dmlc--dgl/examples/pytorch/dgi/train.py

import argparse, time

import dgl
import networkx as nx
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from dgi import Classifier, DGI
from dgl import DGLGraph
from dgl.data import load_data, register_data_args


def evaluate(model, features, labels, mask):
    model.eval()
    with torch.no_grad():
        logits = model(features)
        logits = logits[mask]
        labels = labels[mask]
        _, indices = torch.max(logits, dim=1)
        correct = torch.sum(indices == labels)
        return correct.item() * 1.0 / len(labels)


def main(args):
    # load and preprocess dataset
    data = load_data(args)
    g = data[0]
    features = torch.FloatTensor(g.ndata["feat"])
    labels = torch.LongTensor(g.ndata["label"])
    if hasattr(torch, "BoolTensor"):
        train_mask = torch.BoolTensor(g.ndata["train_mask"])
        val_mask = torch.BoolTensor(g.ndata["val_mask"])
        test_mask = torch.BoolTensor(g.ndata["test_mask"])
    else:
        train_mask = torch.ByteTensor(g.ndata["train_mask"])
        val_mask = torch.ByteTensor(g.ndata["val_mask"])
        test_mask = torch.ByteTensor(g.ndata["test_mask"])
    in_feats = features.shape[1]
    n_classes = data.num_classes
    n_edges = g.num_edges()

    if args.gpu < 0:
        cuda = False
    else:
        cuda = True
        torch.cuda.set_device(args.gpu)
        features = features.cuda()
        labels = labels.cuda()
        train_mask = train_mask.cuda()
        val_mask = val_mask.cuda()
        test_mask = test_mask.cuda()

    # add self loop
    if args.self_loop:
        g = dgl.remove_self_loop(g)
        g = dgl.add_self_loop(g)
    n_edges = g.num_edges()

    if args.gpu >= 0:
        g = g.to(args.gpu)
    # create DGI model
    dgi = DGI(
        g,
        in_feats,
        args.n_hidden,
        args.n_layers,
        nn.PReLU(args.n_hidden),
        args.dropout,
    )

    if cuda:
        dgi.cuda()

    dgi_optimizer = torch.optim.Adam(
        dgi.parameters(), lr=args.dgi_lr, weight_decay=args.weight_decay
    )

    # train deep graph infomax
    cnt_wait = 0
    best = 1e9
    best_t = 0
    mean = 0
    for epoch in range(args.n_dgi_epochs):
        dgi.train()
        if epoch >= 3:
            t0 = time.time()

        dgi_optimizer.zero_grad()
        loss = dgi(features)
        loss.backward()
        dgi_optimizer.step()

        if loss < best:
            best = loss
            best_t = epoch
            cnt_wait = 0
            torch.save(dgi.state_dict(), "best_dgi.pkl")
        else:
            cnt_wait += 1

        if cnt_wait == args.patience:
            print("Early stopping!")
            break

        if epoch >= 3:
            mean = (mean * (epoch - 3) + (time.time() - t0)) / (epoch - 2)

            print(
                "Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | "
                "ETputs(KTEPS) {:.2f}".format(
                    epoch, mean, loss.item(), n_edges / mean / 1000
                )
            )

    # create classifier model
    classifier = Classifier(args.n_hidden, n_classes)
    if cuda:
        classifier.cuda()

    classifier_optimizer = torch.optim.Adam(
        classifier.parameters(),
        lr=args.classifier_lr,
        weight_decay=args.weight_decay,
    )

    # train classifier
    print("Loading {}th epoch".format(best_t))
    dgi.load_state_dict(torch.load("best_dgi.pkl", weights_only=False))
    embeds = dgi.encoder(features, corrupt=False)
    embeds = embeds.detach()
    mean = 0
    for epoch in range(args.n_classifier_epochs):
        classifier.train()
        if epoch >= 3:
            t0 = time.time()

        classifier_optimizer.zero_grad()
        preds = classifier(embeds)
        loss = F.nll_loss(preds[train_mask], labels[train_mask])
        loss.backward()
        classifier_optimizer.step()

        if epoch >= 3:
            mean = (mean * (epoch - 3) + (time.time() - t0)) / (epoch - 2)

            acc = evaluate(classifier, embeds, labels, val_mask)
            print(
                "Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
                "ETputs(KTEPS) {:.2f}".format(
                    epoch,
                    mean,
                    loss.item(),
                    acc,
                    n_edges / mean / 1000,
                )
            )

    print()
    acc = evaluate(classifier, embeds, labels, test_mask)
    print("Test Accuracy {:.4f}".format(acc))


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="DGI")
    register_data_args(parser)
    parser.add_argument(
        "--dropout", type=float, default=0.0, help="dropout probability"
    )
    parser.add_argument("--gpu", type=int, default=-1, help="gpu")
    parser.add_argument(
        "--dgi-lr", type=float, default=1e-3, help="dgi learning rate"
    )
    parser.add_argument(
        "--classifier-lr",
        type=float,
        default=1e-2,
        help="classifier learning rate",
    )
    parser.add_argument(
        "--n-dgi-epochs",
        type=int,
        default=300,
        help="number of training epochs",
    )
    parser.add_argument(
        "--n-classifier-epochs",
        type=int,
        default=300,
        help="number of training epochs",
    )
    parser.add_argument(
        "--n-hidden", type=int, default=512, help="number of hidden gcn units"
    )
    parser.add_argument(
        "--n-layers", type=int, default=1, help="number of hidden gcn layers"
    )
    parser.add_argument(
        "--weight-decay", type=float, default=0.0, help="Weight for L2 loss"
    )
    parser.add_argument(
        "--patience", type=int, default=20, help="early stop patience condition"
    )
    parser.add_argument(
        "--self-loop",
        action="store_true",
        help="graph self-loop (default=False)",
    )
    parser.set_defaults(self_loop=False)
    args = parser.parse_args()
    print(args)

    main(args)