public static void Run(VGGModel modelType)
{
OpenFileDialog ofd = new OpenFileDialog {
Filter = "画像ファイル(*.jpg;*.png;*.gif;*.bmp)|*.jpg;*.png;*.gif;*.bmp|すべてのファイル(*.*)|*.*"
};
if (ofd.ShowDialog() == DialogResult.OK)
{
int vggId = (int)modelType;
Console.WriteLine("Model Loading.");
string modelFilePath = InternetFileDownloader.Donwload(Urls[vggId], FileNames[vggId], Hashes[vggId]);
List <Function> vggNet = CaffemodelDataLoader.ModelLoad(modelFilePath);
string[] classList = File.ReadAllLines(CLASS_LIST_PATH);
//GPUを初期化
for (int i = 0; i < vggNet.Count - 1; i++)
{
if (vggNet[i] is CPU.Convolution2D || vggNet[i] is CPU.Linear || vggNet[i] is CPU.MaxPooling2D)
{
vggNet[i] = (Function)CLConverter.Convert(vggNet[i]);
}
}
FunctionStack nn = new FunctionStack(vggNet.ToArray());
//層を圧縮
nn.Compress();
Console.WriteLine("Model Loading done.");
do
{
//ネットワークへ入力する前に解像度を 224px x 224px x 3ch にしておく
Bitmap baseImage = new Bitmap(ofd.FileName);
Bitmap resultImage = new Bitmap(224, 224, PixelFormat.Format24bppRgb);
Graphics g = Graphics.FromImage(resultImage);
g.DrawImage(baseImage, 0, 0, 224, 224);
g.Dispose();
Real[] bias = { -123.68, -116.779, -103.939 }; //補正値のチャンネル順は入力画像に従う(標準的なBitmapならRGB)
NdArray imageArray = BitmapConverter.Image2NdArray(resultImage, false, true, bias);
Console.WriteLine("Start predict.");
Stopwatch sw = Stopwatch.StartNew();
NdArray result = nn.Predict(imageArray)[0];
sw.Stop();
Console.WriteLine("Result Time : " +
(sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") +
"μｓ");
int maxIndex = Array.IndexOf(result.Data, result.Data.Max());
Console.WriteLine("[" + result.Data[maxIndex] + "] : " + classList[maxIndex]);
} while (ofd.ShowDialog() == DialogResult.OK);
}
}
public static void Run()
{
OpenFileDialog ofd = new OpenFileDialog
{
Filter = "画像ファイル(*.jpg;*.png;*.gif;*.bmp)|*.jpg;*.png;*.gif;*.bmp|すべてのファイル(*.*)|*.*"
};
if (ofd.ShowDialog() == DialogResult.OK)
{
Console.WriteLine("Model Loading.");
string modelFilePath = InternetFileDownloader.Donwload(DOWNLOAD_URL, MODEL_FILE, MODEL_FILE_HASH);
List <Function <Real> > alexNet = CaffemodelDataLoader.ModelLoad <Real>(modelFilePath);
string[] classList = File.ReadAllLines(CLASS_LIST_PATH);
//GPUを初期化
for (int i = 0; i < alexNet.Count - 1; i++)
{
if (alexNet[i] is CPU.Convolution2D <Real> || alexNet[i] is CPU.Linear <Real> || alexNet[i] is CPU.MaxPooling2D <Real> )
{
alexNet[i] = (Function <Real>)CLConverter.Convert(alexNet[i]);
}
}
FunctionStack <Real> nn = new FunctionStack <Real>(alexNet.ToArray());
//層を圧縮
nn.Compress();
Console.WriteLine("Model Loading done.");
do
{
//ネットワークへ入力する前に解像度を 224px x 224px x 3ch にしておく
Bitmap baseImage = new Bitmap(ofd.FileName);
Bitmap resultImage = new Bitmap(227, 227, PixelFormat.Format24bppRgb);
Graphics g = Graphics.FromImage(resultImage);
g.DrawImage(baseImage, 0, 0, 227, 227);
g.Dispose();
Real[] bias = new Real[] { -123.68f, -116.779f, -103.939f }; //補正値のチャンネル順は入力画像に従う
NdArray <Real> imageArray = BitmapConverter.Image2NdArray(resultImage, false, true, bias);
Console.WriteLine("Start predict.");
