Python tensorflow.core.framework.node_def_pb2.NodeDef() Examples

def __init__(self,
               node=None,
               name=None,
               inputs=None,
               outputs=None,
               attr=None,
               domain=None,
               op_type=None):
    # storing a reference to the original protobuf object
    if node is None:
      self.node = None
      self.name = name or ""
      self.inputs = inputs or []
      self.attr = attr or {}
      self.domain = domain or ""
      self.op_type = op_type or ""
      self.outputs = outputs or self.get_outputs_names()
    elif isinstance(node, (OnnxNode, NodeProto)):
      self._load_onnx_node(node)
    elif isinstance(node, NodeDef):
      self._load_tf_node(node) 

def values_from_const(node_def):
  """Extracts the values from a const NodeDef as a numpy ndarray.

  Args:
    node_def: Const NodeDef that has the values we want to access.

  Returns:
    Numpy ndarray containing the values.

  Raises:
    ValueError: If the node isn't a Const.
  """
  if node_def.op != "Const":
    raise ValueError(
        "Node named '%s' should be a Const op for values_from_const." %
        node_def.name)
  input_tensor = node_def.attr["value"].tensor
  tensor_value = tensor_util.MakeNdarray(input_tensor)
  return tensor_value 

def node_from_map(node_map, name):
  """Pulls a node def from a dictionary for a given name.

  Args:
    node_map: Dictionary containing an entry indexed by name for every node.
    name: Identifies the node we want to find.

  Returns:
    NodeDef of the node with the given name.

  Raises:
    ValueError: If the node isn't present in the dictionary.
  """
  stripped_name = node_name_from_input(name)
  if stripped_name not in node_map:
    raise ValueError("No node named '%s' found in map." % name)
  return node_map[stripped_name] 

def _as_node_def_input(self):
    """Return a value to use for the NodeDef "input" attribute.

    The returned string can be used in a NodeDef "input" attribute
    to indicate that the NodeDef uses this Tensor as input.

    Raises:
      ValueError: if this Tensor's Operation does not have a name.

    Returns:
      a string.
    """
    if not self._op.name:
      raise ValueError("Operation was not named: %s" % self._op)
    if self._value_index == 0:
      return self._op.name
    else:
      return "%s:%d" % (self._op.name, self._value_index) 

def values_from_const(node_def):
  """Extracts the values from a const NodeDef as a numpy ndarray.

  Args:
    node_def: Const NodeDef that has the values we want to access.

  Returns:
    Numpy ndarray containing the values.

  Raises:
    ValueError: If the node isn't a Const.
  """
  if node_def.op != "Const":
    raise ValueError(
        "Node named '%s' should be a Const op for values_from_const." %
        node_def.name)
  input_tensor = node_def.attr["value"].tensor
  tensor_value = tensor_util.MakeNdarray(input_tensor)
  return tensor_value 

def node_from_map(node_map, name):
  """Pulls a node def from a dictionary for a given name.

  Args:
    node_map: Dictionary containing an entry indexed by name for every node.
    name: Identifies the node we want to find.

  Returns:
    NodeDef of the node with the given name.

  Raises:
    ValueError: If the node isn't present in the dictionary.
  """
  stripped_name = node_name_from_input(name)
  if stripped_name not in node_map:
    raise ValueError("No node named '%s' found in map." % name)
  return node_map[stripped_name] 

def _as_node_def_input(self):
    """Return a value to use for the NodeDef "input" attribute.

    The returned string can be used in a NodeDef "input" attribute
    to indicate that the NodeDef uses this Tensor as input.

    Raises:
      ValueError: if this Tensor's Operation does not have a name.

