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.IcoSpMaConv(in_feats, out_feats, kernel_size, stride=1, pad=0)[source]¶

Define the convolutional layer on icosahedron discretized sphere using spherical 2-d mapping & circular padding.

See also

IcoDiNeConv, IcoRePaConv

Notes

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

Examples

>>> import torch
>>> from surfify.nn import IcoSpMaConv
>>> module = IcoSpMaConv(
        in_feats=8, out_feats=16, kernel_size=3, stride=2, pad=1)
>>> proj_ico_x = torch.zeros((10, 8, 194, 194))
>>> proj_ico_x = module(proj_ico_x)
>>> proj_ico_x.shape

Init IcoSpMaConv.

Parameters:

in_feats : int

input features/channels.

out_feats : int

output features/channels.

kernel_size : int or tuple

the convolutional kernel size.

stride : int or tuple, default 1

controls the stride for the cross-correlation.

pad : int or tuple (pad_azimuth, pad_elevation), default 0

the size of the padding.

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.