config.md

# Learn about Configs

MMDetection3D and other OpenMMLab repositories use [MMEngine's config system](https://mmengine.readthedocs.io/en/latest/tutorials/config.html). It has a modular and inheritance design, which is convenient to conduct various experiments.
If you wish to inspect the config file, you may run `python tools/misc/print_config.py /PATH/TO/CONFIG` to see the complete config.

## Config File Content

MMDetection3D uses a modular design, all modules with different functions can be configured through the config. Taking PointPillars as an example, we will introduce each field in the config according to different function modules.

### Model config

In mmdetection3d's config, we use `model` to setup detection algorithm components. In addition to neural network components such as `voxel_encoder`, `backbone` etc, it also requires `data_preprocessor`, `train_cfg`, and `test_cfg`. `data_preprocessor` is responsible for processing a batch of data output by dataloader. `train_cfg`, and `test_cfg` in the model config are for training and testing hyperparameters of the components.

```python
model = dict(
    type='VoxelNet',
    data_preprocessor=dict(
        type='Det3DDataPreprocessor',
        voxel=True,
        voxel_layer=dict(
            max_num_points=32,
            point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1],
            voxel_size=[0.16, 0.16, 4],
            max_voxels=(16000, 40000))),
    voxel_encoder=dict(
        type='PillarFeatureNet',
        in_channels=4,
        feat_channels=[64],
        with_distance=False,
        voxel_size=[0.16, 0.16, 4],
        point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1]),
    middle_encoder=dict(
        type='PointPillarsScatter', in_channels=64, output_shape=[496, 432]),
    backbone=dict(
        type='SECOND',
        in_channels=64,
        layer_nums=[3, 5, 5],
        layer_strides=[2, 2, 2],
        out_channels=[64, 128, 256]),
    neck=dict(
        type='SECONDFPN',
        in_channels=[64, 128, 256],
        upsample_strides=[1, 2, 4],
        out_channels=[128, 128, 128]),
    bbox_head=dict(
        type='Anchor3DHead',
        num_classes=3,
        in_channels=384,
        feat_channels=384,
        use_direction_classifier=True,
        assign_per_class=True,
        anchor_generator=dict(
            type='AlignedAnchor3DRangeGenerator',
            ranges=[[0, -39.68, -0.6, 69.12, 39.68, -0.6],
                    [0, -39.68, -0.6, 69.12, 39.68, -0.6],
                    [0, -39.68, -1.78, 69.12, 39.68, -1.78]],
            sizes=[[0.8, 0.6, 1.73], [1.76, 0.6, 1.73], [3.9, 1.6, 1.56]],
            rotations=[0, 1.57],
            reshape_out=False),
        diff_rad_by_sin=True,
        bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
        loss_cls=dict(
            type='mmdet.FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(
            type='mmdet.SmoothL1Loss',
            beta=0.1111111111111111,
            loss_weight=2.0),
        loss_dir=dict(
            type='mmdet.CrossEntropyLoss', use_sigmoid=False,
            loss_weight=0.2)),
    train_cfg=dict(
        assigner=[
            dict(
                type='Max3DIoUAssigner',
                iou_calculator=dict(type='mmdet3d.BboxOverlapsNearest3D'),
                pos_iou_thr=0.5,
                neg_iou_thr=0.35,
                min_pos_iou=0.35,
                ignore_iof_thr=-1),
            dict(
                type='Max3DIoUAssigner',
                iou_calculator=dict(type='mmdet3d.BboxOverlapsNearest3D'),
                pos_iou_thr=0.5,
                neg_iou_thr=0.35,
                min_pos_iou=0.35,
                ignore_iof_thr=-1),
            dict(
                type='Max3DIoUAssigner',
                iou_calculator=dict(type='mmdet3d.BboxOverlapsNearest3D'),
                pos_iou_thr=0.6,
                neg_iou_thr=0.45,
                min_pos_iou=0.45,
                ignore_iof_thr=-1)
        ],
        allowed_border=0,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        use_rotate_nms=True,
        nms_across_levels=False,
        nms_thr=0.01,
        score_thr=0.1,
        min_bbox_size=0,
        nms_pre=100,
        max_num=50))
```

