OpenGait/configs/skeletongait

SkeletonGait: Gait Recognition Using Skeleton Maps

This paper has been accepted by AAAI 2023.

Step 1: Generating Heatmap

Leveraging the power of Distributed Data Parallel (DDP), we've streamlined the heatmap generation process. Below is the script to initiate the generation:

CUDA_VISIBLE_DEVICES=0,1,2,3 \
python -m torch.distributed.launch \
--nproc_per_node=4 \
datasets/pretreatment_heatmap.py \
--pose_data_path=<your pose .pkl files path> \
--save_root=<your_path> \
--dataset_name=<dataset_name>

Parameter Guide:

• --pose_data_path: Specifies the directory containing the pose data files (.pkl, ID-Level). This is required.
• --save_root: Designates the root directory for storing the generated heatmap files (.pkl, ID-Level). This is required.
• --dataset_name: The name of the dataset undergoing preprocessing. This is required.
• --ext_name: An optional suffix for the 'save_root' directory to facilitate identification. Defaults to an empty string.
• --heatmap_cfg_path: Path to the configuration file of the heatmap generator. The default setting is configs/skeletongait/pretreatment_heatmap.yaml.

Optional

Step 2: Creating Symbolic Links for Heatmap and Silhouette Data

The script to symlink heatmaps and silouettes is as follows:

python datasets/ln_sil_heatmap.py \
--heatmap_data_path=<path_to_your_heatmap_folder> \
--silhouette_data_path=<path_to_your_silhouette_folder> \
--output_path=<path_to_your_output_folder>

Parameter Guide:

• --heatmap_data_path: The absolute path to your heatmap data. This is required.
• --silhouette_data_path: The absolute path to your silhouette data. This is required.
• --output_path: Designates the directory for linked output data. This is required.
• --dataset_pkl_ext_name: An optional parameter to specify the extension for .pkl silhouette files. Defaults to .pkl.

Step3: Training SkeletonGait or SkeletonGait++

The script to SkeletonGait is as follows:

CUDA_VISIBLE_DEVICES=0,1,2,3 \
 python -m torch.distributed.launch \
 --nproc_per_node=4 opengait/main.py \
 --cfgs ./configs/skeletongait/skeletongait_Gait3D.yaml \
 --phase train --log_to_file

The script to SkeletonGait++ is as follows:

CUDA_VISIBLE_DEVICES=0,1,2,3 \
 python -m torch.distributed.launch \
 --nproc_per_node=4 opengait/main.py \
 --cfgs ./configs/skeletongait/skeletongait++_Gait3D.yaml \
 --phase train --log_to_file