[Fix] Add typehint for DarknetBottleneck

mmdet/models/layers/csp_layer.py

@@ -20,30 +20,32 @@ class DarknetBottleneck(BaseModule):
     Args:
         in_channels (int): The input channels of this Module.
         out_channels (int): The output channels of this Module.
-        expansion (int): The kernel size of the convolution. Default: 0.5
+        expansion (float): The kernel size of the convolution.
+            Defaults to 0.5.
         add_identity (bool): Whether to add identity to the out.
-            Default: True
+            Defaults to True.
         use_depthwise (bool): Whether to use depthwise separable convolution.
-            Default: False
-        conv_cfg (dict): Config dict for convolution layer. Default: None,
+            Defaults to False.
+        conv_cfg (dict): Config dict for convolution layer. Defaults to None,
             which means using conv2d.
         norm_cfg (dict): Config dict for normalization layer.
-            Default: dict(type='BN').
+            Defaults to dict(type='BN').
         act_cfg (dict): Config dict for activation layer.
-            Default: dict(type='Swish').
+            Defaults to dict(type='Swish').
     """
 
     def __init__(self,
-                 in_channels,
-                 out_channels,
-                 expansion=0.5,
-                 add_identity=True,
-                 use_depthwise=False,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
-                 act_cfg=dict(type='Swish'),
-                 init_cfg=None):
-        super().__init__(init_cfg)
+                 in_channels: int,
+                 out_channels: int,
+                 expansion: float = 0.5,
+                 add_identity: bool = True,
+                 use_depthwise: bool = False,
+                 conv_cfg: OptConfigType = None,
+                 norm_cfg: ConfigType = dict(
+                     type='BN', momentum=0.03, eps=0.001),
+                 act_cfg: ConfigType = dict(type='Swish'),
+                 init_cfg: OptMultiConfig = None) -> None:
+        super().__init__(init_cfg=init_cfg)
         hidden_channels = int(out_channels * expansion)
         conv = DepthwiseSeparableConvModule if use_depthwise else ConvModule
         self.conv1 = ConvModule(
@@ -65,7 +67,8 @@ def __init__(self,
         self.add_identity = \
             add_identity and in_channels == out_channels
 
-    def forward(self, x):
+    def forward(self, x: Tensor) -> Tensor:
+        """Forward function."""
         identity = x
         out = self.conv1(x)
         out = self.conv2(out)
@@ -136,6 +139,7 @@ def __init__(self,
             add_identity and in_channels == out_channels
 
     def forward(self, x: Tensor) -> Tensor:
+        """Forward function."""
         identity = x
         out = self.conv1(x)
         out = self.conv2(out)
@@ -153,22 +157,22 @@ class CSPLayer(BaseModule):
         in_channels (int): The input channels of the CSP layer.
         out_channels (int): The output channels of the CSP layer.
         expand_ratio (float): Ratio to adjust the number of channels of the
-            hidden layer. Default: 0.5
-        num_blocks (int): Number of blocks. Default: 1
+            hidden layer. Defaults to 0.5.
+        num_blocks (int): Number of blocks. Defaults to 1.
         add_identity (bool): Whether to add identity in blocks.
-            Default: True
+            Defaults to True.
         use_cspnext_block (bool): Whether to use CSPNeXt block.
             Defaults to False.
         use_depthwise (bool): Whether to use depthwise separable convolution in
-            blocks. Default: False
+            blocks. Defaults to False.
         channel_attention (bool): Whether to add channel attention in each
             stage. Defaults to True.
         conv_cfg (dict, optional): Config dict for convolution layer.
-            Default: None, which means using conv2d.
+            Defaults to None, which means using conv2d.
         norm_cfg (dict): Config dict for normalization layer.
-            Default: dict(type='BN')
+            Defaults to dict(type='BN')
         act_cfg (dict): Config dict for activation layer.
-            Default: dict(type='Swish')
+            Defaults to dict(type='Swish')
         init_cfg (:obj:`ConfigDict` or dict or list[dict] or
             list[:obj:`ConfigDict`], optional): Initialization config dict.
             Defaults to None.
@@ -229,6 +233,7 @@ def __init__(self,
             self.attention = ChannelAttention(2 * mid_channels)
 
     def forward(self, x: Tensor) -> Tensor:
+        """Forward function."""
         x_short = self.short_conv(x)
 
         x_main = self.main_conv(x)