| |
| from typing import List, Optional |
|
|
| from mmengine.structures import BaseDataElement, InstanceData, PixelData |
|
|
|
|
| class DetDataSample(BaseDataElement): |
| """A data structure interface of MMDetection. They are used as interfaces |
| between different components. |
| |
| The attributes in ``DetDataSample`` are divided into several parts: |
| |
| - ``proposals``(InstanceData): Region proposals used in two-stage |
| detectors. |
| - ``gt_instances``(InstanceData): Ground truth of instance annotations. |
| - ``pred_instances``(InstanceData): Instances of detection predictions. |
| - ``pred_track_instances``(InstanceData): Instances of tracking |
| predictions. |
| - ``ignored_instances``(InstanceData): Instances to be ignored during |
| training/testing. |
| - ``gt_panoptic_seg``(PixelData): Ground truth of panoptic |
| segmentation. |
| - ``pred_panoptic_seg``(PixelData): Prediction of panoptic |
| segmentation. |
| - ``gt_sem_seg``(PixelData): Ground truth of semantic segmentation. |
| - ``pred_sem_seg``(PixelData): Prediction of semantic segmentation. |
| |
| Examples: |
| >>> import torch |
| >>> import numpy as np |
| >>> from mmengine.structures import InstanceData |
| >>> from mmdet.structures import DetDataSample |
| |
| >>> data_sample = DetDataSample() |
| >>> img_meta = dict(img_shape=(800, 1196), |
| ... pad_shape=(800, 1216)) |
| >>> 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) |
| <DetDataSample( |
| |
| META INFORMATION |
| |
| DATA FIELDS |
| gt_instances: <InstanceData( |
| |
| META INFORMATION |
| pad_shape: (800, 1216) |
| img_shape: (800, 1196) |
| |
| 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 = InstanceData(metainfo=img_meta) |
| >>> pred_instances.bboxes = torch.rand((5, 4)) |
| >>> pred_instances.scores = torch.rand((5,)) |
| >>> data_sample = DetDataSample(pred_instances=pred_instances) |
| >>> assert 'pred_instances' in data_sample |
| |
| >>> pred_track_instances = InstanceData(metainfo=img_meta) |
| >>> pred_track_instances.bboxes = torch.rand((5, 4)) |
| >>> pred_track_instances.scores = torch.rand((5,)) |
| >>> data_sample = DetDataSample( |
| ... pred_track_instances=pred_track_instances) |
| >>> assert 'pred_track_instances' in data_sample |
| |
| >>> data_sample = DetDataSample() |
| >>> 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 |
| |
| >>> data_sample = DetDataSample() |
| >>> gt_panoptic_seg_data = dict(panoptic_seg=torch.rand(2, 4)) |
| >>> gt_panoptic_seg = PixelData(**gt_panoptic_seg_data) |
| >>> data_sample.gt_panoptic_seg = gt_panoptic_seg |
| >>> print(data_sample) |
| <DetDataSample( |
| |
| META INFORMATION |
| |
| DATA FIELDS |
| _gt_panoptic_seg: <BaseDataElement( |
| |
| META INFORMATION |
| |
| DATA FIELDS |
| panoptic_seg: tensor([[0.7586, 0.1262, 0.2892, 0.9341], |
| [0.3200, 0.7448, 0.1052, 0.5371]]) |
| ) at 0x7f66c2bb7730> |
| gt_panoptic_seg: <BaseDataElement( |
| |
| META INFORMATION |
| |
| DATA FIELDS |
| panoptic_seg: tensor([[0.7586, 0.1262, 0.2892, 0.9341], |
| [0.3200, 0.7448, 0.1052, 0.5371]]) |
| ) at 0x7f66c2bb7730> |
| ) at 0x7f66c2bb7280> |
| >>> data_sample = DetDataSample() |
| >>> gt_segm_seg_data = dict(segm_seg=torch.rand(2, 2, 2)) |
| >>> gt_segm_seg = PixelData(**gt_segm_seg_data) |
