def test_pipeline(root_path):
# parse options, set distributed setting, set ramdom seed
opt = parse_options(root_path, is_train=False)
torch.backends.cudnn.benchmark = True
# torch.backends.cudnn.deterministic = True
# mkdir and initialize loggers
make_exp_dirs(opt)
log_file = osp.join(opt['path']['log'],
f"test_{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))
# create test dataset and dataloader
test_loaders = []
for phase, dataset_opt in sorted(opt['datasets'].items()):
test_set = build_dataset(dataset_opt)
test_loader = build_dataloader(test_set,
dataset_opt,
num_gpu=opt['num_gpu'],
dist=opt['dist'],
sampler=None,
seed=opt['manual_seed'])
logger.info(
f"Number of test images in {dataset_opt['name']}: {len(test_set)}")
test_loaders.append(test_loader)
# create model
model = build_model(opt)
for test_loader in test_loaders:
test_set_name = test_loader.dataset.opt['name']
logger.info(f'Testing {test_set_name}...')
model.validation(test_loader,
current_iter=opt['name'],
tb_logger=None,
save_img=opt['val']['save_img'])
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']}, 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()
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()