import datetime
import logging
import math
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 (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('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 random 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']}, 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 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)