undercovercd/Swap-Face-Model

1import datetime2import logging3import math4import time5import torch6from os import path as osp7 8from basicsr.data import build_dataloader, build_dataset9from basicsr.data.data_sampler import EnlargedSampler10from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher11from basicsr.models import build_model12from basicsr.utils import (AvgTimer, MessageLogger, check_resume, get_env_info, get_root_logger, get_time_str,13                           init_tb_logger, init_wandb_logger, make_exp_dirs, mkdir_and_rename, scandir)14from basicsr.utils.options import copy_opt_file, dict2str, parse_options15 16 17def init_tb_loggers(opt):18    # initialize wandb logger before tensorboard logger to allow proper sync19    if (opt['logger'].get('wandb') is not None) and (opt['logger']['wandb'].get('project')20                                                     is not None) and ('debug' not in opt['name']):21        assert opt['logger'].get('use_tb_logger') is True, ('should turn on tensorboard when using wandb')22        init_wandb_logger(opt)23    tb_logger = None24    if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name']:25        tb_logger = init_tb_logger(log_dir=osp.join(opt['root_path'], 'tb_logger', opt['name']))26    return tb_logger27 28 29def create_train_val_dataloader(opt, logger):30    # create train and val dataloaders31    train_loader, val_loaders = None, []32    for phase, dataset_opt in opt['datasets'].items():33        if phase == 'train':34            dataset_enlarge_ratio = dataset_opt.get('dataset_enlarge_ratio', 1)35            train_set = build_dataset(dataset_opt)36            train_sampler = EnlargedSampler(train_set, opt['world_size'], opt['rank'], dataset_enlarge_ratio)37            train_loader = build_dataloader(38                train_set,39                dataset_opt,40                num_gpu=opt['num_gpu'],41                dist=opt['dist'],42                sampler=train_sampler,43                seed=opt['manual_seed'])44 45            num_iter_per_epoch = math.ceil(46                len(train_set) * dataset_enlarge_ratio / (dataset_opt['batch_size_per_gpu'] * opt['world_size']))47            total_iters = int(opt['train']['total_iter'])48            total_epochs = math.ceil(total_iters / (num_iter_per_epoch))49            logger.info('Training statistics:'50                        f'\n\tNumber of train images: {len(train_set)}'51                        f'\n\tDataset enlarge ratio: {dataset_enlarge_ratio}'52                        f'\n\tBatch size per gpu: {dataset_opt["batch_size_per_gpu"]}'53                        f'\n\tWorld size (gpu number): {opt["world_size"]}'54                        f'\n\tRequire iter number per epoch: {num_iter_per_epoch}'55                        f'\n\tTotal epochs: {total_epochs}; iters: {total_iters}.')56        elif phase.split('_')[0] == 'val':57            val_set = build_dataset(dataset_opt)58            val_loader = build_dataloader(59                val_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed'])60            logger.info(f'Number of val images/folders in {dataset_opt["name"]}: {len(val_set)}')61            val_loaders.append(val_loader)62        else:63            raise ValueError(f'Dataset phase {phase} is not recognized.')64 65    return train_loader, train_sampler, val_loaders, total_epochs, total_iters66 67 68def load_resume_state(opt):69    resume_state_path = None70    if opt['auto_resume']:71        state_path = osp.join('experiments', opt['name'], 'training_states')72        if osp.isdir(state_path):73            states = list(scandir(state_path, suffix='state', recursive=False, full_path=False))74            if len(states) != 0:75                states = [float(v.split('.state')[0]) for v in states]76                resume_state_path = osp.join(state_path, f'{max(states):.0f}.state')77                opt['path']['resume_state'] = resume_state_path78    else:79        if opt['path'].get('resume_state'):80            resume_state_path = opt['path']['resume_state']81 82    if resume_state_path is None:83        resume_state = None84    else:85        device_id = torch.cuda.current_device()86        resume_state = torch.load(resume_state_path, map_location=lambda storage, loc: storage.cuda(device_id))87        check_resume(opt, resume_state['iter'])88    return resume_state89 90 91def train_pipeline(root_path):92    # parse options, set distributed setting, set random seed93    opt, args = parse_options(root_path, is_train=True)94    opt['root_path'] = root_path95 96    torch.backends.cudnn.benchmark = True97    # torch.backends.cudnn.deterministic = True98 99    # load resume states if necessary100    resume_state = load_resume_state(opt)101    # mkdir for experiments and logger102    if resume_state is None:103        make_exp_dirs(opt)104        if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name'] and opt['rank'] == 0:105            