完整代码如下：

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



class Fusionloss(nn.Module):
    def __init__(self):
        super(Fusionloss, self).__init__()
        self.sobelconv=Sobelxy()

    def forward(self,image_vis,image_ir,generate_img):
        image_y=image_vis[:,:1,:,:]
        x_in_max=torch.max(image_y,image_ir)
        loss_in=F.l1_loss(x_in_max,generate_img)
        y_grad=self.sobelconv(image_y)
        ir_grad=self.sobelconv(image_ir)
        generate_img_grad=self.sobelconv(generate_img)
        x_grad_joint=torch.max(y_grad,ir_grad)
        loss_grad=F.l1_loss(x_grad_joint,generate_img_grad)
        loss_total=loss_in+10*loss_grad
        return loss_total,loss_in,loss_grad

class Sobelxy(nn.Module):
    def __init__(self):
        super(Sobelxy, self).__init__()
        kernelx = [[-1, 0, 1],
                  [-2,0 , 2],
                  [-1, 0, 1]]
        kernely = [[1, 2, 1],
                  [0,0 , 0],
                  [-1, -2, -1]]
        kernelx = torch.FloatTensor(kernelx).unsqueeze(0).unsqueeze(0)
        kernely = torch.FloatTensor(kernely).unsqueeze(0).unsqueeze(0)
        self.weightx = nn.Parameter(data=kernelx, requires_grad=False).cuda()
        self.weighty = nn.Parameter(data=kernely, requires_grad=False).cuda()
    def forward(self,x):
        sobelx=F.conv2d(x, self.weightx, padding=1)
        sobely=F.conv2d(x, self.weighty, padding=1)
        return torch.abs(sobelx)+torch.abs(sobely)


def cc(img1, img2):
    eps = torch.finfo(torch.float32).eps
    """Correlation coefficient for (N, C, H, W) image; torch.float32 [0.,1.]."""
    N, C, _, _ = img1.shape
    img1 = img1.reshape(N, C, -1)
    img2 = img2.reshape(N, C, -1)
    img1 = img1 - img1.mean(dim=-1, keepdim=True)
    img2 = img2 - img2.mean(dim=-1, keepdim=True)
    cc = torch.sum(img1 * img2, dim=-1) / (eps + torch.sqrt(torch.sum(img1 **
                                                                      2, dim=-1)) * torch.sqrt(torch.sum(img2**2, dim=-1)))
    cc = torch.clamp(cc, -1., 1.)
    return cc.mean()

其实loss和net的原理是一样的，都要继承nn.Module，然后有forward函数，通过实例化对象调用forward函数来进行计算啊啥的。

这个就是计算了融合图像的梯度损失和强度损失，不过是和两张源图像两者之间更大的每一点的梯度/强度进行计算。和原文中说的一样。