pytorch ssim损失函数

在PyTorch中，可以使用 `torch_ssim` 函数来计算结构相似性指数（SSIM）。首先，需要导入相关的库：

import torch
import torch.nn.functional as F
from torch.autograd import Variable
from math import exp

然后，可以定义 `ssim` 函数：

def ssim(img1, img2, window_size=11, size_average=True):
    # Set some constants
    C1 = (0.01 ** 2)
    C2 = (0.03 ** 2)

    # Create a 1D Gaussian kernel
    window = torch.Tensor(gaussian(window_size, 1.5)).unsqueeze(1).unsqueeze(2)
    window = window / window.sum()

    # Calculate means
    mu1 = F.conv2d(img1, window, padding=window_size//2, groups=img1.size(1))
    mu2 = F.conv2d(img2, window, padding=window_size//2, groups=img1.size(1))

    # Calculate variances and covariances
    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=img1.size(1)) - mu1_sq
    sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size//2, groups=img1.size(1)) - mu2_sq
    sigma12 = F.conv2d(img1 * img2, window, padding=window_size//2, groups=img1.size(1)) - mu1_mu2

    # Calculate SSIM formula variables
    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)
    
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()

在这个函数中，我们首先创建一个1D高斯核，然后计算两个图像的均值和方差，并计算它们之间的协方差。最后，使用SSIM公式计算两个图像之间的相似度。如果 `size_average` 参数设置为True，则函数返回所有像素的平均值，否则返回每个像素的值。

可以使用以下代码调用函数：

img1 = Variable(torch.rand(1, 3, 256, 256))
img2 = Variable(torch.rand(1, 3, 256, 256))

if torch.cuda.is_available():
    img1 = img1.cuda()
    img2 = img2.cuda()

ssim_value = ssim(img1, img2)

print(ssim_value)

这将打印出两个图像之间的SSIM值。