### 代码表示
MODEL_PATH = '/data/nvme1/weights/Qwen2_5-VL-3B-Instruct'
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor, Qwen2_5_VLForConditionalGeneration
from qwen_vl_utils import process_vision_info
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_PATH, torch_dtype="auto", device_map="auto"
)
print(model)

Qwen2_5_VLForConditionalGeneration(
  (visual): Qwen2_5_VisionTransformerPretrainedModel(
    (patch_embed): Qwen2_5_VisionPatchEmbed(
      (proj): Conv3d(3, 1280, kernel_size=(2, 14, 14), stride=(2, 14, 14), bias=False)
    )
    (rotary_pos_emb): Qwen2_5_VisionRotaryEmbedding()
    (blocks): ModuleList(
      (0-31): 32 x Qwen2_5_VLVisionBlock(
        (norm1): Qwen2RMSNorm((1280,), eps=1e-06)
        (norm2): Qwen2RMSNorm((1280,), eps=1e-06)
        (attn): Qwen2_5_VLVisionSdpaAttention(
          (qkv): Linear(in_features=1280, out_features=3840, bias=True)
          (proj): Linear(in_features=1280, out_features=1280, bias=True)
        )
        (mlp): Qwen2_5_VLMLP(
          (gate_proj): Linear(in_features=1280, out_features=3420, bias=True)
          (up_proj): Linear(in_features=1280, out_features=3420, bias=True)
          (down_proj): Linear(in_features=3420, out_features=1280, bias=True)
          (act_fn): SiLU()
        )
      )
    )
    (merger): Qwen2_5_VLPatchMerger(
      (ln_q): Qwen2RMSNorm((1280,), eps=1e-06)
      (mlp): Sequential(
        (0): Linear(in_features=5120, out_features=5120, bias=True)
        (1): GELU(approximate='none')
        (2): Linear(in_features=5120, out_features=2048, bias=True)
      )
    )
  )
  (model): Qwen2_5_VLModel(
    (embed_tokens): Embedding(151936, 2048)
    (layers): ModuleList(
      (0-35): 36 x Qwen2_5_VLDecoderLayer(
        (self_attn): Qwen2_5_VLSdpaAttention(
          (q_proj): Linear(in_features=2048, out_features=2048, bias=True)
          (k_proj): Linear(in_features=2048, out_features=256, bias=True)
          (v_proj): Linear(in_features=2048, out_features=256, bias=True)
          (o_proj): Linear(in_features=2048, out_features=2048, bias=False)
          (rotary_emb): Qwen2_5_VLRotaryEmbedding()
        )
        (mlp): Qwen2MLP(
          (gate_proj): Linear(in_features=2048, out_features=11008, bias=False)
          (up_proj): Linear(in_features=2048, out_features=11008, bias=False)
          (down_proj): Linear(in_features=11008, out_features=2048, bias=False)
          (act_fn): SiLU()
        )
        (input_layernorm): Qwen2RMSNorm((2048,), eps=1e-06)
        (post_attention_layernorm): Qwen2RMSNorm((2048,), eps=1e-06)
      )
    )
    (norm): Qwen2RMSNorm((2048,), eps=1e-06)
    (rotary_emb): Qwen2_5_VLRotaryEmbedding()
  )
  (lm_head): Linear(in_features=2048, out_features=151936, bias=False)
)

modelscope download --model Qwen/Qwen2.5-VL-3B-Instruct  --local_dir /data/nvme1/weights/Qwen/Qwen2.5-VL-3B-Instruct

modelscope download --dataset Tina12345/textVQA_groundingtask_bbox  --local_dir /data/nvme0/textvqa_bbox

from modelscope.msdatasets import MsDataset
ds =  MsDataset.load('Tina12345/textVQA_groundingtask_bbox', subset_name='default', split='train', cache_dir="./data")

modelscope download --dataset Tina12345/textVQA_groundingtask_bbox  --local_dir /data/nvme0/textvqa_bbox

{
    'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x681 at 0x7FA58E1DB340>, 
    'question': 'what is the name of the company on the card?', 
    'answer': ['blink', 'intergrative nutrition', 'blink', 'blink', 'blink', 'blink', 'blink', 'blink', 'blink', 'blink'], 
    'dataset_id': '36269', 
    'bbox': [712.0, 255.0, 64.0, 43.0]
}

{"image": ["./data/test/003001.jpg"], "query": "what is written on the ghost?", "response": "{\"bbox_2d\": [460, 635, 513, 669]}"}

