T
- data type for output()
output@Operator(group="nn") public final class SpaceToBatch<T> extends PrimitiveOp implements Operand<T>
This is a legacy version of the more general SpaceToBatchND.
Zero-pads and then rearranges (permutes) blocks of spatial data into batch. More specifically, this op outputs a copy of the input tensor where values from the `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.
operation
Modifier and Type | Method and Description |
---|---|
Output<T> |
asOutput()
Returns the symbolic handle of a tensor.
|
static <T,U extends Number> |
create(Scope scope,
Operand<T> input,
Operand<U> paddings,
Long blockSize)
Factory method to create a class wrapping a new SpaceToBatch operation.
|
Output<T> |
output() |
equals, hashCode, op, toString
public static <T,U extends Number> SpaceToBatch<T> create(Scope scope, Operand<T> input, Operand<U> paddings, Long blockSize)
scope
- current scopeinput
- 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:
[batchblock_sizeblock_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.blockSize
- public Output<T> asOutput()
Operand
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.
asOutput
in interface Operand<T>
OperationBuilder.addInput(Output)
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