) + #logger.info("Create criterion: {}".format(criterion)) + optimizer = get_optimizer(self_model, config["optimizer"]) + #logger.info("Create optimizer: {}".format(optimizer)) + scheduler = get_scheduler(optimizer, config["lr_scheduler"]) + #logger.info("Create scheduler: {}".format(config["lr_scheduler"])) + resumed_epoch = load_state( + self_model, args.resume, args.checkpoint_load, 0, optimizer, scheduler, + ) + box = { + 'self_poison_train_loader': self_poison_train_loader, + 'self_model': self_model, + 'criterion': criterion, + 'optimizer': optimizer, + 'scheduler': scheduler, + 'resumed_epoch': resumed_epoch + } + return box \ No newline at end of file diff --git a/utils/defense_utils/dbd/utils_db/setup.py b/utils/defense_utils/dbd/utils_db/setup.py new file mode 100755 index 0000000..909296b --- /dev/null +++ b/utils/defense_utils/dbd/utils_db/setup.py @@ -0,0 +1,164 @@ +import logging +import random +import os +import platform +import shutil +import sys +import time + +import numpy as np +import torch +import yaml + + +def load_config(config_path): + """Load config file from `config_path`. + + Args: + config_path (str): Configuration file path, which must be in `config` dir, e.g., + `./config/inner_dir/example.yaml` and `config/inner_dir/example`. + + Returns: + config (dict): Configuration dict. + inner_dir (str): Directory between `config/` and configuration file. If `config_path` + doesn't contain `inner_dir`, return empty string. + config_name (str): Configuration filename. + """ + assert os.path.exists(config_path) + config_hierarchy = config_path.split("/") + if config_hierarchy[0] != ".": + if config_hierarchy[0] != "config": + raise RuntimeError( + "Configuration file {} must be in config dir".format(config_path) + ) + if len(config_hierarchy) > 2: + inner_dir = os.path.join(*config_hierarchy[1:-1]) + else: + inner_dir = "" + else: + # if config_hierarchy[1] != "config_z": + # raise RuntimeError( + # "Configuration file {} must be in config dir".format(config_path) + # ) + if len(config_hierarchy) > 3: + inner_dir = os.path.join(*config_hierarchy[2:-1]) + else: + inner_dir = "" + print("Load configuration file from {}:".format(config_path)) + with open(config_path, "r") as f: + config = yaml.safe_load(f) + config_name = config_hierarchy[-1].split(".yaml")[0] + + return config, inner_dir, config_name + + +def get_saved_dir(config, inner_dir, config_name, resume=""): + """Get the directory to save for corresponding `config`. + + .. note:: If `saved_dir` in config is already exists and resume is `False`, + it will remove `saved_dir`. + + Args: + config (dict): Configuration dict. + inner_dir (str): Directory between `config/` and configuration file. + config_name (str): Configuration filename. + resume (str): Path to checkpoint or False which means training from scratch (default: ""). + + Returns: + saved_dir (str): The directory to save. + log_dir (str): The directory to save logs. + """ + assert os.path.exists(config["saved_dir"]) + saved_dir = os.path.join(config["saved_dir"], inner_dir, config_name) + if os.path.exists(saved_dir) and resume == "False": + print("Delete existing {} for not resuming.".format(saved_dir)) + shutil.rmtree(saved_dir) + if not os.path.exists(saved_dir): + os.makedirs(saved_dir) + log_dir = os.path.join(saved_dir, "log") + if not os.path.exists(log_dir): + os.makedirs(log_dir) + + return saved_dir, log_dir + + +def get_storage_dir(config, inner_dir, config_name, resume=""): + """Get the storage and checkpoint directory for corresponding `config`. + + .. note:: If `storage_dir` in config is already exists and resume is `False`, + it will remove `storage_dir`. + + Args: + config (dict): Configuration dict. + inner_dir (str): Directory between `config/` and configuration file. + config_name (str): Configuration filename. + resume (str): Path to checkpoint or False which means training from scratch (default: ""). + + Returns: + storage_dir (str): Storage directory. + ckpt_dir (str): Checkpoint directory. + """ + assert os.path.exists(config["storage_dir"]) + storage_dir = os.path.join(config["storage_dir"], inner_dir, config_name) + if os.path.exists(storage_dir) and resume == "False": + print("Delete existing {} for not resuming.".format(storage_dir)) + shutil.rmtree(storage_dir) + if not os.path.exists(storage_dir): + os.makedirs(storage_dir) + ckpt_dir = os.path.join(storage_dir, "checkpoint") + if not os.path.exists(ckpt_dir): + os.mkdir(ckpt_dir) + record_dir = os.path.join(storage_dir, "record") + if not os.path.exists(record_dir): + os.mkdir(record_dir) + + return storage_dir, ckpt_dir, record_dir + + +class NoOp: + def __getattr__(self, *args): + def no_op(*args, **kwargs): + """Accept every signature by doing non-operation. + """ + pass + + return no_op + + +def get_logger(log_dir, log_name, resume, is_rank0=True): + # Only log rank 0 in ddp training. + if is_rank0: + logger = logging.getLogger(__name__) + logger.setLevel(level=logging.INFO) + + # StreamHandler + stream_handler = logging.StreamHandler(sys.stdout) + stream_handler.setLevel(level=logging.INFO) + logger.addHandler(stream_handler) + + # FileHandler + if resume == "False": + mode = "w+" + else: + mode = "a+" + file_handler = logging.FileHandler(os.path.join(log_dir, log_name), mode=mode) + file_handler.setLevel(level=logging.INFO) + logger.addHandler(file_handler) + start_time = time.asctime(time.localtime(time.time())) + logger.info("Start at: {} at: {}".format(start_time, platform.node())) + else: + logger = NoOp() + + return logger + + +def set_seed(seed=None, deterministic=True, benchmark=False): + """See https://pytorch.org/docs/stable/notes/randomness.html + for detailed informations. + """ + if seed is not None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.backends.cudnn.deterministic = deterministic + torch.backends.cudnn.benchmark = benchmark diff --git a/utils/defense_utils/dbd/utils_db/trainer/log.py b/utils/defense_utils/dbd/utils_db/trainer/log.py new file mode 100755 index 0000000..98db9e3 --- /dev/null +++ b/utils/defense_utils/dbd/utils_db/trainer/log.py @@ -0,0 +1,96 @@ +import os + +import pandas as pd +import torch +from tabulate import tabulate + + +def tabulate_step_meter(batch_idx, num_batches, num_intervals, meter_list, logger): + """Tabulate current average value of meters every `step_interval`. + + Args: + batch_idx (int): The batch index in an epoch. + num_batches (int): The number of batch in an epoch. + num_intervals (int): The number of interval to tabulate. + meter_list (list or tuple of AverageMeter): A list of meters. + logger (logging.logger): The logger. + """ + step_interval = int(num_batches / num_intervals) + if batch_idx % step_interval == 0: + step_meter = {"Iteration": ["{}/{}".format(batch_idx, num_batches)]} + for m in meter_list: + step_meter[m.name] = [m.batch_avg] + table = tabulate(step_meter, headers="keys", tablefmt="github", floatfmt=".5f") + if batch_idx == 0: + table = table.split("\n") + table = "\n".join([table[1]] + table) + else: + table = table.split("\n")[2] + logger.info(table) + + +def tabulate_epoch_meter(elapsed_time, meter_list, logger): + """Tabulate total average value of meters every epoch. + + Args: + eplased_time (float): The elapsed time of a epoch. + meter_list (list or tuple of AverageMeter): A list of meters. + logger (logging.logger): The logger. + """ + epoch_meter = {m.name: [m.total_avg] for m in meter_list} + epoch_meter["time"] = [elapsed_time] + table = tabulate(epoch_meter, headers="keys", tablefmt="github", floatfmt=".5f") + table = table.split("\n") + table = "\n".join([table[1]] + table) + logger.info(table) + + +def result2csv(result, log_dir): + for k in result.keys(): + file_path = os.path.join(log_dir, k + ".csv") + if not os.path.exists(file_path): + df = pd.DataFrame.from_records([result[k]]) + df.to_csv(file_path, index=False) + else: + with open(file_path) as f: + df = pd.read_csv(f) + df = df.append(result[k], ignore_index=True) + df.to_csv(file_path, index=False) + + +class AverageMeter(object): + """Computes and stores the average and current value. + + Modified from https://github.com/pytorch/examples/blob/master/imagenet/main.py + """ + + def __init__(self, name, fmt=None): + self.name = name + self.reset() + + def reset(self): + self.batch_avg = 0 + self.total_avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, avg, n=1): + self.batch_avg = avg + self.sum += avg * n + self.count += n + self.total_avg = self.sum / self.count + + +class Record(object): + def __init__(self, name, size): + self.name = name + self.size = size + self.reset() + + def reset(self): + self.ptr = 0 + self.data = torch.zeros(self.size) + + def update(self, batch_data): + self.data[self.ptr : self.ptr + len(batch_data)] = batch_data + self.ptr += len(batch_data) diff --git a/utils/defense_utils/dbd/utils_db/trainer/semi.py b/utils/defense_utils/dbd/utils_db/trainer/semi.py new file mode 100755 index 0000000..a8c7ba3 --- /dev/null +++ b/utils/defense_utils/dbd/utils_db/trainer/semi.py @@ -0,0 +1,161 @@ +"""Modified from https://github.com/YU1ut/MixMatch-pytorch. +""" + +import time + +import numpy as np +import torch + +from .log import AverageMeter, tabulate_step_meter, tabulate_epoch_meter + + +def linear_rampup(current, rampup_length): + if rampup_length == 0: + return 1.0 + else: + current = np.clip(current / rampup_length, 0.0, 1.0) + return float(current) + + +class WeightEMA(object): + def __init__(self, model, ema_model, lr, alpha=0.999): + self.model = model + self.ema_model = ema_model + self.alpha = alpha + self.params = list(model.state_dict().values()) + self.ema_params = list(ema_model.state_dict().values()) + self.wd = 0.02 * lr + + for param, ema_param in zip(self.params, self.ema_params): + param.data.copy_(ema_param.data) + + def step(self): + one_minus_alpha = 1.0 - self.alpha + for param, ema_param in zip(self.params, self.ema_params): + if ema_param.dtype == torch.float32: + ema_param.mul_(self.alpha) + ema_param.add_(param * one_minus_alpha) + # customized weight decay + param.mul_(1 - self.wd) + + +def interleave_offsets(batch, nu): + groups = [batch // (nu + 1)] * (nu + 1) + for x in range(batch - sum(groups)): + groups[-x - 1] += 1 + offsets = [0] + for g in groups: + offsets.append(offsets[-1] + g) + assert offsets[-1] == batch + + return offsets + + +def interleave(xy, batch): + nu = len(xy) - 1 + offsets = interleave_offsets(batch, nu) + xy = [[v[offsets[p] : offsets[p + 1]] for p in range(nu + 1)] for v in xy] + for i in range(1, nu + 1): + xy[0][i], xy[i][i] = xy[i][i], xy[0][i] + + return [torch.cat(v, dim=0) for v in xy] + + +def mixmatch_train( + args, model, xloader, uloader, criterion, optimizer, epoch, logger, **kwargs, +): + loss_meter = AverageMeter("loss") + xloss_meter = AverageMeter("xloss") + uloss_meter = AverageMeter("uloss") + lambda_u_meter = AverageMeter("lambda_u") + meter_list = [loss_meter, xloss_meter, uloss_meter, lambda_u_meter] + + xiter = iter(xloader) + uiter = iter(uloader) + + model.train() + gpu = next(model.parameters()).device + start = time.time() + for batch_idx in range(kwargs["train_iteration"]): + try: + xbatch = next(xiter) + xinput, xtarget = xbatch["img"], xbatch["target"] + except: + xiter = iter(xloader) + xbatch = next(xiter) + xinput, xtarget = xbatch["img"], xbatch["target"] + + try: + ubatch = next(uiter) + uinput1, uinput2 = ubatch["img1"], ubatch["img2"] + except: + uiter = iter(uloader) + ubatch = next(uiter) + uinput1, uinput2 = ubatch["img1"], ubatch["img2"] + + batch_size = xinput.size(0) + xtarget = torch.zeros(batch_size, args.num_classes).scatter_( + 1, xtarget.view(-1, 1).long(), 1 + ) + xinput = xinput.cuda(gpu, non_blocking=True) + xtarget = xtarget.cuda(gpu, non_blocking=True) + uinput1 = uinput1.cuda(gpu, non_blocking=True) + uinput2 = uinput2.cuda(gpu, non_blocking=True) + + with torch.no_grad(): + # compute guessed labels of unlabel samples + uoutput1 = model(uinput1) + uoutput2 = model(uinput2) + p = (torch.softmax(uoutput1, dim=1) + torch.softmax(uoutput2, dim=1)) / 2 + pt = p ** (1 / kwargs["temperature"]) + utarget = pt / pt.sum(dim=1, keepdim=True) + utarget = utarget.detach() + + # mixup + all_input = torch.cat([xinput, uinput1, uinput2], dim=0) + all_target = torch.cat([xtarget, utarget, utarget], dim=0) + l = np.random.beta(kwargs["alpha"], kwargs["alpha"]) + l = max(l, 1 - l) + idx = torch.randperm(all_input.size(0)) + input_a, input_b = all_input, all_input[idx] + target_a, target_b = all_target, all_target[idx] + mixed_input = l * input_a + (1 - l) * input_b + mixed_target = l * target_a + (1 - l) * target_b + + # interleave labeled and unlabeled samples between batches to get correct batchnorm calculation + mixed_input = list(torch.split(mixed_input, batch_size)) + mixed_input = interleave(mixed_input, batch_size) + + logit = [model(mixed_input[0])] + for input in mixed_input[1:]: + logit.append(model(input)) + + # put interleaved samples back + logit = interleave(logit, batch_size) + xlogit = logit[0] + ulogit = torch.cat(logit[1:], dim=0) + + Lx, Lu, lambda_u = criterion( + xlogit, + mixed_target[:batch_size], + ulogit, + mixed_target[batch_size:], + epoch + batch_idx / kwargs["train_iteration"], + ) + loss = Lx + lambda_u * Lu + optimizer.zero_grad() + loss.backward() + optimizer.step() + # ema_optimizer.step() + + loss_meter.update(loss.item()) + xloss_meter.update(Lx.item()) + uloss_meter.update(Lu.item()) + lambda_u_meter.update(lambda_u) + tabulate_step_meter(batch_idx, kwargs["train_iteration"], 3, meter_list, logger) + + logger.info("MixMatch training summary:") + tabulate_epoch_meter(time.time() - start, meter_list, logger) + result = {m.name: m.total_avg for m in meter_list} + + return result diff --git a/utils/defense_utils/dbd/utils_db/trainer/simclr.py b/utils/defense_utils/dbd/utils_db/trainer/simclr.py new file mode 100755 index 0000000..77b5fed --- /dev/null +++ b/utils/defense_utils/dbd/utils_db/trainer/simclr.py @@ -0,0 +1,233 @@ +import time + +import torch +from torch.cuda.amp import autocast +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel + +from .log import AverageMeter, Record, tabulate_step_meter, tabulate_epoch_meter +from .utils import GatherLayer + + +def simclr_train(model, loader, criterion, optimizer, logger, amp=False): + loss_meter = AverageMeter("loss") + meter_list = [loss_meter] + + model.train() + gpu = next(model.parameters()).device + ddp = isinstance(model, DistributedDataParallel) + if amp: + scaler = GradScaler() + else: + scaler = None + start_time = time.time() + for batch_idx, batch in enumerate(loader): + img1, img2 = batch["img1"], batch["img2"] + data = torch.cat([img1.unsqueeze(1), img2.unsqueeze(1)], dim=1) + b, c, h, w = img1.size() + data = data.view(-1, c, h, w) + data = data.cuda(gpu, non_blocking=True) + + optimizer.zero_grad() + if amp: + with autocast(): + output = model(data).view(b, 2, -1) + if ddp: + output = torch.cat(GatherLayer.apply(output), dim=0) + loss = criterion(output) + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + else: + output = model(data).view(b, 2, -1) + if ddp: + output = torch.cat(GatherLayer.apply(output), dim=0) + loss = criterion(output) + loss.backward() + optimizer.step() + + loss_meter.update(loss.item()) + + tabulate_step_meter(batch_idx, len(loader), 3, meter_list, logger) + + logger.info("Training summary:") + tabulate_epoch_meter(time.time() - start_time, meter_list, logger) + result = {m.name: m.total_avg for m in meter_list} + + del loss, data, output + torch.cuda.empty_cache() + return result + + +def linear_train(model, loader, criterion, optimizer, logger): + loss_meter = AverageMeter("loss") + acc_meter = AverageMeter("acc") + meter_list = [loss_meter, acc_meter] + + # Freeze the backbone. + for param in model.backbone.parameters(): + param.require_grad = False + model.train() + gpu = next(model.parameters()).device + start_time = time.time() + for batch_idx, batch in enumerate(loader): + data = batch["img"].cuda(gpu, non_blocking=True) + target = batch["target"].cuda(gpu, non_blocking=True) + with torch.no_grad(): + feature = model.backbone(data) + output = model.linear(feature) + criterion.reduction = "mean" + loss = criterion(output, target) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + loss_meter.update(loss.item()) + pred = output.argmax(dim=1, keepdim=True) + truth = pred.view_as(target).eq(target) + acc_meter.update((torch.sum(truth).float() / len(truth)).item()) + + tabulate_step_meter(batch_idx, len(loader), 3, meter_list, logger) + + # Unfreeze the backbone. + for param in model.backbone.parameters(): + param.require_grad = True + logger.info("Linear training summary:") + tabulate_epoch_meter(time.time() - start_time, meter_list, logger) + result = {m.name: m.total_avg for m in meter_list} + return result + + +def linear_test(model, loader, criterion, logger): + loss_meter = AverageMeter("loss") + acc_meter = AverageMeter("acc") + meter_list = [loss_meter, acc_meter] + + model.eval() + gpu = next(model.parameters()).device + start_time = time.time() + for batch_idx, batch in enumerate(loader): + data = batch["img"].cuda(gpu, non_blocking=True) + target = batch["target"].cuda(gpu, non_blocking=True) + with torch.no_grad(): + output = model(data) + criterion.reduction = "mean" + loss = criterion(output, target) + + loss_meter.update(loss.item()) + pred = output.argmax(dim=1, keepdim=True) + truth = pred.view_as(target).eq(target) + acc_meter.update((torch.sum(truth).float() / len(truth)).item()) + + tabulate_step_meter(batch_idx, len(loader), 2, meter_list, logger) + + logger.info("Linear test summary:") + tabulate_epoch_meter(time.time() - start_time, meter_list, logger) + result = {m.name: m.total_avg for m in meter_list} + + return result + + +def poison_linear_train(model, loader, criterion, optimizer, logger, frozen=True): + loss_meter = AverageMeter("loss") + poison_loss_meter = AverageMeter("poison loss") + clean_loss_meter = AverageMeter("clean loss") + acc_meter = AverageMeter("acc") + poison_acc_meter = AverageMeter("poison acc") + clean_acc_meter = AverageMeter("clean acc") + meter_list = [ + loss_meter, + poison_loss_meter, + clean_loss_meter, + acc_meter, + poison_acc_meter, + clean_acc_meter, + ] + + if frozen: + # Freeze the backbone. + for param in model.backbone.parameters(): + param.require_grad = False + model.train() + gpu = next(model.parameters()).device + start_time = time.time() + for batch_idx, batch in enumerate(loader): + data = batch["img"].cuda(gpu, non_blocking=True) + target = batch["target"].cuda(gpu, non_blocking=True) + if frozen: + with torch.no_grad(): + feature = model.backbone(data) + else: + feature = model.backbone(data) + output = model.linear(feature) + criterion.reduction = "none" + raw_loss = criterion(output, target) + criterion.reduction = "mean" + loss = criterion(output, target) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + loss_meter.update(loss.item()) + pred = output.argmax(dim=1, keepdim=True) + truth = pred.view_as(target).eq(target) + acc_meter.update((torch.sum(truth).float() / len(truth)).item()) + poison_idx = torch.nonzero(batch["poison"], as_tuple=True) + clean_idx = torch.nonzero(batch["poison"] - 1, as_tuple=True) + # Not every batch contains poison data. + if len(poison_idx[0]) != 0: + poison_loss_meter.update(torch.mean(raw_loss[poison_idx]).item()) + poison_acc_meter.update( + (torch.sum(truth[poison_idx]).float() / len(truth[poison_idx])).item() + ) + clean_loss_meter.update(torch.mean(raw_loss[clean_idx]).item()) + clean_acc_meter.update( + (torch.sum(truth[clean_idx]).float() / len(truth[clean_idx])).item() + ) + + tabulate_step_meter(batch_idx, len(loader), 3, meter_list, logger) + + if frozen: + # Unfreeze the backbone. + for param in model.backbone.parameters(): + param.require_grad = True + logger.info("Linear training summary:") + tabulate_epoch_meter(time.time() - start_time, meter_list, logger) + result = {m.name: m.total_avg for m in meter_list} + + return result + + +def poison_linear_record(model, loader, criterion): + num_data = len(loader.dataset) + target_record = Record("target", num_data) + poison_record = Record("poison", num_data) + origin_record = Record("origin", num_data) + loss_record = Record("loss", num_data) + feature_record = Record("feature", (num_data, model.backbone.feature_dim)) + record_list = [ + target_record, + poison_record, + origin_record, + loss_record, + feature_record, + ] + + model.eval() + gpu = next(model.parameters()).device + for _, batch in enumerate(loader): + data = batch["img"].cuda(gpu, non_blocking=True) + target = batch["target"].cuda(gpu, non_blocking=True) + with torch.no_grad(): + feature = model.backbone(data) + output = model.linear(feature) + criterion.reduction = "none" + raw_loss = criterion(output, target) + + target_record.update(batch["target"]) + poison_record.update(batch["poison"]) + origin_record.update(batch["origin"]) + loss_record.update(raw_loss.cpu()) + feature_record.update(feature.cpu()) + + return record_list diff --git a/utils/defense_utils/dbd/utils_db/trainer/supervise.py b/utils/defense_utils/dbd/utils_db/trainer/supervise.py new file mode 100755 index 0000000..3bb0c73 --- /dev/null +++ b/utils/defense_utils/dbd/utils_db/trainer/supervise.py @@ -0,0 +1,110 @@ +import time + +import torch +from torch.cuda.amp import autocast +from torch.cuda.amp import GradScaler + +from .log import AverageMeter, tabulate_epoch_meter, tabulate_step_meter + + +def poison_train(model, loader, criterion, optimizer, logger, amp=False): + loss_meter = AverageMeter("loss") + poison_loss_meter = AverageMeter("poison loss") + clean_loss_meter = AverageMeter("clean loss") + acc_meter = AverageMeter("acc") + poison_acc_meter = AverageMeter("poison acc") + clean_acc_meter = AverageMeter("clean acc") + meter_list = [ + loss_meter, + poison_loss_meter, + clean_loss_meter, + acc_meter, + poison_acc_meter, + clean_acc_meter, + ] + + model.train() + gpu = next(model.parameters()).device + if amp: + scaler = GradScaler() + else: + scaler = None + start_time = time.time() + for batch_idx, batch in enumerate(loader): + data = batch["img"].cuda(gpu, non_blocking=True) + target = batch["target"].cuda(gpu, non_blocking=True) + + optimizer.zero_grad() + if amp: + with autocast(): + output = model(data) + criterion.reduction = "none" + raw_loss = criterion(output, target) + criterion.reduction = "mean" + loss = criterion(output, target) + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + else: + output = model(data) + criterion.reduction = "none" + raw_loss = criterion(output, target) + criterion.reduction = "mean" + loss = criterion(output, target) + loss.backward() + optimizer.step() + + loss_meter.update(loss.item()) + pred = output.argmax(dim=1, keepdim=True) + truth = pred.view_as(target).eq(target) + acc_meter.update((torch.sum(truth).float() / len(truth)).item()) + poison_idx = torch.nonzero(batch["poison"], as_tuple=True) + clean_idx = torch.nonzero(batch["poison"] - 1, as_tuple=True) + # Not every batch contains poison data. + if len(poison_idx[0]) != 0: + poison_loss_meter.update(torch.mean(raw_loss[poison_idx]).item()) + poison_acc_meter.update( + (torch.sum(truth[poison_idx]).float() / len(truth[poison_idx])).item() + ) + clean_loss_meter.update(torch.mean(raw_loss[clean_idx]).item()) + clean_acc_meter.update( + (torch.sum(truth[clean_idx]).float() / len(truth[clean_idx])).item() + ) + + tabulate_step_meter(batch_idx, len(loader), 3, meter_list, logger) + + logger.info("Poison training summary:") + tabulate_epoch_meter(time.time() - start_time, meter_list, logger) + result = {m.name: m.total_avg for m in meter_list} + + return result + + +def test(model, loader, criterion, logger): + loss_meter = AverageMeter("loss") + acc_meter = AverageMeter("acc") + meter_list = [loss_meter, acc_meter] + + model.eval() + gpu = next(model.parameters()).device + start_time = time.time() + for batch_idx, batch in enumerate(loader): + data = batch["img"].cuda(gpu, non_blocking=True) + target = batch["target"].cuda(gpu, non_blocking=True) + with torch.no_grad(): + output = model(data) + criterion.reduction = "mean" + loss = criterion(output, target) + + loss_meter.update(loss.item()) + pred = output.argmax(dim=1, keepdim=True) + truth = pred.view_as(target).eq(target) + acc_meter.update((torch.sum(truth).float() / len(truth)).item()) + + tabulate_step_meter(batch_idx, len(loader), 2, meter_list, logger) + + logger.info("Test summary:") + tabulate_epoch_meter(time.time() - start_time, meter_list, logger) + result = {m.name: m.total_avg for m in meter_list} + + return result diff --git a/utils/defense_utils/dbd/utils_db/trainer/utils.py b/utils/defense_utils/dbd/utils_db/trainer/utils.py new file mode 100755 index 0000000..f8d8419 --- /dev/null +++ b/utils/defense_utils/dbd/utils_db/trainer/utils.py @@ -0,0 +1,23 @@ +import torch +import torch.distributed as dist + + +class GatherLayer(torch.autograd.Function): + """Gather tensors from all process, supporting backward propagation. + + Borrowed from https://github.com/open-mmlab/OpenSelfSup. + """ + + @staticmethod + def forward(ctx, input): + ctx.save_for_backward(input) + output = [torch.zeros_like(input) for _ in range(dist.get_world_size())] + dist.all_gather(output, input) + return tuple(output) + + @staticmethod + def backward(ctx, *grads): + (input,) = ctx.saved_tensors + grad_out = torch.zeros_like(input) + grad_out[:] = grads[dist.get_rank()] + return grad_out diff --git a/utils/defense_utils/dbr/__init__.py b/utils/defense_utils/dbr/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/utils/defense_utils/dbr/br_loss.py b/utils/defense_utils/dbr/br_loss.py new file mode 100644 index 0000000..ec65808 --- /dev/null +++ b/utils/defense_utils/dbr/br_loss.py @@ -0,0 +1,186 @@ +# Modified from https://github.com/HobbitLong/SupContrast + +from __future__ import print_function + +import torch +import torch.nn as nn +import numpy + + +class SupConLoss(nn.Module): + def __init__(self, temperature=0.07, contrast_mode='all', + base_temperature=0.07): + super(SupConLoss, self).__init__() + self.temperature = temperature + self.contrast_mode = contrast_mode + self.base_temperature = base_temperature + + def forward(self, features, labels=None, gt_labels=None, mask=None, isCleans=None): + """Compute loss for model. + Args: + features: hidden vector of shape [bsz, n_views, ...]. + labels: label of shape [bsz]. + gt_labels: ground-truth label of shape [bsz]. + mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j is the positive of sample i. Can be asymmetric. + isCleans: is-clean sign of shape [bsz], isCleans{i}=1 if sample i is genuinely clean. + Returns: + A loss scalar. + """ + device = (torch.device('cuda') + if features.is_cuda + else torch.device('cpu')) + + if len(features.shape) < 3: + raise ValueError('`features` needs to be [bsz, n_views, ...],' + 'at least 3 dimensions are required') + if len(features.shape) > 3: + features = features.view(features.shape[0], features.shape[1], -1) + + batch_size = features.shape[0] + if labels is not None and mask is not None: + raise ValueError('Cannot define both `labels` and `mask`') + elif labels is None and mask is None: # SimCLR (contrastive learning) + mask = torch.eye(batch_size, dtype=torch.float32).to(device) + elif labels is not None: # SupCon (supervised contrastive learning) + labels = labels.contiguous().view(-1, 1) + if labels.shape[0] != batch_size: + raise ValueError('Num of labels does not match num of features') + # set the positives of each sample as its own augmented version and the augmented versions of samples with the same label + mask = torch.eq(labels, labels.T).float().to(device) # mask: positive==1 + else: + mask = mask.float().to(device) + + contrast_count = features.shape[1] + contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0) + if self.contrast_mode == 'one': + anchor_feature = features[:, 0] + anchor_count = 1 + elif self.contrast_mode == 'all': + anchor_feature = contrast_feature + anchor_count = contrast_count + else: + raise ValueError('Unknown mode: {}'.format(self.contrast_mode)) + + # compute logits + anchor_dot_contrast = torch.div( + torch.matmul(anchor_feature, contrast_feature.T), + self.temperature) + # for numerical stability + logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True) + logits = anchor_dot_contrast - logits_max.detach() + + # tile mask + mask = mask.repeat(anchor_count, contrast_count) + # mask-out self-contrast cases + logits_mask = torch.scatter( + torch.ones_like(mask), + 1, + torch.arange(batch_size * anchor_count).view(-1, 1).to(device), + 0 + ) + mask = mask * logits_mask # mask_{i,j}=1 if sample j is the positive of sample i. + + # compute log_prob + exp_logits = torch.exp(logits) * logits_mask + log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True)) + + # compute mean of log-likelihood over positive + mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1) + + # loss + loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos + loss = loss.view(anchor_count, batch_size).mean() + + return loss + + +class SupConLoss_Consistency(nn.Module): + def __init__(self, temperature=0.07, contrast_mode='all', + base_temperature=0.07): + super(SupConLoss_Consistency, self).__init__() + self.temperature = temperature + self.contrast_mode = contrast_mode + self.base_temperature = base_temperature + + def forward(self, features, labels=None, flags=None, mask=None): + """Compute loss for model. + Args: + features: hidden vector of shape [bsz, n_views, ...]. + labels: label of shape [bsz]. + gt_labels: ground-truth label of shape [bsz]. + mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j is the positive of sample i. Can be asymmetric. + isCleans: is-clean sign of shape [bsz], isCleans{i}=1 if sample i is genuinely clean. + Returns: + A loss scalar. + """ + device = (torch.device('cuda') + if features.is_cuda + else torch.device('cpu')) + + if len(features.shape) < 3: + raise ValueError('`features` needs to be [bsz, n_views, ...],' + 'at least 3 dimensions are required') + if len(features.shape) > 3: + features = features.view(features.shape[0], features.shape[1], -1) + + batch_size = features.shape[0] + if labels is not None and mask is not None: + raise ValueError('Cannot define both `labels` and `mask`') + elif labels is None and mask is None: # SimCLR (contrastive learning) + mask = torch.eye(batch_size, dtype=torch.float32).to(device) + elif labels is not None: # SS-CTL (semi-supervised contrastive learning) + labels = labels.contiguous().view(-1, 1) + if labels.shape[0] != batch_size: + raise ValueError('Num of labels does not match num of features') + # set the positive of a poisoned sample / an uncertain sample as its own augmented version + # set the positives of a clean sample as its own augmented version and the augmented versions of samples with the same label + mask = torch.eq(labels, labels.T).float().to(device) + nonclean_idx = torch.where(flags!=0)[0] # poisoned samples and uncertain samples + mask[nonclean_idx, :] = 0 + mask[nonclean_idx, nonclean_idx] = 1 + else: + mask = mask.float().to(device) + + contrast_count = features.shape[1] + contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0) + if self.contrast_mode == 'one': + anchor_feature = features[:, 0] + anchor_count = 1 + elif self.contrast_mode == 'all': + anchor_feature = contrast_feature + anchor_count = contrast_count + else: + raise ValueError('Unknown mode: {}'.format(self.contrast_mode)) + + # compute logits + anchor_dot_contrast = torch.div( + torch.matmul(anchor_feature, contrast_feature.T), + self.temperature) + # for numerical stability + logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True) + logits = anchor_dot_contrast - logits_max.detach() + + # tile mask + mask = mask.repeat(anchor_count, contrast_count) + # isCleans_mask = isCleans_mask.repeat(anchor_count, contrast_count) + # mask-out self-contrast cases + logits_mask = torch.scatter( + torch.ones_like(mask), + 1, + torch.arange(batch_size * anchor_count).view(-1, 1).to(device), + 0 + ) + mask = mask * logits_mask # mask_{i,j}=1 if sample j is the positive of sample i. + + # compute log_prob + exp_logits = torch.exp(logits) * logits_mask + log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True)) + + # compute mean of log-likelihood over positive + mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1) + + # loss + loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos + loss = loss.view(anchor_count, batch_size).mean() + + return loss diff --git a/utils/defense_utils/dbr/dataloader_bd.py b/utils/defense_utils/dbr/dataloader_bd.py new file mode 100644 index 0000000..981ddc4 --- /dev/null +++ b/utils/defense_utils/dbr/dataloader_bd.py @@ -0,0 +1,220 @@ +# Modified from https://github.com/bboylyg/NAD/blob/main/data_loader.py + +import os +import csv +import random +import numpy as np +from PIL import Image +from tqdm import tqdm +import time +import sys +from matplotlib import image as mlt +import cv2 + +import torch +import torch.utils.data as data +import torch.nn.functional as F +import torchvision +import torchvision.transforms as transforms +import torchvision.datasets as datasets + + + +class TwoCropTransform: + """Create two crops of the same image""" + def __init__(self, transform): + self.transform = transform + + def __call__(self, x): + return [self.transform(x), self.transform(x)] + +class TransformThree: + def __init__(self, transform1, transform2, transform3): + self.transform1 = transform1 + self.transform2 = transform2 + self.transform3 = transform3 + + def __call__(self, inp): + out1 = self.transform1(inp) + out2 = self.transform2(inp) + out3 = self.transform3(inp) + return out1, out2, out3 + + +class Dataset_npy(torch.utils.data.Dataset): + def __init__(self, full_dataset=None, transform=None): + self.dataset = full_dataset + self.transform = transform + self.dataLen = len(self.dataset) + + def __getitem__(self, index): + image = self.dataset[index][0] + label = self.dataset[index][1] + flag = self.dataset[index][2] + + if self.transform: + image = self.transform(image) + # print(type(image), image.shape) + return image, label, flag + + def __len__(self): + return self.dataLen + + + +def normalization(opt, inputs): + output = inputs.clone() + if opt.dataset == "cifar10": + f = transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]) + elif opt.dataset == "mnist": + f = transforms.Normalize([0.5], [0.5]) + elif opt.dataset == 'tiny': + f = transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]) + elif opt.dataset == "gtsrb" or opt.dataset == "celeba": + # pass + return output + elif opt.dataset == 'imagenet': + f = transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]) + elif opt.dataset == "cifar100": + f = transforms.Normalize([0.5070751592371323, 0.48654887331495095, 0.4409178433670343], [0.2673342858792401, 0.2564384629170883, 0.27615047132568404]) + else: + raise Exception("Invalid Dataset") + for i in range(inputs.shape[0]): + output[i] = f(inputs[i]) + return output + + +def get_transform_br(opt, train=True): + ### transform1 ### + transforms_list = [] + transforms_list.append(transforms.Resize((opt.input_height, opt.input_width))) + transforms_list.append(transforms.ToTensor()) + transforms1 = transforms.Compose(transforms_list) + + if train == False: + return transforms1 + + ### transform2 ### + transforms_list = [] + transforms_list.append(transforms.Resize((opt.input_height, opt.input_width))) + if train: + if opt.dataset == 'cifar10' or opt.dataset == 'gtsrb': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + transforms_list.append(transforms.RandomHorizontalFlip()) + elif opt.dataset == 'cifar100': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + transforms_list.append(transforms.RandomHorizontalFlip()) + transforms_list.append(transforms.RandomRotation(15)) + elif opt.dataset == "imagenet": + transforms_list.append(transforms.RandomRotation(20)) + transforms_list.append(transforms.RandomHorizontalFlip(0.5)) + elif opt.dataset == "tiny": + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=8)) + transforms_list.append(transforms.RandomHorizontalFlip()) + transforms_list.append(transforms.ToTensor()) + transforms2 = transforms.Compose(transforms_list) + + ### transform3 ### + transforms_list = [] + transforms_list.append(transforms.Resize((opt.input_height, opt.input_width))) + if opt.trans1 == 'rotate': + transforms_list.append(transforms.RandomRotation(180)) + elif opt.trans1 == 'affine': + transforms_list.append(transforms.RandomAffine(degrees=0, translate=(0.2, 0.2))) + elif opt.trans1 == 'flip': + transforms_list.append(transforms.RandomHorizontalFlip(p=1.0)) + elif opt.trans1 == 'crop': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + elif opt.trans1 == 'blur': + transforms_list.append(transforms.GaussianBlur(kernel_size=15, sigma=(0.1, 2.0))) + elif opt.trans1 == 'erase': + transforms_list.append(transforms.ToTensor()) + transforms_list.append(transforms.RandomErasing(p=1.0, scale=(0.2, 0.3), ratio=(0.5, 1.0), value='random')) + transforms_list.append(transforms.ToPILImage()) + + if opt.trans2 == 'rotate': + transforms_list.append(transforms.RandomRotation(180)) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'affine': + transforms_list.append(transforms.RandomAffine(degrees=0, translate=(0.2, 0.2))) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'flip': + transforms_list.append(transforms.RandomHorizontalFlip(p=1.0)) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'crop': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'blur': + transforms_list.append(transforms.GaussianBlur(kernel_size=15, sigma=(0.1, 2.0))) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'erase': + transforms_list.append(transforms.ToTensor()) + transforms_list.append(transforms.RandomErasing(p=1.0, scale=(0.2, 0.3), ratio=(0.5, 1.0), value='random')) + elif opt.trans2 == 'none': + transforms_list.append(transforms.ToTensor()) + + transforms3 = transforms.Compose(transforms_list) + + return transforms1, transforms2, transforms3 + + + +def get_br_train_loader(opt): + transforms_list = [ + transforms.ToPILImage(), + transforms.RandomResizedCrop(size=opt.size, scale=(0.2, 1.)), + transforms.RandomHorizontalFlip(), + transforms.RandomApply([ + transforms.ColorJitter(0.4, 0.4, 0.4, 0.1) + ], p=0.8), + transforms.RandomGrayscale(p=0.2), + transforms.ToTensor() + ] + + # construct data loader + if opt.dataset == 'cifar10': + mean = (0.4914, 0.4822, 0.4465) + std = (0.2023, 0.1994, 0.2010) + elif opt.dataset == 'cifar100': + mean = (0.5071, 0.4867, 0.4408) + std = (0.2675, 0.2565, 0.2761) + elif opt.dataset == "mnist": + mean = [0.5,] + std = [0.5,] + elif opt.dataset == 'tiny': + mean = (0.4802, 0.4481, 0.3975) + std = (0.2302, 0.2265, 0.2262) + elif opt.dataset == 'imagenet': + mean = (0.4802, 0.4481, 0.3975) + std = (0.2302, 0.2265, 0.2262) + elif opt.dataset == 'gtsrb': + mean = None + elif opt.dataset == 'path': + mean = eval(opt.mean) + std = eval(opt.std) + else: + raise ValueError('dataset not supported: {}'.format(opt.dataset)) + + if mean != None: + normalize = transforms.Normalize(mean=mean, std=std) + transforms_list.append(normalize) + + train_transform = transforms.Compose(transforms_list) + + folder_path = folder_path = f'{opt.save_path}/d-br/data_produce' + data_path_clean = os.path.join(folder_path, 'clean_samples.npy') + data_path_poison = os.path.join(folder_path, 'poison_samples.npy') + data_path_suspicious = os.path.join(folder_path, 'suspicious_samples.npy') + + clean_data = np.load(data_path_clean, allow_pickle=True) + poison_data = np.load(data_path_poison, allow_pickle=True) + suspicious_data = np.load(data_path_suspicious, allow_pickle=True) + all_data = np.concatenate((clean_data, poison_data, suspicious_data), axis=0) + + train_dataset = Dataset_npy(full_dataset=all_data, transform=TwoCropTransform(train_transform)) + train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=opt.batch_size, shuffle=True) + + return train_loader + + + diff --git a/utils/defense_utils/dbr/models/resnet_cifar10.py b/utils/defense_utils/dbr/models/resnet_cifar10.py new file mode 100644 index 0000000..f922506 --- /dev/null +++ b/utils/defense_utils/dbr/models/resnet_cifar10.py @@ -0,0 +1,305 @@ +# Source: https://github.com/huyvnphan/PyTorch_CIFAR10 + +import torch +import torch.nn as nn +import os + +__all__ = [ + "ResNet", + "resnet18", + "resnet34", + "resnet50", +] + + +def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): + """3x3 convolution with padding""" + return nn.Conv2d( + in_planes, + out_planes, + kernel_size=3, + stride=stride, + padding=dilation, + groups=groups, + bias=False, + dilation=dilation, + ) + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__( + self, + inplanes, + planes, + stride=1, + downsample=None, + groups=1, + base_width=64, + dilation=1, + norm_layer=None, + ): + super(BasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError("BasicBlock only supports groups=1 and base_width=64") + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__( + self, + inplanes, + planes, + stride=1, + downsample=None, + groups=1, + base_width=64, + dilation=1, + norm_layer=None, + ): + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = int(planes * (base_width / 64.0)) * groups + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, stride, groups, dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + def __init__( + self, + block, + layers, + num_classes=10, + zero_init_residual=False, + groups=1, + width_per_group=64, + replace_stride_with_dilation=None, + norm_layer=None, + ): + super(ResNet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError( + "replace_stride_with_dilation should be None " + "or a 3-element tuple, got {}".format(replace_stride_with_dilation) + ) + self.groups = groups + self.base_width = width_per_group + + # CIFAR10: kernel_size 7 -> 3, stride 2 -> 1, padding 3->1 + self.conv1 = nn.Conv2d( + 3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False + ) + # END + + self.bn1 = norm_layer(self.inplanes) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer( + block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0] + ) + self.layer3 = self._make_layer( + block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1] + ) + self.layer4 = self._make_layer( + block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2] + ) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False): + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append( + block( + self.inplanes, + planes, + stride, + downsample, + self.groups, + self.base_width, + previous_dilation, + norm_layer, + ) + ) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append( + block( + self.inplanes, + planes, + groups=self.groups, + base_width=self.base_width, + dilation=self.dilation, + norm_layer=norm_layer, + ) + ) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.reshape(x.size(0), -1) + x = self.fc(x) + + return x + + +def _resnet(arch, block, layers, pretrained, progress, device, **kwargs): + model = ResNet(block, layers, **kwargs) + if pretrained: + script_dir = os.path.dirname(__file__) + state_dict = torch.load( + script_dir + "/state_dicts/" + arch + ".pt", map_location=device + ) + model.load_state_dict(state_dict) + return model + + +def resnet18(pretrained=False, progress=True, device="cpu", **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet( + "resnet18", BasicBlock, [2, 2, 2, 2], pretrained, progress, device, **kwargs + ) + + +def resnet34(pretrained=False, progress=True, device="cpu", **kwargs): + """Constructs a ResNet-34 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet( + "resnet34", BasicBlock, [3, 4, 6, 3], pretrained, progress, device, **kwargs + ) + + +def resnet50(pretrained=False, progress=True, device="cpu", **kwargs): + """Constructs a ResNet-50 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet( + "resnet50", Bottleneck, [3, 4, 6, 3], pretrained, progress, device, **kwargs + ) diff --git a/utils/defense_utils/dbr/models/resnet_cifar100.py b/utils/defense_utils/dbr/models/resnet_cifar100.py new file mode 100644 index 0000000..19fd1e8 --- /dev/null +++ b/utils/defense_utils/dbr/models/resnet_cifar100.py @@ -0,0 +1,155 @@ +# Source: https://github.com/weiaicunzai/pytorch-cifar100 + +import torch +import torch.nn as nn + +class BasicBlock(nn.Module): + """Basic Block for resnet 18 and resnet 34 + + """ + + #BasicBlock and BottleNeck block + #have different output size + #we use class attribute expansion + #to distinct + expansion = 1 + + def __init__(self, in_channels, out_channels, stride=1): + super().__init__() + + #residual function + self.residual_function = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(out_channels * BasicBlock.expansion) + ) + + #shortcut + self.shortcut = nn.Sequential() + + #the shortcut output dimension is not the same with residual function + #use 1*1 convolution to match the dimension + if stride != 1 or in_channels != BasicBlock.expansion * out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(out_channels * BasicBlock.expansion) + ) + + def forward(self, x): + return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x)) + +class BottleNeck(nn.Module): + """Residual block for resnet over 50 layers + + """ + expansion = 4 + def __init__(self, in_channels, out_channels, stride=1): + super().__init__() + self.residual_function = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels * BottleNeck.expansion), + ) + + self.shortcut = nn.Sequential() + + if stride != 1 or in_channels != out_channels * BottleNeck.expansion: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels * BottleNeck.expansion) + ) + + def forward(self, x): + return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x)) + +class ResNet(nn.Module): + + def __init__(self, block, num_block, num_classes=100): + super().__init__() + + self.in_channels = 64 + + self.conv1 = nn.Sequential( + nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True)) + #we use a different inputsize than the original paper + #so conv2_x's stride is 1 + self.conv2_x = self._make_layer(block, 64, num_block[0], 1) + self.conv3_x = self._make_layer(block, 128, num_block[1], 2) + self.conv4_x = self._make_layer(block, 256, num_block[2], 2) + self.conv5_x = self._make_layer(block, 512, num_block[3], 2) + self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + def _make_layer(self, block, out_channels, num_blocks, stride): + """make resnet layers(by layer i didnt mean this 'layer' was the + same as a neuron netowork layer, ex. conv layer), one layer may + contain more than one residual block + + Args: + block: block type, basic block or bottle neck block + out_channels: output depth channel number of this layer + num_blocks: how many blocks per layer + stride: the stride of the first block of this layer + + Return: + return a resnet layer + """ + + # we have num_block blocks per layer, the first block + # could be 1 or 2, other blocks would always be 1 + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_channels, out_channels, stride)) + self.in_channels = out_channels * block.expansion + + return nn.Sequential(*layers) + + def forward(self, x): + output = self.conv1(x) + output = self.conv2_x(output) + output = self.conv3_x(output) + output = self.conv4_x(output) + output = self.conv5_x(output) + output = self.avg_pool(output) + output = output.view(output.size(0), -1) + output = self.fc(output) + + return output + +def resnet18(): + """ return a ResNet 18 object + """ + return ResNet(BasicBlock, [2, 2, 2, 2]) + +def resnet34(): + """ return a ResNet 34 object + """ + return ResNet(BasicBlock, [3, 4, 6, 3]) + +def resnet50(): + """ return a ResNet 50 object + """ + return ResNet(BottleNeck, [3, 4, 6, 3]) + +def resnet101(): + """ return a ResNet 101 object + """ + return ResNet(BottleNeck, [3, 4, 23, 3]) + +def resnet152(): + """ return a ResNet 152 object + """ + return ResNet(BottleNeck, [3, 8, 36, 3]) + + + diff --git a/utils/defense_utils/dbr/models/resnet_super.py b/utils/defense_utils/dbr/models/resnet_super.py new file mode 100644 index 0000000..6fc1c21 --- /dev/null +++ b/utils/defense_utils/dbr/models/resnet_super.py @@ -0,0 +1,213 @@ +# Source: https://github.com/HobbitLong/SupContrast + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1, is_last=False): + super(BasicBlock, self).__init__() + self.is_last = is_last + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + preact = out + out = F.relu(out) + if self.is_last: + return out, preact + else: + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, in_planes, planes, stride=1, is_last=False): + super(Bottleneck, self).__init__() + self.is_last = is_last + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(self.expansion * planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + out += self.shortcut(x) + preact = out + out = F.relu(out) + if self.is_last: + return out, preact + else: + return out + + +# class ResNet(nn.Module): +# def __init__(self, block, num_blocks, in_channel=3, zero_init_residual=False): +# super(ResNet, self).__init__() +# self.in_planes = 64 + +# self.conv1 = nn.Conv2d(in_channel, 64, kernel_size=3, stride=1, padding=1, +# bias=False) +# self.bn1 = nn.BatchNorm2d(64) +# self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) +# self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) +# self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) +# self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) +# self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + +# for m in self.modules(): +# if isinstance(m, nn.Conv2d): +# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') +# elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): +# nn.init.constant_(m.weight, 1) +# nn.init.constant_(m.bias, 0) + +# # Zero-initialize the last BN in each residual branch, +# # so that the residual branch starts with zeros, and each residual block behaves +# # like an identity. This improves the model by 0.2~0.3% according to: +# # https://arxiv.org/abs/1706.02677 +# if zero_init_residual: +# for m in self.modules(): +# if isinstance(m, Bottleneck): +# nn.init.constant_(m.bn3.weight, 0) +# elif isinstance(m, BasicBlock): +# nn.init.constant_(m.bn2.weight, 0) + +# def _make_layer(self, block, planes, num_blocks, stride): +# strides = [stride] + [1] * (num_blocks - 1) +# layers = [] +# for i in range(num_blocks): +# stride = strides[i] +# layers.append(block(self.in_planes, planes, stride)) +# self.in_planes = planes * block.expansion +# return nn.Sequential(*layers) + +# def forward(self, x, layer=100): +# out = F.relu(self.bn1(self.conv1(x))) +# out = self.layer1(out) +# out = self.layer2(out) +# out = self.layer3(out) +# out = self.layer4(out) +# out = self.avgpool(out) +# out = torch.flatten(out, 1) +# return out + + +# def resnet18(**kwargs): +# return ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + + +# def resnet34(**kwargs): +# return ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) + + +# def resnet50(**kwargs): +# return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) + + +# def resnet101(**kwargs): +# return ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) + + +# model_dict = { +# 'resnet18': [resnet18, 512], +# 'resnet34': [resnet34, 512], +# 'resnet50': [resnet50, 2048], +# 'resnet101': [resnet101, 2048], +# } + + +class LinearBatchNorm(nn.Module): + """Implements BatchNorm1d by BatchNorm2d, for SyncBN purpose""" + def __init__(self, dim, affine=True): + super(LinearBatchNorm, self).__init__() + self.dim = dim + self.bn = nn.BatchNorm2d(dim, affine=affine) + + def forward(self, x): + x = x.view(-1, self.dim, 1, 1) + x = self.bn(x) + x = x.view(-1, self.dim) + return x + + +class SupConResNet(nn.Module): + """backbone + projection head""" + def __init__(self, encoder, dim_in, head='mlp', feat_dim=128): + super(SupConResNet, self).__init__() + self.encoder = encoder + + if head == 'linear': + self.head = nn.Linear(dim_in, feat_dim) + elif head == 'mlp': + self.head = nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.ReLU(inplace=True), + nn.Linear(dim_in, feat_dim) + ) + else: + raise NotImplementedError( + 'head not supported: {}'.format(head)) + + def forward(self, x): + feat = self.encoder(x) + feat = F.normalize(self.head(feat), dim=1) + return feat + + +class SupCEResNet(nn.Module): + """encoder + classifier""" + def __init__(self, encoder, num_classes=10): + super(SupCEResNet, self).__init__() + self.encoder = encoder + dim_in = list(encoder.named_modules())[-1][1].in_features + self.fc = nn.Linear(dim_in, num_classes) + + def forward(self, x): + return self.fc(self.encoder(x)) + + +# class LinearClassifier(nn.Module): +# """Linear classifier""" +# def __init__(self, encoder, num_classes=10): +# super(LinearClassifier, self).__init__() +# dim_in = list(encoder.named_modules())[-1][1].in_features +# self.fc = nn.Linear(dim_in, num_classes) + +# def forward(self, features): +# return self.fc(features) +class LinearClassifier(nn.Module): + """Linear classifier""" + def __init__(self, feat_dim, num_classes=10): + super(LinearClassifier, self).__init__() + self.fc = nn.Linear(feat_dim, num_classes) + + def forward(self, features): + return self.fc(features) diff --git a/utils/defense_utils/dbr/sd.py b/utils/defense_utils/dbr/sd.py new file mode 100644 index 0000000..0e93605 --- /dev/null +++ b/utils/defense_utils/dbr/sd.py @@ -0,0 +1,179 @@ +import sys +import os +from tqdm import tqdm +import numpy as np +import argparse +import torch +from torch import nn +import logging +sys.path.append("./") +sys.path.append(os.getcwd()) +print(os.getcwd()) +from utils.defense_utils.dbr.dataloader_bd import normalization + +def calculate_consistency(args, dataloader, model): + f_path = os.path.join(args.save_path, 'data_produce') + if not os.path.exists(f_path): + os.makedirs(f_path) + f_all = os.path.join(f_path,'all.txt') + f_clean = os.path.join(f_path,'clean.txt') + f_poison = os.path.join(f_path,'poison.txt') + if os.path.exists(f_all): + with open(f_all,'a+') as test: + test.truncate(0) + test.close() + with open(f_clean,'a+') as test: + test.truncate(0) + test.close() + with open(f_poison,'a+') as test: + test.truncate(0) + test.close() + + model.eval() + for i, (inputs, labels, _, is_bd, gt_labels) in enumerate(dataloader): + inputs1, inputs2 = inputs[0], inputs[2] + inputs1, inputs2 = normalization(args, inputs1), normalization(args, inputs2) # Normalize + inputs1, inputs2, labels, gt_labels = inputs1.to(args.device), inputs2.to(args.device), labels.to(args.device), gt_labels.to(args.device) + clean_idx, poison_idx = torch.where(is_bd == False), torch.where(is_bd == True) + + ### Feature ### + # if hasattr(model, "module"): # abandon FC layer + # features_out = list(model.module.children())[:-1] + # else: + # features_out = list(model.children())[:-1] + # modelout = nn.Sequential(*features_out).to(args.device) + # features1, features2 = modelout(inputs1), modelout(inputs2) + + features1, features2 = model(inputs1), model(inputs2) + features1, features2 = features1.view(features1.size(0), -1), features2.view(features2.size(0), -1) + + ### Calculate consistency ### + feature_consistency = torch.mean((features1 - features2)**2, dim=1) + + ### Save ### + draw_features = feature_consistency.detach().cpu().numpy() + draw_clean_features = feature_consistency[clean_idx].detach().cpu().numpy() + draw_poison_features = feature_consistency[poison_idx].detach().cpu().numpy() + + with open(f_all, 'ab') as f: + np.savetxt(f, draw_features, delimiter=" ") + with open(f_clean, 'ab') as f: + np.savetxt(f, draw_clean_features, delimiter=" ") + with open(f_poison, 'ab') as f: + np.savetxt(f, draw_poison_features, delimiter=" ") + return + +def calculate_gamma(args): + + f_path = os.path.join(args.save_path, 'data_produce') + f_all = os.path.join(f_path,'all.txt') + + all_data = np.loadtxt(f_all) + all_size = all_data.shape[0] # 50000 + + clean_size = int(all_size * args.clean_ratio) # 10000 + poison_size = int(all_size * args.poison_ratio) # 2500 + + new_data = np.sort(all_data) # in ascending order + gamma_low = new_data[clean_size] + gamma_high = new_data[all_size-poison_size] + print("gamma_low: ", gamma_low) + print("gamma_high: ", gamma_high) + return gamma_low, gamma_high + +def separate_samples(args, trainloader, model): + gamma_low, gamma_high = args.gamma_low, args.gamma_high + model.eval() + clean_samples, poison_samples, suspicious_samples = [], [], [] + + clean_idx_list = [] + poison_idx_list = [] + suspicious_idx_list = [] + for i, (inputs, labels, original_index, _, gt_labels) in enumerate(trainloader): + if args.debug and i==10001: + break + print("Processing samples:", i*args.batch_size) + inputs1, inputs2 = inputs[0], inputs[2] + inputs1, inputs2 = normalization(args, inputs1), normalization(args, inputs2) + inputs1, inputs2 = inputs1.to(args.device), inputs2.to(args.device) + ### Features ### + features1, features2 = model(inputs1), model(inputs2) + features1, features2 = features1.view(features1.size(0), -1), features2.view(features2.size(0), -1) + + ### Compare consistency ### + feature_consistency = torch.mean((features1 - features2)**2, dim=1) + # feature_consistency = feature_consistency.detach().cpu().numpy() + + ### Separate samples ### + clean_idx_list += original_index[torch.where(feature_consistency <= gamma_low)[0]] + poison_idx_list += original_index[torch.where(feature_consistency >= gamma_high)[0]] + suspicious_idx_list += original_index[torch.where((feature_consistency > gamma_low) & (feature_consistency < gamma_high))[0]] + + ### Save samples original index list### + + folder_path = os.path.join(args.save_path, 'data_produce') + data_path_clean = os.path.join(folder_path, 'clean_samples.npy') + data_path_poison = os.path.join(folder_path, 'poison_samples.npy') + data_path_suspicious = os.path.join(folder_path, 'suspicious_samples.npy') + np.save(data_path_clean, clean_idx_list) + np.save(data_path_poison, poison_idx_list) + np.save(data_path_suspicious, suspicious_idx_list) + logging.info(f"Clean, poison, suspicious samples: {len(clean_idx_list)} {len(poison_idx_list)} {len(suspicious_idx_list)}") + +def separate_samples_back(args, trainloader, model): + gamma_low, gamma_high = args.gamma_low, args.gamma_high + model.eval() + clean_samples, poison_samples, suspicious_samples = [], [], [] + + for i, (inputs, labels, _, _, gt_labels) in enumerate(trainloader): + if args.debug and i==10001: + break + if i % 1000 == 0: + print("Processing samples:", i) + inputs1, inputs2 = inputs[0], inputs[2] + + ### Prepare for saved ### + img = inputs1 + img = img.squeeze() + target = labels.squeeze() + img = np.transpose((img * 255).cpu().numpy(), (1, 2, 0)).astype('uint8') + target = target.cpu().numpy() + + inputs1, inputs2 = normalization(args, inputs1), normalization(args, inputs2) # Normalize + inputs1, inputs2, labels, gt_labels = inputs1.to(args.device), inputs2.to(args.device), labels.to(args.device), gt_labels.to(args.device) + + ### Features ### + # if hasattr(model, "module"): # abandon FC layer + # features_out = list(model.module.children())[:-1] + # else: + # features_out = list(model.children())[:-1] + # modelout = nn.Sequential(*features_out).to(args.device) + # features1, features2 = modelout(inputs1), modelout(inputs2) + features1, features2 = model(inputs1), model(inputs2) + features1, features2 = features1.view(features1.size(0), -1), features2.view(features2.size(0), -1) + + ### Compare consistency ### + feature_consistency = torch.mean((features1 - features2)**2, dim=1) + # feature_consistency = feature_consistency.detach().cpu().numpy() + + ### Separate samples ### + if feature_consistency.item() <= gamma_low: + flag = 0 + clean_samples.append((img, target, flag)) + elif feature_consistency.item() >= gamma_high: + flag = 2 + poison_samples.append((img, target, flag)) + else: + flag = 1 + suspicious_samples.append((img, target, flag)) + + ### Save samples ### + + folder_path = os.path.join(args.save_path, 'data_produce') + + data_path_clean = os.path.join(folder_path, 'clean_samples.npy') + data_path_poison = os.path.join(folder_path, 'poison_samples.npy') + data_path_suspicious = os.path.join(folder_path, 'suspicious_samples.npy') + np.save(data_path_clean, clean_samples) + np.save(data_path_poison, poison_samples) + np.save(data_path_suspicious, suspicious_samples) diff --git a/utils/defense_utils/dbr/utils_br.py b/utils/defense_utils/dbr/utils_br.py new file mode 100644 index 0000000..07506ae --- /dev/null +++ b/utils/defense_utils/dbr/utils_br.py @@ -0,0 +1,166 @@ +import math +import torch.optim as optim +import torch +import numpy as np +import os +import sys +import time + +def warmup_learning_rate(args, epoch, batch_id, total_batches, optimizer): + if args.warm and epoch <= args.warm_epochs: + p = (batch_id + (epoch - 1) * total_batches) / \ + (args.warm_epochs * total_batches) + lr = args.warmup_from + p * (args.warmup_to - args.warmup_from) + + for param_group in optimizer.param_groups: + param_group['lr'] = lr + +def set_optimizer(opt, model): + optimizer = optim.SGD(model.parameters(), + lr=opt.lr, + momentum=0.9, + weight_decay=5e-4) + return optimizer + +def adjust_learning_rate(args, optimizer, epoch): + lr = args.learning_rate + if args.cosine: + eta_min = lr * (args.lr_decay_rate ** 3) + lr = eta_min + (lr - eta_min) * ( + 1 + math.cos(math.pi * epoch / args.epochs)) / 2 + else: + steps = np.sum(epoch > np.asarray(args.lr_decay_epochs)) + if steps > 0: + lr = lr * (args.lr_decay_rate ** steps) + + for param_group in optimizer.param_groups: + param_group['lr'] = lr + +def save_model(model, optimizer, opt, epoch, save_file): + print('==> Saving...') + state = { + 'opt': opt, + 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), + 'epoch': epoch, + } + torch.save(state, save_file) + print('==> Successfully saved!') + del state + +def accuracy(output, target, topk=(1,)): # output: (256,10); target: (256) + """Computes the accuracy over the k top predictions for the specified values of k""" + with torch.no_grad(): + maxk = max(topk) # 5 + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) # pred: (256,5) + pred = pred.t() # (5,256) + correct = pred.eq(target.view(1, -1).expand_as(pred)) # (5,256) + + res = [] + + for k in topk: + # correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) + correct_k = torch.flatten(correct[:k]).float().sum(0, keepdim=True) + res.append(correct_k.mul_(1.0 / batch_size)) + return res + +def format_time(seconds): + days = int(seconds / 3600/24) + seconds = seconds - days*3600*24 + hours = int(seconds / 3600) + seconds = seconds - hours*3600 + minutes = int(seconds / 60) + seconds = seconds - minutes*60 + secondsf = int(seconds) + seconds = seconds - secondsf + millis = int(seconds*1000) + + f = '' + i = 1 + if days > 0: + f += str(days) + 'D' + i += 1 + if hours > 0 and i <= 2: + f += str(hours) + 'h' + i += 1 + if minutes > 0 and i <= 2: + f += str(minutes) + 'm' + i += 1 + if secondsf > 0 and i <= 2: + f += str(secondsf) + 's' + i += 1 + if millis > 0 and i <= 2: + f += str(millis) + 'ms' + i += 1 + if f == '': + f = '0ms' + return f + +# _, term_width = os.popen('stty size', 'r').read().split() +term_width = 80 +term_width = int(term_width) +TOTAL_BAR_LENGTH = 65. +last_time = time.time() +begin_time = last_time + +def progress_bar(current, total, msg=None): + global last_time, begin_time + if current == 0: + begin_time = time.time() # Reset for new bar. + + cur_len = int(TOTAL_BAR_LENGTH*current/total) + rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1 + + sys.stdout.write(' [') + for i in range(cur_len): + sys.stdout.write('=') + sys.stdout.write('>') + for i in range(rest_len): + sys.stdout.write('.') + sys.stdout.write(']') + + cur_time = time.time() + step_time = cur_time - last_time + last_time = cur_time + tot_time = cur_time - begin_time + + L = [] + L.append(' Step: %s' % format_time(step_time)) + L.append(' | Total: %s' % format_time(tot_time)) + if msg: + L.append(' | ' + msg) + + msg = ''.join(L) + sys.stdout.write(msg) + for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3): + sys.stdout.write(' ') + + # Go back to the center of the bar. + for i in range(term_width-int(TOTAL_BAR_LENGTH/2)+2): + sys.stdout.write('\b') + sys.stdout.write(' %d/%d ' % (current+1, total)) + + if current < total-1: + sys.stdout.write('\r') + else: + sys.stdout.write('\n') + sys.stdout.flush() + +class AverageMeter(object): + """Computes and stores the average and current value""" + def __init__(self): + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count diff --git a/utils/defense_utils/dde/dde_model/__init__.py b/utils/defense_utils/dde/dde_model/__init__.py new file mode 100644 index 0000000..c2e05a1 --- /dev/null +++ b/utils/defense_utils/dde/dde_model/__init__.py @@ -0,0 +1,11 @@ +from .vgg_dde import * +from .dde_batchnorm import * +from .preact_dde import * +from .mobilenet_dde import * +from .eff_dde import * +from .den_dde import * +from .dde_layernorm import * +# from .vit_new_dde import * +# from .conv_new_dde import * +from .vit_dde import * +from .conv_dde import * \ No newline at end of file diff --git a/utils/defense_utils/dde/dde_model/conv_dde.py b/utils/defense_utils/dde/dde_model/conv_dde.py new file mode 100644 index 0000000..4e53624 --- /dev/null +++ b/utils/defense_utils/dde/dde_model/conv_dde.py @@ -0,0 +1,272 @@ +from functools import partial +from typing import Any, Callable, Dict, List, Optional, Sequence + +import torch +from torch import nn, Tensor +from torch.nn import functional as F + +from torchvision._internally_replaced_utils import load_state_dict_from_url +from torchvision.ops.misc import ConvNormActivation +from torchvision.ops.stochastic_depth import StochasticDepth +from torchvision.utils import _log_api_usage_once + +from defense.dde import dde_model + +__all__ = [ + "ConvNeXt", + "convnext_tiny", + "convnext_small", + "convnext_base", + "convnext_large", +] + + +_MODELS_URLS: Dict[str, Optional[str]] = { + "convnext_tiny": "https://download.pytorch.org/models/convnext_tiny-983f1562.pth", + "convnext_small": "https://download.pytorch.org/models/convnext_small-0c510722.pth", + "convnext_base": "https://download.pytorch.org/models/convnext_base-6075fbad.pth", + "convnext_large": "https://download.pytorch.org/models/convnext_large-ea097f82.pth", +} + + +class LayerNorm2d(nn.LayerNorm): + def forward(self, x: Tensor) -> Tensor: + x = x.permute(0, 2, 3, 1) + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + x = x.permute(0, 3, 1, 2) + return x + + +class Permute(nn.Module): + def __init__(self, dims: List[int]): + super().