/* 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 "paddle/fluid/inference/tensorrt/convert/op_converter.h" namespace paddle { namespace framework { class Scope; namespace proto { class OpDesc; } // namespace proto } // namespace framework } // namespace paddle namespace paddle { namespace inference { namespace tensorrt { /* * ReshapeOp */ class ReshapeOpConverter : public OpConverter { public: void operator()(const framework::proto::OpDesc& op, const framework::Scope& scope, bool test_mode) override { framework::OpDesc op_desc(op, nullptr); // Declare inputs auto* input = engine_->GetITensor(op_desc.Input("X")[0]); const std::vector& shape = BOOST_GET_CONST(std::vector, op_desc.GetAttr("shape")); int nbDims_num = shape.size(); nvinfer1::Dims reshape_dim; if (engine_->with_dynamic_shape()) { // running the TRT Dynamic Shape mode reshape_dim.nbDims = nbDims_num; for (int i = 0; i < nbDims_num; ++i) { reshape_dim.d[i] = shape[i]; } } else { // running the TRT Static Shape mode reshape_dim.nbDims = nbDims_num - 1; for (int i = 0; i < nbDims_num - 1; ++i) { reshape_dim.d[i] = shape[i + 1]; } } auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input); layer->setReshapeDimensions(reshape_dim); auto output_name = op_desc.Output("Out")[0]; RreplenishLayerAndOutput(layer, "reshape", {output_name}, test_mode); } }; } // namespace tensorrt } // namespace inference } // namespace paddle REGISTER_TRT_OP_CONVERTER(reshape, ReshapeOpConverter);