!14245 reduce lite code Codespell error number to 0

From: @sishuikang
Reviewed-by: @zhanghaibo5,@zhang_xue_tong
Signed-off-by: @zhang_xue_tong

model_zoo/official/lite/image_segmentation/app/src/main/java/com/mindspore/imagesegmentation/MainActivity.java

@@ -286,7 +286,7 @@ public class MainActivity extends AppCompatActivity implements OnBackgroundImage
              progressBar.setVisibility(View.VISIBLE);
             new Thread(() -> {
                 isRunningModel = true;
-                modelTrackingResult = trackingMobile.execut(originBitmap);
+                modelTrackingResult = trackingMobile.execute(originBitmap);
                 if (modelTrackingResult != null) {
                     isRunningModel = false;
                     lastOriginBitmap = originBitmap;

model_zoo/official/lite/image_segmentation/app/src/main/java/com/mindspore/imagesegmentation/help/TrackingMobile.java

@@ -84,19 +84,19 @@ public class TrackingMobile {
         }
         msConfig.free();
 
-        // Complile graph.
+        // Complie graph.
         if (!session.compileGraph(model)) {
             Log.e(TAG, "Compile graph failed");
             model.freeBuffer();
             return;
         }
 
-        // Note: when use model.freeBuffer(), the model can not be complile graph again.
+        // Note: when use model.freeBuffer(), the model can not be complie graph again.
         model.freeBuffer();
 
     }
 
-    public ModelTrackingResult execut(Bitmap bitmap) {
+    public ModelTrackingResult execute(Bitmap bitmap) {
         // Set input tensor values.
         List<MSTensor> inputs = session.getInputs();
         if (inputs.size() != 1) {
@@ -135,8 +135,8 @@ public class TrackingMobile {
             int batch = output.getShape()[0];
             int channel = output.getShape()[1];
             int weight = output.getShape()[2];
-            int hight = output.getShape()[3];
-            int plane = weight * hight;
+            int height = output.getShape()[3];
+            int plane = weight * height;
 
             for (int n = 0; n < batch; n++) {
                 for (int c = 0; c < channel; c++) {

model_zoo/official/lite/posenet/README.en.md

@@ -187,14 +187,14 @@ The inference code process of bone detection demo is as follows. For details abo
     - Load the model file and build a computational graph for inference.
 
         ```java
-        // Complile graph.
+        // Compile graph.
         if (!session.compileGraph(model)) {
             Log.e("MS_LITE", "Compile graph failed");
             model.freeBuffer();
             return false;
         }
 
-        // Note: when use model.freeBuffer(), the model can not be complile graph again.
+        // Note: when use model.freeBuffer(), the model can not be compile graph again.
         model.freeBuffer();
         ```
 

model_zoo/official/lite/posenet/README.md

@@ -190,14 +190,14 @@
     - 加载模型文件并构建用于推理的计算图
 
         ```java
-        // Complile graph.
+        // Compile graph.
         if (!session.compileGraph(model)) {
             Log.e("MS_LITE", "Compile graph failed");
             model.freeBuffer();
             return false;
         }
 
-        // Note: when use model.freeBuffer(), the model can not be complile graph again.
+        // Note: when use model.freeBuffer(), the model can not be compile graph again.
         model.freeBuffer();
         ```
 

model_zoo/official/lite/posenet/app/src/main/java/com/mindspore/posenetdemo/Posenet.java

@@ -115,14 +115,14 @@ public class Posenet {
         }
         msConfig.free();
 
-        // Complile graph.
+        // Compile graph.
         if (!session.compileGraph(model)) {
             Log.e("MS_LITE", "Compile graph failed");
             model.freeBuffer();
             return false;
         }
 
-        // Note: when use model.freeBuffer(), the model can not be complile graph again.
+        // Note: when use model.freeBuffer(), the model can not be compile graph again.
         model.freeBuffer();
 
         return true;
@@ -198,60 +198,12 @@ public class Posenet {
                 String.format("Interpreter took %.2f ms", 1.0f * lastInferenceTimeNanos / 1_000_000)
         );
 
