Python tf_inspect.isclass函数代码示例

def _traverse_internal(root, visit, stack, path):
  """Internal helper for traverse."""

  # Only traverse modules and classes
  if not tf_inspect.isclass(root) and not tf_inspect.ismodule(root):
    return

  try:
    children = tf_inspect.getmembers(root)

    # Add labels for duplicate values in Enum.
    if tf_inspect.isclass(root) and issubclass(root, enum.Enum):
      for enum_member in root.__members__.items():
        if enum_member not in children:
          children.append(enum_member)
      children = sorted(children)
  except ImportError:
    # On some Python installations, some modules do not support enumerating
    # members (six in particular), leading to import errors.
    children = []

  new_stack = stack + [root]
  visit(path, root, children)
  for name, child in children:
    # Do not descend into built-in modules
    if tf_inspect.ismodule(
        child) and child.__name__ in sys.builtin_module_names:
      continue

    # Break cycles
    if any(child is item for item in new_stack):  # `in`, but using `is`
      continue

    child_path = path + '.' + name if path else name
    _traverse_internal(child, visit, new_stack, child_path)

  def _process_variable_assignment(self, source, targets):
    # Special case: constructors.
    if isinstance(source, gast.Call):
      func = source.func
      if anno.hasanno(func, 'live_val'):
        func_obj = anno.getanno(func, 'live_val')
        if tf_inspect.isclass(func_obj):
          anno.setanno(source, 'is_constructor', True)
          anno.setanno(source, 'type', func_obj)
          anno.setanno(source, 'type_fqn', anno.getanno(func, 'fqn'))
          # TODO(mdan): Raise an error if constructor has side effects.
          # We can have a whitelist of no-side-effects constructors.
          # We can also step inside the constructor and further analyze.

    # Multiple targets mean multiple assignment.
    for target in targets:
      # Tuple target means unpacking.
      if isinstance(target, gast.Tuple):
        for i, target_item in enumerate(target.elts):
          # Two cases here:
          #   1. Static unpacking, e.g. a, b = c, d
          #   2. Dynamic unpacking, e.g. a, b = c
          # The former case is optimized away.
          if isinstance(source, (gast.Tuple, gast.List)):
            source_item = source.elts[i]
          else:
            source_item = gast.Subscript(source, gast.Index(i), ctx=None)
          self._process_variable_assignment(source_item, (target_item,))
      elif isinstance(target, (gast.Name, gast.Attribute)):
        target_symbol = anno.getanno(target, anno.Basic.QN)
        self.scope.setval(target_symbol, source)
      else:
        raise ValueError(
            'assignment target has unknown type: %s' % target_item)

  def __call__(self, parent_name, parent, children):
    """Visitor interface, see `tensorflow/tools/common:traverse` for details.

    This method is called for each symbol found in a traversal using
    `tensorflow/tools/common:traverse`. It should not be called directly in
    user code.

    Args:
      parent_name: The fully qualified name of a symbol found during traversal.
      parent: The Python object referenced by `parent_name`.
      children: A list of `(name, py_object)` pairs enumerating, in alphabetical
        order, the children (as determined by `tf_inspect.getmembers`) of
          `parent`. `name` is the local name of `py_object` in `parent`.

    Raises:
      RuntimeError: If this visitor is called with a `parent` that is not a
        class or module.
    """
    parent_name = self._add_prefix(parent_name)
    self._index[parent_name] = parent
    self._tree[parent_name] = []

    if not (tf_inspect.ismodule(parent) or tf_inspect.isclass(parent)):
      raise RuntimeError('Unexpected type in visitor -- %s: %r' % (parent_name,
                                                                   parent))

    for i, (name, child) in enumerate(list(children)):
      # Don't document __metaclass__
      if name in ['__metaclass__']:
        del children[i]
        continue

      full_name = '.'.join([parent_name, name]) if parent_name else name
      self._index[full_name] = child
      self._tree[parent_name].append(name)

  def from_visitor(cls, visitor, doc_index, **kwargs):
    """A factory function for building a ReferenceResolver from a visitor.

