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We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright 2020 SenseTime\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n\n\nDETR\n\nCopyright 2020 - present, Facebook, Inc\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n" }, { "path": "README.md", "content": "# MOTR: End-to-End Multiple-Object Tracking with TRansformer\n\n\n\n\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/motr-end-to-end-multiple-object-tracking-with/multi-object-tracking-on-mot17)](https://paperswithcode.com/sota/multi-object-tracking-on-mot17?p=motr-end-to-end-multiple-object-tracking-with)\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/motr-end-to-end-multiple-object-tracking-with/multi-object-tracking-on-mot16)](https://paperswithcode.com/sota/multi-object-tracking-on-mot16?p=motr-end-to-end-multiple-object-tracking-with)\n\n\n\nThis repository is an official implementation of the paper [MOTR: End-to-End Multiple-Object Tracking with TRansformer](https://arxiv.org/pdf/2105.03247.pdf).\n\n## Introduction\n\n**TL; DR.** MOTR is a fully end-to-end multiple-object tracking framework based on Transformer. It directly outputs the tracks within the video sequences without any association procedures.\n\n
\n\n
\n\n**Abstract.** The key challenge in multiple-object tracking task is temporal modeling of the object under track. Existing tracking-by-detection methods adopt simple heuristics, such as spatial or appearance similarity. Such methods, in spite of their commonality, are overly simple and lack the ability to learn temporal variations from data in an end-to-end manner.In this paper, we present MOTR, a fully end-to-end multiple-object tracking framework. It learns to model the long-range temporal variation of the objects. It performs temporal association implicitly and avoids previous explicit heuristics. Built upon DETR, MOTR introduces the concept of \"track query\". Each track query models the entire track of an object. It is transferred and updated frame-by-frame to perform iterative predictions in a seamless manner. Tracklet-aware label assignment is proposed for one-to-one assignment between track queries and object tracks. Temporal aggregation network together with collective average loss is further proposed to enhance the long-range temporal relation. Experimental results show that MOTR achieves competitive performance and can serve as a strong Transformer-based baseline for future research.\n\n## Updates\n- (2021/09/23) Report BDD100K results and release corresponding codes [motr_bdd100k](https://github.com/megvii-model/MOTR/tree/motr_bdd100k). \n- (2022/02/09) Higher performance achieved by not clipping the bounding boxes inside the image.\n- (2022/02/11) Add checkpoint support for training on RTX 2080ti.\n- (2022/02/11) Report [DanceTrack](https://github.com/DanceTrack/DanceTrack) results and [scripts](configs/r50_motr_train_dance.sh).\n- (2022/05/12) Higher performance achieved by removing the public detection filtering (filter_pub_det) trick.\n- (2022/07/04) MOTR is accepted by ECCV 2022.\n\n## Main Results\n\n### MOT17\n\n| **Method** | **Dataset** | **Train Data** | **HOTA** | **DetA** | **AssA** | **MOTA** | **IDF1** | **IDS** | **URL** |\n| :--------: | :---------: | :------------------: | :------: | :------: | :------: | :------: | :------: | :-----: | :-----------------------------------------------------------------------------------------: |\n| MOTR | MOT17 | MOT17+CrowdHuman Val | 57.8 | 60.3 | 55.7 | 73.4 | 68.6 | 2439 | [model](https://drive.google.com/file/d/1K9AbtzTCBNsOD8LYA1k16kf4X0uJi8PC/view?usp=sharing) |\n\n### DanceTrack\n\n| **Method** | **Dataset** | **Train Data** | **HOTA** | **DetA** | **AssA** | **MOTA** | **IDF1** | **URL** |\n| :--------: | :---------: | :------------: | :------: | :------: | :------: | :------: | :------: | :-----------------------------------------------------------------------------------------: |\n| MOTR | DanceTrack | DanceTrack | 54.2 | 73.5 | 40.2 | 79.7 | 51.5 | [model](https://drive.google.com/file/d/1zs5o1oK8diafVfewRl3heSVQ7-XAty3J/view?usp=sharing) |\n\n### BDD100K\n\n| **Method** | **Dataset** | **Train Data** | **MOTA** | **IDF1** | **IDS** | **URL** |\n| :--------: | :---------: | :------------: | :------: | :------: | :-----: | :-----------------------------------------------------------------------------------------: |\n| MOTR | BDD100K | BDD100K | 32.0 | 43.5 | 3493 | [model](https://drive.google.com/file/d/13fsTj9e6Hk7qVcybWi1X5KbZEsFCHa6e/view?usp=sharing) |\n\n*Note:*\n\n1. MOTR on MOT17 and DanceTrack is trained on 8 NVIDIA RTX 2080ti GPUs.\n2. The training time for MOT17 is about 2.5 days on V100 or 4 days on RTX 2080ti;\n3. The inference speed is about 7.5 FPS for resolution 1536x800;\n4. All models of MOTR are trained with ResNet50 with pre-trained weights on COCO dataset.\n\n\n## Installation\n\nThe codebase is built on top of [Deformable DETR](https://github.com/fundamentalvision/Deformable-DETR).\n\n### Requirements\n\n* Linux, CUDA>=9.2, GCC>=5.4\n \n* Python>=3.7\n\n We recommend you to use Anaconda to create a conda environment:\n ```bash\n conda create -n deformable_detr python=3.7 pip\n ```\n Then, activate the environment:\n ```bash\n conda activate deformable_detr\n ```\n \n* PyTorch>=1.5.1, torchvision>=0.6.1 (following instructions [here](https://pytorch.org/))\n\n For example, if your CUDA version is 9.2, you could install pytorch and torchvision as following:\n ```bash\n conda install pytorch=1.5.1 torchvision=0.6.1 cudatoolkit=9.2 -c pytorch\n ```\n \n* Other requirements\n ```bash\n pip install -r requirements.txt\n ```\n\n* Build MultiScaleDeformableAttention\n ```bash\n cd ./models/ops\n sh ./make.sh\n ```\n\n## Usage\n\n### Dataset preparation\n\n1. Please download [MOT17 dataset](https://motchallenge.net/) and [CrowdHuman dataset](https://www.crowdhuman.org/) and organize them like [FairMOT](https://github.com/ifzhang/FairMOT) as following:\n\n```\n.\n├── crowdhuman\n│   ├── images\n│   └── labels_with_ids\n├── MOT15\n│   ├── images\n│   ├── labels_with_ids\n│   ├── test\n│   └── train\n├── MOT17\n│   ├── images\n│   ├── labels_with_ids\n├── DanceTrack\n│   ├── train\n│   ├── test\n├── bdd100k\n│   ├── images\n│ ├── track\n│ ├── train\n│ ├── val\n│   ├── labels\n│ ├── track\n│ ├── train\n│ ├── val\n\n```\n\n2. For BDD100K dataset, you can use the following script to generate txt file:\n\n\n```bash \ncd datasets/data_path\npython3 generate_bdd100k_mot.py\ncd ../../\n```\n\n### Training and Evaluation\n\n#### Training on single node\n\nYou can download COCO pretrained weights from [Deformable DETR](https://github.com/fundamentalvision/Deformable-DETR). Then training MOTR on 8 GPUs as following:\n\n```bash \nsh configs/r50_motr_train.sh\n\n```\n\n#### Evaluation on MOT15\n\nYou can download the pretrained model of MOTR (the link is in \"Main Results\" session), then run following command to evaluate it on MOT15 train dataset:\n\n```bash \nsh configs/r50_motr_eval.sh\n\n```\n\nFor visual in demo video, you can enable 'vis=True' in eval.py like:\n```bash \ndet.detect(vis=True)\n\n```\n\n#### Evaluation on MOT17\n\nYou can download the pretrained model of MOTR (the link is in \"Main Results\" session), then run following command to evaluate it on MOT17 test dataset (submit to server):\n\n```bash\nsh configs/r50_motr_submit.sh\n\n```\n#### Evaluation on BDD100K\n\nFor BDD100K dataset, please refer [motr_bdd100k](https://github.com/megvii-model/MOTR/tree/motr_bdd100k). \n\n\n#### Test on Video Demo\n\nWe also provide a demo interface which allows for a quick processing of a given video.\n\n```bash\nEXP_DIR=exps/e2e_motr_r50_joint\npython3 demo.py \\\n --meta_arch motr \\\n --dataset_file e2e_joint \\\n --epoch 200 \\\n --with_box_refine \\\n --lr_drop 100 \\\n --lr 2e-4 \\\n --lr_backbone 2e-5 \\\n --pretrained ${EXP_DIR}/motr_final.pth \\\n --output_dir ${EXP_DIR} \\\n --batch_size 1 \\\n --sample_mode 'random_interval' \\\n --sample_interval 10 \\\n --sampler_steps 50 90 120 \\\n --sampler_lengths 2 3 4 5 \\\n --update_query_pos \\\n --merger_dropout 0 \\\n --dropout 0 \\\n --random_drop 0.1 \\\n --fp_ratio 0.3 \\\n --query_interaction_layer 'QIM' \\\n --extra_track_attn \\\n --resume ${EXP_DIR}/motr_final.pth \\\n --input_video figs/demo.avi\n```\n\n## Citing MOTR\nIf you find MOTR useful in your research, please consider citing:\n```bibtex\n@inproceedings{zeng2021motr,\n title={MOTR: End-to-End Multiple-Object Tracking with TRansformer},\n author={Zeng, Fangao and Dong, Bin and Zhang, Yuang and Wang, Tiancai and Zhang, Xiangyu and Wei, Yichen},\n booktitle={European Conference on Computer Vision (ECCV)},\n year={2022}\n}\n```\n" }, { "path": "benchmark.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\n\"\"\"\nBenchmark inference speed of Deformable DETR.\n\"\"\"\nimport os\nimport time\nimport argparse\n\nimport torch\n\nfrom main import get_args_parser as get_main_args_parser\nfrom models import build_model\nfrom datasets import build_dataset\nfrom util.misc import nested_tensor_from_tensor_list\n\n\ndef get_benckmark_arg_parser():\n parser = argparse.ArgumentParser('Benchmark inference speed of Deformable DETR.')\n parser.add_argument('--num_iters', type=int, default=300, help='total iters to benchmark speed')\n parser.add_argument('--warm_iters', type=int, default=5, help='ignore first several iters that are very slow')\n parser.add_argument('--batch_size', type=int, default=1, help='batch size in inference')\n parser.add_argument('--resume', type=str, help='load the pre-trained checkpoint')\n return parser\n\n\n@torch.no_grad()\ndef measure_average_inference_time(model, inputs, num_iters=100, warm_iters=5):\n ts = []\n for iter_ in range(num_iters):\n torch.cuda.synchronize()\n t_ = time.perf_counter()\n model(inputs)\n torch.cuda.synchronize()\n t = time.perf_counter() - t_\n if iter_ >= warm_iters:\n ts.append(t)\n print(ts)\n return sum(ts) / len(ts)\n\n\ndef benchmark():\n args, _ = get_benckmark_arg_parser().parse_known_args()\n main_args = get_main_args_parser().parse_args(_)\n assert args.warm_iters < args.num_iters and args.num_iters > 0 and args.warm_iters >= 0\n assert args.batch_size > 0\n assert args.resume is None or os.path.exists(args.resume)\n dataset = build_dataset('val', main_args)\n model, _, _ = build_model(main_args)\n model.cuda()\n model.eval()\n if args.resume is not None:\n ckpt = torch.load(args.resume, map_location=lambda storage, loc: storage)\n model.load_state_dict(ckpt['model'])\n inputs = nested_tensor_from_tensor_list([dataset.__getitem__(0)[0].cuda() for _ in range(args.batch_size)])\n t = measure_average_inference_time(model, inputs, args.num_iters, args.warm_iters)\n return 1.0 / t * args.batch_size\n\n\nif __name__ == '__main__':\n fps = benchmark()\n print(f'Inference Speed: {fps:.1f} FPS')\n\n" }, { "path": "configs/r50_deformable_detr.sh", "content": "#!/usr/bin/env bash\n# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n\nset -x\n\nEXP_DIR=exps/r50_deformable_detr\nPY_ARGS=${@:1}\n\npython -u main.py \\\n --output_dir ${EXP_DIR} \\\n ${PY_ARGS}\n" }, { "path": "configs/r50_deformable_detr_plus_iterative_bbox_refinement.sh", "content": "#!/usr/bin/env bash\n# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n\nset -x\n\nEXP_DIR=exps/r50_deformable_detr_plus_iterative_bbox_refinement\nPY_ARGS=${@:1}\n\npython -u main.py \\\n --output_dir ${EXP_DIR} \\\n --with_box_refine \\\n ${PY_ARGS}\n" }, { "path": "configs/r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh", "content": "#!/usr/bin/env bash\n# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n\nset -x\n\nEXP_DIR=exps/r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage\nPY_ARGS=${@:1}\n\npython -u main.py \\\n --output_dir ${EXP_DIR} \\\n --with_box_refine \\\n --two_stage \\\n ${PY_ARGS}\n" }, { "path": "configs/r50_deformable_detr_single_scale.sh", "content": "#!/usr/bin/env bash\n# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n\nset -x\n\nEXP_DIR=exps/r50_deformable_detr_single_scale\nPY_ARGS=${@:1}\n\npython -u main.py \\\n --num_feature_levels 1 \\\n --output_dir ${EXP_DIR} \\\n ${PY_ARGS}\n" }, { "path": "configs/r50_deformable_detr_single_scale_dc5.sh", "content": "#!/usr/bin/env bash\n# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n\nset -x\n\nEXP_DIR=exps/r50_deformable_detr_single_scale_dc5\nPY_ARGS=${@:1}\n\npython -u main.py \\\n --num_feature_levels 1 \\\n --dilation \\\n --output_dir ${EXP_DIR} \\\n ${PY_ARGS}\n" }, { "path": "configs/r50_motr_demo.sh", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n\nEXP_DIR=exps/e2e_motr_r50_joint\npython3 demo.py \\\n --meta_arch motr \\\n --dataset_file e2e_joint \\\n --epoch 200 \\\n --with_box_refine \\\n --lr_drop 100 \\\n --lr 2e-4 \\\n --lr_backbone 2e-5 \\\n --pretrained ${EXP_DIR}/motr_final.pth \\\n --output_dir ${EXP_DIR} \\\n --batch_size 1 \\\n --sample_mode 'random_interval' \\\n --sample_interval 10 \\\n --sampler_steps 50 90 120 \\\n --sampler_lengths 2 3 4 5 \\\n --update_query_pos \\\n --merger_dropout 0 \\\n --dropout 0 \\\n --random_drop 0.1 \\\n --fp_ratio 0.3 \\\n --query_interaction_layer 'QIM' \\\n --extra_track_attn \\\n --resume ${EXP_DIR}/motr_final.pth \\\n --input_video figs/demo.avi" }, { "path": "configs/r50_motr_eval.sh", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n\n# for MOT17\n\n# EXP_DIR=exps/e2e_motr_r50_joint\n# python3 eval.py \\\n# --meta_arch motr \\\n# --dataset_file e2e_joint \\\n# --epoch 200 \\\n# --with_box_refine \\\n# --lr_drop 100 \\\n# --lr 2e-4 \\\n# --lr_backbone 2e-5 \\\n# --pretrained ${EXP_DIR}/motr_final.pth \\\n# --output_dir ${EXP_DIR} \\\n# --batch_size 1 \\\n# --sample_mode 'random_interval' \\\n# --sample_interval 10 \\\n# --sampler_steps 50 90 120 \\\n# --sampler_lengths 2 3 4 5 \\\n# --update_query_pos \\\n# --merger_dropout 0 \\\n# --dropout 0 \\\n# --random_drop 0.1 \\\n# --fp_ratio 0.3 \\\n# --query_interaction_layer 'QIM' \\\n# --extra_track_attn \\\n# --data_txt_path_train ./datasets/data_path/joint.train \\\n# --data_txt_path_val ./datasets/data_path/mot17.train \\\n# --resume ${EXP_DIR}/motr_final.pth \\" }, { "path": "configs/r50_motr_submit.sh", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n\nEXP_DIR=exps/e2e_motr_r50_joint\npython3 submit.py \\\n --meta_arch motr \\\n --dataset_file e2e_joint \\\n --epoch 200 \\\n --with_box_refine \\\n --lr_drop 100 \\\n --lr 2e-4 \\\n --lr_backbone 2e-5 \\\n --pretrained ${EXP_DIR}/motr_final.pth \\\n --output_dir ${EXP_DIR} \\\n --batch_size 1 \\\n --sample_mode 'random_interval' \\\n --sample_interval 10 \\\n --sampler_steps 50 90 150 \\\n --sampler_lengths 2 3 4 5 \\\n --update_query_pos \\\n --merger_dropout 0 \\\n --dropout 0 \\\n --random_drop 0.1 \\\n --fp_ratio 0.3 \\\n --query_interaction_layer 'QIM' \\\n --extra_track_attn \\\n --data_txt_path_train ./datasets/data_path/joint.train \\\n --data_txt_path_val ./datasets/data_path/mot17.train \\\n --resume ${EXP_DIR}/motr_final.pth \\\n --exp_name pub_submit_17" }, { "path": "configs/r50_motr_submit_dance.sh", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n\nEXP_DIR=exps/e2e_motr_r50_dance\npython3 submit_dance.py \\\n --meta_arch motr \\\n --dataset_file e2e_joint \\\n --mot_path /data/datasets \\\n --epoch 200 \\\n --with_box_refine \\\n --lr_drop 100 \\\n --lr 2e-4 \\\n --lr_backbone 2e-5 \\\n --output_dir ${EXP_DIR} \\\n --batch_size 1 \\\n --sample_mode 'random_interval' \\\n --sample_interval 10 \\\n --sampler_steps 50 90 150 \\\n --sampler_lengths 2 3 4 5 \\\n --update_query_pos \\\n --merger_dropout 0 \\\n --dropout 0 \\\n --random_drop 0.1 \\\n --fp_ratio 0.3 \\\n --query_interaction_layer 'QIM' \\\n --extra_track_attn \\\n --data_txt_path_train ./datasets/data_path/joint.train \\\n --data_txt_path_val ./datasets/data_path/mot17.train \\\n --resume ${EXP_DIR}/checkpoint.pth \\\n --exp_name tracker\n" }, { "path": "configs/r50_motr_train.sh", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n\n\n# for MOT17\n\n# PRETRAIN=coco_model_final.pth\n# EXP_DIR=exps/e2e_motr_r50_joint\n# python3 -m torch.distributed.launch --nproc_per_node=8 \\\n# --use_env main.py \\\n# --meta_arch motr \\\n# --use_checkpoint \\\n# --dataset_file e2e_joint \\\n# --epoch 200 \\\n# --with_box_refine \\\n# --lr_drop 100 \\\n# --lr 2e-4 \\\n# --lr_backbone 2e-5 \\\n# --pretrained ${PRETRAIN} \\\n# --output_dir ${EXP_DIR} \\\n# --batch_size 1 \\\n# --sample_mode 'random_interval' \\\n# --sample_interval 10 \\\n# --sampler_steps 50 90 150 \\\n# --sampler_lengths 2 3 4 5 \\\n# --update_query_pos \\\n# --merger_dropout 0 \\\n# --dropout 0 \\\n# --random_drop 0.1 \\\n# --fp_ratio 0.3 \\\n# --query_interaction_layer 'QIM' \\\n# --extra_track_attn \\\n# --data_txt_path_train ./datasets/data_path/joint.train \\\n# --data_txt_path_val ./datasets/data_path/mot17.train \\" }, { "path": "configs/r50_motr_train_dance.sh", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n\n\n# for MOT17\n\nPRETRAIN=r50_deformable_detr_plus_iterative_bbox_refinement-checkpoint.pth\nEXP_DIR=exps/e2e_motr_r50_dance\npython3 -m torch.distributed.launch --nproc_per_node=8 \\\n --use_env main.py \\\n --meta_arch motr \\\n --use_checkpoint \\\n --dataset_file e2e_dance \\\n --epoch 20 \\\n --with_box_refine \\\n --lr_drop 10 \\\n --lr 2e-4 \\\n --lr_backbone 2e-5 \\\n --pretrained ${PRETRAIN} \\\n --output_dir ${EXP_DIR} \\\n --batch_size 1 \\\n --sample_mode 'random_interval' \\\n --sample_interval 10 \\\n --sampler_steps 5 9 15 \\\n --sampler_lengths 2 3 4 5 \\\n --update_query_pos \\\n --merger_dropout 0 \\\n --dropout 0 \\\n --random_drop 0.1 \\\n --fp_ratio 0.3 \\\n --query_interaction_layer 'QIM' \\\n --extra_track_attn \\\n --data_txt_path_train ./datasets/data_path/joint.train \\\n --data_txt_path_val ./datasets/data_path/mot17.train \\\n |& tee ${EXP_DIR}/output.log\n" }, { "path": "datasets/__init__.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\nimport torch.utils.data\nimport torchvision\n\nfrom .coco import build as build_coco\nfrom .detmot import build as build_e2e_mot\nfrom .dance import build as build_e2e_dance\nfrom .static_detmot import build as build_e2e_static_mot\nfrom .joint import build as build_e2e_joint\nfrom .torchvision_datasets import CocoDetection\n\ndef get_coco_api_from_dataset(dataset):\n for _ in range(10):\n # if isinstance(dataset, torchvision.datasets.CocoDetection):\n # break\n if isinstance(dataset, torch.utils.data.Subset):\n dataset = dataset.dataset\n if isinstance(dataset, CocoDetection):\n return dataset.coco\n\n\ndef build_dataset(image_set, args):\n if args.dataset_file == 'coco':\n return build_coco(image_set, args)\n if args.dataset_file == 'coco_panoptic':\n # to avoid making panopticapi required for coco\n from .coco_panoptic import build as build_coco_panoptic\n return build_coco_panoptic(image_set, args)\n if args.dataset_file == 'e2e_joint':\n return build_e2e_joint(image_set, args)\n if args.dataset_file == 'e2e_static_mot':\n return build_e2e_static_mot(image_set, args)\n if args.dataset_file == 'e2e_mot':\n return build_e2e_mot(image_set, args)\n if args.dataset_file == 'e2e_dance':\n return build_e2e_dance(image_set, args)\n raise ValueError(f'dataset {args.dataset_file} not supported')\n" }, { "path": "datasets/coco.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\n\"\"\"\nCOCO dataset which returns image_id for evaluation.\n\nMostly copy-paste from https://github.com/pytorch/vision/blob/13b35ff/references/detection/coco_utils.py\n\"\"\"\nfrom pathlib import Path\n\nimport torch\nimport torch.utils.data\nfrom pycocotools import mask as coco_mask\n\nfrom .torchvision_datasets import CocoDetection as TvCocoDetection\nfrom util.misc import get_local_rank, get_local_size\nimport datasets.transforms as T\n\n\nclass CocoDetection(TvCocoDetection):\n def __init__(self, img_folder, ann_file, transforms, return_masks, cache_mode=False, local_rank=0, local_size=1):\n super(CocoDetection, self).__init__(img_folder, ann_file,\n cache_mode=cache_mode, local_rank=local_rank, local_size=local_size)\n self._transforms = transforms\n self.prepare = ConvertCocoPolysToMask(return_masks)\n\n def __getitem__(self, idx):\n img, target = super(CocoDetection, self).__getitem__(idx)\n image_id = self.ids[idx]\n target = {'image_id': image_id, 'annotations': target}\n img, target = self.prepare(img, target)\n if self._transforms is not None:\n img, target = self._transforms(img, target)\n return img, target\n\n\ndef convert_coco_poly_to_mask(segmentations, height, width):\n masks = []\n for polygons in segmentations:\n rles = coco_mask.frPyObjects(polygons, height, width)\n mask = coco_mask.decode(rles)\n if len(mask.shape) < 3:\n mask = mask[..., None]\n mask = torch.as_tensor(mask, dtype=torch.uint8)\n mask = mask.any(dim=2)\n masks.append(mask)\n if masks:\n masks = torch.stack(masks, dim=0)\n else:\n masks = torch.zeros((0, height, width), dtype=torch.uint8)\n return masks\n\n\nclass ConvertCocoPolysToMask(object):\n def __init__(self, return_masks=False):\n self.return_masks = return_masks\n\n def __call__(self, image, target):\n w, h = image.size\n\n image_id = target[\"image_id\"]\n image_id = torch.tensor([image_id])\n\n anno = target[\"annotations\"]\n\n anno = [obj for obj in anno if 'iscrowd' not in obj or obj['iscrowd'] == 0]\n\n boxes = [obj[\"bbox\"] for obj in anno]\n # guard against no boxes via resizing\n boxes = torch.as_tensor(boxes, dtype=torch.float32).reshape(-1, 4)\n boxes[:, 2:] += boxes[:, :2]\n boxes[:, 0::2].clamp_(min=0, max=w)\n boxes[:, 1::2].clamp_(min=0, max=h)\n\n classes = [obj[\"category_id\"] for obj in anno]\n classes = torch.tensor(classes, dtype=torch.int64)\n\n if self.return_masks:\n segmentations = [obj[\"segmentation\"] for obj in anno]\n masks = convert_coco_poly_to_mask(segmentations, h, w)\n\n keypoints = None\n if anno and \"keypoints\" in anno[0]:\n keypoints = [obj[\"keypoints\"] for obj in anno]\n keypoints = torch.as_tensor(keypoints, dtype=torch.float32)\n num_keypoints = keypoints.shape[0]\n if num_keypoints:\n keypoints = keypoints.view(num_keypoints, -1, 3)\n\n keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])\n boxes = boxes[keep]\n classes = classes[keep]\n if self.return_masks:\n masks = masks[keep]\n if keypoints is not None:\n keypoints = keypoints[keep]\n\n target = {}\n target[\"boxes\"] = boxes\n target[\"labels\"] = classes\n if self.return_masks:\n target[\"masks\"] = masks\n target[\"image_id\"] = image_id\n if keypoints is not None:\n target[\"keypoints\"] = keypoints\n\n # for conversion to coco api\n area = torch.tensor([obj[\"area\"] for obj in anno])\n iscrowd = torch.tensor([obj[\"iscrowd\"] if \"iscrowd\" in obj else 0 for obj in anno])\n target[\"area\"] = area[keep]\n target[\"iscrowd\"] = iscrowd[keep]\n\n target[\"orig_size\"] = torch.as_tensor([int(h), int(w)])\n target[\"size\"] = torch.as_tensor([int(h), int(w)])\n\n return image, target\n\n\ndef make_coco_transforms(image_set):\n\n normalize = T.Compose([\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ])\n\n scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]\n\n if image_set == 'train':\n return T.Compose([\n T.RandomHorizontalFlip(),\n T.RandomSelect(\n T.RandomResize(scales, max_size=1333),\n T.Compose([\n T.RandomResize([400, 500, 600]),\n T.RandomSizeCrop(384, 600),\n T.RandomResize(scales, max_size=1333),\n ])\n ),\n normalize,\n ])\n\n if image_set == 'val':\n return T.Compose([\n T.RandomResize([800], max_size=1333),\n normalize,\n ])\n\n raise ValueError(f'unknown {image_set}')\n\n\ndef build(image_set, args):\n root = Path(args.coco_path)\n assert root.exists(), f'provided COCO path {root} does not exist'\n mode = 'instances'\n PATHS = {\n \"train\": (root / \"train2017\", root / \"annotations\" / f'{mode}_train2017.json'),\n \"val\": (root / \"val2017\", root / \"annotations\" / f'{mode}_val2017.json'),\n }\n\n img_folder, ann_file = PATHS[image_set]\n dataset = CocoDetection(img_folder, ann_file, transforms=make_coco_transforms(image_set), return_masks=args.masks,\n cache_mode=args.cache_mode, local_rank=get_local_rank(), local_size=get_local_size())\n return dataset\n" }, { "path": "datasets/coco_eval.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\n\"\"\"\nCOCO evaluator that works in distributed mode.\n\nMostly copy-paste from https://github.com/pytorch/vision/blob/edfd5a7/references/detection/coco_eval.py\nThe difference is that there is less copy-pasting from pycocotools\nin the end of the file, as python3 can suppress prints with contextlib\n\"\"\"\nimport os\nimport contextlib\nimport copy\nimport numpy as np\nimport torch\n\nfrom pycocotools.cocoeval import COCOeval\nfrom pycocotools.coco import COCO\nimport pycocotools.mask as mask_util\n\nfrom util.misc import all_gather\n\n\nclass CocoEvaluator(object):\n def __init__(self, coco_gt, iou_types):\n assert isinstance(iou_types, (list, tuple))\n coco_gt = copy.deepcopy(coco_gt)\n self.coco_gt = coco_gt\n\n self.iou_types = iou_types\n self.coco_eval = {}\n for iou_type in iou_types:\n self.coco_eval[iou_type] = COCOeval(coco_gt, iouType=iou_type)\n\n self.img_ids = []\n self.eval_imgs = {k: [] for k in iou_types}\n\n def update(self, predictions):\n img_ids = list(np.unique(list(predictions.keys())))\n self.img_ids.extend(img_ids)\n\n for iou_type in self.iou_types:\n results = self.prepare(predictions, iou_type)\n\n # suppress pycocotools prints\n with open(os.devnull, 'w') as devnull:\n print(\"self.coco_gt={}\".format(self.coco_gt))\n with contextlib.redirect_stdout(devnull):\n coco_dt = COCO.loadRes(self.coco_gt, results) if results else COCO()\n coco_eval = self.coco_eval[iou_type]\n\n coco_eval.cocoDt = coco_dt\n coco_eval.params.imgIds = list(img_ids)\n img_ids, eval_imgs = evaluate(coco_eval)\n\n self.eval_imgs[iou_type].append(eval_imgs)\n\n def synchronize_between_processes(self):\n for iou_type in self.iou_types:\n self.eval_imgs[iou_type] = np.concatenate(self.eval_imgs[iou_type], 2)\n create_common_coco_eval(self.coco_eval[iou_type], self.img_ids, self.eval_imgs[iou_type])\n\n def accumulate(self):\n for coco_eval in self.coco_eval.values():\n coco_eval.accumulate()\n\n def summarize(self):\n for iou_type, coco_eval in self.coco_eval.items():\n print(\"IoU metric: {}\".format(iou_type))\n coco_eval.summarize()\n\n def prepare(self, predictions, iou_type):\n if iou_type == \"bbox\":\n return self.prepare_for_coco_detection(predictions)\n elif iou_type == \"segm\":\n return self.prepare_for_coco_segmentation(predictions)\n elif iou_type == \"keypoints\":\n return self.prepare_for_coco_keypoint(predictions)\n else:\n raise ValueError(\"Unknown iou type {}\".format(iou_type))\n\n def prepare_for_coco_detection(self, predictions):\n coco_results = []\n for original_id, prediction in predictions.items():\n if len(prediction) == 0:\n continue\n\n boxes = prediction[\"boxes\"]\n boxes = convert_to_xywh(boxes).tolist()\n scores = prediction[\"scores\"].tolist()\n labels = prediction[\"labels\"].tolist()\n\n coco_results.extend(\n [\n {\n \"image_id\": original_id,\n \"category_id\": labels[k],\n \"bbox\": box,\n \"score\": scores[k],\n }\n for k, box in enumerate(boxes)\n ]\n )\n return coco_results\n\n def prepare_for_coco_segmentation(self, predictions):\n coco_results = []\n for original_id, prediction in predictions.items():\n if len(prediction) == 0:\n continue\n\n scores = prediction[\"scores\"]\n labels = prediction[\"labels\"]\n masks = prediction[\"masks\"]\n\n masks = masks > 0.5\n\n scores = prediction[\"scores\"].tolist()\n labels = prediction[\"labels\"].tolist()\n\n rles = [\n mask_util.encode(np.array(mask[0, :, :, np.newaxis], dtype=np.uint8, order=\"F\"))[0]\n for mask in masks\n ]\n for rle in rles:\n rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\")\n\n coco_results.extend(\n [\n {\n \"image_id\": original_id,\n \"category_id\": labels[k],\n \"segmentation\": rle,\n \"score\": scores[k],\n }\n for k, rle in enumerate(rles)\n ]\n )\n return coco_results\n\n def prepare_for_coco_keypoint(self, predictions):\n coco_results = []\n for original_id, prediction in predictions.items():\n if len(prediction) == 0:\n continue\n\n boxes = prediction[\"boxes\"]\n boxes = convert_to_xywh(boxes).tolist()\n scores = prediction[\"scores\"].tolist()\n labels = prediction[\"labels\"].tolist()\n keypoints = prediction[\"keypoints\"]\n keypoints = keypoints.flatten(start_dim=1).tolist()\n\n coco_results.extend(\n [\n {\n \"image_id\": original_id,\n \"category_id\": labels[k],\n 'keypoints': keypoint,\n \"score\": scores[k],\n }\n for k, keypoint in enumerate(keypoints)\n ]\n )\n return coco_results\n\n\ndef convert_to_xywh(boxes):\n xmin, ymin, xmax, ymax = boxes.unbind(1)\n return torch.stack((xmin, ymin, xmax - xmin, ymax - ymin), dim=1)\n\n\ndef merge(img_ids, eval_imgs):\n all_img_ids = all_gather(img_ids)\n all_eval_imgs = all_gather(eval_imgs)\n\n merged_img_ids = []\n for p in all_img_ids:\n merged_img_ids.extend(p)\n\n merged_eval_imgs = []\n for p in all_eval_imgs:\n merged_eval_imgs.append(p)\n\n merged_img_ids = np.array(merged_img_ids)\n merged_eval_imgs = np.concatenate(merged_eval_imgs, 2)\n\n # keep only unique (and in sorted order) images\n merged_img_ids, idx = np.unique(merged_img_ids, return_index=True)\n merged_eval_imgs = merged_eval_imgs[..., idx]\n\n return merged_img_ids, merged_eval_imgs\n\n\ndef create_common_coco_eval(coco_eval, img_ids, eval_imgs):\n img_ids, eval_imgs = merge(img_ids, eval_imgs)\n img_ids = list(img_ids)\n eval_imgs = list(eval_imgs.flatten())\n\n coco_eval.evalImgs = eval_imgs\n coco_eval.params.imgIds = img_ids\n coco_eval._paramsEval = copy.deepcopy(coco_eval.params)\n\n\n#################################################################\n# From pycocotools, just removed the prints and fixed\n# a Python3 bug about unicode not defined\n#################################################################\n\n\ndef evaluate(self):\n '''\n Run per image evaluation on given images and store results (a list of dict) in self.evalImgs\n :return: None\n '''\n # tic = time.time()\n # print('Running per image evaluation...')\n p = self.params\n # add backward compatibility if useSegm is specified in params\n if p.useSegm is not None:\n p.iouType = 'segm' if p.useSegm == 1 else 'bbox'\n print('useSegm (deprecated) is not None. Running {} evaluation'.format(p.iouType))\n # print('Evaluate annotation type *{}*'.format(p.iouType))\n p.imgIds = list(np.unique(p.imgIds))\n if p.useCats:\n p.catIds = list(np.unique(p.catIds))\n p.maxDets = sorted(p.maxDets)\n self.params = p\n\n self._prepare()\n # loop through images, area range, max detection number\n catIds = p.catIds if p.useCats else [-1]\n\n if p.iouType == 'segm' or p.iouType == 'bbox':\n computeIoU = self.computeIoU\n elif p.iouType == 'keypoints':\n computeIoU = self.computeOks\n self.ious = {\n (imgId, catId): computeIoU(imgId, catId)\n for imgId in p.imgIds\n for catId in catIds}\n\n evaluateImg = self.evaluateImg\n maxDet = p.maxDets[-1]\n evalImgs = [\n evaluateImg(imgId, catId, areaRng, maxDet)\n for catId in catIds\n for areaRng in p.areaRng\n for imgId in p.imgIds\n ]\n # this is NOT in the pycocotools code, but could be done outside\n evalImgs = np.asarray(evalImgs).reshape(len(catIds), len(p.areaRng), len(p.imgIds))\n self._paramsEval = copy.deepcopy(self.params)\n # toc = time.time()\n # print('DONE (t={:0.2f}s).'.format(toc-tic))\n return p.imgIds, evalImgs\n\n#################################################################\n# end of straight copy from pycocotools, just removing the prints\n#################################################################\n" }, { "path": "datasets/coco_panoptic.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\nimport json\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nfrom PIL import Image\n\nfrom panopticapi.utils import rgb2id\nfrom util.box_ops import masks_to_boxes\n\nfrom .coco import make_coco_transforms\n\n\nclass CocoPanoptic:\n def __init__(self, img_folder, ann_folder, ann_file, transforms=None, return_masks=True):\n with open(ann_file, 'r') as f:\n self.coco = json.load(f)\n\n # sort 'images' field so that they are aligned with 'annotations'\n # i.e., in alphabetical order\n self.coco['images'] = sorted(self.coco['images'], key=lambda x: x['id'])\n # sanity check\n if \"annotations\" in self.coco:\n for img, ann in zip(self.coco['images'], self.coco['annotations']):\n assert img['file_name'][:-4] == ann['file_name'][:-4]\n\n self.img_folder = img_folder\n self.ann_folder = ann_folder\n self.ann_file = ann_file\n self.transforms = transforms\n self.return_masks = return_masks\n\n def __getitem__(self, idx):\n ann_info = self.coco['annotations'][idx] if \"annotations\" in self.coco else self.coco['images'][idx]\n img_path = Path(self.img_folder) / ann_info['file_name'].replace('.png', '.jpg')\n ann_path = Path(self.ann_folder) / ann_info['file_name']\n\n img = Image.open(img_path).convert('RGB')\n w, h = img.size\n if \"segments_info\" in ann_info:\n masks = np.asarray(Image.open(ann_path), dtype=np.uint32)\n masks = rgb2id(masks)\n\n ids = np.array([ann['id'] for ann in ann_info['segments_info']])\n masks = masks == ids[:, None, None]\n\n masks = torch.as_tensor(masks, dtype=torch.uint8)\n labels = torch.tensor([ann['category_id'] for ann in ann_info['segments_info']], dtype=torch.int64)\n\n target = {}\n target['image_id'] = torch.tensor([ann_info['image_id'] if \"image_id\" in ann_info else ann_info[\"id\"]])\n if self.return_masks:\n target['masks'] = masks\n target['labels'] = labels\n\n target[\"boxes\"] = masks_to_boxes(masks)\n\n target['size'] = torch.as_tensor([int(h), int(w)])\n target['orig_size'] = torch.as_tensor([int(h), int(w)])\n if \"segments_info\" in ann_info:\n for name in ['iscrowd', 'area']:\n target[name] = torch.tensor([ann[name] for ann in ann_info['segments_info']])\n\n if self.transforms is not None:\n img, target = self.transforms(img, target)\n\n return img, target\n\n def __len__(self):\n return len(self.coco['images'])\n\n def get_height_and_width(self, idx):\n img_info = self.coco['images'][idx]\n height = img_info['height']\n width = img_info['width']\n return height, width\n\n\ndef build(image_set, args):\n img_folder_root = Path(args.coco_path)\n ann_folder_root = Path(args.coco_panoptic_path)\n assert img_folder_root.exists(), f'provided COCO path {img_folder_root} does not exist'\n assert ann_folder_root.exists(), f'provided COCO path {ann_folder_root} does not exist'\n mode = 'panoptic'\n PATHS = {\n \"train\": (\"train2017\", Path(\"annotations\") / f'{mode}_train2017.json'),\n \"val\": (\"val2017\", Path(\"annotations\") / f'{mode}_val2017.json'),\n }\n\n img_folder, ann_file = PATHS[image_set]\n img_folder_path = img_folder_root / img_folder\n ann_folder = ann_folder_root / f'{mode}_{img_folder}'\n ann_file = ann_folder_root / ann_file\n\n dataset = CocoPanoptic(img_folder_path, ann_folder, ann_file,\n transforms=make_coco_transforms(image_set), return_masks=args.masks)\n\n return dataset\n" }, { "path": "datasets/dance.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\"\"\"\nMOT dataset which returns image_id for evaluation.\n\"\"\"\nfrom collections import defaultdict\nimport json\nimport os\nfrom pathlib import Path\nimport cv2\nimport numpy as np\nimport torch\nimport torch.utils.data\nimport os.path as osp\nfrom PIL import Image, ImageDraw\nimport copy\nimport datasets.transforms as T\nfrom models.structures import Instances\n\nfrom random import choice, randint\n\n\nclass DetMOTDetection:\n def __init__(self, args, data_txt_path: str, seqs_folder, dataset2transform):\n self.args = args\n self.dataset2transform = dataset2transform\n self.num_frames_per_batch = max(args.sampler_lengths)\n self.sample_mode = args.sample_mode\n self.sample_interval = args.sample_interval\n self.video_dict = {}\n self.split_dir = os.path.join(args.mot_path, \"DanceTrack\", \"train\")\n\n self.labels_full = defaultdict(lambda : defaultdict(list))\n for vid in os.listdir(self.split_dir):\n if 'DPM' in vid or 'FRCNN' in vid:\n print(f'filter {vid}')\n continue\n gt_path = os.path.join(self.split_dir, vid, 'gt', 'gt.txt')\n for l in open(gt_path):\n t, i, *xywh, mark, label = l.strip().split(',')[:8]\n t, i, mark, label = map(int, (t, i, mark, label))\n if mark == 0:\n continue\n if label in [3, 4, 5, 6, 9, 10, 11]: # Non-person\n continue\n else:\n crowd = False\n x, y, w, h = map(float, (xywh))\n self.labels_full[vid][t].append([x, y, w, h, i, crowd])\n vid_files = list(self.labels_full.keys())\n\n self.indices = []\n self.vid_tmax = {}\n for vid in vid_files:\n self.video_dict[vid] = len(self.video_dict)\n t_min = min(self.labels_full[vid].keys())\n t_max = max(self.labels_full[vid].keys()) + 1\n self.vid_tmax[vid] = t_max - 1\n for t in range(t_min, t_max - self.num_frames_per_batch):\n self.indices.append((vid, t))\n\n self.sampler_steps: list = args.sampler_steps\n self.lengths: list = args.sampler_lengths\n print(\"sampler_steps={} lenghts={}\".format(self.sampler_steps, self.lengths))\n self.period_idx = 0\n\n def set_epoch(self, epoch):\n self.current_epoch = epoch\n if self.sampler_steps is None or len(self.sampler_steps) == 0:\n # fixed sampling length.\n return\n\n for i in range(len(self.sampler_steps)):\n if epoch >= self.sampler_steps[i]:\n self.period_idx = i + 1\n print(\"set epoch: epoch {} period_idx={}\".format(epoch, self.period_idx))\n self.num_frames_per_batch = self.lengths[self.period_idx]\n\n def step_epoch(self):\n # one epoch finishes.\n print(\"Dataset: epoch {} finishes\".format(self.current_epoch))\n self.set_epoch(self.current_epoch + 1)\n\n @staticmethod\n def _targets_to_instances(targets: dict, img_shape) -> Instances:\n gt_instances = Instances(tuple(img_shape))\n gt_instances.boxes = targets['boxes']\n gt_instances.labels = targets['labels']\n gt_instances.obj_ids = targets['obj_ids']\n gt_instances.area = targets['area']\n return gt_instances\n\n def load_crowd(self):\n path, boxes, crowd = choice(self.crowd_gts)\n img = Image.open(path)\n\n w, h = img._size\n boxes = torch.tensor(boxes, dtype=torch.float32)\n areas = boxes[..., 2:].prod(-1)\n boxes[:, 2:] += boxes[:, :2]\n target = {\n 'boxes': boxes,\n 'labels': torch.zeros((len(boxes), ), dtype=torch.long),\n 'iscrowd': torch.as_tensor(crowd),\n 'image_id': torch.tensor([0]),\n 'area': areas,\n 'obj_ids': torch.arange(len(boxes)),\n 'size': torch.as_tensor([h, w]),\n 'orig_size': torch.as_tensor([h, w]),\n 'dataset': \"CrowdHuman\",\n }\n return [img], [target]\n\n def _pre_single_frame(self, vid, idx: int):\n img_path = os.path.join(self.split_dir, vid, 'img1', f'{idx:08d}.jpg')\n img = Image.open(img_path)\n targets = {}\n w, h = img._size\n assert w > 0 and h > 0, \"invalid image {} with shape {} {}\".format(img_path, w, h)\n obj_idx_offset = self.video_dict[vid] * 100000 # 100000 unique ids is enough for a video.\n\n targets['dataset'] = 'MOT17'\n targets['boxes'] = []\n targets['area'] = []\n targets['iscrowd'] = []\n targets['labels'] = []\n targets['obj_ids'] = []\n targets['image_id'] = torch.as_tensor(idx)\n targets['size'] = torch.as_tensor([h, w])\n targets['orig_size'] = torch.as_tensor([h, w])\n for *xywh, id, crowd in self.labels_full[vid][idx]:\n targets['boxes'].append(xywh)\n targets['area'].append(xywh[2] * xywh[3])\n targets['iscrowd'].append(crowd)\n targets['labels'].append(0)\n targets['obj_ids'].append(id + obj_idx_offset)\n\n targets['area'] = torch.as_tensor(targets['area'])\n targets['iscrowd'] = torch.as_tensor(targets['iscrowd'])\n targets['labels'] = torch.as_tensor(targets['labels'])\n targets['obj_ids'] = torch.as_tensor(targets['obj_ids'], dtype=torch.float64)\n targets['boxes'] = torch.as_tensor(targets['boxes'], dtype=torch.float32).reshape(-1, 4)\n targets['boxes'][:, 2:] += targets['boxes'][:, :2]\n return img, targets\n\n def _get_sample_range(self, start_idx):\n\n # take default sampling method for normal dataset.\n assert self.sample_mode in ['fixed_interval', 'random_interval'], 'invalid sample mode: {}'.format(self.sample_mode)\n if self.sample_mode == 'fixed_interval':\n sample_interval = self.sample_interval\n elif self.sample_mode == 'random_interval':\n sample_interval = np.random.randint(1, self.sample_interval + 1)\n default_range = start_idx, start_idx + (self.num_frames_per_batch - 1) * sample_interval + 1, sample_interval\n return default_range\n\n def pre_continuous_frames(self, vid, indices):\n return zip(*[self._pre_single_frame(vid, i) for i in indices])\n\n def sample_indices(self, vid, f_index):\n assert self.sample_mode == 'random_interval'\n rate = randint(1, self.sample_interval + 1)\n tmax = self.vid_tmax[vid]\n ids = [f_index + rate * i for i in range(self.num_frames_per_batch)]\n return [min(i, tmax) for i in ids]\n\n def __getitem__(self, idx):\n vid, f_index = self.indices[idx]\n indices = self.sample_indices(vid, f_index)\n images, targets = self.pre_continuous_frames(vid, indices)\n dataset_name = targets[0]['dataset']\n transform = self.dataset2transform[dataset_name]\n if transform is not None:\n images, targets = transform(images, targets)\n gt_instances = []\n for img_i, targets_i in zip(images, targets):\n gt_instances_i = self._targets_to_instances(targets_i, img_i.shape[1:3])\n gt_instances.append(gt_instances_i)\n return {\n 'imgs': images,\n 'gt_instances': gt_instances,\n }\n\n def __len__(self):\n return len(self.indices)\n\n\nclass DetMOTDetectionValidation(DetMOTDetection):\n def __init__(self, args, seqs_folder, dataset2transform):\n args.data_txt_path = args.val_data_txt_path\n super().__init__(args, seqs_folder, dataset2transform)\n\n\ndef make_transforms_for_mot17(image_set, args=None):\n\n normalize = T.MotCompose([\n T.MotToTensor(),\n T.MotNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ])\n scales = [608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992]\n\n if image_set == 'train':\n return T.MotCompose([\n T.MotRandomHorizontalFlip(),\n T.MotRandomSelect(\n T.MotRandomResize(scales, max_size=1536),\n T.MotCompose([\n T.MotRandomResize([800, 1000, 1200]),\n T.FixedMotRandomCrop(800, 1200),\n T.MotRandomResize(scales, max_size=1536),\n ])\n ),\n normalize,\n ])\n\n if image_set == 'val':\n return T.MotCompose([\n T.MotRandomResize([800], max_size=1333),\n normalize,\n ])\n\n raise ValueError(f'unknown {image_set}')\n\n\ndef build_dataset2transform(args, image_set):\n mot17_train = make_transforms_for_mot17('train', args)\n mot17_test = make_transforms_for_mot17('val', args)\n\n dataset2transform_train = {'MOT17': mot17_train}\n dataset2transform_val = {'MOT17': mot17_test}\n if image_set == 'train':\n return dataset2transform_train\n elif image_set == 'val':\n return dataset2transform_val\n else:\n raise NotImplementedError()\n\n\ndef build(image_set, args):\n root = Path(args.mot_path)\n assert root.exists(), f'provided MOT path {root} does not exist'\n dataset2transform = build_dataset2transform(args, image_set)\n if image_set == 'train':\n data_txt_path = args.data_txt_path_train\n dataset = DetMOTDetection(args, data_txt_path=data_txt_path, seqs_folder=root, dataset2transform=dataset2transform)\n if image_set == 'val':\n data_txt_path = args.data_txt_path_val\n dataset = DetMOTDetection(args, data_txt_path=data_txt_path, seqs_folder=root, dataset2transform=dataset2transform)\n return dataset\n" }, { "path": "datasets/data_path/bdd100k.val", "content": 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}, { "path": "datasets/data_path/gen_bdd100k_mot.py", "content": "import os \nimport numpy as np \nimport json\nimport cv2\nfrom tqdm import tqdm\nfrom collections import defaultdict\n\n\ndef convert(img_dir, split, label_dir, save_label_dir, filter_crowd=False, filter_ignore=False):\n cat2id = {'train':6, 'car':3, 'bus':5, 'other person': 1, 'rider':2, 'pedestrian':1, 'other vehicle':3, 'motorcycle':7, 'bicycle':8, 'trailer':4, 'truck':4}\n\n coco = defaultdict(list)\n coco[\"categories\"] = [\n {\"supercategory\": \"human\", \"id\": 1, \"name\": \"pedestrian\"},\n {\"supercategory\": \"human\", \"id\": 2, \"name\": \"rider\"},\n {\"supercategory\": \"vehicle\", \"id\": 3, \"name\": \"car\"},\n {\"supercategory\": \"vehicle\", \"id\": 4, \"name\": \"truck\"},\n {\"supercategory\": \"vehicle\", \"id\": 5, \"name\": \"bus\"},\n {\"supercategory\": \"vehicle\", \"id\": 6, \"name\": \"train\"},\n {\"supercategory\": \"bike\", \"id\": 7, \"name\": \"motorcycle\"},\n {\"supercategory\": \"bike\", \"id\": 8, \"name\": \"bicycle\"},\n ]\n attr_id_dict = {\n frame[\"name\"]: frame[\"id\"] for frame in coco[\"categories\"]\n }\n\n all_categories = set()\n img_dir = os.path.join(img_dir, split)\n label_dir = os.path.join(label_dir, split)\n vids = os.listdir(img_dir)\n for vid in tqdm(vids):\n txt_label_dir = os.path.join(save_label_dir, split, vid)\n os.makedirs(txt_label_dir, exist_ok=True)\n annos = json.load(open(os.path.join(label_dir, vid+'.json'), 'r'))\n for anno in annos:\n name = anno['name']\n labels = anno['labels']\n videoName = anno['videoName']\n frameIndex = anno['frameIndex']\n img = cv2.imread(os.path.join(img_dir, vid, name))\n seq_height, seq_width, _ = img.shape\n if len(labels) < 1:\n continue\n # for label in labels:\n # category = label['category']\n # all_categories.add(category)\n with open(os.path.join(txt_label_dir, name.replace('jpg', 'txt')), 'w') as f:\n for label in labels:\n obj_id = label['id']\n category = label['category']\n attributes = label['attributes']\n is_crowd = attributes['crowd']\n\n if filter_crowd and is_crowd:\n continue\n if filter_ignore and (category not in attr_id_dict.keys()):\n continue\n\n box2d = label['box2d']\n x1 = box2d['x1']\n x2 = box2d['x2']\n y1 = box2d['y1']\n y2 = box2d['y2']\n w = x2-x1\n h = y2-y1\n cx = (x1+x2) / 2\n cy = (y1+y2) / 2\n label_str = '{:d} {:d} {:.6f} {:.6f} {:.6f} {:.6f}\\n'.format(\n cat2id[category], int(obj_id), cx / seq_width, cy / seq_height, w / seq_width, h / seq_height)\n f.write(label_str)\n # print(f'all categories are {all_categories}.')\n\ndef generate_txt(img_dir,label_dir,txt_path='bdd100k.train',split='train'):\n img_dir = os.path.join(img_dir, split)\n label_dir = os.path.join(label_dir, split)\n all_vids = os.listdir(img_dir)\n all_frames = []\n for vid in tqdm(all_vids):\n fids = os.listdir(os.path.join(img_dir, vid))\n fids.sort()\n for fid in fids:\n if os.path.exists(os.path.join(label_dir, vid, fid.replace('jpg', 'txt'))):\n all_frames.append(f'images/track/{split}/{vid}/{fid}')\n with open(txt_path, 'w') as f:\n for frame in all_frames:\n f.write(frame+'\\n')\n\n'''no filter'''\n# img_dir = '/data/Dataset/bdd100k/bdd100k/images/track'\n# label_dir = '/data/Dataset/bdd100k/bdd100k/labels/box_track_20'\n# save_label_dir = '/data/Dataset/bdd100k/bdd100k/labels/track'\n# split = 'train'\n# convert(img_dir, split, label_dir, save_label_dir)\n\n# img_dir = '/data/Dataset/bdd100k/bdd100k/images/track'\n# label_dir = '/data/Dataset/bdd100k/bdd100k/labels/box_track_20'\n# save_label_dir = '/data/Dataset/bdd100k/bdd100k/labels/track'\n# split = 'val'\n# convert(img_dir, split, label_dir, save_label_dir)\n\n# img_dir = '/data/Dataset/bdd100k/bdd100k/images/track'\n# label_dir = '/data/Dataset/bdd100k/bdd100k/labels/box_track_20'\n# save_label_dir = '/data/Dataset/bdd100k/bdd100k/labels/track'\n# split = 'train'\n# generate_txt(img_dir,save_label_dir,txt_path='bdd100k.train',split='train')\n\n# img_dir = '/data/Dataset/bdd100k/bdd100k/images/track'\n# label_dir = '/data/Dataset/bdd100k/bdd100k/labels/box_track_20'\n# save_label_dir = '/data/Dataset/bdd100k/bdd100k/labels/track'\n# split = 'val'\n# generate_txt(img_dir,save_label_dir,txt_path='bdd100k.val',split='val')\n\n\n'''for filter'''\n# img_dir = '/data/Dataset/bdd100k/bdd100k/images/track'\n# label_dir = '/data/Dataset/bdd100k/bdd100k/labels/box_track_20'\n# save_label_dir = '/data/Dataset/bdd100k/bdd100k/filter_labels/track'\n# split = 'train'\n# convert(img_dir, split, label_dir, save_label_dir, filter_crowd=True, filter_ignore=True)\n\n# img_dir = '/data/Dataset/bdd100k/bdd100k/images/track'\n# label_dir = '/data/Dataset/bdd100k/bdd100k/labels/box_track_20'\n# save_label_dir = '/data/Dataset/bdd100k/bdd100k/filter_labels/track'\n# split = 'val'\n# convert(img_dir, split, label_dir, save_label_dir, filter_crowd=True, filter_ignore=True)\n\n# img_dir = '/data/Dataset/bdd100k/bdd100k/images/track'\n# label_dir = '/data/Dataset/bdd100k/bdd100k/labels/box_track_20'\n# save_label_dir = '/data/Dataset/bdd100k/bdd100k/filter_labels/track'\n# split = 'train'\n# generate_txt(img_dir,save_label_dir,txt_path='filter.bdd100k.train',split='train')\n\n# img_dir = '/data/Dataset/bdd100k/bdd100k/images/track'\n# label_dir = '/data/Dataset/bdd100k/bdd100k/labels/box_track_20'\n# save_label_dir = '/data/Dataset/bdd100k/bdd100k/filter_labels/track'\n# split = 'val'\n# generate_txt(img_dir,save_label_dir,txt_path='filter.bdd100k.val',split='val')\n\n" }, { "path": "datasets/data_path/gen_labels_15.py", "content": "import os.path as osp\nimport os\nimport numpy as np\nimport cv2\nfrom tqdm import tqdm\n\ndef mkdirs(d):\n if not osp.exists(d):\n os.makedirs(d)\n\nseq_root = '/data/workspace/datasets/mot/MOT15/images/train'\nlabel_root = '/data/workspace/datasets/mot/MOT15/labels_with_ids/train'\nmkdirs(label_root)\nseqs = ['ADL-Rundle-6', 'ETH-Bahnhof', 'KITTI-13', 'PETS09-S2L1', 'TUD-Stadtmitte', 'ADL-Rundle-8', 'KITTI-17',\n 'ETH-Pedcross2', 'ETH-Sunnyday', 'TUD-Campus', 'Venice-2']\n\ntid_curr = 0\ntid_last = -1\nfor seq in tqdm(seqs):\n\n # seq_info = open(osp.join(seq_root, seq, 'seqinfo.ini')).read()\n # seq_width = int(seq_info[seq_info.find('imWidth=') + 8:seq_info.find('\\nimHeight')])\n # seq_height = int(seq_info[seq_info.find('imHeight=') + 9:seq_info.find('\\nimExt')])\n\n all_imgs = os.listdir(osp.join(seq_root, seq, 'img1'))\n fm = cv2.imread(osp.join(seq_root, seq, 'img1', all_imgs[0]))\n seq_height, seq_width, c = fm.shape\n\n gt_txt = osp.join(seq_root, seq, 'gt', 'gt.txt')\n gt = np.loadtxt(gt_txt, dtype=np.float64, delimiter=',')\n idx = np.lexsort(gt.T[:2, :])\n gt = gt[idx, :]\n\n seq_label_root = osp.join(label_root, seq, 'img1')\n mkdirs(seq_label_root)\n\n for fid, tid, x, y, w, h, mark, _, _, _ in gt:\n if mark == 0:\n continue\n fid = int(fid)\n tid = int(tid)\n if not tid == tid_last:\n tid_curr += 1\n tid_last = tid\n x += w / 2\n y += h / 2\n label_fpath = osp.join(seq_label_root, '{:06d}.txt'.format(fid))\n label_str = '0 {:d} {:.6f} {:.6f} {:.6f} {:.6f}\\n'.format(\n tid_curr, x / seq_width, y / seq_height, w / seq_width, h / seq_height)\n with open(label_fpath, 'a') as f:\n f.write(label_str)" }, { "path": "datasets/data_path/gen_labels_16.py", "content": "import os.path as osp\nimport os\nimport numpy as np\ndef mkdirs(d):\n if not osp.exists(d):\n os.makedirs(d)\n\nseq_root = '/data/workspace/datasets/mot/MOT16/images/train'\nlabel_root = '/data/workspace/datasets/mot/MOT16/labels_with_ids/train'\nmkdirs(label_root)\nseqs = [s for s in os.listdir(seq_root)]\n\ntid_curr = 0\ntid_last = -1\nfor seq in seqs:\n seq_info = open(osp.join(seq_root, seq, 'seqinfo.ini')).read()\n seq_width = int(seq_info[seq_info.find('imWidth=') + 8:seq_info.find('\\nimHeight')])\n seq_height = int(seq_info[seq_info.find('imHeight=') + 9:seq_info.find('\\nimExt')])\n\n gt_txt = osp.join(seq_root, seq, 'gt', 'gt.txt')\n gt = np.loadtxt(gt_txt, dtype=np.float64, delimiter=',')\n idx = np.lexsort(gt.T[:2, :])\n gt = gt[idx, :]\n\n seq_label_root = osp.join(label_root, seq, 'img1')\n mkdirs(seq_label_root)\n\n for fid, tid, x, y, w, h, mark, _, _ in gt:\n if mark == 0:\n continue\n fid = int(fid)\n tid = int(tid)\n if not tid == tid_last:\n tid_curr += 1\n tid_last = tid\n x += w / 2\n y += h / 2\n label_fpath = osp.join(seq_label_root, '{:06d}.txt'.format(fid))\n label_str = '0 {:d} {:.6f} {:.6f} {:.6f} {:.6f}\\n'.format(\n tid_curr, x / seq_width, y / seq_height, w / seq_width, h / seq_height)\n with open(label_fpath, 'a') as f:\n f.write(label_str)" }, { "path": "datasets/data_path/prepare.py", "content": "import os\nfrom functools import partial\nfrom typing import List\n\n\ndef solve_MOT_train(root, year):\n assert year in [15, 16, 17]\n dataset_path = 'MOT{}/images/train'.format(year)\n data_root = os.path.join(root, dataset_path)\n if year == 17:\n video_paths = []\n for video_name in os.listdir(data_root):\n if 'SDP' in video_name:\n video_paths.append(video_name)\n else:\n video_paths = os.listdir(data_root)\n\n frames = []\n for video_name in video_paths:\n files = os.listdir(os.path.join(data_root, video_name, 'img1'))\n files.sort()\n for i in range(1, len(files) + 1):\n frames.append(os.path.join(dataset_path, video_name, 'img1', '%06d.jpg' % i))\n return frames\n\n\ndef solve_CUHK(root):\n dataset_path = 'ethz/CUHK-SYSU'\n data_root = os.path.join(root, dataset_path)\n file_names = os.listdir(os.path.join(data_root, 'images'))\n file_names.sort()\n\n frames = []\n for i in range(len(file_names)):\n if os.path.exists(os.path.join(root, 'ethz/CUHK-SYSU/labels_with_ids', f's{i + 1}.txt')):\n if os.path.exists(os.path.join(root, 'ethz/CUHK-SYSU/images', f's{i + 1}.jpg')):\n frames.append(os.path.join('ethz/CUHK-SYSU/images', f's{i + 1}.jpg'))\n return frames\n\ndef solve_ETHZ(root):\n dataset_path = 'ethz/ETHZ'\n data_root = os.path.join(root, dataset_path)\n video_paths = []\n for name in os.listdir(data_root):\n if name not in ['eth01', 'eth03']:\n video_paths.append(name)\n\n frames = []\n for video_path in video_paths:\n files = os.listdir(os.path.join(data_root, video_path, 'images'))\n files.sort()\n for img_name in files:\n if os.path.exists(os.path.join(data_root, video_path, 'labels_with_ids', img_name.replace('.png', '.txt'))):\n if os.path.exists(os.path.join(data_root, video_path, 'images', img_name)):\n frames.append(os.path.join('ethz/ETHZ', video_path, 'images', img_name))\n return frames\n\n\ndef solve_PRW(root):\n dataset_path = 'ethz/PRW'\n data_root = os.path.join(root, dataset_path)\n frame_paths = os.listdir(os.path.join(data_root, 'images'))\n frame_paths.sort()\n frames = []\n for i in range(len(frame_paths)):\n if os.path.exists(os.path.join(data_root, 'labels_with_ids', frame_paths[i].split('.')