op_param.h

/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
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==============================================================================*/

#pragma once

#include "common/log.h"
#include "common/type_define.h"
#include "framework/lod_tensor.h"
#include "framework/scope.h"
#include "framework/tensor.h"
#include "framework/variable.h"

namespace paddle_mobile {
namespace operators {

using namespace framework;

class OpParam : PaddleMobileObject {
  public:
  protected:
    template <typename T>
    static T *InputFrom(const VariableNameMap &inputs, const Scope &scope) {
        return GetVarValue<T>("Input", inputs, scope);
    }

    template <typename T>
    static T *InputXFrom(const VariableNameMap &inputs, const Scope &scope) {
        return GetVarValue<T>("X", inputs, scope);
    }

    template <typename T>
    static T *InputYFrom(const VariableNameMap &inputs, const Scope &scope) {
        return GetVarValue<T>("Y", inputs, scope);
    }

    template <typename T>
    static T *InputBiasFrom(const VariableNameMap &inputs, const Scope &scope) {
        return GetVarValue<T>("Bias", inputs, scope);
    }
    template <typename T>
    static T *InputVarianceFrom(const VariableNameMap &inputs,
                                const Scope &scope) {
        return GetVarValue<T>("Variance", inputs, scope);
    }
    template <typename T>
    static T *InputMeanFrom(const VariableNameMap &inputs, const Scope &scope) {
        return GetVarValue<T>("Mean", inputs, scope);
    }
    template <typename T>
    static T *InputScaleFrom(const VariableNameMap &inputs,
                             const Scope &scope) {
        return GetVarValue<T>("Scale", inputs, scope);
    }

    template <typename T>
    static std::vector<T *> InputMultiFrom(const VariableNameMap &inputs,
                                           const Scope &scope) {
        return GetMultiVarValue<T>("X", inputs, scope);
    }

    template <typename T>
    static T *OutputFrom(const VariableNameMap &outputs, const Scope &scope) {
        return GetVarValue<T>("Output", outputs, scope);
    }

    template <typename T>
    static T *OutFrom(const VariableNameMap &outputs, const Scope &scope) {
        return GetVarValue<T>("Out", outputs, scope);
    }

    template <typename T>
    static T *OutputYFrom(const VariableNameMap &outputs, const Scope &scope) {
        return GetVarValue<T>("Y", outputs, scope);
    }

    template <typename T>
    static T *MidOutFrom(const VariableNameMap &outputs, const Scope &scope) {
        return GetVarValue<T>("MidOut", outputs, scope);
    }

    template <typename T>
    static T *FilterFrom(const VariableNameMap &inputs, const Scope &scope) {
        return GetVarValue<T>("Filter", inputs, scope);
    }

    template <typename T>
    static const T GetAttr(const std::string &key, const AttributeMap &map) {
        return ((Attribute)map.at(key)).Get<T>();
    }

    template <typename T>
    static T *GetVarValue(const std::string &key,
                          const VariableNameMap &var_map, const Scope &scope) {
        auto var_vec = var_map.at(key);
        if (!var_vec.empty()) {
            //      std::cout << " get var value -- " << var_vec[0] <<
            //      std::endl;
            auto var = scope.FindVar(var_vec[0]);
            return var->GetMutable<T>();
        } else {
            return nullptr;
        }
    }

    template <typename T>
    static std::vector<T *> GetMultiVarValue(const std::string &key,
                                             const VariableNameMap &var_map,
                                             const Scope &scope) {
        auto var_vecs = var_map.at(key);
        assert(var_vecs.size() > 1);
        std::vector<T *> var_res;
        for (auto &var_vec : var_vecs) {
            auto var = scope.FindVar(var_vec);
            var_res.push_back(var->GetMutable<T>());
        }
        return var_res;
    }
};

