Training Error in LiDAR stream

[zccjjj]

Hello, thanks for your excellent work, when I tried to train the lidar stream, I got an error and I can not solve it, please help me with some useful advice, thanks very much!

My Environment:

sys.platform: linux
Python: 3.8.3 (default, Jul  2 2020, 16:21:59) [GCC 7.3.0]
CUDA available: True
GPU 0,1,2,3,4,5,6: NVIDIA TITAN RTX
CUDA_HOME: /usr/local/cuda-10.0
NVCC: Cuda compilation tools, release 10.0, V10.0.130
GCC: gcc (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609
PyTorch: 1.7.0
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.1
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
  - CuDNN 7.6.3
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.8.0
OpenCV: 4.6.0
MMCV: 1.3.8
MMCV Compiler: GCC 5.4
MMCV CUDA Compiler: 10.0
MMDetection: 2.11.0
MMDetection3D: 0.11.0+be0cb2e

when I run ./tools/dist_train.sh configs/bevfusion/lidar_stream/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py 1
I got

import DCN failed
2022-08-07 13:26:24,943 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.8.3 (default, Jul  2 2020, 16:21:59) [GCC 7.3.0]
CUDA available: True
GPU 0,1,2,3,4,5,6: NVIDIA TITAN RTX
CUDA_HOME: /usr/local/cuda-10.0
NVCC: Cuda compilation tools, release 10.0, V10.0.130
GCC: gcc (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609
PyTorch: 1.7.0
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.1
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
  - CuDNN 7.6.3
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.8.0
OpenCV: 4.6.0
MMCV: 1.3.8
MMCV Compiler: GCC 5.4
MMCV CUDA Compiler: 10.0
MMDetection: 2.11.0
MMDetection3D: 0.11.0+be0cb2e
------------------------------------------------------------

