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Applying MogaNet to Pose Estimation

This repo is a PyTorch implementation of applying MogaNet to 2D human pose estimation on COCO. The code is based on MMPose. For more details, see Efficient Multi-order Gated Aggregation Network (ICLR 2024).

Note

Please note that we simply follow the hyper-parameters of PVT and Swin which may not be the optimal ones for MogaNet. Feel free to tune the hyper-parameters to get better performance.

Environement Setup

Install MMPose from souce code, or follow the following steps. This experiment uses MMPose>=0.29.0, and we reproduced the results with MMPose v0.29.0 and Pytorch==1.10.

pip install openmim
mim install mmcv-full
pip install mmpose

Note: Since we write MogaNet backbone code of detection, segmentation, and pose estimation in the same file, it also works for MMDetection and MMSegmentation through @BACKBONES.register_module(). Please continue to install MMDetection or MMSegmentation for further usage.

Data preparation

Download COCO2017 and prepare COCO experiments according to the guidelines in MMPose.

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Results and models on COCO

Notes: All the models use ImageNet-1K pre-trained backbones and can also be downloaded by Baidu Cloud (z8mf) at MogaNet/COCO_Pose. The params (M) and FLOPs (G) are measured by get_flops with 256 $\times$ 192 or 384 $\times$ 288 resolutions.

python get_flops.py /path/to/config --shape 256 192

MogaNet + Top-Down

We provide results of MogaNet and popular architectures (Swin, ConvNeXt, and Uniformer) in comparison.

Backbone Input Size Params FLOPs AP AP50 AP75 AR ARM ARL Config Download
MogaNet-XT 256x192 5.6M 1.8G 72.1 89.7 80.1 77.7 73.6 83.6 config log | model
MogaNet-XT 384x288 5.6M 4.2G 74.7 90.1 81.3 79.9 75.9 85.9 config log | model
MogaNet-T 256x192 8.1M 2.2G 73.2 90.1 81.0 78.8 74.9 84.4 config log | model
MogaNet-T 384x288 8.1M 4.9G 75.7 90.6 82.6 80.9 76.8 86.7 config log | model
MogaNet-S 256x192 29.0M 6.0G 74.9 90.7 82.8 80.1 75.7 86.3 config log | model
MogaNet-S 384x288 29.0M 13.5G 76.4 91.0 83.3 81.4 77.1 87.7 config log | model
MogaNet-B 256x192 47.4M 10.9G 75.3 90.9 83.3 80.7 76.4 87.1 config log | model
MogaNet-B 384x288 47.4M 24.4G 77.3 91.4 84.0 82.2 77.9 88.5 config log | model

MetaFormers + Top-Down

Backbone Input Size Params FLOPs AP AP50 AP75 AR ARM ARL Config Download
Swin-T 256x192 32.8M 6.1G 72.4 90.1 80.6 78.2 74.0 84.3 config model | log
Swin-B 256x192 93.0M 18.6G 73.7 90.4 82.0 79.8 74.9 85.7 config model | log
Swin-B 384x288 93.0M 40.1G 75.9 91.0 83.2 78.8 76.5 87.5 config model | log
Swin-L 256x192 203.4M 40.3G 74.3 90.6 82.1 79.8 75.5 86.2 config model | log
Swin-L 384x288 203.4M 86.9G 76.3 91.2 83.0 81.4 77.0 87.9 config model | log
ConvNeXt-T 256x192 33.0M 5.5G 73.2 90.0 80.9 78.8 74.5 85.1 config log | model
ConvNeXt-T 384x288 33.0M 12.5G 75.3 90.4 82.1 80.5 76.1 86.8 config log | model
ConvNeXt-S 256x192 54.7M 9.7G 73.7 90.3 81.9 79.3 75.0 85.5 config log | model
ConvNeXt-S 384x288 54.7M 21.8G 75.8 90.7 83.1 81.0 76.8 87.1 config log | model
ConvNeXt-B 256x192 93.9M 16.3G 74.0 90.7 82.1 79.5 75.2 85.7 config log | model
ConvNeXt-B 384x288 93.9M 36.6G 75.9 90.6 83.1 81.1 76.5 87.7 config log | model
UniFormer-S 256x192 25.2M 4.7G 74.0 90.3 82.2 79.5 66.8 76.7 config log | model
UniFormer-S 384x288 25.2M 11.1G 75.9 90.6 83.4 81.4 68.6 79.0 config log | model
UniFormer-B 256x192 53.5M 9.2G 75.0 90.6 83.0 80.4 67.8 77.7 config log | model
UniFormer-B 384x288 53.5M 14.8G 76.7 90.8 84.0 81.4 69.3 79.7 config log | model

Demo

We provide some demos according to MMPose. Please use inference_demo or run the python tools with following script:

cd demo
python top_down_img_demo.py path/to/config path/to/checkpoint --img-root coco2017_val --json-file ../data/coco/annotations/person_keypoints_val2017.json --show

Training

We train the model on a single node with 8 GPUs by default (a batch size of 32 $\times$ 8 for Top-Down). Start training with the config as:

PORT=29001 bash dist_train.sh /path/to/config 8

Evaluation

To evaluate the trained model on a single node with 8 GPUs, run:

bash dist_test.sh /path/to/config /path/to/checkpoint 8 --out results.pkl --eval mAP

Citation

If you find this repository helpful, please consider citing:

@inproceedings{iclr2024MogaNet,
  title={Efficient Multi-order Gated Aggregation Network},
  author={Siyuan Li and Zedong Wang and Zicheng Liu and Cheng Tan and Haitao Lin and Di Wu and Zhiyuan Chen and Jiangbin Zheng and Stan Z. Li},
  booktitle={International Conference on Learning Representations},
  year={2024}
}

Acknowledgment

Our segmentation implementation is mainly based on the following codebases. We gratefully thank the authors for their wonderful works.

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