+++ /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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