.. SPDX-FileCopyrightText: 2020-2021 Intel Corporation .. .. SPDX-License-Identifier: CC-BY-4.0 ----------------------- ConvolutionBackpropData ----------------------- **Versioned name**: *ConvolutionBackpropData-1* **Category**: Convolution **Short description**: Computes the gradients of a Convolution operation with respect to the input. Also known as a Deconvolution or a Transposed Convolution. **Detailed description**: ConvolutionBackpropData takes the gradient tensor of output, weights tensor and output shape (optional) to compute the gradient of input. The shape of the input gradient should either be specified as an input 1D integer tensor or be determined by the attribute ``output_shape``. ConvolutionBackpropData accepts the same set of attributes as a regular Convolution operation, but they are interpreted in a "backward way", so they are applied to the output of ConvolutionBackpropData, but not to the input. Refer to a regular Convolution operation for detailed description of each attribute. If ``auto_pad`` is specified, ``pads_begin`` and ``pads_end`` will be ignored, In this case pads are determined based on the next formulas to correctly align input and output tensors: .. code-block:: cpp total_padding[i] = stride[i] * (X_i - 1) + ((K_i - 1) * dilations[i] + 1) - output_shape[i] + output_padding[i] if auto_pads != SAME_UPPER: pads_begin[i] = total_padding[i] // 2 pads_end[i] = total_padding[i] - pads_begin[i] else: pads_end[i] = total_padding[i] // 2 pads_begin[i] = total_padding[i] - pads_end[i] **Attributes** * *strides* * **Description**: *strides* has the same definition as *strides* for a regular Convolution but applied in the backward way. * **Range of values**: positive s64 values. * **Type**: s64[] * **Required**: *yes* * *pads_begin* * **Description**: *pads_begin* has the same definition as *pads_begin* for a regular Convolution but applied in the backward way. May be omitted specified, in which case pads are calculated automatically. * **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* has the same definition as *pads_end* for a regular Convolution but applied in the backward way. May be omitted, in which case pads are calculated automatically. * **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* has the same definition as *dilations* for a regular Convolution but applied in the backward way. * **Range of values**: positive s64 values. * **Type**: s64[] * **Required**: *yes* * *auto_pad* * **Description**: *auto_pad* has the same definition as *auto_pad* for a regular Convolution but applied in the backward way. * *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* (S means HW for 2D convolution, DHW for 3D convolution) * **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 convolution, DHW for 3D convolution) * **Type**: string * **Default value**: *XIO* * **Required**: *no* * *output_shape* * **Description**: *output_shape* denotes the shape of the output tensor. * **Type**: s64[] * **Required**: *no* **Inputs**: * **1**: ``output_delta`` - the gradient tensor with respect to the output. **Required**. * **Type**: T * **2**: ``filter`` -- convolution filter tensor. The format is specified by *filter_format* attribute. The shape of filter is :math:`(out_channels, in_channels / groups, spatial_shape)` for OIX format or :math:`(spatial_shape, in_channels / groups, out_channels)` for XIO format. :math:`in_channels` and :math:`out_channels` must both be divisible by *groups* attribute. **Required.** * **Type**: T * **3**: ``output_shape`` is 1D integer tensor that specifies shape of the output. **Optional**. If specified, *output_shape* attribute will be ignored. If not specified, users should define *output_shape* through attribute. *padding amount* can be deduced from relation of input and output spatial shapes according to formulas in the description. * **Type**: s32 **Outputs**: * **1**: ``input_delta`` - the gradient tensor with respect to the input of Convolution. * **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.