wehub-resource-sync
139 lines
4.0 KiB
Python
139 lines
4.0 KiB
Python
import argparse
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import dgl
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import dgl.nn as dglnn
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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 import AddSelfLoop
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from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset
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class GAT(nn.Module):
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def __init__(self, in_size, hid_size, out_size, heads):
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super().__init__()
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self.gat_layers = nn.ModuleList()
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# two-layer GAT
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self.gat_layers.append(
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dglnn.GATConv(
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in_size,
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hid_size,
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heads[0],
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feat_drop=0.6,
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attn_drop=0.6,
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activation=F.elu,
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)
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)
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self.gat_layers.append(
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dglnn.GATConv(
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hid_size * heads[0],
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out_size,
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heads[1],
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feat_drop=0.6,
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attn_drop=0.6,
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activation=None,
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)
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)
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def forward(self, g, inputs):
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h = inputs
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for i, layer in enumerate(self.gat_layers):
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h = layer(g, h)
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if i == 1: # last layer
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h = h.mean(1)
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else: # other layer(s)
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h = h.flatten(1)
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return h
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def evaluate(g, features, labels, mask, model):
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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)
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def train(g, features, labels, masks, model):
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# define train/val samples, loss function and optimizer
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train_mask = masks[0]
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val_mask = masks[1]
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loss_fcn = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=5e-3, weight_decay=5e-4)
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# training loop
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for epoch in range(200):
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model.train()
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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(g, features, labels, val_mask, model)
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print(
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"Epoch {:05d} | Loss {:.4f} | Accuracy {:.4f} ".format(
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epoch, loss.item(), acc
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)
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)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--dataset",
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type=str,
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default="cora",
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help="Dataset name ('cora', 'citeseer', 'pubmed').",
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)
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parser.add_argument(
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"--dt",
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type=str,
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default="float",
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help="data type(float, bfloat16)",
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)
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args = parser.parse_args()
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print(f"Training with DGL built-in GATConv module.")
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# load and preprocess dataset
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transform = (
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AddSelfLoop()
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) # by default, it will first remove self-loops to prevent duplication
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if args.dataset == "cora":
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data = CoraGraphDataset(transform=transform)
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elif args.dataset == "citeseer":
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data = CiteseerGraphDataset(transform=transform)
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elif args.dataset == "pubmed":
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data = PubmedGraphDataset(transform=transform)
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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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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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g = g.int().to(device)
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features = g.ndata["feat"]
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labels = g.ndata["label"]
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masks = g.ndata["train_mask"], g.ndata["val_mask"], g.ndata["test_mask"]
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# create GAT model
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in_size = features.shape[1]
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out_size = data.num_classes
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model = GAT(in_size, 8, out_size, heads=[8, 1]).to(device)
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# convert model and graph to bfloat16 if needed
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if args.dt == "bfloat16":
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g = dgl.to_bfloat16(g)
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features = features.to(dtype=torch.bfloat16)
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model = model.to(dtype=torch.bfloat16)
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# model training
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print("Training...")
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train(g, features, labels, masks, model)
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# test the model
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print("Testing...")
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acc = evaluate(g, features, labels, masks[2], model)
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print("Test accuracy {:.4f}".format(acc))
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