def _onnx_graph_to_tensorflow_rep(cls, graph_def, opset, strict):
""" Convert ONNX graph to TensorflowRep.
:param graph_def: ONNX GraphProto object.
:param opset: ONNX OperatorSetIdProto list.
:param strict: whether to enforce semantic equivalence between the original model
and the converted tensorflow model.
:return: TensorflowRep object.
"""
handlers = cls._get_handlers(opset)
tf_rep_graph = tf.Graph()
with tf_rep_graph.as_default():
# initializer: TensorProtos representing the values to initialize
# a given tensor.
# initialized: A list of names of the initialized tensors.
if graph_def.initializer:
input_dict_items = cls._onnx_initializer_to_input_dict_items(
graph_def.initializer)
initialized = {init.name for init in graph_def.initializer}
else:
input_dict_items = []
initialized = set()
# creating placeholders for currently unknown inputs
for value_info in graph_def.input:
if value_info.name in initialized:
continue
shape = list(d.dim_value if (
d.dim_value > 0 and d.dim_param == "") else None
for d in value_info.type.tensor_type.shape.dim)
value_info_name = value_info.name.replace(
":", "_tf_") + "_" + get_unique_suffix(
) if ":" in value_info.name else value_info.name
x = tf.compat.v1.placeholder(data_type.onnx2tf(
value_info.type.tensor_type.elem_type),
name=value_info_name,
shape=shape)
input_dict_items.append((value_info.name, x))
# tensor dict: this dictionary is a map from variable names
# to the latest produced TF tensors of the given name.
# This dictionary will get updated as we build the graph to
# record the names of newly produced tensors.
tensor_dict = dict(input_dict_items)
# Since tensor dict may be updated, we need to keep a copy
# of the original input dict where we track the earliest
# defined tensors so we can have access to the placeholders
# to feed in input tensors when we run the graph.
input_dict = dict(input_dict_items)
for node in graph_def.node:
onnx_node = OnnxNode(node)
output_ops = cls._onnx_node_to_tensorflow_op(onnx_node,
tensor_dict,
handlers,
opset=opset,
strict=strict)
curr_node_output_map = dict(zip(onnx_node.outputs, output_ops))
tensor_dict.update(curr_node_output_map)
tf_rep = TensorflowRep()
tf_rep.graph = tf_rep_graph
tf_rep.inputs = [
value_info.name for value_info in graph_def.input
if value_info.name not in initialized
]
tf_rep.outputs = [value_info.name for value_info in graph_def.output]
tf_rep.tensor_dict = tensor_dict
return tf_rep
def _onnx_graph_to_tensorflow_rep(cls, graph_def, opset, strict, **kwargs):
""" Convert ONNX graph to TensorflowRep.
:param graph_def: ONNX GraphProto object.
:param opset: ONNX OperatorSetIdProto list.
:param strict: whether to enforce semantic equivalence between the original model
and the converted tensorflow model.
:kwargs: additional arguements to generate tensor_dict for model debugging
:return: TensorflowRep object.
"""
# To generate tensor_dict or not, default is False
gen_tensor_dict = kwargs[
'gen_tensor_dict'] if 'gen_tensor_dict' in kwargs else False
# User provided input tensors, in the case the model inputs have unknown shapes
input_tensor_dict = kwargs[
'input_tensor_dict'] if 'input_tensor_dict' in kwargs else dict()
handlers = cls._get_handlers(opset)
