af61e2fbb45c53df3ff2a91f81a2026a8f66d90d,python/dgl/nn/pytorch/conv/gmmconv.py,GMMConv,forward,#GMMConv#Any#Any#Any#,89

Before Change

            The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
            is the output feature size.
        
        graph = graph.local_var()
        graph.ndata["h"] = self.fc(feat).view(-1, self._n_kernels, self._out_feats)
        E = graph.number_of_edges()
        // compute gaussian weight
        gaussian = -0.5 * ((pseudo.view(E, 1, self._dim) -

After Change

            The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
            is the output feature size.
        
        with graph.local_scope():
            feat_src, feat_dst = expand_as_pair(feat)
            graph.srcdata["h"] = self.fc(feat_src).view(-1, self._n_kernels, self._out_feats)
            E = graph.number_of_edges()
            // compute gaussian weight
            gaussian = -0.5 * ((pseudo.view(E, 1, self._dim) -
                                self.mu.view(1, self._n_kernels, self._dim)) ** 2)
            gaussian = gaussian * (self.inv_sigma.view(1, self._n_kernels, self._dim) ** 2)
            gaussian = th.exp(gaussian.sum(dim=-1, keepdim=True)) // (E, K, 1)
            graph.edata["w"] = gaussian
            graph.update_all(fn.u_mul_e("h", "w", "m"), self._reducer("m", "h"))
            rst = graph.dstdata["h"].sum(1)
            // residual connection
            if self.res_fc is not None:
                rst = rst + self.res_fc(feat_dst)
            // bias
            if self.bias is not None:
                rst = rst + self.bias
            return rst

Instances