mkdir_and_rename(osp.join(opt['root_path'], 'tb_logger', opt['name']))106 107    # copy the yml file to the experiment root108    copy_opt_file(args.opt, opt['path']['experiments_root'])109 110    # WARNING: should not use get_root_logger in the above codes, including the called functions111    # Otherwise the logger will not be properly initialized112    log_file = osp.join(opt['path']['log'], f"train_{opt['name']}_{get_time_str()}.log")113    logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file)114    logger.info(get_env_info())115    logger.info(dict2str(opt))116    # initialize wandb and tb loggers117    tb_logger = init_tb_loggers(opt)118 119    # create train and validation dataloaders120    result = create_train_val_dataloader(opt, logger)121    train_loader, train_sampler, val_loaders, total_epochs, total_iters = result122 123    # create model124    model = build_model(opt)125    if resume_state:  # resume training126        model.resume_training(resume_state)  # handle optimizers and schedulers127        logger.info(f"Resuming training from epoch: {resume_state['epoch']}, iter: {resume_state['iter']}.")128        start_epoch = resume_state['epoch']129        current_iter = resume_state['iter']130    else:131        start_epoch = 0132        current_iter = 0133 134    # create message logger (formatted outputs)135    msg_logger = MessageLogger(opt, current_iter, tb_logger)136 137    # dataloader prefetcher138    prefetch_mode = opt['datasets']['train'].get('prefetch_mode')139    if prefetch_mode is None or prefetch_mode == 'cpu':140        prefetcher = CPUPrefetcher(train_loader)141    elif prefetch_mode == 'cuda':142        prefetcher = CUDAPrefetcher(train_loader, opt)143        logger.info(f'Use {prefetch_mode} prefetch dataloader')144        if opt['datasets']['train'].get('pin_memory') is not True:145            raise ValueError('Please set pin_memory=True for CUDAPrefetcher.')146    else:147        raise ValueError(f"Wrong prefetch_mode {prefetch_mode}. Supported ones are: None, 'cuda', 'cpu'.")148 149    # training150    logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter}')151    data_timer, iter_timer = AvgTimer(), AvgTimer()152    start_time = time.time()153 154    for epoch in range(start_epoch, total_epochs + 1):155        train_sampler.set_epoch(epoch)156        prefetcher.reset()157        train_data = prefetcher.next()158 159        while train_data is not None:160            data_timer.record()161 162            current_iter += 1163            if current_iter > total_iters:164                break165            # update learning rate166            model.update_learning_rate(current_iter, warmup_iter=opt['train'].get('warmup_iter', -1))167            # training168            model.feed_data(train_data)169            model.optimize_parameters(current_iter)170            iter_timer.record()171            if current_iter == 1:172                # reset start time in msg_logger for more accurate eta_time173                # not work in resume mode174                msg_logger.reset_start_time()175            # log176            if current_iter % opt['logger']['print_freq'] == 0:177                log_vars = {'epoch': epoch, 'iter': current_iter}178                log_vars.update({'lrs': model.get_current_learning_rate()})179                log_vars.update({'time': iter_timer.get_avg_time(), 'data_time': data_timer.get_avg_time()})180                log_vars.update(model.get_current_log())181                msg_logger(log_vars)182 183            # save models and training states184            if current_iter % opt['logger']['save_checkpoint_freq'] == 0:185                logger.info('Saving models and training states.')186                model.save(epoch, current_iter)187 188            # validation189            if opt.get('val') is not None and (current_iter % opt['val']['val_freq'] == 0):190                if len(val_loaders) > 1:191                    logger.warning('Multiple validation datasets are *only* supported by SRModel.')192                for val_loader in val_loaders:193                    model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])194 195            data_timer.start()196            iter_timer.start()197            train_data = prefetcher.next()198        # end of iter199 200    # end of epoch201 202    consumed_time = str(datetime.timedelta(seconds=int(time.time() - start_time)))203    logger.info(f'End of training. Time consumed: {consumed_time}')204    logger.info('Save the latest model.')205    model.save(epoch=-1, current_iter=-1)  # -1 stands for the latest206    if opt.get('val') is not None:207        for val_loader in val_loaders:208            model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])209    if tb_logger:210        tb_logger.close()211 212 213if __name__ == '__main__':214    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))215    train_pipeline(root_path)216