mmdetection中在cocoapi打印每个类别指标

mmdetection中，在使用cocoAPI测试模型时，打印出每个类别的指标并格式化输出（训练时验证可用）：
双击shift搜索 summarize 找到 cocoeval.py 下的 _summarize( ) 函数，结尾添加以下函数：

def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ):
     p = self.params
     iStr = ' {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>4d} ] = {:<8.3f}'
     titleStr = 'Average Precision' if ap == 1 else 'Average Recall'
     typeStr = '(AP)' if ap==1 else '(AR)'
     iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \
         if iouThr is None else '{:0.2f}'.format(iouThr)

     aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]
     mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]
     if ap == 1:
         # dimension of precision: [TxRxKxAxM]
         s = self.eval['precision']
         # IoU
         if iouThr is not None:
             t = np.where(iouThr == p.iouThrs)[0]
             s = s[t]
         s = s[:,:,:,aind,mind]
     else:
         # dimension of recall: [TxKxAxM]
         s = self.eval['recall']
         if iouThr is not None:
             t = np.where(iouThr == p.iouThrs)[0]
             s = s[t]
         s = s[:,:,aind,mind]
     if len(s[s>-1])==0:
         mean_s = -1
     else:
         mean_s = np.mean(s[s>-1])
     # print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))

     # 添加的代码=============================================
     category_dimension = 1 + int(ap)
     if s.shape[category_dimension] > 1:
         iStr += ", per category = ["
         # iStr += ", per category = {}"
         if ap == 1:
             mean_axis = (0, 1)
         else:
             mean_axis = (0,)
         per_category_mean_s = np.mean(s, axis=mean_axis).flatten()
         print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s), end='')
         for i in range(len(per_category_mean_s)):
             if i == len(per_category_mean_s) - 1:
                 end_str = ''
             else:
                 end_str = ', '
             print(f'{per_category_mean_s[i]:<9.3f}', end=end_str)
         print(']')
         # with np.printoptions(precision=3, suppress=True, sign=" ", floatmode="fixed"):
         #     print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s, per_category_mean_s))
     else:
         print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s, ""))
     # 添加的代码=============================================

    return mean_s

再往下翻找到_summarizeDets()函数，并作如下添加：

def _summarizeDets():
    # 添加的代码=============================================
    if len(self.cocoDt.cats) > 1:
        cats = [self.cocoDt.cats[i]['name'] for i in range(len(self.cocoDt.cats))]
        iStr_title = ' {:>78}    , cats name    = ['
        print(iStr_title.format('Mean'), end='')
        for i in range(len(cats)):
            if i == len(cats) - 1:
                end_str = ''
            else:
                end_str = ', '
            print(f'{cats[i]:<9}', end=end_str)
        print(']')
    # 添加的代码=============================================

    stats = np.zeros((12,))
    stats[0] = _summarize(1)
    stats[1] = _summarize(1, iouThr=.5, maxDets=self.params.maxDets[2])
    stats[2] = _summarize(1, iouThr=.75, maxDets=self.params.maxDets[2])
    stats[3] = _summarize(1, areaRng='small', maxDets=self.params.maxDets[2])
    stats[4] = _summarize(1, areaRng='medium', maxDets=self.params.maxDets[2])
    stats[5] = _summarize(1, areaRng='large', maxDets=self.params.maxDets[2])
    stats[6] = _summarize(0, maxDets=self.params.maxDets[0])
    stats[7] = _summarize(0, maxDets=self.params.maxDets[1])
    stats[8] = _summarize(0, maxDets=self.params.maxDets[2])
    stats[9] = _summarize(0, areaRng='small', maxDets=self.params.maxDets[2])
    stats[10] = _summarize(0, areaRng='medium', maxDets=self.params.maxDets[2])
    stats[11] = _summarize(0, areaRng='large', maxDets=self.params.maxDets[2])
    return stats