[Doc] Add Migration documentation (#2066)
* wip * [Doc] Migration * [wip] add package changes * remove zoo stat * add dataset migration * add package changes * revise based on comments * revise based on comments * typo * fix * add engine requirements * revise mmcv dependencypull/2187/head
Miao Zheng
GitHub
parent
a6a5a50518
commit
92ebba6c0e
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@ -30,7 +30,7 @@ optimizers
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:members:
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mmseg.evaluation
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--------------
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-----------------
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metrics
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^^^^^^^^^^
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@ -75,18 +75,13 @@ necks
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.. automodule:: mmseg.models.necks
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:members:
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mmseg.ops
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--------------
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.. automodule:: mmseg.ops
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:members:
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mmseg.registry
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--------------
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.. automodule:: mmseg.registry
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:members:
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mmseg.structures
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--------------
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-----------------
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structures
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^^^^^^^^^^
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@ -104,6 +99,6 @@ mmseg.utils
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:members:
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mmseg.visualization
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--------------
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----------------------
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.. automodule:: mmseg.visualization
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:members:
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@ -24,7 +24,7 @@ Welcome to MMSegmentation's documentation!
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:maxdepth: 1
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:caption: Migration
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migration.md
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migration/index.rst
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.. toctree::
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:caption: API Reference
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@ -1 +0,0 @@
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# Migration from MMSegmentation 0.x
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@ -0,0 +1,8 @@
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Migration
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***************
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.. toctree::
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:maxdepth: 1
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interface.md
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package.md
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@ -0,0 +1,441 @@
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# Migration from MMSegmentation 0.x
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## Introduction
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This guide describes the fundamental differences between MMSegmentation 0.x and MMSegmentation 1.x in terms of behaviors and the APIs, and how these all relate to your migration journey.
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## New dependencies
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MMSegmentation 1.x depends on some new packages, you can prepare a new clean environment and install again according to the [installation tutorial](get_started.md).
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Or install the below packages manually.
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1. [MMEngine](https://github.com/open-mmlab/mmengine): MMEngine is the core the OpenMMLab 2.0 architecture, and we splited many compentents unrelated to computer vision from MMCV to MMEngine.
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2. [MMCV](https://github.com/open-mmlab/mmcv): The computer vision package of OpenMMLab. This is not a new dependency, but you need to upgrade it to above **2.0.0rc1** version.
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3. [MMClassification](https://github.com/open-mmlab/mmclassification)(Optional): The image classification toolbox and benchmark of OpenMMLab. This is not a new dependency, but you need to upgrade it to above **1.0.0rc0** version.
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## Train launch
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The main improvement of OpenMMLab 2.0 is releasing MMEngine which provides universal and powerful runner for unified interfaces to launch training jobs.
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Compared with MMSeg0.x, MMSeg1.x provides fewer command line arguments in `tools/train.py`
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<table class="docutils">
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<tr>
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<td>Function</td>
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<td>Original</td>
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<td>New</td>
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</tr>
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<tr>
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<td>Loading pre-trained checkpoint</td>
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<td>--load_from=$CHECKPOINT</td>
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<td>--cfg-options load_from=$CHECKPOINT</td>
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</tr>
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<tr>
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<td>Resuming Train from specific checkpoint</td>
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<td>--resume-from=$CHECKPOINT</td>
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<td>--resume=$CHECKPOINT</td>
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</tr>
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<tr>
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<td>Resuming Train from the latest checkpoint</td>
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<td>--auto-resume</td>
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<td>--resume='auto'</td>
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</tr>
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<tr>
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<td>Whether not to evaluate the checkpoint during training</td>
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<td>--no-validate</td>
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<td>--cfg-options val_cfg=None val_dataloader=None val_evaluator=None</td>
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</tr>
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<tr>
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<td>Training device assignment</td>
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<td>--gpu-id=$DEVICE_ID</td>
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<td>-</td>
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</tr>
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<tr>
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<td>Whether or not set different seeds for different ranks</td>
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<td>--diff-seed</td>
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<td>--cfg-options randomness.diff_rank_seed=True</td>
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</tr>
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<td>Whether to set deterministic options for CUDNN backend</td>
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<td>--deterministic</td>
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<td>--cfg-options randomness.deterministic=True</td>
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</table>
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## Configuration file
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### Model settings
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No changes in `model.backbone`, `model.neck`, `model.decode_head` and `model.losses` fields.
