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, kernel_size)[source]

Bases: torch.nn.Module

The decoder of the Spherical UNet.

forward(x_enc0, x_enc1, x_enc2, x_enc3, x_enc4)[source]

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)[source]

Bases: torch.nn.Module

Building Block calling a SphericalChebBNPool block then a SphericalCheb.

forward(x)[source]

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)[source]

Bases: torch.nn.Module

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

forward(x, concat_data)[source]

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, kernel_size)[source]

Bases: torch.nn.Module

Encoder for the Spherical UNet.

forward(x)[source]

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.EncoderTemporalConv(pooling, laps, sequence_length, kernel_size)[source]

Bases: deepsphere.models.spherical_unet.encoder.Encoder

Encoder for the Spherical UNet temporality with convolution.

class deepsphere.models.spherical_unet.encoder.SphericalChebBN2(in_channels, middle_channels, out_channels, lap, kernel_size)[source]

Bases: torch.nn.Module

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

forward(x)[source]

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)[source]

Bases: torch.nn.Module

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

forward(x)[source]

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, kernel_size, ratio=1)[source]

Bases: torch.nn.Module

Spherical GCNN Autoencoder.

forward(x)[source]

Forward Pass.

Parameters

x (torch.Tensor) – input to be forwarded.

Returns

output

Return type

torch.Tensor

class deepsphere.models.spherical_unet.unet_model.SphericalUNetTemporalConv(pooling_class, N, depth, laplacian_type, sequence_length, kernel_size, ratio=1)[source]

Bases: deepsphere.models.spherical_unet.unet_model.SphericalUNet

Spherical GCNN Autoencoder with temporality by means of convolution over time.

forward(x)[source]

Forward Pass.

Parameters

x (torch.Tensor) – input to be forwarded.

Returns

output

Return type

torch.Tensor

class deepsphere.models.spherical_unet.unet_model.SphericalUNetTemporalLSTM(pooling_class, N, depth, laplacian_type, sequence_length, kernel_size, ratio=1)[source]

Bases: deepsphere.models.spherical_unet.unet_model.SphericalUNet

Sphericall GCNN Autoencoder with LSTM.

forward(x)[source]

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)[source]

Bases: torch.nn.Module

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

forward(x)[source]

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)[source]

Bases: torch.nn.Module

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

forward(x)[source]

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