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deeplab2 / evaluation /test_utils_test.py

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	# coding=utf-8
	# Copyright 2021 The Deeplab2 Authors.
	#
	# 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 test_utils."""
	import numpy as np
	import tensorflow as tf

	from deeplab2.evaluation import test_utils


	class TestUtilsTest(tf.test.TestCase):

	def test_read_test_image(self):
	image_array = test_utils.read_test_image('team_pred_class.png')
	self.assertSequenceEqual(image_array.shape, (231, 345, 4))

	def test_reads_segmentation_with_color_map(self):
	rgb_to_semantic_label = {(0, 0, 0): 0, (0, 0, 255): 1, (255, 0, 0): 23}
	labels = test_utils.read_segmentation_with_rgb_color_map(
	'team_pred_class.png', rgb_to_semantic_label)

	input_image = test_utils.read_test_image('team_pred_class.png')
	np.testing.assert_array_equal(
	labels == 0,
	np.logical_and(input_image[:, :, 0] == 0, input_image[:, :, 2] == 0))
	np.testing.assert_array_equal(labels == 1, input_image[:, :, 2] == 255)
	np.testing.assert_array_equal(labels == 23, input_image[:, :, 0] == 255)

	def test_reads_gt_segmentation(self):
	instance_label_to_semantic_label = {
	0: 0,
	47: 1,
	97: 1,
	133: 1,
	150: 1,
	174: 1,
	198: 23,
	215: 1,
	244: 1,
	255: 1,
	}
	instances, classes = test_utils.panoptic_segmentation_with_class_map(
	'team_gt_instance.png', instance_label_to_semantic_label)

	expected_label_shape = (231, 345)
	self.assertSequenceEqual(instances.shape, expected_label_shape)
	self.assertSequenceEqual(classes.shape, expected_label_shape)
	np.testing.assert_array_equal(instances == 0, classes == 0)
	np.testing.assert_array_equal(instances == 198, classes == 23)
	np.testing.assert_array_equal(
	np.logical_and(instances != 0, instances != 198), classes == 1)


	if __name__ == '__main__':
	tf.test.main()