2022-08-07 13:26:27,683 - mmdet - INFO - Distributed training: True
2022-08-07 13:26:30,617 - mmdet - INFO - Config:
voxel_size = [0.25, 0.25, 8]
model = dict(
    type='MVXFasterRCNN',
    pts_voxel_layer=dict(
        max_num_points=64,
        point_cloud_range=[-50, -50, -5, 50, 50, 3],
        voxel_size=[0.25, 0.25, 8],
        max_voxels=(30000, 40000)),
    pts_voxel_encoder=dict(
        type='HardVFE',
        in_channels=4,
        feat_channels=[64, 64],
        with_distance=False,
        voxel_size=[0.25, 0.25, 8],
        with_cluster_center=True,
        with_voxel_center=True,
        point_cloud_range=[-50, -50, -5, 50, 50, 3],
        norm_cfg=dict(type='naiveSyncBN1d', eps=0.001, momentum=0.01)),
    pts_middle_encoder=dict(
        type='PointPillarsScatter', in_channels=64, output_shape=[400, 400]),
    pts_backbone=dict(
        type='SECOND',
        in_channels=64,
        norm_cfg=dict(type='naiveSyncBN2d', eps=0.001, momentum=0.01),
        layer_nums=[3, 5, 5],
        layer_strides=[2, 2, 2],
        out_channels=[64, 128, 256]),
    pts_neck=dict(
        type='SECONDFPN',
        norm_cfg=dict(type='naiveSyncBN2d', eps=0.001, momentum=0.01),
        in_channels=[64, 128, 256],
        upsample_strides=[1, 2, 4],
        out_channels=[128, 128, 128]),
    pts_bbox_head=dict(
        type='Anchor3DHead',
        num_classes=10,
        in_channels=384,
        feat_channels=384,
        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],
                   [2.4560939, 6.73778078, 2.73004906],
                   [2.87427237, 12.01320693, 3.81509561],
                   [0.60058911, 1.68452161, 1.27192197],
                   [0.66344886, 0.7256437, 1.75748069],
                   [0.39694519, 0.40359262, 1.06232151],
                   [2.49008838, 0.48578221, 0.98297065]],
            custom_values=[0, 0],
            rotations=[0, 1.57],
            reshape_out=True),
        assigner_per_size=False,
        diff_rad_by_sin=True,
        dir_offset=0.7854,
        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=0.1111111111111111, loss_weight=1.0),
        loss_dir=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
    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)))
point_cloud_range = [-50, -50, -5, 50, 50, 3]
class_names = [
    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = dict(
    use_lidar=True,
    use_camera=False,
    use_radar=False,
    use_map=False,
    use_external=False)
file_client_args = dict(backend='disk')
train_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=5,
        file_client_args=dict(backend='disk')),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=10,
        file_client_args=dict(backend='disk')),
    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
    dict(
        type='PointsRangeFilter', point_cloud_range=[-50, -50, -5, 50, 50, 3]),
    dict(
        type='ObjectRangeFilter', point_cloud_range=[-50, -50, -5, 50, 50, 3]),
    dict(
        type='ObjectNameFilter',
        classes=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ]),
    dict(type='PointShuffle'),
    dict(
        type='DefaultFormatBundle3D',
        class_names=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ]),
    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=5,
        file_client_args=dict(backend='disk')),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=10,
        file_client_args=dict(backend='disk')),
    dict(
        type='MultiScaleFlipAug3D',
        img_scale=(1333, 800),
        pts_scale_ratio=1,
        flip=False,
        transforms=[
            dict(
                type='GlobalRotScaleTrans',
                rot_range=[0, 0],
                scale_ratio_range=[1.0, 1.0],
                translation_std=[0, 0, 0]),
            dict(type='RandomFlip3D'),
            dict(
                type='PointsRangeFilter',
                point_cloud_range=[-50, -50, -5, 50, 50, 3]),
            dict(
                type='DefaultFormatBundle3D',
                class_names=[
                    'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
                    'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
                    'barrier'
                ],
                with_label=False),
            dict(type='Collect3D', keys=['points'])
        ])
]
data = dict(
    samples_per_gpu=4,
    workers_per_gpu=4,
    train=dict(
        type='NuScenesDataset',
        data_root='data/nuscenes/',
        ann_file='data/nuscenes/nuscenes_infos_train.pkl',
        pipeline=[
            dict(
                type='LoadPointsFromFile',
                coord_type='LIDAR',
                load_dim=5,
                use_dim=5,
                file_client_args=dict(backend='disk')),
            dict(
                type='LoadPointsFromMultiSweeps',
                sweeps_num=10,
                file_client_args=dict(backend='disk')),
            dict(
                type='LoadAnnotations3D',
                with_bbox_3d=True,
                with_label_3d=True),
            dict(
                type='PointsRangeFilter',
                point_cloud_range=[-50, -50, -5, 50, 50, 3]),
            dict(
                type='ObjectRangeFilter',
                point_cloud_range=[-50, -50, -5, 50, 50, 3]),
            dict(
