From ed8dcb4c61dd5faad07bc7bae05f96b38e9af6e5 Mon Sep 17 00:00:00 2001
From: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>
Date: Mon, 7 Mar 2022 19:35:37 +0800
Subject: [PATCH] fix type hint in hooks (#106)
---
mmengine/hooks/checkpoint_hook.py | 63 ++++++++----------
mmengine/hooks/empty_cache_hook.py | 22 +++---
mmengine/hooks/hook.py | 92 +++++++++++---------------
mmengine/hooks/iter_timer_hook.py | 37 +++++------
mmengine/hooks/optimizer_hook.py | 31 ++++-----
mmengine/hooks/param_scheduler_hook.py | 12 ++--
mmengine/hooks/sampler_seed_hook.py | 13 ++--
mmengine/hooks/sync_buffer_hook.py | 6 +-
8 files changed, 122 insertions(+), 154 deletions(-)
diff --git a/mmengine/hooks/checkpoint_hook.py b/mmengine/hooks/checkpoint_hook.py
index af6c1de3..a312307f 100644
--- a/mmengine/hooks/checkpoint_hook.py
+++ b/mmengine/hooks/checkpoint_hook.py
@@ -9,6 +9,8 @@ from mmengine.fileio import FileClient
from mmengine.registry import HOOKS
from .hook import Hook
+DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataSample]]]
+
@HOOKS.register_module()
class CheckpointHook(Hook):
@@ -65,7 +67,7 @@ class CheckpointHook(Hook):
self.sync_buffer = sync_buffer
self.file_client_args = file_client_args
- def before_run(self, runner: object) -> None:
+ def before_run(self, runner) -> None:
"""Finish all operations, related to checkpoint.
This function will get the appropriate file client, and the directory
@@ -75,7 +77,7 @@ class CheckpointHook(Hook):
runner (Runner): The runner of the training process.
"""
if not self.out_dir:
- self.out_dir = runner.work_dir # type: ignore
+ self.out_dir = runner.work_dir
self.file_client = FileClient.infer_client(self.file_client_args,
self.out_dir)
@@ -84,17 +86,13 @@ class CheckpointHook(Hook):
# `self.out_dir` is set so the final `self.out_dir` is the
# concatenation of `self.out_dir` and the last level directory of
# `runner.work_dir`
- if self.out_dir != runner.work_dir: # type: ignore
- basename = osp.basename(
- runner.work_dir.rstrip( # type: ignore
- osp.sep))
+ if self.out_dir != runner.work_dir:
+ basename = osp.basename(runner.work_dir.rstrip(osp.sep))
self.out_dir = self.file_client.join_path(
- self.out_dir, # type: ignore
- basename)
+ self.out_dir, basename) # type: ignore # noqa: E501
- runner.logger.info(( # type: ignore
- f'Checkpoints will be saved to {self.out_dir} by '
- f'{self.file_client.name}.'))
+ runner.logger.info((f'Checkpoints will be saved to {self.out_dir} by '
+ f'{self.file_client.name}.'))
# disable the create_symlink option because some file backends do not
# allow to create a symlink
@@ -109,7 +107,7 @@ class CheckpointHook(Hook):
else:
self.args['create_symlink'] = self.file_client.allow_symlink
- def after_train_epoch(self, runner: object) -> None:
+ def after_train_epoch(self, runner) -> None:
"""Save the checkpoint and synchronize buffers after each epoch.
Args:
@@ -124,46 +122,40 @@ class CheckpointHook(Hook):
if self.every_n_epochs(
runner, self.interval) or (self.save_last
and self.is_last_epoch(runner)):
- runner.logger.info( # type: ignore
- f'Saving checkpoint at \
- {runner.epoch + 1} epochs') # type: ignore
+ runner.logger.info(f'Saving checkpoint at \
+ {runner.epoch + 1} epochs')
if self.sync_buffer:
pass
# TODO
self._save_checkpoint(runner)
# TODO Add master_only decorator
- def _save_checkpoint(self, runner: object) -> None:
+ def _save_checkpoint(self, runner) -> None:
"""Save the current checkpoint and delete outdated checkpoint.