    Returns:
      a string.
    """
    if not self._op.name:
      raise ValueError("Operation was not named: %s" % self._op)
    if self._value_index == 0:
      return self._op.name
    else:
      return "%s:%d" % (self._op.name, self._value_index) 

def _len_node_outputs(self, node_def):
        assert isinstance(node_def, node_def_pb2.NodeDef), 'node_def type %s' % type(node_def)
        op_def = self._op_defs[node_def.op]
        len_outputs = 0
        for output_argdef in op_def.output_arg:
            if output_argdef.number_attr:
                # A sequence of tensors with the same type
                len_outputs += node_def.attr[output_argdef.number_attr].i
            elif output_argdef.type_list_attr:
                # A sequence of tensors
                len_outputs += len(node_def.attr[output_argdef.type_list_attr].list.type)
            else:
                # A single tensor
                len_outputs += 1

        return len_outputs 

def extend_mapping_from_nodedef(self, single_nodedef, replica_nodedef):
        assert isinstance(single_nodedef, node_def_pb2.NodeDef), \
                'single nodedef type is %s' % type(single_nodedef)
        assert isinstance(replica_nodedef, node_def_pb2.NodeDef), \
                'replica nodedef type is %s' % type(replica_nodedef)

        def _append_mapping(tensor_or_op_name, replica_name):
            if tensor_or_op_name not in self._mapping:
                self._mapping[tensor_or_op_name] = []
            assert isinstance(self._mapping[tensor_or_op_name], list)
            self._mapping[tensor_or_op_name].append(replica_name)

        _append_mapping(single_nodedef.name, replica_nodedef.name)

        for i in range(self._len_node_outputs(single_nodedef)):
            single_tensor_name = '%s:%d' % (single_nodedef.name, i)
            replica_tensor_name = '%s:%d' % (replica_nodedef.name, i)
            _append_mapping(single_tensor_name, replica_tensor_name) 

def _local_device_setter(worker_device, ps_devices, ps_strategy):
  """A device setter that puts distributes Var/Ops to PS/workers."""
  ps_ops = ['Variable', 'VariableV2', 'VarHandleOp']

  def local_device_chooser(op):
    current_device = framework_device.DeviceSpec.from_string(op.device or '')

    node_def = op if isinstance(op, node_def_pb2.NodeDef) else op.node_def
    if node_def.op in ps_ops:
      ps_device_spec = framework_device.DeviceSpec.from_string(
          '{}'.format(ps_devices[ps_strategy(op)]))

      ps_device_spec.merge_from(current_device)
      return ps_device_spec.to_string()
    else:
      worker_device_spec = framework_device.DeviceSpec.from_string(
          worker_device or '')
      worker_device_spec.merge_from(current_device)
      return worker_device_spec.to_string()

  return local_device_chooser 

def get_stats_for_node_def(graph, node, statistic_type):
  """Looks up the node's statistics function in the registry and calls it.

  This function takes a Graph object and a NodeDef from a GraphDef, and if
  there's an associated statistics method, calls it and returns a result. If no
  function has been registered for the particular node type, it returns an empty
  statistics object.

  Args:
    graph: A Graph object that's been set up with the node's graph.
    node: A NodeDef describing the operator.
    statistic_type: A string identifying the statistic we're interested in.
  Returns:
    An OpStats object containing information about resource usage.
  """

  try:
    stats_func = _stats_registry.lookup(node.op + "," + statistic_type)
    result = stats_func(graph, node)
  except LookupError:
    result = OpStats(statistic_type)
  return result 

def build(self):
        for i, layer in enumerate(self.model.node):
            self.layer_map[layer.name] = TensorflowGraphNode(layer)
            self.layer_name_map[layer.name] = layer.name
            for pred in layer.input:
                if pred not in self.layer_map:
                    if not pred.split(':')[0] in self.layer_map: #test
                        new_node = NodeDef()
                        new_node.name = pred
                        new_node.op = "NoOp"
                        self.layer_map[pred] = TensorflowGraphNode(new_node)
                        self.layer_name_map[pred] = pred

                self.tf_make_connection(pred, layer.name)

        super(TensorflowGraph, self).build() 

