/**************************************************************************** * * 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_TRANSPOSE_LAYOUT_INFERENCE_H_ #define TIM_LAYOUT_INFER_TRANSPOSE_LAYOUT_INFERENCE_H_ #include "tim/vx/ops/transpose.h" #include "ops/op_layout_inference.h" #include "permute_vector.h" #include "direct_map_op_impl.h" namespace tim { namespace transform { class TransposeLayoutInfer : public OpLayoutInfer { public: TransposeLayoutInfer( const std::shared_ptr op, std::shared_ptr& context) : OpLayoutInfer(op, context) {} void OnInputs( std::vector>& next_tensors) override { auto src_input = op_->impl()->InputsTensor()[0]; auto infer_input = context_->GetMapedTensor(src_input); auto input_pv = context_->GetPermuteVector(src_input); std::vector perm(op_->impl()->node()->nn_param.permute.dim_num); memcpy(perm.data(), op_->impl()->node()->nn_param.permute.perm, op_->impl()->node()->nn_param.permute.dim_num * sizeof(uint32_t)); IPermuteVectorPtr perm_pv = MakeShared(perm.size()); for (uint32_t i = 0; i < perm.size(); i++) { perm_pv->At(i) = perm[i]; } IPermuteVectorPtr final_pv = input_pv->Reverse()->Add(perm_pv); if (final_pv->IsAligned()) { //skip transpose op by insert a dummy reshape // context_->UpdateTensorMap(op_->impl()->OutputsTensor()[0], infer_input); auto reshape_op = context_->infer_graph_->CreateOperation( op_->impl()->OutputsTensor()[0]->GetShape()); reshape_op->BindInput(infer_input); auto reshape_out = CreateOutputsTensor(final_pv); reshape_op->BindOutput(reshape_out[0]); } else { auto transpose_op = context_->infer_graph_->CreateOperation( final_pv->AsStdVec()); transpose_op->BindInput(infer_input); auto infer_out = CreateOutputsTensor(final_pv); transpose_op->BindOutput(infer_out[0]); } context_->SetPermuteVector(op_->impl()->OutputsTensor()[0], MakeShared(perm.size())); next_tensors.push_back(op_->impl()->OutputsTensor()[0]); } }; } // namespace transform } // namespace tim #endif