更新模型、修改Promise和Callback调用方式、处理告警

Signed-off-by: Nprincipal87 <nierong3@huawei.com>

ai/mindspore/mindsporectest/src/ohos_common.cpp

@@ -165,11 +165,12 @@ bool allclose(float *a, float *b, uint64_t count, float rtol = 1e-05,
     if (i == count - 1) {
       printf("     ……\n");
       printf("\n *** Total fail_count: %u\n", fail_count);
+      if (fail_count != 0) {
         printf("\n     fabs(a[i] - b[i])/(fabs(b[i])+1) : %f\n",
               tol / fail_count);
         printf("\n     fabs(a[i] - b[i]) : %f\n", tol1 / fail_count);
         printf("\n     fabs(a[i] - b[i])/fabs(b[i]) : %f\n", tol2 / fail_count);
-
+      }
       c = c + count;
       printf("\n     avg : %f\n", sum / count);
       printf("\n     min : %f\n", minimum);
@@ -254,10 +255,12 @@ bool allclose_int8(uint8_t *a, uint8_t *b, uint64_t count, float rtol = 1e-05,
     if (i == count - 1) {
       printf("     ……\n");
       printf("\n *** Total fail_count: %u\n", fail_count);
+      if (fail_count != 0) {
         printf("\n     fabs(a[i] - b[i])/(fabs(b[i])+1) : %f\n",
               tol / fail_count);
         printf("\n     fabs(a[i] - b[i]) : %f\n", tol1 / fail_count);
         printf("\n     fabs(a[i] - b[i])/fabs(b[i]) : %f\n", tol2 / fail_count);
+      }
 
       c = c + count;
       printf("\n     avg : %f\n", sum / count);

ai/mindspore/mindsporejstest/src/main/ets/test/Ability.test.ets

@@ -132,11 +132,9 @@ export default function abilityTest() {
       let modelName = 'ml_face_isface.ms';
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await syscontext.resourceManager.getRawFileContent(modelName).then(async (model_buffer) => {
-          let modelBuffer = model_buffer;
+      let modelBuffer = await syscontext.resourceManager.getRawFileContent(modelName);
       let context: mindSporeLite.Context = {};
       context.target = ["cpu", "nnrt"];
       context.nnrt = {};
@@ -145,7 +143,7 @@ export default function abilityTest() {
       context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
       context.cpu.precisionMode = "preferred_fp16";
       context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
-          await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, context).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, context)
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -169,7 +167,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-            await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -178,17 +176,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-            }).catch(err => {
-              console.log("predict catch: ", err);
-            })
-          }).catch(err => {
-            console.log("loadModel catch: ", err);
-          })
-        }).catch(err => {
-          console.log("getRawFileContent catch: ", err);
-        })
-      })
     })
 
 
@@ -199,12 +189,11 @@ export default function abilityTest() {
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
       console.log('=========MSLITE loadModel start=====');
       let file = fs.openSync(model_file, fs.OpenMode.READ_ONLY);
-        await mindSporeLite.loadModelFromFd(file.fd).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFd(file.fd);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -213,7 +202,7 @@ export default function abilityTest() {
       expect(input_name.toString()).assertEqual("data");
       let input_shape = modelInputs[0].shape;
       console.log(input_shape.toString());
-          expect(input_shape.toString()).assertEqual("1,48,48,3")
+      expect(input_shape.toString()).assertEqual("1,48,48,3");
       let input_elementNum = modelInputs[0].elementNum;
       console.log(input_elementNum.toString());
       expect(input_elementNum.toString()).assertEqual("6912");
@@ -228,7 +217,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -237,17 +226,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-          }).catch(err => {
-            console.log("predict catch: ", err);
-          })
-
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
     // 正常场景：ModelBuild，调用loadModelFromFile方法，正常推理
@@ -256,8 +237,7 @@ export default function abilityTest() {
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
       let context:mindSporeLite.Context={};
       context.target = ["cpu", "nnrt"];
@@ -268,7 +248,7 @@ export default function abilityTest() {
       context.cpu.precisionMode = "preferred_fp16";
       context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
       console.log('=========MSLITE loadModel start=====');
-        await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -292,7 +272,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -302,16 +282,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-          }).catch(err => {
-            console.log("predict catch: ", err);
-          })
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
 
