opencv/modules/dnn/perf/perf_convolution3d.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 Conv3DParam_t {
    int kernel[3];
    struct BlobShape { int dims[5]; } shapeIn;
    int outCN;
    int groups;
    int stride[3];
    int dilation[3];
    int pad[6];
    const char* padMode;
    bool hasBias;
    double declared_flops;
};
// Details: #12142
static const Conv3DParam_t testConvolution3DConfigs[] = {
    {{3, 3, 3}, {{1, 6, 10, 38, 50}}, 6, 1, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "VALID", true, 26956800.},
    {{3, 3, 3}, {{1, 2, 19, 19, 19}}, 2, 2, {2, 2, 2}, {1, 1, 1}, {1, 1, 1, 1, 1, 1}, "", true, 218000.},
    {{3, 3, 3}, {{1, 2, 25, 19, 19}}, 2, 2, {1, 2, 2}, {1, 1, 1}, {2, 2, 2, 2, 2, 2}, "SAME", false, 545000.},
    {{3, 3, 3}, {{1, 11, 9, 150, 200}}, 11, 1, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "VALID", true, 1342562760.},
    {{3, 3, 3}, {{1, 10, 98, 10, 10}}, 10, 1, {1, 1, 1}, {1, 1, 1}, {1, 0, 1, 1, 0,1}, "SAME", false, 53018000.},
    {{5, 5, 5}, {{1, 6, 19, 19, 19}}, 6, 2, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "", false, 30395250.},
    {{5, 5, 5}, {{1, 4, 50, 19, 19}}, 4, 1, {2, 2, 2}, {1, 1, 1}, {1, 1, 1, 1, 1, 1}, "VALID", false, 5893888.},
    {{5, 5, 5}, {{1, 3, 75, 75, 100}}, 3, 1, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "SAME", true, 1267312500.},
    {{5, 5, 5}, {{1, 2, 21, 75, 100}}, 2, 1, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "", true, 116103744.},
    {{5, 5, 5}, {{1, 4, 40, 75, 75}}, 4, 1, {2, 2, 2}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "", false, 93405312.},
    {{7, 7, 7}, {{1, 6, 15, 19, 19}}, 6, 1, {2, 1, 1}, {1, 1, 1}, {3, 3, 3, 3, 3, 3}, "SAME", true, 71339376.},
    {{7, 7, 7}, {{1, 2, 38, 38, 38}}, 2, 1, {1, 2, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "", false, 44990464.},
    {{1, 1, 1}, {{1, 4, 9, 10, 10}}, 4, 1, {1, 1, 2}, {1, 1, 1}, {1, 1, 1, 1, 1, 1}, "VALID", false, 16200.},
    {{3, 1, 4}, {{1, 14, 5, 10, 10}}, 14, 1, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "SAME", false, 2359000.},
    {{1, 1, 1}, {{1, 8, 1, 10, 10}}, 8, 8, {1, 1, 1}, {1, 1, 1}, {1, 1, 1, 1, 1, 1}, "", true, 58752.},
    {{3, 4, 2}, {{1, 4, 8, 10, 10}}, 4, 4, {1, 2, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "", true, 166752.}
};

struct Conv3DParamID
{
    enum {
        CONV_0 = 0,
        CONV_100 = 16,
        CONV_LAST = sizeof(testConvolution3DConfigs) / sizeof(testConvolution3DConfigs[0])
    };
    int val_;                                                                  \
    Conv3DParamID(int val = 0) : val_(val) {}
    operator int() const { return val_; }
    static ::testing::internal::ParamGenerator<Conv3DParamID> all()
    {
#if 0
        enum { NUM = (int)CONV_LAST };
#else
        enum { NUM = (int)CONV_100 };
#endif
        Conv3DParamID v_[NUM]; for (int i = 0; i < NUM; ++i) { v_[i] = Conv3DParamID(i); } // reduce generated code size
        return ::testing::ValuesIn(v_, v_ + NUM);
    }
};                                                                                  \
static inline void PrintTo(const Conv3DParamID& v, std::ostream* os)
{
    CV_Assert((int)v >= 0); CV_Assert((int)v < Conv3DParamID::CONV_LAST);
    const Conv3DParam_t& p = testConvolution3DConfigs[(int)v];

