def shake_shake_fgsm():
aparams = HParams()
aparams.attack = "fgsm"
aparams.attack_epsilons = [(i+1) * 0.1 for i in range(12)]
aparams.add_hparam("clip_min", 0.0)
aparams.add_hparam("clip_max", 255.0)
return aparams
def resnet_fgsm():
aparams = HParams()
aparams.attack = "fgsm"
aparams.epsilon_name = "eps"
aparams.attack_epsilons = [i * 0.8 for i in range(20)]
aparams.add_hparam("clip_min", 0.0)
aparams.add_hparam("clip_max", 255.0)
return aparams
def transformer_weight_mjc():
hp = HParams()
hp.add_hparam("strategy", "weight")
hp.add_hparam("black_list", ["logits", "bias"])
hp.add_hparam("white_list", ["attention"])
hp.add_hparam("sparsities", [0.1 * i for i in range(8)])
hp.add_hparam("weight_sparsities", [0.1 * i for i in range(8)])
return hp
def resnet_weight_mjc():
hp = HParams()
hp.add_hparam("strategy", "weight")
hp.add_hparam("black_list", ["logits", "bias"])
hp.add_hparam("white_list", ["conv2d"])
hp.add_hparam("sparsities", [0.3 * i for i in range(1)])
hp.add_hparam("weight_sparsities", [0.3 * i for i in range(1)])
return hp
def resnet_weight():
hp = HParams()
hp.add_hparam("strategy", "weight")
hp.add_hparam("black_list", ["logits", "bias"])
hp.add_hparam("white_list", ["td_conv"])
hp.add_hparam("sparsities", [0.1 * i for i in range(10)])
return hp