                type='ObjectNameFilter',
                classes=[
                    'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
                    'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
                    'barrier'
                ]),
            dict(type='PointShuffle'),
            dict(
                type='DefaultFormatBundle3D',
                class_names=[
                    'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
                    'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
                    'barrier'
                ]),
            dict(
                type='Collect3D',
                keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
        ],
        classes=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ],
        modality=dict(
            use_lidar=True,
            use_camera=False,
            use_radar=False,
            use_map=False,
            use_external=False),
        test_mode=False,
        box_type_3d='LiDAR'),
    val=dict(
        type='NuScenesDataset',
        data_root='data/nuscenes/',
        ann_file='data/nuscenes/nuscenes_infos_val.pkl',
        pipeline=[
            dict(
                type='LoadPointsFromFile',
                coord_type='LIDAR',
                load_dim=5,
                use_dim=5,
                file_client_args=dict(backend='disk')),
            dict(
                type='LoadPointsFromMultiSweeps',
                sweeps_num=10,
                file_client_args=dict(backend='disk')),
            dict(
                type='MultiScaleFlipAug3D',
                img_scale=(1333, 800),
                pts_scale_ratio=1,
                flip=False,
                transforms=[
                    dict(
                        type='GlobalRotScaleTrans',
                        rot_range=[0, 0],
                        scale_ratio_range=[1.0, 1.0],
                        translation_std=[0, 0, 0]),
                    dict(type='RandomFlip3D'),
                    dict(
                        type='PointsRangeFilter',
                        point_cloud_range=[-50, -50, -5, 50, 50, 3]),
                    dict(
                        type='DefaultFormatBundle3D',
                        class_names=[
                            'car', 'truck', 'trailer', 'bus',
                            'construction_vehicle', 'bicycle', 'motorcycle',
                            'pedestrian', 'traffic_cone', 'barrier'
                        ],
                        with_label=False),
                    dict(type='Collect3D', keys=['points'])
                ])
        ],
        classes=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ],
        modality=dict(
            use_lidar=True,
            use_camera=False,
            use_radar=False,
            use_map=False,
            use_external=False),
        test_mode=True,
        box_type_3d='LiDAR'),
    test=dict(
        type='NuScenesDataset',
        data_root='data/nuscenes/',
        ann_file='data/nuscenes/nuscenes_infos_val.pkl',
        pipeline=[
            dict(
                type='LoadPointsFromFile',
                coord_type='LIDAR',
                load_dim=5,
                use_dim=5,
                file_client_args=dict(backend='disk')),
            dict(
                type='LoadPointsFromMultiSweeps',
                sweeps_num=10,
                file_client_args=dict(backend='disk')),
            dict(
                type='MultiScaleFlipAug3D',
                img_scale=(1333, 800),
                pts_scale_ratio=1,
                flip=False,
                transforms=[
                    dict(
                        type='GlobalRotScaleTrans',
                        rot_range=[0, 0],
                        scale_ratio_range=[1.0, 1.0],
                        translation_std=[0, 0, 0]),
                    dict(type='RandomFlip3D'),
                    dict(
                        type='PointsRangeFilter',
                        point_cloud_range=[-50, -50, -5, 50, 50, 3]),
                    dict(
                        type='DefaultFormatBundle3D',
                        class_names=[
                            'car', 'truck', 'trailer', 'bus',
                            'construction_vehicle', 'bicycle', 'motorcycle',
                            'pedestrian', 'traffic_cone', 'barrier'
                        ],
                        with_label=False),
                    dict(type='Collect3D', keys=['points'])
                ])
        ],
        classes=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ],
        modality=dict(
            use_lidar=True,
            use_camera=False,
            use_radar=False,
            use_map=False,
            use_external=False),
        test_mode=True,
        box_type_3d='LiDAR'))
evaluation = dict(interval=24)
optimizer = dict(type='AdamW', lr=0.001, weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=1000,
    warmup_ratio=0.001,
    step=[20, 23])
momentum_config = None
total_epochs = 24
checkpoint_config = dict(interval=1)
log_config = dict(
    interval=50,
    hooks=[dict(type='TextLoggerHook'),
           dict(type='TensorboardLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d'
load_from = None
resume_from = None
workflow = [('train', 1)]
gpu_ids = range(0, 1)

