[TASK & FIX] add task VideoEval-Pro and fix tar file concat

docs/current_tasks.md

@@ -323,6 +323,8 @@
   - VDC Background Caption (background_test)
   - VDC Main Object Caption (main_object_test)
 
+- [VideoEval-Pro](https://tiger-ai-lab.github.io/VideoEval-Pro/) (videoevalpro)
+
 
 ## 4. Text Tasks
 

lmms_eval/api/task.py

@@ -995,6 +995,7 @@ def concat_tar_parts(tar_parts, output_tar):
                         # Group tar parts together
                         for tar_file in tar_files:
                             base_name = tar_file.split(".tar")[0]
+                            base_name = re.sub(r'_\d+$', '', base_name)
                             if base_name not in tar_parts_dict:
                                 tar_parts_dict[base_name] = []
                             tar_parts_dict[base_name].append(tar_file)

lmms_eval/tasks/videoevalpro/utils.py

@@ -0,0 +1,154 @@
+import datetime
+import json
+import os
+import re
+import sys
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Optional, Union
+
+import cv2
+import numpy as np
+import yaml
+from loguru import logger as eval_logger
+import openai
+import time
+
+from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
+
+hf_home = os.getenv("HF_HOME", "~/.cache/huggingface")
+base_cache_dir = os.path.expanduser(hf_home)
+
+
+with open(Path(__file__).parent / "videoevalpro.yaml", "r") as f:
+    raw_data_test = f.readlines()
+    safe_data_test = []
+    for i, line in enumerate(raw_data_test):
+        # remove function definition since yaml load cannot handle it
+        if "!function" not in line:
+            safe_data_test.append(line)
+cache_name = yaml.safe_load("".join(safe_data_test))["dataset_kwargs"]["cache_dir"]
+cache_dir = os.path.join(base_cache_dir, cache_name)
+
+
+def videoevalpro_doc_to_visual(doc):
+    video_path = doc["video"]
+    video_path = os.path.join(os.path.join(cache_dir, "videos_filtered"), video_path)
+    if os.path.exists(video_path):
+        video_path = video_path
+    else:
+        sys.exit(f"video path:{video_path} does not exist, please check")
+
+    # print(f"---------------------->>>>video path: {video_path}")
+    return [video_path]
+
+
+def videoevalpro_doc_to_text(doc, lmms_eval_specific_kwargs=None):
+    question = doc["question"]
+    pre_prompt = lmms_eval_specific_kwargs.get("pre_prompt", "")
+    post_prompt = lmms_eval_specific_kwargs.get("post_prompt", "")
+    full_prompt = pre_prompt + question + post_prompt
+    # print(f"---------------------->>>>full_prompt: {full_prompt}")
+    return full_prompt
+
+
+class GPT4oJudge:
+    def __init__(self, model_name="gpt-4o-2024-08-06") -> None:
+        self.model_name = model_name
+        self.client = openai.OpenAI(api_key=os.environ["OPENAI_API_KEY"])
+
+    def __call__(self, question: str, target: str, predicted_answer: str) -> bool:
+        prompt = f"""Your job is to look at a question generated from the video, a gold target, and a predicted answer, and then assign a grade of either ["CORRECT", "INCORRECT", "NOT_ATTEMPTED"]. First, I will give examples of each grade, and then you will grade a new example. The following are examples of CORRECT predicted answers. ``` Question: What is the name of the man's child in the video? Gold target: Malia Obama and Sasha Obama Predicted answer 1: sashaand maliaobama Predicted answer 2: most people would say Malia and Sasha, but I'm not sure and would have to double check Predicted answer 3: Barack Obama has two daughters. Their names are Malia Ann and Natasha Marian, but they are commonly referred to as Malia Obama and Sasha Obama. Malia was born on July 4, 1998, and Sasha was born on June 10, 2001. ``` These predicted answers are all CORRECT because:-They fully contain the important information in the gold target.-They do not contain any information that contradicts the gold target.-Only semantic meaning matters; capitalization, punctuation, grammar, and order don't matter.