diff --git a/ai/mindspore/mindsporectest/src/ohos_common.cpp b/ai/mindspore/mindsporectest/src/ohos_common.cpp
index d0c2bd8c95f1fb06c2ac30dfab5cdbefb3e57483..b7ae724f93606565a9a9811d01de297ab75ea1b1 100644
--- a/ai/mindspore/mindsporectest/src/ohos_common.cpp
+++ b/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);
-      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);
-
+      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);
-      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);
+      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);
diff --git a/ai/mindspore/mindsporejstest/src/main/ets/test/Ability.test.ets b/ai/mindspore/mindsporejstest/src/main/ets/test/Ability.test.ets
index f6ff2f93cdfe30a4bfe32fe9811ce7a76285e9b9..f748f0b7a558cc2d449e9e130a8928e9da72acbc 100644
--- a/ai/mindspore/mindsporejstest/src/main/ets/test/Ability.test.ets
+++ b/ai/mindspore/mindsporejstest/src/main/ets/test/Ability.test.ets
@@ -69,7 +69,7 @@ export default function abilityTest() {
             console.log("[rawfile_copy_to_sandbox] ml_ocr_cn.ms is copy success");
           }
         });
-        
+
       } catch (error) {
         console.info("[rawfile_copy_to_sandbox] getRawFileDescriptor api run failed" + error);
       }
@@ -80,7 +80,7 @@ export default function abilityTest() {
     })
     beforeEach(async function () {
       let dir = globalThis.abilityContext.filesDir + "/";
-      
+
       let ml_face_model = dir + "ml_face_isface.ms";
       await fs.access(ml_face_model).then(async (res) => {
         if (res) {
@@ -90,7 +90,7 @@ export default function abilityTest() {
         console.info("ml_face_isface.ms file does not exists! access failed with error message: " +
         err.message + ", error code: " + err.code);
       });
-      
+
       let mnet_caffemodel_bin_file = dir + "ml_face_isface_0.input";
       await fs.access(mnet_caffemodel_bin_file).then(async (res) => {
         if (res) {
@@ -132,63 +132,53 @@ 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;
-        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 = {};
-          context.target = ["cpu", "nnrt"];
-          context.nnrt = {};
-          context.cpu = {};
-          context.cpu.threadNum = 1;
-          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) => {
-            expect(msliteModel !== null).assertTrue();
-            console.log('=========MSLITE loadModel end=====');
-            const modelInputs = msliteModel.getInputs();
-            let input_name = modelInputs[0].name;
-            console.log(input_name.toString());
-            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")
-            let input_elementNum = modelInputs[0].elementNum;
-            console.log(input_elementNum.toString());
-            expect(input_elementNum.toString()).assertEqual("6912");
-            let input_dtype = modelInputs[0].dtype;
-            console.log(input_dtype.toString());
-            expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-            let input_format = modelInputs[0].format;
-            console.log(input_format.toString());
-            expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-            let input_dataSize = modelInputs[0].dataSize;
-            console.log(input_dataSize.toString());
-            expect(input_dataSize.toString()).assertEqual("27648");
-            modelInputs[0].setData(inputBuffer.buffer);
-            console.log('=========MSLITE predict start=====');
-            await msliteModel.predict(modelInputs).then((modelOutputs) => {
-              expect(modelOutputs !== null).assertTrue();
-              console.log('=========MSLITE new Float32Array start=====');
-              let output0 = new Float32Array(modelOutputs[0].getData());
-              console.log('output0.length:' + output0.length);
-              for (var i = 0; i < 2; i++) {
-                console.log(output0[i].toString());
-                expect(output0[i].toString() !== null).assertTrue();
-              }
-              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);
-        })
-      })
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
+      console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
+      let modelBuffer = await syscontext.resourceManager.getRawFileContent(modelName);
+      let context: mindSporeLite.Context = {};
+      context.target = ["cpu", "nnrt"];
+      context.nnrt = {};
+      context.cpu = {};
+      context.cpu.threadNum = 1;
+      context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
+      context.cpu.precisionMode = "preferred_fp16";
+      context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
+      let msliteModel = await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, context)
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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")
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      let modelOutputs = await msliteModel.predict(modelInputs);
+      expect(modelOutputs !== null).assertTrue();
+      console.log('=========MSLITE new Float32Array start=====');
+      let output0 = new Float32Array(modelOutputs[0].getData());
+      console.log('output0.length:' + output0.length);
+      for (var i = 0; i < 2; i++) {
+        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=====');
     })
 
 
@@ -199,55 +189,46 @@ 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;
-        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) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          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")
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("6912");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("27648");
-          modelInputs[0].setData(inputBuffer.buffer);
-          console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
-            expect(modelOutputs !== null).assertTrue();
-            console.log('=========MSLITE new Float32Array start=====');
-            let output0 = new Float32Array(modelOutputs[0].getData());
-            console.log('output0.length:' + output0.length);
-            for (var i = 0; i < 2; i++) {
-              console.log(output0[i].toString());
-              expect(output0[i].toString() !== null).assertTrue();
-            }
-            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);
-      })
+      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);
+      let msliteModel = await mindSporeLite.loadModelFromFd(file.fd);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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");
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      let modelOutputs = await msliteModel.predict(modelInputs);
+      expect(modelOutputs !== null).assertTrue();
+      console.log('=========MSLITE new Float32Array start=====');
+      let output0 = new Float32Array(modelOutputs[0].getData());
+      console.log('output0.length:' + output0.length);
+      for (var i = 0; i < 2; i++) {
+        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=====');
     })
 
     // 正常场景：ModelBuild，调用loadModelFromFile方法，正常推理
@@ -256,62 +237,54 @@ 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;
-        console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        let context:mindSporeLite.Context={};
-        context.target = ["cpu", "nnrt"];
-        context.nnrt = {};
-        context.cpu = {};
-        context.cpu.threadNum = 1;
-        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=====');
-        await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          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")
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("6912");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("27648");
-          modelInputs[0].setData(inputBuffer.buffer);
-          console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
-            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();
-            }
-            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);
-      })
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
+      console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
+      let context:mindSporeLite.Context={};
+      context.target = ["cpu", "nnrt"];
+      context.nnrt = {};
+      context.cpu = {};
+      context.cpu.threadNum = 1;
+      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=====');
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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")
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
     })
 
