/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ #include #include #include "glog/logging.h" #include "paddle/fluid/framework/op_desc.h" #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" #include "paddle/fluid/inference/tensorrt/engine.h" #include "paddle/fluid/inference/tensorrt/helper.h" #include "paddle/fluid/platform/enforce.h" namespace paddle { namespace framework { class Scope; namespace proto { class OpDesc; } // namespace proto } // namespace framework } // namespace paddle namespace paddle { namespace inference { namespace tensorrt { class TopKOpConverter : public OpConverter { public: TopKOpConverter() {} void operator()(const framework::proto::OpDesc& op, const framework::Scope& scope, bool test_mode) override { // Here the two nullptr looks strange, that's because the // framework::OpDesc's constructor is strange. framework::OpDesc op_desc(op, nullptr); auto* input_tensor = engine_->GetITensor(op_desc.Input("X")[0]); const int k = op_desc.HasAttr("k") ? BOOST_GET_CONST(int, op_desc.GetAttr("k")) : 1.0f; nvinfer1::Dims input_dims = input_tensor->getDimensions(); int axis = input_dims.nbDims; nvinfer1::ITopKLayer* layer = TRT_ENGINE_ADD_LAYER(engine_, TopK, *input_tensor, nvinfer1::TopKOperation::kMAX, k, 1 << (axis - 1)); std::vector output_names; output_names.push_back(op_desc.Output("Out").front()); output_names.push_back(op_desc.Output("Indices").front()); RreplenishLayerAndOutput(layer, "top_k", output_names, test_mode); } }; class TopKv2OpConverter : public OpConverter { public: TopKv2OpConverter() {} void operator()(const framework::proto::OpDesc& op, const framework::Scope& scope, bool test_mode) override { // Here the two nullptr looks strange, that's because the // framework::OpDesc's constructor is strange. framework::OpDesc op_desc(op, nullptr); auto* input_tensor = engine_->GetITensor(op_desc.Input("X")[0]); const int k = op_desc.HasAttr("k") ? BOOST_GET_CONST(int, op_desc.GetAttr("k")) : 1.0f; const int axis = op_desc.HasAttr("axis") ? BOOST_GET_CONST(int, op_desc.GetAttr("axis")) : 1.0f; const bool largest = op_desc.HasAttr("largest") ? BOOST_GET_CONST(bool, op_desc.GetAttr("largest")) : true; auto flag = largest ? nvinfer1::TopKOperation::kMAX : nvinfer1::TopKOperation::kMIN; nvinfer1::ITopKLayer* layer = nullptr; if (axis == -1) { nvinfer1::Dims input_dims = input_tensor->getDimensions(); layer = TRT_ENGINE_ADD_LAYER(engine_, TopK, *input_tensor, flag, k, 1 << (input_dims.nbDims - 1)); } else { if (engine_->with_dynamic_shape()) { layer = TRT_ENGINE_ADD_LAYER(engine_, TopK, *input_tensor, flag, k, 1 << axis); } else { layer = TRT_ENGINE_ADD_LAYER(engine_, TopK, *input_tensor, flag, k, 1 << (axis - 1)); } } std::vector output_names; output_names.push_back(op_desc.Output("Out").front()); output_names.push_back(op_desc.Output("Indices").front()); RreplenishLayerAndOutput(layer, "top_k_v2", output_names, test_mode); } }; } // namespace tensorrt } // namespace inference } // namespace paddle REGISTER_TRT_OP_CONVERTER(top_k, TopKOpConverter); REGISTER_TRT_OP_CONVERTER(top_k_v2, TopKv2OpConverter);