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deep-tempest/KAIR/main_test_usrnet.py

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import os.path
import cv2
import logging
import time
import os
import numpy as np
from datetime import datetime
from collections import OrderedDict
from scipy.io import loadmat
#import hdf5storage
from scipy import ndimage
from scipy.signal import convolve2d
import torch
from utils import utils_deblur
from utils import utils_logger
from utils import utils_sisr as sr
from utils import utils_image as util
from models.network_usrnet import USRNet as net
'''
Spyder (Python 3.6)
PyTorch 1.4.0
Windows 10 or Linux
Kai Zhang (cskaizhang@gmail.com)
github: https://github.com/cszn/USRNet
https://github.com/cszn/KAIR
If you have any question, please feel free to contact with me.
Kai Zhang (e-mail: cskaizhang@gmail.com)
by Kai Zhang (12/March/2020)
'''
"""
# --------------------------------------------
testing code of USRNet for the Table 1 in the paper
@inproceedings{zhang2020deep,
title={Deep unfolding network for image super-resolution},
author={Zhang, Kai and Van Gool, Luc and Timofte, Radu},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={0--0},
year={2020}
}
# --------------------------------------------
|--model_zoo # model_zoo
|--usrgan # model_name, optimized for perceptual quality
|--usrnet # model_name, optimized for PSNR
|--usrgan_tiny # model_name, tiny model optimized for perceptual quality
|--usrnet_tiny # model_name, tiny model optimized for PSNR
|--testsets # testsets
|--set5 # testset_name
|--set14
|--urban100
|--bsd100
|--srbsd68 # already cropped
|--results # results
|--srbsd68_usrnet # result_name = testset_name + '_' + model_name
|--srbsd68_usrgan
|--srbsd68_usrnet_tiny
|--srbsd68_usrgan_tiny
# --------------------------------------------
"""
def main():
# ----------------------------------------
# Preparation
# ----------------------------------------
model_name = 'usrnet' # 'usrgan' | 'usrnet' | 'usrgan_tiny' | 'usrnet_tiny'
testset_name = 'set5' # test set, 'set5' | 'srbsd68'
test_sf = [4] if 'gan' in model_name else [2, 3, 4] # scale factor, from {1,2,3,4}
show_img = False # default: False
save_L = True # save LR image
save_E = True # save estimated image
save_LEH = False # save zoomed LR, E and H images
# ----------------------------------------
# load testing kernels
# ----------------------------------------
# kernels = hdf5storage.loadmat(os.path.join('kernels', 'kernels.mat'))['kernels']
kernels = loadmat(os.path.join('kernels', 'kernels_12.mat'))['kernels']
n_channels = 1 if 'gray' in model_name else 3 # 3 for color image, 1 for grayscale image
model_pool = 'model_zoo' # fixed
testsets = 'testsets' # fixed
results = 'results' # fixed
noise_level_img = 0 # fixed: 0, noise level for LR image
noise_level_model = noise_level_img # fixed, noise level of model, default 0
result_name = testset_name + '_' + model_name
model_path = os.path.join(model_pool, model_name+'.pth')
# ----------------------------------------
# L_path = H_path, E_path, logger
# ----------------------------------------
L_path = os.path.join(testsets, testset_name) # L_path and H_path, fixed, for Low-quality images
E_path = os.path.join(results, result_name) # E_path, fixed, for Estimated images
util.mkdir(E_path)
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)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# ----------------------------------------
# load model
# ----------------------------------------
if 'tiny' in model_name:
model = net(n_iter=6, h_nc=32, in_nc=4, out_nc=3, nc=[16, 32, 64, 64],
nb=2, act_mode="R", downsample_mode='strideconv', upsample_mode="convtranspose")
else:
model = net(n_iter=8, h_nc=64, in_nc=4, out_nc=3, nc=[64, 128, 256, 512],
nb=2, act_mode="R", downsample_mode='strideconv', upsample_mode="convtranspose")
