def main():
xp = np
using_gpu = args.gpu_device >= 0
if using_gpu:
cuda.get_device(args.gpu_device).use()
xp = cupy
hyperparams = Hyperparameters(args.snapshot_path)
hyperparams.print()
num_bins_x = 2.0**hyperparams.num_bits_x
encoder = Glow(hyperparams, hdf5_path=args.snapshot_path)
if using_gpu:
encoder.to_gpu()
total = hyperparams.levels + 1
fig = plt.figure(figsize=(4 * total, 4))
subplots = []
for n in range(total):
subplot = fig.add_subplot(1, total, n + 1)
subplots.append(subplot)
def reverse_step(z, sampling=True):
if isinstance(z, list):
factorized_z = z
else:
factorized_z = encoder.factor_z(z)
assert len(factorized_z) == len(encoder.blocks)
out = None
sum_logdet = 0
for block, zi in zip(encoder.blocks[::-1], factorized_z[::-1]):
out, logdet = block.reverse_step(
out,
gaussian_eps=zi,
squeeze_factor=encoder.hyperparams.squeeze_factor,
sampling=sampling)
sum_logdet += logdet
return out, sum_logdet
with chainer.no_backprop_mode() and encoder.reverse() as decoder:
while True:
base_z = xp.random.normal(0,
args.temperature,
size=(
1,
3,
) + hyperparams.image_size,
dtype="float32")
factorized_z = encoder.factor_z(base_z)
rev_x, _ = decoder.reverse_step(factorized_z)
rev_x_img = make_uint8(rev_x.data[0], num_bins_x)
subplots[0].imshow(rev_x_img, interpolation="none")
z = xp.copy(base_z)
factorized_z = encoder.factor_z(z)
for n in range(hyperparams.levels - 1):
factorized_z[n] = xp.random.normal(0,
args.temperature,
size=factorized_z[n].shape,
dtype="float32")
rev_x, _ = decoder.reverse_step(factorized_z)
rev_x_img = make_uint8(rev_x.data[0], num_bins_x)
subplots[1].imshow(rev_x_img, interpolation="none")
# for n in range(hyperparams.levels):
# z = xp.copy(base_z)
# factorized_z = encoder.factor_z(z)
# for m in range(n + 1):
# factorized_z[m] = xp.random.normal(
# 0,
# args.temperature,
# size=factorized_z[m].shape,
# dtype="float32")
# # factorized_z[m] = xp.zeros_like(factorized_z[m])
# out = None
# for k, (block, zi) in enumerate(
# zip(encoder.blocks[::-1], factorized_z[::-1])):
# sampling = False
# out, _ = block.reverse_step(
# out,
# gaussian_eps=zi,
# squeeze_factor=encoder.hyperparams.squeeze_factor,
# sampling=sampling)
# rev_x = out
# rev_x_img = make_uint8(rev_x.data[0], num_bins_x)
# subplots[n + 1].imshow(rev_x_img, interpolation="none")
for n in range(hyperparams.levels):
z = xp.copy(base_z)
factorized_z = encoder.factor_z(z)
factorized_z[n] = xp.random.normal(0,
args.temperature,
size=factorized_z[n].shape,
dtype="float32")
factorized_z[n] = xp.zeros_like(factorized_z[n])
out = None
for k, (block, zi) in enumerate(
zip(encoder.blocks[::-1], factorized_z[::-1])):
sampling = False if k == hyperparams.levels - n - 1 else True
out, _ = block.reverse_step(
out,
gaussian_eps=zi,
squeeze_factor=encoder.hyperparams.squeeze_factor,
sampling=sampling)
rev_x = out
rev_x_img = make_uint8(rev_x.data[0], num_bins_x)
subplots[n + 1].imshow(rev_x_img, interpolation="none")
plt.pause(.01)
def main():
xp = np
using_gpu = args.gpu_device >= 0
if using_gpu:
cuda.get_device(args.gpu_device).use()
xp = cupy
hyperparams = Hyperparameters(args.snapshot_path)
hyperparams.print()
num_bins_x = 2.0**hyperparams.num_bits_x
encoder = Glow(hyperparams, hdf5_path=args.snapshot_path)
if using_gpu:
encoder.to_gpu()
total = hyperparams.levels
fig = plt.figure(figsize=(4 * total, 4))
subplots = []
for n in range(total):
subplot = fig.add_subplot(1, total, n + 1)
subplots.append(subplot)
with chainer.no_backprop_mode() and encoder.reverse() as decoder:
while True:
seed = int(time.time())
for level in range(1, hyperparams.levels):
xp.random.seed(seed)
z = xp.random.normal(0,
args.temperature,
size=(
1,
3,
) + hyperparams.image_size,
dtype="float32")
factorized_z = glow.nn.functions.factor_z(
z, level + 1, squeeze_factor=hyperparams.squeeze_factor)
out = glow.nn.functions.unsqueeze(
factorized_z.pop(-1),
factor=hyperparams.squeeze_factor,
module=xp)
for n, zi in enumerate(factorized_z[::-1]):
block = encoder.blocks[level - n - 1]
out, _ = block.reverse_step(
out,
gaussian_eps=zi,
squeeze_factor=hyperparams.squeeze_factor)
rev_x = out
rev_x_img = make_uint8(rev_x.data[0], num_bins_x)
subplot = subplots[level - 1]
subplot.imshow(rev_x_img, interpolation="none")
subplot.set_title("level = {}".format(level))
# original #levels
xp.random.seed(seed)
z = xp.random.normal(0,
args.temperature,
size=(
1,
3,
) + hyperparams.image_size,
dtype="float32")
factorized_z = encoder.factor_z(z)
rev_x, _ = decoder.reverse_step(factorized_z)
rev_x_img = make_uint8(rev_x.data[0], num_bins_x)
subplot = subplots[-1]
subplot.imshow(rev_x_img, interpolation="none")
subplot.set_title("level = {}".format(hyperparams.levels))
plt.pause(.01)