stable-diffusion-finetune/ldm/modules/evaluate/ssim.py

# 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)