public void AveragePoolingLayer_Forward()
{
var bottom = new Tensor(1, 1, 3, 3);
var filler = new ConstantFiller(2.0d);
filler.Fill(bottom);
var layer = new AveragePoolingLayer(3, 1, 1);
layer.Setup(bottom, top);
Assert.Equal(1, top.Num);
Assert.Equal(1, top.Channels);
Assert.Equal(3, top.Height);
Assert.Equal(3, top.Width);
layer.Forward(bottom, top);
using (var topCpu = top.OnCpu())
{
var topData = topCpu.Data;
AssertInRange(8.0d / 9, topData[0]);
AssertInRange(4.0d / 3, topData[1]);
AssertInRange(8.0d / 9, topData[2]);
AssertInRange(4.0d / 3, topData[3]);
AssertInRange(2.0d, topData[4]);
AssertInRange(4.0d / 3, topData[5]);
AssertInRange(8.0d / 9, topData[6]);
AssertInRange(4.0d / 3, topData[7]);
AssertInRange(8.0d / 9, topData[8]);
}
}
public void AveragePoolingLayer_Forward()
{
var bottom = new Tensor(1, 1, 3, 3);
var filler = new ConstantFiller(2.0d);
filler.Fill(bottom);
var layer = new AveragePoolingLayer(3, 1, 1);
layer.Setup(bottom, top);
Assert.Equal(1, top.Num);
Assert.Equal(1, top.Channels);
Assert.Equal(3, top.Height);
Assert.Equal(3, top.Width);
layer.Forward(bottom, top);
using (var topCpu = top.OnCpu())
{
var topData = topCpu.Data;
AssertInRange(8.0d / 9, topData[0]);
AssertInRange(4.0d / 3, topData[1]);
AssertInRange(8.0d / 9, topData[2]);
AssertInRange(4.0d / 3, topData[3]);
AssertInRange(2.0d, topData[4]);
AssertInRange(4.0d / 3, topData[5]);
AssertInRange(8.0d / 9, topData[6]);
AssertInRange(4.0d / 3, topData[7]);
AssertInRange(8.0d / 9, topData[8]);
}
}
public void SetUp()
{
IDataFiller dataFiller = new ConstantFiller();
IDataRepository dataRepository = new Task1.Data.DataRepository(dataFiller, dataContext);
bookEvent1 = new BookRent(dataContext.ReadersList[3], dataContext.BookStatesList[0], new DateTime(2017, 11, 14));
bookEvent2 = new BookReturn(dataContext.ReadersList[3], dataContext.BookStatesList[0], new DateTime(2017, 11, 14));
}
public void AveragePoolingLayer_BackwardGradient( Size kernel, Size stride, Size padding )
{
var filler = new ConstantFiller(2.0d);
filler.Fill(bottom);
var checker = new GradientChecker(1e-2f, 1e-2f);
var layer = new AveragePoolingLayer(kernel, stride, padding);
checker.CheckExhaustive(layer, bottom, top);
}
public void FillTest()
{
constantFiller = new ConstantFiller();
dataRepository = new DataRepository(constantFiller);
Assert.AreEqual(5, dataRepository.GetAllClients().Count());
Assert.AreEqual(5, dataRepository.GetAllShoes().Count());
Assert.AreEqual(6, dataRepository.GetAllShoesPairs().Count());
Assert.AreEqual(6, dataRepository.GetAllTransactions().Count());
}
public void AveragePoolingLayer_BackwardGradient(Size kernel, Size stride, Size padding)
{
var filler = new ConstantFiller(2.0d);
filler.Fill(bottom);
var checker = new GradientChecker(1e-2f, 1e-2f);
var layer = new AveragePoolingLayer(kernel, stride, padding);
checker.CheckExhaustive(layer, bottom, top);
}
public void Filler_Constant()
{
var blob = new Tensor(2, 3, 4, 5);
var config = new ConstantFillerConfiguration(10.0d);
var filler = new ConstantFiller(config);
filler.Fill(blob);
using (var blobCpu = blob.OnCpu())
{
int count = blobCpu.Count;
var data = blobCpu.Data;
for (int i = 0; i < count; i++)
Assert.Equal(data[i], 10.0d);
}
}
public void Filler_Constant()
{
var blob = new Tensor(2, 3, 4, 5);
var config = new ConstantFillerConfiguration(10.0d);
var filler = new ConstantFiller(config);
filler.Fill(blob);
using (var blobCpu = blob.OnCpu())
{
int count = blobCpu.Count;
var data = blobCpu.Data;
for (int i = 0; i < count; i++)
{
Assert.Equal(data[i], 10.0d);
}
}
}
public void DataContextSerializationTest()
{
DataContext dcOrig = new DataContext();
string filename = "dataContextTest.txt";
{
Serializator ser = new Serializator();