Args:
runner (Runner): The runner of the training process.
"""
- runner.save_checkpoint( # type: ignore
- self.out_dir,
- save_optimizer=self.save_optimizer,
- **self.args)
- if runner.meta is not None: # type: ignore
+ runner.save_checkpoint(
+ self.out_dir, save_optimizer=self.save_optimizer, **self.args)
+ if runner.meta is not None:
if self.by_epoch:
cur_ckpt_filename = self.args.get(
- 'filename_tmpl',
- 'epoch_{}.pth').format(runner.epoch + 1) # type: ignore
+ 'filename_tmpl', 'epoch_{}.pth').format(runner.epoch + 1)
else:
cur_ckpt_filename = self.args.get(
- 'filename_tmpl',
- 'iter_{}.pth').format(runner.iter + 1) # type: ignore
- runner.meta.setdefault('hook_msgs', dict()) # type: ignore
- runner.meta['hook_msgs'][ # type: ignore
- 'last_ckpt'] = self.file_client.join_path(
- self.out_dir, cur_ckpt_filename) # type: ignore
+ 'filename_tmpl', 'iter_{}.pth').format(runner.iter + 1)
+ runner.meta.setdefault('hook_msgs', dict())
+ runner.meta['hook_msgs']['last_ckpt'] = self.file_client.join_path(
+ self.out_dir, cur_ckpt_filename) # type: ignore
# remove other checkpoints
if self.max_keep_ckpts > 0:
if self.by_epoch:
name = 'epoch_{}.pth'
- current_ckpt = runner.epoch + 1 # type: ignore
+ current_ckpt = runner.epoch + 1
else:
name = 'iter_{}.pth'
- current_ckpt = runner.iter + 1 # type: ignore
+ current_ckpt = runner.iter + 1
redundant_ckpts = range(
current_ckpt - self.max_keep_ckpts * self.interval, 0,
-self.interval)
@@ -178,8 +170,8 @@ class CheckpointHook(Hook):
def after_train_iter(
self,
- runner: object,
- data_batch: Optional[Sequence[Tuple[Any, BaseDataSample]]] = None,
+ runner,
+ data_batch: DATA_BATCH = None,
outputs: Optional[Sequence[BaseDataSample]] = None) -> None:
"""Save the checkpoint and synchronize buffers after each iteration.
@@ -199,9 +191,8 @@ class CheckpointHook(Hook):
if self.every_n_iters(
runner, self.interval) or (self.save_last
and self.is_last_iter(runner)):
- runner.logger.info( # type: ignore
- f'Saving checkpoint at \
- {runner.iter + 1} iterations') # type: ignore
+ runner.logger.info(f'Saving checkpoint at \
+ {runner.iter + 1} iterations')
if self.sync_buffer:
pass
# TODO
diff --git a/mmengine/hooks/empty_cache_hook.py b/mmengine/hooks/empty_cache_hook.py
index 44bf53ec..2f621131 100644
--- a/mmengine/hooks/empty_cache_hook.py
+++ b/mmengine/hooks/empty_cache_hook.py
@@ -1,5 +1,5 @@
# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Optional, Sequence
+from typing import Any, Optional, Sequence, Tuple
import torch
@@ -7,6 +7,8 @@ from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
+DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataSample]]]
+
@HOOKS.register_module()
class EmptyCacheHook(Hook):
@@ -33,35 +35,35 @@ class EmptyCacheHook(Hook):
self._after_iter = after_iter
def after_iter(self,
- runner: object,
- data_batch: Optional[Sequence[BaseDataSample]] = None,
+ runner,
+ data_batch: DATA_BATCH = None,
outputs: Optional[Sequence[BaseDataSample]] = None) -> None:
"""Empty cache after an iteration.
