configs/final/all_warmup.yaml

###
# Pretrain on all data with all losses
###

# Experiment general info
name: "FinalOVPretrain"
rng_seed: 42
num_gpu: 1
mode: "warmup"
note: ""
num_views: 32
# Choose keywords to feature your saving directory
naming_keywords: ["dataloader.batchsize", "task", "note", "time"]
base_dir: "results"
exp_dir: ""
save_frequency: 10

resume: False

stage: ['warmup', 'pretrain']
is_pretrain: True

debug:
  flag: False
  debug_size: 20
  hard_debug: False

logger:
  name: "wandb"
  entity: "yem"
  project: "3DReason"

# dataset details
data:
  note: "all"
  # train: ['ScanNetSpatialRefer','ARKitSceneSpatialRefer','MultiScanSpatialRefer','HMSpatialRefer','RScanSpatialRefer']
  warmup: ['LSUNSpatialRefer']
  train: ['ScanNetSpatialRefer']
  val: ['ScanNetSpatialRefer']
  test: ['ScanNetSpatialRefer']
  # train: ['ScanNetSpatialRefer']
  # val:  ['ScanNetSpatialRefer']
  # test: ['ScanNetSpatialRefer']
  args:
    max_obj_len: 80
    max_seq_len: 50
    num_points: 1024
    pc_type: 'pred'
    sem_type: '607'
    filter_lang: False
    txt_mask_ratio: 0.15
    pc_mask_ratio: 0.1
    rot_aug: True
    mask_strategy: random
    use_scene_cap: False
    max_scene_cap_len: 600
  ScanNetSpatialRefer:
    train:
      # sources: [ 'scanrefer', 'referit3d', 'sgrefer', 'sgcaption' ]
      sources: ['scannet_view_cap']
      referit3d:
        anno_type: ['sr3d', 'nr3d']
        sr3d_plus_aug: True
      sgrefer:
        anno_type: [ 'rel2_gpt', 'rel2_template', 'relm_gpt', 'relm_template', 'star_gpt', 'star_template'] #
      sgcaption:
        anno_type: ['gpt', 'template']
    val:
      sources: ['scannet_view_cap']
      referit3d:
        anno_type: ['sr3d'] # 'nr3d', 'sr3d'
        sr3d_plus_aug: False
      sgrefer:
        anno_type: ['template'] # 'template', 'gpt_chain'
      sgcaption:
        anno_type: ['gpt']
    test:
      sources: ['scannet_view_cap']
      referit3d:
        anno_type: ['sr3d'] # 'nr3d', 'sr3d'
        sr3d_plus_aug: False
      sgrefer:
        anno_type: ['template'] # 'template', 'gpt', 'gpt_chain'
      sgcaption:
        anno_type: ['gpt']
  LSUNSpatialRefer:
    warmup:
      sources: ['lsun_view_cap']
  RScanSpatialRefer:
    train:
      sources: ['3rscan_view_cap']
  MultiScanSpatialRefer:
    train:
      sources: ['anno','rel2_template','rel2_gpt','relm_template','relm_gpt','star_template','star_gpt']
    val:
      sources: [ 'anno', 'rel2_template', 'relm_gpt', 'relm_template', 'star_template', 'star_gpt' ]
    test:
      sources: [ 'anno', 'rel2_template', 'relm_gpt', 'relm_template', 'star_template', 'star_gpt' ]
  ARKitSceneSpatialRefer:
    train:
      sources: ['arkitscenes_view_cap']
  HMSpatialRefer:
    train:
      sources: ['anno','rel2_template','rel2_gpt','relm_template','relm_gpt','star_template','star_gpt']
    val:
      sources: [ 'anno', 'rel2_template', 'relm_gpt', 'relm_template', 'star_template' ]
    test:
      sources: [ 'anno', 'rel2_template', 'relm_gpt', 'relm_template', 'star_template' ]
  use_voxel: False
  scan_family_base: "../PointMapVerse/existing_datasets/ScanNet"
  lsun_base: "../PointMapVerse/existing_datasets/lsun"
  rscan_base: "../PointMapVerse/existing_datasets/3RScan"
  arkitscene_base: '../PointMapVerse/existing_datasets/Arkitscenes'
  multiscan_base: '../PointMapVerse/existing_datasets/MultiScan'
  hm_base: '../PointMapVerse/existing_datasets/HM3D'
  procthor_base: '../PointMapVerse/existing_datasets/ProcThor'
  s3d_base: '../PointMapVerse/existing_datasets/Structured3D'

