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Commit 5de2dd5f authored by Cyril Moineau's avatar Cyril Moineau
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Merge branch 'MaxPoolingAttr' into 'main' 

[MaxPooling] Add support for ceil_mode parameter.

See merge request !36
parents f9ca57a0 ac2168cc
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1 merge request!36[MaxPooling] Add support for ceil_mode parameter.
Pipeline #33151 passed
Stage: static_analysis
    Stage: build
      Stage: test
        Stage: coverage
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          Inline Side-by-side
          include/aidge/operator/MaxPooling.hpp
          + 24
          − 11
          View file @ 5de2dd5f
          ......@@ -26,14 +26,15 @@
          #include "aidge/utils/Types.h"
          namespace Aidge {
          enum class MaxPoolingAttr { StrideDims, KernelDims };
          enum class MaxPoolingAttr { StrideDims, KernelDims, CeilMode };
          template <DimIdx_t DIM>
          class MaxPooling_Op : public Operator,
          public Registrable<MaxPooling_Op<DIM>, std::string, std::unique_ptr<OperatorImpl>(const MaxPooling_Op<DIM> &)>,
          public StaticAttributes<MaxPoolingAttr,
          std::array<DimSize_t, DIM>,
          std::array<DimSize_t, DIM>> {
          std::array<DimSize_t, DIM>,
          bool> {
          private:
          // FIXME: change accessibility
          std::shared_ptr<Tensor> mInput = std::make_shared<Tensor>();
          ......@@ -46,15 +47,18 @@ public:
          using Attributes_ = StaticAttributes<MaxPoolingAttr,
          std::array<DimSize_t, DIM>,
          std::array<DimSize_t, DIM>>;
          std::array<DimSize_t, DIM>,
          bool>;
          template <MaxPoolingAttr e>
          using attr = typename Attributes_::template attr<e>;
          constexpr MaxPooling_Op(const std::array<DimSize_t, DIM> &kernel_dims,
          const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1))
          const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
          bool ceil_mode = false)
          : Operator(Type),
          Attributes_(attr<MaxPoolingAttr::StrideDims>(stride_dims),
          attr<MaxPoolingAttr::KernelDims>(kernel_dims)),
          attr<MaxPoolingAttr::KernelDims>(kernel_dims),
          attr<MaxPoolingAttr::CeilMode>(ceil_mode)),
          mOutput(std::make_shared<Tensor>()) {
          setDatatype(DataType::Float32);
          }
          ......@@ -93,9 +97,16 @@ public:
          if (!mInput->empty()) {
          std::array<DimSize_t, DIM + 2> outputDims = {};
          std::function<float(float)> roundingFunction;
          if (this->template getAttr<MaxPoolingAttr::CeilMode>()) {
          roundingFunction = [](float x) { return std::ceil(x); };
          } else {
          roundingFunction = [](float x) { return std::floor(x); };
          }
          for (std::size_t dim = 0; dim < this->template getAttr<MaxPoolingAttr::KernelDims>().size() ; ++dim) {
          outputDims[dim+2] = 1 + static_cast<DimSize_t>(
          std::floor(static_cast<float>(mInput->dims()[dim+2] -
          roundingFunction(static_cast<float>(mInput->dims()[dim+2] -
          this->template getAttr<MaxPoolingAttr::KernelDims>()[dim]) /
          static_cast<float>(this->template getAttr<MaxPoolingAttr::StrideDims>()[dim])));
          }
          ......@@ -169,9 +180,10 @@ public:
          template <std::array<DimSize_t, 1>::size_type DIM>
          inline std::shared_ptr<Node> MaxPooling(const std::array<DimSize_t, DIM> &kernel_dims,
          const std::string& name = "",
          const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1)) {
          const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
          bool ceil_mode=false) {
          static_assert(DIM<=MaxDim,"Too many kernel dimensions required by MaxPooling, not supported");
          return std::make_shared<Node>(std::make_shared<MaxPooling_Op<static_cast<DimIdx_t>(DIM)>>(kernel_dims, stride_dims), name);
          return std::make_shared<Node>(std::make_shared<MaxPooling_Op<static_cast<DimIdx_t>(DIM)>>(kernel_dims, stride_dims, ceil_mode), name);
          }
          // helper with C-style array instead of std::array for kernel_dims to allow automatic template DIM deduction
          ......@@ -179,15 +191,16 @@ template <DimSize_t DIM>
          inline std::shared_ptr<Node> MaxPooling(
          DimSize_t const (&kernel_dims)[DIM],
