official/projects/centernet/losses/centernet_losses_test.py
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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"""Tests for losses of centernet model."""
import numpy as np
import tensorflow as tf, tf_keras
from official.projects.centernet.losses import centernet_losses
LOG_2 = np.log(2)
LOG_3 = np.log(3)
class L1LocalizationLossTest(tf.test.TestCase):
def test_returns_correct_loss(self):
def graph_fn():
loss = centernet_losses.L1LocalizationLoss()
pred = [[0.1, 0.2], [0.7, 0.5]]
target = [[0.9, 1.0], [0.1, 0.4]]
weights = [[1.0, 0.0], [1.0, 1.0]]
return loss(pred, target, weights=weights)
computed_value = graph_fn()
self.assertAllClose(computed_value, [[0.8, 0.0], [0.6, 0.1]], rtol=1e-6)
class PenaltyReducedLogisticFocalLossTest(tf.test.TestCase):
"""Testing loss function."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._prediction = np.array([
# First batch
[[1 / 2, 1 / 4, 3 / 4],
[3 / 4, 1 / 3, 1 / 3]],
# Second Batch
[[0.0, 1.0, 1 / 2],
[3 / 4, 2 / 3, 1 / 3]]], np.float32)
self._prediction = np.log(self._prediction / (1 - self._prediction))
self._target = np.array([
# First batch
[[1.0, 0.91, 1.0],
[0.36, 0.84, 1.0]],
# Second Batch
[[0.01, 1.0, 0.75],
[0.96, 1.0, 1.0]]], np.float32)
def test_returns_correct_loss(self):
def graph_fn(prediction, target):
weights = tf.constant([
[[1.0], [1.0]],
[[1.0], [1.0]],
])
loss = centernet_losses.PenaltyReducedLogisticFocalLoss(
alpha=2.0, beta=0.5)
computed_value = loss(prediction, target, weights=weights)
return computed_value
computed_value = graph_fn(self._prediction, self._target)
expected_value = np.array([
# First batch
[[1 / 4 * LOG_2,
0.3 * 0.0625 * (2 * LOG_2 - LOG_3),
1 / 16 * (2 * LOG_2 - LOG_3)],
[0.8 * 9 / 16 * 2 * LOG_2,
0.4 * 1 / 9 * (LOG_3 - LOG_2),
4 / 9 * LOG_3]],
# Second Batch
[[0.0,
0.0,
1 / 2 * 1 / 4 * LOG_2],
[0.2 * 9 / 16 * 2 * LOG_2,
1 / 9 * (LOG_3 - LOG_2),
4 / 9 * LOG_3]]])
self.assertAllClose(expected_value, computed_value, rtol=1e-3, atol=1e-3)
def test_returns_correct_loss_weighted(self):
def graph_fn(prediction, target):
weights = tf.constant([
[[1.0, 0.0, 1.0], [0.0, 0.0, 1.0]],
[[1.0, 1.0, 1.0], [0.0, 0.0, 0.0]],
])
loss = centernet_losses.PenaltyReducedLogisticFocalLoss(
alpha=2.0, beta=0.5)
computed_value = loss(prediction, target, weights=weights)
return computed_value
computed_value = graph_fn(self._prediction, self._target)
expected_value = np.array([
# First batch
[[1 / 4 * LOG_2,
0.0,
1 / 16 * (2 * LOG_2 - LOG_3)],
[0.0,
0.0,
4 / 9 * LOG_3]],
# Second Batch
[[0.0,
0.0,
1 / 2 * 1 / 4 * LOG_2],
[0.0,
0.0,
0.0]]])
self.assertAllClose(expected_value, computed_value, rtol=1e-3, atol=1e-3)
if __name__ == '__main__':
tf.test.main()