[0] + '.txt')):\n if os.path.exists(os.path.join(data_root, 'images', frame_paths[i])):\n frames.append(os.path.join(dataset_path, 'images', frame_paths[i]))\n return frames\n\n\ndataset_catalog = {\n 'MOT15': partial(solve_MOT_train, year=15),\n 'MOT16': partial(solve_MOT_train, year=16),\n 'MOT17': partial(solve_MOT_train, year=17),\n 'CUHK-SYSU': solve_CUHK,\n 'ETHZ': solve_ETHZ,\n 'PRW': solve_PRW,\n}\n\n\ndef solve(dataset_list: List[str], root, save_path):\n all_frames = []\n for dataset_name in dataset_list:\n dataset_frames = dataset_catalog[dataset_name](root)\n print(\"solve {} frames from dataset:{} \".format(len(dataset_frames), dataset_name))\n all_frames.extend(dataset_frames)\n print(\"totally {} frames are solved.\".format(len(all_frames)))\n with open(save_path, 'w') as f:\n for u in all_frames:\n line = '{}'.format(u) + '\\n'\n f.writelines(line)\n\nroot = '/data/workspace/datasets/mot' \nsave_path = '/data/workspace/detr-mot/datasets/data_path/mot17.train' # for fangao\ndataset_list = ['MOT17', ]\n\nsolve(dataset_list, root, save_path)\n" }, { "path": "datasets/data_prefetcher.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\nimport torch\nfrom functools import partial\nfrom models.structures import Instances\n\ndef to_cuda(samples, targets, device):\n samples = samples.to(device, non_blocking=True)\n targets = [{k: v.to(device, non_blocking=True) for k, v in t.items()} for t in targets]\n return samples, targets\n\n\ndef tensor_to_cuda(tensor: torch.Tensor, device):\n return tensor.to(device)\n\n\ndef is_tensor_or_instances(data):\n return isinstance(data, torch.Tensor) or isinstance(data, Instances)\n\n\ndef data_apply(data, check_func, apply_func):\n if isinstance(data, dict):\n for k in data.keys():\n if check_func(data[k]):\n data[k] = apply_func(data[k])\n elif isinstance(data[k], dict) or isinstance(data[k], list):\n data_apply(data[k], check_func, apply_func)\n else:\n raise ValueError()\n elif isinstance(data, list):\n for i in range(len(data)):\n if check_func(data[i]):\n data[i] = apply_func(data[i])\n elif isinstance(data[i], dict) or isinstance(data[i], list):\n data_apply(data[i], check_func, apply_func)\n else:\n raise ValueError(\"invalid type {}\".format(type(data[i])))\n else:\n raise ValueError(\"invalid type {}\".format(type(data)))\n return data\n\n\ndef data_dict_to_cuda(data_dict, device):\n return data_apply(data_dict, is_tensor_or_instances, partial(tensor_to_cuda, device=device))\n\n\nclass data_prefetcher():\n def __init__(self, loader, device, prefetch=True):\n self.loader = iter(loader)\n self.prefetch = prefetch\n self.device = device\n if prefetch:\n self.stream = torch.cuda.Stream()\n self.preload()\n\n def preload(self):\n try:\n self.next_samples, self.next_targets = next(self.loader)\n except StopIteration:\n self.next_samples = None\n self.next_targets = None\n return\n # if record_stream() doesn't work, another option is to make sure device inputs are created\n # on the main stream.\n # self.next_input_gpu = torch.empty_like(self.next_input, device='cuda')\n # self.next_target_gpu = torch.empty_like(self.next_target, device='cuda')\n # Need to make sure the memory allocated for next_* is not still in use by the main stream\n # at the time we start copying to next_*:\n # self.stream.wait_stream(torch.cuda.current_stream())\n with torch.cuda.stream(self.stream):\n self.next_samples, self.next_targets = to_cuda(self.next_samples, self.next_targets, self.device)\n # more code for the alternative if record_stream() doesn't work:\n # copy_ will record the use of the pinned source tensor in this side stream.\n # self.next_input_gpu.copy_(self.next_input, non_blocking=True)\n # self.next_target_gpu.copy_(self.next_target, non_blocking=True)\n # self.next_input = self.next_input_gpu\n # self.next_target = self.next_target_gpu\n\n # With Amp, it isn't necessary to manually convert data to half.\n # if args.fp16:\n # self.next_input = self.next_input.half()\n # else:\n\n def next(self):\n if self.prefetch:\n torch.cuda.current_stream().wait_stream(self.stream)\n samples = self.next_samples\n targets = self.next_targets\n if samples is not None:\n samples.record_stream(torch.cuda.current_stream())\n if targets is not None:\n for t in targets:\n for k, v in t.items():\n v.record_stream(torch.cuda.current_stream())\n self.preload()\n else:\n try:\n samples, targets = next(self.loader)\n samples, targets = to_cuda(samples, targets, self.device)\n except StopIteration:\n print(\"catch_stop_iter\")\n samples = None\n targets = None\n\n return samples, targets\n" }, { "path": "datasets/detmot.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\"\"\"\nMOT dataset which returns image_id for evaluation.\n\"\"\"\nfrom pathlib import Path\nimport cv2\nimport numpy as np\nimport torch\nimport torch.utils.data\nimport os.path as osp\nfrom PIL import Image, ImageDraw\nimport copy\nimport datasets.transforms as T\nfrom models.structures import Instances\n\n\nclass DetMOTDetection:\n def __init__(self, args, data_txt_path: str, seqs_folder, transforms):\n self.args = args\n self._transforms = transforms\n self.num_frames_per_batch = max(args.sampler_lengths)\n self.sample_mode = args.sample_mode\n self.sample_interval = args.sample_interval\n self.vis = args.vis\n self.video_dict = {}\n\n with open(data_txt_path, 'r') as file:\n self.img_files = file.readlines()\n self.img_files = [osp.join(seqs_folder, x.split(',')[0].strip()) for x in self.img_files]\n self.img_files = list(filter(lambda x: len(x) > 0, self.img_files))\n self.label_files = [(x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt'))\n for x in self.img_files]\n # The number of images per sample: 1 + (num_frames - 1) * interval.\n # The number of valid samples: num_images - num_image_per_sample + 1.\n self.item_num = len(self.img_files) - (self.num_frames_per_batch - 1) * self.sample_interval\n\n self._register_videos()\n\n # video sampler.\n self.sampler_steps: list = args.sampler_steps\n self.lengths: list = args.sampler_lengths\n print(\"sampler_steps={} lenghts={}\".format(self.sampler_steps, self.lengths))\n if self.sampler_steps is not None and len(self.sampler_steps) > 0:\n # Enable sampling length adjustment.\n assert len(self.lengths) > 0\n assert len(self.lengths) == len(self.sampler_steps) + 1\n for i in range(len(self.sampler_steps) - 1):\n assert self.sampler_steps[i] < self.sampler_steps[i + 1]\n self.item_num = len(self.img_files) - (self.lengths[-1] - 1) * self.sample_interval\n self.period_idx = 0\n self.num_frames_per_batch = self.lengths[0]\n self.current_epoch = 0\n\n def _register_videos(self):\n for label_name in self.label_files:\n video_name = '/'.join(label_name.split('/')[:-1])\n if video_name not in self.video_dict:\n print(\"register {}-th video: {} \".format(len(self.video_dict) + 1, video_name))\n self.video_dict[video_name] = len(self.video_dict)\n assert len(self.video_dict) <= 300\n\n def set_epoch(self, epoch):\n self.current_epoch = epoch\n if self.sampler_steps is None or len(self.sampler_steps) == 0:\n # fixed sampling length.\n return\n\n for i in range(len(self.sampler_steps)):\n if epoch >= self.sampler_steps[i]:\n self.period_idx = i + 1\n print(\"set epoch: epoch {} period_idx={}\".format(epoch, self.period_idx))\n self.num_frames_per_batch = self.lengths[self.period_idx]\n\n def step_epoch(self):\n # one epoch finishes.\n print(\"Dataset: epoch {} finishes\".format(self.current_epoch))\n self.set_epoch(self.current_epoch + 1)\n\n @staticmethod\n def _targets_to_instances(targets: dict, img_shape) -> Instances:\n gt_instances = Instances(tuple(img_shape))\n gt_instances.boxes = targets['boxes']\n gt_instances.labels = targets['labels']\n gt_instances.obj_ids = targets['obj_ids']\n gt_instances.area = targets['area']\n return gt_instances\n\n def _pre_single_frame(self, idx: int):\n img_path = self.img_files[idx]\n label_path = self.label_files[idx]\n img = Image.open(img_path)\n targets = {}\n w, h = img._size\n assert w > 0 and h > 0, \"invalid image {} with shape {} {}\".format(img_path, w, h)\n if osp.isfile(label_path):\n labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 6)\n\n # normalized cewh to pixel xyxy format\n labels = labels0.copy()\n labels[:, 2] = w * (labels0[:, 2] - labels0[:, 4] / 2)\n labels[:, 3] = h * (labels0[:, 3] - labels0[:, 5] / 2)\n labels[:, 4] = w * (labels0[:, 2] + labels0[:, 4] / 2)\n labels[:, 5] = h * (labels0[:, 3] + labels0[:, 5] / 2)\n else:\n raise ValueError('invalid label path: {}'.format(label_path))\n video_name = '/'.join(label_path.split('/')[:-1])\n obj_idx_offset = self.video_dict[video_name] * 100000 # 100000 unique ids is enough for a video.\n targets['boxes'] = []\n targets['area'] = []\n targets['iscrowd'] = []\n targets['labels'] = []\n targets['obj_ids'] = []\n targets['image_id'] = torch.as_tensor(idx)\n targets['size'] = torch.as_tensor([h, w])\n targets['orig_size'] = torch.as_tensor([h, w])\n for label in labels:\n targets['boxes'].append(label[2:6].tolist())\n targets['area'].append(label[4] * label[5])\n targets['iscrowd'].append(0)\n targets['labels'].append(0)\n obj_id = label[1] + obj_idx_offset if label[1] >= 0 else label[1]\n targets['obj_ids'].append(obj_id) # relative id\n\n targets['area'] = torch.as_tensor(targets['area'])\n targets['iscrowd'] = torch.as_tensor(targets['iscrowd'])\n targets['labels'] = torch.as_tensor(targets['labels'])\n targets['obj_ids'] = torch.as_tensor(targets['obj_ids'])\n targets['boxes'] = torch.as_tensor(targets['boxes'], dtype=torch.float32).reshape(-1, 4)\n targets['boxes'][:, 0::2].clamp_(min=0, max=w)\n targets['boxes'][:, 1::2].clamp_(min=0, max=h)\n return img, targets\n\n def _get_sample_range(self, start_idx):\n\n # take default sampling method for normal dataset.\n assert self.sample_mode in ['fixed_interval', 'random_interval'], 'invalid sample mode: {}'.format(self.sample_mode)\n if self.sample_mode == 'fixed_interval':\n sample_interval = self.sample_interval\n elif self.sample_mode == 'random_interval':\n sample_interval = np.random.randint(1, self.sample_interval + 1)\n default_range = start_idx, start_idx + (self.num_frames_per_batch - 1) * sample_interval + 1, sample_interval\n return default_range\n\n def pre_continuous_frames(self, start, end, interval=1):\n targets = []\n images = []\n for i in range(start, end, interval):\n img_i, targets_i = self._pre_single_frame(i)\n images.append(img_i)\n targets.append(targets_i)\n return images, targets\n\n def __getitem__(self, idx):\n sample_start, sample_end, sample_interval = self._get_sample_range(idx)\n images, targets = self.pre_continuous_frames(sample_start, sample_end, sample_interval)\n data = {}\n if self._transforms is not None:\n images, targets = self._transforms(images, targets)\n gt_instances = []\n for img_i, targets_i in zip(images, targets):\n gt_instances_i = self._targets_to_instances(targets_i, img_i.shape[1:3])\n gt_instances.append(gt_instances_i)\n data.update({\n 'imgs': images,\n 'gt_instances': gt_instances,\n })\n if self.args.vis:\n data['ori_img'] = [target_i['ori_img'] for target_i in targets]\n return data\n\n def __len__(self):\n return self.item_num\n\n\nclass DetMOTDetectionValidation(DetMOTDetection):\n def __init__(self, args, seqs_folder, transforms):\n args.data_txt_path = args.val_data_txt_path\n super().__init__(args, seqs_folder, transforms)\n\n\ndef make_detmot_transforms(image_set, args=None):\n normalize = T.MotCompose([\n T.MotToTensor(),\n T.MotNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ])\n\n scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]\n\n if image_set == 'train':\n color_transforms = []\n scale_transforms = [\n T.MotRandomHorizontalFlip(),\n T.MotRandomResize(scales, max_size=1333),\n normalize,\n ]\n\n return T.MotCompose(color_transforms + scale_transforms)\n\n if image_set == 'val':\n return T.MotCompose([\n T.MotRandomResize([800], max_size=1333),\n normalize,\n ])\n\n raise ValueError(f'unknown {image_set}')\n\n\ndef build(image_set, args):\n root = Path(args.mot_path)\n assert root.exists(), f'provided MOT path {root} does not exist'\n transforms = make_detmot_transforms(image_set, args)\n if image_set == 'train':\n data_txt_path = args.data_txt_path_train\n dataset = DetMOTDetection(args, data_txt_path=data_txt_path, seqs_folder=root, transforms=transforms)\n if image_set == 'val':\n data_txt_path = args.data_txt_path_val\n dataset = DetMOTDetection(args, data_txt_path=data_txt_path, seqs_folder=root, transforms=transforms)\n return dataset\n\n" }, { "path": "datasets/joint.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\"\"\"\nMOT dataset which returns image_id for evaluation.\n\"\"\"\nfrom pathlib import Path\nimport cv2\nimport numpy as np\nimport torch\nimport torch.utils.data\nimport os.path as osp\nfrom PIL import Image, ImageDraw\nimport copy\nimport datasets.transforms as T\nfrom models.structures import Instances\n\n\nclass DetMOTDetection:\n def __init__(self, args, data_txt_path: str, seqs_folder, dataset2transform):\n self.args = args\n self.dataset2transform = dataset2transform\n self.num_frames_per_batch = max(args.sampler_lengths)\n self.sample_mode = args.sample_mode\n self.sample_interval = args.sample_interval\n self.vis = args.vis\n self.video_dict = {}\n\n with open(data_txt_path, 'r') as file:\n self.img_files = file.readlines()\n self.img_files = [osp.join(seqs_folder, x.strip()) for x in self.img_files]\n self.img_files = list(filter(lambda x: len(x) > 0, self.img_files))\n\n self.label_files = [(x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt'))\n for x in self.img_files]\n # The number of images per sample: 1 + (num_frames - 1) * interval.\n # The number of valid samples: num_images - num_image_per_sample + 1.\n self.item_num = len(self.img_files) - (self.num_frames_per_batch - 1) * self.sample_interval\n\n self._register_videos()\n\n # video sampler.\n self.sampler_steps: list = args.sampler_steps\n self.lengths: list = args.sampler_lengths\n print(\"sampler_steps={} lenghts={}\".format(self.sampler_steps, self.lengths))\n if self.sampler_steps is not None and len(self.sampler_steps) > 0:\n # Enable sampling length adjustment.\n assert len(self.lengths) > 0\n assert len(self.lengths) == len(self.sampler_steps) + 1\n for i in range(len(self.sampler_steps) - 1):\n assert self.sampler_steps[i] < self.sampler_steps[i + 1]\n self.item_num = len(self.img_files) - (self.lengths[-1] - 1) * self.sample_interval\n self.period_idx = 0\n self.num_frames_per_batch = self.lengths[0]\n self.current_epoch = 0\n\n def _register_videos(self):\n for label_name in self.label_files:\n video_name = '/'.join(label_name.split('/')[:-1])\n if video_name not in self.video_dict:\n print(\"register {}-th video: {} \".format(len(self.video_dict) + 1, video_name))\n self.video_dict[video_name] = len(self.video_dict)\n # assert len(self.video_dict) <= 300\n\n def set_epoch(self, epoch):\n self.current_epoch = epoch\n if self.sampler_steps is None or len(self.sampler_steps) == 0:\n # fixed sampling length.\n return\n\n for i in range(len(self.sampler_steps)):\n if epoch >= self.sampler_steps[i]:\n self.period_idx = i + 1\n print(\"set epoch: epoch {} period_idx={}\".format(epoch, self.period_idx))\n self.num_frames_per_batch = self.lengths[self.period_idx]\n\n def step_epoch(self):\n # one epoch finishes.\n print(\"Dataset: epoch {} finishes\".format(self.current_epoch))\n self.set_epoch(self.current_epoch + 1)\n\n @staticmethod\n def _targets_to_instances(targets: dict, img_shape) -> Instances:\n gt_instances = Instances(tuple(img_shape))\n gt_instances.boxes = targets['boxes']\n gt_instances.labels = targets['labels']\n gt_instances.obj_ids = targets['obj_ids']\n gt_instances.area = targets['area']\n return gt_instances\n\n def _pre_single_frame(self, idx: int):\n img_path = self.img_files[idx]\n label_path = self.label_files[idx]\n if 'crowdhuman' in img_path:\n img_path = img_path.replace('.jpg', '.png')\n img = Image.open(img_path)\n targets = {}\n w, h = img._size\n assert w > 0 and h > 0, \"invalid image {} with shape {} {}\".format(img_path, w, h)\n if osp.isfile(label_path):\n labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 6)\n\n # normalized cewh to pixel xyxy format\n labels = labels0.copy()\n labels[:, 2] = w * (labels0[:, 2] - labels0[:, 4] / 2)\n labels[:, 3] = h * (labels0[:, 3] - labels0[:, 5] / 2)\n labels[:, 4] = w * (labels0[:, 2] + labels0[:, 4] / 2)\n labels[:, 5] = h * (labels0[:, 3] + labels0[:, 5] / 2)\n else:\n raise ValueError('invalid label path: {}'.format(label_path))\n video_name = '/'.join(label_path.split('/')[:-1])\n obj_idx_offset = self.video_dict[video_name] * 1000000 # 1000000 unique ids is enough for a video.\n if 'crowdhuman' in img_path:\n targets['dataset'] = 'CrowdHuman'\n elif 'MOT17' in img_path:\n targets['dataset'] = 'MOT17'\n else:\n raise NotImplementedError()\n targets['boxes'] = []\n targets['area'] = []\n targets['iscrowd'] = []\n targets['labels'] = []\n targets['obj_ids'] = []\n targets['image_id'] = torch.as_tensor(idx)\n targets['size'] = torch.as_tensor([h, w])\n targets['orig_size'] = torch.as_tensor([h, w])\n for label in labels:\n targets['boxes'].append(label[2:6].tolist())\n targets['area'].append(label[4] * label[5])\n targets['iscrowd'].append(0)\n targets['labels'].append(0)\n obj_id = label[1] + obj_idx_offset if label[1] >= 0 else label[1]\n targets['obj_ids'].append(obj_id) # relative id\n\n targets['area'] = torch.as_tensor(targets['area'])\n targets['iscrowd'] = torch.as_tensor(targets['iscrowd'])\n targets['labels'] = torch.as_tensor(targets['labels'])\n targets['obj_ids'] = torch.as_tensor(targets['obj_ids'])\n targets['boxes'] = torch.as_tensor(targets['boxes'], dtype=torch.float32).reshape(-1, 4)\n return img, targets\n\n def _get_sample_range(self, start_idx):\n\n # take default sampling method for normal dataset.\n assert self.sample_mode in ['fixed_interval', 'random_interval'], 'invalid sample mode: {}'.format(self.sample_mode)\n if self.sample_mode == 'fixed_interval':\n sample_interval = self.sample_interval\n elif self.sample_mode == 'random_interval':\n sample_interval = np.random.randint(1, self.sample_interval + 1)\n default_range = start_idx, start_idx + (self.num_frames_per_batch - 1) * sample_interval + 1, sample_interval\n return default_range\n\n def pre_continuous_frames(self, start, end, interval=1):\n targets = []\n images = []\n for i in range(start, end, interval):\n img_i, targets_i = self._pre_single_frame(i)\n images.append(img_i)\n targets.append(targets_i)\n return images, targets\n\n def __getitem__(self, idx):\n sample_start, sample_end, sample_interval = self._get_sample_range(idx)\n images, targets = self.pre_continuous_frames(sample_start, sample_end, sample_interval)\n data = {}\n dataset_name = targets[0]['dataset']\n transform = self.dataset2transform[dataset_name]\n if transform is not None:\n images, targets = transform(images, targets)\n gt_instances = []\n for img_i, targets_i in zip(images, targets):\n gt_instances_i = self._targets_to_instances(targets_i, img_i.shape[1:3])\n gt_instances.append(gt_instances_i)\n data.update({\n 'imgs': images,\n 'gt_instances': gt_instances,\n })\n if self.args.vis:\n data['ori_img'] = [target_i['ori_img'] for target_i in targets]\n return data\n\n def __len__(self):\n return self.item_num\n\n\nclass DetMOTDetectionValidation(DetMOTDetection):\n def __init__(self, args, seqs_folder, dataset2transform):\n args.data_txt_path = args.val_data_txt_path\n super().__init__(args, seqs_folder, dataset2transform)\n\n\n\ndef make_transforms_for_mot17(image_set, args=None):\n\n normalize = T.MotCompose([\n T.MotToTensor(),\n T.MotNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ])\n scales = [608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992]\n\n if image_set == 'train':\n return T.MotCompose([\n T.MotRandomHorizontalFlip(),\n T.MotRandomSelect(\n T.MotRandomResize(scales, max_size=1536),\n T.MotCompose([\n T.MotRandomResize([400, 500, 600]),\n T.FixedMotRandomCrop(384, 600),\n T.MotRandomResize(scales, max_size=1536),\n ])\n ),\n normalize,\n ])\n\n if image_set == 'val':\n return T.MotCompose([\n T.MotRandomResize([800], max_size=1333),\n normalize,\n ])\n\n raise ValueError(f'unknown {image_set}')\n\n\ndef make_transforms_for_crowdhuman(image_set, args=None):\n\n normalize = T.MotCompose([\n T.MotToTensor(),\n T.MotNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ])\n scales = [608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992]\n\n if image_set == 'train':\n return T.MotCompose([\n T.MotRandomHorizontalFlip(),\n T.FixedMotRandomShift(bs=1),\n T.MotRandomSelect(\n T.MotRandomResize(scales, max_size=1536),\n T.MotCompose([\n T.MotRandomResize([400, 500, 600]),\n T.FixedMotRandomCrop(384, 600),\n T.MotRandomResize(scales, max_size=1536),\n ])\n ),\n normalize,\n\n ])\n\n if image_set == 'val':\n return T.MotCompose([\n T.MotRandomResize([800], max_size=1333),\n normalize,\n ])\n\n raise ValueError(f'unknown {image_set}')\n\n\ndef build_dataset2transform(args, image_set):\n mot17_train = make_transforms_for_mot17('train', args)\n mot17_test = make_transforms_for_mot17('val', args)\n\n crowdhuman_train = make_transforms_for_crowdhuman('train', args)\n dataset2transform_train = {'MOT17': mot17_train, 'CrowdHuman': crowdhuman_train}\n dataset2transform_val = {'MOT17': mot17_test, 'CrowdHuman': mot17_test}\n if image_set == 'train':\n return dataset2transform_train\n elif image_set == 'val':\n return dataset2transform_val\n else:\n raise NotImplementedError()\n\n\ndef build(image_set, args):\n root = Path(args.mot_path)\n assert root.exists(), f'provided MOT path {root} does not exist'\n dataset2transform = build_dataset2transform(args, image_set)\n if image_set == 'train':\n data_txt_path = args.data_txt_path_train\n dataset = DetMOTDetection(args, data_txt_path=data_txt_path, seqs_folder=root, dataset2transform=dataset2transform)\n if image_set == 'val':\n data_txt_path = args.data_txt_path_val\n dataset = DetMOTDetection(args, data_txt_path=data_txt_path, seqs_folder=root, dataset2transform=dataset2transform)\n return dataset\n\n" }, { "path": "datasets/panoptic_eval.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\nimport json\nimport os\n\nimport util.misc as utils\n\ntry:\n from panopticapi.evaluation import pq_compute\nexcept ImportError:\n pass\n\n\nclass PanopticEvaluator(object):\n def __init__(self, ann_file, ann_folder, output_dir=\"panoptic_eval\"):\n self.gt_json = ann_file\n self.gt_folder = ann_folder\n if utils.is_main_process():\n if not os.path.exists(output_dir):\n os.mkdir(output_dir)\n self.output_dir = output_dir\n self.predictions = []\n\n def update(self, predictions):\n for p in predictions:\n with open(os.path.join(self.output_dir, p[\"file_name\"]), \"wb\") as f:\n f.write(p.pop(\"png_string\"))\n\n self.predictions += predictions\n\n def synchronize_between_processes(self):\n all_predictions = utils.all_gather(self.predictions)\n merged_predictions = []\n for p in all_predictions:\n merged_predictions += p\n self.predictions = merged_predictions\n\n def summarize(self):\n if utils.is_main_process():\n json_data = {\"annotations\": self.predictions}\n predictions_json = os.path.join(self.output_dir, \"predictions.json\")\n with open(predictions_json, \"w\") as f:\n f.write(json.dumps(json_data))\n return pq_compute(self.gt_json, predictions_json, gt_folder=self.gt_folder, pred_folder=self.output_dir)\n return None\n" }, { "path": "datasets/samplers.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\nimport os\nimport math\nimport torch\nimport torch.distributed as dist\nfrom torch.utils.data.sampler import Sampler\n\n\nclass DistributedSampler(Sampler):\n \"\"\"Sampler that restricts data loading to a subset of the dataset.\n It is especially useful in conjunction with\n :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each\n process can pass a DistributedSampler instance as a DataLoader sampler,\n and load a subset of the original dataset that is exclusive to it.\n .. note::\n Dataset is assumed to be of constant size.\n Arguments:\n dataset: Dataset used for sampling.\n num_replicas (optional): Number of processes participating in\n distributed training.\n rank (optional): Rank of the current process within num_replicas.\n \"\"\"\n\n def __init__(self, dataset, num_replicas=None, rank=None, local_rank=None, local_size=None, shuffle=True):\n if num_replicas is None:\n if not dist.is_available():\n raise RuntimeError(\"Requires distributed package to be available\")\n num_replicas = dist.get_world_size()\n if rank is None:\n if not dist.is_available():\n raise RuntimeError(\"Requires distributed package to be available\")\n rank = dist.get_rank()\n self.dataset = dataset\n self.num_replicas = num_replicas\n self.rank = rank\n self.epoch = 0\n self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))\n self.total_size = self.num_samples * self.num_replicas\n self.shuffle = shuffle\n\n def __iter__(self):\n if self.shuffle:\n # deterministically shuffle based on epoch\n g = torch.Generator()\n g.manual_seed(self.epoch)\n indices = torch.randperm(len(self.dataset), generator=g).tolist()\n else:\n indices = torch.arange(len(self.dataset)).tolist()\n\n # add extra samples to make it evenly divisible\n indices += indices[: (self.total_size - len(indices))]\n assert len(indices) == self.total_size\n\n # subsample\n offset = self.num_samples * self.rank\n indices = indices[offset : offset + self.num_samples]\n assert len(indices) == self.num_samples\n\n return iter(indices)\n\n def __len__(self):\n return self.num_samples\n\n def set_epoch(self, epoch):\n self.epoch = epoch\n\n\nclass NodeDistributedSampler(Sampler):\n \"\"\"Sampler that restricts data loading to a subset of the dataset.\n It is especially useful in conjunction with\n :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each\n process can pass a DistributedSampler instance as a DataLoader sampler,\n and load a subset of the original dataset that is exclusive to it.\n .. note::\n Dataset is assumed to be of constant size.\n Arguments:\n dataset: Dataset used for sampling.\n num_replicas (optional): Number of processes participating in\n distributed training.\n rank (optional): Rank of the current process within num_replicas.\n \"\"\"\n\n def __init__(self, dataset, num_replicas=None, rank=None, local_rank=None, local_size=None, shuffle=True):\n if num_replicas is None:\n if not dist.is_available():\n raise RuntimeError(\"Requires distributed package to be available\")\n num_replicas = dist.get_world_size()\n if rank is None:\n if not dist.is_available():\n raise RuntimeError(\"Requires distributed package to be available\")\n rank = dist.get_rank()\n if local_rank is None:\n local_rank = int(os.environ.get('LOCAL_RANK', 0))\n if local_size is None:\n local_size = int(os.environ.get('LOCAL_SIZE', 1))\n self.dataset = dataset\n self.shuffle = shuffle\n self.num_replicas = num_replicas\n self.num_parts = local_size\n self.rank = rank\n self.local_rank = local_rank\n self.epoch = 0\n self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))\n self.total_size = self.num_samples * self.num_replicas\n\n self.total_size_parts = self.num_samples * self.num_replicas // self.num_parts\n\n def __iter__(self):\n if self.shuffle:\n # deterministically shuffle based on epoch\n g = torch.Generator()\n g.manual_seed(self.epoch)\n indices = torch.randperm(len(self.dataset), generator=g).tolist()\n else:\n indices = torch.arange(len(self.dataset)).tolist()\n indices = [i for i in indices if i % self.num_parts == self.local_rank]\n\n # add extra samples to make it evenly divisible\n indices += indices[:(self.total_size_parts - len(indices))]\n assert len(indices) == self.total_size_parts\n\n # subsample\n indices = indices[self.rank // self.num_parts:self.total_size_parts:self.num_replicas // self.num_parts]\n assert len(indices) == self.num_samples\n\n return iter(indices)\n\n def __len__(self):\n return self.num_samples\n\n def set_epoch(self, epoch):\n self.epoch = epoch\n" }, { "path": "datasets/static_detmot.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\"\"\"\nMOT dataset which returns image_id for evaluation.\n\"\"\"\nfrom pathlib import Path\nimport cv2\nimport numpy as np\nimport torch\nimport torch.utils.data\nimport os.path as osp\nfrom PIL import Image, ImageDraw\nimport copy\nimport datasets.transforms as T\nfrom models.structures import Instances\n\n\nclass DetMOTDetection:\n def __init__(self, args, data_txt_path: str, seqs_folder, transforms):\n self.args = args\n self._transforms = transforms\n self.num_frames_per_batch = max(args.sampler_lengths)\n self.sample_mode = args.sample_mode\n self.sample_interval = args.sample_interval\n self.vis = args.vis\n self.video_dict = {}\n\n with open(data_txt_path, 'r') as file:\n self.img_files = file.readlines()\n self.img_files = [osp.join(seqs_folder, x.strip()) for x in self.img_files]\n self.img_files = list(filter(lambda x: len(x) > 0, self.img_files))\n self.label_files = [(x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt'))\n for x in self.img_files]\n # The number of images per sample: 1 + (num_frames - 1) * interval.\n # The number of valid samples: num_images - num_image_per_sample + 1.\n self.item_num = len(self.img_files) - (self.num_frames_per_batch - 1) * self.sample_interval\n\n self._register_videos()\n\n # video sampler.\n self.sampler_steps: list = args.sampler_steps\n self.lengths: list = args.sampler_lengths\n print(\"sampler_steps={} lenghts={}\".format(self.sampler_steps, self.lengths))\n if self.sampler_steps is not None and len(self.sampler_steps) > 0:\n # Enable sampling length adjustment.\n assert len(self.lengths) > 0\n assert len(self.lengths) == len(self.sampler_steps) + 1\n for i in range(len(self.sampler_steps) - 1):\n assert self.sampler_steps[i] < self.sampler_steps[i + 1]\n self.item_num = len(self.img_files) - (self.lengths[-1] - 1) * self.sample_interval\n self.period_idx = 0\n self.num_frames_per_batch = self.lengths[0]\n self.current_epoch = 0\n\n def _register_videos(self):\n for label_name in self.label_files:\n video_name = '/'.join(label_name.split('/')[:-1])\n if video_name not in self.video_dict:\n print(\"register {}-th video: {} \".format(len(self.video_dict) + 1, video_name))\n self.video_dict[video_name] = len(self.video_dict)\n assert len(self.video_dict) <= 300\n\n def set_epoch(self, epoch):\n self.current_epoch = epoch\n if self.sampler_steps is None or len(self.sampler_steps) == 0:\n # fixed sampling length.\n return\n\n for i in range(len(self.sampler_steps)):\n if epoch >= self.sampler_steps[i]:\n self.period_idx = i + 1\n print(\"set epoch: epoch {} period_idx={}\".format(epoch, self.period_idx))\n self.num_frames_per_batch = self.lengths[self.period_idx]\n\n def step_epoch(self):\n # one epoch finishes.\n print(\"Dataset: epoch {} finishes\".format(self.current_epoch))\n self.set_epoch(self.current_epoch + 1)\n\n @staticmethod\n def _targets_to_instances(targets: dict, img_shape) -> Instances:\n gt_instances = Instances(tuple(img_shape))\n gt_instances.boxes = targets['boxes']\n gt_instances.labels = targets['labels']\n gt_instances.obj_ids = targets['obj_ids']\n gt_instances.area = targets['area']\n return gt_instances\n\n def _pre_single_frame(self, idx: int):\n img_path = self.img_files[idx].replace('.jpg', '.png')\n label_path = self.label_files[idx]\n img = Image.open(img_path)\n targets = {}\n w, h = img._size\n assert w > 0 and h > 0, \"invalid image {} with shape {} {}\".format(img_path, w, h)\n if osp.isfile(label_path):\n labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 6)\n\n # normalized cewh to pixel xyxy format\n labels = labels0.copy()\n labels[:, 2] = w * (labels0[:, 2] - labels0[:, 4] / 2)\n labels[:, 3] = h * (labels0[:, 3] - labels0[:, 5] / 2)\n labels[:, 4] = w * (labels0[:, 2] + labels0[:, 4] / 2)\n labels[:, 5] = h * (labels0[:, 3] + labels0[:, 5] / 2)\n else:\n raise ValueError('invalid label path: {}'.format(label_path))\n video_name = '/'.join(label_path.split('/')[:-1])\n obj_idx_offset = self.video_dict[video_name] * 100000 # 100000 unique ids is enough for a video.\n targets['boxes'] = []\n targets['area'] = []\n targets['iscrowd'] = []\n targets['labels'] = []\n targets['obj_ids'] = []\n targets['image_id'] = torch.as_tensor(idx)\n targets['size'] = torch.as_tensor([h, w])\n targets['orig_size'] = torch.as_tensor([h, w])\n for label in labels:\n targets['boxes'].append(label[2:6].tolist())\n targets['area'].append(label[4] * label[5])\n targets['iscrowd'].append(0)\n targets['labels'].append(0)\n obj_id = label[1] + obj_idx_offset if label[1] >= 0 else label[1]\n targets['obj_ids'].append(obj_id) # relative id\n\n targets['area'] = torch.as_tensor(targets['area'])\n targets['iscrowd'] = torch.as_tensor(targets['iscrowd'])\n targets['labels'] = torch.as_tensor(targets['labels'])\n targets['obj_ids'] = torch.as_tensor(targets['obj_ids'])\n targets['boxes'] = torch.as_tensor(targets['boxes'], dtype=torch.float32).reshape(-1, 4)\n targets['boxes'][:, 0::2].clamp_(min=0, max=w)\n targets['boxes'][:, 1::2].clamp_(min=0, max=h)\n return img, targets\n\n def _get_sample_range(self, start_idx):\n\n # take default sampling method for normal dataset.\n assert self.sample_mode in ['fixed_interval', 'random_interval'], 'invalid sample mode: {}'.format(self.sample_mode)\n if self.sample_mode == 'fixed_interval':\n sample_interval = self.sample_interval\n elif self.sample_mode == 'random_interval':\n sample_interval = np.random.randint(1, self.sample_interval + 1)\n default_range = start_idx, start_idx + (self.num_frames_per_batch - 1) * sample_interval + 1, sample_interval\n return default_range\n\n def pre_continuous_frames(self, idx):\n targets = []\n images = []\n # repeat current img and target for twice\n for i in range(self.num_frames_per_batch):\n img_i, targets_i = self._pre_single_frame(idx)\n images.append(img_i)\n targets.append(targets_i)\n return images, targets\n\n def __getitem__(self, idx):\n images, targets = self.pre_continuous_frames(idx)\n data = {}\n if self._transforms is not None:\n images, targets = self._transforms(images, targets)\n gt_instances = []\n for img_i, targets_i in zip(images, targets):\n gt_instances_i = self._targets_to_instances(targets_i, img_i.shape[1:3])\n gt_instances.append(gt_instances_i)\n data.update({\n 'imgs': images,\n 'gt_instances': gt_instances,\n })\n if self.args.vis:\n data['ori_img'] = [target_i['ori_img'] for target_i in targets]\n return data\n\n def __len__(self):\n return self.item_num\n\n\nclass DetMOTDetectionValidation(DetMOTDetection):\n def __init__(self, args, seqs_folder, transforms):\n args.data_txt_path = args.val_data_txt_path\n super().__init__(args, seqs_folder, transforms)\n\n\n\ndef make_detmot_transforms(image_set, args=None):\n normalize = T.MotCompose([\n T.MotToTensor(),\n T.MotNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ])\n\n scales = [608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992]\n\n if image_set == 'train':\n color_transforms = []\n if args.cj:\n print('Training with RandomColorJitter.')\n color_transforms.append(T.MoTColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0))\n if not args.crop:\n scale_transforms = [\n T.MotRandomHorizontalFlip(),\n T.FixedMotRandomShift(bs=1),\n T.MotRandomResize(scales, max_size=1536),\n normalize,\n ]\n else:\n print('Training with RandomCrop.')\n scale_transforms = [\n T.MotRandomHorizontalFlip(),\n T.FixedMotRandomShift(bs=1),\n T.MotRandomSelect(\n T.MotRandomResize(scales, max_size=1536),\n T.MotCompose([\n T.MotRandomResize([400, 500, 600]),\n T.FixedMotRandomCrop(384, 600),\n T.MotRandomResize(scales, max_size=1536),\n ])\n ),\n normalize,\n ]\n\n return T.MotCompose(color_transforms + scale_transforms)\n\n if image_set == 'val':\n return T.MotCompose([\n T.MotRandomResize([800], max_size=1536),\n normalize,\n ])\n\n raise ValueError(f'unknown {image_set}')\n\n\ndef build(image_set, args):\n root = Path(args.mot_path)\n assert root.exists(), f'provided MOT path {root} does not exist'\n transforms = make_detmot_transforms(image_set, args)\n if image_set == 'train':\n data_txt_path = args.data_txt_path_train\n dataset = DetMOTDetection(args, data_txt_path=data_txt_path, seqs_folder=root, transforms=transforms)\n if image_set == 'val':\n data_txt_path = args.data_txt_path_val\n dataset = DetMOTDetection(args, data_txt_path=data_txt_path, seqs_folder=root, transforms=transforms)\n return dataset\n\n" }, { "path": "datasets/torchvision_datasets/__init__.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\nfrom .coco import CocoDetection\n" }, { "path": "datasets/torchvision_datasets/coco.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\n\"\"\"\nCopy-Paste from torchvision, but add utility of caching images on memory\n\"\"\"\nfrom torchvision.datasets.vision import VisionDataset\nfrom PIL import Image\nimport os\nimport os.path\nimport tqdm\nfrom io import BytesIO\n\n\nclass CocoDetection(VisionDataset):\n \"\"\"`MS Coco Detection `_ Dataset.\n Args:\n root (string): Root directory where images are downloaded to.\n annFile (string): Path to json annotation file.\n transform (callable, optional): A function/transform that takes in an PIL image\n and returns a transformed version. E.g, ``transforms.ToTensor``\n target_transform (callable, optional): A function/transform that takes in the\n target and transforms it.\n transforms (callable, optional): A function/transform that takes input sample and its target as entry\n and returns a transformed version.\n \"\"\"\n\n def __init__(self, root, annFile, transform=None, target_transform=None, transforms=None,\n cache_mode=False, local_rank=0, local_size=1):\n super(CocoDetection, self).__init__(root, transforms, transform, target_transform)\n from pycocotools.coco import COCO\n self.coco = COCO(annFile)\n self.ids = list(sorted(self.coco.imgs.keys()))\n self.cache_mode = cache_mode\n self.local_rank = local_rank\n self.local_size = local_size\n if cache_mode:\n self.cache = {}\n self.cache_images()\n\n def cache_images(self):\n self.cache = {}\n for index, img_id in zip(tqdm.trange(len(self.ids)), self.ids):\n if index % self.local_size != self.local_rank:\n continue\n path = self.coco.loadImgs(img_id)[0]['file_name']\n with open(os.path.join(self.root, path), 'rb') as f:\n self.cache[path] = f.read()\n\n def get_image(self, path):\n if self.cache_mode:\n if path not in self.cache.keys():\n with open(os.path.join(self.root, path), 'rb') as f:\n self.cache[path] = f.read()\n return Image.open(BytesIO(self.cache[path])).convert('RGB')\n return Image.open(os.path.join(self.root, path)).convert('RGB')\n\n def __getitem__(self, index):\n \"\"\"\n Args:\n index (int): Index\n Returns:\n tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``.\n \"\"\"\n coco = self.coco\n img_id = self.ids[index]\n ann_ids = coco.getAnnIds(imgIds=img_id)\n target = coco.loadAnns(ann_ids)\n\n path = coco.loadImgs(img_id)[0]['file_name']\n\n img = self.get_image(path)\n if self.transforms is not None:\n img, target = self.transforms(img, target)\n\n return img, target\n\n def __len__(self):\n return len(self.ids)\n" }, { "path": "datasets/transforms.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\"\"\"\nTransforms and data augmentation for both image + bbox.\n\"\"\"\nimport copy\nimport random\nimport PIL\nimport torch\nimport torchvision.transforms as T\nimport torchvision.transforms.functional as F\nfrom PIL import Image, ImageDraw\nfrom util.box_ops import box_xyxy_to_cxcywh\nfrom util.misc import interpolate\nimport numpy as np\nimport os \n\n\n\ndef crop_mot(image, target, region):\n cropped_image = F.crop(image, *region)\n\n target = target.copy()\n i, j, h, w = region\n\n # should we do something wrt the original size?\n target[\"size\"] = torch.tensor([h, w])\n\n fields = [\"labels\", \"area\", \"iscrowd\", \"obj_ids\"]\n\n if \"boxes\" in target:\n boxes = target[\"boxes\"]\n cropped_boxes = boxes - torch.as_tensor([j, i, j, i])\n target[\"boxes\"] = cropped_boxes.reshape(-1, 4)\n fields.append(\"boxes\")\n\n if \"masks\" in target:\n # FIXME should we update the area here if there are no boxes?\n target['masks'] = target['masks'][:, i:i + h, j:j + w]\n fields.append(\"masks\")\n\n # remove elements for which the boxes or masks that have zero area\n if \"boxes\" in target or \"masks\" in target:\n # favor boxes selection when defining which elements to keep\n # this is compatible with previous implementation\n if \"boxes\" in target:\n cropped_boxes = target['boxes'].reshape(-1, 2, 2)\n max_size = torch.as_tensor([w, h], dtype=torch.float32)\n cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)\n cropped_boxes = cropped_boxes.clamp(min=0)\n keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)\n else:\n keep = target['masks'].flatten(1).any(1)\n\n for field in fields:\n target[field] = target[field][keep]\n\n return cropped_image, target\n\n\ndef random_shift(image, target, region, sizes):\n oh, ow = sizes\n # step 1, shift crop and re-scale image firstly\n cropped_image = F.crop(image, *region)\n cropped_image = F.resize(cropped_image, sizes)\n\n target = target.copy()\n i, j, h, w = region\n\n # should we do something wrt the original size?\n target[\"size\"] = torch.tensor([h, w])\n\n fields = [\"labels\", \"area\", \"iscrowd\", \"obj_ids\"]\n\n if \"boxes\" in target:\n boxes = target[\"boxes\"]\n cropped_boxes = boxes - torch.as_tensor([j, i, j, i])\n cropped_boxes *= torch.as_tensor([ow / w, oh / h, ow / w, oh / h])\n target[\"boxes\"] = cropped_boxes.reshape(-1, 4)\n fields.append(\"boxes\")\n\n if \"masks\" in target:\n # FIXME should we update the area here if there are no boxes?\n target['masks'] = target['masks'][:, i:i + h, j:j + w]\n fields.append(\"masks\")\n\n # remove elements for which the boxes or masks that have zero area\n if \"boxes\" in target or \"masks\" in target:\n # favor boxes selection when defining which elements to keep\n # this is compatible with previous implementation\n if \"boxes\" in target:\n cropped_boxes = target['boxes'].reshape(-1, 2, 2)\n max_size = torch.as_tensor([w, h], dtype=torch.float32)\n cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)\n cropped_boxes = cropped_boxes.clamp(min=0)\n keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)\n else:\n keep = target['masks'].flatten(1).any(1)\n\n for field in fields:\n target[field] = target[field][keep]\n\n return cropped_image, target\n\n\ndef crop(image, target, region):\n cropped_image = F.crop(image, *region)\n\n target = target.copy()\n i, j, h, w = region\n\n # should we do something wrt the original size?\n target[\"size\"] = torch.tensor([h, w])\n\n fields = [\"labels\", \"area\", \"iscrowd\"]\n if 'obj_ids' in target:\n fields.append('obj_ids')\n\n if \"boxes\" in target:\n boxes = target[\"boxes\"]\n max_size = torch.as_tensor([w, h], dtype=torch.float32)\n cropped_boxes = boxes - torch.as_tensor([j, i, j, i])\n cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)\n cropped_boxes = cropped_boxes.clamp(min=0)\n\n area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)\n target[\"boxes\"] = cropped_boxes.reshape(-1, 4)\n target[\"area\"] = area\n fields.append(\"boxes\")\n\n if \"masks\" in target:\n # FIXME should we update the area here if there are no boxes?\n target['masks'] = target['masks'][:, i:i + h, j:j + w]\n fields.append(\"masks\")\n\n # remove elements for which the boxes or masks that have zero area\n if \"boxes\" in target or \"masks\" in target:\n # favor boxes selection when defining which elements to keep\n # this is compatible with previous implementation\n if \"boxes\" in target:\n cropped_boxes = target['boxes'].reshape(-1, 2, 2)\n keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)\n else:\n keep = target['masks'].flatten(1).any(1)\n\n for field in fields:\n target[field] = target[field][keep]\n\n return cropped_image, target\n\n\ndef hflip(image, target):\n flipped_image = F.hflip(image)\n\n w, h = image.size\n\n target = target.copy()\n if \"boxes\" in target:\n boxes = target[\"boxes\"]\n boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor([w, 0, w, 0])\n target[\"boxes\"] = boxes\n\n if \"masks\" in target:\n target['masks'] = target['masks'].flip(-1)\n\n return flipped_image, target\n\n\ndef resize(image, target, size, max_size=None):\n # size can be min_size (scalar) or (w, h) tuple\n\n def get_size_with_aspect_ratio(image_size, size, max_size=None):\n w, h = image_size\n if max_size is not None:\n min_original_size = float(min((w, h)))\n max_original_size = float(max((w, h)))\n if max_original_size / min_original_size * size > max_size:\n size = int(round(max_size * min_original_size / max_original_size))\n\n if (w <= h and w == size) or (h <= w and h == size):\n return (h, w)\n\n if w < h:\n ow = size\n oh = int(size * h / w)\n else:\n oh = size\n ow = int(size * w / h)\n\n return (oh, ow)\n\n def get_size(image_size, size, max_size=None):\n if isinstance(size, (list, tuple)):\n return size[::-1]\n else:\n return get_size_with_aspect_ratio(image_size, size, max_size)\n\n size = get_size(image.size, size, max_size)\n rescaled_image = F.resize(image, size)\n\n if target is None:\n return rescaled_image, None\n\n ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))\n ratio_width, ratio_height = ratios\n\n target = target.copy()\n if \"boxes\" in target:\n boxes = target[\"boxes\"]\n scaled_boxes = boxes * torch.as_tensor([ratio_width, ratio_height, ratio_width, ratio_height])\n target[\"boxes\"] = scaled_boxes\n\n if \"area\" in target:\n area = target[\"area\"]\n scaled_area = area * (ratio_width * ratio_height)\n target[\"area\"] = scaled_area\n\n h, w = size\n target[\"size\"] = torch.tensor([h, w])\n\n if \"masks\" in target:\n target['masks'] = interpolate(\n target['masks'][:, None].float(), size, mode=\"nearest\")[:, 0] > 0.5\n\n return rescaled_image, target\n\n\ndef pad(image, target, padding):\n # assumes that we only pad on the bottom right corners\n padded_image = F.pad(image, (0, 0, padding[0], padding[1]))\n if target is None:\n return padded_image, None\n target = target.copy()\n # should we do something wrt the original size?\n target[\"size\"] = torch.tensor(padded_image[::-1])\n if \"masks\" in target:\n target['masks'] = torch.nn.functional.pad(target['masks'], (0, padding[0], 0, padding[1]))\n return padded_image, target\n\n\nclass RandomCrop(object):\n def __init__(self, size):\n self.size = size\n\n def __call__(self, img, target):\n region = T.RandomCrop.get_params(img, self.size)\n return crop(img, target, region)\n\n\nclass MotRandomCrop(RandomCrop):\n def __call__(self, imgs: list, targets: list):\n ret_imgs = []\n ret_targets = []\n region = T.RandomCrop.get_params(imgs[0], self.size)\n for img_i, targets_i in zip(imgs, targets):\n img_i, targets_i = crop(img_i, targets_i, region)\n ret_imgs.append(img_i)\n ret_targets.append(targets_i)\n return ret_imgs, ret_targets\n\nclass FixedMotRandomCrop(object):\n def __init__(self, min_size: int, max_size: int):\n self.min_size = min_size\n self.max_size = max_size\n\n def __call__(self, imgs: list, targets: list):\n ret_imgs = []\n ret_targets = []\n w = random.randint(self.min_size, min(imgs[0].width, self.max_size))\n h = random.randint(self.min_size, min(imgs[0].height, self.max_size))\n region = T.RandomCrop.get_params(imgs[0], [h, w])\n for img_i, targets_i in zip(imgs, targets):\n img_i, targets_i = crop_mot(img_i, targets_i, region)\n ret_imgs.append(img_i)\n ret_targets.append(targets_i)\n return ret_imgs, ret_targets\n\nclass MotRandomShift(object):\n def __init__(self, bs=1):\n self.bs = bs\n\n def __call__(self, imgs: list, targets: list):\n ret_imgs = copy.deepcopy(imgs)\n ret_targets = copy.deepcopy(targets)\n\n n_frames = len(imgs)\n select_i = random.choice(list(range(n_frames)))\n w, h = imgs[select_i].size\n\n xshift = (100 * torch.rand(self.bs)).int()\n xshift *= (torch.randn(self.bs) > 0.0).int() * 2 - 1 \n yshift = (100 * torch.rand(self.bs)).int()\n yshift *= (torch.randn(self.bs) > 0.0).int() * 2 - 1\n ymin = max(0, -yshift[0])\n ymax = min(h, h - yshift[0])\n xmin = max(0, -xshift[0])\n xmax = min(w, w - xshift[0])\n\n region = (int(ymin), int(xmin), int(ymax-ymin), int(xmax-xmin))\n ret_imgs[select_i], ret_targets[select_i] = random_shift(imgs[select_i], targets[select_i], region, (h,w)) \n \n return ret_imgs, ret_targets\n\n\nclass FixedMotRandomShift(object):\n def __init__(self, bs=1, padding=50):\n self.bs = bs\n self.padding = padding\n\n def __call__(self, imgs: list, targets: list):\n ret_imgs = []\n ret_targets = []\n\n n_frames = len(imgs)\n w, h = imgs[0].size\n xshift = (self.padding * torch.rand(self.bs)).int() + 1\n xshift *= (torch.randn(self.bs) > 0.0).int() * 2 - 1\n yshift = (self.padding * torch.rand(self.bs)).int() + 1\n yshift *= (torch.randn(self.bs) > 0.0).int() * 2 - 1\n ret_imgs.append(imgs[0])\n ret_targets.append(targets[0])\n for i in range(1, n_frames):\n ymin = max(0, -yshift[0])\n ymax = min(h, h - yshift[0])\n xmin = max(0, -xshift[0])\n xmax = min(w, w - xshift[0])\n prev_img = ret_imgs[i-1].copy()\n prev_target = copy.deepcopy(ret_targets[i-1])\n region = (int(ymin), int(xmin), int(ymax - ymin), int(xmax - xmin))\n img_i, target_i = random_shift(prev_img, prev_target, region, (h, w))\n ret_imgs.append(img_i)\n ret_targets.append(target_i)\n\n return ret_imgs, ret_targets\n\n\nclass RandomSizeCrop(object):\n def __init__(self, min_size: int, max_size: int):\n self.min_size = min_size\n self.max_size = max_size\n\n def __call__(self, img: PIL.Image.Image, target: dict):\n w = random.randint(self.min_size, min(img.width, self.max_size))\n h = random.randint(self.min_size, min(img.height, self.max_size))\n region = T.RandomCrop.get_params(img, [h, w])\n return crop(img, target, region)\n\n\nclass MotRandomSizeCrop(RandomSizeCrop):\n def __call__(self, imgs, targets):\n w = random.randint(self.min_size, min(imgs[0].width, self.max_size))\n h = random.randint(self.min_size, min(imgs[0].height, self.max_size))\n region = T.RandomCrop.get_params(imgs[0], [h, w])\n ret_imgs = []\n ret_targets = []\n for img_i, targets_i in zip(imgs, targets):\n img_i, targets_i = crop(img_i, targets_i, region)\n ret_imgs.append(img_i)\n ret_targets.append(targets_i)\n return ret_imgs, ret_targets\n\n\nclass CenterCrop(object):\n def __init__(self, size):\n self.size = size\n\n def __call__(self, img, target):\n image_width, image_height = img.size\n crop_height, crop_width = self.size\n crop_top = int(round((image_height - crop_height) / 2.))\n crop_left = int(round((image_width - crop_width) / 2.))\n return crop(img, target, (crop_top, crop_left, crop_height, crop_width))\n\n\nclass MotCenterCrop(CenterCrop):\n def __call__(self, imgs, targets):\n image_width, image_height = imgs[0].size\n crop_height, crop_width = self.size\n crop_top = int(round((image_height - crop_height) / 2.))\n crop_left = int(round((image_width - crop_width) / 2.))\n ret_imgs = []\n ret_targets = []\n for img_i, targets_i in zip(imgs, targets):\n img_i, targets_i = crop(img_i, targets_i, (crop_top, crop_left, crop_height, crop_width))\n ret_imgs.append(img_i)\n ret_targets.append(targets_i)\n return ret_imgs, ret_targets\n\n\nclass RandomHorizontalFlip(object):\n def __init__(self, p=0.5):\n self.p = p\n\n def __call__(self, img, target):\n if random.random() < self.p:\n return hflip(img, target)\n return img, target\n\n\nclass MotRandomHorizontalFlip(RandomHorizontalFlip):\n def __call__(self, imgs, targets):\n if random.random() < self.p:\n ret_imgs = []\n ret_targets = []\n for img_i, targets_i in zip(imgs, targets):\n img_i, targets_i = hflip(img_i, targets_i)\n ret_imgs.append(img_i)\n ret_targets.append(targets_i)\n return ret_imgs, ret_targets\n return imgs, targets\n\n\nclass RandomResize(object):\n def __init__(self, sizes, max_size=None):\n assert isinstance(sizes, (list, tuple))\n self.sizes = sizes\n self.max_size = max_size\n\n def __call__(self, img, target=None):\n size = random.choice(self.sizes)\n return resize(img, target, size, self.max_size)\n\n\nclass MotRandomResize(RandomResize):\n def __call__(self, imgs, targets):\n size = random.choice(self.sizes)\n ret_imgs = []\n ret_targets = []\n for img_i, targets_i in zip(imgs, targets):\n img_i, targets_i = resize(img_i, targets_i, size, self.max_size)\n ret_imgs.append(img_i)\n ret_targets.append(targets_i)\n return ret_imgs, ret_targets\n\n\nclass RandomPad(object):\n def __init__(self, max_pad):\n self.max_pad = max_pad\n\n def __call__(self, img, target):\n pad_x = random.randint(0, self.max_pad)\n pad_y = random.randint(0, self.max_pad)\n return pad(img, target, (pad_x, pad_y))\n\n\nclass MotRandomPad(RandomPad):\n def __call__(self, imgs, targets):\n pad_x = random.randint(0, self.max_pad)\n pad_y = random.randint(0, self.max_pad)\n ret_imgs = []\n ret_targets = []\n for img_i, targets_i in zip(imgs, targets):\n img_i, target_i = pad(img_i, targets_i, (pad_x, pad_y))\n ret_imgs.append(img_i)\n ret_targets.append(targets_i)\n return ret_imgs, ret_targets\n\n\nclass RandomSelect(object):\n \"\"\"\n Randomly selects between transforms1 and transforms2,\n with probability p for transforms1 and (1 - p) for transforms2\n \"\"\"\n def __init__(self, transforms1, transforms2, p=0.5):\n self.transforms1 = transforms1\n self.transforms2 = transforms2\n self.p = p\n\n def __call__(self, img, target):\n if random.random() < self.p:\n return self.transforms1(img, target)\n return self.transforms2(img, target)\n\n\nclass MotRandomSelect(RandomSelect):\n \"\"\"\n Randomly selects between transforms1 and transforms2,\n with probability p for transforms1 and (1 - p) for transforms2\n \"\"\"\n def __call__(self, imgs, targets):\n if random.random() < self.p:\n return self.transforms1(imgs, targets)\n return self.transforms2(imgs, targets)\n\n\nclass ToTensor(object):\n def __call__(self, img, target):\n return F.to_tensor(img), target\n\n\nclass MotToTensor(ToTensor):\n def __call__(self, imgs, targets):\n ret_imgs = []\n for img in imgs:\n ret_imgs.append(F.to_tensor(img))\n return ret_imgs, targets\n\n\nclass RandomErasing(object):\n\n def __init__(self, *args, **kwargs):\n self.eraser = T.RandomErasing(*args, **kwargs)\n\n def __call__(self, img, target):\n return self.eraser(img), target\n\n\nclass MotRandomErasing(RandomErasing):\n def __call__(self, imgs, targets):\n # TODO: Rewrite this part to ensure the data augmentation is same to each image.\n ret_imgs = []\n for img_i, targets_i in zip(imgs, targets):\n ret_imgs.append(self.eraser(img_i))\n return ret_imgs, targets\n\n\nclass MoTColorJitter(T.ColorJitter):\n def __call__(self, imgs, targets):\n transform = self.get_params(self.brightness, self.contrast,\n self.saturation, self.hue)\n ret_imgs = []\n for img_i, targets_i in zip(imgs, targets):\n ret_imgs.append(transform(img_i))\n return ret_imgs, targets\n\n\nclass Normalize(object):\n def __init__(self, mean, std):\n self.mean = mean\n self.std = std\n\n def __call__(self, image, target=None):\n if target is not None:\n target['ori_img'] = image.clone()\n image = F.normalize(image, mean=self.mean, std=self.std)\n if target is None:\n return image, None\n target = target.copy()\n h, w = image.shape[-2:]\n if \"boxes\" in target:\n boxes = target[\"boxes\"]\n boxes = box_xyxy_to_cxcywh(boxes)\n boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)\n target[\"boxes\"] = boxes\n return image, target\n\n\nclass MotNormalize(Normalize):\n def __call__(self, imgs, targets=None):\n ret_imgs = []\n ret_targets = []\n for i in range(len(imgs)):\n img_i = imgs[i]\n targets_i = targets[i] if targets is not None else None\n img_i, targets_i = super().__call__(img_i, targets_i)\n ret_imgs.append(img_i)\n ret_targets.append(targets_i)\n return ret_imgs, ret_targets\n\n\nclass Compose(object):\n def __init__(self, transforms):\n self.transforms = transforms\n\n def __call__(self, image, target):\n for t in self.transforms:\n image, target = t(image, target)\n return image, target\n\n def __repr__(self):\n format_string = self.__class__.__name__ + \"(\"\n for t in self.transforms:\n format_string += \"\\n\"\n format_string += \" {0}\".format(t)\n format_string += \"\\n)\"\n return format_string\n\n\nclass MotCompose(Compose):\n def __call__(self, imgs, targets):\n for t in self.transforms:\n imgs, targets = t(imgs, targets)\n return imgs, targets\n" }, { "path": "demo.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\nfrom __future__ import print_function\nimport glob\nimport math\nimport os\nimport os.path as osp\nimport random\nimport time\nfrom collections import OrderedDict\nimport torchvision.transforms.functional as F\nimport cv2\nimport numpy as np\nimport torch\nimport argparse\nimport torchvision.transforms.functional as F\nfrom tqdm import tqdm\nfrom pathlib import Path\nfrom PIL import Image, ImageDraw\nfrom models import build_model\nfrom util.tool import load_model\nfrom main import get_args_parser\nfrom torch.nn.functional import interpolate\nfrom typing import List\nimport shutil\nfrom models.structures import Instances\n\nnp.random.seed(2020)\n\nCOLORS_10 = [(144, 238, 144), (178, 34, 34), (221, 160, 221), (0, 255, 0), (0, 128, 0), (210, 105, 30), (220, 20, 60),\n (192, 192, 192), (255, 228, 196), (50, 205, 50), (139, 0, 139), (100, 149, 237), (138, 43, 226),\n (238, 130, 238),\n (255, 0, 255), (0, 100, 0), (127, 255, 0), (255, 0, 255), (0, 0, 205), (255, 140, 0), (255, 239, 213),\n (199, 21, 133), (124, 252, 0), (147, 112, 219), (106, 90, 205), (176, 196, 222), (65, 105, 225),\n (173, 255, 47),\n (255, 20, 147), (219, 112, 147), (186, 85, 211), (199, 21, 133), (148, 0, 211), (255, 99, 71),\n (144, 238, 144),\n (255, 255, 0), (230, 230, 250), (0, 0, 255), (128, 128, 0), (189, 183, 107), (255, 255, 224),\n (128, 128, 128),\n (105, 105, 105), (64, 224, 208), (205, 133, 63), (0, 128, 128), (72, 209, 204), (139, 69, 19),\n (255, 245, 238),\n (250, 240, 230), (152, 251, 152), (0, 255, 255), (135, 206, 235), (0, 191, 255), (176, 224, 230),\n (0, 250, 154),\n (245, 255, 250), (240, 230, 140), (245, 222, 179), (0, 139, 139), (143, 188, 143), (255, 0, 0),\n (240, 128, 128),\n (102, 205, 170), (60, 179, 113), (46, 139, 87), (165, 42, 42), (178, 34, 34), (175, 238, 238),\n (255, 248, 220),\n (218, 165, 32), (255, 250, 240), (253, 245, 230), (244, 164, 96), (210, 105, 30)]\n\n\ndef plot_one_box(x, img, color=None, label=None, score=None, line_thickness=None):\n # Plots one bounding box on image img\n\n # tl = line_thickness or round(\n # 0.002 * max(img.shape[0:2])) + 1 # line thickness\n tl = 2\n color = color or [random.randint(0, 255) for _ in range(3)]\n c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))\n cv2.rectangle(img, c1, c2, color, thickness=tl)\n if label:\n tf = max(tl - 1, 1) # font thickness\n t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]\n c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3\n cv2.rectangle(img, c1, c2, color, -1) # filled\n cv2.putText(img,\n label, (c1[0], c1[1] - 2),\n 0,\n tl / 3, [225, 255, 255],\n thickness=tf,\n lineType=cv2.LINE_AA)\n if score is not None:\n cv2.putText(img, score, (c1[0], c1[1] + 30), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)\n return img\n\n\ndef draw_bboxes(ori_img, bbox, identities=None, offset=(0, 0), cvt_color=False):\n if cvt_color:\n ori_img = cv2.cvtColor(np.asarray(ori_img), cv2.COLOR_RGB2BGR)\n img = ori_img\n for i, box in enumerate(bbox):\n x1, y1, x2, y2 = [int(i) for i in box[:4]]\n x1 += offset[0]\n x2 += offset[0]\n y1 += offset[1]\n y2 += offset[1]\n if len(box) > 4:\n score = '{:.2f}'.format(box[4])\n else:\n score = None\n # box text and bar\n id = int(identities[i]) if identities is not None else 0\n color = COLORS_10[id % len(COLORS_10)]\n label = '{:d}'.format(id)\n # t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0]\n img = plot_one_box([x1, y1, x2, y2], img, color, label, score=score)\n return img\n\n\ndef draw_points(img: np.ndarray, points: np.ndarray, color=(255, 255, 255)) -> np.ndarray:\n assert len(points.shape) == 2 and points.shape[1] == 2, 'invalid points shape: {}'.format(points.shape)\n for i, (x, y) in enumerate(points):\n if i >= 300:\n color = (0, 255, 0)\n cv2.circle(img, (int(x), int(y)), 2, color=color, thickness=2)\n return img\n\nclass LoadVideo: # for inference\n def __init__(self, path, img_size=(1536, 800)):\n if not os.path.isfile(path):\n raise FileExistsError\n \n self.cap = cv2.VideoCapture(path) \n self.frame_rate = int(round(self.cap.get(cv2.CAP_PROP_FPS)))\n self.seq_w = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n self.seq_h = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n self.vn = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))\n\n self.width = img_size[0]\n self.height = img_size[1]\n self.count = 0\n\n self.mean = [0.485, 0.456, 0.406]\n self.std = [0.229, 0.224, 0.225]\n\n print('Lenth of the video: {:d} frames'.format(self.vn))\n\n def __iter__(self):\n self.count = -1\n return self\n\n def __next__(self):\n self.count += 1\n if self.count == len(self):\n raise StopIteration\n # Read image\n res, img = self.cap.read() # BGR\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB\n assert img is not None, 'Failed to load frame {:d}'.format(self.count)\n\n cur_img, ori_img = self.init_img(img)\n return self.count, cur_img, ori_img\n\n def init_img(self, img):\n ori_img = img.copy()\n self.seq_h, self.seq_w = img.shape[:2]\n scale = self.height / min(self.seq_h, self.seq_w)\n if max(self.seq_h, self.seq_w) * scale > self.width:\n scale = self.width / max(self.seq_h, self.seq_w)\n target_h = int(self.seq_h * scale)\n target_w = int(self.seq_w * scale)\n img = cv2.resize(img, (target_w, target_h))\n img = F.normalize(F.to_tensor(img), self.mean, self.std)\n img = img.unsqueeze(0)\n return img, ori_img\n\n def __len__(self):\n return self.vn # number of files\n\nclass MOTR(object):\n def update(self, dt_instances: Instances):\n ret = []\n for i in range(len(dt_instances)):\n label = dt_instances.labels[i]\n if label == 0:\n id = dt_instances.obj_idxes[i]\n box_with_score = np.concatenate([dt_instances.boxes[i], dt_instances.scores[i:i+1]], axis=-1)\n ret.append(np.concatenate((box_with_score, [id + 1])).reshape(1, -1)) # +1 as MOT benchmark requires positive\n if len(ret) > 0:\n return np.concatenate(ret)\n return np.empty((0, 6))\n\nclass Detector(object):\n def __init__(self, args):\n\n self.args = args\n\n # build model and load weights\n self.model, _, _ = build_model(args)\n checkpoint = torch.load(args.resume, map_location='cpu')\n self.model = load_model(self.model, args.resume)\n self.model = self.model.cuda()\n self.model.eval()\n\n # mkidr save_dir\n vid_name, prefix = args.input_video.split('/')[-1].split('.')\n self.save_root = os.path.join(args.output_dir, 'results', vid_name)\n Path(self.save_root).mkdir(parents=True, exist_ok=True)\n self.save_img_root = os.path.join(self.save_root, 'imgs')\n Path(self.save_img_root).mkdir(parents=True, exist_ok=True)\n self.txt_root = os.path.join(self.save_root, f'{vid_name}.txt')\n self.vid_root = os.path.join(self.save_root, args.input_video.split('/')[-1])\n\n # build dataloader and tracker\n self.dataloader = LoadVideo(args.input_video)\n self.tr_tracker = MOTR()\n\n @staticmethod\n def filter_dt_by_score(dt_instances: Instances, prob_threshold: float) -> Instances:\n keep = dt_instances.scores > prob_threshold\n return dt_instances[keep]\n\n @staticmethod\n def filter_dt_by_area(dt_instances: Instances, area_threshold: float) -> Instances:\n wh = dt_instances.boxes[:, 2:4] - dt_instances.boxes[:, 0:2]\n areas = wh[:, 0] * wh[:, 1]\n keep = areas > area_threshold\n return dt_instances[keep]\n\n @staticmethod\n def write_results(txt_path, frame_id, bbox_xyxy, identities):\n save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\\n'\n with open(txt_path, 'a') as f:\n for xyxy, track_id in zip(bbox_xyxy, identities):\n if track_id < 0 or track_id is None:\n continue\n x1, y1, x2, y2 = xyxy\n w, h = x2 - x1, y2 - y1\n line = save_format.format(frame=int(frame_id), id=int(track_id), x1=x1, y1=y1, w=w, h=h)\n f.write(line)\n\n @staticmethod\n def visualize_img_with_bbox(img_path, img, dt_instances: Instances, ref_pts=None, gt_boxes=None):\n img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n if dt_instances.has('scores'):\n img_show = draw_bboxes(img, np.concatenate([dt_instances.boxes, dt_instances.scores.reshape(-1, 1)], axis=-1), dt_instances.obj_idxes)\n else:\n img_show = draw_bboxes(img, dt_instances.boxes, dt_instances.obj_idxes)\n if ref_pts is not None:\n img_show = draw_points(img_show, ref_pts)\n if gt_boxes is not None:\n img_show = draw_bboxes(img_show, gt_boxes, identities=np.ones((len(gt_boxes), )) * -1)\n cv2.imwrite(img_path, img_show)\n return img_show\n\n def run(self, prob_threshold=0.7, area_threshold=100, vis=True, dump=True):\n # save as video\n fps = self.dataloader.frame_rate\n videowriter = cv2.VideoWriter(self.vid_root, cv2.VideoWriter_fourcc('M','J','P','G'), fps, (self.dataloader.seq_w, self.dataloader.seq_h))\n track_instances = None\n fid = 0\n for _, cur_img, ori_img in tqdm(self.dataloader):\n if track_instances is not None:\n track_instances.remove('boxes')\n track_instances.remove('labels')\n\n res = self.model.inference_single_image(cur_img.cuda().float(), (self.dataloader.seq_h, self.dataloader.seq_w), track_instances)\n track_instances = res['track_instances']\n dt_instances = track_instances.to(torch.device('cpu'))\n\n # filter det instances by score.\n dt_instances = self.filter_dt_by_score(dt_instances, prob_threshold)\n dt_instances = self.filter_dt_by_area(dt_instances, area_threshold)\n\n if vis:\n vis_img_path = os.path.join(self.save_img_root, '{:06d}.jpg'.format(fid))\n vis_img = self.visualize_img_with_bbox(vis_img_path, ori_img, dt_instances)\n videowriter.write(vis_img)\n\n if dump:\n tracker_outputs = self.tr_tracker.update(dt_instances)\n self.write_results(txt_path=self.txt_root,\n frame_id=(fid+1),\n bbox_xyxy=tracker_outputs[:, :4],\n identities=tracker_outputs[:, 5])\n fid += 1\n videowriter.release()\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])\n args = parser.parse_args()\n if args.output_dir:\n Path(args.output_dir).mkdir(parents=True, exist_ok=True)\n\n detector = Detector(args)\n detector.run()" }, { "path": "engine.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\n\"\"\"\nTrain and eval functions used in main.py\n\"\"\"\nimport cv2\nimport math\nimport numpy as np\nimport os\nimport sys\nfrom typing import Iterable\n\nimport torch\nimport util.misc as utils\nfrom util import box_ops\n\nfrom torch import Tensor\nfrom util.plot_utils import draw_boxes, draw_ref_pts, image_hwc2chw\nfrom datasets.coco_eval import CocoEvaluator\nfrom datasets.panoptic_eval import PanopticEvaluator\nfrom datasets.data_prefetcher import data_prefetcher, data_dict_to_cuda\n\n\ndef train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,\n data_loader: Iterable, optimizer: torch.optim.Optimizer,\n device: torch.device, epoch: int, max_norm: float = 0):\n model.train()\n criterion.train()\n metric_logger = utils.MetricLogger(delimiter=\" \")\n metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))\n metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))\n metric_logger.add_meter('grad_norm', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))\n header = 'Epoch: [{}]'.format(epoch)\n print_freq = 10\n\n prefetcher = data_prefetcher(data_loader, device, prefetch=True)\n samples, targets = prefetcher.next()\n\n # for samples, targets in metric_logger.log_every(data_loader, print_freq, header):\n for _ in metric_logger.log_every(range(len(data_loader)), print_freq, header):\n outputs = model(samples)\n\n loss_dict = criterion(outputs, targets)\n weight_dict = criterion.weight_dict\n losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)\n\n # reduce losses over all GPUs for logging purposes\n loss_dict_reduced = utils.reduce_dict(loss_dict)\n loss_dict_reduced_unscaled = {f'{k}_unscaled': v\n for k, v in loss_dict_reduced.items()}\n loss_dict_reduced_scaled = {k: v * weight_dict[k]\n for k, v in loss_dict_reduced.items() if k in weight_dict}\n losses_reduced_scaled = sum(loss_dict_reduced_scaled.values())\n\n loss_value = losses_reduced_scaled.item()\n\n if not math.isfinite(loss_value):\n print(\"Loss is {}, stopping training\".format(loss_value))\n print(loss_dict_reduced)\n sys.exit(1)\n\n optimizer.zero_grad()\n losses.backward()\n if max_norm > 0:\n grad_total_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)\n else:\n grad_total_norm = utils.get_total_grad_norm(model.parameters(), max_norm)\n optimizer.step()\n\n metric_logger.update(loss=loss_value, **loss_dict_reduced_scaled, **loss_dict_reduced_unscaled)\n metric_logger.update(class_error=loss_dict_reduced['class_error'])\n metric_logger.update(lr=optimizer.param_groups[0][\"lr\"])\n metric_logger.update(grad_norm=grad_total_norm)\n\n samples, targets = prefetcher.next()\n # gather the stats from all processes\n metric_logger.synchronize_between_processes()\n print(\"Averaged stats:\", metric_logger)\n return {k: meter.global_avg for k, meter in metric_logger.meters.items()}\n\n\ndef train_one_epoch_mot(model: torch.nn.Module, criterion: torch.nn.Module,\n data_loader: Iterable, optimizer: torch.optim.Optimizer,\n device: torch.device, epoch: int, max_norm: float = 0):\n model.train()\n criterion.train()\n metric_logger = utils.MetricLogger(delimiter=\" \")\n metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))\n # metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))\n metric_logger.add_meter('grad_norm', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))\n header = 'Epoch: [{}]'.format(epoch)\n print_freq = 10\n\n # for samples, targets in metric_logger.log_every(data_loader, print_freq, header):\n for data_dict in metric_logger.log_every(data_loader, print_freq, header):\n data_dict = data_dict_to_cuda(data_dict, device)\n outputs = model(data_dict)\n\n\n loss_dict = criterion(outputs, data_dict)\n # print(\"iter {} after model\".format(cnt-1))\n weight_dict = criterion.weight_dict\n losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)\n\n # reduce losses over all GPUs for logging purposes\n loss_dict_reduced = utils.reduce_dict(loss_dict)\n # loss_dict_reduced_unscaled = {f'{k}_unscaled': v\n # for k, v in loss_dict_reduced.items()}\n loss_dict_reduced_scaled = {k: v * weight_dict[k]\n for k, v in loss_dict_reduced.items() if k in weight_dict}\n losses_reduced_scaled = sum(loss_dict_reduced_scaled.values())\n\n loss_value = losses_reduced_scaled.item()\n\n if not math.isfinite(loss_value):\n print(\"Loss is {}, stopping training\".format(loss_value))\n print(loss_dict_reduced)\n sys.exit(1)\n\n optimizer.zero_grad()\n losses.backward()\n if max_norm > 0:\n grad_total_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)\n else:\n grad_total_norm = utils.get_total_grad_norm(model.parameters(), max_norm)\n optimizer.step()\n\n # metric_logger.update(loss=loss_value, **loss_dict_reduced_scaled, **loss_dict_reduced_unscaled)\n metric_logger.update(loss=loss_value, **loss_dict_reduced_scaled)\n # metric_logger.update(class_error=loss_dict_reduced['class_error'])\n metric_logger.update(lr=optimizer.param_groups[0][\"lr\"])\n metric_logger.update(grad_norm=grad_total_norm)\n # gather the stats from all processes\n\n metric_logger.synchronize_between_processes()\n print(\"Averaged stats:\", metric_logger)\n return {k: meter.global_avg for k, meter in metric_logger.meters.items()}\n\n\n@torch.no_grad()\ndef evaluate(model, criterion, postprocessors, data_loader, base_ds, device, output_dir):\n model.eval()\n criterion.eval()\n\n metric_logger = utils.MetricLogger(delimiter=\" \")\n metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))\n header = 'Test:'\n\n iou_types = tuple(k for k in ('segm', 'bbox') if k in postprocessors.keys())\n coco_evaluator = CocoEvaluator(base_ds, iou_types)\n # coco_evaluator.coco_eval[iou_types[0]].params.iouThrs = [0, 0.1, 0.5, 0.75]\n\n panoptic_evaluator = None\n if 'panoptic' in postprocessors.keys():\n panoptic_evaluator = PanopticEvaluator(\n data_loader.dataset.ann_file,\n data_loader.dataset.ann_folder,\n output_dir=os.path.join(output_dir, \"panoptic_eval\"),\n )\n\n for samples, targets in metric_logger.log_every(data_loader, 10, header):\n samples = samples.to(device)\n targets = [{k: v.to(device) for k, v in t.items()} for t in targets]\n\n outputs = model(samples)\n loss_dict = criterion(outputs, targets)\n weight_dict = criterion.weight_dict\n\n # reduce losses over all GPUs for logging purposes\n loss_dict_reduced = utils.reduce_dict(loss_dict)\n loss_dict_reduced_scaled = {k: v * weight_dict[k]\n for k, v in loss_dict_reduced.items() if k in weight_dict}\n # loss_dict_reduced_unscaled = {f'{k}_unscaled': v\n # for k, v in loss_dict_reduced.items()}\n # metric_logger.update(loss=sum(loss_dict_reduced_scaled.values()),\n # **loss_dict_reduced_scaled,\n # **loss_dict_reduced_unscaled)\n metric_logger.update(loss=sum(loss_dict_reduced_scaled.values()),\n **loss_dict_reduced_scaled,\n )\n metric_logger.update(class_error=loss_dict_reduced['class_error'])\n\n orig_target_sizes = torch.stack([t[\"orig_size\"] for t in targets], dim=0)\n results = postprocessors['bbox'](outputs, orig_target_sizes)\n if 'segm' in postprocessors.keys():\n target_sizes = torch.stack([t[\"size\"] for t in targets], dim=0)\n results = postprocessors['segm'](results, outputs, orig_target_sizes, target_sizes)\n res = {target['image_id'].item(): output for target, output in zip(targets, results)}\n if coco_evaluator is not None:\n coco_evaluator.update(res)\n\n if panoptic_evaluator is not None:\n res_pano = postprocessors[\"panoptic\"](outputs, target_sizes, orig_target_sizes)\n for i, target in enumerate(targets):\n image_id = target[\"image_id\"].item()\n file_name = f\"{image_id:012d}.png\"\n res_pano[i][\"image_id\"] = image_id\n res_pano[i][\"file_name\"] = file_name\n\n panoptic_evaluator.update(res_pano)\n\n # gather the stats from all processes\n metric_logger.synchronize_between_processes()\n print(\"Averaged stats:\", metric_logger)\n if coco_evaluator is not None:\n coco_evaluator.synchronize_between_processes()\n if panoptic_evaluator is not None:\n panoptic_evaluator.synchronize_between_processes()\n\n # accumulate predictions from all images\n if coco_evaluator is not None:\n coco_evaluator.accumulate()\n coco_evaluator.summarize()\n panoptic_res = None\n if panoptic_evaluator is not None:\n panoptic_res = panoptic_evaluator.summarize()\n stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()}\n if coco_evaluator is not None:\n if 'bbox' in postprocessors.keys():\n stats['coco_eval_bbox'] = coco_evaluator.coco_eval['bbox'].stats.tolist()\n if 'segm' in postprocessors.keys():\n stats['coco_eval_masks'] = coco_evaluator.coco_eval['segm'].stats.tolist()\n if panoptic_res is not None:\n stats['PQ_all'] = panoptic_res[\"All\"]\n stats['PQ_th'] = panoptic_res[\"Things\"]\n stats['PQ_st'] = panoptic_res[\"Stuff\"]\n return stats, coco_evaluator\n" }, { "path": "eval.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\"\"\"\n SORT: A Simple, Online and Realtime Tracker\n Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai\n This program is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n This program is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n You should have received a copy of the GNU General Public License\n along with this program. If not, see .\n\"\"\"\nfrom __future__ import print_function\n\nimport os\nimport numpy as np\nimport random\nimport argparse\nimport torchvision.transforms.functional as F\nimport torch\nimport cv2\nfrom tqdm import tqdm\nfrom pathlib import Path\nfrom PIL import Image, ImageDraw\nfrom models import build_model\nfrom util.tool import load_model\nfrom main import get_args_parser\nfrom torch.nn.functional import interpolate\nfrom typing import List\nfrom util.evaluation import Evaluator\nimport motmetrics as mm\nimport shutil\n\nfrom models.structures import Instances\n\nnp.random.seed(2020)\n\nCOLORS_10 = [(144, 238, 144), (178, 34, 34), (221, 160, 221), (0, 255, 0), (0, 128, 0), (210, 105, 30), (220, 20, 60),\n (192, 192, 192), (255, 228, 196), (50, 205, 50), (139, 0, 139), (100, 149, 237), (138, 43, 226),\n (238, 130, 238),\n (255, 0, 255), (0, 100, 0), (127, 255, 0), (255, 0, 255), (0, 0, 205), (255, 140, 0), (255, 239, 213),\n (199, 21, 133), (124, 252, 0), (147, 112, 219), (106, 90, 205), (176, 196, 222), (65, 105, 225),\n (173, 255, 47),\n (255, 20, 147), (219, 112, 147), (186, 85, 211), (199, 21, 133), (148, 0, 211), (255, 99, 71),\n (144, 238, 144),\n (255, 255, 0), (230, 230, 250), (0, 0, 255), (128, 128, 0), (189, 183, 107), (255, 255, 224),\n (128, 128, 128),\n (105, 105, 105), (64, 224, 208), (205, 133, 63), (0, 128, 128), (72, 209, 204), (139, 69, 19),\n (255, 245, 238),\n (250, 240, 230), (152, 251, 152), (0, 255, 255), (135, 206, 235), (0, 191, 255), (176, 224, 230),\n (0, 250, 154),\n (245, 255, 250), (240, 230, 140), (245, 222, 179), (0, 139, 139), (143, 188, 143), (255, 0, 0),\n (240, 128, 128),\n (102, 205, 170), (60, 179, 113), (46, 139, 87), (165, 42, 42), (178, 34, 34), (175, 238, 238),\n (255, 248, 220),\n (218, 165, 32), (255, 250, 240), (253, 245, 230), (244, 164, 96), (210, 105, 30)]\n\n\ndef plot_one_box(x, img, color=None, label=None, score=None, line_thickness=None):\n # Plots one bounding box on image img\n\n # tl = line_thickness or round(\n # 0.002 * max(img.shape[0:2])) + 1 # line thickness\n tl = 2\n color = color or [random.randint(0, 255) for _ in range(3)]\n c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))\n cv2.rectangle(img, c1, c2, color, thickness=tl)\n if label:\n tf = max(tl - 1, 1) # font thickness\n t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]\n c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3\n cv2.rectangle(img, c1, c2, color, -1) # filled\n cv2.putText(img,\n label, (c1[0], c1[1] - 2),\n 0,\n tl / 3, [225, 255, 255],\n thickness=tf,\n lineType=cv2.LINE_AA)\n if score is not None:\n cv2.putText(img, score, (c1[0], c1[1] + 30), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)\n return img\n\n\ndef draw_bboxes(ori_img, bbox, identities=None, offset=(0, 0), cvt_color=False):\n if cvt_color:\n ori_img = cv2.cvtColor(np.asarray(ori_img), cv2.COLOR_RGB2BGR)\n img = ori_img\n for i, box in enumerate(bbox):\n x1, y1, x2, y2 = [int(i) for i in box[:4]]\n x1 += offset[0]\n x2 += offset[0]\n y1 += offset[1]\n y2 += offset[1]\n if len(box) > 4:\n score = '{:.2f}'.format(box[4])\n else:\n score = None\n # box text and bar\n id = int(identities[i]) if identities is not None else 0\n color = COLORS_10[id % len(COLORS_10)]\n label = '{:d}'.format(id)\n # t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0]\n img = plot_one_box([x1, y1, x2, y2], img, color, label, score=score)\n return img\n\n\ndef draw_points(img: np.ndarray, points: np.ndarray, color=(255, 255, 255)) -> np.ndarray:\n assert len(points.shape) == 2 and points.shape[1] == 2, 'invalid points shape: {}'.format(points.shape)\n for i, (x, y) in enumerate(points):\n if i >= 300:\n color = (0, 255, 0)\n cv2.circle(img, (int(x), int(y)), 2, color=color, thickness=2)\n return img\n\n\ndef tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:\n return tensor.detach().cpu().numpy()\n\n\nclass Track(object):\n track_cnt = 0\n\n def __init__(self, box):\n self.box = box\n self.time_since_update = 0\n self.id = Track.track_cnt\n Track.track_cnt += 1\n self.miss = 0\n\n def miss_one_frame(self):\n self.miss += 1\n\n def clear_miss(self):\n self.miss = 0\n\n def update(self, box):\n self.box = box\n self.clear_miss()\n\n\nclass MOTR(object):\n def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):\n pass\n\n def update(self, dt_instances: Instances):\n ret = []\n for i in range(len(dt_instances)):\n label = dt_instances.labels[i]\n if label == 0:\n id = dt_instances.obj_idxes[i]\n box_with_score = np.concatenate([dt_instances.boxes[i], dt_instances.scores[i:i+1]], axis=-1)\n ret.append(np.concatenate((box_with_score, [id + 1])).reshape(1, -1)) # +1 as MOT benchmark requires positive\n\n if len(ret) > 0:\n return np.concatenate(ret)\n return np.empty((0, 6))\n\n\ndef load_label(label_path: str, img_size: tuple) -> dict:\n labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 6)\n h, w = img_size\n # Normalized cewh to pixel xyxy format\n labels = labels0.copy()\n labels[:, 2] = w * (labels0[:, 2] - labels0[:, 4] / 2)\n labels[:, 3] = h * (labels0[:, 3] - labels0[:, 5] / 2)\n labels[:, 4] = w * (labels0[:, 2] + labels0[:, 4] / 2)\n labels[:, 5] = h * (labels0[:, 3] + labels0[:, 5] / 2)\n targets = {'boxes': [], 'labels': [], 'area': []}\n num_boxes = len(labels)\n\n visited_ids = set()\n for label in labels[:num_boxes]:\n obj_id = label[1]\n if obj_id in visited_ids:\n continue\n visited_ids.add(obj_id)\n targets['boxes'].append(label[2:6].tolist())\n targets['area'].append(label[4] * label[5])\n targets['labels'].append(0)\n targets['boxes'] = np.asarray(targets['boxes'])\n targets['area'] = np.asarray(targets['area'])\n targets['labels'] = np.asarray(targets['labels'])\n return targets\n\n\ndef filter_pub_det(res_file, pub_det_file, filter_iou=False):\n frame_boxes = {}\n with open(pub_det_file, 'r') as f:\n lines = f.readlines()\n for line in lines:\n if len(line) == 0:\n continue\n elements = line.strip().split(',')\n frame_id = int(elements[0])\n x1, y1, w, h = elements[2:6]\n x1, y1, w, h = float(x1), float(y1), float(w), float(h)\n x2 = x1 + w - 1\n y2 = y1 + h - 1\n if frame_id not in frame_boxes:\n frame_boxes[frame_id] = []\n frame_boxes[frame_id].append([x1, y1, x2, y2])\n\n for frame, boxes in frame_boxes.items():\n frame_boxes[frame] = np.array(boxes)\n\n ids = {}\n num_filter_box = 0\n with open(res_file, 'r') as f:\n lines = list(f.readlines())\n with open(res_file, 'w') as f:\n for line in lines:\n if len(line) == 0:\n continue\n elements = line.strip().split(',')\n frame_id, obj_id = elements[:2]\n frame_id = int(frame_id)\n obj_id = int(obj_id)\n x1, y1, w, h = elements[2:6]\n x1, y1, w, h = float(x1), float(y1), float(w), float(h)\n x2 = x1 + w - 1\n y2 = y1 + h - 1\n if obj_id not in ids:\n # track initialization.\n if frame_id not in frame_boxes:\n num_filter_box += 1\n print(\"filter init box {} {}\".format(frame_id, obj_id))\n continue\n pub_dt_boxes = frame_boxes[frame_id]\n dt_box = np.array([[x1, y1, x2, y2]])\n if filter_iou:\n max_iou = bbox_iou(dt_box, pub_dt_boxes).max()\n if max_iou < 0.5:\n num_filter_box += 1\n print(\"filter init box {} {}\".format(frame_id, obj_id))\n continue\n else:\n pub_dt_centers = (pub_dt_boxes[:, :2] + pub_dt_boxes[:, 2:4]) * 0.5\n x_inside = (dt_box[0, 0] <= pub_dt_centers[:, 0]) & (dt_box[0, 2] >= pub_dt_centers[:, 0])\n y_inside = (dt_box[0, 1] <= pub_dt_centers[:, 1]) & (dt_box[0, 3] >= pub_dt_centers[:, 1])\n center_inside: np.ndarray = x_inside & y_inside\n if not center_inside.any():\n num_filter_box += 1\n print(\"filter init box {} {}\".format(frame_id, obj_id))\n continue\n print(\"save init track {} {}\".format(frame_id, obj_id))\n ids[obj_id] = True\n f.write(line)\n\n print(\"totally {} boxes are filtered.\".format(num_filter_box))\n\n\nclass Detector(object):\n def __init__(self, args, model=None, seq_num=2):\n\n self.args = args\n self.detr = model\n\n self.seq_num = seq_num\n img_list = os.listdir(os.path.join(self.args.mot_path, 'MOT15/images/train', self.seq_num, 'img1'))\n img_list = [os.path.join(self.args.mot_path, 'MOT15/images/train', self.seq_num, 'img1', _) for _ in img_list if\n ('jpg' in _) or ('png' in _)]\n\n self.img_list = sorted(img_list)\n self.img_len = len(self.img_list)\n self.tr_tracker = MOTR()\n\n '''\n common settings\n '''\n self.img_height = 800\n self.img_width = 1536\n self.mean = [0.485, 0.456, 0.406]\n self.std = [0.229, 0.224, 0.225]\n\n self.save_path = os.path.join(self.args.output_dir, 'results/{}'.format(seq_num))\n os.makedirs(self.save_path, exist_ok=True)\n\n self.predict_path = os.path.join(self.args.output_dir, 'preds', self.seq_num)\n os.makedirs(self.predict_path, exist_ok=True)\n if os.path.exists(os.path.join(self.predict_path, 'gt.txt')):\n os.remove(os.path.join(self.predict_path, 'gt.txt'))\n\n def load_img_from_file(self, f_path):\n label_path = f_path.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt')\n cur_img = cv2.imread(f_path)\n cur_img = cv2.cvtColor(cur_img, cv2.COLOR_BGR2RGB)\n targets = load_label(label_path, cur_img.shape[:2]) if os.path.exists(label_path) else None\n return cur_img, targets\n\n def init_img(self, img):\n ori_img = img.copy()\n self.seq_h, self.seq_w = img.shape[:2]\n scale = self.img_height / min(self.seq_h, self.seq_w)\n if max(self.seq_h, self.seq_w) * scale > self.img_width:\n scale = self.img_width / max(self.seq_h, self.seq_w)\n target_h = int(self.seq_h * scale)\n target_w = int(self.seq_w * scale)\n img = cv2.resize(img, (target_w, target_h))\n img = F.normalize(F.to_tensor(img), self.mean, self.std)\n img = img.unsqueeze(0)\n return img, ori_img\n\n @staticmethod\n def filter_dt_by_score(dt_instances: Instances, prob_threshold: float) -> Instances:\n keep = dt_instances.scores > prob_threshold\n return dt_instances[keep]\n\n @staticmethod\n def filter_dt_by_area(dt_instances: Instances, area_threshold: float) -> Instances:\n wh = dt_instances.boxes[:, 2:4] - dt_instances.boxes[:, 0:2]\n areas = wh[:, 0] * wh[:, 1]\n keep = areas > area_threshold\n return dt_instances[keep]\n\n @staticmethod\n def write_results(txt_path, frame_id, bbox_xyxy, identities):\n save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\\n'\n with open(txt_path, 'a') as f:\n for xyxy, track_id in zip(bbox_xyxy, identities):\n if track_id < 0 or track_id is None:\n continue\n x1, y1, x2, y2 = xyxy\n w, h = x2 - x1, y2 - y1\n line = save_format.format(frame=int(frame_id), id=int(track_id), x1=x1, y1=y1, w=w, h=h)\n f.write(line)\n\n def eval_seq(self):\n data_root = os.path.join(self.args.mot_path, 'MOT15/images/train')\n result_filename = os.path.join(self.predict_path, 'gt.txt')\n evaluator = Evaluator(data_root, self.seq_num)\n accs = evaluator.eval_file(result_filename)\n return accs\n\n @staticmethod\n def visualize_img_with_bbox(img_path, img, dt_instances: Instances, ref_pts=None, gt_boxes=None):\n img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n if dt_instances.has('scores'):\n img_show = draw_bboxes(img, np.concatenate([dt_instances.boxes, dt_instances.scores.reshape(-1, 1)], axis=-1), dt_instances.obj_idxes)\n else:\n img_show = draw_bboxes(img, dt_instances.boxes, dt_instances.obj_idxes)\n if ref_pts is not None:\n img_show = draw_points(img_show, ref_pts)\n if gt_boxes is not None:\n img_show = draw_bboxes(img_show, gt_boxes, identities=np.ones((len(gt_boxes), )) * -1)\n cv2.imwrite(img_path, img_show)\n\n def detect(self, prob_threshold=0.7, area_threshold=100, vis=False):\n total_dts = 0\n track_instances = None\n max_id = 0\n for i in tqdm(range(0, self.img_len)):\n img, targets = self.load_img_from_file(self.img_list[i])\n cur_img, ori_img = self.init_img(img)\n\n # track_instances = None\n if track_instances is not None:\n track_instances.remove('boxes')\n track_instances.remove('labels')\n\n res = self.detr.inference_single_image(cur_img.cuda().float(), (self.seq_h, self.seq_w), track_instances)\n track_instances = res['track_instances']\n max_id = max(max_id, track_instances.obj_idxes.max().item())\n\n all_ref_pts = tensor_to_numpy(res['ref_pts'][0, :, :2])\n dt_instances = track_instances.to(torch.device('cpu'))\n\n # filter det instances by score.\n dt_instances = self.filter_dt_by_score(dt_instances, prob_threshold)\n dt_instances = self.filter_dt_by_area(dt_instances, area_threshold)\n\n total_dts += len(dt_instances)\n\n if vis:\n # for visual\n cur_vis_img_path = os.path.join(self.save_path, 'frame_{}.jpg'.format(i))\n gt_boxes = None\n self.visualize_img_with_bbox(cur_vis_img_path, ori_img, dt_instances, ref_pts=all_ref_pts, gt_boxes=gt_boxes)\n\n tracker_outputs = self.tr_tracker.update(dt_instances)\n self.write_results(txt_path=os.path.join(self.predict_path, 'gt.txt'),\n frame_id=(i + 1),\n bbox_xyxy=tracker_outputs[:, :4],\n identities=tracker_outputs[:, 5])\n print(\"totally {} dts max_id={}\".format(total_dts, max_id))\n\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])\n args = parser.parse_args()\n if args.output_dir:\n Path(args.output_dir).mkdir(parents=True, exist_ok=True)\n\n # load model and weights\n detr, _, _ = build_model(args)\n checkpoint = torch.load(args.resume, map_location='cpu')\n detr = load_model(detr, args.resume)\n detr = detr.cuda()\n detr.eval()\n\n seq_nums = ['ADL-Rundle-6', 'ETH-Bahnhof', 'KITTI-13', 'PETS09-S2L1', 'TUD-Stadtmitte', 'ADL-Rundle-8', 'KITTI-17',\n 'ETH-Pedcross2', 'ETH-Sunnyday', 'TUD-Campus', 'Venice-2']\n accs = []\n seqs = []\n\n for seq_num in seq_nums:\n print(\"solve {}\".format(seq_num))\n det = Detector(args, model=detr, seq_num=seq_num)\n det.detect(vis=True)\n accs.append(det.eval_seq())\n seqs.append(seq_num)\n\n metrics = mm.metrics.motchallenge_metrics\n mh = mm.metrics.create()\n summary = Evaluator.get_summary(accs, seqs, metrics)\n strsummary = mm.io.render_summary(\n summary,\n formatters=mh.formatters,\n namemap=mm.io.motchallenge_metric_names\n )\n print(strsummary)\n with open(\"eval_log.txt\", 'a') as f:\n print(strsummary, file=f)\n" }, { "path": "main.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\n\nimport argparse\nimport datetime\nimport json\nimport random\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader\nimport datasets\n\nfrom util.motdet_eval import motdet_evaluate, detmotdet_evaluate\nfrom util.tool import load_model\nimport util.misc as utils\nimport datasets.samplers as samplers\nfrom datasets import build_dataset, get_coco_api_from_dataset\nfrom engine import evaluate, train_one_epoch, train_one_epoch_mot\nfrom models import build_model\n\n\ndef get_args_parser():\n parser = argparse.ArgumentParser('Deformable DETR Detector', add_help=False)\n parser.add_argument('--lr', default=2e-4, type=float)\n parser.add_argument('--lr_backbone_names', default=[\"backbone.0\"], type=str, nargs='+')\n parser.add_argument('--lr_backbone', default=2e-5, type=float)\n parser.add_argument('--lr_linear_proj_names', default=['reference_points', 'sampling_offsets',], type=str, nargs='+')\n parser.add_argument('--lr_linear_proj_mult', default=0.1, type=float)\n parser.add_argument('--batch_size', default=2, type=int)\n parser.add_argument('--weight_decay', default=1e-4, type=float)\n parser.add_argument('--epochs', default=50, type=int)\n parser.add_argument('--lr_drop', default=40, type=int)\n parser.add_argument('--save_period', default=50, type=int)\n parser.add_argument('--lr_drop_epochs', default=None, type=int, nargs='+')\n parser.add_argument('--clip_max_norm', default=0.1, type=float,\n help='gradient clipping max norm')\n\n parser.add_argument('--meta_arch', default='deformable_detr', type=str)\n\n parser.add_argument('--sgd', action='store_true')\n\n # Variants of Deformable DETR\n parser.add_argument('--with_box_refine', default=False, action='store_true')\n parser.add_argument('--two_stage', default=False, action='store_true')\n parser.add_argument('--accurate_ratio', default=False, action='store_true')\n\n\n # Model parameters\n parser.add_argument('--frozen_weights', type=str, default=None,\n help=\"Path to the pretrained model. If set, only the mask head will be trained\")\n parser.add_argument('--num_anchors', default=1, type=int)\n\n # * Backbone\n parser.add_argument('--backbone', default='resnet50', type=str,\n help=\"Name of the convolutional backbone to use\")\n parser.add_argument('--enable_fpn', action='store_true')\n parser.add_argument('--dilation', action='store_true',\n help=\"If true, we replace stride with dilation in the last convolutional block (DC5)\")\n parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'),\n help=\"Type of positional embedding to use on top of the image features\")\n parser.add_argument('--position_embedding_scale', default=2 * np.pi, type=float,\n help=\"position / size * scale\")\n parser.add_argument('--num_feature_levels', default=4, type=int, help='number of feature levels')\n\n # * Transformer\n parser.add_argument('--enc_layers', default=6, type=int,\n help=\"Number of encoding layers in the transformer\")\n parser.add_argument('--dec_layers', default=6, type=int,\n help=\"Number of decoding layers in the transformer\")\n parser.add_argument('--dim_feedforward', default=1024, type=int,\n help=\"Intermediate size of the feedforward layers in the transformer blocks\")\n parser.add_argument('--hidden_dim', default=256, type=int,\n help=\"Size of the embeddings (dimension of the transformer)\")\n parser.add_argument('--dropout', default=0.1, type=float,\n help=\"Dropout applied in the transformer\")\n parser.add_argument('--nheads', default=8, type=int,\n help=\"Number of attention heads inside the transformer's attentions\")\n parser.add_argument('--num_queries', default=300, type=int,\n help=\"Number of query slots\")\n parser.add_argument('--dec_n_points', default=4, type=int)\n parser.add_argument('--enc_n_points', default=4, type=int)\n parser.add_argument('--decoder_cross_self', default=False, action='store_true')\n parser.add_argument('--sigmoid_attn', default=False, action='store_true')\n parser.add_argument('--crop', action='store_true')\n parser.add_argument('--cj', action='store_true')\n parser.add_argument('--extra_track_attn', action='store_true')\n parser.add_argument('--loss_normalizer', action='store_true')\n parser.add_argument('--max_size', default=1333, type=int)\n parser.add_argument('--val_width', default=800, type=int)\n parser.add_argument('--filter_ignore', action='store_true')\n\n # * Segmentation\n parser.add_argument('--masks', action='store_true',\n help=\"Train segmentation head if the flag is provided\")\n\n # Loss\n parser.add_argument('--no_aux_loss', dest='aux_loss', action='store_false',\n help=\"Disables auxiliary decoding losses (loss at each layer)\")\n\n # * Matcher\n parser.add_argument('--mix_match', action='store_true',)\n parser.add_argument('--set_cost_class', default=2, type=float,\n help=\"Class coefficient in the matching cost\")\n parser.add_argument('--set_cost_bbox', default=5, type=float,\n help=\"L1 box coefficient in the matching cost\")\n parser.add_argument('--set_cost_giou', default=2, type=float,\n help=\"giou box coefficient in the matching cost\")\n\n # * Loss coefficients\n parser.add_argument('--mask_loss_coef', default=1, type=float)\n parser.add_argument('--dice_loss_coef', default=1, type=float)\n parser.add_argument('--cls_loss_coef', default=2, type=float)\n parser.add_argument('--bbox_loss_coef', default=5, type=float)\n parser.add_argument('--giou_loss_coef', default=2, type=float)\n parser.add_argument('--focal_alpha', default=0.25, type=float)\n\n # dataset parameters\n parser.add_argument('--dataset_file', default='coco')\n parser.add_argument('--gt_file_train', type=str)\n parser.add_argument('--gt_file_val', type=str)\n parser.add_argument('--coco_path', default='/data/workspace/detectron2/datasets/coco/', type=str)\n parser.add_argument('--coco_panoptic_path', type=str)\n parser.add_argument('--remove_difficult', action='store_true')\n\n parser.add_argument('--output_dir', default='',\n help='path where to save, empty for no saving')\n parser.add_argument('--device', default='cuda',\n help='device to use for training / testing')\n parser.add_argument('--seed', default=42, type=int)\n parser.add_argument('--resume', default='', help='resume from checkpoint')\n parser.add_argument('--start_epoch', default=0, type=int, metavar='N',\n help='start epoch')\n parser.add_argument('--eval', action='store_true')\n parser.add_argument('--vis', action='store_true')\n parser.add_argument('--num_workers', default=2, type=int)\n parser.add_argument('--pretrained', default=None, help='resume from checkpoint')\n parser.add_argument('--cache_mode', default=False, action='store_true', help='whether to cache images on memory')\n\n # end-to-end mot settings.\n parser.add_argument('--mot_path', default='/data/Dataset/mot', type=str)\n parser.add_argument('--input_video', default='figs/demo.mp4', type=str)\n parser.add_argument('--data_txt_path_train',\n default='./datasets/data_path/detmot17.train', type=str,\n help=\"path to dataset txt split\")\n parser.add_argument('--data_txt_path_val',\n default='./datasets/data_path/detmot17.train', type=str,\n help=\"path to dataset txt split\")\n parser.add_argument('--img_path', default='data/valid/JPEGImages/')\n\n parser.add_argument('--query_interaction_layer', default='QIM', type=str,\n help=\"\")\n parser.add_argument('--sample_mode', type=str, default='fixed_interval')\n parser.add_argument('--sample_interval', type=int, default=1)\n parser.add_argument('--random_drop', type=float, default=0)\n parser.add_argument('--fp_ratio', type=float, default=0)\n parser.add_argument('--merger_dropout', type=float, default=0.1)\n parser.add_argument('--update_query_pos', action='store_true')\n\n parser.add_argument('--sampler_steps', type=int, nargs='*')\n parser.add_argument('--sampler_lengths', type=int, nargs='*')\n parser.add_argument('--exp_name', default='submit', type=str)\n parser.add_argument('--memory_bank_score_thresh', type=float, default=0.)\n parser.add_argument('--memory_bank_len', type=int, default=4)\n parser.add_argument('--memory_bank_type', type=str, default=None)\n parser.add_argument('--memory_bank_with_self_attn', action='store_true', default=False)\n\n parser.add_argument('--use_checkpoint', action='store_true', default=False)\n return parser\n\n\ndef main(args):\n utils.init_distributed_mode(args)\n print(\"git:\\n {}\\n\".format(utils.get_sha()))\n\n if args.frozen_weights is not None:\n assert args.masks, \"Frozen training is meant for segmentation only\"\n print(args)\n\n device = torch.device(args.device)\n\n # fix the seed for reproducibility\n seed = args.seed + utils.get_rank()\n torch.manual_seed(seed)\n np.random.seed(seed)\n random.seed(seed)\n\n model, criterion, postprocessors = build_model(args)\n model.to(device)\n\n model_without_ddp = model\n n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)\n print('number of params:', n_parameters)\n\n dataset_train = build_dataset(image_set='train', args=args)\n dataset_val = build_dataset(image_set='val', args=args)\n\n if args.distributed:\n if args.cache_mode:\n sampler_train = samplers.NodeDistributedSampler(dataset_train)\n sampler_val = samplers.NodeDistributedSampler(dataset_val, shuffle=False)\n else:\n sampler_train = samplers.DistributedSampler(dataset_train)\n sampler_val = samplers.DistributedSampler(dataset_val, shuffle=False)\n else:\n sampler_train = torch.utils.data.RandomSampler(dataset_train)\n sampler_val = torch.utils.data.SequentialSampler(dataset_val)\n\n batch_sampler_train = torch.utils.data.BatchSampler(\n sampler_train, args.batch_size, drop_last=True)\n if args.dataset_file in ['e2e_mot', 'e2e_dance', 'mot', 'ori_mot', 'e2e_static_mot', 'e2e_joint']:\n collate_fn = utils.mot_collate_fn\n else:\n collate_fn = utils.collate_fn\n data_loader_train = DataLoader(dataset_train, batch_sampler=batch_sampler_train,\n collate_fn=collate_fn, num_workers=args.num_workers,\n pin_memory=True)\n data_loader_val = DataLoader(dataset_val, args.batch_size, sampler=sampler_val,\n drop_last=False, collate_fn=collate_fn, num_workers=args.num_workers,\n pin_memory=True)\n\n def match_name_keywords(n, name_keywords):\n out = False\n for b in name_keywords:\n if b in n:\n out = True\n break\n return out\n\n param_dicts = [\n {\n \"params\":\n [p for n, p in model_without_ddp.named_parameters()\n if not match_name_keywords(n, args.lr_backbone_names) and not match_name_keywords(n, args.lr_linear_proj_names) and p.requires_grad],\n \"lr\": args.lr,\n },\n {\n \"params\": [p for n, p in model_without_ddp.named_parameters() if match_name_keywords(n, args.lr_backbone_names) and p.requires_grad],\n \"lr\": args.lr_backbone,\n },\n {\n \"params\": [p for n, p in model_without_ddp.named_parameters() if match_name_keywords(n, args.lr_linear_proj_names) and p.requires_grad],\n \"lr\": args.lr * args.lr_linear_proj_mult,\n }\n ]\n if args.sgd:\n optimizer = torch.optim.SGD(param_dicts, lr=args.lr, momentum=0.9,\n weight_decay=args.weight_decay)\n else:\n optimizer = torch.optim.AdamW(param_dicts, lr=args.lr,\n weight_decay=args.weight_decay)\n lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, args.lr_drop)\n\n if args.distributed:\n model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)\n model_without_ddp = model.module\n\n if args.dataset_file == \"coco_panoptic\":\n # We also evaluate AP during panoptic training, on original coco DS\n coco_val = datasets.coco.build(\"val\", args)\n base_ds = get_coco_api_from_dataset(coco_val)\n else:\n base_ds = get_coco_api_from_dataset(dataset_val)\n\n if args.frozen_weights is not None:\n checkpoint = torch.load(args.frozen_weights, map_location='cpu')\n model_without_ddp.detr.load_state_dict(checkpoint['model'])\n\n if args.pretrained is not None:\n model_without_ddp = load_model(model_without_ddp, args.pretrained)\n\n output_dir = Path(args.output_dir)\n if args.resume:\n if args.resume.startswith('https'):\n checkpoint = torch.hub.load_state_dict_from_url(\n args.resume, map_location='cpu', check_hash=True)\n else:\n checkpoint = torch.load(args.resume, map_location='cpu')\n missing_keys, unexpected_keys = model_without_ddp.load_state_dict(checkpoint['model'], strict=False)\n unexpected_keys = [k for k in unexpected_keys if not (k.endswith('total_params') or k.endswith('total_ops'))]\n if len(missing_keys) > 0:\n print('Missing Keys: {}'.format(missing_keys))\n if len(unexpected_keys) > 0:\n print('Unexpected Keys: {}'.format(unexpected_keys))\n if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:\n import copy\n p_groups = copy.deepcopy(optimizer.param_groups)\n optimizer.load_state_dict(checkpoint['optimizer'])\n for pg, pg_old in zip(optimizer.param_groups, p_groups):\n pg['lr'] = pg_old['lr']\n pg['initial_lr'] = pg_old['initial_lr']\n # print(optimizer.param_groups)\n lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])\n # todo: this is a hack for doing experiment that resume from checkpoint and also modify lr scheduler (e.g., decrease lr in advance).\n args.override_resumed_lr_drop = True\n if args.override_resumed_lr_drop:\n print('Warning: (hack) args.override_resumed_lr_drop is set to True, so args.lr_drop would override lr_drop in resumed lr_scheduler.')\n lr_scheduler.step_size = args.lr_drop\n lr_scheduler.base_lrs = list(map(lambda group: group['initial_lr'], optimizer.param_groups))\n lr_scheduler.step(lr_scheduler.last_epoch)\n args.start_epoch = checkpoint['epoch'] + 1\n \n if args.eval:\n test_stats, coco_evaluator = evaluate(model, criterion, postprocessors,\n data_loader_val, base_ds, device, args.output_dir)\n if args.output_dir:\n utils.save_on_master(coco_evaluator.coco_eval[\"bbox\"].eval, output_dir / \"eval.pth\")\n return\n\n print(\"Start training\")\n start_time = time.time()\n\n train_func = train_one_epoch\n if args.dataset_file in ['e2e_mot', 'e2e_dance', 'mot', 'ori_mot', 'e2e_static_mot', 'e2e_joint']:\n train_func = train_one_epoch_mot\n dataset_train.set_epoch(args.start_epoch)\n dataset_val.set_epoch(args.start_epoch)\n for epoch in range(args.start_epoch, args.epochs):\n if args.distributed:\n sampler_train.set_epoch(epoch)\n train_stats = train_func(\n model, criterion, data_loader_train, optimizer, device, epoch, args.clip_max_norm)\n lr_scheduler.step()\n if args.output_dir:\n checkpoint_paths = [output_dir / 'checkpoint.pth']\n # extra checkpoint before LR drop and every 5 epochs\n if (epoch + 1) % args.lr_drop == 0 or (epoch + 1) % args.save_period == 0 or (((args.epochs >= 100 and (epoch + 1) > 100) or args.epochs < 100) and (epoch + 1) % 5 == 0):\n checkpoint_paths.append(output_dir / f'checkpoint{epoch:04}.pth')\n for checkpoint_path in checkpoint_paths:\n utils.save_on_master({\n 'model': model_without_ddp.state_dict(),\n 'optimizer': optimizer.state_dict(),\n 'lr_scheduler': lr_scheduler.state_dict(),\n 'epoch': epoch,\n 'args': args,\n }, checkpoint_path)\n \n if args.dataset_file not in ['e2e_mot', 'e2e_dance', 'mot', 'ori_mot', 'e2e_static_mot', 'e2e_joint']:\n test_stats, coco_evaluator = evaluate(\n model, criterion, postprocessors, data_loader_val, base_ds, device, args.output_dir\n )\n\n log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},\n **{f'test_{k}': v for k, v in test_stats.items()},\n 'epoch': epoch,\n 'n_parameters': n_parameters}\n\n if args.output_dir and utils.is_main_process():\n with (output_dir / \"log.txt\").open(\"a\") as f:\n f.write(json.dumps(log_stats) + \"\\n\")\n\n # for evaluation logs\n if coco_evaluator is not None:\n (output_dir / 'eval').mkdir(exist_ok=True)\n if \"bbox\" in coco_evaluator.coco_eval:\n filenames = ['latest.pth']\n if epoch % 50 == 0:\n filenames.append(f'{epoch:03}.pth')\n for name in filenames:\n torch.save(coco_evaluator.coco_eval[\"bbox\"].eval,\n output_dir / \"eval\" / name)\n if args.dataset_file in ['e2e_mot', 'e2e_dance', 'mot', 'ori_mot', 'e2e_static_mot', 'e2e_joint']:\n dataset_train.step_epoch()\n dataset_val.step_epoch()\n total_time = time.time() - start_time\n total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n print('Training time {}'.format(total_time_str))\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser('Deformable DETR training and evaluation script', parents=[get_args_parser()])\n args = parser.parse_args()\n if args.output_dir:\n Path(args.output_dir).mkdir(parents=True, exist_ok=True)\n main(args)\n" }, { "path": "models/__init__.py", "content": "# ------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\nfrom .deformable_detr import build as build_deformable_detr\nfrom .motr import build as build_motr\n\n\ndef build_model(args):\n arch_catalog = {\n 'deformable_detr': build_deformable_detr,\n 'motr': build_motr,\n }\n assert args.meta_arch in arch_catalog, 'invalid arch: {}'.format(args.meta_arch)\n build_func = arch_catalog[args.meta_arch]\n return build_func(args)\n\n" }, { "path": "models/backbone.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\n\"\"\"\nBackbone modules.\n\"\"\"\nfrom collections import OrderedDict\nimport torch.nn as nn\nimport torch\nimport torch.nn.functional as F\nimport torchvision\nfrom torch import nn\nfrom torchvision.models._utils import IntermediateLayerGetter\nfrom typing import Dict, List\n\nfrom util.misc import NestedTensor, is_main_process\nfrom .position_encoding import build_position_encoding\n\nclass FrozenBatchNorm2d(torch.nn.Module):\n \"\"\"\n BatchNorm2d where the batch statistics and the affine parameters are fixed.\n\n Copy-paste from torchvision.misc.ops with added eps before rqsrt,\n without which any other models than torchvision.models.resnet[18,34,50,101]\n produce nans.\n \"\"\"\n\n def __init__(self, n, eps=1e-5):\n super(FrozenBatchNorm2d, self).__init__()\n self.register_buffer(\"weight\", torch.ones(n))\n self.register_buffer(\"bias\", torch.zeros(n))\n self.register_buffer(\"running_mean\", torch.zeros(n))\n self.register_buffer(\"running_var\", torch.ones(n))\n self.eps = eps\n\n def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,\n missing_keys, unexpected_keys, error_msgs):\n num_batches_tracked_key = prefix + 'num_batches_tracked'\n if num_batches_tracked_key in state_dict:\n del state_dict[num_batches_tracked_key]\n\n super(FrozenBatchNorm2d, self)._load_from_state_dict(\n state_dict, prefix, local_metadata, strict,\n missing_keys, unexpected_keys, error_msgs)\n\n def forward(self, x):\n # move reshapes to the beginning\n # to make it fuser-friendly\n w = self.weight.reshape(1, -1, 1, 1)\n b = self.bias.reshape(1, -1, 1, 1)\n rv = self.running_var.reshape(1, -1, 1, 1)\n rm = self.running_mean.reshape(1, -1, 1, 1)\n eps = self.eps\n scale = w * (rv + eps).rsqrt()\n bias = b - rm * scale\n return x * scale + bias\n\n\nclass BackboneBase(nn.Module):\n\n def __init__(self, backbone: nn.Module, train_backbone: bool, return_interm_layers: bool):\n super().__init__()\n for name, parameter in backbone.named_parameters():\n if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name:\n parameter.requires_grad_(False)\n \n if return_interm_layers:\n return_layers = {\"layer2\": \"0\", \"layer3\": \"1\", \"layer4\": \"2\"}\n self.strides = [8, 16, 32]\n self.num_channels = [512, 1024, 2048]\n else:\n return_layers = {'layer4': \"0\"}\n self.strides = [32]\n self.num_channels = [2048]\n self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)\n\n def forward(self, tensor_list: NestedTensor):\n xs = self.body(tensor_list.tensors)\n out: Dict[str, NestedTensor] = {}\n for name, x in xs.items():\n m = tensor_list.mask\n assert m is not None\n mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]\n out[name] = NestedTensor(x, mask)\n return out\n\n\nclass Backbone(BackboneBase):\n \"\"\"ResNet backbone with frozen BatchNorm.