Method forward

models/dat.py:133–159

(self, x)

            self.drop_path.append(DropPath(drop_path_rate[i]) if drop_path_rate[i] > 0.0 else nn.Identity())

    def forward(self, x):

        x = self.proj(x)

        for d in range(self.depths):
            
            if self.use_lpu:
                x0 = x
                x = self.local_perception_units[d](x.contiguous())
                x = x + x0

            if self.stage_spec[d] == 'X':
                x0 = x
                x = self.attns[d](x)
                x = self.mlps[d](self.ln_cnvnxt[str(d)](x))
                x = self.drop_path[d](x) + x0
            else:
                x0 = x
                x, pos, ref = self.attns[d](self.layer_norms[2 * d](x))
                x = self.layer_scales[2 * d](x)
                x = self.drop_path[d](x) + x0
                x0 = x
                x = self.mlps[d](self.layer_norms[2 * d + 1](x))
                x = self.layer_scales[2 * d + 1](x)
                x = self.drop_path[d](x) + x0

        return x


class DAT(nn.Module):