ssim.py

import torch

import torch.nn.functional as F

from torch.autograd import Variable

import numpy as np

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 _ssim(img1, img2, window, window_size, channel, 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 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):

        (_, 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):

    (_, 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)