Python tensorflow.python.util.compat.bytes_or_text_types() Examples

The following are 27 code examples of tensorflow.python.util.compat.bytes_or_text_types(). 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 tensorflow.python.util.compat , or try the search function .
Example #1
Source File: check_ops.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up 
def assert_proper_iterable(values):
  """Static assert that values is a "proper" iterable.

  `Ops` that expect iterables of `Tensor` can call this to validate input.
  Useful since `Tensor`, `ndarray`, byte/text type are all iterables themselves.

  Args:
    values:  Object to be checked.

  Raises:
    TypeError:  If `values` is not iterable or is one of
      `Tensor`, `SparseTensor`, `np.array`, `tf.compat.bytes_or_text_types`.
  """
  unintentional_iterables = (
      (ops.Tensor, sparse_tensor.SparseTensor, np.ndarray)
      + compat.bytes_or_text_types
  )
  if isinstance(values, unintentional_iterables):
    raise TypeError(
        'Expected argument "values" to be a "proper" iterable.  Found: %s' %
        type(values))

  if not hasattr(values, '__iter__'):
    raise TypeError(
        'Expected argument "values" to be iterable.  Found: %s' % type(values))
Example #2
Source File: check_ops.py    From keras-lambda with MIT License 6 votes vote down vote up 
def assert_proper_iterable(values):
  """Static assert that values is a "proper" iterable.

  `Ops` that expect iterables of `Tensor` can call this to validate input.
  Useful since `Tensor`, `ndarray`, byte/text type are all iterables themselves.

  Args:
    values:  Object to be checked.

  Raises:
    TypeError:  If `values` is not iterable or is one of
      `Tensor`, `SparseTensor`, `np.array`, `tf.compat.bytes_or_text_types`.
  """
  unintentional_iterables = (
      (ops.Tensor, sparse_tensor.SparseTensor, np.ndarray)
      + compat.bytes_or_text_types
  )
  if isinstance(values, unintentional_iterables):
    raise TypeError(
        'Expected argument "values" to be a "proper" iterable.  Found: %s' %
        type(values))

  if not hasattr(values, '__iter__'):
    raise TypeError(
        'Expected argument "values" to be iterable.  Found: %s' % type(values))
Example #3
Source File: check_ops.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 6 votes vote down vote up 
def assert_proper_iterable(values):
  """Static assert that values is a "proper" iterable.

  `Ops` that expect iterables of `Tensor` can call this to validate input.
  Useful since `Tensor`, `ndarray`, byte/text type are all iterables themselves.

  Args:
    values:  Object to be checked.

  Raises:
    TypeError:  If `values` is not iterable or is one of
      `Tensor`, `SparseTensor`, `np.array`, `tf.compat.bytes_or_text_types`.
  """
  unintentional_iterables = (
      (ops.Tensor, sparse_tensor.SparseTensor, np.ndarray)
      + compat.bytes_or_text_types
  )
  if isinstance(values, unintentional_iterables):
    raise TypeError(
        'Expected argument "values" to be a "proper" iterable.  Found: %s' %
        type(values))

  if not hasattr(values, '__iter__'):
    raise TypeError(
        'Expected argument "values" to be iterable.  Found: %s' % type(values))
Example #4
Source File: check_ops.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
def assert_proper_iterable(values):
  """Static assert that values is a "proper" iterable.

  `Ops` that expect iterables of `Tensor` can call this to validate input.
  Useful since `Tensor`, `ndarray`, byte/text type are all iterables themselves.

  Args:
    values:  Object to be checked.

  Raises:
    TypeError:  If `values` is not iterable or is one of
      `Tensor`, `SparseTensor`, `np.array`, `tf.compat.bytes_or_text_types`.
  """
  unintentional_iterables = (
      (ops.Tensor, sparse_tensor.SparseTensor, np.ndarray)
      + compat.bytes_or_text_types
  )
  if isinstance(values, unintentional_iterables):
    raise TypeError(
        'Expected argument "values" to be a "proper" iterable.  Found: %s' %
        type(values))

  if not hasattr(values, '__iter__'):
    raise TypeError(
        'Expected argument "values" to be iterable.  Found: %s' % type(values))
Example #5
Source File: check_ops.py    From lambda-packs with MIT License 6 votes vote down vote up 
def assert_proper_iterable(values):
  """Static assert that values is a "proper" iterable.

  `Ops` that expect iterables of `Tensor` can call this to validate input.
  Useful since `Tensor`, `ndarray`, byte/text type are all iterables themselves.

  Args:
    values:  Object to be checked.

