Got an error when I was trying to train swin-t on my customized data:list index out of range

I customized my own datasets following the tutorial but got the following error when I tied to train it.

2022-04-15 10:58:50,949 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 2080 Ti
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 10.2, V10.2.89
GCC: gcc (Ubuntu 5.3.1-14ubuntu2) 5.3.1 20160413
PyTorch: 1.6.0
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 v1.5.0 (Git Hash e2ac1fac44c5078ca927cb9b90e1b3066a0b2ed0)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.2
  - 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 7.6.5
  - 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-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, USE_STATIC_DISPATCH=OFF, 

TorchVision: 0.7.0
OpenCV: 4.5.5
MMCV: 1.4.7
MMCV Compiler: GCC 5.3
MMCV CUDA Compiler: 10.2
MMDetection: 2.23.0+
------------------------------------------------------------

2022-04-15 10:58:51,950 - mmdet - INFO - Distributed training: False
2022-04-15 10:58:52,998 - mmdet - INFO - Config:
model = dict(
    type='MaskRCNN',
    backbone=dict(
        type='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=(0, 1, 2, 3),
        with_cp=False,
        convert_weights=True,
        init_cfg=dict(
            type='Pretrained',
            checkpoint=
            'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'
        )),
    neck=dict(
        type='FPN',
        in_channels=[96, 192, 384, 768],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    roi_head=dict(
        type='StandardRoIHead',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='Shared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=13,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0.0, 0.0, 0.0, 0.0],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=False,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
        mask_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        mask_head=dict(
            type='FCNMaskHead',
            num_convs=4,
            in_channels=256,
            conv_out_channels=256,
            num_classes=13,
            loss_mask=dict(
                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            mask_size=28,
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        rpn=dict(
            nms_pre=1000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100,
            mask_thr_binary=0.5)))
dataset_type = 'COCODataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type='CocoDataset',
        ann_file=
        '/home/nichang/Documents/Lab/mmdetection-master/configs/Caries_detection/data/train.json',
        img_prefix=
        '/home/nichang/Documents/Lab/mmdetection-master/configs/Caries_detection/data/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(
                type='Collect',
                keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
        ],
        classes=('Canine rel', 'Molar rel', 'Residual root', 'Residual crown',
                 'Caries', 'Demineral', 'Peri-disease', 'Calculus', 'Stain',
                 'Wedge', 'Deposit', 'Bad image', 'Overjet')),
    val=dict(
        type='CocoDataset',
        ann_file=
        '/home/nichang/Documents/Lab/mmdetection-master/configs/Caries_detection/data/val.json',
        img_prefix=
        '/home/nichang/Documents/Lab/mmdetection-master/configs/Caries_detection/data/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        classes=('Canine rel', 'Molar rel', 'Residual root', 'Residual crown',
                 'Caries', 'Demineral', 'Peri-disease', 'Calculus', 'Stain',
                 'Wedge', 'Deposit', 'Bad image', 'Overjet')),
    test=dict(
        type='CocoDataset',
        ann_file=
        '/home/nichang/Documents/Lab/mmdetection-master/configs/Caries_detection/data/test.json',
        img_prefix=
        '/home/nichang/Documents/Lab/mmdetection-master/configs/Caries_detection/data/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        classes=('Canine rel', 'Molar rel', 'Residual root', 'Residual crown',
                 'Caries', 'Demineral', 'Peri-disease', 'Calculus', 'Stain',
                 'Wedge', 'Deposit', 'Bad image', 'Overjet')))
evaluation = dict(metric=['bbox', 'segm'])
optimizer = dict(
    type='AdamW',
    lr=0.0001,
    betas=(0.9, 0.999),
    weight_decay=0.05,
    paramwise_cfg=dict(
        custom_keys=dict(
            absolute_pos_embed=dict(decay_mult=0.0),
            relative_position_bias_table=dict(decay_mult=0.0),
            norm=dict(decay_mult=0.0))))
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=1000,
    warmup_ratio=0.001,
    step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'checkpoints/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco_20210903_104808-b92c91f1.pth'
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'
classes = ('Canine rel', 'Molar rel', 'Residual root', 'Residual crown',
           'Caries', 'Demineral', 'Peri-disease', 'Calculus', 'Stain', 'Wedge',
           'Deposit', 'Bad image', 'Overjet')
work_dir = './work_dirs/mask_swin_transformer'
auto_resume = False
gpu_ids = [0]

