dmlc--dgl
Chang Liu
93 行
3.7 KiB
Python
93 行
3.7 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torchmetrics.functional import accuracy
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import dgl
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from dgl.data.rdf import AIFBDataset, MUTAGDataset, BGSDataset, AMDataset
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from dgl.nn.pytorch import RelGraphConv
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import argparse
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class RGCN(nn.Module):
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def __init__(self, num_nodes, h_dim, out_dim, num_rels):
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super().__init__()
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self.emb = nn.Embedding(num_nodes, h_dim)
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# two-layer RGCN
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self.conv1 = RelGraphConv(h_dim, h_dim, num_rels, regularizer='basis',
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num_bases=num_rels, self_loop=False)
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self.conv2 = RelGraphConv(h_dim, out_dim, num_rels, regularizer='basis',
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num_bases=num_rels, self_loop=False)
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def forward(self, g):
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x = self.emb.weight
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h = F.relu(self.conv1(g, x, g.edata[dgl.ETYPE], g.edata['norm']))
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h = self.conv2(g, h, g.edata[dgl.ETYPE], g.edata['norm'])
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return h
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def evaluate(g, target_idx, labels, test_mask, model):
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test_idx = torch.nonzero(test_mask, as_tuple=False).squeeze()
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model.eval()
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with torch.no_grad():
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logits = model(g)
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logits = logits[target_idx]
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return accuracy(logits[test_idx].argmax(dim=1), labels[test_idx]).item()
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def train(g, target_idx, labels, train_mask, model):
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# define train idx, loss function and optimizer
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train_idx = torch.nonzero(train_mask, as_tuple=False).squeeze()
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loss_fcn = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-2, weight_decay=5e-4)
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model.train()
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for epoch in range(50):
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logits = model(g)
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logits = logits[target_idx]
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loss = loss_fcn(logits[train_idx], labels[train_idx])
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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acc = accuracy(logits[train_idx].argmax(dim=1), labels[train_idx]).item()
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print("Epoch {:05d} | Loss {:.4f} | Train Accuracy {:.4f} "
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. format(epoch, loss.item(), acc))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='RGCN for entity classification')
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parser.add_argument("--dataset", type=str, default="aifb",
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help="Dataset name ('aifb', 'mutag', 'bgs', 'am').")
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args = parser.parse_args()
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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print(f'Training with DGL built-in RGCN module.')
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# load and preprocess dataset
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if args.dataset == 'aifb':
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data = AIFBDataset()
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elif args.dataset == 'mutag':
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data = MUTAGDataset()
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elif args.dataset == 'bgs':
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data = BGSDataset()
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elif args.dataset == 'am':
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data = AMDataset()
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else:
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raise ValueError('Unknown dataset: {}'.format(args.dataset))
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g = data[0]
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g = g.int().to(device)
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num_rels = len(g.canonical_etypes)
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category = data.predict_category
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labels = g.nodes[category].data.pop('labels')
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train_mask = g.nodes[category].data.pop('train_mask')
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test_mask = g.nodes[category].data.pop('test_mask')
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# calculate normalization weight for each edge, and find target category and node id
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for cetype in g.canonical_etypes:
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g.edges[cetype].data['norm'] = dgl.norm_by_dst(g, cetype).unsqueeze(1)
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category_id = g.ntypes.index(category)
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g = dgl.to_homogeneous(g, edata=['norm'])
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node_ids = torch.arange(g.num_nodes()).to(device)
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target_idx = node_ids[g.ndata[dgl.NTYPE] == category_id]
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# create RGCN model
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in_size = g.num_nodes() # featureless with one-hot encoding
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out_size = data.num_classes
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model = RGCN(in_size, 16, out_size, num_rels).to(device)
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train(g, target_idx, labels, train_mask, model)
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acc = evaluate(g, target_idx, labels, test_mask, model)
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print("Test accuracy {:.4f}".format(acc))
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