From a7ae18469595f679832cf11bf637a2c5f9c84513 Mon Sep 17 00:00:00 2001
From: marcinic <marcinic@bc.edu>
Date: Thu, 7 Jun 2018 13:54:05 -0400
Subject: [PATCH 1/2] fixed tab error

---
 imagenet/model.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/imagenet/model.py b/imagenet/model.py
index 9adb692..4d5014e 100644
--- a/imagenet/model.py
+++ b/imagenet/model.py
@@ -26,7 +26,7 @@ def __init__(self, sess, image_size=108, is_crop=True,
                  devices=None,
                  disable_vbn=False,
                  sample_size=64,
-		 out_init_b=0.,
+                 out_init_b=0.,
                  out_stddev=.15):
         """
 
@@ -44,8 +44,8 @@ def __init__(self, sess, image_size=108, is_crop=True,
         self.disable_vbn = disable_vbn
         self.devices = devices
         self.d_label_smooth = d_label_smooth
-	self.out_init_b = out_init_b
-	self.out_stddev = out_stddev
+        self.out_init_b = out_init_b
+        self.out_stddev = out_stddev
         self.config = config
         self.generator_target_prob = generator_target_prob
         if generator is not None:

From ced649797858fe282ecc8bb02faf961de97109d2 Mon Sep 17 00:00:00 2001
From: marcinic <marcinic@bc.edu>
Date: Thu, 7 Jun 2018 15:46:59 -0400
Subject: [PATCH 2/2] updated print syntax to python 3

---
 imagenet/__pycache__/model.cpython-36.pyc | Bin 0 -> 18659 bytes
 imagenet/__pycache__/ops.cpython-36.pyc   | Bin 0 -> 15391 bytes
 imagenet/__pycache__/utils.cpython-36.pyc | Bin 0 -> 8703 bytes
 imagenet/convert_imagenet_to_records.py   |   5 ++---
 imagenet/discriminator.py                 |  22 +++++++++++-----------
 imagenet/generator.py                     |  10 +++++-----
 imagenet/model.py                         |   2 +-
 imagenet/ops.py                           |  12 ++++++------
 8 files changed, 25 insertions(+), 26 deletions(-)
 create mode 100644 imagenet/__pycache__/model.cpython-36.pyc
 create mode 100644 imagenet/__pycache__/ops.cpython-36.pyc
 create mode 100644 imagenet/__pycache__/utils.cpython-36.pyc

diff --git a/imagenet/__pycache__/model.cpython-36.pyc b/imagenet/__pycache__/model.cpython-36.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cac743305fee505ea0ff6cb359d4008c8af9b93e
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diff --git a/imagenet/__pycache__/ops.cpython-36.pyc b/imagenet/__pycache__/ops.cpython-36.pyc
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diff --git a/imagenet/convert_imagenet_to_records.py b/imagenet/convert_imagenet_to_records.py
index 79d4122..c1c24af 100644
--- a/imagenet/convert_imagenet_to_records.py
+++ b/imagenet/convert_imagenet_to_records.py
@@ -38,7 +38,7 @@ def main(argv):
     writer = tf.python_io.TFRecordWriter(outfile)
 
     for i, f in enumerate(files):
-        print i
+        print(i)
         image = get_image(f, IMSIZE, is_crop=True, resize_w=IMSIZE)
         image = colorize(image)
         assert image.shape == (IMSIZE, IMSIZE, 3)
@@ -52,7 +52,7 @@ def main(argv):
         class_str = f.split('/')[-2]
         label = str_to_int[class_str]
         if i % 1 == 0:
-            print i, '\t',label
+            print(i+'\t'+label)
         example = tf.train.Example(features=tf.train.Features(feature={
             'height': _int64_feature(IMSIZE),
             'width': _int64_feature(IMSIZE),
@@ -66,4 +66,3 @@ def main(argv):
 
 if __name__ == "__main__":
     tf.app.run()
-
diff --git a/imagenet/discriminator.py b/imagenet/discriminator.py
index 57e9708..219ad4e 100644
--- a/imagenet/discriminator.py
+++ b/imagenet/discriminator.py
@@ -44,37 +44,37 @@ def discriminator(self, image, reuse=False, y=None, prefix=""):
             mean=0.0,
             stddev=.1)
 
-    print "Discriminator shapes"
-    print "image: ", image.get_shape()
+    print("Discriminator shapes")
+    print("image: {0}".format(image.get_shape()))
     def tower(bn, suffix):
         assert not self.y_dim
-        print "\ttower "+suffix
+        print("\ttower "+suffix)
         h0 = lrelu(bn(conv2d(noisy_image, self.df_dim, name='d_h0_conv' + suffix, d_h=2, d_w=2,
             k_w=3, k_h=3), "d_bn_0" + suffix))
-        print "\th0 ", h0.get_shape()
+        print("\th0 {0}".format(h0.get_shape()))
         h1 = lrelu(bn(conv2d(h0, self.df_dim * 2, name='d_h1_conv' + suffix, d_h=2, d_w=2,
             k_w=3, k_h=3), "d_bn_1" + suffix))
-        print "\th1 ", h1.get_shape()
+        print("\th1 {0}".format(h1.get_shape()))
         h2 = lrelu(bn(conv2d(h1, self.df_dim * 4, name='d_h2_conv' + suffix, d_h=2, d_w=2,
             k_w=3, k_h=3), "d_bn_2" + suffix))
-        print "\th2 ", h2.get_shape()
+        print("\th2 {0}", h2.get_shape())
 
