support python3 in video classification

fluid/video_classification/reader.py

 import os
+import sys
 import math
 import random
 import functools
-import cPickle
-from cStringIO import StringIO
+try:
+    import cPickle as pickle
+    from cStringIO import StringIO
+except ImportError:
+    import pickle
+    from io import BytesIO
 import numpy as np
 import paddle
 from PIL import Image, ImageEnhance
 
 random.seed(0)
 
-DATA_DIM = 224
-
 THREAD = 8
 BUF_SIZE = 1024
 
@@ -22,17 +25,13 @@ INFER_LIST = 'data/test.list'
 img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
 img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
 
+python_ver = sys.version_info
 
 def imageloader(buf):
     if isinstance(buf, str):
-        tempbuff = StringIO()
-        tempbuff.write(buf)
-        tempbuff.seek(0)
-        img = Image.open(tempbuff)
-    elif isinstance(buf, collections.Sequence):
-        img = Image.open(StringIO(buf[-1]))
-    else:
         img = Image.open(StringIO(buf))
+    else:
+        img = Image.open(BytesIO(buf))
 
     return img.convert('RGB')
 
@@ -98,7 +97,7 @@ def group_center_crop(img_group, target_size):
 
 def video_loader(frames, nsample, mode):
     videolen = len(frames)
-    average_dur = videolen / nsample
+    average_dur = videolen // nsample
 
     imgs = []
     for i in range(nsample):
@@ -111,12 +110,12 @@ def video_loader(frames, nsample, mode):
                 idx = i
         else:
             if average_dur >= 1:
-                idx = (average_dur - 1) / 2
+                idx = (average_dur - 1) // 2
                 idx += i * average_dur
             else:
                 idx = i
 
-        imgbuf = frames[idx % videolen]
+        imgbuf = frames[int(idx % videolen)]
         img = imageloader(imgbuf)
         imgs.append(img)
 
@@ -125,7 +124,10 @@ def video_loader(frames, nsample, mode):
 
 def decode_pickle(sample, mode, seg_num, short_size, target_size):
     pickle_path = sample[0]
-    data_loaded = cPickle.load(open(pickle_path))
+    if python_ver < (3, 0):
+        data_loaded = pickle.load(open(pickle_path, 'rb'))
+    else:
+        data_loaded = pickle.load(open(pickle_path, 'rb'), encoding='bytes')
     vid, label, frames = data_loaded
 
     imgs = video_loader(frames, seg_num, mode)

fluid/video_classification/resnet.py

@@ -22,7 +22,7 @@ class TSN_ResNet():
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
-            padding=(filter_size - 1) / 2,
+            padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
             bias_attr=False)

fluid/video_classification/train.py

@@ -91,7 +91,7 @@ def train(args):
         fluid.io.load_vars(exe, pretrained_model, vars=vars)
 
     # reader
-    train_reader = paddle.batch(reader.train(seg_num), batch_size=batch_size)
+    train_reader = paddle.batch(reader.train(seg_num), batch_size=batch_size, drop_last=True)
     # test in single GPU
     test_reader = paddle.batch(reader.test(seg_num), batch_size=batch_size / 16)
     feeder = fluid.DataFeeder(place=place, feed_list=[image, label])

fluid/video_classification/utility.py

@@ -17,6 +17,7 @@ from __future__ import division
 from __future__ import print_function
 import distutils.util
 import numpy as np
+import six
 from paddle.fluid import core
 
 
@@ -36,7 +37,7 @@ def print_arguments(args):
     :type args: argparse.Namespace
     """
     print("-----------  Configuration Arguments -----------")
-    for arg, value in sorted(vars(args).iteritems()):
+    for arg, value in sorted(six.iteritems(vars(args))):
         print("%s: %s" % (arg, value))
     print("------------------------------------------------")