From be9971781e00b99bdb482dd594b2b2dd01b1aa51 Mon Sep 17 00:00:00 2001
From: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com>
Date: Mon, 7 Mar 2022 14:00:05 +0800
Subject: [PATCH] [Fix]: Change the type of runner in docstring to Runner
 (#103)

* [Fix]: Change after inter and epoch to after train iter and epoch

* [Fix]: Add new UT to param scheduler hook

* [Fix]: Change the type of runner in docstring to Runner

Co-authored-by: Your <you@example.com>
---
 mmengine/hooks/checkpoint_hook.py            |  8 +--
 mmengine/hooks/hook.py                       | 52 ++++++++++----------
 mmengine/hooks/optimizer_hook.py             |  4 +-
 mmengine/hooks/param_scheduler_hook.py       | 16 +++---
 mmengine/hooks/sampler_seed_hook.py          |  2 +-
 tests/test_hook/test_param_scheduler_hook.py |  4 +-
 6 files changed, 43 insertions(+), 43 deletions(-)

diff --git a/mmengine/hooks/checkpoint_hook.py b/mmengine/hooks/checkpoint_hook.py
index 89c742f3..af6c1de3 100644
--- a/mmengine/hooks/checkpoint_hook.py
+++ b/mmengine/hooks/checkpoint_hook.py
@@ -72,7 +72,7 @@ class CheckpointHook(Hook):
         to save these checkpoints of the model.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         if not self.out_dir:
             self.out_dir = runner.work_dir  # type: ignore
@@ -113,7 +113,7 @@ class CheckpointHook(Hook):
         """Save the checkpoint and synchronize buffers after each epoch.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         if not self.by_epoch:
             return
@@ -137,7 +137,7 @@ class CheckpointHook(Hook):
         """Save the current checkpoint and delete outdated checkpoint.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         runner.save_checkpoint(  # type: ignore
             self.out_dir,
@@ -184,7 +184,7 @@ class CheckpointHook(Hook):
         """Save the checkpoint and synchronize buffers after each iteration.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             data_batch (Sequence[Tuple[Any, BaseDataSample]], optional): Data
                 from dataloader. Defaults to None.
             outputs (Sequence[BaseDataSample], optional): Outputs from model.
diff --git a/mmengine/hooks/hook.py b/mmengine/hooks/hook.py
index 2582bc7b..684196de 100644
--- a/mmengine/hooks/hook.py
+++ b/mmengine/hooks/hook.py
@@ -17,7 +17,7 @@ class Hook:
         operations before the training process.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         pass
 
@@ -26,7 +26,7 @@ class Hook:
         operations after the training process.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         pass
 
@@ -35,7 +35,7 @@ class Hook:
         operations before each epoch.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         pass
 
@@ -44,7 +44,7 @@ class Hook:
         operations after each epoch.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         pass
 
@@ -57,7 +57,7 @@ class Hook:
         operations before each iter.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             data_batch (Sequence[Tuple[Any, BaseDataSample]], optional):
                 Data from dataloader. Defaults to None.
         """
@@ -72,7 +72,7 @@ class Hook:
         operations after each epoch.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             data_batch (Sequence[Tuple[Any, BaseDataSample]], optional):
                 Data from dataloader. Defaults to None.
             outputs (Sequence[BaseDataSample], optional): Outputs from model.
@@ -85,7 +85,7 @@ class Hook:
         operations before saving the checkpoint.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             checkpoints (dict): Model's checkpoint.
         """
         pass
@@ -95,7 +95,7 @@ class Hook:
         operations after loading the checkpoint.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             checkpoints (dict): Model's checkpoint.
         """
         pass
@@ -105,7 +105,7 @@ class Hook:
         operations before each training epoch.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         self.before_epoch(runner)
 
@@ -114,7 +114,7 @@ class Hook:
         operations before each validation epoch.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         self.before_epoch(runner)
 
@@ -123,7 +123,7 @@ class Hook:
         operations before each test epoch.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         self.before_epoch(runner)
 
@@ -132,7 +132,7 @@ class Hook:
         operations after each training epoch.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         self.after_epoch(runner)
 
@@ -141,7 +141,7 @@ class Hook:
         operations after each validation epoch.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         self.after_epoch(runner)
 
@@ -150,7 +150,7 @@ class Hook:
         operations after each test epoch.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         self.after_epoch(runner)
 
@@ -163,7 +163,7 @@ class Hook:
         operations before each training iteration.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             data_batch (Sequence[Tuple[Any, BaseDataSample]], optional):
                 Data from dataloader. Defaults to None.
         """
@@ -178,7 +178,7 @@ class Hook:
         operations before each validation iteration.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             data_batch (Sequence[Tuple[Any, BaseDataSample]], optional):
                 Data from dataloader. Defaults to None.
         """
@@ -193,7 +193,7 @@ class Hook:
         operations before each test iteration.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             data_batch (Sequence[Tuple[Any, BaseDataSample]], optional):
                 Data from dataloader. Defaults to None.
         """
@@ -208,7 +208,7 @@ class Hook:
         operations after each training iteration.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             data_batch (Sequence[Tuple[Any, BaseDataSample]], optional):
                 Data from dataloader. Defaults to None.
             outputs (Sequence[BaseDataSample], optional): Outputs from model.
@@ -225,7 +225,7 @@ class Hook:
         operations after each validation iteration.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             data_batch (Sequence[Tuple[Any, BaseDataSample]], optional):
                 Data from dataloader. Defaults to None.
             outputs (Sequence[BaseDataSample], optional): Outputs from
@@ -242,7 +242,7 @@ class Hook:
         operations after each test iteration.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             data_batch (Sequence[Tuple[Any, BaseDataSample]], optional):
                 Data from dataloader. Defaults to None.
             outputs (Sequence[BaseDataSample], optional): Outputs from model.
@@ -254,7 +254,7 @@ class Hook:
         """Test whether or not current epoch can be evenly divided by n.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             n (int): Whether or not current epoch can be evenly divided by n.
 
