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Diffstat (limited to 'third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Pooling.h')
-rw-r--r-- | third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Pooling.h | 442 |
1 files changed, 442 insertions, 0 deletions
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Pooling.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Pooling.h new file mode 100644 index 0000000000..8dea22806c --- /dev/null +++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Pooling.h @@ -0,0 +1,442 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. +#ifndef EIGEN_CXX11_NEURAL_NETWORKS_POOLING_H +#define EIGEN_CXX11_NEURAL_NETWORKS_POOLING_H + +#include "Patch3d.h" + +namespace Eigen { + +/** SpatialMaxPooling + * \ingroup CXX11_NeuralNetworks_Module + * + * \brief Applies a max-pooling over a multichannel input image. + * + * The input parameter is expected to be a with a rank of 4 (channels, height, width, others in col-major, and the reverse of that in row-major). + * + * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be channels, height, width, and others (in col-major, and the reverse of that if the input was row-major). + * + * The order of the width and height dimensions can be swapped if needed. + * +*/ +#if !defined(EIGEN_HAS_INDEX_LIST) +template <typename Input> +EIGEN_ALWAYS_INLINE +static const TensorReshapingOp<const Eigen::DSizes<typename internal::traits<Input>::Index, internal::traits<Input>::NumDimensions>, const TensorReductionOp<internal::MaxReducer<typename internal::remove_const<typename internal::traits<Input>::Scalar>::type>, const Eigen::array<int, 2>, const TensorImagePatchOp<Dynamic, Dynamic, const Input> > > +#else +template <typename Input> +EIGEN_ALWAYS_INLINE +static const TensorReshapingOp<const Eigen::DSizes<typename internal::traits<Input>::Index, internal::traits<Input>::NumDimensions>, const TensorReductionOp<internal::MaxReducer<typename internal::remove_const<typename internal::traits<Input>::Scalar>::type>, typename internal::conditional<internal::traits<Input>::Layout == ColMajor, const Eigen::IndexList<Eigen::type2index<1>, Eigen::type2index<2> >, const Eigen::IndexList<Eigen::type2index<2>, Eigen::type2index<3> > >::type, const TensorImagePatchOp<Dynamic, Dynamic, const Input> > > +#endif +SpatialMaxPooling(const Input& input, DenseIndex patchRows, DenseIndex patchCols, + DenseIndex strideRows, DenseIndex strideCols, const PaddingType padding_type, + DenseIndex in_strideRows = 1, DenseIndex in_strideCols = 1) +{ + EIGEN_STATIC_ASSERT(internal::traits<Input>::NumDimensions == 4, YOU_MADE_A_PROGRAMMING_MISTAKE); + + typedef typename internal::traits<Input>::Index TensorIndex; + TensorRef<Tensor<typename internal::traits<Input>::Scalar, internal::traits<Input>::NumDimensions, internal::traits<Input>::Layout, TensorIndex> > in(input); + + const DenseIndex patchRowsEff = patchRows + (patchRows - 1) * (in_strideRows - 1); + const DenseIndex patchColsEff = patchCols + (patchCols - 1) * (in_strideCols - 1); + + static const bool isColMajor = (internal::traits<Input>::Layout == ColMajor); + static const int idxRows = isColMajor ? 1 : 2; + static const int idxCols = isColMajor ? 2 : 1; + + // Molds the output of the reduction into the shape expected by the user. + // (assuming col-major): + // - 1st dim: channels + // - 2nd dim: output height + // - 3rd dim: output width + // - 4th dim and beyond: everything else including batch size + Eigen::DSizes<TensorIndex, internal::traits<Input>::NumDimensions> post_reduce_dims; + post_reduce_dims[0] = in.dimension(0); + if (padding_type == PADDING_VALID) { + post_reduce_dims[idxRows] = numext::ceil((in.dimension(idxRows) - patchRowsEff + 1.f) / static_cast<float>(strideRows)); + post_reduce_dims[idxCols] = numext::ceil((in.dimension(idxCols) - patchColsEff + 1.f) / static_cast<float>(strideCols)); + } else { + post_reduce_dims[idxRows] = numext::ceil(in.dimension(idxRows) / static_cast<float>(strideRows)); + post_reduce_dims[idxCols] = numext::ceil(in.dimension(idxCols) / static_cast<float>(strideCols)); + } + post_reduce_dims[3] = in.dimension(3); + +#if !defined(EIGEN_HAS_INDEX_LIST) + // nvcc doesn't support cxx11 + Eigen::array<int, 2> reduction_dims; + if (isColMajor) { + reduction_dims[0] = 1; + reduction_dims[1] = 2; + } else { + reduction_dims[0] = 2; + reduction_dims[1] = 3; + } +#else + // Take advantage of cxx11 to give the compiler information it can use to + // optimize the code. + typename internal::conditional<internal::traits<Input>::Layout == ColMajor, const Eigen::IndexList<Eigen::type2index<1>, Eigen::type2index<2> >, const Eigen::IndexList<Eigen::type2index<2>, Eigen::type2index<3> > >::type reduction_dims; +#endif + + return input.extract_image_patches(patchRows, patchCols, strideRows, strideCols, in_strideRows, in_strideCols, padding_type, -Eigen::NumTraits<typename internal::remove_const<typename internal::traits<Input>::Scalar>::type>::highest()).maximum(reduction_dims).reshape(post_reduce_dims); +} + +/** CuboidMaxPooling + * \ingroup CXX11_NeuralNetworks_Module + * + * \brief Applies a max-pooling over a multichannel input volume. + * + * The input parameter is expected to be a tensor with a rank of 5 (channels, depth, height, width, others in col-major, and the reverse of that in row-major). + * + * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be channels, depth, height, width, and others (in col-major, and the reverse of that if the input was row-major). + * + * The order of the depth, width and height dimensions can be swapped if needed. + * +*/ +#if !defined(EIGEN_HAS_INDEX_LIST) +template <typename Input> +EIGEN_ALWAYS_INLINE static const TensorReshapingOp< + const Eigen::DSizes<DenseIndex, internal::traits<Input>::NumDimensions>, + const TensorReductionOp< + internal::MaxReducer<float>, const Eigen::array<int, 1>, + const TensorReshapingOp< + const Eigen::DSizes<DenseIndex, 3>, + const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Input> > > > +#else +template <typename Input> +EIGEN_ALWAYS_INLINE static const TensorReshapingOp< + const Eigen::DSizes<DenseIndex, internal::traits<Input>::NumDimensions>, + const TensorReductionOp< + internal::MaxReducer<float>, + const Eigen::IndexList<Eigen::type2index<1> >, + const TensorReshapingOp< + const Eigen::DSizes<DenseIndex, 3>, + const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Input> > > > +#endif +CuboidMaxPooling(const Input& input, DenseIndex patchPlanes, + DenseIndex patchRows, DenseIndex patchCols, + DenseIndex stridePlanes, DenseIndex strideRows, + DenseIndex strideCols, const PaddingType padding_type) { + EIGEN_STATIC_ASSERT(internal::traits<Input>::NumDimensions == 5, YOU_MADE_A_PROGRAMMING_MISTAKE); + static const bool isColMajor = (internal::traits<Input>::Layout == ColMajor); + + typedef typename internal::traits<Input>::Index TensorIndex; + TensorRef<Tensor<typename internal::traits<Input>::Scalar, internal::traits<Input>::NumDimensions, internal::traits<Input>::Layout, TensorIndex> > in(input); + + static const int idxPlanes = isColMajor ? 