/**************************************************************************** * * Copyright (c) 2020 Vivante Corporation * * 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 TIM_VX_OPS_DECONV_H_ #define TIM_VX_OPS_DECONV_H_ #include #include "tim/vx/builtin_op.h" namespace tim { namespace vx { namespace ops { /** * ## DeConv2d * * Performs the transpose of 2-D convolution operation. * * This operation is sometimes called "deconvolution" after Deconvolutional Networks, * but is actually the transpose (gradient) of Conv2D rather than an actual deconvolution. * * - oc_count_ : the out channel count for weight tensor. * - pad_type : SAME, VALID or AUTO. * - ksize : the height and width for weight tensor. * - padding : AUTO, VALID or SAME. * - pad : pad value for each spatial axis. * - stride : stride along each spatial axis. * - output_padding : specifying the amount of padding along the height and width of * the output tensor. * - group : the feature count of each group. * - input_layout : Layout for input, WHCN by default. * - kernel_layout: Layout for kernel, WHIO by default. */ class DeConv2d : public BuiltinOp { public: DeConv2d(Graph* graph, int32_t oc_count_, PadType pad_type, const std::array& ksize, const std::array& stride, const std::array& output_padding, DataLayout input_layout = DataLayout::WHCN, DataLayout kernel_layout = DataLayout::WHIcOc); DeConv2d(Graph* graph, int32_t oc_count_, PadType pad_type, const std::array& ksize, const std::array& stride, const std::array& output_padding, const std::array& pad, const uint32_t group = 1, DataLayout input_layout = DataLayout::WHCN, DataLayout kernel_layout = DataLayout::WHIcOc); DataLayout KernelDataLayout() { return kernel_layout_; } std::shared_ptr Clone(std::shared_ptr& graph) const override; protected: const uint32_t oc_count_; const PadType pad_type_; const std::array ksize_; const std::array stride_; const std::array output_padding_; const std::array pad_; const uint32_t group_; const DataLayout kernel_layout_; }; } // namespace ops } // namespace vx } // namespace tim #endif /* TIM_VX_OPS_DECONV_H_ */