import torch
import torch.nn.functional as F
from torch.autograd import Variable
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 smooth_gaussian(img, window_size=7):
(_, channel, _, _) = img.size()
window = create_window(window_size, channel)
if img.is_cuda:
window = window.cuda(img.get_device())
window = window.type_as(img)
return F.conv2d(img, window, padding = window_size//2, groups = channel)
def _ssim(img1, img2, window, window_size, channel):
window = window.transpose(0,1) / channel
mu1 = F.conv2d(img1, window, padding = window_size//2, groups = 1)
mu2 = F.conv2d(img2, window, padding = window_size//2, groups = 1)
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 = 1) - mu1_sq
sigma2_sq = F.conv2d(img2*img2, window, padding = window_size//2, groups = 1) - mu2_sq
sigma12 = F.conv2d(img1*img2, window, padding = window_size//2, groups = 1) - mu1_mu2
scale = 1
C1 = (scale * 0.01)**2
C2 = (scale * 0.03)**2
ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))
return ssim_map
def _ssim1(img1, img2, window, window_size, channel):
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
scale = 1
C1 = (scale * 0.01)**2
C2 = (scale * 0.03)**2
ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))
return ssim_map
def _ssim0(img1, img2, window, window_size, channel):
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
dimg1 = (img1 - mu1)
dimg2 = (img2 - mu2)
sigma1_sq = F.conv2d(dimg1*dimg1, window, padding = window_size//2, groups = channel)
sigma2_sq = F.conv2d(dimg2*dimg2, window, padding = window_size//2, groups = channel)
sigma12 = F.conv2d(dimg1*dimg2, window, padding = window_size//2, groups = channel)
scale = 1
C1 = (scale * 0.01)**2
C2 = (scale * 0.03)**2
ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))
return ssim_map
def ssim(img1, img2, window_size = 11):
(_, 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)
class SSIM(torch.nn.Module):
def __init__(self, window_size = 11):
super(SSIM, self).__init__()
self.window_size = window_size
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)
