diff options
Diffstat (limited to 'third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h')
| -rw-r--r-- | third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h | 351 |
1 files changed, 0 insertions, 351 deletions
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h deleted file mode 100644 index 0f4ada246c..0000000000 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h +++ /dev/null @@ -1,351 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Ke Yang <yangke@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_BACKWARD_SPATIAL_CONVOLUTIONS_H -#define EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_SPATIAL_CONVOLUTIONS_H - -namespace Eigen { - -/** SpatialConvolutionBackwardInput - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Computes the backprop for the input of a 2D convolution. - * - * The output_backward parameter is expected to be a tensor with a rank of 3 or more (channels, height, width, and optionally others) - * The kernel parameter is expected to be a 4D tensor (filters, channels, kernel_height, kernel_width) - * The output_backward and the kernel must both be in col-major layout. The result will also be in col-major layout. - * - * If in_stride > 1, then applies convolution with holes (aka atrous convolution), sampling every in_stride input pixels. - * - * The result can be assigned to a tensor of rank equal to the rank of the output_backward. The dimensions of the result will be filters, height, width (and others if applicable). - * - * It is possible to swap the order of the width and height dimensions provided that the same order is used in the input, the kernel, and the output. - * - */ - -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, 4>, const Kernel> >, const TensorReshapingOp<const DSizes<typename internal::traits<OutputBackward>::Index, 3>, const TensorImagePatchOp<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 TensorImagePatchOp<Dynamic, Dynamic, const OutputBackward> >, const TensorReshapingOp<const DSizes<typename internal::traits<OutputBackward>::Index, 3>, const TensorReverseOp<const array<bool, 4>, const Kernel> > > > >::type -SpatialConvolutionBackwardInput(const Kernel& kernel, const OutputBackward& output_backward, typename internal::traits<OutputBackward>::Index inputRows, typename internal::traits<OutputBackward>::Index inputCols, const DenseIndex stride = 1, const DenseIndex in_stride = 1) { - - typedef typename internal::traits<OutputBackward>::Index TensorIndex; - TensorRef<Tensor<typename internal::traits<Kernel>::Scalar, internal::traits<Kernel>::NumDimensions, internal::traits<Kernel>::Layout, TensorIndex> > kern(kernel); - TensorRef<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()[3]; - // Number of channels. This is the same as the input depth. - const TensorIndex kernelChannels = isColMajor ? kern.dimensions()[1] : kern.dimensions()[2]; - const TensorIndex kernelRows = isColMajor ? kern.dimensions()[2] : kern.dimensions()[1]; - const TensorIndex kernelCols = isColMajor ? kern.dimensions()[3] : kern.dimensions()[0]; - - // This is the effective kernel size, taking into account the (in_stride - 1) zero-values - // inserted between consecutive kernel elements in atrous convolution - const TensorIndex kernelRowsEff = kernelRows + (kernelRows - 1) * (in_stride - 1); - const TensorIndex kernelColsEff = kernelCols + (kernelCols - 1) * (in_stride - 1); - - const TensorIndex outputRows = isColMajor ? output_backward.dimension(1) : output_backward.dimension(NumDims - 2); - const TensorIndex outputCols = isColMajor ? output_backward.dimension(2) : output_backward.dimension(NumDims - 3); - - // Computing the forward padding - const TensorIndex forward_pad_top = ((outputRows - 1) * stride + kernelRowsEff - inputRows) / 2; - const TensorIndex forward_pad_left = ((outputCols - 1) * stride + kernelColsEff - inputCols) / 2; - - const TensorIndex padding_top = kernelRowsEff - 1 - forward_pad_top; - const TensorIndex padding_left = kernelColsEff - 1 - forward_pad_left; - const TensorIndex padding_bottom = inputRows + kernelRowsEff - 1 - (outputRows - 1) * stride - 1 - padding_top; - const TensorIndex padding_right = inputCols + kernelColsEff - 1 - (outputCols - 1) * stride - 1 - padding_left; - - 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_rows X patch_cols - // We need to reverse the kernel along dimensions corresponding to rows and - // cols. - // TODO(yangke): we can make things slightly faster by collapsing the dimensions - // where we don't reverse. Try that once we have a faster compiler. - array<bool, 4> kernel_reverse; - if (isColMajor) { - kernel_reverse[0] = false; - kernel_reverse[1] = false; - kernel_reverse[2] = true; - kernel_reverse[3] = true; - } else { - kernel_reverse[0] = true; - kernel_reverse[1] = true; - kernel_reverse[2] = false; - kernel_reverse[3] = false; - } - - DSizes<TensorIndex, 3> kernel_dims; - if (isColMajor) { - kernel_dims[0] = kernelFilters; - kernel_dims[1] = kernelChannels; - kernel_dims[2] = kernelRows * kernelCols; - } else { - kernel_dims[0] = kernelRows * kernelCols; - kernel_dims[1] = kernelChannels; - kernel_dims[2] = kernelFilters; - } - - // The output_backward has dimensions out_depth 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_rows * patch_cols) X (input_rows * input_cols * OTHERS) - DSizes<TensorIndex, 3> pre_contract_dims; - if (isColMajor) { - pre_contract_dims[0] = kernelFilters; - pre_contract_dims[1] = kernelRows * kernelCols; - pre_contract_dims[2] = inputRows * inputCols; - for (int i = 3; i < NumDims; ++i) { - pre_contract_dims[2] *= out.dimension(i); - } - } else { - pre_contract_dims[2] = kernelFilters; - pre_contract_dims[1] = kernelRows * kernelCols; - pre_contract_dims[0] = inputRows * inputCols; - for (int i = 0; i < NumDims - 3; ++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_rows X input_cols X OTHERS - DSizes<TensorIndex, NumDims> post_contract_dims; - if (isColMajor) { - post_contract_dims[0] = kernelChannels; - post_contract_dims[1] = inputRows; - post_contract_dims[2] = inputCols; - for (int i = 3; i < NumDims; ++i) { - post_contract_dims[i] = out.dimension(i); - } - } else { - post_contract_dims[NumDims - 1] = kernelChannels; - post_contract_dims[NumDims - 2] = inputRows; - post_contract_dims[NumDims - 3] = inputCols; - for (int i = 0; i < NumDims - 3; ++i) { - post_contract_dims[i] = out.dimension(i); - } - } - - return choose(Cond<internal::traits<OutputBackward>::Layout == ColMajor>(), - kernel.reverse(kernel_reverse).reshape(kernel_dims).contract(output_backward.extract_image_patches(kernelRows, kernelCols, 1, 1, in_stride, in_stride, stride, stride, padding_top, padding_bottom, padding_left, padding_right, 0).reshape(pre_contract_dims), contract_dims).reshape(post_contract_dims), - output_backward.extract_image_patches(kernelRows, kernelCols, 1, 1, in_stride, in_stride, stride, stride, padding_top, padding_bottom, padding_left, padding_right, 0).reshape(pre_contract_dims).contract(kernel.reverse(kernel_reverse).reshape(kernel_dims), contract_dims).reshape(post_contract_dims)); -} - - -/** SpatialConvolutionBackwardKernel - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Computes the backprop for the filter of a 2D convolution. - * - * The output_backward parameter is expected to be a tensor with a rank of 3 or more (channels, height, width, and optionally others) - * The kernel parameter is expected to be a 4D tensor (filters, channels, kernel_height, kernel_width) - * The output_backward and the kernel must both be in col-major layout. The result will also be in col-major layout. - * - * If in_stride > 1, then applies convolution with holes (aka atrous convolution), sampling every in_stride input pixels. - * - * The result can be assigned to a tensor of rank equal to the rank of the output_backward. The dimensions of the result will be filters, height, width (and others if applicable). - * - * It is possible to swap the order of the width and height dimensions provided that the same order is used in the input, the kernel, and the output. - * - */ -// TODO(gpapan): Resolve a bug in TensorContractionInputMapper at SpatialConvolutions.h that yangke circumvented by using .reshape().reshape(). -// This can significantly accelerate SpatialConvolutionBackwardKernel. - -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, 4>, const TensorReverseOp<const array<bool, 4>, const TensorReshapingOp<const DSizes<typename internal::traits<OutputBackward>::Index, 4>, 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 TensorReshapingOp<const DSizes<typename internal::traits<OutputBackward>::Index, 4>, const TensorImagePatchOp<Dynamic, Dynamic, const OutputBackward> > > > > > >, - const TensorShufflingOp<const array<typename internal::traits<OutputBackward>::Index, 4>, const TensorReverseOp<const array<bool, 4>, const TensorReshapingOp<const DSizes<typename internal::traits<OutputBackward>::Index, 4>, const TensorContractionOp<const array<IndexPair<typename internal::traits<Input>::Index>, 2>, const TensorReshapingOp<const DSizes<typename internal::traits<OutputBackward>::Index, 4>, const TensorReshapingOp<const DSizes<typename internal::traits<OutputBackward>::Index, 4>, const TensorImagePatchOp<Dynamic, Dynamic, const OutputBackward> > >, const TensorReshapingOp<const DSizes<typename internal::traits<Input>::Index, 3>, const Input> > > > > >::type -SpatialConvolutionBackwardKernel(const Input& input, const OutputBackward& output_backward, typename internal::traits<Input>::Index kernelRows, typename internal::traits<Input>::Index kernelCols, const DenseIndex stride = 1, const DenseIndex in_stride = 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); - - // stride and in_stride cannot both be larger than 1 - eigen_assert(!