├── .gitignore ├── README.md ├── license.txt ├── mAP_evaluation.py └── tmp ├── gt_data.json ├── parallel.png ├── pred_data.json └── single.png /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Parallel mAP_evaluation 2 | 3 | This repo parallelizes [mAP_evaluation](https://github.com/lyft/nuscenes-devkit/blob/master/lyft_dataset_sdk/eval/detection/mAP_evaluation.py) using python's multiprocessing module. 4 | 5 | As we know, in Lyft's 3d object detection challenge, the evaluation metric mAP is calculated as mean of mAPs for IoU thresholds 0.5, 0.55, 0.6, ... 0.95, (see [here](https://github.com/lyft/nuscenes-devkit/blob/master/lyft_dataset_sdk/eval/detection/mAP_evaluation)) looping over these 10 thresholds one by one can be time consuming process. Here's how it looks when you do so: 6 | 7 | 8 | 9 | Only one hyperthread is fully utilized, rest are idle. 10 | 11 | In this repo, you can find the parallelized implementation of mAP evaluation (`mAP_evaluation.py`) which uses Python's inbuilt multiprocessing module to compute APs for all 10 IoUs parallelly and simultaneously. Here's how it looks using parallelized version: 12 | 13 | 14 | 15 | The parallel implementation is ~10x faster than the for loop implementation. 16 | 17 | 18 | ## Requirements 19 | 20 | * lyft's devkit sdk [link](https://github.com/lyft/nuscenes-devkit/) 21 | * fire 22 | * pathlib 23 | * numpy 24 | 25 | ## Instructions 26 | 27 | As official mAP_evaluation script, this script also expects the predictions and ground truth to be in the format: 28 | 29 | `pred_file`: json file, predictions in global frame, in the format of: 30 | 31 | ``` 32 | predictions = [{ 33 | 'sample_token': '0f0e3ce89d2324d8b45aa55a7b4f8207fbb039a550991a5149214f98cec136ac', 34 | 'translation': [971.8343488872263, 1713.6816097857359, -25.82534357061308], 35 | 'size': [2.519726579986132, 7.810161372666739, 3.483438286096803], 36 | 'rotation': [0.10913582721095375, 0.04099572636992043, 0.01927712319721745, 1.029328402625659], 37 | 'name': 'car', 38 | 'score': 0.3077029437237213 39 | }] 40 | 41 | ``` 42 | 43 | `gt_file`: ground truth annotations in global frame, in the format of: 44 | 45 | ``` 46 | gt = [{ 47 | 'sample_token': '0f0e3ce89d2324d8b45aa55a7b4f8207fbb039a550991a5149214f98cec136ac', 48 | 'translation': [974.2811881299899, 1714.6815014457964, -23.689857123368846], 49 | 'size': [1.796, 4.488, 1.664], 50 | 'rotation': [0.14882026466054782, 0, 0, 0.9888642620837121], 51 | 'name': 'car' 52 | }] 53 | ``` 54 | 55 | `output_dir`: a directory to save the final results. 56 | 57 | I've given a sample `pred_file` and `gt_file` in the `tmp` folder of this repository. 58 | Here's how you can run the evaluation script: 59 | 60 | ``` 61 | python mAP_evaluation.py --gt_file="tmp/gt_data.json" --pred_file="tmp/pred_data.json" --output_dir="tmp/" 62 | ``` 63 | 64 | 65 | After this command finishes, you'll find metric_summary.json file in tmp containing the mAPs of all the iou thresholds as well as the overall mAP. 66 | 67 | 68 | Enjoy! 69 | -------------------------------------------------------------------------------- /license.txt: -------------------------------------------------------------------------------- 1 | Attribution-NonCommercial-ShareAlike 4.0 International 2 | 3 | ======================================================================= 4 | 5 | Creative Commons Corporation ("Creative Commons") is not a law firm and 6 | does not provide legal services or legal advice. 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For 434 | the avoidance of doubt, this paragraph does not form part of the 435 | public licenses. 436 | 437 | Creative Commons may be contacted at creativecommons.org. 438 | -------------------------------------------------------------------------------- /mAP_evaluation.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Parallel 3D mAP calculation for the data in nuScenes format. 3 | 4 | File formats: 5 | 6 | `pred_file`: json file, predictions in global frame, in the format of: 7 | 8 | predictions = [{ 9 | 'sample_token': '0f0e3ce89d2324d8b45aa55a7b4f8207fbb039a550991a5149214f98cec136ac', 10 | 'translation': [971.8343488872263, 1713.6816097857359, -25.82534357061308], 11 | 'size': [2.519726579986132, 7.810161372666739, 3.483438286096803], 12 | 'rotation': [0.10913582721095375, 0.04099572636992043, 0.01927712319721745, 1.029328402625659], 13 | 'name': 'car', 14 | 'score': 0.3077029437237213 15 | }] 16 | 17 | `gt_file`: ground truth annotations in global frame, in the format of: 18 | 19 | 20 | gt = [{ 21 | 'sample_token': '0f0e3ce89d2324d8b45aa55a7b4f8207fbb039a550991a5149214f98cec136ac', 22 | 'translation': [974.2811881299899, 1714.6815014457964, -23.689857123368846], 23 | 'size': [1.796, 4.488, 1.664], 24 | 'rotation': [0.14882026466054782, 0, 0, 0.9888642620837121], 25 | 'name': 'car' 26 | }] 27 | 28 | NOTICE both are lists of dicts (annotations). 