GAN项目实战 使用CycleGAN将苹果变成橙子Pytorch版

一、原理

原理参考：一文看懂CycleGAN网络原理以及论文精华解读

二、数据集下载以及加载

cycleGAN训练数据集，苹果橙子数据集 APPLE 2 ORANGE

# 加载训练数据
apples_path = glob.glob('data/trainA/*.jpg')
oranges_path = glob.glob('data/trainB/*.jpg')


transform = transforms.Compose([transforms.ToTensor(),  # 0-1归一化
                                transforms.Normalize(0.5, 0.5),  # -1，1
                                ])

class AppleOrangeDataset(data.Dataset):
    def __init__(self, img_path):
        self.img_path = img_path

    def __getitem__(self, index):
        img_path = self.img_path[index]
        pil_img = Image.open(img_path)
        pil_img = transform(pil_img)
        return pil_img
    def __len__(self):
        return len(self.img_path)

apple_dataset = AppleOrangeDataset(apples_path)
oranges_dataset = AppleOrangeDataset(oranges_path)

三、基于Unet结构定义上 / 下采样模块

生成器结构和判别器结构 与pix2pixGAN中完全相同，具体可参考：

GAN实战之Pytorch使用pix2pixGAN生成建筑物Label to Facade

class Downsample(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Downsample, self).__init__()
        self.conv_relu = nn.Sequential(
                            nn.Conv2d(in_channels, out_channels,
                                      kernel_size=3, stride=2, padding=1),
                            nn.LeakyReLU(inplace=True),
            )
        self.bn = nn.InstanceNorm2d(out_channels)
    def forward(self, x, is_bn=True):

            x = self.conv_relu(x)
            if is_bn:
                x = self.bn(x)
            return x



class Upsample(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Upsample, self).__init__()
        self.upconv_relu = nn.Sequential(
            nn.ConvTranspose2d(in_channels, out_channels,
                               kernel_size=3,
                               stride=2,
                               padding=1,
                               output_padding=1),
            nn.LeakyReLU(inplace=True)
        )
        self.bn = nn.InstanceNorm2d(out_channels)

    def forward(self, x, is_drop=False):
            x = self.upconv_relu(x)
            x = self.bn(x)
            if is_drop:
                x = F.dropout2d(x)
            return x

四、生成器结构

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.down1 = D