@@ -321,15 +294,14 @@ export default function abilityTest() {
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
       let context:mindSporeLite.Context={};
       context.target = ["cpu"];
       context.cpu = {
         "threadNum": 4,
       }
-        await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -353,7 +325,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -363,16 +335,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-          }).catch(err => {
-            console.log("predict catch: ", err);
-          })
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
 
@@ -382,15 +347,14 @@ export default function abilityTest() {
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
       let context:mindSporeLite.Context={};
       context.target = ["cpu"];
       context.cpu = {
         "threadNum": 2,
       }
-        await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -414,7 +378,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -424,16 +388,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-          }).catch(err => {
-            console.log("predict catch: ", err);
-          })
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
     //正常场景：Context设置CPU，1线程
@@ -442,15 +399,14 @@ export default function abilityTest() {
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength)
       let context:mindSporeLite.Context={};
       context.target = ["cpu"];
       context.cpu = {
         "threadNum": 1,
       }
-        await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -474,7 +430,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -484,16 +440,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-          }).catch(err => {
-            console.log("predict catch: ", err);
-          })
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
 
@@ -503,15 +452,14 @@ export default function abilityTest() {
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
       let context:mindSporeLite.Context={};
       context.target = ["cpu"];
       context.cpu = {
         "threadNum": 0,
       }
-        await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -535,7 +483,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -545,16 +493,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-          }).catch(err => {
-            console.log("predict catch: ", err);
-          })
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
 
@@ -564,15 +505,14 @@ export default function abilityTest() {
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
       let context:mindSporeLite.Context={};
       context.target = ["cpu"];
       context.cpu={};
       context.cpu.threadAffinityMode = 3;
       console.log("MSLITE api test: set threadAffinityMode=3.");
-        await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -596,7 +536,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -606,16 +546,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-          }).catch(err => {
-            console.log("predict catch: ", err);
-          })
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
 
@@ -625,15 +558,14 @@ export default function abilityTest() {
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
       let context:mindSporeLite.Context={};
       context.target = ["cpu"];
       context.cpu={};
       context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.LITTLE_CORES_FIRST;
       console.log("MSLITE api test: set threadAffinityMode=2.");
-        await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -657,7 +589,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -667,16 +599,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-          }).catch(err => {
-            console.log("predict catch: ", err);
-          })
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
 
@@ -686,15 +611,14 @@ export default function abilityTest() {
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength)
       let context:mindSporeLite.Context={};
       context.target = ["cpu"];
       context.cpu={};
       context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
       console.log("MSLITE api test: set threadAffinityMode=1.");
-        await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -718,7 +642,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -728,16 +652,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-          }).catch(err => {
-            console.log("predict catch: ", err);
-          })
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
     //正常场景：Context设置CPU，绑核设置为0，不绑核
@@ -746,15 +663,14 @@ export default function abilityTest() {
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
       let context:mindSporeLite.Context={};
       context.target = ["cpu"];
       context.cpu={};
       context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.NO_AFFINITIES;
       console.log("MSLITE api test: set threadAffinityMode=0.");
-        await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -778,7 +694,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -788,16 +704,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-          }).catch(err => {
-            console.log("predict catch: ", err);
-          })
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
 
@@ -808,15 +717,14 @@ export default function abilityTest() {
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
       let context:mindSporeLite.Context={};
       context.target = ["cpu"];
       context.cpu={};
       context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
       console.log("MSLITE api test: set threadAffinityCoreList=[0, 1, 2, 3].");
-        await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -840,7 +748,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -850,16 +758,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-          }).catch(err => {
-            console.log("predict catch: ", err);
-          })
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
     //异常场景：ModelBuild，调用model path方法，path为空
@@ -904,10 +805,9 @@ export default function abilityTest() {
       let inputName = 'ml_ocr_cn_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_ocr_cn.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -955,7 +855,7 @@ export default function abilityTest() {
       expect(input_dataSize2.toString()).assertEqual("65536");
       modelInputs2[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs2).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs2);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -965,16 +865,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-          }).catch(err => {
-            console.log("predict catch: ", err);
-          })
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
 
@@ -984,10 +877,9 @@ export default function abilityTest() {
       let inputName = 'ml_ocr_cn_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_ocr_cn.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -1035,7 +927,7 @@ export default function abilityTest() {
       expect(input_dataSize2.toString()).assertEqual("65536");
       modelInputs2[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs2).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs2);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -1045,16 +937,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-          }).catch(err => {
-            console.log("predict catch: ", err);
-          })
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
 
