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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# 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 object_detection.core.freezable_batch_norm."""
import numpy as np
import tensorflow as tf
from object_detection.core import freezable_batch_norm
class FreezableBatchNormTest(tf.test.TestCase):
"""Tests for FreezableBatchNorm operations."""
def _build_model(self, training=None):
model = tf.keras.models.Sequential()
norm = freezable_batch_norm.FreezableBatchNorm(training=training,
input_shape=(10,),
momentum=0.8)
model.add(norm)
return model, norm
def _train_freezable_batch_norm(self, training_mean, training_var):
model, _ = self._build_model()
model.compile(loss='mse', optimizer='sgd')
# centered on training_mean, variance training_var
train_data = np.random.normal(
loc=training_mean,
scale=training_var,
size=(1000, 10))
model.fit(train_data, train_data, epochs=4, verbose=0)
return model.weights
def test_batchnorm_freezing_training_true(self):
with self.test_session():
training_mean = 5.0
training_var = 10.0
testing_mean = -10.0
testing_var = 5.0
# Initially train the batch norm, and save the weights
trained_weights = self._train_freezable_batch_norm(training_mean,
training_var)
# Load the batch norm weights, freezing training to True.
# Apply the batch norm layer to testing data and ensure it is normalized
# according to the batch statistics.
model, norm = self._build_model(training=True)
for trained_weight, blank_weight in zip(trained_weights, model.weights):
weight_copy = blank_weight.assign(tf.keras.backend.eval(trained_weight))
tf.keras.backend.eval(weight_copy)
# centered on testing_mean, variance testing_var
test_data = np.random.normal(
loc=testing_mean,
scale=testing_var,
size=(1000, 10))
out_tensor = norm(tf.convert_to_tensor(test_data, dtype=tf.float32))
out = tf.keras.backend.eval(out_tensor)
out -= tf.keras.backend.eval(norm.beta)
out /= tf.keras.backend.eval(norm.gamma)
np.testing.assert_allclose(out.mean(), 0.0, atol=1.5e-1)
np.testing.assert_allclose(out.std(), 1.0, atol=1.5e-1)
def test_batchnorm_freezing_training_false(self):
with self.test_session():
training_mean = 5.0
training_var = 10.0
testing_mean = -10.0
testing_var = 5.0
# Initially train the batch norm, and save the weights
trained_weights = self._train_freezable_batch_norm(training_mean,
training_var)
# Load the batch norm back up, freezing training to False.
# Apply the batch norm layer to testing data and ensure it is normalized
# according to the training data's statistics.
model, norm = self._build_model(training=False)
for trained_weight, blank_weight in zip(trained_weights, model.weights):
weight_copy = blank_weight.assign(tf.keras.backend.eval(trained_weight))
tf.keras.backend.eval(weight_copy)
# centered on testing_mean, variance testing_var
test_data = np.random.normal(
loc=testing_mean,
scale=testing_var,
size=(1000, 10))
out_tensor = norm(tf.convert_to_tensor(test_data, dtype=tf.float32))
out = tf.keras.backend.eval(out_tensor)
out -= tf.keras.backend.eval(norm.beta)
out /= tf.keras.backend.eval(norm.gamma)
out *= training_var
out += (training_mean - testing_mean)
out /= testing_var
np.testing.assert_allclose(out.mean(), 0.0, atol=1.5e-1)
np.testing.assert_allclose(out.std(), 1.0, atol=1.5e-1)
if __name__ == '__main__':
tf.test.main()
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