[Refactor]: Modify val_interval and val_begin to be the attributes of TrainLoop. (#274)

* Modify val_interval and val_begin to be the attributes of TrainLoop.

* update doc

* fix lint

* type hint

docs/zh_cn/tutorials/runner.md

@@ -152,8 +152,13 @@ val_evaluator = dict(type='Accuracy')
 test_evaluator = dict(type='Accuracy')
 
 # 训练、验证、测试流程配置
-train_cfg = dict(by_epoch=True, max_epochs=100)
-val_cfg = dict(interval=1)  # 每隔一个 epoch 进行一次验证
+train_cfg = dict(
+    by_epoch=True,
+    max_epochs=100,
+    val_begin=20,  # 从第 20 个 epoch 开始验证
+    val_interval=1  # 每隔一个 epoch 进行一次验证
+)
+val_cfg = dict()
 test_cfg = dict()
 
 # 自定义钩子

mmengine/runner/loops.py

@@ -20,15 +20,24 @@ class EpochBasedTrainLoop(BaseLoop):
         dataloader (Dataloader or dict): A dataloader object or a dict to
             build a dataloader.
         max_epochs (int): Total training epochs.
+        val_begin (int): The epoch that begins validating.
+            Defaults to 1.
+        val_interval (int): Validation interval. Defaults to 1.
     """
 
-    def __init__(self, runner, dataloader: Union[DataLoader, Dict],
-                 max_epochs: int) -> None:
+    def __init__(self,
+                 runner,
+                 dataloader: Union[DataLoader, Dict],
+                 max_epochs: int,
+                 val_begin: int = 1,
+                 val_interval: int = 1) -> None:
         super().__init__(runner, dataloader)
         self._max_epochs = max_epochs
         self._max_iters = max_epochs * len(self.dataloader)
         self._epoch = 0
         self._iter = 0
+        self.val_begin = val_begin
+        self.val_interval = val_interval
         if hasattr(self.dataloader.dataset, 'metainfo'):
             self.runner.visualizer.dataset_meta = \
                 self.dataloader.dataset.metainfo
@@ -66,8 +75,8 @@ class EpochBasedTrainLoop(BaseLoop):
             self.run_epoch()
 
             if (self.runner.val_loop is not None
-                    and self._epoch >= self.runner.val_loop.begin
-                    and self._epoch % self.runner.val_loop.interval == 0):
+                    and self._epoch >= self.val_begin
+                    and self._epoch % self.val_interval == 0):
                 self.runner.val_loop.run()
 
         self.runner.call_hook('after_train')
@@ -111,15 +120,24 @@ class IterBasedTrainLoop(BaseLoop):
         dataloader (Dataloader or dict): A dataloader object or a dict to
             build a dataloader.
         max_iters (int): Total training iterations.
+        val_begin (int): The iteration that begins validating.
+            Defaults to 1.
+        val_interval (int): Validation interval. Defaults to 1000.
     """
 
-    def __init__(self, runner, dataloader: Union[DataLoader, Dict],
-                 max_iters: int) -> None:
+    def __init__(self,
+                 runner,
+                 dataloader: Union[DataLoader, Dict],
+                 max_iters: int,
+                 val_begin: int = 1,
+                 val_interval: int = 1000) -> None:
         super().__init__(runner, dataloader)
         self._max_iters = max_iters
         self._max_epochs = 1  # for compatibility with EpochBasedTrainLoop
         self._epoch = 0
         self._iter = 0
+        self.val_begin = val_begin
+        self.val_interval = val_interval
         if hasattr(self.dataloader.dataset, 'metainfo'):
             self.runner.visualizer.dataset_meta = \
                 self.dataloader.dataset.metainfo
@@ -163,8 +181,8 @@ class IterBasedTrainLoop(BaseLoop):
             self.run_iter(data_batch)
 
             if (self.runner.val_loop is not None
-                    and self._iter >= self.runner.val_begin
-                    and self._iter % self.runner.val_interval == 0):
+                    and self._iter >= self.val_begin
+                    and self._iter % self.val_interval == 0):
                 self.runner.val_loop.run()
 
         self.runner.call_hook('after_train_epoch')
@@ -198,16 +216,10 @@ class ValLoop(BaseLoop):
         dataloader (Dataloader or dict): A dataloader object or a dict to
             build a dataloader.
         evaluator (Evaluator or dict or list): Used for computing metrics.
-        interval (int): Validation interval. Defaults to 1.
-        begin (int): The epoch/iteration that begins validating. Defaults to 1.
     """
 
