openvino/docs/ops/movement/BatchToSpace_2.md

BatchToSpace

Versioned name: BatchToSpace-2

Category: Data movement

Short description: The BatchToSpace operation reshapes the "batch" dimension 0 into N - 1 dimensions of shape block_shape + [batch] and interleaves these blocks back into the grid defined by the spatial dimensions [1, ..., N - 1] to obtain a result with the same rank as data input. The spatial dimensions of this intermediate result are then optionally cropped according to crops_begin and crops_end to produce the output. This is the reverse of the SpaceToBatch operation.

Detailed description:

The BatchToSpace operation is similar to the TensorFlow* operation BatchToSpaceND

The operation is equivalent to the following transformation of the input tensors data with shape [batch, D_1, D_2 ... D_{N-1}] and block_shape, crops_begin, crops_end of shape [N] to Y output tensor.

note: B_0 is expected to be 1.
x' = reshape(`data`, [B_1, ..., B_{N - 1}, batch / (B_1 * ... B_{N - 1}), D_1, D_2, ..., D_{N - 1}]), where B_i = block_shape[i]

x'' = transpose(x', [N, N + 1, 0, N + 2, 1, ..., N + N - 1, N - 1])

x''' = reshape(x'', [batch / (B_1 * ... * B_{N - 1}), D_1 * B_1, D_2 * B_2, ... , D_{N - 1} * B_{N - 1}])

Crop the start and end of dimensions according to crops_begin, crops_end to produce the output of shape: note: crops_begin[0], crops_end[0] are expected to be 0. y = [batch / (B_1 * ... * B_{N - 1}), crop(D_1 * B_1, crops_begin[1], crops_end[1]), crop(D_2 * B_2, crops_begin[2], crops_end[2]), ... , crop(D_{N - 1} * B_{N - 1}, crops_begin[N - 1], crops_end[N - 1])]

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 of data input shape. All values must be >= 1.block_shape[0] is expected to be 1. Required.
• 3: crops_begin - input 1-D tensor of T2 type with shape [N] that is equal to the size of data input shape. All values must be non-negative. crops_begin specifies the amount to crop from the beginning along each axis of data input . It is required that crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i]. crops_begin[0] is expected to be 0. Required.
• 4: crops_end - input 1-D tensor of T2 type with shape [N] that is equal to the size of data input shape. All values must be non-negative. crops_end specifies the amount to crop from the ending along each axis of data input. It is required that crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i]. crops_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 * 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] of the same type as data input.

Types

• T1: any supported type.
• T2: any supported integer type.

Example

<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>