From c01bdfade67674da4b4cfa75c42409fdc6b6bea8 Mon Sep 17 00:00:00 2001 From: Abdur Date: Fri, 14 Jun 2024 02:50:16 +0530 Subject: [PATCH] Medical Report Generation And VQA (Adapting XrayGPT to Any Modality) --- .DS_Store | Bin 0 -> 8196 bytes .gitignore | 6 +- README-DATASET.md | 37 -- README.md | 185 ++++------ Screenshot WebApp.png | Bin 0 -> 1019156 bytes Thumbs.db | Bin 0 -> 60416 bytes env.yml | 317 ------------------ eval_configs/xraygpt_eval.yaml | 4 +- hard_requirements.txt | 259 ++++++++++++++ images/.DS_Store | Bin 0 -> 6148 bytes train.py | 1 + train_configs/xraygpt_openi_finetune.yaml | 20 +- xraygpt/configs/datasets/openi/defaults.yaml | 2 +- xraygpt/conversation/conversation.py | 1 + xraygpt/datasets/datasets/base_dataset.py | 3 +- xraygpt/datasets/datasets/caption_datasets.py | 10 +- xraygpt/datasets/datasets/openi_dataset.py | 10 +- xraygpt/models/mini_gpt4.py | 40 ++- xraygpt/runners/runner_base.py | 6 +- 19 files changed, 387 insertions(+), 514 deletions(-) create mode 100644 .DS_Store delete mode 100644 README-DATASET.md create mode 100644 Screenshot WebApp.png create mode 100644 Thumbs.db delete mode 100644 env.yml create mode 100644 hard_requirements.txt create mode 100644 images/.DS_Store diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..58fabd6fe34c36de2886d423501775d169db1892 GIT binary patch literal 8196 zcmeHM%Wl&^6ur}iCRM4(qJ=Ciu?-@L)T9+c3Lz|5Az=gyK*7dQYGHe#*l89~BrkdV zPsJDT1&HtA%%fFt8kJSb<6ddzI5YS9`W!o++zAnh-ZW?u)rhEqrm(t$?h;e;sx8@u z9k~e%3eE5Hh{0;~Wl@FysMHCtI_#ky~1xn~7ff&Wqg^?q>B z6m~7<2KB20oh|`@B@AoBG3o#v<5=uk%nj;LoYPbf!iEZ4Vh9tCc}KYeyB2eUCY*!` zCt))STcHS@9dV9!C($*?JuAQpEGwXD_c8_KQHFM*ewRLGzWU=ROtVJghuGLG-PkI_ zcFJ$vV>xp(FPo-4Z}M7wdLd=xpB;Pt%WyawRQDdsB=f>#IMD%NFoesSS78#!Sx-)r zAl0$1nb?L6sEbxwHzwbwycjeCa!-uE4-ySWWCzio)c#V&*%68&Tl1L^aWKb-& zD5My7V1yl%)a;<>D>+^J9k@N8H_r7xIy$&a|Bpt+mHK}wGAgjWMbBsqrU^Zzdvt*2 zf_sdysKEbkegdyxShVa)5l`4Vx@3S5r@n - -The MIMIC-CXR is a publicly available collection of chest radiographs associated with free-text radiology reports. It consists of 377,110 images and 227,827 associated reports, which are used for both training and testing purposes. The dataset is de-identified to satisfy the US Health Insurance Portability and Accountability Act of 1996 (HIPAA) Safe Harbor requirements. Protected health information (PHI) has been removed. You can download the raw images from [here](https://physionet.org/content/mimic-cxr-jpg/2.0.0/) and the raw reports from [here](https://physionet.org/content/mimic-cxr/2.0.0/). The OpenI dataset is a collection of chest x-ray images from the Indiana University hospital network comprises of 6,459 images and 3,955 reports. You can download OpenI dataset and raw reports from [here](https://openi.nlm.nih.gov/faq#collection). - -### Preprocessing -
- -To generate concise and coherent medical summaries from the unstructured reports, we performed the following pre-processing steps for both datasets: - -+ Removal of incomplete reports lacking finding or impression sections. -+ Elimination of reports with finding sections containing less than 10 words. -+ Exclusion of reports with impression sections containing less than 2 words. - -In addition, utilizing the power of gpt-3.5-turbo, we implemented the following pre-processing techniques to ensure high-quality summaries per image: - -+ Elimination of sentences containing comparisons to the patient's prior medical history. -+ Removal of de-defined symbols "__" while preserving the original meaning. -+ As our training relies on image-text pairs, we excluded the provided view from the summary. -+ We combine the clean findings and impression to generate an interactive and high-quality summary. - -Following these steps, we obtained a set of filtered training reports consisting of 114,690 reports associated with 241k training images based on Mimic-CXR dataset. Also, we obtained 3,403 high-quality summaries that used for training based on OpenI dataset. - -Here is an example before and after the proposed pre-processing: - -**Input findings:** -PA and lateral views of the chest were provided demonstrating no focal consolidation, effusion or pneumothorax. Cardiomediastinal silhouette appears normal and stable.  There is a compression deformity involving a mid thoracic vertebral body, which appears new from the prior chest radiograph of ___.  No free air below the right hemidiaphragm. There are tiny surgical clips in the left base of neck, likely indicating prior thyroid surgery. - -**Input Impression:** -No acute intrathoracic process.  