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.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
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20250514-en/dataset_info.json

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+{"splits": ["test"]}

20250514-en/test.parquet

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+oid sha256:4dfe8b14147debfc64c3d1e00f09671032defd99b21e3293e34a2dfeb2c6ae13
+size 133444

20250514-zh/dataset_info.json

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+{"splits": ["test"]}

20250514-zh/test.parquet

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+version https://git-lfs.github.com/spec/v1
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evaluations/README.md

@@ -0,0 +1,5 @@
+# Evaluations of SFE
+
+We evaluate the SFE dataset using lmms-eval. The evaluation codes are listed in this folder.
+
+We only provide the evaluation scripts for GPT series and InternVL series. Evaluation scripts for other models follow the same format and can be easily adapted.

evaluations/scripts/chatgpt.sh

@@ -0,0 +1,28 @@
+export HF_HOME=
+export HF_TOKEN=
+export OPENAI_API_KEY=
+export OPENAI_API_BASE=
+
+export API_TYPE="openai"
+export MODEL_VERSION="gpt-4o-2024-11-20"
+
+# export AZURE_OPENAI_API_KEY=""
+# export AZURE_OPENAI_API_BASE=""
+# export AZURE_OPENAI_API_VERSION="2023-07-01-preview"
+
+# pip install git+https://github.com/EvolvingLMMs-Lab/lmms-eval.git
+
+# GPT Series
+export FILE_NAME="lmms_eval_gpt4o_en.json"
+python3 -m lmms_eval \
+    --model openai_compatible \
+    --model_args model_version=gpt-4o-2024-11-20,azure_openai=False \
+    --tasks sfe-en \
+    --batch_size 1
+
+export FILE_NAME="lmms_eval_gpt4o_zh.json"
+python3 -m lmms_eval \
+    --model openai_compatible \
+    --model_args model_version=gpt-4o-2024-11-20,azure_openai=False \
+    --tasks sfe-zh \
+    --batch_size 1

evaluations/scripts/internvl3.sh

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+export HF_HOME=
+export HF_TOKEN=
+export OPENAI_API_KEY=
+export OPENAI_API_BASE=
+
+export API_TYPE="openai"
+export MODEL_VERSION="gpt-4o-2024-11-20"
+
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+
+export VLLM_WORKER_MULTIPROC_METHOD=spawn
+# export NCCL_BLOCKING_WAIT=1
+# export NCCL_TIMEOUT=18000000
+# export NCCL_DEBUG=DEBUG
+
+# pip install git+https://github.com/EvolvingLMMs-Lab/lmms-eval.git
+export FILE_NAME="lmms_eval_internvl3-78b_en.json"
+python3 -m lmms_eval \
+    --model vllm \
+    --model_args model_version=<MODEL_PATH>,tensor_parallel_size=8 \
+    --tasks sfe-en \
+    --batch_size 1 \
+    --log_samples \
+    --log_samples_suffix vllm
+
+export FILE_NAME="lmms_eval_internvl3-78b_zh.json"
+python3 -m lmms_eval \
+    --model vllm \
+    --model_args model_version=<MODEL_PATH>,tensor_parallel_size=8 \
+    --tasks sfe-en \
+    --batch_size 1 \
+    --log_samples \
+    --log_samples_suffix vllm
+

