调试

app/controller/AlgorithmController.py

@@ -102,8 +102,8 @@ def start_download_pt():
         @bp.route('/start_download_pt', methods=['get'])
         def wrapped_function_start_download_pt():
             param = request.args.get('param')
-            func(param)
-            return output_wrapped(0, 'success', '成功')
+            data = func(param)
+            return output_wrapped(0, 'success', data)
 
         return wrapped_function_start_download_pt
 
@@ -309,9 +309,6 @@ def Export_model_RODY(params_str):
         zipf.write(file, arcname=Path(file).name)  # 将torchscript和onnx模型压缩
 
     return exp_outputPath
-    zipf.write(modellist[1], arcname=modellist[1])
-    zip_inputpath = os.path.join(exp_outputPath, "inference_model")
-    zip_outputPath = os.path.join(exp_outputPath, "inference_model.zip")
 
 
 @obtain_train_param()
@@ -397,7 +394,7 @@ def returnDownloadParams():
     params_list = [
         {"index": 0, "name": "exp_inputPath", "value": 'E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt',
          "description": '转化模型输入路径',
-         "default": '/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/new磁环检测test_183504733393264640_R-DDM_11.pt/',
+         "default": 'E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt/',
          "type": "S", 'show': False},
         {"index": 1, "name": "device", "value": 'gpu', "description": 'CPU或GPU', "default": 'gpu', "type": "S",
          'show': False}