Create get_results.py

get_results.py

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+import json
+import sys
+import numpy as np
+from utils import *
+from tqdm import tqdm
+from pydantic import BaseModel
+from openai import OpenAI
+from typing import List
+import multiprocessing as mp
+from functools import partial
+from glob import glob
+
+
+class AnswerScore(BaseModel):
+    score: int
+
+data_functions = {
+    # medical
+    "medicalmmt": medicalmmt_eval,
+    "medicalMMMU": medicalMMMU_eval,
+    "medicalMMMUPro": medicalMMMU_eval,
+
+    # cultural
+    "celebvqa": celebvqa_eval,
+    "foodvqa": foodvqa_eval,
+    "countriesvqa": countriesvqa_eval,
+
+    # agro
+    "agrovqa": agrovqa_eval,
+
+    # chart and diagrams
+    "iconqa": iconqa_eval,
+    "chartqa": chartqa_eval,
+    "diagramsMMMU": diagramsMMMU_eval,
+    "diagramsvqa": diagramsvqa_eval,
+    "tablesvqa": tablesvqa_eval,
+
+    # video
+    "culturevideovqa": culturevideovqa_eval,
+    "videomme": videomme_eval,
+
+    # ocr
+    "ocrisi": ocrisi_eval,
+    "khatt": khatt_eval,
+    "isidocvqa": isidocvqa_eval,
+    "patddocvqa": patddocvqa_eval,
+    "patsocr": patsocr_eval,
+    "evarest": evarest_eval,
+    "historicalbooks": historicalbooks_eval,
+    "arabicocr": arabicocr_eval,
+    
+    # vqa
+    "mme": mme_eval,
+    "mmbench": mmbench_eval,
+    "vqammt": vqammt_eval,
+    "seed": seed_eval,
+    "mmmu": mmmu_eval,
+    "countbench": countbench_eval,
+    "hallucinationmmt": hallucinationmmt_eval,
+    "pope": pope_eval,
+    "scienceqa": scienceqa_eval,
+    "examsv": examsv_eval,
+    "gqa": gqa_eval,
+    "vizwiz": vizwiz_eval,
+    "infographicsvqa": infographicsvqa_eval,
+    "blink": blink_eval,
+    "realworldqa": realworldqa_eval,
+    "mutliimagemmt": mutliimagemmt_eval,
+    "muribench": muribench_eval,
+    "objectcoco": objectcoco_eval,  
+}
+
+fuzz = {
+    "mtvqa": mtvqa_user_prompt,
+    "geochat": geochat_user_prompt,
+    "mathvista": mathvista_user_prompt,
+    "vqav2": vqav2_user_prompt,
+}
+
+medical_data = ["medicalmmt", "medicalMMMU", "medicalMMMUPro"]
+medical_results = {}
+
+cultural_data = ["celebvqa", "foodvqa", "countriesvqa"]
+cultural_results = {}
+
+agro_data = ["agrovqa"]
+agro_results = {}
+
+charts_data = ["iconqa", "chartqa", "diagramsMMMU", "diagramsvqa", "tablesvqa"]
+charts_results = {}
+
+remotesensing_data = ["geochat"]
+remotesensing_results = {}
+
+video_data = ["culturevideovqa", "videomme"]
+video_results = {}
+
+ocr_data = ["ocrisi", "khatt", "isidocvqa", "patddocvqa", "patsocr", "mtvqa", "evarest", "historicalbooks", "arabicocr"]
+ocr_results = {}
+
+vqa_data = ["mme", "mmbench", "vqammt", "seed", "mmmu", "countbench", "hallucinationmmt", "pope", "mathvista", "scienceqa", "examsv", "gqa", "vizwiz", "vqav2", "infographicsvqa", "blink", "realworldqa", "mutliimagemmt", "muribench", "objectcoco"]
+vqa_results = {}
+
+
+def eval_gpt(row, user_prompt):
+    client = OpenAI(
+        api_key="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"
+    )
+    question = row['question'].split("\n")[0]
+    pred = row['pred_answer']
+    pred = pred.split("assistant\n")[-1].strip()
+    gt = row['answer']
+
+    messages = [
+        {
+            "role": "system",
+            "content": fuzz_eval_system_prompt,
+        },
+        {
+            "role": "user",
+            "content": user_prompt.format(question=question, pred=pred, gt=gt)
+        },
+    ]
+
+    completion = client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=messages,
+        max_tokens=300,
+        tools=[
+            {
+                "type": "function",
+                "function": {
+                    "name": "answer_score",
+                    "description": "Provide a [0, 1] score to the semantic similarity between two sentences",
+                    "parameters": AnswerScore.model_json_schema(),
+                },
+            }
+        ],
