Fix Python 3 Syntax Errors (en masse)

research/real_nvp/real_nvp_multiscale_dataset.py

@@ -23,11 +23,14 @@ $ python real_nvp_multiscale_dataset.py \
 --data_path [DATA_PATH]
 """
 
+from __future__ import print_function
+
 import time
 from datetime import datetime
 import os
 
 import numpy
+from six.moves import xrange
 import tensorflow as tf
 
 from tensorflow import gfile
@@ -1435,10 +1438,10 @@ class RealNVP(object):
             n_equal = int(n_equal)
             n_dash = bar_len - n_equal
             progress_bar = "[" + "=" * n_equal + "-" * n_dash + "]\r"
-            print progress_bar,
+            print(progress_bar, end=' ')
             cost = self.bit_per_dim.eval()
             eval_costs.append(cost)
-        print ""
+        print("")
         return float(numpy.mean(eval_costs))
 
 
@@ -1467,7 +1470,7 @@ def train_model(hps, logdir):
 
             ckpt_state = tf.train.get_checkpoint_state(logdir)
             if ckpt_state and ckpt_state.model_checkpoint_path:
-                print "Loading file %s" % ckpt_state.model_checkpoint_path
+                print("Loading file %s" % ckpt_state.model_checkpoint_path)
                 saver.restore(sess, ckpt_state.model_checkpoint_path)
 
             # Start the queue runners.
@@ -1499,8 +1502,8 @@ def train_model(hps, logdir):
                     format_str = ('%s: step %d, loss = %.2f '
                                   '(%.1f examples/sec; %.3f '
                                   'sec/batch)')
-                    print format_str % (datetime.now(), global_step_val, loss,
-                                        examples_per_sec, duration)
+                    print(format_str % (datetime.now(), global_step_val, loss,
+                                        examples_per_sec, duration))
 
                 if should_eval_summaries:
                     summary_str = outputs[-1]
@@ -1542,24 +1545,24 @@ def evaluate(hps, logdir, traindir, subset="valid", return_val=False):
                 while True:
                     ckpt_state = tf.train.get_checkpoint_state(traindir)
                     if not (ckpt_state and ckpt_state.model_checkpoint_path):
-                        print "No model to eval yet at %s" % traindir
+                        print("No model to eval yet at %s" % traindir)
                         time.sleep(30)
                         continue
-                    print "Loading file %s" % ckpt_state.model_checkpoint_path
+                    print("Loading file %s" % ckpt_state.model_checkpoint_path)
                     saver.restore(sess, ckpt_state.model_checkpoint_path)
 
                     current_step = tf.train.global_step(sess, eval_model.step)
                     if current_step == previous_global_step:
-                        print "Waiting for the checkpoint to be updated."
+                        print("Waiting for the checkpoint to be updated.")
                         time.sleep(30)
                         continue
                     previous_global_step = current_step
 
-                    print "Evaluating..."
+                    print("Evaluating...")
                     bit_per_dim = eval_model.eval_epoch(hps)
-                    print ("Epoch: %d, %s -> %.3f bits/dim"
-                           % (current_step, subset, bit_per_dim))
-                    print "Writing summary..."
+                    print("Epoch: %d, %s -> %.3f bits/dim"
+                          % (current_step, subset, bit_per_dim))
+                    print("Writing summary...")
                     summary = tf.Summary()
                     summary.value.extend(
                         [tf.Summary.Value(
@@ -1597,7 +1600,7 @@ def sample_from_model(hps, logdir, traindir):
                     ckpt_state = tf.train.get_checkpoint_state(traindir)
                     if not (ckpt_state and ckpt_state.model_checkpoint_path):
                         if not initialized:
-                            print "No model to eval yet at %s" % traindir
+                            print("No model to eval yet at %s" % traindir)
                             time.sleep(30)
                             continue
                     else:
@@ -1607,7 +1610,7 @@ def sample_from_model(hps, logdir, traindir):
 
                     current_step = tf.train.global_step(sess, eval_model.step)
                     if current_step == previous_global_step:
-                        print "Waiting for the checkpoint to be updated."
+                        print("Waiting for the checkpoint to be updated.")
                         time.sleep(30)
                         continue
                     previous_global_step = current_step

research/street/python/vgsl_model.py

@@ -14,6 +14,8 @@
 # ==============================================================================
 
