Python preprocessing.lenet_preprocessing方法代碼示例

本文整理匯總了Python中preprocessing.lenet_preprocessing方法的典型用法代碼示例。如果您正苦於以下問題：Python preprocessing.lenet_preprocessing方法的具體用法？Python preprocessing.lenet_preprocessing怎麽用？Python preprocessing.lenet_preprocessing使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類preprocessing的用法示例。

在下文中一共展示了preprocessing.lenet_preprocessing方法的11個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: get_preprocessing

# 需要導入模塊: import preprocessing [as 別名]
# 或者: from preprocessing import lenet_preprocessing [as 別名]
def get_preprocessing(name, is_training=False):
  """Returns preprocessing_fn(image, height, width, **kwargs).

  Args:
    name: The name of the preprocessing function.
    is_training: `True` if the model is being used for training and `False`
      otherwise.

  Returns:
    preprocessing_fn: A function that preprocessing a single image (pre-batch).
      It has the following signature:
        image = preprocessing_fn(image, output_height, output_width, ...).

  Raises:
    ValueError: If Preprocessing `name` is not recognized.
  """
  preprocessing_fn_map = {
      'cifarnet': cifarnet_preprocessing,
      'inception': inception_preprocessing,
      'inception_v1': inception_preprocessing,
      'inception_v2': inception_preprocessing,
      'inception_v3': inception_preprocessing,
      'inception_v4': inception_preprocessing,
      'inception_resnet_v2': inception_preprocessing,
      'lenet': lenet_preprocessing,
      'mobilenet_v1': inception_preprocessing,
      'resnet_v1_50': vgg_preprocessing,
      'resnet_v1_101': vgg_preprocessing,
      'resnet_v1_152': vgg_preprocessing,
      'resnet_v1_200': vgg_preprocessing,
      'resnet_v2_50': vgg_preprocessing,
      'resnet_v2_101': vgg_preprocessing,
      'resnet_v2_152': vgg_preprocessing,
      'resnet_v2_200': vgg_preprocessing,
      'vgg': vgg_preprocessing,
      'vgg_a': vgg_preprocessing,
      'vgg_16': vgg_preprocessing,
      'vgg_19': vgg_preprocessing,
  }

  if name not in preprocessing_fn_map:
    raise ValueError('Preprocessing name [%s] was not recognized' % name)

  def preprocessing_fn(image, output_height, output_width, **kwargs):
    return preprocessing_fn_map[name].preprocess_image(
        image, output_height, output_width, is_training=is_training, **kwargs)

  return preprocessing_fn

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:50，代碼來源:preprocessing_factory.py

示例2: get_preprocessing

# 需要導入模塊: import preprocessing [as 別名]
# 或者: from preprocessing import lenet_preprocessing [as 別名]
def get_preprocessing(name, is_training=False):
  """Returns preprocessing_fn(image, height, width, **kwargs).

  Args:
    name: The name of the preprocessing function.
    is_training: `True` if the model is being used for training and `False`
      otherwise.

  Returns:
    preprocessing_fn: A function that preprocessing a single image (pre-batch).
      It has the following signature:
        image = preprocessing_fn(image, output_height, output_width, ...).

  Raises:
    ValueError: If Preprocessing `name` is not recognized.
  """
  preprocessing_fn_map = {
      'cifarnet': cifarnet_preprocessing,
      'inception': inception_preprocessing,
      'inception_v1': inception_preprocessing,
      'inception_v2': inception_preprocessing,
      'inception_v3': inception_preprocessing,
      'inception_v4': inception_preprocessing,
      'inception_resnet_v2': inception_preprocessing,
      'lenet': lenet_preprocessing,
      'mobilenet_v1': inception_preprocessing,
      'resnet_v1_50': vgg_preprocessing,
      'resnet_v1_101': vgg_preprocessing,
      'resnet_v1_152': vgg_preprocessing,
      'vgg': vgg_preprocessing,
      'vgg_a': vgg_preprocessing,
      'vgg_16': vgg_preprocessing,
      'vgg_19': vgg_preprocessing,
  }

