/* Copyright (c) 2020 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 { /* * ClipOp */ class ClipOpConverter : public OpConverter { public: void operator()(const framework::proto::OpDesc& op, const framework::Scope& scope, bool test_mode) override { #if IS_TRT_VERSION_GE(5130) VLOG(3) << "convert a paddle clip op to tensorrt IActivationLayer."; framework::OpDesc op_desc(op, nullptr); // Declare inputs auto* input = engine_->GetITensor(op_desc.Input("X")[0]); float min = BOOST_GET_CONST(float, op_desc.GetAttr("min")); float max = BOOST_GET_CONST(float, op_desc.GetAttr("max")); auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Activation, *input, nvinfer1::ActivationType::kCLIP); layer->setAlpha(min); layer->setBeta(max); auto output_name = op_desc.Output("Out")[0]; RreplenishLayerAndOutput(layer, "clip", {output_name}, test_mode); #else PADDLE_THROW( platform::errors::Fatal("clip TRT converter is only supported on TRT " "5.1.3.0 or higher version.")); #endif } }; } // namespace tensorrt } // namespace inference } // namespace paddle REGISTER_TRT_OP_CONVERTER(clip, ClipOpConverter);