configs_gan_free_ffhq.yml

  Introduction : 'GAN-free methods for gradient inversion attack.'
  
  optim : 'GAN_free'    #optim method, ['GAN_based', 'GAN_free']
  cost_fn : 'sim_cmpr0'   #the type of loss function 
  set_seed :       # random seed. Following geiping et. al., we do not give random seed for GAN-free methods.
  indices : 'def'      #decide which part of gradients to be involved in the final gradients match loss.
  weights : 'equal'    #weight of every graident scalar's matching loss
  init : 'randn'     #how we initial the original latent code.
  model : 'ResNet18'   #FL model
  restarts : 4

  num_images : 1   # the number of images to reconstruct at a batch
  num_exp : 10     # the number of experiments
  target_id : 0
  lr : 0.1            #learning rate for Yin et al.
  total_variation : 0.0001     #the coefficient of total variation 
  image_norm : 0.000001    #the coefficient of norm regularizer for Yin et al.
  group_lazy : 0.01    #choose if we use group lazy regularization for Yin et al.
  
  bn_stat : 0       #choose if we use bn statistic to regularizer 

  max_iterations : 15000    #Maximum number of iterations for reconstruction.

  gias_lr : 0.00001   #For biggan, we'd better choose smaller learning rate. 

  # For input data
  generative_model : ''
  gen_dataset : 'FFHQ64'    # ['ImageNet64', 'FFHQ64']
  dataset : 'FFHQ64'       
  data_path : '/depot/ninghui/data/ffhq-dataset/ffhq_dataset/'    # specify your dataset path

  #For output data
  exp_name : 'ex1_gan_free_ffhq'   #Same latent space search
  output_dir : '/scratch/gilbreth/zhan4057/gradinv/results_gan_free_ffhq/'

  #Choice for GAN-free methods
  geiping : true
  yin : false

  # training epoch
  train_epochs: 100
  our_num_tries : 20
  #Defense parameter
  defense_method : 'orthogonal'
  defense_setting : 
    noise : null
    clipping : 4
    compression : 20
    representation : null
    orthogonal : null

  # The pre-trained StyleGAN checkpoint
  ckpt: None

  #LR pace for training
  lr_same_pace: false