2022-04-15 10:58:52,998 - mmdet - INFO - Set random seed to 2132726593, deterministic: False
2022-04-15 10:58:53,283 - mmdet - INFO - load checkpoint from http path: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth
2022-04-15 10:58:53,393 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2022-04-15 10:58:53,407 - mmdet - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01}
2022-04-15 10:58:53,411 - mmdet - INFO - initialize Shared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'distribution': 'uniform', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]
loading annotations into memory...
Done (t=0.02s)
creating index...
index created!
fatal: not a git repository (or any of the parent directories): .git
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
2022-04-15 10:58:54,806 - mmdet - INFO - load checkpoint from local path: checkpoints/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco_20210903_104808-b92c91f1.pth
2022-04-15 10:58:54,941 - mmdet - WARNING - The model and loaded state dict do not match exactly

size mismatch for roi_head.bbox_head.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([14, 1024]).
size mismatch for roi_head.bbox_head.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([14]).
size mismatch for roi_head.bbox_head.fc_reg.weight: copying a param with shape torch.Size([320, 1024]) from checkpoint, the shape in current model is torch.Size([52, 1024]).
size mismatch for roi_head.bbox_head.fc_reg.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([52]).
size mismatch for roi_head.mask_head.conv_logits.weight: copying a param with shape torch.Size([80, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([13, 256, 1, 1]).
size mismatch for roi_head.mask_head.conv_logits.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([13]).
unexpected key in source state_dict: backbone.stages.2.blocks.6.norm1.weight, backbone.stages.2.blocks.6.norm1.bias, backbone.stages.2.blocks.6.attn.w_msa.relative_position_bias_table, backbone.stages.2.blocks.6.attn.w_msa.relative_position_index, backbone.stages.2.blocks.6.attn.w_msa.qkv.weight, backbone.stages.2.blocks.6.attn.w_msa.qkv.bias, backbone.stages.2.blocks.6.attn.w_msa.proj.weight, backbone.stages.2.blocks.6.attn.w_msa.proj.bias, backbone.stages.2.blocks.6.norm2.weight, backbone.stages.2.blocks.6.norm2.bias, backbone.stages.2.blocks.6.ffn.layers.0.0.weight, backbone.stages.2.blocks.6.ffn.layers.0.0.bias, backbone.stages.2.blocks.6.ffn.layers.1.weight, backbone.stages.2.blocks.6.ffn.layers.1.bias, backbone.stages.2.blocks.7.norm1.weight, backbone.stages.2.blocks.7.norm1.bias, backbone.stages.2.blocks.7.attn.w_msa.relative_position_bias_table, backbone.stages.2.blocks.7.attn.w_msa.relative_position_index, backbone.stages.2.blocks.7.attn.w_msa.qkv.weight, backbone.stages.2.blocks.7.attn.w_msa.qkv.bias, backbone.stages.2.blocks.7.attn.w_msa.proj.weight, backbone.stages.2.blocks.7.attn.w_msa.proj.bias, backbone.stages.2.blocks.7.norm2.weight, backbone.stages.2.blocks.7.norm2.bias, backbone.stages.2.blocks.7.ffn.layers.0.0.weight, backbone.stages.2.blocks.7.ffn.layers.0.0.bias, backbone.stages.2.blocks.7.ffn.layers.1.weight, backbone.stages.2.blocks.7.ffn.layers.1.bias, backbone.stages.2.blocks.8.norm1.weight, backbone.stages.2.blocks.8.norm1.bias, backbone.stages.2.blocks.8.attn.w_msa.relative_position_bias_table, backbone.stages.2.blocks.8.attn.w_msa.relative_position_index, backbone.stages.2.blocks.8.attn.w_msa.qkv.weight, backbone.stages.2.blocks.8.attn.w_msa.qkv.bias, backbone.stages.2.blocks.8.attn.w_msa.proj.weight, backbone.stages.2.blocks.8.attn.w_msa.proj.bias, backbone.stages.2.blocks.8.norm2.weight, backbone.stages.2.blocks.8.norm2.bias, backbone.stages.2.blocks.8.ffn.layers.0.0.weight, backbone.stages.2.blocks.8.ffn.layers.0.0.bias, backbone.stages.2.blocks.8.ffn.layers.1.weight, backbone.stages.2.blocks.8.ffn.layers.1.bias, backbone.stages.2.blocks.9.norm1.weight, backbone.stages.2.blocks.9.norm1.bias, backbone.stages.2.blocks.9.attn.w_msa.relative_position_bias_table, backbone.stages.2.blocks.9.attn.w_msa.relative_position_index, backbone.stages.2.blocks.9.attn.w_msa.qkv.weight, backbone.stages.2.blocks.9.attn.w_msa.qkv.bias, backbone.stages.2.blocks.9.attn.w_msa.proj.weight, backbone.stages.2.blocks.9.attn.w_msa.proj.bias, backbone.stages.2.blocks.9.norm2.weight, backbone.stages.2.blocks.9.norm2.bias, backbone.stages.2.blocks.9.ffn.layers.0.0.weight, backbone.stages.2.blocks.9.ffn.layers.0.0.bias, backbone.stages.2.blocks.9.ffn.layers.1.weight, backbone.stages.2.blocks.9.ffn.layers.1.bias, backbone.stages.2.blocks.10.norm1.weight, backbone.stages.2.blocks.10.norm1.bias, backbone.stages.2.blocks.10.attn.w_msa.relative_position_bias_table, backbone.stages.2.blocks.10.attn.w_msa.relative_position_index, backbone.stages.2.blocks.10.attn.w_msa.qkv.weight, backbone.stages.2.blocks.10.attn.w_msa.qkv.bias, backbone.stages.2.blocks.10.attn.w_msa.proj.weight, backbone.stages.2.blocks.10.attn.w_msa.proj.bias, backbone.stages.2.blocks.10.norm2.weight, backbone.stages.2.blocks.10.norm2.bias, backbone.stages.2.blocks.10.ffn.layers.0.0.weight, backbone.stages.2.blocks.10.ffn.layers.0.0.bias, backbone.stages.2.blocks.10.ffn.layers.1.weight, backbone.stages.2.blocks.10.ffn.layers.1.bias, backbone.stages.2.blocks.11.norm1.weight, backbone.stages.2.blocks.11.norm1.bias, backbone.stages.2.blocks.11.attn.w_msa.relative_position_bias_table, backbone.stages.2.blocks.11.attn.w_msa.relative_position_index, backbone.stages.2.blocks.11.attn.w_msa.qkv.weight, backbone.stages.2.blocks.11.attn.w_msa.qkv.bias, backbone.stages.2.blocks.11.attn.w_msa.proj.weight, backbone.stages.2.blocks.11.attn.w_msa.proj.bias, 