[Enhance] Add RuntimeInfoHook to update runtime information. (#254)

* [Enhance] Add RuntimeInfoHook to update runtime information.

* move lr to runtime info

* docstring

* resolve comments

* update ut and doc

docs/zh_cn/tutorials/hook.md

@@ -194,6 +194,7 @@ MMEngine 提供了很多内置的钩子，将钩子分为两类，分别是默
 
 |         名称          |             用途              |        优先级        |
 | :-----------------: | :-------------------------: | :---------------: |
+|   RuntimeInfoHook   |    向 message hub 更新运行时信息    |  VERY_HIGH (10)   |
 |    OptimizerHook    |         反向传播以及参数更新          |     HIGH (30)     |
 | DistSamplerSeedHook | 确保分布式 Sampler 的 shuffle 生效  |    NORMAL (50)    |
 |   SyncBuffersHook   |        同步模型的 buffer         |    NORMAL (50)    |
@@ -219,12 +220,13 @@ MMEngine 提供了很多内置的钩子，将钩子分为两类，分别是默
 from mmengine import Runner
 
 default_hooks = dict(
-    optimizer=dict(type='OptimizerHook'),
-    timer=dict(type='IterTimerHook',
+    runtime_info=dict(type='RuntimeInfoHook'),
+    optimizer=dict(type='OptimizerHook', grad_clip=None),
+    timer=dict(type='IterTimerHook'),
     sampler_seed=dict(type='DistSamplerSeedHook'),
-    logger=dict(type='TextLoggerHook'),
-    param_scheduler=dict(type='ParamSchedulerHook')),
-    checkpoint=dict(type='CheckpointHook', interval=1)
+    logger=dict(type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', interval=1),
 )
 
 custom_hooks = [
@@ -381,6 +383,11 @@ config = dict(type='EmptyCacheHook', before_epoch=False, after_epoch=True, after
 config = dict(type='SyncBuffersHook')
 ```
 
+### RuntimeInfoHook
+
+`RuntimeInfoHook` 会在执行器的不同钩子位点将当前的运行时信息（如 epoch、iter、max_epochs、max_iters、lr、metrics等）更新至 message hub 中，
+以便其他无法访问执行器的模块能够获取到这些信息。
+
 ## 添加自定义钩子
 
 如果 MMEngine 提供的默认钩子不能满足需求，用户可以自定义钩子，只需继承钩子基类并重写相应的位点方法。

mmengine/hooks/__init__.py

@@ -8,11 +8,12 @@ from .logger_hook import LoggerHook
 from .naive_visualization_hook import NaiveVisualizationHook
 from .optimizer_hook import OptimizerHook
 from .param_scheduler_hook import ParamSchedulerHook
+from .runtime_info_hook import RuntimeInfoHook
 from .sampler_seed_hook import DistSamplerSeedHook
 from .sync_buffer_hook import SyncBuffersHook
 
 __all__ = [
     'Hook', 'IterTimerHook', 'DistSamplerSeedHook', 'ParamSchedulerHook',
     'OptimizerHook', 'SyncBuffersHook', 'EmptyCacheHook', 'CheckpointHook',
-    'LoggerHook', 'NaiveVisualizationHook', 'EMAHook'
+    'LoggerHook', 'NaiveVisualizationHook', 'EMAHook', 'RuntimeInfoHook'
 ]

mmengine/hooks/ema_hook.py

@@ -1,6 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import itertools
-from typing import Optional
+from typing import Dict, Optional
 
 from mmengine.model import is_model_wrapper
 from mmengine.registry import HOOKS, MODELS
@@ -23,6 +23,8 @@ class EMAHook(Hook):
             Defaults to 'ExponentialMovingAverage'
     """
 
+    priority = 'NORMAL'
+
     def __init__(self, ema_type: str = 'ExponentialMovingAverage', **kwargs):
         self.ema_cfg = dict(type=ema_type, **kwargs)
 
@@ -48,7 +50,9 @@ class EMAHook(Hook):
         validation."""
         self._swap_ema_parameters()
 
-    def after_val_epoch(self, runner) -> None:
+    def after_val_epoch(self,
+                        runner,
+                        metrics: Optional[Dict[str, float]] = None) -> None:
         """We recover source model's parameter from ema model after
         validation."""
         self._swap_ema_parameters()
@@ -58,7 +62,9 @@ class EMAHook(Hook):
         test."""
         self._swap_ema_parameters()
 
-    def after_test_epoch(self, runner) -> None:
+    def after_test_epoch(self,
+                         runner,
+                         metrics: Optional[Dict[str, float]] = None) -> None:
         """We recover source model's parameter from ema model after test."""
         self._swap_ema_parameters()
 

mmengine/hooks/hook.py

@@ -1,5 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from typing import Optional, Sequence, Union
+from typing import Dict, Optional, Sequence, Union
 
 from mmengine.data import BaseDataElement
 
@@ -146,21 +146,31 @@ class Hook:
         """
         self._after_epoch(runner, mode='train')
 
-    def after_val_epoch(self, runner) -> None:
+    def after_val_epoch(self,
+                        runner,
+                        metrics: Optional[Dict[str, float]] = None) -> None:
         """All subclasses should override this method, if they need any
         operations after each validation epoch.
 
         Args:
             runner (Runner): The runner of the validation process.
+            metrics (Dict[str, float], optional): Evaluation results of all
+                metrics on validation dataset. The keys are the names of the
+                metrics, and the values are corresponding results.
         """
         self._after_epoch(runner, mode='val')
 
-    def after_test_epoch(self, runner) -> None:
+    def after_test_epoch(self,
+                         runner,
+                         metrics: Optional[Dict[str, float]] = None) -> None:
         """All subclasses should override this method, if they need any
         operations after each test epoch.
 
         Args:
             runner (Runner): The runner of the testing process.
+            metrics (Dict[str, float], optional): Evaluation results of all
+                metrics on test dataset. The keys are the names of the
+                metrics, and the values are corresponding results.
         """
         self._after_epoch(runner, mode='test')
 

mmengine/hooks/logger_hook.py

@@ -2,7 +2,7 @@
 import os
 import os.path as osp
 from pathlib import Path
-from typing import Optional, Sequence, Union
+from typing import Dict, Optional, Sequence, Union
 
 from mmengine.data import BaseDataElement
 from mmengine.fileio import FileClient
@@ -188,11 +188,17 @@ class LoggerHook(Hook):
                 runner, batch_idx, 'test')
             runner.logger.info(log_str)
 
-    def after_val_epoch(self, runner) -> None:
-        """Record logs after validation epoch.
+    def after_val_epoch(self,
+                        runner,
+                        metrics: Optional[Dict[str, float]] = None) -> None:
+        """All subclasses should override this method, if they need any
+        operations after each validation epoch.
 
         Args:
             runner (Runner): The runner of the validation process.
+            metrics (Dict[str, float], optional): Evaluation results of all
+                metrics on validation dataset. The keys are the names of the
+                metrics, and the values are corresponding results.
         """
         tag, log_str = runner.log_processor.get_log_after_epoch(
             runner, len(runner.val_dataloader), 'val')
@@ -200,11 +206,17 @@ class LoggerHook(Hook):
         runner.visualizer.add_scalars(
             tag, step=runner.iter, file_path=self.json_log_path)
 
-    def after_test_epoch(self, runner) -> None:
-        """Record logs after testing epoch.
+    def after_test_epoch(self,
+                         runner,
+                         metrics: Optional[Dict[str, float]] = None) -> None:
+        """All subclasses should override this method, if they need any
+        operations after each test epoch.
 
         Args:
             runner (Runner): The runner of the testing process.
+            metrics (Dict[str, float], optional): Evaluation results of all
+                metrics on test dataset. The keys are the names of the
+                metrics, and the values are corresponding results.
         """
         _, log_str = runner.log_processor.get_log_after_epoch(
             runner, len(runner.test_dataloader), 'test')

mmengine/hooks/optimizer_hook.py

@@ -84,9 +84,6 @@ class OptimizerHook(Hook):
                 we keep ``outputs`` here. Defaults to None.
         """
         runner.optimizer.zero_grad()
-        runner.message_hub.update_scalar(
-            'train/lr', runner.optimizer.param_groups[0]['lr'])
-
         if self.detect_anomalous_params:
             self.detect_anomalous_parameters(runner.outputs['loss'], runner)
         runner.outputs['loss'].backward()

mmengine/hooks/runtime_info_hook.py

@@ -0,0 +1,87 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Dict, Optional, Sequence
+
+from mmengine.registry import HOOKS
+from .hook import Hook
+
+DATA_BATCH = Optional[Sequence[dict]]
+
+
+@HOOKS.register_module()
+class RuntimeInfoHook(Hook):
+    """A hook that updates runtime information into message hub.
