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[CodeCamp2023-504]Add a new script to support the WBF
Co-authored-by: huanghaian <[email protected]>
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| # 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 | ||
| """ | ||
|
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||
| import argparse | ||
| import os.path as osp | ||
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| 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 | ||
|
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||
| IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', | ||
| '.tiff', '.webp') | ||
|
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| 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') | ||
|
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| args = parser.parse_args() | ||
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| if args.no_save_vis and args.no_save_pred: | ||
| args.out_dir = '' | ||
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| return args | ||
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| def main(): | ||
| args = parse_args() | ||
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| results = [] | ||
| cfg_visualizer = None | ||
| dataset_meta = None | ||
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| 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) | ||
|
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| for i, (config, | ||
| checkpoint) in enumerate(zip(args.config, args.checkpoints)): | ||
| inferencer = DetInferencer( | ||
| config, checkpoint, device=args.device, palette=args.palette) | ||
|
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| result_raw = inferencer( | ||
| inputs=inputs, | ||
| batch_size=args.batch_size, | ||
| no_save_vis=True, | ||
| pred_score_thr=args.pred_score_thr) | ||
|
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| 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']))] | ||
|
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| 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']) | ||
|
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| visualizer = VISUALIZERS.build(cfg_visualizer) | ||
| visualizer.dataset_meta = dataset_meta | ||
|
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| 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) | ||
|
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| pred_instances = InstanceData() | ||
| pred_instances.bboxes = bboxes | ||
| pred_instances.scores = scores | ||
| pred_instances.labels = labels | ||
|
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| fusion_result = DetDataSample(pred_instances=pred_instances) | ||
|
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| img_name = filename_list[i] | ||
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| 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) | ||
|
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| out_file = osp.join(args.out_dir, 'vis', | ||
| img_name) if not args.no_save_vis else None | ||
|
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| 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) | ||
|
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| if not args.no_save_vis: | ||
| print_log(f'results have been saved at {args.out_dir}') | ||
|
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| if __name__ == '__main__': | ||
| main() |
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