import datetime import logging import math import time import torch import warnings warnings.filterwarnings("ignore") from os import path as osp from basicsr.data import build_dataloader, build_dataset from basicsr.data.data_sampler import EnlargedSampler from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher from basicsr.models import build_model from basicsr.utils import (AvgTimer, MessageLogger, check_resume, get_env_info, get_root_logger, get_time_str, init_tb_logger, init_wandb_logger, make_exp_dirs, mkdir_and_rename, scandir) from basicsr.utils.options import copy_opt_file, dict2str, parse_options def init_tb_loggers(opt): # initialize wandb logger before tensorboard logger to allow proper sync if (opt['logger'].get('wandb') is not None) and (opt['logger']['wandb'].get('project') is not None) and ('debug' not in opt['name']): assert opt['logger'].get('use_tb_logger') is True, ('should turn on tensorboard when using wandb') init_wandb_logger(opt) tb_logger = None if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name']: tb_logger = init_tb_logger(log_dir=osp.join(opt['root_path'], 'tb_logger', opt['name'])) return tb_logger def create_train_val_dataloader(opt, logger): # create train and val dataloaders train_loader, val_loaders = None, [] for phase, dataset_opt in opt['datasets'].items(): if phase == 'train': dataset_enlarge_ratio = dataset_opt.get('dataset_enlarge_ratio', 1) train_set = build_dataset(dataset_opt) train_sampler = EnlargedSampler(train_set, opt['world_size'], opt['rank'], dataset_enlarge_ratio) train_loader = build_dataloader( train_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=train_sampler, seed=opt['manual_seed']) num_iter_per_epoch = math.ceil( len(train_set) * dataset_enlarge_ratio / (dataset_opt['batch_size_per_gpu'] * opt['world_size'])) total_iters = int(opt['train']['total_iter']) total_epochs = math.ceil(total_iters / (num_iter_per_epoch)) logger.info('Training statistics:' f'\n\tNumber of train images: {len(train_set)}' f'\n\tDataset enlarge ratio: {dataset_enlarge_ratio}' f'\n\tBatch size per gpu: {dataset_opt["batch_size_per_gpu"]}' f'\n\tWorld size (gpu number): {opt["world_size"]}' f'\n\tRequire iter number per epoch: {num_iter_per_epoch}' f'\n\tTotal epochs: {total_epochs}; iters: {total_iters}.') elif phase.split('_')[0] == 'val': val_set = build_dataset(dataset_opt) val_loader = build_dataloader( val_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed']) logger.info(f'Number of val images/folders in {dataset_opt["name"]}: {len(val_set)}') val_loaders.append(val_loader) else: raise ValueError(f'Dataset phase {phase} is not recognized.') return train_loader, train_sampler, val_loaders, total_epochs, total_iters def load_resume_state(opt): resume_state_path = None if opt['auto_resume']: state_path = osp.join(opt['root_path'], 'experiments', opt['name'], 'training_states') if osp.isdir(state_path): states = list(scandir(state_path, suffix='state', recursive=False, full_path=False)) if len(states) != 0: states = [float(v.split('.state')[0]) for v in states] resume_state_path = osp.join(state_path, f'{max(states):.0f}.state') opt['path']['resume_state'] = resume_state_path else: if opt['path'].get('resume_state'): resume_state_path = opt['path']['resume_state'] if resume_state_path is None: resume_state = None else: device_id = torch.cuda.current_device() resume_state = torch.load(resume_state_path, map_location=lambda storage, loc: storage.cuda(device_id)) check_resume(opt, resume_state['iter']) return resume_state def train_pipeline(root_path): # parse options, set distributed setting, set ramdom seed opt, args = parse_options(root_path, is_train=True) opt['root_path'] = root_path torch.backends.cudnn.benchmark = True # torch.backends.cudnn.deterministic = True # load resume states if necessary resume_state = load_resume_state(opt) # mkdir for experiments and logger if resume_state is None: make_exp_dirs(opt) if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name'] and opt['rank'] == 0: mkdir_and_rename(osp.join(opt['root_path'], 