cifar_resnet_2b.yaml

# This is an example configuration file that contains most useful parameter settings.
model:
  name: resnet2b  # This model is defined in model_defs.py. Add your own model definitions there.
  path: models/cifar10_resnet/resnet2b.pth  # Path to PyTorch checkpoint.
data:
  dataset: CIFAR  # Dataset name. This is just the standard CIFAR-10 test set defined in the "load_verification_dataset()" function in utils.py
  mean: [0.4914, 0.4822, 0.4465]  # Mean for normalization.
  std: [0.2471, 0.2435, 0.2616]  # Std for normalization.
  start: 0  # First example to verify in dataset.
  end: 100  # Last example to verify in dataset. We verify 100 examples in this test.
specification:
  norm: .inf  # Linf norm (can also be 2 or 1).
  epsilon: 0.00784313725490196  # epsilon=2./255.
attack:  # Currently attack is only implemented for Linf norm.
  pgd_steps: 100  # Increase for a stronger attack. A PGD attack will be used before verification to filter on non-robust data examples.
  pgd_restarts: 30  # Increase for a stronger attack.
solver:
  batch_size: 2048  # Number of subdomains to compute in parallel in bound solver. Decrease if you run out of memory.
  alpha-crown:
    iteration: 100   # Number of iterations for alpha-CROWN optimization. Alpha-CROWN is used to compute all intermediate layer bounds before branch and bound starts.
    lr_alpha: 0.1    # Learning rate for alpha in alpha-CROWN. The default (0.1) is typically ok.
  beta-crown:
    lr_alpha: 0.01  # Learning rate for optimizing the alpha parameters, the default (0.01) is typically ok, but you can try to tune this parameter to get better lower bound.
    lr_beta: 0.05  # Learning rate for optimizing the beta parameters, the default (0.05) is typically ok, but you can try to tune this parameter to get better lower bound.
    iteration: 20  # Number of iterations for beta-CROWN optimization. 20 is often sufficient, 50 or 100 can also be used.
bab:
  timeout: 120  # Timeout threshold for branch and bound. Increase for verifying more points.
  branching:  # Parameters for branching heuristics.
    reduceop: min  # Reduction function for the branching heuristic scores, min or max. Using max can be better on some models.
    method: kfsb  # babsr is fast but less accurate; fsb is slow but most accurate; kfsb is usually a balance.
    candidates: 3  # Number of candidates to consider in fsb and kfsb. More leads to slower but better branching. 3 is typically good enough.