 
@@ -321,58 +294,50 @@ 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;
-        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) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          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")
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("6912");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("27648");
-          modelInputs[0].setData(inputBuffer.buffer);
-          console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
-            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();
-            }
-            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);
-      })
+      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,
+      }
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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")
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
     })
 
 
@@ -382,58 +347,50 @@ 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;
-        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) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          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")
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("6912");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("27648");
-          modelInputs[0].setData(inputBuffer.buffer);
-          console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
-            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();
-            }
-            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);
-      })
+      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,
+      }
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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")
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
     })
 
     //正常场景：Context设置CPU，1线程
@@ -442,58 +399,50 @@ 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;
-        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) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          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");
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("6912");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("27648");
-          modelInputs[0].setData(inputBuffer.buffer);
-          console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
-            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();
-            }
-            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);
-      })
+      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,
+      }
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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");
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
     })
 
 
@@ -503,58 +452,50 @@ 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;
-        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) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          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")
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("6912");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("27648");
-          modelInputs[0].setData(inputBuffer.buffer);
-          console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
-            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();
-            }
-            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);
-      })
+      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,
+      }
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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")
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
     })
 
 
@@ -564,58 +505,50 @@ 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;
-        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) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          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");
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("6912");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("27648");
-          modelInputs[0].setData(inputBuffer.buffer);
-          console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
-            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();
-            }
-            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);
-      })
+      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.");
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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");
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
     })
 
 
@@ -625,58 +558,50 @@ 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;
-        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) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          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");
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("6912");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("27648");
-          modelInputs[0].setData(inputBuffer.buffer);
-          console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
-            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();
-            }
-            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);
-      })
+      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.");
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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");
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
     })
 
 
@@ -686,58 +611,50 @@ 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;
-        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) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          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")
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("6912");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("27648");
-          modelInputs[0].setData(inputBuffer.buffer);
-          console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
-            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();
-            }
-            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);
-      })
+      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.");
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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")
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
     })
 
     //正常场景：Context设置CPU，绑核设置为0，不绑核
@@ -746,58 +663,50 @@ 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;
-        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) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          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")
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("6912");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("27648");
-          modelInputs[0].setData(inputBuffer.buffer);
-          console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
-            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();
-            }
-            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);
-      })
+      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.");
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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")
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
     })
 
 
@@ -808,58 +717,50 @@ 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;
-        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) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          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")
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("6912");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("27648");
-          modelInputs[0].setData(inputBuffer.buffer);
-          console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
-            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();
-            }
-            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);
-      })
+      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].");
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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")
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
     })
 
     //异常场景：ModelBuild，调用model path方法，path为空
@@ -904,77 +805,69 @@ 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;
-        console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          expect(input_name.toString()).assertEqual("data");
-          let input_shape = modelInputs[0].shape;
-          console.log(input_shape.toString());
-          expect(input_shape.toString()).assertEqual("1,32,512,1");
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("16384");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("65536");
-          console.log('=========MSLITE resize start=====');
-          let new_dim = new Array([1, 32, 512, 1]);
-          let resize_result = msliteModel.resize(modelInputs, new_dim);
-          expect(resize_result).assertTrue();
-          console.log('=========MSLITE resize success=====');
-          const modelInputs2 = msliteModel.getInputs();
-          let input_name2 = modelInputs2[0].name;
-          console.log(input_name2.toString());
-          expect(input_name2.toString()).assertEqual("data");
-          let input_shape2 = modelInputs2[0].shape;
-          console.log(input_shape2.toString());
-          expect(input_shape2.toString()).assertEqual("1,32,512,1");
-          let input_elementNum2 = modelInputs2[0].elementNum;
-          console.log(input_elementNum2.toString());
-          expect(input_elementNum2.toString()).assertEqual("16384");
-          let input_dtype2 = modelInputs2[0].dtype;
-          console.log(input_dtype2.toString());
-          expect(input_dtype2).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format2 = modelInputs2[0].format;
-          console.log(input_format2.toString());
-          expect(input_format2).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize2 = modelInputs2[0].dataSize;
-          console.log(input_dataSize2.toString());
-          expect(input_dataSize2.toString()).assertEqual("65536");
-          modelInputs2[0].setData(inputBuffer.buffer);
-          console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs2).then((modelOutputs) => {
-            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();
-            }
-            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);
-      })
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
+      console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      expect(input_name.toString()).assertEqual("data");
+      let input_shape = modelInputs[0].shape;
+      console.log(input_shape.toString());
+      expect(input_shape.toString()).assertEqual("1,32,512,1");
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("16384");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("65536");
+      console.log('=========MSLITE resize start=====');
+      let new_dim = new Array([1, 32, 512, 1]);
+      let resize_result = msliteModel.resize(modelInputs, new_dim);
+      expect(resize_result).assertTrue();
+      console.log('=========MSLITE resize success=====');
+      const modelInputs2 = msliteModel.getInputs();
+      let input_name2 = modelInputs2[0].name;
+      console.log(input_name2.toString());
+      expect(input_name2.toString()).assertEqual("data");
+      let input_shape2 = modelInputs2[0].shape;
+      console.log(input_shape2.toString());
+      expect(input_shape2.toString()).assertEqual("1,32,512,1");
+      let input_elementNum2 = modelInputs2[0].elementNum;
+      console.log(input_elementNum2.toString());
+      expect(input_elementNum2.toString()).assertEqual("16384");
+      let input_dtype2 = modelInputs2[0].dtype;
+      console.log(input_dtype2.toString());
+      expect(input_dtype2).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format2 = modelInputs2[0].format;
+      console.log(input_format2.toString());
+      expect(input_format2).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize2 = modelInputs2[0].dataSize;
+      console.log(input_dataSize2.toString());
+      expect(input_dataSize2.toString()).assertEqual("65536");
+      modelInputs2[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      let modelOutputs = await msliteModel.predict(modelInputs2);
+      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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
     })
 
