调试

app/controller/AlgorithmController.py

@@ -242,7 +242,7 @@ from app import file_tool
 
 
 # 启动训练
-@start_train_algorithm()
+#@start_train_algorithm()
 def train_R0DY(params_str, id):
     from app.yolov5.train_server import train_start
     params = TrainParams()
@@ -314,7 +314,7 @@ def train_R0DY(params_str, id):
     # zip_outputPath = os.path.join(exp_outputPath, "inference_model.zip")
 
 
-@obtain_train_param()
+#@obtain_train_param()
 def returnTrainParams():
     # nvmlInit()
     # gpuDeviceCount = nvmlDeviceGetCount()  # 获取Nvidia GPU块数
@@ -338,7 +338,7 @@ def returnTrainParams():
         {"index": 7, "name": "CLASS_NAMES", "value": ['hole', '456'], "description": '类别名称', "default": '', "type": "L",
          "items": '',
          'show': False},
-        {"index": 8, "name": "DatasetDir", "value": "E:/aicheck/data_set/11442136178662604800/ori/",
+        {"index": 8, "name": "DatasetDir", "value": "E:/aicheck/data_set/11442136178662604800/ori",
          "description": '数据集路径',
          "default": "./app/maskrcnn/datasets/test", "type": "S", 'show': False}  # ORI_PATH
     ]

app/file_tool.py

@@ -179,7 +179,7 @@ def get_file(ori_path: str, type_list: Union[object,str]):
     test_files = []
     # 训练、测试比例强制9：1
     for img in imgs[0:1]:
-        path = ori_path + '/images/' +img
+        path = ori_path + '/images/' +img #'/images/'
         # print(os.path.exists(path))
         print('图像路径',path)
         if os.path.exists(path):
@@ -187,7 +187,7 @@ def get_file(ori_path: str, type_list: Union[object,str]):
             print('1111')
             #label = ori_path + 'labels/' + os.path.split(path)[1]
             (filename1, extension) = os.path.splitext(img)  # 文件名与后缀名分开
-            label = ori_path + '/labels/' + filename1 + '.json'
+            label = ori_path + '/labels/' + filename1 + '.json' #'/labels/'
             print('标签',label)
             if label is not None:
                 #train_files.append(label)

app/yolov5/data/coco128.yaml

@@ -3,5 +3,5 @@ train: E:/aicheck/data_set/11442136178662604800/trained/images/train/
 val: E:/aicheck/data_set/11442136178662604800/trained/images/val/
 test: null
 names:
-  0: logo
-  1: 3C
+  0: hole
+  1: '456'

app/yolov5/train_server.py

@@ -304,7 +304,7 @@ def train(hyp, opt, device, data_list,id,callbacks):  # hyp is path/to/hyp.yaml
                     num_train_img=train_num,
                     train_mod_savepath=best)
 
-    # @algorithm_process_value_websocket()
+    @algorithm_process_value_websocket()
     def report_cellback(i, num_epochs, reportAccu):
         report.rate_of_progess = ((i + 1) / num_epochs) * 100
         report.progress = (i + 1)
@@ -470,7 +470,7 @@ def train(hyp, opt, device, data_list,id,callbacks):  # hyp is path/to/hyp.yaml
         print('##############',best)
         for f in best:
             print('##################',f)
-            if os.path.exists(f):
+            if os.path.exists(best):
                 strip_optimizer(f)  # strip optimizers
                 if f is best:
                     LOGGER.info(f'\nValidating {f}...')