__init__() + self.dims = dims + + def forward(self, x): + return torch.permute(x, self.dims) + + +class CNBlock(nn.Module): + def __init__( + self, + dim, + layer_scale: float, + stochastic_depth_prob: float, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + if norm_layer is None: + norm_layer = partial(dde_model.LayerNorm_DDE, eps=1e-6) + + self.block = nn.Sequential( + nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim, bias=True), + Permute([0, 2, 3, 1]), + norm_layer(dim), + nn.Linear(in_features=dim, out_features=4 * dim, bias=True), + nn.GELU(), + nn.Linear(in_features=4 * dim, out_features=dim, bias=True), + Permute([0, 3, 1, 2]), + ) + self.layer_scale = nn.Parameter(torch.ones(dim, 1, 1) * layer_scale) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + + def forward(self, input: Tensor) -> Tensor: + result = self.layer_scale * self.block(input) + result = self.stochastic_depth(result) + result += input + return result + + +class CNBlockConfig: + # Stores information listed at Section 3 of the ConvNeXt paper + def __init__( + self, + input_channels: int, + out_channels: Optional[int], + num_layers: int, + ) -> None: + self.input_channels = input_channels + self.out_channels = out_channels + self.num_layers = num_layers + + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "input_channels={input_channels}" + s += ", out_channels={out_channels}" + s += ", num_layers={num_layers}" + s += ")" + return s.format(**self.__dict__) + + +class ConvNeXt(nn.Module): + def __init__( + self, + block_setting: List[CNBlockConfig], + stochastic_depth_prob: float = 0.0, + layer_scale: float = 1e-6, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any, + ) -> None: + super().__init__() + _log_api_usage_once(self) + + if not block_setting: + raise ValueError("The block_setting should not be empty") + elif not (isinstance(block_setting, Sequence) and all([isinstance(s, CNBlockConfig) for s in block_setting])): + raise TypeError("The block_setting should be List[CNBlockConfig]") + + if block is None: + block = CNBlock + + if norm_layer is None: + norm_layer = partial(dde_model.LayerNorm2D_DDE, eps=1e-6) + + layers: List[nn.Module] = [] + + # Stem + firstconv_output_channels = block_setting[0].input_channels + layers.append( + ConvNormActivation( + 3, + firstconv_output_channels, + kernel_size=4, + stride=4, + padding=0, + norm_layer=norm_layer, + activation_layer=None, + bias=True, + ) + ) + + total_stage_blocks = sum(cnf.num_layers for cnf in block_setting) + stage_block_id = 0 + for cnf in block_setting: + # Bottlenecks + stage: List[nn.Module] = [] + for _ in range(cnf.num_layers): + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * stage_block_id / (total_stage_blocks - 1.0) + stage.append(block(cnf.input_channels, layer_scale, sd_prob)) + stage_block_id += 1 + layers.append(nn.Sequential(*stage)) + if cnf.out_channels is not None: + # Downsampling + layers.append( + nn.Sequential( + norm_layer(cnf.input_channels), + nn.Conv2d(cnf.input_channels, cnf.out_channels, kernel_size=2, stride=2), + ) + ) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + + lastblock = block_setting[-1] + lastconv_output_channels = ( + lastblock.out_channels if lastblock.out_channels is not None else lastblock.input_channels + ) + self.classifier = nn.Sequential( + norm_layer(lastconv_output_channels), nn.Flatten(1), nn.Linear(lastconv_output_channels, num_classes) + ) + + for m in self.modules(): + if isinstance(m, (nn.Conv2d, nn.Linear)): + nn.init.trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + x = self.avgpool(x) + x = self.classifier(x) + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _convnext( + arch: str, + block_setting: List[CNBlockConfig], + stochastic_depth_prob: float, + pretrained: bool, + progress: bool, + **kwargs: Any, +) -> ConvNeXt: + model = ConvNeXt(block_setting, stochastic_depth_prob=stochastic_depth_prob, **kwargs) + if pretrained: + if arch not in _MODELS_URLS: + raise ValueError(f"No checkpoint is available for model type {arch}") + state_dict = load_state_dict_from_url(_MODELS_URLS[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def convnext_tiny(*, pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ConvNeXt: + r"""ConvNeXt Tiny model architecture from the + `"A ConvNet for the 2020s" `_ paper. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + block_setting = [ + CNBlockConfig(96, 192, 3), + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 9), + CNBlockConfig(768, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.1) + return _convnext("convnext_tiny", block_setting, stochastic_depth_prob, pretrained, progress, **kwargs) + + +def convnext_small(*, pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ConvNeXt: + r"""ConvNeXt Small model architecture from the + `"A ConvNet for the 2020s" `_ paper. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + block_setting = [ + CNBlockConfig(96, 192, 3), + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 27), + CNBlockConfig(768, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.4) + return _convnext("convnext_small", block_setting, stochastic_depth_prob, pretrained, progress, **kwargs) + + +def convnext_base(*, pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ConvNeXt: + r"""ConvNeXt Base model architecture from the + `"A ConvNet for the 2020s" `_ paper. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + block_setting = [ + CNBlockConfig(128, 256, 3), + CNBlockConfig(256, 512, 3), + CNBlockConfig(512, 1024, 27), + CNBlockConfig(1024, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5) + return _convnext("convnext_base", block_setting, stochastic_depth_prob, pretrained, progress, **kwargs) + + +def convnext_large(*, pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ConvNeXt: + r"""ConvNeXt Large model architecture from the + `"A ConvNet for the 2020s" `_ paper. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + block_setting = [ + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 3), + CNBlockConfig(768, 1536, 27), + CNBlockConfig(1536, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5) + return _convnext("convnext_large", block_setting, stochastic_depth_prob, pretrained, progress, **kwargs) diff --git a/utils/defense_utils/dde/dde_model/conv_new_dde.py b/utils/defense_utils/dde/dde_model/conv_new_dde.py new file mode 100644 index 0000000..22350d3 --- /dev/null +++ b/utils/defense_utils/dde/dde_model/conv_new_dde.py @@ -0,0 +1,404 @@ +from functools import partial +from typing import Any, Callable, List, Optional, Sequence + +import torch +from torch import nn, Tensor +from torch.nn import functional as F + +from torchvision.ops.misc import Conv2dNormActivation, Permute +from torchvision.ops.stochastic_depth import StochasticDepth +from torchvision.transforms._presets import ImageClassification +from torchvision.utils import _log_api_usage_once +from torchvision.models._api import WeightsEnum, Weights +from torchvision.models._meta import _IMAGENET_CATEGORIES +from torchvision.models._utils import handle_legacy_interface, _ovewrite_named_param + +from defense.dde import dde_model + + +__all__ = [ + "ConvNeXt", + "ConvNeXt_Tiny_Weights", + "ConvNeXt_Small_Weights", + "ConvNeXt_Base_Weights", + "ConvNeXt_Large_Weights", + "convnext_tiny", + "convnext_small", + "convnext_base", + "convnext_large", +] + + +class LayerNorm2d(nn.LayerNorm): + def forward(self, x: Tensor) -> Tensor: + x = x.permute(0, 2, 3, 1) + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + x = x.permute(0, 3, 1, 2) + return x + + +class CNBlock(nn.Module): + def __init__( + self, + dim, + layer_scale: float, + stochastic_depth_prob: float, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + if norm_layer is None: + norm_layer = partial(dde_model.LayerNorm_DDE, eps=1e-6) + + self.block = nn.Sequential( + nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim, bias=True), + Permute([0, 2, 3, 1]), + norm_layer(dim), + nn.Linear(in_features=dim, out_features=4 * dim, bias=True), + nn.GELU(), + nn.Linear(in_features=4 * dim, out_features=dim, bias=True), + Permute([0, 3, 1, 2]), + ) + self.layer_scale = nn.Parameter(torch.ones(dim, 1, 1) * layer_scale) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + + def forward(self, input: Tensor) -> Tensor: + result = self.layer_scale * self.block(input) + result = self.stochastic_depth(result) + result += input + return result + + +class CNBlockConfig: + # Stores information listed at Section 3 of the ConvNeXt paper + def __init__( + self, + input_channels: int, + out_channels: Optional[int], + num_layers: int, + ) -> None: + self.input_channels = input_channels + self.out_channels = out_channels + self.num_layers = num_layers + + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "input_channels={input_channels}" + s += ", out_channels={out_channels}" + s += ", num_layers={num_layers}" + s += ")" + return s.format(**self.__dict__) + + +class ConvNeXt(nn.Module): + def __init__( + self, + block_setting: List[CNBlockConfig], + stochastic_depth_prob: float = 0.0, + layer_scale: float = 1e-6, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any, + ) -> None: + super().__init__() + _log_api_usage_once(self) + + if not block_setting: + raise ValueError("The block_setting should not be empty") + elif not (isinstance(block_setting, Sequence) and all([isinstance(s, CNBlockConfig) for s in block_setting])): + raise TypeError("The block_setting should be List[CNBlockConfig]") + + if block is None: + block = CNBlock + + if norm_layer is None: + norm_layer = partial(dde_model.LayerNorm2D_DDE, eps=1e-6) + + layers: List[nn.Module] = [] + + # Stem + firstconv_output_channels = block_setting[0].input_channels + layers.append( + Conv2dNormActivation( + 3, + firstconv_output_channels, + kernel_size=4, + stride=4, + padding=0, + norm_layer=norm_layer, + activation_layer=None, + bias=True, + ) + ) + + total_stage_blocks = sum(cnf.num_layers for cnf in block_setting) + stage_block_id = 0 + for cnf in block_setting: + # Bottlenecks + stage: List[nn.Module] = [] + for _ in range(cnf.num_layers): + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * stage_block_id / (total_stage_blocks - 1.0) + stage.append(block(cnf.input_channels, layer_scale, sd_prob)) + stage_block_id += 1 + layers.append(nn.Sequential(*stage)) + if cnf.out_channels is not None: + # Downsampling + layers.append( + nn.Sequential( + norm_layer(cnf.input_channels), + nn.Conv2d(cnf.input_channels, cnf.out_channels, kernel_size=2, stride=2), + ) + ) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + + lastblock = block_setting[-1] + lastconv_output_channels = ( + lastblock.out_channels if lastblock.out_channels is not None else lastblock.input_channels + ) + self.classifier = nn.Sequential( + norm_layer(lastconv_output_channels), nn.Flatten(1), nn.Linear(lastconv_output_channels, num_classes) + ) + + for m in self.modules(): + if isinstance(m, (nn.Conv2d, nn.Linear)): + nn.init.trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + x = self.avgpool(x) + x = self.classifier(x) + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _convnext( + block_setting: List[CNBlockConfig], + stochastic_depth_prob: float, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> ConvNeXt: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = ConvNeXt(block_setting, stochastic_depth_prob=stochastic_depth_prob, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress)) + + return model + + +_COMMON_META = { + "min_size": (32, 32), + "categories": _IMAGENET_CATEGORIES, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#convnext", + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, +} + + +class ConvNeXt_Tiny_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_tiny-983f1562.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=236), + meta={ + **_COMMON_META, + "num_params": 28589128, + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.520, + "acc@5": 96.146, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ConvNeXt_Small_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_small-0c510722.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=230), + meta={ + **_COMMON_META, + "num_params": 50223688, + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.616, + "acc@5": 96.650, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ConvNeXt_Base_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_base-6075fbad.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 88591464, + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.062, + "acc@5": 96.870, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ConvNeXt_Large_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_large-ea097f82.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 197767336, + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.414, + "acc@5": 96.976, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Tiny_Weights.IMAGENET1K_V1)) +def convnext_tiny(*, weights: Optional[ConvNeXt_Tiny_Weights] = None, progress: bool = True, **kwargs: Any) -> ConvNeXt: + """ConvNeXt Tiny model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Tiny_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Tiny_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Tiny_Weights + :members: + """ + weights = ConvNeXt_Tiny_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(96, 192, 3), + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 9), + CNBlockConfig(768, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.1) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Small_Weights.IMAGENET1K_V1)) +def convnext_small( + *, weights: Optional[ConvNeXt_Small_Weights] = None, progress: bool = True, **kwargs: Any +) -> ConvNeXt: + """ConvNeXt Small model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Small_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Small_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Small_Weights + :members: + """ + weights = ConvNeXt_Small_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(96, 192, 3), + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 27), + CNBlockConfig(768, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.4) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Base_Weights.IMAGENET1K_V1)) +def convnext_base(*, weights: Optional[ConvNeXt_Base_Weights] = None, progress: bool = True, **kwargs: Any) -> ConvNeXt: + """ConvNeXt Base model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Base_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Base_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Base_Weights + :members: + """ + weights = ConvNeXt_Base_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(128, 256, 3), + CNBlockConfig(256, 512, 3), + CNBlockConfig(512, 1024, 27), + CNBlockConfig(1024, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Large_Weights.IMAGENET1K_V1)) +def convnext_large( + *, weights: Optional[ConvNeXt_Large_Weights] = None, progress: bool = True, **kwargs: Any +) -> ConvNeXt: + """ConvNeXt Large model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Large_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Large_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Large_Weights + :members: + """ + weights = ConvNeXt_Large_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 3), + CNBlockConfig(768, 1536, 27), + CNBlockConfig(1536, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) diff --git a/utils/defense_utils/dde/dde_model/dde_batchnorm.py b/utils/defense_utils/dde/dde_model/dde_batchnorm.py new file mode 100644 index 0000000..4e91446 --- /dev/null +++ b/utils/defense_utils/dde/dde_model/dde_batchnorm.py @@ -0,0 +1,23 @@ +# This code is based on: +# https://pytorch.org/docs/stable/_modules/torch/nn/modules/batchnorm.html#BatchNorm2d +# only perturbing weights + +import torch +from torch import Tensor +import torch.nn as nn +import torch.nn.functional as F +import torch.nn.init as init +from torch.nn.parameter import Parameter + + +class BatchNorm2d_DDE(nn.BatchNorm2d): + def __init__(self, num_features): + super().__init__(num_features) + self.batch_feats = None + self.collect_feats = False + + def forward(self, x): + if self.collect_feats: + self.batch_feats = x.reshape(x.shape[0], x.shape[1], -1).max(-1)[0].permute(1, 0).reshape(x.shape[1], -1) + output = super().forward(x) + return output diff --git a/utils/defense_utils/dde/dde_model/dde_layernorm.py b/utils/defense_utils/dde/dde_model/dde_layernorm.py new file mode 100644 index 0000000..5177dd2 --- /dev/null +++ b/utils/defense_utils/dde/dde_model/dde_layernorm.py @@ -0,0 +1,45 @@ +# This code is based on: +# https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html +# only perturbing weights + +import torch +from torch import Tensor +import torch.nn as nn +import torch.nn.functional as F +import torch.nn.init as init +from torch.nn.parameter import Parameter +from torch import Tensor, Size +from typing import Union, List, Tuple + +_shape_t = Union[int, List[int], Size] + +class LayerNorm_DDE(nn.LayerNorm): + def __init__(self, normalized_shape: _shape_t, eps: float = 1e-5, elementwise_affine: bool = True, + device=None, dtype=None): + super().__init__(normalized_shape = normalized_shape, eps = eps, elementwise_affine = elementwise_affine, + device = device, dtype = dtype) + self.batch_feats = None + self.collect_feats = False + + def forward(self, x): + if self.collect_feats: + self.batch_feats = x.reshape(x.shape[0], x.shape[-1], -1).max(-1)[0].permute(1, 0).reshape(x.shape[-1], -1) + output = super().forward(x) + return output + +class LayerNorm2D_DDE(nn.LayerNorm): + def __init__(self, normalized_shape: _shape_t, eps: float = 1e-5, elementwise_affine: bool = True, + device=None, dtype=None): + super().__init__(normalized_shape = normalized_shape, eps = eps, elementwise_affine = elementwise_affine, + device = device, dtype = dtype) + self.batch_feats = None + self.collect_feats = False + + def forward(self, x): + x = x.permute(0, 2, 3, 1) + if self.collect_feats: + self.batch_feats = x.reshape(x.shape[0], x.shape[-1], -1).max(-1)[0].permute(1, 0).reshape(x.shape[-1], -1) + output = super().forward(x) + output = output.permute(0, 3, 1, 2) + return output + diff --git a/utils/defense_utils/dde/dde_model/den_dde.py b/utils/defense_utils/dde/dde_model/den_dde.py new file mode 100644 index 0000000..4681366 --- /dev/null +++ b/utils/defense_utils/dde/dde_model/den_dde.py @@ -0,0 +1,322 @@ +import re +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from collections import OrderedDict +from torchvision._internally_replaced_utils import load_state_dict_from_url +from typing import Any, Callable, List, Optional, Sequence +from torch import Tensor +from typing import Any, List, Tuple + + +__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161'] + +model_urls = { + 'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth', + 'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth', + 'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth', + 'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth', +} + + +class _DenseLayer(nn.Module): + def __init__( + self, + num_input_features: int, + growth_rate: int, + bn_size: int, + drop_rate: float, + memory_efficient: bool = False, + norm_layer: Optional[Callable[..., nn.Module]] = None + ) -> None: + super(_DenseLayer, self).__init__() + self.norm1: norm_layer + self.add_module('norm1', norm_layer(num_input_features)) + self.relu1: nn.ReLU + self.add_module('relu1', nn.ReLU(inplace=True)) + self.conv1: nn.Conv2d + self.add_module('conv1', nn.Conv2d(num_input_features, bn_size * + growth_rate, kernel_size=1, stride=1, + bias=False)) + self.norm2: norm_layer + self.add_module('norm2', norm_layer(bn_size * growth_rate)) + self.relu2: nn.ReLU + self.add_module('relu2', nn.ReLU(inplace=True)) + self.conv2: nn.Conv2d + self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate, + kernel_size=3, stride=1, padding=1, + bias=False)) + self.drop_rate = float(drop_rate) + self.memory_efficient = memory_efficient + + def bn_function(self, inputs: List[Tensor]) -> Tensor: + concated_features = torch.cat(inputs, 1) + bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features))) # noqa: T484 + return bottleneck_output + + # todo: rewrite when torchscript supports any + def any_requires_grad(self, input: List[Tensor]) -> bool: + for tensor in input: + if tensor.requires_grad: + return True + return False + + @torch.jit.unused # noqa: T484 + def call_checkpoint_bottleneck(self, input: List[Tensor]) -> Tensor: + def closure(*inputs): + return self.bn_function(inputs) + + return cp.checkpoint(closure, *input) + + @torch.jit._overload_method # noqa: F811 + def forward(self, input: List[Tensor]) -> Tensor: + pass + + @torch.jit._overload_method # noqa: F811 + def forward(self, input: Tensor) -> Tensor: + pass + + # torchscript does not yet support *args, so we overload method + # allowing it to take either a List[Tensor] or single Tensor + def forward(self, input: Tensor) -> Tensor: # noqa: F811 + if isinstance(input, Tensor): + prev_features = [input] + else: + prev_features = input + + if self.memory_efficient and self.any_requires_grad(prev_features): + if torch.jit.is_scripting(): + raise Exception("Memory Efficient not supported in JIT") + + bottleneck_output = self.call_checkpoint_bottleneck(prev_features) + else: + bottleneck_output = self.bn_function(prev_features) + + new_features = self.conv2(self.relu2(self.norm2(bottleneck_output))) + if self.drop_rate > 0: + new_features = F.dropout(new_features, p=self.drop_rate, + training=self.training) + return new_features + + +class _DenseBlock(nn.ModuleDict): + _version = 2 + + def __init__( + self, + num_layers: int, + num_input_features: int, + bn_size: int, + growth_rate: int, + drop_rate: float, + memory_efficient: bool = False, + norm_layer: Optional[Callable[..., nn.Module]] = None + ) -> None: + super(_DenseBlock, self).__init__() + for i in range(num_layers): + layer = _DenseLayer( + num_input_features + i * growth_rate, + growth_rate=growth_rate, + bn_size=bn_size, + drop_rate=drop_rate, + memory_efficient=memory_efficient, + norm_layer = norm_layer + ) + self.add_module('denselayer%d' % (i + 1), layer) + + def forward(self, init_features: Tensor) -> Tensor: + features = [init_features] + for name, layer in self.items(): + new_features = layer(features) + features.append(new_features) + return torch.cat(features, 1) + + +class _Transition(nn.Sequential): + def __init__(self, num_input_features: int, num_output_features: int, norm_layer: Optional[Callable[..., nn.Module]] ) -> None: + super(_Transition, self).__init__() + self.add_module('norm', norm_layer(num_input_features)) + self.add_module('relu', nn.ReLU(inplace=True)) + self.add_module('conv', nn.Conv2d(num_input_features, num_output_features, + kernel_size=1, stride=1, bias=False)) + self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2)) + + +class DenseNet(nn.Module): + r"""Densenet-BC model class, based on + `"Densely Connected Convolutional Networks" `_. + + Args: + growth_rate (int) - how many filters to add each layer (`k` in paper) + block_config (list of 4 ints) - how many layers in each pooling block + num_init_features (int) - the number of filters to learn in the first convolution layer + bn_size (int) - multiplicative factor for number of bottle neck layers + (i.e. bn_size * k features in the bottleneck layer) + drop_rate (float) - dropout rate after each dense layer + num_classes (int) - number of classification classes + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + + def __init__( + self, + growth_rate: int = 32, + block_config: Tuple[int, int, int, int] = (6, 12, 24, 16), + num_init_features: int = 64, + bn_size: int = 4, + drop_rate: float = 0, + num_classes: int = 1000, + memory_efficient: bool = False, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + if norm_layer is None: + norm_layer = nn.BatchNorm2d + + super(DenseNet, self).__init__() + + # First convolution + self.features = nn.Sequential(OrderedDict([ + ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, + padding=3, bias=False)), + ('norm0', norm_layer(num_init_features)), + ('relu0', nn.ReLU(inplace=True)), + ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)), + ])) + + # Each denseblock + num_features = num_init_features + for i, num_layers in enumerate(block_config): + block = _DenseBlock( + num_layers=num_layers, + num_input_features=num_features, + bn_size=bn_size, + growth_rate=growth_rate, + drop_rate=drop_rate, + memory_efficient=memory_efficient, + norm_layer = norm_layer + ) + self.features.add_module('denseblock%d' % (i + 1), block) + num_features = num_features + num_layers * growth_rate + if i != len(block_config) - 1: + trans = _Transition(num_input_features=num_features, + num_output_features=num_features // 2, norm_layer=norm_layer) + self.features.add_module('transition%d' % (i + 1), trans) + num_features = num_features // 2 + + # Final batch norm + self.features.add_module('norm5', norm_layer(num_features)) + + # Linear layer + self.classifier = nn.Linear(num_features, num_classes) + + # Official init from torch repo. + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.constant_(m.bias, 0) + + def forward(self, x: Tensor) -> Tensor: + features = self.features(x) + out = F.relu(features, inplace=True) + out = F.adaptive_avg_pool2d(out, (1, 1)) + out = torch.flatten(out, 1) + out = self.classifier(out) + return out + + +def _load_state_dict(model: nn.Module, model_url: str, progress: bool) -> None: + # '.'s are no longer allowed in module names, but previous _DenseLayer + # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'. + # They are also in the checkpoints in model_urls. This pattern is used + # to find such keys. + pattern = re.compile( + r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$') + + state_dict = load_state_dict_from_url(model_url, progress=progress) + for key in list(state_dict.keys()): + res = pattern.match(key) + if res: + new_key = res.group(1) + res.group(2) + state_dict[new_key] = state_dict[key] + del state_dict[key] + model.load_state_dict(state_dict) + + +def _densenet( + arch: str, + growth_rate: int, + block_config: Tuple[int, int, int, int], + num_init_features: int, + pretrained: bool, + progress: bool, + **kwargs: Any +) -> DenseNet: + model = DenseNet(growth_rate, block_config, num_init_features, **kwargs) + if pretrained: + _load_state_dict(model, model_urls[arch], progress) + return model + + +def densenet121(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-121 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress, + **kwargs) + + +def densenet161(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-161 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress, + **kwargs) + + +def densenet169(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-169 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet169', 32, (6, 12, 32, 32), 64, pretrained, progress, + **kwargs) + + +def densenet201(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-201 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress, + **kwargs) diff --git a/utils/defense_utils/dde/dde_model/eff_dde.py b/utils/defense_utils/dde/dde_model/eff_dde.py new file mode 100644 index 0000000..07f6ba0 --- /dev/null +++ b/utils/defense_utils/dde/dde_model/eff_dde.py @@ -0,0 +1,350 @@ +import copy +import math +import torch + +from functools import partial +from torch import nn, Tensor +from typing import Any, Callable, List, Optional, Sequence + +from torchvision._internally_replaced_utils import load_state_dict_from_url +from torchvision.ops.misc import ConvNormActivation, SqueezeExcitation +from torchvision.models._utils import _make_divisible +from torchvision.ops import StochasticDepth + + +__all__ = ["EfficientNet", "efficientnet_b0", "efficientnet_b1", "efficientnet_b2", "efficientnet_b3", + "efficientnet_b4", "efficientnet_b5", "efficientnet_b6", "efficientnet_b7"] + + +model_urls = { + # Weights ported from https://github.com/rwightman/pytorch-image-models/ + "efficientnet_b0": "https://download.pytorch.org/models/efficientnet_b0_rwightman-3dd342df.pth", + "efficientnet_b1": "https://download.pytorch.org/models/efficientnet_b1_rwightman-533bc792.pth", + "efficientnet_b2": "https://download.pytorch.org/models/efficientnet_b2_rwightman-bcdf34b7.pth", + "efficientnet_b3": "https://download.pytorch.org/models/efficientnet_b3_rwightman-cf984f9c.pth", + "efficientnet_b4": "https://download.pytorch.org/models/efficientnet_b4_rwightman-7eb33cd5.pth", + # Weights ported from https://github.com/lukemelas/EfficientNet-PyTorch/ + "efficientnet_b5": "https://download.pytorch.org/models/efficientnet_b5_lukemelas-b6417697.pth", + "efficientnet_b6": "https://download.pytorch.org/models/efficientnet_b6_lukemelas-c76e70fd.pth", + "efficientnet_b7": "https://download.pytorch.org/models/efficientnet_b7_lukemelas-dcc49843.pth", +} + + +class MBConvConfig: + # Stores information listed at Table 1 of the EfficientNet paper + def __init__(self, + expand_ratio: float, kernel: int, stride: int, + input_channels: int, out_channels: int, num_layers: int, + width_mult: float, depth_mult: float) -> None: + self.expand_ratio = expand_ratio + self.kernel = kernel + self.stride = stride + self.input_channels = self.adjust_channels(input_channels, width_mult) + self.out_channels = self.adjust_channels(out_channels, width_mult) + self.num_layers = self.adjust_depth(num_layers, depth_mult) + + def __repr__(self) -> str: + s = self.__class__.__name__ + '(' + s += 'expand_ratio={expand_ratio}' + s += ', kernel={kernel}' + s += ', stride={stride}' + s += ', input_channels={input_channels}' + s += ', out_channels={out_channels}' + s += ', num_layers={num_layers}' + s += ')' + return s.format(**self.__dict__) + + @staticmethod + def adjust_channels(channels: int, width_mult: float, min_value: Optional[int] = None) -> int: + return _make_divisible(channels * width_mult, 8, min_value) + + @staticmethod + def adjust_depth(num_layers: int, depth_mult: float): + return int(math.ceil(num_layers * depth_mult)) + + +class MBConv(nn.Module): + def __init__(self, cnf: MBConvConfig, stochastic_depth_prob: float, norm_layer: Callable[..., nn.Module], + se_layer: Callable[..., nn.Module] = SqueezeExcitation) -> None: + super().__init__() + + if not (1 <= cnf.stride <= 2): + raise ValueError('illegal stride value') + + self.use_res_connect = cnf.stride == 1 and cnf.input_channels == cnf.out_channels + + layers: List[nn.Module] = [] + activation_layer = nn.SiLU + + # expand + expanded_channels = cnf.adjust_channels(cnf.input_channels, cnf.expand_ratio) + if expanded_channels != cnf.input_channels: + layers.append(ConvNormActivation(cnf.input_channels, expanded_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=activation_layer)) + + # depthwise + layers.append(ConvNormActivation(expanded_channels, expanded_channels, kernel_size=cnf.kernel, + stride=cnf.stride, groups=expanded_channels, + norm_layer=norm_layer, activation_layer=activation_layer)) + + # squeeze and excitation + squeeze_channels = max(1, cnf.input_channels // 4) + layers.append(se_layer(expanded_channels, squeeze_channels, activation=partial(nn.SiLU, inplace=True))) + + # project + layers.append(ConvNormActivation(expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, + activation_layer=None)) + + self.block = nn.Sequential(*layers) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + self.out_channels = cnf.out_channels + + def forward(self, input: Tensor) -> Tensor: + result = self.block(input) + if self.use_res_connect: + result = self.stochastic_depth(result) + result += input + return result + + +class EfficientNet(nn.Module): + def __init__( + self, + inverted_residual_setting: List[MBConvConfig], + dropout: float, + stochastic_depth_prob: float = 0.2, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any + ) -> None: + """ + EfficientNet main class + + Args: + inverted_residual_setting (List[MBConvConfig]): Network structure + dropout (float): The droupout probability + stochastic_depth_prob (float): The stochastic depth probability + num_classes (int): Number of classes + block (Optional[Callable[..., nn.Module]]): Module specifying inverted residual building block for mobilenet + norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use + """ + super().__init__() + + if not inverted_residual_setting: + raise ValueError("The inverted_residual_setting should not be empty") + elif not (isinstance(inverted_residual_setting, Sequence) and + all([isinstance(s, MBConvConfig) for s in inverted_residual_setting])): + raise TypeError("The inverted_residual_setting should be List[MBConvConfig]") + + if block is None: + block = MBConv + + if norm_layer is None: + norm_layer = nn.BatchNorm2d + + layers: List[nn.Module] = [] + + # building first layer + firstconv_output_channels = inverted_residual_setting[0].input_channels + layers.append(ConvNormActivation(3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer, + activation_layer=nn.SiLU)) + + # building inverted residual blocks + total_stage_blocks = sum([cnf.num_layers for cnf in inverted_residual_setting]) + stage_block_id = 0 + for cnf in inverted_residual_setting: + stage: List[nn.Module] = [] + for _ in range(cnf.num_layers): + # copy to avoid modifications. shallow copy is enough + block_cnf = copy.copy(cnf) + + # overwrite info if not the first conv in the stage + if stage: + block_cnf.input_channels = block_cnf.out_channels + block_cnf.stride = 1 + + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * float(stage_block_id) / total_stage_blocks + + stage.append(block(block_cnf, sd_prob, norm_layer)) + stage_block_id += 1 + + layers.append(nn.Sequential(*stage)) + + # building last several layers + lastconv_input_channels = inverted_residual_setting[-1].out_channels + lastconv_output_channels = 4 * lastconv_input_channels + layers.append(ConvNormActivation(lastconv_input_channels, lastconv_output_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=nn.SiLU)) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Sequential( + nn.Dropout(p=dropout, inplace=True), + nn.Linear(lastconv_output_channels, num_classes), + ) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + init_range = 1.0 / math.sqrt(m.out_features) + nn.init.uniform_(m.weight, -init_range, init_range) + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.classifier(x) + + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _efficientnet_conf(width_mult: float, depth_mult: float, **kwargs: Any) -> List[MBConvConfig]: + bneck_conf = partial(MBConvConfig, width_mult=width_mult, depth_mult=depth_mult) + inverted_residual_setting = [ + bneck_conf(1, 3, 1, 32, 16, 1), + bneck_conf(6, 3, 2, 16, 24, 2), + bneck_conf(6, 5, 2, 24, 40, 2), + bneck_conf(6, 3, 2, 40, 80, 3), + bneck_conf(6, 5, 1, 80, 112, 3), + bneck_conf(6, 5, 2, 112, 192, 4), + bneck_conf(6, 3, 1, 192, 320, 1), + ] + return inverted_residual_setting + + +def _efficientnet_model( + arch: str, + inverted_residual_setting: List[MBConvConfig], + dropout: float, + pretrained: bool, + progress: bool, + **kwargs: Any +) -> EfficientNet: + model = EfficientNet(inverted_residual_setting, dropout, **kwargs) + if pretrained: + if model_urls.get(arch, None) is None: + raise ValueError("No checkpoint is available for model type {}".format(arch)) + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def efficientnet_b0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B0 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.0, **kwargs) + return _efficientnet_model("efficientnet_b0", inverted_residual_setting, 0.2, pretrained, progress, **kwargs) + + +def efficientnet_b1(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B1 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.1, **kwargs) + return _efficientnet_model("efficientnet_b1", inverted_residual_setting, 0.2, pretrained, progress, **kwargs) + + +def efficientnet_b2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B2 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.1, depth_mult=1.2, **kwargs) + return _efficientnet_model("efficientnet_b2", inverted_residual_setting, 0.3, pretrained, progress, **kwargs) + + +def efficientnet_b3(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B3 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.2, depth_mult=1.4, **kwargs) + return _efficientnet_model("efficientnet_b3", inverted_residual_setting, 0.3, pretrained, progress, **kwargs) + + +def efficientnet_b4(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B4 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.4, depth_mult=1.8, **kwargs) + return _efficientnet_model("efficientnet_b4", inverted_residual_setting, 0.4, pretrained, progress, **kwargs) + + +def efficientnet_b5(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B5 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.6, depth_mult=2.2, **kwargs) + return _efficientnet_model("efficientnet_b5", inverted_residual_setting, 0.4, pretrained, progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), **kwargs) + + +def efficientnet_b6(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B6 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.8, depth_mult=2.6, **kwargs) + return _efficientnet_model("efficientnet_b6", inverted_residual_setting, 0.5, pretrained, progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), **kwargs) + + +def efficientnet_b7(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B7 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=2.0, depth_mult=3.1, **kwargs) + return _efficientnet_model("efficientnet_b7", inverted_residual_setting, 0.5, pretrained, progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), **kwargs) diff --git a/utils/defense_utils/dde/dde_model/mobilenet_dde.py b/utils/defense_utils/dde/dde_model/mobilenet_dde.py new file mode 100644 index 0000000..966c15e --- /dev/null +++ b/utils/defense_utils/dde/dde_model/mobilenet_dde.py @@ -0,0 +1,272 @@ +import warnings +import torch + +from functools import partial +from torch import nn, Tensor +from typing import Any, Callable, List, Optional, Sequence + +from torchvision._internally_replaced_utils import load_state_dict_from_url +from torchvision.ops.misc import ConvNormActivation, SqueezeExcitation as SElayer +from torchvision.models._utils import _make_divisible + + +__all__ = ["MobileNetV3", "mobilenet_v3_large", "mobilenet_v3_small"] + + +model_urls = { + "mobilenet_v3_large": "https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth", + "mobilenet_v3_small": "https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth", +} + + +class SqueezeExcitation(SElayer): + """DEPRECATED + """ + def __init__(self, input_channels: int, squeeze_factor: int = 4): + squeeze_channels = _make_divisible(input_channels // squeeze_factor, 8) + super().__init__(input_channels, squeeze_channels, scale_activation=nn.Hardsigmoid) + self.relu = self.activation + delattr(self, 'activation') + warnings.warn( + "This SqueezeExcitation class is deprecated and will be removed in future versions. " + "Use torchvision.ops.misc.SqueezeExcitation instead.", FutureWarning) + + +class InvertedResidualConfig: + # Stores information listed at Tables 1 and 2 of the MobileNetV3 paper + def __init__(self, input_channels: int, kernel: int, expanded_channels: int, out_channels: int, use_se: bool, + activation: str, stride: int, dilation: int, width_mult: float): + self.input_channels = self.adjust_channels(input_channels, width_mult) + self.kernel = kernel + self.expanded_channels = self.adjust_channels(expanded_channels, width_mult) + self.out_channels = self.adjust_channels(out_channels, width_mult) + self.use_se = use_se + self.use_hs = activation == "HS" + self.stride = stride + self.dilation = dilation + + @staticmethod + def adjust_channels(channels: int, width_mult: float): + return _make_divisible(channels * width_mult, 8) + + +class InvertedResidual(nn.Module): + # Implemented as described at section 5 of MobileNetV3 paper + def __init__(self, cnf: InvertedResidualConfig, norm_layer: Callable[..., nn.Module], + se_layer: Callable[..., nn.Module] = partial(SElayer, scale_activation=nn.Hardsigmoid)): + super().__init__() + if not (1 <= cnf.stride <= 2): + raise ValueError('illegal stride value') + + self.use_res_connect = cnf.stride == 1 and cnf.input_channels == cnf.out_channels + + layers: List[nn.Module] = [] + activation_layer = nn.Hardswish if cnf.use_hs else nn.ReLU + + # expand + if cnf.expanded_channels != cnf.input_channels: + layers.append(ConvNormActivation(cnf.input_channels, cnf.expanded_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=activation_layer)) + + # depthwise + stride = 1 if cnf.dilation > 1 else cnf.stride + layers.append(ConvNormActivation(cnf.expanded_channels, cnf.expanded_channels, kernel_size=cnf.kernel, + stride=stride, dilation=cnf.dilation, groups=cnf.expanded_channels, + norm_layer=norm_layer, activation_layer=activation_layer)) + if cnf.use_se: + squeeze_channels = _make_divisible(cnf.expanded_channels // 4, 8) + layers.append(se_layer(cnf.expanded_channels, squeeze_channels)) + + # project + layers.append(ConvNormActivation(cnf.expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, + activation_layer=None)) + + self.block = nn.Sequential(*layers) + self.out_channels = cnf.out_channels + self._is_cn = cnf.stride > 1 + + def forward(self, input: Tensor) -> Tensor: + result = self.block(input) + if self.use_res_connect: + result += input + return result + + +class MobileNetV3(nn.Module): + + def __init__( + self, + inverted_residual_setting: List[InvertedResidualConfig], + last_channel: int, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any + ) -> None: + """ + MobileNet V3 main class + + Args: + inverted_residual_setting (List[InvertedResidualConfig]): Network structure + last_channel (int): The number of channels on the penultimate layer + num_classes (int): Number of classes + block (Optional[Callable[..., nn.Module]]): Module specifying inverted residual building block for mobilenet + norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use + """ + super().__init__() + + if not inverted_residual_setting: + raise ValueError("The inverted_residual_setting should not be empty") + elif not (isinstance(inverted_residual_setting, Sequence) and + all([isinstance(s, InvertedResidualConfig) for s in inverted_residual_setting])): + raise TypeError("The inverted_residual_setting should be List[InvertedResidualConfig]") + + if block is None: + block = InvertedResidual + + if norm_layer is None: + norm_layer = partial(nn.BatchNorm2d, eps=0.001, momentum=0.01) + + layers: List[nn.Module] = [] + + # building first layer + firstconv_output_channels = inverted_residual_setting[0].input_channels + layers.append(ConvNormActivation(3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer, + activation_layer=nn.Hardswish)) + + # building inverted residual blocks + for cnf in inverted_residual_setting: + layers.append(block(cnf, norm_layer)) + + # building last several layers + lastconv_input_channels = inverted_residual_setting[-1].out_channels + lastconv_output_channels = 6 * lastconv_input_channels + layers.append(ConvNormActivation(lastconv_input_channels, lastconv_output_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=nn.Hardswish)) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Sequential( + nn.Linear(lastconv_output_channels, last_channel), + nn.Hardswish(inplace=True), + nn.Dropout(p=0.2, inplace=True), + nn.Linear(last_channel, num_classes), + ) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.classifier(x) + + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _mobilenet_v3_conf(arch: str, width_mult: float = 1.0, reduced_tail: bool = False, dilated: bool = False, + **kwargs: Any): + reduce_divider = 2 if reduced_tail else 1 + dilation = 2 if dilated else 1 + + bneck_conf = partial(InvertedResidualConfig, width_mult=width_mult) + adjust_channels = partial(InvertedResidualConfig.adjust_channels, width_mult=width_mult) + + if arch == "mobilenet_v3_large": + inverted_residual_setting = [ + bneck_conf(16, 3, 16, 16, False, "RE", 1, 1), + bneck_conf(16, 3, 64, 24, False, "RE", 2, 1), # C1 + bneck_conf(24, 3, 72, 24, False, "RE", 1, 1), + bneck_conf(24, 5, 72, 40, True, "RE", 2, 1), # C2 + bneck_conf(40, 5, 120, 40, True, "RE", 1, 1), + bneck_conf(40, 5, 120, 40, True, "RE", 1, 1), + bneck_conf(40, 3, 240, 80, False, "HS", 2, 1), # C3 + bneck_conf(80, 3, 200, 80, False, "HS", 1, 1), + bneck_conf(80, 3, 184, 80, False, "HS", 1, 1), + bneck_conf(80, 3, 184, 80, False, "HS", 1, 1), + bneck_conf(80, 3, 480, 112, True, "HS", 1, 1), + bneck_conf(112, 3, 672, 112, True, "HS", 1, 1), + bneck_conf(112, 5, 672, 160 // reduce_divider, True, "HS", 2, dilation), # C4 + bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1, dilation), + bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1, dilation), + ] + last_channel = adjust_channels(1280 // reduce_divider) # C5 + elif arch == "mobilenet_v3_small": + inverted_residual_setting = [ + bneck_conf(16, 3, 16, 16, True, "RE", 2, 1), # C1 + bneck_conf(16, 3, 72, 24, False, "RE", 2, 1), # C2 + bneck_conf(24, 3, 88, 24, False, "RE", 1, 1), + bneck_conf(24, 5, 96, 40, True, "HS", 2, 1), # C3 + bneck_conf(40, 5, 240, 40, True, "HS", 1, 1), + bneck_conf(40, 5, 240, 40, True, "HS", 1, 1), + bneck_conf(40, 5, 120, 48, True, "HS", 1, 1), + bneck_conf(48, 5, 144, 48, True, "HS", 1, 1), + bneck_conf(48, 5, 288, 96 // reduce_divider, True, "HS", 2, dilation), # C4 + bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1, dilation), + bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1, dilation), + ] + last_channel = adjust_channels(1024 // reduce_divider) # C5 + else: + raise ValueError("Unsupported model type {}".format(arch)) + + return inverted_residual_setting, last_channel + + +def _mobilenet_v3_model( + arch: str, + inverted_residual_setting: List[InvertedResidualConfig], + last_channel: int, + pretrained: bool, + progress: bool, + **kwargs: Any +): + model = MobileNetV3(inverted_residual_setting, last_channel, **kwargs) + if pretrained: + if model_urls.get(arch, None) is None: + raise ValueError("No checkpoint is available for model type {}".format(arch)) + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def mobilenet_v3_large(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MobileNetV3: + """ + Constructs a large MobileNetV3 architecture from + `"Searching for MobileNetV3" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + arch = "mobilenet_v3_large" + inverted_residual_setting, last_channel = _mobilenet_v3_conf(arch, **kwargs) + return _mobilenet_v3_model(arch, inverted_residual_setting, last_channel, pretrained, progress, **kwargs) + + +def mobilenet_v3_small(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MobileNetV3: + """ + Constructs a small MobileNetV3 architecture from + `"Searching for MobileNetV3" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + arch = "mobilenet_v3_small" + inverted_residual_setting, last_channel = _mobilenet_v3_conf(arch, **kwargs) + return _mobilenet_v3_model(arch, inverted_residual_setting, last_channel, pretrained, progress, **kwargs) diff --git a/utils/defense_utils/dde/dde_model/preact_dde.py b/utils/defense_utils/dde/dde_model/preact_dde.py new file mode 100644 index 0000000..14b07c1 --- /dev/null +++ b/utils/defense_utils/dde/dde_model/preact_dde.py @@ -0,0 +1,135 @@ +"""Pre-activation ResNet in PyTorch. + +Reference: +[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Identity Mappings in Deep Residual Networks. arXiv:1603.05027 +""" +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class PreActBlock(nn.Module): + """Pre-activation version of the BasicBlock.""" + + expansion = 1 + + def __init__(self, in_planes, planes, stride=1, norm_layer = None): + if norm_layer is None: + norm_layer = nn.BatchNorm2d + super(PreActBlock, self).__init__() + self.bn1 = norm_layer(in_planes) + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = norm_layer(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.ind = None + + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False) + ) + + def forward(self, x): + out = F.relu(self.bn1(x)) + shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x + out = self.conv1(out) + out = self.conv2(F.relu(self.bn2(out))) + if self.ind is not None: + out += shortcut[:, self.ind, :, :] + else: + out += shortcut + return out + + +class PreActBottleneck(nn.Module): + """Pre-activation version of the original Bottleneck module.""" + + expansion = 4 + + def __init__(self, in_planes, planes, stride=1): + super(PreActBottleneck, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) + + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False) + ) + + def forward(self, x): + out = F.relu(self.bn1(x)) + shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x + out = self.conv1(out) + out = self.conv2(F.relu(self.bn2(out))) + out = self.conv3(F.relu(self.bn3(out))) + out += shortcut + return out + + +class PreActResNet(nn.Module): + def __init__(self, block, num_blocks, num_classes=10, norm_layer=None): + super(PreActResNet, self).__init__() + self.in_planes = 64 + if norm_layer is None: + norm_layer = nn.BatchNorm2d + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1, norm_layer=norm_layer) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2, norm_layer=norm_layer) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2, norm_layer=norm_layer) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2, norm_layer=norm_layer) + self.avgpool = nn.AdaptiveAvgPool2d((1,1)) + # self.feature_dim = 512 + self.linear = nn.Linear(512 * block.expansion, num_classes) + + def _make_layer(self, block, planes, num_blocks, stride, norm_layer): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride, norm_layer)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + out = self.conv1(x) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = self.avgpool(out) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + + +def PreActResNet18(num_classes=10, norm_layer=nn.BatchNorm2d): + return PreActResNet(PreActBlock, [2, 2, 2, 2], num_classes=num_classes, norm_layer=norm_layer) + + +def PreActResNet34(): + return PreActResNet(PreActBlock, [3, 4, 6, 3]) + + +def PreActResNet50(): + return PreActResNet(PreActBottleneck, [3, 4, 6, 3]) + + +def PreActResNet101(): + return PreActResNet(PreActBottleneck, [3, 4, 23, 3]) + + +def PreActResNet152(): + return PreActResNet(PreActBottleneck, [3, 8, 36, 3]) + + +def test(): + net = PreActResNet18() + y = net((torch.randn(1, 3, 32, 32))) + print(y.size()) + + +# test() diff --git a/utils/defense_utils/dde/dde_model/vgg_dde.py b/utils/defense_utils/dde/dde_model/vgg_dde.py new file mode 100644 index 0000000..cf83341 --- /dev/null +++ b/utils/defense_utils/dde/dde_model/vgg_dde.py @@ -0,0 +1,200 @@ +import torch +import torch.nn as nn +from torchvision._internally_replaced_utils import load_state_dict_from_url +from typing import Union, List, Dict, Any, cast + + +__all__ = [ + 'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', + 'vgg19_bn', 'vgg19', +] + + +model_urls = { + 'vgg11': 'https://download.pytorch.org/models/vgg11-8a719046.pth', + 'vgg13': 'https://download.pytorch.org/models/vgg13-19584684.pth', + 'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth', + 'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth', + 'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth', + 'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth', + 'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth', + 'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth', +} + + +class VGG(nn.Module): + + def __init__( + self, + features: nn.Module, + num_classes: int = 1000, + init_weights: bool = True + ) -> None: + super(VGG, self).__init__() + self.features = features + self.avgpool = nn.AdaptiveAvgPool2d((7, 7)) + self.classifier = nn.Sequential( + nn.Linear(512 * 7 * 7, 4096), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(4096, 4096), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(4096, num_classes), + ) + if init_weights: + self._initialize_weights() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.features(x) + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.classifier(x) + return x + + def _initialize_weights(self) -> None: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.constant_(m.bias, 0) + + +def make_layers(cfg: List[Union[str, int]], batch_norm: bool = False, norm_layer = None) -> nn.Sequential: + if norm_layer is None: + norm_layer = nn.BatchNorm2d + layers: List[nn.Module] = [] + in_channels = 3 + for v in cfg: + if v == 'M': + layers += [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + v = cast(int, v) + conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) + if batch_norm: + layers += [conv2d, norm_layer(v), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = v + return nn.Sequential(*layers) + + +cfgs: Dict[str, List[Union[str, int]]] = { + 'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], + 'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], + 'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], + 'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'], +} + + +def _vgg(arch: str, cfg: str, batch_norm: bool, pretrained: bool, progress: bool, norm_layer, **kwargs: Any) -> VGG: + if pretrained: + kwargs['init_weights'] = False + model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm, norm_layer = norm_layer), **kwargs) + if pretrained: + state_dict = load_state_dict_from_url(model_urls[arch], + progress=progress) + model.load_state_dict(state_dict) + return model + + +def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 11-layer model (configuration "A") from + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg11', 'A', False, pretrained, progress, **kwargs) + + +def vgg11_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 11-layer model (configuration "A") with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg11_bn', 'A', True, pretrained, progress, **kwargs) + + +def vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 13-layer model (configuration "B") + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg13', 'B', False, pretrained, progress, **kwargs) + + +def vgg13_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 13-layer model (configuration "B") with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg13_bn', 'B', True, pretrained, progress, **kwargs) + + +def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 16-layer model (configuration "D") + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg16', 'D', False, pretrained, progress, **kwargs) + + +def vgg16_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 16-layer model (configuration "D") with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg16_bn', 'D', True, pretrained, progress, **kwargs) + + +def vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 19-layer model (configuration "E") + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg19', 'E', False, pretrained, progress, **kwargs) + + +def vgg19_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 19-layer model (configuration 'E') with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg19_bn', 'E', True, pretrained, progress, **kwargs) diff --git a/utils/defense_utils/dde/dde_model/vit_dde.py b/utils/defense_utils/dde/dde_model/vit_dde.py new file mode 100644 index 0000000..fa6c4a9 --- /dev/null +++ b/utils/defense_utils/dde/dde_model/vit_dde.py @@ -0,0 +1,470 @@ +import math +from collections import OrderedDict +from functools import partial +from typing import Any, Callable, List, NamedTuple, Optional + +import torch +import torch.nn as nn + +from torchvision._internally_replaced_utils import load_state_dict_from_url +from torchvision.ops.misc import ConvNormActivation +from torchvision.utils import _log_api_usage_once + +from utils.defense_utils.dde import dde_model + +__all__ = [ + "VisionTransformer", + "vit_b_16", + "vit_b_32", + "vit_l_16", + "vit_l_32", +] + +model_urls = { + "vit_b_16": "https://download.pytorch.org/models/vit_b_16-c867db91.pth", + "vit_b_32": "https://download.pytorch.org/models/vit_b_32-d86f8d99.pth", + "vit_l_16": "https://download.pytorch.org/models/vit_l_16-852ce7e3.pth", + "vit_l_32": "https://download.pytorch.org/models/vit_l_32-c7638314.pth", +} + + +class ConvStemConfig(NamedTuple): + out_channels: int + kernel_size: int + stride: int + norm_layer: Callable[..., nn.Module] = dde_model.LayerNorm_DDE + activation_layer: Callable[..., nn.Module] = nn.ReLU + + +class MLPBlock(nn.Sequential): + """Transformer MLP block.""" + + def __init__(self, in_dim: int, mlp_dim: int, dropout: float): + super().__init__() + self.linear_1 = nn.Linear(in_dim, mlp_dim) + self.act = nn.GELU() + self.dropout_1 = nn.Dropout(dropout) + self.linear_2 = nn.Linear(mlp_dim, in_dim) + self.dropout_2 = nn.Dropout(dropout) + + nn.init.xavier_uniform_(self.linear_1.weight) + nn.init.xavier_uniform_(self.linear_2.weight) + nn.init.normal_(self.linear_1.bias, std=1e-6) + nn.init.normal_(self.linear_2.bias, std=1e-6) + + +class EncoderBlock(nn.Module): + """Transformer encoder block.""" + + def __init__( + self, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float, + attention_dropout: float, + norm_layer: Callable[..., torch.nn.Module] = partial(dde_model.LayerNorm_DDE, eps=1e-6), + ): + super().