372 lines
13 KiB
Python
372 lines
13 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import copy
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import os
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import os.path as osp
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import time
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import warnings
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from datetime import datetime
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from pathlib import Path
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import mmcv
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import torch
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from mmcv import Config, DictAction
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from mmcv.runner import get_dist_info, init_dist
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from mmcls import __version__
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from mmcls.apis import init_random_seed, set_random_seed, train_model
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from mmcls.datasets import build_dataset
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from mmcls.models import build_classifier
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from mmcls.utils import collect_env, get_root_logger, load_json_log
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TEST_METRICS = ('precision', 'recall', 'f1_score', 'support', 'mAP', 'CP',
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'CR', 'CF1', 'OP', 'OR', 'OF1', 'accuracy')
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prog_description = """K-Fold cross-validation.
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To start a 5-fold cross-validation experiment:
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python tools/kfold-cross-valid.py $CONFIG --num-splits 5
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To resume a 5-fold cross-validation from an interrupted experiment:
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python tools/kfold-cross-valid.py $CONFIG --num-splits 5 --resume-from work_dirs/fold2/latest.pth
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To summarize a 5-fold cross-validation:
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python tools/kfold-cross-valid.py $CONFIG --num-splits 5 --summary
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""" # noqa: E501
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def parse_args():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.RawDescriptionHelpFormatter,
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description=prog_description)
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parser.add_argument('config', help='train config file path')
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parser.add_argument(
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'--num-splits', type=int, help='The number of all folds.')
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parser.add_argument(
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'--fold',
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type=int,
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help='The fold used to do validation. '
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'If specify, only do an experiment of the specified fold.')
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parser.add_argument(
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'--summary',
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action='store_true',
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help='Summarize the k-fold cross-validation results.')
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parser.add_argument('--work-dir', help='the dir to save logs and models')
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parser.add_argument(
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'--resume-from', help='the checkpoint file to resume from')
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parser.add_argument(
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'--no-validate',
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action='store_true',
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help='whether not to evaluate the checkpoint during training')
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group_gpus = parser.add_mutually_exclusive_group()
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group_gpus.add_argument('--device', help='device used for training')
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group_gpus.add_argument(
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'--gpus',
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type=int,
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help='(Deprecated, please use --gpu-id) number of gpus to use '
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'(only applicable to non-distributed training)')
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group_gpus.add_argument(
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'--gpu-ids',
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type=int,
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nargs='+',
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help='(Deprecated, please use --gpu-id) ids of gpus to use '
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'(only applicable to non-distributed training)')
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group_gpus.add_argument(
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'--gpu-id',
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type=int,
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default=0,
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help='id of gpu to use '
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'(only applicable to non-distributed training)')
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parser.add_argument('--seed', type=int, default=None, help='random seed')
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parser.add_argument(
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'--deterministic',
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action='store_true',
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help='whether to set deterministic options for CUDNN backend.')
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parser.add_argument(
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'--cfg-options',
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nargs='+',
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action=DictAction,
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help='override some settings in the used config, the key-value pair '
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'in xxx=yyy format will be merged into config file. If the value to '
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'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
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'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
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'Note that the quotation marks are necessary and that no white space '
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'is allowed.')
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parser.add_argument(
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'--launcher',
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choices=['none', 'pytorch', 'slurm', 'mpi'],
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default='none',
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help='job launcher')
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parser.add_argument('--local-rank', type=int, default=0)
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args = parser.parse_args()
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if 'LOCAL_RANK' not in os.environ:
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os.environ['LOCAL_RANK'] = str(args.local_rank)
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return args
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def copy_config(old_cfg):
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"""deepcopy a Config object."""