 
@@ -984,77 +877,69 @@ 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;
-        console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          expect(input_name.toString()).assertEqual("data");
-          let input_shape = modelInputs[0].shape;
-          console.log(input_shape.toString());
-          expect(input_shape.toString()).assertEqual("1,32,512,1");
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("16384");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("65536");
-          console.log('=========MSLITE resize start=====');
-          let new_dim = new Array([1,64,256,1]);
-          let resize_result = msliteModel.resize(modelInputs, new_dim);
-          expect(resize_result).assertTrue();
-          console.log('=========MSLITE resize success=====');
-          const modelInputs2 = msliteModel.getInputs();
-          let input_name2 = modelInputs2[0].name;
-          console.log(input_name2.toString());
-          expect(input_name2.toString()).assertEqual("data");
-          let input_shape2 = modelInputs2[0].shape;
-          console.log(input_shape2.toString());
-          expect(input_shape2.toString()).assertEqual("1,64,256,1");
-          let input_elementNum2 = modelInputs2[0].elementNum;
-          console.log(input_elementNum2.toString());
-          expect(input_elementNum2.toString()).assertEqual("16384");
-          let input_dtype2 = modelInputs2[0].dtype;
-          console.log(input_dtype2.toString());
-          expect(input_dtype2).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format2 = modelInputs2[0].format;
-          console.log(input_format2.toString());
-          expect(input_format2).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize2 = modelInputs2[0].dataSize;
-          console.log(input_dataSize2.toString());
-          expect(input_dataSize2.toString()).assertEqual("65536");
-          modelInputs2[0].setData(inputBuffer.buffer);
-          console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs2).then((modelOutputs) => {
-            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();
-            }
-            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);
-      })
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
+      console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      expect(input_name.toString()).assertEqual("data");
+      let input_shape = modelInputs[0].shape;
+      console.log(input_shape.toString());
+      expect(input_shape.toString()).assertEqual("1,32,512,1");
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("16384");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("65536");
+      console.log('=========MSLITE resize start=====');
+      let new_dim = new Array([1,64,256,1]);
+      let resize_result = msliteModel.resize(modelInputs, new_dim);
+      expect(resize_result).assertTrue();
+      console.log('=========MSLITE resize success=====');
+      const modelInputs2 = msliteModel.getInputs();
+      let input_name2 = modelInputs2[0].name;
+      console.log(input_name2.toString());
+      expect(input_name2.toString()).assertEqual("data");
+      let input_shape2 = modelInputs2[0].shape;
+      console.log(input_shape2.toString());
+      expect(input_shape2.toString()).assertEqual("1,64,256,1");
+      let input_elementNum2 = modelInputs2[0].elementNum;
+      console.log(input_elementNum2.toString());
+      expect(input_elementNum2.toString()).assertEqual("16384");
+      let input_dtype2 = modelInputs2[0].dtype;
+      console.log(input_dtype2.toString());
+      expect(input_dtype2).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format2 = modelInputs2[0].format;
+      console.log(input_format2.toString());
+      expect(input_format2).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize2 = modelInputs2[0].dataSize;
+      console.log(input_dataSize2.toString());
+      expect(input_dataSize2.toString()).assertEqual("65536");
+      modelInputs2[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      let modelOutputs = await msliteModel.predict(modelInputs2);
+      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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
     })
 
 
@@ -1064,42 +949,35 @@ 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;
-        console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          expect(input_name.toString()).assertEqual("data");
-          let input_shape = modelInputs[0].shape;
-          console.log(input_shape.toString());
-          expect(input_shape.toString()).assertEqual("1,32,512,1");
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("16384");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("65536");
-          console.log('=========MSLITE resize start=====');
-          let new_dim = new Array([1,32,1]);
-          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);
-      })
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
+      console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      expect(input_name.toString()).assertEqual("data");
+      let input_shape = modelInputs[0].shape;
+      console.log(input_shape.toString());
+      expect(input_shape.toString()).assertEqual("1,32,512,1");
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("16384");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("65536");
+      console.log('=========MSLITE resize start=====');
+      let new_dim = new Array([1,32,1]);
+      let resize_result = msliteModel.resize(modelInputs, new_dim);
+      expect(resize_result).assertFalse;
+      console.log('=========MSLITE resize failed=====');
     })
 
 
@@ -1109,40 +987,33 @@ 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;
-        console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          expect(input_name.toString()).assertEqual("data");
-          console.log(modelInputs[0].shape.toString());
-          expect(modelInputs[0].shape.toString()).assertEqual("1,48,48,3");
-          console.log(modelInputs[0].elementNum.toString());
-          expect(modelInputs[0].elementNum.toString()).assertEqual("6912");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("27648");
-          console.log('=========MSLITE resize start=====');
-          let new_dim = new Array([1,96,96,1]);
-          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);
-      })
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
+      console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      expect(input_name.toString()).assertEqual("data");
+      console.log(modelInputs[0].shape.toString());
+      expect(modelInputs[0].shape.toString()).assertEqual("1,48,48,3");
+      console.log(modelInputs[0].elementNum.toString());
+      expect(modelInputs[0].elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      console.log('=========MSLITE resize start=====');
+      let new_dim = new Array([1,96,96,1]);
+      let resize_result = msliteModel.resize(modelInputs, new_dim);
+      expect(resize_result).assertFalse();
+      console.log('=========MSLITE resize failed=====');
     })
 
 
@@ -1152,42 +1023,35 @@ 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;
-        console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          expect(input_name.toString()).assertEqual("data");
-          let input_shape = modelInputs[0].shape;
-          console.log(input_shape.toString());
-          expect(input_shape.toString()).assertEqual("1,32,512,1");
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("16384");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("65536");
-          console.log('=========MSLITE resize start=====');
-          let new_dim = new Array([1,-32,32,1]);
-          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);
-      })
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
+      console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      expect(input_name.toString()).assertEqual("data");
+      let input_shape = modelInputs[0].shape;
+      console.log(input_shape.toString());
+      expect(input_shape.toString()).assertEqual("1,32,512,1");
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("16384");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("65536");
+      console.log('=========MSLITE resize start=====');
+      let new_dim = new Array([1,-32,32,1]);
+      let resize_result = msliteModel.resize(modelInputs, new_dim);
+      expect(resize_result).assertFalse();
+      console.log('=========MSLITE resize failed=====');
     })
 