          const std::string& name = "",
          const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1)) {
          const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
          bool ceil_mode = false) {
          static_assert(DIM<=MaxDim,"Too many kernel dimensions required by MaxPooling, not supported");
          return MaxPooling(to_array(kernel_dims), name, stride_dims);
          return MaxPooling(to_array(kernel_dims), name, stride_dims, ceil_mode);
          }
          } // namespace Aidge
          namespace {
          template <>
          const char *const EnumStrings<Aidge::MaxPoolingAttr>::data[] = {"StrideDims", "KernelDims"};
          const char *const EnumStrings<Aidge::MaxPoolingAttr>::data[] = {"StrideDims", "KernelDims", "CeilMode"};
          }
          #endif /* AIDGE_CORE_OPERATOR_MAXPOOLING_H_ */
          include/aidge/operator/MetaOperatorDefs.hpp
          + 6
          − 4
          View file @ 5de2dd5f
          ......@@ -115,11 +115,12 @@ template <std::array<DimSize_t, 1>::size_type DIM>
          inline std::shared_ptr<Node> PaddedMaxPooling(const std::array<DimSize_t, DIM> &kernel_dims,
          const std::string& name = "",
          const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
          const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0))
          const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0),
          bool ceil_mode = false)
          {
          auto graph = Sequential({
          Pad<DIM>(padding_dims, (!name.empty()) ? name + "_pad" : ""),
          MaxPooling(kernel_dims, (!name.empty()) ? name + "_maxpooling" : "", stride_dims)
          MaxPooling(kernel_dims, (!name.empty()) ? name + "_maxpooling" : "", stride_dims, ceil_mode)
          });
          return MetaOperator("PaddedMaxPooling", graph, name);
          ......@@ -131,9 +132,10 @@ inline std::shared_ptr<Node> PaddedMaxPooling(
          DimSize_t const (&kernel_dims)[DIM],
          const std::string& name = "",
          const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t,DIM>(1),
          const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0))
          const std::array<DimSize_t, 2*DIM> &padding_dims = create_array<DimSize_t,2*DIM>(0),
          bool ceil_mode= false)
          {
          return PaddedMaxPooling(to_array(kernel_dims), name, stride_dims, padding_dims);
          return PaddedMaxPooling(to_array(kernel_dims), name, stride_dims, padding_dims, ceil_mode);
          }
          } // namespace Aidge
          ......
          python_binding/operator/pybind_MaxPooling.cpp
          + 9
          − 8
          View file @ 5de2dd5f
          ......@@ -30,22 +30,26 @@ template <DimIdx_t DIM> void declare_MaxPoolingOp(py::module &m) {
          m, ("MaxPoolingOp" + std::to_string(DIM) + "D").c_str(),
          py::multiple_inheritance())
          .def(py::init<const std::array<DimSize_t, DIM> &,
          const std::array<DimSize_t, DIM> &>(),
          const std::array<DimSize_t, DIM> &,
          bool>(),
          py::arg("kernel_dims"),
          py::arg("stride_dims"))
          py::arg("stride_dims"),
          py::arg("ceil_mode"))
          .def("get_inputs_name", &MaxPooling_Op<DIM>::getInputsName)
          .def("get_outputs_name", &MaxPooling_Op<DIM>::getOutputsName);
          m.def(("MaxPooling" + std::to_string(DIM) + "D").c_str(), [](const std::vector<DimSize_t>& kernel_dims,
          const std::string& name,
          const std::vector<DimSize_t> &stride_dims) {
          const std::vector<DimSize_t> &stride_dims,
          bool ceil_mode) {
          AIDGE_ASSERT(kernel_dims.size() == DIM, "kernel_dims size [%ld] does not match DIM [%d]", kernel_dims.size(), DIM);
          AIDGE_ASSERT(stride_dims.size() == DIM, "stride_dims size [%ld] does not match DIM [%d]", stride_dims.size(), DIM);
          return MaxPooling<DIM>(to_array<DIM>(kernel_dims.begin()), name, to_array<DIM>(stride_dims.begin()));
          return MaxPooling<DIM>(to_array<DIM>(kernel_dims.begin()), name, to_array<DIM>(stride_dims.begin()), ceil_mode);
          }, py::arg("kernel_dims"),
          py::arg("name") = "",
          py::arg("stride_dims") = std::vector<DimSize_t>(DIM,1));
          py::arg("stride_dims") = std::vector<DimSize_t>(DIM,1),
          py::arg("ceil_mode") = false);
          }
          ......@@ -55,8 +59,5 @@ void init_MaxPooling(py::module &m) {
          declare_MaxPoolingOp<2>(m);
          declare_MaxPoolingOp<3>(m);
          // FIXME:
          // m.def("MaxPooling1D", static_cast<NodeAPI(*)(const char*, int, int, int const
          // (&)[1])>(&MaxPooling));
          }
          } // namespace Aidge
          python_binding/operator/pybind_MetaOperatorDefs.cpp
          + 14
          − 14
          View file @ 5de2dd5f
          ......@@ -28,7 +28,7 @@ template <DimIdx_t DIM> void declare_PaddedConvOp(py::module &m) {
          m.def(("PaddedConv" + std::to_string(DIM) + "D").c_str(), [](DimSize_t in_channels,
          DimSize_t out_channels,
          const std::vector<DimSize_t>& kernel_dims,
          const std::string& name,
          const std::string& name,
          const std::vector<DimSize_t> &stride_dims,
          const std::vector<DimSize_t> &padding_dims,
          const std::vector<DimSize_t> &dilation_dims)
          ......@@ -50,7 +50,7 @@ template <DimIdx_t DIM> void declare_PaddedConvOp(py::module &m) {
          template <DimIdx_t DIM> void declare_PaddedConvDepthWiseOp(py::module &m) {
          m.def(("PaddedConvDepthWise" + std::to_string(DIM) + "D").c_str(), [](const std::vector<DimSize_t>& kernel_dims,
          const std::string& name,
          const std::string& name,
          const std::vector<DimSize_t> &stride_dims,
          const std::vector<DimSize_t> &padding_dims,
          const std::vector<DimSize_t> &dilation_dims)
          ......@@ -66,12 +66,12 @@ template <DimIdx_t DIM> void declare_PaddedConvDepthWiseOp(py::module &m) {
          py::arg("stride_dims") = std::vector<DimSize_t>(DIM,1),
          py::arg("padding_dims") = std::vector<DimSize_t>(2*DIM,0),
          py::arg("dilation_dims") = std::vector<DimSize_t>(DIM,1));
          }
          template <DimIdx_t DIM> void declare_PaddedAvgPoolingOp(py::module &m) {
          m.def(("PaddedAvgPooling" + std::to_string(DIM) + "D").c_str(), [](const std::vector<DimSize_t>& kernel_dims,
          const std::string& name,
          const std::string& name,
          const std::vector<DimSize_t> &stride_dims,
          const std::vector<DimSize_t> &padding_dims)
          {
          ......@@ -84,25 +84,27 @@ template <DimIdx_t DIM> void declare_PaddedAvgPoolingOp(py::module &m) {
          py::arg("name") = "",
          py::arg("stride_dims") = std::vector<DimSize_t>(DIM,1),
          py::arg("padding_dims") = std::vector<DimSize_t>(2*DIM,0));
          }
          template <DimIdx_t DIM> void declare_PaddedMaxPoolingOp(py::module &m) {
          m.def(("PaddedMaxPooling" + std::to_string(DIM) + "D").c_str(), [](const std::vector<DimSize_t>& kernel_dims,
          const std::string& name,
          const std::string& name,
          const std::vector<DimSize_t> &stride_dims,
          const std::vector<DimSize_t> &padding_dims)
          const std::vector<DimSize_t> &padding_dims,
          bool ceil_mode)
          {
          AIDGE_ASSERT(kernel_dims.size() == DIM, "kernel_dims size [%ld] does not match DIM [%d]", kernel_dims.size(), DIM);
          AIDGE_ASSERT(stride_dims.size() == DIM, "stride_dims size [%ld] does not match DIM [%d]", stride_dims.size(), DIM);
          AIDGE_ASSERT(padding_dims.size() == 2*DIM, "padding_dims size [%ld] does not match DIM [%d]", padding_dims.size(), 2*DIM);
          return PaddedMaxPooling<DIM>(to_array<DIM>(kernel_dims.begin()), name, to_array<DIM>(stride_dims.begin()), to_array<2*DIM>(padding_dims.begin()));
          return PaddedMaxPooling<DIM>(to_array<DIM>(kernel_dims.begin()), name, to_array<DIM>(stride_dims.begin()), to_array<2*DIM>(padding_dims.begin()), ceil_mode);
          }, py::arg("kernel_dims"),
          py::arg("name") = "",
          py::arg("stride_dims") = std::vector<DimSize_t>(DIM,1),
          py::arg("padding_dims") = std::vector<DimSize_t>(2*DIM,0));
          py::arg("padding_dims") = std::vector<DimSize_t>(2*DIM,0),
          py::arg("ceil_mode") = false);
          }
          void init_MetaOperatorDefs(py::module &m) {
          ......@@ -118,9 +120,7 @@ void init_MetaOperatorDefs(py::module &m) {
          declare_PaddedMaxPoolingOp<1>(m);
          declare_PaddedMaxPoolingOp<2>(m);
          declare_PaddedMaxPoolingOp<3>(m);
          // FIXME:
          // m.def("Conv1D", static_cast<NodeAPI(*)(const char*, int, int, int const
          // (&)[1])>(&Conv));
          }
          } // namespace Aidge
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