openvino/docs/ops/movement/DepthToSpace_1.md

## DepthToSpace <a name="DepthToSpace"></a> {#openvino_docs_ops_movement_DepthToSpace_1}

**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](https://github.com/onnx/onnx/blob/master/docs/Operators.md#DepthToSpace) 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**

```xml
<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>
```