///得到训练模型在测试集上的检测结果,结果保存在results.pkl文件中
python tools/test.py configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py work_dirs/1/epoch_147.pth --out results.pkl///得到行归一化后的混淆矩阵,
python tools/analysis_tools/confusion_matrix.py configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py ./results.pkl ./  --show//precise recall ACC等可基于混淆矩阵算出来///绘制PR曲线图
在根目录下创建PR.py文件，复制下列代码
=======================================================import os
import mmcv
import numpy as np
import matplotlib.pyplot as pltfrom pycocotools.coco import COCO
from pycocotools.cocoeval import COCOevalfrom mmcv import Config
from mmdet.datasets import build_datasetdef 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 datasetif isinstance(cfg.data.test, dict):cfg.data.test.test_mode = Trueelif isinstance(cfg.data.test, list):for ds_cfg in cfg.data.test:ds_cfg.test_mode = True# build datasetdataset = build_dataset(cfg.data.test)# load result file in pkl formatpkl_results = mmcv.load(result_file)# convert pkl file (list[list | tuple | ndarray]) to jsonjson_results, _ = dataset.format_results(pkl_results)# initialize COCO instancecoco = COCO(annotation_file=cfg.data.test.ann_file)coco_gt = cocococo_dt = coco_gt.loadRes(json_results[metric])# initialize COCOeval instancecoco_eval = COCOeval(coco_gt, coco_dt, metric)coco_eval.evaluate()coco_eval.accumulate()coco_eval.summarize()# extract eval dataprecisions = coco_eval.eval["precision"]'''precisions[T, R, K, A, M]T: iou thresholds [0.5 : 0.05 : 0.95], idx from 0 to 9R: recall thresholds [0 : 0.01 : 1], idx from 0 to 100K: 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 curveplt.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="./configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py", result_file="results.pkl", metric="bbox")

mmdetection 输出各类别Ap值

//mmdetection/mmdet/datasets/coco.py"第353行
def evaluate(self,results,metric='bbox',logger=None,jsonfile_prefix=None,classwise=True,proposal_nums=(100, 300, 1000),iou_thrs=[0.5],metric_items=None):
//测试和训练都会输出类别AP
python tools/analysis_tools/eval_metric.py configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py result.pkl --eval bbox
//如下所示
+----------+-------+----------+-------+----------+-------+
| category | AP    | category | AP    | category | AP    |
+----------+-------+----------+-------+----------+-------+
| weed1    | 0.958 | weed2    | 1.000 | weed3    | 0.957 |
+----------+-------+----------+-------+----------+-------+

///绘制loss收敛曲线
///–work_dirs设置自己模型得到的log.json文件，
///–out后面是保存的位置以及文件名字，可以保存为jpg/png等格式,
/// –key后面可以跟更多参数，但这些参数要在json文件里面有的python tools/analysis_tools/analyze_logs.py plot_curve ./work_dirs/cascade_rcnn_r50_fpn_1x_coco/20210510_140522.log.json --keys loss --out ./plot_result/1.png

• fps参数

• fps代表的是每秒检测的帧数，也就是推理速度，数值越高代表处理的速度越快，在工程应用中是需要考虑的
  首先将训练好的pth文件放入checkpoints文件夹中，之后会使用到benchmark.py这个文件（使用更高版本时候可能会报错，建议从V2.11拷贝过去），该文件用于记录fps

python ./tools/analysis_tools/benchmark.py ./configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py ./checkpoints/epoch_100.pth