def __init__(self,
                 ch_in,
                 ch_out,
                 filter_size=3,
                 stride=1,
                 groups=1,
                 padding=0,
                 norm_type='bn',
                 norm_decay=0.,
                 act="leaky",
                 freeze_norm=False,
                 data_format='NCHW',
                 name=''):
        """
        conv + bn + activation layer

        Args:
            ch_in (int): input channel
            ch_out (int): output channel
            filter_size (int): filter size, default 3
            stride (int): stride, default 1
            groups (int): number of groups of conv layer, default 1
            padding (int): padding size, default 0
            norm_type (str): batch norm type, default bn
            norm_decay (str): decay for weight and bias of batch norm layer, default 0.
            act (str): activation function type, default 'leaky', which means leaky_relu
            freeze_norm (bool): whether to freeze norm, default False
            data_format (str): data format, NCHW or NHWC
        """
        super(ConvBNLayer, self).__init__()

        self.conv = nn.Conv2D(
            in_channels=ch_in,
            out_channels=ch_out,
            kernel_size=filter_size,
            stride=stride,
            padding=padding,
            groups=groups,
            data_format=data_format,
            bias_attr=False)
        self.batch_norm = batch_norm(
            ch_out,
            norm_type=norm_type,
            norm_decay=norm_decay,
            freeze_norm=freeze_norm,
            data_format=data_format)
        self.act = act

        conv + bn + activation layer

        Args:
            ch_in (int): input channel
            ch_out (int): output channel
            filter_size (int): filter size, default 3
            stride (int): stride, default 1
            groups (int): number of groups of conv layer, default 1
            padding (int): padding size, default 0
            norm_type (str): batch norm type, default bn
            norm_decay (str): decay for weight and bias of batch norm layer, default 0.
            act (str): activation function type, default 'leaky', which means leaky_relu
            freeze_norm (bool): whether to freeze norm, default False
            data_format (str): data format, NCHW or NHWC
        
ppdet/modeling/backbones/darknet.py