Skip to content

Instantly share code, notes, and snippets.

@Taehun

Taehun/category_per_class.patch

Created July 13, 2021 08:25
Show Gist options
  • Save Taehun/03c7127e0f50a0b27f746ff1ccba66f9 to your computer and use it in GitHub Desktop.
Save Taehun/03c7127e0f50a0b27f746ff1ccba66f9 to your computer and use it in GitHub Desktop.
Download ZIP
Add mAP for category per class to pycocotools
Raw
category_per_class.patch
--- cocoeval.py 2021-07-13 17:17:22.000000000 +0900
+++ cocoeval_new.py 2021-07-13 17:21:29.000000000 +0900
@@ -494,6 +494,157 @@
def __str__(self):
self.summarize()
+
+ # add for per category metric from here
+ def summarize_per_category(self):
+ '''
+ Compute and display summary metrics for evaluation results *per category*.
+ Note this functin can *only* be applied on the default parameter setting
+ '''
+ def _summarize_single_category(ap=1, iouThr=None, categoryId=None, areaRng='all', maxDets=100):
+ p = self.params
+ iStr = ' {:<18} {} @[ CategoryId={:>3d} | IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}'
+ 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]
+ if categoryId is not None:
+ category_index = [i for i, i_catId in enumerate(p.catIds) if i_catId == categoryId]
+ s = s[:,:,category_index,aind,mind]
+ else:
+ 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]
+ if categoryId is not None:
+ category_index = [i for i, i_catId in enumerate(p.catIds) if i_catId == categoryId]
+ s = s[:,category_index,aind,mind]
+ else:
+ 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, catId, iouStr, areaRng, maxDets, mean_s))
+ return mean_s
+
+ def _summarizeDets_per_category():
+ category_stats = np.zeros((12,len(self.params.catIds)))
+ for category_index, category_id in enumerate(self.params.catIds):
+ category_stats[0][category_index] = _summarize_single_category(1,
+ categoryId=category_id)
+ category_stats[1][category_index] = _summarize_single_category(1,
+ iouThr=.5,
+ maxDets=self.params.maxDets[2],
+ categoryId=category_id)
+ category_stats[2][category_index] = _summarize_single_category(1,
+ iouThr=.75,
+ maxDets=self.params.maxDets[2],
+ categoryId=category_id)
+ category_stats[3][category_index] = _summarize_single_category(1,
+ areaRng='small',
+ maxDets=self.params.maxDets[2],
+ categoryId=category_id)
+ category_stats[4][category_index] = _summarize_single_category(1,
+ areaRng='medium',
+ maxDets=self.params.maxDets[2],
+ categoryId=category_id)
+ category_stats[5][category_index] = _summarize_single_category(1,
+ areaRng='large',
+ maxDets=self.params.maxDets[2],
+ categoryId=category_id)
+ category_stats[6][category_index] = _summarize_single_category(0,
+ maxDets=self.params.maxDets[0],
+ categoryId=category_id)
+ category_stats[7][category_index] = _summarize_single_category(0,
+ maxDets=self.params.maxDets[1],
+ categoryId=category_id)
+ category_stats[8][category_index] = _summarize_single_category(0,
+ maxDets=self.params.maxDets[2],
+ categoryId=category_id)
+ category_stats[9][category_index] = _summarize_single_category(0,
+ areaRng='small',
+ maxDets=self.params.maxDets[2],
+ categoryId=category_id)
+ category_stats[10][category_index] = _summarize_single_category(0,
+ areaRng='medium',
+ maxDets=self.params.maxDets[2],
+ categoryId=category_id)
+ category_stats[11][category_index] = _summarize_single_category(0,
+ areaRng='large',
+ maxDets=self.params.maxDets[2],
+ categoryId=category_id)
+ return category_stats
+
+ def _summarizeKps_per_category():
+ category_stats = np.zeros((10,len(self.params.catIds)))
+ for category_index, category_id in self.params.catIds:
+ category_stats[0][category_index] = _summarize_single_category(1,
+ maxDets=20,
+ categoryId=category_id)
+ category_stats[1][category_index] = _summarize_single_category(1,
+ maxDets=20,
+ iouThr=.5,
+ categoryId=category_id)
+ category_stats[2][category_index] = _summarize_single_category(1,
+ maxDets=20,
+ iouThr=.75,
+ categoryId=category_id)
+ category_stats[3][category_index] = _summarize_single_category(1,
+ maxDets=20,
+ areaRng='medium',
+ categoryId=category_id)
+ category_stats[4][category_index] = _summarize_single_category(1,
+ maxDets=20,
+ areaRng='large',
+ categoryId=category_id)
+ category_stats[5][category_index] = _summarize_single_category(0,
+ maxDets=20,
+ categoryId=category_id)
+ category_stats[6][category_index] = _summarize_single_category(0,
+ maxDets=20,
+ iouThr=.5,
+ categoryId= category_id)
+ category_stats[7][category_index] = _summarize_single_category(0,
+ maxDets=20,
+ iouThr=.75,
+ categoryId=category_id)
+ category_stats[8][category_index] = _summarize_single_category(0,
+ maxDets=20,
+ areaRng='medium',
+ categoryId=category_id)
+ category_stats[9][category_index] = _summarize_single_category(0,
+ maxDets=20,
+ areaRng='large',
+ categoryId=category_id)
+ return category_stats
+
+ if not self.eval:
+ raise Exception('Please run accumulate() first')
+ iouType = self.params.iouType
+ if iouType == 'segm' or iouType == 'bbox':
+ summarize_per_category = _summarizeDets_per_category
+ elif iouType == 'keypoints':
+ summarize_per_category = _summarizeKps_per_category
+ self.category_stats = summarize_per_category()
+
+ def __str__(self):
+ self.summarize_per_category()
+ # add for metric per category end here
class Params:
'''
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment