update

competitions/runner.py

@@ -161,14 +161,22 @@ class JobRunner:
             repo_type="dataset",
         )
         final_score = self._calculate_score(evel_result)
-        score = final_score["all"]["psnr"] * 0.4 + final_score["all"]["ssim"] * 0.3 - final_score["all"]["lpips"] * 0.3
+        score = {
+            "score": final_score["all"]["psnr"] / 100 * 0.4 + final_score["all"]["ssim"] * 0.3 + (1 - final_score["all"]["lpips"]) * 0.3,
+            "psnr": final_score["all"]["psnr"],
+            "ssim": final_score["all"]["ssim"],
+            "lpips": final_score["all"]["lpips"],
+        }
+        for key in score.keys():
+            score[key] = np.round(score[key], 3)
+
         submission_api.update_submission_data(
             team_id=team_id,
             submission_id=submission_id,
             data={
                 "status": SubmissionStatus.SUCCESS.value,
                 "final_score": final_score,
-                "score": np.round(score, 3),
+                "score": score,
             }
         )
 

competitions/submissions.py

@@ -82,7 +82,7 @@ class Submissions:
                 "selected": False,
                 "public_score": {},
                 "private_score": {},
-                "score": 0,
+                "score": {},
                 "error_message": "",
                 "hardware": self.hardware,
             }
@@ -150,6 +150,7 @@ class Submissions:
 
         # stringify public_score column
         submissions_df["public_score"] = submissions_df["public_score"].apply(json.dumps)
+        submissions_df["score"] = submissions_df["score"].apply(json.dumps)
 
         if private:
             submissions_df["private_score"] = submissions_df["private_score"].apply(json.dumps)

competitions/utils.py

@@ -557,7 +557,7 @@ class LeaderboardApi:
         """
         all_scores = self._get_all_scores()
         if not all_scores:
-            return pd.DataFrame(columns=["team_id", "team_name", "score"])
+            return pd.DataFrame(columns=["team_id", "team_name", "psnr", "ssim", "lpips", "score"])
 
         df = pd.DataFrame(all_scores)
         df = df.sort_values(by=["score"], ascending=[False])
@@ -594,7 +594,10 @@ class LeaderboardApi:
                 all_scores.append({
                     "team_id": team_id,
                     "team_name": team_metadata[team_id]["name"],
-                    "score": sub["score"],
+                    "psnr": sub["score"]["psnr"],
+                    "ssim": sub["score"]["ssim"],
+                    "lpips": sub["score"]["lpips"],
+                    "score": sub["score"]["score"],
                 })
         return all_scores