"""
将数据集格式转换成多模态模型监督微调格式，格式如下所示，保存文件格式为jsonl格式：
{
    "image": "demo/COCO_train2014_000000580957.jpg",
    "conversations": [
        {
            "from": "human",
            "value": "<image>\nLocate house in this image and output the bbox coordinates in JSON format."
        },
        {
            "from": "gpt",
            "value": "{\n"bbox_2d": [135, 114, 1016, 672]\n}"
        }
    ]
}
该格式是参考qwen2.5-vl-finetune文件中提到的Grounding Example所示。
原数据集格式为：
{
    'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x681 at 0x7FA58E1DB340>, 
    'question': 'what is the name of the company on the card?', 
    'answer': ['blink', 'intergrative nutrition', 'blink', 'blink', 'blink', 'blink', 'blink', 'blink', 'blink', 'blink'], 
    'dataset_id': '36269', 
    'bbox': [712.0, 255.0, 64.0, 43.0]
}
"""
import json
import os
from tqdm import tqdm
from datasets import load_dataset
import math
"""
Qwen2.5-VL使用绝对协调为调整大小的图像。对于原始绝对坐标，应该乘以调整大小的高度和宽度，然后除以其原始高度和宽度。
具体代码官网给了，链接：https://github.com/QwenLM/Qwen2.5-VL/blob/main/qwen-vl-finetune/tools/process_bbox.ipynb
可以参考官方的链接
"""
# This is the resize function of Qwen2.5-VL
def smart_resize(
    height: int, width: int, factor: int = 28, min_pixels: int = 56 * 56, max_pixels: int = 14 * 14 * 4 * 1280
):
    """Rescales the image so that the following conditions are met:
    1. Both dimensions (height and width) are divisible by 'factor'.
    2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
    3. The aspect ratio of the image is maintained as closely as possible.
    """
    if height < factor or width < factor:
        raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor}")
    elif max(height, width) / min(height, width) > 200:
        raise ValueError(
            f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}"
        )
    h_bar = round(height / factor) * factor
    w_bar = round(width / factor) * factor
    if h_bar * w_bar > max_pixels:
        beta = math.sqrt((height * width) / max_pixels)
        h_bar = math.floor(height / beta / factor) * factor
        w_bar = math.floor(width / beta / factor) * factor
    elif h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (height * width))
        h_bar = math.ceil(height * beta / factor) * factor
        w_bar = math.ceil(width * beta / factor) * factor
    return h_bar, w_bar
def convert_to_qwen25vl_format(bbox, orig_height, orig_width, factor=28, min_pixels=56*56, max_pixels=14*14*4*1280):
    new_height, new_width = smart_resize(orig_height, orig_width, factor, min_pixels, max_pixels)
    scale_w = new_width / orig_width
    scale_h = new_height / orig_height
    x1, y1, x2, y2 = bbox
    x1_new = round(x1 * scale_w)
    y1_new = round(y1 * scale_h)
    x2_new = round(x2 * scale_w)
    y2_new = round(y2 * scale_h)
    x1_new = max(0, min(x1_new, new_width - 1))
    y1_new = max(0, min(y1_new, new_height - 1))
    x2_new = max(0, min(x2_new, new_width - 1))
    y2_new = max(0, min(y2_new, new_height - 1))
    return [x1_new, y1_new, x2_new, y2_new]
def convert_to_sft_format(data_path,save_path,type='train'):
    # 加载数据集
    dataset = load_dataset(data_path,split='train')
    # 每个数据保存到一个jsonl文件中，并且图片的话要另外放到一起
    if not os.path.exists(save_path):
        os.makedirs(save_path)
    # 创建 JSONL 文件
    jsonl_file = os.path.join(save_path, f"{type}.jsonl")
    with open(jsonl_file, 'w', encoding='utf-8') as jsonl_out:
        # 遍历数据集并保存图片，其他的部分信息保存成jsonl文件
        for idx,sample in tqdm(enumerate(dataset),total=len(dataset)):
            if type == 'train':
                if idx >= 3000:  # 判断是否处理到3000条数据
                    break
            elif type == 'test':
                # 判断是否处理到3001到3100条数据
                if idx < 3000 or idx >= 3100:
                    continue
            # 保存图片
            image = sample['image']
            # 生成文件名（格式为 000001.jpg, 000002.jpg 等）
            filename = f"{idx + 1:06d}.jpg"  # 使用 6 位数字格式化文件名
            jpg_path = os.path.join(save_path, type)
            if not os.path.exists(jpg_path):
                os.makedirs(jpg_path)
            output_path = os.path.join(jpg_path, filename)
            # 保存图片
            image.save(output_path)
            # 保存其他信息
            # 坐标信息
            old_bbox = sample['bbox']
            #### 这里需要将坐标转换成Qwen2.5-VL的坐标格式   
            image_width, image_height = image.size
            x1, y1, w, h = old_bbox
            new_bboxes = [x1, y1, x1 + w, y1 + h]
            # 转换坐标
            qwen25_bboxes = convert_to_qwen25vl_format(new_bboxes, image_height, image_width)
            bbox_dict = {"bbox_2d": qwen25_bboxes}
            formatted_json = json.dumps(bbox_dict, indent=None)
            data = {
                "image":[output_path],
                "query":sample['question'],
                "response":formatted_json,
            }
            # 将数据写入 JSONL 文件
            # 将每条数据写入 JSONL 文件
            jsonl_out.write(json.dumps(data, ensure_ascii=False) + '\n')
    print(f"All images and data have been saved to {save_path} and {jsonl_file}")
# 示例调用
convert_to_sft_format(data_path='/home/lixinyu/data/textvqa_bbox', save_path='./data', type='test')

## 该数据是我之前错误格式下保存的，我们主要看下bbox
{
    "image": [
        "./data/test/003003.jpg"
    ],
    "query": "what numbered album is the photo from?",
    "response": "{\"bbox_2d\": [948.0, 633.0, 59.0, 21.0]}"
}

