--- /dev/null
+# Copyright 2017 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 MobileNet 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 mobilenet_v1
+
+slim = tf.contrib.slim
+
+
+class MobilenetV1Test(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 = mobilenet_v1.mobilenet_v1(inputs, num_classes)
+ self.assertTrue(logits.op.name.startswith('MobilenetV1/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))
+ net, end_points = mobilenet_v1.mobilenet_v1_base(inputs)
+ self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_13'))
+ self.assertListEqual(net.get_shape().as_list(),
+ [batch_size, 7, 7, 1024])
+ expected_endpoints = ['Conv2d_0',
+ 'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
+ 'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
+ 'Conv2d_3_depthwise', 'Conv2d_3_pointwise',
+ 'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
+ 'Conv2d_5_depthwise', 'Conv2d_5_pointwise',
+ 'Conv2d_6_depthwise', 'Conv2d_6_pointwise',
+ 'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
+ 'Conv2d_8_depthwise', 'Conv2d_8_pointwise',
+ 'Conv2d_9_depthwise', 'Conv2d_9_pointwise',
+ 'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
+ 'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
+ 'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
+ 'Conv2d_13_depthwise', 'Conv2d_13_pointwise']
+ self.assertItemsEqual(end_points.keys(), expected_endpoints)
+
+ def testBuildOnlyUptoFinalEndpoint(self):
+ batch_size = 5
+ height, width = 224, 224
+ endpoints = ['Conv2d_0',
+ 'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
+ 'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
+ 'Conv2d_3_depthwise', 'Conv2d_3_pointwise',
+ 'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
+ 'Conv2d_5_depthwise', 'Conv2d_5_pointwise',
+ 'Conv2d_6_depthwise', 'Conv2d_6_pointwise',
+ 'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
+ 'Conv2d_8_depthwise', 'Conv2d_8_pointwise',
+ 'Conv2d_9_depthwise', 'Conv2d_9_pointwise',
+ 'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
+ 'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
+ 'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
+ 'Conv2d_13_depthwise', 'Conv2d_13_pointwise']
+ for index, endpoint in enumerate(endpoints):
+ with tf.Graph().as_default():
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ out_tensor, end_points = mobilenet_v1.mobilenet_v1_base(
+ inputs, final_endpoint=endpoint)
+ self.assertTrue(out_tensor.op.name.startswith(
+ 'MobilenetV1/' + endpoint))
+ self.assertItemsEqual(endpoints[:index+1], end_points)
+
+ def testBuildCustomNetworkUsingConvDefs(self):
+ batch_size = 5
+ height, width = 224, 224
+ conv_defs = [
+ mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
+ mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
+ mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
+ mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
+ ]
+
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ net, end_points = mobilenet_v1.mobilenet_v1_base(
+ inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
+ self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
+ self.assertListEqual(net.get_shape().as_list(),
+ [batch_size, 56, 56, 512])
+ expected_endpoints = ['Conv2d_0',
+ 'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
+ 'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
+ 'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
+ self.assertItemsEqual(end_points.keys(), expected_endpoints)
+
+ def testBuildAndCheckAllEndPointsUptoConv2d_13(self):
+ batch_size = 5
+ height, width = 224, 224
+
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
+ normalizer_fn=slim.batch_norm):
+ _, end_points = mobilenet_v1.mobilenet_v1_base(
+ inputs, final_endpoint='Conv2d_13_pointwise')
+ endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32],
+ 'Conv2d_1_depthwise': [batch_size, 112, 112, 32],
+ 'Conv2d_1_pointwise': [batch_size, 112, 112, 64],
+ 'Conv2d_2_depthwise': [batch_size, 56, 56, 64],
+ 'Conv2d_2_pointwise': [batch_size, 56, 56, 128],
+ 'Conv2d_3_depthwise': [batch_size, 56, 56, 128],
+ 'Conv2d_3_pointwise': [batch_size, 56, 56, 128],
+ 'Conv2d_4_depthwise': [batch_size, 28, 28, 128],
+ 'Conv2d_4_pointwise': [batch_size, 28, 28, 256],
+ 'Conv2d_5_depthwise': [batch_size, 28, 28, 256],
+ 'Conv2d_5_pointwise': [batch_size, 28, 28, 256],
+ 'Conv2d_6_depthwise': [batch_size, 14, 14, 256],
+ 'Conv2d_6_pointwise': [batch_size, 14, 14, 512],
+ 'Conv2d_7_depthwise': [batch_size, 14, 14, 512],
+ 'Conv2d_7_pointwise': [batch_size, 14, 14, 512],
+ 'Conv2d_8_depthwise': [batch_size, 14, 14, 512],
+ 'Conv2d_8_pointwise': [batch_size, 14, 14, 512],
+ 'Conv2d_9_depthwise': [batch_size, 14, 14, 512],
+ 'Conv2d_9_pointwise': [batch_size, 14, 14, 512],
+ 'Conv2d_10_depthwise': [batch_size, 14, 14, 512],
+ 'Conv2d_10_pointwise': [batch_size, 14, 14, 512],
+ 'Conv2d_11_depthwise': [batch_size, 14, 14, 512],
+ 'Conv2d_11_pointwise': [batch_size, 14, 14, 512],
+ 'Conv2d_12_depthwise': [batch_size, 7, 7, 512],
+ 'Conv2d_12_pointwise': [batch_size, 7, 7, 1024],
+ 'Conv2d_13_depthwise': [batch_size, 7, 7, 1024],
+ 'Conv2d_13_pointwise': [batch_size, 7, 7, 1024]}
+ self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+ for endpoint_name, expected_shape in endpoints_shapes.iteritems():
+ self.assertTrue(endpoint_name in end_points)
+ self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+ expected_shape)
+
+ def testOutputStride16BuildAndCheckAllEndPointsUptoConv2d_13(self):
+ batch_size = 5
+ height, width = 224, 224
+ output_stride = 16
+
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
+ normalizer_fn=slim.batch_norm):
+ _, end_points = mobilenet_v1.mobilenet_v1_base(
+ inputs, output_stride=output_stride,
+ final_endpoint='Conv2d_13_pointwise')
+ endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32],
+ 'Conv2d_1_depthwise': [batch_size, 112, 112, 32],
+ 'Conv2d_1_pointwise': [batch_size, 112, 112, 64],
+ 'Conv2d_2_depthwise': [batch_size, 56, 56, 64],
+ 'Conv2d_2_pointwise': [batch_size, 56, 56, 128],
+ 'Conv2d_3_depthwise': [batch_size, 56, 56, 128],
+ 'Conv2d_3_pointwise': [batch_size, 56, 56, 128],
+ 'Conv2d_4_depthwise': [batch_size, 28, 28, 128],
+ 'Conv2d_4_pointwise': [batch_size, 28, 28, 256],
+ 'Conv2d_5_depthwise': [batch_size, 28, 28, 256],
+ 'Conv2d_5_pointwise': [batch_size, 28, 28, 256],
+ 'Conv2d_6_depthwise': [batch_size, 14, 14, 256],
+ 'Conv2d_6_pointwise': [batch_size, 14, 14, 512],
+ 'Conv2d_7_depthwise': [batch_size, 14, 14, 512],
+ 'Conv2d_7_pointwise': [batch_size, 14, 14, 512],
+ 'Conv2d_8_depthwise': [batch_size, 14, 14, 512],
+ 'Conv2d_8_pointwise': [batch_size, 14, 14, 512],
+ 'Conv2d_9_depthwise': [batch_size, 14, 14, 512],
+ 'Conv2d_9_pointwise': [batch_size, 14, 14, 512],
+ 'Conv2d_10_depthwise': [batch_size, 14, 14, 512],
+ 'Conv2d_10_pointwise': [batch_size, 14, 14, 512],
+ 'Conv2d_11_depthwise': [batch_size, 14, 14, 512],
+ 'Conv2d_11_pointwise': [batch_size, 14, 14, 512],
+ 'Conv2d_12_depthwise': [batch_size, 14, 14, 512],
+ 'Conv2d_12_pointwise': [batch_size, 14, 14, 1024],
+ 'Conv2d_13_depthwise': [batch_size, 14, 14, 1024],
+ 'Conv2d_13_pointwise': [batch_size, 14, 14, 1024]}
+ self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+ for endpoint_name, expected_shape in endpoints_shapes.iteritems():
+ self.assertTrue(endpoint_name in end_points)
+ self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+ expected_shape)
+
+ def testOutputStride8BuildAndCheckAllEndPointsUptoConv2d_13(self):
+ batch_size = 5
+ height, width = 224, 224
+ output_stride = 8
+
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
+ normalizer_fn=slim.batch_norm):
+ _, end_points = mobilenet_v1.mobilenet_v1_base(
+ inputs, output_stride=output_stride,
+ final_endpoint='Conv2d_13_pointwise')
+ endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32],
+ 'Conv2d_1_depthwise': [batch_size, 112, 112, 32],
+ 'Conv2d_1_pointwise': [batch_size, 112, 112, 64],
+ 'Conv2d_2_depthwise': [batch_size, 56, 56, 64],
+ 'Conv2d_2_pointwise': [batch_size, 56, 56, 128],
+ 'Conv2d_3_depthwise': [batch_size, 56, 56, 128],
+ 'Conv2d_3_pointwise': [batch_size, 56, 56, 128],
+ 'Conv2d_4_depthwise': [batch_size, 28, 28, 128],
+ 'Conv2d_4_pointwise': [batch_size, 28, 28, 256],
+ 'Conv2d_5_depthwise': [batch_size, 28, 28, 256],
+ 'Conv2d_5_pointwise': [batch_size, 28, 28, 256],
+ 'Conv2d_6_depthwise': [batch_size, 28, 28, 256],
+ 'Conv2d_6_pointwise': [batch_size, 28, 28, 512],
+ 'Conv2d_7_depthwise': [batch_size, 28, 28, 512],
+ 'Conv2d_7_pointwise': [batch_size, 28, 28, 512],
+ 'Conv2d_8_depthwise': [batch_size, 28, 28, 512],
+ 'Conv2d_8_pointwise': [batch_size, 28, 28, 512],
+ 'Conv2d_9_depthwise': [batch_size, 28, 28, 512],
+ 'Conv2d_9_pointwise': [batch_size, 28, 28, 512],
+ 'Conv2d_10_depthwise': [batch_size, 28, 28, 512],
+ 'Conv2d_10_pointwise': [batch_size, 28, 28, 512],
+ 'Conv2d_11_depthwise': [batch_size, 28, 28, 512],
+ 'Conv2d_11_pointwise': [batch_size, 28, 28, 512],
+ 'Conv2d_12_depthwise': [batch_size, 28, 28, 512],
+ 'Conv2d_12_pointwise': [batch_size, 28, 28, 1024],
+ 'Conv2d_13_depthwise': [batch_size, 28, 28, 1024],
+ 'Conv2d_13_pointwise': [batch_size, 28, 28, 1024]}
+ self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+ for endpoint_name, expected_shape in endpoints_shapes.iteritems():
+ self.assertTrue(endpoint_name in end_points)
+ self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+ expected_shape)
+
+ def testBuildAndCheckAllEndPointsApproximateFaceNet(self):
+ batch_size = 5
+ height, width = 128, 128
+
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
+ normalizer_fn=slim.batch_norm):
+ _, end_points = mobilenet_v1.mobilenet_v1_base(
+ inputs, final_endpoint='Conv2d_13_pointwise', depth_multiplier=0.75)
+ # For the Conv2d_0 layer FaceNet has depth=16
+ endpoints_shapes = {'Conv2d_0': [batch_size, 64, 64, 24],
+ 'Conv2d_1_depthwise': [batch_size, 64, 64, 24],
+ 'Conv2d_1_pointwise': [batch_size, 64, 64, 48],
+ 'Conv2d_2_depthwise': [batch_size, 32, 32, 48],
+ 'Conv2d_2_pointwise': [batch_size, 32, 32, 96],
+ 'Conv2d_3_depthwise': [batch_size, 32, 32, 96],
+ 'Conv2d_3_pointwise': [batch_size, 32, 32, 96],
+ 'Conv2d_4_depthwise': [batch_size, 16, 16, 96],
+ 'Conv2d_4_pointwise': [batch_size, 16, 16, 192],
+ 'Conv2d_5_depthwise': [batch_size, 16, 16, 192],
+ 'Conv2d_5_pointwise': [batch_size, 16, 16, 192],
+ 'Conv2d_6_depthwise': [batch_size, 8, 8, 192],
+ 'Conv2d_6_pointwise': [batch_size, 8, 8, 384],
+ 'Conv2d_7_depthwise': [batch_size, 8, 8, 384],
+ 'Conv2d_7_pointwise': [batch_size, 8, 8, 384],
+ 'Conv2d_8_depthwise': [batch_size, 8, 8, 384],
+ 'Conv2d_8_pointwise': [batch_size, 8, 8, 384],
+ 'Conv2d_9_depthwise': [batch_size, 8, 8, 384],
+ 'Conv2d_9_pointwise': [batch_size, 8, 8, 384],
+ 'Conv2d_10_depthwise': [batch_size, 8, 8, 384],
+ 'Conv2d_10_pointwise': [batch_size, 8, 8, 384],
+ 'Conv2d_11_depthwise': [batch_size, 8, 8, 384],
+ 'Conv2d_11_pointwise': [batch_size, 8, 8, 384],
+ 'Conv2d_12_depthwise': [batch_size, 4, 4, 384],
+ 'Conv2d_12_pointwise': [batch_size, 4, 4, 768],
+ 'Conv2d_13_depthwise': [batch_size, 4, 4, 768],