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Add `model.data_preprocessor` field to configure the `DataPreProcessor`, including:
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- `mean`(Sequence, optional): The pixel mean of R, G, B channels. Defaults to None.
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- `std`(Sequence, optional): The pixel standard deviation of R, G, B channels. Defaults to None.
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- `size`(Sequence, optional): Fixed padding size.
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- `size_divisor` (int, optional): The divisor of padded size.
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- `seg_pad_val` (float, optional): Padding value of segmentation map. Default: 255.
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- `padding_mode` (str): Type of padding. Default: 'constant'.
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- constant: pads with a constant value, this value is specified with pad_val.
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- `bgr_to_rgb` (bool): whether to convert image from BGR to RGB.Defaults to False.
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- `rgb_to_bgr` (bool): whether to convert image from RGB to RGB. Defaults to False.
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**Note:**
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Please refer [models documentation](../advanced_guides/models.md) for more details.
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### Dataset settings
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Changes in **data**:
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The original `data` field is split to `train_dataloader`, `val_dataloader` and `test_dataloader`. This allows us to configure them in fine-grained. For example, you can specify different sampler and batch size during training and test.
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The `samples_per_gpu` is renamed to `batch_size`.
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The `workers_per_gpu` is renamed to `num_workers`.
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<table class="docutils">
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<tr>
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<td>Original</td>
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<td>
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```python
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data = dict(
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samples_per_gpu=4,
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workers_per_gpu=4,
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train=dict(...),
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val=dict(...),
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test=dict(...),
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)
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```
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</td>
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<tr>
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<td>New</td>
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<td>
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```python
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train_dataloader = dict(
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batch_size=4,
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num_workers=4,
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dataset=dict(...),
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sampler=dict(type='DefaultSampler', shuffle=True) # necessary
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)
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val_dataloader = dict(
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batch_size=4,
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num_workers=4,
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dataset=dict(...),
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sampler=dict(type='DefaultSampler', shuffle=False) # necessary
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)
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test_dataloader = val_dataloader
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```
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</td>
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</tr>
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</table>
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Changes in **pipeline**
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- The original formatting transforms **`ToTensor`**、**`ImageToTensor`**、**`Collect`** are combined as [`PackSegInputs`](mmseg.datasets.transforms.PackSegInputs)
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- We don't recommend to do **`Normalize`** and **Pad** in the dataset pipeline. Please remove it from pipelines and set it in the `data_preprocessor` field.
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- The original **`Resize`** in MMSeg 1.x has been changed to **`RandomResize`** and the input arguments `img_scale` is renamed to `scale`, and the default value of `keep_ratio` is modified to False.
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**Note:**
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We move some work of data transforms to the data preprocessor, like normalization, see [the documentation](package.md) for more details.
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<table class="docutils">
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<tr>
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<td>Original</td>
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<td>
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```python
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', reduce_zero_label=True),
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dict(type='Resize', img_scale=(2560, 640), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
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```
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</td>
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<tr>
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<td>New</td>
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<td>
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```python
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', reduce_zero_label=True),
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dict(
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type='RandomResize',
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scale=(2560, 640),
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ratio_range=(0.5, 2.0),
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keep_ratio=True),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='PackSegInputs')
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]
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```
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</td>
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</tr>
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</table>
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Changes in **`evaluation`**:
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- The **`evaluation`** field is split to `val_evaluator` and `test_evaluator`. And it won't support `interval` and `save_best` arguments.
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The `interval` is moved to `train_cfg.val_interval`, and the `save_best`
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is moved to `default_hooks.checkpoint.save_best`. `pre_eval` has been removed.
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- `'mIoU'` has been changed to `'IoUMetric'`.
