def D_info_gan_stylegan2(
images_in, # First input: Images [minibatch, channel, height, width].
labels_in, # Second input: Labels [minibatch, label_size].
num_channels=3, # Number of input color channels. Overridden based on dataset.
resolution=1024, # Input resolution. Overridden based on dataset.
label_size=0, # Dimensionality of the labels, 0 if no labels. Overridden based on dataset.
fmap_base=16 << 10, # Overall multiplier for the number of feature maps.
fmap_decay=1.0, # log2 feature map reduction when doubling the resolution.
fmap_min=1, # Minimum number of feature maps in any layer.
fmap_max=512, # Maximum number of feature maps in any layer.
architecture='resnet', # Architecture: 'orig', 'skip', 'resnet'.
nonlinearity='lrelu', # Activation function: 'relu', 'lrelu', etc.
mbstd_group_size=4, # Group size for the minibatch standard deviation layer, 0 = disable.
mbstd_num_features=1, # Number of features for the minibatch standard deviation layer.
dtype='float32', # Data type to use for activations and outputs.
resample_kernel=[
1, 3, 3, 1
], # Low-pass filter to apply when resampling activations. None = no filtering.
**_kwargs): # Ignore unrecognized keyword args.
resolution_log2 = int(np.log2(resolution))
assert resolution == 2**resolution_log2 and resolution >= 4
def nf(stage):
return np.clip(int(fmap_base / (2.0**(stage * fmap_decay))), fmap_min,
fmap_max)
assert architecture in ['orig', 'skip', 'resnet']
act = nonlinearity
images_in.set_shape([None, num_channels, resolution, resolution])
labels_in.set_shape([None, label_size])
images_in = tf.cast(images_in, dtype)
labels_in = tf.cast(labels_in, dtype)
# Building blocks for main layers.
def fromrgb(x, y, res): # res = 2..resolution_log2
with tf.variable_scope('FromRGB'):
t = apply_bias_act(conv2d_layer(y, fmaps=nf(res - 1), kernel=1),
act=act)
return t if x is None else x + t
def block(x, res): # res = 2..resolution_log2
t = x
with tf.variable_scope('Conv0'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(res - 1), kernel=3),
act=act)
with tf.variable_scope('Conv1_down'):
x = apply_bias_act(conv2d_layer(x,
fmaps=nf(res - 2),
kernel=3,
down=True,
resample_kernel=resample_kernel),
act=act)
if architecture == 'resnet':
with tf.variable_scope('Skip'):
t = conv2d_layer(t,
fmaps=nf(res - 2),
kernel=1,
down=True,
resample_kernel=resample_kernel)
x = (x + t) * (1 / np.sqrt(2))
return x
def downsample(y):
with tf.variable_scope('Downsample'):
return downsample_2d(y, k=resample_kernel)
# Main layers.
x = None
y = images_in
for res in range(resolution_log2, 2, -1):
with tf.variable_scope('%dx%d' % (2**res, 2**res)):
if architecture == 'skip' or res == resolution_log2:
x = fromrgb(x, y, res)
x = block(x, res)
if architecture == 'skip':
y = downsample(y)
# Final layers.
with tf.variable_scope('4x4'):
if architecture == 'skip':
x = fromrgb(x, y, 2)
if mbstd_group_size > 1:
with tf.variable_scope('MinibatchStddev'):
x = minibatch_stddev_layer(x, mbstd_group_size,
mbstd_num_features)
with tf.variable_scope('Conv'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(1), kernel=3), act=act)
with tf.variable_scope('Dense0'):
hidden = apply_bias_act(dense_layer(x, fmaps=nf(0)), act=act)
# Output layer with label conditioning from "Which Training Methods for GANs do actually Converge?"
with tf.variable_scope('Output'):
x = apply_bias_act(
dense_layer(hidden, fmaps=max(labels_in.shape[1], 1)))
if labels_in.shape[1] > 0:
x = tf.reduce_sum(x * labels_in, axis=1, keepdims=True)
scores_out = x
# Output.
assert scores_out.dtype == tf.as_dtype(dtype)
scores_out = tf.identity(scores_out, name='scores_out')
hidden = tf.identity(hidden, name='hidden')
return scores_out, hidden
def vc_head(
fake1, # First input: generated image from z [minibatch, channel, height, width].
fake2, # Second input: hidden features from z + delta(z) [minibatch, channel, height, width].
num_channels=3, # Number of input color channels. Overridden based on dataset.
resolution=1024, # Input resolution. Overridden based on dataset.
dlatent_size=10,
D_global_size=0,
fmap_base=16 <<
10, # Overall multiplier for the number of feature maps.
fmap_decay=1.0, # log2 feature map reduction when doubling the resolution.
fmap_min=1, # Minimum number of feature maps in any layer.
fmap_max=512, # Maximum number of feature maps in any layer.
architecture='resnet', # Architecture: 'orig', 'skip', 'resnet'.
nonlinearity='lrelu', # Activation function: 'relu', 'lrelu', etc.
mbstd_group_size=4, # Group size for the minibatch standard deviation layer, 0 = disable.
mbstd_num_features=1, # Number of features for the minibatch standard deviation layer.
dtype='float32', # Data type to use for activations and outputs.
resample_kernel=[
1, 3, 3, 1
], # Low-pass filter to apply when resampling activations. None = no filtering.
connect_mode='concat', # How fake1 and fake2 connected.
**_kwargs): # Ignore unrecognized keyword args.
resolution_log2 = int(np.log2(resolution))
assert resolution == 2**resolution_log2 and resolution >= 4
def nf(stage):
return np.clip(int(fmap_base / (2.0**(stage * fmap_decay))), fmap_min,
fmap_max)
assert architecture in ['orig', 'skip', 'resnet']
act = nonlinearity
fake1.set_shape([None, num_channels, resolution, resolution])
fake2.set_shape([None, num_channels, resolution, resolution])
fake1 = tf.cast(fake1, dtype)
fake2 = tf.cast(fake2, dtype)
if connect_mode == 'diff':
images_in = fake1 - fake2
elif connect_mode == 'concat':
images_in = tf.concat([fake1, fake2], axis=1)
# Building blocks for main layers.
def fromrgb(x, y, res): # res = 2..resolution_log2
with tf.variable_scope('FromRGB'):
t = apply_bias_act(conv2d_layer(y, fmaps=nf(res - 1), kernel=1),
act=act)
return t if x is None else x + t
def block(x, res): # res = 2..resolution_log2
t = x
with tf.variable_scope('Conv0'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(res - 1), kernel=3),
act=act)
with tf.variable_scope('Conv1_down'):
x = apply_bias_act(conv2d_layer(x,
fmaps=nf(res - 2),
kernel=3,
down=True,
resample_kernel=resample_kernel),
act=act)
if architecture == 'resnet':
with tf.variable_scope('Skip'):
t = conv2d_layer(t,
fmaps=nf(res - 2),
kernel=1,
down=True,
resample_kernel=resample_kernel)
x = (x + t) * (1 / np.sqrt(2))
return x
def downsample(y):
with tf.variable_scope('Downsample'):
return downsample_2d(y, k=resample_kernel)
# Main layers.
x = None
y = images_in
for res in range(resolution_log2, 2, -1):
with tf.variable_scope('%dx%d' % (2**res, 2**res)):
if architecture == 'skip' or res == resolution_log2:
x = fromrgb(x, y, res)
x = block(x, res)
if architecture == 'skip':
y = downsample(y)
# Final layers.
with tf.variable_scope('4x4'):
if architecture == 'skip':
x = fromrgb(x, y, 2)
if mbstd_group_size > 1:
with tf.variable_scope('MinibatchStddev'):
x = minibatch_stddev_layer(x, mbstd_group_size,
mbstd_num_features)
with tf.variable_scope('Conv'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(1), kernel=3), act=act)
with tf.variable_scope('Dense0'):
x = apply_bias_act(dense_layer(x, fmaps=nf(0)), act=act)
# Output layer with label conditioning from "Which Training Methods for GANs do actually Converge?"
with tf.variable_scope('Output'):
with tf.variable_scope('Dense_VC'):
x = apply_bias_act(
dense_layer(x,
fmaps=(D_global_size +
(dlatent_size - D_global_size))))
# Output.
assert x.dtype == tf.as_dtype(dtype)
return x