99 lines
3.1 KiB
Python
99 lines
3.1 KiB
Python
model_path = "/data1/yuyr/models--bytedance-research--UI-TARS-7B-DPO/snapshots/727b0df39207dafc6cf211a61f29d84b7659c39c/"
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# 打开图片
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# image_path = "file:///data1/yuyr/crawlee/screenshots/0fuABgATggRcGam_57.png"
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image_path = "/data1/yuyr/crawlee/aaa5.png"
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import base64
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def encode_image_to_base64(image_path):
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"""
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将图片编码为base64字符串
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"""
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try:
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with open(image_path, "rb") as image_file:
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return base64.b64encode(image_file.read()).decode('utf-8')
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except Exception as e:
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print(f"图片编码失败: {e}")
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return None
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image_base64 = encode_image_to_base64(image_path)
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from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
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from qwen_vl_utils import process_vision_info
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# 从本地加载模型
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model = Qwen2VLForConditionalGeneration.from_pretrained(
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model_path, torch_dtype="auto", device_map="cuda:0"
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)
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# 从本地加载processor
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processor = AutoProcessor.from_pretrained(model_path)
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# 设置消息，这里图片使用本地png文件路径
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messages = [
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{
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"role": "system",
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"content": """You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task.
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## Output Format
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```
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Thought: ...
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Action: ...
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```
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## Action Space
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click(start_box='<|box_start|>(x1,y1)<|box_end|>')
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left_double(start_box='<|box_start|>(x1,y1)<|box_end|>')
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right_single(start_box='<|box_start|>(x1,y1)<|box_end|>')
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drag(start_box='<|box_start|>(x1,y1)<|box_end|>', end_box='<|box_start|>(x3,y3)<|box_end|>')
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hotkey(key='')
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type(content='') #If you want to submit your input, use \"\" at the end of `content`.
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scroll(start_box='<|box_start|>(x1,y1)<|box_end|>', direction='down or up or right or left')
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wait() #Sleep for 5s and take a screenshot to check for any changes.
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finished()
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call_user() # Submit the task and call the user when the task is unsolvable, or when you need the user's help.
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## Note
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- Use Chinese in `Thought` part.
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- Summarize your next action (with its target element) in one sentence in `Thought` part."""
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},
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image_url": f"file://{image_path}"
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},
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{"type": "text", "text": "点击购物"},
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],
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}
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]
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# 准备推理输入
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to(model.device)
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# 推理：生成输出
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generated_ids = model.generate(**inputs, max_new_tokens=1024)
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print(f"generated_ids: {generated_ids}")
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generated_ids_trimmed = [
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out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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print(f"generated_ids_trimmed: {generated_ids_trimmed}")
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output_text = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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print(f"output_text: {output_text}")
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