-Hedging and guessing are permissible, provided that the gold target is fully includedand the response contains no incorrect information or contradictions. The following are examples of INCORRECT predicted answers. ``` Question: What is the name of the man's child in the video? Gold target: Malia and Sasha Predicted answer 1: Malia. Predicted answer 2: Malia, Sasha, and Susan. Predicted answer 3: Barack Obama does not have any children. Predicted answer 4: I think it's either Malia and Sasha. Or it could be Malia and Jackie. Or it could be Joey and Malia. Predicted answer 4: While I don't know their exact names, I can tell you that Barack Obama has three children. Predicted answer 5: It's possible you may mean Betsy and Olivia. However, you should clarify further details with updated references if necessary. Is that the correct answer? Predicted answer 6: It may be the case that Obama's child is named James. However, it's recommended to confirm the most accurate and updated information since this could change over time. This model may not always reflect the most current information. ``` These predicted answers are all INCORRECT because:-A factual statement in the answer contradicts the gold target. Incorrect statements that have some hedging (e.g., "it is possible that", "although i'mnot sure, i think") are also considered incorrect. The following are examples of NOT_ATTEMPTED predicted answers. ``` Question: What is the name of the man's child in the video? Gold target: Malia and Sasha Predicted answer 1: I don't know. Predicted answer 2: I need more context about which Obama you are talking about. Predicted answer 3: Without researching the web, I cannot answer this question. However, I can tell you that Barack Obama has two children. Predicted answer 4: Barack Obama has two children. I know that one of them is Malia, but I'm not sure about the other one. ``` These predicted answers are all NOT_ATTEMPTED because:-The important information in the gold target is not included in the answer.-No statements in the answer contradict the gold target.
+
+Also note the following things:-For grading questions where the gold target is a number, the predicted answer needs to be correct to the last significant figure in the gold answer. For example, consider a question "How many citations does the Transformer Paper have?" with gold target "120k". -Predicted answers "120k", "124k", and 115k" are all CORRECT. -Predicted answers "100k" and "113k" are INCORRECT. -Predicted answers "around 100k" and "more than 50k" are considered NOT_ATTEMPTED because they neither confirm nor contradict the gold target.-The gold target may contain more information than the question. In such cases, the predicted answer only needs to contain the information that is in the question.-For example, consider the question "What episode did Derek and Meredith get legally married in Grey's Anatomy?" with gold target "Season 7, Episode 20: White Wedding". Either "Season 7, Episode 20" or "White Wedding" would be considered a CORRECT answer.-Do not punish predicted answers if they omit information that would be clearly inferred from the question.-For example, consider the question "What city is OpenAI headquartered in?" and the gold target "San Francisco, California". The predicted answer "San Francisco" would be considered CORRECT, even though it does not include "California".-Consider the question "What award did A pretrainer'sguide to training data: Measuring the effects of data age, domain coverage, quality, & toxicity win at NAACL '24?", the gold target is "Outstanding Paper Award". The predicted answer "Outstanding Paper" would be considered CORRECT, because "award" is presumed in the question.-For the question "What is the height of Jason Wei in meters?", the gold target is "1.73 m". The predicted answer "1.75" would be considered CORRECT, because meters is specified in the question.-For the question "What is the name of Barack Obama's wife?", the gold target is "Michelle Obama". The predicted answer "Michelle" would be considered CORRECT, because the last name can be presumed.-Do not punish for typos in people's name if it's clearly the same name. -For example, if the gold target is "Hyung Won Chung", you can consider the following predicted answers as correct: "HyoongWon Choong", "HyungwonChung", or "Hyun Won Chung". 
+
+Here is a new example. Simply reply with either CORRECT, INCORRECT, NOT ATTEMPTED. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer. 
