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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for segmentation_losses."""
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.vision.losses import segmentation_losses
class SegmentationLossTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
(True, False, 1.),
(True, True, 0.5),
(False, True, 1.),
)
def testSegmentationLoss(self, use_groundtruth_dimension,
use_binary_cross_entropy, top_k_percent_pixels):
# [batch, height, width, num_layers]: [2, 3, 4, 1]
labels = tf.random.uniform([2, 3, 4, 1], minval=0, maxval=6, dtype=tf.int32)
# [batch, height, width, num_classes]: [2, 3, 4, 6]
logits = tf.random.uniform([2, 3, 4, 6],
minval=-1,
maxval=1,
dtype=tf.float32)
loss = segmentation_losses.SegmentationLoss(
label_smoothing=0.,
class_weights=[],
ignore_label=255,
use_groundtruth_dimension=use_groundtruth_dimension,
use_binary_cross_entropy=use_binary_cross_entropy,
top_k_percent_pixels=top_k_percent_pixels)(logits, labels)
self.assertEqual(tf.rank(loss), 0)
def testSegmentationLossTopK(self):
labels = tf.constant([[[[0], [0]], [[0], [2]]]])
logits = tf.constant([[[[100., 0., 0.], [100., 0, 0.]],
[[100., 0., 0.], [0., 1., 0.]]]])
loss = segmentation_losses.SegmentationLoss(
label_smoothing=0.,
class_weights=[],
ignore_label=255,
use_groundtruth_dimension=True,
top_k_percent_pixels=0.5)(logits, labels)
self.assertAllClose(loss, 0.775718, atol=1e-4)
def testSegmentationLossTopKWithIgnoreLabel(self):
labels = tf.constant([[[[0], [0]], [[0], [2]]]])
logits = tf.constant([[[[100., 0., 0.], [100., 0, 0.]],
[[100., 0., 0.], [0., 1., 0.]]]])
loss = segmentation_losses.SegmentationLoss(
label_smoothing=0.,
class_weights=[],
ignore_label=0,
use_groundtruth_dimension=True,
top_k_percent_pixels=0.5)(logits, labels)
self.assertAllClose(loss, 1.551429, atol=1e-4)
def testSegmentationLossGroundTruthIsMattingMap(self):
# [batch, height, width, num_layers]: [2, 3, 4, 1]
labels = tf.random.uniform([2, 3, 4, 1],
minval=0,
maxval=1,
dtype=tf.float32)
# [batch, height, width, num_classes]: [2, 3, 4, 2]
logits = tf.random.uniform([2, 3, 4, 2],
minval=-1,
maxval=1,
dtype=tf.float32)
loss = segmentation_losses.SegmentationLoss(
label_smoothing=0.,
class_weights=[],
ignore_label=255,
use_groundtruth_dimension=True,
use_binary_cross_entropy=False,
top_k_percent_pixels=1.)(logits, labels)
self.assertEqual(tf.rank(loss), 0)
if __name__ == '__main__':
tf.test.main()
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