训练卡住debug

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