Python compat.as_str方法代碼示例

# 需要導入模塊: from tensorflow.python.util import compat [as 別名]
# 或者: from tensorflow.python.util.compat import as_str [as 別名]
def strip_name_scope(name, export_scope):
  """Removes name scope from a name.

  Args:
    name: A `string` name.
    export_scope: Optional `string`. Name scope to remove.

  Returns:
    Name with name scope removed, or the original name if export_scope
    is None.
  """
  if export_scope:
    try:
      # Strips export_scope/, export_scope///,
      # ^export_scope/, loc:@export_scope/.
      str_to_replace = r"([\^]|loc:@|^)" + export_scope + r"[\/]+(.*)"
      return re.sub(str_to_replace, r"\1\2", compat.as_str(name), count=1)
    except TypeError as e:
      # If the name is not of a type we can process, simply return it.
      logging.warning(e)
      return name
  else:
    return name 

# 需要導入模塊: from tensorflow.python.util import compat [as 別名]
# 或者: from tensorflow.python.util.compat import as_str [as 別名]
def prepend_name_scope(name, import_scope):
  """Prepends name scope to a name.

  Args:
    name: A `string` name.
    import_scope: Optional `string`. Name scope to add.

  Returns:
    Name with name scope added, or the original name if import_scope
    is None.
  """
  if import_scope:
    try:
      str_to_replace = r"([\^]|loc:@|^)(.*)"
      return re.sub(str_to_replace, r"\1" + import_scope + r"/\2",
                    compat.as_str(name))
    except TypeError as e:
      # If the name is not of a type we can process, simply return it.
      logging.warning(e)
      return name
  else:
    return name


# pylint: disable=g-doc-return-or-yield 

# 需要導入模塊: from tensorflow.python.util import compat [as 別名]
# 或者: from tensorflow.python.util.compat import as_str [as 別名]
def lookup(self, name):
    """Looks up "name".

    Args:
      name: a string specifying the registry key for the candidate.
    Returns:
      Registered object if found
    Raises:
      LookupError: if "name" has not been registered.
    """
    name = compat.as_str(name)
    if name in self._registry:
      return self._registry[name][_TYPE_TAG]
    else:
      raise LookupError(
          "%s registry has no entry for: %s" % (self._name, name)) 

# 需要導入模塊: from tensorflow.python.util import compat [as 別名]
# 或者: from tensorflow.python.util.compat import as_str [as 別名]
def strip_name_scope(name, export_scope):
  """Removes name scope from a name.

  Args:
    name: A `string` name.
    export_scope: Optional `string`. Name scope to remove.

  Returns:
    Name with name scope removed, or the original name if export_scope
    is None.
  """
  if export_scope:
    # Strips export_scope/, export_scope///,
    # ^export_scope/, loc:@export_scope/.
    str_to_replace = r"([\^]|loc:@|^)" + export_scope + r"[\/]+(.*)"
    return re.sub(str_to_replace, r"\1\2", compat.as_str(name), count=1)
  else:
    return name 

# 需要導入模塊: from tensorflow.python.util import compat [as 別名]
# 或者: from tensorflow.python.util.compat import as_str [as 別名]
def prepend_name_scope(name, import_scope):
  """Prepends name scope to a name.

  Args:
    name: A `string` name.
    import_scope: Optional `string`. Name scope to add.

  Returns:
    Name with name scope added, or the original name if import_scope
    is None.
  """
  if import_scope:
    str_to_replace = r"([\^]|loc:@|^)(.*)"
    return re.sub(str_to_replace, r"\1" + import_scope + r"/\2",
                  compat.as_str(name))
  else:
    return name


# pylint: disable=g-doc-return-or-yield 

# 需要導入模塊: from tensorflow.python.util import compat [as 別名]
# 或者: from tensorflow.python.util.compat import as_str [as 別名]
def assert_equal_graph_def(actual, expected):
  """Asserts that two `GraphDef`s are (mostly) the same.

  Compares two `GraphDef` protos for equality, ignoring versions and ordering of
  nodes, attrs, and control inputs.  Node names are used to match up nodes
  between the graphs, so the naming of nodes must be consistent.

  Args:
    actual: The `GraphDef` we have.
    expected: The `GraphDef` we expected.

