test_elementwise_add_op.cpp

/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
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==============================================================================*/
#pragma once
#include "../test_include.h"
#include "operators/elementwise_add_op.h"

namespace paddle_mobile {
namespace framework {

template <typename Dtype> class TestElementwiseAddOp {
  public:
    explicit 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();
        //  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() ==
                //                        "elementwise_add") {
                //                            if
                //                            (op->GetAttrMap().at("axis").Get<int>()
                //                            != -1) {
                //                                DLOG << "attr: axis =
                //                                "
                //                                     <<
                //                                     op->GetAttrMap().at("axis").Get<int>();
                //                            }
                //                        }
                //                        DLOG << "op:" << op->Type();
                if (op->Type() == "elementwise_add" &&
                    op->Input("X")[0] == "batch_norm_2.tmp_2") {
                    DLOG << " elementwise_add 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 << " Input Y is : " << op->Input("Y")[0];
                    DLOG << " Output Out is : " << op->Output("Out")[0];
                    Attribute axis_attr = op->GetAttrMap().at("axis");
                    int axis = axis_attr.Get<int>();
                    DLOG << " Attr axis is : " << axis;

                    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");
        auto *output_tensor = con_output->GetMutable<Tensor>();
        output_tensor->mutable_data<float>({1, 3, 224, 224});
        //  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_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];
            DLOG << "op -> run()";
            op->Run();
        }
    }
};

template class TestElementwiseAddOp<CPU>;
} // namespace framework
} // namespace paddle_mobile
int main() {
    DLOG << "----------**********----------";
    DLOG << "begin to run ElementAddOp Test";
    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));
    auto *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));
    auto *inputy_ptr = inputy.data<float>();

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

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

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

    DLOG << inputx_ptr[226] << " + " << inputy_ptr[2] << " = "
         << output_add_ptr[226];
    return 0;
}