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/*
* Copyright (c) 2019 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_CLDIRECTDECONVOLUTIONLAYER_H__
#define __ARM_COMPUTE_CLDIRECTDECONVOLUTIONLAYER_H__
#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h"
#include "arm_compute/runtime/CL/functions/CLDeconvolutionLayerUpsample.h"
#include "arm_compute/runtime/CL/functions/CLReverse.h"
#include "arm_compute/runtime/CL/functions/CLTranspose.h"
#include "arm_compute/runtime/CL/CLTensor.h"
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/runtime/IMemoryManager.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include <memory>
namespace arm_compute
{
class ICLTensor;
/** 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 perform a 1x1
* convolution pass. Input stride defines how many zeroes we should put between each element of the input and pad is the amount of padding.
*
* The relation between input to output is as follows:
* \f[
* width\_output = (width\_input - 1) \cdot stride\_x - 2 \cdot padding\_x + kernel\_x
* \f]
* \f[
* height\_output = (height\_input - 1) \cdot stride\_y - 2 \cdot padding\_y + kernel\_y
* \f]
*
* where:
* width_input is the size of the first input dimension.
* height_input 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.
* stride_x and stride_y is the input stride of the first and second dimension.
*
* The weights used by Deconvolution are supposed to be the same as the ones used for Convolution. Therefore, it will be necessary to use the weights in the
* reverse order to perform an actual convolution. This is achieved by using the @ref CPPFlipWeightsKernel.
*
* This function calls the following OpenCL kernels/functions:
*
* -# @ref CLDeconvolutionLayerUpsample
* -# @ref CLConvolutionLayer
*
* And the following CPP kernels:
* -# @ref CLReverse
*
*/
class CLDirectDeconvolutionLayer : public IFunction
{
public:
/** Constructor */
CLDirectDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
CLDirectDeconvolutionLayer(const CLDirectDeconvolutionLayer &) = delete;
/** Default move constructor */
CLDirectDeconvolutionLayer(CLDirectDeconvolutionLayer &&) = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
CLDirectDeconvolutionLayer &operator=(const CLDirectDeconvolutionLayer &) = delete;
/** Default move assignment operator */
CLDirectDeconvolutionLayer &operator=(CLDirectDeconvolutionLayer &&) = 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: QASYMM8/F16/F32.
* @param[in] weights The 4d weights 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[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] weights_info (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel.
*
*/
void configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, const WeightsInfo &weights_info = WeightsInfo());
/** Static function to check if given info will lead to a valid configuration of @ref CLDirectDeconvolutionLayer
*
* @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: QASYMM8/F16/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] weights_info (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel.
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info,
const WeightsInfo &weights_info = WeightsInfo());
// Inherited methods overridden:
void run() override;
void prepare() override;
private:
MemoryGroup _memory_group;
CLDeconvolutionLayerUpsample _scale_f;
CLConvolutionLayer _conv_f;
CLReverse _flip_weights;
CLTensor _scaled_output;
ICLTensor *_original_weights;
CLTensor _weights_flipped;
CLTensor _flip_axis;
bool _is_prepared;
};
} // namespace arm_compute
#endif /* __ARM_COMPUTE_CLDECONVOLUTIONLAYER_H__ */
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