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2022-04-15 10:58:54,949 - mmdet - INFO - Start running, host: nichang@nichang-System-Product-Name, work_dir: /home/nichang/Documents/Lab/mmdetection-master/work_dirs/mask_swin_transformer
2022-04-15 10:58:54,949 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) StepLrUpdaterHook                  
(NORMAL      ) CheckpointHook                     
(LOW         ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_epoch:
(VERY_HIGH   ) StepLrUpdaterHook                  
(NORMAL      ) NumClassCheckHook                  
(LOW         ) IterTimerHook                      
(LOW         ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_iter:
(VERY_HIGH   ) StepLrUpdaterHook                  
(LOW         ) IterTimerHook                      
(LOW         ) EvalHook                           
 -------------------- 
after_train_iter:
(ABOVE_NORMAL) OptimizerHook                      
(NORMAL      ) CheckpointHook                     
(LOW         ) IterTimerHook                      
(LOW         ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
after_train_epoch:
(NORMAL      ) CheckpointHook                     
(LOW         ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_val_epoch:
(NORMAL      ) NumClassCheckHook                  
(LOW         ) IterTimerHook                      
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_epoch:
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
after_run:
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
2022-04-15 10:58:54,949 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs
2022-04-15 10:58:54,950 - mmdet - INFO - Checkpoints will be saved to /home/nichang/Documents/Lab/mmdetection-master/work_dirs/mask_swin_transformer by HardDiskBackend.
Traceback (most recent call last):
  File "tools/train.py", line 220, in <module>
    main()
  File "tools/train.py", line 216, in main
    meta=meta)
  File "/home/nichang/Documents/Lab/mmdetection-master/mmdet/apis/train.py", line 208, in train_detector
    runner.run(data_loaders, cfg.workflow)
  File "/home/nichang/mmcv/mmcv/runner/epoch_based_runner.py", line 127, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/home/nichang/mmcv/mmcv/runner/epoch_based_runner.py", line 47, in train
    for i, data_batch in enumerate(self.data_loader):
  File "/home/nichang/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 363, in __next__
    data = self._next_data()
  File "/home/nichang/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 989, in _next_data
    return self._process_data(data)
  File "/home/nichang/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1014, in _process_data
    data.reraise()
  File "/home/nichang/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/_utils.py", line 395, in reraise
    raise self.exc_type(msg)
IndexError: Caught IndexError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/nichang/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 185, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/nichang/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/nichang/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/nichang/Documents/Lab/mmdetection-master/mmdet/datasets/custom.py", line 218, in __getitem__
    data = self.prepare_train_img(idx)
  File "/home/nichang/Documents/Lab/mmdetection-master/mmdet/datasets/custom.py", line 241, in prepare_train_img
    return self.pipeline(results)
  File "/home/nichang/Documents/Lab/mmdetection-master/mmdet/datasets/pipelines/compose.py", line 41, in __call__
    data = t(data)
  File "/home/nichang/Documents/Lab/mmdetection-master/mmdet/datasets/pipelines/loading.py", line 399, in __call__
    results = self._load_masks(results)
  File "/home/nichang/Documents/Lab/mmdetection-master/mmdet/datasets/pipelines/loading.py", line 351, in _load_masks
    [self._poly2mask(mask, h, w) for mask in gt_masks], h, w)
  File "/home/nichang/Documents/Lab/mmdetection-master/mmdet/datasets/pipelines/loading.py", line 351, in <listcomp>
    [self._poly2mask(mask, h, w) for mask in gt_masks], h, w)
  File "/home/nichang/Documents/Lab/mmdetection-master/mmdet/datasets/pipelines/loading.py", line 307, in _poly2mask
    rles = maskUtils.frPyObjects(mask_ann, img_h, img_w)
  File "pycocotools/_mask.pyx", line 292, in pycocotools._mask.frPyObjects
IndexError: list index out of range

