[Bug] Use KIELocalVisualizer,I get an error

[Dark1Forest]

Prerequisite

• I have searched Issues and Discussions but cannot get the expected help.
• The bug has not been fixed in the latest version (0.x) or latest version (1.x).

Task

I'm using the official example scripts/configs for the officially supported tasks/models/datasets.

Branch

main branch https://github.com/open-mmlab/mmocr

Environment

Package Version

---

addict 2.4.0
asynctest 0.13.0
attrs 23.2.0
boto3 1.34.79
botocore 1.34.79
certifi 2024.2.2
charset-normalizer 3.3.2
clearml 1.15.0
click 8.1.7
codecov 2.1.13
colorama 0.4.6
contourpy 1.2.1
coverage 7.4.4
cycler 0.12.1
exceptiongroup 1.2.0
flake8 7.0.0
fonttools 4.51.0
furl 2.1.3
idna 3.6
imageio 2.34.0
imgaug 0.4.0
importlib_metadata 7.1.0
iniconfig 2.0.0
interrogate 1.5.0
isort 5.13.2
jmespath 1.0.1
jsonschema 4.21.1
jsonschema-specifications 2023.12.1
kiwisolver 1.4.5
kwarray 0.6.18
lanms_neo 1.0.2
lazy_loader 0.4
lmdb 1.4.1
markdown-it-py 3.0.0
matplotlib 3.8.4
mccabe 0.7.0
mdurl 0.1.2
mmcv 2.0.1
mmdet 3.1.0
mmengine 0.10.3
mmocr 1.0.1
networkx 3.3
numpy 1.26.4
opencv-python 4.9.0.80
orderedmultidict 1.0.1
packaging 24.0
parameterized 0.9.0
pathlib2 2.3.7.post1
pillow 10.3.0
pip 23.3.1
platformdirs 4.2.0
pluggy 1.4.0
psutil 5.9.8
py 1.11.0
pyclipper 1.3.0.post5
pycocotools 2.0.7
pycodestyle 2.11.1
pyflakes 3.2.0
Pygments 2.17.2
PyJWT 2.8.0
pyparsing 3.1.2
pytest 8.1.1
pytest-cov 5.0.0
pytest-runner 6.0.1
python-dateutil 2.9.0.post0
PyYAML 6.0.1
rapidfuzz 3.8.0
referencing 0.34.0
requests 2.31.0
rich 13.7.1
rpds-py 0.18.0
s3transfer 0.10.1
scikit-image 0.22.0
scipy 1.13.0
setuptools 68.2.2
shapely 2.0.3
six 1.16.0
tabulate 0.9.0
termcolor 2.4.0
terminaltables 3.1.10
tifffile 2024.2.12
toml 0.10.2
tomli 2.0.1
torch 1.13.1+cu117
torchaudio 0.13.1+cu117
torchvision 0.14.1+cu117
tqdm 4.66.2
typing_extensions 4.11.0
ubelt 1.3.5
urllib3 2.2.1
wheel 0.41.2
xdoctest 1.1.3
yapf 0.40.2
zipp 3.18.1

Reproduces the problem - code sample

_base_ = [
    '../_base_/default_runtime.py',
    # '../_base_/datasets/wildreceipt-openset.py',
    # '../_base_/schedules/schedule_adam_60e.py',
    # '_base_sdmgr_novisual.py',
    '_base_sdmgr_unet16.py',
]

node_num_classes = 5  # 4 classes: bg, key, value and other
edge_num_classes = 2  # edge connectivity
key_node_idx = 1
value_node_idx = 2
model = dict(
    backbone=dict(type='UNet', base_channels=16),
    roi_extractor=dict(
        type='mmdet.SingleRoIExtractor',
        roi_layer=dict(type='RoIAlign', output_size=7),
        featmap_strides=[1]),
    data_preprocessor=dict(
        type='ImgDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True,
        pad_size_divisor=32),
)

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadKIEAnnotations'),
    dict(type='Resize', scale=(512, 1024), keep_ratio=True),
    dict(type='PackKIEInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadKIEAnnotations'),
    dict(type='Resize', scale=(512, 1024), keep_ratio=True),
    dict(type='PackKIEInputs', meta_keys=('img_path', )),
]

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)

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)


optim_wrapper = dict(
    type='OptimWrapper', optimizer=dict(type='Adam', weight_decay=0.0001))
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=60, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
    dict(type='MultiStepLR', milestones=[40, 50], end=60),
]

train_dataloader = dict(
    batch_size=4,
    num_workers=1,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=wildreceipt_openset_train)
val_dataloader = dict(
    batch_size=1,
    num_workers=1,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=wildreceipt_openset_test)
test_dataloader = val_dataloader

# test_evaluator = val_evaluator
auto_scale_lr = dict(base_batch_size=4)


default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=100),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=5),
    # checkpoint=dict(interval=5, max_keep_ckpts=3, save_best='auto', type='CheckpointHook'),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    sync_buffer=dict(type='SyncBuffersHook'),
    visualization=dict(
        type='VisualizationHook',
        interval=5,
        enable=True,
        show=False,
        draw_gt=True,
        draw_pred=True,
    ),
)

clear_ml_init = dict(
    reuse_last_task_id=False,
    continue_last_task=False,
)
vis_backends = [
    dict(type="ClearMLVisBackend", init_kwargs=clear_ml_init)
]

visualizer = dict(
    type='KIELocalVisualizer',
    # type='TextSpottingLocalVisualizer',
    name='visualizer',
    vis_backends=vis_backends,
    is_openset=True,

    save_dir="/mnt/project/mmocr/work_dirs"
)

Reproduces the problem - command or script

CUDA_VISIBLE_DEVICES=0 python tools/train.py configs/kie/sdmgr/sdmgr_novisual_60e_wildreceipt-openset_honkai.py --amp

Reproduces the problem - error message

Traceback (most recent call last):
File "/mnt/project/mmocr/tools/train.py", line 114, in
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 102, in run
self.runner.val_loop.run()
File "/data/conda/envs/mmocr/lib/python3.10/site-packages/mmengine/runner/loops.py", line 371, in run
self.run_iter(idx, data_batch)
File "/data/conda/envs/mmocr/lib/python3.10/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/data/conda/envs/mmocr/lib/python3.10/site-packages/mmengine/runner/loops.py", line 394, in run_iter
self.runner.call_hook(
File "/data/conda/envs/mmocr/lib/python3.10/site-packages/mmengine/runner/runner.py", line 1841, in call_hook
raise TypeError(f'{e} in {hook}') from None
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first. in <mmocr.engine.hooks.visualization_hook.VisualizationHook object at 0x7f01f55f6080>

Additional information

No response