\"\"\"\n def __init__(self, name: str,\n train_backbone: bool,\n return_interm_layers: bool,\n dilation: bool,):\n norm_layer = FrozenBatchNorm2d\n backbone = getattr(torchvision.models, name)(\n replace_stride_with_dilation=[False, False, dilation],\n pretrained=is_main_process(), norm_layer=norm_layer)\n assert name not in ('resnet18', 'resnet34'), \"number of channels are hard coded\"\n super().__init__(backbone, train_backbone, return_interm_layers)\n if dilation:\n self.strides[-1] = self.strides[-1] // 2\n\n\nclass Joiner(nn.Sequential):\n def __init__(self, backbone, position_embedding):\n super().__init__(backbone, position_embedding)\n self.strides = backbone.strides\n self.num_channels = backbone.num_channels\n\n def forward(self, tensor_list: NestedTensor):\n xs = self[0](tensor_list)\n out: List[NestedTensor] = []\n pos = []\n for name, x in sorted(xs.items()):\n out.append(x)\n\n # position encoding\n for x in out:\n pos.append(self[1](x).to(x.tensors.dtype))\n\n return out, pos\n\n\ndef build_backbone(args):\n position_embedding = build_position_encoding(args)\n train_backbone = args.lr_backbone > 0\n return_interm_layers = args.masks or (args.num_feature_levels > 1)\n backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation)\n model = Joiner(backbone, position_embedding)\n return model\n" }, { "path": "models/deformable_detr.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\n\"\"\"\nDeformable DETR model and criterion classes.\n\"\"\"\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\nimport math\n\nfrom util import box_ops\nfrom util.misc import (NestedTensor, nested_tensor_from_tensor_list,\n accuracy, get_world_size, interpolate,\n is_dist_avail_and_initialized, inverse_sigmoid)\n\nfrom .backbone import build_backbone\nfrom .matcher import build_matcher\nfrom .segmentation import (DETRsegm, PostProcessPanoptic, PostProcessSegm,\n dice_loss, sigmoid_focal_loss)\nfrom .deformable_transformer import build_deforamble_transformer\nimport copy\n\n\ndef _get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\nclass DeformableDETR(nn.Module):\n \"\"\" This is the Deformable DETR module that performs object detection \"\"\"\n def __init__(self, backbone, transformer, num_classes, num_queries, num_feature_levels,\n aux_loss=True, with_box_refine=False, two_stage=False, accurate_ratio=False):\n \"\"\" Initializes the model.\n Parameters:\n backbone: torch module of the backbone to be used. See backbone.py\n transformer: torch module of the transformer architecture. See transformer.py\n num_classes: number of object classes\n num_queries: number of object queries, ie detection slot. This is the maximal number of objects\n DETR can detect in a single image. For COCO, we recommend 100 queries.\n aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.\n with_box_refine: iterative bounding box refinement\n two_stage: two-stage Deformable DETR\n \"\"\"\n super().__init__()\n self.num_queries = num_queries\n self.transformer = transformer\n hidden_dim = transformer.d_model\n self.class_embed = nn.Linear(hidden_dim, num_classes)\n self.bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)\n self.num_feature_levels = num_feature_levels\n self.accurate_ratio = accurate_ratio\n if not two_stage:\n self.query_embed = nn.Embedding(num_queries, hidden_dim*2)\n if num_feature_levels > 1:\n num_backbone_outs = len(backbone.strides)\n input_proj_list = []\n for _ in range(num_backbone_outs):\n in_channels = backbone.num_channels[_]\n input_proj_list.append(nn.Sequential(\n nn.Conv2d(in_channels, hidden_dim, kernel_size=1),\n nn.GroupNorm(32, hidden_dim),\n ))\n for _ in range(num_feature_levels - num_backbone_outs):\n input_proj_list.append(nn.Sequential(\n nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),\n nn.GroupNorm(32, hidden_dim),\n ))\n in_channels = hidden_dim\n self.input_proj = nn.ModuleList(input_proj_list)\n else:\n self.input_proj = nn.ModuleList([\n nn.Sequential(\n nn.Conv2d(backbone.num_channels[0], hidden_dim, kernel_size=1),\n nn.GroupNorm(32, hidden_dim),\n )])\n self.backbone = backbone\n self.aux_loss = aux_loss\n self.with_box_refine = with_box_refine\n self.two_stage = two_stage\n\n prior_prob = 0.01\n bias_value = -math.log((1 - prior_prob) / prior_prob)\n self.class_embed.bias.data = torch.ones(num_classes) * bias_value\n nn.init.constant_(self.bbox_embed.layers[-1].weight.data, 0)\n nn.init.constant_(self.bbox_embed.layers[-1].bias.data, 0)\n for proj in self.input_proj:\n nn.init.xavier_uniform_(proj[0].weight, gain=1)\n nn.init.constant_(proj[0].bias, 0)\n\n # if two-stage, the last class_embed and bbox_embed is for region proposal generation\n num_pred = (transformer.decoder.num_layers + 1) if two_stage else transformer.decoder.num_layers\n if with_box_refine:\n self.class_embed = _get_clones(self.class_embed, num_pred)\n self.bbox_embed = _get_clones(self.bbox_embed, num_pred)\n nn.init.constant_(self.bbox_embed[0].layers[-1].bias.data[2:], -2.0)\n # hack implementation for iterative bounding box refinement\n self.transformer.decoder.bbox_embed = self.bbox_embed\n else:\n nn.init.constant_(self.bbox_embed.layers[-1].bias.data[2:], -2.0)\n self.class_embed = nn.ModuleList([self.class_embed for _ in range(num_pred)])\n self.bbox_embed = nn.ModuleList([self.bbox_embed for _ in range(num_pred)])\n self.transformer.decoder.bbox_embed = None\n if two_stage:\n # hack implementation for two-stage\n self.transformer.decoder.class_embed = self.class_embed\n for box_embed in self.bbox_embed:\n nn.init.constant_(box_embed.layers[-1].bias.data[2:], 0.0)\n\n @staticmethod\n def _get_valid_ratio(mask):\n _, H, W = mask.shape\n valid_H = torch.sum(~mask[:, :, 0], 1)\n valid_W = torch.sum(~mask[:, 0, :], 1)\n valid_ratio_h = valid_H.float() / H\n valid_ratio_w = valid_W.float() / W\n valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)\n return valid_ratio\n\n def forward(self, samples: NestedTensor):\n \"\"\" The forward expects a NestedTensor, which consists of:\n - samples.tensor: batched images, of shape [batch_size x 3 x H x W]\n - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels\n\n It returns a dict with the following elements:\n - \"pred_logits\": the classification logits (including no-object) for all queries.\n Shape= [batch_size x num_queries x (num_classes + 1)]\n - \"pred_boxes\": The normalized boxes coordinates for all queries, represented as\n (center_x, center_y, height, width). These values are normalized in [0, 1],\n relative to the size of each individual image (disregarding possible padding).\n See PostProcess for information on how to retrieve the unnormalized bounding box.\n - \"aux_outputs\": Optional, only returned when auxilary losses are activated. It is a list of\n dictionnaries containing the two above keys for each decoder layer.\n \"\"\"\n if not isinstance(samples, NestedTensor):\n samples = nested_tensor_from_tensor_list(samples)\n\n features, pos = self.backbone(samples)\n\n srcs = []\n masks = []\n for l, feat in enumerate(features):\n src, mask = feat.decompose()\n srcs.append(self.input_proj[l](src))\n masks.append(mask)\n assert mask is not None\n if self.num_feature_levels > len(srcs):\n _len_srcs = len(srcs)\n for l in range(_len_srcs, self.num_feature_levels):\n if l == _len_srcs:\n src = self.input_proj[l](features[-1].tensors)\n else:\n src = self.input_proj[l](srcs[-1])\n m = samples.mask\n mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]\n pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)\n srcs.append(src)\n masks.append(mask)\n pos.append(pos_l)\n\n query_embeds = None\n if not self.two_stage:\n query_embeds = self.query_embed.weight\n valid_ratio = None\n if self.accurate_ratio:\n valid_ratio = self._get_valid_ratio(samples.mask)\n\n hs, init_reference, inter_references, enc_outputs_class, enc_outputs_coord_unact = self.transformer(srcs, masks, pos, query_embeds, valid_ratio=valid_ratio\n )\n\n outputs_classes = []\n outputs_coords = []\n for lvl in range(hs.shape[0]):\n if lvl == 0:\n reference = init_reference\n else:\n reference = inter_references[lvl - 1]\n reference = inverse_sigmoid(reference)\n outputs_class = self.class_embed[lvl](hs[lvl])\n tmp = self.bbox_embed[lvl](hs[lvl])\n if reference.shape[-1] == 4:\n tmp += reference\n else:\n assert reference.shape[-1] == 2\n tmp[..., :2] += reference\n outputs_coord = tmp.sigmoid()\n outputs_classes.append(outputs_class)\n outputs_coords.append(outputs_coord)\n outputs_class = torch.stack(outputs_classes)\n outputs_coord = torch.stack(outputs_coords)\n if not self.two_stage:\n ref_pts = torch.cat([init_reference[None], inter_references[:, :, :, :2]])\n out = {'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord[-1], 'ref_pts': ref_pts, 'logits_all': outputs_class, 'boxes_all': outputs_coord}\n else:\n out = {'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord[-1]}\n if self.aux_loss:\n out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord)\n\n if self.two_stage:\n enc_outputs_coord = enc_outputs_coord_unact.sigmoid()\n out['enc_outputs'] = {'pred_logits': enc_outputs_class, 'pred_boxes': enc_outputs_coord}\n return out\n\n @torch.jit.unused\n def _set_aux_loss(self, outputs_class, outputs_coord):\n # this is a workaround to make torchscript happy, as torchscript\n # doesn't support dictionary with non-homogeneous values, such\n # as a dict having both a Tensor and a list.\n return [{'pred_logits': a, 'pred_boxes': b}\n for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]\n\n\nclass SetCriterion(nn.Module):\n \"\"\" This class computes the loss for DETR.\n The process happens in two steps:\n 1) we compute hungarian assignment between ground truth boxes and the outputs of the model\n 2) we supervise each pair of matched ground-truth / prediction (supervise class and box)\n \"\"\"\n def __init__(self, num_classes, matcher, weight_dict, losses, focal_alpha=0.25):\n \"\"\" Create the criterion.\n Parameters:\n num_classes: number of object categories, omitting the special no-object category\n matcher: module able to compute a matching between targets and proposals\n weight_dict: dict containing as key the names of the losses and as values their relative weight.\n losses: list of all the losses to be applied. See get_loss for list of available losses.\n focal_alpha: alpha in Focal Loss\n \"\"\"\n super().__init__()\n self.num_classes = num_classes\n self.matcher = matcher\n self.weight_dict = weight_dict\n self.losses = losses\n self.focal_alpha = focal_alpha\n\n def loss_labels(self, outputs, targets, indices, num_boxes, log=True):\n \"\"\"Classification loss (NLL)\n targets dicts must contain the key \"labels\" containing a tensor of dim [nb_target_boxes]\n \"\"\"\n assert 'pred_logits' in outputs\n src_logits = outputs['pred_logits']\n\n idx = self._get_src_permutation_idx(indices)\n target_classes_o = torch.cat([t[\"labels\"][J] for t, (_, J) in zip(targets, indices)])\n target_classes = torch.full(src_logits.shape[:2], self.num_classes,\n dtype=torch.int64, device=src_logits.device)\n target_classes[idx] = target_classes_o\n\n target_classes_onehot = torch.zeros([src_logits.shape[0], src_logits.shape[1], src_logits.shape[2] + 1],\n dtype=src_logits.dtype, layout=src_logits.layout, device=src_logits.device)\n target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1)\n\n target_classes_onehot = target_classes_onehot[:,:,:-1]\n loss_ce = sigmoid_focal_loss(src_logits, target_classes_onehot, num_boxes, alpha=self.focal_alpha, gamma=2) * src_logits.shape[1]\n losses = {'loss_ce': loss_ce}\n\n if log:\n # TODO this should probably be a separate loss, not hacked in this one here\n losses['class_error'] = 100 - accuracy(src_logits[idx], target_classes_o)[0]\n return losses\n\n @torch.no_grad()\n def loss_cardinality(self, outputs, targets, indices, num_boxes):\n \"\"\" Compute the cardinality error, ie the absolute error in the number of predicted non-empty boxes\n This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients\n \"\"\"\n pred_logits = outputs['pred_logits']\n device = pred_logits.device\n tgt_lengths = torch.as_tensor([len(v[\"labels\"]) for v in targets], device=device)\n # Count the number of predictions that are NOT \"no-object\" (which is the last class)\n card_pred = (pred_logits.argmax(-1) != pred_logits.shape[-1] - 1).sum(1)\n card_err = F.l1_loss(card_pred.float(), tgt_lengths.float())\n losses = {'cardinality_error': card_err}\n return losses\n\n def loss_boxes(self, outputs, targets, indices, num_boxes):\n \"\"\"Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss\n targets dicts must contain the key \"boxes\" containing a tensor of dim [nb_target_boxes, 4]\n The target boxes are expected in format (center_x, center_y, h, w), normalized by the image size.\n \"\"\"\n assert 'pred_boxes' in outputs\n idx = self._get_src_permutation_idx(indices)\n src_boxes = outputs['pred_boxes'][idx]\n target_boxes = torch.cat([t['boxes'][i] for t, (_, i) in zip(targets, indices)], dim=0)\n\n loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction='none')\n\n losses = {}\n losses['loss_bbox'] = loss_bbox.sum() / num_boxes\n\n loss_giou = 1 - torch.diag(box_ops.generalized_box_iou(\n box_ops.box_cxcywh_to_xyxy(src_boxes),\n box_ops.box_cxcywh_to_xyxy(target_boxes)))\n losses['loss_giou'] = loss_giou.sum() / num_boxes\n return losses\n\n def loss_masks(self, outputs, targets, indices, num_boxes):\n \"\"\"Compute the losses related to the masks: the focal loss and the dice loss.\n targets dicts must contain the key \"masks\" containing a tensor of dim [nb_target_boxes, h, w]\n \"\"\"\n assert \"pred_masks\" in outputs\n\n src_idx = self._get_src_permutation_idx(indices)\n tgt_idx = self._get_tgt_permutation_idx(indices)\n\n src_masks = outputs[\"pred_masks\"]\n\n # TODO use valid to mask invalid areas due to padding in loss\n target_masks, valid = nested_tensor_from_tensor_list([t[\"masks\"] for t in targets]).decompose()\n target_masks = target_masks.to(src_masks)\n\n src_masks = src_masks[src_idx]\n # upsample predictions to the target size\n src_masks = interpolate(src_masks[:, None], size=target_masks.shape[-2:],\n mode=\"bilinear\", align_corners=False)\n src_masks = src_masks[:, 0].flatten(1)\n\n target_masks = target_masks[tgt_idx].flatten(1)\n\n losses = {\n \"loss_mask\": sigmoid_focal_loss(src_masks, target_masks, num_boxes),\n \"loss_dice\": dice_loss(src_masks, target_masks, num_boxes),\n }\n return losses\n\n def _get_src_permutation_idx(self, indices):\n # permute predictions following indices\n batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])\n src_idx = torch.cat([src for (src, _) in indices])\n return batch_idx, src_idx\n\n def _get_tgt_permutation_idx(self, indices):\n # permute targets following indices\n batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])\n tgt_idx = torch.cat([tgt for (_, tgt) in indices])\n return batch_idx, tgt_idx\n\n def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs):\n loss_map = {\n 'labels': self.loss_labels,\n 'cardinality': self.loss_cardinality,\n 'boxes': self.loss_boxes,\n 'masks': self.loss_masks\n }\n assert loss in loss_map, f'do you really want to compute {loss} loss?'\n return loss_map[loss](outputs, targets, indices, num_boxes, **kwargs)\n\n def forward(self, outputs, targets):\n \"\"\" This performs the loss computation.\n Parameters:\n outputs: dict of tensors, see the output specification of the model for the format\n targets: list of dicts, such that len(targets) == batch_size.\n The expected keys in each dict depends on the losses applied, see each loss' doc\n \"\"\"\n outputs_without_aux = {k: v for k, v in outputs.items() if k != 'aux_outputs' and k != 'enc_outputs'}\n\n # Retrieve the matching between the outputs of the last layer and the targets\n indices = self.matcher(outputs_without_aux, targets)\n\n # Compute the average number of target boxes accross all nodes, for normalization purposes\n num_boxes = sum(len(t[\"labels\"]) for t in targets)\n num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)\n if is_dist_avail_and_initialized():\n torch.distributed.all_reduce(num_boxes)\n num_boxes = torch.clamp(num_boxes / get_world_size(), min=1).item()\n\n # Compute all the requested losses\n losses = {}\n for loss in self.losses:\n kwargs = {}\n losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes, **kwargs))\n\n # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.\n if 'aux_outputs' in outputs:\n for i, aux_outputs in enumerate(outputs['aux_outputs']):\n indices = self.matcher(aux_outputs, targets)\n for loss in self.losses:\n if loss == 'masks':\n # Intermediate masks losses are too costly to compute, we ignore them.\n continue\n kwargs = {}\n if loss == 'labels':\n # Logging is enabled only for the last layer\n kwargs['log'] = False\n l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_boxes, **kwargs)\n l_dict = {k + f'_{i}': v for k, v in l_dict.items()}\n losses.update(l_dict)\n\n if 'enc_outputs' in outputs:\n enc_outputs = outputs['enc_outputs']\n bin_targets = copy.deepcopy(targets)\n for bt in bin_targets:\n bt['labels'] = torch.zeros_like(bt['labels'])\n indices = self.matcher(enc_outputs, bin_targets)\n for loss in self.losses:\n if loss == 'masks':\n # Intermediate masks losses are too costly to compute, we ignore them.\n continue\n kwargs = {}\n if loss == 'labels':\n # Logging is enabled only for the last layer\n kwargs['log'] = False\n l_dict = self.get_loss(loss, enc_outputs, bin_targets, indices, num_boxes, **kwargs)\n l_dict = {k + f'_enc': v for k, v in l_dict.items()}\n losses.update(l_dict)\n\n return losses\n\n\nclass PostProcess(nn.Module):\n \"\"\" This module converts the model's output into the format expected by the coco api\"\"\"\n\n @torch.no_grad()\n def forward(self, outputs, target_sizes):\n \"\"\" Perform the computation\n Parameters:\n outputs: raw outputs of the model\n target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch\n For evaluation, this must be the original image size (before any data augmentation)\n For visualization, this should be the image size after data augment, but before padding\n \"\"\"\n out_logits, out_bbox = outputs['pred_logits'], outputs['pred_boxes']\n\n assert len(out_logits) == len(target_sizes)\n assert target_sizes.shape[1] == 2\n\n prob = out_logits.sigmoid()\n topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 100, dim=1)\n scores = topk_values\n topk_boxes = topk_indexes // out_logits.shape[2]\n labels = topk_indexes % out_logits.shape[2]\n boxes = box_ops.box_cxcywh_to_xyxy(out_bbox)\n boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1,1,4))\n\n # and from relative [0, 1] to absolute [0, height] coordinates\n img_h, img_w = target_sizes.unbind(1)\n scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)\n boxes = boxes * scale_fct[:, None, :]\n\n results = [{'scores': s, 'labels': l, 'boxes': b} for s, l, b in zip(scores, labels, boxes)]\n\n return results\n\n\nclass MLP(nn.Module):\n \"\"\" Very simple multi-layer perceptron (also called FFN)\"\"\"\n\n def __init__(self, input_dim, hidden_dim, output_dim, num_layers):\n super().__init__()\n self.num_layers = num_layers\n h = [hidden_dim] * (num_layers - 1)\n self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))\n\n def forward(self, x):\n for i, layer in enumerate(self.layers):\n x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n return x\n\n\ndef build(args):\n num_classes = 20 if args.dataset_file != 'coco' else 91\n if args.dataset_file == \"coco_panoptic\":\n num_classes = 250\n if args.dataset_file == 'mot' or args.dataset_file == 'mot_ori':\n num_classes = 1\n if args.dataset_file == 'e2e_mot':\n num_classes = 1\n if args.dataset_file == 'e2e_dance':\n num_classes = 1\n device = torch.device(args.device)\n\n backbone = build_backbone(args)\n transformer = build_deforamble_transformer(args)\n model = DeformableDETR(\n backbone,\n transformer,\n num_classes=num_classes,\n num_queries=args.num_queries,\n num_feature_levels=args.num_feature_levels,\n aux_loss=args.aux_loss,\n with_box_refine=args.with_box_refine,\n two_stage=args.two_stage,\n accurate_ratio=args.accurate_ratio,\n )\n if args.masks:\n model = DETRsegm(model, freeze_detr=(args.frozen_weights is not None))\n matcher = build_matcher(args)\n weight_dict = {'loss_ce': args.cls_loss_coef, 'loss_bbox': args.bbox_loss_coef}\n weight_dict['loss_giou'] = args.giou_loss_coef\n if args.masks:\n weight_dict[\"loss_mask\"] = args.mask_loss_coef\n weight_dict[\"loss_dice\"] = args.dice_loss_coef\n # TODO this is a hack\n if args.aux_loss:\n aux_weight_dict = {}\n for i in range(args.dec_layers - 1):\n aux_weight_dict.update({k + f'_{i}': v for k, v in weight_dict.items()})\n aux_weight_dict.update({k + f'_enc': v for k, v in weight_dict.items()})\n weight_dict.update(aux_weight_dict)\n\n losses = ['labels', 'boxes', 'cardinality']\n if args.masks:\n losses += [\"masks\"]\n # num_classes, matcher, weight_dict, losses, focal_alpha=0.25\n if args.mix_match:\n criterion = MixSetCriterion(num_classes, matcher, weight_dict, losses, focal_alpha=args.focal_alpha)\n else:\n criterion = SetCriterion(num_classes, matcher, weight_dict, losses, focal_alpha=args.focal_alpha)\n criterion.to(device)\n postprocessors = {'bbox': PostProcess()}\n if args.masks:\n postprocessors['segm'] = PostProcessSegm()\n if args.dataset_file == \"coco_panoptic\":\n is_thing_map = {i: i <= 90 for i in range(201)}\n postprocessors[\"panoptic\"] = PostProcessPanoptic(is_thing_map, threshold=0.85)\n\n return model, criterion, postprocessors\n" }, { "path": "models/deformable_transformer.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\nimport copy\nfrom typing import Optional, List\nimport math\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, Tensor\nfrom torch.nn.init import xavier_uniform_, constant_, uniform_, normal_\n\nfrom models.structures import Boxes, matched_boxlist_iou, pairwise_iou\n\nfrom util.misc import inverse_sigmoid\nfrom util.box_ops import box_cxcywh_to_xyxy\nfrom models.ops.modules import MSDeformAttn\n\n\nclass DeformableTransformer(nn.Module):\n def __init__(self, d_model=256, nhead=8,\n num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=1024, dropout=0.1,\n activation=\"relu\", return_intermediate_dec=False,\n num_feature_levels=4, dec_n_points=4, enc_n_points=4,\n two_stage=False, two_stage_num_proposals=300, decoder_self_cross=True, sigmoid_attn=False,\n local_self_attn=False, new_frame_adaptor=False):\n super().__init__()\n\n self.new_frame_adaptor = None\n self.d_model = d_model\n self.nhead = nhead\n self.two_stage = two_stage\n self.two_stage_num_proposals = two_stage_num_proposals\n\n encoder_layer = DeformableTransformerEncoderLayer(d_model, dim_feedforward,\n dropout, activation,\n num_feature_levels, nhead, enc_n_points,\n sigmoid_attn=sigmoid_attn)\n self.encoder = DeformableTransformerEncoder(encoder_layer, num_encoder_layers)\n\n decoder_layer = DeformableTransformerDecoderLayer(d_model, dim_feedforward,\n dropout, activation,\n num_feature_levels, nhead, dec_n_points, decoder_self_cross,\n sigmoid_attn=sigmoid_attn, local_self_attn=local_self_attn)\n self.decoder = DeformableTransformerDecoder(decoder_layer, num_decoder_layers, return_intermediate_dec)\n\n self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))\n\n if two_stage:\n self.enc_output = nn.Linear(d_model, d_model)\n self.enc_output_norm = nn.LayerNorm(d_model)\n self.pos_trans = nn.Linear(d_model * 2, d_model * 2)\n self.pos_trans_norm = nn.LayerNorm(d_model * 2)\n else:\n self.reference_points = nn.Linear(d_model, 2)\n\n self._reset_parameters()\n\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n for m in self.modules():\n if isinstance(m, MSDeformAttn):\n m._reset_parameters()\n if not self.two_stage:\n xavier_uniform_(self.reference_points.weight.data, gain=1.0)\n constant_(self.reference_points.bias.data, 0.)\n normal_(self.level_embed)\n\n def get_proposal_pos_embed(self, proposals):\n num_pos_feats = 128\n temperature = 10000\n scale = 2 * math.pi\n\n dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device)\n dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)\n # N, L, 4\n proposals = proposals.sigmoid() * scale\n # N, L, 4, 128\n pos = proposals[:, :, :, None] / dim_t\n # N, L, 4, 64, 2\n pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2)\n return pos\n\n def gen_encoder_output_proposals(self, memory, memory_padding_mask, spatial_shapes):\n N_, S_, C_ = memory.shape\n base_scale = 4.0\n proposals = []\n _cur = 0\n for lvl, (H_, W_) in enumerate(spatial_shapes):\n mask_flatten_ = memory_padding_mask[:, _cur:(_cur + H_ * W_)].view(N_, H_, W_, 1)\n valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)\n valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)\n\n grid_y, grid_x = torch.meshgrid(torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),\n torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device))\n grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)\n\n scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)\n grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale\n wh = torch.ones_like(grid) * 0.05 * (2.0 ** lvl)\n proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)\n proposals.append(proposal)\n _cur += (H_ * W_)\n output_proposals = torch.cat(proposals, 1)\n output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)\n output_proposals = torch.log(output_proposals / (1 - output_proposals))\n output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))\n output_proposals = output_proposals.masked_fill(~output_proposals_valid, float('inf'))\n\n output_memory = memory\n output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))\n output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))\n output_memory = self.enc_output_norm(self.enc_output(output_memory))\n return output_memory, output_proposals\n\n def get_valid_ratio(self, mask):\n _, H, W = mask.shape\n valid_H = torch.sum(~mask[:, :, 0], 1)\n valid_W = torch.sum(~mask[:, 0, :], 1)\n valid_ratio_h = valid_H.float() / H\n valid_ratio_w = valid_W.float() / W\n valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)\n return valid_ratio\n\n def forward(self, srcs, masks, pos_embeds, query_embed=None, ref_pts=None, valid_ratio=None):\n assert self.two_stage or query_embed is not None\n\n # prepare input for encoder\n src_flatten = []\n mask_flatten = []\n lvl_pos_embed_flatten = []\n spatial_shapes = []\n for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):\n bs, c, h, w = src.shape\n spatial_shape = (h, w)\n spatial_shapes.append(spatial_shape)\n src = src.flatten(2).transpose(1, 2)\n mask = mask.flatten(1)\n pos_embed = pos_embed.flatten(2).transpose(1, 2)\n lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)\n lvl_pos_embed_flatten.append(lvl_pos_embed)\n src_flatten.append(src)\n mask_flatten.append(mask)\n src_flatten = torch.cat(src_flatten, 1)\n mask_flatten = torch.cat(mask_flatten, 1)\n lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)\n spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)\n level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))\n if valid_ratio is None:\n valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)\n else:\n valid_ratios = valid_ratio.unsqueeze(0).repeat(len(masks), 1, 1)\n\n # encoder\n memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten)\n # prepare input for decoder\n bs, _, c = memory.shape\n if self.two_stage:\n output_memory, output_proposals = self.gen_encoder_output_proposals(memory, mask_flatten, spatial_shapes)\n\n # hack implementation for two-stage Deformable DETR\n enc_outputs_class = self.decoder.class_embed[self.decoder.num_layers](output_memory)\n enc_outputs_coord_unact = self.decoder.bbox_embed[self.decoder.num_layers](output_memory) + output_proposals\n\n topk = self.two_stage_num_proposals\n topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1]\n topk_coords_unact = torch.gather(enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4))\n topk_coords_unact = topk_coords_unact.detach()\n reference_points = topk_coords_unact.sigmoid()\n init_reference_out = reference_points\n pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact)))\n query_embed, tgt = torch.split(pos_trans_out, c, dim=2)\n else:\n query_embed, tgt = torch.split(query_embed, c, dim=1)\n query_embed = query_embed.unsqueeze(0).expand(bs, -1, -1)\n tgt = tgt.unsqueeze(0).expand(bs, -1, -1)\n if ref_pts is None:\n reference_points = self.reference_points(query_embed).sigmoid()\n else:\n reference_points = ref_pts.unsqueeze(0).repeat(bs, 1, 1).sigmoid()\n init_reference_out = reference_points\n # decoder\n hs, inter_references = self.decoder(tgt, reference_points, memory,\n spatial_shapes, level_start_index, valid_ratios, query_embed, mask_flatten)\n\n inter_references_out = inter_references\n if self.two_stage:\n return hs, init_reference_out, inter_references_out, enc_outputs_class, enc_outputs_coord_unact\n return hs, init_reference_out, inter_references_out, None, None\n\n\nclass DeformableTransformerEncoderLayer(nn.Module):\n def __init__(self,\n d_model=256, d_ffn=1024,\n dropout=0.1, activation=\"relu\",\n n_levels=4, n_heads=8, n_points=4, sigmoid_attn=False):\n super().__init__()\n\n # self attention\n self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points, sigmoid_attn=sigmoid_attn)\n self.dropout1 = nn.Dropout(dropout)\n self.norm1 = nn.LayerNorm(d_model)\n\n # ffn\n self.linear1 = nn.Linear(d_model, d_ffn)\n self.activation = _get_activation_fn(activation)\n self.dropout2 = nn.Dropout(dropout)\n self.linear2 = nn.Linear(d_ffn, d_model)\n self.dropout3 = nn.Dropout(dropout)\n self.norm2 = nn.LayerNorm(d_model)\n\n @staticmethod\n def with_pos_embed(tensor, pos):\n return tensor if pos is None else tensor + pos\n\n def forward_ffn(self, src):\n src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))\n src = src + self.dropout3(src2)\n src = self.norm2(src)\n return src\n\n def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):\n # self attention\n src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)\n src = src + self.dropout1(src2)\n src = self.norm1(src)\n\n # ffn\n src = self.forward_ffn(src)\n\n return src\n\n\nclass DeformableTransformerEncoder(nn.Module):\n def __init__(self, encoder_layer, num_layers):\n super().__init__()\n self.layers = _get_clones(encoder_layer, num_layers)\n self.num_layers = num_layers\n\n @staticmethod\n def get_reference_points(spatial_shapes, valid_ratios, device):\n reference_points_list = []\n for lvl, (H_, W_) in enumerate(spatial_shapes):\n\n ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),\n torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))\n ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)\n ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)\n ref = torch.stack((ref_x, ref_y), -1)\n reference_points_list.append(ref)\n reference_points = torch.cat(reference_points_list, 1)\n reference_points = reference_points[:, :, None] * valid_ratios[:, None]\n return reference_points\n\n def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):\n output = src\n reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)\n for _, layer in enumerate(self.layers):\n output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)\n\n return output\n\n\nclass DeformableTransformerDecoderLayer(nn.Module):\n def __init__(self, d_model=256, d_ffn=1024,\n dropout=0.1, activation=\"relu\",\n n_levels=4, n_heads=8, n_points=4, self_cross=True, sigmoid_attn=False, local_self_attn=False):\n super().__init__()\n\n self.self_cross = self_cross\n self.local_self_attn = local_self_attn\n self.num_head = n_heads\n # cross attention\n self.cross_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points, sigmoid_attn=sigmoid_attn)\n self.dropout1 = nn.Dropout(dropout)\n self.norm1 = nn.LayerNorm(d_model)\n\n # self attention\n self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)\n self.dropout2 = nn.Dropout(dropout)\n self.norm2 = nn.LayerNorm(d_model)\n\n # ffn\n self.linear1 = nn.Linear(d_model, d_ffn)\n self.activation = _get_activation_fn(activation)\n self.dropout3 = nn.Dropout(dropout)\n self.linear2 = nn.Linear(d_ffn, d_model)\n self.dropout4 = nn.Dropout(dropout)\n self.norm3 = nn.LayerNorm(d_model)\n\n @staticmethod\n def with_pos_embed(tensor, pos):\n return tensor if pos is None else tensor + pos\n\n def forward_ffn(self, tgt):\n tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout4(tgt2)\n tgt = self.norm3(tgt)\n return tgt\n\n def _forward_self_attn(self, tgt, query_pos, attn_mask=None):\n q = k = self.with_pos_embed(tgt, query_pos)\n if attn_mask is not None:\n tgt2 = self.self_attn(q.transpose(0, 1), k.transpose(0, 1), tgt.transpose(0, 1),\n attn_mask=attn_mask)[0].transpose(0, 1)\n else:\n tgt2 = self.self_attn(q.transpose(0, 1), k.transpose(0, 1), tgt.transpose(0, 1))[0].transpose(0, 1)\n tgt = tgt + self.dropout2(tgt2)\n return self.norm2(tgt)\n\n def _forward_self_cross(self, tgt, query_pos, reference_points, src, src_spatial_shapes, level_start_index,\n src_padding_mask=None, attn_mask=None):\n\n # self attention\n tgt = self._forward_self_attn(tgt, query_pos, attn_mask)\n # cross attention\n tgt2 = self.cross_attn(self.with_pos_embed(tgt, query_pos),\n reference_points,\n src, src_spatial_shapes, level_start_index, src_padding_mask)\n tgt = tgt + self.dropout1(tgt2)\n tgt = self.norm1(tgt)\n\n # ffn\n tgt = self.forward_ffn(tgt)\n\n return tgt\n\n def _forward_cross_self(self, tgt, query_pos, reference_points, src, src_spatial_shapes, level_start_index,\n src_padding_mask=None, attn_mask=None):\n # cross attention\n tgt2 = self.cross_attn(self.with_pos_embed(tgt, query_pos),\n reference_points,\n src, src_spatial_shapes, level_start_index, src_padding_mask)\n tgt = tgt + self.dropout1(tgt2)\n tgt = self.norm1(tgt)\n # self attention\n tgt = self._forward_self_attn(tgt, query_pos, attn_mask)\n # ffn\n tgt = self.forward_ffn(tgt)\n\n return tgt\n\n def forward(self, tgt, query_pos, reference_points, src, src_spatial_shapes, level_start_index, src_padding_mask=None):\n attn_mask = None\n if self.self_cross:\n return self._forward_self_cross(tgt, query_pos, reference_points, src, src_spatial_shapes,\n level_start_index, src_padding_mask, attn_mask)\n return self._forward_cross_self(tgt, query_pos, reference_points, src, src_spatial_shapes, level_start_index,\n src_padding_mask, attn_mask)\n\n\nclass DeformableTransformerDecoder(nn.Module):\n def __init__(self, decoder_layer, num_layers, return_intermediate=False):\n super().__init__()\n self.layers = _get_clones(decoder_layer, num_layers)\n self.num_layers = num_layers\n self.return_intermediate = return_intermediate\n # hack implementation for iterative bounding box refinement and two-stage Deformable DETR\n self.bbox_embed = None\n self.class_embed = None\n\n def forward(self, tgt, reference_points, src, src_spatial_shapes, src_level_start_index, src_valid_ratios,\n query_pos=None, src_padding_mask=None):\n output = tgt\n\n intermediate = []\n intermediate_reference_points = []\n for lid, layer in enumerate(self.layers):\n if reference_points.shape[-1] == 4:\n reference_points_input = reference_points[:, :, None] \\\n * torch.cat([src_valid_ratios, src_valid_ratios], -1)[:, None]\n else:\n assert reference_points.shape[-1] == 2\n reference_points_input = reference_points[:, :, None] * src_valid_ratios[:, None]\n output = layer(output, query_pos, reference_points_input, src, src_spatial_shapes, src_level_start_index, src_padding_mask)\n\n # hack implementation for iterative bounding box refinement\n if self.bbox_embed is not None:\n tmp = self.bbox_embed[lid](output)\n if reference_points.shape[-1] == 4:\n new_reference_points = tmp + inverse_sigmoid(reference_points)\n new_reference_points = new_reference_points.sigmoid()\n else:\n assert reference_points.shape[-1] == 2\n new_reference_points = tmp\n new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points)\n new_reference_points = new_reference_points.sigmoid()\n reference_points = new_reference_points.detach()\n\n if self.return_intermediate:\n intermediate.append(output)\n intermediate_reference_points.append(reference_points)\n\n if self.return_intermediate:\n return torch.stack(intermediate), torch.stack(intermediate_reference_points)\n\n return output, reference_points\n\n\ndef _get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(F\"activation should be relu/gelu, not {activation}.\")\n\n\ndef build_deforamble_transformer(args):\n return DeformableTransformer(\n d_model=args.hidden_dim,\n nhead=args.nheads,\n num_encoder_layers=args.enc_layers,\n num_decoder_layers=args.dec_layers,\n dim_feedforward=args.dim_feedforward,\n dropout=args.dropout,\n activation=\"relu\",\n return_intermediate_dec=True,\n num_feature_levels=args.num_feature_levels,\n dec_n_points=args.dec_n_points,\n enc_n_points=args.enc_n_points,\n two_stage=args.two_stage,\n two_stage_num_proposals=args.num_queries,\n decoder_self_cross=not args.decoder_cross_self,\n sigmoid_attn=args.sigmoid_attn,\n local_self_attn=args.local_self_attn,\n new_frame_adaptor=args.new_frame_adaptor,\n )\n\n\n" }, { "path": "models/deformable_transformer_plus.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\nimport copy\nfrom typing import Optional, List\nimport math\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, Tensor\nfrom torch.nn.init import xavier_uniform_, constant_, uniform_, normal_\n\nfrom models.structures import Boxes, matched_boxlist_iou, pairwise_iou\n\nfrom util.misc import inverse_sigmoid\nfrom util.box_ops import box_cxcywh_to_xyxy\nfrom models.ops.modules import MSDeformAttn\n\n\nclass DeformableTransformer(nn.Module):\n def __init__(self, d_model=256, nhead=8,\n num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=1024, dropout=0.1,\n activation=\"relu\", return_intermediate_dec=False,\n num_feature_levels=4, dec_n_points=4, enc_n_points=4,\n two_stage=False, two_stage_num_proposals=300, decoder_self_cross=True, sigmoid_attn=False,\n extra_track_attn=False):\n super().__init__()\n\n self.new_frame_adaptor = None\n self.d_model = d_model\n self.nhead = nhead\n self.two_stage = two_stage\n self.two_stage_num_proposals = two_stage_num_proposals\n\n encoder_layer = DeformableTransformerEncoderLayer(d_model, dim_feedforward,\n dropout, activation,\n num_feature_levels, nhead, enc_n_points,\n sigmoid_attn=sigmoid_attn)\n self.encoder = DeformableTransformerEncoder(encoder_layer, num_encoder_layers)\n\n decoder_layer = DeformableTransformerDecoderLayer(d_model, dim_feedforward,\n dropout, activation,\n num_feature_levels, nhead, dec_n_points, decoder_self_cross,\n sigmoid_attn=sigmoid_attn, extra_track_attn=extra_track_attn)\n self.decoder = DeformableTransformerDecoder(decoder_layer, num_decoder_layers, return_intermediate_dec)\n\n self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))\n\n if two_stage:\n self.enc_output = nn.Linear(d_model, d_model)\n self.enc_output_norm = nn.LayerNorm(d_model)\n self.pos_trans = nn.Linear(d_model * 2, d_model * 2)\n self.pos_trans_norm = nn.LayerNorm(d_model * 2)\n else:\n self.reference_points = nn.Linear(d_model, 2)\n\n self._reset_parameters()\n\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n for m in self.modules():\n if isinstance(m, MSDeformAttn):\n m._reset_parameters()\n if not self.two_stage:\n xavier_uniform_(self.reference_points.weight.data, gain=1.0)\n constant_(self.reference_points.bias.data, 0.)\n normal_(self.level_embed)\n\n def get_proposal_pos_embed(self, proposals):\n num_pos_feats = 128\n temperature = 10000\n scale = 2 * math.pi\n\n dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device)\n dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)\n # N, L, 4\n proposals = proposals.sigmoid() * scale\n # N, L, 4, 128\n pos = proposals[:, :, :, None] / dim_t\n # N, L, 4, 64, 2\n pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2)\n return pos\n\n def gen_encoder_output_proposals(self, memory, memory_padding_mask, spatial_shapes):\n N_, S_, C_ = memory.shape\n base_scale = 4.0\n proposals = []\n _cur = 0\n for lvl, (H_, W_) in enumerate(spatial_shapes):\n mask_flatten_ = memory_padding_mask[:, _cur:(_cur + H_ * W_)].view(N_, H_, W_, 1)\n valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)\n valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)\n\n grid_y, grid_x = torch.meshgrid(torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),\n torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device))\n grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)\n\n scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)\n grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale\n wh = torch.ones_like(grid) * 0.05 * (2.0 ** lvl)\n proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)\n proposals.append(proposal)\n _cur += (H_ * W_)\n output_proposals = torch.cat(proposals, 1)\n output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)\n output_proposals = torch.log(output_proposals / (1 - output_proposals))\n output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))\n output_proposals = output_proposals.masked_fill(~output_proposals_valid, float('inf'))\n\n output_memory = memory\n output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))\n output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))\n output_memory = self.enc_output_norm(self.enc_output(output_memory))\n return output_memory, output_proposals\n\n def get_valid_ratio(self, mask):\n _, H, W = mask.shape\n valid_H = torch.sum(~mask[:, :, 0], 1)\n valid_W = torch.sum(~mask[:, 0, :], 1)\n valid_ratio_h = valid_H.float() / H\n valid_ratio_w = valid_W.float() / W\n valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)\n return valid_ratio\n\n def forward(self, srcs, masks, pos_embeds, query_embed=None, ref_pts=None):\n assert self.two_stage or query_embed is not None\n\n # prepare input for encoder\n src_flatten = []\n mask_flatten = []\n lvl_pos_embed_flatten = []\n spatial_shapes = []\n for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):\n bs, c, h, w = src.shape\n spatial_shape = (h, w)\n spatial_shapes.append(spatial_shape)\n src = src.flatten(2).transpose(1, 2)\n mask = mask.flatten(1)\n pos_embed = pos_embed.flatten(2).transpose(1, 2)\n lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)\n lvl_pos_embed_flatten.append(lvl_pos_embed)\n src_flatten.append(src)\n mask_flatten.append(mask)\n src_flatten = torch.cat(src_flatten, 1)\n mask_flatten = torch.cat(mask_flatten, 1)\n lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)\n spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)\n level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))\n valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)\n\n # encoder\n memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten)\n # prepare input for decoder\n bs, _, c = memory.shape\n if self.two_stage:\n output_memory, output_proposals = self.gen_encoder_output_proposals(memory, mask_flatten, spatial_shapes)\n\n # hack implementation for two-stage Deformable DETR\n enc_outputs_class = self.decoder.class_embed[self.decoder.num_layers](output_memory)\n enc_outputs_coord_unact = self.decoder.bbox_embed[self.decoder.num_layers](output_memory) + output_proposals\n\n topk = self.two_stage_num_proposals\n topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1]\n topk_coords_unact = torch.gather(enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4))\n topk_coords_unact = topk_coords_unact.detach()\n reference_points = topk_coords_unact.sigmoid()\n init_reference_out = reference_points\n pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact)))\n query_embed, tgt = torch.split(pos_trans_out, c, dim=2)\n else:\n query_embed, tgt = torch.split(query_embed, c, dim=1)\n query_embed = query_embed.unsqueeze(0).expand(bs, -1, -1)\n tgt = tgt.unsqueeze(0).expand(bs, -1, -1)\n \n if ref_pts is None:\n reference_points = self.reference_points(query_embed).sigmoid()\n else:\n reference_points = ref_pts.unsqueeze(0).repeat(bs, 1, 1).sigmoid()\n init_reference_out = reference_points\n # decoder\n hs, inter_references = self.decoder(tgt, reference_points, memory,\n spatial_shapes, level_start_index, valid_ratios, query_embed, mask_flatten)\n\n inter_references_out = inter_references\n if self.two_stage:\n return hs, init_reference_out, inter_references_out, enc_outputs_class, enc_outputs_coord_unact\n return hs, init_reference_out, inter_references_out, None, None\n\n\nclass DeformableTransformerEncoderLayer(nn.Module):\n def __init__(self,\n d_model=256, d_ffn=1024,\n dropout=0.1, activation=\"relu\",\n n_levels=4, n_heads=8, n_points=4, sigmoid_attn=False):\n super().__init__()\n\n # self attention\n self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points, sigmoid_attn=sigmoid_attn)\n self.dropout1 = nn.Dropout(dropout)\n self.norm1 = nn.LayerNorm(d_model)\n\n # ffn\n self.linear1 = nn.Linear(d_model, d_ffn)\n self.activation = _get_activation_fn(activation)\n self.dropout2 = nn.Dropout(dropout)\n self.linear2 = nn.Linear(d_ffn, d_model)\n self.dropout3 = nn.Dropout(dropout)\n self.norm2 = nn.LayerNorm(d_model)\n\n @staticmethod\n def with_pos_embed(tensor, pos):\n return tensor if pos is None else tensor + pos\n\n def forward_ffn(self, src):\n src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))\n src = src + self.dropout3(src2)\n src = self.norm2(src)\n return src\n\n def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):\n # self attention\n src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)\n src = src + self.dropout1(src2)\n src = self.norm1(src)\n\n # ffn\n src = self.forward_ffn(src)\n return src\n\n\nclass DeformableTransformerEncoder(nn.Module):\n def __init__(self, encoder_layer, num_layers):\n super().__init__()\n self.layers = _get_clones(encoder_layer, num_layers)\n self.num_layers = num_layers\n\n @staticmethod\n def get_reference_points(spatial_shapes, valid_ratios, device):\n reference_points_list = []\n for lvl, (H_, W_) in enumerate(spatial_shapes):\n\n ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),\n torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))\n ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)\n ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)\n ref = torch.stack((ref_x, ref_y), -1)\n reference_points_list.append(ref)\n reference_points = torch.cat(reference_points_list, 1)\n reference_points = reference_points[:, :, None] * valid_ratios[:, None]\n return reference_points\n\n def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):\n output = src\n reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)\n for _, layer in enumerate(self.layers):\n output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)\n\n return output\n\n\nclass DeformableTransformerDecoderLayer(nn.Module):\n def __init__(self, d_model=256, d_ffn=1024,\n dropout=0.1, activation=\"relu\",\n n_levels=4, n_heads=8, n_points=4, self_cross=True, sigmoid_attn=False, extra_track_attn=False):\n super().__init__()\n\n self.self_cross = self_cross\n self.num_head = n_heads\n\n # cross attention\n self.cross_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points, sigmoid_attn=sigmoid_attn)\n self.dropout1 = nn.Dropout(dropout)\n self.norm1 = nn.LayerNorm(d_model)\n\n self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)\n self.dropout2 = nn.Dropout(dropout)\n self.norm2 = nn.LayerNorm(d_model)\n\n # ffn\n self.linear1 = nn.Linear(d_model, d_ffn)\n self.activation = _get_activation_fn(activation)\n self.dropout3 = nn.Dropout(dropout)\n self.linear2 = nn.Linear(d_ffn, d_model)\n self.dropout4 = nn.Dropout(dropout)\n self.norm3 = nn.LayerNorm(d_model)\n\n # update track query_embed\n self.extra_track_attn = extra_track_attn\n if self.extra_track_attn:\n print('Training with Extra Self Attention in Every Decoder.', flush=True)\n self.update_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)\n self.dropout5 = nn.Dropout(dropout)\n self.norm4 = nn.LayerNorm(d_model)\n\n if self_cross:\n print('Training with Self-Cross Attention.')\n else:\n print('Training with Cross-Self Attention.')\n\n @staticmethod\n def with_pos_embed(tensor, pos):\n return tensor if pos is None else tensor + pos\n\n def forward_ffn(self, tgt):\n tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout4(tgt2)\n tgt = self.norm3(tgt)\n return tgt\n\n def _forward_self_attn(self, tgt, query_pos, attn_mask=None):\n if self.extra_track_attn:\n tgt = self._forward_track_attn(tgt, query_pos)\n\n q = k = self.with_pos_embed(tgt, query_pos)\n if attn_mask is not None:\n tgt2 = self.self_attn(q.transpose(0, 1), k.transpose(0, 1), tgt.transpose(0, 1),\n attn_mask=attn_mask)[0].transpose(0, 1)\n else:\n tgt2 = self.self_attn(q.transpose(0, 1), k.transpose(0, 1), tgt.transpose(0, 1))[0].transpose(0, 1)\n tgt = tgt + self.dropout2(tgt2)\n return self.norm2(tgt)\n\n def _forward_track_attn(self, tgt, query_pos):\n q = k = self.with_pos_embed(tgt, query_pos)\n if q.shape[1] > 300:\n tgt2 = self.update_attn(q[:, 300:].transpose(0, 1),\n k[:, 300:].transpose(0, 1),\n tgt[:, 300:].transpose(0, 1))[0].transpose(0, 1)\n tgt = torch.cat([tgt[:, :300],self.norm4(tgt[:, 300:]+self.dropout5(tgt2))], dim=1)\n return tgt\n\n def _forward_self_cross(self, tgt, query_pos, reference_points, src, src_spatial_shapes, level_start_index,\n src_padding_mask=None, attn_mask=None):\n\n # self attention\n tgt = self._forward_self_attn(tgt, query_pos, attn_mask)\n # cross attention\n tgt2 = self.cross_attn(self.with_pos_embed(tgt, query_pos),\n reference_points,\n src, src_spatial_shapes, level_start_index, src_padding_mask)\n tgt = tgt + self.dropout1(tgt2)\n tgt = self.norm1(tgt)\n\n # ffn\n tgt = self.forward_ffn(tgt)\n\n return tgt\n\n def _forward_cross_self(self, tgt, query_pos, reference_points, src, src_spatial_shapes, level_start_index,\n src_padding_mask=None, attn_mask=None):\n # cross attention\n tgt2 = self.cross_attn(self.with_pos_embed(tgt, query_pos),\n reference_points,\n src, src_spatial_shapes, level_start_index, src_padding_mask)\n tgt = tgt + self.dropout1(tgt2)\n tgt = self.norm1(tgt)\n # self attention\n tgt = self._forward_self_attn(tgt, query_pos, attn_mask)\n # ffn\n tgt = self.forward_ffn(tgt)\n\n return tgt\n\n def forward(self, tgt, query_pos, reference_points, src, src_spatial_shapes, level_start_index, src_padding_mask=None):\n attn_mask = None\n if self.self_cross:\n return self._forward_self_cross(tgt, query_pos, reference_points, src, src_spatial_shapes,\n level_start_index, src_padding_mask, attn_mask)\n return self._forward_cross_self(tgt, query_pos, reference_points, src, src_spatial_shapes, level_start_index,\n src_padding_mask, attn_mask)\n\n\nclass DeformableTransformerDecoder(nn.Module):\n def __init__(self, decoder_layer, num_layers, return_intermediate=False):\n super().__init__()\n self.layers = _get_clones(decoder_layer, num_layers)\n self.num_layers = num_layers\n self.return_intermediate = return_intermediate\n # hack implementation for iterative bounding box refinement and two-stage Deformable DETR\n self.bbox_embed = None\n self.class_embed = None\n\n def forward(self, tgt, reference_points, src, src_spatial_shapes, src_level_start_index, src_valid_ratios,\n query_pos=None, src_padding_mask=None):\n output = tgt\n\n intermediate = []\n intermediate_reference_points = []\n for lid, layer in enumerate(self.layers):\n if reference_points.shape[-1] == 4:\n reference_points_input = reference_points[:, :, None] \\\n * torch.cat([src_valid_ratios, src_valid_ratios], -1)[:, None]\n else:\n assert reference_points.shape[-1] == 2\n reference_points_input = reference_points[:, :, None] * src_valid_ratios[:, None]\n output = layer(output, query_pos, reference_points_input, src, src_spatial_shapes, src_level_start_index, src_padding_mask)\n\n # hack implementation for iterative bounding box refinement\n if self.bbox_embed is not None:\n tmp = self.bbox_embed[lid](output)\n if reference_points.shape[-1] == 4:\n new_reference_points = tmp + inverse_sigmoid(reference_points)\n new_reference_points = new_reference_points.sigmoid()\n else:\n assert reference_points.shape[-1] == 2\n new_reference_points = tmp\n new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points)\n new_reference_points = new_reference_points.sigmoid()\n reference_points = new_reference_points.detach()\n\n if self.return_intermediate:\n intermediate.append(output)\n intermediate_reference_points.append(reference_points)\n\n if self.return_intermediate:\n return torch.stack(intermediate), torch.stack(intermediate_reference_points)\n\n return output, reference_points\n\n\ndef _get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return nn.ReLU(True)\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(F\"activation should be relu/gelu, not {activation}.\")\n\n\n\ndef build_deforamble_transformer(args):\n return DeformableTransformer(\n d_model=args.hidden_dim,\n nhead=args.nheads,\n num_encoder_layers=args.enc_layers,\n num_decoder_layers=args.dec_layers,\n dim_feedforward=args.dim_feedforward,\n dropout=args.dropout,\n activation=\"relu\",\n return_intermediate_dec=True,\n num_feature_levels=args.num_feature_levels,\n dec_n_points=args.dec_n_points,\n enc_n_points=args.enc_n_points,\n two_stage=args.two_stage,\n two_stage_num_proposals=args.num_queries,\n decoder_self_cross=not args.decoder_cross_self,\n sigmoid_attn=args.sigmoid_attn,\n extra_track_attn=args.extra_track_attn,\n )\n\n\n" }, { "path": "models/matcher.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\n\"\"\"\nModules to compute the matching cost and solve the corresponding LSAP.\n\"\"\"\nimport torch\nfrom scipy.optimize import linear_sum_assignment\nfrom torch import nn\n\nfrom util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou\nfrom models.structures import Instances\n\n\nclass HungarianMatcher(nn.Module):\n \"\"\"This class computes an assignment between the targets and the predictions of the network\n\n For efficiency reasons, the targets don't include the no_object. Because of this, in general,\n there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,\n while the others are un-matched (and thus treated as non-objects).