def onnx1_4to1_6(model: onnx.ModelProto) -> onnx.ModelProto:
"""Update ir_version from 4 to 6 and update opset from 9 to 11.
Args:
model (onnx.ModelProto): input onnx model.
Returns:
onnx.ModelProto: updated onnx model.
"""
graph = model.graph
if model.opset_import[0].version == 11:
print("(Stop) the input model is already opset 11, no need to upgrade")
exit(1)
# deal with empty node name issue
other.add_name_to_node(graph)
# simplify the node param type from initializer to constant
replacing.replace_initializer_with_Constant(graph)
# Modify the nodes.
replace_min_max_attribute_to_const_node_in_clip_node(graph)
replace_all_attribute_to_const_node_in_slice_node(graph)
replace_all_attribute_to_const_node_in_pad_node(graph)
upsampling_to_resize(graph)
other.topological_sort(graph)
# Change model properties.
model.ir_version = 6
model.opset_import[0].version = 11
model = onnx.utils.polish_model(model)
return model
def make_model(graph: GraphProto, **kwargs: Any) -> ModelProto:
"""Construct a ModelProto
Arguments:
graph (GraphProto): *make_graph* returns
**kwargs: any attribute to add to the returned instance
Returns:
ModelProto
"""
model = ModelProto()
# Touch model.ir_version so it is stored as the version from which it is
# generated.
model.ir_version = IR_VERSION
model.graph.CopyFrom(graph)
opset_imports: Optional[Sequence[OperatorSetIdProto]] = None
opset_imports = kwargs.pop('opset_imports', None) # type: ignore
if opset_imports is not None:
model.opset_import.extend(opset_imports)
else:
# Default import
imp = model.opset_import.add()
imp.version = defs.onnx_opset_version()
functions: Optional[Sequence[FunctionProto]] = None
functions = kwargs.pop('functions', None) # type: ignore
if functions is not None:
model.functions.extend(functions)
for k, v in kwargs.items():
# TODO: Does this work with repeated fields?
setattr(model, k, v)
return model
def make_model(graph, **kwargs):
model = ModelProto()
# Touch model.ir_version so it is stored as the version from which it is
# generated.
model.ir_version = IR_VERSION
model.graph.CopyFrom(graph)
for k, v in kwargs.items():
setattr(model, k, v)
return model
def test_version_exists(self):
model = ModelProto()
# When we create it, graph should not have a version string.
self.assertFalse(model.HasField('ir_version'))
# We should touch the version so it is annotated with the current
# ir version of the running ONNX
model.ir_version = IR_VERSION
model_string = model.SerializeToString()
model.ParseFromString(model_string)
self.assertTrue(model.HasField('ir_version'))
# Check if the version is correct.
self.assertEqual(model.ir_version, IR_VERSION)
def test_version_exists(self): # type: () -> None
model = ModelProto()
# When we create it, graph should not have a version string.
self.assertFalse(model.HasField('ir_version'))
# We should touch the version so it is annotated with the current
# ir version of the running ONNX
model.ir_version = IR_VERSION
model_string = model.SerializeToString()
model.ParseFromString(model_string)
self.assertTrue(model.HasField('ir_version'))
# Check if the version is correct.
self.assertEqual(model.ir_version, IR_VERSION)
def create_model(self):
mp = ModelProto()
mp.ir_version = ONNX_IR_VERSION
op = mp.opset_import.add()
op.domain = "" # empty string indicates ONNX domain
op.version = ONNX_OPSET_VERSION
# nn_opset = mp.opset_import.add()
# nn_opset.domain = NNABLA_DOMAIN
# nn_opset.version = NNABLA_OPSET_VERSION
mp.producer_name = PRODUCER_NAME
mp.producer_version = PRODUCER_VERSION
mp.domain = NNABLA_DOMAIN
self._model_proto = mp
def nnp_model_to_onnx_protobuf(nnp, batch_size):
mp = ModelProto()
mp.ir_version = ONNX_IR_VERSION
op = mp.opset_import.add()
op.domain = "" # empty string indicates ONNX domain
op.version = ONNX_OPSET_VERSION
# nn_opset = mp.opset_import.add()
# nn_opset.domain = NNABLA_DOMAIN
# nn_opset.version = NNABLA_OPSET_VERSION
mp.producer_name = PRODUCER_NAME
mp.producer_version = PRODUCER_VERSION
mp.domain = NNABLA_DOMAIN
nnp_model_to_onnx_graph(mp.graph, nnp, batch_size)
return mp
def make_model(graph, **kwargs):
model = ModelProto()
# Touch model.ir_version so it is stored as the version from which it is
# generated.
model.ir_version = IR_VERSION
model.graph.CopyFrom(graph)
if 'opset_import' in kwargs:
model.opset_import.extend(kwargs['opset_import'])
else:
# Default import
imp = model.opset_import.add()
imp.version = defs.onnx_opset_version()
for k, v in kwargs.items():
# TODO: Does this work with repeated fields?
setattr(model, k, v)
return model
def make_model(graph, **kwargs): # type: (GraphProto, **Any) -> ModelProto
model = ModelProto()
# Touch model.ir_version so it is stored as the version from which it is
# generated.
model.ir_version = IR_VERSION
model.graph.CopyFrom(graph)
opset_imports = None # type: Optional[Sequence[OperatorSetIdProto]]
opset_imports = kwargs.pop('opset_imports', None) # type: ignore
if opset_imports is not None:
model.opset_import.extend(opset_imports)
else:
# Default import
imp = model.opset_import.add()
imp.version = defs.onnx_opset_version()
for k, v in kwargs.items():
# TODO: Does this work with repeated fields?
setattr(model, k, v)
return model
def make_model(graph, **kwargs): # type: (GraphProto, **Any) -> ModelProto
model = ModelProto()
# Touch model.ir_version so it is stored as the version from which it is
# generated.
model.ir_version = IR_VERSION
model.graph.CopyFrom(graph)
opset_imports = None # type: Optional[Sequence[OperatorSetIdProto]]
opset_imports = kwargs.pop('opset_imports', None) # type: ignore
if opset_imports is not None:
model.opset_import.extend(opset_imports)
else:
# Default import
imp = model.opset_import.add()
imp.version = defs.onnx_opset_version()
for k, v in kwargs.items():
# TODO: Does this work with repeated fields?
setattr(model, k, v)
return model
def test_load(self):
# Create a model proto.
model = ModelProto()
model.ir_version = IR_VERSION
model_string = model.SerializeToString()
# Test if input is string
loaded_model = onnx.load_from_string(model_string)
self.assertTrue(model == loaded_model)
# Test if input has a read function
f = io.BytesIO(model_string)
loaded_model = onnx.load(f)
self.assertTrue(model == loaded_model)
# Test if input is a file name
f = tempfile.NamedTemporaryFile(delete=False)
f.write(model_string)
f.close()
loaded_model = onnx.load(f.name)
self.assertTrue(model == loaded_model)
os.remove(f.name)
def _simple_model(self):
# Create a model proto.
model = ModelProto()
model.ir_version = IR_VERSION
return model
def _simple_model(self): # type: () -> ModelProto
# Create a ModelProto.
model = ModelProto()
model.ir_version = IR_VERSION
return model
def _simple_model(self): # type: () -> ModelProto
# Create a ModelProto.
model = ModelProto()
model.ir_version = IR_VERSION
return model
#!/usr/bin/env python import io import onnx import os import tempfile from onnx import AttributeProto, NodeProto, GraphProto, ModelProto, IR_VERSION # Create a model proto. model = ModelProto() model.ir_version = IR_VERSION model_string = model.SerializeToString() # Test if input is string loaded_model = onnx.load_from_string(model_string) assert model == loaded_model # Test if input has a read function f = io.BytesIO(model_string) loaded_model = onnx.load(f) assert model == loaded_model # Test if input is a file name f = tempfile.NamedTemporaryFile(delete=False) f.write(model_string) f.close() loaded_model = onnx.load(f.name) assert model == loaded_model os.remove(f.name) try:
