1、训练

python tools/train.py configs/fcos/fcosrdweed3.py  

2、测试
这一步要加–out=result.pkl，才能计算准确率和召回率

python tools/test.py configs/fcos/fcosrddweed3.py work_dirs/fcosrddweed3/epoch_300.pth --out=resultfcos.pkl

3、计算准确率和召回率
在tools/analysis_tools/confusion_matrix.py代码下面加上：

	TP = np.diag(confusion_matrix)
    FP = np.sum(confusion_matrix, axis=0) - TP
    FN = np.sum(confusion_matrix, axis=1) - TP

    precision = TP / (TP + FP)
    recall = TP / (TP + FN)
    average_precision = np.mean(precision)
    average_recall = np.mean(recall)
    f1 = 2 * (average_precision * average_recall) / (average_precision + average_recall)
    #print("AP ", average_precision)
    #print("AR", average_recall)
    #print("F1", f1)
    print("P",precision)
    print("R",recall)

运行：

python tools/analysis_tools/confusion_matrix.py configs/fcos/fcosrddweed3.py resultfcos.pkl  ./ 

拿下：