  if name not in preprocessing_fn_map:
    raise ValueError('Preprocessing name [%s] was not recognized' % name)

  def preprocessing_fn(image, output_height, output_width, **kwargs):
    return preprocessing_fn_map[name].preprocess_image(
        image, output_height, output_width, is_training=is_training, **kwargs)

  return preprocessing_fn

開發者ID:ih-lab，項目名稱:STORK，代碼行數:45，代碼來源:preprocessing_factory.py

示例3: get_preprocessing

# 需要導入模塊: import preprocessing [as 別名]
# 或者: from preprocessing import lenet_preprocessing [as 別名]
def get_preprocessing(name, is_training=False):
  """Returns preprocessing_fn(image, height, width, **kwargs).

  Args:
    name: The name of the preprocessing function.
    is_training: `True` if the model is being used for training and `False`
      otherwise.

  Returns:
    preprocessing_fn: A function that preprocessing a single image (pre-batch).
      It has the following signature:
        image = preprocessing_fn(image, output_height, output_width, ...).

  Raises:
    ValueError: If Preprocessing `name` is not recognized.
  """
  preprocessing_fn_map = {
      'cifarnet': cifarnet_preprocessing,
      'inception': inception_preprocessing,
      'inception_v1': inception_preprocessing,
      'inception_v2': inception_preprocessing,
      'inception_v3': inception_preprocessing,
      'inception_v4': inception_preprocessing,
      'inception_resnet_v2': inception_preprocessing,
      'lenet': lenet_preprocessing,
      'mobilenet_v1': inception_preprocessing,
      'nasnet_mobile': inception_preprocessing,
      'nasnet_large': inception_preprocessing,
      'resnet_v1_50': vgg_preprocessing,
      'resnet_v1_101': vgg_preprocessing,
      'resnet_v1_152': vgg_preprocessing,
      'resnet_v1_200': vgg_preprocessing,
      'resnet_v2_50': vgg_preprocessing,
      'resnet_v2_101': vgg_preprocessing,
      'resnet_v2_152': vgg_preprocessing,
      'resnet_v2_200': vgg_preprocessing,
      'vgg': vgg_preprocessing,
      'vgg_a': vgg_preprocessing,
      'vgg_16': vgg_preprocessing,
      'vgg_19': vgg_preprocessing,
  }

  if name not in preprocessing_fn_map:
    raise ValueError('Preprocessing name [%s] was not recognized' % name)

  def preprocessing_fn(image, output_height, output_width, **kwargs):
    return preprocessing_fn_map[name].preprocess_image(
        image, output_height, output_width, is_training=is_training, **kwargs)

  return preprocessing_fn

開發者ID:SrikanthVelpuri，項目名稱:tf-pose，代碼行數:52，代碼來源:preprocessing_factory.py

示例4: get_preprocessing

# 需要導入模塊: import preprocessing [as 別名]
# 或者: from preprocessing import lenet_preprocessing [as 別名]
def get_preprocessing(name, is_training=False):
  """Returns preprocessing_fn(image, height, width, **kwargs).

  Args:
    name: The name of the preprocessing function.
    is_training: `True` if the model is being used for training and `False`
      otherwise.

  Returns:
    preprocessing_fn: A function that preprocessing a single image (pre-batch).
      It has the following signature:
        image = preprocessing_fn(image, output_height, output_width, ...).

  Raises:
    ValueError: If Preprocessing `name` is not recognized.
  """
  preprocessing_fn_map = {
      'cifarnet': cifarnet_preprocessing,
      'inception': inception_preprocessing,
      'inception_v1': inception_preprocessing,
      'inception_v2': inception_preprocessing,
      'inception_v3': inception_preprocessing,
      'inception_v4': inception_preprocessing,
      'inception_resnet_v2': inception_preprocessing,
      'lenet': lenet_preprocessing,
      'mobilenet_v1': inception_preprocessing,
      'mobilenet_v2': inception_preprocessing,
      'mobilenet_v2_035': inception_preprocessing,
      'mobilenet_v2_140': inception_preprocessing,
      'nasnet_mobile': inception_preprocessing,
      'nasnet_large': inception_preprocessing,
      'pnasnet_mobile': inception_preprocessing,
      'pnasnet_large': inception_preprocessing,
      'resnet_v1_50': vgg_preprocessing,
      'resnet_v1_101': vgg_preprocessing,
      'resnet_v1_152': vgg_preprocessing,
      'resnet_v1_200': vgg_preprocessing,
      'resnet_v2_50': vgg_preprocessing,
      'resnet_v2_101': vgg_preprocessing,
      'resnet_v2_152': vgg_preprocessing,
      'resnet_v2_200': vgg_preprocessing,
      'vgg': vgg_preprocessing,
      'vgg_a': vgg_preprocessing,
      'vgg_16': vgg_preprocessing,
      'vgg_19': vgg_preprocessing,
  }

