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

"""
Graph Representation Learning via Hard Attention Networks in DGL using Adam optimization.
References
----------
Paper: https://arxiv.org/abs/1907.04652
"""

import argparse
import time

import dgl

import numpy as np
import torch
import torch.nn.functional as F
from dgl.data import (
    CiteseerGraphDataset,
    CoraGraphDataset,
    PubmedGraphDataset,
    register_data_args,
)
from hgao import HardGAT
from utils import EarlyStopping


def accuracy(logits, labels):
    _, indices = torch.max(logits, dim=1)
    correct = torch.sum(indices == labels)
    return correct.item() * 1.0 / len(labels)


def evaluate(model, features, labels, mask):
    model.eval()
    with torch.no_grad():
        logits = model(features)
        logits = logits[mask]
        labels = labels[mask]
        return accuracy(logits, labels)


def main(args):
    # load and preprocess dataset
    if args.dataset == "cora":
        data = CoraGraphDataset()
    elif args.dataset == "citeseer":
        data = CiteseerGraphDataset()
    elif args.dataset == "pubmed":
        data = PubmedGraphDataset()
    else:
        raise ValueError("Unknown dataset: {}".format(args.dataset))

    if args.num_layers <= 0:
        raise ValueError("num layer must be positive int")
    g = data[0]
    if args.gpu < 0:
        cuda = False
    else:
        cuda = True
        g = g.to(args.gpu)

    features = g.ndata["feat"]
    labels = g.ndata["label"]
    train_mask = g.ndata["train_mask"]
    val_mask = g.ndata["val_mask"]
    test_mask = g.ndata["test_mask"]
    num_feats = features.shape[1]
    n_classes = data.num_classes
    n_edges = g.num_edges()
    print(
        """----Data statistics------'
      #Edges %d
      #Classes %d
      #Train samples %d
      #Val samples %d
      #Test samples %d"""
        % (
            n_edges,
            n_classes,
            train_mask.int().sum().item(),
            val_mask.int().sum().item(),
            test_mask.int().sum().item(),
        )
    )

    # add self loop
    g = dgl.remove_self_loop(g)
    g = dgl.add_self_loop(g)
    n_edges = g.num_edges()
    # create model
    heads = ([args.num_heads] * args.num_layers) + [args.num_out_heads]
    model = HardGAT(
        g,
        args.num_layers,
        num_feats,
        args.num_hidden,
        n_classes,
        heads,
        F.elu,
        args.in_drop,
        args.attn_drop,
        args.negative_slope,
        args.residual,
        args.k,
    )
    print(model)
    if args.early_stop:
        stopper = EarlyStopping(patience=100)
    if cuda:
        model.cuda()
    loss_fcn = torch.nn.CrossEntropyLoss()

    # use optimizer
    optimizer = torch.optim.Adam(
        model.parameters(), lr=args.lr, weight_decay=args.weight_decay
    )

    # initialize graph
    mean = 0
    for epoch in range(args.epochs):
        model.train()
        if epoch >= 3:
            t0 = time.time()
        # forward
        logits = model(features)
        loss = loss_fcn(logits[train_mask], labels[train_mask])

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if epoch >= 3:
            mean = (mean * (epoch - 3) + (time.time() - t0)) / (epoch - 2)
            train_acc = accuracy(logits[train_mask], labels[train_mask])

            if args.fastmode:
                val_acc = accuracy(logits[val_mask], labels[val_mask])
            else:
                val_acc = evaluate(model, features, labels, val_mask)
                if args.early_stop:
                    if stopper.step(val_acc, model):
                        break

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

    print()
    if args.early_stop:
        model.load_state_dict(
            torch.load("es_checkpoint.pt", weights_only=False)
        )
    acc = evaluate(model, features, labels, test_mask)
    print("Test Accuracy {:.4f}".format(acc))


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="GAT")
    register_data_args(parser)
    parser.add_argument(
        "--gpu",
        type=int,
        default=-1,
        help="which GPU to use. Set -1 to use CPU.",
    )
    parser.add_argument(
        "--epochs", type=int, default=200, help="number of training epochs"
    )
    parser.add_argument(
        "--num-heads",
        type=int,
        default=8,
        help="number of hidden attention heads",
    )
    parser.add_argument(
        "--num-out-heads",
        type=int,
        default=1,
        help="number of output attention heads",
    )
    parser.add_argument(
        "--num-layers", type=int, default=1, help="number of hidden layers"
    )
    parser.add_argument(
        "--num-hidden", type=int, default=8, help="number of hidden units"
    )
    parser.add_argument(
        "--residual",
        action="store_true",
        default=False,
        help="use residual connection",
    )
    parser.add_argument(
        "--in-drop", type=float, default=0.6, help="input feature dropout"
    )
    parser.add_argument(
        "--attn-drop", type=float, default=0.6, help="attention dropout"
    )
    parser.add_argument("--lr", type=float, default=0.01, help="learning rate")
    parser.add_argument(
        "--weight-decay", type=float, default=5e-4, help="weight decay"
    )
    parser.add_argument(
        "--negative-slope",
        type=float,
        default=0.2,
        help="the negative slope of leaky relu",
    )
    parser.add_argument(
        "--early-stop",
        action="store_true",
        default=False,
        help="indicates whether to use early stop or not",
    )
    parser.add_argument(
        "--fastmode",
        action="store_true",
        default=False,
        help="skip re-evaluate the validation set",
    )
    parser.add_argument(
        "--k",
        type=int,
        default=8,
        help="top k neighor for attention calculation",
    )
    args = parser.parse_args()
    print(args)

    main(args)