Stopwatch sw = Stopwatch.StartNew();
NdArray <Real> result = nn.Predict(imageArray)[0];
sw.Stop();
Console.WriteLine("Result Time : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μｓ");
int maxIndex = Array.IndexOf(result.Data, result.Data.Max());
Console.WriteLine("[" + result.Data[maxIndex] + "] : " + classList[maxIndex]);
} while (ofd.ShowDialog() == DialogResult.OK);
}
}
static void SwitchGPU(CPU.FunctionStack <Real> functionStack)
{
for (int i = 0; i < functionStack.Functions.Length; i++)
{
if (functionStack.Functions[i] is CPU.Convolution2D <Real> || functionStack.Functions[i] is CPU.Linear <Real> || functionStack.Functions[i] is CPU.MaxPooling2D <Real> )
{
functionStack.Functions[i] = (Function <Real>)CLConverter.Convert(functionStack.Functions[i]);
}
if (functionStack.Functions[i] is SplitFunction <Real> splitFunction)
{
for (int j = 0; j < splitFunction.SplitedFunctions.Length; j++)
{
SwitchGPU(splitFunction.SplitedFunctions[j]);
}
}
}
//ブロック単位で層の圧縮を実行
functionStack.Compress();
}
public static void Run(VGGModel modelType)
{
OpenFileDialog ofd = new OpenFileDialog {
Filter = "画像ファイル(*.jpg;*.png;*.gif;*.bmp)|*.jpg;*.png;*.gif;*.bmp|すべてのファイル(*.*)|*.*"
};
if (ofd.ShowDialog() == DialogResult.OK)
{
int vggId = (int)modelType;
Console.WriteLine("Model Loading.");
string modelFilePath = InternetFileDownloader.Donwload(Urls[vggId], FileNames[vggId], Hashes[vggId]);
List <Function <Real> > vggNet = OnnxmodelDataLoader.LoadNetWork <Real>(modelFilePath);
string[] classList = File.ReadAllLines(CLASS_LIST_PATH);
//GPUを初期化
for (int i = 0; i < vggNet.Count - 1; i++)
{
if (vggNet[i] is CPU.Convolution2D <Real> || vggNet[i] is CPU.Linear <Real> || vggNet[i] is CPU.MaxPooling2D <Real> )
{
vggNet[i] = (Function <Real>)CLConverter.Convert(vggNet[i]);
}
}
FunctionStack <Real> nn = new FunctionStack <Real>(vggNet.ToArray());
//層を圧縮
nn.Compress();
Console.WriteLine("Model Loading done.");
do
{
//ネットワークへ入力する前に解像度を 224px x 224px x 3ch にしておく
Bitmap baseImage = new Bitmap(ofd.FileName);
Bitmap resultImage = new Bitmap(224, 224, PixelFormat.Format24bppRgb);
Graphics g = Graphics.FromImage(resultImage);
g.DrawImage(baseImage, 0, 0, 224, 224);
g.Dispose();
Real[] mean = new Real[] { 0.485f, 0.456f, 0.406f };
Real[] std = new Real[] { 0.229f, 0.224f, 0.225f };
NdArray <Real> imageArray = BitmapConverter.Image2NdArray <Real>(resultImage);
int dataSize = imageArray.Shape[1] * imageArray.Shape[2];
for (int ch = 0; ch < imageArray.Shape[0]; ch++)
{
for (int i = 0; i < dataSize; i++)
{
imageArray.Data[ch * dataSize + i] = (imageArray.Data[ch * dataSize + i] - mean[ch]) / std[ch];
}
}
Console.WriteLine("Start predict.");
Stopwatch sw = Stopwatch.StartNew();
NdArray <Real> result = nn.Predict(imageArray)[0];
sw.Stop();
Console.WriteLine("Result Time : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μｓ");
int maxIndex = Array.IndexOf(result.Data, result.Data.Max());
Console.WriteLine("[" + result.Data[maxIndex] + "] : " + classList[maxIndex]);
} while (ofd.ShowDialog() == DialogResult.OK);
}
}
