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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()
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