263 lines
11 KiB
Python
263 lines
11 KiB
Python
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# 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 nets.inception_v2."""
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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 numpy as np
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import tensorflow as tf
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from nets import inception
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slim = tf.contrib.slim
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class InceptionV2Test(tf.test.TestCase):
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def testBuildClassificationNetwork(self):
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batch_size = 5
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height, width = 224, 224
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num_classes = 1000
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inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, end_points = inception.inception_v2(inputs, num_classes)
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self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, num_classes])
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self.assertTrue('Predictions' in end_points)
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self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
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[batch_size, num_classes])
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def testBuildBaseNetwork(self):
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batch_size = 5
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height, width = 224, 224
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inputs = tf.random_uniform((batch_size, height, width, 3))
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mixed_5c, end_points = inception.inception_v2_base(inputs)
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self.assertTrue(mixed_5c.op.name.startswith('InceptionV2/Mixed_5c'))
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self.assertListEqual(mixed_5c.get_shape().as_list(),
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[batch_size, 7, 7, 1024])
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expected_endpoints = ['Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b',
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'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a',
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'Mixed_5b', 'Mixed_5c', 'Conv2d_1a_7x7',
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'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3',
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'MaxPool_3a_3x3']
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self.assertItemsEqual(end_points.keys(), expected_endpoints)
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def testBuildOnlyUptoFinalEndpoint(self):
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batch_size = 5
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height, width = 224, 224
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endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
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'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
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'Mixed_4a', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
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'Mixed_5a', 'Mixed_5b', 'Mixed_5c']
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for index, endpoint in enumerate(endpoints):
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with tf.Graph().as_default():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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out_tensor, end_points = inception.inception_v2_base(
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inputs, final_endpoint=endpoint)
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self.assertTrue(out_tensor.op.name.startswith(
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'InceptionV2/' + endpoint))
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self.assertItemsEqual(endpoints[:index+1], end_points)
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def testBuildAndCheckAllEndPointsUptoMixed5c(self):
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batch_size = 5
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height, width = 224, 224
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inputs = tf.random_uniform((batch_size, height, width, 3))
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_, end_points = inception.inception_v2_base(inputs,
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final_endpoint='Mixed_5c')
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endpoints_shapes = {'Mixed_3b': [batch_size, 28, 28, 256],
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'Mixed_3c': [batch_size, 28, 28, 320],
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'Mixed_4a': [batch_size, 14, 14, 576],
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'Mixed_4b': [batch_size, 14, 14, 576],
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'Mixed_4c': [batch_size, 14, 14, 576],
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'Mixed_4d': [batch_size, 14, 14, 576],
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'Mixed_4e': [batch_size, 14, 14, 576],
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'Mixed_5a': [batch_size, 7, 7, 1024],
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'Mixed_5b': [batch_size, 7, 7, 1024],
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'Mixed_5c': [batch_size, 7, 7, 1024],
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'Conv2d_1a_7x7': [batch_size, 112, 112, 64],
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'MaxPool_2a_3x3': [batch_size, 56, 56, 64],
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'Conv2d_2b_1x1': [batch_size, 56, 56, 64],
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'Conv2d_2c_3x3': [batch_size, 56, 56, 192],
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'MaxPool_3a_3x3': [batch_size, 28, 28, 192]}
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self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
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for endpoint_name in endpoints_shapes:
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expected_shape = endpoints_shapes[endpoint_name]
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self.assertTrue(endpoint_name in end_points)
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self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
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expected_shape)
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def testModelHasExpectedNumberOfParameters(self):
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batch_size = 5
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height, width = 224, 224
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inputs = tf.random_uniform((batch_size, height, width, 3))
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with slim.arg_scope(inception.inception_v2_arg_scope()):
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inception.inception_v2_base(inputs)
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total_params, _ = slim.model_analyzer.analyze_vars(
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slim.get_model_variables())
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self.assertAlmostEqual(10173112, total_params)
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def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
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batch_size = 5
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height, width = 224, 224
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num_classes = 1000
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inputs = tf.random_uniform((batch_size, height, width, 3))
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_, end_points = inception.inception_v2(inputs, num_classes)
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endpoint_keys = [key for key in end_points.keys()
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if key.startswith('Mixed') or key.startswith('Conv')]
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_, end_points_with_multiplier = inception.inception_v2(
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inputs, num_classes, scope='depth_multiplied_net',
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depth_multiplier=0.5)
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for key in endpoint_keys:
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original_depth = end_points[key].get_shape().as_list()[3]
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new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
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self.assertEqual(0.5 * original_depth, new_depth)
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def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
