exp_root_dir: "outputs"
name: "diffusion_gs_scene_re10k_256_stage1"
tag: "${rmspace:${system.shape_model_type}+lr${system.optimizer.args.lr},_}"
seed: 0
# resume: outputs/image-to-shape-diffusion/clip-dino-rgb-pixart-lr2e4-ddim-refine-sum-scaleup-from-190k-norm-scale-up-1024-new-vae/michelangelo-autoencoder+n12288+noise0.0+pfeat3+normembFalse+lr0.0002+qkvbiasFalse+nfreq8+ln_postTrue/ckpts/last.ckpt


data_type: "Re10k-datamodule"
data:
  local_dir: "DATAPATH_TO_RE10K_TRAIN_FULLLIST"
  local_eval_dir: "DATAPATH_TO_RE10K_EVAL_FULLLIST"
  sel_views: 3
  eval_subset: 16
  sel_views_train: 4
  training_res: [256, 256]
  batch_size: 24
  eval_batch_size: 1
  num_workers: 4
  

system_type: "diffusion-gs-scene-system"
system:
  num_inference_steps: 30
  save_intermediate_video: True
  #validate_metric: False

  shape_model_type: "diffusion-gs-model-scene"
  shape_model:
    width: 1024
    in_channels: 9 #rgb+plucker
    patch_size: 8
    n_gaussians: 2
    dim_heads: 64
    num_layers: 24
    range_setting_near: 0 
    range_setting_far: 500 
    prior_distribution: 'gaussian' #sphere_gaussian
    ray_pe_type: 'plk'
    use_flash: true
    use_checkpoint: true

  noise_scheduler_type: "diffusionGS.models.scheduler.ddim_scheduler.DDIMScheduler"
  noise_scheduler:
    num_train_timesteps: 1000
    prediction_type: "sample"

  loggers:
    wandb:
      enable: false
      project: "diffusionGS"
      name: image-to-shape-diffusion+${name}+${tag}

  loss:
    loss_type: "mse"
    lambda_diffusion: 1. #[150, 0., 1., 151] #1.
    lambda_lpips: 0.1 #[150, 0., 0.5, 151]
    lambda_ssim: 0.0
    lambda_pointsdist: 0.0 #[150, 1., 0., 151]
    lambda_xyz: 0. #[100, 0., 1., 101]
    lambda_depth: 0. #[100, 0., 0.25, 101]

  optimizer:
    name: AdamW
    args:
      lr: 3.e-5
      betas: [0.9, 0.95]
      eps: 1.e-6
      weight_decay: 0.05

  scheduler:
    name: CosineAnnealingLR
    args:
      T_max: 500000
      eta_min: 1e-6

trainer:
  num_nodes: 1
  max_epochs: 1000000
  log_every_n_steps: 5
  num_sanity_val_steps: 1
  #val_check_interval: 5000
  check_val_every_n_epoch: 5
  accumulate_grad_batches: 1
  gradient_clip_val: 0.5
  enable_progress_bar: true
  precision: 16-mixed
  strategy: 'ddp_find_unused_parameters_true' #_find_unused_parameters_true

checkpoint:
  save_last: true
  save_top_k: -1
  every_n_train_steps: 5000