test_realworldqa_vqa.py

import os
import sys
import argparse
from pathlib import Path
from PIL import Image
from typing import Any
import torch
import torchvision.transforms as T
from datasets import load_dataset
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
os.environ["GRADIO_TEMP_DIR"] = "./tmp"
from jodi_pipeline import JodiPipeline
from model.postprocess import (
    ImagePostProcessor, LineartPostProcessor, EdgePostProcessor, DepthPostProcessor,
    NormalPostProcessor, AlbedoPostProcessor, SegADE20KPostProcessor, OpenposePostProcessor,
)
from transformers import (
    Qwen2VLForConditionalGeneration,
    Qwen2_5_VLForConditionalGeneration,
    Qwen3VLForConditionalGeneration,
    Qwen3VLMoeForConditionalGeneration
)
from transformers import AutoProcessor, Trainer
from pathlib import Path
import itertools
import ast
import re
from PIL import Image
import json
def clean_question(q: str) -> str:
    if not isinstance(q, str):
        q = str(q)
    # 删除 <image 1>、<image1>、<image 2> 等占位符    q = re.sub(r"<\s*image\s*\d+\s*>", "", q, flags=re.IGNORECASE)
    # 再清理多余空白
    q = re.sub(r"\s+", " ", q).strip()
    return q
def dump_image(image, save_root):
    os.makedirs(save_root, exist_ok=True)
    save_path = os.path.join(save_root, "input.jpg")
    image.convert("RGB").save(save_path, format="JPEG", quality=95)    
    return save_path

def concatenate_images(image_paths, save_path, images_per_row=None, image_format="png"):
    """    将多个图像拼接成一张大图并保存。
    Args:        image_paths: List[str] 图像路径列表
        save_path: 保存路径（包括文件名）        images_per_row: 每行图像数量（默认为全部在一行）
        image_format: 保存格式
    """
    from PIL import Image
    import io
    # 读取图像
    images = [Image.open(p).convert("RGB") for p in image_paths]

    if images_per_row is None:
        images_per_row = len(images)

    # 调整尺寸（可选）
    target_size = min(1024, images[0].size[0])
    images = [img.resize((target_size, target_size)) for img in images]

    # 拼接
    widths, heights = zip(*(img.size for img in images))
    max_width = max(widths)
    rows = (len(images) + images_per_row - 1) // images_per_row
    total_height = sum(heights[:images_per_row]) * rows

    new_im = Image.new("RGB", (max_width * images_per_row, total_height))
    y_offset = 0
    for i in range(0, len(images), images_per_row):
        row_imgs = images[i:i + images_per_row]
        x_offset = 0
        for img in row_imgs:
            new_im.paste(img, (x_offset, y_offset))
            x_offset += max_width
        y_offset += heights[0]

    os.makedirs(os.path.dirname(save_path), exist_ok=True)
    new_im.save(save_path, format=image_format.upper())
    print(f"🧩 Saved merged image → {save_path}")
    return save_path


def build_vqa_message(root, prompt, question):
    """
    Build Qwen3-VL message for multimodal or single-image VQA.
    Now explicitly tags each modality image before feeding into Qwen3-VL,
    so that the model can distinguish RGB, edge, depth, normal, etc.
    """

    root_path = Path(root)

    # ---------- 单图像情况 ----------
    if root_path.is_file() and root_path.suffix.lower() in [".jpg", ".jpeg", ".png"]:
        image_path = str(root_path)
        text_prompt = (
            f"You are given one RGB image and a text description of the same scene.\n"
            f"Scene description: \"{prompt}\"\n\n"
            f"Now analyze the image carefully and answer the following question based only on what is visible.\n"
            f"Do NOT guess or add details not supported by the image.\n"
            f"Question: \"{question}\"\n"
            "<image>"
        )
        messages = [
            {
                "role": "user",
                "content": [
                    {"type": "image", "image": image_path},
                    {"type": "text", "text": text_prompt},
                ],
            }
        ]
        return messages

    # ---------- 多模态文件夹情况 ----------
    modality_names = [
        "image",
        "annotation_lineart",
        "annotation_edge",
        "annotation_depth",
        "annotation_normal",
        "annotation_albedo",
        "annotation_seg_12colors",
        "annotation_openpose",
    ]

    # 检查存在的模态文件
    available = []
    for name in modality_names:
        for ext in [".png", ".jpg", ".jpeg"]:
            path = Path(root) / f"{name}{ext}"
            if path.exists():
                available.append((name, str(path)))
                break

