T - data type for output output@Operator public final class SpaceToBatchNd<T extends TType> extends RawOp implements Operand<T>
[1, ..., M] of the input into a
grid of blocks of shape block_shape, and interleaves these blocks with the
"batch" dimension (0) such that in the output, the spatial dimensions
[1, ..., M] correspond to the position within the grid, and the batch
dimension combines both the position within a spatial block and the original
batch position. Prior to division into blocks, the spatial dimensions of the
input are optionally zero padded according to paddings. See below for a
precise description.
This operation is equivalent to the following steps:
Zero-pad the start and end of dimensions [1, ..., M] of the
input according to paddings to produce padded of shape padded_shape.
Reshape padded to reshaped_padded of shape:
[batch] + [padded_shape[1] / block_shape[0], block_shape[0], ..., padded_shape[M] / block_shape[M-1], block_shape[M-1]] + remaining_shape
Permute dimensions of reshaped_padded to produce
permuted_reshaped_padded of shape:
block_shape + [batch] + [padded_shape[1] / block_shape[0], ..., padded_shape[M] / block_shape[M-1]] + remaining_shape
Reshape permuted_reshaped_padded to flatten block_shape into the batch
dimension, producing an output tensor of shape:
[batch * prod(block_shape)] + [padded_shape[1] / block_shape[0], ..., padded_shape[M] / block_shape[M-1]] + remaining_shape
Some examples:
(1) For the following input of shape [1, 2, 2, 1], block_shape = [2, 2], and
paddings = [[0, 0], [0, 0]]:
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], block_shape = [2, 2], and
paddings = [[0, 0], [0, 0]]:
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], block_shape = [2, 2], and
paddings = [[0, 0], [0, 0]]:
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], block_shape = [2, 2], and
paddings = [[0, 0], [2, 0]]:
x = [[[[1], [2], [3], [4]],
[[5], [6], [7], [8]]],
[[[9], [10], [11], [12]],
[[13], [14], [15], [16]]]]
The output tensor has shape [8, 1, 3, 1] and value:
x = [[[[0], [1], [3]]], [[[0], [9], [11]]],
[[[0], [2], [4]]], [[[0], [10], [12]]],
[[[0], [5], [7]]], [[[0], [13], [15]]],
[[[0], [6], [8]]], [[[0], [14], [16]]]]
Among others, this operation is useful for reducing atrous convolution into regular convolution.
| Modifier and Type | Class and Description |
|---|---|
static class |
SpaceToBatchNd.Inputs<T extends TType> |
| Modifier and Type | Field and Description |
|---|---|
static String |
OP_NAME
The name of this op, as known by TensorFlow core engine
|
| Constructor and Description |
|---|
SpaceToBatchNd(Operation operation) |
| Modifier and Type | Method and Description |
|---|---|
Output<T> |
asOutput()
Returns the symbolic handle of the tensor.
|
static <T extends TType> |
create(Scope scope,
Operand<T> input,
Operand<? extends TNumber> blockShape,
Operand<? extends TNumber> paddings)
Factory method to create a class wrapping a new SpaceToBatchND operation.
|
Output<T> |
output()
Gets output.
|
public static final String OP_NAME
public SpaceToBatchNd(Operation operation)
@Endpoint(describeByClass=true) public static <T extends TType> SpaceToBatchNd<T> create(Scope scope, Operand<T> input, Operand<? extends TNumber> blockShape, Operand<? extends TNumber> paddings)
T - data type for SpaceToBatchND output and operandsscope - current scopeinput - N-D with shape input_shape = [batch] + spatial_shape + remaining_shape,
where spatial_shape has M dimensions.blockShape - 1-D with shape [M], all values must be >= 1.paddings - 2-D with shape [M, 2], all values must be >= 0.
paddings[i] = [pad_start, pad_end] specifies the padding for input dimension
i + 1, which corresponds to spatial dimension i. It is required that
block_shape[i] divides input_shape[i + 1] + pad_start + pad_end.public Output<T> asOutput()
OperandInputs 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 extends TType>OperationBuilder.addInput(Output)Copyright © 2015–2022. All rights reserved.