/* * Copyright (c) 2019-2021 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 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 @ref CLReverse. * * 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 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. * * Valid data layouts: * - NHWC * - NCHW * * Valid data type configurations: * |src0 |src1 |src2 |dst | * |:--------------|:------------------|:------|:--------------| * |F16 |F16 |F16 |F16 | * |F32 |F32 |F32 |F32 | * |QASYMM8 |QASYMM8 |S32 |QASYMM8 | * |QASYMM8_SIGNED |QASYMM8_SIGNED |S32 |QASYMM8_SIGNED | * |QASYMM8 |QSYMM8_PER_CHANNEL |S32 |QASYMM8 | * |QASYMM8_SIGNED |QSYMM8_PER_CHANNEL |S32 |QASYMM8_SIGNED | * * @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_SIGNED/QASYMM8/F16/F32. * @param[in] weights The 4d weights with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input or QSYMM8_PER_CHANNEL if @p input is QASYMM8/QASYMM8_SIGNED. * @param[in] bias (Optional) The biases have one dimension. * Data type supported: Should match @p input data type, except for input of QASYMM8 and QASYMM8_SIGNED type where biases should be of S32 type * @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 opencl::kernels::ClWeightsReshapeKernel. * */ void configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, const WeightsInfo &weights_info = WeightsInfo()); /** Set the input, weights, biases and output tensors. * * @param[in] compile_context The compile context to be used. * @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_SIGNED/QASYMM8/F16/F32. * @param[in] weights The 4d weights with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input or QSYMM8_PER_CHANNEL if @p input is QASYMM8/QASYMM8_SIGNED. * @param[in] bias (Optional) The biases have one dimension. * Data type supported: Should match @p input data type, except for input of QASYMM8 and QASYMM8_SIGNED type where biases should be of S32 type * @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 opencl::kernels::ClWeightsReshapeKernel. * */ void configure(const CLCompileContext &compile_context, 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_SIGNED/QASYMM8/F16/F32. * @param[in] weights The 4d weights info with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input or QSYMM8_PER_CHANNEL if @p input is QASYMM8/QASYMM8_SIGNED. * @param[in] bias (Optional) The biases have one dimension. * Data type supported: Should match @p input data type, except for input of QASYMM8 and QASYMM8_SIGNED type where biases should be of S32 type * @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 opencl::kernels::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 */