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diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/CuboidConvolution.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/CuboidConvolution.h
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--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/CuboidConvolution.h
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-#ifndef EIGEN_CXX11_SRC_NEURAL_NETWORKS_CUBOID_CONVOLUTION_H
-#define EIGEN_CXX11_SRC_NEURAL_NETWORKS_CUBOID_CONVOLUTION_H
-
-#include "Patch3d.h"
-
-namespace Eigen {
-
-/** CuboidConvolution
- * \ingroup CXX11_NeuralNetworks_Module
- *
- * \brief Applies a 3D convolution over a multichannel input voxel block.
- *
- * The input parameter is expected to be a tensor with a rank of 4 or more (channels, depth, height, width, and optionally others).
- * The kernel parameter is expected to be a 5D tensor (filters, channels, kernel_depth, kernel_height, kernel_width).
- * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be filters, depth, height, width (and others if applicable).
- *
- * The input and kernel have to be in the same layout, and both row-major and
- * col-major are supported. The shapes given above are for col-major layout.
- * For row-major, all dimensions should be reversed.
- *
- * It is possible to swap the order of the depth, width, and height dimensions provided that the same order is used in the input, the kernel, and the output.
- */
-template <typename Input, typename Kernel>
-EIGEN_ALWAYS_INLINE
-static const typename internal::conditional <
- internal::traits<Input>::Layout == ColMajor,
- TensorReshapingOp<
- const DSizes<typename internal::traits<Input>::Index,
- internal::traits<Input>::NumDimensions>,
- const TensorContractionOp<
- const array<IndexPair<typename internal::traits<Input>::Index>, 1>,
- const TensorReshapingOp<
- const DSizes<typename internal::traits<Input>::Index, 2>,
- const Kernel>,
- const TensorReshapingOp<
- const DSizes<typename internal::traits<Input>::Index, 2>,
- const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic,
- const Input> > > >,
- TensorReshapingOp<
- const DSizes<typename internal::traits<Input>::Index,
- internal::traits<Input>::NumDimensions>,
- const TensorContractionOp<
- const array<IndexPair<typename internal::traits<Input>::Index>, 1>,
- const TensorReshapingOp<
- const DSizes<typename internal::traits<Input>::Index, 2>,
- const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic,
- const Input> > ,
- const TensorReshapingOp<
- const DSizes<typename internal::traits<Input>::Index, 2>,
- const Kernel> > > >::type
-CuboidConvolution(const Input& input, const Kernel& kernel,
- const DenseIndex stridePlanes = 1,
- const DenseIndex strideRows = 1,
- const DenseIndex strideCols = 1,
- const PaddingType padding_type = PADDING_SAME) {
- 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);
- TensorRef<Tensor<typename internal::traits<Kernel>::Scalar, internal::traits<Kernel>::NumDimensions, internal::traits<Kernel>::Layout, TensorIndex> > kern(kernel);
-
- EIGEN_STATIC_ASSERT(internal::traits<Input>::Layout == internal::traits<Kernel>::Layout, YOU_MADE_A_PROGRAMMING_MISTAKE);
- static const bool isColMajor = (internal::traits<Input>::Layout == ColMajor);
- static const int NumDims = internal::traits<Input>::NumDimensions;
-
- // Number of filters to apply. This is the same as the output depth of the result.
- const TensorIndex kernelFilters = isColMajor ? kern.dimensions()[0] : kern.dimensions()[4];
- const TensorIndex kernelChannels = isColMajor ? kern.dimensions()[1] : kern.dimensions()[3];
-
- // Spatial size of the kernel.