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<table class="docutils">
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<tr>
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<td>Original</td>
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<td>
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```python
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evaluation = dict(interval=2000, metric='mIoU', pre_eval=True)
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```
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</td>
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<tr>
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<td>New</td>
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<td>
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```python
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val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
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test_evaluator = val_evaluator
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```
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</td>
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</tr>
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</table>
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### Optimizer and Schedule settings
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Changes in **`optimizer`** and **`optimizer_config`**:
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- Now we use `optim_wrapper` field to specify all configuration about the optimization process. And the
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`optimizer` is a sub field of `optim_wrapper` now.
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- `paramwise_cfg` is also a sub field of `optim_wrapper`, instead of `optimizer`.
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- `optimizer_config` is removed now, and all configurations of it are moved to `optim_wrapper`.
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- `grad_clip` is renamed to `clip_grad`.
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<table class="docutils">
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<tr>
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<td>Original</td>
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<td>
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```python
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optimizer = dict(type='AdamW', lr=0.0001, weight_decay=0.0005)
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optimizer_config = dict(grad_clip=dict(max_norm=1, norm_type=2))
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```
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</td>
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<tr>
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<td>New</td>
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<td>
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```python
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0005),
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clip_grad=dict(max_norm=1, norm_type=2))
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```
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</td>
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</tr>
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</table>
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Changes in **`lr_config`**:
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- The `lr_config` field is removed and we use new `param_scheduler` to replace it.
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- The `warmup` related arguments are removed, since we use schedulers combination to implement this
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functionality.
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The new schedulers combination mechanism is very flexible, and you can use it to design many kinds of learning
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rate / momentum curves. See [the tutorial](TODO) for more details.
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<table class="docutils">
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<tr>
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<td>Original</td>
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<td>
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```python
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lr_config = dict(
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policy='poly',
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warmup='linear',
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warmup_iters=1500,
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warmup_ratio=1e-6,
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power=1.0,
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min_lr=0.0,
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by_epoch=False)
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```
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</td>
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<tr>
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<td>New</td>
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<td>
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```python
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param_scheduler = [
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dict(
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type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
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dict(
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type='PolyLR',
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power=1.0,
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begin=1500,
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end=160000,
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eta_min=0.0,
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by_epoch=False,
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)
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]
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```
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</td>
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</tr>
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</table>
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Changes in **`runner`**:
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Most configuration in the original `runner` field is moved to `train_cfg`, `val_cfg` and `test_cfg`, which
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configure the loop in training, validation and test.
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<table class="docutils">
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<tr>
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<td>Original</td>
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<td>
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```python
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runner = dict(type='IterBasedRunner', max_iters=20000)
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```
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</td>
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<tr>
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<td>New</td>
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<td>
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```python
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# The `val_interval` is the original `evaluation.interval`.
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train_cfg = dict(type='IterBasedTrainLoop', max_iters=20000, val_interval=2000)
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val_cfg = dict(type='ValLoop') # Use the default validation loop.
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test_cfg = dict(type='TestLoop') # Use the default test loop.
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```
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</td>
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</tr>
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</table>
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In fact, in OpenMMLab 2.0, we introduced `Loop` to control the behaviors in training, validation and test. The functionalities of `Runner` are also changed. You can find more details of [runner tutorial](https://github.com/open-mmlab/mmengine/blob/main/docs/en/design/runner.md)
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in [MMEngine](https://github.com/open-mmlab/mmengine/).
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### Runtime settings
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Changes in **`checkpoint_config`** and **`log_config`**:
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The `checkpoint_config` are moved to `default_hooks.checkpoint` and the `log_config` are moved to `default_hooks.logger`.
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And we move many hooks settings from the script code to the `default_hooks` field in the runtime configuration.
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```python
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default_hooks = dict(
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# record the time of every iterations.
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timer=dict(type='IterTimerHook'),
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# print log every 50 iterations.
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logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
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# enable the parameter scheduler.
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param_scheduler=dict(type='ParamSchedulerHook'),
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# save checkpoint every 2000 iterations.
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checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=2000),
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# set sampler seed in distributed environment.
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sampler_seed=dict(type='DistSamplerSeedHook'),
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# validation results visualization.
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visualization=dict(type='SegVisualizationHook'))
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```
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In addition, we split the original logger to logger and visualizer. The logger is used to record
|
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information and the visualizer is used to show the logger in different backends, like terminal and TensorBoard.
