-
Notifications
You must be signed in to change notification settings - Fork 9.4k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[CodeCamp2023-504]Add a new script to support the WBF
Co-authored-by: huanghaian <[email protected]>
- Loading branch information
1 parent
59b0fc5
commit 769c810
Showing
6 changed files
with
754 additions
and
1 deletion.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,212 @@ | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| """Support for multi-model fusion, and currently only the Weighted Box Fusion | ||
| (WBF) fusion method is supported. | ||
| References: https://github.com/ZFTurbo/Weighted-Boxes-Fusion | ||
| Example: | ||
| python demo/demo_multi_model.py demo/demo.jpg \ | ||
| ./configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_1x_coco.py \ | ||
| ./configs/retinanet/retinanet_r50-caffe_fpn_1x_coco.py \ | ||
| --checkpoints \ | ||
| https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth \ # noqa | ||
| https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth \ | ||
| --weights 1 2 | ||
| """ | ||
|
|
||
| import argparse | ||
| import os.path as osp | ||
|
|
||
| import mmcv | ||
| import mmengine | ||
| from mmengine.fileio import isdir, join_path, list_dir_or_file | ||
| from mmengine.logging import print_log | ||
| from mmengine.structures import InstanceData | ||
|
|
||
| from mmdet.apis import DetInferencer | ||
| from mmdet.models.utils import weighted_boxes_fusion | ||
| from mmdet.registry import VISUALIZERS | ||
| from mmdet.structures import DetDataSample | ||
|
|
||
| IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', | ||
| '.tiff', '.webp') | ||
|
|
||
|
|
||
| def parse_args(): | ||
| parser = argparse.ArgumentParser( | ||
| description='MMDetection multi-model inference demo') | ||
| parser.add_argument( | ||
| 'inputs', type=str, help='Input image file or folder path.') | ||
| parser.add_argument( | ||
| 'config', | ||
| type=str, | ||
| nargs='*', | ||
| help='Config file(s), support receive multiple files') | ||
| parser.add_argument( | ||
| '--checkpoints', | ||
| type=str, | ||
| nargs='*', | ||
| help='Checkpoint file(s), support receive multiple files, ' | ||
| 'remember to correspond to the above config', | ||
| ) | ||
| parser.add_argument( | ||
| '--weights', | ||
| type=float, | ||
| nargs='*', | ||
| default=None, | ||
| help='weights for each model, remember to ' | ||
| 'correspond to the above config') | ||
| parser.add_argument( | ||
| '--fusion-iou-thr', | ||
| type=float, | ||
| default=0.55, | ||
| help='IoU value for boxes to be a match in wbf') | ||
| parser.add_argument( | ||
| '--skip-box-thr', | ||
| type=float, | ||
| default=0.0, | ||
| help='exclude boxes with score lower than this variable in wbf') | ||
| parser.add_argument( | ||
| '--conf-type', | ||
| type=str, | ||
| default='avg', # avg, max, box_and_model_avg, absent_model_aware_avg | ||
| help='how to calculate confidence in weighted boxes in wbf') | ||
| parser.add_argument( | ||
| '--out-dir', | ||
| type=str, | ||
| default='outputs', | ||
| help='Output directory of images or prediction results.') | ||
| parser.add_argument( | ||
| '--device', default='cuda:0', help='Device used for inference') | ||
| parser.add_argument( | ||
| '--pred-score-thr', | ||
| type=float, | ||
| default=0.3, | ||
| help='bbox score threshold') | ||
| parser.add_argument( | ||
| '--batch-size', type=int, default=1, help='Inference batch size.') | ||
| parser.add_argument( | ||
| '--show', | ||
| action='store_true', | ||
| help='Display the image in a popup window.') | ||
| parser.add_argument( | ||
