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PyTorch Implementation for "TransPose: Keypoint localization via Transformer", ICCV 2021.

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Introduction

TransPose is a human pose estimation model based on a CNN feature extractor, a Transformer Encoder, and a prediction head. Given an image, the attention layers built in Transformer can efficiently capture long-range spatial relationships between keypoints and explain what dependencies the predicted keypoints locations highly rely on.

Architecture

[arxiv 2012.14214] [paper] [demo-notebook]

TransPose: Keypoint Localization via Transformer, Sen Yang, Zhibin Quan, Mu Nie, Wankou Yang, ICCV 2021

Model Zoo

We choose two types of CNNs as the backbone candidates: ResNet and HRNet. The derived convolutional blocks are ResNet-Small, HRNet-Small-W32, and HRNet-Small-W48.

Model Backbone #Attention layers d h #Heads #Params AP (coco val gt bbox) Download
TransPose-R-A3 ResNet-S 3 256 1024 8 5.2Mb 73.8 model
TransPose-R-A4 ResNet-S 4 256 1024 8 6.0Mb 75.1 model
TransPose-H-S HRNet-S-W32 4 64 128 1 8.0Mb 76.1 model
TransPose-H-A4 HRNet-S-W48 4 96 192 1 17.3Mb 77.5 model
TransPose-H-A6 HRNet-S-W48 6 96 192 1 17.5Mb 78.1 model

Quick use

You can directly load TransPose-R-A4 or TransPose-H-A4 models with pretrained weights on COCO train2017 dataset from Torch Hub, simply by:

import torch

tpr = torch.hub.load('yangsenius/TransPose:main', 'tpr_a4_256x192', pretrained=True)
tph = torch.hub.load('yangsenius/TransPose:main', 'tph_a4_256x192', pretrained=True)

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Model Input size FPS* GFLOPs AP Ap .5 AP .75 AP (M) AP (L) AR AR .5 AR .75 AR (M) AR (L)
TransPose-R-A3 256x192 141 8.0 0.717 0.889 0.788 0.680 0.786 0.771 0.930 0.836 0.727 0.835
TransPose-R-A4 256x192 138 8.9 0.726 0.891 0.799 0.688 0.798 0.780 0.931 0.845 0.735 0.844
TransPose-H-S 256x192 45 10.2 0.742 0.896 0.808 0.706 0.810 0.795 0.935 0.855 0.752 0.856
TransPose-H-A4 256x192 41 17.5 0.753 0.900 0.818 0.717 0.821 0.803 0.939 0.861 0.761 0.865
TransPose-H-A6 256x192 38 21.8 0.758 0.901 0.821 0.719 0.828 0.808 0.939 0.864 0.764 0.872

Note:

  • we computed the average FPS* of testing 100 samples from coco val dataset (with batchsize=1) on a single NVIDIA 2080Ti GPU. The FPS may fluctuate up and down at different tests.
  • We trained our different models on different hardware platforms: 1 x RTX2080Ti GPUs (TP-R-A4), 4 x TiTan XP GPUs (TP-H-S, TP-H-A4), and 4 x Tesla P40 GPUs (TP-H-A6).

Results on COCO test-dev2017 with detector having human AP of 60.9 on COCO test-dev2017 dataset

Model Input size #Params GFLOPs AP Ap .5 AP .75 AP (M) AP (L) AR AR .5 AR .75 AR (M) AR (L)
TransPose-H-S 256x192 8.0M 10.2 0.734 0.916 0.811 0.701 0.793 0.786 0.950 0.856 0.745 0.843
TransPose-H-A4 256x192 17.3M 17.5 0.747 0.919 0.822 0.714 0.807 0.799 0.953 0.866 0.758 0.854
TransPose-H-A6 256x192 17.5M 21.8 0.750 0.922 0.823 0.713 0.811 0.801 0.954 0.867 0.759 0.859

Visualization

Jupyter Notebook Demo

Given an input image, a pretrained TransPose model, and the predicted locations, we can visualize the spatial dependencies of the predicted locations with threshold for the attention scores.

TransPose-R-A4 with threshold=0.00 example

TransPose-R-A4 with threshold=0.01

TransPose-H-A4 with threshold=0.00 example

TransPose-H-A4 with threshold=0.00075 example

Getting started

Installation

  1. Clone this repository, and we'll call the directory that you cloned as ${POSE_ROOT}

    git clone https://github.com/yangsenius/TransPose.git
  2. Install PyTorch>=1.6 and torchvision>=0.7 from the PyTorch official website

  3. Install package dependencies. Make sure the python environment >=3.7

    pip install -r requirements.txt
  4. Make output (training models and files) and log (tensorboard log) directories under ${POSE_ROOT} & Make libs

    mkdir output log
    cd ${POSE_ROOT}/lib
    make
  5. Download pretrained models from the releases of this repo to the specified directory

    ${POSE_ROOT}
     `-- models
         `-- pytorch
             |-- imagenet
             |   |-- hrnet_w32-36af842e.pth
             |   |-- hrnet_w48-8ef0771d.pth
             |   |-- resnet50-19c8e357.pth
             |-- transpose_coco
             |   |-- tp_r_256x192_enc3_d256_h1024_mh8.pth
             |   |-- tp_r_256x192_enc4_d256_h1024_mh8.pth
             |   |-- tp_h_32_256x192_enc4_d64_h128_mh1.pth
             |   |-- tp_h_48_256x192_enc4_d96_h192_mh1.pth
             |   |-- tp_h_48_256x192_enc6_d96_h192_mh1.pth    

Data Preparation

We follow the steps of HRNet to prepare the COCO train/val/test dataset and the annotations. The detected person results are downloaded from OneDrive or GoogleDrive. Please download or link them to ${POSE_ROOT}/data/coco/, and make them look like this:

${POSE_ROOT}/data/coco/
|-- annotations
|   |-- person_keypoints_train2017.json
|   `-- person_keypoints_val2017.json
|-- person_detection_results
|   |-- COCO_val2017_detections_AP_H_56_person.json
|   `-- COCO_test-dev2017_detections_AP_H_609_person.json
`-- images
	|-- train2017
	|   |-- 000000000009.jpg
	|   |-- ... 
	`-- val2017
		|-- 000000000139.jpg
		|-- ... 

Traing & Testing

Testing on COCO val2017 dataset

python tools/test.py --cfg experiments/coco/transpose_r/TP_R_256x192_d256_h1024_enc4_mh8.yaml TEST.USE_GT_BBOX True

Training on COCO train2017 dataset

python tools/train.py --cfg experiments/coco/transpose_r/TP_R_256x192_d256_h1024_enc4_mh8.yaml

Acknowledgements

Great thanks for these papers and their open-source codes:HRNet, DETR, DarkPose

License

This repository is released under the MIT LICENSE.

Citation

If you find this repository useful please give it a star 🌟 or consider citing our work:

@inproceedings{yang2021transpose,
  title={TransPose: Keypoint Localization via Transformer},
  author={Yang, Sen and Quan, Zhibin and Nie, Mu and Yang, Wankou},
  booktitle={IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021}
}

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