public void AddFollower(Chainer c)
{
if(!HasFollower(c))
followers.Add(c);
}
public void AVGPoolingRandomTest()
{
Python.Initialize();
Chainer.Initialize();
int batchCount = Mother.Dice.Next(1, 5);
int chCount = Mother.Dice.Next(1, 5);
int wideSize = Mother.Dice.Next(8, 32);
int heightSize = Mother.Dice.Next(8, 32);
int kWidth = Mother.Dice.Next(1, 5);
int kHeight = Mother.Dice.Next(1, 5);
int strideX = Mother.Dice.Next(1, 5);
int strideY = Mother.Dice.Next(1, 5);
int padX = Mother.Dice.Next(0, 5);
int padY = Mother.Dice.Next(0, 5);
int outputHeight = (int)Math.Floor((heightSize - kHeight + padY * 2.0) / strideY) + 1;
int outputWidth = (int)Math.Floor((wideSize - kWidth + padX * 2.0) / strideX) + 1;
Real[,,,] input = (Real[, , , ])Initializer.GetRealNdArray(new[] { batchCount, chCount, heightSize, wideSize });
Real[,,,] dummyGy = (Real[, , , ])Initializer.GetRealNdArray(new[] { batchCount, chCount, outputHeight, outputWidth });
//Chainer
NChainer.AveragePooling2D <Real> cMaxPooling2D = new NChainer.AveragePooling2D <Real>(
new[] { kHeight, kWidth },
new[] { strideY, strideX },
new[] { padY, padX }
);
Variable <Real> cX = new Variable <Real>(Real.ToBaseNdArray(input));
Variable <Real> cY = cMaxPooling2D.Forward(cX);
cY.Grad = Real.ToBaseNdArray(dummyGy);
cY.Backward();
//KelpNet
KelpNet.AveragePooling2D maxPooling2D = new KelpNet.AveragePooling2D(
new[] { kWidth, kHeight },
new[] { strideX, strideY },
new[] { padX, padY });
NdArray x = new NdArray(Real.ToRealArray(input), new[] { chCount, heightSize, wideSize }, batchCount);
NdArray y = maxPooling2D.Forward(x)[0];
y.Grad = Real.ToRealArray(dummyGy);
y.Backward();
Real[] cYdata = Real.ToRealArray((Real[, , , ])cY.Data.Copy());
Real[] cXgrad = Real.ToRealArray((Real[, , , ])cX.Grad.Copy());
//許容範囲を算出
double delta = 0.00001;
Assert.AreEqual(cYdata.Length, y.Data.Length);
Assert.AreEqual(cXgrad.Length, x.Grad.Length);
//y
for (int i = 0; i < y.Data.Length; i++)
{
if (cYdata[i] < float.Epsilon && cYdata[i] > -float.Epsilon)
{
Assert.AreEqual(cYdata[i], y.Data[i], delta);
}
}
//x.grad
for (int i = 0; i < x.Grad.Length; i++)
{
Assert.AreEqual(cXgrad[i], x.Grad[i], delta);
}
}
private Expression(Chainer prev, bool overload)
: base(null)
{
}
private ExecArgument(Chainer arg)
: base(arg)
{
TryTakeAll(arg);
}
public void LRNRandomTest()
{
Python.Initialize();
Chainer.Initialize();
int n = Mother.Dice.Next(2, 7);
float k = (float)Mother.Dice.NextDouble() * 3;
int batchCount = Mother.Dice.Next(1, 5);
int ch = Mother.Dice.Next(1, 5);
int width = Mother.Dice.Next(1, 16);
int height = Mother.Dice.Next(1, 16);
Real[,,,] input = (Real[, , , ])Initializer.GetRealNdArray(new[] { batchCount, ch, height, width });
Real[,,,] dummyGy = (Real[, , , ])Initializer.GetRealNdArray(new[] { batchCount, ch, height, width });
//chainer
NChainer.LocalResponseNormalization <Real> cLocalResponseNormalization = new NChainer.LocalResponseNormalization <Real>(n, k);
Variable <Real> cX = new Variable <Real>(Real.ToBaseNdArray(input));
Variable <Real> cY = cLocalResponseNormalization.Forward(cX);
cY.Grad = Real.ToBaseNdArray(dummyGy);
cY.Backward();
//kelpnet
KelpNet.LRN lrn = new LRN(n, k);
NdArray x = new NdArray(Real.ToRealArray(input), new[] { ch, height, width }, batchCount);
NdArray y = lrn.Forward(x)[0];
y.Grad = Real.ToRealArray(dummyGy);
y.Backward();
Real[] cYdata = Real.ToRealArray((Real[, , , ])cY.Data);
Real[] cXgrad = Real.ToRealArray((Real[, , , ])cX.Grad);
//許容範囲を算出
double delta = 0.00001;
//y
Assert.AreEqual(cYdata.Length, y.Data.Length);
for (int i = 0; i < y.Data.Length; i++)
{
Assert.AreEqual(cYdata[i], y.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);
}
}
internal FullJoinChainer(Chainer prev, Table table)
: base(prev, table)
{
}
private GroupingArgument(Chainer arg)
: base(arg)
{
TryTakeAll(arg);
}
public void LRNRandomTest()
{
Python.Initialize();
Chainer.Initialize();
int n = Mother.Dice.Next(2, 7);
float k = (float)Mother.Dice.NextDouble() * 3;
int batchCount = Mother.Dice.Next(1, 5);
int ch = Mother.Dice.Next(1, 5);
