[Docs] Fix the format of the docstring (#1573)

* [Docs] Fix the format of docstring

* fix format

mmcv/cnn/bricks/activation.py

@@ -83,6 +83,7 @@ def build_activation_layer(cfg):
 
     Args:
         cfg (dict): The activation layer config, which should contain:
+
             - type (str): Layer type.
             - layer args: Args needed to instantiate an activation layer.
 

mmcv/cnn/bricks/norm.py

@@ -83,8 +83,8 @@ def build_norm_layer(cfg, num_features, postfix=''):
             to create named layer.
 
     Returns:
-        (str, nn.Module): The first element is the layer name consisting of
-            abbreviation and postfix, e.g., bn1, gn. The second element is the
+        tuple[str, nn.Module]: The first element is the layer name consisting
+        of abbreviation and postfix, e.g., bn1, gn. The second element is the
         created norm layer.
     """
     if not isinstance(cfg, dict):

mmcv/cnn/bricks/plugin.py

@@ -57,15 +57,15 @@ def build_plugin_layer(cfg, postfix='', **kwargs):
 
     Args:
         cfg (None or dict): cfg should contain:
-            type (str): identify plugin layer type.
-            layer args: args needed to instantiate a plugin layer.
+
+            - type (str): identify plugin layer type.
+            - layer args: args needed to instantiate a plugin layer.
         postfix (int, str): appended into norm abbreviation to
             create named layer. Default: ''.
 
     Returns:
-        tuple[str, nn.Module]:
-            name (str): abbreviation + postfix
-            layer (nn.Module): created plugin layer
+        tuple[str, nn.Module]: The first one is the concatenation of
+        abbreviation and postfix. The second is the created plugin layer.
     """
     if not isinstance(cfg, dict):
         raise TypeError('cfg must be a dict')

mmcv/cnn/utils/flops_counter.py

@@ -48,8 +48,8 @@ def get_model_complexity_info(model,
 
     Supported layers are listed as below:
         - Convolutions: ``nn.Conv1d``, ``nn.Conv2d``, ``nn.Conv3d``.
-        - Activations: ``nn.ReLU``, ``nn.PReLU``, ``nn.ELU``, ``nn.LeakyReLU``,
-            ``nn.ReLU6``.
+        - Activations: ``nn.ReLU``, ``nn.PReLU``, ``nn.ELU``,
+          ``nn.LeakyReLU``, ``nn.ReLU6``.
         - Poolings: ``nn.MaxPool1d``, ``nn.MaxPool2d``, ``nn.MaxPool3d``,
           ``nn.AvgPool1d``, ``nn.AvgPool2d``, ``nn.AvgPool3d``,
           ``nn.AdaptiveMaxPool1d``, ``nn.AdaptiveMaxPool2d``,

mmcv/image/geometric.py

@@ -464,13 +464,13 @@ def impad(img,
             - constant: pads with a constant value, this value is specified
               with pad_val.
             - edge: pads with the last value at the edge of the image.
-            - reflect: pads with reflection of image without repeating the
-                last value on the edge. For example, padding [1, 2, 3, 4]
-                with 2 elements on both sides in reflect mode will result
-                in [3, 2, 1, 2, 3, 4, 3, 2].
-            - symmetric: pads with reflection of image repeating the last
-                value on the edge. For example, padding [1, 2, 3, 4] with
-                2 elements on both sides in symmetric mode will result in
+            - reflect: pads with reflection of image without repeating the last
+              value on the edge. For example, padding [1, 2, 3, 4] with 2
+              elements on both sides in reflect mode will result in
+              [3, 2, 1, 2, 3, 4, 3, 2].
+            - symmetric: pads with reflection of image repeating the last value
+              on the edge. For example, padding [1, 2, 3, 4] with 2 elements on
+              both sides in symmetric mode will result in
               [2, 1, 1, 2, 3, 4, 4, 3]
 
     Returns:

mmcv/ops/scatter_points.py

@@ -25,11 +25,10 @@ class _DynamicScatter(Function):
                 'mean'. Default: 'max'.
 
         Returns:
-            tuple[torch.Tensor]: tuple[torch.Tensor]: A tuple contains two
-            elements. The first one is the voxel features with shape [M, C]
-            which are respectively reduced from input features that share
-            the same voxel coordinates . The second is voxel coordinates
-            with shape [M, ndim].
+            tuple[torch.Tensor]: A tuple contains two elements. The first one
+            is the voxel features with shape [M, C] which are respectively
+            reduced from input features that share the same voxel coordinates.
+            The second is voxel coordinates with shape [M, ndim].
         """
         results = ext_module.dynamic_point_to_voxel_forward(
             feats, coors, reduce_type)
@@ -89,11 +88,10 @@ class DynamicScatter(nn.Module):
                 multi-dim voxel index) of each points.
 
