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DiffusionGS-Object
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exp_root_dir: "outputs"
name: "diffusion_gs_obja_4views_relposes"
tag: "${rmspace:${system.shape_model_type}+lr${system.optimizer.args.lr},_}"
seed: 0

data_type: "Objaverse-datamodule"
data:
  local_dir: "DATAPATH_TO_GOBJAVERSE_JSON"
  image_dir: "DATAPATH_TO_GOBJAVERSE"
  gen_idxs: [30,33,36,39] ##
  sel_views: 6
  gen_views: 4
  training_res: [256, 256]
  batch_size: 4
  num_workers: 4
  norm_camera: True
  norm_radius: 3. #1.8
  gen_rel_idxs: True
  

system_type: "diffusion-gs-system"
system:
  num_inference_steps: 30

  shape_model_type: "diffusion-gs-model"
  shape_model:
    width: 1024
    in_channels: 9 #rgb+plucker
    patch_size: 8
    n_gaussians: 2
    dim_heads: 64
    num_layers: 24
    prior_distribution: 'gaussian' #sphere_gaussian
    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: [150, 0., 1., 151]
    lambda_lpips: [150, 0., 0.5, 151]
    lambda_ssim: 0.0
    lambda_pointsdist: [150, 1., 0., 151]
    lambda_xyz: [150, 0., 0.025, 151]
    lambda_depth: 0.

  optimizer:
    name: AdamW
    args:
      lr: 1.e-5
      betas: [0.9, 0.99]
      eps: 1.e-8

  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
  check_val_every_n_epoch: 2
  accumulate_grad_batches: 1
  gradient_clip_val: 0.5
  enable_progress_bar: true
  precision: 16-mixed
  strategy: 'ddp_find_unused_parameters_true'

checkpoint:
  save_last: true
  save_top_k: -1
  every_n_train_steps: 1000