Skip to content

GitLab

Commit 5c60119d authored by mashun1's avatar mashun1
Browse files

uitars

parents
Pipeline #2683 canceled with stages
Expand all Hide whitespace changes
Inline Side-by-side
Showing with 563 additions and 0 deletions
Dockerfile 0 → 100644
View file @ 5c60119d
FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10
\ No newline at end of file
LICENSE 0 → 100644
View file @ 5c60119d
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
README.md 0 → 100644
View file @ 5c60119d
# UI-TARS
## 论文
`UI-TARS:Pioneering Automated GUI Interaction with Native Agents`
* https://arxiv.org/pdf/2501.12326
## 模型结构
该模型基于Qwen-2-VL，并在此基础上继续训练，以获得UI-TARS。
![alt text](readme_imgs/arch.png)
## 算法原理
增强的GUI感知（Enhanced GUI Perception）
UI-TARS通过构建大规模GUI截图和元数据集，训练模型能够更精准地理解界面元素（按钮、文本框等）、它们的布局关系和状态变化。它不仅能识别元素，还能进行细粒度的界面描述、状态转移描述和视觉问答，以增强对复杂GUI环境的感知能力。
统一的动作建模（Unified Action Modeling）
论文提出了一个统一动作空间，把不同平台（Web、桌面、移动端）上等价的操作（如点击、输入、拖拽）统一标准化。并通过大规模多步操作轨迹（multi-step action traces）数据进行训练，使模型能够连贯地完成复杂多步任务。
系统2推理（System-2 Reasoning）
UI-TARS引入了显式的“思考（Thoughts）”机制，在每步操作之前生成推理步骤，进行任务分解、长期一致性维护、里程碑识别、试错反思等多种推理模式。这种设计让UI-TARS不仅能快速反应（System-1），还能有意识地规划和调整（System-2），应对动态变化和复杂任务。
迭代式自我改进（Iterative Training with Reflective Online Traces）
UI-TARS使用数百台虚拟机在线自动采集、筛选并反思自己的交互轨迹。通过不断地数据迭代采集、错误分析、修正训练，模型能够持续学习，适应新环境和任务，极大减少了对人工标注的依赖。
![alt text](readme_imgs/alg.png)
## 环境配置
### Docker（方法一）
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10
docker run --shm-size 500g --network=host --name=uitars --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
pip install transformers==4.49.0
pip install opencv-python
### Dockerfile（方法二）
docker build -t <IMAGE_NAME>:<TAG> .
docker run --shm-size 500g --network=host --name=uitars --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
pip install transformers==4.49.0
pip install opencv-python
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装： https://developer.hpccube.com/tool/
```
dtk: 25.04
python: 3.10
torch: 2.4.1
deepspeed: 0.14.2
flash-attn: 2.6.1
triton: 3.0.0
```
2、其他非特殊库直接按照requirements.txt安装
```
pip install transformers==4.49.0
pip install opencv-python
```
## 数据集
无
## 训练
无
## 推理
```bash
# 运行前需修改文件内参数
# TARS-1.5 推荐
python inference_15.py
# TARS
python inference.py
```
注意：`参考data/test_messages.json`构建自己的数据集，并使用`utils/generate_new_data.py`处理数据。
## result
![alt text](readme_imgs/result1.png)
### 精度
无
## 应用场景
### 算法类别
`对话问答`
### 热点应用行业
`电商,教育,广媒`
## 预训练权重
下载后的模型放在 `checkpoints` 目录（自行创建）
| Model | url |
|-----------|------------------|
| UI-TARS-1.5-7B 推荐 | [huggingface](https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B) |
| UI-TARS-2B-SFT | [huggingface](https://huggingface.co/ByteDance-Seed/UI-TARS-2B-SFT) |
| UI-TARS-7B-SFT | [huggingface](https://huggingface.co/ByteDance-Seed/UI-TARS-7B-SFT) |
| UI-TARS-7B-DPO | [huggingface](https://huggingface.co/ByteDance-Seed/UI-TARS-7B-DPO) |
| UI-TARS-72B-SFT| [huggingface](https://huggingface.co/ByteDance-Seed/UI-TARS-72B-SFT) |
| UI-TARS-72B-DPO| [huggingface](https://huggingface.co/ByteDance-Seed/UI-TARS-72B-DPO) |
## 源码仓库及问题反馈
* https://developer.sourcefind.cn/codes/modelzoo/ui-tars_pytorch
## 参考资料
* https://github.com/bytedance/UI-TARS
README_coordinates.md 0 → 100644
View file @ 5c60119d
# Tutorial: Processing Model Coordinate Outputs
**Note**: For complete action space parsing, please refer to [OSWorld `uitars_agent.py`](https://github.com/xlang-ai/OSWorld/blob/main/mm_agents/uitars_agent.py). This tutorial assumes that we have already obtained the raw coordinate output from the model and will process it to determine the actual position in the image that the model intends to click.
