ncalab.models.applications.classification.classificationNCA

Classes

ClassificationNCAModel

Abstract base class for NCA models.

Module Contents

class ncalab.models.applications.classification.classificationNCA.ClassificationNCAModel(device: torch.device, num_image_channels: int, num_hidden_channels: int, num_classes: int, fire_rate: float = 0.8, hidden_size: int = 128, use_alive_mask: bool = False, pixel_wise_loss: bool = False, num_learned_filters: int = 2, filter_padding: Literal['zero', 'reflect', 'replicate', 'circular'] = 'reflect', use_laplace: bool = False, kernel_size: int = 3, pad_noise: bool = False, use_temporal_encoding: bool = False, use_classifier: bool = True, class_names: List[str] | None = None, avg_pool_size: int = 8, lambda_hidden: float = 0, **kwargs)

Bases: ncalab.models.basicNCA.AbstractNCAModel

Abstract base class for NCA models.

BasicNCAModel is a composition of an NCA backbone model (called “rule”), and an (optional) head module for downstream tasks.

Parameters:

  • device – Pytorch device descriptor.

  • num_image_channels – _description_

  • num_hidden_channels – _description_

  • num_classes – _description_

  • fire_rate – Fire rate for stochastic weight update. Defaults to 0.8.

  • hidden_size – Number of neurons in hidden layer. Defaults to 128.

  • use_alive_mask – Whether to use alive masking (channel 3) during training. Defaults to False.

  • pixel_wise_loss – Whether a prediction per pixel is desired, like in self-classifying MNIST. Defaults to False.

  • num_learned_filters – Number of learned filters. If zero, use two sobel filters instead. Defaults to 2.

  • filter_padding – Padding type to use. Might affect reliance on spatial cues. Defaults to “circular”.

  • pad_noise – Whether to pad input image tensor with noise in hidden / output channels

_num_classes
pixel_wise_loss = False
use_classifier = True
avg_pool_size = 8
lambda_hidden = 0
metrics
focal_loss
property num_classes: int
classify(image: torch.Tensor, steps: int = 100, reduce: bool = False) → torch.Tensor

Predict classification for an input image.

Parameters:

  • image – Input image.

  • steps – Inference steps. Defaults to 100.

  • reduce – Return a single softmax probability. Defaults to False.

Returns:

Single class index or vector of logits.

loss(pred: ncalab.prediction.Prediction, label: torch.Tensor) → Dict[str, torch.Tensor]

Return the classification loss.

Parameters:

  • pred – Prediction.

  • label – Ground truth.

Returns:

Dictionary of identifiers mapped to computed losses.

post_prediction(prediction: ncalab.prediction.Prediction) → ncalab.prediction.Prediction