  if name not in preprocessing_fn_map:
    raise ValueError('Preprocessing name [%s] was not recognized' % name)

  def preprocessing_fn(image, output_height, output_width, **kwargs):
    return preprocessing_fn_map[name].preprocess_image(
        image, output_height, output_width, is_training=is_training, **kwargs)

  return preprocessing_fn

開發者ID:andrewekhalel，項目名稱:edafa，代碼行數:57，代碼來源:preprocessing_factory.py

示例5: get_preprocessing

# 需要導入模塊: import preprocessing [as 別名]
# 或者: from preprocessing import lenet_preprocessing [as 別名]
def get_preprocessing(name, is_training=False):
  """Returns preprocessing_fn(image, height, width, **kwargs).

  Args:
    name: The name of the preprocessing function.
    is_training: `True` if the model is being used for training and `False`
      otherwise.

  Returns:
    preprocessing_fn: A function that preprocessing a single image (pre-batch).
      It has the following signature:
        image = preprocessing_fn(image, output_height, output_width, ...).

  Raises:
    ValueError: If Preprocessing `name` is not recognized.
  """
  preprocessing_fn_map = {
      'cifarnet': cifarnet_preprocessing,
      'inception': inception_preprocessing,
      'inception_v1': inception_preprocessing,
      'inception_v2': inception_preprocessing,
      'inception_v3': inception_preprocessing,
      'inception_v4': inception_preprocessing,
      'inception_resnet_v2': inception_preprocessing,
      'lenet': lenet_preprocessing,
      'resnet_v1_50': vgg_preprocessing,
      'resnet_v1_101': vgg_preprocessing,
      'resnet_v1_152': vgg_preprocessing,
      'resnet_v2_50': vgg_preprocessing,
      'resnet_v2_101': vgg_preprocessing,
      'resnet_v2_152': vgg_preprocessing,
      'vgg': vgg_preprocessing,
      'vgg_a': vgg_preprocessing,
      'vgg_16': vgg_preprocessing,
      'vgg_19': vgg_preprocessing,
  }

  if name not in preprocessing_fn_map:
    raise ValueError('Preprocessing name [%s] was not recognized' % name)

  def preprocessing_fn(image, output_height, output_width, **kwargs):
    return preprocessing_fn_map[name].preprocess_image(
        image, output_height, output_width, is_training=is_training, **kwargs)

  return preprocessing_fn

開發者ID:legolas123，項目名稱:cv-tricks.com，代碼行數:47，代碼來源:preprocessing_factory.py

示例6: get_preprocessing

# 需要導入模塊: import preprocessing [as 別名]
# 或者: from preprocessing import lenet_preprocessing [as 別名]
def get_preprocessing(name, is_training=False):
  """Returns preprocessing_fn(image, height, width, **kwargs).

  Args:
    name: The name of the preprocessing function.
    is_training: `True` if the model is being used for training and `False`
      otherwise.

  Returns:
    preprocessing_fn: A function that preprocessing a single image (pre-batch).
      It has the following signature:
        image = preprocessing_fn(image, output_height, output_width, ...).

