论文标题：DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
论文地址：https://arxiv.org/pdf/2006.02334
代码地址：https://github.com/joe-siyuan-qiao/DetectoRS/blob/612916ba89ad6452b07ae52d3a6ec8d34a792608/mmdet/ops/saconv.py

代码：

def forward(self, x):
        # pre-context
        avg_x = torch.nn.functional.adaptive_avg_pool2d(x, output_size=1)
        avg_x = self.pre_context(avg_x)
        avg_x = avg_x.expand_as(x)
        x = x + avg_x
        # switch
        avg_x = torch.nn.functional.pad(x, pad=(2, 2, 2, 2), mode="reflect")
        avg_x = torch.nn.functional.avg_pool2d(avg_x, kernel_size=5, stride=1, padding=0)
        switch = self.switch(avg_x)
        # sac
        weight = self._get_weight(self.weight)
        if self.use_deform:
            offset = self.offset_s(avg_x)
            out_s = deform_conv(
                    x,
                    offset,
                    weight,
                    self.stride,
                    self.padding,
                    self.dilation,
                    self.groups,
                    1)
        else:
            out_s = super().conv2d_forward(x, weight)
        ori_p = self.padding
        ori_d = self.dilation
        self.padding = tuple(3 * p for p in self.padding)
        self.dilation = tuple(3 * d for d in self.dilation)
        weight = weight + self.weight_diff
        if self.use_deform:
            offset = self.offset_l(avg_x)
            out_l = deform_conv(
                    x,
                    offset,
                    weight,
                    self.stride,
                    self.padding,
                    self.dilation,
                    self.groups,
                    1)
        else:
            out_l = super().conv2d_forward(x, weight)
        out = switch * out_s + (1 - switch) * out_l
        self.padding = ori_p
        self.dilation = ori_d
        # post-context
        avg_x = torch.nn.functional.adaptive_avg_pool2d(out, output_size=1)
        avg_x = self.post_context(avg_x)
        avg_x = avg_x.expand_as(out)
        out = out + avg_x
        return out

out = switch * out_s + (1 - switch) * out_l 实现门控操作