  Raises:
    AssertionError: If the `GraphDef`s do not match.
    TypeError: If either argument is not a `GraphDef`.
  """
  if not isinstance(actual, graph_pb2.GraphDef):
    raise TypeError("Expected tf.GraphDef for actual, got %s" %
                    type(actual).__name__)
  if not isinstance(expected, graph_pb2.GraphDef):
    raise TypeError("Expected tf.GraphDef for expected, got %s" %
                    type(expected).__name__)
  diff = pywrap_tensorflow.EqualGraphDefWrapper(actual.SerializeToString(),
                                                expected.SerializeToString())
  if diff:
    raise AssertionError(compat.as_str(diff)) 

# 需要導入模塊: from tensorflow.python.util import compat [as 別名]
# 或者: from tensorflow.python.util.compat import as_str [as 別名]
def assert_equal_graph_def(actual, expected, checkpoint_v2=False):
  """Asserts that two `GraphDef`s are (mostly) the same.

  Compares two `GraphDef` protos for equality, ignoring versions and ordering of
  nodes, attrs, and control inputs.  Node names are used to match up nodes
  between the graphs, so the naming of nodes must be consistent.

  Args:
    actual: The `GraphDef` we have.
    expected: The `GraphDef` we expected.
    checkpoint_v2: boolean determining whether to ignore randomized attribute
        values that appear in V2 checkpoints.

  Raises:
    AssertionError: If the `GraphDef`s do not match.
    TypeError: If either argument is not a `GraphDef`.
  """
  if not isinstance(actual, graph_pb2.GraphDef):
    raise TypeError("Expected tf.GraphDef for actual, got %s" %
                    type(actual).__name__)
  if not isinstance(expected, graph_pb2.GraphDef):
    raise TypeError("Expected tf.GraphDef for expected, got %s" %
                    type(expected).__name__)

  if checkpoint_v2:
    _strip_checkpoint_v2_randomized(actual)
    _strip_checkpoint_v2_randomized(expected)

  diff = pywrap_tensorflow.EqualGraphDefWrapper(actual.SerializeToString(),
                                                expected.SerializeToString())
  if diff:
    raise AssertionError(compat.as_str(diff)) 

# 需要導入模塊: from tensorflow.python.util import compat [as 別名]
# 或者: from tensorflow.python.util.compat import as_str [as 別名]
def _CanonicalInputName(input_name):
  input_name = compat.as_str(input_name)
  if _IsControlInput(input_name):
    return input_name
  input_op_name, output_index = _ParseTensorName(input_name)
  return '%s:%d' % (input_op_name, output_index) 

# 需要導入模塊: from tensorflow.python.util import compat [as 別名]
# 或者: from tensorflow.python.util.compat import as_str [as 別名]
def _init_from_proto(self, hparam_def):
    """Creates a new HParams from `HParamDef` protocol buffer.

    Args:
      hparam_def: `HParamDef` protocol buffer.
    """
    assert isinstance(hparam_def, hparam_pb2.HParamDef)
    for name, value in hparam_def.hparam.items():
      kind = value.WhichOneof('kind')
      if kind.endswith('_value'):
        # Single value.
        if kind.startswith('int64'):
          # Setting attribute value to be 'int' to ensure the type is compatible
          # with both Python2 and Python3.
          self.add_hparam(name, int(getattr(value, kind)))
        elif kind.startswith('bytes'):
          # Setting attribute value to be 'str' to ensure the type is compatible
          # with both Python2 and Python3. UTF-8 encoding is assumed.
          self.add_hparam(name, compat.as_str(getattr(value, kind)))
        else:
          self.add_hparam(name, getattr(value, kind))
      else:
        # List of values.
        if kind.startswith('int64'):
          # Setting attribute value to be 'int' to ensure the type is compatible
          # with both Python2 and Python3.
          self.add_hparam(name, [int(v) for v in getattr(value, kind).value])
        elif kind.startswith('bytes'):
          # Setting attribute value to be 'str' to ensure the type is compatible
          # with both Python2 and Python3. UTF-8 encoding is assumed.
          self.add_hparam(name, [compat.as_str(v)
                                 for v in getattr(value, kind).value])
        else:
          self.add_hparam(name, [v for v in getattr(value, kind).value]) 

# 需要導入模塊: from tensorflow.python.util import compat [as 別名]
# 或者: from tensorflow.python.util.compat import as_str [as 別名]
def assert_equal_graph_def(actual, expected, checkpoint_v2=False):
  """Asserts that two `GraphDef`s are (mostly) the same.

