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deeplab2 / trainer /runner_utils_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 runner_utils.py."""

	import os

	import numpy as np
	import tensorflow as tf

	from google.protobuf import text_format
	from deeplab2 import config_pb2
	from deeplab2.data import dataset
	from deeplab2.model import deeplab
	from deeplab2.trainer import runner_utils
	# resources dependency

	_CONFIG_PATH = 'deeplab2/configs/example'


	def _read_proto_file(filename, proto):
	filename = filename # OSS: removed internal filename loading.
	with tf.io.gfile.GFile(filename, 'r') as proto_file:
	return text_format.ParseLines(proto_file, proto)


	def _create_model_from_test_proto(file_name,
	dataset_name='coco_panoptic'):
	proto_filename = os.path.join(_CONFIG_PATH, file_name)
	config = _read_proto_file(proto_filename, config_pb2.ExperimentOptions())
	return deeplab.DeepLab(config,
	dataset.MAP_NAME_TO_DATASET_INFO[dataset_name]
	), config


	class RunnerUtilsTest(tf.test.TestCase):

	def test_check_if_variable_in_backbone_with_max_deeplab(self):
	model, experiment_options = _create_model_from_test_proto(
	'example_coco_max_deeplab.textproto', dataset_name='coco_panoptic')
	train_crop_size = tuple(
	experiment_options.train_dataset_options.crop_size)
	input_tensor = tf.random.uniform(
	shape=(2, train_crop_size[0], train_crop_size[1], 3))
	_ = model(input_tensor, training=True)

	encoder = model.checkpoint_items['encoder']
	encoder_variable_names = [x.name for x in encoder.trainable_variables]
	encoder_name = experiment_options.model_options.backbone.name

	num_backbone_params = 0
	backbone_optimizer_inputs = []
	for variable in model.trainable_weights:
	if runner_utils.check_if_variable_in_backbone(variable, encoder_name,
	encoder_variable_names):
	backbone_optimizer_inputs.append(variable)
	num_backbone_params += np.prod(variable.get_shape().as_list())
	# The number of Tensors in the backbone. We use this number in addition to
	# the number of parameters as a check of correctness.
	self.assertLen(backbone_optimizer_inputs, 301)
	# The same number of parameters as max_deeplab_s_backbone.
	self.assertEqual(num_backbone_params, 41343424)


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