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fixed tab error #36

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fixed tab error #36

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a7ae184
fixed tab error
marcinic Jun 7, 2018
ced6497
updated print syntax to python 3
marcinic Jun 7, 2018
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Binary file added imagenet/__pycache__/model.cpython-36.pyc
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Binary file added imagenet/__pycache__/ops.cpython-36.pyc
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Binary file added imagenet/__pycache__/utils.cpython-36.pyc
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5 changes: 2 additions & 3 deletions imagenet/convert_imagenet_to_records.py
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Expand Up @@ -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)
Expand All @@ -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),
Expand All @@ -66,4 +66,3 @@ def main(argv):

if __name__ == "__main__":
tf.app.run()

22 changes: 11 additions & 11 deletions imagenet/discriminator.py
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Expand Up @@ -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))

Expand Down
10 changes: 5 additions & 5 deletions imagenet/generator.py
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Expand Up @@ -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,
Expand Down Expand Up @@ -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:
Expand All @@ -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:
Expand All @@ -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:
Expand All @@ -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)

Expand Down
8 changes: 4 additions & 4 deletions imagenet/model.py
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Expand Up @@ -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):
"""

Expand All @@ -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:
Expand Down Expand Up @@ -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


Expand Down
12 changes: 6 additions & 6 deletions imagenet/ops.py
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Expand Up @@ -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()
Expand Down Expand Up @@ -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)
Expand Down