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open-mmlab / mmcv

OpenMMLab Computer Vision Foundation
https://mmcv.readthedocs.io/en/latest/
Apache License 2.0
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[Bug] #2947

Open China-pzy opened 1 year ago

China-pzy commented 1 year ago

Prerequisite

Environment

System environment: sys.platform: linux Python: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] CUDA available: True numpy_random_seed: 543195228 GPU 0,1: NVIDIA GeForce RTX 3090 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.8.1+cu111 PyTorch compiling details: PyTorch built with:

Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: none Distributed training: False GPU number: 1

Reproduces the problem - code sample

default_scope = 'mmdet3d' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=1), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='Det3DVisualizationHook')) env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True) log_level = 'INFO' load_from = None resume = False custom_imports = dict( imports=['projects.BEVFusion.bevfusion'], allow_failed_imports=False) voxel_size = [0.075, 0.075, 0.2] point_cloud_range = [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0] class_names = [ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' ] metainfo = dict(classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' ]) dataset_type = 'NuScenesDataset' data_root = '/ssh/data/nuscenes/v1.0-mini/' data_prefix = dict( pts='samples/LIDAR_TOP', CAM_FRONT='samples/CAM_FRONT', CAM_FRONT_LEFT='samples/CAM_FRONT_LEFT', CAM_FRONT_RIGHT='samples/CAM_FRONT_RIGHT', CAM_BACK='samples/CAM_BACK', CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT', CAM_BACK_LEFT='samples/CAM_BACK_LEFT', sweeps='sweeps/LIDAR_TOP') input_modality = dict(use_lidar=True, use_camera=True) backend_args = None model = dict( type='BEVFusion', data_preprocessor=dict( type='Det3DDataPreprocessor', pad_size_divisor=32, voxelize_cfg=dict( max_num_points=10, point_cloud_range=[-54.0, -54.0, -5.0, 54.0, 54.0, 3.0], voxel_size=[0.075, 0.075, 0.2], max_voxels=[120000, 160000], voxelize_reduce=True), mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=False), pts_voxel_encoder=dict(type='HardSimpleVFE', num_features=5), pts_middle_encoder=dict( type='BEVFusionSparseEncoder', in_channels=5, sparse_shape=[1440, 1440, 41], order=('conv', 'norm', 'act'), norm_cfg=dict(type='BN1d', eps=0.001, momentum=0.01), encoder_channels=((16, 16, 32), (32, 32, 64), (64, 64, 128), (128, 128)), encoder_paddings=((0, 0, 1), (0, 0, 1), (0, 0, (1, 1, 0)), (0, 0)), block_type='basicblock'), pts_backbone=dict( type='SECOND', in_channels=256, out_channels=[128, 256], layer_nums=[5, 5], layer_strides=[1, 2], norm_cfg=dict(type='BN', eps=0.001, momentum=0.01), conv_cfg=dict(type='Conv2d', bias=False)), pts_neck=dict( type='SECONDFPN', in_channels=[128, 256], out_channels=[256, 256], upsample_strides=[1, 2], norm_cfg=dict(type='BN', eps=0.001, momentum=0.01), upsample_cfg=dict(type='deconv', bias=False), use_conv_for_no_stride=True), bbox_head=dict( type='TransFusionHead', num_proposals=200, auxiliary=True, in_channels=512, hidden_channel=128, num_classes=10, nms_kernel_size=3, bn_momentum=0.1, num_decoder_layers=1, decoder_layer=dict( type='TransformerDecoderLayer', self_attn_cfg=dict(embed_dims=128, num_heads=8, dropout=0.1), cross_attn_cfg=dict(embed_dims=128, num_heads=8, dropout=0.1), ffn_cfg=dict( embed_dims=128, feedforward_channels=256, num_fcs=2, ffn_drop=0.1, act_cfg=dict(type='ReLU', inplace=True)), norm_cfg=dict(type='LN'), pos_encoding_cfg=dict(input_channel=2, num_pos_feats=128)), train_cfg=dict( dataset='nuScenes', point_cloud_range=[-54.0, -54.0, -5.0, 54.0, 54.0, 3.0], grid_size=[1440, 1440, 41], voxel_size=[0.075, 0.075, 