main_train_avatarposer.py

'''
# --------------------------------------------
# main training code
# --------------------------------------------
# AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing (ECCV 2022)
# https://github.com/eth-siplab/AvatarPoser
# Jiaxi Jiang (jiaxi.jiang@inf.ethz.ch)
# Sensing, Interaction & Perception Lab,
# Department of Computer Science, ETH Zurich
'''

import os.path
import math
import argparse
import random
import numpy as np
from collections import OrderedDict
import logging
import torch
from torch.utils.data import DataLoader
from utils import utils_logger
from utils import utils_option as option
from data.select_dataset import define_Dataset
from models.select_model import define_Model
from utils import utils_transform
import pickle
from utils import utils_visualize as vis

save_animation = False
resolution = (800,800)

def main(json_path='options/train_avatarposer.json'):

    '''
    # ----------------------------------------
    # Step--1 (prepare opt)
    # ----------------------------------------
    '''

    parser = argparse.ArgumentParser()
    parser.add_argument('-opt', type=str, default=json_path, help='Path to option JSON file.')

    opt = option.parse(parser.parse_args().opt, is_train=True)

    paths = (path for key, path in opt['path'].items() if 'pretrained' not in key)
    if isinstance(paths, str):
        if not os.path.exists(paths):
            os.makedirs(paths)
    else:
        for path in paths:
            if not os.path.exists(path):
                os.makedirs(path)

    # ----------------------------------------
    # update opt
    # ----------------------------------------
    # -->-->-->-->-->-->-->-->-->-->-->-->-->-
    init_iter, init_path_G = option.find_last_checkpoint(opt['path']['models'], net_type='G')
    opt['path']['pretrained_netG'] = init_path_G
    current_step = init_iter

    # --<--<--<--<--<--<--<--<--<--<--<--<--<-

    # ----------------------------------------
    # save opt to  a '../option.json' file
    # ----------------------------------------
    option.save(opt)

    # ----------------------------------------
    # return None for missing key
    # ----------------------------------------
    opt = option.dict_to_nonedict(opt)

    # ----------------------------------------
    # configure logger
    # ----------------------------------------
    logger_name = 'train'
    utils_logger.logger_info(logger_name, os.path.join(opt['path']['log'], logger_name+'.log'))
    logger = logging.getLogger(logger_name)
    logger.info(option.dict2str(opt))
    # ----------------------------------------
    # seed
    # ----------------------------------------
    seed = opt['train']['manual_seed']
    if seed is None:
        seed = random.randint(1, 10000)
    logger.info('Random seed: {}'.format(seed))
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)


    '''
    # ----------------------------------------
    # Step--2 (creat dataloader)
    # ----------------------------------------
    '''

    # ----------------------------------------
    # 1) create_dataset
    # 2) creat_dataloader for train and test
    # ----------------------------------------
    dataset_type = opt['datasets']['train']['dataset_type']
    for phase, dataset_opt in opt['datasets'].items():

        if phase == 'train':
            train_set = define_Dataset(dataset_opt)
            train_size = int(math.ceil(len(train_set) / dataset_opt['dataloader_batch_size']))
            logger.info('Number of train images: {:,d}, iters: {:,d}'.format(len(train_set), train_size))
            train_loader = DataLoader(train_set,
                                      batch_size=dataset_opt['dataloader_batch_size'],
                                      shuffle=dataset_opt['dataloader_shuffle'],
                                      num_workers=dataset_opt['dataloader_num_workers'],
                                      drop_last=True,
                                      pin_memory=True)
        elif phase == 'test':
            test_set = define_Dataset(dataset_opt)
            test_loader = DataLoader(test_set, batch_size=dataset_opt['dataloader_batch_size'],
                                     shuffle=False, num_workers=1,
                                     drop_last=False, pin_memory=True)
        else:
            raise NotImplementedError("Phase [%s] is not recognized." % phase)

    '''
    # ----------------------------------------
    # Step--3 (initialize model)
    # ----------------------------------------
    '''

    model = define_Model(opt)

    if opt['merge_bn'] and current_step > opt['merge_bn_startpoint']:
        logger.info('^_^ -----merging bnorm----- ^_^')
        model.merge_bnorm_test()

    logger.info(model.info_network())
    model.init_train()
    logger.info(model.info_params())

    '''
    # ----------------------------------------
    # Step--4 (main training)
    # ----------------------------------------
    '''
    for epoch in range(1000000):  # keep running
        for i, train_data in enumerate(train_loader):

            current_step += 1
            # -------------------------------
            # 1) feed patch pairs
            # -------------------------------
            
            model.feed_data(train_data)

            # -------------------------------
            # 2) optimize parameters
            # -------------------------------
            model.optimize_parameters(current_step)

            # -------------------------------
            # 3) update learning rate
            # -------------------------------
            model.update_learning_rate(current_step)


            # -------------------------------
            # merge bnorm
            # -------------------------------
            if opt['merge_bn'] and opt['merge_bn_startpoint'] == current_step:
                logger.info('^_^ -----merging bnorm----- ^_^')
                model.merge_bnorm_train()
                model.print_network()

            # -------------------------------
            # 4) training information
            # -------------------------------
            if current_step % opt['train']['checkpoint_print'] == 0:
                logs = model.current_log()  # such as loss
                message = '<epoch:{:3d}, iter:{:8,d}, lr:{:.3e}> '.format(epoch, current_step, model.current_learning_rate())
                for k, v in logs.items():  # merge log information into message
                    message += '{:s}: {:.3e} '.format(k, v)
                logger.info(message)

            # -------------------------------
            # 5) save model
            # -------------------------------
            if current_step % opt['train']['checkpoint_save'] == 0:
                logger.info('Saving the model.')
                model.save(current_step)

            # -------------------------------
            # 6) testing
            # -------------------------------
            if current_step % opt['train']['checkpoint_test'] == 0:


                pos_error = []
                vel_error = []
                pos_error_hands = []

                for index, test_data in enumerate(test_loader):
                    logger.info("testing the sample {}/{}".format(index, len(test_loader)))

                    model.feed_data(test_data, test=True)

                    model.test()

                    body_parms_pred = model.current_prediction()
                    body_parms_gt = model.current_gt()
                    predicted_angle = body_parms_pred['pose_body']
                    predicted_position = body_parms_pred['position']
                    predicted_body = body_parms_pred['body']

                    gt_angle = body_parms_gt['pose_body']
                    gt_position = body_parms_gt['position']
                    gt_body = body_parms_gt['body']



                    if index in [0, 10, 20] and save_animation:
                        video_dir = os.path.join(opt['path']['images'], str(index))
                        if not os.path.exists(video_dir):
                            os.makedirs(video_dir)

                        save_video_path_gt = os.path.join(video_dir, 'gt.avi')
                        if not os.path.exists(save_video_path_gt):
                            vis.save_animation(body_pose=gt_body, savepath=save_video_path_gt, bm = model.bm, fps=60, resolution = resolution)

                        save_video_path = os.path.join(video_dir, '{:d}.avi'.format(current_step))
                        vis.save_animation(body_pose=predicted_body, savepath=save_video_path, bm = model.bm, fps=60, resolution = resolution)


                    predicted_position = predicted_position#.cpu().numpy()
                    gt_position = gt_position#.cpu().numpy()

                    predicted_angle = predicted_angle.reshape(body_parms_pred['pose_body'].shape[0],-1,3)                    
                    gt_angle = gt_angle.reshape(body_parms_gt['pose_body'].shape[0],-1,3)


                    pos_error_ = torch.mean(torch.sqrt(torch.sum(torch.square(gt_position-predicted_position),axis=-1)))
                    pos_error_hands_ = torch.mean(torch.sqrt(torch.sum(torch.square(gt_position-predicted_position),axis=-1))[...,[20,21]])

                    gt_velocity = (gt_position[1:,...] - gt_position[:-1,...])*60
                    predicted_velocity = (predicted_position[1:,...] - predicted_position[:-1,...])*60
                    vel_error_ = torch.mean(torch.sqrt(torch.sum(torch.square(gt_velocity-predicted_velocity),axis=-1)))

                    pos_error.append(pos_error_)
                    vel_error.append(vel_error_)

                    pos_error_hands.append(pos_error_hands_)



                pos_error = sum(pos_error)/len(pos_error)
                vel_error = sum(vel_error)/len(vel_error)
                pos_error_hands = sum(pos_error_hands)/len(pos_error_hands)


                # testing log
                logger.info('<epoch:{:3d}, iter:{:8,d}, Average positional error [cm]: {:<.5f}, Average velocity error [cm/s]: {:<.5f}, Average positional error at hand [cm]: {:<.5f}\n'.format(epoch, current_step,pos_error*100, vel_error*100, pos_error_hands*100))


    logger.info('Saving the final model.')
    model.save('latest')
    logger.info('End of training.')


if __name__ == '__main__':
    main()