Encoders#
Abstract class for feature encoders.
- class topobenchmarkx.nn.encoders.base.AbstractFeatureEncoder[source]#
Abstract class to define a custom feature encoder.
Class to apply BaseEncoder to the features of higher order structures.
- class topobenchmarkx.nn.encoders.all_cell_encoder.AllCellFeatureEncoder(in_channels, out_channels, proj_dropout=0, selected_dimensions=None, **kwargs)[source]#
Encoder class to apply BaseEncoder.
The BaseEncoder is applied to the features of higher order structures. The class creates a BaseEncoder for each dimension specified in selected_dimensions. Then during the forward pass, the BaseEncoders are applied to the features of the corresponding dimensions.
- Parameters:
- in_channelslist[int]
Input dimensions for the features.
- out_channelslist[int]
Output dimensions for the features.
- proj_dropoutfloat, optional
Dropout for the BaseEncoders (default: 0).
- selected_dimensionslist[int], optional
List of indexes to apply the BaseEncoders to (default: None).
- **kwargsdict, optional
Additional keyword arguments.
- forward(data: Data) Data[source]#
Forward pass.
The method applies the BaseEncoders to the features of the selected_dimensions.
- Parameters:
- datatorch_geometric.data.Data
Input data object which should contain x_{i} features for each i in the selected_dimensions.
- Returns:
- torch_geometric.data.Data
Output data object with updated x_{i} features.
- class topobenchmarkx.nn.encoders.all_cell_encoder.BaseEncoder(in_channels, out_channels, dropout=0)[source]#
Base encoder class used by AllCellFeatureEncoder.
This class uses two linear layers with GraphNorm, Relu activation function, and dropout between the two layers.
- Parameters:
- in_channelsint
Dimension of input features.
- out_channelsint
Dimensions of output features.
- dropoutfloat, optional
Percentage of channels to discard between the two linear layers (default: 0).
- forward(x: Tensor, batch: Tensor) Tensor[source]#
Forward pass of the encoder.
It applies two linear layers with GraphNorm, Relu activation function, and dropout between the two layers.
- Parameters:
- xtorch.Tensor
Input tensor of dimensions [N, in_channels].
- batchtorch.Tensor
The batch vector which assigns each element to a specific example.
- Returns:
- torch.Tensor
Output tensor of shape [N, out_channels].