int width = Mother.Dice.Next(1, 16);
int height = Mother.Dice.Next(1, 16);
Real[,,,] input = Initializer.GetRandomValues <Real[, , , ]>(batchCount, ch, height, width);
Real[,,,] dummyGy = Initializer.GetRandomValues <Real[, , , ]>(batchCount, ch, height, width);
//chainer
LocalResponseNormalization <Real> cLocalResponseNormalization = new LocalResponseNormalization <Real>(n, k);
Variable <Real> cX = new Variable <Real>(input);
Variable <Real> cY = cLocalResponseNormalization.Forward(cX);
cY.Grad = dummyGy;
cY.Backward();
//kelpnet
LRN <Real> lrn = new LRN <Real>(n, k);
NdArray <Real> x = new NdArray <Real>(input, asBatch: true);
NdArray <Real> y = lrn.Forward(x)[0];
y.Grad = dummyGy.Flatten();
y.Backward();
Real[] cYdata = ((Real[, , , ])cY.Data).Flatten();
Real[] cXgrad = ((Real[, , , ])cX.Grad).Flatten();
//許容範囲を算出
Real delta = 0.00001f;
//y
Assert.AreEqual(cYdata.Length, y.Data.Length);
for (int i = 0; i < y.Data.Length; i++)
{
Assert.AreEqual(cYdata[i], y.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);
}
}
internal PivotAsChainer(Chainer prev, string alias)
: base(prev, alias)
{
CheckNullOrEmptyAliasAndThrow(alias);
((PivotChainer)Prev).SetAlias(this);
}
public void SwishRandomTest()
{
Python.Initialize();
Chainer.Initialize();
int ioCount = Mother.Dice.Next(1, 50);
int batchCount = Mother.Dice.Next(1, 5);
Real[,] input = Initializer.GetRandomValues <Real[, ]>(batchCount, ioCount);
Real[,] dummyGy = Initializer.GetRandomValues <Real[, ]>(batchCount, ioCount);
Real beta = (Real)Mother.Dice.NextDouble();
//Chainer
NChainer.Swish <Real> cSwish = new NChainer.Swish <Real>(new[] { ioCount }, beta);
Variable <Real> cX = new Variable <Real>(input);
Variable <Real> cY = cSwish.Forward(cX);
cY.Grad = dummyGy;
cY.Backward();
//KelpNet
Swish <Real> swish = new Swish <Real>(new[] { ioCount }, beta);
NdArray <Real> x = new NdArray <Real>(input, asBatch: true);
NdArray <Real> y = swish.Forward(x)[0];
y.Grad = dummyGy.Flatten();
y.Backward();
Real[] cYdata = ((Real[, ])cY.Data).Flatten();
Real[] cXgrad = ((Real[, ])cX.Grad).Flatten();
Real[] cbgrad = (Real[])cSwish.beta.Grad;
//許容範囲を算出
Real delta = 0.00001f;
//y
Assert.AreEqual(cYdata.Length, y.Data.Length);
for (int i = 0; i < y.Data.Length; i++)
{
Assert.AreEqual(cYdata[i], y.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);
}
//b.grad
Assert.AreEqual(cbgrad.Length, swish.Beta.Grad.Length);
for (int i = 0; i < swish.Beta.Grad.Length; i++)
{
Assert.AreEqual(cbgrad[i], swish.Beta.Grad[i], delta);
}
}
internal FromAsChainer(Chainer prev, int alias)
: base(prev, alias)
{
((TableChainer)prev).SetAlias(this);
}
public void TrainTest(bool isTtrain, bool finetune)
{
Python.Initialize();
Chainer.Initialize();
int batchCount = Mother.Dice.Next(1, 50);
int ioCount = Mother.Dice.Next(1, 50);
Real[,] input = (Real[, ])Initializer.GetRealNdArray(new[] { batchCount, ioCount });
Real[,] dummyGy = (Real[, ])Initializer.GetRealNdArray(new[] { batchCount, ioCount });
Real[] gamma = Initializer.GetRealArray(ioCount);
Real[] beta = Initializer.GetRealArray(ioCount);
Real[] avgMean = Initializer.GetRealArray(ioCount);
Real[] avgVar = Initializer.GetRealArray(ioCount);
//Chainer
Chainer.Config["train"] = isTtrain;
NChainer.BatchNormalization <Real> cBatchNormalization = new NChainer.BatchNormalization <Real>(ioCount, dtype: Real.Type);
cBatchNormalization.gamma = new Variable <Real>(Real.ToBaseNdArray(gamma));
cBatchNormalization.beta = new Variable <Real>(Real.ToBaseNdArray(beta));
cBatchNormalization.avgMean = Real.ToBaseNdArray(avgMean);
cBatchNormalization.avgVar = Real.ToBaseNdArray(avgVar);
Variable <Real> cX = new Variable <Real>(Real.ToBaseNdArray(input));
Variable <Real> cY = cBatchNormalization.Forward(cX, finetune);
cY.Grad = Real.ToBaseNdArray(dummyGy);
cY.Backward();
//KelpNet
KelpNet.BatchNormalization batchNormalization = new BatchNormalization(ioCount, train: isTtrain, finetune: finetune);
batchNormalization.Gamma.Data = Real.ToRealArray(gamma);
batchNormalization.Beta.Data = Real.ToRealArray(beta);
