201 lines
7.4 KiB
Python
201 lines
7.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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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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import mmcv
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import torch
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from mmcls.apis import set_random_seed
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from mmcls.models import build_classifier
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from mmcv import 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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from mmfewshot.classification.apis import (multi_gpu_meta_test,
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single_gpu_meta_test)
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from mmfewshot.classification.datasets import (build_dataset,
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build_meta_test_dataloader)
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from mmfewshot.utils import get_root_logger
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def parse_args():
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parser = argparse.ArgumentParser(description='mmcls test model')
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parser.add_argument('config', help='test config file path')
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parser.add_argument('checkpoint', help='checkpoint file')
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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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'--metrics',
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type=str,
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nargs='+',
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help='evaluation metrics, which depends on the dataset, e.g., '
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'"accuracy", "precision", "recall", "f1_score", "support" for single '
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'label dataset, and "mAP", "CP", "CR", "CF1", "OP", "OR", "OF1" for '
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'multi-label dataset')
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parser.add_argument(
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'--show-task-results',
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action='store_true',
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help='whether to show results of each task')
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parser.add_argument('--tmpdir', help='tmp dir for writing some results')
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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='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.')
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parser.add_argument(
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'--metric-options',
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nargs='+',
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action=DictAction,
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default={},
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help='custom options for evaluation, the key-value pair in xxx=yyy '
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'format will be parsed as a dict metric_options for dataset.evaluate()'
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' 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('--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('--local_rank', type=int, default=0)
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parser.add_argument(
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'--device', default=None, help='device used for testing. (Deprecated)')
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parser.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 testing)')
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parser.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 testing)')
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parser.add_argument(
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'--show_task_results',
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action='store_true',
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help='whether to record eval result of each task.')
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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.device:
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warnings.warn(
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'--device is deprecated. To use cpu to test, please '
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'refers to https://mmclassification.readthedocs.io/en/latest/'
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'getting_started.html#inference-with-pretrained-models')
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assert args.metrics or args.out, \
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'Please specify at least one of output path and evaluation metrics.'
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return args
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def main():
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args = parse_args()
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cfg = mmcv.Config.fromfile(args.config)
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if args.options is not None:
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cfg.merge_from_dict(args.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.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 testing. Use the first GPU '
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'in `gpu_ids` now.')
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else:
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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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rank, _ = get_dist_info()
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# create work_dir
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mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
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# dump config
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cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
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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}_test.log')
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logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
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# set random seeds
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if args.seed is None:
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args.seed = 0
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logger.info(f'Set random seed to {args.seed}, '
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f'deterministic: {args.deterministic}')
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set_random_seed(args.seed, deterministic=args.deterministic)
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dataset = build_dataset(cfg.data.test)
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meta_test_cfg = cfg.data.test.meta_test_cfg
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(support_data_loader, query_data_loader,
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all_data_loader) = build_meta_test_dataloader(dataset, meta_test_cfg)
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# build the model and load checkpoint
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model = build_classifier(cfg.model)
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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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if rank == 0:
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logger.info(f'load from checkpoint: {args.checkpoint} ')
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load_checkpoint(model, args.checkpoint, map_location='cpu')
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if not distributed:
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if args.device == 'cpu':
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model = model.cpu()
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else:
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model = MMDataParallel(model, device_ids=[0])
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meta_eval_results = single_gpu_meta_test(
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model,
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meta_test_cfg.num_episodes,
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support_data_loader,
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query_data_loader,
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all_data_loader,
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meta_test_cfg=meta_test_cfg,
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logger=logger,
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eval_kwargs=dict(metric=cfg.evaluation.metric),
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show_task_results=args.show_task_results)
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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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meta_eval_results = multi_gpu_meta_test(
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model,
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meta_test_cfg.num_episodes,
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support_data_loader,
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query_data_loader,
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all_data_loader,
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meta_test_cfg=meta_test_cfg,
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logger=logger,
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eval_kwargs=dict(metric=cfg.evaluation.metric),
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show_task_results=args.show_task_results)
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if rank == 0:
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logger.info(f'Checkpoint: {args.checkpoint}')
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for k, v in meta_eval_results.items():
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logger.info(f'{k} : {v:.2f}')
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if __name__ == '__main__':
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main()
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