2022-08-07 13:26:30,618 - mmdet - INFO - Set random seed to 0, deterministic: False
create hard
create hard
2022-08-07 13:26:30,677 - mmdet - INFO - Model:
MVXFasterRCNN(
  (pts_voxel_layer): Voxelization(voxel_size=[0.25, 0.25, 8], point_cloud_range=[-50, -50, -5, 50, 50, 3], max_num_points=64, max_voxels=(30000, 40000))
  (pts_voxel_encoder): HardVFE(
    (scatter): DynamicScatter(voxel_size=[0.25, 0.25, 8], point_cloud_range=[-50, -50, -5, 50, 50, 3], average_points=True)
    (vfe_layers): ModuleList(
      (0): VFELayer(
        (norm): NaiveSyncBatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (linear): Linear(in_features=10, out_features=64, bias=False)
      )
      (1): VFELayer(
        (norm): NaiveSyncBatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (linear): Linear(in_features=128, out_features=64, bias=False)
      )
    )
  )
  (pts_middle_encoder): PointPillarsScatter()
  (pts_backbone): SECOND(
    (blocks): ModuleList(
      (0): Sequential(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): NaiveSyncBatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
        (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): NaiveSyncBatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (5): ReLU(inplace=True)
        (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (7): NaiveSyncBatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (8): ReLU(inplace=True)
        (9): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (10): NaiveSyncBatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (11): ReLU(inplace=True)
      )
      (1): Sequential(
        (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): NaiveSyncBatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
        (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): NaiveSyncBatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (5): ReLU(inplace=True)
        (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (7): NaiveSyncBatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (8): ReLU(inplace=True)
        (9): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (10): NaiveSyncBatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (11): ReLU(inplace=True)
        (12): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (13): NaiveSyncBatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (14): ReLU(inplace=True)
        (15): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (16): NaiveSyncBatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (17): ReLU(inplace=True)
      )
      (2): Sequential(
        (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): NaiveSyncBatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): NaiveSyncBatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (5): ReLU(inplace=True)
        (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (7): NaiveSyncBatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (8): ReLU(inplace=True)
        (9): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (10): NaiveSyncBatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (11): ReLU(inplace=True)
        (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (13): NaiveSyncBatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (14): ReLU(inplace=True)
        (15): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (16): NaiveSyncBatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (17): ReLU(inplace=True)
      )
    )
  )
  (pts_neck): SECONDFPN(
    (deblocks): ModuleList(
      (0): Sequential(
        (0): ConvTranspose2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): NaiveSyncBatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (1): Sequential(
        (0): ConvTranspose2d(128, 128, kernel_size=(2, 2), stride=(2, 2), bias=False)
        (1): NaiveSyncBatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (2): Sequential(
        (0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(4, 4), bias=False)
        (1): NaiveSyncBatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
    )
  )
  (pts_bbox_head): Anchor3DHead(
    (loss_cls): FocalLoss()
    (loss_bbox): SmoothL1Loss()
    (loss_dir): CrossEntropyLoss()
    (conv_cls): Conv2d(384, 140, kernel_size=(1, 1), stride=(1, 1))
    (conv_reg): Conv2d(384, 126, kernel_size=(1, 1), stride=(1, 1))
    (conv_dir_cls): Conv2d(384, 28, kernel_size=(1, 1), stride=(1, 1))
  )
)
noise setting:
/root/BEVFusion/mmdetection-2.11.0/mmdet/apis/train.py:95: UserWarning: config is now expected to have a `runner` section, please set `runner` in your config.
  warnings.warn(
noise setting:
2022-08-07 13:26:33,548 - mmdet - INFO - Start running, host: root@zhangcaiji, work_dir: /root/BEVFusion/work_dirs/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d
2022-08-07 13:26:33,548 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) StepLrUpdaterHook
(NORMAL      ) CheckpointHook
(NORMAL      ) DistEvalHook
(VERY_LOW    ) TextLoggerHook
(VERY_LOW    ) TensorboardLoggerHook