__init__() + self.num_heads = num_heads + + # Attention block + self.ln_1 = norm_layer(hidden_dim) + self.self_attention = nn.MultiheadAttention(hidden_dim, num_heads, dropout=attention_dropout, batch_first=True) + self.dropout = nn.Dropout(dropout) + + # MLP block + self.ln_2 = norm_layer(hidden_dim) + self.mlp = MLPBlock(hidden_dim, mlp_dim, dropout) + + def forward(self, input: torch.Tensor): + torch._assert(input.dim() == 3, f"Expected (seq_length, batch_size, hidden_dim) got {input.shape}") + x = self.ln_1(input) + x, _ = self.self_attention(query=x, key=x, value=x, need_weights=False) + x = self.dropout(x) + x = x + input + + y = self.ln_2(x) + y = self.mlp(y) + return x + y + + +class Encoder(nn.Module): + """Transformer Model Encoder for sequence to sequence translation.""" + + def __init__( + self, + seq_length: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float, + attention_dropout: float, + norm_layer: Callable[..., torch.nn.Module] = partial(dde_model.LayerNorm_DDE, eps=1e-6), + ): + super().__init__() + # Note that batch_size is on the first dim because + # we have batch_first=True in nn.MultiAttention() by default + self.pos_embedding = nn.Parameter(torch.empty(1, seq_length, hidden_dim).normal_(std=0.02)) # from BERT + self.dropout = nn.Dropout(dropout) + layers: OrderedDict[str, nn.Module] = OrderedDict() + for i in range(num_layers): + layers[f"encoder_layer_{i}"] = EncoderBlock( + num_heads, + hidden_dim, + mlp_dim, + dropout, + attention_dropout, + norm_layer, + ) + self.layers = nn.Sequential(layers) + self.ln = norm_layer(hidden_dim) + + def forward(self, input: torch.Tensor): + torch._assert(input.dim() == 3, f"Expected (batch_size, seq_length, hidden_dim) got {input.shape}") + input = input + self.pos_embedding + return self.ln(self.layers(self.dropout(input))) + + +class VisionTransformer(nn.Module): + """Vision Transformer as per https://arxiv.org/abs/2010.11929.""" + + def __init__( + self, + image_size: int, + patch_size: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float = 0.0, + attention_dropout: float = 0.0, + num_classes: int = 1000, + representation_size: Optional[int] = None, + norm_layer: Callable[..., torch.nn.Module] = partial(dde_model.LayerNorm_DDE, eps=1e-6), + conv_stem_configs: Optional[List[ConvStemConfig]] = None, + ): + super().__init__() + _log_api_usage_once(self) + torch._assert(image_size % patch_size == 0, "Input shape indivisible by patch size!") + self.image_size = image_size + self.patch_size = patch_size + self.hidden_dim = hidden_dim + self.mlp_dim = mlp_dim + self.attention_dropout = attention_dropout + self.dropout = dropout + self.num_classes = num_classes + self.representation_size = representation_size + self.norm_layer = norm_layer + + if conv_stem_configs is not None: + # As per https://arxiv.org/abs/2106.14881 + seq_proj = nn.Sequential() + prev_channels = 3 + for i, conv_stem_layer_config in enumerate(conv_stem_configs): + seq_proj.add_module( + f"conv_bn_relu_{i}", + ConvNormActivation( + in_channels=prev_channels, + out_channels=conv_stem_layer_config.out_channels, + kernel_size=conv_stem_layer_config.kernel_size, + stride=conv_stem_layer_config.stride, + norm_layer=conv_stem_layer_config.norm_layer, + activation_layer=conv_stem_layer_config.activation_layer, + ), + ) + prev_channels = conv_stem_layer_config.out_channels + seq_proj.add_module( + "conv_last", nn.Conv2d(in_channels=prev_channels, out_channels=hidden_dim, kernel_size=1) + ) + self.conv_proj: nn.Module = seq_proj + else: + self.conv_proj = nn.Conv2d( + in_channels=3, out_channels=hidden_dim, kernel_size=patch_size, stride=patch_size + ) + + seq_length = (image_size // patch_size) ** 2 + + # Add a class token + self.class_token = nn.Parameter(torch.zeros(1, 1, hidden_dim)) + seq_length += 1 + + self.encoder = Encoder( + seq_length, + num_layers, + num_heads, + hidden_dim, + mlp_dim, + dropout, + attention_dropout, + norm_layer, + ) + self.seq_length = seq_length + + heads_layers: OrderedDict[str, nn.Module] = OrderedDict() + if representation_size is None: + heads_layers["head"] = nn.Linear(hidden_dim, num_classes) + else: + heads_layers["pre_logits"] = nn.Linear(hidden_dim, representation_size) + heads_layers["act"] = nn.Tanh() + heads_layers["head"] = nn.Linear(representation_size, num_classes) + + self.heads = nn.Sequential(heads_layers) + + if isinstance(self.conv_proj, nn.Conv2d): + # Init the patchify stem + fan_in = self.conv_proj.in_channels * self.conv_proj.kernel_size[0] * self.conv_proj.kernel_size[1] + nn.init.trunc_normal_(self.conv_proj.weight, std=math.sqrt(1 / fan_in)) + if self.conv_proj.bias is not None: + nn.init.zeros_(self.conv_proj.bias) + elif self.conv_proj.conv_last is not None and isinstance(self.conv_proj.conv_last, nn.Conv2d): + # Init the last 1x1 conv of the conv stem + nn.init.normal_( + self.conv_proj.conv_last.weight, mean=0.0, std=math.sqrt(2.0 / self.conv_proj.conv_last.out_channels) + ) + if self.conv_proj.conv_last.bias is not None: + nn.init.zeros_(self.conv_proj.conv_last.bias) + + if hasattr(self.heads, "pre_logits") and isinstance(self.heads.pre_logits, nn.Linear): + fan_in = self.heads.pre_logits.in_features + nn.init.trunc_normal_(self.heads.pre_logits.weight, std=math.sqrt(1 / fan_in)) + nn.init.zeros_(self.heads.pre_logits.bias) + + if isinstance(self.heads.head, nn.Linear): + nn.init.zeros_(self.heads.head.weight) + nn.init.zeros_(self.heads.head.bias) + + def _process_input(self, x: torch.Tensor) -> torch.Tensor: + n, c, h, w = x.shape + p = self.patch_size + torch._assert(h == self.image_size, "Wrong image height!") + torch._assert(w == self.image_size, "Wrong image width!") + n_h = h // p + n_w = w // p + + # (n, c, h, w) -> (n, hidden_dim, n_h, n_w) + x = self.conv_proj(x) + # (n, hidden_dim, n_h, n_w) -> (n, hidden_dim, (n_h * n_w)) + x = x.reshape(n, self.hidden_dim, n_h * n_w) + + # (n, hidden_dim, (n_h * n_w)) -> (n, (n_h * n_w), hidden_dim) + # The self attention layer expects inputs in the format (N, S, E) + # where S is the source sequence length, N is the batch size, E is the + # embedding dimension + x = x.permute(0, 2, 1) + + return x + + def forward(self, x: torch.Tensor): + # Reshape and permute the input tensor + x = self._process_input(x) + n = x.shape[0] + + # Expand the class token to the full batch + batch_class_token = self.class_token.expand(n, -1, -1) + x = torch.cat([batch_class_token, x], dim=1) + + x = self.encoder(x) + + # Classifier "token" as used by standard language architectures + x = x[:, 0] + + x = self.heads(x) + + return x + + +def _vision_transformer( + arch: str, + patch_size: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + pretrained: bool, + progress: bool, + **kwargs: Any, +) -> VisionTransformer: + image_size = kwargs.pop("image_size", 224) + + model = VisionTransformer( + image_size=image_size, + patch_size=patch_size, + num_layers=num_layers, + num_heads=num_heads, + hidden_dim=hidden_dim, + mlp_dim=mlp_dim, + **kwargs, + ) + + if pretrained: + if arch not in model_urls: + raise ValueError(f"No checkpoint is available for model type '{arch}'!") + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + + return model + + +def vit_b_16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_b_16 architecture from + `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vision_transformer( + arch="vit_b_16", + patch_size=16, + num_layers=12, + num_heads=12, + hidden_dim=768, + mlp_dim=3072, + pretrained=pretrained, + progress=progress, + **kwargs, + ) + + +def vit_b_32(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_b_32 architecture from + `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vision_transformer( + arch="vit_b_32", + patch_size=32, + num_layers=12, + num_heads=12, + hidden_dim=768, + mlp_dim=3072, + pretrained=pretrained, + progress=progress, + **kwargs, + ) + + +def vit_l_16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_l_16 architecture from + `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vision_transformer( + arch="vit_l_16", + patch_size=16, + num_layers=24, + num_heads=16, + hidden_dim=1024, + mlp_dim=4096, + pretrained=pretrained, + progress=progress, + **kwargs, + ) + + +def vit_l_32(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_l_32 architecture from + `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vision_transformer( + arch="vit_l_32", + patch_size=32, + num_layers=24, + num_heads=16, + hidden_dim=1024, + mlp_dim=4096, + pretrained=pretrained, + progress=progress, + **kwargs, + ) + + +def interpolate_embeddings( + image_size: int, + patch_size: int, + model_state: "OrderedDict[str, torch.Tensor]", + interpolation_mode: str = "bicubic", + reset_heads: bool = False, +) -> "OrderedDict[str, torch.Tensor]": + """This function helps interpolating positional embeddings during checkpoint loading, + especially when you want to apply a pre-trained model on images with different resolution. + + Args: + image_size (int): Image size of the new model. + patch_size (int): Patch size of the new model. + model_state (OrderedDict[str, torch.Tensor]): State dict of the pre-trained model. + interpolation_mode (str): The algorithm used for upsampling. Default: bicubic. + reset_heads (bool): If true, not copying the state of heads. Default: False. + + Returns: + OrderedDict[str, torch.Tensor]: A state dict which can be loaded into the new model. + """ + # Shape of pos_embedding is (1, seq_length, hidden_dim) + pos_embedding = model_state["encoder.pos_embedding"] + n, seq_length, hidden_dim = pos_embedding.shape + if n != 1: + raise ValueError(f"Unexpected position embedding shape: {pos_embedding.shape}") + + new_seq_length = (image_size // patch_size) ** 2 + 1 + + # Need to interpolate the weights for the position embedding. + # We do this by reshaping the positions embeddings to a 2d grid, performing + # an interpolation in the (h, w) space and then reshaping back to a 1d grid. + if new_seq_length != seq_length: + # The class token embedding shouldn't be interpolated so we split it up. + seq_length -= 1 + new_seq_length -= 1 + pos_embedding_token = pos_embedding[:, :1, :] + pos_embedding_img = pos_embedding[:, 1:, :] + + # (1, seq_length, hidden_dim) -> (1, hidden_dim, seq_length) + pos_embedding_img = pos_embedding_img.permute(0, 2, 1) + seq_length_1d = int(math.sqrt(seq_length)) + torch._assert(seq_length_1d * seq_length_1d == seq_length, "seq_length is not a perfect square!") + + # (1, hidden_dim, seq_length) -> (1, hidden_dim, seq_l_1d, seq_l_1d) + pos_embedding_img = pos_embedding_img.reshape(1, hidden_dim, seq_length_1d, seq_length_1d) + new_seq_length_1d = image_size // patch_size + + # Perform interpolation. + # (1, hidden_dim, seq_l_1d, seq_l_1d) -> (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) + new_pos_embedding_img = nn.functional.interpolate( + pos_embedding_img, + size=new_seq_length_1d, + mode=interpolation_mode, + align_corners=True, + ) + + # (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) -> (1, hidden_dim, new_seq_length) + new_pos_embedding_img = new_pos_embedding_img.reshape(1, hidden_dim, new_seq_length) + + # (1, hidden_dim, new_seq_length) -> (1, new_seq_length, hidden_dim) + new_pos_embedding_img = new_pos_embedding_img.permute(0, 2, 1) + new_pos_embedding = torch.cat([pos_embedding_token, new_pos_embedding_img], dim=1) + + model_state["encoder.pos_embedding"] = new_pos_embedding + + if reset_heads: + model_state_copy: "OrderedDict[str, torch.Tensor]" = OrderedDict() + for k, v in model_state.items(): + if not k.startswith("heads"): + model_state_copy[k] = v + model_state = model_state_copy + + return model_state diff --git a/utils/defense_utils/dde/dde_model/vit_new_dde.py b/utils/defense_utils/dde/dde_model/vit_new_dde.py new file mode 100644 index 0000000..bd709a5 --- /dev/null +++ b/utils/defense_utils/dde/dde_model/vit_new_dde.py @@ -0,0 +1,854 @@ +import math +from collections import OrderedDict +from functools import partial +from typing import Any, Callable, List, NamedTuple, Optional, Dict + +import torch +import torch.nn as nn + +from torchvision.ops.misc import Conv2dNormActivation, MLP +from torchvision.transforms._presets import ImageClassification, InterpolationMode +from torchvision.utils import _log_api_usage_once +from torchvision.models._api import WeightsEnum, Weights +from torchvision.models._meta import _IMAGENET_CATEGORIES +from torchvision.models._utils import handle_legacy_interface, _ovewrite_named_param + +from defense.dde import dde_model + + +__all__ = [ + "VisionTransformer", + "ViT_B_16_Weights", + "ViT_B_32_Weights", + "ViT_L_16_Weights", + "ViT_L_32_Weights", + "ViT_H_14_Weights", + "vit_b_16", + "vit_b_32", + "vit_l_16", + "vit_l_32", + "vit_h_14", +] + + +class ConvStemConfig(NamedTuple): + out_channels: int + kernel_size: int + stride: int + norm_layer: Callable[..., nn.Module] = dde_model.LayerNorm_DDE + activation_layer: Callable[..., nn.Module] = nn.ReLU + + +class MLPBlock(MLP): + """Transformer MLP block.""" + + _version = 2 + + def __init__(self, in_dim: int, mlp_dim: int, dropout: float): + super().__init__(in_dim, [mlp_dim, in_dim], activation_layer=nn.GELU, inplace=None, dropout=dropout) + + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if m.bias is not None: + nn.init.normal_(m.bias, std=1e-6) + + def _load_from_state_dict( + self, + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ): + version = local_metadata.get("version", None) + + if version is None or version < 2: + # Replacing legacy MLPBlock with MLP. See https://github.com/pytorch/vision/pull/6053 + for i in range(2): + for type in ["weight", "bias"]: + old_key = f"{prefix}linear_{i+1}.{type}" + new_key = f"{prefix}{3*i}.{type}" + if old_key in state_dict: + state_dict[new_key] = state_dict.pop(old_key) + + super()._load_from_state_dict( + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ) + + +class EncoderBlock(nn.Module): + """Transformer encoder block.""" + + def __init__( + self, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float, + attention_dropout: float, + norm_layer: Callable[..., torch.nn.Module] = partial(dde_model.LayerNorm_DDE, eps=1e-6), + ): + super().__init__() + self.num_heads = num_heads + + # Attention block + self.ln_1 = norm_layer(hidden_dim) + self.self_attention = nn.MultiheadAttention(hidden_dim, num_heads, dropout=attention_dropout, batch_first=True) + self.dropout = nn.Dropout(dropout) + + # MLP block + self.ln_2 = norm_layer(hidden_dim) + self.mlp = MLPBlock(hidden_dim, mlp_dim, dropout) + + def forward(self, input: torch.Tensor): + torch._assert(input.dim() == 3, f"Expected (batch_size, seq_length, hidden_dim) got {input.shape}") + x = self.ln_1(input) + x, _ = self.self_attention(query=x, key=x, value=x, need_weights=False) + x = self.dropout(x) + x = x + input + + y = self.ln_2(x) + y = self.mlp(y) + return x + y + + +class Encoder(nn.Module): + """Transformer Model Encoder for sequence to sequence translation.""" + + def __init__( + self, + seq_length: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float, + attention_dropout: float, + norm_layer: Callable[..., torch.nn.Module] = partial(dde_model.LayerNorm_DDE, eps=1e-6), + ): + super().__init__() + # Note that batch_size is on the first dim because + # we have batch_first=True in nn.MultiAttention() by default + self.pos_embedding = nn.Parameter(torch.empty(1, seq_length, hidden_dim).normal_(std=0.02)) # from BERT + self.dropout = nn.Dropout(dropout) + layers: OrderedDict[str, nn.Module] = OrderedDict() + for i in range(num_layers): + layers[f"encoder_layer_{i}"] = EncoderBlock( + num_heads, + hidden_dim, + mlp_dim, + dropout, + attention_dropout, + norm_layer, + ) + self.layers = nn.Sequential(layers) + self.ln = norm_layer(hidden_dim) + + def forward(self, input: torch.Tensor): + torch._assert(input.dim() == 3, f"Expected (batch_size, seq_length, hidden_dim) got {input.shape}") + input = input + self.pos_embedding + return self.ln(self.layers(self.dropout(input))) + + +class VisionTransformer(nn.Module): + """Vision Transformer as per https://arxiv.org/abs/2010.11929.""" + + def __init__( + self, + image_size: int, + patch_size: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float = 0.0, + attention_dropout: float = 0.0, + num_classes: int = 1000, + representation_size: Optional[int] = None, + norm_layer: Callable[..., torch.nn.Module] = partial(dde_model.LayerNorm_DDE, eps=1e-6), + conv_stem_configs: Optional[List[ConvStemConfig]] = None, + ): + super().__init__() + _log_api_usage_once(self) + torch._assert(image_size % patch_size == 0, "Input shape indivisible by patch size!") + self.image_size = image_size + self.patch_size = patch_size + self.hidden_dim = hidden_dim + self.mlp_dim = mlp_dim + self.attention_dropout = attention_dropout + self.dropout = dropout + self.num_classes = num_classes + self.representation_size = representation_size + self.norm_layer = norm_layer + + if conv_stem_configs is not None: + # As per https://arxiv.org/abs/2106.14881 + seq_proj = nn.Sequential() + prev_channels = 3 + for i, conv_stem_layer_config in enumerate(conv_stem_configs): + seq_proj.add_module( + f"conv_bn_relu_{i}", + Conv2dNormActivation( + in_channels=prev_channels, + out_channels=conv_stem_layer_config.out_channels, + kernel_size=conv_stem_layer_config.kernel_size, + stride=conv_stem_layer_config.stride, + norm_layer=conv_stem_layer_config.norm_layer, + activation_layer=conv_stem_layer_config.activation_layer, + ), + ) + prev_channels = conv_stem_layer_config.out_channels + seq_proj.add_module( + "conv_last", nn.Conv2d(in_channels=prev_channels, out_channels=hidden_dim, kernel_size=1) + ) + self.conv_proj: nn.Module = seq_proj + else: + self.conv_proj = nn.Conv2d( + in_channels=3, out_channels=hidden_dim, kernel_size=patch_size, stride=patch_size + ) + + seq_length = (image_size // patch_size) ** 2 + + # Add a class token + self.class_token = nn.Parameter(torch.zeros(1, 1, hidden_dim)) + seq_length += 1 + + self.encoder = Encoder( + seq_length, + num_layers, + num_heads, + hidden_dim, + mlp_dim, + dropout, + attention_dropout, + norm_layer, + ) + self.seq_length = seq_length + + heads_layers: OrderedDict[str, nn.Module] = OrderedDict() + if representation_size is None: + heads_layers["head"] = nn.Linear(hidden_dim, num_classes) + else: + heads_layers["pre_logits"] = nn.Linear(hidden_dim, representation_size) + heads_layers["act"] = nn.Tanh() + heads_layers["head"] = nn.Linear(representation_size, num_classes) + + self.heads = nn.Sequential(heads_layers) + + if isinstance(self.conv_proj, nn.Conv2d): + # Init the patchify stem + fan_in = self.conv_proj.in_channels * self.conv_proj.kernel_size[0] * self.conv_proj.kernel_size[1] + nn.init.trunc_normal_(self.conv_proj.weight, std=math.sqrt(1 / fan_in)) + if self.conv_proj.bias is not None: + nn.init.zeros_(self.conv_proj.bias) + elif self.conv_proj.conv_last is not None and isinstance(self.conv_proj.conv_last, nn.Conv2d): + # Init the last 1x1 conv of the conv stem + nn.init.normal_( + self.conv_proj.conv_last.weight, mean=0.0, std=math.sqrt(2.0 / self.conv_proj.conv_last.out_channels) + ) + if self.conv_proj.conv_last.bias is not None: + nn.init.zeros_(self.conv_proj.conv_last.bias) + + if hasattr(self.heads, "pre_logits") and isinstance(self.heads.pre_logits, nn.Linear): + fan_in = self.heads.pre_logits.in_features + nn.init.trunc_normal_(self.heads.pre_logits.weight, std=math.sqrt(1 / fan_in)) + nn.init.zeros_(self.heads.pre_logits.bias) + + if isinstance(self.heads.head, nn.Linear): + nn.init.zeros_(self.heads.head.weight) + nn.init.zeros_(self.heads.head.bias) + + def _process_input(self, x: torch.Tensor) -> torch.Tensor: + n, c, h, w = x.shape + p = self.patch_size + torch._assert(h == self.image_size, "Wrong image height!") + torch._assert(w == self.image_size, "Wrong image width!") + n_h = h // p + n_w = w // p + + # (n, c, h, w) -> (n, hidden_dim, n_h, n_w) + x = self.conv_proj(x) + # (n, hidden_dim, n_h, n_w) -> (n, hidden_dim, (n_h * n_w)) + x = x.reshape(n, self.hidden_dim, n_h * n_w) + + # (n, hidden_dim, (n_h * n_w)) -> (n, (n_h * n_w), hidden_dim) + # The self attention layer expects inputs in the format (N, S, E) + # where S is the source sequence length, N is the batch size, E is the + # embedding dimension + x = x.permute(0, 2, 1) + + return x + + def forward(self, x: torch.Tensor): + # Reshape and permute the input tensor + x = self._process_input(x) + n = x.shape[0] + + # Expand the class token to the full batch + batch_class_token = self.class_token.expand(n, -1, -1) + x = torch.cat([batch_class_token, x], dim=1) + + x = self.encoder(x) + + # Classifier "token" as used by standard language architectures + x = x[:, 0] + + x = self.heads(x) + + return x + + +def _vision_transformer( + patch_size: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> VisionTransformer: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + assert weights.meta["min_size"][0] == weights.meta["min_size"][1] + _ovewrite_named_param(kwargs, "image_size", weights.meta["min_size"][0]) + image_size = kwargs.pop("image_size", 224) + + model = VisionTransformer( + image_size=image_size, + patch_size=patch_size, + num_layers=num_layers, + num_heads=num_heads, + hidden_dim=hidden_dim, + mlp_dim=mlp_dim, + **kwargs, + ) + + if weights: + model.load_state_dict(weights.get_state_dict(progress=progress)) + + return model + + +_COMMON_META: Dict[str, Any] = { + "categories": _IMAGENET_CATEGORIES, +} + +_COMMON_SWAG_META = { + **_COMMON_META, + "recipe": "https://github.com/facebookresearch/SWAG", + "license": "https://github.com/facebookresearch/SWAG/blob/main/LICENSE", +} + + +class ViT_B_16_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_16-c867db91.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 86567656, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_b_16", + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.072, + "acc@5": 95.318, + } + }, + "_docs": """ + These weights were trained from scratch by using a modified version of `DeIT + `_'s training recipe. + """, + }, + ) + IMAGENET1K_SWAG_E2E_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_16_swag-9ac1b537.pth", + transforms=partial( + ImageClassification, + crop_size=384, + resize_size=384, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "num_params": 86859496, + "min_size": (384, 384), + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.304, + "acc@5": 97.650, + } + }, + "_docs": """ + These weights are learnt via transfer learning by end-to-end fine-tuning the original + `SWAG `_ weights on ImageNet-1K data. + """, + }, + ) + IMAGENET1K_SWAG_LINEAR_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_16_lc_swag-4e70ced5.pth", + transforms=partial( + ImageClassification, + crop_size=224, + resize_size=224, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "recipe": "https://github.com/pytorch/vision/pull/5793", + "num_params": 86567656, + "min_size": (224, 224), + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.886, + "acc@5": 96.180, + } + }, + "_docs": """ + These weights are composed of the original frozen `SWAG `_ trunk + weights and a linear classifier learnt on top of them trained on ImageNet-1K data. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_B_32_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_32-d86f8d99.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 88224232, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_b_32", + "_metrics": { + "ImageNet-1K": { + "acc@1": 75.912, + "acc@5": 92.466, + } + }, + "_docs": """ + These weights were trained from scratch by using a modified version of `DeIT + `_'s training recipe. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_L_16_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_16-852ce7e3.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=242), + meta={ + **_COMMON_META, + "num_params": 304326632, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_l_16", + "_metrics": { + "ImageNet-1K": { + "acc@1": 79.662, + "acc@5": 94.638, + } + }, + "_docs": """ + These weights were trained from scratch by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + IMAGENET1K_SWAG_E2E_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_16_swag-4f3808c9.pth", + transforms=partial( + ImageClassification, + crop_size=512, + resize_size=512, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "num_params": 305174504, + "min_size": (512, 512), + "_metrics": { + "ImageNet-1K": { + "acc@1": 88.064, + "acc@5": 98.512, + } + }, + "_docs": """ + These weights are learnt via transfer learning by end-to-end fine-tuning the original + `SWAG `_ weights on ImageNet-1K data. + """, + }, + ) + IMAGENET1K_SWAG_LINEAR_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_16_lc_swag-4d563306.pth", + transforms=partial( + ImageClassification, + crop_size=224, + resize_size=224, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "recipe": "https://github.com/pytorch/vision/pull/5793", + "num_params": 304326632, + "min_size": (224, 224), + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.146, + "acc@5": 97.422, + } + }, + "_docs": """ + These weights are composed of the original frozen `SWAG `_ trunk + weights and a linear classifier learnt on top of them trained on ImageNet-1K data. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_L_32_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_32-c7638314.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 306535400, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_l_32", + "_metrics": { + "ImageNet-1K": { + "acc@1": 76.972, + "acc@5": 93.07, + } + }, + "_docs": """ + These weights were trained from scratch by using a modified version of `DeIT + `_'s training recipe. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_H_14_Weights(WeightsEnum): + IMAGENET1K_SWAG_E2E_V1 = Weights( + url="https://download.pytorch.org/models/vit_h_14_swag-80465313.pth", + transforms=partial( + ImageClassification, + crop_size=518, + resize_size=518, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "num_params": 633470440, + "min_size": (518, 518), + "_metrics": { + "ImageNet-1K": { + "acc@1": 88.552, + "acc@5": 98.694, + } + }, + "_docs": """ + These weights are learnt via transfer learning by end-to-end fine-tuning the original + `SWAG `_ weights on ImageNet-1K data. + """, + }, + ) + IMAGENET1K_SWAG_LINEAR_V1 = Weights( + url="https://download.pytorch.org/models/vit_h_14_lc_swag-c1eb923e.pth", + transforms=partial( + ImageClassification, + crop_size=224, + resize_size=224, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "recipe": "https://github.com/pytorch/vision/pull/5793", + "num_params": 632045800, + "min_size": (224, 224), + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.708, + "acc@5": 97.730, + } + }, + "_docs": """ + These weights are composed of the original frozen `SWAG `_ trunk + weights and a linear classifier learnt on top of them trained on ImageNet-1K data. + """, + }, + ) + DEFAULT = IMAGENET1K_SWAG_E2E_V1 + + +@handle_legacy_interface(weights=("pretrained", ViT_B_16_Weights.IMAGENET1K_V1)) +def vit_b_16(*, weights: Optional[ViT_B_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_b_16 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_B_16_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_B_16_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_B_16_Weights + :members: + """ + weights = ViT_B_16_Weights.verify(weights) + + return _vision_transformer( + patch_size=16, + num_layers=12, + num_heads=12, + hidden_dim=768, + mlp_dim=3072, + weights=weights, + progress=progress, + **kwargs, + ) + + +@handle_legacy_interface(weights=("pretrained", ViT_B_32_Weights.IMAGENET1K_V1)) +def vit_b_32(*, weights: Optional[ViT_B_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_b_32 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_B_32_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_B_32_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_B_32_Weights + :members: + """ + weights = ViT_B_32_Weights.verify(weights) + + return _vision_transformer( + patch_size=32, + num_layers=12, + num_heads=12, + hidden_dim=768, + mlp_dim=3072, + weights=weights, + progress=progress, + **kwargs, + ) + + +@handle_legacy_interface(weights=("pretrained", ViT_L_16_Weights.IMAGENET1K_V1)) +def vit_l_16(*, weights: Optional[ViT_L_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_l_16 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_L_16_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_L_16_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_L_16_Weights + :members: + """ + weights = ViT_L_16_Weights.verify(weights) + + return _vision_transformer( + patch_size=16, + num_layers=24, + num_heads=16, + hidden_dim=1024, + mlp_dim=4096, + weights=weights, + progress=progress, + **kwargs, + ) + + +@handle_legacy_interface(weights=("pretrained", ViT_L_32_Weights.IMAGENET1K_V1)) +def vit_l_32(*, weights: Optional[ViT_L_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_l_32 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_L_32_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_L_32_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_L_32_Weights + :members: + """ + weights = ViT_L_32_Weights.verify(weights) + + return _vision_transformer( + patch_size=32, + num_layers=24, + num_heads=16, + hidden_dim=1024, + mlp_dim=4096, + weights=weights, + progress=progress, + **kwargs, + ) + + +def vit_h_14(*, weights: Optional[ViT_H_14_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_h_14 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_H_14_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_H_14_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_H_14_Weights + :members: + """ + weights = ViT_H_14_Weights.verify(weights) + + return _vision_transformer( + patch_size=14, + num_layers=32, + num_heads=16, + hidden_dim=1280, + mlp_dim=5120, + weights=weights, + progress=progress, + **kwargs, + ) + + +def interpolate_embeddings( + image_size: int, + patch_size: int, + model_state: "OrderedDict[str, torch.Tensor]", + interpolation_mode: str = "bicubic", + reset_heads: bool = False, +) -> "OrderedDict[str, torch.Tensor]": + """This function helps interpolating positional embeddings during checkpoint loading, + especially when you want to apply a pre-trained model on images with different resolution. + + Args: + image_size (int): Image size of the new model. + patch_size (int): Patch size of the new model. + model_state (OrderedDict[str, torch.Tensor]): State dict of the pre-trained model. + interpolation_mode (str): The algorithm used for upsampling. Default: bicubic. + reset_heads (bool): If true, not copying the state of heads. Default: False. + + Returns: + OrderedDict[str, torch.Tensor]: A state dict which can be loaded into the new model. + """ + # Shape of pos_embedding is (1, seq_length, hidden_dim) + pos_embedding = model_state["encoder.pos_embedding"] + n, seq_length, hidden_dim = pos_embedding.shape + if n != 1: + raise ValueError(f"Unexpected position embedding shape: {pos_embedding.shape}") + + new_seq_length = (image_size // patch_size) ** 2 + 1 + + # Need to interpolate the weights for the position embedding. + # We do this by reshaping the positions embeddings to a 2d grid, performing + # an interpolation in the (h, w) space and then reshaping back to a 1d grid. + if new_seq_length != seq_length: + # The class token embedding shouldn't be interpolated so we split it up. + seq_length -= 1 + new_seq_length -= 1 + pos_embedding_token = pos_embedding[:, :1, :] + pos_embedding_img = pos_embedding[:, 1:, :] + + # (1, seq_length, hidden_dim) -> (1, hidden_dim, seq_length) + pos_embedding_img = pos_embedding_img.permute(0, 2, 1) + seq_length_1d = int(math.sqrt(seq_length)) + if seq_length_1d * seq_length_1d != seq_length: + raise ValueError( + f"seq_length is not a perfect square! Instead got seq_length_1d * seq_length_1d = {seq_length_1d * seq_length_1d } and seq_length = {seq_length}" + ) + + # (1, hidden_dim, seq_length) -> (1, hidden_dim, seq_l_1d, seq_l_1d) + pos_embedding_img = pos_embedding_img.reshape(1, hidden_dim, seq_length_1d, seq_length_1d) + new_seq_length_1d = image_size // patch_size + + # Perform interpolation. + # (1, hidden_dim, seq_l_1d, seq_l_1d) -> (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) + new_pos_embedding_img = nn.functional.interpolate( + pos_embedding_img, + size=new_seq_length_1d, + mode=interpolation_mode, + align_corners=True, + ) + + # (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) -> (1, hidden_dim, new_seq_length) + new_pos_embedding_img = new_pos_embedding_img.reshape(1, hidden_dim, new_seq_length) + + # (1, hidden_dim, new_seq_length) -> (1, new_seq_length, hidden_dim) + new_pos_embedding_img = new_pos_embedding_img.permute(0, 2, 1) + new_pos_embedding = torch.cat([pos_embedding_token, new_pos_embedding_img], dim=1) + + model_state["encoder.pos_embedding"] = new_pos_embedding + + if reset_heads: + model_state_copy: "OrderedDict[str, torch.Tensor]" = OrderedDict() + for k, v in model_state.items(): + if not k.startswith("heads"): + model_state_copy[k] = v + model_state = model_state_copy + + return model_state + + +# The dictionary below is internal implementation detail and will be removed in v0.15 +from torchvision.models._utils import _ModelURLs + + +model_urls = _ModelURLs( + { + "vit_b_16": ViT_B_16_Weights.IMAGENET1K_V1.url, + "vit_b_32": ViT_B_32_Weights.IMAGENET1K_V1.url, + "vit_l_16": ViT_L_16_Weights.IMAGENET1K_V1.url, + "vit_l_32": ViT_L_32_Weights.IMAGENET1K_V1.url, + } +) diff --git a/utils/defense_utils/dst/__init__.py b/utils/defense_utils/dst/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/utils/defense_utils/dst/dataloader_bd.py b/utils/defense_utils/dst/dataloader_bd.py new file mode 100644 index 0000000..27770f5 --- /dev/null +++ b/utils/defense_utils/dst/dataloader_bd.py @@ -0,0 +1,404 @@ +# Modified from https://github.com/bboylyg/NAD/blob/main/data_loader.py + +import os +import csv +import random +import numpy as np +from PIL import Image +from tqdm import tqdm +import time +import sys +from matplotlib import image as mlt +import cv2 +import logging + +import torch +import torch.utils.data as data +import torch.nn.functional as F +import torchvision +import torchvision.transforms as transforms +import torchvision.datasets as datasets + +# from utils.bd_dataset import prepro_cls_DatasetBD + + +class TwoCropTransform: + """Create two crops of the same image""" + def __init__(self, transform): + self.transform = transform + + def __call__(self, x): + return [self.transform(x), self.transform(x)] + +class TransformThree: + def __init__(self, transform1, transform2, transform3): + self.transform1 = transform1 + self.transform2 = transform2 + self.transform3 = transform3 + + def __call__(self, inp): + out1 = self.transform1(inp) + out2 = self.transform2(inp) + out3 = self.transform3(inp) + return out1, out2, out3 + + + + +def normalization(opt, inputs): + output = inputs.clone() + if opt.dataset == "cifar10": + f = transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]) + elif opt.dataset == "mnist": + f = transforms.Normalize([0.5], [0.5]) + elif opt.dataset == 'tiny': + f = transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]) + elif opt.dataset == "gtsrb" or opt.dataset == "celeba": + # pass + return output + elif opt.dataset == 'imagenet': + f = transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]) + elif opt.dataset == "cifar100": + f = transforms.Normalize([0.5070751592371323, 0.48654887331495095, 0.4409178433670343], [0.2673342858792401, 0.2564384629170883, 0.27615047132568404]) + else: + raise Exception("Invalid Dataset") + for i in range(inputs.shape[0]): + output[i] = f(inputs[i]) + return output + + +def get_transform_st(opt, train=True): + ### transform1 ### + transforms_list = [] + transforms_list.append(transforms.Resize((opt.input_height, opt.input_width))) + transforms_list.append(transforms.ToTensor()) + transforms1 = transforms.Compose(transforms_list) + + if train == False: + return transforms1 + + ### transform2 ### + transforms_list = [] + transforms_list.append(transforms.Resize((opt.input_height, opt.input_width))) + if train: + if opt.dataset == 'cifar10' or opt.dataset == 'gtsrb': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + transforms_list.append(transforms.RandomHorizontalFlip()) + elif opt.dataset == 'cifar100': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + transforms_list.append(transforms.RandomHorizontalFlip()) + transforms_list.append(transforms.RandomRotation(15)) + elif opt.dataset == "imagenet": + transforms_list.append(transforms.RandomRotation(20)) + transforms_list.append(transforms.RandomHorizontalFlip(0.5)) + elif opt.dataset == "tiny": + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=8)) + transforms_list.append(transforms.RandomHorizontalFlip()) + transforms_list.append(transforms.ToTensor()) + transforms2 = transforms.Compose(transforms_list) + + ### transform3 ### + transforms_list = [] + transforms_list.append(transforms.Resize((opt.input_height, opt.input_width))) + if opt.trans1 == 'rotate': + transforms_list.append(transforms.RandomRotation(180)) + elif opt.trans1 == 'affine': + transforms_list.append(transforms.RandomAffine(degrees=0, translate=(0.2, 0.2))) + elif opt.trans1 == 'flip': + transforms_list.append(transforms.RandomHorizontalFlip(p=1.0)) + elif opt.trans1 == 'crop': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + elif opt.trans1 == 'blur': + transforms_list.append(transforms.GaussianBlur(kernel_size=15, sigma=(0.1, 2.0))) + elif opt.trans1 == 'erase': + transforms_list.append(transforms.ToTensor()) + transforms_list.append(transforms.RandomErasing(p=1.0, scale=(0.2, 0.3), ratio=(0.5, 1.0), value='random')) + transforms_list.append(transforms.ToPILImage()) + + if opt.trans2 == 'rotate': + transforms_list.append(transforms.RandomRotation(180)) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'affine': + transforms_list.append(transforms.RandomAffine(degrees=0, translate=(0.2, 0.2))) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'flip': + transforms_list.append(transforms.RandomHorizontalFlip(p=1.0)) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'crop': + transforms_list.append(transforms.RandomCrop((opt.input_height, opt.input_width), padding=4)) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'blur': + transforms_list.append(transforms.GaussianBlur(kernel_size=15, sigma=(0.1, 2.0))) + transforms_list.append(transforms.ToTensor()) + elif opt.trans2 == 'erase': + transforms_list.append(transforms.ToTensor()) + transforms_list.append(transforms.RandomErasing(p=1.0, scale=(0.2, 0.3), ratio=(0.5, 1.0), value='random')) + elif opt.trans2 == 'none': + transforms_list.append(transforms.ToTensor()) + + transforms3 = transforms.Compose(transforms_list) + + return transforms1, transforms2, transforms3 + +# def get_sd_dataloader(args,result): +# x = result['bd_train']['x'] +# y = result['bd_train']['y'] +# data_bd_train = list(zip(x,y)) + +# ### train_dataset and train_dataloader +# transform1, transform2, transform3 = get_transform_st(args, train=True) + +# poisoned_train = prepro_cls_DatasetBD( +# full_dataset_without_transform=data_bd_train, +# poison_idx=np.zeros(len(data_bd_train)), # one-hot to determine which image may take bd_transform +# bd_image_pre_transform=None, +# bd_label_pre_transform=None, +# ori_image_transform_in_loading=TransformThree(transform1, transform2, transform3), +# ori_label_transform_in_loading=None, +# add_details_in_preprocess=True, +# ) + +# bd_trainloader = torch.utils.data.DataLoader(poisoned_train, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=True) + +# ### test_dataset and test_dataloader +# transform = get_transform_st(args, train=False) +# x = result['bd_test']['x'] +# y = result['bd_test']['y'] +# data_bd_test = list(zip(x,y)) + +# data_bd_testset = prepro_cls_DatasetBD( +# full_dataset_without_transform=data_bd_test, +# poison_idx=np.zeros(len(data_bd_test)), # one-hot to determine which image may take bd_transform +# bd_image_pre_transform=None, +# bd_label_pre_transform=None, +# ori_image_transform_in_loading=transform, +# ori_label_transform_in_loading=None, +# add_details_in_preprocess=True, +# ) +# bd_testloader = torch.utils.data.DataLoader(data_bd_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + +# transform = get_transform_st(args, train=False) +# x = result['clean_test']['x'] +# y = result['clean_test']['y'] +# data_clean_test = list(zip(x,y)) +# data_clean_testset = prepro_cls_DatasetBD( +# full_dataset_without_transform=data_clean_test, +# poison_idx=np.zeros(len(data_clean_test)), # one-hot to determine which image may take bd_transform +# bd_image_pre_transform=None, +# bd_label_pre_transform=None, +# ori_image_transform_in_loading=transform, +# ori_label_transform_in_loading=None, +# add_details_in_preprocess=True, +# ) +# clean_testloader = torch.utils.data.DataLoader(data_clean_testset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True) + +# # return bd_trainloader, bd_testloader, clean_testloader +# return bd_trainloader + + +class dataset_wrapper_with_flag(torch.utils.data.Dataset): + ''' + idea from https://stackoverflow.com/questions/1443129/completely-wrap-an-object-in-python + ''' + + def __init__(self, obj, flag, transform=None): + + self.wrapped_dataset = obj + (self.clean_idx_list, self.poison_idx_list, self.suspicious_idx_list) = flag + self.transform = transform + def __getattr__(self, attr): + if attr in self.__dict__: + return getattr(self, attr) + return getattr(self.wrapped_dataset, attr) + + def __getitem__(self, index): + img, label, original_index, poison_or_not, original_target= self.wrapped_dataset[index] + if original_index in self.clean_idx_list: + flag = 0 + elif original_index in self.poison_idx_list: + flag = 2 + elif original_index in self.suspicious_idx_list: + flag = 1 + if self.transform: + img = self.transform(img) + return (img, label, flag) + + def __len__(self): + return len(self.wrapped_dataset) + +# class Dataset_npy(torch.utils.data.Dataset): +# def __init__(self, full_dataset=None, transform=None): +# self.dataset = full_dataset +# self.transform = transform +# self.dataLen = len(self.dataset) + +# def __getitem__(self, index): +# image = self.dataset[index][0] +# label = self.dataset[index][1] +# flag = self.dataset[index][2] + +# if self.transform: +# image = self.transform(image) +# # print(type(image), image.shape) +# return image, label, flag + +# def __len__(self): +# return self.dataLen + +def get_st_train_loader(opt, dataset, module='sscl'): + transforms_list = [ + transforms.RandomResizedCrop(size=opt.input_height, scale=(0.2, 1.)), + transforms.RandomHorizontalFlip(), + transforms.RandomApply([ + transforms.ColorJitter(0.4, 0.4, 0.4, 0.1) + ], p=0.8), + transforms.RandomGrayscale(p=0.2), + transforms.ToTensor() + ] + + # construct data loader + if opt.dataset == 'cifar10': + mean = (0.4914, 0.4822, 0.4465) + std = (0.2023, 0.1994, 0.2010) + elif opt.dataset == 'cifar100': + mean = (0.5071, 0.4867, 0.4408) + std = (0.2675, 0.2565, 0.2761) + elif opt.dataset == "mnist": + mean = [0.5,] + std = [0.5,] + elif opt.dataset == 'tiny': + mean = (0.4802, 0.4481, 0.3975) + std = (0.2302, 0.2265, 0.2262) + elif opt.dataset == 'imagenet': + mean = (0.4802, 0.4481, 0.3975) + std = (0.2302, 0.2265, 0.2262) + elif opt.dataset == 'gtsrb': + mean = None + elif opt.dataset == 'path': + mean = eval(opt.mean) + std = eval(opt.std) + else: + raise ValueError('dataset not supported: {}'.format(opt.dataset)) + + if mean != None: + normalize = transforms.Normalize(mean=mean, std=std) + transforms_list.append(normalize) + + train_transform = transforms.Compose(transforms_list) + + folder_path = folder_path = f'{opt.save_path}data_produce' + clean_idx_list = np.load(os.path.join(folder_path, 'clean_samples.npy')) + poison_idx_list = np.load(os.path.join(folder_path, 'poison_samples.npy')) + suspicious_idx_list = np.load(os.path.join(folder_path, 'suspicious_samples.npy')) + logging.info(f'Num of clean, poison and suspicious: {len(clean_idx_list)}, {len(poison_idx_list)}, {len(suspicious_idx_list)}') + + if module == 'mixed_ce': + train_dataset = dataset_wrapper_with_flag(dataset.wrapped_dataset, flag=[clean_idx_list,poison_idx_list,suspicious_idx_list],transform=train_transform) + elif module == 'sscl': + train_dataset = dataset_wrapper_with_flag(dataset.wrapped_dataset, flag=[clean_idx_list,poison_idx_list,suspicious_idx_list], transform=TwoCropTransform(train_transform)) + else: + raise ValueError('module not specified') + train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=opt.batch_size, shuffle=True) + + return train_loader + +# def get_st_train_loader_back(opt, module='sscl'): +# transforms_list = [ +# transforms.ToPILImage(), +# transforms.RandomResizedCrop(size=opt.input_height, scale=(0.2, 1.)), +# transforms.RandomHorizontalFlip(), +# transforms.RandomApply([ +# transforms.ColorJitter(0.4, 0.4, 0.4, 0.1) +# ], p=0.8), +# transforms.RandomGrayscale(p=0.2), +# transforms.ToTensor() +# ] + +# # construct data loader +# if opt.dataset == 'cifar10': +# mean = (0.4914, 0.4822, 0.4465) +# std = (0.2023, 0.1994, 0.2010) +# elif opt.dataset == 'cifar100': +# mean = (0.5071, 0.4867, 0.4408) +# std = (0.2675, 0.2565, 0.2761) +# elif opt.dataset == "mnist": +# mean = [0.5,] +# std = [0.5,] +# elif opt.dataset == 'tiny': +# mean = (0.4802, 0.4481, 0.3975) +# std = (0.2302, 0.2265, 0.2262) +# elif opt.dataset == 'imagenet': +# mean = (0.4802, 0.4481, 0.3975) +# std = (0.2302, 0.2265, 0.2262) +# elif opt.dataset == 'gtsrb': +# mean = None +# elif opt.dataset == 'path': +# mean = eval(opt.mean) +# std = eval(opt.std) +# else: +# raise ValueError('dataset not supported: {}'.format(opt.dataset)) + +# if mean != None: +# normalize = transforms.Normalize(mean=mean, std=std) +# transforms_list.append(normalize) + +# train_transform = transforms.Compose(transforms_list) + +# folder_path = folder_path = f'{opt.save_path}data_produce' +# data_path_clean = os.path.join(folder_path, 'clean_samples.npy') +# data_path_poison = os.path.join(folder_path, 'poison_samples.npy') +# data_path_suspicious = os.path.join(folder_path, 'suspicious_samples.npy') +# if opt.debug: +# # data_path_poison = os.path.join(folder_path, 'suspicious_samples.npy') +# opt.batch_size = 5 + +# clean_data = np.load(data_path_clean, allow_pickle=True) +# poison_data = np.load(data_path_poison, allow_pickle=True) +# suspicious_data = np.load(data_path_suspicious, allow_pickle=True) +# logging.info(f'Num of clean, poison and suspicious: {clean_data.shape[0]}, {poison_data.shape[0]}, {suspicious_data.shape[0]}') +# all_data = np.concatenate((clean_data, poison_data, suspicious_data), axis=0) +# if module == 'mixed_ce': +# train_dataset = Dataset_npy(full_dataset=all_data, transform=train_transform) +# elif module == 'sscl': +# train_dataset = Dataset_npy(full_dataset=all_data, transform=TwoCropTransform(train_transform)) +# else: +# raise ValueError('module not specified') +# train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=opt.batch_size, shuffle=True) + +# return train_loader + +# def get_st_val_loader(opt): +# # construct data loader +# if opt.dataset == 'cifar10': +# mean = (0.4914, 0.4822, 0.4465) +# std = (0.2023, 0.1994, 0.2010) +# elif opt.dataset == 'cifar100': +# mean = (0.5071, 0.4867, 0.4408) +# std = (0.2675, 0.2565, 0.2761) +# elif opt.dataset == "mnist": +# mean = [0.5] +# std = [0.5] +# elif opt.dataset == 'tiny': +# mean = (0.4802, 0.4481, 0.3975) +# std = (0.2302, 0.2265, 0.2262) +# elif opt.dataset == 'imagenet': +# mean = (0.4802, 0.4481, 0.3975) +# std = (0.2302, 0.2265, 0.2262) +# elif opt.dataset == 'gtsrb': +# mean = None +# elif opt.dataset == 'path': +# mean = eval(opt.mean) +# std = eval(opt.std) +# else: +# raise ValueError('dataset not supported: {}'.format(opt.dataset)) +# transforms_list = [transforms.ToTensor(),] +# if mean != None: +# normalize = transforms.Normalize(mean=mean, std=std) +# transforms_list.append(normalize) + +# val_transform = transforms.Compose(transforms_list) +# val_loader = torch.utils.data.DataLoader( +# val_dataset, batch_size=256, shuffle=False, +# num_workers=8, pin_memory=True) + +# return val_loader \ No newline at end of file diff --git a/utils/defense_utils/dst/models/__pycache__/resnet_super.cpython-38.pyc b/utils/defense_utils/dst/models/__pycache__/resnet_super.cpython-38.pyc new file mode 100644 index 0000000..41f627e Binary files /dev/null and b/utils/defense_utils/dst/models/__pycache__/resnet_super.cpython-38.pyc differ diff --git a/utils/defense_utils/dst/models/__pycache__/resnet_super.cpython-39.pyc b/utils/defense_utils/dst/models/__pycache__/resnet_super.cpython-39.pyc new file mode 100644 index 0000000..dc70662 Binary files /dev/null and b/utils/defense_utils/dst/models/__pycache__/resnet_super.cpython-39.pyc differ diff --git a/utils/defense_utils/dst/models/resnet_cifar10.py b/utils/defense_utils/dst/models/resnet_cifar10.py new file mode 100644 index 0000000..f922506 --- /dev/null +++ b/utils/defense_utils/dst/models/resnet_cifar10.py @@ -0,0 +1,305 @@ +# Source: https://github.com/huyvnphan/PyTorch_CIFAR10 + +import torch +import torch.nn as nn +import os + +__all__ = [ + "ResNet", + "resnet18", + "resnet34", + "resnet50", +] + + +def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): + """3x3 convolution with padding""" + return nn.Conv2d( + in_planes, + out_planes, + kernel_size=3, + stride=stride, + padding=dilation, + groups=groups, + bias=False, + dilation=dilation, + ) + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__( + self, + inplanes, + planes, + stride=1, + downsample=None, + groups=1, + base_width=64, + dilation=1, + norm_layer=None, + ): + super(BasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError("BasicBlock only supports groups=1 and base_width=64") + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__( + self, + inplanes, + planes, + stride=1, + downsample=None, + groups=1, + base_width=64, + dilation=1, + norm_layer=None, + ): + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = int(planes * (base_width / 64.0)) * groups + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, stride, groups, dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + def __init__( + self, + block, + layers, + num_classes=10, + zero_init_residual=False, + groups=1, + width_per_group=64, + replace_stride_with_dilation=None, + norm_layer=None, + ): + super(ResNet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError( + "replace_stride_with_dilation should be None " + "or a 3-element tuple, got {}".format(replace_stride_with_dilation) + ) + self.groups = groups + self.base_width = width_per_group + + # CIFAR10: kernel_size 7 -> 3, stride 2 -> 1, padding 3->1 + self.conv1 = nn.Conv2d( + 3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False + ) + # END + + self.bn1 = norm_layer(self.inplanes) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer( + block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0] + ) + self.layer3 = self._make_layer( + block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1] + ) + self.layer4 = self._make_layer( + block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2] + ) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False): + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append( + block( + self.inplanes, + planes, + stride, + downsample, + self.groups, + self.base_width, + previous_dilation, + norm_layer, + ) + ) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append( + block( + self.inplanes, + planes, + groups=self.groups, + base_width=self.base_width, + dilation=self.dilation, + norm_layer=norm_layer, + ) + ) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.reshape(x.size(0), -1) + x = self.fc(x) + + return x + + +def _resnet(arch, block, layers, pretrained, progress, device, **kwargs): + model = ResNet(block, layers, **kwargs) + if pretrained: + script_dir = os.path.dirname(__file__) + state_dict = torch.load( + script_dir + "/state_dicts/" + arch + ".pt", map_location=device + ) + model.load_state_dict(state_dict) + return model + + +def resnet18(pretrained=False, progress=True, device="cpu", **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet( + "resnet18", BasicBlock, [2, 2, 2, 2], pretrained, progress, device, **kwargs + ) + + +def resnet34(pretrained=False, progress=True, device="cpu", **kwargs): + """Constructs a ResNet-34 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet( + "resnet34", BasicBlock, [3, 4, 6, 3], pretrained, progress, device, **kwargs + ) + + +def resnet50(pretrained=False, progress=True, device="cpu", **kwargs): + """Constructs a ResNet-50 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet( + "resnet50", Bottleneck, [3, 4, 6, 3], pretrained, progress, device, **kwargs + ) diff --git a/utils/defense_utils/dst/models/resnet_cifar100.py b/utils/defense_utils/dst/models/resnet_cifar100.py new file mode 100644 index 0000000..19fd1e8 --- /dev/null +++ b/utils/defense_utils/dst/models/resnet_cifar100.py @@ -0,0 +1,155 @@ +# Source: https://github.com/weiaicunzai/pytorch-cifar100 + +import torch +import torch.nn as nn + +class BasicBlock(nn.Module): + """Basic Block for resnet 18 and resnet 34 + + """ + + #BasicBlock and BottleNeck block + #have different output size + #we use class attribute expansion + #to distinct + expansion = 1 + + def __init__(self, in_channels, out_channels, stride=1): + super().__init__() + + #residual function + self.residual_function = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(out_channels * BasicBlock.expansion) + ) + + #shortcut + self.shortcut = nn.Sequential() + + #the shortcut output dimension is not the same with residual function + #use 1*1 convolution to match the dimension + if stride != 1 or in_channels != BasicBlock.expansion * out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(out_channels * BasicBlock.expansion) + ) + + def forward(self, x): + return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x)) + +class BottleNeck(nn.Module): + """Residual block for resnet over 50 layers + + """ + expansion = 4 + def __init__(self, in_channels, out_channels, stride=1): + super().__init__() + self.residual_function = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels * BottleNeck.expansion), + ) + + self.shortcut = nn.Sequential() + + if stride != 1 or in_channels != out_channels * BottleNeck.expansion: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels * BottleNeck.expansion) + ) + + def forward(self, x): + return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x)) + +class ResNet(nn.Module): + + def __init__(self, block, num_block, num_classes=100): + super().__init__() + + self.in_channels = 64 + + self.conv1 = nn.Sequential( + nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True)) + #we use a different inputsize than the original paper + #so conv2_x's stride is 1 + self.conv2_x = self._make_layer(block, 64, num_block[0], 1) + self.conv3_x = self._make_layer(block, 128, num_block[1], 2) + self.conv4_x = self._make_layer(block, 256, num_block[2], 2) + self.conv5_x = self._make_layer(block, 512, num_block[3], 2) + self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + def _make_layer(self, block, out_channels, num_blocks, stride): + """make resnet layers(by layer i didnt mean this 'layer' was the + same as a neuron netowork layer, ex. conv layer), one layer may + contain more than one residual block + + Args: + block: block type, basic block or bottle neck block + out_channels: output depth channel number of this layer + num_blocks: how many blocks per layer + stride: the stride of the first block of this layer + + Return: + return a resnet layer + """ + + # we have num_block blocks per layer, the first block + # could be 1 or 2, other blocks would always be 1 + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_channels, out_channels, stride)) + self.in_channels = out_channels * block.expansion + + return nn.Sequential(*layers) + + def forward(self, x): + output = self.conv1(x) + output = self.conv2_x(output) + output = self.conv3_x(output) + output = self.conv4_x(output) + output = self.conv5_x(output) + output = self.avg_pool(output) + output = output.view(output.size(0), -1) + output = self.fc(output) + + return output + +def resnet18(): + """ return a ResNet 18 object + """ + return ResNet(BasicBlock, [2, 2, 2, 2]) + +def resnet34(): + """ return a ResNet 34 object + """ + return ResNet(BasicBlock, [3, 4, 6, 3]) + +def resnet50(): + """ return a ResNet 50 object + """ + return ResNet(BottleNeck, [3, 4, 6, 3]) + +def resnet101(): + """ return a ResNet 101 object + """ + return ResNet(BottleNeck, [3, 4, 23, 3]) + +def resnet152(): + """ return a ResNet 152 object + """ + return ResNet(BottleNeck, [3, 8, 36, 3]) + + + diff --git a/utils/defense_utils/dst/models/resnet_super.py b/utils/defense_utils/dst/models/resnet_super.py new file mode 100644 index 0000000..6fc1c21 --- /dev/null +++ b/utils/defense_utils/dst/models/resnet_super.py @@ -0,0 +1,213 @@ +# Source: https://github.com/HobbitLong/SupContrast + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1, is_last=False): + super(BasicBlock, self).__init__() + self.is_last = is_last + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + preact = out + out = F.relu(out) + if self.is_last: + return out, preact + else: + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, in_planes, planes, stride=1, is_last=False): + super(Bottleneck, self).__init__() + self.is_last = is_last + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(self.expansion * planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + out += self.shortcut(x) + preact = out + out = F.relu(out) + if self.is_last: + return out, preact + else: + return out + + +# class ResNet(nn.Module): +# def __init__(self, block, num_blocks, in_channel=3, zero_init_residual=False): +# super(ResNet, self).__init__() +# self.in_planes = 64 + +# self.conv1 = nn.Conv2d(in_channel, 64, kernel_size=3, stride=1, padding=1, +# bias=False) +# self.bn1 = nn.BatchNorm2d(64) +# self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) +# self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) +# self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) +# self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) +# self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + +# for m in self.modules(): +# if isinstance(m, nn.Conv2d): +# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') +# elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): +# nn.init.constant_(m.weight, 1) +# nn.init.constant_(m.bias, 0) + +# # Zero-initialize the last BN in each residual branch, +# # so that the residual branch starts with zeros, and each residual block behaves +# # like an identity. This improves the model by 0.2~0.3% according to: +# # https://arxiv.org/abs/1706.02677 +# if zero_init_residual: +# for m in self.modules(): +# if isinstance(m, Bottleneck): +# nn.init.constant_(m.bn3.weight, 0) +# elif isinstance(m, BasicBlock): +# nn.init.constant_(m.bn2.weight, 0) + +# def _make_layer(self, block, planes, num_blocks, stride): +# strides = [stride] + [1] * (num_blocks - 1) +# layers = [] +# for i in range(num_blocks): +# stride = strides[i] +# layers.append(block(self.in_planes, planes, stride)) +# self.in_planes = planes * block.expansion +# return nn.Sequential(*layers) + +# def forward(self, x, layer=100): +# out = F.relu(self.bn1(self.conv1(x))) +# out = self.layer1(out) +# out = self.layer2(out) +# out = self.layer3(out) +# out = self.layer4(out) +# out = self.avgpool(out) +# out = torch.flatten(out, 1) +# return out + + +# def resnet18(**kwargs): +# return ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + + +# def resnet34(**kwargs): +# return ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) + + +# def resnet50(**kwargs): +# return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) + + +# def resnet101(**kwargs): +# return ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) + + +# model_dict = { +# 'resnet18': [resnet18, 512], +# 'resnet34': [resnet34, 512], +# 'resnet50': [resnet50, 2048], +# 'resnet101': [resnet101, 2048], +# } + + +class LinearBatchNorm(nn.Module): + """Implements BatchNorm1d by BatchNorm2d, for SyncBN purpose""" + def __init__(self, dim, affine=True): + super(LinearBatchNorm, self).__init__() + self.dim = dim + self.bn = nn.BatchNorm2d(dim, affine=affine) + + def forward(self, x): + x = x.view(-1, self.dim, 1, 1) + x = self.bn(x) + x = x.view(-1, self.dim) + return x + + +class SupConResNet(nn.Module): + """backbone + projection head""" + def __init__(self, encoder, dim_in, head='mlp', feat_dim=128): + super(SupConResNet, self).__init__() + self.encoder = encoder + + if head == 'linear': + self.head = nn.Linear(dim_in, feat_dim) + elif head == 'mlp': + self.head = nn.Sequential( + nn.Linear(dim_in, dim_in), + nn.ReLU(inplace=True), + nn.Linear(dim_in, feat_dim) + ) + else: + raise NotImplementedError( + 'head not supported: {}'.format(head)) + + def forward(self, x): + feat = self.encoder(x) + feat = F.normalize(self.head(feat), dim=1) + return feat + + +class SupCEResNet(nn.Module): + """encoder + classifier""" + def __init__(self, encoder, num_classes=10): + super(SupCEResNet, self).__init__() + self.encoder = encoder + dim_in = list(encoder.named_modules())[-1][1].in_features + self.fc = nn.Linear(dim_in, num_classes) + + def forward(self, x): + return self.fc(self.encoder(x)) + + +# class LinearClassifier(nn.Module): +# """Linear classifier""" +# def __init__(self, encoder, num_classes=10): +# super(LinearClassifier, self).__init__() +# dim_in = list(encoder.named_modules())[-1][1].in_features +# self.fc = nn.Linear(dim_in, num_classes) + +# def forward(self, features): +# return self.fc(features) +class LinearClassifier(nn.Module): + """Linear classifier""" + def __init__(self, feat_dim, num_classes=10): + super(LinearClassifier, self).