---
## Steps:
1. **Visualize the Model's Output Coordinates**
Use the code provided below to process the model's output and visualize the coordinates on the image.
## Code Example
```python
# Assume model output
model_raw_response = """Thought: xxx
Action: click(start_box='(197,525)')"""
# Please use re to parse the coordinate values
model_output_width = 197
model_output_height = 525
from PIL import Image
import matplotlib.pyplot as plt
import json
import base64
from io import BytesIO
from PIL import Image
import math
IMAGE_FACTOR = 28
MIN_PIXELS = 100 * 28 * 28
MAX_PIXELS = 16384 * 28 * 28
MAX_RATIO = 200
VIDEO_MIN_PIXELS = 128 * 28 * 28
VIDEO_MAX_PIXELS = 768 * 28 * 28
FRAME_FACTOR = 2
FPS = 2.0
FPS_MIN_FRAMES = 4
FPS_MAX_FRAMES = 768
def round_by_factor(number: int, factor: int) -> int:
"""Returns the closest integer to 'number' that is divisible by 'factor'."""
return round(number / factor) * factor
def ceil_by_factor(number: int, factor: int) -> int:
"""Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
return math.ceil(number / factor) * factor
def floor_by_factor(number: int, factor: int) -> int:
"""Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
return math.floor(number / factor) * factor
def smart_resize(
height: int, width: int, factor: int = IMAGE_FACTOR, min_pixels: int = MIN_PIXELS, max_pixels: int = MAX_PIXELS
) -> tuple[int, int]:
"""
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 max(height, width) / min(height, width) > MAX_RATIO:
raise ValueError(
f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}"
)
h_bar = max(factor, round_by_factor(height, factor))
w_bar = max(factor, round_by_factor(width, factor))
if h_bar * w_bar > max_pixels:
beta = math.sqrt((height * width) / max_pixels)
h_bar = floor_by_factor(height / beta, factor)
w_bar = floor_by_factor(width / beta, factor)
elif h_bar * w_bar < min_pixels:
beta = math.sqrt(min_pixels / (height * width))
h_bar = ceil_by_factor(height * beta, factor)
w_bar = ceil_by_factor(width * beta, factor)
return h_bar, w_bar
# Open the image
img = Image.open('./data/coordinate_process_image.png')
width, height = img.size
print(f'Original coordinate: {width}, {height}')
# Calculate the new dimensions
new_height, new_width = smart_resize(height, width)
new_coordinate = (int(model_output_width/new_width * width), int(model_output_height/new_height * height))
print(f'Resized dimensions: {new_width}, {new_height}')
print(new_coordinate)
# Display the image
plt.imshow(img)
plt.scatter([new_coordinate[0]], [new_coordinate[1]], c='red', s=50) # Mark the point with a red dot
plt.title('Visualize Coordinate')
plt.axis('off') # Set to 'off' to hide the axes
plt.savefig('./data/coordinate_process_image_som.png', dpi=350)
```
2. The output SOM image should look like this:
![Output SOM Image](./data/coordinate_process_image_som.png)
\ No newline at end of file
README_deploy.md 0 → 100644
View file @ 5c60119d
# UI-TARS 1.5 HuggingFace Endpoint Deployment Guide
## 1. HuggingFace Inference Endpoints Cloud Deployment
We use HuggingFace's Inference Endpoints platform to quickly deploy a cloud-based model.
