rename xmap to xmap_readers and remove 'from cv2 import resize' in image.py

python/paddle/v2/dataset/flowers.py

@@ -105,7 +105,8 @@ def reader_creator(data_file,
             for sample, label in itertools.izip(data, batch['label']):
                 yield sample, int(label)
 
-    return paddle.reader.xmap(mapper, reader, cpu_count(), buffered_size)
+    return paddle.reader.xmap_readers(mapper, reader,
+                                      cpu_count(), buffered_size)
 
 
 def train(mapper=default_mapper, buffered_size=1024):

python/paddle/v2/image.py

@@ -3,8 +3,6 @@ try:
     import cv2
 except ImportError:
     cv2 = None
-
-from cv2 import resize
 import os
 import tarfile
 import cPickle
@@ -164,7 +162,7 @@ def resize_short(im, size):
         h_new = size * h / w
     else:
         w_new = size * w / h
-    im = resize(im, (h_new, w_new), interpolation=cv2.INTER_CUBIC)
+    im = cv2.resize(im, (h_new, w_new), interpolation=cv2.INTER_CUBIC)
     return im
 
 

python/paddle/v2/reader/decorator.py

@@ -14,7 +14,7 @@
 
 __all__ = [
     'map_readers', 'buffered', 'compose', 'chain', 'shuffle',
-    'ComposeNotAligned', 'firstn', 'xmap'
+    'ComposeNotAligned', 'firstn', 'xmap_readers'
 ]
 
 import itertools
@@ -230,7 +230,7 @@ class XmapEndSignal():
     pass
 
 
-def xmap(mapper, reader, process_num, buffer_size):
+def xmap_readers(mapper, reader, process_num, buffer_size):
     """
     Use multiprocess to map samples from reader by a mapper defined by user.
     And this function contains a buffered decorator.