Method __init__

ppocr/modeling/backbones/rec_pphgnetv2.py:1149–1194

(
        self,
        in_channels,
        mid_channels,
        out_channels,
        kernel_size=3,
        layer_num=6,
        identity=False,
        light_block=True,
        use_lab=False,
        lr_mult=1.0,
    )

    """

    def __init__(
        self,
        in_channels,
        mid_channels,
        out_channels,
        kernel_size=3,
        layer_num=6,
        identity=False,
        light_block=True,
        use_lab=False,
        lr_mult=1.0,
    ):
        super().__init__()
        self.identity = identity

        self.layers = nn.LayerList()
        block_type = "LightConvBNAct" if light_block else "ConvBNAct"
        for i in range(layer_num):
            self.layers.append(
                eval(block_type)(
                    in_channels=in_channels if i == 0 else mid_channels,
                    out_channels=mid_channels,
                    stride=1,
                    kernel_size=kernel_size,
                    use_lab=use_lab,
                    lr_mult=lr_mult,
                )
            )
        # feature aggregation
        total_channels = in_channels + layer_num * mid_channels
        self.aggregation_squeeze_conv = ConvBNAct(
            in_channels=total_channels,
            out_channels=out_channels // 2,
            kernel_size=1,
            stride=1,
            use_lab=use_lab,
            lr_mult=lr_mult,
        )
        self.aggregation_excitation_conv = ConvBNAct(
            in_channels=out_channels // 2,
            out_channels=out_channels,
            kernel_size=1,
            stride=1,
            use_lab=use_lab,
            lr_mult=lr_mult,
        )

    def forward(self, x):
        identity = x