SpaceToBatch#
Versioned name: SpaceToBatch-2
Category: Data movement
Short description: The SpaceToBatch operation divides “spatial” dimensions [1, ..., N - 1]
of the data
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, ..., N - 1]
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 pads_begin
and pads_end
.
Detailed description:
The operation is equivalent to the following transformation of the input tensor data
of shape [batch, D_1, D_2 ... D_{N - 1}]
and block_shape
, pads_begin
, pads_end
of shapes [N]
to Y output tensor.
Zero-pad the start and end of dimensions \([D_0, \dots, D_{N - 1}]\) of the input according to pads_begin
and pads_end
:
where
\(P_i\) = pads_begin[i] + pads_end[i]
\(B_i\) = block_shape[i]
\(P_0\) for batch dimension is expected to be 0 (no-padding)
\(B_0\) for batch is ignored
Attributes
No attributes available.
Inputs
1:
data
- input N-D tensor[batch, D_1, D_2 ... D_{N - 1}]
of T1 type with rank >= 2. Required.2:
block_shape
- input 1-D tensor of T2 type with shape[N]
that is equal to the size ofdata
input shape. All values must be >= 1.block_shape[0]
is expected to be 1. Required.3:
pads_begin
- input 1-D tensor of T2 type with shape[N]
that is equal to the size ofdata
input shape. All values must be non-negative.pads_begin
specifies the padding for the beginning along each axis ofdata
input . It is required thatblock_shape[i]
dividesdata_shape[i] + pads_begin[i] + pads_end[i]
.pads_begin[0]
is expected to be 0. Required.4:
pads_end
- input 1-D tensor of T2 type with shape[N]
that is equal to the size ofdata
input shape. All values must be non-negative.pads_end
specifies the padding for the ending along each axis ofdata
input. It is required thatblock_shape[i]
dividesdata_shape[i] + pads_begin[i] + pads_end[i]
.pads_end[0]
is expected to be 0. Required.
Outputs
1: N-D tensor with shape
[batch * block_shape[0] * block_shape[1] * ... * block_shape[N - 1], (D_1 + pads_begin[1] + pads_end[1]) / block_shape[1], (D_2 + pads_begin[2] + pads_end[2]) / block_shape[2], ..., (D_{N -1} + pads_begin[N - 1] + pads_end[N - 1]) / block_shape[N - 1]
of the same type asdata
input.
Types
T1: any supported type.
T2: any supported integer type.
Example
<layer type="SpaceToBatch" ...>
<input>
<port id="0"> <!-- data -->
<dim>2</dim> <!-- batch -->
<dim>6</dim> <!-- spatial dimension 1 -->
<dim>10</dim> <!-- spatial dimension 2 -->
<dim>3</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"> <!-- pads_begin value: [0, 0, 1, 0, 0] -->
<dim>5</dim>
</port>
<port id="3"> <!-- pads_end value: [0, 0, 1, 0, 0] -->
<dim>5</dim>
</port>
</input>
<output>
<port id="3">
<dim>48</dim> <!-- data.shape[0] * block_shape.shape[0] * block_shape.shape[1] *... * block_shape.shape[4] -->
<dim>3</dim> <!-- (data.shape[1] + pads_begin[1] + pads_end[1]) / block_shape.shape[1] -->
<dim>3</dim> <!-- (data.shape[2] + pads_begin[2] + pads_end[2]) / block_shape.shape[2] -->
<dim>1</dim> <!-- (data.shape[3] + pads_begin[3] + pads_end[3]) / block_shape.shape[3] -->
<dim>3</dim> <!-- (data.shape[4] + pads_begin[4] + pads_end[4]) / block_shape.shape[4] -->
</port>
</output>
</layer>