将下列生成器改造成能够匹配edge-connect中的修补模式的预训练模型键值的结构：class Generator(nn.Module): def __init__(self): super(Generator, self).__init__() self.encoder = nn.Sequential( nn.Conv2d(3, 64, 3, stride=2, padding=1), nn.BatchNorm2d(64), nn.LeakyReLU(0.2), nn.Conv2d(64, 128, 3, stride=2, padding=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2), nn.Conv2d(128, 256, 3, stride=2, padding=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2), nn.Conv2d(256, 512, 3, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2), nn.Conv2d(512, 4000, 1), nn.BatchNorm2d(4000), nn.LeakyReLU(0.2) ) self.decoder = nn.Sequential( nn.ConvTranspose2d(4000, 512, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2), nn.ConvTranspose2d(512, 256, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2), nn.ConvTranspose2d(256, 128, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2), nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(64), nn.LeakyReLU(0.2), nn.ConvTranspose2d(64, 3, 3, stride=1, padding=1), nn.Tanh() ) def forward(self, x): x = self.encoder(x) x = self.decoder(x) return x

可以将生成器改为以下结构，使其可以匹配edge-connect中的修补模式的预训练模型键值的结构：

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, 3, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.2),
            nn.Conv2d(64, 128, 3, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2),
            nn.Conv2d(128, 256, 3, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2),
            nn.Conv2d(256, 512, 3, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2),
            nn.Conv2d(512, 4000, 1),
            nn.BatchNorm2d(4000),
            nn.LeakyReLU(0.2)
        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(4000, 512, 3, stride=2, padding=1, output_padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2),
            nn.ConvTranspose2d(512, 256, 3, stride=2, padding=1, output_padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2),
            nn.ConvTranspose2d(256, 128, 3, stride=2, padding=1, output_padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2),
            nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.2),
            nn.ConvTranspose2d(64, 3, 3, stride=1, padding=1),
            nn.Tanh()
        )
        self.edge_connect_keys = nn.ModuleList([nn.Linear(4000, 4000) for _ in range(4)])
        self.edge_connect_values = nn.ModuleList([nn.Linear(4000, 4000) for _ in range(4)])

    def forward(self, x, mask=None):
        x = self.encoder(x)
        if mask is not None:
            for i in range(4):
                x = self.edge_connect_keys[i](x[mask == i])
                x = x[mask == i]
                x = self.edge_connect_values[i](x)
            x = x.view(x.size(0), -1, 1, 1)
        x = self.decoder(x)
        return x

其中，增加了 `edge_connect_keys` 和 `edge_connect_values` 两个模块列表，用于存储4个方向的修补模式的预训练模型键值。在 `forward` 方法中，如果有 mask，就根据 mask 的值将 x 分别传入 edge_connect_keys 和 edge_connect_values 中进行处理，并将结果合并。最后再将结果传入 decoder 中进行解码。