# -*- coding: utf-8 -*-
# Copyright 2019 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.
# ==============================================================================
"""Utilities for handling Keras model in graph plugin.
Two canonical types of Keras model are Functional and Sequential.
A model can be serialized as JSON and deserialized to reconstruct a model.
This utility helps with dealing with the serialized Keras model.
They have distinct structures to the configurations in shapes below:
Functional:
config
name: Name of the model. If not specified, it is 'model' with
an optional suffix if there are more than one instance.
input_layers: Keras.layers.Inputs in the model.
output_layers: Layer names that are outputs of the model.
layers: list of layer configurations.
layer: [*]
inbound_nodes: inputs to this layer.
Sequential:
config
name: Name of the model. If not specified, it is 'sequential' with
an optional suffix if there are more than one instance.
layers: list of layer configurations.
layer: [*]
[*]: Note that a model can be a layer.
Please refer to https://github.com/tensorflow/tfjs-layers/blob/master/src/keras_format/model_serialization.ts
for more complete definition.
"""
from tensorboard.compat.proto.graph_pb2 import GraphDef
from tensorboard.compat.tensorflow_stub import dtypes
def _walk_layers(keras_layer):
"""Walks the nested keras layer configuration in preorder.
Args:
keras_layer: Keras configuration from model.to_json.
Yields:
A tuple of (name_scope, layer_config).
name_scope: a string representing a scope name, similar to that of tf.name_scope.
layer_config: a dict representing a Keras layer configuration.
"""
yield ("", keras_layer)
if keras_layer.get("config").get("layers"):
name_scope = keras_layer.get("config").get("name")
for layer in keras_layer.get("config").get("layers"):
for (sub_name_scope, sublayer) in _walk_layers(layer):
sub_name_scope = (
"%s/%s" % (name_scope, sub_name_scope)
if sub_name_scope
else name_scope
)
yield (sub_name_scope, sublayer)
def _scoped_name(name_scope, node_name):
"""Returns scoped name for a node as a string in the form '<scope>/<node
name>'.
Args:
name_scope: a string representing a scope name, similar to that of tf.name_scope.
node_name: a string representing the current node name.
Returns
A string representing a scoped name.
"""
if name_scope:
return "%s/%s" % (name_scope, node_name)
return node_name
def _is_model(layer):
"""Returns True if layer is a model.
Args:
layer: a dict representing a Keras model configuration.
Returns:
bool: True if layer is a model.
"""
return layer.get("config").get("layers") is not None
def _norm_to_list_of_layers(maybe_layers):
"""Normalizes to a list of layers.
Args:
maybe_layers: A list of data[1] or a list of list of data.
Returns:
List of list of data.
[1]: A Functional model has fields 'inbound_nodes' and 'output_layers' which can
look like below:
- ['in_layer_name', 0, 0]
- [['in_layer_is_model', 1, 0], ['in_layer_is_model', 1, 1]]
The data inside the list seems to describe [name, size, index].
"""
return (
maybe_layers if isinstance(maybe_layers[0], (list,)) else [maybe_layers]
)
def _update_dicts(
name_scope,
model_layer,
input_to_in_layer,
model_name_to_output,
prev_node_name,
):
"""Updates input_to_in_layer, model_name_to_output, and prev_node_name
based on the model_layer.
Args:
name_scope: a string representing a scope name, similar to that of tf.name_scope.
model_layer: a dict representing a Keras model configuration.
input_to_in_layer: a dict mapping Keras.layers.Input to inbound layer.
model_name_to_output: a dict mapping Keras Model name to output layer of the model.
prev_node_name: a string representing a previous, in sequential model layout,
node name.
Returns:
A tuple of (input_to_in_layer, model_name_to_output, prev_node_name).
input_to_in_layer: a dict mapping Keras.layers.Input to inbound layer.
model_name_to_output: a dict mapping Keras Model name to output layer of the model.
prev_node_name: a string representing a previous, in sequential model layout,
node name.