Args:
- runner (object): The runner of the training process.
- data_batch (Sequence[BaseDataSample]): Data from dataloader.
- Defaults to None.
+ runner (Runner): The runner of the training process.
+ data_batch (Sequence[Tuple[Any, BaseDataSample]], optional): Data
+ from dataloader. Defaults to None.
outputs (Sequence[BaseDataSample]): Outputs from model.
Defaults to None.
"""
if self._after_iter:
torch.cuda.empty_cache()
- def before_epoch(self, runner: object) -> None:
+ def before_epoch(self, runner) -> None:
"""Empty cache before an epoch.
Args:
- runner (object): The runner of the training process.
+ runner (Runner): The runner of the training process.
"""
if self._before_epoch:
torch.cuda.empty_cache()
- def after_epoch(self, runner: object) -> None:
+ def after_epoch(self, runner) -> None:
"""Empty cache after an epoch.
Args:
- runner (object): The runner of the training process.
+ runner (Runner): The runner of the training process.
"""
if self._after_epoch:
torch.cuda.empty_cache()
diff --git a/mmengine/hooks/hook.py b/mmengine/hooks/hook.py
index 684196de..f2d52fc9 100644
--- a/mmengine/hooks/hook.py
+++ b/mmengine/hooks/hook.py
@@ -3,6 +3,8 @@ from typing import Any, Optional, Sequence, Tuple
from mmengine.data import BaseDataSample
+DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataSample]]]
+
class Hook:
"""Base hook class.
@@ -12,7 +14,7 @@ class Hook:
priority = 'NORMAL'
- def before_run(self, runner: object) -> None:
+ def before_run(self, runner) -> None:
"""All subclasses should override this method, if they need any
operations before the training process.
@@ -21,7 +23,7 @@ class Hook:
"""
pass
- def after_run(self, runner: object) -> None:
+ def after_run(self, runner) -> None:
"""All subclasses should override this method, if they need any
operations after the training process.
@@ -30,7 +32,7 @@ class Hook:
"""
pass
- def before_epoch(self, runner: object) -> None:
+ def before_epoch(self, runner) -> None:
"""All subclasses should override this method, if they need any
operations before each epoch.
@@ -39,7 +41,7 @@ class Hook:
"""
pass
- def after_epoch(self, runner: object) -> None:
+ def after_epoch(self, runner) -> None:
"""All subclasses should override this method, if they need any
operations after each epoch.
@@ -48,11 +50,7 @@ class Hook:
"""
pass
- def before_iter(
- self,
- runner: object,
- data_batch: Optional[Sequence[Tuple[Any,
- BaseDataSample]]] = None) -> None:
+ def before_iter(self, runner, data_batch: DATA_BATCH = None) -> None:
"""All subclasses should override this method, if they need any
operations before each iter.
@@ -64,9 +62,8 @@ class Hook:
pass
def after_iter(self,
- runner: object,
- data_batch: Optional[Sequence[Tuple[
- Any, BaseDataSample]]] = None,
+ runner,
+ data_batch: DATA_BATCH = None,
outputs: Optional[Sequence[BaseDataSample]] = None) -> None:
"""All subclasses should override this method, if they need any
operations after each epoch.
@@ -80,7 +77,7 @@ class Hook:
"""
pass
- def before_save_checkpoint(self, runner: object, checkpoint: dict) -> None:
+ def before_save_checkpoint(self, runner, checkpoint: dict) -> None:
"""All subclasses should override this method, if they need any
operations before saving the checkpoint.
@@ -90,7 +87,7 @@ class Hook:
"""
pass
- def after_load_checkpoint(self, runner: object, checkpoint: dict) -> None:
+ def after_load_checkpoint(self, runner, checkpoint: dict) -> None:
"""All subclasses should override this method, if they need any
operations after loading the checkpoint.
@@ -100,7 +97,7 @@ class Hook:
"""
pass
- def before_train_epoch(self, runner: object) -> None:
+ def before_train_epoch(self, runner) -> None:
"""All subclasses should override this method, if they need any
operations before each training epoch.
@@ -109,7 +106,7 @@ class Hook:
"""
self.before_epoch(runner)
- def before_val_epoch(self, runner: object) -> None:
+ def before_val_epoch(self, runner) -> None:
"""All subclasses should override this method, if they need any
operations before each validation epoch.
@@ -118,7 +115,7 @@ class Hook:
"""
self.before_epoch(runner)
- def before_test_epoch(self, runner: object) -> None:
+ def before_test_epoch(self, runner) -> None:
"""All subclasses should override this method, if they need any
operations before each test epoch.
@@ -127,7 +124,7 @@ class Hook:
"""
self.before_epoch(runner)
- def after_train_epoch(self, runner: object) -> None:
+ def after_train_epoch(self, runner) -> None:
"""All subclasses should override this method, if they need any
operations after each training epoch.
@@ -136,7 +133,7 @@ class Hook:
"""
self.after_epoch(runner)
- def after_val_epoch(self, runner: object) -> None:
+ def after_val_epoch(self, runner) -> None:
"""All subclasses should override this method, if they need any
operations after each validation epoch.
@@ -145,7 +142,7 @@ class Hook:
"""
self.after_epoch(runner)
- def after_test_epoch(self, runner: object) -> None:
+ def after_test_epoch(self, runner) -> None:
"""All subclasses should override this method, if they need any
operations after each test epoch.
@@ -154,11 +151,7 @@ class Hook:
"""
self.after_epoch(runner)
- def before_train_iter(
- self,
- runner: object,
- data_batch: Optional[Sequence[Tuple[Any,
- BaseDataSample]]] = None) -> None:
+ def before_train_iter(self, runner, data_batch: DATA_BATCH = None) -> None:
"""All subclasses should override this method, if they need any
operations before each training iteration.
@@ -169,11 +162,7 @@ class Hook:
"""
self.before_iter(runner, data_batch=None)
- def before_val_iter(
- self,
- runner: object,
- data_batch: Optional[Sequence[Tuple[Any,
- BaseDataSample]]] = None) -> None:
+ def before_val_iter(self, runner, data_batch: DATA_BATCH = None) -> None:
"""All subclasses should override this method, if they need any
operations before each validation iteration.
@@ -184,11 +173,7 @@ class Hook:
"""
self.before_iter(runner, data_batch=None)
- def before_test_iter(
- self,
- runner: object,
- data_batch: Optional[Sequence[Tuple[Any,
- BaseDataSample]]] = None) -> None:
+ def before_test_iter(self, runner, data_batch: DATA_BATCH = None) -> None:
"""All subclasses should override this method, if they need any
operations before each test iteration.
@@ -201,8 +186,8 @@ class Hook:
def after_train_iter(
self,
- runner: object,
- data_batch: Optional[Sequence[Tuple[Any, BaseDataSample]]] = None,
+ runner,
+ data_batch: DATA_BATCH = None,
outputs: Optional[Sequence[BaseDataSample]] = None) -> None:
"""All subclasses should override this method, if they need any
operations after each training iteration.
@@ -218,8 +203,8 @@ class Hook:
def after_val_iter(
self,
- runner: object,
- data_batch: Optional[Sequence[Tuple[Any, BaseDataSample]]] = None,
+ runner,
+ data_batch: DATA_BATCH = None,
outputs: Optional[Sequence[BaseDataSample]] = None) -> None:
"""All subclasses should override this method, if they need any
operations after each validation iteration.
@@ -235,8 +220,8 @@ class Hook:
def after_test_iter(
self,
- runner: object,
- data_batch: Optional[Sequence[Tuple[Any, BaseDataSample]]] = None,
+ runner,
+ data_batch: DATA_BATCH = None,
outputs: Optional[Sequence[BaseDataSample]] = None) -> None:
"""All subclasses should override this method, if they need any
operations after each test iteration.
