ncalab.visualization

Submodules

Attributes

`animator_style_dark`
`animator_styles`

Classes

`AnimatorStyle`
`Animator`	Responsible for rendering NCA predictions as GIFs.
`Prediction`	Stores the result of an NCA prediction, including the number of steps it took.
`Visual`	Base class for tensorboard visuals.
`VisualBinaryImageClassification`	Base class for tensorboard visuals.
`VisualRGBImageClassification`	Base class for tensorboard visuals.
`VisualMultiImageClassification`	Base class for tensorboard visuals.
`VisualBinaryImageSegmentation`	Base class for tensorboard visuals.
`VisualDepthEstimation`	Base class for tensorboard visuals.
`VisualGrowing`	Base class for tensorboard visuals.

Functions

`draw_segmentation_overlay`(image, mask, style)
`unwrap`(x)	Panics if x is None, otherwise returns x.
`abbreviate_label`(L[, max_len])
`show_image_row`(ax, images[, vmin, vmax, cmap, ...])	Shows a row of images next to each other.

Package Contents

class ncalab.visualization.AnimatorStyle(color_background, color_overlay, color_title, color_progress, underline: bool = True, progress_h: int = 3)

color_background

color_overlay

color_title

color_progress

underline = True

progress_h = 3

apply(fig, ax)

ncalab.visualization.animator_style_dark

ncalab.visualization.animator_styles

ncalab.visualization.draw_segmentation_overlay(image, mask, style)

class ncalab.visualization.Animator(nca: ncalab.models.AbstractNCAModel, seed: torch.Tensor, steps: int | None = None, interval: int = 100, repeat: bool = True, repeat_delay: int = 10000, overlay: bool = False, show_timestep: bool = True, hidden: bool = False, show_input: bool = False, style: str | AnimatorStyle = 'dark')

Responsible for rendering NCA predictions as GIFs.

Parameters:

nca (ncalab.AbstractNCAModel) – NCA model instance
seed (torch.Tensor) – Input image for the NCA model
steps (int, optional) – Number of NCA prediction steps per sample, defaults to 100
interval (int, optional) – Time of each frame (milliseconds), defaults to 100
repeat (bool, optional) – Whether to loop the animation, defaults to True
repeat_delay (int, optional) – Time after which the animation is repeated (milliseconds), defaults to 10000
overlay (bool, optional) – Whether to overlay output channel (segmentation mask), defaults to False
show_timestep (bool, optional) – Whether to display timestep in caption, defaults to True

animation_fig

save(path: str | pathlib.Path)

Save generated figure as GIF

Parameters:: path (str | Path) – Output path

class ncalab.visualization.Prediction(model, steps: int, output_image: torch.Tensor, logits: torch.Tensor, head_prediction: torch.Tensor | None = None)

Stores the result of an NCA prediction, including the number of steps it took.

Sequences are typically stored by BasicNCAModel’s “record” function, and are returned as a list of Prediction objects.

Constructor is typically not called explicitly. Rather, the forward pass of BasicNCAModel (and its subclasses) is responsible for filling its attributes.

Parameters:

model (ncalab.BasicNCAModel) – Reference to model used for prediction.
steps (int) – Number of steps taken for the prediction.
output_image (torch.Tensor) – Output image tensor.

model

steps

output_image

_output_array: numpy.ndarray | None = None

logits

_logits_array: numpy.ndarray | None = None

head_prediction = None

_head_prediction_array: numpy.ndarray | None = None

property image_channels: torch.Tensor

Convenience property to access the image channels as a Tensor.

Returns:: BCWH Tensor
Return type:: torch.Tensor

property hidden_channels: torch.Tensor

Convenience property to access the hidden channels as a Tensor.

Returns:: BCWH Tensor
Return type:: torch.Tensor

property output_channels: torch.Tensor

Convenience property to access the output channels as a Tensor.

Returns:: BCWH Tensor
Return type:: torch.Tensor

property output_array: numpy.ndarray

Convenience property to access the whole output image in the format of a numpy array. Brings the entire tensor to CPU on demand, and only at the first call.

Returns:: Numpy array in BCWH format
Return type:: np.ndarray

property image_channels_np: numpy.ndarray

Convenience property to access the output image channels in the format of a numpy array. Brings the entire tensor to CPU on demand, and only at the first call.

Returns:: Numpy array in BCWH format
Return type:: np.ndarray

property hidden_channels_np: numpy.ndarray

Convenience property to access the hidden image channels in the format of a numpy array. Brings the entire tensor to CPU on demand, and only at the first call.

Returns:: Numpy array in BCWH format
Return type:: np.ndarray

property output_channels_np: numpy.ndarray

Convenience property to access the image’s output channels in the format of a numpy array. Brings the entire tensor to CPU on demand, and only at the first call.

Returns:: Numpy array in BCWH format
Return type:: np.ndarray

property head_prediction_array: numpy.ndarray | None

property logits_array: numpy.ndarray

ncalab.visualization.unwrap(x: Any)

Panics if x is None, otherwise returns x.

This is a useful shorthand for cases such as x = unwrap(some_object).do_something() in which we are 99% certain that some_object is not None and want to avoid a mypy complaint.

Parameters:: x (Any) – Any kind of object.
Raises:: RuntimeError – If x is None.
Returns:: Just passes through the input x if it is not None.

ncalab.visualization.abbreviate_label(L, max_len=8)

ncalab.visualization.show_image_row(ax, images, vmin=None, vmax=None, cmap=None, overlays=None, overlay_vmin=None, overlay_vmax=None, overlay_cmap=None, label: str = '', colorbar: bool = False, x_index: bool = False, normalize: bool = False)

Shows a row of images next to each other.

Parameters:

ax – Axis object.
images – List of grayscale, RGB or RGBA images, can be CWH or WHC.
vmin – Minimum value to clip channel values, defaults to None
vmax – Maximum value to clip channel values, defaults to None
cmap – matplotlib colormap to apply, defaults to None
overlays – _description_, defaults to None
overlay_vmin – _description_, defaults to None
overlay_vmax – _description_, defaults to None
overlay_cmap – _description_, defaults to None
label – y-axis label next to first image, defaults to “”
colorbar – Whether to display a colorbar next to the last image, defaults to False
x_index – Whether to show the batch index below each image, defaults to False
normalize – Whether to normalize images across batch

class ncalab.visualization.Visual