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new_cfg = Config()
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_cfg_dict = copy.deepcopy(old_cfg._cfg_dict)
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_filename = copy.deepcopy(old_cfg._filename)
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_text = copy.deepcopy(old_cfg._text)
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super(Config, new_cfg).__setattr__('_cfg_dict', _cfg_dict)
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super(Config, new_cfg).__setattr__('_filename', _filename)
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super(Config, new_cfg).__setattr__('_text', _text)
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return new_cfg
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def train_single_fold(args, cfg, fold, distributed, seed):
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# create the work_dir for the fold
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work_dir = osp.join(cfg.work_dir, f'fold{fold}')
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cfg.work_dir = work_dir
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# create work_dir
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mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
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# wrap the dataset cfg
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train_dataset = dict(
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type='KFoldDataset',
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fold=fold,
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dataset=cfg.data.train,
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num_splits=args.num_splits,
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seed=seed,
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)
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val_dataset = dict(
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type='KFoldDataset',
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fold=fold,
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# Use the same dataset with training.
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dataset=copy.deepcopy(cfg.data.train),
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num_splits=args.num_splits,
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seed=seed,
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test_mode=True,
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)
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val_dataset['dataset']['pipeline'] = cfg.data.val.pipeline
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cfg.data.train = train_dataset
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cfg.data.val = val_dataset
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cfg.data.test = val_dataset
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# dump config
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stem, suffix = osp.basename(args.config).rsplit('.', 1)
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cfg.dump(osp.join(cfg.work_dir, f'{stem}_fold{fold}.{suffix}'))
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# init the logger before other steps
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timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
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log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
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logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
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# init the meta dict to record some important information such as
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# environment info and seed, which will be logged
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meta = dict()
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# log env info
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env_info_dict = collect_env()
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env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
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dash_line = '-' * 60 + '\n'
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logger.info('Environment info:\n' + dash_line + env_info + '\n' +
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dash_line)
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meta['env_info'] = env_info
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# log some basic info
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logger.info(f'Distributed training: {distributed}')
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logger.info(f'Config:\n{cfg.pretty_text}')
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logger.info(
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f'-------- Cross-validation: [{fold+1}/{args.num_splits}] -------- ')
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# set random seeds
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# Use different seed in different folds
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logger.info(f'Set random seed to {seed + fold}, '
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f'deterministic: {args.deterministic}')
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set_random_seed(seed + fold, deterministic=args.deterministic)
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cfg.seed = seed + fold
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meta['seed'] = seed + fold
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model = build_classifier(cfg.model)
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model.init_weights()
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datasets = [build_dataset(cfg.data.train)]
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if len(cfg.workflow) == 2:
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val_dataset = copy.deepcopy(cfg.data.val)
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val_dataset.pipeline = cfg.data.train.pipeline
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datasets.append(build_dataset(val_dataset))
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meta.update(
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dict(
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mmcls_version=__version__,
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config=cfg.pretty_text,
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CLASSES=datasets[0].CLASSES,
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kfold=dict(fold=fold, num_splits=args.num_splits)))
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# add an attribute for visualization convenience
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train_model(
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model,
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datasets,
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cfg,
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distributed=distributed,
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validate=(not args.no_validate),
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timestamp=timestamp,
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device='cpu' if args.device == 'cpu' else 'cuda',
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meta=meta)
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def summary(args, cfg):
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summary = dict()
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for fold in range(args.num_splits):
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work_dir = Path(cfg.work_dir) / f'fold{fold}'
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# Find the latest training log
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log_files = list(work_dir.glob('*.log.json'))
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if len(log_files) == 0:
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continue
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log_file = sorted(log_files)[-1]
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date = datetime.fromtimestamp(log_file.lstat().st_mtime)
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summary[fold] = {'date': date.strftime('%Y-%m-%d %H:%M:%S')}
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# Find the latest eval log
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json_log = load_json_log(log_file)
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epochs = sorted(list(json_log.keys()))
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eval_log = {}
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def is_metric_key(key):
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for metric in TEST_METRICS:
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if metric in key:
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return True
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return False
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for epoch in epochs[::-1]:
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if any(is_metric_key(k) for k in json_log[epoch].keys()):
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eval_log = json_log[epoch]
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break
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summary[fold]['epoch'] = epoch
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summary[fold]['metric'] = {
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k: v[0] # the value is a list with only one item.