         Returns:
@@ -267,7 +267,7 @@ class Hook:
         n.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             n (int): Whether or not current inner iteration can be evenly
                 divided by n.
 
@@ -282,7 +282,7 @@ class Hook:
         """Test whether or not current iteration can be evenly divided by n.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             n (int): Whether or not current iteration can be
                 evenly divided by n.
 
@@ -296,7 +296,7 @@ class Hook:
         """Check whether the current epoch reaches the `max_epochs` or not.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
 
         Returns:
             bool: whether the end of current epoch or not.
@@ -307,7 +307,7 @@ class Hook:
         """Test whether or not current epoch is the last epoch.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
 
         Returns:
             bool: bool: Return True if the current epoch reaches the
@@ -319,7 +319,7 @@ class Hook:
         """Test whether or not current epoch is the last iteration.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
 
         Returns:
             bool: whether or not current iteration is the last iteration.
diff --git a/mmengine/hooks/optimizer_hook.py b/mmengine/hooks/optimizer_hook.py
index a22cb1bb..bbccd667 100644
--- a/mmengine/hooks/optimizer_hook.py
+++ b/mmengine/hooks/optimizer_hook.py
@@ -73,7 +73,7 @@ class OptimizerHook(Hook):
         - Update model parameters with gradients.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             data_batch (Sequence[Tuple[Any, BaseDataSample]], optional): Data
                 from dataloader. In order to keep this interface consistent
                 with other hooks, we keep ``data_batch`` here.
@@ -105,7 +105,7 @@ class OptimizerHook(Hook):
 
         Args:
             loss (torch.Tensor): The loss of current iteration.
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         logger = runner.logger  # type: ignore
         parameters_in_graph = set()
diff --git a/mmengine/hooks/param_scheduler_hook.py b/mmengine/hooks/param_scheduler_hook.py
index e99d9251..fe162d12 100644
--- a/mmengine/hooks/param_scheduler_hook.py
+++ b/mmengine/hooks/param_scheduler_hook.py
@@ -13,15 +13,15 @@ class ParamSchedulerHook(Hook):
 
     priority = 'LOW'
 
-    def after_iter(self,
-                   runner: object,
-                   data_batch: Optional[Sequence[Tuple[
-                       Any, BaseDataSample]]] = None,
-                   outputs: Optional[Sequence[BaseDataSample]] = None) -> None:
+    def after_train_iter(
+            self,
+            runner: object,
+            data_batch: Optional[Sequence[Tuple[Any, BaseDataSample]]] = None,
+            outputs: Optional[Sequence[BaseDataSample]] = None) -> None:
         """Call step function for each scheduler after each iteration.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
             data_batch (Sequence[Tuple[Any, BaseDataSample]], optional): Data
                 from dataloader. In order to keep this interface consistent
                 with other hooks, we keep ``data_batch`` here.
@@ -34,11 +34,11 @@ class ParamSchedulerHook(Hook):
             if not scheduler.by_epoch:
                 scheduler.step()
 
-    def after_epoch(self, runner: object) -> None:
+    def after_train_epoch(self, runner: object) -> None:
         """Call step function for each scheduler after each epoch.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         for scheduler in runner.schedulers:  # type: ignore
             if scheduler.by_epoch:
diff --git a/mmengine/hooks/sampler_seed_hook.py b/mmengine/hooks/sampler_seed_hook.py
index 6d665172..0896636d 100644
--- a/mmengine/hooks/sampler_seed_hook.py
+++ b/mmengine/hooks/sampler_seed_hook.py
@@ -18,7 +18,7 @@ class DistSamplerSeedHook(Hook):
         """Set the seed for sampler and batch_sampler.
 
         Args:
-            runner (object): The runner of the training process.
+            runner (Runner): The runner of the training process.
         """
         if hasattr(runner.data_loader.sampler, 'set_epoch'):  # type: ignore
             # in case the data loader uses `SequentialSampler` in Pytorch
diff --git a/tests/test_hook/test_param_scheduler_hook.py b/tests/test_hook/test_param_scheduler_hook.py
index 75f12c4a..f944d3ed 100644
--- a/tests/test_hook/test_param_scheduler_hook.py
+++ b/tests/test_hook/test_param_scheduler_hook.py
@@ -13,7 +13,7 @@ class TestParamSchedulerHook:
         scheduler.step = Mock()
         scheduler.by_epoch = False
         Runner.schedulers = [scheduler]
-        Hook.after_iter(Runner)
+        Hook.after_train_iter(Runner)
         scheduler.step.assert_called()
 
     def test_after_epoch(self):
@@ -23,5 +23,5 @@ class TestParamSchedulerHook:
         scheduler.step = Mock()
         scheduler.by_epoch = True
         Runner.schedulers = [scheduler]
-        Hook.after_epoch(Runner)
+        Hook.after_train_epoch(Runner)
         scheduler.step.assert_called()
-- 
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