parser.add_argument('--k', type=int, default=500, help="Number mixture components in MoG prior")
parser.add_argument('--iter_max', type=int, default=20000, help="Number of training iterations")
parser.add_argument('--iter_save', type=int, default=10000, help="Save model every n iterations")
parser.add_argument('--run', type=int, default=0, help="Run ID. In case you want to run replicates")
args = parser.parse_args()
layout = [
('model={:s}', 'gmvae'),
('z={:02d}', args.z),
('k={:03d}', args.k),
('run={:04d}', args.run)
]
model_name = '_'.join([t.format(v) for (t, v) in layout])
pprint(vars(args))
print('Model name:', model_name)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_loader, labeled_subset, _ = ut.get_mnist_data(device, use_test_subset=True)
gmvae = GMVAE(z_dim=args.z, k=args.k, name=model_name).to(device)
ut.load_model_by_name(gmvae, global_step=args.iter_max)
ut.evaluate_lower_bound(gmvae, labeled_subset, run_iwae=False)
samples = torch.reshape(gmvae.sample_x(200), (10, 20, 28, 28))
f, axarr = plt.subplots(10,20)
for i in range(samples.shape[0]):
for j in range(samples.shape[1]):
axarr[i,j].imshow(samples[i,j].detach().numpy())
axarr[i,j].axis('off')
plt.show()
test_loader = torch.utils.data.DataLoader(testgen,
batch_size=1,
shuffle=False,
num_workers=0)
full_loader = torch.utils.data.DataLoader(dc,
batch_size=1,
shuffle=False,
num_workers=0)
#raise Exception("Stop")
#train_loader, labeled_subset, test_loader = ut.get_mnist_data_and_test(device, use_test_subset=True)
gmvae = GMVAE(nn='popv',
encode_dim=len(dc[0]),
z_dim=args.z,
k=args.k,
name=model_name).to(device)
if args.train:
writer = ut.prepare_writer(model_name, overwrite_existing=True)
train(model=gmvae,
train_loader=train_loader,
labeled_subset=None,
device=device,
tqdm=tqdm.tqdm,
writer=writer,
iter_max=args.iter_max,
iter_save=args.iter_save)
ut.evaluate_lower_bound(gmvae,
None,
parser.add_argument('--run', type=int, default=0, help="Run ID. In case you want to run replicates")
parser.add_argument('--train', type=int, default=1, help="Flag for training")
args = parser.parse_args()
layout = [
('model={:s}', 'gmvae'),
('z={:02d}', args.z),
('k={:03d}', args.k),
('run={:04d}', args.run)
]
model_name = '_'.join([t.format(v) for (t, v) in layout])
pprint(vars(args))
print('Model name:', model_name)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_loader, labeled_subset, _ = ut.get_mnist_data(device, use_test_subset=True)
gmvae = GMVAE(z_dim=args.z, k=args.k, name=model_name).to(device)
if args.train:
writer = ut.prepare_writer(model_name, overwrite_existing=True)
train(model=gmvae,
train_loader=train_loader,
labeled_subset=labeled_subset,
device=device,
tqdm=tqdm.tqdm,
writer=writer,
iter_max=args.iter_max,
iter_save=args.iter_save)
ut.evaluate_lower_bound(gmvae, labeled_subset, run_iwae=args.train == 2)
else:
ut.load_model_by_name(gmvae, global_step=args.iter_max, device=device)
parser.add_argument('--sample', type=int, default=0, help="Flag for smapling")
args = parser.parse_args()
layout = [
('model={:s}', 'gmvae'),
('z={:02d}', args.z),
('k={:03d}', args.k),
('run={:04d}', args.run)
]
model_name = '_'.join([t.format(v) for (t, v) in layout])
pprint(vars(args))
print('Model name:', model_name)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_loader, labeled_subset, _ = ut.get_mnist_data(device, use_test_subset=True)
gmvae = GMVAE(z_dim=args.z, k=args.k, name=model_name).to(device)
if args.train:
writer = ut.prepare_writer(model_name, overwrite_existing=True)
train(model=gmvae,
train_loader=train_loader,
labeled_subset=labeled_subset,
device=device,
tqdm=tqdm.tqdm,
writer=writer,
iter_max=args.iter_max,
iter_save=args.iter_save)
ut.evaluate_lower_bound(gmvae, labeled_subset, run_iwae=args.train == 2)
else:
ut.load_model_by_name(gmvae, global_step=args.iter_max)
parser.add_argument('--run',
type=int,
default=0,
help="Run ID. In case you want to run replicates")
parser.add_argument('--train', type=int, default=1, help="Flag for training")
args = parser.parse_args()
layout = [('model={:s}', 'gmvae'), ('z={:02d}', args.z), ('k={:03d}', args.k),
('run={:04d}', args.run)]
model_name = '_'.join([t.format(v) for (t, v) in layout])
pprint(vars(args))
print('Model name:', model_name)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_loader, labeled_subset, _ = ut.get_mnist_data(device,
use_test_subset=True)
gmvae = GMVAE(z_dim=args.z, k=args.k, name=model_name).to(device)
if args.train:
writer = ut.prepare_writer(model_name, overwrite_existing=True)
print(args.train)
train(model=gmvae,
train_loader=train_loader,
labeled_subset=labeled_subset,
device=device,
tqdm=tqdm.tqdm,
writer=writer,
iter_max=args.iter_max,
iter_save=args.iter_save)
ut.evaluate_lower_bound(gmvae, labeled_subset, run_iwae=args.train == 2)
x = gmvae.sample_x(100).view(100, 1, 28, 28)
parser.add_argument('--run',
type=int,
default=0,
help="Run ID. In case you want to run replicates")
parser.add_argument('--train', type=int, default=1, help="Flag for training")
args = parser.parse_args()
layout = [('model={:s}', 'gmvae'), ('z={:02d}', args.z), ('k={:03d}', args.k),
('run={:04d}', args.run)]
model_name = '_'.join([t.format(v) for (t, v) in layout])
pprint(vars(args))
print('Model name:', model_name)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_loader, labeled_subset, _ = ut.get_mnist_data(device,
use_test_subset=True)
gmvae = GMVAE(z_dim=args.z, k=args.k, name=model_name).to(device)
if args.train:
writer = ut.prepare_writer(model_name, overwrite_existing=True)
train(model=gmvae,
train_loader=train_loader,
labeled_subset=labeled_subset,
device=device,
tqdm=tqdm.tqdm,
writer=writer,
iter_max=args.iter_max,
iter_save=args.iter_save)
ut.evaluate_lower_bound(gmvae, labeled_subset, run_iwae=args.train == 2)
else:
ut.load_model_by_name(gmvae, global_step=args.iter_max, device=device)