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- import torch
- import torch.nn.functional as F
- from math import exp
- import numpy as np
-
-
- 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=1):
- _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
- _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
- window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()
- return window
-
-
- def ssim(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None):
- # Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh).
- if val_range is None:
- if torch.max(img1) > 128:
- max_val = 255
- else:
- max_val = 1
-
- if torch.min(img1) < -0.5:
- min_val = -1
- else:
- min_val = 0
- L = max_val - min_val
- else:
- L = val_range
-
- padd = 0
- (_, channel, height, width) = img1.size()
- if window is None:
- real_size = min(window_size, height, width)
- window = create_window(real_size, channel=channel).to(img1.device)
-
- mu1 = F.conv2d(img1, window, padding=padd, groups=channel)
- mu2 = F.conv2d(img2, window, padding=padd, groups=channel)
-
- mu1_sq = mu1.pow(2)
- mu2_sq = mu2.pow(2)
- mu1_mu2 = mu1 * mu2
-
- sigma1_sq = F.conv2d(img1 * img1, window, padding=padd, groups=channel) - mu1_sq
- sigma2_sq = F.conv2d(img2 * img2, window, padding=padd, groups=channel) - mu2_sq
- sigma12 = F.conv2d(img1 * img2, window, padding=padd, groups=channel) - mu1_mu2
-
- C1 = (0.01 * L) ** 2
- C2 = (0.03 * L) ** 2
-
- v1 = 2.0 * sigma12 + C2
- v2 = sigma1_sq + sigma2_sq + C2
- cs = torch.mean(v1 / v2) # contrast sensitivity
-
- ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2)
-
- if size_average:
- ret = ssim_map.mean()
- else:
- ret = ssim_map.mean(1).mean(1).mean(1)
-
- if full:
- return ret, cs
- return ret
-
-
- def msssim(img1, img2, window_size=11, size_average=True, val_range=None, normalize=False):
- device = img1.device
- weights = torch.FloatTensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333]).to(device)
- levels = weights.size()[0]
- mssim = []
- mcs = []
- for _ in range(levels):
- sim, cs = ssim(img1, img2, window_size=window_size, size_average=size_average, full=True, val_range=val_range)
- mssim.append(sim)
- mcs.append(cs)
-
- img1 = F.avg_pool2d(img1, (2, 2))
- img2 = F.avg_pool2d(img2, (2, 2))
-
- mssim = torch.stack(mssim)
- mcs = torch.stack(mcs)
-
- # Normalize (to avoid NaNs during training unstable models, not compliant with original definition)
- if normalize:
- mssim = (mssim + 1) / 2
- mcs = (mcs + 1) / 2
-
- pow1 = mcs ** weights
- pow2 = mssim ** weights
- # From Matlab implementation https://ece.uwaterloo.ca/~z70wang/research/iwssim/
- output = torch.prod(pow1[:-1] * pow2[-1])
- return output
-
-
- # Classes to re-use window
- class SSIM(torch.nn.Module):
- def __init__(self, window_size=11, size_average=True, val_range=None):
- super(SSIM, self).__init__()
- self.window_size = window_size
- self.size_average = size_average
- self.val_range = val_range
-
- # Assume 1 channel for SSIM
- self.channel = 1
- self.window = create_window(window_size)
-
- def forward(self, img1, img2):
- (_, channel, _, _) = img1.size()
-
- if channel == self.channel and self.window.dtype == img1.dtype:
- window = self.window
- else:
- window = create_window(self.window_size, channel).to(img1.device).type(img1.dtype)
- self.window = window
- self.channel = channel
-
- return ssim(img1, img2, window=window, window_size=self.window_size, size_average=self.size_average)
-
- class MSSSIM(torch.nn.Module):
- def __init__(self, window_size=11, size_average=True, channel=3):
- super(MSSSIM, self).__init__()
- self.window_size = window_size
- self.size_average = size_average
- self.channel = channel
-
- def forward(self, img1, img2):
- # TODO: store window between calls if possible
- return msssim(img1, img2, window_size=self.window_size, size_average=self.size_average)
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