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SimplePose

Code and pre-trained models for our paper, “Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation”, accepted by AAAI-2020.

Also this repo serves as the Part B of our paper. The Part A is available at this link.

• Update

  An improved project is released at this link.

  A Chinese note at Zhihu

• More powerful backbone with higher-res outputs

Introduction

A bottom-up approach for the problem of multi-person pose estimation.

Contents

1. Training
2. Evaluation
3. Demo

Project Features

• Implement the models using Pytorch in auto mixed-precision (using Nvidia Apex).
• Support training on multiple GPUs (over 90% GPU usage rate on each GPU card).
• Fast data preparing and augmentation during training (generating about 40 samples per second on signle CPU process and much more if wrapped by DataLoader Class).
• Focal L2 loss.
• Multi-scale supervision.
• This project can also serve as a detailed practice to the green hand in Pytorch.

Prepare

1. Install packages:

   Python=3.6, Pytorch>1.0, Nvidia Apex and other packages needed.

2. Download the COCO dataset.

3. Download the pre-trained models (default configuration: download the pretrained model snapshotted at epoch 52 provided as follow).

   Download Link: BaiduCloud

   Alternatively, download the pre-trained model without optimizer checkpoint only for the default configuration via GoogleDrive

4. Change the paths in the code according to your environment.

Run a Demo

python demo_image.py

Inference Speed

The speed of our system is tested on the MS-COCO test-dev dataset.

• Inference speed of our 4-stage IMHN with 512 × 512 input on one 2080TI GPU: 38.5 FPS (100% GPU-Util).
• Processing speed of the keypoint assignment algorithm part that is implemented in pure Python and a single process on Intel Xeon E5-2620 CPU: 5.2 FPS (has not been well accelerated).

Evaluation Steps

The corresponding code is in pure python without multiprocess for now.

python evaluate.py

Results on MSCOCO 2017 test-dev subset (focal L2 loss with gamma=2):

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.685
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.867
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.749
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.664
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.719
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.728
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.892
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.782
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.688
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.784

Training Steps

Before training, prepare the training data using ''SimplePose/data/coco_masks_hdf5.py''.

Multiple GUPs are recommended to use to speed up the training process, but we support different training options.

• Most code has been provided already, you can train the model with.

  1. 'train.py': single training process on one GPU only.
  2. 'train_parallel.py': signle training process on multiple GPUs using Dataparallel.
  3. 'train_distributed.py' (recommended): multiple training processes on multiple GPUs using Distributed Training:

python -m torch.distributed.launch --nproc_per_node=4 train_distributed.py

Note: The loss_model_parrel.py is for train.py and train_parallel.py, while the loss_model.py is for train_distributed.py and train_distributed_SWA.py. They are different in dividing the batch size. Please refer to the code about the different choices.

For distributed training, the real batch_size = batch_size_in_config* × GPU_Num (world_size actually). For others, the real batch_size = batch_size_in_config*. The differences come from the different mechanisms of data parallel training and distributed training.

Referred Repositories (mainly)

• Realtime Multi-Person Pose Estimation version 1
• Realtime Multi-Person Pose Estimation version 2
• Realtime Multi-Person Pose Estimation version 3
• Associative Embedding
• NVIDIA/apex

Recommend Repositories

Faster Version: Chun-Ming Su has rebuilt and improved the post-processing speed of this repo using C++, and the improved system can run up to 7~8 FPS using a single scale with flipping on a 2080 TI GPU. Many thanks to Chun-Ming Su.

Citation

Please kindly cite this paper in your publications if it helps your research.

@inproceedings{li2020simple,
  title={Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation.},
  author={Li, Jia and Su, Wen and Wang, Zengfu},
  booktitle={AAAI},
  pages={11354--11361},
  year={2020}
}