GaitPart

GaitPart is a CVPR 2020 paper.

NOTE

This repo is not official, but almost reproduces the same recognition accuracy on CASIA-B dataset like the paper does. This repo is based on GaitSet

Prerequisites

• Python 3.6
• PyTorch 1.5
• CUDA 10.2

Getting started

Installation

pip install -r requirement.txt

Dataset & Preparation

Download CASIA-B Dataset

!!! ATTENTION !!! ATTENTION !!! ATTENTION !!!

Before training or test, please make sure you have prepared the dataset by this two steps:

• Step1: Organize the directory as: your_dataset_path/subject_ids/walking_conditions/views. E.g. CASIA-B/001/nm-01/000/.
• Step2: Cut and align the raw silhouettes with pretreatment.py. (See pretreatment for details.) Welcome to try different ways of pretreatment but note that the silhouettes after pretreatment MUST have a size of 64x64.

Futhermore, you also can test our code on OU-MVLP Dataset. The number of channels and the training batchsize is slightly different for this dataset. For more detail, please refer to our paper.

Pretreatment

Pretreatment your dataset by

python pretreatment.py --input_path='root_path_of_raw_dataset' --output_path='root_path_for_output'

• --input_path (NECESSARY) Root path of raw dataset.
• --output_path (NECESSARY) Root path for output.
• --log_file Log file path. #Default: './pretreatment.log'
• --log If set as True, all logs will be saved. Otherwise, only warnings and errors will be saved. #Default: False
• --worker_num How many subprocesses to use for data pretreatment. Default: 1

Configuration

In config.py, you might want to change the following settings:

• dataset_path (NECESSARY) root path of the dataset (for the above example, it is "gaitdata")
• WORK_PATH path to save/load checkpoints
• CUDA_VISIBLE_DEVICES indices of GPUs

Train

Train a model by

python train.py

• --cache if set as TRUE all the training data will be loaded at once before the training start. This will accelerate the training. Note that if this arg is set as FALSE, samples will NOT be kept in the memory even they have been used in the former iterations. #Default: TRUE

Evaluation

Evaluate the trained model by

python test.py

• --iter iteration of the checkpoint to load. #Default: 80000
• --batch_size batch size of the parallel test. #Default: 1
• --cache if set as TRUE all the test data will be loaded at once before the transforming start. This might accelerate the testing. #Default: FALSE

It will output Rank@1 of all three walking conditions. Note that the test is parallelizable. To conduct a faster evaluation, you could use --batch_size to change the batch size for test.

Result on CASIA-B dataset

I do experiments on CASIA-B dataset, 74 for training and 50 for testing, totally train 80000 iters, the accuracies are in the following

	NM	BG	CL
GaitPart	96.2%	91.5%	78.7%
Ours	96.0%	90.6%	77.8%