static void Main(string[] args)
{
Console.WriteLine("<<<Mamecog sample>>>");
// Conv2DとDenseのインスタンスを生成する
Conv2D conv1 = new Conv2D(32, 1, 3, 3);
Conv2D conv2 = new Conv2D(64, 32, 3, 3);
Dense dense = new Dense(10, 1600);
// Conv2DとDenseのカーネルとバイアスをファイルから読み込む
conv1.LoadKernelAndBias("conv2d_k.bin", "conv2d_b.bin");
conv2.LoadKernelAndBias("conv2d_1_k.bin", "conv2d_1_b.bin");
dense.LoadKernelAndBias("dense_k.bin", "dense_b.bin");
// 各層の入出力の格納先を用意する
LayerData2D input0 = new LayerData2D(1, 28, 28);
LayerData2D conv1output = new LayerData2D(32, 26, 26);
LayerData2D pool1output = new LayerData2D(32, 13, 13);
LayerData2D conv2output = new LayerData2D(64, 11, 11);
LayerData2D pool2output = new LayerData2D(64, 5, 5);
float[] pool2flatten = new float[64 * 5 * 5];
float[] denseOutput = new float[10];
// テスト用の入力データを用意する
Console.WriteLine("<<<Input>>>");
input0.Cells = testInput;
input0.PrintCellValues();
// 各層の出力を順に計算する
conv1.Conv(conv1output, input0, false);
conv1.ReLU(conv1output);
MaxPool2D.Calc(pool1output, conv1output, 2);
conv2.Conv(conv2output, pool1output, false);
conv2.ReLU(conv2output);
MaxPool2D.Calc(pool2output, conv2output, 2);
pool2output.Flatten(pool2flatten);
dense.Calc(denseOutput, pool2flatten);
dense.Softmax(denseOutput);
// Dense層の出力を表示する
Console.WriteLine("<<<Output>>>");
for (int i = 0; i < denseOutput.Length; i++)
{
Console.WriteLine("Dense[" + i.ToString() + "] = " + denseOutput[i].ToString("F4"));
}
}
static void Main(string[] args)
{
// テスト用の入力データを用意する
Console.WriteLine("テスト画像読み込み");
LayerData2D input1 = new LayerData2D(3, 224, 224);
string inputFilename = "test_input.png"; // 224x224ピクセルのRGB画像
using (Bitmap inputImage = new Bitmap(Image.FromFile(inputFilename)))
{
Debug.Assert(inputImage.Height == 224);
Debug.Assert(inputImage.Width == 224);
for (int y = 0; y < inputImage.Height; y++)
{
for (int x = 0; x < inputImage.Width; x++)
{
Color pixelData = inputImage.GetPixel(x, y);
float r = (float)pixelData.R - 123.68f;
float g = (float)pixelData.G - 116.779f;
float b = (float)pixelData.B - 103.939f;
input1.SetVal(0, y, x, b); // BGR
input1.SetVal(1, y, x, g);
input1.SetVal(2, y, x, r);
}
}
}
// Conv2DとDenseのインスタンスを生成する
Conv2D block1Conv1 = new Conv2D(64, 3, 3, 3);
Conv2D block1Conv2 = new Conv2D(64, 64, 3, 3);
Conv2D block2Conv1 = new Conv2D(128, 64, 3, 3);
Conv2D block2Conv2 = new Conv2D(128, 128, 3, 3);
Conv2D block3Conv1 = new Conv2D(256, 128, 3, 3);
Conv2D block3Conv2 = new Conv2D(256, 256, 3, 3);
Conv2D block3Conv3 = new Conv2D(256, 256, 3, 3);
Conv2D block4Conv1 = new Conv2D(512, 256, 3, 3);
Conv2D block4Conv2 = new Conv2D(512, 512, 3, 3);
Conv2D block4Conv3 = new Conv2D(512, 512, 3, 3);
Conv2D block5Conv1 = new Conv2D(512, 512, 3, 3);
Conv2D block5Conv2 = new Conv2D(512, 512, 3, 3);
Conv2D block5Conv3 = new Conv2D(512, 512, 3, 3);
Dense fc1 = new Dense(4096, 25088);
Dense fc2 = new Dense(4096, 4096);
Dense predictions = new Dense(1000, 4096);
// Conv2DとDenseのカーネルとバイアスをファイルから読み込む
Console.WriteLine("学習済みモデル読み込み");
block1Conv1.LoadKernelAndBias("block1_conv1_k.bin", "block1_conv1_b.bin");
block1Conv2.LoadKernelAndBias("block1_conv2_k.bin", "block1_conv2_b.bin");
block2Conv1.LoadKernelAndBias("block2_conv1_k.bin", "block2_conv1_b.bin");
block2Conv2.LoadKernelAndBias("block2_conv2_k.bin", "block2_conv2_b.bin");
block3Conv1.LoadKernelAndBias("block3_conv1_k.bin", "block3_conv1_b.bin");
block3Conv2.LoadKernelAndBias("block3_conv2_k.bin", "block3_conv2_b.bin");
block3Conv3.LoadKernelAndBias("block3_conv3_k.bin", "block3_conv3_b.bin");
block4Conv1.LoadKernelAndBias("block4_conv1_k.bin", "block4_conv1_b.bin");
block4Conv2.LoadKernelAndBias("block4_conv2_k.bin", "block4_conv2_b.bin");
block4Conv3.LoadKernelAndBias("block4_conv3_k.bin", "block4_conv3_b.bin");
block5Conv1.LoadKernelAndBias("block5_conv1_k.bin", "block5_conv1_b.bin");
block5Conv2.LoadKernelAndBias("block5_conv2_k.bin", "block5_conv2_b.bin");
