def _onnx_node_to_tensorflow_op(cls,
node,
tensor_dict,
handlers=None,
opset=None,
strict=True):
"""
Convert onnx node to tensorflow op.
Args:
node: Onnx node object.
tensor_dict: Tensor dict of graph.
opset: Opset version of the operator set. Default 0 means using latest version.
strict: whether to enforce semantic equivalence between the original model
and the converted tensorflow model, defaults to True (yes, enforce semantic equivalence).
Changing to False is strongly discouraged.
Returns:
Tensorflow op
"""
handlers = handlers or cls._get_handlers(opset)
handler = handlers[node.domain].get(node.op_type, None)
if handler:
return handler.handle(node, tensor_dict=tensor_dict, strict=strict)
else:
exception.OP_UNIMPLEMENTED_EXCEPT(node.op_type)
def handle(cls, node, **kwargs):
""" Main method in handler. It will find corresponding versioned handle method,
whose name format is `version_%d`. So prefix `version_` is reserved in onnx-tensorflow.
DON'T use it for other purpose.
:param node: NodeProto for backend or TensorflowNode for frontend.
:param kwargs: Other args.
:return: NodeProto for frontend or TensorflowNode for backend.
"""
ver_handle = getattr(cls, "version_{}".format(cls.SINCE_VERSION), None)
if ver_handle:
cls.args_check(node, **kwargs)
return ver_handle(node, **kwargs)
exception.OP_UNIMPLEMENTED_EXCEPT(node.op_type, cls.SINCE_VERSION)
return None
def tensorflow_graph_to_onnx_graph(cls,
graph_def,
output,
opset=((defs.ONNX_DOMAIN,
defs.onnx_opset_version()), ),
name="graph",
ignore_unimplemented=False):
"""Converts a Tensorflow Graph Proto to an ONNX graph
This function converts a Tensorflow Graph proto to an equivalent
representation of ONNX graph.
:param graph_def: Tensorflow Graph Proto object.
:param output: List of Tensorflow NodeDef object specifying which nodes
to be taken as outputs of the ONNX graph.
:param opset: Opset, which should be ((str domain: int version number),).
:param name: The name of the output ONNX Graph.
:param ignore_unimplemented: Convert to ONNX model and ignore all the operators
that are not currently supported by onnx-tensorflow.
This is an experimental feature. By enabling this feature,
the graph would not be guaranteed to match the ONNX specifications.
:returns: The equivalent ONNX Graph Proto object.
"""
onnx_graph = OnnxGraph(name)
exception.IGNORE_UNIMPLEMENTED = ignore_unimplemented
opset_dict = {}
for domain, version in opset:
if domain == "ai.onnx":
domain = defs.ONNX_DOMAIN
opset_dict[domain] = version
handlers = get_all_frontend_handlers(opset_dict)
node_tup = [(node.name, TensorflowNode(node))
for node in graph_def.node]
for name, node in node_tup:
if node.op_type == "Placeholder":
onnx_graph.add_input_proto(node)
elif node.op_type == "Const":
onnx_graph.add_const(node)
onnx_graph.add_const_proto(node)
onnx_graph.add_input_proto(node)
else:
onnx_graph.add_value_info_proto(node)
handler = handlers.get(node.domain, {}).get(node.op_type, None)
node_proto = None
if handler:
node_proto = handler.handle(
node,
consts=onnx_graph.consts,
node_dict=dict(node_tup),
data_type_cast_map=onnx_graph.data_type_cast_map)
else:
exception.OP_UNIMPLEMENTED_EXCEPT(
node.op_type,
domain=None
if node.domain in handlers else node.domain)
if node_proto is None:
node_proto = FrontendHandler.make_node_from_tf_node(
node, op_type=node.op_type, should_check=False)
onnx_graph.add_node_proto(node_proto)
for o in output:
output_node = TensorflowNode(o)
onnx_graph.add_output_proto(output_node)
return onnx_graph.make_graph_proto()
def tensorflow_graph_to_onnx_graph(cls,
tf_graph,
opset=((defs.ONNX_DOMAIN,
defs.onnx_opset_version()), ),
ignore_unimplemented=False):
"""Converts a TensorflowGraph to an ONNX graph
This function converts a TensorflowGraph to an equivalent
representation of ONNX graph.
:param tf_graph: TensorflowGraph object.
:param opset: Opset, which should be ((str domain: int version number),).
:param ignore_unimplemented: Convert to ONNX model and ignore all the operators
that are not currently supported by onnx-tensorflow.
This is an experimental feature. By enabling this feature,
the graph would not be guaranteed to match the ONNX specifications.
:returns: The equivalent ONNX Graph Proto object.
"""
onnx_graph = OnnxGraph(tf_graph.graph_name)
exception.IGNORE_UNIMPLEMENTED = ignore_unimplemented
training_ops_to_remove = ["RandomShuffleQueueV2"]
opset_dict = {}
for domain, version in opset:
if domain == "ai.onnx":
domain = defs.ONNX_DOMAIN
opset_dict[domain] = version
handlers = get_all_frontend_handlers(opset_dict)
node_tup = [(n.name, n) for n in tf_graph.nodes]
for name, node in node_tup:
if node.op_type == "Placeholder":
onnx_graph.add_input_proto(node)
elif node.op_type == "Const":
onnx_graph.add_const(node)
onnx_graph.add_const_proto(node)
onnx_graph.add_input_proto(node)
elif node.op_type in training_ops_to_remove:
logger.info(
"A training op with name {} type {} has been removed.".
format(node.name, node.op_type))
elif node.op_type == "QueueDequeueManyV2":
num_output = len(node.attr["_output_shapes"])
for index, shape, onnx_type in zip(
range(num_output), node.attr["_output_shapes"],
node.attr["component_types"]):
onnx_graph.add_input_proto_explicit(node.name + ":" +
str(index),
shape,
onnx_dtype=onnx_type)
else:
onnx_graph.add_value_info_proto(node)
handler = handlers.get(node.domain, {}).get(node.op_type, None)
node_proto = None
if handler:
node_proto = handler.handle(
node,
consts=onnx_graph.consts,
node_dict=dict(node_tup),
data_type_cast_map=onnx_graph.data_type_cast_map)
else:
exception.OP_UNIMPLEMENTED_EXCEPT(
node.op_type,
domain=None
if node.domain in handlers else node.domain)
if node_proto is None:
node_proto = FrontendHandler.make_node_from_tf_node(
node, op_type=node.op_type, should_check=False)
onnx_graph.add_node_proto(node_proto)
for o in tf_graph.outputs:
output_node = tf_graph.get_node_by_name(o)
onnx_graph.add_output_proto(output_node)
return onnx_graph.make_graph_proto()
