使用cuda版torch进行训练和推理

app/application/app.py

@@ -1,6 +1,7 @@
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 import asyncio
+import torch
 
 from app.application.token_middleware import TokenMiddleware
 from app.application.logger_middleware import LoggerMiddleware

app/service/project_detect_service.py

@@ -1,5 +1,4 @@
 import time
-
 from sqlalchemy.orm import Session
 from typing import List
 from fastapi import UploadFile
@@ -20,6 +19,7 @@ from app.util import os_utils as os
 from app.util import random_utils as ru
 from app.config.config_reader import yolo_url
 from app.websocket.web_socket_server import room_manager
+from app.common.redis_cli import redis_conn
 
 
 def add_detect(detect_in: ProjectDetectIn, session: Session):
@@ -160,8 +160,12 @@ async def run_detect_img(weights: str, source: str, project: str, name: str, log
     """
     yolo_path = os.file_path(yolo_url, 'detect.py')
     room = 'detect_' + str(detect_id)
-    await room_manager.send_to_room(room, f"stdout: 模型训练开始，请稍等。。。\n")
+    await room_manager.send_to_room(room, f"AiCheck: 模型训练开始，请稍等。。。\n")
     commend = ["python", '-u', yolo_path, "--weights", weights, "--source", source, "--name", name, "--project", project, "--save-txt"]
+    is_gpu = redis_conn.get('is_gpu')
+    # 判断是否存在cuda版本
+    if is_gpu == 'True':
+        commend.append("--device", "0")
     # 启动子进程
     with subprocess.Popen(
             commend,
@@ -209,6 +213,10 @@ async def run_detect_rtsp(weights_pt: str, rtsp_url: str, data: str, detect_id:
     room = 'detect_rtsp_' + str(detect_id)
     # 选择设备（CPU 或 GPU）
     device = select_device('cpu')
+    is_gpu = redis_conn.get('is_gpu')
+    # 判断是否存在cuda版本
+    if is_gpu == 'True':
+        device = select_device('cuda:0')
 
     # 加载模型
     model = DetectMultiBackend(weights_pt, device=device, dnn=False, data=data, fp16=False)

app/service/project_train_service.py

@@ -12,12 +12,14 @@ from app.util import random_utils as ru
 from app.config.config_reader import datasets_url, runs_url, images_url, yolo_url
 from app.websocket.web_socket_server import room_manager
 from app.util.csv_utils import read_csv
+from app.common.redis_cli import redis_conn
 
 from sqlalchemy.orm import Session
 from typing import List
 from fastapi import UploadFile
 import yaml
 import subprocess
+import torch
 
 
 def add_project(info: ProjectInfoIn, session: Session, user_id: int):
@@ -180,9 +182,7 @@ def run_train_yolo(project_info: ProjectInfoOut, train_in: ProjectTrainIn, sessi
     return data, project, name
 
 
-async def run_commend(data: str, project: str,
+async def run_commend(data: str, project: str, name: str, epochs: int, patience: int, weights: str, project_id: int, session: Session):
-                name: str, epochs: int, patience: int, weights: str,
-                project_id: int, session: Session):
     """
     执行训练
     :param data: 训练数据集
@@ -197,13 +197,20 @@ async def run_commend(data: str, project: str,
     """
     yolo_path = os.file_path(yolo_url, 'train.py')
     room = 'train_' + str(project_id)
-    await room_manager.send_to_room(room, f"stdout: 模型训练开始，请稍等。。。\n")
+    await room_manager.send_to_room(room, f"AiCheck: 模型训练开始，请稍等。。。\n")
     commend = ["python", '-u', yolo_path, "--data=" + data, "--project=" + project, "--name=" + name,
-               "--epochs=" + str(epochs), "--batch-size=4", "--exist-ok", "--patience=" + str(patience)]
+               "--epochs=" + str(epochs), "--batch-size=8", "--exist-ok", "--patience=" + str(patience)]
+
+    # 增加权重文件，在之前训练的基础上重新巡逻
     if weights != '' and weights is not None:
         train_info = ptc.get_train(int(weights), session)
         if train_info is not None:
             commend.append("--weights=" + train_info.best_pt)
+
+    is_gpu = redis_conn.get('is_gpu')
+    # 判断是否存在cuda版本
+    if is_gpu == 'True':
+        commend.append("--device=0")
     # 启动子进程
     with subprocess.Popen(
             commend,
@@ -212,7 +219,7 @@ async def run_commend(data: str, project: str,
             stdout=subprocess.PIPE,
             stderr=subprocess.STDOUT,  # 这里可以显示yolov5训练过程中出现的进度条等信息
             text=True,  # 缓存内容为文本，避免后续编码显示问题
-            encoding='utf-8',
+            encoding='latin1',
     ) as process:
         while process.poll() is None:
             line = process.stdout.readline()

main.py

@@ -1,5 +1,12 @@
 import uvicorn
+import torch
+
+from app.common.redis_cli import redis_conn
 from app.application.app import my_app
 
+
 if __name__ == '__main__':
-    uvicorn.run("main:my_app", host='0.0.0.0', port=8080, reload=True)
+    # 在主线程初始化cuda
+    is_gpu = torch.cuda.is_available()
+    redis_conn.set('is_gpu', str(is_gpu))
+    uvicorn.run("main:my_app", host='0.0.0.0', port=8080, reload=False)

requirements_gpu.txt

@@ -24,7 +24,7 @@ python-socketio == 5.12.1
 # BASE ------------------------------------------------------------------------
 gitpython>=3.1.30
 matplotlib>=3.3
-numpy==2.0.2
+numpy==1.24.0 # cuda版本的numpy需要降一下版本
 opencv-python>=4.1.1
 pillow>=10.3.0
 psutil  # system resources
@@ -32,12 +32,11 @@ PyYAML>=5.3.1
 requests>=2.32.2
 scipy==1.13.1
 thop>=0.1.1  # FLOPs computation
-torch --extra-index-url https://download.pytorch.org/whl/cu121
+torch==2.3.1+cu121 --extra-index-url https://mirrors.aliyun.com/pytorch-wheels/cu121/
-torchvision --extra-index-url https://download.pytorch.org/whl/cu121
+torchvision==0.18.1+cu121 --extra-index-url https://mirrors.aliyun.com/pytorch-wheels/cu121/
-torchaudio --extra-index-url https://download.pytorch.org/whl/cu121
 tqdm>=4.66.3
-ultralytics>=8.2.34  # https://ultralytics.com
+ultralytics==8.3.75  # https://ultralytics.com
 
 # Plotting --------------------------------------------------------------------
 pandas==2.2.3
-seaborn>=0.11.0
+seaborn==0.11.0 # 对应的这个依赖也需要降