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feat_operator_convtranspose

Merged feat_operator_convtranspose
feat_operator_convtranspose into dev
Merged Grégoire Kubler requested to merge feat_operator_convtranspose into dev
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include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp
+ 119
− 54
@@ -450,16 +450,15 @@ REGISTRAR(ConvImpl1D_cpu,
* @param output_ Output Tensor.
*/
template <class I, class W, class B, class O>
void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
const std::array<DimSize_t, 2>& dilationDims,
const std::array<DimSize_t, 2>& kernelDims,
const std::array<DimSize_t, 4> &inputDims,
DimSize_t outChannels,
const void *input_,
const void *weights_,
const void *biases_,
void *output_)
{
void ConvImpl2D_cpu_forward_kernel(const array<DimSize_t, 2> &strideDims,
const array<DimSize_t, 2> &dilationDims,
const array<DimSize_t, 2> &kernelDims,
const array<DimSize_t, 4> &inputDims,
DimSize_t outChannels,
const void *input_,
const void *weights_,
const void *biases_,
void *output_) {
// FIXME: missing convolution attributes as arguments
const I *input = static_cast<const I *>(input_);
const W *weights = static_cast<const W *>(weights_);
@@ -467,59 +466,102 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
O *output = static_cast<O *>(output_);
// output H size
const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
const std::size_t oxSize =
static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[2] - dilated_kernel_x + strideDims[0]) /
static_cast<float>(strideDims[0])));
const DimSize_t dilated_kernel_x =
dilationDims[0] * (kernelDims[0] - 1) + 1;
const std::size_t oxSize = static_cast<std::size_t>(std::floor(
static_cast<float>(inputDims[2] - dilated_kernel_x + strideDims[0]) /
static_cast<float>(strideDims[0])));
// output W size
const DimSize_t dilated_kernel_y = dilationDims[1]*(kernelDims[1] - 1) + 1;
const std::size_t oySize =
static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[3] - dilated_kernel_y + strideDims[1]) /
static_cast<float>(strideDims[1])));
const DimSize_t dilated_kernel_y =
dilationDims[1] * (kernelDims[1] - 1) + 1;
const std::size_t oySize = static_cast<std::size_t>(std::floor(
static_cast<float>(inputDims[3] - dilated_kernel_y + strideDims[1]) /
static_cast<float>(strideDims[1])));
// TODO: kernel computation
// output (batch, outCh, Xout, Yout)
// input (batch, inCh, Xin, Yin)
// weight (outCh, inCh, kernelX, kernelY)
// does not take Dilation attribute into account
const std::size_t outChannels_s = oxSize * oySize;
const std::size_t outChannels_s = oxSize * oySize;
if (dilated_kernel_x == 3 && dilated_kernel_y == 3) {
for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
for (std::size_t outCh = 0; outCh < outChannels; ++outCh) {
// If bias = nullptr, set B(0)
B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
std::fill(output, output+outChannels_s, biasVal);
std::fill(output, output + outChannels_s, biasVal);
for (std::size_t inCh = 0; inCh < inputDims[1]; ++inCh) {
std::size_t iIndex = (inCh + batch*inputDims[1]) * inputDims[2] * inputDims[3];
const std::size_t wIndex = (inCh + outCh*inputDims[1]) * 9;
if (strideDims[0] == 1 && strideDims[1]==1) {
for (std::size_t ox = 0, oIndex = 0; ox < oxSize; ++ox, oIndex+=oySize, iIndex-=inputDims[3]) {
std::size_t iIndex = (inCh + batch * inputDims[1]) *
inputDims[2] * inputDims[3];
const std::size_t wIndex =
(inCh + outCh * inputDims[1]) * 9;
if (strideDims[0] == 1 && strideDims[1] == 1) {
for (std::size_t ox = 0, oIndex = 0; ox < oxSize;
++ox, oIndex += oySize, iIndex -= inputDims[3]) {
for (std::size_t oy = 0; oy < oySize; ++oy) {
output[oIndex + oy] += weights[wIndex+0]*input[iIndex+oy]+weights[wIndex+1]*input[iIndex+oy+1]+weights[wIndex+2]*input[iIndex+oy+2];
output[oIndex + oy] +=
weights[wIndex + 0] * input[iIndex + oy] +
weights[wIndex + 1] *
input[iIndex + oy + 1] +
weights[wIndex + 2] *
input[iIndex + oy + 2];
}
iIndex+=inputDims[3];
iIndex += inputDims[3];
for (std::size_t oy = 0; oy < oySize; ++oy) {
output[oIndex + oy] += weights[wIndex+3]*input[iIndex+oy]+weights[wIndex+4]*input[iIndex+oy+1]+weights[wIndex+5]*input[iIndex+oy+2];
output[oIndex + oy] +=
weights[wIndex + 3] * input[iIndex + oy] +
weights[wIndex + 4] *
input[iIndex + oy + 1] +
weights[wIndex + 5] *
input[iIndex + oy + 2];
}
iIndex+=inputDims[3];
iIndex += inputDims[3];
for (std::size_t oy = 0; oy < oySize; ++oy) {
output[oIndex + oy] += weights[wIndex+6]*input[iIndex+oy]+weights[wIndex+7]*input[iIndex+oy+1]+weights[wIndex+8]*input[iIndex+oy+2];
output[oIndex + oy] +=
weights[wIndex + 6] * input[iIndex + oy] +
weights[wIndex + 7] *
input[iIndex + oy + 1] +
weights[wIndex + 8] *
input[iIndex + oy + 2];
}
}
} else {
for (std::size_t ox = 0, oIndex = 0; ox < oxSize; ++ox, oIndex+=oySize, iIndex+=(strideDims[0]-2)*inputDims[3]) {
for (std::size_t ox = 0, oIndex = 0; ox < oxSize; ++ox,
oIndex += oySize,
iIndex += (strideDims[0] -
2) * inputDims[3]) {
for (std::size_t oy = 0; oy < oySize; ++oy) {
output[oIndex + oy] += weights[wIndex+0]*input[iIndex+oy*strideDims[1]]+weights[wIndex+1]*input[iIndex+oy*strideDims[1]+1]+weights[wIndex+2]*input[iIndex+oy*strideDims[1]+2];
output[oIndex + oy] +=
weights[wIndex + 0] *
input[iIndex + oy * strideDims[1]] +
weights[wIndex + 1] *
input[iIndex + oy * strideDims[1] +
1] +
weights[wIndex + 2] *
input[iIndex + oy * strideDims[1] + 2];
}
iIndex+=inputDims[3];
iIndex += inputDims[3];
for (std::size_t oy = 0; oy < oySize; ++oy) {
output[oIndex + oy] += weights[wIndex+3]*input[iIndex+oy*strideDims[1]]+weights[wIndex+4]*input[iIndex+oy*strideDims[1]+1]+weights[wIndex+5]*input[iIndex+oy*strideDims[1]+2];
output[oIndex + oy] +=
weights[wIndex + 3] *
input[iIndex + oy * strideDims[1]] +
weights[wIndex + 4] *
input[iIndex + oy * strideDims[1] +
1] +
weights[wIndex + 5] *
input[iIndex + oy * strideDims[1] + 2];
}
iIndex+=inputDims[3];
iIndex += inputDims[3];
for (std::size_t oy = 0; oy < oySize; ++oy) {
output[oIndex + oy] += weights[wIndex+6]*input[iIndex+oy*strideDims[1]]+weights[wIndex+7]*input[iIndex+oy*strideDims[1]+1]+weights[wIndex+8]*input[iIndex+oy*strideDims[1]+2];
output[oIndex + oy] +=
weights[wIndex + 6] *
input[iIndex + oy * strideDims[1]] +
weights[wIndex + 7] *
input[iIndex + oy * strideDims[1] +
1] +
weights[wIndex + 8] *
input[iIndex + oy * strideDims[1] + 2];
}
}
}
@@ -532,18 +574,26 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
for (std::size_t outCh = 0; outCh < outChannels; ++outCh) {
// If bias = nullptr, set B(0)
