mirror of
https://github.com/open-mmlab/mmfewshot.git
synced 2025-06-03 14:49:43 +08:00
265 lines
10 KiB
Python
265 lines
10 KiB
Python
"""Test multiple run few shot models."""
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import argparse
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import copy
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import os
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import warnings
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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.parallel import MMDataParallel, MMDistributedDataParallel
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from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
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wrap_fp16_model)
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import mmfewshot # noqa: F401, F403
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from mmfewshot.detection.datasets import (build_dataloader, build_dataset,
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get_copy_dataset_type)
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from mmfewshot.detection.models import build_detector
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def parse_args():
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parser = argparse.ArgumentParser(
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description='MMFewShot test (and eval) models for multi train')
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parser.add_argument('config', help='test config file path')
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parser.add_argument(
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'times', type=int, help='number of experiments to test')
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parser.add_argument(
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'--checkpoint',
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type=str,
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default='latest',
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help='name of checkpoint to test')
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parser.add_argument(
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'--start',
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default=0,
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type=int,
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help='number of resume experiment times')
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parser.add_argument(
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'--work-dir', default=None, help='work directory for experiments')
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parser.add_argument('--out', help='output result file in pickle format')
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parser.add_argument(
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'--eval',
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type=str,
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nargs='+',
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help='evaluation metrics, which depends on the dataset '
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'of specific task_type, e.g., "bbox","segm", "proposal" for '
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'COCO, and "mAP", "recall" for PASCAL VOC in'
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'MMDet or "accuracy", "precision", "recall", "f1_score", '
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'"support" for single label dataset, and "mAP", "CP", "CR",'
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'"CF1", "OP", "OR", "OF1" for '
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'multi-label dataset in MMCLS')
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parser.add_argument(
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'--gpu-collect',
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action='store_true',
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help='whether to use gpu to collect results.')
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parser.add_argument(
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'--tmpdir',
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help='tmp directory used for collecting results from multiple '
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'workers, available when gpu-collect is not specified')
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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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'--options',
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nargs='+',
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action=DictAction,
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help='custom options for evaluation, the key-value pair in xxx=yyy '
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'format will be kwargs for dataset.evaluate() function (deprecate), '
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'change to --eval-options instead.')
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parser.add_argument(
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'--eval-options',
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nargs='+',
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action=DictAction,
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help='custom options for evaluation, the key-value pair in xxx=yyy '
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'format will be kwargs for dataset.evaluate() function')
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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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if args.options and args.eval_options:
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raise ValueError(
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'--options and --eval-options cannot be both '
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'specified, --options is deprecated in favor of --eval-options')
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if args.options:
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warnings.warn('--options is deprecated in favor of --eval-options')
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args.eval_options = args.options
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args.cfg_options = args.options
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return args
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def main():
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args = parse_args()
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if args.work_dir is None:
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args.work_dir = os.path.join(
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'./work_dirs',
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os.path.splitext(os.path.basename(args.config))[0])
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assert args.out or args.eval, (
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'Please specify at least one operation (save/eval the '
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'results / save the results) with the argument "--out", "--eval"')
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if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
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raise ValueError('The output file must be a pkl file.')
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base_cfg = Config.fromfile(args.config)
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if args.cfg_options is not None:
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base_cfg.merge_from_dict(args.cfg_options)
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# import modules from string list.
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if base_cfg.get('custom_imports', None):
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from mmcv.utils import import_modules_from_strings
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import_modules_from_strings(**base_cfg['custom_imports'])
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# set cudnn_benchmark
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if base_cfg.get('cudnn_benchmark', False):
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torch.backends.cudnn.benchmark = True
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base_cfg.model.pretrained = None
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# currently only support single images testing
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samples_per_gpu = base_cfg.data.test.pop('samples_per_gpu', 1)
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assert samples_per_gpu == 1, 'currently only support single images testing'
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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, **base_cfg.dist_params)
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rank, _ = get_dist_info()
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eval_result_list = []
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# build the dataloader
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dataset = build_dataset(base_cfg.data.test)
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# currently only support single images testing
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data_loader = build_dataloader(
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dataset,
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samples_per_gpu=samples_per_gpu,
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workers_per_gpu=base_cfg.data.workers_per_gpu,
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dist=distributed,
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shuffle=False)
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for i in range(args.start, args.times):
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cfg = copy.deepcopy(base_cfg)
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work_dir = os.path.join(args.work_dir, f'times_{i}')
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cfg.work_dir = work_dir
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# pop frozen_parameters
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cfg.model.pop('frozen_parameters', None)
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# build the model and load checkpoint
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cfg.model.train_cfg = None
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model = build_detector(cfg.model)
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checkpoint_path = os.path.join(work_dir, f'{args.checkpoint}.pth')
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checkpoint = load_checkpoint(
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model, checkpoint_path, map_location='cpu')
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fp16_cfg = cfg.get('fp16', None)
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if fp16_cfg is not None:
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wrap_fp16_model(model)
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# for meta-learning methods which require support template dataset
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# for model initialization.
