Add evaluation script.

evaluation.py

@@ -0,0 +1,113 @@
+import os, llava, argparse
+import numpy as np
+from mmengine import load, dump
+from tqdm import tqdm
+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".',
+}
+
+QUESTION_POOL = {
+    "instruction": None,
+    "physical_laws": [
+        "Violation of Newton's Law: Objects move without any external force.",
+        "Violation of the Law of Conservation of Mass or Solid Constitutive Law: Objects deform or distort irregularly.",
+        "Violation of Fluid Constitutive Law: Liquids flow in an unnatural or irregular manner.",
+        "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.",
+    ],
+    "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.add_argument(
+        "--judge",
+        type=str,
+        help="Path to judge model checkpoint.",
+    )
+    parser.add_argument(
+        "--video_path",
+        type=str,
+        help="Path to the generated video directory.",
+    )
+    parser.add_argument(
+        "--save_path",
+        type=str,
+        help="Path to save evaluation results.",
+    )
+    parser.add_argument("--cot", action="store_true", help="Enable or disable Chain-of-Thought output.")
+    args = parser.parse_args()
+
+    validation_set = load("./worldmodelbench.json")
+    save_name = args.judge.replace("/", "_")
+    if args.cot:
+        save_name += "_cot"
+    results = None
+    if os.path.exists(save_name):
+        results = load(save_name)
+        preds = results["preds"]
+        accs = results["accs"]
+    else:
+        model = llava.load(args.judge)
+        video_root = args.video_path
+
+        preds = dict()
+        accs = defaultdict(list)
+        for vid, v_i in tqdm(enumerate(validation_set), total=len(validation_set)):
+            video_name = v_i["first_frame"].split("/")[-1].split(".")[0]
+            video = os.path.join(video_root, video_name + ".mp4")
+            video = llava.Video(video)
+            ## traverse criterions
+            for k in ["instruction", "physical_laws", "commonsense"]:
+                preds_i = []
+                prompt_template = PROMPT_TEMPLATES[k]
+                qs = QUESTION_POOL[k]
+                if qs is not None:
+                    accs_i = []
+                    for q in qs:
+                        if k == "physical_laws":
+                            text_prompt = prompt_template.format(physical_laws=k.lower())
+                        else:
+                            text_prompt = prompt_template.format(commonsense=k.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")
+                        pred = model.generate_content([video, text_prompt])
+                        preds_i.append(pred)
+                        accs_i.append("no" in pred.lower())
+                    accs[k].append(np.mean(accs_i))
+                else:
+                    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")
+                    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)
+                if video_name not in preds:
+                    preds[video_name] = dict()
+                preds[video_name][k] = preds_i
+    ## Print results
+    for k, v in accs.items():
+        if isinstance(v, list):
+            print(f"{k} accuracy: {np.mean(v) * 100}%.")
+        else:
+            print(f"{k} accuracy: {v}%.")
+    if results is None:
+        results = {"preds": preds, "accs": accs}
+        dump(results, f"./{save_name}.json", indent=4)
+