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from typing import List, Optional |
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from mmengine.structures import BaseDataElement, InstanceData, PixelData |
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class DetDataSample(BaseDataElement): |
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"""A data structure interface of MMDetection. They are used as interfaces |
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between different components. |
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The attributes in ``DetDataSample`` are divided into several parts: |
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- ``proposals``(InstanceData): Region proposals used in two-stage |
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detectors. |
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- ``gt_instances``(InstanceData): Ground truth of instance annotations. |
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- ``pred_instances``(InstanceData): Instances of model predictions. |
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- ``ignored_instances``(InstanceData): Instances to be ignored during |
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training/testing. |
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- ``gt_panoptic_seg``(PixelData): Ground truth of panoptic |
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segmentation. |
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- ``pred_panoptic_seg``(PixelData): Prediction of panoptic |
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segmentation. |
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- ``gt_sem_seg``(PixelData): Ground truth of semantic segmentation. |
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- ``pred_sem_seg``(PixelData): Prediction of semantic segmentation. |
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Examples: |
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>>> import torch |
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>>> import numpy as np |
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>>> from mmengine.structures import InstanceData |
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>>> from mmdet.structures import DetDataSample |
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>>> data_sample = DetDataSample() |
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>>> img_meta = dict(img_shape=(800, 1196), |
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... pad_shape=(800, 1216)) |
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>>> gt_instances = InstanceData(metainfo=img_meta) |
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>>> gt_instances.bboxes = torch.rand((5, 4)) |
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>>> gt_instances.labels = torch.rand((5,)) |
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>>> data_sample.gt_instances = gt_instances |
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>>> assert 'img_shape' in data_sample.gt_instances.metainfo_keys() |
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>>> len(data_sample.gt_instances) |
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5 |
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>>> print(data_sample) |
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<DetDataSample( |
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META INFORMATION |
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DATA FIELDS |
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gt_instances: <InstanceData( |
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META INFORMATION |
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pad_shape: (800, 1216) |
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img_shape: (800, 1196) |
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DATA FIELDS |
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labels: tensor([0.8533, 0.1550, 0.5433, 0.7294, 0.5098]) |
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bboxes: |
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tensor([[9.7725e-01, 5.8417e-01, 1.7269e-01, 6.5694e-01], |
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[1.7894e-01, 5.1780e-01, 7.0590e-01, 4.8589e-01], |
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[7.0392e-01, 6.6770e-01, 1.7520e-01, 1.4267e-01], |
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[2.2411e-01, 5.1962e-01, 9.6953e-01, 6.6994e-01], |
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[4.1338e-01, 2.1165e-01, 2.7239e-04, 6.8477e-01]]) |
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) at 0x7f21fb1b9190> |
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) at 0x7f21fb1b9880> |
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>>> pred_instances = InstanceData(metainfo=img_meta) |
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>>> pred_instances.bboxes = torch.rand((5, 4)) |
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>>> pred_instances.scores = torch.rand((5,)) |
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>>> data_sample = DetDataSample(pred_instances=pred_instances) |
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>>> assert 'pred_instances' in data_sample |
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>>> data_sample = DetDataSample() |
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>>> gt_instances_data = dict( |
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... bboxes=torch.rand(2, 4), |
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... labels=torch.rand(2), |
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... masks=np.random.rand(2, 2, 2)) |
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>>> gt_instances = InstanceData(**gt_instances_data) |
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>>> data_sample.gt_instances = gt_instances |
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>>> assert 'gt_instances' in data_sample |
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>>> assert 'masks' in data_sample.gt_instances |
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>>> data_sample = DetDataSample() |
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>>> gt_panoptic_seg_data = dict(panoptic_seg=torch.rand(2, 4)) |
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>>> gt_panoptic_seg = PixelData(**gt_panoptic_seg_data) |
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>>> data_sample.gt_panoptic_seg = gt_panoptic_seg |
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>>> print(data_sample) |
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<DetDataSample( |
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META INFORMATION |
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DATA FIELDS |
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_gt_panoptic_seg: <BaseDataElement( |
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META INFORMATION |
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DATA FIELDS |
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panoptic_seg: tensor([[0.7586, 0.1262, 0.2892, 0.9341], |
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[0.3200, 0.7448, 0.1052, 0.5371]]) |
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) at 0x7f66c2bb7730> |
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gt_panoptic_seg: <BaseDataElement( |
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META INFORMATION |
