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Diffstat (limited to 'third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h')
| -rw-r--r-- | third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h | 523 |
1 files changed, 0 insertions, 523 deletions
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h deleted file mode 100644 index 12ce23444c..0000000000 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h +++ /dev/null @@ -1,523 +0,0 @@ -#ifndef EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_CUBOID_CONVOLUTIONS_H -#define EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_CUBOID_CONVOLUTIONS_H - -#include "Patch3d.h" - -namespace Eigen { - -/** CuboidConvolutionBackwardInput - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Computes the backprop for the input of a 3D convolution. - * - * The output_backward 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) - * output_backward and kernel have to be in the same layout. - * - * The dimensions of the result will be filters, depth, height, width (and others if applicable). - * - * 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. - * - * All dimension orders above are given for col-major, and should be reversed for row-major. - */ - -template <typename OutputBackward, typename Kernel> -EIGEN_ALWAYS_INLINE static const typename internal::conditional< - internal::traits<OutputBackward>::Layout == ColMajor, - TensorReshapingOp< - const DSizes<typename internal::traits<OutputBackward>::Index, - internal::traits<OutputBackward>::NumDimensions>, - const TensorContractionOp< - const array< IndexPair<typename internal::traits<OutputBackward>::Index>, 2>, - const TensorReshapingOp< - const DSizes< typename internal::traits<OutputBackward>::Index, 3>, - const TensorReverseOp<const array<bool, 5>, const Kernel> - >, - const TensorReshapingOp< - const DSizes< typename internal::traits<OutputBackward>::Index, 3>, - const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const OutputBackward> - > - > - >, - TensorReshapingOp< - const DSizes<typename internal::traits<OutputBackward>::Index, - internal::traits<OutputBackward>::NumDimensions>, - const TensorContractionOp< - const array< IndexPair<typename internal::traits<OutputBackward>::Index>, 2>, - const TensorReshapingOp< - const DSizes< typename internal::traits<OutputBackward>::Index, 3>, - const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const OutputBackward> - >, - const TensorReshapingOp< - const DSizes<typename internal::traits<OutputBackward>::Index, 3>, - const TensorReverseOp<const array<bool, 5>, const Kernel> - > - > - > ->::type -CuboidConvolutionBackwardInput( - const Kernel& kernel, const OutputBackward& output_backward, - typename internal::traits<OutputBackward>::Index inputPlanes, - typename internal::traits<OutputBackward>::Index inputRows, - typename internal::traits<OutputBackward>::Index inputCols, - const DenseIndex stridePlanes = 1, const DenseIndex strideRows = 1, - const DenseIndex strideCols = 1) { - typedef typename internal::traits<OutputBackward>::Index TensorIndex; - const TensorRef<const Tensor<typename internal::traits<Kernel>::Scalar, internal::traits<Kernel>::NumDimensions, internal::traits<Kernel>::Layout, TensorIndex> > kern(kernel); - const TensorRef<const Tensor<typename internal::traits<OutputBackward>::Scalar, internal::traits<OutputBackward>::NumDimensions, internal::traits<OutputBackward>::Layout, TensorIndex> > out(output_backward); - - EIGEN_STATIC_ASSERT(internal::traits<Kernel>::Layout == internal::traits<OutputBackward>::Layout, YOU_MADE_A_PROGRAMMING_MISTAKE); - - static const bool isColMajor = (internal::traits<OutputBackward>::Layout == ColMajor); - - static const int NumDims = internal::traits<OutputBackward>::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]; - // Number of channels. This is the same as the input depth. - const TensorIndex kernelChannels = isColMajor ? kern.dimensions()[1] : kern.dimensions()[3]; - const TensorIndex kernelPlanes = 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]; - - const TensorIndex