// Copyright (C) 2015  Davis E. King (davis@dlib.net)
// License: Boost Software License   See LICENSE.txt for the full license.


#include <sstream>
#include <string>
#include <cstdlib>
#include <ctime>
#include <vector>
#include "../dnn.h"

#include "tester.h"


namespace  
{

    using namespace test;
    using namespace dlib;
    using namespace dlib::tt;
    using namespace std;

    logger dlog("test.dnn");

// ----------------------------------------------------------------------------------------

    template <typename T>
    float compare_gradients (
        const tensor& t,
        T grad
    )
    {
        float max_error = 0;
        auto p = t.host();
        for (size_t i = 0; i < t.size(); ++i)
        {
            max_error = std::max(max_error, std::abs(p[i]-grad(i)));
        }
        return max_error;
    }

// ----------------------------------------------------------------------------------------

    void test_tanh()
    {
        print_spinner();
        resizable_tensor src(5,5), dest(5,5), gradient_input(5,5);
        src = matrix_cast<float>(gaussian_randm(5,5, 0));
        dest = matrix_cast<float>(gaussian_randm(5,5, 1));
        gradient_input = matrix_cast<float>(gaussian_randm(5,5, 2));



        auto grad_src = [&](long idx) {
            auto f = [&](float eps) {
                const float old = src.host()[idx];
                src.host()[idx] += eps;
                tanh(dest, src);
                float result = dot(gradient_input, dest);
                src.host()[idx] = old;
                return result;
            };
            const float eps = 0.01;
            return (f(+eps)-f(-eps))/(2*eps);
        };

        resizable_tensor src_grad;
        src_grad.copy_size(src);
        src_grad = 0;

        tanh(dest, src);
        tanh_gradient(src_grad, dest, gradient_input);

        auto grad_error = compare_gradients(src_grad, grad_src);
        dlog << LINFO << "src error: " << grad_error;
        DLIB_TEST(grad_error < 0.001);
    }

    void test_sigmoid()
    {
        print_spinner();
        resizable_tensor src(5,5), dest(5,5), gradient_input(5,5);
        src = matrix_cast<float>(gaussian_randm(5,5, 0));
        dest = matrix_cast<float>(gaussian_randm(5,5, 1));
        gradient_input = matrix_cast<float>(gaussian_randm(5,5, 2));



        auto grad_src = [&](long idx) {
            auto f = [&](float eps) {
                const float old = src.host()[idx];
                src.host()[idx] += eps;
                sigmoid(dest, src);
                float result = dot(gradient_input, dest);
                src.host()[idx] = old;
                return result;
            };
            const float eps = 0.01;
            return (f(+eps)-f(-eps))/(2*eps);
        };

        resizable_tensor src_grad;
        src_grad.copy_size(src);
        src_grad = 0;

        sigmoid(dest, src);
        sigmoid_gradient(src_grad, dest, gradient_input);

        auto grad_error = compare_gradients(src_grad, grad_src);
        dlog << LINFO << "src error: " << grad_error;
        DLIB_TEST(grad_error < 0.001);
    }

    void test_softmax()
    {
        print_spinner();
        resizable_tensor src(5,5), dest(5,5), gradient_input(5,5);
        src = matrix_cast<float>(gaussian_randm(5,5, 0));
        dest = matrix_cast<float>(gaussian_randm(5,5, 1));
        gradient_input = matrix_cast<float>(gaussian_randm(5,5, 2));



        auto grad_src = [&](long idx) {
            auto f = [&](float eps) {
                const float old = src.host()[idx];
                src.host()[idx] += eps;
                tt::softmax(dest, src);
                float result = dot(gradient_input, dest);
                src.host()[idx] = old;
                return result;
            };
            const float eps = 0.01;
            return (f(+eps)-f(-eps))/(2*eps);
        };

        resizable_tensor src_grad;
        src_grad.copy_size(src);
        src_grad = 0;

        tt::softmax(dest, src);
        softmax_gradient(src_grad, dest, gradient_input);

