3dssd_4xb4_kitti-3d-car.py

_base_ = [
    '../_base_/models/3dssd.py', '../_base_/datasets/kitti-3d-car.py',
    '../_base_/default_runtime.py'
]

# dataset settings
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Car']
point_cloud_range = [0, -40, -5, 70, 40, 3]
input_modality = dict(use_lidar=True, use_camera=False)
db_sampler = dict(
    data_root=data_root,
    info_path=data_root + 'kitti_dbinfos_train.pkl',
    rate=1.0,
    prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
    classes=class_names,
    sample_groups=dict(Car=15),
    points_loader=dict(
        type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4))

file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
# file_client_args = dict(
#     backend='petrel', path_mapping=dict(data='s3://kitti_data/'))

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='PointsRangeFilter', point_cloud_range=point_cloud_range),
    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
    dict(type='ObjectSample', db_sampler=db_sampler),
    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
    dict(
        type='ObjectNoise',
        num_try=100,
        translation_std=[1.0, 1.0, 0],
        global_rot_range=[0.0, 0.0],
        rot_range=[-1.0471975511965976, 1.0471975511965976]),
    dict(
        type='GlobalRotScaleTrans',
        rot_range=[-0.78539816, 0.78539816],
        scale_ratio_range=[0.9, 1.1]),
    # 3DSSD can get a higher performance without this transform
    # dict(type='BackgroundPointsFilter', bbox_enlarge_range=(0.5, 2.0, 0.5)),
    dict(type='PointSample', num_points=16384),
    dict(
        type='Pack3DDetInputs',
        keys=['points', 'gt_bboxes_3d', 'gt_labels_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., 1.],
                translation_std=[0, 0, 0]),
            dict(type='RandomFlip3D'),
            dict(
                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
            dict(type='PointSample', num_points=16384),
        ]),
    dict(type='Pack3DDetInputs', keys=['points'])
]

train_dataloader = dict(
    batch_size=4, dataset=dict(dataset=dict(pipeline=train_pipeline, )))
test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))

# model settings
model = dict(
    bbox_head=dict(
        num_classes=1,
        bbox_coder=dict(
            type='AnchorFreeBBoxCoder', num_dir_bins=12, with_rot=True)))

# optimizer
lr = 0.002  # max learning rate
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='AdamW', lr=lr, weight_decay=0.),
    clip_grad=dict(max_norm=35, norm_type=2),
)

# training schedule for 1x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=80, val_interval=2)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

# learning rate
param_scheduler = [
    dict(
        type='MultiStepLR',
        begin=0,
        end=80,
        by_epoch=True,
        milestones=[45, 60],
        gamma=0.1)
]