[Doc] Add Data Structures and Elements (#2070)
* [WIP][Doc] Add Data Structures and Elements * fix * add * fix * add chinses doc * refactor * fix * fix typo * fix * fix * fix typo * Update docs/en/advanced_guides/structures.md * Update docs/en/advanced_guides/structures.md Co-authored-by: Miao Zheng <76149310+MeowZheng@users.noreply.github.com>pull/2187/head
MengzhangLI
GitHub
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# Structures
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To unify input and output interfaces between different models and modules, OpenMMLab 2.0 MMEngine defines an abstract data structure,
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it has implemented basic functions of `Create`, `Read`, `Update`, `Delete`, supported data transferring among different types of devices
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and tensor-like or dictionary-like operations such as `.cpu()`, `.cuda()`, `.get()` and `.detach()`.
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More details can be found [here](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/data_element.md).
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MMSegmentation also follows this interface protocol and defines `SegDataSample` which is used to encapsulate the data of semantic segmentation task.
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## Semantic Segmentation Data SegDataSample
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[SegDataSample](mmseg.structures.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.
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| Field | Type | Description |
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| -------------- | ------------------------- | ------------------------------------------ |
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| gt_sem_seg | [`PixelData`](#pixeldata) | Annotation information. |
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| pred_instances | [`PixelData`](#pixeldata) | The predicted result. |
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| seg_logits | [`PixelData`](#pixeldata) | The raw (non-normalized) predicted result. |
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The following sample code demonstrates the use of `SegDataSample`.
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```python
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import torch
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from mmengine.structures import PixelData
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from mmseg.structures import SegDataSample
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img_meta = dict(img_shape=(4, 4, 3),
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pad_shape=(4, 4, 3))
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data_sample = SegDataSample()
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# defining gt_segmentations for encapsulate the ground truth data
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gt_segmentations = PixelData(metainfo=img_meta)
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gt_segmentations.data = torch.randint(0, 2, (1, 4, 4))
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# add and process property in SegDataSample
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data_sample.gt_sem_seg = gt_segmentations
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assert 'gt_sem_seg' in data_sample
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assert 'sem_seg' in data_sample.gt_sem_seg
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assert 'img_shape' in data_sample.gt_sem_seg.metainfo_keys()
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print(data_sample.gt_sem_seg.shape)
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'''
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(4, 4)
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'''
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print(data_sample)
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'''
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<SegDataSample(
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META INFORMATION
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DATA FIELDS
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gt_sem_seg: <PixelData(
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META INFORMATION
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img_shape: (4, 4, 3)
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pad_shape: (4, 4, 3)
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DATA FIELDS
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data: tensor([[[1, 1, 1, 0],
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[1, 0, 1, 1],
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[1, 1, 1, 1],
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[0, 1, 0, 1]]])
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) at 0x1c2b4156460>
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) at 0x1c2aae44d60>
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'''
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# delete and change property in SegDataSample
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data_sample = SegDataSample()
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gt_segmentations = PixelData(metainfo=img_meta)
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gt_segmentations.data = torch.randint(0, 2, (1, 4, 4))
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data_sample.gt_sem_seg = gt_segmentations
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data_sample.gt_sem_seg.set_metainfo(dict(img_shape=(4,4,9), pad_shape=(4,4,9)))
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del data_sample.gt_sem_seg.img_shape
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# Tensor-like operations
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data_sample = SegDataSample()
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gt_segmentations = PixelData(metainfo=img_meta)
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gt_segmentations.data = torch.randint(0, 2, (1, 4, 4))
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cuda_gt_segmentations = gt_segmentations.cuda()
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cuda_gt_segmentations = gt_segmentations.to('cuda:0')
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cpu_gt_segmentations = cuda_gt_segmentations.cpu()
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cpu_gt_segmentations = cuda_gt_segmentations.to('cpu')
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```
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## Customize New Property in SegDataSample
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If you want to customize new property in `SegDataSample`, you may follow [SegDataSample](https://github.com/open-mmlab/mmsegmentation/blob/1.x/mmseg/structures/seg_data_sample.py) below:
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```python
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class SegDataSample(BaseDataElement):
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...
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@property
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def xxx_property(self) -> xxxData:
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return self._xxx_property
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@xxx_property.setter
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def xxx_property(self, value: xxxData) -> None:
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self.set_field(value, '_xxx_property', dtype=xxxData)
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@xxx_property.deleter
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def xxx_property(self) -> None:
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del self._xxx_property
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```
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Then a new property would be added to `SegDataSample`.
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@ -16,7 +16,7 @@ class SegDataSample(BaseDataElement):
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>>> import torch
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>>> import numpy as np
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>>> from mmengine.structures import PixelData
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>>> from mmseg.core import SegDataSample
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>>> from mmseg.structures import SegDataSample
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>>> data_sample = SegDataSample()
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>>> img_meta = dict(img_shape=(4, 4, 3),
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