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| import os.path | |
| import math | |
| import argparse | |
| import time | |
| import random | |
| import numpy as np | |
| from collections import OrderedDict | |
| import logging | |
| import torch | |
| from torch.utils.data import DataLoader | |
| from utils import utils_logger | |
| from utils import utils_image as util | |
| from utils import utils_option as option | |
| from data.select_dataset import define_Dataset | |
| from models.select_model import define_Model | |
| ''' | |
| # -------------------------------------------- | |
| # training code for DnCNN | |
| # -------------------------------------------- | |
| # Kai Zhang (cskaizhang@gmail.com) | |
| # github: https://github.com/cszn/KAIR | |
| # https://github.com/cszn/DnCNN | |
| # | |
| # Reference: | |
| @article{zhang2017beyond, | |
| title={Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising}, | |
| author={Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei}, | |
| journal={IEEE Transactions on Image Processing}, | |
| volume={26}, | |
| number={7}, | |
| pages={3142--3155}, | |
| year={2017}, | |
| publisher={IEEE} | |
| } | |
| # -------------------------------------------- | |
| # https://github.com/xinntao/BasicSR | |
| # -------------------------------------------- | |
| ''' | |
| def main(json_path='options/train_dncnn.json'): | |
| ''' | |
| # ---------------------------------------- | |
| # Step--1 (prepare opt) | |
| # ---------------------------------------- | |
| ''' | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('-opt', type=str, default=json_path, help='Path to option JSON file.') | |
| opt = option.parse(parser.parse_args().opt, is_train=True) | |
| util.mkdirs((path for key, path in opt['path'].items() if 'pretrained' not in key)) | |
| # ---------------------------------------- | |
| # update opt | |
| # ---------------------------------------- | |
| # -->-->-->-->-->-->-->-->-->-->-->-->-->- | |
| init_iter, init_path_G = option.find_last_checkpoint(opt['path']['models'], net_type='G') | |
| opt['path']['pretrained_netG'] = init_path_G | |
| current_step = init_iter | |
| border = 0 | |
| # --<--<--<--<--<--<--<--<--<--<--<--<--<- | |
| # ---------------------------------------- | |
| # save opt to a '../option.json' file | |
| # ---------------------------------------- | |
| option.save(opt) | |
| # ---------------------------------------- | |
| # return None for missing key | |
| # ---------------------------------------- | |
| opt = option.dict_to_nonedict(opt) | |
| # ---------------------------------------- | |
| # configure logger | |
| # ---------------------------------------- | |
| logger_name = 'train' | |
| utils_logger.logger_info(logger_name, os.path.join(opt['path']['log'], logger_name+'.log')) | |
| logger = logging.getLogger(logger_name) | |
| logger.info(option.dict2str(opt)) | |
| # ---------------------------------------- | |
| # seed | |
| # ---------------------------------------- | |
| seed = opt['train']['manual_seed'] | |
| if seed is None: | |
| seed = random.randint(1, 10000) | |
| logger.info('Random seed: {}'.format(seed)) | |
| random.seed(seed) | |
| np.random.seed(seed) | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed_all(seed) | |
| ''' | |
| # ---------------------------------------- | |
| # Step--2 (creat dataloader) | |
| # ---------------------------------------- | |
| ''' | |
| # ---------------------------------------- | |
| # 1) create_dataset | |
| # 2) creat_dataloader for train and test | |
| # ---------------------------------------- | |
| dataset_type = opt['datasets']['train']['dataset_type'] | |
| for phase, dataset_opt in opt['datasets'].items(): | |
| if phase == 'train': | |
| train_set = define_Dataset(dataset_opt) | |
| train_size = int(math.ceil(len(train_set) / dataset_opt['dataloader_batch_size'])) | |
| logger.info('Number of train images: {:,d}, iters: {:,d}'.format(len(train_set), train_size)) | |
| train_loader = DataLoader(train_set, | |
| batch_size=dataset_opt['dataloader_batch_size'], | |
| shuffle=dataset_opt['dataloader_shuffle'], | |
