When training svtr，use dataset ：Each image sample should have one text annotation only，get ：Each image sample should have one text annotation only

[Dark1Forest]

this is my config：

_base_ = [
    '_base_svtr-tiny.py',
    '../_base_/schedules/schedule_adam_base.py',
]

train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=20, val_interval=1)

optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(
        type='AdamW',
        lr=5 / (10 ** 4) * 2048 / 2048,
        betas=(0.9, 0.99),
        eps=8e-8,
        weight_decay=0.05))

param_scheduler = [
    dict(
        type='LinearLR',
        start_factor=0.5,
        end_factor=1.,
        end=2,
        verbose=False,
        convert_to_iter_based=True),
    dict(
        type='CosineAnnealingLR',
        T_max=19,
        begin=2,
        end=20,
        verbose=False,
        convert_to_iter_based=True),
]

wildreceipt_openset_train = [
    dict(
        type='WildReceiptDataset',
        metainfo=dict(category=[
            dict(id=0, name='relic_entry'),
            dict(id=1, name='relic_entry_main'),
            dict(id=2, name='relic_level'),
            dict(id=3, name='relic_parts'),
            dict(id=3, name='relic_suit')
        ]),
        ann_file='/data/dataset/mmdetection/HonkaiKIELabel/result_w.json',
        # pipeline=train_pipeline
        pipeline=None,
    )
]

wildreceipt_openset_test = [
    dict(
        type='WildReceiptDataset',
        metainfo=dict(category=[
            dict(id=0, name='relic_entry'),
            dict(id=1, name='relic_entry_main'),
            dict(id=2, name='relic_level'),
            dict(id=3, name='relic_parts'),
            dict(id=3, name='relic_suit')
        ]),
        ann_file='/data/dataset/mmdetection/HonkaiKIELabel/result_w.json',
        test_mode=True,
        # pipeline=test_pipeline
        pipeline=None,
    )
]

train_dataloader = dict(
    batch_size=512,
    num_workers=24,
    persistent_workers=True,
    pin_memory=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type='ConcatDataset',
        datasets=wildreceipt_openset_train,
        # pipeline=train_pipeline
        pipeline=_base_.test_pipeline
    )
)

val_dataloader = dict(
    batch_size=128,
    num_workers=8,
    persistent_workers=True,
    pin_memory=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='ConcatDataset',
        datasets=wildreceipt_openset_test,
        pipeline=_base_.test_pipeline
    )
)
test_dataloader = val_dataloader

Error：

Traceback (most recent call last):
  File "/mnt/project/mmocr/tools/train.py", line 114, in <module>
    main()
  File "/mnt/project/mmocr/tools/train.py", line 110, in main
    runner.train()
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/mmengine/runner/runner.py", line 1777, in train
    model = self.train_loop.run()  # type: ignore
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/mmengine/runner/loops.py", line 96, in run
    self.run_epoch()
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/mmengine/runner/loops.py", line 111, in run_epoch
    for idx, data_batch in enumerate(self.dataloader):
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 628, in __next__
    data = self._next_data()
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1333, in _next_data
    return self._process_data(data)
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1359, in _process_data
    data.reraise()
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/torch/_utils.py", line 543, in reraise
    raise exception
AssertionError: Caught AssertionError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 302, in _worker_loop
    data = fetcher.fetch(index)
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 58, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 58, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/mmengine/dataset/dataset_wrapper.py", line 171, in __getitem__
    return self.datasets[dataset_idx][sample_idx]
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py", line 410, in __getitem__
    data = self.prepare_data(idx)
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py", line 793, in prepare_data
    return self.pipeline(data_info)
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py", line 60, in __call__
    data = t(data)
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/mmcv/transforms/base.py", line 12, in __call__
    return self.transform(results)
  File "/data/conda/envs/mmocr/lib/python3.10/site-packages/mmocr/datasets/transforms/formatting.py", line 202, in transform
    assert len(
AssertionError: Each image sample should have one text annotation only