config_NCF.yaml

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# 1 Basic settings(In general, there is no need to change these settings.)#
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# attack mode: 'untargeted' or 'targeted(least-likely)' or 'targeted(random)' eta
attack_mode: !!str untargeted  
# Types of returned adversarial samples. 'float' or 'int'
return_type: float   
model_mode: !!str 'torch'  # torch|tf|timm
local: True  # Does it run locally? (Default: True)
device: 'cuda:0'

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# 2 Set up the test model and data. 
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white_models_name: ['inception_v3'] 
black_models_name: ['vgg19', 'resnet18', 'mobilenet_v2', 'densenet121', 'resnet50'] # Black box and white box cannot have the same model!

label_path: 'dataset/labels.txt'
img_dir: 'dataset/images'
masks_path: 'segm/masks.npy'  
color_dir: 'dataset/lib_299'  

output_dir: 'adv'
data_range: [1,1001]  # [start, end)
batch_size: !!int 20
images_size: 224

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# 3 NCF 
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color_mode: !!str Lab
num_reset: !!int 10  # The number of initialization reset (Default: 10).
eta: !!int 50  # The number of random searches (Default: 50).
num_iter: !!int 15  # The iteration of neighborhood search (Default: 15).
T_step: !!float 0.013  # The iterative step size of T (Default: 0.013).

momentum: !!float 0.6  # (Default: 0.6)