configs/detection3d/bevformer/bevformer_base_r101_dcn_nuscenes.py [225:309]:
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    pin_memory=True,
    # shuffler_sampler=dict(type='DistributedGroupSampler'),
    # nonshuffler_sampler=dict(type='DistributedSampler'),
    train=dict(
        type=dataset_type,
        data_source=dict(
            type='Det3dSourceNuScenes',
            data_root=data_root,
            ann_file=data_root + 'nuscenes_infos_temporal_train.pkl',
            pipeline=[
                dict(
                    type='LoadMultiViewImageFromFiles',
                    to_float32=True,
                    backend='turbojpeg'),
                dict(
                    type='LoadAnnotations3D',
                    with_bbox_3d=True,
                    with_label_3d=True,
                    with_attr_label=False)
            ],
            classes=CLASSES,
            modality=input_modality,
            test_mode=False,
            use_valid_flag=True,
            # 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'),
        pipeline=train_pipeline,
        queue_length=queue_length,
    ),
    val=dict(
        imgs_per_gpu=1,
        type=dataset_type,
        data_source=dict(
            type='Det3dSourceNuScenes',
            data_root=data_root,
            ann_file=data_root + 'nuscenes_infos_temporal_val.pkl',
            pipeline=[
                dict(
                    type='LoadMultiViewImageFromFiles',
                    to_float32=True,
                    backend='turbojpeg')
            ],
            classes=CLASSES,
            modality=input_modality,
            test_mode=True),
        pipeline=test_pipeline))

paramwise_cfg = {'img_backbone': dict(lr_mult=0.1)}
optimizer = dict(
    type='AdamW', lr=2e-4, paramwise_options=paramwise_cfg, weight_decay=0.01)

optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
    policy='CosineAnnealing',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=1.0 / 3,
    min_lr_ratio=1e-3)
total_epochs = 24

eval_config = dict(initial=False, interval=1, gpu_collect=False)
eval_pipelines = [
    dict(
        mode='test',
        data=data['val'],
        dist_eval=True,
        evaluators=[
            dict(
                type='NuScenesEvaluator',
                classes=CLASSES,
                result_names=['pts_bbox'])
        ],
    )
]

load_from = 'https://github.com/zhiqi-li/storage/releases/download/v1.0/r101_dcn_fcos3d_pretrain.pth'
log_config = dict(
    interval=50,
    hooks=[dict(type='TextLoggerHook'),
           dict(type='TensorboardLoggerHook')])

checkpoint_config = dict(interval=1)
cudnn_benchmark = True
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configs/detection3d/bevformer/bevformer_base_r101_dcn_nuscenes_blancehybrid.py [227:310]:
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    pin_memory=True,
    # shuffler_sampler=dict(type='DistributedGroupSampler'),
    # nonshuffler_sampler=dict(type='DistributedSampler'),
    train=dict(
        type=dataset_type,
        data_source=dict(
            type='Det3dSourceNuScenes',
            data_root=data_root,
            ann_file=data_root + 'nuscenes_infos_temporal_train.pkl',
            pipeline=[
                dict(
                    type='LoadMultiViewImageFromFiles',
                    to_float32=True,
                    backend='turbojpeg'),
                dict(
                    type='LoadAnnotations3D',
                    with_bbox_3d=True,
                    with_label_3d=True,
                    with_attr_label=False)
            ],
            classes=CLASSES,
            modality=input_modality,
            test_mode=False,
            use_valid_flag=True,
            # 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'),
        pipeline=train_pipeline,
        queue_length=queue_length,
    ),
    val=dict(
        imgs_per_gpu=1,
        type=dataset_type,
        data_source=dict(
            type='Det3dSourceNuScenes',
            data_root=data_root,
            ann_file=data_root + 'nuscenes_infos_temporal_val.pkl',
            pipeline=[
                dict(
                    type='LoadMultiViewImageFromFiles',
                    to_float32=True,
                    backend='turbojpeg')
            ],
            classes=CLASSES,
            modality=input_modality,
            test_mode=True),
        pipeline=test_pipeline))

paramwise_cfg = {'img_backbone': dict(lr_mult=0.1)}
optimizer = dict(
    type='AdamW', lr=2e-4, paramwise_options=paramwise_cfg, weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
    policy='CosineAnnealing',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=1.0 / 3,
    min_lr_ratio=1e-3)
total_epochs = 24

eval_config = dict(initial=False, interval=1, gpu_collect=False)
eval_pipelines = [
    dict(
        mode='test',
        data=data['val'],
        dist_eval=True,
        evaluators=[
            dict(
                type='NuScenesEvaluator',
                classes=CLASSES,
                result_names=['pts_bbox'])
        ],
    )
]

load_from = 'https://github.com/zhiqi-li/storage/releases/download/v1.0/r101_dcn_fcos3d_pretrain.pth'
log_config = dict(
    interval=50,
    hooks=[dict(type='TextLoggerHook'),
           dict(type='TensorboardLoggerHook')])

checkpoint_config = dict(interval=1)
cudnn_benchmark = True
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