写一个简单的SNGAN网络

好的，以下是一个简单的SNGAN网络的代码实现：

import torch
import torch.nn as nn

class SNGANGenerator(nn.Module):
    def __init__(self, z_dim=100, image_size=64, num_channels=3, num_filters=64):
        super(SNGANGenerator, self).__init__()

        self.image_size = image_size
        self.num_channels = num_channels
        self.num_filters = num_filters
        self.z_dim = z_dim

        self.linear = nn.Linear(z_dim, self.num_filters * 8 * self.image_size // 8 * self.image_size // 8)

        self.blocks = nn.Sequential(
            nn.BatchNorm2d(self.num_filters * 8),
            nn.Upsample(scale_factor=2),
            nn.Conv2d(self.num_filters * 8, self.num_filters * 4, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(self.num_filters * 4),
            nn.ReLU(inplace=True),

            nn.Upsample(scale_factor=2),
            nn.Conv2d(self.num_filters * 4, self.num_filters * 2, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(self.num_filters * 2),
            nn.ReLU(inplace=True),

            nn.Upsample(scale_factor=2),
            nn.Conv2d(self.num_filters * 2, self.num_filters, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(self.num_filters),
            nn.ReLU(inplace=True),

            nn.Upsample(scale_factor=2),
            nn.Conv2d(self.num_filters, self.num_channels, kernel_size=3, stride=1, padding=1),
            nn.Tanh()
        )

    def forward(self, noise):
        x = self.linear(noise)
        x = x.view(-1, self.num_filters * 8, self.image_size // 8, self.image_size // 8)
        x = self.blocks(x)
        return x


class SNGANDiscriminator(nn.Module):
    def __init__(self, image_size=64, num_channels=3, num_filters=64):
        super(SNGANDiscriminator, self).__init__()

        self.image_size = image_size
        self.num_channels = num_channels
        self.num_filters = num_filters

        self.blocks = nn.Sequential(
            nn.Conv2d(self.num_channels, self.num_filters, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(self.num_filters),
            nn.ReLU(inplace=True),

            nn.Conv2d(self.num_filters, self.num_filters * 2, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(self.num_filters * 2),
            nn.ReLU(inplace=True),

            nn.Conv2d(self.num_filters * 2, self.num_filters * 4, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(self.num_filters * 4),
            nn.ReLU(inplace=True),

            nn.Conv2d(self.num_filters * 4, self.num_filters * 8, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(self.num_filters * 8),
            nn.ReLU(inplace=True)
        )

        self.linear = nn.Linear(self.num_filters * 8 * self.image_size // 8 * self.image_size // 8, 1)

    def forward(self, img):
        x = self.blocks(img)
        x = x.view(-1, self.num_filters * 8 * self.image_size // 8 * self.image_size // 8)
        x = self.linear(x)
        return x

这个SNGAN网络包含一个生成器 `SNGANGenerator` 和一个判别器 `SNGANDiscriminator`，其中生成器接受一个噪声向量作为输入，输出一个图像。判别器接受一个图像作为输入，输出一个标量，表示这个图像是真实的还是生成的。这个网络使用的是 SNGAN 的思想，即在每一层卷积之后都加入了批标准化技术。