def run():
experiment_name = 'cifar'
img_size = 32
epoch_size = 250
batch_size = 64
train_feed, test_feed, _ = dataset.cifar.feeds(split='test',
batch_size=batch_size,
epoch_size=epoch_size)
model, experiment_name = aegan.build_model(
experiment_name,
img_size,
n_hidden=256,
recon_depth=6,
recon_vs_gan_weight=1e-5,
real_vs_gen_weight=0.5,
discriminate_ae_recon=False,
discriminate_sample_z=True,
)
print('experiment_name: %s' % experiment_name)
output_dir = os.path.join('out', experiment_name)
aegan.train(model,
output_dir,
train_feed,
test_feed,
n_epochs=200,
lr_start=0.025)
model_path = os.path.join(output_dir, 'arch.pickle')
print('Saving model to disk')
print(model_path)
with open(model_path, 'wb') as f:
pickle.dump(model, f)
def run():
experiment_name = 'lfw'
img_size = 64
epoch_size = 250
batch_size = 64
np.random.seed(1)
train_feed, test_feed = dataset.lfw.feeds(
alignment='landmarks', crop_size=150, rescale_size=img_size,
batch_size=batch_size, epoch_size=epoch_size, n_augment=250000,
split='test',
)
model, experiment_name = aegan.build_model(
experiment_name, img_size, n_hidden=128, recon_depth=9,
recon_vs_gan_weight=1e-6, real_vs_gen_weight=0.33,
discriminate_ae_recon=False, discriminate_sample_z=True,
)
print('experiment_name: %s' % experiment_name)
output_dir = os.path.join('out', experiment_name)
aegan.train(
model, output_dir, train_feed, test_feed,
)
model_path = os.path.join(output_dir, 'arch.pickle')
print('Saving model to disk')
print(model_path)
with open(model_path, 'wb') as f:
pickle.dump(model, f)
def run():
experiment_name = 'cifar'
img_size = 32
epoch_size = 250
batch_size = 64
train_feed, test_feed, _ = dataset.cifar.feeds(
split='test', batch_size=batch_size, epoch_size=epoch_size
)
model, experiment_name = aegan.build_model(
experiment_name, img_size, n_hidden=256, recon_depth=6,
recon_vs_gan_weight=1e-5, real_vs_gen_weight=0.5,
discriminate_ae_recon=False, discriminate_sample_z=True,
)
print('experiment_name: %s' % experiment_name)
output_dir = os.path.join('out', experiment_name)
aegan.train(
model, output_dir, train_feed, test_feed, n_epochs=200, lr_start=0.025
)
model_path = os.path.join(output_dir, 'arch.pickle')
print('Saving model to disk')
print(model_path)
with open(model_path, 'wb') as f:
pickle.dump(model, f)
def run():
experiment_name = 'stl'
img_size = 64
epoch_size = 250
batch_size = 64
train_feed = dataset.stl.unlabeled_feed(
img_size, batch_size=batch_size, epoch_size=epoch_size,
n_augment=int(5e5)
)
_, test_feed = dataset.stl.supervised_feed(img_size)
model, experiment_name = aegan.build_model(
experiment_name, img_size, n_hidden=128, recon_depth=9,
recon_vs_gan_weight=1e-6, real_vs_gen_weight=0.5,
discriminate_ae_recon=False, discriminate_sample_z=True,
)
print('experiment_name: %s' % experiment_name)
output_dir = os.path.join('out', experiment_name)
aegan.train(
model, output_dir, train_feed, test_feed, n_epochs=200,
)
model_path = os.path.join(output_dir, 'arch.pickle')
print('Saving model to disk')
print(model_path)
with open(model_path, 'wb') as f:
pickle.dump(model, f)
def run():
experiment_name = 'stl'
