python类cifarnet_preprocessing()的实例源码-面圈网

preprocessing_factory.py 文件源码项目：the-neural-perspective 作者: GokuMohandas 项目源码文件源码阅读 24 收藏 0 点赞 0 评论 0

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_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

preprocessing_factory.py 文件源码项目：Densenet 作者: bysowhat 项目源码文件源码阅读 23 收藏 0 点赞 0 评论 0

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,
      'densenet_40': cifarnet_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

preprocessing_factory.py 文件源码项目：the-neural-perspective 作者: johnsonc 项目源码文件源码阅读 20 收藏 0 点赞 0 评论 0

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_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

preprocessing_factory.py 文件源码项目：terngrad 作者: wenwei202 项目源码文件源码阅读 23 收藏 0 点赞 0 评论 0

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

preprocessing_factory.py 文件源码项目：tensorflow_yolo2 作者: wenxichen 项目源码文件源码阅读 25 收藏 0 点赞 0 评论 0

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

preprocessing_factory.py 文件源码项目：Classification_Nets 作者: BobLiu20 项目源码文件源码阅读 23 收藏 0 点赞 0 评论 0

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

preprocessing_factory.py 文件源码项目：shuttleNet 作者: shiyemin 项目源码文件源码阅读 22 收藏 0 点赞 0 评论 0

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

preprocessing_factory.py 文件源码项目：fast-neural-style 作者: coder-james 项目源码文件源码阅读 25 收藏 0 点赞 0 评论 0

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

preprocessing_factory.py 文件源码项目：MobileNet 作者: Zehaos 项目源码文件源码阅读 21 收藏 0 点赞 0 评论 0

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

preprocessing_factory.py 文件源码项目：TensorFlowOnSpark 作者: yahoo 项目源码文件源码阅读 22 收藏 0 点赞 0 评论 0

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

preprocessing_factory.py 文件源码项目：Densenet 作者: bysowhat 项目源码文件源码阅读 21 收藏 0 点赞 0 评论 0

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

preprocessing_factory.py 文件源码项目：hops-tensorflow 作者: hopshadoop 项目源码文件源码阅读 19 收藏 0 点赞 0 评论 0

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

preprocessing_factory.py 文件源码项目：places365-tf 作者: baileyqbb 项目源码文件源码阅读 21 收藏 0 点赞 0 评论 0

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_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,
      'xception': xception_preprocessing,
      'resnext_50': vgg_preprocessing,
      'resnext_101': vgg_preprocessing,
      'resnext_152': vgg_preprocessing,
      'resnext_200': vgg_preprocessing,
      'shufflenet_50_g4_d272': vgg_preprocessing,
      'shufflenet_50_g4_d136': 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