         Returns:
-            tuple[torch.Tensor]: tuple[torch.Tensor]: A tuple contains two
-            elements. The first one is the voxel features with shape [M, C]
-            which are respectively reduced from input features that share
-            the same voxel coordinates . The second is voxel coordinates
-            with shape [M, ndim].
+            tuple[torch.Tensor]: A tuple contains two elements. The first one
+            is the voxel features with shape [M, C] which are respectively
+            reduced from input features that share the same voxel coordinates.
+            The second is voxel coordinates with shape [M, ndim].
         """
         reduce = 'mean' if self.average_points else 'max'
         return dynamic_scatter(points.contiguous(), coors.contiguous(), reduce)
@@ -107,11 +105,10 @@ class DynamicScatter(nn.Module):
                 multi-dim voxel index) of each points.
 
         Returns:
-            tuple[torch.Tensor]:tuple[torch.Tensor]: A tuple contains two
-            elements. The first one is the voxel features with shape [M, C]
-            which are respectively reduced from input features that share
-            the same voxel coordinates . The second is voxel coordinates
-            with shape [M, ndim].
+            tuple[torch.Tensor]: A tuple contains two elements. The first one
+            is the voxel features with shape [M, C] which are respectively
+            reduced from input features that share the same voxel coordinates.
+            The second is voxel coordinates with shape [M, ndim].
         """
         if coors.size(-1) == 3:
             return self.forward_single(points, coors)

mmcv/runner/base_module.py

@@ -19,12 +19,11 @@ class BaseModule(nn.Module, metaclass=ABCMeta):
     ``torch.nn.Module``, ``BaseModule`` mainly adds three attributes.
 
     - ``init_cfg``: the config to control the initialization.
-        - ``init_weights``: The function of parameter
-            initialization and recording initialization
-            information.
-        - ``_params_init_info``: Used to track the parameter
-            initialization information. This attribute only
-            exists during executing the ``init_weights``.
+    - ``init_weights``: The function of parameter initialization and recording
+      initialization information.
+    - ``_params_init_info``: Used to track the parameter initialization
+      information. This attribute only exists during executing the
+      ``init_weights``.
 
     Args:
         init_cfg (dict, optional): Initialization config dict.

mmcv/runner/base_runner.py

@@ -207,8 +207,8 @@ class BaseRunner(metaclass=ABCMeta):
 
         Returns:
             list[float] | dict[str, list[float]]: Current learning rates of all
-                param groups. If the runner has a dict of optimizers, this
-                method will return a dict.
+            param groups. If the runner has a dict of optimizers, this method
+            will return a dict.
         """
         if isinstance(self.optimizer, torch.optim.Optimizer):
             lr = [group['lr'] for group in self.optimizer.param_groups]
@@ -226,8 +226,8 @@ class BaseRunner(metaclass=ABCMeta):
 
         Returns:
             list[float] | dict[str, list[float]]: Current momentums of all
-                param groups. If the runner has a dict of optimizers, this
-                method will return a dict.
+            param groups. If the runner has a dict of optimizers, this method
+            will return a dict.
         """
 
         def _get_momentum(optimizer):
@@ -287,7 +287,7 @@ class BaseRunner(metaclass=ABCMeta):
             hook_cfg (dict): Hook config. It should have at least keys 'type'
               and 'priority' indicating its type and priority.
 
-        Notes:
+        Note:
             The specific hook class to register should not use 'type' and
             'priority' arguments during initialization.
         """

mmcv/runner/hooks/ema.py

@@ -13,8 +13,8 @@ class EMAHook(Hook):
 
         .. math::
 
-            \text{Xema\_{t+1}} = (1 - \text{momentum}) \times
-            \text{Xema\_{t}} +  \text{momentum} \times X_t
+            Xema\_{t+1} = (1 - \text{momentum}) \times
+            Xema\_{t} +  \text{momentum} \times X_t
 
     Args:
         momentum (float): The momentum used for updating ema parameter.

mmcv/runner/hooks/logger/neptune.py

@@ -12,19 +12,21 @@ class NeptuneLoggerHook(LoggerHook):
 
     Args:
         init_kwargs (dict): a dict contains the initialization keys as below:
+
             - project (str): Name of a project in a form of
-                namespace/project_name. If None, the value of
-                NEPTUNE_PROJECT environment variable will be taken.
-            - api_token (str): User’s API token.
-                If None, the value of NEPTUNE_API_TOKEN environment
-                variable will be taken. Note: It is strongly recommended
-                to use NEPTUNE_API_TOKEN environment variable rather than
-                placing your API token in plain text in your source code.
-            - name (str, optional, default is 'Untitled'): Editable name of
-                the run. Name is displayed in the run's Details and in
-                Runs table as a column.
-            Check https://docs.neptune.ai/api-reference/neptune#init for
-                more init arguments.
+              namespace/project_name. If None, the value of NEPTUNE_PROJECT
+              environment variable will be taken.
+            - api_token (str): User’s API token. If None, the value of
+              NEPTUNE_API_TOKEN environment variable will be taken. Note: It is
+              strongly recommended to use NEPTUNE_API_TOKEN environment
+              variable rather than placing your API token in plain text in your
+              source code.
+            - name (str, optional, default is 'Untitled'): Editable name of the
+              run. Name is displayed in the run's Details and in Runs table as
+              a column.
+
+            Check https://docs.neptune.ai/api-reference/neptune#init for more
+            init arguments.
         interval (int): Logging interval (every k iterations).
         ignore_last (bool): Ignore the log of last iterations in each epoch
             if less than `interval`.