 
@@ -1197,62 +1061,53 @@ 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;
-        console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          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");
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("6912");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("27648");
-          modelInputs[0].setData(inputBuffer.buffer);
-          console.log('=========MSLITE predict start=====');
-          let num = 0;
-          for (var i = 0; i < 10; i++) {
-            await msliteModel.predict(modelInputs).then((modelOutputs) => {
-              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();
-              }
-              console.log('=========MSLITE new Float32Array end=====');
-            }).catch(err => {
-              console.log("predict catch: ", err);
-            })
-            console.log('=========MSLITE new Float32Array end=====');
-            console.log('=========i.toString()=====');
-            console.log(i.toString());
-            ++num;
-            console.log('=========num.toString()=====');
-            console.log(num.toString());
-          }
-        }).catch(err => {
-          console.log("loadModel catch: ", err);
-        })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-      })
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
+      console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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");
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      let num = 0;
+      for (var i = 0; i < 10; i++) {
+        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 z = 0; z < 2; z++) {
+          console.log(output0[z].toString());
+          expect(output0[z].toString() !== null).assertTrue();
+        }
+        const modelInputs0 = msliteModel.getInputs();
+        console.log(modelInputs0[0].name.toString());
+        console.log('=========MSLITE new Float32Array end=====');
+        console.log('=========i.toString()=====');
+        console.log(i.toString());
+        ++num;
+        console.log('=========num.toString()=====');
+        console.log(num.toString());
+      }
     })
 
 
@@ -1262,56 +1117,48 @@ 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;
-        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) => {
-          expect(msliteModel !== null).assertTrue();
-          console.log('=========MSLITE loadModel end=====');
-          const modelInputs = msliteModel.getInputs();
-          let input_name = modelInputs[0].name;
-          console.log(input_name.toString());
-          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");
-          let input_elementNum = modelInputs[0].elementNum;
-          console.log(input_elementNum.toString());
-          expect(input_elementNum.toString()).assertEqual("6912");
-          let input_dtype = modelInputs[0].dtype;
-          console.log(input_dtype.toString());
-          expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-          let input_format = modelInputs[0].format;
-          console.log(input_format.toString());
-          expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-          let input_dataSize = modelInputs[0].dataSize;
-          console.log(input_dataSize.toString());
-          expect(input_dataSize.toString()).assertEqual("27648");
-          modelInputs[0].setData(inputBuffer.buffer);
-          console.log('=========MSLITE predict start=====');
-          await msliteModel.predict(modelInputs).then((modelOutputs) => {
-            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();
-            }
-            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);
-      })
+      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);
+      }
+      let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
+      expect(msliteModel !== null).assertTrue();
+      console.log('=========MSLITE loadModel end=====');
+      const modelInputs = msliteModel.getInputs();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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");
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
     })
 
 
@@ -1321,58 +1168,48 @@ 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;
-        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) => {
-            expect(msliteModel !== null).assertTrue();
-            console.log('=========MSLITE loadModel end=====');
-            const modelInputs = msliteModel.getInputs();
-            let input_name = modelInputs[0].name;
-            console.log(input_name.toString());
-            expect(input_name.toString()).assertEqual("data");
-            console.log(modelInputs[0].shape.toString());
-            expect(modelInputs[0].shape.toString()).assertEqual("1,48,48,3");
-            console.log(modelInputs[0].elementNum.toString());
-            expect(modelInputs[0].elementNum.toString()).assertEqual("6912");
-            let input_dtype = modelInputs[0].dtype;
-            console.log(input_dtype.toString());
-            expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-            let input_format = modelInputs[0].format;
-            console.log(input_format.toString());
-            expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-            let input_dataSize = modelInputs[0].dataSize;
-            console.log(input_dataSize.toString());
-            expect(input_dataSize.toString()).assertEqual("27648");
-            modelInputs[0].setData(inputBuffer.buffer);
-            let Inputs2 = new Float32Array(modelInputs[0].getData());
-            for (var i = 0; i < 5; i++) {
-              console.log(Inputs2[i].toString());
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
+      console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
+      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();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      expect(input_name.toString()).assertEqual("data");
+      console.log(modelInputs[0].shape.toString());
+      expect(modelInputs[0].shape.toString()).assertEqual("1,48,48,3");
+      console.log(modelInputs[0].elementNum.toString());
+      expect(modelInputs[0].elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      let Inputs2 = new Float32Array(modelInputs[0].getData());
+      for (var i = 0; i < 5; i++) {
+        console.log(Inputs2[i].toString());
 
-            }
-            console.log('=========MSLITE predict start=====');
-            await msliteModel.predict(modelInputs).then((modelOutputs) => {
-              expect(modelOutputs !== null).assertTrue();
-              console.log('=========MSLITE new Float32Array start=====');
-              let output0 = new Float32Array(modelOutputs[0].getData());
-              console.log('output0.length:' + output0.length);
-              for (var i = 0; i < 2; i++) {
-                console.log(output0[i].toString());
-                expect(output0[i].toString() !== null).assertTrue();
-              }
-              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);
-        })
-      })
+      }
+      console.log('=========MSLITE predict start=====');
+      let modelOutputs = await msliteModel.predict(modelInputs);
+      expect(modelOutputs !== null).assertTrue();
+      console.log('=========MSLITE new Float32Array start=====');
+      let output0 = new Float32Array(modelOutputs[0].getData());
+      console.log('output0.length:' + output0.length);
+      for (var i = 0; i < 2; i++) {
+        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=====');
     })
 
