dmlc--dgl/examples/mxnet/gat/train.py

"""
Graph Attention Networks in DGL using SPMV optimization.
Multiple heads are also batched together for faster training.
References
----------
Paper: https://arxiv.org/abs/1710.10903
Author's code: https://github.com/PetarV-/GAT
Pytorch implementation: https://github.com/Diego999/pyGAT
"""

import argparse
import time

import dgl

import mxnet as mx
import networkx as nx
import numpy as np
from dgl.data import (
    CiteseerGraphDataset,
    CoraGraphDataset,
    PubmedGraphDataset,
    register_data_args,
)
from gat import GAT
from mxnet import gluon
from utils import EarlyStopping


def elu(data):
    return mx.nd.LeakyReLU(data, act_type="elu")


def evaluate(model, features, labels, mask):
    logits = model(features)
    logits = logits[mask].asnumpy().squeeze()
    val_labels = labels[mask].asnumpy().squeeze()
    max_index = np.argmax(logits, axis=1)
    accuracy = np.sum(np.where(max_index == val_labels, 1, 0)) / len(val_labels)
    return accuracy


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))

    g = data[0]
    if args.gpu < 0:
        cuda = False
        ctx = mx.cpu(0)
    else:
        cuda = True
        ctx = mx.gpu(args.gpu)
        g = g.to(ctx)

    features = g.ndata["feat"]
    labels = mx.nd.array(g.ndata["label"], dtype="float32", ctx=ctx)
    mask = g.ndata["train_mask"]
    mask = mx.nd.array(np.nonzero(mask.asnumpy())[0], ctx=ctx)
    val_mask = g.ndata["val_mask"]
    val_mask = mx.nd.array(np.nonzero(val_mask.asnumpy())[0], ctx=ctx)
    test_mask = g.ndata["test_mask"]
    test_mask = mx.nd.array(np.nonzero(test_mask.asnumpy())[0], ctx=ctx)
    in_feats = features.shape[1]
    n_classes = data.num_classes
    n_edges = data.graph.number_of_edges()

    g = dgl.remove_self_loop(g)
    g = dgl.add_self_loop(g)
    # create model
    heads = ([args.num_heads] * args.num_layers) + [args.num_out_heads]
    model = GAT(
        g,
        args.num_layers,
        in_feats,
        args.num_hidden,
        n_classes,
        heads,
        elu,
        args.in_drop,
        args.attn_drop,
        args.alpha,
        args.residual,
    )

    if args.early_stop:
        stopper = EarlyStopping(patience=100)
    model.initialize(ctx=ctx)

    # use optimizer
    trainer = gluon.Trainer(
        model.collect_params(), "adam", {"learning_rate": args.lr}
    )

    dur = []
    for epoch in range(args.epochs):
        if epoch >= 3:
            t0 = time.time()
        # forward
        with mx.autograd.record():
            logits = model(features)
            loss = mx.nd.softmax_cross_entropy(
                logits[mask].squeeze(), labels[mask].squeeze()
            )
            loss.backward()
        trainer.step(mask.shape[0])

        if epoch >= 3:
            dur.append(time.time() - t0)
        print(
            "Epoch {:05d} | Loss {:.4f} | Time(s) {:.4f} | ETputs(KTEPS) {:.2f}".format(
                epoch,
                loss.asnumpy()[0],
                np.mean(dur),
                n_edges / np.mean(dur) / 1000,
            )
        )
        val_accuracy = evaluate(model, features, labels, val_mask)
        print("Validation Accuracy {:.4f}".format(val_accuracy))
        if args.early_stop:
            if stopper.step(val_accuracy, model):
                break
    print()

    if args.early_stop:
        model.load_parameters("model.param")
    test_accuracy = evaluate(model, features, labels, test_mask)
    print("Test Accuracy {:.4f}".format(test_accuracy))


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=0.005, help="learning rate")
    parser.add_argument(
        "--weight-decay", type=float, default=5e-4, help="weight decay"
    )
    parser.add_argument(
        "--alpha",
        type=float,
        default=0.2,
        help="the negative slop of leaky relu",
    )
    parser.add_argument(
        "--early-stop",
        action="store_true",
        default=False,
        help="indicates whether to use early stop or not",
    )
    args = parser.parse_args()
    print(args)

    main(args)