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deeplab2 / model /encoder /axial_resnet_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 axial_resnet."""

	import numpy as np
	import tensorflow as tf

	from deeplab2.model.encoder import axial_resnet


	class AxialResNetTest(tf.test.TestCase):

	def test_axial_resnet_correct_output_shape(self):
	model = axial_resnet.AxialResNet('max_deeplab_s')
	endpoints = model(tf.zeros([2, 65, 65, 3]), training=False)
	self.assertListEqual(endpoints['backbone_output'].get_shape().as_list(),
	[2, 5, 5, 2048])
	self.assertListEqual(
	endpoints['transformer_class_feature'].get_shape().as_list(),
	[2, 128, 256])
	self.assertListEqual(
	endpoints['transformer_mask_feature'].get_shape().as_list(),
	[2, 128, 256])
	self.assertListEqual(endpoints['feature_panoptic'].get_shape().as_list(),
	[2, 17, 17, 256])
	self.assertListEqual(endpoints['feature_semantic'].get_shape().as_list(),
	[2, 5, 5, 2048])
	num_params = np.sum(
	[np.prod(v.get_shape().as_list()) for v in model.trainable_weights])
	self.assertEqual(num_params, 61726624)

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