+        tool_choice={"type": "function", "function": {"name": "answer_score"}},
+    )
+
+    vqa_answer = AnswerScore.model_validate_json(
+        completion.choices[0].message.tool_calls[0].function.arguments
+    )
+    return {
+        'index': row['index'],
+        'question': question,
+        'pred_answer': pred,
+        'answer': gt,
+        'evaluation': vqa_answer.score
+    }
+
+def process_chunk(user_prompt, chunk):
+    d = []
+    for row in chunk:
+        try:
+            d.append(eval_gpt(row, user_prompt))
+        except Exception as e:
+            print("ERROR", e)
+            continue
+    return d 
+
+def fuzz_eval(user_prompt, data):
+    num_cores = mp.cpu_count()
+    chunk_size = len(data) // num_cores
+    chunks = [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)]
+    pool = mp.Pool(num_cores)
+    results = []
+    process_chunk_f = partial(process_chunk, user_prompt)
+    with tqdm(total=len(data)) as pbar:
+        for chunk_result in pool.imap_unordered(process_chunk_f, chunks):
+            results.extend(chunk_result)
+            pbar.update(len(chunk_result))
+
+    pool.close()
+    pool.join()
+
+    correct_count = sum(1 for item in results if item['evaluation'] == 1)
+    total_count = len(results)
+    return round(correct_count * 100 / total_count, 2)
+
+
+MODEL = "peacock"
+files = glob(f"results/{MODEL}_*.json")
+for file in files:
+    name = file.split(f"_")[-1].replace(".json", "")
+    print(name)
+    with open(file, "r") as f:
+        data = json.load(f)
+    if len(data) == 0: continue
+    
+    accuracy = 0
+    if name in fuzz:
+        accuracy = fuzz_eval(fuzz[name], data)
+    else:
+        tot = 0
+        for r in data:
+            tot += data_functions[name](r["pred_answer"], r["answer"])
+        accuracy = round(tot * 100 / len(data), 2)
+        print(f"{name}: {tot} / {len(data)} -> {accuracy:.2f}")
+
+    if name in medical_data:
+        medical_results[name] = accuracy
+    elif name in cultural_data:
+        cultural_results[name] = accuracy
+    elif name in agro_data:
+        agro_results[name] = accuracy
+    elif name in charts_data:
+        charts_results[name] = accuracy
+    elif name in remotesensing_data:
+        remotesensing_results[name] = accuracy
+    elif name in video_data:
+        video_results[name] = accuracy
+    elif name in ocr_data:
+        ocr_results[name] = accuracy
+    elif name in vqa_data:
+        vqa_results[name] = accuracy
+
+
+from pprint import pprint
+print("\nMedical Results")
+pprint(medical_results)
+if len(medical_results) > 0:
+    print("Medical average:", round(sum(list(medical_results.values())) / len(medical_results), 2))
+
+print("\ncultural Results")
+pprint(cultural_results)
+if len(cultural_results) > 0:
+    print("cultural average:", round(sum(list(cultural_results.values())) / len(cultural_results), 2))
+
+print("\nagro Results")
+pprint(agro_results)
+if len(agro_results) > 0:
+    print("agro average:", round(sum(list(agro_results.values())) / len(agro_results), 2))
+
+print("\ncharts Results")
+pprint(charts_results)
+if len(charts_results) > 0:
+    print("charts average:", round(sum(list(charts_results.values())) / len(charts_results), 2))
+
+print("\nremotesensing Results")
+pprint(remotesensing_results)
+if len(remotesensing_results) > 0:
+    print("remotesensing average:", round(sum(list(remotesensing_results.values())) / len(remotesensing_results), 2))
+
+print("\nvideo Results")
+pprint(video_results)
+if len(video_results) > 0:
+    print("video average:", round(sum(list(video_results.values())) / len(video_results), 2))
+
+print("\nocr Results")
+pprint(ocr_results)
+if len(ocr_results) > 0:
+    print("ocr average:", round(sum(list(ocr_results.values())) / len(ocr_results), 2))
+
+print("\nvqa Results")
+pprint(vqa_results)
+if len(vqa_results) > 0:
+    print("vqa average:", round(sum(list(vqa_results.values())) / len(vqa_results), 2))