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https://github.com/kennethwdk/CID
Code for "Contextual Instance Decoupling for Robust Multi-Person Pose Estimation", CVPR 2022 Oral
https://github.com/kennethwdk/CID
Last synced: 2 months ago
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Code for "Contextual Instance Decoupling for Robust Multi-Person Pose Estimation", CVPR 2022 Oral
- Host: GitHub
- URL: https://github.com/kennethwdk/CID
- Owner: kennethwdk
- License: mit
- Created: 2022-03-25T02:44:53.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-06-19T05:38:16.000Z (over 2 years ago)
- Last Synced: 2024-08-04T22:13:19.147Z (6 months ago)
- Language: Python
- Homepage:
- Size: 829 KB
- Stars: 50
- Watchers: 1
- Forks: 11
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-human-pose-estimation - [code - Person_Pose_Estimation_CVPR_2022_paper.pdf) - CVPR 22, CID (Multi-Person 2D Pose Estimation / 2022)
README
# Contextual Instance Decoupling for Robust Multi-Person Pose Estimation
[[`Paper`](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Contextual_Instance_Decoupling_for_Robust_Multi-Person_Pose_Estimation_CVPR_2022_paper.pdf)]
> [Contextual Instance Decoupling for Robust Multi-Person Pose Estimation](https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Contextual_Instance_Decoupling_for_Robust_Multi-Person_Pose_Estimation_CVPR_2022_paper.html)
> Dongkai Wang, Shiliang Zhang
> CVPR 2022 Oral
## Installation
### 1. Clone code
```shell
git clone https://github.com/kennethwdk/CID
cd ./CID
```
### 2. Create a conda environment for this repo
```shell
conda create -n CID python=3.9
conda activate CID
```
### 3. Install PyTorch >= 1.6.0 following official instruction, *e.g.*,
```shell
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
```
There is no requirement for cudatoolkit version for CID, so just use the newest version.
### 4. Install other dependency python packages
```shell
pip install -r requirements.txt
```
### 5. Prepare dataset
Download [COCO ](https://cocodataset.org/#home), [CrowdPose](https://github.com/Jeff-sjtu/CrowdPose) and [OCHuman](https://github.com/liruilong940607/OCHumanApi) from website and put the zip file under the directory following below structure, (xxx.json) denotes their original name.
```
./data
|── coco
│ └── annotations
| | └──coco_train.json(person_keypoints_train2017.json)
| | └──coco_val.json(person_keypoints_val2017.json)
| | └──coco_test.json(image_info_test-dev2017.json)
| └── images
| | └──train2017
| | | └──000000000009.jpg
| | └──val2017
| | | └──000000000139.jpg
| | └──test2017
| | | └──000000000001.jpg
├── crowdpose
│ └── annotations
| | └──crowdpose_trainval.json(refer to DEKR, link:https://github.com/HRNet/DEKR)
| | └──crowdpose_test.json
| └── images
| | └──100000.jpg
├── ochuman
│ └── annotations
| | └──ochuman_val.json(ochuman_coco_format_val_range_0.00_1.00.json)
| | └──ochuman_test.json(ochuman_coco_format_test_range_0.00_1.00.json)
| └── images
| | └──000001.jpg
```
## Usage
### 1. Download trained model
* [COCO](https://1drv.ms/u/s!AhpKYLhXKpH7gv8RepyMU_iU5uhxhg?e=ygs4Me)
* [CrowdPose](https://1drv.ms/u/s!AhpKYLhXKpH7gv8RepyMU_iU5uhxhg?e=ygs4Me)
* [OCHuman](https://1drv.ms/u/s!AhpKYLhXKpH7gv8RepyMU_iU5uhxhg?e=ygs4Me)
* [ImageNet pre-trained](https://1drv.ms/u/s!AhpKYLhXKpH7gv8RepyMU_iU5uhxhg?e=ygs4Me)
### 2. Evaluate Model
Change the checkpoint path by modifying `TEST.MODEL_FILE` option in *.yaml* or command line.
`--gpus` option specifies the gpu ids for evaluation, multiple ids denotes multiple gpu evaluation.