def quantize_nodes_recursively(self, current_node):
    """The entry point for quantizing nodes to eight bit and back."""
    if self.already_visited[current_node.name]:
      return
    self.already_visited[current_node.name] = True
    for input_node_name in current_node.input:
      input_node_name = node_name_from_input(input_node_name)
      input_node = self.nodes_map[input_node_name]
      self.quantize_nodes_recursively(input_node)
    nodes_to_quantize = ["Conv2D", "BiasAdd", "MatMul"]
    if any(current_node.op in s for s in nodes_to_quantize):
      for input_name in current_node.input:
        input_name = node_name_from_input(input_name)
        input_node = self.nodes_map[input_name]
        self.quantize_node(input_node)
      self.quantize_node(current_node)
    else:
      new_node = node_def_pb2.NodeDef()
      new_node.CopyFrom(current_node)
      self.add_output_graph_node(new_node) 

def quantize_nodes_recursively(self, current_node):
    """The entry point for quantizing nodes to eight bit and back."""
    if self.already_visited[current_node.name]:
      return
    self.already_visited[current_node.name] = True
    for input_node_name in current_node.input:
      input_node_name = node_name_from_input(input_node_name)
      input_node = self.nodes_map[input_node_name]
      self.quantize_nodes_recursively(input_node)
    nodes_to_quantize = ["Conv2D", "BiasAdd", "MatMul"]
    if any(current_node.op in s for s in nodes_to_quantize):
      for input_name in current_node.input:
        input_name = node_name_from_input(input_name)
        input_node = self.nodes_map[input_name]
        self.quantize_node(input_node)
      self.quantize_node(current_node)
    else:
      new_node = node_def_pb2.NodeDef()
      new_node.CopyFrom(current_node)
      self.add_output_graph_node(new_node) 

def is_fused_op(node: NodeDef, op_name: Text, activation: Text) -> bool:
    """
    Return whether a node represents a fused TF operation.

    Args:
        node: Node defintion
        op_name: Fused operation name (e.g. 'MatMul')
        activation: Name of the fused activation function (e.g. 'Relu')

    Returns:
        `True`, iff the node is a fused operation with the given activation
    """
    if node.op == f'_Fused{op_name}' and 'fused_ops' in node.attr:
        fused_ops = node.attr['fused_ops'].list.s
        return (len(fused_ops) == 2
                and fused_ops[0] in (b'BiasAdd', b'BiasAddV1')
                and fused_ops[1] == activation)
    return False 

def quantize_nodes_recursively(self, current_node):
    """The entry point for quantizing nodes to eight bit and back."""
    if self.already_visited[current_node.name]:
      return
    self.already_visited[current_node.name] = True
    for input_node_name in current_node.input:
      input_node_name = node_name_from_input(input_node_name)
      input_node = self.nodes_map[input_node_name]
      self.quantize_nodes_recursively(input_node)
    nodes_to_quantize = ["Conv2D", "BiasAdd", "MatMul"]
    if any(current_node.op in s for s in nodes_to_quantize):
      for input_name in current_node.input:
        input_name = node_name_from_input(input_name)
        input_node = self.nodes_map[input_name]
        self.quantize_node(input_node)
      self.quantize_node(current_node)
    else:
      new_node = node_def_pb2.NodeDef()
      new_node.CopyFrom(current_node)
      self.add_output_graph_node(new_node) 

def quantize_nodes_recursively(self, current_node):
    """The entry point for quantizing nodes to eight bit and back."""
    if self.already_visited[current_node.name]:
      return
    self.already_visited[current_node.name] = True
    for input_node_name in current_node.input:
      input_node_name = node_name_from_input(input_node_name)
      input_node = self.nodes_map[input_node_name]
      self.quantize_nodes_recursively(input_node)
    nodes_to_quantize = ["Conv2D", "BiasAdd", "MatMul"]
    if any(current_node.op in s for s in nodes_to_quantize):
      for input_name in current_node.input:
        input_name = node_name_from_input(input_name)
        input_node = self.nodes_map[input_name]
        self.quantize_node(input_node)
      self.quantize_node(current_node)
    else:
      new_node = node_def_pb2.NodeDef()
      new_node.CopyFrom(current_node)
      self.add_output_graph_node(new_node) 