@@ -1064,10 +949,9 @@ export default function abilityTest() {
       let inputName = 'ml_ocr_cn_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_ocr_cn.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -1094,12 +978,6 @@ export default function abilityTest() {
       let resize_result = msliteModel.resize(modelInputs, new_dim);
       expect(resize_result).assertFalse;
       console.log('=========MSLITE resize failed=====');
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
 
@@ -1109,10 +987,9 @@ export default function abilityTest() {
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -1137,12 +1014,6 @@ export default function abilityTest() {
       let resize_result = msliteModel.resize(modelInputs, new_dim);
       expect(resize_result).assertFalse();
       console.log('=========MSLITE resize failed=====');
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
 
@@ -1152,10 +1023,9 @@ export default function abilityTest() {
       let inputName = 'ml_ocr_cn_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_ocr_cn.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -1182,12 +1052,6 @@ export default function abilityTest() {
       let resize_result = msliteModel.resize(modelInputs, new_dim);
       expect(resize_result).assertFalse();
       console.log('=========MSLITE resize failed=====');
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
 
@@ -1197,10 +1061,9 @@ export default function abilityTest() {
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -1226,20 +1089,18 @@ export default function abilityTest() {
       console.log('=========MSLITE predict start=====');
       let num = 0;
       for (var i = 0; i < 10; i++) {
-            await msliteModel.predict(modelInputs).then((modelOutputs) => {
+        let modelOutputs = await msliteModel.predict(modelInputs);
         expect(modelOutputs !== null).assertTrue();
         console.log('=========MSLITE new Float32Array start=====');
         let output0 = new Float32Array(modelOutputs[0].getData());
         expect(output0.length).assertLarger(0);
         console.log('output0.length:' + output0.length);
-              for (var i = 0; i < 2; i++) {
-                console.log(output0[i].toString());
-                expect(output0[i].toString() !== null).assertTrue();
+        for (var z = 0; z < 2; z++) {
+          console.log(output0[z].toString());
+          expect(output0[z].toString() !== null).assertTrue();
         }
-              console.log('=========MSLITE new Float32Array end=====');
-            }).catch(err => {
-              console.log("predict catch: ", err);
-            })
+        const modelInputs0 = msliteModel.getInputs();
+        console.log(modelInputs0[0].name.toString());
         console.log('=========MSLITE new Float32Array end=====');
         console.log('=========i.toString()=====');
         console.log(i.toString());
@@ -1247,12 +1108,6 @@ export default function abilityTest() {
         console.log('=========num.toString()=====');
         console.log(num.toString());
       }
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
 
@@ -1262,13 +1117,12 @@ export default function abilityTest() {
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
       let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
       for (var i = 0; i < 10; i++) {
         mindSporeLite.loadModelFromFile(model_file);
       }
-        await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -1292,7 +1146,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -1302,16 +1156,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-          }).catch(err => {
-            console.log("predict catch: ", err);
-          })
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
     })
 
 
@@ -1321,12 +1168,10 @@ export default function abilityTest() {
       let modelName = 'ml_face_isface.ms';
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await syscontext.resourceManager.getRawFileContent(modelName).then(async (model_buffer) => {
-          let modelBuffer = model_buffer;
-          await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer).then(async (msliteModel) => {
+      let modelBuffer = await syscontext.resourceManager.getRawFileContent(modelName);
+      let msliteModel = await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -1353,7 +1198,7 @@ export default function abilityTest() {
 
       }
       console.log('=========MSLITE predict start=====');
-            await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -1362,17 +1207,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-            }).catch(err => {
-              console.log("predict catch: ", err);
-            })
-          }).catch(err => {
-            console.log("loadModel catch: ", err);
-          })
-        }).catch(err => {
-          console.log("getRawFileContent catch: ", err);
-        })
-      })
     })
 