### Dataset and evaluator config

[Dataloaders](https://pytorch.org/docs/stable/data.html?highlight=data%20loader#torch.utils.data.DataLoader) are required for the training, validation, and testing of the [runner](https://mmengine.readthedocs.io/en/latest/tutorials/runner.html). Dataset and data pipeline need to be set to build the dataloader. Due to the complexity of this part, we use intermediate variables to simplify the writing of dataloader configs.

```python
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
point_cloud_range = [0, -39.68, -3, 69.12, 39.68, 1]
input_modality = dict(use_lidar=True, use_camera=False)
metainfo = dict(classes=['Pedestrian', 'Cyclist', 'Car'])
db_sampler = dict(
    data_root='data/kitti/',
    info_path='data/kitti/kitti_dbinfos_train.pkl',
    rate=1.0,
    prepare=dict(
        filter_by_difficulty=[-1],
        filter_by_min_points=dict(Car=5, Pedestrian=5, Cyclist=5)),
    classes=['Pedestrian', 'Cyclist', 'Car'],
    sample_groups=dict(Car=15, Pedestrian=15, Cyclist=15),
    points_loader=dict(
        type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4))
train_pipeline = [
    dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
    dict(
        type='ObjectSample',
        db_sampler=dict(
            data_root='data/kitti/',
            info_path='data/kitti/kitti_dbinfos_train.pkl',
            rate=1.0,
            prepare=dict(
                filter_by_difficulty=[-1],
                filter_by_min_points=dict(Car=5, Pedestrian=5, Cyclist=5)),
            classes=['Pedestrian', 'Cyclist', 'Car'],
            sample_groups=dict(Car=15, Pedestrian=15, Cyclist=15),
            points_loader=dict(
                type='LoadPointsFromFile',
                coord_type='LIDAR',
                load_dim=4,
                use_dim=4)),
        use_ground_plane=True),
    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
    dict(
        type='GlobalRotScaleTrans',
        rot_range=[-0.78539816, 0.78539816],
        scale_ratio_range=[0.95, 1.05]),
    dict(
        type='PointsRangeFilter',
        point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1]),
    dict(
        type='ObjectRangeFilter',
        point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1]),
    dict(type='PointShuffle'),
    dict(
        type='Pack3DDetInputs',
        keys=['points', 'gt_labels_3d', 'gt_bboxes_3d'])
]
test_pipeline = [
    dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
    dict(
        type='MultiScaleFlipAug3D',
        img_scale=(1333, 800),
        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'),
            dict(
                type='PointsRangeFilter',
                point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1])
        ]),
    dict(type='Pack3DDetInputs', keys=['points'])
]
eval_pipeline = [
    dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
    dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
    batch_size=6,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type='RepeatDataset',
        times=2,
        dataset=dict(
            type='KittiDataset',
            data_root='data/kitti/',
            ann_file='kitti_infos_train.pkl',
            data_prefix=dict(pts='training/velodyne_reduced'),
            pipeline=[
                dict(
                    type='LoadPointsFromFile',
                    coord_type='LIDAR',
                    load_dim=4,
                    use_dim=4),
                dict(
                    type='LoadAnnotations3D',
                    with_bbox_3d=True,
                    with_label_3d=True),
                dict(
                    type='ObjectSample',
                    db_sampler=dict(
                        data_root='data/kitti/',
                        info_path='data/kitti/kitti_dbinfos_train.pkl',
                        rate=1.0,
                        prepare=dict(
                            filter_by_difficulty=[-1],
                            filter_by_min_points=dict(
                                Car=5, Pedestrian=5, Cyclist=5)),
                        classes=['Pedestrian', 'Cyclist', 'Car'],
                        sample_groups=dict(Car=15, Pedestrian=15, Cyclist=15),
                        points_loader=dict(
                            type='LoadPointsFromFile',
                            coord_type='LIDAR',
                            load_dim=4,
                            use_dim=4)),
                    use_ground_plane=True),
                dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
                dict(
                    type='GlobalRotScaleTrans',
                    rot_range=[-0.78539816, 0.78539816],
                    scale_ratio_range=[0.95, 1.05]),
                dict(
                    type='PointsRangeFilter',
                    point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1]),
                dict(
                    type='ObjectRangeFilter',
                    point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1]),
                dict(type='PointShuffle'),
                dict(
                    type='Pack3DDetInputs',
                    keys=['points', 'gt_labels_3d', 'gt_bboxes_3d'])
            ],
            modality=dict(use_lidar=True, use_camera=False),
            test_mode=False,
            metainfo=dict(classes=['Pedestrian', 'Cyclist', 'Car']),
            box_type_3d='LiDAR')))
val_dataloader = dict(
    batch_size=1,
    num_workers=1,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='KittiDataset',
        data_root='data/kitti/',
        data_prefix=dict(pts='training/velodyne_reduced'),
        ann_file='kitti_infos_val.pkl',
        pipeline=[
            dict(
                type='LoadPointsFromFile',
                coord_type='LIDAR',
                load_dim=4,
                use_dim=4),
            dict(
                type='MultiScaleFlipAug3D',
                img_scale=(1333, 800),
                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'),
                    dict(
                        type='PointsRangeFilter',
                        point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1])
                ]),
            dict(type='Pack3DDetInputs', keys=['points'])
        ],
        modality=dict(use_lidar=True, use_camera=False),
        test_mode=True,
        metainfo=dict(classes=['Pedestrian', 'Cyclist', 'Car']),
        box_type_3d='LiDAR'))
test_dataloader = dict(
    batch_size=1,
    num_workers=1,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='KittiDataset',
        data_root='data/kitti/',
        data_prefix=dict(pts='training/velodyne_reduced'),
        ann_file='kitti_infos_val.pkl',
        pipeline=[
            dict(
                type='LoadPointsFromFile',
                coord_type='LIDAR',
                load_dim=4,
                use_dim=4),
            dict(
                type='MultiScaleFlipAug3D',
                img_scale=(1333, 800),
                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'),
                    dict(
                        type='PointsRangeFilter',
                        point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1])
                ]),
            dict(type='Pack3DDetInputs', keys=['points'])
        ],
        modality=dict(use_lidar=True, use_camera=False),
        test_mode=True,
        metainfo=dict(classes=['Pedestrian', 'Cyclist', 'Car']),
        box_type_3d='LiDAR'))
```