def local_device_setter(num_devices=1,
                        ps_device_type='cpu',
                        worker_device='/cpu:0',
                        ps_ops=None,
                        ps_strategy=None):
  if ps_ops == None:
    ps_ops = ['Variable', 'VariableV2', 'VarHandleOp']

  if ps_strategy is None:
    ps_strategy = device_setter._RoundRobinStrategy(num_devices)
  if not six.callable(ps_strategy):
    raise TypeError("ps_strategy must be callable")

  def _local_device_chooser(op):
    current_device = pydev.DeviceSpec.from_string(op.device or "")

    node_def = op if isinstance(op, node_def_pb2.NodeDef) else op.node_def
    if node_def.op in ps_ops:
      ps_device_spec = pydev.DeviceSpec.from_string(
          '/{}:{}'.format(ps_device_type, ps_strategy(op)))

      ps_device_spec.merge_from(current_device)
      return ps_device_spec.to_string()
    else:
      worker_device_spec = pydev.DeviceSpec.from_string(worker_device or "")
      worker_device_spec.merge_from(current_device)
      return worker_device_spec.to_string()
  return _local_device_chooser 

def local_device_setter(num_devices=1,
                        ps_device_type='cpu',
                        worker_device='/cpu:0',
                        ps_ops=None,
                        ps_strategy=None):
    if ps_ops == None:
        ps_ops = ['Variable', 'VariableV2', 'VarHandleOp']

    if ps_strategy is None:
        ps_strategy = device_setter._RoundRobinStrategy(num_devices)
    if not six.callable(ps_strategy):
        raise TypeError("ps_strategy must be callable")

    def _local_device_chooser(op):
        current_device = pydev.DeviceSpec.from_string(op.device or "")

        node_def = op if isinstance(op, node_def_pb2.NodeDef) else op.node_def
        if node_def.op in ps_ops:
            ps_device_spec = pydev.DeviceSpec.from_string(
                  '/{}:{}'.format(ps_device_type, ps_strategy(op)))

            ps_device_spec.merge_from(current_device)
            return ps_device_spec.to_string()
        else:
            worker_device_spec = pydev.DeviceSpec.from_string(worker_device or "")
            worker_device_spec.merge_from(current_device)
            return worker_device_spec.to_string()
    return _local_device_chooser 

def eightbitize_placeholder_node(self, current_node):
    """Replaces a placeholder node with a quint8 placeholder node+dequantize."""
    name = current_node.name

    # Convert the placeholder into a quantized type.
    output_node = node_def_pb2.NodeDef()
    output_node.CopyFrom(current_node)
    set_attr_dtype(output_node, "dtype", dtypes.quint8)
    output_node.name += "_original_input"
    self.add_output_graph_node(output_node)

    # Add a dequantize to convert back to float.
    dequantize_node = create_node("Dequantize", name, [
        output_node.name, "quantized_input_min_value",
        "quantized_input_max_value"
    ])
    set_attr_dtype(dequantize_node, "T", dtypes.quint8)
    set_attr_string(dequantize_node, "mode", b"MIN_FIRST")
    self.add_output_graph_node(dequantize_node)

    # For the descent over the graph to work, the dequantize node must be named
    # current_node.name.  However, for the feeding of the graph to work, the
    # placeholder must have the name current_node.name; so record a final set
    # of renames to apply after all processing has been done.
    self.final_node_renames[output_node.name] = name
    self.final_node_renames[dequantize_node.name] = name + "_dequantize" 

def strip_unused(input_graph_def, input_node_names, output_node_names,
                 placeholder_type_enum):
  """Removes unused nodes from a GraphDef.