## 缩放图了之后观察坐标值和图像的对应关系
import math
# This is the resize function of Qwen2.5-VL
def smart_resize(
    height: int, width: int, factor: int = 28, min_pixels: int = 56 * 56, max_pixels: int = 14 * 14 * 4 * 1280
):
    """Rescales the image so that the following conditions are met:
    1. Both dimensions (height and width) are divisible by 'factor'.
    2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
    3. The aspect ratio of the image is maintained as closely as possible.
    """
    if height < factor or width < factor:
        raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor}")
    elif max(height, width) / min(height, width) > 200:
        raise ValueError(
            f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}"
        )
    h_bar = round(height / factor) * factor
    w_bar = round(width / factor) * factor
    if h_bar * w_bar > max_pixels:
        beta = math.sqrt((height * width) / max_pixels)
        h_bar = math.floor(height / beta / factor) * factor
        w_bar = math.floor(width / beta / factor) * factor
    elif h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (height * width))
        h_bar = math.ceil(height * beta / factor) * factor
        w_bar = math.ceil(width * beta / factor) * factor
    return h_bar, w_bar
def convert_to_qwen25vl_format(bbox, orig_height, orig_width, factor=28, min_pixels=56*56, max_pixels=14*14*4*1280):
    new_height, new_width = smart_resize(orig_height, orig_width, factor, min_pixels, max_pixels)
    scale_w = new_width / orig_width
    scale_h = new_height / orig_height
    x1, y1, x2, y2 = bbox
    x1_new = round(x1 * scale_w)
    y1_new = round(y1 * scale_h)
    x2_new = round(x2 * scale_w)
    y2_new = round(y2 * scale_h)
    x1_new = max(0, min(x1_new, new_width - 1))
    y1_new = max(0, min(y1_new, new_height - 1))
    x2_new = max(0, min(x2_new, new_width - 1))
    y2_new = max(0, min(y2_new, new_height - 1))
    return [x1_new, y1_new, x2_new, y2_new]
new_bboxes = [x1, y1, x1+w, y1+h]
print(f"新的坐标是{new_bboxes}")
print(f"转换后的坐标是{convert_to_qwen25vl_format(new_bboxes, image.shape[0], image.shape[1])}")

modelscope
qwen_vl_utils
transformers
datasets==3.6.0
peft
diffusers
torch==2.5.1 
torchvision==0.20.1 
torchaudio==2.5.1
swanlab
deepspeed
trl

pip install -r requirements.txt

DatasetDict({
    train: Dataset({
        features: ['image', 'query', 'response'],
        num_rows: 3000
    })
    test: Dataset({
        features: ['image', 'query', 'response'],
        num_rows: 100
    })
})

################
  # Dataset
  ################
  # 1、读取保存的jsonl文件，使用datasets.load_dataset生成的数据集即是Dataset格式，符合hf使用标准格式
  train_dataset = datasets.load_dataset("json", data_files=data_args.train_dataset_name)
  test_dataset = datasets.load_dataset("json", data_files=data_args.test_dataset_name)
  # 2、创建 DatasetDict，这部分只是为了后续读取测试数据方便，因此把train和test放在一起
  raw_dataset = datasets.DatasetDict({
    "train": train_dataset["train"],
    "test": test_dataset["train"]
  })
  print(raw_dataset)
  # 3、固定数据集格式用于后面批处理数据集
  def preporocess_textvqa(example):
    return {
      "image": example["image"],
      "user": example["query"],
      "assistant": example["response"],
    }
  raw_dataset = raw_dataset.map(
    preporocess_textvqa,
    remove_columns=raw_dataset["train"].column_names,
    desc="Preprocessing textvqa dataset",
  )
# 4、Trainer数据集调用
train_dataset=raw_dataset["train"],
eval_dataset=(
            raw_dataset["test"] if training_args.eval_strategy != "no" else None
        ),

# 缩放图像的大小，同时因为grounding任务，需要同时缩放坐标
def resize_with_max_side(image, max_side_length):
    # 获取原始尺寸
    width, height = image.size
    # 计算缩放比例
    scale = min(max_side_length / width, max_side_length / height)
    # 计算新的尺寸
    new_width = int(width * scale)
    new_height = int(height * scale)
    # 调整图像大小
    resized_image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
    return resized_image, scale

def resize_bbox(bbox, scale):
    # 缩放矩形框坐标
    return [int(coord * scale) for coord in bbox]

question = example["user"]
answer = example["assistant"]
# 需要读取图像，需要确保是RGB图像
image_path = example['image'][0]
image = Image.open(image_path)
# 输出缩放后的图像以及缩放倍率
image, scale = resize_with_max_side(
  image, max_side_length=self.max_img_side_length
)
# 缩放answer的坐标值
# answer是一个json字符串，解析成字典
answer = json.loads(answer)
answer = {"bbox_2d": resize_bbox(answer["bbox_2d"],scale)}
# 转化新的answer
answer = json.dumps(answer, indent=None)
根据得到的image、question、answer经过大模型加载为tokens格式：
prompt = "Please enclose the corresponding positions using coordinate boxes. Examples of coordinate value formats: [x1,y1,x2,y2]"
question = '<image>\n'+ question+prompt
messages = [
  {
    "role": "user",
    "content": [
      {"type": "image"},
      {"type": "text", "text": question},
    ],
  }
]
prompt = self.processor.tokenizer.apply_chat_template(
  messages, tokenize=False, add_generation_prompt=True
)
answer = f"{answer}<|im_end|>\n"
input_ids = self.processor(
  images=[image],
  text=prompt + answer,
  return_tensors="pt",
  max_length=self.max_seq_length,
  truncation=False,
  padding=False,
)
answer_ids = self.processor.tokenizer(
  answer, add_special_tokens=False, return_tensors="pt"
)
ignore_ids_len = len(input_ids["input_ids"][0]) - len(
  answer_ids["input_ids"][0]
)
input_ids["labels"] = torch.cat(
  [
    torch.tensor([IGNORE_INDEX] * ignore_ids_len).unsqueeze(0),
    answer_ids["input_ids"],
  ],
  dim=1,
)

