Method __init__

surya/detection/model/encoderdecoder.py:98–130

(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size=3,
        stride=1,
        use_bias=False,
        norm_layer=(nn.BatchNorm2d, nn.BatchNorm2d),
        act_layer=(nn.ReLU6, None),
    )

class DSConv(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size=3,
        stride=1,
        use_bias=False,
        norm_layer=(nn.BatchNorm2d, nn.BatchNorm2d),
        act_layer=(nn.ReLU6, None),
    ):
        super(DSConv, self).__init__()
        use_bias = val2tuple(use_bias, 2)
        norm_layer = val2tuple(norm_layer, 2)
        act_layer = val2tuple(act_layer, 2)

        self.depth_conv = ConvNormAct(
            in_channels,
            in_channels,
            kernel_size,
            stride,
            groups=in_channels,
            norm_layer=norm_layer[0],
            act_layer=act_layer[0],
            bias=use_bias[0],
        )
        self.point_conv = ConvNormAct(
            in_channels,
            out_channels,
            1,
            norm_layer=norm_layer[1],
            act_layer=act_layer[1],
            bias=use_bias[1],
        )

    def forward(self, x):
        x = self.depth_conv(x)