@@ -250,7 +235,7 @@ class Hook:
"""
self.after_iter(runner, data_batch=None, outputs=None)
- def every_n_epochs(self, runner: object, n: int) -> bool:
+ def every_n_epochs(self, runner, n: int) -> bool:
"""Test whether or not current epoch can be evenly divided by n.
Args:
@@ -260,9 +245,9 @@ class Hook:
Returns:
bool: whether or not current epoch can be evenly divided by n.
"""
- return (runner.epoch + 1) % n == 0 if n > 0 else False # type: ignore
+ return (runner.epoch + 1) % n == 0 if n > 0 else False
- def every_n_inner_iters(self, runner: object, n: int) -> bool:
+ def every_n_inner_iters(self, runner, n: int) -> bool:
"""Test whether or not current inner iteration can be evenly divided by
n.
@@ -275,10 +260,9 @@ class Hook:
bool: whether or not current inner iteration can be evenly
divided by n.
"""
- return (runner.inner_iter + # type: ignore
- 1) % n == 0 if n > 0 else False
+ return (runner.inner_iter + 1) % n == 0 if n > 0 else False
- def every_n_iters(self, runner: object, n: int) -> bool:
+ def every_n_iters(self, runner, n: int) -> bool:
"""Test whether or not current iteration can be evenly divided by n.
Args:
@@ -290,9 +274,9 @@ class Hook:
bool: Return True if the current iteration can be evenly divided
by n, otherwise False.
"""
- return (runner.iter + 1) % n == 0 if n > 0 else False # type: ignore
+ return (runner.iter + 1) % n == 0 if n > 0 else False
- def end_of_epoch(self, runner: object) -> bool:
+ def end_of_epoch(self, runner) -> bool:
"""Check whether the current epoch reaches the `max_epochs` or not.
Args:
@@ -301,9 +285,9 @@ class Hook:
Returns:
bool: whether the end of current epoch or not.
"""
- return runner.inner_iter + 1 == len(runner.data_loader) # type: ignore
+ return runner.inner_iter + 1 == len(runner.data_loader)
- def is_last_epoch(self, runner: object) -> bool:
+ def is_last_epoch(self, runner) -> bool:
"""Test whether or not current epoch is the last epoch.
Args:
@@ -313,9 +297,9 @@ class Hook:
bool: bool: Return True if the current epoch reaches the
`max_epochs`, otherwise False.
"""
- return runner.epoch + 1 == runner._max_epochs # type: ignore
+ return runner.epoch + 1 == runner._max_epochs
- def is_last_iter(self, runner: object) -> bool:
+ def is_last_iter(self, runner) -> bool:
"""Test whether or not current epoch is the last iteration.
Args:
@@ -324,4 +308,4 @@ class Hook:
Returns:
bool: whether or not current iteration is the last iteration.
"""
- return runner.iter + 1 == runner._max_iters # type: ignore
+ return runner.iter + 1 == runner._max_iters
diff --git a/mmengine/hooks/iter_timer_hook.py b/mmengine/hooks/iter_timer_hook.py
index 3c637056..d1d6404f 100644
--- a/mmengine/hooks/iter_timer_hook.py
+++ b/mmengine/hooks/iter_timer_hook.py
@@ -1,11 +1,13 @@
# Copyright (c) OpenMMLab. All rights reserved.
import time
-from typing import Optional, Sequence
+from typing import Any, Optional, Sequence, Tuple
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
+DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataSample]]]
+
@HOOKS.register_module()
class IterTimerHook(Hook):
@@ -16,45 +18,38 @@ class IterTimerHook(Hook):
priority = 'NORMAL'
- def before_epoch(self, runner: object) -> None:
+ def before_epoch(self, runner) -> None:
"""Record time flag before start a epoch.
Args:
- runner (object): The runner of the training process.
+ runner (Runner): The runner of the training process.