 --------------------
before_train_epoch:
(VERY_HIGH   ) StepLrUpdaterHook
(NORMAL      ) DistSamplerSeedHook
(NORMAL      ) DistEvalHook
(LOW         ) IterTimerHook
(VERY_LOW    ) TextLoggerHook
(VERY_LOW    ) TensorboardLoggerHook
 --------------------
before_train_iter:
(VERY_HIGH   ) StepLrUpdaterHook
(LOW         ) IterTimerHook
 --------------------
after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(NORMAL      ) CheckpointHook
(NORMAL      ) DistEvalHook
(LOW         ) IterTimerHook
(VERY_LOW    ) TextLoggerHook
(VERY_LOW    ) TensorboardLoggerHook
 --------------------
after_train_epoch:
(NORMAL      ) CheckpointHook
(NORMAL      ) DistEvalHook
(VERY_LOW    ) TextLoggerHook
(VERY_LOW    ) TensorboardLoggerHook
 --------------------
before_val_epoch:
(NORMAL      ) DistSamplerSeedHook
(LOW         ) IterTimerHook
(VERY_LOW    ) TextLoggerHook
(VERY_LOW    ) TensorboardLoggerHook
 --------------------
before_val_iter:
(LOW         ) IterTimerHook
 --------------------
after_val_iter:
(LOW         ) IterTimerHook
 --------------------
after_val_epoch:
(VERY_LOW    ) TextLoggerHook
(VERY_LOW    ) TensorboardLoggerHook
 --------------------
after_run:
(VERY_LOW    ) TensorboardLoggerHook
 --------------------
2022-08-07 13:26:33,548 - mmdet - INFO - workflow: [('train', 1)], max: 24 epochs
Traceback (most recent call last):
  File "./tools/train.py", line 316, in <module>
    main()
  File "./tools/train.py", line 305, in main
    train_detector(
  File "/root/BEVFusion/mmdetection-2.11.0/mmdet/apis/train.py", line 170, in train_detector
    runner.run(data_loaders, cfg.workflow)
  File "/root/anaconda3/envs/BEVFusion_ali/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/root/anaconda3/envs/BEVFusion_ali/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train
    self.run_iter(data_batch, train_mode=True, **kwargs)
  File "/root/anaconda3/envs/BEVFusion_ali/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 29, in run_iter
    outputs = self.model.train_step(data_batch, self.optimizer,
  File "/root/anaconda3/envs/BEVFusion_ali/lib/python3.8/site-packages/mmcv/parallel/distributed.py", line 51, in train_step
    output = self.module.train_step(*inputs[0], **kwargs[0])
  File "/root/BEVFusion/mmdetection-2.11.0/mmdet/models/detectors/base.py", line 247, in train_step
    losses = self(**data)
  File "/root/anaconda3/envs/BEVFusion_ali/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/root/anaconda3/envs/BEVFusion_ali/lib/python3.8/site-packages/mmcv/runner/fp16_utils.py", line 97, in new_func
    return old_func(*args, **kwargs)
  File "/root/BEVFusion/mmdet3d/models/detectors/base.py", line 58, in forward
    return self.forward_train(**kwargs)
  File "/root/BEVFusion/mmdet3d/models/detectors/mvx_two_stage.py", line 295, in forward_train
    img_feats, pts_feats = self.extract_feat(
  File "/root/BEVFusion/mmdet3d/models/detectors/mvx_two_stage.py", line 230, in extract_feat
    pts_feats = self.extract_pts_feat(points, img_feats, img_metas)
  File "/root/BEVFusion/mmdet3d/models/detectors/mvx_two_stage.py", line 214, in extract_pts_feat
    voxels, num_points, coors = self.voxelize(pts) # torch.Size([13909, 64, 4]) torch.Size([13909]) torch.Size([13909, 4])
  File "/root/anaconda3/envs/BEVFusion_ali/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 26, in decorate_context
    return func(*args, **kwargs)
  File "/root/anaconda3/envs/BEVFusion_ali/lib/python3.8/site-packages/mmcv/runner/fp16_utils.py", line 184, in new_func
    return old_func(*args, **kwargs)
  File "/root/BEVFusion/mmdet3d/models/detectors/mvx_two_stage.py", line 247, in voxelize
    res_voxels, res_coors, res_num_points = self.pts_voxel_layer(res)
  File "/root/anaconda3/envs/BEVFusion_ali/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/root/BEVFusion/mmdet3d/ops/voxel/voxelize.py", line 112, in forward
    return voxelization(input, self.voxel_size, self.point_cloud_range,
  File "/root/BEVFusion/mmdet3d/ops/voxel/voxelize.py", line 51, in forward
    voxel_num = hard_voxelize(points, voxels, coors,
RuntimeError: CUDA error: invalid device function
Traceback (most recent call last):
  File "/root/anaconda3/envs/BEVFusion_ali/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/root/anaconda3/envs/BEVFusion_ali/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/root/anaconda3/envs/BEVFusion_ali/lib/python3.8/site-packages/torch/distributed/launch.py", line 260, in <module>
    main()
  File "/root/anaconda3/envs/BEVFusion_ali/lib/python3.8/site-packages/torch/distributed/launch.py", line 255, in main
    raise subprocess.CalledProcessError(returncode=process.returncode,
subprocess.CalledProcessError: Command '['/root/anaconda3/envs/BEVFusion_ali/bin/python', '-u', './tools/train.py', '--local_rank=0', 'configs/bevfusion/lidar_stream/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py', '--launcher', 'pytorch']' died with <Signals.SIGSEGV: 11>.

