[Feature]: Add sampler seed hook (#64)

* [Feature]: Add sampler seed hook

* [Fix]: Add call with to UT

docs/zh_cn/tutorials/evaluator.md

@@ -97,7 +97,7 @@ import numpy as np
 
 @EVALUATORS.register_module()
 class Accuracy(BaseEvaluator):
-   
+
     def process(self, data_samples: Dict, predictions: Dict):
         """Process one batch of data and predictions. The processed
         Results should be stored in `self.results`, which will be used

docs/zh_cn/tutorials/logging.md

@@ -276,7 +276,7 @@ class ModuleA:
 class ModuleB:
     def __init__(self):
         self.instance = GlobalAccessible.get_instance(current=True)
-        
+
     def run(self):
         print(f'moduleB: {self.instance.instance_name} is called')
 

mmengine/hooks/__init__.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from .hook import Hook
 from .iter_timer_hook import IterTimerHook
+from .sampler_seed_hook import DistSamplerSeedHook
 
-__all__ = ['Hook', 'IterTimerHook']
+__all__ = ['Hook', 'IterTimerHook', 'DistSamplerSeedHook']

mmengine/hooks/sampler_seed_hook.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.registry import HOOKS
+from .hook import Hook
+
+
+@HOOKS.register_module()
+class DistSamplerSeedHook(Hook):
+    """Data-loading sampler for distributed training.
+
+    When distributed training, it is only useful in conjunction with
+    :obj:`EpochBasedRunner`, while :obj:`IterBasedRunner` achieves the same
+    purpose with :obj:`IterLoader`.
+    """
+
+    def before_epoch(self, runner: object) -> None:
+        """Set the seed for sampler and batch_sampler.
+
+        Args:
+            runner (object): 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
+            runner.data_loader.sampler.set_epoch(runner.epoch)  # type: ignore
+        elif hasattr(
+                runner.data_loader.batch_sampler.sampler,  # type: ignore
+                'set_epoch'):
+            # batch sampler in pytorch warps the sampler as its attributes.
+            runner.data_loader.batch_sampler.sampler.set_epoch(  # type: ignore
+                runner.epoch)  # type: ignore

tests/test_hook/test_sampler_seed_hook.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from unittest.mock import Mock
+
+from mmengine.hooks import DistSamplerSeedHook
+
+
+class TestDistSamplerSeedHook:
+
+    def test_before_epoch(self):
+
+        hook = DistSamplerSeedHook()
+        # Test dataset sampler
+        runner = Mock()
+        runner.epoch = 1
+        runner.data_loader = Mock()
+        runner.data_loader.sampler = Mock()
+        runner.data_loader.sampler.set_epoch = Mock()
+        hook.before_epoch(runner)
+        runner.data_loader.sampler.set_epoch.assert_called()
+        # Test batch sampler
+        runner = Mock()
+        runner.data_loader = Mock()
+        runner.data_loader.sampler = Mock(spec_set=True)
+        runner.data_loader.batch_sampler = Mock()
+        runner.data_loader.batch_sampler.sampler = Mock()
+        runner.data_loader.batch_sampler.sampler.set_epoch = Mock()
+        hook.before_epoch(runner)
+        runner.data_loader.batch_sampler.sampler.set_epoch.assert_called()