Method forward

models/drconv.py:89–101

(self, input, mask)

        self.kwargs = kwargs
        self.act = nn.Sigmoid()
    def forward(self, input, mask):
        kernel = self.conv_kernel(input)
        kernel = kernel.view(kernel.size(0), -1, kernel.size(2), kernel.size(3)) # B x (r*in*out) x W X H
        output = self.corr(input, kernel, **self.kwargs) # B x (r*out) x W x H
        output = output.view(output.size(0), self.region_num, -1, output.size(2), output.size(3)) # B x r x out x W x H

        mask = F.interpolate(mask.detach(), size=input.size()[2:], mode='nearest')
        mask = mask.unsqueeze(1) 
        inv_msak = 1 - mask
        guide_mask = torch.cat((mask, inv_msak), 1)

        output = torch.sum(output * guide_mask, dim=1)
        return output