position_ids, _ = self.get_rope_index_2(
  self.processor.image_processor.merge_size,
  input_ids["input_ids"],
  input_ids["image_grid_thw"],
)
input_ids["position_ids"] = position_ids

# padding
if len(input_ids["labels"]) < self.max_seq_length:
  input_ids["input_ids"] = torch.cat(
    [
      input_ids["input_ids"],
      torch.tensor(
        [self.processor.tokenizer.pad_token_id]
        * (self.max_seq_length - len(input_ids["input_ids"]))
      ).unsqueeze(0),
    ],
    dim=1,
  )
  input_ids["labels"] = torch.cat(
    [
      input_ids["labels"],
      torch.tensor(
        [IGNORE_INDEX]
        * (self.max_seq_length - len(input_ids["labels"]))
      ).unsqueeze(0),
    ],
    dim=1,
  )
  input_ids["attention_mask"] = input_ids["input_ids"].ne(
    self.processor.tokenizer.pad_token_id
  )
  # padding position_ids
  pad_length = self.max_seq_length - input_ids["position_ids"].shape[2]
  input_ids["position_ids"] = torch.nn.functional.pad(
    input_ids["position_ids"], (0, pad_length), "constant", 1
  )

# truncate
if len(input_ids["input_ids"][0]) > self.max_seq_length:
  input_ids["input_ids"] = input_ids["input_ids"][
    :, : self.max_seq_length
  ]
  input_ids["labels"] = input_ids["labels"][:, : self.max_seq_length]
  input_ids["attention_mask"] = input_ids["attention_mask"][
    :, : self.max_seq_length
  ]
  input_ids["position_ids"] = input_ids["position_ids"][
    :, : self.max_seq_length
  ]

batch_input_ids = {
  "input_ids": torch.cat(
    [input_ids["input_ids"] for input_ids in batch_input_ids], dim=0
  ),
  "attention_mask": torch.cat(
    [input_ids["attention_mask"] for input_ids in batch_input_ids], dim=0
  ),
  "labels": torch.cat(
    [input_ids["labels"] for input_ids in batch_input_ids], dim=0
  ),
  "pixel_values": torch.cat(
    [input_ids["pixel_values"] for input_ids in batch_input_ids], dim=0
  ),
  "image_grid_thw": torch.cat(
    [input_ids["image_grid_thw"] for input_ids in batch_input_ids], dim=0
  ),
  "position_ids": torch.cat(
    [input_ids["position_ids"] for input_ids in batch_input_ids], dim=1
  ),
}
return batch_input_ids