  Compares two `GraphDef` protos for equality, ignoring versions and ordering of
  nodes, attrs, and control inputs.  Node names are used to match up nodes
  between the graphs, so the naming of nodes must be consistent.

  Args:
    actual: The `GraphDef` we have.
    expected: The `GraphDef` we expected.
    checkpoint_v2: boolean determining whether to ignore randomized attribute
        values that appear in V2 checkpoints.

  Raises:
    AssertionError: If the `GraphDef`s do not match.
    TypeError: If either argument is not a `GraphDef`.
  """
  if not isinstance(actual, graph_pb2.GraphDef):
    raise TypeError("Expected tf.GraphDef for actual, got %s" %
                    type(actual).__name__)
  if not isinstance(expected, graph_pb2.GraphDef):
    raise TypeError("Expected tf.GraphDef for expected, got %s" %
                    type(expected).__name__)

  if checkpoint_v2:
    _strip_checkpoint_v2_randomized(actual)
    _strip_checkpoint_v2_randomized(expected)

  diff = pywrap_tensorflow.EqualGraphDefWrapper(actual.SerializeToString(),
                                                expected.SerializeToString())
  if diff:
    raise AssertionError(compat.as_str(diff))


# Matches attributes named via _SHARDED_SUFFIX in
# tensorflow/python/training/saver.py 

# 需要導入模塊: from tensorflow.python.util import compat [as 別名]
# 或者: from tensorflow.python.util.compat import as_str [as 別名]
def _init_from_proto(self, hparam_def):
    """Creates a new HParams from `HParamDef` protocol buffer.
    Args:
      hparam_def: `HParamDef` protocol buffer.
    """
    assert isinstance(hparam_def, hparam_pb2.HParamDef)
    for name, value in hparam_def.hparam.items():
      kind = value.WhichOneof('kind')
      if kind.endswith('_value'):
        # Single value.
        if kind.startswith('int64'):
          # Setting attribute value to be 'int' to ensure the type is compatible
          # with both Python2 and Python3.
          self.add_hparam(name, int(getattr(value, kind)))
        elif kind.startswith('bytes'):
          # Setting attribute value to be 'str' to ensure the type is compatible
          # with both Python2 and Python3. UTF-8 encoding is assumed.
          self.add_hparam(name, compat.as_str(getattr(value, kind)))
        else:
          self.add_hparam(name, getattr(value, kind))
      else:
        # List of values.
        if kind.startswith('int64'):
          # Setting attribute value to be 'int' to ensure the type is compatible
          # with both Python2 and Python3.
          self.add_hparam(name, [int(v) for v in getattr(value, kind).value])
        elif kind.startswith('bytes'):
          # Setting attribute value to be 'str' to ensure the type is compatible
          # with both Python2 and Python3. UTF-8 encoding is assumed.
          self.add_hparam(
              name, [compat.as_str(v) for v in getattr(value, kind).value])
        else:
          self.add_hparam(name, [v for v in getattr(value, kind).value]) 

# 需要導入模塊: from tensorflow.python.util import compat [as 別名]
# 或者: from tensorflow.python.util.compat import as_str [as 別名]
def gpu_device_name():
  """Returns the name of a GPU device if available or the empty string."""
  for x in device_lib.list_local_devices():
    if x.device_type == "GPU" or x.device_type == "SYCL":
      return compat.as_str(x.name)
  return "" 

# 需要導入模塊: from tensorflow.python.util import compat [as 別名]
# 或者: from tensorflow.python.util.compat import as_str [as 別名]
def _set_c_attrs(self, attrs):
    """Sets `attrs` as attributes of self._c_func.

    Requires that self._c_func is not None.

