forked from mrq/DL-Art-School
311 lines
14 KiB
Python
311 lines
14 KiB
Python
import os
|
|
import math
|
|
import argparse
|
|
import random
|
|
import logging
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
import torch.multiprocessing as mp
|
|
from data.data_sampler import DistIterSampler
|
|
|
|
import options.options as option
|
|
from utils import util
|
|
from data import create_dataloader, create_dataset
|
|
from models import create_model
|
|
|
|
|
|
def init_dist(backend='nccl', **kwargs):
|
|
"""initialization for distributed training"""
|
|
if mp.get_start_method(allow_none=True) != 'spawn':
|
|
mp.set_start_method('spawn')
|
|
rank = int(os.environ['RANK'])
|
|
num_gpus = torch.cuda.device_count()
|
|
torch.cuda.set_device(rank % num_gpus)
|
|
dist.init_process_group(backend=backend, **kwargs)
|
|
|
|
|
|
def main():
|
|
#### options
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('-opt', type=str, help='Path to option YAML file.')
|
|
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
|
|
help='job launcher')
|
|
parser.add_argument('--local_rank', type=int, default=0)
|
|
args = parser.parse_args()
|
|
opt = option.parse(args.opt, is_train=True)
|
|
|
|
#### distributed training settings
|
|
if args.launcher == 'none': # disabled distributed training
|
|
opt['dist'] = False
|
|
rank = -1
|
|
print('Disabled distributed training.')
|
|
else:
|
|
opt['dist'] = True
|
|
init_dist()
|
|
world_size = torch.distributed.get_world_size()
|
|
rank = torch.distributed.get_rank()
|
|
|
|
#### loading resume state if exists
|
|
if opt['path'].get('resume_state', None):
|
|
# distributed resuming: all load into default GPU
|
|
device_id = torch.cuda.current_device()
|
|
resume_state = torch.load(opt['path']['resume_state'],
|
|
map_location=lambda storage, loc: storage.cuda(device_id))
|
|
option.check_resume(opt, resume_state['iter']) # check resume options
|
|
else:
|
|
resume_state = None
|
|
|
|
#### mkdir and loggers
|
|
if rank <= 0: # normal training (rank -1) OR distributed training (rank 0)
|
|
if resume_state is None:
|
|
util.mkdir_and_rename(
|
|
opt['path']['experiments_root']) # rename experiment folder if exists
|
|
util.mkdirs((path for key, path in opt['path'].items() if not key == 'experiments_root'
|
|
and 'pretrain_model' not in key and 'resume' not in key))
|
|
|
|
# config loggers. Before it, the log will not work
|
|
util.setup_logger('base', opt['path']['log'], 'train_' + opt['name'], level=logging.INFO,
|
|
screen=True, tofile=True)
|
|
logger = logging.getLogger('base')
|
|
logger.info(option.dict2str(opt))
|
|
# tensorboard logger
|
|
if opt['use_tb_logger'] and 'debug' not in opt['name']:
|
|
version = float(torch.__version__[0:3])
|
|
if version >= 1.1: # PyTorch 1.1
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
else:
|
|
logger.info(
|
|
'You are using PyTorch {}. Tensorboard will use [tensorboardX]'.format(version))
|
|
from tensorboardX import SummaryWriter
|
|
tb_logger = SummaryWriter(log_dir='../tb_logger/' + opt['name'])
|
|
else:
|
|
util.setup_logger('base', opt['path']['log'], 'train', level=logging.INFO, screen=True)
|
|
logger = logging.getLogger('base')
|
|
|
|
# convert to NoneDict, which returns None for missing keys
|
|
opt = option.dict_to_nonedict(opt)
|
|
|
|
#### random seed
|
|
seed = opt['train']['manual_seed']
|
|
if seed is None:
|
|
seed = random.randint(1, 10000)
|
|
if rank <= 0:
|
|
logger.info('Random seed: {}'.format(seed))
|
|
util.set_random_seed(seed)
|
|
|
|
torch.backends.cudnn.benchmark = True
|
|
# torch.backends.cudnn.deterministic = True
|
|
|
|
#### create train and val dataloader
|
|
dataset_ratio = 200 # enlarge the size of each epoch
|
|
for phase, dataset_opt in opt['datasets'].items():
|
|