[Evaluators](https://mmengine.readthedocs.io/en/latest/tutorials/metric_and_evaluator.html) are used to compute the metrics of the trained model on the validation and testing datasets. The config of evaluators consists of one or a list of metric configs:

```python
val_evaluator = dict(
    type='KittiMetric',
    ann_file='data/kitti/kitti_infos_val.pkl',
    metric='bbox')
test_evaluator = dict(
    type='KittiMetric',
    ann_file='data/kitti/kitti_infos_val.pkl',
    metric='bbox')
```

### Training and testing config

MMEngine's runner uses Loop to control the training, validation, and testing processes.
Users can set the maximum training epochs and validation intervals with these fields.

```python
train_cfg = dict(
    type='EpochBasedTrainLoop',
    max_epochs=80,
    val_interval=2)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
```

### Optimization config

`optim_wrapper` is the field to configure optimization related settings. The optimizer wrapper not only provides the functions of the optimizer, but also supports functions such as gradient clipping, mixed precision training, etc. Find more in [optimizer wrapper tutorial](https://mmengine.readthedocs.io/en/latest/tutorials/optimizer.html).

```python
optim_wrapper = dict(  # Optimizer wrapper config
    type='OptimWrapper',  # Optimizer wrapper type, switch to AmpOptimWrapper to enable mixed precision training.
    optimizer=dict(  # Optimizer config. Support all kinds of optimizers in PyTorch. Refer to https://pytorch.org/docs/stable/optim.html#algorithms
        type='AdamW', lr=0.001, betas=(0.95, 0.99), weight_decay=0.01),
    clip_grad=dict(max_norm=35, norm_type=2))  # Gradient clip option. Set None to disable gradient clip. Find usage in https://mmengine.readthedocs.io/en/latest/tutorials
```

`param_scheduler` is a field that configures methods of adjusting optimization hyperparameters such as learning rate and momentum. Users can combine multiple schedulers to create a desired parameter adjustment strategy. Find more in [parameter scheduler tutorial](https://mmengine.readthedocs.io/en/latest/tutorials/param_scheduler.html) and [parameter scheduler API documents](TODO)

```python
param_scheduler = [
    dict(
        type='CosineAnnealingLR',
        T_max=32.0,
        eta_min=0.01,
        begin=0,
        end=32.0,
        by_epoch=True,
        convert_to_iter_based=True),
    dict(
        type='CosineAnnealingLR',
        T_max=48.0,
        eta_min=1.0000000000000001e-07,
        begin=32.0,
        end=80,
        by_epoch=True,
        convert_to_iter_based=True),
    dict(
        type='CosineAnnealingMomentum',
        T_max=32.0,
        eta_min=0.8947368421052632,
        begin=0,
        end=32.0,
        by_epoch=True,
        convert_to_iter_based=True),
    dict(
        type='CosineAnnealingMomentum',
        T_max=48.0,
        eta_min=1,
        begin=32.0,
        end=80,
        convert_to_iter_based=True)
]
```

### Hook config

Users can attach hooks to training, validation, and testing loops to insert some operations during running. There are two different hook fields, one is `default_hooks` and the other is `custom_hooks`.

