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()
('model={:s}', 'vae'),
('z={:02d}', args.z),
('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)
vae = VAE(z_dim=args.z, name=model_name).to(device)
if args.train:
writer = ut.prepare_writer(model_name, overwrite_existing=True)
train(model=vae,
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(vae, labeled_subset, run_iwae=args.train == 2)
x = vae.sample_x(100).view(100, 1, 28, 28)
db.printTensor(x)
ImUtil.showBatch(x, show=True)
input('Press key to exit')
else:
ut.load_model_by_name(vae, global_step=args.iter_max)
ut.evaluate_lower_bound(vae, labeled_subset, run_iwae=True)
parser.add_argument('--train', type=int, default=1, help="Flag for training")
args = parser.parse_args()
layout = [('model={:s}', 'gvae'), ('x-dim={:03d}', args.x_dim),
('z-dim={:02d}', args.z_dim), ('z-num={:02d}', args.z_num),
('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)
gvae = GVAE(x_dim=args.x_dim,
z_dim=args.z_dim,
z_num=args.z_num,
name=model_name).to(device)
if args.train:
writer = ut.prepare_writer(model_name, overwrite_existing=True)
train(model=gvae,
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(gvae, labeled_subset)
else:
ut.load_model_by_name(gvae, global_step=args.iter_max)
ut.evaluate_lower_bound(gvae, labeled_subset)
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,
run_iwae=args.train == 2,
ox=torch.tensor(testgen))
evaluate(model=gmvae,
test_loader=test_loader,
labeled_subset=None,
device=device,
tqdm=tqdm.tqdm,
iter_max=len(testgen),
iter_save=args.iter_save,
samples=testsamples,
meta=meta,
group_by=color_by,
group_on=group_on)
else:
#writer = ut.prepare_writer(model_name, overwrite_existing=True)
device=device,
tqdm=tqdm.tqdm,
writer=writer,
lr=args.lr,
lr_gamma=args.lr_gamma,
lr_milestones=lr_milestones,
iw=args.iw,
iter_max=args.iter_max,
iter_save=args.iter_save)
model.set_to_eval()
val_set = ut.get_load_data(device,
split='val',
in_memory=True,
log_normal=True,
shift_scale=shift_scale)
ut.evaluate_lower_bound(model, val_set, run_iwae=(args.iw > 1))
else:
ut.load_model_by_name(model, global_step=args.iter_max)
if args.mode in ['val', 'test']:
model.set_to_eval()
val_set = ut.get_load_data(device,
split=args.mode,
in_memory=True,
log_normal=True,
shift_scale=shift_scale)
ut.evaluate_lower_bound(model, val_set, run_iwae=(args.iw > 1))
make_image_load(model, log_normal=True, shift_scale=shift_scale["use"])
make_image_load_z(model, log_normal=True, shift_scale=shift_scale["use"])
