opencv/modules/dnn/perf/perf_layer.cpp

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.

#include "perf_precomp.hpp"
#include <opencv2/dnn/shape_utils.hpp>

namespace opencv_test {

struct Layer_Slice : public TestBaseWithParam<tuple<Backend, Target> >
{
    template<int DIMS>
    void test_slice(const int* inputShape, const int* begin, const int* end)
    {
        int backendId = get<0>(GetParam());
        int targetId = get<1>(GetParam());

        Mat input(DIMS, inputShape, CV_32FC1, Scalar::all(0));
        for (int i = 0; i < (int)input.total(); ++i)
            input.ptr<float>()[i] = (float)(i & 4095);

        std::vector<Range> range(DIMS);
        for (int i = 0; i < DIMS; ++i)
            range[i] = Range(begin[i], end[i]);

        Net net;
        LayerParams lp;
        lp.type = "Slice";
        lp.name = "testLayer";
        lp.set("begin", DictValue::arrayInt<int*>((int*)&begin[0], DIMS));
        lp.set("end", DictValue::arrayInt<int*>((int*)&end[0], DIMS));
        net.addLayerToPrev(lp.name, lp.type, lp);

        // warmup
        {
            net.setInput(input);
            net.setPreferableBackend(backendId);
            net.setPreferableTarget(targetId);
            Mat out = net.forward();

            EXPECT_GT(cv::norm(out, NORM_INF), 0);
#if 0
            //normAssert(out, input(range));
            cout << input(range).clone().reshape(1, 1) << endl;
            cout << out.reshape(1, 1) << endl;
#endif
        }

        TEST_CYCLE()
        {
            Mat res = net.forward();
        }

        SANITY_CHECK_NOTHING();
    }
};

static std::set<std::string> nary_eltwise_cuda_deny_ops = {"add", "equal", "greater", "less", "mean", "mul", "pow", "sub"};

struct Layer_NaryEltwise : public TestBaseWithParam<tuple<Backend, Target> >
{
    void test_layer(const std::vector<int>& a_shape, const std::vector<int>& b_shape, const String op, bool isRef = false)
    {
        int backendId = get<0>(GetParam());
        int targetId = get<1>(GetParam());

        if (!isRef && backendId == DNN_BACKEND_CUDA)
        {
            if (a_shape != b_shape)
                throw SkipTestException("The test is skipped because inputs with different shapes are not supported.");
            if (nary_eltwise_cuda_deny_ops.find(op) != nary_eltwise_cuda_deny_ops.end())
                throw SkipTestException("The operator '" + op + "' is skipped because is not support with cuda currently.");
        }
        Mat a(a_shape, CV_32FC1);
        Mat b(b_shape, CV_32FC1);

        Scalar mean = 0.f;
        Scalar std = 1.f;
        randn(a, mean, std);
        randn(b, mean, std);


        Net net;
        LayerParams lp;
        if (isRef)
            lp.type = "Eltwise";
        else
            lp.type = "NaryEltwise";
        lp.name = "testLayer";
        lp.set("operation", op);
        int id = net.addLayerToPrev(lp.name, lp.type, lp);
        net.connect(0, 1, id, 1);

        // warmup
        {
            std::vector<String> inpNames(2);
            inpNames[0] = "a";
            inpNames[1] = "b";
            net.setInputsNames(inpNames);
            net.setInput(a, inpNames[0]);
            net.setInput(b, inpNames[1]);

            net.setPreferableBackend(backendId);
            net.setPreferableTarget(targetId);
            Mat out = net.forward();
        }

        TEST_CYCLE()
        {
            Mat res = net.forward();
        }

        SANITY_CHECK_NOTHING();
    }

    int N = 8;
    int C = 256;
    int H = 128;
    int W = 100;
};


PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_add)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "add");
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_div)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "div");
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_div)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "div", true);
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_equal)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "equal");
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_greater)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "greater");
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_less)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "less");
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_max)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "max");
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_max)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "max", true);
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_mean)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "mean");
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_min)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "min");
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_min)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "min", true);
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_mul)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "mul");
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_mul)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "prod", true);
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_pow)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "pow");
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_sub)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "sub");
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_sum)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "sum");
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_sum)
{
    test_layer({N, C, H, W}, {N, C, H, W}, "sum", true);
}

PERF_TEST_P_(Layer_NaryEltwise, NCHW_C_sum)
{
    test_layer({N, C, H, W}, {C, 1, 1}, "sum");
}

PERF_TEST_P_(Layer_NaryEltwise, NHWC_C)
{
    test_layer({N, H, W, C}, {1, C}, "sum");
}

PERF_TEST_P_(Layer_Slice, YOLOv4_tiny_1)
{
    const int inputShape[4] = {1, 64, 104, 104};
    const int begin[] = {0, 32, 0, 0};
    const int end[] = {1, 64, 104, 104};
    test_slice<4>(inputShape, begin, end);
}

PERF_TEST_P_(Layer_Slice, YOLOv4_tiny_2)
{
    const int inputShape[4] = {1, 128, 52, 52};
    const int begin[] = {0, 64, 0, 0};
    const int end[] = {1, 128, 52, 52};
    test_slice<4>(inputShape, begin, end);
}

PERF_TEST_P_(Layer_Slice, YOLOv4_tiny_3)
{
    const int inputShape[4] = {1, 256, 26, 26};
    const int begin[] = {0, 128, 0, 0};
    const int end[] = {1, 256, 26, 26};
    test_slice<4>(inputShape, begin, end);
}


PERF_TEST_P_(Layer_Slice, FastNeuralStyle_eccv16)
{
    const int inputShape[4] = {1, 128, 80, 100};
    const int begin[] = {0, 0, 2, 2};
    const int end[] = {1, 128, 76, 96};
    test_slice<4>(inputShape, begin, end);
}

struct Layer_Scatter : public TestBaseWithParam<tuple<Backend, Target> >
{
    void test_layer(const std::vector<int>& shape, const String reduction = "none", int axis = 0)
    {
        int backendId = get<0>(GetParam());
        int targetId = get<1>(GetParam());

        Mat data(shape, CV_32FC1);
        Mat indices(shape, CV_32FC1);
        Mat updates(shape, CV_32FC1);

        Scalar mean = 0.f;
        Scalar std = 1.f;
        randn(data, mean, std);
        randu(indices, 0, shape[axis]);
        randn(updates, mean, std);

        indices.convertTo(indices, CV_32SC1, 1, -1);

        Net net;
        LayerParams lp;
        lp.type = "Scatter";
        lp.name = "testLayer";
        lp.set("reduction", reduction);
        lp.set("axis", axis);

        int id = net.addLayerToPrev(lp.name, lp.type, lp);
        net.connect(0, 0, id, 0);
        net.connect(0, 1, id, 1);
        net.connect(0, 2, id, 2);

        // warmup
        {
            std::vector<String> inpNames(3);
            inpNames[0] = "data";
            inpNames[1] = "indices";
            inpNames[2] = "updates";
            net.setInputsNames(inpNames);
            net.setInput(data, inpNames[0]);
            net.setInput(indices, inpNames[1]);
            net.setInput(updates, inpNames[2]);

            net.setPreferableBackend(backendId);
            net.setPreferableTarget(targetId);
            Mat out = net.forward();
        }

        TEST_CYCLE()
        {
            Mat res = net.forward();
        }

        SANITY_CHECK_NOTHING();
    }

    int N = 8;
    int C = 256;
    int H = 128;
    int W = 100;
};

PERF_TEST_P_(Layer_Scatter, DISABLED_Scatter)
{
    test_layer({N, C, H, W});
}

PERF_TEST_P_(Layer_Scatter, DISABLED_Scatter_add)
{
    test_layer({N, C, H, W}, "add");
}

struct Layer_ScatterND : public TestBaseWithParam<tuple<Backend, Target> >
{
    void test_layer(const std::vector<int>& shape, const String reduction = "none")
    {
        int backendId = get<0>(GetParam());
        int targetId = get<1>(GetParam());

        std::vector<int> indices_shape(shape);
        indices_shape.push_back(int(shape.size()));
        Mat data(shape, CV_32FC1);
        Mat indices(indices_shape, CV_32FC1);
        Mat updates(shape, CV_32FC1);

        Scalar mean = 0.f;
        Scalar std = 1.f;
        randn(data, mean, std);
        randn(updates, mean, std);

        // initialize the indices with index tuples like [0...N, 0...C, 0...H, 0...W]
        std::vector<int> current_index_tuple(shape.size());
        int total = data.total();
        std::vector<int> indices_step;
        for (int i = 0; i < indices.dims; i++)
        {
            int step = indices.step.p[i] / sizeof(float);
            indices_step.push_back(step);
        }
        int t, j, idx, offset_at_idx, offset;
        for (int i = 0; i < total; i++)
        {
            t = i;
            for (j = shape.size() - 1; j >= 0; j--)
            {
                idx = t / shape[j];
                offset_at_idx = (int)(t - idx * shape[j]);
                current_index_tuple[j] = offset_at_idx;
                t = idx;
            }

            offset = 0;
            for (j = 0; j < shape.size(); j++)
                offset += current_index_tuple[j] * indices_step[j];

            for (j = 0; j < shape.size(); j++)
                indices.at<float>(offset + j) = current_index_tuple[j];
        }

        Net net;
        LayerParams lp;
        lp.type = "ScatterND";
        lp.name = "testLayer";
        lp.set("reduction", reduction);

        int id = net.addLayerToPrev(lp.name, lp.type, lp);
        net.connect(0, 0, id, 0);
        net.connect(0, 1, id, 1);
        net.connect(0, 2, id, 2);

        // warmup
        {
            std::vector<String> inpNames(3);
            inpNames[0] = "data";
            inpNames[1] = "indices";
            inpNames[2] = "updates";
            net.setInputsNames(inpNames);
            net.setInput(data, inpNames[0]);
            net.setInput(indices, inpNames[1]);
            net.setInput(updates, inpNames[2]);

            net.setPreferableBackend(backendId);
            net.setPreferableTarget(targetId);
            Mat out = net.forward();
        }

        TEST_CYCLE()
        {
            Mat res = net.forward();
        }

        SANITY_CHECK_NOTHING();
    }

    int N = 8;
    int C = 256;
    int H = 128;
    int W = 100;
};

PERF_TEST_P_(Layer_ScatterND, DISABLED_ScatterND)
{
    test_layer({N, C, H ,W});
}

PERF_TEST_P_(Layer_ScatterND, DISABLED_ScatterND_add)
{
    test_layer({N, C, H , W}, "add");
}

INSTANTIATE_TEST_CASE_P(/**/, Layer_Slice, dnnBackendsAndTargets(false, false));
INSTANTIATE_TEST_CASE_P(/**/, Layer_NaryEltwise, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
#ifdef HAVE_CUDA
INSTANTIATE_TEST_CASE_P(CUDA, Layer_NaryEltwise, testing::Values(std::make_tuple(DNN_BACKEND_CUDA, DNN_TARGET_CUDA)));
#endif
INSTANTIATE_TEST_CASE_P(/**/, Layer_Scatter, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
INSTANTIATE_TEST_CASE_P(/**/, Layer_ScatterND, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));

} // namespace