__init__() + self.fc = nn.Linear(feat_dim, num_classes) + + def forward(self, features): + return self.fc(features) diff --git a/utils/defense_utils/dst/sd.py b/utils/defense_utils/dst/sd.py new file mode 100644 index 0000000..d2412f6 --- /dev/null +++ b/utils/defense_utils/dst/sd.py @@ -0,0 +1,179 @@ +import sys +import os +from tqdm import tqdm +import numpy as np +import argparse +import torch +from torch import nn +sys.path.append("./") +sys.path.append(os.getcwd()) +print(os.getcwd()) +from utils.defense_utils.dst.dataloader_bd import normalization +import logging + +def calculate_consistency(args, dataloader, model): + f_path = os.path.join(args.save_path, 'data_produce') + if not os.path.exists(f_path): + os.makedirs(f_path) + f_all = os.path.join(f_path,'all.txt') + f_clean = os.path.join(f_path,'clean.txt') + f_poison = os.path.join(f_path,'poison.txt') + if os.path.exists(f_all): + with open(f_all,'a+') as test: + test.truncate(0) + test.close() + with open(f_clean,'a+') as test: + test.truncate(0) + test.close() + with open(f_poison,'a+') as test: + test.truncate(0) + test.close() + + model.eval() + for i, (inputs, labels, _, is_bd, gt_labels) in enumerate(dataloader): + inputs1, inputs2 = inputs[0], inputs[2] + inputs1, inputs2 = normalization(args, inputs1), normalization(args, inputs2) # Normalize + inputs1, inputs2, labels, gt_labels = inputs1.to(args.device), inputs2.to(args.device), labels.to(args.device), gt_labels.to(args.device) + clean_idx, poison_idx = torch.where(is_bd == False), torch.where(is_bd == True) + + ### Feature ### + # if hasattr(model, "module"): # abandon FC layer + # features_out = list(model.module.children())[:-1] + # else: + # features_out = list(model.children())[:-1] + # modelout = nn.Sequential(*features_out).to(args.device) + # features1, features2 = modelout(inputs1), modelout(inputs2) + + features1, features2 = model(inputs1), model(inputs2) + features1, features2 = features1.view(features1.size(0), -1), features2.view(features2.size(0), -1) + + ### Calculate consistency ### + feature_consistency = torch.mean((features1 - features2)**2, dim=1) + + ### Save ### + draw_features = feature_consistency.detach().cpu().numpy() + draw_clean_features = feature_consistency[clean_idx].detach().cpu().numpy() + draw_poison_features = feature_consistency[poison_idx].detach().cpu().numpy() + + with open(f_all, 'ab') as f: + np.savetxt(f, draw_features, delimiter=" ") + with open(f_clean, 'ab') as f: + np.savetxt(f, draw_clean_features, delimiter=" ") + with open(f_poison, 'ab') as f: + np.savetxt(f, draw_poison_features, delimiter=" ") + return + +def calculate_gamma(args): + + f_path = os.path.join(args.save_path, 'data_produce') + f_all = os.path.join(f_path,'all.txt') + + all_data = np.loadtxt(f_all) + all_size = all_data.shape[0] # 50000 + + clean_size = int(all_size * args.clean_ratio) # 10000 + poison_size = int(all_size * args.poison_ratio) # 2500 + + new_data = np.sort(all_data) # in ascending order + gamma_low = new_data[clean_size] + gamma_high = new_data[all_size-poison_size] + print("gamma_low: ", gamma_low) + print("gamma_high: ", gamma_high) + return gamma_low, gamma_high + +def separate_samples(args, trainloader, model): + gamma_low, gamma_high = args.gamma_low, args.gamma_high + model.eval() + clean_samples, poison_samples, suspicious_samples = [], [], [] + + clean_idx_list = [] + poison_idx_list = [] + suspicious_idx_list = [] + for i, (inputs, labels, original_index, _, gt_labels) in enumerate(trainloader): + if args.debug and i==10001: + break + print("Processing samples:", i*args.batch_size) + inputs1, inputs2 = inputs[0], inputs[2] + inputs1, inputs2 = normalization(args, inputs1), normalization(args, inputs2) + inputs1, inputs2 = inputs1.to(args.device), inputs2.to(args.device) + ### Features ### + features1, features2 = model(inputs1), model(inputs2) + features1, features2 = features1.view(features1.size(0), -1), features2.view(features2.size(0), -1) + + ### Compare consistency ### + feature_consistency = torch.mean((features1 - features2)**2, dim=1) + # feature_consistency = feature_consistency.detach().cpu().numpy() + + ### Separate samples ### + clean_idx_list += original_index[torch.where(feature_consistency <= gamma_low)[0]] + poison_idx_list += original_index[torch.where(feature_consistency >= gamma_high)[0]] + suspicious_idx_list += original_index[torch.where((feature_consistency > gamma_low) & (feature_consistency < gamma_high))[0]] + + ### Save samples original index list### + + folder_path = os.path.join(args.save_path, 'data_produce') + data_path_clean = os.path.join(folder_path, 'clean_samples.npy') + data_path_poison = os.path.join(folder_path, 'poison_samples.npy') + data_path_suspicious = os.path.join(folder_path, 'suspicious_samples.npy') + np.save(data_path_clean, clean_idx_list) + np.save(data_path_poison, poison_idx_list) + np.save(data_path_suspicious, suspicious_idx_list) + logging.info(f"Clean, poison, suspicious samples: {len(clean_idx_list)} {len(poison_idx_list)} {len(suspicious_idx_list)}") + +def separate_samples_back(args, trainloader, model): + gamma_low, gamma_high = args.gamma_low, args.gamma_high + model.eval() + clean_samples, poison_samples, suspicious_samples = [], [], [] + + for i, (inputs, labels, _, _, gt_labels) in enumerate(trainloader): + if args.debug and i==10001: + break + if i % 1000 == 0: + print("Processing samples:", i) + inputs1, inputs2 = inputs[0], inputs[2] + + ### Prepare for saved ### + img = inputs1 + img = img.squeeze() + target = labels.squeeze() + img = np.transpose((img * 255).cpu().numpy(), (1, 2, 0)).astype('uint8') + target = target.cpu().numpy() + + inputs1, inputs2 = normalization(args, inputs1), normalization(args, inputs2) # Normalize + inputs1, inputs2, labels, gt_labels = inputs1.to(args.device), inputs2.to(args.device), labels.to(args.device), gt_labels.to(args.device) + + ### Features ### + # if hasattr(model, "module"): # abandon FC layer + # features_out = list(model.module.children())[:-1] + # else: + # features_out = list(model.children())[:-1] + # modelout = nn.Sequential(*features_out).to(args.device) + # features1, features2 = modelout(inputs1), modelout(inputs2) + features1, features2 = model(inputs1), model(inputs2) + features1, features2 = features1.view(features1.size(0), -1), features2.view(features2.size(0), -1) + + ### Compare consistency ### + feature_consistency = torch.mean((features1 - features2)**2, dim=1) + # feature_consistency = feature_consistency.detach().cpu().numpy() + + ### Separate samples ### + if feature_consistency.item() <= gamma_low: + flag = 0 + clean_samples.append((img, target, flag)) + elif feature_consistency.item() >= gamma_high: + flag = 2 + poison_samples.append((img, target, flag)) + else: + flag = 1 + suspicious_samples.append((img, target, flag)) + + ### Save samples ### + + folder_path = os.path.join(args.save_path, 'data_produce') + + data_path_clean = os.path.join(folder_path, 'clean_samples.npy') + data_path_poison = os.path.join(folder_path, 'poison_samples.npy') + data_path_suspicious = os.path.join(folder_path, 'suspicious_samples.npy') + np.save(data_path_clean, clean_samples) + np.save(data_path_poison, poison_samples) + np.save(data_path_suspicious, suspicious_samples) diff --git a/utils/defense_utils/dst/st_loss.py b/utils/defense_utils/dst/st_loss.py new file mode 100644 index 0000000..3b183c8 --- /dev/null +++ b/utils/defense_utils/dst/st_loss.py @@ -0,0 +1,191 @@ +# Modified from https://github.com/HobbitLong/SupContrast + +from __future__ import print_function + +import torch +import torch.nn as nn +import numpy + + +class SupConLoss(nn.Module): + def __init__(self, temperature=0.07, contrast_mode='all', + base_temperature=0.07,device=None): + super(SupConLoss, self).__init__() + self.temperature = temperature + self.contrast_mode = contrast_mode + self.base_temperature = base_temperature + self.device =device + + def forward(self, features, labels=None, gt_labels=None, mask=None, isCleans=None): + """Compute loss for model. + Args: + features: hidden vector of shape [bsz, n_views, ...]. + labels: label of shape [bsz]. + gt_labels: ground-truth label of shape [bsz]. + mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j is the positive of sample i. Can be asymmetric. + isCleans: is-clean sign of shape [bsz], isCleans{i}=1 if sample i is genuinely clean. + Returns: + A loss scalar. + """ + if self.device is None: + device = (torch.device('cuda') if features.is_cuda else torch.device('cpu')) + else: + device = self.device + + if len(features.shape) < 3: + raise ValueError('`features` needs to be [bsz, n_views, ...],' + 'at least 3 dimensions are required') + if len(features.shape) > 3: + features = features.view(features.shape[0], features.shape[1], -1) + + batch_size = features.shape[0] + if labels is not None and mask is not None: + raise ValueError('Cannot define both `labels` and `mask`') + elif labels is None and mask is None: # SimCLR (contrastive learning) + mask = torch.eye(batch_size, dtype=torch.float32).to(device) + elif labels is not None: # SupCon (supervised contrastive learning) + labels = labels.contiguous().view(-1, 1) + if labels.shape[0] != batch_size: + raise ValueError('Num of labels does not match num of features') + # set the positives of each sample as its own augmented version and the augmented versions of samples with the same label + mask = torch.eq(labels, labels.T).float().to(device) # mask: positive==1 + else: + mask = mask.float().to(device) + + contrast_count = features.shape[1] + contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0) + if self.contrast_mode == 'one': + anchor_feature = features[:, 0] + anchor_count = 1 + elif self.contrast_mode == 'all': + anchor_feature = contrast_feature + anchor_count = contrast_count + else: + raise ValueError('Unknown mode: {}'.format(self.contrast_mode)) + + # compute logits + anchor_dot_contrast = torch.div( + torch.matmul(anchor_feature, contrast_feature.T), + self.temperature) + # for numerical stability + logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True) + logits = anchor_dot_contrast - logits_max.detach() + + # tile mask + mask = mask.repeat(anchor_count, contrast_count) + # mask-out self-contrast cases + logits_mask = torch.scatter( + torch.ones_like(mask), + 1, + torch.arange(batch_size * anchor_count).view(-1, 1).to(device), + 0 + ) + mask = mask * logits_mask # mask_{i,j}=1 if sample j is the positive of sample i. + + # compute log_prob + exp_logits = torch.exp(logits) * logits_mask + log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True)) + + # compute mean of log-likelihood over positive + mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1) + + # loss + loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos + loss = loss.view(anchor_count, batch_size).mean() + + return loss + + +class SupConLoss_Consistency(nn.Module): + def __init__(self, temperature=0.07, contrast_mode='all', + base_temperature=0.07,device=None): + super(SupConLoss_Consistency, self).__init__() + self.temperature = temperature + self.contrast_mode = contrast_mode + self.base_temperature = base_temperature + self.device =device + + + def forward(self, features, labels=None, flags=None, mask=None): + """Compute loss for model. + Args: + features: hidden vector of shape [bsz, n_views, ...]. + labels: label of shape [bsz]. + gt_labels: ground-truth label of shape [bsz]. + mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j is the positive of sample i. Can be asymmetric. + isCleans: is-clean sign of shape [bsz], isCleans{i}=1 if sample i is genuinely clean. + Returns: + A loss scalar. + """ + if self.device is None: + device = (torch.device('cuda') if features.is_cuda else torch.device('cpu')) + else: + device = self.device + + if len(features.shape) < 3: + raise ValueError('`features` needs to be [bsz, n_views, ...],' + 'at least 3 dimensions are required') + if len(features.shape) > 3: + features = features.view(features.shape[0], features.shape[1], -1) + + batch_size = features.shape[0] + if labels is not None and mask is not None: + raise ValueError('Cannot define both `labels` and `mask`') + elif labels is None and mask is None: # SimCLR (contrastive learning) + mask = torch.eye(batch_size, dtype=torch.float32).to(device) + elif labels is not None: # SS-CTL (semi-supervised contrastive learning) + labels = labels.contiguous().view(-1, 1) + if labels.shape[0] != batch_size: + raise ValueError('Num of labels does not match num of features') + # set the positive of a poisoned sample / an uncertain sample as its own augmented version + # set the positives of a clean sample as its own augmented version and the augmented versions of samples with the same label + mask = torch.eq(labels, labels.T).float().to(device) + nonclean_idx = torch.where(flags!=0)[0] # poisoned samples and uncertain samples + mask[nonclean_idx, :] = 0 + mask[nonclean_idx, nonclean_idx] = 1 + else: + mask = mask.float().to(device) + + contrast_count = features.shape[1] + contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0) + if self.contrast_mode == 'one': + anchor_feature = features[:, 0] + anchor_count = 1 + elif self.contrast_mode == 'all': + anchor_feature = contrast_feature + anchor_count = contrast_count + else: + raise ValueError('Unknown mode: {}'.format(self.contrast_mode)) + + # compute logits + anchor_dot_contrast = torch.div( + torch.matmul(anchor_feature, contrast_feature.T), + self.temperature) + # for numerical stability + logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True) + logits = anchor_dot_contrast - logits_max.detach() + + # tile mask + mask = mask.repeat(anchor_count, contrast_count) + # isCleans_mask = isCleans_mask.repeat(anchor_count, contrast_count) + # mask-out self-contrast cases + logits_mask = torch.scatter( + torch.ones_like(mask), + 1, + torch.arange(batch_size * anchor_count).view(-1, 1).to(device), + 0 + ) + mask = mask * logits_mask # mask_{i,j}=1 if sample j is the positive of sample i. + + # compute log_prob + exp_logits = torch.exp(logits) * logits_mask + log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True)) + + # compute mean of log-likelihood over positive + mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1) + + # loss + loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos + loss = loss.view(anchor_count, batch_size).mean() + + return loss diff --git a/utils/defense_utils/dst/utils_st.py b/utils/defense_utils/dst/utils_st.py new file mode 100644 index 0000000..d515ae8 --- /dev/null +++ b/utils/defense_utils/dst/utils_st.py @@ -0,0 +1,119 @@ +import math +import torch.optim as optim +import torch +import numpy as np +import sys, os +sys.path.append(os.getcwd()) +sys.path.append('../') + +# def set_args(args,module='sscl'): +# if module == 'sscl': +# args.batch_size = 512 +# args.learning_rate = 0.5 +# args.temp = 0.1 +# args.epochs = 200 +# args.num_workers = 16 +# args.method = 'SupCon' # choices = ['SupCon', 'SimCLR'] +# args.consine = True + +# elif module == 'mixed_ce': +# args.batch_size = 512 +# args.learning_rate = 5 +# args.epochs = 10 +# args.num_workers = 16 +# args.consine = False + +# if args.batch_size > 256: +# args.warm = True +# if args.warm: +# args.warmup_from = 0.01 +# args.warm_epochs = 10 +# if args.cosine: +# eta_min = args.learning_rate * (args.lr_decay_rate ** 3) +# args.warmup_to = eta_min + (args.learning_rate - eta_min) * ( +# 1 + math.cos(math.pi * args.warm_epochs / args.epochs)) / 2 +# else: +# args.warmup_to = args.learning_rate +# if args.debug: +# args.epochs = 2 +# return args + +def warmup_learning_rate(args, epoch, batch_id, total_batches, optimizer): + if args.warm and epoch <= args.warm_epochs: + p = (batch_id + (epoch - 1) * total_batches) / \ + (args.warm_epochs * total_batches) + lr = args.warmup_from + p * (args.warmup_to - args.warmup_from) + + for param_group in optimizer.param_groups: + param_group['lr'] = lr + +def set_optimizer(opt, model,lr=None): + if lr == None: + lr = opt.lr + optimizer = optim.SGD(model.parameters(), + lr=lr, + momentum=0.9, + weight_decay=5e-4) + return optimizer + +def adjust_learning_rate(args, optimizer, epoch): + lr = args.learning_rate + if args.cosine: + eta_min = lr * (args.lr_decay_rate ** 3) + lr = eta_min + (lr - eta_min) * ( + 1 + math.cos(math.pi * epoch / args.epochs)) / 2 + else: + steps = np.sum(epoch > np.asarray(args.lr_decay_epochs)) + if steps > 0: + lr = lr * (args.lr_decay_rate ** steps) + + for param_group in optimizer.param_groups: + param_group['lr'] = lr + + +def save_model(model, optimizer, opt, epoch, save_file): + print('==> Saving...') + state = { + 'opt': opt, + 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), + 'epoch': epoch, + } + torch.save(state, save_file) + print('==> Successfully saved!') + del state + +def accuracy(output, target, topk=(1,)): # output: (256,10); target: (256) + """Computes the accuracy over the k top predictions for the specified values of k""" + with torch.no_grad(): + maxk = max(topk) # 5 + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) # pred: (256,5) + pred = pred.t() # (5,256) + correct = pred.eq(target.view(1, -1).expand_as(pred)) # (5,256) + + res = [] + + for k in topk: + # correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) + correct_k = torch.flatten(correct[:k]).float().sum(0, keepdim=True) + res.append(correct_k.mul_(1. / batch_size)) + return res + +class AverageMeter(object): + """Computes and stores the average and current value""" + def __init__(self): + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count diff --git a/utils/defense_utils/mbns/mbns_model/__init__.py b/utils/defense_utils/mbns/mbns_model/__init__.py new file mode 100644 index 0000000..731fc57 --- /dev/null +++ b/utils/defense_utils/mbns/mbns_model/__init__.py @@ -0,0 +1,11 @@ +from .vgg_mbns import * +from .mbns_batchnorm import * +from .preact_mbns import * +from .mobilenet_mbns import * +from .eff_mbns import * +from .den_mbns import * +from .mbns_layernorm import * +# from .vit_new_mbns import * +# from .conv_new_mbns import * +from .vit_mbns import * +from .conv_mbns import * \ No newline at end of file diff --git a/utils/defense_utils/mbns/mbns_model/conv_mbns.py b/utils/defense_utils/mbns/mbns_model/conv_mbns.py new file mode 100644 index 0000000..7d58a9d --- /dev/null +++ b/utils/defense_utils/mbns/mbns_model/conv_mbns.py @@ -0,0 +1,273 @@ +from functools import partial +from typing import Any, Callable, Dict, List, Optional, Sequence + +import torch +from torch import nn, Tensor +from torch.nn import functional as F + +from torchvision._internally_replaced_utils import load_state_dict_from_url +from torchvision.ops.misc import ConvNormActivation +from torchvision.ops.stochastic_depth import StochasticDepth +from torchvision.utils import _log_api_usage_once + +from defense.mbns import mbns_model + + +__all__ = [ + "ConvNeXt", + "convnext_tiny", + "convnext_small", + "convnext_base", + "convnext_large", +] + + +_MODELS_URLS: Dict[str, Optional[str]] = { + "convnext_tiny": "https://download.pytorch.org/models/convnext_tiny-983f1562.pth", + "convnext_small": "https://download.pytorch.org/models/convnext_small-0c510722.pth", + "convnext_base": "https://download.pytorch.org/models/convnext_base-6075fbad.pth", + "convnext_large": "https://download.pytorch.org/models/convnext_large-ea097f82.pth", +} + + +class LayerNorm2d(nn.LayerNorm): + def forward(self, x: Tensor) -> Tensor: + x = x.permute(0, 2, 3, 1) + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + x = x.permute(0, 3, 1, 2) + return x + + +class Permute(nn.Module): + def __init__(self, dims: List[int]): + super().__init__() + self.dims = dims + + def forward(self, x): + return torch.permute(x, self.dims) + + +class CNBlock(nn.Module): + def __init__( + self, + dim, + layer_scale: float, + stochastic_depth_prob: float, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + if norm_layer is None: + norm_layer = partial(mbns_model.LayerNorm_MBNS, eps=1e-6) + + self.block = nn.Sequential( + nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim, bias=True), + Permute([0, 2, 3, 1]), + norm_layer(dim), + nn.Linear(in_features=dim, out_features=4 * dim, bias=True), + nn.GELU(), + nn.Linear(in_features=4 * dim, out_features=dim, bias=True), + Permute([0, 3, 1, 2]), + ) + self.layer_scale = nn.Parameter(torch.ones(dim, 1, 1) * layer_scale) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + + def forward(self, input: Tensor) -> Tensor: + result = self.layer_scale * self.block(input) + result = self.stochastic_depth(result) + result += input + return result + + +class CNBlockConfig: + # Stores information listed at Section 3 of the ConvNeXt paper + def __init__( + self, + input_channels: int, + out_channels: Optional[int], + num_layers: int, + ) -> None: + self.input_channels = input_channels + self.out_channels = out_channels + self.num_layers = num_layers + + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "input_channels={input_channels}" + s += ", out_channels={out_channels}" + s += ", num_layers={num_layers}" + s += ")" + return s.format(**self.__dict__) + + +class ConvNeXt(nn.Module): + def __init__( + self, + block_setting: List[CNBlockConfig], + stochastic_depth_prob: float = 0.0, + layer_scale: float = 1e-6, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any, + ) -> None: + super().__init__() + _log_api_usage_once(self) + + if not block_setting: + raise ValueError("The block_setting should not be empty") + elif not (isinstance(block_setting, Sequence) and all([isinstance(s, CNBlockConfig) for s in block_setting])): + raise TypeError("The block_setting should be List[CNBlockConfig]") + + if block is None: + block = CNBlock + + if norm_layer is None: + norm_layer = partial(mbns_model.LayerNorm2D_MBNS, eps=1e-6) + + layers: List[nn.Module] = [] + + # Stem + firstconv_output_channels = block_setting[0].input_channels + layers.append( + ConvNormActivation( + 3, + firstconv_output_channels, + kernel_size=4, + stride=4, + padding=0, + norm_layer=norm_layer, + activation_layer=None, + bias=True, + ) + ) + + total_stage_blocks = sum(cnf.num_layers for cnf in block_setting) + stage_block_id = 0 + for cnf in block_setting: + # Bottlenecks + stage: List[nn.Module] = [] + for _ in range(cnf.num_layers): + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * stage_block_id / (total_stage_blocks - 1.0) + stage.append(block(cnf.input_channels, layer_scale, sd_prob)) + stage_block_id += 1 + layers.append(nn.Sequential(*stage)) + if cnf.out_channels is not None: + # Downsampling + layers.append( + nn.Sequential( + norm_layer(cnf.input_channels), + nn.Conv2d(cnf.input_channels, cnf.out_channels, kernel_size=2, stride=2), + ) + ) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + + lastblock = block_setting[-1] + lastconv_output_channels = ( + lastblock.out_channels if lastblock.out_channels is not None else lastblock.input_channels + ) + self.classifier = nn.Sequential( + norm_layer(lastconv_output_channels), nn.Flatten(1), nn.Linear(lastconv_output_channels, num_classes) + ) + + for m in self.modules(): + if isinstance(m, (nn.Conv2d, nn.Linear)): + nn.init.trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + x = self.avgpool(x) + x = self.classifier(x) + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _convnext( + arch: str, + block_setting: List[CNBlockConfig], + stochastic_depth_prob: float, + pretrained: bool, + progress: bool, + **kwargs: Any, +) -> ConvNeXt: + model = ConvNeXt(block_setting, stochastic_depth_prob=stochastic_depth_prob, **kwargs) + if pretrained: + if arch not in _MODELS_URLS: + raise ValueError(f"No checkpoint is available for model type {arch}") + state_dict = load_state_dict_from_url(_MODELS_URLS[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def convnext_tiny(*, pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ConvNeXt: + r"""ConvNeXt Tiny model architecture from the + `"A ConvNet for the 2020s" `_ paper. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + block_setting = [ + CNBlockConfig(96, 192, 3), + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 9), + CNBlockConfig(768, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.1) + return _convnext("convnext_tiny", block_setting, stochastic_depth_prob, pretrained, progress, **kwargs) + + +def convnext_small(*, pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ConvNeXt: + r"""ConvNeXt Small model architecture from the + `"A ConvNet for the 2020s" `_ paper. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + block_setting = [ + CNBlockConfig(96, 192, 3), + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 27), + CNBlockConfig(768, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.4) + return _convnext("convnext_small", block_setting, stochastic_depth_prob, pretrained, progress, **kwargs) + + +def convnext_base(*, pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ConvNeXt: + r"""ConvNeXt Base model architecture from the + `"A ConvNet for the 2020s" `_ paper. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + block_setting = [ + CNBlockConfig(128, 256, 3), + CNBlockConfig(256, 512, 3), + CNBlockConfig(512, 1024, 27), + CNBlockConfig(1024, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5) + return _convnext("convnext_base", block_setting, stochastic_depth_prob, pretrained, progress, **kwargs) + + +def convnext_large(*, pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ConvNeXt: + r"""ConvNeXt Large model architecture from the + `"A ConvNet for the 2020s" `_ paper. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + block_setting = [ + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 3), + CNBlockConfig(768, 1536, 27), + CNBlockConfig(1536, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5) + return _convnext("convnext_large", block_setting, stochastic_depth_prob, pretrained, progress, **kwargs) diff --git a/utils/defense_utils/mbns/mbns_model/conv_new_mbns.py b/utils/defense_utils/mbns/mbns_model/conv_new_mbns.py new file mode 100644 index 0000000..ca6d895 --- /dev/null +++ b/utils/defense_utils/mbns/mbns_model/conv_new_mbns.py @@ -0,0 +1,404 @@ +from functools import partial +from typing import Any, Callable, List, Optional, Sequence + +import torch +from torch import nn, Tensor +from torch.nn import functional as F + +from torchvision.ops.misc import Conv2dNormActivation, Permute +from torchvision.ops.stochastic_depth import StochasticDepth +from torchvision.transforms._presets import ImageClassification +from torchvision.utils import _log_api_usage_once +from torchvision.models._api import WeightsEnum, Weights +from torchvision.models._meta import _IMAGENET_CATEGORIES +from torchvision.models._utils import handle_legacy_interface, _ovewrite_named_param + +from defense.mbns import mbns_model + + +__all__ = [ + "ConvNeXt", + "ConvNeXt_Tiny_Weights", + "ConvNeXt_Small_Weights", + "ConvNeXt_Base_Weights", + "ConvNeXt_Large_Weights", + "convnext_tiny", + "convnext_small", + "convnext_base", + "convnext_large", +] + + +class LayerNorm2d(nn.LayerNorm): + def forward(self, x: Tensor) -> Tensor: + x = x.permute(0, 2, 3, 1) + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + x = x.permute(0, 3, 1, 2) + return x + + +class CNBlock(nn.Module): + def __init__( + self, + dim, + layer_scale: float, + stochastic_depth_prob: float, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + if norm_layer is None: + norm_layer = partial(mbns_model.LayerNorm_MBNS, eps=1e-6) + + self.block = nn.Sequential( + nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim, bias=True), + Permute([0, 2, 3, 1]), + norm_layer(dim), + nn.Linear(in_features=dim, out_features=4 * dim, bias=True), + nn.GELU(), + nn.Linear(in_features=4 * dim, out_features=dim, bias=True), + Permute([0, 3, 1, 2]), + ) + self.layer_scale = nn.Parameter(torch.ones(dim, 1, 1) * layer_scale) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + + def forward(self, input: Tensor) -> Tensor: + result = self.layer_scale * self.block(input) + result = self.stochastic_depth(result) + result += input + return result + + +class CNBlockConfig: + # Stores information listed at Section 3 of the ConvNeXt paper + def __init__( + self, + input_channels: int, + out_channels: Optional[int], + num_layers: int, + ) -> None: + self.input_channels = input_channels + self.out_channels = out_channels + self.num_layers = num_layers + + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "input_channels={input_channels}" + s += ", out_channels={out_channels}" + s += ", num_layers={num_layers}" + s += ")" + return s.format(**self.__dict__) + + +class ConvNeXt(nn.Module): + def __init__( + self, + block_setting: List[CNBlockConfig], + stochastic_depth_prob: float = 0.0, + layer_scale: float = 1e-6, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any, + ) -> None: + super().__init__() + _log_api_usage_once(self) + + if not block_setting: + raise ValueError("The block_setting should not be empty") + elif not (isinstance(block_setting, Sequence) and all([isinstance(s, CNBlockConfig) for s in block_setting])): + raise TypeError("The block_setting should be List[CNBlockConfig]") + + if block is None: + block = CNBlock + + if norm_layer is None: + norm_layer = partial(mbns_model.LayerNorm2D_MBNS, eps=1e-6) + + layers: List[nn.Module] = [] + + # Stem + firstconv_output_channels = block_setting[0].input_channels + layers.append( + Conv2dNormActivation( + 3, + firstconv_output_channels, + kernel_size=4, + stride=4, + padding=0, + norm_layer=norm_layer, + activation_layer=None, + bias=True, + ) + ) + + total_stage_blocks = sum(cnf.num_layers for cnf in block_setting) + stage_block_id = 0 + for cnf in block_setting: + # Bottlenecks + stage: List[nn.Module] = [] + for _ in range(cnf.num_layers): + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * stage_block_id / (total_stage_blocks - 1.0) + stage.append(block(cnf.input_channels, layer_scale, sd_prob)) + stage_block_id += 1 + layers.append(nn.Sequential(*stage)) + if cnf.out_channels is not None: + # Downsampling + layers.append( + nn.Sequential( + norm_layer(cnf.input_channels), + nn.Conv2d(cnf.input_channels, cnf.out_channels, kernel_size=2, stride=2), + ) + ) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + + lastblock = block_setting[-1] + lastconv_output_channels = ( + lastblock.out_channels if lastblock.out_channels is not None else lastblock.input_channels + ) + self.classifier = nn.Sequential( + norm_layer(lastconv_output_channels), nn.Flatten(1), nn.Linear(lastconv_output_channels, num_classes) + ) + + for m in self.modules(): + if isinstance(m, (nn.Conv2d, nn.Linear)): + nn.init.trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + x = self.avgpool(x) + x = self.classifier(x) + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _convnext( + block_setting: List[CNBlockConfig], + stochastic_depth_prob: float, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> ConvNeXt: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = ConvNeXt(block_setting, stochastic_depth_prob=stochastic_depth_prob, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress)) + + return model + + +_COMMON_META = { + "min_size": (32, 32), + "categories": _IMAGENET_CATEGORIES, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#convnext", + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, +} + + +class ConvNeXt_Tiny_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_tiny-983f1562.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=236), + meta={ + **_COMMON_META, + "num_params": 28589128, + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.520, + "acc@5": 96.146, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ConvNeXt_Small_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_small-0c510722.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=230), + meta={ + **_COMMON_META, + "num_params": 50223688, + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.616, + "acc@5": 96.650, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ConvNeXt_Base_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_base-6075fbad.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 88591464, + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.062, + "acc@5": 96.870, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ConvNeXt_Large_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_large-ea097f82.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 197767336, + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.414, + "acc@5": 96.976, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Tiny_Weights.IMAGENET1K_V1)) +def convnext_tiny(*, weights: Optional[ConvNeXt_Tiny_Weights] = None, progress: bool = True, **kwargs: Any) -> ConvNeXt: + """ConvNeXt Tiny model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Tiny_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Tiny_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Tiny_Weights + :members: + """ + weights = ConvNeXt_Tiny_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(96, 192, 3), + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 9), + CNBlockConfig(768, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.1) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Small_Weights.IMAGENET1K_V1)) +def convnext_small( + *, weights: Optional[ConvNeXt_Small_Weights] = None, progress: bool = True, **kwargs: Any +) -> ConvNeXt: + """ConvNeXt Small model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Small_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Small_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Small_Weights + :members: + """ + weights = ConvNeXt_Small_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(96, 192, 3), + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 27), + CNBlockConfig(768, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.4) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Base_Weights.IMAGENET1K_V1)) +def convnext_base(*, weights: Optional[ConvNeXt_Base_Weights] = None, progress: bool = True, **kwargs: Any) -> ConvNeXt: + """ConvNeXt Base model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Base_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Base_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Base_Weights + :members: + """ + weights = ConvNeXt_Base_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(128, 256, 3), + CNBlockConfig(256, 512, 3), + CNBlockConfig(512, 1024, 27), + CNBlockConfig(1024, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Large_Weights.IMAGENET1K_V1)) +def convnext_large( + *, weights: Optional[ConvNeXt_Large_Weights] = None, progress: bool = True, **kwargs: Any +) -> ConvNeXt: + """ConvNeXt Large model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Large_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Large_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Large_Weights + :members: + """ + weights = ConvNeXt_Large_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 3), + CNBlockConfig(768, 1536, 27), + CNBlockConfig(1536, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) diff --git a/utils/defense_utils/mbns/mbns_model/den_mbns.py b/utils/defense_utils/mbns/mbns_model/den_mbns.py new file mode 100644 index 0000000..4681366 --- /dev/null +++ b/utils/defense_utils/mbns/mbns_model/den_mbns.py @@ -0,0 +1,322 @@ +import re +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from collections import OrderedDict +from torchvision._internally_replaced_utils import load_state_dict_from_url +from typing import Any, Callable, List, Optional, Sequence +from torch import Tensor +from typing import Any, List, Tuple + + +__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161'] + +model_urls = { + 'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth', + 'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth', + 'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth', + 'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth', +} + + +class _DenseLayer(nn.Module): + def __init__( + self, + num_input_features: int, + growth_rate: int, + bn_size: int, + drop_rate: float, + memory_efficient: bool = False, + norm_layer: Optional[Callable[..., nn.Module]] = None + ) -> None: + super(_DenseLayer, self).__init__() + self.norm1: norm_layer + self.add_module('norm1', norm_layer(num_input_features)) + self.relu1: nn.ReLU + self.add_module('relu1', nn.ReLU(inplace=True)) + self.conv1: nn.Conv2d + self.add_module('conv1', nn.Conv2d(num_input_features, bn_size * + growth_rate, kernel_size=1, stride=1, + bias=False)) + self.norm2: norm_layer + self.add_module('norm2', norm_layer(bn_size * growth_rate)) + self.relu2: nn.ReLU + self.add_module('relu2', nn.ReLU(inplace=True)) + self.conv2: nn.Conv2d + self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate, + kernel_size=3, stride=1, padding=1, + bias=False)) + self.drop_rate = float(drop_rate) + self.memory_efficient = memory_efficient + + def bn_function(self, inputs: List[Tensor]) -> Tensor: + concated_features = torch.cat(inputs, 1) + bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features))) # noqa: T484 + return bottleneck_output + + # todo: rewrite when torchscript supports any + def any_requires_grad(self, input: List[Tensor]) -> bool: + for tensor in input: + if tensor.requires_grad: + return True + return False + + @torch.jit.unused # noqa: T484 + def call_checkpoint_bottleneck(self, input: List[Tensor]) -> Tensor: + def closure(*inputs): + return self.bn_function(inputs) + + return cp.checkpoint(closure, *input) + + @torch.jit._overload_method # noqa: F811 + def forward(self, input: List[Tensor]) -> Tensor: + pass + + @torch.jit._overload_method # noqa: F811 + def forward(self, input: Tensor) -> Tensor: + pass + + # torchscript does not yet support *args, so we overload method + # allowing it to take either a List[Tensor] or single Tensor + def forward(self, input: Tensor) -> Tensor: # noqa: F811 + if isinstance(input, Tensor): + prev_features = [input] + else: + prev_features = input + + if self.memory_efficient and self.any_requires_grad(prev_features): + if torch.jit.is_scripting(): + raise Exception("Memory Efficient not supported in JIT") + + bottleneck_output = self.call_checkpoint_bottleneck(prev_features) + else: + bottleneck_output = self.bn_function(prev_features) + + new_features = self.conv2(self.relu2(self.norm2(bottleneck_output))) + if self.drop_rate > 0: + new_features = F.dropout(new_features, p=self.drop_rate, + training=self.training) + return new_features + + +class _DenseBlock(nn.ModuleDict): + _version = 2 + + def __init__( + self, + num_layers: int, + num_input_features: int, + bn_size: int, + growth_rate: int, + drop_rate: float, + memory_efficient: bool = False, + norm_layer: Optional[Callable[..., nn.Module]] = None + ) -> None: + super(_DenseBlock, self).__init__() + for i in range(num_layers): + layer = _DenseLayer( + num_input_features + i * growth_rate, + growth_rate=growth_rate, + bn_size=bn_size, + drop_rate=drop_rate, + memory_efficient=memory_efficient, + norm_layer = norm_layer + ) + self.add_module('denselayer%d' % (i + 1), layer) + + def forward(self, init_features: Tensor) -> Tensor: + features = [init_features] + for name, layer in self.items(): + new_features = layer(features) + features.append(new_features) + return torch.cat(features, 1) + + +class _Transition(nn.Sequential): + def __init__(self, num_input_features: int, num_output_features: int, norm_layer: Optional[Callable[..., nn.Module]] ) -> None: + super(_Transition, self).__init__() + self.add_module('norm', norm_layer(num_input_features)) + self.add_module('relu', nn.ReLU(inplace=True)) + self.add_module('conv', nn.Conv2d(num_input_features, num_output_features, + kernel_size=1, stride=1, bias=False)) + self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2)) + + +class DenseNet(nn.Module): + r"""Densenet-BC model class, based on + `"Densely Connected Convolutional Networks" `_. + + Args: + growth_rate (int) - how many filters to add each layer (`k` in paper) + block_config (list of 4 ints) - how many layers in each pooling block + num_init_features (int) - the number of filters to learn in the first convolution layer + bn_size (int) - multiplicative factor for number of bottle neck layers + (i.e. bn_size * k features in the bottleneck layer) + drop_rate (float) - dropout rate after each dense layer + num_classes (int) - number of classification classes + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + + def __init__( + self, + growth_rate: int = 32, + block_config: Tuple[int, int, int, int] = (6, 12, 24, 16), + num_init_features: int = 64, + bn_size: int = 4, + drop_rate: float = 0, + num_classes: int = 1000, + memory_efficient: bool = False, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + if norm_layer is None: + norm_layer = nn.BatchNorm2d + + super(DenseNet, self).__init__() + + # First convolution + self.features = nn.Sequential(OrderedDict([ + ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, + padding=3, bias=False)), + ('norm0', norm_layer(num_init_features)), + ('relu0', nn.ReLU(inplace=True)), + ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)), + ])) + + # Each denseblock + num_features = num_init_features + for i, num_layers in enumerate(block_config): + block = _DenseBlock( + num_layers=num_layers, + num_input_features=num_features, + bn_size=bn_size, + growth_rate=growth_rate, + drop_rate=drop_rate, + memory_efficient=memory_efficient, + norm_layer = norm_layer + ) + self.features.add_module('denseblock%d' % (i + 1), block) + num_features = num_features + num_layers * growth_rate + if i != len(block_config) - 1: + trans = _Transition(num_input_features=num_features, + num_output_features=num_features // 2, norm_layer=norm_layer) + self.features.add_module('transition%d' % (i + 1), trans) + num_features = num_features // 2 + + # Final batch norm + self.features.add_module('norm5', norm_layer(num_features)) + + # Linear layer + self.classifier = nn.Linear(num_features, num_classes) + + # Official init from torch repo. + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.constant_(m.bias, 0) + + def forward(self, x: Tensor) -> Tensor: + features = self.features(x) + out = F.relu(features, inplace=True) + out = F.adaptive_avg_pool2d(out, (1, 1)) + out = torch.flatten(out, 1) + out = self.classifier(out) + return out + + +def _load_state_dict(model: nn.Module, model_url: str, progress: bool) -> None: + # '.'s are no longer allowed in module names, but previous _DenseLayer + # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'. + # They are also in the checkpoints in model_urls. This pattern is used + # to find such keys. + pattern = re.compile( + r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$') + + state_dict = load_state_dict_from_url(model_url, progress=progress) + for key in list(state_dict.keys()): + res = pattern.match(key) + if res: + new_key = res.group(1) + res.group(2) + state_dict[new_key] = state_dict[key] + del state_dict[key] + model.load_state_dict(state_dict) + + +def _densenet( + arch: str, + growth_rate: int, + block_config: Tuple[int, int, int, int], + num_init_features: int, + pretrained: bool, + progress: bool, + **kwargs: Any +) -> DenseNet: + model = DenseNet(growth_rate, block_config, num_init_features, **kwargs) + if pretrained: + _load_state_dict(model, model_urls[arch], progress) + return model + + +def densenet121(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-121 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress, + **kwargs) + + +def densenet161(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-161 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress, + **kwargs) + + +def densenet169(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-169 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet169', 32, (6, 12, 32, 32), 64, pretrained, progress, + **kwargs) + + +def densenet201(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-201 model from + `"Densely Connected Convolutional Networks" `_. + The required minimum input size of the model is 29x29. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress, + **kwargs) diff --git a/utils/defense_utils/mbns/mbns_model/eff_mbns.py b/utils/defense_utils/mbns/mbns_model/eff_mbns.py new file mode 100644 index 0000000..07f6ba0 --- /dev/null +++ b/utils/defense_utils/mbns/mbns_model/eff_mbns.py @@ -0,0 +1,350 @@ +import copy +import math +import torch + +from functools import partial +from torch import nn, Tensor +from typing import Any, Callable, List, Optional, Sequence + +from torchvision._internally_replaced_utils import load_state_dict_from_url +from torchvision.ops.misc import ConvNormActivation, SqueezeExcitation +from torchvision.models._utils import _make_divisible +from torchvision.ops import StochasticDepth + + +__all__ = ["EfficientNet", "efficientnet_b0", "efficientnet_b1", "efficientnet_b2", "efficientnet_b3", + "efficientnet_b4", "efficientnet_b5", "efficientnet_b6", "efficientnet_b7"] + + +model_urls = { + # Weights ported from https://github.com/rwightman/pytorch-image-models/ + "efficientnet_b0": "https://download.pytorch.org/models/efficientnet_b0_rwightman-3dd342df.pth", + "efficientnet_b1": "https://download.pytorch.org/models/efficientnet_b1_rwightman-533bc792.pth", + "efficientnet_b2": "https://download.pytorch.org/models/efficientnet_b2_rwightman-bcdf34b7.pth", + "efficientnet_b3": "https://download.pytorch.org/models/efficientnet_b3_rwightman-cf984f9c.pth", + "efficientnet_b4": "https://download.pytorch.org/models/efficientnet_b4_rwightman-7eb33cd5.pth", + # Weights ported from https://github.com/lukemelas/EfficientNet-PyTorch/ + "efficientnet_b5": "https://download.pytorch.org/models/efficientnet_b5_lukemelas-b6417697.pth", + "efficientnet_b6": "https://download.pytorch.org/models/efficientnet_b6_lukemelas-c76e70fd.pth", + "efficientnet_b7": "https://download.pytorch.org/models/efficientnet_b7_lukemelas-dcc49843.pth", +} + + +class MBConvConfig: + # Stores information listed at Table 1 of the EfficientNet paper + def __init__(self, + expand_ratio: float, kernel: int, stride: int, + input_channels: int, out_channels: int, num_layers: int, + width_mult: float, depth_mult: float) -> None: + self.expand_ratio = expand_ratio + self.kernel = kernel + self.stride = stride + self.input_channels = self.adjust_channels(input_channels, width_mult) + self.out_channels = self.adjust_channels(out_channels, width_mult) + self.num_layers = self.adjust_depth(num_layers, depth_mult) + + def __repr__(self) -> str: + s = self.__class__.__name__ + '(' + s += 'expand_ratio={expand_ratio}' + s += ', kernel={kernel}' + s += ', stride={stride}' + s += ', input_channels={input_channels}' + s += ', out_channels={out_channels}' + s += ', num_layers={num_layers}' + s += ')' + return s.format(**self.__dict__) + + @staticmethod + def adjust_channels(channels: int, width_mult: float, min_value: Optional[int] = None) -> int: + return _make_divisible(channels * width_mult, 8, min_value) + + @staticmethod + def adjust_depth(num_layers: int, depth_mult: float): + return int(math.ceil(num_layers * depth_mult)) + + +class MBConv(nn.Module): + def __init__(self, cnf: MBConvConfig, stochastic_depth_prob: float, norm_layer: Callable[..., nn.Module], + se_layer: Callable[..., nn.Module] = SqueezeExcitation) -> None: + super().__init__() + + if not (1 <= cnf.stride <= 2): + raise ValueError('illegal stride value') + + self.use_res_connect = cnf.stride == 1 and cnf.input_channels == cnf.out_channels + + layers: List[nn.Module] = [] + activation_layer = nn.SiLU + + # expand + expanded_channels = cnf.adjust_channels(cnf.input_channels, cnf.expand_ratio) + if expanded_channels != cnf.input_channels: + layers.append(ConvNormActivation(cnf.input_channels, expanded_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=activation_layer)) + + # depthwise + layers.append(ConvNormActivation(expanded_channels, expanded_channels, kernel_size=cnf.kernel, + stride=cnf.stride, groups=expanded_channels, + norm_layer=norm_layer, activation_layer=activation_layer)) + + # squeeze and excitation + squeeze_channels = max(1, cnf.input_channels // 4) + layers.append(se_layer(expanded_channels, squeeze_channels, activation=partial(nn.SiLU, inplace=True))) + + # project + layers.append(ConvNormActivation(expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, + activation_layer=None)) + + self.block = nn.Sequential(*layers) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + self.out_channels = cnf.out_channels + + def forward(self, input: Tensor) -> Tensor: + result = self.block(input) + if self.use_res_connect: + result = self.stochastic_depth(result) + result += input + return result + + +class EfficientNet(nn.Module): + def __init__( + self, + inverted_residual_setting: List[MBConvConfig], + dropout: float, + stochastic_depth_prob: float = 0.2, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any + ) -> None: + """ + EfficientNet main class + + Args: + inverted_residual_setting (List[MBConvConfig]): Network structure + dropout (float): The droupout probability + stochastic_depth_prob (float): The stochastic depth probability + num_classes (int): Number of classes + block (Optional[Callable[..., nn.Module]]): Module specifying inverted residual building block for mobilenet + norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use + """ + super().__init__() + + if not inverted_residual_setting: + raise ValueError("The inverted_residual_setting should not be empty") + elif not (isinstance(inverted_residual_setting, Sequence) and + all([isinstance(s, MBConvConfig) for s in inverted_residual_setting])): + raise TypeError("The inverted_residual_setting should be List[MBConvConfig]") + + if block is None: + block = MBConv + + if norm_layer is None: + norm_layer = nn.BatchNorm2d + + layers: List[nn.Module] = [] + + # building first layer + firstconv_output_channels = inverted_residual_setting[0].input_channels + layers.append(ConvNormActivation(3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer, + activation_layer=nn.SiLU)) + + # building inverted residual blocks + total_stage_blocks = sum([cnf.num_layers for cnf in inverted_residual_setting]) + stage_block_id = 0 + for cnf in inverted_residual_setting: + stage: List[nn.Module] = [] + for _ in range(cnf.num_layers): + # copy to avoid modifications. shallow copy is enough + block_cnf = copy.copy(cnf) + + # overwrite info if not the first conv in the stage + if stage: + block_cnf.input_channels = block_cnf.out_channels + block_cnf.stride = 1 + + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * float(stage_block_id) / total_stage_blocks + + stage.append(block(block_cnf, sd_prob, norm_layer)) + stage_block_id += 1 + + layers.append(nn.Sequential(*stage)) + + # building last several layers + lastconv_input_channels = inverted_residual_setting[-1].out_channels + lastconv_output_channels = 4 * lastconv_input_channels + layers.append(ConvNormActivation(lastconv_input_channels, lastconv_output_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=nn.SiLU)) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Sequential( + nn.Dropout(p=dropout, inplace=True), + nn.Linear(lastconv_output_channels, num_classes), + ) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + init_range = 1.0 / math.sqrt(m.out_features) + nn.init.uniform_(m.weight, -init_range, init_range) + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.classifier(x) + + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _efficientnet_conf(width_mult: float, depth_mult: float, **kwargs: Any) -> List[MBConvConfig]: + bneck_conf = partial(MBConvConfig, width_mult=width_mult, depth_mult=depth_mult) + inverted_residual_setting = [ + bneck_conf(1, 3, 1, 32, 16, 1), + bneck_conf(6, 3, 2, 16, 24, 2), + bneck_conf(6, 5, 2, 24, 40, 2), + bneck_conf(6, 3, 2, 40, 80, 3), + bneck_conf(6, 5, 1, 80, 112, 3), + bneck_conf(6, 5, 2, 112, 192, 4), + bneck_conf(6, 3, 1, 192, 320, 1), + ] + return inverted_residual_setting + + +def _efficientnet_model( + arch: str, + inverted_residual_setting: List[MBConvConfig], + dropout: float, + pretrained: bool, + progress: bool, + **kwargs: Any +) -> EfficientNet: + model = EfficientNet(inverted_residual_setting, dropout, **kwargs) + if pretrained: + if model_urls.get(arch, None) is None: + raise ValueError("No checkpoint is available for model type {}".format(arch)) + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def efficientnet_b0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B0 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.0, **kwargs) + return _efficientnet_model("efficientnet_b0", inverted_residual_setting, 0.2, pretrained, progress, **kwargs) + + +def efficientnet_b1(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B1 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.1, **kwargs) + return _efficientnet_model("efficientnet_b1", inverted_residual_setting, 0.2, pretrained, progress, **kwargs) + + +def efficientnet_b2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B2 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.1, depth_mult=1.2, **kwargs) + return _efficientnet_model("efficientnet_b2", inverted_residual_setting, 0.3, pretrained, progress, **kwargs) + + +def efficientnet_b3(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B3 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.2, depth_mult=1.4, **kwargs) + return _efficientnet_model("efficientnet_b3", inverted_residual_setting, 0.3, pretrained, progress, **kwargs) + + +def efficientnet_b4(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B4 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.4, depth_mult=1.8, **kwargs) + return _efficientnet_model("efficientnet_b4", inverted_residual_setting, 0.4, pretrained, progress, **kwargs) + + +def efficientnet_b5(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B5 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.6, depth_mult=2.2, **kwargs) + return _efficientnet_model("efficientnet_b5", inverted_residual_setting, 0.4, pretrained, progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), **kwargs) + + +def efficientnet_b6(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B6 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=1.8, depth_mult=2.6, **kwargs) + return _efficientnet_model("efficientnet_b6", inverted_residual_setting, 0.5, pretrained, progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), **kwargs) + + +def efficientnet_b7(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet: + """ + Constructs a EfficientNet B7 architecture from + `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + inverted_residual_setting = _efficientnet_conf(width_mult=2.0, depth_mult=3.1, **kwargs) + return _efficientnet_model("efficientnet_b7", inverted_residual_setting, 0.5, pretrained, progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), **kwargs) diff --git a/utils/defense_utils/mbns/mbns_model/mbns_batchnorm.py b/utils/defense_utils/mbns/mbns_model/mbns_batchnorm.py new file mode 100644 index 0000000..e64b021 --- /dev/null +++ b/utils/defense_utils/mbns/mbns_model/mbns_batchnorm.py @@ -0,0 +1,25 @@ +# This code is based on: +# https://pytorch.org/docs/stable/_modules/torch/nn/modules/batchnorm.html#BatchNorm2d +# only perturbing weights + +import torch +from torch import Tensor +import torch.nn as nn +import torch.nn.functional as F +import torch.nn.init as init +from torch.nn.parameter import Parameter + + +class BatchNorm2d_MBNS(nn.BatchNorm2d): + def __init__(self, num_features): + super().__init__(num_features) + self.batch_var = 0 + self.batch_mean = 0 + self.collect_stats = False + + def forward(self, x): + output = super().forward(x) + if self.collect_stats: + self.batch_var = x.var((0,2,3)) + self.batch_mean = x.mean((0,2,3)) + return output diff --git a/utils/defense_utils/mbns/mbns_model/mbns_layernorm.py b/utils/defense_utils/mbns/mbns_model/mbns_layernorm.py new file mode 100644 index 0000000..3378e7c --- /dev/null +++ b/utils/defense_utils/mbns/mbns_model/mbns_layernorm.py @@ -0,0 +1,69 @@ +# This code is based on: +# https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html +# only perturbing weights + +import torch +from torch import Tensor +import torch.nn as nn +import torch.nn.functional as F +import torch.nn.init as init +from torch.nn.parameter import Parameter +from torch import Tensor, Size +from typing import Union, List, Tuple + +_shape_t = Union[int, List[int], Size] + +class LayerNorm_MBNS(nn.LayerNorm): + def __init__(self, normalized_shape: _shape_t, eps: float = 1e-5, elementwise_affine: bool = True, + device=None, dtype=None): + super().__init__(normalized_shape = normalized_shape, eps = eps, elementwise_affine = elementwise_affine, + device = device, dtype = dtype) + self.batch_var_clean = 0 + self.batch_mean_clean = 0 + self.batch_var_bd = 0 + self.batch_mean_bd = 0 + self.collect_stats = False + self.collect_stats_clean = False + self.collect_stats_bd = False + + def forward(self, x): + if self.collect_stats: + if self.collect_stats_clean: + feats = x.reshape(x.shape[-1],-1) + self.batch_var_clean = feats.var(-1) + self.batch_mean_clean = feats.mean(-1) + elif self.collect_stats_bd: + feats = x.reshape(x.shape[-1],-1) + self.batch_var_bd = feats.var(-1) + self.batch_mean_bd = feats.mean(-1) + output = super().forward(x) + return output + +class LayerNorm2D_MBNS(nn.LayerNorm): + def __init__(self, normalized_shape: _shape_t, eps: float = 1e-5, elementwise_affine: bool = True, + device=None, dtype=None): + super().__init__(normalized_shape = normalized_shape, eps = eps, elementwise_affine = elementwise_affine, + device = device, dtype = dtype) + self.batch_var_clean = 0 + self.batch_mean_clean = 0 + self.batch_var_bd = 0 + self.batch_mean_bd = 0 + self.collect_stats = False + self.collect_stats_clean = False + self.collect_stats_bd = False + + def forward(self, x): + x = x.permute(0, 2, 3, 1) + if self.collect_stats: + if self.collect_stats_clean: + feats = x.reshape(x.shape[-1],-1) + self.batch_var_clean = feats.var(-1) + self.batch_mean_clean = feats.mean(-1) + elif self.collect_stats_bd: + feats = x.reshape(x.shape[-1],-1) + self.batch_var_bd = feats.var(-1) + self.batch_mean_bd = feats.mean(-1) + output = super().forward(x) + output = output.permute(0, 3, 1, 2) + return output + diff --git a/utils/defense_utils/mbns/mbns_model/mobilenet_mbns.py b/utils/defense_utils/mbns/mbns_model/mobilenet_mbns.py new file mode 100644 index 0000000..966c15e --- /dev/null +++ b/utils/defense_utils/mbns/mbns_model/mobilenet_mbns.py @@ -0,0 +1,272 @@ +import warnings +import torch + +from functools import partial +from torch import nn, Tensor +from typing import Any, Callable, List, Optional, Sequence + +from torchvision._internally_replaced_utils import load_state_dict_from_url +from torchvision.ops.misc import ConvNormActivation, SqueezeExcitation as SElayer +from torchvision.models._utils import _make_divisible + + +__all__ = ["MobileNetV3", "mobilenet_v3_large", "mobilenet_v3_small"] + + +model_urls = { + "mobilenet_v3_large": "https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth", + "mobilenet_v3_small": "https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth", +} + + +class SqueezeExcitation(SElayer): + """DEPRECATED + """ + def __init__(self, input_channels: int, squeeze_factor: int = 4): + squeeze_channels = _make_divisible(input_channels // squeeze_factor, 8) + super().__init__(input_channels, squeeze_channels, scale_activation=nn.Hardsigmoid) + self.relu = self.activation + delattr(self, 'activation') + warnings.warn( + "This SqueezeExcitation class is deprecated and will be removed in future versions. " + "Use torchvision.ops.misc.SqueezeExcitation instead.", FutureWarning) + + +class InvertedResidualConfig: + # Stores information listed at Tables 1 and 2 of the MobileNetV3 paper + def __init__(self, input_channels: int, kernel: int, expanded_channels: int, out_channels: int, use_se: bool, + activation: str, stride: int, dilation: int, width_mult: float): + self.input_channels = self.adjust_channels(input_channels, width_mult) + self.kernel = kernel + self.expanded_channels = self.adjust_channels(expanded_channels, width_mult) + self.out_channels = self.adjust_channels(out_channels, width_mult) + self.use_se = use_se + self.use_hs = activation == "HS" + self.stride = stride + self.dilation = dilation + + @staticmethod + def adjust_channels(channels: int, width_mult: float): + return _make_divisible(channels * width_mult, 8) + + +class InvertedResidual(nn.Module): + # Implemented as described at section 5 of MobileNetV3 paper + def __init__(self, cnf: InvertedResidualConfig, norm_layer: Callable[..., nn.Module], + se_layer: Callable[..., nn.Module] = partial(SElayer, scale_activation=nn.Hardsigmoid)): + super().__init__() + if not (1 <= cnf.stride <= 2): + raise ValueError('illegal stride value') + + self.use_res_connect = cnf.stride == 1 and cnf.input_channels == cnf.out_channels + + layers: List[nn.Module] = [] + activation_layer = nn.Hardswish if cnf.use_hs else nn.ReLU + + # expand + if cnf.expanded_channels != cnf.input_channels: + layers.append(ConvNormActivation(cnf.input_channels, cnf.expanded_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=activation_layer)) + + # depthwise + stride = 1 if cnf.dilation > 1 else cnf.stride + layers.append(ConvNormActivation(cnf.expanded_channels, cnf.expanded_channels, kernel_size=cnf.kernel, + stride=stride, dilation=cnf.dilation, groups=cnf.expanded_channels, + norm_layer=norm_layer, activation_layer=activation_layer)) + if cnf.use_se: + squeeze_channels = _make_divisible(cnf.expanded_channels // 4, 8) + layers.append(se_layer(cnf.expanded_channels, squeeze_channels)) + + # project + layers.append(ConvNormActivation(cnf.expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, + activation_layer=None)) + + self.block = nn.Sequential(*layers) + self.out_channels = cnf.out_channels + self._is_cn = cnf.stride > 1 + + def forward(self, input: Tensor) -> Tensor: + result = self.block(input) + if self.use_res_connect: + result += input + return result + + +class MobileNetV3(nn.Module): + + def __init__( + self, + inverted_residual_setting: List[InvertedResidualConfig], + last_channel: int, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any + ) -> None: + """ + MobileNet V3 main class + + Args: + inverted_residual_setting (List[InvertedResidualConfig]): Network structure + last_channel (int): The number of channels on the penultimate layer + num_classes (int): Number of classes + block (Optional[Callable[..., nn.Module]]): Module specifying inverted residual building block for mobilenet + norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use + """ + super().