Merge pull request #1472 from TeslaZhao/develop

python pipeline support lod input

python/pipeline/operator.py

@@ -1589,13 +1589,15 @@ class RequestOp(Op):
             tensor: one tensor in request.tensors.
 
         Returns:
-            np.ndnumpy
+            np_data: np.ndnumpy, the tensor data is converted to numpy.
+            lod_info: np.ndnumpy, lod info of the tensor data, None default.
         """
         if tensor is None or tensor.elem_type is None or tensor.name is None:
             _LOGGER.error("input params of tensor is wrong. tensor: {}".format(
                 tensor))
             return None
 
+        # Set dim shape
         dims = []
         if tensor.shape is None:
             dims.append(1)
@@ -1603,6 +1605,11 @@ class RequestOp(Op):
             for one_dim in tensor.shape:
                 dims.append(one_dim)
 
+        # Set up 2-d lod tensor
+        np_lod = None
+        if len(tensor.lod) > 0:
+            np_lod = np.array(tensor.lod).astype(int32).reshape(2, -1)
+
         np_data = None
         _LOGGER.info("proto_to_numpy, name:{}, type:{}, dims:{}".format(
             tensor.name, tensor.elem_type, dims))
@@ -1648,7 +1655,7 @@ class RequestOp(Op):
                 "Sorry, the type {} of tensor {} is not supported.".format(
                     tensor.elem_type, tensor.name))
 
-        return np_data
+        return np_data, np_lod
 
     def unpack_request_package(self, request):
         """
@@ -1705,7 +1712,10 @@ class RequestOp(Op):
 
                 dict_data[name] = new_string
             else:
-                dict_data[name] = self.proto_tensor_2_numpy(one_tensor)
+                np_data, np_lod = self.proto_tensor_2_numpy(one_tensor)
+                dict_data[name] = np_data
+                if np_lod is not None:
+                    dict_data[name + ".lod"] = np_lod
 
         _LOGGER.info("RequestOp unpack one request. log_id:{}, clientip:{} \
             name:{}, method:{}, time:{}"

python/pipeline/pipeline_client.py

@@ -98,6 +98,12 @@ class PipelineClient(object):
         else:
             # pack tensor format
             for key, value in feed_dict.items():
+
+                # skipping the lod feed_var.
+                # The declare of lod feed_var must be hebind the feed_var.
+                if ".lod" in key:
+                    continue
+
                 one_tensor = req.tensors.add()
                 one_tensor.name = key
 
@@ -114,6 +120,13 @@ class PipelineClient(object):
                     for one_dim in value.shape:
                         one_tensor.shape.append(one_dim)
 
+                    # set lod info, must be list type.
+                    lod_key = key + ".lod"
+                    if lod_key in feed_dict:
+                        lod_list = feed_dict.get(lod_key)
+                        if lod_list is not None:
+                            one_tensor.lod.extend(lod_list)
+
                     # packed into bytes
                     if use_tensor_bytes is True:
                         np_bytes = BytesIO()