Merge branch 'master' into patch-1

research/cognitive_mapping_and_planning/src/utils.py

@@ -17,6 +17,7 @@ r"""Generaly Utilities.
 """
 
 import numpy as np, cPickle, os, time
+from six.moves import xrange
 import src.file_utils as fu
 import logging
 
@@ -93,12 +94,12 @@ def tic_toc_print(interval, string):
   global tic_toc_print_time_old
   if 'tic_toc_print_time_old' not in globals():
     tic_toc_print_time_old = time.time()
-    print string
+    print(string)
   else:
     new_time = time.time()
     if new_time - tic_toc_print_time_old > interval:
       tic_toc_print_time_old = new_time;
-      print string
+      print(string)
 
 def mkdir_if_missing(output_dir):
   if not fu.exists(output_dir):
@@ -126,7 +127,7 @@ def load_variables(pickle_file_name):
 def voc_ap(rec, prec):
   rec = rec.reshape((-1,1))
   prec = prec.reshape((-1,1))
-  z = np.zeros((1,1)) 
+  z = np.zeros((1,1))
   o = np.ones((1,1))
   mrec = np.vstack((z, rec, o))
   mpre = np.vstack((z, prec, z))
@@ -165,4 +166,3 @@ def calc_pr(gt, out, wt=None):
 
   ap = voc_ap(rec, prec)
   return ap, rec, prec
-

research/compression/entropy_coder/core/entropy_coder_single.py

@@ -58,7 +58,7 @@ def main(_):
   #iteration = FLAGS.iteration
 
   if not tf.gfile.Exists(FLAGS.input_codes):
-    print '\nInput codes not found.\n'
+    print('\nInput codes not found.\n')
     return
 
   with tf.gfile.FastGFile(FLAGS.input_codes, 'rb') as code_file:

research/compression/entropy_coder/core/entropy_coder_train.py

@@ -171,7 +171,7 @@ def train():
               'code_length': model.average_code_length
           }
           np_tensors = sess.run(tf_tensors, feed_dict=feed_dict)
-          print np_tensors['code_length']
+          print(np_tensors['code_length'])
 
       sv.Stop()
 

research/compression/entropy_coder/dataset/gen_synthetic_dataset.py

@@ -18,6 +18,7 @@
 import os
 
 import numpy as np
+from six.moves import xrange
 import tensorflow as tf
 
 import synthetic_model

research/compression/entropy_coder/dataset/synthetic_model.py

@@ -16,6 +16,7 @@
 """Binary code sample generator."""
 
 import numpy as np
+from six.moves import xrange
 
 
 _CRC_LINE = [

research/compression/entropy_coder/lib/block_util.py

@@ -21,6 +21,7 @@ from __future__ import unicode_literals
 import math
 
 import numpy as np
+import six
 import tensorflow as tf
 
 
@@ -39,7 +40,7 @@ class RsqrtInitializer(object):
         1.0 / sqrt(product(shape[dims]))
       **kwargs: Extra keyword arguments to pass to tf.truncated_normal.
     """
-    if isinstance(dims, (int, long)):
+    if isinstance(dims, six.integer_types):
       self._dims = [dims]
     else:
       self._dims = dims
@@ -73,7 +74,7 @@ class RectifierInitializer(object):
         sqrt(scale / product(shape[dims])).
       **kwargs: Extra keyword arguments to pass to tf.truncated_normal.
     """
-    if isinstance(dims, (int, long)):
+    if isinstance(dims, six.integer_types):
       self._dims = [dims]
     else:
       self._dims = dims

research/compression/entropy_coder/lib/blocks_masked_conv2d.py

@@ -16,6 +16,7 @@
 """Define some typical masked 2D convolutions."""
 
 import numpy as np
+from six.moves import xrange
 import tensorflow as tf
 
 import block_util

research/compression/entropy_coder/lib/blocks_masked_conv2d_test.py

@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import unicode_literals
 
 import numpy as np
+from six.moves import xrange
 import tensorflow as tf
 
 import blocks_masked_conv2d

research/compression/entropy_coder/lib/blocks_std_test.py

@@ -22,6 +22,7 @@ import math
 import os
 
 import numpy as np
+from six.moves import xrange
 import tensorflow as tf
 
 import blocks_std

research/delf/delf/python/feature_io.py

@@ -25,6 +25,7 @@ from __future__ import print_function
 from delf import feature_pb2
 from delf import datum_io
 import numpy as np
+from six.moves import xrange
 import tensorflow as tf
 
 

research/differential_privacy/dp_sgd/dp_mnist/dp_mnist.py

@@ -22,6 +22,7 @@ import sys
 import time
 
 import numpy as np
+from six.moves import xrange
 import tensorflow as tf
 
 from differential_privacy.dp_sgd.dp_optimizer import dp_optimizer

research/differential_privacy/dp_sgd/per_example_gradients/per_example_gradients.py

@@ -17,6 +17,7 @@
 
 import collections
 
+from six.moves import xrange
 import tensorflow as tf
 
 OrderedDict = collections.OrderedDict

research/differential_privacy/multiple_teachers/aggregation.py

@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
+from six.moves import xrange
 
 
 def labels_from_probs(probs):
@@ -127,5 +128,3 @@ def aggregation_most_frequent(logits):
     result[i] = np.argmax(label_counts)
 
   return np.asarray(result, dtype=np.int32)
-
-

research/differential_privacy/multiple_teachers/analysis.py

@@ -41,6 +41,7 @@ python analysis.py
 import os
 import math
 import numpy as np
+from six.moves import xrange
 import tensorflow as tf
 
 from differential_privacy.multiple_teachers.input import maybe_download
@@ -139,7 +140,7 @@ def logmgf_exact(q, priv_eps, l):
     try:
       log_t = math.log(t)
     except ValueError:
-      print "Got ValueError in math.log for values :" + str((q, priv_eps, l, t))
+      print("Got ValueError in math.log for values :" + str((q, priv_eps, l, t)))
       log_t = priv_eps * l
   else:
     log_t = priv_eps * l
@@ -171,7 +172,7 @@ def sens_at_k(counts, noise_eps, l, k):
   """
   counts_sorted = sorted(counts, reverse=True)
   if 0.5 * noise_eps * l > 1:
-    print "l too large to compute sensitivity"
+    print("l too large to compute sensitivity")
     return 0
   # Now we can assume that at k, gap remains positive
   # or we have reached the point where logmgf_exact is
@@ -268,8 +269,8 @@ def main(unused_argv):
   # Solving gives eps = (alpha - ln (delta))/l
   eps_list_nm = (total_log_mgf_nm - math.log(delta)) / l_list
 
-  print "Epsilons (Noisy Max): " + str(eps_list_nm)
-  print "Smoothed sensitivities (Noisy Max): " + str(total_ss_nm / l_list)
+  print("Epsilons (Noisy Max): " + str(eps_list_nm))
+  print("Smoothed sensitivities (Noisy Max): " + str(total_ss_nm / l_list))
 
   # If beta < eps / 2 ln (1/delta), then adding noise Lap(1) * 2 SS/eps
   # is eps,delta DP
@@ -280,12 +281,12 @@ def main(unused_argv):
   # Print the first one's scale
   ss_eps = 2.0 * beta * math.log(1/delta)
   ss_scale = 2.0 / ss_eps
-  print "To get an " + str(ss_eps) + "-DP estimate of epsilon, "
-  print "..add noise ~ " + str(ss_scale)
-  print "... times " + str(total_ss_nm / l_list)
-  print "Epsilon = " + str(min(eps_list_nm)) + "."
+  print("To get an " + str(ss_eps) + "-DP estimate of epsilon, ")
+  print("..add noise ~ " + str(ss_scale))
+  print("... times " + str(total_ss_nm / l_list))
+  print("Epsilon = " + str(min(eps_list_nm)) + ".")
   if min(eps_list_nm) == eps_list_nm[-1]:
-    print "Warning: May not have used enough values of l"
+    print("Warning: May not have used enough values of l")
 
   # Data independent bound, as mechanism is
   # 2*noise_eps DP.
@@ -294,7 +295,7 @@ def main(unused_argv):
       [logmgf_exact(1.0, 2.0 * noise_eps, l) for l in l_list])
 
   data_ind_eps_list = (data_ind_log_mgf - math.log(delta)) / l_list
-  print "Data independent bound = " + str(min(data_ind_eps_list)) + "."
+  print("Data independent bound = " + str(min(data_ind_eps_list)) + ".")
 
   return
 

research/differential_privacy/multiple_teachers/deep_cnn.py

@@ -20,6 +20,7 @@ from __future__ import print_function
 from datetime import datetime
 import math
 import numpy as np
+from six.moves import xrange
 import tensorflow as tf
 import time
 
