def generator(x, phase, reuse=None):
with tf.variable_scope('gen', reuse=reuse):
with arg_scope([dense], bn=True, phase=phase, activation=tf.nn.relu), \
arg_scope([conv2d_transpose], bn=True, phase=phase, activation=tf.nn.relu):
if args.dense:
raise NotImplementedError('yep')
# Level 0: 1
l0 = tf.reshape(x, [-1, 1, 1, 128])
# Level 1: 1 -> 4, 8, 16
a1 = conv2d_transpose(l0, 64, 1, 1)
a2 = conv2d_transpose(l0, 64, 1, 1)
a3 = conv2d_transpose(l0, 64, 1, 1)
l1 = a1
# Level 2: 4 -> 8, 16
b2 = conv2d_transpose(l1, 64, )
# Level 2: 8 -> 16, 32
b2 = conv2d_transpose(l1, 64, 3, 2)
l2 = tf.concat([a2, b2], -1)
# Level 3: 16 -> 32
c3 = conv2d_transpose(l2, 64, 3, 2)
l3 = tf.concat([a3, b3, c3], -1)
else:
x = dense(x, 4 * 4 * 512)
x = tf.reshape(x, [-1, 4, 4, 512])
x = conv2d_transpose(x, 256, 5, 2)
x = conv2d_transpose(x, 128, 5, 2)
x = conv2d_transpose(x, 1, 5, 2, bn=False, activation=tf.nn.tanh)
return x
def generator(z, y, phase, scope='gen', reuse=None, internal_update=False):
with tf.variable_scope(scope, reuse=reuse):
with arg_scope([dense, conv2d_transpose], bn=True, phase=phase, activation=leaky_relu), \
arg_scope([batch_norm], internal_update=internal_update):
x = tf.concat([z, y], 1)
x = dense(x, 4 * 4 * 512)
x = tf.reshape(x, [-1, 4, 4, 512])
x = conv2d_transpose(x, 256, 5, 2)
x = conv2d_transpose(x, 128, 5, 2)
x = conv2d_transpose(x, 3, 5, 2, bn=False, activation=tf.nn.tanh)
return x
def generator(x, y, phase, reuse=None):
with tf.variable_scope('gen', reuse=reuse):
with arg_scope([dense], bn=True, phase=phase, activation=tf.nn.relu), \
arg_scope([conv2d_transpose], bn=True, phase=phase, activation=tf.nn.relu):
if y is not None:
x = tf.concat([x, y], 1)
x = dense(x, 4 * 4 * 512)
x = tf.reshape(x, [-1, 4, 4, 512])
x = conv2d_transpose(x, 256, 5, 2)
x = conv2d_transpose(x, 128, 5, 2)
x = wnconv2d_transpose(x, 3, 5, 2, bn=False, activation=tf.nn.tanh, scale=True)
return x
