radames/Text2Human-API

1import argparse2import logging3import os4import os.path as osp5import random6import time7 8import torch9 10from data.segm_attr_dataset import DeepFashionAttrSegmDataset11from models import create_model12from utils.logger import MessageLogger, get_root_logger, init_tb_logger13from utils.options import dict2str, dict_to_nonedict, parse14from utils.util import make_exp_dirs15 16 17def main():18    # options19    parser = argparse.ArgumentParser()20    parser.add_argument('-opt', type=str, help='Path to option YAML file.')21    args = parser.parse_args()22    opt = parse(args.opt, is_train=True)23 24    # mkdir and loggers25    make_exp_dirs(opt)26    log_file = osp.join(opt['path']['log'], f"train_{opt['name']}.log")27    logger = get_root_logger(28        logger_name='base', log_level=logging.INFO, log_file=log_file)29    logger.info(dict2str(opt))30    # initialize tensorboard logger31    tb_logger = None32    if opt['use_tb_logger'] and 'debug' not in opt['name']:33        tb_logger = init_tb_logger(log_dir='./tb_logger/' + opt['name'])34 35    # convert to NoneDict, which returns None for missing keys36    opt = dict_to_nonedict(opt)37 38    # set up data loader39    train_dataset = DeepFashionAttrSegmDataset(40        img_dir=opt['train_img_dir'],41        segm_dir=opt['segm_dir'],42        pose_dir=opt['pose_dir'],43        ann_dir=opt['train_ann_file'],44        xflip=True)45    train_loader = torch.utils.data.DataLoader(46        dataset=train_dataset,47        batch_size=opt['batch_size'],48        shuffle=True,49        num_workers=opt['num_workers'],50        persistent_workers=True,51        drop_last=True)52    logger.info(f'Number of train set: {len(train_dataset)}.')53    opt['max_iters'] = opt['num_epochs'] * len(54        train_dataset) // opt['batch_size']55 56    val_dataset = DeepFashionAttrSegmDataset(57        img_dir=opt['train_img_dir'],58        segm_dir=opt['segm_dir'],59        pose_dir=opt['pose_dir'],60        ann_dir=opt['val_ann_file'])61    val_loader = torch.utils.data.DataLoader(62        dataset=val_dataset, batch_size=opt['batch_size'], shuffle=False)63    logger.info(f'Number of val set: {len(val_dataset)}.')64 65    test_dataset = DeepFashionAttrSegmDataset(66        img_dir=opt['test_img_dir'],67        segm_dir=opt['segm_dir'],68        pose_dir=opt['pose_dir'],69        ann_dir=opt['test_ann_file'])70    test_loader = torch.utils.data.DataLoader(71        dataset=test_dataset, batch_size=opt['batch_size'], shuffle=False)72    logger.info(f'Number of test set: {len(test_dataset)}.')73 74    current_iter = 075 76    model = create_model(opt)77 78    data_time, iter_time = 0, 079    current_iter = 080 81    # create message logger (formatted outputs)82    msg_logger = MessageLogger(opt, current_iter, tb_logger)83 84    for epoch in range(opt['num_epochs']):85        lr = model.update_learning_rate(epoch, current_iter)86 87        for _, batch_data in enumerate(train_loader):88            data_time = time.time() - data_time89 90            current_iter += 191 92            model.feed_data(batch_data)93            model.optimize_parameters()94 95            iter_time = time.time() - iter_time96            if current_iter % opt['print_freq'] == 0:97                log_vars = {'epoch': epoch, 'iter': current_iter}98                log_vars.update({'lrs': [lr]})99                log_vars.update({'time': iter_time, 'data_time': data_time})100                log_vars.update(model.get_current_log())101                msg_logger(log_vars)102 103            data_time = time.time()104            iter_time = time.time()105 106        if epoch % opt['val_freq'] == 0 and epoch != 0:107            save_dir = f'{opt["path"]["visualization"]}/valset/epoch_{epoch:03d}'  # noqa108            os.makedirs(save_dir, exist_ok=opt['debug'])109            model.inference(val_loader, save_dir)110 111            save_dir = f'{opt["path"]["visualization"]}/testset/epoch_{epoch:03d}'  # noqa112            os.makedirs(save_dir, exist_ok=opt['debug'])113            model.inference(test_loader, save_dir)114 115            # save model116            model.save_network(117                model._denoise_fn,118                f'{opt["path"]["models"]}/sampler_epoch{epoch}.pth')119 120 121if __name__ == '__main__':122    main()123