def aggregate_stats(model_list,
device,
shape,
classes=10,
batches=20,
batch_size=100,
out_seeds=False):
stats = []
if out_seeds:
if shape[0] == 1:
dataset = 'MNIST'
else:
dataset = 'CIFAR10'
out_loader = iter(dl.TinyImages(dataset, batch_size=batch_size))
for _ in range(batches):
if out_seeds:
seed = next(out_loader)[0].to(device)
else:
seed = torch.rand((batch_size, ) + tuple(shape), device=device)
batch_stats = []
for i, model in enumerate(model_list):
model.eval()
batch_stats.append(
model(seed).max(1)[0].exp().detach().cpu().clone())
batch_stats = torch.stack(batch_stats, 0)
stats.append(batch_stats.clone())
stats = torch.cat(stats, -1)
return stats
def __init__(self, augm_flag=True, batch_size=128):
self.data_name = 'CIFAR10'
self.base_model = resnet.ResNet18()
self.train_loader = dl.CIFAR10(train=True, batch_size=batch_size, augm_flag=augm_flag)
self.cali_loader = dl.CIFAR10(train=True, batch_size=batch_size, augm_flag=False)
self.test_loader = dl.CIFAR10(train=False)
self.dim = 3072
self.loaders = [('SVHN', dl.SVHN(train=False)),
('CIFAR100', dl.CIFAR100(train=False)),
('LSUN_CR', dl.LSUN_CR(train=False)),
('Imagenet-',dl.ImageNetMinusCifar10(train=False)),
# ('TinyImages', dl.TinyImages(self.data_name, batch_size=batch_size, train=False)),
('Noise', dl.Noise(dataset='CIFAR10', batch_size=batch_size)),
('UniformNoise', dl.UniformNoise(dataset=self.data_name, batch_size=batch_size))]
self.tinyimage_loader = dl.TinyImages(self.data_name, batch_size=100)
self.data_used = 50000
self.epsilon = 0.3
self.lr = 0.1
self.classes = 10
def __init__(self, augm_flag=True, batch_size=128):
self.data_name = 'FMNIST'
self.base_model = resnet.ResNet18(num_of_channels=1)
self.train_loader = dl.FMNIST(train=True, batch_size=batch_size, augm_flag=augm_flag)
self.cali_loader = dl.FMNIST(train=True, batch_size=batch_size, augm_flag=False)
self.test_loader = dl.FMNIST(train=False, augm_flag=False)
self.dim = 784
self.loaders = [('MNIST', dl.MNIST(train=False)),
('EMNIST', dl.EMNIST(train=False)),
('GrayCIFAR10', dl.GrayCIFAR10(train=False)),
# ('TinyImages', dl.TinyImages(self.data_name, batch_size=batch_size, train=False)),
('Noise', dl.Noise(dataset=self.data_name, batch_size=batch_size)),
('UniformNoise', dl.UniformNoise(dataset=self.data_name, batch_size=batch_size))]
self.tinyimage_loader = dl.TinyImages(self.data_name, batch_size=100)
self.data_used = 60000
self.epsilon = 0.3
self.lr = 0.1
self.classes = 10
def aggregate_adv_stats_out(model_list, gmm, gmm_out, device, shape, classes=10,
batches=10, batch_size=100, steps=200, out_seeds=None,
restarts=10, alpha=1., lam=1.):
pca = models.MyPCA(gmm.metric.comp_vecs.t(), gmm.metric.singular_values, shape)
f = 1.1
b = lam * (f-1.) / (classes-f)
bounds = []
stats = []
samples = []
seeds = []
if out_seeds is not None:
if type(out_seeds)!=torch.Tensor:
if shape[0]==1:
dataset = 'MNIST'
else:
dataset = 'CIFAR10'
out_loader = iter(dl.TinyImages(dataset, batch_size=batch_size, train=False))
for idx in range(batches):
if out_seeds is not None:
if type(out_seeds)!=torch.Tensor:
seed = next(out_loader)[0].to(device)
else:
seed = out_seeds[idx].to(device)
else:
seed = torch.rand((batch_size,) + tuple(shape), device=device)
batch_bounds = []
batch_samples = []
for x in seed:
a = gmm_helpers.get_b_out(0., x, gmm, gmm_out, b)
if a>=0:
batch_bounds.append(0.)
else:
batch_bounds.append( scipy.optimize.brentq(gmm_helpers.get_b_out, 0,
10000., args = (x, gmm, gmm_out, b),
maxiter=10000) )
batch_bounds = torch.tensor(batch_bounds, device=device)
bounds.append(batch_bounds.clone().cpu())
batch_stats = []
for i, model in enumerate(model_list):
model.eval()
adv_noise, _ = adv.gen_pca_noise(model, device, seed, pca,
epsilon=batch_bounds, perturb=True,
restarts=restarts, steps=steps, alpha=alpha)
out = model(adv_noise).max(1)[0].detach().cpu().clone()
batch_stats.append(out)
batch_samples.append(adv_noise.detach().cpu())
seeds.append(seed.cpu())
batch_samples = torch.stack(batch_samples, 0)
batch_stats = torch.stack(batch_stats, 0)
stats.append(batch_stats.clone())
samples.append(batch_samples.clone())
seeds = torch.stack(seeds, 0)
samples = torch.stack(samples, 0)
stats = torch.cat(stats, -1)
bounds = torch.cat(bounds, 0)
return stats, bounds, seeds, samples
'params': model.parameters(),
'lr': lr,
'weight_decay': hps.decay
}]
if hps.dataset == 'MNIST':
optimizer = optim.Adam(param_groups)
else:
optimizer = optim.SGD(param_groups, momentum=0.9)
lam = model.loglam.data.exp().item() if hps.use_gmm else np.exp(hps.loglam)
writer = SummaryWriter('logs/' + saving_string + str(datetime.datetime.now()))
if hps.fit_out:
noise_loader = iter(dl.TinyImages(hps.dataset, shuffle=False))
else:
noise_loader = None
for epoch in range(hps.epochs):
if epoch + 1 in [50, 75, 90]:
for group in optimizer.param_groups:
group['lr'] *= .1
# group['weight_decay'] *= .1
if hps.train_type == 'ACET':
if epoch < 3:
lam = 0.001
else:
lam = model.loglam.data.exp().item() if hps.use_gmm else np.exp(
hps.loglam)
gmm.mu.data = ( (gmm.mu.data * metric.singular_values_sqrt[None,:] )
@ metric.comp_vecs.t().inverse() )
saving_string = ('SavedModels/GMM/gmm_' + hps.dataset
+'_n' + str(n)
+'_data_used' + str(hps.data_used)
+'_augm_flag' + str(hps.augm_flag))
if hps.PCA:
saving_string += '_PCA'
torch.save(gmm, saving_string + '.pth')
out_loader = dl.TinyImages(hps.dataset)
X = []
for idx, (x, f) in enumerate(out_loader):
if idx>200:
break;
X.append(x.view(-1,dim))
X = torch.cat(X, 0)
if hps.PCA:
X = ( ([email protected]_vecs.t()) / metric.singular_values_sqrt[None,:] )
for n in hps.n: