Added support for convolution and reshaping of tensors.

unsupported/Eigen/CXX11/Tensor

@@ -40,6 +40,8 @@
 #include "unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h"
 #include "unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h"
 #include "unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h"
+#include "unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h"
+#include "unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h"
 #include "unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h"
 #include "unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h"
 

unsupported/Eigen/CXX11/src/Tensor/TensorBase.h

@@ -203,6 +203,13 @@ class TensorBase
       return TensorContractionOp<const Dimensions, const Derived, const OtherDerived>(derived(), other.derived(), dims);
     }
 
+    // Convolutions.
+    template<typename KernelDerived, typename Dimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorConvolutionOp<const Dimensions, const Derived, const KernelDerived>
+    convolve(const KernelDerived& kernel, const Dimensions& dims) const {
+      return TensorConvolutionOp<const Dimensions, const Derived, const KernelDerived>(derived(), kernel.derived(), dims);
+    }
+
     // Coefficient-wise ternary operators.
     template<typename ThenDerived, typename ElseDerived>
     inline const TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>
@@ -210,6 +217,13 @@ class TensorBase
       return TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>(derived(), thenTensor.derived(), elseTensor.derived());
     }
 
+    // Morphing operators (slicing tbd).
+    template <typename NewDimensions>
+    inline const TensorReshapingOp<const Derived, const NewDimensions>
+    reshape(const NewDimensions& newDimensions) const {
+      return TensorReshapingOp<const Derived, const NewDimensions>(derived(), newDimensions);
+    }
+
     // Select the device on which to evaluate the expression.
     template <typename DeviceType>
     TensorDevice<Derived, DeviceType> device(const DeviceType& device) {

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

unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h

@@ -21,9 +21,9 @@ template<typename NullaryOp, typename PlainObjectType> class TensorCwiseNullaryO
 template<typename UnaryOp, typename XprType> class TensorCwiseUnaryOp;
 template<typename BinaryOp, typename LeftXprType, typename RightXprType> class TensorCwiseBinaryOp;
 template<typename IfXprType, typename ThenXprType, typename ElseXprType> class TensorSelectOp;
-template <typename XprType> class TensorReductionOp;
 template<typename Dimensions, typename LeftXprType, typename RightXprType> class TensorContractionOp;
-
+template<typename Dimensions, typename InputXprType, typename KernelXprType> class TensorConvolutionOp;
+template<typename NewDimensions, typename XprType> class TensorReshapingOp;
 template<typename ExpressionType, typename DeviceType> class TensorDevice;
 
 // Move to internal?

unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h

@@ -0,0 +1,119 @@
+// 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_MORPHING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H
+
+namespace Eigen {
+
+/** \class TensorReshaping
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor reshaping class.
+  *
+  *
+  */
+namespace internal {
+template<typename XprType, typename NewDimensions>
+struct traits<TensorReshapingOp<XprType, NewDimensions> >
+{
+  // Type promotion to handle the case where the types of the lhs and the rhs are different.
+  typedef typename XprType::Scalar Scalar;
+  typedef typename internal::packet_traits<Scalar>::type Packet;
+  typedef typename traits<XprType>::StorageKind StorageKind;
+  typedef typename traits<XprType>::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+};
+
+template<typename XprType, typename NewDimensions>
+struct eval<TensorReshapingOp<XprType, NewDimensions>, Eigen::Dense>
+{
+  typedef const TensorReshapingOp<XprType, NewDimensions>& type;
+};
+
+template<typename XprType, typename NewDimensions>
+struct nested<TensorReshapingOp<XprType, NewDimensions>, 1, typename eval<TensorReshapingOp<XprType, NewDimensions> >::type>
+{
+  typedef TensorReshapingOp<XprType, NewDimensions> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename XprType, typename NewDimensions>
+class TensorReshapingOp : public TensorBase<TensorReshapingOp<XprType, NewDimensions> >
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorReshapingOp>::Scalar Scalar;
+  typedef typename Eigen::internal::traits<TensorReshapingOp>::Packet Packet;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename XprType::PacketReturnType PacketReturnType;
+  typedef typename Eigen::internal::nested<TensorReshapingOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorReshapingOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorReshapingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReshapingOp(const XprType& expr, const NewDimensions& dims)
+      : m_xpr(expr), m_dims(dims) {}
+
+    EIGEN_DEVICE_FUNC
+    const NewDimensions& dimensions() const { return m_dims; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const NewDimensions m_dims;
+};
+
+
+template<typename ArgType, typename NewDimensions>
+struct TensorEvaluator<const TensorReshapingOp<ArgType, NewDimensions> >
+{
+  typedef TensorReshapingOp<ArgType, NewDimensions> XprType;
+
+  enum {
+    IsAligned = TensorEvaluator<ArgType>::IsAligned,
+    PacketAccess = TensorEvaluator<ArgType>::PacketAccess,
+  };
+
+  TensorEvaluator(const XprType& op)
+      : m_impl(op.expression()), m_dimensions(op.dimensions())
+  { }
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename XprType::PacketReturnType PacketReturnType;
+
+  const NewDimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    return m_impl.coeff(index);
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    return m_impl.template packet<LoadMode>(index);
+  }
+
+ private:
+  NewDimensions m_dimensions;
+  TensorEvaluator<ArgType> m_impl;
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H

unsupported/test/cxx11_tensor_convolution.cpp

@@ -0,0 +1,70 @@
+// 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/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+
+static void test_evals()
+{
+  Tensor<float, 2> input(3, 3);
+  Tensor<float, 1> kernel(2);
+
+  input.setRandom();
+  kernel.setRandom();
+
+  Tensor<float, 2> result(2,3);
+  result.setZero();
+  Eigen::array<Tensor<float, 2>::Index, 1> dims3({0});
+
+  TensorEvaluator<decltype(input.convolve(kernel, dims3))> eval(input.convolve(kernel, dims3));
+  eval.evalTo(result.data());
+  EIGEN_STATIC_ASSERT(TensorEvaluator<decltype(input.convolve(kernel, dims3))>::NumDims==2ul, YOU_MADE_A_PROGRAMMING_MISTAKE);
+  VERIFY_IS_EQUAL(eval.dimensions()[0], 2);
+  VERIFY_IS_EQUAL(eval.dimensions()[1], 3);
+
+  VERIFY_IS_APPROX(result(0,0), input(0,0)*kernel(0) + input(1,0)*kernel(1));  // index 0
+  VERIFY_IS_APPROX(result(0,1), input(0,1)*kernel(0) + input(1,1)*kernel(1));  // index 2
+  VERIFY_IS_APPROX(result(0,2), input(0,2)*kernel(0) + input(1,2)*kernel(1));  // index 4
+  VERIFY_IS_APPROX(result(1,0), input(1,0)*kernel(0) + input(2,0)*kernel(1));  // index 1
+  VERIFY_IS_APPROX(result(1,1), input(1,1)*kernel(0) + input(2,1)*kernel(1));  // index 3
+  VERIFY_IS_APPROX(result(1,2), input(1,2)*kernel(0) + input(2,2)*kernel(1));  // index 5
+}
+
+
+static void test_expr()
+{
+  Tensor<float, 2> input(3, 3);
+  Tensor<float, 2> kernel(2, 2);
+  input.setRandom();
+  kernel.setRandom();
+
+  Tensor<float, 2> result(2,2);
+  Eigen::array<ptrdiff_t, 2> dims({0, 1});
+  result = input.convolve(kernel, dims);
+
+  VERIFY_IS_APPROX(result(0,0), input(0,0)*kernel(0,0) + input(0,1)*kernel(0,1) +
+                                input(1,0)*kernel(1,0) + input(1,1)*kernel(1,1));
+  VERIFY_IS_APPROX(result(0,1), input(0,1)*kernel(0,0) + input(0,2)*kernel(0,1) +
+                                input(1,1)*kernel(1,0) + input(1,2)*kernel(1,1));
+  VERIFY_IS_APPROX(result(1,0), input(1,0)*kernel(0,0) + input(1,1)*kernel(0,1) +
+                                input(2,0)*kernel(1,0) + input(2,1)*kernel(1,1));
+  VERIFY_IS_APPROX(result(1,1), input(1,1)*kernel(0,0) + input(1,2)*kernel(0,1) +
+                                input(2,1)*kernel(1,0) + input(2,2)*kernel(1,1));
+}
+
+
+void test_cxx11_tensor_convolution()
+{
+  CALL_SUBTEST(test_evals());
+  CALL_SUBTEST(test_expr());
+}