yolo中断训练功能添加

app/configs/global_var.py

@@ -0,0 +1,27 @@
+"""
+@Time ： 2022/11/15 10:13
+@Auth ： 东
+@File ：global_var.py
+@IDE ：PyCharm
+@Motto：ABC(Always Be Coding)
+@Desc：
+
+"""
+
+
+def _init():  # 初始化
+    global _global_dict
+    _global_dict = {}
+
+
+def set_value(key, value):
+    # 定义一个全局变量
+    _global_dict[key] = value
+
+
+def get_value(key):
+    # 获得一个全局变量，不存在则提示读取对应变量失败
+    try:
+        return _global_dict[key]
+    except:
+        print('读取' + key + '失败\r\n')

app/controller/AlgorithmController.py

@@ -287,11 +287,11 @@ from app.schemas.TrainResult import DetectProcessValueDice, DetectReport
 from app import file_tool
 
 
-def error_return(id: str):
+def error_return(id: str, data):
     """
     算法出错，返回
     """
-    data_res = {'code': 2, "type": 'error', 'msg': 'fail', 'data': None}
+    data_res = {'code': 2, "type": 'error', 'msg': 'fail', 'data': data}
     manager.send_message_proj_json(message=data_res, id=id)
 
 # 启动训练
@@ -310,8 +310,10 @@ def train_R0DY(params_str, id):
     device = params.get('device').value
     try:
         train_start(weights, savemodel, epoches, img_size, batch_size, device, data_list, id)
-    except:
+        print("train down!")
-        error_return(id=id)
+    except Exception as e:
+        print(repr(e))
+        error_return(id=id,data=repr(e))
 
 
 # 启动验证程序

app/schemas/TrainResult.py

@@ -32,6 +32,7 @@ class Report(BaseModel):
     train_mod_savepath: str = Field(..., description="模型保存路径")
     start_time: datetime.date = Field(datetime.datetime.now(), description="开始时间")
     end_time: datetime.date = Field(datetime.datetime.now(), description="结束时间")
+    alg_code: str = Field(..., description="模型编码")
 
 
 class ReportDict(BaseModel):

app/yolov5/train_server.py

@@ -61,7 +61,7 @@ from app.yolov5.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel,
                                smart_resume, torch_distributed_zero_first)
 from app.schemas.TrainResult import Report, ProcessValueList
 from app.controller.AlgorithmController import algorithm_process_value_websocket
-from app.controller.AlgorithmController import ifKillDict
+from app.configs import global_var
 from app.utils.websocket_tool import manager
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
 RANK = int(os.getenv('RANK', -1))
@@ -304,7 +304,8 @@ def train(hyp, opt, device, data_list,id,callbacks):  # hyp is path/to/hyp.yaml
     report = Report(rate_of_progess=0, precision=[process_value_list],
                     id=id, sum=epochs, progress=0,
                     num_train_img=train_num,
-                    train_mod_savepath=best)
+                    train_mod_savepath=best,
+                    alg_code="R-ODY")
 
     def kill_return():
         """
@@ -325,8 +326,9 @@ def train(hyp, opt, device, data_list,id,callbacks):  # hyp is path/to/hyp.yaml
     ###################结束#######################
     for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
         #callbacks.run('on_train_epoch_start')
-        global ifKillDict
+        print("start get global_var")
-        ifkill = ifKillDict['id']
+        ifkill = global_var.get_value(report.id)
+        print("get global_var down:",ifkill)
         if ifkill:
             kill_return()
             break
@@ -350,6 +352,12 @@ def train(hyp, opt, device, data_list,id,callbacks):  # hyp is path/to/hyp.yaml
         optimizer.zero_grad()
         for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------
             #callbacks.run('on_train_batch_start')
+            print("start get global_var")
+            ifkill = global_var.get_value(report.id)
+            print("get global_var down:",ifkill)
+            if ifkill:
+                kill_return()
+                break
             if targets.shape[0] == 0:
                 targets = [[0.00000, 5.00000, 0.97002, 0.24679, 0.05995, 0.05553],
                            [0.00000, 7.00000, 0.95097, 0.32007, 0.04188, 0.02549],