evaluations/tasks/sfe/sfe-en.yaml

@@ -0,0 +1,52 @@
+dataset_path: DATASET_PATH_EN
+task: "sfe-en"
+test_split: test
+output_type: generate_until
+doc_to_visual: !function utils.sfe_doc_to_visual
+doc_to_text: !function utils.sfe_doc_to_text
+doc_to_target: "answer"
+process_results: !function utils.sfe_process_results
+
+generation_kwargs:
+  max_new_tokens: 1024
+
+metric_list:
+  - metric: all_info
+    aggregation: !function utils.sfe_save_results
+    higher_is_better: true
+  - metric: rouge_score
+    aggregation: !function utils.sfe_aggregate_rouge_results
+    higher_is_better: true
+  - metric: bert_score
+    aggregation: !function utils.sfe_aggregate_bertscore_results
+    higher_is_better: true
+  - metric: bleu_score
+    aggregation: !function utils.sfe_aggregate_bleuscore_results
+    higher_is_better: true
+  - metric: meteor_score
+    aggregation: !function utils.sfe_aggregate_meteor_score_results
+    higher_is_better: true
+  - metric: llm_score
+    aggregation: !function utils.sfe_aggregate_judge_results
+    higher_is_better: true
+  - metric: execute_succ_rate
+    aggregation: !function utils.sfe_aggregate_execute_succ_rate_results
+    higher_is_better: true
+  - metric: iou_score
+    aggregation: !function utils.sfe_aggregate_iou_score_results
+    higher_is_better: true
+  - metric: [email protected]
+    aggregation: !function utils.sfe_aggregate_acc01_results
+    higher_is_better: true
+  - metric: [email protected]
+    aggregation: !function utils.sfe_aggregate_acc03_results
+    higher_is_better: true
+  - metric: [email protected]
+    aggregation: !function utils.sfe_aggregate_acc05_results
+    higher_is_better: true
+  - metric: [email protected]
+    aggregation: !function utils.sfe_aggregate_acc07_results
+    higher_is_better: true
+  - metric: [email protected]
+    aggregation: !function utils.sfe_aggregate_acc09_results
+    higher_is_better: true

evaluations/tasks/sfe/sfe-zh.yaml

@@ -0,0 +1,52 @@
+dataset_path: DATASET_PATH_ZH
+task: "sfe-zh"
+test_split: test
+output_type: generate_until
+doc_to_visual: !function utils.sfe_doc_to_visual
+doc_to_text: !function utils.sfe_doc_to_text
+doc_to_target: "answer"
+process_results: !function utils.sfe_process_results
+
+generation_kwargs:
+  max_new_tokens: 1024
+
+metric_list:
+  - metric: all_info
+    aggregation: !function utils.sfe_save_results
+    higher_is_better: true
+  - metric: rouge_score
+    aggregation: !function utils.sfe_aggregate_rouge_results
+    higher_is_better: true
+  - metric: bert_score
+    aggregation: !function utils.sfe_aggregate_bertscore_results
+    higher_is_better: true
+  - metric: bleu_score
+    aggregation: !function utils.sfe_aggregate_bleuscore_results
+    higher_is_better: true
+  - metric: meteor_score
+    aggregation: !function utils.sfe_aggregate_meteor_score_results
+    higher_is_better: true
+  - metric: llm_score
+    aggregation: !function utils.sfe_aggregate_judge_results
+    higher_is_better: true
+  - metric: execute_succ_rate
+    aggregation: !function utils.sfe_aggregate_execute_succ_rate_results
+    higher_is_better: true
+  - metric: iou_score
+    aggregation: !function utils.sfe_aggregate_iou_score_results
+    higher_is_better: true
+  - metric: [email protected]
+    aggregation: !function utils.sfe_aggregate_acc01_results
+    higher_is_better: true
+  - metric: [email protected]
+    aggregation: !function utils.sfe_aggregate_acc03_results
+    higher_is_better: true
+  - metric: [email protected]
+    aggregation: !function utils.sfe_aggregate_acc05_results
+    higher_is_better: true
+  - metric: [email protected]
+    aggregation: !function utils.sfe_aggregate_acc07_results
+    higher_is_better: true
+  - metric: [email protected]
+    aggregation: !function utils.sfe_aggregate_acc09_results
+    higher_is_better: true

