Deep learning for NeuroImaging in Python.

Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the gallery for the big picture.

class nidl.volume.backbones.alexnet3d.AlexNet(n_embedding: int = 128, in_channels: int = 1)[source]¶

Bases: Module

3D AlexNet architecture adapted from Abrol et al., 2021. See https://doi.org/10.5281/zenodo.4309677 for details.

Parameters:

n_embedding : int, default=128

the size of the embedding space.

in_channels : int, default=1

the number of input channels.

Notes

Small adaptation for representation learning: the final regressor head is replaced by simple linear layer.

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(x)[source]¶

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.