  Raises:
    TypeError:  If `values` is not iterable or is one of
      `Tensor`, `SparseTensor`, `np.array`, `tf.compat.bytes_or_text_types`.
  """
  unintentional_iterables = (
      (ops.Tensor, sparse_tensor.SparseTensor, np.ndarray)
      + compat.bytes_or_text_types
  )
  if isinstance(values, unintentional_iterables):
    raise TypeError(
        'Expected argument "values" to be a "proper" iterable.  Found: %s' %
        type(values))

  if not hasattr(values, '__iter__'):
    raise TypeError(
        'Expected argument "values" to be iterable.  Found: %s' % type(values))
Example #6
Source File: tensor_shape.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def __init__(self, value):
    """Creates a new Dimension with the given value."""
    if value is None:
      self._value = None
    else:
      self._value = int(value)
      if (not isinstance(value, compat.bytes_or_text_types)
          and self._value != value):
        raise ValueError("Ambiguous dimension: %s" % value)
      if self._value < 0:
        raise ValueError("Dimension %d must be >= 0" % self._value)
Example #7
Source File: tensor_util.py    From keras-lambda with MIT License 5 votes vote down vote up 
def _FilterStr(v):
  if isinstance(v, (list, tuple)):
    return _FirstNotNone([_FilterStr(x) for x in v])
  if isinstance(v, compat.bytes_or_text_types):
    return None
  else:
    return _NotNone(v)
Example #8
Source File: op_def_library.py    From keras-lambda with MIT License 5 votes vote down vote up 
def _MakeStr(v, arg_name):
  if not isinstance(v, compat.bytes_or_text_types):
    raise TypeError("Expected string for argument '%s' not %s." %
                    (arg_name, repr(v)))
  return compat.as_bytes(v)  # Convert unicode strings to bytes.
Example #9
Source File: tensor_shape.py    From keras-lambda with MIT License 5 votes vote down vote up 
def __init__(self, dims):
    """Creates a new TensorShape with the given dimensions.

    Args:
      dims: A list of Dimensions, or None if the shape is unspecified.
        DEPRECATED: A single integer is treated as a singleton list.

    Raises:
      TypeError: If dims cannot be converted to a list of dimensions.
    """
    # TODO(irving): Eliminate the single integer special case.
    if dims is None:
      self._dims = None
    elif isinstance(dims, compat.bytes_or_text_types):
      raise TypeError("A string has ambiguous TensorShape, please wrap in a "
                       "list or convert to an int: %s" % dims)
    elif isinstance(dims, tensor_shape_pb2.TensorShapeProto):
      if dims.unknown_rank:
        self._dims = None
      else:
        self._dims = [
            # Protos store variable-size dimensions as -1
            as_dimension(dim.size if dim.size != -1 else None)
            for dim in dims.dim]
    elif isinstance(dims, TensorShape):
      self._dims = dims.dims
    else:
      try:
        dims_iter = iter(dims)
      except TypeError:
        # Treat as a singleton dimension
        self._dims = [as_dimension(dims)]
      else:
        # Got a list of dimensions
        self._dims = [as_dimension(d) for d in dims_iter]
Example #10
Source File: tensor_shape.py    From keras-lambda with MIT License 5 votes vote down vote up 
def __init__(self, value):
    """Creates a new Dimension with the given value."""
    if value is None:
      self._value = None
    else:
      self._value = int(value)
      if (not isinstance(value, compat.bytes_or_text_types)
          and self._value != value):
        raise ValueError("Ambiguous dimension: %s" % value)
      if self._value < 0:
        raise ValueError("Dimension %d must be >= 0" % self._value)
Example #11
Source File: op_def_library.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def _MakeStr(v, arg_name):
  if not isinstance(v, compat.bytes_or_text_types):
    raise TypeError("Expected string for argument '%s' not %s." %
                    (arg_name, repr(v)))
  return compat.as_bytes(v)  # Convert unicode strings to bytes.
Example #12
Source File: tensor_shape.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def __init__(self, dims):
    """Creates a new TensorShape with the given dimensions.

    Args:
      dims: A list of Dimensions, or None if the shape is unspecified.
        DEPRECATED: A single integer is treated as a singleton list.

    Raises:
      TypeError: If dims cannot be converted to a list of dimensions.
    """
    # TODO(irving): Eliminate the single integer special case.
    if dims is None:
      self._dims = None
    elif isinstance(dims, compat.bytes_or_text_types):
      raise TypeError("A string has ambiguous TensorShape, please wrap in a "
                      "list or convert to an int: %s" % dims)
    elif isinstance(dims, tensor_shape_pb2.TensorShapeProto):
      if dims.unknown_rank:
        self._dims = None
      else:
        self._dims = [
            # Protos store variable-size dimensions as -1
            as_dimension(dim.size if dim.size != -1 else None)
            for dim in dims.dim
        ]
    elif isinstance(dims, TensorShape):
      self._dims = dims.dims
    else:
      try:
        dims_iter = iter(dims)
      except TypeError:
        # Treat as a singleton dimension
        self._dims = [as_dimension(dims)]
      else:
        # Got a list of dimensions
        self._dims = [as_dimension(d) for d in dims_iter]
Example #13
Source File: tensor_shape.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def __init__(self, value):
    """Creates a new Dimension with the given value."""
    if value is None:
      self._value = None
    else:
      self._value = int(value)
      if (not isinstance(value, compat.bytes_or_text_types) and
          self._value != value):
        raise ValueError("Ambiguous dimension: %s" % value)
      if self._value < 0:
        raise ValueError("Dimension %d must be >= 0" % self._value)
Example #14
Source File: execute.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def make_str(v, arg_name):
  if not isinstance(v, compat.bytes_or_text_types):
    raise TypeError("Expected string for argument '%s' not %s." %
                    (arg_name, repr(v)))
  return compat.as_bytes(v)  # Convert unicode strings to bytes.
Example #15
Source File: tensor_util.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def _FilterStr(v):
  if isinstance(v, (list, tuple)):
    return _FirstNotNone([_FilterStr(x) for x in v])
  if isinstance(v, compat.bytes_or_text_types):
    return None
  else:
    return _NotNone(v)
Example #16
Source File: op_def_library.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def _MakeStr(v, arg_name):
  if not isinstance(v, compat.bytes_or_text_types):
    raise TypeError("Expected string for argument '%s' not %s." %
                    (arg_name, repr(v)))
  return compat.as_bytes(v)  # Convert unicode strings to bytes.
Example #17
Source File: tensor_shape.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def __init__(self, dims):
    """Creates a new TensorShape with the given dimensions.

    Args:
      dims: A list of Dimensions, or None if the shape is unspecified.
        DEPRECATED: A single integer is treated as a singleton list.

    Raises:
      TypeError: If dims cannot be converted to a list of dimensions.
    """
    # TODO(irving): Eliminate the single integer special case.
    if dims is None:
      self._dims = None
    elif isinstance(dims, compat.bytes_or_text_types):
      raise TypeError("A string has ambiguous TensorShape, please wrap in a "
                       "list or convert to an int: %s" % dims)
    elif isinstance(dims, tensor_shape_pb2.TensorShapeProto):
      if dims.unknown_rank:
        self._dims = None
      else:
        self._dims = [
            # Protos store variable-size dimensions as -1
            as_dimension(dim.size if dim.size != -1 else None)
            for dim in dims.dim]
    elif isinstance(dims, TensorShape):
      self._dims = dims.dims
    else:
      try:
        dims_iter = iter(dims)
      except TypeError:
        # Treat as a singleton dimension
        self._dims = [as_dimension(dims)]
      else:
        # Got a list of dimensions
        self._dims = [as_dimension(d) for d in dims_iter]
Example #18
Source File: session_context.py    From parallax with Apache License 2.0 5 votes vote down vote up 
def _convert_feed(self, feed_dict):

        def _feed_fn(feed):
            for tensor_type, _, _, feed_fn in session._REGISTERED_EXPANSIONS:
                if isinstance(feed, tensor_type):
                    return feed_fn(feed)
            raise TypeError('Feed argument %r has invalid type %r' % (feed,
                                                                   type(feed)))
        if feed_dict:
            new_feed_dict = {}
            for feed, feed_val in feed_dict.items():
                if isinstance(feed, compat.bytes_or_text_types):
                    new_feeds = self._read_converted_names(feed)
                    if isinstance(new_feeds, list):
                        for i in range(self._num_replicas_per_worker):
                            new_feed_dict[new_feeds[i]] = feed_val[i]
                    else:
                        new_feed_dict[new_feeds] = feed_val
                else:
                    for subfeed in _feed_fn(feed):
                        new_subfeeds = self._read_converted_names(subfeed)
                        if isinstance(new_subfeeds, list):
                            for i in range(self._num_replicas_per_worker):
                                new_feed_dict[new_subfeeds[i]] = feed_val[i]
                        else:
                            new_feed_dict[new_subfeeds] = feed_val
            return new_feed_dict
        else:
            return feed_dict
Example #19
Source File: session_context.py    From parallax with Apache License 2.0 5 votes vote down vote up 
def _read_converted_names(self, target):
        if isinstance(target, compat.bytes_or_text_types):
            target_name = target
        else:
            target_name = target.name
        if target_name in self._replica_dict:
            return self._replica_dict[target_name]
        else:
            return target
Example #20
Source File: tensor_util.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def _FilterStr(v):
  if isinstance(v, (list, tuple)):
    return _FirstNotNone([_FilterStr(x) for x in v])
  if isinstance(v, compat.bytes_or_text_types):
    return None
  else:
    return _NotNone(v)
Example #21
Source File: op_def_library.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def _MakeStr(v, arg_name):
  if not isinstance(v, compat.bytes_or_text_types):
    raise TypeError("Expected string for argument '%s' not %s." %
                    (arg_name, repr(v)))
  return compat.as_bytes(v)  # Convert unicode strings to bytes.
Example #22
Source File: tensor_shape.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def __init__(self, dims):
    """Creates a new TensorShape with the given dimensions.

    Args:
      dims: A list of Dimensions, or None if the shape is unspecified.
        DEPRECATED: A single integer is treated as a singleton list.

    Raises:
      TypeError: If dims cannot be converted to a list of dimensions.
    """
    # TODO(irving): Eliminate the single integer special case.
    if dims is None:
      self._dims = None
    elif isinstance(dims, compat.bytes_or_text_types):
      raise TypeError("A string has ambiguous TensorShape, please wrap in a "
                       "list or convert to an int: %s" % dims)
    elif isinstance(dims, tensor_shape_pb2.TensorShapeProto):
      if dims.unknown_rank:
        self._dims = None
      else:
        self._dims = [
            # Protos store variable-size dimensions as -1
            as_dimension(dim.size if dim.size != -1 else None)
            for dim in dims.dim]
    elif isinstance(dims, TensorShape):
      self._dims = dims.dims
    else:
      try:
        dims_iter = iter(dims)
      except TypeError:
        # Treat as a singleton dimension
        self._dims = [as_dimension(dims)]
      else:
        # Got a list of dimensions
        self._dims = [as_dimension(d) for d in dims_iter]
Example #23
Source File: tensor_shape.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def __init__(self, value):
    """Creates a new Dimension with the given value."""
    if value is None:
      self._value = None
    else:
      self._value = int(value)
      if (not isinstance(value, compat.bytes_or_text_types)
          and self._value != value):
        raise ValueError("Ambiguous dimension: %s" % value)
      if self._value < 0:
        raise ValueError("Dimension %d must be >= 0" % self._value)
Example #24
Source File: tensor_util.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _FilterStr(v):
  if isinstance(v, (list, tuple)):
    return _FirstNotNone([_FilterStr(x) for x in v])
  if isinstance(v, compat.bytes_or_text_types):
    return None
  else:
    return _NotNone(v)
Example #25
Source File: op_def_library.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _MakeStr(v, arg_name):
  if not isinstance(v, compat.bytes_or_text_types):
    raise TypeError("Expected string for argument '%s' not %s." %
                    (arg_name, repr(v)))
  return compat.as_bytes(v)  # Convert unicode strings to bytes.
Example #26
Source File: tensor_shape.py    From lambda-packs with MIT License 5 votes vote down vote up 
def __init__(self, dims):
    """Creates a new TensorShape with the given dimensions.

    Args:
      dims: A list of Dimensions, or None if the shape is unspecified.
        DEPRECATED: A single integer is treated as a singleton list.

    Raises:
      TypeError: If dims cannot be converted to a list of dimensions.
    """
    # TODO(irving): Eliminate the single integer special case.
    if dims is None:
      self._dims = None
    elif isinstance(dims, compat.bytes_or_text_types):
      raise TypeError("A string has ambiguous TensorShape, please wrap in a "
                      "list or convert to an int: %s" % dims)
    elif isinstance(dims, tensor_shape_pb2.TensorShapeProto):
      if dims.unknown_rank:
        self._dims = None
      else:
        self._dims = [
            # Protos store variable-size dimensions as -1
            as_dimension(dim.size if dim.size != -1 else None)
            for dim in dims.dim
        ]
    elif isinstance(dims, TensorShape):
      self._dims = dims.dims
    else:
      try:
        dims_iter = iter(dims)
      except TypeError:
        # Treat as a singleton dimension
        self._dims = [as_dimension(dims)]
      else:
        # Got a list of dimensions
        self._dims = [as_dimension(d) for d in dims_iter]
Example #27
Source File: tensor_shape.py    From lambda-packs with MIT License 5 votes vote down vote up 
def __init__(self, value):
    """Creates a new Dimension with the given value."""
    if value is None:
      self._value = None
    else:
      self._value = int(value)
      if (not isinstance(value, compat.bytes_or_text_types) and
          self._value != value):
        raise ValueError("Ambiguous dimension: %s" % value)
      if self._value < 0:
        raise ValueError("Dimension %d must be >= 0" % self._value)