-        // Get output tensor values.
-        List<MSTensor> heatmaps_list = session.getOutputsByNodeName("Conv2D-27");
-        if (heatmaps_list == null) {
-            return null;
-        }
-        MSTensor heatmaps_tensors = heatmaps_list.get(0);
+        float[][][][] heatmaps = runConv2Dfor27();
+        float[][][][] offsets = runConv2Dfor28();
 
-        float[] heatmaps_results = heatmaps_tensors.getFloatData();
-        int[] heatmapsShape = heatmaps_tensors.getShape(); //1, 9, 9 ,17
-
-        float[][][][] heatmaps = new float[heatmapsShape[0]][][][];
-        for (int x = 0; x < heatmapsShape[0]; x++) {  // heatmapsShape[0] =1
-            float[][][] arrayThree = new float[heatmapsShape[1]][][];
-            for (int y = 0; y < heatmapsShape[1]; y++) {  // heatmapsShape[1] = 9
-                float[][] arrayTwo = new float[heatmapsShape[2]][];
-                for (int z = 0; z < heatmapsShape[2]; z++) { //heatmapsShape[2] = 9
-                    float[] arrayOne = new float[heatmapsShape[3]]; //heatmapsShape[3] = 17
-                    for (int i = 0; i < heatmapsShape[3]; i++) {
-                        int n = i + z * heatmapsShape[3] + y * heatmapsShape[2] * heatmapsShape[3] + x * heatmapsShape[1] * heatmapsShape[2] * heatmapsShape[3];
-                        arrayOne[i] = heatmaps_results[n]; //1*9*9*17  ??
-                    }
-                    arrayTwo[z] = arrayOne;
-                }
-                arrayThree[y] = arrayTwo;
-            }
-            heatmaps[x] = arrayThree;
-        }
-
-
-        List<MSTensor> offsets_list = session.getOutputsByNodeName("Conv2D-28");
-        if (offsets_list == null) {
+        if (heatmaps == null || offsets ==null){
             return null;
         }
-        MSTensor offsets_tensors = offsets_list.get(0);
-        float[] offsets_results = offsets_tensors.getFloatData();
-        int[] offsetsShapes = offsets_tensors.getShape();
-
-        float[][][][] offsets = new float[offsetsShapes[0]][][][];
-        for (int x = 0; x < offsetsShapes[0]; x++) {
-            float[][][] offsets_arrayThree = new float[offsetsShapes[1]][][];
-            for (int y = 0; y < offsetsShapes[1]; y++) {
-                float[][] offsets_arrayTwo = new float[offsetsShapes[2]][];
-                for (int z = 0; z < offsetsShapes[2]; z++) {
-                    float[] offsets_arrayOne = new float[offsetsShapes[3]];
-                    for (int i = 0; i < offsetsShapes[3]; i++) {
-                        int n = i + z * offsetsShapes[3] + y * offsetsShapes[2] * offsetsShapes[3] + x * offsetsShapes[1] * offsetsShapes[2] * offsetsShapes[3];
-                        offsets_arrayOne[i] = offsets_results[n];
-                    }
-                    offsets_arrayTwo[z] = offsets_arrayOne;
-                }
-                offsets_arrayThree[y] = offsets_arrayTwo;
-            }
-            offsets[x] = offsets_arrayThree;
-        }
 
         int height = ((Object[]) heatmaps[0]).length;  //9
         int width = ((Object[]) heatmaps[0][0]).length; //9
@@ -288,8 +240,8 @@ public class Posenet {
             int positionY = (int) position.first;
             int positionX = (int) position.second;
 
-            yCoords[i] =  ((float) positionY / (float) (height - 1) * bitmap.getHeight() + offsets[0][positionY][positionX][i]);
-            xCoords[i] =  ((float) positionX / (float) (width - 1) * bitmap.getWidth() + offsets[0][positionY][positionX][i + numKeypoints]);
+            yCoords[i] = ((float) positionY / (float) (height - 1) * bitmap.getHeight() + offsets[0][positionY][positionX][i]);
+            xCoords[i] = ((float) positionX / (float) (width - 1) * bitmap.getWidth() + offsets[0][positionY][positionX][i + numKeypoints]);
             confidenceScores[i] = sigmoid(heatmaps[0][positionY][positionX][i]);
         }
 
