# Train config file

settings:
  general:
    seed: 0
    cuda_visible_devices: "0,1,2,3,4,5,6,7"
    device: cuda
    parallel: True
    test_dir: ""
    print: True
    web_api: 
      host: "127.0.0.1"
      port: 8888

  experiment:
    name: test_score_eval
    root_dir: "experiments"
    use_checkpoint: False
    epoch: -1 # -1 stands for last epoch
    max_epochs: 5000
    save_checkpoint_interval: 1
    test_first: True       
    use_cache: False
    small_batch_overfit: False
    small_batch_size: 100
    small_batch_times: 100

  train:
    optimizer:
      type: adam
      lr: 0.0001
    losses: # loss type : weight
      gf_loss: 1.0
    dataset:
      name: synthetic_train_sample
      source: nbv1
      data_type: sample
      synthetic: True
      ratio: 1.0
      batch_size: 80
      num_workers: 8

  test:
    batch_size: 16
    frequency: 1
    dataset_list:
      - name: synthetic_test_sample
        source: nbv1
        data_type: sample
        synthetic: True
        eval_list:
          - delta_pose
          - grasp_improvement
        ratio: 0.01
        batch_size: 16
        num_workers: 8

  pipeline: # module_type: name
    pts_encoder: pointnet
    view_finder: gradient_field
    rgb_encoder: dinov2

datasets:
  general:
    data_dir: "/mnt/d/Datasets"
    score_limit: 0.3
    target_pts_num: 1024
    scene_pts_num: 16384
    canonical: False
    image_size: 480
    

modules:
  general:
    pts_channels: 3
    feature_dim: 1024
    per_point_feature: False
  pts_encoder:
    pointnet:
    pointnet++:
      params_name: light
    pointnet++rgb:
      params_name: light
      target_layer: 3
      rgb_feat_dim: 384
  view_finder:
    gradient_field:
      pose_mode: rot_matrix
      regression_head: Rx_Ry
      sample_mode: ode
      sample_repeat: 50
      sampling_steps: 500
      sde_mode: ve
  rgb_encoder:
    dinov2:
      model_name: "dinov2_vits14"