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