Apply 2to3 to current paddle main python code

Author: velconia

paddle/fluid/pybind/pybind.cc

@@ -394,8 +394,10 @@ All parameter, weight, gradient are variables in Paddle.
     InferenceOptimize(*(origin.Proto()), &pruned_desc);
     return new ProgramDesc(pruned_desc);
   });
-  m.def("empty_var_name", []() { return framework::kEmptyVarName; });
-  m.def("grad_var_suffix", []() { return framework::kGradVarSuffix; });
+  m.def("empty_var_name",
+        []() { return std::string(framework::kEmptyVarName); });
+  m.def("grad_var_suffix",
+        []() { return std::string(framework::kGradVarSuffix); });
   m.def_submodule(
        "var_names",
        "The module will return special predefined variable name in Paddle")

python/paddle/dataset/cifar.py

@@ -28,7 +28,7 @@
 
 """
 
-import cPickle
+import pickle
 import itertools
 import numpy
 import paddle.dataset.common
@@ -48,7 +48,7 @@ def read_batch(batch):
         data = batch['data']
         labels = batch.get('labels', batch.get('fine_labels', None))
         assert labels is not None
-        for sample, label in itertools.izip(data, labels):
+        for sample, label in zip(data, labels):
             yield (sample / 255.0).astype(numpy.float32), int(label)
 
     def reader():
@@ -58,7 +58,7 @@ def reader():
 
             while True:
                 for name in names:
-                    batch = cPickle.load(f.extractfile(name))
+                    batch = pickle.load(f.extractfile(name))
                     for item in read_batch(batch):
                         yield item
                 if not cycle:

python/paddle/dataset/common.py

@@ -20,9 +20,9 @@
 import sys
 import importlib
 import paddle.dataset
-import cPickle
+import pickle
 import glob
-import cPickle as pickle
+import pickle as pickle
 
 __all__ = [
     'DATA_HOME',
@@ -75,13 +75,13 @@ def download(url, module_name, md5sum, save_name=None):
     retry_limit = 3
     while not (os.path.exists(filename) and md5file(filename) == md5sum):
         if os.path.exists(filename):
-            print "file md5", md5file(filename), md5sum
+            print(("file md5", md5file(filename), md5sum))
         if retry < retry_limit:
             retry += 1
         else:
             raise RuntimeError("Cannot download {0} within retry limit {1}".
                                format(url, retry_limit))
-        print "Cache file %s not found, downloading %s" % (filename, url)
+        print(("Cache file %s not found, downloading %s" % (filename, url)))
         r = requests.get(url, stream=True)
         total_length = r.headers.get('content-length')
 
@@ -104,8 +104,9 @@ def download(url, module_name, md5sum, save_name=None):
 
 
 def fetch_all():
-    for module_name in filter(lambda x: not x.startswith("__"),
-                              dir(paddle.dataset)):
+    for module_name in [
+            x for x in dir(paddle.dataset) if not x.startswith("__")
+    ]:
         if "fetch" in dir(
                 importlib.import_module("paddle.dataset.%s" % module_name)):
             getattr(
@@ -114,8 +115,9 @@ def fetch_all():
 
 
 def fetch_all_recordio(path):
-    for module_name in filter(lambda x: not x.startswith("__"),
-                              dir(paddle.dataset)):
+    for module_name in [
+            x for x in dir(paddle.dataset) if not x.startswith("__")
+    ]:
         if "convert" in dir(
                 importlib.import_module("paddle.dataset.%s" % module_name)) and \
                 not module_name == "common":
@@ -126,7 +128,7 @@ def fetch_all_recordio(path):
                 "convert")(ds_path)
 
 
-def split(reader, line_count, suffix="%05d.pickle", dumper=cPickle.dump):
+def split(reader, line_count, suffix="%05d.pickle", dumper=pickle.dump):
     """
     you can call the function as:
 
@@ -167,7 +169,7 @@ def split(reader, line_count, suffix="%05d.pickle", dumper=cPickle.dump):
 def cluster_files_reader(files_pattern,
                          trainer_count,
                          trainer_id,
-                         loader=cPickle.load):
+                         loader=pickle.load):
     """
     Create a reader that yield element from the given files, select
     a file set according trainer count and trainer_id
@@ -188,7 +190,7 @@ def reader():
         my_file_list = []
         for idx, fn in enumerate(file_list):
             if idx % trainer_count == trainer_id:
-                print "append file: %s" % fn
+                print(("append file: %s" % fn))
                 my_file_list.append(fn)
         for fn in my_file_list:
             with open(fn, "r") as f:
@@ -221,7 +223,7 @@ def write_data(indx_f, lines):
         for l in lines:
             # FIXME(Yancey1989):
             # dumps with protocol: pickle.HIGHEST_PROTOCOL
-            writer.write(cPickle.dumps(l))
+            writer.write(pickle.dumps(l))
         writer.close()
 
     lines = []

python/paddle/dataset/conll05.py

@@ -87,12 +87,12 @@ def reader():
             sentences = []
             labels = []
             one_seg = []
-            for word, label in itertools.izip(words_file, props_file):
+            for word, label in zip(words_file, props_file):
                 word = word.strip()
                 label = label.strip().split()
 
                 if len(label) == 0:  # end of sentence
-                    for i in xrange(len(one_seg[0])):
+                    for i in range(len(one_seg[0])):
                         a_kind_lable = [x[i] for x in one_seg]
                         labels.append(a_kind_lable)
 

python/paddle/dataset/flowers.py

@@ -28,10 +28,10 @@
 http://www.robots.ox.ac.uk/~vgg/publications/papers/nilsback08.{pdf,ps.gz}.
 
