set_visualizer

code_loader.leap_binder.set_visualizer

The leap_binder.set_visualizer binding function points to Visualizer Function. In addition, it defines the visualizer_type and name for reference.

code_loader.leap_binder.set_visualizer(
    name=str,
    function=VisualizerCallableInterface,
    visualizer_type=LeapDataType,
    heatmap_visualizer=Optional[Callable[[npt.NDArray[np.float32]], npt.NDArray[np.float32]]]
)

Args
function	(VisualizerCallableInterface) This parameter points to the Visualizer Function mentioned above.
name	(str) with the given name of the input, e.g. image.
visualizer_type	(LeapDataType) This property sets the type of the data to be visualized by the visualizer.
heatmap_visualizer	(optional) Callable[[npt.NDArray[np.float32]], npt.NDArray[np.float32]] This parameter points to a function that modifies the heatmap data before visualization.

Examples

Basic Usage

import numpy as np
from code_loader.contract.visualizer_classes import LeapText
from code_loader import leap_binder
...

def text_visualizer_func(data: np.ndarray) -> LeapText:
    tokenizer = leap_binder.custom_tokenizer
    text = tokenizer.sequences_to_texts(data)
    return LeapText(text)
    
leap_binder.set_visualizer(
    function=text_visualizer_func,
    visualizer_type=LeapText.type,
    name='text_from_token'
)

The decoder_classes pages contain additional, decoder specific, examples. Moreover, full script usage can be found in Integration Script.

Resize Image and Heatmap

When changing the original data shape in the visualizer function, we need to reshape the heatmap data that is projected. heatmap_visualizer points to a function that modifies the heatmap data respectively. Consider the following example:

from code_loader.contract.visualizer_classes import LeapImage
from code_loader import le

def resized_image_visualizer(data: npt.NDArray[np.float32]) -> LeapImage:
    return LeapImage(np.resize(data, (256, 512, 3)))

def resized_image_visualizer_heatmap(data: npt.NDArray[np.float32]) -> npt.NDArray[np.float32]:
    # data is the heatmap with original size (origin_W, origin_H)
    return np.resize(data, (256, 512))    # we reshape to the resized shape


leap_binder.set_visualizer(
    function=resized_image_visualizer,
    name='resized_image_visualizer',
    visualizer_type=LeapImage.type,
    heatmap_visualizer=resized_image_visualizer_heatmap
)

Guides

Full examples can be found in the Dataset Integration section of the following guides:

• MNIST Guide
• IMDB Guide