_base_ = [
    '../_base_/datasets/kitti-3d-3class.py',
    '../_base_/schedules/cyclic_40e.py', '../_base_/default_runtime.py'
]
voxel_size = [0.05, 0.05, 0.1]
point_cloud_range = [0, -40, -3, 70.4, 40, 1]

model = dict(
    type='VoxelNet',
    voxel_layer=dict(
        max_num_points=5,
        point_cloud_range=point_cloud_range,
        voxel_size=[0.05, 0.05, 0.1],
        max_voxels=(40000, 40000)),
    voxel_encoder=dict(type='HardSimpleVFE', num_features=3),
    middle_encoder=dict(
        type='CustomSparseEncoder',
        in_channels=3,
        output_channels=32,
        encoder_strides=((1, ), (2, 1, 1), (2, 1, 1), ((2, 1, 1), 1, 1)),
        sparse_shape=[41, 1600, 1408],
        order=('conv', 'norm', 'act'),
        norm_cfg=dict(type='SyncBN', eps=1e-3, momentum=0.01),
        with_final_bnrelu=False),
    backbone=dict(
        type='BEVHourglass',
        in_channels=160,  # 160 = 32 * 5
        out_channels=64,
        norm_cfg=dict(type='SyncBN'),
        output_prehg_feat=False),
    bbox_head=dict(
        type='LIGAAnchor3DHead',
        num_classes=3,
        in_channels=64,
        feat_channels=64,
        num_convs=2,
        use_direction_classifier=True,
        diff_rad_by_sin=True,
        dir_offset=0.7854,
        anchor_generator=dict(
            type='Anchor3DRangeGenerator',
            ranges=[[0, -40, -1.78, 70.4, 40, -1.78],
                    [0, -40, -0.6, 70.4, 40, -0.6],
                    [0, -40, -0.6, 70.4, 40, -0.6]],
            sizes=[[3.9, 1.6, 1.56], [0.8, 0.6, 1.73], [1.76, 0.6, 1.73]],
            rotations=[0, 1.57],
            reshape_out=False),
        bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
        loss_cls=dict(  # TODO: add weightedsmoothl1loss, iouloss
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=2.0),
        loss_dir=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2),
        norm_cfg=dict(type='SyncBN')),
    # model training and testing settings
    train_cfg=dict(
        assigner=[
            dict(  # for Car
                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),
            dict(  # for Pedestrian
                type='MaxIoUAssigner',
                iou_calculator=dict(type='BboxOverlapsNearest3D'),
                pos_iou_thr=0.5,
                neg_iou_thr=0.35,
                min_pos_iou=0.35,
                ignore_iof_thr=-1),
            dict(  # for Cyclist
                type='MaxIoUAssigner',
                iou_calculator=dict(type='BboxOverlapsNearest3D'),
                pos_iou_thr=0.5,
                neg_iou_thr=0.35,
                min_pos_iou=0.35,
                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=4096,
        max_num=500))

# dataset setting
class_names = ['Car', 'Pedestrian', 'Cyclist']
data_root = 'data/kitti/'
# 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/kitti/':
        's3://openmmlab/datasets/detection3d/kitti/',
        'data/kitti/':
        's3://openmmlab/datasets/detection3d/kitti/'
    }))
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, Pedestrian=10, Cyclist=10)),
    classes=class_names,
    sample_groups=dict(Car=12, Pedestrian=6, Cyclist=6))

train_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=4,
        use_dim=4,
        file_client_args=file_client_args),
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=True,
        with_label_3d=True,
        file_client_args=file_client_args),
    dict(type='ObjectSample', db_sampler=db_sampler),
    dict(
        type='ObjectNoise',
        num_try=100,
        translation_std=[1.0, 1.0, 0.5],
        global_rot_range=[0.0, 0.0],
        rot_range=[-0.78539816, 0.78539816]),
    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,
        file_client_args=file_client_args),
    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'])
        ])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=4,
        use_dim=4,
        file_client_args=file_client_args),
    dict(
        type='DefaultFormatBundle3D',
        class_names=class_names,
        with_label=False),
    dict(type='Collect3D', keys=['points'])
]

data = dict(
    samples_per_gpu=6,
    workers_per_gpu=4,
    train=dict(
        type='RepeatDataset',
        times=2,
        dataset=dict(
            pipeline=train_pipeline,
            classes=class_names,
            test_mode=False,
            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
            box_type_3d='LiDAR')),
    val=dict(
        pipeline=test_pipeline,
        classes=class_names,
        test_mode=True,
        box_type_3d='LiDAR'),
    test=dict(
        pipeline=test_pipeline,
        classes=class_names,
        test_mode=True,
        box_type_3d='LiDAR'))

evaluation = dict(interval=1, pipeline=eval_pipeline)