img_size = 64
epoch_size = 250
batch_size = 64
train_feed = dataset.stl.unlabeled_feed(img_size,
batch_size=batch_size,
epoch_size=epoch_size,
n_augment=int(5e5))
_, test_feed = dataset.stl.supervised_feed(img_size)
model, experiment_name = aegan.build_model(
experiment_name,
img_size,
n_hidden=128,
recon_depth=9,
recon_vs_gan_weight=1e-6,
real_vs_gen_weight=0.5,
discriminate_ae_recon=False,
discriminate_sample_z=True,
)
print('experiment_name: %s' % experiment_name)
output_dir = os.path.join('out', experiment_name)
aegan.train(
model,
output_dir,
train_feed,
test_feed,
n_epochs=200,
)
model_path = os.path.join(output_dir, 'arch.pickle')
print('Saving model to disk')
print(model_path)
with open(model_path, 'wb') as f:
pickle.dump(model, f)
def run():
experiment_name = 'cifar'
img_size = 32
epoch_size = 250
batch_size = 64
train_input, test_input, _ = dataset.cifar.inputs(
split='test', batch_size=batch_size, epoch_size=epoch_size
)
model, experiment_name = aegan.build_model(
experiment_name, img_size, n_hidden=128, recon_depth=6,
recon_vs_gan_weight=5e-6, real_vs_gen_weight=0.66,
)
print('experiment_name: %s' % experiment_name)
output_dir = os.path.join('out', experiment_name)
aegan.train(
model, output_dir, train_input, test_input,
)
model_path = os.path.join(output_dir, 'arch.pickle')
print('Saving model to disk')
print(model_path)
with open(model_path, 'wb') as f:
pickle.dump(model, f)
def run():
experiment_name = 'celeba'
img_size = 64
epoch_size = 250
batch_size = 64
n_augment = int(6e5)
train_feed, test_feed = dataset.celeba.feeds(
img_size,
split='test',
batch_size=batch_size,
epoch_size=epoch_size,
n_augment=n_augment,
)
n_hidden = 128
model, experiment_name = aegan.build_model(
experiment_name,
img_size,
n_hidden=n_hidden,
recon_depth=9,
recon_vs_gan_weight=1e-6,
real_vs_gen_weight=0.5,
discriminate_ae_recon=False,
discriminate_sample_z=True,
)
print('experiment_name: %s' % experiment_name)
output_dir = os.path.join('out', experiment_name)
aegan.train(
model,
output_dir,
train_feed,
test_feed,
n_epochs=250,
lr_start=0.025,
)
model_path = os.path.join(output_dir, 'arch.pickle')
print('Saving model to disk')
print(model_path)
with open(model_path, 'wb') as f:
pickle.dump(model, f)
print('Extracting visual attribute vectors')
model.phase = 'test'
train_feed, test_feed = dataset.celeba.feeds(
img_size,
batch_size=batch_size,
epoch_size=epoch_size,
with_attributes=True,
split='test',
)
n_attr_imgs = 10000
x = img_transform(train_feed.x[:n_attr_imgs], to_bc01=False)
y = train_feed.y[:n_attr_imgs]
z = model.encode(x)
all_attributes = list(dp.dataset.CelebA().attribute_names)
selected_attributes = [
'Bald',
'Bangs',
'Black_Hair',
'Blond_Hair',
'Bushy_Eyebrows',
'Eyeglasses',
'Gray_Hair',
'Heavy_Makeup',
'High_Cheekbones',
'Male',
'Mustache',
'Pale_Skin',
'Rosy_Cheeks',
'Smiling',
'Straight_Hair',
'Wavy_Hair',
'Wearing_Lipstick',
'Young',
]
attr_idxs = [all_attributes.index(attr) for attr in selected_attributes]
attr_vecs = []
for attr_idx in attr_idxs:
on_mask = y[:, attr_idx] == 1.0
off_mask = np.logical_not(on_mask)
