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/*
* Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef __ARM_COMPUTE_NEDECONVOLUTIONLAYER_H__
#define __ARM_COMPUTE_NEDECONVOLUTIONLAYER_H__
#include "arm_compute/runtime/CPP/functions/CPPUpsample.h"
#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h"
#include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/runtime/IMemoryManager.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include "arm_compute/runtime/Tensor.h"
#include <memory>
namespace arm_compute
{
/** Function to run the deconvolution layer.
*
* Deconvolution Layer is the backward pass of Convolution Layer. First we transform the input depending on the stride and pad info and then perfrom a 1x1
* convolution pass. Input stride defines how many zeroes we should put between each element of the input, pad is the amount of padding and finaly a is a user
* specified value where a < stride - 1 that increases the padding top and right of the input image.
*
* The relation between input to output is as follows:
* width_output = round((width_input − 1) ∗ (stride_x - 1) − 2 ∗ padding_x + kernel_x + inner_border_right )
* height_output = round((height_input − 1) ∗ (stride_y - 1) − 2 ∗ padding_y + kernel_y + inner_border_top )
*
* where
* width is the size of the first input dimension.
* height is the size of the second input dimension.
* width_output is the size of the first output dimension.
* height_output is the size of the second output dimension.
* kernel_x and kernel_y are the convolution sizes in x and y.
* inner_border_right and inner_border_top the number of zeros added to the top and right edges of the input.
* stride_x and stride_y is the input stride of the first and second dimension.
*
* This function calls the following NEON kernels:
*
* -# @ref NEDirectConvolutionLayer
*
*/
class NEDeconvolutionLayer : public IFunction
{
public:
/** Default constructor */
NEDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEDeconvolutionLayer(const NEDeconvolutionLayer &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEDeconvolutionLayer &operator=(const NEDeconvolutionLayer &) = delete;
/** Allow instances of this class to be moved */
NEDeconvolutionLayer(NEDeconvolutionLayer &&) = default;
/** Allow instances of this class to be moved */
NEDeconvolutionLayer &operator=(NEDeconvolutionLayer &&) = default;
/** Default destructor */
virtual ~NEDeconvolutionLayer() = default;
/** Set the input, weights, biases and output tensors.
*
* @param[in,out] input Input tensor. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: F32.
* @param[in] weights The 4d weights with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
* @param[in] bias Optional, ignored if NULL. The biases have one dimension. Data type supported: Same as @p input.
* @param[out] output Output tensor. The output has the same number of dimensions as the @p input.
* @param[in] info Contains padding and policies to be used in the deconvolution, this is decribed in @ref PadStrideInfo.
* @param[in] inner_border_right The number of zeros added to right edge of the input.
* @param[in] inner_border_top The number of zeros added to top edge of the input.
*
*/
void configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &info,
unsigned int inner_border_right, unsigned int inner_border_top);
/** Static function to check if given info will lead to a valid configuration of @ref NEDeconvolutionLayer
*
* @param[in] input Input tensor info. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: F32.
* @param[in] weights The 4d weights info with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
* @param[in] bias (Optional) The biases have one dimension. Data type supported: Same as @p input.
* @param[in] output Output tensor info. The output has the same number of dimensions as the @p input.
* @param[in] info Contains padding and policies to be used in the deconvolution, this is decribed in @ref PadStrideInfo.
* @param[in] inner_border_right The number of zeros added to right edge of the input.
* @param[in] inner_border_top The number of zeros added to top edge of the input.
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &info,
unsigned int inner_border_right, unsigned int inner_border_top);
// Inherited methods overridden:
void run() override;
private:
MemoryGroup _memory_group;
NEConvolutionLayer _conv_f;
CPPUpsample _upsample_f;
Tensor _scaled_output;
ITensor *_input;
PadStrideInfo _info;
std::pair<unsigned int, unsigned int> _inner_border;
};
} // arm_compute
#endif /* __ARM_COMPUTE_NEDECONVOLUTIONLAYER_H__ */
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