B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
std::fill(output, output+outChannels_s, biasVal);
std::fill(output, output + outChannels_s, biasVal);
for (std::size_t inCh = 0; inCh < inputDims[1]; ++inCh) {
std::size_t iIndex = (inCh + batch*inputDims[1]) * inputDims[2] * inputDims[3];
const std::size_t wIndex = (inCh + outCh*inputDims[1]);
std::size_t iIndex = (inCh + batch * inputDims[1]) *
inputDims[2] * inputDims[3];
const std::size_t wIndex = (inCh + outCh * inputDims[1]);
if (strideDims[0] == 1 && strideDims[1] == 1) {
for (std::size_t oIndex = 0; oIndex < oxSize*oySize; ++oIndex, ++iIndex) {
for (std::size_t oIndex = 0; oIndex < oxSize * oySize;
++oIndex, ++iIndex) {
output[oIndex] += weights[wIndex] * input[iIndex];
}
} else {
for (std::size_t ox = 0, oIndex = 0; ox < oxSize; ++ox, oIndex+=oySize, iIndex+=inputDims[3]*strideDims[0]) {
for (std::size_t oy = 0, iy = 0; oy < oySize; ++oy, iy+=strideDims[1]) {
output[oIndex + oy] += weights[wIndex+0]*input[iIndex+iy];
} else {
for (std::size_t ox = 0, oIndex = 0; ox < oxSize;
++ox,
oIndex += oySize,
iIndex +=
inputDims[3] * strideDims[0]) {
for (std::size_t oy = 0, iy = 0; oy < oySize;
++oy, iy += strideDims[1]) {
output[oIndex + oy] +=
weights[wIndex + 0] * input[iIndex + iy];
}
}
}
@@ -556,21 +606,36 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
for (std::size_t outCh = 0; outCh < outChannels; ++outCh) {
// If bias = nullptr, set B(0)
B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
std::fill(output, output+outChannels_s, biasVal);
std::fill(output, output + outChannels_s, biasVal);
for (std::size_t inCh = 0; inCh < inputDims[1]; ++inCh) {
std::size_t iIndex_channel = (inCh + batch*inputDims[1]) * inputDims[2] * inputDims[3];
const std::size_t wIndex = (inCh + outCh*inputDims[1]) * kernelDims[0] * kernelDims[1];
std::size_t iIndex_channel =
(inCh + batch * inputDims[1]) * inputDims[2] *
inputDims[3];
const std::size_t wIndex = (inCh + outCh * inputDims[1]) *
kernelDims[0] * kernelDims[1];
// loop over each ouput line
for (std::size_t ox = 0, oIndex = 0; ox < oxSize; ++ox, oIndex+=oySize, iIndex_channel+=inputDims[3]*strideDims[0]) {
for (std::size_t ox = 0, oIndex = 0; ox < oxSize;
++ox,
oIndex += oySize,
iIndex_channel +=
inputDims[3] * strideDims[0]) {
// loop over associated input line
for (std::size_t ky = 0, ix = 0; ky < kernelDims[0]; ++ky, ix += inputDims[3]*dilationDims[0]) {
for (std::size_t ky = 0, ix = 0; ky < kernelDims[0];
++ky, ix += inputDims[3] * dilationDims[0]) {
// loop over the entire line
for (std::size_t oy = 0, iy = 0; oy < oySize; ++oy, iy+=strideDims[1]) {
const std::size_t iIndex = iIndex_channel + ix + iy;
// loop over elements assosicated with one output
for (std::size_t kx = 0; kx < kernelDims[0]; ++kx) {
output[oIndex + oy] += weights[wIndex+kernelDims[0]*ky+kx]*input[iIndex+kx*dilationDims[1]];
for (std::size_t oy = 0, iy = 0; oy < oySize;
++oy, iy += strideDims[1]) {
const std::size_t iIndex =
iIndex_channel + ix + iy;
// loop over elements assosicated with one
// output
for (std::size_t kx = 0; kx < kernelDims[0];
++kx) {
output[oIndex + oy] +=
weights[wIndex + kernelDims[0] * ky +
kx] *
input[iIndex + kx * dilationDims[1]];
}
}
}

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