precompute=True

Part 1 (2018)
12

Hello @jeremy

I see that we are chopping off the last two layres of resnet34

<class 'torch.nn.modules.conv.Conv2d'>
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>
<class 'torch.nn.modules.activation.ReLU'>
<class 'torch.nn.modules.pooling.MaxPool2d'>
<class 'torch.nn.modules.container.Sequential'>
<class 'torch.nn.modules.container.Sequential'>
<class 'torch.nn.modules.container.Sequential'>
<class 'torch.nn.modules.container.Sequential'>
                --- cut off ---
<class 'torch.nn.modules.pooling.AvgPool2d'>
<class 'torch.nn.modules.linear.Linear'>

And then we are adding a bunch of other layers (almost double).

<class 'torch.nn.modules.conv.Conv2d'>
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>
<class 'torch.nn.modules.activation.ReLU'>
<class 'torch.nn.modules.pooling.MaxPool2d'>
<class 'torch.nn.modules.container.Sequential'>
<class 'torch.nn.modules.container.Sequential'>
<class 'torch.nn.modules.container.Sequential'>
<class 'torch.nn.modules.container.Sequential'>
                --- add ---
<class 'fastai.layers.AdaptiveConcatPool2d'>
<class 'fastai.layers.Flatten'>
<class 'torch.nn.modules.batchnorm.BatchNorm1d'>
<class 'torch.nn.modules.dropout.Dropout'>
<class 'torch.nn.modules.linear.Linear'>
<class 'torch.nn.modules.activation.ReLU'>
<class 'torch.nn.modules.batchnorm.BatchNorm1d'>
<class 'torch.nn.modules.dropout.Dropout'>
<class 'torch.nn.modules.linear.Linear'>
<class 'torch.nn.modules.activation.LogSoftmax'>

I wanted to get an insight into why these are laid out the way they are, I believe these are important from transfer learning point of view. Is this something you will take up coming Monday? (In the meantime I am digging further)

–

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