# Copyright (c) OpenMMLab. All rights reserved.
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 model 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
>>> 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 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]