supports predict of string list input

python/paddle_serving_app/local_predict.py

@@ -157,12 +157,12 @@ class LocalPredictor(object):
             "use_trt:{}, use_lite:{}, use_xpu:{}, precision:{}, use_calib:{}, "
             "use_mkldnn:{}, mkldnn_cache_capacity:{}, mkldnn_op_list:{}, "
             "mkldnn_bf16_op_list:{}, use_feed_fetch_ops:{}, "
-            "use_ascend_cl:{}, min_subgraph_size:{}, dynamic_shape_info:{}".format(
-                model_path, use_gpu, gpu_id, use_profile, thread_num, mem_optim,
-                ir_optim, use_trt, use_lite, use_xpu, precision, use_calib,
-                use_mkldnn, mkldnn_cache_capacity, mkldnn_op_list,
-                mkldnn_bf16_op_list, use_feed_fetch_ops, use_ascend_cl,
-                min_subgraph_size, dynamic_shape_info))
+            "use_ascend_cl:{}, min_subgraph_size:{}, dynamic_shape_info:{}".
+            format(model_path, use_gpu, gpu_id, use_profile, thread_num,
+                   mem_optim, ir_optim, use_trt, use_lite, use_xpu, precision,
+                   use_calib, use_mkldnn, mkldnn_cache_capacity, mkldnn_op_list,
+                   mkldnn_bf16_op_list, use_feed_fetch_ops, use_ascend_cl,
+                   min_subgraph_size, dynamic_shape_info))
 
         self.feed_names_ = [var.alias_name for var in model_conf.feed_var]
         self.fetch_names_ = [var.alias_name for var in model_conf.fetch_var]
@@ -224,10 +224,10 @@ class LocalPredictor(object):
                     use_calib_mode=use_calib)
 
                 if len(dynamic_shape_info):
-                     config.set_trt_dynamic_shape_info(
-                         dynamic_shape_info['min_input_shape'], 
-                         dynamic_shape_info['max_input_shape'], 
-                         dynamic_shape_info['opt_input_shape'])       
+                    config.set_trt_dynamic_shape_info(
+                        dynamic_shape_info['min_input_shape'],
+                        dynamic_shape_info['max_input_shape'],
+                        dynamic_shape_info['opt_input_shape'])
         # set lite
         if use_lite:
             config.enable_lite_engine(
@@ -315,7 +315,8 @@ class LocalPredictor(object):
         # Assemble the input data of paddle predictor, and filter invalid inputs. 
         input_names = self.predictor.get_input_names()
         for name in input_names:
-            if isinstance(feed[name], list):
+            if isinstance(feed[name], list) and not isinstance(feed[name][0],
+                                                               str):
                 feed[name] = np.array(feed[name]).reshape(self.feed_shapes_[
                     name])
             if self.feed_types_[name] == 0:
@@ -342,6 +343,9 @@ class LocalPredictor(object):
                 feed[name] = feed[name].astype("complex64")
             elif self.feed_types_[name] == 11:
                 feed[name] = feed[name].astype("complex128")
+            elif isinstance(feed[name], list) and isinstance(feed[name][0],
+                                                             str):
+                pass
             else:
                 raise ValueError("local predictor receives wrong data type")
 

python/pipeline/channel.py

@@ -34,6 +34,7 @@ from .error_catch import CustomExceptionCode as ChannelDataErrcode
 
 _LOGGER = logging.getLogger(__name__)
 
+
 class ChannelDataType(enum.Enum):
     """
     Channel data type
@@ -167,7 +168,8 @@ class ChannelData(object):
         elif isinstance(npdata, dict):
             # batch_size = 1
             for _, value in npdata.items():
-                if not isinstance(value, np.ndarray):
+                if not isinstance(value, np.ndarray) and not (isinstance(
+                        value, list) and isinstance(value[0], str)):
                     error_code = ChannelDataErrcode.TYPE_ERROR.value
                     error_info = "Failed to check data: the value " \
                             "of data must be np.ndarray, but get {}.".format(