Extract the 2048 vector of a fine-tuned Inception V3 on test set

[Mona_Jalal]

In my model, I do see that avgpool layer shows 2048. However, when I try this solution from @ptrblck it doesn’t recognize the syntax.

      (branch3x3dbl_3b): BasicConv2d(
        (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)
        (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (branch_pool): BasicConv2d(
        (conv): Conv2d(2048, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
    (dropout): Dropout(p=0.5, inplace=False)
    (fc): Linear(in_features=2048, out_features=2, bias=True)
  )
)>

I tried:
model.avgpool.register_forward_hook(get_activation('avgpool'))

How can l load my best model as a feature extractor/evaluator?

I’ve created a small code snippet using a forward hook to store one activation from fc2: class MyModel(nn.Module): def __init__(self): super(MyModel, self).__init__() self.cl1 = nn.Linear(25, 60) self.cl2 = nn.Linear(60, 16) self.fc1 = nn.Linear(16, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = F.relu(self.cl1(x)) x = F.relu(self.cl2(x)) x = F.relu(self.fc1(x)) …