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deeplab2 / model /decoder /max_deeplab_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 max_deeplab."""

	import tensorflow as tf

	from deeplab2 import common
	from deeplab2 import config_pb2
	from deeplab2.model.decoder import max_deeplab


	def _create_max_deeplab_example_proto(num_non_void_classes=19):
	semantic_decoder = config_pb2.DecoderOptions(
	feature_key='feature_semantic', atrous_rates=[6, 12, 18])
	auxiliary_semantic_head = config_pb2.HeadOptions(
	output_channels=num_non_void_classes, head_channels=256)
	pixel_space_head = config_pb2.HeadOptions(
	output_channels=128, head_channels=256)
	max_deeplab_options = config_pb2.ModelOptions.MaXDeepLabOptions(
	pixel_space_head=pixel_space_head,
	auxiliary_semantic_head=auxiliary_semantic_head)
	# Add features from lowest to highest.
	max_deeplab_options.auxiliary_low_level.add(
	feature_key='res3', channels_project=64)
	max_deeplab_options.auxiliary_low_level.add(
	feature_key='res2', channels_project=32)
	return config_pb2.ModelOptions(
	decoder=semantic_decoder, max_deeplab=max_deeplab_options)


	class MaXDeeplabTest(tf.test.TestCase):

	def test_max_deeplab_decoder_output_shape(self):
	num_non_void_classes = 19
	num_mask_slots = 127
	model_options = _create_max_deeplab_example_proto(
	num_non_void_classes=num_non_void_classes)
	decoder = max_deeplab.MaXDeepLab(
	max_deeplab_options=model_options.max_deeplab,
	ignore_label=255,
	decoder_options=model_options.decoder)

	input_dict = {
	'res2':
	tf.random.uniform([2, 17, 17, 256]),
	'res3':
	tf.random.uniform([2, 9, 9, 512]),
	'transformer_class_feature':
	tf.random.uniform([2, num_mask_slots, 256]),
	'transformer_mask_feature':
	tf.random.uniform([2, num_mask_slots, 256]),
	'feature_panoptic':
	tf.random.uniform([2, 17, 17, 256]),
	'feature_semantic':
	tf.random.uniform([2, 5, 5, 2048])
	}
	resulting_dict = decoder(input_dict)
	self.assertListEqual(
	resulting_dict[common.PRED_SEMANTIC_LOGITS_KEY].shape.as_list(),
	[2, 17, 17, 19]) # Stride 4
	self.assertListEqual(
	resulting_dict[
	common.PRED_PIXEL_SPACE_NORMALIZED_FEATURE_KEY].shape.as_list(),
	[2, 17, 17, 128]) # Stride 4
	self.assertListEqual(
	resulting_dict[
	common.PRED_TRANSFORMER_CLASS_LOGITS_KEY].shape.as_list(),
	# Non-void classes and a void class.
	[2, num_mask_slots, num_non_void_classes + 1])
	self.assertListEqual(
	resulting_dict[common.PRED_PIXEL_SPACE_MASK_LOGITS_KEY].shape.as_list(),
	[2, 17, 17, num_mask_slots]) # Stride 4.


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