Merge pull request #1008 from TeslaZhao/develop

Fix Bugs for batching predict of pipeline

python/pipeline/operator.py

@@ -709,11 +709,39 @@ class Op(object):
             # combine samples to batch
             one_input = preped_data_dict[data_ids[0]]
             feed_batch = []
+            feed_dict = {}
             input_offset = None
+            cur_offset = 0
+            input_offset_dict = {}
+
             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))
+                if len(data_ids) == 1:
+                    feed_batch = [
+                        preped_data_dict[data_id] for data_id in data_ids
+                    ]
+                else:
+                    for data_id in data_ids:
+                        for key, val in preped_data_dict[data_id].items():
+                            has_val = feed_dict.get(key)
+                            if has_val is None:
+                                feed_dict[key] = val
+                                continue
+                            # merge 2 np.arrray
+                            if isinstance(val, np.ndarray):
+                                feed_dict[key] = np.append(
+                                    feed_dict[key], val, axis=0)
+                    feed_batch.append(feed_dict)
+
+                for data_id in data_ids:
+                    start = cur_offset
+                    for key, val in preped_data_dict[data_id].items():
+                        if isinstance(val, (list, np.ndarray)):
+                            cur_offset += len(val)
+                        else:
+                            cur_offset += 1
+                        break
+                    input_offset_dict[data_id] = [start, cur_offset]
             elif isinstance(one_input, list):
                 # batch input
                 input_offset = [0]
@@ -796,8 +824,10 @@ class Op(object):
                 for name in var_names:
                     lod_offset_name = "{}.lod".format(name)
                     if lod_offset_name in var_names:
-                        _LOGGER.debug("(log_id={}) {} {} is LodTensor".format(
-                            typical_logid, op_info_prefix, name))
+                        _LOGGER.debug(
+                            "(log_id={}) {} {} is LodTensor. lod_offset_name:{}".
+                            format(typical_logid, op_info_prefix, name,
+                                   lod_offset_name))
                         lod_var_names.add(name)
                         lod_offset_names.add(lod_offset_name)
 
@@ -812,8 +842,8 @@ class Op(object):
                         lod_offset_name = "{}.lod".format(name)
                         lod_offset = midped_batch[lod_offset_name]
                         for idx, data_id in enumerate(data_ids):
-                            data_offset_left = input_offset[idx]
-                            data_offset_right = input_offset[idx + 1]
+                            data_offset_left = input_offset_dict[data_id][0]
+                            data_offset_right = input_offset_dict[data_id][1]
                             lod_offset_left = lod_offset[data_offset_left]
                             lod_offset_right = lod_offset[data_offset_right]
                             midped_data_dict[data_id][name] = value[
@@ -823,8 +853,8 @@ class Op(object):
                     else:
                         # normal tensor
                         for idx, data_id in enumerate(data_ids):
-                            left = input_offset[idx]
-                            right = input_offset[idx + 1]
+                            left = input_offset_dict[data_id][0]
+                            right = input_offset_dict[data_id][1]
                             midped_data_dict[data_id][name] = value[left:right]
         else:
             midped_data_dict = preped_data_dict