[Bug] ImportError: DLL load failed while importing _ext: 找不到指定的程序。

Prerequisite

[X] I have searched Issues and Discussions but cannot get the expected help.
[X] The bug has not been fixed in the latest version(https://github.com/open-mmlab/mmcv).

Environment

addict 2.4.0 aliyun-python-sdk-core 2.13.36 aliyun-python-sdk-kms 2.16.2 appdirs 1.4.4 brotlipy 0.7.0 certifi 2023.7.22 cffi 1.15.1 charset-normalizer 3.2.0 click 8.1.7 cloudpickle 2.2.1 colorama 0.4.6 contourpy 1.1.1 crcmod 1.7 cryptography 41.0.3 cycler 0.11.0 Cython 3.0.2 cytoolz 0.12.0 dask 2023.4.1 fonttools 4.42.1 fsspec 2023.4.0 future 0.18.3 idna 3.4 imagecodecs 2023.1.23 imageio 2.26.0 imgaug 0.4.0 d:\imgaug-master\imgaug-master importlib-metadata 6.8.0 importlib-resources 6.0.1 jmespath 0.10.0 kiwisolver 1.4.5 lmdb 1.4.1 locket 1.0.0 Markdown 3.4.4 markdown-it-py 3.0.0 matplotlib 3.7.3 mdurl 0.1.2 mkl-fft 1.3.8 mkl-random 1.2.4 mkl-service 2.4.0 mmcv-full 1.4.2 mmdet 2.19.1 d:\mmdetection-2.19.1\mmdetection-2.19.1 mmocr 1.0.1 d:\mmocr-main\mmocr-main model-index 0.1.11 networkx 3.1 numpy 1.24.4 opencv-python 4.8.0.76 opendatalab 0.0.10 openmim 0.3.9 openxlab 0.0.25 ordered-set 4.1.0 oss2 2.17.0 packaging 23.1 pandas 2.0.3 partd 1.4.0 Pillow 10.0.1 pip 20.0.2 platformdirs 3.10.0 pooch 1.4.0 pyclipper 1.3.0.post5 pycocotools 2.0.7 pycocotools-windows 2.0.0.2 pycparser 2.21 pycryptodome 3.19.0 Pygments 2.16.1 pyOpenSSL 23.2.0 pyparsing 3.1.1 PySocks 1.7.1 python-dateutil 2.8.2 pytz 2023.3.post1 PyWavelets 1.4.1 pywin32 306 PyYAML 6.0.1 rapidfuzz 3.3.0 regex 2023.8.8 requests 2.31.0 rich 13.4.2 scikit-image 0.19.3 scipy 1.10.1 setuptools 68.0.0 shapely 2.0.1 six 1.16.0 tabulate 0.9.0 terminaltables 3.1.10 tifffile 2021.7.2 tomli 2.0.1 toolz 0.12.0 torch 1.5.0+cu101 torchvision 0.6.0+cu101 tqdm 4.65.2 typing-extensions 4.7.1 tzdata 2023.3 urllib3 1.26.16 wheel 0.38.4 win-inet-pton 1.1.0 yapf 0.40.1 zipp 3.16.2

Reproduces the problem - code sample

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

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.apis import set_random_seed, train_detector from mmdet.datasets import build_dataset from mmdet.models import build_detector from mmdet.utils import collect_env, get_root_logger

def parse_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() 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(): args = parse_args()

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 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
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
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)

# 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
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)
# 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()

Reproduces the problem - command or script

(newpoint) D:\PointTinyBenchmark-master\PointTinyBenchmark-master\TOV_mmdetection\tools>python train.py

Reproduces the problem - error message

from mmdet.apis import set_random_seed, train_detector from mmcv.ops import RoIPool from .assign_score_withk import assign_score_withk ImportError: DLL load failed while importing _ext: 找不到指定的程序。

Additional information

No response

open-mmlab / mmcv