[Type Refactor] Merge TType and Tensor instances as a single entity

tensorflow-core/tensorflow-core-api/src/bazel/op_generator/op_specs.cc

@@ -152,8 +152,7 @@ std::pair<Type, Type> TypeResolver::TypesOf(const OpDef_AttrDef& attr_def,
  types = MakeTypePair(Type::Class("Shape", "org.tensorflow.ndarray"));
 
  } else if (attr_type == "tensor") {
- types = MakeTypePair(Type::Class("Tensor", "org.tensorflow")
- .add_parameter(Type::Wildcard()));
+ types = MakeTypePair(Type::Class("Tensor", "org.tensorflow"));
 
  } else if (attr_type == "type") {
  Type type = *iterable_out ? Type::Wildcard() : NextGeneric();

tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/Ops.java

@@ -24,7 +24,6 @@
 import org.tensorflow.EagerSession;
 import org.tensorflow.ExecutionEnvironment;
 import org.tensorflow.Operand;
-import org.tensorflow.Tensor;
 import org.tensorflow.ndarray.BooleanNdArray;
 import org.tensorflow.ndarray.ByteNdArray;
 import org.tensorflow.ndarray.DoubleNdArray;
@@ -349,10 +348,10 @@ public final class Ops {
 
  public final SignalOps signal;
 
- public final TrainOps train;
-
  public final QuantizationOps quantization;
 
+ public final TrainOps train;
+
  private final Scope scope;
 
  private Ops(Scope scope) {
@@ -374,8 +373,8 @@ private Ops(Scope scope) {
  math = new MathOps(this);
  audio = new AudioOps(this);
  signal = new SignalOps(this);
- train = new TrainOps(this);
  quantization = new QuantizationOps(this);
+ train = new TrainOps(this);
  }
 
  /**
@@ -1708,17 +1707,6 @@ public Constant<TInt64> constant(Shape shape) {
  return Constant.tensorOf(scope, shape);
  }
 
- /**
- * Create a constant from a Tensor.
- *
- * @param scope is a scope used to add the underlying operation.
- * @param tensor a Tensor holding the constant value
- * @return a constant of the same data type as `tensor`
- */
- public <T extends TType> Constant<T> constant(Tensor<T> tensor) {
- return Constant.create(scope, tensor);
- }
-
  /**
  * Creates a constant of {@code String} elements, using the given charset.
  *
@@ -1879,6 +1867,20 @@ public <T extends TType> Constant<T> constant(DataType<T> type, Shape shape,
  return Constant.tensorOf(scope, type, shape, data);
  }
 
+ /**
+ * Create a constant by making an immutable copy of {@code tensor}.
+ *
+ * <p>Note: this endpoint cannot be simply called {@code constant} since it will conflict with
+ * other endpoints accepting an NdArray in parameter {e.g. {@link #tensorOf(Scope, FloatNdArray)}}.
+ *
+ * @param scope is a scope used to add the underlying operation.
+ * @param tensor a Tensor holding the constant value
+ * @return a constant of the same data type as `tensor`
+ */
+ public <T extends TType> Constant<T> constantOf(T tensor) {
+ return Constant.create(scope, tensor);
+ }
+
  /**
  * This op consumes a lock created by `MutexLock`.
  * <p>

tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/summary/ImageSummary.java

@@ -99,13 +99,13 @@ public Options maxImages(Long maxImages) {
  /**
  * @param badColor Color to use for pixels with non-finite values.
  */
- public Options badColor(Tensor<?> badColor) {
+ public Options badColor(Tensor badColor) {
  this.badColor = badColor;
  return this;
  }
 
  private Long maxImages;
- private Tensor<?> badColor;
+ private Tensor badColor;
 
  private Options() {
  }
@@ -150,7 +150,7 @@ public static Options maxImages(Long maxImages) {
  /**
  * @param badColor Color to use for pixels with non-finite values.
  */
- public static Options badColor(Tensor<?> badColor) {
+ public static Options badColor(Tensor badColor) {
  return new Options().badColor(badColor);
  }
 

tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/AbstractOperation.java

@@ -86,5 +86,5 @@ public String toString() {
  * @param outputIdx index of the output of this operation
  * @return output tensor
  */
- abstract Tensor<?> tensor(int outputIdx);
+ abstract Tensor tensor(int outputIdx);
 }

tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/ConcreteFunction.java

@@ -34,7 +34,7 @@
  *
  * <pre>{@code
  * ConcreteFunction myFunction = savedModelBundle.function("myFunctionSignatureName");
- * Map<String, Tensor<?>> outputTensorMap = myFunction.call(inputTensorMap);
+ * Map<String, Tensor> outputTensorMap = myFunction.call(inputTensorMap);
  * }</pre>
  */
 public class ConcreteFunction implements AutoCloseable {
@@ -61,8 +61,8 @@ public class ConcreteFunction implements AutoCloseable {
  *
  * public static void main(String args[]) {
  * try (ConcreteFunction function = ConcreteFunction.create(MyModel::addTwo);
- * Tensor<TFloat32> x = TFloat32.scalarOf(2.0f)) {
- * assertEquals(4.0f, function.call(x).expect(TFloat32.DTYPE).data().getFloat());
+ * TFloat32 x = TFloat32.scalarOf(2.0f)) {
+ * assertEquals(4.0f, ((TFloat32)function.call(x)).getFloat());
  * }
  * }
  * }
@@ -97,8 +97,8 @@ public static ConcreteFunction create(Function<Ops, Signature> functionBuilder)
  * Signature signature = Signature.builder().input("x", input).output("y", output).build();
  *
  * try (ConcreteFunction f = ConcreteFunction.create(signature, g);
- * Tensor<TFloat32> x = TFloat32.scalarOf(2.0f)) {
- * assertEquals(4.0f, function.call(x).expect(TFloat32.DTYPE).data().getFloat());
+ * TFloat32 x = TFloat32.scalarOf(2.0f)) {
+ * assertEquals(4.0f, ((TFloat32)function.call(x)).getFloat());
  * }
  * // Graph g is still valid at this point
  * }
@@ -129,8 +129,8 @@ public static ConcreteFunction create(Signature signature, Graph graph) {
  * // Auto-closing the function just as an example but this is not required since it has
  * // no effect
  * try (ConcreteFunction f = ConcreteFunction.create(signature, s);
- * Tensor<TFloat32> t = TFloat32.scalarOf(2.0f)) {
- * assertEquals(4.0f, function.call(x).expect(TFloat32.DTYPE).data().getFloat());
+ * TFloat32 t = TFloat32.scalarOf(2.0f)) {
+ * assertEquals(4.0f, ((TFloat32)function.call(x)).getFloat());
  * }
  * // Session s is still valid at this point
  * }
@@ -163,14 +163,14 @@ public Signature signature() {
  * @return output tensors resulting from the execution of the function,
  * mapped by their signature name
  */
- public Map<String, Tensor<?>> call(Map<String, Tensor<?>> arguments)
+ public Map<String, Tensor> call(Map<String, Tensor> arguments)
  throws IllegalArgumentException {
 
  final SignatureDef signatureDef = signature.asSignatureDef();
  final Session.Runner runner = session.runner();
 
  signatureDef.getInputsMap().forEach((argName, t) -> {
- Tensor<?> tensor = arguments.get(argName);
+ Tensor tensor = arguments.get(argName);
  if (tensor == null) {
  throw new IllegalArgumentException(String.format("Missing argument [%s]", argName));
  }
@@ -180,10 +180,10 @@ public Map<String, Tensor<?>> call(Map<String, Tensor<?>> arguments)
  Map<String, TensorInfo> outputToNode = signatureDef.getOutputsMap();
  outputToNode.values().forEach(t -> runner.fetch(t.getName()));
 
