train.py

"""
Graph Attention Networks in DGL using SPMV optimization.
Multiple heads are also batched together for faster training.
Compared with the original paper, this code does not implement
early stopping.
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 networkx as nx
import numpy as np
import tensorflow as tf
from dgl.data import (
    CiteseerGraphDataset,
    CoraGraphDataset,
    PubmedGraphDataset,
    register_data_args,
)
from gat import GAT
from utils import EarlyStopping


def accuracy(logits, labels):
    indices = tf.math.argmax(logits, axis=1)
    acc = tf.reduce_mean(tf.cast(indices == labels, dtype=tf.float32))
    return acc.numpy().item()


def evaluate(model, features, labels, mask):
    logits = model(features, training=False)
    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))

    g = data[0]
    if args.gpu < 0:
        device = "/cpu:0"
    else:
        device = "/gpu:{}".format(args.gpu)
        g = g.to(device)

    with tf.device(device):
        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.number_of_edges()
        print(
            """----Data statistics------'
        #Edges %d
        #Classes %d
        #Train samples %d
        #Val samples %d
        #Test samples %d"""
            % (
                n_edges,
                n_classes,
                train_mask.numpy().sum(),
                val_mask.numpy().sum(),
                test_mask.numpy().sum(),
            )
        )

        g = dgl.remove_self_loop(g)
        g = dgl.add_self_loop(g)
        n_edges = g.number_of_edges()
        # create model
        heads = ([args.num_heads] * args.num_layers) + [args.num_out_heads]
        model = GAT(
            g,
            args.num_layers,
            num_feats,
            args.num_hidden,
            n_classes,
            heads,
            tf.nn.elu,
            args.in_drop,
            args.attn_drop,
            args.negative_slope,
            args.residual,
        )
        print(model)
        if args.early_stop:
            stopper = EarlyStopping(patience=100)

        # loss_fcn = tf.keras.losses.SparseCategoricalCrossentropy(
        #     from_logits=False)
        loss_fcn = tf.nn.sparse_softmax_cross_entropy_with_logits

        # use optimizer
        optimizer = tf.keras.optimizers.Adam(
            learning_rate=args.lr, epsilon=1e-8
        )

        # initialize graph
        dur = []
        for epoch in range(args.epochs):
            if epoch >= 3:
                t0 = time.time()
            # forward
            with tf.GradientTape() as tape:
                tape.watch(model.trainable_weights)
                logits = model(features, training=True)
                loss_value = tf.reduce_mean(
                    loss_fcn(
                        labels=labels[train_mask], logits=logits[train_mask]
                    )
                )
                # Manually Weight Decay
                # We found Tensorflow has a different implementation on weight decay
                # of Adam(W) optimizer with PyTorch. And this results in worse results.
                # Manually adding weights to the loss to do weight decay solves this problem.
                for weight in model.trainable_weights:
                    loss_value = loss_value + args.weight_decay * tf.nn.l2_loss(
                        weight
                    )

                grads = tape.gradient(loss_value, model.trainable_weights)
                optimizer.apply_gradients(zip(grads, model.trainable_weights))

            if epoch >= 3:
                dur.append(time.time() - t0)

            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,
                    np.mean(dur),
                    loss_value.numpy().item(),
                    train_acc,
                    val_acc,
                    n_edges / np.mean(dur) / 1000,
                )
            )

        print()
        if args.early_stop:
            model.load_weights("es_checkpoint.pb")
        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=0.005, 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",
    )
    args = parser.parse_args()
    print(args)

    main(args)