+++ /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.inception_v4."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-from nets import inception
-
-
-class InceptionTest(tf.test.TestCase):
-
- def testBuildLogits(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_v4(inputs, num_classes)
- auxlogits = end_points['AuxLogits']
- predictions = end_points['Predictions']
- self.assertTrue(auxlogits.op.name.startswith('InceptionV4/AuxLogits'))
- self.assertListEqual(auxlogits.get_shape().as_list(),
- [batch_size, num_classes])
- self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, num_classes])
- self.assertTrue(predictions.op.name.startswith(
- 'InceptionV4/Logits/Predictions'))
- self.assertListEqual(predictions.get_shape().as_list(),
- [batch_size, num_classes])
-
- def testBuildWithoutAuxLogits(self):
- batch_size = 5
- height, width = 299, 299
- num_classes = 1000
- inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, endpoints = inception.inception_v4(inputs, num_classes,
- create_aux_logits=False)
- self.assertFalse('AuxLogits' in endpoints)
- self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, num_classes])
-
- def testAllEndPointsShapes(self):
- batch_size = 5
- height, width = 299, 299
- num_classes = 1000
- inputs = tf.random_uniform((batch_size, height, width, 3))
- _, end_points = inception.inception_v4(inputs, num_classes)
- 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],
- 'Mixed_3a': [batch_size, 73, 73, 160],
- 'Mixed_4a': [batch_size, 71, 71, 192],
- 'Mixed_5a': [batch_size, 35, 35, 384],
- # 4 x Inception-A blocks
- 'Mixed_5b': [batch_size, 35, 35, 384],
- 'Mixed_5c': [batch_size, 35, 35, 384],
- 'Mixed_5d': [batch_size, 35, 35, 384],
- 'Mixed_5e': [batch_size, 35, 35, 384],
- # Reduction-A block
- 'Mixed_6a': [batch_size, 17, 17, 1024],
- # 7 x Inception-B blocks
- 'Mixed_6b': [batch_size, 17, 17, 1024],
- 'Mixed_6c': [batch_size, 17, 17, 1024],
- 'Mixed_6d': [batch_size, 17, 17, 1024],
- 'Mixed_6e': [batch_size, 17, 17, 1024],
- 'Mixed_6f': [batch_size, 17, 17, 1024],
- 'Mixed_6g': [batch_size, 17, 17, 1024],
- 'Mixed_6h': [batch_size, 17, 17, 1024],
- # Reduction-A block
- 'Mixed_7a': [batch_size, 8, 8, 1536],
- # 3 x Inception-C blocks
- 'Mixed_7b': [batch_size, 8, 8, 1536],
- 'Mixed_7c': [batch_size, 8, 8, 1536],
- 'Mixed_7d': [batch_size, 8, 8, 1536],
- # Logits and predictions
- 'AuxLogits': [batch_size, num_classes],
- 'PreLogitsFlatten': [batch_size, 1536],
- 'Logits': [batch_size, num_classes],
- 'Predictions': [batch_size, num_classes]}
- 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 testBuildBaseNetwork(self):
- batch_size = 5
- height, width = 299, 299
- inputs = tf.random_uniform((batch_size, height, width, 3))
- net, end_points = inception.inception_v4_base(inputs)
- self.assertTrue(net.op.name.startswith(
- 'InceptionV4/Mixed_7d'))
- self.assertListEqual(net.get_shape().as_list(), [batch_size, 8, 8, 1536])
- expected_endpoints = [
- 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a',
- 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
- 'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
- 'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
- 'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
- self.assertItemsEqual(end_points.keys(), expected_endpoints)
- for name, op in end_points.iteritems():
- self.assertTrue(op.name.startswith('InceptionV4/' + name))
-
- def testBuildOnlyUpToFinalEndpoint(self):
- batch_size = 5
- height, width = 299, 299
- all_endpoints = [
- 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a',
- 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
- 'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
- 'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
- 'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
- for index, endpoint in enumerate(all_endpoints):
- with tf.Graph().as_default():
- inputs = tf.random_uniform((batch_size, height, width, 3))
- out_tensor, end_points = inception.inception_v4_base(
- inputs, final_endpoint=endpoint)
- self.assertTrue(out_tensor.op.name.startswith(
- 'InceptionV4/' + endpoint))
- self.assertItemsEqual(all_endpoints[:index+1], end_points)
-
- def testVariablesSetDevice(self):
- batch_size = 5
- height, width = 299, 299
- num_classes = 1000
- inputs = tf.random_uniform((batch_size, height, width, 3))
- # Force all Variables to reside on the device.
- with tf.variable_scope('on_cpu'), tf.device('/cpu:0'):
- inception.inception_v4(inputs, num_classes)
- with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
- inception.inception_v4(inputs, num_classes)
- for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'):
- self.assertDeviceEqual(v.device, '/cpu:0')
- for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'):
- self.assertDeviceEqual(v.device, '/gpu: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_v4(inputs, num_classes)
- self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, num_classes])
- pre_pool = end_points['Mixed_7d']
- self.assertListEqual(pre_pool.get_shape().as_list(),
- [batch_size, 3, 3, 1536])
-
- def testUnknownBatchSize(self):
- batch_size = 1
- height, width = 299, 299
- num_classes = 1000
- with self.test_session() as sess:
- inputs = tf.placeholder(tf.float32, (None, height, width, 3))
- logits, _ = inception.inception_v4(inputs, num_classes)
- self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
- self.assertListEqual(logits.get_shape().as_list(),
- [None, num_classes])
- images = tf.random_uniform((batch_size, height, width, 3))
- 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
- with self.test_session() as sess:
- eval_inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, _ = inception.inception_v4(eval_inputs,
- num_classes,
- is_training=False)
- predictions = tf.argmax(logits, 1)
- 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
- with self.test_session() as sess:
- train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
- inception.inception_v4(train_inputs, num_classes)
- eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
- logits, _ = inception.inception_v4(eval_inputs,
- num_classes,
- is_training=False,
- reuse=True)
- predictions = tf.argmax(logits, 1)
- sess.run(tf.global_variables_initializer())
- output = sess.run(predictions)
- self.assertEquals(output.shape, (eval_batch_size,))
-
-
-if __name__ == '__main__':
- tf.test.main()
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