From 6015fd35e5cb8f3a06486802682b5a3fd5542ecd Mon Sep 17 00:00:00 2001
From: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>
Date: Tue, 28 Jun 2022 11:04:55 +0800
Subject: [PATCH] Fix docstring format (#337)
---
mmengine/logging/log_processor.py | 7 +++---
mmengine/logging/logger.py | 1 +
mmengine/model/__init__.py | 16 ++++++-------
mmengine/model/averaged_model.py | 37 ++++++++++++++++++-------------
4 files changed, 34 insertions(+), 27 deletions(-)
diff --git a/mmengine/logging/log_processor.py b/mmengine/logging/log_processor.py
index 4b743668..d1fd8a4b 100644
--- a/mmengine/logging/log_processor.py
+++ b/mmengine/logging/log_processor.py
@@ -27,7 +27,8 @@ class LogProcessor:
custom_cfg (list[dict], optional): Contains multiple log config dict,
in which key means the data source name of log and value means the
statistic method and corresponding arguments used to count the
- data source. Defaults to None
+ data source. Defaults to None.
+
- If custom_cfg is None, all logs will be formatted via default
methods, such as smoothing loss by default window_size. If
custom_cfg is defined as a list of config dict, for example:
@@ -35,12 +36,12 @@ class LogProcessor:
window_size='global')]. It means the log item ``loss`` will be
counted as global mean and additionally logged as ``global_loss``
(defined by ``log_name``). If ``log_name`` is not defined in
- config dict, the original logged key will be overwritten.
+ config dict, the original logged key will be overwritten.
- The original log item cannot be overwritten twice. Here is
an error example:
[dict(data_src=loss, method='mean', window_size='global'),
- dict(data_src=loss, method='mean', window_size='epoch')].
+ dict(data_src=loss, method='mean', window_size='epoch')].
Both log config dict in custom_cfg do not have ``log_name`` key,
which means the loss item will be overwritten twice.
diff --git a/mmengine/logging/logger.py b/mmengine/logging/logger.py
index 85025dba..059110c4 100644
--- a/mmengine/logging/logger.py
+++ b/mmengine/logging/logger.py
@@ -246,6 +246,7 @@ def print_log(msg,
logger (Logger or str, optional): If the type of logger is
``logging.Logger``, we directly use logger to log messages.
Some special loggers are:
+
- "silent": No message will be printed.
- "current": Use latest created logger to log message.
- other str: Instance name of logger. The corresponding logger
diff --git a/mmengine/model/__init__.py b/mmengine/model/__init__.py
index 8b8203fb..3da94604 100644
--- a/mmengine/model/__init__.py
+++ b/mmengine/model/__init__.py
@@ -1,8 +1,8 @@
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.utils.parrots_wrapper import TORCH_VERSION
from mmengine.utils.version_utils import digit_version
-from .averaged_model import (ExponentialMovingAverage, MomentumAnnealingEMA,
- StochasticWeightAverage)
+from .averaged_model import (BaseAveragedModel, ExponentialMovingAverage,
+ MomentumAnnealingEMA, StochasticWeightAverage)
from .base_model import BaseDataPreprocessor, BaseModel, ImgDataPreprocessor
from .base_module import BaseModule, ModuleDict, ModuleList, Sequential
from .utils import detect_anomalous_params, merge_dict, stack_batch
@@ -10,12 +10,12 @@ from .wrappers import (MMDistributedDataParallel,
MMSeparateDistributedDataParallel, is_model_wrapper)
__all__ = [
- 'MMDistributedDataParallel', 'is_model_wrapper', 'StochasticWeightAverage',
- 'ExponentialMovingAverage', 'MomentumAnnealingEMA', 'BaseModel',
- 'BaseDataPreprocessor', 'ImgDataPreprocessor',
- 'MMSeparateDistributedDataParallel', 'BaseModule', 'stack_batch',
- 'merge_dict', 'detect_anomalous_params', 'ModuleList', 'ModuleDict',
- 'Sequential'
+ 'MMDistributedDataParallel', 'is_model_wrapper', 'BaseAveragedModel',
+ 'StochasticWeightAverage', 'ExponentialMovingAverage',
+ 'MomentumAnnealingEMA', 'BaseModel', 'BaseDataPreprocessor',
+ 'ImgDataPreprocessor', 'MMSeparateDistributedDataParallel', 'BaseModule',
+ 'stack_batch', 'merge_dict', 'detect_anomalous_params', 'ModuleList',
+ 'ModuleDict', 'Sequential'
]
if digit_version(TORCH_VERSION) >= digit_version('1.11.0'):
diff --git a/mmengine/model/averaged_model.py b/mmengine/model/averaged_model.py
index e2f75e99..8a47e8e0 100644
--- a/mmengine/model/averaged_model.py
+++ b/mmengine/model/averaged_model.py
@@ -17,25 +17,28 @@ class BaseAveragedModel(nn.Module):
training neural networks. This class implements the averaging process
for a model. All subclasses must implement the `avg_func` method.
