# model settings
voxel_size = [0.05, 0.05, 0.1]
point_cloud_range = [0, -40, -3, 70.4, 40, 1]

model = dict(
    type='PartA2',
    voxel_layer=dict(
        max_num_points=5,  # max_points_per_voxel
        point_cloud_range=point_cloud_range,
        voxel_size=voxel_size,
        max_voxels=(16000, 40000)  # (training, testing) max_voxels
    ),
    voxel_encoder=dict(type='HardSimpleVFE'),
    middle_encoder=dict(
        type='SparseUNet',
        in_channels=4,
        sparse_shape=[41, 1600, 1408],
        order=('conv', 'norm', 'act')),
    backbone=dict(
        type='SECOND',
        in_channels=256,
        layer_nums=[5, 5],
        layer_strides=[1, 2],
        out_channels=[128, 256]),
    neck=dict(
        type='SECONDFPN',
        in_channels=[128, 256],
        upsample_strides=[1, 2],
        out_channels=[256, 256]),
    rpn_head=dict(
        type='PartA2RPNHead',
        num_classes=3,
        in_channels=512,
        feat_channels=512,
        use_direction_classifier=True,
        anchor_generator=dict(
            type='Anchor3DRangeGenerator',
            ranges=[[0, -40.0, -0.6, 70.4, 40.0, -0.6],
                    [0, -40.0, -0.6, 70.4, 40.0, -0.6],
                    [0, -40.0, -1.78, 70.4, 40.0, -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,
        assigner_per_size=True,
        assign_per_class=True,
        bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
        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)),
    roi_head=dict(
        type='PartAggregationROIHead',
        num_classes=3,
        semantic_head=dict(
            type='PointwiseSemanticHead',
            in_channels=16,
            extra_width=0.2,
            seg_score_thr=0.3,
            num_classes=3,
            loss_seg=dict(
                type='FocalLoss',
                use_sigmoid=True,
                reduction='sum',
                gamma=2.0,
                alpha=0.25,
                loss_weight=1.0),
            loss_part=dict(
                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
        seg_roi_extractor=dict(
            type='Single3DRoIAwareExtractor',
            roi_layer=dict(
                type='RoIAwarePool3d',
                out_size=14,
                max_pts_per_voxel=128,
                mode='max')),
        part_roi_extractor=dict(
            type='Single3DRoIAwareExtractor',
            roi_layer=dict(
                type='RoIAwarePool3d',
                out_size=14,
                max_pts_per_voxel=128,
                mode='avg')),
        bbox_head=dict(
            type='PartA2BboxHead',
            num_classes=3,
            seg_in_channels=16,
            part_in_channels=4,
            seg_conv_channels=[64, 64],
            part_conv_channels=[64, 64],
            merge_conv_channels=[128, 128],
            down_conv_channels=[128, 256],
            bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
            shared_fc_channels=[256, 512, 512, 512],
            cls_channels=[256, 256],
            reg_channels=[256, 256],
            dropout_ratio=0.1,
            roi_feat_size=14,
            with_corner_loss=True,
            loss_bbox=dict(
                type='SmoothL1Loss',
                beta=1.0 / 9.0,
                reduction='sum',
                loss_weight=1.0),
            loss_cls=dict(
                type='CrossEntropyLoss',
                use_sigmoid=True,
                reduction='sum',
                loss_weight=1.0))),
    # model training and testing settings
    train_cfg=dict(
        rpn=dict(
            assigner=[
                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),
                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)
            ],
            allowed_border=0,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=9000,
            nms_post=512,
            max_num=512,
            nms_thr=0.8,
            score_thr=0,
            use_rotate_nms=False),
        rcnn=dict(
            assigner=[
                dict(  # for Pedestrian
                    type='MaxIoUAssigner',
                    iou_calculator=dict(
                        type='BboxOverlaps3D', coordinate='lidar'),
                    pos_iou_thr=0.55,
                    neg_iou_thr=0.55,
                    min_pos_iou=0.55,
                    ignore_iof_thr=-1),
                dict(  # for Cyclist
                    type='MaxIoUAssigner',
                    iou_calculator=dict(
                        type='BboxOverlaps3D', coordinate='lidar'),
                    pos_iou_thr=0.55,
                    neg_iou_thr=0.55,
                    min_pos_iou=0.55,
                    ignore_iof_thr=-1),
                dict(  # for Car
                    type='MaxIoUAssigner',
                    iou_calculator=dict(
                        type='BboxOverlaps3D', coordinate='lidar'),
                    pos_iou_thr=0.55,
                    neg_iou_thr=0.55,
                    min_pos_iou=0.55,
                    ignore_iof_thr=-1)
            ],
            sampler=dict(
                type='IoUNegPiecewiseSampler',
                num=128,
                pos_fraction=0.55,
                neg_piece_fractions=[0.8, 0.2],
                neg_iou_piece_thrs=[0.55, 0.1],
                neg_pos_ub=-1,
                add_gt_as_proposals=False,
                return_iou=True),
            cls_pos_thr=0.75,
            cls_neg_thr=0.25)),
    test_cfg=dict(
        rpn=dict(
            nms_pre=1024,
            nms_post=100,
            max_num=100,
            nms_thr=0.7,
            score_thr=0,
            use_rotate_nms=True),
        rcnn=dict(
            use_rotate_nms=True,
            use_raw_score=True,
            nms_thr=0.01,
            score_thr=0.1)))
