def __init__(
self,
name: str,
user_func: Callable[[Any], Any],
protocol: Protocol = None,
inputs: ModelDataType = None,
outputs: ModelDataType = None,
):
self._name = name
self._user_func = user_func
self.protocol = protocol
input_args = []
output_args = []
if inputs is None and outputs is None:
if user_func is not None:
hints = get_type_hints(user_func)
for k, v in hints.items():
if k == "return":
if isinstance(v, typing._GenericAlias):
targs = v.__args__
for targ in targs:
output_args.append(ModelDataArg(ty=targ))
else:
output_args.append(ModelDataArg(ty=v))
else:
input_args.append(ModelDataArg(name=k, ty=v))
else:
if type(outputs) == Dict:
for k, v in outputs.items():
output_args.append(ModelDataArg(name=k, ty=v))
elif type(outputs) == Tuple:
for ty in list(outputs):
output_args.append(ModelDataArg(ty=ty))
else:
output_args.append(ModelDataArg(ty=outputs))
if type(inputs) == Dict:
for k, v in inputs.items():
input_args.append(ModelDataArg(name=k, ty=v))
elif type(inputs) == Tuple:
for ty in list(inputs):
input_args.append(ModelDataArg(ty=ty))
else:
input_args.append(ModelDataArg(ty=inputs))
self.inputs: ModelDataArgs = ModelDataArgs(args=input_args)
self.outputs: ModelDataArgs = ModelDataArgs(args=output_args)
self.cls = None
def test_v2_from_protocol_response():
res = {
"outputs": [{
"name": "a",
"data": [97, 98, 99],
"datatype": "BYTES"
}]
}
modelTyArgs = ModelDataArgs(args=[ModelDataArg(ty=str, name=None)])
v2 = V2Protocol()
res = v2.from_protocol_response(res, modelTyArgs)
def test_model_data_arg():
m = ModelDataArg(ty=str)
s = m.json()
j = json.loads(s)
assert j["ty"] == "builtins.str"
m = ModelDataArg(ty=np.ndarray)
s = m.json()
j = json.loads(s)
assert j["ty"] == "numpy.ndarray"
m = ModelDataArg(**j)
def _get_args(
self, inputs: ModelDataType = None, outputs: ModelDataType = None
) -> Tuple[ModelDataArgs, ModelDataArgs]:
input_args = []
output_args = []
if inputs is None and outputs is None:
if self._user_func is not None:
hints = get_type_hints(self._user_func)
for k, v in hints.items():
if k == "return":
if hasattr(v, "__args__"):
# NOTE: If `__args__` are present, assume this as a
# `typing.Generic`, like `Tuple`
targs = v.__args__
for targ in targs:
output_args.append(ModelDataArg(ty=targ))
else:
output_args.append(ModelDataArg(ty=v))
else:
input_args.append(ModelDataArg(name=k, ty=v))
else:
if isinstance(outputs, dict):
for k, v in outputs.items():
output_args.append(ModelDataArg(name=k, ty=v))
elif isinstance(outputs, tuple):
for ty in list(outputs):
output_args.append(ModelDataArg(ty=ty))
else:
output_args.append(ModelDataArg(ty=outputs))
if isinstance(inputs, dict):
for k, v in inputs.items():
input_args.append(ModelDataArg(name=k, ty=v))
elif isinstance(inputs, tuple):
for ty in list(inputs):
input_args.append(ModelDataArg(ty=ty))
else:
input_args.append(ModelDataArg(ty=inputs))
return ModelDataArgs(args=input_args), ModelDataArgs(args=output_args)
def _get_args(self,
inputs: ModelDataType = None,
outputs: ModelDataType = None
) -> Tuple[ModelDataArgs, ModelDataArgs]:
input_args = []
output_args = []
if inputs is None and outputs is None:
if self._user_func is not None:
hints = get_type_hints(self._user_func)
for k, v in hints.items():
if k == "return":
if isinstance(v, typing._GenericAlias):
targs = v.__args__
for targ in targs:
output_args.append(ModelDataArg(ty=targ))
else:
output_args.append(ModelDataArg(ty=v))
else:
input_args.append(ModelDataArg(name=k, ty=v))
else:
if type(outputs) == Dict:
for k, v in outputs.items():
output_args.append(ModelDataArg(name=k, ty=v))
elif type(outputs) == Tuple:
for ty in list(outputs):
output_args.append(ModelDataArg(ty=ty))
else:
output_args.append(ModelDataArg(ty=outputs))
if type(inputs) == Dict:
for k, v in inputs.items():
input_args.append(ModelDataArg(name=k, ty=v))
elif type(inputs) == Tuple:
for ty in list(inputs):
input_args.append(ModelDataArg(ty=ty))
else:
input_args.append(ModelDataArg(ty=inputs))
return ModelDataArgs(args=input_args), ModelDataArgs(args=output_args)
def test_model_spec():
ms = ModelSpec(
model_details=ModelDetails(
name="test",
local_folder="",
uri="",
platform=ModelFramework.XGBoost,
inputs=ModelDataArgs(args=[ModelDataArg(ty=str)]),
outputs=ModelDataArgs(args=[]),
),
protocol=V2Protocol(),
runtime_options=KFServingOptions().local_options,
)
s = ms.json()
j = json.loads(s)
ms2 = ModelSpec(**j)
assert isinstance(ms2.protocol, V2Protocol)
assert ms2.model_details.inputs.args[0].ty == str
def test_model_data_args():
args = ModelDataArgs(args=[ModelDataArg(ty=str)])
s = args.json()
j = json.loads(s)
assert j["args"][0]["ty"] == "builtins.str"