-    def __init__(self,
-                 runner,
-                 dataloader: Union[DataLoader, Dict],
-                 evaluator: Union[Evaluator, Dict, List],
-                 interval: int = 1,
-                 begin: int = 1) -> None:
+    def __init__(self, runner, dataloader: Union[DataLoader, Dict],
+                 evaluator: Union[Evaluator, Dict, List]) -> None:
         super().__init__(runner, dataloader)
 
         if isinstance(evaluator, dict) or is_list_of(evaluator, dict):
@@ -223,8 +235,6 @@ class ValLoop(BaseLoop):
                 f'Dataset {self.dataloader.dataset.__class__.__name__} has no '
                 'metainfo. ``dataset_meta`` in evaluator, metric and '
                 'visualizer will be None.')
-        self.interval = interval
-        self.begin = begin
 
     def run(self):
         """Launch validation."""

mmengine/runner/runner.py

@@ -189,8 +189,8 @@ class Runner:
         >>>     param_scheduler=dict(type='MultiStepLR', milestones=[1, 2]),
         >>>     val_evaluator=dict(type='ToyEvaluator'),
         >>>     test_evaluator=dict(type='ToyEvaluator'),
-        >>>     train_cfg=dict(by_epoch=True, max_epochs=3),
-        >>>     val_cfg=dict(interval=1),
+        >>>     train_cfg=dict(by_epoch=True, max_epochs=3, val_interval=1),
+        >>>     val_cfg=dict(),
         >>>     test_cfg=dict(),
         >>>     custom_hooks=[],
         >>>     default_hooks=dict(
@@ -573,13 +573,13 @@ class Runner:
     @property
     def val_interval(self):
         """int: Interval to run validation during training."""
-        return self.val_loop.interval
+        return self.train_loop.val_interval
 
     @property
     def val_begin(self):
         """int: The epoch/iteration to start running validation during
         training."""
-        return self.val_loop.begin
+        return self.train_loop.val_begin
 
     def setup_env(self, env_cfg: Dict) -> None:
         """Setup environment.
@@ -1285,10 +1285,10 @@ class Runner:
         Examples of ``loop``:
 
             # `ValLoop` will be used
-            loop = dict(interval=1)
+            loop = dict()
 
             # custom validation loop
-            loop = dict(type='CustomValLoop', interval=1)
+            loop = dict(type='CustomValLoop')
 
         Args:
             loop (BaseLoop or dict): A validation loop or a dict to build
@@ -1317,9 +1317,7 @@ class Runner:
             loop = ValLoop(
                 runner=self,
                 dataloader=self._val_dataloader,
-                evaluator=self._val_evaluator,  # type: ignore
-                **loop_cfg,
-            )  # type: ignore
+                evaluator=self._val_evaluator)  # type: ignore
 
         return loop  # type: ignore
 

tests/test_hook/test_ema_hook.py

@@ -82,8 +82,8 @@ class TestEMAHook(TestCase):
             work_dir=self.temp_dir.name,
             optim_wrapper=OptimWrapper(
                 torch.optim.Adam(ToyModel().parameters())),
-            train_cfg=dict(by_epoch=True, max_epochs=2),
-            val_cfg=dict(interval=1),
+            train_cfg=dict(by_epoch=True, max_epochs=2, val_interval=1),
+            val_cfg=dict(),
             default_hooks=dict(logger=None),
             custom_hooks=[dict(type='EMAHook', )],
             experiment_name='test1')

tests/test_runner/test_runner.py

@@ -176,7 +176,7 @@ class CustomTrainLoop(BaseLoop):
 @LOOPS.register_module()
 class CustomValLoop(BaseLoop):
 