1 : 3; + static const int idxRows = 2; + static const int idxCols = isColMajor ? 3 : 1; + + // Molds the output of the reduction into the shape expected by the used + // (assuming col-major): + // - 1st dim: channels + // - 2nd dim: output depth + // - 3rd dim: output height + // - 4th dim: output width + // - 5th dim and beyond: everything else including batch size + Eigen::DSizes<DenseIndex, internal::traits<Input>::NumDimensions> post_reduce_dims; + post_reduce_dims[0] = in.dimension(0); + if (padding_type == PADDING_VALID) { + post_reduce_dims[idxPlanes] = numext::ceil((in.dimension(idxPlanes) - patchPlanes + 1.f) / static_cast<float>(stridePlanes)); + post_reduce_dims[idxRows] = numext::ceil((in.dimension(idxRows) - patchRows + 1.f) / static_cast<float>(strideRows)); + post_reduce_dims[idxCols] = numext::ceil((in.dimension(idxCols) - patchCols + 1.f) / static_cast<float>(strideCols)); + } else { + post_reduce_dims[idxPlanes] = numext::ceil(in.dimension(idxPlanes) / static_cast<float>(stridePlanes)); + post_reduce_dims[idxRows] = numext::ceil(in.dimension(idxRows) / static_cast<float>(strideRows)); + post_reduce_dims[idxCols] = numext::ceil(in.dimension(idxCols) / static_cast<float>(strideCols)); + } + post_reduce_dims[4] = in.dimension(4); + + Eigen::DSizes<DenseIndex, 3> pre_reduce_dims; + pre_reduce_dims[1] = patchRows * patchCols * patchPlanes; + if (isColMajor) { + pre_reduce_dims[0] = post_reduce_dims[0]; + pre_reduce_dims[2] = post_reduce_dims[1] * post_reduce_dims[2] * post_reduce_dims[3] * post_reduce_dims[4]; + } else { + pre_reduce_dims[0] = post_reduce_dims[0] * post_reduce_dims[1] * post_reduce_dims[2] * post_reduce_dims[3]; + pre_reduce_dims[2] = post_reduce_dims[4]; + } + +#if !defined(EIGEN_HAS_INDEX_LIST) + // nvcc doesn't support cxx11 + Eigen::array<int, 1> reduction_dims; + reduction_dims[0] = 1; +#else + // Take advantage of cxx11 to give the compiler information it can use to + // optimize the code. + Eigen::IndexList<Eigen::type2index<1> > reduction_dims; +#endif + return input.extract_volume_patches(patchPlanes, patchRows, patchCols, + stridePlanes, strideRows, strideCols, + padding_type, -Eigen::NumTraits<float>::highest()) + .reshape(pre_reduce_dims) + .maximum(reduction_dims) + .reshape(post_reduce_dims); +} + + +/** SpatialAvgPooling + * \ingroup CXX11_NeuralNetworks_Module + * + * \brief Applies an average pooling over a multichannel input image. + * + * The input parameter is expected to be a tensor with a rank of 4 (channels, height, width, others in col-major, and the reverse of that in row-major). + * + * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be channels, height, width, and others (in col-major, and the reverse of that if the input was row-major). + * + * The order of the width and height dimensions can be swapped if needed. + * +*/ +namespace internal { + +template <typename T> struct AvgPoolMeanReducer +{ +#if (EIGEN_ARCH_i386 || EIGEN_ARCH_x86_64 || defined (EIGEN_USE_GPU) || defined(__CUDACC__) || defined(__CUDA_ARCH__)) + // We only support packet access for floats. + static const bool PacketAccess = internal::is_same<T, float>::value; +#else + static const bool PacketAccess = false; +#endif + static const bool IsStateful = true; + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE AvgPoolMeanReducer() : scalarCount_(0) { + typedef typename packet_traits<T>::type Packet; + packetCount_ = pset1<Packet>(0.0); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) { + if (t != -Eigen::NumTraits<T>::highest()) { + (*accum) = (*accum) + t; + scalarCount_++; + } + } + + +#if (!defined (EIGEN_USE_GPU) || !defined(__CUDACC__) || !defined(__CUDA_ARCH__)) +#ifdef EIGEN_VECTORIZE_AVX +#define pequal(a,b) _mm256_cmp_ps(a,b,_CMP_EQ_UQ) +#define psel(a,b,false_mask) _mm256_blendv_ps(a,b,false_mask) +#else +#define pequal(a,b) _mm_cmpeq_ps(a,b) +#define psel(a,b,false_mask) _mm_or_ps(_mm_andnot_ps(false_mask, a), _mm_and_ps(false_mask, b)) +#endif + + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) { + reducePacketWithType(static_cast<T>(0), p, accum); + } + + template <typename Packet> + void reducePacketWithType(T, const Packet& p, Packet* accum) { + Packet skip_mask = pequal(p, pset1<Packet>(-Eigen::NumTraits<T>::highest())); + (*accum) = padd<Packet>(*accum, psel(p, pset1<Packet>(0), skip_mask)); + packetCount_ = padd<Packet>(packetCount_, psel(pset1<Packet>(1), pset1<Packet>(0), skip_mask)); + } + +#else +#define pequal(a,b) make_float4(a.x == b.x ? 1.f : 0, a.y == b.y ? 1.f : 0, a.z == b.z ? 1.f : 0, a.w == b.w ? 1.f : 0) +#define psel(a,b,c) make_float4(c.x ? b.x : a.x, c.y ? b.y : a.y, c.z ? b.z : a.z, c.w ? b.w : a.w) + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const float4& p, float4* accum) { + float4 skip_mask = pequal(p, pset1<float4>(-Eigen::NumTraits<float>::highest())); + (*accum) = padd<float4>(*accum, psel(p, pset1<float4>(0), skip_mask)); + packetCount_ = padd<float4>(packetCount_, psel(pset1<float4>(1), pset1<float4>(0), skip_mask)); + } + +#endif + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const { + return static_cast<T>(0); + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const { + return pset1<Packet>(0); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const { + eigen_assert(scalarCount_ > 0); + return accum / scalarCount_; + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const { + return pdiv(vaccum, packetCount_); + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const { + return (saccum + predux(vaccum)) / (scalarCount_ + predux(packetCount_)); + } + + protected: + typedef typename packet_traits<T>::type Packet; + int scalarCount_; + Packet packetCount_; +}; + +} // namespace internal + +#if !defined(EIGEN_HAS_INDEX_LIST) +template <typename Input> +EIGEN_ALWAYS_INLINE +static const TensorReshapingOp<const Eigen::DSizes<typename internal::traits<Input>::Index, internal::traits<Input>::NumDimensions>, const TensorReductionOp<internal::AvgPoolMeanReducer<typename internal::remove_const<typename internal::traits<Input>::Scalar>::type>, const Eigen::array<int, 2>, const TensorImagePatchOp<Dynamic, Dynamic, const Input> > > +#else +template <typename Input> +EIGEN_ALWAYS_INLINE +static const TensorReshapingOp<const Eigen::DSizes<typename internal::traits<Input>::Index, internal::traits<Input>::NumDimensions>, const TensorReductionOp<internal::AvgPoolMeanReducer<typename internal::remove_const<typename internal::traits<Input>::Scalar>::type>, typename internal::conditional<internal::traits<Input>::Layout == ColMajor, const Eigen::IndexList<Eigen::type2index<1>, Eigen::type2index<2> >, const Eigen::IndexList<Eigen::type2index<2>, Eigen::type2index<3> > >::type, const TensorImagePatchOp<Dynamic, Dynamic, const Input> > > +#endif +SpatialAvgPooling(const Input& input, DenseIndex patchRows, DenseIndex patchCols, + DenseIndex strideRows, DenseIndex strideCols, const PaddingType padding_type, + DenseIndex in_strideRows = 1, DenseIndex in_strideCols = 1) +{ + EIGEN_STATIC_ASSERT(internal::traits<Input>::NumDimensions == 4, YOU_MADE_A_PROGRAMMING_MISTAKE); + + typedef typename internal::traits<Input>::Index TensorIndex; + TensorRef<Tensor<typename internal::traits<Input>::Scalar, internal::traits<Input>::NumDimensions, internal::traits<Input>::Layout, TensorIndex> > in(input); + + const DenseIndex patchRowsEff = patchRows + (patchRows - 1) * (in_strideRows - 1); + const DenseIndex patchColsEff = patchCols + (patchCols - 1) * (in_strideCols - 1); + + static const bool isColMajor = (internal::traits<Input>::Layout == ColMajor); + static const int idxRows = isColMajor ? 