This file contains some instructions on how to write a .json configuration file for the branch and bound framework.
We will use bab_configs/oval21_vnncomp21.json as an example file.
Further information can be found in the parsing algorithm, implemented within bab_from_json of tools/bab_tools/bab_runner.py.
The bounding algorithms to be stratified (see section 6.2 from https://arxiv.org/pdf/2101.05844.pdf) are passed as a
list of dictionaries, in order of increasing computational cost, using the nets entry of the bounding key.
bounding_algorithm can be either prox, to use the proximal solver from Bunel et al. 2020b,
propagation for linear bound propagation methods (including a beta-CROWN Wang et al. 2021 implementation),
dual-anderson for algorithms on the convex hull of the ReLU with the preceding linear layer, from De Palma et al. 2024.
batch_size indicates the largest number of subproblems to split at once. Twice at many will be bounded upon. Reduce this in case of memory errors.
auto_iters automatically increases the number of iterations of a bounding algorithm, as described in https://arxiv.org/abs/2104.06718.
The params defined within each dictionary refer to the hyper-parameters of each bounding algorithms,
which are used in the solver.py files (see main README).
ibs accepts a dictionary for the bounding algorithm to be used for pre-activation bounds (under loose_ib), and whether these bounds should be kept fixed during the procedure (fixed_ib).
Keep joint_ib (only supported by propagation) equal to false for the best performance under our implementation.
upper_bounding refers to the parameters of the adversarial attack being employed. Only MI-FGSM (mi_fgsm) is currently supported.
branching refers to the branching strategy. For instance FSB is the strategy from https://arxiv.org/abs/2104.06718, which accepts a bounding algorithm to do the filtering. I would keep it as shown in the example (best bounds between the method from Wong and Kolter and CROWN).
SR would run the strategy from https://arxiv.org/abs/1909.06588.
Other options can be found within the initilizer of BranchingChoice in plnn/branch_and_bound/branching_scores.py.