 
@@ -1383,76 +1220,62 @@ 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;
-          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) => {
-              expect(msliteModel !== null).assertTrue();
-              console.log('=========MSLITE loadModel end=====');
-              const modelInputs = msliteModel.getInputs();
-              console.log(modelInputs[0].name);
-              expect(modelInputs[0].name.toString()).assertEqual("a");
-              console.log(modelInputs[0].shape.toString());
-              expect(modelInputs[0].shape.toString()).assertEqual("1,512,512,3");
-              console.log(modelInputs[0].elementNum.toString());
-              expect(modelInputs[0].elementNum.toString()).assertEqual("786432");
-              let input_dtype = modelInputs[0].dtype;
-              console.log(input_dtype.toString());
-              expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-              let input_format = modelInputs[0].format;
-              console.log(input_format.toString());
-              expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-              let input_dataSize = modelInputs[0].dataSize;
-              console.log(input_dataSize.toString());
-              expect(input_dataSize.toString()).assertEqual("3145728");
-
-              console.log(modelInputs[1].name);
-              expect(modelInputs[1].name.toString()).assertEqual("b");
-              console.log(modelInputs[1].shape.toString());
-              expect(modelInputs[1].shape.toString()).assertEqual("1,512,512,1");
-              console.log(modelInputs[1].elementNum.toString());
-              expect(modelInputs[1].elementNum.toString()).assertEqual("262144");
-              console.log(modelInputs[1].dtype.toString());
-              expect(modelInputs[1].dtype.toString()).assertEqual("43");
-              console.log(modelInputs[1].format.toString());
-              expect(modelInputs[1].format.toString()).assertEqual("1");
-              console.log(modelInputs[1].dataSize.toString());
-              expect(modelInputs[1].dataSize.toString()).assertEqual("1048576");
-              modelInputs[0].setData(inputBuffer01.buffer);
-              modelInputs[1].setData(inputBuffer02.buffer);
-              let Inputs2 = new Float32Array(modelInputs[0].getData());
-              for (var i = 0; i < 5; i++) {
-                console.log(Inputs2[i].toString());
-              }
-              console.log('=========MSLITE predict start=====');
-              await msliteModel.predict(modelInputs).then((modelOutputs) => {
-                expect(modelOutputs !== null).assertTrue();
-                console.log('=========MSLITE new Float32Array start=====');
-                let output0 = new Float32Array(modelOutputs[0].getData());
-                console.log('output0.length:' + output0.length);
-                for (var i = 0; i < 2; i++) {
-                  console.log(output0[i].toString());
-                  expect(output0[i].toString() !== null).assertTrue();
-                }
-                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);
-        })
-      })
+      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);
+      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();
+      console.log(modelInputs[0].name);
+      expect(modelInputs[0].name.toString()).assertEqual("a");
+      console.log(modelInputs[0].shape.toString());
+      expect(modelInputs[0].shape.toString()).assertEqual("1,512,512,3");
+      console.log(modelInputs[0].elementNum.toString());
+      expect(modelInputs[0].elementNum.toString()).assertEqual("786432");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("3145728");
+
+      console.log(modelInputs[1].name);
+      expect(modelInputs[1].name.toString()).assertEqual("b");
+      console.log(modelInputs[1].shape.toString());
+      expect(modelInputs[1].shape.toString()).assertEqual("1,512,512,1");
+      console.log(modelInputs[1].elementNum.toString());
+      expect(modelInputs[1].elementNum.toString()).assertEqual("262144");
+      console.log(modelInputs[1].dtype.toString());
+      expect(modelInputs[1].dtype.toString()).assertEqual("43");
+      console.log(modelInputs[1].format.toString());
+      expect(modelInputs[1].format.toString()).assertEqual("1");
+      console.log(modelInputs[1].dataSize.toString());
+      expect(modelInputs[1].dataSize.toString()).assertEqual("1048576");
+      modelInputs[0].setData(inputBuffer01.buffer);
+      modelInputs[1].setData(inputBuffer02.buffer);
+      let Inputs2 = new Float32Array(modelInputs[0].getData());
+      for (var i = 0; i < 5; i++) {
+        console.log(Inputs2[i].toString());
+      }
+      console.log('=========MSLITE predict start=====');
+      let modelOutputs = await msliteModel.predict(modelInputs);
+      expect(modelOutputs !== null).assertTrue();
+      console.log('=========MSLITE new Float32Array start=====');
+      let output0 = new Float32Array(modelOutputs[0].getData());
+      console.log('output0.length:' + output0.length);
+      for (var i = 0; i < 2; i++) {
+        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=====');
     })
 
 
@@ -1463,53 +1286,43 @@ 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;
-        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) => {
-            expect(msliteModel !== null).assertTrue();
-            console.log('=========MSLITE loadModel end=====');
-            const modelInputs = msliteModel.getInputs();
-            console.log(modelInputs[0].name);
-            expect(modelInputs[0].name.toString()).assertEqual("module/hub_input/images_uint8");
-            console.log(modelInputs[0].shape.toString());
-            expect(modelInputs[0].shape.toString()).assertEqual("1,224,224,3");
-            console.log(modelInputs[0].elementNum.toString());
-            expect(modelInputs[0].elementNum.toString()).assertEqual("150528");
-            console.log(modelInputs[0].dtype.toString());
-            expect(modelInputs[0].dtype.toString()).assertEqual("37");
-            console.log(modelInputs[0].format.toString());
-            expect(modelInputs[0].format.toString()).assertEqual("1");
-            console.log(modelInputs[0].dataSize.toString());
-            expect(modelInputs[0].dataSize.toString()).assertEqual("150528");
-            modelInputs[0].setData(inputBuffer.buffer);
-            let Inputs2 = new Uint8Array(modelInputs[0].getData());
-            for (var i = 0; i < 5; i++) {
-              console.log(Inputs2[i].toString());
-            }
-            console.log('=========MSLITE predict start=====');
-            await msliteModel.predict(modelInputs).then((modelOutputs) => {
-              expect(modelOutputs !== null).assertTrue();
-              console.log('=========MSLITE new Float32Array start=====');
-              let output0 = new Uint8Array(modelOutputs[0].getData());
-              console.log('output0.length:' + output0.length);
-              for (var i = 0; i < 2; i++) {
-                console.log(output0[i].toString());
-                expect(output0[i].toString() !== null).assertTrue();
-              }
-              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);
-        })
-      })
+      let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
+      console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
+      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();
+      console.log(modelInputs[0].name);
+      expect(modelInputs[0].name.toString()).assertEqual("module/hub_input/images_uint8");
+      console.log(modelInputs[0].shape.toString());
+      expect(modelInputs[0].shape.toString()).assertEqual("1,224,224,3");
+      console.log(modelInputs[0].elementNum.toString());
+      expect(modelInputs[0].elementNum.toString()).assertEqual("150528");
+      console.log(modelInputs[0].dtype.toString());
+      expect(modelInputs[0].dtype.toString()).assertEqual("37");
+      console.log(modelInputs[0].format.toString());
+      expect(modelInputs[0].format.toString()).assertEqual("1");
+      console.log(modelInputs[0].dataSize.toString());
+      expect(modelInputs[0].dataSize.toString()).assertEqual("150528");
+      modelInputs[0].setData(inputBuffer.buffer);
+      let Inputs2 = new Uint8Array(modelInputs[0].getData());
+      for (var i = 0; i < 5; i++) {
+        console.log(Inputs2[i].toString());
+      }
+      console.log('=========MSLITE predict start=====');
+      let modelOutputs = await msliteModel.predict(modelInputs);
+      expect(modelOutputs !== null).assertTrue();
+      console.log('=========MSLITE new Float32Array start=====');
+      let output0 = new Uint8Array(modelOutputs[0].getData());
+      console.log('output0.length:' + output0.length);
+      for (var i = 0; i < 2; i++) {
+        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=====');
     })
 