        auto grad_error = compare_gradients(src_grad, grad_src);
        dlog << LINFO << "src error: " << grad_error;
        DLIB_TEST(grad_error < 0.001);
    }

    void test_batch_normalize()
    {
        print_spinner();
        resizable_tensor src(5,5), gamma(1,5), beta(1,5), dest, means, vars, gradient_input(5,5);
        src = matrix_cast<float>(gaussian_randm(5,5, 0));
        gamma = matrix_cast<float>(gaussian_randm(1,5, 1));
        beta = matrix_cast<float>(gaussian_randm(1,5, 2));
        gradient_input = matrix_cast<float>(gaussian_randm(5,5, 3));

        gamma = 1;
        beta = 0;

        batch_normalize(dest, means, vars, src, gamma, beta);


        auto grad_src = [&](long idx) {
            auto f = [&](float eps) {
                const float old = src.host()[idx];
                src.host()[idx] += eps;
                batch_normalize(dest, means, vars, src, gamma, beta);
                float result = dot(gradient_input, dest);
                src.host()[idx] = old;
                return result;
            };
            const float eps = 0.01;
            return (f(+eps)-f(-eps))/(2*eps);
        };
        auto grad_gamma = [&](long idx) {
            auto f = [&](float eps) {
                const float old = gamma.host()[idx];
                gamma.host()[idx] += eps;
                batch_normalize(dest, means, vars, src, gamma, beta);
                float result = dot(gradient_input, dest);
                gamma.host()[idx] = old;
                return result;
            };
            const float eps = 0.01;
            return (f(+eps)-f(-eps))/(2*eps);
        };
        auto grad_beta = [&](long idx) {
            auto f = [&](float eps) {
                const float old = beta.host()[idx];
                beta.host()[idx] += eps;
                batch_normalize(dest, means, vars, src, gamma, beta);
                float result = dot(gradient_input, dest);
                beta.host()[idx] = old;
                return result;
            };
            const float eps = 0.01;
            return (f(+eps)-f(-eps))/(2*eps);
        };

        resizable_tensor src_grad, gamma_grad, beta_grad;
        src_grad.copy_size(src);
        gamma_grad.copy_size(gamma);
        beta_grad.copy_size(beta);
        src_grad = 0;
        gamma_grad = 0;
        beta_grad = 0;

        batch_normalize_gradient bng;
        bng(gradient_input, means, vars, src, gamma, src_grad, gamma_grad, beta_grad);

        auto grad_error = compare_gradients(src_grad, grad_src);
        dlog << LINFO << "src error: " << grad_error;
        DLIB_TEST(grad_error < 0.001);

        grad_error = compare_gradients(gamma_grad, grad_gamma);
        dlog << LINFO << "gamma error: " << grad_error;
        DLIB_TEST(grad_error < 0.001);

        grad_error = compare_gradients(beta_grad, grad_beta);
        dlog << LINFO << "beta error: " << grad_error;
        DLIB_TEST(grad_error < 0.001);
    }

    void test_batch_normalize_conv()
    {
        print_spinner();
        resizable_tensor src(5,5,4,4), gamma(1,5), beta(1,5), dest, means, vars, gradient_input(5,5,4,4);
        src = matrix_cast<float>(gaussian_randm(5,5*4*4, 0));
        gamma = matrix_cast<float>(gaussian_randm(1,5, 1));
        beta = matrix_cast<float>(gaussian_randm(1,5, 2));
        gradient_input = matrix_cast<float>(gaussian_randm(5,5*4*4, 3));

        gamma = 1;
        beta = 0;

        batch_normalize_conv(dest, means, vars, src, gamma, beta);


