mogen/datasets/human_body_prior/train/README.md

Train VPoser from Scratch

To train your own VPoser with new configuration duplicate the provided V02_05 folder while setting a new experiment ID and change the settings as you desire. First you would need to download the AMASS dataset, then following the data preparation tutorial prepare the data for training. Following is a code snippet for training that can be found in the example training experiment:

import glob
import os.path as osp

from human_body_prior.tools.configurations import load_config
from human_body_prior.train.vposer_trainer import train_vposer_once

def main():
    expr_id = 'V02_05'

    default_ps_fname = glob.glob(osp.join(osp.dirname(__file__), '*.yaml'))[0]

    vp_ps = load_config(default_ps_fname)

    vp_ps.train_parms.batch_size = 128

    vp_ps.general.expr_id = expr_id

    total_jobs = []
    total_jobs.append(vp_ps.toDict().copy())

    print('#training_jobs to be done: {}'.format(len(total_jobs)))
    if len(total_jobs) == 0:
        print('No jobs to be done')
        return

    for job in total_jobs:
        train_vposer_once(job)

The above code uses yaml configuration files to handle experiment settings. It loads the default settings in .yaml and overloads it with your new args.

The training code, will dump a log file along with tensorboard readable events file.