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batch_size = 5
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height, width = 224, 224
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num_classes = 1000
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inputs = tf.random_uniform((batch_size, height, width, 3))
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_, end_points = inception.inception_v2(inputs, num_classes)
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endpoint_keys = [key for key in end_points.keys()
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if key.startswith('Mixed') or key.startswith('Conv')]
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_, end_points_with_multiplier = inception.inception_v2(
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inputs, num_classes, scope='depth_multiplied_net',
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depth_multiplier=2.0)
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for key in endpoint_keys:
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original_depth = end_points[key].get_shape().as_list()[3]
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new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
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self.assertEqual(2.0 * original_depth, new_depth)
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def testRaiseValueErrorWithInvalidDepthMultiplier(self):
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batch_size = 5
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height, width = 224, 224
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num_classes = 1000
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inputs = tf.random_uniform((batch_size, height, width, 3))
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with self.assertRaises(ValueError):
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_ = inception.inception_v2(inputs, num_classes, depth_multiplier=-0.1)
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with self.assertRaises(ValueError):
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_ = inception.inception_v2(inputs, num_classes, depth_multiplier=0.0)
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def testHalfSizeImages(self):
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batch_size = 5
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height, width = 112, 112
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num_classes = 1000
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inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, end_points = inception.inception_v2(inputs, num_classes)
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self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, num_classes])
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pre_pool = end_points['Mixed_5c']
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self.assertListEqual(pre_pool.get_shape().as_list(),
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[batch_size, 4, 4, 1024])
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def testUnknownImageShape(self):
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tf.reset_default_graph()
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batch_size = 2
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height, width = 224, 224
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num_classes = 1000
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input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
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with self.test_session() as sess:
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inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
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logits, end_points = inception.inception_v2(inputs, num_classes)
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self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, num_classes])
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pre_pool = end_points['Mixed_5c']
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feed_dict = {inputs: input_np}
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tf.initialize_all_variables().run()
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pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
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self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
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def testUnknowBatchSize(self):
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batch_size = 1
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height, width = 224, 224
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num_classes = 1000
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inputs = tf.placeholder(tf.float32, (None, height, width, 3))
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logits, _ = inception.inception_v2(inputs, num_classes)
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self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
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self.assertListEqual(logits.get_shape().as_list(),
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[None, num_classes])
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images = tf.random_uniform((batch_size, height, width, 3))
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with self.test_session() as sess:
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sess.run(tf.initialize_all_variables())
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output = sess.run(logits, {inputs: images.eval()})
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self.assertEquals(output.shape, (batch_size, num_classes))
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def testEvaluation(self):
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batch_size = 2
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height, width = 224, 224
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num_classes = 1000
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eval_inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, _ = inception.inception_v2(eval_inputs, num_classes,
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is_training=False)
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predictions = tf.argmax(logits, 1)
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with self.test_session() as sess:
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sess.run(tf.initialize_all_variables())
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output = sess.run(predictions)
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self.assertEquals(output.shape, (batch_size,))
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def testTrainEvalWithReuse(self):
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train_batch_size = 5
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eval_batch_size = 2
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height, width = 150, 150
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num_classes = 1000
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train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
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inception.inception_v2(train_inputs, num_classes)
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eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
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logits, _ = inception.inception_v2(eval_inputs, num_classes, reuse=True)
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predictions = tf.argmax(logits, 1)
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with self.test_session() as sess:
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sess.run(tf.initialize_all_variables())
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output = sess.run(predictions)
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self.assertEquals(output.shape, (eval_batch_size,))
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def testLogitsNotSqueezed(self):
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num_classes = 25
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images = tf.random_uniform([1, 224, 224, 3])
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logits, _ = inception.inception_v2(images,
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num_classes=num_classes,
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spatial_squeeze=False)
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with self.test_session() as sess:
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tf.initialize_all_variables().run()
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logits_out = sess.run(logits)
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self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
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if __name__ == '__main__':
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tf.test.main()
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