基于密集残差的图像超分辨实战

由于计算资源有限，这里使用的尺度增强的训练方法。也就是采取的预上采样的方式。

import torch
import torch.nn as nn
from math import sqrt
 
def conv_block(in_channel, out_channel):
    layer = nn.Sequential(
        nn.ReLU(),
        nn.Conv2d(in_channel, out_channel, kernel_size=3, padding=1, bias=False)
    )
    return layer
 
class dense_block(nn.Module):
    def __init__(self, in_channel, growth_rate,out_channel, num_layers):
        super(dense_block, self).__init__()
        block = []
        channel = in_channel
        for i in range(num_layers):
            block.append(conv_block(channel, growth_rate))
            channel += growth_rate
        self.net = nn.Sequential(*block)
        self.conv1=nn.Sequential(
            nn.ReLU(),
            nn.Conv2d(in_channels=(num_layers)*growth_rate+in_channel,out_channels=out_channel,kernel_size=1,bias=False)
        )
 
    def forward(self, x):
        for layer in