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deeplab2 / data /data_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 data_utils."""

	import io
	import numpy as np
	from PIL import Image
	import tensorflow as tf

	from deeplab2.data import data_utils


	def _encode_png_image(image):
	"""Helper method to encode input image in PNG format."""
	buffer = io.BytesIO()
	Image.fromarray(image).save(buffer, format='png')
	return buffer.getvalue()


	class DataUtilsTest(tf.test.TestCase):

	def _create_test_image(self, height, width):
	rng = np.random.RandomState(319281498)
	return rng.randint(0, 255, size=(height, width, 3), dtype=np.uint8)

	def test_encode_and_decode(self):
	"""Checks decode created tf.Example for semantic segmentation."""
	test_image_height = 20
	test_image_width = 15
	filename = 'dummy'

	image = self._create_test_image(test_image_height, test_image_width)
	# Take the last channel as dummy label.
	label = image[..., 0]

	example = data_utils.create_tfexample(
	image_data=_encode_png_image(image),
	image_format='png', filename=filename,
	label_data=_encode_png_image(label), label_format='png')

	# Parse created example, expect getting identical results.
	parser = data_utils.SegmentationDecoder(is_panoptic_dataset=False)
	parsed_tensors = parser(example.SerializeToString())

	self.assertIn('image', parsed_tensors)
	self.assertIn('image_name', parsed_tensors)
	self.assertIn('label', parsed_tensors)
	self.assertEqual(filename, parsed_tensors['image_name'])
	np.testing.assert_array_equal(image, parsed_tensors['image'].numpy())
	# Decoded label is a 3-D array with last dimension of 1.
	decoded_label = parsed_tensors['label'].numpy()
	np.testing.assert_array_equal(label, decoded_label[..., 0])

	def test_encode_and_decode_panoptic(self):
	test_image_height = 31
	test_image_width = 17
	filename = 'dummy'

	image = self._create_test_image(test_image_height, test_image_width)
	# Create dummy panoptic label in np.int32 dtype.
	label = np.dot(image.astype(np.int32), [1, 256, 256 * 256]).astype(np.int32)
	example = data_utils.create_tfexample(
	image_data=_encode_png_image(image),
	image_format='png', filename=filename,
	label_data=label.tostring(), label_format='raw')

	parser = data_utils.SegmentationDecoder(is_panoptic_dataset=True)
	parsed_tensors = parser(example.SerializeToString())

	self.assertIn('image', parsed_tensors)
	self.assertIn('image_name', parsed_tensors)
	self.assertIn('label', parsed_tensors)
	self.assertEqual(filename, parsed_tensors['image_name'])
	np.testing.assert_array_equal(image, parsed_tensors['image'].numpy())
	# Decoded label is a 3-D array with last dimension of 1.
	decoded_label = parsed_tensors['label'].numpy()
	np.testing.assert_array_equal(label, decoded_label[..., 0])


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