wehub-resource-sync
49 lines
1.7 KiB
C++
49 lines
1.7 KiB
C++
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
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namespace paddle::inference::tensorrt {
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/*
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* Where Op
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*/
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class WhereOpConverter : public OpConverter {
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public:
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void operator()(const framework::proto::OpDesc& op,
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const framework::Scope& scope,
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bool test_mode) override {
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VLOG(3) << "convert a where op to tensorrt where layer";
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framework::OpDesc op_desc(op, nullptr);
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std::string input_x_name = op_desc.Input("X").front();
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std::string condition_name = op_desc.Input("Condition").front();
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std::string input_y_name = op_desc.Input("Y").front();
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std::string output_name = op_desc.Output("Out").front();
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const auto input_x_tensor = engine_->GetITensor(input_x_name);
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const auto condition_tensor = engine_->GetITensor(condition_name);
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const auto input_y_tensor = engine_->GetITensor(input_y_name);
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auto layer = TRT_ENGINE_ADD_LAYER(
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engine_, Select, *condition_tensor, *input_x_tensor, *input_y_tensor);
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ReplenishLayerAndOutput(layer, "where", {output_name}, test_mode);
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}
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};
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} // namespace paddle::inference::tensorrt
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REGISTER_TRT_OP_CONVERTER(where, WhereOpConverter);
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