    def __init__(self,
                 ch_in,
                 ch_out,
                 count,
                 norm_type='bn',
                 norm_decay=0.,
                 freeze_norm=False,
                 name=None,
                 data_format='NCHW'):
        """
        Blocks layer, which consist of some BaickBlock layers
        Args:
            ch_in (int): input channel
            ch_out (int): output channel
            count (int): number of BasicBlock layer
            norm_type (str): batch norm type, default bn
            norm_decay (str): decay for weight and bias of batch norm layer, default 0.
            freeze_norm (bool): whether to freeze norm, default False
            name (str): layer name
            data_format (str): data format, NCHW or NHWC
        """
        super(Blocks, self).__init__()

        self.basicblock0 = BasicBlock(
            ch_in,
            ch_out,
            norm_type=norm_type,
            norm_decay=norm_decay,
            freeze_norm=freeze_norm,
            data_format=data_format)
        # self.res_out_list = nn.Sequential()
        self.res_out_list = nn.ModuleList()
        for i in range(1, count):
            block_name = '{}_{}'.format(name, i)
            # res_out = self.add_sublayer(
            #     block_name,
            #     BasicBlock(
            #         ch_out * 2,
            #         ch_out,
            #         norm_type=norm_type,
            #         norm_decay=norm_decay,
            #         freeze_norm=freeze_norm,
            #         data_format=data_format))
            # self.res_out_list.append(res_out)
            res_out = BasicBlock(
                    ch_out * 2,
                    ch_out,
                    norm_type=norm_type,
                    norm_decay=norm_decay,
                    freeze_norm=freeze_norm,
                    data_format=data_format)
            self.res_out_list.add_module(block_name, res_out)
        self.ch_out = ch_out
Blocks layer, which consist of some BaickBlock layers
Args:
ch_in (int): input channel
ch_out (int): output channel
count (int): number of BasicBlock layer
norm_type (str): batch norm type, default bn
norm_decay (str): decay for weight and bias of batch norm layer, default 0.
freeze_norm (bool): whether to freeze norm, default False
name (str): layer name
data_format (str): data format, NCHW or NHWC
ptstructure/layout/ptppyolov2/ppyolov2_darknet.py