text: _base_ = ["fcaf3d.py"]
  n_points = 100000
  
  model = dict(neck_with_head=dict(n_classes=2, n_reg_outs=8))
  
  dataset_type = "SUNRGBDSSGDataset"
  data_root = "/svl/u/alanlou/data/sunrgbd_data/"
  # bottom_cabinet and microwave.
  class_names = ("1", "2")
  train_pipeline = [
      dict(
          type="LoadPointsFromFile",
          coord_type="DEPTH",
          shift_height=False,
          load_dim=6,
          use_dim=[0, 1, 2, 3, 4, 5],
      ),
      dict(type="LoadAnnotations3D"),
      dict(type="IndoorPointSample", num_points=n_points),
      dict(type="RandomFlip3D", sync_2d=False, flip_ratio_bev_horizontal=0.5),
      dict(
          type="GlobalRotScaleTrans",
          rot_range=[-0.523599, 0.523599],
          scale_ratio_range=[0.85, 1.15],
          translation_std=[0.1, 0.1, 0.1],
          shift_height=False,
      ),
      dict(type="DefaultFormatBundle3D", class_names=class_names),
      dict(type="Collect3D", keys=["points", "gt_bboxes_3d", "gt_labels_3d"]),
  ]
  test_pipeline = [
      dict(
          type="LoadPointsFromFile",
          coord_type="DEPTH",
          shift_height=False,
          load_dim=6,
          use_dim=[0, 1, 2, 3, 4, 5],
      ),
      dict(
          type="MultiScaleFlipAug3D",
          img_scale=(512, 512),
          pts_scale_ratio=1,
          flip=False,
          transforms=[
              dict(
                  type="GlobalRotScaleTrans",
                  rot_range=[0, 0],
                  scale_ratio_range=[1.0, 1.0],
                  translation_std=[0, 0, 0],
              ),
              dict(
                  type="RandomFlip3D",
                  sync_2d=False,
                  flip_ratio_bev_horizontal=0.5,
                  flip_ratio_bev_vertical=0.5,
              ),
              dict(type="IndoorPointSample", num_points=n_points),
              dict(
                  type="DefaultFormatBundle3D", class_names=class_names, with_label=False
              ),
              dict(type="Collect3D", keys=["points"]),
          ],
      ),
  ]
  data = dict(
      samples_per_gpu=2,
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          modality=dict(use_camera=False, use_lidar=True),
          data_root=data_root,
          ann_file=data_root + "sunrgbd_infos_train.pkl",
          pipeline=train_pipeline,
          filter_empty_gt=True,
          classes=class_names,
          box_type_3d="Depth",
      ),
      val=dict(
          type=dataset_type,
          modality=dict(use_camera=False, use_lidar=True),
          data_root=data_root,
          ann_file=data_root + "sunrgbd_infos_val.pkl",
          pipeline=test_pipeline,
          classes=class_names,
          test_mode=True,
          box_type_3d="Depth",
      ),
      test=dict(
          type=dataset_type,
          modality=dict(use_camera=False, use_lidar=True),
          data_root=data_root,
          ann_file=data_root + "sunrgbd_infos_val.pkl",
          pipeline=test_pipeline,
          classes=class_names,
          test_mode=True,
          box_type_3d="Depth",
      ),
  )
  