private static int DetectShuffleNetV2(NcnnDotNet.OpenCV.Mat bgr, List <float> clsScores)
{
using (var shuffleNetV2 = new Net())
{
if (Ncnn.IsSupportVulkan)
{
shuffleNetV2.Opt.UseVulkanCompute = true;
}
// https://github.com/miaow1988/ShuffleNet_V2_pytorch_caffe
// models can be downloaded from https://github.com/miaow1988/ShuffleNet_V2_pytorch_caffe/releases
shuffleNetV2.LoadParam("shufflenet_v2_x0.5.param");
shuffleNetV2.LoadModel("shufflenet_v2_x0.5.bin");
using var @in = Mat.FromPixelsResize(bgr.Data, PixelType.Bgr, bgr.Cols, bgr.Rows, 224, 224);
var normVals = new[] { 1 / 255.0f, 1 / 255.0f, 1 / 255.0f };
@in.SubstractMeanNormalize(null, normVals);
using var ex = shuffleNetV2.CreateExtractor();
ex.Input("data", @in);
using var @out = new Mat();
ex.Extract("fc", @out);
// manually call softmax on the fc output
// convert result into probability
// skip if your model already has softmax operation
{
using var softmax = Ncnn.CreateLayer("Softmax");
using var pd = new ParamDict();
softmax.LoadParam(pd);
softmax.ForwardInplace(@out, shuffleNetV2.Opt);
}
using var @out2 = @out.Reshape(@out.W * @out.H * @out.C);
clsScores.Capacity = @out2.W;
for (var j = 0; j < @out2.W; j++)
{
clsScores.Add(@out2[j]);
}
}
return(0);
}
