ncalab.models.wrappers.cascadeNCA

Classes

CascadeNCA

Chain multiple instances of the same NCA model, operating at different

Functions

`upscale`(→ torch.Tensor)	Upsamples an image.
`downscale`(→ torch.Tensor)	Downsamples an image.

Module Contents

ncalab.models.wrappers.cascadeNCA.upscale(image: torch.Tensor, scale: float, mode: str = 'nearest') → torch.Tensor

Upsamples an image.

Parameters:

image (torch.Tensor) – Image tensor in BCWH order.
scale (float) – Scale factor. Must be >= 1.0
mode (str) – Interpolation mode, defaults to “nearest”.

Returns:

Image tensor in BCWH order.

Return type:

torch.Tensor

ncalab.models.wrappers.cascadeNCA.downscale(image: torch.Tensor, scale: float, mode: str = 'bilinear') → torch.Tensor

Downsamples an image.

Parameters:

image (torch.Tensor) – Image tensor in BCWH order.
scale (float) – Scale factor, must be >= 1.0
mode – Interpolation mode, defaults to “bilinear”.

Returns:

Image tensor in BCWH order.

Return type:

torch.Tensor

class ncalab.models.wrappers.cascadeNCA.CascadeNCA(wrapped: ncalab.models.basicNCA.AbstractNCAModel, scales: List[int], steps: List[int], single_model: bool = True)

Bases: ncalab.models.basicNCA.AbstractNCAModel

Chain multiple instances of the same NCA model, operating at different image scales.

The idea is to use this model as a wrapper and drop-in replacement for an existing model. For instance, if we created a model nca = SegmentationNCA(…) and all code to interface with it, we could instead write cascade = CascadeNCA(SegmentationNCA(…), scales, steps) without the need for adjusting any of the interfacing code.

This is still highly experimental. In the future, we’ll work on a cleaner interface for this.

Parameters:

wrapped (ncalab.AbstractNCAModel) – Backbone model based on AbstractNCAModel.
scales (List[int]) – List of scales to operate at, e.g. [4, 2, 1].
steps (List[int]) – List of number of NCA inference time steps.
single_model (bool) – Only train a single instance of the NCA model

loss

Compute loss. Needs to be overloaded by any subclass. Please note that the returned dict needs to hold “total” key in which the total loss is stored, which is typically a weighted sum of other losses. The total loss is backpropagated, whereas the other losses are sent to tensorboard.

Parameters:

[torch.Tensor] (label) – Input image, BCWH.
[torch.Tensor] – Ground truth, BCWH.

Returns:

Dictionary of identifiers mapped to computed losses.

finetune

Prepare model for fine tuning by freezing everything except the final layer, and setting to “train” mode.

Param:: freeze_head

prepare_input

Preprocess input. Intended to be overwritten by subclass, if preprocessing is necessary.

Parameters:: [torch.Tensor] (x) – Input tensor to preprocess.
Returns:: Processed tensor.

head

metrics

wrapped

scales

steps

single_model = True

models: List[ncalab.models.basicNCA.AbstractNCAModel]

forward(x: torch.Tensor, *args, **kwargs) → ncalab.prediction.Prediction

Parameters:

x (torch.Tensor) – Input image tensor, BCWH.
steps (torch.Tensor) – Unused, as steps are defined in constructor.

Returns:

Prediction object

Return type:

Prediction

record(image: torch.Tensor, steps: int | Tuple[int, int] | None = None) → List[ncalab.prediction.Prediction]

Records predictions for all time steps and returns the resulting sequence of predictions.

Takes care of scaling the image in between steps.

Parameters:: image (torch.Tensor) – Input image, BCWH.
Returns:: List of Prediction objects.
Return type:: List[Prediction]