Merge branch 'master' of https://gitea.star-rising.cn/xkrs_manan/RODY

app/controller/AlgorithmController.py

@@ -297,6 +297,7 @@ def error_return(id: str, data):
 # 启动训练
 @start_train_algorithm()
 def train_R0DY(params_str, id):
+    print(params_str)
     from app.yolov5.train_server import train_start
     params = TrainParams()
     params.read_from_str(params_str)
@@ -308,12 +309,12 @@ def train_R0DY(params_str, id):
     epoches = params.get('epochnum').value
     batch_size = params.get('batch_size').value
     device = params.get('device').value
-    try:
+    #try:
     train_start(weights, savemodel, epoches, img_size, batch_size, device, data_list, id)
     print("train down!")
-    except Exception as e:
-        print(repr(e))
-        error_return(id=id,data=repr(e))
+    # except Exception as e:
+    #     print(repr(e))
+    #     error_return(id=id,data=repr(e))
 
 
 # 启动验证程序

app/yolov5/train_server.py

@@ -93,6 +93,7 @@ def train(hyp, opt, device, data_list,id,callbacks):  # hyp is path/to/hyp.yaml
     #将数据路径写到yaml文件中
     #data_list = file_tool.get_file(proj_no=pro)
     # print(data_list)
+    print("get in train()")
     yaml_rewrite(file=opt.data, data_list=data_list)
 
     save_dir, epochs,batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \
@@ -209,6 +210,7 @@ def train(hyp, opt, device, data_list,id,callbacks):  # hyp is path/to/hyp.yaml
     ema = ModelEMA(model) if RANK in {-1, 0} else None
 
     # Resume
+    print("Resume")
     best_fitness, start_epoch = 0.0, 0
     if pretrained:
         if resume:
@@ -226,6 +228,7 @@ def train(hyp, opt, device, data_list,id,callbacks):  # hyp is path/to/hyp.yaml
         model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
         LOGGER.info('Using SyncBatchNorm()')
 
+    print("Trainloader")
     # Trainloader
     train_loader, dataset = create_dataloader(train_path,
                                               imgsz,
@@ -283,6 +286,7 @@ def train(hyp, opt, device, data_list,id,callbacks):  # hyp is path/to/hyp.yaml
     model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc  # attach class weights
     model.names = names
 
+    print("Start training")
     # Start training
     t0 = time.time()
     nb = len(train_loader)  # number of batches