DepthToSpace

Versioned name: DepthToSpace-1

Category: Data movement

Short description: DepthToSpace operation rearranges data from the depth dimension of the input tensor into spatial dimensions of the output tensor.

Attributes

• block_size
  • Description: block_size specifies the size of the value block to be moved. The depth dimension size must be evenly divided by block_size ^ (len(input.shape) - 2).
  • Range of values: a positive integer
  • Type: int
  • Default value: 1
  • Required: no
• mode
  • Description: specifies how the input depth dimension is split to block coordinates and the new depth dimension.
  • Range of values:
    • blocks_first: the input depth is divided to [block_size, ..., block_size, new_depth]
    • depth_first: the input depth is divided to [new_depth, block_size, ..., block_size]
  • Type: string
  • Default value: None
  • Required: yes

Inputs

• 1: data - input tensor of any type with rank >= 3. Required.

Outputs

• 1: permuted tensor with shape [N, C / block_size ^ K, D1 * block_size, D2 * block_size, ..., DK * block_size].

Detailed description

DepthToSpace operation permutes elements from the input tensor with shape [N, C, D1, D2, ..., DK], to the output tensor where values from the input depth dimension (features) C are moved to spatial blocks in D1, ..., DK. Refer to the ONNX* specification for an example of the 4D input tensor case.

The operation is equivalent to the following transformation of the input tensor data with K spatial dimensions of shape [N, C, D1, D2, ..., DK] to Y output tensor. If mode = blocks_first:

x' = reshape(data, [N, block_size, block_size, ..., block_size, C / (block_size ^ K), D1, D2, ..., DK])

x'' = transpose(x', [0,  K + 1,  K + 2, 1, K + 3, 2, K + 4, 3, ..., K + (K + 1), K])

y = reshape(x'', [N, C / (block_size ^ K), D1 * block_size, D2 * block_size, D3 * block_size, ..., DK * block_size])

If mode = depth_first:

x' = reshape(data, [N, C / (block_size ^ K), block_size, block_size, ..., block_size, D1, D2, ..., DK])

x'' = transpose(x', [0,  1,  K + 2, 2, K + 3, 3, K + 4, 4, ..., K + (K + 1), K + 1])

y = reshape(x'', [N, C / (block_size ^ K), D1 * block_size, D2 * block_size, D3 * block_size, ..., DK * block_size])

Example

<layer type="DepthToSpace" ...>
    <data block_size="2" mode="blocks_first"/>
    <input>
        <port id="0">
            <dim>5</dim>
            <dim>28</dim>
            <dim>2</dim>
            <dim>3</dim>
        </port>
    </input>
    <output>
        <port id="1">
            <dim>5</dim>  <!-- data.shape[0] -->
            <dim>7</dim>  <!-- data.shape[1] / (block_size ^ 2) -->
            <dim>4</dim>  <!-- data.shape[2] * block_size -->
            <dim>6</dim>  <!-- data.shape[3] * block_size -->
        </port>
    </output>
</layer>