modules/dnn/src/layers/layers_common.cpp - 4.x - Alx89/OpenCV

modules/dnn/src/layers/layers_common.cpp
268 строк10 KB
Vadim Pisarevsky
Merge pull request #24709 from vpisarev:winograd_mode
22 дек 2023, 09:22
22 дек 2023, 09:22853e5df
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#include "../precomp.hpp"
#include "layers_common.hpp"

namespace cv
{
namespace dnn
{

namespace util
{

std::string makeName(const std::string& str1, const std::string& str2)
{
    return str1 + str2;
}

bool getParameter(const LayerParams &params, const std::string& nameBase, const std::string& nameAll,
                  std::vector<size_t>& parameter, bool hasDefault = false, const std::vector<size_t>& defaultValue = std::vector<size_t>(2, 0))
{
    std::string nameH = makeName(nameBase, std::string("_h"));
    std::string nameW = makeName(nameBase, std::string("_w"));
    std::string nameAll_ = nameAll;
    if (nameAll_ == "")
        nameAll_ = nameBase;

    if (params.has(nameH) && params.has(nameW))
    {
        CV_Assert(params.get<int>(nameH) >= 0 && params.get<int>(nameW) >= 0);
        parameter.push_back(params.get<int>(nameH));
        parameter.push_back(params.get<int>(nameW));
        return true;
    }
    else
    {
        if (params.has(nameAll_))
        {
            DictValue param = params.get(nameAll_);
            for (int i = 0; i < param.size(); i++) {
                CV_Assert(param.get<int>(i) >= 0);
                parameter.push_back(param.get<int>(i));
            }
            if (parameter.size() == 1)
                parameter.resize(2, parameter[0]);
            return true;
        }
        else
        {
            if (hasDefault)
            {
                parameter = defaultValue;
                return true;
            }
            else
            {
                return false;
            }
        }
    }
}

void getKernelSize(const LayerParams &params, std::vector<size_t>& kernel)
{
    if (!util::getParameter(params, "kernel", "kernel_size", kernel))
        CV_Error(cv::Error::StsBadArg, "kernel_size (or kernel_h and kernel_w) not specified");

    for (int i = 0; i < kernel.size(); i++)
        CV_Assert(kernel[i] > 0);
}

void getStrideAndPadding(const LayerParams &params, std::vector<size_t>& pads_begin, std::vector<size_t>& pads_end,
                         std::vector<size_t>& strides, cv::String& padMode, size_t kernel_size = 2)
{
    if (params.has("pad_l") && params.has("pad_t") && params.has("pad_r") && params.has("pad_b")) {
        CV_Assert(params.get<int>("pad_t") >= 0 && params.get<int>("pad_l") >= 0 &&
                  params.get<int>("pad_b") >= 0 && params.get<int>("pad_r") >= 0);
        pads_begin.push_back(params.get<int>("pad_t"));
        pads_begin.push_back(params.get<int>("pad_l"));
        pads_end.push_back(params.get<int>("pad_b"));
        pads_end.push_back(params.get<int>("pad_r"));
    }
    else {
        util::getParameter(params, "pad", "pad", pads_begin, true, std::vector<size_t>(kernel_size, 0));
        if (pads_begin.size() < 4)
            pads_end = pads_begin;
        else
        {
            pads_end = std::vector<size_t>(pads_begin.begin() + pads_begin.size() / 2, pads_begin.end());
            pads_begin.resize(pads_begin.size() / 2);
        }
        CV_Assert(pads_begin.size() == pads_end.size());
    }
    util::getParameter(params, "stride", "stride", strides, true, std::vector<size_t>(kernel_size, 1));

    padMode = "";
    if (params.has("pad_mode"))
    {
        padMode = params.get<String>("pad_mode");
    }

    for (int i = 0; i < strides.size(); i++)
        CV_Assert(strides[i] > 0);
}
}

void getPoolingKernelParams(const LayerParams &params, std::vector<size_t>& kernel, std::vector<bool>& globalPooling,
                            std::vector<size_t>& pads_begin, std::vector<size_t>& pads_end,
                            std::vector<size_t>& strides, cv::String &padMode)
{
    bool is_global = params.get<bool>("global_pooling", false);
    globalPooling.assign({
        params.get<bool>("global_pooling_d", is_global),
        params.get<bool>("global_pooling_h", is_global),
        params.get<bool>("global_pooling_w", is_global)
    });

    if (globalPooling[0] || globalPooling[1] || globalPooling[2])
    {
        util::getStrideAndPadding(params, pads_begin, pads_end, strides, padMode);
        if ((globalPooling[0] && params.has("kernel_d")) ||
            (globalPooling[1] && params.has("kernel_h")) ||
            (globalPooling[2] && params.has("kernel_w")) ||
            params.has("kernel_size")) {
            CV_Error(cv::Error::StsBadArg, "In global_pooling mode, kernel_size (or kernel_h and kernel_w) cannot be specified");
        }