    Args:
      visitor: an instance of `DocGeneratorVisitor`
      doc_index: a dictionary mapping document names to references objects with
        "title" and "url" fields
      **kwargs: all remaining args are passed to the constructor
    Returns:
      an instance of `ReferenceResolver` ()
    """
    is_class = {
        name: tf_inspect.isclass(visitor.index[name])
        for name, obj in visitor.index.items()
    }

    is_module = {
        name: tf_inspect.ismodule(visitor.index[name])
        for name, obj in visitor.index.items()
    }

    return cls(
        duplicate_of=visitor.duplicate_of,
        doc_index=doc_index,
        is_class=is_class,
        is_module=is_module,
        **kwargs)

  def compiled_function_name(self,
                             original_name,
                             live_object=None,
                             owner_type=None):
    """See call_trees.FunctionNamer.compiled_function_name."""
    if live_object is not None and live_object in self.renamed_calls:
      return self.renamed_calls[live_object]

    if not self.recursive:
      new_name = original_name
    elif owner_type is None or owner_type in self.partial_types:
      # Top level functions: rename
      new_name_root = 'tf__%s' % original_name
      new_name = new_name_root
      n = 0
      while new_name in self.global_namespace:
        n += 1
        new_name = '%s_%d' % (new_name_root, n)
    else:
      if tf_inspect.isclass(owner_type):
        # Class members: do not rename (the entire class will be renamed)
        new_name = original_name
      else:
        raise NotImplementedError('Member function "%s" of non-class type: %s' %
                                  (original_name, owner_type))

    if live_object is not None:
      self.renamed_calls[live_object] = new_name
    self.generated_names.add(new_name)
    return new_name

  def _process_variable_assignment(self, source, targets):
    if isinstance(source, gast.Call):
      func = source.func
      if anno.hasanno(func, 'live_val'):
        func_obj = anno.getanno(func, 'live_val')
        if tf_inspect.isclass(func_obj):
          anno.setanno(source, 'is_constructor', True)
          anno.setanno(source, 'type', func_obj)
          anno.setanno(source, 'type_fqn', anno.getanno(func, 'fqn'))
          # TODO(mdan): Raise an error if constructor has side effects.
          # We can have a whitelist of no-side-effects constructors.
          # We can also step inside the constructor and further analyze.

    for t in targets:
      if isinstance(t, gast.Tuple):
        for i, e in enumerate(t.elts):
          self.scope.setval(e.id,
                            gast.Subscript(
                                source, gast.Index(i), ctx=gast.Store()))
      elif isinstance(t, gast.Name):
        self.scope.setval(t.id, source)
      elif isinstance(t, gast.Attribute):
        if not (isinstance(t.value, gast.Name) and t.value.id == 'self'):
          raise ValueError(
              'Dont know how to handle assignment to attributes of objects'
              ' other than "self": [%s].%s' % (t.value, t.attr))
      else:
        raise ValueError('Dont know how to handle assignment to %s' % t)

def is_supported_type_for_deprecation(symbol):
  # Exclude Exception subclasses since users should be able to
  # "except" the same type of exception that was "raised" (i.e. we
  # shouldn't wrap it with deprecation alias).
  # Also, exclude subclasses of namedtuples for now.
  return tf_inspect.isfunction(symbol) or (
      tf_inspect.isclass(symbol) and not issubclass(symbol, Exception)
      and tuple not in tf_inspect.getmro(symbol))

  def __call__(self, path, parent, children):
    # The path to the object.
    lib_path = 'tensorflow.%s' % path if path else 'tensorflow'

