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# Source code for mmseg.models.decode_heads.gc_head

    import torch
    from mmcv.cnn import ContextBlock

    from ..builder import HEADS
    from .fcn_head import FCNHead

    @HEADS.register_module()
    class GCHead(FCNHead):
        """GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond.

        This head is the implementation of `GCNet
        <https://arxiv.org/abs/1904.11492>`_.

        Args:
            ratio (float): Multiplier of channels ratio. Default: 1/4.
            pooling_type (str): The pooling type of context aggregation.
                Options are 'att', 'avg'. Default: 'avg'.
            fusion_types (tuple[str]): The fusion type for feature fusion.
                Options are 'channel_add', 'channel_mul'. Defautl: ('channel_add',)
        """

        def __init__(self,
                     ratio=1 / 4.,
                     pooling_type='att',
                     fusion_types=('channel_add', ),
                     **kwargs):
            super(GCHead, self).__init__(num_convs=2, **kwargs)
            self.ratio = ratio
            self.pooling_type = pooling_type
            self.fusion_types = fusion_types
            self.gc_block = ContextBlock(
                in_channels=self.channels,
                ratio=self.ratio,
                pooling_type=self.pooling_type,
                fusion_types=self.fusion_types)

        def forward(self, inputs):
            """Forward function."""
            x = self._transform_inputs(inputs)
            output = self.convs[0](x)
            output = self.gc_block(output)
            output = self.convs[1](output)
            if self.concat_input:
                output = self.conv_cat(torch.cat([x, output], dim=1))
            output = self.cls_seg(output)
            return output