`default_hooks` is a dict of hook configs. `default_hooks` are the hooks must required at runtime. They have default priority which should not be modified. If not set, runner will use the default values. To disable a default hook, users can set its config to `None`.

```python
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=50),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=1),
    sampler_seed=dict(type='DistSamplerSeedHook'))
```

### Runtime config

```python
default_scope = 'mmdet3d'  # The default registry scope to find modules. Refer to https://mmengine.readthedocs.io/en/latest/tutorials/registry.html

env_cfg = dict(
    cudnn_benchmark=False,  # Whether to enable cudnn benchmark
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),  # Use fork to start multi-processing threads. 'fork' usually faster than 'spawn' but maybe unsafe. See discussion in https://github.com/pytorch/pytorch/issues/1355
    dist_cfg=dict(backend='nccl'))  # Distribution configs
vis_backends = [dict(type='LocalVisBackend')]  # Visualization backends.
visualizer = dict(
    type='Det3DLocalVisualizer',
    vis_backends=[dict(type='LocalVisBackend')],
    name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = None
resume = False
```

## Config file inheritance

There are 4 basic component types under `configs/_base_`, dataset, model, schedule, default_runtime.
Many methods could be easily constructed with one of each like SECOND, PointPillars, PartA2, and VoteNet.
The configs that are composed by components from `_base_` are called _primitive_.

For all configs under the same folder, it is recommended to have only **one** _primitive_ config. All other configs should inherit from the _primitive_ config. In this way, the maximum of inheritance level is 3.

For easy understanding, we recommend contributors to inherit from exiting methods.
For example, if some modification is made based on PointPillars, user may first inherit the basic PointPillars structure by specifying `_base_ = ../pointpillars/pointpillars_hv_fpn_sbn-all_8xb4_2x_nus-3d.py`, and then modify the necessary fields in the config files.

If you are building an entirely new method that does not share the structure with any of the existing methods, you may create a folder `xxx_rcnn` under `configs`,

Please refer to [mmengine config tutorial](https://mmengine.readthedocs.io/en/latest/tutorials/config.html) for detailed documentation.

### Ignore some fields in the base configs

Sometimes, you may set `_delete_=True` to ignore some of fields in base configs.
You may refer to [mmcv](https://mmcv.readthedocs.io/en/latest/utils.html#inherit-from-base-config-with-ignored-fields) for simple illustration.

In MMDetection3D, for example, to change the FPN neck of PointPillars with the following config.

```python
model = dict(
    type='MVXFasterRCNN',
    data_preprocessor=dict(voxel_layer=dict(...)),
    pts_voxel_encoder=dict(...),
    pts_middle_encoder=dict(...),
    pts_backbone=dict(...),
    pts_neck=dict(
        type='FPN',
        norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
        act_cfg=dict(type='ReLU'),
        in_channels=[64, 128, 256],
        out_channels=256,
        start_level=0,
        num_outs=3),
    pts_bbox_head=dict(...))
```

`FPN` and `SECONDFPN` use different keywords to construct.

```python
_base_ = '../_base_/models/pointpillars_hv_fpn_nus.py'
model = dict(
    pts_neck=dict(
        _delete_=True,
        type='SECONDFPN',
        norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
        in_channels=[64, 128, 256],
        upsample_strides=[1, 2, 4],
        out_channels=[128, 128, 128]),
    pts_bbox_head=dict(...))
```

The `_delete_=True` would replace all old keys in `pts_neck` field with new keys.

