test_mul_op.cpp

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

namespace paddle_mobile {
namespace framework {

template <typename Dtype> class TestMulOp {
  public:
    explicit TestMulOp(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() == "mul" &&
                    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 << " Input Y is : " << op->Input("Y")[0];
                    DLOG << " Output Out is : " << op->Output("Out")[0];
                    DLOG << "x_num_col_dims : "
                         << op->GetAttrMap().at("x_num_col_dims").Get<int>();
                    DLOG << "y_num_col_dims : "
                         << op->GetAttrMap().at("y_num_col_dims").Get<int>();

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

    std::shared_ptr<Tensor> predict_mul(Tensor &t1, Tensor &t2) {
        // feed
        auto scope = program_.scope;
        Variable *x_feed_value = scope->Var("pool2d_0.tmp_0");
        auto tensor_x = x_feed_value->GetMutable<Tensor>();
        tensor_x->ShareDataWith(t1);

        Variable *y_feed_value = scope->Var("fc_0.w_0");
        auto tensor_y = y_feed_value->GetMutable<Tensor>();
        tensor_y->ShareDataWith(t2);

        Variable *con_output = scope->Var("fc_0.tmp_0");
        auto *output_tensor = con_output->GetMutable<Tensor>();
        output_tensor->mutable_data<float>({3, 3});
        //  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_mul(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_mul(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 TestMulOp<CPU>;
} // namespace framework
} // namespace paddle_mobile

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

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

    /// input y (2,3)
    paddle_mobile::framework::Tensor inputy;
    SetupTensor<float>(&inputy, {2, 3}, static_cast<float>(0),
                       static_cast<float>(1));
    auto *inputy_ptr = inputy.data<float>();

    paddle_mobile::framework::TestMulOp<paddle_mobile::CPU> testMulOp(program);

    auto output_mul = testMulOp.predict_mul(inputx, inputy);
    auto *output_mul_ptr = output_mul->data<float>();

    auto dimx_1 = inputx.numel() / inputx.dims()[0];
    DLOG << " inputx : ";
    for (int i = 0; i < inputx.dims()[0]; ++i) {
        for (int j = 0; j < dimx_1; ++j) {
            DLOGF("%f ", inputx_ptr[i * dimx_1 + j]);
        }
        DLOGF("\n");
    }

    auto dimy_1 = inputy.numel() / inputy.dims()[0];
    DLOG << " inputy : ";
    for (int i = 0; i < inputy.dims()[0]; ++i) {
        for (int j = 0; j < dimy_1; ++j) {
            DLOGF("%f ", inputy_ptr[i * dimx_1 + j]);
        }
        DLOGF("\n");
    }

    auto dim_output_1 = output_mul->numel() / output_mul->dims()[0];
    DLOG << " output : ";
    for (int i = 0; i < output_mul->dims()[0]; ++i) {
        for (int j = 0; j < dim_output_1; ++j) {
            DLOGF("%f ", output_mul_ptr[i * dimy_1 + j]);
        }
        DLOGF("\n");
    }

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

    DLOG << inputx_ptr[0] << " x " << inputy_ptr[0] << " + " << inputx_ptr[1]
         << " x " << inputy_ptr[0 + 3] << " = " << output_mul_ptr[0];
    return 0;
}