bound-propagation 0.2.0

Bound propagation in Pytorch

Bound propagation

Linear and interval bound propagation in Pytorch with easy-to-use API, GPU support, and heavy parallization. Initially made as an alternative to the original CROWN implementation which featured only Numpy, lots of for-loops, and a cumbersome API.

To install:

pip install bound-propagation

Supported bound propagation methods:

• Interval Bound Propagation (IBP)
• CROWN
• CROWN-IBP

For the examples below assume the following network definition:

from torch import nn
from bound_propagation import crown, crown_ibp, ibp

# The decorators _must_ be on a subclass of nn.Sequential

@crown
@crown_ibp
@ibp
class Network(nn.Sequential):
    def __init__(self, *args):
        if args:
            # To support __get_index__ of nn.Sequential when slice indexing
            # CROWN (and implicitly CROWN-IBP) is doing this underlying
            super().__init__(*args)
        else:
            in_size = 30
            classes = 10

            super().__init__(
                nn.Linear(in_size, 16),
                nn.Tanh(),
                nn.Linear(16, 16),
                nn.Tanh(),
                nn.Linear(16, classes)
            )

net = Network()

Alternatively, you can add the functions to your network by calling the functions with an instance of your network:

from torch import nn
from bound_propagation import crown, crown_ibp, ibp

class Network(nn.Sequential):
    def __init__(self, *args):
        if args:
            # To support __get_index__ of nn.Sequential when slice indexing
            # CROWN (and implicitly CROWN-IBP) is doing this underlying
            super().__init__(*args)
        else:
            in_size = 30
            classes = 10

            super().__init__(
                nn.Linear(in_size, 16),
                nn.Tanh(),
                nn.Linear(16, 16),
                nn.Tanh(),
                nn.Linear(16, classes)
            )

# The instance _must_ be an nn.Sequential or a subclass thereof
net = crown(crown_ibp(ibp(Network())))

The method also works with nn.Sigmoid and nn.ReLU.

Interval bounds

To get interval bounds for either IBP, CROWN, or CROWN-IBP:

x = torch.rand(100, 30)
epsilon = 0.1
lower, upper = x - epsilon, x + epsilon

ibp_bounds = net.ibp(lower, upper)
crown_bounds = net.crown_interval(lower, upper)
crown_ibp_bounds = net.crown_ibp_interval(lower, upper)

Linear bounds

To get linear bounds for either CROWN or CROWN-IBP:

x = torch.rand(100, 30)
epsilon = 0.1
lower, upper = x - epsilon, x + epsilon

crown_bounds = net.crown_linear(lower, upper)
crown_ibp_bounds = net.crown_ibp_linear(lower, upper)

Authors

• Frederik Baymler Mathiesen - PhD student @ TU Delft