.. SPDX-FileCopyrightText: 2022 Intel Corporation
..
.. SPDX-License-Identifier: CC-BY-4.0

----------------------------
ConvTransposeBackpropData
----------------------------

**Versioned name**: *ConvTransposeBackpropData-1*

**Category**: Convolution

**Short description**: Computes the gradients of a ConvTranspose operation with
respect to the input.

**Detailed description**:

ConvTransposeBackpropData takes the gradient tensor of output and filter to
compute the gradient of input.

**Attributes**

* *strides*

  * **Description**: *strides* controls the stride along each spatial axis.
  * **Range of values**: positive s64 values.
  * **Type**: s64[]
  * **Required**: *yes*

* *pads_begin*

  * **Description**: *pads_begin* controls the amount of implicit zero padding
    of each spatial axis.
  * **Range of values**: Non-negative s64 values.
  * **Type**: s64[]
  * **Required**: *yes*
  * **Note**: the attribute is ignored when *auto_pad* attribute is specified.

* *pads_end*

  * **Description**: *pads_end* controls the amount of implicit zero padding of
    each spatial axis.
  * **Range of values**: Non-negative s64 values.
  * **Type**: s64[]
  * **Required**: *yes*
  * **Note**: the attribute is ignored when *auto_pad* attribute is specified.

* *dilations*

  * **Description**: *dilations* controls the spacing between the kernel points.
  * **Range of values**: positive s64 values.
  * **Type**: s64[]
  * **Required**: *yes*

* *auto_pad*

  * **Description**: *auto_pad* describes how the padding is calculated.

    * *none (not specified)*: use explicit padding values from ``pads_begin``
      and ``pads_end``.
    * *same_upper (same_lower)* the input is padded to match the output size.
      In case of odd padding value an extra padding is added at the end
      (at the beginning).
    * *valid* - do not use padding.

  * **Type**: string
  * **Default value**: *none*
  * **Required**: *no*
  * **Note**: *pads_begin* and *pads_end* attributes are ignored when *auto_pad*
    is specified.

* *output_padding*

  * **Description**: *output_padding* adds additional amount of padding per
    each spatial axis in the ``output`` tensor. It unlocks more elements in the
    output allowing them to be computed. Elements are added at the higher
    coordinate indices for the spatial dimensions. Number of elements in
    *output_padding* list matches the number of spatial dimensions in ``data``
    and ``output`` tensors.
  * **Range of values**: Non-negative s64 values.
  * **Type**: s64[]
  * **Default value**: all zeros
  * **Required**: *no*

* *groups*

  * **Description**: *groups* denotes the number of groups input channels and
    output channels are divided into. In_channels and out_channels must both be
    divisible by groups.
  * **Range of values**: A positive s64 value.
  * **Type**: s64
  * **Default value**: 1
  * **Required**: *no*

* *data_format*

  * **Description**: *data_format* denotes the data format of the input and
    output data.
  * **Range of values**: *NXC* or *NCX* (X means HW for 2D ConvTranspose, DHW
    for 3D ConvTranspose)
  * **Type**: string
  * **Default value**: *NXC*
  * **Required**: *no*

* *filter_format*

  * **Description**: *filter_format* denotes the data format of the filter.
  * **Range of values**: *XIO* or *OIX* (X means HW for 2D ConvTranspose, DHW
    for 3D ConvTranspose)
  * **Type**: string
  * **Default value**: *XIO*
  * **Required**: *no*

**Inputs**:

* **1**: ``output_delta`` - gradients tensor with respect to the output of the
  ConvTranspose. **Required**.

  * **Type**: T

* **2**: ``filter`` --  ConvTranspose filter tensor. The format is specified by
  *filter_format* attribute. The shape of filter is
  ::math::`(out_channels / groups, in_channels, spatial_shape)` for OIX format
  or ::math::`(spatial_shape, in_channels, out_channels / groups)` for XIO
  format. ::math::`in_channels` and ::math::`out_channels` must both be
  divisible by groups. **Required.**

  * **Type**: T

**Outputs**:

* **1**: ``input_delta`` - gradient tensor with respect to the input data of the
  ConvTranspose. The format is specified by *data_format* attribute.

  * **Type**: T

**Types**:

* **T**: f32, f16, bf16.
* **Note**: Inputs and outputs have the same data type denoted by *T*. For
  example, if input is f32 tensor, then all other tensors have f32 data type.