138 lines
4.8 KiB
Python
138 lines
4.8 KiB
Python
# model_manager.py
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"""RL 模型管理器：从云端扫描和加载强化学习模型。
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纯业务逻辑，无 UI 依赖。通过回调与 UI 层通信。
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"""
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import threading
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import io
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import requests
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import torch
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from stable_baselines3 import SAC
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# 关键：禁用 PyTorch 内部多线程，防止在 PyInstaller daemon 线程中 segfault
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torch.set_num_threads(1)
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from api import device_post, the_folder
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class ModelManager:
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"""管理 RL 模型的云端扫描与加载"""
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def __init__(self):
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self.model_file_map = {} # 文件名 → fileID 映射
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self.rl_model = None # 加载的 SAC 模型实例
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self._on_log = None
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self._on_models_loaded = None
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self._on_load_complete = None
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# ---- 回调设置 ----
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def set_log_callback(self, callback):
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"""设置日志回调: callback(message: str)"""
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self._on_log = callback
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def set_models_loaded_callback(self, callback):
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"""设置模型列表加载完成回调: callback(file_names: list)"""
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self._on_models_loaded = callback
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def set_load_complete_callback(self, callback):
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"""设置模型加载完成回调: callback(success: bool, message: str)"""
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self._on_load_complete = callback
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def log(self, message):
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if self._on_log:
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self._on_log(message)
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# ---- 模型扫描 ----
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def scan_models(self):
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"""异步扫描云端模型文件夹，完成后回调通知"""
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def fetch_models():
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try:
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payload = {"type": "listModels", "folder": f"{the_folder}/model_config"}
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result = device_post(payload, timeout=10)
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if result.get("success"):
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files = result.get("files", [])
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file_list = result.get("fileList", [])
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self.model_file_map = {
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item.get("fileName"): item.get("fileID")
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for item in file_list if item.get("fileName")
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}
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self.log("模型列表刷新成功")
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if self._on_models_loaded:
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self._on_models_loaded(files)
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else:
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err = result.get('errMsg', '未知错误')
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self.log(f"获取模型列表失败: {err}")
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except Exception as e:
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self.log(f"扫描模型异常: {str(e)}")
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threading.Thread(target=fetch_models, daemon=True).start()
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# ---- 模型加载 ----
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def load_model(self, model_name: str):
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"""异步从云端加载指定的 RL 模型
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Args:
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model_name: 模型文件名
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"""
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if not model_name or model_name == "无模型文件":
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self.log("错误：请先选择一个有效的模型")
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return
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def download_and_load():
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try:
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file_id = self.model_file_map.get(model_name)
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if not file_id:
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self.log("模型加载失败: 缺少 fileID，请先刷新模型列表")
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return
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self.log(f"正在加载模型: {model_name}...")
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# 获取临时下载 URL
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payload = {"type": "downloadModel", "fileID": file_id}
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result = device_post(payload, timeout=15)
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if not result.get("success"):
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err = result.get('errMsg', '未知错误')
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self.log(f"模型加载异常: {err}")
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return
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url = result['url']
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# 下载模型文件
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model_resp = requests.get(url, timeout=30)
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if model_resp.status_code != 200:
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self.log(f"模型加载异常: HTTP {model_resp.status_code}")
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return
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model_bytes = model_resp.content
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# 直接加载到内存
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model_stream = io.BytesIO(model_bytes)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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self.rl_model = SAC.load(model_stream, device=device)
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self.log(f"成功加载模型: {model_name}")
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if self._on_load_complete:
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self._on_load_complete(True, f"成功加载模型: {model_name}")
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except Exception as e:
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msg = str(e)
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self.log(f"加载模型失败: {msg}")
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if self._on_load_complete:
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self._on_load_complete(False, f"加载失败: {msg}")
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threading.Thread(target=download_and_load, daemon=True).start()
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def is_model_loaded(self) -> bool:
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"""检查模型是否已加载"""
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return self.rl_model is not None
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