(self, cfg, in_channels)
Arguments:
            cfg              : config
            in_channels (int): number of channels of the input feature
Arguments:
            cfg              : config
            in_channels (int): number of channels of the input feature
Arguments : cfg : config in_channels ( int ) : number of channels of the input feature
def __init__(self, cfg, in_channels):
        """
        Arguments:
            cfg              : config
            in_channels (int): number of channels of the input feature
        """
        super(RPNHeadFeatureSingleConv, self).__init__()
        self.conv = nn.Conv2d(
            in_channels, in_channels, kernel_size=3, stride=1, padding=1
        )

        for l in [self.conv]:
            torch.nn.init.normal_(l.weight, std=0.01)
            torch.nn.init.constant_(l.bias, 0)

        self.out_channels = in_channels
def __init__ ( self , cfg , in_channels ) : super ( RPNHeadFeatureSingleConv , self ) . __init__ ( ) self . conv = nn . Conv2d ( in_channels , in_channels , kernel_size = 3 , stride = 1 , padding = 1 ) for l in [ self . conv ] : torch . nn . init . normal_ ( l . weight , std = 0.01 ) torch . nn . init . constant_ ( l . bias , 0 ) self . out_channels = in_channels