__init__() + + if not inverted_residual_setting: + raise ValueError("The inverted_residual_setting should not be empty") + elif not (isinstance(inverted_residual_setting, Sequence) and + all([isinstance(s, InvertedResidualConfig) for s in inverted_residual_setting])): + raise TypeError("The inverted_residual_setting should be List[InvertedResidualConfig]") + + if block is None: + block = InvertedResidual + + if norm_layer is None: + norm_layer = partial(nn.BatchNorm2d, eps=0.001, momentum=0.01) + + layers: List[nn.Module] = [] + + # building first layer + firstconv_output_channels = inverted_residual_setting[0].input_channels + layers.append(ConvNormActivation(3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer, + activation_layer=nn.Hardswish)) + + # building inverted residual blocks + for cnf in inverted_residual_setting: + layers.append(block(cnf, norm_layer)) + + # building last several layers + lastconv_input_channels = inverted_residual_setting[-1].out_channels + lastconv_output_channels = 6 * lastconv_input_channels + layers.append(ConvNormActivation(lastconv_input_channels, lastconv_output_channels, kernel_size=1, + norm_layer=norm_layer, activation_layer=nn.Hardswish)) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Sequential( + nn.Linear(lastconv_output_channels, last_channel), + nn.Hardswish(inplace=True), + nn.Dropout(p=0.2, inplace=True), + nn.Linear(last_channel, num_classes), + ) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.classifier(x) + + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _mobilenet_v3_conf(arch: str, width_mult: float = 1.0, reduced_tail: bool = False, dilated: bool = False, + **kwargs: Any): + reduce_divider = 2 if reduced_tail else 1 + dilation = 2 if dilated else 1 + + bneck_conf = partial(InvertedResidualConfig, width_mult=width_mult) + adjust_channels = partial(InvertedResidualConfig.adjust_channels, width_mult=width_mult) + + if arch == "mobilenet_v3_large": + inverted_residual_setting = [ + bneck_conf(16, 3, 16, 16, False, "RE", 1, 1), + bneck_conf(16, 3, 64, 24, False, "RE", 2, 1), # C1 + bneck_conf(24, 3, 72, 24, False, "RE", 1, 1), + bneck_conf(24, 5, 72, 40, True, "RE", 2, 1), # C2 + bneck_conf(40, 5, 120, 40, True, "RE", 1, 1), + bneck_conf(40, 5, 120, 40, True, "RE", 1, 1), + bneck_conf(40, 3, 240, 80, False, "HS", 2, 1), # C3 + bneck_conf(80, 3, 200, 80, False, "HS", 1, 1), + bneck_conf(80, 3, 184, 80, False, "HS", 1, 1), + bneck_conf(80, 3, 184, 80, False, "HS", 1, 1), + bneck_conf(80, 3, 480, 112, True, "HS", 1, 1), + bneck_conf(112, 3, 672, 112, True, "HS", 1, 1), + bneck_conf(112, 5, 672, 160 // reduce_divider, True, "HS", 2, dilation), # C4 + bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1, dilation), + bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1, dilation), + ] + last_channel = adjust_channels(1280 // reduce_divider) # C5 + elif arch == "mobilenet_v3_small": + inverted_residual_setting = [ + bneck_conf(16, 3, 16, 16, True, "RE", 2, 1), # C1 + bneck_conf(16, 3, 72, 24, False, "RE", 2, 1), # C2 + bneck_conf(24, 3, 88, 24, False, "RE", 1, 1), + bneck_conf(24, 5, 96, 40, True, "HS", 2, 1), # C3 + bneck_conf(40, 5, 240, 40, True, "HS", 1, 1), + bneck_conf(40, 5, 240, 40, True, "HS", 1, 1), + bneck_conf(40, 5, 120, 48, True, "HS", 1, 1), + bneck_conf(48, 5, 144, 48, True, "HS", 1, 1), + bneck_conf(48, 5, 288, 96 // reduce_divider, True, "HS", 2, dilation), # C4 + bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1, dilation), + bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1, dilation), + ] + last_channel = adjust_channels(1024 // reduce_divider) # C5 + else: + raise ValueError("Unsupported model type {}".format(arch)) + + return inverted_residual_setting, last_channel + + +def _mobilenet_v3_model( + arch: str, + inverted_residual_setting: List[InvertedResidualConfig], + last_channel: int, + pretrained: bool, + progress: bool, + **kwargs: Any +): + model = MobileNetV3(inverted_residual_setting, last_channel, **kwargs) + if pretrained: + if model_urls.get(arch, None) is None: + raise ValueError("No checkpoint is available for model type {}".format(arch)) + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def mobilenet_v3_large(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MobileNetV3: + """ + Constructs a large MobileNetV3 architecture from + `"Searching for MobileNetV3" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + arch = "mobilenet_v3_large" + inverted_residual_setting, last_channel = _mobilenet_v3_conf(arch, **kwargs) + return _mobilenet_v3_model(arch, inverted_residual_setting, last_channel, pretrained, progress, **kwargs) + + +def mobilenet_v3_small(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MobileNetV3: + """ + Constructs a small MobileNetV3 architecture from + `"Searching for MobileNetV3" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + arch = "mobilenet_v3_small" + inverted_residual_setting, last_channel = _mobilenet_v3_conf(arch, **kwargs) + return _mobilenet_v3_model(arch, inverted_residual_setting, last_channel, pretrained, progress, **kwargs) diff --git a/utils/defense_utils/mbns/mbns_model/preact_mbns.py b/utils/defense_utils/mbns/mbns_model/preact_mbns.py new file mode 100644 index 0000000..14b07c1 --- /dev/null +++ b/utils/defense_utils/mbns/mbns_model/preact_mbns.py @@ -0,0 +1,135 @@ +"""Pre-activation ResNet in PyTorch. + +Reference: +[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Identity Mappings in Deep Residual Networks. arXiv:1603.05027 +""" +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class PreActBlock(nn.Module): + """Pre-activation version of the BasicBlock.""" + + expansion = 1 + + def __init__(self, in_planes, planes, stride=1, norm_layer = None): + if norm_layer is None: + norm_layer = nn.BatchNorm2d + super(PreActBlock, self).__init__() + self.bn1 = norm_layer(in_planes) + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = norm_layer(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.ind = None + + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False) + ) + + def forward(self, x): + out = F.relu(self.bn1(x)) + shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x + out = self.conv1(out) + out = self.conv2(F.relu(self.bn2(out))) + if self.ind is not None: + out += shortcut[:, self.ind, :, :] + else: + out += shortcut + return out + + +class PreActBottleneck(nn.Module): + """Pre-activation version of the original Bottleneck module.""" + + expansion = 4 + + def __init__(self, in_planes, planes, stride=1): + super(PreActBottleneck, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) + + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False) + ) + + def forward(self, x): + out = F.relu(self.bn1(x)) + shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x + out = self.conv1(out) + out = self.conv2(F.relu(self.bn2(out))) + out = self.conv3(F.relu(self.bn3(out))) + out += shortcut + return out + + +class PreActResNet(nn.Module): + def __init__(self, block, num_blocks, num_classes=10, norm_layer=None): + super(PreActResNet, self).__init__() + self.in_planes = 64 + if norm_layer is None: + norm_layer = nn.BatchNorm2d + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1, norm_layer=norm_layer) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2, norm_layer=norm_layer) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2, norm_layer=norm_layer) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2, norm_layer=norm_layer) + self.avgpool = nn.AdaptiveAvgPool2d((1,1)) + # self.feature_dim = 512 + self.linear = nn.Linear(512 * block.expansion, num_classes) + + def _make_layer(self, block, planes, num_blocks, stride, norm_layer): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride, norm_layer)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + out = self.conv1(x) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = self.avgpool(out) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + + +def PreActResNet18(num_classes=10, norm_layer=nn.BatchNorm2d): + return PreActResNet(PreActBlock, [2, 2, 2, 2], num_classes=num_classes, norm_layer=norm_layer) + + +def PreActResNet34(): + return PreActResNet(PreActBlock, [3, 4, 6, 3]) + + +def PreActResNet50(): + return PreActResNet(PreActBottleneck, [3, 4, 6, 3]) + + +def PreActResNet101(): + return PreActResNet(PreActBottleneck, [3, 4, 23, 3]) + + +def PreActResNet152(): + return PreActResNet(PreActBottleneck, [3, 8, 36, 3]) + + +def test(): + net = PreActResNet18() + y = net((torch.randn(1, 3, 32, 32))) + print(y.size()) + + +# test() diff --git a/utils/defense_utils/mbns/mbns_model/vgg_mbns.py b/utils/defense_utils/mbns/mbns_model/vgg_mbns.py new file mode 100644 index 0000000..cf83341 --- /dev/null +++ b/utils/defense_utils/mbns/mbns_model/vgg_mbns.py @@ -0,0 +1,200 @@ +import torch +import torch.nn as nn +from torchvision._internally_replaced_utils import load_state_dict_from_url +from typing import Union, List, Dict, Any, cast + + +__all__ = [ + 'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', + 'vgg19_bn', 'vgg19', +] + + +model_urls = { + 'vgg11': 'https://download.pytorch.org/models/vgg11-8a719046.pth', + 'vgg13': 'https://download.pytorch.org/models/vgg13-19584684.pth', + 'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth', + 'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth', + 'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth', + 'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth', + 'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth', + 'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth', +} + + +class VGG(nn.Module): + + def __init__( + self, + features: nn.Module, + num_classes: int = 1000, + init_weights: bool = True + ) -> None: + super(VGG, self).__init__() + self.features = features + self.avgpool = nn.AdaptiveAvgPool2d((7, 7)) + self.classifier = nn.Sequential( + nn.Linear(512 * 7 * 7, 4096), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(4096, 4096), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(4096, num_classes), + ) + if init_weights: + self._initialize_weights() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.features(x) + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.classifier(x) + return x + + def _initialize_weights(self) -> None: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.constant_(m.bias, 0) + + +def make_layers(cfg: List[Union[str, int]], batch_norm: bool = False, norm_layer = None) -> nn.Sequential: + if norm_layer is None: + norm_layer = nn.BatchNorm2d + layers: List[nn.Module] = [] + in_channels = 3 + for v in cfg: + if v == 'M': + layers += [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + v = cast(int, v) + conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) + if batch_norm: + layers += [conv2d, norm_layer(v), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = v + return nn.Sequential(*layers) + + +cfgs: Dict[str, List[Union[str, int]]] = { + 'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], + 'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], + 'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], + 'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'], +} + + +def _vgg(arch: str, cfg: str, batch_norm: bool, pretrained: bool, progress: bool, norm_layer, **kwargs: Any) -> VGG: + if pretrained: + kwargs['init_weights'] = False + model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm, norm_layer = norm_layer), **kwargs) + if pretrained: + state_dict = load_state_dict_from_url(model_urls[arch], + progress=progress) + model.load_state_dict(state_dict) + return model + + +def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 11-layer model (configuration "A") from + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg11', 'A', False, pretrained, progress, **kwargs) + + +def vgg11_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 11-layer model (configuration "A") with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg11_bn', 'A', True, pretrained, progress, **kwargs) + + +def vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 13-layer model (configuration "B") + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg13', 'B', False, pretrained, progress, **kwargs) + + +def vgg13_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 13-layer model (configuration "B") with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg13_bn', 'B', True, pretrained, progress, **kwargs) + + +def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 16-layer model (configuration "D") + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg16', 'D', False, pretrained, progress, **kwargs) + + +def vgg16_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 16-layer model (configuration "D") with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg16_bn', 'D', True, pretrained, progress, **kwargs) + + +def vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 19-layer model (configuration "E") + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg19', 'E', False, pretrained, progress, **kwargs) + + +def vgg19_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: + r"""VGG 19-layer model (configuration 'E') with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_. + The required minimum input size of the model is 32x32. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg('vgg19_bn', 'E', True, pretrained, progress, **kwargs) diff --git a/utils/defense_utils/mbns/mbns_model/vit_mbns.py b/utils/defense_utils/mbns/mbns_model/vit_mbns.py new file mode 100644 index 0000000..9305d61 --- /dev/null +++ b/utils/defense_utils/mbns/mbns_model/vit_mbns.py @@ -0,0 +1,470 @@ +import math +from collections import OrderedDict +from functools import partial +from typing import Any, Callable, List, NamedTuple, Optional + +import torch +import torch.nn as nn + +from torchvision._internally_replaced_utils import load_state_dict_from_url +from torchvision.ops.misc import ConvNormActivation +from torchvision.utils import _log_api_usage_once + +from defense.mbns import mbns_model + +__all__ = [ + "VisionTransformer", + "vit_b_16", + "vit_b_32", + "vit_l_16", + "vit_l_32", +] + +model_urls = { + "vit_b_16": "https://download.pytorch.org/models/vit_b_16-c867db91.pth", + "vit_b_32": "https://download.pytorch.org/models/vit_b_32-d86f8d99.pth", + "vit_l_16": "https://download.pytorch.org/models/vit_l_16-852ce7e3.pth", + "vit_l_32": "https://download.pytorch.org/models/vit_l_32-c7638314.pth", +} + + +class ConvStemConfig(NamedTuple): + out_channels: int + kernel_size: int + stride: int + norm_layer: Callable[..., nn.Module] = mbns_model.LayerNorm_MBNS + activation_layer: Callable[..., nn.Module] = nn.ReLU + + +class MLPBlock(nn.Sequential): + """Transformer MLP block.""" + + def __init__(self, in_dim: int, mlp_dim: int, dropout: float): + super().__init__() + self.linear_1 = nn.Linear(in_dim, mlp_dim) + self.act = nn.GELU() + self.dropout_1 = nn.Dropout(dropout) + self.linear_2 = nn.Linear(mlp_dim, in_dim) + self.dropout_2 = nn.Dropout(dropout) + + nn.init.xavier_uniform_(self.linear_1.weight) + nn.init.xavier_uniform_(self.linear_2.weight) + nn.init.normal_(self.linear_1.bias, std=1e-6) + nn.init.normal_(self.linear_2.bias, std=1e-6) + + +class EncoderBlock(nn.Module): + """Transformer encoder block.""" + + def __init__( + self, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float, + attention_dropout: float, + norm_layer: Callable[..., torch.nn.Module] = partial(mbns_model.LayerNorm_MBNS, eps=1e-6), + ): + super().__init__() + self.num_heads = num_heads + + # Attention block + self.ln_1 = norm_layer(hidden_dim) + self.self_attention = nn.MultiheadAttention(hidden_dim, num_heads, dropout=attention_dropout, batch_first=True) + self.dropout = nn.Dropout(dropout) + + # MLP block + self.ln_2 = norm_layer(hidden_dim) + self.mlp = MLPBlock(hidden_dim, mlp_dim, dropout) + + def forward(self, input: torch.Tensor): + torch._assert(input.dim() == 3, f"Expected (seq_length, batch_size, hidden_dim) got {input.shape}") + x = self.ln_1(input) + x, _ = self.self_attention(query=x, key=x, value=x, need_weights=False) + x = self.dropout(x) + x = x + input + + y = self.ln_2(x) + y = self.mlp(y) + return x + y + + +class Encoder(nn.Module): + """Transformer Model Encoder for sequence to sequence translation.""" + + def __init__( + self, + seq_length: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float, + attention_dropout: float, + norm_layer: Callable[..., torch.nn.Module] = partial(mbns_model.LayerNorm_MBNS, eps=1e-6), + ): + super().__init__() + # Note that batch_size is on the first dim because + # we have batch_first=True in nn.MultiAttention() by default + self.pos_embedding = nn.Parameter(torch.empty(1, seq_length, hidden_dim).normal_(std=0.02)) # from BERT + self.dropout = nn.Dropout(dropout) + layers: OrderedDict[str, nn.Module] = OrderedDict() + for i in range(num_layers): + layers[f"encoder_layer_{i}"] = EncoderBlock( + num_heads, + hidden_dim, + mlp_dim, + dropout, + attention_dropout, + norm_layer, + ) + self.layers = nn.Sequential(layers) + self.ln = norm_layer(hidden_dim) + + def forward(self, input: torch.Tensor): + torch._assert(input.dim() == 3, f"Expected (batch_size, seq_length, hidden_dim) got {input.shape}") + input = input + self.pos_embedding + return self.ln(self.layers(self.dropout(input))) + + +class VisionTransformer(nn.Module): + """Vision Transformer as per https://arxiv.org/abs/2010.11929.""" + + def __init__( + self, + image_size: int, + patch_size: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float = 0.0, + attention_dropout: float = 0.0, + num_classes: int = 1000, + representation_size: Optional[int] = None, + norm_layer: Callable[..., torch.nn.Module] = partial(mbns_model.LayerNorm_MBNS, eps=1e-6), + conv_stem_configs: Optional[List[ConvStemConfig]] = None, + ): + super().__init__() + _log_api_usage_once(self) + torch._assert(image_size % patch_size == 0, "Input shape indivisible by patch size!") + self.image_size = image_size + self.patch_size = patch_size + self.hidden_dim = hidden_dim + self.mlp_dim = mlp_dim + self.attention_dropout = attention_dropout + self.dropout = dropout + self.num_classes = num_classes + self.representation_size = representation_size + self.norm_layer = norm_layer + + if conv_stem_configs is not None: + # As per https://arxiv.org/abs/2106.14881 + seq_proj = nn.Sequential() + prev_channels = 3 + for i, conv_stem_layer_config in enumerate(conv_stem_configs): + seq_proj.add_module( + f"conv_bn_relu_{i}", + ConvNormActivation( + in_channels=prev_channels, + out_channels=conv_stem_layer_config.out_channels, + kernel_size=conv_stem_layer_config.kernel_size, + stride=conv_stem_layer_config.stride, + norm_layer=conv_stem_layer_config.norm_layer, + activation_layer=conv_stem_layer_config.activation_layer, + ), + ) + prev_channels = conv_stem_layer_config.out_channels + seq_proj.add_module( + "conv_last", nn.Conv2d(in_channels=prev_channels, out_channels=hidden_dim, kernel_size=1) + ) + self.conv_proj: nn.Module = seq_proj + else: + self.conv_proj = nn.Conv2d( + in_channels=3, out_channels=hidden_dim, kernel_size=patch_size, stride=patch_size + ) + + seq_length = (image_size // patch_size) ** 2 + + # Add a class token + self.class_token = nn.Parameter(torch.zeros(1, 1, hidden_dim)) + seq_length += 1 + + self.encoder = Encoder( + seq_length, + num_layers, + num_heads, + hidden_dim, + mlp_dim, + dropout, + attention_dropout, + norm_layer, + ) + self.seq_length = seq_length + + heads_layers: OrderedDict[str, nn.Module] = OrderedDict() + if representation_size is None: + heads_layers["head"] = nn.Linear(hidden_dim, num_classes) + else: + heads_layers["pre_logits"] = nn.Linear(hidden_dim, representation_size) + heads_layers["act"] = nn.Tanh() + heads_layers["head"] = nn.Linear(representation_size, num_classes) + + self.heads = nn.Sequential(heads_layers) + + if isinstance(self.conv_proj, nn.Conv2d): + # Init the patchify stem + fan_in = self.conv_proj.in_channels * self.conv_proj.kernel_size[0] * self.conv_proj.kernel_size[1] + nn.init.trunc_normal_(self.conv_proj.weight, std=math.sqrt(1 / fan_in)) + if self.conv_proj.bias is not None: + nn.init.zeros_(self.conv_proj.bias) + elif self.conv_proj.conv_last is not None and isinstance(self.conv_proj.conv_last, nn.Conv2d): + # Init the last 1x1 conv of the conv stem + nn.init.normal_( + self.conv_proj.conv_last.weight, mean=0.0, std=math.sqrt(2.0 / self.conv_proj.conv_last.out_channels) + ) + if self.conv_proj.conv_last.bias is not None: + nn.init.zeros_(self.conv_proj.conv_last.bias) + + if hasattr(self.heads, "pre_logits") and isinstance(self.heads.pre_logits, nn.Linear): + fan_in = self.heads.pre_logits.in_features + nn.init.trunc_normal_(self.heads.pre_logits.weight, std=math.sqrt(1 / fan_in)) + nn.init.zeros_(self.heads.pre_logits.bias) + + if isinstance(self.heads.head, nn.Linear): + nn.init.zeros_(self.heads.head.weight) + nn.init.zeros_(self.heads.head.bias) + + def _process_input(self, x: torch.Tensor) -> torch.Tensor: + n, c, h, w = x.shape + p = self.patch_size + torch._assert(h == self.image_size, "Wrong image height!") + torch._assert(w == self.image_size, "Wrong image width!") + n_h = h // p + n_w = w // p + + # (n, c, h, w) -> (n, hidden_dim, n_h, n_w) + x = self.conv_proj(x) + # (n, hidden_dim, n_h, n_w) -> (n, hidden_dim, (n_h * n_w)) + x = x.reshape(n, self.hidden_dim, n_h * n_w) + + # (n, hidden_dim, (n_h * n_w)) -> (n, (n_h * n_w), hidden_dim) + # The self attention layer expects inputs in the format (N, S, E) + # where S is the source sequence length, N is the batch size, E is the + # embedding dimension + x = x.permute(0, 2, 1) + + return x + + def forward(self, x: torch.Tensor): + # Reshape and permute the input tensor + x = self._process_input(x) + n = x.shape[0] + + # Expand the class token to the full batch + batch_class_token = self.class_token.expand(n, -1, -1) + x = torch.cat([batch_class_token, x], dim=1) + + x = self.encoder(x) + + # Classifier "token" as used by standard language architectures + x = x[:, 0] + + x = self.heads(x) + + return x + + +def _vision_transformer( + arch: str, + patch_size: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + pretrained: bool, + progress: bool, + **kwargs: Any, +) -> VisionTransformer: + image_size = kwargs.pop("image_size", 224) + + model = VisionTransformer( + image_size=image_size, + patch_size=patch_size, + num_layers=num_layers, + num_heads=num_heads, + hidden_dim=hidden_dim, + mlp_dim=mlp_dim, + **kwargs, + ) + + if pretrained: + if arch not in model_urls: + raise ValueError(f"No checkpoint is available for model type '{arch}'!") + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + + return model + + +def vit_b_16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_b_16 architecture from + `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vision_transformer( + arch="vit_b_16", + patch_size=16, + num_layers=12, + num_heads=12, + hidden_dim=768, + mlp_dim=3072, + pretrained=pretrained, + progress=progress, + **kwargs, + ) + + +def vit_b_32(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_b_32 architecture from + `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vision_transformer( + arch="vit_b_32", + patch_size=32, + num_layers=12, + num_heads=12, + hidden_dim=768, + mlp_dim=3072, + pretrained=pretrained, + progress=progress, + **kwargs, + ) + + +def vit_l_16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_l_16 architecture from + `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vision_transformer( + arch="vit_l_16", + patch_size=16, + num_layers=24, + num_heads=16, + hidden_dim=1024, + mlp_dim=4096, + pretrained=pretrained, + progress=progress, + **kwargs, + ) + + +def vit_l_32(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_l_32 architecture from + `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vision_transformer( + arch="vit_l_32", + patch_size=32, + num_layers=24, + num_heads=16, + hidden_dim=1024, + mlp_dim=4096, + pretrained=pretrained, + progress=progress, + **kwargs, + ) + + +def interpolate_embeddings( + image_size: int, + patch_size: int, + model_state: "OrderedDict[str, torch.Tensor]", + interpolation_mode: str = "bicubic", + reset_heads: bool = False, +) -> "OrderedDict[str, torch.Tensor]": + """This function helps interpolating positional embeddings during checkpoint loading, + especially when you want to apply a pre-trained model on images with different resolution. + + Args: + image_size (int): Image size of the new model. + patch_size (int): Patch size of the new model. + model_state (OrderedDict[str, torch.Tensor]): State dict of the pre-trained model. + interpolation_mode (str): The algorithm used for upsampling. Default: bicubic. + reset_heads (bool): If true, not copying the state of heads. Default: False. + + Returns: + OrderedDict[str, torch.Tensor]: A state dict which can be loaded into the new model. + """ + # Shape of pos_embedding is (1, seq_length, hidden_dim) + pos_embedding = model_state["encoder.pos_embedding"] + n, seq_length, hidden_dim = pos_embedding.shape + if n != 1: + raise ValueError(f"Unexpected position embedding shape: {pos_embedding.shape}") + + new_seq_length = (image_size // patch_size) ** 2 + 1 + + # Need to interpolate the weights for the position embedding. + # We do this by reshaping the positions embeddings to a 2d grid, performing + # an interpolation in the (h, w) space and then reshaping back to a 1d grid. + if new_seq_length != seq_length: + # The class token embedding shouldn't be interpolated so we split it up. + seq_length -= 1 + new_seq_length -= 1 + pos_embedding_token = pos_embedding[:, :1, :] + pos_embedding_img = pos_embedding[:, 1:, :] + + # (1, seq_length, hidden_dim) -> (1, hidden_dim, seq_length) + pos_embedding_img = pos_embedding_img.permute(0, 2, 1) + seq_length_1d = int(math.sqrt(seq_length)) + torch._assert(seq_length_1d * seq_length_1d == seq_length, "seq_length is not a perfect square!") + + # (1, hidden_dim, seq_length) -> (1, hidden_dim, seq_l_1d, seq_l_1d) + pos_embedding_img = pos_embedding_img.reshape(1, hidden_dim, seq_length_1d, seq_length_1d) + new_seq_length_1d = image_size // patch_size + + # Perform interpolation. + # (1, hidden_dim, seq_l_1d, seq_l_1d) -> (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) + new_pos_embedding_img = nn.functional.interpolate( + pos_embedding_img, + size=new_seq_length_1d, + mode=interpolation_mode, + align_corners=True, + ) + + # (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) -> (1, hidden_dim, new_seq_length) + new_pos_embedding_img = new_pos_embedding_img.reshape(1, hidden_dim, new_seq_length) + + # (1, hidden_dim, new_seq_length) -> (1, new_seq_length, hidden_dim) + new_pos_embedding_img = new_pos_embedding_img.permute(0, 2, 1) + new_pos_embedding = torch.cat([pos_embedding_token, new_pos_embedding_img], dim=1) + + model_state["encoder.pos_embedding"] = new_pos_embedding + + if reset_heads: + model_state_copy: "OrderedDict[str, torch.Tensor]" = OrderedDict() + for k, v in model_state.items(): + if not k.startswith("heads"): + model_state_copy[k] = v + model_state = model_state_copy + + return model_state diff --git a/utils/defense_utils/mbns/mbns_model/vit_new_mbns.py b/utils/defense_utils/mbns/mbns_model/vit_new_mbns.py new file mode 100644 index 0000000..f336a06 --- /dev/null +++ b/utils/defense_utils/mbns/mbns_model/vit_new_mbns.py @@ -0,0 +1,854 @@ +import math +from collections import OrderedDict +from functools import partial +from typing import Any, Callable, List, NamedTuple, Optional, Dict + +import torch +import torch.nn as nn + +from torchvision.ops.misc import Conv2dNormActivation, MLP +from torchvision.transforms._presets import ImageClassification, InterpolationMode +from torchvision.utils import _log_api_usage_once +from torchvision.models._api import WeightsEnum, Weights +from torchvision.models._meta import _IMAGENET_CATEGORIES +from torchvision.models._utils import handle_legacy_interface, _ovewrite_named_param + +from defense.mbns import mbns_model + + +__all__ = [ + "VisionTransformer", + "ViT_B_16_Weights", + "ViT_B_32_Weights", + "ViT_L_16_Weights", + "ViT_L_32_Weights", + "ViT_H_14_Weights", + "vit_b_16", + "vit_b_32", + "vit_l_16", + "vit_l_32", + "vit_h_14", +] + + +class ConvStemConfig(NamedTuple): + out_channels: int + kernel_size: int + stride: int + norm_layer: Callable[..., nn.Module] = mbns_model.LayerNorm_MBNS + activation_layer: Callable[..., nn.Module] = nn.ReLU + + +class MLPBlock(MLP): + """Transformer MLP block.""" + + _version = 2 + + def __init__(self, in_dim: int, mlp_dim: int, dropout: float): + super().__init__(in_dim, [mlp_dim, in_dim], activation_layer=nn.GELU, inplace=None, dropout=dropout) + + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if m.bias is not None: + nn.init.normal_(m.bias, std=1e-6) + + def _load_from_state_dict( + self, + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ): + version = local_metadata.get("version", None) + + if version is None or version < 2: + # Replacing legacy MLPBlock with MLP. See https://github.com/pytorch/vision/pull/6053 + for i in range(2): + for type in ["weight", "bias"]: + old_key = f"{prefix}linear_{i+1}.{type}" + new_key = f"{prefix}{3*i}.{type}" + if old_key in state_dict: + state_dict[new_key] = state_dict.pop(old_key) + + super()._load_from_state_dict( + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ) + + +class EncoderBlock(nn.Module): + """Transformer encoder block.""" + + def __init__( + self, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float, + attention_dropout: float, + norm_layer: Callable[..., torch.nn.Module] = partial(mbns_model.LayerNorm_MBNS, eps=1e-6), + ): + super().__init__() + self.num_heads = num_heads + + # Attention block + self.ln_1 = norm_layer(hidden_dim) + self.self_attention = nn.MultiheadAttention(hidden_dim, num_heads, dropout=attention_dropout, batch_first=True) + self.dropout = nn.Dropout(dropout) + + # MLP block + self.ln_2 = norm_layer(hidden_dim) + self.mlp = MLPBlock(hidden_dim, mlp_dim, dropout) + + def forward(self, input: torch.Tensor): + torch._assert(input.dim() == 3, f"Expected (batch_size, seq_length, hidden_dim) got {input.shape}") + x = self.ln_1(input) + x, _ = self.self_attention(query=x, key=x, value=x, need_weights=False) + x = self.dropout(x) + x = x + input + + y = self.ln_2(x) + y = self.mlp(y) + return x + y + + +class Encoder(nn.Module): + """Transformer Model Encoder for sequence to sequence translation.""" + + def __init__( + self, + seq_length: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float, + attention_dropout: float, + norm_layer: Callable[..., torch.nn.Module] = partial(mbns_model.LayerNorm_MBNS, eps=1e-6), + ): + super().__init__() + # Note that batch_size is on the first dim because + # we have batch_first=True in nn.MultiAttention() by default + self.pos_embedding = nn.Parameter(torch.empty(1, seq_length, hidden_dim).normal_(std=0.02)) # from BERT + self.dropout = nn.Dropout(dropout) + layers: OrderedDict[str, nn.Module] = OrderedDict() + for i in range(num_layers): + layers[f"encoder_layer_{i}"] = EncoderBlock( + num_heads, + hidden_dim, + mlp_dim, + dropout, + attention_dropout, + norm_layer, + ) + self.layers = nn.Sequential(layers) + self.ln = norm_layer(hidden_dim) + + def forward(self, input: torch.Tensor): + torch._assert(input.dim() == 3, f"Expected (batch_size, seq_length, hidden_dim) got {input.shape}") + input = input + self.pos_embedding + return self.ln(self.layers(self.dropout(input))) + + +class VisionTransformer(nn.Module): + """Vision Transformer as per https://arxiv.org/abs/2010.11929.""" + + def __init__( + self, + image_size: int, + patch_size: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float = 0.0, + attention_dropout: float = 0.0, + num_classes: int = 1000, + representation_size: Optional[int] = None, + norm_layer: Callable[..., torch.nn.Module] = partial(mbns_model.LayerNorm_MBNS, eps=1e-6), + conv_stem_configs: Optional[List[ConvStemConfig]] = None, + ): + super().__init__() + _log_api_usage_once(self) + torch._assert(image_size % patch_size == 0, "Input shape indivisible by patch size!") + self.image_size = image_size + self.patch_size = patch_size + self.hidden_dim = hidden_dim + self.mlp_dim = mlp_dim + self.attention_dropout = attention_dropout + self.dropout = dropout + self.num_classes = num_classes + self.representation_size = representation_size + self.norm_layer = norm_layer + + if conv_stem_configs is not None: + # As per https://arxiv.org/abs/2106.14881 + seq_proj = nn.Sequential() + prev_channels = 3 + for i, conv_stem_layer_config in enumerate(conv_stem_configs): + seq_proj.add_module( + f"conv_bn_relu_{i}", + Conv2dNormActivation( + in_channels=prev_channels, + out_channels=conv_stem_layer_config.out_channels, + kernel_size=conv_stem_layer_config.kernel_size, + stride=conv_stem_layer_config.stride, + norm_layer=conv_stem_layer_config.norm_layer, + activation_layer=conv_stem_layer_config.activation_layer, + ), + ) + prev_channels = conv_stem_layer_config.out_channels + seq_proj.add_module( + "conv_last", nn.Conv2d(in_channels=prev_channels, out_channels=hidden_dim, kernel_size=1) + ) + self.conv_proj: nn.Module = seq_proj + else: + self.conv_proj = nn.Conv2d( + in_channels=3, out_channels=hidden_dim, kernel_size=patch_size, stride=patch_size + ) + + seq_length = (image_size // patch_size) ** 2 + + # Add a class token + self.class_token = nn.Parameter(torch.zeros(1, 1, hidden_dim)) + seq_length += 1 + + self.encoder = Encoder( + seq_length, + num_layers, + num_heads, + hidden_dim, + mlp_dim, + dropout, + attention_dropout, + norm_layer, + ) + self.seq_length = seq_length + + heads_layers: OrderedDict[str, nn.Module] = OrderedDict() + if representation_size is None: + heads_layers["head"] = nn.Linear(hidden_dim, num_classes) + else: + heads_layers["pre_logits"] = nn.Linear(hidden_dim, representation_size) + heads_layers["act"] = nn.Tanh() + heads_layers["head"] = nn.Linear(representation_size, num_classes) + + self.heads = nn.Sequential(heads_layers) + + if isinstance(self.conv_proj, nn.Conv2d): + # Init the patchify stem + fan_in = self.conv_proj.in_channels * self.conv_proj.kernel_size[0] * self.conv_proj.kernel_size[1] + nn.init.trunc_normal_(self.conv_proj.weight, std=math.sqrt(1 / fan_in)) + if self.conv_proj.bias is not None: + nn.init.zeros_(self.conv_proj.bias) + elif self.conv_proj.conv_last is not None and isinstance(self.conv_proj.conv_last, nn.Conv2d): + # Init the last 1x1 conv of the conv stem + nn.init.normal_( + self.conv_proj.conv_last.weight, mean=0.0, std=math.sqrt(2.0 / self.conv_proj.conv_last.out_channels) + ) + if self.conv_proj.conv_last.bias is not None: + nn.init.zeros_(self.conv_proj.conv_last.bias) + + if hasattr(self.heads, "pre_logits") and isinstance(self.heads.pre_logits, nn.Linear): + fan_in = self.heads.pre_logits.in_features + nn.init.trunc_normal_(self.heads.pre_logits.weight, std=math.sqrt(1 / fan_in)) + nn.init.zeros_(self.heads.pre_logits.bias) + + if isinstance(self.heads.head, nn.Linear): + nn.init.zeros_(self.heads.head.weight) + nn.init.zeros_(self.heads.head.bias) + + def _process_input(self, x: torch.Tensor) -> torch.Tensor: + n, c, h, w = x.shape + p = self.patch_size + torch._assert(h == self.image_size, "Wrong image height!") + torch._assert(w == self.image_size, "Wrong image width!") + n_h = h // p + n_w = w // p + + # (n, c, h, w) -> (n, hidden_dim, n_h, n_w) + x = self.conv_proj(x) + # (n, hidden_dim, n_h, n_w) -> (n, hidden_dim, (n_h * n_w)) + x = x.reshape(n, self.hidden_dim, n_h * n_w) + + # (n, hidden_dim, (n_h * n_w)) -> (n, (n_h * n_w), hidden_dim) + # The self attention layer expects inputs in the format (N, S, E) + # where S is the source sequence length, N is the batch size, E is the + # embedding dimension + x = x.permute(0, 2, 1) + + return x + + def forward(self, x: torch.Tensor): + # Reshape and permute the input tensor + x = self._process_input(x) + n = x.shape[0] + + # Expand the class token to the full batch + batch_class_token = self.class_token.expand(n, -1, -1) + x = torch.cat([batch_class_token, x], dim=1) + + x = self.encoder(x) + + # Classifier "token" as used by standard language architectures + x = x[:, 0] + + x = self.heads(x) + + return x + + +def _vision_transformer( + patch_size: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> VisionTransformer: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + assert weights.meta["min_size"][0] == weights.meta["min_size"][1] + _ovewrite_named_param(kwargs, "image_size", weights.meta["min_size"][0]) + image_size = kwargs.pop("image_size", 224) + + model = VisionTransformer( + image_size=image_size, + patch_size=patch_size, + num_layers=num_layers, + num_heads=num_heads, + hidden_dim=hidden_dim, + mlp_dim=mlp_dim, + **kwargs, + ) + + if weights: + model.load_state_dict(weights.get_state_dict(progress=progress)) + + return model + + +_COMMON_META: Dict[str, Any] = { + "categories": _IMAGENET_CATEGORIES, +} + +_COMMON_SWAG_META = { + **_COMMON_META, + "recipe": "https://github.com/facebookresearch/SWAG", + "license": "https://github.com/facebookresearch/SWAG/blob/main/LICENSE", +} + + +class ViT_B_16_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_16-c867db91.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 86567656, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_b_16", + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.072, + "acc@5": 95.318, + } + }, + "_docs": """ + These weights were trained from scratch by using a modified version of `DeIT + `_'s training recipe. + """, + }, + ) + IMAGENET1K_SWAG_E2E_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_16_swag-9ac1b537.pth", + transforms=partial( + ImageClassification, + crop_size=384, + resize_size=384, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "num_params": 86859496, + "min_size": (384, 384), + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.304, + "acc@5": 97.650, + } + }, + "_docs": """ + These weights are learnt via transfer learning by end-to-end fine-tuning the original + `SWAG `_ weights on ImageNet-1K data. + """, + }, + ) + IMAGENET1K_SWAG_LINEAR_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_16_lc_swag-4e70ced5.pth", + transforms=partial( + ImageClassification, + crop_size=224, + resize_size=224, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "recipe": "https://github.com/pytorch/vision/pull/5793", + "num_params": 86567656, + "min_size": (224, 224), + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.886, + "acc@5": 96.180, + } + }, + "_docs": """ + These weights are composed of the original frozen `SWAG `_ trunk + weights and a linear classifier learnt on top of them trained on ImageNet-1K data. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_B_32_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_32-d86f8d99.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 88224232, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_b_32", + "_metrics": { + "ImageNet-1K": { + "acc@1": 75.912, + "acc@5": 92.466, + } + }, + "_docs": """ + These weights were trained from scratch by using a modified version of `DeIT + `_'s training recipe. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_L_16_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_16-852ce7e3.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=242), + meta={ + **_COMMON_META, + "num_params": 304326632, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_l_16", + "_metrics": { + "ImageNet-1K": { + "acc@1": 79.662, + "acc@5": 94.638, + } + }, + "_docs": """ + These weights were trained from scratch by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + IMAGENET1K_SWAG_E2E_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_16_swag-4f3808c9.pth", + transforms=partial( + ImageClassification, + crop_size=512, + resize_size=512, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "num_params": 305174504, + "min_size": (512, 512), + "_metrics": { + "ImageNet-1K": { + "acc@1": 88.064, + "acc@5": 98.512, + } + }, + "_docs": """ + These weights are learnt via transfer learning by end-to-end fine-tuning the original + `SWAG `_ weights on ImageNet-1K data. + """, + }, + ) + IMAGENET1K_SWAG_LINEAR_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_16_lc_swag-4d563306.pth", + transforms=partial( + ImageClassification, + crop_size=224, + resize_size=224, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "recipe": "https://github.com/pytorch/vision/pull/5793", + "num_params": 304326632, + "min_size": (224, 224), + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.146, + "acc@5": 97.422, + } + }, + "_docs": """ + These weights are composed of the original frozen `SWAG `_ trunk + weights and a linear classifier learnt on top of them trained on ImageNet-1K data. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_L_32_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_32-c7638314.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 306535400, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_l_32", + "_metrics": { + "ImageNet-1K": { + "acc@1": 76.972, + "acc@5": 93.07, + } + }, + "_docs": """ + These weights were trained from scratch by using a modified version of `DeIT + `_'s training recipe. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_H_14_Weights(WeightsEnum): + IMAGENET1K_SWAG_E2E_V1 = Weights( + url="https://download.pytorch.org/models/vit_h_14_swag-80465313.pth", + transforms=partial( + ImageClassification, + crop_size=518, + resize_size=518, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "num_params": 633470440, + "min_size": (518, 518), + "_metrics": { + "ImageNet-1K": { + "acc@1": 88.552, + "acc@5": 98.694, + } + }, + "_docs": """ + These weights are learnt via transfer learning by end-to-end fine-tuning the original + `SWAG `_ weights on ImageNet-1K data. + """, + }, + ) + IMAGENET1K_SWAG_LINEAR_V1 = Weights( + url="https://download.pytorch.org/models/vit_h_14_lc_swag-c1eb923e.pth", + transforms=partial( + ImageClassification, + crop_size=224, + resize_size=224, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "recipe": "https://github.com/pytorch/vision/pull/5793", + "num_params": 632045800, + "min_size": (224, 224), + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.708, + "acc@5": 97.730, + } + }, + "_docs": """ + These weights are composed of the original frozen `SWAG `_ trunk + weights and a linear classifier learnt on top of them trained on ImageNet-1K data. + """, + }, + ) + DEFAULT = IMAGENET1K_SWAG_E2E_V1 + + +@handle_legacy_interface(weights=("pretrained", ViT_B_16_Weights.IMAGENET1K_V1)) +def vit_b_16(*, weights: Optional[ViT_B_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_b_16 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_B_16_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_B_16_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_B_16_Weights + :members: + """ + weights = ViT_B_16_Weights.verify(weights) + + return _vision_transformer( + patch_size=16, + num_layers=12, + num_heads=12, + hidden_dim=768, + mlp_dim=3072, + weights=weights, + progress=progress, + **kwargs, + ) + + +@handle_legacy_interface(weights=("pretrained", ViT_B_32_Weights.IMAGENET1K_V1)) +def vit_b_32(*, weights: Optional[ViT_B_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_b_32 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_B_32_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_B_32_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_B_32_Weights + :members: + """ + weights = ViT_B_32_Weights.verify(weights) + + return _vision_transformer( + patch_size=32, + num_layers=12, + num_heads=12, + hidden_dim=768, + mlp_dim=3072, + weights=weights, + progress=progress, + **kwargs, + ) + + +@handle_legacy_interface(weights=("pretrained", ViT_L_16_Weights.IMAGENET1K_V1)) +def vit_l_16(*, weights: Optional[ViT_L_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_l_16 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_L_16_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_L_16_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_L_16_Weights + :members: + """ + weights = ViT_L_16_Weights.verify(weights) + + return _vision_transformer( + patch_size=16, + num_layers=24, + num_heads=16, + hidden_dim=1024, + mlp_dim=4096, + weights=weights, + progress=progress, + **kwargs, + ) + + +@handle_legacy_interface(weights=("pretrained", ViT_L_32_Weights.IMAGENET1K_V1)) +def vit_l_32(*, weights: Optional[ViT_L_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_l_32 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_L_32_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_L_32_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_L_32_Weights + :members: + """ + weights = ViT_L_32_Weights.verify(weights) + + return _vision_transformer( + patch_size=32, + num_layers=24, + num_heads=16, + hidden_dim=1024, + mlp_dim=4096, + weights=weights, + progress=progress, + **kwargs, + ) + + +def vit_h_14(*, weights: Optional[ViT_H_14_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_h_14 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_H_14_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_H_14_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_H_14_Weights + :members: + """ + weights = ViT_H_14_Weights.verify(weights) + + return _vision_transformer( + patch_size=14, + num_layers=32, + num_heads=16, + hidden_dim=1280, + mlp_dim=5120, + weights=weights, + progress=progress, + **kwargs, + ) + + +def interpolate_embeddings( + image_size: int, + patch_size: int, + model_state: "OrderedDict[str, torch.Tensor]", + interpolation_mode: str = "bicubic", + reset_heads: bool = False, +) -> "OrderedDict[str, torch.Tensor]": + """This function helps interpolating positional embeddings during checkpoint loading, + especially when you want to apply a pre-trained model on images with different resolution. + + Args: + image_size (int): Image size of the new model. + patch_size (int): Patch size of the new model. + model_state (OrderedDict[str, torch.Tensor]): State dict of the pre-trained model. + interpolation_mode (str): The algorithm used for upsampling. Default: bicubic. + reset_heads (bool): If true, not copying the state of heads. Default: False. + + Returns: + OrderedDict[str, torch.Tensor]: A state dict which can be loaded into the new model. + """ + # Shape of pos_embedding is (1, seq_length, hidden_dim) + pos_embedding = model_state["encoder.pos_embedding"] + n, seq_length, hidden_dim = pos_embedding.shape + if n != 1: + raise ValueError(f"Unexpected position embedding shape: {pos_embedding.shape}") + + new_seq_length = (image_size // patch_size) ** 2 + 1 + + # Need to interpolate the weights for the position embedding. + # We do this by reshaping the positions embeddings to a 2d grid, performing + # an interpolation in the (h, w) space and then reshaping back to a 1d grid. + if new_seq_length != seq_length: + # The class token embedding shouldn't be interpolated so we split it up. + seq_length -= 1 + new_seq_length -= 1 + pos_embedding_token = pos_embedding[:, :1, :] + pos_embedding_img = pos_embedding[:, 1:, :] + + # (1, seq_length, hidden_dim) -> (1, hidden_dim, seq_length) + pos_embedding_img = pos_embedding_img.permute(0, 2, 1) + seq_length_1d = int(math.sqrt(seq_length)) + if seq_length_1d * seq_length_1d != seq_length: + raise ValueError( + f"seq_length is not a perfect square! Instead got seq_length_1d * seq_length_1d = {seq_length_1d * seq_length_1d } and seq_length = {seq_length}" + ) + + # (1, hidden_dim, seq_length) -> (1, hidden_dim, seq_l_1d, seq_l_1d) + pos_embedding_img = pos_embedding_img.reshape(1, hidden_dim, seq_length_1d, seq_length_1d) + new_seq_length_1d = image_size // patch_size + + # Perform interpolation. + # (1, hidden_dim, seq_l_1d, seq_l_1d) -> (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) + new_pos_embedding_img = nn.functional.interpolate( + pos_embedding_img, + size=new_seq_length_1d, + mode=interpolation_mode, + align_corners=True, + ) + + # (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) -> (1, hidden_dim, new_seq_length) + new_pos_embedding_img = new_pos_embedding_img.reshape(1, hidden_dim, new_seq_length) + + # (1, hidden_dim, new_seq_length) -> (1, new_seq_length, hidden_dim) + new_pos_embedding_img = new_pos_embedding_img.permute(0, 2, 1) + new_pos_embedding = torch.cat([pos_embedding_token, new_pos_embedding_img], dim=1) + + model_state["encoder.pos_embedding"] = new_pos_embedding + + if reset_heads: + model_state_copy: "OrderedDict[str, torch.Tensor]" = OrderedDict() + for k, v in model_state.items(): + if not k.startswith("heads"): + model_state_copy[k] = v + model_state = model_state_copy + + return model_state + + +# The dictionary below is internal implementation detail and will be removed in v0.15 +from torchvision.models._utils import _ModelURLs + + +model_urls = _ModelURLs( + { + "vit_b_16": ViT_B_16_Weights.IMAGENET1K_V1.url, + "vit_b_32": ViT_B_32_Weights.IMAGENET1K_V1.url, + "vit_l_16": ViT_L_16_Weights.IMAGENET1K_V1.url, + "vit_l_32": ViT_L_32_Weights.IMAGENET1K_V1.url, + } +) diff --git a/utils/defense_utils/sam/__init__.py b/utils/defense_utils/sam/__init__.py new file mode 100755 index 0000000..10bfeae --- /dev/null +++ b/utils/defense_utils/sam/__init__.py @@ -0,0 +1,3 @@ +from .sam import SAM +from .scheduler import * +from .util import * diff --git a/utils/defense_utils/sam/sam.py b/utils/defense_utils/sam/sam.py new file mode 100755 index 0000000..61542f3 --- /dev/null +++ b/utils/defense_utils/sam/sam.py @@ -0,0 +1,188 @@ +import torch +from .util import enable_running_stats, disable_running_stats +import contextlib +from torch.distributed import ReduceOp + +class SAM(torch.optim.Optimizer): + def __init__(self, params, base_optimizer, model, sam_alpha, rho_scheduler, adaptive=False, perturb_eps=1e-12, grad_reduce='mean', **kwargs): + defaults = dict(adaptive=adaptive, **kwargs) + super(SAM, self).__init__(params, defaults) + self.model = model + self.base_optimizer = base_optimizer + self.param_groups = self.base_optimizer.param_groups + self.adaptive = adaptive + self.rho_scheduler = rho_scheduler + self.perturb_eps = perturb_eps + self.alpha = sam_alpha + + # initialize self.rho_t + self.update_rho_t() + + # set up reduction for gradient across workers + if grad_reduce.lower() == 'mean': + if hasattr(ReduceOp, 'AVG'): + self.grad_reduce = ReduceOp.AVG + self.manual_average = False + else: # PyTorch <= 1.11.0 does not have AVG, need to manually average across processes + self.grad_reduce = ReduceOp.SUM + self.manual_average = True + elif grad_reduce.lower() == 'sum': + self.grad_reduce = ReduceOp.SUM + self.manual_average = False + else: + raise ValueError('"grad_reduce" should be one of ["mean", "sum"].') + + @torch.no_grad() + def update_rho_t(self): + self.rho_t = self.rho_scheduler.step() + return self.rho_t + + @torch.no_grad() + def perturb_weights(self, rho=0.0): + grad_norm = self._grad_norm( weight_adaptive = self.adaptive ) + for group in self.param_groups: + scale = rho / (grad_norm + self.perturb_eps) + + for p in group["params"]: + if p.grad is None: continue + self.state[p]["old_g"] = p.grad.data.clone() + e_w = p.grad * scale.to(p) + if self.adaptive: + e_w *= torch.pow(p, 2) + p.add_(e_w) # climb to the local maximum "w + e(w)" + self.state[p]['e_w'] = e_w + + @torch.no_grad() + def unperturb(self): + for group in self.param_groups: + for p in group['params']: + if 'e_w' in self.state[p].keys(): + p.data.sub_(self.state[p]['e_w']) + + @torch.no_grad() + def gradient_decompose(self, alpha=0.0): + # calculate inner product + inner_prod = 0.0 + for group in self.param_groups: + for p in group['params']: + if p.grad is None: continue + inner_prod += torch.sum( + self.state[p]['old_g'] * p.grad.data + ) + + # get norm + new_grad_norm = self._grad_norm() + old_grad_norm = self._grad_norm(by='old_g') + + # get cosine + cosine = inner_prod / (new_grad_norm * old_grad_norm + self.perturb_eps) + + # gradient decomposition + for group in self.param_groups: + for p in group['params']: + if p.grad is None: continue + vertical = self.state[p]['old_g'] - cosine * old_grad_norm * p.grad.data / (new_grad_norm + self.perturb_eps) + p.grad.data.add_( vertical, alpha=-alpha) + + @torch.no_grad() + def _sync_grad(self): + if torch.distributed.is_initialized(): # synchronize final gardients + for group in self.param_groups: + for p in group['params']: + if p.grad is None: continue + if self.manual_average: + torch.distributed.all_reduce(p.grad, op=self.grad_reduce) + world_size = torch.distributed.get_world_size() + p.grad.div_(float(world_size)) + else: + torch.distributed.all_reduce(p.grad, op=self.grad_reduce) + return + + @torch.no_grad() + def _grad_norm(self, by=None, weight_adaptive=False): + #shared_device = self.param_groups[0]["params"][0].device # put everything on the same device, in case of model parallelism + if not by: + norm = torch.norm( + torch.stack([ + ( (torch.abs(p.data) if weight_adaptive else 1.0) * p.grad).norm(p=2) + for group in self.param_groups for p in group["params"] + if p.grad is not None + ]), + p=2 + ) + else: + norm = torch.norm( + torch.stack([ + ( (torch.abs(p.data) if weight_adaptive else 1.0) * self.state[p][by]).norm(p=2) + for group in self.param_groups for p in group["params"] + if p.grad is not None + ]), + p=2 + ) + return norm + + def load_state_dict(self, state_dict): + super().load_state_dict(state_dict) + self.base_optimizer.param_groups = self.param_groups + + def maybe_no_sync(self): + if torch.distributed.is_initialized(): + return self.model.no_sync() + else: + return contextlib.ExitStack() + + @torch.no_grad() + def set_closure(self, loss_fn, inputs, targets, **kwargs): + # create self.forward_backward_func, which is a function such that + # self.forward_backward_func() automatically performs forward and backward passes. + # This function does not take any arguments, and the inputs and targets data + # should be pre-set in the definition of partial-function + + def get_grad(): + self.base_optimizer.zero_grad() + with torch.enable_grad(): + outputs = self.model(inputs) + loss = loss_fn(outputs, targets, **kwargs) + loss_value = loss.data.clone().detach() + loss.backward() + return outputs, loss_value + + self.forward_backward_func = get_grad + + @torch.no_grad() + def step(self, closure=None): + + if closure: + get_grad = closure + else: + get_grad = self.forward_backward_func + + with self.maybe_no_sync(): + # get gradient + outputs, loss_value = get_grad() + + # perturb weights + self.perturb_weights(rho=self.rho_t) + + # disable running stats for second pass + disable_running_stats(self.model) + + # get gradient at perturbed weights + get_grad() + + # decompose and get new update direction + self.gradient_decompose(self.alpha) + + # unperturb + self.unperturb() + + # synchronize gradients across workers + self._sync_grad() + + # update with new directions + self.base_optimizer.step() + + # enable running stats + enable_running_stats(self.model) + + return outputs, loss_value diff --git a/utils/defense_utils/sam/scheduler.py b/utils/defense_utils/sam/scheduler.py new file mode 100755 index 0000000..56378a5 --- /dev/null +++ b/utils/defense_utils/sam/scheduler.py @@ -0,0 +1,108 @@ + +import math +import numpy as np + +class ProportionScheduler: + def __init__(self, pytorch_lr_scheduler, max_lr, min_lr, max_value, min_value): + """ + This scheduler outputs a value that evolves proportional to pytorch_lr_scheduler, e.g. + (value - min_value) / (max_value - min_value) = (lr - min_lr) / (max_lr - min_lr) + """ + self.t = 0 + self.pytorch_lr_scheduler = pytorch_lr_scheduler + self.max_lr = max_lr + self.min_lr = min_lr + self.max_value = max_value + self.min_value = min_value + + assert (max_lr > min_lr) or ((max_lr==min_lr) and (max_value==min_value)), "Current scheduler for `value` is scheduled to evolve proportionally to `lr`," \ + "e.g. `(lr - min_lr) / (max_lr - min_lr) = (value - min_value) / (max_value - min_value)`. Please check `max_lr >= min_lr` and `max_value >= min_value`;" \ + "if `max_lr==min_lr` hence `lr` is constant with step, please set 'max_value == min_value' so 'value' is constant with step." + + assert max_value >= min_value + + self.step() # take 1 step during initialization to get self._last_lr + + def lr(self): + return self._last_lr[0] + + def step(self): + self.t += 1 + if hasattr(self.pytorch_lr_scheduler, "_last_lr"): + lr = self.pytorch_lr_scheduler._last_lr[0] + else: + lr = self.pytorch_lr_scheduler.optimizer.param_groups[0]['lr'] + + if self.max_lr > self.min_lr: + value = self.min_value + (self.max_value - self.min_value) * (lr - self.min_lr) / (self.max_lr - self.min_lr) + else: + value = self.max_value + + self._last_lr = [value] + return value + +class SchedulerBase: + def __init__(self, T_max, max_value, min_value=0.0, init_value=0.0, warmup_steps=0, optimizer=None): + super(SchedulerBase, self).__init__() + self.t = 0 + self.min_value = min_value + self.max_value = max_value + self.init_value = init_value + self.warmup_steps = warmup_steps + self.total_steps = T_max + + # record current value in self._last_lr to match API from torch.optim.lr_scheduler + self._last_lr = [init_value] + + # If optimizer is not None, will set learning rate to all trainable parameters in optimizer. + # If optimizer is None, only output the value of lr. + self.optimizer = optimizer + + def step(self): + if self.t < self.warmup_steps: + value = self.init_value + (self.max_value - self.init_value) * self.t / self.warmup_steps + elif self.t == self.warmup_steps: + value = self.max_value + else: + value = self.step_func() + self.t += 1 + + # apply the lr to optimizer if it's provided + if self.optimizer is not None: + for param_group in self.optimizer.param_groups: + param_group['lr'] = value + + self._last_lr = [value] + return value + + def step_func(self): + pass + + def lr(self): + return self._last_lr[0] + +class LinearScheduler(SchedulerBase): + def step_func(self): + value = self.max_value + (self.min_value - self.max_value) * (self.t - self.warmup_steps) / ( + self.total_steps - self.warmup_steps) + return value + +class CosineScheduler(SchedulerBase): + def step_func(self): + phase = (self.t-self.warmup_steps) / (self.total_steps-self.warmup_steps) * math.pi + value = self.min_value + (self.max_value-self.min_value) * (np.cos(phase) + 1.) / 2.0 + return value + +class PolyScheduler(SchedulerBase): + def __init__(self, poly_order=-0.5, *args, **kwargs): + super(PolyScheduler, self).__init__(*args, **kwargs) + self.poly_order = poly_order + assert poly_order<=0, "Please check poly_order<=0 so that the scheduler decreases with steps" + + def step_func(self): + value = self.min_value + (self.max_value-self.min_value) * (self.t - self.warmup_steps)**self.poly_order + return value + + + + diff --git a/utils/defense_utils/sam/util.py b/utils/defense_utils/sam/util.py new file mode 100755 index 0000000..70ce37e --- /dev/null +++ b/utils/defense_utils/sam/util.py @@ -0,0 +1,29 @@ +import torch +import torch.nn as nn +from torch.nn.modules.batchnorm import _BatchNorm +import torch.nn.functional as F + +def disable_running_stats(model): + def _disable(module): + if isinstance(module, _BatchNorm): + module.backup_momentum = module.momentum + module.momentum = 0 + + model.apply(_disable) + +def enable_running_stats(model): + def _enable(module): + if isinstance(module, _BatchNorm) and hasattr(module, "backup_momentum"): + module.momentum = module.backup_momentum + + model.apply(_enable) + + +def smooth_crossentropy(pred, gold, smoothing=0.1): + n_class = pred.size(1) + + one_hot = torch.full_like(pred, fill_value=smoothing / (n_class - 1)) + one_hot.scatter_(dim=1, index=gold.unsqueeze(1), value=1.0 - smoothing) + log_prob = F.log_softmax(pred, dim=1) + + return F.kl_div(input=log_prob, target=one_hot, reduction='none').sum(-1) diff --git a/utils/log_assist.py b/utils/log_assist.py new file mode 100755 index 0000000..01a6f41 --- /dev/null +++ b/utils/log_assist.py @@ -0,0 +1,16 @@ +import subprocess + +def get_git_info(): + + info = {} + + info['status'] = (subprocess.check_output(['git', 'status']).decode('ascii').strip()) + + info['last 3 log'] = (subprocess.check_output(['git', 'log', '-n', '3']).decode('ascii').strip()) + + if 'modified:' in subprocess.check_output(['git', 'status']).decode('ascii').strip(): + info['git hash'] = None + else: + info['git hash'] = (f"--git_hash {subprocess.check_output(['git', 'rev-parse', 'HEAD']).decode('ascii').strip()}") + + return info \ No newline at end of file diff --git a/utils/metric.py b/utils/metric.py new file mode 100644 index 0000000..87af663 --- /dev/null +++ b/utils/metric.py @@ -0,0 +1,100 @@ +'''Define some commonly used metric for evaluation''' + +import numpy as np +import torch + + +'''clean accuracy (C-Acc) (i.e., the prediction accuracy of clean samples),''' +def clean_accuracy(pred, label): + '''Compute the accuracy of clean samples''' + if isinstance(pred, torch.Tensor): + pred = pred.cpu().numpy() + if isinstance(label, torch.Tensor): + label = label.cpu().numpy() + if isinstance(pred, list): + pred = np.array(pred) + if isinstance(label, list): + label = np.array(label) + + return np.mean((pred == label)) + +def clean_accuracy_per_class(pred, label, num_classes): + '''Compute the accuracy of clean samples per class''' + if isinstance(pred, torch.Tensor): + pred = pred.cpu().numpy() + if isinstance(label, torch.Tensor): + label = label.cpu().numpy() + if isinstance(pred, list): + pred = np.array(pred) + if isinstance(label, list): + label = np.array(label) + + accuracy = [] + for i in range(num_classes): + accuracy.append(np.mean((pred[label == i] == i))) + return accuracy + + +'''attack success rate (ASR) (i.e., the prediction accuracy of poisoned samples to the target class)''' +def attack_success_rate(pred, target_label): + '''Compute the attack success rate''' + + return clean_accuracy(pred, target_label) + +def attack_success_rate_per_class(pred, target_label, num_classes): + '''Compute the attack success rate per class''' + return clean_accuracy_per_class(pred, target_label, num_classes) + + + +'''robust accuracy (R-Acc) (i.e., the prediction accuracy of poisoned samples to the original class)''' +def robust_accuracy(bd_pred, ori_label): + '''Compute the robust accuracy''' + return clean_accuracy(bd_pred, ori_label) + +def robust_accuracy_per_class(bd_pred, ori_label, num_classes): + '''Compute the robust accuracy per class''' + return clean_accuracy_per_class(bd_pred, ori_label, num_classes) + + +'''Defense Effectiveness Rate (DER) ( DER = [max(0,ΔASR) − max(0,ΔACC) + 1]/2, where ΔACC = C-Acc_bd − C-Acc_defnse and ΔASR = ASR_bd − ASR_defnse)''' + +def defense_effectiveness_rate(bd_pred, defense_pred, ori_label, target_label): + '''Compute the defense effectiveness rate''' + return (max(0, attack_success_rate(bd_pred, target_label) - attack_success_rate(defense_pred, target_label)) - max(0, clean_accuracy(bd_pred, ori_label) - clean_accuracy(defense_pred, ori_label)) + 1) / 2 + +def defense_effectiveness_rate_per_class(bd_pred, defense_pred, ori_label, target_label, num_classes): + '''Compute the defense effectiveness rate per class''' + der = [] + asr_bd = attack_success_rate_per_class(bd_pred, target_label, num_classes) + asr_defense = attack_success_rate_per_class(defense_pred, target_label, num_classes) + acc_bd = clean_accuracy_per_class(bd_pred, ori_label, num_classes) + acc_defense = clean_accuracy_per_class(defense_pred, ori_label, num_classes) + for i in range(num_classes): + der.append((max(0, asr_bd[i] - asr_defense[i]) - max(0, acc_bd[i] - acc_defense[i]) + 1) / 2) + return der + +def defense_effectiveness_rate_simplied(acc_bd, acc_defnese, asr_bd, asr_defense): + '''Compute the defense effectiveness rate''' + return (max(0, asr_bd - asr_defense) - max(0, acc_bd - acc_defnese) + 1) / 2 + +'''Robust Improvement Rate (DER) ( DER = [max(0,-ΔRA) − max(0,ΔACC) + 1]/2, where ΔRA = RA_bd − RA_defnse and ΔASR = ASR_bd − ASR_defnse)''' + +def robust_improvement_rate(bd_pred, defense_pred, ori_label): + '''Compute the robust improvement rate''' + return (max(0, -robust_accuracy(bd_pred, ori_label) + robust_accuracy(defense_pred, ori_label)) - max(0, clean_accuracy(bd_pred, ori_label) - clean_accuracy(defense_pred, ori_label)) + 1) / 2 + +def robust_improvement_rate_per_class(bd_pred, defense_pred, ori_label, num_classes): + '''Compute the robust improvement rate per class''' + rir = [] + ra_bd = robust_accuracy_per_class(bd_pred, ori_label, num_classes) + ra_defense = robust_accuracy_per_class(defense_pred, ori_label, num_classes) + acc_bd = clean_accuracy_per_class(bd_pred, ori_label, num_classes) + acc_defense = clean_accuracy_per_class(defense_pred, ori_label, num_classes) + for i in range(num_classes): + rir.append((max(0, -ra_bd[i] + ra_defense[i]) - max(0, acc_bd[i] - acc_defense[i]) + 1) / 2) + return rir + +def robust_improvement_rate_simplied(acc_bd, acc_defnese, ra_bd, ra_defense): + '''Compute the robust improvement rate''' + return (max(0, -ra_bd + ra_defense) - max(0, acc_bd - acc_defnese) + 1) / 2 \ No newline at end of file diff --git a/utils/nCHW_nHWC.py b/utils/nCHW_nHWC.py new file mode 100755 index 0000000..ea63d28 --- /dev/null +++ b/utils/nCHW_nHWC.py @@ -0,0 +1,16 @@ +''' +This script aims to do transformation between nCHW and nHWC +please note that these two function both need to have 3 or 4 dimension, + PIL or list DOES NOT SUPPORT ! +''' +def nCHW_to_nHWC(images): + if images.shape.