    # 可读名称映射
    readable_map = {
        "image": "RGB image",
        "annotation_lineart": "line drawing",
        "annotation_edge": "edge map",
        "annotation_depth": "depth map",
        "annotation_normal": "normal map",
        "annotation_albedo": "albedo map",
        "annotation_seg_12colors": "segmentation map",
        "annotation_openpose": "human pose map",
    }

    present_modalities = [readable_map[n] for n, _ in available]

    # ---------- 指令文本 ----------
    text_prompt = (
        f"You are given multiple visual modalities of the same scene, including: {', '.join(present_modalities)}. "
        f"The **RGB image** is the primary and most reliable modality that truly represents the scene. "
        f"Other modalities (e.g., depth, normal, segmentation) may contain small errors or artifacts, "
        f"so use them only as optional references for additional context. "
        f"Each modality provides complementary information about the same visual content:\n"
        f"- The line drawing highlights object outlines, shapes, and fine structures.\n"
        f"- The edge map emphasizes boundaries and contours.\n"
        f"- The depth map reveals spatial distances, perspective, and 3D relationships.\n"
        f"- The normal map shows surface orientation and geometric curvature.\n"
        f"- The albedo map presents true surface color without illumination or shadows.\n"
        f"- The segmentation map divides the scene into semantic regions and object categories.\n"
        f"- The human pose map indicates body orientation, structure, and articulation.\n\n"
        f"Together, these modalities offer a unified, rich understanding of the scene.\n"
        f"Scene description: \"{prompt}\"\n\n"
        f"Please answer the following question using visual reasoning primarily grounded in the RGB image, "
        f"while cross-checking with other modalities (e.g., edge or depth) when relevant.\n"
        f"If multiple correct answers are possible, choose the most precise and visually supported one.\n\n"
        f"Question: \"{question}\"\n"
    )

    # ---------- 构建内容序列（模态锚定） ----------
    content = []
    for name, path in available:
        readable = readable_map.get(name, "visual input")
        # 在每张图像前显式标注模态类型
        content.append({"type": "text", "text": f"This is the {readable}."})
        content.append({"type": "image", "image": path})

    # 最后加入主指令
    content.append({"type": "text", "text": text_prompt})

    messages = [{"role": "user", "content": content}]
    return messages




def build_multimodal_message(root, coarse_caption="a generic scene", feedback=""):
    """
    Build Qwen3-VL message for multi-modal caption refinement.
    Explicitly binds each image to its modality name (RGB, edge, depth, etc.)
    so Qwen3-VL can reason over them correctly and refine the caption faithfully.
    """

    modality_names = [
        "image",
        "annotation_lineart",
        "annotation_edge",
        "annotation_depth",
        "annotation_normal",
        "annotation_albedo",
        "annotation_seg_12colors",
        "annotation_openpose",
    ]

    # --- 检查存在的模态 ---
    available = []
    for name in modality_names:
        for ext in [".png", ".jpg", ".jpeg"]:
            path = Path(root) / f"{name}{ext}"
            if path.exists():
                available.append((name, str(path)))
                break

    # --- 构建模态说明 ---
    readable_map = {
        "image": "RGB image",
        "annotation_lineart": "line drawing",
        "annotation_edge": "edge map",
        "annotation_depth": "depth map",
        "annotation_normal": "normal map",
        "annotation_albedo": "albedo map",
        "annotation_seg_12colors": "segmentation map",
        "annotation_openpose": "human pose map",
    }

    present_modalities = [readable_map[n] for n, _ in available]

    # --- 构造文本指令 ---

    # --- 构建消息内容：在每个图像前加模态标识 ---
   

    content = []

