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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 aspp."""
	import tensorflow as tf

	from deeplab2.model.decoder import aspp
	from deeplab2.utils import test_utils


	class AsppTest(tf.test.TestCase):

	def test_aspp_pool_error(self):
	pool = aspp.ASPPPool(output_channels=64, name='')

	# Should pass without an error.
	pool.set_pool_size((None, None))

	with self.assertRaises(ValueError):
	# Should raise an error.
	pool.set_pool_size((2, None))

	def test_aspp_conv_atrous_rate_shape(self):
	atrous_rates = [2, 6, 12, 18]
	for rate in atrous_rates:
	conv = aspp.ASPPConv(output_channels=64, atrous_rate=rate, name='')
	input_tensor = tf.random.uniform(shape=(2, 12, 12, 3))

	output = conv(input_tensor)
	expected_shape = [2, 12, 12, 64]
	self.assertListEqual(output.shape.as_list(), expected_shape)

	def test_aspp_conv_non_negative(self):
	conv = aspp.ASPPConv(output_channels=12, atrous_rate=2, name='')
	input_tensor = tf.random.uniform(shape=(2, 17, 17, 3))

	output = conv(input_tensor)
	self.assertTrue((output.numpy() >= 0.0).all())

	def test_aspp_pool_shape(self):
	pool = aspp.ASPPPool(output_channels=64, name='')
	input_tensor = tf.random.uniform(shape=(2, 12, 12, 3))

	output = pool(input_tensor)
	expected_shape = [2, 12, 12, 64]
	self.assertListEqual(output.shape.as_list(), expected_shape)

	def test_aspp_pool_non_negative(self):
	pool = aspp.ASPPPool(output_channels=12, name='')
	input_tensor = tf.random.uniform(shape=(2, 17, 17, 3))

	output = pool(input_tensor)
	self.assertTrue((output.numpy() >= 0.0).all())

	def test_aspp_wrong_atrous_rate(self):
	with self.assertRaises(ValueError):
	_ = aspp.ASPP(output_channels=64, atrous_rates=[1, 2, 3, 4])

	@test_utils.test_all_strategies
	def test_aspp_shape(self, strategy):
	with strategy.scope():
	for bn_layer in test_utils.NORMALIZATION_LAYERS:
	aspp_layer = aspp.ASPP(
	output_channels=64, atrous_rates=[6, 12, 18], bn_layer=bn_layer)
	input_tensor = tf.random.uniform(shape=(2, 32, 32, 3))

	output = aspp_layer(input_tensor)
	expected_shape = [2, 32, 32, 64]
	self.assertListEqual(output.shape.as_list(), expected_shape)

	def test_aspp_non_negative(self):
	aspp_layer = aspp.ASPP(output_channels=32, atrous_rates=[4, 8, 16])
	input_tensor = tf.random.uniform(shape=(2, 32, 32, 3))

	output = aspp_layer(input_tensor)
	self.assertTrue((output.numpy() >= 0.0).all())

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