Note: This code is only used for academic purposes, people cannot use this code for anything that might be considered commercial use.

OpenGait is a flexible and extensible gait recognition project provided by the Shiqi Yu Group and supported in part by WATRIX.AI. Just the pre-beta version is released now, and more documentations as well as the reproduced methods will be offered as soon as possible.

Highlighted features:

• Multiple Models Support: We reproduced several SOTA methods, and reached the same or even better performance.
• DDP Support: The officially recommended Distributed Data Parallel (DDP) mode is used during the training and testing phases.
• AMP Support: The Auto Mixed Precision (AMP) option is available.
• Nice log: We use tensorboard and logging to log everything, which looks pretty.

The results in the parentheses are mentioned in the papers

Note:

• All the models were tested on CASIA-B (Rank@1, excluding identical-view cases).
• The shown result of GLN is implemented without compact block.
• Only 2 RTX6000 are used during the inference phase.
• The results on OUMVLP will be released soon. It's inference process just cost about 90 secs(Baseline & 8 RTX6000).

1. clone this repo.

   git clone https://github.com/ShiqiYu/OpenGait.git
   

2. Install dependenices:

   • pytorch >= 1.6
   • torchvision
   • pyyaml
   • tensorboard
   • opencv-python
   • tqdm

   Install dependenices by Anaconda:

   conda install tqdm pyyaml tensorboard opencv
   conda install pytorch==1.6.0 torchvision -c pytorch
   

   Or, Install dependenices by pip:

   pip install tqdm pyyaml tensorboard opencv-python
   pip install torch==1.6.0 torchvision==0.7.0
   

See prepare dataset.

Train a model by

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 lib/main.py --cfgs ./config/baseline.yaml --phase train

• python -m torch.distributed.launch Our implementation uses DistributedDataParallel.
• --nproc_per_node The number of gpu to use, it must equal the length of CUDA_VISIBLE_DEVICES.
• --cfgs The path of config file.
• --phase Specified as train.
• --iter You can specify a number of iterations or use restore_hint in the configuration file and resume training from there.
• --log_to_file If specified, log will be written on disk simultaneously.

You can run commands in train.sh for training different models.

Use trained model to evaluate by

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 lib/main.py --cfgs ./config/baseline.yaml --phase test

• --phase Specified as test.
• --iter You can specify a number of iterations or or use restore_hint in the configuration file and restore model from there.

Tip: Other arguments are the same as train phase.

You can run commands in test.sh for testing different models.

If you want customize your own model, see here.

• Some models may not be compatible with AMP, you can disable it by setting enable_float16 False.
• In DDP mode, zombie processes may occur when the program terminates abnormally. You can use this command kill $(ps aux | grep main.py | grep -v grep | awk '{print $2}') to clear them.
• We implemented the functionality of testing while training, but it slightly affected the results. None of our published models use this functionality. You can disable it by setting with_test False.

Open Gait Team (OGT)

• Chao Fan (樊超)
• Chuanfu Shen (沈川福)
• Junhao Liang (梁峻豪)

• GLN: Saihui Hou (侯赛辉)
• GaitGL: Beibei Lin (林贝贝)