    text_prompt = ("you are given multiple visual modalities of the same scene, including: {', '.join(present_modalities)}.\n"
    f"Each modality provides a different aspect of visual information about the same scene.\n\n"
    f"### Modality Information:\n"
    f"- **RGB image:** shows colors, textures, lighting, and overall appearance.\n"
    f"- **Line drawing:** reveals outlines, object contours, and structural details.\n"
    f"- **Edge map:** highlights strong edges and object boundaries.\n"
    f"- **Depth map:** encodes per-object spatial distance and perspective. "
    f"For each main object, estimate its approximate physical distance from the camera or ground reference "
    f"in **meters**. "
    f"If multiple objects are visible, provide numeric distances rather than qualitative terms like "
    f"'closer' or 'farther'.\n"
    f"- **Normal map:** provides surface orientation and facing direction.\n"
    f"- **Albedo map:** shows true surface color unaffected by lighting or shadows.\n"
    f"- **Segmentation map:** divides the image into semantic regions and object categories.\n"
    f"- **Human pose map:** depicts human keypoints, poses, and orientations if present.\n\n"
    f"### Your Task:\n"
    f"Refine the coarse caption into a detailed, modality-wise visual description. "
    f"For each available modality listed above, generate one corresponding description paragraph "
    f"based only on what that modality shows.\n\n"
    f"### Rules:\n"
    f"1. Follow the order and modality names given in 'Modality Information'.\n"
    f"2. Start each paragraph with the modality name (e.g., 'RGB image:').\n"
    f"3. Describe only what is visible in that modality—do NOT merge or summarize multiple modalities.\n"
    f"4. Use **numeric distance estimates in meters** for the depth map whenever possible.\n"
    f"5. Use clear and factual language (no imagination or hallucination).\n"
    #f"6. You may use the following feedback for improvement: '{feedback}'\n\n"
    f"### Coarse Caption:\n'{coarse_caption}'\n\n"
    f"Now, according to the 'Modality Information' above, write one detailed description for each available modality below."
    )

    for name, path in available:
        readable = readable_map.get(name, "visual input")
        content.append({
            "type": "text",
            "text": f"This is the {readable}, which provides {get_modality_description(name)}."
        })
        content.append({"type": "image", "image": path})

    # 最后附上总任务说明
    content.append({"type": "text", "text": text_prompt})

    messages = [{"role": "user", "content": content}]
    return messages


def get_modality_description(name: str) -> str:
    """为每个模态生成一句说明，用于提示模型理解模态功能"""
    desc_map = {
        "image": "the main visual appearance of the scene, including color, texture, and lighting",
        "annotation_lineart": "structural outlines, object contours, and fine geometry",
        "annotation_edge": "strong boundaries and contrast edges between objects",
        "annotation_depth": "distance and perspective information for spatial understanding",
        "annotation_normal": "surface orientation and geometric curvature cues",
        "annotation_albedo": "pure surface color without lighting or shading effects",
        "annotation_seg_12colors": "semantic regions and object categories",
        "annotation_openpose": "human body keypoints, joints, and orientation",
    }
    return desc_map.get(name, "complementary visual evidence")




# ------------------------------
# Argument Parser
# ------------------------------
def get_parser():
    parser = argparse.ArgumentParser(description="Run JODI inference without Gradio UI.")
    parser.add_argument("--text_model_path", type=str, default='Qwen/Qwen3-VL-8B-Instruct',
                        help="Path to model checkpoint.")
    parser.add_argument("--config", type=str, default="./configs/inference.yaml", help="Path to config file.")
    parser.add_argument("--model_path", type=str, default='hf://VIPL-GENUN/Jodi/Jodi.pth',
                        help="Path to model checkpoint.")
    parser.add_argument("--model_name_or_path", type=str, default='Qwen/Qwen3-VL-8B-Instruct',
                        help="Path to model checkpoint.")
    parser.add_argument("--data_path", type=str, default="/home/efs/mjw/mjw/dataset/dataset/realworldqa/images",
                        help="Prompt text for generation.")
    parser.add_argument("--json", type=str, default="/home/efs/mjw/mjw/dataset/dataset/realworldqa/annotations.json",
                        help="Optional negative prompt.")
    parser.add_argument("--temp_dir", type=str, default="/home/efs/mjw/mjw/dataset/dataset/tmp",
                        help="Prompt text for generation.")
    parser.add_argument("--negative_prompt", type=str, default="", help="Optional negative prompt.")
    parser.add_argument("--question", type=str, default="how many cars in this image?",
                        help="Optional negative prompt.")
    parser.add_argument("--steps", type=int, default=20, help="Number of inference steps.")
    parser.add_argument("--iters", type=int, default=10, help="Number of inference steps.")
    parser.add_argument("--guidance_scale", type=float, default=4.5)
    parser.add_argument("--seed", type=int, default=41)
    parser.add_argument("--output_dir", type=str, default="./vqa_realworld_outputs", help="Directory to save results.")
    return parser


# ------------------------------
# Main Inference Function
# ------------------------------

@torch.inference_mode()
def init_i2t(model, processor, image_path, iter_num, vqa_id, max_length=300):
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": image_path,
                },
                {"type": "text", "text": f"Describe this image."},
            ],
        }
    ]

    inputs = processor.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True, return_dict=True, return_tensors="pt"
    )
    inputs = inputs.to(model.device)

