Rename method from_raw_data to from_xr_tensors

tests/test_converters.py

@@ -299,7 +299,7 @@ def test_create_sample_from_raw_data(self):
         arr_2 = np.arange(64 * 64, dtype=np.int64).reshape(64, 64)
         tensor_2 = xr.DataArray(arr_2, dims=["x", "y"])
         tensors_ids = ["input1", "input2"]
-        actual_sample = Sample.from_raw_data(tensors_ids, [tensor_1, tensor_2])
+        actual_sample = Sample.from_xr_tensors(tensors_ids, [tensor_1, tensor_2])
 
         expected_dict = {tensors_ids[0]: tensor_1, tensors_ids[1]: tensor_2}
         expected_sample = Sample(expected_dict)
@@ -311,7 +311,7 @@ def test_sample_to_pb_tensors(self):
         arr_2 = np.arange(64 * 64, dtype=np.int64).reshape(64, 64)
         tensor_2 = xr.DataArray(arr_2, dims=["x", "y"])
         tensors_ids = ["input1", "input2"]
-        sample = Sample.from_raw_data(tensors_ids, [tensor_1, tensor_2])
+        sample = Sample.from_xr_tensors(tensors_ids, [tensor_1, tensor_2])
 
         pb_tensor_1 = inference_pb2.Tensor(
             dtype="int64",

tiktorch/converters.py

@@ -44,7 +44,7 @@ def from_pb_tensors(cls, pb_tensors: List[inference_pb2.Tensor]) -> Sample:
         return Sample({tensor.tensorId: pb_tensor_to_xarray(tensor) for tensor in pb_tensors})
 
     @classmethod
-    def from_raw_data(cls, tensor_ids: List[str], tensors_data: List[xr.DataArray]):
+    def from_xr_tensors(cls, tensor_ids: List[str], tensors_data: List[xr.DataArray]):
         assert len(tensor_ids) == len(tensors_data)
         return Sample({tensor_id: tensor_data for tensor_id, tensor_data in zip(tensor_ids, tensors_data)})
 

tiktorch/server/grpc/inference_servicer.py

@@ -80,7 +80,7 @@ def Predict(self, request: inference_pb2.PredictRequest, context) -> inference_p
         tensor_validator.check_tensors(input_sample)
         res = session.client.api.forward(input_sample)
         output_tensor_ids = [tensor.name for tensor in session.client.output_specs]
-        output_sample = Sample.from_raw_data(output_tensor_ids, res)
+        output_sample = Sample.from_xr_tensors(output_tensor_ids, res)
         return inference_pb2.PredictResponse(tensors=output_sample.to_pb_tensors())
 
     def _getModelSession(self, context, modelSessionId: str) -> ISession: