def vgg(pretrained = False): layers = [] in_channels = 3 for v in base: if v == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] elif v == 'C': layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)] else: conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) layers += [conv2d, nn.ReLU(inplace=True)] in_channels = v # 19, 19, 512 -> 19, 19, 512 pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) # 19, 19, 512 -> 19, 19, 1024 conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6) # 19, 19, 1024 -> 19, 19, 1024 conv7 = nn.Conv2d(1024, 1024, kernel_size=1) layers += [pool5, conv6, nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)] model = nn.ModuleList(layers) if pretrained: state_dict = load_state_dict_from_url("https://download.pytorch.org/models/vgg16-397923af.pth", model_dir="./model_data") state_dict = {k.replace('features.', '') : v for k, v in state_dict.items()} model.load_state_dict(state_dict, strict = False) return model 给出将conv1、conv2、conv3、conv4、conv5进行融合的代码，并且将融合后的代码统一为conv1的尺寸

好的，下面给出一种可能的实现方式：

class VGG(nn.Module):
    def __init__(self, pretrained=False):
        super(VGG, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv5 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        
        # conv1, conv2, conv3, conv4, conv5特征融合
        self.conv1_5 = nn.Conv2d(512*5, 64, kernel_size=3, padding=1)
        
        self.conv6 = nn.Conv2d(64, 1024, kernel_size=3, padding=6, dilation=6)
        self.conv7 = nn.Conv2d(1024, 1024, kernel_size=1)
        
        self.relu = nn.ReLU(inplace=True)
        
        if pretrained:
            state_dict = load_state_dict_from_url("https://download.pytorch.org/models/vgg16-397923af.pth", model_dir="./model_data")
            state_dict = {k.replace('features.', '') : v for k, v in state_dict.items()}
            self.load_state_dict(state_dict, strict = False)
            
    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x1 = x.clone()
        x = self.pool(self.relu(self.conv2(x)))
        x2 = x.clone()
        x = self.pool(self.relu(self.conv3(x)))
        x3 = x.clone()
        x = self.pool(self.relu(self.conv4(x)))
        x4 = x.clone()
        x = self.pool(self.relu(self.conv5(x)))
        x5 = x.clone()
        
        # conv1, conv2, conv3, conv4, conv5特征融合
        x = torch.cat([x1, x2, x3, x4, x5], dim=1)
        x = self.relu(self.conv1_5(x))

        x = self.pool(x)
        x = self.relu(self.conv6(x))
        x = self.relu(self.conv7(x))
        x = self.pool(x)
        
        return x

这个实现中，在forward中对conv1-conv5的输出进行了融合，具体来说，将它们的输出在通道维度上拼接起来，然后经过一个大小为3x3，padding为1的卷积层(conv1_5)进行特征融合，并将输出送入后面的层。融合后的输出统一为conv1的尺寸。