mmfewshot/tools/classification/test.py

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import time

import mmcv
import torch
from mmcls.apis import set_random_seed
from mmcls.models import build_classifier
from mmcv import DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
                         wrap_fp16_model)

from mmfewshot.classification.apis import (multi_gpu_meta_test,
                                           single_gpu_meta_test)
from mmfewshot.classification.datasets import (build_dataset,
                                               build_meta_test_dataloader)
from mmfewshot.utils import get_root_logger


def parse_args():
    parser = argparse.ArgumentParser(description='mmcls test model')
    parser.add_argument('config', help='test config file path')
    parser.add_argument('checkpoint', help='checkpoint file')
    parser.add_argument('--work-dir', help='the dir to save logs and models')
    parser.add_argument(
        '--metrics',
        type=str,
        nargs='+',
        help='evaluation metrics, which depends on the dataset, e.g., '
        '"accuracy", "precision", "recall", "f1_score", "support" for single '
        'label dataset, and "mAP", "CP", "CR", "CF1", "OP", "OR", "OF1" for '
        'multi-label dataset')
    parser.add_argument(
        '--show-task-results',
        action='store_true',
        help='whether to show results of each task')
    parser.add_argument('--tmpdir', help='tmp dir for writing some results')
    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.')
    parser.add_argument(
        '--metric-options',
        nargs='+',
        action=DictAction,
        default={},
        help='custom options for evaluation, the key-value pair in xxx=yyy '
        'format will be parsed as a dict metric_options for dataset.evaluate()'
        ' function.')
    parser.add_argument(
        '--launcher',
        choices=['none', 'pytorch', 'slurm', 'mpi'],
        default='none',
        help='job launcher')
    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('--local_rank', type=int, default=0)
    parser.add_argument(
        '--device',
        choices=['cpu', 'cuda'],
        default='cuda',
        help='device used for testing')
    args = parser.parse_args()
    if 'LOCAL_RANK' not in os.environ:
        os.environ['LOCAL_RANK'] = str(args.local_rank)
    return args


def main():
    args = parse_args()

    cfg = mmcv.Config.fromfile(args.config)
    if args.options is not None:
        cfg.merge_from_dict(args.options)
    # 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])

    # 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)
    rank, _ = get_dist_info()
    # 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}_test.log')
    logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)

    # set random seeds
    if args.seed is None:
        args.seed = 0
    logger.info(f'Set random seed to {args.seed}, '
                f'deterministic: {args.deterministic}')
    set_random_seed(args.seed, deterministic=args.deterministic)

    dataset = build_dataset(cfg.data.test)
    meta_test_cfg = cfg.data.test.meta_test_cfg
    (support_data_loader, query_data_loader,
     all_data_loader) = build_meta_test_dataloader(dataset, meta_test_cfg)

    # build the model and load checkpoint
    model = build_classifier(cfg.model)
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        wrap_fp16_model(model)
    if rank == 0:
        logger.info(f'load from checkpoint: {args.checkpoint} ')
    load_checkpoint(model, args.checkpoint, map_location='cpu')
    if not distributed:
        if args.device == 'cpu':
            model = model.cpu()
        else:
            model = MMDataParallel(model, device_ids=[0])
        meta_eval_results = single_gpu_meta_test(
            model,
            meta_test_cfg.num_episodes,
            support_data_loader,
            query_data_loader,
            all_data_loader,
            meta_test_cfg=meta_test_cfg,
            logger=logger,
            eval_kwargs=dict(metric=cfg.evaluation.metric),
            show_task_results=args.show_task_results)
    else:
        model = MMDistributedDataParallel(
            model.cuda(),
            device_ids=[torch.cuda.current_device()],
            broadcast_buffers=False)
        meta_eval_results = multi_gpu_meta_test(
            model,
            meta_test_cfg.num_episodes,
            support_data_loader,
            query_data_loader,
            all_data_loader,
            meta_test_cfg=meta_test_cfg,
            logger=logger,
            eval_kwargs=dict(metric=cfg.evaluation.metric),
            show_task_results=args.show_task_results)

    if rank == 0:
        logger.info(f'Checkpoint: {args.checkpoint}')
        for k, v in meta_eval_results.items():
            logger.info(f'{k} : {v:.2f}')


if __name__ == '__main__':
    main()