import os.path import logging import re import numpy as np from collections import OrderedDict import torch from utils import utils_logger from utils import utils_image as util from utils import utils_model ''' Spyder (Python 3.6) PyTorch 1.1.0 Windows 10 or Linux Kai Zhang (cskaizhang@gmail.com) github: https://github.com/cszn/KAIR If you have any question, please feel free to contact with me. Kai Zhang (e-mail: cskaizhang@gmail.com) (github: https://github.com/cszn/KAIR) by Kai Zhang (12/Dec./2019) ''' """ # -------------------------------------------- testing demo for RRDB-ESRGAN https://github.com/xinntao/ESRGAN @inproceedings{wang2018esrgan, title={Esrgan: Enhanced super-resolution generative adversarial networks}, author={Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Change Loy, Chen}, booktitle={European Conference on Computer Vision (ECCV)}, pages={0--0}, year={2018} } @inproceedings{ledig2017photo, title={Photo-realistic single image super-resolution using a generative adversarial network}, author={Ledig, Christian and Theis, Lucas and Husz{\'a}r, Ferenc and Caballero, Jose and Cunningham, Andrew and Acosta, Alejandro and Aitken, Andrew and Tejani, Alykhan and Totz, Johannes and Wang, Zehan and others}, booktitle={IEEE conference on computer vision and pattern recognition}, pages={4681--4690}, year={2017} } # -------------------------------------------- |--model_zoo # model_zoo |--msrresnet_x4_gan # model_name, optimized for perceptual quality |--msrresnet_x4_psnr # model_name, optimized for PSNR |--testset # testsets |--set5 # testset_name |--srbsd68 |--results # results |--set5_msrresnet_x4_gan # result_name = testset_name + '_' + model_name |--set5_msrresnet_x4_psnr # -------------------------------------------- """ def main(): # ---------------------------------------- # Preparation # ---------------------------------------- model_name = 'msrresnet_x4_psnr' # 'msrresnet_x4_gan' | 'msrresnet_x4_psnr' testset_name = 'set5' # test set, 'set5' | 'srbsd68' need_degradation = True # default: True x8 = False # default: False, x8 to boost performance, default: False sf = [int(s) for s in re.findall(r'\d+', model_name)][0] # scale factor show_img = False # default: False task_current = 'sr' # 'dn' for denoising | 'sr' for super-resolution n_channels = 3 # fixed model_pool = 'model_zoo' # fixed testsets = 'testsets' # fixed results = 'results' # fixed noise_level_img = 0 # fixed: 0, noise level for LR image result_name = testset_name + '_' + model_name border = sf if task_current == 'sr' else 0 # shave boader to calculate PSNR and SSIM model_path = os.path.join(model_pool, model_name+'.pth') # ---------------------------------------- # L_path, E_path, H_path # ---------------------------------------- L_path = os.path.join(testsets, testset_name) # L_path, for Low-quality images H_path = L_path # H_path, for High-quality images E_path = os.path.join(results, result_name) # E_path, for Estimated images util.mkdir(E_path) if H_path == L_path: need_degradation = True logger_name = result_name utils_logger.logger_info(logger_name, log_path=os.path.join(E_path, logger_name+'.log')) logger = logging.getLogger(logger_name) need_H = True if H_path is not None else False device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # ---------------------------------------- # load model # ---------------------------------------- from models.network_msrresnet import MSRResNet1 as net model = net(in_nc=n_channels, out_nc=n_channels, nc=64, nb=16, upscale=4) model.load_state_dict(torch.load(model_path), strict=True) model.eval() for k, v in model.named_parameters(): v.requires_grad = False model = model.to(device) logger.info('Model path: {:s}'.format(model_path)) number_parameters = sum(map(lambda x: x.numel(), model.parameters())) logger.info('Params number: {}'.format(number_parameters)) test_results = OrderedDict() test_results['psnr'] = [] test_results['ssim'] = [] test_results['psnr_y'] = [] test_results['ssim_y'] = [] logger.info('model_name:{}, image sigma:{}'.format(model_name, noise_level_img)) logger.info(L_path) L_paths = util.get_image_paths(L_path) H_paths = util.get_image_paths(H_path) if need_H else None for idx, img in enumerate(L_paths): # ------------------------------------ # (1) img_L # ------------------------------------ img_name, ext = os.path.splitext(os.path.basename(img)) # logger.info('{:->4d}--> {:>10s}'.format(idx+1, img_name+ext)) img_L = util.imread_uint(img, n_channels=n_channels) img_L = util.uint2single(img_L) # degradation process, bicubic downsampling if need_degradation: img_L = util.modcrop(img_L, sf) img_L = util.imresize_np(img_L, 1/sf) # img_L = util.uint2single(util.single2uint(img_L)) # np.random.seed(seed=0) # for reproducibility # img_L += np.random.normal(0, noise_level_img/255., img_L.shape) util.imshow(util.single2uint(img_L), title='LR image with noise level {}'.format(noise_level_img)) if show_img else None img_L = util.single2tensor4(img_L) img_L = img_L.to(device) # ------------------------------------ # (2) img_E # ------------------------------------ if not x8: img_E = model(img_L) else: img_E = utils_model.test_mode(model, img_L, mode=3, sf=sf) img_E = util.tensor2uint(img_E) if need_H: # -------------------------------- # (3) img_H # -------------------------------- img_H = util.imread_uint(H_paths[idx], n_channels=n_channels) img_H = img_H.squeeze() img_H = util.modcrop(img_H, sf) # -------------------------------- # PSNR and SSIM # -------------------------------- psnr = util.calculate_psnr(img_E, img_H, border=border) ssim = util.calculate_ssim(img_E, img_H, border=border) test_results['psnr'].append(psnr) test_results['ssim'].append(ssim) logger.info('{:s} - PSNR: {:.2f} dB; SSIM: {:.4f}.'.format(img_name+ext, psnr, ssim)) util.imshow(np.concatenate([img_E, img_H], axis=1), title='Recovered / Ground-truth') if show_img else None if np.ndim(img_H) == 3: # RGB image img_E_y = util.rgb2ycbcr(img_E, only_y=True) img_H_y = util.rgb2ycbcr(img_H, only_y=True) psnr_y = util.calculate_psnr(img_E_y, img_H_y, border=border) ssim_y = util.calculate_ssim(img_E_y, img_H_y, border=border) test_results['psnr_y'].append(psnr_y) test_results['ssim_y'].append(ssim_y) # ------------------------------------ # save results # ------------------------------------ util.imsave(img_E, os.path.join(E_path, img_name+'.png')) if need_H: ave_psnr = sum(test_results['psnr']) / len(test_results['psnr']) ave_ssim = sum(test_results['ssim']) / len(test_results['ssim']) logger.info('Average PSNR/SSIM(RGB) - {} - x{} --PSNR: {:.2f} dB; SSIM: {:.4f}'.format(result_name, sf, ave_psnr, ave_ssim)) if np.ndim(img_H) == 3: ave_psnr_y = sum(test_results['psnr_y']) / len(test_results['psnr_y']) ave_ssim_y = sum(test_results['ssim_y']) / len(test_results['ssim_y']) logger.info('Average PSNR/SSIM( Y ) - {} - x{} - PSNR: {:.2f} dB; SSIM: {:.4f}'.format(result_name, sf, ave_psnr_y, ave_ssim_y)) if __name__ == '__main__': main()