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deeplab2 / model /decoder /deeplabv3_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 deeplabv3."""

	import numpy as np
	import tensorflow as tf

	from deeplab2 import common
	from deeplab2 import config_pb2
	from deeplab2.model.decoder import deeplabv3
	from deeplab2.utils import test_utils


	def _create_deeplabv3_model(feature_key, decoder_channels, aspp_channels,
	atrous_rates, num_classes, **kwargs):
	decoder_options = config_pb2.DecoderOptions(
	feature_key=feature_key,
	decoder_channels=decoder_channels,
	aspp_channels=aspp_channels,
	atrous_rates=atrous_rates)
	deeplabv3_options = config_pb2.ModelOptions.DeeplabV3Options(
	num_classes=num_classes)
	return deeplabv3.DeepLabV3(decoder_options, deeplabv3_options, **kwargs)


	class Deeplabv3Test(tf.test.TestCase):

	def test_deeplabv3_feature_key_not_present(self):
	deeplabv3_decoder = _create_deeplabv3_model(
	feature_key='not_in_features_dict',
	aspp_channels=64,
	decoder_channels=48,
	atrous_rates=[6, 12, 18],
	num_classes=80)
	input_dict = dict()
	input_dict['not_the_same_key'] = tf.random.uniform(shape=(2, 65, 65, 32))

	with self.assertRaises(KeyError):
	_ = deeplabv3_decoder(input_dict)

	def test_deeplabv3_output_shape(self):
	list_of_num_classes = [2, 19, 133]
	for num_classes in list_of_num_classes:
	deeplabv3_decoder = _create_deeplabv3_model(
	feature_key='not_used',
	aspp_channels=64,
	decoder_channels=48,
	atrous_rates=[6, 12, 18],
	num_classes=num_classes)
	input_tensor = tf.random.uniform(shape=(2, 65, 65, 32))
	expected_shape = [2, 65, 65, num_classes]

	logit_tensor = deeplabv3_decoder(input_tensor)
	self.assertListEqual(
	logit_tensor[common.PRED_SEMANTIC_LOGITS_KEY].shape.as_list(),
	expected_shape)

	@test_utils.test_all_strategies
	def test_sync_bn(self, strategy):
	input_tensor = tf.random.uniform(shape=(2, 65, 65, 32))
	with strategy.scope():
	for bn_layer in test_utils.NORMALIZATION_LAYERS:
	deeplabv3_decoder = _create_deeplabv3_model(
	feature_key='not_used',
	aspp_channels=64,
	decoder_channels=48,
	atrous_rates=[6, 12, 18],
	num_classes=19,
	bn_layer=bn_layer)
	_ = deeplabv3_decoder(input_tensor)

	def test_deeplabv3_feature_extraction_consistency(self):
	deeplabv3_decoder = _create_deeplabv3_model(
	aspp_channels=64,
	decoder_channels=48,
	atrous_rates=[6, 12, 18],
	num_classes=80,
	feature_key='feature_key')
	input_tensor = tf.random.uniform(shape=(2, 65, 65, 32))
	input_dict = dict()
	input_dict['feature_key'] = input_tensor

	reference_logits_tensor = deeplabv3_decoder(input_tensor, training=False)
	logits_tensor_to_compare = deeplabv3_decoder(input_dict, training=False)

	np.testing.assert_equal(
	reference_logits_tensor[common.PRED_SEMANTIC_LOGITS_KEY].numpy(),
	logits_tensor_to_compare[common.PRED_SEMANTIC_LOGITS_KEY].numpy())

	def test_deeplabv3_pool_size_setter(self):
	deeplabv3_decoder = _create_deeplabv3_model(
	feature_key='not_used',
	aspp_channels=64,
	decoder_channels=48,
	atrous_rates=[6, 12, 18],
	num_classes=80)
	pool_size = (10, 10)
	deeplabv3_decoder.set_pool_size(pool_size)

	self.assertTupleEqual(deeplabv3_decoder._aspp._aspp_pool._pool_size,
	pool_size)

	def test_deeplabv3_pool_size_resetter(self):
	deeplabv3_decoder = _create_deeplabv3_model(
	feature_key='not_used',
	aspp_channels=64,
	decoder_channels=48,
	atrous_rates=[6, 12, 18],
	num_classes=80)
	pool_size = (None, None)
	deeplabv3_decoder.reset_pooling_layer()

	self.assertTupleEqual(deeplabv3_decoder._aspp._aspp_pool._pool_size,
	pool_size)

	def test_deeplabv3_ckpt_items(self):
	deeplabv3_decoder = _create_deeplabv3_model(
	feature_key='not_used',
	aspp_channels=64,
	decoder_channels=48,
	atrous_rates=[6, 12, 18],
	num_classes=80)
	ckpt_dict = deeplabv3_decoder.checkpoint_items
	self.assertIn(common.CKPT_DEEPLABV3_ASPP, ckpt_dict)
	self.assertIn(common.CKPT_DEEPLABV3_CLASSIFIER_CONV_BN_ACT, ckpt_dict)
	self.assertIn(common.CKPT_SEMANTIC_LAST_LAYER, ckpt_dict)


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