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1.配置环境
2.数据集准备
3.模型下载
4.注册SwanLab
5.微调
6.训练过程可视化
1.配置环境
本博客使用的是2B模型，所以仅用了单卡3090，若大一点的模型，自行根据实际情况准备显卡
安装Python>=3.8
安装Qwen2-VL必要的库
pip install modelscope==1.18.0
pip install transformers==4.46.2
pip install accelerate==1.1.1
pip install datasets==2.18.0
pip install peft==0.13.2
pip install qwen-vl-utils==0.0.8
2.数据集准备
本博客使用的是coco_2014_caption数据集中的部分，json格式如下：
数据集下载可以使用如下同样会产生一个csv：
from modelscope.msdatasets import MsDataset
import os
import pandas as pdMAX_DATA_NUMBER = 500if not os.path.exists('coco_2014_caption'):# 从modelscope下载COCO 2014图像描述数据集ds = MsDataset.load('modelscope/coco_2014_caption', subset_name='coco_2014_caption', split='train')print(len(ds))total = min(MAX_DATA_NUMBER, len(ds))os.makedirs('coco_2014_caption', exist_ok=True)image_paths = []captions = []for i in range(total):# 获取每个样本的信息item = ds[i]image_id = item['image_id']caption = item['caption']image = item['image']# 保存图片并记录路径image_path = os.path.abspath(f'coco_2014_caption/{image_id}.jpg')image.save(image_path)# 将路径和描述添加到列表中image_paths.append(image_path)captions.append(caption)# 每处理50张图片打印一次进度if (i + 1) % 50 == 0:print(f'Processing {i+1}/{total} images ({(i+1)/total*100:.1f}%)')# 将图片路径和描述保存为CSV文件df = pd.DataFrame({'image_path': image_paths,'caption': captions})# 将数据保存为CSV文件df.to_csv('./coco-2024-dataset.csv', index=False)print(f'数据处理完成，共处理了{total}张图片')else:print('coco_2014_caption目录已存在,跳过数据处理步骤')
我们需要得到json，所以执行下面脚本得到：
import pandas as pd
import json# 载入CSV文件
df = pd.read_csv('./coco-2024-dataset.csv')
conversations = []# 添加对话数据
for i in range(len(df)):conversations.append({"id": f"identity_{i+1}","conversations": [{"from": "user","value": f"COCO Yes: <|vision_start|>{df.iloc[i]['image_path']}<|vision_end|>"},{"from": "assistant", "value": df.iloc[i]['caption']}]})# 保存为Json
with open('data_vl.json', 'w', encoding='utf-8') as f:json.dump(conversations, f, ensure_ascii=False, indent=2)
以上则是数据集准备，若使用自定义数据集则根据上面的格式准备！！！
3.模型下载
本博客使用魔塔社区中的Qwen2-VL-2B模型
from modelscope import snapshot_download, AutoTokenizer
from transformers import TrainingArguments, Trainer, DataCollatorForSeq2Seq, Qwen2VLForConditionalGeneration, AutoProcessor
import torchmodel_dir = snapshot_download("Qwen/Qwen2-VL-2B-Instruct", cache_dir="./", revision="master")
tokenizer = AutoTokenizer.from_pretrained("./Qwen/Qwen2-VL-2B-Instruct/", use_fast=False, trust_remote_code=True)
model = Qwen2VLForConditionalGeneration.from_pretrained("./Qwen/Qwen2-VL-2B-Instruct/", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True,)
model.enable_input_require_grads()
4.注册SwanLab
为了微调时随时查看各项指标，注册SwanLab,复制key,粘贴到运行过程中，如下图：
5.微调
微调前保证当前文件夹下面包含以下：
train.py代码如下：
import torch
from datasets import Dataset
from modelscope import snapshot_download, AutoTokenizer
from swanlab.integration.transformers import SwanLabCallback
from qwen_vl_utils import process_vision_info
from peft import LoraConfig, TaskType, get_peft_model, PeftModel
from transformers import (TrainingArguments,Trainer,DataCollatorForSeq2Seq,Qwen2VLForConditionalGeneration,AutoProcessor,
)
import swanlab
import jsondef process_func(example):"""将数据集进行预处理"""MAX_LENGTH = 8192input_ids, attention_mask, labels = [], [], []conversation = example["conversations"]input_content = conversation[0]["value"]output_content = conversation[1]["value"]file_path = input_content.split("<|vision_start|>")[1].split("<|vision_end|>")[0] # 获取图像路径messages = [{"role": "user","content": [{"type": "image","image": f"{file_path}","resized_height": 280,"resized_width": 280,},{"type": "text", "text": "COCO Yes:"},],}]text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) # 获取文本image_inputs, video_inputs = process_vision_info(messages) # 获取数据数据（预处理过）inputs = processor(text=[text],images=image_inputs,videos=video_inputs,padding=True,return_tensors="pt",)inputs = {key: value.tolist() for key, value in inputs.items()} #tensor -> list,为了方便拼接instruction = inputsresponse = tokenizer(f"{output_content}", add_special_tokens=False)input_ids = (instruction["input_ids"][0] + response["input_ids"] + [tokenizer.pad_token_id])attention_mask = instruction["attention_mask"][0] + response["attention_mask"] + [1]labels = ([-100] * len(instruction["input_ids"][0])+ response["input_ids"]+ [tokenizer.pad_token_id])if len(input_ids) > MAX_LENGTH: # 做一个截断input_ids = input_ids[:MAX_LENGTH]attention_mask = attention_mask[:MAX_LENGTH]labels = labels[:MAX_LENGTH]input_ids = torch.tensor(input_ids)attention_mask = torch.tensor(attention_mask)labels = torch.tensor(labels)inputs['pixel_values'] = torch.tensor(inputs['pixel_values'])inputs['image_grid_thw'] = torch.tensor(inputs['image_grid_thw']).squeeze(0) #由（1,h,w)变换为（h,w）return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels,"pixel_values": inputs['pixel_values'], "image_grid_thw": inputs['image_grid_thw']}def predict(messages, model):# 准备推理text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)image_inputs, video_inputs = process_vision_info(messages)inputs = processor(text=[text],images=image_inputs,videos=video_inputs,padding=True,return_tensors="pt",)inputs = inputs.to("cuda")# 生成输出generated_ids = model.generate(**inputs, max_new_tokens=128)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)return output_text[0]# 在modelscope上下载Qwen2-VL模型到本地目录下
model_dir = snapshot_download("Qwen/Qwen2-VL-2B-Instruct", cache_dir="./", revision="master")# 使用Transformers加载模型权重
tokenizer = AutoTokenizer.from_pretrained("./Qwen/Qwen2-VL-2B-Instruct/", use_fast=False, trust_remote_code=True)
processor = AutoProcessor.from_pretrained("./Qwen/Qwen2-VL-2B-Instruct")model = Qwen2VLForConditionalGeneration.from_pretrained("./Qwen/Qwen2-VL-2B-Instruct/", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True,)
model.enable_input_require_grads() # 开启梯度检查点时，要执行该方法# 处理数据集：读取json文件
# 拆分成训练集和测试集，保存为data_vl_train.json和data_vl_test.json
train_json_path = "data_vl.json"
with open(train_json_path, 'r') as f:data = json.load(f)train_data = data[:-4]test_data = data[-4:]with open("data_vl_train.json", "w") as f:json.dump(train_data, f)with open("data_vl_test.json", "w") as f:json.dump(test_data, f)train_ds = Dataset.from_json("data_vl_train.json")
train_dataset = train_ds.map(process_func)# 配置LoRA
config = LoraConfig(task_type=TaskType.CAUSAL_LM,target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],inference_mode=False, # 训练模式r=64, # Lora 秩lora_alpha=16, # Lora alaph，具体作用参见 Lora 原理lora_dropout=0.05, # Dropout 比例bias="none",
)# 获取LoRA模型
peft_model = get_peft_model(model, config)# 配置训练参数
args = TrainingArguments(output_dir="./output/Qwen2-VL-2B",per_device_train_batch_size=4,gradient_accumulation_steps=4,logging_steps=10,logging_first_step=5,num_train_epochs=2,save_steps=100,learning_rate=1e-4,save_on_each_node=True,gradient_checkpointing=True,report_to="none",
)# 设置SwanLab回调
swanlab_callback = SwanLabCallback(project="Qwen2-VL-finetune",experiment_name="qwen2-vl-coco2014",config={"model": "https://modelscope.cn/models/Qwen/Qwen2-VL-2B-Instruct","dataset": "https://modelscope.cn/datasets/modelscope/coco_2014_caption/quickstart","github": "https://github.com/datawhalechina/self-llm","prompt": "COCO Yes: ","train_data_number": len(train_data),"lora_rank": 64,"lora_alpha": 16,"lora_dropout": 0.1,},
)# 配置Trainer
trainer = Trainer(model=peft_model,args=args,train_dataset=train_dataset,data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),callbacks=[swanlab_callback],
)# 开启模型训练
trainer.train()# 配置测试参数
val_config = LoraConfig(task_type=TaskType.CAUSAL_LM,target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],inference_mode=True, # 训练模式r=64, # Lora 秩lora_alpha=16, # Lora alaph，具体作用参见 Lora 原理lora_dropout=0.05, # Dropout 比例bias="none",
)# 获取测试模型
val_peft_model = PeftModel.from_pretrained(model, model_id="./output/Qwen2-VL-2B/checkpoint-62", config=val_config)# 读取测试数据
with open("data_vl_test.json", "r") as f:test_dataset = json.load(f)test_image_list = []
for item in test_dataset:input_image_prompt = item["conversations"][0]["value"]# 去掉前后的<|vision_start|>和<|vision_end|>origin_image_path = input_image_prompt.split("<|vision_start|>")[1].split("<|vision_end|>")[0]messages = [{"role": "user", "content": [{"type": "image", "image": origin_image_path},{"type": "text","text": "COCO Yes:"}]}]response = predict(messages, val_peft_model)messages.append({"role": "assistant", "content": f"{response}"})print(messages[-1])test_image_list.append(swanlab.Image(origin_image_path, caption=response))swanlab.log({"Prediction": test_image_list})swanlab.finish()
6.训练过程可视化
