From b595b988131d59fce756af50f9e177b376075fde Mon Sep 17 00:00:00 2001 From: Dheemant Dixit <102575300+Dheemant-Dixit@users.noreply.github.com> Date: Thu, 24 Aug 2023 06:13:22 +0530 Subject: [PATCH] Added GOT10K (#147) * GOT10K dataset added * Added got10k * Added got10k and some changes * Changes to got10k and requirements --------- Co-authored-by: EC2 Default User --- experiments/datasets/load_got10k.ipynb | 231 ++++++++++++++++++++++ requirements.txt | 3 +- trailmet/datasets/__init__.py | 1 + trailmet/datasets/tracking/__init__.py | 53 +++++ trailmet/datasets/tracking/got10kdata.py | 239 +++++++++++++++++++++++ 5 files changed, 526 insertions(+), 1 deletion(-) create mode 100644 experiments/datasets/load_got10k.ipynb create mode 100644 trailmet/datasets/tracking/__init__.py create mode 100644 trailmet/datasets/tracking/got10kdata.py diff --git a/experiments/datasets/load_got10k.ipynb b/experiments/datasets/load_got10k.ipynb new file mode 100644 index 0000000..bc364d1 --- /dev/null +++ b/experiments/datasets/load_got10k.ipynb @@ -0,0 +1,231 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "sys.path.append(\"./../../\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import matplotlib.pyplot as plt\n", + "from torchvision import transforms\n", + "from trailmet.datasets.tracking import TrackingDatasetFactory" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "root_dir = \"./../../../got10kdata/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "got_dataset = TrackingDatasetFactory.create_dataset(\n", + " name=\"got10k\", root=root_dir, split_types=[\"train\", \"val\", \"test\"]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "got_dataset[\"train\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'train_size': 93350, 'val_size': 1800, 'test_size': 180, 'note': ''}\n" + ] + } + ], + "source": [ + "print(got_dataset[\"info\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train samples: 93350\n", + "Val samples: 1800\n", + "Test samples: 180\n" + ] + } + ], + "source": [ + "# getting the size of the different splits of the data\n", + "print(\"Train samples: \", got_dataset[\"info\"][\"train_size\"])\n", + "print(\"Val samples: \", got_dataset[\"info\"][\"val_size\"])\n", + "print(\"Test samples: \", got_dataset[\"info\"][\"test_size\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No. of training batches: 1459\n", + "No. of validation batches: 29\n", + "No. of test batches: 3\n" + ] + } + ], + "source": [ + "# Construct dataloaders\n", + "train_loader = torch.utils.data.DataLoader(\n", + " got_dataset[\"train\"],\n", + " batch_size=64,\n", + " sampler=got_dataset[\"train_sampler\"],\n", + " num_workers=0,\n", + ")\n", + "val_loader = torch.utils.data.DataLoader(\n", + " got_dataset[\"val\"],\n", + " batch_size=64,\n", + " sampler=got_dataset[\"val_sampler\"],\n", + " num_workers=0,\n", + ")\n", + "test_loader = torch.utils.data.DataLoader(\n", + " got_dataset[\"test\"],\n", + " batch_size=64,\n", + " sampler=got_dataset[\"test_sampler\"],\n", + " num_workers=0,\n", + ")\n", + "\n", + "print(\"No. of training batches: \", len(train_loader))\n", + "print(\"No. of validation batches: \", len(val_loader))\n", + "print(\"No. of test batches: \", len(test_loader))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[395., 340., 532., 407.]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "got_dataset[\"test\"][0][1]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature batch shape: torch.Size([64, 3, 127, 127])\n", + "Labels batch shape: torch.Size([64, 3, 239, 239])\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Display image and label.\n", + "train_features, train_labels = next(iter(train_loader))\n", + "print(f\"Feature batch shape: {train_features.size()}\")\n", + "print(f\"Labels batch shape: {train_labels.size()}\")\n", + "img = train_features[7, 0, :, :].squeeze()\n", + "label = train_labels[0]\n", + "plt.imshow(img, cmap=\"gray\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "trailmet", + "language": "python", + "name": "trailmet" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/requirements.txt b/requirements.txt index f8fcd2c..ccc1625 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ codecarbon==2.2.3 -pandas==2.0.2 +pandas==2.0.3 tqdm==4.65.0 timm==0.9.2 