  Raises:
    ValueError: If Preprocessing `name` is not recognized.
  """
  preprocessing_fn_map = {
      'cifarnet': cifarnet_preprocessing,
      'inception': inception_preprocessing,
      'inception_v1': inception_preprocessing,
      'inception_v2': inception_preprocessing,
      'inception_v3': inception_preprocessing,
      'inception_v4': inception_preprocessing,
      'inception_resnet_v2': inception_preprocessing,
      'lenet': lenet_preprocessing,
      'mobilenet_v1': inception_preprocessing,
      'nasnet_mobile': inception_preprocessing,
      'nasnet_large': inception_preprocessing,
      'pnasnet_large': inception_preprocessing,
      'resnet_v1_50': vgg_preprocessing,
      'resnet_v1_101': vgg_preprocessing,
      'resnet_v1_152': vgg_preprocessing,
      'resnet_v1_200': vgg_preprocessing,
      'resnet_v2_50': vgg_preprocessing,
      'resnet_v2_101': vgg_preprocessing,
      'resnet_v2_152': vgg_preprocessing,
      'resnet_v2_200': vgg_preprocessing,
      'vgg': vgg_preprocessing,
      'vgg_a': vgg_preprocessing,
      'vgg_16': vgg_preprocessing,
      'vgg_19': vgg_preprocessing,
  }

  if name not in preprocessing_fn_map:
    raise ValueError('Preprocessing name [%s] was not recognized' % name)

  def preprocessing_fn(image, output_height, output_width, **kwargs):
    return preprocessing_fn_map[name].preprocess_image(
        image, output_height, output_width, is_training=is_training, **kwargs)

  return preprocessing_fn

開發者ID:kobiso，項目名稱:CBAM-tensorflow-slim，代碼行數:53，代碼來源:preprocessing_factory.py

示例7: get_preprocessing

# 需要導入模塊: import preprocessing [as 別名]
# 或者: from preprocessing import lenet_preprocessing [as 別名]
def get_preprocessing(name, is_training=False):
  """Returns preprocessing_fn(image, height, width, **kwargs).

  Args:
    name: The name of the preprocessing function.
    is_training: `True` if the model is being used for training and `False`
      otherwise.

  Returns:
    preprocessing_fn: A function that preprocessing a single image (pre-batch).
      It has the following signature:
        image = preprocessing_fn(image, output_height, output_width, ...).

  Raises:
    ValueError: If Preprocessing `name` is not recognized.
  """
  preprocessing_fn_map = {
      'cifarnet': cifarnet_preprocessing,
      'inception': inception_preprocessing,
      'inception_v1': inception_preprocessing,
      'inception_v2': inception_preprocessing,
      'inception_v3': inception_preprocessing,
      'inception_v4': inception_preprocessing,
      'inception_resnet_v2': inception_preprocessing,
      'lenet': lenet_preprocessing,
      'resnet_v1_50': vgg_preprocessing,
      'resnet_v1_101': vgg_preprocessing,
      'resnet_v1_152': vgg_preprocessing,
      'resnet_v2_50': vgg_preprocessing,
      'resnet_v2_101': vgg_preprocessing,
      'resnet_v2_152': vgg_preprocessing,
      'vgg': vgg_preprocessing,
      'vgg_a': vgg_preprocessing,
      'vgg_16': vgg_preprocessing,
      'vgg_19': vgg_preprocessing,
      'mobilenet': mobilenet_preprocessing,
      'mobilenetdet': mobilenetdet_preprocessing
  }

  if name not in preprocessing_fn_map:
    raise ValueError('Preprocessing name [%s] was not recognized' % name)

  def preprocessing_fn(image, output_height, output_width, **kwargs):
    return preprocessing_fn_map[name].preprocess_image(
        image, output_height, output_width, is_training=is_training, **kwargs)

  return preprocessing_fn

開發者ID:Zehaos，項目名稱:MobileNet，代碼行數:49，代碼來源:preprocessing_factory.py

示例8: get_preprocessing

# 需要導入模塊: import preprocessing [as 別名]
# 或者: from preprocessing import lenet_preprocessing [as 別名]
def get_preprocessing(name, is_training=False):
  """Returns preprocessing_fn(image, height, width, **kwargs).

  Args:
    name: The name of the preprocessing function.
    is_training: `True` if the model is being used for training and `False`
      otherwise.

