Merge branch 'develop' of github.com:PaddlePaddle/models into fix-638

fluid/DeepASR/data_utils/data_reader.py

@@ -122,8 +122,6 @@ class DataReader(object):
                                  corresponding description file.
         label_file_list (str): File containing paths of label data file and 
                                corresponding description file.
-        frame_dim (int): The final feature dimension of one frame after all 
-                         augmentation applied.
         drop_frame_len (int): Samples whose label length above the value will be
                               dropped.
         process_num (int): Number of processes for processing data.
@@ -141,11 +139,9 @@ class DataReader(object):
                        suppressed. If set to 1, all complaints will be printed.
     """
 
-    def __init__(
-            self,
+    def __init__(self,
                  feature_file_list,
                  label_file_list,
-            frame_dim=120 * 11,  # @TODO augmentor is responsible for the value
                  drop_frame_len=512,
                  process_num=10,
                  sample_buffer_size=1024,
@@ -156,7 +152,6 @@ class DataReader(object):
                  verbose=0):
         self._feature_file_list = feature_file_list
         self._label_file_list = label_file_list
-        self._frame_dim = frame_dim
         self._drop_frame_len = drop_frame_len
         self._shuffle_block_num = shuffle_block_num
         self._block_info_list = None
@@ -308,8 +303,9 @@ class DataReader(object):
 
     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")
+            assert len(batch_samples)
+            frame_dim = batch_samples[0][0].shape[1]
+            batch_feature = np.zeros((lod[-1], frame_dim), dtype="float32")
             batch_label = np.zeros((lod[-1], 1), dtype="int64")
             start = 0
             for sample in batch_samples: