public Convolution2D(Variable <T> data, int kernelH, int kernelW, int numFilter)
{
Util.EnsureTrue(data.Shape.Rank == 4);
Util.EnsureTrue(data.Shape[1] > 0);
Util.EnsureTrue(data.Shape[2] > 0);
Util.EnsureTrue(data.Shape[3] > 0);
var numInputFilter = data.Shape[1];
var numOutputFilter = numFilter;
var height = data.Shape[2];
var width = data.Shape[3];
// fixed padding and stride now
ConvolutionDesc = new ConvolutionDescriptor();
ConvolutionDesc.Set2D(0, 0, 1, 1, 1, 1, ConvolutionMode.CROSS_CORRELATION);
using (var dataDesc = new TensorDescriptor())
using (var weightDesc = new FilterDescriptor())
{
var dataType = Dnn.DataTypeOf <T>();
var tempN = 100; // for temp mini batch size
dataDesc.Set4D(dataType, TensorFormat.CUDNN_TENSOR_NCHW, tempN, (int)numInputFilter, (int)height, (int)width);
weightDesc.Set4D(dataType, TensorFormat.CUDNN_TENSOR_NCHW, numOutputFilter, (int)numInputFilter, kernelH, kernelW);
// get output dimension
int n, c, h, w;
ConvolutionDesc.Get2DForwardOutputDim(dataDesc, weightDesc, out n, out c, out h, out w);
//Console.WriteLine($"{c},{h},{w}");
// Create variables
var scale = Sqrt(3.0.AsScalar <T>() / ((double)(numInputFilter * kernelH * kernelW)).AsScalar <T>());
Data = data;
Weight = Parameter(scale * (2.0.AsScalar <T>() * RandomUniform <T>(Shape.Create(numOutputFilter, numInputFilter, kernelH, kernelW), 0UL, 0UL) - 1.0.AsScalar <T>()));
Bias = Parameter(Fill(Shape.Create(c), ScalarOps.Conv <T>(0.1)));
Output = Variable <T>(PartialShape.Create(-1, c, h, w));
Workspace1 = AuxVariable <byte>();
Workspace2 = AuxVariable <byte>();
AddInput(Data);
AddInput(Weight);
AddInput(Bias);
AddOutput(Output);
AddAuxVar(Workspace1);
AddAuxVar(Workspace2);
}
}
// cuDNN fields setup
private void SetupCuDnnInfo()
{
ConvolutionDescription.Set2D(OperationInfo.VerticalPadding, OperationInfo.HorizontalPadding, OperationInfo.VerticalStride, OperationInfo.HorizontalStride, 1, 1, (Alea.cuDNN.ConvolutionMode)OperationInfo.Mode);
FilterDescription.Set4D(DataType.FLOAT, TensorFormat.CUDNN_TENSOR_NCHW, OutputInfo.Channels, KernelInfo.Channels, KernelInfo.Height, KernelInfo.Width);
BiasDescription.Set4D(DataType.FLOAT, TensorFormat.CUDNN_TENSOR_NCHW, 1, OutputInfo.Channels, 1, 1);
}