115 lines
5.0 KiB
Markdown
115 lines
5.0 KiB
Markdown
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# Package structures changes
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This section is included if you are curious about what has changed between MMSeg 0.x and 1.x.
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<table>
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<tr>
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<td>MMSegmentation 0.x</td>
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<td>MMSegmentation 1.x</td>
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</tr>
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<tr>
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<td>mmseg.api</td>
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<td>mmseg.api</td>
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</tr>
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<tr>
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<td bgcolor=#fcf7f7>- mmseg.core</td>
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<td bgcolor=#ecf4eb>+ mmseg.engine</td>
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</tr>
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<tr>
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<td>mmseg.datasets</td>
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<td>mmseg.datasets</td>
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</tr>
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<tr>
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<td>mmseg.models</td>
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<td>mmseg.models</td>
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</tr>
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<tr>
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<td bgcolor=#fcf7f7>- mmseg.ops</td>
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<td bgcolor=#ecf4eb>+ mmseg.structure</td>
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</tr>
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<tr>
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<td>mmseg.utils</td>
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<td>mmseg.utils</td>
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</tr>
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<tr>
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<td></td>
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<td bgcolor=#ecf4eb>+ mmseg.evaluation</td>
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</tr>
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<tr>
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<td></td>
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<td bgcolor=#ecf4eb>+ mmseg.registry</td>
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<tr>
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</table>
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## Removed packages
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### `mmseg.core`
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In OpenMMLab 2.0, `core` package has been removed. `hooks` and `optimizers` of `core` are moved in `mmseg.engine`, and `evaluation` in `core` is mmseg.evaluation currently.
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## `mmseg.ops`
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`ops` package included `encoding` and `wrappers`, which are moved in `mmseg.models.utils`.
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## Added packages
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### `mmseg.engine`
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OpenMMLab 2.0 adds a new foundational library for training deep learning, MMEngine. It servers as the training engine of all OpenMMLab codebases.
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`engine` package of mmseg is some customized modules for semantic segmentation task, like `SegVisualizationHook` which works for visualizing segmentation mask.
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### `mmseg.structure`
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In OpenMMLab 2.0, we designed data structure for computer vision task, and in mmseg, we implements `SegDataSample` in `structure` package.
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### `mmseg.evaluation`
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We move all evaluation metric in `mmseg.evaluation`.
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### `mmseg.registry`
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We moved registry implementations for all kinds of modules in MMSegmentation in `mmseg.registry`.
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## Modified packages
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### `mmseg.apis`
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OpenMMLab 2.0 tries to support unified interface for multitasking of Computer Vision,
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and releases much stronger [`Runner`](https://github.com/open-mmlab/mmengine/blob/main/docs/en/design/runner.md),
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so MMSeg 1.x removed modules in `train.py` and `test.py` renamed `init_segmentor` to `init_model` and `inference_segmentor` to `inference_model`
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Here is the changes of `mmseg.apis`:
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| Function | Changes |
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| :-------------------: | :---------------------------------------------- |
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| `init_segmentor` | Renamed to `init_model` |
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| `inference_segmentor` | Rename to `inference_segmentor` |
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| `show_result_pyplot` | Implemented based on `SegLocalVisualizer` |
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| `train_model` | Removed, use `runner.train` to train. |
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| `multi_gpu_test` | Removed, use `runner.test` to test. |
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| `single_gpu_test` | Removed, use `runner.test` to test. |
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| `set_random_seed` | Removed, use `mmengine.runner.set_random_seed`. |
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| `init_random_seed` | Removed, use `mmengine.dist.sync_random_seed`. |
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### `mmseg.datasets`
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OpenMMLab 2.0 defines the `BaseDataset` to function and interface of dataset, and MMSegmentation 1.x also follow this protocol and defines the `BaseSegDataset` inherited from `BaseDataset`. MMCV 2.x collects general data transforms for multiple tasks e.g. classification, detection, segmentation, so MMSegmentation 1.x uses these data transforms and removes them from mmseg.datasets
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| Packages/Modules | Changes |
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| :-------------------: | :------------------------------------------------------------------------------------------ |
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| `mmseg.pipelines` | Renamed to `mmseg.transforms` |
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| `mmseg.sampler` | Move in `mmengine.dataset.sampler` |
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| `CustomDataset` | Renamed to `BaseDataset` and inherited from `BaseDataset` in MMEngine |
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| `DefaultFormatBundle` | Replaced with `PackSegInputs` |
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| `LoadImageFromFile` | Move in `mmcv.transforms.LoadImageFromFile` |
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| `LoadAnnotations` | Moved in `mmcv.transforms.LoadAnnotations` |
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| `Resize` | Moved in `mmcv.transforms` and split into `Resize`, `RandomResize` and `RandomChoiseResize` |
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| `RandomFlip` | Moved in `mmcv.transforms.RandomFlip` |
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| `Pad` | Moved in `mmcv.transforms.Pad` |
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| `Normalize` | Moved in `mmcv.transforms.Normalize` |
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| `Compose` | Moved in `mmcv.transforms.Compose` |
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| `ImageToTensor` | Moved in `mmcv.transforms.ImageToTensor` |
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### `mmseg.models`
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`models` has not changed a lot, just added the `encoding` and `wrappers` from previous `mmseg.ops`
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