Merge pull request #854 from wangjiawei04/lod_tensor

set fetch var lod tensor true forcefully

core/general-server/op/general_response_op.cpp

@@ -155,9 +155,11 @@ int GeneralResponseOp::inference() {
       }
 
       if (model_config->_is_lod_fetch[idx]) {
-        for (int j = 0; j < in->at(idx).lod[0].size(); ++j) {
-          fetch_p->mutable_tensor_array(var_idx)->add_lod(
-              in->at(idx).lod[0][j]);
+        if (in->at(idx).lod.size() > 0) {
+          for (int j = 0; j < in->at(idx).lod[0].size(); ++j) {
+            fetch_p->mutable_tensor_array(var_idx)->add_lod(
+                in->at(idx).lod[0][j]);
+          }
         }
       }
 

python/paddle_serving_client/__init__.py

@@ -354,8 +354,9 @@ class Client(object):
                                 name))
                     result_map[name].shape = shape
                     if name in self.lod_tensor_set:
-                        result_map["{}.lod".format(
-                            name)] = result_batch_handle.get_lod(mi, name)
+                        tmp_lod = result_batch_handle.get_lod(mi, name)
+                        if np.size(tmp_lod) > 0:
+                            result_map["{}.lod".format(name)] = tmp_lod
                 elif self.fetch_names_to_type_[name] == float32_type:
                     result_map[name] = result_batch_handle.get_float_by_name(
                         mi, name)
@@ -367,9 +368,9 @@ class Client(object):
                     shape = result_batch_handle.get_shape(mi, name)
                     result_map[name].shape = shape
                     if name in self.lod_tensor_set:
-                        result_map["{}.lod".format(
-                            name)] = result_batch_handle.get_lod(mi, name)
-
+                        tmp_lod = result_batch_handle.get_lod(mi, name)
+                        if np.size(tmp_lod) > 0:
+                            result_map["{}.lod".format(name)] = tmp_lod
                 elif self.fetch_names_to_type_[name] == int32_type:
                     # result_map[name] will be py::array(numpy array)
                     result_map[name] = result_batch_handle.get_int32_by_name(
@@ -382,8 +383,9 @@ class Client(object):
                     shape = result_batch_handle.get_shape(mi, name)
                     result_map[name].shape = shape
                     if name in self.lod_tensor_set:
-                        result_map["{}.lod".format(
-                            name)] = result_batch_handle.get_lod(mi, name)
+                        tmp_lod = result_batch_handle.get_lod(mi, name)
+                        if np.size(tmp_lod) > 0:
+                            result_map["{}.lod".format(name)] = tmp_lod
             multi_result_map.append(result_map)
         ret = None
         if len(model_engine_names) == 1:

python/paddle_serving_client/io/__init__.py

@@ -74,7 +74,8 @@ def save_model(server_model_folder,
         fetch_var = model_conf.FetchVar()
         fetch_var.alias_name = key
         fetch_var.name = fetch_var_dict[key].name
-        fetch_var.is_lod_tensor = fetch_var_dict[key].lod_level >= 1
+        #fetch_var.is_lod_tensor = fetch_var_dict[key].lod_level >= 1
+        fetch_var.is_lod_tensor = 1
         if fetch_var_dict[key].dtype == core.VarDesc.VarType.INT64:
             fetch_var.fetch_type = 0
         if fetch_var_dict[key].dtype == core.VarDesc.VarType.FP32: