|
--- |
|
license: apache-2.0 |
|
library_name: transformers |
|
base_model: OpenGVLab/InternVL2-4B |
|
pipeline_tag: image-text-to-text |
|
--- |
|
|
|
# OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis |
|
|
|
<div align="center"> |
|
|
|
[\[🏠Homepage\]](https://qiushisun.github.io/OS-Genesis-Home/) [\[💻Code\]](https://github.com/OS-Copilot/OS-Genesis) [\[📝Paper\]](https://arxiv.org/abs/2412.19723) [\[🤗Models\]](https://huggingface.co/collections/OS-Copilot/os-genesis-6768d4b6fffc431dbf624c2d)[\[🤗Data\]](https://huggingface.co/collections/OS-Copilot/os-genesis-6768d4b6fffc431dbf624c2d) |
|
|
|
</div> |
|
|
|
## Overview |
|
 |
|
|
|
We introduce OS-Genesis, an interaction-driven pipeline that synthesizes high-quality and diverse GUI agent trajectory data without human supervision. By leveraging reverse task synthesis, OS-Genesis enables effective training of GUI agents to achieve superior performance on dynamic benchmarks such as AndroidWorld and WebArena. |
|
|
|
## Quick Start |
|
OS-Genesis-4B-AC is a mobile action model finetuned from [InternVL2-4B](https://huggingface.co/OpenGVLab/InternVL2-4B). |
|
|
|
### OS-Genesis AC Family Models |
|
In the following table, we provide an overview of the OS-Genesis AC Family Models used for evaluating the AndroidControl Benchmark. |
|
|
|
| Model Name | Base Model | Training Data | HF Link | |
|
| :-------------: | :-------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | :---------------------------------------------------------: | |
|
| OS-Genesis-4B-AC | [InternVL2-4B](https://huggingface.co/OpenGVLab/InternVL2-4B) | [OS-Genesis-ac-training-data](https://huggingface.co/datasets/OS-Copilot/OS-Genesis-mobile-data/blob/main/os_genesis_ac_training_data.jsonl) | [🤗 link](https://huggingface.co/OS-Copilot/OS-Genesis-4B-AC) | |
|
| OS-Genesis-7B-AC | [Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) | [OS-Genesis-ac-training-data](https://huggingface.co/datasets/OS-Copilot/OS-Genesis-mobile-data/blob/main/os_genesis_ac_training_data.jsonl) | [🤗 link](https://huggingface.co/OS-Copilot/OS-Genesis-7B-AC) | |
|
| OS-Genesis-8B-AC | [InternVL2-8B](https://huggingface.co/OpenGVLab/InternVL2-8B) | [OS-Genesis-ac-training-data](https://huggingface.co/datasets/OS-Copilot/OS-Genesis-mobile-data/blob/main/os_genesis_ac_training_data.jsonl) | [🤗 link](https://huggingface.co/OS-Copilot/OS-Genesis-8B-AC) | |
|
|
|
|
|
### Inference Example |
|
First, install the `transformers` library: |
|
|
|
``` |
|
pip install transformers |
|
``` |
|
|
|
For additional dependencies, please refer to the [InternVL2 documentation](https://internvl.readthedocs.io/en/latest/get_started/installation.html). |
|
|
|
For evaluating the AndroidControl Benchmark, please refer to the [**evaluation code**](https://github.com/OS-Copilot/OS-Genesis/tree/main/evaluation/android_control). |
|
|
|
Inference code example: |
|
```python |
|
import numpy as np |
|
import torch |
|
import torchvision.transforms as T |
|
from PIL import Image |
|
from torchvision.transforms.functional import InterpolationMode |
|
from transformers import AutoModel, AutoTokenizer |
|
IMAGENET_MEAN = (0.485, 0.456, 0.406) |
|
IMAGENET_STD = (0.229, 0.224, 0.225) |
|
|
|
def build_transform(input_size): |
|
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD |
|
transform = T.Compose([ |
|
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), |
|
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), |
|
T.ToTensor(), |
|
T.Normalize(mean=MEAN, std=STD) |
|
]) |
|
return transform |
|
|
|
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): |
|
best_ratio_diff = float('inf') |
|
best_ratio = (1, 1) |
|
area = width * height |
|
for ratio in target_ratios: |
|