 """String network description language to define network layouts."""
+from __future__ import print_function
+
 import re
 import time
 
@@ -170,7 +172,7 @@ def Eval(train_dir,
           _AddRateToSummary('Sequence error rate', rates.sequence_error, step,
                             sw)
           sw.flush()
-          print 'Error rates=', rates
+          print('Error rates=', rates)
         else:
           raise ValueError('Non-softmax decoder evaluation not implemented!')
       if eval_interval_secs:

research/swivel/prep.py

@@ -61,6 +61,7 @@ import os
 import struct
 import sys
 
+from six.moves import xrange
 import tensorflow as tf
 
 flags = tf.app.flags
@@ -118,7 +119,7 @@ def create_vocabulary(lines):
   if not num_words:
     raise Exception('empty vocabulary')
 
-  print 'vocabulary contains %d tokens' % num_words
+  print('vocabulary contains %d tokens' % num_words)
 
   vocab = vocab[:num_words]
   return [tok for tok, n in vocab]
@@ -309,7 +310,7 @@ def main(_):
   write_vocab_and_sums(vocab, sums, 'row_vocab.txt', 'row_sums.txt')
   write_vocab_and_sums(vocab, sums, 'col_vocab.txt', 'col_sums.txt')
 
-  print 'done!'
+  print('done!')
 
 
 if __name__ == '__main__':

research/swivel/text2bin.py

@@ -49,7 +49,7 @@ import sys
 try:
   opts, args = getopt(
       sys.argv[1:], 'o:v:', ['output=', 'vocab='])
-except GetoptError, e:
+except GetoptError as e:
   print >> sys.stderr, e
   sys.exit(2)
 

research/syntaxnet/dragnn/python/graph_builder.py

@@ -28,7 +28,7 @@ from syntaxnet.util import check
 
 try:
   tf.NotDifferentiable('ExtractFixedFeatures')
-except KeyError, e:
+except KeyError as e:
   logging.info(str(e))
 
 

research/syntaxnet/syntaxnet/conll2tree.py

@@ -88,12 +88,12 @@ def main(unused_argv):
         sentence.ParseFromString(d)
         tr = asciitree.LeftAligned()
         d = to_dict(sentence)
-        print 'Input: %s' % sentence.text
-        print 'Parse:'
+        print('Input: %s' % sentence.text)
+        print('Parse:')
         tr_str = tr(d)
         pat = re.compile(r'\s*@\d+$')
         for tr_ln in tr_str.splitlines():
-          print pat.sub('', tr_ln)
+          print(pat.sub('', tr_ln))
 
       if finished:
         break

research/tcn/alignment.py

@@ -76,9 +76,9 @@ def compute_average_alignment(
         alignment = np.mean(
             np.abs(np.array(times_i)-np.array(times_j))/float(seq_len))
         all_alignments.append(alignment)
-        print 'alignment so far %f' % alignment
+        print('alignment so far %f' % alignment)
   average_alignment = np.mean(all_alignments)
-  print 'Average alignment %f' % average_alignment
+  print('Average alignment %f' % average_alignment)
   summ = tf.Summary(value=[tf.Summary.Value(
       tag='validation/alignment', simple_value=average_alignment)])
   summary_writer.add_summary(summ, int(training_step))

research/tcn/dataset/webcam.py

@@ -58,6 +58,7 @@ matplotlib.use('TkAgg')
 from matplotlib import animation  # pylint: disable=g-import-not-at-top
 import matplotlib.pyplot as plt
 import numpy as np
+from six.moves import input
 import tensorflow as tf
 tf.logging.set_verbosity(tf.logging.INFO)
 
@@ -438,7 +439,7 @@ def main(_):
       tf.logging.info('About to write to:')
       for v in view_dirs:
         tf.logging.info(v)
-      raw_input('Press Enter to continue...')
+      input('Press Enter to continue...')
     except SyntaxError:
       pass