[zccjjj]

I have solved this problem by reinstalling the environment!

sys.platform: linux
Python: 3.8.3 (default, Jul  2 2020, 16:21:59) [GCC 7.3.0]
CUDA available: True
GPU 0,1,2,3,4,5,6: NVIDIA TITAN RTX
CUDA_HOME: /usr/local/cuda-10.0
NVCC: Cuda compilation tools, release 10.0, V10.0.130
GCC: gcc (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609
PyTorch: 1.7.0+cu101
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.1
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75
  - CuDNN 7.6.3
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.8.1+cu101
OpenCV: 4.6.0
MMCV: 1.4.0
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.1
MMDetection: 2.11.0
MMDetection3D: 0.11.0+be0cb2e

[bysota]

@zccjjj ，after installing the enviroment, an error occurs as below when running the command：
./tools/dist_train.sh configs/bevfusion/lidar_stream/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py 1

ImportError: cannot import name 'ball_query_ext' from partially initialized module 'mmdet3d.ops.ball_query' (most likely due to a circular import)

could u help to give some detailed installation steps for this issue? thanks a lot.

The enviroment:
sys.platform: linux
Python: 3.8.3 (default, Jul 2 2020, 16:21:59) [GCC 7.3.0]
CUDA available: True
GPU 0,1,2,3,4,5,6,7: GeForce RTX 2080 Ti
CUDA_HOME: /usr/local/cuda-10.0
NVCC: Cuda compilation tools, release 10.0, V10.0.130
GCC: gcc (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0
PyTorch: 1.7.0+cu101
PyTorch compiling details: PyTorch built with:

• GCC 7.3
• C++ Version: 201402
• Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• NNPACK is enabled
• CPU capability usage: AVX2
• CUDA Runtime 10.1
• NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75
• CuDNN 7.6.3
• Magma 2.5.2
• Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.8.1+cu101
OpenCV: 4.6.0
MMCV: 1.4.0
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.1
MMDetection: 2.11.0
MMDetection3D: 0.11.0+be0cb2e

[GYGWG]

@zccjjj ，after installing the enviroment, an error occurs as below when running the command： ./tools/dist_train.sh configs/bevfusion/lidar_stream/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py 1

ImportError: cannot import name 'ball_query_ext' from partially initialized module 'mmdet3d.ops.ball_query' (most likely due to a circular import)

could u help to give some detailed installation steps for this issue? thanks a lot.

The enviroment: sys.platform: linux Python: 3.8.3 (default, Jul 2 2020, 16:21:59) [GCC 7.3.0] CUDA available: True GPU 0,1,2,3,4,5,6,7: GeForce RTX 2080 Ti CUDA_HOME: /usr/local/cuda-10.0 NVCC: Cuda compilation tools, release 10.0, V10.0.130 GCC: gcc (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0 PyTorch: 1.7.0+cu101 PyTorch compiling details: PyTorch built with:

• GCC 7.3
• C++ Version: 201402
• Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• NNPACK is enabled
• CPU capability usage: AVX2
• CUDA Runtime 10.1
• NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75
• CuDNN 7.6.3
• Magma 2.5.2
• Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.8.1+cu101 OpenCV: 4.6.0 MMCV: 1.4.0 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 10.1 MMDetection: 2.11.0 MMDetection3D: 0.11.0+be0cb2e

Hi @bysota, have you fixed it? I got the exactly same problem. Thx

[buaazeus]

@GYGWG @bysota
Hello，have you fixed this issue?
ball_query_ext import error.
I got this issue as well.
Thank you.

[zjufkq]

I have solved this problem by reinstalling the environment!

sys.platform: linux
Python: 3.8.3 (default, Jul  2 2020, 16:21:59) [GCC 7.3.0]
CUDA available: True
GPU 0,1,2,3,4,5,6: NVIDIA TITAN RTX
CUDA_HOME: /usr/local/cuda-10.0
NVCC: Cuda compilation tools, release 10.0, V10.0.130
GCC: gcc (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609
PyTorch: 1.7.0+cu101
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.1
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75
  - CuDNN 7.6.3
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.8.1+cu101
OpenCV: 4.6.0
MMCV: 1.4.0
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.1
MMDetection: 2.11.0
MMDetection3D: 0.11.0+be0cb2e

How do you install mmdet3d==0.11.0 on torch==1.7.0? I have been reporting errors. seek help

[eyabesbes]

I have solved this problem by reinstalling the environment!

sys.platform: linux
Python: 3.8.3 (default, Jul  2 2020, 16:21:59) [GCC 7.3.0]
CUDA available: True
GPU 0,1,2,3,4,5,6: NVIDIA TITAN RTX
CUDA_HOME: /usr/local/cuda-10.0
NVCC: Cuda compilation tools, release 10.0, V10.0.130
GCC: gcc (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609
PyTorch: 1.7.0+cu101
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.1
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75
  - CuDNN 7.6.3
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.8.1+cu101
OpenCV: 4.6.0
MMCV: 1.4.0
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.1
MMDetection: 2.11.0
MMDetection3D: 0.11.0+be0cb2e

How do you install mmdet3d==0.11.0 on torch==1.7.0? I have been reporting errors. seek help

Hi,
I have been reporting the same errors I couldn't install mmdet3d ==0.11.0 on torch==1.7.0.

did you figure out how to solve this issue ?