### Use intermediate variables in configs

Some intermediate variables are used in the configs files, like `train_pipeline`/`test_pipeline` in datasets.
It's worth noting that when modifying intermediate variables in the children configs, user needs to pass the intermediate variables into corresponding fields again.
For example, we would like to use multi scale strategy to train and test a PointPillars. `train_pipeline`/`test_pipeline` are intermediate variable we would like modify.

```python
_base_ = './nus-3d.py'
train_pipeline = [
    dict(
        type='LoadPointsFromFile',
        load_dim=5,
        use_dim=5,
        file_client_args=file_client_args),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=10,
        file_client_args=file_client_args),
    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
    dict(
        type='GlobalRotScaleTrans',
        rot_range=[-0.3925, 0.3925],
        scale_ratio_range=[0.95, 1.05],
        translation_std=[0, 0, 0]),
    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
    dict(type='ObjectNameFilter', classes=class_names),
    dict(type='PointShuffle'),
    dict(
        type='Pack3DDetInputs',
        keys=['points', 'gt_labels_3d', 'gt_bboxes_3d'])
]
test_pipeline = [
    dict(
        type='LoadPointsFromFile',
        load_dim=5,
        use_dim=5,
        file_client_args=file_client_args),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=10,
        file_client_args=file_client_args),
    dict(
        type='MultiScaleFlipAug3D',
        img_scale=(1333, 800),
        pts_scale_ratio=[0.95, 1.0, 1.05],
        flip=False,
        transforms=[
            dict(
                type='GlobalRotScaleTrans',
                rot_range=[0, 0],
                scale_ratio_range=[1., 1.],
                translation_std=[0, 0, 0]),
            dict(type='RandomFlip3D'),
            dict(
                type='PointsRangeFilter', point_cloud_range=point_cloud_range)
        ]),
      dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
```

### Reuse variables in \_base\_ file

If the users want to reuse the variables in the base file, they can get a copy of the corresponding variable by using `{{_base_.xxx}}`. E.g:

```python
_base_ = './pointpillars_hv_secfpn_8xb6_160e_kitti-3d-3class.py'

a = {{_base_.model}} # variable `a` is equal to the `model` defined in `_base_`
```

## Modify Config Through Script Arguments

When submitting jobs using "tools/train.py" or "tools/test.py", you may specify `--cfg-options` to in-place modify the config.

- Update config keys of dict chains.

  The config options can be specified following the order of the dict keys in the original config.
  For example, `--cfg-options model.backbone.norm_eval=False` changes the all BN modules in model backbones to `train` mode.

- Update keys inside a list of configs.

  Some config dicts are composed as a list in your config. For example, the training pipeline `train_dataloader.dataset.pipeline` is normally a list
  e.g. `[dict(type='LoadImageFromFile'), ...]`. If you want to change `'LoadImageFromFile'` to `'LoadImageFromNDArray'` in the pipeline,
  you may specify `--cfg-options data.train.pipeline.0.type=LoadImageFromNDArray`.

- Update values of list/tuples.

  If the value to be updated is a list or a tuple. For example, the config file normally sets `model.data_preprocessor.mean=[123.675, 116.28, 103.53]`. If you want to
  change the mean values, you may specify `--cfg-options model.data_preprocessor.mean="[127,127,127]"`. Note that the quotation mark `"` is necessary to
  support list/tuple data types, and that **NO** white space is allowed inside the quotation marks in the specified value.

## Config Name Style

We follow the below style to name config files. Contributors are advised to follow the same style.

```
{algorithm name}_{model component names [component1]_[component2]_[...]}_{training settings}_{training dataset information}_{testing dataset information}.py
```

The file name is divided to five parts. All parts and components are connected with `_` and words of each part or component should be connected with `-`.

- `{algorithm name}`: The name of the algorithm. It can be a detector name such as `pointpillars`, `fcos3d`, etc.
- `{model component names}`: Names of the components used in the algorithm such as voxel_encoder, backbone, neck, etc. For example, `second_secfpn_head-dcn-circlenms` means using SECOND's SparseEncoder, SECONDFPN and a detection head with DCN and circle NMS.
- `{training settings}`: Information of training settings such as batch size, augmentations, loss trick, scheduler, and epochs/iterations. For example: `8xb4-tta-cyclic-20e` means using 8-gpus x 4-samples-per-gpu, test time augmentation, cyclic annealing learning rate, and train 20 epochs.
  Some abbreviations:
  - `{gpu x batch_per_gpu}`: GPUs and samples per GPU. `bN` indicates N batch size per GPU. E.g. `4xb4` is the short term of 4-gpus x 4-samples-per-gpu.
  - `{schedule}`: training schedule, options are `schedule-2x`, `schedule-3x`, `cyclic-20e`, etc.
    `schedule-2x` and `schedule-3x` mean 24 epochs and 36 epochs respectively.
    `cyclic-20e` means 20 epochs respectively.
- `{training dataset information}`: Training dataset names like `kitti-3d-3class`, `nus-3d`, `s3dis-seg`, `scannet-seg`, `waymoD5-3d-car`. Here `3d` means dataset used for 3d object detection, and `seg` means dataset used for point cloud segmentation.
- `{testing dataset information}` (optional): Testing dataset name for models trained on one dataset but tested on another. If not mentioned, it means the model was trained and tested on the same dataset type.