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for k, v in eval_log.items() if is_metric_key(k)
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}
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show_summary(args, summary)
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def show_summary(args, summary_data):
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try:
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from rich.console import Console
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from rich.table import Table
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except ImportError:
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raise ImportError('Please run `pip install rich` to install '
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'package `rich` to draw the table.')
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console = Console()
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table = Table(title=f'{args.num_splits}-fold Cross-validation Summary')
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table.add_column('Fold')
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metrics = summary_data[0]['metric'].keys()
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for metric in metrics:
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table.add_column(metric)
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table.add_column('Epoch')
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table.add_column('Date')
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for fold in range(args.num_splits):
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row = [f'{fold+1}']
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if fold not in summary_data:
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table.add_row(*row)
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continue
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for metric in metrics:
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metric_value = summary_data[fold]['metric'].get(metric, '')
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def format_value(value):
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if isinstance(value, float):
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return f'{value:.2f}'
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if isinstance(value, (list, tuple)):
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return str([format_value(i) for i in value])
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else:
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return str(value)
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row.append(format_value(metric_value))
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row.append(str(summary_data[fold]['epoch']))
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row.append(summary_data[fold]['date'])
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table.add_row(*row)
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console.print(table)
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def main():
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args = parse_args()
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cfg = Config.fromfile(args.config)
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if args.cfg_options is not None:
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cfg.merge_from_dict(args.cfg_options)
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# set cudnn_benchmark
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if cfg.get('cudnn_benchmark', False):
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torch.backends.cudnn.benchmark = True
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# work_dir is determined in this priority: CLI > segment in file > filename
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if args.work_dir is not None:
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# update configs according to CLI args if args.work_dir is not None
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cfg.work_dir = args.work_dir
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elif cfg.get('work_dir', None) is None:
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# use config filename as default work_dir if cfg.work_dir is None
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cfg.work_dir = osp.join('./work_dirs',
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osp.splitext(osp.basename(args.config))[0])
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if args.summary:
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summary(args, cfg)
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return
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# resume from the previous experiment
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if args.resume_from is not None:
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cfg.resume_from = args.resume_from
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resume_kfold = torch.load(cfg.resume_from).get('meta',
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{}).get('kfold', None)
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if resume_kfold is None:
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raise RuntimeError(
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'No "meta" key in checkpoints or no "kfold" in the meta dict. '
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'Please check if the resume checkpoint from a k-fold '
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'cross-valid experiment.')
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resume_fold = resume_kfold['fold']
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assert args.num_splits == resume_kfold['num_splits']
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else:
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resume_fold = 0
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if args.gpus is not None:
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cfg.gpu_ids = range(1)
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warnings.warn('`--gpus` is deprecated because we only support '
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'single GPU mode in non-distributed training. '
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'Use `gpus=1` now.')
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if args.gpu_ids is not None:
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cfg.gpu_ids = args.gpu_ids[0:1]
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warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
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'Because we only support single GPU mode in '
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'non-distributed training. Use the first GPU '
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'in `gpu_ids` now.')
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if args.gpus is None and args.gpu_ids is None:
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cfg.gpu_ids = [args.gpu_id]
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# init distributed env first, since logger depends on the dist info.
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if args.launcher == 'none':
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distributed = False
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else:
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distributed = True
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init_dist(args.launcher, **cfg.dist_params)
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_, world_size = get_dist_info()
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cfg.gpu_ids = range(world_size)
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# init a unified random seed
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seed = init_random_seed(args.seed)
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# create work_dir
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mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
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if args.fold is not None:
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folds = [args.fold]
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else:
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folds = range(resume_fold, args.num_splits)
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for fold in folds:
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cfg_ = copy_config(cfg)
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if fold != resume_fold:
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cfg_.resume_from = None
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train_single_fold(args, cfg_, fold, distributed, seed)
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if args.fold is None:
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summary(args, cfg)
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if __name__ == '__main__':
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main()
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