if phase == 'train':
|
|
train_set = create_dataset(dataset_opt)
|
|
train_size = int(math.ceil(len(train_set) / dataset_opt['batch_size']))
|
|
total_iters = int(opt['train']['niter'])
|
|
total_epochs = int(math.ceil(total_iters / train_size))
|
|
if opt['dist']:
|
|
train_sampler = DistIterSampler(train_set, world_size, rank, dataset_ratio)
|
|
total_epochs = int(math.ceil(total_iters / (train_size * dataset_ratio)))
|
|
else:
|
|
train_sampler = None
|
|
train_loader = create_dataloader(train_set, dataset_opt, opt, train_sampler)
|
|
if rank <= 0:
|
|
logger.info('Number of train images: {:,d}, iters: {:,d}'.format(
|
|
len(train_set), train_size))
|
|
logger.info('Total epochs needed: {:d} for iters {:,d}'.format(
|
|
total_epochs, total_iters))
|
|
elif phase == 'val':
|
|
val_set = create_dataset(dataset_opt)
|
|
val_loader = create_dataloader(val_set, dataset_opt, opt, None)
|
|
if rank <= 0:
|
|
logger.info('Number of val images in [{:s}]: {:d}'.format(
|
|
dataset_opt['name'], len(val_set)))
|
|
else:
|
|
raise NotImplementedError('Phase [{:s}] is not recognized.'.format(phase))
|
|
assert train_loader is not None
|
|
|
|
#### create model
|
|
model = create_model(opt)
|
|
|
|
#### resume training
|
|
if resume_state:
|
|
logger.info('Resuming training from epoch: {}, iter: {}.'.format(
|
|
resume_state['epoch'], resume_state['iter']))
|
|
|
|
start_epoch = resume_state['epoch']
|
|
current_step = resume_state['iter']
|
|
model.resume_training(resume_state) # handle optimizers and schedulers
|
|
else:
|
|
current_step = 0
|
|
start_epoch = 0
|
|
|
|
#### training
|
|
logger.info('Start training from epoch: {:d}, iter: {:d}'.format(start_epoch, current_step))
|
|
for epoch in range(start_epoch, total_epochs + 1):
|
|
if opt['dist']:
|
|
train_sampler.set_epoch(epoch)
|
|
for _, train_data in enumerate(train_loader):
|
|
current_step += 1
|
|
if current_step > total_iters:
|
|
break
|
|
#### update learning rate
|
|
model.update_learning_rate(current_step, warmup_iter=opt['train']['warmup_iter'])
|
|
|
|
#### training
|
|
model.feed_data(train_data)
|
|
model.optimize_parameters(current_step)
|
|
|
|
#### log
|
|
if current_step % opt['logger']['print_freq'] == 0:
|
|
logs = model.get_current_log()
|
|
message = '[epoch:{:3d}, iter:{:8,d}, lr:('.format(epoch, current_step)
|
|
for v in model.get_current_learning_rate():
|
|
message += '{:.3e},'.format(v)
|
|
message += ')] '
|
|
for k, v in logs.items():
|
|
message += '{:s}: {:.4e} '.format(k, v)
|
|
# tensorboard logger
|
|
if opt['use_tb_logger'] and 'debug' not in opt['name']:
|
|
if rank <= 0:
|
|
tb_logger.add_scalar(k, v, current_step)
|
|
if rank <= 0:
|
|
logger.info(message)
|
|
#### validation
|
|
if opt['datasets'].get('val', None) and current_step % opt['train']['val_freq'] == 0:
|
|
if opt['model'] in ['sr', 'srgan'] and rank <= 0: # image restoration validation
|
|
# does not support multi-GPU validation
|
|
pbar = util.ProgressBar(len(val_loader))
|
|
avg_psnr = 0.
|
|
idx = 0
|
|
for val_data in val_loader:
|
|
idx += 1
|
|
img_name = os.path.splitext(os.path.basename(val_data['LQ_path'][0]))[0]
|
|
img_dir = os.path.join(opt['path']['val_images'], img_name)
|
|
util.mkdir(img_dir)
|
|
|
|
model.feed_data(val_data)
|
|
model.test()
|
|
|
|
visuals = model.get_current_visuals()
|
|
sr_img = util.tensor2img(visuals['rlt']) # uint8
|
|
gt_img = util.tensor2img(visuals['GT']) # uint8
|
|
|
|
# Save SR images for reference
|
|
save_img_path = os.path.join(img_dir,
|
|
'{:s}_{:d}.png'.format(img_name, current_step))
|
|
util.save_img(sr_img, save_img_path)
|
|
|
|
# calculate PSNR
|
|
sr_img, gt_img = util.crop_border([sr_img, gt_img], opt['scale'])
|
|
avg_psnr += util.calculate_psnr(sr_img, gt_img)
|
|
