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main.py
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main.py
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import argparse
import logging
import yaml
import os
import torch
import numpy as np
import torch.utils.tensorboard as tb
from runner import Diffusion, HilbertDiffusion
from datasets import get_dataset
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=None, 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=False,
default="",
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(
"--sample",
action="store_true",
help="Whether to produce samples from the model",
)
parser.add_argument(
"--resume", action="store_true", help="Whether to resume training"
)
parser.add_argument(
"--fid",
default=None,
choices=['train', 'test', None],
help="type of fid calculation",
)
parser.add_argument(
"--sample_type",
default='sde',
choices=['sde', 'ode', 'sde_imputation', 'sde_super_resolution' ,None],
help="sampling method",
)
parser.add_argument(
"--nfe", type=int, default=1000, help="number of function evaluations"
)
parser.add_argument(
"--prior",
default='hdm',
choices=['hdm', 'ihdm'],
help='Prior for sampling'
)
parser.add_argument(
"-i",
"--image_folder",
type=str,
default="images",
help="The folder name of samples",
)
parser.add_argument(
"--degrade_type",
default='blur',
choices=['blur', 'pixelate'],
help='Type of generating degraded images'
)
parser.add_argument(
"--distributed",
action='store_true',
help='Whether to use distributed data parallel or not'
)
args = parser.parse_args()
args.log_path = os.path.join(args.exp, "logs", args.doc)
args.image_folder = os.path.join( args.exp, "samples", args.image_folder)
if not os.path.exists(args.image_folder):
os.makedirs(args.image_folder)
if args.distributed and torch.distributed.is_available():
args.local_rank = int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
else:
args.local_rank = 0
# parse config file
with open(os.path.join("configs", args.config), "r") as f:
config = yaml.safe_load(f)
new_config = dict2namespace(config)
if args.local_rank == 0:
if not os.path.exists(args.image_folder):
os.makedirs(args.image_folder)
tb_path = os.path.join(args.exp, "tensorboard", args.doc)
if not args.sample:
if not args.resume:
if os.path.exists(args.log_path):
pass
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, "samples"), exist_ok=True)
args.image_folder = os.path.join(
args.exp, "samples", args.image_folder
)
if not os.path.exists(args.image_folder):
os.makedirs(args.image_folder)
else:
if not args.fid:
overwrite = False
# 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
if args.seed is not None:
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))
dataset, test_dataset = get_dataset(config)
if config.data.modality == '1D':
runner = HilbertDiffusion(args, config, dataset, test_dataset)
else:
runner = Diffusion(args, config, dataset, test_dataset)
if args.sample:
runner.sample()
else:
runner.train()
runner.sample()
if __name__ == "__main__":
main()