@@ -600,5 +601,3 @@ def softmax_preds(images, ckpt_path, return_logits=False):
   tf.reset_default_graph()
 
   return preds
-
-

research/differential_privacy/multiple_teachers/input.py

@@ -24,6 +24,7 @@ import numpy as np
 import os
 from scipy.io import loadmat as loadmat
 from six.moves import urllib
+from six.moves import xrange
 import sys
 import tarfile
 

research/differential_privacy/multiple_teachers/train_student.py

@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
+from six.moves import xrange
 import tensorflow as tf
 
 from differential_privacy.multiple_teachers import aggregation

research/differential_privacy/privacy_accountant/python/gaussian_moments.py

@@ -40,12 +40,15 @@ To verify that the I1 >= I2 (see comments in GaussianMomentsAccountant in
 accountant.py for the context), run the same loop above with verify=True
 passed to compute_log_moment.
 """
+from __future__ import print_function
+
 import math
 import sys
 
 import numpy as np
 import scipy.integrate as integrate
 import scipy.stats
+from six.moves import xrange
 from sympy.mpmath import mp
 
 
@@ -108,10 +111,10 @@ def compute_a(sigma, q, lmbd, verbose=False):
   a_lambda_exact = ((1.0 - q) * a_lambda_first_term_exact +
                     q * a_lambda_second_term_exact)
   if verbose:
-    print "A: by binomial expansion    {} = {} + {}".format(
+    print("A: by binomial expansion    {} = {} + {}".format(
         a_lambda_exact,
         (1.0 - q) * a_lambda_first_term_exact,
-        q * a_lambda_second_term_exact)
+        q * a_lambda_second_term_exact))
   return _to_np_float64(a_lambda_exact)
 
 
@@ -125,8 +128,8 @@ def compute_b(sigma, q, lmbd, verbose=False):
   b_fn = lambda z: (np.power(mu0(z) / mu(z), lmbd) -
                     np.power(mu(-z) / mu0(z), lmbd))
   if verbose:
-    print "M =", m
-    print "f(-M) = {} f(M) = {}".format(b_fn(-m), b_fn(m))
+    print("M =", m)
+    print("f(-M) = {} f(M) = {}".format(b_fn(-m), b_fn(m)))
     assert b_fn(-m) < 0 and b_fn(m) < 0
 
   b_lambda_int1_fn = lambda z: (mu0(z) *
@@ -140,9 +143,9 @@ def compute_b(sigma, q, lmbd, verbose=False):
   b_bound = a_lambda_m1 + b_int1 - b_int2
 
   if verbose:
-    print "B: by numerical integration", b_lambda
-    print "B must be no more than     ", b_bound
-  print b_lambda, b_bound
+    print("B: by numerical integration", b_lambda)
+    print("B must be no more than     ", b_bound)
+  print(b_lambda, b_bound)
   return _to_np_float64(b_lambda)
 
 
@@ -188,10 +191,10 @@ def compute_a_mp(sigma, q, lmbd, verbose=False):
   a_lambda_second_term = integral_inf_mp(a_lambda_second_term_fn)
 
   if verbose:
-    print "A: by numerical integration {} = {} + {}".format(
+    print("A: by numerical integration {} = {} + {}".format(
         a_lambda,
         (1 - q) * a_lambda_first_term,
-        q * a_lambda_second_term)
+        q * a_lambda_second_term))
 
   return _to_np_float64(a_lambda)
 
@@ -210,8 +213,8 @@ def compute_b_mp(sigma, q, lmbd, verbose=False):
   b_fn = lambda z: ((mu0(z) / mu(z)) ** lmbd_int -
                     (mu(-z) / mu0(z)) ** lmbd_int)
   if verbose:
-    print "M =", m
-    print "f(-M) = {} f(M) = {}".format(b_fn(-m), b_fn(m))
+    print("M =", m)
+    print("f(-M) = {} f(M) = {}".format(b_fn(-m), b_fn(m)))
     assert b_fn(-m) < 0 and b_fn(m) < 0
 
   b_lambda_int1_fn = lambda z: mu0(z) * (mu0(z) / mu(z)) ** lmbd_int
@@ -223,8 +226,8 @@ def compute_b_mp(sigma, q, lmbd, verbose=False):
   b_bound = a_lambda_m1 + b_int1 - b_int2
 
   if verbose:
-    print "B by numerical integration", b_lambda
-    print "B must be no more than    ", b_bound
+    print("B by numerical integration", b_lambda)
+    print("B must be no more than    ", b_bound)
   assert b_lambda < b_bound + 1e-5
   return _to_np_float64(b_lambda)
 

research/domain_adaptation/domain_separation/dsn_eval.py

@@ -19,6 +19,7 @@
 import math
 
 import numpy as np
+from six.moves import xrange
 import tensorflow as tf
 
 from domain_adaptation.datasets import dataset_factory

research/gan/cifar/util.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
 tfgan = tf.contrib.gan