def predict_raw(self, request):
print("Predict raw")
default_data_type = request.data.WhichOneof("data_oneof")
print(default_data_type)
if default_data_type == "tftensor" and self.grpc:
tfrequest = predict_pb2.PredictRequest()
tfrequest.model_spec.name = self.model_name
tfrequest.model_spec.signature_name = self.signature_name
tfrequest.inputs[self.model_input].CopyFrom(request.data.tftensor)
result = self.stub.Predict(tfrequest)
print(result)
datadef = prediction_pb2.DefaultData(
tftensor=result.outputs[self.model_output])
return prediction_pb2.SeldonMessage(data=datadef)
elif default_data_type == "jsonData":
predictions = self.predict(request.jsonData, features_names=[])
return prediction_pb2.SeldonMessage(jsonData=predictions)
else:
features = get_data_from_proto(request)
datadef = request.data
data_type = request.WhichOneof("data_oneof")
predictions = self.predict(features, datadef.names)
predictions = np.array(predictions)
if data_type == "data":
default_data_type = request.data.WhichOneof("data_oneof")
else:
default_data_type = "tensor"
class_names = []
data = array_to_grpc_datadef(predictions, class_names,
default_data_type)
return prediction_pb2.SeldonMessage(data=data)
def test_get_data_from_proto_tftensor():
arr = np.array([[1], [2]])
datadef = prediction_pb2.DefaultData(tftensor=tf.make_tensor_proto(arr))
request = prediction_pb2.SeldonMessage(data=datadef)
arr: np.ndarray = scu.get_data_from_proto(request)
assert arr.shape == (2, 1)
assert arr[0][0] == 1
assert arr[1][0] == 2
def test_get_data_from_proto_ndarray():
arr = np.array([[1], [2]])
lv = scu.array_to_list_value(arr)
datadef = prediction_pb2.DefaultData(ndarray=lv)
request = prediction_pb2.SeldonMessage(data=datadef)
arr: np.ndarray = scu.get_data_from_proto(request)
assert arr.shape == (2, 1)
assert arr[0][0] == 1
assert arr[1][0] == 2
def test_get_data_from_proto_tensor():
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr))
request = prediction_pb2.SeldonMessage(data=datadef)
arr: np.ndarray = scu.get_data_from_proto(request)
assert arr.shape == (2, 1)
assert arr[0][0] == 1
assert arr[1][0] == 2
def predict_raw(self, request):
log.debug("Predict raw")
request_data_type = request.WhichOneof("data_oneof")
default_data_type = request.data.WhichOneof("data_oneof")
log.debug(str(request_data_type), str(default_data_type))
if default_data_type == "tftensor" and self.grpc:
tfrequest = predict_pb2.PredictRequest()
tfrequest.model_spec.name = self.model_name
tfrequest.model_spec.signature_name = self.signature_name
tfrequest.inputs[self.model_input].CopyFrom(request.data.tftensor)
result = self.stub.Predict(tfrequest)
log.debug(result)
datadef = prediction_pb2.DefaultData(
tftensor=result.outputs[self.model_output])
return prediction_pb2.SeldonMessage(data=datadef)
elif request_data_type == "jsonData":
features = get_data_from_proto(request)
predictions = self.predict(features, features_names=[])
try:
sm = json_to_seldon_message({"jsonData": predictions})
except ParseError as e:
sm = prediction_pb2.SeldonMessage(strData=predictions)
return sm
else:
features = get_data_from_proto(request)
datadef = request.data
predictions = self.predict(features, datadef.names)
predictions = np.array(predictions)
if request_data_type is not "data":
default_data_type = "tensor"
class_names = []
data = array_to_grpc_datadef(predictions, class_names,
default_data_type)
return prediction_pb2.SeldonMessage(data=data)
