Add codeformer and update license
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Felipe Daragon
225
basicsr/train.py
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225
basicsr/train.py
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import argparse
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import datetime
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import logging
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import math
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import copy
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import random
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import time
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import torch
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from os import path as osp
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from basicsr.data import build_dataloader, build_dataset
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from basicsr.data.data_sampler import EnlargedSampler
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from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher
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from basicsr.models import build_model
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from basicsr.utils import (MessageLogger, check_resume, get_env_info, get_root_logger, init_tb_logger,
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init_wandb_logger, make_exp_dirs, mkdir_and_rename, set_random_seed)
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from basicsr.utils.dist_util import get_dist_info, init_dist
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from basicsr.utils.options import dict2str, parse
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import warnings
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# ignore UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`.
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warnings.filterwarnings("ignore", category=UserWarning)
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def parse_options(root_path, is_train=True):
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, required=True, help='Path to option YAML file.')
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parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none', help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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args = parser.parse_args()
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opt = parse(args.opt, root_path, is_train=is_train)
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# distributed settings
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if args.launcher == 'none':
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opt['dist'] = False
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print('Disable distributed.', flush=True)
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else:
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opt['dist'] = True
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if args.launcher == 'slurm' and 'dist_params' in opt:
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init_dist(args.launcher, **opt['dist_params'])
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else:
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init_dist(args.launcher)
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opt['rank'], opt['world_size'] = get_dist_info()
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# random seed
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seed = opt.get('manual_seed')
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if seed is None:
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seed = random.randint(1, 10000)
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opt['manual_seed'] = seed
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set_random_seed(seed + opt['rank'])
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return opt
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def init_loggers(opt):
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log_file = osp.join(opt['path']['log'], f"train_{opt['name']}.log")
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logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
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logger.info(get_env_info())
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logger.info(dict2str(opt))
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# initialize wandb logger before tensorboard logger to allow proper sync:
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if (opt['logger'].get('wandb') is not None) and (opt['logger']['wandb'].get('project') is not None):
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assert opt['logger'].get('use_tb_logger') is True, ('should turn on tensorboard when using wandb')
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init_wandb_logger(opt)
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tb_logger = None
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if opt['logger'].get('use_tb_logger'):
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tb_logger = init_tb_logger(log_dir=osp.join('tb_logger', opt['name']))
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return logger, tb_logger
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def create_train_val_dataloader(opt, logger):
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# create train and val dataloaders
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train_loader, val_loader = None, None
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for phase, dataset_opt in opt['datasets'].items():
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if phase == 'train':
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dataset_enlarge_ratio = dataset_opt.get('dataset_enlarge_ratio', 1)
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train_set = build_dataset(dataset_opt)
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train_sampler = EnlargedSampler(train_set, opt['world_size'], opt['rank'], dataset_enlarge_ratio)
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train_loader = build_dataloader(
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train_set,
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dataset_opt,
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num_gpu=opt['num_gpu'],
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dist=opt['dist'],
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sampler=train_sampler,
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seed=opt['manual_seed'])
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num_iter_per_epoch = math.ceil(
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len(train_set) * dataset_enlarge_ratio / (dataset_opt['batch_size_per_gpu'] * opt['world_size']))
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total_iters = int(opt['train']['total_iter'])
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total_epochs = math.ceil(total_iters / (num_iter_per_epoch))
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logger.info('Training statistics:'
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f'\n\tNumber of train images: {len(train_set)}'
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f'\n\tDataset enlarge ratio: {dataset_enlarge_ratio}'
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f'\n\tBatch size per gpu: {dataset_opt["batch_size_per_gpu"]}'
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f'\n\tWorld size (gpu number): {opt["world_size"]}'
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f'\n\tRequire iter number per epoch: {num_iter_per_epoch}'
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f'\n\tTotal epochs: {total_epochs}; iters: {total_iters}.')
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elif phase == 'val':
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val_set = build_dataset(dataset_opt)
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val_loader = build_dataloader(
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val_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed'])
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logger.info(f'Number of val images/folders in {dataset_opt["name"]}: ' f'{len(val_set)}')
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else:
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raise ValueError(f'Dataset phase {phase} is not recognized.')
