Merge branch 'master' into master

research/real_nvp/celeba_formatting.py

@@ -30,6 +30,8 @@ python celeba_formatting.py \
 
 """
 
+from __future__ import print_function
+
 import os
 import os.path
 
@@ -70,7 +72,7 @@ def main():
     writer = tf.python_io.TFRecordWriter(file_out)
     for example_idx, img_fn in enumerate(img_fn_list):
         if example_idx % 1000 == 0:
-            print example_idx, "/", num_examples
+            print(example_idx, "/", num_examples)
         image_raw = scipy.ndimage.imread(os.path.join(fn_root, img_fn))
         rows = image_raw.shape[0]
         cols = image_raw.shape[1]

research/real_nvp/imnet_formatting.py

@@ -34,6 +34,8 @@ done
 
 """
 
+from __future__ import print_function
+
 import os
 import os.path
 
@@ -73,10 +75,10 @@ def main():
             file_out = "%s_%05d.tfrecords"
             file_out = file_out % (FLAGS.file_out,
                                    example_idx // n_examples_per_file)
-            print "Writing on:", file_out
+            print("Writing on:", file_out)
             writer = tf.python_io.TFRecordWriter(file_out)
         if example_idx % 1000 == 0:
-            print example_idx, "/", num_examples
+            print(example_idx, "/", num_examples)
         image_raw = scipy.ndimage.imread(os.path.join(fn_root, img_fn))
         rows = image_raw.shape[0]
         cols = image_raw.shape[1]

research/real_nvp/lsun_formatting.py

@@ -29,6 +29,7 @@ python lsun_formatting.py \
     --fn_root [LSUN_FOLDER]
 
 """
+from __future__ import print_function
 
 import os
 import os.path
@@ -68,10 +69,10 @@ def main():
             file_out = "%s_%05d.tfrecords"
             file_out = file_out % (FLAGS.file_out,
                                    example_idx // n_examples_per_file)
-            print "Writing on:", file_out
+            print("Writing on:", file_out)
             writer = tf.python_io.TFRecordWriter(file_out)
         if example_idx % 1000 == 0:
-            print example_idx, "/", num_examples
+            print(example_idx, "/", num_examples)
         image_raw = numpy.array(Image.open(os.path.join(fn_root, img_fn)))
         rows = image_raw.shape[0]
         cols = image_raw.shape[1]

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/real_nvp/real_nvp_utils.py

@@ -19,6 +19,7 @@ r"""Utility functions for Real NVP.
 # pylint: disable=dangerous-default-value
 
 import numpy
+from six.moves import xrange
 import tensorflow as tf
 from tensorflow.python.framework import ops
 

research/slim/datasets/build_imagenet_data.py

@@ -94,6 +94,7 @@ import threading
 
 import google3
 import numpy as np
+from six.moves import xrange
 import tensorflow as tf
 
 tf.app.flags.DEFINE_string('train_directory', '/tmp/',

research/slim/datasets/preprocess_imagenet_validation_data.py

@@ -51,6 +51,7 @@ from __future__ import print_function
 import os
 import os.path
 import sys
+from six.moves import xrange
 
 
 if __name__ == '__main__':

research/slim/datasets/process_bounding_boxes.py

@@ -85,6 +85,7 @@ import glob
 import os.path
 import sys
 import xml.etree.ElementTree as ET
+from six.moves import xrange
 
 
 class BoundingBox(object):

research/slim/nets/cyclegan.py

@@ -18,7 +18,7 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
-
+from six.moves import xrange
 import tensorflow as tf
 
 layers = tf.contrib.layers

research/slim/nets/dcgan.py

@@ -19,6 +19,7 @@ from __future__ import print_function
 
 from math import log
 
+from six.moves import xrange
 import tensorflow as tf
 slim = tf.contrib.slim
 

research/slim/nets/dcgan_test.py

@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from six.moves import xrange
 import tensorflow as tf
 from nets import dcgan
 

research/street/python/decoder.py

@@ -25,6 +25,7 @@ import collections
 import re
 
 import errorcounter as ec
+from six.moves import xrange
 import tensorflow as tf
 
 # Named tuple Part describes a part of a multi (1 or more) part code that

research/street/python/shapes.py

@@ -24,6 +24,7 @@ tensor_dim: gets a shape dimension as a constant integer if known otherwise a
   runtime usable tensor value.
 tensor_shape: returns the full shape of a tensor as the tensor_dim.
 """
+from six.moves import xrange
 import tensorflow as tf
 
 

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/street/python/vgslspecs.py

@@ -23,6 +23,7 @@ from string import maketrans
 
 import nn_ops
 import shapes
+from six.moves import xrange
 import tensorflow as tf
 import tensorflow.contrib.slim as slim
 

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/dragnn_model_saver_lib_test.py

@@ -45,7 +45,7 @@ class DragnnModelSaverLibTest(test_util.TensorFlowTestCase):
     master_spec = spec_pb2.MasterSpec()
     root_dir = os.path.join(FLAGS.test_srcdir,
                             'dragnn/python')
-    with file(os.path.join(root_dir, 'testdata', spec_path), 'r') as fin:
+    with open(os.path.join(root_dir, 'testdata', spec_path), 'r') as fin:
       text_format.Parse(fin.read().replace('TOPDIR', root_dir), master_spec)
       return master_spec
 

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/dragnn/python/graph_builder_test.py

@@ -20,6 +20,7 @@ import os.path
 
 
 import numpy as np
+from six.moves import xrange
 import tensorflow as tf
 
 from google.protobuf import text_format
@@ -245,7 +246,7 @@ class GraphBuilderTest(test_util.TensorFlowTestCase):
     master_spec = spec_pb2.MasterSpec()
     testdata = os.path.join(FLAGS.test_srcdir,
                             'dragnn/core/testdata')
-    with file(os.path.join(testdata, spec_path), 'r') as fin:
+    with open(os.path.join(testdata, spec_path), 'r') as fin:
       text_format.Parse(fin.read().replace('TESTDATA', testdata), master_spec)
       return master_spec