/* * Copyright (c) 2017-2019 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_GCWEIGHTSRESHAPEKERNEL_H #define ARM_COMPUTE_GCWEIGHTSRESHAPEKERNEL_H #include "arm_compute/core/GLES_COMPUTE/IGCKernel.h" namespace arm_compute { /** GLES Compute kernel to perform reshaping on the weights used by convolution and locally connected layer * * Rearranges each 3-dimensional kernel to a single row leading to a matrix with linearized kernels. * In combination with the @ref GCIm2ColKernel can transform a convolution to a matrix multiplication. * * For example assuming a 3D weight kernel of 3x3 dimensions and depth of 2 we have: * @f[ * \left( \begin{array}{ccc} * a000 & a001 & a002 \\ * a010 & a011 & a012 \\ * a020 & a021 & a022 \\ * \end{array} \right) * \left( \begin{array}{ccc} * a100 & a101 & a102 \\ * a110 & a111 & a112 \\ * a120 & a121 & a122 \\ * \end{array} \right) * \rightarrow * \left( \begin{array}{ccccccccc} * a000 & a001 & a002 & a010 & a011 & a012 & a020 & a021 & a022 & a100 & a101 & a102 & a110 & a111 & a112 & a120 & a121 & a122 \\ * \end{array} \right) * @f] */ class GCWeightsReshapeKernel : public IGCKernel { public: /** Constructor.*/ GCWeightsReshapeKernel(); /** Prevent instances of this class from being copied (As this class contains pointers) */ GCWeightsReshapeKernel(const GCWeightsReshapeKernel &) = delete; /** Prevent instances of this class from being copied (As this class contains pointers) */ GCWeightsReshapeKernel &operator=(const GCWeightsReshapeKernel &) = delete; /** Allow instances of this class to be moved */ GCWeightsReshapeKernel(GCWeightsReshapeKernel &&) = default; /** Allow instances of this class to be moved */ GCWeightsReshapeKernel &operator=(GCWeightsReshapeKernel &&) = default; /** Default destructor */ ~GCWeightsReshapeKernel() = default; /** Set the input and output of the kernel. * * @param[in] input The input tensor to convert. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] if shared, * and 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, batches] if unshared. Data types supported: F16, F32 * @param[in] biases The shared biases tensor to append. Bias is 1D tensor with dimensions [OFM] if shared and 2D tensor with * dimensions [OFM, batches] if unshared. Data types supported: Same as @p input * @warning Appending biases to weights reshaped matrix is not supported for quantized asymmetric types. * @param[out] output The output tensor. Should be a 2D Tensor. Data types supported: Same as @p input */ void configure(const IGCTensor *input, const IGCTensor *biases, IGCTensor *output); // Inherited methods overridden: void run(const Window &window) override; private: const IGCTensor *_input; const IGCTensor *_biases; IGCTensor *_output; }; } // namespace arm_compute #endif /*ARM_COMPUTE_GCWEIGHTSRESHAPEKERNEL_H */