\n \"\"\"\n\n def __init__(self,\n cost_class: float = 1,\n cost_bbox: float = 1,\n cost_giou: float = 1):\n \"\"\"Creates the matcher\n\n Params:\n cost_class: This is the relative weight of the classification error in the matching cost\n cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost\n cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost\n \"\"\"\n super().__init__()\n self.cost_class = cost_class\n self.cost_bbox = cost_bbox\n self.cost_giou = cost_giou\n assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, \"all costs cant be 0\"\n\n def forward(self, outputs, targets, use_focal=True):\n \"\"\" Performs the matching\n\n Params:\n outputs: This is a dict that contains at least these entries:\n \"pred_logits\": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits\n \"pred_boxes\": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates\n\n targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:\n \"labels\": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth\n objects in the target) containing the class labels\n \"boxes\": Tensor of dim [num_target_boxes, 4] containing the target box coordinates\n\n Returns:\n A list of size batch_size, containing tuples of (index_i, index_j) where:\n - index_i is the indices of the selected predictions (in order)\n - index_j is the indices of the corresponding selected targets (in order)\n For each batch element, it holds:\n len(index_i) = len(index_j) = min(num_queries, num_target_boxes)\n \"\"\"\n with torch.no_grad():\n bs, num_queries = outputs[\"pred_logits\"].shape[:2]\n\n # We flatten to compute the cost matrices in a batch\n if use_focal:\n out_prob = outputs[\"pred_logits\"].flatten(0, 1).sigmoid()\n else:\n out_prob = outputs[\"pred_logits\"].flatten(0, 1).softmax(-1) # [batch_size * num_queries, num_classes]\n out_bbox = outputs[\"pred_boxes\"].flatten(0, 1) # [batch_size * num_queries, 4]\n\n # Also concat the target labels and boxes\n if isinstance(targets[0], Instances):\n tgt_ids = torch.cat([gt_per_img.labels for gt_per_img in targets])\n tgt_bbox = torch.cat([gt_per_img.boxes for gt_per_img in targets])\n else:\n tgt_ids = torch.cat([v[\"labels\"] for v in targets])\n tgt_bbox = torch.cat([v[\"boxes\"] for v in targets])\n\n # Compute the classification cost.\n if use_focal:\n alpha = 0.25\n gamma = 2.0\n neg_cost_class = (1 - alpha) * (out_prob ** gamma) * (-(1 - out_prob + 1e-8).log())\n pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log())\n cost_class = pos_cost_class[:, tgt_ids] - neg_cost_class[:, tgt_ids]\n else:\n # Compute the classification cost. Contrary to the loss, we don't use the NLL,\n # but approximate it in 1 - proba[target class].\n # The 1 is a constant that doesn't change the matching, it can be ommitted.\n cost_class = -out_prob[:, tgt_ids]\n\n # Compute the L1 cost between boxes\n cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)\n\n # Compute the giou cost betwen boxes\n cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox),\n box_cxcywh_to_xyxy(tgt_bbox))\n\n # Final cost matrix\n C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou\n C = C.view(bs, num_queries, -1).cpu()\n\n if isinstance(targets[0], Instances):\n sizes = [len(gt_per_img.boxes) for gt_per_img in targets]\n else:\n sizes = [len(v[\"boxes\"]) for v in targets]\n\n indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))]\n return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]\n\n\ndef build_matcher(args):\n return HungarianMatcher(cost_class=args.set_cost_class,\n cost_bbox=args.set_cost_bbox,\n cost_giou=args.set_cost_giou)\n" }, { "path": "models/memory_bank.py", "content": "# ------------------------------------------------------------------------\r\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\r\n# ------------------------------------------------------------------------\r\n\r\nimport torch\r\nimport torch.nn.functional as F\r\nfrom torch import nn, Tensor\r\n\r\nfrom typing import List\r\n\r\nfrom models.structures import Instances\r\n\r\n\r\nclass MemoryBank(nn.Module):\r\n def __init__(self, args, dim_in, hidden_dim, dim_out):\r\n super().__init__()\r\n self._build_layers(args, dim_in, hidden_dim, dim_out)\r\n for p in self.parameters():\r\n if p.dim() > 1:\r\n nn.init.xavier_uniform_(p)\r\n\r\n def _build_layers(self, args, dim_in, hidden_dim, dim_out):\r\n self.save_thresh = args.memory_bank_score_thresh\r\n self.save_period = 3\r\n self.max_his_length = args.memory_bank_len\r\n\r\n self.save_proj = nn.Linear(dim_in, dim_in)\r\n\r\n self.temporal_attn = nn.MultiheadAttention(dim_in, 8, dropout=0)\r\n self.temporal_fc1 = nn.Linear(dim_in, hidden_dim)\r\n self.temporal_fc2 = nn.Linear(hidden_dim, dim_in)\r\n self.temporal_norm1 = nn.LayerNorm(dim_in)\r\n self.temporal_norm2 = nn.LayerNorm(dim_in)\r\n\r\n self.track_cls = nn.Linear(dim_in, 1)\r\n\r\n self.self_attn = None\r\n if args.memory_bank_with_self_attn:\r\n self.spatial_attn = nn.MultiheadAttention(dim_in, 8, dropout=0)\r\n self.spatial_fc1 = nn.Linear(dim_in, hidden_dim)\r\n self.spatial_fc2 = nn.Linear(hidden_dim, dim_in)\r\n self.spatial_norm1 = nn.LayerNorm(dim_in)\r\n self.spatial_norm2 = nn.LayerNorm(dim_in)\r\n else:\r\n self.spatial_attn = None\r\n\r\n def update(self, track_instances):\r\n embed = track_instances.output_embedding[:, None] #( N, 1, 256)\r\n scores = track_instances.scores\r\n mem_padding_mask = track_instances.mem_padding_mask\r\n device = embed.device\r\n\r\n save_period = track_instances.save_period\r\n if self.training:\r\n saved_idxes = scores > 0\r\n else:\r\n saved_idxes = (save_period == 0) & (scores > self.save_thresh)\r\n # saved_idxes = (save_period == 0)\r\n save_period[save_period > 0] -= 1\r\n save_period[saved_idxes] = self.save_period\r\n\r\n saved_embed = embed[saved_idxes]\r\n if len(saved_embed) > 0:\r\n prev_embed = track_instances.mem_bank[saved_idxes]\r\n save_embed = self.save_proj(saved_embed)\r\n mem_padding_mask[saved_idxes] = torch.cat([mem_padding_mask[saved_idxes, 1:], torch.zeros((len(saved_embed), 1), dtype=torch.bool, device=device)], dim=1)\r\n track_instances.mem_bank = track_instances.mem_bank.clone()\r\n track_instances.mem_bank[saved_idxes] = torch.cat([prev_embed[:, 1:], save_embed], dim=1)\r\n\r\n def _forward_spatial_attn(self, track_instances):\r\n if len(track_instances) == 0:\r\n return track_instances\r\n\r\n embed = track_instances.output_embedding\r\n dim = embed.shape[-1]\r\n query_pos = track_instances.query_pos[:, :dim]\r\n k = q = (embed + query_pos)\r\n v = embed\r\n embed2 = self.spatial_attn(\r\n q[:, None],\r\n k[:, None],\r\n v[:, None]\r\n )[0][:, 0]\r\n embed = self.spatial_norm1(embed + embed2)\r\n embed2 = self.spatial_fc2(F.relu(self.spatial_fc1(embed)))\r\n embed = self.spatial_norm2(embed + embed2)\r\n track_instances.output_embedding = embed\r\n return track_instances\r\n\r\n def _forward_track_cls(self, track_instances):\r\n track_instances.track_scores = self.track_cls(track_instances.output_embedding)[..., 0]\r\n return track_instances\r\n\r\n def _forward_temporal_attn(self, track_instances):\r\n if len(track_instances) == 0:\r\n return track_instances\r\n\r\n dim = track_instances.query_pos.shape[1]\r\n key_padding_mask = track_instances.mem_padding_mask\r\n\r\n valid_idxes = key_padding_mask[:, -1] == 0\r\n embed = track_instances.output_embedding[valid_idxes] # (n, 256)\r\n\r\n if len(embed) > 0:\r\n prev_embed = track_instances.mem_bank[valid_idxes]\r\n key_padding_mask = key_padding_mask[valid_idxes]\r\n embed2 = self.temporal_attn(\r\n embed[None], # (num_track, dim) to (1, num_track, dim)\r\n prev_embed.transpose(0, 1), # (num_track, mem_len, dim) to (mem_len, num_track, dim)\r\n prev_embed.transpose(0, 1),\r\n key_padding_mask=key_padding_mask,\r\n )[0][0]\r\n\r\n embed = self.temporal_norm1(embed + embed2)\r\n embed2 = self.temporal_fc2(F.relu(self.temporal_fc1(embed)))\r\n embed = self.temporal_norm2(embed + embed2)\r\n track_instances.output_embedding = track_instances.output_embedding.clone()\r\n track_instances.output_embedding[valid_idxes] = embed\r\n\r\n return track_instances\r\n\r\n def forward_temporal_attn(self, track_instances):\r\n return self._forward_temporal_attn(track_instances)\r\n\r\n def forward(self, track_instances: Instances, update_bank=True) -> Instances:\r\n track_instances = self._forward_temporal_attn(track_instances)\r\n if update_bank:\r\n self.update(track_instances)\r\n if self.spatial_attn is not None:\r\n track_instances = self._forward_spatial_attn(track_instances)\r\n if self.track_cls is not None:\r\n track_instances = self._forward_track_cls(track_instances)\r\n return track_instances\r\n\r\n\r\ndef build_memory_bank(args, dim_in, hidden_dim, dim_out):\r\n name = args.memory_bank_type\r\n memory_banks = {\r\n 'MemoryBank': MemoryBank,\r\n }\r\n assert name in memory_banks\r\n return memory_banks[name](args, dim_in, hidden_dim, dim_out)\r\n" }, { "path": "models/motr.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\"\"\"\nDETR model and criterion classes.\n\"\"\"\nimport copy\nimport math\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, Tensor\nfrom typing import List\n\nfrom util import box_ops, checkpoint\nfrom util.misc import (NestedTensor, nested_tensor_from_tensor_list,\n accuracy, get_world_size, interpolate, get_rank,\n is_dist_avail_and_initialized, inverse_sigmoid)\n\nfrom models.structures import Instances, Boxes, pairwise_iou, matched_boxlist_iou\n\nfrom .backbone import build_backbone\nfrom .matcher import build_matcher\nfrom .deformable_transformer_plus import build_deforamble_transformer\nfrom .qim import build as build_query_interaction_layer\nfrom .memory_bank import build_memory_bank\nfrom .deformable_detr import SetCriterion, MLP\nfrom .segmentation import sigmoid_focal_loss\n\n\nclass ClipMatcher(SetCriterion):\n def __init__(self, num_classes,\n matcher,\n weight_dict,\n losses):\n \"\"\" Create the criterion.\n Parameters:\n num_classes: number of object categories, omitting the special no-object category\n matcher: module able to compute a matching between targets and proposals\n weight_dict: dict containing as key the names of the losses and as values their relative weight.\n eos_coef: relative classification weight applied to the no-object category\n losses: list of all the losses to be applied. See get_loss for list of available losses.\n \"\"\"\n super().__init__(num_classes, matcher, weight_dict, losses)\n self.num_classes = num_classes\n self.matcher = matcher\n self.weight_dict = weight_dict\n self.losses = losses\n self.focal_loss = True\n self.losses_dict = {}\n self._current_frame_idx = 0\n\n def initialize_for_single_clip(self, gt_instances: List[Instances]):\n self.gt_instances = gt_instances\n self.num_samples = 0\n self.sample_device = None\n self._current_frame_idx = 0\n self.losses_dict = {}\n\n def _step(self):\n self._current_frame_idx += 1\n\n def calc_loss_for_track_scores(self, track_instances: Instances):\n frame_id = self._current_frame_idx - 1\n gt_instances = self.gt_instances[frame_id]\n outputs = {\n 'pred_logits': track_instances.track_scores[None],\n }\n device = track_instances.track_scores.device\n\n num_tracks = len(track_instances)\n src_idx = torch.arange(num_tracks, dtype=torch.long, device=device)\n tgt_idx = track_instances.matched_gt_idxes # -1 for FP tracks and disappeared tracks\n\n track_losses = self.get_loss('labels',\n outputs=outputs,\n gt_instances=[gt_instances],\n indices=[(src_idx, tgt_idx)],\n num_boxes=1)\n self.losses_dict.update(\n {'frame_{}_track_{}'.format(frame_id, key): value for key, value in\n track_losses.items()})\n\n def get_num_boxes(self, num_samples):\n num_boxes = torch.as_tensor(num_samples, dtype=torch.float, device=self.sample_device)\n if is_dist_avail_and_initialized():\n torch.distributed.all_reduce(num_boxes)\n num_boxes = torch.clamp(num_boxes / get_world_size(), min=1).item()\n return num_boxes\n\n def get_loss(self, loss, outputs, gt_instances, indices, num_boxes, **kwargs):\n loss_map = {\n 'labels': self.loss_labels,\n 'cardinality': self.loss_cardinality,\n 'boxes': self.loss_boxes,\n }\n assert loss in loss_map, f'do you really want to compute {loss} loss?'\n return loss_map[loss](outputs, gt_instances, indices, num_boxes, **kwargs)\n\n def loss_boxes(self, outputs, gt_instances: List[Instances], indices: List[tuple], num_boxes):\n \"\"\"Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss\n targets dicts must contain the key \"boxes\" containing a tensor of dim [nb_target_boxes, 4]\n The target boxes are expected in format (center_x, center_y, h, w), normalized by the image size.\n \"\"\"\n # We ignore the regression loss of the track-disappear slots.\n #TODO: Make this filter process more elegant.\n filtered_idx = []\n for src_per_img, tgt_per_img in indices:\n keep = tgt_per_img != -1\n filtered_idx.append((src_per_img[keep], tgt_per_img[keep]))\n indices = filtered_idx\n idx = self._get_src_permutation_idx(indices)\n src_boxes = outputs['pred_boxes'][idx]\n target_boxes = torch.cat([gt_per_img.boxes[i] for gt_per_img, (_, i) in zip(gt_instances, indices)], dim=0)\n\n # for pad target, don't calculate regression loss, judged by whether obj_id=-1\n target_obj_ids = torch.cat([gt_per_img.obj_ids[i] for gt_per_img, (_, i) in zip(gt_instances, indices)], dim=0) # size(16)\n mask = (target_obj_ids != -1)\n\n loss_bbox = F.l1_loss(src_boxes[mask], target_boxes[mask], reduction='none')\n loss_giou = 1 - torch.diag(box_ops.generalized_box_iou(\n box_ops.box_cxcywh_to_xyxy(src_boxes[mask]),\n box_ops.box_cxcywh_to_xyxy(target_boxes[mask])))\n\n losses = {}\n losses['loss_bbox'] = loss_bbox.sum() / num_boxes\n losses['loss_giou'] = loss_giou.sum() / num_boxes\n\n return losses\n\n def loss_labels(self, outputs, gt_instances: List[Instances], indices, num_boxes, log=False):\n \"\"\"Classification loss (NLL)\n targets dicts must contain the key \"labels\" containing a tensor of dim [nb_target_boxes]\n \"\"\"\n src_logits = outputs['pred_logits']\n idx = self._get_src_permutation_idx(indices)\n target_classes = torch.full(src_logits.shape[:2], self.num_classes,\n dtype=torch.int64, device=src_logits.device)\n # The matched gt for disappear track query is set -1.\n labels = []\n for gt_per_img, (_, J) in zip(gt_instances, indices):\n labels_per_img = torch.ones_like(J)\n # set labels of track-appear slots to 0.\n if len(gt_per_img) > 0:\n labels_per_img[J != -1] = gt_per_img.labels[J[J != -1]]\n labels.append(labels_per_img)\n target_classes_o = torch.cat(labels)\n target_classes[idx] = target_classes_o\n if self.focal_loss:\n gt_labels_target = F.one_hot(target_classes, num_classes=self.num_classes + 1)[:, :, :-1] # no loss for the last (background) class\n gt_labels_target = gt_labels_target.to(src_logits)\n loss_ce = sigmoid_focal_loss(src_logits.flatten(1),\n gt_labels_target.flatten(1),\n alpha=0.25,\n gamma=2,\n num_boxes=num_boxes, mean_in_dim1=False)\n loss_ce = loss_ce.sum()\n else:\n loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight)\n losses = {'loss_ce': loss_ce}\n\n if log:\n # TODO this should probably be a separate loss, not hacked in this one here\n losses['class_error'] = 100 - accuracy(src_logits[idx], target_classes_o)[0]\n\n return losses\n\n def match_for_single_frame(self, outputs: dict):\n outputs_without_aux = {k: v for k, v in outputs.items() if k != 'aux_outputs'}\n\n gt_instances_i = self.gt_instances[self._current_frame_idx] # gt instances of i-th image.\n track_instances: Instances = outputs_without_aux['track_instances']\n pred_logits_i = track_instances.pred_logits # predicted logits of i-th image.\n pred_boxes_i = track_instances.pred_boxes # predicted boxes of i-th image.\n\n obj_idxes = gt_instances_i.obj_ids\n obj_idxes_list = obj_idxes.detach().cpu().numpy().tolist()\n obj_idx_to_gt_idx = {obj_idx: gt_idx for gt_idx, obj_idx in enumerate(obj_idxes_list)}\n outputs_i = {\n 'pred_logits': pred_logits_i.unsqueeze(0),\n 'pred_boxes': pred_boxes_i.unsqueeze(0),\n }\n\n # step1. inherit and update the previous tracks.\n num_disappear_track = 0\n for j in range(len(track_instances)):\n obj_id = track_instances.obj_idxes[j].item()\n # set new target idx.\n if obj_id >= 0:\n if obj_id in obj_idx_to_gt_idx:\n track_instances.matched_gt_idxes[j] = obj_idx_to_gt_idx[obj_id]\n else:\n num_disappear_track += 1\n track_instances.matched_gt_idxes[j] = -1 # track-disappear case.\n else:\n track_instances.matched_gt_idxes[j] = -1\n\n full_track_idxes = torch.arange(len(track_instances), dtype=torch.long).to(pred_logits_i.device)\n matched_track_idxes = (track_instances.obj_idxes >= 0) # occu \n prev_matched_indices = torch.stack(\n [full_track_idxes[matched_track_idxes], track_instances.matched_gt_idxes[matched_track_idxes]], dim=1).to(\n pred_logits_i.device)\n\n # step2. select the unmatched slots.\n # note that the FP tracks whose obj_idxes are -2 will not be selected here.\n unmatched_track_idxes = full_track_idxes[track_instances.obj_idxes == -1]\n\n # step3. select the untracked gt instances (new tracks).\n tgt_indexes = track_instances.matched_gt_idxes\n tgt_indexes = tgt_indexes[tgt_indexes != -1]\n\n tgt_state = torch.zeros(len(gt_instances_i)).to(pred_logits_i.device)\n tgt_state[tgt_indexes] = 1\n untracked_tgt_indexes = torch.arange(len(gt_instances_i)).to(pred_logits_i.device)[tgt_state == 0]\n # untracked_tgt_indexes = select_unmatched_indexes(tgt_indexes, len(gt_instances_i))\n untracked_gt_instances = gt_instances_i[untracked_tgt_indexes]\n\n def match_for_single_decoder_layer(unmatched_outputs, matcher):\n new_track_indices = matcher(unmatched_outputs,\n [untracked_gt_instances]) # list[tuple(src_idx, tgt_idx)]\n\n src_idx = new_track_indices[0][0]\n tgt_idx = new_track_indices[0][1]\n # concat src and tgt.\n new_matched_indices = torch.stack([unmatched_track_idxes[src_idx], untracked_tgt_indexes[tgt_idx]],\n dim=1).to(pred_logits_i.device)\n return new_matched_indices\n\n # step4. do matching between the unmatched slots and GTs.\n unmatched_outputs = {\n 'pred_logits': track_instances.pred_logits[unmatched_track_idxes].unsqueeze(0),\n 'pred_boxes': track_instances.pred_boxes[unmatched_track_idxes].unsqueeze(0),\n }\n new_matched_indices = match_for_single_decoder_layer(unmatched_outputs, self.matcher)\n\n # step5. update obj_idxes according to the new matching result.\n track_instances.obj_idxes[new_matched_indices[:, 0]] = gt_instances_i.obj_ids[new_matched_indices[:, 1]].long()\n track_instances.matched_gt_idxes[new_matched_indices[:, 0]] = new_matched_indices[:, 1]\n\n # step6. calculate iou.\n active_idxes = (track_instances.obj_idxes >= 0) & (track_instances.matched_gt_idxes >= 0)\n active_track_boxes = track_instances.pred_boxes[active_idxes]\n if len(active_track_boxes) > 0:\n gt_boxes = gt_instances_i.boxes[track_instances.matched_gt_idxes[active_idxes]]\n active_track_boxes = box_ops.box_cxcywh_to_xyxy(active_track_boxes)\n gt_boxes = box_ops.box_cxcywh_to_xyxy(gt_boxes)\n track_instances.iou[active_idxes] = matched_boxlist_iou(Boxes(active_track_boxes), Boxes(gt_boxes))\n\n # step7. merge the unmatched pairs and the matched pairs.\n matched_indices = torch.cat([new_matched_indices, prev_matched_indices], dim=0)\n\n # step8. calculate losses.\n self.num_samples += len(gt_instances_i) + num_disappear_track\n self.sample_device = pred_logits_i.device\n for loss in self.losses:\n new_track_loss = self.get_loss(loss,\n outputs=outputs_i,\n gt_instances=[gt_instances_i],\n indices=[(matched_indices[:, 0], matched_indices[:, 1])],\n num_boxes=1)\n self.losses_dict.update(\n {'frame_{}_{}'.format(self._current_frame_idx, key): value for key, value in new_track_loss.items()})\n\n if 'aux_outputs' in outputs:\n for i, aux_outputs in enumerate(outputs['aux_outputs']):\n unmatched_outputs_layer = {\n 'pred_logits': aux_outputs['pred_logits'][0, unmatched_track_idxes].unsqueeze(0),\n 'pred_boxes': aux_outputs['pred_boxes'][0, unmatched_track_idxes].unsqueeze(0),\n }\n new_matched_indices_layer = match_for_single_decoder_layer(unmatched_outputs_layer, self.matcher)\n matched_indices_layer = torch.cat([new_matched_indices_layer, prev_matched_indices], dim=0)\n for loss in self.losses:\n if loss == 'masks':\n # Intermediate masks losses are too costly to compute, we ignore them.\n continue\n l_dict = self.get_loss(loss,\n aux_outputs,\n gt_instances=[gt_instances_i],\n indices=[(matched_indices_layer[:, 0], matched_indices_layer[:, 1])],\n num_boxes=1, )\n self.losses_dict.update(\n {'frame_{}_aux{}_{}'.format(self._current_frame_idx, i, key): value for key, value in\n l_dict.items()})\n self._step()\n return track_instances\n\n def forward(self, outputs, input_data: dict):\n # losses of each frame are calculated during the model's forwarding and are outputted by the model as outputs['losses_dict].\n losses = outputs.pop(\"losses_dict\")\n num_samples = self.get_num_boxes(self.num_samples)\n for loss_name, loss in losses.items():\n losses[loss_name] /= num_samples\n return losses\n\n\nclass RuntimeTrackerBase(object):\n def __init__(self, score_thresh=0.7, filter_score_thresh=0.6, miss_tolerance=5):\n self.score_thresh = score_thresh\n self.filter_score_thresh = filter_score_thresh\n self.miss_tolerance = miss_tolerance\n self.max_obj_id = 0\n\n def clear(self):\n self.max_obj_id = 0\n\n def update(self, track_instances: Instances):\n track_instances.disappear_time[track_instances.scores >= self.score_thresh] = 0\n for i in range(len(track_instances)):\n if track_instances.obj_idxes[i] == -1 and track_instances.scores[i] >= self.score_thresh:\n # print(\"track {} has score {}, assign obj_id {}\".format(i, track_instances.scores[i], self.max_obj_id))\n track_instances.obj_idxes[i] = self.max_obj_id\n self.max_obj_id += 1\n elif track_instances.obj_idxes[i] >= 0 and track_instances.scores[i] < self.filter_score_thresh:\n track_instances.disappear_time[i] += 1\n if track_instances.disappear_time[i] >= self.miss_tolerance:\n # Set the obj_id to -1.\n # Then this track will be removed by TrackEmbeddingLayer.\n track_instances.obj_idxes[i] = -1\n\n\nclass TrackerPostProcess(nn.Module):\n \"\"\" This module converts the model's output into the format expected by the coco api\"\"\"\n def __init__(self):\n super().__init__()\n\n @torch.no_grad()\n def forward(self, track_instances: Instances, target_size) -> Instances:\n \"\"\" Perform the computation\n Parameters:\n outputs: raw outputs of the model\n target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch\n For evaluation, this must be the original image size (before any data augmentation)\n For visualization, this should be the image size after data augment, but before padding\n \"\"\"\n out_logits = track_instances.pred_logits\n out_bbox = track_instances.pred_boxes\n\n prob = out_logits.sigmoid()\n # prob = out_logits[...,:1].sigmoid()\n scores, labels = prob.max(-1)\n\n # convert to [x0, y0, x1, y1] format\n boxes = box_ops.box_cxcywh_to_xyxy(out_bbox)\n # and from relative [0, 1] to absolute [0, height] coordinates\n img_h, img_w = target_size\n scale_fct = torch.Tensor([img_w, img_h, img_w, img_h]).to(boxes)\n boxes = boxes * scale_fct[None, :]\n\n track_instances.boxes = boxes\n track_instances.scores = scores\n track_instances.labels = labels\n track_instances.remove('pred_logits')\n track_instances.remove('pred_boxes')\n return track_instances\n\n\ndef _get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\nclass MOTR(nn.Module):\n def __init__(self, backbone, transformer, num_classes, num_queries, num_feature_levels, criterion, track_embed,\n aux_loss=True, with_box_refine=False, two_stage=False, memory_bank=None, use_checkpoint=False):\n \"\"\" Initializes the model.\n Parameters:\n backbone: torch module of the backbone to be used. See backbone.py\n transformer: torch module of the transformer architecture. See transformer.py\n num_classes: number of object classes\n num_queries: number of object queries, ie detection slot. This is the maximal number of objects\n DETR can detect in a single image. For COCO, we recommend 100 queries.\n aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.\n with_box_refine: iterative bounding box refinement\n two_stage: two-stage Deformable DETR\n \"\"\"\n super().__init__()\n self.num_queries = num_queries\n self.track_embed = track_embed\n self.transformer = transformer\n hidden_dim = transformer.d_model\n self.num_classes = num_classes\n self.class_embed = nn.Linear(hidden_dim, num_classes)\n self.bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)\n self.num_feature_levels = num_feature_levels\n self.use_checkpoint = use_checkpoint\n if not two_stage:\n self.query_embed = nn.Embedding(num_queries, hidden_dim * 2)\n if num_feature_levels > 1:\n num_backbone_outs = len(backbone.strides)\n input_proj_list = []\n for _ in range(num_backbone_outs):\n in_channels = backbone.num_channels[_]\n input_proj_list.append(nn.Sequential(\n nn.Conv2d(in_channels, hidden_dim, kernel_size=1),\n nn.GroupNorm(32, hidden_dim),\n ))\n for _ in range(num_feature_levels - num_backbone_outs):\n input_proj_list.append(nn.Sequential(\n nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),\n nn.GroupNorm(32, hidden_dim),\n ))\n in_channels = hidden_dim\n self.input_proj = nn.ModuleList(input_proj_list)\n else:\n self.input_proj = nn.ModuleList([\n nn.Sequential(\n nn.Conv2d(backbone.num_channels[0], hidden_dim, kernel_size=1),\n nn.GroupNorm(32, hidden_dim),\n )])\n self.backbone = backbone\n self.aux_loss = aux_loss\n self.with_box_refine = with_box_refine\n self.two_stage = two_stage\n\n prior_prob = 0.01\n bias_value = -math.log((1 - prior_prob) / prior_prob)\n self.class_embed.bias.data = torch.ones(num_classes) * bias_value\n nn.init.constant_(self.bbox_embed.layers[-1].weight.data, 0)\n nn.init.constant_(self.bbox_embed.layers[-1].bias.data, 0)\n for proj in self.input_proj:\n nn.init.xavier_uniform_(proj[0].weight, gain=1)\n nn.init.constant_(proj[0].bias, 0)\n\n # if two-stage, the last class_embed and bbox_embed is for region proposal generation\n num_pred = (transformer.decoder.num_layers + 1) if two_stage else transformer.decoder.num_layers\n if with_box_refine:\n self.class_embed = _get_clones(self.class_embed, num_pred)\n self.bbox_embed = _get_clones(self.bbox_embed, num_pred)\n nn.init.constant_(self.bbox_embed[0].layers[-1].bias.data[2:], -2.0)\n # hack implementation for iterative bounding box refinement\n self.transformer.decoder.bbox_embed = self.bbox_embed\n else:\n nn.init.constant_(self.bbox_embed.layers[-1].bias.data[2:], -2.0)\n self.class_embed = nn.ModuleList([self.class_embed for _ in range(num_pred)])\n self.bbox_embed = nn.ModuleList([self.bbox_embed for _ in range(num_pred)])\n self.transformer.decoder.bbox_embed = None\n if two_stage:\n # hack implementation for two-stage\n self.transformer.decoder.class_embed = self.class_embed\n for box_embed in self.bbox_embed:\n nn.init.constant_(box_embed.layers[-1].bias.data[2:], 0.0)\n self.post_process = TrackerPostProcess()\n self.track_base = RuntimeTrackerBase()\n self.criterion = criterion\n self.memory_bank = memory_bank\n self.mem_bank_len = 0 if memory_bank is None else memory_bank.max_his_length\n\n def _generate_empty_tracks(self):\n track_instances = Instances((1, 1))\n num_queries, dim = self.query_embed.weight.shape # (300, 512)\n device = self.query_embed.weight.device\n track_instances.ref_pts = self.transformer.reference_points(self.query_embed.weight[:, :dim // 2])\n track_instances.query_pos = self.query_embed.weight\n track_instances.output_embedding = torch.zeros((num_queries, dim >> 1), device=device)\n track_instances.obj_idxes = torch.full((len(track_instances),), -1, dtype=torch.long, device=device)\n track_instances.matched_gt_idxes = torch.full((len(track_instances),), -1, dtype=torch.long, device=device)\n track_instances.disappear_time = torch.zeros((len(track_instances), ), dtype=torch.long, device=device)\n track_instances.iou = torch.zeros((len(track_instances),), dtype=torch.float, device=device)\n track_instances.scores = torch.zeros((len(track_instances),), dtype=torch.float, device=device)\n track_instances.track_scores = torch.zeros((len(track_instances),), dtype=torch.float, device=device)\n track_instances.pred_boxes = torch.zeros((len(track_instances), 4), dtype=torch.float, device=device)\n track_instances.pred_logits = torch.zeros((len(track_instances), self.num_classes), dtype=torch.float, device=device)\n\n mem_bank_len = self.mem_bank_len\n track_instances.mem_bank = torch.zeros((len(track_instances), mem_bank_len, dim // 2), dtype=torch.float32, device=device)\n track_instances.mem_padding_mask = torch.ones((len(track_instances), mem_bank_len), dtype=torch.bool, device=device)\n track_instances.save_period = torch.zeros((len(track_instances), ), dtype=torch.float32, device=device)\n\n return track_instances.to(self.query_embed.weight.device)\n\n def clear(self):\n self.track_base.clear()\n\n @torch.jit.unused\n def _set_aux_loss(self, outputs_class, outputs_coord):\n # this is a workaround to make torchscript happy, as torchscript\n # doesn't support dictionary with non-homogeneous values, such\n # as a dict having both a Tensor and a list.\n return [{'pred_logits': a, 'pred_boxes': b, }\n for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]\n\n def _forward_single_image(self, samples, track_instances: Instances):\n features, pos = self.backbone(samples)\n src, mask = features[-1].decompose()\n assert mask is not None\n\n srcs = []\n masks = []\n for l, feat in enumerate(features):\n src, mask = feat.decompose()\n srcs.append(self.input_proj[l](src))\n masks.append(mask)\n assert mask is not None\n\n if self.num_feature_levels > len(srcs):\n _len_srcs = len(srcs)\n for l in range(_len_srcs, self.num_feature_levels):\n if l == _len_srcs:\n src = self.input_proj[l](features[-1].tensors)\n else:\n src = self.input_proj[l](srcs[-1])\n m = samples.mask\n mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]\n pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)\n srcs.append(src)\n masks.append(mask)\n pos.append(pos_l)\n\n hs, init_reference, inter_references, enc_outputs_class, enc_outputs_coord_unact = self.transformer(srcs, masks, pos, track_instances.query_pos, ref_pts=track_instances.ref_pts)\n\n outputs_classes = []\n outputs_coords = []\n for lvl in range(hs.shape[0]):\n if lvl == 0:\n reference = init_reference\n else:\n reference = inter_references[lvl - 1]\n reference = inverse_sigmoid(reference)\n outputs_class = self.class_embed[lvl](hs[lvl])\n tmp = self.bbox_embed[lvl](hs[lvl])\n if reference.shape[-1] == 4:\n tmp += reference\n else:\n assert reference.shape[-1] == 2\n tmp[..., :2] += reference\n outputs_coord = tmp.sigmoid()\n outputs_classes.append(outputs_class)\n outputs_coords.append(outputs_coord)\n outputs_class = torch.stack(outputs_classes)\n outputs_coord = torch.stack(outputs_coords)\n\n ref_pts_all = torch.cat([init_reference[None], inter_references[:, :, :, :2]], dim=0)\n out = {'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord[-1], 'ref_pts': ref_pts_all[5]}\n if self.aux_loss:\n out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord)\n out['hs'] = hs[-1]\n return out\n \n def _post_process_single_image(self, frame_res, track_instances, is_last):\n with torch.no_grad():\n if self.training:\n track_scores = frame_res['pred_logits'][0, :].sigmoid().max(dim=-1).values\n else:\n track_scores = frame_res['pred_logits'][0, :, 0].sigmoid()\n\n track_instances.scores = track_scores\n track_instances.pred_logits = frame_res['pred_logits'][0]\n track_instances.pred_boxes = frame_res['pred_boxes'][0]\n track_instances.output_embedding = frame_res['hs'][0]\n if self.training:\n # the track id will be assigned by the mather.\n frame_res['track_instances'] = track_instances\n track_instances = self.criterion.match_for_single_frame(frame_res)\n else:\n # each track will be assigned an unique global id by the track base.\n self.track_base.update(track_instances)\n if self.memory_bank is not None:\n track_instances = self.memory_bank(track_instances)\n # track_instances.track_scores = track_instances.track_scores[..., 0]\n # track_instances.scores = track_instances.track_scores.sigmoid()\n if self.training:\n self.criterion.calc_loss_for_track_scores(track_instances)\n tmp = {}\n tmp['init_track_instances'] = self._generate_empty_tracks()\n tmp['track_instances'] = track_instances\n if not is_last:\n out_track_instances = self.track_embed(tmp)\n frame_res['track_instances'] = out_track_instances\n else:\n frame_res['track_instances'] = None\n return frame_res\n\n @torch.no_grad()\n def inference_single_image(self, img, ori_img_size, track_instances=None):\n if not isinstance(img, NestedTensor):\n img = nested_tensor_from_tensor_list(img)\n if track_instances is None:\n track_instances = self._generate_empty_tracks()\n res = self._forward_single_image(img,\n track_instances=track_instances)\n res = self._post_process_single_image(res, track_instances, False)\n\n track_instances = res['track_instances']\n track_instances = self.post_process(track_instances, ori_img_size)\n ret = {'track_instances': track_instances}\n if 'ref_pts' in res:\n ref_pts = res['ref_pts']\n img_h, img_w = ori_img_size\n scale_fct = torch.Tensor([img_w, img_h]).to(ref_pts)\n ref_pts = ref_pts * scale_fct[None]\n ret['ref_pts'] = ref_pts\n return ret\n\n def forward(self, data: dict):\n if self.training:\n self.criterion.initialize_for_single_clip(data['gt_instances'])\n frames = data['imgs'] # list of Tensor.\n outputs = {\n 'pred_logits': [],\n 'pred_boxes': [],\n }\n\n track_instances = self._generate_empty_tracks()\n keys = list(track_instances._fields.keys())\n for frame_index, frame in enumerate(frames):\n frame.requires_grad = False\n is_last = frame_index == len(frames) - 1\n if self.use_checkpoint and frame_index < len(frames) - 2:\n def fn(frame, *args):\n frame = nested_tensor_from_tensor_list([frame])\n tmp = Instances((1, 1), **dict(zip(keys, args)))\n frame_res = self._forward_single_image(frame, tmp)\n return (\n frame_res['pred_logits'],\n frame_res['pred_boxes'],\n frame_res['ref_pts'],\n frame_res['hs'],\n *[aux['pred_logits'] for aux in frame_res['aux_outputs']],\n *[aux['pred_boxes'] for aux in frame_res['aux_outputs']]\n )\n\n args = [frame] + [track_instances.get(k) for k in keys]\n params = tuple((p for p in self.parameters() if p.requires_grad))\n tmp = checkpoint.CheckpointFunction.apply(fn, len(args), *args, *params)\n frame_res = {\n 'pred_logits': tmp[0],\n 'pred_boxes': tmp[1],\n 'ref_pts': tmp[2],\n 'hs': tmp[3],\n 'aux_outputs': [{\n 'pred_logits': tmp[4+i],\n 'pred_boxes': tmp[4+5+i],\n } for i in range(5)],\n }\n else:\n frame = nested_tensor_from_tensor_list([frame])\n frame_res = self._forward_single_image(frame, track_instances)\n frame_res = self._post_process_single_image(frame_res, track_instances, is_last)\n\n track_instances = frame_res['track_instances']\n outputs['pred_logits'].append(frame_res['pred_logits'])\n outputs['pred_boxes'].append(frame_res['pred_boxes'])\n\n if not self.training:\n outputs['track_instances'] = track_instances\n else:\n outputs['losses_dict'] = self.criterion.losses_dict\n return outputs\n\n\ndef build(args):\n dataset_to_num_classes = {\n 'coco': 91,\n 'coco_panoptic': 250,\n 'e2e_mot': 1,\n 'e2e_dance': 1,\n 'e2e_joint': 1,\n 'e2e_static_mot': 1,\n }\n assert args.dataset_file in dataset_to_num_classes\n num_classes = dataset_to_num_classes[args.dataset_file]\n device = torch.device(args.device)\n\n backbone = build_backbone(args)\n\n transformer = build_deforamble_transformer(args)\n d_model = transformer.d_model\n hidden_dim = args.dim_feedforward\n query_interaction_layer = build_query_interaction_layer(args, args.query_interaction_layer, d_model, hidden_dim, d_model*2)\n\n img_matcher = build_matcher(args)\n num_frames_per_batch = max(args.sampler_lengths)\n weight_dict = {}\n for i in range(num_frames_per_batch):\n weight_dict.update({\"frame_{}_loss_ce\".format(i): args.cls_loss_coef,\n 'frame_{}_loss_bbox'.format(i): args.bbox_loss_coef,\n 'frame_{}_loss_giou'.format(i): args.giou_loss_coef,\n })\n\n # TODO this is a hack\n if args.aux_loss:\n for i in range(num_frames_per_batch):\n for j in range(args.dec_layers - 1):\n weight_dict.update({\"frame_{}_aux{}_loss_ce\".format(i, j): args.cls_loss_coef,\n 'frame_{}_aux{}_loss_bbox'.format(i, j): args.bbox_loss_coef,\n 'frame_{}_aux{}_loss_giou'.format(i, j): args.giou_loss_coef,\n })\n if args.memory_bank_type is not None and len(args.memory_bank_type) > 0:\n memory_bank = build_memory_bank(args, d_model, hidden_dim, d_model * 2)\n for i in range(num_frames_per_batch):\n weight_dict.update({\"frame_{}_track_loss_ce\".format(i): args.cls_loss_coef})\n else:\n memory_bank = None\n losses = ['labels', 'boxes']\n criterion = ClipMatcher(num_classes, matcher=img_matcher, weight_dict=weight_dict, losses=losses)\n criterion.to(device)\n postprocessors = {}\n model = MOTR(\n backbone,\n transformer,\n track_embed=query_interaction_layer,\n num_feature_levels=args.num_feature_levels,\n num_classes=num_classes,\n num_queries=args.num_queries,\n aux_loss=args.aux_loss,\n criterion=criterion,\n with_box_refine=args.with_box_refine,\n two_stage=args.two_stage,\n memory_bank=memory_bank,\n use_checkpoint=args.use_checkpoint,\n )\n return model, criterion, postprocessors\n" }, { "path": "models/ops/functions/__init__.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\nfrom .ms_deform_attn_func import MSDeformAttnFunction\n\n" }, { "path": "models/ops/functions/ms_deform_attn_func.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport torch\nimport torch.nn.functional as F\nfrom torch.autograd import Function\nfrom torch.autograd.function import once_differentiable\n\nimport MultiScaleDeformableAttention as MSDA\n\n\nclass MSDeformAttnFunction(Function):\n @staticmethod\n def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step):\n ctx.im2col_step = im2col_step\n output = MSDA.ms_deform_attn_forward(\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step)\n ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights)\n return output\n\n @staticmethod\n @once_differentiable\n def backward(ctx, grad_output):\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors\n grad_value, grad_sampling_loc, grad_attn_weight = \\\n MSDA.ms_deform_attn_backward(\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step)\n\n return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None\n\n\ndef ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights):\n # for debug and test only,\n # need to use cuda version instead\n N_, S_, M_, D_ = value.shape\n _, Lq_, M_, L_, P_, _ = sampling_locations.shape\n value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)\n sampling_grids = 2 * sampling_locations - 1\n sampling_value_list = []\n for lid_, (H_, W_) in enumerate(value_spatial_shapes):\n # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_\n value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_)\n # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2\n sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1)\n # N_*M_, D_, Lq_, P_\n sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_,\n mode='bilinear', padding_mode='zeros', align_corners=False)\n sampling_value_list.append(sampling_value_l_)\n # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_)\n attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_)\n output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_)\n return output.transpose(1, 2).contiguous()\n" }, { "path": "models/ops/make.sh", "content": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------------------------------\n# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n# ------------------------------------------------------------------------------------------------\n\npython setup.py build install\n" }, { "path": "models/ops/modules/__init__.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\nfrom .ms_deform_attn import MSDeformAttn\n" }, { "path": "models/ops/modules/ms_deform_attn.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport warnings\nimport math\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.nn.init import xavier_uniform_, constant_\n\nfrom ..functions import MSDeformAttnFunction\n\n\ndef _is_power_of_2(n):\n if (not isinstance(n, int)) or (n < 0):\n raise ValueError(\"invalid input for _is_power_of_2: {} (type: {})\".format(n, type(n)))\n return (n & (n-1) == 0) and n != 0\n\n\nclass MSDeformAttn(nn.Module):\n def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4, sigmoid_attn=False):\n \"\"\"\n Multi-Scale Deformable Attention Module\n :param d_model hidden dimension\n :param n_levels number of feature levels\n :param n_heads number of attention heads\n :param n_points number of sampling points per attention head per feature level\n \"\"\"\n super().__init__()\n if d_model % n_heads != 0:\n raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads))\n _d_per_head = d_model // n_heads\n # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation\n if not _is_power_of_2(_d_per_head):\n warnings.warn(\"You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 \"\n \"which is more efficient in our CUDA implementation.\")\n\n self.im2col_step = 64\n self.sigmoid_attn = sigmoid_attn\n\n self.d_model = d_model\n self.n_levels = n_levels\n self.n_heads = n_heads\n self.n_points = n_points\n\n self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)\n self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)\n self.value_proj = nn.Linear(d_model, d_model)\n self.output_proj = nn.Linear(d_model, d_model)\n\n self._reset_parameters()\n\n def _reset_parameters(self):\n constant_(self.sampling_offsets.weight.data, 0.)\n thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)\n grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)\n grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1)\n for i in range(self.n_points):\n grid_init[:, :, i, :] *= i + 1\n with torch.no_grad():\n self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))\n constant_(self.attention_weights.weight.data, 0.)\n constant_(self.attention_weights.bias.data, 0.)\n xavier_uniform_(self.value_proj.weight.data)\n constant_(self.value_proj.bias.data, 0.)\n xavier_uniform_(self.output_proj.weight.data)\n constant_(self.output_proj.bias.data, 0.)\n\n def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None):\n \"\"\"\n :param query (N, Length_{query}, C)\n :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area\n or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes\n :param input_flatten (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l, C)\n :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]\n :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}]\n :param input_padding_mask (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l), True for padding elements, False for non-padding elements\n\n :return output (N, Length_{query}, C)\n \"\"\"\n N, Len_q, _ = query.shape\n N, Len_in, _ = input_flatten.shape\n assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in\n\n value = self.value_proj(input_flatten)\n if input_padding_mask is not None:\n value.masked_fill_(input_padding_mask[..., None], float(0))\n value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads)\n sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2)\n attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)\n if self.sigmoid_attn:\n attention_weights = attention_weights.sigmoid().view(N, Len_q, self.n_heads, self.n_levels, self.n_points)\n else:\n attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)\n # N, Len_q, n_heads, n_levels, n_points, 2\n if reference_points.shape[-1] == 2:\n sampling_locations = reference_points[:, :, None, :, None, :] \\\n + sampling_offsets / input_spatial_shapes[None, None, None, :, None, (1, 0)]\n elif reference_points.shape[-1] == 4:\n sampling_locations = reference_points[:, :, None, :, None, :2] \\\n + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5\n else:\n raise ValueError(\n 'Last dim of reference_points must be 2 or 4, but get {} instead.'.format(reference_points.shape[-1]))\n output = MSDeformAttnFunction.apply(\n value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step)\n output = self.output_proj(output)\n return output\n" }, { "path": "models/ops/setup.py", "content": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------------------------------\n# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n# ------------------------------------------------------------------------------------------------\n\nimport os\nimport glob\n\nimport torch\n\nfrom torch.utils.cpp_extension import CUDA_HOME\nfrom torch.utils.cpp_extension import CppExtension\nfrom torch.utils.cpp_extension import CUDAExtension\n\nfrom setuptools import find_packages\nfrom setuptools import setup\n\nrequirements = [\"torch\", \"torchvision\"]\n\ndef get_extensions():\n this_dir = os.path.dirname(os.path.abspath(__file__))\n extensions_dir = os.path.join(this_dir, \"src\")\n\n main_file = glob.glob(os.path.join(extensions_dir, \"*.cpp\"))\n source_cpu = glob.glob(os.path.join(extensions_dir, \"cpu\", \"*.cpp\"))\n source_cuda = glob.glob(os.path.join(extensions_dir, \"cuda\", \"*.cu\"))\n\n sources = main_file + source_cpu\n extension = CppExtension\n extra_compile_args = {\"cxx\": []}\n define_macros = []\n\n if torch.cuda.is_available() and CUDA_HOME is not None:\n extension = CUDAExtension\n sources += source_cuda\n define_macros += [(\"WITH_CUDA\", None)]\n extra_compile_args[\"nvcc\"] = [\n \"-DCUDA_HAS_FP16=1\",\n \"-D__CUDA_NO_HALF_OPERATORS__\",\n \"-D__CUDA_NO_HALF_CONVERSIONS__\",\n \"-D__CUDA_NO_HALF2_OPERATORS__\",\n ]\n else:\n raise NotImplementedError('Cuda is not availabel')\n\n sources = [os.path.join(extensions_dir, s) for s in sources]\n include_dirs = [extensions_dir]\n ext_modules = [\n extension(\n \"MultiScaleDeformableAttention\",\n sources,\n include_dirs=include_dirs,\n define_macros=define_macros,\n extra_compile_args=extra_compile_args,\n )\n ]\n return ext_modules\n\nsetup(\n name=\"MultiScaleDeformableAttention\",\n version=\"1.0\",\n author=\"Weijie Su\",\n url=\"https://github.com/fundamentalvision/Deformable-DETR\",\n description=\"PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention\",\n packages=find_packages(exclude=(\"configs\", \"tests\",)),\n ext_modules=get_extensions(),\n cmdclass={\"build_ext\": torch.utils.cpp_extension.BuildExtension},\n)\n" }, { "path": "models/ops/src/cpu/ms_deform_attn_cpu.cpp", "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#include \n\n#include \n#include \n\n\nat::Tensor\nms_deform_attn_cpu_forward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const int im2col_step)\n{\n AT_ERROR(\"Not implement on cpu\");\n}\n\nstd::vector\nms_deform_attn_cpu_backward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const at::Tensor &grad_output,\n const int im2col_step)\n{\n AT_ERROR(\"Not implement on cpu\");\n}\n\n" }, { "path": "models/ops/src/cpu/ms_deform_attn_cpu.h", "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#pragma once\n#include \n\nat::Tensor\nms_deform_attn_cpu_forward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const int im2col_step);\n\nstd::vector\nms_deform_attn_cpu_backward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const at::Tensor &grad_output,\n const int im2col_step);\n\n\n" }, { "path": "models/ops/src/cuda/ms_deform_attn_cuda.cu", "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#include \n#include \"cuda/ms_deform_im2col_cuda.cuh\"\n\n#include \n#include \n#include \n#include \n\n\nat::Tensor ms_deform_attn_cuda_forward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const int im2col_step)\n{\n AT_ASSERTM(value.is_contiguous(), \"value tensor has to be contiguous\");\n AT_ASSERTM(spatial_shapes.is_contiguous(), \"spatial_shapes tensor has to be contiguous\");\n AT_ASSERTM(level_start_index.is_contiguous(), \"level_start_index tensor has to be contiguous\");\n AT_ASSERTM(sampling_loc.is_contiguous(), \"sampling_loc tensor has to be contiguous\");\n AT_ASSERTM(attn_weight.is_contiguous(), \"attn_weight tensor has to be contiguous\");\n\n AT_ASSERTM(value.type().is_cuda(), \"value must be a CUDA tensor\");\n AT_ASSERTM(spatial_shapes.type().is_cuda(), \"spatial_shapes must be a CUDA tensor\");\n AT_ASSERTM(level_start_index.type().is_cuda(), \"level_start_index must be a CUDA tensor\");\n AT_ASSERTM(sampling_loc.type().is_cuda(), \"sampling_loc must be a CUDA tensor\");\n AT_ASSERTM(attn_weight.type().is_cuda(), \"attn_weight must be a CUDA tensor\");\n\n const int batch = value.size(0);\n const int spatial_size = value.size(1);\n const int num_heads = value.size(2);\n const int channels = value.size(3);\n\n const int num_levels = spatial_shapes.size(0);\n\n const int num_query = sampling_loc.size(1);\n const int num_point = sampling_loc.size(4);\n\n const int im2col_step_ = std::min(batch, im2col_step);\n\n AT_ASSERTM(batch % im2col_step_ == 0, \"batch(%d) must divide im2col_step(%d)\", batch, im2col_step_);\n \n auto output = at::zeros({batch, num_query, num_heads, channels}, value.options());\n\n const int batch_n = im2col_step_;\n auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});\n auto per_value_size = spatial_size * num_heads * channels;\n auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;\n auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;\n for (int n = 0; n < batch/im2col_step_; ++n)\n {\n auto columns = output_n.select(0, n);\n AT_DISPATCH_FLOATING_TYPES(value.type(), \"ms_deform_attn_forward_cuda\", ([&] {\n ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),\n value.data() + n * im2col_step_ * per_value_size,\n spatial_shapes.data(),\n level_start_index.data(),\n sampling_loc.data() + n * im2col_step_ * per_sample_loc_size,\n attn_weight.data() + n * im2col_step_ * per_attn_weight_size,\n batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,\n columns.data());\n\n }));\n }\n\n output = output.view({batch, num_query, num_heads*channels});\n\n return output;\n}\n\n\nstd::vector ms_deform_attn_cuda_backward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const at::Tensor &grad_output,\n const int im2col_step)\n{\n\n AT_ASSERTM(value.is_contiguous(), \"value tensor has to be contiguous\");\n AT_ASSERTM(spatial_shapes.is_contiguous(), \"spatial_shapes tensor has to be contiguous\");\n AT_ASSERTM(level_start_index.is_contiguous(), \"level_start_index tensor has to be contiguous\");\n AT_ASSERTM(sampling_loc.is_contiguous(), \"sampling_loc tensor has to be contiguous\");\n AT_ASSERTM(attn_weight.is_contiguous(), \"attn_weight tensor has to be contiguous\");\n AT_ASSERTM(grad_output.is_contiguous(), \"grad_output tensor has to be contiguous\");\n\n AT_ASSERTM(value.type().is_cuda(), \"value must be a CUDA tensor\");\n AT_ASSERTM(spatial_shapes.type().is_cuda(), \"spatial_shapes must be a CUDA tensor\");\n AT_ASSERTM(level_start_index.type().is_cuda(), \"level_start_index must be a CUDA tensor\");\n AT_ASSERTM(sampling_loc.type().is_cuda(), \"sampling_loc must be a CUDA tensor\");\n AT_ASSERTM(attn_weight.type().is_cuda(), \"attn_weight must be a CUDA tensor\");\n AT_ASSERTM(grad_output.type().is_cuda(), \"grad_output must be a CUDA tensor\");\n\n const int batch = value.size(0);\n const int spatial_size = value.size(1);\n const int num_heads = value.size(2);\n const int channels = value.size(3);\n\n const int num_levels = spatial_shapes.size(0);\n\n const int num_query = sampling_loc.size(1);\n const int num_point = sampling_loc.size(4);\n\n const int im2col_step_ = std::min(batch, im2col_step);\n\n AT_ASSERTM(batch % im2col_step_ == 0, \"batch(%d) must divide im2col_step(%d)\", batch, im2col_step_);\n\n auto grad_value = at::zeros_like(value);\n auto grad_sampling_loc = at::zeros_like(sampling_loc);\n auto grad_attn_weight = at::zeros_like(attn_weight);\n\n const int batch_n = im2col_step_;\n auto per_value_size = spatial_size * num_heads * channels;\n auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;\n auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;\n auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});\n \n for (int n = 0; n < batch/im2col_step_; ++n)\n {\n auto grad_output_g = grad_output_n.select(0, n);\n AT_DISPATCH_FLOATING_TYPES(value.type(), \"ms_deform_attn_backward_cuda\", ([&] {\n ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),\n grad_output_g.data(),\n value.data() + n * im2col_step_ * per_value_size,\n spatial_shapes.data(),\n level_start_index.data(),\n sampling_loc.data() + n * im2col_step_ * per_sample_loc_size,\n attn_weight.data() + n * im2col_step_ * per_attn_weight_size,\n batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,\n grad_value.data() + n * im2col_step_ * per_value_size,\n grad_sampling_loc.data() + n * im2col_step_ * per_sample_loc_size,\n grad_attn_weight.data() + n * im2col_step_ * per_attn_weight_size);\n\n }));\n }\n\n return {\n grad_value, grad_sampling_loc, grad_attn_weight\n };\n}" }, { "path": "models/ops/src/cuda/ms_deform_attn_cuda.h", "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#pragma once\n#include \n\nat::Tensor ms_deform_attn_cuda_forward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const int im2col_step);\n\nstd::vector ms_deform_attn_cuda_backward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const at::Tensor &grad_output,\n const int im2col_step);\n\n" }, { "path": "models/ops/src/cuda/ms_deform_im2col_cuda.cuh", "content": "/*!