Python nets.inception.inception_v2_base() Examples

The following are 30 code examples of nets.inception.inception_v2_base(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module nets.inception , or try the search function .
Example #1
Source File: inception_v2_test.py    From edafa with MIT License 6 votes vote down vote up 
def testBuildEndPointsWithUseSeparableConvolutionFalse(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs)

    endpoint_keys = [
        key for key in end_points.keys()
        if key.startswith('Mixed') or key.startswith('Conv')
    ]

    _, end_points_with_replacement = inception.inception_v2_base(
        inputs, use_separable_conv=False)

    # The endpoint shapes must be equal to the original shape even when the
    # separable convolution is replaced with a normal convolution.
    for key in endpoint_keys:
      original_shape = end_points[key].get_shape().as_list()
      self.assertTrue(key in end_points_with_replacement)
      new_shape = end_points_with_replacement[key].get_shape().as_list()
      self.assertListEqual(original_shape, new_shape)
Example #2
Source File: inception_v2_test.py    From garbage-object-detection-tensorflow with MIT License 6 votes vote down vote up 
def testBuildEndPointsWithUseSeparableConvolutionFalse(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs)

    endpoint_keys = [
        key for key in end_points.keys()
        if key.startswith('Mixed') or key.startswith('Conv')
    ]

    _, end_points_with_replacement = inception.inception_v2_base(
        inputs, use_separable_conv=False)

    # The endpoint shapes must be equal to the original shape even when the
    # separable convolution is replaced with a normal convolution.
    for key in endpoint_keys:
      original_shape = end_points[key].get_shape().as_list()
      self.assertTrue(key in end_points_with_replacement)
      new_shape = end_points_with_replacement[key].get_shape().as_list()
      self.assertListEqual(original_shape, new_shape)
Example #3
Source File: inception_v2_test.py    From CBAM-tensorflow-slim with MIT License 6 votes vote down vote up 
def testBuildEndPointsWithUseSeparableConvolutionFalse(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs)

    endpoint_keys = [
        key for key in end_points.keys()
        if key.startswith('Mixed') or key.startswith('Conv')
    ]

    _, end_points_with_replacement = inception.inception_v2_base(
        inputs, use_separable_conv=False)

    # The endpoint shapes must be equal to the original shape even when the
    # separable convolution is replaced with a normal convolution.
    for key in endpoint_keys:
      original_shape = end_points[key].get_shape().as_list()
      self.assertTrue(key in end_points_with_replacement)
      new_shape = end_points_with_replacement[key].get_shape().as_list()
      self.assertListEqual(original_shape, new_shape)
Example #4
Source File: inception_v2_test.py    From CVTron with Apache License 2.0 6 votes vote down vote up 
def testBuildEndPointsWithUseSeparableConvolutionFalse(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs)

    endpoint_keys = [
        key for key in end_points.keys()
        if key.startswith('Mixed') or key.startswith('Conv')
    ]

    _, end_points_with_replacement = inception.inception_v2_base(
        inputs, use_separable_conv=False)

    # The endpoint shapes must be equal to the original shape even when the
    # separable convolution is replaced with a normal convolution.
    for key in endpoint_keys:
      original_shape = end_points[key].get_shape().as_list()
      self.assertTrue(key in end_points_with_replacement)
      new_shape = end_points_with_replacement[key].get_shape().as_list()
      self.assertListEqual(original_shape, new_shape)
Example #5
Source File: inception_v2_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 6 votes vote down vote up 
def testBuildEndPointsWithUseSeparableConvolutionFalse(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs)

    endpoint_keys = [
        key for key in end_points.keys()
        if key.startswith('Mixed') or key.startswith('Conv')
    ]

    _, end_points_with_replacement = inception.inception_v2_base(
        inputs, use_separable_conv=False)

    # The endpoint shapes must be equal to the original shape even when the
    # separable convolution is replaced with a normal convolution.
    for key in endpoint_keys:
      original_shape = end_points[key].get_shape().as_list()
      self.assertTrue(key in end_points_with_replacement)
      new_shape = end_points_with_replacement[key].get_shape().as_list()
      self.assertListEqual(original_shape, new_shape)
Example #6
Source File: inception_v2_test.py    From DeepLab_v3 with MIT License 6 votes vote down vote up 
def testBuildEndPointsWithUseSeparableConvolutionFalse(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs)

    endpoint_keys = [
        key for key in end_points.keys()
        if key.startswith('Mixed') or key.startswith('Conv')
    ]

    _, end_points_with_replacement = inception.inception_v2_base(
        inputs, use_separable_conv=False)