batchNormalization.AvgMean.Data = Real.ToRealArray(avgMean);
batchNormalization.AvgVar.Data = Real.ToRealArray(avgVar);
NdArray x = new NdArray(Real.ToRealArray(input), new[] { ioCount }, batchCount);
NdArray y = batchNormalization.Forward(x)[0];
y.Grad = Real.ToRealArray(dummyGy);
y.Backward();
Real[] cYdata = Real.ToRealArray((Real[, ])cY.Data);
Real[] cXgrad = Real.ToRealArray((Real[, ])cX.Grad);
Real[] cGammaGrad = Real.ToRealArray((Real[])cBatchNormalization.gamma.Grad);
Real[] cBetaGrad = Real.ToRealArray((Real[])cBatchNormalization.beta.Grad);
//許容範囲を算出
double delta = 0.00001;
//y
Assert.AreEqual(cYdata.Length, y.Data.Length);
for (int i = 0; i < y.Data.Length; i++)
{
Assert.AreEqual(cYdata[i], y.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);
}
//gamma.grad
Assert.AreEqual(cGammaGrad.Length, batchNormalization.Gamma.Grad.Length);
for (int i = 0; i < batchNormalization.Gamma.Grad.Length; i++)
{
Assert.AreEqual(cGammaGrad[i], batchNormalization.Gamma.Grad[i], delta);
}
//beta.grad
Assert.AreEqual(cBetaGrad.Length, batchNormalization.Beta.Grad.Length);
for (int i = 0; i < batchNormalization.Beta.Grad.Length; i++)
{
Assert.AreEqual(cBetaGrad[i], batchNormalization.Beta.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);
}
}
private Udf(Chainer prev)
: base(null)
{
}
public void RandomTest()
{
Python.Initialize();
Chainer.Initialize();
//Make random value.
Real[] val = { 1 + Mother.Dice.Next() };
//Chainer
Variable <Real> cX = new Variable <Real>(Real.ToBaseArray(val));
//Add
Variable <Real> cAdd = 2 + cX + cX + 2;
cAdd.Backward();
Real[] pyGadd = (Real[])cX.Grad;
//Mul
Variable <Real> cMul = 2 * cX * cX * 3;
cMul.Backward();
Real[] pyGmul = (Real[])cX.Grad;
//Sub
Variable <Real> cSub = 30 - cX - cX - 2;
cSub.Backward();
Real[] pyGsub = (Real[])cX.Grad;
//Div
Variable <Real> cDiv = 50 / cX / cX / 2;
cDiv.Backward();
Real[] pyGdiv = (Real[])cX.Grad;
//KelpNet
NdArray x = new NdArray(val);
//Add
NdArray add = 2 + x + x + 2;
add.Backward();
Real[] gadd = x.Grad.ToArray(); //このToArrayはコピーのため
//mul
NdArray mul = 2 * x * x * 3;
mul.Backward();
Real[] gmul = x.Grad.ToArray();
//sub
NdArray sub = 30 - x - x - 2;
sub.Backward();
Real[] gsub = x.Grad.ToArray();
//mul
NdArray div = 50 / x / x / 2;
div.Backward();
Real[] gdiv = x.Grad.ToArray();
//Check
CollectionAssert.AreEqual(pyGadd, gadd);
CollectionAssert.AreEqual(pyGmul, gmul);
CollectionAssert.AreEqual(pyGsub, gsub);
CollectionAssert.AreEqual(pyGdiv, gdiv);
}
internal ApplyAsChainer(Chainer prev, string alias)
: base(prev, alias)
{
CheckNullOrEmptyAliasAndThrow(alias);
((TableChainer)prev).SetAliasByClient(this);
}
internal CaseValueChainer(Chainer prev, ScalarArgument checkValue)
: base(prev)
{
checkValue = checkValue ?? Designer.Null;
TryTake(checkValue, TakeProperty.Database, TakeProperty.Build);
}
internal Procedure(Chainer prev, bool isInternal = false)
: base(prev, ObjectType.Procedure, isInternal)
{
var root = GetRoot();
Build = (buildContext, buildArgs) =>
{
if (buildArgs.Executable != null && buildArgs.Executable.Exception != null)
{
throw buildArgs.Executable.Exception;
}
if (Sql != null)
{
return(Sql);
}
StringBuilder sql = new StringBuilder();
// append transaction begin block
if (root.IsEmbeddedTransaction)
{
BeginEmbeddedTransactionWrapper(root, sql, Text.Reserved.QtTransactionCount, Text.Reserved.QtTransactionSave);
}
if (HasTVP)
{
InjectTableVariables(sql, buildArgs, false);
}
// build
sql.Append(BuildChain(buildContext, buildArgs));
sql.TrimEnd();
if (!(prev is EndChainer))
{
sql.TerminateSingle();
}
// append transaction end block
if (root.IsEmbeddedTransaction)
{
EndEmbeddedTransactionWrapper(sql, Text.Reserved.QtTransactionCount, Text.Reserved.QtTransactionSave);
}
// cache compiled code only, non-compiled should not be cached
if (buildArgs.Executable == null)
{
Sql = sql.ToString();
}
CompiledSql = Sql ?? sql.ToString();
return(CompiledSql);
};
CheckAndThrow();
// detect TVP
if (root.AllParams.Where(param => param.DT == DT.TableVariable).Any())
{
HasTVP = true;
}
// compiled property is set to false if a procedure has TVP or inliners
