elementwise_add_op_test.h

/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
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The above copyright notice and this permission notice shall be included in all
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==============================================================================*/
#pragma once
#include "operators/elementwise_add_op.h"
#include "test_include.h"

namespace paddle_mobile {
namespace framework {

template <typename Dtype> class TestElementwiseAddOp {
public:
  TestElementwiseAddOp(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();
    //  std::cout << " **block size " << blocks.size() << std::endl;
    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();
      //    std::cout << " ops " << ops.size() << std::endl;
      for (int j = 0; j < ops.size(); ++j) {
        std::shared_ptr<OpDesc> op = ops[j];
        if (op->Type() == "elementwise_add") {
          if (op->GetAttrMap().at("axis").Get<int>() != -1) {
            std::cout << "attr: axis = "
                      << op->GetAttrMap().at("axis").Get<int>() << std::endl;
          }
        }
        std::cout << "op:" << op->Type() << std::endl;
        if (op->Type() == "elementwise_add" &&
            op->Input("X")[0] == "batch_norm_2.tmp_2") {
          std::cout << " elementwise_add attr size: " << op->GetAttrMap().size()
                    << std::endl;
          std::cout << " inputs size: " << op->GetInputs().size() << std::endl;
          std::cout << " outputs size: " << op->GetOutputs().size()
                    << std::endl;
          std::cout << " Input X is : " << op->Input("X")[0] << std::endl;
          std::cout << " Input Y is : " << op->Input("Y")[0] << std::endl;
          std::cout << " Output Out is : " << op->Output("Out")[0] << std::endl;
          Attribute axis_attr = op->GetAttrMap().at("axis");
          int axis = axis_attr.Get<int>();
          std::cout << " Attr axis is : " << axis << std::endl;

          std::shared_ptr<operators::ElementwiseAddOp<Dtype, float>> add =
              std::make_shared<operators::ElementwiseAddOp<Dtype, float>>(
                  op->Type(), op->GetInputs(), op->GetOutputs(),
                  op->GetAttrMap(), program_.scope);
          ops_of_block_[*block_desc.get()].push_back(add);
        }
      }
    }
  }

  std::shared_ptr<Tensor> predict_add(Tensor &t1, Tensor &t2) {
    // feed
    auto scope = program_.scope;
    Variable *x_feed_value = scope->Var("batch_norm_2.tmp_2");
    auto tensor_x = x_feed_value->GetMutable<Tensor>();
    tensor_x->ShareDataWith(t1);

    Variable *y_feed_value = scope->Var("batch_norm_0.tmp_3");
    auto tensor_y = y_feed_value->GetMutable<Tensor>();
    tensor_y->ShareDataWith(t2);

    Variable *con_output = scope->Var("elementwise_add_0.tmp_0");
    Tensor *output_tensor = con_output->GetMutable<Tensor>();
    output_tensor->mutable_data<float>({1, 3, 224, 224});
    //  std::cout << typeid(output_tensor).name() << std::endl;
    //  std::cout << "output_tensor dims: " << output_tensor->dims() <<
    //  std::endl;

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

    predict_add(t1, t2, 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_add(const Tensor &t1, const Tensor &t2, 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];
      std::cout << "op -> run()" << std::endl;
      op->Run();
    }
  }
};

template class TestElementwiseAddOp<CPU>;
} // namespace framework

namespace test {
void testElementwiseAdd() {
  paddle_mobile::Loader<paddle_mobile::CPU> loader;
  auto program = loader.Load(std::string(
      "../../test/models/image_classification_resnet.inference.model"));

  /// input x (1,3,224,224)
  paddle_mobile::framework::Tensor inputx;
  SetupTensor<float>(&inputx, {1, 3, 224, 224}, static_cast<float>(0),
                     static_cast<float>(1));
  float *inputx_ptr = inputx.data<float>();
  /// input y (224,)
  paddle_mobile::framework::Tensor inputy;
  SetupTensor<float>(&inputy, {224}, static_cast<float>(0),
                     static_cast<float>(1));
  float *inputy_ptr = inputy.data<float>();

  paddle_mobile::framework::TestElementwiseAddOp<paddle_mobile::CPU>
      testElementwiseAddOp(program);

  auto output_add = testElementwiseAddOp.predict_add(inputx, inputy);
  float *output_add_ptr = output_add->data<float>();
  for (int j = 0; j < output_add->numel(); ++j) {
    // std::cout << "value of output: " << output_add_ptr[j] << std::endl;
  }

  /// output (1,3,224,224)
  std::cout << "output memory size : " << output_add->memory_size()
            << std::endl;
  std::cout << "output numel : " << output_add->numel() << std::endl;

  std::cout << inputx_ptr[226] << " + " << inputy_ptr[2] << " = "
            << output_add_ptr[226] << std::endl;
}
} // namespace test
} // namespace paddle_mobile