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Repository: megvii-model/MOTR
Branch: main
Commit: 8690da339215
Files: 82
Total size: 3.2 MB
Directory structure:
gitextract_mj3tn03w/
├── .gitignore
├── LICENSE
├── README.md
├── benchmark.py
├── configs/
│ ├── r50_deformable_detr.sh
│ ├── r50_deformable_detr_plus_iterative_bbox_refinement.sh
│ ├── r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh
│ ├── r50_deformable_detr_single_scale.sh
│ ├── r50_deformable_detr_single_scale_dc5.sh
│ ├── r50_motr_demo.sh
│ ├── r50_motr_eval.sh
│ ├── r50_motr_submit.sh
│ ├── r50_motr_submit_dance.sh
│ ├── r50_motr_train.sh
│ └── r50_motr_train_dance.sh
├── datasets/
│ ├── __init__.py
│ ├── coco.py
│ ├── coco_eval.py
│ ├── coco_panoptic.py
│ ├── dance.py
│ ├── data_path/
│ │ ├── bdd100k.val
│ │ ├── crowdhuman.val
│ │ ├── gen_bdd100k_mot.py
│ │ ├── gen_labels_15.py
│ │ ├── gen_labels_16.py
│ │ └── prepare.py
│ ├── data_prefetcher.py
│ ├── detmot.py
│ ├── joint.py
│ ├── panoptic_eval.py
│ ├── samplers.py
│ ├── static_detmot.py
│ ├── torchvision_datasets/
│ │ ├── __init__.py
│ │ └── coco.py
│ └── transforms.py
├── demo.py
├── engine.py
├── eval.py
├── main.py
├── models/
│ ├── __init__.py
│ ├── backbone.py
│ ├── deformable_detr.py
│ ├── deformable_transformer.py
│ ├── deformable_transformer_plus.py
│ ├── matcher.py
│ ├── memory_bank.py
│ ├── motr.py
│ ├── ops/
│ │ ├── functions/
│ │ │ ├── __init__.py
│ │ │ └── ms_deform_attn_func.py
│ │ ├── make.sh
│ │ ├── modules/
│ │ │ ├── __init__.py
│ │ │ └── ms_deform_attn.py
│ │ ├── setup.py
│ │ ├── src/
│ │ │ ├── cpu/
│ │ │ │ ├── ms_deform_attn_cpu.cpp
│ │ │ │ └── ms_deform_attn_cpu.h
│ │ │ ├── cuda/
│ │ │ │ ├── ms_deform_attn_cuda.cu
│ │ │ │ ├── ms_deform_attn_cuda.h
│ │ │ │ └── ms_deform_im2col_cuda.cuh
│ │ │ ├── ms_deform_attn.h
│ │ │ └── vision.cpp
│ │ └── test.py
│ ├── position_encoding.py
│ ├── qim.py
│ ├── relu_dropout.py
│ ├── segmentation.py
│ └── structures/
│ ├── __init__.py
│ ├── boxes.py
│ └── instances.py
├── requirements.txt
├── submit.py
├── submit_dance.py
├── tools/
│ ├── launch.py
│ ├── run_dist_launch.sh
│ └── run_dist_slurm.sh
└── util/
├── __init__.py
├── box_ops.py
├── checkpoint.py
├── evaluation.py
├── misc.py
├── motdet_eval.py
├── plot_utils.py
└── tool.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
__pycache__/
*.pth
*.train
exps/
build/
*.egg
*.egg-info
*.mp4
================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# MOTR: End-to-End Multiple-Object Tracking with TRansformer
</div>
[](https://paperswithcode.com/sota/multi-object-tracking-on-mot17?p=motr-end-to-end-multiple-object-tracking-with)
[](https://paperswithcode.com/sota/multi-object-tracking-on-mot16?p=motr-end-to-end-multiple-object-tracking-with)
</div>
This repository is an official implementation of the paper [MOTR: End-to-End Multiple-Object Tracking with TRansformer](https://arxiv.org/pdf/2105.03247.pdf).
## Introduction
**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.
<div style="align: center">
<img src=./figs/motr.png/>
</div>
**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.
## Updates
- (2021/09/23) Report BDD100K results and release corresponding codes [motr_bdd100k](https://github.com/megvii-model/MOTR/tree/motr_bdd100k).
- (2022/02/09) Higher performance achieved by not clipping the bounding boxes inside the image.
- (2022/02/11) Add checkpoint support for training on RTX 2080ti.
- (2022/02/11) Report [DanceTrack](https://github.com/DanceTrack/DanceTrack) results and [scripts](configs/r50_motr_train_dance.sh).
- (2022/05/12) Higher performance achieved by removing the public detection filtering (filter_pub_det) trick.
- (2022/07/04) MOTR is accepted by ECCV 2022.
## Main Results
### MOT17
| **Method** | **Dataset** | **Train Data** | **HOTA** | **DetA** | **AssA** | **MOTA** | **IDF1** | **IDS** | **URL** |
| :--------: | :---------: | :------------------: | :------: | :------: | :------: | :------: | :------: | :-----: | :-----------------------------------------------------------------------------------------: |
| 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) |
### DanceTrack
| **Method** | **Dataset** | **Train Data** | **HOTA** | **DetA** | **AssA** | **MOTA** | **IDF1** | **URL** |
| :--------: | :---------: | :------------: | :------: | :------: | :------: | :------: | :------: | :-----------------------------------------------------------------------------------------: |
| 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) |
### BDD100K
| **Method** | **Dataset** | **Train Data** | **MOTA** | **IDF1** | **IDS** | **URL** |
| :--------: | :---------: | :------------: | :------: | :------: | :-----: | :-----------------------------------------------------------------------------------------: |
| MOTR | BDD100K | BDD100K | 32.0 | 43.5 | 3493 | [model](https://drive.google.com/file/d/13fsTj9e6Hk7qVcybWi1X5KbZEsFCHa6e/view?usp=sharing) |
*Note:*
1. MOTR on MOT17 and DanceTrack is trained on 8 NVIDIA RTX 2080ti GPUs.
2. The training time for MOT17 is about 2.5 days on V100 or 4 days on RTX 2080ti;
3. The inference speed is about 7.5 FPS for resolution 1536x800;
4. All models of MOTR are trained with ResNet50 with pre-trained weights on COCO dataset.
## Installation
The codebase is built on top of [Deformable DETR](https://github.com/fundamentalvision/Deformable-DETR).
### Requirements
* Linux, CUDA>=9.2, GCC>=5.4
* Python>=3.7
We recommend you to use Anaconda to create a conda environment:
```bash
conda create -n deformable_detr python=3.7 pip
```
Then, activate the environment:
```bash
conda activate deformable_detr
```
* PyTorch>=1.5.1, torchvision>=0.6.1 (following instructions [here](https://pytorch.org/))
For example, if your CUDA version is 9.2, you could install pytorch and torchvision as following:
```bash
conda install pytorch=1.5.1 torchvision=0.6.1 cudatoolkit=9.2 -c pytorch
```
* Other requirements
```bash
pip install -r requirements.txt
```
* Build MultiScaleDeformableAttention
```bash
cd ./models/ops
sh ./make.sh
```
## Usage
### Dataset preparation
1. 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:
```
.
├── crowdhuman
│ ├── images
│ └── labels_with_ids
├── MOT15
│ ├── images
│ ├── labels_with_ids
│ ├── test
│ └── train
├── MOT17
│ ├── images
│ ├── labels_with_ids
├── DanceTrack
│ ├── train
│ ├── test
├── bdd100k
│ ├── images
│ ├── track
│ ├── train
│ ├── val
│ ├── labels
│ ├── track
│ ├── train
│ ├── val
```
2. For BDD100K dataset, you can use the following script to generate txt file:
```bash
cd datasets/data_path
python3 generate_bdd100k_mot.py
cd ../../
```
### Training and Evaluation
#### Training on single node
You can download COCO pretrained weights from [Deformable DETR](https://github.com/fundamentalvision/Deformable-DETR). Then training MOTR on 8 GPUs as following:
```bash
sh configs/r50_motr_train.sh
```
#### Evaluation on MOT15
You 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:
```bash
sh configs/r50_motr_eval.sh
```
For visual in demo video, you can enable 'vis=True' in eval.py like:
```bash
det.detect(vis=True)
```
#### Evaluation on MOT17
You 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):
```bash
sh configs/r50_motr_submit.sh
```
#### Evaluation on BDD100K
For BDD100K dataset, please refer [motr_bdd100k](https://github.com/megvii-model/MOTR/tree/motr_bdd100k).
#### Test on Video Demo
We also provide a demo interface which allows for a quick processing of a given video.
```bash
EXP_DIR=exps/e2e_motr_r50_joint
python3 demo.py \
--meta_arch motr \
--dataset_file e2e_joint \
--epoch 200 \
--with_box_refine \
--lr_drop 100 \
--lr 2e-4 \
--lr_backbone 2e-5 \
--pretrained ${EXP_DIR}/motr_final.pth \
--output_dir ${EXP_DIR} \
--batch_size 1 \
--sample_mode 'random_interval' \
--sample_interval 10 \
--sampler_steps 50 90 120 \
--sampler_lengths 2 3 4 5 \
--update_query_pos \
--merger_dropout 0 \
--dropout 0 \
--random_drop 0.1 \
--fp_ratio 0.3 \
--query_interaction_layer 'QIM' \
--extra_track_attn \
--resume ${EXP_DIR}/motr_final.pth \
--input_video figs/demo.avi
```
## Citing MOTR
If you find MOTR useful in your research, please consider citing:
```bibtex
@inproceedings{zeng2021motr,
title={MOTR: End-to-End Multiple-Object Tracking with TRansformer},
author={Zeng, Fangao and Dong, Bin and Zhang, Yuang and Wang, Tiancai and Zhang, Xiangyu and Wei, Yichen},
booktitle={European Conference on Computer Vision (ECCV)},
year={2022}
}
```
================================================
FILE: benchmark.py
================================================
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
"""
Benchmark inference speed of Deformable DETR.
"""
import os
import time
import argparse
import torch
from main import get_args_parser as get_main_args_parser
from models import build_model
from datasets import build_dataset
from util.misc import nested_tensor_from_tensor_list
def get_benckmark_arg_parser():
parser = argparse.ArgumentParser('Benchmark inference speed of Deformable DETR.')
