588 lines
23 KiB
Markdown
588 lines
23 KiB
Markdown
# Migration from MMClassification 0.x
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We introduce some modifications in MMClassification 1.x, and some of them are BC-breading. To migrate your projects from MMClassification 0.x smoothly, please read this tutorial.
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## New dependencies
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MMClassification 1.x depends on some new packages, you can prepare a new clean environment and install again
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according to the [install tutorial](./get_started.md). 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,
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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
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dependency, but you need to upgrade it to above `2.0.0rc0` version.
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3. [rich](https://github.com/Textualize/rich): A terminal formatting package, and we use it to beautify some
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outputs in the terminal.
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## Configuration files
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In MMClassification 1.x, we refactored the structure of configuration files, and the original files are not usable.
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<!-- TODO: migration tool -->
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In this section, we will introduce all changes of the configuration files. And we assume you already have
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ideas of the [config files](./user_guides/config.md).
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### Model settings
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No changes in `model.backbone`, `model.neck` and `model.head` fields.
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Changes in **`model.train_cfg`**:
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- `BatchMixup` is renamed to [`Mixup`](Mixup).
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- `BatchCutMix` is renamed to [`CutMix`](CutMix).
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- `BatchResizeMix` is renamed to [`ResizeMix`](ResizeMix).
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- The `prob` argument is removed from all augments settings, and you can use the `probs` field in `train_cfg` to
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specify probabilities of every augemnts. If no `probs` field, randomly choose one by the same probability.
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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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model = dict(
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...
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train_cfg=dict(augments=[
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dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
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dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
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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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<td>New</td>
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<td>
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```python
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model = dict(
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...
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train_cfg=dict(augments=[
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dict(type='Mixup', alpha=0.8), dict(type='CutMix', alpha=1.0)]
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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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### Data settings
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Changes in **`data`**:
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- The original `data` field is splited to `train_dataloader`, `val_dataloader` and
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`test_dataloader`. This allows us to configure them in fine-grained. For example,
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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=32,
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workers_per_gpu=2,
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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=32,
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num_workers=2,
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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=32,
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num_workers=2,
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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 [`PackClsInputs`](PackClsInputs).
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- We don't recommend to do **`Normalize`** in the dataset pipeline. Please remove it from pipelines and set it in the `data_preprocessor` field.
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- The argument `flip_prob` in [**`RandomFlip`**](RandomFlip) is renamed to `flip`.
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- The argument `size` in [**`RandomCrop`**](RandomCrop) is renamed to `crop_size`.
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- The argument `size` in [**`RandomResizedCrop`**](RandomResizedCrop) is renamed to `scale`.
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- The argument `size` in [**`Resize`**](Resize) is renamed to `scale`. And `Resize` won't support size like `(256, -1)`, please use [`ResizeEdge`](ResizeEdge) to replace it.
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- The argument `policies` in [**`AutoAugment`**](AutoAugment) and [**`RandAugment`**](RandAugment) supports using string to specify preset policies. `AutoAugment` supports "imagenet" and `RandAugment` supports "timm_increasing".
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- **`RandomResizedCrop`** and **`CenterCrop`** won't supports `efficientnet_style`, and please use [`EfficientnNetRandomCrop`](EfficientnNetRandomCrop) and [`EfficientNetCenterCrop`](EfficientNetCenterCrop) to replace them.
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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](mmcls.models.utils.data_preprocessor) for
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more details.
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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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```python
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='RandomResizedCrop', size=224),
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dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='ToTensor', keys=['gt_label']),
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dict(type='Collect', keys=['img', 'gt_label'])
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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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data_preprocessor = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='RandomResizedCrop', scale=224),
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dict(type='RandomFlip', prob=0.5, direction='horizontal'),
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dict(type='PackClsInputs'),
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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 splited to `val_evaluator` and `test_evaluator`. And it won't supports `interval` and `save_best` arguments.
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The `interval` is moved to `train_cfg.val_interval`, see [the schedule settings](./user_guides/config.md#schedule-settings) and the `save_best`
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is moved to `default_hooks.checkpoint.save_best`, see [the runtime settings](./user_guides/config.md#runtime-settings).
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- The 'accuracy' metric is renamed to [`Accuracy`](mmcls.evaluation.Accuracy).
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- The 'precision'，'recall'，'f1-score' and 'support' are combined as [`SingleLabelMetric`](mmcls.evaluation.SingleLabelMetric), and use `items` argument to specify to calculate which metric.
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- The 'mAP' is renamed to [`AveragePrecision`](mmcls.evaluation.AveragePrecision).
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- The 'CP', 'CR', 'CF1', 'OP', 'OR', 'OF1' are combined as [`MultiLabelMetric`](mmcls.evaluation.MultiLabelMetric), and use `items` and `average` arguments to specify to calculate which metric.
