251 lines
8.7 KiB
Python
251 lines
8.7 KiB
Python
import os.path
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import math
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import argparse
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import time
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import random
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import numpy as np
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from collections import OrderedDict
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import logging
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import torch
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from torch.utils.data import DataLoader
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from utils import utils_logger
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from utils import utils_image as util
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from utils import utils_option as option
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from data.select_dataset import define_Dataset
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from models.select_model import define_Model
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'''
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# --------------------------------------------
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# training code for DnCNN
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# --------------------------------------------
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# Kai Zhang (cskaizhang@gmail.com)
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# github: https://github.com/cszn/KAIR
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# https://github.com/cszn/DnCNN
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#
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# Reference:
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@article{zhang2017beyond,
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title={Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising},
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author={Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei},
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journal={IEEE Transactions on Image Processing},
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volume={26},
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number={7},
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pages={3142--3155},
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year={2017},
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publisher={IEEE}
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}
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# --------------------------------------------
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# https://github.com/xinntao/BasicSR
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# --------------------------------------------
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'''
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def main(json_path='options/train_dncnn.json'):
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'''
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# ----------------------------------------
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# Step--1 (prepare opt)
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# ----------------------------------------
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'''
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, default=json_path, help='Path to option JSON file.')
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opt = option.parse(parser.parse_args().opt, is_train=True)
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util.mkdirs((path for key, path in opt['path'].items() if 'pretrained' not in key))
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# ----------------------------------------
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# update opt
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# ----------------------------------------
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# -->-->-->-->-->-->-->-->-->-->-->-->-->-
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init_iter, init_path_G = option.find_last_checkpoint(opt['path']['models'], net_type='G')
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opt['path']['pretrained_netG'] = init_path_G
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current_step = init_iter
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border = 0
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# --<--<--<--<--<--<--<--<--<--<--<--<--<-
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# ----------------------------------------
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# save opt to a '../option.json' file
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# ----------------------------------------
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option.save(opt)
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# ----------------------------------------
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# return None for missing key
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# ----------------------------------------
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opt = option.dict_to_nonedict(opt)
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# ----------------------------------------
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# configure logger
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# ----------------------------------------
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logger_name = 'train'
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utils_logger.logger_info(logger_name, os.path.join(opt['path']['log'], logger_name+'.log'))
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logger = logging.getLogger(logger_name)
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logger.info(option.dict2str(opt))
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# ----------------------------------------
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# seed
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# ----------------------------------------
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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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logger.info('Random seed: {}'.format(seed))
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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'''
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# ----------------------------------------
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# Step--2 (creat dataloader)
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# ----------------------------------------
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'''
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# ----------------------------------------
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# 1) create_dataset
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# 2) creat_dataloader for train and test
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# ----------------------------------------
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dataset_type = opt['datasets']['train']['dataset_type']
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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 = define_Dataset(dataset_opt)
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train_size = int(math.ceil(len(train_set) / dataset_opt['dataloader_batch_size']))
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logger.info('Number of train images: {:,d}, iters: {:,d}'.format(len(train_set), train_size))
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train_loader = DataLoader(train_set,
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batch_size=dataset_opt['dataloader_batch_size'],
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shuffle=dataset_opt['dataloader_shuffle'],
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num_workers=dataset_opt['dataloader_num_workers'],
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drop_last=True,
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pin_memory=True)
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elif phase == 'test':
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test_set = define_Dataset(dataset_opt)
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test_loader = DataLoader(test_set, batch_size=1,
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shuffle=False, num_workers=1,
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drop_last=False, pin_memory=True)
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else:
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raise NotImplementedError("Phase [%s] is not recognized." % phase)
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'''
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# ----------------------------------------
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# Step--3 (initialize model)
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# ----------------------------------------
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'''
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model = define_Model(opt)
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if opt['merge_bn'] and current_step > opt['merge_bn_startpoint']:
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logger.info('^_^ -----merging bnorm----- ^_^')
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model.merge_bnorm_test()
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logger.info(model.info_network())
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model.init_train()
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logger.info(model.info_params())
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'''
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# ----------------------------------------
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# Step--4 (main training)
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# ----------------------------------------
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'''
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for epoch in range(1000000): # keep running
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for i, train_data in enumerate(train_loader):
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current_step += 1
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if dataset_type == 'dnpatch' and current_step % 20000 == 0: # for 'train400'
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train_loader.dataset.update_data()
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# -------------------------------
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# 1) update learning rate
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# -------------------------------
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model.update_learning_rate(current_step)
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# -------------------------------
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# 2) feed patch pairs
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# -------------------------------
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model.feed_data(train_data)
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# -------------------------------
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# 3) optimize parameters
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# -------------------------------
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model.optimize_parameters(current_step)
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# -------------------------------
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# merge bnorm
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# -------------------------------
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if opt['merge_bn'] and opt['merge_bn_startpoint'] == current_step:
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logger.info('^_^ -----merging bnorm----- ^_^')
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model.merge_bnorm_train()
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model.print_network()
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# -------------------------------
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# 4) training information
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# -------------------------------
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if current_step % opt['train']['checkpoint_print'] == 0:
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logs = model.current_log() # such as loss
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message = '<epoch:{:3d}, iter:{:8,d}, lr:{:.3e}> '.format(epoch, current_step, model.current_learning_rate())
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for k, v in logs.items(): # merge log information into message
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message += '{:s}: {:.3e} '.format(k, v)
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logger.info(message)
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# -------------------------------
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# 5) save model
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# -------------------------------
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if current_step % opt['train']['checkpoint_save'] == 0:
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logger.info('Saving the model.')
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model.save(current_step)
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# -------------------------------
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# 6) testing
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# -------------------------------
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if current_step % opt['train']['checkpoint_test'] == 0:
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avg_psnr = 0.0
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idx = 0
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for test_data in test_loader:
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idx += 1
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image_name_ext = os.path.basename(test_data['L_path'][0])
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img_name, ext = os.path.splitext(image_name_ext)
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img_dir = os.path.join(opt['path']['images'], img_name)
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util.mkdir(img_dir)
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model.feed_data(test_data)
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model.test()
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visuals = model.current_visuals()
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E_img = util.tensor2uint(visuals['E'])
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H_img = util.tensor2uint(visuals['H'])
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# -----------------------
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# save estimated image E
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# -----------------------
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save_img_path = os.path.join(img_dir, '{:s}_{:d}.png'.format(img_name, current_step))
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util.imsave(E_img, save_img_path)
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# -----------------------
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# calculate PSNR
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# -----------------------
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current_psnr = util.calculate_psnr(E_img, H_img, border=border)
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logger.info('{:->4d}--> {:>10s} | {:<4.2f}dB'.format(idx, image_name_ext, current_psnr))
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avg_psnr += current_psnr
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avg_psnr = avg_psnr / idx
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# testing log
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logger.info('<epoch:{:3d}, iter:{:8,d}, Average PSNR : {:<.2f}dB\n'.format(epoch, current_step, avg_psnr))
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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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if __name__ == '__main__':
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main()
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