Added support for convolution and reshaping of tensors.

1 files changed, 206 insertions, 0 deletions

diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h b/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h
new file mode 100644
index 000000000..ca2e0e562
--- /dev/null
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h
@@ -0,0 +1,206 @@
+// 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_TENSOR_TENSOR_CONVOLUTION_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
+
+namespace Eigen {
+
+/** \class TensorConvolution
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor convolution class.
+  *
+  *
+  */
+namespace internal {
+template<typename Dimensions, typename InputXprType, typename KernelXprType>
+struct traits<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> >
+{
+  // Type promotion to handle the case where the types of the lhs and the rhs are different.
+  typedef typename internal::promote_storage_type<typename InputXprType::Scalar,
+                                                  typename KernelXprType::Scalar>::ret Scalar;
+  typedef typename internal::packet_traits<Scalar>::type Packet;
+  typedef typename promote_storage_type<typename traits<InputXprType>::StorageKind,
+                                        typename traits<KernelXprType>::StorageKind>::ret StorageKind;
+  typedef typename promote_index_type<typename traits<InputXprType>::Index,
+                                      typename traits<KernelXprType>::Index>::type Index;
+  typedef typename InputXprType::Nested LhsNested;
+  typedef typename KernelXprType::Nested RhsNested;
+  typedef typename remove_reference<LhsNested>::type _LhsNested;
+  typedef typename remove_reference<RhsNested>::type _RhsNested;
+};
+
+template<typename Dimensions, typename InputXprType, typename KernelXprType>
+struct eval<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>, Eigen::Dense>
+{
+  typedef const TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>& type;
+};
+
+template<typename Dimensions, typename InputXprType, typename KernelXprType>
+struct nested<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>, 1, typename eval<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> >::type>
+{
+  typedef TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename Indices, typename InputXprType, typename KernelXprType>
+class TensorConvolutionOp : public TensorBase<TensorConvolutionOp<Indices, InputXprType, KernelXprType> >
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorConvolutionOp>::Scalar Scalar;
+  typedef typename Eigen::internal::traits<TensorConvolutionOp>::Packet Packet;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename internal::promote_storage_type<typename InputXprType::CoeffReturnType,
+                                                  typename KernelXprType::CoeffReturnType>::ret CoeffReturnType;
+  typedef typename internal::promote_storage_type<typename InputXprType::PacketReturnType,
+                                                  typename KernelXprType::PacketReturnType>::ret PacketReturnType;
+  typedef typename Eigen::internal::nested<TensorConvolutionOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorConvolutionOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorConvolutionOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConvolutionOp(const InputXprType& input, const KernelXprType& kernel, const Indices& dims)
+      : m_input_xpr(input), m_kernel_xpr(kernel), m_indices(dims) {}
+
+    EIGEN_DEVICE_FUNC
+    const Indices& indices() const { return m_indices; }
+
+    /** \returns the nested expressions */
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename InputXprType::Nested>::type&
+    inputExpression() const { return m_input_xpr; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename KernelXprType::Nested>::type&
+    kernelExpression() const { return m_kernel_xpr; }
+
+  protected:
+    typename InputXprType::Nested m_input_xpr;
+    typename KernelXprType::Nested m_kernel_xpr;
+    const Indices m_indices;
+};
+
+
+template<typename Indices, typename InputArgType, typename KernelArgType>
+struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType> >
+{
+  typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType;
+
+  static const int NumDims = TensorEvaluator<InputArgType>::Dimensions::count;
+  static const int KernelDims = Indices::size;
+  typedef typename XprType::Index Index;
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  enum {
+    IsAligned = TensorEvaluator<InputArgType>::IsAligned & TensorEvaluator<KernelArgType>::IsAligned,
+    PacketAccess = /*TensorEvaluator<InputArgType>::PacketAccess & TensorEvaluator<KernelArgType>::PacketAccess */
+                   false,
+  };
+
+  TensorEvaluator(const XprType& op)
+      : m_inputImpl(op.inputExpression()), m_kernelImpl(op.kernelExpression()), m_dimensions(op.inputExpression().dimensions())
+  {
+    const typename TensorEvaluator<InputArgType>::Dimensions& input_dims = m_inputImpl.dimensions();
+    const typename TensorEvaluator<KernelArgType>::Dimensions& kernel_dims = m_kernelImpl.dimensions();
+
+    for (int i = 0; i < NumDims; ++i) {
+      if (i > 0) {
+        m_inputStride[i] = m_inputStride[i-1] * input_dims[i-1];
+      } else {
+        m_inputStride[0] = 1;
+      }
+    }
+
+    for (int i = 0; i < KernelDims; ++i) {
+      const Index index = op.indices()[i];
+      const Index input_dim = input_dims[index];
+      const Index kernel_dim = kernel_dims[i];
+      const Index result_dim = input_dim - kernel_dim + 1;
+      m_dimensions[index] = result_dim;
+
+      if (i > 0) {
+        m_kernelStride[i] = m_kernelStride[i-1] * kernel_dims[i-1];
+      } else {
+        m_kernelStride[0] = 1;
+      }
+      m_indexStride[i] = m_inputStride[index];
+    }
+
+    for (int i = 0; i < NumDims; ++i) {
+      if (i > 0) {
+        m_outputStride[i] = m_outputStride[i-1] * m_dimensions[i-1];
+      } else {
+        m_outputStride[0] = 1;
+      }
+    }
+  }
+
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename XprType::PacketReturnType PacketReturnType;
+
+  const Dimensions& dimensions() const { return m_dimensions; }
+
+  void evalTo(typename XprType::Scalar* buffer) const {
+    for (int i = 0; i < dimensions().TotalSize(); ++i) {
+      buffer[i] += coeff(i);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    Index startInput = 0;
+    for (int i = NumDims - 1; i >= 0; --i) {
+      const Index idx = index / m_outputStride[i];
+      startInput += idx * m_inputStride[i];
+      index -= idx * m_outputStride[i];
+    }
+
+    CoeffReturnType result = CoeffReturnType(0);
+    convolve(startInput, 0, 0, result);
+    return result;
+  }
+
+  /* TODO: vectorization
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
+  {
+    assert(false);
+  }*/
+
+  EIGEN_DEVICE_FUNC void convolve(Index firstIndex, Index firstKernel, int DimIndex, CoeffReturnType& accum) const {
+    for (int j = 0; j < m_kernelImpl.dimensions()[DimIndex]; ++j) {
+      const Index input = firstIndex + j * m_indexStride[DimIndex];
+      const Index kernel = firstKernel + j * m_kernelStride[DimIndex];
+      if (DimIndex < KernelDims-1) {
+        convolve(input, kernel, DimIndex+1, accum);
+      } else {
+
+        accum += m_inputImpl.coeff(input) * m_kernelImpl.coeff(kernel);
+      }
+    }
+  }
+
+ private:
+  array<Index, NumDims> m_inputStride;
+  array<Index, NumDims> m_outputStride;
+
+  array<Index, KernelDims> m_indexStride;
+  array<Index, KernelDims> m_kernelStride;
+  Dimensions m_dimensions;
+  TensorEvaluator<InputArgType> m_inputImpl;
+  TensorEvaluator<KernelArgType> m_kernelImpl;
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H