CropAndResizeGradImage

public final class CropAndResizeGradImage

Computes the gradient of the crop_and_resize op wrt the input image tensor.

Nested Classes

class CropAndResizeGradImage.Options Optional attributes for CropAndResizeGradImage  

Constants

String OP_NAME The name of this op, as known by TensorFlow core engine

Public Methods

Output<T>
asOutput()
Returns the symbolic handle of the tensor.
static <T extends TNumber> CropAndResizeGradImage<T>
create(Scope scope, Operand<TFloat32> grads, Operand<TFloat32> boxes, Operand<TInt32> boxInd, Operand<TInt32> imageSize, Class<T> T, Options... options)
Factory method to create a class wrapping a new CropAndResizeGradImage operation.
static CropAndResizeGradImage.Options
method(String method)
Output<T>
output()
A 4-D tensor of shape `[batch, image_height, image_width, depth]`.

Inherited Methods

org.tensorflow.op.RawOp
final boolean
equals(Object obj)
final int
hashCode()
Operation
op()
Return this unit of computation as a single Operation.
final String
toString()
boolean
equals(Object arg0)
final Class<?>
getClass()
int
hashCode()
final void
notify()
final void
notifyAll()
String
toString()
final void
wait(long arg0, int arg1)
final void
wait(long arg0)
final void
wait()
org.tensorflow.op.Op
abstract ExecutionEnvironment
env()
Return the execution environment this op was created in.
abstract Operation
op()
Return this unit of computation as a single Operation.
org.tensorflow.Operand
abstract Output<T>
asOutput()
Returns the symbolic handle of the tensor.
abstract T
asTensor()
Returns the tensor at this operand.
abstract Shape
shape()
Returns the (possibly partially known) shape of the tensor referred to by the Output of this operand.
abstract Class<T>
type()
Returns the tensor type of this operand
org.tensorflow.ndarray.Shaped
abstract int
rank()
abstract Shape
shape()
abstract long
size()
Computes and returns the total size of this container, in number of values.

Constants

public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

Constant Value: "CropAndResizeGradImage"

Public Methods

public Output<T> asOutput ()

Returns the symbolic handle of the tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static CropAndResizeGradImage<T> create (Scope scope, Operand<TFloat32> grads, Operand<TFloat32> boxes, Operand<TInt32> boxInd, Operand<TInt32> imageSize, Class<T> T, Options... options)

Factory method to create a class wrapping a new CropAndResizeGradImage operation.

Parameters
scope current scope
grads A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
boxes A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor specifies the coordinates of a box in the `box_ind[i]` image and is specified in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the `[0, 1]` interval of normalized image height is mapped to `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled crop is an up-down flipped version of the original image. The width dimension is treated similarly. Normalized coordinates outside the `[0, 1]` range are allowed, in which case we use `extrapolation_value` to extrapolate the input image values.
boxInd A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. The value of `box_ind[i]` specifies the image that the `i`-th box refers to.
imageSize A 1-D tensor with value `[batch, image_height, image_width, depth]` containing the original image size. Both `image_height` and `image_width` need to be positive.
options carries optional attributes values
Returns
  • a new instance of CropAndResizeGradImage

public static CropAndResizeGradImage.Options method (String method)

Parameters
method A string specifying the interpolation method. Only 'bilinear' is supported for now.

public Output<T> output ()

A 4-D tensor of shape `[batch, image_height, image_width, depth]`.