+
+    E.g. ``epoch``, ``iter``, ``max_epochs``, and ``max_iters`` for the
+    training state. Components that cannot access the runner can get runtime
+    information through the message hub.
+    """
+
+    priority = 'VERY_HIGH'
+
+    def before_run(self, runner) -> None:
+        """Initialize runtime information."""
+        runner.message_hub.update_info('epoch', runner.epoch)
+        runner.message_hub.update_info('iter', runner.iter)
+        runner.message_hub.update_info('max_epochs', runner.max_epochs)
+        runner.message_hub.update_info('max_iters', runner.max_iters)
+
+    def before_train(self, runner) -> None:
+        """Update resumed training state."""
+        runner.message_hub.update_info('epoch', runner.epoch)
+        runner.message_hub.update_info('iter', runner.iter)
+
+    def before_train_epoch(self, runner) -> None:
+        """Update current epoch information before every epoch."""
+        runner.message_hub.update_info('epoch', runner.epoch)
+
+    def before_train_iter(self,
+                          runner,
+                          batch_idx: int,
+                          data_batch: DATA_BATCH = None) -> None:
+        """Update current iter and learning rate information before every
+        iteration."""
+        runner.message_hub.update_info('iter', runner.iter)
+        runner.message_hub.update_scalar(
+            'train/lr', runner.optimizer.param_groups[0]['lr'])
+
+    def after_train_iter(self,
+                         runner,
+                         batch_idx: int,
+                         data_batch: DATA_BATCH = None,
+                         outputs: Optional[dict] = None) -> None:
+        """Update ``log_vars`` in model outputs every iteration."""
+        if outputs is not None:
+            for key, value in outputs['log_vars'].items():
+                runner.message_hub.update_scalar(f'train/{key}', value)
+
+    def after_val_epoch(self,
+                        runner,
+                        metrics: Optional[Dict[str, float]] = None) -> None:
+        """All subclasses should override this method, if they need any
+        operations after each validation epoch.
+
+        Args:
+            runner (Runner): The runner of the validation process.
+            metrics (Dict[str, float], optional): Evaluation results of all
+                metrics on validation dataset. The keys are the names of the
+                metrics, and the values are corresponding results.
+        """
+        if metrics is not None:
+            for key, value in metrics.items():
+                runner.message_hub.update_scalar(f'val/{key}', value)
+
+    def after_test_epoch(self,
+                         runner,
+                         metrics: Optional[Dict[str, float]] = None) -> None:
+        """All subclasses should override this method, if they need any
+        operations after each test epoch.
+
+        Args:
+            runner (Runner): The runner of the testing process.
+            metrics (Dict[str, float], optional): Evaluation results of all
+                metrics on test dataset. The keys are the names of the
+                metrics, and the values are corresponding results.
+        """
+        if metrics is not None:
+            for key, value in metrics.items():
+                runner.message_hub.update_scalar(f'test/{key}', value)

mmengine/runner/loops.py

@@ -80,8 +80,6 @@ class EpochBasedTrainLoop(BaseLoop):
 
         self.runner.call_hook('after_train_epoch')
         self._epoch += 1
-        # To allow components that cannot access runner to get current epoch.
-        self.runner.message_hub.update_info('epoch', self._epoch)
 
     def run_iter(self, idx, data_batch: Sequence[dict]) -> None:
         """Iterate one min-batch.
@@ -94,10 +92,6 @@ class EpochBasedTrainLoop(BaseLoop):
         # outputs should be a dict containing one or multiple loss tensors
         self.runner.outputs = self.runner.model(data_batch, return_loss=True)
 
-        # TODO, should move to LoggerHook
-        for key, value in self.runner.outputs['log_vars'].items():
-            self.runner.message_hub.update_scalar(f'train/{key}', value)
-
         self.runner.call_hook(
             'after_train_iter',
             batch_idx=idx,
@@ -105,9 +99,6 @@ class EpochBasedTrainLoop(BaseLoop):
             outputs=self.runner.outputs)
 
         self._iter += 1
-        # To allow components that cannot access runner to get current
-        # iteration.
