public void ConvUtils_Batch_Col2Im()
{
var batchSize = 5;
var filterHeight = 2;
var filterWidth = 2;
var stride = 1;
var padding = 0;
var inputWidth = 3;
var inputHeight = 3;
var inputDepth = 3;
var filterGridWidth = ConvUtils.GetFilterGridLength(inputWidth, filterWidth, stride, padding, BorderMode.Valid);
var filterGridHeight = ConvUtils.GetFilterGridLength(inputHeight, filterHeight, stride, padding, BorderMode.Valid);
var k = filterWidth * filterHeight * inputDepth;
var n = filterGridWidth * filterGridHeight * batchSize;
var fanIn = inputWidth * inputHeight * inputDepth;
var input = Matrix <float> .Build.Random(k, n, 42);
var actual = Matrix <float> .Build.Dense(batchSize, fanIn);
ConvUtils.Batch_Col2Im(input, inputDepth, inputHeight, inputWidth,
filterHeight, filterWidth, padding, padding, stride, stride, BorderMode.Valid, actual);
Trace.WriteLine(actual.ToString());
Trace.WriteLine(string.Join(",", actual.ToColumnMajorArray()));
var expected = Matrix <float> .Build.Dense(batchSize, fanIn, new float[] { 0.408388f, -0.3281617f, -0.163763f, -0.7540793f, -0.8690567f, -0.8093507f, 0.2888344f, -1.777985f, -2.136633f, 2.92046f, -2.021355f, -0.4799407f, -0.6079422f, 0.5664175f, 1.640147f, 0.2616988f, -0.4687745f, -0.7903177f, 1.407904f, 0.1495381f, -1.212453f, 0.6085976f, -0.7663184f, -0.05670342f, 1.895431f, -0.6066797f, -0.2541801f, -0.01155096f, 1.438064f, -1.349128f, 1.942754f, 0.5057944f, -1.907569f, -0.5227588f, 0.5727027f, -1.167249f, 0.2078037f, 2.980192f, 0.4892522f, -0.6720377f, 0.9384909f, -0.9973568f, 0.5546624f, 1.710745f, 1.995577f, -0.734176f, -2.817736f, -0.8027026f, -0.7883626f, -1.275902f, -0.5054669f, 0.3228757f, 3.105314f, -0.3089013f, 1.549119f, -0.5383296f, 1.401819f, 1.837471f, 0.1251182f, -0.7002729f, 0.07180786f, -0.9396007f, 0.6037194f, -0.7305622f, 1.063156f, 4.591741f, 0.4193244f, -1.031005f, -3.045349f, 0.4254266f, 0.6900162f, -2.136511f, -1.578628f, 0.7839373f, 1.781849f, 0.1622419f, -0.6845301f, -1.676224f, 1.028266f, 0.9345228f, 0.789884f, 1.158841f, 1.703116f, -0.8997472f, -1.423375f, -0.1056926f, -0.08005979f, 1.399474f, -0.05612089f, -0.722365f, -0.6606446f, 0.08791012f, -1.749763f, 0.685056f, 0.3641174f, 0.2083111f, -0.5394329f, 1.846675f, 0.5931945f, -1.26804f, -1.087396f, 0.5506561f, -1.644088f, -0.8753259f, -1.839462f, 0.5598704f, -2.054844f, 1.20434f, -3.263947f, 1.221963f, -0.5145022f, -1.402665f, 1.101824f, 0.4248552f, -2.63849f, 1.160408f, 2.130142f, 0.3172536f, 1.109406f, 0.9979748f, 0.2864983f, 0.00849107f, -2.00572f, 1.178588f, -0.3127078f, -1.662103f, -1.043834f, 1.065703f, -0.9702578f, -0.1781971f, -1.362978f, 0.4443011f, -1.050083f, 0.6755545f, -1.088875f });
MatrixAsserts.AreEqual(expected, actual);
}
/// <summary>
///
/// </summary>
/// <param name="delta"></param>
/// <returns></returns>
public Matrix <float> Backward(Matrix <float> delta)
{
// Reshape delta to fit with data layout in im2col
ConvUtils.ReshapeConvolutionsToRowMajor(delta, InputDepth, InputHeight, InputWidth,
FilterWidth, FilterHeight, m_padHeight, m_padWidth, m_stride, m_stride, BorderMode, m_deltaInReshape);
// Calculate gradients for weights and biases
m_deltaInReshape.TransposeAndMultiply(Im2Cols, WeightsGradients);
m_deltaInReshape.SumRows(BiasGradients);
// calcualte delta for next layer.
Weights.TransposeThisAndMultiply(m_deltaInReshape, Im2Cols);
// convert back to original layout
m_delta.Clear();
ConvUtils.Batch_Col2Im(Im2Cols, InputDepth, InputHeight, InputWidth,
FilterHeight, FilterWidth, m_padHeight, m_padWidth, m_stride, m_stride, BorderMode, m_delta);
return(m_delta);
}