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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 | 433 |
1 files changed, 0 insertions, 433 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 deleted file mode 100644 index 942b060ba7..0000000000 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Pooling.h +++ /dev/null @@ -1,433 +0,0 @@ -// 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(__CUDACC__) - // 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_++; - } - } - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const { - return static_cast<T>(0); - } - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const { - eigen_assert(scalarCount_ > 0); - return accum / scalarCount_; - } - -#if (EIGEN_ARCH_i386 || EIGEN_ARCH_x86_64) && !defined(__CUDACC__) -#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)); - } - - template <typename Packet> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const { - return pset1<Packet>(0); - } - - 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_)); - } -#endif - - 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 |