        auto grad_src = [&](long idx) {
            auto f = [&](float eps) {
                const float old = src.host()[idx];
                src.host()[idx] += eps;
                batch_normalize_conv(dest, means, vars, src, gamma, beta);
                float result = dot(gradient_input, dest);
                src.host()[idx] = old;
                return result;
            };
            const float eps = 0.01;
            return (f(+eps)-f(-eps))/(2*eps);
        };
        auto grad_gamma = [&](long idx) {
            auto f = [&](float eps) {
                const float old = gamma.host()[idx];
                gamma.host()[idx] += eps;
                batch_normalize_conv(dest, means, vars, src, gamma, beta);
                float result = dot(gradient_input, dest);
                gamma.host()[idx] = old;
                return result;
            };
            const float eps = 0.01;
            return (f(+eps)-f(-eps))/(2*eps);
        };
        auto grad_beta = [&](long idx) {
            auto f = [&](float eps) {
                const float old = beta.host()[idx];
                beta.host()[idx] += eps;
                batch_normalize_conv(dest, means, vars, src, gamma, beta);
                float result = dot(gradient_input, dest);
                beta.host()[idx] = old;
                return result;
            };
            const float eps = 0.01;
            return (f(+eps)-f(-eps))/(2*eps);
        };


        resizable_tensor src_grad, gamma_grad, beta_grad;
        src_grad.copy_size(src);
        gamma_grad.copy_size(gamma);
        beta_grad.copy_size(beta);
        src_grad = 0;
        gamma_grad = 0;
        beta_grad = 0;

        batch_normalize_conv_gradient bng;
        bng(gradient_input, means, vars, src, gamma, src_grad, gamma_grad, beta_grad);


        auto grad_error = compare_gradients(src_grad, grad_src);
        dlog << LINFO << "src error: " << grad_error;
        DLIB_TEST(grad_error < 0.001);

        grad_error = compare_gradients(gamma_grad, grad_gamma);
        dlog << LINFO << "gamma error: " << grad_error;
        DLIB_TEST(grad_error < 0.001);

        grad_error = compare_gradients(beta_grad, grad_beta);
        dlog << LINFO << "beta error: " << grad_error;
        DLIB_TEST(grad_error < 0.001);

    }

// ----------------------------------------------------------------------------------------

    void test_basic_tensor_ops()
    {
        print_spinner();
        resizable_tensor dest, src(3,4), A(1,4), B(1,4);
        src = 2;
        dest.copy_size(src);
        affine_transform(dest, src, 2, 3);
        dlog << LINFO << mat(dest);
        matrix<float> truth1(3,4), truth2(3,4);

        truth1 = 7;
        truth2 = 7, 10,  7,  7,
        7, 10,  7,  7,
        7, 10,  7,  7;
        DLIB_TEST(max(abs(truth1-mat(dest))) < 1e-5);

        A = 2;
        B = 3;
        A.host()[1] = 3;
        B.host()[1] = 4;
        dest = 0;
        affine_transform(dest, src, A, B);
        dlog << LINFO << mat(dest);
        DLIB_TEST(max(abs(truth2-mat(dest))) < 1e-5);

        A.set_size(3,4);
        B.set_size(3,4);
        A = matrix_cast<float>(gaussian_randm(3,4, 1));
        B = matrix_cast<float>(gaussian_randm(3,4, 2));
        affine_transform(dest, src, A, B);
        dlog << LINFO << mat(dest);
        matrix<float> truth3 = pointwise_multiply(mat(src), mat(A)) + mat(B);
        DLIB_TEST(max(abs(truth3-mat(dest))) < 1e-5);

        matrix<float> truth4 = pointwise_multiply(mat(A), mat(B));
        multiply(A, B);
        DLIB_TEST(max(abs(truth4-mat(A))) < 1e-5);

        matrix<float> truth5 = mat(B) > 0.1;
        dlog << LINFO << truth5;
        threshold(B, 0.1);
        DLIB_TEST(max(abs(truth5-mat(B))) < 1e-5);
    }

// ----------------------------------------------------------------------------------------

    class dnn_tester : public tester
    {
    public:
        dnn_tester (
        ) :
            tester ("test_dnn",
                "Runs tests on the deep neural network tools.")
        {}

        void perform_test (
        )
        {
            test_tanh();
            test_softmax();
            test_sigmoid();
            test_batch_normalize();
            test_batch_normalize_conv();
            test_basic_tensor_ops();
        }
    } a;

}