        kernel.resize(3);
        kernel[0] = params.get<int>("kernel_d", 1);
        kernel[1] = params.get<int>("kernel_h", 1);
        kernel[2] = params.get<int>("kernel_w", 1);

        for (int i = 0, j = globalPooling.size() - pads_begin.size(); i < pads_begin.size(); i++, j++) {
            if ((pads_begin[i] != 0 || pads_end[i] != 0) && globalPooling[j])
                CV_Error(cv::Error::StsBadArg, "In global_pooling mode, pads must be = 0");
        }
        for (int i = 0, j = globalPooling.size() - strides.size(); i < strides.size(); i++, j++) {
            if (strides[i] != 1 && globalPooling[j])
                CV_Error(cv::Error::StsBadArg, "In global_pooling mode, strides must be = 1");
        }
    }
    else
    {
        util::getKernelSize(params, kernel);
        util::getStrideAndPadding(params, pads_begin, pads_end, strides, padMode, kernel.size());
    }
}

void getConvolutionKernelParams(const LayerParams &params, std::vector<size_t>& kernel, std::vector<size_t>& pads_begin,
                                std::vector<size_t>& pads_end, std::vector<size_t>& strides,
                                std::vector<size_t>& dilations, cv::String &padMode, std::vector<size_t>& adjust_pads,
                                bool& useWinograd)
{
    util::getKernelSize(params, kernel);
    util::getStrideAndPadding(params, pads_begin, pads_end, strides, padMode, kernel.size());
    util::getParameter(params, "dilation", "dilation", dilations, true, std::vector<size_t>(kernel.size(), 1));
    util::getParameter(params, "adj", "adj", adjust_pads, true, std::vector<size_t>(kernel.size(), 0));
    useWinograd = params.get<bool>("use_winograd", useWinograd);

    for (int i = 0; i < dilations.size(); i++)
        CV_Assert(dilations[i] > 0);
}

// From TensorFlow code:
// Total padding on rows and cols is
// Pr = (R' - 1) * S + Kr - R
// Pc = (C' - 1) * S + Kc - C
// where (R', C') are output dimensions, (R, C) are input dimensions, S
// is stride, (Kr, Kc) are filter dimensions.
// We pad Pr/2 on the left and Pr - Pr/2 on the right, Pc/2 on the top
// and Pc - Pc/2 on the bottom.  When Pr or Pc is odd, this means
// we pad more on the right and bottom than on the top and left.
void getConvPoolOutParams(const std::vector<int>& inp, const std::vector<size_t>& kernel,
                          const std::vector<size_t>& stride, const String &padMode,
                          const std::vector<size_t>& dilation, std::vector<int>& out)
{
    if (padMode == "VALID")
    {
        for (int i = 0; i < inp.size(); i++)
            out.push_back((inp[i] - dilation[i] * (kernel[i] - 1) - 1 + stride[i]) / stride[i]);
    }
    else if (padMode == "SAME")
    {
        for (int i = 0; i < inp.size(); i++)
            out.push_back((inp[i] - 1 + stride[i]) / stride[i]);
    }
    else
    {
        CV_Error(Error::StsError, "Unsupported padding mode");
    }
}

void getConvPoolPaddings(const std::vector<int>& inp, const std::vector<size_t>& kernel,
                         const std::vector<size_t>& strides, const String &padMode,
                         std::vector<size_t>& pads_begin, std::vector<size_t>& pads_end)
{
    if (padMode == "SAME" || padMode == "VALID")
    {
        pads_begin.assign(kernel.size(), 0);
        pads_end.assign(kernel.size(), 0);
    }
    if (padMode == "SAME")
    {
        CV_Assert_N(kernel.size() == strides.size(), kernel.size() == inp.size());
        for (int i = 0; i < pads_begin.size(); i++) {
            // There are test cases with stride > kernel.
            if (strides[i] <= kernel[i])
            {
                int pad = (kernel[i] - 1 - (inp[i] - 1 + strides[i]) % strides[i]) / 2;
                pads_begin[i] = pads_end[i] = pad;
            }
        }
    }
}

double getWeightScale(const Mat& weightsMat)
{
    double realMin, realMax;

    cv::minMaxIdx(weightsMat, &realMin, &realMax);
    realMin = std::min(realMin, 0.0);
    realMax = std::max(realMax, 0.0);

    return (realMax == realMin) ? 1.0 : std::max(-realMin, realMax)/127;
}

}
}