if (HasTVP || root.AllParams.Where(param => param.DT.IsInliner()).Any())
{
compiled = false;
}
if (compiled)
{
Build(new BuildContext(this), new BuildArgs(null));
}
}
internal FullJoinChainer(Chainer prev, IOpenView table)
: base(prev, table)
{
}
internal OrderByChainer(Chainer prev, OrderingArgument[] columns)
: base(prev)
{
_Body(columns);
}
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 RandomTest(bool gpuEnable)
{
Python.Initialize();
Chainer.Initialize();
int inputCount = Mother.Dice.Next(1, 50);
int outputCount = Mother.Dice.Next(1, 50);
int batchCount = Mother.Dice.Next(1, 5);
Real[,] input = (Real[, ])Initializer.GetRealNdArray(new[] { batchCount, inputCount });
Real[,] dummyGy = (Real[, ])Initializer.GetRealNdArray(new[] { batchCount, outputCount });
Real[,] w = (Real[, ])Initializer.GetRealNdArray(new[] { outputCount, inputCount });
Real[] b = Initializer.GetRealArray(outputCount);
//Chainer
NChainer.Linear <Real> cLinear = new NChainer.Linear <Real>(inputCount, outputCount, false, Real.ToBaseNdArray(w), Real.ToBaseArray(b));
Variable <Real> cX = new Variable <Real>(Real.ToBaseNdArray(input));
Variable <Real> cY = cLinear.Forward(cX);
cY.Grad = Real.ToBaseNdArray(dummyGy);
cY.Backward();
//KelpNet
KelpNet.Linear linear = new KelpNet.Linear(inputCount, outputCount, false, w, b, gpuEnable: gpuEnable);
NdArray x = new NdArray(Real.ToRealArray(input), new[] { inputCount }, batchCount);
NdArray y = linear.Forward(x)[0];
y.Grad = Real.ToRealArray(dummyGy);
y.Backward();
Real[] cYdata = Real.ToRealArray((Real[, ])cY.Data);
Real[] cXgrad = Real.ToRealArray((Real[, ])cX.Grad);
Real[] cWgrad = Real.ToRealArray((Real[, ])cLinear.W.Grad);
Real[] cbgrad = (Real[])cLinear.b.Grad;
//許容範囲を算出
double delta = 0.00001;
//y
Assert.AreEqual(cYdata.Length, y.Data.Length);
for (int i = 0; i < y.Data.Length; i++)
{
Assert.AreEqual(cYdata[i], y.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);
}
//W.grad
Assert.AreEqual(cWgrad.Length, linear.Weight.Grad.Length);
for (int i = 0; i < linear.Weight.Grad.Length; i++)
{
Assert.AreEqual(cWgrad[i], linear.Weight.Grad[i], delta);
}
//b.grad
Assert.AreEqual(cbgrad.Length, linear.Bias.Grad.Length);
for (int i = 0; i < linear.Bias.Grad.Length; i++)
{
Assert.AreEqual(cbgrad[i], linear.Bias.Grad[i], delta);
}
}
public void SGDRandomTest()
{
Python.Initialize();
Chainer.Initialize();
int inputCount = Mother.Dice.Next(2, 50);
int outputCount = Mother.Dice.Next(2, 50);
int batchCount = Mother.Dice.Next(1, 5);
Real[,] input = Initializer.GetRandomValues <Real[, ]>(batchCount, inputCount);
Real[,] dummyGy = Initializer.GetRandomValues <Real[, ]>(batchCount, outputCount);
Real[,] w = Initializer.GetRandomValues <Real[, ]>(outputCount, inputCount);
Real[] b = Initializer.GetRandomValues <Real[]>(outputCount);
//Chainer
Linear <Real> cLinear = new Linear <Real>(inputCount, outputCount, false, w, b);
NChainer.SGD <Real> cSgd = new NChainer.SGD <Real>();
cSgd.Setup(cLinear);
Variable <Real> cX = new Variable <Real>(input);
Variable <Real> cY = cLinear.Forward(cX);
cY.Grad = dummyGy;
cY.Backward();
cSgd.Update();
//KelpNet
CL.Linear <Real> linear = new CL.Linear <Real>(inputCount, outputCount, false, w, b);
KelpNet.SGD <Real> sgd = new SGD <Real>();
sgd.SetUp(linear);
NdArray <Real> x = new NdArray <Real>(input, asBatch: true);
NdArray <Real> y = linear.Forward(x)[0];
y.Grad = dummyGy.Flatten();
y.Backward();
sgd.Update();
Real[] cW = ((Real[, ])cLinear.W.Data).Flatten();
Real[] cb = (Real[])cLinear.b.Data;
//許容範囲を算出
Real delta = 0.00001f;
//W.grad
Assert.AreEqual(cW.Length, linear.Weight.Data.Length);
for (int i = 0; i < linear.Weight.Data.Length; i++)
{
Assert.AreEqual(cW[i], linear.Weight.Data[i], delta);
}
//b.grad
Assert.AreEqual(cb.Length, linear.Bias.Data.Length);
for (int i = 0; i < linear.Bias.Data.Length; i++)
{
Assert.AreEqual(cb[i], linear.Bias.Data[i], delta);
}
}
public void SwishRandomTest()
{
Python.Initialize();
Chainer.Initialize();
int ioCount = Mother.Dice.Next(1, 50);
int batchCount = Mother.Dice.Next(1, 5);
Real[,] input = (Real[, ])Initializer.GetRealNdArray(new[] { batchCount, ioCount });
Real[,] dummyGy = (Real[, ])Initializer.GetRealNdArray(new[] { batchCount, ioCount });
Real beta = Mother.Dice.NextDouble();
//Chainer
NChainer.Swish <Real> cSwish = new NChainer.Swish <Real>(new[] { ioCount }, beta);
Variable <Real> cX = new Variable <Real>(Real.ToBaseNdArray(input));
Variable <Real> cY = cSwish.Forward(cX);
cY.Grad = Real.ToBaseNdArray(dummyGy);
cY.Backward();
//KelpNet
KelpNet.Swish swish = new KelpNet.Swish(new[] { ioCount }, beta);
NdArray x = new NdArray(Real.ToRealArray(input), new[] { ioCount }, batchCount);
NdArray y = swish.Forward(x)[0];
y.Grad = Real.ToRealArray(dummyGy);
y.Backward();
Real[] cYdata = Real.ToRealArray((Real[, ])cY.Data);
Real[] cXgrad = Real.ToRealArray((Real[, ])cX.Grad);
Real[] cbgrad = (Real[])cSwish.beta.Grad;
//許容範囲を算出
double delta = 0.00001;
//y
Assert.AreEqual(cYdata.Length, y.Data.Length);
for (int i = 0; i < y.Data.Length; i++)
{
Assert.AreEqual(cYdata[i], y.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);
}
//b.grad
Assert.AreEqual(cbgrad.Length, swish.beta.Grad.Length);
for (int i = 0; i < swish.beta.Grad.Length; i++)
{
Assert.AreEqual(cbgrad[i], swish.beta.Grad[i], delta);
}
}
internal ConditionChainer(Chainer prev, PredicateGroup predicateGroup)
: base(prev)
{
PredicateGroup = predicateGroup;
}
public Wave(Dictionary <string, System.Object> json)
{
canvas = GameObject.Find("Canvas").transform;
System.Random rand = new System.Random();
enemies = new List <GameObject> ();
//note: ints in MiniJSON come out as longs, so must be cast twice
levelID = (int)(long)json ["levelID"];
waveID = (int)(long)json ["waveID"];
maxTime = (int)(long)json ["maxMilliseconds"];
interval = (int)(long)json ["minimumInterval"];
List <System.Object> enemyjson = (List <System.Object>)json ["enemies"];
//step one: create a randomized list of spawn times
int slots = maxTime / interval;
//Debug.Log ("wave "+ waveID + " slots: " + slots);
int occupants = enemyjson.Count;
//create bool array to randomize
List <bool> timeslots = new List <bool>();
for (int i = 0; i < slots; i++)
{
if (i < occupants)
{
timeslots.Add(true);
}
else
{
timeslots.Add(false);
}
}
//randomize this array (fisher-yates shuffle)
for (int i = slots - 1; i > 0; i--)
{
int j = rand.Next(i + 1);
bool temp = timeslots[i];
timeslots[i] = timeslots[j];
timeslots[j] = temp;
}
//make sure one enemy spawns immediately
if (!timeslots[0])
{
for (int i = 1; i < slots; i++)
{
if (timeslots[i])
{
timeslots[0] = true;
timeslots[i] = false;
break;
}
}
}
//create corresponding array of random-ish long bonuses to positions
List <long> timeChaos = new List <long>();
for (int i = 0; i < timeslots.Count; i++)
{
timeChaos.Add(0);
}
for (int i = 0; i < timeChaos.Count; i++)
{
if (timeslots[i]) //if an enemy should spawn in this timeframe,
//get the previous bonus to make sure you're the min distance away
{
long previous = 0;
if (i > 0)
{
previous = timeChaos[i - 1];
}
//create random spawn time within the time slot allotted
long chaos = rand.Next((int)previous, (int)interval);
timeChaos[i] = chaos;
}
}
//check to make sure nothing went wrong and timeslots and timechaos are both of length slots
if (timeslots.Count != slots)
{
Debug.Log("timeslots is wrong length! slots is " + slots + " but timeslots length is " + timeslots.Count);
}
if (timeChaos.Count != slots)
{
Debug.Log("timechaos is wrong length! slots is " + slots + " but timechaos length is " + timeChaos.Count);
}
//finally, create final list of spawn times
List <long> spawntimesInMillis = new List <long>();
for (int i = 0; i < timeslots.Count; i++)
{
if (timeslots[i])
{
long spawntime = i * interval;
spawntime += timeChaos[i];
spawntimesInMillis.Add(spawntime);
}
}
//for(int i = 0; i < spawntimesInMillis.Count; i++){
// Debug.Log ("wave "+ waveID + " spawntime "+i +": " + spawntimesInMillis[i]);
//}
//check to make sure nothing went wrong and spawntimes is of length occupants
if (spawntimesInMillis.Count != enemyjson.Count)
