meta['cut'] contains the information to know where to cut the model of its final layers, to only keep the encoder (no max/average pooling or final linear layers). meta['split'] contains the information of how to split those layers for differential learning rates (where we give the lower learning rate, the middle ones or the higher one).
This is a factory method so cls is the class you are using, here Learner (but it could be a subclass of Learner).
The DynamicUnet already has UnetBlock, you don’t need to pass them.
I have one question about the Unet Code: I have noticed that DynamicUnet is using F.interpolate to do the upsampling instead of transpose convolutions, also, just at the end of the model there is a transpose convolution. Can you please give me some light on why was this the choice (interpolate instead of transpose convs) and why we are still using a tranpose conv at the end.
I’m doing some segmentation on 3D medical images and want to incorporate good practices. Thanks .