 
@@ -1383,15 +1220,12 @@ export default function abilityTest() {
       let inputName01 = 'ml_video_edit_face_cutout_portraitSeg_deconv_0.input';
       let inputName02 = 'ml_video_edit_face_cutout_portraitSeg_deconv_1.input';
       let syscontext = globalThis.context;
-      await syscontext.resourceManager.getRawFileContent(inputName01).then(async (buffer) => {
-        let inputBuffer01 = buffer;
-        await syscontext.resourceManager.getRawFileContent(inputName02).then(async (buffer) => {
-          let inputBuffer02 = buffer;
+      let inputBuffer01 = await syscontext.resourceManager.getRawFileContent(inputName01);
+      let inputBuffer02 = await syscontext.resourceManager.getRawFileContent(inputName02);
       console.log('=========MSLITE success, input01 bin bytelength: ' + inputBuffer01.byteLength);
       console.log('=========MSLITE success, input02 bin bytelength: ' + inputBuffer02.byteLength);
-          await syscontext.resourceManager.getRawFileContent(modelName).then(async (model_buffer) => {
-            let modelBuffer = model_buffer;
-            await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer).then(async (msliteModel) => {
+      let modelBuffer = await syscontext.resourceManager.getRawFileContent(modelName);
+      let msliteModel = await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -1430,7 +1264,7 @@ export default function abilityTest() {
         console.log(Inputs2[i].toString());
       }
       console.log('=========MSLITE predict start=====');
-              await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -1439,20 +1273,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-              }).catch(err => {
-                console.log("predict catch: ", err);
-              })
-            }).catch(err => {
-              console.log("loadModel catch: ", err);
-            })
-          }).catch(err => {
-            console.log("getRawFileContent catch: ", err);
-          })
-        }).catch(err => {
-          console.log("getRawFileContent catch: ", err);
-        })
-      })
     })
 
 
@@ -1463,12 +1286,10 @@ export default function abilityTest() {
       let modelName = 'aiy_vision_classifier_plants_V1_3.ms';
       let inputName = 'aiy_vision_classifier_plants_V1_3_0.input';
       let syscontext = globalThis.context;
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await syscontext.resourceManager.getRawFileContent(modelName).then(async (model_buffer) => {
-          let modelBuffer = model_buffer;
-          await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer).then(async (msliteModel) => {
+      let modelBuffer = await syscontext.resourceManager.getRawFileContent(modelName);
+      let msliteModel = await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -1490,7 +1311,7 @@ export default function abilityTest() {
         console.log(Inputs2[i].toString());
       }
       console.log('=========MSLITE predict start=====');
-            await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Uint8Array(modelOutputs[0].getData());
@@ -1499,17 +1320,9 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-            }).catch(err => {
-              console.log("predict catch: ", err);
-            })
-          }).catch(err => {
-            console.log("loadModel catch: ", err);
-          })
-        }).catch(err => {
-          console.log("getRawFileContent catch: ", err);
-        })
-      })
     })
 
 
@@ -1521,18 +1334,14 @@ export default function abilityTest() {
       let inputName02 = 'ml_headpose_pb2tflite_1.input';
       let inputName03 = 'ml_headpose_pb2tflite_2.input';
       let syscontext = globalThis.context
-      await syscontext.resourceManager.getRawFileContent(inputName01).then(async (buffer) => {
-        let inputBuffer01 = buffer;
-        await syscontext.resourceManager.getRawFileContent(inputName02).then(async (buffer) => {
-          let inputBuffer02 = buffer;
-          await syscontext.resourceManager.getRawFileContent(inputName03).then(async (buffer) => {
-            let inputBuffer03 = buffer;
+      let inputBuffer01 = await syscontext.resourceManager.getRawFileContent(inputName01);
+      let inputBuffer02 = await syscontext.resourceManager.getRawFileContent(inputName02);
+      let inputBuffer03 = await syscontext.resourceManager.getRawFileContent(inputName03);
       console.log('=========MSLITE success, input01 bin bytelength: ' + inputBuffer01.byteLength);
       console.log('=========MSLITE success, input02 bin bytelength: ' + inputBuffer02.byteLength);
       console.log('=========MSLITE success, input03 bin bytelength: ' + inputBuffer03.byteLength);
-            await syscontext.resourceManager.getRawFileContent(modelName).then(async (model_buffer) => {
-              let modelBuffer = model_buffer;
-              await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer).then(async (msliteModel) => {
+      let modelBuffer = await syscontext.resourceManager.getRawFileContent(modelName);
+      let msliteModel = await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer);
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -1585,7 +1394,7 @@ export default function abilityTest() {
         console.log(Inputs2[i].toString());
       }
       console.log('=========MSLITE predict start=====');
-                await msliteModel.predict(modelInputs).then((modelOutputs) => {
+      let modelOutputs = await msliteModel.predict(modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -1594,36 +1403,19 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
-                }).catch(err => {
-                  console.log("predict catch: ", err);
-                })
-              }).catch(err => {
-                console.log("loadModel catch: ", err);
-              })
-            }).catch(err => {
-              console.log("getRawFileContent catch: ", err);
-            })
-          }).catch(err => {
-            console.log("getRawFileContent catch: ", err);
-          })
-        }).catch(err => {
-          console.log("getRawFileContent catch: ", err);
-        })
-      })
     })
 
 
     // 正常场景：调用loadModelFromFile callback接口设置context
     it('Test_load_model_param_model_path_callback', 0, async function (done) {
       console.log("MSLITE api test: loadModel param model file.Test_load_model_param_model_path_callback");
-      let inputName = 'ml_face_isface_0.input';
-      let syscontext = globalThis.context;
+      function load_model_from_file() {
+        return new Promise((resolve) => {
           let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
-        console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        let context:mindSporeLite.Context={};
+          let context: mindSporeLite.Context = {};
           context.target = ["cpu"];
           context.cpu = {
             "threadNum": 4,
@@ -1631,10 +1423,26 @@ export default function abilityTest() {
           context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
           context.cpu.precisionMode = "preferred_fp16";
           context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
-        console.log('=========MSLITE loadModel start=====');
-
           mindSporeLite.loadModelFromFile(model_file, context, (msliteModel) => {
-          try {
+            resolve(msliteModel);
+          })
+        })
+      }
+      function case_predict(msliteModel, modelInputs) {
+        return new Promise((resolve) => {
+          msliteModel.predict(modelInputs, (modelOutputs) => {
+            resolve(modelOutputs);
+          })
+        })
+      }
+
+      let inputName = 'ml_face_isface_0.input';
+      let syscontext = globalThis.context;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
+      console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
+      console.log('=========MSLITE loadModel start=====');
+      let msliteModel = null;
+      msliteModel = await load_model_from_file();
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -1658,8 +1466,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-            msliteModel.predict(modelInputs, (modelOutputs) => {
-              try {
+      let modelOutputs = await case_predict(msliteModel, modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -1669,40 +1476,22 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
       done();
-              } catch (error) {
-                console.info("predict catch: " + error);
-                done();
-              }
-            })
-            for (var j = 0; j < 10000; j++){
-              console.log("wait predict end:" + j.toString());
-            }
-          } catch (error) {
-            console.info("loadModel catch: " + error);
-            done();
-          }
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-        done();
-      })
     })
 
 
     // 正常场景：调用loadModelFromBuffer callback接口设置context
     it('Test_load_model_param_model_buffer_callback', 0, async function (done) {
       console.log("MSLITE api test: loadModel param model file.Test_load_model_param_model_buffer_callback");
-      let inputName = 'ml_face_isface_0.input';
-      let syscontext = globalThis.context;
+      function load_model_from_buffer() {
+        return new Promise((resolve) => {
           let modelName = 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
-        console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await syscontext.resourceManager.getRawFileContent(modelName).then(async (model_buffer) => {
-          let modelBuffer = model_buffer;
-          let context:mindSporeLite.Context={};
+          syscontext.resourceManager.getRawFileContent(modelName).then((model_buffer) => {
+            let modelBuffer = model_buffer
+            let context: mindSporeLite.Context = {};
             context.target = ["cpu"];
             context.cpu = {
               "threadNum": 4,
@@ -1710,10 +1499,28 @@ export default function abilityTest() {
             context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
             context.cpu.precisionMode = "preferred_fp16";
             context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
-          console.log('=========MSLITE loadModel start=====');
-
             mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, context, (msliteModel) => {
-            try {
+              resolve(msliteModel);
+            })
+          })
+        })
+      }
+      function case_predict(msliteModel, modelInputs) {
+        return new Promise((resolve) => {
+          msliteModel.predict(modelInputs, (modelOutputs) => {
+            resolve(modelOutputs);
+          })
+        })
+      }
+      let inputName = 'ml_face_isface_0.input';
+      let syscontext = globalThis.context;
+
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
+      console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
+
+      console.log('=========MSLITE loadModel start=====');
+      let msliteModel = null;
+      msliteModel = await load_model_from_buffer();
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -1737,8 +1544,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-              msliteModel.predict(modelInputs, (modelOutputs) => {
-                try {
+      let modelOutputs = await case_predict(msliteModel, modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -1748,41 +1554,19 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
       done();
-                } catch (error) {
-                  console.info("predict catch: " + error);
-                  done();
-                }
-              })
-              for (var j = 0; j < 10000; j++){
-                console.log("wait predict end:" + j.toString());
-              }
-            } catch (error) {
-              console.info("loadModel catch: " + error);
-              done();
-            }
-          })
-        }).catch(err => {
-          console.log("getRawFileContent catch: ", err);
-          done();
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-        done();
-      })
     })
 
