pytorch:size mimatch,m1:[7168*7],m2:[25088*4096]

目标:迁移vgg16,固定卷积池化层以及前两层全连接层,在分类层的最后一层前面再加两层全连接层

原来的代码:

class CNN(nn.Module):
    def __init__(self, model):
        super(CNN, self).__init__()
        self.vgg_layer = nn.Sequential(
        *list(model.children())[:-1],
        *list(model.classifier.children())[:-1]
        )
                
        self.Linear_layer = nn.Sequential(
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 10),      
        )

    def forward(self, x):
        x = self.vgg_layer(x)
        x = x.view(x.size(0), -1)
        x = self.Linear_layer(x) 
       
        return x

输出后出现错误,如题。
输出了网络,形式上好像没有什么错误,于是将sequential里面的分开,然后分别输出x,即

class CNN(nn.Module):
    def __init__(self, model):
        super(CNN, self).__init__()
        self.vgg_layer1 = nn.Sequential(*list(model.children())[:-1])
        self.vgg_layer2 = nn.Sequential(*list(model.classifier.children())[:-1])
        
        self.Linear_layer = nn.Sequential(
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 10),      
        )
        ones = torch.Tensor(np.ones([10,10]))
        self.xi_layer = nn.Linear(10, 1, bias=False)
        self.xi_layer.weight=torch.nn.Parameter(ones)

    def forward(self, x):
        x = self.vgg_layer1(x)
        print(x.shape)
        x = self.vgg_layer2(x)
        x = x.view(x.size(0), -1)
        x = self.Linear_layer(x) #(1, 10)
        x = self.xi_layer(x)        
        return x

输出的x本来应该是(255×7×7)最后输出发现是(2×255×7×7)维度明显不对,原来是少写了一行

        x = self.vgg_layer1(x)
        x = x.view(x.size(0), -1)
        x = self.vgg_layer2(x)
        x = x.view(x.size(0), -1)

再输出就能发现是正确的(255×7×7),问题解决!

注意:
1.出现维度问题的时候一定要记得输出每一层的输出或者维度,更便于发现问题;
2.全连接层后面不要忘了加x = x.view(x.size(0), -1)。

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