morphoclass.models.cnnet module¶

Classification of persistence images using a CNN.

This module includes a convolutional model and a corresponding trainer class.

class morphoclass.models.cnnet.CNNEmbedder¶

Bases: torch.nn.modules.module.Module

The embedder part of the CNNet classifier.

forward(data)¶

Do the forward pass.

Parameters: data (torch_geometric.data.Batch | torch.Tensor) – A batch of the MorphologyDataset dataset.
Returns: x – The embedding of the persistence image. The dimension of the tensor is (n_batch, 3 * (image_size // 4)**2).
Return type: torch.Tensor

class morphoclass.models.cnnet.CNNet(n_classes, image_size=100, bn=False)¶

Bases: torch.nn.modules.module.Module

Convolutional net for classifying persistence images.

The provided persistence images should be square greyscale images and can be obtained from persistence diagrams by applying Gaussian KDE.

Parameters

n_classes (int) – The number of classes.
image_size (int) – The width or height of the input images. The images are assumed to be square.
bn (bool) – If true then 1d batch normalization and a relu are applied to the flattened embeddings before the FC layer.

forward(data)¶

Do the forward pass.

Parameters: data (torch_geometric.data.Batch) – A batch of MorphologyDataset dataset.
Returns: logits – The predicted logits of shape (n_images, n_classes)
Return type: torch.Tensor

loss_acc(data)¶

Get loss and accuracy.

Parameters

data (torch_geometric.data.Batch) – A batch of MorphologyDataset dataset.

Returns