public CudnnTensorDescriptorParameters(CudnnType type, CudnnTensorFormat format, int n, int c, int h, int w)
{
if (n < 1 || c < 1 || h < 1 || w < 1)
{
throw new ArgumentException("At least one of the parameters n, c, h, w was negative.");
}
this.Type = type;
this.Num = n;
this.Channels = c;
this.Height = h;
this.Width = w;
this.NumStride = h * w * c;
if (format == CudnnTensorFormat.MajorRow)
{
this.ChannelsStride = h * w;
this.HeightStride = w;
this.WidthStride = 1;
}
else
{
this.ChannelsStride = 1;
this.HeightStride = w * c;
this.WidthStride = c;
}
}
public static extern CudnnStatus cudnnSetTensor4dDescriptor(CudnnTensorDescriptorHandle tensorDesc,
CudnnTensorFormat format,
CudnnType dataType, // image data type
int n, // number of inputs (batch size)
int c, // number of input feature maps
int h, // height of input section
int w); // width of input section
public static extern CudnnStatus cudnnGetFilterDescriptor(CudnnFilterDescriptorHandle filterDescriptor,
out CudnnType dataType, // image data type
out int k, // number of output feature maps
out int c, // number of input feature maps
out int h, // height of each input filter
out int w // width of each input filter
);
public void Cudnn_Descriptors_ConstructTensorWithMajorRow()
{
var param = new CudnnTensorDescriptorParameters(CudnnType.Float, CudnnTensorFormat.MajorRow, 10, 5, 2, 4);
using (var tensor = CudnnContext.CreateTensor(param))
using (var tensor2 = CudnnContext.CreateTensor())
{
Assert.True(tensor.IsInitialized);
Assert.False(tensor2.IsInitialized);
CudnnContext.Invoke(() => CudnnNativeMethods.cudnnSetTensor4dDescriptor(tensor2.Handle, CudnnTensorFormat.MajorRow, CudnnType.Float, 10, 5, 2, 4));
CudnnType dataType = default(CudnnType);
int n1 = 0, c1 = 0, h1 = 0, w1 = 0;
int nStride1 = 0, cStride1 = 0, hStride1 = 0, wStride1 = 0;
CudnnContext.Invoke(() => CudnnNativeMethods.cudnnGetTensor4dDescriptor(tensor.Handle, out dataType, out n1, out c1, out h1, out w1, out nStride1, out cStride1, out hStride1, out wStride1));
int n2 = 0, c2 = 0, h2 = 0, w2 = 0;
int nStride2 = 0, cStride2 = 0, hStride2 = 0, wStride2 = 0;
CudnnContext.Invoke(() => CudnnNativeMethods.cudnnGetTensor4dDescriptor(tensor2.Handle, out dataType, out n2, out c2, out h2, out w2, out nStride2, out cStride2, out hStride2, out wStride2));
Assert.Equal(n2, n1);
Assert.Equal(c2, c1);
Assert.Equal(h2, h1);
Assert.Equal(w2, w1);
Assert.Equal(nStride2, nStride1);
Assert.Equal(cStride2, cStride1);
Assert.Equal(hStride2, hStride1);
Assert.Equal(wStride2, wStride1);
}
}
public static extern CudnnStatus cudnnGetTensor4dDescriptor(CudnnTensorDescriptorHandle tensorDesc,
out CudnnType dataType, // image data type
out int n, // number of inputs (batch size)
out int c, // number of input feature maps
out int h, // height of input section
out int w,
out int nStride,
out int cStride,
out int hStride,
out int wStride);
public static extern CudnnStatus cudnnSetTensor4dDescriptorEx(CudnnTensorDescriptorHandle tensorDesc,
CudnnType dataType, // image data type
int n, // number of inputs (batch size)
int c, // number of input feature maps
int h, // height of input section
int w,
int nStride,
int cStride,
int hStride,
int wStride);
public CudnnFilterDescriptorParameters(CudnnType type, int output, int input, int h, int w)
{
if (output < 1 || input < 1 || h < 1 || w < 1)
{
throw new ArgumentException("At least one of the parameters output, input, h, w was negative.");
}
Contract.EndContractBlock();
this.Type = type;
this.Output = output;
this.Input = input;
this.Height = h;
this.Width = w;
}
public void Cudnn_Descriptors_ConstructFilterWithSetup(CudnnFilterDescriptorParameters param)
{
using (var filter = CudnnContext.CreateFilter(param))
{
Assert.True(filter.IsInitialized);
CudnnType dataType = default(CudnnType);
int k = 0, c = 0, h = 0, w = 0;
CudnnContext.Invoke(() => CudnnNativeMethods.cudnnGetFilterDescriptor(filter.Handle, out dataType, out k, out c, out h, out w));
Assert.Equal(filter.Parameters.Type, dataType);
Assert.Equal(filter.Parameters.Output, k);
Assert.Equal(filter.Parameters.Input, c);
Assert.Equal(filter.Parameters.Height, h);
Assert.Equal(filter.Parameters.Width, w);
}
}
private static void CheckIfCompatible(CudnnType type, params ITypeAware[] parameters)
{
Contract.Requires(parameters != null);
Contract.ForAll <ITypeAware>(parameters, x => x != null && x.Type == type);
foreach (var param in parameters)
{
if (param == null)
{
throw new ArgumentNullException("Parameter is null");
}
if (param.Type != type)
{
throw new ArgumentException(string.Format("One of the descriptors with type {0} is not CudnnType.{1}", param.GetType().Name, type.ToString()));
