def discriminator(x,
progress,
num_filters_fn,
resolution_schedule,
num_blocks=None,
kernel_size=3,
simple_arch=False,
scope='progressive_gan_discriminator',
reuse=None):
"""Discriminator network for the progressive GAN model.
Args:
x: A `Tensor`of NHWC format representing images of size `resolution`.
progress: A scalar float `Tensor` of training progress.
num_filters_fn: A function that maps `block_id` to # of filters for the
block.
resolution_schedule: An object of `ResolutionSchedule`.
num_blocks: An integer of number of blocks. None means maximum number of
blocks, i.e. `resolution.schedule.num_resolutions`. Defaults to None.
kernel_size: An integer of convolution kernel size.
simple_arch: Bool, use a simple architecture.
scope: A string or variable scope.
reuse: Whether to reuse `scope`. Defaults to None which means to inherit
the reuse option of the parent scope.
Returns:
A `Tensor` of model output and a dictionary of model end points.
"""
he_init = tf_slim.variance_scaling_initializer()
if num_blocks is None:
num_blocks = resolution_schedule.num_resolutions
def _conv2d(scope, x, kernel_size, filters, padding='SAME'):
return layers.custom_conv2d(
x=x,
filters=filters,
kernel_size=kernel_size,
padding=padding,
activation=tf.nn.leaky_relu,
he_initializer_slope=0.0,
scope=scope)
def _from_rgb(x, block_id):
return _conv2d('from_rgb', x, 1, num_filters_fn(block_id))
if resolution_schedule.scale_mode == 'H':
strides = (resolution_schedule.scale_base, 1)
else:
strides = (resolution_schedule.scale_base,
resolution_schedule.scale_base)
end_points = {}
with tf.variable_scope(scope, reuse=reuse):
x0 = x
end_points['rgb'] = x0
lods = []
for block_id in range(num_blocks, 0, -1):
with tf.variable_scope(block_name(block_id)):
scale = resolution_schedule.scale_factor(block_id)
lod = resolution_schedule.downscale(x0, scale)
end_points['downscaled_rgb_{}'.format(block_id)] = lod
if simple_arch:
lod = tf.layers.conv2d(
lod,
num_filters_fn(block_id),
kernel_size=1,
padding='SAME',
name='from_rgb',
kernel_initializer=he_init)
lod = tf.nn.relu(lod)
else:
lod = _from_rgb(lod, block_id)
# alpha_i is used to replace lod_select.
alpha = _discriminator_alpha(block_id, progress)
end_points['alpha_{}'.format(block_id)] = alpha
lods.append((lod, alpha))
lods_iter = iter(lods)
x, _ = next(lods_iter)
for block_id in range(num_blocks, 1, -1):
with tf.variable_scope(block_name(block_id)):
if simple_arch:
x = tf.layers.conv2d(
x,
num_filters_fn(block_id-1),
strides=strides,
kernel_size=kernel_size,
padding='SAME',
name='conv',
kernel_initializer=he_init)
x = tf.nn.relu(x)
else:
x = _conv2d('conv0', x, kernel_size, num_filters_fn(block_id))
x = _conv2d('conv1', x, kernel_size, num_filters_fn(block_id - 1))
x = resolution_schedule.downscale(x, resolution_schedule.scale_base)
lod, alpha = next(lods_iter)
x = alpha * lod + (1.0 - alpha) * x
with tf.variable_scope(block_name(1)):
x = layers.scalar_concat(x, layers.minibatch_mean_stddev(x))
if simple_arch:
x = tf.reshape(x, [tf.shape(x)[0], -1]) # flatten
x = tf.layers.dense(x, num_filters_fn(0), name='last_conv',
kernel_initializer=he_init)
x = tf.reshape(x, [tf.shape(x)[0], 1, 1, num_filters_fn(0)])
x = tf.nn.relu(x)
else:
x = _conv2d('conv0', x, kernel_size, num_filters_fn(1))
x = _conv2d('conv1', x, resolution_schedule.start_resolutions,
num_filters_fn(0), 'VALID')
end_points['last_conv'] = x
if simple_arch:
logits = tf.layers.dense(x, 1, name='logits',
kernel_initializer=he_init)
else:
logits = layers.custom_dense(x=x, units=1, scope='logits')
end_points['logits'] = logits
return logits, end_points
def discriminator(x,
progress,
num_filters_fn,
resolution_schedule,
num_blocks=None,
kernel_size=3,
simple_arch=False,
scope='progressive_gan_discriminator',
reuse=None):
"""Discriminator network for the progressive GAN model.