@@ -311,4 +263,72 @@ public class Posenet {
 
         return person;
     }
+
+    private float[][][][] runConv2Dfor27() {
+        // Get output tensor values.
+        List<MSTensor> heatmaps_list = session.getOutputsByNodeName("Conv2D-27");
+        if (heatmaps_list == null) {
+            return null;
+        }
+        MSTensor heatmaps_tensors = heatmaps_list.get(0);
+
+        float[] heatmaps_results = heatmaps_tensors.getFloatData();
+        int[] heatmapsShape = heatmaps_tensors.getShape(); //1, 9, 9 ,17
+
+        if (heatmapsShape[0] < 0 || heatmapsShape[1] < 0 || heatmapsShape[2] < 0 || heatmapsShape[3] < 0) {
+            return null;
+        }
+
+        float[][][][] heatmaps = new float[heatmapsShape[0]][][][];
+        for (int x = 0; x < heatmapsShape[0]; x++) {  // heatmapsShape[0] =1
+            float[][][] arrayThree = new float[heatmapsShape[1]][][];
+            for (int y = 0; y < heatmapsShape[1]; y++) {  // heatmapsShape[1] = 9
+                float[][] arrayTwo = new float[heatmapsShape[2]][];
+                for (int z = 0; z < heatmapsShape[2]; z++) { //heatmapsShape[2] = 9
+                    float[] arrayOne = new float[heatmapsShape[3]]; //heatmapsShape[3] = 17
+                    for (int i = 0; i < heatmapsShape[3]; i++) {
+                        int n = i + z * heatmapsShape[3] + y * heatmapsShape[2] * heatmapsShape[3] + x * heatmapsShape[1] * heatmapsShape[2] * heatmapsShape[3];
+                        arrayOne[i] = heatmaps_results[n]; //1*9*9*17  ??
+                    }
+                    arrayTwo[z] = arrayOne;
+                }
+                arrayThree[y] = arrayTwo;
+            }
+            heatmaps[x] = arrayThree;
+        }
+        return heatmaps;
+    }
+
+
+    private float[][][][] runConv2Dfor28() {
+        List<MSTensor> offsets_list = session.getOutputsByNodeName("Conv2D-28");
+        if (offsets_list == null) {
+            return null;
+        }
+        MSTensor offsets_tensors = offsets_list.get(0);
+        float[] offsets_results = offsets_tensors.getFloatData();
+        int[] offsetsShapes = offsets_tensors.getShape();
+
+        if (offsetsShapes[0] < 0 || offsetsShapes[1] < 0 || offsetsShapes[2] < 0 || offsetsShapes[3] < 0) {
+            return null;
+        }
+        float[][][][] offsets = new float[offsetsShapes[0]][][][];
+        for (int x = 0; x < offsetsShapes[0]; x++) {
+            float[][][] offsets_arrayThree = new float[offsetsShapes[1]][][];
+            for (int y = 0; y < offsetsShapes[1]; y++) {
+                float[][] offsets_arrayTwo = new float[offsetsShapes[2]][];
+                for (int z = 0; z < offsetsShapes[2]; z++) {
+                    float[] offsets_arrayOne = new float[offsetsShapes[3]];
+                    for (int i = 0; i < offsetsShapes[3]; i++) {
+                        int n = i + z * offsetsShapes[3] + y * offsetsShapes[2] * offsetsShapes[3] + x * offsetsShapes[1] * offsetsShapes[2] * offsetsShapes[3];
+                        offsets_arrayOne[i] = offsets_results[n];
+                    }
+                    offsets_arrayTwo[z] = offsets_arrayOne;
+                }
+                offsets_arrayThree[y] = offsets_arrayTwo;
+            }
+            offsets[x] = offsets_arrayThree;
+        }
+        return offsets;
+    }
 }

model_zoo/official/lite/style_transfer/README.en.md

@@ -154,7 +154,7 @@ The inference code process of style transfer demo is as follows. For details abo
     - Load the model file and build a computational graph for inference.
 
