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with '#' will be ignored, and an empty message aborts the commit. On branch master Your branch is ahead of 'origin/master' by 2 commits. (use "git push" to publish your local commits) Changes to be committed: modified: .gitignore modified: README.md new file: bashes/auto-wifi-connect.service new file: bashes/auto-wifi-connect.sh deleted: keyboard_control.py new file: my_model/image.png new file: path_follower_demo.py new file: scripts/PIDtracking.py new file: scripts/__pycache__/publish_sine_path.cpython-310.pyc new file: scripts/publish_sine_path.py modified: src/gc_navigation2_slamtoolbox/params/gc_navigation_slam.yaml modified: src/gc_navigation2_slamtoolbox/params/gc_navigation_slam.yaml.bak new file: src/gc_navigation2_slamtoolbox/params/gc_navigation_slam.yaml.bak2 modified: src/origincar_base/config/ekf.yaml new file: src/origincar_base/config/ekf.yaml.bak modified: src/origincar_base/launch/base_serial.launch.py new file: src/origincar_base/launch/base_serial.launch.py.bak modified: src/origincar_base/launch/origincar_bringup.launch.py new file: src/past_control/CMakeLists.txt new file: src/past_control/config/past_control.yaml new file: src/past_control/include/past_control/tools.h new file: src/past_control/launch/past_control.launch.py new file: src/past_control/msg/Obstacle.msg new file: src/past_control/msg/ObstacleArray.msg new file: src/past_control/package.xml new file: src/past_control/src/lane_follower_node.cpp new file: src/past_control/src/obstacle_detector_node.cpp new file: src/past_control/src/racing_orchestrator.cpp new file: src/planner/CMakeLists.txt new file: src/planner/config/planner.yaml new file: src/planner/launch/planner.launch.py new file: src/planner/package.xml new file: src/planner/src/planner_version.cpp modified: src/qr_detection/src/qr_dete_depth.cpp new file: src/racing_control/CMakeLists.txt new file: src/racing_control/include/racing_control/racing_control.hpp new file: src/racing_control/package.xml new file: src/racing_control/src/racing_control.cpp modified: src/vlm_detect/setup.py new file: src/vlm_detect/vlm_detect/__pycache__/__init__.cpython-310.pyc new file: src/vlm_detect/vlm_detect/__pycache__/tts_node.cpython-310.pyc new file: src/vlm_detect/vlm_detect/test_publisher.py new file: src/vlm_detect/vlm_detect/tts_node.py modified: src/vlm_detect/vlm_detect/vlm_node.py new file: tools/measure_turning_radius.py new file: tools/set_volume.py new file: tools/udp_to_cmdvel.py new file: tools/windows_keyboard_control.py new file: vlm_server.py new file: "\350\260\203\350\257\225\350\256\260\345\275\225.Assets/1.png" renamed: "\350\260\203\350\257\225\350\256\260\345\275\225.log" -> "\350\260\203\350\257\225\350\256\260\345\275\225.md"
2026-06-22 17:16:45 +08:00
parent a9bfeef59b
commit 779b32362a
51 changed files with 3974 additions and 398 deletions
--- a/vlm_server.py
+++ b/vlm_server.py
@@ -0,0 +1,207 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+VLM 图生文推理服务 — AndesVL-1B (千问体系)
+为 RDK X5 提供 OpenAI 兼容 API（纯 CPU 推理）
+
+启动方式: python vlm_server.py
+API 地址: http://192.168.10.210:8000
+"""
+
+import base64
+import io
+import time
+import uuid
+import logging
+import sys
+from contextlib import asynccontextmanager
+
+import torch
+import uvicorn
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from PIL import Image
+from transformers import AutoModel, AutoTokenizer, AutoImageProcessor
+
+# ========== 配置 ==========
+MODEL_PATH = "/home/root/models/AndesVL-1B-Instruct"   # RDK X5 上模型路径
+HOST = "0.0.0.0"
+PORT = 8000
+
+# ========== 日志 ==========
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    handlers=[logging.StreamHandler(sys.stdout)],
+)
+logger = logging.getLogger("vlm_server")
+
+# ========== 全局变量 ==========
+model = None
+tokenizer = None
+image_processor = None
+
+
+def load_model():
+    """加载 AndesVL-1B 模型"""
+    global model, tokenizer, image_processor
+
+    logger.info(f"模型路径: {MODEL_PATH}")
+    logger.info("加载 tokenizer & image processor...")