| '--no-save-vis', | ||
| action='store_true', | ||
| help='Do not save detection vis results') | ||
| parser.add_argument( | ||
| '--no-save-pred', | ||
| action='store_true', | ||
| help='Do not save detection json results') | ||
| parser.add_argument( | ||
| '--palette', | ||
| default='none', | ||
| choices=['coco', 'voc', 'citys', 'random', 'none'], | ||
| help='Color palette used for visualization') | ||
|
|
||
| args = parser.parse_args() | ||
|
|
||
| if args.no_save_vis and args.no_save_pred: | ||
| args.out_dir = '' | ||
|
|
||
| return args | ||
|
|
||
|
|
||
| def main(): | ||
| args = parse_args() | ||
|
|
||
| results = [] | ||
| cfg_visualizer = None | ||
| dataset_meta = None | ||
|
|
||
| inputs = [] | ||
| filename_list = [] | ||
| if isdir(args.inputs): | ||
| dir = list_dir_or_file( | ||
| args.inputs, list_dir=False, suffix=IMG_EXTENSIONS) | ||
| for filename in dir: | ||
| img = mmcv.imread(join_path(args.inputs, filename)) | ||
| inputs.append(img) | ||
| filename_list.append(filename) | ||
| else: | ||
| img = mmcv.imread(args.inputs) | ||
| inputs.append(img) | ||
| img_name = osp.basename(args.inputs) | ||
| filename_list.append(img_name) | ||
|
|
||
| for i, (config, | ||
| checkpoint) in enumerate(zip(args.config, args.checkpoints)): | ||
| inferencer = DetInferencer( | ||
| config, checkpoint, device=args.device, palette=args.palette) | ||
|
|
||
| result_raw = inferencer( | ||
| inputs=inputs, | ||
| batch_size=args.batch_size, | ||
| no_save_vis=True, | ||
| pred_score_thr=args.pred_score_thr) | ||
|
|
||
| if i == 0: | ||
| cfg_visualizer = inferencer.cfg.visualizer | ||
| dataset_meta = inferencer.model.dataset_meta | ||
| results = [{ | ||
| 'bboxes_list': [], | ||
| 'scores_list': [], | ||
| 'labels_list': [] | ||
| } for _ in range(len(result_raw['predictions']))] | ||
|
|
||
| for res, raw in zip(results, result_raw['predictions']): | ||
| res['bboxes_list'].append(raw['bboxes']) | ||
| res['scores_list'].append(raw['scores']) | ||
| res['labels_list'].append(raw['labels']) | ||
|
|
||
| visualizer = VISUALIZERS.build(cfg_visualizer) | ||
| visualizer.dataset_meta = dataset_meta | ||
|
|
||
| for i in range(len(results)): | ||
| bboxes, scores, labels = weighted_boxes_fusion( | ||
| results[i]['bboxes_list'], | ||
| results[i]['scores_list'], | ||
| results[i]['labels_list'], | ||
| weights=args.weights, | ||
| iou_thr=args.fusion_iou_thr, | ||
| skip_box_thr=args.skip_box_thr, | ||
| conf_type=args.conf_type) | ||
|
|
||
| pred_instances = InstanceData() | ||
| pred_instances.bboxes = bboxes | ||
| pred_instances.scores = scores | ||
| pred_instances.labels = labels | ||
|
|
||
| fusion_result = DetDataSample(pred_instances=pred_instances) | ||
|
|
||
| img_name = filename_list[i] | ||
|
|
||
| if not args.no_save_pred: | ||
| out_json_path = ( | ||
| args.out_dir + '/preds/' + img_name.split('.')[0] + '.json') | ||
| mmengine.dump( | ||
| { | ||
| 'labels': labels.tolist(), | ||
| 'scores': scores.tolist(), | ||
| 'bboxes': bboxes.tolist() | ||
| }, out_json_path) | ||
|
|
||
| out_file = osp.join(args.out_dir, 'vis', | ||
| img_name) if not args.no_save_vis else None | ||
|
|
||
| visualizer.add_datasample( | ||
| img_name, | ||
| inputs[i][..., ::-1], | ||
| data_sample=fusion_result, | ||
| show=args.show, | ||
| draw_gt=False, | ||
| wait_time=0, | ||
| pred_score_thr=args.pred_score_thr, | ||
| out_file=out_file) | ||
|
|
||
| if not args.no_save_vis: | ||
| print_log(f'results have been saved at {args.out_dir}') | ||
|
|
||
|
|
||
| if __name__ == '__main__': | ||
| main() |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.