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:
For the examples below assume the following network definition:
from torch import nn
from bound_propagation import BoundModelFactory, HyperRectangle
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()
factory = BoundModelFactory()
net = factory.build(net)
The method also works with nn.Sigmoid and nn.ReLU, and the three custom layers Residual, Cat, and Parallel.
Interval bounds
To get interval bounds for either IBP, CROWN, or CROWN-IBP:
x = torch.rand(100, 30)
epsilon = 0.1
input_bounds = HyperRectangle.from_eps(x, epsilon)
ibp_bounds = net.ibp(input_bounds)
crown_bounds = net.crown(input_bounds).concretize()
crown_ibp_bounds = net.crown(input_bounds).concretize()
Linear bounds
To get linear bounds for either CROWN or CROWN-IBP:
x = torch.rand(100, 30)
epsilon = 0.1
input_bounds = HyperRectangle.from_eps(x, epsilon)
crown_bounds = net.crown(input_bounds)
crown_ibp_bounds = net.crown(input_bounds)
Authors
- Frederik Baymler Mathiesen - PhD student @ TU Delft
1 maintainer
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