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
+    image_processor = AutoImageProcessor.from_pretrained(
+        MODEL_PATH, trust_remote_code=True
+    )
+
+    logger.info("加载 AndesVL-1B 模型 (fp32 CPU, 约 18 秒)...")
+    t0 = time.time()
+    model = AutoModel.from_pretrained(
+        MODEL_PATH,
+        trust_remote_code=True,
+        torch_dtype=torch.float32,
+        low_cpu_mem_usage=True,
+    )
+    model.eval()
+    logger.info(f"模型加载完成，耗时 {time.time() - t0:.1f}s")
+    logger.info("AndesVL-1B 推理服务就绪！")
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    load_model()
+    yield
+
+
+# ========== FastAPI 应用 ==========
+app = FastAPI(title="VLM Server (AndesVL-1B)", lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+# ========== Pydantic 模型 (OpenAI 格式) ==========
+class ImageUrl(BaseModel):
+    url: str
+
+
+class ContentPart(BaseModel):
+    type: str
+    text: str | None = None
+    image_url: ImageUrl | None = None
+
+
+class Message(BaseModel):
+    role: str
+    content: str | list[ContentPart]
+
+
+class ChatCompletionRequest(BaseModel):
+    model: str
+    messages: list[Message]
+    max_tokens: int = 100
+    temperature: float | None = None
+
+
+# ========== 辅助函数 ==========
+def decode_base64_image(data_url: str) -> Image.Image:
+    """从 data URL 解码图像"""
+    if "," in data_url:
+        base64_str = data_url.split(",", 1)[1]
+    else:
+        base64_str = data_url
+    image_bytes = base64.b64decode(base64_str)
+    return Image.open(io.BytesIO(image_bytes)).convert("RGB")
+
+
+def build_messages(messages: list[Message]) -> list[dict]:
+    """OpenAI 格式 → AndesVL chat 格式"""
+    result = []
+    for msg in messages:
+        if isinstance(msg.content, str):
+            result.append({"role": msg.role, "content": msg.content})
+        else:
+            content_list = []
+            for part in msg.content:
+                if part.type == "text" and part.text:
+                    content_list.append({"type": "text", "text": part.text})
+                elif part.type == "image_url" and part.image_url:
+                    image = decode_base64_image(part.image_url.url)
+                    content_list.append({"type": "image", "image": image})
+            result.append({"role": msg.role, "content": content_list})
+    return result
+
+
+# ========== API 路由 ==========
+@app.get("/v1/models")
+async def list_models():
+    return {
+        "object": "list",
+        "data": [
+            {
+                "id": "./OpenGVLab/InternVL3-1B/",
+                "object": "model",
+                "created": 1700000000,
+                "owned_by": "local",
+            }
+        ],
+    }
+
+
+@app.post("/v1/chat/completions")
+async def chat_completions(request: ChatCompletionRequest):
+    try:
+        messages = build_messages(request.messages)
+        logger.info(f"收到请求, model={request.model}, max_tokens={request.max_tokens}")
+
+        start_time = time.time()
+        response_text = model.chat(
+            messages,
+            tokenizer,
+            image_processor,
+            max_new_tokens=request.max_tokens,
+        )
+        elapsed = time.time() - start_time
+        logger.info(f"推理完成，耗时 {elapsed:.1f}s, 结果: {response_text[:50]}...")
+
+        return {
+            "id": f"chatcmpl-{uuid.uuid4().hex[:12]}",
+            "object": "chat.completion",
+            "created": int(time.time()),
+            "model": request.model,
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {"role": "assistant", "content": response_text},
+                    "finish_reason": "stop",
+                }
+            ],
+            "usage": {
+                "prompt_tokens": 0,
+                "completion_tokens": 0,
+                "total_tokens": 0,
+            },
+        }
+
+    except Exception as e:
+        logger.error(f"推理出错: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/health")
+async def health():
+    return {"status": "ok", "model_loaded": model is not None}
+
+
+# ========== 入口 ==========
+if __name__ == "__main__":
+    logger.info("=" * 50)
+    logger.info("VLM 图生文推理服务启动中...")
+    logger.info(f"监听: http://{HOST}:{PORT}")
+    logger.info("=" * 50)
+    uvicorn.run(app, host=HOST, port=PORT, log_level="info")