    # The endpoint shapes must be equal to the original shape even when the
    # separable convolution is replaced with a normal convolution.
    for key in endpoint_keys:
      original_shape = end_points[key].get_shape().as_list()
      self.assertTrue(key in end_points_with_replacement)
      new_shape = end_points_with_replacement[key].get_shape().as_list()
      self.assertListEqual(original_shape, new_shape)
Example #7
Source File: inception_v2_test.py    From ctw-baseline with MIT License 6 votes vote down vote up 
def testBuildEndPointsWithUseSeparableConvolutionFalse(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs)

    endpoint_keys = [
        key for key in end_points.keys()
        if key.startswith('Mixed') or key.startswith('Conv')
    ]

    _, end_points_with_replacement = inception.inception_v2_base(
        inputs, use_separable_conv=False)

    # The endpoint shapes must be equal to the original shape even when the
    # separable convolution is replaced with a normal convolution.
    for key in endpoint_keys:
      original_shape = end_points[key].get_shape().as_list()
      self.assertTrue(key in end_points_with_replacement)
      new_shape = end_points_with_replacement[key].get_shape().as_list()
      self.assertListEqual(original_shape, new_shape)
Example #8
Source File: inception_v2_test.py    From tf-pose with Apache License 2.0 6 votes vote down vote up 
def testBuildEndPointsWithUseSeparableConvolutionFalse(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs)

    endpoint_keys = [
        key for key in end_points.keys()
        if key.startswith('Mixed') or key.startswith('Conv')
    ]

    _, end_points_with_replacement = inception.inception_v2_base(
        inputs, use_separable_conv=False)

    # The endpoint shapes must be equal to the original shape even when the
    # separable convolution is replaced with a normal convolution.
    for key in endpoint_keys:
      original_shape = end_points[key].get_shape().as_list()
      self.assertTrue(key in end_points_with_replacement)
      new_shape = end_points_with_replacement[key].get_shape().as_list()
      self.assertListEqual(original_shape, new_shape)
Example #9
Source File: inception_v2_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def testBuildEndPointsWithUseSeparableConvolutionFalse(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs)

    endpoint_keys = [
        key for key in end_points.keys()
        if key.startswith('Mixed') or key.startswith('Conv')
    ]

    _, end_points_with_replacement = inception.inception_v2_base(
        inputs, use_separable_conv=False)

