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65 lines
1.5 KiB
65 lines
1.5 KiB
using Microsoft.ML.OnnxRuntime.Tensors;
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using SixLabors.ImageSharp;
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using ZeroLevel.NN.Models;
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/*
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INPUT
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Image, name: data, shape: 1, 3, 112, 112, format: B, C, H, W, where:
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B - batch size
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C - channel
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H - height
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W - width
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Channel order is BGR.
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OUTPUT
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Face embeddings, name: fc1, shape: 1, 512, output data format: B, C, where:
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B - batch size
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C - row-vector of 512 floating points values, face embeddings
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INPUT NORMALIZATION
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img -= 127.5
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img /= 128
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OUTPUT NORMALIZATION
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NORM - vector length = 1
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*/
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namespace ZeroLevel.NN
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{
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public sealed class ArcFaceNet
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: SSDNN, IEncoder
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{
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private const int INPUT_WIDTH = 112;
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private const int INPUT_HEIGHT = 112;
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public ArcFaceNet(string modelPath)
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: base(modelPath)
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{
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}
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public int InputW => INPUT_WIDTH;
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public int InputH => INPUT_HEIGHT;
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public float[] Predict(Image image)
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{
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var input = MakeInput(image,
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new ImagePreprocessorOptions(INPUT_WIDTH, INPUT_HEIGHT, PredictorChannelType.ChannelFirst)
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.ApplyAxeInversion());
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return Predict(input);
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}
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public float[] Predict(Tensor<float> input)
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{
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float[] embedding = null;
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Extract(new Dictionary<string, Tensor<float>> { { "data", input } }, d =>
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{
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embedding = d.First().Value.ToArray();
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});
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Norm(embedding);
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return embedding;
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}
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}
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}
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