\n**************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************\n* Modified from DCN (https://github.com/msracver/Deformable-ConvNets)\n* Copyright (c) 2018 Microsoft\n**************************************************************************\n*/\n\n#include \n#include \n#include \n\n#include \n#include \n\n#include \n\n#define CUDA_KERNEL_LOOP(i, n) \\\n for (int i = blockIdx.x * blockDim.x + threadIdx.x; \\\n i < (n); \\\n i += blockDim.x * gridDim.x)\n\nconst int CUDA_NUM_THREADS = 1024;\ninline int GET_BLOCKS(const int N, const int num_threads)\n{\n return (N + num_threads - 1) / num_threads;\n}\n\n\ntemplate \n__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data, \n const int &height, const int &width, const int &nheads, const int &channels,\n const scalar_t &h, const scalar_t &w, const int &m, const int &c)\n{\n const int h_low = floor(h);\n const int w_low = floor(w);\n const int h_high = h_low + 1;\n const int w_high = w_low + 1;\n\n const scalar_t lh = h - h_low;\n const scalar_t lw = w - w_low;\n const scalar_t hh = 1 - lh, hw = 1 - lw;\n\n const int w_stride = nheads * channels;\n const int h_stride = width * w_stride;\n const int h_low_ptr_offset = h_low * h_stride;\n const int h_high_ptr_offset = h_low_ptr_offset + h_stride;\n const int w_low_ptr_offset = w_low * w_stride;\n const int w_high_ptr_offset = w_low_ptr_offset + w_stride;\n const int base_ptr = m * channels + c;\n\n scalar_t v1 = 0;\n if (h_low >= 0 && w_low >= 0)\n {\n const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;\n v1 = bottom_data[ptr1];\n }\n scalar_t v2 = 0;\n if (h_low >= 0 && w_high <= width - 1)\n {\n const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;\n v2 = bottom_data[ptr2];\n }\n scalar_t v3 = 0;\n if (h_high <= height - 1 && w_low >= 0)\n {\n const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;\n v3 = bottom_data[ptr3];\n }\n scalar_t v4 = 0;\n if (h_high <= height - 1 && w_high <= width - 1)\n {\n const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;\n v4 = bottom_data[ptr4];\n }\n\n const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;\n\n const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);\n return val;\n}\n\n\ntemplate \n__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data, \n const int &height, const int &width, const int &nheads, const int &channels,\n const scalar_t &h, const scalar_t &w, const int &m, const int &c,\n const scalar_t &top_grad,\n const scalar_t &attn_weight,\n scalar_t* &grad_value, \n scalar_t* grad_sampling_loc,\n scalar_t* grad_attn_weight)\n{\n const int h_low = floor(h);\n const int w_low = floor(w);\n const int h_high = h_low + 1;\n const int w_high = w_low + 1;\n\n const scalar_t lh = h - h_low;\n const scalar_t lw = w - w_low;\n const scalar_t hh = 1 - lh, hw = 1 - lw;\n\n const int w_stride = nheads * channels;\n const int h_stride = width * w_stride;\n const int h_low_ptr_offset = h_low * h_stride;\n const int h_high_ptr_offset = h_low_ptr_offset + h_stride;\n const int w_low_ptr_offset = w_low * w_stride;\n const int w_high_ptr_offset = w_low_ptr_offset + w_stride;\n const int base_ptr = m * channels + c;\n\n const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;\n const scalar_t top_grad_value = top_grad * attn_weight;\n scalar_t grad_h_weight = 0, grad_w_weight = 0;\n\n scalar_t v1 = 0;\n if (h_low >= 0 && w_low >= 0)\n {\n const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;\n v1 = bottom_data[ptr1];\n grad_h_weight -= hw * v1;\n grad_w_weight -= hh * v1;\n atomicAdd(grad_value+ptr1, w1*top_grad_value);\n }\n scalar_t v2 = 0;\n if (h_low >= 0 && w_high <= width - 1)\n {\n const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;\n v2 = bottom_data[ptr2];\n grad_h_weight -= lw * v2;\n grad_w_weight += hh * v2;\n atomicAdd(grad_value+ptr2, w2*top_grad_value);\n }\n scalar_t v3 = 0;\n if (h_high <= height - 1 && w_low >= 0)\n {\n const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;\n v3 = bottom_data[ptr3];\n grad_h_weight += hw * v3;\n grad_w_weight -= lh * v3;\n atomicAdd(grad_value+ptr3, w3*top_grad_value); \n }\n scalar_t v4 = 0;\n if (h_high <= height - 1 && w_high <= width - 1)\n {\n const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;\n v4 = bottom_data[ptr4];\n grad_h_weight += lw * v4;\n grad_w_weight += lh * v4;\n atomicAdd(grad_value+ptr4, w4*top_grad_value);\n }\n\n const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);\n *grad_attn_weight = top_grad * val;\n *grad_sampling_loc = width * grad_w_weight * top_grad_value;\n *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value;\n}\n\n\ntemplate \n__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data, \n const int &height, const int &width, const int &nheads, const int &channels,\n const scalar_t &h, const scalar_t &w, const int &m, const int &c,\n const scalar_t &top_grad,\n const scalar_t &attn_weight,\n scalar_t* &grad_value, \n scalar_t* grad_sampling_loc,\n scalar_t* grad_attn_weight)\n{\n const int h_low = floor(h);\n const int w_low = floor(w);\n const int h_high = h_low + 1;\n const int w_high = w_low + 1;\n\n const scalar_t lh = h - h_low;\n const scalar_t lw = w - w_low;\n const scalar_t hh = 1 - lh, hw = 1 - lw;\n\n const int w_stride = nheads * channels;\n const int h_stride = width * w_stride;\n const int h_low_ptr_offset = h_low * h_stride;\n const int h_high_ptr_offset = h_low_ptr_offset + h_stride;\n const int w_low_ptr_offset = w_low * w_stride;\n const int w_high_ptr_offset = w_low_ptr_offset + w_stride;\n const int base_ptr = m * channels + c;\n\n const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;\n const scalar_t top_grad_value = top_grad * attn_weight;\n scalar_t grad_h_weight = 0, grad_w_weight = 0;\n\n scalar_t v1 = 0;\n if (h_low >= 0 && w_low >= 0)\n {\n const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;\n v1 = bottom_data[ptr1];\n grad_h_weight -= hw * v1;\n grad_w_weight -= hh * v1;\n atomicAdd(grad_value+ptr1, w1*top_grad_value);\n }\n scalar_t v2 = 0;\n if (h_low >= 0 && w_high <= width - 1)\n {\n const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;\n v2 = bottom_data[ptr2];\n grad_h_weight -= lw * v2;\n grad_w_weight += hh * v2;\n atomicAdd(grad_value+ptr2, w2*top_grad_value);\n }\n scalar_t v3 = 0;\n if (h_high <= height - 1 && w_low >= 0)\n {\n const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;\n v3 = bottom_data[ptr3];\n grad_h_weight += hw * v3;\n grad_w_weight -= lh * v3;\n atomicAdd(grad_value+ptr3, w3*top_grad_value); \n }\n scalar_t v4 = 0;\n if (h_high <= height - 1 && w_high <= width - 1)\n {\n const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;\n v4 = bottom_data[ptr4];\n grad_h_weight += lw * v4;\n grad_w_weight += lh * v4;\n atomicAdd(grad_value+ptr4, w4*top_grad_value);\n }\n\n const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);\n atomicAdd(grad_attn_weight, top_grad * val); \n atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value);\n atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value);\n}\n\n\ntemplate \n__global__ void ms_deformable_im2col_gpu_kernel(const int n,\n const scalar_t *data_value, \n const int64_t *data_spatial_shapes,\n const int64_t *data_level_start_index, \n const scalar_t *data_sampling_loc,\n const scalar_t *data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point,\n scalar_t *data_col)\n{\n CUDA_KERNEL_LOOP(index, n)\n {\n int _temp = index;\n const int c_col = _temp % channels;\n _temp /= channels;\n const int sampling_index = _temp; \n const int m_col = _temp % num_heads;\n _temp /= num_heads;\n const int q_col = _temp % num_query;\n _temp /= num_query;\n const int b_col = _temp;\n\n scalar_t *data_col_ptr = data_col + index;\n int data_weight_ptr = sampling_index * num_levels * num_point;\n int data_loc_w_ptr = data_weight_ptr << 1;\n const int qid_stride = num_heads * channels;\n const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n scalar_t col = 0;\n \n for (int l_col=0; l_col < num_levels; ++l_col)\n {\n const int level_start_id = data_level_start_index[l_col];\n const int spatial_h_ptr = l_col << 1;\n const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride);\n for (int p_col=0; p_col < num_point; ++p_col)\n {\n const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n const scalar_t h_im = loc_h * spatial_h - 0.5;\n const scalar_t w_im = loc_w * spatial_w - 0.5;\n\n if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n {\n col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight;\n }\n\n data_weight_ptr += 1;\n data_loc_w_ptr += 2;\n }\n }\n *data_col_ptr = col;\n }\n}\n\ntemplate \n__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n,\n const scalar_t *grad_col,\n const scalar_t *data_value,\n const int64_t *data_spatial_shapes,\n const int64_t *data_level_start_index, \n const scalar_t *data_sampling_loc,\n const scalar_t *data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point,\n scalar_t *grad_value,\n scalar_t *grad_sampling_loc,\n scalar_t *grad_attn_weight)\n{\n CUDA_KERNEL_LOOP(index, n)\n {\n __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];\n __shared__ scalar_t cache_grad_attn_weight[blockSize];\n unsigned int tid = threadIdx.x;\n int _temp = index;\n const int c_col = _temp % channels;\n _temp /= channels;\n const int sampling_index = _temp; \n const int m_col = _temp % num_heads;\n _temp /= num_heads;\n const int q_col = _temp % num_query;\n _temp /= num_query;\n const int b_col = _temp;\n\n const scalar_t top_grad = grad_col[index];\n\n int data_weight_ptr = sampling_index * num_levels * num_point;\n int data_loc_w_ptr = data_weight_ptr << 1;\n const int grad_sampling_ptr = data_weight_ptr;\n grad_sampling_loc += grad_sampling_ptr << 1;\n grad_attn_weight += grad_sampling_ptr;\n const int grad_weight_stride = 1;\n const int grad_loc_stride = 2;\n const int qid_stride = num_heads * channels;\n const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n for (int l_col=0; l_col < num_levels; ++l_col)\n {\n const int level_start_id = data_level_start_index[l_col];\n const int spatial_h_ptr = l_col << 1;\n const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n for (int p_col=0; p_col < num_point; ++p_col)\n {\n const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n const scalar_t h_im = loc_h * spatial_h - 0.5;\n const scalar_t w_im = loc_w * spatial_w - 0.5;\n *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;\n *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;\n *(cache_grad_attn_weight+threadIdx.x)=0;\n if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n {\n ms_deform_attn_col2im_bilinear(\n data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n top_grad, weight, grad_value_ptr, \n cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);\n }\n \n __syncthreads();\n if (tid == 0)\n {\n scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];\n int sid=2;\n for (unsigned int tid = 1; tid < blockSize; ++tid)\n {\n _grad_w += cache_grad_sampling_loc[sid];\n _grad_h += cache_grad_sampling_loc[sid + 1];\n _grad_a += cache_grad_attn_weight[tid];\n sid += 2;\n }\n \n \n *grad_sampling_loc = _grad_w;\n *(grad_sampling_loc + 1) = _grad_h;\n *grad_attn_weight = _grad_a;\n }\n __syncthreads();\n\n data_weight_ptr += 1;\n data_loc_w_ptr += 2;\n grad_attn_weight += grad_weight_stride;\n grad_sampling_loc += grad_loc_stride;\n }\n }\n }\n}\n\n\ntemplate \n__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n,\n const scalar_t *grad_col,\n const scalar_t *data_value,\n const int64_t *data_spatial_shapes,\n const int64_t *data_level_start_index, \n const scalar_t *data_sampling_loc,\n const scalar_t *data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point,\n scalar_t *grad_value,\n scalar_t *grad_sampling_loc,\n scalar_t *grad_attn_weight)\n{\n CUDA_KERNEL_LOOP(index, n)\n {\n __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];\n __shared__ scalar_t cache_grad_attn_weight[blockSize];\n unsigned int tid = threadIdx.x;\n int _temp = index;\n const int c_col = _temp % channels;\n _temp /= channels;\n const int sampling_index = _temp; \n const int m_col = _temp % num_heads;\n _temp /= num_heads;\n const int q_col = _temp % num_query;\n _temp /= num_query;\n const int b_col = _temp;\n\n const scalar_t top_grad = grad_col[index];\n\n int data_weight_ptr = sampling_index * num_levels * num_point;\n int data_loc_w_ptr = data_weight_ptr << 1;\n const int grad_sampling_ptr = data_weight_ptr;\n grad_sampling_loc += grad_sampling_ptr << 1;\n grad_attn_weight += grad_sampling_ptr;\n const int grad_weight_stride = 1;\n const int grad_loc_stride = 2;\n const int qid_stride = num_heads * channels;\n const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n for (int l_col=0; l_col < num_levels; ++l_col)\n {\n const int level_start_id = data_level_start_index[l_col];\n const int spatial_h_ptr = l_col << 1;\n const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n for (int p_col=0; p_col < num_point; ++p_col)\n {\n const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n const scalar_t h_im = loc_h * spatial_h - 0.5;\n const scalar_t w_im = loc_w * spatial_w - 0.5;\n *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;\n *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;\n *(cache_grad_attn_weight+threadIdx.x)=0;\n if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n {\n ms_deform_attn_col2im_bilinear(\n data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n top_grad, weight, grad_value_ptr, \n cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);\n }\n \n __syncthreads();\n\n for (unsigned int s=blockSize/2; s>0; s>>=1)\n {\n if (tid < s) {\n const unsigned int xid1 = tid << 1;\n const unsigned int xid2 = (tid + s) << 1;\n cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];\n cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];\n cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];\n }\n __syncthreads();\n }\n\n if (tid == 0)\n { \n *grad_sampling_loc = cache_grad_sampling_loc[0];\n *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];\n *grad_attn_weight = cache_grad_attn_weight[0];\n }\n __syncthreads();\n\n data_weight_ptr += 1;\n data_loc_w_ptr += 2;\n grad_attn_weight += grad_weight_stride;\n grad_sampling_loc += grad_loc_stride;\n }\n }\n }\n}\n\n\ntemplate \n__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n,\n const scalar_t *grad_col,\n const scalar_t *data_value,\n const int64_t *data_spatial_shapes,\n const int64_t *data_level_start_index, \n const scalar_t *data_sampling_loc,\n const scalar_t *data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point,\n scalar_t *grad_value,\n scalar_t *grad_sampling_loc,\n scalar_t *grad_attn_weight)\n{\n CUDA_KERNEL_LOOP(index, n)\n {\n extern __shared__ int _s[];\n scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;\n scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;\n unsigned int tid = threadIdx.x;\n int _temp = index;\n const int c_col = _temp % channels;\n _temp /= channels;\n const int sampling_index = _temp; \n const int m_col = _temp % num_heads;\n _temp /= num_heads;\n const int q_col = _temp % num_query;\n _temp /= num_query;\n const int b_col = _temp;\n\n const scalar_t top_grad = grad_col[index];\n\n int data_weight_ptr = sampling_index * num_levels * num_point;\n int data_loc_w_ptr = data_weight_ptr << 1;\n const int grad_sampling_ptr = data_weight_ptr;\n grad_sampling_loc += grad_sampling_ptr << 1;\n grad_attn_weight += grad_sampling_ptr;\n const int grad_weight_stride = 1;\n const int grad_loc_stride = 2;\n const int qid_stride = num_heads * channels;\n const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n for (int l_col=0; l_col < num_levels; ++l_col)\n {\n const int level_start_id = data_level_start_index[l_col];\n const int spatial_h_ptr = l_col << 1;\n const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n for (int p_col=0; p_col < num_point; ++p_col)\n {\n const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n const scalar_t h_im = loc_h * spatial_h - 0.5;\n const scalar_t w_im = loc_w * spatial_w - 0.5;\n *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;\n *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;\n *(cache_grad_attn_weight+threadIdx.x)=0;\n if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n {\n ms_deform_attn_col2im_bilinear(\n data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n top_grad, weight, grad_value_ptr, \n cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);\n }\n \n __syncthreads();\n if (tid == 0)\n {\n scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];\n int sid=2;\n for (unsigned int tid = 1; tid < blockDim.x; ++tid)\n {\n _grad_w += cache_grad_sampling_loc[sid];\n _grad_h += cache_grad_sampling_loc[sid + 1];\n _grad_a += cache_grad_attn_weight[tid];\n sid += 2;\n }\n \n \n *grad_sampling_loc = _grad_w;\n *(grad_sampling_loc + 1) = _grad_h;\n *grad_attn_weight = _grad_a;\n }\n __syncthreads();\n\n data_weight_ptr += 1;\n data_loc_w_ptr += 2;\n grad_attn_weight += grad_weight_stride;\n grad_sampling_loc += grad_loc_stride;\n }\n }\n }\n}\n\ntemplate \n__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n,\n const scalar_t *grad_col,\n const scalar_t *data_value,\n const int64_t *data_spatial_shapes,\n const int64_t *data_level_start_index, \n const scalar_t *data_sampling_loc,\n const scalar_t *data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point,\n scalar_t *grad_value,\n scalar_t *grad_sampling_loc,\n scalar_t *grad_attn_weight)\n{\n CUDA_KERNEL_LOOP(index, n)\n {\n extern __shared__ int _s[];\n scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;\n scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;\n unsigned int tid = threadIdx.x;\n int _temp = index;\n const int c_col = _temp % channels;\n _temp /= channels;\n const int sampling_index = _temp; \n const int m_col = _temp % num_heads;\n _temp /= num_heads;\n const int q_col = _temp % num_query;\n _temp /= num_query;\n const int b_col = _temp;\n\n const scalar_t top_grad = grad_col[index];\n\n int data_weight_ptr = sampling_index * num_levels * num_point;\n int data_loc_w_ptr = data_weight_ptr << 1;\n const int grad_sampling_ptr = data_weight_ptr;\n grad_sampling_loc += grad_sampling_ptr << 1;\n grad_attn_weight += grad_sampling_ptr;\n const int grad_weight_stride = 1;\n const int grad_loc_stride = 2;\n const int qid_stride = num_heads * channels;\n const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n for (int l_col=0; l_col < num_levels; ++l_col)\n {\n const int level_start_id = data_level_start_index[l_col];\n const int spatial_h_ptr = l_col << 1;\n const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n for (int p_col=0; p_col < num_point; ++p_col)\n {\n const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n const scalar_t h_im = loc_h * spatial_h - 0.5;\n const scalar_t w_im = loc_w * spatial_w - 0.5;\n *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;\n *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;\n *(cache_grad_attn_weight+threadIdx.x)=0;\n if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n {\n ms_deform_attn_col2im_bilinear(\n data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n top_grad, weight, grad_value_ptr, \n cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);\n }\n \n __syncthreads();\n\n for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)\n {\n if (tid < s) {\n const unsigned int xid1 = tid << 1;\n const unsigned int xid2 = (tid + s) << 1;\n cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];\n cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];\n cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];\n if (tid + (s << 1) < spre)\n {\n cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];\n cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];\n cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];\n } \n }\n __syncthreads();\n }\n\n if (tid == 0)\n {\n *grad_sampling_loc = cache_grad_sampling_loc[0];\n *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];\n *grad_attn_weight = cache_grad_attn_weight[0];\n }\n __syncthreads();\n\n data_weight_ptr += 1;\n data_loc_w_ptr += 2;\n grad_attn_weight += grad_weight_stride;\n grad_sampling_loc += grad_loc_stride;\n }\n }\n }\n}\n\ntemplate \n__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n,\n const scalar_t *grad_col,\n const scalar_t *data_value,\n const int64_t *data_spatial_shapes,\n const int64_t *data_level_start_index, \n const scalar_t *data_sampling_loc,\n const scalar_t *data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point,\n scalar_t *grad_value,\n scalar_t *grad_sampling_loc,\n scalar_t *grad_attn_weight)\n{\n CUDA_KERNEL_LOOP(index, n)\n {\n extern __shared__ int _s[];\n scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;\n scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;\n unsigned int tid = threadIdx.x;\n int _temp = index;\n const int c_col = _temp % channels;\n _temp /= channels;\n const int sampling_index = _temp; \n const int m_col = _temp % num_heads;\n _temp /= num_heads;\n const int q_col = _temp % num_query;\n _temp /= num_query;\n const int b_col = _temp;\n\n const scalar_t top_grad = grad_col[index];\n\n int data_weight_ptr = sampling_index * num_levels * num_point;\n int data_loc_w_ptr = data_weight_ptr << 1;\n const int grad_sampling_ptr = data_weight_ptr;\n grad_sampling_loc += grad_sampling_ptr << 1;\n grad_attn_weight += grad_sampling_ptr;\n const int grad_weight_stride = 1;\n const int grad_loc_stride = 2;\n const int qid_stride = num_heads * channels;\n const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n for (int l_col=0; l_col < num_levels; ++l_col)\n {\n const int level_start_id = data_level_start_index[l_col];\n const int spatial_h_ptr = l_col << 1;\n const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n for (int p_col=0; p_col < num_point; ++p_col)\n {\n const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n const scalar_t h_im = loc_h * spatial_h - 0.5;\n const scalar_t w_im = loc_w * spatial_w - 0.5;\n *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;\n *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;\n *(cache_grad_attn_weight+threadIdx.x)=0;\n if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n {\n ms_deform_attn_col2im_bilinear(\n data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n top_grad, weight, grad_value_ptr, \n cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);\n }\n \n __syncthreads();\n\n for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)\n {\n if (tid < s) {\n const unsigned int xid1 = tid << 1;\n const unsigned int xid2 = (tid + s) << 1;\n cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];\n cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];\n cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];\n if (tid + (s << 1) < spre)\n {\n cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];\n cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];\n cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];\n }\n }\n __syncthreads();\n }\n\n if (tid == 0)\n {\n atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]);\n atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]);\n atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]);\n }\n __syncthreads();\n\n data_weight_ptr += 1;\n data_loc_w_ptr += 2;\n grad_attn_weight += grad_weight_stride;\n grad_sampling_loc += grad_loc_stride;\n }\n }\n }\n}\n\n\ntemplate \n__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n,\n const scalar_t *grad_col,\n const scalar_t *data_value,\n const int64_t *data_spatial_shapes,\n const int64_t *data_level_start_index, \n const scalar_t *data_sampling_loc,\n const scalar_t *data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point,\n scalar_t *grad_value,\n scalar_t *grad_sampling_loc,\n scalar_t *grad_attn_weight)\n{\n CUDA_KERNEL_LOOP(index, n)\n {\n int _temp = index;\n const int c_col = _temp % channels;\n _temp /= channels;\n const int sampling_index = _temp; \n const int m_col = _temp % num_heads;\n _temp /= num_heads;\n const int q_col = _temp % num_query;\n _temp /= num_query;\n const int b_col = _temp;\n\n const scalar_t top_grad = grad_col[index];\n\n int data_weight_ptr = sampling_index * num_levels * num_point;\n int data_loc_w_ptr = data_weight_ptr << 1;\n const int grad_sampling_ptr = data_weight_ptr;\n grad_sampling_loc += grad_sampling_ptr << 1;\n grad_attn_weight += grad_sampling_ptr;\n const int grad_weight_stride = 1;\n const int grad_loc_stride = 2;\n const int qid_stride = num_heads * channels;\n const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n for (int l_col=0; l_col < num_levels; ++l_col)\n {\n const int level_start_id = data_level_start_index[l_col];\n const int spatial_h_ptr = l_col << 1;\n const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n for (int p_col=0; p_col < num_point; ++p_col)\n {\n const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n const scalar_t h_im = loc_h * spatial_h - 0.5;\n const scalar_t w_im = loc_w * spatial_w - 0.5;\n if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n {\n ms_deform_attn_col2im_bilinear_gm(\n data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n top_grad, weight, grad_value_ptr, \n grad_sampling_loc, grad_attn_weight);\n }\n data_weight_ptr += 1;\n data_loc_w_ptr += 2;\n grad_attn_weight += grad_weight_stride;\n grad_sampling_loc += grad_loc_stride;\n }\n }\n }\n}\n\n\ntemplate \nvoid ms_deformable_im2col_cuda(cudaStream_t stream,\n const scalar_t* data_value,\n const int64_t* data_spatial_shapes, \n const int64_t* data_level_start_index, \n const scalar_t* data_sampling_loc,\n const scalar_t* data_attn_weight,\n const int batch_size,\n const int spatial_size, \n const int num_heads, \n const int channels, \n const int num_levels, \n const int num_query,\n const int num_point,\n scalar_t* data_col)\n{\n const int num_kernels = batch_size * num_query * num_heads * channels;\n const int num_actual_kernels = batch_size * num_query * num_heads * channels;\n const int num_threads = CUDA_NUM_THREADS;\n ms_deformable_im2col_gpu_kernel\n <<>>(\n num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, \n batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col);\n \n cudaError_t err = cudaGetLastError();\n if (err != cudaSuccess)\n {\n printf(\"error in ms_deformable_im2col_cuda: %s\\n\", cudaGetErrorString(err));\n }\n\n}\n\ntemplate \nvoid ms_deformable_col2im_cuda(cudaStream_t stream,\n const scalar_t* grad_col,\n const scalar_t* data_value,\n const int64_t * data_spatial_shapes,\n const int64_t * data_level_start_index,\n const scalar_t * data_sampling_loc,\n const scalar_t * data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point, \n scalar_t* grad_value,\n scalar_t* grad_sampling_loc,\n scalar_t* grad_attn_weight)\n{\n const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels;\n const int num_kernels = batch_size * num_query * num_heads * channels;\n const int num_actual_kernels = batch_size * num_query * num_heads * channels;\n if (channels > 1024)\n {\n if ((channels & 1023) == 0)\n {\n ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n }\n else\n {\n ms_deformable_col2im_gpu_kernel_gm\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n }\n }\n else{\n switch(channels)\n {\n case 1:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 2:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 4:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 8:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 16:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 32:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 64:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 128:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 256:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 512:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 1024:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n default:\n if (channels < 64)\n {\n ms_deformable_col2im_gpu_kernel_shm_reduce_v1\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n }\n else\n {\n ms_deformable_col2im_gpu_kernel_shm_reduce_v2\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n }\n }\n }\n cudaError_t err = cudaGetLastError();\n if (err != cudaSuccess)\n {\n printf(\"error in ms_deformable_col2im_cuda: %s\\n\", cudaGetErrorString(err));\n }\n\n}" }, { "path": "models/ops/src/ms_deform_attn.h", "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#pragma once\n\n#include \"cpu/ms_deform_attn_cpu.h\"\n\n#ifdef WITH_CUDA\n#include \"cuda/ms_deform_attn_cuda.h\"\n#endif\n\n\nat::Tensor\nms_deform_attn_forward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const int im2col_step)\n{\n if (value.type().is_cuda())\n {\n#ifdef WITH_CUDA\n return ms_deform_attn_cuda_forward(\n value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step);\n#else\n AT_ERROR(\"Not compiled with GPU support\");\n#endif\n }\n AT_ERROR(\"Not implemented on the CPU\");\n}\n\nstd::vector\nms_deform_attn_backward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const at::Tensor &grad_output,\n const int im2col_step)\n{\n if (value.type().is_cuda())\n {\n#ifdef WITH_CUDA\n return ms_deform_attn_cuda_backward(\n value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step);\n#else\n AT_ERROR(\"Not compiled with GPU support\");\n#endif\n }\n AT_ERROR(\"Not implemented on the CPU\");\n}\n\n" }, { "path": "models/ops/src/vision.cpp", "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#include \"ms_deform_attn.h\"\n\nPYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {\n m.def(\"ms_deform_attn_forward\", &ms_deform_attn_forward, \"ms_deform_attn_forward\");\n m.def(\"ms_deform_attn_backward\", &ms_deform_attn_backward, \"ms_deform_attn_backward\");\n}\n" }, { "path": "models/ops/test.py", "content": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------------------------------\n# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n# ------------------------------------------------------------------------------------------------\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport time\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import gradcheck\n\nfrom functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch\n\n\nN, M, D = 1, 2, 2\nLq, L, P = 2, 2, 2\nshapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()\nlevel_start_index = torch.cat((shapes.new_zeros((1, )), shapes.prod(1).cumsum(0)[:-1]))\nS = sum([(H*W).item() for H, W in shapes])\n\n\ntorch.manual_seed(3)\n\n\n@torch.no_grad()\ndef check_forward_equal_with_pytorch_double():\n value = torch.rand(N, S, M, D).cuda() * 0.01\n sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()\n attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5\n attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)\n im2col_step = 2\n output_pytorch = ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double()).detach().cpu()\n output_cuda = MSDeformAttnFunction.apply(value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step).detach().cpu()\n fwdok = torch.allclose(output_cuda, output_pytorch)\n max_abs_err = (output_cuda - output_pytorch).abs().max()\n max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()\n\n print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')\n\n\n@torch.no_grad()\ndef check_forward_equal_with_pytorch_float():\n value = torch.rand(N, S, M, D).cuda() * 0.01\n sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()\n attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5\n attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)\n im2col_step = 2\n output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu()\n output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu()\n fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)\n max_abs_err = (output_cuda - output_pytorch).abs().max()\n max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()\n\n print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')\n\n\ndef check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True):\n\n value = torch.rand(N, S, M, channels).cuda() * 0.01\n sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()\n attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5\n attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)\n im2col_step = 2\n func = MSDeformAttnFunction.apply\n\n value.requires_grad = grad_value\n sampling_locations.requires_grad = grad_sampling_loc\n attention_weights.requires_grad = grad_attn_weight\n\n gradok = gradcheck(func, (value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step))\n\n print(f'* {gradok} check_gradient_numerical(D={channels})')\n\n\nif __name__ == '__main__':\n check_forward_equal_with_pytorch_double()\n check_forward_equal_with_pytorch_float()\n\n for channels in [30, 32, 64, 71, 1025, 2048, 3096]:\n check_gradient_numerical(channels, True, True, True)\n\n\n\n" }, { "path": "models/position_encoding.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\n\"\"\"\nVarious positional encodings for the transformer.\n\"\"\"\nimport math\nimport torch\nfrom torch import nn\n\nfrom util.misc import NestedTensor\n\n\nclass PositionEmbeddingSine(nn.Module):\n \"\"\"\n This is a more standard version of the position embedding, very similar to the one\n used by the Attention is all you need paper, generalized to work on images.\n \"\"\"\n def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):\n super().__init__()\n self.num_pos_feats = num_pos_feats\n self.temperature = temperature\n self.normalize = normalize\n if scale is not None and normalize is False:\n raise ValueError(\"normalize should be True if scale is passed\")\n if scale is None:\n scale = 2 * math.pi\n self.scale = scale\n\n def forward(self, tensor_list: NestedTensor):\n x = tensor_list.tensors\n mask = tensor_list.mask\n assert mask is not None\n not_mask = ~mask\n y_embed = not_mask.cumsum(1, dtype=torch.float32)\n x_embed = not_mask.cumsum(2, dtype=torch.float32)\n if self.normalize:\n eps = 1e-6\n y_embed = (y_embed - 0.5) / (y_embed[:, -1:, :] + eps) * self.scale\n x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale\n\n dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n pos_x = x_embed[:, :, :, None] / dim_t\n pos_y = y_embed[:, :, :, None] / dim_t\n pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)\n pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)\n pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n return pos\n\n\nclass PositionEmbeddingLearned(nn.Module):\n \"\"\"\n Absolute pos embedding, learned.\n \"\"\"\n def __init__(self, num_pos_feats=256):\n super().__init__()\n self.row_embed = nn.Embedding(50, num_pos_feats)\n self.col_embed = nn.Embedding(50, num_pos_feats)\n self.reset_parameters()\n\n def reset_parameters(self):\n nn.init.uniform_(self.row_embed.weight)\n nn.init.uniform_(self.col_embed.weight)\n\n def forward(self, tensor_list: NestedTensor):\n x = tensor_list.tensors\n h, w = x.shape[-2:]\n i = torch.arange(w, device=x.device)\n j = torch.arange(h, device=x.device)\n x_emb = self.col_embed(i)\n y_emb = self.row_embed(j)\n pos = torch.cat([\n x_emb.unsqueeze(0).repeat(h, 1, 1),\n y_emb.unsqueeze(1).repeat(1, w, 1),\n ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1)\n return pos\n\n\ndef build_position_encoding(args):\n N_steps = args.hidden_dim // 2\n if args.position_embedding in ('v2', 'sine'):\n # TODO find a better way of exposing other arguments\n position_embedding = PositionEmbeddingSine(N_steps, normalize=True)\n elif args.position_embedding in ('v3', 'learned'):\n position_embedding = PositionEmbeddingLearned(N_steps)\n else:\n raise ValueError(f\"not supported {args.position_embedding}\")\n\n return position_embedding\n" }, { "path": "models/qim.py", "content": "# ------------------------------------------------------------------------\r\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\r\n# ------------------------------------------------------------------------\r\n\r\nimport random\r\nimport torch\r\nfrom torch import nn, Tensor\r\nfrom torch.nn import functional as F\r\nfrom typing import Optional, List\r\n\r\nfrom util import box_ops\r\nfrom util.misc import inverse_sigmoid\r\nfrom models.structures import Boxes, Instances, pairwise_iou\r\n\r\n\r\ndef random_drop_tracks(track_instances: Instances, drop_probability: float) -> Instances:\r\n if drop_probability > 0 and len(track_instances) > 0:\r\n keep_idxes = torch.rand_like(track_instances.scores) > drop_probability\r\n track_instances = track_instances[keep_idxes]\r\n return track_instances\r\n\r\n\r\nclass QueryInteractionBase(nn.Module):\r\n def __init__(self, args, dim_in, hidden_dim, dim_out):\r\n super().__init__()\r\n self.args = args\r\n self._build_layers(args, dim_in, hidden_dim, dim_out)\r\n self._reset_parameters()\r\n\r\n def _build_layers(self, args, dim_in, hidden_dim, dim_out):\r\n raise NotImplementedError()\r\n\r\n def _reset_parameters(self):\r\n for p in self.parameters():\r\n if p.dim() > 1:\r\n nn.init.xavier_uniform_(p)\r\n\r\n def _select_active_tracks(self, data: dict) -> Instances:\r\n raise NotImplementedError()\r\n\r\n def _update_track_embedding(self, track_instances):\r\n raise NotImplementedError()\r\n\r\n\r\nclass FFN(nn.Module):\r\n def __init__(self, d_model, d_ffn, dropout=0):\r\n super().__init__()\r\n self.linear1 = nn.Linear(d_model, d_ffn)\r\n self.activation = nn.ReLU(True)\r\n self.dropout1 = nn.Dropout(dropout)\r\n self.linear2 = nn.Linear(d_ffn, d_model)\r\n self.dropout2 = nn.Dropout(dropout)\r\n self.norm = nn.LayerNorm(d_model)\r\n\r\n def forward(self, tgt):\r\n tgt2 = self.linear2(self.dropout1(self.activation(self.linear1(tgt))))\r\n tgt = tgt + self.dropout2(tgt2)\r\n tgt = self.norm(tgt)\r\n return tgt\r\n\r\n\r\nclass QueryInteractionModule(QueryInteractionBase):\r\n def __init__(self, args, dim_in, hidden_dim, dim_out):\r\n super().__init__(args, dim_in, hidden_dim, dim_out)\r\n self.random_drop = args.random_drop\r\n self.fp_ratio = args.fp_ratio\r\n self.update_query_pos = args.update_query_pos\r\n\r\n def _build_layers(self, args, dim_in, hidden_dim, dim_out):\r\n dropout = args.merger_dropout\r\n\r\n self.self_attn = nn.MultiheadAttention(dim_in, 8, dropout)\r\n self.linear1 = nn.Linear(dim_in, hidden_dim)\r\n self.dropout = nn.Dropout(dropout)\r\n self.linear2 = nn.Linear(hidden_dim, dim_in)\r\n\r\n if args.update_query_pos:\r\n self.linear_pos1 = nn.Linear(dim_in, hidden_dim)\r\n self.linear_pos2 = nn.Linear(hidden_dim, dim_in)\r\n self.dropout_pos1 = nn.Dropout(dropout)\r\n self.dropout_pos2 = nn.Dropout(dropout)\r\n self.norm_pos = nn.LayerNorm(dim_in)\r\n\r\n self.linear_feat1 = nn.Linear(dim_in, hidden_dim)\r\n self.linear_feat2 = nn.Linear(hidden_dim, dim_in)\r\n self.dropout_feat1 = nn.Dropout(dropout)\r\n self.dropout_feat2 = nn.Dropout(dropout)\r\n self.norm_feat = nn.LayerNorm(dim_in)\r\n\r\n self.norm1 = nn.LayerNorm(dim_in)\r\n self.norm2 = nn.LayerNorm(dim_in)\r\n if args.update_query_pos:\r\n self.norm3 = nn.LayerNorm(dim_in)\r\n\r\n self.dropout1 = nn.Dropout(dropout)\r\n self.dropout2 = nn.Dropout(dropout)\r\n if args.update_query_pos:\r\n self.dropout3 = nn.Dropout(dropout)\r\n self.dropout4 = nn.Dropout(dropout)\r\n\r\n self.activation = nn.ReLU(True)\r\n\r\n def _random_drop_tracks(self, track_instances: Instances) -> Instances:\r\n return random_drop_tracks(track_instances, self.random_drop)\r\n\r\n def _add_fp_tracks(self, track_instances: Instances, active_track_instances: Instances) -> Instances:\r\n inactive_instances = track_instances[track_instances.obj_idxes < 0]\r\n\r\n # add fp for each active track in a specific probability.\r\n fp_prob = torch.ones_like(active_track_instances.scores) * self.fp_ratio\r\n selected_active_track_instances = active_track_instances[torch.bernoulli(fp_prob).bool()]\r\n\r\n if len(inactive_instances) > 0 and len(selected_active_track_instances) > 0:\r\n num_fp = len(selected_active_track_instances)\r\n if num_fp >= len(inactive_instances):\r\n fp_track_instances = inactive_instances\r\n else:\r\n inactive_boxes = Boxes(box_ops.box_cxcywh_to_xyxy(inactive_instances.pred_boxes))\r\n selected_active_boxes = Boxes(box_ops.box_cxcywh_to_xyxy(selected_active_track_instances.pred_boxes))\r\n ious = pairwise_iou(inactive_boxes, selected_active_boxes)\r\n # select the fp with the largest IoU for each active track.\r\n fp_indexes = ious.max(dim=0).indices\r\n\r\n # remove duplicate fp.\r\n fp_indexes = torch.unique(fp_indexes)\r\n fp_track_instances = inactive_instances[fp_indexes]\r\n\r\n merged_track_instances = Instances.cat([active_track_instances, fp_track_instances])\r\n return merged_track_instances\r\n\r\n return active_track_instances\r\n\r\n def _select_active_tracks(self, data: dict) -> Instances:\r\n track_instances: Instances = data['track_instances']\r\n if self.training:\r\n active_idxes = (track_instances.obj_idxes >= 0) & (track_instances.iou > 0.5)\r\n active_track_instances = track_instances[active_idxes]\r\n # set -2 instead of -1 to ensure that these tracks will not be selected in matching.\r\n active_track_instances = self._random_drop_tracks(active_track_instances)\r\n if self.fp_ratio > 0:\r\n active_track_instances = self._add_fp_tracks(track_instances, active_track_instances)\r\n else:\r\n active_track_instances = track_instances[track_instances.obj_idxes >= 0]\r\n\r\n return active_track_instances\r\n\r\n def _update_track_embedding(self, track_instances: Instances) -> Instances:\r\n if len(track_instances) == 0:\r\n return track_instances\r\n dim = track_instances.query_pos.shape[1]\r\n out_embed = track_instances.output_embedding\r\n query_pos = track_instances.query_pos[:, :dim // 2]\r\n query_feat = track_instances.query_pos[:, dim//2:]\r\n q = k = query_pos + out_embed\r\n\r\n tgt = out_embed\r\n tgt2 = self.self_attn(q[:, None], k[:, None], value=tgt[:, None])[0][:, 0]\r\n tgt = tgt + self.dropout1(tgt2)\r\n tgt = self.norm1(tgt)\r\n\r\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\r\n tgt = tgt + self.dropout2(tgt2)\r\n tgt = self.norm2(tgt)\r\n\r\n if self.update_query_pos:\r\n query_pos2 = self.linear_pos2(self.dropout_pos1(self.activation(self.linear_pos1(tgt))))\r\n query_pos = query_pos + self.dropout_pos2(query_pos2)\r\n query_pos = self.norm_pos(query_pos)\r\n track_instances.query_pos[:, :dim // 2] = query_pos\r\n\r\n query_feat2 = self.linear_feat2(self.dropout_feat1(self.activation(self.linear_feat1(tgt))))\r\n query_feat = query_feat + self.dropout_feat2(query_feat2)\r\n query_feat = self.norm_feat(query_feat)\r\n track_instances.query_pos[:, dim//2:] = query_feat\r\n\r\n track_instances.ref_pts = inverse_sigmoid(track_instances.pred_boxes[:, :2].detach().clone())\r\n return track_instances\r\n\r\n def forward(self, data) -> Instances:\r\n active_track_instances = self._select_active_tracks(data)\r\n active_track_instances = self._update_track_embedding(active_track_instances)\r\n init_track_instances: Instances = data['init_track_instances']\r\n merged_track_instances = Instances.cat([init_track_instances, active_track_instances])\r\n return merged_track_instances\r\n\r\n\r\ndef build(args, layer_name, dim_in, hidden_dim, dim_out):\r\n interaction_layers = {\r\n 'QIM': QueryInteractionModule,\r\n }\r\n assert layer_name in interaction_layers, 'invalid query interaction layer: {}'.format(layer_name)\r\n return interaction_layers[layer_name](args, dim_in, hidden_dim, dim_out)\r\n" }, { "path": "models/relu_dropout.py", "content": "# https://gist.github.com/vadimkantorov/360ece06de4fd2641fa9ed1085f76d48\nimport torch\n\nclass ReLUDropout(torch.nn.Dropout):\n def forward(self, input):\n return relu_dropout(input, p=self.p, training=self.training, inplace=self.inplace)\n\ndef relu_dropout(x, p=0, inplace=False, training=False):\n if not training or p == 0:\n return x.clamp_(min=0) if inplace else x.clamp(min=0)\n\n mask = (x < 0) | (torch.rand_like(x) > 1 - p)\n return x.masked_fill_(mask, 0).div_(1 - p) if inplace else x.masked_fill(mask, 0).div(1 - p)\n" }, { "path": "models/segmentation.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\n\"\"\"\nThis file provides the definition of the convolutional heads used to predict masks, as well as the losses\n\"\"\"\nimport io\nfrom collections import defaultdict\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom PIL import Image\n\nimport util.box_ops as box_ops\nfrom util.misc import NestedTensor, interpolate, nested_tensor_from_tensor_list\n\ntry:\n from panopticapi.utils import id2rgb, rgb2id\nexcept ImportError:\n pass\n\n\nclass DETRsegm(nn.Module):\n def __init__(self, detr, freeze_detr=False):\n super().__init__()\n self.detr = detr\n\n if freeze_detr:\n for p in self.parameters():\n p.requires_grad_(False)\n\n hidden_dim, nheads = detr.transformer.d_model, detr.transformer.nhead\n self.bbox_attention = MHAttentionMap(hidden_dim, hidden_dim, nheads, dropout=0)\n self.mask_head = MaskHeadSmallConv(hidden_dim + nheads, [1024, 512, 256], hidden_dim)\n\n def forward(self, samples: NestedTensor):\n if not isinstance(samples, NestedTensor):\n samples = nested_tensor_from_tensor_list(samples)\n features, pos = self.detr.backbone(samples)\n\n bs = features[-1].tensors.shape[0]\n\n src, mask = features[-1].decompose()\n src_proj = self.detr.input_proj(src)\n hs, memory = self.detr.transformer(src_proj, mask, self.detr.query_embed.weight, pos[-1])\n\n outputs_class = self.detr.class_embed(hs)\n outputs_coord = self.detr.bbox_embed(hs).sigmoid()\n out = {\"pred_logits\": outputs_class[-1], \"pred_boxes\": outputs_coord[-1]}\n if self.detr.aux_loss:\n out[\"aux_outputs\"] = [\n {\"pred_logits\": a, \"pred_boxes\": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])\n ]\n\n # FIXME h_boxes takes the last one computed, keep this in mind\n bbox_mask = self.bbox_attention(hs[-1], memory, mask=mask)\n\n seg_masks = self.mask_head(src_proj, bbox_mask, [features[2].tensors, features[1].tensors, features[0].tensors])\n outputs_seg_masks = seg_masks.view(bs, self.detr.num_queries, seg_masks.shape[-2], seg_masks.shape[-1])\n\n out[\"pred_masks\"] = outputs_seg_masks\n return out\n\n\nclass MaskHeadSmallConv(nn.Module):\n \"\"\"\n Simple convolutional head, using group norm.\n Upsampling is done using a FPN approach\n \"\"\"\n\n def __init__(self, dim, fpn_dims, context_dim):\n super().__init__()\n\n inter_dims = [dim, context_dim // 2, context_dim // 4, context_dim // 8, context_dim // 16, context_dim // 64]\n self.lay1 = torch.nn.Conv2d(dim, dim, 3, padding=1)\n self.gn1 = torch.nn.GroupNorm(8, dim)\n self.lay2 = torch.nn.Conv2d(dim, inter_dims[1], 3, padding=1)\n self.gn2 = torch.nn.GroupNorm(8, inter_dims[1])\n self.lay3 = torch.nn.Conv2d(inter_dims[1], inter_dims[2], 3, padding=1)\n self.gn3 = torch.nn.GroupNorm(8, inter_dims[2])\n self.lay4 = torch.nn.Conv2d(inter_dims[2], inter_dims[3], 3, padding=1)\n self.gn4 = torch.nn.GroupNorm(8, inter_dims[3])\n self.lay5 = torch.nn.Conv2d(inter_dims[3], inter_dims[4], 3, padding=1)\n self.gn5 = torch.nn.GroupNorm(8, inter_dims[4])\n self.out_lay = torch.nn.Conv2d(inter_dims[4], 1, 3, padding=1)\n\n self.dim = dim\n\n self.adapter1 = torch.nn.Conv2d(fpn_dims[0], inter_dims[1], 1)\n self.adapter2 = torch.nn.Conv2d(fpn_dims[1], inter_dims[2], 1)\n self.adapter3 = torch.nn.Conv2d(fpn_dims[2], inter_dims[3], 1)\n\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n nn.init.kaiming_uniform_(m.weight, a=1)\n nn.init.constant_(m.bias, 0)\n\n def forward(self, x, bbox_mask, fpns):\n def expand(tensor, length):\n return tensor.unsqueeze(1).repeat(1, int(length), 1, 1, 1).flatten(0, 1)\n\n x = torch.cat([expand(x, bbox_mask.shape[1]), bbox_mask.flatten(0, 1)], 1)\n\n x = self.lay1(x)\n x = self.gn1(x)\n x = F.relu(x)\n x = self.lay2(x)\n x = self.gn2(x)\n x = F.relu(x)\n\n cur_fpn = self.adapter1(fpns[0])\n if cur_fpn.size(0) != x.size(0):\n cur_fpn = expand(cur_fpn, x.size(0) / cur_fpn.size(0))\n x = cur_fpn + F.interpolate(x, size=cur_fpn.shape[-2:], mode=\"nearest\")\n x = self.lay3(x)\n x = self.gn3(x)\n x = F.relu(x)\n\n cur_fpn = self.adapter2(fpns[1])\n if cur_fpn.size(0) != x.size(0):\n cur_fpn = expand(cur_fpn, x.size(0) / cur_fpn.size(0))\n x = cur_fpn + F.interpolate(x, size=cur_fpn.shape[-2:], mode=\"nearest\")\n x = self.lay4(x)\n x = self.gn4(x)\n x = F.relu(x)\n\n cur_fpn = self.adapter3(fpns[2])\n if cur_fpn.size(0) != x.size(0):\n cur_fpn = expand(cur_fpn, x.size(0) / cur_fpn.size(0))\n x = cur_fpn + F.interpolate(x, size=cur_fpn.shape[-2:], mode=\"nearest\")\n x = self.lay5(x)\n x = self.gn5(x)\n x = F.relu(x)\n\n x = self.out_lay(x)\n return x\n\n\nclass MHAttentionMap(nn.Module):\n \"\"\"This is a 2D attention module, which only returns the attention softmax (no multiplication by value)\"\"\"\n\n def __init__(self, query_dim, hidden_dim, num_heads, dropout=0, bias=True):\n super().__init__()\n self.num_heads = num_heads\n self.hidden_dim = hidden_dim\n self.dropout = nn.Dropout(dropout)\n\n self.q_linear = nn.Linear(query_dim, hidden_dim, bias=bias)\n self.k_linear = nn.Linear(query_dim, hidden_dim, bias=bias)\n\n nn.init.zeros_(self.k_linear.bias)\n nn.init.zeros_(self.q_linear.bias)\n nn.init.xavier_uniform_(self.k_linear.weight)\n nn.init.xavier_uniform_(self.q_linear.weight)\n self.normalize_fact = float(hidden_dim / self.num_heads) ** -0.5\n\n def forward(self, q, k, mask=None):\n q = self.q_linear(q)\n k = F.conv2d(k, self.k_linear.weight.unsqueeze(-1).unsqueeze(-1), self.k_linear.bias)\n qh = q.view(q.shape[0], q.shape[1], self.num_heads, self.hidden_dim // self.num_heads)\n kh = k.view(k.shape[0], self.num_heads, self.hidden_dim // self.num_heads, k.shape[-2], k.shape[-1])\n weights = torch.einsum(\"bqnc,bnchw->bqnhw\", qh * self.normalize_fact, kh)\n\n if mask is not None:\n weights.masked_fill_(mask.unsqueeze(1).unsqueeze(1), float(\"-inf\"))\n weights = F.softmax(weights.flatten(2), dim=-1).view_as(weights)\n weights = self.dropout(weights)\n return weights\n\n\ndef dice_loss(inputs, targets, num_boxes):\n \"\"\"\n Compute the DICE loss, similar to generalized IOU for masks\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n \"\"\"\n inputs = inputs.sigmoid()\n inputs = inputs.flatten(1)\n numerator = 2 * (inputs * targets).sum(1)\n denominator = inputs.sum(-1) + targets.sum(-1)\n loss = 1 - (numerator + 1) / (denominator + 1)\n return loss.sum() / num_boxes\n\n\ndef sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, mean_in_dim1=True):\n \"\"\"\n Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n alpha: (optional) Weighting factor in range (0,1) to balance\n positive vs negative examples. Default = -1 (no weighting).\n gamma: Exponent of the modulating factor (1 - p_t) to\n balance easy vs hard examples.\n Returns:\n Loss tensor\n \"\"\"\n prob = inputs.sigmoid()\n ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=\"none\")\n p_t = prob * targets + (1 - prob) * (1 - targets)\n loss = ce_loss * ((1 - p_t) ** gamma)\n\n if alpha >= 0:\n alpha_t = alpha * targets + (1 - alpha) * (1 - targets)\n loss = alpha_t * loss\n if mean_in_dim1:\n return loss.mean(1).sum() / num_boxes\n else:\n return loss.sum() / num_boxes\n\n\nclass PostProcessSegm(nn.Module):\n def __init__(self, threshold=0.5):\n super().__init__()\n self.threshold = threshold\n\n @torch.no_grad()\n def forward(self, results, outputs, orig_target_sizes, max_target_sizes):\n assert len(orig_target_sizes) == len(max_target_sizes)\n max_h, max_w = max_target_sizes.max(0)[0].tolist()\n outputs_masks = outputs[\"pred_masks\"].squeeze(2)\n outputs_masks = F.interpolate(outputs_masks, size=(max_h, max_w), mode=\"bilinear\", align_corners=False)\n outputs_masks = (outputs_masks.sigmoid() > self.threshold).cpu()\n\n for i, (cur_mask, t, tt) in enumerate(zip(outputs_masks, max_target_sizes, orig_target_sizes)):\n img_h, img_w = t[0], t[1]\n results[i][\"masks\"] = cur_mask[:, :img_h, :img_w].unsqueeze(1)\n results[i][\"masks\"] = F.interpolate(\n results[i][\"masks\"].float(), size=tuple(tt.tolist()), mode=\"nearest\"\n ).byte()\n\n return results\n\n\nclass PostProcessPanoptic(nn.Module):\n \"\"\"This class converts the output of the model to the final panoptic result, in the format expected by the\n coco panoptic API \"\"\"\n\n def __init__(self, is_thing_map, threshold=0.85):\n \"\"\"\n Parameters:\n is_thing_map: This is a whose keys are the class ids, and the values a boolean indicating whether\n the class is a thing (True) or a stuff (False) class\n threshold: confidence threshold: segments with confidence lower than this will be deleted\n \"\"\"\n super().__init__()\n self.threshold = threshold\n self.is_thing_map = is_thing_map\n\n def forward(self, outputs, processed_sizes, target_sizes=None):\n \"\"\" This function computes the panoptic prediction from the model's predictions.\n Parameters:\n outputs: This is a dict coming directly from the model. See the model doc for the content.\n processed_sizes: This is a list of tuples (or torch tensors) of sizes of the images that were passed to the\n model, ie the size after data augmentation but before batching.\n target_sizes: This is a list of tuples (or torch tensors) corresponding to the requested final size\n of each prediction. If left to None, it will default to the processed_sizes\n \"\"\"\n if target_sizes is None:\n target_sizes = processed_sizes\n assert len(processed_sizes) == len(target_sizes)\n out_logits, raw_masks, raw_boxes = outputs[\"pred_logits\"], outputs[\"pred_masks\"], outputs[\"pred_boxes\"]\n assert len(out_logits) == len(raw_masks) == len(target_sizes)\n preds = []\n\n def to_tuple(tup):\n if isinstance(tup, tuple):\n return tup\n return tuple(tup.cpu().tolist())\n\n for cur_logits, cur_masks, cur_boxes, size, target_size in zip(\n out_logits, raw_masks, raw_boxes, processed_sizes, target_sizes\n ):\n # we filter empty queries and detection below threshold\n scores, labels = cur_logits.softmax(-1).max(-1)\n keep = labels.ne(outputs[\"pred_logits\"].shape[-1] - 1) & (scores > self.threshold)\n cur_scores, cur_classes = cur_logits.softmax(-1).max(-1)\n cur_scores = cur_scores[keep]\n cur_classes = cur_classes[keep]\n cur_masks = cur_masks[keep]\n cur_masks = interpolate(cur_masks[None], to_tuple(size), mode=\"bilinear\").squeeze(0)\n cur_boxes = box_ops.box_cxcywh_to_xyxy(cur_boxes[keep])\n\n h, w = cur_masks.shape[-2:]\n assert len(cur_boxes) == len(cur_classes)\n\n # It may be that we have several predicted masks for the same stuff class.