main.py

import argparse
import logging
import os
import shutil
import sys
import traceback

import numpy as np
import torch
import torch.utils.tensorboard as tb
import yaml

from runners.diffusion import Diffusion
from utils import dict2namespace

torch.set_printoptions(sci_mode=False)


def parse_args_and_config():
    parser = argparse.ArgumentParser(description=globals()["__doc__"])

    parser.add_argument(
        "--config", type=str, required=True, help="Path to the config file"
    )
    parser.add_argument("--seed", type=int, default=1234, help="Random seed")
    parser.add_argument(
        "--exp", type=str, default="exp", help="Path for saving running related data."
    )
    parser.add_argument(
        "--doc",
        type=str,
        required=True,
        help="A string for documentation purpose. "
        "Will be the name of the log folder.",
    )
    parser.add_argument(
        "--comment", type=str, default="", help="A string for experiment comment"
    )
    parser.add_argument(
        "--verbose",
        type=str,
        default="info",
        help="Verbose level: info | debug | warning | critical",
    )
    parser.add_argument("--test", action="store_true", help="Whether to test the model")
    parser.add_argument(
        "--sample",
        action="store_true",
        help="Whether to produce samples from the model",
    )
    parser.add_argument("--fid", action="store_true")
    parser.add_argument("--interpolation", action="store_true")
    parser.add_argument(
        "--resume_training", action="store_true", help="Whether to resume training"
    )
    parser.add_argument(
        "-i",
        "--image_folder",
        type=str,
        default="images",
        help="The folder name of samples",
    )
    parser.add_argument(
        "--ni",
        action="store_true",
        help="No interaction. Suitable for Slurm Job launcher",
    )
    parser.add_argument("--use_pretrained", action="store_true")
    parser.add_argument(
        "--sample_type",
        type=str,
        default="generalized",
        help="sampling approach (generalized or ddpm_noisy)",
    )
    parser.add_argument(
        "--skip_type",
        type=str,
        default="uniform",
        help="skip according to (uniform or quadratic)",
    )
    parser.add_argument(
        "--timesteps", type=int, default=1000, help="number of steps involved"
    )
    parser.add_argument(
        "--eta",
        type=float,
        default=0.0,
        help="eta used to control the variances of sigma",
    )
    parser.add_argument(
        "--sequence",
        type=int,
        default=None,
        help="while sample the sequence, number of intermediates in each case",
    )
    args = parser.parse_args()
    args.log_path = os.path.join(args.exp, "logs", args.doc)

    # parse config file
    with open(os.path.join("configs", args.config), "r") as f:
        config = yaml.safe_load(f)
    new_config = dict2namespace(config)

    tb_path = os.path.join(args.exp, "tensorboard", args.doc)

    if not args.test and not args.sample:
        if not args.resume_training:
            if os.path.exists(args.log_path):
                overwrite = False
                if args.ni:
                    overwrite = True
                else:
                    response = input("Folder already exists. Overwrite? (Y/N)")
                    if response.upper() == "Y":
                        overwrite = True

                if overwrite:
                    shutil.rmtree(args.log_path)
                    shutil.rmtree(tb_path)
                    os.makedirs(args.log_path)
                    if os.path.exists(tb_path):
                        shutil.rmtree(tb_path)
                else:
                    print("Folder exists. Program halted.")
                    sys.exit(0)
            else:
                os.makedirs(args.log_path)

            with open(os.path.join(args.log_path, "config.yml"), "w") as f:
                yaml.dump(new_config, f, default_flow_style=False)

        new_config.tb_logger = tb.SummaryWriter(log_dir=tb_path)
        # setup logger
        level = getattr(logging, args.verbose.upper(), None)
        if not isinstance(level, int):
            raise ValueError("level {} not supported".format(args.verbose))

        handler1 = logging.StreamHandler()
        handler2 = logging.FileHandler(os.path.join(args.log_path, "stdout.txt"))
        formatter = logging.Formatter(
            "%(levelname)s - %(filename)s - %(asctime)s - %(message)s"
        )
        handler1.setFormatter(formatter)
        handler2.setFormatter(formatter)
        logger = logging.getLogger()
        logger.addHandler(handler1)
        logger.addHandler(handler2)
        logger.setLevel(level)

    else:
        level = getattr(logging, args.verbose.upper(), None)
        if not isinstance(level, int):
            raise ValueError("level {} not supported".format(args.verbose))

        handler1 = logging.StreamHandler()
        formatter = logging.Formatter(
            "%(levelname)s - %(filename)s - %(asctime)s - %(message)s"
        )
        handler1.setFormatter(formatter)
        logger = logging.getLogger()
        logger.addHandler(handler1)
        logger.setLevel(level)

        if args.sample:
            os.makedirs(os.path.join(args.exp, "image_samples"), exist_ok=True)
            args.image_folder = os.path.join(
                args.exp, "image_samples", args.image_folder
            )
            if not os.path.exists(args.image_folder):
                os.makedirs(args.image_folder)
            else:
                if not (args.fid or args.interpolation):
                    overwrite = False
                    if args.ni:
                        overwrite = True
                    else:
                        response = input(
                            f"Image folder {args.image_folder} already exists. Overwrite? (Y/N)"
                        )
                        if response.upper() == "Y":
                            overwrite = True

                    if overwrite:
                        shutil.rmtree(args.image_folder)
                        os.makedirs(args.image_folder)
                    else:
                        print("Output image folder exists. Program halted.")
                        sys.exit(0)

    # add device
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    logging.info("Using device: {}".format(device))
    new_config.device = device

    # set random seed
    torch.manual_seed(args.seed)
    np.random.seed(args.seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(args.seed)

    torch.backends.cudnn.benchmark = True

    return args, new_config


def main():
    args, config = parse_args_and_config()
    logging.info("Writing log file to {}".format(args.log_path))
    logging.info("Exp instance id = {}".format(os.getpid()))
    logging.info("Exp comment = {}".format(args.comment))

    try:
        runner = Diffusion(args, config)
        if args.sample:
            runner.sample()
        elif args.test:
            runner.test()
        else:
            runner.train()
    except Exception:
        logging.error(traceback.format_exc())

    return 0


if __name__ == "__main__":
    sys.exit(main())