evaluations/tasks/sfe/utils.py

@@ -0,0 +1,633 @@
+import ast
+import json
+import os
+import random
+import re
+import time
+from collections import defaultdict
+from pathlib import Path
+import copy
+import math
+
+from PIL import Image
+
+import numpy as np
+import requests
+import yaml
+from loguru import logger as eval_logger
+from openai import AzureOpenAI, OpenAI
+
+from rouge_score import rouge_scorer
+from bert_score import score
+import pymeteor.pymeteor as pymeteor
+
+from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
+from nltk.translate.meteor_score import meteor_score
+
+from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
+
+import torch
+
+NUM_SECONDS_TO_SLEEP = 5
+API_TYPE = os.getenv("API_TYPE", "openai")
+MODEL_VERSION = os.getenv("MODEL_VERSION", "gpt-4o-2024-08-06")
+FILE_NAME = os.getenv("FILE_NAME", "sfe_test.json")
+
+JUDGE_RULES = """You are a strict evaluator assessing answer correctness. You must score the model's prediction on a scale from 0 to 9, where 0 represents an entirely incorrect answer and 9 indicates a highly correct answer.
+# Input
+Question:
+```
+{question}
+```
+Ground Truth Answer:
+```
+{answer}
+```
+Model Prediction:
+```
+{pred}
+```
+
+
+# Evaluation Rules
+- The model prediction may contain the reasoning process, you should spot the final answer from it.
+- For multiple-choice questions: Assign a higher score if the predicted answer matches the ground truth, either by option letters or content. Include partial credit for answers that are close in content.
+- For exact match and open-ended questions:
+  * Assign a high score if the prediction matches the answer semantically, considering variations in format.
+  * Deduct points for partially correct answers or those with incorrect additional information.
+- Ignore minor differences in formatting, capitalization, or spacing since the model may explain in a different way.
+- Treat numerical answers as correct if they match within reasonable precision
+- For questions requiring units, both value and unit must be correct
+
+# Scoring Guide
+Provide a single integer from 0 to 9 to reflect your judgment of the answer's correctness.
+
+# Strict Output format example
+4"""
+
+
+if API_TYPE == "openai":
+    API_URL = os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1")
+    API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")
+    client = OpenAI(base_url=API_URL, api_key=API_KEY)
+elif API_TYPE == "azure":
+    API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken")
+    API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY")
+    client = AzureOpenAI(azure_endpoint=API_URL, api_version="2023-07-01-preview", api_key=API_KEY)
+
+
+scorer = rouge_scorer.RougeScorer(['rougeL'], use_stemmer=True)
+
+
+def get_chat_response(content: str, max_tokens: int, retries: int = 5):
+    global MODEL_VERSION
+    global client
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful and precise assistant for checking the correctness of the answer.",
+        },
+        {"role": "user", "content": content},
+    ]
+
+    payload = {
+        "model": MODEL_VERSION,
+        "messages": messages,
+        "temperature": 0.0,
+        "max_tokens": max_tokens,
+    }
+
+    for attempt in range(retries):
+        try:
+            response = client.chat.completions.create(**payload)
+            content = response.choices[0].message.content.strip()
+            return content
+        except requests.exceptions.RequestException as e:
+            eval_logger.warning(f"Request failed on attempt {attempt+1}: {e}")
+            time.sleep(NUM_SECONDS_TO_SLEEP)
+            if attempt == retries - 1:
+                eval_logger.error(f"Failed to get response after {retries} attempts")
+                return ""
+        except Exception as e:
+            eval_logger.error(f"Error on attempt {attempt+1}: {e}")
+            return ""
+
+
+def parse_float_sequence_within(input_str):
+    pattern_in_bracket = r"\[(.*)\]"
+    match = re.search(pattern_in_bracket, input_str)
+
+    if not match:
+        return None
+
+    inside_str = match.group(1)
+    groups = inside_str.split(";")
+
+    bboxs = []
+    for group in groups:
+        floats = group.split(",")
+        if len(floats) != 4:
+            continue
