Fix docstring format (#337)

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.
 

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

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'):

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))