168a88e57070871eef5a9fcdad3ed1a4d708d7bd,examples/pytorch/ogb/ogbn-products/graphsage/main.py,SAGE,inference,#SAGE#Any#Any#Any#Any#,80

Before Change

        for l, layer in enumerate(self.layers):
            y = th.zeros(g.number_of_nodes(), self.n_hidden if l != len(self.layers) - 1 else self.n_classes)

            for start in tqdm.trange(0, len(nodes), batch_size):
                end = start + batch_size
                batch_nodes = nodes[start:end]
                block = dgl.to_block(dgl.in_subgraph(g, batch_nodes), batch_nodes)
                input_nodes = block.srcdata[dgl.NID]

                h = x[input_nodes].to(device)
                h_dst = h[:block.number_of_dst_nodes()]
                h = layer(block, (h, h_dst))
                if l != len(self.layers) - 1:
                    h = self.activation(h)
                    h = self.dropout(h)

                y[start:end] = h.cpu()

            x = y
        return y

def prepare_mp(g):

After Change

        for l, layer in enumerate(self.layers):
            y = th.zeros(g.number_of_nodes(), self.n_hidden if l != len(self.layers) - 1 else self.n_classes)

            sampler = dgl.sampling.MultiLayerNeighborSampler([None])
            dataloader = dgl.sampling.NodeDataLoader(
                g,
                th.arange(g.number_of_nodes()),
                sampler,
                batch_size=args.batch_size,
                shuffle=True,
                drop_last=False,
                num_workers=args.num_workers)

            for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader):
                block = blocks[0]

                h = x[input_nodes].to(device)
                h_dst = h[:block.number_of_dst_nodes()]
                h = layer(block, (h, h_dst))
                if l != len(self.layers) - 1:
                    h = self.activation(h)
                    h = self.dropout(h)

                y[output_nodes] = h.cpu()

            x = y
        return y

def prepare_mp(g):

Instances