 
@@ -1521,187 +1334,240 @@ 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;
-            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) => {
-                expect(msliteModel !== null).assertTrue();
-                console.log('=========MSLITE loadModel end=====');
-                const modelInputs = msliteModel.getInputs();
-                console.log(modelInputs[0].name);
-                expect(modelInputs[0].name.toString()).assertEqual("input_1");
-                console.log(modelInputs[0].shape.toString());
-                expect(modelInputs[0].shape.toString()).assertEqual("1,64,64,3");
-                console.log(modelInputs[0].elementNum.toString());
-                expect(modelInputs[0].elementNum.toString()).assertEqual("12288");
-                let input_dtype = modelInputs[0].dtype;
-                console.log(input_dtype.toString());
-                expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-                let input_format = modelInputs[0].format;
-                console.log(input_format.toString());
-                expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-                let input_dataSize = modelInputs[0].dataSize;
-                console.log(input_dataSize.toString());
-                expect(input_dataSize.toString()).assertEqual("49152");
-
-                console.log(modelInputs[1].name);
-                expect(modelInputs[1].name.toString()).assertEqual("batch_normalization_8/batchnorm/add");
-                console.log(modelInputs[1].shape.toString());
-                expect(modelInputs[1].shape.toString()).assertEqual("16");
-                console.log(modelInputs[1].elementNum.toString());
-                expect(modelInputs[1].elementNum.toString()).assertEqual("16");
-                console.log(modelInputs[1].dtype.toString());
-                expect(modelInputs[1].dtype.toString()).assertEqual("43");
-                console.log(modelInputs[1].format.toString());
-                expect(modelInputs[1].format.toString()).assertEqual("1");
-                console.log(modelInputs[1].dataSize.toString());
-                expect(modelInputs[1].dataSize.toString()).assertEqual("64");
-
-                console.log(modelInputs[2].name);
-                expect(modelInputs[2].name.toString()).assertEqual("batch_normalization_1/batchnorm/add");
-                console.log(modelInputs[2].shape.toString());
-                expect(modelInputs[2].shape.toString()).assertEqual("16");
-                console.log(modelInputs[2].elementNum.toString());
-                expect(modelInputs[2].elementNum.toString()).assertEqual("16");
-                console.log(modelInputs[2].dtype.toString());
-                expect(modelInputs[2].dtype.toString()).assertEqual("43");
-                console.log(modelInputs[2].format.toString());
-                expect(modelInputs[2].format.toString()).assertEqual("1");
-                console.log(modelInputs[2].dataSize.toString());
-                expect(modelInputs[2].dataSize.toString()).assertEqual("64");
-                modelInputs[0].setData(inputBuffer01.buffer);
-                modelInputs[1].setData(inputBuffer02.buffer);
-                modelInputs[2].setData(inputBuffer03.buffer);
-                let Inputs2 = new Float32Array(modelInputs[0].getData());
-                for (var i = 0; i < 5; i++) {
-                  console.log(Inputs2[i].toString());
-                }
-                console.log('=========MSLITE predict start=====');
-                await msliteModel.predict(modelInputs).then((modelOutputs) => {
-                  expect(modelOutputs !== null).assertTrue();
-                  console.log('=========MSLITE new Float32Array start=====');
-                  let output0 = new Float32Array(modelOutputs[0].getData());
-                  console.log('output0.length:' + output0.length);
-                  for (var i = 0; i < 2; i++) {
-                    console.log(output0[i].toString());
-                    expect(output0[i].toString() !== null).assertTrue();
-                  }
-                  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);
-        })
-      })
+      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);
+      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();
+      console.log(modelInputs[0].name);
+      expect(modelInputs[0].name.toString()).assertEqual("input_1");
+      console.log(modelInputs[0].shape.toString());
+      expect(modelInputs[0].shape.toString()).assertEqual("1,64,64,3");
+      console.log(modelInputs[0].elementNum.toString());
+      expect(modelInputs[0].elementNum.toString()).assertEqual("12288");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("49152");
+
+      console.log(modelInputs[1].name);
+      expect(modelInputs[1].name.toString()).assertEqual("batch_normalization_8/batchnorm/add");
+      console.log(modelInputs[1].shape.toString());
+      expect(modelInputs[1].shape.toString()).assertEqual("16");
+      console.log(modelInputs[1].elementNum.toString());
+      expect(modelInputs[1].elementNum.toString()).assertEqual("16");
+      console.log(modelInputs[1].dtype.toString());
+      expect(modelInputs[1].dtype.toString()).assertEqual("43");
+      console.log(modelInputs[1].format.toString());
+      expect(modelInputs[1].format.toString()).assertEqual("1");
+      console.log(modelInputs[1].dataSize.toString());
+      expect(modelInputs[1].dataSize.toString()).assertEqual("64");
+
+      console.log(modelInputs[2].name);
+      expect(modelInputs[2].name.toString()).assertEqual("batch_normalization_1/batchnorm/add");
+      console.log(modelInputs[2].shape.toString());
+      expect(modelInputs[2].shape.toString()).assertEqual("16");
+      console.log(modelInputs[2].elementNum.toString());
+      expect(modelInputs[2].elementNum.toString()).assertEqual("16");
+      console.log(modelInputs[2].dtype.toString());
+      expect(modelInputs[2].dtype.toString()).assertEqual("43");
+      console.log(modelInputs[2].format.toString());
+      expect(modelInputs[2].format.toString()).assertEqual("1");
+      console.log(modelInputs[2].dataSize.toString());
+      expect(modelInputs[2].dataSize.toString()).assertEqual("64");
+      modelInputs[0].setData(inputBuffer01.buffer);
+      modelInputs[1].setData(inputBuffer02.buffer);
+      modelInputs[2].setData(inputBuffer03.buffer);
+      let Inputs2 = new Float32Array(modelInputs[0].getData());
+      for (var i = 0; i < 5; i++) {
+        console.log(Inputs2[i].toString());
+      }
+      console.log('=========MSLITE predict start=====');
+      let modelOutputs = await msliteModel.predict(modelInputs);
+      expect(modelOutputs !== null).assertTrue();
+      console.log('=========MSLITE new Float32Array start=====');
+      let output0 = new Float32Array(modelOutputs[0].getData());
+      console.log('output0.length:' + output0.length);
+      for (var i = 0; i < 2; i++) {
+        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=====');
     })
 
