Hugging Face's logo

Spaces:
lambdalabs
/
LambdaSuperRes
Running

App Files Community
1
LambdaSuperRes
File size: 8,165 Bytes 
2514fb4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
 
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
        https://github.com/cszn/DPSR

@inproceedings{zhang2019deep,
  title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
  author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
  pages={1671--1681},
  year={2019}
}

% If you have any question, please feel free to contact with me.
% Kai Zhang (e-mail: cskaizhang@gmail.com; github: https://github.com/cszn)

by Kai Zhang (12/Dec./2019)
'''

"""
# --------------------------------------------
testing code for the super-resolver prior of DPSR
# --------------------------------------------
|--model_zoo             # model_zoo
   |--dpsr_x2            # model_name, optimized for PSNR
   |--dpsr_x3 
   |--dpsr_x4
   |--dpsr_x4_gan        # model_name, optimized for perceptual quality
|--testset               # testsets
   |--set5               # testset_name
   |--srbsd68
|--results               # results
   |--set5_dpsr_x2       # result_name = testset_name + '_' + model_name
   |--set5_dpsr_x3
   |--set5_dpsr_x4
   |--set5_dpsr_x4_gan
   |--srbsd68_dpsr_x4_gan
# --------------------------------------------
"""


def main():

    # ----------------------------------------
    # Preparation
    # ----------------------------------------

    noise_level_img = 0                  # default: 0, noise level for LR image
    noise_level_model = noise_level_img  # noise level for model    
    model_name = 'dpsr_x4_gan'           # 'dpsr_x2' | 'dpsr_x3' | 'dpsr_x4' | 'dpsr_x4_gan'
    testset_name = 'set5'                # test set,  'set5' | 'srbsd68'
    need_degradation = True              # default: True
    x8 = False                           # default: False, x8 to boost performance
    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
    nc = 96                   # fixed, number of channels
    nb = 16                   # fixed, number of conv layers
    model_pool = 'model_zoo'  # fixed
    testsets = 'testsets'     # fixed
    results = 'results'       # fixed
    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_dpsr import MSRResNet_prior as net
    model = net(in_nc=n_channels+1, out_nc=n_channels, nc=nc, nb=nb, upscale=sf, act_mode='R', upsample_mode='pixelshuffle')
    model.load_state_dict(torch.load(model_path), strict=False)
    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:{}, model sigma:{}, image sigma:{}'.format(model_name, noise_level_img, noise_level_model))
    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 + Gaussian noise
        if need_degradation:
            img_L = util.modcrop(img_L, sf)
            img_L = util.imresize_np(img_L, 1/sf)
            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)
        noise_level_map = torch.full((1, 1, img_L.size(2), img_L.size(3)), noise_level_model/255.).type_as(img_L)
        img_L = torch.cat((img_L, noise_level_map), dim=1)
        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()