    *os << "GFLOPS=" << cv::format("%.3f", p.declared_flops * 1e-9)
        << ", K=[" << p.kernel[0] << " x " << p.kernel[1]  << " x " << p.kernel[2] << "]"
        << ", IN={" << p.shapeIn.dims[0] << ", " << p.shapeIn.dims[1] << ", " << p.shapeIn.dims[2] << ", " << p.shapeIn.dims[3] << ", " << p.shapeIn.dims[4] << "}"
        << ", OCN=" << p.outCN;
    if (p.groups > 1)
       *os << ", G=" << p.groups;
    if (p.stride[0] * p.stride[1] * p.stride[2] != 1)
        *os << ", S=[" << p.stride[0] << " x " << p.stride[1]  << " x " << p.stride[2] << "]";
    if (p.dilation[0] * p.dilation[1] * p.dilation[2] != 1)
        *os << ", D=["  << p.dilation[0] << " x " << p.dilation[1]  << " x " << p.dilation[2] << "]";
    if (p.pad[0] != 0 && p.pad[1] != 0 && p.pad[2] != 0 &&
        p.pad[3] != 0 && p.pad[4] != 0 && p.pad[5] != 0)
        *os << ", P=(" << p.pad[0] << ", " << p.pad[3] << ") x ("
                       << p.pad[1] << ", " << p.pad[4] << ") x ("
                       << p.pad[2] << ", " << p.pad[5] << ")";
    if (!((std::string)p.padMode).empty())
        *os << ", PM=" << ((std::string)p.padMode);
    if (p.hasBias)
        *os << ", BIAS";
}


typedef tuple<Conv3DParamID, tuple<Backend, Target> > Conv3DTestParam_t;
typedef TestBaseWithParam<Conv3DTestParam_t> Conv3D;

PERF_TEST_P_(Conv3D, conv3d)
{
    int test_id = (int)get<0>(GetParam());
    ASSERT_GE(test_id, 0); ASSERT_LT(test_id, Conv3DParamID::CONV_LAST);
    const Conv3DParam_t& params = testConvolution3DConfigs[test_id];
    double declared_flops = params.declared_flops;

    DictValue kernel   = DictValue::arrayInt(&params.kernel[0], 3);
    DictValue stride   = DictValue::arrayInt(&params.stride[0], 3);
    DictValue pad      = DictValue::arrayInt(&params.pad[0], 6);
    DictValue dilation = DictValue::arrayInt(&params.dilation[0], 3);

    MatShape inputShape = MatShape(params.shapeIn.dims, params.shapeIn.dims + 5);
    int outChannels = params.outCN;
    int groups = params.groups;
    std::string padMode(params.padMode);

    bool hasBias = params.hasBias;
    Backend backendId = get<0>(get<1>(GetParam()));
    Target targetId = get<1>(get<1>(GetParam()));

    if (targetId != DNN_TARGET_CPU && backendId != DNN_BACKEND_CUDA)
        throw SkipTestException("Only CPU and CUDA is supported");

    int inChannels = inputShape[1];

    int sz[] = {outChannels, inChannels / groups, params.kernel[0], params.kernel[1], params.kernel[2]};
    Mat weights(5, &sz[0], CV_32F);
    randu(weights, -1.0f, 1.0f);

    LayerParams lp;
    lp.set("kernel_size", kernel);
    lp.set("pad", pad);
    if (!padMode.empty())
        lp.set("pad_mode", padMode);

    lp.set("stride", stride);
    lp.set("dilation", dilation);
    lp.set("num_output", outChannels);
    lp.set("group", groups);
    lp.set("bias_term", hasBias);
    lp.type = "Convolution";
    lp.name = "testLayer";
    lp.blobs.push_back(weights);

    if (hasBias)
    {
        Mat bias(1, outChannels, CV_32F);
        randu(bias, -1.0f, 1.0f);
        lp.blobs.push_back(bias);
    }
    int inpSz[] = {1, inChannels, inputShape[2], inputShape[3], inputShape[4]};
    Mat input(5, &inpSz[0], CV_32F);
    randu(input, -1.0f, 1.0f);

    Net net;
    net.addLayerToPrev(lp.name, lp.type, lp);

    net.setInput(input);
    net.setPreferableBackend(backendId);
    net.setPreferableTarget(targetId);

    Mat output = net.forward();

    MatShape netInputShape = shape(input);
    size_t weightsMemory = 0, blobsMemory = 0;
    net.getMemoryConsumption(netInputShape, weightsMemory, blobsMemory);
    int64 flops = net.getFLOPS(netInputShape);
    CV_Assert(flops > 0);

    std::cout
        << "IN=" << divUp(input.total() * input.elemSize(), 1u<<10) << " Kb " << netInputShape
        << "    OUT=" << divUp(output.total() * output.elemSize(), 1u<<10) << " Kb " << shape(output)
        << "    Weights(parameters): " << divUp(weightsMemory, 1u<<10) << " Kb"
        << "    MFLOPS=" << flops * 1e-6 << std::endl;

    TEST_CYCLE()
    {
        Mat res = net.forward();
    }
    EXPECT_NEAR(flops, declared_flops, declared_flops * 1e-6);
    SANITY_CHECK_NOTHING();
}

INSTANTIATE_TEST_CASE_P(/**/, Conv3D, Combine(
    Conv3DParamID::all(),
    dnnBackendsAndTargets(false, false)  // defined in ../test/test_common.hpp
));

} // namespace