fix(evaluator): fix attr bug in COCOEvaluator (#1067)

tools/eval.py

@@ -143,7 +143,7 @@ def main(exp, args, num_gpu):
     logger.info("Model Structure:\n{}".format(str(model)))
 
     evaluator = exp.get_evaluator(args.batch_size, is_distributed, args.test, args.legacy)
-    evaluator.per_class_mAP = True
+    evaluator.per_class_AP = True
     evaluator.per_class_AR = True
 
     torch.cuda.set_device(rank)

yolox/evaluators/coco_evaluator.py

@@ -50,8 +50,8 @@ def per_class_AR_table(coco_eval, class_names=COCO_CLASSES, headers=["class", "A
     return table
 
 
-def per_class_mAP_table(coco_eval, class_names=COCO_CLASSES, headers=["class", "AP"], colums=6):
-    per_class_mAP = {}
+def per_class_AP_table(coco_eval, class_names=COCO_CLASSES, headers=["class", "AP"], colums=6):
+    per_class_AP = {}
     precisions = coco_eval.eval["precision"]
     # dimension of precisions: [TxRxKxAxM]
     # precision has dims (iou, recall, cls, area range, max dets)
@@ -63,10 +63,10 @@ def per_class_mAP_table(coco_eval, class_names=COCO_CLASSES, headers=["class", "
         precision = precisions[:, :, idx, 0, -1]
         precision = precision[precision > -1]
         ap = np.mean(precision) if precision.size else float("nan")
-        per_class_mAP[name] = float(ap * 100)
+        per_class_AP[name] = float(ap * 100)
 
-    num_cols = min(colums, len(per_class_mAP) * len(headers))
-    result_pair = [x for pair in per_class_mAP.items() for x in pair]
+    num_cols = min(colums, len(per_class_AP) * len(headers))
+    result_pair = [x for pair in per_class_AP.items() for x in pair]
     row_pair = itertools.zip_longest(*[result_pair[i::num_cols] for i in range(num_cols)])
     table_headers = headers * (num_cols // len(headers))
     table = tabulate(
@@ -89,7 +89,7 @@ class COCOEvaluator:
         nmsthre: float,
         num_classes: int,
         testdev: bool = False,
-        per_class_mAP: bool = False,
+        per_class_AP: bool = False,
         per_class_AR: bool = False,
     ):
         """
@@ -100,7 +100,8 @@ class COCOEvaluator:
             confthre: confidence threshold ranging from 0 to 1, which
                 is defined in the config file.
             nmsthre: IoU threshold of non-max supression ranging from 0 to 1.
-            per_class_mAP: Show per class mAP during evalution or not. Default to False.
+            per_class_AP: Show per class AP during evalution or not. Default to False.
+            per_class_AR: Show per class AR during evalution or not. Default to False.
         """
         self.dataloader = dataloader
         self.img_size = img_size
@@ -108,7 +109,8 @@ class COCOEvaluator:
         self.nmsthre = nmsthre
         self.num_classes = num_classes
         self.testdev = testdev
-        self.per_class_mAP = per_class_mAP
+        self.per_class_AP = per_class_AP
+        self.per_class_AR = per_class_AR
 
     def evaluate(
         self,
@@ -278,8 +280,8 @@ class COCOEvaluator:
             with contextlib.redirect_stdout(redirect_string):
                 cocoEval.summarize()
             info += redirect_string.getvalue()
-            if self.per_class_mAP:
-                info += "per class mAP:\n" + per_class_mAP_table(cocoEval) + "\n"
+            if self.per_class_AP:
+                info += "per class AP:\n" + per_class_AP_table(cocoEval) + "\n"
             if self.per_class_AR:
                 info += "per class AR:\n" + per_class_AR_table(cocoEval) + "\n"
             return cocoEval.stats[0], cocoEval.stats[1], info