Spaces:
Build error
Build error
File size: 14,141 Bytes
98f685a |
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)
|