test_batchnorm_op.cpp

/* 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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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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SOFTWARE.
==============================================================================*/
#pragma once
#include "../test_include.h"
#include "operators/batchnorm_op.h"

namespace paddle_mobile {
namespace framework {

template <typename Dtype> class TestBatchNormOp {
  public:
    explicit TestBatchNormOp(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() == "batch_norm" &&
                    op->Input("X")[0] == "conv2d_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 Mean is : " << op->Input("Mean")[0];
                    DLOG << " Input Variance is : " << op->Input("Variance")[0];
                    DLOG << " Input Scale is : " << op->Input("Scale")[0];
                    DLOG << " Input Bias is : " << op->Input("Bias")[0];
                    DLOG << " Output Y is : " << op->Output("Y")[0];
                    DLOG << " epsilon : "
                         << op->GetAttrMap().at("epsilon").Get<float>();
                    std::shared_ptr<operators::BatchNormOp<Dtype, float>> lrn =
                        std::make_shared<operators::BatchNormOp<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_bn(Tensor &t1, Tensor &t2, Tensor &t3,
                                       Tensor &t4, Tensor &t5) {
        // feed
        auto scope = program_.scope;
        Variable *x1_feed_value = scope->Var("conv2d_0.tmp_0");
        auto tensor_x1 = x1_feed_value->GetMutable<Tensor>();
        tensor_x1->ShareDataWith(t1);

        Variable *mean_feed_value = scope->Var("batch_norm_0.w_1");
        auto tensor_mean = mean_feed_value->GetMutable<Tensor>();
        tensor_mean->ShareDataWith(t2);

        Variable *scale_feed_value = scope->Var("batch_norm_0.w_0");
        auto tensor_scale = scale_feed_value->GetMutable<Tensor>();
        tensor_scale->ShareDataWith(t3);

        Variable *variance_feed_value = scope->Var("batch_norm_0.w_2");
        auto tensor_variance = variance_feed_value->GetMutable<Tensor>();
        tensor_variance->ShareDataWith(t4);

        Variable *bias_feed_value = scope->Var("batch_norm_0.b_0");
        auto tensor_bias = bias_feed_value->GetMutable<Tensor>();
        tensor_bias->ShareDataWith(t5);

        Variable *output = scope->Var("batch_norm_0.tmp_2");
        auto *output_tensor = output->GetMutable<Tensor>();
        output_tensor->mutable_data<float>({4, 10, 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_bn(t1, t2, t3, t4, t5, 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_bn(const Tensor &t1, const Tensor &t2, const Tensor &t3,
                    const Tensor &t4, const Tensor &t5, 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 TestBatchNormOp<CPU>;
} // namespace framework
} // namespace paddle_mobile

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

    /// input x (4,10,2,2)
    paddle_mobile::framework::Tensor inputx1;
    SetupTensor<float>(&inputx1, {4, 10, 2, 2}, static_cast<float>(0),
                       static_cast<float>(1));
    auto *inputx1_ptr = inputx1.data<float>();

    paddle_mobile::framework::Tensor mean;
    SetupTensor<float>(&mean, {10}, static_cast<float>(0),
                       static_cast<float>(1));
    auto *mean_ptr = mean.data<float>();

    paddle_mobile::framework::Tensor scale;
    SetupTensor<float>(&scale, {10}, static_cast<float>(0),
                       static_cast<float>(1));
    auto *scale_ptr = scale.data<float>();

    paddle_mobile::framework::Tensor variance;
    SetupTensor<float>(&variance, {10}, static_cast<float>(0),
                       static_cast<float>(1));
    auto *variance_ptr = variance.data<float>();

    paddle_mobile::framework::Tensor bias;
    SetupTensor<float>(&bias, {10}, static_cast<float>(0),
                       static_cast<float>(1));
    auto *bias_ptr = bias.data<float>();

    paddle_mobile::framework::TestBatchNormOp<paddle_mobile::CPU>
        testBatchNormOp(program);

    auto output_bn =
        testBatchNormOp.predict_bn(inputx1, mean, scale, variance, bias);
    auto *output_bn_ptr = output_bn->data<float>();

    /// [2, 5, 1, 0]
    DLOG << " (" << inputx1_ptr[102] << " - " << mean_ptr[5] << ")/(("
         << variance_ptr[5] << " + 0.00001"
         << ")^0.5)* " << scale_ptr[5] << " + " << bias_ptr[5] << " = ";
    DLOG << output_bn_ptr[102];

    return 0;
}