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 TV_Loss(torch.nn.Module):

    def __init__(self):
        super(TV_Loss, self).__init__()

    def forward(self, IA, IF):
        r = IA - IF
        h = r.shape[2]
        w = r.shape[3]
        tv1 = torch.pow((r[:, :, 1:, :] - r[:, :, :h - 1, :]), 2).mean()
        tv2 = torch.pow((r[:, :, :, 1:] - r[:, :, :, :w - 1]), 2).mean()
        return tv1 + tv2


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)