1 : 2; + static const int idxCols = isColMajor ? 2 : 1; + + // Molds the output of the reduction into the shape expected by the user. + // (assuming col-major): + // - 1st dim: channels + // - 2nd dim: output height + // - 3rd dim: output width + // - 4th dim and beyond: everything else including batch size + Eigen::DSizes<TensorIndex, internal::traits<Input>::NumDimensions> post_reduce_dims; + post_reduce_dims[0] = in.dimension(0); + if (padding_type == PADDING_VALID) { + post_reduce_dims[idxRows] = numext::ceil((in.dimension(idxRows) - patchRowsEff + 1.f) / static_cast<float>(strideRows)); + post_reduce_dims[idxCols] = numext::ceil((in.dimension(idxCols) - patchColsEff + 1.f) / static_cast<float>(strideCols)); + } else { + post_reduce_dims[idxRows] = numext::ceil(in.dimension(idxRows) / static_cast<float>(strideRows)); + post_reduce_dims[idxCols] = numext::ceil(in.dimension(idxCols) / static_cast<float>(strideCols)); + } + post_reduce_dims[3] = in.dimension(3); + + typedef typename internal::remove_const<typename internal::traits<Input>::Scalar>::type CoeffReturnType; + internal::AvgPoolMeanReducer<CoeffReturnType> mean_with_nan; + +#if !defined(EIGEN_HAS_INDEX_LIST) + // nvcc doesn't support cxx11 + Eigen::array<int, 2> reduction_dims; + if (isColMajor) { + reduction_dims[0] = 1; + reduction_dims[1] = 2; + } else { + reduction_dims[0] = 2; + reduction_dims[1] = 3; + } +#else + // Take advantage of cxx11 to give the compiler information it can use to + // optimize the code. + typename internal::conditional<internal::traits<Input>::Layout == ColMajor, const Eigen::IndexList<Eigen::type2index<1>, Eigen::type2index<2> >, const Eigen::IndexList<Eigen::type2index<2>, Eigen::type2index<3> > >::type reduction_dims; +#endif + return input.extract_image_patches(patchRows, patchCols, strideRows, strideCols, in_strideRows, in_strideCols, padding_type, -Eigen::NumTraits<typename internal::remove_const<typename internal::traits<Input>::Scalar>::type>::highest()).reduce(reduction_dims, mean_with_nan).reshape(post_reduce_dims); +} + + +/** CuboidAvgPooling + * \ingroup CXX11_NeuralNetworks_Module + * + * \brief Applies an average pooling over a multichannel input volume. + * + * The input parameter is expected to be a tensor with a rank of 5 (channels, depth, height, width, others, and the reverse of that in row-major). + * + * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be channels, depth, width, and others (in col-major, and the reverse of that if the input was row-major). + * + * The order of the depth, width and height dimensions can be swapped if needed. + * +*/ +#if !defined(EIGEN_HAS_INDEX_LIST) +template <typename Input> +EIGEN_ALWAYS_INLINE static const TensorReshapingOp< + const Eigen::DSizes<DenseIndex, internal::traits<Input>::NumDimensions>, + const TensorReductionOp< + internal::AvgPoolMeanReducer<float>, const Eigen::array<int, 1>, + const TensorReshapingOp< + const Eigen::DSizes<DenseIndex, 3>, + const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Input> > > > +#else +template <typename Input> +EIGEN_ALWAYS_INLINE static const