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KIEDataSample

class mmocr.structures.KIEDataSample(*, metainfo=None, **kwargs)[源代码]

A data structure interface of MMOCR. They are used as interfaces between different components.

The attributes in KIEDataSample are divided into two parts:

  • ``gt_instances``(InstanceData): Ground truth of instance annotations.

  • ``pred_instances``(InstanceData): Instances of model predictions.

实际案例

>>> import torch
>>> import numpy as np
>>> from mmengine.structures import InstanceData
>>> from mmocr.data import KIEDataSample
>>> # gt_instances
>>> data_sample = KIEDataSample()
>>> img_meta = dict(img_shape=(800, 1196, 3),
...                 pad_shape=(800, 1216, 3))
>>> gt_instances = InstanceData(metainfo=img_meta)
>>> gt_instances.bboxes = torch.rand((5, 4))
>>> gt_instances.labels = torch.rand((5,))
>>> data_sample.gt_instances = gt_instances
>>> assert 'img_shape' in data_sample.gt_instances.metainfo_keys()
>>> len(data_sample.gt_instances)
5
>>> print(data_sample)
<KIEDataSample(
    META INFORMATION
    DATA FIELDS
    gt_instances: <InstanceData(
            META INFORMATION
            pad_shape: (800, 1216, 3)
            img_shape: (800, 1196, 3)
            DATA FIELDS
            labels: tensor([0.8533, 0.1550, 0.5433, 0.7294, 0.5098])
            bboxes:
            tensor([[9.7725e-01, 5.8417e-01, 1.7269e-01, 6.5694e-01],
                    [1.7894e-01, 5.1780e-01, 7.0590e-01, 4.8589e-01],
                    [7.0392e-01, 6.6770e-01, 1.7520e-01, 1.4267e-01],
                    [2.2411e-01, 5.1962e-01, 9.6953e-01, 6.6994e-01],
                    [4.1338e-01, 2.1165e-01, 2.7239e-04, 6.8477e-01]])
        ) at 0x7f21fb1b9190>
) at 0x7f21fb1b9880>
>>> # pred_instances
>>> pred_instances = InstanceData(metainfo=img_meta)
>>> pred_instances.bboxes = torch.rand((5, 4))
>>> pred_instances.scores = torch.rand((5,))
>>> data_sample = KIEDataSample(pred_instances=pred_instances)
>>> assert 'pred_instances' in data_sample
>>> data_sample = KIEDataSample()
>>> gt_instances_data = dict(
...                        bboxes=torch.rand(2, 4),
...                        labels=torch.rand(2))
>>> gt_instances = InstanceData(**gt_instances_data)
>>> data_sample.gt_instances = gt_instances
>>> assert 'gt_instances' in data_sample
参数

metainfo (Optional[dict]) –

返回类型

None

property gt_instances: mmengine.structures.instance_data.InstanceData

groundtruth instances.

Type

InstanceData

property pred_instances: mmengine.structures.instance_data.InstanceData

prediction instances.

Type

InstanceData

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