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if cfg.data.get('model_init', None) is not None:
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cfg.data.model_init.pop('copy_from_train_dataset')
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model_init_samples_per_gpu = cfg.data.model_init.pop(
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'samples_per_gpu', 1)
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model_init_workers_per_gpu = cfg.data.model_init.pop(
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'workers_per_gpu', 1)
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if cfg.data.model_init.get('ann_cfg', None) is None:
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assert checkpoint['meta'].get('model_init_ann_cfg',
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None) is not None
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cfg.data.model_init.type = \
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get_copy_dataset_type(cfg.data.model_init.type)
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cfg.data.model_init.ann_cfg = \
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checkpoint['meta']['model_init_ann_cfg']
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model_init_dataset = build_dataset(cfg.data.model_init)
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# disable dist to make all rank get same data
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model_init_dataloader = build_dataloader(
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model_init_dataset,
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samples_per_gpu=model_init_samples_per_gpu,
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workers_per_gpu=model_init_workers_per_gpu,
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dist=False,
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shuffle=False)
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# old versions did not save class info in checkpoints,
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# this walkaround is for backward compatibility
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if 'CLASSES' in checkpoint.get('meta', {}):
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model.CLASSES = checkpoint['meta']['CLASSES']
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else:
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model.CLASSES = dataset.CLASSES
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if not distributed:
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model = MMDataParallel(model, device_ids=[0])
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show_kwargs = dict(show_score_thr=args.show_score_thr)
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if cfg.data.get('model_init', None) is not None:
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from mmfewshot.detection.apis import (single_gpu_model_init,
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single_gpu_test)
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single_gpu_model_init(model, model_init_dataloader)
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else:
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from mmdet.apis.test import single_gpu_test
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outputs = single_gpu_test(model, data_loader, args.show,
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args.show_dir, **show_kwargs)
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else:
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model = MMDistributedDataParallel(
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model.cuda(),
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device_ids=[torch.cuda.current_device()],
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broadcast_buffers=False)
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if cfg.data.get('model_init', None) is not None:
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from mmfewshot.detection.apis import (multi_gpu_model_init,
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multi_gpu_test)
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multi_gpu_model_init(model, model_init_dataloader)
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else:
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from mmdet.apis.test import multi_gpu_test
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outputs = multi_gpu_test(
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model,
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data_loader,
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args.tmpdir,
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args.gpu_collect,
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)
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rank, _ = get_dist_info()
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if rank == 0:
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if args.out:
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print(f'\nwriting results to {args.out}')
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mmcv.dump(outputs, args.out)
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kwargs = {} if args.eval_options is None else args.eval_options
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if args.eval:
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eval_kwargs = cfg.get('evaluation', {}).copy()
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# hard-code way to remove EvalHook args
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for key in [
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'interval', 'tmpdir', 'start', 'gpu_collect',
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'save_best', 'rule'
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]:
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eval_kwargs.pop(key, None)
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eval_kwargs.update(dict(metric=args.eval, **kwargs))
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eval_result = dataset.evaluate(outputs, **eval_kwargs)
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print(eval_result)
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eval_result_list.append(eval_result)
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if i == (args.times - 1):
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num_results = len(eval_result_list)
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if num_results == 0:
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print('zero experiment tested')
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else:
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avg_results = dict()
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for k in eval_result_list[0].keys():
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avg_results[f'Avg {k}'] = sum([
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eval_result_list[i][k] for i in range(num_results)
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]) / num_results
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mmcv.dump(avg_results,
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os.path.join(args.work_dir, 'avg_result.json'))
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print(avg_results)
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if __name__ == '__main__':
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main()
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