outputPlanes = isColMajor ? out.dimensions()[1] : out.dimensions()[NumDims - 2]; - const TensorIndex outputRows = isColMajor ? out.dimensions()[2] : out.dimensions()[NumDims - 3]; - const TensorIndex outputCols = isColMajor ? out.dimensions()[3] : out.dimensions()[NumDims - 4]; - - TensorIndex forward_pad_z, forward_pad_y, forward_pad_x; - const TensorIndex size_z = ceil(inputPlanes / static_cast<float>(stridePlanes)); - const TensorIndex size_y = ceil(inputRows / static_cast<float>(strideRows)); - const TensorIndex size_x = ceil(inputCols / static_cast<float>(strideCols)); - - // Infer padding type. - if (size_z == outputPlanes && size_y == outputRows && size_x == outputCols) { - // SAME padding. - const TensorIndex dz = size_z * stridePlanes + kernelPlanes - 1 - inputPlanes; - const TensorIndex dy = size_y * strideRows + kernelRows - 1 - inputRows; - const TensorIndex dx = size_x * strideCols + kernelCols - 1 - inputCols; - - forward_pad_z = dz - dz / 2; - forward_pad_y = dy - dy / 2; - forward_pad_x = dx - dx / 2; - } else { - // VALID padding. - forward_pad_z = 0; - forward_pad_y = 0; - forward_pad_x = 0; - } - const TensorIndex padding_ztop = kernelPlanes - 1 - forward_pad_z; - const TensorIndex padding_top = kernelRows - 1 - forward_pad_y; - const TensorIndex padding_left = kernelCols - 1 - forward_pad_x; - - const TensorIndex padding_zbottom = inputPlanes + kernelPlanes - 1 - (outputPlanes - 1) * stridePlanes - 1 - padding_ztop; - const TensorIndex padding_bottom = inputRows + kernelRows - 1 - (outputRows - 1) * strideRows - 1 - padding_top; - const TensorIndex padding_right = inputCols + kernelCols - 1 - (outputCols - 1) * strideCols - 1 - padding_left; - - eigen_assert(padding_ztop >= 0); - eigen_assert(padding_zbottom >= 0); - eigen_assert(padding_top >= 0); - eigen_assert(padding_left >= 0); - eigen_assert(padding_bottom >= 0); - eigen_assert(padding_right >= 0); - - // The kernel has dimensions filters X channels X patch_planes X patch_rows X patch_cols. - // We need to reverse the kernel along the spatial dimensions. - array<bool, 5> kernel_reverse; - if (isColMajor) { - kernel_reverse[0] = false; - kernel_reverse[1] = false; - kernel_reverse[2] = true; - kernel_reverse[3] = true; - kernel_reverse[4] = true; - } else { - kernel_reverse[0] = true; - kernel_reverse[1] = true; - kernel_reverse[2] = true; - kernel_reverse[3] = false; - kernel_reverse[4] = false; - } - - DSizes<TensorIndex, 3> kernel_dims; - if (isColMajor) { - kernel_dims[0] = kernelFilters; - kernel_dims[1] = kernelChannels; - kernel_dims[2] = kernelRows * kernelCols * kernelPlanes; - } else { - kernel_dims[0] = kernelRows * kernelCols * kernelPlanes; - kernel_dims[1] = kernelChannels; - kernel_dims[2] = kernelFilters; - } - - // The output_backward has dimensions out_depth X out_planes X out_rows X out_cols X OTHERS - // When we extract the image patches from output_backward, it will have dimensions: - // out_depth X (patch_planes * patch_rows * patch_cols) X (input_planes * input_rows * input_cols * OTHERS) - DSizes<TensorIndex, 3> pre_contract_dims; - if (isColMajor) { - pre_contract_dims[0] = kernelFilters; - pre_contract_dims[1] = kernelRows * kernelCols * kernelPlanes; - pre_contract_dims[2] = inputRows * inputCols * inputPlanes; - for (int i = 4; i < NumDims; ++i) { - pre_contract_dims[2] *= out.dimension(i); - } - } else { - pre_contract_dims[2] = kernelFilters; - pre_contract_dims[1] = kernelRows * kernelCols * kernelPlanes; - pre_contract_dims[0] = inputRows * inputCols * inputPlanes; - for (int i = 0; i < NumDims - 4; ++i) { - pre_contract_dims[0] *= out.dimension(i); - } - } - - // We will contract along dimensions (0, 2) in kernel and (0, 1) in - // output_backward, if this is col-major, and - // dimensions (0, 2) in kernel and (1, 2) in output_backward, if this row-major. - array<IndexPair<TensorIndex>, 2> contract_dims; - if (isColMajor) { - // col-major: kernel.contract(output.patches) - contract_dims[0] = IndexPair<TensorIndex>(0, 0); - contract_dims[1] = IndexPair<TensorIndex>(2, 1); - } else { - // row-major: output.patches.contract(kernel) - contract_dims[0] = IndexPair<TensorIndex>(1, 0); - contract_dims[1] = IndexPair<TensorIndex>(2, 2); - } - - // Post contraction, the dimensions of the input_backprop is - // channels X input_planes X input_rows X input_cols X OTHERS - DSizes<TensorIndex, NumDims> post_contract_dims; - if (isColMajor) { - post_contract_dims[0] = kernelChannels; - post_contract_dims[1] = inputPlanes; - post_contract_dims[2] = inputRows; - post_contract_dims[3] = inputCols; - for (int i = 4; i < NumDims; ++i) { - post_contract_dims[i] = out.dimension(i); - } - } else { - post_contract_dims[NumDims - 1] = kernelChannels; - post_contract_dims[NumDims - 2] = inputPlanes; - post_contract_dims[NumDims - 3] = inputRows; - post_contract_dims[NumDims - 4] = inputCols; - for (int i = 0; i < NumDims - 4; ++i) { - post_contract_dims[i] = out.dimension(i); - } - } - - DSizes<TensorIndex, NumDims> strides; - for (int i = 0; i < NumDims; i++) { - strides[i] = 1; - } - if (isColMajor) { - strides[1] = stridePlanes; - strides[2] = strideRows; - strides[3] = strideCols; - } else { - strides[NumDims - 2] = stridePlanes; - strides[NumDims - 3] = strideRows; - strides[NumDims - 4] = strideCols; - } - - return choose( - Cond<internal::traits<OutputBackward>::Layout == ColMajor>(), - kernel.reverse(kernel_reverse) - .reshape(kernel_dims) - .contract( - output_backward.extract_volume_patches(kernelPlanes, kernelRows, kernelCols, - 1, 1, 1, stridePlanes, strideRows, strideCols, - padding_ztop, padding_zbottom, - padding_top, padding_bottom, - padding_left, padding_right) - .reshape(pre_contract_dims), - contract_dims) - .reshape(post_contract_dims), - output_backward.extract_volume_patches(kernelPlanes, kernelRows, kernelCols, - 1, 1, 1, stridePlanes, strideRows, strideCols, - padding_ztop, padding_zbottom, - padding_top, padding_bottom, - padding_left, padding_right) - .reshape(pre_contract_dims) - .contract(kernel.reverse(kernel_reverse).reshape(kernel_dims), - contract_dims) - .reshape(post_contract_dims)); -} - - -/** CuboidConvolutionBackwardKernel - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Computes the backprop for the filter of a 3D convolution. - * - * The output_backward 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 4D tensor (filters, channels, kernel_depth, kernel_height, kernel_width) - * output_backward and kernel have to be in the same layout. - * - * The dimensions of the result will be filters, depth, height, width (and others if applicable). - * - * 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. - * - * All dimension orders above are given for col-major, and should be reversed for row-major. - */ -template <typename OutputBackward, typename Input> -EIGEN_ALWAYS_INLINE static const typename internal::conditional< - internal::traits<OutputBackward>::Layout == ColMajor, - const TensorShufflingOp< - const array<typename internal::traits<OutputBackward>::Index, 5>, - const TensorReverseOp< - const array<bool, 5>, - const TensorReshapingOp< - const DSizes<typename internal::traits<OutputBackward>::Index, 5>, - const TensorContractionOp< - const array< IndexPair<typename internal::traits<Input>::Index>, 2>, - const TensorReshapingOp< - const DSizes<typename internal::traits<Input>::Index, 3>, - const Input>, - const TensorReshapingOp< - const DSizes< typename internal::traits<OutputBackward>::Index, 4>, - const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const OutputBackward> - > - > - > - > - >, - const TensorShufflingOp< - const array<typename internal::traits<OutputBackward>::Index, 5>, - const TensorReverseOp< - const array<bool, 5>, - const TensorReshapingOp< - const DSizes<typename internal::traits<OutputBackward>::Index, 5>, - const TensorContractionOp< - const array< IndexPair<typename internal::traits<Input>::Index>, 2>, - const TensorReshapingOp< - const DSizes< typename internal::traits<OutputBackward>::Index, 4>, - const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const OutputBackward> - >, - const TensorReshapingOp< - const DSizes<typename internal::traits<Input>::Index, 3>, - const Input - > - > - > - > - > ->::type -CuboidConvolutionBackwardKernel( - const Input& input, const OutputBackward& output_backward, - typename internal::traits<Input>::Index kernelPlanes, - typename internal::traits<Input>::Index kernelRows, - typename internal::traits<Input>::Index kernelCols, - const DenseIndex stridePlanes = 1, - const DenseIndex strideRows = 1, - const DenseIndex strideCols = 1) { - 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<OutputBackward>::Scalar, internal::traits<OutputBackward>::NumDimensions, internal::traits<OutputBackward>::Layout, TensorIndex> > out(output_backward); - - EIGEN_STATIC_ASSERT(internal::traits<Input>::Layout == internal::traits<OutputBackward>::Layout, YOU_MADE_A_PROGRAMMING_MISTAKE); - - static const bool isColMajor = (internal::traits<Input>::Layout == ColMajor); - - static const int NumDims = internal::traits<Input>::NumDimensions; - EIGEN_STATIC_ASSERT(internal::traits<Input>::NumDimensions == internal::traits<OutputBackward>::NumDimensions, YOU_MADE_A_PROGRAMMING_MISTAKE); - - 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 TensorIndex outputPlanes = isColMajor ? out.dimension(1) : out.dimension(NumDims - 2); - const TensorIndex outputRows = isColMajor ? out.dimension(2) : out.dimension(NumDims - 3); - const TensorIndex outputCols = isColMajor ? out.dimension(3) : out.dimension(NumDims - 4); - - const TensorIndex kernelFilters = isColMajor ? out.dimension(0) : out.dimension(NumDims - 1); - const TensorIndex kernelChannels = isColMajor ? in.dimension(0) : in.dimension(NumDims - 1); - - TensorIndex forward_pad_z, forward_pad_y, forward_pad_x; - const TensorIndex size_z = ceil(inputPlanes / static_cast<float>(stridePlanes)); - const TensorIndex size_y = ceil(inputRows / static_cast<float>(strideRows)); - const TensorIndex size_x = ceil(inputCols / static_cast<float>(strideCols)); - - // Infer padding type. - if (size_z == outputPlanes && size_y == outputRows && size_x == outputCols) { - // SAME padding. - const TensorIndex dz = size_z * stridePlanes + kernelPlanes - 1 - inputPlanes; - const TensorIndex dy = size_y * strideRows + kernelRows - 1 - inputRows; - const TensorIndex dx = size_x * strideCols + kernelCols - 1 - inputCols; - - forward_pad_z = dz - dz / 2; - forward_pad_y = dy - dy / 2; - forward_pad_x = dx - dx / 2; - } else { - // VALID padding. - forward_pad_z = 0; - forward_pad_y = 0; - forward_pad_x = 0; - } - - const TensorIndex padding_ztop = kernelPlanes - 1 - forward_pad_z; - const TensorIndex padding_top = kernelRows - 1 - forward_pad_y; - const TensorIndex padding_left = kernelCols - 1 - forward_pad_x; - - const TensorIndex padding_zbottom = inputPlanes + kernelPlanes - 1 - (outputPlanes - 1) * stridePlanes - 1 - padding_ztop; - const TensorIndex padding_bottom = inputRows + kernelRows - 1 - (outputRows - 1) * strideRows - 1 - padding_top; - const TensorIndex padding_right = inputCols + kernelCols - 1 - (outputCols - 1) * strideCols - 1 - padding_left; - - eigen_assert(padding_ztop >= 0); - eigen_assert(padding_zbottom >= 0); - eigen_assert(padding_top >= 0); - eigen_assert(padding_left >= 0); - eigen_assert(padding_bottom >= 0); - eigen_assert(padding_right >= 0); - - // The output_backward has dimensions out_depth X out_plaens X out_rows X out_cols X OTHERS - // When we extract the image patches from output_backward (with input as the - // kernel), it will have dimensions - // (out_depth) X (input_planes * input_rows * input_cols) X (kernel_planes * kernel_rows * kernel_cols) X OTHERS - DSizes<TensorIndex, 4> pre_contract_dims; - if (isColMajor) { - pre_contract_dims[0] = kernelFilters; - pre_contract_dims[1] = inputRows * inputCols * inputPlanes; - pre_contract_dims[2] = kernelRows * kernelCols * kernelPlanes; - pre_contract_dims[3] = 1; - for (int i = 4; i < NumDims; ++i) { - pre_contract_dims[3] *= out.dimension(i); - } - } else { - pre_contract_dims[3] = kernelFilters; - pre_contract_dims[2] = inputRows * inputCols * inputPlanes; - pre_contract_dims[1] = kernelRows * kernelCols * kernelPlanes; - pre_contract_dims[0] = 1; - for (int i = 0; i < NumDims - 4; ++i) { - pre_contract_dims[0] *= out.dimension(i); - } - } - - // The input has dimensions in_depth X (input_planes * input_rows * input_cols) X OTHERS - DSizes<TensorIndex, 3> input_dims; - if (isColMajor) { - input_dims[0] = kernelChannels; - input_dims[1] = inputRows * inputCols * inputPlanes; - input_dims[2] = 1; - for (int i = 4; i < NumDims; ++i) { - input_dims[2] *= in.dimension(i); - } - eigen_assert(input_dims[2] == pre_contract_dims[3]); - } else { - input_dims[2] = kernelChannels; - input_dims[1] = inputRows * inputCols * inputPlanes; - input_dims[0] = 1; - for (int i = 0; i < NumDims - 4; ++i) { - input_dims[0] *= in.dimension(i); - } - eigen_assert(input_dims[0] == pre_contract_dims[0]); - } - - // We will contract along dimensions (1, 2) in in and (1, 3) in out, if - // this is col-major. - // For row-major, it's dimensions (0, 1) in in and (0, 2) in out. - array<IndexPair<TensorIndex>, 2> contract_dims; - if (isColMajor) { - // col-major: in.contract(output.patches) - contract_dims[0] = IndexPair<TensorIndex>(1, 1); - contract_dims[1] = IndexPair<TensorIndex>(2, 3); - } else { - // row-major: output.patches.contract(in) - contract_dims[0] = IndexPair<TensorIndex>(0, 0); - contract_dims[1] = IndexPair<TensorIndex>(2, 1); - } - - // After the contraction, the kernel will have dimension - // in_depth X out_depth X kernel_patches X kernel_rows X kernel_cols - // We will need to shuffle the first two dimensions and reverse the spatial dimensions. - // The end shape is: - // out_depth X in_shape X kernel_planes X kernel_rows X kernel_cols - - // This is the shape of the kernel *before* the shuffling. - DSizes<TensorIndex, 5> kernel_dims; - if (isColMajor) { - kernel_dims[0] = kernelChannels; - kernel_dims[1] = kernelFilters; - kernel_dims[2] = kernelPlanes; - kernel_dims[3] = kernelRows; - kernel_dims[4] = kernelCols; - } else { - kernel_dims[0] = kernelCols; - kernel_dims[1] = kernelRows; - kernel_dims[2] = kernelPlanes; - kernel_dims[3] = kernelFilters; - kernel_dims[4] = kernelChannels; - } - - // Flip filters and channels. - array<TensorIndex, 5> kernel_shuffle; - if (isColMajor) { - kernel_shuffle[0] = 1; - kernel_shuffle[1] = 0; - kernel_shuffle[2] = 2; - kernel_shuffle[3] = 3; - kernel_shuffle[4] = 4; - } else { - kernel_shuffle[0] = 0; - kernel_shuffle[1] = 1; - kernel_shuffle[2] = 2; - kernel_shuffle[3] = 4; - kernel_shuffle[4] = 3; - } - - // Reverse the spatial dimensions. - array<bool, 5> kernel_reverse; - if (isColMajor) { - kernel_reverse[0] = false; - kernel_reverse[1] = false; - kernel_reverse[2] = true; - kernel_reverse[3] = true; - kernel_reverse[4] = true; - } else { - kernel_reverse[0] = true; - kernel_reverse[1] = true; - kernel_reverse[2] = true; - kernel_reverse[3] = false; - kernel_reverse[4] = false; - } - - DSizes<TensorIndex, NumDims> strides; - for (int i = 0; i < NumDims; i++) { - strides[i] = 1; - } - if (isColMajor) { - strides[1] = stridePlanes; - strides[2] = strideRows; - strides[3] = strideCols; - } else { - strides[NumDims - 2] = stridePlanes; - strides[NumDims - 3] = strideRows; - strides[NumDims - 4] = strideCols; - } - return choose( - Cond<internal::traits<Input>::Layout == ColMajor>(), - input.reshape(input_dims) - .contract( - output_backward.extract_volume_patches( - inputPlanes, inputRows, inputCols, 1, - 1, 1, stridePlanes, strideRows, strideCols, - - padding_ztop, padding_zbottom, padding_top, - padding_bottom, padding_left, padding_right) - .reshape(pre_contract_dims), - contract_dims) - .reshape(kernel_dims) - .reverse(kernel_reverse) - .shuffle(kernel_shuffle), - output_backward.extract_volume_patches( - inputPlanes, inputRows, inputCols, 1, 1, 1, - stridePlanes, strideRows, strideCols, padding_ztop, - padding_zbottom, padding_top, padding_bottom, - padding_left, padding_right) - .reshape(pre_contract_dims) - .contract(input.reshape(input_dims), contract_dims) - .reshape(kernel_dims) - .reverse(kernel_reverse) - .shuffle(kernel_shuffle)); -} - -} // end namespace Eigen - -#endif // EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_CUBOID_CONVOLUTIONS_H |