ncalab.models.applications.classification

Submodules

Classes

ClassificationNCAModel

Abstract base class for NCA models.

ClassificationNCAHead

Base class for all neural network modules.

Package Contents

class ncalab.models.applications.classification.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
class ncalab.models.applications.classification.ClassificationNCAHead(num_hidden_channels: int, num_classes: int, device: torch.device, avg_pool_size: int, hidden_size: int = 32)

Bases: ncalab.models.basicNCA.abstractNCAhead.AbstractNCAHead

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

Initialize internal Module state, shared by both nn.Module and ScriptModule.

num_hidden_channels
num_classes
device
avg_pool_size
classifier
forward(x)
Parameters:

x (torch.Tensor) – Input tensor

Returns:

NotImplemented, subclasses are required to implement this method.

freeze(freeze_last: bool = False)

Freeze head weights.

Parameters:

freeze_last (bool, optional) – Whether to freeze the last layer (if applicable), defaults to True

Returns:

NotImplemented, subclasses are required to implement this method.