EditGuard / options /train_editguard_bit.yml
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#### general settings
name: train_ibsn_bit_64
use_tb_logger: true
model: MIMO-VRN-h
distortion: sr
scale: 4
gpu_ids: [0, 1]
gop: 1
num_image: 1
addnoise: False
noisesigma: 0.05
addjpeg: False
jpegfactor: 90
addpossion: False
sdinpaint: False
controlnetinpaint: False
sdxl: False
repaint: False
sdprompt: False
sdxlprompt: False
faceswap: False
hide: True
bithide: True
degrade_shuffle: True
prompt: True
prompt_len: 3
message_length: 64
losstype: mse
mode: bit
#### datasets
datasets:
train:
name: Vimeo90K
mode: train
interval_list: [1]
random_reverse: false
border_mode: false
data_path: /userhome/train2017
txt_path: /userhome/train2017.txt
dataroot_LQ: ~/vimeo90k/vimeo90k_train_LR7frames.lmdb
cache_keys: Vimeo90K_train_keys.pkl
num_image: 1
N_frames: 7
use_shuffle: true
n_workers: 24
batch_size: 4
GT_size: 400
LQ_size: 36
use_flip: true
use_rot: true
color: RGB
val:
num_image: 1
name: Vid4
mode: test
data_path: ../dataset/valAGE-Set
txt_path: ../dataset/sep_vallist.txt
N_frames: 1
padding: 'new_info'
pred_interval: -1
#### network structures
network_G:
which_model_G:
subnet_type: DBNet
in_nc: 12
out_nc: 12
block_num: [6, 6]
scale: 2
init: xavier_group
block_num_rbm: 8
block_num_trans: 4
#### path
path:
pretrain_model_G:
models: ckp/base
strict_load: true
resume_state: ~
#### training settings: learning rate scheme, loss
train:
lr_G: !!float 1e-4
beta1: 0.9
beta2: 0.5
niter: 250000
warmup_iter: -1 # no warm up
lr_scheme: MultiStepLR
lr_steps: [30000, 100000, 250000]
lr_gamma: 0.5
pixel_criterion_forw: l2
pixel_criterion_back: l1
manual_seed: 10
val_freq: !!float 500 #!!float 5e3
lambda_fit_forw: 1.
lambda_rec_back: 1
lambda_center: 0
lambda_msg: !!float 100 # 500000
progressive: False
weight_decay_G: !!float 1e-12
gradient_clipping: 10
#### logger
logger:
print_freq: 100
save_checkpoint_freq: !!float 500