Support new task mmworld (#269)

* support new task mmworld

* Apply linting fixes

lmms_eval/tasks/mmworld/mmworld.yaml

@@ -0,0 +1,37 @@
+dataset_path: MMWorld/MMWorld
+dataset_kwargs:
+  token: True
+  cache_dir: mmworld
+  video: True
+  # From_YouTube: True
+task: mmworld
+test_split: train
+output_type: generate_until
+doc_to_visual: !function utils.mmworld_doc_to_visual
+doc_to_text: !function utils.mmworld_doc_to_text
+doc_to_target: "answer"
+generation_kwargs:
+  max_new_tokens: 16
+  temperature: 0
+  top_p: 1.0
+  num_beams: 1
+  do_sample: false
+# The return value of process_results will be used by metrics
+process_results: !function utils.mmworld_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: mmworld_accuracy
+    aggregation: !function utils.mmworld_aggregate_results
+    higher_is_better: true
+lmms_eval_specific_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "\nAnswer with the option's letter from the given choices directly."
+  gpt4v:
+    pre_prompt: ""
+    post_prompt: "\nAnswer the question with A, B, C, or D."
+  xcomposer2_4khd:
+    pre_prompt: "[UNUSED_TOKEN_146]user\n"
+    post_prompt: " Answer this question with A, B, C, or D.[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"
+metadata:
+  - version: 0.0

