@Namespace(value="tensorflow::ops") @NoOffset @Properties(inherit=tensorflow.class) public class SpaceToBatch extends Pointer
height
and width
dimensions are moved to the batch
dimension. After
the zero-padding, both height
and width
of the input must be divisible by the
block size.
Arguments:
* scope: A Scope object
* input: 4-D with shape [batch, height, width, depth]
.
* paddings: 2-D tensor of non-negative integers with shape [2, 2]
. It specifies
the padding of the input with zeros across the spatial dimensions as follows:
paddings = [[pad_top, pad_bottom], [pad_left, pad_right]]
The effective spatial dimensions of the zero-padded input tensor will be:
height_pad = pad_top + height + pad_bottom
width_pad = pad_left + width + pad_right
The attr block_size
must be greater than one. It indicates the block size.
* Non-overlapping blocks of size block_size x block size
in the height and
width dimensions are rearranged into the batch dimension at each location.
* The batch of the output tensor is batch * block_size * block_size
.
* Both height_pad and width_pad must be divisible by block_size.
The shape of the output will be:
[batch*block_size*block_size, height_pad/block_size, width_pad/block_size,
depth]
Some examples:
(1) For the following input of shape [1, 2, 2, 1]
and block_size of 2:
x = [[[[1], [2]], [[3], [4]]]]
The output tensor has shape [4, 1, 1, 1]
and value:
[[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
(2) For the following input of shape [1, 2, 2, 3]
and block_size of 2:
x = [[[[1, 2, 3], [4, 5, 6]],
[[7, 8, 9], [10, 11, 12]]]]
The output tensor has shape [4, 1, 1, 3]
and value:
[[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]]
(3) For the following input of shape [1, 4, 4, 1]
and block_size of 2:
x = [[[[1], [2], [3], [4]],
[[5], [6], [7], [8]],
[[9], [10], [11], [12]],
[[13], [14], [15], [16]]]]
The output tensor has shape [4, 2, 2, 1]
and value:
x = [[[[1], [3]], [[9], [11]]],
[[[2], [4]], [[10], [12]]],
[[[5], [7]], [[13], [15]]],
[[[6], [8]], [[14], [16]]]]
(4) For the following input of shape [2, 2, 4, 1]
and block_size of 2:
x = [[[[1], [2], [3], [4]],
[[5], [6], [7], [8]]],
[[[9], [10], [11], [12]],
[[13], [14], [15], [16]]]]
The output tensor has shape [8, 1, 2, 1]
and value:
x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]],
[[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]]
Among others, this operation is useful for reducing atrous convolution into
regular convolution.
Returns:
* Output
: The output tensor.Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter
Constructor and Description |
---|
SpaceToBatch(Pointer p)
Pointer cast constructor.
|
SpaceToBatch(Scope scope,
Input input,
Input paddings,
long block_size) |
Modifier and Type | Method and Description |
---|---|
Input |
asInput() |
Output |
asOutput() |
Node |
node() |
Operation |
operation() |
SpaceToBatch |
operation(Operation setter) |
Output |
output() |
SpaceToBatch |
output(Output setter) |
address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, getDirectBufferAddress, getPointer, getPointer, getPointer, getPointer, hashCode, interruptDeallocatorThread, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetAddress, offsetof, offsetof, parseBytes, physicalBytes, physicalBytesInaccurate, position, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, sizeof, toString, totalBytes, totalCount, totalPhysicalBytes, withDeallocator, zero
public SpaceToBatch(Pointer p)
Pointer(Pointer)
.public Node node()
public SpaceToBatch operation(Operation setter)
public SpaceToBatch output(Output setter)
Copyright © 2022. All rights reserved.