Adapt dam to py3

fluid/deep_attention_matching_net/model.py

-import cPickle as pickle
+import six
 import numpy as np
 import paddle.fluid as fluid
 import utils.layers as layers
@@ -22,7 +22,7 @@ class Net(object):
         mask_cache = dict() if self.use_mask_cache else None
 
         turns_data = []
-        for i in xrange(self._max_turn_num):
+        for i in six.moves.xrange(self._max_turn_num):
             turn = fluid.layers.data(
                 name="turn_%d" % i,
                 shape=[self._max_turn_len, 1],
@@ -30,7 +30,7 @@ class Net(object):
             turns_data.append(turn)
 
         turns_mask = []
-        for i in xrange(self._max_turn_num):
+        for i in six.moves.xrange(self._max_turn_num):
             turn_mask = fluid.layers.data(
                 name="turn_mask_%d" % i,
                 shape=[self._max_turn_len, 1],
@@ -57,7 +57,7 @@ class Net(object):
         Hr = response_emb
         Hr_stack = [Hr]
 
-        for index in range(self._stack_num):
+        for index in six.moves.xrange(self._stack_num):
             Hr = layers.block(
                 name="response_self_stack" + str(index),
                 query=Hr,
@@ -71,7 +71,7 @@ class Net(object):
 
         # context part
         sim_turns = []
-        for t in xrange(self._max_turn_num):
+        for t in six.moves.xrange(self._max_turn_num):
             Hu = fluid.layers.embedding(
                 input=turns_data[t],
                 size=[self._vocab_size + 1, self._emb_size],
@@ -81,7 +81,7 @@ class Net(object):
                     initializer=fluid.initializer.Normal(scale=0.1)))
             Hu_stack = [Hu]
 
-            for index in range(self._stack_num):
+            for index in six.moves.range(self._stack_num):
                 # share parameters
                 Hu = layers.block(
                     name="turn_self_stack" + str(index),
@@ -97,7 +97,7 @@ class Net(object):
             # cross attention 
             r_a_t_stack = []
             t_a_r_stack = []
-            for index in range(self._stack_num + 1):
+            for index in six.moves.range(self._stack_num + 1):
                 t_a_r = layers.block(
                     name="t_attend_r_" + str(index),
                     query=Hu_stack[index],
@@ -127,7 +127,7 @@ class Net(object):
                 t_a_r = fluid.layers.stack(t_a_r_stack, axis=1)
                 r_a_t = fluid.layers.stack(r_a_t_stack, axis=1)
             else:
-                for index in xrange(len(t_a_r_stack)):
+                for index in six.moves.xrange(len(t_a_r_stack)):
                     t_a_r_stack[index] = fluid.layers.unsqueeze(
                         input=t_a_r_stack[index], axes=[1])
                     r_a_t_stack[index] = fluid.layers.unsqueeze(
@@ -144,7 +144,7 @@ class Net(object):
         if self.use_stack_op:
             sim = fluid.layers.stack(sim_turns, axis=2)
         else:
-            for index in xrange(len(sim_turns)):
+            for index in six.moves.xrange(len(sim_turns)):
                 sim_turns[index] = fluid.layers.unsqueeze(
                     input=sim_turns[index], axes=[2])
             # sim shape: [batch_size, 2*(stack_num+2), max_turn_num, max_turn_len, max_turn_len]

fluid/deep_attention_matching_net/test_and_evaluate.py

 import os
+import six
 import numpy as np
 import time
 import argparse
@@ -167,7 +168,7 @@ def test(args):
     score_path = os.path.join(args.save_path, 'score.txt')
     score_file = open(score_path, 'w')
 
-    for it in xrange(test_batch_num // dev_count):
+    for it in six.moves.xrange(test_batch_num // dev_count):
         feed_list = []
         for dev in xrange(dev_count):
             index = it * dev_count + dev

fluid/deep_attention_matching_net/train_and_evaluate.py

 import os
+import six
 import numpy as np
 import time
 import argparse
@@ -6,9 +7,13 @@ import multiprocessing
 import paddle
 import paddle.fluid as fluid
 import utils.reader as reader
-import cPickle as pickle
 from utils.util import print_arguments
 
+try:
+    import cPickle as pickle  #python 2
+except ImportError as e:
+    import pickle  #python 3
+
 from model import Net
 
 
@@ -172,12 +177,12 @@ def train(args):
     print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))
 
     step = 0
-    for epoch in xrange(args.num_scan_data):
+    for epoch in six.moves.xrange(args.num_scan_data):
         shuffle_train = reader.unison_shuffle(train_data)
         train_batches = reader.build_batches(shuffle_train, data_conf)
 
         ave_cost = 0.0
-        for it in xrange(batch_num // dev_count):
+        for it in six.moves.xrange(batch_num // dev_count):
             feed_list = []
             for dev in xrange(dev_count):
                 index = it * dev_count + dev
@@ -200,7 +205,8 @@ def train(args):
             if (args.save_path is not None) and (step % save_step == 0):
                 save_path = os.path.join(args.save_path, "step_" + str(step))
                 print("Save model at step %d ... " % step)
-                print(time.strftime('%Y-%m-%d %H:%M:%S',
+                print(
+                    time.strftime('%Y-%m-%d %H:%M:%S',
                                   time.localtime(time.time())))
                 fluid.io.save_persistables(exe, save_path)
 
@@ -235,7 +241,8 @@ def train(args):
                     for p_at in result:
                         out_file.write(str(p_at) + '\n')
                 print('finish evaluation')
-                print(time.strftime('%Y-%m-%d %H:%M:%S',
+                print(
+                    time.strftime('%Y-%m-%d %H:%M:%S',
                                   time.localtime(time.time())))
 
 

fluid/deep_attention_matching_net/utils/reader.py

-import cPickle as pickle
 import numpy as np
 
+try:
+    import cPickle as pickle  #python 2
+except ImportError as e:
+    import pickle  #python 3
+
 
 def unison_shuffle(data, seed=None):
     if seed is not None:

fluid/deep_attention_matching_net/utils/util.py

+import six
+
+
 def print_arguments(args):
     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('------------------------------------------------')