ncalab.models.applications.segmentation

Submodules

Classes

SegmentationNCAModel

Model used for image segmentation.

Package Contents

class ncalab.models.applications.segmentation.SegmentationNCAModel(device: torch.device, num_image_channels: int = 3, num_hidden_channels: int = 16, num_classes: int = 1, fire_rate: float = 0.8, hidden_size: int = 128, num_learned_filters: int = 2, pad_noise: bool = False, filter_padding: Literal['zero', 'reflect', 'replicate', 'circular'] = 'circular', lambda_hidden: float = 0.001, **kwargs)

Bases: ncalab.models.basicNCA.AbstractNCAModel

Model used for image segmentation.

Uses Dice score as the default validation metric. Currently, only binary segmentation masks are supported.

Parameters:

  • [torch.device] (device) – Compute device.

  • [int] (learned_filters) – Number of image channels. Defaults to 3.

  • [int] – Number of hidden channels. Defaults to 16.

  • [int] – Number of classes. Defaults to 1.

  • [float] (fire_rate) – NCA fire rate. Defaults to 0.8.

  • [int] – Number of neurons in hidden layer. Defaults to 128.

  • [int] – Number of learned filters. If 0, use sobel. Defaults to 2.

  • [bool] (pad_noise) – Whether to pad input images with noise. Defaults to True.

  • [str] (filter_padding) – Padding type to use. Might affect reliance on spatial cues. Defaults to “circular”.

num_classes = 1
metrics
lambda_hidden = 0.001
bce_loss
dice_loss
loss(pred: ncalab.prediction.Prediction, label: torch.Tensor) → Dict[str, torch.Tensor]

Compute Dice loss.

Parameters:

  • pred – Prediction.

  • label – Ground truth.

Returns:

Dictionary of identifiers mapped to computed losses.

_post_forward_step(x: torch.Tensor) → torch.Tensor
post_prediction(prediction: ncalab.prediction.Prediction) → ncalab.prediction.Prediction