Awesome
GaitGL
NOTE
This repo is based on GaitSet
Prerequisites
- Python 3.7
- PyTorch 1.1
- CUDA 10.2
Dataset & Preparation
Download OU-MVLP 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.OUMVLP/00001/00/000/
. - Step2: Cut and align the raw silhouettes with
pretreatment_oumvlp.py
. the silhouettes after pretreatment MUST have a size of 64x64.
Pretreatment
pretreatment_oumvlp.py
uses the alignment method in
this paper.
Pretreatment your dataset by
python pretreatment_oumvlp.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
Train
Train a model by
python train.py
'batch_size': (32, 8), 'frame_num': 30, 'total_iter': 250000.The learning rate is 1e − 4 in the first 150K iterations, and then is changed into 1e − 5 for the rest of 100K iterations.
--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_oumvlp.py
--iter
iteration of the checkpoint to load. #Default: 250000--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
CAISA-E
Dataset & Preparation
Function generate_test_gallery() generate_train_gallery() generate_test_probe() from pt_casiae.py
Train
OUMVLP Pre-training parameters need to be added. Train a model by
python train.py
'batch_size': (12, 8), 'frame_num': 64, 'total_iter': 15000. The learning rate is 1e − 4 in the first 10K iterations, and then is changed into 1e − 5 for the rest of 5K iterations.
Test
Training parameters. Test a model by using Function testout() from pt_casiae.py
python pt_casiae.py
Citation
Please cite these papers in your publications if it helps your research:
@article{linlearning,
title={Learning Effective Representations from Global and Local Features for Cross-View Gait Recognition},
author={Lin, Beibei and Zhang, Shunli and Yu, Xin and Kong, Chuihan and Wan, Chenwei}
}