| | 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) |
| | |
| | if cfg.get('custom_imports', None): |
| | from mmcv.utils import import_modules_from_strings |
| | import_modules_from_strings(**cfg['custom_imports']) |
| | |
| | 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 |
| |
|
| | |
| | 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: |
| | |
| | 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) |
| |
|
| | |
| | if args.launcher == 'none': |
| | distributed = False |
| | else: |
| | distributed = True |
| | init_dist(args.launcher, **cfg.dist_params) |
| |
|
| | |
| | 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) |
| |
|
| | |
| | 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) |
| | |
| | |
| | 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() |
| | |
| | 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() |
| |
|