parser.add_argument('--num_iters', type=int, default=300, help='total iters to benchmark speed')
parser.add_argument('--warm_iters', type=int, default=5, help='ignore first several iters that are very slow')
parser.add_argument('--batch_size', type=int, default=1, help='batch size in inference')
parser.add_argument('--resume', type=str, help='load the pre-trained checkpoint')
return parser
@torch.no_grad()
def measure_average_inference_time(model, inputs, num_iters=100, warm_iters=5):
ts = []
for iter_ in range(num_iters):
torch.cuda.synchronize()
t_ = time.perf_counter()
model(inputs)
torch.cuda.synchronize()
t = time.perf_counter() - t_
if iter_ >= warm_iters:
ts.append(t)
print(ts)
return sum(ts) / len(ts)
def benchmark():
args, _ = get_benckmark_arg_parser().parse_known_args()
main_args = get_main_args_parser().parse_args(_)
assert args.warm_iters < args.num_iters and args.num_iters > 0 and args.warm_iters >= 0
assert args.batch_size > 0
assert args.resume is None or os.path.exists(args.resume)
dataset = build_dataset('val', main_args)
model, _, _ = build_model(main_args)
model.cuda()
model.eval()
if args.resume is not None:
ckpt = torch.load(args.resume, map_location=lambda storage, loc: storage)
model.load_state_dict(ckpt['model'])
inputs = nested_tensor_from_tensor_list([dataset.__getitem__(0)[0].cuda() for _ in range(args.batch_size)])
t = measure_average_inference_time(model, inputs, args.num_iters, args.warm_iters)
return 1.0 / t * args.batch_size
if __name__ == '__main__':
fps = benchmark()
print(f'Inference Speed: {fps:.1f} FPS')
================================================
FILE: configs/r50_deformable_detr.sh
================================================
#!/usr/bin/env bash
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
set -x
EXP_DIR=exps/r50_deformable_detr
PY_ARGS=${@:1}
python -u main.py \
--output_dir ${EXP_DIR} \
${PY_ARGS}
================================================
FILE: configs/r50_deformable_detr_plus_iterative_bbox_refinement.sh
================================================
#!/usr/bin/env bash
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
set -x
EXP_DIR=exps/r50_deformable_detr_plus_iterative_bbox_refinement
PY_ARGS=${@:1}
python -u main.py \
--output_dir ${EXP_DIR} \
--with_box_refine \
${PY_ARGS}
================================================
FILE: configs/r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh
================================================
#!/usr/bin/env bash
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
set -x
EXP_DIR=exps/r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage
PY_ARGS=${@:1}
python -u main.py \
--output_dir ${EXP_DIR} \
--with_box_refine \
--two_stage \
${PY_ARGS}
================================================
FILE: configs/r50_deformable_detr_single_scale.sh
================================================
#!/usr/bin/env bash
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
set -x
EXP_DIR=exps/r50_deformable_detr_single_scale
PY_ARGS=${@:1}
python -u main.py \
--num_feature_levels 1 \
--output_dir ${EXP_DIR} \
${PY_ARGS}
================================================
FILE: configs/r50_deformable_detr_single_scale_dc5.sh
================================================
#!/usr/bin/env bash
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
set -x
EXP_DIR=exps/r50_deformable_detr_single_scale_dc5
PY_ARGS=${@:1}
python -u main.py \
--num_feature_levels 1 \
--dilation \
--output_dir ${EXP_DIR} \
${PY_ARGS}
================================================
FILE: configs/r50_motr_demo.sh
================================================
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
EXP_DIR=exps/e2e_motr_r50_joint
python3 demo.py \
--meta_arch motr \
--dataset_file e2e_joint \
--epoch 200 \
--with_box_refine \
--lr_drop 100 \
--lr 2e-4 \
--lr_backbone 2e-5 \
--pretrained ${EXP_DIR}/motr_final.pth \
--output_dir ${EXP_DIR} \
--batch_size 1 \
--sample_mode 'random_interval' \
--sample_interval 10 \
--sampler_steps 50 90 120 \
--sampler_lengths 2 3 4 5 \
--update_query_pos \
--merger_dropout 0 \
--dropout 0 \
--random_drop 0.1 \
--fp_ratio 0.3 \
--query_interaction_layer 'QIM' \
--extra_track_attn \
--resume ${EXP_DIR}/motr_final.pth \
--input_video figs/demo.avi
================================================
FILE: configs/r50_motr_eval.sh
================================================
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
# for MOT17
# EXP_DIR=exps/e2e_motr_r50_joint
# python3 eval.py \
# --meta_arch motr \
# --dataset_file e2e_joint \
# --epoch 200 \
# --with_box_refine \
# --lr_drop 100 \
# --lr 2e-4 \
# --lr_backbone 2e-5 \
# --pretrained ${EXP_DIR}/motr_final.pth \
# --output_dir ${EXP_DIR} \
# --batch_size 1 \
# --sample_mode 'random_interval' \
# --sample_interval 10 \
# --sampler_steps 50 90 120 \
# --sampler_lengths 2 3 4 5 \
# --update_query_pos \
# --merger_dropout 0 \
# --dropout 0 \
# --random_drop 0.1 \
# --fp_ratio 0.3 \
# --query_interaction_layer 'QIM' \
# --extra_track_attn \
# --data_txt_path_train ./datasets/data_path/joint.train \
# --data_txt_path_val ./datasets/data_path/mot17.train \
# --resume ${EXP_DIR}/motr_final.pth \
================================================
FILE: configs/r50_motr_submit.sh
================================================
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
EXP_DIR=exps/e2e_motr_r50_joint
python3 submit.py \
--meta_arch motr \
--dataset_file e2e_joint \
--epoch 200 \
--with_box_refine \
--lr_drop 100 \
--lr 2e-4 \
--lr_backbone 2e-5 \
--pretrained ${EXP_DIR}/motr_final.pth \
--output_dir ${EXP_DIR} \
--batch_size 1 \
--sample_mode 'random_interval' \
--sample_interval 10 \
--sampler_steps 50 90 150 \
--sampler_lengths 2 3 4 5 \
--update_query_pos \
--merger_dropout 0 \
--dropout 0 \
--random_drop 0.1 \
--fp_ratio 0.3 \
--query_interaction_layer 'QIM' \
--extra_track_attn \
--data_txt_path_train ./datasets/data_path/joint.train \
--data_txt_path_val ./datasets/data_path/mot17.train \
--resume ${EXP_DIR}/motr_final.pth \
--exp_name pub_submit_17
================================================
FILE: configs/r50_motr_submit_dance.sh
================================================
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
EXP_DIR=exps/e2e_motr_r50_dance
python3 submit_dance.py \
--meta_arch motr \
--dataset_file e2e_joint \
--mot_path /data/datasets \
--epoch 200 \
--with_box_refine \
--lr_drop 100 \
--lr 2e-4 \
--lr_backbone 2e-5 \
--output_dir ${EXP_DIR} \
--batch_size 1 \
--sample_mode 'random_interval' \
--sample_interval 10 \
--sampler_steps 50 90 150 \
--sampler_lengths 2 3 4 5 \
--update_query_pos \
--merger_dropout 0 \
--dropout 0 \
--random_drop 0.1 \
--fp_ratio 0.3 \
--query_interaction_layer 'QIM' \
--extra_track_attn \
--data_txt_path_train ./datasets/data_path/joint.train \
--data_txt_path_val ./datasets/data_path/mot17.train \
--resume ${EXP_DIR}/checkpoint.pth \
--exp_name tracker
================================================
FILE: configs/r50_motr_train.sh
================================================
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
# for MOT17
# PRETRAIN=coco_model_final.pth
# EXP_DIR=exps/e2e_motr_r50_joint
# python3 -m torch.distributed.launch --nproc_per_node=8 \
# --use_env main.py \
# --meta_arch motr \
# --use_checkpoint \
# --dataset_file e2e_joint \
# --epoch 200 \
# --with_box_refine \
# --lr_drop 100 \
# --lr 2e-4 \
# --lr_backbone 2e-5 \
# --pretrained ${PRETRAIN} \
# --output_dir ${EXP_DIR} \
# --batch_size 1 \
# --sample_mode 'random_interval' \
# --sample_interval 10 \
# --sampler_steps 50 90 150 \
# --sampler_lengths 2 3 4 5 \
# --update_query_pos \
# --merger_dropout 0 \
# --dropout 0 \
# --random_drop 0.1 \
# --fp_ratio 0.3 \
# --query_interaction_layer 'QIM' \
# --extra_track_attn \
# --data_txt_path_train ./datasets/data_path/joint.train \
# --data_txt_path_val ./datasets/data_path/mot17.train \
================================================
FILE: configs/r50_motr_train_dance.sh
================================================
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
# for MOT17
PRETRAIN=r50_deformable_detr_plus_iterative_bbox_refinement-checkpoint.pth
EXP_DIR=exps/e2e_motr_r50_dance
python3 -m torch.distributed.launch --nproc_per_node=8 \
--use_env main.py \
--meta_arch motr \
--use_checkpoint \
--dataset_file e2e_dance \
--epoch 20 \
--with_box_refine \
--lr_drop 10 \
--lr 2e-4 \
--lr_backbone 2e-5 \
--pretrained ${PRETRAIN} \
--output_dir ${EXP_DIR} \
--batch_size 1 \
--sample_mode 'random_interval' \
--sample_interval 10 \
--sampler_steps 5 9 15 \
--sampler_lengths 2 3 4 5 \
--update_query_pos \
--merger_dropout 0 \
--dropout 0 \
--random_drop 0.1 \
--fp_ratio 0.3 \
--query_interaction_layer 'QIM' \
--extra_track_attn \
--data_txt_path_train ./datasets/data_path/joint.train \
--data_txt_path_val ./datasets/data_path/mot17.train \
|& tee ${EXP_DIR}/output.log
================================================
FILE: datasets/__init__.py
================================================
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
import torch.utils.data
import torchvision
from .coco import build as build_coco
from .detmot import build as build_e2e_mot
from .dance import build as build_e2e_dance
from .static_detmot import build as build_e2e_static_mot
from .joint import build as build_e2e_joint
from .torchvision_datasets import CocoDetection
def get_coco_api_from_dataset(dataset):
for _ in range(10):
# if isinstance(dataset, torchvision.datasets.CocoDetection):
# break
if isinstance(dataset, torch.utils.data.Subset):
dataset = dataset.dataset
if isinstance(dataset, CocoDetection):
return dataset.coco
def build_dataset(image_set, args):
if args.dataset_file == 'coco':
return build_coco(image_set, args)
if args.dataset_file == 'coco_panoptic':
# to avoid making panopticapi required for coco
from .coco_panoptic import build as build_coco_panoptic
return build_coco_panoptic(image_set, args)
if args.dataset_file == 'e2e_joint':
return build_e2e_joint(image_set, args)
if args.dataset_file == 'e2e_static_mot':
return build_e2e_static_mot(image_set, args)
if args.dataset_file == 'e2e_mot':
return build_e2e_mot(image_set, args)
if args.dataset_file == 'e2e_dance':
return build_e2e_dance(image_set, args)
raise ValueError(f'dataset {args.dataset_file} not supported')
================================================
FILE: datasets/coco.py
================================================
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
"""
COCO dataset which returns image_id for evaluation.
Mostly copy-paste from https://github.com/pytorch/vision/blob/13b35ff/references/detection/coco_utils.py
"""
from pathlib import Path
import torch
import torch.utils.data
from pycocotools import mask as coco_mask
from .torchvision_datasets import CocoDetection as TvCocoDetection
from util.misc import get_local_rank, get_local_size
import datasets.transforms as T
class CocoDetection(TvCocoDetection):
def __init__(self, img_folder, ann_file, transforms, return_masks, cache_mode=False, local_rank=0, local_size=1):
super(CocoDetection, self).__init__(img_folder, ann_file,
cache_mode=cache_mode, local_rank=local_rank, local_size=local_size)
self._transforms = transforms
self.prepare = ConvertCocoPolysToMask(return_masks)
def __getitem__(self, idx):
img, target = super(CocoDetection, self).__getitem__(idx)
image_id = self.ids[idx]
target = {'image_id': image_id, 'annotations': target}
img, target = self.prepare(img, target)
if self._transforms is not None:
img, target = self._transforms(img, target)
return img, target
def convert_coco_poly_to_mask(segmentations, height, width):
masks = []
for polygons in segmentations:
rles = coco_mask.frPyObjects(polygons, height, width)
mask = coco_mask.decode(rles)
if len(mask.shape) < 3:
mask = mask[..., None]
mask = torch.as_tensor(mask, dtype=torch.uint8)
mask = mask.any(dim=2)
masks.append(mask)
if masks:
masks = torch.stack(masks, dim=0)
else:
masks = torch.zeros((0, height, width), dtype=torch.uint8)
return masks
class ConvertCocoPolysToMask(object):
def __init__(self, return_masks=False):
self.return_masks = return_masks
def __call__(self, image, target):
w, h = image.size
image_id = target["image_id"]
image_id = torch.tensor([image_id])
anno = target["annotations"]
anno = [obj for obj in anno if 'iscrowd' not in obj or obj['iscrowd'] == 0]
boxes = [obj["bbox"] for obj in anno]
# guard against no boxes via resizing
boxes = torch.as_tensor(boxes, dtype=torch.float32).reshape(-1, 4)
boxes[:, 2:] += boxes[:, :2]
boxes[:, 0::2].clamp_(min=0, max=w)
boxes[:, 1::2].clamp_(min=0, max=h)
classes = [obj["category_id"] for obj in anno]
classes = torch.tensor(classes, dtype=torch.int64)
if self.return_masks:
segmentations = [obj["segmentation"] for obj in anno]
masks = convert_coco_poly_to_mask(segmentations, h, w)
keypoints = None
if anno and "keypoints" in anno[0]:
keypoints = [obj["keypoints"] for obj in anno]
keypoints = torch.as_tensor(keypoints, dtype=torch.float32)
num_keypoints = keypoints.shape[0]
if num_keypoints:
keypoints = keypoints.view(num_keypoints, -1, 3)
keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
boxes = boxes[keep]
classes = classes[keep]
if self.return_masks:
masks = masks[keep]
if keypoints is not None:
keypoints = keypoints[keep]
target = {}
target["boxes"] = boxes
target["labels"] = classes
if self.return_masks:
target["masks"] = masks
target["image_id"] = image_id
if keypoints is not None:
target["keypoints"] = keypoints
# for conversion to coco api
area = torch.tensor([obj["area"] for obj in anno])
iscrowd = torch.tensor([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in anno])
target["area"] = area[keep]
target["iscrowd"] = iscrowd[keep]
target["orig_size"] = torch.as_tensor([int(h), int(w)])
target["size"] = torch.as_tensor([int(h), int(w)])
return image, target
def make_coco_transforms(image_set):
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
if image_set == 'train':
return T.Compose([
T.RandomHorizontalFlip(),
T.RandomSelect(
T.RandomResize(scales, max_size=1333),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
T.RandomResize(scales, max_size=1333),
])
),
normalize,
])
if image_set == 'val':
return T.Compose([
T.RandomResize([800], max_size=1333),
normalize,
])
raise ValueError(f'unknown {image_set}')
def build(image_set, args):
root = Path(args.coco_path)
assert root.exists(), f'provided COCO path {root} does not exist'
mode = 'instances'
PATHS = {
"train": (root / "train2017", root / "annotations" / f'{mode}_train2017.json'),
"val": (root / "val2017", root / "annotations" / f'{mode}_val2017.json'),
}
img_folder, ann_file = PATHS[image_set]
dataset = CocoDetection(img_folder, ann_file, transforms=make_coco_transforms(image_set), return_masks=args.masks,
cache_mode=args.cache_mode, local_rank=get_local_rank(), local_size=get_local_size())
return dataset
================================================
FILE: datasets/coco_eval.py
================================================
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
"""
COCO evaluator that works in distributed mode.
Mostly copy-paste from https://github.com/pytorch/vision/blob/edfd5a7/references/detection/coco_eval.py
The difference is that there is less copy-pasting from pycocotools
in the end of the file, as python3 can suppress prints with contextlib
"""
import os
import contextlib
import copy
import numpy as np
import torch
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from util.misc import all_gather
class CocoEvaluator(object):
def __init__(self, coco_gt, iou_types):
assert isinstance(iou_types, (list, tuple))
coco_gt = copy.deepcopy(coco_gt)
self.coco_gt = coco_gt
self.iou_types = iou_types
self.coco_eval = {}
for iou_type in iou_types:
self.coco_eval[iou_type] = COCOeval(coco_gt, iouType=iou_type)
self.img_ids = []
self.eval_imgs = {k: [] for k in iou_types}
def update(self, predictions):
img_ids = list(np.unique(list(predictions.keys())))
self.img_ids.extend(img_ids)
for iou_type in self.iou_types:
results = self.prepare(predictions, iou_type)
# suppress pycocotools prints
with open(os.devnull, 'w') as devnull:
print("self.coco_gt={}".format(self.coco_gt))
with contextlib.redirect_stdout(devnull):
coco_dt = COCO.loadRes(self.coco_gt, results) if results else COCO()
coco_eval = self.coco_eval[iou_type]
coco_eval.cocoDt = coco_dt
coco_eval.params.imgIds = list(img_ids)
img_ids, eval_imgs = evaluate(coco_eval)
self.eval_imgs[iou_type].append(eval_imgs)
def synchronize_between_processes(self):
for iou_type in self.iou_types:
self.eval_imgs[iou_type] = np.concatenate(self.eval_imgs[iou_type], 2)
create_common_coco_eval(self.coco_eval[iou_type], self.img_ids, self.eval_imgs[iou_type])
def accumulate(self):
for coco_eval in self.coco_eval.values():
coco_eval.accumulate()
def summarize(self):
for iou_type, coco_eval in self.coco_eval.items():
print("IoU metric: {}".format(iou_type))
coco_eval.summarize()
def prepare(self, predictions, iou_type):
if iou_type == "bbox":
return self.prepare_for_coco_detection(predictions)
elif iou_type == "segm":
return self.prepare_for_coco_segmentation(predictions)
elif iou_type == "keypoints":
return self.prepare_for_coco_keypoint(predictions)
else:
raise ValueError("Unknown iou type {}".format(iou_type))
def prepare_for_coco_detection(self, predictions):
coco_results = []
for original_id, prediction in predictions.items():
if len(prediction) == 0:
continue
boxes = prediction["boxes"]
boxes = convert_to_xywh(boxes).tolist()
scores = prediction["scores"].tolist()
labels = prediction["labels"].tolist()
coco_results.extend(
[
{
"image_id": original_id,
"category_id": labels[k],
"bbox": box,
"score": scores[k],
}
for k, box in enumerate(boxes)
]
)
return coco_results
def prepare_for_coco_segmentation(self, predictions):
coco_results = []
for original_id, prediction in predictions.items():
if len(prediction) == 0:
continue
scores = prediction["scores"]
labels = prediction["labels"]
masks = prediction["masks"]
masks = masks > 0.5
scores = prediction["scores"].tolist()
labels = prediction["labels"].tolist()
rles = [
mask_util.encode(np.array(mask[0, :, :, np.newaxis], dtype=np.uint8, order="F"))[0]
for mask in masks
]
for rle in rles:
rle["counts"] = rle["counts"].decode("utf-8")
coco_results.extend(
[
{
"image_id": original_id,
"category_id": labels[k],
"segmentation": rle,
"score": scores[k],
}
for k, rle in enumerate(rles)
]
)
return coco_results
def prepare_for_coco_keypoint(self, predictions):
coco_results = []
for original_id, prediction in predictions.items():
if len(prediction) == 0:
continue
boxes = prediction["boxes"]
boxes = convert_to_xywh(boxes).tolist()
scores = prediction["scores"].tolist()
labels = prediction["labels"].tolist()
keypoints = prediction["keypoints"]
keypoints = keypoints.flatten(start_dim=1).tolist()
coco_results.extend(
[
{
"image_id": original_id,
"category_id": labels[k],
'keypoints': keypoint,
"score": scores[k],
}
for k, keypoint in enumerate(keypoints)
]
)
return coco_results
def convert_to_xywh(boxes):
xmin, ymin, xmax, ymax = boxes.unbind(1)
return torch.stack((xmin, ymin, xmax - xmin, ymax - ymin), dim=1)
def merge(img_ids, eval_imgs):
all_img_ids = all_gather(img_ids)
all_eval_imgs = all_gather(eval_imgs)
merged_img_ids = []
for p in all_img_ids:
merged_img_ids.extend(p)
merged_eval_imgs = []
for p in all_eval_imgs:
merged_eval_imgs.append(p)
merged_img_ids = np.array(merged_img_ids)
merged_eval_imgs = np.concatenate(merged_eval_imgs, 2)
# keep only unique (and in sorted order) images
merged_img_ids, idx = np.unique(merged_img_ids, return_index=True)
merged_eval_imgs = merged_eval_imgs[..., idx]
return merged_img_ids, merged_eval_imgs
def create_common_coco_eval(coco_eval, img_ids, eval_imgs):
img_ids, eval_imgs = merge(img_ids, eval_imgs)
img_ids = list(img_ids)
eval_imgs = list(eval_imgs.flatten())
coco_eval.evalImgs = eval_imgs
coco_eval.params.imgIds = img_ids
coco_eval._paramsEval = copy.deepcopy(coco_eval.params)
#################################################################
# From pycocotools, just removed the prints and fixed
# a Python3 bug about unicode not defined
#################################################################
def evaluate(self):
'''
Run per image evaluation on given images and store results (a list of dict) in self.evalImgs
:return: None
'''
# tic = time.time()
# print('Running per image evaluation...')
p = self.params
# add backward compatibility if useSegm is specified in params
if p.useSegm is not None:
p.iouType = 'segm' if p.useSegm == 1 else 'bbox'
print('useSegm (deprecated) is not None. Running {} evaluation'.format(p.iouType))
# print('Evaluate annotation type *{}*'.format(p.iouType))
p.imgIds = list(np.unique(p.imgIds))
if p.useCats:
p.catIds = list(np.unique(p.catIds))
p.maxDets = sorted(p.maxDets)
self.params = p
self._prepare()
# loop through images, area range, max detection number
catIds = p.catIds if p.useCats else [-1]
if p.iouType == 'segm' or p.iouType == 'bbox':
computeIoU = self.computeIoU
elif p.iouType == 'keypoints':
computeIoU = self.computeOks
self.ious = {
(imgId, catId): computeIoU(imgId, catId)
for imgId in p.imgIds
for catId in catIds}
evaluateImg = self.evaluateImg
maxDet = p.maxDets[-1]
evalImgs = [
evaluateImg(imgId, catId, areaRng, maxDet)
for catId in catIds
for areaRng in p.areaRng
for imgId in p.imgIds
]
# this is NOT in the pycocotools code, but could be done outside
evalImgs = np.asarray(evalImgs).reshape(len(catIds), len(p.areaRng), len(p.imgIds))
self._paramsEval = copy.deepcopy(self.params)
# toc = time.time()
# print('DONE (t={:0.2f}s).'.format(toc-tic))
return p.imgIds, evalImgs
#################################################################
# end of straight copy from pycocotools, just removing the prints
#################################################################
================================================
FILE: datasets/coco_panoptic.py
================================================
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
import json
from pathlib import Path
import numpy as np
import torch
from PIL import Image
from panopticapi.utils import rgb2id
from util.box_ops import masks_to_boxes
from .coco import make_coco_transforms
class CocoPanoptic:
def __init__(self, img_folder, ann_folder, ann_file, transforms=None, return_masks=True):
with open(ann_file, 'r') as f:
self.coco = json.load(f)
# sort 'images' field so that they are aligned with 'annotations'
# i.e., in alphabetical order
self.coco['images'] = sorted(self.coco['images'], key=lambda x: x['id'])
# sanity check
if "annotations" in self.coco:
for img, ann in zip(self.coco['images'], self.coco['annotations']):
assert img['file_name'][:-4] == ann['file_name'][:-4]
self.img_folder = img_folder
self.ann_folder = ann_folder
self.ann_file = ann_file
self.transforms = transforms
self.return_masks = return_masks
def __getitem__(self, idx):
ann_info = self.coco['annotations'][idx] if "annotations" in self.coco else self.coco['images'][idx]
img_path = Path(self.img_folder) / ann_info['file_name'].replace('.png', '.jpg')
ann_path = Path(self.ann_folder) / ann_info['file_name']
img = Image.open(img_path).convert('RGB')
w, h = img.size
if "segments_info" in ann_info:
masks = np.asarray(Image.open(ann_path), dtype=np.uint32)
masks = rgb2id(masks)
ids = np.array([ann['id'] for ann in ann_info['segments_info']])
masks = masks == ids[:, None, None]
masks = torch.as_tensor(masks, dtype=torch.uint8)
labels = torch.tensor([ann['category_id'] for ann in ann_info['segments_info']], dtype=torch.int64)
target = {}
target['image_id'] = torch.tensor([ann_info['image_id'] if "image_id" in ann_info else ann_info["id"]])
if self.return_masks:
target['masks'] = masks
target['labels'] = labels
target["boxes"] = masks_to_boxes(masks)
target['size'] = torch.as_tensor([int(h), int(w)])
target['orig_size'] = torch.as_tensor([int(h), int(w)])
if "segments_info" in ann_info:
for name in ['iscrowd', 'area']:
target[name] = torch.tensor([ann[name] for ann in ann_info['segments_info']])
if self.transforms is not None:
img, target = self.transforms(img, target)
return img, target
def __len__(self):
return len(self.coco['images'])
def get_height_and_width(self, idx):
img_info = self.coco['images'][idx]
height = img_info['height']
width = img_info['width']
return height, width
def build(image_set, args):
img_folder_root = Path(args.coco_path)
ann_folder_root = Path(args.coco_panoptic_path)
assert img_folder_root.exists(), f'provided COCO path {img_folder_root} does not exist'
assert ann_folder_root.exists(), f'provided COCO path {ann_folder_root} does not exist'
mode = 'panoptic'
PATHS = {
"train": ("train2017", Path("annotations") / f'{mode}_train2017.json'),
"val": ("val2017", Path("annotations") / f'{mode}_val2017.json'),
}
img_folder, ann_file = PATHS[image_set]
img_folder_path = img_folder_root / img_folder
ann_folder = ann_folder_root / f'{mode}_{img_folder}'
ann_file = ann_folder_root / ann_file
dataset = CocoPanoptic(img_folder_path, ann_folder, ann_file,
transforms=make_coco_transforms(image_set), return_masks=args.masks)
return dataset
================================================
FILE: datasets/dance.py
================================================
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
"""
MOT dataset which returns image_id for evaluation.
"""
from collections import defaultdict
import json
import os
from pathlib import Path
import cv2
import numpy as np
import torch
import torch.utils.data
import os.path as osp
from PIL import Image, ImageDraw
import copy
import datasets.transforms as T
from models.structures import Instances
from random import choice, randint
class DetMOTDetection:
def __init__(self, args, data_txt_path: str, seqs_folder, dataset2transform):
self.args = args
self.dataset2transform = dataset2transform
self.num_frames_per_batch = max(args.sampler_lengths)
self.sample_mode = args.sample_mode
self.sample_interval = args.sample_interval
self.video_dict = {}
self.split_dir = os.path.join(args.mot_path, "DanceTrack", "train")
self.labels_full = defaultdict(lambda : defaultdict(list))
for vid in os.listdir(self.split_dir):
if 'DPM' in vid or 'FRCNN' in vid:
print(f'filter {vid}')
continue
gt_path = os.path.join(self.split_dir, vid, 'gt', 'gt.txt')
for l in open(gt_path):
t, i, *xywh, mark, label = l.strip().split(',')[:8]
t, i, mark, label = map(int, (t, i, mark, label))
if mark == 0:
continue
if label in [3, 4, 5, 6, 9, 10, 11]: # Non-person
continue
else:
crowd = False
x, y, w, h = map(float, (xywh))
self.labels_full[vid][t].append([x, y, w, h, i, crowd])
vid_files = list(self.labels_full.keys())
self.indices = []
self.vid_tmax = {}
for vid in vid_files:
self.video_dict[vid] = len(self.video_dict)
t_min = min(self.labels_full[vid].keys())
t_max = max(self.labels_full[vid].keys()) + 1
self.vid_tmax[vid] = t_max - 1
for t in range(t_min, t_max - self.num_frames_per_batch):
self.indices.append((vid, t))
self.sampler_steps: list = args.sampler_steps
self.lengths: list = args.sampler_lengths
print("sampler_steps={} lenghts={}".format(self.sampler_steps, self.lengths))
self.period_idx = 0
def set_epoch(self, epoch):
self.current_epoch = epoch
if self.sampler_steps is None or len(self.sampler_steps) == 0:
# fixed sampling length.
return
for i in range(len(self.sampler_steps)):
if epoch >= self.sampler_steps[i]:
self.period_idx = i + 1
print("set epoch: epoch {} period_idx={}".format(epoch, self.period_idx))
self.num_frames_per_batch = self.lengths[self.period_idx]
def step_epoch(self):
# one epoch finishes.
print("Dataset: epoch {} finishes".format(self.current_epoch))
self.set_epoch(self.current_epoch + 1)
@staticmethod
def _targets_to_instances(targets: dict, img_shape) -> Instances:
gt_instances = Instances(tuple(img_shape))
gt_instances.boxes = targets['boxes']
gt_instances.labels = targets['labels']
gt_instances.obj_ids = targets['obj_ids']
gt_instances.area = targets['area']
return gt_instances
def load_crowd(self):
path, boxes, crowd = choice(self.crowd_gts)
img = Image.open(path)
w, h = img._size
boxes = torch.tensor(boxes, dtype=torch.float32)
areas = boxes[..., 2:].prod(-1)
boxes[:, 2:] += boxes[:, :2]
target = {
'boxes': boxes,
'labels': torch.zeros((len(boxes), ), dtype=torch.long),
'iscrowd': torch.as_tensor(crowd),
'image_id': torch.tensor([0]),
'area': areas,
'obj_ids': torch.arange(len(boxes)),
'size': torch.as_tensor([h, w]),
'orig_size': torch.as_tensor([h, w]),
'dataset': "CrowdHuman",
}
return [img], [target]
def _pre_single_frame(self, vid, idx: int):
img_path = os.path.join(self.split_dir, vid, 'img1', f'{idx:08d}.jpg')
img = Image.open(img_path)
targets = {}
w, h = img._size
assert w > 0 and h > 0, "invalid image {} with shape {} {}".format(img_path, w, h)
obj_idx_offset = self.video_dict[vid] * 100000 # 100000 unique ids is enough for a video.
