mirror of
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343 lines
8.5 KiB
Markdown
343 lines
8.5 KiB
Markdown
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# 从 EpochBased 切换至 IterBased
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MMEngine 支持两种训练模式，基于轮次的 EpochBased 方式和基于迭代次数的 IterBased 方式，这两种方式在下游算法库均有使用，例如 [MMDetection](https://github.com/open-mmlab/mmdetection) 默认使用 EpochBased 方式，[MMSegmentation](https://github.com/open-mmlab/mmsegmentation) 默认使用 IterBased 方式。
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MMEngine 很多模块默认以 EpochBased 的模式执行，例如 `ParamScheduler`, `LoggerHook`, `CheckpointHook` 等，常见的 EpochBased 配置写法如下：
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```python
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param_scheduler = dict(
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type='MultiStepLR',
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milestones=[6, 8]
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by_epoch=True # by_epoch 默认为 True，这边显式的写出来只是为了方便对比
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)
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default_hooks = dict(
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logger=dict(type='LoggerHook'),
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checkpoint=dict(type='CheckpointHook', interval=2),
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)
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train_cfg = dict(
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by_epoch=True, # by_epoch 默认为 True，这边显式的写出来只是为了方便对比
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max_epochs=10,
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val_interval=2
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)
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log_processor = dict(
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by_epoch=True
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) # log_processor 的 by_epoch 默认为 True，这边显式的写出来只是为了方便对比， 实际上不需要设置
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runner = Runner(
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model=ResNet18(),
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work_dir='./work_dir',
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train_dataloader=train_dataloader_cfg,
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optim_wrapper=dict(optimizer=dict(type='SGD', lr=0.001, momentum=0.9)),
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param_scheduler=param_scheduler
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default_hooks=default_hooks,
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log_processor=log_processor,
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train_cfg=train_cfg,
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resume=True,
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)
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```
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如果想按照 iter 训练模型，需要做以下改动：
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1. 将 `train_cfg` 中的 `by_epoch` 设置为 `False`，同时将 `max_iters` 设置为训练的总 iter 数，`val_iterval` 设置为验证间隔的 iter 数。
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```python
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train_cfg = dict(
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by_epoch=False,
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max_iters=10000,
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val_interval=2000
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)
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```
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2. 将 `default_hooks` 中的 `logger` 的 `log_metric_by_epoch` 设置为 False， `checkpoint` 的 `by_epoch` 设置为 `False`。
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```python
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default_hooks = dict(
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logger=dict(type='LoggerHook', log_metric_by_epoch=False),
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checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=2000),
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)
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```
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3. 将 `param_scheduler` 中的 `by_epoch` 设置为 `False`，并將 `epoch` 相关的参数换算成 `iter`
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```python
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param_scheduler = dict(
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type='MultiStepLR',
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milestones=[6000, 8000],
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by_epoch=False,
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)
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```
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除了这种方式，如果你能保证 IterBasedTraining 和 EpochBasedTraining 总 iter 数一致，直接设置 `convert_to_iter_based` 为 `True` 即可。
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```python
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param_scheduler = dict(
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type='MultiStepLR',
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milestones=[6, 8]
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convert_to_iter_based=True
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)
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```
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4. 将 `log_processor` 的 `by_epoch` 设置为 `False`。
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```python
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log_processor = dict(
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by_epoch=False
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)
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```
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以 [15 分钟教程训练 CIFAR10 数据集](../get_started/15_minutes.md)为例：
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<table class="docutils">
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<thead>
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<tr>
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<th>Step</th>
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<th>Training by epoch</th>
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<th>Training by iteration</th>
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<tbody>
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<tr>
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<td>Build model</td>
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<td colspan="2"><div>
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```python
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import torch.nn.functional as F
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import torchvision
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from mmengine.model import BaseModel
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class MMResNet50(BaseModel):
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def __init__(self):
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super().__init__()
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self.resnet = torchvision.models.resnet50()
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def forward(self, imgs, labels, mode):
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x = self.resnet(imgs)
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if mode == 'loss':
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return {'loss': F.cross_entropy(x, labels)}
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elif mode == 'predict':
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return x, labels
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```
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</td>
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</div>
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</tr>
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<tr>
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<td>Build dataloader</td>
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<td colspan="2">
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```python
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import torchvision.transforms as transforms
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from torch.utils.data import DataLoader
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norm_cfg = dict(mean=[0.491, 0.482, 0.447], std=[0.202, 0.199, 0.201])
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train_dataloader = DataLoader(
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batch_size=32,
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shuffle=True,
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dataset=torchvision.datasets.CIFAR10(
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'data/cifar10',
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train=True,
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download=True,
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transform=transforms.Compose([
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transforms.RandomCrop(32, padding=4),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize(**norm_cfg)])))
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val_dataloader = DataLoader(
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batch_size=32,
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shuffle=False,
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dataset=torchvision.datasets.CIFAR10(
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'data/cifar10',
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train=False,
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download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize(**norm_cfg)])))
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```
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</td>
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</tr>
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<tr>
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<td>Prepare metric</td>
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<td colspan="2">
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```python
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from mmengine.evaluator import BaseMetric
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class Accuracy(BaseMetric):