{
Debug.Log("spawntimes and enemies don't match! (" + spawntimesInMillis.Count + "/" + enemyjson.Count + ")");
}
//shuffle the enemy order (fisher-yates)
for (int i = enemyjson.Count - 1; i > 0; i--)
{
int j = rand.Next(i + 1);
System.Object temp = enemyjson[i];
enemyjson[i] = enemyjson[j];
enemyjson[j] = temp;
}
for (int i = 0; i < enemyjson.Count; i++)
{
System.Object enemy = enemyjson[i];
Dictionary <string, System.Object> enemydict = (Dictionary <string, System.Object>)enemy;
//would load from bestiary using (string)enemydict["enemyID"], but no bestiary yet
//long spawntimeInMillis = (long)enemydict["spawntime"];
string filename = (string)enemydict["enemyID"];
int track = (int)(long)enemydict["trackID"];
int trackpos = 0;
if (enemydict.ContainsKey("trackpos"))
{
trackpos = (int)(long)enemydict["trackpos"];
}
//make enemy
GameObject enemyspawn = GameObject.Instantiate(Resources.Load("Prefabs/MainCanvas/Enemy")) as GameObject;
//Debug.Log("we're setting it to the spawn layer");
//Debug.Log (Dial.spawnLayer == null);
enemyspawn.transform.SetParent(Dial.spawnLayer, false);
enemyspawn.SetActive(false);
Enemy ec = enemyspawn.GetComponent <Enemy>();
FileLoader fl = new FileLoader("JSONData" + Path.DirectorySeparatorChar + "Bestiary", filename);
string actualenemyjson = fl.Read();
Dictionary <string, System.Object> actualenemydict = Json.Deserialize(actualenemyjson) as Dictionary <string, System.Object>;
string enemytype = (string)actualenemydict["enemyType"];
if (enemytype.Equals("Chainers"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
Chainer c = enemyobj.AddComponent <Chainer>() as Chainer;
float chaindelay = (float)(double)actualenemydict["delay"];
c.delay = chaindelay;
ec = c;
}
else if (enemytype.Equals("TipOfTheSpear"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
TipOfTheSpear tots = enemyobj.AddComponent <TipOfTheSpear>() as TipOfTheSpear;
float chaindelay = (float)(double)actualenemydict["delay"];
tots.SetDelay(chaindelay);
tots.leader = true;
ec = tots;
}
else if (enemytype.Equals("WallOfDoom"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
WallOfDoom wod = enemyobj.AddComponent <WallOfDoom>() as WallOfDoom;
ec = wod;
}
else if (enemytype.Equals("TheDiversion"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
Diversion d = enemyobj.AddComponent <Diversion>() as Diversion;
float chaindelay = (float)(double)actualenemydict["delay"];
d.SetDelay(chaindelay);
ec = d;
}
else if (enemytype.Equals("MeatShield"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
MeatShield ms = enemyobj.AddComponent <MeatShield>() as MeatShield;
float chaindelay = (float)(double)actualenemydict["delay"];
ms.SetDelay(chaindelay);
ms.leader = true;
ec = ms;
}
else if (enemytype.Equals("Splitter"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
Splitter s = enemyobj.AddComponent <Splitter>() as Splitter;
ec = s;
}
else if (enemytype.Equals("Blob"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
Blob b = enemyobj.AddComponent <Blob>() as Blob;
ec = b;
}
else if (enemytype.Equals("Megasplit"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
Megasplit ms = enemyobj.AddComponent <Megasplit>() as Megasplit;
ec = ms;
}
else if (enemytype.Equals("Melder"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
Melder m = enemyobj.AddComponent <Melder>() as Melder;
ec = m;
}
else if (enemytype.Equals("BigSplit"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
BigSplit bs = enemyobj.AddComponent <BigSplit>() as BigSplit;
ec = bs;
}
else if (enemytype.Equals("Junior"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
Junior j = enemyobj.AddComponent <Junior>() as Junior;
ec = j;
}
else if (enemytype.Equals("Cheater"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
Cheater ch = enemyobj.AddComponent <Cheater>() as Cheater;
ec = ch;
}
else if (enemytype.Equals("Spite"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
Spite s = enemyobj.AddComponent <Spite>() as Spite;
ec = s;
}
else if (enemytype.Equals("Executor"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