targets['dataset'] = 'MOT17'
targets['boxes'] = []
targets['area'] = []
targets['iscrowd'] = []
targets['labels'] = []
targets['obj_ids'] = []
targets['image_id'] = torch.as_tensor(idx)
targets['size'] = torch.as_tensor([h, w])
targets['orig_size'] = torch.as_tensor([h, w])
for *xywh, id, crowd in self.labels_full[vid][idx]:
targets['boxes'].append(xywh)
targets['area'].append(xywh[2] * xywh[3])
targets['iscrowd'].append(crowd)
targets['labels'].append(0)
targets['obj_ids'].append(id + obj_idx_offset)
targets['area'] = torch.as_tensor(targets['area'])
targets['iscrowd'] = torch.as_tensor(targets['iscrowd'])
targets['labels'] = torch.as_tensor(targets['labels'])
targets['obj_ids'] = torch.as_tensor(targets['obj_ids'], dtype=torch.float64)
targets['boxes'] = torch.as_tensor(targets['boxes'], dtype=torch.float32).reshape(-1, 4)
targets['boxes'][:, 2:] += targets['boxes'][:, :2]
return img, targets
def _get_sample_range(self, start_idx):
# take default sampling method for normal dataset.
assert self.sample_mode in ['fixed_interval', 'random_interval'], 'invalid sample mode: {}'.format(self.sample_mode)
if self.sample_mode == 'fixed_interval':
sample_interval = self.sample_interval
elif self.sample_mode == 'random_interval':
sample_interval = np.random.randint(1, self.sample_interval + 1)
default_range = start_idx, start_idx + (self.num_frames_per_batch - 1) * sample_interval + 1, sample_interval
return default_range
def pre_continuous_frames(self, vid, indices):
return zip(*[self._pre_single_frame(vid, i) for i in indices])
def sample_indices(self, vid, f_index):
assert self.sample_mode == 'random_interval'
rate = randint(1, self.sample_interval + 1)
tmax = self.vid_tmax[vid]
ids = [f_index + rate * i for i in range(self.num_frames_per_batch)]
return [min(i, tmax) for i in ids]
def __getitem__(self, idx):
vid, f_index = self.indices[idx]
indices = self.sample_indices(vid, f_index)
images, targets = self.pre_continuous_frames(vid, indices)
dataset_name = targets[0]['dataset']
transform = self.dataset2transform[dataset_name]
if transform is not None:
images, targets = transform(images, targets)
gt_instances = []
for img_i, targets_i in zip(images, targets):
gt_instances_i = self._targets_to_instances(targets_i, img_i.shape[1:3])
gt_instances.append(gt_instances_i)
return {
'imgs': images,
'gt_instances': gt_instances,
}
def __len__(self):
return len(self.indices)
class DetMOTDetectionValidation(DetMOTDetection):
def __init__(self, args, seqs_folder, dataset2transform):
args.data_txt_path = args.val_data_txt_path
super().__init__(args, seqs_folder, dataset2transform)
def make_transforms_for_mot17(image_set, args=None):
normalize = T.MotCompose([
T.MotToTensor(),
T.MotNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992]
if image_set == 'train':
return T.MotCompose([
T.MotRandomHorizontalFlip(),
T.MotRandomSelect(
T.MotRandomResize(scales, max_size=1536),
T.MotCompose([
T.MotRandomResize([800, 1000, 1200]),
T.FixedMotRandomCrop(800, 1200),
T.MotRandomResize(scales, max_size=1536),
])
),
normalize,
])
if image_set == 'val':
return T.MotCompose([
T.MotRandomResize([800], max_size=1333),
normalize,
])
raise ValueError(f'unknown {image_set}')
def build_dataset2transform(args, image_set):
mot17_train = make_transforms_for_mot17('train', args)
mot17_test = make_transforms_for_mot17('val', args)
dataset2transform_train = {'MOT17': mot17_train}
dataset2transform_val = {'MOT17': mot17_test}
if image_set == 'train':
return dataset2transform_train
elif image_set == 'val':
return dataset2transform_val
else:
raise NotImplementedError()
def build(image_set, args):
root = Path(args.mot_path)
assert root.exists(), f'provided MOT path {root} does not exist'
dataset2transform = build_dataset2transform(args, image_set)
if image_set == 'train':
data_txt_path = args.data_txt_path_train
dataset = DetMOTDetection(args, data_txt_path=data_txt_path, seqs_folder=root, dataset2transform=dataset2transform)
if image_set == 'val':
data_txt_path = args.data_txt_path_val
dataset = DetMOTDetection(args, data_txt_path=data_txt_path, seqs_folder=root, dataset2transform=dataset2transform)
return dataset
================================================
FILE: datasets/data_path/bdd100k.val
================================================
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gitextract_mj3tn03w/
├── .gitignore
├── LICENSE
├── README.md
├── benchmark.py
├── configs/
│ ├── r50_deformable_detr.sh
│ ├── r50_deformable_detr_plus_iterative_bbox_refinement.sh
│ ├── r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh
│ ├── r50_deformable_detr_single_scale.sh
│ ├── r50_deformable_detr_single_scale_dc5.sh
│ ├── r50_motr_demo.sh
│ ├── r50_motr_eval.sh
│ ├── r50_motr_submit.sh
│ ├── r50_motr_submit_dance.sh
│ ├── r50_motr_train.sh
│ └── r50_motr_train_dance.sh
├── datasets/
│ ├── __init__.py
│ ├── coco.py
│ ├── coco_eval.py
│ ├── coco_panoptic.py
│ ├── dance.py
│ ├── data_path/
│ │ ├── bdd100k.val
│ │ ├── crowdhuman.val
│ │ ├── gen_bdd100k_mot.py
│ │ ├── gen_labels_15.py
│ │ ├── gen_labels_16.py
│ │ └── prepare.py
│ ├── data_prefetcher.py
│ ├── detmot.py
│ ├── joint.py
│ ├── panoptic_eval.py
│ ├── samplers.py
│ ├── static_detmot.py
│ ├── torchvision_datasets/
│ │ ├── __init__.py
│ │ └── coco.py
│ └── transforms.py
├── demo.py
├── engine.py
├── eval.py
├── main.py
├── models/
│ ├── __init__.py
│ ├── backbone.py
│ ├── deformable_detr.py
│ ├── deformable_transformer.py
│ ├── deformable_transformer_plus.py
│ ├── matcher.py
│ ├── memory_bank.py
│ ├── motr.py
│ ├── ops/
│ │ ├── functions/
│ │ │ ├── __init__.py
│ │ │ └── ms_deform_attn_func.py
│ │ ├── make.sh
│ │ ├── modules/
│ │ │ ├── __init__.py
│ │ │ └── ms_deform_attn.py
│ │ ├── setup.py
│ │ ├── src/
│ │ │ ├── cpu/
│ │ │ │ ├── ms_deform_attn_cpu.cpp
│ │ │ │ └── ms_deform_attn_cpu.h
│ │ │ ├── cuda/
│ │ │ │ ├── ms_deform_attn_cuda.cu
│ │ │ │ ├── ms_deform_attn_cuda.h
│ │ │ │ └── ms_deform_im2col_cuda.cuh
│ │ │ ├── ms_deform_attn.h
│ │ │ └── vision.cpp
│ │ └── test.py
│ ├── position_encoding.py
│ ├── qim.py
│ ├── relu_dropout.py
│ ├── segmentation.py
│ └── structures/
│ ├── __init__.py
│ ├── boxes.py
│ └── instances.py
├── requirements.txt
├── submit.py
├── submit_dance.py
├── tools/
│ ├── launch.py
│ ├── run_dist_launch.sh
│ └── run_dist_slurm.sh
└── util/
├── __init__.py
├── box_ops.py
├── checkpoint.py
├── evaluation.py
├── misc.py
├── motdet_eval.py
├── plot_utils.py
└── tool.py
SYMBOL INDEX (643 symbols across 53 files)
FILE: benchmark.py
function get_benckmark_arg_parser (line 27) | def get_benckmark_arg_parser():
function measure_average_inference_time (line 37) | def measure_average_inference_time(model, inputs, num_iters=100, warm_it...
function benchmark (line 51) | def benchmark():
FILE: datasets/__init__.py
function get_coco_api_from_dataset (line 21) | def get_coco_api_from_dataset(dataset):
function build_dataset (line 31) | def build_dataset(image_set, args):
FILE: datasets/coco.py
class CocoDetection (line 28) | class CocoDetection(TvCocoDetection):
method __init__ (line 29) | def __init__(self, img_folder, ann_file, transforms, return_masks, cac...
method __getitem__ (line 35) | def __getitem__(self, idx):
function convert_coco_poly_to_mask (line 45) | def convert_coco_poly_to_mask(segmentations, height, width):
class ConvertCocoPolysToMask (line 62) | class ConvertCocoPolysToMask(object):
method __init__ (line 63) | def __init__(self, return_masks=False):
method __call__ (line 66) | def __call__(self, image, target):
function make_coco_transforms (line 127) | def make_coco_transforms(image_set):
function build (line 159) | def build(image_set, args):
FILE: datasets/coco_eval.py
class CocoEvaluator (line 32) | class CocoEvaluator(object):
method __init__ (line 33) | def __init__(self, coco_gt, iou_types):
method update (line 46) | def update(self, predictions):
method synchronize_between_processes (line 66) | def synchronize_between_processes(self):
method accumulate (line 71) | def accumulate(self):
method summarize (line 75) | def summarize(self):
method prepare (line 80) | def prepare(self, predictions, iou_type):
method prepare_for_coco_detection (line 90) | def prepare_for_coco_detection(self, predictions):
method prepare_for_coco_segmentation (line 114) | def prepare_for_coco_segmentation(self, predictions):
method prepare_for_coco_keypoint (line 149) | def prepare_for_coco_keypoint(self, predictions):
function convert_to_xywh (line 176) | def convert_to_xywh(boxes):
function merge (line 181) | def merge(img_ids, eval_imgs):
function create_common_coco_eval (line 203) | def create_common_coco_eval(coco_eval, img_ids, eval_imgs):
function evaluate (line 219) | def evaluate(self):
FILE: datasets/coco_panoptic.py
class CocoPanoptic (line 25) | class CocoPanoptic:
method __init__ (line 26) | def __init__(self, img_folder, ann_folder, ann_file, transforms=None, ...
method __getitem__ (line 44) | def __getitem__(self, idx):
method __len__ (line 80) | def __len__(self):
method get_height_and_width (line 83) | def get_height_and_width(self, idx):
function build (line 90) | def build(image_set, args):
FILE: datasets/dance.py
class DetMOTDetection (line 31) | class DetMOTDetection:
method __init__ (line 32) | def __init__(self, args, data_txt_path: str, seqs_folder, dataset2tran...
method set_epoch (line 75) | def set_epoch(self, epoch):
method step_epoch (line 87) | def step_epoch(self):
method _targets_to_instances (line 93) | def _targets_to_instances(targets: dict, img_shape) -> Instances:
method load_crowd (line 101) | def load_crowd(self):
method _pre_single_frame (line 122) | def _pre_single_frame(self, vid, idx: int):
method _get_sample_range (line 154) | def _get_sample_range(self, start_idx):
method pre_continuous_frames (line 165) | def pre_continuous_frames(self, vid, indices):
method sample_indices (line 168) | def sample_indices(self, vid, f_index):
method __getitem__ (line 175) | def __getitem__(self, idx):
method __len__ (line 192) | def __len__(self):
class DetMOTDetectionValidation (line 196) | class DetMOTDetectionValidation(DetMOTDetection):
method __init__ (line 197) | def __init__(self, args, seqs_folder, dataset2transform):
function make_transforms_for_mot17 (line 202) | def make_transforms_for_mot17(image_set, args=None):
function build_dataset2transform (line 233) | def build_dataset2transform(args, image_set):
function build (line 247) | def build(image_set, args):
FILE: datasets/data_path/gen_bdd100k_mot.py
function convert (line 9) | def convert(img_dir, split, label_dir, save_label_dir, filter_crowd=Fals...