 """
-import cPickle
+import pickle
 import itertools
 import functools
-from common import download
+from .common import download
 import tarfile
 import scipy.io as scio
 from paddle.dataset.image import *
@@ -116,10 +116,10 @@ def reader():
                 file = file.strip()
                 batch = None
                 with open(file, 'r') as f:
-                    batch = cPickle.load(f)
+                    batch = pickle.load(f)
                 data = batch['data']
                 labels = batch['label']
-                for sample, label in itertools.izip(data, batch['label']):
+                for sample, label in zip(data, batch['label']):
                     yield sample, int(label) - 1
             if not cycle:
                 break

python/paddle/dataset/image.py

@@ -36,7 +36,7 @@
     cv2 = None
 import os
 import tarfile
-import cPickle
+import pickle
 
 __all__ = [
     "load_image_bytes", "load_image", "resize_short", "to_chw", "center_crop",
@@ -86,21 +86,21 @@ def batch_images_from_tar(data_file,
                 output = {}
                 output['label'] = labels
                 output['data'] = data
-                cPickle.dump(
+                pickle.dump(
                     output,
                     open('%s/batch_%d' % (out_path, file_id), 'w'),
-                    protocol=cPickle.HIGHEST_PROTOCOL)
+                    protocol=pickle.HIGHEST_PROTOCOL)
                 file_id += 1
                 data = []
                 labels = []
     if len(data) > 0:
         output = {}
         output['label'] = labels
         output['data'] = data
-        cPickle.dump(
+        pickle.dump(
             output,
             open('%s/batch_%d' % (out_path, file_id), 'w'),
-            protocol=cPickle.HIGHEST_PROTOCOL)
+            protocol=pickle.HIGHEST_PROTOCOL)
 
     with open(meta_file, 'a') as meta:
         for file in os.listdir(out_path):

python/paddle/dataset/imdb.py

@@ -42,13 +42,13 @@ def tokenize(pattern):
         # sequential access of member files, other than
         # tarfile.extractfile, which does random access and might
         # destroy hard disks.
-        tf = tarf.next()
+        tf = next(tarf)
         while tf != None:
             if bool(pattern.match(tf.name)):
                 # newline and punctuations removal and ad-hoc tokenization.
                 yield tarf.extractfile(tf).read().rstrip("\n\r").translate(
                     None, string.punctuation).lower().split()
-            tf = tarf.next()
+            tf = next(tarf)
 
 
 def build_dict(pattern, cutoff):
@@ -62,11 +62,11 @@ def build_dict(pattern, cutoff):
             word_freq[word] += 1
 
     # Not sure if we should prune less-frequent words here.
-    word_freq = filter(lambda x: x[1] > cutoff, word_freq.items())
+    word_freq = [x for x in list(word_freq.items()) if x[1] > cutoff]
 
     dictionary = sorted(word_freq, key=lambda x: (-x[1], x[0]))
     words, _ = list(zip(*dictionary))
-    word_idx = dict(zip(words, xrange(len(words))))
+    word_idx = dict(list(zip(words, list(range(len(words))))))
     word_idx['<unk>'] = len(words)
     return word_idx
 

python/paddle/dataset/imikolov.py

@@ -64,11 +64,11 @@ def build_dict(min_word_freq=50):
             # remove <unk> for now, since we will set it as last index
             del word_freq['<unk>']
 
-        word_freq = filter(lambda x: x[1] > min_word_freq, word_freq.items())
+        word_freq = [x for x in list(word_freq.items()) if x[1] > min_word_freq]
 
         word_freq_sorted = sorted(word_freq, key=lambda x: (-x[1], x[0]))
         words, _ = list(zip(*word_freq_sorted))
-        word_idx = dict(zip(words, xrange(len(words))))
+        word_idx = dict(list(zip(words, list(range(len(words))))))
         word_idx['<unk>'] = len(words)
 
     return word_idx

python/paddle/dataset/mnist.py

@@ -65,7 +65,7 @@ def reader():
 
                 images = images / 255.0 * 2.0 - 1.0
 
-                for i in xrange(buffer_size):
+                for i in range(buffer_size):
                     yield images[i, :], int(labels[i])
         finally:
             try:

python/paddle/dataset/movielens.py

@@ -187,15 +187,15 @@ def max_movie_id():
     Get the maximum value of movie id.
     """
     __initialize_meta_info__()
-    return reduce(__max_index_info__, MOVIE_INFO.viewvalues()).index
+    return reduce(__max_index_info__, list(MOVIE_INFO.values())).index
 
 
 def max_user_id():
     """
     Get the maximum value of user id.
     """
     __initialize_meta_info__()
-    return reduce(__max_index_info__, USER_INFO.viewvalues()).index
+    return reduce(__max_index_info__, list(USER_INFO.values())).index
 
 
 def __max_job_id_impl__(a, b):
@@ -210,7 +210,7 @@ def max_job_id():
     Get the maximum value of job id.
     """
     __initialize_meta_info__()
-    return reduce(__max_job_id_impl__, USER_INFO.viewvalues()).job_id
+    return reduce(__max_job_id_impl__, list(USER_INFO.values())).job_id
 
 
 def movie_categories():
@@ -243,7 +243,7 @@ def unittest():
     for test_count, _ in enumerate(test()()):
         pass
 
-    print train_count, test_count
+    print((train_count, test_count))
 
 
 def fetch():