add to Tensor symmetric methods getDataAsIntArray, getDataAsByteArray (#25183)

Summary:
Tensor has getDataAsFloatArray(), we also support Int and Byte Tensors,
adding symmetric methods for Int and Byte, that will throw
IllegalStateException if called for not appropriate type
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25183

Reviewed By: dreiss

Differential Revision: D17052674

Pulled By: IvanKobzarev

fbshipit-source-id: 1d44944461ad008e202e382152cd0690c61124f4

android/.gitignore

@@ -2,6 +2,7 @@ local.properties
 **/*.iml
 .gradle
 gradlew*
+gradle/wrapper
 .idea/*
 .externalNativeBuild
 build

android/pytorch_android/src/androidTest/java/org/pytorch/PytorchInstrumentedTests.java

@@ -214,6 +214,47 @@ public class PytorchInstrumentedTests {
     module.runMethod("test_undefined_method_throws_exception");
   }
 
+  @Test
+  public void testTensorMethods() {
+    long[] dims = new long[] {1, 3, 224, 224};
+    final int numel = (int) Tensor.numElements(dims);
+    int[] ints = new int[numel];
+    float[] floats = new float[numel];
+
+    byte[] bytes = new byte[numel];
+    for (int i = 0; i < numel; i++) {
+      bytes[i] = (byte) ((i % 255) - 128);
+      ints[i] = i;
+      floats[i] = i / 1000.f;
+    }
+
+    Tensor tensorBytes = Tensor.newByteTensor(dims, bytes);
+    assertTrue(tensorBytes.isByteTensor());
+    assertArrayEquals(bytes, tensorBytes.getDataAsByteArray());
+
+    Tensor tensorInts = Tensor.newIntTensor(dims, ints);
+    assertTrue(tensorInts.isIntTensor());
+    assertArrayEquals(ints, tensorInts.getDataAsIntArray());
+
+    Tensor tensorFloats = Tensor.newFloatTensor(dims, floats);
+    assertTrue(tensorFloats.isFloatTensor());
+    float[] floatsOut = tensorFloats.getDataAsFloatArray();
+    assertTrue(floatsOut.length == numel);
+    for (int i = 0; i < numel; i++) {
+      assertTrue(floats[i] == floatsOut[i]);
+    }
+  }
+
+  @Test(expected = IllegalStateException.class)
+  public void testTensorIllegalStateOnWrongType() {
+    long[] dims = new long[] {1, 3, 224, 224};
+    final int numel = (int) Tensor.numElements(dims);
+    float[] floats = new float[numel];
+    Tensor tensorFloats = Tensor.newFloatTensor(dims, floats);
+    assertTrue(tensorFloats.isFloatTensor());
+    tensorFloats.getDataAsByteArray();
+  }
+
   private static String assetFilePath(String assetName) throws IOException {
     final Context appContext = InstrumentationRegistry.getInstrumentation().getTargetContext();
     File file = new File(appContext.getFilesDir(), assetName);

android/pytorch_android/src/main/java/org/pytorch/Tensor.java

@@ -128,9 +128,31 @@ public abstract class Tensor {
     return result;
   }
 
+  public byte[] getDataAsByteArray() {
+    throw new IllegalStateException(
+        "Tensor of type " + getClass().getSimpleName() + " cannot return data as byte array.");
+  }
+
+  public int[] getDataAsIntArray() {
+    throw new IllegalStateException(
+        "Tensor of type " + getClass().getSimpleName() + " cannot return data as int array.");
+  }
+
   public float[] getDataAsFloatArray() {
     throw new IllegalStateException(
-        "Tensor of type " + getClass().getSimpleName() + " cannot " + "return data as float.");
+        "Tensor of type " + getClass().getSimpleName() + " cannot return data as float array.");
+  }
+
+  public boolean isByteTensor() {
+    return TYPE_CODE_BYTE == getTypeCode();
+  }
+
+  public boolean isIntTensor() {
+    return TYPE_CODE_INT32 == getTypeCode();
+  }
+
+  public boolean isFloatTensor() {
+    return TYPE_CODE_FLOAT32 == getTypeCode();
   }
 
   abstract int getTypeCode();
@@ -200,6 +222,7 @@ public abstract class Tensor {
       return data;
     }
 
+    @Override
     public int[] getDataAsIntArray() {
       data.rewind();
       int[] arr = new int[data.remaining()];
@@ -233,6 +256,7 @@ public abstract class Tensor {
       return data;
     }
 
+    @Override
     public byte[] getDataAsByteArray() {
       data.rewind();
       byte[] arr = new byte[data.remaining()];