(self, in_channels, inner_channels=128, fpem_repeat=2, **kwargs)
PANnet
        :param in_channels: 基础网络输出的维度
PANnet
        :param in_channels: 基础网络输出的维度
PANnet : param in_channels : 基础网络输出的维度
def __init__(self, in_channels, inner_channels=128, fpem_repeat=2, **kwargs):
        """
        PANnet
        :param in_channels: 基础网络输出的维度
        """
        super().__init__()
        self.conv_out = inner_channels
        inplace = True
        # reduce layers
        self.reduce_conv_c2 = ConvBnRelu(in_channels[0], inner_channels, kernel_size=1, inplace=inplace)
        self.reduce_conv_c3 = ConvBnRelu(in_channels[1], inner_channels, kernel_size=1, inplace=inplace)
        self.reduce_conv_c4 = ConvBnRelu(in_channels[2], inner_channels, kernel_size=1, inplace=inplace)
        self.reduce_conv_c5 = ConvBnRelu(in_channels[3], inner_channels, kernel_size=1, inplace=inplace)
        self.fpems = nn.ModuleList()
        for i in range(fpem_repeat):
            self.fpems.append(FPEM(self.conv_out))
        self.out_channels = self.conv_out * 4
def __init__ ( self , in_channels , inner_channels = 128 , fpem_repeat = 2 , * * kwargs ) : super ( ) . __init__ ( ) self . conv_out = inner_channels inplace = True # reduce layers self . reduce_conv_c2 = ConvBnRelu ( in_channels [ 0 ] , inner_channels , kernel_size = 1 , inplace = inplace ) self . reduce_conv_c3 = ConvBnRelu ( in_channels [ 1 ] , inner_channels , kernel_size = 1 , inplace = inplace ) self . reduce_conv_c4 = ConvBnRelu ( in_channels [ 2 ] , inner_channels , kernel_size = 1 , inplace = inplace ) self . reduce_conv_c5 = ConvBnRelu ( in_channels [ 3 ] , inner_channels , kernel_size = 1 , inplace = inplace ) self . fpems = nn . ModuleList ( ) for i in range ( fpem_repeat ) : self . fpems . append ( FPEM ( self . conv_out ) ) self . out_channels = self . conv_out * 4