+++ /dev/null
-# Copyright 2016 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 slim_nets.inception_v1."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-import tensorflow as tf
-
-from nets import inception
-
-slim = tf.contrib.slim
-
-
-class InceptionV3Test(tf.test.TestCase):
-
- def testBuildClassificationNetwork(self):
- batch_size = 5
- height, width = 299, 299
- num_classes = 1000
-
- inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, end_points = inception.inception_v3(inputs, num_classes)
- self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, num_classes])
- self.assertTrue('Predictions' in end_points)
- self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
- [batch_size, num_classes])
-
- def testBuildBaseNetwork(self):
- batch_size = 5
- height, width = 299, 299
-
- inputs = tf.random_uniform((batch_size, height, width, 3))
- final_endpoint, end_points = inception.inception_v3_base(inputs)
- self.assertTrue(final_endpoint.op.name.startswith(
- 'InceptionV3/Mixed_7c'))
- self.assertListEqual(final_endpoint.get_shape().as_list(),
- [batch_size, 8, 8, 2048])
- expected_endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
- 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3',
- 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
- 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
- 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c']
- self.assertItemsEqual(end_points.keys(), expected_endpoints)
-
- def testBuildOnlyUptoFinalEndpoint(self):
- batch_size = 5
- height, width = 299, 299
- endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
- 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3',
- 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
- 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
- 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c']
-
- for index, endpoint in enumerate(endpoints):
- with tf.Graph().as_default():
- inputs = tf.random_uniform((batch_size, height, width, 3))
- out_tensor, end_points = inception.inception_v3_base(
- inputs, final_endpoint=endpoint)
- self.assertTrue(out_tensor.op.name.startswith(
- 'InceptionV3/' + endpoint))
- self.assertItemsEqual(endpoints[:index+1], end_points)
-
- def testBuildAndCheckAllEndPointsUptoMixed7c(self):
- batch_size = 5
- height, width = 299, 299
-
- inputs = tf.random_uniform((batch_size, height, width, 3))
- _, end_points = inception.inception_v3_base(
- inputs, final_endpoint='Mixed_7c')
- endpoints_shapes = {'Conv2d_1a_3x3': [batch_size, 149, 149, 32],
- 'Conv2d_2a_3x3': [batch_size, 147, 147, 32],
- 'Conv2d_2b_3x3': [batch_size, 147, 147, 64],
- 'MaxPool_3a_3x3': [batch_size, 73, 73, 64],
- 'Conv2d_3b_1x1': [batch_size, 73, 73, 80],
- 'Conv2d_4a_3x3': [batch_size, 71, 71, 192],
- 'MaxPool_5a_3x3': [batch_size, 35, 35, 192],
- 'Mixed_5b': [batch_size, 35, 35, 256],
- 'Mixed_5c': [batch_size, 35, 35, 288],
- 'Mixed_5d': [batch_size, 35, 35, 288],
- 'Mixed_6a': [batch_size, 17, 17, 768],
- 'Mixed_6b': [batch_size, 17, 17, 768],
- 'Mixed_6c': [batch_size, 17, 17, 768],
- 'Mixed_6d': [batch_size, 17, 17, 768],
- 'Mixed_6e': [batch_size, 17, 17, 768],
- 'Mixed_7a': [batch_size, 8, 8, 1280],
- 'Mixed_7b': [batch_size, 8, 8, 2048],
- 'Mixed_7c': [batch_size, 8, 8, 2048]}
- self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
- for endpoint_name in endpoints_shapes:
- expected_shape = endpoints_shapes[endpoint_name]
- self.assertTrue(endpoint_name in end_points)
- self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
- expected_shape)
-
- def testModelHasExpectedNumberOfParameters(self):
- batch_size = 5
- height, width = 299, 299
- inputs = tf.random_uniform((batch_size, height, width, 3))
- with slim.arg_scope(inception.inception_v3_arg_scope()):
- inception.inception_v3_base(inputs)
- total_params, _ = slim.model_analyzer.analyze_vars(
- slim.get_model_variables())
- self.assertAlmostEqual(21802784, total_params)
-
- def testBuildEndPoints(self):
- batch_size = 5
- height, width = 299, 299
- num_classes = 1000
-
- inputs = tf.random_uniform((batch_size, height, width, 3))
- _, end_points = inception.inception_v3(inputs, num_classes)
- self.assertTrue('Logits' in end_points)
- logits = end_points['Logits']
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, num_classes])
- self.assertTrue('AuxLogits' in end_points)
- aux_logits = end_points['AuxLogits']
- self.assertListEqual(aux_logits.get_shape().as_list(),
- [batch_size, num_classes])
- self.assertTrue('Mixed_7c' in end_points)
- pre_pool = end_points['Mixed_7c']
- self.assertListEqual(pre_pool.get_shape().as_list(),
- [batch_size, 8, 8, 2048])
- self.assertTrue('PreLogits' in end_points)
- pre_logits = end_points['PreLogits']
- self.assertListEqual(pre_logits.get_shape().as_list(),
- [batch_size, 1, 1, 2048])
-
- def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
- batch_size = 5
- height, width = 299, 299
