File size: 14,141 Bytes
b93970c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
# '''
# https://github.com/One-sixth/ms_ssim_pytorch/blob/master/ssim.py
# '''
#
# import torch
# import torch.jit
# import torch.nn.functional as F
#
#
# @torch.jit.script
# def create_window(window_size: int, sigma: float, channel: int):
#     '''
#     Create 1-D gauss kernel
#     :param window_size: the size of gauss kernel
#     :param sigma: sigma of normal distribution
#     :param channel: input channel
#     :return: 1D kernel
#     '''
#     coords = torch.arange(window_size, dtype=torch.float)
#     coords -= window_size // 2
#
#     g = torch.exp(-(coords ** 2) / (2 * sigma ** 2))
#     g /= g.sum()
#
#     g = g.reshape(1, 1, 1, -1).repeat(channel, 1, 1, 1)
#     return g
#
#
# @torch.jit.script
# def _gaussian_filter(x, window_1d, use_padding: bool):
#     '''
#     Blur input with 1-D kernel
#     :param x: batch of tensors to be blured
#     :param window_1d: 1-D gauss kernel
#     :param use_padding: padding image before conv
#     :return: blured tensors
#     '''
#     C = x.shape[1]
#     padding = 0
#     if use_padding:
#         window_size = window_1d.shape[3]
#         padding = window_size // 2
#     out = F.conv2d(x, window_1d, stride=1, padding=(0, padding), groups=C)
#     out = F.conv2d(out, window_1d.transpose(2, 3), stride=1, padding=(padding, 0), groups=C)
#     return out
#
#
# @torch.jit.script
# def ssim(X, Y, window, data_range: float, use_padding: bool = False):
#     '''
#     Calculate ssim index for X and Y
#     :param X: images [B, C, H, N_bins]
#     :param Y: images [B, C, H, N_bins]
#     :param window: 1-D gauss kernel
#     :param data_range: value range of input images. (usually 1.0 or 255)
#     :param use_padding: padding image before conv
#     :return:
#     '''
#
#     K1 = 0.01
#     K2 = 0.03
#     compensation = 1.0
#
#     C1 = (K1 * data_range) ** 2
#     C2 = (K2 * data_range) ** 2
#
#     mu1 = _gaussian_filter(X, window, use_padding)
#     mu2 = _gaussian_filter(Y, window, use_padding)
#     sigma1_sq = _gaussian_filter(X * X, window, use_padding)
#     sigma2_sq = _gaussian_filter(Y * Y, window, use_padding)
#     sigma12 = _gaussian_filter(X * Y, window, use_padding)
#
#     mu1_sq = mu1.pow(2)
#     mu2_sq = mu2.pow(2)
#     mu1_mu2 = mu1 * mu2
#
#     sigma1_sq = compensation * (sigma1_sq - mu1_sq)
#     sigma2_sq = compensation * (sigma2_sq - mu2_sq)
#     sigma12 = compensation * (sigma12 - mu1_mu2)
#
#     cs_map = (2 * sigma12 + C2) / (sigma1_sq + sigma2_sq + C2)
#     # Fixed the issue that the negative value of cs_map caused ms_ssim to output Nan.
#     cs_map = cs_map.clamp_min(0.)
#     ssim_map = ((2 * mu1_mu2 + C1) / (mu1_sq + mu2_sq + C1)) * cs_map
#
#     ssim_val = ssim_map.mean(dim=(1, 2, 3))  # reduce along CHW
#     cs = cs_map.mean(dim=(1, 2, 3))
#
#     return ssim_val, cs
#
#
# @torch.jit.script
# def ms_ssim(X, Y, window, data_range: float, weights, use_padding: bool = False, eps: float = 1e-8):
#     '''
#     interface of ms-ssim
#     :param X: a batch of images, (N,C,H,W)
#     :param Y: a batch of images, (N,C,H,W)
#     :param window: 1-D gauss kernel
#     :param data_range: value range of input images. (usually 1.0 or 255)
#     :param weights: weights for different levels
#     :param use_padding: padding image before conv
#     :param eps: use for avoid grad nan.
#     :return:
#     '''
#     levels = weights.shape[0]
#     cs_vals = []
#     ssim_vals = []
#     for _ in range(levels):
#         ssim_val, cs = ssim(X, Y, window=window, data_range=data_range, use_padding=use_padding)
#         # Use for fix a issue. When c = a ** b and a is 0, c.backward() will cause the a.grad become inf.
#         ssim_val = ssim_val.clamp_min(eps)
#         cs = cs.clamp_min(eps)
#         cs_vals.append(cs)
#
#         ssim_vals.append(ssim_val)
#         padding = (X.shape[2] % 2, X.shape[3] % 2)
#         X = F.avg_pool2d(X, kernel_size=2, stride=2, padding=padding)
#         Y = F.avg_pool2d(Y, kernel_size=2, stride=2, padding=padding)
#
#     cs_vals = torch.stack(cs_vals, dim=0)
#     ms_ssim_val = torch.prod((cs_vals[:-1] ** weights[:-1].unsqueeze(1)) * (ssim_vals[-1] ** weights[-1]), dim=0)
#     return ms_ssim_val
#
#
# class SSIM(torch.jit.ScriptModule):
#     __constants__ = ['data_range', 'use_padding']
#
#     def __init__(self, window_size=11, window_sigma=1.5, data_range=255., channel=3, use_padding=False):
#         '''
#         :param window_size: the size of gauss kernel
#         :param window_sigma: sigma of normal distribution
#         :param data_range: value range of input images. (usually 1.0 or 255)
#         :param channel: input channels (default: 3)
#         :param use_padding: padding image before conv
#         '''
#         super().__init__()
#         assert window_size % 2 == 1, 'Window size must be odd.'
#         window = create_window(window_size, window_sigma, channel)
#         self.register_buffer('window', window)
#         self.data_range = data_range
#         self.use_padding = use_padding
#
#     @torch.jit.script_method
#     def forward(self, X, Y):
#         r = ssim(X, Y, window=self.window, data_range=self.data_range, use_padding=self.use_padding)
#         return r[0]
#
#
# class MS_SSIM(torch.jit.ScriptModule):
#     __constants__ = ['data_range', 'use_padding', 'eps']
#
#     def __init__(self, window_size=11, window_sigma=1.5, data_range=255., channel=3, use_padding=False, weights=None,
#                  levels=None, eps=1e-8):
#         '''
#         class for ms-ssim
#         :param window_size: the size of gauss kernel
#         :param window_sigma: sigma of normal distribution
#         :param data_range: value range of input images. (usually 1.0 or 255)
#         :param channel: input channels
#         :param use_padding: padding image before conv
#         :param weights: weights for different levels. (default [0.0448, 0.2856, 0.3001, 0.2363, 0.1333])
#         :param levels: number of downsampling
#         :param eps: Use for fix a issue. When c = a ** b and a is 0, c.backward() will cause the a.grad become inf.
