Visualizer Function
Tensorleap enables visualizations of data within the model graph by connecting a Visualizer block to a node's output tensor. This allows to visualize the model's input, prediction, ground truth, or any inner tensor.
By default, these naive Visualizers are provided (in the UI):
Image
Graph
Numeric
HorizontalBar
Text
ImageMask
TextMask
These naive visualizers shows the data as-is, but in many cases, there is a need to write our own Visualizer Function to make sense of the data. For example:
Converting tokenized data to text words
Custom post-processing
Draw landmarks on images
Apply transforms
Example a visualizer function:
import numpy as np
from code_loader.contract.visualizer_classes import LeapText
from code_loader.contract.enums import LeapDataType
...
@tensorleap_custom_visualizer(name="input_visualizer", visualizer_type=LeapDataType.Text)
def input_visualizer(input_ids: np.ndarray) -> LeapText:
input_ids = np.squeeze(input_ids)
text = decode_token_ids(input_ids)
return LeapText(text)
The @tensorleap_custom_visualizerdecorator registers each visualizer into the Tensorleap integration.
These functions should return one of the following types defined in visualizer_classes.
The visualizer_classes pages contain additional, visualizer specific, examples. Moreover, full script usage can be found at Integration Script.
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