__len__() == 3: + return images.transpose((1,2,0)) + elif images.shape.__len__() == 4: + return images.transpose((0, 2, 3, 1)) + +def nHWC_to_nCHW(images): + if images.shape.__len__() == 3: + return images.transpose((2,0,1)) + elif images.shape.__len__() == 4: + return images.transpose((0, 3, 1, 2)) \ No newline at end of file diff --git a/utils/prefetch.py b/utils/prefetch.py new file mode 100755 index 0000000..4e7fe02 --- /dev/null +++ b/utils/prefetch.py @@ -0,0 +1,67 @@ +from functools import partial + +import numpy as np +import torch +from torchvision import transforms + +def prefetch_transform(transform): + """Remove ``ToTensor`` and ``Normalize`` in ``transform``.""" + transform_list = [] + normalize = False + for t in transform.transforms: + if "Normalize" in str(type(t)): + normalize = True + if not normalize: + raise KeyError("No Normalize in transform: {}".format(transform)) + for t in transform.transforms: + if not ("ToTensor" or "Normalize" in str(type(t))): + transform_list.append(t) + if "Normalize" in str(type(t)): + mean, std = t.mean, t.std + + transform_list += [ + partial(np.array, dtype = np.uint8), + partial(np.rollaxis, axis = 2), + torch.from_numpy, + ] + + transform = transforms.Compose(transform_list) + + return transform, mean, std + +class PrefetchLoader: + """A data loader wrapper for prefetching data along with ``ToTensor`` and `Normalize` + transformations. + + Modified from https://github.com/open-mmlab/OpenSelfSup. + """ + + def __init__(self, loader, mean, std): + self.loader = loader + self.raw_mean = mean + self.raw_std = std + def __iter__(self): + stream = torch.cuda.Stream() + first = True + self.mean = torch.tensor([x * 255 for x in self.raw_mean]).cuda().view(1, 3, 1, 1) + self.std = torch.tensor([x * 255 for x in self.raw_std]).cuda().view(1, 3, 1, 1) + for next_item in self.loader: + with torch.cuda.stream(stream): + img = next_item[0].cuda(non_blocking=True) + next_item[0] = img.float().sub_(self.mean).div_(self.std) + if not first: + yield item + else: + first = False + torch.cuda.current_stream().wait_stream(stream) + item = next_item + yield item + def __len__(self): + return len(self.loader) + @property + def sampler(self): + return self.loader.sampler + @property + def dataset(self): + return self.loader.dataset + diff --git a/utils/pytorch_ssim/__init__.py b/utils/pytorch_ssim/__init__.py new file mode 100755 index 0000000..738e803 --- /dev/null +++ b/utils/pytorch_ssim/__init__.py @@ -0,0 +1,73 @@ +import torch +import torch.nn.functional as F +from torch.autograd import Variable +import numpy as np +from math import exp + +def gaussian(window_size, sigma): + gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)]) + return gauss/gauss.sum() + +def create_window(window_size, channel): + _1D_window = gaussian(window_size, 1.5).unsqueeze(1) + _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) + window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous()) + return window + +def _ssim(img1, img2, window, window_size, channel, size_average = True): + mu1 = F.conv2d(img1, window, padding = window_size//2, groups = channel) + mu2 = F.conv2d(img2, window, padding = window_size//2, groups = channel) + + mu1_sq = mu1.pow(2) + mu2_sq = mu2.pow(2) + mu1_mu2 = mu1*mu2 + + sigma1_sq = F.conv2d(img1*img1, window, padding = window_size//2, groups = channel) - mu1_sq + sigma2_sq = F.conv2d(img2*img2, window, padding = window_size//2, groups = channel) - mu2_sq + sigma12 = F.conv2d(img1*img2, window, padding = window_size//2, groups = channel) - mu1_mu2 + + C1 = 0.01**2 + C2 = 0.03**2 + + ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2)) + + if size_average: + return ssim_map.mean() + else: + return ssim_map.mean(1).mean(1).mean(1) + +class SSIM(torch.nn.Module): + def __init__(self, window_size = 11, size_average = True): + super(SSIM, self).__init__() + self.window_size = window_size + self.size_average = size_average + self.channel = 1 + self.window = create_window(window_size, self.channel) + + def forward(self, img1, img2): + (_, channel, _, _) = img1.size() + + if channel == self.channel and self.window.data.type() == img1.data.type(): + window = self.window + else: + window = create_window(self.window_size, channel) + + if img1.is_cuda: + window = window.cuda(img1.get_device()) + window = window.type_as(img1) + + self.window = window + self.channel = channel + + + return _ssim(img1, img2, window, self.window_size, channel, self.size_average) + +def ssim(img1, img2, window_size = 11, size_average = True): + (_, channel, _, _) = img1.size() + window = create_window(window_size, channel) + + if img1.is_cuda: + window = window.cuda(img1.get_device()) + window = window.type_as(img1) + + return _ssim(img1, img2, window, window_size, channel, size_average) diff --git a/utils/pytorch_ssim/readme.md b/utils/pytorch_ssim/readme.md new file mode 100755 index 0000000..c24dcbf --- /dev/null +++ b/utils/pytorch_ssim/readme.md @@ -0,0 +1 @@ +this implementation is from https://github.com/Po-Hsun-Su/pytorch-ssim diff --git a/utils/quick_learning_utils/quick_learning_trainer.py b/utils/quick_learning_utils/quick_learning_trainer.py new file mode 100644 index 0000000..0113923 --- /dev/null +++ b/utils/quick_learning_utils/quick_learning_trainer.py @@ -0,0 +1,1143 @@ +# This script is for trainer. This is a warpper for training process. + +import datetime +import enum +from tqdm import tqdm +import torch.nn.functional as F +import scipy +from time import time +import pandas as pd +import torch +import numpy as np +from typing import * +from collections import deque +from pprint import pformat +import random +from statistics import mean, mode +import sys +import logging +from torch.utils.data import Dataset, DataLoader +sys.path.append('../') + + +def last_and_valid_max(col: pd.Series): + ''' + find last not None value and max valid (not None or np.nan) value for each column + :param col: + :return: + ''' + return pd.Series( + index=[ + 'last', 'valid_max', 'exist_nan_value' + ], + data=[ + col[~col.isna()].iloc[-1], pd.to_numeric(col, + errors='coerce').max(), any(i == 'nan_value' for i in col) + ]) + + +class Metric_Aggregator(object): + ''' + aggregate the metric to log automatically + ''' + + def __init__(self): + self.history = [] + + def __call__(self, + one_metric: dict): + # drop pair with None as value + one_metric = {k: v for k, v in one_metric.items() if v is not None} + one_metric = { + k: ( + "nan_value" if v is np.nan or torch.tensor( + v).isnan().item() else v # turn nan to str('nan_value') + ) for k, v in one_metric.items() + } + self.history.append(one_metric) + logging.info( + pformat( + one_metric + ) + ) + + def to_dataframe(self): + self.df = pd.DataFrame(self.history, dtype=object) + logging.info("return df with np.nan and None converted by str()") + return self.df + + def summary(self): + ''' + do summary for dataframe of record + :return: + eg. + ,train_epoch_num,train_acc_clean + last,100.0,96.68965148925781 + valid_max,100.0,96.70848846435547 + exist_nan_value,False,False + + ''' + if 'df' not in self.__dict__: + logging.info('No df found in Metric_Aggregator, generate now') + self.to_dataframe() + logging.info("return df with np.nan and None converted by str()") + return self.df.apply(last_and_valid_max) + +def compute_grad_batch(model, loss_fn, sample, target): + + prediction = model(sample) + loss = loss_fn(prediction, target) + grad_list = torch.autograd.grad(loss, list(model.parameters())) + return torch.concat([layer_grad.flatten() for layer_grad in grad_list]).flatten().detach() + + +def compute_grad(model, loss_fn, sample, target): + + sample = sample.unsqueeze(0) # prepend batch dimension for processing + target = target.unsqueeze(0) + + prediction = model(sample) + loss = loss_fn(prediction, target) + grad_list = torch.autograd.grad(loss, list(model.parameters())) + return torch.concat([layer_grad.flatten() for layer_grad in grad_list]).flatten().detach() + + +def compute_sample_grads_sums(model, loss_fn, data, targets, additional_info, num_class, grad_epoch): + """ manually process each sample with per sample gradient, naive implementation """ + if targets.max() >= num_class: + raise ValueError("targets max value should be less than num_class") + + batch_size = data.shape[0] + grad_sum = 0 + grad_sum_squared = 0 + grad_sum_clean = 0 + grad_sum_squared_clean = 0 + grad_sum_bd = 0 + grad_sum_squared_bd = 0 + grad_sum_class = [0 for _ in range(num_class)] + grad_sum_squared_class = [0 for _ in range(num_class)] + + ori_idx, poi_indicator, ori_target = additional_info + grad_dis_clean = [] + grad_dis_bd = [] + grad_dis_class = [[] for _ in range(num_class)] + + grad_cos_clean = [] + grad_cos_bd = [] + grad_cos_class = [[] for _ in range(num_class)] + + for i in range(batch_size): + grad_i = compute_grad(model, loss_fn, data[i], targets[i]) + grad_sum += grad_i + grad_sum_squared += grad_i.square() + if poi_indicator[i] == 1: + grad_sum_bd += grad_i + grad_sum_squared_bd += grad_i.square() + grad_dis_bd.append(torch.linalg.norm(grad_i.flatten())) + grad_cos_bd.append(F.cosine_similarity(grad_i.flatten(), grad_epoch.flatten(),dim=0)) + else: + grad_sum_clean += grad_i + grad_sum_squared_clean += grad_i.square() + grad_dis_clean.append(torch.linalg.norm(grad_i.flatten())) + grad_cos_clean.append(F.cosine_similarity(grad_i.flatten(), grad_epoch.flatten(),dim=0)) + + grad_sum_class[targets[i]] += grad_i + grad_sum_squared_class[targets[i]] += grad_i.square() + grad_dis_class[targets[i]].append( + torch.linalg.norm(grad_i.flatten())) + + grad_cos_class[targets[i]].append( + F.cosine_similarity(grad_i.flatten(), grad_epoch.flatten(),dim=0)) + + # To avoid GPU memory overflow, we use numpy instead of torch. + # grad_sum = grad_sum.cpu().numpy() + # grad_sum_squared = grad_sum_squared.cpu().numpy() + # grad_sum_clean = grad_sum_clean.cpu().numpy() + # grad_sum_squared_clean = grad_sum_squared_clean.cpu().numpy() + # grad_sum_bd = grad_sum_bd.cpu().numpy() + # grad_sum_squared_bd = grad_sum_squared_bd.cpu().numpy() + return grad_sum, grad_sum_squared, grad_sum_clean, grad_sum_squared_clean, grad_sum_bd, grad_sum_squared_bd, grad_dis_clean, grad_dis_bd, grad_sum_class, grad_sum_squared_class, grad_dis_class, grad_cos_clean, grad_cos_bd, grad_cos_class + +# def compute_sample_grads_sums_op(model, loss_fn, data, targets): +# """ manually process each sample with per sample gradient """ +# model = GradSampleModule(model) +# batch_size = data.shape[0] +# grad_sum = 0 +# grad_sum_squared = 0 + +# # zero grad and grad_sample +# model.zero_grad() + +# output = model(data) +# loss = loss_fn(output, targets) +# loss.backward() +# grad_sum = torch.cat([p.grad_sample.sum(dim=0).view(-1) for p in model.parameters()]).cpu().numpy() +# grad_sum_squared = torch.cat([p.grad_sample.square().sum(dim=0).view(-1) for p in model.parameters()]).cpu().numpy() + + +# return grad_sum, grad_sum_squared + +# Modified from https://pytorch.org/functorch/stable/notebooks/per_sample_grads.html + +# from opacus.grad_sample import GradSampleModule +# from functorch import make_functional_with_buffers, vmap, grad + +def compute_sample_grads_sums_vmap(model, loss_fn, data, targets, additional_info): + """ manually process each sample with per sample gradient, efficient implementation via """ + fmodel, params, buffers = make_functional_with_buffers(model) + + def compute_loss_stateless_model(params, buffers, sample, target): + batch = sample.unsqueeze(0) + targets = target.unsqueeze(0) + + predictions = fmodel(params, buffers, batch) + loss = loss_fn(predictions, targets) + return loss + + ft_compute_grad = grad(compute_loss_stateless_model) + ft_compute_sample_grad = vmap(ft_compute_grad, in_dims=(None, None, 0, 0)) + ft_per_sample_grads = ft_compute_sample_grad( + params, buffers, data, targets) + # ft_per_sample_grads is of form sample x params x shape of grad + # reshape to sample x params + grad_arrays = [] + for ft_grad in ft_per_sample_grads: + grad_arrays.append(ft_grad.detach().cpu( + ).numpy().reshape(ft_grad.shape[0], -1)) + grad_matrix = np.concatenate(grad_arrays, axis=1) + + batch_size = data.shape[0] + grad_sum = 0 + grad_sum_squared = 0 + grad_sum_clean = 0 + grad_sum_squared_clean = 0 + grad_sum_bd = 0 + grad_sum_squared_bd = 0 + + ori_idx, poi_indicator, ori_target = additional_info + poi_indicator_np = poi_indicator.cpu().numpy().reshape(-1) + + grad_sum = grad_matrix.sum(axis=0) + grad_sum_squared = np.square(grad_matrix).sum(axis=0) + print(grad_sum_squared.shape) + grad_sum_bd = grad_matrix[poi_indicator_np == 1].sum(axis=0) + grad_sum_squared_bd = np.square( + grad_matrix[poi_indicator_np == 1]).sum(axis=0) + + grad_sum_clean = grad_matrix[poi_indicator_np == 0].sum(axis=0) + grad_sum_squared_clean = np.square( + grad_matrix[poi_indicator_np == 0]).sum(axis=0) + + # for i in range(batch_size): + # grad_i_np = grad_matrix[i] + # grad_sum += grad_i_np + # grad_sum_squared += grad_i_np ** 2 + # if poi_indicator[i] == 1: + # grad_sum_bd += grad_i_np + # grad_sum_squared_bd += grad_i_np ** 2 + # else: + # grad_sum_clean += grad_i_np + # grad_sum_squared_clean += grad_i_np ** 2 + + return grad_sum, grad_sum_squared, grad_sum_clean, grad_sum_squared_clean, grad_sum_bd, grad_sum_squared_bd + + +class ModelTrainerCLS(): + def __init__(self, model, amp=False, args = None): + self.model = model + self.amp = amp + self.args = args + # get the value of each parameter + self.init_weights = [] + for p in self.model.cuda().parameters(): + self.init_weights.append(p.data.clone()) + + def init_or_continue_train(self, + train_data, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + continue_training_path: Optional[str] = None, + only_load_model: bool = False, + ) -> None: + ''' + config the training process, from 0 or continue previous. + The requirement for saved file please refer to save_all_state_to_path + :param train_data: train_data_loader, only if when you need of number of batch, you need to input it. Otherwise just skip. + :param end_epoch_num: end training epoch number, if not continue training process, then equal to total training epoch + :param criterion: loss function used + :param optimizer: optimizer + :param scheduler: scheduler + :param device: device + :param continue_training_path: where to load files for continue training process + :param only_load_model: only load the model, do not load other settings and random state. + + ''' + + model = self.model + + model.to(device) + model.train() + + # train and update + + self.criterion = criterion + self.optimizer = optimizer + self.scheduler = scheduler + self.scaler = torch.cuda.amp.GradScaler(enabled=self.amp) + + if continue_training_path is not None: + logging.info(f"No batch info will be used. Cannot continue from specific batch!") + # start_epoch, start_batch = self.load_from_path(continue_training_path, device, only_load_model) + # if (start_epoch is None) or (start_batch is None): + # self.start_epochs, self.end_epochs = 0, end_epoch_num + # self.start_batch = 0 + # else: + # batch_num = len(train_data) + # self.start_epochs, self.end_epochs = start_epoch + ((start_batch + 1)//batch_num), end_epoch_num + # self.start_batch = (start_batch + 1) % batch_num + start_epoch, _ = self.load_from_path(continue_training_path, device, only_load_model) + self.start_epochs, self.end_epochs = start_epoch, end_epoch_num + else: + self.start_epochs, self.end_epochs = 0, end_epoch_num + # self.start_batch = 0 + + logging.info(f'All setting done, train from epoch {self.start_epochs} to epoch {self.end_epochs}') + + logging.info( + pformat(f"self.amp:{self.amp}," + + f"self.criterion:{self.criterion}," + + f"self.optimizer:{self.optimizer}," + + f"self.scheduler:{self.scheduler.state_dict() if self.scheduler is not None else None}," + + f"self.scaler:{self.scaler.state_dict() if self.scaler is not None else None})") + ) + + def get_model_params(self): + return self.model.cpu().state_dict() + + def set_model_params(self, model_parameters): + self.model.load_state_dict(model_parameters) + + def save_all_state_to_path(self, + path: str, + epoch: Optional[int] = None, + batch: Optional[int] = None, + only_model_state_dict: bool = False) -> None: + ''' + save all information needed to continue training, include 3 random state in random, numpy and torch + :param path: where to save + :param epoch: which epoch when save + :param batch: which batch index when save + :param only_model_state_dict: only save the model, drop all other information + ''' + + save_dict = { + 'epoch_num_when_save': epoch, + 'batch_num_when_save': batch, + 'random_state': random.getstate(), + 'np_random_state': np.random.get_state(), + 'torch_random_state': torch.random.get_rng_state(), + 'model_state_dict': self.get_model_params(), + 'optimizer_state_dict': self.optimizer.state_dict(), + 'scheduler_state_dict': self.scheduler.state_dict() if self.scheduler is not None else None, + 'criterion_state_dict': self.criterion.state_dict(), + "scaler": self.scaler.state_dict(), + } \ + if only_model_state_dict == False else self.get_model_params() + + torch.save( + save_dict, + path, + ) + + def load_from_path(self, + path: str, + device, + only_load_model: bool = False + ) -> [Optional[int], Optional[int]]: + ''' + + :param path: + :param device: map model to which device + :param only_load_model: only_load_model or not? + ''' + + self.model = self.model.to(device) + + load_dict = torch.load( + path, map_location=device + ) + + logging.info( + f"loading... keys:{load_dict.keys()}, only_load_model:{only_load_model}") + + attr_list = [ + 'epoch_num_when_save', + 'batch_num_when_save', + 'random_state', + 'np_random_state', + 'torch_random_state', + 'model_state_dict', + 'optimizer_state_dict', + 'scheduler_state_dict', + 'criterion_state_dict', + ] + + if all([key_name in load_dict for key_name in attr_list]): + # all required key can find in load dict + # AND only_load_model == False + if only_load_model == False: + random.setstate(load_dict['random_state']) + np.random.set_state(load_dict['np_random_state']) + torch.random.set_rng_state( + load_dict['torch_random_state'].cpu()) # since may map to cuda + + self.model.load_state_dict( + load_dict['model_state_dict'] + ) + self.optimizer.load_state_dict( + load_dict['optimizer_state_dict'] + ) + if self.scheduler is not None: + self.scheduler.load_state_dict( + load_dict['scheduler_state_dict'] + ) + self.criterion.load_state_dict( + load_dict['criterion_state_dict'] + ) + if 'scaler' in load_dict: + self.scaler.load_state_dict( + load_dict["scaler"] + ) + logging.info( + f'load scaler done. scaler={load_dict["scaler"]}') + logging.info('all state load successful') + return load_dict['epoch_num_when_save'], load_dict['batch_num_when_save'] + else: + self.model.load_state_dict( + load_dict['model_state_dict'], + ) + logging.info('only model state_dict load') + return None, None + + else: # only state_dict + + if 'model_state_dict' in load_dict: + self.model.load_state_dict( + load_dict['model_state_dict'], + ) + logging.info('only model state_dict load') + return None, None + else: + self.model.load_state_dict( + load_dict, + ) + logging.info('only model state_dict load') + return None, None + + # def grad_info_op(self, test_data, device): + # model = self.model + # model.to(device) + # model.eval() + + # metrics = { + # 'GSNR': 0, + # 'test_total': 0, + # 'grad_mean': 0, + # 'grad_var': 0, + # 'grad_norm': 0, + # } + + # criterion = self.criterion.to(device) + # epoch_grad_sum = 0 + # epoch_grad_sum_squared = 0 + # test_total = 0 + # print('Collecting gradients info: GSNR') + # for batch_idx, (x, target, *additional_info) in tqdm(enumerate(test_data)): + # x = x.to(device) + # target = target.to(device) + # batch_grad_sum, batch_grad_sum_squared = compute_sample_grads_sums_op(model, criterion, x, target) + # epoch_grad_sum += batch_grad_sum + # epoch_grad_sum_squared += batch_grad_sum_squared + # test_total += target.size(0) + + # grad_var = epoch_grad_sum_squared / test_total - (epoch_grad_sum / test_total) ** 2 + # metrics['GSNR'] = np.mean((epoch_grad_sum / test_total)**2 / grad_var) + # metrics['test_total'] = test_total + # metrics['grad_mean'] = np.mean(epoch_grad_sum / test_total) + # metrics['grad_var'] = np.mean(grad_var) + # metrics['grad_norm'] = np.linalg.norm(epoch_grad_sum / test_total) + + # return metrics + + def grad_info(self, test_data, device, epoch, save_folder_path): + model = self.model + model.to(device) + model.eval() + + metrics = { + 'GSNR': 0, + 'test_total': 0, + 'grad_mean': 0, + 'grad_var': 0, + 'grad_norm': 0, + 'GSNR_clean': 0, + 'clean_total': 0, + 'clean_grad_mean': 0, + 'clean_grad_var': 0, + 'clean_grad_norm': 0, + 'GSNR_bd': 0, + 'bd_total': 0, + 'bd_grad_mean': 0, + 'bd_grad_var': 0, + 'bd_grad_norm': 0, + 'cosine_tot_clean': 0, + 'cosine_tot_bd': 0, + 'cosine_clean_bd': 0, + } + num_class = 10 + + criterion = self.criterion.to(device) + epoch_grad_sum = 0 + epoch_grad_sum_squared = 0 + + epoch_grad_sum_clean = 0 + epoch_grad_sum_squared_clean = 0 + + epoch_grad_sum_bd = 0 + epoch_grad_sum_squared_bd = 0 + + epoch_grad_sum_class = [0 for _ in range(num_class)] + epoch_grad_sum_squared_class = [0 for _ in range(num_class)] + + test_total = 0 + clean_total = 1e-12 # avoid zero division + bd_total = 1e-12 # avoid zero division + class_total = [1e-12 for _ in range(10)] # avoid zero division + print('Collecting gradients info: GSNR') + grad_dis_clean_total = [] + grad_dis_bd_total = [] + + grad_dis_class_total = [[] for _ in range(num_class)] + + grad_cos_clean_total = [] + grad_cos_bd_total = [] + + grad_cos_class_total = [[] for _ in range(num_class)] + # collect mean grad vector + grad_epoch = 0 + for batch_idx, (x, target, *additional_info) in tqdm(enumerate(test_data)): + x = x.to(device) + target = target.to(device) + grad_batch_i = compute_grad_batch(model, self.criterion, x, target) + grad_epoch+=grad_batch_i + grad_epoch = grad_epoch/(batch_idx+1) + + + for batch_idx, (x, target, *additional_info) in tqdm(enumerate(test_data)): + x = x.to(device) + target = target.to(device) + + # batch_grad_sum, batch_grad_sum_squared, batch_grad_sum_clean, batch_grad_sum_squared_clean, batch_grad_sum_bd, batch_grad_sum_squared_bd = compute_sample_grads_sums_vmap(model, criterion, x, target, additional_info) + batch_grad_sum, batch_grad_sum_squared, batch_grad_sum_clean, batch_grad_sum_squared_clean, batch_grad_sum_bd, batch_grad_sum_squared_bd, batch_grad_dis_clean, batch_grad_dis_bd, batch_grad_sum_class, batch_grad_sum_squared_class, batch_grad_dis_class, batch_grad_cos_clean, batch_grad_cos_bd, batch_grad_cos_class = compute_sample_grads_sums( + model, criterion, x, target, additional_info, num_class, grad_epoch) + + epoch_grad_sum += batch_grad_sum + epoch_grad_sum_squared += batch_grad_sum_squared + + epoch_grad_sum_clean += batch_grad_sum_clean + epoch_grad_sum_squared_clean += batch_grad_sum_squared_clean + + epoch_grad_sum_bd += batch_grad_sum_bd + epoch_grad_sum_squared_bd += batch_grad_sum_squared_bd + + test_total += target.size(0) + bd_total += torch.sum(additional_info[1]).item() + clean_total += target.size(0) - \ + torch.sum(additional_info[1]).item() + + grad_dis_clean_total += batch_grad_dis_clean + grad_dis_bd_total += batch_grad_dis_bd + + grad_cos_clean_total += batch_grad_cos_clean + grad_cos_bd_total += batch_grad_cos_bd + + for c_i in range(num_class): + epoch_grad_sum_class[c_i] += batch_grad_sum_class[c_i] + epoch_grad_sum_squared_class[c_i] += batch_grad_sum_squared_class[c_i] + grad_dis_class_total[c_i]+=batch_grad_dis_class[c_i] + grad_cos_class_total[c_i]+=batch_grad_cos_class[c_i] + class_total[c_i] += len(batch_grad_dis_class[c_i]) + + grad_dis_clean_total_numpy = np.array( + [i.cpu().numpy() for i in grad_dis_clean_total]) + grad_dis_bd_total_numpy = np.array( + [i.cpu().numpy() for i in grad_dis_bd_total]) + + grad_cos_clean_total_numpy = np.array( + [i.cpu().numpy() for i in grad_cos_clean_total]) + grad_cos_bd_total_numpy = np.array( + [i.cpu().numpy() for i in grad_cos_bd_total]) + + grad_dis_class_numpy = [] + grad_cos_class_numpy = [] + for c_i in range(num_class): + temp_grad_class = grad_dis_class_total[c_i] + grad_dis_class_numpy.append(np.array([i.cpu().numpy() for i in temp_grad_class])) + + temp_grad_class = grad_cos_class_total[c_i] + grad_cos_class_numpy.append(np.array([i.cpu().numpy() for i in temp_grad_class])) + + current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") + np.save(f'./{save_folder_path}/{epoch}_grad_dis_clean_total.npy', + grad_dis_clean_total_numpy) + np.save(f'./{save_folder_path}/{epoch}_grad_dis_bd_total.npy', + grad_dis_bd_total_numpy) + np.save(f'./{save_folder_path}/{epoch}_grad_cos_clean_total.npy', + grad_cos_clean_total_numpy) + np.save(f'./{save_folder_path}/{epoch}_grad_cos_bd_total.npy', + grad_cos_bd_total_numpy) + + for c_i in range(num_class): + np.save(f'./{save_folder_path}/{epoch}_grad_dis_class_{c_i}.npy', + grad_dis_class_numpy[c_i]) + np.save(f'./{save_folder_path}/{epoch}_grad_cos_class_{c_i}.npy', + grad_cos_class_numpy[c_i]) + + + mean_grad = epoch_grad_sum / test_total + mean_grad_clean = epoch_grad_sum_clean / clean_total + mean_grad_bd = epoch_grad_sum_bd / bd_total + mean_grad_class = [epoch_grad_sum_class[c_i] / + class_total[c_i] for c_i in range(num_class)] + + grad_var = epoch_grad_sum_squared / test_total - mean_grad.square()+1e-16 + grad_var_clean = epoch_grad_sum_squared_clean / \ + clean_total - mean_grad_clean.square()+1e-16 + grad_var_bd = epoch_grad_sum_squared_bd / \ + bd_total - mean_grad_bd.square()+1e-16 + grad_var_class = [epoch_grad_sum_squared_class[c_i] / class_total[c_i] - + mean_grad_class[c_i].square()+1e-16 for c_i in range(num_class)] + + metrics['GSNR'] = torch.mean(mean_grad.square() / grad_var).item() + print(torch.mean(mean_grad.square()).item()) + print(torch.mean(grad_var).item()) + print(torch.min(mean_grad.square()).item()) + print(torch.min(grad_var).item()) + metrics['test_total'] = test_total + metrics['grad_mean'] = torch.mean(mean_grad).item() + metrics['grad_var'] = torch.mean(grad_var).item() + metrics['grad_norm'] = torch.linalg.norm(mean_grad).item() + + metrics['GSNR_clean'] = torch.mean( + mean_grad_clean.square() / grad_var_clean).item() + metrics['clean_total'] = clean_total + metrics['clean_grad_mean'] = torch.mean(mean_grad_clean).item() + metrics['clean_grad_var'] = torch.mean(grad_var_clean).item() + metrics['clean_grad_norm'] = torch.linalg.norm(mean_grad_clean).item() + + metrics['GSNR_bd'] = torch.mean( + mean_grad_bd.square() / grad_var_bd).item() + metrics['bd_total'] = bd_total + metrics['bd_grad_mean'] = torch.mean(mean_grad_bd).item() + metrics['bd_grad_var'] = torch.mean(grad_var_bd).item() + metrics['bd_grad_norm'] = torch.linalg.norm(mean_grad_bd).item() + + + + # compute the cosine similarity between the clean and bd gradients + metrics['cosine_tot_clean'] = F.cosine_similarity( + mean_grad_clean, mean_grad, dim=0).item() + metrics['cosine_tot_bd'] = F.cosine_similarity( + mean_grad_bd, mean_grad, dim=0).item() + metrics['cosine_clean_bd'] = F.cosine_similarity( + mean_grad_clean, mean_grad_bd, dim=0).item() + + + for c_i in range(num_class): + metrics[f'GSNR_class_{c_i}'] = torch.mean(mean_grad_class[c_i].square()/grad_var_class[c_i]).item() + metrics[f'class_{c_i}_total'] =class_total[c_i] + metrics[f'class_{c_i}_grad_mean'] = torch.mean(mean_grad_class[c_i]).item() + metrics[f'class_{c_i}_grad_var'] = torch.mean(grad_var_class[c_i]).item() + metrics[f'class_{c_i}_grad_norm'] = torch.linalg.norm(mean_grad_class[c_i]).item() + metrics[f'cosine_{c_i}_total'] = F.cosine_similarity(mean_grad_class[c_i], mean_grad, dim=0).item() + + + + # To avoid GPU memory overflow, use numpy instead of tensor + + # mean_grad = mean_grad.detach().cpu().numpy() + # mean_grad_clean = mean_grad_clean.detach().cpu().numpy() + # mean_grad_bd = mean_grad_bd.detach().cpu().numpy() + + # grad_var = grad_var.detach().cpu().numpy() + # grad_var_clean = grad_var_clean.detach().cpu().numpy() + # grad_var_bd = grad_var_bd.detach().cpu().numpy() + # metrics['GSNR'] = np.mean((mean_grad)**2 / grad_var) + # metrics['test_total'] = test_total + # metrics['grad_mean'] = np.mean(mean_grad) + # metrics['grad_var'] = np.mean(grad_var) + # metrics['grad_norm'] = np.linalg.norm(test_total) + + # metrics['GSNR_clean'] = np.mean((mean_grad_clean)**2 / grad_var_clean) + # metrics['clean_total'] = clean_total + # metrics['clean_grad_mean'] = np.mean(mean_grad_clean) + # metrics['clean_grad_var'] = np.mean(grad_var_clean) + # metrics['clean_grad_norm'] = np.linalg.norm(mean_grad_clean) + + # metrics['GSNR_bd'] = np.mean((mean_grad_bd)**2 / grad_var_bd) + # metrics['bd_total'] = bd_total + # metrics['bd_grad_mean'] = np.mean(mean_grad_bd) + # metrics['bd_grad_var'] = np.mean(grad_var_bd) + # metrics['bd_grad_norm'] = np.linalg.norm(mean_grad_bd) + # # compute the cosine similarity between the clean and bd gradients + # metrics['cosine_tot_clean'] = scipy.spatial.distance.cosine(mean_grad_clean, mean_grad) + # metrics['cosine_tot_bd'] = scipy.spatial.distance.cosine(mean_grad_bd, mean_grad) + # metrics['cosine_clean_bd'] = scipy.spatial.distance.cosine(mean_grad_clean, mean_grad_bd) + + return metrics + def test_train(self, test_data, device): + model = self.model + model.to(device) + model.eval() + + metrics = { + 'clean_correct': 0, + 'clean_loss': 0, + 'clean_total': 0, + 'clean_acc': 0, + 'bd_correct': 0, + 'bd_loss': 0, + 'bd_total': 0, + 'bd_acc': 0, + } + criterion_sep = torch.nn.CrossEntropyLoss(reduction='none') + criterion = self.criterion.to(device) + + with torch.no_grad(): + for batch_idx, (x, target, *additional_info) in enumerate(test_data): + ori_idx, poi_indicator, ori_target = additional_info + poi_indicator = poi_indicator.to(device) + x = x.to(device) + target = target.to(device) + pred = model(x) + loss = criterion_sep(pred, target.long()) + loss_clean = torch.sum(loss*(1-poi_indicator)) + loss_bd = torch.sum(loss*poi_indicator) + + # logging.info(list(zip(additional_info[0].cpu().numpy(), pred.detach().cpu().numpy(), + # target.detach().cpu().numpy(), ))) + + _, predicted = torch.max(pred, -1) + correct = predicted.eq(target) + correct_clean = torch.sum(correct*(1-poi_indicator)) + correct_bd = torch.sum(correct*poi_indicator) + + metrics['clean_correct'] += correct_clean.item() + metrics['clean_loss'] += loss_clean.item() + metrics['clean_total'] += torch.sum(1-poi_indicator).item() + metrics['bd_correct'] += correct_bd.item() + metrics['bd_loss'] += loss_bd.item() + metrics['bd_total'] += torch.sum(poi_indicator).item() + + metrics['clean_acc'] += metrics['clean_correct']/metrics['clean_total'] + metrics['bd_acc'] += metrics['bd_correct']/metrics['bd_total'] + + return metrics + + def test(self, test_data, device): + model = self.model + model.to(device) + model.eval() + + metrics = { + 'test_correct': 0, + 'test_loss': 0, + 'test_total': 0, + # 'detail_list' : [], + } + + criterion = self.criterion.to(device) + + with torch.no_grad(): + for batch_idx, (x, target, *additional_info) in enumerate(test_data): + x = x.to(device) + target = target.to(device) + pred = model(x) + loss = criterion(pred, target.long()) + + # logging.info(list(zip(additional_info[0].cpu().numpy(), pred.detach().cpu().numpy(), + # target.detach().cpu().numpy(), ))) + + _, predicted = torch.max(pred, -1) + correct = predicted.eq(target).sum() + + metrics['test_correct'] += correct.item() + metrics['test_loss'] += loss.item() * target.size(0) + metrics['test_total'] += target.size(0) + + return metrics + + # @resource_check + def train_one_batch(self, x, labels, device): + clip_weight = True + eps = self.args.weight_eps + self.model.train() + self.model.to(device) + + x, labels = x.to(device), labels.to(device) + + with torch.cuda.amp.autocast(enabled=self.amp): + log_probs = self.model(x) + loss = self.criterion(log_probs, labels.long()) + self.scaler.scale(loss).backward() + self.scaler.step(self.optimizer) + self.scaler.update() + self.optimizer.zero_grad() + + batch_loss = loss.item() * labels.size(0) + + if clip_weight: + # print('Do Clipping...') + with torch.no_grad(): + for idx, param in enumerate(self.model.parameters()): + param.clamp_(self.init_weights[idx]-eps, self.init_weights[idx]+eps) + + + return batch_loss + + def train_one_epoch(self, train_data, device): + startTime = time() + batch_loss = [] + total_batch = len(train_data) + for batch_idx, (x, labels, *additional_info) in enumerate(train_data): + # if batch_idx % 1 == 0: + # for possi_i in range(1000000): + # import os + # if os.path.exists(f'/workspace/weishaokui/BackdoorBench/record/model_{possi_i}.pt'): + # pass + # else: + # torch.save(self.model.state_dict(), f'/workspace/weishaokui/BackdoorBench/record/model_{possi_i}.pt') + # break + batch_loss.append(self.train_one_batch(x, labels, device)) + one_epoch_loss = sum(batch_loss) + if self.scheduler is not None: + if isinstance(self.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + # here since ReduceLROnPlateau need the train loss to decide next step setting. + self.scheduler.step(one_epoch_loss) + else: + self.scheduler.step() + + endTime = time() + + logging.info( + f"one epoch training part done, use time = {endTime - startTime} s") + + return one_epoch_loss + + def train(self, train_data, end_epoch_num, + criterion, + optimizer, + scheduler, device, frequency_save, save_folder_path, + save_prefix, + continue_training_path: Optional[str] = None, + only_load_model: bool = False, ): + ''' + + simplest train algorithm with init function put inside. + + :param train_data: train_data_loader + :param end_epoch_num: end training epoch number, if not continue training process, then equal to total training epoch + :param criterion: loss function used + :param optimizer: optimizer + :param scheduler: scheduler + :param device: device + :param frequency_save: how many epoch to save model and random states information once + :param save_folder_path: folder path to save files + :param save_prefix: for saved files, the prefix of file name + :param continue_training_path: where to load files for continue training process + :param only_load_model: only load the model, do not load other settings and random state. + ''' + + self.init_or_continue_train( + train_data, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + continue_training_path, + only_load_model + ) + epoch_loss = [] + for epoch in range(self.start_epochs, self.end_epochs): + one_epoch_loss = self.train_one_epoch(train_data, device) + epoch_loss.append(one_epoch_loss) + logging.info(f'train, epoch_loss: {epoch_loss[-1]}') + if frequency_save != 0 and epoch % frequency_save == frequency_save - 1: + logging.info(f'saved. epoch:{epoch}') + self.save_all_state_to_path( + epoch=epoch, + path=f"{save_folder_path}/{save_prefix}_epoch_{epoch}.pt") + + def train_with_test_each_epoch(self, + train_data, + test_data, + adv_test_data, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + frequency_save, + save_folder_path, + save_prefix, + continue_training_path: Optional[str] = None, + only_load_model: bool = False, + ): + ''' + train with test on benign and backdoor dataloader for each epoch + + :param train_data: train_data_loader + :param test_data: benign test data + :param adv_test_data: backdoor poisoned test data (for ASR) + :param end_epoch_num: end training epoch number, if not continue training process, then equal to total training epoch + :param criterion: loss function used + :param optimizer: optimizer + :param scheduler: scheduler + :param device: device + :param frequency_save: how many epoch to save model and random states information once + :param save_folder_path: folder path to save files + :param save_prefix: for saved files, the prefix of file name + :param continue_training_path: where to load files for continue training process + :param only_load_model: only load the model, do not load other settings and random state. + ''' + collect_grad = False + agg = Metric_Aggregator() + self.init_or_continue_train( + train_data, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + continue_training_path, + only_load_model + ) + epoch_loss = [] + for epoch in range(self.start_epochs, self.end_epochs): + one_epoch_loss = self.train_one_epoch(train_data, device) + epoch_loss.append(one_epoch_loss) + logging.info( + f'train_with_test_each_epoch, epoch:{epoch} ,epoch_loss: {epoch_loss[-1]}') + + train_metrics = self.test(train_data, device) + # metric_info = { + # 'epoch': epoch, + # 'train acc': train_metrics['test_correct'] / train_metrics['test_total'], + # 'train loss': train_metrics['test_loss'], + # } + # agg(metric_info) + + metrics = self.test(test_data, device) + # metric_info = { + # 'epoch': epoch, + # 'benign acc': metrics['test_correct'] / metrics['test_total'], + # 'benign loss': metrics['test_loss'], + # } + # agg(metric_info) + + adv_metrics = self.test(adv_test_data, device) + # adv_metric_info = { + # 'epoch': epoch, + # 'ASR': adv_metrics['test_correct'] / adv_metrics['test_total'], + # 'backdoor loss': adv_metrics['test_loss'], + # } + # agg(adv_metric_info) + +# grad_metric = self.grad_info(train_data, device) + if collect_grad: + grad_metric = self.grad_info( + train_data, device, epoch, save_folder_path) + + # metric_info = { + # 'epoch': epoch, + # 'train acc': train_metrics['test_correct'] / train_metrics['test_total'], + # 'train loss': train_metrics['test_loss'], + # 'test benign acc': metrics['test_correct'] / metrics['test_total'], + # 'test benign loss': metrics['test_loss'], + # 'test ASR': adv_metrics['test_correct'] / adv_metrics['test_total'], + # 'test backdoor loss': adv_metrics['test_loss'], + # 'GSNR': grad_metric['GSNR'], + # 'grad_mean': grad_metric['grad_mean'], + # 'grad_var': grad_metric['grad_var'], + # 'grad_norm': grad_metric['grad_norm'], + # 'GSNR_clean': grad_metric['GSNR_clean'], + # 'clean_grad_mean': grad_metric['clean_grad_mean'], + # 'clean_grad_var': grad_metric['clean_grad_var'], + # 'clean_grad_norm': grad_metric['clean_grad_norm'], + # 'GSNR_bd': grad_metric['GSNR_bd'], + # 'bd_grad_mean': grad_metric['bd_grad_mean'], + # 'bd_grad_var': grad_metric['bd_grad_var'], + # 'bd_grad_norm': grad_metric['bd_grad_norm'], + # 'cosine_tot_clean': grad_metric['cosine_tot_clean'], + # 'cosine_tot_bd': grad_metric['cosine_tot_bd'], + # 'cosine_clean_bd': grad_metric['cosine_clean_bd'], + # } + + metric_info = { + 'epoch': epoch, + 'train acc': train_metrics['test_correct'] / train_metrics['test_total'], + 'train loss': train_metrics['test_loss'], + 'test benign acc': metrics['test_correct'] / metrics['test_total'], + 'test benign loss': metrics['test_loss'], + 'test ASR': adv_metrics['test_correct'] / adv_metrics['test_total'], + 'test backdoor loss': adv_metrics['test_loss'] + } + + if collect_grad: + # combine all metrics + metric_info.update(grad_metric) + + agg(metric_info) + + if frequency_save != 0 and epoch % frequency_save == frequency_save - 1: + logging.info(f'saved. epoch:{epoch}') + self.save_all_state_to_path( + epoch=epoch, + path=f"{save_folder_path}/{save_prefix}_epoch_{epoch}.pt") + # logging.info(f"training, epoch:{epoch}, batch:{batch_idx},batch_loss:{loss.item()}") + agg.to_dataframe().to_csv( + f"{save_folder_path}/{save_prefix}_df.csv") + agg.summary().to_csv( + f"{save_folder_path}/{save_prefix}_df_summary.csv") + + def train_with_test_each_epoch_v2(self, + train_data, + test_dataloader_dict, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + frequency_save, + save_folder_path, + save_prefix, + continue_training_path: Optional[str] = None, + only_load_model: bool = False, + ): + ''' + v2 can feed many test_dataloader, so easier for test with multiple dataloader. + + only change the test data part, instead of predetermined 2 dataloader, you can input any number of dataloader to test + with { + test_name (will show in log): test dataloader + } + in log you will see acc and loss for each test dataloader + + :param test_dataloader_dict: { name : dataloader } + + :param train_data: train_data_loader + :param end_epoch_num: end training epoch number, if not continue training process, then equal to total training epoch + :param criterion: loss function used + :param optimizer: optimizer + :param scheduler: scheduler + :param device: device + :param frequency_save: how many epoch to save model and random states information once + :param save_folder_path: folder path to save files + :param save_prefix: for saved files, the prefix of file name + :param continue_training_path: where to load files for continue training process + :param only_load_model: only load the model, do not load other settings and random state. + ''' + agg = Metric_Aggregator() + self.init_or_continue_train( + train_data, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + continue_training_path, + only_load_model + ) + epoch_loss = [] + for epoch in range(self.start_epochs, self.end_epochs): + one_epoch_loss = self.train_one_epoch(train_data, device) + epoch_loss.append(one_epoch_loss) + logging.info( + f'train_with_test_each_epoch, epoch:{epoch} ,epoch_loss: {epoch_loss[-1]}') + + for dl_name, test_dataloader in test_dataloader_dict.items(): + metrics = self.test(test_dataloader, device) + metric_info = { + 'epoch': epoch, + f'{dl_name} acc': metrics['test_correct'] / metrics['test_total'], + f'{dl_name} loss': metrics['test_loss'], + } + + + metrics_train = self.test_train(train_data, device) + metric_info.update(metrics_train) + agg(metric_info) + + if frequency_save != 0 and epoch % frequency_save == frequency_save - 1: + logging.info(f'saved. epoch:{epoch}') + self.save_all_state_to_path( + epoch=epoch, + path=f"{save_folder_path}/{save_prefix}_epoch_{epoch}.pt") + # logging.info(f"training, epoch:{epoch}, batch:{batch_idx},batch_loss:{loss.item()}") + agg.to_dataframe().to_csv(f"{save_folder_path}/{save_prefix}_df.csv") + agg.summary().to_csv(f"{save_folder_path}/{save_prefix}_df_summary.csv") + + def train_with_test_each_epoch_v2_sp(self, + batch_size, + train_dataset, + test_dataset_dict, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + frequency_save, + save_folder_path, + save_prefix, + continue_training_path: Optional[str] = None, + only_load_model: bool = False, + ): + + ''' + Nothing different, just be simplified to accept dataset instead. + ''' + train_data = DataLoader( + dataset = train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True, + ) + + test_dataloader_dict = { + name : DataLoader( + dataset = test_dataset, + batch_size=batch_size, + shuffle=False, + drop_last=False, + ) + for name, test_dataset in test_dataset_dict.items() + } + + self.train_with_test_each_epoch_v2( + train_data, + test_dataloader_dict, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + frequency_save, + save_folder_path, + save_prefix, + continue_training_path, + only_load_model, + ) diff --git a/utils/quick_learning_utils/quick_learning_trainer_v2.py b/utils/quick_learning_utils/quick_learning_trainer_v2.py new file mode 100644 index 0000000..d6606dc --- /dev/null +++ b/utils/quick_learning_utils/quick_learning_trainer_v2.py @@ -0,0 +1,456 @@ +import sys, logging +sys.path.append('../') +import random +from pprint import pformat +from typing import * +import numpy as np +import torch +import pandas as pd +from time import time +from copy import deepcopy +from torch.utils.data import DataLoader +import matplotlib.pyplot as plt + +from utils.prefetch import PrefetchLoader, prefetch_transform +from utils.bd_dataset import prepro_cls_DatasetBD + + +import torch.nn.functional as F +from utils.trainer_cls import BackdoorModelTrainer, all_acc + +import datetime +import enum +from tqdm import tqdm + +def compute_grad_batch(model, loss_fn, sample, target): + + prediction = model(sample) + loss = loss_fn(prediction, target) + grad_list = torch.autograd.grad(loss, list(model.parameters())) + return torch.concat([layer_grad.flatten() for layer_grad in grad_list]).flatten().detach() + + +def compute_grad(model, loss_fn, sample, target): + + sample = sample.unsqueeze(0) # prepend batch dimension for processing + target = target.unsqueeze(0) + + prediction = model(sample) + loss = loss_fn(prediction, target) + grad_list = torch.autograd.grad(loss, list(model.parameters())) + return torch.concat([layer_grad.flatten() for layer_grad in grad_list]).flatten().detach() + + +def compute_sample_grads_sums(model, loss_fn, data, targets, additional_info, num_class, grad_epoch): + """ manually process each sample with per sample gradient, naive implementation """ + if targets.max() >= num_class: + raise ValueError("targets max value should be less than num_class") + + batch_size = data.shape[0] + grad_sum = 0 + grad_sum_squared = 0 + grad_sum_clean = 0 + grad_sum_squared_clean = 0 + grad_sum_bd = 0 + grad_sum_squared_bd = 0 + grad_sum_class = [0 for _ in range(num_class)] + grad_sum_squared_class = [0 for _ in range(num_class)] + + ori_idx, poi_indicator, ori_target = additional_info + grad_dis_clean = [] + grad_dis_bd = [] + grad_dis_class = [[] for _ in range(num_class)] + + grad_cos_clean = [] + grad_cos_bd = [] + grad_cos_class = [[] for _ in range(num_class)] + + for i in range(batch_size): + grad_i = compute_grad(model, loss_fn, data[i], targets[i]) + grad_sum += grad_i + grad_sum_squared += grad_i.square() + if poi_indicator[i] == 1: + grad_sum_bd += grad_i + grad_sum_squared_bd += grad_i.square() + grad_dis_bd.append(torch.linalg.norm(grad_i.flatten())) + grad_cos_bd.append(F.cosine_similarity(grad_i.flatten(), grad_epoch.flatten(),dim=0)) + else: + grad_sum_clean += grad_i + grad_sum_squared_clean += grad_i.square() + grad_dis_clean.append(torch.linalg.norm(grad_i.flatten())) + grad_cos_clean.append(F.cosine_similarity(grad_i.flatten(), grad_epoch.flatten(),dim=0)) + + grad_sum_class[targets[i]] += grad_i + grad_sum_squared_class[targets[i]] += grad_i.square() + grad_dis_class[targets[i]].append( + torch.linalg.norm(grad_i.flatten())) + + grad_cos_class[targets[i]].append( + F.cosine_similarity(grad_i.flatten(), grad_epoch.flatten(),dim=0)) + + # To avoid GPU memory overflow, we use numpy instead of torch. + # grad_sum = grad_sum.cpu().numpy() + # grad_sum_squared = grad_sum_squared.cpu().numpy() + # grad_sum_clean = grad_sum_clean.cpu().numpy() + # grad_sum_squared_clean = grad_sum_squared_clean.cpu().numpy() + # grad_sum_bd = grad_sum_bd.cpu().numpy() + # grad_sum_squared_bd = grad_sum_squared_bd.cpu().numpy() + return grad_sum, grad_sum_squared, grad_sum_clean, grad_sum_squared_clean, grad_sum_bd, grad_sum_squared_bd, grad_dis_clean, grad_dis_bd, grad_sum_class, grad_sum_squared_class, grad_dis_class, grad_cos_clean, grad_cos_bd, grad_cos_class + + +class QuickLearningBackdoorModelTrainer(BackdoorModelTrainer): + def __init__(self, model): + super().__init__(model) + logging.debug("This class REQUIRE bd dataset to implement overwrite methods. This is NOT a general class for all cls task.") + + def train_with_test_each_epoch_on_mix(self, + train_dataloader, + clean_test_dataloader, + bd_test_dataloader, + total_epoch_num, + criterion, + optimizer, + scheduler, + amp, + device, + frequency_save, + save_folder_path, + save_prefix, + prefetch, + prefetch_transform_attr_name, + non_blocking, + ): + + test_dataloader_dict = { + "clean_test_dataloader":clean_test_dataloader, + "bd_test_dataloader":bd_test_dataloader, + } + + self.set_with_dataloader( + train_dataloader, + test_dataloader_dict, + criterion, + optimizer, + scheduler, + device, + amp, + + frequency_save, + save_folder_path, + save_prefix, + + prefetch, + prefetch_transform_attr_name, + non_blocking, + ) + + train_loss_list = [] + train_mix_acc_list = [] + train_asr_list = [] + train_ra_list = [] + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + + for epoch in range(total_epoch_num): + + train_epoch_loss_avg_over_batch, \ + train_epoch_predict_list, \ + train_epoch_label_list, \ + train_epoch_original_index_list, \ + train_epoch_poison_indicator_list, \ + train_epoch_original_targets_list = self.train_one_epoch_on_mix(verbose=1) + + train_mix_acc = all_acc(train_epoch_predict_list, train_epoch_label_list) + + train_bd_idx = torch.where(train_epoch_poison_indicator_list == 1)[0] + train_clean_idx = torch.where(train_epoch_poison_indicator_list == 0)[0] + train_clean_acc = all_acc( + train_epoch_predict_list[train_clean_idx], + train_epoch_label_list[train_clean_idx], + ) + train_asr = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_label_list[train_bd_idx], + ) + train_ra = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_original_targets_list[train_bd_idx], + ) + + clean_metrics, \ + clean_test_epoch_predict_list, \ + clean_test_epoch_label_list, \ + = self.test_given_dataloader(self.test_dataloader_dict["clean_test_dataloader"], verbose=1) + + clean_test_loss_avg_over_batch = clean_metrics["test_loss_avg_over_batch"] + test_acc = clean_metrics["test_acc"] + + bd_metrics, \ + bd_test_epoch_predict_list, \ + bd_test_epoch_label_list, \ + bd_test_epoch_original_index_list, \ + bd_test_epoch_poison_indicator_list, \ + bd_test_epoch_original_targets_list = self.test_given_dataloader_on_mix(self.test_dataloader_dict["bd_test_dataloader"], verbose=1) + + bd_test_loss_avg_over_batch = bd_metrics["test_loss_avg_over_batch"] + test_asr = all_acc(bd_test_epoch_predict_list, bd_test_epoch_label_list) + test_ra = all_acc(bd_test_epoch_predict_list, bd_test_epoch_original_targets_list) + + grad_metric = self.grad_info( + self.train_dataloader, device, epoch, save_folder_path) + + info = { + "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch, + "train_acc": train_mix_acc, + "train_acc_clean_only": train_clean_acc, + "train_asr_bd_only": train_asr, + "train_ra_bd_only": train_ra, + + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch" : bd_test_loss_avg_over_batch, + "test_acc" : test_acc, + "test_asr" : test_asr, + "test_ra" : test_ra, + } + info.update(grad_metric) + + self.agg( + info, + ) + + train_loss_list.append(train_epoch_loss_avg_over_batch) + train_mix_acc_list.append(train_mix_acc) + train_asr_list.append(train_asr) + train_ra_list.append(train_ra) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + + self.plot_loss( + train_loss_list, + clean_test_loss_list, + bd_test_loss_list, + ) + + self.plot_acc_like_metric( + train_mix_acc_list, + train_asr_list, + train_ra_list, + test_acc_list, + test_asr_list, + test_ra_list, + ) + + self.agg_save_dataframe() + + self.agg_save_summary() + + return train_loss_list, \ + train_mix_acc_list, \ + train_asr_list, \ + train_ra_list, \ + clean_test_loss_list, \ + bd_test_loss_list, \ + test_acc_list, \ + test_asr_list, \ + test_ra_list + + def grad_info(self, test_data, device, epoch, save_folder_path): + model = self.model + model.to(device) + model.eval() + + metrics = { + 'GSNR': 0, + 'test_total': 0, + 'grad_mean': 0, + 'grad_var': 0, + 'grad_norm': 0, + 'GSNR_clean': 0, + 'clean_total': 0, + 'clean_grad_mean': 0, + 'clean_grad_var': 0, + 'clean_grad_norm': 0, + 'GSNR_bd': 0, + 'bd_total': 0, + 'bd_grad_mean': 0, + 'bd_grad_var': 0, + 'bd_grad_norm': 0, + 'cosine_tot_clean': 0, + 'cosine_tot_bd': 0, + 'cosine_clean_bd': 0, + } + num_class = 10 + + criterion = self.criterion.to(device) + epoch_grad_sum = 0 + epoch_grad_sum_squared = 0 + + epoch_grad_sum_clean = 0 + epoch_grad_sum_squared_clean = 0 + + epoch_grad_sum_bd = 0 + epoch_grad_sum_squared_bd = 0 + + epoch_grad_sum_class = [0 for _ in range(num_class)] + epoch_grad_sum_squared_class = [0 for _ in range(num_class)] + + test_total = 0 + clean_total = 1e-12 # avoid zero division + bd_total = 1e-12 # avoid zero division + class_total = [1e-12 for _ in range(10)] # avoid zero division + print('Collecting gradients info: GSNR') + grad_dis_clean_total = [] + grad_dis_bd_total = [] + + grad_dis_class_total = [[] for _ in range(num_class)] + + grad_cos_clean_total = [] + grad_cos_bd_total = [] + + grad_cos_class_total = [[] for _ in range(num_class)] + # collect mean grad vector + grad_epoch = 0 + for batch_idx, (x, target, *additional_info) in tqdm(enumerate(test_data)): + x = x.to(device) + target = target.to(device) + grad_batch_i = compute_grad_batch(model, self.criterion, x, target) + grad_epoch+=grad_batch_i + grad_epoch = grad_epoch/(batch_idx+1) + + + for batch_idx, (x, target, *additional_info) in tqdm(enumerate(test_data)): + x = x.to(device) + target = target.to(device) + + # batch_grad_sum, batch_grad_sum_squared, batch_grad_sum_clean, batch_grad_sum_squared_clean, batch_grad_sum_bd, batch_grad_sum_squared_bd = compute_sample_grads_sums_vmap(model, criterion, x, target, additional_info) + batch_grad_sum, batch_grad_sum_squared, batch_grad_sum_clean, batch_grad_sum_squared_clean, batch_grad_sum_bd, batch_grad_sum_squared_bd, batch_grad_dis_clean, batch_grad_dis_bd, batch_grad_sum_class, batch_grad_sum_squared_class, batch_grad_dis_class, batch_grad_cos_clean, batch_grad_cos_bd, batch_grad_cos_class = compute_sample_grads_sums( + model, criterion, x, target, additional_info, num_class, grad_epoch) + + epoch_grad_sum += batch_grad_sum + epoch_grad_sum_squared += batch_grad_sum_squared + + epoch_grad_sum_clean += batch_grad_sum_clean + epoch_grad_sum_squared_clean += batch_grad_sum_squared_clean + + epoch_grad_sum_bd += batch_grad_sum_bd + epoch_grad_sum_squared_bd += batch_grad_sum_squared_bd + + test_total += target.size(0) + bd_total += torch.sum(additional_info[1]).item() + clean_total += target.size(0) - \ + torch.sum(additional_info[1]).item() + + grad_dis_clean_total += batch_grad_dis_clean + grad_dis_bd_total += batch_grad_dis_bd + + grad_cos_clean_total += batch_grad_cos_clean + grad_cos_bd_total += batch_grad_cos_bd + + for c_i in range(num_class): + epoch_grad_sum_class[c_i] += batch_grad_sum_class[c_i] + epoch_grad_sum_squared_class[c_i] += batch_grad_sum_squared_class[c_i] + grad_dis_class_total[c_i]+=batch_grad_dis_class[c_i] + grad_cos_class_total[c_i]+=batch_grad_cos_class[c_i] + class_total[c_i] += len(batch_grad_dis_class[c_i]) + + grad_dis_clean_total_numpy = np.array( + [i.cpu().numpy() for i in grad_dis_clean_total]) + grad_dis_bd_total_numpy = np.array( + [i.cpu().numpy() for i in grad_dis_bd_total]) + + grad_cos_clean_total_numpy = np.array( + [i.cpu().numpy() for i in grad_cos_clean_total]) + grad_cos_bd_total_numpy = np.array( + [i.cpu().numpy() for i in grad_cos_bd_total]) + + grad_dis_class_numpy = [] + grad_cos_class_numpy = [] + for c_i in range(num_class): + temp_grad_class = grad_dis_class_total[c_i] + grad_dis_class_numpy.append(np.array([i.cpu().numpy() for i in temp_grad_class])) + + temp_grad_class = grad_cos_class_total[c_i] + grad_cos_class_numpy.append(np.array([i.cpu().numpy() for i in temp_grad_class])) + + current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") + np.save(f'./{save_folder_path}/{epoch}_grad_dis_clean_total.npy', + grad_dis_clean_total_numpy) + np.save(f'./{save_folder_path}/{epoch}_grad_dis_bd_total.npy', + grad_dis_bd_total_numpy) + np.save(f'./{save_folder_path}/{epoch}_grad_cos_clean_total.npy', + grad_cos_clean_total_numpy) + np.save(f'./{save_folder_path}/{epoch}_grad_cos_bd_total.npy', + grad_cos_bd_total_numpy) + + for c_i in range(num_class): + np.save(f'./{save_folder_path}/{epoch}_grad_dis_class_{c_i}.npy', + grad_dis_class_numpy[c_i]) + np.save(f'./{save_folder_path}/{epoch}_grad_cos_class_{c_i}.npy', + grad_cos_class_numpy[c_i]) + + + mean_grad = epoch_grad_sum / test_total + mean_grad_clean = epoch_grad_sum_clean / clean_total + mean_grad_bd = epoch_grad_sum_bd / bd_total + mean_grad_class = [epoch_grad_sum_class[c_i] / + class_total[c_i] for c_i in range(num_class)] + + grad_var = epoch_grad_sum_squared / test_total - mean_grad.square()+1e-16 + grad_var_clean = epoch_grad_sum_squared_clean / \ + clean_total - mean_grad_clean.square()+1e-16 + grad_var_bd = epoch_grad_sum_squared_bd / \ + bd_total - mean_grad_bd.square()+1e-16 + grad_var_class = [epoch_grad_sum_squared_class[c_i] / class_total[c_i] - + mean_grad_class[c_i].square()+1e-16 for c_i in range(num_class)] + + metrics['GSNR'] = torch.mean(mean_grad.square() / grad_var).item() + print(torch.mean(mean_grad.square()).item()) + print(torch.mean(grad_var).item()) + print(torch.min(mean_grad.square()).item()) + print(torch.min(grad_var).item()) + metrics['test_total'] = test_total + metrics['grad_mean'] = torch.mean(mean_grad).item() + metrics['grad_var'] = torch.mean(grad_var).item() + metrics['grad_norm'] = torch.linalg.norm(mean_grad).item() + + metrics['GSNR_clean'] = torch.mean( + mean_grad_clean.square() / grad_var_clean).item() + metrics['clean_total'] = clean_total + metrics['clean_grad_mean'] = torch.mean(mean_grad_clean).item() + metrics['clean_grad_var'] = torch.mean(grad_var_clean).item() + metrics['clean_grad_norm'] = torch.linalg.norm(mean_grad_clean).item() + + metrics['GSNR_bd'] = torch.mean( + mean_grad_bd.square() / grad_var_bd).item() + metrics['bd_total'] = bd_total + metrics['bd_grad_mean'] = torch.mean(mean_grad_bd).item() + metrics['bd_grad_var'] = torch.mean(grad_var_bd).item() + metrics['bd_grad_norm'] = torch.linalg.norm(mean_grad_bd).item() + + + + # compute the cosine similarity between the clean and bd gradients + metrics['cosine_tot_clean'] = F.cosine_similarity( + mean_grad_clean, mean_grad, dim=0).item() + metrics['cosine_tot_bd'] = F.cosine_similarity( + mean_grad_bd, mean_grad, dim=0).item() + metrics['cosine_clean_bd'] = F.cosine_similarity( + mean_grad_clean, mean_grad_bd, dim=0).item() + + + for c_i in range(num_class): + metrics[f'GSNR_class_{c_i}'] = torch.mean(mean_grad_class[c_i].square()/grad_var_class[c_i]).item() + metrics[f'class_{c_i}_total'] =class_total[c_i] + metrics[f'class_{c_i}_grad_mean'] = torch.mean(mean_grad_class[c_i]).item() + metrics[f'class_{c_i}_grad_var'] = torch.mean(grad_var_class[c_i]).item() + metrics[f'class_{c_i}_grad_norm'] = torch.linalg.norm(mean_grad_class[c_i]).item() + metrics[f'cosine_{c_i}_total'] = F.cosine_similarity(mean_grad_class[c_i], mean_grad, dim=0).item() + + return metrics \ No newline at end of file diff --git a/utils/save_load_attack.py b/utils/save_load_attack.py new file mode 100755 index 0000000..bf79ec3 --- /dev/null +++ b/utils/save_load_attack.py @@ -0,0 +1,258 @@ +''' +This script aims to save and load the attack result as a bridge between attack and defense files. + +Model, clean data, backdoor data and all infomation needed to reconstruct will be saved. + +Note that in default, only the poisoned part of backdoor dataset will be saved to save space. + +Jun 12th update: + change save_load to adapt to alternative save method. + But notice that this method assume the bd_train after reconstruct MUST have the SAME length with clean_train. + +''' +import copy +import logging, time + +from typing import Optional +import torch, os +from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform +import numpy as np +from copy import deepcopy +from pprint import pformat +from typing import Union + +from utils.aggregate_block.dataset_and_transform_generate import dataset_and_transform_generate + +def summary_dict(input_dict): + ''' + Input a dict, this func will do summary for it. + deepcopy to make sure no influence for summary + :return: + ''' + input_dict = deepcopy(input_dict) + summary_dict_return = dict() + for k,v in input_dict.items(): + if isinstance(v, dict): + summary_dict_return[k] = summary_dict(v) + elif isinstance(v, torch.Tensor) or isinstance(v, np.ndarray): + summary_dict_return[k] = { + 'shape':v.shape, + 'min':v.min(), + 'max':v.max(), + } + elif isinstance(v, list): + summary_dict_return[k] = { + 'len':v.