+        try:
+            bboxs.append([float(f) for f in floats])
+        except Exception as e:
+            continue
+
+    if len(bboxs) == 0:
+        return None
+
+    return bboxs
+
+
+def compute_iou(box1, box2):
+    """
+    Compute the Intersection over Union (IoU) of two bounding boxes.
+
+    Parameters:
+    - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max].
+    - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max].
+
+    Returns:
+    - float: IoU of box1 and box2.
+    """
+    # Determine the coordinates of the intersection rectangle
+    x_left = max(box1[0], box2[0])
+    y_top = max(box1[1], box2[1])
+    x_right = min(box1[2], box2[2])
+    y_bottom = min(box1[3], box2[3])
+
+    # Compute the area of intersection
+    intersection_area = max(0, x_right - x_left) * max(0, y_bottom - y_top)
+
+    # Compute the area of both bounding boxes
+    box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
+    box2_area = (box2[2] - box2[0]) * (box2[3] - box2[1])
+
+    # Compute the area of the union
+    union_area = box1_area + box2_area - intersection_area
+
+    # Compute the Intersection over Union
+    iou = intersection_area / union_area
+
+    return iou
+
+
+def greedy_iou(answers, preds):
+    score = 0.0
+    n_answer, n_pred = len(answers), len(preds)
+    selected = []
+    for pred in preds:
+        if len(selected) == n_answer:
+            break
+        _scores = [compute_iou(answer, pred) if i not in selected else -1 for i, answer in enumerate(answers)]
+        max_index = _scores.index(max(_scores))
+        score += max(_scores)
+        selected.append(max_index)
+
+    return score / n_answer
+
+
+
+def construct_prompt(doc):
+    description = f"You are an expert in {doc['field']} and need to solve the following question."
+    if doc["question_type"] == "mcq":
+        description += "\nThe question is a multiple-choice question. Answer with the option letter from the given choices."
+    elif doc["question_type"] == "exact_match":
+        description += "\nThe question is an exact match question. Answer the question using a single word or phrase."
+    elif doc["question_type"] == "open_ended":
+        description += "\nThe question is an open-ended question. Answer the question using a phrase."
+    else:
+        raise ValueError(f"Unknown question type: {doc['question_type']}")
+
+    question = doc["question"]
+    question = f"{description}\n\n{question}"
+    if doc["question_type"] == "mcq":
+        parsed_options = "\n".join(doc["options"])
+        question = f"{question}\n{parsed_options}"
+    elif doc["question_type"] == "exact_match":
+        question = f"{question}"
+    elif doc["question_type"] == "open_ended":
+        question = f"{question}"
+    else:
+        raise ValueError(f"Unknown question type: {doc['question_type']}")
+
+    return question
+
+
+def sfe_doc_to_text(doc, lmms_eval_specific_kwargs=None):
+    if lmms_eval_specific_kwargs is None:
+        question = construct_prompt(doc)
+    else:
+        question = construct_prompt(doc, lmms_eval_specific_kwargs["multiple_choice_prompt"], lmms_eval_specific_kwargs["open_ended_prompt"], lmms_eval_specific_kwargs["prompt_type"])
+    return question
+
+
+def sfe_doc_to_visual(doc):
+    question = construct_prompt(doc)
+    images = doc["images"]
+    visual = [Image.open(image).convert("RGB") for image in images]
+    return visual
+
+def sfe_doc_to_visual_claude(doc):
+    images = doc["images"]
+    visual = []
+    for image in images:
+        img = Image.open(image).convert("RGB")
+        if max(img.size) > 8000:
+            scale = 8000 / max(img.size)
+            img = img.resize((min(int(img.size[0] * scale), 8000), min(int(img.size[1] * scale), 8000)), Image.LANCZOS)
+        visual.append(img)
+    return visual
+
+
+def sfe_doc_to_visual_doubao(doc):
+    images = doc["images"]
+    visual = []
+    for image in images:
+        img = Image.open(image).convert("RGB")
+        if img.size[0] * img.size[1] > 36000000:
+            scale = 36000000 / (img.size[0] * img.size[1])
+            img = img.resize((math.floor(img.size[0] * scale), math.floor(img.size[1] * scale)), Image.LANCZOS)
+        visual.append(img)
+    return visual
+
+
+def sfe_process_results(doc, results):