  Returns:
    preprocessing_fn: A function that preprocessing a single image (pre-batch).
      It has the following signature:
        image = preprocessing_fn(image, output_height, output_width, ...).

  Raises:
    ValueError: If Preprocessing `name` is not recognized.
  """
  preprocessing_fn_map = {
      'cifarnet': cifarnet_preprocessing,
      'inception': inception_preprocessing,
      'inception_v1': inception_preprocessing,
      'inception_v2': inception_preprocessing,
      'inception_v3': inception_preprocessing,
      'inception_v4': inception_preprocessing,
      'inception_resnet_v2': inception_preprocessing,
      'inception_resnet_v2_rnn': inception_preprocessing,
      'lenet': lenet_preprocessing,
      'googlenet': googlenet_preprocessing,
      'googlenet_rnn': googlenet_preprocessing,
      'resnet_v1_50': vgg_preprocessing,
      'resnet_v1_101': vgg_preprocessing,
      'resnet_v1_152': vgg_preprocessing,
      'vgg': vgg_preprocessing,
      'vgg_a': vgg_preprocessing,
      'vgg_16': vgg_preprocessing,
      'vgg_19': vgg_preprocessing,
  }

  if name not in preprocessing_fn_map:
    raise ValueError('Preprocessing name [%s] was not recognized' % name)

  def preprocessing_fn(image, output_height, output_width, **kwargs):
    return preprocessing_fn_map[name].preprocess_image(
        image, output_height, output_width, is_training=is_training, **kwargs)

  return preprocessing_fn

開發者ID:shiyemin，項目名稱:shuttleNet，代碼行數:47，代碼來源:preprocessing_factory.py

示例9: get_preprocessing

# 需要導入模塊: import preprocessing [as 別名]
# 或者: from preprocessing import lenet_preprocessing [as 別名]
def get_preprocessing(name, is_training=False):
  """Returns preprocessing_fn(image, height, width, **kwargs).

  Args:
    name: The name of the preprocessing function.
    is_training: `True` if the model is being used for training and `False`
      otherwise.

  Returns:
    preprocessing_fn: A function that preprocessing a single image (pre-batch).
      It has the following signature:
        image = preprocessing_fn(image, output_height, output_width, ...).

  Raises:
    ValueError: If Preprocessing `name` is not recognized.
  """
  preprocessing_fn_map = {
      'cifarnet': cifarnet_preprocessing,
      'inception': inception_preprocessing,
      'inception_v1': inception_preprocessing,
      'inception_v2': inception_preprocessing,
      'inception_v3': inception_preprocessing,
      'inception_v4': inception_preprocessing,
      'inception_resnet_v2': inception_preprocessing,
      'lenet': lenet_preprocessing,
      'mobilenet_v1': inception_preprocessing,
      'nasnet_mobile': inception_preprocessing,
      'nasnet_large': inception_preprocessing,
      'resnet_v1_50': vgg_preprocessing,
      'resnet_v1_101': vgg_preprocessing,
      'resnet_v1_152': vgg_preprocessing,
      'resnet_v1_200': vgg_preprocessing,
      'resnet_v2_50': vgg_preprocessing,
      'resnet_v2_101': vgg_preprocessing,
      'resnet_v2_152': vgg_preprocessing,
      'resnet_v2_200': vgg_preprocessing,
      'vgg': vgg_preprocessing,
      'vgg_a': vgg_preprocessing,
      'vgg_16': vgg_preprocessing,
      'vgg_19': vgg_preprocessing,
      'danbooru': danbooru_preprocessing
  }
  if name is None or name == 'fully_connected':
    tf.logging.info('No preprocessing.')
    return None

  if name not in preprocessing_fn_map:
    raise ValueError('Preprocessing name [%s] was not recognized' % name)

  def preprocessing_fn(image, output_height, output_width, **kwargs):
    return preprocessing_fn_map[name].preprocess_image(
        image, output_height, output_width, is_training=is_training, **kwargs)

  return preprocessing_fn

開發者ID:jerryli27，項目名稱:TwinGAN，代碼行數:56，代碼來源:preprocessing_factory.py

示例10: get_preprocessing

# 需要導入模塊: import preprocessing [as 別名]
# 或者: from preprocessing import lenet_preprocessing [as 別名]
def get_preprocessing(name, is_training=False):
  """Returns preprocessing_fn(image, height, width, **kwargs).

  Args:
    name: The name of the preprocessing function.
    is_training: `True` if the model is being used for training and `False`
      otherwise.

  Returns:
    preprocessing_fn: A function that preprocessing a single image (pre-batch).
      It has the following signature:
        image = preprocessing_fn(image, output_height, output_width, ...).

  Raises:
    ValueError: If Preprocessing `name` is not recognized.
  """
  preprocessing_fn_map = {
      'cifarnet': cifarnet_preprocessing,
      'inception': inception_preprocessing,
      'inception_v1': inception_preprocessing,
      'inception_v2': inception_preprocessing,
      'inception_v3': inception_preprocessing,
      'inception_v4': inception_preprocessing,
      'inception_resnet_v2': inception_preprocessing,
      'lenet': lenet_preprocessing,
      'resnet_v1_50': vgg_preprocessing,
      'resnet_v1_101': vgg_preprocessing,
      'resnet_v1_152': vgg_preprocessing,
      'vgg': vgg_preprocessing,
      'vgg_a': vgg_preprocessing,
      'vgg_16': vgg_preprocessing,
      'vgg_19': vgg_preprocessing,
  }

  if name not in preprocessing_fn_map:
    raise ValueError('Preprocessing name [%s] was not recognized' % name)

  def preprocessing_fn(image, output_height, output_width, **kwargs):
    return preprocessing_fn_map[name].preprocess_image(
        image, output_height, output_width, is_training=is_training, **kwargs)

  return preprocessing_fn

開發者ID:coderSkyChen，項目名稱:Action_Recognition_Zoo，代碼行數:44，代碼來源:preprocessing_factory.py

示例11: get_preprocessing

# 需要導入模塊: import preprocessing [as 別名]
# 或者: from preprocessing import lenet_preprocessing [as 別名]
def get_preprocessing(name, is_training=False):
  """Returns preprocessing_fn(image, height, width, **kwargs).

  Args:
    name: The name of the preprocessing function.
    is_training: `True` if the model is being used for training and `False`
      otherwise.

  Returns:
    preprocessing_fn: A function that preprocessing a single image (pre-batch).
      It has the following signature:
        image = preprocessing_fn(image, output_height, output_width, ...).

  Raises:
    ValueError: If Preprocessing `name` is not recognized.
  """
  preprocessing_fn_map = {
      'cifarnet': cifarnet_preprocessing,
      'inception': inception_preprocessing,
      'inception_v1': inception_preprocessing,
      'inception_v2': inception_preprocessing,
      'inception_v3': inception_preprocessing,
      'inception_v4': inception_preprocessing,
      'inception_resnet_v2': inception_preprocessing,
      'lenet': lenet_preprocessing,
      'resnet_v1_50': vgg_preprocessing,
      'resnet_v1_101': vgg_preprocessing,
      'resnet_v1_152': vgg_preprocessing,
      'resnet_v2_50': vgg_preprocessing,
      'resnet_v2_101': vgg_preprocessing,
      'resnet_v2_152': vgg_preprocessing,
      'vgg': vgg_preprocessing,
      'vgg_a': vgg_preprocessing,
      'vgg_16': vgg_preprocessing,
      'vgg_19': vgg_preprocessing,
      'off': off_preprocessing,
  }

  if name not in preprocessing_fn_map:
    raise ValueError('Preprocessing name [%s] was not recognized' % name)

  def preprocessing_fn(image, output_height, output_width, **kwargs):
    return preprocessing_fn_map[name].preprocess_image(
        image, output_height, output_width, is_training=is_training, **kwargs)

  return preprocessing_fn

開發者ID:reallongnguyen，項目名稱:Optical-Flow-Guided-Feature，代碼行數:48，代碼來源:preprocessing_factory.py

注：本文中的preprocessing.lenet_preprocessing方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。