- List<Tensor<?>> resultTensors = runner.run();
+ List<Tensor> resultTensors = runner.run();
  try {
- ListIterator<Tensor<?>> resultTensorIter = resultTensors.listIterator();
- Map<String, Tensor<?>> returnMap = new HashMap<String, Tensor<?>>();
+ ListIterator<Tensor> resultTensorIter = resultTensors.listIterator();
+ Map<String, Tensor> returnMap = new HashMap<String, Tensor>();
 
  // Use the output names as present in the signature definition
  for (String nodeName: outputToNode.keySet()) {
@@ -193,7 +193,7 @@ public Map<String, Tensor<?>> call(Map<String, Tensor<?>> arguments)
 
  } catch (Exception e) {
  // Release tensors before throwing exception
- for (Tensor<?> t : resultTensors) {
+ for (Tensor t : resultTensors) {
  t.close();
  }
  throw e;
@@ -210,7 +210,7 @@ public Map<String, Tensor<?>> call(Map<String, Tensor<?>> arguments)
  * @throws IllegalArgumentException if there are multiple input or output parameters defined
  * in the function
  */
- public Tensor<?> call(Tensor<?> tensor) throws IllegalArgumentException {
+ public Tensor call(Tensor tensor) throws IllegalArgumentException {
  final SignatureDef signatureDef = signature.asSignatureDef();
 
  if (signatureDef.getInputsCount() != 1) {

tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/DataType.java

@@ -16,7 +16,6 @@
 package org.tensorflow;
 
 import org.tensorflow.internal.c_api.TF_Tensor;
-import org.tensorflow.ndarray.Shape;
 import org.tensorflow.types.TBfloat16;
 import org.tensorflow.types.TBool;
 import org.tensorflow.types.TFloat16;
@@ -35,13 +34,17 @@ public final class DataType<T extends TType> {
  public interface TensorMapper<T> {
 
  /**
- * Maps tensor memory to a data structure for manipulating elements of this type.
+ * Maps the tensor memory to a n-dimensional typed data space.
  *
- * @param nativeTensor pointer to the native tensor
- * @param shape the shape of the tensor
- * @return data structure of elements of this type
+ * <p>This method is designed to be invoked internally by this library only, in order to pass the
+ * native handle of {@code tensor} as {@code nativeHandle} (and since only classes from the
+ * {@code org.tensorflow} package can retrieve such handle).
+ *
+ * @param tensor the tensor to map in its raw nature
+ * @param nativeHandle native handle of the tensor
+ * @return a typed tensor of type {@code T}
  */
- T apply(TF_Tensor nativeTensor, Shape shape);
+ T apply(RawTensor tensor, TF_Tensor nativeHandle);
  }
 
  /**
@@ -158,13 +161,13 @@ int nativeCode() {
  }
 
  /**
- * Maps a tensor to a data structure for manipulating elements of this type.
+ * Maps a raw tensor to a typed tensor.
  *
  * @param tensor tensor to map
  * @return data structure of elements of this type
  */
- T map(Tensor<T> tensor) {
- return tensorMapper.apply(tensor.nativeHandle(), tensor.shape());
+ T map(RawTensor tensor) {
+ return tensorMapper.apply(tensor, tensor.nativeHandle());
  }
 
  private final int nativeCode;

tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/EagerOperation.java

@@ -91,7 +91,7 @@ public TFE_TensorHandle getUnsafeNativeHandle(int outputIndex) {
  public Shape shape(int outputIndex) {
  // If the tensor of this output has already been resolved, return its shape.
  // Otherwise, retrieve the tensor shape from the native library.
- Tensor<?> tensor = outputTensors.get(outputIndex);
+ Tensor tensor = outputTensors.get(outputIndex);
  if (tensor != null) {
  return tensor.shape();
  }
@@ -107,7 +107,7 @@ public Shape shape(int outputIndex) {
  public DataType<?> dtype(int outputIndex) {
  // If the tensor of this output has already been resolved, return its datatype.
  // Otherwise, retrieve the tensor datatype from the native library.
- Tensor<?> tensor = outputTensors.get(outputIndex);
+ Tensor tensor = outputTensors.get(outputIndex);
  if (tensor != null) {
  return tensor.dataType();
  }
@@ -116,8 +116,8 @@ public DataType<?> dtype(int outputIndex) {
  }
 
