import argparse import os import warnings import mmcv import torch from mmcv import Config, DictAction from mmcv.parallel import MMDataParallel, MMDistributedDataParallel from mmcv.runner import (get_dist_info, init_dist, load_checkpoint, wrap_fp16_model) from mmdet.datasets import replace_ImageToTensor import mmfewshot # noqa: F401, F403 from mmfewshot.detection.apis import (multi_gpu_model_init, single_gpu_model_init) from mmfewshot.detection.apis.test import multi_gpu_test, single_gpu_test from mmfewshot.detection.datasets import build_dataloader, build_dataset from mmfewshot.detection.models import build_detector def parse_args(): parser = argparse.ArgumentParser( description='MMFewShot test (and eval) a model') parser.add_argument('config', help='test config file path') parser.add_argument('checkpoint', help='checkpoint file') parser.add_argument('--out', help='output result file in pickle format') parser.add_argument( '--eval', type=str, nargs='+', help='evaluation metrics, which depends on the dataset, e.g., "bbox",' ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC') parser.add_argument('--show', action='store_true', help='show results') parser.add_argument( '--show-dir', help='directory where painted images will be saved') parser.add_argument( '--show-score-thr', type=float, default=0.3, help='score threshold (default: 0.3)') parser.add_argument( '--gpu-collect', action='store_true', help='whether to use gpu to collect results.') parser.add_argument( '--tmpdir', help='tmp directory used for collecting results from multiple ' 'workers, available when gpu-collect is not specified') 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( '--options', nargs='+', action=DictAction, help='custom options for evaluation, the key-value pair in xxx=yyy ' 'format will be kwargs for dataset.evaluate() function (deprecate), ' 'change to --eval-options instead.') parser.add_argument( '--eval-options', nargs='+', action=DictAction, help='custom options for evaluation, the key-value pair in xxx=yyy ' 'format will be kwargs for dataset.evaluate() function') parser.add_argument( '--launcher', choices=['none', 'pytorch', 'slurm', 'mpi'], default='none', help='job launcher') parser.add_argument('--local_rank', type=int, default=0) args = parser.parse_args() if 'LOCAL_RANK' not in os.environ: os.environ['LOCAL_RANK'] = str(args.local_rank) if args.options and args.eval_options: raise ValueError( '--options and --eval-options cannot be both ' 'specified, --options is deprecated in favor of --eval-options') if args.options: warnings.warn('--options is deprecated in favor of --eval-options') args.eval_options = args.options return args def main(): args = parse_args() assert args.out or args.eval or args.show \ or args.show_dir, ( 'Please specify at least one operation (save/eval/show the ' 'results / save the results) with the argument "--out", "--eval"', '"--show" or "--show-dir"') if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') 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 cfg.model.pretrained = None # in case the test dataset is concatenated samples_per_gpu = 1 if isinstance(cfg.data.test, dict): cfg.data.test.test_mode = True samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) if samples_per_gpu > 1: # Replace 'ImageToTensor' to 'DefaultFormatBundle' cfg.data.test.pipeline = replace_ImageToTensor( cfg.data.test.pipeline) elif isinstance(cfg.data.test, list): for ds_cfg in cfg.data.test: ds_cfg.test_mode = True samples_per_gpu = max( [ds_cfg.pop('samples_per_gpu', 1) for ds_cfg in cfg.data.test]) if samples_per_gpu > 1: for ds_cfg in cfg.data.test: ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline) # 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) # currently only support single images testing samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) assert samples_per_gpu == 1, 'currently only support single images testing' # build the dataloader dataset = build_dataset(cfg.data.test) data_loader = build_dataloader( dataset, samples_per_gpu=samples_per_gpu, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False, round_up=False) # for meta-learning methods which require support template dataset # for model initialization. if cfg.data.get('model_init', None) is not None: model_init_samples_per_gpu = cfg.data.model_init.pop( 'samples_per_gpu', 1) model_init_workers_per_gpu = cfg.data.model_init.pop( 'workers_per_gpu', 1) assert cfg.data.model_init.get('ann_cfg', None) is not None, \ 'during testing ann_cfg of support template can not be None' model_init_dataset = build_dataset(cfg.data.model_init) # disable dist to make all rank get same data model_init_dataloader = build_dataloader( model_init_dataset, samples_per_gpu=model_init_samples_per_gpu, workers_per_gpu=model_init_workers_per_gpu, dist=False, shuffle=False) # pop frozen_parameters cfg.model.pop('frozen_parameters', None) # build the model and load checkpoint cfg.model.train_cfg = None model = build_detector(cfg.model) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if 'CLASSES' in checkpoint.get('meta', {}): model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES if not distributed: model = MMDataParallel(model, device_ids=[0]) if cfg.task_type == 'mmdet': show_kwargs = dict(show_score_thr=args.show_score_thr) elif cfg.task_type == 'mmcls': show_kwargs = {} if args.show_options is None \ else args.show_options if cfg.data.get('model_init', None) is not None: single_gpu_model_init(model, model_init_dataloader) outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, **show_kwargs) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) if cfg.data.get('model_init', None) is not None: multi_gpu_model_init(model, model_init_dataloader) outputs = multi_gpu_test( model, data_loader, args.tmpdir, args.gpu_collect, ) rank, _ = get_dist_info() if rank == 0: if args.out: print(f'\nwriting results to {args.out}') mmcv.dump(outputs, args.out) kwargs = {} if args.eval_options is None else args.eval_options if args.eval: eval_kwargs = cfg.get('evaluation', {}).copy() # hard-code way to remove EvalHook args for key in [ 'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best', 'rule' ]: eval_kwargs.pop(key, None) eval_kwargs.update(dict(metric=args.eval, **kwargs)) print(dataset.evaluate(outputs, **eval_kwargs)) if __name__ == '__main__': main()