This class creates a copy of the provided module :attr:`model`
- on the device :attr:`device` and allows computing running averages of the
+ on the :attr:`device` and allows computing running averages of the
parameters of the :attr:`model`.
+
The code is referenced from: https://github.com/pytorch/pytorch/blob/master/torch/optim/swa_utils.py.
+
Different from the `AveragedModel` in PyTorch, we use in-place operation
to improve the parameter updating speed, which is about 5 times faster
than the non-in-place version.
In mmengine, we provide two ways to use the model averaging:
+
1. Use the model averaging module in hook:
- We provide an EMAHook to apply the model averaging during training.
- Add ``custom_hooks=[dict(type='EMAHook')]`` to the config or the runner.
- The hook is implemented in mmengine/hooks/ema_hook.py
+ We provide an EMAHook to apply the model averaging during training.
+ Add ``custom_hooks=[dict(type='EMAHook')]`` to the config or the runner.
+ The hook is implemented in mmengine/hooks/ema_hook.py
2. Use the model averaging module directly in the algorithm. Take the ema
teacher in semi-supervise as an example:
- >>> from mmengine.model import ExponentialMovingAverage
- >>> student = ResNet(depth=50)
- >>> # use ema model as teacher
- >>> ema_teacher = ExponentialMovingAverage(student)
+ >>> from mmengine.model import ExponentialMovingAverage
+ >>> student = ResNet(depth=50)
+ >>> # use ema model as teacher
+ >>> ema_teacher = ExponentialMovingAverage(student)
Args:
model (nn.Module): The model to be averaged.
@@ -134,7 +137,7 @@ class StochasticWeightAverage(BaseAveragedModel):
@MODELS.register_module()
class ExponentialMovingAverage(BaseAveragedModel):
- """Implements the exponential moving average (EMA) of the model.
+ r"""Implements the exponential moving average (EMA) of the model.
All parameters are updated by the formula as below:
@@ -145,9 +148,10 @@ class ExponentialMovingAverage(BaseAveragedModel):
Args:
model (nn.Module): The model to be averaged.
momentum (float): The momentum used for updating ema parameter.
- Ema's parameter are updated with the formula:
- `averaged_param = (1-momentum) * averaged_param + momentum *
- source_param`. Defaults to 0.0002.
+ Defaults to 0.0002.
+ Ema's parameter are updated with the formula
+ :math:`averaged\_param = (1-momentum) * averaged\_param +
+ momentum * source\_param`.
interval (int): Interval between two updates. Defaults to 1.
device (torch.device, optional): If provided, the averaged model will
be stored on the :attr:`device`. Defaults to None.
@@ -184,14 +188,15 @@ class ExponentialMovingAverage(BaseAveragedModel):
@MODELS.register_module()
class MomentumAnnealingEMA(ExponentialMovingAverage):
- """Exponential moving average (EMA) with momentum annealing strategy.
+ r"""Exponential moving average (EMA) with momentum annealing strategy.
Args:
model (nn.Module): The model to be averaged.
momentum (float): The momentum used for updating ema parameter.
- Ema's parameter are updated with the formula:
- `averaged_param = (1-momentum) * averaged_param + momentum *
- source_param`. Defaults to 0.0002.
+ Defaults to 0.0002.
+ Ema's parameter are updated with the formula
+ :math:`averaged\_param = (1-momentum) * averaged\_param +
+ momentum * source\_param`.
gamma (int): Use a larger momentum early in training and gradually
annealing to a smaller value to update the ema model smoothly. The
momentum is calculated as max(momentum, gamma / (gamma + steps))
--
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