def test_proto_array_to_tftensor():
arr = np.array([[1, 2, 3], [4, 5, 6]])
datadef = array_to_grpc_datadef(arr, [], "tftensor")
print(datadef)
assert datadef.tftensor.tensor_shape.dim[0].size == 2
assert datadef.tftensor.tensor_shape.dim[1].size == 3
assert datadef.tftensor.dtype == 9
def TransformOutput(self, request, context):
if hasattr(self.user_model, "transform_output_grpc"):
return self.user_model.transform_output_grpc(request)
else:
features = get_data_from_proto(request)
datadef = request.data
data_type = request.WhichOneof("data_oneof")
# Construct meta data
meta = prediction_pb2.Meta()
metaJson = {}
tags = get_custom_tags(self.user_model)
if tags:
metaJson["tags"] = tags
metrics = get_custom_metrics(self.user_model)
if metrics:
metaJson["metrics"] = metrics
json_format.ParseDict(metaJson, meta)
transformed = transform_output(self.user_model, features,
datadef.names)
if isinstance(transformed, np.ndarray) or data_type == "data":
transformed = np.array(transformed)
class_names = get_class_names(self.user_model, datadef.names)
if data_type == "data":
default_data_type = request.data.WhichOneof("data_oneof")
else:
default_data_type = "tensor"
data = array_to_grpc_datadef(transformed, class_names,
default_data_type)
return prediction_pb2.SeldonMessage(data=data, meta=meta)
else:
return prediction_pb2.SeldonMessage(binData=transformed,
meta=meta)
def predict_grpc(self, request):
print("Predict grpc called")
default_data_type = request.data.WhichOneof("data_oneof")
print(default_data_type)
if default_data_type == "tftensor":
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)
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 len(predictions.shape) > 1:
class_names = get_class_names(self, predictions.shape[1])
else:
class_names = []
if data_type == "data":
default_data_type = request.data.WhichOneof("data_oneof")
else:
default_data_type = "tensor"
data = array_to_grpc_datadef(predictions, class_names,
default_data_type)
return prediction_pb2.SeldonMessage(data=data)
def Route(self, request, context):
if hasattr(self.user_model, "route_grpc"):
return self.user_model.route_grpc(request)
else:
datadef = request.data
features = grpc_datadef_to_array(datadef)
routing = np.array([[route(self.user_model, features, datadef.names)]])
# TODO: check that predictions is 2 dimensional
class_names = []
data = array_to_grpc_datadef(
routing, class_names, request.data.WhichOneof("data_oneof"))
# Construct meta data
meta = prediction_pb2.Meta()
metaJson = {}
tags = get_custom_tags(self.user_model)
if tags:
metaJson["tags"] = tags
metrics = get_custom_metrics(self.user_model)
if metrics:
metaJson["metrics"] = metrics
json_format.ParseDict(metaJson, meta)
return prediction_pb2.SeldonMessage(data=data, meta=meta)
def Predict(self, request, context):
if hasattr(self.user_model, "predict_grpc"):
return self.user_model.predict_grpc(request)
else:
features = get_data_from_proto(request)
meta = get_meta_from_proto(request)
datadef = request.data
data_type = request.WhichOneof("data_oneof")
predictions = predict(self.user_model,
features,
datadef.names,
meta=meta)
# Construct meta data
meta = prediction_pb2.Meta()
metaJson = {}
tags = get_custom_tags(self.user_model)
if tags:
metaJson["tags"] = tags
metrics = get_custom_metrics(self.user_model)
if metrics:
metaJson["metrics"] = metrics
json_format.ParseDict(metaJson, meta)
if isinstance(predictions, np.ndarray) or data_type == "data":
predictions = np.array(predictions)
if len(predictions.shape) > 1:
class_names = get_class_names(self.user_model,
predictions.shape[1])
else:
class_names = []
if data_type == "data":
default_data_type = request.data.WhichOneof("data_oneof")
else:
default_data_type = "tensor"
data = array_to_grpc_datadef(predictions, class_names,
default_data_type)
return prediction_pb2.SeldonMessage(data=data, meta=meta)
else:
return prediction_pb2.SeldonMessage(binData=predictions,
meta=meta)
def Aggregate(self, request, context):
if hasattr(self.user_model, "aggregate_grpc"):
return self.user_model.aggregate_grpc(request)
else:
features_list = []
names_list = []
for msg in request.seldonMessages:
features = get_data_from_proto(msg)
features_list.append(features)
names_list.append(msg.data.names)
data_type = request.seldonMessages[0].WhichOneof("data_oneof")
aggregated = aggregate(self.user_model, features_list, names_list)
# Construct meta data
meta = prediction_pb2.Meta()
metaJson = {}
tags = get_custom_tags(self.user_model)
if tags:
metaJson["tags"] = tags
metrics = get_custom_metrics(self.user_model)
if metrics:
metaJson["metrics"] = metrics
json_format.ParseDict(metaJson, meta)
if isinstance(aggregated, np.ndarray) or data_type == "data":
aggregated = np.array(aggregated)
feature_names = get_feature_names(self.user_model, [])
if data_type == "data":
default_data_type = request.seldonMessages[
0].data.WhichOneof("data_oneof")
else:
default_data_type = "tensor"
data = array_to_grpc_datadef(aggregated, feature_names,
default_data_type)
return prediction_pb2.SeldonMessage(data=data, meta=meta)
else:
return prediction_pb2.SeldonMessage(binData=aggregated,
meta=meta)