# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # pylint: disable=protected-access # pylint: disable=g-classes-have-attributes """Contains the `Node` class.""" import collections import copy import json import numpy as np from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.saving.saved_model import json_utils from tensorflow.python.keras.utils import tf_utils from tensorflow.python.util import nest _CONSTANT_VALUE = '_CONSTANT_VALUE' class Node: """A `Node` describes the connectivity between two layers. Each time a layer is connected to some new input, a node is added to `layer._inbound_nodes`. Each time the output of a layer is used by another layer, a node is added to `layer._outbound_nodes`. Args: layer: The Layer for the Layer.__call__ this node represents. call_args: The positional arguments the Layer was called with. call_kwargs: The keyword arguments the Layer was called with. outputs: The outputs of the Layer.__call__ """ def __init__(self, layer, call_args=None, call_kwargs=None, outputs=None): call_args = [] if call_args is None else call_args call_kwargs = {} if call_kwargs is None else call_kwargs outputs = [] if outputs is None else outputs self.layer = layer self.is_input = not call_args and not call_kwargs # These arguments are user-provided. Copy the structures here so that # future user modifications do not affect the node's metadata. # We copy using map_structure rather than python's shallow or deep copy, # because the args can be data structures (so shallow copy is # insufficient), but individual values might not support copy.copy # or be too expensive to deep copy. call_args = nest.map_structure(lambda t: t, call_args) call_kwargs = nest.map_structure(lambda t: t, call_kwargs) self.outputs = nest.map_structure(lambda t: t, outputs) self.call_args = call_args self.call_kwargs = call_kwargs # Cached for performance. self._flat_arguments = nest.flatten((self.call_args, self.call_kwargs)) # Used to avoid expensive `nest` operations in the most common case. self._single_positional_tensor_passed = (not self.call_kwargs and len( self.call_args) == 1 and tensor_util.is_tf_type(self.call_args[0])) if not ops.executing_eagerly_outside_functions(): # Create TensorFlowOpLayers if needed (in TF1) for obj in self._flat_arguments: if (isinstance(obj, ops.Tensor) and base_layer_utils.needs_keras_history( obj, ignore_call_context=True)): base_layer_utils.create_keras_history(obj) self._keras_inputs = [] self._keras_inputs_ids_and_indices = [] for i, ele in enumerate(self._flat_arguments): if is_keras_tensor(ele): self._keras_inputs.append(ele) kt_id = str(id(ele)) kt_index = i self._keras_inputs_ids_and_indices.append((kt_id, kt_index)) # Wire up Node to Layers. self.layer._inbound_nodes.append(self) for kt in self.keras_inputs: inbound_layer = kt._keras_history.layer if inbound_layer is not None: # `None` for `Input` tensors. inbound_layer._outbound_nodes.append(self) # Set metadata on outputs. node_index = len(self.layer._inbound_nodes) - 1 for i, tensor in enumerate(nest.flatten(outputs)): tensor._keras_history = KerasHistory( layer=layer, node_index=node_index, tensor_index=i) # Cached for performance. self.flat_input_ids = [str(id(t)) for t in self._keras_inputs] self.flat_output_ids = [str(id(t)) for t in nest.flatten(self.outputs)] @property def keras_inputs(self): """Tensors input to this node that can be traced back to a `keras.Input`.""" return self._keras_inputs @property def parent_nodes(self): """Returns all the `Node`s whose output this node immediately depends on.""" node_deps = [] for kt in self.keras_inputs: layer = kt._keras_history.layer node_index = kt._keras_history.node_index if layer is not None: # `None` for `Input` tensors. node_deps.append(layer._inbound_nodes[node_index]) return node_deps def iterate_inbound(self): """Yields tuples representing the data inbound from other nodes. Yields: tuples like: (inbound_layer, node_index, tensor_index, tensor). """ for kt in self.keras_inputs: keras_history = kt._keras_history layer = keras_history.layer node_index = keras_history.node_index tensor_index = keras_history.tensor_index yield layer, node_index, tensor_index, kt def map_arguments(self, tensor_dict): """Maps Keras Tensors to computed Tensors using `tensor_dict`.""" if self._single_positional_tensor_passed: # Performance optimization for most common case. kt_id, _ = self._keras_inputs_ids_and_indices[0] return (tensor_dict[kt_id].pop(),), {} else: flat_arguments = copy.copy(self._flat_arguments) for kt_id, kt_index in self._keras_inputs_ids_and_indices: flat_arguments[kt_index] = tensor_dict[kt_id].pop() args, kwargs = nest.pack_sequence_as((self.call_args, self.call_kwargs), flat_arguments) return args, kwargs def serialize(self, make_node_key, node_conversion_map): """Serializes `Node` for Functional API's `get_config`.""" # Serialization still special-cases first argument. args, kwargs = self.call_args, self.call_kwargs inputs, args, kwargs = self.layer._split_out_first_arg(args, kwargs) # Treat everything other than first argument as a kwarg. arguments = dict(zip(self.layer._call_fn_args[1:], args)) arguments.update(kwargs) kwargs = arguments def _serialize_keras_tensor(t): """Serializes a single Tensor passed to `call`.""" if hasattr(t, '_keras_history'): kh = t._keras_history node_index = kh.node_index node_key = make_node_key(kh.layer.name, node_index) new_node_index = node_conversion_map.get(node_key, 0) return [kh.layer.name, new_node_index, kh.tensor_index] if isinstance(t, np.ndarray): return t.tolist() if isinstance(t, ops.Tensor): return backend.get_value(t).tolist() return t kwargs = nest.map_structure(_serialize_keras_tensor, kwargs) try: json.dumps(kwargs, default=json_utils.get_json_type) except TypeError: kwarg_types = nest.map_structure(type, kwargs) raise TypeError('Layer ' + self.layer.name + ' was passed non-JSON-serializable arguments. ' + 'Arguments had types: ' + str(kwarg_types) + '. They cannot be serialized out ' 'when saving the model.') # `kwargs` is added to each Tensor in the first arg. This should be # changed in a future version of the serialization format. def serialize_first_arg_tensor(t): if is_keras_tensor(t): kh = t._keras_history node_index = kh.node_index node_key = make_node_key(kh.layer.name, node_index) new_node_index = node_conversion_map.get(node_key, 0) data = [kh.layer.name, new_node_index, kh.tensor_index, kwargs] else: # If an element in the first call argument did not originate as a # keras tensor and is a constant value, we save it using the format # ['_CONSTANT_VALUE', -1, serializaed_tensor_or_python_constant] # (potentially including serialized kwargs in an optional 4th argument data = [_CONSTANT_VALUE, -1, _serialize_keras_tensor(t), kwargs] return tf_utils.ListWrapper(data) data = nest.map_structure(serialize_first_arg_tensor, inputs) if (not nest.is_nested(data) and not self.layer._preserve_input_structure_in_config): data = [data] data = tf_utils.convert_inner_node_data(data) return data ############################################################# # Properties for Backwards compatibility. # These only check the first input argument # As nodes are internal, they may be removed in the future. ############################################################# @property def input_tensors(self): if self.is_input: return [self.outputs] # Used in `Layer.input`. return self.call_args[0] @property def output_tensors(self): if self.is_input: return [self.outputs] # Used in `Layer.input`. return self.outputs @property def input_shapes(self): input_shapes = nest.map_structure(backend.int_shape, self.input_tensors) if len(input_shapes) == 1 and not self.is_input: return input_shapes[0] return input_shapes @property def output_shapes(self): return nest.map_structure(backend.int_shape, self.output_tensors) @property def outbound_layer(self): return self.layer @property def inbound_layers(self): if self.is_input: return [] inbound_layers = nest.map_structure(lambda t: t._keras_history.layer, self.call_args[0]) return inbound_layers class KerasHistory( collections.namedtuple('KerasHistory', ['layer', 'node_index', 'tensor_index'])): """Tracks the Layer call that created a Tensor, for Keras Graph Networks. During construction of Keras Graph Networks, this metadata is added to each Tensor produced as the output of a Layer, starting with an `InputLayer`. This allows Keras to track how each Tensor was produced, and this information is later retraced by the `keras.engine.Network` class to reconstruct the Keras Graph Network. Attributes: layer: The Layer that produced the Tensor. node_index: The specific call to the Layer that produced this Tensor. Layers can be called multiple times in order to share weights. A new node is created every time a Layer is called. tensor_index: The output index for this Tensor. Always zero if the Layer that produced this Tensor only has one output. Nested structures of Tensors are deterministically assigned an index via `nest.flatten`. """ # Added to maintain memory and performance characteristics of `namedtuple` # while subclassing. __slots__ = () def is_keras_tensor(obj): return hasattr(obj, '_keras_history')