| >>> data_sample.gt_segm_seg = gt_segm_seg |
| >>> assert 'gt_segm_seg' in data_sample |
| >>> assert 'segm_seg' in data_sample.gt_segm_seg |
| """ |
|
|
| @property |
| def proposals(self) -> InstanceData: |
| return self._proposals |
|
|
| @proposals.setter |
| def proposals(self, value: InstanceData): |
| self.set_field(value, '_proposals', dtype=InstanceData) |
|
|
| @proposals.deleter |
| def proposals(self): |
| del self._proposals |
|
|
| @property |
| def gt_instances(self) -> InstanceData: |
| return self._gt_instances |
|
|
| @gt_instances.setter |
| def gt_instances(self, value: InstanceData): |
| self.set_field(value, '_gt_instances', dtype=InstanceData) |
|
|
| @gt_instances.deleter |
| def gt_instances(self): |
| del self._gt_instances |
|
|
| @property |
| def pred_instances(self) -> InstanceData: |
| return self._pred_instances |
|
|
| @pred_instances.setter |
| def pred_instances(self, value: InstanceData): |
| self.set_field(value, '_pred_instances', dtype=InstanceData) |
|
|
| @pred_instances.deleter |
| def pred_instances(self): |
| del self._pred_instances |
|
|
| |
| |
| |
| @property |
| def pred_track_instances(self) -> InstanceData: |
| return self._pred_track_instances |
|
|
| @pred_track_instances.setter |
| def pred_track_instances(self, value: InstanceData): |
| self.set_field(value, '_pred_track_instances', dtype=InstanceData) |
|
|
| @pred_track_instances.deleter |
| def pred_track_instances(self): |
| del self._pred_track_instances |
|
|
| @property |
| def ignored_instances(self) -> InstanceData: |
| return self._ignored_instances |
|
|
| @ignored_instances.setter |
| def ignored_instances(self, value: InstanceData): |
| self.set_field(value, '_ignored_instances', dtype=InstanceData) |
|
|
| @ignored_instances.deleter |
| def ignored_instances(self): |
| del self._ignored_instances |
|
|
| @property |
| def gt_panoptic_seg(self) -> PixelData: |
| return self._gt_panoptic_seg |
|
|
| @gt_panoptic_seg.setter |
| def gt_panoptic_seg(self, value: PixelData): |
| self.set_field(value, '_gt_panoptic_seg', dtype=PixelData) |
|
|
| @gt_panoptic_seg.deleter |
| def gt_panoptic_seg(self): |
| del self._gt_panoptic_seg |
|
|
| @property |
| def pred_panoptic_seg(self) -> PixelData: |
| return self._pred_panoptic_seg |
|
|
| @pred_panoptic_seg.setter |
| def pred_panoptic_seg(self, value: PixelData): |
| self.set_field(value, '_pred_panoptic_seg', dtype=PixelData) |
|
|
| @pred_panoptic_seg.deleter |
| def pred_panoptic_seg(self): |
| del self._pred_panoptic_seg |
|
|
| @property |
| def gt_sem_seg(self) -> PixelData: |
| return self._gt_sem_seg |
|
|
| @gt_sem_seg.setter |
| def gt_sem_seg(self, value: PixelData): |
| self.set_field(value, '_gt_sem_seg', dtype=PixelData) |
|
|
| @gt_sem_seg.deleter |
| def gt_sem_seg(self): |
| del self._gt_sem_seg |
|
|
| @property |
| def pred_sem_seg(self) -> PixelData: |
| return self._pred_sem_seg |
|
|
| @pred_sem_seg.setter |
| def pred_sem_seg(self, value: PixelData): |
| self.set_field(value, '_pred_sem_seg', dtype=PixelData) |
|
|
| @pred_sem_seg.deleter |
| def pred_sem_seg(self): |
| del self._pred_sem_seg |
|
|
|
|
| SampleList = List[DetDataSample] |
| OptSampleList = Optional[SampleList] |
|
|
