Met a ValueError of handling dataset during training a vfnet model of object detection

[PoplarBoat]

Hello,dear developers! I did meet a trouble,and need your helps!

Checklist

1. I have searched related issues but cannot get the expected help.
2. I have read the FAQ documentation but cannot get the expected help.
3. The bug has not been fixed in the latest version.

Describe the bug
When I am training a vfnet with a dataset of coco format,I met a problem.
Ouput was as follows:
loading annotations into memory...
Done (t=0.13s)
creating index...
index created!
Traceback (most recent call last):
File "E:\mmdetection\mmdetection\tools\train.py", line 121, in
main()
File "E:\mmdetection\mmdetection\tools\train.py", line 117, in main
runner.train()
File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\runner\runner.py", line 1728, in train
self._train_loop = self.build_train_loop(
File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\runner\runner.py", line 1520, in build_train_loop
loop = LOOPS.build(
File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\registry\registry.py", line 570, in build
return self.build_func(cfg, *args, **kwargs, registry=self)
File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\registry\build_functions.py", line 123, in build_from_cfg
obj = obj_cls(**args) # type: ignore
File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\runner\loops.py", line 46, in init
super().init(runner, dataloader)
File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\runner\base_loop.py", line 26, in init
self.dataloader = runner.build_dataloader(
File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\runner\runner.py", line 1370, in build_dataloader
dataset = DATASETS.build(dataset_cfg)
File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\registry\registry.py", line 570, in build
return self.build_func(cfg, *args, **kwargs, registry=self)
File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\registry\build_functions.py", line 123, in build_from_cfg
obj = obj_cls(**args) # type: ignore
File "E:\mmdetection\mmdetection\mmdet\datasets\base_det_dataset.py", line 51, in init
super().init(*args, **kwargs)
File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\dataset\base_dataset.py", line 247, in init
self.full_init()
File "E:\mmdetection\mmdetection\mmdet\datasets\base_det_dataset.py", line 89, in full_init
self.data_bytes, self.data_address = self._serialize_data()
File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\dataset\base_dataset.py", line 768, in _serialize_data
data_bytes = np.concatenate(data_list)
File "<array_function internals>", line 200, in concatenate
ValueError: need at least one array to concatenate

Reproduction

1. What command or script did you run?

python tools/train.py configs/vfnet/vfnet_r50_fpn_ms-2x_coco.py

2. Did you make any modifications on the code or config? Did you understand what you have modified?
   I didn't make any modification except the dataset path in the configs/base/datasets/coco_detection.py.
   Yes,I understand what I have modified.

3. What dataset did you use?
   Environment

4. Please run python mmdet/utils/collect_env.py to collect necessary environment information and paste it here.
   sys.platform: win32
   Python: 3.8.20 (default, Oct 3 2024, 15:19:54) [MSC v.1929 64 bit (AMD64)]
   CUDA available: True
   MUSA available: False
   numpy_random_seed: 2147483648
   GPU 0: NVIDIA GeForce MX350
   CUDA_HOME: E:\cuda_toolkit
   NVCC: Cuda compilation tools, release 11.8, V11.8.89
   GCC: n/a
   PyTorch: 2.0.1+cu118
   PyTorch compiling details: PyTorch built with:

• C++ Version: 199711
• MSVC 193431937
• Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
• OpenMP 2019
• LAPACK is enabled (usually provided by MKL)
• CPU capability usage: AVX2
• CUDA Runtime 11.8
• 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=c
  ompute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90;-gencode;arch=compute_37,code=compute_37
• CuDNN 8.7
• Magma 2.5.4
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=C:/actions-runner/_wor
  k/pytorch/pytorch/builder/windows/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /w /bigobj /FS -DUSE_PTHREADPOOL
  -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE, LA
  PACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=OFF, TORCH_VERSION=2.0.1, USE_CUDA
  =ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=OFF, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.15.2+cu118
OpenCV: 4.11.0
MMEngine: 0.10.6
MMDetection: 3.2.0+cfd5d3a

Error traceback

E:\Anaconda\envs\openmmlab_330\python.exe E:\mmdetection\mmdetection\tools\train.py configs/vfnet/vfnet_r50_fpn_ms-2x_coco.py 
02/19 15:13:02 - mmengine - INFO - 
------------------------------------------------------------
System environment:
    sys.platform: win32
    Python: 3.8.20 (default, Oct  3 2024, 15:19:54) [MSC v.1929 64 bit (AMD64)]
    CUDA available: True
    MUSA available: False
    numpy_random_seed: 1411509375
    GPU 0: NVIDIA GeForce MX350
    CUDA_HOME: E:\cuda_toolkit
    NVCC: Cuda compilation tools, release 11.8, V11.8.89
    GCC: n/a
    PyTorch: 2.0.1+cu118
    PyTorch compiling details: PyTorch built with:
  - C++ Version: 199711
  - MSVC 193431937
  - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
  - OpenMP 2019
  - LAPACK is enabled (usually provided by MKL)
  - CPU capability usage: AVX2
  - CUDA Runtime 11.8
  - 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_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90;-gencode;arch=compute_37,code=compute_37
  - CuDNN 8.7
  - Magma 2.5.4
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=C:/actions-runner/_work/pytorch/pytorch/builder/windows/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /w /bigobj /FS -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=OFF, TORCH_VERSION=2.0.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=OFF, USE_OPENMP=ON, USE_ROCM=OFF, 