         h3 = lrelu(bn(conv2d(h2, self.df_dim*4, name='d_h3_conv' + suffix, d_h=1, d_w=1,
             k_w=3, k_h=3), "d_bn_3" + suffix))
-        print "\th3 ", h3.get_shape()
+        print("\th3 {0}".format(h3.get_shape()))
         h4 = lrelu(bn(conv2d(h3, self.df_dim*4, name='d_h4_conv' + suffix, d_h=1, d_w=1,
             k_w=3, k_h=3), "d_bn_4" + suffix))
-        print "\th4 ", h4.get_shape()
+        print("\th4 {0}".format(h4.get_shape()))
         h5 = lrelu(bn(conv2d(h4, self.df_dim*8, name='d_h5_conv' + suffix, d_h=2, d_w=2,
             k_w=3, k_h=3), "d_bn_5" + suffix))
-        print "\th5 ", h5.get_shape()
+        print("\th5 {0}".format(h5.get_shape())
 
         h6 = lrelu(bn(conv2d(h5, self.df_dim*8, name='d_h6_conv' + suffix,
             k_w=3, k_h=3), "d_bn_6" + suffix))
-        print "\th6 ", h6.get_shape()
+        print("\th6 {0}".format(h6.get_shape())
         # return tf.reduce_mean(h6, [1, 2])
         h6_reshaped = tf.reshape(h6, [batch_size, -1])
-        print '\th6_reshaped: ', h6_reshaped.get_shape()
+        print('\th6_reshaped: {0}'.format(h6_reshaped.get_shape()))
 
         h7 = lrelu(bn(linear(h6_reshaped, self.df_dim * 40, scope="d_h7" + suffix), "d_bn_7" + suffix))
 
diff --git a/imagenet/generator.py b/imagenet/generator.py
index f4a9860..867019c 100644
--- a/imagenet/generator.py
+++ b/imagenet/generator.py
@@ -21,7 +21,7 @@ def make_z(shape, minval, maxval, name, dtype):
                                 initializer=tf.random_uniform_initializer(minval, maxval),
                                 trainable=False)
                     if z.device != "/device:GPU:0":
-                        print "z.device is " + str(z.device)
+                        print("z.device is " + str(z.device))
                         assert False
             else:
                 z = tf.random_uniform(shape,
@@ -109,7 +109,7 @@ def reuse_wrapper(packed, *args):
         else:
             h3_name = "h3_relu_reuse"
         h3 = tf.nn.relu(dcgan.vbn(h3, "g_vbn_3"), name=h3_name)
-        print "h3 shape: ", h3.get_shape()
+        print("h3 shape: {0}".format(h3.get_shape()))
 
         quarter = dcgan.gf_dim // 4
         if quarter == 0:
@@ -127,7 +127,7 @@ def reuse_wrapper(packed, *args):
                 name='g_h4', with_w=make_vars),
             'h4_w', 'h4_b')
         h4 = tf.nn.relu(dcgan.vbn(h4, "g_vbn_4"))
-        print "h4 shape: ", h4.get_shape()
+        print("h4 shape: {0}".format(h4.get_shape()))
 
         eighth = dcgan.gf_dim // 8
         if eighth == 0:
@@ -143,7 +143,7 @@ def reuse_wrapper(packed, *args):
                 name='g_h5', with_w=make_vars),
             'h5_w', 'h5_b')
         h5 = tf.nn.relu(dcgan.vbn(h5, "g_vbn_5"))
-        print "h5 shape: ", h5.get_shape()
+        print("h5 shape: {0}".format(h5.get_shape()))
 
         sixteenth = dcgan.gf_dim // 16
         if sixteenth == 0:
@@ -161,7 +161,7 @@ def reuse_wrapper(packed, *args):
                 init_bias=dcgan.out_init_b,
                 stddev=dcgan.out_stddev),
             'h6_w', 'h6_b')
-        print 'h6 shape: ', h6.get_shape()
+        print('h6 shape: {0}'.format(h6.get_shape()))
 
         out = tf.nn.tanh(h6)
 
diff --git a/imagenet/model.py b/imagenet/model.py
index 4d5014e..fe9e01d 100644
--- a/imagenet/model.py
+++ b/imagenet/model.py
@@ -226,7 +226,7 @@ def load(self, checkpoint_dir):
             self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))
             return True
         else:
-            print "Bad checkpoint: ", ckpt
+            print("Bad checkpoint: {0}".format(ckpt))
             return False
 
 
diff --git a/imagenet/ops.py b/imagenet/ops.py
index 0662954..8d536a6 100644
--- a/imagenet/ops.py
+++ b/imagenet/ops.py
@@ -66,7 +66,7 @@ def __init__(self, name="batch_norm", epsilon=1e-5, momentum=0.1,
             global TRAIN_MODE
             self.train = TRAIN_MODE
             self.ema = tf.train.ExponentialMovingAverage(decay=0.9)
-            print "initing %s in train: %s" % (scope.name, self.train)
+            print ("initing {0} in train: {1}".format(scope.name, self.train))
 
     def __call__(self, x):
         shape = x.get_shape()
@@ -167,11 +167,11 @@ def special_deconv2d(input_, output_shape,
 
         def check_shape(h_size, im_size, stride):
             if h_size != (im_size + stride - 1) // stride:
-                print "Need h_size == (im_size + stride - 1) // stride"
-                print "h_size: ", h_size
-                print "im_size: ", im_size
-                print "stride: ", stride
-                print "(im_size + stride - 1) / float(stride): ", (im_size + stride - 1) / float(stride)
+                print ("Need h_size == (im_size + stride - 1) // stride")
+                print ("h_size: {0}".format(h_size))
+                print ("im_size: {0}".format(im_size))
+                print ("stride: {0}".format(stride))
+                print ("(im_size + stride - 1) / float(stride): {0}".format((im_size + stride - 1) / float(stride)))
                 raise ValueError()
 
         check_shape(int(input_.get_shape()[1]), output_shape[1] + k_h, d_h)