你能否给出pytorch实现self attention gan的代码

我可以回答这个问题，以下是pytorch实现self attention gan的代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class SelfAttention(nn.Module):
    def __init__(self, in_dim):
        super(SelfAttention, self).__init__()
        self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
        self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
        self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
        self.gamma = nn.Parameter(torch.zeros(1))

    def forward(self, x):
        batch_size, C, H, W = x.size()
        proj_query = self.query_conv(x).view(batch_size, -1, H*W).permute(, 2, 1)
        proj_key = self.key_conv(x).view(batch_size, -1, H*W)
        energy = torch.bmm(proj_query, proj_key)
        attention = F.softmax(energy, dim=-1)
        proj_value = self.value_conv(x).view(batch_size, -1, H*W)
        out = torch.bmm(proj_value, attention.permute(, 2, 1))
        out = out.view(batch_size, C, H, W)
        out = self.gamma * out + x
        return out

class Generator(nn.Module):
    def __init__(self, z_dim=100, image_size=64, conv_dim=64):
        super(Generator, self).__init__()
        self.image_size = image_size
        self.fc = nn.Linear(z_dim, (image_size//16)*(image_size//16)*8*conv_dim)
        self.conv1 = nn.ConvTranspose2d(8*conv_dim, 4*conv_dim, 4, 2, 1)
        self.conv2 = nn.ConvTranspose2d(4*conv_dim, 2*conv_dim, 4, 2, 1)
        self.conv3 = nn.ConvTranspose2d(2*conv_dim, conv_dim, 4, 2, 1)
        self.conv4 = nn.ConvTranspose2d(conv_dim, 3, 4, 2, 1)
        self.bn1 = nn.BatchNorm2d(8*conv_dim)
        self.bn2 = nn.BatchNorm2d(4*conv_dim)
        self.bn3 = nn.BatchNorm2d(2*conv_dim)
        self.bn4 = nn.BatchNorm2d(conv_dim)
        self.attn1 = SelfAttention(8*conv_dim)
        self.attn2 = SelfAttention(4*conv_dim)

    def forward(self, z):
        out = self.fc(z)
        out = out.view(-1, 8*self.conv_dim, self.image_size//16, self.image_size//16)
        out = F.relu(self.bn1(out))
        out = F.relu(self.bn2(self.conv1(out)))
        out = self.attn1(out)
        out = F.relu(self.bn3(self.conv2(out)))
        out = self.attn2(out)
        out = F.relu(self.bn4(self.conv3(out)))
        out = torch.tanh(self.conv4(out))
        return out

class Discriminator(nn.Module):
    def __init__(self, image_size=64, conv_dim=64):
        super(Discriminator, self).__init__()
        self.conv1 = nn.Conv2d(3, conv_dim, 4, 2, 1)
        self.conv2 = nn.Conv2d(conv_dim, 2*conv_dim, 4, 2, 1)
        self.conv3 = nn.Conv2d(2*conv_dim, 4*conv_dim, 4, 2, 1)
        self.conv4 = nn.Conv2d(4*conv_dim, 1, 4, 1, )
        self.bn1 = nn.BatchNorm2d(conv_dim)
        self.bn2 = nn.BatchNorm2d(2*conv_dim)
        self.bn3 = nn.BatchNorm2d(4*conv_dim)
        self.attn1 = SelfAttention(conv_dim)
        self.attn2 = SelfAttention(2*conv_dim)

    def forward(self, x):
        out = F.leaky_relu(self.conv1(x), .2)
        out = self.attn1(out)
        out = F.leaky_relu(self.bn2(self.conv2(out)), .2)
        out = self.attn2(out)
        out = F.leaky_relu(self.bn3(self.conv3(out)), .2)
        out = self.conv4(out)
        return out.view(-1, 1)

希望能对你有所帮助！