diff --git a/include/aidge/backend/cpu/operator/SliceImpl.hpp b/include/aidge/backend/cpu/operator/SliceImpl.hpp
index 69dd88bb5e6e463b1122bc46b294f3220c88a905..41d5367f8397d867270f2134f70890f5fc576393 100644
--- a/include/aidge/backend/cpu/operator/SliceImpl.hpp
+++ b/include/aidge/backend/cpu/operator/SliceImpl.hpp
@@ -26,95 +26,26 @@ namespace Aidge {
// class Slice_Op;
// compute kernel registry for forward and backward
-template <DimIdx_t DIM>
class SliceImplForward_cpu
- : public Registrable<SliceImplForward_cpu<DIM>, std::tuple<DataType>,
- void(const typename Slice_Op<DIM>::Attrs&,
- const std::array<std::size_t, DIM>,
+ : public Registrable<SliceImplForward_cpu, std::tuple<DataType>,
+ void(const typename Slice_Op::Attrs&,
+ const std::vector<std::size_t>,
const void*,
void*)> {};
-template <DimIdx_t DIM>
class SliceImplBackward_cpu
- : public Registrable<SliceImplBackward_cpu<DIM>, std::tuple<DataType>,
- void(const typename Slice_Op<DIM>::Attrs&,
- const std::array<std::size_t, DIM>,
+ : public Registrable<SliceImplBackward_cpu, std::tuple<DataType>,
+ void(const typename Slice_Op::Attrs&,
+ const std::vector<std::size_t>,
const void*,
void*)> {};
-template <DimIdx_t DIM>
-class SliceImpl_cpu : public OperatorImpl {
- public:
- SliceImpl_cpu(const Slice_Op<DIM>& op) : OperatorImpl(op) {}
-
- static std::unique_ptr<SliceImpl_cpu<DIM>> create(const Slice_Op<DIM>& op) {
- return std::make_unique<SliceImpl_cpu<DIM>>(op);
- }
-
- public:
- NbElts_t getNbRequiredData(const IOIndex_t /*inputIdx*/) const override final {
- assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0)) && "requires valid input");
-
- // Requires the whole tensors
- const auto& inputDims = std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dims();
-
- return std::accumulate(inputDims.begin(), inputDims.end(), static_cast<NbElts_t>(1),
- std::multiplies<NbElts_t>());
- }
- NbElts_t getNbRequiredProtected(const IOIndex_t /*inputIdx*/) const override final { return 0; }
- NbElts_t getRequiredMemory(const IOIndex_t outputIdx,
- const std::vector<DimSize_t>& inputsSize) const override final {
- (void)outputIdx;
- (void)inputsSize;
- const auto& outputDims = std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dims();
- return std::accumulate(outputDims.begin(), outputDims.end(), static_cast<NbElts_t>(1),
- std::multiplies<NbElts_t>());
- }
- NbElts_t getNbConsumedData(const IOIndex_t /*inputIdx*/) const override final {
- return mNbConsumedData[0];
- }
- NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final {
- return mNbProducedData[0];
- }
- void updateConsummerProducer() override final {
- // each input is consumed by the minimum amount for a forward pass
- mNbConsumedData[0] += getNbRequiredData(0);
-
- mNbProducedData[0] += getRequiredMemory(0, {});
- }
-
- void forward() {
- // FIXME: uncomment the following code once memory handling will work
- assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0)) && "missing input #0");
-
- // Find the correct kernel type
- auto kernelFunc = Registrar<SliceImplForward_cpu<DIM>>::create(
- {std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType()});
-
- // Call kernel
- kernelFunc(std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->template dims<DIM>(),
- std::get<1>(std::static_pointer_cast<const Slice_Op<DIM>&>(mOp).getStaticAttributes()),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->getImpl()->rawPtr(),
- std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->getImpl()->rawPtr()
- );
-