mmcv/utils/config.py

@@ -344,7 +344,7 @@ class Config:
                config str. Only py/yml/yaml/json type are supported now!
 
         Returns:
-            obj:`Config`: Config obj.
+            :obj:`Config`: Config obj.
         """
         if file_format not in ['.py', '.json', '.yaml', '.yml']:
             raise IOError('Only py/yml/yaml/json type are supported now!')
@@ -561,7 +561,7 @@ class Config:
             >>> assert cfg_dict == dict(
             ...     model=dict(backbone=dict(depth=50, with_cp=True)))
 
-            # Merge list element
+            >>> # Merge list element
             >>> cfg = Config(dict(pipeline=[
             ...     dict(type='LoadImage'), dict(type='LoadAnnotations')]))
             >>> options = dict(pipeline={'0': dict(type='SelfLoadImage')})

mmcv/utils/path.py

@@ -40,7 +40,7 @@ def scandir(dir_path, suffix=None, recursive=False, case_sensitive=True):
     """Scan a directory to find the interested files.
 
     Args:
-        dir_path (str | obj:`Path`): Path of the directory.
+        dir_path (str | :obj:`Path`): Path of the directory.
         suffix (str | tuple(str), optional): File suffix that we are
             interested in. Default: None.
         recursive (bool, optional): If set to True, recursively scan the

mmcv/utils/registry.py

@@ -59,6 +59,7 @@ class Registry:
     """A registry to map strings to classes.
 
     Registered object could be built from registry.
+
     Example:
         >>> MODELS = Registry('models')
         >>> @MODELS.register_module()
@@ -128,16 +129,15 @@ class Registry:
         The name of the package where registry is defined will be returned.
 
         Example:
-            # in mmdet/models/backbone/resnet.py
+            >>> # in mmdet/models/backbone/resnet.py
             >>> MODELS = Registry('models')
             >>> @MODELS.register_module()
             >>> class ResNet:
             >>>     pass
             The scope of ``ResNet`` will be ``mmdet``.
 
-
         Returns:
-            scope (str): The inferred scope name.
+            str: The inferred scope name.
         """
         # inspect.stack() trace where this function is called, the index-2
         # indicates the frame where `infer_scope()` is called
@@ -158,8 +158,8 @@ class Registry:
             None, 'ResNet'
 
         Return:
-            scope (str, None): The first scope.
-            key (str): The remaining key.
+            tuple[str | None, str]: The former element is the first scope of
+            the key, which can be ``None``. The latter is the remaining key.
         """
         split_index = key.find('.')
         if split_index != -1:

mmcv/utils/timer.py

@@ -12,8 +12,7 @@ class TimerError(Exception):
 class Timer:
     """A flexible Timer class.
 
-    :Example:
-
+    Examples:
         >>> import time
         >>> import mmcv
         >>> with mmcv.Timer():
@@ -64,7 +63,8 @@ class Timer:
     def since_start(self):
         """Total time since the timer is started.
 
-        Returns (float): Time in seconds.
+        Returns:
+            float: Time in seconds.
         """
         if not self._is_running:
             raise TimerError('timer is not running')
@@ -77,7 +77,8 @@ class Timer:
         Either :func:`since_start` or :func:`since_last_check` is a checking
         operation.
 
-        Returns (float): Time in seconds.
+        Returns:
+            float: Time in seconds.
         """
         if not self._is_running:
             raise TimerError('timer is not running')
@@ -95,8 +96,7 @@ def check_time(timer_id):
     This method is suitable for running a task on a list of items. A timer will
     be registered when the method is called for the first time.
 
-    :Example:
-
+    Examples:
         >>> import time
         >>> import mmcv
         >>> for i in range(1, 6):
@@ -109,7 +109,7 @@ def check_time(timer_id):
         5.000
 
     Args:
-        timer_id (str): Timer identifier.
+        str: Timer identifier.
     """
     if timer_id not in _g_timers:
         _g_timers[timer_id] = Timer()

mmcv/video/io.py

@@ -50,8 +50,7 @@ class VideoReader:
     the second time, there is no need to decode again if it is stored in the
     cache.
 
-    :Example:
-
+    Examples:
         >>> import mmcv
         >>> v = mmcv.VideoReader('sample.mp4')
         >>> len(v)  # get the total frame number with `len()`