+    question_type = doc["question_type"]
+
+    parsed_preds = []
+
+    rough_scores = []
+    bertscore_scores = []
+    bleu_scores = []
+    meteor_scores = []
+    llm_scores = []
+
+    execute_success_rate = []
+    iou_scores = []
+
+    assert len(results) == 1, f"Expected one result, got {len(results)}"
+    for pred in results:
+        formatted_question = construct_prompt(doc)
+        answer = doc["answer"]
+
+        if doc["id"].split("/")[0].lower() in ["e011", "e012"]:
+            answer_bboxs = parse_float_sequence_within(answer)
+            pred_bboxs = parse_float_sequence_within(pred)
+
+            if pred_bboxs is not None:
+                execute_success_rate.append(1)
+                iou_score = greedy_iou(answer_bboxs, pred_bboxs)
+                iou_scores.append(iou_score)
+            else:
+                execute_success_rate.append(0)
+                iou_scores.append(-1)
+            
+            rough_scores.append(-1)
+            bertscore_scores.append(-1)
+            bleu_scores.append(-1)
+            meteor_scores.append(-1)
+            llm_scores.append(-1)
+        else:
+            if question_type == "open_ended":
+                try:
+                    rouge_score = scorer.score(answer, pred)
+                    rough_scores.append(rouge_score["rougeL"].fmeasure)
+                except:
+                    rough_scores.append(0.)
+
+                try:
+                    bertscore = score([answer], [pred], lang="multi", device="cuda" if torch.cuda.is_available() else "cpu")[2].item()
+                    bertscore_scores.append(bertscore)
+                except:
+                    bertscore_scores.append(0.)
+                
+                try:
+                    chencherry = SmoothingFunction()
+                    bleu_score = sentence_bleu([answer.strip().split()], pred.strip().split(), smoothing_function=chencherry.method1)
+                    bleu_scores.append(bleu_score)
+                except:
+                    bleu_scores.append(0.)
+                
+                try:
+                    meteor_score = meteor_score([answer.strip().split()], pred.strip().split())
+                    meteor_scores.append(meteor_score)
+                except:
+                    meteor_scores.append(0.)
+            else:
+                rough_scores.append(-1)
+                bertscore_scores.append(-1)
+                bleu_scores.append(-1)
+                meteor_scores.append(-1)
+
+            # llm_as_a_judge
+            llm_judge_prompt = JUDGE_RULES.format(question=formatted_question, answer=answer, pred=pred)
+            llm_judge_score = get_chat_response(llm_judge_prompt, max_tokens=20, retries=3)
+            llm_scores.append(llm_judge_score)
+
+            execute_success_rate.append(-1)
+            iou_scores.append(-1)
+
+        parsed_preds.append(pred)
+
+    all_info = {
+        "id": doc["id"], 
+        "field": doc["field"], 
+        "question_type": doc["question_type"], 
+        "answer": doc["answer"], 
+        "parsed_pred": parsed_preds, 
+        "rouge_score": rough_scores,
+        "bertscore": bertscore_scores,
+        "bleu_score": bleu_scores,
+        "meteor_score": meteor_scores,
+        "llm_score": llm_scores,
+        "execute_success_rate": execute_success_rate,
+        "iou_score": iou_scores,
+    }
+
+    rouge_score_info = {
+        "id": doc["id"], 
+        "field": doc["field"], 
+        "question_type": doc["question_type"], 
+        "answer": doc["answer"], 
+        "parsed_pred": parsed_preds, 
+        "rouge_score": rough_scores,
+    }
+
+    bert_score_info = {
+        "id": doc["id"], 
+        "field": doc["field"], 
+        "question_type": doc["question_type"], 
+        "answer": doc["answer"], 
+        "parsed_pred": parsed_preds, 
+        "bertscore": bertscore_scores,
+    }
+
+    bleu_score_info = {
+        "id": doc["id"], 
+        "field": doc["field"], 
+        "question_type": doc["question_type"], 
+        "answer": doc["answer"], 
+        "parsed_pred": parsed_preds, 
+        "bleu_score": bleu_scores,
+    }
+
+    meteor_score_info = {
+        "id": doc["id"], 
+        "field": doc["field"], 
+        "question_type": doc["question_type"], 
