test_lrn_op.cpp

#pragma once
#include "../test_include.h"
#include "operators/lrn_op.h"

namespace paddle_mobile {
namespace framework {

template <typename Dtype> class TestLrnOp {
public:
  explicit TestLrnOp(const Program<Dtype> p) : program_(p) {
    if (use_optimize_) {
      to_predict_program_ = program_.optimizeProgram;
    } else {
      to_predict_program_ = program_.originProgram;
    }

    const std::vector<std::shared_ptr<BlockDesc>> blocks =
        to_predict_program_->Blocks();
    //  DLOG << " **block size " << blocks.size();
    for (int i = 0; i < blocks.size(); ++i) {
      std::shared_ptr<BlockDesc> block_desc = blocks[i];
      std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
      //    DLOG << " ops " << ops.size();
      for (int j = 0; j < ops.size(); ++j) {
        std::shared_ptr<OpDesc> op = ops[j];
        if (op->Type() == "lrn" && op->Input("X")[0] == "pool2d_0.tmp_0") {
          DLOG << " mul attr size: " << op->GetAttrMap().size();
          DLOG << " inputs size: " << op->GetInputs().size();
          DLOG << " outputs size: " << op->GetOutputs().size();
          DLOG << " Input X is : " << op->Input("X")[0];
          DLOG << " Output Out is : " << op->Output("Out")[0];
          DLOG << " n : " << op->GetAttrMap().at("n").Get<int>();
          DLOG << " alpha : " << op->GetAttrMap().at("alpha").Get<float>();
          DLOG << " beta : " << op->GetAttrMap().at("beta").Get<float>();
          DLOG << " k : " << op->GetAttrMap().at("k").Get<float>();
          std::shared_ptr<operators::LrnOp<Dtype, float>> lrn =
              std::make_shared<operators::LrnOp<Dtype, float>>(
                  op->Type(), op->GetInputs(), op->GetOutputs(),
                  op->GetAttrMap(), program_.scope);
          ops_of_block_[*block_desc.get()].push_back(lrn);
        }
      }
    }
  }

  std::shared_ptr<Tensor> predict_lrn(Tensor &t1) {
    // feed
    auto scope = program_.scope;
    Variable *x1_feed_value = scope->Var("pool2d_0.tmp_0");
    auto tensor_x1 = x1_feed_value->GetMutable<Tensor>();
    tensor_x1->ShareDataWith(t1);

    Variable *con_output = scope->Var("pool1_norm1.tmp_1");
    auto *output_tensor = con_output->GetMutable<Tensor>();
    output_tensor->mutable_data<float>({3, 4, 2, 2});
    //  DLOG << typeid(output_tensor).name();
    //  DLOG << "output_tensor dims: " << output_tensor->dims();

    std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
    out_tensor.reset(output_tensor);

    predict_lrn(t1, 0);
    return out_tensor;
  }

private:
  const framework::Program<Dtype> program_;
  std::shared_ptr<ProgramDesc> to_predict_program_;
  std::map<framework::BlockDesc,
           std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
      ops_of_block_;
  bool use_optimize_ = false;

  void predict_lrn(const Tensor &t1, int block_id) {
    std::shared_ptr<BlockDesc> to_predict_block =
        to_predict_program_->Block(block_id);
    for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
      auto op = ops_of_block_[*to_predict_block.get()][j];
      DLOG << "op -> run()";
      op->Run();
    }
  }
};

template class TestLrnOp<CPU>;
} // namespace framework
} // namespace paddle_mobile

int main() {
  DLOG << "----------**********----------";
  DLOG << "begin to run LrnOp Test";
  paddle_mobile::Loader<paddle_mobile::CPU> loader;
  auto program = loader.Load(std::string("../../test/models/googlenet"));

  /// input x (3,4,2,2)
  paddle_mobile::framework::Tensor inputx1;
  SetupTensor<float>(&inputx1, {3, 4, 2, 2}, static_cast<float>(0),
                     static_cast<float>(1));
  auto *inputx1_ptr = inputx1.data<float>();

  paddle_mobile::framework::TestLrnOp<paddle_mobile::CPU> testLrnOp(program);

  auto output_lrn = testLrnOp.predict_lrn(inputx1);
  auto *output_lrn_ptr = output_lrn->data<float>();

  DLOG << " LrnOp input: ";
  for (int i = 0; i < 3; i++) {
    for (int j = 0; j < 4; j++) {
      for (int c = 0; c < 2; c++) {
        for (int d = 0; d < 2; d++) {
          DLOGF("%f ", inputx1_ptr[i * 16 + j * 4 + c * 2 + d]);
        }
        DLOGF("\n");
      }
      DLOGF("\n");
    }
    DLOGF("\n");
  }
  DLOG << " LrnOp output: ";
  for (int i = 0; i < 3; i++) {
    for (int j = 0; j < 4; j++) {
      for (int c = 0; c < 2; c++) {
        for (int d = 0; d < 2; d++) {
          DLOGF("%f ", output_lrn_ptr[i * 16 + j * 4 + c * 2 + d]);
        }
        DLOGF("\n");
      }
      DLOGF("\n");
    }
    DLOGF("\n");
  }
  DLOG << inputx1_ptr[0] << " / ((1 + 0.00002 * ( " << inputx1_ptr[0] << "^2 + "
       << inputx1_ptr[4] << "^2 + " << inputx1_ptr[8] << "^2 ))^0.75) = ";
  DLOG << output_lrn_ptr[0];
  return 0;
}