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| import argparse | |
| import logging | |
| import os | |
| import os.path as osp | |
| import random | |
| import time | |
| import torch | |
| from data.segm_attr_dataset import DeepFashionAttrSegmDataset | |
| from models import create_model | |
| from utils.logger import MessageLogger, get_root_logger, init_tb_logger | |
| from utils.options import dict2str, dict_to_nonedict, parse | |
| from utils.util import make_exp_dirs | |
| def main(): | |
| # options | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('-opt', type=str, help='Path to option YAML file.') | |
| args = parser.parse_args() | |
| opt = parse(args.opt, is_train=True) | |
| # mkdir and loggers | |
| make_exp_dirs(opt) | |
| log_file = osp.join(opt['path']['log'], f"train_{opt['name']}.log") | |
| logger = get_root_logger( | |
| logger_name='base', log_level=logging.INFO, log_file=log_file) | |
| logger.info(dict2str(opt)) | |
| # initialize tensorboard logger | |
| tb_logger = None | |
| if opt['use_tb_logger'] and 'debug' not in opt['name']: | |
| tb_logger = init_tb_logger(log_dir='./tb_logger/' + opt['name']) | |
| # convert to NoneDict, which returns None for missing keys | |
| opt = dict_to_nonedict(opt) | |
| # set up data loader | |
| train_dataset = DeepFashionAttrSegmDataset( | |
| img_dir=opt['train_img_dir'], | |
| segm_dir=opt['segm_dir'], | |
| pose_dir=opt['pose_dir'], | |
| ann_dir=opt['train_ann_file'], | |
| xflip=True) | |
| train_loader = torch.utils.data.DataLoader( | |
| dataset=train_dataset, | |
| batch_size=opt['batch_size'], | |
| shuffle=True, | |
| num_workers=opt['num_workers'], | |
| persistent_workers=True, | |
| drop_last=True) | |
| logger.info(f'Number of train set: {len(train_dataset)}.') | |
| opt['max_iters'] = opt['num_epochs'] * len( | |
| train_dataset) // opt['batch_size'] | |
| val_dataset = DeepFashionAttrSegmDataset( | |
| img_dir=opt['train_img_dir'], | |
| segm_dir=opt['segm_dir'], | |
| pose_dir=opt['pose_dir'], | |
| ann_dir=opt['val_ann_file']) | |
| val_loader = torch.utils.data.DataLoader( | |
| dataset=val_dataset, batch_size=opt['batch_size'], shuffle=False) | |
| logger.info(f'Number of val set: {len(val_dataset)}.') | |
| test_dataset = DeepFashionAttrSegmDataset( | |
| img_dir=opt['test_img_dir'], | |
| segm_dir=opt['segm_dir'], | |
| pose_dir=opt['pose_dir'], | |
| ann_dir=opt['test_ann_file']) | |
| test_loader = torch.utils.data.DataLoader( | |
| dataset=test_dataset, batch_size=opt['batch_size'], shuffle=False) | |
| logger.info(f'Number of test set: {len(test_dataset)}.') | |
| current_iter = 0 | |
| model = create_model(opt) | |
| data_time, iter_time = 0, 0 | |
| current_iter = 0 | |
| # create message logger (formatted outputs) | |
| msg_logger = MessageLogger(opt, current_iter, tb_logger) | |
| for epoch in range(opt['num_epochs']): | |
| lr = model.update_learning_rate(epoch, current_iter) | |
| for _, batch_data in enumerate(train_loader): | |
| data_time = time.time() - data_time | |
| current_iter += 1 | |
| model.feed_data(batch_data) | |
| model.optimize_parameters() | |
| iter_time = time.time() - iter_time | |
| if current_iter % opt['print_freq'] == 0: | |
| log_vars = {'epoch': epoch, 'iter': current_iter} | |
| log_vars.update({'lrs': [lr]}) | |
| log_vars.update({'time': iter_time, 'data_time': data_time}) | |
| log_vars.update(model.get_current_log()) | |
| msg_logger(log_vars) | |
| data_time = time.time() | |
| iter_time = time.time() | |
| if epoch % opt['val_freq'] == 0 and epoch != 0: | |
| save_dir = f'{opt["path"]["visualization"]}/valset/epoch_{epoch:03d}' # noqa | |
| os.makedirs(save_dir, exist_ok=opt['debug']) | |
| model.inference(val_loader, save_dir) | |
| save_dir = f'{opt["path"]["visualization"]}/testset/epoch_{epoch:03d}' # noqa | |
| os.makedirs(save_dir, exist_ok=opt['debug']) | |
| model.inference(test_loader, save_dir) | |
| # save model | |
| model.save_network( | |
| model._denoise_fn, | |
| f'{opt["path"]["models"]}/sampler_epoch{epoch}.pth') | |
| if __name__ == '__main__': | |
| main() | |