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return train_loader, train_sampler, val_loader, total_epochs, total_iters
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def train_pipeline(root_path):
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# parse options, set distributed setting, set ramdom seed
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opt = parse_options(root_path, is_train=True)
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torch.backends.cudnn.benchmark = True
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# torch.backends.cudnn.deterministic = True
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# load resume states if necessary
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if opt['path'].get('resume_state'):
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device_id = torch.cuda.current_device()
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resume_state = torch.load(
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opt['path']['resume_state'], map_location=lambda storage, loc: storage.cuda(device_id))
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else:
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resume_state = None
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# mkdir for experiments and logger
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if resume_state is None:
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make_exp_dirs(opt)
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if opt['logger'].get('use_tb_logger') and opt['rank'] == 0:
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mkdir_and_rename(osp.join('tb_logger', opt['name']))
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# initialize loggers
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logger, tb_logger = init_loggers(opt)
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# create train and validation dataloaders
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result = create_train_val_dataloader(opt, logger)
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train_loader, train_sampler, val_loader, total_epochs, total_iters = result
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# create model
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if resume_state: # resume training
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check_resume(opt, resume_state['iter'])
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model = build_model(opt)
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model.resume_training(resume_state) # handle optimizers and schedulers
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logger.info(f"Resuming training from epoch: {resume_state['epoch']}, " f"iter: {resume_state['iter']}.")
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start_epoch = resume_state['epoch']
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current_iter = resume_state['iter']
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else:
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model = build_model(opt)
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start_epoch = 0
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current_iter = 0
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# create message logger (formatted outputs)
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msg_logger = MessageLogger(opt, current_iter, tb_logger)
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# dataloader prefetcher
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prefetch_mode = opt['datasets']['train'].get('prefetch_mode')
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if prefetch_mode is None or prefetch_mode == 'cpu':
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prefetcher = CPUPrefetcher(train_loader)
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elif prefetch_mode == 'cuda':
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prefetcher = CUDAPrefetcher(train_loader, opt)
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logger.info(f'Use {prefetch_mode} prefetch dataloader')
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if opt['datasets']['train'].get('pin_memory') is not True:
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raise ValueError('Please set pin_memory=True for CUDAPrefetcher.')
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else:
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raise ValueError(f'Wrong prefetch_mode {prefetch_mode}.' "Supported ones are: None, 'cuda', 'cpu'.")
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# training
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logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter+1}')
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data_time, iter_time = time.time(), time.time()
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start_time = time.time()
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for epoch in range(start_epoch, total_epochs + 1):
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train_sampler.set_epoch(epoch)
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prefetcher.reset()
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train_data = prefetcher.next()
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while train_data is not None:
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data_time = time.time() - data_time
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current_iter += 1
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if current_iter > total_iters:
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break
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# update learning rate
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model.update_learning_rate(current_iter, warmup_iter=opt['train'].get('warmup_iter', -1))
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# training
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model.feed_data(train_data)
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model.optimize_parameters(current_iter)
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iter_time = time.time() - iter_time
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# log
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if current_iter % opt['logger']['print_freq'] == 0:
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log_vars = {'epoch': epoch, 'iter': current_iter}
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log_vars.update({'lrs': model.get_current_learning_rate()})
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log_vars.update({'time': iter_time, 'data_time': data_time})
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log_vars.update(model.get_current_log())
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msg_logger(log_vars)
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# save models and training states
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if current_iter % opt['logger']['save_checkpoint_freq'] == 0:
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logger.info('Saving models and training states.')
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model.save(epoch, current_iter)
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# validation
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if opt.get('val') is not None and opt['datasets'].get('val') is not None \
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and (current_iter % opt['val']['val_freq'] == 0):
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model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])
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data_time = time.time()
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iter_time = time.time()
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train_data = prefetcher.next()
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# end of iter
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# end of epoch
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consumed_time = str(datetime.timedelta(seconds=int(time.time() - start_time)))
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logger.info(f'End of training. Time consumed: {consumed_time}')
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logger.info('Save the latest model.')
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model.save(epoch=-1, current_iter=-1) # -1 stands for the latest
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if opt.get('val') is not None and opt['datasets'].get('val'):
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model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])
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if tb_logger:
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tb_logger.close()
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if __name__ == '__main__':
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root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
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train_pipeline(root_path)
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