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DATA FIELDS |
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panoptic_seg: tensor([[0.7586, 0.1262, 0.2892, 0.9341], |
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[0.3200, 0.7448, 0.1052, 0.5371]]) |
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) at 0x7f66c2bb7730> |
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) at 0x7f66c2bb7280> |
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>>> data_sample = DetDataSample() |
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>>> gt_segm_seg_data = dict(segm_seg=torch.rand(2, 2, 2)) |
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>>> gt_segm_seg = PixelData(**gt_segm_seg_data) |
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>>> data_sample.gt_segm_seg = gt_segm_seg |
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>>> assert 'gt_segm_seg' in data_sample |
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>>> assert 'segm_seg' in data_sample.gt_segm_seg |
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""" |
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@property |
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def proposals(self) -> InstanceData: |
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return self._proposals |
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@proposals.setter |
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def proposals(self, value: InstanceData): |
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self.set_field(value, '_proposals', dtype=InstanceData) |
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@proposals.deleter |
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def proposals(self): |
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del self._proposals |
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@property |
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def gt_instances(self) -> InstanceData: |
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return self._gt_instances |
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@gt_instances.setter |
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def gt_instances(self, value: InstanceData): |
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self.set_field(value, '_gt_instances', dtype=InstanceData) |
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@gt_instances.deleter |
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def gt_instances(self): |
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del self._gt_instances |
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@property |
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def pred_instances(self) -> InstanceData: |
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return self._pred_instances |
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@pred_instances.setter |
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def pred_instances(self, value: InstanceData): |
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self.set_field(value, '_pred_instances', dtype=InstanceData) |
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@pred_instances.deleter |
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def pred_instances(self): |
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del self._pred_instances |
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@property |
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def ignored_instances(self) -> InstanceData: |
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return self._ignored_instances |
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@ignored_instances.setter |
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def ignored_instances(self, value: InstanceData): |
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self.set_field(value, '_ignored_instances', dtype=InstanceData) |
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@ignored_instances.deleter |
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def ignored_instances(self): |
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del self._ignored_instances |
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@property |
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def gt_panoptic_seg(self) -> PixelData: |
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return self._gt_panoptic_seg |
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@gt_panoptic_seg.setter |
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def gt_panoptic_seg(self, value: PixelData): |
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self.set_field(value, '_gt_panoptic_seg', dtype=PixelData) |
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@gt_panoptic_seg.deleter |
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def gt_panoptic_seg(self): |
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del self._gt_panoptic_seg |
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@property |
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def pred_panoptic_seg(self) -> PixelData: |
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return self._pred_panoptic_seg |
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@pred_panoptic_seg.setter |
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def pred_panoptic_seg(self, value: PixelData): |
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self.set_field(value, '_pred_panoptic_seg', dtype=PixelData) |
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@pred_panoptic_seg.deleter |
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def pred_panoptic_seg(self): |
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del self._pred_panoptic_seg |
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@property |
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def gt_sem_seg(self) -> PixelData: |
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return self._gt_sem_seg |
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@gt_sem_seg.setter |
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def gt_sem_seg(self, value: PixelData): |
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self.set_field(value, '_gt_sem_seg', dtype=PixelData) |
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@gt_sem_seg.deleter |
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def gt_sem_seg(self): |
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del self._gt_sem_seg |
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@property |
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def pred_sem_seg(self) -> PixelData: |
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return self._pred_sem_seg |
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@pred_sem_seg.setter |
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def pred_sem_seg(self, value: PixelData): |
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self.set_field(value, '_pred_sem_seg', dtype=PixelData) |
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@pred_sem_seg.deleter |
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def pred_sem_seg(self): |
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del self._pred_sem_seg |
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SampleList = List[DetDataSample] |
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OptSampleList = Optional[SampleList] |
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