main.py

import argparse
import json
import logging
import os
from time import time
import dgl
import torch
import torch.nn
import torch.nn.functional as F
from dgl.data import LegacyTUDataset
from dgl.dataloading import GraphDataLoader
from torch.utils.data import random_split

from network import get_sag_network
from utils import get_stats


def parse_args():
    parser = argparse.ArgumentParser(description="Self-Attention Graph Pooling")
    parser.add_argument("--dataset", type=str, default="DD",
                        choices=["DD", "PROTEINS", "NCI1", "NCI109", "Mutagenicity"],
                        help="DD/PROTEINS/NCI1/NCI109/Mutagenicity")
    parser.add_argument("--batch_size", type=int, default=128,
                        help="batch size")
    parser.add_argument("--lr", type=float, default=5e-4,
                        help="learning rate")
    parser.add_argument("--weight_decay", type=float, default=1e-4,
                        help="weight decay")
    parser.add_argument("--pool_ratio", type=float, default=0.5,
                        help="pooling ratio")
    parser.add_argument("--hid_dim", type=int, default=128,
                        help="hidden size")
    parser.add_argument("--dropout", type=float, default=0.5,
                        help="dropout ratio")
    parser.add_argument("--epochs", type=int, default=100000,
                        help="max number of training epochs")
    parser.add_argument("--patience", type=int, default=50,
                        help="patience for early stopping")
    parser.add_argument("--device", type=int, default=-1,
                        help="device id, -1 for cpu")
    parser.add_argument("--architecture", type=str, default="hierarchical",
                        choices=["hierarchical", "global"],
                        help="model architecture")
    parser.add_argument("--dataset_path", type=str, default="./dataset",
                        help="path to dataset")
    parser.add_argument("--conv_layers", type=int, default=3,
                        help="number of conv layers")
    parser.add_argument("--print_every", type=int, default=10,
                        help="print trainlog every k epochs, -1 for silent training")
    parser.add_argument("--num_trials", type=int, default=1,
                        help="number of trials")
    parser.add_argument("--output_path", type=str, default="./output")
    
    args = parser.parse_args()

    # device
    args.device = "cpu" if args.device == -1 else "cuda:{}".format(args.device)
    if not torch.cuda.is_available():
        logging.warning("CUDA is not available, use CPU for training.")
        args.device = "cpu"

    # print every
    if args.print_every == -1:
        args.print_every = args.epochs + 1

    # paths
    if not os.path.exists(args.dataset_path):
        os.makedirs(args.dataset_path)
    if not os.path.exists(args.output_path):
        os.makedirs(args.output_path)
    name = "Data={}_Hidden={}_Arch={}_Pool={}_WeightDecay={}_Lr={}.log".format(
        args.dataset, args.hid_dim, args.architecture, args.pool_ratio, args.weight_decay, args.lr)
    args.output_path = os.path.join(args.output_path, name)

    return args


def train(model:torch.nn.Module, optimizer, trainloader, device):
    model.train()
    total_loss = 0.
    num_batches = len(trainloader)
    for batch in trainloader:
        optimizer.zero_grad()
        batch_graphs, batch_labels = batch
        for (key, value) in batch_graphs.ndata.items():
            batch_graphs.ndata[key] = value.float()
        batch_graphs = batch_graphs.to(device)
        batch_labels = batch_labels.long().to(device)
        out = model(batch_graphs)
        loss = F.nll_loss(out, batch_labels)
        loss.backward()
        optimizer.step()

        total_loss += loss.item()
    
    return total_loss / num_batches


@torch.no_grad()
def test(model:torch.nn.Module, loader, device):
    model.eval()
    correct = 0.
    loss = 0.
    num_graphs = len(loader)
    for batch in loader:
        batch_graphs, batch_labels = batch
        for (key, value) in batch_graphs.ndata.items():
            batch_graphs.ndata[key] = value.float()
        batch_graphs = batch_graphs.to(device)
        batch_labels = batch_labels.long().to(device)
        out = model(batch_graphs)
        pred = out.argmax(dim=1)
        loss += F.nll_loss(out, batch_labels, reduction="sum").item()
        correct += pred.eq(batch_labels).sum().item()
    return correct / num_graphs, loss / num_graphs


def main(args):
    # Step 1: Prepare graph data and retrieve train/validation/test index ============================= #
    dataset = LegacyTUDataset(args.dataset, raw_dir=args.dataset_path)

    # add self loop. We add self loop for each graph here since the function "add_self_loop" does not
    # support batch graph.
    for i in range(len(dataset)):
        dataset.graph_lists[i] = dgl.add_self_loop(dataset.graph_lists[i])

    num_training = int(len(dataset) * 0.8)
    num_val = int(len(dataset) * 0.1)
    num_test = len(dataset) - num_val - num_training
    train_set, val_set, test_set = random_split(dataset, [num_training, num_val, num_test])

    train_loader = GraphDataLoader(train_set, batch_size=args.batch_size, shuffle=True, num_workers=6)
    val_loader = GraphDataLoader(val_set, batch_size=args.batch_size, num_workers=2)
    test_loader = GraphDataLoader(test_set, batch_size=args.batch_size, num_workers=2)

    device = torch.device(args.device)
    
    # Step 2: Create model =================================================================== #
    num_feature, num_classes, _ = dataset.statistics()
    model_op = get_sag_network(args.architecture)
    model = model_op(in_dim=num_feature, hid_dim=args.hid_dim, out_dim=num_classes,
                     num_convs=args.conv_layers, pool_ratio=args.pool_ratio, dropout=args.dropout).to(device)
    args.num_feature = int(num_feature)
    args.num_classes = int(num_classes)

    # Step 3: Create training components ===================================================== #
    optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)

    # Step 4: training epoches =============================================================== #
    bad_cound = 0
    best_val_loss = float("inf")
    final_test_acc = 0.
    best_epoch = 0
    train_times = []
    for e in range(args.epochs):
        s_time = time()
        train_loss = train(model, optimizer, train_loader, device)
        train_times.append(time() - s_time)
        val_acc, val_loss = test(model, val_loader, device)
        test_acc, _ = test(model, test_loader, device)
        if best_val_loss > val_loss:
            best_val_loss = val_loss
            final_test_acc = test_acc
            bad_cound = 0
            best_epoch = e + 1
        else:
            bad_cound += 1
        if bad_cound >= args.patience:
            break
        
        if (e + 1) % args.print_every == 0:
            log_format = "Epoch {}: loss={:.4f}, val_acc={:.4f}, final_test_acc={:.4f}"
            print(log_format.format(e + 1, train_loss, val_acc, final_test_acc))
    print("Best Epoch {}, final test acc {:.4f}".format(best_epoch, final_test_acc))
    return final_test_acc, sum(train_times) / len(train_times)


if __name__ == "__main__":
    args = parse_args()
    res = []
    train_times = []
    for i in range(args.num_trials):
        print("Trial {}/{}".format(i + 1, args.num_trials))
        acc, train_time = main(args)
        res.append(acc)
        train_times.append(train_time)

    mean, err_bd = get_stats(res)
    print("mean acc: {:.4f}, error bound: {:.4f}".format(mean, err_bd))

    out_dict = {"hyper-parameters": vars(args),
                "result": "{:.4f}(+-{:.4f})".format(mean, err_bd),
                "train_time": "{:.4f}".format(sum(train_times) / len(train_times))}

    with open(args.output_path, "w") as f:
        json.dump(out_dict, f, sort_keys=True, indent=4)