    # Inference: Generation of the output
    generated_ids = model.generate(**inputs, max_new_tokens=max_length)
    generated_ids_trimmed = [
        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    output_text = processor.batch_decode(
        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
    )
    print(output_text)

    os.makedirs(args.output_dir, exist_ok=True)
    save_dir = Path(args.output_dir) / vqa_id / f"iteration_{iter_num}"
    save_dir.mkdir(parents=True, exist_ok=True)
    caption_path = Path(save_dir) / f"caption.txt"
    with open(caption_path, "w", encoding="utf-8") as f:
        f.write(output_text[0].strip())

    return output_text[0]


@torch.inference_mode()
def evaluate_consistency(image_path, model, processor, caption, max_length=256):

    # --- 构造 Qwen 输入 ---
    eval_prompt = f"""
    You are an image-text alignment evaluator.
    You are given one RGB image and a description that may include references
    to multiple visual modalities (e.g., depth map, normal map, segmentation map, etc.).
    These terms are just analytical perspectives of the same scene — they should not reduce
    the consistency score. Focus only on whether the described visual content matches what
    is visible in the RGB image.
    Your task:
    1. Judge how accurately the text describes what is visually present in the image.
    2. Ignore mentions of modality names (such as 'depth map' or 'normal map').
    3. Provide a consistency score between 0.0 (completely mismatched) and 1.0 (perfect match).
    4. Provide one short feedback sentence suggesting how to make the description better aligned.
    Return JSON strictly in this format:
    {{"Consistency": <float 0-1>, "Feedback": "<sentence>"}}
    Description: "{caption}"
    <image>
    """

    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": image_path},
                {"type": "text", "text": eval_prompt},
            ],
        }
    ]

    # --- 推理 ---
    inputs = processor.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_dict=True,
        return_tensors="pt"
    ).to(model.device)

    out_ids = model.generate(**inputs, max_new_tokens=max_length)
    out_trim = [o[len(i):] for i, o in zip(inputs.input_ids, out_ids)]
    text = processor.batch_decode(out_trim, skip_special_tokens=True)[0]

    # --- 解析输出 ---
    try:
        data = json.loads(re.search(r"\{.*\}", text, re.S).group(0))
        score = float(data.get("Consistency", 0))
        feedback = data.get("Feedback", "")
    except Exception:
        score, feedback = 0.0, text.strip()

    print(f"🧮 [Image Consistency] {score:.3f} | Feedback: {feedback}")
    return score, feedback


@torch.inference_mode()
def text_refine(root, model, processor, prompt, feedback, iter_num, vqa_id, max_length=300):
    messages = build_multimodal_message(root, prompt, feedback)
    inputs = processor.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_dict=True,
        return_tensors="pt"
    )
    inputs = inputs.to(model.device)

    # Inference: Generation of the output
    generated_ids = model.generate(**inputs, max_new_tokens=max_length)
    generated_ids_trimmed = [
        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    output_text = processor.batch_decode(
        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
    )
    print(output_text)

    os.makedirs(args.output_dir, exist_ok=True)
    save_dir = Path(args.output_dir) / vqa_id / f"iteration_{iter_num}"
    save_dir.mkdir(parents=True, exist_ok=True)
    caption_path = Path(save_dir) / f"caption.txt"
    with open(caption_path, "w", encoding="utf-8") as f:
        f.write(output_text[0].strip())
    return output_text[0]

@torch.inference_mode()
def vqa(root, model, processor, prompt, question, vqa_id, max_length=300):
    messages = build_vqa_message(root, prompt, question)
    print(messages)
    inputs = processor.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_dict=True,
        return_tensors="pt"
    )
    inputs = inputs.to(model.device)
    generated_ids = model.generate(**inputs, max_new_tokens=max_length)
    generated_ids_trimmed = [
        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
    output_text = processor.batch_decode(
        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
    )
    print(output_text)
    os.makedirs(args.output_dir, exist_ok=True)
    save_dir = Path(args.output_dir) / vqa_id / 'vqa_answer'
    save_dir.mkdir(parents=True, exist_ok=True)
    caption_path = Path(save_dir) / f"caption.txt"
    with open(caption_path, "w", encoding="utf-8") as f:
        f.write(output_text[0].strip())
    return output_text[0]