- num_classes = 1000
-
- inputs = tf.random_uniform((batch_size, height, width, 3))
- _, end_points = inception.inception_v3(inputs, num_classes)
-
- endpoint_keys = [key for key in end_points.keys()
- if key.startswith('Mixed') or key.startswith('Conv')]
-
- _, end_points_with_multiplier = inception.inception_v3(
- inputs, num_classes, scope='depth_multiplied_net',
- depth_multiplier=0.5)
-
- for key in endpoint_keys:
- original_depth = end_points[key].get_shape().as_list()[3]
- new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
- self.assertEqual(0.5 * original_depth, new_depth)
-
- def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
- batch_size = 5
- height, width = 299, 299
- num_classes = 1000
-
- inputs = tf.random_uniform((batch_size, height, width, 3))
- _, end_points = inception.inception_v3(inputs, num_classes)
-
- endpoint_keys = [key for key in end_points.keys()
- if key.startswith('Mixed') or key.startswith('Conv')]
-
- _, end_points_with_multiplier = inception.inception_v3(
- inputs, num_classes, scope='depth_multiplied_net',
- depth_multiplier=2.0)
-
- for key in endpoint_keys:
- original_depth = end_points[key].get_shape().as_list()[3]
- new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
- self.assertEqual(2.0 * original_depth, new_depth)
-
- def testRaiseValueErrorWithInvalidDepthMultiplier(self):
- batch_size = 5
- height, width = 299, 299
- num_classes = 1000
-
- inputs = tf.random_uniform((batch_size, height, width, 3))
- with self.assertRaises(ValueError):
- _ = inception.inception_v3(inputs, num_classes, depth_multiplier=-0.1)
- with self.assertRaises(ValueError):
- _ = inception.inception_v3(inputs, num_classes, depth_multiplier=0.0)
-
- def testHalfSizeImages(self):
- batch_size = 5
- height, width = 150, 150
- num_classes = 1000
-
- inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, end_points = inception.inception_v3(inputs, num_classes)
- self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, num_classes])
- pre_pool = end_points['Mixed_7c']
- self.assertListEqual(pre_pool.get_shape().as_list(),
- [batch_size, 3, 3, 2048])
-
- def testUnknownImageShape(self):
- tf.reset_default_graph()
- batch_size = 2
- height, width = 299, 299
- num_classes = 1000
- input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
- with self.test_session() as sess:
- inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
- logits, end_points = inception.inception_v3(inputs, num_classes)
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, num_classes])
- pre_pool = end_points['Mixed_7c']
- feed_dict = {inputs: input_np}
- tf.global_variables_initializer().run()
- pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
- self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])
-
- def testUnknowBatchSize(self):
- batch_size = 1
- height, width = 299, 299
- num_classes = 1000
-
- inputs = tf.placeholder(tf.float32, (None, height, width, 3))
- logits, _ = inception.inception_v3(inputs, num_classes)
- self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
- self.assertListEqual(logits.get_shape().as_list(),
- [None, num_classes])
- images = tf.random_uniform((batch_size, height, width, 3))
-
- with self.test_session() as sess:
- sess.run(tf.global_variables_initializer())
- output = sess.run(logits, {inputs: images.eval()})
- self.assertEquals(output.shape, (batch_size, num_classes))
-
- def testEvaluation(self):
- batch_size = 2
- height, width = 299, 299
- num_classes = 1000
-
- eval_inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, _ = inception.inception_v3(eval_inputs, num_classes,
- is_training=False)
- predictions = tf.argmax(logits, 1)
-
- with self.test_session() as sess:
- sess.run(tf.global_variables_initializer())
- output = sess.run(predictions)
- self.assertEquals(output.shape, (batch_size,))
-
- def testTrainEvalWithReuse(self):
- train_batch_size = 5
- eval_batch_size = 2
- height, width = 150, 150
- num_classes = 1000
-
- train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
- inception.inception_v3(train_inputs, num_classes)
- eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
- logits, _ = inception.inception_v3(eval_inputs, num_classes,
- is_training=False, reuse=True)
- predictions = tf.argmax(logits, 1)
-
- with self.test_session() as sess:
- sess.run(tf.global_variables_initializer())
- output = sess.run(predictions)
- self.assertEquals(output.shape, (eval_batch_size,))
-
- def testLogitsNotSqueezed(self):
- num_classes = 25
- images = tf.random_uniform([1, 299, 299, 3])
- logits, _ = inception.inception_v3(images,
- num_classes=num_classes,
- spatial_squeeze=False)
-
- with self.test_session() as sess:
- tf.global_variables_initializer().run()
- logits_out = sess.run(logits)
- self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
-
-
-if __name__ == '__main__':
- tf.test.main()
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