TensorReshapingOp< + const Eigen::DSizes<DenseIndex, internal::traits<Input>::NumDimensions>, + const TensorReductionOp< + internal::AvgPoolMeanReducer<float>, + const Eigen::IndexList<Eigen::type2index<1> >, + const TensorReshapingOp< + const Eigen::DSizes<DenseIndex, 3>, + const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Input> > > > +#endif +CuboidAvgPooling(const Input& input, DenseIndex patchPlanes, + DenseIndex patchRows, DenseIndex patchCols, + DenseIndex stridePlanes, DenseIndex strideRows, + DenseIndex strideCols, const PaddingType padding_type) { + EIGEN_STATIC_ASSERT(internal::traits<Input>::NumDimensions == 5, YOU_MADE_A_PROGRAMMING_MISTAKE); + static const bool isColMajor = (internal::traits<Input>::Layout == ColMajor); + + typedef typename internal::traits<Input>::Index TensorIndex; + TensorRef<Tensor<typename internal::traits<Input>::Scalar, internal::traits<Input>::NumDimensions, internal::traits<Input>::Layout, TensorIndex> > in(input); + + static const int idxPlanes = isColMajor ? 1 : 3; + static const int idxRows = 2; + static const int idxCols = isColMajor ? 3 : 1; + // Molds the output of the reduction into the shape expected by the used + // (assuming col-major): + // - 1st dim: channels + // - 2nd dim: outupt depth + // - 3rd dim: output height + // - 4th dim: output width + // - 5th dim and beyond: everything else including batch size + Eigen::DSizes<DenseIndex, internal::traits<Input>::NumDimensions> post_reduce_dims; + post_reduce_dims[0] = in.dimension(0); + if (padding_type == PADDING_VALID) { + post_reduce_dims[idxPlanes] = numext::ceil((in.dimension(idxPlanes) - patchPlanes + 1.f) / static_cast<float>(stridePlanes)); + post_reduce_dims[idxRows] = numext::ceil((in.dimension(idxRows) - patchRows + 1.f) / static_cast<float>(strideRows)); + post_reduce_dims[idxCols] = numext::ceil((in.dimension(idxCols) - patchCols + 1.f) / static_cast<float>(strideCols)); + } else { + post_reduce_dims[idxPlanes] = numext::ceil(in.dimension(idxPlanes) / static_cast<float>(stridePlanes)); + post_reduce_dims[idxRows] = numext::ceil(in.dimension(idxRows) / static_cast<float>(strideRows)); + post_reduce_dims[idxCols] = numext::ceil(in.dimension(idxCols) / static_cast<float>(strideCols)); + } + post_reduce_dims[4] = in.dimension(4); + + Eigen::DSizes<DenseIndex, 3> pre_reduce_dims; + pre_reduce_dims[1] = patchRows * patchCols * patchPlanes; + if (isColMajor) { + pre_reduce_dims[0] = post_reduce_dims[0]; + pre_reduce_dims[2] = post_reduce_dims[1] * post_reduce_dims[2] * post_reduce_dims[3] * post_reduce_dims[4]; + } else { + pre_reduce_dims[0] = post_reduce_dims[0] * post_reduce_dims[1] * post_reduce_dims[2] * post_reduce_dims[3]; + pre_reduce_dims[2] = post_reduce_dims[4]; + } + + typedef typename internal::remove_const<typename internal::traits<Input>::Scalar>::type CoeffReturnType; + internal::AvgPoolMeanReducer<CoeffReturnType> mean_with_nan; + +#if !defined(EIGEN_HAS_INDEX_LIST) + // nvcc doesn't support cxx11 + Eigen::array<int, 1> reduction_dims; + reduction_dims[0] = 1; +#else + // Take advantage of cxx11 to give the compiler information it can use to + // optimize the code. + Eigen::IndexList<Eigen::type2index<1> > reduction_dims; +#endif + return input.extract_volume_patches(patchPlanes, patchRows, patchCols, + stridePlanes, strideRows, strideCols, + padding_type, -Eigen::NumTraits<float>::highest()) + .reshape(pre_reduce_dims) + .reduce(reduction_dims, mean_with_nan) + .reshape(post_reduce_dims); +} + +} // end namespace Eigen + +#endif // EIGEN_CXX11_NEURAL_NETWORKS_POOLING_H |