多尺度特征融合图像超分

from torch import nn
import torch

import os
os.environ['CUDA_VISIBLE_DEVICES']='1'

class CharbonnierLoss(nn.Module):
    """L1 Charbonnierloss."""
    def __init__(self):
        super(CharbonnierLoss, self).__init__()
        self.eps = 1e-6
    def forward(self, X, Y):
        diff = torch.add(X, -Y)
        error = torch.sqrt(diff * diff + self.eps)
        # print(error)
        loss = torch.sum(error)
        return loss

class Conv_ReLU_Block(nn.Module):
    def __init__(self,in_channels=64,out_channels=64,kernel_size=3):
        super(Conv_ReLU_Block, self).__init__()
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=1, padding=kernel_size//2, bias=False)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        return self.relu(self.conv(x))

#递归多尺度残