@Namespace(value="tensorflow::ops") @NoOffset public static class tensorflow.CropAndResizeGradBoxes extends Pointer
[num_boxes, crop_height, crop_width, depth].
* image: A 4-D tensor of shape [batch, image_height, image_width, depth].
Both image_height and image_width need to be positive.
* 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_valueto extrapolate the input image values.
* box_ind: 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.
Optional attributes (see Attrs):
* method: A string specifying the interpolation method. Only 'bilinear' is
supported for now.
Returns:
* Output: A 2-D tensor of shape [num_boxes, 4]{@code .| Modifier and Type | Class and Description |
|---|---|
static class |
tensorflow.CropAndResizeGradBoxes.Attrs
Optional attribute setters for CropAndResizeGradBoxes
|
Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator| Constructor and Description |
|---|
CropAndResizeGradBoxes(Pointer p)
Pointer cast constructor.
|
CropAndResizeGradBoxes(tensorflow.Scope scope,
tensorflow.Input grads,
tensorflow.Input image,
tensorflow.Input boxes,
tensorflow.Input box_ind) |
CropAndResizeGradBoxes(tensorflow.Scope scope,
tensorflow.Input grads,
tensorflow.Input image,
tensorflow.Input boxes,
tensorflow.Input box_ind,
tensorflow.CropAndResizeGradBoxes.Attrs attrs) |
| Modifier and Type | Method and Description |
|---|---|
tensorflow.Input |
asInput() |
tensorflow.Output |
asOutput() |
static tensorflow.CropAndResizeGradBoxes.Attrs |
Method(BytePointer x) |
static tensorflow.CropAndResizeGradBoxes.Attrs |
Method(String x) |
tensorflow.Node |
node() |
tensorflow.Operation |
operation() |
tensorflow.CropAndResizeGradBoxes |
operation(tensorflow.Operation operation) |
tensorflow.Output |
output() |
tensorflow.CropAndResizeGradBoxes |
output(tensorflow.Output output) |
address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, position, put, realloc, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zeropublic CropAndResizeGradBoxes(Pointer p)
Pointer.Pointer(Pointer).public CropAndResizeGradBoxes(@Const @ByRef tensorflow.Scope scope, @ByVal tensorflow.Input grads, @ByVal tensorflow.Input image, @ByVal tensorflow.Input boxes, @ByVal tensorflow.Input box_ind)
public CropAndResizeGradBoxes(@Const @ByRef tensorflow.Scope scope, @ByVal tensorflow.Input grads, @ByVal tensorflow.Input image, @ByVal tensorflow.Input boxes, @ByVal tensorflow.Input box_ind, @Const @ByRef tensorflow.CropAndResizeGradBoxes.Attrs attrs)
@ByVal @Name(value="operator tensorflow::Output") public tensorflow.Output asOutput()
@ByVal @Name(value="operator tensorflow::Input") public tensorflow.Input asInput()
public tensorflow.Node node()
@ByVal public static tensorflow.CropAndResizeGradBoxes.Attrs Method(@tensorflow.StringPiece BytePointer x)
@ByVal public static tensorflow.CropAndResizeGradBoxes.Attrs Method(@tensorflow.StringPiece String x)
@ByRef public tensorflow.Operation operation()
public tensorflow.CropAndResizeGradBoxes operation(tensorflow.Operation operation)
@ByRef public tensorflow.Output output()
public tensorflow.CropAndResizeGradBoxes output(tensorflow.Output output)
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