def process_node(self, node: fbs.Node, value_name_to_typeinfo: dict):
type0 = value_name_to_typestr(node.Inputs(0), value_name_to_typeinfo)
type1 = value_name_to_typestr(node.Inputs(1), value_name_to_typeinfo)
type2 = value_name_to_typestr(node.Inputs(2), value_name_to_typeinfo)
# types in kernel registration are ordered this way: input (T1), output (T3), depth (T2)
key = (type0, type2, type1)
self._triples.add(key)
def process_node(self, node: fbs.Node, value_name_to_typeinfo: dict):
'''
Process a Node and record info on the types used.
:param node: Node from ORT format model
:param value_name_to_typeinfo: Map of value names to TypeInfo instances
'''
optype = node.OpType().decode()
domain = node.Domain().decode(
) or 'ai.onnx' # empty domain defaults to ai.onnx
key = _create_op_key(domain, optype)
op_processor = self._get_op_processor(key)
if op_processor:
op_processor.process_node(node, value_name_to_typeinfo)
def process_node(self, node: fbs.Node, value_name_to_typeinfo: dict):
for i in self._input_types.keys():
if i >= node.InputsLength():
# Some operators have fewer inputs in earlier versions where data that was as an attribute
# become an input in later versions to allow it to be dynamically provided. Allow for that.
# e.g. Slice-1 had attributes for the indices, and Slice-10 moved those to be inputs
# raise RuntimeError('Node has {} outputs. Tracker for {} incorrectly configured as it requires {}.'
# .format(node.OutputsLength(), self.name, o))
pass
else:
type_str = value_name_to_typestr(node.Inputs(i),
value_name_to_typeinfo)
self._input_types[i].add(type_str)
for o in self._output_types.keys():
# Don't know of any ops where the number of outputs changed across versions, so require a valid length
if o >= node.OutputsLength():
raise RuntimeError(
'Node has {} outputs. Tracker for {} incorrectly configured as it requires {}.'
.format(node.OutputsLength(), self.name, o))
type_str = value_name_to_typestr(node.Outputs(o),
value_name_to_typeinfo)
self._output_types[o].add(type_str)
def _dump_node(self, node: fbs.Node):
optype = node.OpType().decode()
domain = node.Domain().decode(
) or 'ai.onnx' # empty domain defaults to ai.onnx
inputs = [
node.Inputs(i).decode() for i in range(0, node.InputsLength())
]
outputs = [
node.Outputs(i).decode() for i in range(0, node.OutputsLength())
]
print(f'{node.Index()}:{node.Name().decode()}({domain}:{optype}) '
f'inputs=[{",".join(inputs)} outputs=[{",".join(outputs)}]')
def process_node(self, node: fbs.Node, value_name_to_typeinfo: dict):
for i in self._input_types.keys():
if i >= node.InputsLength():
raise RuntimeError(
'Node has {} inputs. Tracker for {} incorrectly configured as it requires {}.'
.format(node.InputsLength(), self.name, i))
type_str = value_name_to_typestr(node.Inputs(i),
value_name_to_typeinfo)
self._input_types[i].add(type_str)
for o in self._output_types.keys():
if o >= node.OutputsLength():
raise RuntimeError(
'Node has {} outputs. Tracker for {} incorrectly configured as it requires {}.'
.format(node.OutputsLength(), self.name, o))
type_str = value_name_to_typestr(node.Outputs(o),
value_name_to_typeinfo)
self._output_types[o].add(type_str)
def process_node(self, node: fbs.Node, value_name_to_typeinfo: dict):
type0 = value_name_to_typestr(node.Inputs(0), value_name_to_typeinfo)
type1 = value_name_to_typestr(node.Inputs(1), value_name_to_typeinfo)
type2 = value_name_to_typestr(node.Inputs(2), value_name_to_typeinfo)
key = '{}_{}_{}'.format(type0, type1, type2)
self._triples.add(key)