-        self.runner.message_hub.update_info('iter', self._iter)
 
 
 @LOOPS.register_module()
@@ -188,19 +179,12 @@ class IterBasedTrainLoop(BaseLoop):
         # outputs should be a dict containing loss tensor
         self.runner.outputs = self.runner.model(data_batch, return_loss=True)
 
-        # TODO
-        for key, value in self.runner.outputs['log_vars'].items():
-            self.runner.message_hub.update_scalar(f'train/{key}', value)
-
         self.runner.call_hook(
             'after_train_iter',
             batch_idx=self._iter,
             data_batch=data_batch,
             outputs=self.runner.outputs)
         self._iter += 1
-        # To allow components that cannot access runner to get current
-        # iteration.
-        self.runner.message_hub.update_info('iter', self._iter)
 
 
 @LOOPS.register_module()
@@ -247,10 +231,8 @@ class ValLoop(BaseLoop):
 
         # compute metrics
         metrics = self.evaluator.evaluate(len(self.dataloader.dataset))
-        for key, value in metrics.items():
-            self.runner.message_hub.update_scalar(f'val/{key}', value)
 
-        self.runner.call_hook('after_val_epoch')
+        self.runner.call_hook('after_val_epoch', metrics=metrics)
         self.runner.call_hook('after_val')
 
     @torch.no_grad()
@@ -312,10 +294,8 @@ class TestLoop(BaseLoop):
 
         # compute metrics
         metrics = self.evaluator.evaluate(len(self.dataloader.dataset))
-        for key, value in metrics.items():
-            self.runner.message_hub.update_scalar(f'test/{key}', value)
 
-        self.runner.call_hook('after_test_epoch')
+        self.runner.call_hook('after_test_epoch', metrics=metrics)
         self.runner.call_hook('after_test')
 
     @torch.no_grad()

mmengine/runner/runner.py

@@ -1353,6 +1353,8 @@ class Runner:
         +----------------------+-------------------------+
         | Hooks                | Priority                |
         +======================+=========================+
+        | RuntimeInfoHook      | VERY_HIGH (10)          |
+        +----------------------+-------------------------+
         | OptimizerHook        | HIGH (30)               |
         +----------------------+-------------------------+
         | IterTimerHook        | NORMAL (40)             |
@@ -1370,6 +1372,7 @@ class Runner:
         default::
 
             default_hooks = dict(
+                runtime_info=dict(type='RuntimeInfoHook'),
                 optimizer=dict(type='OptimizerHook', grad_clip=None),
                 timer=dict(type='IterTimerHook'),
                 sampler_seed=dict(type='DistSamplerSeedHook'),
@@ -1392,6 +1395,7 @@ class Runner:
                 to be registered.
         """
         default_hooks: dict = dict(
+            runtime_info=dict(type='RuntimeInfoHook'),
             optimizer=dict(type='OptimizerHook', grad_clip=None),
             timer=dict(type='IterTimerHook'),
             logger=dict(type='LoggerHook'),

tests/test_hook/test_hook.py

@@ -74,12 +74,12 @@ class TestHook:
     def test_after_val_epoch(self):
         hook = Hook()
         runner = Mock()
-        hook.after_val_epoch(runner)
+        hook.after_val_epoch(runner, {})
 
     def test_after_test_epoch(self):
         hook = Hook()
         runner = Mock()
-        hook.after_test_epoch(runner)
+        hook.after_test_epoch(runner, {})
 
     def test_before_train_iter(self):
         hook = Hook()

tests/test_hook/test_runtime_info_hook.py

@@ -0,0 +1,93 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from unittest import TestCase
+from unittest.mock import Mock
+
+from mmengine.hooks import RuntimeInfoHook
+from mmengine.logging import MessageHub
+
+
+class TestRuntimeInfoHook(TestCase):
+
+    def test_before_run(self):
+        message_hub = MessageHub.get_instance(
+            'runtime_info_hook_test_before_run')
+        runner = Mock()
+        runner.epoch = 3
+        runner.iter = 30
+        runner.max_epochs = 4
+        runner.max_iters = 40
+        runner.message_hub = message_hub
+        hook = RuntimeInfoHook()
+        hook.before_run(runner)
+        self.assertEqual(message_hub.get_info('epoch'), 3)
+        self.assertEqual(message_hub.get_info('iter'), 30)