matplotlib==3.7.1 @@ -11,3 +11,4 @@ scikit-learn==1.2.2 pytest==7.3.1 torch_pruning==1.1.9 wandb==0.15.4 +got10k==0.1.3 diff --git a/trailmet/datasets/__init__.py b/trailmet/datasets/__init__.py index 4a89614..450b656 100644 --- a/trailmet/datasets/__init__.py +++ b/trailmet/datasets/__init__.py @@ -20,3 +20,4 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. from .classification import DatasetFactory +from .tracking import TrackingDatasetFactory diff --git a/trailmet/datasets/tracking/__init__.py b/trailmet/datasets/tracking/__init__.py new file mode 100644 index 0000000..0064ff1 --- /dev/null +++ b/trailmet/datasets/tracking/__init__.py @@ -0,0 +1,53 @@ +# MIT License +# +# Copyright (c) 2023 Transmute AI 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. +from .got10kdata import GOT10kDataset + + +class TrackingDatasetFactory(object): + """This class forms the generic wrapper for the different dataset classes. + + The module includes utilities to load datasets, including methods to load + and fetch popular reference datasets. + """ + + @staticmethod + def create_dataset(**kwargs): + """ + Args: + name(string): dataset name 'CIFAR10', 'CIFAR100', 'ImageNet', 'CHEST', + root(string): Root directory of dataset where directory + cifar-10-batches-py exists or will be saved + to if download is set to True. + Return: + dataset(tuple): dataset + """ + assert 'name' in kwargs, 'should provide dataset name' + name = kwargs['name'] + assert 'root' in kwargs, 'should provide dataset root' + if 'got10k' == name: + obj_dfactory = GOT10kDataset(**kwargs) + else: + raise Exception(f"unknown dataset{kwargs['name']}") + dataset = obj_dfactory.stack_dataset() + dataset = obj_dfactory.build_dict_info() + + return dataset diff --git a/trailmet/datasets/tracking/got10kdata.py b/trailmet/datasets/tracking/got10kdata.py new file mode 100644 index 0000000..43f9f54 --- /dev/null +++ b/trailmet/datasets/tracking/got10kdata.py @@ -0,0 +1,239 @@ +# MIT License +# +# Copyright (c) 2023 Transmute AI 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. +# https://github.com/got-10k/siamfc +from __future__ import absolute_import, division, print_function + +import os +import sys +import numpy as np +import torch +from collections import namedtuple +from torch.utils.data import Dataset +from torch.utils.data.sampler import SubsetRandomSampler +from torchvision.transforms import Compose, CenterCrop, RandomCrop, ToTensor +from PIL import Image, ImageStat, ImageOps + +from got10k.datasets import GOT10k + + +class RandomStretch(object): + + def __init__(self, max_stretch=0.05, interpolation='bilinear'): + assert interpolation in ['bilinear', 'bicubic'] + self.max_stretch = max_stretch + self.interpolation = interpolation + + def __call__(self, img): + scale = 1.0 + np.random.uniform(-self.max_stretch, self.max_stretch) + size = np.round(np.array(img.size, float) * scale).astype(int) + if self.interpolation == 'bilinear': + method = Image.BILINEAR + elif self.interpolation == 'bicubic': + method = Image.BICUBIC + return img.resize(tuple(size), method) + + +class Pairwise(Dataset): + + def __init__(self, seq_dataset, **kargs): + super(Pairwise, self).__init__() + self.cfg = self.parse_args(**kargs) + + self.seq_dataset = seq_dataset + self.indices = np.random.permutation(len(seq_dataset)) + # augmentation for exemplar and instance images + self.transform_z = Compose([ + RandomStretch(max_stretch=0.05), + CenterCrop(self.cfg.instance_sz - 8), + RandomCrop(self.cfg.instance_sz - 2 * 8), + CenterCrop(self.cfg.exemplar_sz), + ToTensor(), + ]) + self.transform_x = Compose([ + RandomStretch(max_stretch=0.05), + CenterCrop(self.cfg.instance_sz - 8), + RandomCrop(self.cfg.instance_sz - 2 * 8), + ToTensor(), + ]) + + def parse_args(self, **kargs): + # default parameters + cfg = { + 'pairs_per_seq': 10, + 'max_dist': 100, + 'exemplar_sz': 127, + 'instance_sz': 255, + 'context': 0.5, + } + + for key, val in kargs.items(): + if key in cfg: + cfg.update({key: val}) + return namedtuple('GenericDict', cfg.keys())(**cfg) + + def __getitem__(self, index): + index = self.indices[index % len(self.seq_dataset)] + img_files, anno = self.seq_dataset[index] + + # remove too small objects + valid = anno[:, 2:].prod(axis=1) >= 10 + img_files = np.array(img_files)[valid] + anno = anno[valid, :] + + rand_z, rand_x = self._sample_pair(len(img_files)) + + exemplar_image = Image.open(img_files[rand_z]) + instance_image = Image.open(img_files[rand_x]) + exemplar_image = self._crop_and_resize(exemplar_image, anno[rand_z]) + instance_image = self._crop_and_resize(instance_image, anno[rand_x]) + exemplar_image = 255.0 * self.transform_z(exemplar_image) + instance_image = 255.0 * self.transform_x(instance_image) + + return exemplar_image, instance_image + + def __len__(self): + return self.cfg.pairs_per_seq * len(self.seq_dataset) + + def _sample_pair(self, n): + assert n > 0 + if n == 1: + return 0, 0 + elif n == 2: + return 0, 1 + else: + max_dist = min(n - 1, self.cfg.max_dist) + rand_dist = np.random.choice(max_dist) + 1 + rand_z = np.random.choice(n - rand_dist) + rand_x = rand_z + rand_dist + + return rand_z, rand_x + + def _crop_and_resize(self, image, box): + # convert box to 0-indexed and center based + box = np.array( + [ + box[0] - 1 + (box[2] - 1) / 2, + box[1] - 1 + (box[3] - 1) / 2, + box[2], + box[3], + ], + dtype=np.float32, + ) + center, target_sz = box[:2], box[2:] + + # exemplar and search sizes + context = self.cfg.context * np.sum(target_sz) + z_sz = np.sqrt(np.prod(target_sz + context)) + x_sz = z_sz * self.cfg.instance_sz / self.cfg.exemplar_sz + + # convert box to corners (0-indexed) + size = round(x_sz) + corners = np.concatenate(( + np.round(center - (size - 1) / 2), + np.round(center - (size - 1) / 2) + size, + )) + corners = np.round(corners).astype(int) + + # pad image if necessary + pads = np.concatenate((-corners[:2], corners[2:] - image.size)) + npad = max(0, int(pads.max())) + if npad > 0: + avg_color = ImageStat.Stat(image).mean + # PIL doesn't support float RGB image + avg_color = tuple(int(round(c)) for c in avg_color) + image = ImageOps.expand(image, border=npad, fill=avg_color) + + # crop image patch + corners = tuple((corners + npad).astype(int)) + patch = image.crop(corners) + + # resize to instance_sz + out_size = (self.cfg.instance_sz, self.cfg.instance_sz) + patch = patch.resize(out_size, Image.BILINEAR) + + return patch + + +class GOT10kDataset: + + def __init__( + self, + name=None, + root=None, + split_types=None, + shuffle=True, + random_seed=None, + ): + self.name = name + self.shuffle = shuffle + self.dataset_dict = {} + + for item in split_types: + dataset_type = item + data = GOT10k(root, subset=dataset_type) + if item != 'test': + data = Pairwise(data) + self.dataset_dict[dataset_type] = data + + def build_dict_info(self): + """ + Behavior: + This function creates info key in the output dictionary. The info key contains details related to the size + of the training, validation and test datasets. Further, it can be used to define any additional information + necessary for the user. + Returns: + dataset_dict (dict): Updated with info key that contains details related to the data splits + """ + self.dataset_dict['info'] = {} + self.dataset_dict['info']['train_size'] = len( + self.dataset_dict['train']) + self.dataset_dict['info']['val_size'] = len(self.dataset_dict['val']) + self.dataset_dict['info']['test_size'] = len(self.dataset_dict['test']) + self.dataset_dict['info']['note'] = '' + return self.dataset_dict + + def stack_dataset(self): + """ + Behavior: + This function stacks the three dataset objects (train, val and test) in a single dictionary together with + their samplers. For cases where the no validation set is explicitly available, the split is performed here. + Returns: + dataset_dict (dict): The keys of the dictionary are "train_datset", "val_dataset" + and "test_dataset" and the values are object of dataset containing train, + val and test respectively. + """ + + # defining the samplers + self.dataset_dict['train_sampler'] = None + self.dataset_dict['val_sampler'] = None + self.dataset_dict['test_sampler'] = None + + if self.name == 'got10k': + self.train_idx, self.valid_idx = range( + len(self.dataset_dict['train'])), range( + len(self.dataset_dict['val'])) + train_sampler = SubsetRandomSampler(self.train_idx) + valid_sampler = SubsetRandomSampler(self.valid_idx) + self.dataset_dict['train_sampler'] = train_sampler + self.dataset_dict['val_sampler'] = valid_sampler + + return self.dataset_dict