test_i2t_coco2.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
import json
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 re

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_multimodal_message(root, coarse_caption="a generic scene", feedback=''):
    """
    Build Qwen3-VL message for multi-modal caption refinement.
    Automatically detects available modalities under root.
    """
    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:
        # 优先匹配 .png 或 .jpg
        for ext in [".png", ".jpg", ".jpeg"]:
            path = Path(root) / f"{name}{ext}"
            if path.exists():
                available.append(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[m] for m in modality_names if any(str(Path(root)/f"{m}{ext}") in available for ext in [".png",".jpg",".jpeg"])]

    # --- 构造文本指令 ---
    text_prompt = (
        f"You are given multiple modalities of the same scene, including: {', '.join(present_modalities)}. "
        f"Each modality provides distinct types of visual information that together describe the same subject: "
        f"- The RGB image provides color, texture, lighting, and the overall visual appearance. "
        f"- The line drawing reveals detailed structural outlines, shapes, and proportions. "
        f"- The edge map highlights object boundaries and contours. "
        f"- The depth map shows spatial distance, perspective, and 3D depth relationships. "
        f"- The normal map captures fine surface orientation, curvature, and geometric details. "
        f"- The albedo map shows true surface colors without lighting or shadow effects. "
        f"- The segmentation map provides semantic regions and object boundaries for scene composition. "
        f"- The human pose map shows body structure, orientation, and posture of subjects. "
        f"For each provided modality image, analyze it according to the above definitions and describe "
        f"the specific visual information it contributes in this particular case. "
        f"Use all available information together to produce one unified, richly detailed, and realistic description of the scene. "
        f"Do NOT describe each modality separately or mention modality names. "
        f"Focus on merging their information into a single coherent image description. "
        #f"the subject’s appearance, lighting, form, and spatial depth. "
        f"Consider the following feedback when refining your description: '{feedback}'. "
        f"Refine the coarse caption into a more detailed and accurate image description. "
        f"Coarse caption: '{coarse_caption}' " +
        " ".join(["<image>"] * len(available))
    )

    # --- 构建 Qwen3-VL 消息格式 ---
    messages = [
        {
            "role": "user",
            "content": [{"type": "image", "image": path} for path in available]
                      + [{"type": "text", "text": text_prompt}],
        }
    ]
    return messages

# ------------------------------
# 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("--image_root", type=str, default="/home/efs/mjw/mjw/dataset/dataset/COCO_Karpathy", help="Prompt text for generation.")
    parser.add_argument("--json_path", type=str, default="/home/efs/mjw/mjw/dataset/dataset/COCO_Karpathy/karpathy_test.json", help="Prompt text for generation.")
    parser.add_argument("--negative_prompt", type=str, default="", 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=42)
    parser.add_argument("--output_dir", type=str, default="./example_coco_i2t_outputs", help="Directory to save results.")
    return parser


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

@torch.inference_mode()
def init_i2t(model, processor, image_path, iter_num, name, max_length=300):
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": image_path,
                },
                {"type": "text", "text": "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) / name / 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_caption(image_path, model, processor, caption, max_length=256):
    """
    Evaluate how well the generated caption truthfully describes the given image.
    """
    eval_prompt = f"""
    You are an image–caption alignment evaluator and factuality advisor.
    Given one RGB image and a textual caption, evaluate how well the caption
    truthfully and comprehensively describes what is visually shown.

    Caption: "{caption}"

    ## Evaluation focus
    - Describe whether all **objects, attributes, and relations** mentioned in the caption are actually visible.
    - The caption should only include what is clearly seen in the image — no imaginary or hallucinated content.
    - The caption should also cover the **main visible objects** and their essential attributes (color, count, relative position) if possible.
    - If the caption adds nonexistent objects or attributes, reduce the score sharply (<0.6).
    - If the caption omits minor details but remains overall faithful, keep a moderate score (~0.8–0.9).
    - If the caption perfectly matches and fully reflects the visual scene, score near 1.0.

    ## Feedback instruction
    Provide **one short constructive feedback sentence** to improve the caption.
    - Focus on what should be *added, adjusted, or rephrased* for truthfulness.
    - Do NOT mention errors or missing things directly (avoid "not", "no", "missing", "wrong", "fail").
    - Start with a verb such as "Add", "Replace", "Adjust", "Rephrase", "Include", "Describe".
    - Example:
        - If the caption says "a cat and a dog" but only a cat is visible → "Remove the dog and describe only the cat."
        - If the caption omits a visible red car → "Add the red car on the right side of the road."
        - If the color or quantity is inaccurate → "Replace with the correct color and number as seen."

    Return JSON only:
    {{
        "Consistency": <float 0–1>,
        "Feedback": "<one short sentence suggesting how to make the caption more accurate>"
    }}

    <image>
    """

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

    print(f'eval:{messages}')

    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]

    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" → Overall={score:.3f}")
    #print(f"💡 Feedback: {feedback}")
    return score, feedback



@torch.inference_mode()
def text_refine(root, model, processor, prompt, feedback, iter_num, name, max_length=300):
    messages = build_multimodal_message(root, prompt, feedback)
    print(f'refine message:{messages}')
    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) / name / 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 image_refine(prompt, images, role, pipe, iter_num, modality_names, generator, height, width, name):

    #print(f"🚀 Generating with prompt: {prompt}")
    #prompt = args.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,
    )

    # 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) / name/ 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


# ------------------------------
# Entry Point
# ------------------------------
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)
    import glob
    image_root = args.image_root
    json_path = args.json_path

    with open(json_path, "r") as f:
        data = json.load(f)

    save_image_names = os.listdir("/home/efs/mjw/mjw/code/Jodi/coco_i2t_outputs/val2014")
    image_names = [item["image_path"] for item in data][4021:]

    for image_name in image_names[246:369]:

        if image_name in save_image_names:
            print(f'already got {image_name} in ', f'our {save_image_names}')

        image_path = os.path.join(image_root, image_name)
        image = Image.open(image_path).convert("RGB")
        width, height = image.size

        control_images = [image] + [None] * pipe.num_conditions

        role=[1] + [0] * pipe.num_conditions
        print(role)
    
        max_length  = 1024
        prompt = init_i2t(model, processor, image_path, 0, image_name, max_length)

        score, feedback = evaluate_caption(image_path, model, processor, prompt)
		
        for step in range(1, args.iters):
            generator = torch.Generator(device=device).manual_seed(args.seed)
            save_dir = image_refine(prompt, control_images, role, pipe, step, modality_names, generator, height, width, image_name)
            max_length += 100
            prompt = text_refine(save_dir, model, processor, prompt, feedback, step, image_name, max_length)
            score, feedback = evaluate_caption(image_path, model, processor, prompt)