/Improved-Body-Parts

Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation

Primary LanguagePython

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 "Multi-Person Pose Estimation using Body Parts" (under review). The Part A is available at this link.

Introduction

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

heatmap

network

Contents

  1. Training
  2. Evaluation
  3. Demo

Project Features

  • Implement the models using Pytorch in auto mixed-precision (using Nvidia Apex).
  • Supprot 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 warpped 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

examples

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

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 form the different mechanisms of data parallel training and distrubited training.

Referred Repositories (mainly)

Citation

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

@inproceedings{li2019simple,
	title={Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation},
	author={Jia Li and Wen Su and Zengfu Wang},
	booktitle = {arXiv preprint arXiv:1911.10529},
	year={2019}
}