##############################################################################################
########################################## Model #############################################
##############################################################################################
point_cloud_range = [0, -39.68, -3, 69.12, 39.68, 1]
voxel_size = [0.16, 0.16, 4]
model = dict(
    type='PVGNet',
    voxel_layer=dict(
        max_num_points=32,
        point_cloud_range=point_cloud_range,
        voxel_size=voxel_size,
        max_voxels=(16000, 40000)),
    voxel_encoder=dict(
        type='PillarFeatureNet',
        in_channels=4,
        feat_channels=[64],
        with_distance=False,
        voxel_size=voxel_size,
        point_cloud_range=point_cloud_range),
    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]),
    # VFE
    voxel_layer2=dict(
        max_num_points=32,
        point_cloud_range=point_cloud_range,
        voxel_size=[0.32, 0.32, 4],
        max_voxels=(10000, 10000)),
    voxel_encoder2=dict(
        type='HardVFE',
        in_channels=3,
        feat_channels=[128],
        with_distance=False,
        voxel_size=[0.32, 0.32, 4],
        point_cloud_range=point_cloud_range),
    bbox_head=dict(
        type='PVGHead',
        anchor_cfg =dict(size=[1.6, 3.9, 1.56],
                         rotation=[1.57]),
        num_classes=1,
        in_channels=515,
        feat_channels=515,
        use_direction_classifier=True,
        diff_rad_by_sin=True,
        bbox_coder=dict(type='PVGNetBBoxCoder', normalize=False),
        fg_weight=1,
        loss_cls=dict(
            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)))
# model training and testing settings
train_cfg = dict(
    assigner=dict(type='InBoxAssigner'),
    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=0.5,
    min_bbox_size=0,
    max_num=50)
##############################################################################################
######################################## Dataset #############################################
##############################################################################################
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Car']
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))

file_client_args = dict(backend='disk')

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='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
    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'])
            # dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
        ])
]

data = dict(
    samples_per_gpu=6,
    workers_per_gpu=4,
    train=dict(
        type='RepeatDataset',
        times=2,
        dataset=dict(
            type=dataset_type,
            data_root=data_root,
            ann_file=data_root + 'kitti_infos_train.pkl',
            # ann_file=data_root + 'kitti_infos_debug.pkl',
            split='training',
            pts_prefix='velodyne_reduced',
            pipeline=train_pipeline,
            modality=input_modality,
            classes=class_names,
            test_mode=False,
            box_type_3d='LiDAR')),
    val=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file=data_root + 'kitti_infos_val.pkl',
        # ann_file=data_root + 'kitti_infos_train.pkl',
        # ann_file=data_root + 'kitti_infos_debug.pkl',
        split='training',
        pts_prefix='velodyne_reduced',
        pipeline=test_pipeline,
        modality=input_modality,
        classes=class_names,
        test_mode=True,
        box_type_3d='LiDAR'),
    test=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file=data_root + 'kitti_infos_val.pkl',
        # ann_file=data_root + 'kitti_infos_train.pkl',
        split='training',
        pts_prefix='velodyne_reduced',
        pipeline=test_pipeline,
        modality=input_modality,
        classes=class_names,
        test_mode=True,
        box_type_3d='LiDAR'))
evaluation = dict(interval=2)
##############################################################################################
######################################## Schedule ############################################
##############################################################################################
lr = 0.0005
optimizer = dict(type='AdamW', lr=lr, betas=(0.95, 0.99), weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
    policy='cyclic',
    target_ratio=(10, 1e-4),
    cyclic_times=1,
    step_ratio_up=0.4,
)
momentum_config = dict(
    policy='cyclic',
    target_ratio=(0.85 / 0.95, 1),
    cyclic_times=1,
    step_ratio_up=0.4,
)
total_epochs = 80
##############################################################################################
######################################## Runtime #############################################
##############################################################################################
checkpoint_config = dict(interval=2)
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(type='TensorboardLoggerHook')
    ])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = None
load_from = None
resume_from = None
workflow = [('train', 1)]


find_unused_parameters = True