"""
layer_config = model_layer.get("config")
if not layer_config.get("layers"):
raise ValueError("layer is not a model.")
node_name = _scoped_name(name_scope, layer_config.get("name"))
input_layers = layer_config.get("input_layers")
output_layers = layer_config.get("output_layers")
inbound_nodes = model_layer.get("inbound_nodes")
is_functional_model = bool(input_layers and output_layers)
# In case of [1] and the parent model is functional, current layer
# will have the 'inbound_nodes' property.
is_parent_functional_model = bool(inbound_nodes)
if is_parent_functional_model and is_functional_model:
for (input_layer, inbound_node) in zip(input_layers, inbound_nodes):
input_layer_name = _scoped_name(node_name, input_layer)
inbound_node_name = _scoped_name(name_scope, inbound_node[0])
input_to_in_layer[input_layer_name] = inbound_node_name
elif is_parent_functional_model and not is_functional_model:
# Sequential model can take only one input. Make sure inbound to the
# model is linked to the first layer in the Sequential model.
prev_node_name = _scoped_name(name_scope, inbound_nodes[0][0][0])
elif (
not is_parent_functional_model
and prev_node_name
and is_functional_model
):
assert len(input_layers) == 1, (
"Cannot have multi-input Functional model when parent model "
"is not Functional. Number of input layers: %d" % len(input_layer)
)
input_layer = input_layers[0]
input_layer_name = _scoped_name(node_name, input_layer)
input_to_in_layer[input_layer_name] = prev_node_name
if is_functional_model and output_layers:
layers = _norm_to_list_of_layers(output_layers)
layer_names = [_scoped_name(node_name, layer[0]) for layer in layers]
model_name_to_output[node_name] = layer_names
else:
last_layer = layer_config.get("layers")[-1]
last_layer_name = last_layer.get("config").get("name")
output_node = _scoped_name(node_name, last_layer_name)
model_name_to_output[node_name] = [output_node]
return (input_to_in_layer, model_name_to_output, prev_node_name)
def keras_model_to_graph_def(keras_layer):
"""Returns a GraphDef representation of the Keras model in a dict form.
Note that it only supports models that implemented to_json().
Args:
keras_layer: A dict from Keras model.to_json().
Returns:
A GraphDef representation of the layers in the model.
"""
input_to_layer = {}
model_name_to_output = {}
g = GraphDef()
# Sequential model layers do not have a field "inbound_nodes" but
# instead are defined implicitly via order of layers.
prev_node_name = None
for (name_scope, layer) in _walk_layers(keras_layer):
if _is_model(layer):
(
input_to_layer,
model_name_to_output,
prev_node_name,
) = _update_dicts(
name_scope,
layer,
input_to_layer,
model_name_to_output,
prev_node_name,
)
continue
layer_config = layer.get("config")
node_name = _scoped_name(name_scope, layer_config.get("name"))
node_def = g.node.add()
node_def.name = node_name
if layer.get("class_name") is not None:
keras_cls_name = layer.get("class_name").encode("ascii")
node_def.attr["keras_class"].s = keras_cls_name
dtype_or_policy = layer_config.get("dtype")
# Skip dtype processing if this is a dict, since it's presumably a instance of
# tf/keras/mixed_precision/Policy rather than a single dtype.
# TODO(#5548): parse the policy dict and populate the dtype attr with the variable dtype.
if dtype_or_policy is not None and not isinstance(
dtype_or_policy, dict
):
tf_dtype = dtypes.as_dtype(layer_config.get("dtype"))
node_def.attr["dtype"].type = tf_dtype.as_datatype_enum
if layer.get("inbound_nodes") is not None:
for maybe_inbound_node in layer.get("inbound_nodes"):
inbound_nodes = _norm_to_list_of_layers(maybe_inbound_node)
for [name, size, index, _] in inbound_nodes:
inbound_name = _scoped_name(name_scope, name)
# An input to a layer can be output from a model. In that case, the name
# of inbound_nodes to a layer is a name of a model. Remap the name of the
# model to output layer of the model. Also, since there can be multiple
# outputs in a model, make sure we pick the right output_layer from the model.
inbound_node_names = model_name_to_output.get(
inbound_name, [inbound_name]
)
# There can be multiple inbound_nodes that reference the
# same upstream layer. This causes issues when looking for
# a particular index in that layer, since the indices
# captured in `inbound_nodes` doesn't necessarily match the
# number of entries in the `inbound_node_names` list. To
# avoid IndexErrors, we just use the last element in the
# `inbound_node_names` in this situation.
# Note that this is a quick hack to avoid IndexErrors in
# this situation, and might not be an appropriate solution
# to this problem in general.
input_name = (
inbound_node_names[index]
if index < len(inbound_node_names)
else inbound_node_names[-1]
)
node_def.input.append(input_name)
elif prev_node_name is not None:
node_def.input.append(prev_node_name)
if node_name in input_to_layer:
node_def.input.append(input_to_layer.get(node_name))
prev_node_name = node_def.name
return g