data_aug:
  aug_list: ['scene_aug']
  scene_aug:
    translation:
      enabled: False
      value: [1.0, 1.0, 1.0]
      p: 1.0
    scaling:
      enabled: False
      p: 1.0
      value: [0.9, 1.1]
    flip:
      enabled: False
      p: 0.5
    rotation:
      enabled: True
      p: 1.0
      axis_align: True
      value: [0.0, 0.0, 1.0]
      shuffle: True
    color_jitter: False
    order_shuffle: False
  obj_aug:
    translation:
      enabled: False
      value: [0.1, 0.1, 0.1]
      p: 1.0
    rotation:
      enabled: False
      p: 1.0
      axis_align: False
      value: [0.0, 0.0, 0.1]
      shuffle: True
    random_jitter:
      enabled: False
      value: 0.01
      accord_to_size: False
      p: 1.0
    pts_shuffle: True

# task details: 'Pretrain', 'scanqa', 'spatialrefer'
task: 'Pretrain'
# 'MaskDatasetWrapper', 'ScanFamilyDatasetWrapper', 'MaskMVDatasetWrapper'
data_wrapper:
  warmup: 'SceneDatasetWrapper'
  train: 'SceneDatasetWrapper'
  val: 'SceneDatasetWrapper'
  test: 'SceneDatasetWrapper'

# Training details
trainer: "OpenVocabTrainer"
ckpt_path: ""
pretrain_ckpt_path: ""

# dataloader details
dataloader:
  batchsize: 256
  num_workers: 2
  balance_dataset: False
  filter_empty_annotations: False

solver:
  gradient_accumulation_steps: 1
  epochs_per_save: 20
  epochs_per_eval: 1
  lr: 1e-4
  grad_norm: 5.0
  warmup_epochs: 100
  epochs: 100
  optim:
    name: 'AdamW'
    args:
      betas: [0.9, 0.98]
      weight_decay: 0.05
  sched:
    name: 'warmup_cosine'
    args:
      warmup_steps: 500
      minimum_ratio: 0.1
eval:
  train:
    name: 'PretrainEval'
  val:
    name: 'ScanReferEval'
  save: False


# Model details
model:
  name: OpenVocab
  language:
    # This part could be further optimized to be using
    # huggingface yaml config files
    name: 'SigLIPLanguageEncoder' 
    args:
      weights: 'fg-clip-base'
      # hidden_size: 768
      # num_hidden_layers: 4
      # num_attention_heads: 12
      # type_vocab_size: 2
    lr: 1e-4
  vision:
    name: 'fg-clip-base'
    args:
        backbone: 'pointnet++'
        hidden_size: 768
        freeze: True
        path: 'pretrained_weights/pointnetpp-open-bert'
        num_attention_heads: 12
        spatial_dim: 5
        num_layers: 4
        dim_loc: 6
        dim_feedforward: 2048
        attn_type: spatial
        pairwise_rel_type: 'center'
        use_matmul_label: False
        lang_type: 'bert'
        lang_path: 'pretrained_weights/607_text_embeddings'
    lr: 1e-4
  grounding:
    name: 'UnifiedSpatialCrossEncoderV2'
    args:
      hidden_size: 768
      num_attention_heads: 12
      num_layers: 4
      dim_feedforward: 2048
      dim_loc: 6
    lr: 1e-4
  inter: before
  heads:
    head_list: []
    pretrain_head:
      name: 'OVPretrainHead'
      args:
        hidden_size: 768
        vocab_size: 30522
  loss_type: 'ListLoss'
  loss_list: [
    'WarmUpPM_loss'
  ]
  vis_loss_list: [
    'WarmUpPM_loss'
  ]