1 # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
7 # http://www.apache.org/licenses/LICENSE-2.0
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 # ==============================================================================
15 """Tests for slim_nets.inception_v1."""
17 from __future__ import absolute_import
18 from __future__ import division
19 from __future__ import print_function
22 import tensorflow as tf
24 from nets import inception
26 slim = tf.contrib.slim
29 class InceptionV1Test(tf.test.TestCase):
31 def testBuildClassificationNetwork(self):
33 height, width = 224, 224
36 inputs = tf.random_uniform((batch_size, height, width, 3))
37 logits, end_points = inception.inception_v1(inputs, num_classes)
38 self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
39 self.assertListEqual(logits.get_shape().as_list(),
40 [batch_size, num_classes])
41 self.assertTrue('Predictions' in end_points)
42 self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
43 [batch_size, num_classes])
45 def testBuildBaseNetwork(self):
47 height, width = 224, 224
49 inputs = tf.random_uniform((batch_size, height, width, 3))
50 mixed_6c, end_points = inception.inception_v1_base(inputs)
51 self.assertTrue(mixed_6c.op.name.startswith('InceptionV1/Mixed_5c'))
52 self.assertListEqual(mixed_6c.get_shape().as_list(),
53 [batch_size, 7, 7, 1024])
54 expected_endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
55 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b',
56 'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c',
57 'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2',
58 'Mixed_5b', 'Mixed_5c']
59 self.assertItemsEqual(end_points.keys(), expected_endpoints)
61 def testBuildOnlyUptoFinalEndpoint(self):
63 height, width = 224, 224
64 endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
65 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
66 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
67 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
69 for index, endpoint in enumerate(endpoints):
70 with tf.Graph().as_default():
71 inputs = tf.random_uniform((batch_size, height, width, 3))
72 out_tensor, end_points = inception.inception_v1_base(
73 inputs, final_endpoint=endpoint)
74 self.assertTrue(out_tensor.op.name.startswith(
75 'InceptionV1/' + endpoint))
76 self.assertItemsEqual(endpoints[:index+1], end_points)
78 def testBuildAndCheckAllEndPointsUptoMixed5c(self):
80 height, width = 224, 224
82 inputs = tf.random_uniform((batch_size, height, width, 3))
83 _, end_points = inception.inception_v1_base(inputs,
84 final_endpoint='Mixed_5c')
85 endpoints_shapes = {'Conv2d_1a_7x7': [5, 112, 112, 64],
86 'MaxPool_2a_3x3': [5, 56, 56, 64],
87 'Conv2d_2b_1x1': [5, 56, 56, 64],
88 'Conv2d_2c_3x3': [5, 56, 56, 192],
89 'MaxPool_3a_3x3': [5, 28, 28, 192],
90 'Mixed_3b': [5, 28, 28, 256],
91 'Mixed_3c': [5, 28, 28, 480],
92 'MaxPool_4a_3x3': [5, 14, 14, 480],
93 'Mixed_4b': [5, 14, 14, 512],
94 'Mixed_4c': [5, 14, 14, 512],
95 'Mixed_4d': [5, 14, 14, 512],
96 'Mixed_4e': [5, 14, 14, 528],
97 'Mixed_4f': [5, 14, 14, 832],
98 'MaxPool_5a_2x2': [5, 7, 7, 832],
99 'Mixed_5b': [5, 7, 7, 832],
100 'Mixed_5c': [5, 7, 7, 1024]}
102 self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
103 for endpoint_name in endpoints_shapes:
104 expected_shape = endpoints_shapes[endpoint_name]
105 self.assertTrue(endpoint_name in end_points)
106 self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
109 def testModelHasExpectedNumberOfParameters(self):
111 height, width = 224, 224
112 inputs = tf.random_uniform((batch_size, height, width, 3))
113 with slim.arg_scope(inception.inception_v1_arg_scope()):
114 inception.inception_v1_base(inputs)
115 total_params, _ = slim.model_analyzer.analyze_vars(
116 slim.get_model_variables())
117 self.assertAlmostEqual(5607184, total_params)
119 def testHalfSizeImages(self):
121 height, width = 112, 112
123 inputs = tf.random_uniform((batch_size, height, width, 3))
124 mixed_5c, _ = inception.inception_v1_base(inputs)
125 self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c'))
126 self.assertListEqual(mixed_5c.get_shape().as_list(),
127 [batch_size, 4, 4, 1024])
129 def testUnknownImageShape(self):
130 tf.reset_default_graph()
132 height, width = 224, 224
134 input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
135 with self.test_session() as sess:
136 inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
137 logits, end_points = inception.inception_v1(inputs, num_classes)
138 self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
139 self.assertListEqual(logits.get_shape().as_list(),
140 [batch_size, num_classes])
141 pre_pool = end_points['Mixed_5c']
142 feed_dict = {inputs: input_np}
143 tf.global_variables_initializer().run()
144 pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
145 self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
147 def testUnknowBatchSize(self):
149 height, width = 224, 224
152 inputs = tf.placeholder(tf.float32, (None, height, width, 3))
153 logits, _ = inception.inception_v1(inputs, num_classes)
154 self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
155 self.assertListEqual(logits.get_shape().as_list(),
157 images = tf.random_uniform((batch_size, height, width, 3))
159 with self.test_session() as sess:
160 sess.run(tf.global_variables_initializer())
161 output = sess.run(logits, {inputs: images.eval()})
162 self.assertEquals(output.shape, (batch_size, num_classes))
164 def testEvaluation(self):
166 height, width = 224, 224
169 eval_inputs = tf.random_uniform((batch_size, height, width, 3))
170 logits, _ = inception.inception_v1(eval_inputs, num_classes,
172 predictions = tf.argmax(logits, 1)
174 with self.test_session() as sess:
175 sess.run(tf.global_variables_initializer())
176 output = sess.run(predictions)
177 self.assertEquals(output.shape, (batch_size,))
179 def testTrainEvalWithReuse(self):
182 height, width = 224, 224
185 train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
186 inception.inception_v1(train_inputs, num_classes)
187 eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
188 logits, _ = inception.inception_v1(eval_inputs, num_classes, reuse=True)
189 predictions = tf.argmax(logits, 1)
191 with self.test_session() as sess:
192 sess.run(tf.global_variables_initializer())
193 output = sess.run(predictions)
194 self.assertEquals(output.shape, (eval_batch_size,))
196 def testLogitsNotSqueezed(self):
198 images = tf.random_uniform([1, 224, 224, 3])
199 logits, _ = inception.inception_v1(images,
200 num_classes=num_classes,
201 spatial_squeeze=False)
203 with self.test_session() as sess:
204 tf.global_variables_initializer().run()
205 logits_out = sess.run(logits)
206 self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
209 if __name__ == '__main__':