# lightning.pytorch==2.1.3
seed_everything: 31
trainer:
  accelerator: auto
  strategy: auto
  devices: auto
  num_nodes: 1
  precision: null
  logger: null
  callbacks:
  - class_path: callbacks.SaveImageCallback
    init_args:
      save_interval: 0
      final_dir: out
  fast_dev_run: false
  max_epochs: 10
  min_epochs: 10
  max_steps: -1
  min_steps: null
  max_time: null
  limit_train_batches: null
  limit_val_batches: 50
  limit_test_batches: null
  limit_predict_batches: null
  overfit_batches: 0.0
  val_check_interval: null
  check_val_every_n_epoch: 1
  num_sanity_val_steps: null
  log_every_n_steps: 3
  enable_checkpointing: null
  enable_progress_bar: null
  enable_model_summary: null
  accumulate_grad_batches: 1
  gradient_clip_val: null
  gradient_clip_algorithm: null
  deterministic: null
  benchmark: null
  inference_mode: true
  use_distributed_sampler: true
  profiler: null
  detect_anomaly: false
  barebones: false
  plugins: null
  sync_batchnorm: false
  reload_dataloaders_every_n_epochs: 0
  default_root_dir: null
model:
  transform: tanh
  width: 256
  depth: 8
  bias: true
  alpha: 0.0
data:
  val_size: 10000
  train_size: 10000
  batch_size: 256
  num_workers: 3
ckpt_path: null
optimizer:
  class_path: torch.optim.AdamW
  init_args:
    lr: 0.001
    betas:
    - 0.9
    - 0.999
    eps: 1.0e-08
    weight_decay: 0.01
    amsgrad: false
    maximize: false
    foreach: null
    capturable: false
    differentiable: false
    fused: null
lr_scheduler:
  class_path: lightning.pytorch.cli.ReduceLROnPlateau
  init_args:
    monitor: hp_metric
    mode: min
    factor: 0.05
    patience: 5
    threshold: 0.0001
    threshold_mode: rel
    cooldown: 10
    min_lr: 0.0
    eps: 1.0e-08
    verbose: true