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.models.vae.HemiFusionEncoder(input_channels, input_dim, latent_dim, conv_flts=(64, 128, 128, 256, 256), fusion_level=1, activation='LeakyReLU', batch_norm=False)[source]¶

Init class.

Parameters:

input_channels : int, default 1

the number of input channels.

input_dim : int, default 192

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

latent_dim : int, default 64

the size of the latent space it encodes to.

conv_flts : list of int

the size of convolutional filters.

fusion_level : int, default 1

at which max pooling level left and right hemisphere data are concatenated.

activation : str, default ‘LeakyReLU’

activation function’s class name in pytorch’s nn module to use after each convolution

batch_norm : bool, default False

optionally uses batch normalization after each convolution

forward(x)[source]¶

The encoder.

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:

q(z | x) : Normal (batch_size, <latent_dim>)

a Normal distribution.