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Commit 7c94c8b0 authored by Maxence Naud's avatar Maxence Naud
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Remove the need to specify the number of dimensions for the input of Slice_Op

parent f0100a63
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    Stage: test
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      include/aidge/backend/cpu/operator/SliceImpl.hpp
      + 12
      − 156
      View file @ 7c94c8b0
      ......@@ -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
      include/aidge/backend/cpu/operator/SliceImpl_forward_kernels.hpp
      + 26
      − 49
      View file @ 7c94c8b0
      ......@@ -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_ */
      src/operator/SliceImpl.cpp
      + 16
      − 192
      View file @ 7c94c8b0
      ......@@ -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
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