133 lines
6.7 KiB
C++
133 lines
6.7 KiB
C++
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_NEDECONVOLUTIONLAYER_H
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#define ARM_COMPUTE_NEDECONVOLUTIONLAYER_H
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#include "arm_compute/runtime/CPP/functions/CPPUpsample.h"
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#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h"
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#include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h"
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#include "arm_compute/runtime/NEON/functions/NEReverse.h"
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#include "arm_compute/core/Types.h"
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#include "arm_compute/runtime/IFunction.h"
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#include "arm_compute/runtime/IMemoryManager.h"
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#include "arm_compute/runtime/MemoryGroup.h"
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#include "arm_compute/runtime/Tensor.h"
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#include <memory>
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namespace arm_compute
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{
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/** Function to run the deconvolution layer.
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*
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* 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
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* 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
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* specified value where a < stride - 1 that increases the padding top and right of the input image.
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*
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* The relation between input to output is as follows:
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* \f[
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* width\_output = (width\_input - 1) \cdot stride\_x - 2 \cdot padding\_x + kernel\_x
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* \f]
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* \f[
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* height\_output = (height\_input - 1) \cdot stride\_y - 2 \cdot padding\_y + kernel\_y
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* \f]
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*
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* where
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* width is the size of the first input dimension.
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* height is the size of the second input dimension.
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* width_output is the size of the first output dimension.
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* height_output is the size of the second output dimension.
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* kernel_x and kernel_y are the convolution sizes in x and y.
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* stride_x and stride_y is the input stride of the first and second dimension.
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*
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* 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
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* reverse order to perform an actual convolution. This is achieved by using @ref NEReverse.
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*
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* This function calls the following NEON kernels/functions:
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*
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* -# @ref CPPUpsample
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* -# @ref NEConvolutionLayer
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* -# @ref NEPermute
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* -# @ref NEReverse
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*
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*/
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class NEDeconvolutionLayer : public IFunction
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{
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public:
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/** Constructor */
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NEDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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NEDeconvolutionLayer(const NEDeconvolutionLayer &) = delete;
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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NEDeconvolutionLayer &operator=(const NEDeconvolutionLayer &) = delete;
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/** Prevent instances of this class from being moved (As this class contains pointers) */
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NEDeconvolutionLayer(NEDeconvolutionLayer &&) = delete;
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/** Prevent instances of this class from being moved (As this class contains pointers) */
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NEDeconvolutionLayer &operator=(NEDeconvolutionLayer &&) = delete;
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/** Default destructor */
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virtual ~NEDeconvolutionLayer() = default;
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/** Set the input, weights, biases and output tensors.
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*
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* @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/F16/QASYMM8/QASYMM8_SIGNED.
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* @param[in] weights The 4d weights with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
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* @param[in] bias Optional, ignored if NULL. The biases have one dimension. Data type supported: Data types supported: S32 for QASYMM8 and QASYMM8_SIGNED input, F32 for F32 input, F16 for F16 input.
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* @param[out] output Output tensor. The output has the same number of dimensions as the @p input.
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* @param[in] info Contains padding and policies to be used in the deconvolution, this is decribed in @ref PadStrideInfo.
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*
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*/
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void configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &info);
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/** Static function to check if given info will lead to a valid configuration of @ref NEDeconvolutionLayer
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*
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* @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/F16/QASYMM8/QASYMM8_SIGNED.
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* @param[in] weights The 4d weights info with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
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* @param[in] bias (Optional) The biases have one dimension. Data type supported: Data types supported: S32 for QASYMM8 and QASYMM8_SIGNED input, F32 for F32 input, F16 for F16 input.
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* @param[in] output Output tensor info. The output has the same number of dimensions as the @p input.
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* @param[in] info Contains padding and policies to be used in the deconvolution, this is decribed in @ref PadStrideInfo.
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*
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* @return a status
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*/
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static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &info);
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// Inherited methods overridden:
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void run() override;
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void prepare() override;
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private:
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MemoryGroup _memory_group;
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NEConvolutionLayer _conv_f;
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CPPUpsample _upsample_f;
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NEReverse _flip_weights;
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Tensor _scaled_output;
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Tensor _weights_flipped;
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Tensor _flip_axis;
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const ITensor *_original_weights;
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ITensor *_input;
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PadStrideInfo _info;
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bool _is_prepared;
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};
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} // arm_compute
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#endif /* ARM_COMPUTE_NEDECONVOLUTIONLAYER_H */
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