"""Structural Similarity Index (SSIM) utilities."""
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
import math
import torch.nn as nn
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def gaussian(window_size, sigma):
"""Create a 1D Gaussian kernel for SSIM windows."""
gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
return gauss / gauss.sum()
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def create_window(window_size, channel):
"""Create a 2D SSIM window tensor."""
_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
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def create_window_3D(window_size, channel):
"""Create a 3D SSIM window tensor."""
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
_2D_window = _1D_window.mm(_1D_window.t())
_3D_window = _1D_window.mm(_2D_window.reshape(1, -1)).reshape(window_size, window_size,
window_size).float().unsqueeze(0).unsqueeze(0)
window = Variable(_3D_window.expand(channel, 1, window_size, window_size, window_size).contiguous())
return window
def _ssim(img1, img2, window, window_size, channel, size_average=True):
"""Compute 2D SSIM for a pair of images.
Args:
img1: Tensor (B, C, H, W).
img2: Tensor (B, C, H, W).
window: SSIM window tensor.
window_size: Window size.
channel: Number of channels.
size_average: If True, returns scalar mean.
Returns:
SSIM value (scalar or per-image).
"""
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).mean(1).mean(1)
def _ssim_3D(img1, img2, window, window_size, channel, size_average=True):
"""Compute 3D SSIM for a pair of volumes.
Args:
img1: Tensor (B, C, H, W, D).
img2: Tensor (B, C, H, W, D).
window: SSIM window tensor.
window_size: Window size.
channel: Number of channels.
size_average: If True, returns scalar mean.
Returns:
SSIM value (scalar or per-image).
"""
mu1 = F.conv3d(img1, window, padding=window_size // 2, groups=channel)
mu2 = F.conv3d(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.conv3d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
sigma2_sq = F.conv3d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
sigma12 = F.conv3d(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).mean(1).mean(1)
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class SSIM2D(torch.nn.Module):
def __init__(self, window_size=11, size_average=True):
super(SSIM2D, self).__init__()
self.window_size = window_size
self.size_average = size_average
self.channel = 1
self.window = create_window(window_size, self.channel)
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def forward(self, img1, img2):
"""Compute 2D SSIM between two images.
Args:
img1: Tensor (B, C, H, W).
img2: Tensor (B, C, H, W).
Returns:
SSIM score.
"""
(_, 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)
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class SSIM3D(torch.nn.Module):
def __init__(self, window_size=11, size_average=True):
super(SSIM3D, self).__init__()
self.window_size = window_size
self.size_average = size_average
self.channel = 1
self.window = create_window_3D(window_size, self.channel)
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def forward(self, img1, img2):
"""Compute 3D SSIM between two volumes.
Args:
img1: Tensor (B, C, H, W, D).
img2: Tensor (B, C, H, W, D).
Returns:
SSIM score.
"""
(_, channel, _, _, _) = img1.size()
if channel == self.channel and self.window.data.type() == img1.data.type():
window = self.window
else:
window = create_window_3D(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 1-_ssim_3D(img1, img2, window, self.window_size, channel, self.size_average)
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def ssim(img1, img2, window_size=11, size_average=True):
(_, channel, _, _) = img1.size()
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)
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def ssim3D(img1, img2, window_size=11, size_average=True):
(_, channel, _, _, _) = img1.size()
window = create_window_3D(window_size, channel)
if img1.is_cuda:
window = window.cuda(img1.get_device())
window = window.type_as(img1)
return _ssim_3D(img1, img2, window, window_size, channel, size_average)