@tensorleap_integration_test

code_loader.inner_leap_binder.leapbinder_decorators.tensorleap_integration_test

The tensorleap_integration_test decorates Tensorleap's integration test, and is used to instruct the platform on what code interfaces should be used when analyzing your model and to locally test the validity of a Tensorleap integration script.

@tensorleap_integration_test()
def integration_test(idx: int, subset: PreprocessResponse) -> None:
    pass

Examples

Basic Usage

import os
from code_loader.plot_functions.visualize import visualize

from leap_binder import (input_encoder, preprocess_func_leap, gt_encoder,
                         combined_bar, metrics, image_visualizer, categorical_crossentropy_loss,
                         metadata_sample_index, metadata_one_hot_digit, metadata_euclidean_distance_from_class_centroid)
import tensorflow as tf
from code_loader.inner_leap_binder.leapbinder_decorators import integration_test

...

@tensorleap_integration_test()
def integration_test(idx, subset):
    # Get input and GT
    image = input_encoder(idx, subset)
    gt = gt_encoder(idx, subset)

    # Load Model and infer
    cnn = load_model()
    y_pred = cnn([image])

    # Visualize the inputs and outputs of the model
    horizontal_bar_vis = combined_bar(y_pred, gt)
    img_vis = image_visualizer(image)

    visualize(img_vis)
    visualize(horizontal_bar_vis)

    # Compute metrics and loss
    metric_res = metrics(y_pred)
    loss_res = categorical_crossentropy_loss(gt, y_pred)
    print(metric_res)
    print(loss_res)

    # Compute metadata
    m1 = metadata_sample_index(idx, subset)
    m2 = metadata_one_hot_digit(idx, subset)
    m3 = metadata_euclidean_distance_from_class_centroid(idx, subset)
    print(m1)
    print(m2)
    print(m3)
    # here the user can return whatever he wants


if __name__ == '__main__':
    num_samples_to_test = 3
    train, val = preprocess_func_leap()
    for i in range(num_samples_to_test):
        integration_test(i, train)
        integration_test(i, val)