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 surfify.nn.modules.IcoRePaConv(in_feats, out_feats, neighs)[source]¶

Define the convolutional layer on icosahedron discretized sphere using rectagular filter in tangent plane.

See also

IcoDiNeConv, IcoSpMaConv

Notes

Debuging messages can be displayed by changing the log level using setup_logging(level='debug').

Examples

>>> import torch
>>> from surfify.nn import IcoRePaConv
>>> from surfify.utils import icosahedron, neighbors_rec
>>> ico2_vertices, ico2_triangles = icosahedron(order=2)
>>> neighbors = neighbors_rec(
        ico2_vertices, ico2_triangles, size=5, zoom=5)[:2]
>>> module = IcoRePaConv(
        in_feats=8, out_feats=8, neighs=neighbors)
>>> ico2_x = torch.zeros((10, 8, len(ico2_vertices)))
>>> ico2_x = module(ico2_x)
>>> ico2_x.shape

Init IcoRePaConv.

Parameters:

in_feats : int

input features/channels.

out_feats : int

output features/channels.

neighs : 2-uplet

neigh_indices: array (N, k, 3) - the neighbors indices. neigh_weights: array (N, k, 3) - the neighbors distances.

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.