[Docs] Add the document for the transition between IterBasedTraining and EpochBasedTraining

docs/en/common_usage/epoch_to_iter.md

@@ -0,0 +1,341 @@
+# EpochBasedTraining to IterBasedTraining
+
+Many modules in MMEngine default to training models by epoch, such as `ParamScheduler`, `LoggerHook`, `CheckPointHook`, etc. Therefore, you need to adjust the configuration of these modules if you want to train by iteration. For example, a commonly used epoch based configuration is as follows:
+
+```python
+param_scheduler = dict(
+    type='MultiStepLR',
+    milestones=[6, 8]
+    by_epoch=True  # by_epoch is True by default
+)
+
+default_hooks = dict(
+    logger=dict(type='LoggerHook', log_metric_by_epoch=True),  # log_metric_by_epoch is True by default
+    checkpoint=dict(type='CheckpointHook', interval=2, by_epoch=True),  # by_epoch is True by default
+)
+
+train_cfg = dict(
+    by_epoch=True,  # set by_epoch=True or type='EpochBasedTrainLoop'
+    max_epochs=10,
+    val_interval=2
+)
+
+log_processor = dict(
+    by_epoch=True
+)  # This is the default configuration, and just set it here for comparison.
+
+runner = Runner(
+    model=ResNet18(),
+    work_dir='./work_dir',
+    # Assuming train_dataloader is configured with an epoch-based sampler
+    train_dataloader=train_dataloader_cfg,
+    optim_wrapper=dict(optimizer=dict(type='SGD', lr=0.001, momentum=0.9)),
+    param_scheduler=param_scheduler
+    default_hooks=default_hooks,
+    log_processor=log_processor,
+    train_cfg=train_cfg,
+    resume=True,
+)
+```
+
+There are four steps to convert the above configuration to iteration based training:
+
+1. Set `by_epoch` in `train_cfg` to False, and set `max_iters` to the total number of training iterations and `val_interval` to the interval between validation iterations.
+
+   ```python
+   train_cfg = dict(
+       by_epoch=False,
+       max_iters=10000,
+       val_interval=2000
+     )
+   ```
+
+2. Set `log_metric_by_epoch` to `False` in logger and `by_epoch` to `False` in checkpoint.
+
+   ```python
+   default_hooks = dict(
+       logger=dict(type='LoggerHook', log_metric_by_epoch=False),
+       checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=2000),
+   )
+   ```
+
+3. Set `by_epoch` in param_scheduler to `False` and convert any epoch-related parameters to iteration.
+
+   ```python
+   param_scheduler = dict(
+       type='MultiStepLR',
+       milestones=[6000, 8000],
+       by_epoch=False,
+   )
+   ```
+
+Alternatively, if you can ensure that the total number of iterations for IterBasedTraining and EpochBasedTraining is the same, simply set `convert_to_iter_based` to True.
+
+```python
+param_scheduler = dict(
+    type='MultiStepLR',
+    milestones=[6, 8]
+    convert_to_iter_based=True
+)
+```
+
+4. Set by_epoch in log_processor to False.
+
+   ```python
+   log_processor = dict(
+       by_epoch=False
+   )
+   ```
+
+Take [training CIFAR10](../get_started/15_minutes.md) as an example:
+
+<table class="docutils">
+<thead>
+  <tr>
+    <th>Step</th>
+    <th>Training by epoch</th>
+    <th>Training by iteration</th>
+<tbody>
+<tr>
+  <td>Build model</td>
+  <td colspan="2"><div>
+
+```python
+import torch.nn.functional as F
+import torchvision
+from mmengine.model import BaseModel
+
+
+class MMResNet50(BaseModel):
+    def __init__(self):
+        super().__init__()
+        self.resnet = torchvision.models.resnet50()
+
+    def forward(self, imgs, labels, mode):
+        x = self.resnet(imgs)
+        if mode == 'loss':
+            return {'loss': F.cross_entropy(x, labels)}
+        elif mode == 'predict':
+            return x, labels
+```
+
+</td>
+  </div>
+</tr>
+
+<tr>
+  <td>Build dataloader</td>
+
+<td colspan="2">
+
+```python
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader
+
+norm_cfg = dict(mean=[0.491, 0.482, 0.447], std=[0.202, 0.199, 0.201])
+train_dataloader = DataLoader(
+    batch_size=32,
+    shuffle=True,
+    dataset=torchvision.datasets.CIFAR10(
+        'data/cifar10',
+        train=True,
+        download=True,
+        transform=transforms.Compose([
+            transforms.RandomCrop(32, padding=4),
+            transforms.RandomHorizontalFlip(),
+            transforms.ToTensor(),
+            transforms.Normalize(**norm_cfg)])))
+
+val_dataloader = DataLoader(
+    batch_size=32,
+    shuffle=False,
+    dataset=torchvision.datasets.CIFAR10(
+        'data/cifar10',
+        train=False,
+        download=True,
+        transform=transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize(**norm_cfg)])))
+```
+
+</td>
+</tr>
+
+<tr>
+  <td>Prepare metric</td>
+  <td colspan="2">
+
+```python
+from mmengine.evaluator import BaseMetric
+
+class Accuracy(BaseMetric):
+    def process(self, data_batch, data_samples):
+        score, gt = data_samples
+        # save the middle result of a batch to `self.results`
+        self.results.append({
+            'batch_size': len(gt),
+            'correct': (score.argmax(dim=1) == gt).sum().cpu(),
+        })
+
+    def compute_metrics(self, results):
+        total_correct = sum(item['correct'] for item in results)
+        total_size = sum(item['batch_size'] for item in results)
+        # return the dict containing the eval results
+        # the key is the name of the metric name
+        return dict(accuracy=100 * total_correct / total_size)
+```
+
+</td>
+  </tr>
+
+<tr>
+  <td>Configure default hooks</td>
+  <td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
+  <div style="overflow-x: auto">
+
+```python
+default_hooks = dict(
+    logger=dict(type='LoggerHook', log_metric_by_epoch=True),
+    checkpoint=dict(type='CheckpointHook', interval=2, by_epoch=True),
+)
+```
+
+</div>
+  </td>
+
+<td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
+  <div style="overflow-x: auto">
+
+```python
+default_hooks = dict(
+    logger=dict(type='LoggerHook', log_metric_by_epoch=False),
+    checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=2000),
+)
+```
+
+</div>
+  </td>
+</tr>
+
+<tr>
+  <td>Configure parameter scheduler</td>
+  <td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
+  <div style="overflow-x: auto">
+
+```python
+param_scheduler = dict(
+    type='MultiStepLR',
+    milestones=[6, 8]
+    by_epoch=True
+)
+```
+
+</div>
+  </td>
+
+<td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
+  <div style="overflow-x: auto">
+
+```python
+param_scheduler = dict(
+    type='MultiStepLR',
+    milestones=[6000, 8000],
+    by_epoch=False,
+)
+```
+
+</div>
+  </td>
+</tr>
+
+<tr>
+  <td>Configure log_processor</td>
+  <td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
+  <div style="overflow-x: auto">
+
+```python
+# The default configuration of log_processor is used for epoch based training.
+# Defining it here additionally is for building runner with the same way.
+log_processor = dict(by_epoch=True)
+```
+
+</div>
+  </td>
+
+<td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
+  <div style="overflow-x: auto">
+
+```python
+log_processor = dict(by_epoch=False)
+```
+
+</div>
+  </td>
+</tr>
+
+<tr>
+  <td>Configure train_cfg</td>
+  <td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
+  <div style="overflow-x: auto">
+
+```python
+train_cfg = dict(
+    by_epoch=True,
+    max_epochs=10,
+    val_interval=2
+)
+```
+
+</div>
+  </td>
+
+<td valign="top" class='two-column-table-wrapper' width="50%" colspan="1">
+  <div style="overflow-x: auto">
+
+```python
+train_cfg = dict(
+    by_epoch=False,
+    max_iters=10000,
+    val_interval=2000
+)
+```
+
+</div>
+  </td>
+</tr>
+
+<tr>
+  <td>Build Runner</td>
+  <td colspan="2">
+
+```python
+from torch.optim import SGD
+from mmengine.runner import Runner
+runner = Runner(
+    model=MMResNet50(),
+    work_dir='./work_dir',
+    train_dataloader=train_dataloader,
+    optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)),
+    train_cfg=train_cfg,
+    log_processor=log_processor,
+    default_hooks=default_hooks,
+    val_dataloader=val_dataloader,
+    val_cfg=dict(),
+    val_evaluator=dict(type=Accuracy),
+)
+runner.train()
+```
+
+</td>
+</tr>
+
+</thead>
+</table>
+
+```{note}
+If the base configuration file has configured a epoch/iteration based sampler for the train_dataloader, then it is necessary to change it to a specified type of sampler in the current configuration file, or set it to None. When the sampler in the dataloader is set to None, MMEngine will choose either the InfiniteSampler (when by_epoch is False) or the DefaultSampler (when by_epoch is True) according to the train_cfg parameter.
+```
+
+```{note}
+If `type` is configured for the `train_cfg` in the base configuration, you must overwrite the type to target type (EpochBasedTrainLoop or IterBasedTrainLoop) rather than simply set `by_epoch` to True/False.
+```

