Extract bottleneck features of Dynamic Unet

Deep Learning
1

Besides dense tasks, one might want to access the deepest layer of DynamicUnet. Ideally, the model should return it too, given a proper flag in the constructor. Given that there is currently no such functionality, how should we access the bottleneck features?

2

Hello,

Extracting the bottleneck features is straightforward; fastai’s hook_output can be utilized to hook the output of a DynamicUnet’s encoder (i.e., the bottleneck features), and after a forward pass, the stored values can be accessed via the hook’s .stored attribute.

encoder = dynamic_unet.layers[0]
encoder_hook = hook_output(encoder)
bottleneck_features = encoder_hook.stored

Is that helpful?