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Jianmin Chen
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Add collections_test
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‎inception/inception/slim/collections_test.py‎

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# Copyright 2016 Google Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for inception."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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from inception.slim import slim
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def get_variables(scope=None):
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return slim.variables.get_variables(scope)
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def get_variables_by_name(name):
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return slim.variables.get_variables_by_name(name)
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class CollectionsTest(tf.test.TestCase):
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def testVariables(self):
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batch_size = 5
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height, width = 299, 299
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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with slim.arg_scope([slim.ops.conv2d],
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batch_norm_params={'decay': 0.9997}):
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slim.inception.inception_v3(inputs)
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self.assertEqual(len(get_variables()), 388)
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self.assertEqual(len(get_variables_by_name('weights')), 98)
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self.assertEqual(len(get_variables_by_name('biases')), 2)
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self.assertEqual(len(get_variables_by_name('beta')), 96)
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self.assertEqual(len(get_variables_by_name('gamma')), 0)
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self.assertEqual(len(get_variables_by_name('moving_mean')), 96)
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self.assertEqual(len(get_variables_by_name('moving_variance')), 96)
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def testVariablesWithoutBatchNorm(self):
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batch_size = 5
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height, width = 299, 299
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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with slim.arg_scope([slim.ops.conv2d],
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batch_norm_params=None):
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slim.inception.inception_v3(inputs)
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self.assertEqual(len(get_variables()), 196)
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self.assertEqual(len(get_variables_by_name('weights')), 98)
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self.assertEqual(len(get_variables_by_name('biases')), 98)
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self.assertEqual(len(get_variables_by_name('beta')), 0)
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self.assertEqual(len(get_variables_by_name('gamma')), 0)
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self.assertEqual(len(get_variables_by_name('moving_mean')), 0)
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self.assertEqual(len(get_variables_by_name('moving_variance')), 0)
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def testVariablesByLayer(self):
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batch_size = 5
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height, width = 299, 299
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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with slim.arg_scope([slim.ops.conv2d],
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batch_norm_params={'decay': 0.9997}):
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slim.inception.inception_v3(inputs)
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self.assertEqual(len(get_variables()), 388)
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self.assertEqual(len(get_variables('conv0')), 4)
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self.assertEqual(len(get_variables('conv1')), 4)
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self.assertEqual(len(get_variables('conv2')), 4)
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self.assertEqual(len(get_variables('conv3')), 4)
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self.assertEqual(len(get_variables('conv4')), 4)
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self.assertEqual(len(get_variables('mixed_35x35x256a')), 28)
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self.assertEqual(len(get_variables('mixed_35x35x288a')), 28)
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self.assertEqual(len(get_variables('mixed_35x35x288b')), 28)
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self.assertEqual(len(get_variables('mixed_17x17x768a')), 16)
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self.assertEqual(len(get_variables('mixed_17x17x768b')), 40)
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self.assertEqual(len(get_variables('mixed_17x17x768c')), 40)
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self.assertEqual(len(get_variables('mixed_17x17x768d')), 40)
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self.assertEqual(len(get_variables('mixed_17x17x768e')), 40)
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self.assertEqual(len(get_variables('mixed_8x8x2048a')), 36)
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self.assertEqual(len(get_variables('mixed_8x8x2048b')), 36)
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self.assertEqual(len(get_variables('logits')), 2)
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self.assertEqual(len(get_variables('aux_logits')), 10)
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def testVariablesToRestore(self):
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batch_size = 5
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height, width = 299, 299
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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with slim.arg_scope([slim.ops.conv2d],
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batch_norm_params={'decay': 0.9997}):
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slim.inception.inception_v3(inputs)
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variables_to_restore = tf.get_collection(
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slim.variables.VARIABLES_TO_RESTORE)
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self.assertEqual(len(variables_to_restore), 388)
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self.assertListEqual(variables_to_restore, get_variables())
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def testVariablesToRestoreWithoutLogits(self):
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batch_size = 5
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height, width = 299, 299
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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with slim.arg_scope([slim.ops.conv2d],
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batch_norm_params={'decay': 0.9997}):
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slim.inception.inception_v3(inputs, restore_logits=False)
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variables_to_restore = tf.get_collection(
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slim.variables.VARIABLES_TO_RESTORE)
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self.assertEqual(len(variables_to_restore), 384)
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def testRegularizationLosses(self):
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batch_size = 5
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height, width = 299, 299
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], weight_decay=0.00004):
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slim.inception.inception_v3(inputs)
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losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
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self.assertEqual(len(losses), len(get_variables_by_name('weights')))
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def testTotalLossWithoutRegularization(self):
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batch_size = 5
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height, width = 299, 299
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num_classes = 1001
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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dense_labels = tf.random_uniform((batch_size, num_classes))
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with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], weight_decay=0):
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logits, end_points = slim.inception.inception_v3(
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inputs,
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num_classes=num_classes)
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# Cross entropy loss for the main softmax prediction.
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slim.losses.cross_entropy_loss(logits,
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dense_labels,
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label_smoothing=0.1,
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weight=1.0)
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# Cross entropy loss for the auxiliary softmax head.
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slim.losses.cross_entropy_loss(end_points['aux_logits'],
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dense_labels,
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label_smoothing=0.1,
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weight=0.4,
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scope='aux_loss')
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losses = tf.get_collection(slim.losses.LOSSES_COLLECTION)
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self.assertEqual(len(losses), 2)
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def testTotalLossWithRegularization(self):
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batch_size = 5
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height, width = 299, 299
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num_classes = 1000
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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dense_labels = tf.random_uniform((batch_size, num_classes))
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with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], weight_decay=0.00004):
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logits, end_points = slim.inception.inception_v3(inputs, num_classes)
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# Cross entropy loss for the main softmax prediction.
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slim.losses.cross_entropy_loss(logits,
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dense_labels,
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label_smoothing=0.1,
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weight=1.0)
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# Cross entropy loss for the auxiliary softmax head.
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slim.losses.cross_entropy_loss(end_points['aux_logits'],
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dense_labels,
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label_smoothing=0.1,
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weight=0.4,
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scope='aux_loss')
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losses = tf.get_collection(slim.losses.LOSSES_COLLECTION)
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self.assertEqual(len(losses), 2)
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reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
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self.assertEqual(len(reg_losses), 98)
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if __name__ == '__main__':
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tf.test.main()

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