技術標籤：深度學習實戰

mmdetection很好很強大，但是在測試完畢儲存pkl結果，並沒有繪製PR曲線的指令碼。 需要我們自己編寫，經研究需要藉助COCO的API

參考文獻：https://zhuanlan.zhihu.com/p/60707912

執行

本指令碼是在mmdetection2.0執行測試後，從測試結果pkl檔案裡，通過COCO API抽取PR繪圖資料。
直接把繪圖指令碼放在mmdetection根目錄下，設定好路徑，執行即可。

程式碼

直接上程式碼
github：https://github.com/xuhuasheng/mmdetection_plot_pr_curve

# =========================================================
 

# @purpose: plot PR curve by COCO API and mmdet API
# @date：   2020/12
# @version: v1.0
# @author： Xu Huasheng
# @github： https://github.com/xuhuasheng/mmdetection_plot_pr_curve
# =========================================================

import os
import mmcv
import numpy as np
import matplotlib.pyplot as
 
 plt

from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval

from mmcv import Config
from mmdet.datasets import (build_dataset, replace_ImageToTensor)

MODEL = "mask_rcnn"
MODEL_NAME = "mask_rcnn_r50_fpn_1x_coco_senet"

CONFIG_FILE = f"configs/{MODEL}/{MODEL_NAME}.py"
 

RESULT_FILE = f"test_result/{MODEL_NAME}/latest.pkl"

def plot_pr_curve(config_file, result_file, metric="bbox"):
    """plot precison-recall curve based on testing results of pkl file.

        Args:
            config_file (list[list | tuple]): config file path.
            result_file (str): pkl file of testing results path.
            metric (str): Metrics to be evaluated. Options are
                'bbox', 'segm'.
    """
    
    cfg = Config.fromfile(config_file)
    # turn on test mode of dataset
    if isinstance(cfg.data.test, dict):
        cfg.data.test.test_mode = True
    elif isinstance(cfg.data.test, list):
        for ds_cfg in cfg.data.test:
            ds_cfg.test_mode = True

    # build dataset
    dataset = build_dataset(cfg.data.test)
    # load result file in pkl format
    pkl_results = mmcv.load(result_file)
    # convert pkl file (list[list | tuple | ndarray]) to json
    json_results, _ = dataset.format_results(pkl_results)
    # initialize COCO instance
    coco = COCO(annotation_file=cfg.data.test.ann_file)
    coco_gt = coco
    coco_dt = coco_gt.loadRes(json_results[metric]) 
    # initialize COCOeval instance
    coco_eval = COCOeval(coco_gt, coco_dt, metric)
    coco_eval.evaluate()
    coco_eval.accumulate()
    coco_eval.summarize()
    # extract eval data
    precisions = coco_eval.eval["precision"]
    '''
    precisions[T, R, K, A, M]
    T: iou thresholds [0.5 : 0.05 : 0.95], idx from 0 to 9
    R: recall thresholds [0 : 0.01 : 1], idx from 0 to 100
    K: category, idx from 0 to ...
    A: area range, (all, small, medium, large), idx from 0 to 3 
    M: max dets, (1, 10, 100), idx from 0 to 2
    '''
    pr_array1 = precisions[0, :, 0, 0, 2] 
    pr_array2 = precisions[1, :, 0, 0, 2] 
    pr_array3 = precisions[2, :, 0, 0, 2] 
    pr_array4 = precisions[3, :, 0, 0, 2] 
    pr_array5 = precisions[4, :, 0, 0, 2] 
    pr_array6 = precisions[5, :, 0, 0, 2] 
    pr_array7 = precisions[6, :, 0, 0, 2] 
    pr_array8 = precisions[7, :, 0, 0, 2] 
    pr_array9 = precisions[8, :, 0, 0, 2] 
    pr_array10 = precisions[9, :, 0, 0, 2] 

    x = np.arange(0.0, 1.01, 0.01)
    # plot PR curve
    plt.plot(x, pr_array1, label="iou=0.5")
    plt.plot(x, pr_array2, label="iou=0.55")
    plt.plot(x, pr_array3, label="iou=0.6")
    plt.plot(x, pr_array4, label="iou=0.65")
    plt.plot(x, pr_array5, label="iou=0.7")
    plt.plot(x, pr_array6, label="iou=0.75")
    plt.plot(x, pr_array7, label="iou=0.8")
    plt.plot(x, pr_array8, label="iou=0.85")
    plt.plot(x, pr_array9, label="iou=0.9")
    plt.plot(x, pr_array10, label="iou=0.95")

    plt.xlabel("recall")
    plt.ylabel("precison")
    plt.xlim(0, 1.0)
    plt.ylim(0, 1.01)
    plt.grid(True)
    plt.legend(loc="lower left")
    plt.show()

if __name__ == "__main__":
    plot_pr_curve(config_file=CONFIG_FILE, result_file=RESULT_FILE, metric="segm")