void ConvolutionLayer::forward(std::vector<Blob*> &inputs, std::vector<Blob> &outputs)
{
Blob &wgtBlob = blobs[0];
for (size_t ii = 0; ii < outputs.size(); ii++)
{
Blob &inpBlob = *inputs[ii];
Blob &outBlob = outputs[ii];
for (int n = 0; n < inpBlob.num(); n++)
{
for (int g = 0; g < group; g++)
{
im2col(inpBlob, n, g);
Mat kerMat(outGroupCn, ksize, wgtBlob.type(), wgtBlob.ptr(g*outGroupCn));
Mat dstMat(outGroupCn, outH*outW, outBlob.type(), outBlob.ptr(n, g*outGroupCn));
gemmCPU(kerMat, colMat, 1, dstMat, 0);
if (bias)
{
float *biasPtr = blobs[1].ptrf() + g*outGroupCn;
Mat biasMat(outGroupCn, 1, CV_32F, biasPtr);
gemmCPU(biasMat, biasOnesMat, 1, dstMat, 1); //TODO: gemv
}
}
}
}
}
void DeConvolutionLayer::forward(std::vector<Blob*> &inputs, std::vector<Blob> &outputs)
{
Blob &wghtBlob = blobs[0];
for (size_t ii = 0; ii < outputs.size(); ii++)
{
Blob &convBlob = *inputs[ii];
Blob &decnBlob = outputs[ii];
for (int n = 0; n < convBlob.num(); n++)
{
for (int g = 0; g < group; g++)
{
Mat dstMat(inpGroupCn, inpH*inpW, decnBlob.type(), decnBlob.ptr(n, g*inpGroupCn));
if (is1x1())
colMat = dstMat;
Mat convMat(outGroupCn, outH*outW, convBlob.type(), convBlob.ptr(n, g*outGroupCn));
Mat wghtMat(outGroupCn, ksize, wghtBlob.type(), wghtBlob.ptr(g*outGroupCn));
gemmCPU(wghtMat, convMat, 1, colMat, 0, GEMM_1_T);
col2im(dstMat);
if (bias)
{
float *biasPtr = blobs[1].ptrf() + g*inpGroupCn;
Mat biasMat(inpGroupCn, 1, CV_32F, biasPtr);
gemmCPU(biasMat, biasOnesMat, 1, dstMat, 1); //TODO: gemv
}
}
}
}
}
glm::mat4 Math::BiasMat(){
glm::mat4 biasMat(
0.5, 0.0, 0.0, 0.0,
0.0, 0.5, 0.0, 0.0,
0.0, 0.0, 0.5, 0.0,
0.5, 0.5, 0.5, 1.0);
return biasMat;
}
void FullyConnectedLayer::forward(std::vector<Blob*> &input, std::vector<Blob> &output)
{
for (size_t i = 0; i < input.size(); i++)
{
int M = (int)input[i]->total(0, axis);
int N = numOutputs;
int K = innerSize;
Mat srcMat(M, K, input[i]->type(), input[i]->ptrf());
Mat weight(N, K, blobs[0].type(), blobs[0].ptrf());
Mat dstMat(M, N, output[i].type(), output[i].ptrf());
//important: for perfomance purposes Caffe stores weights as transposed array
gemmCPU(srcMat, weight, 1, dstMat, 0, GEMM_2_T);
if (bias)
{
Mat biasOnesMat = Mat::ones(M, 1, CV_32F);
Mat biasMat(1, N, CV_32F, blobs[1].ptrf());
gemmCPU(biasOnesMat, biasMat, 1, dstMat, 1);
}
}
}