class ConvParam : OpParam {
  public:
    ConvParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
              const framework::AttributeMap &attrs,
              const framework::Scope &scope) {
        filter_ = FilterFrom<framework::LoDTensor>(inputs, scope);
        input_ = InputFrom<framework::Tensor>(inputs, scope);
        output_ = OutputFrom<framework::Tensor>(outputs, scope);
        strides_ = GetAttr<std::vector<int>>("strides", attrs);
        paddings_ = GetAttr<std::vector<int>>("paddings", attrs);
        dilations_ = GetAttr<std::vector<int>>("dilations", attrs);
        groups = GetAttr<int>("groups", attrs);
    }

    const Tensor *Input() const { return input_; }

    const LoDTensor *Filter() const { return filter_; }

    Tensor *Output() const { return output_; }

    const std::vector<int> &Strides() const { return strides_; }

    const std::vector<int> &Paddings() const { return paddings_; }

    const std::vector<int> &Dilations() const { return dilations_; }

    const int &Groups() const { return groups; }

  private:
    Tensor *input_;
    Tensor *output_;
    LoDTensor *filter_;
    std::vector<int> strides_;
    std::vector<int> paddings_;
    std::vector<int> dilations_;
    int groups;
};

Print &operator<<(Print &printer, const ConvParam &conv_param);

class ElementwiseAddParam : OpParam {
  public:
    ElementwiseAddParam(const VariableNameMap &inputs,
                        const VariableNameMap &outputs,
                        const framework::AttributeMap &attrs,
                        const framework::Scope &scope) {
        input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
        input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
        out_ = OutFrom<framework::Tensor>(outputs, scope);
        axis_ = GetAttr<int>("axis", attrs);
    }

    const Tensor *InputX() const { return input_x_; }

    const Tensor *InputY() const { return input_y_; }

    Tensor *Out() const { return out_; }

    const int &Axis() const { return axis_; }

  private:
    Tensor *input_x_;
    Tensor *input_y_;
    Tensor *out_;
    int axis_;
};

class MulParam : OpParam {
  public:
    MulParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
             const framework::AttributeMap &attrs,
             const framework::Scope &scope) {
        input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
        input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
        out_ = OutFrom<framework::Tensor>(outputs, scope);
        x_num_col_dims_ = GetAttr<int>("x_num_col_dims", attrs);
        y_num_col_dims_ = GetAttr<int>("y_num_col_dims", attrs);
    }

    const Tensor *InputX() const { return input_x_; }

    const Tensor *InputY() const { return input_y_; }

    Tensor *Out() const { return out_; }

    const int &XNumColDims() const { return x_num_col_dims_; }

    const int &YNumColDims() const { return y_num_col_dims_; }

  private:
    Tensor *input_x_;
    Tensor *input_y_;
    Tensor *out_;
    int x_num_col_dims_;
    int y_num_col_dims_;
};

class ConcatParam : public OpParam {
  public:
    ConcatParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
                const framework::AttributeMap &attrs,
                const framework::Scope &scope) {
        inputs_ = InputMultiFrom<framework::Tensor>(inputs, scope);
        out_ = OutFrom<framework::Tensor>(outputs, scope);
        axis_ = GetAttr<int>("axis", attrs);
    }

    std::vector<Tensor *> Inputs() const { return inputs_; }

    Tensor *Out() const { return out_; }

    const int &Axis() const { return axis_; }

  private:
    std::vector<Tensor *> inputs_;
    Tensor *out_;
    int axis_;
};

class LrnParam : public OpParam {
  public:
    LrnParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
             const framework::AttributeMap &attrs,
             const framework::Scope &scope) {
        input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
        out_ = OutFrom<framework::Tensor>(outputs, scope);
        mid_out_ = MidOutFrom<framework::Tensor>(outputs, scope);
        n_ = GetAttr<int>("n", attrs);
        alpha_ = GetAttr<float>("alpha", attrs);
        beta_ = GetAttr<float>("beta", attrs);
        k_ = GetAttr<float>("k", attrs);
        data_format_ = GetAttr<std::string>("data_format", attrs);
    }

