OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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205 lines
7.8 KiB
205 lines
7.8 KiB
import argparse |
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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.cnn import fuse_conv_bn |
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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 mmdet.apis import multi_gpu_test, single_gpu_test |
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from mmdet.datasets import (build_dataloader, build_dataset, |
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replace_ImageToTensor) |
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from mmdet.models import build_detector |
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def parse_args(): |
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parser = argparse.ArgumentParser( |
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description='MMDet test (and eval) a 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('--out', help='output result file in pickle format') |
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parser.add_argument( |
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'--fuse-conv-bn', |
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action='store_true', |
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help='Whether to fuse conv and bn, this will slightly increase' |
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'the inference speed') |
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parser.add_argument( |
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'--format-only', |
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action='store_true', |
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help='Format the output results without perform evaluation. It is' |
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'useful when you want to format the result to a specific format and ' |
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'submit it to the test server') |
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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, e.g., "bbox",' |
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' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC') |
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parser.add_argument('--show', action='store_true', help='show results') |
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parser.add_argument( |
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'--show-dir', help='directory where painted images will be saved') |
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parser.add_argument( |
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'--show-score-thr', |
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type=float, |
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default=0.3, |
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help='score threshold (default: 0.3)') |
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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.') |
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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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return args |
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def main(): |
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args = parse_args() |
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assert args.out or args.eval or args.format_only or args.show \ |
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or args.show_dir, \ |
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('Please specify at least one operation (save/eval/format/show the ' |
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'results / save the results) with the argument "--out", "--eval"' |
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', "--format-only", "--show" or "--show-dir"') |
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if args.eval and args.format_only: |
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raise ValueError('--eval and --format_only cannot be both specified') |
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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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cfg = Config.fromfile(args.config) |
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if args.cfg_options is not None: |
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cfg.merge_from_dict(args.cfg_options) |
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# import modules from string list. |
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if 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(**cfg['custom_imports']) |
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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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cfg.model.pretrained = None |
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if cfg.model.get('neck'): |
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if isinstance(cfg.model.neck, list): |
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for neck_cfg in cfg.model.neck: |
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if neck_cfg.get('rfp_backbone'): |
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if neck_cfg.rfp_backbone.get('pretrained'): |
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neck_cfg.rfp_backbone.pretrained = None |
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elif cfg.model.neck.get('rfp_backbone'): |
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if cfg.model.neck.rfp_backbone.get('pretrained'): |
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cfg.model.neck.rfp_backbone.pretrained = None |
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# in case the test dataset is concatenated |
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if isinstance(cfg.data.test, dict): |
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cfg.data.test.test_mode = True |
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elif isinstance(cfg.data.test, list): |
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for ds_cfg in cfg.data.test: |
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ds_cfg.test_mode = True |
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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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# build the dataloader |
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samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) |
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if samples_per_gpu > 1: |
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# Replace 'ImageToTensor' to 'DefaultFormatBundle' |
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cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline) |
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dataset = build_dataset(cfg.data.test) |
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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=cfg.data.workers_per_gpu, |
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dist=distributed, |
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shuffle=False) |
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# build the model and load checkpoint |
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model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) |
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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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checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') |
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if args.fuse_conv_bn: |
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model = fuse_conv_bn(model) |
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# old versions did not save class info in checkpoints, this walkaround is |
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# for backward compatibility |
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if 'CLASSES' in checkpoint['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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outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, |
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args.show_score_thr) |
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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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outputs = multi_gpu_test(model, data_loader, args.tmpdir, |
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args.gpu_collect) |
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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.format_only: |
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dataset.format_results(outputs, **kwargs) |
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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 ['interval', 'tmpdir', 'start', 'gpu_collect']: |
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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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print(dataset.evaluate(outputs, **eval_kwargs)) |
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if __name__ == '__main__': |
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main()
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