Structures — MMSegmentation 1.2.2 documentation (original) (raw)

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.

Semantic Segmentation Data SegDataSample¶

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')

Customize New Property in SegDataSample¶

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.