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
for key, v in model.named_parameters():
v.requires_grad = False
number_parameters = sum(map(lambda x: x.numel(), model.parameters()))
model = model.to(device)
logger.info('Model path: {:s}'.format(model_path))
logger.info('Params number: {}'.format(number_parameters))
logger.info('Model_name:{}, image sigma:{}'.format(model_name, noise_level_img))
logger.info(L_path)
L_paths = util.get_image_paths(L_path)
# --------------------------------
# read images
# --------------------------------
test_results_ave = OrderedDict()
test_results_ave['psnr_sf_k'] = []
for sf in test_sf:
for k_index in range(kernels.shape[1]):
test_results = OrderedDict()
test_results['psnr'] = []
kernel = kernels[0, k_index].astype(np.float64)
## other kernels
# kernel = utils_deblur.blurkernel_synthesis(h=25) # motion kernel
# kernel = utils_deblur.fspecial('gaussian', 25, 1.6) # Gaussian kernel
# kernel = sr.shift_pixel(kernel, sf) # pixel shift; optional
# kernel /= np.sum(kernel)
util.surf(kernel) if show_img else None
idx = 0
for img in L_paths:
# --------------------------------
# (1) classical degradation, img_L
# --------------------------------
idx += 1
img_name, ext = os.path.splitext(os.path.basename(img))
img_H = util.imread_uint(img, n_channels=n_channels) # HR image, int8
img_H = util.modcrop(img_H, np.lcm(sf,8)) # modcrop
# generate degraded LR image
img_L = ndimage.filters.convolve(img_H, kernel[..., np.newaxis], mode='wrap') # blur
img_L = sr.downsample_np(img_L, sf, center=False) # downsample, standard s-fold downsampler
img_L = util.uint2single(img_L) # uint2single
np.random.seed(seed=0) # for reproducibility
img_L += np.random.normal(0, noise_level_img, img_L.shape) # add AWGN
util.imshow(util.single2uint(img_L)) if show_img else None
x = util.single2tensor4(img_L)
k = util.single2tensor4(kernel[..., np.newaxis])
sigma = torch.tensor(noise_level_model).float().view([1, 1, 1, 1])
[x, k, sigma] = [el.to(device) for el in [x, k, sigma]]
# --------------------------------
# (2) inference
# --------------------------------
x = model(x, k, sf, sigma)
# --------------------------------
# (3) img_E
# --------------------------------
img_E = util.tensor2uint(x)
if save_E:
util.imsave(img_E, os.path.join(E_path, img_name+'_x'+str(sf)+'_k'+str(k_index+1)+'_'+model_name+'.png'))
# --------------------------------
# (4) img_LEH
# --------------------------------
img_L = util.single2uint(img_L)
if save_LEH:
k_v = kernel/np.max(kernel)*1.2
k_v = util.single2uint(np.tile(k_v[..., np.newaxis], [1, 1, 3]))
k_v = cv2.resize(k_v, (3*k_v.shape[1], 3*k_v.shape[0]), interpolation=cv2.INTER_NEAREST)
img_I = cv2.resize(img_L, (sf*img_L.shape[1], sf*img_L.shape[0]), interpolation=cv2.INTER_NEAREST)
img_I[:k_v.shape[0], -k_v.shape[1]:, :] = k_v
img_I[:img_L.shape[0], :img_L.shape[1], :] = img_L
util.imshow(np.concatenate([img_I, img_E, img_H], axis=1), title='LR / Recovered / Ground-truth') if show_img else None
util.imsave(np.concatenate([img_I, img_E, img_H], axis=1), os.path.join(E_path, img_name+'_x'+str(sf)+'_k'+str(k_index+1)+'_LEH.png'))
if save_L:
util.imsave(img_L, os.path.join(E_path, img_name+'_x'+str(sf)+'_k'+str(k_index+1)+'_LR.png'))
psnr = util.calculate_psnr(img_E, img_H, border=sf**2) # change with your own border
test_results['psnr'].append(psnr)
logger.info('{:->4d}--> {:>10s} -- x{:>2d} --k{:>2d} PSNR: {:.2f}dB'.format(idx, img_name+ext, sf, k_index, psnr))
ave_psnr_k = sum(test_results['psnr']) / len(test_results['psnr'])
logger.info('------> Average PSNR(RGB) of ({}) scale factor: ({}), kernel: ({}) sigma: ({}): {:.2f} dB'.format(testset_name, sf, k_index+1, noise_level_model, ave_psnr_k))
test_results_ave['psnr_sf_k'].append(ave_psnr_k)
logger.info(test_results_ave['psnr_sf_k'])
if __name__ == '__main__':
main()
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