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[FEAT] Ensemble 코드 구현 (boostcampaitech6#28)
* Feat : Add ensemble WBF code boostcampaitech6#27 * Feat : multi_model_val log code - - boostcampaitech6#27 * Feat : csv ensemble - - boostcampaitech6#26 * Feat : Modify multi_model_val_another_csv.py boostcampaitech6#27 * "Feat : test ensembel and fix val csv ensemble - -boostcampaitech6#27 * Feat : Modify submission_hook.py for Ensemble boostcampaitech6#27 * Feat : Modify base_config.py for Ensemble boostcampaitech6#27 * Remove : 필요없는 파일 제거 boostcampaitech6#27 --------- Co-authored-by: internationalwe <[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 | ||
| """ | ||
|
|
||
| import argparse | ||
| import os.path as osp | ||
| import os | ||
| from datetime import datetime | ||
|
|
||
| import mmcv | ||
| from mmengine.structures import InstanceData | ||
|
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| from mmdet.apis import DetInferencer | ||
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| # from mmdet.models.utils import weighted_boxes_fusion | ||
|
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| import torch | ||
| from pycocotools.coco import COCO | ||
| from torchmetrics.detection import MeanAveragePrecision | ||
| from tqdm import tqdm | ||
|
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| from ensemble_boxes import * | ||
| import pandas as pd | ||
| import numpy as np | ||
|
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|
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| IMG_EXTENSIONS = ( | ||
| ".jpg", | ||
| ".jpeg", | ||
| ".png", | ||
| ".ppm", | ||
| ".bmp", | ||
| ".pgm", | ||
| ".tif", | ||
| ".tiff", | ||
| ".webp", | ||
| ) | ||
|
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|
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| def parse_args(): | ||
| parser = argparse.ArgumentParser( | ||
| description="MMDetection multi-model inference demo" | ||
| ) | ||
| parser.add_argument( | ||
| "type", | ||
| type=str, | ||
| default="wbf", # avg, max, box_and_model_avg, absent_model_aware_avg | ||
| help="ensemble type", | ||
| ) | ||
| parser.add_argument( | ||
| "csv", | ||
| type=str, | ||
| nargs="*", | ||
| help="CSV file(s), support receive multiple files", | ||
| ) | ||
| 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( | ||
| "--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( | ||
| "--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") | ||
| args = parser.parse_args() | ||
|
|
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| return args | ||
|
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|
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| def main(): | ||
| args = parse_args() | ||
|
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| print(f"Ensemble Type : {args.type}") | ||
| print(f"Ensemble weight : {args.weights}") | ||
| print(f"Ensemble iou thr : {args.fusion_iou_thr}") | ||
| print(f"Ensemble skip box thr : {args.skip_box_thr}") | ||
| print(f"Ensemble wbf type : {args.conf_type}") | ||
|
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||
| now = datetime.now() | ||
| out_path = osp.join(args.out_dir, "result", now.strftime("%Y_%m_%d_%H_%M_%S")) | ||
| os.makedirs(out_path, exist_ok=True) | ||
|
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| image_size = 1024 | ||
| results = [] | ||
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| submission_df = [pd.read_csv(file) for file in args.csv] | ||
| image_ids = submission_df[0]["image_id"].tolist() | ||
|
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| prediction_strings = [] | ||
| file_names = [] | ||
|
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| for i, image_id in tqdm(enumerate(image_ids), total=len(image_ids)): | ||
| prediction_string = "" | ||
| boxes_list = [] | ||
| scores_list = [] | ||
| labels_list = [] | ||
|
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| for df in submission_df: | ||
| predict_string = df[df["image_id"] == image_id][ | ||
| "PredictionString" | ||
| ].tolist()[0] | ||
| predict_list = str(predict_string).split() | ||
|
|
||
| if len(predict_list) == 0 or len(predict_list) == 1: | ||
| continue | ||
|
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| predict_list = np.reshape(predict_list, (-1, 6)) | ||
| box_list = [] | ||
|
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| for bbox in predict_list[:, 2:6].tolist(): | ||
| bbox[0] = float(bbox[0]) / image_size | ||
| bbox[1] = float(bbox[1]) / image_size | ||
| bbox[2] = float(bbox[2]) / image_size | ||
| bbox[3] = float(bbox[3]) / image_size | ||
| box_list.append(bbox) | ||
|
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| boxes_list.append(box_list) | ||
| scores_list.append(list(map(float, predict_list[:, 1].tolist()))) | ||
| labels_list.append(list(map(int, predict_list[:, 0].tolist()))) | ||
| results.append([boxes_list, scores_list, labels_list]) | ||
|
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| if len(boxes_list): | ||
| if args.type == "nms": | ||
| bboxes, scores, labels = nms( | ||
| boxes_list, | ||
| scores_list, | ||
| labels_list, | ||
| weights=args.weights, | ||
| iou_thr=args.fusion_iou_thr, | ||
| ) | ||
| if args.type == "soft_nms": | ||
| bboxes, scores, labels = soft_nms( | ||
| boxes_list, | ||
| scores_list, | ||
| labels_list, | ||
| weights=args.weights, | ||
| iou_thr=args.fusion_iou_thr, | ||
| mode=2, | ||
| sigma=0.5, | ||
| ) | ||
| elif args.type == "nmw": | ||
| bboxes, scores, labels = non_maximum_weighted( | ||
| boxes_list, | ||
| scores_list, | ||
| labels_list, | ||
| weights=args.weights, | ||
| iou_thr=args.fusion_iou_thr, | ||
| skip_box_thr=args.skip_box_thr, | ||
| ) | ||
| elif args.type == "wbf": | ||
| bboxes, scores, labels = weighted_boxes_fusion( | ||
| boxes_list, | ||
| scores_list, | ||
| 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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| for bbox, score, label in zip(bboxes, scores, labels): | ||
| prediction_string += ( | ||
| str(int(label)) | ||
| + " " | ||
| + str(score) | ||
| + " " | ||
| + str(bbox[0] * image_size) | ||
| + " " | ||
| + str(bbox[1] * image_size) | ||
| + " " | ||
| + str(bbox[2] * image_size) | ||
| + " " | ||
| + str(bbox[3] * image_size) | ||
| + " " | ||
| ) | ||
| prediction_strings.append(prediction_string) | ||
| file_names.append(image_id) | ||
|
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| submission = pd.DataFrame() | ||
| submission["PredictionString"] = prediction_strings | ||
| submission["image_id"] = file_names | ||
| submission.to_csv(osp.join(out_path, "ouput.csv"), index=False) | ||
|
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| with open(os.path.join(out_path, "log.txt"), "w") as f: | ||
| f.write("Config & Weight List\n") | ||
| for csv_name in args.csv: | ||
| slice_config_name = csv_name.split("/")[-1] | ||
| f.write(f"{slice_config_name}\n") | ||
| f.write("Ensemble Config\n") | ||
| f.write(f"Ensemble Type : {args.type}\n") | ||
| f.write(f"Ensemble weight : {args.weights}\n") | ||
| f.write(f"Ensemble iou thr : {args.fusion_iou_thr}\n") | ||
| f.write(f"Ensemble skip box thr : {args.skip_box_thr}\n") | ||
| f.write(f"Ensemble wbf type : {args.conf_type}\n") | ||
|
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|
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| if __name__ == "__main__": | ||
| main() |
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