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Commit 1096664a authored by Houssem ROUIS's avatar Houssem ROUIS Committed by Maxence Naud
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add dilations and cielmode to AvgPooling

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include/aidge/backend/cpu/operator/AvgPoolingImpl.hpp
+ 2
− 0
View file @ 1096664a
...@@ -28,8 +28,10 @@ namespace Aidge { ...@@ -28,8 +28,10 @@ namespace Aidge {
using AvgPooling2D_Op = AvgPooling_Op<2>; using AvgPooling2D_Op = AvgPooling_Op<2>;
using AvgPoolingImpl2D_cpu = OperatorImpl_cpu<AvgPooling_Op<2>, using AvgPoolingImpl2D_cpu = OperatorImpl_cpu<AvgPooling_Op<2>,
void(const std::array<DimSize_t, 2>&, void(const std::array<DimSize_t, 2>&,
const std::array<DimSize_t, 2>&,
const std::array<DimSize_t, 2>&, const std::array<DimSize_t, 2>&,
const std::array<DimSize_t, 4>&, const std::array<DimSize_t, 4>&,
bool,
const void *, const void *,
void *)>; void *)>;
......
include/aidge/backend/cpu/operator/AvgPoolingImpl_kernels.hpp
+ 32
− 44
View file @ 1096664a
...@@ -35,66 +35,54 @@ namespace Aidge { ...@@ -35,66 +35,54 @@ namespace Aidge {
template <class I, class O> template <class I, class O>
void AvgPoolingImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims, void AvgPoolingImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
const std::array<DimSize_t, 2>& kernelDims, const std::array<DimSize_t, 2>& kernelDims,
const std::array<DimSize_t, 2>& dilations,
const std::array<DimSize_t, 4> &dims, const std::array<DimSize_t, 4> &dims,
bool ceilMode,
const void *input_, const void *input_,
void *output_) { void *output_) {
// FIXME: missing convolution attributes as arguments
const I *input = static_cast<const I *>(input_); const I *input = static_cast<const I *>(input_);
O *output = static_cast<O *>(output_); O *output = static_cast<O *>(output_);
// Calculate output dimensions based on ceilMode and dilations
auto compute_output_size = [&](DimSize_t inputDim, DimSize_t kernelDim, DimSize_t stride, DimSize_t dilation) {
DimSize_t effectiveKernelDim = (kernelDim - 1) * dilation + 1;
float result = static_cast<float>(inputDim - effectiveKernelDim + stride) / static_cast<float>(stride);
return ceilMode ? static_cast<DimSize_t>(std::ceil(result)) : static_cast<DimSize_t>(std::floor(result));
};
// output H size const std::size_t oxSize = compute_output_size(dims[2], kernelDims[0], strideDims[0], dilations[0]);
const std::size_t oxSize = const std::size_t oySize = compute_output_size(dims[3], kernelDims[1], strideDims[1], dilations[1]);
static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] - kernelDims[0] + strideDims[0]) /
static_cast<float>(strideDims[0])));
// output W size
const std::size_t oySize =
static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] - kernelDims[1] + strideDims[1]) /
static_cast<float>(strideDims[1])));
// TODO: kernel computation
// output (batch, outCh, Xout, Yout)
// input (batch, ch, Xin, Yin)
// weight (outCh, ch, kernelX, kernelY)
// does not take Dilation attribute into account
using signedsize = std::make_signed<std::size_t>::type; using signedsize = std::make_signed<std::size_t>::type;
for (std::size_t batch = 0; batch < dims[0]; ++batch) { for (std::size_t batch = 0; batch < dims[0]; ++batch) {
for (std::size_t ch = 0; ch < dims[1]; ++ch) { for (std::size_t ch = 0; ch < dims[1]; ++ch) {
const std::size_t oIndex = (ch + batch*dims[1]) * oxSize * oySize; const std::size_t oIndex = (ch + batch * dims[1]) * oxSize * oySize;
const std::size_t iIndex = (ch + batch*dims[1]) * dims[2] * dims[3]; const std::size_t iIndex = (ch + batch * dims[1]) * dims[2] * dims[3];
std::fill(output + oIndex, output+(oIndex+oxSize*oySize), 0); std::fill(output + oIndex, output + (oIndex + oxSize * oySize), 0);
for (std::size_t ox = 0; ox < oxSize; ++ox) { for (std::size_t ox = 0; ox < oxSize; ++ox) {
const signedsize difx = static_cast<signedsize>(- ox * strideDims[0]); const signedsize startx = static_cast<signedsize>(ox * strideDims[0]) - (dilations[0] - 1);
const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0))); const std::size_t sxMin = static_cast<std::size_t>(std::max(startx, signedsize(0)));
