BatchToSpace#
Versioned name: BatchToSpace-2
Category: Data movement
Short description: BatchToSpace operation permutes the batch dimension on a given input data
into blocks in the spatial dimensions specified by block_shape
input. The spatial dimensions are then optionally cropped according to crops_begin
and crops_end
inputs to produce the output.
Detailed description
BatchToSpace operation is equivalent to the following operation steps on the input data
with shape [batch, D_1, D_2, ..., D_{N-1}]
and block_shape
, crops_begin
, crops_end
inputs with shape [N]
to produce the output tensor \(y\).
Reshape
data
input to produce a tensor of shape \([B_1, \dots, B_{N - 1}, \frac{batch}{\left(B_1 \times \dots \times B_{N - 1}\right)}, D_1, D_2, \dots, D_{N - 1}]\)
Permute dimensions of \(x^{\prime}\) to produce a tensor of shape \([\frac{batch}{\left(B_1 \times \dots \times B_{N - 1}\right)}, D_1, B_1, D_2, B_2, \dots, D_{N-1}, B_{N - 1}]\)
Reshape \(x^{\prime\prime}\) to produce a tensor of shape \([\frac{batch}{\left(B_1 \times \dots \times B_{N - 1}\right)}, D_1 \times B_1, D_2 \times B_2, \dots, D_{N - 1} \times B_{N - 1}]\)
Crop the start and end of spatial dimensions of \(x^{\prime\prime\prime}\) according to
crops_begin
andcrops_end
inputs to produce the output \(y\) of shape:
Where
\(B_i\) = block_shape[i]
\(B_0\) is expected to be 1
\(CB_i\) = crops_begin[i]
\(CE_i\) = crops_end[i]
\(CB_0\) and \(CE_0\) are expected to be 0
\(CB_i + CE_i \leq D_i \times B_i\)
BatchToSpace operation is the reverse of SpaceToBatch operation.
Attributes: BatchToSpace operation has no attributes.
Inputs
1:
data
- A tensor of type T and rank greater than or equal to 2. Layout is[batch, D_1, D_2 ... D_{N-1}]
(number of batches, spatial axes). Required.2:
block_shape
- Specifies the block sizes ofbatch
axis ofdata
input which are moved to the corresponding spatial axes. A 1D tensor of type T_INT and shape[N]
. All element values must be greater than or equal to 1.block_shape[0]
is expected to be 1. Required.3:
crops_begin
- Specifies the amount to crop from the beginning along each axis ofdata
input. A 1D tensor of type T_INT and shape[N]
. All element values must be greater than or equal to 0.crops_begin[0]
is expected to be 0. Required.4:
crops_end
- Specifies the amount to crop from the ending along each axis ofdata
input. A 1D tensor of type T_INT and shape[N]
. All element values must be greater than or equal to 0.crops_end[0]
is expected to be 0. Required.Note:
N
corresponds to the rank ofdata
input.Note:
batch
axis ofdata
input must be evenly divisible by the cumulative product ofblock_shape
elements.Note: It is required that
crops_begin[i] + crops_end[i] <= block_shape[i] * input_shape[i]
.
Outputs
1: Permuted tensor of type T with the same rank as
data
input tensor, and shape[batch / (block_shape[0] * block_shape[1] * ... * block_shape[N - 1]), D_1 * block_shape[1] - crops_begin[1] - crops_end[1], D_2 * block_shape[2] - crops_begin[2] - crops_end[2], ..., D_{N - 1} * block_shape[N - 1] - crops_begin[N - 1] - crops_end[N - 1]
.
Types
T: any supported type.
T_INT: any supported integer type.
Examples
Example: 2D input tensor data
<layer type="BatchToSpace" ...>
<input>
<port id="0"> <!-- data -->
<dim>10</dim> <!-- batch -->
<dim>2</dim> <!-- spatial dimension 1 -->
</port>
<port id="1"> <!-- block_shape value: [1, 5] -->
<dim>2</dim>
</port>
<port id="2"> <!-- crops_begin value: [0, 2] -->
<dim>2</dim>
</port>
<port id="3"> <!-- crops_end value: [0, 0] -->
<dim>2</dim>
</port>
</input>
<output>
<port id="3">
<dim>2</dim> <!-- data.shape[0] / (block_shape.shape[0] * block_shape.shape[1]) -->
<dim>8</dim> <!-- data.shape[1] * block_shape.shape[1] - crops_begin[1] - crops_end[1]-->
</port>
</output>
</layer>
Example: 5D input tensor data
<layer type="BatchToSpace" ...>
<input>
<port id="0"> <!-- data -->
<dim>48</dim> <!-- batch -->
<dim>3</dim> <!-- spatial dimension 1 -->
<dim>3</dim> <!-- spatial dimension 2 -->
<dim>1</dim> <!-- spatial dimension 3 -->
<dim>3</dim> <!-- spatial dimension 4 -->
</port>
<port id="1"> <!-- block_shape value: [1, 2, 4, 3, 1] -->
<dim>5</dim>
</port>
<port id="2"> <!-- crops_begin value: [0, 0, 1, 0, 0] -->
<dim>5</dim>
</port>
<port id="3"> <!-- crops_end value: [0, 0, 1, 0, 0] -->
<dim>5</dim>
</port>
</input>
<output>
<port id="3">
<dim>2</dim> <!-- data.shape[0] / (block_shape.shape[0] * block_shape.shape[1] * ... * block_shape.shape[4]) -->
<dim>6</dim> <!-- data.shape[1] * block_shape.shape[1] - crops_begin[1] - crops_end[1]-->
<dim>10</dim> <!-- data.shape[2] * block_shape.shape[2] - crops_begin[2] - crops_end[2] -->
<dim>3</dim> <!-- data.shape[3] * block_shape.shape[3] - crops_begin[3] - crops_end[3] -->
<dim>3</dim> <!-- data.shape[4] * block_shape.shape[4] - crops_begin[4] - crops_end[4] -->
</port>
</output>
</layer>