Executor s = enemyobj.AddComponent <Executor>() as Executor;
ec = s;
}
else if (enemytype.Equals("Saboteur"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
Saboteur s = enemyobj.AddComponent <Saboteur>() as Saboteur;
ec = s;
}
else if (enemytype.Equals("Pusher"))
{
GameObject enemyobj = ec.gameObject;
GameObject.Destroy(enemyobj.GetComponent <Enemy>());
Pusher s = enemyobj.AddComponent <Pusher>() as Pusher;
ec = s;
}
//give enemy a filename to load from
ec.SetSrcFileName(filename);
ec.SetTrackID(track);
ec.SetTrackLane(trackpos);
//calculate and set position
float degrees = (track - 1) * 60 + 30; //clockwise of y-axis
degrees += 15 * trackpos; //negative trackpos is left side, positive is right side, 0 is middle
degrees = ((360 - degrees) + 90) % 360; //convert to counterclockwise of x axis
degrees *= Mathf.Deg2Rad;
((RectTransform)enemyspawn.transform).anchoredPosition = new Vector2(Dial.ENEMY_SPAWN_LENGTH * Mathf.Cos(degrees), Dial.ENEMY_SPAWN_LENGTH * Mathf.Sin(degrees));
//set spawn time
ec.SetSpawnTime(spawntimesInMillis[i]);
enemies.Add(enemyspawn);
}
/*foreach (System.Object enemy in enemyjson) {
* //ec.ConfigureEnemy ();
* }*/
}
internal InjectChainer(Chainer prev, string sqlOrInliner)
: base(prev)
{
var root = GetRoot();
var inliner = root.TryGetVariable(sqlOrInliner, out chainException, Variable.SearchType.Inliner);
chainException = null; // reset exception
if (inliner == null)
{
Build = (buildContext, buildArgs) =>
{
return(Text.GenerateSql(200)
.NewLine(sqlOrInliner)
.TerminateSingle()
.ToString());
};
}
// inliner
else
{
Build = (buildContext, buildArgs) =>
{
ParameterArgument inlinerArgument = buildArgs.Executable.GetInlinerArgument(inliner.Name);
if (inlinerArgument == null)
{
return(null);
}
if (inliner.DT == DT.InSql)
{
if (inlinerArgument.DT != DT.InSql)
{
TryThrow(inliner.DT.InvalidInlinerException(GetType().Name, inliner.Name, _sqlInliners));
}
string sql2 = inliner.Name;
if (buildArgs.Executable != null)
{
sql2 = (string)inlinerArgument.Value;
}
return(Text.GenerateSql(500)
.NewLine(sql2)
.TerminateSingle()
.ToString());
}
else if (inliner.DT == DT.InSnippet)
{
if (inlinerArgument.DT != DT.InSnippet)
{
buildContext.TryTakeException(inliner.DT.InvalidInlinerException(GetType().Name,
inliner.Name, _snippetInliners));
return(null);
}
string sql = inliner.Name;
if (buildArgs.Executable != null)
{
var snippet = (Snippet)inlinerArgument.Value;
if (snippet != null)
{
TryTake(snippet);
buildContext.ParamRoot = GetRoot();
sql = snippet.Build(buildContext, buildArgs);
}
else
{
sql = null; // it is allowed that snippet is not passed
}
}
return(sql);
}
else
{
chainException = inliner.DT.InvalidInlinerException(GetType().Name,
inliner.Name, _inliners);
chainException.ObjectName = root.Name;
chainException.Method = Text.Method.Exec;
throw chainException;
}
};
}
}
internal WhereChainer(Chainer prev, Expression expression, PredicateGroup predicateGroup = null)
: base(prev, expression, predicateGroup)
{
Query.Clause.Wheres.Add(this);
}
public void RandomTest(bool gpuEnable)
{
Python.Initialize();
Chainer.Initialize();
int inputCount = Mother.Dice.Next(1, 50);
int outputCount = Mother.Dice.Next(1, 50);
int batchCount = Mother.Dice.Next(1, 5);
Real[,] input = Initializer.GetRandomValues <Real[, ]>(batchCount, inputCount);
Real[,] dummyGy = Initializer.GetRandomValues <Real[, ]>(batchCount, outputCount);
Real[,] w = Initializer.GetRandomValues <Real[, ]>(outputCount, inputCount);
Real[] b = Initializer.GetRandomValues <Real[]>(outputCount);
//Chainer
NChainer.Linear <Real> cLinear = new NChainer.Linear <Real>(inputCount, outputCount, false, w, b);
Variable <Real> cX = new Variable <Real>(input);
Variable <Real> cY = cLinear.Forward(cX);
cY.Grad = dummyGy;
cY.Backward();
//KelpNet
CL.Linear <Real> linear = new CL.Linear <Real>(inputCount, outputCount, false, w, b, gpuEnable: gpuEnable);
NdArray <Real> x = new NdArray <Real>(input, asBatch: true);
NdArray <Real> y = linear.Forward(x)[0];
y.Grad = dummyGy.Flatten();
y.Backward();
