Update src/localization_eval.py

src/localization_eval.py

@@ -9,16 +9,6 @@ from pycocotools.coco import COCO
 from pycocotools.cocoeval import COCOeval
 
 
-import logging
-
-logging.basicConfig(level=logging.INFO) # Configure the root logger
-
-logging.basicConfig(
-    format='%(asctime)s - %(levelname)s - %(message)s',
-    level=logging.INFO
-)
-
-
 class Localization:
     """
     Original Localization class from EvalAI
@@ -41,11 +31,8 @@ class Localization:
         """
         print("In compute_coco_metrics")
         # Load ground truth and submission annotations
-        logging.info(f"gt_annotations : {gt_annotations}\tsub_annotations : {sub_annotations}")
         coco_gt = COCO(gt_annotations)
         coco_sub = coco_gt.loadRes(sub_annotations)
-
-        logging.info(f"COCO GT : {coco_gt}\tCOCO SUB : {coco_sub}")
         
         bbox_mAP = 0.0
         bbox_AP50 = 0.0
@@ -62,8 +49,6 @@ class Localization:
             # CORRECTED: stats[0] is mAP, stats[1] is AP50
             bbox_mAP = coco_eval_bbox.stats[0]
             bbox_AP50 = coco_eval_bbox.stats[1]
-
-            logging.info(f"In [IF self.eval_bbox] condition : Calculated bbox_mAP : {bbox_mAP} and bbox_AP50 : {bbox_AP50}")
         
         if self.eval_segm:
             # Instance Segmentation Evaluation
@@ -75,8 +60,6 @@ class Localization:
             segm_mAP = coco_eval_mask.stats[0]
             segm_AP50 = coco_eval_mask.stats[1]
         
-            logging.info(f"In [IF self.eval_segm] condition : Calculated bbox_mAP : {bbox_mAP} and bbox_AP50 : {bbox_AP50}")
-            
         return bbox_mAP, bbox_AP50, segm_mAP, segm_AP50
 
 
@@ -97,7 +80,6 @@ def evaluate_submission(ground_truth_path, predictions_path):
             predictions = json.load(f)
         
         print("Loaded Predictions file")
-        logging.info(f" [localization_eval.py] Loaded Predictions File")
         
         if not isinstance(predictions, list) or len(predictions) == 0:
             return None
@@ -105,26 +87,22 @@ def evaluate_submission(ground_truth_path, predictions_path):
         # Check required fields
         required_fields = ['image_id', 'category_id', 'bbox', 'score']
         if not all(field in predictions[0] for field in required_fields):
-            logging.info(f"Required Fields not found")
             return None
         print(f"Required Fields Checked")
         
         # Run evaluation
-        logging.info("Initiating Localization Instance....")
         evaluator = Localization(eval_bbox=True, eval_segm=False)
-        logging.info("Evaluation Object Instantiated\nStatring COCO metrics computation")
+        print("Evaluation Object Instantiated")
         bbox_mAP, bbox_AP50, segm_mAP, segm_AP50 = evaluator.compute_coco_metrics(
             ground_truth_path,
             predictions_path
         )
-        logging.info("COCO Metrics computation completed")
-        logging.info(f"bbox_mAP, bbox_AP50, segm_mAP, segm_AP50 : {bbox_mAP, bbox_AP50, segm_mAP, segm_AP50}")
         print(f"bbox score calculated : {bbox_mAP}")
         # Convert mAP to percentage (0-100 scale)
         score = round(bbox_mAP * 100, 2)
         print("Returning Result")
-        return score
+        return float(bbox_mAP), float(bbox_AP50), segm_mAP, segm_AP50
         
     except Exception as e:
-        logging.info(f"Evaluation error: {e}")
+        print(f"Evaluation error: {e}")
         return None