function generate_txt (line 73) | def generate_txt(img_dir,label_dir,txt_path='bdd100k.train',split='train'):
FILE: datasets/data_path/gen_labels_15.py
function mkdirs (line 7) | def mkdirs(d):
FILE: datasets/data_path/gen_labels_16.py
function mkdirs (line 4) | def mkdirs(d):
FILE: datasets/data_path/prepare.py
function solve_MOT_train (line 6) | def solve_MOT_train(root, year):
function solve_CUHK (line 27) | def solve_CUHK(root):
function solve_ETHZ (line 40) | def solve_ETHZ(root):
function solve_PRW (line 59) | def solve_PRW(root):
function solve (line 82) | def solve(dataset_list: List[str], root, save_path):
FILE: datasets/data_prefetcher.py
function to_cuda (line 16) | def to_cuda(samples, targets, device):
function tensor_to_cuda (line 22) | def tensor_to_cuda(tensor: torch.Tensor, device):
function is_tensor_or_instances (line 26) | def is_tensor_or_instances(data):
function data_apply (line 30) | def data_apply(data, check_func, apply_func):
function data_dict_to_cuda (line 52) | def data_dict_to_cuda(data_dict, device):
class data_prefetcher (line 56) | class data_prefetcher():
method __init__ (line 57) | def __init__(self, loader, device, prefetch=True):
method preload (line 65) | def preload(self):
method next (line 93) | def next(self):
FILE: datasets/detmot.py
class DetMOTDetection (line 26) | class DetMOTDetection:
method __init__ (line 27) | def __init__(self, args, data_txt_path: str, seqs_folder, transforms):
method _register_videos (line 63) | def _register_videos(self):
method set_epoch (line 71) | def set_epoch(self, epoch):
method step_epoch (line 83) | def step_epoch(self):
method _targets_to_instances (line 89) | def _targets_to_instances(targets: dict, img_shape) -> Instances:
method _pre_single_frame (line 97) | def _pre_single_frame(self, idx: int):
method _get_sample_range (line 142) | def _get_sample_range(self, start_idx):
method pre_continuous_frames (line 153) | def pre_continuous_frames(self, start, end, interval=1):
method __getitem__ (line 162) | def __getitem__(self, idx):
method __len__ (line 180) | def __len__(self):
class DetMOTDetectionValidation (line 184) | class DetMOTDetectionValidation(DetMOTDetection):
method __init__ (line 185) | def __init__(self, args, seqs_folder, transforms):
function make_detmot_transforms (line 190) | def make_detmot_transforms(image_set, args=None):
function build (line 217) | def build(image_set, args):
FILE: datasets/joint.py
class DetMOTDetection (line 26) | class DetMOTDetection:
method __init__ (line 27) | def __init__(self, args, data_txt_path: str, seqs_folder, dataset2tran...
method _register_videos (line 64) | def _register_videos(self):
method set_epoch (line 72) | def set_epoch(self, epoch):
method step_epoch (line 84) | def step_epoch(self):
method _targets_to_instances (line 90) | def _targets_to_instances(targets: dict, img_shape) -> Instances:
method _pre_single_frame (line 98) | def _pre_single_frame(self, idx: int):
method _get_sample_range (line 149) | def _get_sample_range(self, start_idx):
method pre_continuous_frames (line 160) | def pre_continuous_frames(self, start, end, interval=1):
method __getitem__ (line 169) | def __getitem__(self, idx):
method __len__ (line 189) | def __len__(self):
class DetMOTDetectionValidation (line 193) | class DetMOTDetectionValidation(DetMOTDetection):
method __init__ (line 194) | def __init__(self, args, seqs_folder, dataset2transform):
function make_transforms_for_mot17 (line 200) | def make_transforms_for_mot17(image_set, args=None):
function make_transforms_for_crowdhuman (line 231) | def make_transforms_for_crowdhuman(image_set, args=None):
function build_dataset2transform (line 264) | def build_dataset2transform(args, image_set):
function build (line 279) | def build(image_set, args):
FILE: datasets/panoptic_eval.py
class PanopticEvaluator (line 23) | class PanopticEvaluator(object):
method __init__ (line 24) | def __init__(self, ann_file, ann_folder, output_dir="panoptic_eval"):
method update (line 33) | def update(self, predictions):
method synchronize_between_processes (line 40) | def synchronize_between_processes(self):
method summarize (line 47) | def summarize(self):
FILE: datasets/samplers.py
class DistributedSampler (line 19) | class DistributedSampler(Sampler):
method __init__ (line 34) | def __init__(self, dataset, num_replicas=None, rank=None, local_rank=N...
method __iter__ (line 51) | def __iter__(self):
method __len__ (line 71) | def __len__(self):
method set_epoch (line 74) | def set_epoch(self, epoch):
class NodeDistributedSampler (line 78) | class NodeDistributedSampler(Sampler):
method __init__ (line 93) | def __init__(self, dataset, num_replicas=None, rank=None, local_rank=N...
method __iter__ (line 118) | def __iter__(self):
method __len__ (line 138) | def __len__(self):
method set_epoch (line 141) | def set_epoch(self, epoch):
FILE: datasets/static_detmot.py
class DetMOTDetection (line 26) | class DetMOTDetection:
method __init__ (line 27) | def __init__(self, args, data_txt_path: str, seqs_folder, transforms):
method _register_videos (line 63) | def _register_videos(self):
method set_epoch (line 71) | def set_epoch(self, epoch):
method step_epoch (line 83) | def step_epoch(self):
method _targets_to_instances (line 89) | def _targets_to_instances(targets: dict, img_shape) -> Instances:
method _pre_single_frame (line 97) | def _pre_single_frame(self, idx: int):
method _get_sample_range (line 142) | def _get_sample_range(self, start_idx):
method pre_continuous_frames (line 153) | def pre_continuous_frames(self, idx):
method __getitem__ (line 163) | def __getitem__(self, idx):
method __len__ (line 180) | def __len__(self):
class DetMOTDetectionValidation (line 184) | class DetMOTDetectionValidation(DetMOTDetection):
method __init__ (line 185) | def __init__(self, args, seqs_folder, transforms):
function make_detmot_transforms (line 191) | def make_detmot_transforms(image_set, args=None):
function build (line 238) | def build(image_set, args):
FILE: datasets/torchvision_datasets/coco.py
class CocoDetection (line 23) | class CocoDetection(VisionDataset):
method __init__ (line 36) | def __init__(self, root, annFile, transform=None, target_transform=Non...
method cache_images (line 49) | def cache_images(self):
method get_image (line 58) | def get_image(self, path):
method __getitem__ (line 66) | def __getitem__(self, index):
method __len__ (line 86) | def __len__(self):
FILE: datasets/transforms.py
function crop_mot (line 28) | def crop_mot(image, target, region):
function random_shift (line 69) | def random_shift(image, target, region, sizes):
function crop (line 114) | def crop(image, target, region):
function hflip (line 160) | def hflip(image, target):
function resize (line 177) | def resize(image, target, size, max_size=None):
function pad (line 236) | def pad(image, target, padding):
class RandomCrop (line 249) | class RandomCrop(object):
method __init__ (line 250) | def __init__(self, size):
method __call__ (line 253) | def __call__(self, img, target):
class MotRandomCrop (line 258) | class MotRandomCrop(RandomCrop):
method __call__ (line 259) | def __call__(self, imgs: list, targets: list):
class FixedMotRandomCrop (line 269) | class FixedMotRandomCrop(object):
method __init__ (line 270) | def __init__(self, min_size: int, max_size: int):
method __call__ (line 274) | def __call__(self, imgs: list, targets: list):
class MotRandomShift (line 286) | class MotRandomShift(object):
method __init__ (line 287) | def __init__(self, bs=1):
method __call__ (line 290) | def __call__(self, imgs: list, targets: list):
class FixedMotRandomShift (line 313) | class FixedMotRandomShift(object):
method __init__ (line 314) | def __init__(self, bs=1, padding=50):
method __call__ (line 318) | def __call__(self, imgs: list, targets: list):
class RandomSizeCrop (line 345) | class RandomSizeCrop(object):
method __init__ (line 346) | def __init__(self, min_size: int, max_size: int):
method __call__ (line 350) | def __call__(self, img: PIL.Image.Image, target: dict):
class MotRandomSizeCrop (line 357) | class MotRandomSizeCrop(RandomSizeCrop):
method __call__ (line 358) | def __call__(self, imgs, targets):
class CenterCrop (line 371) | class CenterCrop(object):
method __init__ (line 372) | def __init__(self, size):
method __call__ (line 375) | def __call__(self, img, target):
class MotCenterCrop (line 383) | class MotCenterCrop(CenterCrop):
method __call__ (line 384) | def __call__(self, imgs, targets):
class RandomHorizontalFlip (line 398) | class RandomHorizontalFlip(object):
method __init__ (line 399) | def __init__(self, p=0.5):
method __call__ (line 402) | def __call__(self, img, target):
class MotRandomHorizontalFlip (line 408) | class MotRandomHorizontalFlip(RandomHorizontalFlip):
method __call__ (line 409) | def __call__(self, imgs, targets):
class RandomResize (line 421) | class RandomResize(object):
method __init__ (line 422) | def __init__(self, sizes, max_size=None):
method __call__ (line 427) | def __call__(self, img, target=None):
class MotRandomResize (line 432) | class MotRandomResize(RandomResize):
method __call__ (line 433) | def __call__(self, imgs, targets):
class RandomPad (line 444) | class RandomPad(object):
method __init__ (line 445) | def __init__(self, max_pad):
method __call__ (line 448) | def __call__(self, img, target):
class MotRandomPad (line 454) | class MotRandomPad(RandomPad):
method __call__ (line 455) | def __call__(self, imgs, targets):
class RandomSelect (line 467) | class RandomSelect(object):
method __init__ (line 472) | def __init__(self, transforms1, transforms2, p=0.5):
method __call__ (line 477) | def __call__(self, img, target):
class MotRandomSelect (line 483) | class MotRandomSelect(RandomSelect):
method __call__ (line 488) | def __call__(self, imgs, targets):
class ToTensor (line 494) | class ToTensor(object):
method __call__ (line 495) | def __call__(self, img, target):
class MotToTensor (line 499) | class MotToTensor(ToTensor):
method __call__ (line 500) | def __call__(self, imgs, targets):
class RandomErasing (line 507) | class RandomErasing(object):
method __init__ (line 509) | def __init__(self, *args, **kwargs):
method __call__ (line 512) | def __call__(self, img, target):
class MotRandomErasing (line 516) | class MotRandomErasing(RandomErasing):
method __call__ (line 517) | def __call__(self, imgs, targets):
class MoTColorJitter (line 525) | class MoTColorJitter(T.ColorJitter):
method __call__ (line 526) | def __call__(self, imgs, targets):
class Normalize (line 535) | class Normalize(object):
method __init__ (line 536) | def __init__(self, mean, std):
method __call__ (line 540) | def __call__(self, image, target=None):
class MotNormalize (line 556) | class MotNormalize(Normalize):
method __call__ (line 557) | def __call__(self, imgs, targets=None):
class Compose (line 569) | class Compose(object):
method __init__ (line 570) | def __init__(self, transforms):
method __call__ (line 573) | def __call__(self, image, target):
method __repr__ (line 578) | def __repr__(self):
class MotCompose (line 587) | class MotCompose(Compose):
method __call__ (line 588) | def __call__(self, imgs, targets):
FILE: demo.py
function plot_one_box (line 59) | def plot_one_box(x, img, color=None, label=None, score=None, line_thickn...
function draw_bboxes (line 84) | def draw_bboxes(ori_img, bbox, identities=None, offset=(0, 0), cvt_color...
function draw_points (line 107) | def draw_points(img: np.ndarray, points: np.ndarray, color=(255, 255, 25...
class LoadVideo (line 115) | class LoadVideo: # for inference
method __init__ (line 116) | def __init__(self, path, img_size=(1536, 800)):
method __iter__ (line 135) | def __iter__(self):
method __next__ (line 139) | def __next__(self):
method init_img (line 151) | def init_img(self, img):
method __len__ (line 164) | def __len__(self):
class MOTR (line 167) | class MOTR(object):
method update (line 168) | def update(self, dt_instances: Instances):
class Detector (line 180) | class Detector(object):
method __init__ (line 181) | def __init__(self, args):
method filter_dt_by_score (line 206) | def filter_dt_by_score(dt_instances: Instances, prob_threshold: float)...
method filter_dt_by_area (line 211) | def filter_dt_by_area(dt_instances: Instances, area_threshold: float) ...
method write_results (line 218) | def write_results(txt_path, frame_id, bbox_xyxy, identities):
method visualize_img_with_bbox (line 230) | def visualize_img_with_bbox(img_path, img, dt_instances: Instances, re...
method run (line 243) | def run(self, prob_threshold=0.7, area_threshold=100, vis=True, dump=T...
FILE: engine.py
function train_one_epoch (line 33) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
function train_one_epoch_mot (line 91) | def train_one_epoch_mot(model: torch.nn.Module, criterion: torch.nn.Module,
function evaluate (line 150) | def evaluate(model, criterion, postprocessors, data_loader, base_ds, dev...