\n # In the following, we track the list of masks ids for each stuff class (they are merged later on)\n cur_masks = cur_masks.flatten(1)\n stuff_equiv_classes = defaultdict(lambda: [])\n for k, label in enumerate(cur_classes):\n if not self.is_thing_map[label.item()]:\n stuff_equiv_classes[label.item()].append(k)\n\n def get_ids_area(masks, scores, dedup=False):\n # This helper function creates the final panoptic segmentation image\n # It also returns the area of the masks that appears on the image\n\n m_id = masks.transpose(0, 1).softmax(-1)\n\n if m_id.shape[-1] == 0:\n # We didn't detect any mask :(\n m_id = torch.zeros((h, w), dtype=torch.long, device=m_id.device)\n else:\n m_id = m_id.argmax(-1).view(h, w)\n\n if dedup:\n # Merge the masks corresponding to the same stuff class\n for equiv in stuff_equiv_classes.values():\n if len(equiv) > 1:\n for eq_id in equiv:\n m_id.masked_fill_(m_id.eq(eq_id), equiv[0])\n\n final_h, final_w = to_tuple(target_size)\n\n seg_img = Image.fromarray(id2rgb(m_id.view(h, w).cpu().numpy()))\n seg_img = seg_img.resize(size=(final_w, final_h), resample=Image.NEAREST)\n\n np_seg_img = (\n torch.ByteTensor(torch.ByteStorage.from_buffer(seg_img.tobytes())).view(final_h, final_w, 3).numpy()\n )\n m_id = torch.from_numpy(rgb2id(np_seg_img))\n\n area = []\n for i in range(len(scores)):\n area.append(m_id.eq(i).sum().item())\n return area, seg_img\n\n area, seg_img = get_ids_area(cur_masks, cur_scores, dedup=True)\n if cur_classes.numel() > 0:\n # We know filter empty masks as long as we find some\n while True:\n filtered_small = torch.as_tensor(\n [area[i] <= 4 for i, c in enumerate(cur_classes)], dtype=torch.bool, device=keep.device\n )\n if filtered_small.any().item():\n cur_scores = cur_scores[~filtered_small]\n cur_classes = cur_classes[~filtered_small]\n cur_masks = cur_masks[~filtered_small]\n area, seg_img = get_ids_area(cur_masks, cur_scores)\n else:\n break\n\n else:\n cur_classes = torch.ones(1, dtype=torch.long, device=cur_classes.device)\n\n segments_info = []\n for i, a in enumerate(area):\n cat = cur_classes[i].item()\n segments_info.append({\"id\": i, \"isthing\": self.is_thing_map[cat], \"category_id\": cat, \"area\": a})\n del cur_classes\n\n with io.BytesIO() as out:\n seg_img.save(out, format=\"PNG\")\n predictions = {\"png_string\": out.getvalue(), \"segments_info\": segments_info}\n preds.append(predictions)\n return preds\n" }, { "path": "models/structures/__init__.py", "content": "# ------------------------------------------------------------------------\n# Modified from Detectron2 (https://github.com/facebookresearch/detectron2)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\nfrom .boxes import Boxes, BoxMode, pairwise_iou, pairwise_ioa, matched_boxlist_iou\nfrom .instances import Instances\n\n__all__ = [k for k in globals().keys() if not k.startswith(\"_\")]" }, { "path": "models/structures/boxes.py", "content": "# ------------------------------------------------------------------------\n# Modified from Detectron2 (https://github.com/facebookresearch/detectron2)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\nimport math\nimport numpy as np\nfrom enum import IntEnum, unique\nfrom typing import List, Tuple, Union\nimport torch\nfrom torch import device\n\n_RawBoxType = Union[List[float], Tuple[float, ...], torch.Tensor, np.ndarray]\ndef _maybe_jit_unused(x):\n return x\n\n\n@unique\nclass BoxMode(IntEnum):\n \"\"\"\n Enum of different ways to represent a box.\n \"\"\"\n\n XYXY_ABS = 0\n \"\"\"\n (x0, y0, x1, y1) in absolute floating points coordinates.\n The coordinates in range [0, width or height].\n \"\"\"\n XYWH_ABS = 1\n \"\"\"\n (x0, y0, w, h) in absolute floating points coordinates.\n \"\"\"\n XYXY_REL = 2\n \"\"\"\n Not yet supported!\n (x0, y0, x1, y1) in range [0, 1]. They are relative to the size of the image.\n \"\"\"\n XYWH_REL = 3\n \"\"\"\n Not yet supported!\n (x0, y0, w, h) in range [0, 1]. They are relative to the size of the image.\n \"\"\"\n XYWHA_ABS = 4\n \"\"\"\n (xc, yc, w, h, a) in absolute floating points coordinates.\n (xc, yc) is the center of the rotated box, and the angle a is in degrees ccw.\n \"\"\"\n\n @staticmethod\n def convert(box: _RawBoxType, from_mode: \"BoxMode\", to_mode: \"BoxMode\") -> _RawBoxType:\n \"\"\"\n Args:\n box: can be a k-tuple, k-list or an Nxk array/tensor, where k = 4 or 5\n from_mode, to_mode (BoxMode)\n\n Returns:\n The converted box of the same type.\n \"\"\"\n if from_mode == to_mode:\n return box\n\n original_type = type(box)\n is_numpy = isinstance(box, np.ndarray)\n single_box = isinstance(box, (list, tuple))\n if single_box:\n assert len(box) == 4 or len(box) == 5, (\n \"BoxMode.convert takes either a k-tuple/list or an Nxk array/tensor,\"\n \" where k == 4 or 5\"\n )\n arr = torch.tensor(box)[None, :]\n else:\n # avoid modifying the input box\n if is_numpy:\n arr = torch.from_numpy(np.asarray(box)).clone()\n else:\n arr = box.clone()\n\n assert to_mode not in [BoxMode.XYXY_REL, BoxMode.XYWH_REL] and from_mode not in [\n BoxMode.XYXY_REL,\n BoxMode.XYWH_REL,\n ], \"Relative mode not yet supported!\"\n\n if from_mode == BoxMode.XYWHA_ABS and to_mode == BoxMode.XYXY_ABS:\n assert (\n arr.shape[-1] == 5\n ), \"The last dimension of input shape must be 5 for XYWHA format\"\n original_dtype = arr.dtype\n arr = arr.double()\n\n w = arr[:, 2]\n h = arr[:, 3]\n a = arr[:, 4]\n c = torch.abs(torch.cos(a * math.pi / 180.0))\n s = torch.abs(torch.sin(a * math.pi / 180.0))\n # This basically computes the horizontal bounding rectangle of the rotated box\n new_w = c * w + s * h\n new_h = c * h + s * w\n\n # convert center to top-left corner\n arr[:, 0] -= new_w / 2.0\n arr[:, 1] -= new_h / 2.0\n # bottom-right corner\n arr[:, 2] = arr[:, 0] + new_w\n arr[:, 3] = arr[:, 1] + new_h\n\n arr = arr[:, :4].to(dtype=original_dtype)\n elif from_mode == BoxMode.XYWH_ABS and to_mode == BoxMode.XYWHA_ABS:\n original_dtype = arr.dtype\n arr = arr.double()\n arr[:, 0] += arr[:, 2] / 2.0\n arr[:, 1] += arr[:, 3] / 2.0\n angles = torch.zeros((arr.shape[0], 1), dtype=arr.dtype)\n arr = torch.cat((arr, angles), axis=1).to(dtype=original_dtype)\n else:\n if to_mode == BoxMode.XYXY_ABS and from_mode == BoxMode.XYWH_ABS:\n arr[:, 2] += arr[:, 0]\n arr[:, 3] += arr[:, 1]\n elif from_mode == BoxMode.XYXY_ABS and to_mode == BoxMode.XYWH_ABS:\n arr[:, 2] -= arr[:, 0]\n arr[:, 3] -= arr[:, 1]\n else:\n raise NotImplementedError(\n \"Conversion from BoxMode {} to {} is not supported yet\".format(\n from_mode, to_mode\n )\n )\n\n if single_box:\n return original_type(arr.flatten().tolist())\n if is_numpy:\n return arr.numpy()\n else:\n return arr\n\n\nclass Boxes:\n \"\"\"\n This structure stores a list of boxes as a Nx4 torch.Tensor.\n It supports some common methods about boxes\n (`area`, `clip`, `nonempty`, etc),\n and also behaves like a Tensor\n (support indexing, `to(device)`, `.device`, and iteration over all boxes)\n\n Attributes:\n tensor (torch.Tensor): float matrix of Nx4. Each row is (x1, y1, x2, y2).\n \"\"\"\n\n def __init__(self, tensor: torch.Tensor):\n \"\"\"\n Args:\n tensor (Tensor[float]): a Nx4 matrix. Each row is (x1, y1, x2, y2).\n \"\"\"\n device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device(\"cpu\")\n tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)\n if tensor.numel() == 0:\n # Use reshape, so we don't end up creating a new tensor that does not depend on\n # the inputs (and consequently confuses jit)\n tensor = tensor.reshape((-1, 4)).to(dtype=torch.float32, device=device)\n assert tensor.dim() == 2 and tensor.size(-1) == 4, tensor.size()\n\n self.tensor = tensor\n\n def clone(self) -> \"Boxes\":\n \"\"\"\n Clone the Boxes.\n\n Returns:\n Boxes\n \"\"\"\n return Boxes(self.tensor.clone())\n\n @_maybe_jit_unused\n def to(self, device: torch.device):\n # Boxes are assumed float32 and does not support to(dtype)\n return Boxes(self.tensor.to(device=device))\n\n def area(self) -> torch.Tensor:\n \"\"\"\n Computes the area of all the boxes.\n\n Returns:\n torch.Tensor: a vector with areas of each box.\n \"\"\"\n box = self.tensor\n area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1])\n return area\n\n def clip(self, box_size: Tuple[int, int]) -> None:\n \"\"\"\n Clip (in place) the boxes by limiting x coordinates to the range [0, width]\n and y coordinates to the range [0, height].\n\n Args:\n box_size (height, width): The clipping box's size.\n \"\"\"\n assert torch.isfinite(self.tensor).all(), \"Box tensor contains infinite or NaN!\"\n h, w = box_size\n x1 = self.tensor[:, 0].clamp(min=0, max=w)\n y1 = self.tensor[:, 1].clamp(min=0, max=h)\n x2 = self.tensor[:, 2].clamp(min=0, max=w)\n y2 = self.tensor[:, 3].clamp(min=0, max=h)\n self.tensor = torch.stack((x1, y1, x2, y2), dim=-1)\n\n def nonempty(self, threshold: float = 0.0) -> torch.Tensor:\n \"\"\"\n Find boxes that are non-empty.\n A box is considered empty, if either of its side is no larger than threshold.\n\n Returns:\n Tensor:\n a binary vector which represents whether each box is empty\n (False) or non-empty (True).\n \"\"\"\n box = self.tensor\n widths = box[:, 2] - box[:, 0]\n heights = box[:, 3] - box[:, 1]\n keep = (widths > threshold) & (heights > threshold)\n return keep\n\n def __getitem__(self, item) -> \"Boxes\":\n \"\"\"\n Args:\n item: int, slice, or a BoolTensor\n\n Returns:\n Boxes: Create a new :class:`Boxes` by indexing.\n\n The following usage are allowed:\n\n 1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box.\n 2. `new_boxes = boxes[2:10]`: return a slice of boxes.\n 3. `new_boxes = boxes[vector]`, where vector is a torch.BoolTensor\n with `length = len(boxes)`. Nonzero elements in the vector will be selected.\n\n Note that the returned Boxes might share storage with this Boxes,\n subject to Pytorch's indexing semantics.\n \"\"\"\n if isinstance(item, int):\n return Boxes(self.tensor[item].view(1, -1))\n b = self.tensor[item]\n assert b.dim() == 2, \"Indexing on Boxes with {} failed to return a matrix!\".format(item)\n return Boxes(b)\n\n def __len__(self) -> int:\n return self.tensor.shape[0]\n\n def __repr__(self) -> str:\n return \"Boxes(\" + str(self.tensor) + \")\"\n\n def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor:\n \"\"\"\n Args:\n box_size (height, width): Size of the reference box.\n boundary_threshold (int): Boxes that extend beyond the reference box\n boundary by more than boundary_threshold are considered \"outside\".\n\n Returns:\n a binary vector, indicating whether each box is inside the reference box.\n \"\"\"\n height, width = box_size\n inds_inside = (\n (self.tensor[..., 0] >= -boundary_threshold)\n & (self.tensor[..., 1] >= -boundary_threshold)\n & (self.tensor[..., 2] < width + boundary_threshold)\n & (self.tensor[..., 3] < height + boundary_threshold)\n )\n return inds_inside\n\n def get_centers(self) -> torch.Tensor:\n \"\"\"\n Returns:\n The box centers in a Nx2 array of (x, y).\n \"\"\"\n return (self.tensor[:, :2] + self.tensor[:, 2:]) / 2\n\n def scale(self, scale_x: float, scale_y: float) -> None:\n \"\"\"\n Scale the box with horizontal and vertical scaling factors\n \"\"\"\n self.tensor[:, 0::2] *= scale_x\n self.tensor[:, 1::2] *= scale_y\n\n @classmethod\n @_maybe_jit_unused\n def cat(cls, boxes_list: List[\"Boxes\"]) -> \"Boxes\":\n \"\"\"\n Concatenates a list of Boxes into a single Boxes\n\n Arguments:\n boxes_list (list[Boxes])\n\n Returns:\n Boxes: the concatenated Boxes\n \"\"\"\n assert isinstance(boxes_list, (list, tuple))\n if len(boxes_list) == 0:\n return cls(torch.empty(0))\n assert all([isinstance(box, Boxes) for box in boxes_list])\n\n # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input\n cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))\n return cat_boxes\n\n @property\n def device(self) -> device:\n return self.tensor.device\n\n # type \"Iterator[torch.Tensor]\", yield, and iter() not supported by torchscript\n # https://github.com/pytorch/pytorch/issues/18627\n @torch.jit.unused\n def __iter__(self):\n \"\"\"\n Yield a box as a Tensor of shape (4,) at a time.\n \"\"\"\n yield from self.tensor\n\n\ndef pairwise_intersection(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:\n \"\"\"\n Given two lists of boxes of size N and M,\n compute the intersection area between __all__ N x M pairs of boxes.\n The box order must be (xmin, ymin, xmax, ymax)\n\n Args:\n boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.\n\n Returns:\n Tensor: intersection, sized [N,M].\n \"\"\"\n boxes1, boxes2 = boxes1.tensor, boxes2.tensor\n width_height = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) - torch.max(\n boxes1[:, None, :2], boxes2[:, :2]\n ) # [N,M,2]\n\n width_height.clamp_(min=0) # [N,M,2]\n intersection = width_height.prod(dim=2) # [N,M]\n return intersection\n\n\n# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py\n# with slight modifications\ndef pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:\n \"\"\"\n Given two lists of boxes of size N and M, compute the IoU\n (intersection over union) between **all** N x M pairs of boxes.\n The box order must be (xmin, ymin, xmax, ymax).\n\n Args:\n boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.\n\n Returns:\n Tensor: IoU, sized [N,M].\n \"\"\"\n area1 = boxes1.area() # [N]\n area2 = boxes2.area() # [M]\n inter = pairwise_intersection(boxes1, boxes2)\n\n # handle empty boxes\n iou = torch.where(\n inter > 0,\n inter / (area1[:, None] + area2 - inter),\n torch.zeros(1, dtype=inter.dtype, device=inter.device),\n )\n return iou\n\n\ndef pairwise_ioa(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:\n \"\"\"\n Similar to :func:`pariwise_iou` but compute the IoA (intersection over boxes2 area).\n\n Args:\n boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.\n\n Returns:\n Tensor: IoA, sized [N,M].\n \"\"\"\n area2 = boxes2.area() # [M]\n inter = pairwise_intersection(boxes1, boxes2)\n\n # handle empty boxes\n ioa = torch.where(\n inter > 0, inter / area2, torch.zeros(1, dtype=inter.dtype, device=inter.device)\n )\n return ioa\n\n\ndef matched_boxlist_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:\n \"\"\"\n Compute pairwise intersection over union (IOU) of two sets of matched\n boxes. The box order must be (xmin, ymin, xmax, ymax).\n Similar to boxlist_iou, but computes only diagonal elements of the matrix\n\n Args:\n boxes1: (Boxes) bounding boxes, sized [N,4].\n boxes2: (Boxes) bounding boxes, sized [N,4].\n Returns:\n Tensor: iou, sized [N].\n \"\"\"\n assert len(boxes1) == len(\n boxes2\n ), \"boxlists should have the same\" \"number of entries, got {}, {}\".format(\n len(boxes1), len(boxes2)\n )\n area1 = boxes1.area() # [N]\n area2 = boxes2.area() # [N]\n box1, box2 = boxes1.tensor, boxes2.tensor\n lt = torch.max(box1[:, :2], box2[:, :2]) # [N,2]\n rb = torch.min(box1[:, 2:], box2[:, 2:]) # [N,2]\n wh = (rb - lt).clamp(min=0) # [N,2]\n inter = wh[:, 0] * wh[:, 1] # [N]\n iou = inter / (area1 + area2 - inter) # [N]\n return iou\n" }, { "path": "models/structures/instances.py", "content": "# ------------------------------------------------------------------------\n# Modified from Detectron2 (https://github.com/facebookresearch/detectron2)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\nimport itertools\nfrom typing import Any, Dict, List, Tuple, Union\nimport torch\nimport numpy as np\n\n\nclass Instances:\n \"\"\"\n This class represents a list of instances in an image.\n It stores the attributes of instances (e.g., boxes, masks, labels, scores) as \"fields\".\n All fields must have the same ``__len__`` which is the number of instances.\n\n All other (non-field) attributes of this class are considered private:\n they must start with '_' and are not modifiable by a user.\n\n Some basic usage:\n\n 1. Set/get/check a field:\n\n .. code-block:: python\n\n instances.gt_boxes = Boxes(...)\n print(instances.pred_masks) # a tensor of shape (N, H, W)\n print('gt_masks' in instances)\n\n 2. ``len(instances)`` returns the number of instances\n 3. Indexing: ``instances[indices]`` will apply the indexing on all the fields\n and returns a new :class:`Instances`.\n Typically, ``indices`` is a integer vector of indices,\n or a binary mask of length ``num_instances``\n\n .. code-block:: python\n\n category_3_detections = instances[instances.pred_classes == 3]\n confident_detections = instances[instances.scores > 0.9]\n \"\"\"\n\n def __init__(self, image_size: Tuple[int, int], **kwargs: Any):\n \"\"\"\n Args:\n image_size (height, width): the spatial size of the image.\n kwargs: fields to add to this `Instances`.\n \"\"\"\n self._image_size = image_size\n self._fields: Dict[str, Any] = {}\n for k, v in kwargs.items():\n self.set(k, v)\n\n @property\n def image_size(self) -> Tuple[int, int]:\n \"\"\"\n Returns:\n tuple: height, width\n \"\"\"\n return self._image_size\n\n def __setattr__(self, name: str, val: Any) -> None:\n if name.startswith(\"_\"):\n super().__setattr__(name, val)\n else:\n self.set(name, val)\n\n def __getattr__(self, name: str) -> Any:\n if name == \"_fields\" or name not in self._fields:\n raise AttributeError(\"Cannot find field '{}' in the given Instances!\".format(name))\n return self._fields[name]\n\n def set(self, name: str, value: Any) -> None:\n \"\"\"\n Set the field named `name` to `value`.\n The length of `value` must be the number of instances,\n and must agree with other existing fields in this object.\n \"\"\"\n data_len = len(value)\n if len(self._fields):\n assert (\n len(self) == data_len\n ), \"Adding a field of length {} to a Instances of length {}\".format(data_len, len(self))\n self._fields[name] = value\n\n def has(self, name: str) -> bool:\n \"\"\"\n Returns:\n bool: whether the field called `name` exists.\n \"\"\"\n return name in self._fields\n\n def remove(self, name: str) -> None:\n \"\"\"\n Remove the field called `name`.\n \"\"\"\n del self._fields[name]\n\n def get(self, name: str) -> Any:\n \"\"\"\n Returns the field called `name`.\n \"\"\"\n return self._fields[name]\n\n def get_fields(self) -> Dict[str, Any]:\n \"\"\"\n Returns:\n dict: a dict which maps names (str) to data of the fields\n\n Modifying the returned dict will modify this instance.\n \"\"\"\n return self._fields\n\n # Tensor-like methods\n def to(self, *args: Any, **kwargs: Any) -> \"Instances\":\n \"\"\"\n Returns:\n Instances: all fields are called with a `to(device)`, if the field has this method.\n \"\"\"\n ret = Instances(self._image_size)\n for k, v in self._fields.items():\n if hasattr(v, \"to\"):\n v = v.to(*args, **kwargs)\n ret.set(k, v)\n return ret\n\n def numpy(self):\n ret = Instances(self._image_size)\n for k, v in self._fields.items():\n if hasattr(v, \"numpy\"):\n v = v.numpy()\n ret.set(k, v)\n return ret\n\n def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> \"Instances\":\n \"\"\"\n Args:\n item: an index-like object and will be used to index all the fields.\n\n Returns:\n If `item` is a string, return the data in the corresponding field.\n Otherwise, returns an `Instances` where all fields are indexed by `item`.\n \"\"\"\n if type(item) == int:\n if item >= len(self) or item < -len(self):\n raise IndexError(\"Instances index out of range!\")\n else:\n item = slice(item, None, len(self))\n\n ret = Instances(self._image_size)\n for k, v in self._fields.items():\n ret.set(k, v[item])\n return ret\n\n def __len__(self) -> int:\n for v in self._fields.values():\n # use __len__ because len() has to be int and is not friendly to tracing\n return v.__len__()\n raise NotImplementedError(\"Empty Instances does not support __len__!\")\n\n def __iter__(self):\n raise NotImplementedError(\"`Instances` object is not iterable!\")\n\n @staticmethod\n def cat(instance_lists: List[\"Instances\"]) -> \"Instances\":\n \"\"\"\n Args:\n instance_lists (list[Instances])\n\n Returns:\n Instances\n \"\"\"\n assert all(isinstance(i, Instances) for i in instance_lists)\n assert len(instance_lists) > 0\n if len(instance_lists) == 1:\n return instance_lists[0]\n\n image_size = instance_lists[0].image_size\n for i in instance_lists[1:]:\n assert i.image_size == image_size\n ret = Instances(image_size)\n for k in instance_lists[0]._fields.keys():\n values = [i.get(k) for i in instance_lists]\n v0 = values[0]\n if isinstance(v0, torch.Tensor):\n values = torch.cat(values, dim=0)\n elif isinstance(v0, list):\n values = list(itertools.chain(*values))\n elif hasattr(type(v0), \"cat\"):\n values = type(v0).cat(values)\n else:\n raise ValueError(\"Unsupported type {} for concatenation\".format(type(v0)))\n ret.set(k, values)\n return ret\n\n def __str__(self) -> str:\n s = self.__class__.__name__ + \"(\"\n s += \"num_instances={}, \".format(len(self))\n s += \"image_height={}, \".format(self._image_size[0])\n s += \"image_width={}, \".format(self._image_size[1])\n s += \"fields=[{}])\".format(\", \".join((f\"{k}: {v}\" for k, v in self._fields.items())))\n return s\n\n __repr__ = __str__\n" }, { "path": "requirements.txt", "content": "pycocotools\ntqdm\ncython\nscipy\nmotmetrics\nopencv-python\nseaborn\nlap\n" }, { "path": "submit.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\"\"\"\n SORT: A Simple, Online and Realtime Tracker\n Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai\n This program is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n This program is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n You should have received a copy of the GNU General Public License\n along with this program. If not, see .\n\"\"\"\nfrom __future__ import print_function\n\nimport os\nimport numpy as np\nimport random\nimport argparse\nimport torchvision.transforms.functional as F\nimport torch\nimport cv2\nfrom tqdm import tqdm\nfrom pathlib import Path\nfrom PIL import Image, ImageDraw\nfrom models import build_model\nfrom util.tool import load_model\nfrom main import get_args_parser\nfrom torch.nn.functional import interpolate\nfrom typing import List\nfrom util.evaluation import Evaluator\nimport motmetrics as mm\nimport shutil\n\nfrom models.structures import Instances\nfrom torch.utils.data import Dataset, DataLoader\n\nnp.random.seed(2020)\n\nCOLORS_10 = [(144, 238, 144), (178, 34, 34), (221, 160, 221), (0, 255, 0), (0, 128, 0), (210, 105, 30), (220, 20, 60),\n (192, 192, 192), (255, 228, 196), (50, 205, 50), (139, 0, 139), (100, 149, 237), (138, 43, 226),\n (238, 130, 238),\n (255, 0, 255), (0, 100, 0), (127, 255, 0), (255, 0, 255), (0, 0, 205), (255, 140, 0), (255, 239, 213),\n (199, 21, 133), (124, 252, 0), (147, 112, 219), (106, 90, 205), (176, 196, 222), (65, 105, 225),\n (173, 255, 47),\n (255, 20, 147), (219, 112, 147), (186, 85, 211), (199, 21, 133), (148, 0, 211), (255, 99, 71),\n (144, 238, 144),\n (255, 255, 0), (230, 230, 250), (0, 0, 255), (128, 128, 0), (189, 183, 107), (255, 255, 224),\n (128, 128, 128),\n (105, 105, 105), (64, 224, 208), (205, 133, 63), (0, 128, 128), (72, 209, 204), (139, 69, 19),\n (255, 245, 238),\n (250, 240, 230), (152, 251, 152), (0, 255, 255), (135, 206, 235), (0, 191, 255), (176, 224, 230),\n (0, 250, 154),\n (245, 255, 250), (240, 230, 140), (245, 222, 179), (0, 139, 139), (143, 188, 143), (255, 0, 0),\n (240, 128, 128),\n (102, 205, 170), (60, 179, 113), (46, 139, 87), (165, 42, 42), (178, 34, 34), (175, 238, 238),\n (255, 248, 220),\n (218, 165, 32), (255, 250, 240), (253, 245, 230), (244, 164, 96), (210, 105, 30)]\n\n\ndef plot_one_box(x, img, color=None, label=None, score=None, line_thickness=None):\n # Plots one bounding box on image img\n\n # tl = line_thickness or round(\n # 0.002 * max(img.shape[0:2])) + 1 # line thickness\n tl = 2\n color = color or [random.randint(0, 255) for _ in range(3)]\n c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))\n cv2.rectangle(img, c1, c2, color, thickness=tl)\n if label:\n tf = max(tl - 1, 1) # font thickness\n t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]\n c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3\n cv2.rectangle(img, c1, c2, color, -1) # filled\n cv2.putText(img,\n label, (c1[0], c1[1] - 2),\n 0,\n tl / 3, [225, 255, 255],\n thickness=tf,\n lineType=cv2.LINE_AA)\n if score is not None:\n cv2.putText(img, score, (c1[0], c1[1] + 30), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)\n return img\n\n\n'''\ndeep sort 中的画图方法，在原图上进行作画\n'''\ndef draw_bboxes(ori_img, bbox, identities=None, offset=(0, 0), cvt_color=False):\n if cvt_color:\n ori_img = cv2.cvtColor(np.asarray(ori_img), cv2.COLOR_RGB2BGR)\n img = ori_img\n for i, box in enumerate(bbox):\n x1, y1, x2, y2 = [int(i) for i in box[:4]]\n x1 += offset[0]\n x2 += offset[0]\n y1 += offset[1]\n y2 += offset[1]\n if len(box) > 4:\n score = '{:.2f}'.format(box[4])\n else:\n score = None\n # box text and bar\n id = int(identities[i]) if identities is not None else 0\n color = COLORS_10[id % len(COLORS_10)]\n label = '{:d}'.format(id)\n # t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0]\n img = plot_one_box([x1, y1, x2, y2], img, color, label, score=score)\n return img\n\n\ndef draw_points(img: np.ndarray, points: np.ndarray, color=(255, 255, 255)) -> np.ndarray:\n assert len(points.shape) == 2 and points.shape[1] == 2, 'invalid points shape: {}'.format(points.shape)\n for i, (x, y) in enumerate(points):\n if i >= 300:\n color = (0, 255, 0)\n cv2.circle(img, (int(x), int(y)), 2, color=color, thickness=2)\n return img\n\n\ndef tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:\n return tensor.detach().cpu().numpy()\n\n\nclass Track(object):\n track_cnt = 0\n\n def __init__(self, box):\n self.box = box\n self.time_since_update = 0\n self.id = Track.track_cnt\n Track.track_cnt += 1\n self.miss = 0\n\n def miss_one_frame(self):\n self.miss += 1\n\n def clear_miss(self):\n self.miss = 0\n\n def update(self, box):\n self.box = box\n self.clear_miss()\n\n\nclass MOTR(object):\n def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):\n \"\"\"\n Sets key parameters for SORT\n \"\"\"\n self.max_age = max_age\n self.min_hits = min_hits\n self.iou_threshold = iou_threshold\n self.trackers = []\n self.frame_count = 0\n self.active_trackers = {}\n self.inactive_trackers = {}\n self.disappeared_tracks = []\n\n def _remove_track(self, slot_id):\n self.inactive_trackers.pop(slot_id)\n self.disappeared_tracks.append(slot_id)\n\n def clear_disappeared_track(self):\n self.disappeared_tracks = []\n\n def update(self, dt_instances: Instances):\n \"\"\"\n Params:\n dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]\n Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).\n Returns the a similar array, where the last column is the object ID.\n NOTE: The number of objects returned may differ from the number of detections provided.\n \"\"\"\n self.frame_count += 1\n # get predicted locations from existing trackers.\n dt_idxes = set(dt_instances.obj_idxes.tolist())\n track_idxes = set(self.active_trackers.keys()).union(set(self.inactive_trackers.keys()))\n matched_idxes = dt_idxes.intersection(track_idxes)\n\n unmatched_tracker = track_idxes - matched_idxes\n for track_id in unmatched_tracker:\n # miss in this frame, move to inactive_trackers.\n if track_id in self.active_trackers:\n self.inactive_trackers[track_id] = self.active_trackers.pop(track_id)\n self.inactive_trackers[track_id].miss_one_frame()\n if self.inactive_trackers[track_id].miss > 10:\n self._remove_track(track_id)\n\n for i in range(len(dt_instances)):\n idx = dt_instances.obj_idxes[i]\n bbox = np.concatenate([dt_instances.boxes[i], dt_instances.scores[i:i+1]], axis=-1)\n label = dt_instances.labels[i]\n if label == 0:\n # get a positive track.\n if idx in self.inactive_trackers:\n # set state of track active.\n self.active_trackers[idx] = self.inactive_trackers.pop(idx)\n if idx not in self.active_trackers:\n # create a new track.\n self.active_trackers[idx] = Track(idx)\n self.active_trackers[idx].update(bbox)\n elif label == 1:\n # get an occluded track.\n if idx in self.active_trackers:\n # set state of track inactive.\n self.inactive_trackers[idx] = self.active_trackers.pop(idx)\n if idx not in self.inactive_trackers:\n # It's strange to obtain a new occluded track.\n # TODO: think more rational disposal.\n self.inactive_trackers[idx] = Track(idx)\n self.inactive_trackers[idx].miss_one_frame()\n if self.inactive_trackers[idx].miss > 10:\n self._remove_track(idx)\n\n ret = []\n for i in range(len(dt_instances)):\n label = dt_instances.labels[i]\n if label == 0:\n id = dt_instances.obj_idxes[i]\n box_with_score = np.concatenate([dt_instances.boxes[i], dt_instances.scores[i:i+1]], axis=-1)\n ret.append(np.concatenate((box_with_score, [id + 1])).reshape(1, -1)) # +1 as MOT benchmark requires positive\n\n if len(ret) > 0:\n return np.concatenate(ret)\n return np.empty((0, 6))\n\n\ndef load_label(label_path: str, img_size: tuple) -> dict:\n labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 6)\n h, w = img_size\n # Normalized cewh to pixel xyxy format\n labels = labels0.copy()\n labels[:, 2] = w * (labels0[:, 2] - labels0[:, 4] / 2)\n labels[:, 3] = h * (labels0[:, 3] - labels0[:, 5] / 2)\n labels[:, 4] = w * (labels0[:, 2] + labels0[:, 4] / 2)\n labels[:, 5] = h * (labels0[:, 3] + labels0[:, 5] / 2)\n targets = {'boxes': [], 'labels': [], 'area': []}\n num_boxes = len(labels)\n\n visited_ids = set()\n for label in labels[:num_boxes]:\n obj_id = label[1]\n if obj_id in visited_ids:\n continue\n visited_ids.add(obj_id)\n targets['boxes'].append(label[2:6].tolist())\n targets['area'].append(label[4] * label[5])\n targets['labels'].append(0)\n targets['boxes'] = np.asarray(targets['boxes'])\n targets['area'] = np.asarray(targets['area'])\n targets['labels'] = np.asarray(targets['labels'])\n return targets\n\n\ndef filter_pub_det(res_file, pub_det_file, filter_iou=False):\n frame_boxes = {}\n with open(pub_det_file, 'r') as f:\n lines = f.readlines()\n for line in lines:\n if len(line) == 0:\n continue\n elements = line.strip().split(',')\n frame_id = int(elements[0])\n x1, y1, w, h = elements[2:6]\n x1, y1, w, h = float(x1), float(y1), float(w), float(h)\n x2 = x1 + w - 1\n y2 = y1 + h - 1\n if frame_id not in frame_boxes:\n frame_boxes[frame_id] = []\n frame_boxes[frame_id].append([x1, y1, x2, y2])\n\n for frame, boxes in frame_boxes.items():\n frame_boxes[frame] = np.array(boxes)\n\n ids = {}\n num_filter_box = 0\n with open(res_file, 'r') as f:\n lines = f.readlines()\n with open(res_file, 'w') as f:\n for line in lines:\n if len(line) == 0:\n continue\n elements = line.strip().split(',')\n frame_id, obj_id = elements[:2]\n frame_id = int(frame_id)\n obj_id = int(obj_id)\n x1, y1, w, h = elements[2:6]\n x1, y1, w, h = float(x1), float(y1), float(w), float(h)\n x2 = x1 + w - 1\n y2 = y1 + h - 1\n if obj_id not in ids:\n # track initialization.\n if frame_id not in frame_boxes:\n num_filter_box += 1\n print(\"filter init box {} {}\".format(frame_id, obj_id))\n continue\n pub_dt_boxes = frame_boxes[frame_id]\n dt_box = np.array([[x1, y1, x2, y2]])\n if filter_iou:\n max_iou = bbox_iou(dt_box, pub_dt_boxes).max()\n if max_iou < 0.5:\n num_filter_box += 1\n print(\"filter init box {} {}\".format(frame_id, obj_id))\n continue\n else:\n pub_dt_centers = (pub_dt_boxes[:, :2] + pub_dt_boxes[:, 2:4]) * 0.5\n x_inside = (dt_box[0, 0] <= pub_dt_centers[:, 0]) & (dt_box[0, 2] >= pub_dt_centers[:, 0])\n y_inside = (dt_box[0, 1] <= pub_dt_centers[:, 1]) & (dt_box[0, 3] >= pub_dt_centers[:, 1])\n center_inside:np.ndarray = x_inside & y_inside\n if not center_inside.any():\n num_filter_box += 1\n print(\"filter init box {} {}\".format(frame_id, obj_id))\n continue\n print(\"save init track {} {}\".format(frame_id, obj_id))\n ids[obj_id] = True\n f.write(line)\n\n print(\"totally {} boxes are filtered.\".format(num_filter_box))\n\nclass ListImgDataset(Dataset):\n def __init__(self, img_list) -> None:\n super().__init__()\n self.img_list = img_list\n\n '''\n common settings\n '''\n self.img_height = 800\n self.img_width = 1536\n self.mean = [0.485, 0.456, 0.406]\n self.std = [0.229, 0.224, 0.225]\n\n def load_img_from_file(self, f_path):\n label_path = f_path.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt')\n cur_img = cv2.imread(f_path)\n assert cur_img is not None, f_path\n cur_img = cv2.cvtColor(cur_img, cv2.COLOR_BGR2RGB)\n targets = load_label(label_path, cur_img.shape[:2]) if os.path.exists(label_path) else None\n return cur_img, targets\n\n def init_img(self, img):\n ori_img = img.copy()\n self.seq_h, self.seq_w = img.shape[:2]\n scale = self.img_height / min(self.seq_h, self.seq_w)\n if max(self.seq_h, self.seq_w) * scale > self.img_width:\n scale = self.img_width / max(self.seq_h, self.seq_w)\n target_h = int(self.seq_h * scale)\n target_w = int(self.seq_w * scale)\n img = cv2.resize(img, (target_w, target_h))\n img = F.normalize(F.to_tensor(img), self.mean, self.std)\n img = img.unsqueeze(0)\n return img, ori_img\n\n def __len__(self):\n return len(self.img_list)\n \n def __getitem__(self, index):\n img, targets = self.load_img_from_file(self.img_list[index])\n return self.init_img(img)\n\n\nclass Detector(object):\n def __init__(self, args, model=None, seq_num=2):\n\n self.args = args\n self.detr = model\n\n self.seq_num = seq_num\n img_list = os.listdir(os.path.join(self.args.mot_path, 'MOT17/images/test', self.seq_num, 'img1'))\n img_list = [os.path.join(self.args.mot_path, 'MOT17/images/test', self.seq_num, 'img1', _) for _ in img_list if\n ('jpg' in _) or ('png' in _)]\n\n self.img_list = sorted(img_list)\n self.img_len = len(self.img_list)\n self.tr_tracker = MOTR()\n\n self.save_path = os.path.join(self.args.output_dir, 'results/{}'.format(seq_num))\n os.makedirs(self.save_path, exist_ok=True)\n\n self.predict_path = os.path.join(self.args.output_dir, args.exp_name)\n os.makedirs(self.predict_path, exist_ok=True)\n if os.path.exists(os.path.join(self.predict_path, f'{self.seq_num}.txt')):\n os.remove(os.path.join(self.predict_path, f'{self.seq_num}.txt'))\n\n @staticmethod\n def filter_dt_by_score(dt_instances: Instances, prob_threshold: float) -> Instances:\n keep = dt_instances.scores > prob_threshold\n return dt_instances[keep]\n\n @staticmethod\n def filter_dt_by_area(dt_instances: Instances, area_threshold: float) -> Instances:\n wh = dt_instances.boxes[:, 2:4] - dt_instances.boxes[:, 0:2]\n areas = wh[:, 0] * wh[:, 1]\n keep = areas > area_threshold\n return dt_instances[keep]\n\n @staticmethod\n def write_results(txt_path, frame_id, bbox_xyxy, identities):\n save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\\n'\n with open(txt_path, 'a') as f:\n for xyxy, track_id in zip(bbox_xyxy, identities):\n if track_id < 0 or track_id is None:\n continue\n x1, y1, x2, y2 = xyxy\n w, h = x2 - x1, y2 - y1\n line = save_format.format(frame=int(frame_id), id=int(track_id), x1=x1, y1=y1, w=w, h=h)\n f.write(line)\n\n def eval_seq(self):\n data_root = os.path.join(self.args.mot_path, 'MOT15/images/train')\n result_filename = os.path.join(self.predict_path, 'gt.txt')\n evaluator = Evaluator(data_root, self.seq_num)\n accs = evaluator.eval_file(result_filename)\n return accs\n\n @staticmethod\n def visualize_img_with_bbox(img_path, img, dt_instances: Instances, ref_pts=None, gt_boxes=None):\n if dt_instances.has('scores'):\n img_show = draw_bboxes(img, np.concatenate([dt_instances.boxes, dt_instances.scores.reshape(-1, 1)], axis=-1), dt_instances.obj_idxes)\n else:\n img_show = draw_bboxes(img, dt_instances.boxes, dt_instances.obj_idxes)\n if ref_pts is not None:\n img_show = draw_points(img_show, ref_pts)\n if gt_boxes is not None:\n img_show = draw_bboxes(img_show, gt_boxes, identities=np.ones((len(gt_boxes), )) * -1)\n cv2.imwrite(img_path, img_show)\n\n def detect(self, prob_threshold=0.7, area_threshold=100):\n last_dt_embedding = None\n total_dts = 0\n total_occlusion_dts = 0\n\n track_instances = None\n loader = DataLoader(ListImgDataset(self.img_list), 1, num_workers=2)\n for i, (cur_img, ori_img) in enumerate(tqdm(loader)):\n cur_img, ori_img = cur_img[0], ori_img[0]\n\n # track_instances = None\n if track_instances is not None:\n track_instances.remove('boxes')\n track_instances.remove('labels')\n seq_h, seq_w, _ = ori_img.shape\n\n res = self.detr.inference_single_image(cur_img.cuda().float(), (seq_h, seq_w), track_instances)\n track_instances = res['track_instances']\n\n all_ref_pts = tensor_to_numpy(res['ref_pts'][0, :, :2])\n dt_instances = track_instances.to(torch.device('cpu'))\n\n # filter det instances by score.\n dt_instances = self.filter_dt_by_score(dt_instances, prob_threshold)\n dt_instances = self.filter_dt_by_area(dt_instances, area_threshold)\n\n num_occlusion = (dt_instances.labels == 1).sum()\n dt_instances.scores[dt_instances.labels == 1] *= -1\n total_dts += len(dt_instances)\n total_occlusion_dts += num_occlusion\n\n tracker_outputs = self.tr_tracker.update(dt_instances)\n\n self.write_results(txt_path=os.path.join(self.predict_path, f'{self.seq_num}.txt'),\n frame_id=(i + 1),\n bbox_xyxy=tracker_outputs[:, :4],\n identities=tracker_outputs[:, 5])\n # filter_pub_det(os.path.join(self.predict_path, f'{self.seq_num}.txt'),\n # f'/data/Dataset/mot/MOT17/images/test/{self.seq_num}/det/det.txt')\n print(\"totally {} dts {} occlusion dts\".format(total_dts, total_occlusion_dts))\n\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])\n args = parser.parse_args()\n if args.output_dir:\n Path(args.output_dir).mkdir(parents=True, exist_ok=True)\n\n # load model and weights\n detr, _, _ = build_model(args)\n checkpoint = torch.load(args.resume, map_location='cpu')\n detr = load_model(detr, args.resume)\n detr.eval()\n detr = detr.cuda()\n\n # '''for MOT17 submit''' \n sub_dir = 'MOT17/images/test'\n seq_nums = ['MOT17-01-SDP',\n 'MOT17-03-SDP',\n 'MOT17-06-SDP',\n 'MOT17-07-SDP',\n 'MOT17-08-SDP',\n 'MOT17-12-SDP',\n 'MOT17-14-SDP']\n\n for seq_num in seq_nums:\n det = Detector(args, model=detr, seq_num=seq_num)\n det.detect()\n\n \"\"\"copy reuslts for same sequences\"\"\"\n repeated_seq_nums = ['MOT17-01-DPM',\n 'MOT17-03-DPM',\n 'MOT17-06-DPM',\n 'MOT17-07-DPM',\n 'MOT17-08-DPM',\n 'MOT17-12-DPM',\n 'MOT17-14-DPM',\n 'MOT17-01-FRCNN',\n 'MOT17-03-FRCNN',\n 'MOT17-06-FRCNN',\n 'MOT17-07-FRCNN',\n 'MOT17-08-FRCNN',\n 'MOT17-12-FRCNN',\n 'MOT17-14-FRCNN']\n \n print('copy reuslts for same sequences: ')\n predict_path = os.path.join(args.output_dir, args.exp_name)\n for repeated_seq_nums_i in repeated_seq_nums:\n u, v = repeated_seq_nums_i.split('-')[:-1]\n shutil.copyfile(os.path.join(predict_path, '{}-{}-SDP.txt'.format(u,v)),os.path.join(predict_path,f'{repeated_seq_nums_i}.txt'))\n\n sub_dir = 'MOT17/images/train'\n seq_nums = os.listdir('/data/Dataset/mot/MOT17/images/train')\n accs = []\n seqs = []\n for seq_num in seq_nums:\n shutil.copyfile(os.path.join(args.mot_path, sub_dir, f'{seq_num}/gt/gt.txt'),os.path.join(predict_path,f'{seq_num}.txt'))\n" }, { "path": "submit_dance.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\"\"\"\n SORT: A Simple, Online and Realtime Tracker\n Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai\n This program is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n This program is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n You should have received a copy of the GNU General Public License\n along with this program. If not, see .\n\"\"\"\nfrom __future__ import print_function\n\nimport os\nimport numpy as np\nimport random\nimport argparse\nimport torchvision.transforms.functional as F\nimport torch\nimport cv2\nfrom tqdm import tqdm\nfrom pathlib import Path\nfrom PIL import Image, ImageDraw\nfrom models import build_model\nfrom util.tool import load_model\nfrom main import get_args_parser\nfrom torch.nn.functional import interpolate\nfrom typing import List\nfrom util.evaluation import Evaluator\nimport motmetrics as mm\nimport shutil\n\nfrom models.structures import Instances\nfrom torch.utils.data import Dataset, DataLoader\n\nnp.random.seed(2020)\n\nCOLORS_10 = [(144, 238, 144), (178, 34, 34), (221, 160, 221), (0, 255, 0), (0, 128, 0), (210, 105, 30), (220, 20, 60),\n (192, 192, 192), (255, 228, 196), (50, 205, 50), (139, 0, 139), (100, 149, 237), (138, 43, 226),\n (238, 130, 238),\n (255, 0, 255), (0, 100, 0), (127, 255, 0), (255, 0, 255), (0, 0, 205), (255, 140, 0), (255, 239, 213),\n (199, 21, 133), (124, 252, 0), (147, 112, 219), (106, 90, 205), (176, 196, 222), (65, 105, 225),\n (173, 255, 47),\n (255, 20, 147), (219, 112, 147), (186, 85, 211), (199, 21, 133), (148, 0, 211), (255, 99, 71),\n (144, 238, 144),\n (255, 255, 0), (230, 230, 250), (0, 0, 255), (128, 128, 0), (189, 183, 107), (255, 255, 224),\n (128, 128, 128),\n (105, 105, 105), (64, 224, 208), (205, 133, 63), (0, 128, 128), (72, 209, 204), (139, 69, 19),\n (255, 245, 238),\n (250, 240, 230), (152, 251, 152), (0, 255, 255), (135, 206, 235), (0, 191, 255), (176, 224, 230),\n (0, 250, 154),\n (245, 255, 250), (240, 230, 140), (245, 222, 179), (0, 139, 139), (143, 188, 143), (255, 0, 0),\n (240, 128, 128),\n (102, 205, 170), (60, 179, 113), (46, 139, 87), (165, 42, 42), (178, 34, 34), (175, 238, 238),\n (255, 248, 220),\n (218, 165, 32), (255, 250, 240), (253, 245, 230), (244, 164, 96), (210, 105, 30)]\n\n\ndef plot_one_box(x, img, color=None, label=None, score=None, line_thickness=None):\n # Plots one bounding box on image img\n\n # tl = line_thickness or round(\n # 0.002 * max(img.shape[0:2])) + 1 # line thickness\n tl = 2\n color = color or [random.randint(0, 255) for _ in range(3)]\n c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))\n cv2.rectangle(img, c1, c2, color, thickness=tl)\n if label:\n tf = max(tl - 1, 1) # font thickness\n t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]\n c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3\n cv2.rectangle(img, c1, c2, color, -1) # filled\n cv2.putText(img,\n label, (c1[0], c1[1] - 2),\n 0,\n tl / 3, [225, 255, 255],\n thickness=tf,\n lineType=cv2.LINE_AA)\n if score is not None:\n cv2.putText(img, score, (c1[0], c1[1] + 30), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)\n return img\n\n\n'''\ndeep sort 中的画图方法，在原图上进行作画\n'''\ndef draw_bboxes(ori_img, bbox, identities=None, offset=(0, 0), cvt_color=False):\n if cvt_color:\n ori_img = cv2.cvtColor(np.asarray(ori_img), cv2.COLOR_RGB2BGR)\n img = ori_img\n for i, box in enumerate(bbox):\n x1, y1, x2, y2 = [int(i) for i in box[:4]]\n x1 += offset[0]\n x2 += offset[0]\n y1 += offset[1]\n y2 += offset[1]\n if len(box) > 4:\n score = '{:.2f}'.format(box[4])\n else:\n score = None\n # box text and bar\n id = int(identities[i]) if identities is not None else 0\n color = COLORS_10[id % len(COLORS_10)]\n label = '{:d}'.format(id)\n # t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0]\n img = plot_one_box([x1, y1, x2, y2], img, color, label, score=score)\n return img\n\n\ndef draw_points(img: np.ndarray, points: np.ndarray, color=(255, 255, 255)) -> np.ndarray:\n assert len(points.shape) == 2 and points.shape[1] == 2, 'invalid points shape: {}'.format(points.shape)\n for i, (x, y) in enumerate(points):\n if i >= 300:\n color = (0, 255, 0)\n cv2.circle(img, (int(x), int(y)), 2, color=color, thickness=2)\n return img\n\n\ndef tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:\n return tensor.detach().cpu().numpy()\n\n\nclass Track(object):\n track_cnt = 0\n\n def __init__(self, box):\n self.box = box\n self.time_since_update = 0\n self.id = Track.track_cnt\n Track.track_cnt += 1\n self.miss = 0\n\n def miss_one_frame(self):\n self.miss += 1\n\n def clear_miss(self):\n self.miss = 0\n\n def update(self, box):\n self.box = box\n self.clear_miss()\n\n\nclass MOTR(object):\n def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):\n \"\"\"\n Sets key parameters for SORT\n \"\"\"\n self.max_age = max_age\n self.min_hits = min_hits\n self.iou_threshold = iou_threshold\n self.trackers = []\n self.frame_count = 0\n self.active_trackers = {}\n self.inactive_trackers = {}\n self.disappeared_tracks = []\n\n def _remove_track(self, slot_id):\n self.inactive_trackers.pop(slot_id)\n self.disappeared_tracks.append(slot_id)\n\n def clear_disappeared_track(self):\n self.disappeared_tracks = []\n\n def update(self, dt_instances: Instances):\n \"\"\"\n Params:\n dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]\n Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).\n Returns the a similar array, where the last column is the object ID.\n NOTE: The number of objects returned may differ from the number of detections provided.\n \"\"\"\n self.frame_count += 1\n # get predicted locations from existing trackers.\n dt_idxes = set(dt_instances.obj_idxes.tolist())\n track_idxes = set(self.active_trackers.keys()).union(set(self.inactive_trackers.keys()))\n matched_idxes = dt_idxes.intersection(track_idxes)\n\n unmatched_tracker = track_idxes - matched_idxes\n for track_id in unmatched_tracker:\n # miss in this frame, move to inactive_trackers.\n if track_id in self.active_trackers:\n self.inactive_trackers[track_id] = self.active_trackers.pop(track_id)\n self.inactive_trackers[track_id].miss_one_frame()\n if self.inactive_trackers[track_id].miss > 10:\n self._remove_track(track_id)\n\n for i in range(len(dt_instances)):\n idx = dt_instances.obj_idxes[i]\n bbox = np.concatenate([dt_instances.boxes[i], dt_instances.scores[i:i+1]], axis=-1)\n label = dt_instances.labels[i]\n if label == 0:\n # get a positive track.\n if idx in self.inactive_trackers:\n # set state of track active.\n self.active_trackers[idx] = self.inactive_trackers.pop(idx)\n if idx not in self.active_trackers:\n # create a new track.\n self.active_trackers[idx] = Track(idx)\n self.active_trackers[idx].update(bbox)\n elif label == 1:\n # get an occluded track.\n if idx in self.active_trackers:\n # set state of track inactive.\n self.inactive_trackers[idx] = self.active_trackers.pop(idx)\n if idx not in self.inactive_trackers:\n # It's strange to obtain a new occluded track.\n # TODO: think more rational disposal.\n self.inactive_trackers[idx] = Track(idx)\n self.inactive_trackers[idx].miss_one_frame()\n if self.inactive_trackers[idx].miss > 10:\n self._remove_track(idx)\n\n ret = []\n for i in range(len(dt_instances)):\n label = dt_instances.labels[i]\n if label == 0:\n id = dt_instances.obj_idxes[i]\n box_with_score = np.concatenate([dt_instances.boxes[i], dt_instances.scores[i:i+1]], axis=-1)\n ret.append(np.concatenate((box_with_score, [id + 1])).reshape(1, -1)) # +1 as MOT benchmark requires positive\n\n if len(ret) > 0:\n return np.concatenate(ret)\n return np.empty((0, 6))\n\n\ndef load_label(label_path: str, img_size: tuple) -> dict:\n labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 6)\n h, w = img_size\n # Normalized cewh to pixel xyxy format\n labels = labels0.copy()\n labels[:, 2] = w * (labels0[:, 2] - labels0[:, 4] / 2)\n labels[:, 3] = h * (labels0[:, 3] - labels0[:, 5] / 2)\n labels[:, 4] = w * (labels0[:, 2] + labels0[:, 4] / 2)\n labels[:, 5] = h * (labels0[:, 3] + labels0[:, 5] / 2)\n targets = {'boxes': [], 'labels': [], 'area': []}\n num_boxes = len(labels)\n\n visited_ids = set()\n for label in labels[:num_boxes]:\n obj_id = label[1]\n if obj_id in visited_ids:\n continue\n visited_ids.add(obj_id)\n targets['boxes'].append(label[2:6].tolist())\n targets['area'].append(label[4] * label[5])\n targets['labels'].append(0)\n targets['boxes'] = np.asarray(targets['boxes'])\n targets['area'] = np.asarray(targets['area'])\n targets['labels'] = np.asarray(targets['labels'])\n return targets\n\n\ndef filter_pub_det(res_file, pub_det_file, filter_iou=False):\n frame_boxes = {}\n with open(pub_det_file, 'r') as f:\n lines = f.readlines()\n for line in lines:\n if len(line) == 0:\n continue\n elements = line.strip().split(',')\n frame_id = int(elements[0])\n x1, y1, w, h = elements[2:6]\n x1, y1, w, h = float(x1), float(y1), float(w), float(h)\n x2 = x1 + w - 1\n y2 = y1 + h - 1\n if frame_id not in frame_boxes:\n frame_boxes[frame_id] = []\n frame_boxes[frame_id].append([x1, y1, x2, y2])\n\n for frame, boxes in frame_boxes.items():\n frame_boxes[frame] = np.array(boxes)\n\n ids = {}\n num_filter_box = 0\n with open(res_file, 'r') as f:\n lines = f.readlines()\n with open(res_file, 'w') as f:\n for line in lines:\n if len(line) == 0:\n continue\n elements = line.strip().split(',')\n frame_id, obj_id = elements[:2]\n frame_id = int(frame_id)\n obj_id = int(obj_id)\n x1, y1, w, h = elements[2:6]\n x1, y1, w, h = float(x1), float(y1), float(w), float(h)\n x2 = x1 + w - 1\n y2 = y1 + h - 1\n if obj_id not in ids:\n # track initialization.\n if frame_id not in frame_boxes:\n num_filter_box += 1\n print(\"filter init box {} {}\".format(frame_id, obj_id))\n continue\n pub_dt_boxes = frame_boxes[frame_id]\n dt_box = np.array([[x1, y1, x2, y2]])\n if filter_iou:\n max_iou = bbox_iou(dt_box, pub_dt_boxes).max()\n if max_iou < 0.5:\n num_filter_box += 1\n print(\"filter init box {} {}\".format(frame_id, obj_id))\n continue\n else:\n pub_dt_centers = (pub_dt_boxes[:, :2] + pub_dt_boxes[:, 2:4]) * 0.5\n x_inside = (dt_box[0, 0] <= pub_dt_centers[:, 0]) & (dt_box[0, 2] >= pub_dt_centers[:, 0])\n y_inside = (dt_box[0, 1] <= pub_dt_centers[:, 1]) & (dt_box[0, 3] >= pub_dt_centers[:, 1])\n center_inside:np.ndarray = x_inside & y_inside\n if not center_inside.any():\n num_filter_box += 1\n print(\"filter init box {} {}\".format(frame_id, obj_id))\n continue\n print(\"save init track {} {}\".format(frame_id, obj_id))\n ids[obj_id] = True\n f.write(line)\n\n print(\"totally {} boxes are filtered.\".format(num_filter_box))\n\nclass ListImgDataset(Dataset):\n def __init__(self, img_list) -> None:\n super().__init__()\n self.img_list = img_list\n\n '''\n common settings\n '''\n self.img_height = 800\n self.img_width = 1536\n self.mean = [0.485, 0.456, 0.406]\n self.std = [0.229, 0.224, 0.225]\n\n def load_img_from_file(self, f_path):\n label_path = f_path.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt')\n cur_img = cv2.imread(f_path)\n assert cur_img is not None, f_path\n cur_img = cv2.cvtColor(cur_img, cv2.COLOR_BGR2RGB)\n targets = load_label(label_path, cur_img.shape[:2]) if os.path.exists(label_path) else None\n return cur_img, targets\n\n def init_img(self, img):\n ori_img = img.copy()\n self.seq_h, self.seq_w = img.shape[:2]\n scale = self.img_height / min(self.seq_h, self.seq_w)\n if max(self.seq_h, self.seq_w) * scale > self.img_width:\n scale = self.img_width / max(self.seq_h, self.seq_w)\n target_h = int(self.seq_h * scale)\n target_w = int(self.seq_w * scale)\n img = cv2.resize(img, (target_w, target_h))\n img = F.normalize(F.to_tensor(img), self.mean, self.std)\n img = img.unsqueeze(0)\n return img, ori_img\n\n def __len__(self):\n return len(self.img_list)\n \n def __getitem__(self, index):\n img, targets = self.load_img_from_file(self.img_list[index])\n return self.init_img(img)\n\n\nclass Detector(object):\n def __init__(self, args, model=None, seq_num=2):\n\n self.args = args\n self.detr = model\n\n self.seq_num = seq_num\n img_list = os.listdir(os.path.join(self.args.mot_path, 'DanceTrack/test', self.seq_num, 'img1'))\n img_list = [os.path.join(self.args.mot_path, 'DanceTrack/test', self.seq_num, 'img1', _) for _ in img_list if\n ('jpg' in _) or ('png' in _)]\n\n self.img_list = sorted(img_list)\n self.img_len = len(self.img_list)\n self.tr_tracker = MOTR()\n\n self.save_path = os.path.join(self.args.output_dir, 'results/{}'.format(seq_num))\n os.makedirs(self.save_path, exist_ok=True)\n\n self.predict_path = os.path.join(self.args.output_dir, args.exp_name)\n os.makedirs(self.predict_path, exist_ok=True)\n if os.path.exists(os.path.join(self.predict_path, f'{self.seq_num}.txt')):\n os.remove(os.path.join(self.predict_path, f'{self.seq_num}.txt'))\n\n @staticmethod\n def filter_dt_by_score(dt_instances: Instances, prob_threshold: float) -> Instances:\n keep = dt_instances.scores > prob_threshold\n return dt_instances[keep]\n\n @staticmethod\n def filter_dt_by_area(dt_instances: Instances, area_threshold: float) -> Instances:\n wh = dt_instances.boxes[:, 2:4] - dt_instances.boxes[:, 0:2]\n areas = wh[:, 0] * wh[:, 1]\n keep = areas > area_threshold\n return dt_instances[keep]\n\n @staticmethod\n def write_results(txt_path, frame_id, bbox_xyxy, identities):\n save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\\n'\n with open(txt_path, 'a') as f:\n for xyxy, track_id in zip(bbox_xyxy, identities):\n if track_id < 0 or track_id is None:\n continue\n x1, y1, x2, y2 = xyxy\n w, h = x2 - x1, y2 - y1\n line = save_format.format(frame=int(frame_id), id=int(track_id), x1=x1, y1=y1, w=w, h=h)\n f.write(line)\n\n def eval_seq(self):\n data_root = os.path.join(self.args.mot_path, 'MOT15/images/train')\n result_filename = os.path.join(self.predict_path, 'gt.txt')\n evaluator = Evaluator(data_root, self.seq_num)\n accs = evaluator.eval_file(result_filename)\n return accs\n\n @staticmethod\n def visualize_img_with_bbox(img_path, img, dt_instances: Instances, ref_pts=None, gt_boxes=None):\n if dt_instances.has('scores'):\n img_show = draw_bboxes(img, np.concatenate([dt_instances.boxes, dt_instances.scores.reshape(-1, 1)], axis=-1), dt_instances.obj_idxes)\n else:\n img_show = draw_bboxes(img, dt_instances.boxes, dt_instances.obj_idxes)\n if ref_pts is not None:\n img_show = draw_points(img_show, ref_pts)\n if gt_boxes is not None:\n img_show = draw_bboxes(img_show, gt_boxes, identities=np.ones((len(gt_boxes), )) * -1)\n cv2.imwrite(img_path, img_show)\n\n def detect(self, prob_threshold=0.7, area_threshold=100, vis=False):\n last_dt_embedding = None\n total_dts = 0\n total_occlusion_dts = 0\n\n track_instances = None\n loader = DataLoader(ListImgDataset(self.img_list), 1, num_workers=2)\n with open(os.path.join(self.predict_path, 'gt.txt'), 'w'):\n pass\n for i, (cur_img, ori_img) in enumerate(tqdm(loader)):\n cur_img, ori_img = cur_img[0], ori_img[0]\n\n # track_instances = None\n if track_instances is not None:\n track_instances.remove('boxes')\n track_instances.remove('labels')\n seq_h, seq_w, _ = ori_img.shape\n\n res = self.detr.inference_single_image(cur_img.cuda().float(), (seq_h, seq_w), track_instances)\n track_instances = res['track_instances']\n\n all_ref_pts = tensor_to_numpy(res['ref_pts'][0, :, :2])\n dt_instances = track_instances.to(torch.device('cpu'))\n\n # filter det instances by score.\n dt_instances = self.filter_dt_by_score(dt_instances, prob_threshold)\n dt_instances = self.filter_dt_by_area(dt_instances, area_threshold)\n\n total_dts += len(dt_instances)\n\n if vis:\n # for visual\n cur_vis_img_path = os.path.join(self.save_path, 'frame_{}.jpg'.format(i))\n gt_boxes = None\n self.visualize_img_with_bbox(cur_vis_img_path, ori_img, dt_instances, ref_pts=all_ref_pts, gt_boxes=gt_boxes)\n tracker_outputs = self.tr_tracker.update(dt_instances)\n\n self.write_results(txt_path=os.path.join(self.predict_path, f'{self.seq_num}.txt'),\n frame_id=(i + 1),\n bbox_xyxy=tracker_outputs[:, :4],\n identities=tracker_outputs[:, 5])\n print(\"totally {} dts {} occlusion dts\".format(total_dts, total_occlusion_dts))\n\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])\n args = parser.parse_args()\n if args.output_dir:\n Path(args.output_dir).mkdir(parents=True, exist_ok=True)\n\n # load model and weights\n detr, _, _ = build_model(args)\n checkpoint = torch.load(args.resume, map_location='cpu')\n detr = load_model(detr, args.resume)\n detr.eval()\n detr = detr.cuda()\n\n # '''for MOT17 submit''' \n sub_dir = 'DanceTrack/test'\n seq_nums = os.listdir(os.path.join(args.mot_path, sub_dir))\n\n for seq_num in seq_nums:\n det = Detector(args, model=detr, seq_num=seq_num)\n det.detect()\n" }, { "path": "tools/launch.