train_sudoku.py

import argparse
import os

import numpy as np
import torch
from sudoku import SudokuNN
from sudoku_data import sudoku_dataloader
from torch.optim import Adam


def main(args):
    if args.gpu < 0 or not torch.cuda.is_available():
        device = torch.device("cpu")
    else:
        device = torch.device("cuda", args.gpu)

    model = SudokuNN(num_steps=args.steps, edge_drop=args.edge_drop)

    if args.do_train:
        if not os.path.exists(args.output_dir):
            os.mkdir(args.output_dir)
        model.to(device)
        train_dataloader = sudoku_dataloader(args.batch_size, segment="train")
        dev_dataloader = sudoku_dataloader(args.batch_size, segment="valid")

        opt = Adam(
            model.parameters(), lr=args.lr, weight_decay=args.weight_decay
        )

        best_dev_acc = 0.0
        for epoch in range(args.epochs):
            model.train()
            for i, g in enumerate(train_dataloader):
                g = g.to(device)
                _, loss = model(g)
                opt.zero_grad()
                loss.backward()
                opt.step()
                if i % 100 == 0:
                    print(f"Epoch {epoch}, batch {i}, loss {loss.cpu().data}")

            # dev
            print("\n=========Dev step========")
            model.eval()
            dev_loss = []
            dev_res = []
            for g in dev_dataloader:
                g = g.to(device)
                target = g.ndata["a"]
                target = target.view([-1, 81])

                with torch.no_grad():
                    preds, loss = model(g, is_training=False)
                    preds = preds.view([-1, 81])

                    for i in range(preds.size(0)):
                        dev_res.append(
                            int(torch.equal(preds[i, :], target[i, :]))
                        )

                    dev_loss.append(loss.cpu().detach().data)

            dev_acc = sum(dev_res) / len(dev_res)
            print(f"Dev loss {np.mean(dev_loss)}, accuracy {dev_acc}")
            if dev_acc >= best_dev_acc:
                torch.save(
                    model.state_dict(),
                    os.path.join(args.output_dir, "model_best.bin"),
                )
                best_dev_acc = dev_acc
            print(f"Best dev accuracy {best_dev_acc}\n")

        torch.save(
            model.state_dict(), os.path.join(args.output_dir, "model_final.bin")
        )

    if args.do_eval:
        model_path = os.path.join(args.output_dir, "model_best.bin")
        if not os.path.exists(model_path):
            raise FileNotFoundError("Saved model not Found!")

        model.load_state_dict(torch.load(model_path))
        model.to(device)

        test_dataloader = sudoku_dataloader(args.batch_size, segment="test")

        print("\n=========Test step========")
        model.eval()
        test_loss = []
        test_res = []
        for g in test_dataloader:
            g = g.to(device)
            target = g.ndata["a"]
            target = target.view([-1, 81])

            with torch.no_grad():
                preds, loss = model(g, is_training=False)
                preds = preds
                preds = preds.view([-1, 81])

                for i in range(preds.size(0)):
                    test_res.append(int(torch.equal(preds[i, :], target[i, :])))

                test_loss.append(loss.cpu().detach().data)

        test_acc = sum(test_res) / len(test_res)
        print(f"Test loss {np.mean(test_loss)}, accuracy {test_acc}\n")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Recurrent Relational Network on sudoku task."
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default=None,
        required=True,
        help="The directory to save model",
    )
    parser.add_argument(
        "--do_train", default=False, action="store_true", help="Train the model"
    )
    parser.add_argument(
        "--do_eval",
        default=False,
        action="store_true",
        help="Evaluate the model on test data",
    )
    parser.add_argument(
        "--epochs", type=int, default=100, help="Number of training epochs"
    )
    parser.add_argument("--batch_size", type=int, default=64, help="Batch size")
    parser.add_argument(
        "--edge_drop", type=float, default=0.4, help="Dropout rate at edges."
    )
    parser.add_argument(
        "--steps", type=int, default=32, help="Number of message passing steps."
    )
    parser.add_argument("--gpu", type=int, default=-1, help="gpu")
    parser.add_argument("--lr", type=float, default=2e-4, help="Learning rate")
    parser.add_argument(
        "--weight_decay",
        type=float,
        default=1e-4,
        help="weight decay (L2 penalty)",
    )

    args = parser.parse_args()

    main(args)