+        self.assertEqual(message_hub.get_info('max_epochs'), 4)
+        self.assertEqual(message_hub.get_info('max_iters'), 40)
+
+    def test_before_train(self):
+        message_hub = MessageHub.get_instance(
+            'runtime_info_hook_test_before_train')
+        runner = Mock()
+        runner.epoch = 7
+        runner.iter = 71
+        runner.message_hub = message_hub
+        hook = RuntimeInfoHook()
+        hook.before_train(runner)
+        self.assertEqual(message_hub.get_info('epoch'), 7)
+        self.assertEqual(message_hub.get_info('iter'), 71)
+
+    def test_before_train_epoch(self):
+        message_hub = MessageHub.get_instance(
+            'runtime_info_hook_test_before_train_epoch')
+        runner = Mock()
+        runner.epoch = 9
+        runner.message_hub = message_hub
+        hook = RuntimeInfoHook()
+        hook.before_train_epoch(runner)
+        self.assertEqual(message_hub.get_info('epoch'), 9)
+
+    def test_before_train_iter(self):
+        message_hub = MessageHub.get_instance(
+            'runtime_info_hook_test_before_train_iter')
+        runner = Mock()
+        runner.iter = 9
+        runner.optimizer.param_groups = [{'lr': 0.01}]
+        runner.message_hub = message_hub
+        hook = RuntimeInfoHook()
+        hook.before_train_iter(runner, batch_idx=2, data_batch=None)
+        self.assertEqual(message_hub.get_info('iter'), 9)
+        self.assertEqual(message_hub.get_scalar('train/lr').current(), 0.01)
+
+    def test_after_train_iter(self):
+        message_hub = MessageHub.get_instance(
+            'runtime_info_hook_test_after_train_iter')
+        runner = Mock()
+        runner.message_hub = message_hub
+        hook = RuntimeInfoHook()
+        hook.after_train_iter(
+            runner,
+            batch_idx=2,
+            data_batch=None,
+            outputs={'log_vars': {
+                'loss_cls': 1.111
+            }})
+        self.assertEqual(
+            message_hub.get_scalar('train/loss_cls').current(), 1.111)
+
+    def test_after_val_epoch(self):
+        message_hub = MessageHub.get_instance(
+            'runtime_info_hook_test_after_val_epoch')
+        runner = Mock()
+        runner.message_hub = message_hub
+        hook = RuntimeInfoHook()
+        hook.after_val_epoch(runner, metrics={'acc': 0.8})
+        self.assertEqual(message_hub.get_scalar('val/acc').current(), 0.8)
+
+    def test_after_test_epoch(self):
+        message_hub = MessageHub.get_instance(
+            'runtime_info_hook_test_after_test_epoch')
+        runner = Mock()
+        runner.message_hub = message_hub
+        hook = RuntimeInfoHook()
+        hook.after_test_epoch(runner, metrics={'acc': 0.8})
+        self.assertEqual(message_hub.get_scalar('test/acc').current(), 0.8)

tests/test_runner/test_runner.py

@@ -218,11 +218,14 @@ class TestRunner(TestCase):
             test_cfg=dict(),
             custom_hooks=[],
             default_hooks=dict(
-                timer=dict(type='IterTimerHook'),
-                checkpoint=dict(type='CheckpointHook', interval=1),
-                logger=dict(type='LoggerHook'),
+                runtime_info=dict(type='RuntimeInfoHook'),
                 optimizer=dict(type='OptimizerHook', grad_clip=None),
-                param_scheduler=dict(type='ParamSchedulerHook')),
+                timer=dict(type='IterTimerHook'),
+                logger=dict(type='LoggerHook'),
+                param_scheduler=dict(type='ParamSchedulerHook'),
+                checkpoint=dict(
+                    type='CheckpointHook', interval=1, by_epoch=True),
+                sampler_seed=dict(type='DistSamplerSeedHook')),
             launcher='none',
             env_cfg=dict(dist_cfg=dict(backend='nccl')),
         )
@@ -235,11 +238,13 @@ class TestRunner(TestCase):
             num_workers=0)
         self.iter_based_cfg.train_cfg = dict(by_epoch=False, max_iters=12)
         self.iter_based_cfg.default_hooks = dict(
-            timer=dict(type='IterTimerHook'),
-            checkpoint=dict(type='CheckpointHook', interval=1, by_epoch=False),
-            logger=dict(type='LoggerHook'),
+            runtime_info=dict(type='RuntimeInfoHook'),
