public AveragePooling2D(PyArray <T> kSize, PyArray <T> stride, PyArray <T> pad)
{
this._kSize = kSize;
this._stride = stride;
this._pad = pad;
_maxPooling2D = Chainer.Functions["average_pooling_2d"];
}
public MaxPooling2D(PyArray <T> kSize, PyArray <T> stride, PyArray <T> pad, bool coverAll = true)
{
this._kSize = kSize;
this._stride = stride;
this._pad = pad;
this._coverAll = coverAll;
_maxPooling2D = Chainer.Functions["max_pooling_2d"];
}
private IntPtr WriteDataPort(IntPtr self, IntPtr args)
{
var argTuple = new PyTuple(new PyObject(args, false));
var data = new PyArray(argTuple.Get(0));
var portName = argTuple.Get(1).GetString();
var dataPort = OutputPorts.First(port => port.Name == portName) as NodeSystemLib2.FormatData1D.OutputPortData1D;
var result = data.ToArrayDouble();
dataPort.Buffer.Write(result, 0, result.Length);
return(new PyLong(1, false).Handle);
}
public Deconvolution2D(int inChannels, int outChannels, PyArray <T> kSize, PyArray <T> stride, PyArray <T> pad, bool nobias, PyObject[] outSize, PyArray <T> initialW, PyArray <T> initialBias)
{
_deconvolution2D = Chainer.Links["Deconvolution2D"].Call(inChannels, outChannels, kSize, stride, pad, nobias, PyTuple.Pack(outSize), initialW, initialBias);
_deconvolution2D["cleargrads"].Call();
}
public EmbedID(int inSize, int outSize, PyArray <T> initialW = default(PyArray <T>))
{
_embedID = Chainer.Links["EmbedID"].Call(inSize, outSize, initialW);
_embedID["cleargrads"].Call();
}
public Linear(int inSize, int outSize, bool noBias, PyArray <T> initialW, PyArray <T> initialBias)
{
_linear = Chainer.Links["Linear"].Call(inSize, outSize, noBias, initialW, initialBias);
_linear["cleargrads"].Call();
}
public Variable(PyArray <T> array)
{
_rawData = Chainer.Variable.Call(array);
}
public LSTM(int inSize, int outSize, PyArray <T> lateralInit = default(PyArray <T>), PyArray <T> upwardInit = default(PyArray <T>), PyArray <T> biasInit = default(PyArray <T>), PyArray <T> forgetBiasInit = default(PyArray <T>))
{
_lstm = Chainer.Links["LSTM"].Call(inSize, outSize, lateralInit, upwardInit, biasInit, forgetBiasInit);
_lstm["cleargrads"].Call();
}
public Convolution2D(int inChannels, int outChannels, PyArray <T> kSize, PyArray <T> stride, PyArray <T> pad, bool nobias, PyArray <T> initialW, PyArray <T> initialBias)
{
_convolution2D = Chainer.Links["Convolution2D"].Call(inChannels, outChannels, kSize, stride, pad, nobias, initialW, initialBias);
_convolution2D["cleargrads"].Call();
}
static void Main(string[] args)
{
Python py = new Python();
TestType[,] array =
{
{ 0, 1, 2, 3 },
{ 4, 5, 6, 7 },
{ 8, 9, 10, 11 }
};
//arrayの内容をPythonに送信
PyArray <TestType> x = array;
//pythonで受信したxを表示する
Python.Print(x);
Console.WriteLine("\n> pyBuffer[2, 1] += 100 From C#");
//Pythonの値をC#から変更するクラスを作成
x[2, 1] += 100;
//c#から変更した結果を表示する
Python.Print(x);
//xをPythonのｙに転送
py["y"] = x;
Console.WriteLine("\n> pyBuffer += 1000 From C#");
//xのすべての値に1000を加算する
x += 1000;
//加算したxを表示する
Python.Print(x);
Console.WriteLine("\n> pytest(x) in Python");
//pytest.pyを読み込む
dynamic pyTest = new PyDynamicModule("pytest");
//test内の関数calcを呼び出す
x = pyTest.calc(x);
//関数の結果を表示する
Python.Print(x);
Console.WriteLine("\n> Add array From C#");
//加算用の配列を作る
TestType[] addArray = { 10000, 20000, 30000, 40000 };
//xのすべての値にaddArrayを加算する
x += addArray;
//加算したxを表示する
Python.Print(x);
Console.WriteLine("\n> Set array From C#");
//セット用の配列を作る
TestType[] setArray = { 1111, 2222, 3333, 4444 };
//Pythonの値をC#から変更するクラスを作成
PyArray <TestType[]> pyArrayBuffer = new PyArray <TestType[]>(x);
//x[1]にsetArrayを設定する
pyArrayBuffer[1] = setArray;
//加算したxを表示する
Python.Print(x);
//計算したxをC#で取得
TestType[,] destArrayX = (TestType[, ])x;
//Pythonで宣言したyをC#で取得
PyArray <TestType> pyNdArrayBuffer = py["y"];
TestType[,] destArrayY = (TestType[, ])pyNdArrayBuffer;
//取得したXの中身を表示
Console.WriteLine("\n> Console.WriteLine(x[i, j]) from C#");
for (int i = 0; i < destArrayX.GetLength(0); i++)
{
for (int j = 0; j < destArrayX.GetLength(1); j++)
{
Console.WriteLine(i * destArrayX.GetLength(1) + j + " : " + destArrayX[i, j]);
}
}
//取得したYの中身を表示
Console.WriteLine("\n> Console.WriteLine(y[i, j]) from C#");
for (int i = 0; i < destArrayY.GetLength(0); i++)
{
for (int j = 0; j < destArrayY.GetLength(1); j++)
{
Console.WriteLine(i * destArrayY.GetLength(1) + j + " : " + destArrayY[i, j]);
}
}
Console.WriteLine("Done.");
Console.Read();
}
public override string GetPyCode(PyEmitStyle style)
{
return(string.Format("{0}[{1}]", PyArray.GetPyCode(style), Index.GetPyCode(style)));
}
public PyObject[] Forward(PyObject x, PyArray <int> indices, int axis = 1)
{
return(PyTuple.UnPack(_splitAxis.Call(x, indices, axis)));
}
public Linear(int inSize, int outSize, bool noBias = false, PyArray <T> initialW = default(PyArray <T>), PyArray <T> initialBias = default(PyArray <T>))
{
_linear = Chainer.Links["Linear"].Call(inSize, outSize, noBias, initialW, initialBias);
ClearGrads();
}
