PyTorch toolbox to work with spherical surfaces.

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.models.vgg.SphericalGVGG(input_channels, cfg, n_classes, input_dim=194, hidden_dim=4096, batch_norm=False, init_weights=True)[source]¶

Spherical Grided VGG architecture.

See also

SphericalVGG

Notes

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

__init__(input_channels, cfg, n_classes, input_dim=194, hidden_dim=4096, batch_norm=False, init_weights=True)[source]¶

Init class.

Parameters

input_channels : int

the number of input channels.

cfg : list

the definition of layers where ‘M’ stands for max pooling.

n_classes : int

the number of class in the classification problem.

input_dim : int, default 192

the size of the converted 3-D surface to the 2-D grid.

hidden_dim : int, default 4096

the 2-layer classification MLP number of hidden dims.

batch_norm : bool, default False

wether or not to use batch normalization after a convolution layer.

init_weights : bool, default True

initialize network weights.

forward(left_x, right_x)[source]¶

Forward method.

Parameters

left_x : Tensor (samples, <input_channels>, azimuth, elevation)

input left cortical texture.

right_x : Tensor (samples, <input_channels>, azimuth, elevation)

input right cortical texture.

Returns

out : torch.Tensor