    Args:
      attrs: a dictionary from attribute name to attribute proto value
    """
    for name, attr_value in attrs.items():
      serialized = attr_value.SerializeToString()
      # TODO(skyewm): this creates and deletes a new TF_Status for every attr.
      # It might be worth creating a convenient way to re-use the same status.
      with errors.raise_exception_on_not_ok_status() as status:
        c_api.TF_FunctionSetAttrValueProto(self._c_func, compat.as_str(name),
                                           serialized, status) 

# 需要導入模塊: from tensorflow.python.util import compat [as 別名]
# 或者: from tensorflow.python.util.compat import as_str [as 別名]
def _create_c_op(self, graph, node_def, inputs, control_inputs):
    """Creates a TF_Operation.

    Arguments:
      graph: a `Graph`.
      node_def: `node_def_pb2.NodeDef` for the operation to create.
      inputs: A list of `Tensor`s (corresponding to scalar inputs) and lists of
        `Tensor`s (corresponding to sequence inputs, e.g. "int64 * N",
        "list(int64)"). The length of the list should be equal to the number of
        inputs specified by this operation's op def.
      control_inputs: A list of `Operation`s to set as control dependencies.

    Returns:
      A wrapped TF_Operation*.
    """
    # pylint: disable=protected-access
    op_desc = c_api.TF_NewOperation(graph._c_graph.g,
                                    compat.as_str(node_def.op),
                                    compat.as_str(node_def.name))
    # Add inputs
    for op_input in inputs:
      if isinstance(op_input, (list, tuple)):
        c_api.TF_AddInputList(op_desc, [t._as_tf_output() for t in op_input])
      else:
        c_api.TF_AddInput(op_desc, op_input._as_tf_output())

    # Add control inputs
    for control_input in control_inputs:
      c_api.TF_AddControlInput(op_desc, control_input._c_op)
    # pylint: enable=protected-access

    # Add attrs
    for name, attr_value in node_def.attr.items():
      serialized = attr_value.SerializeToString()
      # TODO(skyewm): this creates and deletes a new TF_Status for every attr.
      # It might be worth creating a convenient way to re-use the same status.
      with errors.raise_exception_on_not_ok_status() as status:
        c_api.TF_SetAttrValueProto(op_desc, compat.as_str(name), serialized,
                                   status)

    with errors.raise_exception_on_not_ok_status() as status:
      c_op = c_api.TF_FinishOperation(op_desc, status)

    return c_op 

# 需要導入模塊: from tensorflow.python.util import compat [as 別名]
# 或者: from tensorflow.python.util.compat import as_str [as 別名]
def _node_def(from_node_def, export_scope, unbound_inputs, clear_devices=False):
  """Create a `NodeDef` proto with export_scope stripped.

  Args:
    from_node_def: A `node_def_pb2.NodeDef` protocol buffer.
    export_scope: A `string` representing the name scope to remove.
    unbound_inputs: An array of unbound input names if they exist.
    clear_devices: Boolean which controls whether to clear device information
      from node_def. Default false.

  Returns:
    A `node_def_pb2.NodeDef` protocol buffer.
  """
  node_def = copy.deepcopy(from_node_def)
  for i, v in enumerate(node_def.input):
    if (export_scope and
        not node_def.input[i].lstrip("^").startswith(export_scope)):
      # Adds "$unbound_inputs_" prefix to the unbound name so they are easily
      # identifiable.
      node_def.input[i] = re.sub(r"([\^]|^)(.*)",
                                 r"\1" + _UNBOUND_INPUT_PREFIX + r"\2",
                                 compat.as_str(v))
      unbound_inputs.append(node_def.input[i])
    else:
      node_def.input[i] = ops.strip_name_scope(v, export_scope)
  node_def.name = compat.as_bytes(
      ops.strip_name_scope(from_node_def.name, export_scope))
  for k, v in six.iteritems(from_node_def.attr):
    if k == "_class":
      new_s = [compat.as_bytes(
          ops.strip_name_scope(s, export_scope)) for s in v.list.s
               if not export_scope or
               compat.as_str(s).split("@")[1].startswith(export_scope)]
      node_def.attr[k].CopyFrom(attr_value_pb2.AttrValue(
          list=attr_value_pb2.AttrValue.ListValue(s=new_s)))
    else:
      node_def.attr[k].CopyFrom(v)

  if clear_devices:
    node_def.device = ""

  return node_def