semi_fcaf3d.py

voxel_size = 0.01

model = dict(
    type='SemiSingleStageSparse3DDetector',
    model_cfg=dict(
        type="SingleStageSparse3DDetector",
        voxel_size=voxel_size,
        backbone=dict(
            type='MEResNet3D',
            in_channels=3,
            depth=34),
        neck_with_head=dict(
            type='Fcaf3DNeckWithHead',
            in_channels=(64, 128, 256, 512),
            out_channels=128,
            pts_threshold=100000,
            n_classes=18,
            n_reg_outs=6,
            voxel_size=voxel_size,
            assigner=dict(
                type='Fcaf3DAssigner',
                limit=27,
                topk=18,
                n_scales=4),
            loss_bbox=dict(type='IoU3DLoss', loss_weight=1.0)),
        train_cfg=dict(),
        test_cfg=dict(
            nms_pre=1000,
            iou_thr=.5,
            score_thr=.01)
    )
)

optimizer = dict(type='AdamW', lr=0.001, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
runner = dict(type='IterBasedRunner', max_iters=12000)
custom_hooks = [dict(type='EmptyCacheHook', after_iter=True)]
lr_config = dict(policy='step', warmup=None, step=[12000, 16500])
evaluation = dict(interval=1000)
checkpoint_config = dict(by_epoch=False, interval=1000, max_keep_ckpts=-1,)
log_config = dict(
    interval=20,
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook')
    ])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = None
load_from = None
resume_from = None
workflow = [('train', 1)]