Interval development of a mid thoracic spine compression fracture. -  - -**High-quality and interactive summary:** -The chest x-ray findings reveal no evidence of focal consolidation, effusion, or pneumothorax. The cardiomediastinal silhouette appears stable and normal. There is a newly developed mid thoracic spine compression fracture but no free air below the right hemidiaphragm. The presence of surgical clips in the left base of the neck suggests prior thyroid surgery. The impression suggests that there is no acute intrathoracic condition detected in the x-ray aside from the new development of mid thoracic spine compression fracture. diff --git a/README.md b/README.md index 83fc02f..7e9ec09 100644 --- a/README.md +++ b/README.md @@ -1,95 +1,70 @@ -# XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models. -![](https://i.imgur.com/waxVImv.png) - -[Omkar Thawakar](https://omkarthawakar.github.io/)* , [Abdelrahman Shaker](https://amshaker.github.io/)* , [Sahal Shaji Mullappilly](https://scholar.google.com/citations?user=LJWxVpUAAAAJ&hl=en)* , [Hisham Cholakkal](https://scholar.google.com/citations?hl=en&user=bZ3YBRcAAAAJ), [Rao Muhammad Anwer](https://scholar.google.com/citations?hl=en&authuser=1&user=_KlvMVoAAAAJ), [Salman Khan](https://salman-h-khan.github.io/), [Jorma Laaksonen](https://scholar.google.com/citations?user=qQP6WXIAAAAJ&hl=en), and [Fahad Shahbaz Khan](https://scholar.google.es/citations?user=zvaeYnUAAAAJ&hl=en). - -*Equal Contribution - -**Mohamed bin Zayed University of Artificial Intelligence, UAE** - - [![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://youtu.be/-zzq7bzbUuY) - - -## :rocket: News -
- -+ Jun-14 : Our technical report is released [here](https://arxiv.org/abs/2306.07971). :fire::fire: -+ May-25 : Our technical report will be released very soon. stay tuned!. -+ May-19 : Our code, models, and pre-processed report summaries are released. - - -## Online Demo -You can try our demo using the provided examples or by uploading your own X-ray here : [Link-1](https://e764abfa8fdc8ad0c8.gradio.live) | [Link-2](https://61adec76d380025b25.gradio.live) | [Link-3](https://c1a70c1631c7cc54cd.gradio.live) . - +# Medical Report Generation (& VQA) using a VLM (XrayGPT-Based). ## About XrayGPT -
-+ XrayGPT aims to stimulate research around automated analysis of chest radiographs based on the given x-ray.  -+ The LLM (Vicuna) is fine-tuned on medical data (100k real conversations between patients and doctors) and ~30k radiology conversations to acquire domain specific and relevant features.  -+ We generate interactive and clean summaries (~217k) from free-text radiology reports of two datasets ([MIMIC-CXR](https://physionet.org/content/mimic-cxr-jpg/2.0.0/) and [OpenI](https://openi.nlm.nih.gov/faq#collection)). These summaries serve to enhance the performance of LLMs through fine-tuning the linear transformation layer on high-quality data. For more details regarding our high-quality summaries, please check [Dataset Creation](README-DATASET.md). -+ We align frozen medical visual encoder (MedClip) with a fune-tuned LLM (Vicuna), using simple linear transformation. +XrayGPT is a state-of-the-art model for chest radiology report generation using large medical vision-language models. Built on top of BLIP-2 and MedCLIP, XrayGPT aligns a frozen visual encoder with a frozen large language model (LLM), Vicuna, using a Q-former. This repository extends XrayGPT for general-purpose medical report generation and Visual Question Answering (VQA). -![overview](images/Overall_architecture_V3.gif) +- [XrayGPT Paper](https://arxiv.org/abs/2306.07971) +- [XrayGPT Repository](https://github.com/mbzuai-oryx/XrayGPT) +## Using This Repository -## Getting Started ### Installation -**1. Prepare the code and the environment** +Due to inconsistencies and incompatibilities among various libraries in the original codebase, a new environment is created to run the code in a Runpod container. The environment is based on Python 3.10, PyTorch 2.0.0, and CUDA 11.8. -Clone the repository and create a anaconda environment +[Runpod Website](https://runpod.io/) + +Use the Runpod Template `pytorch:2.1.0-py3.10-cuda11.8.0` and run the following commands to install the required libraries: ```bash -git clone https://github.com/mbzuai-oryx/XrayGPT.git -cd XrayGPT -conda env create -f env.yml -conda activate xraygpt -``` -OR -```bash -git clone https://github.com/mbzuai-oryx/XrayGPT.git -cd XrayGPT -conda create -n xraygpt python=3.9 -conda activate xraygpt -pip install -r xraygpt_requirements.txt +apt-get update -y && apt-get install zip unzip vim -y +python -m pip install --upgrade pip +pip install gdown +pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu118 +pip install -r hard_requirements.txt --no-deps +pip install pydantic==1.10.7 +pip install hyperframe==5.2.0 +pip install gradio==3.23.0 +pip install safetensors==0.4.3 ``` ### Setup +Below is a brief overview of the steps for fine-tuning the model. Detailed instructions for training from scratch are provided in the original repository. -**1. Prepare the Datasets for training** +#### 1. Prepare the Datasets for Training +Publicly available datasets for medical report generation predominantly focus on chest X-ray reports, often derived from sources like MIMIC-CXR/OpenI. While these datasets are valuable, they lack diversity in terms of medical imaging modalities. To address this limitation and enhance the model's capabilities for multi-modality report generation and Visual Question Answering (VQA), we curated a unique dataset by integrating two distinct datasets: OpenI and ROCO. -Refer the [dataset_creation](README-DATASET.md) for more details. +**OpenI Dataset**: [OpenI](https://openi.nlm.nih.gov/faq) is a well-known resource provided by the Indiana University School of Medicine, comprising chest X-ray images paired with corresponding radiology reports. +- **Kaggle Download:** [Link](https://www.kaggle.com/datasets/raddar/chest-xrays-indiana-university) +- **Description:** Radiology reports and chest X-ray images +- **Samples:** 4,000 +- **Usage:** Report generation (Chest X-ray) -Download the preprocessed annoatations [mimic](https://mbzuaiac-my.sharepoint.com/:u:/g/personal/omkar_thawakar_mbzuai_ac_ae/EZ6500itBIVMnD7sUztdMQMBVWVe7fuF7ta4FV78hpGSwg?e=wyL7Z7) & [openi](https://mbzuaiac-my.sharepoint.com/:u:/g/personal/omkar_thawakar_mbzuai_ac_ae/EVYGprPyzdhOjFlQ2aNJbykBj49SwTGBYmC1uJ7TMswaVQ?e=qdqS8U). -Respective image folders contains the images from the dataset. +**ROCO Dataset**: [ROCO](https://github.com/razorx89/roco-dataset) (Radiology Objects in COntext) is a multimodal medical image dataset enriched with descriptive captions, offering a broader spectrum of medical imaging scenarios. -Following will be the final dataset folder structure: +- **Description:** Multimodal images with detailed descriptive captions +- **Dataset Size:** 8,000 samples (validation split used) +- **Usage:** Enables the model to generalize across various medical imaging modalities beyond chest X-rays. + +By combining the OpenI and ROCO datasets and processing them using the OpenAI API, we created a comprehensive dataset suitable for training our model. The data integration resulted in a structured dataset stored in the `dataset` folder, facilitating efficient training and evaluation processes. + +The final structure of the dataset folder is as follows: ``` dataset -├── mimic -| ├── image -| | ├──abea5eb9-b7c32823-3a14c5ca-77868030-69c83139.jpg -| | ├──427446c1-881f5cce-85191ce1-91a58ba9-0a57d3f5.jpg -| | ..... -| ├──filter_cap.json -├── openi -| ├── image -| | ├──1.jpg -| | ├──2.jpg -| | ..... -| ├──filter_cap.json -... +├── image +| ├──1.jpg +| ├──2.jpg +| ├──3.jpg +| ..... +├── filter_cap.json ``` -**3. Prepare the pretrained Vicuna weights** +#### 2. Prepare the Pretrained Vicuna Weights -We built XrayGPT on the v1 versoin of Vicuna-7B. -We finetuned Vicuna using curated radiology report samples. -Download the Vicuna weights from [vicuna_weights](https://mbzuaiac-my.sharepoint.com/:u:/g/personal/omkar_thawakar_mbzuai_ac_ae/EWoMYn3x7sdEnM2CdJRwWZgBCkMpLM03bk4GR5W0b3KIQQ?e=q6hEBz) -The final weights would be in a single folder in a structure similar to the following: +Download the finetuned version of `Vicuna-7B` from the [original XrayGPT link](https://mbzuaiac-my.sharepoint.com/:u:/g/personal/omkar_thawakar_mbzuai_ac_ae/EWoMYn3x7sdEnM2CdJRwWZgBCkMpLM03bk4GR5W0b3KIQQ?e=q6hEBz). The final weights should be in a single folder with a structure similar to the following: ``` vicuna_weights @@ -100,76 +75,42 @@ vicuna_weights ... ``` -Then, set the path to the vicuna weight in the model config file "xraygpt/configs/models/xraygpt.yaml" at Line 16. - -To finetune Vicuna on radiology samples please download our curated [radiology](https://mbzuaiac-my.sharepoint.com/:u:/g/personal/omkar_thawakar_mbzuai_ac_ae/EXsChX3eN_lJgcrV2fLUU0QBQalFkDtp-mlHNixta_hc4w) and [medical_healthcare](https://mbzuaiac-my.sharepoint.com/:u:/g/personal/omkar_thawakar_mbzuai_ac_ae/Ecm7-uxj045DhHqZTSBsZi4B2Ld77tE-uB7SvvmLNmCW1Q?e=t5YLgi) conversational samples and refer the original Vicuna repo for finetune.[Vicuna_Finetune](https://github.com/lm-sys/FastChat#fine-tuning) +#### 3. Download the Minigpt-4 Checkpoint -**4. Download the pretrained Minigpt-4 checkpoint** +Download the Minigpt-4 checkpoint from the [trained XrayGPT model](https://mbzuaiac-my.sharepoint.com/:u:/g/personal/omkar_thawakar_mbzuai_ac_ae/EbGJZmueJkFAstU965buWs8B7T8tLcks7N-P79gsExRH0Q?e=mVASdV). -Download the pretrained minigpt-4 checkpoints. [ckpt](https://drive.google.com/file/d/1RY9jV0dyqLX-o38LrumkKRh6Jtaop58R/view?pli=1) +### Model Training +Here we fine-tuned a pretrained XrayGPT model on the dataset created above. The model was initially trained on the MIMIC and OpenI datasets in a two-stage training process. - -## 5. Training of XrayGPT - -**A. First mimic pretraining stage** - -In the first pretrained stage, the model is trained using image-text pairs from preprocessed mimic dataset. - -To launch the first stage training, run the following command. In our experiments, we use 4 AMD MI250X GPUs. +Run the following command: ```bash -torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/xraygpt_mimic_pretrain.yaml +python3 train.py --cfg-path train_configs/xraygpt_openi_finetune.yaml ``` -**2. Second openi finetuning stage** +### Launching the Demo -In the second stage, we use a small high quality image-text pair openi dataset preprocessed by us. +Download the pretrained XrayGPT checkpoints from the [link](https://mbzuaiac-my.sharepoint.com/:u:/g/personal/omkar_thawakar_mbzuai_ac_ae/EbGJZmueJkFAstU965buWs8B7T8tLcks7N-P79gsExRH0Q?e=mVASdV) and add this checkpoint in `eval_configs/xraygpt_eval.yaml`. -Run the following command. In our experiments, we use AMD MI250X GPU. +Run the following command to launch the demo: ```bash -torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/xraygpt_openi_finetune.yaml -``` - -### Launching Demo on local machine - - -Download the pretrained xraygpt checkpoints. [link](https://mbzuaiac-my.sharepoint.com/:u:/g/personal/omkar_thawakar_mbzuai_ac_ae/EbGJZmueJkFAstU965buWs8B7T8tLcks7N-P79gsExRH0Q?e=mVASdV) - -Add this ckpt in "eval_configs/xraygpt_eval.yaml". - -Try gradio [demo.py](demo.py) on your local machine with following - -``` -python demo.py --cfg-path eval_configs/xraygpt_eval.yaml --gpu-id 0 +python demo.py --cfg-path eval_configs/xraygpt_eval.yaml --gpu-id 0 ``` -## Examples - | | | -:-------------------------:|:-------------------------: -![example 1](images/image1.jpg) | ![example 2](images/image2.jpg) -![example 3](images/image3.jpg) | ![example 4](images/image4.jpg) +### Important Note +Due to computational constraints, the model was fine-tuned on a very small subset of the newly curated dataset (only 100 samples total). The model is not fully trained, and the results are not accurate. The purpose was to set up the environment and run the code. For better results, please train the model on the complete datasets. -## Acknowledgement -
+## Citation -+ [MiniGPT-4](https://minigpt-4.github.io) Enhancing Vision-language Understanding with Advanced Large Language Models. We built our model on top of MiniGPT-4. -+ [MedCLIP](https://github.com/RyanWangZf/MedCLIP) Contrastive Learning from Unpaired Medical Images and Texts. We used medical aware image encoder from MedCLIP. -+ [BLIP2](https://huggingface.co/docs/transformers/main/model_doc/blip-2) The model architecture of XrayGPT follows BLIP-2. -+ [Lavis](https://github.com/salesforce/LAVIS) This repository is built upon Lavis! -+ [Vicuna](https://github.com/lm-sys/FastChat) The fantastic language ability of Vicuna is just amazing. And it is open-source! +If you use this work, please cite the following paper: -## Citation -If you're using XrayGPT in your research or applications, please cite using this BibTeX: ```bibtex - @article{Omkar2023XrayGPT, - title={XrayGPT: Chest Radiographs Summarization using Large Medical Vision-Language Models}, - author={Omkar Thawkar, Abdelrahman Shaker, Sahal Shaji Mullappilly, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan, Jorma Laaksonen and Fahad Shahbaz Khan}, - journal={arXiv: 2306.07971}, - year={2023} - } -``` - -## License -This repository is licensed under CC BY-NC-SA. Please refer to the license terms [here](https://creativecommons.org/licenses/by-nc-sa/4.0/). +@article{Omkar2023XrayGPT, + title={XrayGPT: Chest Radiographs Summarization using Large Medical Vision-Language Models}, + author={Omkar Thawkar, Abdelrahman Shaker, Sahal Shaji Mullappilly, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan, Jorma Laaksonen and Fahad Shahbaz Khan}, + journal={arXiv: 2306.07971}, + year={2023} +} +``` \ No newline at end of file diff --git a/Screenshot WebApp.png b/Screenshot WebApp.png new file mode 100644 index 0000000000000000000000000000000000000000..1ac17bca6b0eda1588105fd7d0eb61d16c59012a GIT binary patch literal 1019156 zcmeFZc{tSV`#+47RN7Q-WJ?I4>^r4W)U&$rk2^)y+IaU7$g zqhq;u_m%-2-SI&>I;QfYM`%};K4mb{e&|64nm6ex;1?HYC&~7v_nzwN(uvTHkJ2&H zbI=_=c!c&*rRV(T@g4drbccRkXP~2taG?9y4DDQx_Br^AqkRr&e*H4Whto09&d$<4 zFLLRBQ_}9|GW>(`_+aj#e;hNF)1ITdVW@HM9_`oA*2CW374!_~$-*d)qFrEiyK4rb 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ca-certificates=2023.01.10=h06a4308_0 - - certifi=2022.12.7=py39h06a4308_0 - - cffi=1.15.1=py39h5eee18b_3 - - charset-normalizer=2.0.4=pyhd3eb1b0_0 - - cryptography=39.0.1=py39h9ce1e76_0 - - cudatoolkit=11.3.1=h2bc3f7f_2 - - ffmpeg=4.3=hf484d3e_0 - - flit-core=3.8.0=py39h06a4308_0 - - freetype=2.12.1=h4a9f257_0 - - giflib=5.2.1=h5eee18b_3 - - gmp=6.2.1=h295c915_3 - - gnutls=3.6.15=he1e5248_0 - - intel-openmp=2021.4.0=h06a4308_3561 - - jpeg=9e=h5eee18b_1 - - lame=3.100=h7b6447c_0 - - lcms2=2.12=h3be6417_0 - - ld_impl_linux-64=2.38=h1181459_1 - - lerc=3.0=h295c915_0 - - libdeflate=1.17=h5eee18b_0 - - libffi=3.4.2=h6a678d5_6 - - libgcc-ng=11.2.0=h1234567_1 - - libgomp=11.2.0=h1234567_1 - - libiconv=1.16=h7f8727e_2 - - libidn2=2.3.2=h7f8727e_0 - - libpng=1.6.39=h5eee18b_0 - - libstdcxx-ng=11.2.0=h1234567_1 - - libtasn1=4.19.0=h5eee18b_0 - - libtiff=4.5.0=h6a678d5_2 - - libunistring=0.9.10=h27cfd23_0 - - libwebp=1.2.4=h11a3e52_1 - - libwebp-base=1.2.4=h5eee18b_1 - - lz4-c=1.9.4=h6a678d5_0 - - mkl=2021.4.0=h06a4308_640 - - mkl-service=2.4.0=py39h7f8727e_0 - - mkl_fft=1.3.1=py39hd3c417c_0 - - mkl_random=1.2.2=py39h51133e4_0 - - ncurses=6.4=h6a678d5_0 - - nettle=3.7.3=hbbd107a_1 - - numpy=1.23.5=py39h14f4228_0 - - numpy-base=1.23.5=py39h31eccc5_0 - - openh264=2.1.1=h4ff587b_0 - - openssl=1.1.1t=h7f8727e_0 - - pillow=9.4.0=py39h6a678d5_0 - - pip=23.0.1=py39h06a4308_0 - - pycparser=2.21=pyhd3eb1b0_0 - - pyopenssl=23.0.0=py39h06a4308_0 - - pysocks=1.7.1=py39h06a4308_0 - - python=3.9.16=h7a1cb2a_2 - - pytorch-mutex=1.0=cuda - - readline=8.2=h5eee18b_0 - - requests=2.28.1=py39h06a4308_1 - - setuptools=66.0.0=py39h06a4308_0 - - six=1.16.0=pyhd3eb1b0_1 - - sqlite=3.41.2=h5eee18b_0 - - tk=8.6.12=h1ccaba5_0 - - torchaudio=0.12.1=py39_cu113 - - torchvision=0.13.1=py39_cu113 - - typing_extensions=4.4.0=py39h06a4308_0 - - urllib3=1.26.15=py39h06a4308_0 - - wheel=0.38.4=py39h06a4308_0 - - xz=5.2.10=h5eee18b_1 - - zlib=1.2.13=h5eee18b_0 - - zstd=1.5.5=hc292b87_0 - - pip: - - accelerate==0.15.0 - - aiofiles==23.1.0 - - aiohttp==3.8.4 - - aiosignal==1.3.1 - - albumentations==1.3.0 - - altair==4.2.2 - - antlr4-python3-runtime==4.9.3 - - anyio==3.6.2 - - appdirs==1.4.4 - - argon2-cffi==21.3.0 - - argon2-cffi-bindings==21.2.0 - - arrow==1.2.3 - - asttokens==2.2.1 - - async-timeout==4.0.2 - - attrs==22.2.0 - - backcall==0.2.0 - - beautifulsoup4==4.12.2 - - bitsandbytes==0.37.0 - - bleach==6.0.0 - - blis==0.7.9 - - braceexpand==0.1.7 - - cachetools==5.3.0 - - catalogue==2.0.8 - - cchardet==2.1.7 - - chardet==3.0.4 - - click==8.1.3 - - cmake==3.26.3 - - comm==0.1.3 - - confection==0.0.4 - - contourpy==1.0.7 - - cycler==0.11.0 - - cymem==2.0.7 - - dataclasses==0.6 - - datasets==2.12.0 - - debugpy==1.6.7 - - decorator==5.1.1 - - decord==0.6.0 - - defusedxml==0.7.1 - - dill==0.3.6 - - docker-pycreds==0.4.0 - - entrypoints==0.4 - - et-xmlfile==1.1.0 - - evaluate==0.4.0 - - executing==1.2.0 - - exifread-nocycle==3.0.1 - - fairscale==0.4.13 - - fastapi==0.95.1 - - fastchat==0.1 - - fastjsonschema==2.16.3 - - ffmpy==0.3.0 - - filelock==3.9.0 - - fire==0.5.0 - - fonttools==4.38.0 - - fqdn==1.5.1 - - frozenlist==1.3.3 - - fschat==0.1.10 - - fsspec==2022.11.0 - - gensim==4.3.1 - - gitdb==4.0.10 - - gitpython==3.1.31 - - googletrans==3.0.0 - - gradio==3.23.0 - - gradio-client==0.0.8 - - h11==0.9.0 - - h2==3.2.0 - - hiq-python==1.1.12 - - hpack==3.0.0 - - hstspreload==2023.1.1 - - httpcore==0.9.1 - - httpx==0.13.3 - - huggingface-hub==0.13.4 - - hyperframe==5.2.0 - - idna==2.10 - - imageio==2.27.0 - - img2dataset==1.41.0 - - importlib-metadata==6.5.0 - - importlib-resources==5.12.0 - - iopath==0.1.10 - - ipykernel==6.22.0 - - ipython==8.12.0 - - ipython-genutils==0.2.0 - - isoduration==20.11.0 - - jedi==0.18.2 - - jinja2==3.1.2 - - joblib==1.2.0 - - jsonpointer==2.3 - - jsonschema==4.17.3 - - jupyter-client==8.2.0 - - jupyter-core==5.3.0 - - jupyter-events==0.6.3 - - jupyter-server==2.5.0 - - jupyter-server-terminals==0.4.4 - - jupyterlab-pygments==0.2.2 - - kiwisolver==1.4.4 - - langcodes==3.3.0 - - lazy-loader==0.2 - - linkify-it-py==2.0.0 - - lit==16.0.1 - - llvmlite==0.39.1 - - markdown-it-py==2.2.0 - - markdown2==2.4.8 - - markupsafe==2.1.2 - - matplotlib==3.7.0 - - matplotlib-inline==0.1.6 - - mdit-py-plugins==0.3.3 - - mdurl==0.1.2 - - medclip==0.0.3 - - mistune==2.0.5 - - mpmath==1.3.0 - - multidict==6.0.4 - - multiprocess==0.70.14 - - murmurhash==1.0.9 - - nbclassic==0.5.5 - - nbclient==0.7.3 - - nbconvert==7.3.1 - - nbformat==5.8.0 - - nest-asyncio==1.5.6 - - networkx==3.1 - - nltk==3.8.1 - - notebook==6.5.4 - - notebook-shim==0.2.2 - - numba==0.56.4 - - nvidia-cublas-cu11==11.10.3.66 - - nvidia-cuda-cupti-cu11==11.7.101 - - nvidia-cuda-nvrtc-cu11==11.7.99 - - nvidia-cuda-runtime-cu11==11.7.99 - - nvidia-cudnn-cu11==8.5.0.96 - - nvidia-cufft-cu11==10.9.0.58 - - nvidia-curand-cu11==10.2.10.91 - - nvidia-cusolver-cu11==11.4.0.1 - - nvidia-cusparse-cu11==11.7.4.91 - - nvidia-nccl-cu11==2.14.3 - - nvidia-nvtx-cu11==11.7.91 - - omegaconf==2.3.0 - - openai==0.27.0 - - opencv-python==4.7.0.72 - - opencv-python-headless==4.7.0.72 - - openpyxl==3.1.2 - - orjson==3.8.10 - - packaging==23.0 - - pandas==1.5.3 - - pandocfilters==1.5.0 - - parso==0.8.3 - - pathtools==0.1.2 - - pathy==0.10.1 - - peft==0.2.0 - - pexpect==4.8.0 - - pickleshare==0.7.5 - - platformdirs==3.2.0 - - portalocker==2.7.0 - - preshed==3.0.8 - - prometheus-client==0.16.0 - - promise==2.3 - - prompt-toolkit==3.0.38 - - protobuf==3.20.3 - - psutil==5.9.4 - - ptyprocess==0.7.0 - - pure-eval==0.2.2 - - py-itree==0.0.19 - - pyarrow==12.0.0 - - pycocoevalcap==1.2 - - pycocotools==2.0.6 - - pydantic==1.10.7 - - pydub==0.25.1 - - pygments==2.15.1 - - pyllama==0.0.9 - - pynndescent==0.5.9 - - pyparsing==3.0.9 - - pyrsistent==0.19.3 - - python-dateutil==2.8.2 - - python-json-logger==2.0.7 - - python-multipart==0.0.6 - - pytz==2023.3 - - pywavelets==1.4.1 - - pyyaml==6.0 - - pyzmq==25.0.2 - - qudida==0.0.4 - - regex==2022.10.31 - - responses==0.18.0 - - rfc3339-validator==0.1.4 - - rfc3986==1.5.0 - - rfc3986-validator==0.1.1 - - rich==13.3.4 - - scikit-image==0.20.0 - - scikit-learn==1.2.2 - - scipy==1.9.1 - - semantic-version==2.10.0 - - send2trash==1.8.0 - - sentence-transformers==2.2.2 - - sentencepiece==0.1.97 - - sentry-sdk==1.19.1 - - setproctitle==1.3.2 - - shortuuid==1.0.11 - - smart-open==6.3.0 - - smmap==5.0.0 - - sniffio==1.3.0 - - soupsieve==2.4.1 - - spacy==3.5.1 - - spacy-legacy==3.0.12 - - spacy-loggers==1.0.4 - - srsly==2.4.6 - - stack-data==0.6.2 - - starlette==0.26.1 - - svgwrite==1.4.3 - - sympy==1.11.1 - - tenacity==8.2.2 - - termcolor==2.2.0 - - terminado==0.17.1 - - textaugment==1.3.4 - - textblob==0.17.1 - - thinc==8.1.9 - - threadpoolctl==3.1.0 - - tifffile==2023.4.12 - - timm==0.6.13 - - tinycss2==1.2.1 - - tokenizers==0.13.2 - - toolz==0.12.0 - - torch==2.0.0 - - tornado==6.3 - - tqdm==4.64.1 - - traitlets==5.9.0 - - transformers==4.29.0.dev0 - - triton==2.0.0 - - typer==0.7.0 - - tzdata==2023.3 - - uc-micro-py==1.0.1 - - umap-learn==0.5.3 - - uri-template==1.2.0 - - uvicorn==0.21.1 - - wandb==0.12.21 - - wasabi==1.1.1 - - wavedrom==2.0.3.post3 - - wcwidth==0.2.6 - - webcolors==1.13 - - webdataset==0.2.48 - - webencodings==0.5.1 - - websocket-client==1.5.1 - - websockets==11.0.2 - - wget==3.2 - - xxhash==3.2.0 - - yarl==1.8.2 - - zipp==3.14.0 -prefix: /home/omkarthawakar/anaconda3/envs/xraygpt \ No newline at end of file diff --git a/eval_configs/xraygpt_eval.yaml b/eval_configs/xraygpt_eval.yaml index eccc2d9..ca85e93 100644 --- a/eval_configs/xraygpt_eval.yaml +++ b/eval_configs/xraygpt_eval.yaml @@ -5,10 +5,10 @@ model: freeze_qformer: True max_txt_len: 160 end_sym: "###" - low_resource: True + low_resource: False prompt_path: "prompts/alignment.txt" prompt_template: '###Patient: {} ###Doctor: ' - ckpt: './xraygpt/output/path_to_ckpt' + ckpt: './xraygpt/output/xraygpt_openi_finetune/20240613200/checkpoint_12.pth' datasets: diff --git a/hard_requirements.txt b/hard_requirements.txt new file mode 100644 index 0000000..dfa7bf3 --- /dev/null +++ b/hard_requirements.txt @@ -0,0 +1,259 @@ +accelerate==0.15.0 +aiofiles==23.1.0 +aiohttp==3.8.4 +aiosignal==1.3.1 +albucore==0.0.9 +albumentations==1.4.8 +altair==4.2.2 +annotated-types==0.7.0 +antlr4-python3-runtime==4.9.3 +anyio==3.6.2 +appdirs==1.4.4 +argon2-cffi==21.3.0 +argon2-cffi-bindings==21.2.0 +arrow==1.2.3 +asttokens==2.2.1 +async-timeout==4.0.2 +attrs==22.2.0 +backcall==0.2.0 +beautifulsoup4==4.12.2 +bitsandbytes==0.37.0 +bleach==6.0.0 +blis==0.7.9 +braceexpand==0.1.7 +brotlipy==0.7.0 +cachetools==5.3.0 +catalogue==2.0.8 +cchardet==2.1.7 +certifi==2024.6.2 +cffi==1.16.0 +chardet==3.0.4 +charset-normalizer==3.3.2 +click==8.1.7 +cmake==3.26.3 +comm==0.1.3 +confection==0.0.4 +contourpy==1.0.7 +cryptography==42.0.8 +cycler==0.11.0 +cymem==2.0.7 +dataclasses==0.6 +datasets==2.12.0 +debugpy==1.6.7 +decorator==5.1.1 +decord==0.6.0 +defusedxml==0.7.1 +dill==0.3.6 +docker-pycreds==0.4.0 +entrypoints==0.4 +et-xmlfile==1.1.0 +evaluate==0.4.0 +executing==1.2.0 +ExifRead-nocycle==3.0.1 +fairscale==0.4.13 +fastapi==0.95.1 +fastchat==0.1.0 +fastjsonschema==2.16.3 +ffmpy==0.3.0 +filelock==3.9.0 +fire==0.5.0 +flit_core==3.9.0 +fonttools==4.38.0 +fqdn==1.5.1 +frozenlist==1.3.3 +fsspec==2024.6.0 +gensim==4.3.1 +gitdb==4.0.11 +GitPython==3.1.43 +googletrans==3.0.0 +gradio==3.23.0 +gradio_client==0.0.8 +h11==0.14.0 +h2==4.1.0 +hiq-python==1.1.12 +hpack==3.0.0 +hstspreload==2023.1.1 +httpcore==0.9.1 +httpx==0.13.3 +huggingface-hub==0.23.3 +hyperframe==5.2.0 +idna==3.7 +imageio==2.34.1 +img2dataset==1.45.0 +importlib-metadata==6.5.0 +importlib-resources==5.12.0 +iopath==0.1.10 +ipykernel==6.22.0 +ipython==8.12.0 +ipython-genutils==0.2.0 +isoduration==20.11.0 +jedi==0.18.2 +Jinja2==3.1.2 +joblib==1.4.2 +jsonpointer==2.3 +jsonschema==4.17.3 +jupyter-events==0.6.3 +jupyter_client==8.2.0 +jupyter_core==5.3.0 +jupyter_server==2.5.0 +jupyter_server_terminals==0.4.4 +jupyterlab-pygments==0.2.2 +kiwisolver==1.4.4 +langcodes==3.3.0 +lazy_loader==0.4 +linkify-it-py==2.0.0 +lit==16.0.1 +llvmlite==0.39.1 +markdown-it-py==2.2.0 +markdown2==2.4.8 +MarkupSafe==2.1.2 +matplotlib==3.7.0 +matplotlib-inline==0.1.6 +mdit-py-plugins==0.3.3 +mdurl==0.1.2 +MedCLIP==0.0.3 +mistune==2.0.5 +mpmath==1.3.0 +multidict==6.0.4 +multiprocess==0.70.14 +murmurhash==1.0.9 +nbclassic==0.5.5 +nbclient==0.7.3 +nbconvert==7.3.1 +nbformat==5.8.0 +nest-asyncio==1.5.6 +networkx==3.2.1 +nltk==3.8.1 +notebook==6.5.4 +notebook_shim==0.2.2 +numba==0.56.4 +numpy==1.26.4 +nvidia-cublas-cu11==11.10.3.66 +nvidia-cuda-cupti-cu11==11.7.101 +nvidia-cuda-nvrtc-cu11==11.7.99 +nvidia-cuda-runtime-cu11==11.7.99 +nvidia-cudnn-cu11==8.5.0.96 +nvidia-cufft-cu11==10.9.0.58 +nvidia-curand-cu11==10.2.10.91 +nvidia-cusolver-cu11==11.4.0.1 +nvidia-cusparse-cu11==11.7.4.91 +nvidia-nccl-cu11==2.14.3 +nvidia-nvtx-cu11==11.7.91 +omegaconf==2.3.0 +openai==0.27.0 +opencv-python==4.7.0.72 +opencv-python-headless==4.10.0.82 +openpyxl==3.1.2 +orjson==3.8.10 +packaging==24.0 +pandas==2.2.2 +pandocfilters==1.5.0 +parso==0.8.3 +pathtools==0.1.2 +pathy==0.10.1 +peft==0.2.0 +pexpect==4.8.0 +pickleshare==0.7.5 +pillow==10.3.0 +platformdirs==3.2.0 +portalocker==2.7.0 +preshed==3.0.8 +prometheus-client==0.16.0 +promise==2.3 +prompt-toolkit==3.0.38 +protobuf==4.25.3 +psutil==5.9.8 +ptyprocess==0.7.0 +pure-eval==0.2.2 +py-itree==0.0.19 +pyarrow==15.0.2 +pycocoevalcap==1.2 +pycocotools==2.0.6 +pycparser==2.22 +pydantic==2.7.3 +pydantic_core==2.18.4 +pydub==0.25.1 +Pygments==2.15.1 +pyllama==0.0.9 +pynndescent==0.5.9 +pyOpenSSL==24.1.0 +pyparsing==3.0.9 +pyrsistent==0.19.3 +PySocks==1.7.1 +python-dateutil==2.9.0.post0 +python-json-logger==2.0.7 +python-multipart==0.0.6 +pytz==2024.1 +PyWavelets==1.4.1 +PyYAML==6.0.1 +pyzmq==25.0.2 +qudida==0.0.4 +regex==2022.10.31 +requests==2.32.3 +responses==0.18.0 +rfc3339-validator==0.1.4 +rfc3986==1.5.0 +rfc3986-validator==0.1.1 +rich==13.3.4 +scikit-image==0.22.0 +scikit-learn==1.5.0 +scipy==1.13.1 +semantic-version==2.10.0 +Send2Trash==1.8.0 +sentence-transformers==3.0.1 +sentencepiece==0.1.97 +sentry-sdk==2.5.0 +setproctitle==1.3.3 +shortuuid==1.0.11 +six==1.16.0 +smart-open==6.3.0 +smmap==5.0.1 +sniffio==1.3.0 +soupsieve==2.4.1 +spacy==3.5.1 +spacy-legacy==3.0.12 +spacy-loggers==1.0.4 +srsly==2.4.6 +stack-data==0.6.2 +starlette==0.26.1 +svgwrite==1.4.3 +sympy==1.11.1 +tenacity==8.2.2 +termcolor==2.4.0 +terminado==0.17.1 +textaugment==1.3.4 +textblob==0.17.1 +thinc==8.1.9 +threadpoolctl==3.5.0 +tifffile==2024.5.22 +timm==0.6.13 +tinycss2==1.2.1 +tokenizers==0.19.1 +tomli==2.0.1 +toolz==0.12.0 +tornado==6.3 +tqdm==4.66.4 +traitlets==5.9.0 +transformers==4.41.2 +triton==2.0.0 +typer==0.7.0 +typing_extensions==4.12.2 +tzdata==2024.1 +uc-micro-py==1.0.1 +umap-learn==0.5.3 +uri-template==1.2.0 +urllib3==2.2.1 +uvicorn==0.21.1 +wandb==0.17.1 +wasabi==1.1.1 +wavedrom==2.0.3.post3 +wcwidth==0.2.6 +webcolors==1.13 +webdataset==0.2.86 +webencodings==0.5.1 +websocket-client==1.5.1 +websockets==11.0.2 +wget==3.2 +xxhash==3.2.0 +yarl==1.8.2 +zipp==3.14.0 diff --git a/images/.DS_Store b/images/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..1abc6438e67ed2c9d95d1903c1d283f749513cb2 GIT binary patch literal 6148 zcmeHK(TWo>6uoJyZ7D)QS9}ckTCjbvwfHjB`UB3gKIk&e&{7AdlhT=5p%mtE{UJZa z@A2FuUA9!g2U)1x12-qRxi=vvLoy*EHQCGtL|r0k!I*1p6u$xYvz?&kJ>|#VIyfG~hQ6$@9m1HJC#*bu`rA68A|5UYl;`Ik^T^f@mhkBe`rSomnee%ZSWt`^owM|H8bA)_+pXM{OotRBN zD{OA)I@&47A(eTxN-)>zzfARWV^d(#7=Ee!G@GV+A@_eB0 zsQl$xzlQ5PGx-7;htUqB2#W%ufGBYO3iy+))j9vA$O=ROQQ#jdz~_U8F$bP2$M)zz z!M~5(-(Y5-&Tk23?K$vVIYtklTq@Y5D*wb#E**aD;{wl>W0y|K&Wv&V%*ww|l${-Z zZNo_gj-?a@M1f-k>aN-4`~U5q-~Y!+(h>zkf&WSY)tH9U36|vV)|18YU8`ZAU<{6{ m9N(s(Vy|Mv@>To{W&ywE3OMjwIfe(29|0?a6r#YrD)0-H^i<{m literal 0 HcmV?d00001 diff --git a/train.py b/train.py index 2d814a6..7dfa2d3 100644 --- a/train.py +++ b/train.py @@ -91,6 +91,7 @@ def main(): task = tasks.setup_task(cfg) datasets = task.build_datasets(cfg) + print("datasets", datasets) model = task.build_model(cfg) runner = get_runner_class(cfg)( diff --git a/train_configs/xraygpt_openi_finetune.yaml b/train_configs/xraygpt_openi_finetune.yaml index 03990cc..dc00523 100644 --- a/train_configs/xraygpt_openi_finetune.yaml +++ b/train_configs/xraygpt_openi_finetune.yaml @@ -5,9 +5,9 @@ model: freeze_qformer: True max_txt_len: 160 end_sym: "###" - prompt_path: "prompts/alignment.txt" + prompt_path: null prompt_template: '###Patient: {} ###Doctor: ' - ckpt: './xraygpt/pretrained_ckpt/pretrained_minigpt4_7b.pth' + ckpt: './xraygpt_pretrained.pth' datasets: @@ -24,17 +24,19 @@ run: task: image_text_pretrain # optimizer lr_sched: "linear_warmup_cosine_lr" - init_lr: 3e-5 - min_lr: 1e-5 - warmup_lr: 1e-6 + init_lr: 1e-7 + min_lr: 1e-7 + warmup_lr: 1e-7 weight_decay: 0.05 - max_epoch: 1 - iters_per_epoch: 2501 + # max_epoch: 1 + max_epoch: 25 + # iters_per_epoch: 2501 + iters_per_epoch: 100 batch_size_train: 1 batch_size_eval: 1 num_workers: 4 - warmup_steps: 200 + warmup_steps: 0 seed: 42 output_dir: "output/xraygpt_openi_finetune" @@ -48,4 +50,4 @@ run: device: "cuda" world_size: 1 dist_url: "env://" - distributed: True + distributed: True \ No newline at end of file diff --git a/xraygpt/configs/datasets/openi/defaults.yaml b/xraygpt/configs/datasets/openi/defaults.yaml index b249200..0b6e663 100644 --- a/xraygpt/configs/datasets/openi/defaults.yaml +++ b/xraygpt/configs/datasets/openi/defaults.yaml @@ -2,4 +2,4 @@ datasets: openi: data_type: images build_info: - storage: /home/omkarthawakar/fahad/XrayGPT/dataset/openi + storage: /workspace/XrayGPT/dataset/ diff --git a/xraygpt/conversation/conversation.py b/xraygpt/conversation/conversation.py index c6b0300..a21fdc2 100644 --- a/xraygpt/conversation/conversation.py +++ b/xraygpt/conversation/conversation.py @@ -186,6 +186,7 @@ def answer(self, conv, img_list, max_new_tokens=300, num_beams=1, min_length=1, return output_text, output_token.cpu().numpy() def upload_img(self, image, conv, img_list): + print("Processing Image") if isinstance(image, str): # is a image path raw_image = Image.open(image).convert('RGB') image = self.vis_processor(raw_image).unsqueeze(0).to(self.device) diff --git a/xraygpt/datasets/datasets/base_dataset.py b/xraygpt/datasets/datasets/base_dataset.py index ae2a8d0..63a5a96 100644 --- a/xraygpt/datasets/datasets/base_dataset.py +++ b/xraygpt/datasets/datasets/base_dataset.py @@ -24,7 +24,8 @@ def __init__( self.annotation = [] for ann_path in ann_paths: - self.annotation.extend(json.load(open(ann_path, "r"))['annotations']) + # self.annotation.extend(json.load(open(ann_path, "r")) + self.annotation.extend(json.load(open(ann_path, "r"))) self.vis_processor = vis_processor self.text_processor = text_processor diff --git a/xraygpt/datasets/datasets/caption_datasets.py b/xraygpt/datasets/datasets/caption_datasets.py index 4bfc48e..98f34e8 100644 --- a/xraygpt/datasets/datasets/caption_datasets.py +++ b/xraygpt/datasets/datasets/caption_datasets.py @@ -36,7 +36,8 @@ def __init__(self, vis_processor, text_processor, vis_root, ann_paths): self.img_ids = {} n = 0 for ann in self.annotation: - img_id = ann["image_id"] + # img_id = ann["image_id"] + img_id = ann["image"] if img_id not in self.img_ids.keys(): self.img_ids[img_id] = n n += 1 @@ -46,15 +47,18 @@ def __getitem__(self, index): # TODO this assumes image input, not general enough ann = self.annotation[index] - img_file = '{:0>12}.png'.format(ann["image_id"]) - image_path = os.path.join(self.vis_root, img_file) + # img_file = '{:0>12}.png'.format(ann["image_id"]) + # image_path = os.path.join(self.vis_root, img_file) + image_path = ann["image"] image = Image.open(image_path).convert("RGB") image = self.vis_processor(image) caption = self.text_processor(ann["caption"]) + prompt = self.text_processor(ann["prompt"]) return { "image": image, + "prompt": prompt, "text_input": caption, "image_id": self.img_ids[ann["image_id"]], } diff --git a/xraygpt/datasets/datasets/openi_dataset.py b/xraygpt/datasets/datasets/openi_dataset.py index 0471ba6..1adfbd5 100644 --- a/xraygpt/datasets/datasets/openi_dataset.py +++ b/xraygpt/datasets/datasets/openi_dataset.py @@ -39,16 +39,20 @@ def __getitem__(self, index): # TODO this assumes image input, not general enough ann = self.annotation[index] - img_file = '{}.png'.format(ann["image_id"]) - image_path = os.path.join(self.vis_root, img_file) + # img_file = '{}.png'.format(ann["image_id"]) + # image_path = os.path.join(self.vis_root, img_file) + image_path = ann["image"] + image_path = os.path.join("dataset", image_path) image = Image.open(image_path).convert("RGB") image = self.vis_processor(image) caption = ann['caption'] + prompt = ann['prompt'] return { "image": image, "caption":caption, - "image_id": self.img_ids[ann["image_id"]], + "prompt":prompt, + "image_id": self.img_ids[ann["image"]], } diff --git a/xraygpt/models/mini_gpt4.py b/xraygpt/models/mini_gpt4.py index bae69dd..5e7eb21 100644 --- a/xraygpt/models/mini_gpt4.py +++ b/xraygpt/models/mini_gpt4.py @@ -138,7 +138,9 @@ def __init__( print('Load {} training prompts'.format(len(self.prompt_list))) print('Prompt Example \n{}'.format(random.choice(self.prompt_list))) else: - self.prompt_list = [] + print("No prompt file provided. Expecting prompt in the dataset.") + + self.prompt_template = prompt_template # '###Patient: {} ###Doctor: ' print('#'*100) @@ -173,8 +175,10 @@ def encode_img(self, image): return inputs_llama, atts_llama def prompt_wrap(self, img_embeds, atts_img, prompt): - if prompt: + if prompt and type(prompt) == str: batch_size = img_embeds.shape[0] + if batch_size!=1: + raise ValueError("Currently only support batch size 1 for prompt wrapping.") p_before, p_after = prompt.split('') p_before_tokens = self.llama_tokenizer( p_before, return_tensors="pt", add_special_tokens=False).to(img_embeds.device) @@ -191,19 +195,25 @@ def prompt_wrap(self, img_embeds, atts_img, prompt): def forward(self, samples): image = samples["image"] img_embeds, atts_img = self.encode_img(image) - if hasattr(samples, 'question_split'): # VQA dataset - print('VQA Batch') - vqa_prompt = '###Patient: ' - img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img, vqa_prompt) - elif self.prompt_list: - prompt = random.choice(self.prompt_list) - - #for adding finding to prompt - # x, y = prompt.split('###Doctor:') - # x = x + ' '.join(samples['finding']) + y - # prompt = x + '###Doctor:' + y - - img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img, prompt) + prompt = samples.get("prompt") + prompt = '' + prompt[0] + # Format this as per the prompt template + prompt = self.prompt_template.format(prompt) + + img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img, prompt) + # if hasattr(samples, 'question_split'): # VQA dataset + # print('VQA Batch') + # vqa_prompt = '###Patient: ' + # img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img, vqa_prompt) + # elif self.prompt_list: + # prompt = random.choice(self.prompt_list) + + # #for adding finding to prompt + # # x, y = prompt.split('###Doctor:') + # # x = x + ' '.join(samples['finding']) + y + # # prompt = x + '###Doctor:' + y + + # img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img, prompt) self.llama_tokenizer.padding_side = "right" diff --git a/xraygpt/runners/runner_base.py b/xraygpt/runners/runner_base.py index e70b771..b334380 100644 --- a/xraygpt/runners/runner_base.py +++ b/xraygpt/runners/runner_base.py @@ -412,9 +412,9 @@ def train(self): if self.config.run_cfg.distributed: dist.barrier() - # testing phase - test_epoch = "best" if len(self.valid_splits) > 0 else cur_epoch - self.evaluate(cur_epoch=test_epoch, skip_reload=self.evaluate_only) + # # testing phase + # test_epoch = "best" if len(self.valid_splits) > 0 else cur_epoch + # self.evaluate(cur_epoch=test_epoch, skip_reload=self.evaluate_only) total_time = time.time() - start_time total_time_str = str(datetime.timedelta(seconds=int(total_time)))

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