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