FILE: eval.py
function plot_one_box (line 71) | def plot_one_box(x, img, color=None, label=None, score=None, line_thickn...
function draw_bboxes (line 96) | def draw_bboxes(ori_img, bbox, identities=None, offset=(0, 0), cvt_color...
function draw_points (line 119) | def draw_points(img: np.ndarray, points: np.ndarray, color=(255, 255, 25...
function tensor_to_numpy (line 128) | def tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:
class Track (line 132) | class Track(object):
method __init__ (line 135) | def __init__(self, box):
method miss_one_frame (line 142) | def miss_one_frame(self):
method clear_miss (line 145) | def clear_miss(self):
method update (line 148) | def update(self, box):
class MOTR (line 153) | class MOTR(object):
method __init__ (line 154) | def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
method update (line 157) | def update(self, dt_instances: Instances):
function load_label (line 171) | def load_label(label_path: str, img_size: tuple) -> dict:
function filter_pub_det (line 198) | def filter_pub_det(res_file, pub_det_file, filter_iou=False):
class Detector (line 264) | class Detector(object):
method __init__ (line 265) | def __init__(self, args, model=None, seq_num=2):
method load_img_from_file (line 295) | def load_img_from_file(self, f_path):
method init_img (line 302) | def init_img(self, img):
method filter_dt_by_score (line 316) | def filter_dt_by_score(dt_instances: Instances, prob_threshold: float)...
method filter_dt_by_area (line 321) | def filter_dt_by_area(dt_instances: Instances, area_threshold: float) ...
method write_results (line 328) | def write_results(txt_path, frame_id, bbox_xyxy, identities):
method eval_seq (line 339) | def eval_seq(self):
method visualize_img_with_bbox (line 347) | def visualize_img_with_bbox(img_path, img, dt_instances: Instances, re...
method detect (line 359) | def detect(self, prob_threshold=0.7, area_threshold=100, vis=False):
FILE: main.py
function get_args_parser (line 34) | def get_args_parser():
function main (line 183) | def main(args):
FILE: models/__init__.py
function build_model (line 14) | def build_model(args):
FILE: models/backbone.py
class FrozenBatchNorm2d (line 27) | class FrozenBatchNorm2d(torch.nn.Module):
method __init__ (line 36) | def __init__(self, n, eps=1e-5):
method _load_from_state_dict (line 44) | def _load_from_state_dict(self, state_dict, prefix, local_metadata, st...
method forward (line 54) | def forward(self, x):
class BackboneBase (line 67) | class BackboneBase(nn.Module):
method __init__ (line 69) | def __init__(self, backbone: nn.Module, train_backbone: bool, return_i...
method forward (line 85) | def forward(self, tensor_list: NestedTensor):
class Backbone (line 96) | class Backbone(BackboneBase):
method __init__ (line 98) | def __init__(self, name: str,
class Joiner (line 112) | class Joiner(nn.Sequential):
method __init__ (line 113) | def __init__(self, backbone, position_embedding):
method forward (line 118) | def forward(self, tensor_list: NestedTensor):
function build_backbone (line 132) | def build_backbone(args):
FILE: models/deformable_detr.py
function _get_clones (line 33) | def _get_clones(module, N):
class DeformableDETR (line 37) | class DeformableDETR(nn.Module):
method __init__ (line 39) | def __init__(self, backbone, transformer, num_classes, num_queries, nu...
method _get_valid_ratio (line 118) | def _get_valid_ratio(mask):
method forward (line 127) | def forward(self, samples: NestedTensor):
method _set_aux_loss (line 212) | def _set_aux_loss(self, outputs_class, outputs_coord):
class SetCriterion (line 220) | class SetCriterion(nn.Module):
method __init__ (line 226) | def __init__(self, num_classes, matcher, weight_dict, losses, focal_al...
method loss_labels (line 242) | def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
method loss_cardinality (line 269) | def loss_cardinality(self, outputs, targets, indices, num_boxes):
method loss_boxes (line 282) | def loss_boxes(self, outputs, targets, indices, num_boxes):
method loss_masks (line 303) | def loss_masks(self, outputs, targets, indices, num_boxes):
method _get_src_permutation_idx (line 332) | def _get_src_permutation_idx(self, indices):
method _get_tgt_permutation_idx (line 338) | def _get_tgt_permutation_idx(self, indices):
method get_loss (line 344) | def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs):
method forward (line 354) | def forward(self, outputs, targets):
class PostProcess (line 416) | class PostProcess(nn.Module):
method forward (line 420) | def forward(self, outputs, target_sizes):
class MLP (line 451) | class MLP(nn.Module):
method __init__ (line 454) | def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
method forward (line 460) | def forward(self, x):
function build (line 466) | def build(args):
FILE: models/deformable_transformer.py
class DeformableTransformer (line 28) | class DeformableTransformer(nn.Module):
method __init__ (line 29) | def __init__(self, d_model=256, nhead=8,
method _reset_parameters (line 67) | def _reset_parameters(self):
method get_proposal_pos_embed (line 79) | def get_proposal_pos_embed(self, proposals):
method gen_encoder_output_proposals (line 94) | def gen_encoder_output_proposals(self, memory, memory_padding_mask, sp...
method get_valid_ratio (line 126) | def get_valid_ratio(self, mask):
method forward (line 135) | def forward(self, srcs, masks, pos_embeds, query_embed=None, ref_pts=N...
class DeformableTransformerEncoderLayer (line 202) | class DeformableTransformerEncoderLayer(nn.Module):
method __init__ (line 203) | def __init__(self,
method with_pos_embed (line 223) | def with_pos_embed(tensor, pos):
method forward_ffn (line 226) | def forward_ffn(self, src):
method forward (line 232) | def forward(self, src, pos, reference_points, spatial_shapes, level_st...
class DeformableTransformerEncoder (line 244) | class DeformableTransformerEncoder(nn.Module):
method __init__ (line 245) | def __init__(self, encoder_layer, num_layers):
method get_reference_points (line 251) | def get_reference_points(spatial_shapes, valid_ratios, device):
method forward (line 265) | def forward(self, src, spatial_shapes, level_start_index, valid_ratios...
class DeformableTransformerDecoderLayer (line 274) | class DeformableTransformerDecoderLayer(nn.Module):
method __init__ (line 275) | def __init__(self, d_model=256, d_ffn=1024,
method with_pos_embed (line 302) | def with_pos_embed(tensor, pos):
method forward_ffn (line 305) | def forward_ffn(self, tgt):
method _forward_self_attn (line 311) | def _forward_self_attn(self, tgt, query_pos, attn_mask=None):
method _forward_self_cross (line 321) | def _forward_self_cross(self, tgt, query_pos, reference_points, src, s...
method _forward_cross_self (line 338) | def _forward_cross_self(self, tgt, query_pos, reference_points, src, s...
method forward (line 353) | def forward(self, tgt, query_pos, reference_points, src, src_spatial_s...
class DeformableTransformerDecoder (line 362) | class DeformableTransformerDecoder(nn.Module):
method __init__ (line 363) | def __init__(self, decoder_layer, num_layers, return_intermediate=False):
method forward (line 372) | def forward(self, tgt, reference_points, src, src_spatial_shapes, src_...
function _get_clones (line 410) | def _get_clones(module, N):
function _get_activation_fn (line 414) | def _get_activation_fn(activation):
function build_deforamble_transformer (line 425) | def build_deforamble_transformer(args):
FILE: models/deformable_transformer_plus.py
class DeformableTransformer (line 28) | class DeformableTransformer(nn.Module):
method __init__ (line 29) | def __init__(self, d_model=256, nhead=8,
method _reset_parameters (line 67) | def _reset_parameters(self):
method get_proposal_pos_embed (line 79) | def get_proposal_pos_embed(self, proposals):
method gen_encoder_output_proposals (line 94) | def gen_encoder_output_proposals(self, memory, memory_padding_mask, sp...
method get_valid_ratio (line 126) | def get_valid_ratio(self, mask):
method forward (line 135) | def forward(self, srcs, masks, pos_embeds, query_embed=None, ref_pts=N...
class DeformableTransformerEncoderLayer (line 200) | class DeformableTransformerEncoderLayer(nn.Module):
method __init__ (line 201) | def __init__(self,
method with_pos_embed (line 221) | def with_pos_embed(tensor, pos):
method forward_ffn (line 224) | def forward_ffn(self, src):
method forward (line 230) | def forward(self, src, pos, reference_points, spatial_shapes, level_st...
class DeformableTransformerEncoder (line 241) | class DeformableTransformerEncoder(nn.Module):
method __init__ (line 242) | def __init__(self, encoder_layer, num_layers):
method get_reference_points (line 248) | def get_reference_points(spatial_shapes, valid_ratios, device):
method forward (line 262) | def forward(self, src, spatial_shapes, level_start_index, valid_ratios...
class DeformableTransformerDecoderLayer (line 271) | class DeformableTransformerDecoderLayer(nn.Module):
method __init__ (line 272) | def __init__(self, d_model=256, d_ffn=1024,
method with_pos_embed (line 311) | def with_pos_embed(tensor, pos):
method forward_ffn (line 314) | def forward_ffn(self, tgt):
method _forward_self_attn (line 320) | def _forward_self_attn(self, tgt, query_pos, attn_mask=None):
method _forward_track_attn (line 333) | def _forward_track_attn(self, tgt, query_pos):
method _forward_self_cross (line 342) | def _forward_self_cross(self, tgt, query_pos, reference_points, src, s...
method _forward_cross_self (line 359) | def _forward_cross_self(self, tgt, query_pos, reference_points, src, s...
method forward (line 374) | def forward(self, tgt, query_pos, reference_points, src, src_spatial_s...
class DeformableTransformerDecoder (line 383) | class DeformableTransformerDecoder(nn.Module):
method __init__ (line 384) | def __init__(self, decoder_layer, num_layers, return_intermediate=False):
method forward (line 393) | def forward(self, tgt, reference_points, src, src_spatial_shapes, src_...
function _get_clones (line 431) | def _get_clones(module, N):
function _get_activation_fn (line 435) | def _get_activation_fn(activation):
function build_deforamble_transformer (line 447) | def build_deforamble_transformer(args):
FILE: models/matcher.py
class HungarianMatcher (line 23) | class HungarianMatcher(nn.Module):
method __init__ (line 31) | def __init__(self,
method forward (line 48) | def forward(self, outputs, targets, use_focal=True):
function build_matcher (line 119) | def build_matcher(args):
FILE: models/memory_bank.py
class MemoryBank (line 14) | class MemoryBank(nn.Module):
method __init__ (line 15) | def __init__(self, args, dim_in, hidden_dim, dim_out):
method _build_layers (line 22) | def _build_layers(self, args, dim_in, hidden_dim, dim_out):
method update (line 47) | def update(self, track_instances):
method _forward_spatial_attn (line 70) | def _forward_spatial_attn(self, track_instances):
method _forward_track_cls (line 90) | def _forward_track_cls(self, track_instances):
method _forward_temporal_attn (line 94) | def _forward_temporal_attn(self, track_instances):
method forward_temporal_attn (line 122) | def forward_temporal_attn(self, track_instances):
method forward (line 125) | def forward(self, track_instances: Instances, update_bank=True) -> Ins...
function build_memory_bank (line 136) | def build_memory_bank(args, dim_in, hidden_dim, dim_out):
FILE: models/motr.py
class ClipMatcher (line 38) | class ClipMatcher(SetCriterion):
method __init__ (line 39) | def __init__(self, num_classes,
method initialize_for_single_clip (line 60) | def initialize_for_single_clip(self, gt_instances: List[Instances]):
method _step (line 67) | def _step(self):
method calc_loss_for_track_scores (line 70) | def calc_loss_for_track_scores(self, track_instances: Instances):
method get_num_boxes (line 91) | def get_num_boxes(self, num_samples):
method get_loss (line 98) | def get_loss(self, loss, outputs, gt_instances, indices, num_boxes, **...
method loss_boxes (line 107) | def loss_boxes(self, outputs, gt_instances: List[Instances], indices: ...
method loss_labels (line 138) | def loss_labels(self, outputs, gt_instances: List[Instances], indices,...
method match_for_single_frame (line 175) | def match_for_single_frame(self, outputs: dict):
method forward (line 294) | def forward(self, outputs, input_data: dict):
class RuntimeTrackerBase (line 303) | class RuntimeTrackerBase(object):
method __init__ (line 304) | def __init__(self, score_thresh=0.7, filter_score_thresh=0.6, miss_tol...
method clear (line 310) | def clear(self):
method update (line 313) | def update(self, track_instances: Instances):
class TrackerPostProcess (line 328) | class TrackerPostProcess(nn.Module):
method __init__ (line 330) | def __init__(self):
method forward (line 334) | def forward(self, track_instances: Instances, target_size) -> Instances:
function _get_clones (line 364) | def _get_clones(module, N):
class MOTR (line 368) | class MOTR(nn.Module):
method __init__ (line 369) | def __init__(self, backbone, transformer, num_classes, num_queries, nu...
method _generate_empty_tracks (line 454) | def _generate_empty_tracks(self):
method clear (line 477) | def clear(self):
method _set_aux_loss (line 481) | def _set_aux_loss(self, outputs_class, outputs_coord):
method _forward_single_image (line 488) | def _forward_single_image(self, samples, track_instances: Instances):
method _post_process_single_image (line 545) | def _post_process_single_image(self, frame_res, track_instances, is_la...
method inference_single_image (line 580) | def inference_single_image(self, img, ori_img_size, track_instances=No...
method forward (line 600) | def forward(self, data: dict):
function build (line 657) | def build(args):
FILE: models/ops/functions/ms_deform_attn_func.py
class MSDeformAttnFunction (line 24) | class MSDeformAttnFunction(Function):
method forward (line 26) | def forward(ctx, value, value_spatial_shapes, value_level_start_index,...
method backward (line 35) | def backward(ctx, grad_output):
function ms_deform_attn_core_pytorch (line 44) | def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_lo...
FILE: models/ops/modules/ms_deform_attn.py
function _is_power_of_2 (line 27) | def _is_power_of_2(n):
class MSDeformAttn (line 33) | class MSDeformAttn(nn.Module):
method __init__ (line 34) | def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4, sig...
method _reset_parameters (line 66) | def _reset_parameters(self):
method forward (line 82) | def forward(self, query, reference_points, input_flatten, input_spatia...
FILE: models/ops/setup.py
function get_extensions (line 23) | def get_extensions():
FILE: models/ops/src/cpu/ms_deform_attn_cpu.cpp
function ms_deform_attn_cpu_forward (line 17) | at::Tensor
function ms_deform_attn_cpu_backward (line 29) | std::vector<at::Tensor>
FILE: models/ops/src/ms_deform_attn.h
function im2col_step (line 27) | int im2col_step)
FILE: models/ops/src/vision.cpp
function PYBIND11_MODULE (line 13) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
FILE: models/ops/test.py
function check_forward_equal_with_pytorch_double (line 32) | def check_forward_equal_with_pytorch_double():
function check_forward_equal_with_pytorch_float (line 48) | def check_forward_equal_with_pytorch_float():
function check_gradient_numerical (line 63) | def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_...
