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.unet.UpBlock(conv_layer, in_ch, out_ch, conv_neigh_indices, neigh_indices, up_neigh_indices, down_indices, up_mode)[source]¶
Define the upsamping block in spherical UNet: upconv => (conv => BN => ReLU) * 2
Init UpBlock.
- Parameters:
conv_layer : nn.Module
the convolutional layer on icosahedron discretized sphere.
in_ch : int
input features/channels.
out_ch : int
output features/channels.
conv_neigh_indices : tensor, int
conv layer’s filters’ neighborhood indices at sampling i.
neigh_indices : tensor, int
neighborhood indices at sampling i.
up_neigh_indices : array
upsampling neighborhood indices at sampling i + 1.
down_indices : array
downsampling indices at sampling i.
up_mode : str, default ‘interp’
type of upsampling: ‘transpose’ for transpose convolution, ‘interp’ for nearest neighbor linear interpolation, ‘maxpad’ for max pooling shifted zero padding, and ‘zeropad’ for classical zero padding.
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