-    def __init__(self, runner, dataloader, evaluator, interval=1):
+    def __init__(self, runner, dataloader, evaluator):
         super().__init__(runner, dataloader)
         self._runner = runner
 
@@ -246,8 +246,9 @@ class TestRunner(TestCase):
             param_scheduler=dict(type='MultiStepLR', milestones=[1, 2]),
             val_evaluator=dict(type='ToyMetric1'),
             test_evaluator=dict(type='ToyMetric1'),
-            train_cfg=dict(by_epoch=True, max_epochs=3),
-            val_cfg=dict(interval=1, begin=1),
+            train_cfg=dict(
+                by_epoch=True, max_epochs=3, val_interval=1, val_begin=1),
+            val_cfg=dict(),
             test_cfg=dict(),
             custom_hooks=[],
             default_hooks=dict(
@@ -432,11 +433,12 @@ class TestRunner(TestCase):
         runner = Runner(
             model=model,
             work_dir=self.temp_dir,
-            train_cfg=dict(by_epoch=True, max_epochs=3),
+            train_cfg=dict(
+                by_epoch=True, max_epochs=3, val_interval=1, val_begin=1),
             train_dataloader=train_dataloader,
             optim_wrapper=optim_wrapper,
             param_scheduler=MultiStepLR(optim_wrapper, milestones=[1, 2]),
-            val_cfg=dict(interval=1, begin=1),
+            val_cfg=dict(),
             val_dataloader=val_dataloader,
             val_evaluator=ToyMetric1(),
             test_cfg=dict(),
@@ -888,12 +890,12 @@ class TestRunner(TestCase):
             runner.build_test_loop('invalid-type')
 
         # input is a dict and contains type key
-        cfg = dict(type='ValLoop', interval=1, begin=1)
+        cfg = dict(type='ValLoop')
         loop = runner.build_test_loop(cfg)
         self.assertIsInstance(loop, ValLoop)
 
         # input is a dict but does not contain type key
-        cfg = dict(interval=1, begin=1)
+        cfg = dict()
         loop = runner.build_val_loop(cfg)
         self.assertIsInstance(loop, ValLoop)
 
@@ -901,7 +903,7 @@ class TestRunner(TestCase):
         self.assertEqual(id(runner.build_val_loop(loop)), id(loop))
 
         # test custom validation loop
-        cfg = dict(type='CustomValLoop', interval=1)
+        cfg = dict(type='CustomValLoop')
         loop = runner.build_val_loop(cfg)
         self.assertIsInstance(loop, CustomValLoop)
 
@@ -999,7 +1001,7 @@ class TestRunner(TestCase):
         cfg = copy.deepcopy(self.epoch_based_cfg)
         cfg.experiment_name = 'test_train2'
         cfg.custom_hooks = [dict(type='TestEpochHook', priority=50)]
-        cfg.val_cfg = dict(begin=2)
+        cfg.train_cfg = dict(by_epoch=True, max_epochs=3, val_begin=2)
         runner = Runner.from_cfg(cfg)
 
         runner.train()
@@ -1044,7 +1046,8 @@ class TestRunner(TestCase):
         cfg = copy.deepcopy(self.iter_based_cfg)
         cfg.experiment_name = 'test_train3'
         cfg.custom_hooks = [dict(type='TestIterHook', priority=50)]
-        cfg.val_cfg = dict(interval=4, begin=4)
+        cfg.train_cfg = dict(
+            by_epoch=False, max_iters=12, val_interval=4, val_begin=4)
         runner = Runner.from_cfg(cfg)
         runner.train()
 
@@ -1052,6 +1055,8 @@ class TestRunner(TestCase):
 
         self.assertEqual(len(epoch_results), 1)
         self.assertEqual(epoch_results[0], 0)
+        self.assertEqual(runner.val_interval, 4)
+        self.assertEqual(runner.val_begin, 4)
         for result, target, in zip(iter_results, iter_targets):
             self.assertEqual(result, target)
         for result, target, in zip(batch_idx_results, batch_idx_targets):