from typing import Optional, Tuple
import copy
import transformers
import torch
from PIL import Image
import json
IGNORE_INDEX = -100
# 缩放图像的大小，同时因为grounding任务，需要同时缩放坐标
def resize_with_max_side(image, max_side_length):
    # 获取原始尺寸
    width, height = image.size
    # 计算缩放比例
    scale = min(max_side_length / width, max_side_length / height)
    # 计算新的尺寸
    new_width = int(width * scale)
    new_height = int(height * scale)
    # 调整图像大小
    resized_image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
    return resized_image, scale
def resize_bbox(bbox, scale):
    # 缩放矩形框坐标
    return [int(coord * scale) for coord in bbox]
class Qwen2_5VLCollator:
    def __init__(
        self, processor, max_seq_length=1024, max_img_side_length=1024, **kwargs
    ):
        self.processor = processor
        # to fix bug in Qwen2.5VL
        self.processor.tokenizer.chat_template =  "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
        self.max_seq_length = max_seq_length
        self.max_img_side_length = max_img_side_length
    def __call__(self, examples):
        batch_input_ids = []
        for example in examples:
            # 根据数据集格式来，数据集格式如下：
            """
            {"image": ["./data/train/000001.jpg"], "query": "what is the name of the company on the card?", "response": "{\n  \"bbox_2d\": [\n    712.0,\n    255.0,\n    64.0,\n    43.0\n  ]\n}"}
            """
            question = example["user"]
            answer = example["assistant"]
            # 需要读取图像，需要确保是RGB图像
            image_path = example['image'][0]
            image = Image.open(image_path)
            # 输出缩放后的图像以及缩放倍率
            image, scale = resize_with_max_side(
                image, max_side_length=self.max_img_side_length
            )
            # 缩放answer的坐标值
            # answer是一个json字符串，解析成字典
            answer = json.loads(answer)
            answer = {"bbox_2d": resize_bbox(answer["bbox_2d"],scale)}
            # 转化新的answer
            answer = json.dumps(answer, indent=None)
            # 这了不知道是否需要添加prompt
            prompt = "Please enclose the corresponding positions using coordinate boxes. Examples of coordinate value formats: [x1,y1,x2,y2]"
            question = '<image>\n'+ question+prompt
            messages = [
                {
                    "role": "user",
                    "content": [
                        {"type": "image"},
                        {"type": "text", "text": question},
                    ],
                }
            ]
            prompt = self.processor.tokenizer.apply_chat_template(
                messages, tokenize=False, add_generation_prompt=True
            )
            answer = f"{answer}<|im_end|>\n"
            input_ids = self.processor(
                images=[image],
                text=prompt + answer,
                return_tensors="pt",
                max_length=self.max_seq_length,
                truncation=False,
                padding=False,
            )
            answer_ids = self.processor.tokenizer(
                answer, add_special_tokens=False, return_tensors="pt"
            )
            ignore_ids_len = len(input_ids["input_ids"][0]) - len(
                answer_ids["input_ids"][0]
            )
            input_ids["labels"] = torch.cat(
                [
                    torch.tensor([IGNORE_INDEX] * ignore_ids_len).unsqueeze(0),
                    answer_ids["input_ids"],
                ],
                dim=1,
            )
            # position_ids
            position_ids, _ = self.get_rope_index_2(
                self.processor.image_processor.merge_size,
                input_ids["input_ids"],
                input_ids["image_grid_thw"],
            )
            input_ids["position_ids"] = position_ids
            # padding
            if len(input_ids["labels"]) < self.max_seq_length:
                input_ids["input_ids"] = torch.cat(
                    [
                        input_ids["input_ids"],
                        torch.tensor(
                            [self.processor.tokenizer.pad_token_id]
                            * (self.max_seq_length - len(input_ids["input_ids"]))
                        ).unsqueeze(0),
                    ],
                    dim=1,
                )
                input_ids["labels"] = torch.cat(
                    [
                        input_ids["labels"],
                        torch.tensor(
                            [IGNORE_INDEX]
                            * (self.max_seq_length - len(input_ids["labels"]))
                        ).unsqueeze(0),
                    ],
                    dim=1,
                )
                input_ids["attention_mask"] = input_ids["input_ids"].ne(
                    self.processor.tokenizer.pad_token_id
                )
                # padding position_ids
                pad_length = self.max_seq_length - input_ids["position_ids"].shape[2]
                input_ids["position_ids"] = torch.nn.functional.pad(
                    input_ids["position_ids"], (0, pad_length), "constant", 1
                )
            # truncate
            if len(input_ids["input_ids"][0]) > self.max_seq_length:
                input_ids["input_ids"] = input_ids["input_ids"][
                    :, : self.max_seq_length
                ]
                input_ids["labels"] = input_ids["labels"][:, : self.max_seq_length]
                input_ids["attention_mask"] = input_ids["attention_mask"][
                    :, : self.max_seq_length
                ]
                input_ids["position_ids"] = input_ids["position_ids"][
                    :, : self.max_seq_length
                ]
            # batching
            batch_input_ids.append(input_ids)
        batch_input_ids = {
            "input_ids": torch.cat(
                [input_ids["input_ids"] for input_ids in batch_input_ids], dim=0
            ),
            "attention_mask": torch.cat(
                [input_ids["attention_mask"] for input_ids in batch_input_ids], dim=0
            ),
            "labels": torch.cat(
                [input_ids["labels"] for input_ids in batch_input_ids], dim=0
            ),
            "pixel_values": torch.cat(
                [input_ids["pixel_values"] for input_ids in batch_input_ids], dim=0
            ),
            "image_grid_thw": torch.cat(
                [input_ids["image_grid_thw"] for input_ids in batch_input_ids], dim=0
            ),
            "position_ids": torch.cat(
                [input_ids["position_ids"] for input_ids in batch_input_ids], dim=1
            ),
        }
        return batch_input_ids
    def get_rope_index_2(
        self,
        spatial_merge_size: Optional[int] = 2,
        input_ids: Optional[torch.LongTensor] = None,
        image_grid_thw: Optional[torch.LongTensor] = None,
        video_grid_thw: Optional[torch.LongTensor] = None,
        second_per_grid_ts: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Calculate the 3D rope index based on image and video's temporal, height and width in LLM.
        Explanation:
            Each embedding sequence contains vision embedding and text embedding or just contains text embedding.
            For pure text embedding sequence, the rotary position embedding has no difference with mordern LLMs.
            Examples:
                input_ids: [T T T T T], here T is for text.
                temporal position_ids: [0, 1, 2, 3, 4]
                height position_ids: [0, 1, 2, 3, 4]
                width position_ids: [0, 1, 2, 3, 4]
            For vision and text embedding sequence, we calculate 3D rotary position embedding for vision part
            and 1D rotary position embeddin for text part.
            Examples:
                Assume we have a video input with 3 temporal patches, 2 height patches and 2 width patches.
                input_ids: [V V V V V V V V V V V V T T T T T], here V is for vision.
                vision temporal position_ids: [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2]
                vision height position_ids: [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1]
                vision width position_ids: [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
                text temporal position_ids: [3, 4, 5, 6, 7]
                text height position_ids: [3, 4, 5, 6, 7]
                text width position_ids: [3, 4, 5, 6, 7]
                Here we calculate the text start position_ids as the max vision position_ids plus 1.
        Args:
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
                it.
            image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
                The temporal, height and width of feature shape of each image in LLM.
            video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
                The temporal, height and width of feature shape of each video in LLM.
            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.
        Returns:
            position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`)
            mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`)
        """
        image_token_id = 151655
        video_token_id = 151656
        vision_start_token_id = 151652
        mrope_position_deltas = []
        if input_ids is not None and (
            image_grid_thw is not None or video_grid_thw is not None
        ):
            total_input_ids = input_ids
            if attention_mask is None:
                attention_mask = torch.ones_like(total_input_ids)
            position_ids = torch.ones(
                3,
                input_ids.shape[0],
                input_ids.shape[1],
                dtype=input_ids.dtype,
                device=input_ids.device,
            )
            image_index, video_index = 0, 0
            for i, input_ids in enumerate(total_input_ids):
                input_ids = input_ids[attention_mask[i] == 1]
                image_nums, video_nums = 0, 0
                vision_start_indices = torch.argwhere(
                    input_ids == vision_start_token_id
                ).squeeze(1)
                vision_tokens = input_ids[vision_start_indices + 1]
                image_nums = (vision_tokens == image_token_id).sum()
                video_nums = (vision_tokens == video_token_id).sum()
                input_tokens = input_ids.tolist()
                llm_pos_ids_list: list = []
                st = 0
                remain_images, remain_videos = image_nums, video_nums
                for _ in range(image_nums + video_nums):
                    if image_token_id in input_tokens and remain_images > 0:
                        ed_image = input_tokens.index(image_token_id, st)
                    else:
                        ed_image = len(input_tokens) + 1
                    if video_token_id in input_tokens and remain_videos > 0:
                        ed_video = input_tokens.index(video_token_id, st)
                    else:
                        ed_video = len(input_tokens) + 1
                    if ed_image < ed_video:
                        t, h, w = (
                            image_grid_thw[image_index][0],
                            image_grid_thw[image_index][1],
                            image_grid_thw[image_index][2],
                        )
                        image_index += 1
                        remain_images -= 1
                        ed = ed_image
                    else:
                        t, h, w = (
                            video_grid_thw[video_index][0],
                            video_grid_thw[video_index][1],
                            video_grid_thw[video_index][2],
                        )
                        video_index += 1
                        remain_videos -= 1
                        ed = ed_video
                    llm_grid_t, llm_grid_h, llm_grid_w = (
                        t.item(),
                        h.item() // spatial_merge_size,
                        w.item() // spatial_merge_size,
                    )
                    text_len = ed - st
                    st_idx = (
                        llm_pos_ids_list[-1].max() + 1
                        if len(llm_pos_ids_list) > 0
                        else 0
                    )
                    llm_pos_ids_list.append(
                        torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
                    )
                    t_index = (
                        torch.arange(llm_grid_t)
                        .view(-1, 1)
                        .expand(-1, llm_grid_h * llm_grid_w)
                        .flatten()
                    )
                    h_index = (
                        torch.arange(llm_grid_h)
                        .view(1, -1, 1)
                        .expand(llm_grid_t, -1, llm_grid_w)
                        .flatten()
                    )
                    w_index = (
                        torch.arange(llm_grid_w)
                        .view(1, 1, -1)
                        .expand(llm_grid_t, llm_grid_h, -1)
                        .flatten()
                    )
                    llm_pos_ids_list.append(
                        torch.stack([t_index, h_index, w_index]) + text_len + st_idx
                    )
                    st = ed + llm_grid_t * llm_grid_h * llm_grid_w
                if st < len(input_tokens):
                    st_idx = (
                        llm_pos_ids_list[-1].max() + 1
                        if len(llm_pos_ids_list) > 0
                        else 0
                    )
                    text_len = len(input_tokens) - st
                    llm_pos_ids_list.append(
                        torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
                    )
                llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
                position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(
                    position_ids.device
                )
                mrope_position_deltas.append(
                    llm_positions.max() + 1 - len(total_input_ids[i])
                )
            mrope_position_deltas = torch.tensor(
                mrope_position_deltas, device=input_ids.device
            ).unsqueeze(1)
            return position_ids, mrope_position_deltas
        else:
            if attention_mask is not None:
                position_ids = attention_mask.long().cumsum(-1) - 1
                position_ids.masked_fill_(attention_mask == 0, 1)
                position_ids = (
                    position_ids.unsqueeze(0)
                    .expand(3, -1, -1)
                    .to(attention_mask.device)
                )
                max_position_ids = position_ids.max(0, keepdim=False)[0].max(
                    -1, keepdim=True
                )[0]
                mrope_position_deltas = max_position_ids + 1 - attention_mask.shape[-1]
            else:
                position_ids = (
                    torch.arange(input_ids.shape[1], device=input_ids.device)
                    .view(1, 1, -1)
                    .expand(3, input_ids.shape[0], -1)
                )
                mrope_position_deltas = torch.zeros(
                    [input_ids.shape[0], 1],
                    device=input_ids.device,
                    dtype=input_ids.dtype,
                )
            return position_ids, mrope_position_deltas
################
# Data collator map
################
vision_data_collator_map = {
    "Qwen2_5VLCollator": Qwen2_5VLCollator,
}