Real[] cYdata = ((Real[, ])cY.Data).Flatten();
Real[] cXgrad = ((Real[, ])cX.Grad).Flatten();
Real[] cWgrad = ((Real[, ])cLinear.W.Grad).Flatten();
Real[] cbgrad = (Real[])cLinear.b.Grad;
//許容範囲を算出
Real delta = 0.00001f;
//y
Assert.AreEqual(cYdata.Length, y.Data.Length);
for (int i = 0; i < y.Data.Length; i++)
{
Assert.AreEqual(cYdata[i], y.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);
}
//W.grad
Assert.AreEqual(cWgrad.Length, linear.Weight.Grad.Length);
for (int i = 0; i < linear.Weight.Grad.Length; i++)
{
Assert.AreEqual(cWgrad[i], linear.Weight.Grad[i], delta);
}
//b.grad
Assert.AreEqual(cbgrad.Length, linear.Bias.Grad.Length);
for (int i = 0; i < linear.Bias.Grad.Length; i++)
{
Assert.AreEqual(cbgrad[i], linear.Bias.Grad[i], delta);
}
}
// .WhereExists
internal WhereChainer(Chainer prev, INonSelectView nonSelectView, bool exists, PredicateGroup predicateGroup = null)
: base(prev, nonSelectView, exists, predicateGroup)
{
Query.Clause.Wheres.Add(this);
chainMethod = exists ? Text.Method.WhereExists : Text.Method.WhereNotExists;
}
public bool HasFollower(Chainer c)
{
return followers.Contains(c);
}
public void RandomTest(bool gpuEnable)
{
Python.Initialize();
Chainer.Initialize();
int batchCount = Mother.Dice.Next(1, 5);
int inChCount = Mother.Dice.Next(1, 5);
int outChCount = Mother.Dice.Next(1, 5);
int wideSize = Mother.Dice.Next(8, 32);
int heightSize = Mother.Dice.Next(8, 32);
int kWidth = Mother.Dice.Next(1, 5);
int kHeight = Mother.Dice.Next(1, 5);
int strideX = Mother.Dice.Next(1, 5);
int strideY = Mother.Dice.Next(1, 5);
int padX = Mother.Dice.Next(0, 5);
int padY = Mother.Dice.Next(0, 5);
int outputHeight = (heightSize - 1) * strideY + kHeight - padY * 2;
int outputWidth = (wideSize - 1) * strideX + kWidth - padX * 2;
Real[,,,] input = (Real[, , , ])Initializer.GetRealNdArray(new[] { batchCount, inChCount, heightSize, wideSize });
Real[,,,] dummyGy = (Real[, , , ])Initializer.GetRealNdArray(new[] { batchCount, outChCount, outputHeight, outputWidth });
Real[,,,] w = (Real[, , , ])Initializer.GetRealNdArray(new[] { inChCount, outChCount, kHeight, kWidth });
Real[] b = Initializer.GetRealArray(outChCount);
//Chainer
NChainer.Deconvolution2D <Real> cDeconvolotion2D = new NChainer.Deconvolution2D <Real>(
inChCount, outChCount,
new[] { kHeight, kWidth },
new[] { strideY, strideX },
new[] { padY, padX },
false,
new PyObject[] { outputHeight, outputWidth },
Real.ToBaseNdArray(w),
Real.ToBaseArray(b));
Variable <Real> cX = new Variable <Real>(Real.ToBaseNdArray(input));
Variable <Real> cY = cDeconvolotion2D.Forward(cX);
cY.Grad = Real.ToBaseNdArray(dummyGy);
cY.Backward();
//KelpNet
KelpNet.Deconvolution2D deconvolution2D = new KelpNet.Deconvolution2D(
inChCount, outChCount,
new [] { kWidth, kHeight },
new [] { strideX, strideY },
new [] { padX, padY },
false, w, b, gpuEnable: gpuEnable);
NdArray x = new NdArray(Real.ToRealArray(input), new[] { inChCount, heightSize, wideSize }, batchCount);
NdArray y = deconvolution2D.Forward(x)[0];
y.Grad = Real.ToRealArray(dummyGy);
y.Backward();
Real[] cYdata = Real.ToRealArray((Real[, , , ])cY.Data.Copy());
Real[] cXgrad = Real.ToRealArray((Real[, , , ])cX.Grad.Copy());
Real[] cWgrad = Real.ToRealArray((Real[, , , ])cDeconvolotion2D.W.Grad);
Real[] cbgrad = (Real[])cDeconvolotion2D.b.Grad;
//許容範囲を算出
double delta = 0.00001;
Assert.AreEqual(cYdata.Length, y.Data.Length);
Assert.AreEqual(cXgrad.Length, x.Grad.Length);
Assert.AreEqual(cWgrad.Length, deconvolution2D.Weight.Grad.Length);
Assert.AreEqual(cbgrad.Length, deconvolution2D.Bias.Grad.Length);
//y
for (int i = 0; i < y.Data.Length; i++)
{
Assert.AreEqual(cYdata[i], y.Data[i], delta);
}
//x.grad
for (int i = 0; i < x.Grad.Length; i++)
{
Assert.AreEqual(cXgrad[i], x.Grad[i], delta);
}
delta = 0.1;
//W.grad
for (int i = 0; i < deconvolution2D.Weight.Grad.Length; i++)
{
Assert.AreEqual(cWgrad[i], deconvolution2D.Weight.Grad[i], delta);
}
//b.grad
for (int i = 0; i < deconvolution2D.Bias.Grad.Length; i++)
{
Assert.AreEqual(cbgrad[i], deconvolution2D.Bias.Grad[i], delta);
}
}
