def make_uint8(array, bins):
if array.ndim == 4:
array = array[0]
if (array.shape[2] == 3):
return np.uint8(
np.clip(
np.floor((to_cpu(array) + 0.5) * bins) * (255 / bins), 0, 255))
return np.uint8(
np.clip(
np.floor((to_cpu(array.transpose(1, 2, 0)) + 0.5) * bins) *
(255 / bins), 0, 255))
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")
