Python inception.inception_v3_base方法代码示例

# 需要导入模块: from tensorflow.contrib.slim.nets import inception [as 别名]
# 或者: from tensorflow.contrib.slim.nets.inception import inception_v3_base [as 别名]
def conv_tower_fn(self, images, is_training=True, reuse=None):
    """Computes convolutional features using the InceptionV3 model.

    Args:
      images: A tensor of shape [batch_size, height, width, channels].
      is_training: whether is training or not.
      reuse: whether or not the network and its variables should be reused. To
        be able to reuse 'scope' must be given.

    Returns:
      A tensor of shape [batch_size, OH, OW, N], where OWxOH is resolution of
      output feature map and N is number of output features (depends on the
      network architecture).
    """
    mparams = self._mparams['conv_tower_fn']
    logging.debug('Using final_endpoint=%s', mparams.final_endpoint)
    with tf.variable_scope('conv_tower_fn/INCE'):
      if reuse:
        tf.get_variable_scope().reuse_variables()
      with slim.arg_scope(inception.inception_v3_arg_scope()):
        net, _ = inception.inception_v3_base(
            images, final_endpoint=mparams.final_endpoint)
      return net 

# 需要导入模块: from tensorflow.contrib.slim.nets import inception [as 别名]
# 或者: from tensorflow.contrib.slim.nets.inception import inception_v3_base [as 别名]
def conv_tower_fn(self, images, is_training=True, reuse=None):
    """Computes convolutional features using the InceptionV3 model.

    Args:
      images: A tensor of shape [batch_size, height, width, channels].
      is_training: whether is training or not.
      reuse: whether or not the network and its variables should be reused. To
        be able to reuse 'scope' must be given.

    Returns:
      A tensor of shape [batch_size, OH, OW, N], where OWxOH is resolution of
      output feature map and N is number of output features (depends on the
      network architecture).
    """
    mparams = self._mparams['conv_tower_fn']
    logging.debug('Using final_endpoint=%s', mparams.final_endpoint)
    with tf.variable_scope('conv_tower_fn/INCE'):
      if reuse:
        tf.get_variable_scope().reuse_variables()
      with slim.arg_scope(inception.inception_v3_arg_scope()):
        with slim.arg_scope([slim.batch_norm, slim.dropout],
                            is_training=is_training):
          net, _ = inception.inception_v3_base(
            images, final_endpoint=mparams.final_endpoint)
      return net 

# 需要导入模块: from tensorflow.contrib.slim.nets import inception [as 别名]
# 或者: from tensorflow.contrib.slim.nets.inception import inception_v3_base [as 别名]
def testBuildBaseNetwork(self):
    batch_size = 5
    height, width = 299, 299

    inputs = tf.random_uniform((batch_size, height, width, 3))
    final_endpoint, end_points = inception.inception_v3_base(inputs)
    self.assertTrue(final_endpoint.op.name.startswith(
        'InceptionV3/Mixed_7c'))
    self.assertListEqual(final_endpoint.get_shape().as_list(),
                         [batch_size, 8, 8, 2048])
    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_5c', 'Mixed_5d',
                          'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
                          'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c']
    self.assertItemsEqual(end_points.keys(), expected_endpoints) 

# 需要导入模块: from tensorflow.contrib.slim.nets import inception [as 别名]
# 或者: from tensorflow.contrib.slim.nets.inception import inception_v3_base [as 别名]
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_5c', 'Mixed_5d',
                 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
                 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c']

    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_v3_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV3/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points) 

# 需要导入模块: from tensorflow.contrib.slim.nets import inception [as 别名]
# 或者: from tensorflow.contrib.slim.nets.inception import inception_v3_base [as 别名]
def conv_tower_fn(self, images, is_training=True, reuse=None):
    """Computes convolutional features using the InceptionV3 model.

    Args:
      images: A tensor of shape [batch_size, height, width, channels].
      is_training: whether is training or not.
      reuse: whether or not the network and its variables should be reused. To
        be able to reuse 'scope' must be given.

    Returns:
      A tensor of shape [batch_size, OH, OW, N], where OWxOH is resolution of
      output feature map and N is number of output features (depends on the
      network architecture).
    """
    mparams = self._mparams['conv_tower_fn']
    logging.debug('Using final_endpoint=%s', mparams.final_endpoint)
    with tf.variable_scope('conv_tower_fn/INCE'):
      if reuse:
        tf.get_variable_scope().reuse_variables()
      with slim.arg_scope(
        [slim.batch_norm, slim.dropout], is_training=is_training):  
          with slim.arg_scope(inception.inception_v3_arg_scope()):
            net, _ = inception.inception_v3_base(
                images, final_endpoint=mparams.final_endpoint)
      return net 

# 需要导入模块: from tensorflow.contrib.slim.nets import inception [as 别名]
# 或者: from tensorflow.contrib.slim.nets.inception import inception_v3_base [as 别名]
def testBuildAndCheckAllEndPointsUptoMixed7c(self):
    batch_size = 5
    height, width = 299, 299

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3_base(
        inputs, final_endpoint='Mixed_7c')
    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],
                        'MaxPool_3a_3x3': [batch_size, 73, 73, 64],
                        'Conv2d_3b_1x1': [batch_size, 73, 73, 80],
                        'Conv2d_4a_3x3': [batch_size, 71, 71, 192],
                        'MaxPool_5a_3x3': [batch_size, 35, 35, 192],
                        'Mixed_5b': [batch_size, 35, 35, 256],
                        'Mixed_5c': [batch_size, 35, 35, 288],
                        'Mixed_5d': [batch_size, 35, 35, 288],
                        'Mixed_6a': [batch_size, 17, 17, 768],
                        'Mixed_6b': [batch_size, 17, 17, 768],
                        'Mixed_6c': [batch_size, 17, 17, 768],
                        'Mixed_6d': [batch_size, 17, 17, 768],
                        'Mixed_6e': [batch_size, 17, 17, 768],
                        'Mixed_7a': [batch_size, 8, 8, 1280],
                        'Mixed_7b': [batch_size, 8, 8, 2048],
                        'Mixed_7c': [batch_size, 8, 8, 2048]}
    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) 

# 需要导入模块: from tensorflow.contrib.slim.nets import inception [as 别名]
# 或者: from tensorflow.contrib.slim.nets.inception import inception_v3_base [as 别名]
def testModelHasExpectedNumberOfParameters(self):
    batch_size = 5
    height, width = 299, 299
    inputs = tf.random_uniform((batch_size, height, width, 3))
    with slim.arg_scope(inception.inception_v3_arg_scope()):
      inception.inception_v3_base(inputs)
    total_params, _ = slim.model_analyzer.analyze_vars(
        slim.get_model_variables())
    self.assertAlmostEqual(21802784, total_params)