             optimizer=dict(type='OptimizerHook', grad_clip=None),
-            param_scheduler=dict(type='ParamSchedulerHook'))
+            timer=dict(type='IterTimerHook'),
+            logger=dict(type='LoggerHook'),
+            param_scheduler=dict(type='ParamSchedulerHook'),
+            checkpoint=dict(type='CheckpointHook', interval=1, by_epoch=False),
+            sampler_seed=dict(type='DistSamplerSeedHook'))
 
     def tearDown(self):
         shutil.rmtree(self.temp_dir)
@@ -868,9 +873,6 @@ class TestRunner(TestCase):
 
         assert isinstance(runner.train_loop, EpochBasedTrainLoop)
 
-        assert runner.iter == runner.message_hub.get_info('iter')
-        assert runner.epoch == runner.message_hub.get_info('epoch')
-
         for result, target, in zip(epoch_results, epoch_targets):
             self.assertEqual(result, target)
         for result, target, in zip(iter_results, iter_targets):
@@ -903,7 +905,6 @@ class TestRunner(TestCase):
         runner.train()
 
         assert isinstance(runner.train_loop, IterBasedTrainLoop)
-        assert runner.iter == runner.message_hub.get_info('iter')
 
         self.assertEqual(len(epoch_results), 1)
         self.assertEqual(epoch_results[0], 0)
@@ -1035,37 +1036,37 @@ class TestRunner(TestCase):
         runner = Runner.from_cfg(cfg)
         runner._hooks = []
 
-        # register five hooks by default
+        # register 7 hooks by default
         runner.register_default_hooks()
-        self.assertEqual(len(runner._hooks), 6)
+        self.assertEqual(len(runner._hooks), 7)
         # the third registered hook should be `DistSamplerSeedHook`
-        self.assertTrue(isinstance(runner._hooks[2], DistSamplerSeedHook))
+        self.assertTrue(isinstance(runner._hooks[3], DistSamplerSeedHook))
         # the fifth registered hook should be `ParamSchedulerHook`
-        self.assertTrue(isinstance(runner._hooks[4], ParamSchedulerHook))
+        self.assertTrue(isinstance(runner._hooks[5], ParamSchedulerHook))
 
         runner._hooks = []
         # remove `ParamSchedulerHook` from default hooks
         runner.register_default_hooks(hooks=dict(timer=None))
-        self.assertEqual(len(runner._hooks), 5)
-        # `ParamSchedulerHook` was popped so the forth is `CheckpointHook`
-        self.assertTrue(isinstance(runner._hooks[4], CheckpointHook))
+        self.assertEqual(len(runner._hooks), 6)
+        # `ParamSchedulerHook` was popped so the fifth is `CheckpointHook`
+        self.assertTrue(isinstance(runner._hooks[5], CheckpointHook))
 
         # add a new default hook
         runner._hooks = []
         runner.register_default_hooks(hooks=dict(ToyHook=dict(type='ToyHook')))
-        self.assertEqual(len(runner._hooks), 7)
-        self.assertTrue(isinstance(runner._hooks[6], ToyHook))
+        self.assertEqual(len(runner._hooks), 8)
+        self.assertTrue(isinstance(runner._hooks[7], ToyHook))
 
     def test_custom_hooks(self):
         cfg = copy.deepcopy(self.epoch_based_cfg)
         cfg.experiment_name = 'test_custom_hooks'
         runner = Runner.from_cfg(cfg)
 
-        self.assertEqual(len(runner._hooks), 6)
+        self.assertEqual(len(runner._hooks), 7)
         custom_hooks = [dict(type='ToyHook')]
         runner.register_custom_hooks(custom_hooks)
-        self.assertEqual(len(runner._hooks), 7)
-        self.assertTrue(isinstance(runner._hooks[6], ToyHook))
+        self.assertEqual(len(runner._hooks), 8)
+        self.assertTrue(isinstance(runner._hooks[7], ToyHook))
 
     def test_register_hooks(self):
         cfg = copy.deepcopy(self.epoch_based_cfg)
@@ -1075,9 +1076,9 @@ class TestRunner(TestCase):
         runner._hooks = []
         custom_hooks = [dict(type='ToyHook')]
         runner.register_hooks(custom_hooks=custom_hooks)
-        # six default hooks + custom hook (ToyHook)
-        self.assertEqual(len(runner._hooks), 7)
-        self.assertTrue(isinstance(runner._hooks[6], ToyHook))
+        # 7 default hooks + custom hook (ToyHook)
+        self.assertEqual(len(runner._hooks), 8)
+        self.assertTrue(isinstance(runner._hooks[7], ToyHook))
 
     def test_custom_loop(self):
         # test custom loop with additional hook