sam3本地部署

官网地址：https://github.com/facebookresearch/sam3/tree/main

下载：

安装python版本为3.11，我使用 torch 版本是 2.5.0，torch 安装可以参考往期教程，之后安装基础库就行，缺什么安装什么

下载对应的 .whl 文件

https://hf-mirror.com/madbuda/triton-windows-builds

切换下载文件的路径，直接安装

运行效果

app完整代码如下：

# -*- coding: utf-8 -*-""" @Auth ：落花不写码 @File ：app.py @Motto :学习新思想，争做新青年 """importosimportsysimporttorchimportnumpyasnpimportgradioasgrfromPILimportImageimportwarningsfromsam3.model_builderimportbuild_sam3_image_modelfromsam3.model.sam3_image_processorimportSam3Processor# ================= 路径 =================REPO_PATH=r"D:\1-Python\2-DeepLearningProject\modelscope\sam3\sam3-main"CHECKPOINT_PATH=r"E:\2-浏览器下载的文件\sam3.pt"ifREPO_PATHnotinsys.path:sys.path.append(REPO_PATH)warnings.filterwarnings("ignore")os.environ['MODELSCOPE_DISABLE_AST']='1'device="cuda"iftorch.cuda.is_available()else"cpu"print(f"正在加载模型...")model=build_sam3_image_model(checkpoint_path=CHECKPOINT_PATH).to(device)processor=Sam3Processor(model)defsegment_with_sam3(image:Image.Image,text:str,threshold:float,mask_threshold:float):ifimageisNone:returnNone,"请先上传图片。"ifnottext.strip():return(image,[]),"请输入文本描述。"try:withtorch.inference_mode(),torch.autocast(device_type=device,dtype=torch.bfloat16):inference_state=processor.set_image(image)output=processor.set_text_prompt(state=inference_state,prompt=text.strip())masks=output.get("masks")scores=output.get("scores")ifmasksisNoneorlen(scores)==0:return(image,[]),f"未识别到 '{text}'"annotations=[]fori,(mask,score)inenumerate(zip(masks,scores)):ifscore<threshold:continuemask_np=mask.squeeze().cpu().numpy().astype(np.float32)label=f"{text}#{i+1}({score:.2f})"annotations.append((mask_np,label))return(image,annotations),f"找到{len(annotations)}个目标"exceptExceptionase:importtraceback traceback.print_exc()return(image,[]),f"报错:{str(e)}"withgr.Blocks(theme=gr.themes.Soft(),title="SAM3 模型")asdemo:gr.Markdown(""" # SAM3 文本引导万物分割 (源码部署) **Web 界面可视化二次开发 | CSDN：落花不写码** """)withgr.Row():withgr.Column():img_in=gr.Image(label="输入图片",type="pil")prompt=gr.Textbox(label="输入物体名称 (英文)",placeholder="例如: person, dog, car...")withgr.Row():t_slider=gr.Slider(0,1,value=0.5,label="检测阈值")m_slider=gr.Slider(0,1,value=0.5,label="掩码精细度")btn=gr.Button("开始分割",variant="primary")withgr.Column():img_out=gr.AnnotatedImage(label="分割结果")info=gr.Markdown(value="等待任务...")btn.click(fn=segment_with_sam3,inputs=[img_in,prompt,t_slider,m_slider],outputs=[img_out,info])if__name__=="__main__":demo.launch()

目录结构：