- // each input is consumed by the minimum amount for a forward pass
- mNbConsumedData[0] += getNbRequiredData(0);
-
- mNbProducedData[0] += getRequiredMemory(0, {});
- }
-
- void backward() { printf("Not implemented yet.\n"); }
-};
-/******************************************************************************/
-
-template <>
-class SliceImpl_cpu<1> : public OperatorImpl {
+class SliceImpl_cpu : public OperatorImpl {
public:
- SliceImpl_cpu(const Slice_Op<1>& op) : OperatorImpl(op) {}
+ SliceImpl_cpu(const Slice_Op& op) : OperatorImpl(op) {}
- static std::unique_ptr<SliceImpl_cpu<1>> create(const Slice_Op<1>& op) {
- return std::make_unique<SliceImpl_cpu<1>>(op);
+ static std::unique_ptr<SliceImpl_cpu> create(const Slice_Op& op) {
+ return std::make_unique<SliceImpl_cpu>(op);
}
public:
@@ -127,89 +58,14 @@ public:
void updateConsummerProducer() override final;
void forward();
- void backward();
-};
-
-/******************************************************************************/
-
-template <>
-class SliceImpl_cpu<2> : public OperatorImpl {
- public:
- SliceImpl_cpu(const Slice_Op<2>& op) : OperatorImpl(op) {}
- static std::unique_ptr<SliceImpl_cpu<2>> create(const Slice_Op<2>& op) {
- return std::make_unique<SliceImpl_cpu<2>>(op);
- }
-
- public:
- NbElts_t getNbRequiredData(const IOIndex_t /*inputIdx*/) const override final;
- NbElts_t getNbRequiredProtected(const IOIndex_t /*inputIdx*/) const override final;
- NbElts_t getRequiredMemory(const IOIndex_t outputIdx,
- const std::vector<DimSize_t>& inputsSize) const override final;
- NbElts_t getNbConsumedData(const IOIndex_t /*inputIdx*/) const override final;
- NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
- void updateConsummerProducer() override final;
-
- void forward();
- void backward();
-};
-
-/******************************************************************************/
-
-template <>
-class SliceImpl_cpu<3> : public OperatorImpl {
- public:
- SliceImpl_cpu(const Slice_Op<3>& op) : OperatorImpl(op) {}
-
- static std::unique_ptr<SliceImpl_cpu<3>> create(const Slice_Op<3>& op) {
- return std::make_unique<SliceImpl_cpu<3>>(op);
- }
-
- public:
- NbElts_t getNbRequiredData(const IOIndex_t /*inputIdx*/) const override final;
- NbElts_t getNbRequiredProtected(const IOIndex_t /*inputIdx*/) const override final;
- NbElts_t getRequiredMemory(const IOIndex_t outputIdx,
- const std::vector<DimSize_t>& inputsSize) const override final;
- NbElts_t getNbConsumedData(const IOIndex_t /*inputIdx*/) const override final;
- NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
- void updateConsummerProducer() override final;
-
- void forward();
void backward();
};
-/******************************************************************************/
-
-template <>
-class SliceImpl_cpu<4> : public OperatorImpl {
- public:
- SliceImpl_cpu(const Slice_Op<4>& op) : OperatorImpl(op) {}
-
- static std::unique_ptr<SliceImpl_cpu<4>> create(const Slice_Op<4>& op) {
- return std::make_unique<SliceImpl_cpu<4>>(op);
- }
-
- public:
- NbElts_t getNbRequiredData(const IOIndex_t /*inputIdx*/) const override final;
- NbElts_t getNbRequiredProtected(const IOIndex_t /*inputIdx*/) const override final;
- NbElts_t getRequiredMemory(const IOIndex_t outputIdx,
- const std::vector<DimSize_t>& inputsSize) const override final;
- NbElts_t getNbConsumedData(const IOIndex_t /*inputIdx*/) const override final;
- NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
- void updateConsummerProducer() override final;
-
- void forward();
- void backward();
-};
-
-
namespace {
-static Registrar<Slice_Op<1>> registrarSliceImpl_1D_cpu("cpu", Aidge::SliceImpl_cpu<1>::create);
-static Registrar<Slice_Op<2>> registrarSliceImpl_2D_cpu("cpu", Aidge::SliceImpl_cpu<2>::create);
-static Registrar<Slice_Op<3>> registrarSliceImpl_3D_cpu("cpu", Aidge::SliceImpl_cpu<3>::create);
-static Registrar<Slice_Op<4>> registrarSliceImpl_4D_cpu("cpu", Aidge::SliceImpl_cpu<4>::create);
+static Registrar<Slice_Op> registrarSliceImpl_cpu("cpu", Aidge::SliceImpl_cpu::create);
} // namespace
} // namespace Aidge
-#endif /* AIDGE_CPU_OPERATOR_LEAKYRELUIMPL_H_ */
+#endif /* AIDGE_CPU_OPERATOR_LEAKYRELUIMPL_H_ */
\ No newline at end of file
diff --git a/include/aidge/backend/cpu/operator/SliceImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/SliceImpl_forward_kernels.hpp
index bbf4ccbae77089ca75dfee34f5bc5b0dd7d3697d..7eb4b9dc2cb8dddc8b7fdaf4d63b8f1d39d879b0 100644
--- a/include/aidge/backend/cpu/operator/SliceImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/SliceImpl_forward_kernels.hpp
@@ -15,46 +15,47 @@
#include "aidge/utils/Registrar.hpp"
#include "aidge/operator/Slice.hpp"
#include "aidge/backend/cpu/operator/SliceImpl.hpp"
-#include <array>
+#include <vector>
#include <cstddef>
#include "aidge/data/Data.hpp"
namespace Aidge {
-template <class I, std::size_t DIM>
-void SliceImpl_cpu_forward_kernel(const typename Slice_Op<DIM>::Attrs& attrs,
- const std::array<std::size_t, DIM> inputDims,
+template <class I>
+void SliceImpl_cpu_forward_kernel(const typename Slice_Op::Attrs& attrs,
+ const std::vector<std::size_t> inputDims,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_) + std::get<0>(attrs);
I* output = static_cast<I*>(output_);
- const std::array<std::size_t, DIM> slicedDims = std::get<1>(attrs);
+ const std::vector<std::size_t> slicedDims = std::get<1>(attrs);
+ const std::size_t nbDims = slicedDims.size();
// for inputDims = {4,5,5,3} & slicedDims = {3,2,2,1}, substractDims = {1,5,5,3}
- std::array<std::size_t, DIM> substractedDims;
- for (std::size_t i = 0; i < DIM; ++i) {
+ std::vector<std::size_t> substractedDims = std::vector<std::size_t>(nbDims);
+ for (std::size_t i = 0; i < nbDims; ++i) {
substractedDims[i] = inputDims[i] - slicedDims[i];
}
// for slicedDims = {3,2,2,1}, prodSlicedDims = {12,4,2,1}
- std::array<std::size_t, DIM> prodSlicedDims;
- std::array<std::size_t, DIM+1> prodInputDims;
- prodSlicedDims[DIM - 1] = slicedDims[DIM - 1];
- prodInputDims[DIM - 1] = inputDims[DIM - 1];
- prodInputDims[DIM] = 1;
- for (std::size_t i = 2; i <= DIM; ++i) {
- prodSlicedDims[DIM - i] = prodSlicedDims[DIM - i + 1]*slicedDims[DIM - i];
- prodInputDims[DIM - i] = prodInputDims[DIM - i + 1]*inputDims[DIM - i];
+ std::vector<std::size_t> prodSlicedDims = std::vector<std::size_t>(nbDims);
+ std::vector<std::size_t> prodInputDims = std::vector<std::size_t>(nbDims+1);
+ prodSlicedDims[nbDims - 1] = slicedDims[nbDims - 1];
+ prodInputDims[nbDims - 1] = inputDims[nbDims - 1];
+ prodInputDims[nbDims] = 1;
+ for (std::size_t i = 2; i <= nbDims; ++i) {
+ prodSlicedDims[nbDims - i] = prodSlicedDims[nbDims - i + 1]*slicedDims[nbDims - i];
+ prodInputDims[nbDims - i] = prodInputDims[nbDims - i + 1]*inputDims[nbDims - i];
}
std::size_t j = 0;
std::size_t i = 0;
for (; j < prodSlicedDims[0];) {
output[j] = input[i++];
- ++j;
- for (std::size_t idx = DIM - 1; idx > 0; --idx) {
- i += j % prodSlicedDims[idx] == 0 ? substractedDims[idx]*prodInputDims[idx+1] : 0;
+ ++j;
+ for (std::size_t idx = nbDims - 1; idx > 0; --idx) {