const std::size_t sxMax = (static_cast<signedsize>(dims[2]) + difx) < 0 ? 0 : ((dims[2] + difx) > kernelDims[0] ? kernelDims[0] : dims[2] + difx); const std::size_t sxMax = std::min(dims[2], static_cast<std::size_t>(startx + kernelDims[0] * dilations[0]));
for (std::size_t oy = 0; oy < oySize; ++oy) { for (std::size_t oy = 0; oy < oySize; ++oy) {
const signedsize dify = static_cast<signedsize>(- oy * strideDims[1]); const signedsize starty = static_cast<signedsize>(oy * strideDims[1]) - (dilations[1] - 1);
const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0))); const std::size_t syMin = static_cast<std::size_t>(std::max(starty, signedsize(0)));
const std::size_t syMax = (static_cast<signedsize>(dims[3]) + dify) < 0 ? 0 : ((dims[3] + dify) > kernelDims[1] ? kernelDims[1] : dims[3] + dify); const std::size_t syMax = std::min(dims[3], static_cast<std::size_t>(starty + kernelDims[1] * dilations[1]));
const std::size_t oIndexFull = oIndex + ox*oySize + oy;
const std::size_t ix = ox * strideDims[0];
const std::size_t iy = oy * strideDims[1];
if (sxMin == 0 && syMin == 0 && sxMax == 3 && syMax == 3) { const std::size_t oIndexFull = oIndex + ox * oySize + oy;
output[oIndexFull] += static_cast<O>( O sum = static_cast<O>(0);
input[iIndex + (ix+0)*dims[3] + (iy+0)] + std::size_t count = 0;
input[iIndex + (ix+0)*dims[3] + (iy+1)] +
input[iIndex + (ix+0)*dims[3] + (iy+2)] + for (std::size_t sx = sxMin; sx < sxMax; sx += dilations[0]) {
input[iIndex + (ix+1)*dims[3] + (iy+0)] + for (std::size_t sy = syMin; sy < syMax; sy += dilations[1]) {
input[iIndex + (ix+1)*dims[3] + (iy+1)] + sum += static_cast<O>(input[iIndex + sx * dims[3] + sy]);
input[iIndex + (ix+1)*dims[3] + (iy+2)] + ++count;
input[iIndex + (ix+2)*dims[3] + (iy+0)] +
input[iIndex + (ix+2)*dims[3] + (iy+1)] +
input[iIndex + (ix+2)*dims[3] + (iy+2)]) / O(9);
} else {
for (std::size_t sx = sxMin; sx < sxMax; ++sx) {
for (std::size_t sy = syMin; sy < syMax; ++sy) {
output[oIndexFull] += input[iIndex + (ix+sx)*dims[3] + (iy+sy)];
}
} }
// padding not used
output[oIndexFull] /= (sxMax - sxMin) * (syMax - syMin);
} }
output[oIndexFull] = sum / static_cast<O>(count);
} }
} }
} }
......
src/operator/AvgPoolingImpl.cpp
+ 2
− 0
View file @ 1096664a
...@@ -32,7 +32,9 @@ void Aidge::AvgPoolingImpl2D_cpu::forward() { ...@@ -32,7 +32,9 @@ void Aidge::AvgPoolingImpl2D_cpu::forward() {
// Call kernel // Call kernel
impl.forward(op_.strideDims(), impl.forward(op_.strideDims(),
op_.kernelDims(), op_.kernelDims(),
op_.dilations(),
op_.getInput(0)->template dims<4>(), op_.getInput(0)->template dims<4>(),
op_.ceilMode(),
getCPUPtr(op_.getInput(0)), getCPUPtr(op_.getInput(0)),
getCPUPtr(op_.getOutput(0))); getCPUPtr(op_.getOutput(0)));
} }
......
unit_tests/operator/Test_AndImpl.cpp
+ 0
− 23
View file @ 1096664a
...@@ -160,28 +160,6 @@ TEST_CASE("[cpu/operator] And(forward)", "[And][CPU]") { ...@@ -160,28 +160,6 @@ TEST_CASE("[cpu/operator] And(forward)", "[And][CPU]") {
} }
SECTION("Broadcasting") { SECTION("Broadcasting") {
<<<<<<< HEAD
std::shared_ptr<Tensor> input_1 = std::make_shared<Tensor>(Array4D<int,1,3,3,2> {
{ //
{ //
{{10, 20},{22, 23},{20, 20}}, //
{{10, 15},{10, 29},{20, 20}}, //
{{26, 25},{33, 20},{10, 20}} //
} //
} //
}); //
std::shared_ptr<Tensor> input_2 = std::make_shared<Tensor>(Array1D<int,2> {{10, 20}});
std::shared_ptr<Tensor> expectedOutput = std::make_shared<Tensor>(Array4D<int,1,3,3,2> {
{ //
{ //
{{ 1, 1},{ 0, 0},{ 0, 1}}, //
{{ 1, 0},{ 1, 0},{ 0, 1}}, //
{{ 0, 0},{ 0, 1},{ 1, 1}} //
} //
} //
}); //
=======
std::shared_ptr<Tensor> input_1 = std::make_shared<Tensor>(Array4D<float, 1, 2, 2, 2>{ std::shared_ptr<Tensor> input_1 = std::make_shared<Tensor>(Array4D<float, 1, 2, 2, 2>{
{ {
{{{1, 0}, {1, 0}}, {{{1, 0}, {1, 0}},
...@@ -193,7 +171,6 @@ TEST_CASE("[cpu/operator] And(forward)", "[And][CPU]") { ...@@ -193,7 +171,6 @@ TEST_CASE("[cpu/operator] And(forward)", "[And][CPU]") {
{{{1, 0}, {1, 0}}, {{{1, 0}, {1, 0}},
{{1, 0}, {0, 0}}}} {{1, 0}, {0, 0}}}}
}); });
>>>>>>> fix and kernel and unit tests
std::shared_ptr<Node> myAnd = And(); std::shared_ptr<Node> myAnd = And();
auto op = std::static_pointer_cast<OperatorTensor>(myAnd->getOperator()); auto op = std::static_pointer_cast<OperatorTensor>(myAnd->getOperator());
......
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