segmentation/train.py

# --------------------------------------------------------
# InternImage
# Copyright (c) 2022 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------

import argparse
import copy
import os
import os.path as osp
import time
import warnings

import mmcv
import mmcv_custom  # noqa: F401,F403
import mmseg_custom  # noqa: F401,F403
import torch
import torch.distributed as dist
from mmcv.cnn.utils import revert_sync_batchnorm
from mmcv.runner import get_dist_info, init_dist
from mmcv.utils import Config, DictAction, get_git_hash
from mmseg import __version__
from mmseg.apis import init_random_seed, set_random_seed, train_segmentor
from mmseg.datasets import build_dataset
from mmseg.models import build_segmentor
from mmseg.utils import collect_env, get_device, get_root_logger, setup_multi_processes


def parse_args():
    parser = argparse.ArgumentParser(description="Train a segmentor")
    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("--load-from", help="the checkpoint file to load weights from")
    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="(Deprecated, please use --gpu-id) number of gpus to use "
        "(only applicable to non-distributed training)",
    )
    group_gpus.add_argument(
        "--gpu-ids",
        type=int,
        nargs="+",
        help="(Deprecated, please use --gpu-id) ids of gpus to use "
        "(only applicable to non-distributed training)",
    )
    group_gpus.add_argument(
        "--gpu-id",
        type=int,
        default=0,
        help="id of gpu to use (only applicable to non-distributed training)",
    )
    parser.add_argument("--seed", type=int, default=None, help="random seed")
    parser.add_argument(
        "--diff_seed",
        action="store_true",
        help="Whether or not set different seeds for different ranks",
    )
    parser.add_argument(
        "--deterministic",
        action="store_true",
        help="whether to set deterministic options for CUDNN backend.",
    )
    parser.add_argument(
        "--options",
        nargs="+",
        action=DictAction,
        help="--options is deprecated in favor of --cfg_options' and it will "
        "not be supported in version v0.22.0. 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(
        "--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)
    parser.add_argument(
        "--auto-resume",
        action="store_true",
        help="resume from the latest checkpoint automatically.",
    )
    args = parser.parse_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. "
            "--options will not be supported in version v0.22.0."
        )
    if args.options:
        warnings.warn(
            "--options is deprecated in favor of --cfg-options. "
            "--options will not be supported in version v0.22.0."
        )
        args.cfg_options = args.options

    return args


def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)
    # 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 args.load_from is not None:
        cfg.load_from = args.load_from
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    if args.gpus is not None:
        cfg.gpu_ids = range(1)
        warnings.warn(
            "`--gpus` is deprecated because we only support "
            "single GPU mode in non-distributed training. "
            "Use `gpus=1` now."
        )
    if args.gpu_ids is not None:
        cfg.gpu_ids = args.gpu_ids[0:1]
        warnings.warn(
            "`--gpu-ids` is deprecated, please use `--gpu-id`. "
            "Because we only support single GPU mode in "
            "non-distributed training. Use the first GPU "
            "in `gpu_ids` now."
        )
    if args.gpus is None and args.gpu_ids is None:
        cfg.gpu_ids = [args.gpu_id]

    cfg.auto_resume = args.auto_resume

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == "none":
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)
        # gpu_ids is used to calculate iter when resuming checkpoint
        _, 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
    timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime())
    log_file = osp.join(cfg.work_dir, f"{timestamp}.log")
    logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)

    # set multi-process settings
    setup_multi_processes(cfg)

    # 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("Environment info:\n" + dash_line + env_info + "\n" + dash_line)
    meta["env_info"] = env_info

    # log some basic info
    logger.info(f"Distributed training: {distributed}")
    logger.info(f"Config:\n{cfg.pretty_text}")

    # set random seeds
    cfg.device = get_device()
    seed = init_random_seed(args.seed, device=cfg.device)
    seed = seed + dist.get_rank() if args.diff_seed else seed
    logger.info(f"Set random seed to {seed}, deterministic: {args.deterministic}")
    set_random_seed(seed, deterministic=args.deterministic)
    cfg.seed = seed
    meta["seed"] = seed
    meta["exp_name"] = osp.basename(args.config)

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

    # SyncBN is not support for DP
    if not distributed:
        warnings.warn(
            "SyncBN is only supported with DDP. To be compatible with DP, "
            "we convert SyncBN to BN. Please use dist_train.sh which can "
            "avoid this error."
        )
        model = revert_sync_batchnorm(model)

    # logger.info(model)

    datasets = [build_dataset(cfg.data.train)]
    logger.info(f"Built training dataset from config: {cfg.data.train}")
    logger.info(f"Number of samples in training dataset: {len(datasets[0])}")

    if len(datasets[0]) == 0:
        logger.error("Training dataset is EMPTY! Please check the following:")
        logger.error(
            f"1. Cityscapes root path (exists?): {cfg.data.train.get('data_root', 'Not Set')}"
        )
        logger.error(
            f"2. Image directory (resolved path should exist): {cfg.data.train.get('img_dir', 'Not Set')}"
        )
        logger.error(
            f"3. Annotation directory (resolved path should exist): {cfg.data.train.get('ann_dir', 'Not Set')}"
        )
        logger.error(
            f"4. Split file path (exists and readable?): {cfg.data.train.get('split', 'Not Set')}"
        )
        logger.error(
            "   Ensure the paths in the split file are correct relative to img_dir and ann_dir."
        )
        logger.error(
            "   And that the actual image and annotation files exist at the fully resolved paths."
        )
        # Attempt to log resolved paths if the dataset object has them
        # This might not work if the object is a simple list wrapper or if attributes are private
        try:
            dataset_obj = datasets[0]
            if hasattr(dataset_obj, "data_root"):
                logger.error(f"   Dataset effective data_root: {dataset_obj.data_root}")
            if hasattr(dataset_obj, "img_dir"):
                logger.error(f"   Dataset effective img_dir: {dataset_obj.img_dir}")
            if hasattr(dataset_obj, "ann_dir"):
                logger.error(f"   Dataset effective ann_dir: {dataset_obj.ann_dir}")
            if hasattr(dataset_obj, "split"):
                logger.error(f"   Dataset effective split file: {dataset_obj.split}")
        except Exception as e:
            logger.error(
                f"   Could not retrieve effective paths from dataset object: {e}"
            )
        return  # Exit if dataset is empty

    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 mmseg version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmseg_version=f"{__version__}+{get_git_hash()[:7]}",
            config=cfg.pretty_text,
            CLASSES=datasets[0].CLASSES,
            PALETTE=datasets[0].PALETTE,
        )
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES
    # passing checkpoint meta for saving best checkpoint
    meta.update(cfg.checkpoint_config.meta)

    torch.backends.cuda.matmul.allow_tf32 = True
    torch.backends.cudnn.allow_tf32 = True

    train_segmentor(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=(not args.no_validate),
        timestamp=timestamp,
        meta=meta,
    )


if __name__ == "__main__":
    main()