train_indoor.yaml

NEUCONW:
  N_SAMPLES: 8
  N_IMPORTANCE: 16
  UP_SAMPLE_STEP: 2
  N_OUTSIDE: 8
  USE_DISP: False
  PERTURB: 1.0
  NOISE_STD: 1.0

  NEAR_FAR_OVERRIDE: True
  DEPTH_LOSS: False
  S_VAL_BASE: 3
  BOUNDARY_SAMPLES: 10
  SAMPLE_RANGE: 16
  SDF_THRESHOLD: 0.0
  TRAIN_VOXEL_SIZE: 0.08
  UPDATE_FREQ: 5000
  
  N_VOCAB: 5000
  ENCODE_A: True
  N_A: 48
  N_STATIC_HEAD: 1
  ANNEAL_END: 50000

  RENDER_BG: True
  MESH_MASK_LIST: ['sky']
  RAY_MASK_LIST:  None
  ENCODE_A_BG: False
  FLOOR_NORMAL: False
  FLOOR_LABELS: [ 'road' ]

# network config
  SDF_CONFIG:
    d_in: 3
    d_out: 513
    d_hidden: 512
    n_layers: 8
    skip_in: (4,)
    multires: 6
    bias: 0.5
    scale: 1
    geometric_init: True
    weight_norm: True
    inside_outside: True

  COLOR_CONFIG:
    d_in: 9
    d_feature: 512
    mode: "idr"
    d_out: 3
    d_hidden: 256 
    n_layers: 4
    head_channels: 128
    static_head_layers: 2
    weight_norm: True
    multires_view: 4

  S_CONFIG:
    init_val: 0.3

# loss config
  LOSS:
    coef: 1.0
    igr_weight: 0.1
    mask_weight: 0.1
    depth_weight: 0.1
    floor_weight: 0.01

DATASET:
  ROOT_DIR: '/nas/users/chenxi/recon_data/hz322-2_xi/VID_00'
  DATASET_NAME: 'phototourism'
  SPLIT: 'train'

  PHOTOTOURISM:
    IMG_DOWNSCALE: 1
    USE_CACHE: True
    CACHE_DIR: 'cache_sgs'

TRAINER:
  CANONICAL_BS: 4096
  CANONICAL_LR: 1e-4

  OPTIMIZER: "adam" 
  WEIGHT_DECAY: 0 

  LR_SCHEDULER: 'none'
  SAVE_DIR: './checkpoints'
  VAL_FREQ: 10000.0
  SAVE_FREQ: 10000