lmms_eval/tasks/mmworld/utils.py

@@ -0,0 +1,169 @@
+import datetime
+import json
+import os
+import re
+import shutil
+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
+
+from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
+
+DISCIPLINES = ["Tech & Engineering", "Science", "Health & Medicine", "Sports & Arts", "Game", "Business", "Embodied Tasks"]
+
+
+replace_prompt = " Please answer yes or no."
+
+# with open(Path(__file__).parent / "_default_template_yaml", "r") as f:
+#     raw_data = f.readlines()
+#     safe_data = []
+#     for i, line in enumerate(raw_data):
+#         # remove function definition since yaml load cannot handle it
+#         if "!function" not in line:
+#             safe_data.append(line)
+
+#     config = yaml.safe_load("".join(safe_data))
+
+hf_home = os.getenv("HF_HOME", "~/.cache/huggingface/")
+# cache_dir = os.path.join(hf_home, cache_dir)
+# base_cache_dir = config["dataset_kwargs"]["cache_dir"]
+base_cache_dir = os.path.expanduser(hf_home)
+with open(Path(__file__).parent / "mmworld.yaml", "r") as f:
+    raw_data = f.readlines()
+    safe_data = []
+    for i, line in enumerate(raw_data):
+        # remove function definition since yaml load cannot handle it
+        if "!function" not in line:
+            safe_data.append(line)
+cache_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"]["cache_dir"]
+
+
+def extract_and_remove_subfolders(cache_dir):
+    # Walk through all the subdirectories and move files to the root of cache_dir
+    for root, dirs, files in os.walk(cache_dir):
+        for file in files:
+            source = os.path.join(root, file)
+            destination = os.path.join(cache_dir, file)
+            if source != destination:
+                shutil.move(source, destination)
+
+    for root, dirs, files in os.walk(cache_dir, topdown=False):
+        for dir in dirs:
+            os.rmdir(os.path.join(root, dir))
+
+
+def mmworld_doc_to_visual(doc):
+    cache_dir = os.path.join(base_cache_dir, cache_name)
+    extract_and_remove_subfolders(cache_dir)
+    video_path_doc = doc["video_id"].split("/")[-1] + ".mp4"
+    video_path = os.path.join(cache_dir, video_path_doc).replace(".mp4.mp4", ".mp4")
+
+    if os.path.exists(video_path):
+        video_path = video_path
+    elif os.path.exists(video_path.replace("mp4", "MP4")):
+        video_path = video_path.replace("mp4", "MP4")
+    elif os.path.exists(video_path.replace("mp4", "avi")):
+        video_path = video_path.replace("mp4", "avi")
+    elif os.path.exists(os.path.join(cache_dir, "shorts:" + video_path_doc)):
+        video_path = os.path.join(cache_dir, "shorts:" + video_path_doc)
+    elif os.path.exists(os.path.join(cache_dir, "shorts:" + doc["video_id"].split("/")[-1] + ".MP4")):
+        video_path = os.path.join(cache_dir, "shorts:" + doc["video_id"].split("/")[-1] + doc["video_id"] + ".MP4")
+    elif os.path.exists(os.path.join(cache_dir, "shorts:" + doc["video_id"].split("/")[-1] + ".avi")):
+        video_path = os.path.join(cache_dir, "shorts:" + doc["video_id"].split("/")[-1] + ".avi")
+    else:
+        sys.exit(f"video path:{video_path} does not exist, please check")
+
+    return [video_path]
+
+
+def mmworld_doc_to_text(doc, lmms_eval_specific_kwargs=None):
+    option_prompt = "Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option."
+    question = doc["question"]
+    option = str(doc["options"])
+    question = question + "\n" + option
+    post_prompt = lmms_eval_specific_kwargs["post_prompt"] if "post_prompt" in lmms_eval_specific_kwargs else "The best answer is:"
+    full_prompt = option_prompt + "\n" + question + "\n" + post_prompt
+    return full_prompt
+
+
+def extract_characters_regex(s):
+    s = s.strip()
+    answer_prefixes = [
+        "The best answer is",
+        "The correct answer is",
+        "The answer is",
+        "The answer",
+        "The best option is" "The correct option is",
+        "Best answer:" "Best option:",
+    ]
+    for answer_prefix in answer_prefixes:
+        s = s.replace(answer_prefix, "")
+
+    if len(s.split()) > 10 and not re.search("[ABCD]", s):
+        return ""
+
+    matches = re.search(r"[ABCD]", s)
+    if matches is None:
+        return ""
+    return matches[0]
+
+
+def mmworld_process_results(doc, results):
+    """
+    Args:
+        doc: a instance of the eval dataset
+        results: [pred]
+    Returns:
+        a dictionary with key: metric name (in this case videomme score), value: metric value
+    """
+    pred = results[0]
+    pred_ans = extract_characters_regex(pred)
+    # gt_ans = doc["answer"].lower().strip().replace(".", "")
+
+    discipline = doc["discipline"]
+    data_dict = {"video_id": doc["video_id"], "discipline": discipline, "pred_answer": pred_ans, "answer": doc["correct_answer_label"].upper()}
+
+    return {f"mmworld_accuracy": data_dict}
+
+
+def mmworld_aggregate_results(results):
+    """
+    Args:
+        results: a list of values returned by process_results
+    Returns:
+        A score
+    """
+    category2score = {}
+
+    for category in DISCIPLINES:
+        key = f"{category}"
+        category2score[key] = {"correct": 0, "answered": 0}
+
+    for result in results:
+        category = result["discipline"]
+        key = f"{category}"
+        category2score[key]["answered"] += 1
+        category2score[key]["correct"] += result["pred_answer"] == result["answer"]
+
+    for category in DISCIPLINES:
+        total_correct = 0
+        total_answered = 0
+        for k, v in category2score.items():
+            if category in k:
+                total_correct += v["correct"]
+                total_answered += v["answered"]
+        eval_logger.info(f"Evaluation on DISCIPLINES: {category}: {100 * total_correct / total_answered if total_answered > 0 else 0 : .1f}%")
+
+    total_correct = 0
+    total_answered = 0
+    for k, v in category2score.items():
+        total_correct += v["correct"]
+        total_answered += v["answered"]
+    eval_logger.info(f"Overall Performance: {100 * total_correct / total_answered if total_answered > 0 else 0 : .1f}%")
+    return 100 * total_correct / total_answered if total_answered > 0 else 0