cycleGAN代码实现（附详细代码注释）

最近刚刚入门深度学习，试着复现cycleGAN代码。看了一个YouTube博主的cycleGAN代码，自己跟着写了一遍，同时加上了代码注释，希望能帮到同样的入门伙伴

下面的github地址

RRRRRBL/CycleGAN-Detailed-notes-: 内含cycleGAN代码，且有详细代码注释 (github.com)
在这里给出一个生成器的代码

import torch
import torch.nn as nn

class ConvBlock(nn.Module):
def init(self, in_channels, out_channels, down=True, use_act=True, kwargs): # down：下采样，act：激活，kwargs字典参数
super().init()
self.conv = nn.Sequential( # 卷积块，可以完成下采样卷积或者保持原size卷积
nn.Conv2d(in_channels, out_channels, padding_mode='reflect', **kwargs)
if down
else nn.ConvTranspose2d(in_channels, out_channels, **kwargs),
nn.InstanceNorm2d(out_channels), # 标准化
nn.ReLU(inplace=True) if use_act else nn.Identity() # identity不会做任何操作
)

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

class ResidualBlock(nn.Module): # 残差块，不改变size
def init(self, channels):
super().init()
self.block = nn.Sequential(
ConvBlock(channels, channels, kernel_size=3, padding=1),
ConvBlock(channels, channels, use_act=False, kernel_size=3, padding=1)
)

def forward(self, x):
    return x + self.block(x)  # 残差块儿

class Generator(nn.Module):
def init(self, img_channels, num_features=64, num_residuals=9, ): # num_features是通道数的一个公约数,num_residuals残差层数
super(Generator, self).init()
self.initial = nn.Sequential( # 初始化
nn.Conv2d(img_channels, num_features, kernel_size=7, stride=1, padding=3, padding_mode='reflect'),
nn.InstanceNorm2d(num_features),
nn.ReLU(inplace=True), # 原地激活
)
self.down_blocks = nn.ModuleList( # 下采样（增加通道数，减小img尺寸
[
ConvBlock(num_features, num_features * 2, kernel_size=3, stride=2, padding=1),
ConvBlock(num_features * 2, num_features * 4, kernel_size=3, stride=2, padding=1),

        ]
    )
    self.residual_block = nn.Sequential(  # 残差块儿（不改变大小
        *[ResidualBlock(num_features * 4) for _ in range(num_residuals)]
        # *4是因为之前的各类操作得到的变量channel已经是4
        # 是4*num_featurs了，这里调用了九次残差块儿，进行训练，大小一直不变
    )
    self.up_blocks = nn.ModuleList(  # 上采样block channels减小，img变大
        [
            ConvBlock(num_features * 4, num_features * 2, down=False, kernel_size=3, stride=2, padding=1,
                      output_padding=1),
            ConvBlock(num_features * 2, num_features * 1, down=False, kernel_size=3, stride=2, padding=1,
                      output_padding=1),

        ]
    )
    self.last = nn.Conv2d(num_features * 1, img_channels, kernel_size=7, stride=1, padding=3,
                          padding_mode='reflect')

def forward(self, x):
    x = self.initial(x)  # 初始化
    for layer in self.down_blocks:
        x = layer(x)
    x = self.residual_block(x)
    for layer in self.up_blocks:
        x = layer(x)
    return torch.tanh(self.last(x))

'''
观察代码不难发现，在整个生成器的生成过程中，用到的还是简单基础的知识，只是在一些处理选择上比较特殊
代码利用了残差神经网络 和卷积神经网络集合的方式进行训练
def test():
img_channels = 3
img_size = 256
x = torch.randn((2, img_channels, img_size, img_size))
gen = Generator(img_channels, 9)
print(gen(x).shape)

if name == "main":
test()
'''