  Args:
    input_graph_def: A graph with nodes we want to prune.
    input_node_names: A list of the nodes we use as inputs.
    output_node_names: A list of the output nodes.
    placeholder_type_enum: The AttrValue enum for the placeholder data type, or
        a list that specifies one value per input node name.

  Returns:
    A GraphDef with all unnecessary ops removed.
  """
  # Here we replace the nodes we're going to override as inputs with
  # placeholders so that any unused nodes that are inputs to them are
  # automatically stripped out by extract_sub_graph().
  inputs_replaced_graph_def = graph_pb2.GraphDef()
  for node in input_graph_def.node:
    if node.name in input_node_names:
      placeholder_node = node_def_pb2.NodeDef()
      placeholder_node.op = "Placeholder"
      placeholder_node.name = node.name
      if isinstance(placeholder_type_enum, list):
        input_node_index = input_node_names.index(node.name)
        placeholder_node.attr["dtype"].CopyFrom(
            attr_value_pb2.AttrValue(type=placeholder_type_enum[
                input_node_index]))
      else:
        placeholder_node.attr["dtype"].CopyFrom(
            attr_value_pb2.AttrValue(type=placeholder_type_enum))
      if "_output_shapes" in node.attr:
        placeholder_node.attr["_output_shapes"].CopyFrom(node.attr[
            "_output_shapes"])
      inputs_replaced_graph_def.node.extend([placeholder_node])
    else:
      inputs_replaced_graph_def.node.extend([copy.deepcopy(node)])

  output_graph_def = graph_util.extract_sub_graph(inputs_replaced_graph_def,
                                                  output_node_names)
  return output_graph_def 

def create_node(op, name, inputs):
  new_node = node_def_pb2.NodeDef()
  new_node.op = op
  new_node.name = name
  for input_name in inputs:
    new_node.input.extend([input_name])
  return new_node 

def local_device_setter(num_devices=1,
                        ps_device_type='cpu',
                        worker_device='/cpu:0',
                        ps_ops=None,
                        ps_strategy=None):
    if ps_ops == None:
        ps_ops = ['Variable', 'VariableV2', 'VarHandleOp']

    if ps_strategy is None:
        ps_strategy = device_setter._RoundRobinStrategy(num_devices)
    if not six.callable(ps_strategy):
        raise TypeError("ps_strategy must be callable")

    def _local_device_chooser(op):
        current_device = pydev.DeviceSpec.from_string(op.device or "")

        node_def = op if isinstance(op, node_def_pb2.NodeDef) else op.node_def
        if node_def.op in ps_ops:
            ps_device_spec = pydev.DeviceSpec.from_string(
                '/{}:{}'.format(ps_device_type, ps_strategy(op)))

            ps_device_spec.merge_from(current_device)
            return ps_device_spec.to_string()
        else:
            worker_device_spec = pydev.DeviceSpec.from_string(worker_device or "")
            worker_device_spec.merge_from(current_device)
            return worker_device_spec.to_string()

    return _local_device_chooser 

def local_device_setter(num_devices=1,
                        ps_device_type='cpu',
                        worker_device='/cpu:0',
                        ps_ops=None,
                        ps_strategy=None):
  if ps_ops == None:
    ps_ops = ['Variable', 'VariableV2', 'VarHandleOp']

  if ps_strategy is None:
    ps_strategy = device_setter._RoundRobinStrategy(num_devices)
  if not six.callable(ps_strategy):
    raise TypeError("ps_strategy must be callable")

  def _local_device_chooser(op):
    current_device = pydev.DeviceSpec.from_string(op.device or "")

    node_def = op if isinstance(op, node_def_pb2.NodeDef) else op.node_def
    if node_def.op in ps_ops:
      ps_device_spec = pydev.DeviceSpec.from_string(
          '/{}:{}'.format(ps_device_type, ps_strategy(op)))

      ps_device_spec.merge_from(current_device)
      return ps_device_spec.to_string()
    else:
      worker_device_spec = pydev.DeviceSpec.from_string(worker_device or "")
      worker_device_spec.merge_from(current_device)
      return worker_device_spec.to_string()
  return _local_device_chooser 

def local_device_setter(num_devices=1,
                        ps_device_type='cpu',
                        worker_device='/cpu:0',
                        ps_ops=None,
                        ps_strategy=None):
  if ps_ops == None:
    ps_ops = ['Variable', 'VariableV2', 'VarHandleOp']

  if ps_strategy is None:
    ps_strategy = device_setter._RoundRobinStrategy(num_devices)
  if not six.callable(ps_strategy):
    raise TypeError("ps_strategy must be callable")

  def _local_device_chooser(op):
    current_device = pydev.DeviceSpec.from_string(op.device or "")

    node_def = op if isinstance(op, node_def_pb2.NodeDef) else op.node_def
    if node_def.op in ps_ops:
      ps_device_spec = pydev.DeviceSpec.from_string(
          '/{}:{}'.format(ps_device_type, ps_strategy(op)))

      ps_device_spec.merge_from(current_device)
      return ps_device_spec.to_string()
    else:
      worker_device_spec = pydev.DeviceSpec.from_string(worker_device or "")
      worker_device_spec.merge_from(current_device)
      return worker_device_spec.to_string()
  return _local_device_chooser 

def tensor_shape_from_node_def_name(graph, input_name):
    """Convenience function to get a shape from a NodeDef's input string."""
    # To get a tensor, the name must be in the form <input>:<port>, for example
    # 'Mul:0'. The GraphDef input strings don't always have the port specified
    # though, so if there isn't a colon we need to add a default ':0' to the end.
    if ":" not in input_name:
        canonical_name = input_name + ":0"
    else:
        canonical_name = input_name
    tensor = graph.get_tensor_by_name(canonical_name)
    shape = tensor.get_shape()
    return shape 

def _op_nodes(graph_def: GraphDef) -> List[NodeDef]:
    return [node for node in graph_def.node if _is_op_node(node)] 

def _get_shape(node: NodeDef) -> List[int]:
    def shape(attr): return attr.shape.dim
    def size(dim): return dim.size if dim.size > 0 else None
    return [size(dim) for dim in shape(node.attr[c.TFJS_ATTR_SHAPE_KEY])] 

def _node_info(node: NodeDef) -> NodeInfo:
    def dtype(n): return _map_type(n.attr[c.TFJS_ATTR_DTYPE_KEY].type)
    return NodeInfo(name=node.name, shape=_get_shape(node), dtype=dtype(node),
                    tensor=node.name + ':0') 

def local_device_setter(num_devices=1,
                        ps_device_type='cpu',
                        worker_device='/cpu:0',
                        ps_ops=None,
                        ps_strategy=None):
    if ps_ops == None:
        ps_ops = ['Variable', 'VariableV2', 'VarHandleOp']

    if ps_strategy is None:
        ps_strategy = device_setter._RoundRobinStrategy(num_devices)
    if not six.callable(ps_strategy):
        raise TypeError("ps_strategy must be callable")

    def _local_device_chooser(op):
        current_device = pydev.DeviceSpec.from_string(op.device or "")

        node_def = op if isinstance(op, node_def_pb2.NodeDef) else op.node_def
        if node_def.op in ps_ops:
            ps_device_spec = pydev.DeviceSpec.from_string(
                  '/{}:{}'.format(ps_device_type, ps_strategy(op)))

            ps_device_spec.merge_from(current_device)
            return ps_device_spec.to_string()
        else:
            worker_device_spec = pydev.DeviceSpec.from_string(worker_device or "")
            worker_device_spec.merge_from(current_device)
            return worker_device_spec.to_string()
    return _local_device_chooser