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| import argparse | |
| import os | |
| import warnings | |
| import mmcv | |
| import torch | |
| from mmcv import Config, DictAction | |
| from mmcv.cnn import fuse_conv_bn | |
| from mmcv.parallel import MMDataParallel, MMDistributedDataParallel | |
| from mmcv.runner import (get_dist_info, init_dist, load_checkpoint, | |
| wrap_fp16_model) | |
| from mmdet.apis import multi_gpu_test, single_gpu_test | |
| from mmdet.datasets import (build_dataloader, build_dataset, | |
| replace_ImageToTensor) | |
| from mmdet.models import build_detector | |
| def parse_args(): | |
| parser = argparse.ArgumentParser( | |
| description='MMDet 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( | |
| '--fuse-conv-bn', | |
| action='store_true', | |
| help='Whether to fuse conv and bn, this will slightly increase' | |
| 'the inference speed') | |
| parser.add_argument( | |
| '--format-only', | |
| action='store_true', | |
| help='Format the output results without perform evaluation. It is' | |
| 'useful when you want to format the result to a specific format and ' | |
| 'submit it to the test server') | |
| 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.format_only or args.show \ | |
| or args.show_dir, \ | |
| ('Please specify at least one operation (save/eval/format/show the ' | |
| 'results / save the results) with the argument "--out", "--eval"' | |
| ', "--format-only", "--show" or "--show-dir"') | |
| if args.eval and args.format_only: | |
| raise ValueError('--eval and --format_only cannot be both specified') | |
| 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 | |
| if cfg.model.get('neck'): | |
| if isinstance(cfg.model.neck, list): | |
| for neck_cfg in cfg.model.neck: | |
| if neck_cfg.get('rfp_backbone'): | |
| if neck_cfg.rfp_backbone.get('pretrained'): | |
| neck_cfg.rfp_backbone.pretrained = None | |
| elif cfg.model.neck.get('rfp_backbone'): | |
| if cfg.model.neck.rfp_backbone.get('pretrained'): | |
| cfg.model.neck.rfp_backbone.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) | |
| # 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) | |
| # build the model and load checkpoint | |
| cfg.model.train_cfg = None | |
| model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) | |
| 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') | |
| if args.fuse_conv_bn: | |
| model = fuse_conv_bn(model) | |
| # 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]) | |
| outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, | |
| args.show_score_thr) | |
| else: | |
| model = MMDistributedDataParallel( | |
| model.cuda(), | |
| device_ids=[torch.cuda.current_device()], | |
| broadcast_buffers=False) | |
| 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.format_only: | |
| dataset.format_results(outputs, **kwargs) | |
| 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() | |