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train.py
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train.py
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import os
import math
import time
import torch
import random
import logging
import argparse
import datetime
from network import create_model
from options.yaml_opt import parse, dict2str
from dataset.data_sampler import EnlargedSampler
from dataset import create_dataset, create_dataloader
from base_utils.logger import get_root_logger, get_env_info, init_tb_logger, MessageLogger
from base_utils.utils import set_random_seed, get_time_str, check_resume, make_exp_dirs, mkdir_and_rename
torch.cuda.is_available()
def parse_options(is_train=True):
format_str = 'train' if is_train else 'test'
parser = argparse.ArgumentParser()
parser.add_argument(
'-opt', type=str, default='options/{}_option.yml'.format(format_str), help='Path to option YAML file.')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()
opt = parse(args.opt, is_train=is_train)
opt['dist'] = False
# 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_path = opt['path'].get('log_path')
if log_path == None:
log_path = opt['path']['experiments_root']
log_file = os.path.join(log_path,
f"train_{opt['name']}_{get_time_str()}.log")
if os.path.exists(log_path) == False:
os.makedirs(log_path)
logger = get_root_logger(
logger_name='relighting', log_level=logging.INFO, log_file=log_file)
logger.info(get_env_info())
logger.info(dict2str(opt))
# initialize tensorboard logger and wandb logger
tb_logger = None
if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name']:
tb_logger = init_tb_logger(log_dir=opt['path']['tb_logger'])
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 = create_dataset(dataset_opt)
train_sampler = EnlargedSampler(train_set, opt['world_size'],
opt['rank'], dataset_enlarge_ratio)
train_loader = create_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['num_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 = create_dataset(dataset_opt)
val_loader = create_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 main():
# parse options, set distributed setting, set ramdom seed
opt = parse_options(is_train=True)
os.environ["CUDA_VISIBLE_DEVICES"] = opt['gpu_id']
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)
# 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 = create_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 = create_model(opt)
start_epoch = 0
current_iter = 0
# create message logger (formatted outputs)
msg_logger = MessageLogger(opt, current_iter, tb_logger)
# 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)
iter_trainloader = iter(train_loader)
train_data = iter_trainloader.next()
while train_data is not None:
data_time = time.time() - data_time
current_iter += 1
if current_iter > total_iters:
break
model.lr_decay(current_iter)
# 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()
try:
train_data = iter_trainloader.next()
except StopIteration:
train_data = None
# 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__':
main()