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Showing content from https://mmsegmentation.readthedocs.io/en/latest/advanced_guides/structures.html below:

Structures — MMSegmentation 1.2.2 documentation

To unify input and output interfaces between different models and modules, OpenMMLab 2.0 MMEngine defines an abstract data structure, it has implemented basic functions of Create, Read, Update, Delete, supported data transferring among different types of devices and tensor-like or dictionary-like operations such as .cpu(), .cuda(), .get() and .detach(). More details can be found here.

MMSegmentation also follows this interface protocol and defines SegDataSample which is used to encapsulate the data of semantic segmentation task.

SegDataSample includes three main fields gt_sem_seg, pred_sem_seg and seg_logits, which are used to store the annotation information and prediction results respectively.

The following sample code demonstrates the use of SegDataSample.

import torch
from mmengine.structures import PixelData
from mmseg.structures import SegDataSample

img_meta = dict(img_shape=(4, 4, 3),
                 pad_shape=(4, 4, 3))
data_sample = SegDataSample()
# defining gt_segmentations for encapsulate the ground truth data
gt_segmentations = PixelData(metainfo=img_meta)
gt_segmentations.data = torch.randint(0, 2, (1, 4, 4))

# add and process property in SegDataSample
data_sample.gt_sem_seg = gt_segmentations
assert 'gt_sem_seg' in data_sample
assert 'sem_seg' in data_sample.gt_sem_seg
assert 'img_shape' in data_sample.gt_sem_seg.metainfo_keys()
print(data_sample.gt_sem_seg.shape)
'''
(4, 4)
'''
print(data_sample)
'''
<SegDataSample(

    META INFORMATION

    DATA FIELDS
    gt_sem_seg: <PixelData(

            META INFORMATION
            img_shape: (4, 4, 3)
            pad_shape: (4, 4, 3)

            DATA FIELDS
            data: tensor([[[1, 1, 1, 0],
                         [1, 0, 1, 1],
                         [1, 1, 1, 1],
                         [0, 1, 0, 1]]])
        ) at 0x1c2b4156460>
) at 0x1c2aae44d60>
'''

# delete and change property in SegDataSample
data_sample = SegDataSample()
gt_segmentations = PixelData(metainfo=img_meta)
gt_segmentations.data = torch.randint(0, 2, (1, 4, 4))
data_sample.gt_sem_seg = gt_segmentations
data_sample.gt_sem_seg.set_metainfo(dict(img_shape=(4,4,9), pad_shape=(4,4,9)))
del data_sample.gt_sem_seg.img_shape

# Tensor-like operations
data_sample = SegDataSample()
gt_segmentations = PixelData(metainfo=img_meta)
gt_segmentations.data = torch.randint(0, 2, (1, 4, 4))
cuda_gt_segmentations = gt_segmentations.cuda()
cuda_gt_segmentations = gt_segmentations.to('cuda:0')
cpu_gt_segmentations = cuda_gt_segmentations.cpu()
cpu_gt_segmentations = cuda_gt_segmentations.to('cpu')

If you want to customize new property in SegDataSample, you may follow SegDataSample below:

class SegDataSample(BaseDataElement):
    ...

    @property
    def xxx_property(self) -> xxxData:
        return self._xxx_property

    @xxx_property.setter
    def xxx_property(self, value: xxxData) -> None:
        self.set_field(value, '_xxx_property', dtype=xxxData)

    @xxx_property.deleter
    def xxx_property(self) -> None:
        del self._xxx_property

Then a new property would be added to SegDataSample.

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