def cifar_model_deep():
model = nn.Sequential(nn.Conv2d(3, 8, 4, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(8, 8, 3, stride=1, padding=1), nn.ReLU(),
nn.Conv2d(8, 8, 3, stride=1, padding=1), nn.ReLU(),
nn.Conv2d(8, 8, 4, stride=2, padding=1), nn.ReLU(),
Flatten(), nn.Linear(8 * 8 * 8, 100), nn.ReLU(),
nn.Linear(100, 10))
return model
def large_cifar_model():
model = nn.Sequential(nn.Conv2d(3, 32, 3, stride=1, padding=1), nn.ReLU(),
nn.Conv2d(32, 32, 4, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(32, 64, 3, stride=1, padding=1), nn.ReLU(),
nn.Conv2d(64, 64, 4, stride=2, padding=1), nn.ReLU(),
Flatten(), nn.Linear(64 * 8 * 8, 512), nn.ReLU(),
nn.Linear(512, 512), nn.ReLU(), nn.Linear(512, 10))
return model
def cifar_model():
model = nn.Sequential(nn.Conv2d(3, 16, 4, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(16, 32, 4, stride=2, padding=1), nn.ReLU(),
Flatten(), nn.Linear(32 * 8 * 8, 100), nn.ReLU(),
nn.Linear(100, 10))
for m in model.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
m.bias.data.zero_()
return model
def mini_mnist_model():
model = nn.Sequential(
nn.Conv2d(1, 4, 2, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(4, 8, 2, stride=2),
nn.ReLU(),
Flatten(),
nn.Linear(8 * 4 * 4, 50),
nn.ReLU(),
nn.Linear(50, 10),
)
return model
def cifar_model_large():
model = nn.Sequential(nn.Conv2d(3, 32, 3, stride=1, padding=1), nn.ReLU(),
nn.Conv2d(32, 32, 4, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(32, 64, 3, stride=1, padding=1), nn.ReLU(),
nn.Conv2d(64, 64, 4, stride=2, padding=1), nn.ReLU(),
Flatten(), nn.Linear(64 * 8 * 8, 512), nn.ReLU(),
nn.Linear(512, 512), nn.ReLU(), nn.Linear(512, 10))
return model
for m in model.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
m.bias.data.zero_()
return model
def cifar_model():
model = nn.Sequential(nn.Conv2d(3, 16, 4, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(16, 32, 4, stride=2, padding=1), nn.ReLU(),
Flatten(), nn.Linear(32 * 8 * 8, 100), nn.ReLU(),
nn.Linear(100, 10))
return model
def mini_mnist_model_m1():
model = nn.Sequential(nn.Conv2d(1, 8, 2, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(8, 16, 2, stride=2), nn.ReLU(), Flatten(),
nn.Linear(4 * 4 * 16, 50), nn.ReLU(),
nn.Linear(50, 10))
return model
def mnist_model_m1():
model = nn.Sequential(nn.Conv2d(1, 4, 4, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(4, 8, 4, stride=2, padding=1), nn.ReLU(),
Flatten(), nn.Linear(8 * 7 * 7, 50), nn.ReLU(),
nn.Linear(50, 10))
return model
def mnist_model():
model = nn.Sequential(nn.Conv2d(1, 16, 4, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(16, 32, 4, stride=2, padding=1), nn.ReLU(),
Flatten(), nn.Linear(32 * 7 * 7, 100), nn.ReLU(),
nn.Linear(100, 10))
return model
def load_adversarial_problem(filename, cls):
if filename.endswith('mini.pth'):
model = nn.Sequential(
nn.Conv2d(1, 4, 2, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(4, 8, 2, stride=2),
nn.ReLU(),
Flatten(),
nn.Linear(8 * 4 * 4, 50),
nn.ReLU(),
nn.Linear(50, 10),
)
model.load_state_dict(torch.load(filename)['state_dict'][0])
no_grad(model)
dataset = torch.load('./data/mini_mnist_test.pt')
elif filename.endswith('small.pth'):
model = nn.Sequential(nn.Conv2d(1, 16, 4, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(16, 32, 4, stride=2, padding=1),
nn.ReLU(), Flatten(), nn.Linear(32 * 7 * 7, 100),
nn.ReLU(), nn.Linear(100, 10))
model.load_state_dict(torch.load(filename)['state_dict'][0])
no_grad(model)
# from torchvision import datasets, transforms
# ds = datasets.MNIST('./data', train=True, download=True)
# train_ds = {'data': ds.train_data.unsqueeze(1).float()/255.0,
# 'labels': ds.train_labels}
# torch.save(train_ds, './data/mnist_train.pt')
# ds = datasets.MNIST('./data', train=False, download=True)
# test_ds = {'data': ds.test_data.unsqueeze(1).float() / 255.0,
# 'labels': ds.test_labels}
# torch.save(test_ds, './data/mnist_test.pt')
dataset = torch.load('./data/mnist_test.pt')
else:
raise NotImplementedError
data = dataset['data']
labels = dataset['labels']
sample = data[0].type(torch.Tensor().type())
label = int(labels[0])
adv_label = 0
if label == adv_label:
adv_label += 1
eps = 0.1
# Create the input domain to the verification
domain = torch.stack([
torch.clamp(sample - eps, 0, None),
torch.clamp(sample + eps, None, 1.0)
], -1)
# Adjust the convolutional bound so as to make it mono-objective, just for
# the target label.
layers = [lay for lay in model]
assert isinstance(layers[-1], nn.Linear)
old_last = layers[-1]
new_last = nn.Linear(old_last.in_features, 1)
no_grad(new_last)
new_last.weight.copy_(old_last.weight[label] - old_last.weight[adv_label])
new_last.bias.copy_(old_last.bias[label] - old_last.bias[adv_label])
layers[-1] = new_last
return cls(layers), domain