pbar.update('Test {}'.format(img_name))
|
|
|
|
avg_psnr = avg_psnr / idx
|
|
|
|
# log
|
|
logger.info('# Validation # PSNR: {:.4e}'.format(avg_psnr))
|
|
# tensorboard logger
|
|
if opt['use_tb_logger'] and 'debug' not in opt['name']:
|
|
tb_logger.add_scalar('psnr', avg_psnr, current_step)
|
|
else: # video restoration validation
|
|
if opt['dist']:
|
|
# multi-GPU testing
|
|
psnr_rlt = {} # with border and center frames
|
|
if rank == 0:
|
|
pbar = util.ProgressBar(len(val_set))
|
|
for idx in range(rank, len(val_set), world_size):
|
|
val_data = val_set[idx]
|
|
val_data['LQs'].unsqueeze_(0)
|
|
val_data['GT'].unsqueeze_(0)
|
|
folder = val_data['folder']
|
|
idx_d, max_idx = val_data['idx'].split('/')
|
|
idx_d, max_idx = int(idx_d), int(max_idx)
|
|
if psnr_rlt.get(folder, None) is None:
|
|
psnr_rlt[folder] = torch.zeros(max_idx, dtype=torch.float32,
|
|
device='cuda')
|
|
# tmp = torch.zeros(max_idx, dtype=torch.float32, device='cuda')
|
|
model.feed_data(val_data)
|
|
model.test()
|
|
visuals = model.get_current_visuals()
|
|
rlt_img = util.tensor2img(visuals['rlt']) # uint8
|
|
gt_img = util.tensor2img(visuals['GT']) # uint8
|
|
# calculate PSNR
|
|
psnr_rlt[folder][idx_d] = util.calculate_psnr(rlt_img, gt_img)
|
|
|
|
if rank == 0:
|
|
for _ in range(world_size):
|
|
pbar.update('Test {} - {}/{}'.format(folder, idx_d, max_idx))
|
|
# # collect data
|
|
for _, v in psnr_rlt.items():
|
|
dist.reduce(v, 0)
|
|
dist.barrier()
|
|
|
|
if rank == 0:
|
|
psnr_rlt_avg = {}
|
|
psnr_total_avg = 0.
|
|
for k, v in psnr_rlt.items():
|
|
psnr_rlt_avg[k] = torch.mean(v).cpu().item()
|
|
psnr_total_avg += psnr_rlt_avg[k]
|
|
psnr_total_avg /= len(psnr_rlt)
|
|
log_s = '# Validation # PSNR: {:.4e}:'.format(psnr_total_avg)
|
|
for k, v in psnr_rlt_avg.items():
|
|
log_s += ' {}: {:.4e}'.format(k, v)
|
|
logger.info(log_s)
|
|
if opt['use_tb_logger'] and 'debug' not in opt['name']:
|
|
tb_logger.add_scalar('psnr_avg', psnr_total_avg, current_step)
|
|
for k, v in psnr_rlt_avg.items():
|
|
tb_logger.add_scalar(k, v, current_step)
|
|
else:
|
|
pbar = util.ProgressBar(len(val_loader))
|
|
psnr_rlt = {} # with border and center frames
|
|
psnr_rlt_avg = {}
|
|
psnr_total_avg = 0.
|
|
for val_data in val_loader:
|
|
folder = val_data['folder'][0]
|
|
idx_d = val_data['idx'].item()
|
|
# border = val_data['border'].item()
|
|
if psnr_rlt.get(folder, None) is None:
|
|
psnr_rlt[folder] = []
|
|
|
|
model.feed_data(val_data)
|
|
model.test()
|
|
visuals = model.get_current_visuals()
|
|
rlt_img = util.tensor2img(visuals['rlt']) # uint8
|
|
gt_img = util.tensor2img(visuals['GT']) # uint8
|
|
|
|
# calculate PSNR
|
|
psnr = util.calculate_psnr(rlt_img, gt_img)
|
|
psnr_rlt[folder].append(psnr)
|
|
pbar.update('Test {} - {}'.format(folder, idx_d))
|
|
for k, v in psnr_rlt.items():
|
|
psnr_rlt_avg[k] = sum(v) / len(v)
|
|
psnr_total_avg += psnr_rlt_avg[k]
|
|
psnr_total_avg /= len(psnr_rlt)
|
|
log_s = '# Validation # PSNR: {:.4e}:'.format(psnr_total_avg)
|
|
for k, v in psnr_rlt_avg.items():
|
|
log_s += ' {}: {:.4e}'.format(k, v)
|
|
logger.info(log_s)
|
|
if opt['use_tb_logger'] and 'debug' not in opt['name']:
|
|
tb_logger.add_scalar('psnr_avg', psnr_total_avg, current_step)
|
|
for k, v in psnr_rlt_avg.items():
|
|
tb_logger.add_scalar(k, v, current_step)
|
|
|
|
#### save models and training states
|
|
if current_step % opt['logger']['save_checkpoint_freq'] == 0:
|
|
if rank <= 0:
|
|
logger.info('Saving models and training states.')
|
|
model.save(current_step)
|
|
model.save_training_state(epoch, current_step)
|
|
|
|
if rank <= 0:
|
|
logger.info('Saving the final model.')
|
|
model.save('latest')
|
|
logger.info('End of training.')
|
|
tb_logger.close()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|