将以下代码改成残差卷积网络class EmbeddingOmniglot(nn.Module): ''' In this network the input image is supposed to be 28x28 ''' def __init__(self, args, emb_size): super(EmbeddingOmniglot, self).__init__() self.emb_size = emb_size self.nef = 64 self.args = args # input is 1 x 28 x 28 self.conv1 = nn.Conv2d(1, self.nef, 3, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(self.nef) # state size. (nef) x 14 x 14 self.conv2 = nn.Conv2d(self.nef, self.nef, 3, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(self.nef) # state size. (1.5*ndf) x 7 x 7 self.conv3 = nn.Conv2d(self.nef, self.nef, 3, bias=False) self.bn3 = nn.BatchNorm2d(self.nef) # state size. (2*ndf) x 5 x 5 self.conv4 = nn.Conv2d(self.nef, self.nef, 3, bias=False) self.bn4 = nn.BatchNorm2d(self.nef) # state size. (2*ndf) x 3 x 3 self.fc_last = nn.Linear(3 * 3 * self.nef, self.emb_size, bias=False) self.bn_last = nn.BatchNorm1d(self.emb_size) def forward(self, inputs): e1 = F.max_pool2d(self.bn1(self.conv1(inputs)), 2) x = F.leaky_relu(e1, 0.1, inplace=True) e2 = F.max_pool2d(self.bn2(self.conv2(x)), 2) x = F.leaky_relu(e2, 0.1, inplace=True) e3 = self.bn3(self.conv3(x)) x = F.leaky_relu(e3, 0.1, inplace=True) e4 = self.bn4(self.conv4(x)) x = F.leaky_relu(e4, 0.1, inplace=True) x = x.view(-1, 3 * 3 * self.nef) output = F.leaky_relu(self.bn_last(self.fc_last(x))) return [e1, e2, e3, output]

以下是将代码改写成使用残差卷积网络的示例代码：

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out += identity
        out = self.relu(out)

        return out

class EmbeddingOmniglot(nn.Module):
    ''' In this network the input image is supposed to be 28x28 '''

    def __init__(self, args, emb_size):
        super(EmbeddingOmniglot, self).__init__()
        self.emb_size = emb_size
        self.nef = 64
        self.args = args

        # input is 1 x 28 x 28
        self.conv1 = nn.Conv2d(1, self.nef, 3, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.nef)
        # state size. (nef) x 14 x 14
        self.residual_block1 = ResidualBlock(self.nef, self.nef)
        self.residual_block2 = ResidualBlock(self.nef, self.nef)
        # state size. (nef) x 14 x 14
        self.residual_block3 = ResidualBlock(self.nef, self.nef)
        self.residual_block4 = ResidualBlock(self.nef, self.nef)
        # state size. (nef) x 14 x 14

        # state size. (1.5*ndf) x 7 x 7
        self.conv2 = nn.Conv2d(self.nef, self.nef, 3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(self.nef)
        # state size. (2*ndf) x 5 x 5
        self.conv3 = nn.Conv2d(self.nef, self.nef, 3, bias=False)
        self.bn3 = nn.BatchNorm2d(self.nef)
        # state size. (2*ndf) x 3 x 3
        self.fc_last = nn.Linear(3 * 3 * self.nef, self.emb_size, bias=False)
        self.bn_last = nn.BatchNorm1d(self.emb_size)

    def forward(self, inputs):
        x = self.conv1(inputs)
        x = self.bn1(x)
        x = F.relu(x)

        x = self.residual_block1(x)
        x = self.residual_block2(x)
        e1 = F.max_pool2d(x, 2)

        x = self.residual_block3(e1)
        x = self.residual_block4(x)
        e2 = F.max_pool2d(x, 2)

        x = self.conv2(e2)
        x = self.bn2(x)
        x = F.relu(x)

        x = self.conv3(x)
        x = self.bn3(x)
        x = F.relu(x)
        x = x.view(-1, 3 * 3 * self.nef)

        output = self.fc_last(x)
        output = self.bn_last(output)
        output = F.relu(output)

        return [e1, e2, x, output]

这段代码定义了一个使用残差卷积网络的`EmbeddingOmniglot`类。其中`ResidualBlock`类表示残差块。你可以根据需要调整残差块的层数和卷积层的参数。在`forward`方法中，我们使用`ResidualBlock`类创建了残差块，并按照残差连接的方式连接它们。最后，我们对输出进行相应的处理，如最大池化、全连接层和批标准化等。注意，这里的输出顺序与原代码保持一致，但你也可以根据需要进行修改。