CycleGAN模型——pytorch实现

 论文传送门：Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks​​​​​​​

# 输入图像shape默认为(3,256,256)
class Discriminator(nn.Module):  # 定义判别器
    def __init__(self):  # 初始化方法
        super(Discriminator, self).__init__()  # 继承初始化方法
        self.conv1 = nn.Conv2d(3, 64, 4, 2, 1)  # conv
        self.conv2 = nn.Conv2d(64, 128, 4, 2, 1)  # conv
        self.isn2 = nn.InstanceNorm2d(128)  # instancenorm，实例标准化，在图像风格转化任务中，生成图像依赖于某个图像的实例，所以batchnorm并不适用于风格转化任务
        self.conv3 = nn.Conv2d(128, 256, 4, 2, 1)  # conv
        self.isn3 = nn.InstanceNorm2d(256)  # in
        self.conv4 = nn.Conv2d(256, 512, 4, 2, 1)  # conv
        self.isn4 = nn.InstanceNorm2d(512)  # in
        self.conv5 = nn.Con