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# MIT Licence
# Methods to predict the SSIM, taken from
# https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py
from math import exp
import torch
import torch.nn.functional as F
from torch.autograd import Variable
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, mask=None, 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 not (mask is None):
b = mask.size(0)
ssim_map = ssim_map.mean(dim=1, keepdim=True) * mask
ssim_map = ssim_map.view(b, -1).sum(dim=1) / mask.view(b, -1).sum(
dim=1
).clamp(min=1)
return ssim_map
import pdb
pdb.set_trace
if size_average:
return ssim_map.mean()
else:
return ssim_map.mean(1).mean(1).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, mask=None):
(_, 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,
mask,
self.size_average,
)
def ssim(img1, img2, window_size=11, mask=None, 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, mask, size_average)
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