'tb_logger', opt['name'])) # copy the yml file to the experiment root copy_opt_file(args.opt, opt['path']['experiments_root']) # WARNING: should not use get_root_logger in the above codes, including the called functions # Otherwise the logger will not be properly initialized log_file = osp.join(opt['path']['log'], f"train_{opt['name']}_{get_time_str()}.log") logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file) logger.info(get_env_info()) logger.info(dict2str(opt)) # initialize wandb and tb loggers tb_logger = init_tb_loggers(opt) # create train and validation dataloaders result = create_train_val_dataloader(opt, logger) train_loader, train_sampler, val_loaders, total_epochs, total_iters = result # create model model = build_model(opt) if resume_state: # resume training model.resume_training(resume_state) # handle optimizers and schedulers logger.info(f"Resuming training from epoch: {resume_state['epoch']}, " f"iter: {resume_state['iter']}.") start_epoch = resume_state['epoch'] current_iter = resume_state['iter'] else: start_epoch = 0 current_iter = 0 # create message logger (formatted outputs) msg_logger = MessageLogger(opt, current_iter, tb_logger) # dataloader prefetcher prefetch_mode = opt['datasets']['train'].get('prefetch_mode') if prefetch_mode is None or prefetch_mode == 'cpu': prefetcher = CPUPrefetcher(train_loader) elif prefetch_mode == 'cuda': prefetcher = CUDAPrefetcher(train_loader, opt) logger.info(f'Use {prefetch_mode} prefetch dataloader') if opt['datasets']['train'].get('pin_memory') is not True: raise ValueError('Please set pin_memory=True for CUDAPrefetcher.') else: raise ValueError(f'Wrong prefetch_mode {prefetch_mode}.' "Supported ones are: None, 'cuda', 'cpu'.") # training logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter}') data_timer, iter_timer = AvgTimer(), AvgTimer() start_time = time.time() for epoch in range(start_epoch, total_epochs + 1): train_sampler.set_epoch(epoch) prefetcher.reset() train_data = prefetcher.next() while train_data is not None: data_timer.record() current_iter += 1 if current_iter > total_iters: break # update learning rate model.update_learning_rate(current_iter, warmup_iter=opt['train'].get('warmup_iter', -1)) # training model.feed_data(train_data) model.optimize_parameters(current_iter) iter_timer.record() if current_iter == 1: # reset start time in msg_logger for more accurate eta_time # not work in resume mode msg_logger.reset_start_time() # log if current_iter % opt['logger']['print_freq'] == 0: log_vars = {'epoch': epoch, 'iter': current_iter} log_vars.update({'lrs': model.get_current_learning_rate()}) log_vars.update({'time': iter_timer.get_avg_time(), 'data_time': data_timer.get_avg_time()}) log_vars.update(model.get_current_log()) msg_logger(log_vars) # save training images snapshot save_snapshot_freq if opt['logger'][ 'save_snapshot_freq'] is not None and current_iter % opt['logger']['save_snapshot_freq'] == 0: model.save_training_images(current_iter) # save models and training states if current_iter % opt['logger']['save_checkpoint_freq'] == 0: logger.info('Saving models and training states.') model.save(epoch, current_iter) # validation if opt.get('val') is not None and (current_iter % opt['val']['val_freq'] == 0): if len(val_loaders) > 1: logger.warning('Multiple validation datasets are *only* supported by SRModel.') for val_loader in val_loaders: model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img']) data_timer.start() iter_timer.start() train_data = prefetcher.next() # end of iter # end of epoch consumed_time = str(datetime.timedelta(seconds=int(time.time() - start_time))) logger.info(f'End of training. Time consumed: {consumed_time}') logger.info('Save the latest model.') model.save(epoch=-1, current_iter=-1) # -1 stands for the latest if opt.get('val') is not None: for val_loader in val_loaders: model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img']) if tb_logger: tb_logger.close() if __name__ == '__main__': root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir)) train_pipeline(root_path)