#### 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