Ejemplo n.º 1
0
        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"));
            }
        }
Ejemplo n.º 2
0
        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]);
            }
        }