/**************************************************************************** * * 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_REDUCE_LAYOUT_INFERENCE_H_ #define TIM_LAYOUT_INFER_REDUCE_LAYOUT_INFERENCE_H_ #include "tim/vx/ops/reduce.h" #include #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" namespace tim { namespace transform { template class ReduceLayoutInfer : public OpLayoutInfer { ReduceLayoutInfer( const std::shared_ptr op, std::shared_ptr& context) : OpLayoutInfer(op, context) {} void OnInputs( std::vector>& next_tensor) override { auto t_src = op_->impl()->InputsTensor()[0]; auto pv = context_->GetPermuteVector(op_->impl()->InputsTensor()[0]); std::set unique_axis; std::vector new_axis; for (uint32_t i = 0; i < op_->impl()->node()->nn_param.reduce.axis_num; ++i) { int32_t axis = op_->impl()->node()->nn_param.reduce.axis[i]; if (axis < 0) { axis += pv->Rank(); } unique_axis.insert(axis); new_axis.push_back(MapAxis(pv->AsStdVec(), axis)); } auto reduce = context_->infer_graph_->CreateOperation( new_axis, op_->impl()->node()->nn_param.reduce.keep_dim); (*reduce).BindInput(context_->GetMapedTensor(t_src)); if (op_->impl()->node()->nn_param.reduce.keep_dim) { auto otensor_infer = CreateOutputsTensor(pv); (*reduce).BindOuput(otensor_infer[0]); context_->SetPermuteVector(op_->impl()->OutputsTensor()[0], pv); } else { auto out_pv = MakeShared(pv->Rank() - unique_axis.size()); uint32_t j = 0; for (uint32_t i = 0; i < out_pv->Rank(); i++) { if (unique_axis.end() != unique_axis.find(pv->At(i))) continue; uint32_t cnt = 0; for (auto axis : unique_axis) { if (pv->At(i) > (uint32_t)axis) cnt++; } out_pv->At(j) = pv->At(i) - cnt; j++; } auto otensor_infer = CreateOutputsTensor(out_pv); (*reduce).BindOutput(otensor_infer[0]); context_->SetPermuteVector(op_->impl()->OutputsTensor()[0], out_pv); } next_tensor.push_back(op_->impl()->OutputsTensor()[0]); } }; using ReduceMinLayoutInfer = ReduceLayoutInfer; using ReduceMaxLayoutInfer = ReduceLayoutInfer; using ReduceAnyLayoutInfer = ReduceLayoutInfer; using ReduceProdLayoutInfer = ReduceLayoutInfer; using ReduceMeanLayoutInfer = ReduceLayoutInfer; } // namespace transform } // namespace tim #endif