- const TensorIndex kernelDepth = isColMajor ? kern.dimensions()[2] : kern.dimensions()[2];
- const TensorIndex kernelRows = isColMajor ? kern.dimensions()[3] : kern.dimensions()[1];
- const TensorIndex kernelCols = isColMajor ? kern.dimensions()[4] : kern.dimensions()[0];
-
- if (isColMajor) {
- eigen_assert(kernelChannels == in.dimension(0));
- } else {
- eigen_assert(kernelChannels == in.dimension(NumDims - 1));
- }
-
- const TensorIndex inputPlanes = isColMajor ? in.dimension(1) : in.dimension(NumDims - 2);
- const TensorIndex inputRows = isColMajor ? in.dimension(2) : in.dimension(NumDims - 3);
- const TensorIndex inputCols = isColMajor ? in.dimension(3) : in.dimension(NumDims - 4);
-
- const float stride_planes_f = static_cast<float>(stridePlanes);
- const float stride_rows_f = static_cast<float>(strideRows);
- const float stride_cols_f = static_cast<float>(strideCols);
- TensorIndex out_depth;
- TensorIndex out_height;
- TensorIndex out_width;
- switch (padding_type) {
- case PADDING_VALID:
- out_depth = ceil((inputPlanes - kernelDepth + 1.f) / stride_planes_f);
- out_height = ceil((inputRows - kernelRows + 1.f) / stride_rows_f);
- out_width = ceil((inputCols - kernelCols + 1.f) / stride_cols_f);
- break;
- case PADDING_SAME:
- out_depth = ceil(inputPlanes / stride_planes_f);
- out_height = ceil(inputRows / stride_rows_f);
- out_width = ceil(inputCols / stride_cols_f);
- break;
- default:
- eigen_assert(false && "unexpected padding");
- }
-
- DSizes<TensorIndex, 2> kernel_dims;
- if (isColMajor) {
- kernel_dims[0] = kernelFilters;
- kernel_dims[1] = kernelChannels * kernelDepth * kernelRows * kernelCols;
- } else {
- kernel_dims[0] = kernelChannels * kernelDepth * kernelRows * kernelCols;
- kernel_dims[1] = kernelFilters;
- }
-
- // Molds the output of the patch extraction result into a 2D tensor:
- // - the first dimension (dims[0]): the patch values to be multiplied with the kernels
- // - the second dimension (dims[1]): everything else
- DSizes<TensorIndex, 2> pre_contract_dims;
- if (isColMajor) {
- pre_contract_dims[0] = kernelChannels * kernelDepth * kernelRows * kernelCols;
- pre_contract_dims[1] = out_depth * out_height * out_width;
- for (int i = 4; i < NumDims; ++i) {
- pre_contract_dims[1] *= in.dimension(i);
- }
- } else {
- pre_contract_dims[1] = kernelChannels * kernelDepth * kernelRows * kernelCols;
- pre_contract_dims[0] = out_depth * out_height * out_width;
- for (int i = 0; i < NumDims - 4; ++i) {
- pre_contract_dims[0] *= in.dimension(i);
- }
- }
-
- array<IndexPair<TensorIndex>, 1> contract_dims;
- contract_dims[0] = IndexPair<TensorIndex>(1, 0);
-
- // Molds the output of the contraction into the shape expected by the user
- // (assuming ColMajor):
- // - 1st dim: kernel filters
- // - 2nd dim: output depth
- // - 3nd dim: output height
- // - 4rd dim: output width
- // - 5th dim and beyond: everything else including batch size
- DSizes<TensorIndex, NumDims> post_contract_dims;
- if (isColMajor) {
- post_contract_dims[0] = kernelFilters;
- post_contract_dims[1] = out_depth;
- post_contract_dims[2] = out_height;
- post_contract_dims[3] = out_width;
- for (int i = 4; i < NumDims; ++i) {
- post_contract_dims[i] = in.dimension(i);
- }
- } else {
- post_contract_dims[NumDims - 1] = kernelFilters;
- post_contract_dims[NumDims - 2] = out_depth;
- post_contract_dims[NumDims - 3] = out_height;
- post_contract_dims[NumDims - 4] = out_width;
- for (int i = 0; i < NumDims - 4; ++i) {
- post_contract_dims[i] = in.dimension(i);
- }
- }
-
- return choose(
- Cond<internal::traits<Input>::Layout == ColMajor>(),
- kernel.reshape(kernel_dims)
- .contract(input.extract_volume_patches(
- kernelDepth, kernelRows, kernelCols, stridePlanes,
- strideRows, strideCols, padding_type)
- .reshape(pre_contract_dims),
- contract_dims)
- .reshape(post_contract_dims),
- input.extract_volume_patches(kernelDepth, kernelRows, kernelCols,
- stridePlanes, strideRows, strideCols,
- padding_type)
- .reshape(pre_contract_dims)
- .contract(kernel.reshape(kernel_dims), contract_dims)
- .reshape(post_contract_dims));
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_SRC_NEURAL_NETWORKS_CUBOID_CONVOLUTION_H
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