# model settings
_base_ = './hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py'

point_cloud_range = [0, -39.68, -3, 69.12, 39.68, 1]
model = dict(
    bbox_head=dict(
        type='Anchor3DHead',
        num_classes=1,
        anchor_generator=dict(
            _delete_=True,
            type='Anchor3DRangeGenerator',
            ranges=[[0, -39.68, -1.78, 69.12, 39.68, -1.78]],
            sizes=[[1.6, 3.9, 1.56]],
            rotations=[0, 1.57],
            reshape_out=True)))
# model training and testing settings
train_cfg = dict(
    _delete_=True,
    assigner=dict(
        type='MaxIoUAssigner',
        iou_calculator=dict(type='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)

# dataset settings
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Car']
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)),
    sample_groups=dict(Car=15),
    classes=class_names)

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=db_sampler),
    dict(
        type='ObjectNoise',
        num_try=100,
        translation_std=[0.25, 0.25, 0.25],
        global_rot_range=[0.0, 0.0],
        rot_range=[-0.15707963267, 0.15707963267]),
    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=point_cloud_range),
    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
    dict(type='PointShuffle'),
    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='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='DefaultFormatBundle3D',
                class_names=class_names,
                with_label=False),
            dict(type='Collect3D', keys=['points'])
        ])
]

data = dict(
    train=dict(
        type='RepeatDataset',
        times=2,
        dataset=dict(pipeline=train_pipeline, classes=class_names)),
    val=dict(pipeline=test_pipeline, classes=class_names),
    test=dict(pipeline=test_pipeline, classes=class_names))