## 参数设置
################
# Model arguments
################
@dataclass
class ModelArguments:
    auto_model_class: Optional[str] = field(
        default="AutoModelForCausalLM",
        metadata={
            "help": (
                "The auto model class to use for the model. Default is AutoModelForCausalLM."
            )
        },
    )
    model_name_or_path: Optional[str] = field(
        default=None,
        metadata={
            "help": "Path to pretrained model or model identifier from huggingface.co/models."
        },
    )
    processor_name_or_path: Optional[str] = field(
        default=None,
        metadata={
            "help": "Path to pretrained processor or processor identifier from huggingface.co/models."
        },
    )
    trust_remote_code: Optional[bool] = field(
        default=True,
        metadata={
            "help": "Whether to trust the remote code when loading the model and processor. default is True."
        },
    )
    torch_dtype: Optional[str] = field(
        default="bfloat16",
        metadata={"help": "The torch dtype to use for the model. Default is bfloat16."},
    )
    def __post_init__(self):
        if self.processor_name_or_path is None:
            self.processor_name_or_path = self.model_name_or_path
################
# datasets arguments
################
@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """
    train_dataset_name: Optional[str] = field(
        default=None,
        metadata={"help": "The name of the train dataset to use (via the datasets library)."},
    )
    test_dataset_name: Optional[str] = field(
        default=None,
        metadata={"help": "The name of the test dataset to use (via the datasets library)."},
    )
    data_collator: Optional[str] = field(
        default="vision_data_collator",
        metadata={
            "help": (
                "The data collator to use for the dataset. Default is vision_data_collator."
            )
        },
    )
    max_seq_length: Optional[int] = field(
        default=1024,
        metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },
    )
    max_image_side: Optional[int] = field(
        default=256,
        metadata={
            "help": ("The size of the image to use for the dataset. Default is 224.")
        },
    )
################
# lora arguments
################
@dataclass
class LoraArguments:
    use_lora: bool = False
    r: int = 8
    lora_alpha: int = 32
    target_modules: List[str] = field(default_factory=lambda: ["q_proj", "v_proj"])
    bias = "none"
    task_type: str = "CAUSAL_LM"
    lora_dropout: float = 0.05
    inference_mode: bool = False

