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Diffstat (limited to 'third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h')
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diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h new file mode 100644 index 0000000000..12ce23444c --- /dev/null +++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h @@ -0,0 +1,523 @@ +#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 |