```python
# evaluate on coco val set with 2 gpus
python tools/valid.py --cfg experiments/coco.yaml --gpus 0,1 TEST.MODEL_FILE model/coco/checkpoint.pth.tar
# evaluate on coco test-dev set with 2 gpus (submit to codalab)
python tools/infer_coco_testdev.py --cfg experiments/coco.yaml --gpus 0,1 TEST.MODEL_FILE model/coco/checkpoint.pth.tar
# evaluate on crowdpose test set with 2 gpus
python tools/valid.py --cfg experiments/crowdpose.yaml --gpus 0,1 TEST.MODEL_FILE model/crowdpose/checkpoint.pth.tar
# evaluate on ochuman test set with 2 gpus (trained on ochuman val set)
python tools/valid.py --cfg experiments/ochuman_val.yaml --gpus 0,1 TEST.MODEL_FILE model/ochuman/checkpoint.pth.tar
# evaluate on ochuman test set with 2 gpus (trained on coco train set)
python tools/valid.py --cfg experiments/ochuman_coco.yaml --gpus 0,1 TEST.MODEL_FILE model/coco/checkpoint.pth.tar
# evaluate on ochuman val set with 2 gpus (trained on coco train set)
python tools/valid.py --cfg experiments/ochuman_coco.yaml --gpus 0,1 TEST.MODEL_FILE model/coco/checkpoint.pth.tar DATASET.TEST val
```
### 3. Train Model
You need to download HRNet-W32 imagenet pretrained model (see above) and change the checkpoint path by modifying `MODEL.PRETRAINED` in .yaml, and run following commands:
```python
# train on coco with 2 gpus
python tools/train.py --cfg experiments/coco.yaml --gpus 0,1
# train on crowdpose with 2 gpus
python tools/train.py --cfg experiments/crowdpose.yaml --gpus 0,1
# train on ochuman with 2 gpus
python tools/train.py --cfg experiments/ochuman_val.yaml --gpus 0,1
```
The experimental results are obtained by training on two NVIDIA RTX 3090. You can use more gpu cards for model training by specifying gpu ids in `--gpus` optition, *e.g.*, training model on crowdpose on 8 gpu cards by
```python
# train on coco with 8 gpus
python tools/train.py --cfg experiments/coco.yaml --gpus 0,1,2,3,4,5,6,7
```
Note that you should modify corresponding batch size for each gpu by `TRAIN.IMAGES_PER_GPU`.
## Main Results
With the code contained in this repo, you should be able to reproduce the following results.
### Results on COCO val and test-dev set
| Method| Test set | Backbone | Input size | AP | AP.5 | AP .75 | AP (M) | AP (L) |
|----------|----------|----------|------------|-------|-------|--------|--------|--------|
| CID| COCO val | HRNet-W32 | 512 | 69.8 | 88.5 | 76.6 | 64.0 | 78.9 |
| CID| COCO test-dev | HRNet-W32 | 512 | 69.1 | 89.9 | 76.3 | 63.4 | 77.6 |
### Results on CrowdPose test set
| Method| Backbone | Input size | AP | AP .5 | AP .75 | AP (E) | AP (M) | AP (H) |
|----------|----------|------------|-------|-------|--------|--------|--------| --------|
| CID| HRNet-W32 | 512 | 71.2 | 89.8 | 76.7 | 77.9 | 71.9 | 63.8 |
### Results on OCHuman dataset
| Method| Train set | Test set | Backbone | Input size | AP | AP.5 | AP .75 | AR |
|----------|----------|----------|------------|-------|-------|--------|--------|--------|
| CID| OCHuman val | OCHuman test | HRNet-W32 | 512 | 57.7 | 75.5 | 63.3 | 75.7 |
| CID| COCO train | OCHuman val | HRNet-W32 | 512 | 45.7 | 58.8 | 51.1 | 78.3 |
| CID| COCO train | OCHuman test | HRNet-W32 | 512 | 44.6 | 57.5 | 49.3 | 78.0 |
## Citations
If you find this code useful for your research, please cite our paper:
```
@InProceedings{Wang_2022_CVPR,
author = {Wang, Dongkai and Zhang, Shiliang},
title = {Contextual Instance Decoupling for Robust Multi-Person Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {11060-11068}
}
```
## Contact me
If you have any questions about this code or paper, feel free to contact me at
[email protected].
## Acknowledgement
The code is mainly encouraged by [HigherHRNet](https://github.com/HRNet/HigherHRNet-Human-Pose-Estimation) and [DEKR](https://github.com/HRNet/DEKR).