如果把其中的nn.BatchNorm2d(self.num_filters)换成nn.InstanceNorm2d(out_features)效果会更好吗？

在使用 SNGAN 的时候，使用 InstanceNorm 代替 BatchNorm 确实可以得到更好的效果。因为 BatchNorm 的计算是在一个 batch 内进行的，而 InstanceNorm 是在一个单独的样本内进行的，因此 InstanceNorm 更适合用于生成器和判别器中，因为它们的输入不同。

因此，如果你将 `SNGANGenerator` 和 `SNGANDiscriminator` 中的 `nn.BatchNorm2d` 替换为 `nn.InstanceNorm2d`，会得到更好的结果。以下是代码示例：

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.InstanceNorm2d(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.InstanceNorm2d(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.InstanceNorm2d(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.InstanceNorm2d(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.InstanceNorm2d(self.num_filters),
            nn.ReLU(inplace=True),

            nn.Conv2d(self.num_filters, self.num_filters * 2, kernel_size=3, stride=1, padding=1),
            nn.InstanceNorm2d(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.InstanceNorm2d(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.InstanceNorm2d(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

需要注意的是，如果使用 InstanceNorm，需要保证样本的大小是一致的，否则可能会导致效果变差。