ConstantFiller filler = new ConstantFiller();
filler.Fill(dcOrig);
ser.Add(dcOrig);
ser.SetFilename(filename);
ser.Write();
}
{
Serializator ser = new Serializator();
ser.SetFilename(filename);
ser.Read();
DataContext dcNew = (DataContext)ser.GetNext();
Assert.AreEqual(dcOrig.bookDictionary[1].ToString(), dcNew.bookDictionary[1].ToString());
Assert.AreEqual(dcOrig.clientList.Count, dcNew.clientList.Count);
}
}
public void MaxPoolingLayer_BackwardsRectangularWithSquareKernel(int topLayer)
{
Contract.Requires(topLayer > 0);
const int num = 2;
const int channels = 2;
var bottom = new Tensor(num, channels, 3, 5);
var topList = new Tensor[topLayer];
for (int i = 0; i < topLayer; i++)
topList[i] = new Tensor();
var filler = new ConstantFiller(2);
filler.Fill(bottom);
// Input: 2x 2 channels of:
// [2 2 2 2 2]
// [2 2 2 2 2]
// [2 2 2 2 2]
var layer = new MaxPoolingLayer(2, 1, 0);
layer.Setup(new TensorCollection { bottom }, topList);
layer.Forward(new TensorCollection { bottom }, topList);
// Input: 2x 2 channels of:
// [1 1 1 1]
// [0 0 0 0]
using (var topCpu = topList[0].OnCpu())
{
var topDiff = topCpu.Diff;
for (int i = 0; i < 8 * num * channels; i += 8)
{
topDiff[i + 0] = 1;
topDiff[i + 1] = 1;
topDiff[i + 2] = 1;
topDiff[i + 3] = 1;
}
}
// Input: 2x 2 channels of:
// [1 2 2 2 1]
// [1 2 2 2 1]
// [0 0 0 0 0]
layer.Backward(topList, new[] { true }, new TensorCollection { bottom });
using (var bottomCpu = bottom.OnCpu())
{
var bottomDiff = bottomCpu.Diff;
for (int i = 0; i < 15 * num * channels; i += 15)
{
Assert.Equal(1, bottomDiff[i + 0]);
Assert.Equal(2, bottomDiff[i + 1]);
Assert.Equal(2, bottomDiff[i + 2]);
Assert.Equal(2, bottomDiff[i + 3]);
Assert.Equal(1, bottomDiff[i + 4]);
Assert.Equal(1, bottomDiff[i + 5]);
Assert.Equal(2, bottomDiff[i + 6]);
Assert.Equal(2, bottomDiff[i + 7]);
Assert.Equal(2, bottomDiff[i + 8]);
Assert.Equal(1, bottomDiff[i + 9]);
Assert.Equal(0, bottomDiff[i + 10]);
Assert.Equal(0, bottomDiff[i + 11]);
Assert.Equal(0, bottomDiff[i + 12]);
Assert.Equal(0, bottomDiff[i + 13]);
Assert.Equal(0, bottomDiff[i + 14]);
}
}
}
public void MaxPoolingLayer_BackwardsRectangularWithSquareKernel(int topLayer)
{
Contract.Requires(topLayer > 0);
const int num = 2;
const int channels = 2;
var bottom = new Tensor(num, channels, 3, 5);
var topList = new Tensor[topLayer];
for (int i = 0; i < topLayer; i++)
{
topList[i] = new Tensor();
}
var filler = new ConstantFiller(2);
filler.Fill(bottom);
// Input: 2x 2 channels of:
// [2 2 2 2 2]
// [2 2 2 2 2]
// [2 2 2 2 2]
var layer = new MaxPoolingLayer(2, 1, 0);
layer.Setup(new TensorCollection {
bottom
}, topList);
layer.Forward(new TensorCollection {
bottom
}, topList);
// Input: 2x 2 channels of:
// [1 1 1 1]
// [0 0 0 0]
using (var topCpu = topList[0].OnCpu())
{
var topDiff = topCpu.Diff;
for (int i = 0; i < 8 * num * channels; i += 8)
{
topDiff[i + 0] = 1;
topDiff[i + 1] = 1;
topDiff[i + 2] = 1;
topDiff[i + 3] = 1;
}
}
// Input: 2x 2 channels of:
// [1 2 2 2 1]
// [1 2 2 2 1]
// [0 0 0 0 0]
layer.Backward(topList, new[] { true }, new TensorCollection {
bottom
});
using (var bottomCpu = bottom.OnCpu())
{
var bottomDiff = bottomCpu.Diff;
for (int i = 0; i < 15 * num * channels; i += 15)
{
Assert.Equal(1, bottomDiff[i + 0]);
Assert.Equal(2, bottomDiff[i + 1]);
Assert.Equal(2, bottomDiff[i + 2]);
Assert.Equal(2, bottomDiff[i + 3]);
Assert.Equal(1, bottomDiff[i + 4]);
Assert.Equal(1, bottomDiff[i + 5]);
Assert.Equal(2, bottomDiff[i + 6]);
Assert.Equal(2, bottomDiff[i + 7]);
Assert.Equal(2, bottomDiff[i + 8]);
Assert.Equal(1, bottomDiff[i + 9]);
Assert.Equal(0, bottomDiff[i + 10]);
Assert.Equal(0, bottomDiff[i + 11]);
Assert.Equal(0, bottomDiff[i + 12]);
Assert.Equal(0, bottomDiff[i + 13]);
Assert.Equal(0, bottomDiff[i + 14]);
}
}
}