FILE: models/position_encoding.py
class PositionEmbeddingSine (line 22) | class PositionEmbeddingSine(nn.Module):
method __init__ (line 27) | def __init__(self, num_pos_feats=64, temperature=10000, normalize=Fals...
method forward (line 38) | def forward(self, tensor_list: NestedTensor):
class PositionEmbeddingLearned (line 61) | class PositionEmbeddingLearned(nn.Module):
method __init__ (line 65) | def __init__(self, num_pos_feats=256):
method reset_parameters (line 71) | def reset_parameters(self):
method forward (line 75) | def forward(self, tensor_list: NestedTensor):
function build_position_encoding (line 89) | def build_position_encoding(args):
FILE: models/qim.py
function random_drop_tracks (line 16) | def random_drop_tracks(track_instances: Instances, drop_probability: flo...
class QueryInteractionBase (line 23) | class QueryInteractionBase(nn.Module):
method __init__ (line 24) | def __init__(self, args, dim_in, hidden_dim, dim_out):
method _build_layers (line 30) | def _build_layers(self, args, dim_in, hidden_dim, dim_out):
method _reset_parameters (line 33) | def _reset_parameters(self):
method _select_active_tracks (line 38) | def _select_active_tracks(self, data: dict) -> Instances:
method _update_track_embedding (line 41) | def _update_track_embedding(self, track_instances):
class FFN (line 45) | class FFN(nn.Module):
method __init__ (line 46) | def __init__(self, d_model, d_ffn, dropout=0):
method forward (line 55) | def forward(self, tgt):
class QueryInteractionModule (line 62) | class QueryInteractionModule(QueryInteractionBase):
method __init__ (line 63) | def __init__(self, args, dim_in, hidden_dim, dim_out):
method _build_layers (line 69) | def _build_layers(self, args, dim_in, hidden_dim, dim_out):
method _random_drop_tracks (line 103) | def _random_drop_tracks(self, track_instances: Instances) -> Instances:
method _add_fp_tracks (line 106) | def _add_fp_tracks(self, track_instances: Instances, active_track_inst...
method _select_active_tracks (line 133) | def _select_active_tracks(self, data: dict) -> Instances:
method _update_track_embedding (line 147) | def _update_track_embedding(self, track_instances: Instances) -> Insta...
method forward (line 179) | def forward(self, data) -> Instances:
function build (line 187) | def build(args, layer_name, dim_in, hidden_dim, dim_out):
FILE: models/relu_dropout.py
class ReLUDropout (line 4) | class ReLUDropout(torch.nn.Dropout):
method forward (line 5) | def forward(self, input):
function relu_dropout (line 8) | def relu_dropout(x, p=0, inplace=False, training=False):
FILE: models/segmentation.py
class DETRsegm (line 32) | class DETRsegm(nn.Module):
method __init__ (line 33) | def __init__(self, detr, freeze_detr=False):
method forward (line 45) | def forward(self, samples: NestedTensor):
class MaskHeadSmallConv (line 74) | class MaskHeadSmallConv(nn.Module):
method __init__ (line 80) | def __init__(self, dim, fpn_dims, context_dim):
method forward (line 107) | def forward(self, x, bbox_mask, fpns):
class MHAttentionMap (line 148) | class MHAttentionMap(nn.Module):
method __init__ (line 151) | def __init__(self, query_dim, hidden_dim, num_heads, dropout=0, bias=T...
method forward (line 166) | def forward(self, q, k, mask=None):
function dice_loss (line 180) | def dice_loss(inputs, targets, num_boxes):
function sigmoid_focal_loss (line 198) | def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, ...
class PostProcessSegm (line 228) | class PostProcessSegm(nn.Module):
method __init__ (line 229) | def __init__(self, threshold=0.5):
method forward (line 234) | def forward(self, results, outputs, orig_target_sizes, max_target_sizes):
class PostProcessPanoptic (line 251) | class PostProcessPanoptic(nn.Module):
method __init__ (line 255) | def __init__(self, is_thing_map, threshold=0.85):
method forward (line 266) | def forward(self, outputs, processed_sizes, target_sizes=None):
FILE: models/structures/boxes.py
function _maybe_jit_unused (line 14) | def _maybe_jit_unused(x):
class BoxMode (line 19) | class BoxMode(IntEnum):
method convert (line 50) | def convert(box: _RawBoxType, from_mode: "BoxMode", to_mode: "BoxMode"...
class Boxes (line 136) | class Boxes:
method __init__ (line 148) | def __init__(self, tensor: torch.Tensor):
method clone (line 163) | def clone(self) -> "Boxes":
method to (line 173) | def to(self, device: torch.device):
method area (line 177) | def area(self) -> torch.Tensor:
method clip (line 188) | def clip(self, box_size: Tuple[int, int]) -> None:
method nonempty (line 204) | def nonempty(self, threshold: float = 0.0) -> torch.Tensor:
method __getitem__ (line 220) | def __getitem__(self, item) -> "Boxes":
method __len__ (line 244) | def __len__(self) -> int:
method __repr__ (line 247) | def __repr__(self) -> str:
method inside_box (line 250) | def inside_box(self, box_size: Tuple[int, int], boundary_threshold: in...
method get_centers (line 269) | def get_centers(self) -> torch.Tensor:
method scale (line 276) | def scale(self, scale_x: float, scale_y: float) -> None:
method cat (line 285) | def cat(cls, boxes_list: List["Boxes"]) -> "Boxes":
method device (line 305) | def device(self) -> device:
method __iter__ (line 311) | def __iter__(self):
function pairwise_intersection (line 318) | def pairwise_intersection(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
function pairwise_iou (line 342) | def pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
function pairwise_ioa (line 367) | def pairwise_ioa(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
function matched_boxlist_iou (line 387) | def matched_boxlist_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
FILE: models/structures/instances.py
class Instances (line 12) | class Instances:
method __init__ (line 43) | def __init__(self, image_size: Tuple[int, int], **kwargs: Any):
method image_size (line 55) | def image_size(self) -> Tuple[int, int]:
method __setattr__ (line 62) | def __setattr__(self, name: str, val: Any) -> None:
method __getattr__ (line 68) | def __getattr__(self, name: str) -> Any:
method set (line 73) | def set(self, name: str, value: Any) -> None:
method has (line 86) | def has(self, name: str) -> bool:
method remove (line 93) | def remove(self, name: str) -> None:
method get (line 99) | def get(self, name: str) -> Any:
method get_fields (line 105) | def get_fields(self) -> Dict[str, Any]:
method to (line 115) | def to(self, *args: Any, **kwargs: Any) -> "Instances":
method numpy (line 127) | def numpy(self):
method __getitem__ (line 135) | def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "I...
method __len__ (line 155) | def __len__(self) -> int:
method __iter__ (line 161) | def __iter__(self):
method cat (line 165) | def cat(instance_lists: List["Instances"]) -> "Instances":
method __str__ (line 196) | def __str__(self) -> str:
FILE: submit.py
function plot_one_box (line 72) | def plot_one_box(x, img, color=None, label=None, score=None, line_thickn...
function draw_bboxes (line 100) | def draw_bboxes(ori_img, bbox, identities=None, offset=(0, 0), cvt_color...
function draw_points (line 123) | def draw_points(img: np.ndarray, points: np.ndarray, color=(255, 255, 25...
function tensor_to_numpy (line 132) | def tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:
class Track (line 136) | class Track(object):
method __init__ (line 139) | def __init__(self, box):
method miss_one_frame (line 146) | def miss_one_frame(self):
method clear_miss (line 149) | def clear_miss(self):
method update (line 152) | def update(self, box):
class MOTR (line 157) | class MOTR(object):
method __init__ (line 158) | def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
method _remove_track (line 171) | def _remove_track(self, slot_id):
method clear_disappeared_track (line 175) | def clear_disappeared_track(self):
method update (line 178) | def update(self, dt_instances: Instances):
function load_label (line 240) | def load_label(label_path: str, img_size: tuple) -> dict:
function filter_pub_det (line 267) | def filter_pub_det(res_file, pub_det_file, filter_iou=False):
class ListImgDataset (line 332) | class ListImgDataset(Dataset):
method __init__ (line 333) | def __init__(self, img_list) -> None:
method load_img_from_file (line 345) | def load_img_from_file(self, f_path):
method init_img (line 353) | def init_img(self, img):
method __len__ (line 366) | def __len__(self):
method __getitem__ (line 369) | def __getitem__(self, index):
class Detector (line 374) | class Detector(object):
method __init__ (line 375) | def __init__(self, args, model=None, seq_num=2):
method filter_dt_by_score (line 398) | def filter_dt_by_score(dt_instances: Instances, prob_threshold: float)...
method filter_dt_by_area (line 403) | def filter_dt_by_area(dt_instances: Instances, area_threshold: float) ...
method write_results (line 410) | def write_results(txt_path, frame_id, bbox_xyxy, identities):
method eval_seq (line 421) | def eval_seq(self):
method visualize_img_with_bbox (line 429) | def visualize_img_with_bbox(img_path, img, dt_instances: Instances, re...
method detect (line 440) | def detect(self, prob_threshold=0.7, area_threshold=100):
FILE: submit_dance.py
function plot_one_box (line 72) | def plot_one_box(x, img, color=None, label=None, score=None, line_thickn...
function draw_bboxes (line 100) | def draw_bboxes(ori_img, bbox, identities=None, offset=(0, 0), cvt_color...
function draw_points (line 123) | def draw_points(img: np.ndarray, points: np.ndarray, color=(255, 255, 25...
function tensor_to_numpy (line 132) | def tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:
class Track (line 136) | class Track(object):
method __init__ (line 139) | def __init__(self, box):
method miss_one_frame (line 146) | def miss_one_frame(self):
method clear_miss (line 149) | def clear_miss(self):
method update (line 152) | def update(self, box):
class MOTR (line 157) | class MOTR(object):
method __init__ (line 158) | def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
method _remove_track (line 171) | def _remove_track(self, slot_id):
method clear_disappeared_track (line 175) | def clear_disappeared_track(self):
method update (line 178) | def update(self, dt_instances: Instances):
function load_label (line 240) | def load_label(label_path: str, img_size: tuple) -> dict:
function filter_pub_det (line 267) | def filter_pub_det(res_file, pub_det_file, filter_iou=False):
class ListImgDataset (line 332) | class ListImgDataset(Dataset):
method __init__ (line 333) | def __init__(self, img_list) -> None:
method load_img_from_file (line 345) | def load_img_from_file(self, f_path):
method init_img (line 353) | def init_img(self, img):
method __len__ (line 366) | def __len__(self):
method __getitem__ (line 369) | def __getitem__(self, index):
class Detector (line 374) | class Detector(object):
method __init__ (line 375) | def __init__(self, args, model=None, seq_num=2):
method filter_dt_by_score (line 398) | def filter_dt_by_score(dt_instances: Instances, prob_threshold: float)...
method filter_dt_by_area (line 403) | def filter_dt_by_area(dt_instances: Instances, area_threshold: float) ...
method write_results (line 410) | def write_results(txt_path, frame_id, bbox_xyxy, identities):
method eval_seq (line 421) | def eval_seq(self):
method visualize_img_with_bbox (line 429) | def visualize_img_with_bbox(img_path, img, dt_instances: Instances, re...
method detect (line 440) | def detect(self, prob_threshold=0.7, area_threshold=100, vis=False):
FILE: tools/launch.py
function parse_args (line 119) | def parse_args():
function main (line 162) | def main():
FILE: util/box_ops.py
function box_cxcywh_to_xyxy (line 19) | def box_cxcywh_to_xyxy(x):
function box_xyxy_to_cxcywh (line 26) | def box_xyxy_to_cxcywh(x):
function box_iou (line 34) | def box_iou(boxes1, boxes2):
function generalized_box_iou (line 50) | def generalized_box_iou(boxes1, boxes2):
function masks_to_boxes (line 74) | def masks_to_boxes(masks):
FILE: util/checkpoint.py
function detach_variable (line 7) | def detach_variable(inputs):
function check_backward_validity (line 20) | def check_backward_validity(inputs):
class CheckpointFunction (line 25) | class CheckpointFunction(torch.autograd.Function):
method forward (line 27) | def forward(ctx, run_function, length, *args):
method backward (line 36) | def backward(ctx, *output_grads):
FILE: util/evaluation.py
function read_results (line 22) | def read_results(filename, data_type: str, is_gt=False, is_ignore=False):
function read_mot_results (line 59) | def read_mot_results(filename, is_gt, is_ignore):
function unzip_objs (line 104) | def unzip_objs(objs):
class Evaluator (line 113) | class Evaluator(object):
method __init__ (line 114) | def __init__(self, data_root, seq_name, data_type='mot'):
method load_annotations (line 123) | def load_annotations(self):
method reset_accumulator (line 130) | def reset_accumulator(self):
method eval_frame (line 133) | def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
method eval_file (line 171) | def eval_file(self, filename):
method get_summary (line 184) | def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', '...