Args:
x: A `Tensor`of NHWC format representing images of size `resolution`.
progress: A scalar float `Tensor` of training progress.
num_filters_fn: A function that maps `block_id` to # of filters for the
block.
resolution_schedule: An object of `ResolutionSchedule`.
num_blocks: An integer of number of blocks. None means maximum number of
blocks, i.e. `resolution.schedule.num_resolutions`. Defaults to None.
kernel_size: An integer of convolution kernel size.
simple_arch: Bool, use a simple architecture.
scope: A string or variable scope.
reuse: Whether to reuse `scope`. Defaults to None which means to inherit
the reuse option of the parent scope.
Returns:
A `Tensor` of model output and a dictionary of model end points.
"""
he_init = tf.contrib.layers.variance_scaling_initializer()
if num_blocks is None:
num_blocks = resolution_schedule.num_resolutions
def _conv2d(scope, x, kernel_size, filters, padding='SAME'):
return layers.custom_conv2d(
x=x,
filters=filters,
kernel_size=kernel_size,
padding=padding,
activation=tf.nn.leaky_relu,
he_initializer_slope=0.0,
scope=scope)
def _from_rgb(x, block_id):
return _conv2d('from_rgb', x, 1, num_filters_fn(block_id))
if resolution_schedule.scale_mode == 'H':
strides = (resolution_schedule.scale_base, 1)
else:
strides = (resolution_schedule.scale_base,
resolution_schedule.scale_base)
end_points = {}
with tf.variable_scope(scope, reuse=reuse):
x0 = x
end_points['rgb'] = x0
lods = []
for block_id in range(num_blocks, 0, -1):
with tf.variable_scope(block_name(block_id)):
scale = resolution_schedule.scale_factor(block_id)
lod = resolution_schedule.downscale(x0, scale)
end_points['downscaled_rgb_{}'.format(block_id)] = lod
if simple_arch:
lod = tf.layers.conv2d(
lod,
num_filters_fn(block_id),
kernel_size=1,
padding='SAME',
name='from_rgb',
kernel_initializer=he_init)
lod = tf.nn.relu(lod)
else:
lod = _from_rgb(lod, block_id)
# alpha_i is used to replace lod_select.
alpha = _discriminator_alpha(block_id, progress)
end_points['alpha_{}'.format(block_id)] = alpha
lods.append((lod, alpha))
lods_iter = iter(lods)
x, _ = six.next(lods_iter)
for block_id in range(num_blocks, 1, -1):
with tf.variable_scope(block_name(block_id)):
if simple_arch:
x = tf.layers.conv2d(
x,
num_filters_fn(block_id-1),
strides=strides,
kernel_size=kernel_size,
padding='SAME',
name='conv',
kernel_initializer=he_init)
x = tf.nn.relu(x)
else:
x = _conv2d('conv0', x, kernel_size, num_filters_fn(block_id))
x = _conv2d('conv1', x, kernel_size, num_filters_fn(block_id - 1))
x = resolution_schedule.downscale(x, resolution_schedule.scale_base)
lod, alpha = six.next(lods_iter)
x = alpha * lod + (1.0 - alpha) * x
with tf.variable_scope(block_name(1)):
x = layers.scalar_concat(x, layers.minibatch_mean_stddev(x))
if simple_arch:
x = tf.reshape(x, [tf.shape(x)[0], -1]) # flatten
x = tf.layers.dense(x, num_filters_fn(0), name='last_conv',
kernel_initializer=he_init)
x = tf.reshape(x, [tf.shape(x)[0], 1, 1, num_filters_fn(0)])
x = tf.nn.relu(x)
else:
x = _conv2d('conv0', x, kernel_size, num_filters_fn(1))
x = _conv2d('conv1', x, resolution_schedule.start_resolutions,
num_filters_fn(0), 'VALID')
end_points['last_conv'] = x
if simple_arch:
logits = tf.layers.dense(x, 1, name='logits',
kernel_initializer=he_init)
else:
logits = layers.custom_dense(x=x, units=1, scope='logits')
end_points['logits'] = logits
return logits, end_points