deepsphere.models.spherical_unet package

Submodules

deepsphere.models.spherical_unet.decoder module

Decoder for Spherical UNet.

class deepsphere.models.spherical_unet.decoder.Decoder(unpooling, laps)

Bases: torch.nn.modules.module.Module

The decoder of the Spherical UNet.

forward(x_enc0, x_enc1, x_enc2, x_enc3, x_enc4)

Forward Pass.

Parameters:x_enc* (torch.Tensor) – input tensors. Returns:output after forward pass. Return type:torch.Tensor

class deepsphere.models.spherical_unet.decoder.SphericalChebBNPoolCheb(in_channels, middle_channels, out_channels, lap, pooling, kernel_size=3)

Bases: torch.nn.modules.module.Module

Building Block calling a SphericalChebBNPool block then a SphericalCheb.

forward(x)

Forward Pass.

Parameters:x (torch.Tensor) – input [batch x vertices x channels/features] Returns:output [batch x vertices x channels/features] Return type:torch.Tensor

class deepsphere.models.spherical_unet.decoder.SphericalChebBNPoolConcat(in_channels, out_channels, lap, pooling, kernel_size=3)

Bases: torch.nn.modules.module.Module

Building Block calling a SphericalChebBNPool Block then concatenating the output with another tensor and calling a SphericalChebBN block.

forward(x, concat_data)

Forward Pass.

Parameters:

• x (torch.Tensor) – input [batch x vertices x channels/features]
• concat_data (torch.Tensor) – encoder layer output [batch x vertices x channels/features]

Returns:

output [batch x vertices x channels/features]

Return type:

torch.Tensor

deepsphere.models.spherical_unet.encoder module

Encoder for Spherical UNet.

class deepsphere.models.spherical_unet.encoder.Encoder(pooling, laps)

Bases: torch.nn.modules.module.Module

Encoder for the Spherical UNet.

forward(x)

Forward Pass.

Parameters:x (torch.Tensor) – input [batch x vertices x channels/features] Returns:obj: torch.Tensor: output [batch x vertices x channels/features] Return type:x_enc*

class deepsphere.models.spherical_unet.encoder.SphericalChebBN2(in_channels, middle_channels, out_channels, lap, kernel_size=3)

Bases: torch.nn.modules.module.Module

Building Block made of 2 Building Blocks (convolution, batchnorm, activation).

forward(x)

Forward Pass.

Parameters:x (torch.Tensor) – input [batch x vertices x channels/features] Returns:output [batch x vertices x channels/features] Return type:torch.Tensor

class deepsphere.models.spherical_unet.encoder.SphericalChebPool(in_channels, out_channels, lap, pooling, kernel_size=3)

Bases: torch.nn.modules.module.Module

Building Block with a pooling/unpooling and a Chebyshev Convolution.

forward(x)

Forward Pass.

Parameters:x (torch.Tensor) – input [batch x vertices x channels/features] Returns:output [batch x vertices x channels/features] Return type:torch.Tensor

deepsphere.models.spherical_unet.unet_model module

Spherical Graph Convolutional Neural Network with UNet autoencoder architecture.

class deepsphere.models.spherical_unet.unet_model.SphericalUNet(pooling_class, N, depth, laplacian_type, ratio=1)

Bases: torch.nn.modules.module.Module

Spherical GCNN Autoencoder.

forward(x)

Forward Pass.

Parameters:x (torch.Tensor) – input to be forwarded. Returns:output Return type:torch.Tensor

deepsphere.models.spherical_unet.utils module

Layers used in both Encoder and Decoder.

class deepsphere.models.spherical_unet.utils.SphericalChebBN(in_channels, out_channels, lap, kernel_size=3)

Bases: torch.nn.modules.module.Module

Building Block with a Chebyshev Convolution, Batchnormalization, and ReLu activation.

forward(x)

Forward Pass.

Parameters:x (torch.tensor) – input [batch x vertices x channels/features] Returns:output [batch x vertices x channels/features] Return type:torch.tensor

class deepsphere.models.spherical_unet.utils.SphericalChebBNPool(in_channels, out_channels, lap, pooling, kernel_size=3)

Bases: torch.nn.modules.module.Module

Building Block with a pooling/unpooling, a calling the SphericalChebBN block.

forward(x)

Forward Pass.

Parameters:x (torch.tensor) – input [batch x vertices x channels/features] Returns:output [batch x vertices x channels/features] Return type:torch.tensor

Module contents