    TorchVision: 0.15.2+cu118
    OpenCV: 4.11.0
    MMEngine: 0.10.6

Runtime environment:
    cudnn_benchmark: False
    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
    dist_cfg: {'backend': 'nccl'}
    seed: 1411509375
    Distributed launcher: none
    Distributed training: False
    GPU number: 1
------------------------------------------------------------

02/19 15:13:03 - mmengine - INFO - Config:
auto_scale_lr = dict(base_batch_size=16, enable=False)
backend_args = None
data_root = 'data/NEU-DET_coco/'
dataset_type = 'CocoDataset'
default_hooks = dict(
    checkpoint=dict(interval=1, type='CheckpointHook'),
    logger=dict(interval=50, type='LoggerHook'),
    param_scheduler=dict(type='ParamSchedulerHook'),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    timer=dict(type='IterTimerHook'),
    visualization=dict(type='DetVisualizationHook'))
default_scope = 'mmdet'
env_cfg = dict(
    cudnn_benchmark=False,
    dist_cfg=dict(backend='nccl'),
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
launcher = 'none'
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
max_epochs = 24
model = dict(
    backbone=dict(
        depth=50,
        frozen_stages=1,
        init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'),
        norm_cfg=dict(requires_grad=True, type='BN'),
        norm_eval=True,
        num_stages=4,
        out_indices=(
            0,
            1,
            2,
            3,
        ),
        style='pytorch',
        type='ResNet'),
    bbox_head=dict(
        center_sampling=False,
        dcn_on_last_conv=False,
        feat_channels=256,
        in_channels=256,
        loss_bbox=dict(loss_weight=1.5, type='GIoULoss'),
        loss_bbox_refine=dict(loss_weight=2.0, type='GIoULoss'),
        loss_cls=dict(
            alpha=0.75,
            gamma=2.0,
            iou_weighted=True,
            loss_weight=1.0,
            type='VarifocalLoss',
            use_sigmoid=True),
        num_classes=80,
        stacked_convs=3,
        strides=[
            8,
            16,
            32,
            64,
            128,
        ],
        type='VFNetHead',
        use_atss=True,
        use_vfl=True),
    data_preprocessor=dict(
        bgr_to_rgb=True,
        mean=[
            123.675,
            116.28,
            103.53,
        ],
        pad_size_divisor=32,
        std=[
            58.395,
            57.12,
            57.375,
        ],
        type='DetDataPreprocessor'),
    neck=dict(
        add_extra_convs='on_output',
        in_channels=[
            256,
            512,
            1024,
            2048,
        ],
        num_outs=5,
        out_channels=256,
        relu_before_extra_convs=True,
        start_level=1,
        type='FPN'),
    test_cfg=dict(
        max_per_img=100,
        min_bbox_size=0,
        nms=dict(iou_threshold=0.6, type='nms'),
        nms_pre=1000,
        score_thr=0.05),
    train_cfg=dict(
        allowed_border=-1,
        assigner=dict(topk=9, type='ATSSAssigner'),
        debug=False,
        pos_weight=-1),
    type='VFNet')
optim_wrapper = dict(
    clip_grad=None,
    optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0001),
    paramwise_cfg=dict(bias_decay_mult=0.0, bias_lr_mult=2.0),
    type='OptimWrapper')
param_scheduler = [
    dict(begin=0, by_epoch=False, end=500, start_factor=0.1, type='LinearLR'),
    dict(
        begin=0,
        by_epoch=True,
        end=24,
        gamma=0.1,
        milestones=[
            16,
            22,
        ],
        type='MultiStepLR'),
]
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='annotations/instances_val2017.json',
        backend_args=None,
        data_prefix=dict(img='val2017/'),
        data_root='data/NEU-DET_coco/',
        pipeline=[
            dict(backend_args=None, type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                1333,
                800,
            ), type='Resize'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                meta_keys=(
                    'img_id',
                    'img_path',
                    'ori_shape',
                    'img_shape',
                    'scale_factor',
                ),
                type='PackDetInputs'),
        ],
        test_mode=True,
        type='CocoDataset'),
    drop_last=False,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
    ann_file='data/NEU-DET_coco/annotations/instances_val2017.json',
    backend_args=None,
    format_only=False,
    metric='bbox',
    type='CocoMetric')
test_pipeline = [
    dict(backend_args=None, type='LoadImageFromFile'),
    dict(keep_ratio=True, scale=(
        1333,
        800,
    ), type='Resize'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        meta_keys=(
            'img_id',
            'img_path',
            'ori_shape',
            'img_shape',
            'scale_factor',
        ),
        type='PackDetInputs'),
]
train_cfg = dict(max_epochs=24, type='EpochBasedTrainLoop', val_interval=1)
train_dataloader = dict(
    batch_sampler=dict(type='AspectRatioBatchSampler'),
    batch_size=2,
    dataset=dict(
        ann_file='annotations/instances_train2017.json',
        backend_args=None,
        data_prefix=dict(img='train2017/'),
        data_root='data/NEU-DET_coco/',
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        pipeline=[
            dict(backend_args=None, type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                keep_ratio=True,
                scale=[
                    (
                        1333,
                        480,
                    ),
                    (
                        1333,
                        960,
                    ),
                ],
                type='RandomResize'),
            dict(prob=0.5, type='RandomFlip'),
            dict(type='PackDetInputs'),
        ],
        type='CocoDataset'),
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=True, type='DefaultSampler'))
train_pipeline = [
    dict(backend_args=None, type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        keep_ratio=True,
        scale=[
            (
                1333,
                480,
            ),
            (
                1333,
                960,
            ),
        ],
        type='RandomResize'),
    dict(prob=0.5, type='RandomFlip'),
    dict(type='PackDetInputs'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='annotations/instances_val2017.json',
        backend_args=None,
        data_prefix=dict(img='val2017/'),
        data_root='data/NEU-DET_coco/',
        pipeline=[
            dict(backend_args=None, type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                1333,
                800,
            ), type='Resize'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                meta_keys=(
                    'img_id',
                    'img_path',
                    'ori_shape',
                    'img_shape',
                    'scale_factor',
                ),
                type='PackDetInputs'),
        ],
        test_mode=True,
        type='CocoDataset'),
    drop_last=False,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
    ann_file='data/NEU-DET_coco/annotations/instances_val2017.json',
    backend_args=None,
    format_only=False,
    metric='bbox',
    type='CocoMetric')
vis_backends = [
    dict(type='LocalVisBackend'),
]
visualizer = dict(
    name='visualizer',
    type='DetLocalVisualizer',
    vis_backends=[
        dict(type='LocalVisBackend'),
    ])
work_dir = './work_dirs\\vfnet_r50_fpn_ms-2x_coco'