method save_summary (line 201) | def save_summary(summary, filename):
FILE: util/misc.py
function _check_size_scale_factor (line 35) | def _check_size_scale_factor(dim, size, scale_factor):
function _output_size (line 46) | def _output_size(dim, input, size, scale_factor):
class SmoothedValue (line 64) | class SmoothedValue(object):
method __init__ (line 69) | def __init__(self, window_size=20, fmt=None):
method update (line 77) | def update(self, value, n=1):
method synchronize_between_processes (line 82) | def synchronize_between_processes(self):
method median (line 96) | def median(self):
method avg (line 101) | def avg(self):
method global_avg (line 106) | def global_avg(self):
method max (line 110) | def max(self):
method value (line 114) | def value(self):
method __str__ (line 117) | def __str__(self):
function all_gather (line 126) | def all_gather(data):
function reduce_dict (line 169) | def reduce_dict(input_dict, average=True):
class MetricLogger (line 196) | class MetricLogger(object):
method __init__ (line 197) | def __init__(self, delimiter="\t"):
method update (line 201) | def update(self, **kwargs):
method __getattr__ (line 208) | def __getattr__(self, attr):
method __str__ (line 216) | def __str__(self):
method synchronize_between_processes (line 224) | def synchronize_between_processes(self):
method add_meter (line 228) | def add_meter(self, name, meter):
method log_every (line 231) | def log_every(self, iterable, print_freq, header=None):
function get_sha (line 286) | def get_sha():
function collate_fn (line 306) | def collate_fn(batch):
function mot_collate_fn (line 312) | def mot_collate_fn(batch: List[dict]) -> dict:
function _max_by_axis (line 322) | def _max_by_axis(the_list):
function nested_tensor_from_tensor_list (line 331) | def nested_tensor_from_tensor_list(tensor_list: List[Tensor], size_divis...
class NestedTensor (line 358) | class NestedTensor(object):
method __init__ (line 359) | def __init__(self, tensors, mask: Optional[Tensor]):
method to (line 363) | def to(self, device, non_blocking=False):
method record_stream (line 374) | def record_stream(self, *args, **kwargs):
method decompose (line 379) | def decompose(self):
method __repr__ (line 382) | def __repr__(self):
function setup_for_distributed (line 386) | def setup_for_distributed(is_master):
function is_dist_avail_and_initialized (line 401) | def is_dist_avail_and_initialized():
function get_world_size (line 409) | def get_world_size():
function get_rank (line 415) | def get_rank():
function get_local_size (line 421) | def get_local_size():
function get_local_rank (line 427) | def get_local_rank():
function is_main_process (line 433) | def is_main_process():
function save_on_master (line 437) | def save_on_master(*args, **kwargs):
function init_distributed_mode (line 442) | def init_distributed_mode(args):
function accuracy (line 484) | def accuracy(output, target, topk=(1,)):
function interpolate (line 502) | def interpolate(input, size=None, scale_factor=None, mode="nearest", ali...
function get_total_grad_norm (line 524) | def get_total_grad_norm(parameters, norm_type=2):
function inverse_sigmoid (line 532) | def inverse_sigmoid(x, eps=1e-5):
FILE: util/motdet_eval.py
function ap_per_class (line 16) | def ap_per_class(tp, conf, pred_cls, target_cls):
function compute_ap (line 69) | def compute_ap(recall, precision):
function bbox_iou (line 97) | def bbox_iou(box1, box2, x1y1x2y2=False):
function xyxy2xywh (line 126) | def xyxy2xywh(x):
function xywh2xyxy (line 136) | def xywh2xyxy(x):
function motdet_evaluate (line 147) | def motdet_evaluate(model, data_loader, iou_thres=0.5, print_interval=10):
function init_metrics (line 259) | def init_metrics():
function detmotdet_evaluate (line 282) | def detmotdet_evaluate(model, data_loader, device, iou_thres=0.5, print_...
FILE: util/plot_utils.py
function plot_logs (line 27) | def plot_logs(logs, fields=('class_error', 'loss_bbox_unscaled', 'mAP'),...
function plot_precision_recall (line 83) | def plot_precision_recall(files, naming_scheme='iter'):
function draw_boxes (line 117) | def draw_boxes(image: Tensor, boxes: Tensor, color=(0, 255, 0), texts=No...
function draw_ref_pts (line 141) | def draw_ref_pts(image: Tensor, ref_pts: Tensor) -> np.ndarray:
function image_hwc2chw (line 157) | def image_hwc2chw(image: np.ndarray):
FILE: util/tool.py
function load_model (line 15) | def load_model(model, model_path, optimizer=None, resume=False,
Condensed preview — 82 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (3,419K chars).
[
{
"path": ".gitignore",
"chars": 63,
"preview": "__pycache__/\n*.pth\n*.train\nexps/\nbuild/\n*.egg\n*.egg-info\n*.mp4\n"
},
{
"path": "LICENSE",
"chars": 13051,
"preview": "MIT License\n\nCopyright (c) 2021 megvii-model\n\nPermission is hereby granted, free of charge, to any person obtaining a co"
},
{
"path": "README.md",
"chars": 8824,
"preview": "# MOTR: End-to-End Multiple-Object Tracking with TRansformer\n\n\n</div>\n\n[![PWC](https://img.shields.io/endpoint.svg?url=h"
},
{
"path": "benchmark.py",
"chars": 2781,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "configs/r50_deformable_detr.sh",
"chars": 564,
"preview": "#!/usr/bin/env bash\n# ------------------------------------------------------------------------\n# Copyright (c) 2021 megv"
},
{
"path": "configs/r50_deformable_detr_plus_iterative_bbox_refinement.sh",
"chars": 619,
"preview": "#!/usr/bin/env bash\n# ------------------------------------------------------------------------\n# Copyright (c) 2021 megv"
},
{
"path": "configs/r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh",
"chars": 657,
"preview": "#!/usr/bin/env bash\n# ------------------------------------------------------------------------\n# Copyright (c) 2021 megv"
},
{
"path": "configs/r50_deformable_detr_single_scale.sh",
"chars": 606,
"preview": "#!/usr/bin/env bash\n# ------------------------------------------------------------------------\n# Copyright (c) 2021 megv"
},
{
"path": "configs/r50_deformable_detr_single_scale_dc5.sh",
"chars": 627,
"preview": "#!/usr/bin/env bash\n# ------------------------------------------------------------------------\n# Copyright (c) 2021 megv"
},
{
"path": "configs/r50_motr_demo.sh",
"chars": 1102,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "configs/r50_motr_eval.sh",
"chars": 1255,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "configs/r50_motr_submit.sh",
"chars": 1221,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "configs/r50_motr_submit_dance.sh",
"chars": 1209,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "configs/r50_motr_train.sh",
"chars": 1321,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "configs/r50_motr_train_dance.sh",
"chars": 1339,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "datasets/__init__.py",
"chars": 2034,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "datasets/coco.py",
"chars": 6170,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "datasets/coco_eval.py",
"chars": 9359,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "datasets/coco_panoptic.py",
"chars": 4285,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "datasets/dance.py",
"chars": 10178,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "datasets/data_path/bdd100k.val",
"chars": 2558270,
"preview": "images/track/val/b251064f-8d92db81/b251064f-8d92db81-0000001.jpg\nimages/track/val/b251064f-8d92db81/b251064f-8d92db81-00"
},
{
"path": "datasets/data_path/crowdhuman.val",
"chars": 218219,
"preview": "crowdhuman/images/val/273271,c9db000d5146c15.jpg\ncrowdhuman/images/val/273271,1c72c000a2ee47d5.jpg\ncrowdhuman/images/val"
},
{
"path": "datasets/data_path/gen_bdd100k_mot.py",
"chars": 5943,
"preview": "import os \nimport numpy as np \nimport json\nimport cv2\nfrom tqdm import tqdm\nfrom collections import defaultdict\n\n\ndef co"
},
{
"path": "datasets/data_path/gen_labels_15.py",
"chars": 1761,
"preview": "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.exist"
},
{
"path": "datasets/data_path/gen_labels_16.py",
"chars": 1407,
"preview": "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_"
},
{
"path": "datasets/data_path/prepare.py",
"chars": 3527,
"preview": "import os\nfrom functools import partial\nfrom typing import List\n\n\ndef solve_MOT_train(root, year):\n assert year in [1"
},
{
"path": "datasets/data_prefetcher.py",
"chars": 4706,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "datasets/detmot.py",
"chars": 9684,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "datasets/joint.py",
"chars": 11756,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "datasets/panoptic_eval.py",
"chars": 2055,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "datasets/samplers.py",
"chars": 5815,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "datasets/static_detmot.py",
"chars": 10478,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "datasets/torchvision_datasets/__init__.py",
"chars": 665,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "datasets/torchvision_datasets/coco.py",
"chars": 3518,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "datasets/transforms.py",
"chars": 20065,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "demo.py",
"chars": 12045,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "engine.py",
"chars": 10757,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "eval.py",
"chars": 17813,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "main.py",
"chars": 19484,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "models/__init__.py",
"chars": 895,
"preview": "# ------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseT"
},
{
"path": "models/backbone.py",
"chars": 5365,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "models/deformable_detr.py",
"chars": 24855,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "models/deformable_transformer.py",
"chars": 20444,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "models/deformable_transformer_plus.py",
"chars": 21274,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "models/matcher.py",
"chars": 6069,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "models/memory_bank.py",
"chars": 5876,
"preview": "# ------------------------------------------------------------------------\r\n# Copyright (c) 2021 megvii-model. All Right"
},
{
"path": "models/motr.py",
"chars": 35072,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "models/ops/functions/__init__.py",
"chars": 688,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "models/ops/functions/ms_deform_attn_func.py",
"chars": 3388,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "models/ops/make.sh",
"chars": 573,
"preview": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# C"
},
{
"path": "models/ops/modules/__init__.py",
"chars": 674,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "models/ops/modules/ms_deform_attn.py",
"chars": 6479,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "models/ops/setup.py",
"chars": 2559,
"preview": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# C"
},
{
"path": "models/ops/src/cpu/ms_deform_attn_cpu.cpp",
"chars": 1256,
"preview": "/*!\n**************************************************************************************************\n* Deformable DETR"
},
{
"path": "models/ops/src/cpu/ms_deform_attn_cpu.h",
"chars": 1139,
"preview": "/*!\n**************************************************************************************************\n* Deformable DETR"
},
{
"path": "models/ops/src/cuda/ms_deform_attn_cuda.cu",
"chars": 7316,
"preview": "/*!\n**************************************************************************************************\n* Deformable DETR"
},
{
"path": "models/ops/src/cuda/ms_deform_attn_cuda.h",
"chars": 1140,
"preview": "/*!\n**************************************************************************************************\n* Deformable DETR"
},
{
"path": "models/ops/src/cuda/ms_deform_im2col_cuda.cuh",
"chars": 54694,
"preview": "/*!\n**************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 Se"
},
{
"path": "models/ops/src/ms_deform_attn.h",
"chars": 1838,
"preview": "/*!\n**************************************************************************************************\n* Deformable DETR"
},
{
"path": "models/ops/src/vision.cpp",
"chars": 799,
"preview": "/*!\n**************************************************************************************************\n* Deformable DETR"
},
{
"path": "models/ops/test.py",
"chars": 4087,
"preview": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# C"
},
{
"path": "models/position_encoding.py",
"chars": 3914,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "models/qim.py",
"chars": 8456,
"preview": "# ------------------------------------------------------------------------\r\n# Copyright (c) 2021 megvii-model. All Right"
},
{
"path": "models/relu_dropout.py",
"chars": 544,
"preview": "# https://gist.github.com/vadimkantorov/360ece06de4fd2641fa9ed1085f76d48\nimport torch\n\nclass ReLUDropout(torch.nn.Dropou"
},
{
"path": "models/segmentation.py",
"chars": 16142,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "models/structures/__init__.py",
"chars": 478,
"preview": "# ------------------------------------------------------------------------\n# Modified from Detectron2 (https://github.co"
},
{
"path": "models/structures/boxes.py",
"chars": 13994,
"preview": "# ------------------------------------------------------------------------\n# Modified from Detectron2 (https://github.co"
},
{
"path": "models/structures/instances.py",
"chars": 6932,
"preview": "# ------------------------------------------------------------------------\n# Modified from Detectron2 (https://github.co"
},
{
"path": "requirements.txt",
"chars": 67,
"preview": "pycocotools\ntqdm\ncython\nscipy\nmotmetrics\nopencv-python\nseaborn\nlap\n"
},
{
"path": "submit.py",
"chars": 22262,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "submit_dance.py",
"chars": 20879,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "tools/launch.py",
"chars": 9292,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "tools/run_dist_launch.sh",
"chars": 1149,
"preview": "#!/usr/bin/env bash\n# ------------------------------------------------------------------------\n# Copyright (c) 2021 megv"
},
{
"path": "tools/run_dist_slurm.sh",
"chars": 1143,
"preview": "#!/usr/bin/env bash\n# ------------------------------------------------------------------------\n# Copyright (c) 2021 megv"
},
{
"path": "util/__init__.py",
"chars": 632,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "util/box_ops.py",
"chars": 3123,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "util/checkpoint.py",
"chars": 2051,
"preview": "# from: https://github.com/csrhddlam/pytorch-checkpoint\n\nimport torch\nimport warnings\n\n\ndef detach_variable(inputs):\n "
},
{
"path": "util/evaluation.py",
"chars": 7410,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "util/misc.py",
"chars": 18319,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "util/motdet_eval.py",
"chars": 16411,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "util/plot_utils.py",
"chars": 6297,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
},
{
"path": "util/tool.py",
"chars": 3245,
"preview": "# ------------------------------------------------------------------------\n# Copyright (c) 2021 megvii-model. All Rights"
}
]
About this extraction
This page contains the full source code of the megvii-model/MOTR GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 82 files (3.2 MB), approximately 842.4k tokens, and a symbol index with 643 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.