0.2], out_size_factor=8, gaussian_overlap=0.1, min_radius=2, pos_weight=-1, code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2], assigner=dict( type='HungarianAssigner3D', iou_calculator=dict(type='BboxOverlaps3D', coordinate='lidar'), cls_cost=dict( type='mmdet.FocalLossCost', gamma=2.0, alpha=0.25, weight=0.15), reg_cost=dict(type='BBoxBEVL1Cost', weight=0.25), iou_cost=dict(type='IoU3DCost', weight=0.25))), test_cfg=dict( dataset='nuScenes', grid_size=[1440, 1440, 41], out_size_factor=8, voxel_size=[0.075, 0.075], pc_range=[-54.0, -54.0], nms_type=None), common_heads=dict( center=[2, 2], height=[1, 2], dim=[3, 2], rot=[2, 2], vel=[2, 2]), bbox_coder=dict( type='TransFusionBBoxCoder', pc_range=[-54.0, -54.0], post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0], score_threshold=0.0, out_size_factor=8, voxel_size=[0.075, 0.075], code_size=10), loss_cls=dict( type='mmdet.FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, reduction='mean', loss_weight=1.0), loss_heatmap=dict( type='mmdet.GaussianFocalLoss', reduction='mean', loss_weight=1.0), loss_bbox=dict( type='mmdet.L1Loss', reduction='mean', loss_weight=0.25)), img_backbone=dict( type='mmdet.SwinTransformer', embed_dims=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=0.0, attn_drop_rate=0.0, drop_path_rate=0.2, patch_norm=True, out_indices=[1, 2, 3], with_cp=False, convert_weights=True, init_cfg=dict( type='Pretrained', checkpoint='/ssh/mmdetection3d/swin_tiny_patch4_window7_224.pth')), img_neck=dict( type='GeneralizedLSSFPN', in_channels=[192, 384, 768], out_channels=256, start_level=0, num_outs=3, norm_cfg=dict(type='BN2d', requires_grad=True), act_cfg=dict(type='ReLU', inplace=True), upsample_cfg=dict(mode='bilinear', align_corners=False)), view_transform=dict( type='DepthLSSTransform', in_channels=256, out_channels=80, image_size=[256, 704], feature_size=[32, 88], xbound=[-54.0, 54.0, 0.3], ybound=[-54.0, 54.0, 0.3], zbound=[-10.0, 10.0, 20.0], dbound=[1.0, 60.0, 0.5], downsample=2), fusion_layer=dict( type='ConvFuser', in_channels=[80, 256], out_channels=256)) db_sampler = dict( data_root='/ssh/data/nuscenes/v1.0-mini/', info_path='/ssh/data/nuscenes/v1.0-mini/nuscenes_dbinfos_train.pkl', rate=1.0, prepare=dict( filter_by_difficulty=[-1], filter_by_min_points=dict( car=5, truck=5, bus=5, trailer=5, construction_vehicle=5, traffic_cone=5, barrier=5, motorcycle=5, bicycle=5, pedestrian=5)), classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' ], sample_groups=dict( car=2, truck=3, construction_vehicle=7, bus=4, trailer=6, barrier=2, motorcycle=6, bicycle=6, pedestrian=2, traffic_cone=2), points_loader=dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=[0, 1, 2, 3, 4], backend_args=None)) train_pipeline = [ dict( type='BEVLoadMultiViewImageFromFiles', to_float32=True, color_type='color', backend_args=None), dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5, backend_args=None), dict( type='LoadPointsFromMultiSweeps', sweeps_num=9, load_dim=5, use_dim=5, pad_empty_sweeps=True, remove_close=True, backend_args=None), dict( type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, with_attr_label=False), dict( type='ImageAug3D', final_dim=[256, 704], resize_lim=[0.38, 0.55], bot_pct_lim=[0.0, 0.0], rot_lim=[-5.4, 5.4], rand_flip=True, is_train=True), dict( type='BEVFusionGlobalRotScaleTrans', scale_ratio_range=[0.9, 1.1], rot_range=[-0.78539816, 0.78539816], translation_std=0.5), dict(type='BEVFusionRandomFlip3D'), dict( type='PointsRangeFilter', point_cloud_range=[-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]), dict( type='ObjectRangeFilter', point_cloud_range=[-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]), dict( type='ObjectNameFilter', classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' ]), dict( type='GridMask', use_h=True, use_w=True, max_epoch=6, rotate=1, offset=False, ratio=0.5, mode=1, prob=0.0, fixed_prob=True), dict(type='PointShuffle'), dict( type='Pack3DDetInputs', keys=[ 'points', 'img', 'gt_bboxes_3d', 'gt_labels_3d', 'gt_bboxes', 'gt_labels' ], meta_keys=[ 'cam2img', 'ori_cam2img', 'lidar2cam', 'lidar2img', 'cam2lidar', 'ori_lidar2img', 