main.py

"""
Learning Deep Generative Models of Graphs
Paper: https://arxiv.org/pdf/1803.03324.pdf

This implementation works with a minibatch of size 1 only for both training and inference.
"""
import argparse
import datetime
import time

import torch
from model import DGMG
from torch.nn.utils import clip_grad_norm_
from torch.optim import Adam
from torch.utils.data import DataLoader


def main(opts):
    t1 = time.time()

    # Setup dataset and data loader
    if opts["dataset"] == "cycles":
        from cycles import CycleDataset, CycleModelEvaluation, CyclePrinting

        dataset = CycleDataset(fname=opts["path_to_dataset"])
        evaluator = CycleModelEvaluation(
            v_min=opts["min_size"], v_max=opts["max_size"], dir=opts["log_dir"]
        )
        printer = CyclePrinting(
            num_epochs=opts["nepochs"],
            num_batches=opts["ds_size"] // opts["batch_size"],
        )
    else:
        raise ValueError("Unsupported dataset: {}".format(opts["dataset"]))

    data_loader = DataLoader(
        dataset,
        batch_size=1,
        shuffle=True,
        num_workers=0,
        collate_fn=dataset.collate_single,
    )

    # Initialize_model
    model = DGMG(
        v_max=opts["max_size"],
        node_hidden_size=opts["node_hidden_size"],
        num_prop_rounds=opts["num_propagation_rounds"],
    )

    # Initialize optimizer
    if opts["optimizer"] == "Adam":
        optimizer = Adam(model.parameters(), lr=opts["lr"])
    else:
        raise ValueError("Unsupported argument for the optimizer")

    t2 = time.time()

    # Training
    model.train()
    for epoch in range(opts["nepochs"]):
        batch_count = 0
        batch_loss = 0
        batch_prob = 0
        optimizer.zero_grad()

        for i, data in enumerate(data_loader):
            log_prob = model(actions=data)
            prob = log_prob.detach().exp()

            loss = -log_prob / opts["batch_size"]
            prob_averaged = prob / opts["batch_size"]

            loss.backward()

            batch_loss += loss.item()
            batch_prob += prob_averaged.item()
            batch_count += 1

            if batch_count % opts["batch_size"] == 0:
                printer.update(
                    epoch + 1,
                    {"averaged_loss": batch_loss, "averaged_prob": batch_prob},
                )

                if opts["clip_grad"]:
                    clip_grad_norm_(model.parameters(), opts["clip_bound"])

                optimizer.step()

                batch_loss = 0
                batch_prob = 0
                optimizer.zero_grad()

    t3 = time.time()

    model.eval()
    evaluator.rollout_and_examine(model, opts["num_generated_samples"])
    evaluator.write_summary()

    t4 = time.time()

    print("It took {} to setup.".format(datetime.timedelta(seconds=t2 - t1)))
    print(
        "It took {} to finish training.".format(
            datetime.timedelta(seconds=t3 - t2)
        )
    )
    print(
        "It took {} to finish evaluation.".format(
            datetime.timedelta(seconds=t4 - t3)
        )
    )
    print(
        "--------------------------------------------------------------------------"
    )
    print(
        "On average, an epoch takes {}.".format(
            datetime.timedelta(seconds=(t3 - t2) / opts["nepochs"])
        )
    )

    del model.g
    torch.save(model, "./model.pth")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="DGMG")

    # configure
    parser.add_argument("--seed", type=int, default=9284, help="random seed")

    # dataset
    parser.add_argument(
        "--dataset", choices=["cycles"], default="cycles", help="dataset to use"
    )
    parser.add_argument(
        "--path-to-dataset",
        type=str,
        default="cycles.p",
        help="load the dataset if it exists, "
        "generate it and save to the path otherwise",
    )

    # log
    parser.add_argument(
        "--log-dir",
        default="./results",
        help="folder to save info like experiment configuration "
        "or model evaluation results",
    )

    # optimization
    parser.add_argument(
        "--batch-size",
        type=int,
        default=10,
        help="batch size to use for training",
    )
    parser.add_argument(
        "--clip-grad",
        action="store_true",
        default=True,
        help="gradient clipping is required to prevent gradient explosion",
    )
    parser.add_argument(
        "--clip-bound",
        type=float,
        default=0.25,
        help="constraint of gradient norm for gradient clipping",
    )

    args = parser.parse_args()
    from utils import setup

    opts = setup(args)

    main(opts)