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
image_size = (28, 28)
_, test = chainer.datasets.mnist.get_mnist()
images = []
labels = []
for entity in test:
image, label = entity
images.append(image)
labels.append(label)
labels = np.asarray(labels)
images = 255.0 * np.asarray(images).reshape((-1, ) + image_size + (1, ))
if hyperparams.num_image_channels != 1:
images = np.broadcast_to(images, (images.shape[0], ) + image_size +
(hyperparams.num_image_channels, ))
images = preprocess(images, hyperparams.num_bits_x)
# images = images[:200]
# labels = labels[:200]
sections = len(images) // 100
dataset_image = np.split(images, sections)
encoder = Glow(hyperparams, hdf5_path=args.snapshot_path)
if using_gpu:
encoder.to_gpu()
fig = plt.figure(figsize=(8, 8))
t_sne_inputs = []
with chainer.no_backprop_mode():
for n, image_batch in enumerate(dataset_image):
x = to_gpu(image_batch)
x += xp.random.uniform(0, 1.0 / num_bins_x, size=x.shape)
factorized_z_distribution, _ = encoder.forward_step(x)
factorized_z = []
for (zi, mean, ln_var) in factorized_z_distribution:
factorized_z.append(zi)
z = encoder.merge_factorized_z(factorized_z,
factor=hyperparams.squeeze_factor)
z = z.reshape((-1, hyperparams.num_image_channels * 28 * 28))
z = to_cpu(z)
t_sne_inputs.append(z)
t_sne_inputs = np.asanyarray(t_sne_inputs).reshape(
(-1, hyperparams.num_image_channels * 28 * 28))
print(t_sne_inputs.shape)
z_reduced = TSNE(n_components=2,
random_state=0).fit_transform(t_sne_inputs)
print(z_reduced.shape)
plt.scatter(z_reduced[:, 0],
z_reduced[:, 1],
c=labels,
s=1,
cmap="Spectral")
plt.colorbar()
plt.savefig("scatter.png")
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
image_size = (28, 28)
images = chainer.datasets.mnist.get_mnist(withlabel=False)[0]
images = 255.0 * np.asarray(images).reshape((-1, ) + image_size + (1, ))
if hyperparams.num_image_channels != 1:
images = np.broadcast_to(images, (images.shape[0], ) + image_size +
(hyperparams.num_image_channels, ))
images = preprocess(images, hyperparams.num_bits_x)
dataset = glow.dataset.Dataset(images)
iterator = glow.dataset.Iterator(dataset, batch_size=2)
print(tabulate([["#image", len(dataset)]]))
encoder = Glow(hyperparams, hdf5_path=args.snapshot_path)
if using_gpu:
encoder.to_gpu()
total = args.num_steps + 2
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:
for data_indices in iterator:
x = to_gpu(dataset[data_indices])
x += xp.random.uniform(0, 1.0 / num_bins_x, size=x.shape)
factorized_z_distribution, _ = encoder.forward_step(x)
factorized_z = []
for (zi, mean, ln_var) in factorized_z_distribution:
factorized_z.append(zi)
z = encoder.merge_factorized_z(factorized_z)
z_start = z[0]
z_end = z[1]
z_batch = [z_start]
for n in range(args.num_steps):
ratio = n / (args.num_steps - 1)
z_interp = ratio * z_end + (1.0 - ratio) * z_start
z_batch.append(args.temperature * z_interp)
z_batch.append(z_end)
z_batch = xp.stack(z_batch)
rev_x_batch, _ = decoder.reverse_step(z_batch)
for n in range(args.num_steps):
rev_x_img = make_uint8(rev_x_batch.data[n + 1], num_bins_x)
subplots[n + 1].imshow(rev_x_img, interpolation="none")
x_start_img = make_uint8(x[0], num_bins_x)
subplots[0].imshow(x_start_img, interpolation="none")
x_end_img = make_uint8(x[-1], num_bins_x)
subplots[-1].imshow(x_end_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
assert args.dataset_format in ["png", "npy"]
files = Path(args.dataset_path).glob("*.{}".format(args.dataset_format))
if args.dataset_format == "png":
images = []
for filepath in files:
image = np.array(Image.open(filepath)).astype("float32")
image = preprocess(image, hyperparams.num_bits_x)
images.append(image)
assert len(images) > 0
images = np.asanyarray(images)
elif args.dataset_format == "npy":
images = []
for filepath in files:
array = np.load(filepath).astype("float32")
array = preprocess(array, hyperparams.num_bits_x)
images.append(array)
assert len(images) > 0
num_files = len(images)
images = np.asanyarray(images)
images = images.reshape((num_files * images.shape[1], ) +
images.shape[2:])
else:
raise NotImplementedError
dataset = glow.dataset.Dataset(images)
iterator = glow.dataset.Iterator(dataset, batch_size=2)
print(tabulate([["#image", len(dataset)]]))
encoder = Glow(hyperparams, hdf5_path=args.snapshot_path)
if using_gpu:
encoder.to_gpu()
total = args.num_steps + 2
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:
for data_indices in iterator:
x = to_gpu(dataset[data_indices])
x += xp.random.uniform(0, 1.0 / num_bins_x, size=x.shape)
factorized_z_distribution, _ = encoder.forward_step(x)
factorized_z = []
for (zi, mean, ln_var) in factorized_z_distribution:
factorized_z.append(zi)
z = encoder.merge_factorized_z(factorized_z)
z_start = z[0]
z_end = z[1]
z_batch = [args.temperature * z_start]
for n in range(args.num_steps):
ratio = n / (args.num_steps - 1)
z_interp = ratio * z_end + (1.0 - ratio) * z_start
z_batch.append(args.temperature * z_interp)
z_batch.append(args.temperature * z_end)
for z, subplot in zip(z_batch, subplots):
z = z[None, ...]
rev_x, _ = decoder.reverse_step(z)
rev_x_img = make_uint8(rev_x.data[0], num_bins_x)
subplot.imshow(rev_x_img, interpolation="none")
plt.pause(.01)