hrnet-tf

Overview

This is a tensorflow implementation of high-resolution representations for ImageNet classification. The network structure and training hyperparamters are kept the same as the offical pytorch implementation.

Features of this repo

• Low-level implementation of tensorflow
• Multiple GPU training via Horovod
• Support configurable network for HRNet
• Reproduce the close accuracy compared with its offical pytorch implementation.

HRnet structure details

First, the four-resolution feature maps are fed into a bottleneck and the number of output channels are increased to 128, 256, 512, and 1024, respectively. Then, we downsample the high-resolution representations by a 2-strided 3x3 convolution outputting 256 channels and add them to the representations of the second-high-resolution representations. This process is repeated two times to get 1024 channels over the small resolution. Last, we transform 1024 channels to 2048 channels through a 1x1 convolution, followed by a global average pooling operation. The output 2048-dimensional representation is fed into the classifier.

Accuracy of pretrained models

model	#Params	GFLOPs	top-1 error	top-5 error	Link
HRNet-W18-C	21.3M	3.99	24.2%	7.3%	TF-HRNET-W18
HRNet-W30-C	37.7M	7.55	21.9%	6.0%	TF-HRNet-W30

Installation

This repo is built on tensorflow 1.12 and Python 3.6

1. Install dependency

pip install -r requirements.txt

2. [Optional] Follow horovod installation instructions to install horovod to support multiple gpu training.

Data preparision

Please follow instructions to converted imagenet dataset from images to tfrecords. This can accelerate the training speed significantly. After convertion, you will have tfrecords files under data/tfrecords as below

# training files
train-00000-of-01024
train-00001-of-01024
...

# validation files
validation-00000-of-00128
validation-00001-of-00128
...

How to train and eval network

1. Train network with one GPU for HRNet-W30

python top/train.py --net_cfg cfgs/w30_s4.cfg --data_path /path/to/tfrecords  

2. If you want to resume training from saved checkpoint, set resume_training to enable resume training.

python top/train.py --net_cfg cfgs/w30_s4.cfg --data_path /path/to/tfrecords --resume_training

3. Evaluate network. Make sure the checkpoint saved in models.

python top/train.py --net_cfg cfgs/w30_s4.cfg --data_path /path/to/tfrecords --eval_only

4. Training with multiple GPUs. Specify the number of gpus via nb_gpus and extra_args in ./scripts/run_horovod.sh. For example, if you want to train HRNet-w30 by using 4 GPUs, the scripts would be like below

nb_gpus=4

extra_args='--net_cfg cfgs/w30_s4.cfg'

echo "multi-GPU training enabled"
mpirun -np ${nb_gpus} -bind-to none -map-by slot -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH -x PATH \
    -mca pml ob1 -mca btl ^openib \
    python top/train.py --enbl_multi_gpu  

Related Efforts

1. Lot of code to build the dataset and training pipeline refer to pocketflow

Citation

If you find this work or code is helpful in your research, please cite:

@inproceedings{SunXLW19,
  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
  booktitle={CVPR},
  year={2019}
}

@article{SunZJCXLMWLW19,
  title={High-Resolution Representations for Labeling Pixels and Regions},
  author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao 
  and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang},
  journal   = {CoRR},
  volume    = {abs/1904.04514},
  year={2019}
}