+        "answer": doc["answer"], 
+        "parsed_pred": parsed_preds, 
+        "meteor_score": meteor_scores,
+    }
+
+    llm_score_info = {
+        "id": doc["id"], 
+        "field": doc["field"], 
+        "question_type": doc["question_type"], 
+        "answer": doc["answer"], 
+        "parsed_pred": parsed_preds, 
+        "llm_score": llm_scores
+    }
+
+    execute_succ_rate_info = {
+        "id": doc["id"], 
+        "field": doc["field"], 
+        "question_type": doc["question_type"], 
+        "answer": doc["answer"], 
+        "parsed_pred": parsed_preds, 
+        "execute_success_rate": execute_success_rate,
+    }
+
+    iou_score_info = {
+        "id": doc["id"], 
+        "field": doc["field"], 
+        "question_type": doc["question_type"], 
+        "answer": doc["answer"], 
+        "parsed_pred": parsed_preds, 
+        "iou_score": iou_scores,
+    }
+
+    return {
+        "all_info": all_info, 
+        "rouge_score": rouge_score_info, 
+        "bert_score": bert_score_info, 
+        "bleu_score": bleu_score_info, 
+        "meteor_score": meteor_score_info, 
+        "llm_score": llm_score_info,
+        "execute_succ_rate": execute_succ_rate_info,
+        "iou_score": iou_score_info,
+        "[email protected]": iou_score_info,
+        "[email protected]": iou_score_info,
+        "[email protected]": iou_score_info,
+        "[email protected]": iou_score_info,
+        "[email protected]": iou_score_info,
+        }
+
+
+def sfe_save_results(results, args):
+    path = os.path.join("/fs-computility/ai4sData/earth-shared/SFE/lmms-eval/examples/sfe/results", FILE_NAME)
+    with open(path, "w") as f:
+        json.dump(results, f)
+    eval_logger.info(f"Results saved to {path}.")
+
+    return 0.0
+
+
+def sfe_aggregate_rouge_results(results, args):
+    total_score = 0
+    total_cnt = 0
+    for result in results:
+        try:
+            score = float(result["rouge_score"][0])
+            if score < 0:
+                continue
+            total_score += score
+            total_cnt += 1
+        except:
+            eval_logger.warning(f"Failed to convert rouge score to float for {result['id']}: {result['rouge_score'][0]}")
+            total_score += 0
+    return total_score / total_cnt if total_cnt > 0 else -1
+
+
+def sfe_aggregate_bertscore_results(results, args):
+    total_score = 0
+    total_cnt = 0
+    for result in results:
+        try:
+            score = float(result["bertscore"][0])
+            if score < 0:
+                continue
+            total_score += score
+            total_cnt += 1
+        except:
+            eval_logger.warning(f"Failed to convert bert score to float for {result['id']}: {result['bertscore'][0]}")
+            total_score += 0
+    return total_score / total_cnt if total_cnt > 0 else -1
+
+
+def sfe_aggregate_bleuscore_results(results, args):
+    total_score = 0
+    total_cnt = 0
+    for result in results:
+        try:
+            score = float(result["bleu_score"][0])
+            if score < 0:
+                continue
+            total_score += score
+            total_cnt += 1
+        except:
+            eval_logger.warning(f"Failed to convert bleu score to float for {result['id']}: {result['bleu_score'][0]}")
+            total_score += 0
+    return total_score / total_cnt if total_cnt > 0 else -1
+
+
+def sfe_aggregate_meteor_score_results(results, args):
+    total_score = 0
+    total_cnt = 0
+    for result in results:
+        try:
+            score = float(result["meteor_score"][0])
+            if score < 0:
+                continue
+            total_score += score
+            total_cnt += 1
+        except:
+            eval_logger.warning(f"Failed to convert meteor score to float for {result['id']}: {result['meteor_score'][0]}")
+            total_score += 0
+    return total_score / total_cnt if total_cnt > 0 else -1
+    
+
+def sfe_aggregate_judge_results(results, args):
+    total_score = 0
+    total_cnt = 0
+    for result in results:
+        try:
+            item_score = result["llm_score"][0]
+            pattern = r"(\d+)"
+            match = re.search(pattern, item_score)
+
+            if match:
+                item_score = float(match.group(1))