"""
self.t = time.time()
- def before_iter(
- self,
- runner: object,
- data_batch: Optional[Sequence[BaseDataSample]] = None) -> None:
+ def before_iter(self, runner, data_batch: DATA_BATCH = None) -> None:
"""Logging time for loading data and update the time flag.
Args:
- runner (object): The runner of the training process.
- data_batch (Sequence[BaseDataSample]): Data from dataloader.
- Defaults to None.
+ runner (Runner): The runner of the training process.
+ data_batch (Sequence[Tuple[Any, BaseDataSample]], optional): Data
+ from dataloader. Defaults to None.
"""
# TODO: update for new logging system
- runner.log_buffer.update({ # type: ignore
- 'data_time': time.time() - self.t
- })
+ runner.log_buffer.update({'data_time': time.time() - self.t})
def after_iter(self,
- runner: object,
- data_batch: Optional[Sequence[BaseDataSample]] = None,
+ runner,
+ data_batch: DATA_BATCH = None,
outputs: Optional[Sequence[BaseDataSample]] = None) -> None:
"""Logging time for a iteration and update the time flag.
Args:
- runner (object): The runner of the training process.
- data_batch (Sequence[BaseDataSample]): Data from dataloader.
- Defaults to None.
+ runner (Runner): The runner of the training process.
+ data_batch (Sequence[Tuple[Any, BaseDataSample]], optional): Data
+ from dataloader. Defaults to None.
outputs (Sequence[BaseDataSample]): Outputs from model.
Defaults to None.
"""
# TODO: update for new logging system
- runner.log_buffer.update({ # type: ignore
- 'time': time.time() - self.t
- })
+ runner.log_buffer.update({'time': time.time() - self.t})
self.t = time.time()
diff --git a/mmengine/hooks/optimizer_hook.py b/mmengine/hooks/optimizer_hook.py
index bbccd667..3a9dabbc 100644
--- a/mmengine/hooks/optimizer_hook.py
+++ b/mmengine/hooks/optimizer_hook.py
@@ -10,6 +10,8 @@ from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
+DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataSample]]]
+
@HOOKS.register_module()
class OptimizerHook(Hook):
@@ -56,8 +58,8 @@ class OptimizerHook(Hook):
def after_train_iter(
self,
- runner: object,
- data_batch: Optional[Sequence[Tuple[Any, BaseDataSample]]] = None,
+ runner,
+ data_batch: DATA_BATCH = None,
outputs: Optional[Sequence[BaseDataSample]] = None) -> None:
"""All operations need to be finished after each training iteration.
@@ -82,32 +84,27 @@ class OptimizerHook(Hook):
In order to keep this interface consistent with other hooks,
we keep ``outputs`` here. Defaults to None.
"""
- runner.optimizer.zero_grad() # type: ignore
+ runner.optimizer.zero_grad()
if self.detect_anomalous_params:
- self.detect_anomalous_parameters(
- runner.outputs['loss'], # type: ignore
- runner)
- runner.outputs['loss'].backward() # type: ignore
+ self.detect_anomalous_parameters(runner.outputs['loss'], runner)
+ runner.outputs['loss'].backward()
if self.grad_clip is not None:
- grad_norm = self.clip_grads(
- runner.model.parameters()) # type: ignore
+ grad_norm = self.clip_grads(runner.model.parameters())
if grad_norm is not None:
# Add grad norm to the logger
- runner.log_buffer.update( # type: ignore
- {'grad_norm': float(grad_norm)},
- runner.outputs['num_samples']) # type: ignore
- runner.optimizer.step() # type: ignore
+ runner.log_buffer.update({'grad_norm': float(grad_norm)},
+ runner.outputs['num_samples'])
+ runner.optimizer.step()
- def detect_anomalous_parameters(self, loss: torch.Tensor,
- runner: object) -> None:
+ def detect_anomalous_parameters(self, loss: torch.Tensor, runner) -> None:
"""Detect anomalous parameters that are not included in the graph.
Args:
loss (torch.Tensor): The loss of current iteration.
runner (Runner): The runner of the training process.
