wehub-resource-sync
95 lines
2.5 KiB
Python
95 lines
2.5 KiB
Python
import dgl
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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 dgl.nn.pytorch import SAGEConv
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from .. import utils
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class GraphSAGE(nn.Module):
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def __init__(
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self,
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in_feats,
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n_hidden,
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n_classes,
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n_layers,
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activation,
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dropout,
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aggregator_type,
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):
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super(GraphSAGE, self).__init__()
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self.layers = nn.ModuleList()
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self.dropout = nn.Dropout(dropout)
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self.activation = activation
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# input layer
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self.layers.append(SAGEConv(in_feats, n_hidden, aggregator_type))
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# hidden layers
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for i in range(n_layers - 1):
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self.layers.append(SAGEConv(n_hidden, n_hidden, aggregator_type))
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# output layer
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self.layers.append(
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SAGEConv(n_hidden, n_classes, aggregator_type)
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) # activation None
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def forward(self, graph, inputs):
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h = self.dropout(inputs)
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for l, layer in enumerate(self.layers):
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h = layer(graph, h)
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if l != len(self.layers) - 1:
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h = self.activation(h)
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h = self.dropout(h)
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return h
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def evaluate(model, g, features, labels, mask):
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model.eval()
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with torch.no_grad():
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logits = model(g, features)
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logits = logits[mask]
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labels = labels[mask]
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_, indices = torch.max(logits, dim=1)
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correct = torch.sum(indices == labels)
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return correct.item() * 1.0 / len(labels) * 100
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@utils.benchmark("acc")
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@utils.parametrize("data", ["cora", "pubmed"])
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def track_acc(data):
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data = utils.process_data(data)
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device = utils.get_bench_device()
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g = data[0].to(device)
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features = g.ndata["feat"]
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labels = g.ndata["label"]
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train_mask = g.ndata["train_mask"]
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val_mask = g.ndata["val_mask"]
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test_mask = g.ndata["test_mask"]
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in_feats = features.shape[1]
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n_classes = data.num_classes
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g = dgl.remove_self_loop(g)
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g = dgl.add_self_loop(g)
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# create model
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model = GraphSAGE(in_feats, 16, n_classes, 1, F.relu, 0.5, "gcn")
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loss_fcn = torch.nn.CrossEntropyLoss()
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model = model.to(device)
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model.train()
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# optimizer
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-2, weight_decay=5e-4)
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for epoch in range(200):
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logits = model(g, features)
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loss = loss_fcn(logits[train_mask], labels[train_mask])
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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acc = evaluate(model, g, features, labels, test_mask)
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return acc
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