Spaces:
Running
Running
File size: 3,839 Bytes
9bf4bd7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.structures import BaseDataElement, InstanceData
class TextDetDataSample(BaseDataElement):
"""A data structure interface of MMOCR. They are used as interfaces between
different components.
The attributes in ``TextDetDataSample`` are divided into two parts:
- ``gt_instances``(InstanceData): Ground truth of instance annotations.
- ``pred_instances``(InstanceData): Instances of model predictions.
Examples:
>>> import torch
>>> import numpy as np
>>> from mmengine.structures import InstanceData
>>> from mmocr.data import TextDetDataSample
>>> # gt_instances
>>> data_sample = TextDetDataSample()
>>> 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)
<TextDetDataSample(
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 = TextDetDataSample(pred_instances=pred_instances)
>>> assert 'pred_instances' in data_sample
>>> data_sample = TextDetDataSample()
>>> gt_instances_data = dict(
... bboxes=torch.rand(2, 4),
... labels=torch.rand(2),
... masks=np.random.rand(2, 2, 2))
>>> gt_instances = InstanceData(**gt_instances_data)
>>> data_sample.gt_instances = gt_instances
>>> assert 'gt_instances' in data_sample
>>> assert 'masks' in data_sample.gt_instances
"""
@property
def gt_instances(self) -> InstanceData:
"""InstanceData: groundtruth instances."""
return self._gt_instances
@gt_instances.setter
def gt_instances(self, value: InstanceData):
"""gt_instances setter."""
self.set_field(value, '_gt_instances', dtype=InstanceData)
@gt_instances.deleter
def gt_instances(self):
"""gt_instances deleter."""
del self._gt_instances
@property
def pred_instances(self) -> InstanceData:
"""InstanceData: prediction instances."""
return self._pred_instances
@pred_instances.setter
def pred_instances(self, value: InstanceData):
"""pred_instances setter."""
self.set_field(value, '_pred_instances', dtype=InstanceData)
@pred_instances.deleter
def pred_instances(self):
"""pred_instances deleter."""
del self._pred_instances
|