WSI_Seg_HTC_swin_NuCLS.py

import argparse
import copy
import os
import os.path as osp
import time
import warnings
import shutil
import sys
sys.path.insert(0, 'thirdparty/mmdetection')

import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmcv.utils import get_git_hash
from mmdet import __version__
from mmdet.models import build_detector
from mmdet.utils import collect_env

from nuhtc.apis import get_root_logger, set_random_seed, train_detector, single_gpu_test_score
from nuhtc.datasets import build_dataset
from nuhtc.utils import patch_config
warnings.filterwarnings("error", category=UserWarning, message='.*Step must only increase.*')

def parse_args(args=[]):
    parser = argparse.ArgumentParser(description="Train a detector")
    parser.add_argument("config", help="train config file path")
    parser.add_argument("--work-dir", help="the dir to save logs and models")
    parser.add_argument("--resume-from", help="the checkpoint file to resume from")
    parser.add_argument(
        "--no-validate",
        action="store_true",
        help="whether not to evaluate the checkpoint during training",
    )
    group_gpus = parser.add_mutually_exclusive_group()
    group_gpus.add_argument(
        "--gpus",
        type=int,
        help="number of gpus to use " "(only applicable to non-distributed training)",
    )
    group_gpus.add_argument(
        "--gpu-ids",
        type=int,
        nargs="+",
        help="ids of gpus to use " "(only applicable to non-distributed training)",
    )
    parser.add_argument("--seed", type=int, default=None, help="random seed")
    parser.add_argument(
        "--deterministic",
        action="store_true",
        help="whether to set deterministic options for CUDNN backend.",
    )
    parser.add_argument(
        "--options",
        nargs="+",
        action=DictAction,
        help="override some settings in the used config, the key-value pair "
        "in xxx=yyy format will be merged into config file (deprecate), "
        "change to --cfg-options instead.",
    )
    parser.add_argument(
        "--cfg-options",
        nargs="+",
        action=DictAction,
        help="override some settings in the used config, the key-value pair "
        "in xxx=yyy format will be merged into config file. If the value to "
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        "Note that the quotation marks are necessary and that no white space "
        "is allowed.",
    )
    parser.add_argument(
        "--launcher",
        choices=["none", "pytorch", "slurm", "mpi"],
        default="none",
        help="job launcher",
    )
    parser.add_argument("--local_rank", type=int, default=0)
    args = parser.parse_args(args)
    if "LOCAL_RANK" not in os.environ:
        os.environ["LOCAL_RANK"] = str(args.local_rank)

    if args.options and args.cfg_options:
        raise ValueError(
            "--options and --cfg-options cannot be both "
            "specified, --options is deprecated in favor of --cfg-options"
        )
    if args.options:
        warnings.warn("--options is deprecated in favor of --cfg-options")
        args.cfg_options = args.options

    return args

def main():
    timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime())
    args = parse_args(args=['configs/nuhtc/htc_lite_swin_pytorch_fpn_NuCLS_seasaw_CAS.py',
                            '--gpu-ids', '0'])

    cfg = Config.fromfile(args.config)
    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)
    # import modules from string list.
    if cfg.get("custom_imports", None):
        from mmcv.utils import import_modules_from_strings

        import_modules_from_strings(**cfg["custom_imports"])
    # set cudnn_benchmark
    if cfg.get("cudnn_benchmark", False):
        torch.backends.cudnn.benchmark = True

    # work_dir is determined in this priority: CLI > segment in file > filename
    if args.work_dir is not None:
        # update configs according to CLI args if args.work_dir is not None
        cfg.work_dir = args.work_dir
    elif cfg.get("work_dir", None) is None:
        # use config filename as default work_dir if cfg.work_dir is None
        cfg.work_dir = osp.join(
            "./work_dirs", osp.splitext(osp.basename(args.config))[0]
        )

    if not cfg.resume_from and osp.exists(cfg.work_dir):
        cfg.work_dir = f"{cfg.work_dir}_{timestamp}"

    cfg = patch_config(cfg)
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    if args.gpu_ids is not None:
        cfg.gpu_ids = args.gpu_ids
    else:
        cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == "none":
        distributed = False
        # import torch.distributed as dist
        # dist.init_process_group('gloo', init_method=f'file:///tmp/mmdet_nucls_{timestamp}', rank=0, world_size=1)
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)
        # re-set gpu_ids with distributed training mode
        _, world_size = get_dist_info()
        cfg.gpu_ids = range(world_size)
    # create work_dir
    mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
    # dump config
    cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
    # init the logger before other steps

    log_file = osp.join(cfg.work_dir, f"{timestamp}.log")
    logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)

    # init the meta dict to record some important information such as
    # environment info and seed, which will be logged
    meta = dict()
    # log env info
    env_info_dict = collect_env()
    env_info = "\n".join([(f"{k}: {v}") for k, v in env_info_dict.items()])
    dash_line = "-" * 60 + "\n"
    logger.info(logger.handlers)
    logger.info("Environment info:\n" + dash_line + env_info + "\n" + dash_line)
    meta["env_info"] = env_info
    meta["config"] = cfg.pretty_text
    # log some basic info
    logger.info(f"Distributed training: {distributed}")
    logger.info(f"Config:\n{cfg.pretty_text}")

    # set random seeds
    if args.seed is not None:
        logger.info(
            f"Set random seed to {args.seed}, " f"deterministic: {args.deterministic}"
        )
        set_random_seed(args.seed, deterministic=args.deterministic)
    cfg.seed = args.seed
    meta["seed"] = args.seed
    meta["exp_name"] = osp.basename(args.config)

    model = build_detector(
        cfg.model, train_cfg=cfg.get("train_cfg"), test_cfg=cfg.get("test_cfg")
    )
    model.init_weights()

    datasets = [build_dataset(cfg.data.train)]
    if len(cfg.workflow) == 2:
        val_dataset = copy.deepcopy(cfg.data.val)
        val_dataset.pipeline = cfg.data.train.pipeline
        datasets.append(build_dataset(val_dataset))
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__ + get_git_hash()[:7], CLASSES=datasets[0].CLASSES
        )

    cfg.evaluation['test_fn'] = single_gpu_test_score
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES
    train_detector(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=(not args.no_validate),
        timestamp=timestamp,
        meta=meta,
    )

if __name__ == "__main__":
    main()