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\nr\"\"\"\n`torch.distributed.launch` is a module that spawns up multiple distributed\ntraining processes on each of the training nodes.\nThe utility can be used for single-node distributed training, in which one or\nmore processes per node will be spawned. The utility can be used for either\nCPU training or GPU training. If the utility is used for GPU training,\neach distributed process will be operating on a single GPU. This can achieve\nwell-improved single-node training performance. It can also be used in\nmulti-node distributed training, by spawning up multiple processes on each node\nfor well-improved multi-node distributed training performance as well.\nThis will especially be benefitial for systems with multiple Infiniband\ninterfaces that have direct-GPU support, since all of them can be utilized for\naggregated communication bandwidth.\nIn both cases of single-node distributed training or multi-node distributed\ntraining, this utility will launch the given number of processes per node\n(``--nproc_per_node``). If used for GPU training, this number needs to be less\nor euqal to the number of GPUs on the current system (``nproc_per_node``),\nand each process will be operating on a single GPU from *GPU 0 to\nGPU (nproc_per_node - 1)*.\n**How to use this module:**\n1. Single-Node multi-process distributed training\n::\n >>> python -m torch.distributed.launch --nproc_per_node=NUM_GPUS_YOU_HAVE\n YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other\n arguments of your training script)\n2. Multi-Node multi-process distributed training: (e.g. two nodes)\nNode 1: *(IP: 192.168.1.1, and has a free port: 1234)*\n::\n >>> python -m torch.distributed.launch --nproc_per_node=NUM_GPUS_YOU_HAVE\n --nnodes=2 --node_rank=0 --master_addr=\"192.168.1.1\"\n --master_port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3\n and all other arguments of your training script)\nNode 2:\n::\n >>> python -m torch.distributed.launch --nproc_per_node=NUM_GPUS_YOU_HAVE\n --nnodes=2 --node_rank=1 --master_addr=\"192.168.1.1\"\n --master_port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3\n and all other arguments of your training script)\n3. To look up what optional arguments this module offers:\n::\n >>> python -m torch.distributed.launch --help\n**Important Notices:**\n1. This utilty and multi-process distributed (single-node or\nmulti-node) GPU training currently only achieves the best performance using\nthe NCCL distributed backend. Thus NCCL backend is the recommended backend to\nuse for GPU training.\n2. In your training program, you must parse the command-line argument:\n``--local_rank=LOCAL_PROCESS_RANK``, which will be provided by this module.\nIf your training program uses GPUs, you should ensure that your code only\nruns on the GPU device of LOCAL_PROCESS_RANK. This can be done by:\nParsing the local_rank argument\n::\n >>> import argparse\n >>> parser = argparse.ArgumentParser()\n >>> parser.add_argument(\"--local_rank\", type=int)\n >>> args = parser.parse_args()\nSet your device to local rank using either\n::\n >>> torch.cuda.set_device(arg.local_rank) # before your code runs\nor\n::\n >>> with torch.cuda.device(arg.local_rank):\n >>> # your code to run\n3. In your training program, you are supposed to call the following function\nat the beginning to start the distributed backend. You need to make sure that\nthe init_method uses ``env://``, which is the only supported ``init_method``\nby this module.\n::\n torch.distributed.init_process_group(backend='YOUR BACKEND',\n init_method='env://')\n4. In your training program, you can either use regular distributed functions\nor use :func:`torch.nn.parallel.DistributedDataParallel` module. If your\ntraining program uses GPUs for training and you would like to use\n:func:`torch.nn.parallel.DistributedDataParallel` module,\nhere is how to configure it.\n::\n model = torch.nn.parallel.DistributedDataParallel(model,\n device_ids=[arg.local_rank],\n output_device=arg.local_rank)\nPlease ensure that ``device_ids`` argument is set to be the only GPU device id\nthat your code will be operating on. This is generally the local rank of the\nprocess. In other words, the ``device_ids`` needs to be ``[args.local_rank]``,\nand ``output_device`` needs to be ``args.local_rank`` in order to use this\nutility\n5. Another way to pass ``local_rank`` to the subprocesses via environment variable\n``LOCAL_RANK``. This behavior is enabled when you launch the script with\n``--use_env=True``. You must adjust the subprocess example above to replace\n``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher\nwill not pass ``--local_rank`` when you specify this flag.\n.. warning::\n ``local_rank`` is NOT globally unique: it is only unique per process\n on a machine. Thus, don't use it to decide if you should, e.g.,\n write to a networked filesystem. See\n https://github.com/pytorch/pytorch/issues/12042 for an example of\n how things can go wrong if you don't do this correctly.\n\"\"\"\n\n\nimport sys\nimport subprocess\nimport os\nimport socket\nfrom argparse import ArgumentParser, REMAINDER\n\nimport torch\n\n\ndef parse_args():\n \"\"\"\n Helper function parsing the command line options\n @retval ArgumentParser\n \"\"\"\n parser = ArgumentParser(description=\"PyTorch distributed training launch \"\n \"helper utilty that will spawn up \"\n \"multiple distributed processes\")\n\n # Optional arguments for the launch helper\n parser.add_argument(\"--nnodes\", type=int, default=1,\n help=\"The number of nodes to use for distributed \"\n \"training\")\n parser.add_argument(\"--node_rank\", type=int, default=0,\n help=\"The rank of the node for multi-node distributed \"\n \"training\")\n parser.add_argument(\"--nproc_per_node\", type=int, default=1,\n help=\"The number of processes to launch on each node, \"\n \"for GPU training, this is recommended to be set \"\n \"to the number of GPUs in your system so that \"\n \"each process can be bound to a single GPU.\")\n parser.add_argument(\"--master_addr\", default=\"127.0.0.1\", type=str,\n help=\"Master node (rank 0)'s address, should be either \"\n \"the IP address or the hostname of node 0, for \"\n \"single node multi-proc training, the \"\n \"--master_addr can simply be 127.0.0.1\")\n parser.add_argument(\"--master_port\", default=29500, type=int,\n help=\"Master node (rank 0)'s free port that needs to \"\n \"be used for communciation during distributed \"\n \"training\")\n\n # positional\n parser.add_argument(\"training_script\", type=str,\n help=\"The full path to the single GPU training \"\n \"program/script to be launched in parallel, \"\n \"followed by all the arguments for the \"\n \"training script\")\n\n # rest from the training program\n parser.add_argument('training_script_args', nargs=REMAINDER)\n return parser.parse_args()\n\n\ndef main():\n args = parse_args()\n\n # world size in terms of number of processes\n dist_world_size = args.nproc_per_node * args.nnodes\n\n # set PyTorch distributed related environmental variables\n current_env = os.environ.copy()\n current_env[\"MASTER_ADDR\"] = args.master_addr\n current_env[\"MASTER_PORT\"] = str(args.master_port)\n current_env[\"WORLD_SIZE\"] = str(dist_world_size)\n\n processes = []\n\n for local_rank in range(0, args.nproc_per_node):\n # each process's rank\n dist_rank = args.nproc_per_node * args.node_rank + local_rank\n current_env[\"RANK\"] = str(dist_rank)\n current_env[\"LOCAL_RANK\"] = str(local_rank)\n\n cmd = [args.training_script] + args.training_script_args\n\n process = subprocess.Popen(cmd, env=current_env)\n processes.append(process)\n\n for process in processes:\n process.wait()\n if process.returncode != 0:\n raise subprocess.CalledProcessError(returncode=process.returncode,\n cmd=process.args)\n\n\nif __name__ == \"__main__\":\n main()" }, { "path": "tools/run_dist_launch.sh", "content": "#!/usr/bin/env bash\n# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\nset -x\n\nGPUS=$1\nRUN_COMMAND=${@:2}\nif [ $GPUS -lt 8 ]; then\n GPUS_PER_NODE=${GPUS_PER_NODE:-$GPUS}\nelse\n GPUS_PER_NODE=${GPUS_PER_NODE:-8}\nfi\nMASTER_ADDR=${MASTER_ADDR:-\"127.0.0.1\"}\nMASTER_PORT=${MASTER_PORT:-\"29500\"}\nNODE_RANK=${NODE_RANK:-0}\n\nlet \"NNODES=GPUS/GPUS_PER_NODE\"\n\npython3 ./tools/launch.py \\\n --nnodes ${NNODES} \\\n --node_rank ${NODE_RANK} \\\n --master_addr ${MASTER_ADDR} \\\n --master_port ${MASTER_PORT} \\\n --nproc_per_node ${GPUS_PER_NODE} \\\n ${RUN_COMMAND}" }, { "path": "tools/run_dist_slurm.sh", "content": "#!/usr/bin/env bash\n# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\nset -x\n\nPARTITION=$1\nJOB_NAME=$2\nGPUS=$3\nRUN_COMMAND=${@:4}\nif [ $GPUS -lt 8 ]; then\n GPUS_PER_NODE=${GPUS_PER_NODE:-$GPUS}\nelse\n GPUS_PER_NODE=${GPUS_PER_NODE:-8}\nfi\nCPUS_PER_TASK=${CPUS_PER_TASK:-4}\nSRUN_ARGS=${SRUN_ARGS:-\"\"}\n\nsrun -p ${PARTITION} \\\n --job-name=${JOB_NAME} \\\n --gres=gpu:${GPUS_PER_NODE} \\\n --ntasks=${GPUS} \\\n --ntasks-per-node=${GPUS_PER_NODE} \\\n --cpus-per-task=${CPUS_PER_TASK} \\\n --kill-on-bad-exit=1 \\\n ${SRUN_ARGS} \\\n ${RUN_COMMAND}\n\n" }, { "path": "util/__init__.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n" }, { "path": "util/box_ops.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\n\"\"\"\nUtilities for bounding box manipulation and GIoU.\n\"\"\"\nimport torch\nfrom torchvision.ops.boxes import box_area\n\n\ndef box_cxcywh_to_xyxy(x):\n x_c, y_c, w, h = x.unbind(-1)\n b = [(x_c - 0.5 * w), (y_c - 0.5 * h),\n (x_c + 0.5 * w), (y_c + 0.5 * h)]\n return torch.stack(b, dim=-1)\n\n\ndef box_xyxy_to_cxcywh(x):\n x0, y0, x1, y1 = x.unbind(-1)\n b = [(x0 + x1) / 2, (y0 + y1) / 2,\n (x1 - x0), (y1 - y0)]\n return torch.stack(b, dim=-1)\n\n\n# modified from torchvision to also return the union\ndef box_iou(boxes1, boxes2):\n area1 = box_area(boxes1)\n area2 = box_area(boxes2)\n\n lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]\n rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]\n\n wh = (rb - lt).clamp(min=0) # [N,M,2]\n inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]\n\n union = area1[:, None] + area2 - inter\n\n iou = inter / union\n return iou, union\n\n\ndef generalized_box_iou(boxes1, boxes2):\n \"\"\"\n Generalized IoU from https://giou.stanford.edu/\n\n The boxes should be in [x0, y0, x1, y1] format\n\n Returns a [N, M] pairwise matrix, where N = len(boxes1)\n and M = len(boxes2)\n \"\"\"\n # degenerate boxes gives inf / nan results\n # so do an early check\n assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n iou, union = box_iou(boxes1, boxes2)\n\n lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])\n rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])\n\n wh = (rb - lt).clamp(min=0) # [N,M,2]\n area = wh[:, :, 0] * wh[:, :, 1]\n\n return iou - (area - union) / area\n\n\ndef masks_to_boxes(masks):\n \"\"\"Compute the bounding boxes around the provided masks\n\n The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.\n\n Returns a [N, 4] tensors, with the boxes in xyxy format\n \"\"\"\n if masks.numel() == 0:\n return torch.zeros((0, 4), device=masks.device)\n\n h, w = masks.shape[-2:]\n\n y = torch.arange(0, h, dtype=torch.float)\n x = torch.arange(0, w, dtype=torch.float)\n y, x = torch.meshgrid(y, x)\n\n x_mask = (masks * x.unsqueeze(0))\n x_max = x_mask.flatten(1).max(-1)[0]\n x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n y_mask = (masks * y.unsqueeze(0))\n y_max = y_mask.flatten(1).max(-1)[0]\n y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n return torch.stack([x_min, y_min, x_max, y_max], 1)\n" }, { "path": "util/checkpoint.py", "content": "# from: https://github.com/csrhddlam/pytorch-checkpoint\n\nimport torch\nimport warnings\n\n\ndef detach_variable(inputs):\n if isinstance(inputs, tuple):\n out = []\n for inp in inputs:\n x = inp.detach()\n x.requires_grad = inp.requires_grad\n out.append(x)\n return tuple(out)\n else:\n raise RuntimeError(\n \"Only tuple of tensors is supported. Got Unsupported input type: \", type(inputs).__name__)\n\n\ndef check_backward_validity(inputs):\n if not any(inp.requires_grad for inp in inputs):\n warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n\n\nclass CheckpointFunction(torch.autograd.Function):\n @staticmethod\n def forward(ctx, run_function, length, *args):\n ctx.run_function = run_function\n ctx.input_tensors = list(args[:length])\n ctx.input_params = list(args[length:])\n with torch.no_grad():\n output_tensors = ctx.run_function(*ctx.input_tensors)\n return output_tensors\n\n @staticmethod\n def backward(ctx, *output_grads):\n for i in range(len(ctx.input_tensors)):\n temp = ctx.input_tensors[i]\n ctx.input_tensors[i] = temp.detach()\n ctx.input_tensors[i].requires_grad = temp.requires_grad\n with torch.enable_grad():\n output_tensors = ctx.run_function(*ctx.input_tensors)\n to_autograd = []\n for i in range(len(ctx.input_tensors)):\n if ctx.input_tensors[i].requires_grad:\n to_autograd.append(ctx.input_tensors[i])\n output_tensors, output_grads = zip(*filter(lambda t: t[0].requires_grad, zip(output_tensors, output_grads)))\n input_grads = torch.autograd.grad(output_tensors, to_autograd + ctx.input_params, output_grads, allow_unused=True)\n input_grads = list(input_grads)\n for i in range(len(ctx.input_tensors)):\n if not ctx.input_tensors[i].requires_grad:\n input_grads.insert(i, None)\n return (None, None) + tuple(input_grads)\n" }, { "path": "util/evaluation.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\nimport os\nimport numpy as np\nimport copy\nimport motmetrics as mm\nmm.lap.default_solver = 'lap'\nimport os\nfrom typing import Dict\nimport numpy as np\nimport logging\n\ndef read_results(filename, data_type: str, is_gt=False, is_ignore=False):\n if data_type in ('mot', 'lab'):\n read_fun = read_mot_results\n else:\n raise ValueError('Unknown data type: {}'.format(data_type))\n\n return read_fun(filename, is_gt, is_ignore)\n\n# def read_mot_results(filename, is_gt, is_ignore):\n# results_dict = dict()\n# if os.path.isfile(filename):\n# with open(filename, 'r') as f:\n# for line in f.readlines():\n# linelist = line.split(',')\n# if len(linelist) < 7:\n# continue\n# fid = int(linelist[0])\n# if fid < 1:\n# continue\n# results_dict.setdefault(fid, list())\n\n# if is_gt:\n# mark = int(float(linelist[6]))\n# if mark == 0 :\n# continue\n# score = 1\n# elif is_ignore:\n# score = 1\n# else:\n# score = float(linelist[6])\n\n# tlwh = tuple(map(float, linelist[2:6]))\n# target_id = int(float(linelist[1]))\n# results_dict[fid].append((tlwh, target_id, score))\n\n# return results_dict\n\ndef read_mot_results(filename, is_gt, is_ignore):\n valid_labels = {1}\n ignore_labels = {0, 2, 7, 8, 12}\n results_dict = dict()\n if os.path.isfile(filename):\n with open(filename, 'r') as f:\n for line in f.readlines():\n linelist = line.split(',')\n if len(linelist) < 7:\n continue\n fid = int(linelist[0])\n if fid < 1:\n continue\n results_dict.setdefault(fid, list())\n\n if is_gt:\n if 'MOT16-' in filename or 'MOT17-' in filename:\n label = int(float(linelist[7]))\n mark = int(float(linelist[6]))\n if mark == 0 or label not in valid_labels:\n continue\n score = 1\n elif is_ignore:\n if 'MOT16-' in filename or 'MOT17-' in filename:\n label = int(float(linelist[7]))\n vis_ratio = float(linelist[8])\n if label not in ignore_labels and vis_ratio >= 0:\n continue\n elif 'MOT15' in filename:\n label = int(float(linelist[6]))\n if label not in ignore_labels:\n continue\n else:\n continue\n score = 1\n else:\n score = float(linelist[6])\n\n tlwh = tuple(map(float, linelist[2:6]))\n target_id = int(linelist[1])\n\n results_dict[fid].append((tlwh, target_id, score))\n\n return results_dict\n\ndef unzip_objs(objs):\n if len(objs) > 0:\n tlwhs, ids, scores = zip(*objs)\n else:\n tlwhs, ids, scores = [], [], []\n tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4)\n return tlwhs, ids, scores\n\n\nclass Evaluator(object):\n def __init__(self, data_root, seq_name, data_type='mot'):\n\n self.data_root = data_root\n self.seq_name = seq_name\n self.data_type = data_type\n\n self.load_annotations()\n self.reset_accumulator()\n\n def load_annotations(self):\n assert self.data_type == 'mot'\n\n gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt')\n self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True)\n self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True)\n\n def reset_accumulator(self):\n self.acc = mm.MOTAccumulator(auto_id=True)\n\n def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):\n # results\n trk_tlwhs = np.copy(trk_tlwhs)\n trk_ids = np.copy(trk_ids)\n\n # gts\n gt_objs = self.gt_frame_dict.get(frame_id, [])\n gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]\n\n # ignore boxes\n ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])\n ignore_tlwhs = unzip_objs(ignore_objs)[0]\n # remove ignored results\n keep = np.ones(len(trk_tlwhs), dtype=bool)\n iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5)\n if len(iou_distance) > 0:\n match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)\n match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])\n match_ious = iou_distance[match_is, match_js]\n\n match_js = np.asarray(match_js, dtype=int)\n match_js = match_js[np.logical_not(np.isnan(match_ious))]\n keep[match_js] = False\n trk_tlwhs = trk_tlwhs[keep]\n trk_ids = trk_ids[keep]\n\n # get distance matrix\n iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)\n\n # acc\n self.acc.update(gt_ids, trk_ids, iou_distance)\n\n if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'):\n events = self.acc.last_mot_events # only supported by https://github.com/longcw/py-motmetrics\n else:\n events = None\n return events\n\n def eval_file(self, filename):\n self.reset_accumulator()\n\n result_frame_dict = read_results(filename, self.data_type, is_gt=False)\n frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys())))\n for frame_id in frames:\n trk_objs = result_frame_dict.get(frame_id, [])\n trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]\n self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)\n\n return self.acc\n\n @staticmethod\n def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')):\n names = copy.deepcopy(names)\n if metrics is None:\n metrics = mm.metrics.motchallenge_metrics\n metrics = copy.deepcopy(metrics)\n\n mh = mm.metrics.create()\n summary = mh.compute_many(\n accs,\n metrics=metrics,\n names=names,\n generate_overall=True\n )\n\n return summary\n\n @staticmethod\n def save_summary(summary, filename):\n import pandas as pd\n writer = pd.ExcelWriter(filename)\n summary.to_excel(writer)\n writer.save()" }, { "path": "util/misc.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\n\"\"\"\nMisc functions, including distributed helpers.\n\nMostly copy-paste from torchvision references.\n\"\"\"\nimport os\nimport subprocess\nimport time\nfrom collections import defaultdict, deque\nimport datetime\nimport pickle\nfrom typing import Optional, List\n\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\nfrom torch import Tensor\n\n# needed due to empty tensor bug in pytorch and torchvision 0.5\nimport torchvision\nif float(torchvision.__version__[:3]) < 0.5:\n import math\n from torchvision.ops.misc import _NewEmptyTensorOp\n def _check_size_scale_factor(dim, size, scale_factor):\n # type: (int, Optional[List[int]], Optional[float]) -> None\n if size is None and scale_factor is None:\n raise ValueError(\"either size or scale_factor should be defined\")\n if size is not None and scale_factor is not None:\n raise ValueError(\"only one of size or scale_factor should be defined\")\n if not (scale_factor is not None and len(scale_factor) != dim):\n raise ValueError(\n \"scale_factor shape must match input shape. \"\n \"Input is {}D, scale_factor size is {}\".format(dim, len(scale_factor))\n )\n def _output_size(dim, input, size, scale_factor):\n # type: (int, Tensor, Optional[List[int]], Optional[float]) -> List[int]\n assert dim == 2\n _check_size_scale_factor(dim, size, scale_factor)\n if size is not None:\n return size\n # if dim is not 2 or scale_factor is iterable use _ntuple instead of concat\n assert scale_factor is not None and isinstance(scale_factor, (int, float))\n scale_factors = [scale_factor, scale_factor]\n # math.floor might return float in py2.7\n return [\n int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)\n ]\nelif float(torchvision.__version__[:3]) < 0.7:\n from torchvision.ops import _new_empty_tensor\n from torchvision.ops.misc import _output_size\n\n\nclass SmoothedValue(object):\n \"\"\"Track a series of values and provide access to smoothed values over a\n window or the global series average.\n \"\"\"\n\n def __init__(self, window_size=20, fmt=None):\n if fmt is None:\n fmt = \"{median:.4f} ({global_avg:.4f})\"\n self.deque = deque(maxlen=window_size)\n self.total = 0.0\n self.count = 0\n self.fmt = fmt\n\n def update(self, value, n=1):\n self.deque.append(value)\n self.count += n\n self.total += value * n\n\n def synchronize_between_processes(self):\n \"\"\"\n Warning: does not synchronize the deque!\n \"\"\"\n if not is_dist_avail_and_initialized():\n return\n t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')\n dist.barrier()\n dist.all_reduce(t)\n t = t.tolist()\n self.count = int(t[0])\n self.total = t[1]\n\n @property\n def median(self):\n d = torch.tensor(list(self.deque))\n return d.median().item()\n\n @property\n def avg(self):\n d = torch.tensor(list(self.deque), dtype=torch.float32)\n return d.mean().item()\n\n @property\n def global_avg(self):\n return self.total / self.count\n\n @property\n def max(self):\n return max(self.deque)\n\n @property\n def value(self):\n return self.deque[-1]\n\n def __str__(self):\n return self.fmt.format(\n median=self.median,\n avg=self.avg,\n global_avg=self.global_avg,\n max=self.max,\n value=self.value)\n\n\ndef all_gather(data):\n \"\"\"\n Run all_gather on arbitrary picklable data (not necessarily tensors)\n Args:\n data: any picklable object\n Returns:\n list[data]: list of data gathered from each rank\n \"\"\"\n world_size = get_world_size()\n if world_size == 1:\n return [data]\n\n # serialized to a Tensor\n buffer = pickle.dumps(data)\n storage = torch.ByteStorage.from_buffer(buffer)\n tensor = torch.ByteTensor(storage).to(\"cuda\")\n\n # obtain Tensor size of each rank\n local_size = torch.tensor([tensor.numel()], device=\"cuda\")\n size_list = [torch.tensor([0], device=\"cuda\") for _ in range(world_size)]\n dist.all_gather(size_list, local_size)\n size_list = [int(size.item()) for size in size_list]\n max_size = max(size_list)\n\n # receiving Tensor from all ranks\n # we pad the tensor because torch all_gather does not support\n # gathering tensors of different shapes\n tensor_list = []\n for _ in size_list:\n tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=\"cuda\"))\n if local_size != max_size:\n padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=\"cuda\")\n tensor = torch.cat((tensor, padding), dim=0)\n dist.all_gather(tensor_list, tensor)\n\n data_list = []\n for size, tensor in zip(size_list, tensor_list):\n buffer = tensor.cpu().numpy().tobytes()[:size]\n data_list.append(pickle.loads(buffer))\n\n return data_list\n\n\ndef reduce_dict(input_dict, average=True):\n \"\"\"\n Args:\n input_dict (dict): all the values will be reduced\n average (bool): whether to do average or sum\n Reduce the values in the dictionary from all processes so that all processes\n have the averaged results. Returns a dict with the same fields as\n input_dict, after reduction.\n \"\"\"\n world_size = get_world_size()\n if world_size < 2:\n return input_dict\n with torch.no_grad():\n names = []\n values = []\n # sort the keys so that they are consistent across processes\n for k in sorted(input_dict.keys()):\n names.append(k)\n values.append(input_dict[k])\n values = torch.stack(values, dim=0)\n dist.all_reduce(values)\n if average:\n values /= world_size\n reduced_dict = {k: v for k, v in zip(names, values)}\n return reduced_dict\n\n\nclass MetricLogger(object):\n def __init__(self, delimiter=\"\\t\"):\n self.meters = defaultdict(SmoothedValue)\n self.delimiter = delimiter\n\n def update(self, **kwargs):\n for k, v in kwargs.items():\n if isinstance(v, torch.Tensor):\n v = v.item()\n assert isinstance(v, (float, int))\n self.meters[k].update(v)\n\n def __getattr__(self, attr):\n if attr in self.meters:\n return self.meters[attr]\n if attr in self.__dict__:\n return self.__dict__[attr]\n raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n type(self).__name__, attr))\n\n def __str__(self):\n loss_str = []\n for name, meter in self.meters.items():\n loss_str.append(\n \"{}: {}\".format(name, str(meter))\n )\n return self.delimiter.join(loss_str)\n\n def synchronize_between_processes(self):\n for meter in self.meters.values():\n meter.synchronize_between_processes()\n\n def add_meter(self, name, meter):\n self.meters[name] = meter\n\n def log_every(self, iterable, print_freq, header=None):\n i = 0\n if not header:\n header = ''\n start_time = time.time()\n end = time.time()\n iter_time = SmoothedValue(fmt='{avg:.4f}')\n data_time = SmoothedValue(fmt='{avg:.4f}')\n space_fmt = ':' + str(len(str(len(iterable)))) + 'd'\n if torch.cuda.is_available():\n log_msg = self.delimiter.join([\n header,\n '[{0' + space_fmt + '}/{1}]',\n 'eta: {eta}',\n '{meters}',\n 'time: {time}',\n 'data: {data}',\n 'max mem: {memory:.0f}'\n ])\n else:\n log_msg = self.delimiter.join([\n header,\n '[{0' + space_fmt + '}/{1}]',\n 'eta: {eta}',\n '{meters}',\n 'time: {time}',\n 'data: {data}'\n ])\n MB = 1024.0 * 1024.0\n for obj in iterable:\n data_time.update(time.time() - end)\n yield obj\n iter_time.update(time.time() - end)\n if i % print_freq == 0 or i == len(iterable) - 1:\n eta_seconds = iter_time.global_avg * (len(iterable) - i)\n eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n if torch.cuda.is_available():\n print(log_msg.format(\n i, len(iterable), eta=eta_string,\n meters=str(self),\n time=str(iter_time), data=str(data_time),\n memory=torch.cuda.max_memory_allocated() / MB))\n else:\n print(log_msg.format(\n i, len(iterable), eta=eta_string,\n meters=str(self),\n time=str(iter_time), data=str(data_time)))\n i += 1\n end = time.time()\n total_time = time.time() - start_time\n total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n print('{} Total time: {} ({:.4f} s / it)'.format(\n header, total_time_str, total_time / len(iterable)))\n\n\ndef get_sha():\n cwd = os.path.dirname(os.path.abspath(__file__))\n\n def _run(command):\n return subprocess.check_output(command, cwd=cwd).decode('ascii').strip()\n sha = 'N/A'\n diff = \"clean\"\n branch = 'N/A'\n try:\n sha = _run(['git', 'rev-parse', 'HEAD'])\n subprocess.check_output(['git', 'diff'], cwd=cwd)\n diff = _run(['git', 'diff-index', 'HEAD'])\n diff = \"has uncommited changes\" if diff else \"clean\"\n branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD'])\n except Exception:\n pass\n message = f\"sha: {sha}, status: {diff}, branch: {branch}\"\n return message\n\n\ndef collate_fn(batch):\n batch = list(zip(*batch))\n batch[0] = nested_tensor_from_tensor_list(batch[0], size_divisibility=32)\n return tuple(batch)\n\n\ndef mot_collate_fn(batch: List[dict]) -> dict:\n ret_dict = {}\n for key in list(batch[0].keys()):\n assert not isinstance(batch[0][key], Tensor)\n ret_dict[key] = [img_info[key] for img_info in batch]\n if len(ret_dict[key]) == 1:\n ret_dict[key] = ret_dict[key][0]\n return ret_dict\n\n\ndef _max_by_axis(the_list):\n # type: (List[List[int]]) -> List[int]\n maxes = the_list[0]\n for sublist in the_list[1:]:\n for index, item in enumerate(sublist):\n maxes[index] = max(maxes[index], item)\n return maxes\n\n\ndef nested_tensor_from_tensor_list(tensor_list: List[Tensor], size_divisibility: int = 0):\n # TODO make this more general\n if tensor_list[0].ndim == 3:\n # TODO make it support different-sized images\n\n max_size = _max_by_axis([list(img.shape) for img in tensor_list])\n if size_divisibility > 0:\n stride = size_divisibility\n # the last two dims are H,W, both subject to divisibility requirement\n max_size[-1] = (max_size[-1] + (stride - 1)) // stride * stride\n max_size[-2] = (max_size[-2] + (stride - 1)) // stride * stride\n\n # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))\n batch_shape = [len(tensor_list)] + max_size\n b, c, h, w = batch_shape\n dtype = tensor_list[0].dtype\n device = tensor_list[0].device\n tensor = torch.zeros(batch_shape, dtype=dtype, device=device)\n mask = torch.ones((b, h, w), dtype=torch.bool, device=device)\n for img, pad_img, m in zip(tensor_list, tensor, mask):\n pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n m[: img.shape[1], :img.shape[2]] = False\n else:\n raise ValueError('not supported')\n return NestedTensor(tensor, mask)\n\n\nclass NestedTensor(object):\n def __init__(self, tensors, mask: Optional[Tensor]):\n self.tensors = tensors\n self.mask = mask\n\n def to(self, device, non_blocking=False):\n # type: (Device) -> NestedTensor # noqa\n cast_tensor = self.tensors.to(device, non_blocking=non_blocking)\n mask = self.mask\n if mask is not None:\n assert mask is not None\n cast_mask = mask.to(device, non_blocking=non_blocking)\n else:\n cast_mask = None\n return NestedTensor(cast_tensor, cast_mask)\n\n def record_stream(self, *args, **kwargs):\n self.tensors.record_stream(*args, **kwargs)\n if self.mask is not None:\n self.mask.record_stream(*args, **kwargs)\n\n def decompose(self):\n return self.tensors, self.mask\n\n def __repr__(self):\n return str(self.tensors)\n\n\ndef setup_for_distributed(is_master):\n \"\"\"\n This function disables printing when not in master process\n \"\"\"\n import builtins as __builtin__\n builtin_print = __builtin__.print\n\n def print(*args, **kwargs):\n force = kwargs.pop('force', False)\n if is_master or force:\n builtin_print(*args, **kwargs)\n\n __builtin__.print = print\n\n\ndef is_dist_avail_and_initialized():\n if not dist.is_available():\n return False\n if not dist.is_initialized():\n return False\n return True\n\n\ndef get_world_size():\n if not is_dist_avail_and_initialized():\n return 1\n return dist.get_world_size()\n\n\ndef get_rank():\n if not is_dist_avail_and_initialized():\n return 0\n return dist.get_rank()\n\n\ndef get_local_size():\n if not is_dist_avail_and_initialized():\n return 1\n return int(os.environ['LOCAL_SIZE'])\n\n\ndef get_local_rank():\n if not is_dist_avail_and_initialized():\n return 0\n return int(os.environ['LOCAL_RANK'])\n\n\ndef is_main_process():\n return get_rank() == 0\n\n\ndef save_on_master(*args, **kwargs):\n if is_main_process():\n torch.save(*args, **kwargs)\n\n\ndef init_distributed_mode(args):\n if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:\n args.rank = int(os.environ[\"RANK\"])\n args.world_size = int(os.environ['WORLD_SIZE'])\n args.gpu = int(os.environ['LOCAL_RANK'])\n args.dist_url = 'env://'\n os.environ['LOCAL_SIZE'] = str(torch.cuda.device_count())\n elif 'SLURM_PROCID' in os.environ:\n proc_id = int(os.environ['SLURM_PROCID'])\n ntasks = int(os.environ['SLURM_NTASKS'])\n node_list = os.environ['SLURM_NODELIST']\n num_gpus = torch.cuda.device_count()\n addr = subprocess.getoutput(\n 'scontrol show hostname {} | head -n1'.format(node_list))\n os.environ['MASTER_PORT'] = os.environ.get('MASTER_PORT', '29500')\n os.environ['MASTER_ADDR'] = addr\n os.environ['WORLD_SIZE'] = str(ntasks)\n os.environ['RANK'] = str(proc_id)\n os.environ['LOCAL_RANK'] = str(proc_id % num_gpus)\n os.environ['LOCAL_SIZE'] = str(num_gpus)\n args.dist_url = 'env://'\n args.world_size = ntasks\n args.rank = proc_id\n args.gpu = proc_id % num_gpus\n else:\n print('Not using distributed mode')\n args.distributed = False\n return\n\n args.distributed = True\n\n torch.cuda.set_device(args.gpu)\n args.dist_backend = 'nccl'\n print('| distributed init (rank {}): {}'.format(\n args.rank, args.dist_url), flush=True)\n torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,\n world_size=args.world_size, rank=args.rank)\n torch.distributed.barrier()\n setup_for_distributed(args.rank == 0)\n\n\n@torch.no_grad()\ndef accuracy(output, target, topk=(1,)):\n \"\"\"Computes the precision@k for the specified values of k\"\"\"\n if target.numel() == 0:\n return [torch.zeros([], device=output.device)]\n maxk = max(topk)\n batch_size = target.size(0)\n\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n res = []\n for k in topk:\n correct_k = correct[:k].view(-1).float().sum(0)\n res.append(correct_k.mul_(100.0 / batch_size))\n return res\n\n\ndef interpolate(input, size=None, scale_factor=None, mode=\"nearest\", align_corners=None):\n # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor\n \"\"\"\n Equivalent to nn.functional.interpolate, but with support for empty batch sizes.\n This will eventually be supported natively by PyTorch, and this\n class can go away.\n \"\"\"\n if float(torchvision.__version__[:3]) < 0.7:\n if input.numel() > 0:\n return torch.nn.functional.interpolate(\n input, size, scale_factor, mode, align_corners\n )\n\n output_shape = _output_size(2, input, size, scale_factor)\n output_shape = list(input.shape[:-2]) + list(output_shape)\n if float(torchvision.__version__[:3]) < 0.5:\n return _NewEmptyTensorOp.apply(input, output_shape)\n return _new_empty_tensor(input, output_shape)\n else:\n return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)\n\n\ndef get_total_grad_norm(parameters, norm_type=2):\n parameters = list(filter(lambda p: p.grad is not None, parameters))\n norm_type = float(norm_type)\n device = parameters[0].grad.device\n total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]),\n norm_type)\n return total_norm\n\ndef inverse_sigmoid(x, eps=1e-5):\n x = x.clamp(min=0, max=1)\n x1 = x.clamp(min=eps)\n x2 = (1 - x).clamp(min=eps)\n return torch.log(x1/x2)\n\n" }, { "path": "util/motdet_eval.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\nimport torch\nimport numpy as np\nimport time\nimport cv2\n\ndef ap_per_class(tp, conf, pred_cls, target_cls):\n \"\"\" Compute the average precision, given the recall and precision curves.\n Method originally from https://github.com/rafaelpadilla/Object-Detection-Metrics.\n # Arguments\n tp: True positives (list).\n conf: Objectness value from 0-1 (list).\n pred_cls: Predicted object classes (list).\n target_cls: True object classes (list).\n # Returns\n The average precision as computed in py-faster-rcnn.\n \"\"\"\n\n # lists/pytorch to numpy\n tp, conf, pred_cls, target_cls = np.array(tp), np.array(conf), np.array(pred_cls), np.array(target_cls)\n\n # Sort by objectness\n i = np.argsort(-conf)\n tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]\n\n # Find unique classes\n unique_classes = np.unique(np.concatenate((pred_cls, target_cls), 0))\n\n # Create Precision-Recall curve and compute AP for each class\n ap, p, r = [], [], []\n for c in unique_classes:\n i = pred_cls == c\n n_gt = sum(target_cls == c) # Number of ground truth objects\n n_p = sum(i) # Number of predicted objects\n\n if (n_p == 0) and (n_gt == 0):\n continue\n elif (n_p == 0) or (n_gt == 0):\n ap.append(0)\n r.append(0)\n p.append(0)\n else:\n # Accumulate FPs and TPs\n fpc = np.cumsum(1 - tp[i])\n tpc = np.cumsum(tp[i])\n\n # Recall\n recall_curve = tpc / (n_gt + 1e-16)\n r.append(tpc[-1] / (n_gt + 1e-16))\n\n # Precision\n precision_curve = tpc / (tpc + fpc)\n p.append(tpc[-1] / (tpc[-1] + fpc[-1]))\n\n # AP from recall-precision curve\n ap.append(compute_ap(recall_curve, precision_curve))\n\n return np.array(ap), unique_classes.astype('int32'), np.array(r), np.array(p)\n\ndef compute_ap(recall, precision):\n \"\"\" Compute the average precision, given the recall and precision curves.\n Code originally from https://github.com/rbgirshick/py-faster-rcnn.\n # Arguments\n recall: The recall curve (list).\n precision: The precision curve (list).\n # Returns\n The average precision as computed in py-faster-rcnn.\n \"\"\"\n # correct AP calculation\n # first append sentinel values at the end\n\n mrec = np.concatenate(([0.], recall, [1.]))\n mpre = np.concatenate(([0.], precision, [0.]))\n\n # compute the precision envelope\n for i in range(mpre.size - 1, 0, -1):\n mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])\n\n # to calculate area under PR curve, look for points\n # where X axis (recall) changes value\n i = np.where(mrec[1:] != mrec[:-1])[0]\n\n # and sum (\\Delta recall) * prec\n ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])\n return ap\n\n\ndef bbox_iou(box1, box2, x1y1x2y2=False):\n \"\"\"\n Returns the IoU of two bounding boxes\n \"\"\"\n N, M = len(box1), len(box2)\n if x1y1x2y2:\n # Get the coordinates of bounding boxes\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]\n else:\n # Transform from center and width to exact coordinates\n b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2\n b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2\n b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2\n b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2\n\n # get the coordinates of the intersection rectangle\n inter_rect_x1 = torch.max(b1_x1.unsqueeze(1), b2_x1)\n inter_rect_y1 = torch.max(b1_y1.unsqueeze(1), b2_y1)\n inter_rect_x2 = torch.min(b1_x2.unsqueeze(1), b2_x2)\n inter_rect_y2 = torch.min(b1_y2.unsqueeze(1), b2_y2)\n # Intersection area\n inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1, 0) * torch.clamp(inter_rect_y2 - inter_rect_y1, 0)\n # Union Area\n b1_area = ((b1_x2 - b1_x1) * (b1_y2 - b1_y1)).view(-1,1).expand(N,M)\n b2_area = ((b2_x2 - b2_x1) * (b2_y2 - b2_y1)).view(1,-1).expand(N,M)\n\n return inter_area / (b1_area + b2_area - inter_area + 1e-16)\n\ndef xyxy2xywh(x):\n # Convert bounding box format from [x1, y1, x2, y2] to [x, y, w, h]\n y = torch.zeros(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)\n y[:, 0] = (x[:, 0] + x[:, 2]) / 2\n y[:, 1] = (x[:, 1] + x[:, 3]) / 2\n y[:, 2] = x[:, 2] - x[:, 0]\n y[:, 3] = x[:, 3] - x[:, 1]\n return y\n\n\ndef xywh2xyxy(x):\n # Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]\n y = torch.zeros(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)\n y[:, 0] = (x[:, 0] - x[:, 2] / 2)\n y[:, 1] = (x[:, 1] - x[:, 3] / 2)\n y[:, 2] = (x[:, 0] + x[:, 2] / 2)\n y[:, 3] = (x[:, 1] + x[:, 3] / 2)\n return y\n\n\n@torch.no_grad()\ndef motdet_evaluate(model, data_loader, iou_thres=0.5, print_interval=10):\n model.eval()\n mean_mAP, mean_R, mean_P, seen = 0.0, 0.0, 0.0, 0\n print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))\n outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class, jdict = \\\n [], [], [], [], [], [], [], [], []\n AP_accum, AP_accum_count = np.zeros(1), np.zeros(1)\n for batch_i, data in enumerate(data_loader):\n seen += 1\n if(batch_i > 300):\n break\n # [batch_size x 3 x H x W]\n imgs, _ = data[0].decompose()\n # print(\"imgs.shape={}\".format(imgs.shape))\n #dict{'boxes':cxcywh_norm 'labels', size, orig_size}\n targets = data[1][0]\n # img_path = data[2]\n height, width = targets['orig_size'].cpu().numpy().tolist()\n t = time.time()\n output = model(imgs.cuda())\n outputs_class = output['pred_logits'].squeeze()\n if outputs_class.ndim == 1:\n # focal_loss\n outputs_class = outputs_class.unsqueeze(-1)\n outputs_boxes = output['pred_boxes'].squeeze()\n target_boxes = targets['boxes']\n\n # Compute average precision\n if target_boxes is None:\n # If there are labels but no detections mark as zero AP\n if target_boxes.size(0) != 0:\n mAPs.append(0), mR.append(0), mP.append(0)\n continue\n\n # If no labels add number of detections as incorrect\n correct = []\n if target_boxes.size(0) == 0:\n # correct.extend([0 for _ in range(len(detections))])\n mAPs.append(0), mR.append(0), mP.append(0)\n continue\n else:\n target_cls = targets['labels']\n # Extract target boxes as (x1, y1, x2, y2)\n target_boxes = xywh2xyxy(target_boxes)\n target_boxes[:, 0] *= width\n target_boxes[:, 2] *= width\n target_boxes[:, 1] *= height\n target_boxes[:, 3] *= height\n\n outputs_boxes = xywh2xyxy(outputs_boxes)\n outputs_boxes[:, 0] *= width\n outputs_boxes[:, 2] *= width\n outputs_boxes[:, 1] *= height\n outputs_boxes[:, 3] *= height\n\n detected = set()\n print(\"output_boxes.shape={} class.shape={}\".format(outputs_boxes.shape, outputs_class.shape))\n print((outputs_class.sigmoid() > 0.5).sum())\n num_dt = 0\n num_tp = 0\n for *pred_bbox, conf in zip(outputs_boxes, outputs_class):\n obj_pred = 0\n pred_bbox = torch.FloatTensor(pred_bbox[0]).view(1, -1)\n if conf.sigmoid() > 0.5:\n num_dt += 1\n\n # Compute iou with target boxes\n iou = bbox_iou(pred_bbox, target_boxes, x1y1x2y2=True)[0]\n # Extract index of largest overlap\n best_i = np.argmax(iou)\n # If overlap exceeds threshold and classification is correct mark as correct\n if iou[best_i] > iou_thres and obj_pred == int(target_cls[best_i]) and best_i.item() not in detected:\n correct.append(1)\n if conf.sigmoid() > 0.5:\n num_tp += 1\n detected.add(best_i.item())\n else:\n correct.append(0)\n print(\"precision={} recall={}\".format(num_tp / max(1.0, num_dt), num_tp / max(1.0, len(target_boxes))))\n # Compute Average Precision (AP) per class\n AP, AP_class, R, P = ap_per_class(tp=correct,\n conf=outputs_class[:, 0].cpu(),\n pred_cls=np.zeros_like(outputs_class[:, 0].cpu()),\n target_cls=target_cls)\n\n # Accumulate AP per class\n AP_accum_count += np.bincount(AP_class, minlength=1)\n AP_accum += np.bincount(AP_class, minlength=1, weights=AP)\n\n # Compute mean AP across all classes in this image, and append to image list\n mAPs.append(AP.mean())\n mR.append(R.mean())\n mP.append(P.mean())\n\n # Means of all images\n mean_mAP = np.sum(mAPs) / (AP_accum_count + 1E-16)\n mean_R = np.sum(mR) / (AP_accum_count + 1E-16)\n mean_P = np.sum(mP) / (AP_accum_count + 1E-16)\n\n if batch_i % print_interval == 0:\n # Print image mAP and running mean mAP\n print(('%11s%11s' + '%11.3g' * 4 + 's') %\n (seen, 100, mean_P, mean_R, mean_mAP, time.time() - t))\n # Print mAP per class\n print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))\n\n print('AP: %-.4f\\n\\n' % (AP_accum[0] / (AP_accum_count[0] + 1E-16)))\n\n # Return mAP\n return mean_mAP, mean_R, mean_P\n\n\ndef init_metrics():\n mean_mAP, mean_R, mean_P, seen = 0.0, 0.0, 0.0, 0\n outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class, jdict = [], [], [], [], [], [], [], [], []\n AP_accum, AP_accum_count = np.zeros(1), np.zeros(1)\n return {'mean_mAP': mean_mAP,\n 'mean_R': mean_R,\n 'mean_P': mean_P,\n 'seen': seen,\n 'outputs': outputs,\n 'mAPs': mAPs,\n 'mR': mR,\n 'mP': mP,\n 'TP': TP,\n 'confidence': confidence,\n 'pred_class': pred_class,\n 'target_class': target_class,\n 'jdict': jdict,\n 'AP_accum': AP_accum,\n 'AP_accum_count': AP_accum_count,\n }\n\n\n@torch.no_grad()\ndef detmotdet_evaluate(model, data_loader, device, iou_thres=0.5, print_interval=10):\n model.eval()\n print('%11s' * 5 % ('Cur Image', 'Total', 'P', 'R', 'mAP'))\n # TODO: Remove the hard-code 3.\n metrics_list = [init_metrics() for i in range(10)]\n for batch_i, data in enumerate(data_loader):\n if(batch_i > 100):\n break\n\n for key in list(data.keys()):\n if isinstance(data[key], list):\n data[key] = [img_info.to(device) for img_info in data[key]]\n else:\n data[key] = data[key].to(device)\n output = model(data)\n num_frames = len(data['gt_instances'])\n for i in range(num_frames):\n metrics_i = metrics_list[i]\n metrics_i['seen'] += 1\n gt_instances = data['gt_instances'][i].to(torch.device('cpu'))\n\n height, width = gt_instances.image_size\n t = time.time()\n outputs_class = output['pred_logits'][i].squeeze()\n outputs_boxes = output['pred_boxes'][i].squeeze()\n\n if outputs_class.ndim == 1:\n # focal_loss\n outputs_class = outputs_class.unsqueeze(-1)\n\n target_boxes = gt_instances.boxes\n\n # Compute average precision\n if target_boxes is None:\n # If there are labels but no detections mark as zero AP\n if target_boxes.size(0) != 0:\n metrics_i['mAPs'].append(0)\n metrics_i['mR'].append(0)\n metrics_i['mP'].append(0)\n print('cur_target_boxes is None')\n continue\n\n # for cur frame\n # If no labels add number of detections as incorrect\n correct = []\n if target_boxes.size(0) == 0:\n # correct.extend([0 for _ in range(len(detections))])\n metrics_i['mAP'].append(0)\n metrics_i['mR'].append(0)\n metrics_i['mP'].apppend(0)\n print('cur_target_boxes.size(0) == 0')\n continue\n else:\n target_cls = gt_instances.labels\n # Extract target boxes as (x1, y1, x2, y2)\n target_boxes = xywh2xyxy(target_boxes)\n target_boxes[:, 0] *= width\n target_boxes[:, 2] *= width\n target_boxes[:, 1] *= height\n target_boxes[:, 3] *= height\n\n outputs_boxes = xywh2xyxy(outputs_boxes)\n outputs_boxes[:, 0] *= width\n outputs_boxes[:, 2] *= width\n outputs_boxes[:, 1] *= height\n outputs_boxes[:, 3] *= height\n\n detected = []\n for *pred_bbox, conf in zip(outputs_boxes, outputs_class):\n obj_pred = 0\n pred_bbox = torch.FloatTensor(pred_bbox[0]).view(1, -1)\n # Compute iou with target boxes\n iou = bbox_iou(pred_bbox, target_boxes, x1y1x2y2=True)[0]\n # Extract index of largest overlap\n best_i = np.argmax(iou)\n # If overlap exceeds threshold and classification is correct mark as correct\n if iou[best_i] > iou_thres and obj_pred == int(target_cls[best_i]) and best_i not in detected:\n correct.append(1)\n detected.append(best_i)\n else:\n correct.append(0)\n\n # Compute Average Precision (AP) per class\n AP, AP_class, R, P = ap_per_class(tp=correct,\n conf=outputs_class[:, 0].cpu(),\n pred_cls=np.zeros_like(outputs_class[:, 0].cpu()),\n target_cls=target_cls)\n\n # Accumulate AP per class\n metrics_i['AP_accum_count'] += np.bincount(AP_class, minlength=1)\n metrics_i['AP_accum'] += np.bincount(AP_class, minlength=1, weights=AP)\n\n # Compute mean AP across all classes in this image, and append to image list\n metrics_i['mAPs'].append(AP.mean())\n metrics_i['mR'].append(R.mean())\n metrics_i['mP'].append(P.mean())\n\n # Means of all images\n metrics_i['mean_mAP'] = np.sum(metrics_i['mAPs']) / (metrics_i['AP_accum_count'] + 1E-16)\n metrics_i['mean_R'] = np.sum(metrics_i['mR']) / (metrics_i['AP_accum_count'] + 1E-16)\n metrics_i['mean_P'] = np.sum(metrics_i['mP']) / (metrics_i['AP_accum_count'] + 1E-16)\n\n if batch_i % print_interval == 0:\n # Print image mAP and running mean mAP\n seen = metrics_i['seen']\n mean_P = metrics_i['mean_P']\n mean_R = metrics_i['mean_R']\n mean_mAP = metrics_i['mean_mAP']\n print(\"res_frame_{}\".format(i))\n print(('%11s%11s' + '%11.3g' * 4 + 's') % (seen, 100, mean_P, mean_R, mean_mAP, time.time() - t))\n\n # Return mAP\n ret = []\n for i in range(2):\n mean_mAP = metrics_list[i]['mean_mAP']\n mean_R = metrics_list[i]['mean_R']\n mean_P = metrics_list[i]['mean_P']\n ret.append(mean_mAP)\n ret.append(mean_R)\n ret.append(mean_P)\n return ret\n" }, { "path": "util/plot_utils.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\n\n\"\"\"\nPlotting utilities to visualize training logs.\n\"\"\"\nimport cv2\nimport torch\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nfrom torch import Tensor\n\nfrom pathlib import Path, PurePath\n\n\ndef plot_logs(logs, fields=('class_error', 'loss_bbox_unscaled', 'mAP'), ewm_col=0, log_name='log.txt'):\n '''\n Function to plot specific fields from training log(s). Plots both training and test results.\n\n :: Inputs - logs = list containing Path objects, each pointing to individual dir with a log file\n - fields = which results to plot from each log file - plots both training and test for each field.\n - ewm_col = optional, which column to use as the exponential weighted smoothing of the plots\n - log_name = optional, name of log file if different than default 'log.txt'.\n\n :: Outputs - matplotlib plots of results in fields, color coded for each log file.\n - solid lines are training results, dashed lines are test results.\n\n '''\n func_name = \"plot_utils.py::plot_logs\"\n\n # verify logs is a list of Paths (list[Paths]) or single Pathlib object Path,\n # convert single Path to list to avoid 'not iterable' error\n\n if not isinstance(logs, list):\n if isinstance(logs, PurePath):\n logs = [logs]\n print(f\"{func_name} info: logs param expects a list argument, converted to list[Path].\")\n else:\n raise ValueError(f\"{func_name} - invalid argument for logs parameter.\\n \\\n Expect list[Path] or single Path obj, received {type(logs)}\")\n\n # verify valid dir(s) and that every item in list is Path object\n for i, dir in enumerate(logs):\n if not isinstance(dir, PurePath):\n raise ValueError(f\"{func_name} - non-Path object in logs argument of {type(dir)}: \\n{dir}\")\n if dir.exists():\n continue\n raise ValueError(f\"{func_name} - invalid directory in logs argument:\\n{dir}\")\n\n # load log file(s) and plot\n dfs = [pd.read_json(Path(p) / log_name, lines=True) for p in logs]\n\n fig, axs = plt.subplots(ncols=len(fields), figsize=(16, 5))\n\n for df, color in zip(dfs, sns.color_palette(n_colors=len(logs))):\n for j, field in enumerate(fields):\n if field == 'mAP':\n coco_eval = pd.DataFrame(pd.np.stack(df.test_coco_eval.dropna().values)[:, 1]).ewm(com=ewm_col).mean()\n axs[j].plot(coco_eval, c=color)\n else:\n df.interpolate().ewm(com=ewm_col).mean().plot(\n y=[f'train_{field}', f'test_{field}'],\n ax=axs[j],\n color=[color] * 2,\n style=['-', '--']\n )\n for ax, field in zip(axs, fields):\n ax.legend([Path(p).name for p in logs])\n ax.set_title(field)\n\n\ndef plot_precision_recall(files, naming_scheme='iter'):\n if naming_scheme == 'exp_id':\n # name becomes exp_id\n names = [f.parts[-3] for f in files]\n elif naming_scheme == 'iter':\n names = [f.stem for f in files]\n else:\n raise ValueError(f'not supported {naming_scheme}')\n fig, axs = plt.subplots(ncols=2, figsize=(16, 5))\n for f, color, name in zip(files, sns.color_palette(\"Blues\", n_colors=len(files)), names):\n data = torch.load(f)\n # precision is n_iou, n_points, n_cat, n_area, max_det\n precision = data['precision']\n recall = data['params'].recThrs\n scores = data['scores']\n # take precision for all classes, all areas and 100 detections\n precision = precision[0, :, :, 0, -1].mean(1)\n scores = scores[0, :, :, 0, -1].mean(1)\n prec = precision.mean()\n rec = data['recall'][0, :, 0, -1].mean()\n print(f'{naming_scheme} {name}: mAP@50={prec * 100: 05.1f}, ' +\n f'score={scores.mean():0.3f}, ' +\n f'f1={2 * prec * rec / (prec + rec + 1e-8):0.3f}'\n )\n axs[0].plot(recall, precision, c=color)\n axs[1].plot(recall, scores, c=color)\n\n axs[0].set_title('Precision / Recall')\n axs[0].legend(names)\n axs[1].set_title('Scores / Recall')\n axs[1].legend(names)\n return fig, axs\n\n\ndef draw_boxes(image: Tensor, boxes: Tensor, color=(0, 255, 0), texts=None) -> np.ndarray:\n if isinstance(image, Tensor):\n cv_image = image.detach().cpu().numpy()\n else:\n cv_image = image\n if isinstance(boxes, Tensor):\n cv_boxes = boxes.detach().cpu().numpy()\n else:\n cv_boxes = boxes\n\n tl = round(0.002 * max(image.shape[0:2])) + 1 # line thickness\n tf = max(tl - 1, 1)\n for i in range(len(boxes)):\n box = cv_boxes[i]\n x1, y1 = box[0:2]\n x2, y2 = box[2:4]\n cv2.rectangle(cv_image, (int(x1), int(y1)), (int(x2), int(y2)), color=color)\n if texts is not None:\n cv2.putText(cv_image, texts[i], (int(x1), int(y1+10)), 0, tl/3, [225, 255, 255],\n thickness=tf,\n lineType=cv2.LINE_AA)\n return cv_image\n\n\ndef draw_ref_pts(image: Tensor, ref_pts: Tensor) -> np.ndarray:\n if isinstance(image, Tensor):\n cv_image = image.detach().cpu().numpy()\n else:\n cv_image = image\n if isinstance(ref_pts, Tensor):\n cv_pts = ref_pts.detach().cpu().numpy()\n else:\n cv_pts = ref_pts\n for i in range(len(cv_pts)):\n x, y, is_pos = cv_pts[i]\n color = (0, 1, 0) if is_pos else (1, 1, 1)\n cv2.circle(cv_image, (int(x), int(y)), 2, color)\n return cv_image\n\n\ndef image_hwc2chw(image: np.ndarray):\n image = np.ascontiguousarray(image.transpose(2, 0, 1))\n return image\n" }, { "path": "util/tool.py", "content": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# ------------------------------------------------------------------------\n\nimport torch\nimport numpy as np\n\n\ndef load_model(model, model_path, optimizer=None, resume=False,\n lr=None, lr_step=None):\n start_epoch = 0\n checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage)\n print(f'loaded {model_path}')\n state_dict = checkpoint['model']\n model_state_dict = model.state_dict()\n\n # check loaded parameters and created model parameters\n msg = 'If you see this, your model does not fully load the ' + \\\n 'pre-trained weight. Please make sure ' + \\\n 'you set the correct --num_classes for your own dataset.'\n for k in state_dict:\n if k in model_state_dict:\n if state_dict[k].shape != model_state_dict[k].shape:\n print('Skip loading parameter {}, required shape{}, ' \\\n 'loaded shape{}. {}'.format(\n k, model_state_dict[k].shape, state_dict[k].shape, msg))\n if 'class_embed' in k:\n print(\"load class_embed: {} shape={}\".format(k, state_dict[k].shape))\n if model_state_dict[k].shape[0] == 1:\n state_dict[k] = state_dict[k][1:2]\n elif model_state_dict[k].shape[0] == 2:\n state_dict[k] = state_dict[k][1:3]\n elif model_state_dict[k].shape[0] == 3:\n state_dict[k] = state_dict[k][1:4]\n else:\n raise NotImplementedError('invalid shape: {}'.format(model_state_dict[k].shape))\n continue\n state_dict[k] = model_state_dict[k]\n else:\n print('Drop parameter {}.'.format(k) + msg)\n for k in model_state_dict:\n if not (k in state_dict):\n print('No param {}.'.format(k) + msg)\n state_dict[k] = model_state_dict[k]\n model.load_state_dict(state_dict, strict=False)\n\n # resume optimizer parameters\n if optimizer is not None and resume:\n if 'optimizer' in checkpoint:\n optimizer.load_state_dict(checkpoint['optimizer'])\n start_epoch = checkpoint['epoch']\n start_lr = lr\n for step in lr_step:\n if start_epoch >= step:\n start_lr *= 0.1\n for param_group in optimizer.param_groups:\n param_group['lr'] = start_lr\n print('Resumed optimizer with start lr', start_lr)\n else:\n print('No optimizer parameters in checkpoint.')\n if optimizer is not None:\n return model, optimizer, start_epoch\n else:\n return model\n\n\n\n" } ]