| num_workers=dataset_opt['dataloader_num_workers'], | |
| drop_last=True, | |
| pin_memory=True) | |
| elif phase == 'test': | |
| test_set = define_Dataset(dataset_opt) | |
| test_loader = DataLoader(test_set, batch_size=1, | |
| shuffle=False, num_workers=1, | |
| drop_last=False, pin_memory=True) | |
| else: | |
| raise NotImplementedError("Phase [%s] is not recognized." % phase) | |
| ''' | |
| # ---------------------------------------- | |
| # Step--3 (initialize model) | |
| # ---------------------------------------- | |
| ''' | |
| model = define_Model(opt) | |
| if opt['merge_bn'] and current_step > opt['merge_bn_startpoint']: | |
| logger.info('^_^ -----merging bnorm----- ^_^') | |
| model.merge_bnorm_test() | |
| logger.info(model.info_network()) | |
| model.init_train() | |
| logger.info(model.info_params()) | |
| ''' | |
| # ---------------------------------------- | |
| # Step--4 (main training) | |
| # ---------------------------------------- | |
| ''' | |
| for epoch in range(1000000): # keep running | |
| for i, train_data in enumerate(train_loader): | |
| current_step += 1 | |
| if dataset_type == 'dnpatch' and current_step % 20000 == 0: # for 'train400' | |
| train_loader.dataset.update_data() | |
| # ------------------------------- | |
| # 1) update learning rate | |
| # ------------------------------- | |
| model.update_learning_rate(current_step) | |
| # ------------------------------- | |
| # 2) feed patch pairs | |
| # ------------------------------- | |
| model.feed_data(train_data) | |
| # ------------------------------- | |
| # 3) optimize parameters | |
| # ------------------------------- | |
| model.optimize_parameters(current_step) | |
| # ------------------------------- | |
| # merge bnorm | |
| # ------------------------------- | |
| if opt['merge_bn'] and opt['merge_bn_startpoint'] == current_step: | |
| logger.info('^_^ -----merging bnorm----- ^_^') | |
| model.merge_bnorm_train() | |
| model.print_network() | |
| # ------------------------------- | |
| # 4) training information | |
| # ------------------------------- | |
| if current_step % opt['train']['checkpoint_print'] == 0: | |
| logs = model.current_log() # such as loss | |
| message = '<epoch:{:3d}, iter:{:8,d}, lr:{:.3e}> '.format(epoch, current_step, model.current_learning_rate()) | |
| for k, v in logs.items(): # merge log information into message | |
| message += '{:s}: {:.3e} '.format(k, v) | |
| logger.info(message) | |
| # ------------------------------- | |
| # 5) save model | |
| # ------------------------------- | |
| if current_step % opt['train']['checkpoint_save'] == 0: | |
| logger.info('Saving the model.') | |
| model.save(current_step) | |
| # ------------------------------- | |
| # 6) testing | |
| # ------------------------------- | |
| if current_step % opt['train']['checkpoint_test'] == 0: | |
| avg_psnr = 0.0 | |
| idx = 0 | |
| for test_data in test_loader: | |
| idx += 1 | |
| image_name_ext = os.path.basename(test_data['L_path'][0]) | |
| img_name, ext = os.path.splitext(image_name_ext) | |
| img_dir = os.path.join(opt['path']['images'], img_name) | |
| util.mkdir(img_dir) | |
| model.feed_data(test_data) | |
| model.test() | |
| visuals = model.current_visuals() | |
| E_img = util.tensor2uint(visuals['E']) | |
| H_img = util.tensor2uint(visuals['H']) | |
| # ----------------------- | |
| # save estimated image E | |
| # ----------------------- | |
| save_img_path = os.path.join(img_dir, '{:s}_{:d}.png'.format(img_name, current_step)) | |
| util.imsave(E_img, save_img_path) | |
| # ----------------------- | |
| # calculate PSNR | |
| # ----------------------- | |
| current_psnr = util.calculate_psnr(E_img, H_img, border=border) | |
| logger.info('{:->4d}--> {:>10s} | {:<4.2f}dB'.format(idx, image_name_ext, current_psnr)) | |
| avg_psnr += current_psnr | |
| avg_psnr = avg_psnr / idx | |
| # testing log | |
| logger.info('<epoch:{:3d}, iter:{:8,d}, Average PSNR : {:<.2f}dB\n'.format(epoch, current_step, avg_psnr)) | |
| logger.info('Saving the final model.') | |
| model.save('latest') | |
| logger.info('End of training.') | |
| if __name__ == '__main__': | |
| main() | |