146 lines
5.6 KiB
Python
146 lines
5.6 KiB
Python
# Copyright 2016 The TensorFlow Authors. 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 slim.nets.overfeat."""
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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 nets import overfeat
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slim = tf.contrib.slim
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class OverFeatTest(tf.test.TestCase):
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def testBuild(self):
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batch_size = 5
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height, width = 231, 231
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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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logits, _ = overfeat.overfeat(inputs, num_classes)
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self.assertEquals(logits.op.name, 'overfeat/fc8/squeezed')
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, num_classes])
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def testFullyConvolutional(self):
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batch_size = 1
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height, width = 281, 281
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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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logits, _ = overfeat.overfeat(inputs, num_classes, spatial_squeeze=False)
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self.assertEquals(logits.op.name, 'overfeat/fc8/BiasAdd')
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, 2, 2, num_classes])
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def testEndPoints(self):
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batch_size = 5
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height, width = 231, 231
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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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_, end_points = overfeat.overfeat(inputs, num_classes)
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expected_names = ['overfeat/conv1',
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'overfeat/pool1',
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'overfeat/conv2',
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'overfeat/pool2',
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'overfeat/conv3',
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'overfeat/conv4',
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'overfeat/conv5',
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'overfeat/pool5',
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'overfeat/fc6',
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'overfeat/fc7',
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'overfeat/fc8'
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]
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self.assertSetEqual(set(end_points.keys()), set(expected_names))
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def testModelVariables(self):
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batch_size = 5
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height, width = 231, 231
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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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overfeat.overfeat(inputs, num_classes)
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expected_names = ['overfeat/conv1/weights',
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'overfeat/conv1/biases',
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'overfeat/conv2/weights',
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'overfeat/conv2/biases',
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'overfeat/conv3/weights',
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'overfeat/conv3/biases',
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'overfeat/conv4/weights',
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'overfeat/conv4/biases',
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'overfeat/conv5/weights',
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'overfeat/conv5/biases',
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'overfeat/fc6/weights',
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'overfeat/fc6/biases',
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'overfeat/fc7/weights',
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'overfeat/fc7/biases',
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'overfeat/fc8/weights',
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'overfeat/fc8/biases',
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]
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model_variables = [v.op.name for v in slim.get_model_variables()]
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self.assertSetEqual(set(model_variables), set(expected_names))
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def testEvaluation(self):
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batch_size = 2
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height, width = 231, 231
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num_classes = 1000
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with self.test_session():
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eval_inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, _ = overfeat.overfeat(eval_inputs, is_training=False)
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, num_classes])
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predictions = tf.argmax(logits, 1)
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self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
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def testTrainEvalWithReuse(self):
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train_batch_size = 2
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eval_batch_size = 1
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train_height, train_width = 231, 231
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eval_height, eval_width = 281, 281
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num_classes = 1000
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with self.test_session():
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train_inputs = tf.random_uniform(
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(train_batch_size, train_height, train_width, 3))
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logits, _ = overfeat.overfeat(train_inputs)
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self.assertListEqual(logits.get_shape().as_list(),
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[train_batch_size, num_classes])
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tf.get_variable_scope().reuse_variables()
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eval_inputs = tf.random_uniform(
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(eval_batch_size, eval_height, eval_width, 3))
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logits, _ = overfeat.overfeat(eval_inputs, is_training=False,
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spatial_squeeze=False)
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self.assertListEqual(logits.get_shape().as_list(),
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[eval_batch_size, 2, 2, num_classes])
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logits = tf.reduce_mean(logits, [1, 2])
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predictions = tf.argmax(logits, 1)
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self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
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def testForward(self):
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batch_size = 1
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height, width = 231, 231
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with self.test_session() as sess:
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inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, _ = overfeat.overfeat(inputs)
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sess.run(tf.initialize_all_variables())
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output = sess.run(logits)
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self.assertTrue(output.any())
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if __name__ == '__main__':
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tf.test.main()
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