bash scripts/sft_vqa_8gpu-z2.sh configs/SFT_Qwen2_5-VL-3B-Instruct_vqa.yaml

########################################################
# train sft_alpaca.py with 8gpu in deepspeed zero2 bf16
########################################################
accelerate launch \
    --num_processes 8 \
    --main_process_port 25001 \
    --config_file configs/deepspeed_bf16_zero2.yaml \
    sft.py \
    --config $1

compute_environment: LOCAL_MACHINE
debug: false
deepspeed_config:
  deepspeed_multinode_launcher: standard
  gradient_clipping: 1.0
  offload_optimizer_device: none
  offload_param_device: none
  zero3_init_flag: false
  zero_stage: 2
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: 'bf16'
num_machines: 1
num_processes: 8
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false

# 模型设置，参数设置参考 trl.ModelConfig
model_name_or_path: /home/jiangqiushan/test/models/Qwen2.5-VL-3B-Instruct
auto_model_class: "Qwen2_5_VLForConditionalGeneration"
torch_dtype: bfloat16
# 数据集设置，参数设置参考 sft.DataTrainingArguments
train_dataset_name: ./data/train.jsonl
test_dataset_name: ./data/test.jsonl
preprocessing_num_workers: 1
data_collator: "Qwen2_5VLCollator"
max_seq_length: 256
# 训练设置，参数设置参考 transformers.TrainingArguments trl.SFTConfig
## 训练超参数
seed: 2025
data_seed: 2025
remove_unused_columns: False  # 此处需要指定为false
## batchsize、训练次数相关
per_device_train_batch_size: 1
gradient_accumulation_steps: 1
max_steps: 2000
## 学习率相关
learning_rate: 1.0e-5
lr_scheduler_type: cosine
warmup_ratio: 0.1
## 训练效率相关
gradient_checkpointing: false
bf16: true
bf16_full_eval: true
## 验证输出
eval_strategy: steps
eval_steps: 0.1
## 结果输出+日志日志设置
output_dir: /home/jiangqiushan/test/models/SFT_Qwen2_5-VL-3B-Instruct_vqa
save_steps: 0.2
save_total_limit: 1
report_to: swanlab
logging_first_step: true
logging_steps: 0.001

