Update.

evaluation.py

@@ -8,7 +8,7 @@ from collections import defaultdict
 PROMPT_TEMPLATES = {
     "instruction": "Evaluate if this video follows the instruction: '{instruction}'. Use the following scoring criteria:\n\n- 0: The video does not follow the instruction at all.\n- 1: The video includes the correct object but performs the wrong action, or vice versa.\n- 2: The video follows the instruction and shows a tendency toward the intended action but does not fully achieve the goal.\n- 3: The video follows the instruction precisely and successfully achieves the intended goal.\n\nLet's analyze step-by-step and conclude with 'Score: [score]'.",
     "physical_laws": 'Watch the video and determine if it shows any \'{physical_laws}\' Let\'s think step-by-step and conclude with "Yes" or "No".',
-    "commonsense": 'Does the video exhibit \'{commonsense}\'? Let\'s think step-by-step and conclude with "Yes" or "No".',
+    "common_sense": 'Does the video exhibit \'{common_sense}\'? Let\'s think step-by-step and conclude with "Yes" or "No".',
 }
 
 QUESTION_POOL = {
@@ -20,14 +20,16 @@ QUESTION_POOL = {
         "Violation of Non-physical Penetration: Objects unnaturally pass through each other.",
         "Violation of Gravity: Objects behave inconsistently with gravity, such as floating in the air.",
     ],
-    "commonsense": [
+    "common_sense": [
         "Poor Aesthetics: Visually unappealing or low-quality content.",
         "Temporal Inconsistency: Noticeable flickering, choppy motion, or abrupt appearance/disappearance of irrelevant objects.",
     ],
 }
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Script for evaluating the WorldModelBenchmark.")
+    parser = argparse.ArgumentParser(
+        description="Script for evaluating the WorldModelBenchmark."
+    )
     parser.add_argument(
         "--judge",
         type=str,
@@ -43,7 +45,9 @@ if __name__ == "__main__":
         type=str,
         help="Path to save evaluation results.",
     )
-    parser.add_argument("--cot", action="store_true", help="Enable or disable Chain-of-Thought output.")
+    parser.add_argument(
+        "--cot", action="store_true", help="Enable or disable Chain-of-Thought output."
+    )
     args = parser.parse_args()
 
     validation_set = load("./worldmodelbench.json")
@@ -70,7 +74,7 @@ if __name__ == "__main__":
                 continue
             video = llava.Video(video)
             ## Traverse criterions
-            for k in ["instruction", "physical_laws", "commonsense"]:
+            for k in ["instruction", "physical_laws", "common_sense"]:
                 preds_i = []
                 prompt_template = PROMPT_TEMPLATES[k]
                 qs = QUESTION_POOL[k]
@@ -78,42 +82,76 @@ if __name__ == "__main__":
                     accs_i = []
                     for q in qs:
                         if k == "physical_laws":
-                            text_prompt = prompt_template.format(physical_laws=q.lower())
+                            text_prompt = prompt_template.format(
+                                physical_laws=q.lower()
+                            )
                         else:
-                            text_prompt = prompt_template.format(commonsense=q.lower())
+                            text_prompt = prompt_template.format(common_sense=q.lower())
                         if not args.cot:
                             text_prompt = text_prompt.replace(
-                                "Let's think step-by-step and conclude with", "Answer with"
-                            ).replace("Let's analyze step-by-step and conclude with", "Answer with")
+                                "Let's think step-by-step and conclude with",
+                                "Answer with",
+                            ).replace(
+                                "Let's analyze step-by-step and conclude with",
+                                "Answer with",
+                            )
                         pred = model.generate_content([video, text_prompt])
                         preds_i.append(pred)
                         ## Always ask for violations, so a "No" is preferred!
                         accs_i.append("no" in pred.lower())
-                    accs[k].append(np.mean(accs_i))
+                    accs[k].extend(accs_i)
                 else:
-                    text_prompt = prompt_template.format(instruction=v_i["text_instruction"])
+                    text_prompt = prompt_template.format(
+                        instruction=v_i["text_instruction"]
+                    )
                     if not args.cot:
                         text_prompt = text_prompt.replace(
                             "Let's think step-by-step and conclude with", "Answer with"
-                        ).replace("Let's analyze step-by-step and conclude with", "Answer with")
+                        ).replace(
+                            "Let's analyze step-by-step and conclude with",
+                            "Answer with",
+                        )
                     pred = model.generate_content([video, text_prompt])
                     preds_i.append(pred)
                     try:
                         score = float(pred.split(":")[-1].strip(" ."))
                     except:
                         score = 0
-                    accs[k].append(score / 3)
+                    accs[k].append(score)
                 if video_name not in preds:
                     preds[video_name] = dict()
                 preds[video_name][k] = preds_i
+    ## Save results
+    # if results is None:
+    #     results = {"preds": preds, "accs": accs}
+    #     dump(results, f"./{args.save_name}.json", indent=4)
     ## Print results
+    num_insts = len(preds)
+    total_score = 0
     for k, v in accs.items():
-        if isinstance(v, list):
-            print(f"{k} accuracy: {np.mean(v) * 100}%.")
+        print(k + " details:")
+        num_sub = len(v) // num_insts
+        if num_sub == 1:
+            print(f"-- overall score: {np.mean(v):.2f}.")
+            total_score += np.mean(v)
+        elif num_sub == 2:
+            sub_scores = []
+            for i, sub in enumerate(["framewise", "temporal"]):
+                print(f"-- {sub} score: {np.mean(v):.2f}.")
+                sub_scores.append(np.mean(v))
+            print(f"-- overall score: {np.mean(sub_scores):.2f}.")
+            total_score += np.mean(sub_scores)
+        elif num_sub == 5:
+            sub_scores = []
+            for i, sub in enumerate(
+                ["newton", "mass", "fluid", "penetration", "gravity"]
+            ):
+                print(f"-- {sub} score: {np.mean(v):.2f}.")
+                sub_scores.append(np.mean(v))
+            print(f"-- overall score: {np.mean(sub_scores):.2f}.")
+            total_score += np.mean(sub_scores)
         else:
-            print(f"{k} accuracy: {v}%.")
-    ## Save results
-    if results is None:
-        results = {"preds": preds, "accs": accs}
-        dump(results, f"./{args.save_name}.json", indent=4)
+            raise ValueError("Unexpected number of subcategories!")
+
+    print(f"\ntotal score: {total_score:.2f}.")