02/19 15:13:04 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
02/19 15:13:04 - mmengine - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) RuntimeInfoHook                    
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
before_train:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_train_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(NORMAL      ) DistSamplerSeedHook                
 -------------------- 
before_train_iter:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_train_iter:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
after_train_epoch:
(NORMAL      ) IterTimerHook                      
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_val:
(VERY_HIGH   ) RuntimeInfoHook                    
 -------------------- 
before_val_epoch:
(NORMAL      ) IterTimerHook                      
 -------------------- 
before_val_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_val_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) DetVisualizationHook               
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_val_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
after_val:
(VERY_HIGH   ) RuntimeInfoHook                    
 -------------------- 
after_train:
(VERY_HIGH   ) RuntimeInfoHook                    
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_test:
(VERY_HIGH   ) RuntimeInfoHook                    
 -------------------- 
before_test_epoch:
(NORMAL      ) IterTimerHook                      
 -------------------- 
before_test_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_test_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) DetVisualizationHook               
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test:
(VERY_HIGH   ) RuntimeInfoHook                    
 -------------------- 
after_run:
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
loading annotations into memory...
Done (t=0.13s)
creating index...
index created!
Traceback (most recent call last):
  File "E:\mmdetection\mmdetection\tools\train.py", line 121, in <module>
    main()
  File "E:\mmdetection\mmdetection\tools\train.py", line 117, in main
    runner.train()
  File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\runner\runner.py", line 1728, in train
    self._train_loop = self.build_train_loop(
  File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\runner\runner.py", line 1520, in build_train_loop
    loop = LOOPS.build(
  File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\registry\registry.py", line 570, in build
    return self.build_func(cfg, *args, **kwargs, registry=self)
  File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\registry\build_functions.py", line 123, in build_from_cfg
    obj = obj_cls(**args)  # type: ignore
  File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\runner\loops.py", line 46, in __init__
    super().__init__(runner, dataloader)
  File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\runner\base_loop.py", line 26, in __init__
    self.dataloader = runner.build_dataloader(
  File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\runner\runner.py", line 1370, in build_dataloader
    dataset = DATASETS.build(dataset_cfg)
  File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\registry\registry.py", line 570, in build
    return self.build_func(cfg, *args, **kwargs, registry=self)
  File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\registry\build_functions.py", line 123, in build_from_cfg
    obj = obj_cls(**args)  # type: ignore
  File "E:\mmdetection\mmdetection\mmdet\datasets\base_det_dataset.py", line 51, in __init__
    super().__init__(*args, **kwargs)
  File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\dataset\base_dataset.py", line 247, in __init__
    self.full_init()
  File "E:\mmdetection\mmdetection\mmdet\datasets\base_det_dataset.py", line 89, in full_init
    self.data_bytes, self.data_address = self._serialize_data()
  File "E:\Anaconda\envs\openmmlab_330\lib\site-packages\mmengine\dataset\base_dataset.py", line 768, in _serialize_data
    data_bytes = np.concatenate(data_list)
  File "<__array_function__ internals>", line 200, in concatenate
ValueError: need at least one array to concatenate

进程已结束，退出代码为 1

Bug fix
If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated!