def __init__(
        self,
        bottom_up,
        in_features,
        out_channels,
        norm="",
        top_block=None,
        fuse_type="sum",
        square_pad=0,
    ):
        """
        Args:
            bottom_up (Backbone): module representing the bottom up subnetwork.
                Must be a subclass of :class:`Backbone`. The multi-scale feature
                maps generated by the bottom up network, and listed in `in_features`,
                are used to generate FPN levels.
            in_features (list[str]): names of the input feature maps coming
                from the backbone to which FPN is attached. For example, if the
                backbone produces ["res2", "res3", "res4"], any *contiguous* sublist
                of these may be used; order must be from high to low resolution.
            out_channels (int): number of channels in the output feature maps.
            norm (str): the normalization to use.
            top_block (nn.Module or None): if provided, an extra operation will
                be performed on the output of the last (smallest resolution)
                FPN output, and the result will extend the result list. The top_block
                further downsamples the feature map. It must have an attribute
                "num_levels", meaning the number of extra FPN levels added by
                this block, and "in_feature", which is a string representing
                its input feature (e.g., p5).
            fuse_type (str): types for fusing the top down features and the lateral
                ones. It can be "sum" (default), which sums up element-wise; or "avg",
                which takes the element-wise mean of the two.
            square_pad (int): If > 0, require input images to be padded to specific square size.
        """
        super(FPN, self).__init__()
        assert isinstance(bottom_up, Backbone)
        assert in_features, in_features

        # Feature map strides and channels from the bottom up network (e.g. ResNet)
        input_shapes = bottom_up.output_shape()
        strides = [input_shapes[f].stride for f in in_features]
        in_channels_per_feature = [input_shapes[f].channels for f in in_features]

        _assert_strides_are_log2_contiguous(strides)
        lateral_convs = []
        output_convs = []

        use_bias = norm == ""
        for idx, in_channels in enumerate(in_channels_per_feature):
            lateral_norm = get_norm(norm, out_channels)
            output_norm = get_norm(norm, out_channels)

            lateral_conv = Conv2d(
                in_channels, out_channels, kernel_size=1, bias=use_bias, norm=lateral_norm
            )
            output_conv = Conv2d(
                out_channels,
                out_channels,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=use_bias,
                norm=output_norm,
            )
            weight_init.c2_xavier_fill(lateral_conv)
            weight_init.c2_xavier_fill(output_conv)
            stage = int(math.log2(strides[idx]))
            self.add_module("fpn_lateral{}".format(stage), lateral_conv)
            self.add_module("fpn_output{}".format(stage), output_conv)

            lateral_convs.append(lateral_conv)
            output_convs.append(output_conv)
        # Place convs into top-down order (from low to high resolution)
        # to make the top-down computation in forward clearer.
        self.lateral_convs = lateral_convs[::-1]
        self.output_convs = output_convs[::-1]
        self.top_block = top_block
        self.in_features = tuple(in_features)
        self.bottom_up = bottom_up
        # Return feature names are "p<stage>", like ["p2", "p3", ..., "p6"]
        self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in strides}
        # top block output feature maps.
        if self.top_block is not None:
            for s in range(stage, stage + self.top_block.num_levels):
                self._out_feature_strides["p{}".format(s + 1)] = 2 ** (s + 1)

        self._out_features = list(self._out_feature_strides.keys())
        self._out_feature_channels = {k: out_channels for k in self._out_features}
        self._size_divisibility = strides[-1]
        self._square_pad = square_pad
        assert fuse_type in {"avg", "sum"}
        self._fuse_type = fuse_type

        Args:
            bottom_up (Backbone): module representing the bottom up subnetwork.
                Must be a subclass of :class:`Backbone`. The multi-scale feature
                maps generated by the bottom up network, and listed in `in_features`,
                are used to generate FPN levels.
            in_features (list[str]): names of the input feature maps coming
                from the backbone to which FPN is attached. For example, if the
                backbone produces ["res2", "res3", "res4"], any *contiguous* sublist
                of these may be used; order must be from high to low resolution.
            out_channels (int): number of channels in the output feature maps.
            norm (str): the normalization to use.
            top_block (nn.Module or None): if provided, an extra operation will
                be performed on the output of the last (smallest resolution)
                FPN output, and the result will extend the result list. The top_block
                further downsamples the feature map. It must have an attribute
                "num_levels", meaning the number of extra FPN levels added by
                this block, and "in_feature", which is a string representing
                its input feature (e.g., p5).
            fuse_type (str): types for fusing the top down features and the lateral
                ones. It can be "sum" (default), which sums up element-wise; or "avg",
                which takes the element-wise mean of the two.
            square_pad (int): If > 0, require input images to be padded to specific square size.
        
ControlNet-v1-1-nightly/annotator/oneformer/detectron2/modeling/backbone/fpn.py