import math import torch from typing import Callable, List import pandas as pd import json import os from tqdm import tqdm from transformers import AutoTokenizer, AutoModel from utils import * from PIL import Image import shutil from glob import glob import numpy as np from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration, AddedToken from datasets import load_dataset model = InstructBlipForConditionalGeneration.from_pretrained("UBC-NLP/Peacock") processor = InstructBlipProcessor.from_pretrained("UBC-NLP/Peacock") device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) def handle_images1(row: pd.Series) -> List[str]: return [row["image"].convert("RGB")] def handle_images2(row: pd.Series) -> List[str]: return [ row.get(f"image_{i}", None).convert("RGB") for i in range(9) if row.get(f"image_{i}", None) is not None ] def save_images(images: List[str], with_resize: bool = True): for i, image in enumerate(images): if image is None: continue if with_resize: img = image width, height = img.size req_dim = 420 new_width = req_dim if width > height else int((req_dim / height) * width) new_height = int((req_dim / width) * height) if width > height else req_dim img = img.resize((420, 420)) img = img.convert("RGB") img.save(f"temp/image{i}.png") def generate_qwen(prompt: str, images: List[str]) -> str: images = images[:1] save_images(images) inputs = processor(images=Image.open("temp/image0.png").convert("RGB"), text=prompt, return_tensors="pt").to(device) outputs = model.generate( **inputs, do_sample=False, num_beams=1, max_length=256, min_length=2, top_p=0.9, temperature=1, length_penalty=1.0, repetition_penalty=1.5, ) generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip() return generated_text answer_field = "answer" def process_row(row: pd.Series, fn: Callable, fn_images: Callable) -> dict: i, row = row d = {} try: d["index"] = i images = fn_images(row) d["pred_answer"] = generate_qwen(fn(row), images) d["answer"] = str(row[answer_field]) d["question"] = fn(row) print(f"Question: {fn(row)}\nPredicted: {d['pred_answer']}") return d except Exception as e: print(f"Error processing row: {e}") return None name_to_processor = { "mmmu": mmmu_doc_to_text, "mme": mme_doc_to_text, "gqa": gqa_doc_to_text, "realworldqa": realworldqa_doc_to_text, "vqav2": vqav2_doc_to_text, "vizwiz": vizwiz_doc_to_text, "pope": pope_doc_to_text, "countbench": countbench_doc_to_text, "medicalMMMU": medicalMMMU_doc_to_text, "medicalMMMUPro": medicalMMMUPro_doc_to_text, "diagramsMMMU": diagramsMMMU_doc_to_text, "mmbench": mmbench_doc_to_text, "seed": seed_doc_to_text, "medicalmmt": medicalmmt_doc_to_text, "hallucinationmmt": hallucinationmmt_doc_to_text, "vqammt": vqammt_doc_to_text, "mutliimagemmt": mutliimagemmt_doc_to_text, "isidocvqa": isidocvqa_doc_to_text, "patddocvqa": patddocvqa_doc_to_text, "celebvqa": celebvqa_doc_to_text, "countriesvqa": countriesvqa_doc_to_text, "foodvqa": foodvqa_doc_to_text, "objectcoco": objectcoco_doc_to_text, "blink": blink_doc_to_text, "examsv": examsv_doc_to_text, "chartqa": chartqa_doc_to_text, "mtvqa": mtvqa_doc_to_text, "mathvista": mathvista_doc_to_text, "infographicsvqa": infographicsvqa_doc_to_text, "agrovqa": agrovqa_doc_to_text, "diagramsvqa": diagramsvqa_doc_to_text, "tablesvqa": tablesvqa_doc_to_text, "iconqa": iconqa_doc_to_text, "scienceqa": scienceqa_doc_to_text, "ocrisi": ocrisi_doc_to_text, "evarest": evarest_doc_to_text, "historicalbooks": historicalbooks_doc_to_text, "khatt": khatt_doc_to_text, "patsocr": patsocr_doc_to_text, "arabicocr": arabicocr_doc_to_text, "culturevideovqa": culturevideovqa_doc_to_text, "videomme": videomme_doc_to_text, "geochat": geochat_doc_to_text, "muribench": muribench_doc_to_text, } name_to_handle_type = { # "mmmu": handle_images2, # "mme": handle_images1, # "gqa": handle_images1, # "realworldqa": handle_images1, # "vqav2": handle_images1, # "vizwiz": handle_images1, # "pope": handle_images1, # "countbench": handle_images1, # "medicalMMMU": handle_images2, # "medicalMMMUPro": handle_images2, # "diagramsMMMU": handle_images2, # "mmbench": handle_images1, # "seed": handle_images2, "vqammt": handle_images1, "isidocvqa": handle_images1, "patddocvqa": handle_images1, "celebvqa": handle_images1, "countriesvqa": handle_images1, "foodvqa": handle_images1, "objectcoco": handle_images1, "blink": handle_images2, "examsv": handle_images1, "chartqa": handle_images1, "mtvqa": handle_images1, "mathvista": handle_images1, "infographicsvqa": handle_images1, "agrovqa": handle_images1, "diagramsvqa": handle_images1, "tablesvqa": handle_images1, "scienceqa": handle_images1, "geochat": handle_images1, "ocrisi": handle_images1, "evarest": handle_images1, "historicalbooks": handle_images1, "khatt": handle_images1, "patsocr": handle_images1, "hallucinationmmt": handle_images1, "medicalmmt": handle_images1, "arabicocr": handle_images1, # "iconqa": handle_images2, # "culturevideovqa": handle_images2, # "muribench": handle_images2, # "videomme": handle_images2, # "mutliimagemmt": handle_images2, } names = list(name_to_handle_type.keys()) os.makedirs("results", exist_ok=True) os.makedirs("temp", exist_ok=True) for name in tqdm(names): try: ds = load_dataset(f"ahmedheakl/arabicp_{name}", split="train", num_proc=4) except: continue # if os.path.exists(f"results/peacock_{name}.json"): # with open(f"results/peacock_{name}.json", "r", encoding="utf-8") as f: # dd = json.load(f) # if len(dd) >= (len(ds) // 2): continue df = pd.DataFrame(ds) print(f"Evaluating {name} dataset") fn = name_to_processor[name] fn_images = name_to_handle_type[name] results = [] for i in tqdm(range(len(df))): results.append(process_row((i, df.iloc[i]), fn, fn_images)) report = [r for r in results if r is not None] with open(f"results/peacock_{name}.json", "w", encoding="utf-8") as f: json.dump(report, f, ensure_ascii=False, indent=2) shutil.rmtree("temp")