Low precision bug for batch-norm models about alpha-beta-crown HOT 4 CLOSED

def cifar_model_deep(num_classes=10,bn=True,channels=None):
    # cifar deep
    if channels==None:
        channels=[32,64,64,128,128]
    assert(len(channels)==5)
    module_list=[
        nn.Conv2d(3, channels[0], 3, stride=1, padding=1),
        nn.ReLU(),
        nn.Conv2d(channels[0], channels[1], 4, stride=2, padding=1),
        nn.ReLU(),
        nn.Conv2d(channels[1], channels[2], 3, stride=1, padding=1),
        nn.ReLU(),
        nn.Conv2d(channels[2], channels[3], 4, stride=2, padding=1),
        nn.ReLU(),
        nn.Conv2d(channels[3], channels[4], 4, stride=2, padding=1),
        nn.ReLU(),
        # nn.Conv2d(128, 256, 4, stride=2, padding=1),
        # nn.ReLU(),
        Flatten(),
        nn.Linear(channels[4]*4*4, 100),
        nn.ReLU(),
        nn.Linear(100, num_classes)
    ]

    new_module_list=[]
    for m in module_list:
        new_module_list.append(m)
        if bn and isinstance(m,nn.Conv2d):
            new_module_list.append(nn.BatchNorm2d(m.out_channels))

    model=nn.Sequential(*new_module_list)

    print(model)

    return model