vec = (np.mean(z[on_mask, :], axis=0, dtype=float) -
np.mean(z[off_mask, :], axis=0, dtype=float))
attr_vecs.append(vec)
print('Outputting visual attribute vectors')
original_x = test_feed.batches().next()[0]
original_z = model.encode(original_x)
attributes_dir = os.path.join(output_dir, 'attributes')
if not os.path.exists(attributes_dir):
os.mkdir(attributes_dir)
for attr_idx, attr_vec in zip(attr_idxs, attr_vecs):
attr_name = all_attributes[attr_idx].lower()
attrs_z = original_z + attr_vec
attrs_x = model.decode(attrs_z.astype(dp.float_))
attrs_x = img_inverse_transform(attrs_x)
for i, attr_x in enumerate(attrs_x):
path = os.path.join(attributes_dir, '%.3d_%s.png' % (i, attr_name))
sp.misc.imsave(path, attr_x)
def run():
experiment_name = 'celeba'
img_size = 64
epoch_size = 250
batch_size = 64
n_augment = int(6e5)
train_feed, test_feed = dataset.celeba.feeds(
img_size, split='test', batch_size=batch_size, epoch_size=epoch_size,
n_augment=n_augment,
)
n_hidden = 128
model, experiment_name = aegan.build_model(
experiment_name, img_size, n_hidden=n_hidden, recon_depth=9,
recon_vs_gan_weight=1e-6, real_vs_gen_weight=0.5,
discriminate_ae_recon=False, discriminate_sample_z=True,
)
print('experiment_name: %s' % experiment_name)
output_dir = os.path.join('out', experiment_name)
aegan.train(
model, output_dir, train_feed, test_feed, n_epochs=250,
lr_start=0.025,
)
model_path = os.path.join(output_dir, 'arch.pickle')
print('Saving model to disk')
print(model_path)
with open(model_path, 'wb') as f:
pickle.dump(model, f)
print('Extracting visual attribute vectors')
model.phase = 'test'
train_feed, test_feed = dataset.celeba.feeds(
img_size, batch_size=batch_size, epoch_size=epoch_size,
with_attributes=True, split='test',
)
n_attr_imgs = 10000
x = img_transform(train_feed.x[:n_attr_imgs], to_bc01=False)
y = train_feed.y[:n_attr_imgs]
z = model.encode(x)
all_attributes = list(dp.dataset.CelebA().attribute_names)
selected_attributes = [
'Bald', 'Bangs', 'Black_Hair', 'Blond_Hair', 'Bushy_Eyebrows',
'Eyeglasses', 'Gray_Hair', 'Heavy_Makeup', 'High_Cheekbones', 'Male',
'Mustache', 'Pale_Skin', 'Rosy_Cheeks', 'Smiling', 'Straight_Hair',
'Wavy_Hair', 'Wearing_Lipstick', 'Young',
]
attr_idxs = [all_attributes.index(attr) for attr in selected_attributes]
attr_vecs = []
for attr_idx in attr_idxs:
on_mask = y[:, attr_idx] == 1.0
off_mask = np.logical_not(on_mask)
vec = (np.mean(z[on_mask, :], axis=0, dtype=float) -
np.mean(z[off_mask, :], axis=0, dtype=float))
attr_vecs.append(vec)
print('Outputting visual attribute vectors')
original_x = test_feed.batches().next()[0]
original_z = model.encode(original_x)
attributes_dir = os.path.join(output_dir, 'attributes')
if not os.path.exists(attributes_dir):
os.mkdir(attributes_dir)
for attr_idx, attr_vec in zip(attr_idxs, attr_vecs):
attr_name = all_attributes[attr_idx].lower()
attrs_z = original_z + attr_vec
attrs_x = model.decode(attrs_z.astype(dp.float_))
attrs_x = img_inverse_transform(attrs_x)
for i, attr_x in enumerate(attrs_x):
path = os.path.join(attributes_dir, '%.3d_%s.png' % (i, attr_name))
sp.misc.imsave(path, attr_x)