import argparse import datetime import logging import math import random import time import torch 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 (MessageLogger, check_resume, get_env_info, get_root_logger, get_time_str, init_tb_logger, init_wandb_logger, make_exp_dirs, mkdir_and_rename, set_random_seed) from basicsr.utils.dist_util import get_dist_info, init_dist from basicsr.utils.options import dict2str, parse def parse_options(root_path, is_train=True): parser = argparse.ArgumentParser() parser.add_argument('-opt', type=str, required=True, help='Path to option YAML file.') parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none', help='job launcher') parser.add_argument('--local_rank', type=int, default=0) args = parser.parse_args() opt = parse(args.opt, root_path, is_train=is_train) # distributed settings if args.launcher == 'none': opt['dist'] = False print('Disable distributed.', flush=True) else: opt['dist'] = True if args.launcher == 'slurm' and 'dist_params' in opt: init_dist(args.launcher, **opt['dist_params']) else: init_dist(args.launcher) opt['rank'], opt['world_size'] = get_dist_info() # random seed seed = opt.get('manual_seed') if seed is None: seed = random.randint(1, 10000) opt['manual_seed'] = seed set_random_seed(seed + opt['rank']) return opt def init_loggers(opt): 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 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('tb_logger', opt['name'])) return logger, tb_logger def create_train_val_dataloader(opt, logger): # create train and val dataloaders train_loader, val_loader = None, 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 == '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"]}: ' f'{len(val_set)}') else: raise ValueError(f'Dataset phase {phase} is not recognized.') return train_loader, train_sampler, val_loader, total_epochs, total_iters def train_pipeline(root_path): # parse options, set distributed setting, set ramdom seed opt = parse_options(root_path, is_train=True) torch.backends.cudnn.benchmark = True # torch.backends.cudnn.deterministic = True # load resume states if necessary if opt['path'].get('resume_state'): device_id = torch.cuda.current_device() resume_state = torch.load( opt['path']['resume_state'], map_location=lambda storage, loc: storage.cuda(device_id)) else: resume_state = None # 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('tb_logger', opt['name'])) # initialize loggers logger, tb_logger = init_loggers(opt) # create train and validation dataloaders result = create_train_val_dataloader(opt, logger) train_loader, train_sampler, val_loader, total_epochs, total_iters = result # create model if resume_state: # resume training check_resume(opt, resume_state['iter']) model = build_model(opt) 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: model = build_model(opt) 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_time, iter_time = time.time(), time.time() 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_time = time.time() - data_time 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_time = time.time() - iter_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_time, 'data_time': data_time}) log_vars.update(model.get_current_log()) msg_logger(log_vars) # 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): model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img']) data_time = time.time() iter_time = time.time() 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: 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)