    # A small helper method to construct members(children) protos.
    def _AddMember(member_name, member_obj, proto):
      """Add the child object to the object being constructed."""
      _, member_obj = tf_decorator.unwrap(member_obj)
      if member_name == '__init__' or not member_name.startswith('_'):
        if tf_inspect.isroutine(member_obj):
          new_method = proto.member_method.add()
          new_method.name = member_name
          # If member_obj is a python builtin, there is no way to get its
          # argspec, because it is implemented on the C side. It also has no
          # func_code.
          if getattr(member_obj, 'func_code', None):
            new_method.argspec = _SanitizedArgSpec(member_obj)
        else:
          new_member = proto.member.add()
          new_member.name = member_name
          new_member.mtype = str(type(member_obj))

    parent_corner_cases = _CORNER_CASES.get(path, {})

    if path not in _CORNER_CASES or parent_corner_cases:
      # Decide if we have a module or a class.
      if tf_inspect.ismodule(parent):
        # Create a module object.
        module_obj = api_objects_pb2.TFAPIModule()
        for name, child in children:
          if name in parent_corner_cases:
            # If we have an empty entry, skip this object.
            if parent_corner_cases[name]:
              module_obj.member.add(**(parent_corner_cases[name]))
          else:
            _AddMember(name, child, module_obj)

        # Store the constructed module object.
        self._protos[lib_path] = api_objects_pb2.TFAPIObject(
            path=lib_path, tf_module=module_obj)
      elif tf_inspect.isclass(parent):
        # Construct a class.
        class_obj = api_objects_pb2.TFAPIClass()
        class_obj.is_instance.extend(_SanitizedMRO(parent))
        for name, child in children:
          if name in parent_corner_cases:
            # If we have an empty entry, skip this object.
            if parent_corner_cases[name]:
              module_obj.member.add(**(parent_corner_cases[name]))
          else:
            _AddMember(name, child, class_obj)

        # Store the constructed class object.
        self._protos[lib_path] = api_objects_pb2.TFAPIObject(
            path=lib_path, tf_class=class_obj)
      else:
        logging.error('Illegal call to ApiProtoDump::_py_obj_to_proto.'
                      'Object is neither a module nor a class: %s', path)

def entity_to_graph(o, conversion_map, arg_values, arg_types):
  """Compile a Python entity into equivalent TensorFlow.

  The function will also recursively compile all the entities that `o`
  references, updating `dependency_cache`.

  This function is reentrant, and relies on dependency_cache to avoid
  generating duplicate code.

  Args:
    o: A Python entity.
    conversion_map: A ConversionMap object.
    arg_values: A dict containing value hints for symbols like function
        parameters.
    arg_types: A dict containing type hints for symbols like function
        parameters.

  Returns:
    A tuple (ast, new_name, namespace):
        * ast: An AST representing an entity with interface equivalent to `o`,
            but which when executed it creates TF a graph.
        * new_name: The symbol name under which the new entity can be found.
        * namespace: A dict mapping all symbols visible to the converted entity,
            keyed by their symbol name.

  Raises:
    ValueError: if the entity type is not supported.
  """
  if tf_inspect.isclass(o):
    node, name, ns = class_to_graph(o, conversion_map)
  elif tf_inspect.isfunction(o):
    node, name, ns = function_to_graph(o, conversion_map, arg_values, arg_types)
  elif tf_inspect.ismethod(o):
    node, name, ns = function_to_graph(o, conversion_map, arg_values, arg_types)
  else:
    raise ValueError(
        'Entity "%s" has unsupported type "%s". Only functions and classes are '
        'supported for now.' % (o, type(o)))

  conversion_map.add_to_cache(o, node)
  if conversion_map.recursive:
    while True:
      candidate = None
      for obj in conversion_map.name_map.keys():
        if obj not in conversion_map.dependency_cache:
          candidate = obj
          break
      if candidate is None:
        break
      if (hasattr(candidate, 'im_class') and
          getattr(candidate, 'im_class') not in conversion_map.partial_types):
        # Class members are converted with their objects, unless they're
        # only converted partially.
        continue
      entity_to_graph(candidate, conversion_map, {}, {})

  return node, name, ns

def isnamedtuple(f):
  """Returns True if the argument is a namedtuple-like."""
  if not (tf_inspect.isclass(f) and issubclass(f, tuple)):
    return False
  if not hasattr(f, '_fields'):
    return False
  fields = getattr(f, '_fields')
  if not isinstance(fields, tuple):
    return False
  if not all(isinstance(f, str) for f in fields):
    return False
  return True

def convert_entity_to_ast(o, program_ctx):
  """Compile a Python entity into equivalent TensorFlow.

