Class ConvBlock

SRGAN/models.py:11–30

from torchinfo import summary

class ConvBlock(torch.nn.Module):
    def __init__(self,in_channels,out_channels,
                 discriminator = False,use_act = True,
                 use_bn = True,**kwargs):
        super(ConvBlock, self).__init__()
        self.use_act = use_act
        self.cnn = torch.nn.Conv2d(in_channels,out_channels,**kwargs,bias=not use_bn)
        #Identity()表示输入是什么输出就是什么
        #要加深网络，有些层是不改变输入数据的维度的，
        #在增减网络的过程中我们就可以用identity占个位置，这样网络整体层数永远不变，
        self.bn = torch.nn.BatchNorm2d(out_channels) if use_bn else torch.nn.Identity()
        #对于generator使用PReLU
        #对于discriminator使用LeakReLU
        self.act = (
            torch.nn.LeakyReLU(negative_slope=0.2,inplace=True)
            if discriminator else torch.nn.PReLU(num_parameters=out_channels)
        )
    def forward(self,x):
        out = self.act(self.bn(self.cnn(x))) if self.use_act else self.bn(self.cnn(x))
        return out

#采用PixelShuffle进行上采样
class UpsampleBlock(torch.nn.Module):