 
     // 正常场景：调用loadModelFromFd callback接口设置context
     it('Test_load_model_param_model_fd_callback', 0, async function (done) {
       console.log("MSLITE api test: loadModel param model file.Test_load_model_param_model_fd_callback");
-      let inputName = 'ml_face_isface_0.input';
-      let syscontext = globalThis.context;
+      function load_model_from_fd() {
+        return new Promise((resolve) => {
           let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
-        console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
           let file = fs.openSync(model_file, fs.OpenMode.READ_ONLY);
           let context:mindSporeLite.Context={};
           context.target = ["cpu"];
@@ -1792,10 +1576,26 @@ export default function abilityTest() {
           context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
           context.cpu.precisionMode = "preferred_fp16";
           context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
-        console.log('=========MSLITE loadModel start=====');
-
           mindSporeLite.loadModelFromFd(file.fd, context, (msliteModel) => {
-          try {
+            resolve(msliteModel);
+          })
+        })
+      }
+      function case_predict(msliteModel, modelInputs) {
+        return new Promise((resolve) => {
+          msliteModel.predict(modelInputs, (modelOutputs) => {
+            resolve(modelOutputs);
+          })
+        })
+      }
+      let inputName = 'ml_face_isface_0.input';
+      let syscontext = globalThis.context;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
+      console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
+
+      console.log('=========MSLITE loadModel start=====');
+      let msliteModel = null;
+      msliteModel = await load_model_from_fd();
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -1819,8 +1619,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-            msliteModel.predict(modelInputs, (modelOutputs) => {
-              try {
+      let modelOutputs = await case_predict(msliteModel, modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -1830,41 +1629,38 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
       done();
-              } catch (error) {
-                console.info("predict catch: " + error);
-                done();
-              }
-            })
-            for (var j = 0; j < 10000; j++){
-              console.log("wait predict end:" + j.toString());
-            }
-          } catch (error) {
-            console.info("loadModel catch: " + error);
-            done();
-          }
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-        done();
-      })
     })
 
 
     // 正常场景：调用loadModelFromFile callback接口未设置context
     it('Test_load_model_param_model_path_callback_no_context', 0, async function (done) {
       console.log("MSLITE api test: loadModel param model file.Test_load_model_param_model_path_callback_no_context");
+      function load_model_from_file() {
+        return new Promise((resolve) => {
+          let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
+          mindSporeLite.loadModelFromFile(model_file, (msliteModel) => {
+            resolve(msliteModel);
+          })
+        })
+      }
+      function case_predict(msliteModel, modelInputs) {
+        return new Promise((resolve) => {
+          msliteModel.predict(modelInputs, (modelOutputs) => {
+            resolve(modelOutputs);
+          })
+        })
+      }
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
-      let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
       console.log('=========MSLITE loadModel start=====');
-
-        mindSporeLite.loadModelFromFile(model_file, (msliteModel) => {
-          try {
+      let msliteModel = null;
+      msliteModel = await load_model_from_file();
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -1888,8 +1684,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-            msliteModel.predict(modelInputs, (modelOutputs) => {
-              try {
+      let modelOutputs = await case_predict(msliteModel, modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -1899,42 +1694,41 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
       done();
-              } catch (error) {
-                console.info("predict catch: " + error);
-                done();
-              }
-            })
-            for (var j = 0; j < 10000; j++){
-              console.log("wait predict end:" + j.toString());
-            }
-          } catch (error) {
-            console.info("loadModel catch: " + error);
-            done();
-          }
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-        done();
-      })
     })
 