  Args:
    o: A Python entity.
    program_ctx: A ProgramContext object.

  Returns:
    A tuple (ast, new_name, namespace):
        * ast: An AST representing an entity with interface equivalent to `o`,
            but which when executed it creates TF a graph.
        * new_name: The symbol name under which the new entity can be found.
        * namespace: A dict mapping all symbols visible to the converted entity,
            keyed by their symbol name.

  Raises:
    ValueError: if the entity type is not supported.
  """
  logging.log(1, 'Converting %s', o)

  if tf_inspect.isclass(o):
    nodes, name, entity_info = convert_class_to_ast(o, program_ctx)
  elif tf_inspect.isfunction(o):
    nodes, name, entity_info = convert_func_to_ast(o, program_ctx)
  elif tf_inspect.ismethod(o):
    nodes, name, entity_info = convert_func_to_ast(o, program_ctx)
  # TODO(mdan,yashkatariya): Remove when object conversion is implemented.
  elif hasattr(o, '__class__'):
    raise NotImplementedError(
        'Object conversion is not yet supported. If you are '
        'trying to convert code that uses an existing object, '
        'try including the creation of that object in the '
        'conversion. For example, instead of converting the method '
        'of a class, try converting the entire class instead. '
        'See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/'
        'python/autograph/README.md#using-the-functional-api '
        'for more information.')
  else:
    raise ValueError(
        'Entity "%s" has unsupported type "%s". Only functions and classes are '
        'supported for now.' % (o, type(o)))

  if logging.has_verbosity(2):
    logging.log(2, 'Compiled output of %s:\n\n%s\n', o,
                compiler.ast_to_source(nodes))
  if logging.has_verbosity(4):
    for n in nodes:
      logging.log(4, 'Compiled AST of %s:\n\n%s\n\n', o,
                  pretty_printer.fmt(n, color=False))

  return nodes, name, entity_info

def object_to_graph(o, conversion_map, value_hints):
  """Compile a Python object into equivalent TensorFlow.

  The function will also recursively compile all the objects that `o`
  references, updating `dependency_cache`.

  This function is reentrant, and relies on dependency_cache to avoid
  generating duplicate code.

  Args:
    o: A Python object.
    conversion_map: A ConversionMap object.
    value_hints: A dict containing value hints for symbols like function
        parameters.

  Returns:
    A tuple (ast, new_name):
        * ast: An AST representing an object with interface equivalent to `o`,
            but which when executed it creates TF a graph.
        * new_name: The symbol name under which the new object can be found.

  Raises:
    ValueError: if the object is not supported.
  """
  if value_hints is None:
    value_hints = {}

  if tf_inspect.isclass(o):
    node, new_name = class_to_graph(o, conversion_map, value_hints)
  elif tf_inspect.isfunction(o):
    node, new_name = function_to_graph(o, conversion_map, value_hints)
  elif tf_inspect.ismethod(o):
    node, new_name = function_to_graph(o, conversion_map, value_hints)
  else:
    raise ValueError(
        'Entity "%s" has unsupported type "%s". Only functions and classes are '
        'supported for now.' % (o, type(o)))

  conversion_map.add_to_cache(o, node)
  if conversion_map.recursive:
    for obj in conversion_map.name_map.keys():
      if obj not in conversion_map.dependency_cache:
        if (hasattr(obj, 'im_class') and
            getattr(obj, 'im_class') not in conversion_map.partial_types):
          # Class members are converted with their objects, unless they're
          # only converted partially.
          continue
        object_to_graph(obj, conversion_map, None)