def main(data_args, training_args, model_args, lora_args):
    ################
    # Prepare something
    ################
    output_dir = training_args.output_dir
    dir_path, model_name = os.path.split(output_dir)
    new_model_name = device_type + "_" + model_name
    training_args.output_dir = os.path.join(dir_path, new_model_name)
    training_args.run_name = new_model_name
    set_seeds(training_args.seed)
    ################
    # Model init kwargs & Tokenizer
    ################
    # load processor
    processor = AutoProcessor.from_pretrained(
        pretrained_model_name_or_path=model_args.processor_name_or_path,
        trust_remote_code=model_args.trust_remote_code,
        local_files_only=True,
    )
    # load and construct model
    model_class = getattr(transformers, model_args.auto_model_class)  # 动态加载模型类
    if model_class is None:
        raise ValueError(f"Model class {model_args.auto_model_class} is not available.")
    model = model_class.from_pretrained(
        pretrained_model_name_or_path=model_args.model_name_or_path,
        torch_dtype=getattr(torch, model_args.torch_dtype),
        trust_remote_code=model_args.trust_remote_code,
        local_files_only=True,
    )
    if lora_args.use_lora:
        lora_config = LoraConfig(
            r=lora_args.r,
            lora_alpha=lora_args.lora_alpha,
            target_modules=lora_args.target_modules,
            lora_dropout=lora_args.lora_dropout,
            bias=lora_args.bias,
        )
        model = get_peft_model(model, lora_config)
    ################
    # Dataset
    ################
    train_dataset = datasets.load_dataset("json", data_files=data_args.train_dataset_name)
    test_dataset = datasets.load_dataset("json", data_files=data_args.test_dataset_name)
    # 创建 DatasetDict
    raw_dataset = datasets.DatasetDict({
        "train": train_dataset["train"],
        "test": test_dataset["train"]
    })
    print(raw_dataset)
    # data formatting
    def preporocess_textvqa(example):
        return {
            "image": example["image"],
            "user": example["query"],
            "assistant": example["response"],
        }
    raw_dataset = raw_dataset.map(
        preporocess_textvqa,
        remove_columns=raw_dataset["train"].column_names,
        desc="Preprocessing textvqa dataset",
    )
    data_collator = vision_data_collator_map[data_args.data_collator](
        processor=processor,
        max_seq_length=data_args.max_seq_length,
        max_img_side_length=data_args.max_image_side,
    )
    ################
    # Training
    ################
    last_checkpoint = None  # load last checkpoint if available
    if (
        os.path.isdir(training_args.output_dir)
        and not training_args.overwrite_output_dir
    ):
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        print(
            f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
            "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
        )
        # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=raw_dataset["train"],
        eval_dataset=(
            raw_dataset["test"] if training_args.eval_strategy != "no" else None
        ),
        data_collator=data_collator,
    )
    trainer.train(resume_from_checkpoint=last_checkpoint)
    trainer.save_model(training_args.output_dir)
if __name__ == "__main__":
    dataclass_types = (
        DataTrainingArguments,
        TrainingArguments,
        ModelArguments,
        LoraArguments,
    )
    parser = TrlParser(dataclass_types)
    data_args, training_args, model_args, lora_args = parser.parse_args_and_config()
    main(data_args, training_args, model_args, lora_args)

project/
├── configs/
│   ├── deepspeed_bf16_zero2.yaml
│   ├── deepspeed_bf16_zero3.yaml
│   └── SFT_Qwen2_5-VL-3B-Instruct_vqa.yaml
├── scripts/
│   ├── merge_model.py
│   ├── convert2sft_format.py
│   ├── download_data.sh
│   ├── download_model.sh
│   ├── download_dayiwan.sh
│   └── sft_vqa_8gpu-z2.sh
├── README.md
├── requirements.txt
├── sft.py
├── utils.py
└── vision_datacollator.py

# 1、下载模型和数据集
bash download_data.sh
bash download_model.sh
# 2、数据预处理，并保存到本地
python scripts/convert2sft_format.py
# 3、修改超参数，地址为./configs/SFT_Qwen2_5-VL-3B-Instruct_vqa.yaml
# 主要是修改其中的模型地址和保存地址等
# 开启训练
bash scripts/sft_vqa_4gpu-z2.sh configs/SFT_Qwen2_5-VL-3B-Instruct_vqa.yaml

python inference.py

模型输出：
{"bbox_2d": [446, 630, 521, 660]}

# 先读取一个图片然后观察坐标
import cv2
import numpy as np
import matplotlib.pyplot as plt
import json
EXAMPLE_IMAGE_PATH = "./data/test/003001.jpg"
EXAMPLE_TEXT_PATH = "./data/test.jsonl"
# 读取 JSONL 文件并查找符合条件的数据
def find_data_with_image_path(file_path, image_path):
    with open(file_path, 'r', encoding='utf-8') as file:
        for line in file:
            try:
                data = json.loads(line)  # 解析每一行的 JSON 数据
                # 检查是否存在键 "image" 且其值是一个列表，且列表的第一个元素是 EXAMPLE_IMAGE_PATH
                if 'image' in data and isinstance(data['image'], list) and data['image'][0] == image_path:
                    return data
            except json.JSONDecodeError as e:
                print(f"解析 JSON 时出错: {e}")
                continue
    return None
# 调用函数并输出结果
result = find_data_with_image_path(EXAMPLE_TEXT_PATH, EXAMPLE_IMAGE_PATH)
if result:
    print("找到符合条件的数据：")
    print(json.dumps(result, indent=4, ensure_ascii=False))
else:
    print("未找到符合条件的数据。")
print("-"*20)
# 读取图像，然后看看图像的大小和坐标的对应关系
image = cv2.imread(EXAMPLE_IMAGE_PATH)
print(f"图像大小是{image.shape}")
### 所以坐标的话应该是[左上角x, 左上角y, 宽度, 高度]
x1, y1, x2, y2 = [446, 630, 521, 660]
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.imshow(image)
plt.axis('off')
plt.show()

from datasets import load_dataset
DATA_PATH = '/data/nvme0/textvqa_bbox'
ds = load_dataset(DATA_PATH,split='train')

from datasets import load_dataset
DATA_PATH = '/data/nvme0/textvqa_bbox'
ds = load_dataset(DATA_PATH,split='train')