    # The endpoint shapes must be equal to the original shape even when the
    # separable convolution is replaced with a normal convolution.
    for key in endpoint_keys:
      original_shape = end_points[key].get_shape().as_list()
      self.assertTrue(key in end_points_with_replacement)
      new_shape = end_points_with_replacement[key].get_shape().as_list()
      self.assertListEqual(original_shape, new_shape)
Example #10
Source File: inception_v2_test.py    From RetinaNet_Tensorflow_Rotation with MIT License 5 votes vote down vote up 
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_v2_arg_scope()):
      inception.inception_v2_base(inputs)
    total_params, _ = slim.model_analyzer.analyze_vars(
        slim.get_model_variables())
    self.assertAlmostEqual(10173112, total_params)
Example #11
Source File: inception_v2_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def testBuildBaseNetwork(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    mixed_5c, end_points = inception.inception_v2_base(inputs)
    self.assertTrue(mixed_5c.op.name.startswith('InceptionV2/Mixed_5c'))
    self.assertListEqual(mixed_5c.get_shape().as_list(),
                         [batch_size, 7, 7, 1024])
    expected_endpoints = ['Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b',
                          'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a',
                          'Mixed_5b', 'Mixed_5c', 'Conv2d_1a_7x7',
                          'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3',
                          'MaxPool_3a_3x3']
    self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #12
Source File: inception_v2_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
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',
                 'Mixed_4a', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
                 'Mixed_5a', '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_v2_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV2/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points)
Example #13
Source File: inception_v2_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def testBuildAndCheckAllEndPointsUptoMixed5c(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs,
                                                final_endpoint='Mixed_5c')
    endpoints_shapes = {'Mixed_3b': [batch_size, 28, 28, 256],
                        'Mixed_3c': [batch_size, 28, 28, 320],
                        'Mixed_4a': [batch_size, 14, 14, 576],
                        'Mixed_4b': [batch_size, 14, 14, 576],
                        'Mixed_4c': [batch_size, 14, 14, 576],
                        'Mixed_4d': [batch_size, 14, 14, 576],
                        'Mixed_4e': [batch_size, 14, 14, 576],
                        'Mixed_5a': [batch_size, 7, 7, 1024],
                        'Mixed_5b': [batch_size, 7, 7, 1024],
                        'Mixed_5c': [batch_size, 7, 7, 1024],
                        'Conv2d_1a_7x7': [batch_size, 112, 112, 64],
                        'MaxPool_2a_3x3': [batch_size, 56, 56, 64],
                        'Conv2d_2b_1x1': [batch_size, 56, 56, 64],
                        'Conv2d_2c_3x3': [batch_size, 56, 56, 192],
                        'MaxPool_3a_3x3': [batch_size, 28, 28, 192]}
    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)
Example #14
Source File: inception_v2_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
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_v2_arg_scope()):
      inception.inception_v2_base(inputs)
    total_params, _ = slim.model_analyzer.analyze_vars(
        slim.get_model_variables())
    self.assertAlmostEqual(10173112, total_params)
Example #15
Source File: inception_v2_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
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_v2_arg_scope()):
      inception.inception_v2_base(inputs)
    total_params, _ = slim.model_analyzer.analyze_vars(
        slim.get_model_variables())
    self.assertAlmostEqual(10173112, total_params)
Example #16
Source File: inception_v2_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
def testBuildBaseNetwork(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    mixed_5c, end_points = inception.inception_v2_base(inputs)
    self.assertTrue(mixed_5c.op.name.startswith('InceptionV2/Mixed_5c'))
    self.assertListEqual(mixed_5c.get_shape().as_list(),
                         [batch_size, 7, 7, 1024])
    expected_endpoints = ['Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b',
                          'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a',
                          'Mixed_5b', 'Mixed_5c', 'Conv2d_1a_7x7',
                          'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3',
                          'MaxPool_3a_3x3']
    self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #17
Source File: inception_v2_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
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',
                 'Mixed_4a', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
                 'Mixed_5a', '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_v2_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV2/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points.keys())
Example #18
Source File: inception_v2_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
def testBuildAndCheckAllEndPointsUptoMixed5c(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs,
                                                final_endpoint='Mixed_5c')
    endpoints_shapes = {'Mixed_3b': [batch_size, 28, 28, 256],
                        'Mixed_3c': [batch_size, 28, 28, 320],
                        'Mixed_4a': [batch_size, 14, 14, 576],
                        'Mixed_4b': [batch_size, 14, 14, 576],
                        'Mixed_4c': [batch_size, 14, 14, 576],
                        'Mixed_4d': [batch_size, 14, 14, 576],
                        'Mixed_4e': [batch_size, 14, 14, 576],
                        'Mixed_5a': [batch_size, 7, 7, 1024],
                        'Mixed_5b': [batch_size, 7, 7, 1024],
                        'Mixed_5c': [batch_size, 7, 7, 1024],
                        'Conv2d_1a_7x7': [batch_size, 112, 112, 64],
                        'MaxPool_2a_3x3': [batch_size, 56, 56, 64],
                        'Conv2d_2b_1x1': [batch_size, 56, 56, 64],
                        'Conv2d_2c_3x3': [batch_size, 56, 56, 192],
                        'MaxPool_3a_3x3': [batch_size, 28, 28, 192]}
    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)
Example #19
Source File: inception_v2_test.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def testBuildBaseNetwork(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    mixed_5c, end_points = inception.inception_v2_base(inputs)
    self.assertTrue(mixed_5c.op.name.startswith('InceptionV2/Mixed_5c'))
    self.assertListEqual(mixed_5c.get_shape().as_list(),
                         [batch_size, 7, 7, 1024])
    expected_endpoints = ['Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b',
                          'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a',
                          'Mixed_5b', 'Mixed_5c', 'Conv2d_1a_7x7',
                          'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3',
                          'MaxPool_3a_3x3']
    self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #20
Source File: inception_v2_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def testBuildErrorsForDataFormats(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))

    # 'NCWH' data format is not supported.
    with self.assertRaises(ValueError):
      _ = inception.inception_v2_base(inputs, data_format='NCWH')

    # 'NCHW' data format is not supported for separable convolution.
    with self.assertRaises(ValueError):
      _ = inception.inception_v2_base(inputs, data_format='NCHW')
Example #21
Source File: inception_v2_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def testBuildErrorsForDataFormats(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))

    # 'NCWH' data format is not supported.
    with self.assertRaises(ValueError):
      _ = inception.inception_v2_base(inputs, data_format='NCWH')

