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miomio.py

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+"""
+Evaluate Qwen2.5-VL-7B on MCQA-style fire dataset using multiple GPUs for model loading.
+"""
+
+import json
+import re
+import torch
+import os
+import csv
+from PIL import Image
+from tqdm import tqdm
+from transformers import AutoProcessor, AutoModelForVision2Seq
+import logging
+from collections import defaultdict
+from qwen_vl_utils import process_vision_info
+
+# --- configuration ---------------------------------------------------------
+MODEL_ID = "Qwen/Qwen2.5-VL-7B-Instruct"
+DATA_FILE = "/home/muzammal/Projects/Qwen-2.5-VL/qa_dataset_concat_cleaned.json"
+IMAGE_ROOT = "/home/muzammal/Projects/Qwen-2.5-VL/UniFire_11k/UniFire"
+MAX_TOKENS = 12
+LOG_FILE = "missing_images_qwen.log"
+CSV_RESULT_DIM = "vqa_qwen_accuracy_by_dimension.csv"
+CSV_RESULT_SCEN = "vqa_qwen_accuracy_by_scenario.csv"
+
+# --- logging setup --------------------------------------------------------
+logging.basicConfig(filename=LOG_FILE, filemode="w", level=logging.WARNING)
+
+# --- helper functions ------------------------------------------------------
+def build_prompt(question, options):
+    return (
+        "<|image|>\n"
+        "You are given the picture of fire. Answer with the option letter (A, B, C, or D) only from the given choices directly.\n\n"
+        f"Question: {question}\n"
+        f"{options[0]}\n"
+        f"{options[1]}\n"
+        f"{options[2]}\n"
+        f"{options[3]}\n\n"
+        "Answer:"
+    )
+
+def extract_letter(text):
+    m = re.search(r"\b([A-D])\b", text, re.IGNORECASE)
+    return m.group(1).upper() if m else ""
+
+# --- load model & processor with multi-GPU --------------------------------
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,3"  # Use GPU 0,1,3
+
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+model = AutoModelForVision2Seq.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto"
+)
+
+# --- main evaluation loop --------------------------------------------------
+with open(DATA_FILE) as f:
+    dataset = json.load(f)
+
+correct = 0
+results = []
+missing = []
+
+# 统计：按问题维度、场景分类
+dimension_stats = defaultdict(lambda: {"correct": 0, "total": 0}) # 创建默认字典，不用初始化新场景
+scenario_stats = defaultdict(lambda: {"correct": 0, "total": 0}) # 遇到新key，自动初始化为0
+
+total_qa = 0
+
+# Outer evaluation loop
+for scene_path, scene in tqdm(dataset.items(), desc="Evaluating MCQA VQA"):
+    print(f"Processing scene: {scene_path}")
+    image_path = os.path.join(IMAGE_ROOT, scene_path)
+    print(f"Image path: {image_path}")
+
+    try:
+        image = Image.open(image_path).convert("RGB")
+    except Exception as e:
+        logging.warning(f"Missing or unreadable image: {scene_path} - {e}")
+        missing.append(scene_path)
+        continue
+
+    scenario = scene.get("scenario", "Unknown")
+
+    for i, qa in enumerate(scene.get("QA_pairs", [])):
+        qid = f"{scene_path}_{i}"
+        options = qa["options"]
+
+        # Build prompt without <image> tag
+        text_prompt = (
+            "You are given the picture of fire. Answer with the option letter (A, B, C, or D) only from the given choices directly.\n\n"
+            f"Question: {qa['question']}\n"
+            f"{options[0]}\n{options[1]}\n{options[2]}\n{options[3]}\n\nAnswer:"
+        )
+
+        # Build messages with image and text
+        messages = [{
+            "role": "user",
+            "content": [
+                {"type": "image", "image": image},
+                {"type": "text", "text": text_prompt},
+            ]
+        }]
+
+        chat_prompt = processor.apply_chat_template(
+            messages, tokenize=False, add_generation_prompt=True
+        )
+        image_inputs, video_inputs = process_vision_info(messages)
+
+        inputs = processor(
+            text=[chat_prompt],
+            images=image_inputs,
+            videos=video_inputs,
+            return_tensors="pt"
+        ).to(model.device)
+
+        output = model.generate(**inputs, max_new_tokens=MAX_TOKENS)
+        decoded = processor.batch_decode(output, skip_special_tokens=True)[0]
+        assistant_response = decoded.split("assistant\n")[-1].strip() # check the assistant only, not the system message
+
+        pred = extract_letter(assistant_response) # only take the last letter in the "answer" so that the options won't influence the answer extraction
+
+        correct_option = qa["answer"][0].upper()
+        
+        print("="*50)
+        print("[Full decoded output]\n", decoded)
+        print("[Assistant-only response]\n", assistant_response)
+        print("[Extracted Letter]:", pred)
+        print("[Ground Truth]:", correct_option)
+        print("="*50)
+
+
+        is_correct = pred == correct_option
+        correct += is_correct
+        total_qa += 1
+
+        question_text = qa["question"]
+        dimension_stats[question_text]["total"] += 1
+        scenario_stats[scenario]["total"] += 1
+        if is_correct:
+            dimension_stats[question_text]["correct"] += 1
+            scenario_stats[scenario]["correct"] += 1
+
+        results.append({
+            "id": qid,
+            "question": question_text,
+            "scenario": scenario,
+            "gt": correct_option,
+            "pred": pred,
+            "is_correct": is_correct
+        })
+
+
+accuracy = correct / total_qa if total_qa > 0 else 0
+print(f"\nEvaluated {total_qa} QA pairs.")
+print(f"Missing images: {len(missing)} logged in {LOG_FILE}")
+print(f"Overall accuracy: {accuracy:.2%}\n")
+
+# --- write per-dimension accuracy to CSV -----------------------------------
+with open(CSV_RESULT_DIM, mode="w", newline="", encoding="utf-8") as f:
+    writer = csv.writer(f)
+    writer.writerow(["Question Dimension", "Correct", "Total", "Accuracy"])
+    for question, stats in dimension_stats.items():
+        acc = stats["correct"] / stats["total"] if stats["total"] > 0 else 0
+        writer.writerow([question, stats["correct"], stats["total"], f"{acc:.2%}"])
+
+# --- write per-scenario accuracy to CSV ------------------------------------
+with open(CSV_RESULT_SCEN, mode="w", newline="", encoding="utf-8") as f:
+    writer = csv.writer(f)
+    writer.writerow(["Fire Scenario", "Correct", "Total", "Accuracy"])
+    for scenario, stats in scenario_stats.items():
+        acc = stats["correct"] / stats["total"] if stats["total"] > 0 else 0
+        writer.writerow([scenario, stats["correct"], stats["total"], f"{acc:.2%}"])
+
+print(f"Per-dimension accuracy written to {CSV_RESULT_DIM}")
+print(f"Per-scenario accuracy written to {CSV_RESULT_SCEN}")