pytorch图像质量评价代码

以下是基于PyTorch实现的图像质量评价的代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class SSIM(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 = self.create_window(window_size, self.channel)

    def gaussian(self, window_size, sigma):
        gauss = torch.Tensor([math.exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
        return gauss/gauss.sum()

    def create_window(self, window_size, channel):
        _1D_window = self.gaussian(window_size, 1.5).unsqueeze(1)
        _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
        window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()
        return window

    def forward(self, img1, img2):
        (_, channel, _, _) = img1.size()

        if channel == self.channel and self.window.dtype == img1.dtype:
            window = self.window
        else:
            window = self.create_window(self.window_size, channel).to(img1.device).type(img1.dtype)
            self.window = window
            self.channel = channel

        mu1 = F.conv2d(img1, window, padding=self.window_size//2, groups=channel)
        mu2 = F.conv2d(img2, window, padding=self.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=self.window_size//2, groups=channel) - mu1_sq
        sigma2_sq = F.conv2d(img2*img2, window, padding=self.window_size//2, groups=channel) - mu2_sq
        sigma12 = F.conv2d(img1*img2, window, padding=self.window_size//2, groups=channel) - mu1_mu2

        C1 = (0.01*255)**2
        C2 = (0.03*255)**2

        ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2)) / ((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))
        if self.size_average:
            return ssim_map.mean()
        else:
            return ssim_map.mean(1).mean(1).mean(1)

class PSNR(nn.Module):
    def __init__(self, max_value=255.0):
        super(PSNR, self).__init__()
        self.max_value = max_value

    def forward(self, img1, img2):
        mse = torch.mean((img1 - img2) ** 2)
        if mse == 0:
            return 100
        return 10 * torch.log10(self.max_value ** 2 / mse)

其中，SSIM和PSNR分别是评价图像结构相似性和峰值信噪比的指标。可以在训练过程中使用这些指标来评价生成的图像质量。