--- /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_resnet_v2."""
+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
+ with self.test_session():
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ logits, endpoints = inception.inception_resnet_v2(inputs, num_classes)
+ self.assertTrue('AuxLogits' in endpoints)
+ auxlogits = endpoints['AuxLogits']
+ self.assertTrue(
+ auxlogits.op.name.startswith('InceptionResnetV2/AuxLogits'))
+ self.assertListEqual(auxlogits.get_shape().as_list(),
+ [batch_size, num_classes])
+ self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
+ self.assertListEqual(logits.get_shape().as_list(),
+ [batch_size, num_classes])
+
+ def testBuildWithoutAuxLogits(self):
+ batch_size = 5
+ height, width = 299, 299
+ num_classes = 1000
+ with self.test_session():
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ logits, endpoints = inception.inception_resnet_v2(inputs, num_classes,
+ create_aux_logits=False)
+ self.assertTrue('AuxLogits' not in endpoints)
+ self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
+ self.assertListEqual(logits.get_shape().as_list(),
+ [batch_size, num_classes])
+
+ def testBuildEndPoints(self):
+ batch_size = 5
+ height, width = 299, 299
+ num_classes = 1000
+ with self.test_session():
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ _, end_points = inception.inception_resnet_v2(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])
+ pre_pool = end_points['Conv2d_7b_1x1']
+ self.assertListEqual(pre_pool.get_shape().as_list(),
+ [batch_size, 8, 8, 1536])
+
+ def testBuildBaseNetwork(self):
+ batch_size = 5
+ height, width = 299, 299
+
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ net, end_points = inception.inception_resnet_v2_base(inputs)
+ self.assertTrue(net.op.name.startswith('InceptionResnetV2/Conv2d_7b_1x1'))
+ self.assertListEqual(net.get_shape().as_list(),
+ [batch_size, 8, 8, 1536])
+ 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_6a',
+ 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
+ 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_6a',
+ 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
+ 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_resnet_v2_base(
+ inputs, final_endpoint=endpoint)
+ if endpoint != 'PreAuxLogits':
+ self.assertTrue(out_tensor.op.name.startswith(
+ 'InceptionResnetV2/' + endpoint))
+ self.assertItemsEqual(endpoints[:index+1], end_points)
+
+ def testBuildAndCheckAllEndPointsUptoPreAuxLogits(self):
+ batch_size = 5
+ height, width = 299, 299
+
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ _, end_points = inception.inception_resnet_v2_base(
+ inputs, final_endpoint='PreAuxLogits')
+ endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32],
+ 'Conv2d_2a_3x3': [5, 147, 147, 32],
+ 'Conv2d_2b_3x3': [5, 147, 147, 64],
+ 'MaxPool_3a_3x3': [5, 73, 73, 64],
+ 'Conv2d_3b_1x1': [5, 73, 73, 80],
+ 'Conv2d_4a_3x3': [5, 71, 71, 192],
+ 'MaxPool_5a_3x3': [5, 35, 35, 192],
+ 'Mixed_5b': [5, 35, 35, 320],
+ 'Mixed_6a': [5, 17, 17, 1088],
+ 'PreAuxLogits': [5, 17, 17, 1088]
+ }
+
+ 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 testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithAlignedFeatureMaps(self):
+ batch_size = 5
+ height, width = 299, 299
+
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ _, end_points = inception.inception_resnet_v2_base(
+ inputs, final_endpoint='PreAuxLogits', align_feature_maps=True)
+ endpoints_shapes = {'Conv2d_1a_3x3': [5, 150, 150, 32],
+ 'Conv2d_2a_3x3': [5, 150, 150, 32],
+ 'Conv2d_2b_3x3': [5, 150, 150, 64],
+ 'MaxPool_3a_3x3': [5, 75, 75, 64],
+ 'Conv2d_3b_1x1': [5, 75, 75, 80],
+ 'Conv2d_4a_3x3': [5, 75, 75, 192],
+ 'MaxPool_5a_3x3': [5, 38, 38, 192],
+ 'Mixed_5b': [5, 38, 38, 320],
+ 'Mixed_6a': [5, 19, 19, 1088],
+ 'PreAuxLogits': [5, 19, 19, 1088]
+ }
+
+ 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 testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithOutputStrideEight(self):
+ batch_size = 5
+ height, width = 299, 299
+
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ _, end_points = inception.inception_resnet_v2_base(
+ inputs, final_endpoint='PreAuxLogits', output_stride=8)
+ endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32],
+ 'Conv2d_2a_3x3': [5, 147, 147, 32],
+ 'Conv2d_2b_3x3': [5, 147, 147, 64],
+ 'MaxPool_3a_3x3': [5, 73, 73, 64],
+ 'Conv2d_3b_1x1': [5, 73, 73, 80],
+ 'Conv2d_4a_3x3': [5, 71, 71, 192],
+ 'MaxPool_5a_3x3': [5, 35, 35, 192],
+ 'Mixed_5b': [5, 35, 35, 320],
+ 'Mixed_6a': [5, 33, 33, 1088],
+ 'PreAuxLogits': [5, 33, 33, 1088]
+ }
+
+ 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 testVariablesSetDevice(self):
+ batch_size = 5
+ height, width = 299, 299
+ num_classes = 1000
+ with self.test_session():
+ 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_resnet_v2(inputs, num_classes)
+ with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
+ inception.inception_resnet_v2(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
+ with self.test_session():
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ logits, end_points = inception.inception_resnet_v2(inputs, num_classes)
+ self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
+ self.assertListEqual(logits.get_shape().as_list(),
+ [batch_size, num_classes])
+ pre_pool = end_points['Conv2d_7b_1x1']
+ 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_resnet_v2(inputs, num_classes)
+ self.assertTrue(logits.op.name.startswith('InceptionResnetV2/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_resnet_v2(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_resnet_v2(train_inputs, num_classes)
+ eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
+ logits, _ = inception.inception_resnet_v2(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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