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python/paddle/utils/image_multiproc.py

@@ -3,7 +3,8 @@
 from PIL import Image
 from cStringIO import StringIO
 import multiprocessing
-from functools import partial
+import functools
+import itertools
 
 from paddle.utils.image_util import *
 from paddle.trainer.config_parser import logger
@@ -14,10 +15,12 @@
     logger.warning("OpenCV2 is not installed, using PIL to prcoess")
     cv2 = None
 
+__all__ = ["CvTransformer", "PILTransformer", "MultiProcessImageTransformer"]
 
-class CvTransfomer(ImageTransformer):
+
+class CvTransformer(ImageTransformer):
     """
-    CvTransfomer used python-opencv to process image.
+    CvTransformer used python-opencv to process image.
     """
 
     def __init__(
@@ -97,9 +100,9 @@ def transform_from_file(self, file):
         return self.transform(im)
 
 
-class PILTransfomer(ImageTransformer):
+class PILTransformer(ImageTransformer):
     """
-    PILTransfomer used PIL to process image.
+    PILTransformer used PIL to process image.
     """
 
     def __init__(
@@ -170,8 +173,11 @@ def transform_from_file(self, file):
         return self.transform(im)
 
 
-def warpper(cls, (dat, label)):
-    return cls.job(dat, label)
+def job(is_img_string, transformer, (data, label)):
+    if is_img_string:
+        return transformer.transform_from_string(data), label
+    else:
+        return transformer.transform_from_file(data), label
 
 
 class MultiProcessImageTransformer(object):
@@ -238,36 +244,19 @@ def process(settings, file_list):
         :type is_img_string: bool.
         """
 
+        self.procnum = procnum
         self.pool = multiprocessing.Pool(procnum)
         self.is_img_string = is_img_string
         if cv2 is not None:
-            self.transformer = CvTransfomer(resize_size, crop_size, transpose,
-                                            channel_swap, mean, is_train,
-                                            is_color)
-        else:
-            self.transformer = PILTransfomer(resize_size, crop_size, transpose,
+            self.transformer = CvTransformer(resize_size, crop_size, transpose,
                                              channel_swap, mean, is_train,
                                              is_color)
-
-    def run(self, data, label):
-        try:
-            fun = partial(warpper, self)
-            return self.pool.imap_unordered(fun, zip(data, label), chunksize=5)
-        except KeyboardInterrupt:
-            self.pool.terminate()
-        except Exception, e:
-            self.pool.terminate()
-
-    def job(self, data, label):
-        if self.is_img_string:
-            return self.transformer.transform_from_string(data), label
         else:
-            return self.transformer.transform_from_file(data), label
-
-    def __getstate__(self):
-        self_dict = self.__dict__.copy()
-        del self_dict['pool']
-        return self_dict
+            self.transformer = PILTransformer(resize_size, crop_size, transpose,
+                                              channel_swap, mean, is_train,
+                                              is_color)
 
-    def __setstate__(self, state):
-        self.__dict__.update(state)
+    def run(self, data, label):
+        fun = functools.partial(job, self.is_img_string, self.transformer)
+        return self.pool.imap_unordered(
+            fun, itertools.izip(data, label), chunksize=100 * self.procnum)