    # 'NCHW' data format is not supported for separable convolution.
    with self.assertRaises(ValueError):
      _ = inception.inception_v2_base(inputs, data_format='NCHW')
Example #22
Source File: inception_v2_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def testBuildAndCheckAllEndPointsUptoMixed5c(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs,
                                                final_endpoint='Mixed_5c')
    endpoints_shapes = {'Mixed_3b': [batch_size, 28, 28, 256],
                        'Mixed_3c': [batch_size, 28, 28, 320],
                        'Mixed_4a': [batch_size, 14, 14, 576],
                        'Mixed_4b': [batch_size, 14, 14, 576],
                        'Mixed_4c': [batch_size, 14, 14, 576],
                        'Mixed_4d': [batch_size, 14, 14, 576],
                        'Mixed_4e': [batch_size, 14, 14, 576],
                        'Mixed_5a': [batch_size, 7, 7, 1024],
                        'Mixed_5b': [batch_size, 7, 7, 1024],
                        'Mixed_5c': [batch_size, 7, 7, 1024],
                        'Conv2d_1a_7x7': [batch_size, 112, 112, 64],
                        'MaxPool_2a_3x3': [batch_size, 56, 56, 64],
                        'Conv2d_2b_1x1': [batch_size, 56, 56, 64],
                        'Conv2d_2c_3x3': [batch_size, 56, 56, 192],
                        'MaxPool_3a_3x3': [batch_size, 28, 28, 192]}
    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)
Example #23
Source File: inception_v2_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
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',
                 'Mixed_4a', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
                 'Mixed_5a', '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_v2_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV2/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points)
Example #24
Source File: inception_v2_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def testBuildBaseNetwork(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    mixed_5c, end_points = inception.inception_v2_base(inputs)
    self.assertTrue(mixed_5c.op.name.startswith('InceptionV2/Mixed_5c'))
    self.assertListEqual(mixed_5c.get_shape().as_list(),
                         [batch_size, 7, 7, 1024])
    expected_endpoints = ['Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b',
                          'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a',
                          'Mixed_5b', 'Mixed_5c', 'Conv2d_1a_7x7',
                          'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3',
                          'MaxPool_3a_3x3']
    self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #25
Source File: inception_v2_test.py    From cv-tricks.com with MIT License 5 votes vote down vote up 
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_v2_arg_scope()):
      inception.inception_v2_base(inputs)
    total_params, _ = slim.model_analyzer.analyze_vars(
        slim.get_model_variables())
    self.assertAlmostEqual(10173112, total_params)
Example #26
Source File: inception_v2_test.py    From cv-tricks.com with MIT License 5 votes vote down vote up 
def testBuildAndCheckAllEndPointsUptoMixed5c(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2_base(inputs,
                                                final_endpoint='Mixed_5c')
    endpoints_shapes = {'Mixed_3b': [batch_size, 28, 28, 256],
                        'Mixed_3c': [batch_size, 28, 28, 320],
                        'Mixed_4a': [batch_size, 14, 14, 576],
                        'Mixed_4b': [batch_size, 14, 14, 576],
                        'Mixed_4c': [batch_size, 14, 14, 576],
                        'Mixed_4d': [batch_size, 14, 14, 576],
                        'Mixed_4e': [batch_size, 14, 14, 576],
                        'Mixed_5a': [batch_size, 7, 7, 1024],
                        'Mixed_5b': [batch_size, 7, 7, 1024],
                        'Mixed_5c': [batch_size, 7, 7, 1024],
                        'Conv2d_1a_7x7': [batch_size, 112, 112, 64],
                        'MaxPool_2a_3x3': [batch_size, 56, 56, 64],
                        'Conv2d_2b_1x1': [batch_size, 56, 56, 64],
                        'Conv2d_2c_3x3': [batch_size, 56, 56, 192],
                        'MaxPool_3a_3x3': [batch_size, 28, 28, 192]}
    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)
Example #27
Source File: inception_v2_test.py    From cv-tricks.com with MIT License 5 votes vote down vote up 
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',
                 'Mixed_4a', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
                 'Mixed_5a', '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_v2_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV2/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points)
Example #28
Source File: inception_v2_test.py    From cv-tricks.com with MIT License 5 votes vote down vote up 
def testBuildBaseNetwork(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    mixed_5c, end_points = inception.inception_v2_base(inputs)
    self.assertTrue(mixed_5c.op.name.startswith('InceptionV2/Mixed_5c'))
    self.assertListEqual(mixed_5c.get_shape().as_list(),
                         [batch_size, 7, 7, 1024])
    expected_endpoints = ['Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b',
                          'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a',
                          'Mixed_5b', 'Mixed_5c', 'Conv2d_1a_7x7',
                          'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3',
                          'MaxPool_3a_3x3']
    self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #29
Source File: inception_v2_test.py    From edafa with MIT License 5 votes vote down vote up 
def testBuildErrorsForDataFormats(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))

    # 'NCWH' data format is not supported.
    with self.assertRaises(ValueError):
      _ = inception.inception_v2_base(inputs, data_format='NCWH')

    # 'NCHW' data format is not supported for separable convolution.
    with self.assertRaises(ValueError):
      _ = inception.inception_v2_base(inputs, data_format='NCHW')
Example #30
Source File: inception_v2_test.py    From edafa with MIT License 5 votes vote down vote up 
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_v2_arg_scope()):
      inception.inception_v2_base(inputs)
    total_params, _ = slim.model_analyzer.analyze_vars(
        slim.get_model_variables())
    self.assertAlmostEqual(10173112, total_params)