+            else:
+                item_score = 0
+
+            total_score += item_score
+            total_cnt += 1
+        except:
+            eval_logger.warning(f"Failed to convert llm score to int for {result['id']}: {result['llm_score']}")
+            total_score += 0
+    return total_score / total_cnt if total_cnt > 0 else -1
+
+
+def sfe_aggregate_execute_succ_rate_results(results, args):
+    total_score = 0
+    total_cnt = 0
+    for result in results:
+        try:
+            score = float(result["execute_success_rate"][0])
+            if score < 0:
+                continue
+            total_score += score
+            total_cnt += 1
+        except:
+            eval_logger.warning(f"Failed to convert execute success score to float for {result['id']}: {result['execute_success_rate'][0]}")
+            total_score += 0
+    return total_score / total_cnt if total_cnt > 0 else -1
+
+
+def sfe_aggregate_iou_score_results(results, args):
+    total_score = 0
+    total_cnt = 0
+    for result in results:
+        try:
+            score = float(result["iou_score"][0])
+            if score < 0:
+                continue
+            total_score += score
+            total_cnt += 1
+        except:
+            eval_logger.warning(f"Failed to convert execute iou score to float for {result['id']}: {result['iou_score'][0]}")
+            total_score += 0
+    return total_score / total_cnt if total_cnt > 0 else -1
+
+
+def sfe_aggregate_acc01_results(results, args):
+    total_score = 0
+    total_cnt = 0
+    for result in results:
+        try:
+            score = 1.0 if float(result["iou_score"][0]) > 0.1 else 0.0
+            if score < 0:
+                continue
+            total_score += score
+            total_cnt += 1
+        except:
+            eval_logger.warning(f"Failed to convert execute iou score to float for {result['id']}: {result['iou_score'][0]}")
+            total_score += 0
+    return total_score / total_cnt if total_cnt > 0 else -1
+
+
+def sfe_aggregate_acc03_results(results, args):
+    total_score = 0
+    total_cnt = 0
+    for result in results:
+        try:
+            score = 1.0 if float(result["iou_score"][0]) > 0.3 else 0.0
+            if score < 0:
+                continue
+            total_score += score
+            total_cnt += 1
+        except:
+            eval_logger.warning(f"Failed to convert execute iou score to float for {result['id']}: {result['iou_score'][0]}")
+            total_score += 0
+    return total_score / total_cnt if total_cnt > 0 else -1
+
+
+def sfe_aggregate_acc05_results(results, args):
+    total_score = 0
+    total_cnt = 0
+    for result in results:
+        try:
+            score = 1.0 if float(result["iou_score"][0]) > 0.5 else 0.0
+            if score < 0:
+                continue
+            total_score += score
+            total_cnt += 1
+        except:
+            eval_logger.warning(f"Failed to convert execute iou score to float for {result['id']}: {result['iou_score'][0]}")
+            total_score += 0
+    return total_score / total_cnt if total_cnt > 0 else -1
+
+
+def sfe_aggregate_acc07_results(results, args):
+    total_score = 0
+    total_cnt = 0
+    for result in results:
+        try:
+            score = 1.0 if float(result["iou_score"][0]) > 0.7 else 0.0
+            if score < 0:
+                continue
+            total_score += score
+            total_cnt += 1
+        except:
+            eval_logger.warning(f"Failed to convert execute iou score to float for {result['id']}: {result['iou_score'][0]}")
+            total_score += 0
+    return total_score / total_cnt if total_cnt > 0 else -1
+
+
+def sfe_aggregate_acc09_results(results, args):
+    total_score = 0
+    total_cnt = 0
+    for result in results:
+        try:
+            score = 1.0 if float(result["iou_score"][0]) > 0.9 else 0.0
+            if score < 0:
+                continue
+            total_score += score
+            total_cnt += 1
+        except:
+            eval_logger.warning(f"Failed to convert execute iou score to float for {result['id']}: {result['iou_score'][0]}")
+            total_score += 0
+    return total_score / total_cnt if total_cnt > 0 else -1

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