【fix】 修复机器学习ut 报错 review by songym

xilanhuaweidapao · xilanhuaweidapao · commit 01f2a45a8ea2 · 2021-04-29T17:44:51.000+08:00
diff --git a/test/common/thirdparty/ai/BinaryClassificationSpec.js b/test/common/thirdparty/ai/BinaryClassificationSpec.js
@@ -17,12 +17,13 @@ describe('binary classification', () => {
     it('predict', (done) => {
         var params = {
             modelUrl: 'http://fakeurl/model.json',
-            image: document.querySelector('#demo')
+            image: document.querySelector('#demo'),
+            backend: 'cpu'
         };
         var binaryClassification = new BinaryClassification(params);
         expect(binaryClassification).not.toBeNull();
         expect(binaryClassification.modelUrl).toBe(params.modelUrl);
-        expect(binaryClassification.backend).toBe('webgl');
+        expect(binaryClassification.backend).toBe('cpu');
         spyOn(tfconv, 'loadGraphModel').and.callFake((modelUrl) => {
             expect(modelUrl).toBe(params.modelUrl);
             const model = {
diff --git a/test/common/thirdparty/ai/LandcoverClassificationSpec.js b/test/common/thirdparty/ai/LandcoverClassificationSpec.js
@@ -17,12 +17,13 @@ describe('landcover classification', () => {
     it('predict', (done) => {
         var params = {
             modelUrl: 'http://fakeurl/model.json',
-            image: window.document.querySelector('#demo')
+            image: window.document.querySelector('#demo'),
+            backend: 'cpu'
         };
         var landcoverClassification = new LandcoverClassification(params);
         expect(landcoverClassification).not.toBeNull();
         expect(landcoverClassification.modelUrl).toBe(params.modelUrl);
-        expect(landcoverClassification.backend).toBe('webgl');
+        expect(landcoverClassification.backend).toBe('cpu');
         
         spyOn(tfconv, 'loadGraphModel').and.callFake((modelUrl) => {
           expect(modelUrl).toBe(params.modelUrl);
diff --git a/test/common/thirdparty/ai/ObjectDetectionSpec.js b/test/common/thirdparty/ai/ObjectDetectionSpec.js
@@ -17,18 +17,16 @@ describe('object detection', () => {
     it('predict', (done) => {
         var params = {
             modelUrl: 'http://fakeurl/model.json',
-            image: document.querySelector('#demo')
+            image: document.querySelector('#demo'),
+            backend: 'cpu'
         };
         var objectDetection = new ObjectDetection(params);
         expect(objectDetection).not.toBeNull();
         expect(objectDetection.modelUrl).toBe(params.modelUrl);
-        expect(objectDetection.backend).toBe('webgl');
+        expect(objectDetection.backend).toBe('cpu');
         spyOn(tfconv, 'loadGraphModel').and.callFake((modelUrl) => {
             expect(modelUrl).toBe(params.modelUrl);
             const model = {
-                //         const score = result[3].arraySync();
-                // const bbox = result[2].arraySync();
-                // const delta = result[1].arraySync();
                 executeAsync: (x) => {
                     return [
                         {},
diff --git a/test/common/thirdparty/ai/WebMachineLearningSpec.js b/test/common/thirdparty/ai/WebMachineLearningSpec.js
@@ -18,12 +18,13 @@ describe('WebMachineLearning', () => {
     it('predict', (done) => {
         var params = {
             modelUrl: 'ttp://fakeurl/model.json',
-            image: window.document.querySelector('#demo')
+            image: window.document.querySelector('#demo'),
+            backend: 'cpu'
         };
         var binaryClassification = new BinaryClassification(params);
         expect(binaryClassification).not.toBeNull();
         expect(binaryClassification.modelUrl).toBe(params.modelUrl);
-        expect(binaryClassification.backend).toBe('webgl');
+        expect(binaryClassification.backend).toBe('cpu');
         spyOn(tfconv, 'loadGraphModel').and.callFake((modelUrl) => {
             expect(modelUrl).toBe(params.modelUrl);
             const model = {
