fix batch input

python/pipeline/operator.py

@@ -345,7 +345,29 @@ class Op(object):
                         " we selected logid={} (from batch: {}) as a "
                         "representative for logging.".format(
                             data_id, op_info_prefix, typical_logid, data_ids))
-            feed_batch = [preped_data_dict[data_id] for data_id in data_ids]
+
+            # combine samples to batch
+            one_input = preped_data_dict[data_ids[0]]
+            feed_batch = []
+            input_offset = None
+            if isinstance(one_input, dict):
+                # sample input
+                feed_batch = [preped_data_dict[data_id] for data_id in data_ids]
+                input_offset = list(range(len(data_ids) + 1))
+            elif isinstance(one_input, list):
+                # batch input
+                input_offset = [0]
+                for data_id in data_ids:
+                    batch_input = preped_data_dict[data_id]
+                    offset = input_offset[-1] + len(batch_input)
+                    feed_batch += batch_input
+                    input_offset.append(offset)
+            else:
+                _LOGGER.critical(
+                        "{} Failed to process: expect input type is dict or listi"
+                        ", but get {}".format(op_info_prefix, type(one_input)))
+                os._exit(-1)
+
             midped_batch = None
             ecode = ChannelDataEcode.OK.value
             if self._timeout <= 0:
@@ -414,20 +436,28 @@ class Op(object):
                 
                 for idx, data_id in enumerate(data_ids):
                     midped_data_dict[data_id] = {}
-                
+ 
                 for name, value in midped_batch.items():
                     if name in lod_offset_names:
                         continue
                     if name in lod_var_names:
                         # lodtensor
                         lod_offset_name = "{}.lod".format(name)
+                        lod_offset = midped_batch[lod_offset_name]
                         for idx, data_id in enumerate(data_ids):
-                            left = midped_batch[lod_offset_name][idx]
-                            right = midped_batch[lod_offset_name][idx + 1]
-                            midped_data_dict[data_id][name] = value[left:right]
+                            data_offset_left = input_offset[idx]
+                            data_offset_right = input_offset[idx + 1]
+                            lod_offset_left = lod_offset[data_offset_left]
+                            lod_offset_right = lod_offset[data_offset_right]
+                            midped_data_dict[data_id][name] = value[lod_offset_left:lod_offset_right]
+                            midped_data_dict[data_id][lod_offset_name] = \
+                                    lod_offset[data_offset_left:data_offset_right + 1] - lod_offset[data_offset_left]
                     else:
+                        # normal tensor
                         for idx, data_id in enumerate(data_ids):
-                            midped_data_dict[data_id][name] = value[idx]
+                            left = input_offset[idx]
+                            right = input_offset[idx + 1]
+                            midped_data_dict[data_id][name] = value[left:right]
         else:
             midped_data_dict = preped_data_dict
         _LOGGER.debug("{} Succ process".format(op_info_prefix))
@@ -772,7 +802,7 @@ class ResponseOp(Op):
                 feed = channeldata.parse()
                 # ndarray to string:
                 # https://stackoverflow.com/questions/30167538/convert-a-numpy-ndarray-to-stringor-bytes-and-convert-it-back-to-numpy-ndarray
-                np.set_printoptions(threshold=np.nan)
+                np.set_printoptions(threshold=sys.maxsize)
                 for name, var in feed.items():
                     resp.value.append(var.__repr__())
                     resp.key.append(name)