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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(
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interval=1,
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metric='accuracy',
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metric_options=dict(topk=(1, 5))
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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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val_evaluator = dict(type='Accuracy', topk=(1, 5))
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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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<tr>
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<td>Original</td>
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<td>
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```python
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evaluation = dict(
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interval=1,
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metric=['mAP', 'CP', 'OP', 'CR', 'OR', 'CF1', 'OF1'],
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metric_options=dict(thr=0.5),
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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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val_evaluator = [
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dict(type='AveragePrecision'),
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dict(type='MultiLabelMetric',
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items=['precision', 'recall', 'f1-score'],
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average='both',
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thr=0.5),
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]
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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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### 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(
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type='AdamW',
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lr=0.0015,
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weight_decay=0.3,
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paramwise_cfg = dict(
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norm_decay_mult=0.0,
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bias_decay_mult=0.0,
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))
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optimizer_config = dict(grad_clip=dict(max_norm=1.0))
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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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optimizer=dict(type='AdamW', lr=0.0015, weight_decay=0.3),
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paramwise_cfg = dict(
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norm_decay_mult=0.0,
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bias_decay_mult=0.0,
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),
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clip_gard=dict(max_norm=1.0),
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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 **`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='CosineAnnealing',
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min_lr=0,
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warmup='linear',
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warmup_iters=5,
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warmup_ratio=0.01,
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warmup_by_epoch=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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param_scheduler = [
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# warmup
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dict(
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type='LinearLR',
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start_factor=0.01,
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by_epoch=True,
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end=5,
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# Update the learning rate after every iters.
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convert_to_iter_based=True),
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# main learning rate scheduler
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dict(type='CosineAnnealingLR', by_epoch=True, begin=5),
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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='EpochBasedRunner', max_epochs=100)
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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(by_epoch=True, max_epochs=100, val_interval=1)
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val_cfg = dict() # Use the default validation loop.
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test_cfg = dict() # 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. And
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the functionalities of `Runner` are also changed. You can find more details in [the MMEngine tutorials](TODO).
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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 100 iterations.
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logger=dict(type='LoggerHook', interval=100),
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# enable the parameter scheduler.
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param_scheduler=dict(type='ParamSchedulerHook'),
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# save checkpoint per epoch, and automatically save the best checkpoint.
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checkpoint=dict(type='CheckpointHook', interval=1, save_best='auto'),
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# set sampler seed in distributed evrionment.
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sampler_seed=dict(type='DistSamplerSeedHook'),
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# validation results visualization, set True to enable it.
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visualization=dict(type='VisualizationHook', enable=False),
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)
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```
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In addition, we splited 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, TensorBoard
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and Wandb.
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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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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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visualizer = dict(
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type='ClsVisualizer',
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vis_backends=[dict(type='LocalVisBackend'), dict(type='TensorboardVisBackend')],
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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 **`load_from`** and **`resume_from`**:
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- 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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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
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env_cfg = dict(
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# whether to enable cudnn benchmark
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cudnn_benchmark=False,
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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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# set distributed parameters
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dist_cfg=dict(backend='nccl'),
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)
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```
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Changes in **`workflow`**: `workflow` related functionalities are removed.
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New field **`visualizer`**: The visualizer is a new design in OpenMMLab 2.0 architecture. We use a
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visualizer instance in the runner to handle results & log visualization and save to different backends.
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See the [MMEngine tutorial](TODO) for more details.
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```python
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visualizer = dict(
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type='ClsVisualizer',
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vis_backends=[
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dict(type='LocalVisBackend'),
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# Uncomment the below line to save the log and visualization results to TensorBoard.
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# dict(type='TensorboardVisBackend')
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]
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)
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```
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New field **`default_scope`**: The start point to search module for all registries. The `default_scope` in MMClassification is `mmcls`. See [the registry tutorial](TODO) for more details.
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## Packages
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### `mmcls.apis`
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The documentation can be found [here](mmcls.apis).
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| Function | Changes |
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| :------------------: | :---------------------------------------------- |
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| `init_model` | No changes |
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| `inference_model` | No changes |
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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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| `show_result_pyplot` | Waiting for support. |
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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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### `mmcls.core`
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The `mmcls.core` package is renamed to [`mmcls.engine`](mmcls.engine).
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| Sub package | Changes |
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| :-------------: | :-------------------------------------------------------------------------------------------------------------------------------- |
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| `evaluation` | Removed, use the metrics in [`mmcls.evaluation`](mmcls.evaluation). |
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| `hook` | Moved to [`mmcls.engine.hooks`](mmcls.engine.hooks) |
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| `optimizers` | Moved to [`mmcls.engine.optimizers`](mmcls.engine.optimizers) |
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| `utils` | Removed, the distributed environment related functions can be found in the [`mmengine.dist`](mmengine.dist) package. |
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| `visualization` | Removed, the related functionalities are implemented in [`mmengine.visualization.Visualizer`](mmengine.visualization.Visualizer). |
|
||
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The `MMClsWandbHook` in `hooks` package is waiting for implementation.
|
||
|
||
The `CosineAnnealingCooldownLrUpdaterHook` in `hooks` package is removed, and we support this functionality by
|
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the combination of parameter schedulers, see [the tutorial](./advanced_guides/schedule.md).
|
||
|
||
### `mmcls.datasets`
|
||
|
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The documentation can be found [here](mmcls.datasets).
