def disc_spec(x, init=False, nonlinearity='crelu', ema=None, **kwargs):
with arg_scope([nn.conv2d, nn.dense],
counters={},
init=init,
weight_norm=True,
ema=ema):
x = nn.conv2d(x, 128, filter_size=[5, 5], pre_activation=None)
x = nn.conv2d(x,
256,
filter_size=[5, 5],
pre_activation=nonlinearity,
stride=[2, 2])
x = nn.conv2d(x,
512,
filter_size=[5, 5],
pre_activation=nonlinearity,
stride=[2, 2])
x = nn.conv2d(x,
1024,
filter_size=[5, 5],
pre_activation=nonlinearity,
stride=[2, 2])
x = tf.concat([tf.nn.relu(x), tf.nn.relu(-x)], 3)
xs = x.get_shape().as_list()
x = tf.reshape(x, [-1, np.prod(xs[1:])])
x /= tf.sqrt(tf.reduce_sum(tf.square(x), axis=1, keep_dims=True))
# return the features
return x
def gen_spec(batch_size, init=False, nonlinearity='crelu', ema=None, **kwargs):
u = tf.random_uniform(shape=(batch_size, 100), minval=-1., maxval=1.)
with arg_scope([nn.conv2d, nn.dense],
counters={},
init=init,
weight_norm=True,
ema=ema):
x = nn.dense(u, 2 * 4 * 4 * 1024, pre_activation=None)
x, l = tf.split(x, 2, 1)
x *= tf.nn.sigmoid(l) # gated linear unit, one of Alec's tricks
x = tf.reshape(x, shape=(batch_size, 4, 4, 1024))
x = tf.image.resize_nearest_neighbor(x, [8, 8])
x = nn.conv2d(x, 2 * 512, filter_size=[5, 5], pre_activation=None)
x, l = tf.split(x, 2, 3)
x *= tf.nn.sigmoid(l)
x = tf.image.resize_nearest_neighbor(x, [16, 16])
x = nn.conv2d(x, 2 * 256, filter_size=[5, 5], pre_activation=None)
x, l = tf.split(x, 2, 3)
x *= tf.nn.sigmoid(l)
x = tf.image.resize_nearest_neighbor(x, [32, 32])
x = nn.conv2d(x, 2 * 128, filter_size=[5, 5], pre_activation=None)
x, l = tf.split(x, 2, 3)
x *= tf.nn.sigmoid(l)
x = tf.nn.tanh(
nn.conv2d(x,
3,
filter_size=[5, 5],
pre_activation=None,
init_scale=0.1))
return x
def gen_spec(batch_size,
init=False,
layers_per_block=16,
filters_per_layer=16,
nonlinearity='crelu',
ema=None,
**kwargs):
u = [
tf.random_uniform(shape=(batch_size, 100), minval=-1., maxval=1.),
tf.random_uniform(shape=(batch_size, 8, 8, filters_per_layer),
minval=-1.,
maxval=1.),
tf.random_uniform(shape=(batch_size, 16, 16, filters_per_layer),
minval=-1.,
maxval=1.),
tf.random_uniform(shape=(batch_size, 32, 32, filters_per_layer),
minval=-1.,
maxval=1.)
]
with arg_scope([nn.conv2d, nn.dense],
counters={},
init=init,
weight_norm=True,
ema=ema):
def block(x):
if type(x) is not list:
x = [x]
for rep in range(layers_per_block):
x.append(
nn.conv2d(x,
filters_per_layer,
pre_activation=nonlinearity))
return x
def upsample(x):
if type(x) is list:
x = tf.concat(x, 3)
xs = x.get_shape().as_list()
x = tf.image.resize_nearest_neighbor(x, [xs[1] * 2, xs[2] * 2])
x = nn.conv2d(x, xs[3] // 2, pre_activation=nonlinearity)
return x
x = nn.dense(u[0], 8 * 8 * filters_per_layer, pre_activation=None)
x = [tf.reshape(x, shape=(batch_size, 8, 8, filters_per_layer)), u[1]]
x = block(x)
x = upsample(x)
x = [x, u[2]]
x = block(x)
x = upsample(x)
x = [x, u[3]]
x = block(x)
x = tf.nn.tanh(
nn.conv2d(x, 3, pre_activation=nonlinearity, init_scale=0.1))
return x
def upsample(x):
if type(x) is list:
x = tf.concat(x, 3)
xs = x.get_shape().as_list()
x = tf.image.resize_nearest_neighbor(x, [xs[1] * 2, xs[2] * 2])
x = nn.conv2d(x, xs[3] // 2, pre_activation=nonlinearity)
return x
def downsample(x):
if type(x) is not list:
x = [x]
return nn.conv2d(x,
np.sum([int(xi.get_shape()[-1])
for xi in x]) // 2,
pre_activation=nonlinearity,
stride=[2, 2])
def block(x):
if type(x) is not list:
x = [x]
for rep in range(layers_per_block):
x.append(
nn.conv2d(x,
filters_per_layer,
pre_activation=nonlinearity))
return x
def disc_spec(x,
init=False,
layers_per_block=16,
filters_per_layer=16,
nonlinearity='crelu',
ema=None,
**kwargs):
with arg_scope([nn.conv2d, nn.dense],
counters={},
init=init,
weight_norm=True,
ema=ema):
def block(x):
if type(x) is not list:
x = [x]
for rep in range(layers_per_block):
x.append(
nn.conv2d(x,
filters_per_layer,
pre_activation=nonlinearity))
return x
def downsample(x):
if type(x) is not list:
x = [x]
return nn.conv2d(x,
np.sum([int(xi.get_shape()[-1])
for xi in x]) // 2,
pre_activation=nonlinearity,
stride=[2, 2])
x = nn.conv2d(x, 2 * filters_per_layer, pre_activation=None)
x = block(x)
x = downsample(x)
x = block(x)
x = downsample(x)
x = block(x)
x = downsample(x)
x = tf.concat(x, 3)
x = tf.concat([tf.nn.relu(x), tf.nn.relu(-x)], 3)
xs = x.get_shape().as_list()
x = tf.reshape(x, [-1, np.prod(xs[1:])])
x /= tf.sqrt(tf.reduce_sum(tf.square(x), axis=1, keep_dims=True))
# return the features
return x