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="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="ddpm_noisy", help="sampling approach (generalized or ddpm_noisy)", ) 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", 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_ddpm_train() elif args.dataset=='LDFDCT' or args.dataset=='BRATS': runner.sg_ddpm_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())