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- # Copyright (c) OpenMMLab. All rights reserved.
- import argparse
- import copy
- import os
- import os.path as osp
- import time
- import warnings
- import shutil
- import sys
- path = os.path.dirname(os.path.dirname(__file__))
- print(path)
- sys.path.append("/home/kaijie-tang/userdata/qizhi/code_test")
- #os.environ['RANK'] = "0"
- #os.environ['WORLD_SIZE'] = "8"
- #os.environ['MASTER_ADDR'] = "localhost"
- #os.environ['MASTER_PORT'] = "1234"
- import mmcv
- import torch
- #os.environ["CUDA_VISIBLE_DEVICES"] = "0, 1, 2, 3"
- from mmcv import Config, DictAction
- from mmcv.runner import get_dist_info, init_dist
- from mmcv.utils import get_git_hash
- from pycocotools.coco import COCO
-
- from mmdet import __version__
- from mmdet.apis import init_random_seed, 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', default='/home/kaijie-tang/userdata/qizhi/code_test/configs/AD_mlops/AD_mlops_test18.py', help='train config file path')
- parser.add_argument('--work-dir',default='/home/kaijie-tang/outputs/', 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)
- parser.add_argument(
- '--data-path', default='/home/kaijie-tang/adhub/coco2017_train/0.1', help='dataset path')
- parser.add_argument(
- '--batchsize',
- type=int,
- default=8,
- help='training batch size')
- parser.add_argument(
- '--epoch',
- type=int,
- default=5,
- help='training epoch')
- parser.add_argument(
- '--warmup_iters',
- type=int,
- default=500,
- help='training warmup_iters')
- parser.add_argument(
- '--lr',
- type=float,
- default=0.001,
- help='learning rate')
- parser.add_argument('--train_image_size',
- type=list,
- default=[(100, 100)],
- help='train image size')
- parser.add_argument('--test_image_size',
- type=list,
- default=[(100, 100)],
- help='test image size')
- 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
-
- if args.batchsize is not None:
- cfg.data.samples_per_gpu = args.batchsize
- if args.epoch is not None:
- cfg.runner.max_epochs = args.epoch
- if args.warmup_iters is not None:
- cfg.lr_config.warmup_iters = args.warmup_iters
- if args.lr is not None:
- cfg.optimizer.lr = args.lr
- '''if args.train_image_size is not None:
- cfg.train_pipeline[2].img_scale = args.train_image_size
- cfg.data.train.dataset.pipeline[2].img_scale = args.train_image_size'''
- if args.test_image_size is not None:
- cfg.test_pipeline[1].img_scale = args.test_image_size
- cfg.data.val.pipeline[1].img_scale = args.test_image_size
- cfg.data.test.pipeline[1].img_scale = args.test_image_size
- #if on platform, change the classnum fit the user define dataset
- if args.data_path is not None:
- coco_config=COCO(os.path.join(args.data_path,"annotations/instances_annotations.json"))
- cfg.data.train.dataset.img_prefix = os.path.join(args.data_path,"images")
- cfg.data.train.dataset.ann_file = os.path.join(args.data_path,"annotations/instances_annotations.json")
-
- cfg.data.val.img_prefix = os.path.join(args.data_path,"images")
- cfg.data.val.ann_file = os.path.join(args.data_path,"annotations/instances_annotations.json")
-
- cfg.data.test.img_prefix = os.path.join(args.data_path,"images")
- cfg.data.test.ann_file = os.path.join(args.data_path,"annotations/instances_annotations.json")
- cfg.classes = ()
- for cat in coco_config.cats.values():
- cfg.classes = cfg.classes + tuple([cat['name']])
-
- cfg.data.train.dataset.classes = cfg.classes
- cfg.data.val.classes = cfg.classes
- cfg.data.test.classes = cfg.classes
-
- #some model will RepeatDataset to speed up training, make sure all dataset path replace to data_path
- #cfg = Config.fromstring(cfg.dump().replace("ann_file='data/coco/annotations/instances_train2017.json',","ann_file='{}',".format(os.path.join(args.data_path,"annotations/instances_annotations.json"))), ".py")
- #cfg = Config.fromstring(cfg.dump().replace("img_prefix='data/coco/train2017/',","img_prefix='{}',".format(os.path.join(args.data_path,"images"))), ".py")
-
- # replace the classes num fit userdefine dataset
- #cfg = Config.fromstring(cfg.dump().replace("num_classes=80","num_classes={0}".format(len(coco_config.getCatIds()))), ".py")
- cfg.model.bbox_head.num_classes = len(coco_config.getCatIds())
- print(cfg.dump())
- # 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, 'config.py'))
- # 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
- #seed = init_random_seed(args.seed)
- seed = 965702173
- logger.info(f'Set random seed to {seed}, '
- f'deterministic: {args.deterministic}')
- set_random_seed(seed, deterministic=args.deterministic)
- #set_random_seed(seed, deterministic=True)
- cfg.seed = seed
- meta['seed'] = 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)
-
- #启智平台
- shutil.copytree(osp.abspath(osp.join(osp.dirname(__file__),'../../transformer/')), osp.join(args.work_dir, "transformer"))
- shutil.copy(osp.join(args.train_work_dir, "config.py"), osp.join(args.work_dir, "config.py"))
- class_name_file = open(osp.join(args.work_dir, "class_names.txt"), 'w')
- for name in cfg.classes:
- class_name_file.write(name+'\n')
- shutil.copy(osp.abspath(osp.join(osp.dirname(__file__),'serve_desc.yaml')), osp.join(args.work_dir, "serve_desc.yaml"))
-
-
-
- if __name__ == '__main__':
- main()
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