block5Conv3.LoadKernelAndBias("block5_conv3_k.bin", "block5_conv3_b.bin");
fc1.LoadKernelAndBias("fc1_k.bin", "fc1_b.bin");
fc2.LoadKernelAndBias("fc2_k.bin", "fc2_b.bin");
predictions.LoadKernelAndBias("predictions_k.bin", "predictions_b.bin");
// 各層の入出力の格納先を用意する
LayerData2D block1Conv1Output = new LayerData2D(64, 224, 224);
LayerData2D block1Conv2Output = new LayerData2D(64, 224, 224);
LayerData2D block1PoolOutput = new LayerData2D(64, 112, 112);
LayerData2D block2Conv1Output = new LayerData2D(128, 112, 112);
LayerData2D block2Conv2Output = new LayerData2D(128, 112, 112);
LayerData2D block2PoolOutput = new LayerData2D(128, 56, 56);
LayerData2D block3Conv1Output = new LayerData2D(256, 56, 56);
LayerData2D block3Conv2Output = new LayerData2D(256, 56, 56);
LayerData2D block3Conv3Output = new LayerData2D(256, 56, 56);
LayerData2D block3PoolOutput = new LayerData2D(256, 28, 28);
LayerData2D block4Conv1Output = new LayerData2D(512, 28, 28);
LayerData2D block4Conv2Output = new LayerData2D(512, 28, 28);
LayerData2D block4Conv3Output = new LayerData2D(512, 28, 28);
LayerData2D block4PoolOutput = new LayerData2D(512, 14, 14);
LayerData2D block5Conv1Output = new LayerData2D(512, 14, 14);
LayerData2D block5Conv2Output = new LayerData2D(512, 14, 14);
LayerData2D block5Conv3Output = new LayerData2D(512, 14, 14);
LayerData2D block5PoolOutput = new LayerData2D(512, 7, 7);
float[] flattenOutput = new float[25088];
float[] fc1Output = new float[4096];
float[] fc2Output = new float[4096];
float[] predictionsOutput = new float[1000];
// 各層の出力を順に計算する
Console.WriteLine("CNN実行開始");
var sw = new System.Diagnostics.Stopwatch();
sw.Start();
Console.WriteLine("Block 1");
block1Conv1.Conv(block1Conv1Output, input1, true);
block1Conv1.ReLU(block1Conv1Output);
block1Conv2.Conv(block1Conv2Output, block1Conv1Output, true);
block1Conv2.ReLU(block1Conv2Output);
MaxPool2D.Calc(block1PoolOutput, block1Conv2Output, 2);
Console.WriteLine("Block 2");
block2Conv1.Conv(block2Conv1Output, block1PoolOutput, true);
block2Conv1.ReLU(block2Conv1Output);
block2Conv2.Conv(block2Conv2Output, block2Conv1Output, true);
block2Conv2.ReLU(block2Conv2Output);
MaxPool2D.Calc(block2PoolOutput, block2Conv2Output, 2);
Console.WriteLine("Block 3");
block3Conv1.Conv(block3Conv1Output, block2PoolOutput, true);
block3Conv1.ReLU(block3Conv1Output);
block3Conv2.Conv(block3Conv2Output, block3Conv1Output, true);
block3Conv2.ReLU(block3Conv2Output);
block3Conv3.Conv(block3Conv3Output, block3Conv2Output, true);
block3Conv3.ReLU(block3Conv3Output);
MaxPool2D.Calc(block3PoolOutput, block3Conv3Output, 2);
Console.WriteLine("Block 4");
block4Conv1.Conv(block4Conv1Output, block3PoolOutput, true);
block4Conv1.ReLU(block4Conv1Output);
block4Conv2.Conv(block4Conv2Output, block4Conv1Output, true);
block4Conv2.ReLU(block4Conv2Output);
block4Conv3.Conv(block4Conv3Output, block4Conv2Output, true);
block4Conv3.ReLU(block4Conv3Output);
MaxPool2D.Calc(block4PoolOutput, block4Conv3Output, 2);
Console.WriteLine("Block 5");
block5Conv1.Conv(block5Conv1Output, block4PoolOutput, true);
block5Conv1.ReLU(block5Conv1Output);
block5Conv2.Conv(block5Conv2Output, block5Conv1Output, true);
block5Conv2.ReLU(block5Conv2Output);
block5Conv3.Conv(block5Conv3Output, block5Conv2Output, true);
block5Conv3.ReLU(block5Conv3Output);
MaxPool2D.Calc(block5PoolOutput, block5Conv3Output, 2);
Console.WriteLine("FC");
block5PoolOutput.Flatten(flattenOutput);
fc1.Calc(fc1Output, flattenOutput);
fc1.ReLU(fc1Output);
fc2.Calc(fc2Output, fc1Output);
fc2.ReLU(fc2Output);
predictions.Calc(predictionsOutput, fc2Output);
predictions.Softmax(predictionsOutput);
sw.Stop();
TimeSpan ts = sw.Elapsed;
Console.WriteLine("CNN実行完了：実行時間 = {0}", ts);
// VGG16モデルの学習済み1000カテゴリのうち先頭10カテゴリ分の確率を出力
for (int i = 0; i < 10; i++)
{
Console.WriteLine("カテゴリ[{0}] = {1}", i, predictionsOutput[i]);
}
}