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deeplab2 / model /encoder /mobilenet_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 mobilenet."""

	from absl.testing import parameterized

	import tensorflow as tf

	from deeplab2.model import test_utils
	from deeplab2.model.encoder import mobilenet


	class MobilenetTest(tf.test.TestCase, parameterized.TestCase):

	@parameterized.parameters('MobileNetV3Small', 'MobileNetV3Large')
	def test_mobilenetv3_construct_graph(self, model_name):
	tf.keras.backend.set_image_data_format('channels_last')
	input_size = 128

	mobilenet_models = {
	'MobileNetV3Small': mobilenet.MobileNetV3Small,
	'MobileNetV3Large': mobilenet.MobileNetV3Large,
	}
	mobilenet_channels = {
	# The number of filters of layers having outputs been collected
	# for filter_size_scale = 1.0
	'MobileNetV3Small': [16, 24, 48, 576],
	'MobileNetV3Large': [24, 40, 112, 960],
	}
	network = mobilenet_models[str(model_name)](width_multiplier=1.0)

	inputs = tf.ones([1, input_size, input_size, 3])
	endpoints = network(inputs)

	for idx, num_filter in enumerate(mobilenet_channels[model_name]):
	self.assertAllEqual(
	[1, input_size / 2 (idx+2), input_size / 2 (idx+2), num_filter],
	endpoints['res'+str(idx+2)].shape.as_list())

	@parameterized.product(
	model_name=['MobileNetV3Small', 'MobileNetV3Large'],
	output_stride=[4, 8, 16, 32])
	def test_mobilenetv3_atrous_endpoint_shape(self, model_name, output_stride):
	tf.keras.backend.set_image_data_format('channels_last')
	input_size = 321
	batch_size = 2

	mobilenet_models = {
	'MobileNetV3Small': mobilenet.MobileNetV3Small,
	'MobileNetV3Large': mobilenet.MobileNetV3Large,
	}
	stride_spatial_shapes_map = {
	4: [81, 81, 81, 81],
	8: [81, 41, 41, 41],
	16: [81, 41, 21, 21],
	32: [81, 41, 21, 11],
	}
	mobilenet_channels = {
	# The number of filters of layers having outputs been collected
	# for filter_size_scale = 1.0
	'MobileNetV3Small': [16, 24, 48, 576],
	'MobileNetV3Large': [24, 40, 112, 960],
	}
	network = mobilenet_models[str(model_name)](
	width_multiplier=1.0,
	output_stride=output_stride)
	spatial_shapes = stride_spatial_shapes_map[output_stride]

	inputs = tf.ones([batch_size, input_size, input_size, 3])
	endpoints = network(inputs)

	for idx, num_filters in enumerate(mobilenet_channels[model_name]):
	expected_shape = [
	batch_size, spatial_shapes[idx], spatial_shapes[idx], num_filters
	]
	self.assertAllEqual(
	expected_shape,
	endpoints['res'+str(idx+2)].shape.as_list())

	@parameterized.parameters('MobileNetV3Small', 'MobileNetV3Large')
	def test_mobilenet_reload_weights(self, model_name):
	tf.keras.backend.set_image_data_format('channels_last')
	mobilenet_models = {
	'MobileNetV3Small': mobilenet.MobileNetV3Small,
	'MobileNetV3Large': mobilenet.MobileNetV3Large,
	}

	tf.random.set_seed(0)
	pixel_inputs = test_utils.create_test_input(1, 320, 320, 3)

	network1 = mobilenet_models[model_name](
	width_multiplier=1.0,
	output_stride=32,
	name='m1')
	network1(pixel_inputs, False)
	outputs1 = network1(pixel_inputs, False)
	pixel_outputs = outputs1['res5']

	# Feature extraction at the normal network rate.
	network2 = mobilenet_models[model_name](
	width_multiplier=1.0,
	output_stride=32,
	name='m2')
	network2(pixel_inputs, False)
	# Make the two networks use the same weights.
	network2.set_weights(network1.get_weights())
	outputs2 = network2(pixel_inputs, False)
	expected = outputs2['res5']

	self.assertAllClose(network1.get_weights(), network2.get_weights(),
	atol=1e-4, rtol=1e-4)
	self.assertAllClose(pixel_outputs, expected, atol=1e-4, rtol=1e-4)


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