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|
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<table class="docutils">
|
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<tr>
|
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<td>Original</td>
|
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<td>
|
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|
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```python
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log_config = dict(
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interval=100,
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hooks=[
|
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dict(type='TextLoggerHook'),
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dict(type='TensorboardLoggerHook'),
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])
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```
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</td>
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<tr>
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<td>New</td>
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<td>
|
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```python
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default_hooks = dict(
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...
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logger=dict(type='LoggerHook', interval=100),
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)
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vis_backends = [dict(type='LocalVisBackend'),
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dict(type='TensorboardVisBackend')]
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visualizer = dict(
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type='SegLocalVisualizer', vis_backends=vis_backends, name='visualizer')
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```
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|
||||
</td>
|
||||
</tr>
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</table>
|
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Changes in **`load_from`** and **`resume_from`**:
|
||||
|
||||
- The `resume_from` is removed. And we use `resume` and `load_from` to replace it.
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- If `resume=True` and `load_from` is **not None**, resume training from the checkpoint in `load_from`.
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- If `resume=True` and `load_from` is **None**, try to resume from the latest checkpoint in the work directory.
|
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- If `resume=False` and `load_from` is **not None**, only load the checkpoint, not resume training.
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- If `resume=False` and `load_from` is **None**, do not load nor resume.
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|
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Changes in **`dist_params`**: The `dist_params` field is a sub field of `env_cfg` now. And there are some new
|
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configurations in the `env_cfg`.
|
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|
||||
```python
|
||||
env_cfg = dict(
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# whether to enable cudnn benchmark
|
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cudnn_benchmark=False,
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|
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# set multi process parameters
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||||
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
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|
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# set distributed parameters
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dist_cfg=dict(backend='nccl'),
|
||||
)
|
||||
```
|
||||
|
||||
Changes in **`workflow`**: `workflow` related functionalities are removed.
|
||||
|
||||
New field **`visualizer`**: The visualizer is a new design in OpenMMLab 2.0 architecture. We use a
|
||||
visualizer instance in the runner to handle results & log visualization and save to different backends.
|
||||
See the [visualization tutorial](user_guides/visualization.md) for more details.
|
||||
|
||||
New field **`default_scope`**: The start point to search module for all registries. The `default_scope` in MMSegmentation is `mmseg`. See [the registry tutorial](https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/registry.md) for more details.
|
||||
|
|
@ -0,0 +1,114 @@
|
|||
# Package structures changes
|
||||
|
||||
This section is included if you are curious about what has changed between MMSeg 0.x and 1.x.
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<td>MMSegmentation 0.x</td>
|
||||
<td>MMSegmentation 1.x</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>mmseg.api</td>
|
||||
<td>mmseg.api</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td bgcolor=#fcf7f7>- mmseg.core</td>
|
||||
<td bgcolor=#ecf4eb>+ mmseg.engine</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>mmseg.datasets</td>
|
||||
<td>mmseg.datasets</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>mmseg.models</td>
|
||||
<td>mmseg.models</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td bgcolor=#fcf7f7>- mmseg.ops</td>
|
||||
<td bgcolor=#ecf4eb>+ mmseg.structure</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>mmseg.utils</td>
|
||||
<td>mmseg.utils</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td></td>
|
||||
<td bgcolor=#ecf4eb>+ mmseg.evaluation</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td></td>
|
||||
<td bgcolor=#ecf4eb>+ mmseg.registry</td>
|
||||
<tr>
|
||||
</table>
|
||||
|
||||
## Removed packages
|
||||
|
||||
### `mmseg.core`
|
||||
|
||||
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.
|
||||
|
||||
## `mmseg.ops`
|
||||
|
||||
`ops` package included `encoding` and `wrappers`, which are moved in `mmseg.models.utils`.
|
||||
|
||||
## Added packages
|
||||
|
||||
### `mmseg.engine`
|
||||
|
||||
OpenMMLab 2.0 adds a new foundational library for training deep learning, MMEngine. It servers as the training engine of all OpenMMLab codebases.
|
||||
`engine` package of mmseg is some customized modules for semantic segmentation task, like `SegVisualizationHook` which works for visualizing segmentation mask.
|
||||
|
||||
### `mmseg.structure`
|
||||
|
||||
In OpenMMLab 2.0, we designed data structure for computer vision task, and in mmseg, we implements `SegDataSample` in `structure` package.
|
||||
|
||||
### `mmseg.evaluation`
|
||||
|
||||
We move all evaluation metric in `mmseg.evaluation`.
|
||||
|
||||
### `mmseg.registry`
|
||||
|
||||
We moved registry implementations for all kinds of modules in MMSegmentation in `mmseg.registry`.
|
||||
|
||||
## Modified packages
|
||||
|
||||
### `mmseg.apis`
|
||||
|
||||
OpenMMLab 2.0 tries to support unified interface for multitasking of Computer Vision,
|
||||
and releases much stronger [`Runner`](https://github.com/open-mmlab/mmengine/blob/main/docs/en/design/runner.md),
|
||||
so MMSeg 1.x removed modules in `train.py` and `test.py` renamed `init_segmentor` to `init_model` and `inference_segmentor` to `inference_model`
|
||||
Here is the changes of `mmseg.apis`:
|
||||
|
||||
| Function | Changes |
|
||||
| :-------------------: | :---------------------------------------------- |
|
||||
| `init_segmentor` | Renamed to `init_model` |
|
||||
| `inference_segmentor` | Rename to `inference_segmentor` |
|
||||
| `show_result_pyplot` | Implemented based on `SegLocalVisualizer` |
|
||||
| `train_model` | Removed, use `runner.train` to train. |
|
||||
| `multi_gpu_test` | Removed, use `runner.test` to test. |
|
||||
| `single_gpu_test` | Removed, use `runner.test` to test. |
|
||||
| `set_random_seed` | Removed, use `mmengine.runner.set_random_seed`. |
|
||||
| `init_random_seed` | Removed, use `mmengine.dist.sync_random_seed`. |
|
||||
|
||||
### `mmseg.datasets`
|
||||
|
||||
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
|
||||
|
||||
| Packages/Modules | Changes |
|
||||
| :-------------------: | :------------------------------------------------------------------------------------------ |
|
||||
| `mmseg.pipelines` | Renamed to `mmseg.transforms` |
|
||||
| `mmseg.sampler` | Move in `mmengine.dataset.sampler` |
|
||||
| `CustomDataset` | Renamed to `BaseDataset` and inherited from `BaseDataset` in MMEngine |
|
||||
| `DefaultFormatBundle` | Replaced with `PackSegInputs` |
|
||||
| `LoadImageFromFile` | Move in `mmcv.transforms.LoadImageFromFile` |
|
||||
| `LoadAnnotations` | Moved in `mmcv.transforms.LoadAnnotations` |
|
||||
| `Resize` | Moved in `mmcv.transforms` and split into `Resize`, `RandomResize` and `RandomChoiseResize` |
|
||||
| `RandomFlip` | Moved in `mmcv.transforms.RandomFlip` |
|
||||
| `Pad` | Moved in `mmcv.transforms.Pad` |
|
||||
| `Normalize` | Moved in `mmcv.transforms.Normalize` |
|
||||
| `Compose` | Moved in `mmcv.transforms.Compose` |
|
||||
| `ImageToTensor` | Moved in `mmcv.transforms.ImageToTensor` |
|
||||
|
||||
### `mmseg.models`
|
||||
|
||||
`models` has not changed a lot, just added the `encoding` and `wrappers` from previous `mmseg.ops`
|
||||
|
|
@ -75,11 +75,6 @@ necks
|
|||
.. automodule:: mmseg.models.necks
|
||||
:members:
|
||||
|
||||
mmseg.ops
|
||||
--------------
|
||||
.. automodule:: mmseg.ops
|
||||
:members:
|
||||
|
||||
mmseg.registry
|
||||
--------------
|
||||
.. automodule:: mmseg.registry
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
mmcv
|
||||
mmcv>=2.0.0rc0
|
||||
mmengine
|
||||
prettytable
|
||||
torch
|
||||
torchvision
|
||||
|
|
|
|||
Loading…
Reference in New Issue