fast_ddpm_main.py

import argparse
import traceback
import shutil
import logging
import yaml
import sys
import os
import torch
import numpy as np
import torch.utils.tensorboard as tb

from runners.diffusion import Diffusion

torch.set_printoptions(sci_mode=False)


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

    parser.add_argument(
        "--config", type=str, default="pmub_linear.yml", help="Path to the config file"
    )
    parser.add_argument(
        "--dataset", type=str, default="PMUB", help="Name of dataset(LDFDCT, BRATS, PMUB)"
    )
    parser.add_argument("--seed", type=int, default=1244, help="Random seed")
    parser.add_argument(
        "--exp", type=str, default="exp", help="Path for saving running related data."
    )
    parser.add_argument(
        "--doc",
        type=str,
        default="Fast-DDPM_experiments",
        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_false",
        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(
        "--scheduler_type",
        type=str,
        default="uniform",
        help="sample involved time steps according to (uniform or non-uniform)",
    )
    parser.add_argument(
        "--timesteps", type=int, default=100, 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", action="store_true")

    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)

    #   No test No sampling No resume training
    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)

        # Sample from the model
        if args.sample:
            os.makedirs(os.path.join(args.exp, "image_samples"), exist_ok=True)
            if args.fid:
                args.image_folder = os.path.join(
                args.exp, "image_samples", args.doc, "images_fid")
            if args.interpolation:
                args.image_folder = os.path.join(
                args.exp, "image_samples", args.doc, "images_interpolation")
 
            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 dict2namespace(config):
    namespace = argparse.Namespace()
    for key, value in config.items():
        if isinstance(value, dict):
            new_value = dict2namespace(value)
        else:
            new_value = value
        setattr(namespace, key, new_value)
    return namespace


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:
            if args.dataset=='PMUB':
                runner.sr_sample()
            elif args.dataset=='LDFDCT' or args.dataset=='BRATS':
                runner.sg_sample()
            else:
                raise Exception("This script only supports LDFDCT, BRATS and PMUB as sampling dataset. Feel free to add your own.")
        elif args.test:
            runner.test()
        else:
            if args.dataset=='PMUB':
                runner.sr_train()
            elif args.dataset=='LDFDCT' or args.dataset=='BRATS':
                runner.sg_train()
            else:
                raise Exception("This script only supports LDFDCT, BRATS and PMUB as training dataset. Feel free to add your own.")
    except Exception:
        logging.error(traceback.format_exc())

    return 0


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