def _make_deconv_head(
        self,
        in_channels: Union[int, Sequence[int]],
        out_channels: int,
        deconv_type: str = 'heatmap',
        deconv_out_channels: OptIntSeq = (256, 256, 256),
        deconv_kernel_sizes: OptIntSeq = (4, 4, 4),
        deconv_num_groups: OptIntSeq = (16, 16, 16),
        conv_out_channels: OptIntSeq = None,
        conv_kernel_sizes: OptIntSeq = None,
        final_layer: dict = dict(kernel_size=1)
    ) -> nn.Module:
        """Create deconvolutional layers by given parameters."""

        if deconv_type == 'heatmap':
            deconv_head = MODELS.build(
                dict(
                    type='HeatmapHead',
                    in_channels=self.in_channels,
                    out_channels=out_channels,
                    deconv_out_channels=deconv_out_channels,
                    deconv_kernel_sizes=deconv_kernel_sizes,
                    conv_out_channels=conv_out_channels,
                    conv_kernel_sizes=conv_kernel_sizes,
                    final_layer=final_layer))
        else:
            deconv_head = MODELS.build(
                dict(
                    type='ViPNASHead',
                    in_channels=in_channels,
                    out_channels=out_channels,
                    deconv_out_channels=deconv_out_channels,
                    deconv_num_groups=deconv_num_groups,
                    conv_out_channels=conv_out_channels,
                    conv_kernel_sizes=conv_kernel_sizes,
                    final_layer=final_layer))

        return deconv_head
Create deconvolutional layers by given parameters.
mmpose/mmpose/models/heads/coord_cls_heads/simcc_head.py