@torch.inference_mode()
def image_refine(prompt, images, role, pipe, iter_num, modality_names, generator, height, width, image_id):
    # print(f"🚀 Generating with prompt: {prompt}")
    outputs = pipe(
        images=images,
        role=role,
        prompt=prompt,
        negative_prompt=args.negative_prompt,
        height=height,
        width=width,
        num_inference_steps=args.steps,
        guidance_scale=args.guidance_scale,
        num_images_per_prompt=1,
        generator=generator,
        task='t2i'
    )

    # Apply post-processing for each modality
    results = [post_processors[i](outputs[i]) for i in range(1 + pipe.num_conditions)]
    results = torch.stack(results, dim=1).reshape(-1, 3, height, width)
    results = [T.ToPILImage()(res).convert("RGB") for res in results.unbind(0)]

    # --------------------------
    # Save results
    # --------------------------
    os.makedirs(args.output_dir, exist_ok=True)
    save_dir = Path(args.output_dir) / image_id / f"iteration_{iter_num}"
    save_dir.mkdir(parents=True, exist_ok=True)
    for idx, img in enumerate(results):
        name = modality_names[idx]
        save_path = save_dir / f"{name}.png"
        img.save(save_path)
        print(f"💾 Saved {name} → {save_path}")


    merged_path = save_dir / f"merged_iteration_{iter_num}.png"
    concatenate_images([save_dir / f"{name}.png" for name in modality_names], merged_path)
    print(f"\n✅ All results saved in: {save_dir}\n")
    return save_dir

if __name__ == "__main__":
    args = get_parser().parse_args()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    print(f"✅ Using device: {device}")

    processor = AutoProcessor.from_pretrained(
        args.model_name_or_path,
    )

    model = Qwen3VLForConditionalGeneration.from_pretrained(
        args.text_model_path,
        attn_implementation="flash_attention_2",
        dtype=(torch.bfloat16),
    ).to(device)

    pipe = JodiPipeline(args.config)
    pipe.from_pretrained(args.model_path)

    modality_names = [
        "image",
        "annotation_lineart",
        "annotation_edge",
        "annotation_depth",
        "annotation_normal",
        "annotation_albedo",
        "annotation_seg_12colors",
        "annotation_openpose",
    ]

    # Build post-processors
    post_processors: list[Any] = [ImagePostProcessor()]
    for condition in pipe.config.conditions:  # type: ignore
        if condition == "lineart":
            post_processors.append(LineartPostProcessor())
        elif condition == "edge":
            post_processors.append(EdgePostProcessor())
        elif condition == "depth":
            post_processors.append(DepthPostProcessor())
        elif condition == "normal":
            post_processors.append(NormalPostProcessor())
        elif condition == "albedo":
            post_processors.append(AlbedoPostProcessor())
        elif condition == "segmentation":
            post_processors.append(SegADE20KPostProcessor(color_scheme="colors12", only_return_image=True))
        elif condition == "openpose":
            post_processors.append(OpenposePostProcessor())
        else:
            print(f"⚠️ Warning: Unknown condition: {condition}")
            post_processors.append(ImagePostProcessor())

    torch.manual_seed(args.seed)
    generator = torch.Generator(device=device).manual_seed(args.seed)

    with open(args.json, "r", encoding="utf-8") as f:
            annotations = json.load(f)

    for sample in annotations[1:255]:
        image_path = os.path.join(args.data_path, sample["image"])
        image_id = sample["image"].split('.')[0]
        image = Image.open(image_path)
        question = sample["question"]

        control_images = [image.convert('RGB')] + [None] * pipe.num_conditions

        role = [1] + [0] * pipe.num_conditions
        print(role)

        best_dir, best_caption, best_score = '', '', 0.0
        max_length = 1024

        # input_img = Image.open(image_path).convert("RGB")
        width, height = image.size
        print(f'ori width:{width}', f'ori height:{height}')

        prompt = init_i2t(model, processor, image_path, 0, image_id, max_length)
        score, feedback = evaluate_consistency(image_path, model, processor, prompt)

        if score >= best_score:
            best_caption, best_score = prompt, score
            best_dir = image_path

        for step in range(1, args.iters):
            save_dir = image_refine(prompt, control_images, role, pipe, step, modality_names, generator, height, width,
                                    image_id)
            max_length += 100
            prompt = text_refine(save_dir, model, processor, prompt, feedback, step, image_id, max_length)
            score, feedback = evaluate_consistency(image_path, model, processor, prompt)
            
            #if score >= best_score:
            best_caption, best_score = prompt, score
            best_dir = save_dir

        result = vqa(best_dir, model, processor, best_caption, question, image_id, max_length)
        print(f'result:{result}')