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int8 layers and 8-bit quantization support

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SamFC10
2021-08-19 09:56:47 +05:30
parent 95919051e0
commit fa90e14b06
36 changed files with 6401 additions and 67 deletions
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modules/dnn/src/layers/convolution_layer.cpp
+42
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@@ -2083,6 +2083,48 @@ public:
}
#endif
virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
{
// References - https://arxiv.org/pdf/1712.05877.pdf
// Quantized convolution with variable weights is not supported.
if (blobs.empty())
return false;
float inputScale = scales[0][0], outputScale = scales[1][0];
int inputZp = zeropoints[0][0];
params.set("input_zeropoint", inputZp);
Mat weightsQuantized(weightsMat.rows, weightsMat.cols, CV_8S);
Mat biasQuantized(1, numOutput, CV_32S);
Mat outputMultiplier(1, numOutput, CV_32F);
double realMin, realMax, weightsScale;
for( int i = 0; i < numOutput; i++ )
{
// Quantize weights
cv::minMaxIdx(weightsMat.row(i), &realMin, &realMax);
realMin = std::min(realMin, 0.0);
realMax = std::max(realMax, 0.0);
weightsScale = (realMax == realMin) ? 1.0 : std::max(-realMin, realMax)/127;
weightsMat.row(i).convertTo(weightsQuantized.row(i), CV_8S, 1.f/weightsScale);
// Quantize biases
float biasScale = inputScale * weightsScale;
biasQuantized.at<int>(i) = (int)std::round(biasvec[i]/biasScale) - inputZp*(cv::sum(weightsQuantized.row(i))[0]);
// Store multiplier
outputMultiplier.at<float>(i) = biasScale / outputScale;
}
params.blobs.clear();
params.blobs.push_back(weightsQuantized.reshape(1, shape(blobs[0])));
params.blobs.push_back(biasQuantized);
params.blobs.push_back(outputMultiplier);
return true;
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{