My customized config is as follows:

# The new config inherits a base config to highlight the necessary modification
_base_ = 'configs/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py'

# We also need to change the num_classes in head to match the dataset's annotation
model = dict(
    roi_head=dict(
        bbox_head=dict(num_classes=13),
        mask_head=dict(num_classes=13)))

# Modify dataset related settings
dataset_type = 'COCODataset'
classes = ('Canine rel','Molar rel', 'Residual root', 'Residual crown', 'Caries', 'Demineral', 'Peri-disease','Calculus','Stain','Wedge',
           'Deposit','Bad image','Overjet')
data = dict(
    train=dict(
        img_prefix='/home/nichang/Documents/Lab/mmdetection-master/configs/Caries_detection/data/',
        classes=classes,
        ann_file='/home/nichang/Documents/Lab/mmdetection-master/configs/Caries_detection/data/train.json'),
    val=dict(
        img_prefix='/home/nichang/Documents/Lab/mmdetection-master/configs/Caries_detection/data/',
        classes=classes,
        ann_file='/home/nichang/Documents/Lab/mmdetection-master/configs/Caries_detection/data/val.json'),
    test=dict(
        img_prefix='/home/nichang/Documents/Lab/mmdetection-master/configs/Caries_detection/data/',
        classes=classes,
        ann_file='/home/nichang/Documents/Lab/mmdetection-master/configs/Caries_detection/data/test.json'))

# We can use the pre-trained Mask RCNN model to obtain higher performance
load_from = 'checkpoints/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco_20210903_104808-b92c91f1.pth'

I can’t figure out what went wrong, could someone please help?

[abysee]

@abysee how we can create dataset like it? i’m student .can u help me

The owner of the repository had notified the data format and gave an example code for format transforming. You could write a similar code to transfer your own data. https://github.com/open-mmlab/mmdetection/blob/master/docs/en/2_new_data_model.md

[abysee]

Mask rcnn would use the segmentation information so I wrote a parser to fill segmentation with bbox’s information. For example, "bbox":[x1,y1,x2,y2] then segmentation should be "segmentation": [[x1,y1, x2,y1, x2, y2, x2, y1]]'. Or maybe you could try using other backbone that doesn’t require segmentation.

@abysee how did you solve this issue? I am training an object detector with a single class. My JSON looks like this (sample attached):