remove redundant code in kinetics_reader in decode_mp4 and decode_pickle

fluid/PaddleCV/video/datareader/kinetics_reader.py

@@ -135,33 +135,24 @@ def _reader_creator(pickle_list,
     return paddle.reader.xmap_readers(mapper, reader, num_threads, buf_size)
 
 
-def decode_mp4(sample, mode, seg_num, seglen, short_size, target_size,
-               img_mean, img_std):
+def decode_mp4(sample, mode, seg_num, seglen, short_size, target_size, img_mean,
+               img_std):
     sample = sample[0].split(' ')
     mp4_path = sample[0]
     # when infer, we store vid as label
     label = int(sample[1])
+    try:
         imgs = mp4_loader(mp4_path, seg_num, seglen, mode)
-    imgs = group_scale(imgs, short_size)
-
-    if mode == 'train':
-        imgs = group_random_crop(imgs, target_size)
-        imgs = group_random_flip(imgs)
-    else:
-        imgs = group_center_crop(imgs, target_size)
-
-    np_imgs = (np.array(imgs[0]).astype('float32').transpose(
-        (2, 0, 1))).reshape(1, 3, target_size, target_size) / 255
-    for i in range(len(imgs) - 1):
-        img = (np.array(imgs[i + 1]).astype('float32').transpose(
-            (2, 0, 1))).reshape(1, 3, target_size, target_size) / 255
-        np_imgs = np.concatenate((np_imgs, img))
-    imgs = np_imgs
-    imgs -= img_mean
-    imgs /= img_std
-    imgs = np.reshape(imgs, (seg_num, seglen * 3, target_size, target_size))
+        if len(imgs) < 1:
+            logger.error('{} frame length {} less than 1.'.format(mp4_path,
+                                                                  len(imgs)))
+            return None, None
+    except:
+        logger.error('Error when loading {}'.format(mp4_path))
+        return None, None
 
-    return imgs, label
+    return imgs_transform(imgs, label, mode, seg_num, seglen, \
+                 short_size, target_size, img_mean, img_std)
 
 
 def decode_pickle(sample, mode, seg_num, seglen, short_size, target_size,
@@ -175,14 +166,25 @@ def decode_pickle(sample, mode, seg_num, seglen, short_size, target_size,
 
         vid, label, frames = data_loaded
         if len(frames) < 1:
-            logger.info('{} frame length {} less than 1.'.format(pickle_path,
+            logger.error('{} frame length {} less than 1.'.format(pickle_path,
                                                                   len(frames)))
-            raise
+            return None, None
     except:
         logger.info('Error when loading {}'.format(pickle_path))
         return None, None
 
+    if mode == 'train' or mode == 'valid' or mode == 'test':
+        ret_label = label
+    elif mode == 'infer':
+        ret_label = vid
+
     imgs = video_loader(frames, seg_num, seglen, mode)
+    return imgs_transform(imgs, ret_label, mode, seg_num, seglen, \
+                 short_size, target_size, img_mean, img_std)
+
+
+def imgs_transform(imgs, label, mode, seg_num, seglen, short_size, target_size,
+                   img_mean, img_std):
     imgs = group_scale(imgs, short_size)
 
     if mode == 'train':
@@ -202,10 +204,7 @@ def decode_pickle(sample, mode, seg_num, seglen, short_size, target_size,
     imgs /= img_std
     imgs = np.reshape(imgs, (seg_num, seglen * 3, target_size, target_size))
 
-    if mode == 'train' or mode == 'valid' or mode == 'test':
     return imgs, label
-    elif mode == 'infer':
-        return imgs, vid
 
 
 def group_random_crop(img_group, target_size):