+ 'Conv2d_13_pointwise': [batch_size, 4, 4, 768]}
+ self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
+ for endpoint_name, expected_shape in endpoints_shapes.iteritems():
+ 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([slim.conv2d, slim.separable_conv2d],
+ normalizer_fn=slim.batch_norm):
+ mobilenet_v1.mobilenet_v1_base(inputs)
+ total_params, _ = slim.model_analyzer.analyze_vars(
+ slim.get_model_variables())
+ self.assertAlmostEqual(3217920L, total_params)
+
+ def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
+ batch_size = 5
+ height, width = 224, 224
+ num_classes = 1000
+
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ _, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
+
+ endpoint_keys = [key for key in end_points.keys() if key.startswith('Conv')]
+
+ _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1(
+ inputs, num_classes, scope='depth_multiplied_net',
+ depth_multiplier=0.5)
+
+ for key in endpoint_keys:
+ original_depth = end_points[key].get_shape().as_list()[3]
+ new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
+ self.assertEqual(0.5 * original_depth, new_depth)
+
+ def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
+ batch_size = 5
+ height, width = 224, 224
+ num_classes = 1000
+
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ _, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
+
+ endpoint_keys = [key for key in end_points.keys()
+ if key.startswith('Mixed') or key.startswith('Conv')]
+
+ _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1(
+ inputs, num_classes, scope='depth_multiplied_net',
+ depth_multiplier=2.0)
+
+ for key in endpoint_keys:
+ original_depth = end_points[key].get_shape().as_list()[3]
+ new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
+ self.assertEqual(2.0 * original_depth, new_depth)
+
+ def testRaiseValueErrorWithInvalidDepthMultiplier(self):
+ batch_size = 5
+ height, width = 224, 224
+ num_classes = 1000
+
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ with self.assertRaises(ValueError):
+ _ = mobilenet_v1.mobilenet_v1(
+ inputs, num_classes, depth_multiplier=-0.1)
+ with self.assertRaises(ValueError):
+ _ = mobilenet_v1.mobilenet_v1(
+ inputs, num_classes, depth_multiplier=0.0)
+
+ def testHalfSizeImages(self):
+ batch_size = 5
+ height, width = 112, 112
+ num_classes = 1000
+
+ inputs = tf.random_uniform((batch_size, height, width, 3))
+ logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
+ self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
+ self.assertListEqual(logits.get_shape().as_list(),
+ [batch_size, num_classes])
+ pre_pool = end_points['Conv2d_13_pointwise']
+ self.assertListEqual(pre_pool.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 = mobilenet_v1.mobilenet_v1(inputs, num_classes)
+ self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
+ self.assertListEqual(logits.get_shape().as_list(),
+ [batch_size, num_classes])
+ pre_pool = end_points['Conv2d_13_pointwise']
+ 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, _ = mobilenet_v1.mobilenet_v1(inputs, num_classes)
+ self.assertTrue(logits.op.name.startswith('MobilenetV1/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, _ = mobilenet_v1.mobilenet_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 = 150, 150
+ num_classes = 1000
+
+ train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
+ mobilenet_v1.mobilenet_v1(train_inputs, num_classes)
+ eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
+ logits, _ = mobilenet_v1.mobilenet_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, _ = mobilenet_v1.mobilenet_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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