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python - 如何在 PyTorch 中向预训练模型添加层？

问题描述

我想在layer 和after 之后添加layer normalization功能。我希望我的 输出为 200，因此尝试不包含 fc 层而不是它 make new ，但它没有删除我正在使用的预训练模型附带的。AdaptiveAvgPool2dL2 normalizationfc layerfc layerfc layerfc layersgooglenet

我的代码：

class GoogleNet(nn.Module):
    def __init__(self):
        super(GoogleNet,self).__init__()
        self.model = googlenet_pytorch.GoogLeNet.from_pretrained('googlenet')
        
        self.fc = nn.Linear(1024,200, bias=False),
        
    def forward(self, x):
        batch_size ,_,_,_ =x.shape
        x = self.model.extract_features(x)
        x = self.model._avg_pooling(x)
        x = F.layer_norm(x,x.size[1:],elementwise_affine=False)
        x = self.fc(x)
        x = F.normalize(x, p=2, dim=1)
        return x

我得到的输出：

      .....  
      .....  
      .....

(aux1): InceptionAux(
      (conv): BasicConv2d(
        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (fc1): Linear(in_features=2048, out_features=1024, bias=True)
      (fc2): Linear(in_features=1024, out_features=1000, bias=True)
    )
    (aux2): InceptionAux(
      (conv): BasicConv2d(
        (conv): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (fc1): Linear(in_features=2048, out_features=1024, bias=True)
      (fc2): Linear(in_features=1024, out_features=1000, bias=True)
    )
    (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
    (dropout): Dropout(p=0.2, inplace=False)
    (fc): Linear(in_features=1024, out_features=1000, bias=True)
  )
)

我想要的输出：

  ......
  ......
  ......

  (aux1): None
  (aux2): None
  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
  ** layer normalization here**
  (dropout): Dropout(p=0.2, inplace=False)
  (fc): Linear(in_features=1024, out_features=200, bias=False)
  **L2 normalization here**
)

如果有人需要知道这个问题的解决方案代码，在 iacob 的答案的帮助下我解决了它，我将它添加为answer。

标签： pythondeep-learningpytorch

解决方案

根据@iacob对代码进行更正后，我解决了这个问题。

正确的代码：

class GoogleNet(nn.Module):
    def __init__(self):
        super(GoogleNet,self).__init__()
        self.model = googlenet_pytorch.GoogLeNet.from_pretrained('googlenet')
        self.avgpool = nn.AdaptiveAvgPool2d((1,1))
        self.layernorm = nn.LayerNorm(1024,elementwise_affine=True)
        self._fc = nn.Linear(1024,200, bias=False)
    def forward(self, x):
        batch_size ,_,_,_ =x.shape
        x = self.model.extract_features(x)
        x = self.model.avgpool(x)
        x = x.view(-1, 1024)
        x = self.layernorm(x)
        x = self._fc(x)
        x = F.normalize(x, p=2, dim=1)
        return x

在此之后，我创建了一个实例GoogleNet() 并将不需要的层转换为 Identity()。

dep = GoogleNet()
dep.model.aux1 = Identity()
dep.model.aux2 = Identity()
dep.model.avgpool = Identity()
dep.model.dropout = Identity()
dep.model.fc = Identity()

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