--- /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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