+++ /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 InceptionV1Test(tf.test.TestCase):
-
- def testBuildClassificationNetwork(self):
- batch_size = 5
- height, width = 224, 224
- num_classes = 1000
-
- inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, end_points = inception.inception_v1(inputs, num_classes)
- self.assertTrue(logits.op.name.startswith('InceptionV1/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 = 224, 224
-
- inputs = tf.random_uniform((batch_size, height, width, 3))
- mixed_6c, end_points = inception.inception_v1_base(inputs)
- self.assertTrue(mixed_6c.op.name.startswith('InceptionV1/Mixed_5c'))
- self.assertListEqual(mixed_6c.get_shape().as_list(),
- [batch_size, 7, 7, 1024])
- expected_endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
- 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b',
- 'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c',
- 'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2',
- 'Mixed_5b', 'Mixed_5c']
- self.assertItemsEqual(end_points.keys(), expected_endpoints)
-
- def testBuildOnlyUptoFinalEndpoint(self):
- batch_size = 5
- height, width = 224, 224
- endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
- 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
- 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
- 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
- 'Mixed_5c']
- 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_v1_base(
- inputs, final_endpoint=endpoint)
- self.assertTrue(out_tensor.op.name.startswith(
- 'InceptionV1/' + endpoint))
- self.assertItemsEqual(endpoints[:index+1], end_points)
-
- def testBuildAndCheckAllEndPointsUptoMixed5c(self):
- batch_size = 5
- height, width = 224, 224
-
- inputs = tf.random_uniform((batch_size, height, width, 3))
- _, end_points = inception.inception_v1_base(inputs,
- final_endpoint='Mixed_5c')
- endpoints_shapes = {'Conv2d_1a_7x7': [5, 112, 112, 64],
- 'MaxPool_2a_3x3': [5, 56, 56, 64],
- 'Conv2d_2b_1x1': [5, 56, 56, 64],
- 'Conv2d_2c_3x3': [5, 56, 56, 192],
- 'MaxPool_3a_3x3': [5, 28, 28, 192],
- 'Mixed_3b': [5, 28, 28, 256],
- 'Mixed_3c': [5, 28, 28, 480],
- 'MaxPool_4a_3x3': [5, 14, 14, 480],
- 'Mixed_4b': [5, 14, 14, 512],
- 'Mixed_4c': [5, 14, 14, 512],
- 'Mixed_4d': [5, 14, 14, 512],
- 'Mixed_4e': [5, 14, 14, 528],
- 'Mixed_4f': [5, 14, 14, 832],
- 'MaxPool_5a_2x2': [5, 7, 7, 832],
- 'Mixed_5b': [5, 7, 7, 832],
- 'Mixed_5c': [5, 7, 7, 1024]}
-
- 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 = 224, 224
- inputs = tf.random_uniform((batch_size, height, width, 3))
- with slim.arg_scope(inception.inception_v1_arg_scope()):
- inception.inception_v1_base(inputs)
- total_params, _ = slim.model_analyzer.analyze_vars(
- slim.get_model_variables())
- self.assertAlmostEqual(5607184, total_params)
-
- def testHalfSizeImages(self):
- batch_size = 5
- height, width = 112, 112
-
- inputs = tf.random_uniform((batch_size, height, width, 3))
- mixed_5c, _ = inception.inception_v1_base(inputs)
- self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c'))
- self.assertListEqual(mixed_5c.get_shape().as_list(),
- [batch_size, 4, 4, 1024])
-
- def testUnknownImageShape(self):
- tf.reset_default_graph()
- batch_size = 2
- height, width = 224, 224
- 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_v1(inputs, num_classes)
- self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, num_classes])
- pre_pool = end_points['Mixed_5c']
- 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, 7, 7, 1024])
-
- def testUnknowBatchSize(self):
- batch_size = 1
- height, width = 224, 224
- num_classes = 1000
-
- inputs = tf.placeholder(tf.float32, (None, height, width, 3))
- logits, _ = inception.inception_v1(inputs, num_classes)
- self.assertTrue(logits.op.name.startswith('InceptionV1/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 = 224, 224
- num_classes = 1000
-
- eval_inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, _ = inception.inception_v1(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 = 224, 224
- num_classes = 1000
-
- train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
- inception.inception_v1(train_inputs, num_classes)
- eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
- logits, _ = inception.inception_v1(eval_inputs, num_classes, 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, 224, 224, 3])
- logits, _ = inception.inception_v1(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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