def __init__(self,
                 depth=50,
                 variant='b',
                 lr_mult=1.0,
                 groups=1,
                 base_width=64,
                 norm_type='bn',
                 norm_decay=0,
                 dcn_v2=False,
                 freeze_norm=False,
                 std_senet=True):
        """
        SERes5Head layer

        Args:
            depth (int): SENet depth, should be 50, 101, 152
            variant (str): ResNet variant, supports 'a', 'b', 'c', 'd' currently
            lr_mult (list): learning rate ratio of SERes5Head, default as 1.0.
            groups (int): group convolution cardinality
            base_width (int): base width of each group convolution
            norm_type (str): normalization type, 'bn', 'sync_bn' or 'affine_channel'
            norm_decay (float): weight decay for normalization layer weights
            dcn_v2_stages (list): index of stages who select deformable conv v2
            std_senet (bool): whether use senet, default True
            
        """
        super(SERes5Head, self).__init__()
        ch_out = 512
        ch_in = 256 if depth < 50 else 1024
        na = NameAdapter(self)
        block = BottleNeck if depth >= 50 else BasicBlock
        self.res5 = Blocks(
            block,
            ch_in,
            ch_out,
            count=3,
            name_adapter=na,
            stage_num=5,
            variant=variant,
            groups=groups,
            base_width=base_width,
            lr=lr_mult,
            norm_type=norm_type,
            norm_decay=norm_decay,
            freeze_norm=freeze_norm,
            dcn_v2=dcn_v2,
            std_senet=std_senet)
        self.ch_out = ch_out * block.expansion

        SERes5Head layer

        Args:
            depth (int): SENet depth, should be 50, 101, 152
            variant (str): ResNet variant, supports 'a', 'b', 'c', 'd' currently
            lr_mult (list): learning rate ratio of SERes5Head, default as 1.0.
            groups (int): group convolution cardinality
            base_width (int): base width of each group convolution
            norm_type (str): normalization type, 'bn', 'sync_bn' or 'affine_channel'
            norm_decay (float): weight decay for normalization layer weights
            dcn_v2_stages (list): index of stages who select deformable conv v2
            std_senet (bool): whether use senet, default True
            
        
ppdet/modeling/backbones/senet.py