# initializer: TensorProtos representing the values to initialize
# a given tensor.
# initialized: A list of names of the initialized tensors.
if graph_def.initializer:
initialized = {init.name for init in graph_def.initializer}
else:
initialized = set()
input_dict = dict()
module = BackendTFModule(handlers, opset, strict, graph_def, cls)
signatures = dict()
for value_info in graph_def.input:
if value_info.name in initialized:
continue
shape = list(d.dim_value if (
d.dim_value > 0 and d.dim_param == "") else None
for d in value_info.type.tensor_type.shape.dim)
value_info_name = value_info.name.replace(
":", "_tf_") + "_" + get_unique_suffix(
) if ":" in value_info.name else value_info.name
tf_spec = tf.TensorSpec(
shape,
data_type.onnx2tf(value_info.type.tensor_type.elem_type),
value_info_name)
signatures[value_info.name] = tf_spec
if gen_tensor_dict:
x = tf.constant(
0,
dtype=data_type.onnx2tf(
value_info.type.tensor_type.elem_type),
name=value_info_name,
shape=shape
) if value_info.name not in input_tensor_dict else input_tensor_dict[
value_info.name]
input_dict[value_info.name] = x
tf_rep = TensorflowRep()
tf_rep.inputs = [
value_info.name for value_info in graph_def.input
if value_info.name not in initialized
]
tf_rep.outputs = [value_info.name for value_info in graph_def.output]
module.outputs = tf_rep.outputs
tf_rep.tf_module = module
tf_rep.signatures = signatures
tf_rep.tensor_dict = module.gen_tensor_dict(
input_dict) if gen_tensor_dict else None
tf_rep.onnx_op_list = cls._get_onnx_op_list(graph_def)
return tf_rep
def _onnx_graph_to_tensorflow_rep(cls, graph_def, opset, strict, **kwargs):
""" Convert ONNX graph to TensorflowRep.
:param graph_def: ONNX GraphProto object.
:param opset: ONNX OperatorSetIdProto list.
:param strict: whether to enforce semantic equivalence between the original model
and the converted tensorflow model.
:kwargs: additional arguements to generate tensor_dict for model debugging
:return: TensorflowRep object.
"""
# To generate tensor_dict or not, default is False
gen_tensor_dict = kwargs[
'gen_tensor_dict'] if 'gen_tensor_dict' in kwargs else False
# User provided input tensors, in the case the model inputs have unknown shapes
input_tensor_dict = kwargs[
'input_tensor_dict'] if 'input_tensor_dict' in kwargs else dict()
training_mode = kwargs[
'training_mode'] if 'training_mode' in kwargs else False
handlers = cls._get_handlers(opset)
# initializer: TensorProtos representing the values to initialize
# a given tensor.
# initialized: A list of names of the initialized tensors.
if graph_def.initializer:
initialized = {init.name for init in graph_def.initializer}
else:
initialized = set()
input_dict = dict()
module = BackendTFModule(handlers, opset, strict, graph_def, cls)
signatures = dict()
tf_rep_graph = tf.Graph()
with tf_rep_graph.as_default():
for value_info in graph_def.input:
if value_info.name in initialized:
continue
shape = list(d.dim_value if (
d.dim_value > 0 and d.dim_param == "") else None
for d in value_info.type.tensor_type.shape.dim)
value_info_name = value_info.name.replace(
":", "_tf_") + "_" + get_unique_suffix(
) if ":" in value_info.name else value_info.name
tf_spec = tf.TensorSpec(
shape,
data_type.onnx2tf(value_info.type.tensor_type.elem_type),
value_info_name)
signatures[value_info.name] = tf_spec
if gen_tensor_dict or training_mode:
x = tf.compat.v1.placeholder(
data_type.onnx2tf(
value_info.type.tensor_type.elem_type),
name=value_info_name,
shape=shape
) if value_info.name not in input_tensor_dict else input_tensor_dict[
value_info.name]
input_dict[value_info.name] = x
if gen_tensor_dict or training_mode:
input_dict_items = cls._onnx_initializer_to_input_dict_items(
graph_def.initializer, training_mode=True)
tensor_dict = dict(input_dict)
tensor_dict.update(input_dict_items)
tensor_dict[
training_flag_name] = tf.compat.v1.placeholder_with_default(
False, shape=[])
for node in graph_def.node:
onnx_node = OnnxNode(node)
output_ops = cls._onnx_node_to_tensorflow_op(onnx_node,
tensor_dict,
handlers,
opset=opset,
strict=strict)
curr_node_output_map = dict(
zip(onnx_node.outputs, output_ops))
tensor_dict.update(curr_node_output_map)
tf_rep = TensorflowRep()
tf_rep.inputs = [
value_info.name for value_info in graph_def.input
if value_info.name not in initialized
]
tf_rep.outputs = [value_info.name for value_info in graph_def.output]
module.outputs = tf_rep.outputs
tf_rep.tf_module = module
tf_rep.signatures = signatures
if gen_tensor_dict or training_mode:
tf_rep.tensor_dict = tensor_dict
if training_mode:
tf_rep.graph = tf_rep_graph
tf_rep.onnx_op_list = cls._get_onnx_op_list(graph_def)
return tf_rep
