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# Pytorch Multi-Scale Structural Similarity Index (SSIM)
# This code is written by jorge-pessoa (https://github.com/jorge-pessoa/pytorch-msssim)
# MIT licence
import math
from math import exp
import torch
import torch.nn.functional as F
from torch.autograd import Variable
# +++++++++++++++++++++++++++++++++++++
# SSIM
# -------------------------------------
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, full=False):
padd = 0
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 ** 2
C2 = 0.03 ** 2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
v1 = 2.0 * sigma12 + C2
v2 = sigma1_sq + sigma2_sq + C2
cs = torch.mean(v1 / 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
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)
def ssim(img1, img2, window_size=11, size_average=True, full=False):
(_, channel, height, width) = img1.size()
real_size = min(window_size, height, width)
window = create_window(real_size, channel)
if img1.is_cuda:
window = window.cuda(img1.get_device())
window = window.type_as(img1)
return _ssim(img1, img2, window, real_size, channel, size_average, full=full)
def msssim(img1, img2, window_size=11, size_average=True):
# TODO: fix NAN results
if img1.size() != img2.size():
raise RuntimeError('Input images must have the same shape (%s vs. %s).' %
(img1.size(), img2.size()))
if len(img1.size()) != 4:
raise RuntimeError('Input images must have four dimensions, not %d' %
len(img1.size()))
weights = torch.tensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333], dtype=img1.dtype)
if img1.is_cuda:
weights = weights.cuda(img1.get_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)
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)
return (torch.prod(mcs[0:levels - 1] ** weights[0:levels - 1]) *
(mssim[levels - 1] ** weights[levels - 1]))
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)
def calc_psnr(sr, hr, scale=0, benchmark=False):
# adapt from EDSR: https://github.com/thstkdgus35/EDSR-PyTorch
diff = (sr - hr).data
if benchmark:
shave = scale
if diff.size(1) > 1:
convert = diff.new(1, 3, 1, 1)
convert[0, 0, 0, 0] = 65.738
convert[0, 1, 0, 0] = 129.057
convert[0, 2, 0, 0] = 25.064
diff.mul_(convert).div_(256)
diff = diff.sum(dim=1, keepdim=True)
else:
shave = scale + 6
valid = diff[:, :, shave:-shave, shave:-shave]
mse = valid.pow(2).mean()
return -10 * math.log10(mse)
# +++++++++++++++++++++++++++++++++++++
# PSNR
# -------------------------------------
from torch import nn
def psnr(predict, target):
with torch.no_grad():
criteria = nn.MSELoss()
mse = criteria(predict, target)
return -10 * torch.log10(mse)
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