+ i += j % prodSlicedDims[idx] == 0 ? substractedDims[idx]*prodInputDims[idx+1] : 0;
}
}
}
@@ -62,37 +63,13 @@ void SliceImpl_cpu_forward_kernel(const typename Slice_Op<DIM>::Attrs& attrs,
namespace {
// DIM = 1
-static Registrar<SliceImplForward_cpu<1>> registrarSliceImplForward_1D_cpu_Float32(
- {DataType::Float32}, Aidge::SliceImpl_cpu_forward_kernel<float, 1>);
-static Registrar<SliceImplForward_cpu<1>> registrarSliceImplForward_1D_cpu_Int32(
- {DataType::Int32}, Aidge::SliceImpl_cpu_forward_kernel<int, 1>);
-static Registrar<SliceImplForward_cpu<1>> registrarSliceImplForward_1D_cpu_Float64(
- {DataType::Float64}, Aidge::SliceImpl_cpu_forward_kernel<double, 1>);
-
-// DIM = 2
-static Registrar<SliceImplForward_cpu<2>> registrarSliceImplForward_2D_cpu_Float32(
- {DataType::Float32}, Aidge::SliceImpl_cpu_forward_kernel<float, 2>);
-static Registrar<SliceImplForward_cpu<2>> registrarSliceImplForward_2D_cpu_Int32(
- {DataType::Int32}, Aidge::SliceImpl_cpu_forward_kernel<int, 2>);
-static Registrar<SliceImplForward_cpu<2>> registrarSliceImplForward_2D_cpu_Float64(
- {DataType::Float64}, Aidge::SliceImpl_cpu_forward_kernel<double, 2>);
-
-// DIM = 3
-static Registrar<SliceImplForward_cpu<3>> registrarSliceImplForward_3D_cpu_Float32(
- {DataType::Float32}, Aidge::SliceImpl_cpu_forward_kernel<float, 3>);
-static Registrar<SliceImplForward_cpu<3>> registrarSliceImplForward_3D_cpu_Int32(
- {DataType::Int32}, Aidge::SliceImpl_cpu_forward_kernel<int, 3>);
-static Registrar<SliceImplForward_cpu<3>> registrarSliceImplForward_3D_cpu_Float64(
- {DataType::Float64}, Aidge::SliceImpl_cpu_forward_kernel<double, 3>);
-
-// DIM = 4
-static Registrar<SliceImplForward_cpu<4>> registrarSliceImplForward_4D_cpu_Float32(
- {DataType::Float32}, Aidge::SliceImpl_cpu_forward_kernel<float, 4>);
-static Registrar<SliceImplForward_cpu<4>> registrarSliceImplForward_4D_cpu_Int32(
- {DataType::Int32}, Aidge::SliceImpl_cpu_forward_kernel<int, 4>);
-static Registrar<SliceImplForward_cpu<4>> registrarSliceImplForward_4D_cpu_Float64(
- {DataType::Float64}, Aidge::SliceImpl_cpu_forward_kernel<double, 4>);
+static Registrar<SliceImplForward_cpu> registrarSliceImplForward_cpu_Float32(
+ {DataType::Float32}, Aidge::SliceImpl_cpu_forward_kernel<float>);
+static Registrar<SliceImplForward_cpu> registrarSliceImplForward_cpu_Int32(
+ {DataType::Int32}, Aidge::SliceImpl_cpu_forward_kernel<int>);
+static Registrar<SliceImplForward_cpu> registrarSliceImplForward_cpu_Float64(
+ {DataType::Float64}, Aidge::SliceImpl_cpu_forward_kernel<double>);
} // namespace
} // namespace Aidge
-#endif /* AIDGE_CPU_OPERATOR_LEAKYRELUIMPL_FORWARD_KERNEL_H_ */
+#endif /* AIDGE_CPU_OPERATOR_SLICEIMPL_FORWARD_KERNEL_H_ */
diff --git a/src/operator/SliceImpl.cpp b/src/operator/SliceImpl.cpp
index 3ae56e1a4f613a4188dc51659853e07674e74768..b60bbe60188f416f28ff2562875dce6e5ee15bd5 100644
--- a/src/operator/SliceImpl.cpp
+++ b/src/operator/SliceImpl.cpp
@@ -22,231 +22,55 @@
#include <cassert>
#include <tuple>
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<1>::getNbRequiredData(const Aidge::IOIndex_t /*inputIdx*/) const {
- assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0)) && "requires valid input");
-
- // Requires the whole tensors
- return std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->template dims<1>()[0];
-}
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<1>::getNbRequiredProtected(const Aidge::IOIndex_t /*inputIdx*/) const { return 0; }
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<1>::getRequiredMemory(const Aidge::IOIndex_t outputIdx,
