    def __init__(self,
                 ch_in,
                 ch_out,
                 k,
                 pool_size,
                 norm_type,
                 freeze_norm=False,
                 name='',
                 act='leaky',
                 data_format='NCHW'):
        """
        SPP layer, which consist of four pooling layer follwed by conv layer
        Args:
            ch_in (int): input channel of conv layer
            ch_out (int): output channel of conv layer
            k (int): kernel size of conv layer
            norm_type (str): batch norm type
            freeze_norm (bool): whether to freeze norm, default False
            name (str): layer name
            act (str): activation function
            data_format (str): data format, NCHW or NHWC
        """
        super(SPP, self).__init__()
        # self.pool = []
        self.pool = nn.ModuleList()
        self.data_format = data_format
        for i, size in enumerate(pool_size):
            # pool = self.add_sublayer(
            #     '{}.pool1'.format(name),
            #     nn.MaxPool2D(
            #         kernel_size=size,
            #         stride=1,
            #         padding=size // 2,
            #         data_format=data_format,
            #         ceil_mode=False))
            # self.pool.append(pool)
            self.pool.add_module('{}_pool_{}'.format(name, i),
                                 nn.MaxPool2d(
                                     kernel_size=size,
                                     stride=1,
                                     padding=size // 2,
                                     ceil_mode=False,
                                 )
                                 )
        self.conv = ConvBNLayer(
            ch_in,
            ch_out,
            k,
            padding=k // 2,
            norm_type=norm_type,
            freeze_norm=freeze_norm,
            name=name,
            act=act,
            data_format=data_format)
SPP layer, which consist of four pooling layer follwed by conv layer
Args:
ch_in (int): input channel of conv layer
ch_out (int): output channel of conv layer
k (int): kernel size of conv layer
norm_type (str): batch norm type
freeze_norm (bool): whether to freeze norm, default False
name (str): layer name
act (str): activation function
data_format (str): data format, NCHW or NHWC
ptstructure/layout/ptppyolov2/ppyolov2_yolo_fpn.py