wehub-resource-sync
92 lines
3.8 KiB
ReStructuredText
92 lines
3.8 KiB
ReStructuredText
Prepare Data
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============
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In this section, we will prepare the data for the Graphormer model introduced before. We can use any dataset containing :class:`~dgl.DGLGraph` objects and standard PyTorch dataloader to feed the data to the model. The key is to define a collate function to group features of multiple graphs into batches. We show an example of the collate function as follows:
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.. code:: python
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def collate(graphs):
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# compute shortest path features, can be done in advance
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for g in graphs:
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spd, path = dgl.shortest_dist(g, root=None, return_paths=True)
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g.ndata["spd"] = spd
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g.ndata["path"] = path
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num_graphs = len(graphs)
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num_nodes = [g.num_nodes() for g in graphs]
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max_num_nodes = max(num_nodes)
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attn_mask = th.zeros(num_graphs, max_num_nodes, max_num_nodes)
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node_feat = []
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in_degree, out_degree = [], []
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path_data = []
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# Since shortest_dist returns -1 for unreachable node pairs and padded
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# nodes are unreachable to others, distance relevant to padded nodes
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# use -1 padding as well.
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dist = -th.ones(
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(num_graphs, max_num_nodes, max_num_nodes), dtype=th.long
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)
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for i in range(num_graphs):
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# A binary mask where invalid positions are indicated by True.
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# Avoid the case where all positions are invalid.
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attn_mask[i, :, num_nodes[i] + 1 :] = 1
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# +1 to distinguish padded non-existing nodes from real nodes
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node_feat.append(graphs[i].ndata["feat"] + 1)
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# 0 for padding
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in_degree.append(
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th.clamp(graphs[i].in_degrees() + 1, min=0, max=512)
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)
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out_degree.append(
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th.clamp(graphs[i].out_degrees() + 1, min=0, max=512)
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)
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# Path padding to make all paths to the same length "max_len".
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path = graphs[i].ndata["path"]
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path_len = path.size(dim=2)
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# shape of shortest_path: [n, n, max_len]
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max_len = 5
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if path_len >= max_len:
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shortest_path = path[:, :, :max_len]
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else:
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p1d = (0, max_len - path_len)
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# Use the same -1 padding as shortest_dist for
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# invalid edge IDs.
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shortest_path = th.nn.functional.pad(path, p1d, "constant", -1)
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pad_num_nodes = max_num_nodes - num_nodes[i]
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p3d = (0, 0, 0, pad_num_nodes, 0, pad_num_nodes)
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shortest_path = th.nn.functional.pad(shortest_path, p3d, "constant", -1)
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# +1 to distinguish padded non-existing edges from real edges
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edata = graphs[i].edata["feat"] + 1
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# shortest_dist pads non-existing edges (at the end of shortest
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# paths) with edge IDs -1, and th.zeros(1, edata.shape[1]) stands
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# for all padded edge features.
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edata = th.cat(
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(edata, th.zeros(1, edata.shape[1]).to(edata.device)), dim=0
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)
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path_data.append(edata[shortest_path])
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dist[i, : num_nodes[i], : num_nodes[i]] = graphs[i].ndata["spd"]
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# node feat padding
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node_feat = th.nn.utils.rnn.pad_sequence(node_feat, batch_first=True)
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# degree padding
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in_degree = th.nn.utils.rnn.pad_sequence(in_degree, batch_first=True)
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out_degree = th.nn.utils.rnn.pad_sequence(out_degree, batch_first=True)
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return (
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node_feat,
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in_degree,
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out_degree,
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attn_mask,
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th.stack(path_data),
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dist,
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)
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In this example, we also omit details like the addition of a virtual node. For more details, please refer to the `Graphormer example <https://github.com/dmlc/dgl/tree/master/examples/core/Graphormer>`_.
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