- const std::vector<Aidge::DimSize_t>& inputsSize) const {
- (void)outputIdx;
- (void)inputsSize;
- return std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->template dims<1>()[0];
-}
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<1>::getNbConsumedData(const Aidge::IOIndex_t /*inputIdx*/) const {
- return mNbConsumedData[0];
-}
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<1>::getNbProducedData(const Aidge::IOIndex_t /*outputIdx*/) const {
- return mNbProducedData[0];
-}
-
-void Aidge::SliceImpl_cpu<1>::updateConsummerProducer() {
- // each input is consumed by the minimum amount for a forward pass
- mNbConsumedData[0] += getNbRequiredData(0);
-
- mNbProducedData[0] += getRequiredMemory(0, {});
-}
-
-void Aidge::SliceImpl_cpu<1>::forward() {
- // FIXME: uncomment the following code once memory handling will work
- assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0)) && "missing input #0");
-
- // Find the correct kernel type
- auto kernelFunc = Registrar<SliceImplForward_cpu<1>>::create(
- {std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType()});
-
- // Call kernel
- kernelFunc(dynamic_cast<const Slice_Op<1>&>(mOp).getStaticAttributes(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->template dims<1>(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->getImpl()->rawPtr(),
- std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->getImpl()->rawPtr()
- );
-
- // each input is consumed by the minimum amount for a forward pass
- mNbConsumedData[0] += getNbRequiredData(0);
-
- mNbProducedData[0] += getRequiredMemory(0, {});
-}
-
-void Aidge::SliceImpl_cpu<1>::backward() { printf("Not implemented yet.\n"); }
-
-/////////////////////////////////////////////////////////////////////////
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<2>::getNbRequiredData(const Aidge::IOIndex_t /*inputIdx*/) const {
- assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0)) && "requires valid input");
-
- // Requires the whole tensors
- const auto& inputDims = std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->template dims<2>();
- return inputDims[0]*inputDims[1];
-}
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<2>::getNbRequiredProtected(const Aidge::IOIndex_t /*inputIdx*/) const { return 0; }
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<2>::getRequiredMemory(const Aidge::IOIndex_t outputIdx,
- const std::vector<Aidge::DimSize_t>& inputsSize) const {
- (void)outputIdx;
- (void)inputsSize;
- const auto& outputDims = std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->template dims<2>();
- return outputDims[0]*outputDims[1];
-}
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<2>::getNbConsumedData(const Aidge::IOIndex_t /*inputIdx*/) const {
- return mNbConsumedData[0];
-}
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<2>::getNbProducedData(const Aidge::IOIndex_t /*outputIdx*/) const {
- return mNbProducedData[0];
-}
-
-void Aidge::SliceImpl_cpu<2>::updateConsummerProducer() {
- // each input is consumed by the minimum amount for a forward pass
- mNbConsumedData[0] += getNbRequiredData(0);
-
- mNbProducedData[0] += getRequiredMemory(0, {});
-}
-
-void Aidge::SliceImpl_cpu<2>::forward() {
- // FIXME: uncomment the following code once memory handling will work
- assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0)) && "missing input #0");
-
- // Find the correct kernel type
- auto kernelFunc = Registrar<SliceImplForward_cpu<2>>::create(
- {std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType()});
-
- // Call kernel
- kernelFunc(dynamic_cast<const Slice_Op<2>&>(mOp).getStaticAttributes(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->template dims<2>(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->getImpl()->rawPtr(),
- std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->getImpl()->rawPtr()
- );
-
- // each input is consumed by the minimum amount for a forward pass
- mNbConsumedData[0] += getNbRequiredData(0);
-
- mNbProducedData[0] += getRequiredMemory(0, {});
-}
-
-void Aidge::SliceImpl_cpu<2>::backward() { printf("Not implemented yet.\n"); }
-
-////////////////////////////////////////////////////////////////////////////
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<3>::getNbRequiredData(const Aidge::IOIndex_t /*inputIdx*/) const {
- assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0)) && "requires valid input");
-
- // Requires the whole tensors
- const auto& inputDims = std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->template dims<3>();
-
- return std::accumulate(inputDims.begin(), inputDims.end(), static_cast<NbElts_t>(1),
- std::multiplies<NbElts_t>());
-}
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<3>::getNbRequiredProtected(const Aidge::IOIndex_t /*inputIdx*/) const { return 0; }
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<3>::getRequiredMemory(const Aidge::IOIndex_t outputIdx,
- const std::vector<Aidge::DimSize_t>& inputsSize) const {
- (void)outputIdx;
- (void)inputsSize;
- const auto& outputDims = std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->template dims<3>();
- return std::accumulate(outputDims.begin(), outputDims.end(), static_cast<NbElts_t>(1),
- std::multiplies<NbElts_t>());
-}
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<3>::getNbConsumedData(const Aidge::IOIndex_t /*inputIdx*/) const {
- return mNbConsumedData[0];
-}
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<3>::getNbProducedData(const Aidge::IOIndex_t /*outputIdx*/) const {
- return mNbProducedData[0];
-}
-
-void Aidge::SliceImpl_cpu<3>::updateConsummerProducer() {
- // each input is consumed by the minimum amount for a forward pass
- mNbConsumedData[0] += getNbRequiredData(0);
-
- mNbProducedData[0] += getRequiredMemory(0, {});
-}
-
-void Aidge::SliceImpl_cpu<3>::forward() {
- // FIXME: uncomment the following code once memory handling will work
- assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0)) && "missing input #0");
-
- // Find the correct kernel type
- auto kernelFunc = Registrar<SliceImplForward_cpu<3>>::create(
- {std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType()});
-
- // Call kernel
- kernelFunc(dynamic_cast<const Slice_Op<3>&>(mOp).getStaticAttributes(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->template dims<3>(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->getImpl()->rawPtr(),
- std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->getImpl()->rawPtr()
- );
-
- // each input is consumed by the minimum amount for a forward pass
- mNbConsumedData[0] += getNbRequiredData(0);
-
- mNbProducedData[0] += getRequiredMemory(0, {});
-}
-
-void Aidge::SliceImpl_cpu<3>::backward() { printf("Not implemented yet.\n"); }
-
-//////////////////////////////////////////////////////////////////////////////
-
-Aidge::NbElts_t Aidge::SliceImpl_cpu<4>::getNbRequiredData(const Aidge::IOIndex_t /*inputIdx*/) const {
+Aidge::NbElts_t Aidge::SliceImpl_cpu::getNbRequiredData(const Aidge::IOIndex_t /*inputIdx*/) const {
assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0)) && "requires valid input");
// Requires the whole tensors
- const auto& inputDims = std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->template dims<4>();
+ const auto& inputDims = std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dims();
return std::accumulate(inputDims.begin(), inputDims.end(), static_cast<NbElts_t>(1),
std::multiplies<NbElts_t>());
}
-Aidge::NbElts_t Aidge::SliceImpl_cpu<4>::getNbRequiredProtected(const Aidge::IOIndex_t /*inputIdx*/) const { return 0; }
+Aidge::NbElts_t Aidge::SliceImpl_cpu::getNbRequiredProtected(const Aidge::IOIndex_t /*inputIdx*/) const { return 0; }
-Aidge::NbElts_t Aidge::SliceImpl_cpu<4>::getRequiredMemory(const Aidge::IOIndex_t outputIdx,
- const std::vector<Aidge::DimSize_t>& inputsSize) const {
+Aidge::NbElts_t Aidge::SliceImpl_cpu::getRequiredMemory(const Aidge::IOIndex_t outputIdx,
+ const std::vector<Aidge::DimSize_t>& inputsSize) const {
(void)outputIdx;
(void)inputsSize;
- const auto& outputDims = std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->template dims<4>();
+ const auto& outputDims = std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dims();
return std::accumulate(outputDims.begin(), outputDims.end(), static_cast<NbElts_t>(1),
std::multiplies<NbElts_t>());
}
-Aidge::NbElts_t Aidge::SliceImpl_cpu<4>::getNbConsumedData(const Aidge::IOIndex_t /*inputIdx*/) const {
+Aidge::NbElts_t Aidge::SliceImpl_cpu::getNbConsumedData(const Aidge::IOIndex_t /*inputIdx*/) const {
return mNbConsumedData[0];
}
-Aidge::NbElts_t Aidge::SliceImpl_cpu<4>::getNbProducedData(const Aidge::IOIndex_t /*outputIdx*/) const {
+Aidge::NbElts_t Aidge::SliceImpl_cpu::getNbProducedData(const Aidge::IOIndex_t /*outputIdx*/) const {
return mNbProducedData[0];
}
-void Aidge::SliceImpl_cpu<4>::updateConsummerProducer() {
+void Aidge::SliceImpl_cpu::updateConsummerProducer() {
// each input is consumed by the minimum amount for a forward pass
mNbConsumedData[0] += getNbRequiredData(0);
mNbProducedData[0] += getRequiredMemory(0, {});
}
-void Aidge::SliceImpl_cpu<4>::forward() {
+void Aidge::SliceImpl_cpu::forward() {
// FIXME: uncomment the following code once memory handling will work
assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0)) && "missing input #0");
// Find the correct kernel type
- auto kernelFunc = Registrar<SliceImplForward_cpu<4>>::create(
+ auto kernelFunc = Registrar<SliceImplForward_cpu>::create(
{std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType()});
// Call kernel
- kernelFunc(dynamic_cast<const Slice_Op<4>&>(mOp).getStaticAttributes(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->template dims<4>(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->getImpl()->rawPtr(),
- std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->getImpl()->rawPtr()
+ kernelFunc(dynamic_cast<const Slice_Op&>(mOp).getStaticAttributes(),
+ std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dims(),
+ std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->getImpl()->rawPtr(),
+ std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->getImpl()->rawPtr()
);
// each input is consumed by the minimum amount for a forward pass
@@ -255,4 +79,4 @@ void Aidge::SliceImpl_cpu<4>::forward() {
mNbProducedData[0] += getRequiredMemory(0, {});
}
-void Aidge::SliceImpl_cpu<4>::backward() { printf("Not implemented yet.\n"); }
\ No newline at end of file
+void Aidge::SliceImpl_cpu::backward() { printf("Not implemented yet.\n"); }
\ No newline at end of file