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81 lines
2.5 KiB
81 lines
2.5 KiB
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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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#pragma once
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#include <algorithm>
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#include <map>
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#include <memory>
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/lod_tensor.h"
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#include "paddle/fluid/inference/engine.h"
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#include "paddle/fluid/inference/utils/singleton.h"
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#include "framework/core/net/net.h"
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#include "framework/core/types.h"
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#include "framework/graph/graph.h"
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#include "saber/saber_types.h"
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namespace anakin {
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template <typename, Precision, OpRunType>
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class Net;
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namespace graph {
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template <typename, Precision>
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class Graph;
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} // namespace graph
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} // namespace anakin
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namespace paddle {
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namespace inference {
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namespace anakin {
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template <typename TargetT, ::anakin::Precision PrecisionType,
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::anakin::OpRunType RunType = ::anakin::OpRunType::ASYNC>
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class AnakinEngine {
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public:
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explicit AnakinEngine(bool need_summary = false);
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~AnakinEngine();
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void InitGraph();
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void SetInputShape(const std::string &name, std::vector<int> shape);
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void AddOp(const std::string &name, const std::string &type,
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const std::vector<std::string> &inputs,
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const std::vector<std::string> &outputs);
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template <typename T>
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void AddOpAttr(const std::string &op_name, const std::string &attr_name,
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const T &attr_value) {
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PADDLE_ENFORCE(graph_->AddOpAttr(op_name, attr_name, attr_value),
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"Add operation's attribution.");
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}
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std::unique_ptr<AnakinEngine> Clone();
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void Freeze();
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void Optimize();
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void Execute(const std::map<std::string, framework::LoDTensor *> &inputs,
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const std::map<std::string, framework::LoDTensor *> &outputs);
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private:
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using NetT = ::anakin::Net<TargetT, PrecisionType, RunType>;
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using GraphT = ::anakin::graph::Graph<TargetT, PrecisionType>;
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std::unique_ptr<GraphT> graph_;
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std::unique_ptr<NetT> net_;
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};
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} // namespace anakin
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} // namespace inference
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} // namespace paddle
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