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# Input data
resolution: 1024
age_min: 20
age_max: 70
use_realimg: True
# Training hyperparameters
batch_size: 1
epochs: 12
iter_per_epoch: 10000
device: 'cuda'
# Optimizer parameters
optimizer: 'ranger'
lr: 0.0001
beta_1: 0.95
beta_2: 0.999
weight_decay: 0
# Learning rate scheduler
step_size: 10
gamma: 0.1
# Tensorboard log options
image_save_iter: 100
log_iter: 10
# Network setting
use_fs_encoder: True
use_fs_encoder_v2: True
fs_stride: 2
pretrained_weight_for_fs: False
enc_resolution: 256
enc_residual: False
truncation_psi: 1
use_noise: True
randomize_noise: False # If generator use a different random noise at each time of generating a image from z
# Loss setting
use_parsing_net: True
multi_layer_idloss: True
real_image_as_image_loss: False
feature_match_loss: False
feature_match_loss_G: False
use_random_noise: True
optimize_on_z: False
multiscale_lpips: True
# Loss weight
w:
l1: 0
l2: 1
lpips: 0.2
id: 0.1
landmark: 0.1
f_recon: 0.01 |