|
||
|
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| Dataset class | Changes |
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||
| :-----------------------------------------------: | :----------------------------------------------------------------------------- |
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| [`CustomDataset`](CustomDataset) | Add `data_root` argument as the common prefix of `data_prefix` and `ann_file`. |
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| [`ImageNet`](ImageNet) | Same as `CustomDataset`. |
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||
| [`ImageNet21k`](ImageNet21k) | Same as `CustomDataset`. |
|
||
| [`CIFAR10`](CIFAR10) & [`CIFAR100`](CIFAR100) | The `test_mode` argument is a required argument now. |
|
||
| [`MNIST`](MNIST) & [`FashionMNIST`](FashionMNIST) | The `test_mode` argument is a required argument now. |
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||
| [`VOC`](VOC) | Requires `data_root`, `image_set_path` and `test_mode` now. |
|
||
| [`CUB`](CUB) | Requires `data_root` and `test_mode` now. |
|
||
|
||
The `mmcls.datasets.pipelines` is renamed to `mmcls.datasets.transforms`.
|
||
|
||
| Transform class | Changes |
|
||
| :-----------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||
| `LoadImageFromFile` | Removed, use [`mmcv.transforms.LoadImageFromFile`](mmcv.transforms.LoadImageFromFile). |
|
||
| `RandomFlip` | Removed, use [`mmcv.transforms.RandomFlip`](mmcv.transforms.RandomFlip). The argument `flip_prob` is renamed to `prob`. |
|
||
| `RandomCrop` | The argument `size` is renamed to `crop_size`. |
|
||
| `RandomResizedCrop` | The argument `size` is renamed to `scale`. The argument `scale` is renamed to `crop_ratio_range`. Won't support `efficientnet_style`, use [`EfficientNetRandomCrop`](EfficientNetRandomCrop). |
|
||
| `CenterCrop` | Removed, use [`mmcv.transforms.CenterCrop`](mmcv.transforms.CenterCrop). Won't support `efficientnet_style`, use [`EfficientNetCenterCrop`](EfficientNetCenterCrop). |
|
||
| `Resize` | Removed, use [`mmcv.transforms.Resize`](mmcv.transforms.Resize). The argument `size` is renamed to `scale`. Won't support size like `(256, -1)`, use [`ResizeEdge`](ResizeEdge). |
|
||
| `AutoAugment` & `RandomAugment` | The argument `policies` supports using string to specify preset policies. |
|
||
| `Compose` | Removed, use [`mmcv.transforms.Compose`](mmcv.transforms.Compose). |
|
||
|
||
### `mmcls.models`
|
||
|
||
The documentation can be found [here](mmcls.models). The interface of all **backbones**, **necks** and **losses** didn't change.
|
||
|
||
Changes in [`ImageClassifier`](ImageClassifier):
|
||
|
||
| Method of classifiers | Changes |
|
||
| :-------------------: | :---------------------------------------------------------------------------------------------------------------------------------------- |
|
||
| `extract_feat` | No changes |
|
||
| `forward` | Now only accepts three arguments: `inputs`, `data_samples` and `mode`. See [the documentation](ImageClassifier.forward) for more details. |
|
||
| `forward_train` | Replaced by `loss`. |
|
||
| `simple_test` | Replaced by `predict`. |
|
||
| `train_step` | The `optimizer` argument is replaced by `optim_wrapper` and it accepts [`OptimWrapper`](mmengine.optim.OptimWrapper). |
|
||
| `val_step` | The original `val_step` is the same as `train_step`, now it calls `predict`. |
|
||
| `test_step` | New method, and it's the same as `val_step`. |
|
||
|
||
Changes in [heads](mmcls.models.heads):
|
||
|
||
| Method of heads | Changes |
|
||
| :-------------: | :---------------------------------------------------------------------------------------------------------------------------------- |
|
||
| `pre_logits` | No changes |
|
||
| `forward_train` | Replaced by `loss`. |
|
||
| `simple_test` | Replaced by `predict`. |
|
||
| `loss` | It accepts `data_samples` instead of `gt_labels` to calculate loss. The `data_samples` is a list of [ClsDataSample](ClsDataSample). |
|
||
| `forward` | New method, and it returns the output of the classification head without any post-processs like softmax or sigmoid. |
|
||
|
||
### `mmcls.utils`
|
||
|
||
| Function | Changes |
|
||
| :--------------------------: | :------------------------------------------------------------ |
|
||
| `collect_env` | No changes |
|
||
| `get_root_logger` | Removed, use `mmengine.MMLogger.get_current_instance` |
|
||
| `load_json_log` | Waiting for support |
|
||
| `setup_multi_processes` | Removed, use `mmengine.utils.dl_utils.setup_multi_processes`. |
|
||
| `wrap_non_distributed_model` | Removed, we auto wrap the model in the runner. |
|
||
| `wrap_distributed_model` | Removed, we auto wrap the model in the runner. |
|
||
| `auto_select_device` | Removed, we auto select the device in the runner. |
|
||
|
||
### Other changes
|
||
|
||
- We moved the definition of all registries in different packages to the `mmcls.registry` package.
|