GTA1 7B

🚀 强化学习驱动的GUI定位模型

本项目借助强化学习（如GRPO），实现了更精准的GUI元素定位。与依赖冗长思维链推理的方法不同，GRPO直接激励可操作且基于实际的响应，为GUI定位带来了新的解决方案。

🚀 快速开始

模型推理

以下是使用训练好的模型进行推理的代码示例：

from PIL import Image
from qwen_vl_utils import process_vision_info, smart_resize
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
import torch
import re

SYSTEM_PROMPT = '''
You are an expert UI element locator. Given a GUI image and a user's element description, provide the coordinates of the specified element as a single (x,y) point. The image resolution is height {height} and width {width}. For elements with area, return the center point.

Output the coordinate pair exactly:
(x,y)
'''
SYSTEM_PROMPT=SYSTEM_PROMPT.strip()

# Function to extract coordinates from model output
def extract_coordinates(raw_string):
    try:
        matches = re.findall(r"\((-?\d*\.?\d+),\s*(-?\d*\.?\d+)\)", raw_string)
        return [tuple(map(int, match)) for match in matches][0]
    except:
        return 0,0

# Load model and processor
model_path = "HelloKKMe/GTA1-7B"
max_new_tokens = 32

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto"
)
processor = AutoProcessor.from_pretrained(
    model_path,
    min_pixels=3136,
    max_pixels= 4096 * 2160
)

# Load and resize image
image = Image.open("file path")
instruction = "description"  # Instruction for grounding
width, height = image.width, image.height

resized_height, resized_width = smart_resize(
    image.height,
    image.width,
    factor=processor.image_processor.patch_size * processor.image_processor.merge_size,
    min_pixels=processor.image_processor.min_pixels,
    max_pixels=processor.image_processor.max_pixels,
)
resized_image = image.resize((resized_width, resized_height))
scale_x, scale_y = width / resized_width, height / resized_height

# Prepare system and user messages
system_message = {
   "role": "system",
   "content": SYSTEM_PROMPT.format(height=resized_height,width=resized_width)
}

user_message = {
    "role": "user",
    "content": [
        {"type": "image", "image": resized_image},
        {"type": "text", "text": instruction}
    ]
}

# Tokenize and prepare inputs
image_inputs, video_inputs = process_vision_info([system_message, user_message])
text = processor.apply_chat_template([system_message, user_message], tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt")
inputs = inputs.to(model.device)

# Generate prediction
output_ids = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False, temperature=1.0, use_cache=True)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)[0]

# Extract and rescale coordinates
pred_x, pred_y  = extract_coordinates(output_text) 
pred_x*=scale_x
pred_y*=scale_y 
print(pred_x,pred_y)

更多详细信息请参考我们的代码仓库。

✨ 主要特性

• 强化学习驱动：采用GRPO强化学习方法，实现目标对齐，直接激励可操作且基于实际的响应。
• 高精度定位：在多个挑战性数据集上取得了领先的性能。

📚 详细文档

模型性能

我们遵循标准评估协议，在三个具有挑战性的数据集上对模型进行了基准测试。我们的方法在所有开源模型家族中始终取得最佳结果。以下是对比结果：

注意：

• 模型规模以十亿（B）为单位表示参数数量。
• 短横线（—）表示当前结果不可用。
• 上标星号（﹡）表示我们的评估结果。
• UI-TARS-1.5 7B、Qwen2.5-VL-32B-Instruct和Qwen2.5-VL-72B-Instruct作为我们的基线模型。
• ∆ 表示我们的模型相对于基线的性能提升。