public void ConcatRandomTest()
{
Python.Initialize();
Chainer.Initialize();
int batchCount = Mother.Dice.Next(1, 5);
int ch = Mother.Dice.Next(1, 5);
int widthA = Mother.Dice.Next(1, 16);
int widthB = Mother.Dice.Next(1, 16);
int height = Mother.Dice.Next(1, 16);
int axis = 3;
Real[,,,] inputA = Initializer.GetRandomValues <Real[, , , ]>(batchCount, ch, height, widthA);
Real[,,,] inputB = Initializer.GetRandomValues <Real[, , , ]>(batchCount, ch, height, widthB);
Real[,,,] dummyGy = Initializer.GetRandomValues <Real[, , , ]>(batchCount, ch, height, widthA + widthB);
//chainer
NChainer.Concat <Real> cConcat = new NChainer.Concat <Real>(axis);
Variable <Real> cX = new Variable <Real>(inputA);
Variable <Real> cY = new Variable <Real>(inputB);
Variable <Real> cZ = cConcat.Forward(cX, cY);
cZ.Grad = dummyGy;
cZ.Backward();
//KelpNet
Concat <Real> concat = new Concat <Real>(axis - 1);//Chainerと異なり1次元目を暗黙的にBatchとみなさないため
NdArray <Real> x = new NdArray <Real>(inputA, asBatch: true);
NdArray <Real> y = new NdArray <Real>(inputB, asBatch: true);
NdArray <Real> z = concat.Forward(x, y)[0];
z.Grad = dummyGy.Flatten();
z.Backward();
Real[] cZdata = ((Real[, , , ])cZ.Data).Flatten();
//Copyが必要
Real[] cXgrad = ((Real[, , , ])cX.Grad.Copy()).Flatten();
Real[] cYgrad = ((Real[, , , ])cY.Grad.Copy()).Flatten();
//許容範囲を算出
Real delta = 0.00001f;
//z
Assert.AreEqual(cZdata.Length, z.Data.Length);
for (int i = 0; i < y.Data.Length; i++)
{
Assert.AreEqual(cZdata[i], z.Data[i], delta);
}
//x.grad
Assert.AreEqual(cXgrad.Length, x.Grad.Length);
for (int i = 0; i < x.Grad.Length; i++)
{
Assert.AreEqual(cXgrad[i], x.Grad[i], delta);
}
//y.grad
Assert.AreEqual(cYgrad.Length, y.Grad.Length);
for (int i = 0; i < y.Grad.Length; i++)
{
Assert.AreEqual(cYgrad[i], y.Grad[i], delta);
}
}
public void ConcatRandomTest()
{
Python.Initialize();
Chainer.Initialize();
int batchCount = Mother.Dice.Next(1, 5);
int ch = Mother.Dice.Next(1, 5);
int widthA = Mother.Dice.Next(1, 16);
int widthB = Mother.Dice.Next(1, 16);
int height = Mother.Dice.Next(1, 16);
int axis = 3;
Real[,,,] inputA = (Real[, , , ])Initializer.GetRealNdArray(new[] { batchCount, ch, height, widthA });
Real[,,,] inputB = (Real[, , , ])Initializer.GetRealNdArray(new[] { batchCount, ch, height, widthB });
Real[,,,] dummyGy = (Real[, , , ])Initializer.GetRealNdArray(new[] { batchCount, ch, height, widthA + widthB });
//chainer
NChainer.Concat <Real> cConcat = new NChainer.Concat <Real>(axis);
Variable <Real> cX = new Variable <Real>(Real.ToBaseNdArray(inputA));
Variable <Real> cY = new Variable <Real>(Real.ToBaseNdArray(inputB));
Variable <Real> cZ = cConcat.Forward(cX, cY);
cZ.Grad = Real.ToBaseNdArray(dummyGy);
cZ.Backward();
//KelpNet
KelpNet.Concat concat = new Concat(axis - 1);//Chainerと異なり1次元目を暗黙的にBatchとみなさないため
NdArray x = new NdArray(Real.ToRealArray(inputA), new[] { ch, height, widthA }, batchCount);
NdArray y = new NdArray(Real.ToRealArray(inputB), new[] { ch, height, widthB }, batchCount);
NdArray z = concat.Forward(x, y)[0];
z.Grad = Real.ToRealArray(dummyGy);
z.Backward();
Real[] cZdata = Real.ToRealArray((Real[, , , ])cZ.Data);
//Copyが必要
Real[] cXgrad = Real.ToRealArray((Real[, , , ])cX.Grad.Copy());
Real[] cYgrad = Real.ToRealArray((Real[, , , ])cY.Grad.Copy());
//許容範囲を算出
double delta = 0.00001;
//z
Assert.AreEqual(cZdata.Length, z.Data.Length);
for (int i = 0; i < y.Data.Length; i++)
{
Assert.AreEqual(cZdata[i], z.Data[i], delta);
}
//x.grad
Assert.AreEqual(cXgrad.Length, x.Grad.Length);
for (int i = 0; i < x.Grad.Length; i++)
{
Assert.AreEqual(cXgrad[i], x.Grad[i], delta);
}
//y.grad
Assert.AreEqual(cYgrad.Length, y.Grad.Length);
for (int i = 0; i < y.Grad.Length; i++)
{
Assert.AreEqual(cYgrad[i], y.Grad[i], delta);
}
}
