Create eval_peacock.py

eval_peacock.py

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+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")