  return node, new_name

def deserialize_keras_object(identifier,
                             module_objects=None,
                             custom_objects=None,
                             printable_module_name='object'):
  if identifier is None:
    return None
  if isinstance(identifier, dict):
    # In this case we are dealing with a Keras config dictionary.
    config = identifier
    (cls, cls_config) = class_and_config_for_serialized_keras_object(
        config, module_objects, custom_objects, printable_module_name)

    if hasattr(cls, 'from_config'):
      arg_spec = tf_inspect.getfullargspec(cls.from_config)
      custom_objects = custom_objects or {}

      if 'custom_objects' in arg_spec.args:
        return cls.from_config(
            cls_config,
            custom_objects=dict(
                list(_GLOBAL_CUSTOM_OBJECTS.items()) +
                list(custom_objects.items())))
      with CustomObjectScope(custom_objects):
        return cls.from_config(cls_config)
    else:
      # Then `cls` may be a function returning a class.
      # in this case by convention `config` holds
      # the kwargs of the function.
      custom_objects = custom_objects or {}
      with CustomObjectScope(custom_objects):
        return cls(**cls_config)
  elif isinstance(identifier, six.string_types):
    object_name = identifier
    if custom_objects and object_name in custom_objects:
      obj = custom_objects.get(object_name)
    elif object_name in _GLOBAL_CUSTOM_OBJECTS:
      obj = _GLOBAL_CUSTOM_OBJECTS[object_name]
    else:
      obj = module_objects.get(object_name)
      if obj is None:
        raise ValueError('Unknown ' + printable_module_name + ':' + object_name)
    # Classes passed by name are instantiated with no args, functions are
    # returned as-is.
    if tf_inspect.isclass(obj):
      return obj()
    return obj
  else:
    raise ValueError('Could not interpret serialized ' + printable_module_name +
                     ': ' + identifier)

def generate_global_index(library_name, index, reference_resolver):
  """Given a dict of full names to python objects, generate an index page.

  The index page generated contains a list of links for all symbols in `index`
  that have their own documentation page.

  Args:
    library_name: The name for the documented library to use in the title.
    index: A dict mapping full names to python objects.
    reference_resolver: An instance of ReferenceResolver.

  Returns:
    A string containing an index page as Markdown.
  """
  symbol_links = []
  for full_name, py_object in six.iteritems(index):
    if (tf_inspect.ismodule(py_object) or tf_inspect.isfunction(py_object) or
        tf_inspect.isclass(py_object)):
      # In Python 3, unbound methods are functions, so eliminate those.
      if tf_inspect.isfunction(py_object):
        if full_name.count('.') == 0:
          parent_name = ''
        else:
          parent_name = full_name[:full_name.rfind('.')]
        if parent_name in index and tf_inspect.isclass(index[parent_name]):
          # Skip methods (=functions with class parents).
          continue
      symbol_links.append((
          full_name, reference_resolver.python_link(full_name, full_name, '.')))

  lines = ['# All symbols in %s' % library_name, '']
  for _, link in sorted(symbol_links, key=lambda x: x[0]):
    lines.append('*  %s' % link)

  # TODO(markdaoust): use a _ModulePageInfo -> prety_docs.build_md_page()
  return '\n'.join(lines)

def _is_free_function(py_object, full_name, index):
  """Check if input is a free function (and not a class- or static method)."""
  if not tf_inspect.isfunction(py_object):
    return False

  # Static methods are functions to tf_inspect (in 2.7), so check if the parent
  # is a class. If there is no parent, it's not a function.
  if '.' not in full_name:
    return False

  parent_name = full_name.rsplit('.', 1)[0]
  if tf_inspect.isclass(index[parent_name]):
    return False

  return True