  @Override
- public Tensor<?> tensor(int outputIndex) {
- Tensor<?> tensor = outputTensors.get(outputIndex);
+ public Tensor tensor(int outputIndex) {
+ Tensor tensor = outputTensors.get(outputIndex);
  if (tensor == null) {
  tensor = resolveTensor(outputIndex);
  }
@@ -127,21 +127,21 @@ public Tensor<?> tensor(int outputIndex) {
  private final EagerSession session;
  private final String type;
  private final String name;
- private final AtomicReferenceArray<Tensor<?>> outputTensors;
+ private final AtomicReferenceArray<Tensor> outputTensors;
 
- private Tensor<?> resolveTensor(int outputIndex) {
+ private Tensor resolveTensor(int outputIndex) {
  // Take an optimistic approach, where we attempt to resolve the output tensor without locking.
  // If another thread has resolved it meanwhile, release our copy and reuse the existing one
  // instead.
- Tensor<?> tensor = resolveTensorHandle(getUnsafeNativeHandle(outputIndex), session);
+ Tensor tensor = resolveTensorHandle(getUnsafeNativeHandle(outputIndex), session);
  if (!outputTensors.compareAndSet(outputIndex, null, tensor)) {
- session.detach(tensor.nativeHandle());
+ session.detach(tensor.asRawTensor().nativeHandle());
  tensor = outputTensors.get(outputIndex);
  }
  return tensor;
  }
 
- private TFE_Op opHandle;
+ private final TFE_Op opHandle;
  private final TFE_TensorHandle[] outputHandles;
 
  private static void requireOp(TFE_Op handle) {
@@ -156,13 +156,13 @@ private static void requireTensorHandle(TFE_TensorHandle handle) {
  }
  }
 
- private static Tensor<?> resolveTensorHandle(TFE_TensorHandle handle, EagerSession session) {
+ private static Tensor resolveTensorHandle(TFE_TensorHandle handle, EagerSession session) {
  requireTensorHandle(handle);
  try (PointerScope scope = new PointerScope()) {
  TF_Status status = TF_Status.newStatus();
  TF_Tensor tensor = TFE_TensorHandleResolve(handle, status).withDeallocator();
  status.throwExceptionIfNotOK();
- return Tensor.fromHandle(tensor, session);
+ return RawTensor.fromHandle(tensor, session).asTypedTensor();
  }
  }
 

tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/EagerOperationBuilder.java

@@ -175,13 +175,13 @@ public EagerOperationBuilder setAttr(String name, DataType<?>[] values) {
  }
 
  @Override
- public EagerOperationBuilder setAttr(String name, Tensor<?> value) {
- setAttrTensor(opHandle, name, value.nativeHandle());
+ public EagerOperationBuilder setAttr(String name, Tensor value) {
+ setAttrTensor(opHandle, name, value.asRawTensor().nativeHandle());
  return this;
  }
 
  @Override
- public EagerOperationBuilder setAttr(String name, Tensor<?>[] values) {
+ public EagerOperationBuilder setAttr(String name, Tensor[] values) {
  // TODO (karllessard) could be supported by adding this attribute type in the eager C API
  throw new UnsupportedOperationException(
  "Tensor list attributes are not supported in eager mode");

tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/GraphOperation.java

@@ -158,7 +158,7 @@ DataType<?> dtype(int outputIdx) {
  }
 
  @Override
- Tensor<?> tensor(int outputIdx) {
+ Tensor tensor(int outputIdx) {
  throw new IllegalStateException("Graph tensors must be fetched by running a session");
  }