#         '''
#         super().__init__()
#         assert window_size % 2 == 1, 'Window size must be odd.'
#         self.data_range = data_range
#         self.use_padding = use_padding
#         self.eps = eps
#
#         window = create_window(window_size, window_sigma, channel)
#         self.register_buffer('window', window)
#
#         if weights is None:
#             weights = [0.0448, 0.2856, 0.3001, 0.2363, 0.1333]
#         weights = torch.tensor(weights, dtype=torch.float)
#
#         if levels is not None:
#             weights = weights[:levels]
#             weights = weights / weights.sum()
#
#         self.register_buffer('weights', weights)
#
#     @torch.jit.script_method
#     def forward(self, X, Y):
#         return ms_ssim(X, Y, window=self.window, data_range=self.data_range, weights=self.weights,
#                        use_padding=self.use_padding, eps=self.eps)
#
#
# if __name__ == '__main__':
#     print('Simple Test')
#     im = torch.randint(0, 255, (5, 3, 256, 256), dtype=torch.float, device='cuda')
#     img1 = im / 255
#     img2 = img1 * 0.5
#
#     losser = SSIM(data_range=1.).cuda()
#     loss = losser(img1, img2).mean()
#
#     losser2 = MS_SSIM(data_range=1.).cuda()
#     loss2 = losser2(img1, img2).mean()
#
#     print(loss.item())
#     print(loss2.item())
#
# if __name__ == '__main__':
#     print('Training Test')
#     import cv2
#     import torch.optim
#     import numpy as np
#     import imageio
#     import time
#
#     out_test_video = False
#     # 最好不要直接输出gif图,会非常大,最好先输出mkv文件后用ffmpeg转换到GIF
#     video_use_gif = False
#
#     im = cv2.imread('test_img1.jpg', 1)
#     t_im = torch.from_numpy(im).cuda().permute(2, 0, 1).float()[None] / 255.
#
#     if out_test_video:
#         if video_use_gif:
#             fps = 0.5
#             out_wh = (im.shape[1] // 2, im.shape[0] // 2)
#             suffix = '.gif'
#         else:
#             fps = 5
#             out_wh = (im.shape[1], im.shape[0])
#             suffix = '.mkv'
#         video_last_time = time.perf_counter()
#         video = imageio.get_writer('ssim_test' + suffix, fps=fps)
#
#     # 测试ssim
#     print('Training SSIM')
#     rand_im = torch.randint_like(t_im, 0, 255, dtype=torch.float32) / 255.
#     rand_im.requires_grad = True
#     optim = torch.optim.Adam([rand_im], 0.003, eps=1e-8)
#     losser = SSIM(data_range=1., channel=t_im.shape[1]).cuda()
#     ssim_score = 0
#     while ssim_score < 0.999:
#         optim.zero_grad()
#         loss = losser(rand_im, t_im)
#         (-loss).sum().backward()
#         ssim_score = loss.item()
#         optim.step()
#         r_im = np.transpose(rand_im.detach().cpu().numpy().clip(0, 1) * 255, [0, 2, 3, 1]).astype(np.uint8)[0]
#         r_im = cv2.putText(r_im, 'ssim %f' % ssim_score, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2)
#
#         if out_test_video:
#             if time.perf_counter() - video_last_time > 1. / fps:
#                 video_last_time = time.perf_counter()
#                 out_frame = cv2.cvtColor(r_im, cv2.COLOR_BGR2RGB)
#                 out_frame = cv2.resize(out_frame, out_wh, interpolation=cv2.INTER_AREA)
#                 if isinstance(out_frame, cv2.UMat):
#                     out_frame = out_frame.get()
#                 video.append_data(out_frame)
#
#         cv2.imshow('ssim', r_im)
#         cv2.setWindowTitle('ssim', 'ssim %f' % ssim_score)
#         cv2.waitKey(1)
#
#     if out_test_video:
#         video.close()
#
#     # 测试ms_ssim
#     if out_test_video:
#         if video_use_gif:
#             fps = 0.5
#             out_wh = (im.shape[1] // 2, im.shape[0] // 2)
#             suffix = '.gif'
#         else:
#             fps = 5
#             out_wh = (im.shape[1], im.shape[0])
#             suffix = '.mkv'
#         video_last_time = time.perf_counter()
#         video = imageio.get_writer('ms_ssim_test' + suffix, fps=fps)
#
#     print('Training MS_SSIM')
#     rand_im = torch.randint_like(t_im, 0, 255, dtype=torch.float32) / 255.
#     rand_im.requires_grad = True
#     optim = torch.optim.Adam([rand_im], 0.003, eps=1e-8)
#     losser = MS_SSIM(data_range=1., channel=t_im.shape[1]).cuda()
#     ssim_score = 0
#     while ssim_score < 0.999:
#         optim.zero_grad()
#         loss = losser(rand_im, t_im)
#         (-loss).sum().backward()
#         ssim_score = loss.item()
#         optim.step()
#         r_im = np.transpose(rand_im.detach().cpu().numpy().clip(0, 1) * 255, [0, 2, 3, 1]).astype(np.uint8)[0]
#         r_im = cv2.putText(r_im, 'ms_ssim %f' % ssim_score, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2)
#
#         if out_test_video:
#             if time.perf_counter() - video_last_time > 1. / fps:
#                 video_last_time = time.perf_counter()
#                 out_frame = cv2.cvtColor(r_im, cv2.COLOR_BGR2RGB)
#                 out_frame = cv2.resize(out_frame, out_wh, interpolation=cv2.INTER_AREA)
#                 if isinstance(out_frame, cv2.UMat):
#                     out_frame = out_frame.get()
#                 video.append_data(out_frame)
#
#         cv2.imshow('ms_ssim', r_im)
#         cv2.setWindowTitle('ms_ssim', 'ms_ssim %f' % ssim_score)
#         cv2.waitKey(1)
#
#     if out_test_video:
#         video.close()

"""
Adapted from https://github.com/Po-Hsun-Su/pytorch-ssim
"""

import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp


def gaussian(window_size, sigma):
    gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
    return gauss / gauss.sum()


def create_window(window_size, channel):
    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
    window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
    return window


def _ssim(img1, img2, window, window_size, channel, size_average=True):
    mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
    mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)

    mu1_sq = mu1.pow(2)
    mu2_sq = mu2.pow(2)
    mu1_mu2 = mu1 * mu2

    sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
    sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
    sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2

    C1 = 0.01 ** 2
    C2 = 0.03 ** 2

    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))

    if size_average:
        return ssim_map.mean()
    else:
        return ssim_map.mean(1)


class SSIM(torch.nn.Module):
    def __init__(self, window_size=11, size_average=True):
        super(SSIM, self).__init__()
        self.window_size = window_size
        self.size_average = size_average
        self.channel = 1
        self.window = create_window(window_size, self.channel)

    def forward(self, img1, img2):
        (_, channel, _, _) = img1.size()

        if channel == self.channel and self.window.data.type() == img1.data.type():
            window = self.window
        else:
            window = create_window(self.window_size, channel)

            if img1.is_cuda:
                window = window.cuda(img1.get_device())
            window = window.type_as(img1)

            self.window = window
            self.channel = channel

        return _ssim(img1, img2, window, self.window_size, channel, self.size_average)


window = None


def ssim(img1, img2, window_size=11, size_average=True):
    (_, channel, _, _) = img1.size()
    global window
    if window is None:
        window = create_window(window_size, channel)
        if img1.is_cuda:
            window = window.cuda(img1.get_device())
        window = window.type_as(img1)
    return _ssim(img1, img2, window, window_size, channel, size_average)