docs/en/get_started/15_minutes.md

@@ -3,10 +3,11 @@
 In this tutorial, we'll take training a ResNet-50 model on CIFAR-10 dataset as an example. We will build a complete and configurable pipeline for both training and validation in only 80 lines of code with `MMEgnine`.
 The whole process includes the following steps:
 
-1. [Build a Model](#build-a-model)
-2. [Build a Dataset and DataLoader](#build-a-dataset-and-dataloader)
-3. [Build a Evaluation Metrics](#build-a-evaluation-metrics)
-4. [Build a Runner and Run the Task](#build-a-runner-and-run-the-task)
+- [15 minutes to get started with MMEngine](#15-minutes-to-get-started-with-mmengine)
+  - [Build a Model](#build-a-model)
+  - [Build a Dataset and DataLoader](#build-a-dataset-and-dataloader)
+  - [Build a Evaluation Metrics](#build-a-evaluation-metrics)
+  - [Build a Runner and Run the Task](#build-a-runner-and-run-the-task)
 
 ## Build a Model
 

docs/en/index.rst

@@ -23,6 +23,7 @@ You can switch between Chinese and English documents in the lower-left corner of
    common_usage/resume_training.md
    common_usage/speed_up_training.md
    common_usage/save_gpu_memory.md
+   common_usage/epoch_to_iter.md
 
 .. toctree::
    :maxdepth: 3