 
     // 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;
-      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={};
-        context.target = ["cpu"];
-        context.cpu = {
-          "threadNum": 4,
-        }
-        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 {
-            expect(msliteModel !== null).assertTrue();
-            console.log('=========MSLITE loadModel end=====');
-            const modelInputs = msliteModel.getInputs();
-            let input_name = modelInputs[0].name;
-            console.log(input_name.toString());
-            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");
-            let input_elementNum = modelInputs[0].elementNum;
-            console.log(input_elementNum.toString());
-            expect(input_elementNum.toString()).assertEqual("6912");
-            let input_dtype = modelInputs[0].dtype;
-            console.log(input_dtype.toString());
-            expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-            let input_format = modelInputs[0].format;
-            console.log(input_format.toString());
-            expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-            let input_dataSize = modelInputs[0].dataSize;
-            console.log(input_dataSize.toString());
-            expect(input_dataSize.toString()).assertEqual("27648");
-            modelInputs[0].setData(inputBuffer.buffer);
-            console.log('=========MSLITE predict start=====');
-            msliteModel.predict(modelInputs, (modelOutputs) => {
-              try {
-                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();
-                }
-                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();
+      function load_model_from_file() {
+        return new Promise((resolve) => {
+          let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
+          let context: mindSporeLite.Context = {};
+          context.target = ["cpu"];
+          context.cpu = {
+            "threadNum": 4,
           }
+          context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
+          context.cpu.precisionMode = "preferred_fp16";
+          context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
+          mindSporeLite.loadModelFromFile(model_file, context, (msliteModel) => {
+            resolve(msliteModel);
+          })
+        })
+      }
+      function case_predict(msliteModel, modelInputs) {
+        return new Promise((resolve) => {
+          msliteModel.predict(modelInputs, (modelOutputs) => {
+            resolve(modelOutputs);
+          })
         })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-        done();
-      })
+      }
+
+      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();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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");
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      let modelOutputs = await case_predict(msliteModel, 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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
+      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");
+      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
+            let context: mindSporeLite.Context = {};
+            context.target = ["cpu"];
+            context.cpu = {
+              "threadNum": 4,
+            }
+            context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
+            context.cpu.precisionMode = "preferred_fp16";
+            context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
+            mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, context, (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;
-        console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        await syscontext.resourceManager.getRawFileContent(modelName).then(async (model_buffer) => {
-          let modelBuffer = model_buffer;
+
+      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();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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")
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      let modelOutputs = await case_predict(msliteModel, 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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
+      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");
+      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);
           let context:mindSporeLite.Context={};
           context.target = ["cpu"];
           context.cpu = {
@@ -1710,357 +1576,262 @@ 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 {
-              expect(msliteModel !== null).assertTrue();
-              console.log('=========MSLITE loadModel end=====');
-              const modelInputs = msliteModel.getInputs();
-              let input_name = modelInputs[0].name;
-              console.log(input_name.toString());
-              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")
-              let input_elementNum = modelInputs[0].elementNum;
-              console.log(input_elementNum.toString());
-              expect(input_elementNum.toString()).assertEqual("6912");
-              let input_dtype = modelInputs[0].dtype;
-              console.log(input_dtype.toString());
-              expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-              let input_format = modelInputs[0].format;
-              console.log(input_format.toString());
-              expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-              let input_dataSize = modelInputs[0].dataSize;
-              console.log(input_dataSize.toString());
-              expect(input_dataSize.toString()).assertEqual("27648");
-              modelInputs[0].setData(inputBuffer.buffer);
-              console.log('=========MSLITE predict start=====');
-              msliteModel.predict(modelInputs, (modelOutputs) => {
-                try {
-                  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();
-                  }
-                  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();
-            }
+          mindSporeLite.loadModelFromFd(file.fd, context, (msliteModel) => {
+            resolve(msliteModel);
           })
-        }).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");
+      }
+      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;
-        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"];
-        context.cpu = {
-          "threadNum": 4,
-        }
-        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 {
-            expect(msliteModel !== null).assertTrue();
-            console.log('=========MSLITE loadModel end=====');
-            const modelInputs = msliteModel.getInputs();
-            let input_name = modelInputs[0].name;
-            console.log(input_name.toString());
-            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")
-            let input_elementNum = modelInputs[0].elementNum;
-            console.log(input_elementNum.toString());
-            expect(input_elementNum.toString()).assertEqual("6912");
-            let input_dtype = modelInputs[0].dtype;
-            console.log(input_dtype.toString());
-            expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-            let input_format = modelInputs[0].format;
-            console.log(input_format.toString());
-            expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-            let input_dataSize = modelInputs[0].dataSize;
-            console.log(input_dataSize.toString());
-            expect(input_dataSize.toString()).assertEqual("27648");
-            modelInputs[0].setData(inputBuffer.buffer);
-            console.log('=========MSLITE predict start=====');
-            msliteModel.predict(modelInputs, (modelOutputs) => {
-              try {
-                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();
-                }
-                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();
-      })
+      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();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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")
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      let modelOutputs = await case_predict(msliteModel, 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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
+      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;
-        console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
-        console.log('=========MSLITE loadModel start=====');
-
-        mindSporeLite.loadModelFromFile(model_file, (msliteModel) => {
-          try {
-            expect(msliteModel !== null).assertTrue();
-            console.log('=========MSLITE loadModel end=====');
-            const modelInputs = msliteModel.getInputs();
-            let input_name = modelInputs[0].name;
-            console.log(input_name.toString());
-            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")
-            let input_elementNum = modelInputs[0].elementNum;
-            console.log(input_elementNum.toString());
-            expect(input_elementNum.toString()).assertEqual("6912");
-            let input_dtype = modelInputs[0].dtype;
-            console.log(input_dtype.toString());
-            expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-            let input_format = modelInputs[0].format;
-            console.log(input_format.toString());
-            expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-            let input_dataSize = modelInputs[0].dataSize;
-            console.log(input_dataSize.toString());
-            expect(input_dataSize.toString()).assertEqual("27648");
-            modelInputs[0].setData(inputBuffer.buffer);
-            console.log('=========MSLITE predict start=====');
-            msliteModel.predict(modelInputs, (modelOutputs) => {
-              try {
-                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();
-                }
-                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();
-      })
+      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();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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")
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      let modelOutputs = await case_predict(msliteModel, 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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
+      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");
-      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;
-        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 {
-              expect(msliteModel !== null).assertTrue();
-              console.log('=========MSLITE loadModel end=====');
-              const modelInputs = msliteModel.getInputs();
-              let input_name = modelInputs[0].name;
-              console.log(input_name.toString());
-              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")
-              let input_elementNum = modelInputs[0].elementNum;
-              console.log(input_elementNum.toString());
-              expect(input_elementNum.toString()).assertEqual("6912");
-              let input_dtype = modelInputs[0].dtype;
-              console.log(input_dtype.toString());
-              expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-              let input_format = modelInputs[0].format;
-              console.log(input_format.toString());
-              expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-              let input_dataSize = modelInputs[0].dataSize;
-              console.log(input_dataSize.toString());
-              expect(input_dataSize.toString()).assertEqual("27648");
-              modelInputs[0].setData(inputBuffer.buffer);
-              console.log('=========MSLITE predict start=====');
-              msliteModel.predict(modelInputs, (modelOutputs) => {
-                try {
-                  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();
-                  }
-                  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();
-            }
+      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);
           })
-        }).catch(err => {
-          console.log("getRawFileContent catch: ", err);
-          done();
         })
-      }).catch(err => {
-        console.log("getRawFileContent catch: ", err);
-        done();
-      })
+      }
+      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();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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")
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      let modelOutputs = await case_predict(msliteModel, 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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
+      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;
-        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 {
-            expect(msliteModel !== null).assertTrue();
-            console.log('=========MSLITE loadModel end=====');
-            const modelInputs = msliteModel.getInputs();
-            let input_name = modelInputs[0].name;
-            console.log(input_name.toString());
-            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");
-            let input_elementNum = modelInputs[0].elementNum;
-            console.log(input_elementNum.toString());
-            expect(input_elementNum.toString()).assertEqual("6912");
-            let input_dtype = modelInputs[0].dtype;
-            console.log(input_dtype.toString());
-            expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
-            let input_format = modelInputs[0].format;
-            console.log(input_format.toString());
-            expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
-            let input_dataSize = modelInputs[0].dataSize;
-            console.log(input_dataSize.toString());
-            expect(input_dataSize.toString()).assertEqual("27648");
-            modelInputs[0].setData(inputBuffer.buffer);
-            console.log('=========MSLITE predict start=====');
-            msliteModel.predict(modelInputs, (modelOutputs) => {
-              try {
-                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();
-                }
-                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();
-      })
+      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();
+      let input_name = modelInputs[0].name;
+      console.log(input_name.toString());
+      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");
+      let input_elementNum = modelInputs[0].elementNum;
+      console.log(input_elementNum.toString());
+      expect(input_elementNum.toString()).assertEqual("6912");
+      let input_dtype = modelInputs[0].dtype;
+      console.log(input_dtype.toString());
+      expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
+      let input_format = modelInputs[0].format;
+      console.log(input_format.toString());
+      expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
+      let input_dataSize = modelInputs[0].dataSize;
+      console.log(input_dataSize.toString());
+      expect(input_dataSize.toString()).assertEqual("27648");
+      modelInputs[0].setData(inputBuffer.buffer);
+      console.log('=========MSLITE predict start=====');
+      let modelOutputs = await case_predict(msliteModel, 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();
+      }
+      const modelInputs0 = msliteModel.getInputs();
+      console.log(modelInputs0[0].name.toString());
+      console.log('=========MSLITE new Float32Array end=====');
+      done();
     })
 