    const Tensor *InputX() const { return input_x_; }

    Tensor *Out() const { return out_; }

    Tensor *MidOut() const { return mid_out_; }

    const int &N() const { return n_; }

    const float &Alpha() const { return alpha_; }

    const float &Beta() const { return beta_; }

    const float &K() const { return k_; }

    const std::string &DataFormat() const { return data_format_; }

  private:
    Tensor *input_x_;
    Tensor *out_;
    Tensor *mid_out_;
    int n_;
    float alpha_;
    float beta_;
    float k_;
    std::string data_format_;
};
class BatchNormParam : OpParam {
  public:
    BatchNormParam(const VariableNameMap &inputs,
                   const VariableNameMap &outputs,
                   const framework::AttributeMap &attrs,
                   const framework::Scope &scope) {
        input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
        output_y_ = OutputYFrom<framework::Tensor>(outputs, scope);
        input_bias_ = InputBiasFrom<framework::Tensor>(inputs, scope);
        input_mean_ = InputMeanFrom<framework::Tensor>(inputs, scope);
        input_scale_ = InputScaleFrom<framework::Tensor>(inputs, scope);
        input_variance_ = InputVarianceFrom<framework::Tensor>(inputs, scope);
        epsilon_ = GetAttr<float>("epsilon", attrs);
        momentum_ = GetAttr<float>("momentum", attrs);
        is_test_ = GetAttr<bool>("is_test", attrs);
    }

    const Tensor *InputX() const { return input_x_; }

    Tensor *OutputY() const { return output_y_; }

    const Tensor *InputBias() const { return input_bias_; }

    const Tensor *InputMean() const { return input_mean_; }

    const Tensor *InputScale() const { return input_scale_; }

    const Tensor *InputVariance() const { return input_variance_; }

    const float &Epsilon() const { return epsilon_; }

    const float &Momentum() const { return momentum_; }

    const bool &IsTest() const { return is_test_; }

    const std::string &DataFormat() const { return data_format_; }

  private:
    Tensor *input_x_;
    Tensor *output_y_;
    Tensor *input_bias_;
    Tensor *input_mean_;
    Tensor *input_scale_;
    Tensor *input_variance_;
    float epsilon_;
    float momentum_;
    bool is_test_;
    std::string data_format_;
};
class PoolParam : public OpParam {
  public:
    PoolParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
              const framework::AttributeMap &attrs,
              const framework::Scope &scope) {
        input_ = InputXFrom<framework::Tensor>(inputs, scope);

        output_ = OutFrom<framework::Tensor>(outputs, scope);
        pooling_type_ = GetAttr<std::string>("pooling_type", attrs);
        ksize_ = GetAttr<std::vector<int>>("ksize", attrs);
        strides_ = GetAttr<std::vector<int>>("strides", attrs);
        paddings_ = GetAttr<std::vector<int>>("paddings", attrs);
        ceil_mode_ = GetAttr<bool>("ceil_mode", attrs);
        gloabal_pooling_ = GetAttr<bool>("global_pooling", attrs);
    }

    const Tensor *Input() const { return input_; }

    Tensor *Output() const { return output_; }

    const std::string &PoolingType() const { return pooling_type_; }

    const std::vector<int> &Ksize() const { return ksize_; }

    const std::vector<int> &Strides() const { return strides_; }

    const std::vector<int> &Paddings() const { return paddings_; }

    bool isCeilMode() const { return ceil_mode_; }

    bool isGlobalPooling() const { return gloabal_pooling_; }

  private:
    Tensor *input_;
    Tensor *output_;
    std::string pooling_type_;
    std::vector<int> ksize_;
    std::vector<int> strides_;
    std::vector<int> paddings_;
    bool ceil_mode_;
    bool gloabal_pooling_ = false;
};

} // namespace operators
} // namespace paddle_mobile