29 | 30 | ''' 31 | 32 | import json 33 | import fire 34 | from pathlib import Path 35 | import numpy as np 36 | from multiprocessing import Process 37 | 38 | from lyft_dataset_sdk.eval.detection.mAP_evaluation import get_average_precisions 39 | 40 | 41 | def save_AP(gt, predictions, class_names, iou_threshold, output_dir): 42 | ''' computes average precisions (AP) for a given threshold, and saves the metrics in a temp file ''' 43 | # use lyft's provided function to compute AP 44 | AP = get_average_precisions(gt, predictions, class_names, iou_threshold) 45 | # create a dict with keys as class names and values as their respective APs 46 | metric = {c:AP[idx] for idx, c in enumerate(class_names)} 47 | 48 | # save the dict in a temp file 49 | summary_path = output_dir / f'metric_summary_{iou_threshold}.json' 50 | with open(str(summary_path), 'w') as f: 51 | json.dump(metric, f) 52 | 53 | 54 | def get_metric_overall_AP(iou_th_range, output_dir, class_names): 55 | ''' reads temp files and calculates overall per class APs. 56 | 57 | returns: 58 | `metric`: a dict with key as iou thresholds and value as dicts of class and their respective APs, 59 | `overall_AP`: overall AP of each class 60 | ''' 61 | 62 | metric = {} 63 | overall_AP = np.zeros(len(class_names)) 64 | for iou_threshold in iou_th_range: 65 | summary_path = output_dir / f'metric_summary_{iou_threshold}.json' 66 | with open(str(summary_path), 'r') as f: 67 | data = json.load(f) # type(data): dict 68 | metric[iou_threshold] = data 69 | overall_AP += np.array([data[c] for c in class_names]) 70 | summary_path.unlink() # delete this temp file 71 | overall_AP /= len(iou_th_range) 72 | return metric, overall_AP 73 | 74 | 75 | def main(gt_file, pred_file, output_dir): 76 | ''' 77 | Main function to compute mAP, metrics are saved in `metric_summary.json` file 78 | 79 | args: 80 | gt_file: json file path with ground truth annotations 81 | pred_file: json file path with predicted annotations 82 | output_dir: the final computed metrics are saved in this directory as a json file 83 | ''' 84 | print('Starting mAP computation') 85 | 86 | gt_path = Path(gt_file) 87 | pred_path = Path(pred_file) 88 | output_dir = Path(output_dir) 89 | output_dir.mkdir(parents=True, exist_ok=True) 90 | 91 | with open(str(pred_path)) as f: 92 | predictions = json.load(f) 93 | 94 | with open(str(gt_path)) as f: 95 | gt = json.load(f) 96 | 97 | class_names = ['animal', 'bicycle', 'bus', 'car', 'emergency_vehicle', 98 | 'motorcycle', 'other_vehicle', 'pedestrian', 'truck'] 99 | 100 | iou_th_range = np.linspace(0.5, 0.95, 10) # 0.5, 0.55, ..., 0.90, 0.95 101 | 102 | metric = {} 103 | 104 | # create and start parallel processes 105 | processes = [] 106 | for iou_threshold in iou_th_range: 107 | process = Process(target=save_AP, args=(gt, predictions, class_names, iou_threshold, output_dir)) 108 | process.start() 109 | processes.append(process) 110 | 111 | for process in processes: 112 | process.join() 113 | 114 | # get overall metrics 115 | metric, overall_AP = get_metric_overall_AP(iou_th_range, output_dir, class_names) 116 | metric['overall'] = {c: overall_AP[idx] for idx, c in enumerate(class_names)} 117 | metric['mAP'] = np.mean(overall_AP) 118 | 119 | summary_path = Path(output_dir) / 'metric_summary.json' 120 | with open(str(summary_path), 'w') as f: 121 | json.dump(metric, f, indent=4) 122 | 123 | print(f'Done!, Final metrics saved at {str(summary_path)}') 124 | 125 | if __name__ == "__main__": 126 | fire.Fire(main) 127 | 128 | -------------------------------------------------------------------------------- /tmp/parallel.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pyaf/parallel_mAP_evaluation/224cc02914009789ca4c1461b070982b72c03564/tmp/parallel.png -------------------------------------------------------------------------------- /tmp/single.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pyaf/parallel_mAP_evaluation/224cc02914009789ca4c1461b070982b72c03564/tmp/single.png --------------------------------------------------------------------------------