         ```java
-        // Complile graph.
+        // Compile graph.
         if (!Predict_session.compileGraph(style_predict_model)) {
             Log.e("MS_LITE", "Compile style_predict graph failed");
             style_predict_model.freeBuffer();
@@ -164,7 +164,7 @@ The inference code process of style transfer demo is as follows. For details abo
             style_transform_model.freeBuffer();
         }
 
-        // Note: when use model.freeBuffer(), the model can not be complile graph again.
+        // Note: when use model.freeBuffer(), the model can not be compile graph again.
         style_predict_model.freeBuffer();
         style_transform_model.freeBuffer();
         ```
@@ -223,7 +223,7 @@ The inference code process of style transfer demo is as follows. For details abo
         // Get output tensor values.
         List<String> tensorNames = Predict_session.getOutputTensorNames();
         Map<String, MSTensor> outputs = Predict_session.getOutputMapByTensor();
-        Set<Map.Entry<String, MSTensor>> entrys = outputs.entrySet();
+        Set<Map.Entry<String, MSTensor>> entry = outputs.entrySet();
 
         float[] Predict_results = null;
         for (String tensorName : tensorNames) {

model_zoo/official/lite/style_transfer/README.md

@@ -157,7 +157,7 @@
     - 加载模型文件并构建用于推理的计算图
 
         ```java
-        // Complile graph.
+        // Compile graph.
         if (!Predict_session.compileGraph(style_predict_model)) {
             Log.e("MS_LITE", "Compile style_predict graph failed");
             style_predict_model.freeBuffer();
@@ -167,7 +167,7 @@
             style_transform_model.freeBuffer();
         }
 
-        // Note: when use model.freeBuffer(), the model can not be complile graph again.
+        // Note: when use model.freeBuffer(), the model can not be compile graph again.
         style_predict_model.freeBuffer();
         style_transform_model.freeBuffer();
         ```
@@ -225,7 +225,7 @@
         // Get output tensor values.
         List<String> tensorNames = Predict_session.getOutputTensorNames();
         Map<String, MSTensor> outputs = Predict_session.getOutputMapByTensor();
-        Set<Map.Entry<String, MSTensor>> entrys = outputs.entrySet();
+        Set<Map.Entry<String, MSTensor>> entry = outputs.entrySet();
 
         float[] Predict_results = null;
         for (String tensorName : tensorNames) {

model_zoo/official/lite/style_transfer/app/src/main/java/com/mindspore/styletransferdemo/StyleTransferModelExecutor.java

@@ -96,7 +96,7 @@ public class StyleTransferModelExecutor {
         msConfig.free();
 
 
-        // Complile graph.
+        // Compile graph.
         if (!Predict_session.compileGraph(style_predict_model)) {
             Log.e("MS_LITE", "Compile style_predict graph failed");
             style_predict_model.freeBuffer();
@@ -106,7 +106,7 @@ public class StyleTransferModelExecutor {
             style_transform_model.freeBuffer();
         }
 
-        // Note: when use model.freeBuffer(), the model can not be complile graph again.
+        // Note: when use model.freeBuffer(), the model can not be compile graph again.
         style_predict_model.freeBuffer();
         style_transform_model.freeBuffer();
     }
@@ -165,7 +165,7 @@ public class StyleTransferModelExecutor {
         // Get output tensor values.
         List<String> tensorNames = Predict_session.getOutputTensorNames();
         Map<String, MSTensor> outputs = Predict_session.getOutputMapByTensor();
-        Set<Map.Entry<String, MSTensor>> entrys = outputs.entrySet();
+        Set<Map.Entry<String, MSTensor>> entry = outputs.entrySet();
 
         float[] Predict_results = null;
         for (String tensorName : tensorNames) {