'img_aug_matrix', 'box_type_3d', 'sample_idx', 'lidar_path', 'img_path', 'transformation_3d_flow', 'pcd_rotation', 'pcd_scale_factor', 'pcd_trans', 'img_aug_matrix', 'lidar_aug_matrix', 'num_pts_feats' ]) ] test_pipeline = [ dict( type='BEVLoadMultiViewImageFromFiles', to_float32=True, color_type='color', backend_args=None), dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5, backend_args=None), dict( type='LoadPointsFromMultiSweeps', sweeps_num=9, load_dim=5, use_dim=5, pad_empty_sweeps=True, remove_close=True, backend_args=None), dict( type='ImageAug3D', final_dim=[256, 704], resize_lim=[0.48, 0.48], bot_pct_lim=[0.0, 0.0], rot_lim=[0.0, 0.0], rand_flip=False, is_train=False), dict( type='PointsRangeFilter', point_cloud_range=[-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]), dict( type='Pack3DDetInputs', keys=['img', 'points', 'gt_bboxes_3d', 'gt_labels_3d'], meta_keys=[ 'cam2img', 'ori_cam2img', 'lidar2cam', 'lidar2img', 'cam2lidar', 'ori_lidar2img', 'img_aug_matrix', 'box_type_3d', 'sample_idx', 'lidar_path', 'img_path', 'num_pts_feats' ]) ] train_dataloader = dict( batch_size=4, num_workers=4, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='CBGSDataset', dataset=dict( type='NuScenesDataset', data_root='/ssh/data/nuscenes/v1.0-mini/', ann_file='nuscenes_infos_train.pkl', pipeline=[ dict( type='BEVLoadMultiViewImageFromFiles', to_float32=True, color_type='color', backend_args=None), dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5, backend_args=None), dict( type='LoadPointsFromMultiSweeps', sweeps_num=9, load_dim=5, use_dim=5, pad_empty_sweeps=True, remove_close=True, backend_args=None), dict( type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, with_attr_label=False), dict( type='ImageAug3D', final_dim=[256, 704], resize_lim=[0.38, 0.55], bot_pct_lim=[0.0, 0.0], rot_lim=[-5.4, 5.4], rand_flip=True, is_train=True), dict( type='BEVFusionGlobalRotScaleTrans', scale_ratio_range=[0.9, 1.1], rot_range=[-0.78539816, 0.78539816], translation_std=0.5), dict(type='BEVFusionRandomFlip3D'), dict( type='PointsRangeFilter', point_cloud_range=[-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]), dict( type='ObjectRangeFilter', point_cloud_range=[-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]), dict( type='ObjectNameFilter', classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' ]), dict( type='GridMask', use_h=True, use_w=True, max_epoch=6, rotate=1, offset=False, ratio=0.5, mode=1, prob=0.0, fixed_prob=True), dict(type='PointShuffle'), dict( type='Pack3DDetInputs', keys=[ 'points', 'img', 'gt_bboxes_3d', 'gt_labels_3d', 'gt_bboxes', 'gt_labels' ], meta_keys=[ 'cam2img', 'ori_cam2img', 'lidar2cam', 'lidar2img', 'cam2lidar', 'ori_lidar2img', 'img_aug_matrix', 'box_type_3d', 'sample_idx', 'lidar_path', 'img_path', 'transformation_3d_flow', 'pcd_rotation', 'pcd_scale_factor', 'pcd_trans', 'img_aug_matrix', 'lidar_aug_matrix', 'num_pts_feats' ]) ], metainfo=dict(classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' ]), modality=dict(use_lidar=True, use_camera=True), test_mode=False, data_prefix=dict( pts='samples/LIDAR_TOP', CAM_FRONT='samples/CAM_FRONT', CAM_FRONT_LEFT='samples/CAM_FRONT_LEFT', CAM_FRONT_RIGHT='samples/CAM_FRONT_RIGHT', CAM_BACK='samples/CAM_BACK', CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT', CAM_BACK_LEFT='samples/CAM_BACK_LEFT', sweeps='sweeps/LIDAR_TOP'), use_valid_flag=True, box_type_3d='LiDAR'))) val_dataloader = dict( batch_size=1, num_workers=4, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='NuScenesDataset', data_root='/ssh/data/nuscenes/v1.0-mini/', ann_file='nuscenes_infos_val.pkl', pipeline=[ dict( type='BEVLoadMultiViewImageFromFiles', to_float32=True, color_type='color', backend_args=None), dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5, backend_args=None), dict( type='LoadPointsFromMultiSweeps', sweeps_num=9, load_dim=5, use_dim=5, pad_empty_sweeps=True, remove_close=True, backend_args=None), dict( type='ImageAug3D', final_dim=[256, 