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def process(self, data_batch, data_samples):
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score, gt = data_samples
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# save the middle result of a batch to `self.results`
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self.results.append({
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'batch_size': len(gt),
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'correct': (score.argmax(dim=1) == gt).sum().cpu(),
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})
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def compute_metrics(self, results):
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total_correct = sum(item['correct'] for item in results)
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total_size = sum(item['batch_size'] for item in results)
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# return the dict containing the eval results
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# the key is the name of the metric name
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return dict(accuracy=100 * total_correct / total_size)
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```
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</td>
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</tr>
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<tr>
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<td>Configure default hooks</td>
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<td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
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<div style="overflow-x: auto">
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```python
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default_hooks = dict(
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logger=dict(type='LoggerHook', log_metric_by_epoch=True),
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checkpoint=dict(type='CheckpointHook', interval=2, by_epoch=True),
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)
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```
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</div>
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</td>
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<td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
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<div style="overflow-x: auto">
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```python
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default_hooks = dict(
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logger=dict(type='LoggerHook', log_metric_by_epoch=False),
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checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=2000),
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)
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```
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</div>
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</td>
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</tr>
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<tr>
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<td>Configure parameter scheduler</td>
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<td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
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<div style="overflow-x: auto">
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```python
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param_scheduler = dict(
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type='MultiStepLR',
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milestones=[6, 8],
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by_epoch=True,
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)
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```
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</div>
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</td>
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<td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
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<div style="overflow-x: auto">
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```python
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param_scheduler = dict(
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type='MultiStepLR',
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milestones=[6000, 8000],
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by_epoch=False,
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)
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```
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</div>
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</td>
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</tr>
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<tr>
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<td>Configure log_processor</td>
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<td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
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<div style="overflow-x: auto">
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```python
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# The default configuration of log_processor is used for epoch based training.
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# Defining it here additionally is for building runner with the same way.
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log_processor = dict(by_epoch=True)
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```
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</div>
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</td>
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<td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
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<div style="overflow-x: auto">
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```python
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log_processor = dict(by_epoch=False)
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```
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</div>
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</td>
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</tr>
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<tr>
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<td>Configure train_cfg</td>
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<td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
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<div style="overflow-x: auto">
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```python
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train_cfg = dict(
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by_epoch=True,
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max_epochs=10,
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val_interval=2
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)
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```
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</div>
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</td>
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<td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
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<div style="overflow-x: auto">
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```python
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train_cfg = dict(
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by_epoch=False,
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max_iters=10000,
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val_interval=2000
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)
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```
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</div>
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</td>
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</tr>
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<tr>
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<td>Build Runner</td>
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<td colspan="2">
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```python
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from torch.optim import SGD
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from mmengine.runner import Runner
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runner = Runner(
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model=MMResNet50(),
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work_dir='./work_dir',
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train_dataloader=train_dataloader,
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optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)),
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train_cfg=train_cfg,
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log_processor=log_processor,
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default_hooks=default_hooks,
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val_dataloader=val_dataloader,
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val_cfg=dict(),
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val_evaluator=dict(type=Accuracy),
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)
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runner.train()
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```
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</td>
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</tr>
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</thead>
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</table>
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```{note}
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如果基础配置文件为 train_dataloader 配置了基于 iteration/epoch 采样的 sampler，则需要在当前配置文件中将其更改为指定类型的 sampler，或将其设置为 None。当 dataloader 中的 sampler 为 None，MMEngine 或根据 train_cfg 中的 by_epoch 参数选择 `InfiniteSampler`（False） 或 `DefaultSampler`（True）。
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```
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```{note}
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如果基础配置文件在 train_cfg 中指定了 type，那么必须在当前配置文件中将 type 覆盖为（IterBasedTrainLoop 或 EpochBasedTrainLoop），而不能简单的指定 by_epoch 参数。
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```
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