"""
- logger = runner.logger # type: ignore
+ logger = runner.logger
parameters_in_graph = set()
visited = set()
@@ -125,7 +122,7 @@ class OptimizerHook(Hook):
traverse(grad_fn)
traverse(loss.grad_fn)
- for n, p in runner.model.named_parameters(): # type: ignore
+ for n, p in runner.model.named_parameters():
if p not in parameters_in_graph and p.requires_grad:
logger.log(
level=logging.ERROR,
diff --git a/mmengine/hooks/param_scheduler_hook.py b/mmengine/hooks/param_scheduler_hook.py
index fe162d12..095b1bb3 100644
--- a/mmengine/hooks/param_scheduler_hook.py
+++ b/mmengine/hooks/param_scheduler_hook.py
@@ -5,6 +5,8 @@ from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
+DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataSample]]]
+
@HOOKS.register_module()
class ParamSchedulerHook(Hook):
@@ -15,8 +17,8 @@ class ParamSchedulerHook(Hook):
def after_train_iter(
self,
- runner: object,
- data_batch: Optional[Sequence[Tuple[Any, BaseDataSample]]] = None,
+ runner,
+ data_batch: DATA_BATCH = None,
outputs: Optional[Sequence[BaseDataSample]] = None) -> None:
"""Call step function for each scheduler after each iteration.
@@ -30,16 +32,16 @@ class ParamSchedulerHook(Hook):
In order to keep this interface consistent with other hooks, we
keep ``data_batch`` here. Defaults to None.
"""
- for scheduler in runner.schedulers: # type: ignore
+ for scheduler in runner.schedulers:
if not scheduler.by_epoch:
scheduler.step()
- def after_train_epoch(self, runner: object) -> None:
+ def after_train_epoch(self, runner) -> None:
"""Call step function for each scheduler after each epoch.
Args:
runner (Runner): The runner of the training process.
"""
- for scheduler in runner.schedulers: # type: ignore
+ for scheduler in runner.schedulers:
if scheduler.by_epoch:
scheduler.step()
diff --git a/mmengine/hooks/sampler_seed_hook.py b/mmengine/hooks/sampler_seed_hook.py
index 0896636d..d7d243d8 100644
--- a/mmengine/hooks/sampler_seed_hook.py
+++ b/mmengine/hooks/sampler_seed_hook.py
@@ -14,18 +14,15 @@ class DistSamplerSeedHook(Hook):
priority = 'NORMAL'
- def before_epoch(self, runner: object) -> None:
+ def before_epoch(self, runner) -> None:
"""Set the seed for sampler and batch_sampler.
Args:
runner (Runner): The runner of the training process.
"""
- if hasattr(runner.data_loader.sampler, 'set_epoch'): # type: ignore
+ if hasattr(runner.data_loader.sampler, 'set_epoch'):
# 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'):
+ runner.data_loader.sampler.set_epoch(runner.epoch)
+ elif hasattr(runner.data_loader.batch_sampler.sampler, '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
+ runner.data_loader.batch_sampler.sampler.set_epoch(runner.epoch)
diff --git a/mmengine/hooks/sync_buffer_hook.py b/mmengine/hooks/sync_buffer_hook.py
index f62910e8..9aa6402d 100644
--- a/mmengine/hooks/sync_buffer_hook.py
+++ b/mmengine/hooks/sync_buffer_hook.py
@@ -89,11 +89,11 @@ class SyncBuffersHook(Hook):
def __init__(self) -> None:
self.distributed = dist.IS_DIST
- def after_epoch(self, runner: object) -> None:
+ def after_epoch(self, runner) -> None:
"""All-reduce model buffers at the end of each epoch.
Args:
- runner (object): The runner of the training process.
+ runner (Runner): The runner of the training process.
"""
if self.distributed:
- allreduce_params(runner.model.buffers()) # type: ignore
+ allreduce_params(runner.model.buffers())
--
GitLab