704], resize_lim=[0.48, 0.48], bot_pct_lim=[0.0, 0.0], rot_lim=[0.0, 0.0], rand_flip=False, is_train=False), dict( type='PointsRangeFilter', point_cloud_range=[-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]), dict( type='Pack3DDetInputs', keys=['img', 'points', 'gt_bboxes_3d', 'gt_labels_3d'], meta_keys=[ 'cam2img', 'ori_cam2img', 'lidar2cam', 'lidar2img', 'cam2lidar', 'ori_lidar2img', 'img_aug_matrix', 'box_type_3d', 'sample_idx', 'lidar_path', 'img_path', 'num_pts_feats' ]) ], metainfo=dict(classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' ]), modality=dict(use_lidar=True, use_camera=True), data_prefix=dict( pts='samples/LIDAR_TOP', CAM_FRONT='samples/CAM_FRONT', CAM_FRONT_LEFT='samples/CAM_FRONT_LEFT', CAM_FRONT_RIGHT='samples/CAM_FRONT_RIGHT', CAM_BACK='samples/CAM_BACK', CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT', CAM_BACK_LEFT='samples/CAM_BACK_LEFT', sweeps='sweeps/LIDAR_TOP'), test_mode=True, box_type_3d='LiDAR', backend_args=None)) test_dataloader = dict( batch_size=1, num_workers=4, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='NuScenesDataset', data_root='/ssh/data/nuscenes/v1.0-mini/', ann_file='nuscenes_infos_val.pkl', pipeline=[ dict( type='BEVLoadMultiViewImageFromFiles', to_float32=True, color_type='color', backend_args=None), dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5, backend_args=None), dict( type='LoadPointsFromMultiSweeps', sweeps_num=9, load_dim=5, use_dim=5, pad_empty_sweeps=True, remove_close=True, backend_args=None), dict( type='ImageAug3D', final_dim=[256, 704], resize_lim=[0.48, 0.48], bot_pct_lim=[0.0, 0.0], rot_lim=[0.0, 0.0], rand_flip=False, is_train=False), dict( type='PointsRangeFilter', point_cloud_range=[-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]), dict( type='Pack3DDetInputs', keys=['img', 'points', 'gt_bboxes_3d', 'gt_labels_3d'], meta_keys=[ 'cam2img', 'ori_cam2img', 'lidar2cam', 'lidar2img', 'cam2lidar', 'ori_lidar2img', 'img_aug_matrix', 'box_type_3d', 'sample_idx', 'lidar_path', 'img_path', 'num_pts_feats' ]) ], metainfo=dict(classes=[ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' ]), modality=dict(use_lidar=True, use_camera=True), data_prefix=dict( pts='samples/LIDAR_TOP', CAM_FRONT='samples/CAM_FRONT', CAM_FRONT_LEFT='samples/CAM_FRONT_LEFT', CAM_FRONT_RIGHT='samples/CAM_FRONT_RIGHT', CAM_BACK='samples/CAM_BACK', CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT', CAM_BACK_LEFT='samples/CAM_BACK_LEFT', sweeps='sweeps/LIDAR_TOP'), test_mode=True, box_type_3d='LiDAR', backend_args=None)) val_evaluator = dict( type='NuScenesMetric', data_root='/ssh/data/nuscenes/v1.0-mini/', ann_file='/ssh/data/nuscenes/v1.0-mini/nuscenes_infos_val.pkl', metric='bbox', backend_args=None) test_evaluator = dict( type='NuScenesMetric', data_root='/ssh/data/nuscenes/v1.0-mini/', ann_file='/ssh/data/nuscenes/v1.0-mini/nuscenes_infos_val.pkl', metric='bbox', backend_args=None) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='Det3DLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') lr = 0.0001 param_scheduler = [ dict( type='LinearLR', start_factor=0.33333333, by_epoch=False, begin=0, end=500), dict( type='CosineAnnealingLR', begin=0, T_max=6, end=6, by_epoch=True, eta_min_ratio=0.0001, convert_to_iter_based=True), dict( type='CosineAnnealingMomentum', eta_min=0.8947368421052632, begin=0, end=2.4, by_epoch=True, convert_to_iter_based=True), dict( type='CosineAnnealingMomentum', eta_min=1, begin=2.4, end=6, by_epoch=True, convert_to_iter_based=True) ] train_cfg = dict(by_epoch=True, max_epochs=6, val_interval=1) val_cfg = dict() test_cfg = dict() optim_wrapper = dict( type='OptimWrapper', optimizer=dict(type='AdamW', lr=0.0002, weight_decay=0.01), clip_grad=dict(max_norm=35, norm_type=2)) auto_scale_lr = dict(enable=False, base_batch_size=32) launcher = 'none' work_dir = './work_dirs/bevfusion_lidar-cam_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d'

Reproduces the problem - command or script

bash tools/dist_train.sh projects/BEVFusion/configs/bevfusion_lidar-cam_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d.py 8 --cfg-options load_from=${LIDAR_PRETRAINED_CHECKPOINT} model.img_backbone.init_cfg.checkpoint=${IMAGE_PRETRAINED_BACKBONE}

Reproduces the problem - error message

/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmdet/models/task_modules/builder.py:17: UserWarning: build_sampler would be deprecated soon, please use mmdet.registry.TASK_UTILS.build() warnings.warn('build_sampler would be deprecated soon, please use ' Traceback (most recent call last): File "/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg obj = obj_cls(args) # type: ignore File "/ssh/mmdetection3d/projects/BEVFusion/bevfusion/transformer.py", line 30, in init super().init(kwargs) TypeError: init() missing 2 required positional arguments: 'attn_cfgs' and 'feedforward_channels'

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg obj = obj_cls(*args) # type: ignore File "/ssh/mmdetection3d/projects/BEVFusion/bevfusion/transfusion_head.py", line 136, in init self.decoder.append(MODELS.build(decoder_layer)) File "/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/registry/registry.py", line 545, in build return self.build_func(cfg, args, **kwargs, registry=self) File "/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 241, in build_model_from_cfg return build_from_cfg(cfg, registry, default_args) File "/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 135, in build_from_cfg raise type(e)( TypeError: class TransformerDecoderLayer in projects/BEVFusion/bevfusion/transformer.py: init() missing 2 required positional arguments: 'attn_cfgs' and 'feedforward_channels'

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg obj = obj_cls(*args) # type: ignore File "/ssh/mmdetection3d/projects/BEVFusion/bevfusion/bevfusion.py", line 61, in init self.bbox_head = MODELS.build(bbox_head) File "/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/registry/registry.py", line 545, in build return self.build_func(cfg, args, **kwargs, registry=self) File "/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 241, in build_model_from_cfg return build_from_cfg(cfg, registry, default_args) File "/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 135, in build_from_cfg raise type(e)( TypeError: class TransFusionHead in projects/BEVFusion/bevfusion/transfusion_head.py: class TransformerDecoderLayer in projects/BEVFusion/bevfusion/transformer.py: init() missing 2 required positional arguments: 'attn_cfgs' and 'feedforward_channels'

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/ssh/mmdetection3d/tools/train.py", line 135, in main() File "/ssh/mmdetection3d/tools/train.py", line 124, in main runner = Runner.from_cfg(cfg) File "/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/runner/runner.py", line 439, in from_cfg runner = cls( File "/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/runner/runner.py", line 406, in init self.model = self.build_model(model) File "/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/runner/runner.py", line 813, in build_model model = MODELS.build(model) File "/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/registry/registry.py", line 545, in build return self.build_func(cfg, *args, **kwargs, registry=self) File "/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 241, in build_model_from_cfg return build_from_cfg(cfg, registry, default_args) File "/opt/conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 135, in build_from_cfg raise type(e)( TypeError: class BEVFusion in projects/BEVFusion/bevfusion/bevfusion.py: class TransFusionHead in projects/BEVFusion/bevfusion/transfusion_head.py: class TransformerDecoderLayer in projects/BEVFusion/bevfusion/transformer.py: init() missing 2 required positional arguments: 'attn_cfgs' and 'feedforward_channels'

进程已结束,退出代码1

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