142 lines
3.9 KiB
C++
142 lines
3.9 KiB
C++
/**
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* Copyright 2022 Huawei Technologies Co., Ltd
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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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*/
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#ifndef MINDSPORE_INCLUDE_API_NET_H
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#define MINDSPORE_INCLUDE_API_NET_H
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#include <memory>
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#include <vector>
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#include <unordered_set>
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#include <string>
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#include "include/api/types.h"
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#include "include/api/data_type.h"
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#include "include/api/cfg.h"
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namespace mindspore {
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/// \brief Register node or sub network
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#define REG(_name) Register(_name, #_name)
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class Expr;
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class NodeImpl;
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class NetImpl;
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class NodeSet;
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class Graph;
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class NetData;
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class NetBase {
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public:
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NetBase() = default;
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virtual std::vector<Expr *> operator()(const std::vector<Expr *> &inputs) = 0;
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virtual uint32_t type() = 0;
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};
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class Node : public NetBase {
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public:
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Node();
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virtual ~Node();
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/// \brief Create output expression from node
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/// \param[in] name Name of input (like "labels" etc.)
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///
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/// \return Expression
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Expr *Create(std::string name);
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/// \brief Run node on inputs. This operator is used in Net::construct()
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///
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/// \param[in] inputs Inputs expression for the node.
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/// \return Output node expression vector
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std::vector<Expr *> operator()(const std::vector<Expr *> &inputs) override;
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uint32_t type() final;
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private:
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friend NodeImpl;
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std::shared_ptr<NodeImpl> impl_ = nullptr;
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};
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class Net : public NetBase, public std::enable_shared_from_this<Net> {
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public:
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Net();
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virtual ~Net();
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explicit Net(std::string name);
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explicit Net(const Graph &g);
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/// \brief Define the relation between network inputs and outputs
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///
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/// \param[in] inputs expression vector
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///
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/// \return expression vector
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virtual std::vector<Expr *> construct(const std::vector<Expr *> &inputs);
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/// \brief Addition operation
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///
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/// \param[in] inputs Two elements to add
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///
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/// \return expression vector (single element)
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/// \brief Execution operator. Connect inputs to outputs via user defined construct
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///
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/// \return expression vector
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std::vector<Expr *> operator()(const std::vector<Expr *> &inputs);
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void Register(Net *net, std::string &&name);
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void Register(Node *node, std::string &&name);
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/// \brief Find the trainable params for the trained network
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///
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/// \return NodeSet for all trainable nodes
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std::shared_ptr<NodeSet> trainable_params();
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virtual void Add(NetBase *element);
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/// \brief Input shape
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///
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/// \param[in] idx input index
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///
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/// \return Specific input shape vector
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const std::vector<int> InputShape(int idx);
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/// \brief Output shape
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///
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/// \param[in] idx Output index
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///
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/// \return Specific output shape vector
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const std::vector<int> OutputShape(int idx);
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uint32_t type() final;
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private:
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friend NetImpl;
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friend NetData;
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std::shared_ptr<NetImpl> impl_;
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};
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class SoftMaxCrossEntropyCfg {
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public:
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std::string reduction = "mean"; /**< Specifies reduction mode. The optional values are "none", "mean", "sum" */
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};
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class AdamConfig {
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public:
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float learning_rate_ = 1e-3;
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float beta1_ = 0.9;
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float beta2_ = 0.999;
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float eps_ = 1e-08;
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bool use_nesterov_ = false;
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};
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namespace NN {
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Net *NetWithLoss(Net *net, Node *loss);
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Graph *GraphWithLoss(Graph *g, Node *loss);
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Node *Adam(std::shared_ptr<NodeSet> learn, const AdamConfig &cfg);
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Node *SoftmaxCrossEntropy(const SoftMaxCrossEntropyCfg &cfg);
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std::unique_ptr<Node> Input(std::vector<int> dims, DataType data_type = DataType::kNumberTypeFloat32, int fmt = NHWC);
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}; // namespace NN
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} // namespace mindspore
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#endif // MINDSPORE_INCLUDE_API_NET_H
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