Add Interpreter.allocateTensors() call in Java

Normally this isn't necessary in Java, but it is useful in cases where
the output tensor shape is invalidated due to a resize, and the client
needs to know that shape before executing inference.

BUG=152671540
PiperOrigin-RevId: 304701829
Change-Id: Idbfaa956b307d9a24482a7c0731efddcda3e15a3

tensorflow/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java

@@ -314,6 +314,32 @@ public final class Interpreter implements AutoCloseable {
     wrapper.run(inputs, outputs);
   }
 
+  /**
+   * Expicitly updates allocations for all tensors, if necessary.
+   *
+   * <p>This will propagate shapes and memory allocations for all dependent tensors using the input
+   * tensor shape(s) as given.
+   *
+   * <p>Note: This call is *purely optional*. Tensor allocation will occur automatically during
+   * execution if any input tensors have been resized. This call is most useful in determining the
+   * shapes for any output tensors before executing the graph, e.g.,
+   * <pre>{@code
+   * interpreter.resizeInput(0, new int[]{1, 4, 4, 3}));
+   * interpreter.allocateTensors();
+   * FloatBuffer input = FloatBuffer.allocate(interpreter.getInputTensor(0),numElements());
+   * // Populate inputs...
+   * FloatBuffer output = FloatBuffer.allocate(interpreter.getOutputTensor(0).numElements());
+   * interpreter.run(input, output)
+   * // Process outputs...
+   * }</pre>
+   *
+   * @throws IllegalStateException if the graph's tensors could not be successfully allocated.
+   */
+  public void allocateTensors() {
+    checkNotClosed();
+    wrapper.allocateTensors();
+  }
+
   /**
    * Resizes idx-th input of the native model to the given dims.
    *
@@ -373,6 +399,13 @@ public final class Interpreter implements AutoCloseable {
   /**
    * Gets the Tensor associated with the provdied output index.
    *
+   * <p>Note: Output tensor details (e.g., shape) may not be fully populated until after inference
+   * is executed. If you need updated details *before* running inference (e.g., after resizing an
+   * input tensor, which may invalidate output tensor shapes), use {@link #allocateTensors()} to
+   * explicitly trigger allocation and shape propagation. Note that, for graphs with output shapes
+   * that are dependent on input *values*, the output shape may not be fully determined until
+   * running inference.
+   *
    * @throws IllegalArgumentException if {@code outputIndex} is negtive or is not smaller than the
    *     number of model outputs.
    */

tensorflow/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java

@@ -175,6 +175,8 @@ final class NativeInterpreterWrapper implements AutoCloseable {
   /** Resizes dimensions of a specific input. */
   void resizeInput(int idx, int[] dims) {
     if (resizeInput(interpreterHandle, errorHandle, idx, dims)) {
+      // Tensor allocation is deferred until either an explicit `allocateTensors()` call or
+      // `invoke()` avoiding redundant allocations if multiple tensors are simultaneosly resized.
       isMemoryAllocated = false;
       if (inputTensors[idx] != null) {
         inputTensors[idx].refreshShape();
@@ -185,6 +187,23 @@ final class NativeInterpreterWrapper implements AutoCloseable {
   private static native boolean resizeInput(
       long interpreterHandle, long errorHandle, int inputIdx, int[] dims);
 
+  /** Triggers explicit allocation of tensors. */
+  void allocateTensors() {
+    if (isMemoryAllocated) {
+      return;
+    }
+
+    isMemoryAllocated = true;
+    allocateTensors(interpreterHandle, errorHandle);
+    for (int i = 0; i < outputTensors.length; ++i) {
+      if (outputTensors[i] != null) {
+        outputTensors[i].refreshShape();
+      }
+    }
+  }
+
+  private static native long allocateTensors(long interpreterHandle, long errorHandle);
+
   void setUseNNAPI(boolean useNNAPI) {
     useNNAPI(interpreterHandle, useNNAPI);
   }
@@ -385,8 +404,6 @@ final class NativeInterpreterWrapper implements AutoCloseable {
   // List of owned delegates that must be closed when the interpreter is closed.
   private final List<AutoCloseable> ownedDelegates = new ArrayList<>();
 
-  private static native long allocateTensors(long interpreterHandle, long errorHandle);
-
   private static native boolean hasUnresolvedFlexOp(long interpreterHandle);
 
   private static native int getInputTensorIndex(long interpreterHandle, int inputIdx);

tensorflow/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java

@@ -222,6 +222,32 @@ public final class InterpreterTest {
     }
   }
 
+  @Test
+  public void testAllocateTensors() {
+    try (Interpreter interpreter = new Interpreter(MODEL_BUFFER)) {
+      // Redundant allocateTensors() should have no effect.
+      interpreter.allocateTensors();
+
+      // allocateTensors() should propagate resizes.
+      int[] inputDims = {1};
+      assertThat(interpreter.getOutputTensor(0).shape()).isNotEqualTo(inputDims);
+      interpreter.resizeInput(0, inputDims);
+      assertThat(interpreter.getOutputTensor(0).shape()).isNotEqualTo(inputDims);
+      interpreter.allocateTensors();
+      assertThat(interpreter.getOutputTensor(0).shape()).isEqualTo(inputDims);
+
+      // Additional redundant calls should have no effect.
+      interpreter.allocateTensors();
+      assertThat(interpreter.getOutputTensor(0).shape()).isEqualTo(inputDims);
+
+      // Execution should succeed as expected.
+      ByteBuffer input = ByteBuffer.allocateDirect(4).order(ByteOrder.nativeOrder());
+      ByteBuffer output = ByteBuffer.allocateDirect(4).order(ByteOrder.nativeOrder());
+      interpreter.run(input, output);
+      assertThat(interpreter.getOutputTensor(0).shape()).isEqualTo(inputDims);
+    }
+  }
+
   @Test
   public void testUnknownDims() {
     try (Interpreter interpreter = new Interpreter(UNKNOWN_DIMS_MODEL_PATH_BUFFER)) {