}
}
}
public void Cudnn_Descriptors_ConstructTensorWithSetup(CudnnTensorDescriptorParameters param)
{
using (var tensor = CudnnContext.CreateTensor(param))
{
Assert.True(tensor.IsInitialized);
CudnnType dataType = default(CudnnType);
int n = 0, c = 0, h = 0, w = 0;
int nStride = 0, cStride = 0, hStride = 0, wStride = 0;
CudnnContext.Invoke(() => CudnnNativeMethods.cudnnGetTensor4dDescriptor(tensor.Handle, out dataType, out n, out c, out h, out w, out nStride, out cStride, out hStride, out wStride));
Assert.Equal(tensor.Parameters.Num, n);
Assert.Equal(tensor.Parameters.Channels, c);
Assert.Equal(tensor.Parameters.Height, h);
Assert.Equal(tensor.Parameters.Width, w);
Assert.Equal(tensor.Parameters.NumStride, nStride);
Assert.Equal(tensor.Parameters.ChannelsStride, cStride);
Assert.Equal(tensor.Parameters.HeightStride, hStride);
Assert.Equal(tensor.Parameters.WidthStride, wStride);
}
}
public CudnnTensorDescriptorParameters(CudnnType type, int n, int c, int h, int w, int nStride, int cStride, int hStride, int wStride)
{
if (n < 1 || c < 1 || h < 1 || w < 1)
{
throw new ArgumentException("At least one of the parameters n, c, h, w was negative.");
}
if (nStride < 1 || cStride < 1 || hStride < 1 || wStride < 1)
{
throw new ArgumentException("At least one of the parameters nStride, cStride,h Stride, wStride is negative.");
}
this.Type = type;
this.Num = n;
this.Channels = c;
this.Height = h;
this.Width = w;
this.NumStride = nStride;
this.ChannelsStride = cStride;
this.HeightStride = hStride;
this.WidthStride = wStride;
}
public static extern CudnnStatus cudnnSetTensor4dDescriptorEx(CudnnTensorDescriptorHandle tensorDesc,
CudnnType dataType, // image data type
int n, // number of inputs (batch size)
int c, // number of input feature maps
int h, // height of input section
int w,
int nStride,
int cStride,
int hStride,
int wStride);
public static extern CudnnStatus cudnnSetFilterDescriptor(CudnnFilterDescriptorHandle filterDescriptor,
CudnnType dataType, // image data type
int k, // number of output feature maps
int c, // number of input feature maps
int h, // height of each input filter
int w // width of each input filter
);
public CudnnFilterDescriptorParameters(CudnnType type, int output, int input, int h, int w)
{
if (output < 1 || input < 1 || h < 1 || w < 1)
throw new ArgumentException("At least one of the parameters output, input, h, w was negative.");
Contract.EndContractBlock();
this.Type = type;
this.Output = output;
this.Input = input;
this.Height = h;
this.Width = w;
}
public CudnnTensorDescriptorParameters(CudnnType type, CudnnTensorFormat format, int n, int c, int h, int w)
{
if (n < 1 || c < 1 || h < 1 || w < 1)
throw new ArgumentException("At least one of the parameters n, c, h, w was negative.");
this.Type = type;
this.Num = n;
this.Channels = c;
this.Height = h;
this.Width = w;
this.NumStride = h * w * c;
if ( format == CudnnTensorFormat.MajorRow )
{
this.ChannelsStride = h * w;
this.HeightStride = w;
this.WidthStride = 1;
}
else
{
this.ChannelsStride = 1;
this.HeightStride = w * c;
this.WidthStride = c;
}
}
public CudnnTensorDescriptorParameters(CudnnType type, int n, int c, int h, int w, int nStride, int cStride, int hStride, int wStride)
{
if (n < 1 || c < 1 || h < 1 || w < 1)
throw new ArgumentException("At least one of the parameters n, c, h, w was negative.");
if (nStride < 1 || cStride < 1 || hStride < 1 || wStride < 1)
throw new ArgumentException("At least one of the parameters nStride, cStride,h Stride, wStride is negative.");
this.Type = type;
this.Num = n;
this.Channels = c;
this.Height = h;
this.Width = w;
this.NumStride = nStride;
this.ChannelsStride = cStride;
this.HeightStride = hStride;
this.WidthStride = wStride;
}
private static void CheckIfCompatible(CudnnType type, params ITypeAware[] parameters)
{
Contract.Requires(parameters != null);
Contract.ForAll<ITypeAware>(parameters, x => x != null && x.Type == type);
foreach (var param in parameters)
{
if (param == null)
throw new ArgumentNullException("Parameter is null");
if (param.Type != type)
throw new ArgumentException(string.Format("One of the descriptors with type {0} is not CudnnType.{1}", param.GetType().Name, type.ToString()));
}
}
public static extern CudnnStatus cudnnGetTensor4dDescriptor(CudnnTensorDescriptorHandle tensorDesc,
out CudnnType dataType, // image data type
out int n, // number of inputs (batch size)
out int c, // number of input feature maps
out int h, // height of input section
out int w,
out int nStride,
out int cStride,
out int hStride,
out int wStride);
