import argparse import datetime import logging import math import random import time import torch import platform from os import path as osp import warnings 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, 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 # ----------- DEVICE SELECTION ---------- def select_device(prefer_coreml=True): if torch.backends.mps.is_available() and prefer_coreml and platform.system() == "Darwin": print("BasicSR: Using CoreML backend (MPS).") return torch.device("mps") elif torch.cuda.is_available(): print("BasicSR: Using CUDA backend.") return torch.device("cuda") else: print("BasicSR: Using CPU backend.") return torch.device("cpu") DEVICE = select_device(prefer_coreml=True) # ignore UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. warnings.filterwarnings("ignore", category=UserWarning) 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' or DEVICE.type != 'cuda': opt['dist'] = False print('Distributed training disabled.', 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']}.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)) if (opt['logger'].get('wandb') is not None) and (opt['logger']['wandb'].get('project') is not None): assert opt['logger'].get('use_tb_logger') is True init_wandb_logger(opt) tb_logger = None if opt['logger'].get('use_tb_logger'): tb_logger = init_tb_logger(log_dir=osp.join('tb_logger', opt['name'])) return logger, tb_logger def create_train_val_dataloader(opt, logger): 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(f'Training stats:\n\tTrain images: {len(train_set)}\n\tEnlarge ratio: {dataset_enlarge_ratio}\n\tBatch/GPU: {dataset_opt["batch_size_per_gpu"]}\n\tGPUs: {opt["world_size"]}\n\tIters/epoch: {num_iter_per_epoch}\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'Validation items in {dataset_opt["name"]}: {len(val_set)}') else: raise ValueError(f'Dataset phase {phase} not recognized.') return train_loader, train_sampler, val_loader, total_epochs, total_iters def train_pipeline(root_path): opt = parse_options(root_path, is_train=True) if DEVICE.type == 'cuda': torch.backends.cudnn.benchmark = True if opt['path'].get('resume_state'): resume_state = torch.load(opt['path']['resume_state'], map_location=DEVICE) else: resume_state = None if resume_state is None: make_exp_dirs(opt) if opt['logger'].get('use_tb_logger') and opt['rank'] == 0: mkdir_and_rename(osp.join('tb_logger', opt['name'])) logger, tb_logger = init_loggers(opt) train_loader, train_sampler, val_loader, total_epochs, total_iters = create_train_val_dataloader(opt, logger) if resume_state: check_resume(opt, resume_state['iter']) model = build_model(opt).to(DEVICE) model.resume_training(resume_state) logger.info(f"Resuming from epoch {resume_state['epoch']}, iter {resume_state['iter']}") start_epoch = resume_state['epoch'] current_iter = resume_state['iter'] else: model = build_model(opt).to(DEVICE) start_epoch = 0 current_iter = 0 msg_logger = MessageLogger(opt, current_iter, tb_logger) prefetch_mode = opt['datasets']['train'].get('prefetch_mode') if prefetch_mode is None or prefetch_mode == 'cpu' or DEVICE.type in ['cpu', 'mps']: if prefetch_mode == 'cuda' and DEVICE.type == 'mps': logger.warning("CUDA prefetch requested but MPS (CoreML) is in use. Falling back to CPU prefetch.") prefetcher = CPUPrefetcher(train_loader) elif prefetch_mode == 'cuda': if DEVICE.type != 'cuda': logger.warning("CUDA prefetch requested but CUDA unavailable. Using CPU prefetch.") prefetcher = CPUPrefetcher(train_loader) else: if opt['datasets']['train'].get('pin_memory') is not True: raise ValueError('Set pin_memory=True for CUDAPrefetcher.') prefetcher = CUDAPrefetcher(train_loader, opt) logger.info(f'Using CUDA prefetcher') else: raise ValueError(f"Invalid prefetch_mode: {prefetch_mode}. Supported: 'cpu', 'cuda', None") logger.info(f'Start training at epoch {start_epoch}, iter {current_iter + 1}') start_time = time.time() data_time, iter_time = time.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 model.update_learning_rate(current_iter, warmup_iter=opt['train'].get('warmup_iter', -1)) model.feed_data(train_data) model.optimize_parameters(current_iter) iter_time = time.time() - iter_time 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) if current_iter % opt['logger']['save_checkpoint_freq'] == 0: logger.info('Saving model and training state.') model.save(epoch, current_iter) if opt.get('val') and opt['datasets'].get('val') 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() consumed_time = str(datetime.timedelta(seconds=int(time.time() - start_time))) logger.info(f'Training complete. Time: {consumed_time}') logger.info('Saving latest model.') model.save(epoch=-1, current_iter=-1) if opt.get('val') and opt['datasets'].get('val'): 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)