bevf_pp_4x8_2x_nusc_cam.py

_base_ = [
    '../../_base_/datasets/nusc_cam_pp.py',
    '../../_base_/schedules/schedule_2x.py',
    '../../_base_/default_runtime.py'
]
optimizer = dict(_delete_=True, type='AdamW', lr=0.001, betas=(0.9, 0.999), weight_decay=0.05,
                 paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
                                                 'relative_position_bias_table': dict(decay_mult=0.),
                                                 'norm': dict(decay_mult=0.)}))

final_dim=(900, 1600) # HxW
downsample=8
voxel_size = [0.25, 0.25, 8]
model = dict(
    type='BEVF_FasterRCNN',
    camera_stream=True, 
    lss=False,
    grid=0.5, 
    num_views=6,
    final_dim=final_dim,
    downsample=downsample, 
    img_backbone=dict(
        type='CBSwinTransformer',
                embed_dim=96,
                depths=[2, 2, 6, 2],
                num_heads=[3, 6, 12, 24],
                window_size=7,
                mlp_ratio=4.,
                qkv_bias=True,
                qk_scale=None,
                drop_rate=0.,
                attn_drop_rate=0.,
                drop_path_rate=0.2,
                ape=False,
                patch_norm=True,
                out_indices=(0, 1, 2, 3),
                use_checkpoint=False),
    img_neck=dict(
        type='FPNC',
        final_dim=final_dim,
        downsample=downsample, 
        in_channels=[96, 192, 384, 768],
        out_channels=256,
        use_adp=True,
        num_outs=5),
    pts_bbox_head=dict(
        type='Anchor3DHead',
        num_classes=10,
        in_channels=256,
        feat_channels=256,
        use_direction_classifier=True,
        anchor_generator=dict(
            type='AlignedAnchor3DRangeGenerator',
            ranges=[
                [-49.6, -49.6, -1.80032795, 49.6, 49.6, -1.80032795],
                [-49.6, -49.6, -1.74440365, 49.6, 49.6, -1.74440365],
                [-49.6, -49.6, -1.68526504, 49.6, 49.6, -1.68526504],
                [-49.6, -49.6, -1.67339111, 49.6, 49.6, -1.67339111],
                [-49.6, -49.6, -1.61785072, 49.6, 49.6, -1.61785072],
                [-49.6, -49.6, -1.80984986, 49.6, 49.6, -1.80984986],
                [-49.6, -49.6, -1.763965, 49.6, 49.6, -1.763965],
            ],
            sizes=[
                [1.95017717, 4.60718145, 1.72270761],  # car
                [2.4560939, 6.73778078, 2.73004906],  # truck
                [2.87427237, 12.01320693, 3.81509561],  # trailer
                [0.60058911, 1.68452161, 1.27192197],  # bicycle
                [0.66344886, 0.7256437, 1.75748069],  # pedestrian
                [0.39694519, 0.40359262, 1.06232151],  # traffic_cone
                [2.49008838, 0.48578221, 0.98297065],  # barrier
            ],
            custom_values=[0, 0],
            rotations=[0, 1.57],
            reshape_out=True),
        assigner_per_size=False,
        diff_rad_by_sin=True,
        dir_offset=0.7854,  # pi/4
        dir_limit_offset=0,
        bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=9),
        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=1.0),
        loss_dir=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
    # model training and testing settings
    train_cfg=dict(
        pts=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                iou_calculator=dict(type='BboxOverlapsNearest3D'),
                pos_iou_thr=0.6,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                ignore_iof_thr=-1),
            allowed_border=0,
            code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2],
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        pts=dict(
            use_rotate_nms=True,
            nms_across_levels=False,
            nms_pre=1000,
            nms_thr=0.2,
            score_thr=0.05,
            min_bbox_size=0,
            max_num=500)))


data = dict(
    samples_per_gpu=4,
    workers_per_gpu=4,)

load_img_from = 'work_dirs/mask_rcnn_dbswin-t_fpn_3x_nuim_cocopre/epoch_36.pth'
# fp16 = dict(loss_scale=32.0)