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deeplab2 / model /layers /drop_path_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.

	"""Test for drop_path.py."""
	import numpy as np
	import tensorflow as tf

	from deeplab2.model.layers import drop_path

	# Set a fixed random seed.
	tf.random.set_seed(1)


	class DropPathTest(tf.test.TestCase):

	def test_drop_path_keep_prob_one(self):
	# Test drop_path_keep_prob = 1, where output should be equal to input.
	drop_path_keep_prob = 1.0
	input_tensor = tf.random.uniform(shape=(3, 65, 65, 32))
	layer_op = drop_path.DropPath(drop_path_keep_prob)
	output_tensor = layer_op(input_tensor, training=True)
	np.testing.assert_equal(input_tensor.numpy(), output_tensor.numpy())

	def test_not_training_mode(self):
	# Test not training mode, where output should be equal to input.
	drop_path_keep_prob = 0.8
	input_tensor = tf.random.uniform(shape=(3, 65, 65, 32))
	layer_op = drop_path.DropPath(drop_path_keep_prob)
	output_tensor = layer_op(input_tensor, training=False)
	np.testing.assert_equal(input_tensor.numpy(), output_tensor.numpy())

	def test_drop_path(self):
	drop_path_keep_prob = 0.8
	input_tensor = tf.random.uniform(shape=(3, 65, 65, 32))
	layer_op = drop_path.DropPath(drop_path_keep_prob)
	output_tensor = layer_op(input_tensor, training=True)
	self.assertFalse(np.array_equal(input_tensor.numpy(),
	output_tensor.numpy()))

	def test_constant_drop_path_schedule(self):
	keep_prob_for_last_stage = 0.8
	current_stage_keep_prob = drop_path.get_drop_path_keep_prob(
	keep_prob_for_last_stage,
	schedule='constant',
	current_stage=2,
	num_stages=5)
	self.assertEqual(current_stage_keep_prob, keep_prob_for_last_stage)

	def test_linear_drop_path_schedule(self):
	keep_prob_for_last_stage = 0.8
	current_stage_keep_prob = drop_path.get_drop_path_keep_prob(
	keep_prob_for_last_stage,
	schedule='linear',
	current_stage=1,
	num_stages=4)
	self.assertEqual(current_stage_keep_prob, 0.95)

	def test_unknown_drop_path_schedule(self):
	with self.assertRaises(ValueError):
	_ = drop_path.get_drop_path_keep_prob(0.8, 'unknown', 1, 4)


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