 
     // 正常场景：调用loadModelFromBuffer callback接口未设置context
     it('Test_load_model_param_model_buffer_callback_no_context', 0, async function (done) {
       console.log("MSLITE api test: loadModel param model file.Test_load_model_param_model_buffer_callback_no_context");
+      function load_model_from_buffer() {
+        return new Promise((resolve) => {
+          let modelName = 'ml_face_isface.ms';
+          syscontext.resourceManager.getRawFileContent(modelName).then((model_buffer) => {
+            let modelBuffer = model_buffer
+            mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, (msliteModel) => {
+              resolve(msliteModel);
+          })
+          })
+        })
+      }
+      function case_predict(msliteModel, modelInputs) {
+        return new Promise((resolve) => {
+          msliteModel.predict(modelInputs, (modelOutputs) => {
+            resolve(modelOutputs);
+          })
+        })
+      }
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
-      let modelName = 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await syscontext.resourceManager.getRawFileContent(modelName).then(async (model_buffer) => {
-          let modelBuffer = model_buffer;
       console.log('=========MSLITE loadModel start=====');
-          mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, (msliteModel) => {
-            try {
+      let msliteModel = null;
+      msliteModel = await load_model_from_buffer();
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -1958,8 +1752,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-              msliteModel.predict(modelInputs, (modelOutputs) => {
-                try {
+      let modelOutputs = await case_predict(msliteModel, modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -1969,45 +1762,39 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
       done();
-                } catch (error) {
-                  console.info("predict catch: " + error);
-                  done();
-                }
-              })
-              for (var j = 0; j < 10000; j++){
-                console.log("wait predict end:" + j.toString());
-              }
-            } catch (error) {
-              console.info("loadModel catch: " + error);
-              done();
-            }
-          })
-        }).catch(err => {
-          console.log("getRawFileContent catch: ", err);
-          done();
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-        done();
-      })
     })
 
 
     // 正常场景：调用loadModelFromFd callback接口未设置context
     it('Test_load_model_param_model_fd_callback_no_context', 0, async function (done) {
       console.log("MSLITE api test: loadModel param model file.Test_load_model_param_model_fd_callback_no_context");
+      function load_model_from_fd() {
+        return new Promise((resolve) => {
+          let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
+          let file = fs.openSync(model_file, fs.OpenMode.READ_ONLY);
+          mindSporeLite.loadModelFromFd(file.fd, (msliteModel) => {
+            resolve(msliteModel);
+          })
+        })
+      }
+      function case_predict(msliteModel, modelInputs) {
+        return new Promise((resolve) => {
+          msliteModel.predict(modelInputs, (modelOutputs) => {
+            resolve(modelOutputs);
+          })
+        })
+      }
       let inputName = 'ml_face_isface_0.input';
       let syscontext = globalThis.context;
-      let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
-      await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
-        let inputBuffer = buffer;
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
       console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        let file = fs.openSync(model_file, fs.OpenMode.READ_ONLY);
       console.log('=========MSLITE loadModel start=====');
-        mindSporeLite.loadModelFromFd(file.fd, (msliteModel) => {
-          try {
+      let msliteModel = null;
+      msliteModel = await load_model_from_fd();
       expect(msliteModel !== null).assertTrue();
       console.log('=========MSLITE loadModel end=====');
       const modelInputs = msliteModel.getInputs();
@@ -2031,8 +1818,7 @@ export default function abilityTest() {
       expect(input_dataSize.toString()).assertEqual("27648");
       modelInputs[0].setData(inputBuffer.buffer);
       console.log('=========MSLITE predict start=====');
-            msliteModel.predict(modelInputs, (modelOutputs) => {
-              try {
+      let modelOutputs = await case_predict(msliteModel, modelInputs);
       expect(modelOutputs !== null).assertTrue();
       console.log('=========MSLITE new Float32Array start=====');
       let output0 = new Float32Array(modelOutputs[0].getData());
@@ -2042,25 +1828,10 @@ export default function abilityTest() {
         console.log(output0[i].toString());
         expect(output0[i].toString() !== null).assertTrue();
       }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
       console.log('=========MSLITE new Float32Array end=====');
       done();
-              } catch (error) {
-                console.info("predict catch: " + error);
-                done();
-              }
-            })
-            for (var j = 0; j < 10000; j++){
-              console.log("wait predict end:" + j.toString());
-            }
-          } catch (error) {
-            console.info("loadModel catch: " + error);
-            done();
-          }
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-        done();
-      })
     })
 
     // 正常场景：头文件枚举值测试