__len__(), + 'first ten':v[:10], + 'last ten':v[-10:], + } + else: + summary_dict_return[k] = v + return summary_dict_return + +def sample_pil_imgs(pil_image_list, save_folder, num = 5,): + if not os.path.exists(save_folder): + os.makedirs(save_folder) + + select_index = np.random.choice( + len(pil_image_list), + num, + ).tolist() + np.arange(num).tolist() + np.arange(len(pil_image_list) - num, len(pil_image_list)).tolist() + + for ii in select_index : + if 0 <= ii < len(pil_image_list): + pil_image_list[ii].save(f"{save_folder}/{ii}.png") + +def save_attack_result( + model_name : str, + num_classes : int, + model : dict, # the state_dict + data_path : str, + img_size : Union[list, tuple], + clean_data : str, + bd_test : prepro_cls_DatasetBD_v2, # MUST be dataset without transform + save_path : str, + bd_train : Optional[prepro_cls_DatasetBD_v2] = None, # MUST be dataset without transform +): + ''' + + main idea is to loop through the backdoor train and test dataset, and match with the clean dataset + by remove replicated parts, this function can save the space. + + WARNING: keep all dataset with shuffle = False, same order of data samples is the basic of this function !!!! + + :param model_name : str, + :param num_classes : int, + :param model : dict, # the state_dict + :param data_path : str, + :param img_size : list, like [32,32,3] + :param clean_data : str, clean dataset name + :param bd_train : torch.utils.data.Dataset, # dataset without transform !! + :param bd_test : torch.utils.data.Dataset, # dataset without transform + :param save_path : str, + ''' + + save_dict = { + 'model_name': model_name, + 'num_classes' : num_classes, + 'model': model, + 'data_path': data_path, + 'img_size' : img_size, + 'clean_data': clean_data, + 'bd_train': bd_train.retrieve_state() if bd_train is not None else None, + 'bd_test': bd_test.retrieve_state(), + } + + logging.info(f"saving...") + # logging.debug(f"location : {save_path}/attack_result.pt") #, content summary :{pformat(summary_dict(save_dict))}") + + torch.save( + save_dict, + f'{save_path}/attack_result.pt', + ) + + logging.info("Saved, folder path: {}".format(save_path)) + +def save_defense_result( + model_name : str, + num_classes : int, + model : dict, # the state_dict + save_path : str, +): + ''' + + main idea is to loop through the backdoor train and test dataset, and match with the clean dataset + by remove replicated parts, this function can save the space. + + WARNING: keep all dataset with shuffle = False, same order of data samples is the basic of this function !!!! + + :param model_name : str, + :param num_classes : int, + :param model : dict, # the state_dict + :param save_path : str, + ''' + + save_dict = { + 'model_name': model_name, + 'num_classes' : num_classes, + 'model': model, + } + + logging.info(f"saving...") + logging.debug(f"location : {save_path}/defense_result.pt") #, content summary :{pformat(summary_dict(save_dict))}") + + torch.save( + save_dict, + f'{save_path}/defense_result.pt', + ) + + +class Args: + pass + +def load_attack_result( + save_path : str, +): + ''' + This function first replicate the basic steps of generate models and clean train and test datasets + then use the index given in files to replace the samples should be poisoned to re-create the backdoor train and test dataset + + save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path!!! + save_path : the path of "attack_result.pt" + ''' + load_file = torch.load(save_path) + + if all(key in load_file for key in ['model_name', + 'num_classes', + 'model', + 'data_path', + 'img_size', + 'clean_data', + 'bd_train', + 'bd_test', + ]): + + logging.info('key match for attack_result, processing...') + + # model = generate_cls_model(load_file['model_name'], load_file['num_classes']) + # model.load_state_dict(load_file['model']) + + clean_setting = Args() + + clean_setting.dataset = load_file['clean_data'] + + # convert the relative/abs path in attack result to abs path for defense + clean_setting.dataset_path = load_file['data_path'] + logging.warning("save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path") + clean_setting.dataset_path = save_path[:save_path.index('record')] + clean_setting.dataset_path[clean_setting.dataset_path.index('data'):] + + clean_setting.img_size = load_file['img_size'] + + train_dataset_without_transform, \ + train_img_transform, \ + train_label_transform, \ + test_dataset_without_transform, \ + test_img_transform, \ + test_label_transform = dataset_and_transform_generate(clean_setting) + + clean_train_dataset_with_transform = dataset_wrapper_with_transform( + train_dataset_without_transform, + train_img_transform, + train_label_transform, + ) + + clean_test_dataset_with_transform = dataset_wrapper_with_transform( + test_dataset_without_transform, + test_img_transform, + test_label_transform, + ) + + if load_file['bd_train'] is not None: + bd_train_dataset = prepro_cls_DatasetBD_v2(train_dataset_without_transform) + bd_train_dataset.set_state( + load_file['bd_train'] + ) + bd_train_dataset_with_transform = dataset_wrapper_with_transform( + bd_train_dataset, + train_img_transform, + train_label_transform, + ) + else: + logging.info("No bd_train info found.") + bd_train_dataset_with_transform = None + + + bd_test_dataset = prepro_cls_DatasetBD_v2(test_dataset_without_transform) + bd_test_dataset.set_state( + load_file['bd_test'] + ) + bd_test_dataset_with_transform = dataset_wrapper_with_transform( + bd_test_dataset, + test_img_transform, + test_label_transform, + ) + + new_dict = copy.deepcopy(load_file['model']) + for k, v in load_file['model'].items(): + if k.startswith('module.'): + del new_dict[k] + new_dict[k[7:]] = v + + load_file['model'] = new_dict + load_dict = { + 'model_name': load_file['model_name'], + 'model': load_file['model'], + 'clean_train': clean_train_dataset_with_transform, + 'clean_test' : clean_test_dataset_with_transform, + 'bd_train': bd_train_dataset_with_transform, + 'bd_test': bd_test_dataset_with_transform, + } + + print(f"loading...") + + return load_dict + + else: + logging.info(f"loading...") + logging.debug(f"location : {save_path}, content summary :{pformat(summary_dict(load_file))}") + return load_file \ No newline at end of file diff --git a/utils/trainer_cls.py b/utils/trainer_cls.py new file mode 100755 index 0000000..be0aa74 --- /dev/null +++ b/utils/trainer_cls.py @@ -0,0 +1,2041 @@ +# This script is for trainer. This is a warpper for training process. + +import sys, logging +sys.path.append('../') +import random +from pprint import pformat +from typing import * +import numpy as np +import torch +import pandas as pd +from time import time +from copy import deepcopy +from torch.utils.data import DataLoader +import matplotlib.pyplot as plt + +from utils.prefetch import PrefetchLoader, prefetch_transform + + +def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + random.seed(worker_seed) + +class dataloader_generator: + def __init__(self, **kwargs_init): + self.kwargs_init = kwargs_init + def __call__(self, *args, **kwargs_call): + kwargs = deepcopy(self.kwargs_init) + kwargs.update(kwargs_call) + return DataLoader( + *args, + **kwargs + ) + +def last_and_valid_max(col:pd.Series): + ''' + find last not None value and max valid (not None or np.nan) value for each column + :param col: + :return: + ''' + return pd.Series( + index=[ + 'last', 'valid_max', 'exist_nan_value' + ], + data=[ + col[~col.isna()].iloc[-1], pd.to_numeric(col, errors='coerce').max(), any(i == 'nan_value' for i in col) + ]) + +class Metric_Aggregator(object): + ''' + aggregate the metric to log automatically + ''' + def __init__(self): + self.history = [] + def __call__(self, + one_metric : dict): + one_metric = {k : v for k,v in one_metric.items() if v is not None} # drop pair with None as value + one_metric = { + k : ( + "nan_value" if v is np.nan or torch.tensor(v).isnan().item() else v #turn nan to str('nan_value') + ) for k, v in one_metric.items() + } + self.history.append(one_metric) + logging.info( + pformat( + one_metric + ) + ) + def to_dataframe(self): + self.df = pd.DataFrame(self.history, dtype=object) + logging.debug("return df with np.nan and None converted by str()") + return self.df + def summary(self): + ''' + do summary for dataframe of record + :return: + eg. + ,train_epoch_num,train_acc_clean + last,100.0,96.68965148925781 + valid_max,100.0,96.70848846435547 + exist_nan_value,False,False + + ''' + if 'df' not in self.__dict__: + logging.debug('No df found in Metric_Aggregator, generate now') + self.to_dataframe() + logging.debug("return df with np.nan and None converted by str()") + return self.df.apply(last_and_valid_max) + +class ModelTrainerCLS(): + def __init__(self, model, amp = False): + self.model = model + self.amp = amp + + def init_or_continue_train(self, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + continue_training_path: Optional[str] = None, + only_load_model: bool = False, + ) -> None: + ''' + config the training process, from 0 or continue previous. + The requirement for saved file please refer to save_all_state_to_path + :param train_data: train_data_loader, only if when you need of number of batch, you need to input it. Otherwise just skip. + :param end_epoch_num: end training epoch number, if not continue training process, then equal to total training epoch + :param criterion: loss function used + :param optimizer: optimizer + :param scheduler: scheduler + :param device: device + :param continue_training_path: where to load files for continue training process + :param only_load_model: only load the model, do not load other settings and random state. + + ''' + + model = self.model + + model.to(device, non_blocking = True) + model.train() + + # train and update + + self.criterion = criterion + self.optimizer = optimizer + self.scheduler = scheduler + self.scaler = torch.cuda.amp.GradScaler(enabled=self.amp) + + if continue_training_path is not None: + logging.info(f"No batch info will be used. Cannot continue from specific batch!") + + start_epoch, _ = self.load_from_path(continue_training_path, device, only_load_model) + self.start_epochs, self.end_epochs = start_epoch, end_epoch_num + else: + self.start_epochs, self.end_epochs = 0, end_epoch_num + # self.start_batch = 0 + + logging.info(f'All setting done, train from epoch {self.start_epochs} to epoch {self.end_epochs}') + + logging.info( + pformat(f"self.amp:{self.amp}," + + f"self.criterion:{self.criterion}," + + f"self.optimizer:{self.optimizer}," + + f"self.scheduler:{self.scheduler.state_dict() if self.scheduler is not None else None}," + + f"self.scaler:{self.scaler.state_dict() if self.scaler is not None else None})") + ) + def get_model_params(self): + return self.model.cpu().state_dict() + + def set_model_params(self, model_parameters): + self.model.load_state_dict(model_parameters) + + def save_all_state_to_path(self, + path: str, + epoch: Optional[int] = None, + batch: Optional[int] = None, + only_model_state_dict: bool = False) -> None: + ''' + save all information needed to continue training, include 3 random state in random, numpy and torch + :param path: where to save + :param epoch: which epoch when save + :param batch: which batch index when save + :param only_model_state_dict: only save the model, drop all other information + ''' + + save_dict = { + 'epoch_num_when_save': epoch, + 'batch_num_when_save': batch, + 'random_state': random.getstate(), + 'np_random_state': np.random.get_state(), + 'torch_random_state': torch.random.get_rng_state(), + 'model_state_dict': self.get_model_params(), + 'optimizer_state_dict': self.optimizer.state_dict(), + 'scheduler_state_dict': self.scheduler.state_dict() if self.scheduler is not None else None, + 'criterion_state_dict': self.criterion.state_dict(), + "scaler": self.scaler.state_dict(), + } \ + if only_model_state_dict == False else self.get_model_params() + + torch.save( + save_dict, + path, + ) + + def load_from_path(self, + path: str, + device, + only_load_model: bool = False + ) -> [Optional[int], Optional[int]]: + ''' + + :param path: + :param device: map model to which device + :param only_load_model: only_load_model or not? + ''' + + self.model = self.model.to(device, non_blocking = True) + + load_dict = torch.load( + path, map_location=device + ) + + logging.info(f"loading... keys:{load_dict.keys()}, only_load_model:{only_load_model}") + + attr_list = [ + 'epoch_num_when_save', + 'batch_num_when_save', + 'random_state', + 'np_random_state', + 'torch_random_state', + 'model_state_dict', + 'optimizer_state_dict', + 'scheduler_state_dict', + 'criterion_state_dict', + ] + + if all([key_name in load_dict for key_name in attr_list]) : + # all required key can find in load dict + # AND only_load_model == False + if only_load_model == False: + random.setstate(load_dict['random_state']) + np.random.set_state(load_dict['np_random_state']) + torch.random.set_rng_state(load_dict['torch_random_state'].cpu()) # since may map to cuda + + self.model.load_state_dict( + load_dict['model_state_dict'] + ) + self.optimizer.load_state_dict( + load_dict['optimizer_state_dict'] + ) + if self.scheduler is not None: + self.scheduler.load_state_dict( + load_dict['scheduler_state_dict'] + ) + self.criterion.load_state_dict( + load_dict['criterion_state_dict'] + ) + if 'scaler' in load_dict: + self.scaler.load_state_dict( + load_dict["scaler"] + ) + logging.info(f'load scaler done. scaler={load_dict["scaler"]}') + logging.info('all state load successful') + return load_dict['epoch_num_when_save'], load_dict['batch_num_when_save'] + else: + self.model.load_state_dict( + load_dict['model_state_dict'], + ) + logging.info('only model state_dict load') + return None, None + + else: # only state_dict + + if 'model_state_dict' in load_dict: + self.model.load_state_dict( + load_dict['model_state_dict'], + ) + logging.info('only model state_dict load') + return None, None + else: + self.model.load_state_dict( + load_dict, + ) + logging.info('only model state_dict load') + return None, None + + def test(self, test_data, device): + model = self.model + model.to(device, non_blocking = True) + model.eval() + + metrics = { + 'test_correct': 0, + 'test_loss': 0, + 'test_total': 0, + } + + criterion = self.criterion.to(device, non_blocking = True) + + with torch.no_grad(): + for batch_idx, (x, target, *additional_info) in enumerate(test_data): + x = x.to(device, non_blocking = True) + target = target.to(device, non_blocking = True) + pred = model(x) + loss = criterion(pred, target.long()) + + _, predicted = torch.max(pred, -1) + correct = predicted.eq(target).sum() + + metrics['test_correct'] += correct.item() + metrics['test_loss'] += loss.item() * target.size(0) + metrics['test_total'] += target.size(0) + + return metrics + + #@resource_check + def train_one_batch(self, x, labels, device): + + self.model.train() + self.model.to(device, non_blocking = True) + + x, labels = x.to(device, non_blocking = True), labels.to(device, non_blocking = True) + + with torch.cuda.amp.autocast(enabled=self.amp): + log_probs = self.model(x) + loss = self.criterion(log_probs, labels.long()) + self.scaler.scale(loss).backward() + self.scaler.step(self.optimizer) + self.scaler.update() + self.optimizer.zero_grad() + + batch_loss = loss.item() * labels.size(0) + + return batch_loss + + def train_one_epoch(self, train_data, device): + startTime = time() + batch_loss = [] + for batch_idx, (x, labels, *additional_info) in enumerate(train_data): + batch_loss.append(self.train_one_batch(x, labels, device)) + one_epoch_loss = sum(batch_loss) + if self.scheduler is not None: + if isinstance(self.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + # here since ReduceLROnPlateau need the train loss to decide next step setting. + self.scheduler.step(one_epoch_loss) + else: + self.scheduler.step() + + endTime = time() + + logging.info(f"one epoch training part done, use time = {endTime - startTime} s") + + return one_epoch_loss + + def train(self, train_data, end_epoch_num, + criterion, + optimizer, + scheduler, device, frequency_save, save_folder_path, + save_prefix, + continue_training_path: Optional[str] = None, + only_load_model: bool = False, ): + ''' + + simplest train algorithm with init function put inside. + + :param train_data: train_data_loader + :param end_epoch_num: end training epoch number, if not continue training process, then equal to total training epoch + :param criterion: loss function used + :param optimizer: optimizer + :param scheduler: scheduler + :param device: device + :param frequency_save: how many epoch to save model and random states information once + :param save_folder_path: folder path to save files + :param save_prefix: for saved files, the prefix of file name + :param continue_training_path: where to load files for continue training process + :param only_load_model: only load the model, do not load other settings and random state. + ''' + + self.init_or_continue_train( + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + continue_training_path, + only_load_model + ) + epoch_loss = [] + for epoch in range(self.start_epochs, self.end_epochs): + one_epoch_loss = self.train_one_epoch(train_data, device) + epoch_loss.append(one_epoch_loss) + logging.info(f'train, epoch_loss: {epoch_loss[-1]}') + if frequency_save != 0 and epoch % frequency_save == frequency_save - 1: + logging.info(f'saved. epoch:{epoch}') + self.save_all_state_to_path( + epoch=epoch, + path=f"{save_folder_path}/{save_prefix}_epoch_{epoch}.pt") + + def train_with_test_each_epoch(self, + train_data, + test_data, + bd_test_data, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + frequency_save, + save_folder_path, + save_prefix, + continue_training_path: Optional[str] = None, + only_load_model: bool = False, + ): + ''' + train with test on clean and backdoor dataloader for each epoch + + :param train_data: train_data_loader + :param test_data: clean test data + :param adv_test_data: backdoor poisoned test data (for ASR) + :param end_epoch_num: end training epoch number, if not continue training process, then equal to total training epoch + :param criterion: loss function used + :param optimizer: optimizer + :param scheduler: scheduler + :param device: device + :param frequency_save: how many epoch to save model and random states information once + :param save_folder_path: folder path to save files + :param save_prefix: for saved files, the prefix of file name + :param continue_training_path: where to load files for continue training process + :param only_load_model: only load the model, do not load other settings and random state. + ''' + agg = Metric_Aggregator() + self.init_or_continue_train( + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + continue_training_path, + only_load_model + ) + epoch_loss = [] + for epoch in range(self.start_epochs, self.end_epochs): + one_epoch_loss = self.train_one_epoch(train_data, device) + epoch_loss.append(one_epoch_loss) + logging.info(f'train_with_test_each_epoch, epoch:{epoch} ,epoch_loss: {epoch_loss[-1]}') + + metrics = self.test(test_data, device) + metric_info = { + 'epoch': epoch, + 'clean acc': metrics['test_correct'] / metrics['test_total'], + 'clean loss': metrics['test_loss'], + } + agg(metric_info) + + bd_metrics = self.test(bd_test_data, device) + bd_metric_info = { + 'epoch': epoch, + 'ASR': bd_metrics['test_correct'] / bd_metrics['test_total'], + 'backdoor loss': bd_metrics['test_loss'], + } + agg(bd_metric_info) + + if frequency_save != 0 and epoch % frequency_save == frequency_save - 1: + logging.info(f'saved. epoch:{epoch}') + self.save_all_state_to_path( + epoch=epoch, + path=f"{save_folder_path}/{save_prefix}_epoch_{epoch}.pt") + # logging.info(f"training, epoch:{epoch}, batch:{batch_idx},batch_loss:{loss.item()}") + agg.to_dataframe().to_csv(f"{save_folder_path}/{save_prefix}_df.csv") + agg.summary().to_csv(f"{save_folder_path}/{save_prefix}_df_summary.csv") + + def train_with_test_each_epoch_v2(self, + train_data, + test_dataloader_dict, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + frequency_save, + save_folder_path, + save_prefix, + continue_training_path: Optional[str] = None, + only_load_model: bool = False, + ): + ''' + v2 can feed many test_dataloader, so easier for test with multiple dataloader. + + only change the test data part, instead of predetermined 2 dataloader, you can input any number of dataloader to test + with { + test_name (will show in log): test dataloader + } + in log you will see acc and loss for each test dataloader + + :param test_dataloader_dict: { name : dataloader } + + :param train_data: train_data_loader + :param end_epoch_num: end training epoch number, if not continue training process, then equal to total training epoch + :param criterion: loss function used + :param optimizer: optimizer + :param scheduler: scheduler + :param device: device + :param frequency_save: how many epoch to save model and random states information once + :param save_folder_path: folder path to save files + :param save_prefix: for saved files, the prefix of file name + :param continue_training_path: where to load files for continue training process + :param only_load_model: only load the model, do not load other settings and random state. + ''' + agg = Metric_Aggregator() + self.init_or_continue_train( + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + continue_training_path, + only_load_model + ) + epoch_loss = [] + for epoch in range(self.start_epochs, self.end_epochs): + one_epoch_loss = self.train_one_epoch(train_data, device) + epoch_loss.append(one_epoch_loss) + logging.info(f'train_with_test_each_epoch, epoch:{epoch} ,epoch_loss: {epoch_loss[-1]}') + + for dataloader_name, test_dataloader in test_dataloader_dict.items(): + metrics = self.test(test_dataloader, device) + metric_info = { + 'epoch': epoch, + f'{dataloader_name} acc': metrics['test_correct'] / metrics['test_total'], + f'{dataloader_name} loss': metrics['test_loss'], + } + agg(metric_info) + + + if frequency_save != 0 and epoch % frequency_save == frequency_save - 1: + logging.info(f'saved. epoch:{epoch}') + self.save_all_state_to_path( + epoch=epoch, + path=f"{save_folder_path}/{save_prefix}_epoch_{epoch}.pt") + # logging.info(f"training, epoch:{epoch}, batch:{batch_idx},batch_loss:{loss.item()}") + agg.to_dataframe().to_csv(f"{save_folder_path}/{save_prefix}_df.csv") + agg.summary().to_csv(f"{save_folder_path}/{save_prefix}_df_summary.csv") + + def train_with_test_each_epoch_v2_sp(self, + batch_size, + train_dataset, + test_dataset_dict, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + frequency_save, + save_folder_path, + save_prefix, + prefetch=False, + continue_training_path: Optional[str] = None, + only_load_model: bool = False, + ): + + ''' + Nothing different, just be simplified to accept dataset instead. + ''' + train_data = DataLoader( + dataset = train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True, + pin_memory=True, + worker_init_fn=seed_worker, + num_workers=8, + ) + + test_dataloader_dict = { + name : DataLoader( + dataset = test_dataset, + batch_size=batch_size, + shuffle=False, + drop_last=False, + pin_memory=True, + worker_init_fn=seed_worker, + num_workers=8, + ) + for name, test_dataset in test_dataset_dict.items() + } + + if prefetch: + raise SystemError("Due to technical issue, not implemented yet") + + self.train_with_test_each_epoch_v2( + train_data, + test_dataloader_dict, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + frequency_save, + save_folder_path, + save_prefix, + continue_training_path, + only_load_model, + ) + +def all_acc(preds:torch.Tensor, + labels:torch.Tensor,): + if len(preds) == 0 or len(labels) == 0: + logging.warning("zero len array in func all_acc(), return None!") + return None + return preds.eq(labels).sum().item() / len(preds) + +def class_wise_acc( + preds:torch.Tensor, + labels:torch.Tensor, + selected_class: list, +): + assert len(preds) == len(labels) + acc = {class_idx : 0 for class_idx in selected_class} + for c in acc.keys(): + acc[c] = preds.eq(c).sum().item() / len(preds) + return acc + +def given_dataloader_test( + model, + test_dataloader, + criterion, + non_blocking : bool = False, + device = "cpu", + verbose : int = 0 +): + model.to(device, non_blocking=non_blocking) + model.eval() + metrics = { + 'test_correct': 0, + 'test_loss_sum_over_batch': 0, + 'test_total': 0, + } + criterion = criterion.to(device, non_blocking=non_blocking) + + if verbose == 1: + batch_predict_list, batch_label_list = [], [] + + with torch.no_grad(): + for batch_idx, (x, target, *additional_info) in enumerate(test_dataloader): + x = x.to(device, non_blocking=non_blocking) + target = target.to(device, non_blocking=non_blocking) + pred = model(x) + loss = criterion(pred, target.long()) + + _, predicted = torch.max(pred, -1) + correct = predicted.eq(target).sum() + + if verbose == 1: + batch_predict_list.append(predicted.detach().clone().cpu()) + batch_label_list.append(target.detach().clone().cpu()) + + metrics['test_correct'] += correct.item() + metrics['test_loss_sum_over_batch'] += loss.item() + metrics['test_total'] += target.size(0) + + metrics['test_loss_avg_over_batch'] = metrics['test_loss_sum_over_batch']/len(test_dataloader) + metrics['test_acc'] = metrics['test_correct'] / metrics['test_total'] + + if verbose == 0: + return metrics, None, None + elif verbose == 1: + return metrics, torch.cat(batch_predict_list), torch.cat(batch_label_list) + +def test_given_dataloader_on_mix(model, test_dataloader, criterion, device = None, non_blocking=True, verbose = 0): + + + model.to(device, non_blocking=non_blocking) + model.eval() + + metrics = { + 'test_correct': 0, + 'test_loss_sum_over_batch': 0, + 'test_total': 0, + } + + criterion = criterion.to(device, non_blocking=non_blocking) + + if verbose == 1: + batch_predict_list = [] + batch_label_list = [] + batch_original_index_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + + with torch.no_grad(): + for batch_idx, (x, labels, original_index, poison_indicator, original_targets) in enumerate(test_dataloader): + x = x.to(device, non_blocking=non_blocking) + labels = labels.to(device, non_blocking=non_blocking) + pred = model(x) + loss = criterion(pred, labels.long()) + + _, predicted = torch.max(pred, -1) + correct = predicted.eq(labels).sum() + + if verbose == 1: + batch_predict_list.append(predicted.detach().clone().cpu()) + batch_label_list.append(labels.detach().clone().cpu()) + batch_original_index_list.append(original_index.detach().clone().cpu()) + batch_poison_indicator_list.append(poison_indicator.detach().clone().cpu()) + batch_original_targets_list.append(original_targets.detach().clone().cpu()) + + metrics['test_correct'] += correct.item() + metrics['test_loss_sum_over_batch'] += loss.item() + metrics['test_total'] += labels.size(0) + + metrics['test_loss_avg_over_batch'] = metrics['test_loss_sum_over_batch']/len(test_dataloader) + metrics['test_acc'] = metrics['test_correct'] / metrics['test_total'] + + if verbose == 0: + return metrics, \ + None, None, None, None, None + elif verbose == 1: + return metrics, \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_original_index_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + +def validate_list_for_plot(given_list, require_len=None): + + if (require_len is not None) and (len(given_list) == require_len): + pass + else: + return False + + if None in given_list: + return False + + return True + +def general_plot_for_epoch( + labelToListDict : dict, + save_path: str, + ylabel: str, + xlabel: str = "epoch", + y_min = None, + y_max = None, + title: str = "Results", +): + # len of first list + len_of_first_valueList = len(list(labelToListDict.values())[0]) + + '''These line of set color is from https://stackoverflow.com/questions/8389636/creating-over-20-unique-legend-colors-using-matplotlib''' + NUM_COLORS = len(labelToListDict) + cm = plt.get_cmap('gist_rainbow') + fig = plt.figure(figsize=(12.8, 9.6)) # 4x default figsize + ax = fig.add_subplot(111) + ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)]) + + # hese line of set linestyple is from https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html + linestyle_tuple = [ + ('loosely dotted', (0, (1, 10))), + ('dotted', (0, (1, 1))), + ('densely dotted', (0, (1, 1))), + ('long dash with offset', (5, (10, 3))), + ('loosely dashed', (0, (5, 10))), + ('dashed', (0, (5, 5))), + ('densely dashed', (0, (5, 1))), + ('loosely dashdotted', (0, (3, 10, 1, 10))), + ('dashdotted', (0, (3, 5, 1, 5))), + ('densely dashdotted', (0, (3, 1, 1, 1))), + ('dashdotdotted', (0, (3, 5, 1, 5, 1, 5))), + ('loosely dashdotdotted', (0, (3, 10, 1, 10, 1, 10))), + ('densely dashdotdotted', (0, (3, 1, 1, 1, 1, 1)))] + + all_min = np.infty + all_max = -np.infty + for idx, (label, value_list) in enumerate(labelToListDict.items()): + linestyle = linestyle_tuple[ + idx % len(linestyle_tuple) + ][1] + if validate_list_for_plot(value_list, len_of_first_valueList): + plt.plot(range(len(value_list)), value_list, marker=idx%11, linewidth=2, label=label, linestyle=linestyle) + else: + logging.warning(f"list:{label} contains None or len not match") + once_min, once_max = min(value_list), max(value_list) + all_min = once_min if once_min < all_min else all_min + all_max = once_max if once_max > all_max else all_max + + plt.xlabel(xlabel) + plt.ylabel(ylabel) + + plt.ylim( + (all_min, all_max) if (y_min is None) or (y_max is None) else (float(y_min), float(y_max)) + ) + plt.legend() + plt.title(title) + plt.grid() + plt.savefig(save_path) + plt.close() + +def plot_loss( + train_loss_list : list, + clean_test_loss_list : list, + bd_test_loss_list : list, + save_folder_path: str, + save_file_name="loss_metric_plots", + ): + '''These line of set color is from https://stackoverflow.com/questions/8389636/creating-over-20-unique-legend-colors-using-matplotlib''' + NUM_COLORS = 3 + cm = plt.get_cmap('gist_rainbow') + fig = plt.figure(figsize=(12.8, 9.6)) # 4x default figsize + ax = fig.add_subplot(111) + ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)]) + + len_set = len(train_loss_list) + x = range(len_set) + if validate_list_for_plot(train_loss_list, len_set): + plt.plot(x, train_loss_list, marker="o", linewidth=2, label="Train Loss", linestyle="--") + else: + logging.warning("train_loss_list contains None or len not match") + if validate_list_for_plot(clean_test_loss_list, len_set): + plt.plot(x, clean_test_loss_list, marker="v", linewidth=2, label="Test Clean loss", linestyle="-") + else: + logging.warning("clean_test_loss_list contains None or len not match") + if validate_list_for_plot(bd_test_loss_list, len_set): + plt.plot(x, bd_test_loss_list, marker="+", linewidth=2, label="Test Backdoor Loss", linestyle="-.") + else: + logging.warning("bd_test_loss_list contains None or len not match") + + plt.xlabel("Epochs") + plt.ylabel("Loss") + + plt.ylim((0, + max([value for value in # filter None value + train_loss_list + + clean_test_loss_list + + bd_test_loss_list if value is not None]) + )) + plt.legend() + plt.title("Results") + plt.grid() + plt.savefig(f"{save_folder_path}/{save_file_name}.png") + plt.close() + +def plot_acc_like_metric_pure( + train_acc_list: list, + test_acc_list: list, + test_asr_list: list, + test_ra_list: list, + save_folder_path: str, + save_file_name="acc_like_metric_plots", + ): + len_set = len(test_asr_list) + x = range(len(test_asr_list)) + + '''These line of set color is from https://stackoverflow.com/questions/8389636/creating-over-20-unique-legend-colors-using-matplotlib''' + NUM_COLORS = 6 + cm = plt.get_cmap('gist_rainbow') + fig = plt.figure(figsize=(12.8, 9.6)) # 4x default figsize + ax = fig.add_subplot(111) + ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)]) + + + if validate_list_for_plot(train_acc_list, len_set): + plt.plot(x, train_acc_list,marker="o",linewidth=2,label="Train Acc",linestyle="--") + else: + logging.warning("train_acc_list contains None, or len not match") + if validate_list_for_plot(test_acc_list, len_set): + plt.plot(x, test_acc_list, marker="o",linewidth=2,label="Test C-Acc",linestyle="--") + else: + logging.warning("test_acc_list contains None, or len not match") + if validate_list_for_plot(test_asr_list, len_set): + plt.plot(x, test_asr_list, marker="v", linewidth=2, label="Test ASR", linestyle = "-") + else: + logging.warning("test_asr_list contains None, or len not match") + if validate_list_for_plot(test_ra_list, len_set): + plt.plot(x, test_ra_list, marker = "+", linewidth=2, label="Test RA", linestyle = "-.") + else: + logging.warning("test_ra_list contains None, or len not match") + + plt.xlabel("Epochs") + plt.ylabel("ACC") + + plt.ylim((0, 1)) + plt.legend() + plt.title("Results") + plt.grid() + plt.savefig(f"{save_folder_path}/{save_file_name}.png") + plt.close() + + +def plot_acc_like_metric( + train_acc_list: list, + train_asr_list: list, + train_ra_list: list, + test_acc_list: list, + test_asr_list: list, + test_ra_list: list, + save_folder_path: str, + save_file_name="acc_like_metric_plots", + ): + len_set = len(test_asr_list) + x = range(len(test_asr_list)) + + '''These line of set color is from https://stackoverflow.com/questions/8389636/creating-over-20-unique-legend-colors-using-matplotlib''' + NUM_COLORS = 6 + cm = plt.get_cmap('gist_rainbow') + fig = plt.figure(figsize=(12.8, 9.6)) # 4x default figsize + ax = fig.add_subplot(111) + ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)]) + + + if validate_list_for_plot(train_acc_list, len_set): + plt.plot(x, train_acc_list,marker="o",linewidth=2,label="Train Acc",linestyle="--") + else: + logging.warning("train_acc_list contains None, or len not match") + if validate_list_for_plot(train_asr_list, len_set): + plt.plot(x, train_asr_list, marker="v", linewidth=2, label="Train ASR", linestyle="-") + else: + logging.warning("train_asr_list contains None, or len not match") + if validate_list_for_plot(train_ra_list, len_set): + plt.plot(x, train_ra_list, marker="+", linewidth=2, label="Train RA", linestyle = "-.") + else: + logging.warning("train_ra_list contains None, or len not match") + if validate_list_for_plot(test_acc_list, len_set): + plt.plot(x, test_acc_list, marker="o",linewidth=2,label="Test C-Acc",linestyle="--") + else: + logging.warning("test_acc_list contains None, or len not match") + if validate_list_for_plot(test_asr_list, len_set): + plt.plot(x, test_asr_list, marker="v", linewidth=2, label="Test ASR", linestyle = "-") + else: + logging.warning("test_asr_list contains None, or len not match") + if validate_list_for_plot(test_ra_list, len_set): + plt.plot(x, test_ra_list, marker = "+", linewidth=2, label="Test RA", linestyle = "-.") + else: + logging.warning("test_ra_list contains None, or len not match") + + plt.xlabel("Epochs") + plt.ylabel("ACC") + + plt.ylim((0, 1)) + plt.legend() + plt.title("Results") + plt.grid() + plt.savefig(f"{save_folder_path}/{save_file_name}.png") + plt.close() + +class ModelTrainerCLS_v2(): + + def __init__(self, model): + self.model = model + + def set_with_dataloader( + self, + train_dataloader, + test_dataloader_dict, + + criterion, + optimizer, + scheduler, + device, + amp, + + frequency_save, + save_folder_path, + save_prefix, + + prefetch=False, + prefetch_transform_attr_name="transform", + non_blocking=False, + + # continue_training_path: Optional[str] = None, + # only_load_model: bool = False, + ): + + logging.info( + "Do NOT set the settings/parameters attr manually after you start training!" + + "\nYou may break the relationship between them." + ) + + if non_blocking == False: + logging.warning( + "Make sure non_blocking=True if you use pin_memory or prefetch or other tricks depending on non_blocking." + ) + + self.train_dataloader = train_dataloader + self.test_dataloader_dict = test_dataloader_dict + + self.criterion = criterion + self.optimizer = optimizer + self.scheduler = scheduler + self.device = device + self.amp = amp + self.scaler = torch.cuda.amp.GradScaler(enabled=self.amp) + self.non_blocking = non_blocking + + self.frequency_save = frequency_save + self.save_folder_path = save_folder_path + self.save_prefix = save_prefix + + if prefetch: + logging.debug("Converting dataloader to prefetch version.") + + train_dataset = self.train_dataloader.dataset + train_prefetch_transform, train_mean, train_std = prefetch_transform( + getattr(train_dataset, prefetch_transform_attr_name) + ) + setattr(train_dataset, prefetch_transform_attr_name, train_prefetch_transform) + self.train_dataloader = PrefetchLoader( + self.train_dataloader, train_mean, train_std + ) + for name, test_dataloader in self.test_dataloader_dict.items(): + val_dataset = test_dataloader.dataset + val_prefetch_transform, val_mean, val_std = prefetch_transform( + getattr(val_dataset, prefetch_transform_attr_name) + ) + setattr(val_dataset, prefetch_transform_attr_name, val_prefetch_transform) + test_dataloader = PrefetchLoader( + test_dataloader, val_mean, val_std + ) + self.test_dataloader_dict[name] = test_dataloader + + self.batch_num_per_epoch = len(self.train_dataloader) + + self.train_iter = iter(self.train_dataloader) + + # if continue_training_path is not None: + # logging.info(f"No batch info will be used. Cannot continue from specific batch!") + # self.epoch_now, self.batch_now = self.load_from_path(continue_training_path, device, only_load_model) + # assert self.batch_now < self.batch_num_per_epoch + # else: + self.epoch_now, self.batch_now = 0, 0 + + logging.info( + pformat( + f"epoch_now:{self.epoch_now}, batch_now:{self.batch_now}" + + f"self.amp:{self.amp}," + + f"self.criterion:{self.criterion}," + + f"self.optimizer:{self.optimizer}," + + f"self.scheduler:{self.scheduler.state_dict() if self.scheduler is not None else None}," + + f"self.scaler:{self.scaler.state_dict() if self.scaler is not None else None})" + ) + ) + + self.metric_aggregator = Metric_Aggregator() + + self.train_batch_loss_record = [] + + def set_with_dataset( + self, + train_dataset, + test_dataset_dict, + + batch_size, + criterion, + optimizer, + scheduler, + device, + + frequency_save, + save_folder_path, + save_prefix, + + amp = False, + + prefetch=True, + prefetch_transform_attr_name="transform", + non_blocking=True, + pin_memory=True, + worker_init_fn = seed_worker, + num_workers = 4, + + # continue_training_path: Optional[str] = None, + # only_load_model: bool = False, + ): + + train_dataloader = DataLoader( + dataset=train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True, + pin_memory=pin_memory, + worker_init_fn=worker_init_fn, + num_workers=num_workers, + ) + + test_dataloader_dict = { + name: DataLoader( + dataset=test_dataset, + batch_size=batch_size, + shuffle=False, + drop_last=False, + pin_memory=pin_memory, + worker_init_fn=worker_init_fn, + num_workers=num_workers, + ) + for name, test_dataset in test_dataset_dict.items() + } + + self.set_with_dataloader( + train_dataloader = train_dataloader, + test_dataloader_dict = test_dataloader_dict, + + criterion = criterion, + optimizer = optimizer, + scheduler = scheduler, + device = device, + amp = amp, + + frequency_save = frequency_save, + save_folder_path = save_folder_path, + save_prefix = save_prefix, + + prefetch = prefetch, + prefetch_transform_attr_name = prefetch_transform_attr_name, + non_blocking = non_blocking, + + # continue_training_path = continue_training_path, + # only_load_model = only_load_model, + ) + + def convert_to_batch_num(self, epochs = 0, batchs = 0): + return int(epochs * self.batch_num_per_epoch + batchs) + + def get_one_batch(self): + + if self.batch_now == self.batch_num_per_epoch: + + self.epoch_now += 1 + self.batch_now = 0 + + self.train_iter = iter(self.train_dataloader) + + if self.frequency_save != 0 and self.epoch_now % self.frequency_save == self.frequency_save - 1: + logging.info(f'saved. epoch:{self.epoch_now}') + self.save_all_state_to_path( + path=f"{self.save_folder_path}/{self.save_prefix}_epoch_{self.epoch_now}.pt") + + self.agg_save_dataframe() + + self.batch_now += 1 + + return self.train_iter.__next__() + + def get_one_train_epoch_loss_avg_over_batch(self): + if len(self.train_batch_loss_record) >= self.batch_num_per_epoch: + return sum( + self.train_batch_loss_record[-self.batch_num_per_epoch:] + )/self.batch_num_per_epoch + else: + logging.warning("No enough batch loss to get the one epoch loss") + + def one_forward_backward(self, x, labels, device, verbose=0): + + self.model.train() + self.model.to(device, non_blocking=self.non_blocking) + + x, labels = x.to(device, non_blocking=self.non_blocking), labels.to(device, non_blocking=self.non_blocking) + + with torch.cuda.amp.autocast(enabled=self.amp): + log_probs = self.model(x) + loss = self.criterion(log_probs, labels.long()) + self.scaler.scale(loss).backward() + self.scaler.step(self.optimizer) + self.scaler.update() + self.optimizer.zero_grad() + + batch_loss = loss.item() + + if verbose == 1: + batch_predict = torch.max(log_probs, -1)[1].detach().clone().cpu() + return batch_loss, batch_predict + + return batch_loss, None + + def train(self, epochs = 0, batchs = 0): + + train_batch_num = self.convert_to_batch_num(epochs, batchs) + + for idx in range(train_batch_num): + + x, labels, *additional_info = self.get_one_batch() + batch_loss, _ = self.one_forward_backward(x, labels, self.device) + + self.train_batch_loss_record.append(batch_loss) + + if self.batch_now == 0 and self.scheduler is not None: + if isinstance(self.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + # here since ReduceLROnPlateau need the train loss to decide next step setting. + self.scheduler.step(self.get_one_train_epoch_loss_avg_over_batch()) + else: + self.scheduler.step() + + def test_given_dataloader(self, test_dataloader, device = None, verbose = 0): + + if device is None: + device = self.device + + model = self.model + non_blocking = self.non_blocking + + return given_dataloader_test( + model, + test_dataloader, + self.criterion, + non_blocking, + device, + verbose, + ) + + def test_all_inner_dataloader(self): + metrics_dict = {} + for name, test_dataloader in self.test_dataloader_dict.items(): + metrics_dict[name], *other_returns = self.test_given_dataloader( + test_dataloader, + verbose = 0, + ) + return metrics_dict + + def agg(self, info_dict): + info = { + "epoch":self.epoch_now, + "batch":self.batch_now, + } + info.update(info_dict) + self.metric_aggregator( + info + ) + + def train_one_epoch(self, verbose = 0): + + startTime = time() + + batch_loss_list = [] + if verbose == 1: + batch_predict_list = [] + batch_label_list = [] + + for batch_idx in range(self.batch_num_per_epoch): + x, labels, *additional_info = self.get_one_batch() + one_batch_loss, batch_predict = self.one_forward_backward(x, labels, self.device, verbose) + batch_loss_list.append(one_batch_loss) + + if verbose == 1: + batch_predict_list.append(batch_predict.detach().clone().cpu()) + batch_label_list.append(labels.detach().clone().cpu()) + + train_one_epoch_loss_batch_avg = sum(batch_loss_list) / len(batch_loss_list) + if self.scheduler is not None: + if isinstance(self.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + self.scheduler.step(train_one_epoch_loss_batch_avg) + else: + self.scheduler.step() + + endTime = time() + + logging.info(f"one epoch training part done, use time = {endTime - startTime} s") + + if verbose == 0: + return train_one_epoch_loss_batch_avg, None, None + elif verbose == 1: + return train_one_epoch_loss_batch_avg, torch.cat(batch_predict_list), torch.cat(batch_label_list) + + def train_with_test_each_epoch(self, + train_dataloader, + test_dataloader_dict, + total_epoch_num, + criterion, + optimizer, + scheduler, + amp, + device, + frequency_save, + save_folder_path, + save_prefix, + prefetch, + prefetch_transform_attr_name, + non_blocking, + ): + + self.set_with_dataloader( + train_dataloader, + test_dataloader_dict, + + criterion, + optimizer, + scheduler, + device, + amp, + + frequency_save, + save_folder_path, + save_prefix, + + prefetch, + prefetch_transform_attr_name, + non_blocking, + + # continue_training_path, + # only_load_model, + ) + + for epoch in range(total_epoch_num): + + train_one_epoch_loss_batch_avg, train_epoch_predict_list, train_epoch_label_list = self.train_one_epoch(verbose=1) + + info_dict_for_one_epoch = {} + info_dict_for_one_epoch.update( + { + "train_epoch_loss_avg_over_batch" : train_one_epoch_loss_batch_avg, + "train_acc" : all_acc(train_epoch_predict_list, train_epoch_label_list), + } + ) + + for dataloader_name, test_dataloader in test_dataloader_dict.items(): + metrics, *other_returns = self.test_given_dataloader(test_dataloader) + info_dict_for_one_epoch.update( + { + f"{dataloader_name}_{k}" : v for k, v in metrics.items() + } + ) + + self.agg(info_dict_for_one_epoch) + + self.agg_save_summary() + + def agg_save_dataframe(self): + self.metric_aggregator.to_dataframe().to_csv(f"{self.save_folder_path}/{self.save_prefix}_df.csv") + + def agg_save_summary(self): + self.metric_aggregator.summary().to_csv(f"{self.save_folder_path}/{self.save_prefix}_df_summary.csv") + + def get_model_params(self): + return self.model.cpu().state_dict() + + # def set_model_params(self, model_parameters): + # self.model.load_state_dict(model_parameters) + + def save_all_state_to_path(self, + path: str, + only_model_state_dict: bool = False) -> None: + ''' + save all information needed to continue training, include 3 random state in random, numpy and torch + :param path: where to save + :param epoch: which epoch when save + :param batch: which batch index when save + :param only_model_state_dict: only save the model, drop all other information + ''' + + epoch, batch = self.epoch_now, self.batch_now + + save_dict = { + 'epoch_num_when_save': epoch, + 'batch_num_when_save': batch, + 'random_state': random.getstate(), + 'np_random_state': np.random.get_state(), + 'torch_random_state': torch.random.get_rng_state(), + 'model_state_dict': self.get_model_params(), + 'optimizer_state_dict': self.optimizer.state_dict(), + 'scheduler_state_dict': self.scheduler.state_dict() if self.scheduler is not None else None, + 'criterion_state_dict': self.criterion.state_dict(), + "scaler": self.scaler.state_dict(), + } \ + if only_model_state_dict == False else self.get_model_params() + + torch.save( + save_dict, + path, + ) + + # def load_from_path(self, + # path: str, + # device, + # only_load_model: bool = False + # ) -> [Optional[int], Optional[int]]: + # ''' + # + # :param path: + # :param device: map model to which device + # :param only_load_model: only_load_model or not? + # ''' + # + # self.model = self.model.to(device, non_blocking=self.non_blocking) + # + # load_dict = torch.load( + # path, map_location=device + # ) + # + # logging.info(f"loading... keys:{load_dict.keys()}, only_load_model:{only_load_model}") + # + # attr_list = [ + # 'epoch_num_when_save', + # 'batch_num_when_save', + # 'random_state', + # 'np_random_state', + # 'torch_random_state', + # 'model_state_dict', + # 'optimizer_state_dict', + # 'scheduler_state_dict', + # 'criterion_state_dict', + # ] + # + # if all([key_name in load_dict for key_name in attr_list]) : + # # all required key can find in load dict + # # AND only_load_model == False + # if only_load_model == False: + # random.setstate(load_dict['random_state']) + # np.random.set_state(load_dict['np_random_state']) + # torch.random.set_rng_state(load_dict['torch_random_state'].cpu()) # since may map to cuda + # + # self.model.load_state_dict( + # load_dict['model_state_dict'] + # ) + # self.optimizer.load_state_dict( + # load_dict['optimizer_state_dict'] + # ) + # if self.scheduler is not None: + # self.scheduler.load_state_dict( + # load_dict['scheduler_state_dict'] + # ) + # self.criterion.load_state_dict( + # load_dict['criterion_state_dict'] + # ) + # if 'scaler' in load_dict: + # self.scaler.load_state_dict( + # load_dict["scaler"] + # ) + # logging.info(f'load scaler done. scaler={load_dict["scaler"]}') + # logging.info('all state load successful') + # return load_dict['epoch_num_when_save'], load_dict['batch_num_when_save'] + # else: + # self.model.load_state_dict( + # load_dict['model_state_dict'], + # ) + # logging.info('only model state_dict load') + # return None, None + # + # else: # only state_dict + # + # if 'model_state_dict' in load_dict: + # self.model.load_state_dict( + # load_dict['model_state_dict'], + # ) + # logging.info('only model state_dict load') + # return None, None + # else: + # self.model.load_state_dict( + # load_dict, + # ) + # logging.info('only model state_dict load') + # return None, None + # + +class PureCleanModelTrainer(ModelTrainerCLS_v2): + + def __init__(self, model): + super().__init__(model) + logging.debug("This class REQUIRE bd dataset to implement overwrite methods. This is NOT a general class for all cls task.") + + def train_one_epoch_on_mix(self, verbose=0): + + startTime = time() + + batch_loss_list = [] + if verbose == 1: + batch_predict_list = [] + batch_label_list = [] + batch_original_index_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + + for batch_idx in range(self.batch_num_per_epoch): + x, labels, original_index, poison_indicator, original_targets = self.get_one_batch() + one_batch_loss, batch_predict = self.one_forward_backward(x, labels, self.device, verbose) + batch_loss_list.append(one_batch_loss) + + if verbose == 1: + batch_predict_list.append(batch_predict.detach().clone().cpu()) + batch_label_list.append(labels.detach().clone().cpu()) + batch_original_index_list.append(original_index.detach().clone().cpu()) + batch_poison_indicator_list.append(poison_indicator.detach().clone().cpu()) + batch_original_targets_list.append(original_targets.detach().clone().cpu()) + + one_epoch_loss = sum(batch_loss_list) / len(batch_loss_list) + if self.scheduler is not None: + if isinstance(self.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + self.scheduler.step(one_epoch_loss) + else: + self.scheduler.step() + + endTime = time() + + logging.info(f"one epoch training part done, use time = {endTime - startTime} s") + + if verbose == 0: + return one_epoch_loss, \ + None, None, None, None, None + elif verbose == 1: + return one_epoch_loss, \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_original_index_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + + def test_given_dataloader_on_mix(self, test_dataloader, device = None, verbose = 0): + + if device is None: + device = self.device + + model = self.model + model.to(device, non_blocking=self.non_blocking) + model.eval() + + metrics = { + 'test_correct': 0, + 'test_loss_sum_over_batch': 0, + 'test_total': 0, + } + + criterion = self.criterion.to(device, non_blocking=self.non_blocking) + + if verbose == 1: + batch_predict_list = [] + batch_label_list = [] + batch_original_index_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + + with torch.no_grad(): + for batch_idx, (x, labels, original_index, poison_indicator, original_targets) in enumerate(test_dataloader): + x = x.to(device, non_blocking=self.non_blocking) + labels = labels.to(device, non_blocking=self.non_blocking) + pred = model(x) + loss = criterion(pred, labels.long()) + + _, predicted = torch.max(pred, -1) + correct = predicted.eq(labels).sum() + + if verbose == 1: + batch_predict_list.append(predicted.detach().clone().cpu()) + batch_label_list.append(labels.detach().clone().cpu()) + batch_original_index_list.append(original_index.detach().clone().cpu()) + batch_poison_indicator_list.append(poison_indicator.detach().clone().cpu()) + batch_original_targets_list.append(original_targets.detach().clone().cpu()) + + metrics['test_correct'] += correct.item() + metrics['test_loss_sum_over_batch'] += loss.item() + metrics['test_total'] += labels.size(0) + + metrics['test_loss_avg_over_batch'] = metrics['test_loss_sum_over_batch']/len(test_dataloader) + metrics['test_acc'] = metrics['test_correct'] / metrics['test_total'] + + if verbose == 0: + return metrics, \ + None, None, None, None, None + elif verbose == 1: + return metrics, \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_original_index_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + + def train_with_test_each_epoch_on_mix(self, + train_dataloader, + clean_test_dataloader, + bd_test_dataloader, + total_epoch_num, + criterion, + optimizer, + scheduler, + amp, + device, + frequency_save, + save_folder_path, + save_prefix, + prefetch, + prefetch_transform_attr_name, + non_blocking, + ): + + test_dataloader_dict = { + "clean_test_dataloader":clean_test_dataloader, + "bd_test_dataloader":bd_test_dataloader, + } + + self.set_with_dataloader( + train_dataloader, + test_dataloader_dict, + criterion, + optimizer, + scheduler, + device, + amp, + + frequency_save, + save_folder_path, + save_prefix, + + prefetch, + prefetch_transform_attr_name, + non_blocking, + ) + + train_loss_list = [] + train_mix_acc_list = [] + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + + for epoch in range(total_epoch_num): + + train_epoch_loss_avg_over_batch, \ + train_epoch_predict_list, \ + train_epoch_label_list, \ + train_epoch_original_index_list, \ + train_epoch_poison_indicator_list, \ + train_epoch_original_targets_list = self.train_one_epoch_on_mix(verbose=1) + + train_mix_acc = all_acc(train_epoch_predict_list, train_epoch_label_list) + + train_bd_idx = torch.where(train_epoch_poison_indicator_list == 1)[0] + train_clean_idx = torch.where(train_epoch_poison_indicator_list == 0)[0] + + clean_metrics, \ + clean_test_epoch_predict_list, \ + clean_test_epoch_label_list, \ + = self.test_given_dataloader(test_dataloader_dict["clean_test_dataloader"], verbose=1) + + clean_test_loss_avg_over_batch = clean_metrics["test_loss_avg_over_batch"] + test_acc = clean_metrics["test_acc"] + + bd_metrics, \ + bd_test_epoch_predict_list, \ + bd_test_epoch_label_list, \ + bd_test_epoch_original_index_list, \ + bd_test_epoch_poison_indicator_list, \ + bd_test_epoch_original_targets_list = self.test_given_dataloader_on_mix(test_dataloader_dict["bd_test_dataloader"], verbose=1) + + bd_test_loss_avg_over_batch = bd_metrics["test_loss_avg_over_batch"] + test_asr = all_acc(bd_test_epoch_predict_list, bd_test_epoch_label_list) + test_ra = all_acc(bd_test_epoch_predict_list, bd_test_epoch_original_targets_list) + + self.agg( + { + "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch, + "train_acc": train_mix_acc, + + + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch" : bd_test_loss_avg_over_batch, + "test_acc" : test_acc, + "test_asr" : test_asr, + "test_ra" : test_ra, + } + ) + + train_loss_list.append(train_epoch_loss_avg_over_batch) + train_mix_acc_list.append(train_mix_acc) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + + self.plot_loss( + train_loss_list, + clean_test_loss_list, + bd_test_loss_list, + ) + + self.plot_acc_like_metric( + train_mix_acc_list, + test_acc_list, + test_asr_list, + test_ra_list, + ) + + self.agg_save_dataframe() + + self.agg_save_summary() + + return train_loss_list, \ + train_mix_acc_list, \ + clean_test_loss_list, \ + bd_test_loss_list, \ + test_acc_list, \ + test_asr_list, \ + test_ra_list + + def plot_loss( + self, + train_loss_list : list, + clean_test_loss_list : list, + bd_test_loss_list : list, + save_file_name="loss_metric_plots", + ): + + plot_loss( + train_loss_list, + clean_test_loss_list, + bd_test_loss_list, + self.save_folder_path, + save_file_name, + ) + + def plot_acc_like_metric(self, + train_acc_list: list, + test_acc_list: list, + test_asr_list: list, + test_ra_list: list, + save_file_name="acc_like_metric_plots", + ): + + plot_acc_like_metric_pure( + train_acc_list, + test_acc_list, + test_asr_list, + test_ra_list, + self.save_folder_path, + save_file_name, + ) + + def test_current_model(self, test_dataloader_dict, device = None,): + + if device is None: + device = self.device + + model = self.model + model.to(device, non_blocking=self.non_blocking) + model.eval() + + clean_metrics, \ + clean_test_epoch_predict_list, \ + clean_test_epoch_label_list, \ + = self.test_given_dataloader(test_dataloader_dict["clean_test_dataloader"], verbose=1) + + clean_test_loss_avg_over_batch = clean_metrics["test_loss_avg_over_batch"] + test_acc = clean_metrics["test_acc"] + + bd_metrics, \ + bd_test_epoch_predict_list, \ + bd_test_epoch_label_list, \ + bd_test_epoch_original_index_list, \ + bd_test_epoch_poison_indicator_list, \ + bd_test_epoch_original_targets_list = self.test_given_dataloader_on_mix(test_dataloader_dict["bd_test_dataloader"], verbose=1) + + bd_test_loss_avg_over_batch = bd_metrics["test_loss_avg_over_batch"] + test_asr = all_acc(bd_test_epoch_predict_list, bd_test_epoch_label_list) + test_ra = all_acc(bd_test_epoch_predict_list, bd_test_epoch_original_targets_list) + + return clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra + + + + +class BackdoorModelTrainer(ModelTrainerCLS_v2): + + def __init__(self, model): + super().__init__(model) + logging.debug("This class REQUIRE bd dataset to implement overwrite methods. This is NOT a general class for all cls task.") + + def train_one_epoch_on_mix(self, verbose=0): + + startTime = time() + + batch_loss_list = [] + if verbose == 1: + batch_predict_list = [] + batch_label_list = [] + batch_original_index_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + + for batch_idx in range(self.batch_num_per_epoch): + x, labels, original_index, poison_indicator, original_targets = self.get_one_batch() + one_batch_loss, batch_predict = self.one_forward_backward(x, labels, self.device, verbose) + batch_loss_list.append(one_batch_loss) + + if verbose == 1: + batch_predict_list.append(batch_predict.detach().clone().cpu()) + batch_label_list.append(labels.detach().clone().cpu()) + batch_original_index_list.append(original_index.detach().clone().cpu()) + batch_poison_indicator_list.append(poison_indicator.detach().clone().cpu()) + batch_original_targets_list.append(original_targets.detach().clone().cpu()) + + one_epoch_loss = sum(batch_loss_list) / len(batch_loss_list) + if self.scheduler is not None: + if isinstance(self.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + self.scheduler.step(one_epoch_loss) + else: + self.scheduler.step() + + endTime = time() + + logging.info(f"one epoch training part done, use time = {endTime - startTime} s") + + if verbose == 0: + return one_epoch_loss, \ + None, None, None, None, None + elif verbose == 1: + return one_epoch_loss, \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_original_index_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + + def test_given_dataloader_on_mix(self, test_dataloader, device = None, verbose = 0): + + if device is None: + device = self.device + + model = self.model + model.to(device, non_blocking=self.non_blocking) + model.eval() + + metrics = { + 'test_correct': 0, + 'test_loss_sum_over_batch': 0, + 'test_total': 0, + } + + criterion = self.criterion.to(device, non_blocking=self.non_blocking) + + if verbose == 1: + batch_predict_list = [] + batch_label_list = [] + batch_original_index_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + + with torch.no_grad(): + for batch_idx, (x, labels, original_index, poison_indicator, original_targets) in enumerate(test_dataloader): + x = x.to(device, non_blocking=self.non_blocking) + labels = labels.to(device, non_blocking=self.non_blocking) + pred = model(x) + loss = criterion(pred, labels.long()) + + _, predicted = torch.max(pred, -1) + correct = predicted.eq(labels).sum() + + if verbose == 1: + batch_predict_list.append(predicted.detach().clone().cpu()) + batch_label_list.append(labels.detach().clone().cpu()) + batch_original_index_list.append(original_index.detach().clone().cpu()) + batch_poison_indicator_list.append(poison_indicator.detach().clone().cpu()) + batch_original_targets_list.append(original_targets.detach().clone().cpu()) + + metrics['test_correct'] += correct.item() + metrics['test_loss_sum_over_batch'] += loss.item() + metrics['test_total'] += labels.size(0) + + metrics['test_loss_avg_over_batch'] = metrics['test_loss_sum_over_batch']/len(test_dataloader) + metrics['test_acc'] = metrics['test_correct'] / metrics['test_total'] + + if verbose == 0: + return metrics, \ + None, None, None, None, None + elif verbose == 1: + return metrics, \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_original_index_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + + def train_with_test_each_epoch_on_mix(self, + train_dataloader, + clean_test_dataloader, + bd_test_dataloader, + total_epoch_num, + criterion, + optimizer, + scheduler, + amp, + device, + frequency_save, + save_folder_path, + save_prefix, + prefetch, + prefetch_transform_attr_name, + non_blocking, + ): + + test_dataloader_dict = { + "clean_test_dataloader":clean_test_dataloader, + "bd_test_dataloader":bd_test_dataloader, + } + + self.set_with_dataloader( + train_dataloader, + test_dataloader_dict, + criterion, + optimizer, + scheduler, + device, + amp, + + frequency_save, + save_folder_path, + save_prefix, + + prefetch, + prefetch_transform_attr_name, + non_blocking, + ) + + train_loss_list = [] + train_mix_acc_list = [] + train_asr_list = [] + train_ra_list = [] + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + + for epoch in range(total_epoch_num): + + train_epoch_loss_avg_over_batch, \ + train_epoch_predict_list, \ + train_epoch_label_list, \ + train_epoch_original_index_list, \ + train_epoch_poison_indicator_list, \ + train_epoch_original_targets_list = self.train_one_epoch_on_mix(verbose=1) + + train_mix_acc = all_acc(train_epoch_predict_list, train_epoch_label_list) + + train_bd_idx = torch.where(train_epoch_poison_indicator_list == 1)[0] + train_clean_idx = torch.where(train_epoch_poison_indicator_list == 0)[0] + train_clean_acc = all_acc( + train_epoch_predict_list[train_clean_idx], + train_epoch_label_list[train_clean_idx], + ) + train_asr = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_label_list[train_bd_idx], + ) + train_ra = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_original_targets_list[train_bd_idx], + ) + + clean_metrics, \ + clean_test_epoch_predict_list, \ + clean_test_epoch_label_list, \ + = self.test_given_dataloader(self.test_dataloader_dict["clean_test_dataloader"], verbose=1) + + clean_test_loss_avg_over_batch = clean_metrics["test_loss_avg_over_batch"] + test_acc = clean_metrics["test_acc"] + + bd_metrics, \ + bd_test_epoch_predict_list, \ + bd_test_epoch_label_list, \ + bd_test_epoch_original_index_list, \ + bd_test_epoch_poison_indicator_list, \ + bd_test_epoch_original_targets_list = self.test_given_dataloader_on_mix(self.test_dataloader_dict["bd_test_dataloader"], verbose=1) + + bd_test_loss_avg_over_batch = bd_metrics["test_loss_avg_over_batch"] + test_asr = all_acc(bd_test_epoch_predict_list, bd_test_epoch_label_list) + test_ra = all_acc(bd_test_epoch_predict_list, bd_test_epoch_original_targets_list) + + self.agg( + { + "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch, + "train_acc": train_mix_acc, + "train_acc_clean_only": train_clean_acc, + "train_asr_bd_only": train_asr, + "train_ra_bd_only": train_ra, + + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch" : bd_test_loss_avg_over_batch, + "test_acc" : test_acc, + "test_asr" : test_asr, + "test_ra" : test_ra, + } + ) + + train_loss_list.append(train_epoch_loss_avg_over_batch) + train_mix_acc_list.append(train_mix_acc) + train_asr_list.append(train_asr) + train_ra_list.append(train_ra) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + + self.plot_loss( + train_loss_list, + clean_test_loss_list, + bd_test_loss_list, + ) + + self.plot_acc_like_metric( + train_mix_acc_list, + train_asr_list, + train_ra_list, + test_acc_list, + test_asr_list, + test_ra_list, + ) + + self.agg_save_dataframe() + + self.agg_save_summary() + + return train_loss_list, \ + train_mix_acc_list, \ + train_asr_list, \ + train_ra_list, \ + clean_test_loss_list, \ + bd_test_loss_list, \ + test_acc_list, \ + test_asr_list, \ + test_ra_list + + def plot_loss( + self, + train_loss_list : list, + clean_test_loss_list : list, + bd_test_loss_list : list, + save_file_name="loss_metric_plots", + ): + + plot_loss( + train_loss_list, + clean_test_loss_list, + bd_test_loss_list, + self.save_folder_path, + save_file_name, + ) + + def plot_acc_like_metric(self, + train_acc_list: list, + train_asr_list: list, + train_ra_list: list, + test_acc_list: list, + test_asr_list: list, + test_ra_list: list, + save_file_name="acc_like_metric_plots", + ): + + plot_acc_like_metric( + train_acc_list, + train_asr_list, + train_ra_list, + test_acc_list, + test_asr_list, + test_ra_list, + self.save_folder_path, + save_file_name, + ) \ No newline at end of file diff --git a/utils/trainer_cls_bypass.py b/utils/trainer_cls_bypass.py new file mode 100644 index 0000000..a7b30f8 --- /dev/null +++ b/utils/trainer_cls_bypass.py @@ -0,0 +1,2059 @@ +# This script is for trainer. This is a warpper for training process. + +import sys, logging +sys.path.append('../') +import random +from pprint import pformat +from typing import * +import numpy as np +import torch +import pandas as pd +from time import time +from copy import deepcopy +from torch.utils.data import DataLoader +import matplotlib.pyplot as plt +import torch.nn as nn +from utils.prefetch import PrefetchLoader, prefetch_transform + + +def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + random.seed(worker_seed) + +class dataloader_generator: + def __init__(self, **kwargs_init): + self.kwargs_init = kwargs_init + def __call__(self, *args, **kwargs_call): + kwargs = deepcopy(self.kwargs_init) + kwargs.update(kwargs_call) + return DataLoader( + *args, + **kwargs + ) + +def last_and_valid_max(col:pd.Series): + ''' + find last not None value and max valid (not None or np.nan) value for each column + :param col: + :return: + ''' + return pd.Series( + index=[ + 'last', 'valid_max', 'exist_nan_value' + ], + data=[ + col[~col.isna()].iloc[-1], pd.to_numeric(col, errors='coerce').max(), any(i == 'nan_value' for i in col) + ]) + +class Metric_Aggregator(object): + ''' + aggregate the metric to log automatically + ''' + def __init__(self): + self.history = [] + def __call__(self, + one_metric : dict): + one_metric = {k : v for k,v in one_metric.items() if v is not None} # drop pair with None as value + one_metric = { + k : ( + "nan_value" if v is np.nan or torch.tensor(v).isnan().item() else v #turn nan to str('nan_value') + ) for k, v in one_metric.items() + } + self.history.append(one_metric) + logging.info( + pformat( + one_metric + ) + ) + def to_dataframe(self): + self.df = pd.DataFrame(self.history, dtype=object) + logging.debug("return df with np.nan and None converted by str()") + return self.df + def summary(self): + ''' + do summary for dataframe of record + :return: + eg. + ,train_epoch_num,train_acc_clean + last,100.0,96.68965148925781 + valid_max,100.0,96.70848846435547 + exist_nan_value,False,False + + ''' + if 'df' not in self.__dict__: + logging.debug('No df found in Metric_Aggregator, generate now') + self.to_dataframe() + logging.debug("return df with np.nan and None converted by str()") + return self.df.apply(last_and_valid_max) + +class ModelTrainerCLS(): + def __init__(self, model, amp = False): + self.model = model + self.amp = amp + + def init_or_continue_train(self, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + continue_training_path: Optional[str] = None, + only_load_model: bool = False, + ) -> None: + ''' + config the training process, from 0 or continue previous. + The requirement for saved file please refer to save_all_state_to_path + :param train_data: train_data_loader, only if when you need of number of batch, you need to input it. Otherwise just skip. + :param end_epoch_num: end training epoch number, if not continue training process, then equal to total training epoch + :param criterion: loss function used + :param optimizer: optimizer + :param scheduler: scheduler + :param device: device + :param continue_training_path: where to load files for continue training process + :param only_load_model: only load the model, do not load other settings and random state. + + ''' + + model = self.model + + model.to(device, non_blocking = True) + model.train() + + # train and update + + self.criterion = criterion + self.optimizer = optimizer + self.scheduler = scheduler + self.scaler = torch.cuda.amp.GradScaler(enabled=self.amp) + + if continue_training_path is not None: + logging.info(f"No batch info will be used. Cannot continue from specific batch!") + + start_epoch, _ = self.load_from_path(continue_training_path, device, only_load_model) + self.start_epochs, self.end_epochs = start_epoch, end_epoch_num + else: + self.start_epochs, self.end_epochs = 0, end_epoch_num + # self.start_batch = 0 + + logging.info(f'All setting done, train from epoch {self.start_epochs} to epoch {self.end_epochs}') + + logging.info( + pformat(f"self.amp:{self.amp}," + + f"self.criterion:{self.criterion}," + + f"self.optimizer:{self.optimizer}," + + f"self.scheduler:{self.scheduler.state_dict() if self.scheduler is not None else None}," + + f"self.scaler:{self.scaler.state_dict() if self.scaler is not None else None})") + ) + def get_model_params(self): + return self.model.cpu().state_dict() + + def set_model_params(self, model_parameters): + self.model.load_state_dict(model_parameters) + + def save_all_state_to_path(self, + path: str, + epoch: Optional[int] = None, + batch: Optional[int] = None, + only_model_state_dict: bool = False) -> None: + ''' + save all information needed to continue training, include 3 random state in random, numpy and torch + :param path: where to save + :param epoch: which epoch when save + :param batch: which batch index when save + :param only_model_state_dict: only save the model, drop all other information + ''' + + save_dict = { + 'epoch_num_when_save': epoch, + 'batch_num_when_save': batch, + 'random_state': random.getstate(), + 'np_random_state': np.random.get_state(), + 'torch_random_state': torch.random.get_rng_state(), + 'model_state_dict': self.get_model_params(), + 'optimizer_state_dict': self.optimizer.state_dict(), + 'scheduler_state_dict': self.scheduler.state_dict() if self.scheduler is not None else None, + 'criterion_state_dict': self.criterion.state_dict(), + "scaler": self.scaler.state_dict(), + } \ + if only_model_state_dict == False else self.get_model_params() + + torch.save( + save_dict, + path, + ) + + def load_from_path(self, + path: str, + device, + only_load_model: bool = False + ): + ''' + + :param path: + :param device: map model to which device + :param only_load_model: only_load_model or not? + ''' + + self.model = self.model.to(device, non_blocking = True) + + load_dict = torch.load( + path, map_location=device + ) + + logging.info(f"loading... keys:{load_dict.keys()}, only_load_model:{only_load_model}") + + attr_list = [ + 'epoch_num_when_save', + 'batch_num_when_save', + 'random_state', + 'np_random_state', + 'torch_random_state', + 'model_state_dict', + 'optimizer_state_dict', + 'scheduler_state_dict', + 'criterion_state_dict', + ] + + if all([key_name in load_dict for key_name in attr_list]) : + # all required key can find in load dict + # AND only_load_model == False + if only_load_model == False: + random.setstate(load_dict['random_state']) + np.random.set_state(load_dict['np_random_state']) + torch.random.set_rng_state(load_dict['torch_random_state'].cpu()) # since may map to cuda + + self.model.load_state_dict( + load_dict['model_state_dict'] + ) + self.optimizer.load_state_dict( + load_dict['optimizer_state_dict'] + ) + if self.scheduler is not None: + self.scheduler.load_state_dict( + load_dict['scheduler_state_dict'] + ) + self.criterion.load_state_dict( + load_dict['criterion_state_dict'] + ) + if 'scaler' in load_dict: + self.scaler.load_state_dict( + load_dict["scaler"] + ) + logging.info(f'load scaler done. scaler={load_dict["scaler"]}') + logging.info('all state load successful') + return load_dict['epoch_num_when_save'], load_dict['batch_num_when_save'] + else: + self.model.load_state_dict( + load_dict['model_state_dict'], + ) + logging.info('only model state_dict load') + return None, None + + else: # only state_dict + + if 'model_state_dict' in load_dict: + self.model.load_state_dict( + load_dict['model_state_dict'], + ) + logging.info('only model state_dict load') + return None, None + else: + self.model.load_state_dict( + load_dict, + ) + logging.info('only model state_dict load') + return None, None + + def test(self, test_data, device): + model = self.model + model.to(device, non_blocking = True) + model.eval() + + metrics = { + 'test_correct': 0, + 'test_loss': 0, + 'test_total': 0, + } + + criterion = self.criterion.to(device, non_blocking = True) + + with torch.no_grad(): + for batch_idx, (x, target, *additional_info) in enumerate(test_data): + x = x.to(device, non_blocking = True) + target = target.to(device, non_blocking = True) + pred,inter = model(x) + loss = criterion(pred, target.long()) + + _, predicted = torch.max(pred, -1) + correct = predicted.eq(target).sum() + + metrics['test_correct'] += correct.item() + metrics['test_loss'] += loss.item() * target.size(0) + metrics['test_total'] += target.size(0) + + return metrics + + #@resource_check + def train_one_batch(self, x, labels, device): + + self.model.train() + self.model.to(device, non_blocking = True) + + x, labels = x.to(device, non_blocking = True), labels.to(device, non_blocking = True) + + with torch.cuda.amp.autocast(enabled=self.amp): + log_probs,inter = self.model(x) + loss = self.criterion(log_probs, labels.long()) + self.scaler.scale(loss).backward() + self.scaler.step(self.optimizer) + self.scaler.update() + self.optimizer.zero_grad() + + batch_loss = loss.item() * labels.size(0) + + return batch_loss + + def train_one_epoch(self, train_data, device): + startTime = time() + batch_loss = [] + for batch_idx, (x, labels, *additional_info) in enumerate(train_data): + # print(batch_idx) + batch_loss.append(self.train_one_batch(x, labels, device)) + one_epoch_loss = sum(batch_loss) + if self.scheduler is not None: + if isinstance(self.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + # here since ReduceLROnPlateau need the train loss to decide next step setting. + self.scheduler.step(one_epoch_loss) + else: + self.scheduler.step() + + endTime = time() + + logging.info(f"one epoch training part done, use time = {endTime - startTime} s") + + return one_epoch_loss + + def train(self, train_data, end_epoch_num, + criterion, + optimizer, + scheduler, device, frequency_save, save_folder_path, + save_prefix, + continue_training_path: Optional[str] = None, + only_load_model: bool = False, ): + ''' + + simplest train algorithm with init function put inside. + + :param train_data: train_data_loader + :param end_epoch_num: end training epoch number, if not continue training process, then equal to total training epoch + :param criterion: loss function used + :param optimizer: optimizer + :param scheduler: scheduler + :param device: device + :param frequency_save: how many epoch to save model and random states information once + :param save_folder_path: folder path to save files + :param save_prefix: for saved files, the prefix of file name + :param continue_training_path: where to load files for continue training process + :param only_load_model: only load the model, do not load other settings and random state. + ''' + + self.init_or_continue_train( + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + continue_training_path, + only_load_model + ) + epoch_loss = [] + for epoch in range(self.start_epochs, self.end_epochs): + one_epoch_loss = self.train_one_epoch(train_data, device) + epoch_loss.append(one_epoch_loss) + logging.info(f'train, epoch_loss: {epoch_loss[-1]}') + if frequency_save != 0 and epoch % frequency_save == frequency_save - 1: + logging.info(f'saved. epoch:{epoch}') + self.save_all_state_to_path( + epoch=epoch, + path=f"{save_folder_path}/{save_prefix}_epoch_{epoch}.pt") + + def train_with_test_each_epoch(self, + train_data, + test_data, + bd_test_data, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + frequency_save, + save_folder_path, + save_prefix, + continue_training_path: Optional[str] = None, + only_load_model: bool = False, + ): + ''' + train with test on clean and backdoor dataloader for each epoch + + :param train_data: train_data_loader + :param test_data: clean test data + :param adv_test_data: backdoor poisoned test data (for ASR) + :param end_epoch_num: end training epoch number, if not continue training process, then equal to total training epoch + :param criterion: loss function used + :param optimizer: optimizer + :param scheduler: scheduler + :param device: device + :param frequency_save: how many epoch to save model and random states information once + :param save_folder_path: folder path to save files + :param save_prefix: for saved files, the prefix of file name + :param continue_training_path: where to load files for continue training process + :param only_load_model: only load the model, do not load other settings and random state. + ''' + agg = Metric_Aggregator() + self.init_or_continue_train( + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + continue_training_path, + only_load_model + ) + epoch_loss = [] + for epoch in range(self.start_epochs, self.end_epochs): + one_epoch_loss = self.train_one_epoch(train_data, device) + epoch_loss.append(one_epoch_loss) + logging.info(f'train_with_test_each_epoch, epoch:{epoch} ,epoch_loss: {epoch_loss[-1]}') + + metrics = self.test(test_data, device) + metric_info = { + 'epoch': epoch, + 'clean acc': metrics['test_correct'] / metrics['test_total'], + 'clean loss': metrics['test_loss'], + } + agg(metric_info) + + bd_metrics = self.test(bd_test_data, device) + bd_metric_info = { + 'epoch': epoch, + 'ASR': bd_metrics['test_correct'] / bd_metrics['test_total'], + 'backdoor loss': bd_metrics['test_loss'], + } + agg(bd_metric_info) + + if frequency_save != 0 and epoch % frequency_save == frequency_save - 1: + logging.info(f'saved. epoch:{epoch}') + self.save_all_state_to_path( + epoch=epoch, + path=f"{save_folder_path}/{save_prefix}_epoch_{epoch}.pt") + # logging.info(f"training, epoch:{epoch}, batch:{batch_idx},batch_loss:{loss.item()}") + agg.to_dataframe().to_csv(f"{save_folder_path}/{save_prefix}_df.csv") + agg.summary().to_csv(f"{save_folder_path}/{save_prefix}_df_summary.csv") + + def train_with_test_each_epoch_v2(self, + train_data, + test_dataloader_dict, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + frequency_save, + save_folder_path, + save_prefix, + continue_training_path: Optional[str] = None, + only_load_model: bool = False, + ): + ''' + v2 can feed many test_dataloader, so easier for test with multiple dataloader. + + only change the test data part, instead of predetermined 2 dataloader, you can input any number of dataloader to test + with { + test_name (will show in log): test dataloader + } + in log you will see acc and loss for each test dataloader + + :param test_dataloader_dict: { name : dataloader } + + :param train_data: train_data_loader + :param end_epoch_num: end training epoch number, if not continue training process, then equal to total training epoch + :param criterion: loss function used + :param optimizer: optimizer + :param scheduler: scheduler + :param device: device + :param frequency_save: how many epoch to save model and random states information once + :param save_folder_path: folder path to save files + :param save_prefix: for saved files, the prefix of file name + :param continue_training_path: where to load files for continue training process + :param only_load_model: only load the model, do not load other settings and random state. + ''' + agg = Metric_Aggregator() + self.init_or_continue_train( + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + continue_training_path, + only_load_model + ) + epoch_loss = [] + for epoch in range(self.start_epochs, self.end_epochs): + one_epoch_loss = self.train_one_epoch(train_data, device) + epoch_loss.append(one_epoch_loss) + logging.info(f'train_with_test_each_epoch, epoch:{epoch} ,epoch_loss: {epoch_loss[-1]}') + + for dataloader_name, test_dataloader in test_dataloader_dict.items(): + metrics = self.test(test_dataloader, device) + metric_info = { + 'epoch': epoch, + f'{dataloader_name} acc': metrics['test_correct'] / metrics['test_total'], + f'{dataloader_name} loss': metrics['test_loss'], + } + agg(metric_info) + + + if frequency_save != 0 and epoch % frequency_save == frequency_save - 1: + logging.info(f'saved. epoch:{epoch}') + self.save_all_state_to_path( + epoch=epoch, + path=f"{save_folder_path}/{save_prefix}_epoch_{epoch}.pt") + # logging.info(f"training, epoch:{epoch}, batch:{batch_idx},batch_loss:{loss.item()}") + agg.to_dataframe().to_csv(f"{save_folder_path}/{save_prefix}_df.csv") + agg.summary().to_csv(f"{save_folder_path}/{save_prefix}_df_summary.csv") + + def train_with_test_each_epoch_v2_sp(self, + batch_size, + train_dataset, + test_dataset_dict, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + frequency_save, + save_folder_path, + save_prefix, + prefetch=False, + continue_training_path: Optional[str] = None, + only_load_model: bool = False, + ): + + ''' + Nothing different, just be simplified to accept dataset instead. + ''' + train_data = DataLoader( + dataset = train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True, + pin_memory=True, + worker_init_fn=seed_worker, + num_workers=8, + ) + + test_dataloader_dict = { + name : DataLoader( + dataset = test_dataset, + batch_size=batch_size, + shuffle=False, + drop_last=False, + pin_memory=True, + worker_init_fn=seed_worker, + num_workers=8, + ) + for name, test_dataset in test_dataset_dict.items() + } + + if prefetch: + raise SystemError("Due to technical issue, not implemented yet") + + self.train_with_test_each_epoch_v2( + train_data, + test_dataloader_dict, + end_epoch_num, + criterion, + optimizer, + scheduler, + device, + frequency_save, + save_folder_path, + save_prefix, + continue_training_path, + only_load_model, + ) + +def all_acc(preds:torch.Tensor, + labels:torch.Tensor,): + if len(preds) == 0 or len(labels) == 0: + logging.warning("zero len array in func all_acc(), return None!") + return None + return preds.eq(labels).sum().item() / len(preds) + +def class_wise_acc( + preds:torch.Tensor, + labels:torch.Tensor, + selected_class: list, +): + assert len(preds) == len(labels) + acc = {class_idx : 0 for class_idx in selected_class} + for c in acc.keys(): + acc[c] = preds.eq(c).sum().item() / len(preds) + return acc + +def given_dataloader_test( + model, + test_dataloader, + criterion, + non_blocking : bool = False, + device = "cpu", + verbose : int = 0 +): + model.to(device, non_blocking=non_blocking) + model.eval() + metrics = { + 'test_correct': 0, + 'test_loss_sum_over_batch': 0, + 'test_total': 0, + } + criterion = criterion.to(device, non_blocking=non_blocking) + + if verbose == 1: + batch_predict_list, batch_label_list = [], [] + + with torch.no_grad(): + for batch_idx, (x, target, *additional_info) in enumerate(test_dataloader): + x = x.to(device, non_blocking=non_blocking) + # print(x.shape) + target = target.to(device, non_blocking=non_blocking) + pred,inter = model(x) + loss = criterion(pred, target.long()) + + _, predicted = torch.max(pred, -1) + correct = predicted.eq(target).sum() + + if verbose == 1: + batch_predict_list.append(predicted.detach().clone().cpu()) + batch_label_list.append(target.detach().clone().cpu()) + + metrics['test_correct'] += correct.item() + metrics['test_loss_sum_over_batch'] += loss.item() + metrics['test_total'] += target.size(0) + + metrics['test_loss_avg_over_batch'] = metrics['test_loss_sum_over_batch']/len(test_dataloader) + metrics['test_acc'] = metrics['test_correct'] / metrics['test_total'] + + if verbose == 0: + return metrics, None, None + elif verbose == 1: + return metrics, torch.cat(batch_predict_list), torch.cat(batch_label_list) + +def test_given_dataloader_on_mix(model, test_dataloader, criterion, device = None, non_blocking=True, verbose = 0): + + + model.to(device, non_blocking=non_blocking) + model.eval() + + metrics = { + 'test_correct': 0, + 'test_loss_sum_over_batch': 0, + 'test_total': 0, + } + + criterion = criterion.to(device, non_blocking=non_blocking) + + if verbose == 1: + batch_predict_list = [] + batch_label_list = [] + batch_original_index_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + + with torch.no_grad(): + for batch_idx, (x, labels, original_index, poison_indicator, original_targets) in enumerate(test_dataloader): + x = x.to(device, non_blocking=non_blocking) + labels = labels.to(device, non_blocking=non_blocking) + pred,inter = model(x) + loss = criterion(pred, labels.long()) + + _, predicted = torch.max(pred, -1) + correct = predicted.eq(labels).sum() + + if verbose == 1: + batch_predict_list.append(predicted.detach().clone().cpu()) + batch_label_list.append(labels.detach().clone().cpu()) + batch_original_index_list.append(original_index.detach().clone().cpu()) + batch_poison_indicator_list.append(poison_indicator.detach().clone().cpu()) + batch_original_targets_list.append(original_targets.detach().clone().cpu()) + + metrics['test_correct'] += correct.item() + metrics['test_loss_sum_over_batch'] += loss.item() + metrics['test_total'] += labels.size(0) + + metrics['test_loss_avg_over_batch'] = metrics['test_loss_sum_over_batch']/len(test_dataloader) + metrics['test_acc'] = metrics['test_correct'] / metrics['test_total'] + + if verbose == 0: + return metrics, \ + None, None, None, None, None + elif verbose == 1: + return metrics, \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_original_index_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + +def validate_list_for_plot(given_list, require_len=None): + + if (require_len is not None) and (len(given_list) == require_len): + pass + else: + return False + + if None in given_list: + return False + + return True + +def general_plot_for_epoch( + labelToListDict : dict, + save_path: str, + ylabel: str, + xlabel: str = "epoch", + y_min = None, + y_max = None, + title: str = "Results", +): + # len of first list + len_of_first_valueList = len(list(labelToListDict.values())[0]) + + '''These line of set color is from https://stackoverflow.com/questions/8389636/creating-over-20-unique-legend-colors-using-matplotlib''' + NUM_COLORS = len(labelToListDict) + cm = plt.get_cmap('gist_rainbow') + fig = plt.figure(figsize=(12.8, 9.6)) # 4x default figsize + ax = fig.add_subplot(111) + ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)]) + + # hese line of set linestyple is from https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html + linestyle_tuple = [ + ('loosely dotted', (0, (1, 10))), + ('dotted', (0, (1, 1))), + ('densely dotted', (0, (1, 1))), + ('long dash with offset', (5, (10, 3))), + ('loosely dashed', (0, (5, 10))), + ('dashed', (0, (5, 5))), + ('densely dashed', (0, (5, 1))), + ('loosely dashdotted', (0, (3, 10, 1, 10))), + ('dashdotted', (0, (3, 5, 1, 5))), + ('densely dashdotted', (0, (3, 1, 1, 1))), + ('dashdotdotted', (0, (3, 5, 1, 5, 1, 5))), + ('loosely dashdotdotted', (0, (3, 10, 1, 10, 1, 10))), + ('densely dashdotdotted', (0, (3, 1, 1, 1, 1, 1)))] + + all_min = np.infty + all_max = -np.infty + for idx, (label, value_list) in enumerate(labelToListDict.items()): + linestyle = linestyle_tuple[ + idx % len(linestyle_tuple) + ][1] + if validate_list_for_plot(value_list, len_of_first_valueList): + plt.plot(range(len(value_list)), value_list, marker=idx%11, linewidth=2, label=label, linestyle=linestyle) + else: + logging.warning(f"list:{label} contains None or len not match") + once_min, once_max = min(value_list), max(value_list) + all_min = once_min if once_min < all_min else all_min + all_max = once_max if once_max > all_max else all_max + + plt.xlabel(xlabel) + plt.ylabel(ylabel) + + plt.ylim( + (all_min, all_max) if (y_min is None) or (y_max is None) else (float(y_min), float(y_max)) + ) + plt.legend() + plt.title(title) + plt.grid() + plt.savefig(save_path) + plt.close() + +def plot_loss( + train_loss_list : list, + clean_test_loss_list : list, + bd_test_loss_list : list, + save_folder_path: str, + save_file_name="loss_metric_plots", + ): + '''These line of set color is from https://stackoverflow.com/questions/8389636/creating-over-20-unique-legend-colors-using-matplotlib''' + NUM_COLORS = 3 + cm = plt.get_cmap('gist_rainbow') + fig = plt.figure(figsize=(12.8, 9.6)) # 4x default figsize + ax = fig.add_subplot(111) + ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)]) + + len_set = len(train_loss_list) + x = range(len_set) + if validate_list_for_plot(train_loss_list, len_set): + plt.plot(x, train_loss_list, marker="o", linewidth=2, label="Train Loss", linestyle="--") + else: + logging.warning("train_loss_list contains None or len not match") + if validate_list_for_plot(clean_test_loss_list, len_set): + plt.plot(x, clean_test_loss_list, marker="v", linewidth=2, label="Test Clean loss", linestyle="-") + else: + logging.warning("clean_test_loss_list contains None or len not match") + if validate_list_for_plot(bd_test_loss_list, len_set): + plt.plot(x, bd_test_loss_list, marker="+", linewidth=2, label="Test Backdoor Loss", linestyle="-.") + else: + logging.warning("bd_test_loss_list contains None or len not match") + + plt.xlabel("Epochs") + plt.ylabel("Loss") + + plt.ylim((0, + max([value for value in # filter None value + train_loss_list + + clean_test_loss_list + + bd_test_loss_list if value is not None]) + )) + plt.legend() + plt.title("Results") + plt.grid() + plt.savefig(f"{save_folder_path}/{save_file_name}.png") + plt.close() + +def plot_acc_like_metric_pure( + train_acc_list: list, + test_acc_list: list, + test_asr_list: list, + test_ra_list: list, + save_folder_path: str, + save_file_name="acc_like_metric_plots", + ): + len_set = len(test_asr_list) + x = range(len(test_asr_list)) + + '''These line of set color is from https://stackoverflow.com/questions/8389636/creating-over-20-unique-legend-colors-using-matplotlib''' + NUM_COLORS = 6 + cm = plt.get_cmap('gist_rainbow') + fig = plt.figure(figsize=(12.8, 9.6)) # 4x default figsize + ax = fig.add_subplot(111) + ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)]) + + + if validate_list_for_plot(train_acc_list, len_set): + plt.plot(x, train_acc_list,marker="o",linewidth=2,label="Train Acc",linestyle="--") + else: + logging.warning("train_acc_list contains None, or len not match") + if validate_list_for_plot(test_acc_list, len_set): + plt.plot(x, test_acc_list, marker="o",linewidth=2,label="Test C-Acc",linestyle="--") + else: + logging.warning("test_acc_list contains None, or len not match") + if validate_list_for_plot(test_asr_list, len_set): + plt.plot(x, test_asr_list, marker="v", linewidth=2, label="Test ASR", linestyle = "-") + else: + logging.warning("test_asr_list contains None, or len not match") + if validate_list_for_plot(test_ra_list, len_set): + plt.plot(x, test_ra_list, marker = "+", linewidth=2, label="Test RA", linestyle = "-.") + else: + logging.warning("test_ra_list contains None, or len not match") + + plt.xlabel("Epochs") + plt.ylabel("ACC") + + plt.ylim((0, 1)) + plt.legend() + plt.title("Results") + plt.grid() + plt.savefig(f"{save_folder_path}/{save_file_name}.png") + plt.close() + + +def plot_acc_like_metric( + train_acc_list: list, + train_asr_list: list, + train_ra_list: list, + test_acc_list: list, + test_asr_list: list, + test_ra_list: list, + save_folder_path: str, + save_file_name="acc_like_metric_plots", + ): + len_set = len(test_asr_list) + x = range(len(test_asr_list)) + + '''These line of set color is from https://stackoverflow.com/questions/8389636/creating-over-20-unique-legend-colors-using-matplotlib''' + NUM_COLORS = 6 + cm = plt.get_cmap('gist_rainbow') + fig = plt.figure(figsize=(12.8, 9.6)) # 4x default figsize + ax = fig.add_subplot(111) + ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)]) + + + if validate_list_for_plot(train_acc_list, len_set): + plt.plot(x, train_acc_list,marker="o",linewidth=2,label="Train Acc",linestyle="--") + else: + logging.warning("train_acc_list contains None, or len not match") + if validate_list_for_plot(train_asr_list, len_set): + plt.plot(x, train_asr_list, marker="v", linewidth=2, label="Train ASR", linestyle="-") + else: + logging.warning("train_asr_list contains None, or len not match") + if validate_list_for_plot(train_ra_list, len_set): + plt.plot(x, train_ra_list, marker="+", linewidth=2, label="Train RA", linestyle = "-.") + else: + logging.warning("train_ra_list contains None, or len not match") + if validate_list_for_plot(test_acc_list, len_set): + plt.plot(x, test_acc_list, marker="o",linewidth=2,label="Test C-Acc",linestyle="--") + else: + logging.warning("test_acc_list contains None, or len not match") + if validate_list_for_plot(test_asr_list, len_set): + plt.plot(x, test_asr_list, marker="v", linewidth=2, label="Test ASR", linestyle = "-") + else: + logging.warning("test_asr_list contains None, or len not match") + if validate_list_for_plot(test_ra_list, len_set): + plt.plot(x, test_ra_list, marker = "+", linewidth=2, label="Test RA", linestyle = "-.") + else: + logging.warning("test_ra_list contains None, or len not match") + + plt.xlabel("Epochs") + plt.ylabel("ACC") + + plt.ylim((0, 1)) + plt.legend() + plt.title("Results") + plt.grid() + plt.savefig(f"{save_folder_path}/{save_file_name}.png") + plt.close() + +class ModelTrainerCLS_v2(): + + def __init__(self, model, discriminator, optimizer_inter, regularization_ratio): + self.model = model + self.discriminator = discriminator + self.regularization_ratio = regularization_ratio + self.optimizer_inter = optimizer_inter + def set_with_dataloader( + self, + train_dataloader, + test_dataloader_dict, + + criterion, + optimizer, + scheduler, + device, + amp, + + frequency_save, + save_folder_path, + save_prefix, + + prefetch=False, + prefetch_transform_attr_name="transform", + non_blocking=False, + + # continue_training_path: Optional[str] = None, + # only_load_model: bool = False, + ): + + logging.info( + "Do NOT set the settings/parameters attr manually after you start training!" + + "\nYou may break the relationship between them." + ) + + if non_blocking == False: + logging.warning( + "Make sure non_blocking=True if you use pin_memory or prefetch or other tricks depending on non_blocking." + ) + + self.train_dataloader = train_dataloader + self.test_dataloader_dict = test_dataloader_dict + + self.criterion = criterion + self.optimizer = optimizer + self.scheduler = scheduler + self.device = device + self.amp = amp + self.scaler = torch.cuda.amp.GradScaler(enabled=self.amp) + self.non_blocking = non_blocking + + self.frequency_save = frequency_save + self.save_folder_path = save_folder_path + self.save_prefix = save_prefix + + if prefetch: + logging.debug("Converting dataloader to prefetch version.") + + train_dataset = self.train_dataloader.dataset + train_prefetch_transform, train_mean, train_std = prefetch_transform( + getattr(train_dataset, prefetch_transform_attr_name) + ) + setattr(train_dataset, prefetch_transform_attr_name, train_prefetch_transform) + self.train_dataloader = PrefetchLoader( + self.train_dataloader, train_mean, train_std + ) + for name, test_dataloader in self.test_dataloader_dict.items(): + val_dataset = test_dataloader.dataset + val_prefetch_transform, val_mean, val_std = prefetch_transform( + getattr(val_dataset, prefetch_transform_attr_name) + ) + setattr(val_dataset, prefetch_transform_attr_name, val_prefetch_transform) + test_dataloader = PrefetchLoader( + test_dataloader, val_mean, val_std + ) + self.test_dataloader_dict[name] = test_dataloader + + self.batch_num_per_epoch = len(self.train_dataloader) + + self.train_iter = iter(self.train_dataloader) + + # if continue_training_path is not None: + # logging.info(f"No batch info will be used. Cannot continue from specific batch!") + # self.epoch_now, self.batch_now = self.load_from_path(continue_training_path, device, only_load_model) + # assert self.batch_now < self.batch_num_per_epoch + # else: + self.epoch_now, self.batch_now = 0, 0 + + logging.info( + pformat( + f"epoch_now:{self.epoch_now}, batch_now:{self.batch_now}" + + f"self.amp:{self.amp}," + + f"self.criterion:{self.criterion}," + + f"self.optimizer:{self.optimizer}," + + f"self.scheduler:{self.scheduler.state_dict() if self.scheduler is not None else None}," + + f"self.scaler:{self.scaler.state_dict() if self.scaler is not None else None})" + ) + ) + + self.metric_aggregator = Metric_Aggregator() + + self.train_batch_loss_record = [] + + def set_with_dataset( + self, + train_dataset, + test_dataset_dict, + + batch_size, + criterion, + optimizer, + scheduler, + device, + + frequency_save, + save_folder_path, + save_prefix, + + amp = False, + + prefetch=True, + prefetch_transform_attr_name="transform", + non_blocking=True, + pin_memory=True, + worker_init_fn = seed_worker, + num_workers = 4, + + # continue_training_path: Optional[str] = None, + # only_load_model: bool = False, + ): + + train_dataloader = DataLoader( + dataset=train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True, + pin_memory=pin_memory, + worker_init_fn=worker_init_fn, + num_workers=num_workers, + ) + + test_dataloader_dict = { + name: DataLoader( + dataset=test_dataset, + batch_size=batch_size, + shuffle=False, + drop_last=False, + pin_memory=pin_memory, + worker_init_fn=worker_init_fn, + num_workers=num_workers, + ) + for name, test_dataset in test_dataset_dict.items() + } + + self.set_with_dataloader( + train_dataloader = train_dataloader, + test_dataloader_dict = test_dataloader_dict, + + criterion = criterion, + optimizer = optimizer, + scheduler = scheduler, + device = device, + amp = amp, + + frequency_save = frequency_save, + save_folder_path = save_folder_path, + save_prefix = save_prefix, + + prefetch = prefetch, + prefetch_transform_attr_name = prefetch_transform_attr_name, + non_blocking = non_blocking, + + # continue_training_path = continue_training_path, + # only_load_model = only_load_model, + ) + + def convert_to_batch_num(self, epochs = 0, batchs = 0): + return int(epochs * self.batch_num_per_epoch + batchs) + + def get_one_batch(self): + + if self.batch_now == self.batch_num_per_epoch: + + self.epoch_now += 1 + self.batch_now = 0 + + self.train_iter = iter(self.train_dataloader) + + if self.frequency_save != 0 and self.epoch_now % self.frequency_save == self.frequency_save - 1: + logging.info(f'saved. epoch:{self.epoch_now}') + self.save_all_state_to_path( + path=f"{self.save_folder_path}/{self.save_prefix}_epoch_{self.epoch_now}.pt") + + self.agg_save_dataframe() + + self.batch_now += 1 + + return self.train_iter.__next__() + + def get_one_train_epoch_loss_avg_over_batch(self): + if len(self.train_batch_loss_record) >= self.batch_num_per_epoch: + return sum( + self.train_batch_loss_record[-self.batch_num_per_epoch:] + )/self.batch_num_per_epoch + else: + logging.warning("No enough batch loss to get the one epoch loss") + + def one_forward_backward(self, x, labels, poison_indicator,device, verbose=0): + + self.model.train() + self.model.to(device, non_blocking=self.non_blocking) + + self.discriminator.train() + self.discriminator.to(device) + + x, labels = x.to(device, non_blocking=self.non_blocking), labels.to(device, non_blocking=self.non_blocking) + + with torch.cuda.amp.autocast(enabled=self.amp): + log_probs, inter = self.model(x) + + loss_inter = nn.BCELoss()(self.discriminator(inter).reshape(-1),target = poison_indicator.float().to(x.device)) + loss = self.criterion(log_probs, labels.long()) - self.regularization_ratio * loss_inter + loss_inter_dis = nn.BCELoss()(self.discriminator(inter.detach().clone()).reshape(-1),target = poison_indicator.float().to(x.device)) + + + self.scaler.scale(loss).backward() + self.scaler.step(self.optimizer) + self.scaler.update() + self.optimizer_inter.zero_grad() + loss_inter_dis.backward() + self.optimizer_inter.step() + + + self.optimizer.zero_grad() + + batch_loss = loss.item() + + if verbose == 1: + batch_predict = torch.max(log_probs, -1)[1].detach().clone().cpu() + return batch_loss, batch_predict + + return batch_loss, None + + def train(self, epochs = 0, batchs = 0): + + train_batch_num = self.convert_to_batch_num(epochs, batchs) + + for idx in range(train_batch_num): + + x, labels, *additional_info = self.get_one_batch() + batch_loss, _ = self.one_forward_backward(x, labels, self.device) + + self.train_batch_loss_record.append(batch_loss) + + if self.batch_now == 0 and self.scheduler is not None: + if isinstance(self.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + # here since ReduceLROnPlateau need the train loss to decide next step setting. + self.scheduler.step(self.get_one_train_epoch_loss_avg_over_batch()) + else: + self.scheduler.step() + + def test_given_dataloader(self, test_dataloader, device = None, verbose = 0): + + if device is None: + device = self.device + + model = self.model + non_blocking = self.non_blocking + + return given_dataloader_test( + model, + test_dataloader, + self.criterion, + non_blocking, + device, + verbose, + ) + + def test_all_inner_dataloader(self): + metrics_dict = {} + for name, test_dataloader in self.test_dataloader_dict.items(): + metrics_dict[name], *other_returns = self.test_given_dataloader( + test_dataloader, + verbose = 0, + ) + return metrics_dict + + def agg(self, info_dict): + info = { + "epoch":self.epoch_now, + "batch":self.batch_now, + } + info.update(info_dict) + self.metric_aggregator( + info + ) + + def train_one_epoch(self, verbose = 0): + + startTime = time() + + batch_loss_list = [] + if verbose == 1: + batch_predict_list = [] + batch_label_list = [] + + for batch_idx in range(self.batch_num_per_epoch): + x, labels, *additional_info = self.get_one_batch() + one_batch_loss, batch_predict = self.one_forward_backward(x, labels, self.device, verbose) + batch_loss_list.append(one_batch_loss) + + if verbose == 1: + batch_predict_list.append(batch_predict.detach().clone().cpu()) + batch_label_list.append(labels.detach().clone().cpu()) + + train_one_epoch_loss_batch_avg = sum(batch_loss_list) / len(batch_loss_list) + if self.scheduler is not None: + if isinstance(self.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + self.scheduler.step(train_one_epoch_loss_batch_avg) + else: + self.scheduler.step() + + endTime = time() + + logging.info(f"one epoch training part done, use time = {endTime - startTime} s") + + if verbose == 0: + return train_one_epoch_loss_batch_avg, None, None + elif verbose == 1: + return train_one_epoch_loss_batch_avg, torch.cat(batch_predict_list), torch.cat(batch_label_list) + + def train_with_test_each_epoch(self, + train_dataloader, + test_dataloader_dict, + total_epoch_num, + criterion, + optimizer, + scheduler, + amp, + device, + frequency_save, + save_folder_path, + save_prefix, + prefetch, + prefetch_transform_attr_name, + non_blocking, + ): + + self.set_with_dataloader( + train_dataloader, + test_dataloader_dict, + + criterion, + optimizer, + scheduler, + device, + amp, + + frequency_save, + save_folder_path, + save_prefix, + + prefetch, + prefetch_transform_attr_name, + non_blocking, + + # continue_training_path, + # only_load_model, + ) + + for epoch in range(total_epoch_num): + + train_one_epoch_loss_batch_avg, train_epoch_predict_list, train_epoch_label_list = self.train_one_epoch(verbose=1) + + info_dict_for_one_epoch = {} + info_dict_for_one_epoch.update( + { + "train_epoch_loss_avg_over_batch" : train_one_epoch_loss_batch_avg, + "train_acc" : all_acc(train_epoch_predict_list, train_epoch_label_list), + } + ) + + for dataloader_name, test_dataloader in test_dataloader_dict.items(): + metrics, *other_returns = self.test_given_dataloader(test_dataloader) + info_dict_for_one_epoch.update( + { + f"{dataloader_name}_{k}" : v for k, v in metrics.items() + } + ) + + self.agg(info_dict_for_one_epoch) + + self.agg_save_summary() + + def agg_save_dataframe(self): + self.metric_aggregator.to_dataframe().to_csv(f"{self.save_folder_path}/{self.save_prefix}_df.csv") + + def agg_save_summary(self): + self.metric_aggregator.summary().to_csv(f"{self.save_folder_path}/{self.save_prefix}_df_summary.csv") + + def get_model_params(self): + return self.model.cpu().state_dict() + + # def set_model_params(self, model_parameters): + # self.model.load_state_dict(model_parameters) + + def save_all_state_to_path(self, + path: str, + only_model_state_dict: bool = False) -> None: + ''' + save all information needed to continue training, include 3 random state in random, numpy and torch + :param path: where to save + :param epoch: which epoch when save + :param batch: which batch index when save + :param only_model_state_dict: only save the model, drop all other information + ''' + + epoch, batch = self.epoch_now, self.batch_now + + save_dict = { + 'epoch_num_when_save': epoch, + 'batch_num_when_save': batch, + 'random_state': random.getstate(), + 'np_random_state': np.random.get_state(), + 'torch_random_state': torch.random.get_rng_state(), + 'model_state_dict': self.get_model_params(), + 'optimizer_state_dict': self.optimizer.state_dict(), + 'scheduler_state_dict': self.scheduler.state_dict() if self.scheduler is not None else None, + 'criterion_state_dict': self.criterion.state_dict(), + "scaler": self.scaler.state_dict(), + } \ + if only_model_state_dict == False else self.get_model_params() + + torch.save( + save_dict, + path, + ) + + # def load_from_path(self, + # path: str, + # device, + # only_load_model: bool = False + # ) -> [Optional[int], Optional[int]]: + # ''' + # + # :param path: + # :param device: map model to which device + # :param only_load_model: only_load_model or not? + # ''' + # + # self.model = self.model.to(device, non_blocking=self.non_blocking) + # + # load_dict = torch.load( + # path, map_location=device + # ) + # + # logging.info(f"loading... keys:{load_dict.keys()}, only_load_model:{only_load_model}") + # + # attr_list = [ + # 'epoch_num_when_save', + # 'batch_num_when_save', + # 'random_state', + # 'np_random_state', + # 'torch_random_state', + # 'model_state_dict', + # 'optimizer_state_dict', + # 'scheduler_state_dict', + # 'criterion_state_dict', + # ] + # + # if all([key_name in load_dict for key_name in attr_list]) : + # # all required key can find in load dict + # # AND only_load_model == False + # if only_load_model == False: + # random.setstate(load_dict['random_state']) + # np.random.set_state(load_dict['np_random_state']) + # torch.random.set_rng_state(load_dict['torch_random_state'].cpu()) # since may map to cuda + # + # self.model.load_state_dict( + # load_dict['model_state_dict'] + # ) + # self.optimizer.load_state_dict( + # load_dict['optimizer_state_dict'] + # ) + # if self.scheduler is not None: + # self.scheduler.load_state_dict( + # load_dict['scheduler_state_dict'] + # ) + # self.criterion.load_state_dict( + # load_dict['criterion_state_dict'] + # ) + # if 'scaler' in load_dict: + # self.scaler.load_state_dict( + # load_dict["scaler"] + # ) + # logging.info(f'load scaler done. scaler={load_dict["scaler"]}') + # logging.info('all state load successful') + # return load_dict['epoch_num_when_save'], load_dict['batch_num_when_save'] + # else: + # self.model.load_state_dict( + # load_dict['model_state_dict'], + # ) + # logging.info('only model state_dict load') + # return None, None + # + # else: # only state_dict + # + # if 'model_state_dict' in load_dict: + # self.model.load_state_dict( + # load_dict['model_state_dict'], + # ) + # logging.info('only model state_dict load') + # return None, None + # else: + # self.model.load_state_dict( + # load_dict, + # ) + # logging.info('only model state_dict load') + # return None, None + # + +class PureCleanModelTrainer(ModelTrainerCLS_v2): + + def __init__(self, model): + super().__init__(model) + logging.debug("This class REQUIRE bd dataset to implement overwrite methods. This is NOT a general class for all cls task.") + + def train_one_epoch_on_mix(self, verbose=0): + + startTime = time() + + batch_loss_list = [] + if verbose == 1: + batch_predict_list = [] + batch_label_list = [] + batch_original_index_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + + for batch_idx in range(self.batch_num_per_epoch): + x, labels, original_index, poison_indicator, original_targets = self.get_one_batch() + one_batch_loss, batch_predict = self.one_forward_backward(x, labels, self.device, verbose) + batch_loss_list.append(one_batch_loss) + + if verbose == 1: + batch_predict_list.append(batch_predict.detach().clone().cpu()) + batch_label_list.append(labels.detach().clone().cpu()) + batch_original_index_list.append(original_index.detach().clone().cpu()) + batch_poison_indicator_list.append(poison_indicator.detach().clone().cpu()) + batch_original_targets_list.append(original_targets.detach().clone().cpu()) + + one_epoch_loss = sum(batch_loss_list) / len(batch_loss_list) + if self.scheduler is not None: + if isinstance(self.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + self.scheduler.step(one_epoch_loss) + else: + self.scheduler.step() + + endTime = time() + + logging.info(f"one epoch training part done, use time = {endTime - startTime} s") + + if verbose == 0: + return one_epoch_loss, \ + None, None, None, None, None + elif verbose == 1: + return one_epoch_loss, \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_original_index_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + + def test_given_dataloader_on_mix(self, test_dataloader, device = None, verbose = 0): + + if device is None: + device = self.device + + model = self.model + model.to(device, non_blocking=self.non_blocking) + model.eval() + + metrics = { + 'test_correct': 0, + 'test_loss_sum_over_batch': 0, + 'test_total': 0, + } + + criterion = self.criterion.to(device, non_blocking=self.non_blocking) + + if verbose == 1: + batch_predict_list = [] + batch_label_list = [] + batch_original_index_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + + with torch.no_grad(): + for batch_idx, (x, labels, original_index, poison_indicator, original_targets) in enumerate(test_dataloader): + x = x.to(device, non_blocking=self.non_blocking) + labels = labels.to(device, non_blocking=self.non_blocking) + pred = model(x) + loss = criterion(pred, labels.long()) + + _, predicted = torch.max(pred, -1) + correct = predicted.eq(labels).sum() + + if verbose == 1: + batch_predict_list.append(predicted.detach().clone().cpu()) + batch_label_list.append(labels.detach().clone().cpu()) + batch_original_index_list.append(original_index.detach().clone().cpu()) + batch_poison_indicator_list.append(poison_indicator.detach().clone().cpu()) + batch_original_targets_list.append(original_targets.detach().clone().cpu()) + + metrics['test_correct'] += correct.item() + metrics['test_loss_sum_over_batch'] += loss.item() + metrics['test_total'] += labels.size(0) + + metrics['test_loss_avg_over_batch'] = metrics['test_loss_sum_over_batch']/len(test_dataloader) + metrics['test_acc'] = metrics['test_correct'] / metrics['test_total'] + + if verbose == 0: + return metrics, \ + None, None, None, None, None + elif verbose == 1: + return metrics, \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_original_index_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + + def train_with_test_each_epoch_on_mix(self, + train_dataloader, + clean_test_dataloader, + bd_test_dataloader, + total_epoch_num, + criterion, + optimizer, + scheduler, + amp, + device, + frequency_save, + save_folder_path, + save_prefix, + prefetch, + prefetch_transform_attr_name, + non_blocking, + ): + + test_dataloader_dict = { + "clean_test_dataloader":clean_test_dataloader, + "bd_test_dataloader":bd_test_dataloader, + } + + self.set_with_dataloader( + train_dataloader, + test_dataloader_dict, + criterion, + optimizer, + scheduler, + device, + amp, + + frequency_save, + save_folder_path, + save_prefix, + + prefetch, + prefetch_transform_attr_name, + non_blocking, + ) + + train_loss_list = [] + train_mix_acc_list = [] + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + + for epoch in range(total_epoch_num): + + train_epoch_loss_avg_over_batch, \ + train_epoch_predict_list, \ + train_epoch_label_list, \ + train_epoch_original_index_list, \ + train_epoch_poison_indicator_list, \ + train_epoch_original_targets_list = self.train_one_epoch_on_mix(verbose=1) + + train_mix_acc = all_acc(train_epoch_predict_list, train_epoch_label_list) + + train_bd_idx = torch.where(train_epoch_poison_indicator_list == 1)[0] + train_clean_idx = torch.where(train_epoch_poison_indicator_list == 0)[0] + + clean_metrics, \ + clean_test_epoch_predict_list, \ + clean_test_epoch_label_list, \ + = self.test_given_dataloader(test_dataloader_dict["clean_test_dataloader"], verbose=1) + + clean_test_loss_avg_over_batch = clean_metrics["test_loss_avg_over_batch"] + test_acc = clean_metrics["test_acc"] + + bd_metrics, \ + bd_test_epoch_predict_list, \ + bd_test_epoch_label_list, \ + bd_test_epoch_original_index_list, \ + bd_test_epoch_poison_indicator_list, \ + bd_test_epoch_original_targets_list = self.test_given_dataloader_on_mix(test_dataloader_dict["bd_test_dataloader"], verbose=1) + + bd_test_loss_avg_over_batch = bd_metrics["test_loss_avg_over_batch"] + test_asr = all_acc(bd_test_epoch_predict_list, bd_test_epoch_label_list) + test_ra = all_acc(bd_test_epoch_predict_list, bd_test_epoch_original_targets_list) + + self.agg( + { + "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch, + "train_acc": train_mix_acc, + + + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch" : bd_test_loss_avg_over_batch, + "test_acc" : test_acc, + "test_asr" : test_asr, + "test_ra" : test_ra, + } + ) + + train_loss_list.append(train_epoch_loss_avg_over_batch) + train_mix_acc_list.append(train_mix_acc) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + + self.plot_loss( + train_loss_list, + clean_test_loss_list, + bd_test_loss_list, + ) + + self.plot_acc_like_metric( + train_mix_acc_list, + test_acc_list, + test_asr_list, + test_ra_list, + ) + + self.agg_save_dataframe() + + self.agg_save_summary() + + return train_loss_list, \ + train_mix_acc_list, \ + clean_test_loss_list, \ + bd_test_loss_list, \ + test_acc_list, \ + test_asr_list, \ + test_ra_list + + def plot_loss( + self, + train_loss_list : list, + clean_test_loss_list : list, + bd_test_loss_list : list, + save_file_name="loss_metric_plots", + ): + + plot_loss( + train_loss_list, + clean_test_loss_list, + bd_test_loss_list, + self.save_folder_path, + save_file_name, + ) + + def plot_acc_like_metric(self, + train_acc_list: list, + test_acc_list: list, + test_asr_list: list, + test_ra_list: list, + save_file_name="acc_like_metric_plots", + ): + + plot_acc_like_metric_pure( + train_acc_list, + test_acc_list, + test_asr_list, + test_ra_list, + self.save_folder_path, + save_file_name, + ) + + def test_current_model(self, test_dataloader_dict, device = None,): + + if device is None: + device = self.device + + model = self.model + model.to(device, non_blocking=self.non_blocking) + model.eval() + + clean_metrics, \ + clean_test_epoch_predict_list, \ + clean_test_epoch_label_list, \ + = self.test_given_dataloader(test_dataloader_dict["clean_test_dataloader"], verbose=1) + + clean_test_loss_avg_over_batch = clean_metrics["test_loss_avg_over_batch"] + test_acc = clean_metrics["test_acc"] + + bd_metrics, \ + bd_test_epoch_predict_list, \ + bd_test_epoch_label_list, \ + bd_test_epoch_original_index_list, \ + bd_test_epoch_poison_indicator_list, \ + bd_test_epoch_original_targets_list = self.test_given_dataloader_on_mix(test_dataloader_dict["bd_test_dataloader"], verbose=1) + + bd_test_loss_avg_over_batch = bd_metrics["test_loss_avg_over_batch"] + test_asr = all_acc(bd_test_epoch_predict_list, bd_test_epoch_label_list) + test_ra = all_acc(bd_test_epoch_predict_list, bd_test_epoch_original_targets_list) + + return clean_test_loss_avg_over_batch, \ + bd_test_loss_avg_over_batch, \ + test_acc, \ + test_asr, \ + test_ra + + + + +class BackdoorModelTrainer(ModelTrainerCLS_v2): + + def __init__(self, model, discriminator, optimizer_inter,regularization_ratio): + super().__init__(model,discriminator, optimizer_inter,regularization_ratio) + logging.debug("This class REQUIRE bd dataset to implement overwrite methods. This is NOT a general class for all cls task.") + + def train_one_epoch_on_mix(self, verbose=0): + + startTime = time() + + batch_loss_list = [] + if verbose == 1: + batch_predict_list = [] + batch_label_list = [] + batch_original_index_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + + for batch_idx in range(self.batch_num_per_epoch): + # print(batch_idx) + x, labels, original_index, poison_indicator, original_targets = self.get_one_batch() + one_batch_loss, batch_predict = self.one_forward_backward(x, labels, poison_indicator ,self.device, verbose) + batch_loss_list.append(one_batch_loss) + + if verbose == 1: + batch_predict_list.append(batch_predict.detach().clone().cpu()) + batch_label_list.append(labels.detach().clone().cpu()) + batch_original_index_list.append(original_index.detach().clone().cpu()) + batch_poison_indicator_list.append(poison_indicator.detach().clone().cpu()) + batch_original_targets_list.append(original_targets.detach().clone().cpu()) + + one_epoch_loss = sum(batch_loss_list) / len(batch_loss_list) + if self.scheduler is not None: + if isinstance(self.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + self.scheduler.step(one_epoch_loss) + else: + self.scheduler.step() + + endTime = time() + + logging.info(f"one epoch training part done, use time = {endTime - startTime} s") + + if verbose == 0: + return one_epoch_loss, \ + None, None, None, None, None + elif verbose == 1: + return one_epoch_loss, \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_original_index_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + + def test_given_dataloader_on_mix(self, test_dataloader, device = None, verbose = 0): + + if device is None: + device = self.device + + model = self.model + model.to(device, non_blocking=self.non_blocking) + model.eval() + + metrics = { + 'test_correct': 0, + 'test_loss_sum_over_batch': 0, + 'test_total': 0, + } + + criterion = self.criterion.to(device, non_blocking=self.non_blocking) + + if verbose == 1: + batch_predict_list = [] + batch_label_list = [] + batch_original_index_list = [] + batch_poison_indicator_list = [] + batch_original_targets_list = [] + + with torch.no_grad(): + for batch_idx, (x, labels, original_index, poison_indicator, original_targets) in enumerate(test_dataloader): + x = x.to(device, non_blocking=self.non_blocking) + labels = labels.to(device, non_blocking=self.non_blocking) + pred,inter = model(x) + loss = criterion(pred, labels.long()) + + _, predicted = torch.max(pred, -1) + correct = predicted.eq(labels).sum() + + if verbose == 1: + batch_predict_list.append(predicted.detach().clone().cpu()) + batch_label_list.append(labels.detach().clone().cpu()) + batch_original_index_list.append(original_index.detach().clone().cpu()) + batch_poison_indicator_list.append(poison_indicator.detach().clone().cpu()) + batch_original_targets_list.append(original_targets.detach().clone().cpu()) + + metrics['test_correct'] += correct.item() + metrics['test_loss_sum_over_batch'] += loss.item() + metrics['test_total'] += labels.size(0) + + metrics['test_loss_avg_over_batch'] = metrics['test_loss_sum_over_batch']/len(test_dataloader) + metrics['test_acc'] = metrics['test_correct'] / metrics['test_total'] + + if verbose == 0: + return metrics, \ + None, None, None, None, None + elif verbose == 1: + return metrics, \ + torch.cat(batch_predict_list), \ + torch.cat(batch_label_list), \ + torch.cat(batch_original_index_list), \ + torch.cat(batch_poison_indicator_list), \ + torch.cat(batch_original_targets_list) + + def train_with_test_each_epoch_on_mix(self, + train_dataloader, + clean_test_dataloader, + bd_test_dataloader, + total_epoch_num, + criterion, + optimizer, + scheduler, + amp, + device, + frequency_save, + save_folder_path, + save_prefix, + prefetch, + prefetch_transform_attr_name, + non_blocking, + ): + + test_dataloader_dict = { + "clean_test_dataloader":clean_test_dataloader, + "bd_test_dataloader":bd_test_dataloader, + } + + self.set_with_dataloader( + train_dataloader, + test_dataloader_dict, + criterion, + optimizer, + scheduler, + device, + amp, + + frequency_save, + save_folder_path, + save_prefix, + + prefetch, + prefetch_transform_attr_name, + non_blocking, + ) + + train_loss_list = [] + train_mix_acc_list = [] + train_asr_list = [] + train_ra_list = [] + clean_test_loss_list = [] + bd_test_loss_list = [] + test_acc_list = [] + test_asr_list = [] + test_ra_list = [] + + for epoch in range(total_epoch_num): + + train_epoch_loss_avg_over_batch, \ + train_epoch_predict_list, \ + train_epoch_label_list, \ + train_epoch_original_index_list, \ + train_epoch_poison_indicator_list, \ + train_epoch_original_targets_list = self.train_one_epoch_on_mix(verbose=1) + + train_mix_acc = all_acc(train_epoch_predict_list, train_epoch_label_list) + + train_bd_idx = torch.where(train_epoch_poison_indicator_list == 1)[0] + train_clean_idx = torch.where(train_epoch_poison_indicator_list == 0)[0] + train_clean_acc = all_acc( + train_epoch_predict_list[train_clean_idx], + train_epoch_label_list[train_clean_idx], + ) + train_asr = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_label_list[train_bd_idx], + ) + train_ra = all_acc( + train_epoch_predict_list[train_bd_idx], + train_epoch_original_targets_list[train_bd_idx], + ) + + clean_metrics, \ + clean_test_epoch_predict_list, \ + clean_test_epoch_label_list, \ + = self.test_given_dataloader(self.test_dataloader_dict["clean_test_dataloader"], verbose=1) + + clean_test_loss_avg_over_batch = clean_metrics["test_loss_avg_over_batch"] + test_acc = clean_metrics["test_acc"] + + bd_metrics, \ + bd_test_epoch_predict_list, \ + bd_test_epoch_label_list, \ + bd_test_epoch_original_index_list, \ + bd_test_epoch_poison_indicator_list, \ + bd_test_epoch_original_targets_list = self.test_given_dataloader_on_mix(self.test_dataloader_dict["bd_test_dataloader"], verbose=1) + + bd_test_loss_avg_over_batch = bd_metrics["test_loss_avg_over_batch"] + test_asr = all_acc(bd_test_epoch_predict_list, bd_test_epoch_label_list) + test_ra = all_acc(bd_test_epoch_predict_list, bd_test_epoch_original_targets_list) + + self.agg( + { + "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch, + "train_acc": train_mix_acc, + "train_acc_clean_only": train_clean_acc, + "train_asr_bd_only": train_asr, + "train_ra_bd_only": train_ra, + + "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch, + "bd_test_loss_avg_over_batch" : bd_test_loss_avg_over_batch, + "test_acc" : test_acc, + "test_asr" : test_asr, + "test_ra" : test_ra, + } + ) + + train_loss_list.append(train_epoch_loss_avg_over_batch) + train_mix_acc_list.append(train_mix_acc) + train_asr_list.append(train_asr) + train_ra_list.append(train_ra) + + clean_test_loss_list.append(clean_test_loss_avg_over_batch) + bd_test_loss_list.append(bd_test_loss_avg_over_batch) + test_acc_list.append(test_acc) + test_asr_list.append(test_asr) + test_ra_list.append(test_ra) + + self.plot_loss( + train_loss_list, + clean_test_loss_list, + bd_test_loss_list, + ) + + self.plot_acc_like_metric( + train_mix_acc_list, + train_asr_list, + train_ra_list, + test_acc_list, + test_asr_list, + test_ra_list, + ) + + self.agg_save_dataframe() + + self.agg_save_summary() + + return train_loss_list, \ + train_mix_acc_list, \ + train_asr_list, \ + train_ra_list, \ + clean_test_loss_list, \ + bd_test_loss_list, \ + test_acc_list, \ + test_asr_list, \ + test_ra_list + + def plot_loss( + self, + train_loss_list : list, + clean_test_loss_list : list, + bd_test_loss_list : list, + save_file_name="loss_metric_plots", + ): + + plot_loss( + train_loss_list, + clean_test_loss_list, + bd_test_loss_list, + self.save_folder_path, + save_file_name, + ) + + def plot_acc_like_metric(self, + train_acc_list: list, + train_asr_list: list, + train_ra_list: list, + test_acc_list: list, + test_asr_list: list, + test_ra_list: list, + save_file_name="acc_like_metric_plots", + ): + + plot_acc_like_metric( + train_acc_list, + train_asr_list, + train_ra_list, + test_acc_list, + test_asr_list, + test_ra_list, + self.save_folder_path, + save_file_name, + ) \ No newline at end of file