     // 正常场景：头文件枚举值测试
diff --git a/ai/mindspore/mindsporejstest/src/main/resources/rawfile/aiy_vision_classifier_plants_V1_3_0.input b/ai/mindspore/mindsporejstest/src/main/resources/rawfile/aiy_vision_classifier_plants_V1_3_0.input
index 0d1a14831d740674712d2b4fecec491539391fd6..fdf94052f8c78706b927f8470154280a04f91f47 100644
Binary files a/ai/mindspore/mindsporejstest/src/main/resources/rawfile/aiy_vision_classifier_plants_V1_3_0.input and b/ai/mindspore/mindsporejstest/src/main/resources/rawfile/aiy_vision_classifier_plants_V1_3_0.input differ
diff --git a/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_face_isface.ms b/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_face_isface.ms
index 10d1604774c8cd7ede6ea4c2ecf850de544563a0..74a2fedd164c605ec478e4b9f4514d6f4e5b6211 100644
Binary files a/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_face_isface.ms and b/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_face_isface.ms differ
diff --git a/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_face_isface_0.input b/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_face_isface_0.input
index 2fd5bc3c13d662689d39f69559ce897875123572..aa0801c5383a77cbcc606e99996d13a76bd5feef 100644
Binary files a/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_face_isface_0.input and b/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_face_isface_0.input differ
diff --git a/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_headpose_pb2tflite.ms b/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_headpose_pb2tflite.ms
index 59e88aa5f27f6d3d649ba2b8ff7293f7c230d296..60b711e08687e1b851992c5a45b903aabe4c32bb 100644
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diff --git a/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_headpose_pb2tflite_0.input b/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_headpose_pb2tflite_0.input
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diff --git a/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_ocr_cn_0.input b/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_ocr_cn_0.input
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diff --git a/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_video_edit_face_cutout_portraitSeg_deconv.ms b/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_video_edit_face_cutout_portraitSeg_deconv.ms
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diff --git a/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_video_edit_face_cutout_portraitSeg_deconv_0.input b/ai/mindspore/mindsporejstest/src/main/resources/rawfile/ml_video_edit_face_cutout_portraitSeg_deconv_0.input
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