opencv/modules/dnn/perf/perf_convolution1d.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 Conv1DParam_t {
    int kernel;
    struct BlobShape { int dims[3]; } shapeIn;
    int outCN;
    int groups;
    int stride;
    int dilation;
    int pad[2];
    const char* padMode;
    bool hasBias;
    double declared_flops;
};
// Details: #12142
static const Conv1DParam_t testConvolution1DConfigs[] = {
        {3, {{1, 6, 10}}, 6, 1, 1, 1, {0, 0}, "VALID", true, 1776.},
        {3, {{1, 2, 19}}, 2, 2, 2, 1, {1, 1}, "", true, 260.},
        {3, {{1, 2, 25}}, 2, 2, 1, 1, {2, 2}, "SAME", false, 650.},
};

struct Conv1DParamID
{
    enum {
        CONV_0 = 0,
        CONV_LAST = sizeof(testConvolution1DConfigs) / sizeof(testConvolution1DConfigs[0])
    };
    int val_;
    Conv1DParamID(int val = 0) : val_(val) {}
    operator int() const { return val_; }
    static ::testing::internal::ParamGenerator<Conv1DParamID> all()
    {
        enum { NUM = (int)CONV_LAST };
        Conv1DParamID v_[NUM]; for (int i = 0; i < NUM; ++i) { v_[i] = Conv1DParamID(i); } // reduce generated code size
        return ::testing::ValuesIn(v_, v_ + NUM);
    }
};
static inline void PrintTo(const Conv1DParamID& v, std::ostream* os)
{
    CV_Assert((int)v >= 0); CV_Assert((int)v < Conv1DParamID::CONV_LAST);
    const Conv1DParam_t& p = testConvolution1DConfigs[(int)v];

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


typedef tuple<Conv1DParamID, tuple<Backend, Target> > Conv1DTestParam_t;
typedef TestBaseWithParam<Conv1DTestParam_t> Conv1D;

PERF_TEST_P_(Conv1D, conv1d)
{
    int test_id = (int)get<0>(GetParam());
    ASSERT_GE(test_id, 0); ASSERT_LT(test_id, Conv1DParamID::CONV_LAST);
    const Conv1DParam_t& params = testConvolution1DConfigs[test_id];
    double declared_flops = params.declared_flops;

    DictValue kernel   = DictValue::arrayInt(&params.kernel, 1);
    DictValue stride   = DictValue::arrayInt(&params.stride, 1);
    DictValue pad      = DictValue::arrayInt(&params.pad[0], 2);
    DictValue dilation = DictValue::arrayInt(&params.dilation, 1);

    MatShape inputShape = MatShape(params.shapeIn.dims, params.shapeIn.dims + 3);
    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)
        throw SkipTestException("Only CPU is supported");

    int inChannels = inputShape[1];

    int sz[] = {outChannels, inChannels / groups, params.kernel};
    Mat weights(3, &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]};
    Mat input(3, &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);

    // warmup
    Mat output = net.forward();

    MatShape netInputShape = shape(input);
    cv::dnn::MatType netInputType = input.depth();

    bool fp16 = false;
#ifdef HAVE_OPENCL
    fp16 = ocl::Device::getDefault().isExtensionSupported("cl_khr_fp16");
#endif
    if (netInputType == CV_32F && fp16 && targetId == DNN_TARGET_OPENCL_FP16)
        netInputType = CV_16F;
    size_t weightsMemory = 0, blobsMemory = 0;
    net.getMemoryConsumption(netInputShape, netInputType, weightsMemory, blobsMemory);
    int64 flops = net.getFLOPS(netInputShape, netInputType);
    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(/**/, Conv1D, Combine(
        Conv1DParamID::all(),
        dnnBackendsAndTargets(/* withInferenceEngine = */false, /* obsolete_withHalide = */false)  // defined in ../test/test_common.hpp
));

} // namespace