def generator_fn_specgram(inputs, **kwargs):
"""Builds generator network."""
# inputs = (noises, one_hot_labels)
with tf.variable_scope('generator_cond'):
z = tf.concat(inputs, axis=1)
if kwargs['to_rgb_activation'] == 'tanh':
to_rgb_activation = tf.tanh
elif kwargs['to_rgb_activation'] == 'linear':
to_rgb_activation = lambda x: x
fake_images, end_points = networks.generator(
z,
kwargs['progress'],
lambda block_id: _num_filters_fn(block_id, **kwargs),
kwargs['resolution_schedule'],
num_blocks=kwargs['num_blocks'],
kernel_size=kwargs['kernel_size'],
colors=2,
to_rgb_activation=to_rgb_activation,
simple_arch=kwargs['simple_arch'])
shape = fake_images.shape
normalizer = data_normalizer.registry[kwargs['data_normalizer']](kwargs)
fake_images = normalizer.denormalize_op(fake_images)
fake_images.set_shape(shape)
return fake_images, end_points
def generator_fn_specgram(inputs, **kwargs):
"""Builds generator network."""
# inputs = (noises, one_hot_labels)
with tf.variable_scope('generator_cond'):
z = tf.concat(inputs, axis=1)
if kwargs['to_rgb_activation'] == 'tanh':
to_rgb_activation = tf.tanh
elif kwargs['to_rgb_activation'] == 'linear':
to_rgb_activation = lambda x: x
fake_images, end_points = networks.generator(
z,
kwargs['progress'],
lambda block_id: _num_filters_fn(block_id, **kwargs),
kwargs['resolution_schedule'],
num_blocks=kwargs['num_blocks'],
kernel_size=kwargs['kernel_size'],
colors=2,
to_rgb_activation=to_rgb_activation,
simple_arch=kwargs['simple_arch'])
shape = fake_images.shape
normalizer = data_normalizer.registry[kwargs['data_normalizer']](kwargs)
fake_images = normalizer.denormalize_op(fake_images)
fake_images.set_shape(shape)
return fake_images, end_points