### Deployment Steps
1. **Access the Deployment Interface**
- Click [Deploy from Hugging Face](https://endpoints.huggingface.co/catalog)
![Deploy from Hugging Face](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_1_formal.png?download=true)
- Select the model `UI-TARS 1.5 7B` and click **Import Model**
![Import Model](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_2_formal.png?download=true)
2. **Configure Settings**
- **Hardware Configuration**
- In the `Hardware Configuration` section, choose a GPU instance. Here are the recommendations for different model sizes:
- For the 7B model, select `GPU L40S 1GPU 48G` (Recommended: Nvidia L4 / Nvidia A100).
![Hardware Configuration](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_3_formal.png?download=true)
- **Container Configuration**
- Set the following parameters:
- `Max Number of Tokens (per Query)`: 65536
- `Max Batch Prefill Tokens`: 65536
- `Max Input Length (per Query)`: 65537
![Container Configuration](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_4_formal.png?download=true)
- **Environment Variables**
- Add the following environment variables:
- `CUDA_GRAPHS=0` to avoid deployment failures. For details, refer to [issue 2875](https://github.com/huggingface/text-generation-inference/issues/2875).
- `PAYLOAD_LIMIT=8000000` to prevent request failures due to large images. For details, refer to [issue 1802](https://github.com/huggingface/text-generation-inference/issues/1802).
![Environment Variables](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_5_formal.png?download=true)
- **Create Endpoint**
- Click **Create** to set up the endpoint.
![Create Endpoint](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_6_formal.png?download=true)
- **Enter Setup**
- Once the deployment is finished, you will see the confirmation page and need to enter the settings page.
![Complete](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_7_formal.png?download=true)
- **Update URI** -
- Go to the Container page, set the Container URI to ghcr.io/huggingface/text-generation-inference:3.2.1, and **click Update Endpoint to apply the changes**.
![Complete](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_8_formal.png?download=true)
## 2. API Usage Example
### **Python Test Code**
```python
# pip install openai
import io
import re
import json
import base64
from PIL import Image
from io import BytesIO
from openai import OpenAI
def add_box_token(input_string):
# Step 1: Split the string into individual actions
if "Action: " in input_string and "start_box=" in input_string:
suffix = input_string.split("Action: ")[0] + "Action: "
actions = input_string.split("Action: ")[1:]
processed_actions = []
for action in actions:
action = action.strip()
# Step 2: Extract coordinates (start_box or end_box) using regex
coordinates = re.findall(r"(start_box|end_box)='\((\d+),\s*(\d+)\)'", action)
updated_action = action # Start with the original action
for coord_type, x, y in coordinates:
# Convert x and y to integers
updated_action = updated_action.replace(f"{coord_type}='({x},{y})'", f"{coord_type}='<|box_start|>({x},{y})<|box_end|>'")
processed_actions.append(updated_action)
# Step 5: Reconstruct the final string
final_string = suffix + "\n\n".join(processed_actions)
else:
final_string = input_string
return final_string
client = OpenAI(
base_url="https:xxx",
api_key="hf_xxx"
)
result = {}
messages = json.load(open("./data/test_messages.json"))
for message in messages:
if message["role"] == "assistant":
message["content"] = add_box_token(message["content"])
print(message["content"])
chat_completion = client.chat.completions.create(
model="tgi",
messages=messages,
top_p=None,
temperature=0.0,
max_tokens=400,
stream=True,
seed=None,
stop=None,
frequency_penalty=None,
presence_penalty=None
)
response = ""
for message in chat_completion:
response += message.choices[0].delta.content
print(response)
```
### **Expected Output** ###
```python
Thought: 我看到Preferences窗口已经打开了，但这里显示的都是系统资源相关的设置。要设置图片的颜色模式，我得先看看左侧的选项列表。嗯，"Color Management"这个选项看起来很有希望，应该就是处理颜色管理的地方。让我点击它看看里面有什么选项。
Action: click(start_box='(177,549)')
```
\ No newline at end of file
README_v1.md 0 → 100644
View file @ 5c60119d
This diff is collapsed.
data/test_messages.json 0 → 100644
View file @ 5c60119d
This diff is collapsed.
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment