/**************************************************************************** * * 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_LAYOUT_INFER_POOL2D_LAYOUT_INFERENCE_H_ #define TIM_LAYOUT_INFER_POOL2D_LAYOUT_INFERENCE_H_ #include "src/tim/layout_infer/ops/op_layout_inference.h" #include "src/tim/layout_infer/permute_vector.h" #include "src/tim/vx/operation_private.h" #include "tim/vx/ops/pool2d.h" namespace tim { namespace transform { class Pool2dLayoutInfer : public OpLayoutInfer { public: Pool2dLayoutInfer( const std::shared_ptr op, std::shared_ptr& context) : OpLayoutInfer(op, context) {} void OnInputs( std::vector>& next_tensors) override { vx::DataLayout layout = op_->impl()->layout_; auto required_pv = MakeShared(4); if (layout == vx::DataLayout::CWHN) { required_pv = std::make_shared>(kCWHN2WHCN); } auto input_tensors = op_->impl()->InputsTensor(); auto pv = context_->GetPermuteVector(input_tensors[0]); auto final_pv = pv->Reverse()->Add(required_pv); if (!final_pv->IsAligned()) { auto perm_out = InsertPermute(context_->GetMapedTensor(input_tensors[0]), final_pv); context_->UpdateTensorMap(input_tensors[0], perm_out); context_->SetPermuteVector(input_tensors[0], required_pv); } auto pool_type = TranslatePoolType(op_->impl()->node()->nn_param.pool.type); auto round_type = TranslateRoundType(op_->impl()->node()->nn_param.pool.round_type); auto pad_type = TranslatePadType(op_->impl()->node()->nn_param.pool.pad_type); std::array ksize = { op_->impl()->node()->nn_param.pool.ksize[0], op_->impl()->node()->nn_param.pool.ksize[1]}; std::array stride = { op_->impl()->node()->nn_param.pool.stride[0], op_->impl()->node()->nn_param.pool.stride[1]}; auto pool2d = context_->infer_graph_->CreateOperation( pool_type, pad_type, ksize, stride, round_type, vx::DataLayout::WHCN); auto otensor_infer = CreateOutputsTensor(required_pv); (*pool2d).BindInput(context_->GetMapedTensor(input_tensors[0])); (*pool2d).BindOutput(otensor_infer[0]); context_->SetPermuteVector(op_->impl()->OutputsTensor()[0], required_pv); // Add out tensor of src_graph into next_tensor next_tensors.push_back(op_->impl()->OutputsTensor()[0]); } }; } // namespace transform } // namespace tim #endif