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