target_aspect_ratio = ratio[0] / ratio[1] |
|
ratio_diff = abs(aspect_ratio - target_aspect_ratio) |
|
if ratio_diff < best_ratio_diff: |
|
best_ratio_diff = ratio_diff |
|
best_ratio = ratio |
|
elif ratio_diff == best_ratio_diff: |
|
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: |
|
best_ratio = ratio |
|
return best_ratio |
|
|
|
def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False): |
|
orig_width, orig_height = image.size |
|
aspect_ratio = orig_width / orig_height |
|
|
|
# calculate the existing image aspect ratio |
|
target_ratios = set( |
|
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if |
|
i * j <= max_num and i * j >= min_num) |
|
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) |
|
|
|
# find the closest aspect ratio to the target |
|
target_aspect_ratio = find_closest_aspect_ratio( |
|
aspect_ratio, target_ratios, orig_width, orig_height, image_size) |
|
|
|
# calculate the target width and height |
|
target_width = image_size * target_aspect_ratio[0] |
|
target_height = image_size * target_aspect_ratio[1] |
|
blocks = target_aspect_ratio[0] * target_aspect_ratio[1] |
|
|
|
# resize the image |
|
resized_img = image.resize((target_width, target_height)) |
|
processed_images = [] |
|
for i in range(blocks): |
|
box = ( |
|
(i % (target_width // image_size)) * image_size, |
|
(i // (target_width // image_size)) * image_size, |
|
((i % (target_width // image_size)) + 1) * image_size, |
|
((i // (target_width // image_size)) + 1) * image_size |
|
) |
|
# split the image |
|
split_img = resized_img.crop(box) |
|
processed_images.append(split_img) |
|
assert len(processed_images) == blocks |
|
if use_thumbnail and len(processed_images) != 1: |
|
thumbnail_img = image.resize((image_size, image_size)) |
|
processed_images.append(thumbnail_img) |
|
return processed_images |
|
|
|
def load_image(image_file, input_size=448, max_num=12): |
|
image = Image.open(image_file).convert('RGB') |
|
transform = build_transform(input_size=input_size) |
|
images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num) |
|
pixel_values = [transform(image) for image in images] |
|
pixel_values = torch.stack(pixel_values) |
|
return pixel_values |
|
|
|
# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section. |
|
path = 'OS-Copilot/OS-Genesis-4B-AC' |
|
model = AutoModel.from_pretrained( |
|
path, |
|
torch_dtype=torch.bfloat16, |
|
low_cpu_mem_usage=True, |
|
trust_remote_code=True).eval().cuda() |
|
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False) |
|
|
|
# set the max number of tiles in `max_num` |
|
pixel_values = load_image('./web_dfacd48d-d2c2-492f-b94c-41e6a34ea99f.png', max_num=6).to(torch.bfloat16).cuda() |
|
generation_config = dict(max_new_tokens=1024, do_sample=True) |
|
|
|
question = "<image>\nYou are a GUI task expert, I will provide you with a high-level instruction, an action history, a screenshot with its corresponding accessibility tree.\n High-level instruction: {high_level_instruction}\n Action history: {action_history}\n Accessibility tree: {a11y_tree}\n Please generate the low-level thought and action for the next step." |
|
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) |
|
print(f'User: {question}\nAssistant: {response}') |
|
``` |
|
|
|
|
|
## Citation |
|
If you find this repository helpful, feel free to cite our paper: |
|
```bibtex |
|
@article{sun2024genesis, |
|
title={OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis}, |
|
author={Sun, Qiushi and Cheng, Kanzhi and Ding, Zichen and Jin, Chuanyang and Wang, Yian and Xu, Fangzhi and Wu, Zhenyu and Jia, Chengyou and Chen, Liheng and Liu, Zhoumianze and others}, |
|
journal={arXiv preprint arXiv:2412.19723}, |
|
year={2024} |
|
} |
|
``` |
