def dec(x, start_res, end_res, scope='Decoder'):
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
x = ops.dense('fc1', x, fn(4) * 4 * 4, 'NHWC')
x = tf.reshape(x, [-1, 4, 4, fn(4)])
res = 8
prev_x = None
while res <= end_res:
prev_x = x
x = block_up(x, fn(res), 3, rname(res))
res *= 2
res = res // 2
if res > start_res:
t = tf.get_variable(
rname(res) + '_t',
shape=[],
collections=[tf.GraphKeys.GLOBAL_VARIABLES, "lerp"],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
trainable=False)
x1 = ops.to_rgb('rgb_' + rname(res // 2), prev_x, 'NHWC')
x1 = ops.upscale2d(x1, 'NHWC')
x2 = ops.to_rgb('rgb_' + rname(res), x, 'NHWC')
x = ops.lerp_clip(x1, x2, t)
else:
x = ops.to_rgb('rgb_' + rname(res), x, "NHWC")
x_shape = utils.int_shape(x)
assert (end_res == x_shape[1])
assert (end_res == x_shape[2])
return x
def pixel_norm(x, data_format, epsilon=1e-8):
with tf.variable_scope('PixelNorm'):
shape = utils.int_shape(x)
if len(shape) == 2:
axis = 1
else:
axis = 3 if data_format == 'NHWC' else 1
return x * tf.rsqrt(
tf.reduce_mean(tf.square(x), axis=axis, keepdims=True) + epsilon)
def squeeze(self, x):
# x.shape = [N, d, n, 2]
N, d, n, _ = int_shape(x)
return tf.reshape(x, [
N,
d // self.f1,
n // self.f2,
2 * self.f1 * self.f2,
])
def squash(x, data_format, epsilon=1e-8):
with tf.variable_scope('Squash'):
shape = utils.int_shape(x)
if len(shape) == 2:
axis = 1
else:
axis = 3 if data_format == 'NHWC' else 1
squared_norm = tf.reduce_sum(tf.square(x), axis=axis, keepdims=True)
scalar_factor = squared_norm / (
1 + squared_norm) * tf.rsqrt(squared_norm + epsilon)
return x * scalar_factor
def generator(x,
last_layer_resolution,
cfg,
is_training=True,
scope='Generator'):
def rname(resolution):
return str(resolution) + 'x' + str(resolution)
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
with tf.variable_scope("4x4"):
fn4 = cfg.resolution_to_filt_num[4]
x = ops.pixel_norm(x, cfg.data_format)
x = ops.dense('dense', x, 4 * 4 * fn4, cfg.data_format)
if cfg.data_format == 'NHWC':
x = tf.reshape(x, [-1, 4, 4, fn4])
else:
x = tf.reshape(x, [-1, fn4, 4, 4])
x = ops.leaky_relu(x)
x = ops.pixel_norm(x, cfg.data_format)
x = ops.conv2d('conv', x, fn4, 3, cfg.data_format)
x = ops.leaky_relu(x)
x = ops.pixel_norm(x, cfg.data_format)
resolution = 8
prev_x = None
while resolution <= last_layer_resolution:
filt_num = cfg.resolution_to_filt_num[resolution]
prev_x = x
x = gblock(rname(resolution), x, filt_num, cfg.data_format)
resolution *= 2
resolution = resolution // 2
if resolution > cfg.starting_resolution:
t = tf.get_variable(
rname(resolution) + '_t',
shape=[],
collections=[tf.GraphKeys.GLOBAL_VARIABLES, "lerp"],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
trainable=False)
x1 = ops.to_rgb('to_rgb_' + rname(resolution // 2), prev_x,
cfg.data_format)
x1 = ops.upscale2d(x1, cfg.data_format)
x2 = ops.to_rgb('to_rgb_' + rname(resolution), x, cfg.data_format)
x = ops.lerp_clip(x1, x2, t)
else:
x = ops.to_rgb('to_rgb_' + rname(resolution), x, cfg.data_format)
x_shape = utils.int_shape(x)
assert (resolution == x_shape[1 if cfg.data_format == 'NHWC' else 3])
assert (resolution == x_shape[2])
return x
def discriminator(x, resolution, cfg, is_training=True, scope='Discriminator'):
assert (cfg.data_format == 'NCHW' or cfg.data_format == 'NHWC')
def rname(resolution):
return str(resolution) + 'x' + str(resolution)
def fmap(resolution):
return cfg.resolution_to_filt_num[resolution]
x_shape = utils.int_shape(x)
assert (resolution == x_shape[1 if cfg.data_format == 'NHWC' else 3])
assert (resolution == x_shape[2])
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
if resolution > cfg.starting_resolution:
x1 = ops.downscale2d(x, cfg.data_format)
x1 = ops.from_rgb('from_rgb_' + rname(resolution // 2), x1,
fmap(resolution // 2), cfg.data_format)
x2 = ops.from_rgb('from_rgb_' + rname(resolution), x,
fmap(resolution // 2), cfg.data_format)
t = tf.get_variable(
rname(resolution) + '_t',
shape=[],
dtype=tf.float32,
collections=[tf.GraphKeys.GLOBAL_VARIABLES, "lerp"],
initializer=tf.zeros_initializer(),
trainable=False)
num_filters = [fmap(resolution), fmap(resolution // 2)]
x2 = dblock(rname(resolution), x2, num_filters, cfg.data_format)
x = ops.lerp_clip(x1, x2, t)
resolution = resolution // 2
else:
x = ops.from_rgb('from_rgb_' + rname(resolution), x,
fmap(resolution), cfg.data_format)
while resolution >= 4:
if resolution == 4:
x = ops.minibatch_stddev_layer(x, cfg.data_format)
num_filters = [fmap(resolution), fmap(resolution // 2)]
x = dblock(rname(resolution), x, num_filters, cfg.data_format)
resolution = resolution // 2
x = ops.dense('2x2', x, fmap(resolution), cfg.data_format)
x = ops.leaky_relu(x)
x = ops.dense('output', x, 1, cfg.data_format)
return x
def apply_bias(x, data_format):
shape = utils.int_shape(x)
assert (len(shape) == 2 or len(shape) == 4)
if len(shape) == 2:
channels = shape[1]
else:
channels = shape[3] if data_format == 'NHWC' else shape[1]
b = tf.get_variable('bias',
shape=[channels],
initializer=tf.initializers.zeros())
b = tf.cast(b, x.dtype)
if len(x.shape) == 2:
return x + b
else:
if data_format == 'NHWC':
return x + tf.reshape(b, [1, 1, 1, -1])
else:
return x + tf.reshape(b, [1, -1, 1, 1])
def make_pixeldefend(sess, x, pixelcnn_out):
l = pixelcnn_out
xs = int_shape(
x) # true image (i.e. labels) to regress to, e.g. (B,32,32,3)
ls = int_shape(l) # predicted distribution, e.g. (B,32,32,100)
nr_mix = int(
ls[-1] /
10) # here and below: unpacking the params of the mixture of logistics
logit_probs = l[:, :, :, :nr_mix]
l = tf.reshape(l[:, :, :, nr_mix:], xs + [nr_mix * 3])
means = l[:, :, :, :, :nr_mix]
log_scales = tf.maximum(l[:, :, :, :, nr_mix:2 * nr_mix], -7.)
coeffs = tf.nn.tanh(l[:, :, :, :, 2 * nr_mix:3 * nr_mix])
x_ = tf.reshape(x, xs + [1]) + tf.zeros(
xs + [nr_mix]
) # here and below: getting the means and adjusting them based on preceding sub-pixels
m2 = tf.reshape(
means[:, :, :, 1, :] + coeffs[:, :, :, 0, :] * x_[:, :, :, 0, :],
[xs[0], xs[1], xs[2], 1, nr_mix])
m3 = tf.reshape(
means[:, :, :, 2, :] + coeffs[:, :, :, 1, :] * x_[:, :, :, 0, :] +
coeffs[:, :, :, 2, :] * x_[:, :, :, 1, :],
[xs[0], xs[1], xs[2], 1, nr_mix])
means = tf.concat([
tf.reshape(means[:, :, :, 0, :], [xs[0], xs[1], xs[2], 1, nr_mix]), m2,
m3
], 3)
# B is batch size (1)
# H and W are height and width (32)
# C is channels (3)
# M is number of mixtures (10)
# shapes are (B, H, W, C, M)
eval_pts = tf.constant(
np.linspace(-1 + 1. / 256, 1 - 1. / 256, 255, dtype=np.float32))
eval_pts = tf.reshape(eval_pts, (1, 1, 1, 1, 1, -1))
eval_pts = tf.tile(eval_pts, (xs[0], xs[1], xs[2], 3, nr_mix, 1))
log_scales = tf.reshape(log_scales, (xs[0], xs[1], xs[2], 3, nr_mix, 1))
scales = tf.exp(log_scales)
scales = tf.tile(scales, (1, 1, 1, 1, 1, 255))
means = tf.reshape(means, (xs[0], xs[1], xs[2], 3, nr_mix, 1))
means = tf.tile(means, (1, 1, 1, 1, 1, 255))
evals = tf.sigmoid((eval_pts - means) / scales)
eval_upper = tf.concat(
[evals, tf.ones((xs[0], xs[1], xs[2], 3, nr_mix, 1))], axis=5)
eval_lower = tf.concat(
[tf.zeros((xs[0], xs[1], xs[2], 3, nr_mix, 1)), evals], axis=5)
eval_diffs = eval_upper - eval_lower
probs_tiled = tf.nn.softmax(tf.tile(
tf.reshape(logit_probs, (xs[0], xs[1], xs[2], 1, nr_mix, 1)),
(1, 1, 1, 3, 1, 256)),
axis=4)
probs = tf.reduce_sum(eval_diffs * probs_tiled, axis=4)
# input image has elements in [0, 255]
# epsilon is 0-255
def pixeldefend(input_image, eps=16):
purified = 2.0 * np.copy(
input_image) / 255.0 - 1.0 # rescale to [-1, 1]
for yi in range(32):
for xi in range(32):
# we have to do this one channel at a time, due to channel-wise dependencies
for ki in range(3):
p = sess.run(probs, {x: [purified]})
sub = p[0, yi, xi, ki]
curr_val = np.floor(255.0 * (purified[yi, xi, ki] + 1) /
2.0)
feasible = range(int(max(curr_val - eps, 0)),
int(min(curr_val + eps, 255) + 1))
best_p = -1
best_idx = None
for i in feasible:
if sub[i] > best_p:
best_p = sub[i]
best_idx = i
purified[yi, xi, ki] = 2.0 * best_idx / 255.0 - 1.0
return 255.0 * ((purified + 1.0) / 2.0)
return pixeldefend
def make_pixeldefend(sess, x, pixelcnn_out):
l = pixelcnn_out
xs = int_shape(x) # true image (i.e. labels) to regress to, e.g. (B,32,32,3)
ls = int_shape(l) # predicted distribution, e.g. (B,32,32,100)
nr_mix = int(ls[-1] / 10) # here and below: unpacking the params of the mixture of logistics
logit_probs = l[:,:,:,:nr_mix]
l = tf.reshape(l[:,:,:,nr_mix:], xs + [nr_mix*3])
means = l[:,:,:,:,:nr_mix]
log_scales = tf.maximum(l[:,:,:,:,nr_mix:2*nr_mix], -7.)
coeffs = tf.nn.tanh(l[:,:,:,:,2*nr_mix:3*nr_mix])
x_ = tf.reshape(x, xs + [1]) + tf.zeros(xs + [nr_mix]) # here and below: getting the means and adjusting them based on preceding sub-pixels
m2 = tf.reshape(means[:,:,:,1,:] + coeffs[:, :, :, 0, :] * x_[:, :, :, 0, :], [xs[0],xs[1],xs[2],1,nr_mix])
m3 = tf.reshape(means[:, :, :, 2, :] + coeffs[:, :, :, 1, :] * x_[:, :, :, 0, :] + coeffs[:, :, :, 2, :] * x_[:, :, :, 1, :], [xs[0],xs[1],xs[2],1,nr_mix])
means = tf.concat([tf.reshape(means[:,:,:,0,:], [xs[0],xs[1],xs[2],1,nr_mix]), m2, m3],3)
# B is batch size (1)
# H and W are height and width (32)
# C is channels (3)
# M is number of mixtures (10)
# shapes are (B, H, W, C, M)
eval_pts = tf.constant(np.linspace(-1+1./256, 1-1./256, 255, dtype=np.float32))
eval_pts = tf.reshape(eval_pts, (1, 1, 1, 1, 1, -1))
eval_pts = tf.tile(eval_pts, (xs[0],xs[1],xs[2],3,nr_mix,1))
log_scales = tf.reshape(log_scales, (xs[0],xs[1],xs[2],3,nr_mix,1))
scales = tf.exp(log_scales)
scales = tf.tile(scales, (1, 1, 1, 1, 1, 255))
means = tf.reshape(means, (xs[0],xs[1],xs[2],3,nr_mix,1))
means = tf.tile(means, (1, 1, 1, 1, 1, 255))
evals = tf.sigmoid((eval_pts - means) / scales)
eval_upper = tf.concat([evals, tf.ones((xs[0],xs[1],xs[2],3,nr_mix,1))], axis=5)
eval_lower = tf.concat([tf.zeros((xs[0],xs[1],xs[2],3,nr_mix,1)), evals], axis=5)
eval_diffs = eval_upper - eval_lower
probs_tiled = tf.nn.softmax(
tf.tile(tf.reshape(logit_probs, (xs[0],xs[1],xs[2],1,nr_mix,1)), (1,1,1,3,1,256)),
axis=4
)
probs = tf.reduce_sum(eval_diffs * probs_tiled, axis=4)
# input image has elements in [0, 255]
# epsilon is 0-255
def pixeldefend(input_image, eps=16):
purified = 2.0*np.copy(input_image)/255.0 - 1.0 # rescale to [-1, 1]
for yi in range(32):
for xi in range(32):
# we have to do this one channel at a time, due to channel-wise dependencies
for ki in range(3):
p = sess.run(probs, {x: [purified]})
sub = p[0,yi,xi,ki]
curr_val = np.floor(255.0*(purified[yi,xi,ki]+1)/2.0)
feasible = range(int(max(curr_val-eps, 0)), int(min(curr_val+eps, 255)+1))
best_p = -1
best_idx = None
for i in feasible:
if sub[i] > best_p:
best_p = sub[i]
best_idx = i
purified[yi,xi,ki] = 2.0*best_idx/255.0 - 1.0
return 255.0*((purified+1.0)/2.0)
return pixeldefend
def sample(mean, std):
shape = utils.int_shape(mean)
with tf.variable_scope('Sample'):
n = tf.random_normal([shape[0], shape[1]])
return mean + tf.multiply(n, std)
def unsqueeze(self, x):
# x.shape = [N, d/f1, n/f2, 2*f1*f2]
N, d_over_f1, n_over_f2, _ = int_shape(x)
return tf.reshape(x, [N, d_over_f1 * self.f1, n_over_f2 * self.f2, 2])
