Simplify some logic.

fluid/DeepASR/data_utils/data_reader.py

@@ -299,6 +299,18 @@ class DataReader(object):
             w.join()
 
     def batch_iterator(self, batch_size, minimum_batch_size):
+        def batch_to_ndarray(batch_samples, lod):
+            batch_feature = np.zeros(
+                (lod[-1], self._frame_dim), dtype="float32")
+            batch_label = np.zeros((lod[-1], 1), dtype="int64")
+            start = 0
+            for sample in batch_samples:
+                frame_num = sample[0].shape[0]
+                batch_feature[start:start + frame_num, :] = sample[0]
+                batch_label[start:start + frame_num, :] = sample[1]
+                start += frame_num
+            return (batch_feature, batch_label)
+
         def batch_assembling_task(sample_generator, batch_queue):
             batch_samples = []
             lod = [0]
@@ -306,29 +318,15 @@ class DataReader(object):
                 batch_samples.append(sample)
                 lod.append(lod[-1] + sample[0].shape[0])
                 if len(batch_samples) == batch_size:
-                    batch_feature = np.zeros(
-                        (lod[-1], self._frame_dim), dtype="float32")
-                    batch_label = np.zeros((lod[-1], 1), dtype="int64")
-                    start = 0
-                    for sample in batch_samples:
-                        frame_num = sample[0].shape[0]
-                        batch_feature[start:start + frame_num, :] = sample[0]
-                        batch_label[start:start + frame_num, :] = sample[1]
-                        start += frame_num
+                    (batch_feature, batch_label) = batch_to_ndarray(
+                        batch_samples, lod)
                     batch_queue.put((batch_feature, batch_label, lod))
                     batch_samples = []
                     lod = [0]
 
             if len(batch_samples) >= minimum_batch_size:
-                batch_feature = np.zeros(
-                    (lod[-1], self._frame_dim), dtype="float32")
-                batch_label = np.zeros((lod[-1], 1), dtype="int64")
-                start = 0
-                for sample in batch_samples:
-                    frame_num = sample[0].shape[0]
-                    batch_feature[start:start + frame_num, :] = sample[0]
-                    batch_label[start:start + frame_num, :] = sample[1]
-                    start += frame_num
+                (batch_feature, batch_label) = batch_to_ndarray(batch_samples,
+                                                                lod)
                 batch_queue.put((batch_feature, batch_label, lod))
 
             batch_queue.put(EpochEndSignal())