(stride > 1 && in_stride > 1)); - - 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 inputRows = isColMajor ? in.dimension(1) : in.dimension(NumDims - 2); - const TensorIndex inputCols = isColMajor ? in.dimension(2) : in.dimension(NumDims - 3); - - const TensorIndex outputRows = isColMajor ? output_backward.dimension(1) : output_backward.dimension(NumDims - 2); - const TensorIndex outputCols = isColMajor ? output_backward.dimension(2) : output_backward.dimension(NumDims - 3); - - // Number of filters to apply. This is the same as the output depth of the result - const TensorIndex kernelFilters = isColMajor ? out.dimensions()[0] : out.dimensions()[NumDims - 1]; - - // Number of channels. This is the same as the input depth. - const TensorIndex kernelChannels = isColMajor ? in.dimensions()[0] : in.dimensions()[NumDims - 1]; - - // This is the effective kernel size, taking into account the (in_stride - 1) zero-values - // inserted between consecutive kernel elements in atrous convolution - const TensorIndex kernelRowsEff = kernelRows + (kernelRows - 1) * (in_stride - 1); - const TensorIndex kernelColsEff = kernelCols + (kernelCols - 1) * (in_stride - 1); - - // Computing the forward padding - const TensorIndex forward_pad_top = ((outputRows - 1) * stride + kernelRowsEff - inputRows) / 2; - const TensorIndex forward_pad_left = ((outputCols - 1) * stride + kernelColsEff - inputCols) / 2; - - // TODO: factor out the padding computation. - const TensorIndex padding_top = kernelRowsEff - 1 - forward_pad_top; - const TensorIndex padding_left = kernelColsEff - 1 - forward_pad_left; - const TensorIndex padding_bottom = inputRows + kernelRowsEff - 1 - (outputRows - 1) * stride - 1 - padding_top; - const TensorIndex padding_right = inputCols + kernelColsEff - 1 - (outputCols - 1) * stride - 1 - padding_left; - - 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_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_rows * input_cols) X (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; - pre_contract_dims[2] = kernelRows * kernelCols; - pre_contract_dims[3] = 1; - for (int i = 3; i < NumDims; ++i) { - pre_contract_dims[3] *= out.dimension(i); - } - } else { - pre_contract_dims[3] = kernelFilters; - pre_contract_dims[2] = inputRows * inputCols; - pre_contract_dims[1] = kernelRows * kernelCols; - pre_contract_dims[0] = 1; - for (int i = 0; i < NumDims - 3; ++i) { - pre_contract_dims[0] *= out.dimension(i); - } - } - - // The input has dimensions in_depth X (input_rows * input_cols) X OTHERS - DSizes<TensorIndex, 3> input_dims; - if (isColMajor) { - input_dims[0] = kernelChannels; - input_dims[1] = inputRows * inputCols; - input_dims[2] = 1; - for (int i = 3; 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; - input_dims[0] = 1; - for (int i = 0; i < NumDims - 3; ++i) { - input_dims[0] *= in.dimension(i); - } - eigen_assert(input_dims[0] == pre_contract_dims[0]); - } - - // We will contract along dimensions (1, 2) in and (1, 3) in out, if - // this is col-major. - // For row-major, it's dimensions (0, 1) 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_rows X kernel_cols - // We will need to shuffle the first two dimensions and reverse the latter - // two dimensions. - // The end shape is - // out_depth X in_shape X kernel_rows X kernel_cols - - // This is the shape of the kernel *before* the shuffling. - DSizes<TensorIndex, 4> kernel_dims; - if (isColMajor) { - kernel_dims[0] = kernelChannels; - kernel_dims[1] = kernelFilters; - kernel_dims[2] = kernelRows; - kernel_dims[3] = kernelCols; - } else { - kernel_dims[0] = kernelCols; - kernel_dims[1] = kernelRows; - kernel_dims[2] = kernelFilters; - kernel_dims[3] = kernelChannels; - } - - array<TensorIndex, 4> kernel_shuffle; - if (isColMajor) { - kernel_shuffle[0] = 1; - kernel_shuffle[1] = 0; - kernel_shuffle[2] = 2; - kernel_shuffle[3] = 3; - } else { - kernel_shuffle[0] = 0; - kernel_shuffle[1] = 1; - kernel_shuffle[2] = 3; - kernel_shuffle[3] = 2; - } - - array<bool, 4> kernel_reverse; - if (isColMajor) { - kernel_reverse[0] = false; - kernel_reverse[1] = false; - kernel_reverse[2] = true; - kernel_reverse[3] = true; - } else { - kernel_reverse[0] = true; - kernel_reverse[1] = true; - kernel_reverse[2] = false; - kernel_reverse[3] = false; - } - - return choose(Cond<internal::traits<Input>::Layout == ColMajor>(), - input.reshape(input_dims).contract(output_backward.extract_image_patches(inputRows, inputCols, in_stride, in_stride, 1, 1, stride, stride, padding_top, padding_bottom, padding_left, padding_right, 0).reshape(pre_contract_dims).reshape(pre_contract_dims), contract_dims).reshape(kernel_dims).reverse(kernel_reverse).shuffle(kernel_shuffle), - output_backward.extract_image_patches(inputRows, inputCols, in_stride, in_stride, 1, 1, stride, stride, padding_top, padding_bottom, padding_left, padding_right, 0).reshape(pre_contract_dims).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_SPATIAL_CONVOLUTIONS_H |