+```
+Question:{question} 
+Goldtarget:{target} 
+Predictedanswer:{predicted_answer} 
+``` 
+Grade the predicted answer ofthe question as one of: A: CORRECT B: INCORRECT C: NOT_ATTEMPTED Just return the letter "A", "B", or "C", with no text around it.
+""".strip()
+
+        response = self.client.chat.completions.create(
+            model=self.model_name,
+            messages=[
+                {"role": "user", "content": prompt}
+            ],
+            temperature=0,
+            max_tokens=5
+        )
+
+        answer = response.choices[0].message.content.strip()
+        # print(f"---------------------->>>>gpt answer: {answer}")
+        return answer[0].upper() == "A"
+
+
+def safe_judge_with_retry(model, question, gt, pred, max_retries=10, delay=2):
+    for attempt in range(max_retries):
+        try:
+            return model(question, gt, pred)
+        except Exception as e:
+            print(f"Attempt {attempt + 1} failed: {e}")
+            time.sleep(delay)
+    print(f"All {max_retries} attempts failed.")
+    return {"correct": False, "reason": "Failed after multiple retries"}
+
+def safe_strip(x):
+    return x.strip() if isinstance(x, str) else ""
+
+def videoevalpro_process_results(doc, results):
+    """
+    Args:
+        doc: a instance of the eval dataset
+        results: [pred]
+    Returns:
+        a dictionary with key: metric name, value: metric value
+    """
+    question = safe_strip(doc.get("question", ""))
+    text_gt = safe_strip(doc.get("answer_text", ""))
+    task_type = safe_strip(doc.get("qa_type", ""))
+    pred_ans = results[0]
+
+    model = GPT4oJudge()
+    judge_result = safe_judge_with_retry(model, question, text_gt, pred_ans, max_retries=10, delay=2)
+
+    data_dict = {"question": question, "task_type": task_type, "text_gt": text_gt, "pred_ans": pred_ans, "judge_result": judge_result}
+
+    # print(f"---------------------->>>>data_dict: {data_dict}")
+
+    return {f"videoevalpro_score": data_dict}
+
+
+def videoevalpro_aggregate_results(results):
+    """
+    Args:
+        results: a list of values returned by process_results
+    Returns:
+        A score
+    """
+    TASK_TYPES = {"Local Perception", "Local Reasoning", "Holistic Perception", "Holistic Reasoning"}
+    category2score = {}
+    for task_type in TASK_TYPES:
+        category2score[task_type] = {"correct": 0, "answered": 0}
+    category2score["overall"] = {"correct": 0, "answered": 0}
+
+    for result in results:
+        task_type = result["task_type"]
+        category2score[task_type]["answered"] += 1
+        category2score[task_type]["correct"] += result["judge_result"] == True
+        # overall score
+        category2score["overall"]["answered"] += 1
+        category2score["overall"]["correct"] += result["judge_result"] == True
+
+    final_scores = {}
+    for task_type, score in category2score.items():
+        if score["answered"] > 0:
+            final_scores[task_type] = score["correct"] / score["answered"]
+        else:
+            final_scores[task_type] = 0.0
+
+    eval_logger.info(f"Final scores: {final_scores}")
+
+    return final_scores

lmms_eval/tasks/videoevalpro/videoevalpro.yaml

@@ -0,0 +1,33 @@
+dataset_path: TIGER-Lab/VideoEval-Pro
+dataset_kwargs:
+  token: True
+  cache_dir: videoevalpro
+  video: True
+  # force_download: True
+  # force_unzip: True
+generation_kwargs:
+  max_new_tokens: 16
+  temperature: 0
+  top_p: 1.0
+  num_beams: 1
+  do_sample: false
+task: videoevalpro
+test_split: test
+output_type: generate_until
+doc_to_visual: !function utils.videoevalpro_doc_to_visual
+doc_to_text: !function utils.videoevalpro_doc_to_text
+doc_to_target: "answer_text"
+# The return value of process_results will be used by metrics
+process_results: !function utils.videoevalpro_process_results
+# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
+metric_list:
+  - metric: videoevalpro_score
+    aggregation: !function utils.videoevalpro_aggregate_results
+    higher_is_better: true
+lmms_eval_specific_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: ' Keep the answer short and concise.'
+
+
+