def ResidualTabularWPrior(num_classes,
dim_in,
coupling_layers,
k,
means_r=1.,
cov_std=1.,
nperlayer=1,
acc=0.9):
#print(f'Instantiating means with dimension {dim_in}.')
device = torch.device('cuda')
inv_cov_std = torch.ones((num_classes, ), device=device) / cov_std
model = TabularResidualFlow(
in_dim=dim_in, hidden_dim=k,
num_per_block=coupling_layers) #*np.sqrt(1000/dim_in)/3
dist_scaling = np.sqrt(-8 * np.log(1 - acc))
means = utils.get_means('random',
r=means_r * dist_scaling,
num_means=num_classes,
trainloader=None,
shape=(dim_in),
device=device)
means[0] /= means[0].norm()
means[0] *= dist_scaling / 2
means[1] = -means[0]
model.prior = SSLGaussMixture(means, inv_cov_std, device=device)
means_np = means.cpu().numpy()
#print("Pairwise dists:", cdist(means_np, means_np))
return model
def RealNVPTabularWPrior(num_classes,
dim_in,
coupling_layers,
k,
means_r=.8,
cov_std=1.,
nperlayer=1,
acc=0.9):
#print(f'Instantiating means with dimension {dim_in}.')
device = torch.device('cuda')
inv_cov_std = torch.ones((num_classes, ), device=device) / cov_std
model = RealNVPTabular(num_coupling_layers=coupling_layers,
in_dim=dim_in,
hidden_dim=k,
num_layers=1,
dropout=True) #*np.sqrt(1000/dim_in)/3
#dist_scaling = np.sqrt(-8*np.log(1-acc))#np.sqrt(4*np.log(20)/dim_in)#np.sqrt(1000/dim_in)
if num_classes == 2:
means = utils.get_means('random',
r=means_r,
num_means=num_classes,
trainloader=None,
shape=(dim_in),
device=device)
#means = torch.zeros(2,dim_in,device=device)
#means[0,1] = 3.75
dist = 2 * (means[0]**2).sum().sqrt()
means[0] *= 7.5 / dist
means[1] = -means[0]
# means[0] /= means[0].norm()
# means[0] *= dist_scaling/2
# means[1] = - means[0]
model.prior = SSLGaussMixture(means, inv_cov_std, device=device)
means_np = means.cpu().numpy()
else:
means = utils.get_means('random',
r=means_r * .7,
num_means=num_classes,
trainloader=None,
shape=(dim_in),
device=device)
model.prior = SSLGaussMixture(means, inv_cov_std, device=device)
means_np = means.cpu().numpy()
print("Means :", means_np)
print("Pairwise dists:", cdist(means_np, means_np))
return model
if args.resume is not None:
print("Using the means for ckpt")
means = checkpoint['means']
print("Means:", means)
print("Cov std:", cov_std)
means_np = means.cpu().numpy()
print("Pairwise dists:", cdist(means_np, means_np))
if args.means_trainable:
print("Using learnable means")
means = torch.tensor(means_np, requires_grad=True)
mean_imgs = torchvision.utils.make_grid(means.reshape((10, *img_shape)),
nrow=5)
writer.add_image("means", mean_imgs)
prior = SSLGaussMixture(means, device=device)
loss_fn = FlowLoss(prior)
#PAVEL: check why do we need this
param_groups = utils.get_param_groups(net,
args.weight_decay,
norm_suffix='weight_g')
if args.means_trainable:
param_groups.append({'name': 'means', 'params': means})
if args.optimizer == "SGD":
optimizer = optim.SGD(param_groups, lr=args.lr)
elif args.optimizer == "Adam":
optimizer = optim.Adam(param_groups, lr=args.lr)
for epoch in range(start_epoch, args.num_epochs):