Use norms other then linf about alpha-beta-crown HOT 3 OPEN

# 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: 2 
  epsilon: 0.1411764705882353  # epsilon=36./255.
attack:  # Currently attack is only implemented for Linf norm.
  pgd_order: skip
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.

Exception has occurred: RuntimeError
Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [1, 1, 3, 32, 32]
  File "/home/f.brau/alpha-beta-CROWN/complete_verifier/model_defs.py", line 164, in forward
    out = self.relu(self.conv1(x))
  File "/home/f.brau/alpha-beta-CROWN/complete_verifier/auto_LiRPA/bound_general.py", line 110, in __init__
    self.final_shape = model(
  File "/home/f.brau/alpha-beta-CROWN/complete_verifier/beta_CROWN_solver.py", line 55, in __init__
    self.net = BoundedModule(
  File "/home/f.brau/alpha-beta-CROWN/complete_verifier/abcrown.py", line 198, in bab
    self.model = LiRPANet(
  File "/home/f.brau/alpha-beta-CROWN/complete_verifier/abcrown.py", line 398, in complete_verifier
    l, nodes, ret = self.bab(
  File "/home/f.brau/alpha-beta-CROWN/complete_verifier/abcrown.py", line 591, in main
    verified_status = self.complete_verifier(
  File "/home/f.brau/alpha-beta-CROWN/complete_verifier/abcrown.py", line 614, in <module>
    abcrown.main()
RuntimeError: Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [1, 1, 3, 32, 32]```

model:
  name: cnn_4layer_adv
  path: models/sdp/cifar_cnn_a_adv.model
data:
  dataset: CIFAR_SDP
  start: 1
  end: 5
specification:
  epsilon: 0.00784313725  # 2./255.
  norm: 1
attack:
  pgd_restarts: 50
solver:
  batch_size: 2048
  beta-crown:
    iteration: 20
bab:
  timeout: 200
  branching:
    reduceop: max
  cut:
    enabled: True
    cplex_cuts: True
    bab_cut: True
    lr_decay: 0.9

mip_multi_proc: 20, mip_threads: 1, total threads used: 20
Process Process-2:
Traceback (most recent call last):
  File "/home/yusuf/miniconda3/envs/alpha-beta-crown/lib/python3.9/multiprocessing/process.py", line 315, in _bootstrap
    self.run()
  File "/home/yusuf/miniconda3/envs/alpha-beta-crown/lib/python3.9/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/home/yusuf/miniconda3/envs/alpha-beta-crown/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/home/yusuf/Desktop/alpha-beta-CROWN-main/complete_verifier/lp_mip_solver.py", line 1042, in construct_mip_with_model
    build_the_model_mip(model, labels_to_verify=None, save_mps=save_mps, process_dict=process_dict, x=x, intermediate_bounds=intermediate_bounds)
  File "/home/yusuf/Desktop/alpha-beta-CROWN-main/complete_verifier/lp_mip_solver.py", line 1066, in build_the_model_mip
    build_solver_model(m, timeout, mip_multi_proc=mip_multi_proc,
  File "/home/yusuf/Desktop/alpha-beta-CROWN-main/complete_verifier/lp_mip_solver.py", line 638, in build_solver_model
    out_vars = m.net.build_solver_module(
  File "/home/yusuf/Desktop/alpha-beta-CROWN-main/complete_verifier/auto_LiRPA/solver_module.py", line 50, in build_solver_module
    inp_gurobi_vars = self._build_solver_input(roots[i])
  File "/home/yusuf/Desktop/alpha-beta-CROWN-main/complete_verifier/auto_LiRPA/solver_module.py", line 94, in _build_solver_input
    assert node.perturbation.norm == float("inf")
AssertionError

...
...

Remaining spec index tensor([0], device='cuda:0') with bounds tensor([[-0.96139729]], device='cuda:0') need to verify.
Traceback (most recent call last):
  File "/home/yusuf/Desktop/alpha-beta-CROWN-main/complete_verifier/abcrown.py", line 616, in <module>
    abcrown.main()
  File "/home/yusuf/Desktop/alpha-beta-CROWN-main/complete_verifier/abcrown.py", line 595, in main
    verified_status = self.complete_verifier(
  File "/home/yusuf/Desktop/alpha-beta-CROWN-main/complete_verifier/abcrown.py", line 402, in complete_verifier
    l, nodes, ret = self.bab(
  File "/home/yusuf/Desktop/alpha-beta-CROWN-main/complete_verifier/abcrown.py", line 198, in bab
    self.model = LiRPANet(
  File "/home/yusuf/Desktop/alpha-beta-CROWN-main/complete_verifier/beta_CROWN_solver.py", line 55, in __init__
    self.net = BoundedModule(
  File "/home/yusuf/Desktop/alpha-beta-CROWN-main/complete_verifier/auto_LiRPA/bound_general.py", line 110, in __init__
    self.final_shape = model(
  File "/home/yusuf/miniconda3/envs/alpha-beta-crown/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/yusuf/miniconda3/envs/alpha-beta-crown/lib/python3.9/site-packages/torch/nn/modules/container.py", line 204, in forward
    input = module(input)
  File "/home/yusuf/miniconda3/envs/alpha-beta-crown/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/yusuf/miniconda3/envs/alpha-beta-crown/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 463, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "/home/yusuf/miniconda3/envs/alpha-beta-crown/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 459, in _conv_forward
    return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [1, 1, 3, 32, 32]