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eigen/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h
Maozhou, Ge 21a8a2487c fix paddings of TensorVolumePatchOp
2021-01-15 11:51:49 +08:00

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
#ifndef EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H
#define EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H
namespace Eigen {
/** \class TensorVolumePatch
* \ingroup CXX11_Tensor_Module
*
* \brief Patch extraction specialized for processing of volumetric data.
* This assumes that the input has a least 4 dimensions ordered as follows:
* - channels
* - planes
* - rows
* - columns
* - (optional) additional dimensions such as time or batch size.
* Calling the volume patch code with patch_planes, patch_rows, and patch_cols
* is equivalent to calling the regular patch extraction code with parameters
* d, patch_planes, patch_rows, patch_cols, and 1 for all the additional
* dimensions.
*/
namespace internal {
template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
struct traits<TensorVolumePatchOp<Planes, Rows, Cols, XprType> > : public traits<XprType>
{
typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
typedef traits<XprType> XprTraits;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = XprTraits::NumDimensions + 1;
static const int Layout = XprTraits::Layout;
typedef typename XprTraits::PointerType PointerType;
};
template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
struct eval<TensorVolumePatchOp<Planes, Rows, Cols, XprType>, Eigen::Dense>
{
typedef const TensorVolumePatchOp<Planes, Rows, Cols, XprType>& type;
};
template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
struct nested<TensorVolumePatchOp<Planes, Rows, Cols, XprType>, 1, typename eval<TensorVolumePatchOp<Planes, Rows, Cols, XprType> >::type>
{
typedef TensorVolumePatchOp<Planes, Rows, Cols, XprType> type;
};
} // end namespace internal
template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
class TensorVolumePatchOp : public TensorBase<TensorVolumePatchOp<Planes, Rows, Cols, XprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorVolumePatchOp>::Scalar Scalar;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename Eigen::internal::nested<TensorVolumePatchOp>::type Nested;
typedef typename Eigen::internal::traits<TensorVolumePatchOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorVolumePatchOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorVolumePatchOp(const XprType& expr, DenseIndex patch_planes, DenseIndex patch_rows, DenseIndex patch_cols,
DenseIndex plane_strides, DenseIndex row_strides, DenseIndex col_strides,
DenseIndex in_plane_strides, DenseIndex in_row_strides, DenseIndex in_col_strides,
DenseIndex plane_inflate_strides, DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
PaddingType padding_type, Scalar padding_value)
: m_xpr(expr), m_patch_planes(patch_planes), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
m_plane_strides(plane_strides), m_row_strides(row_strides), m_col_strides(col_strides),
m_in_plane_strides(in_plane_strides), m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
m_plane_inflate_strides(plane_inflate_strides), m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
m_padding_explicit(false), m_padding_top_z(0), m_padding_bottom_z(0), m_padding_top(0), m_padding_bottom(0), m_padding_left(0), m_padding_right(0),
m_padding_type(padding_type), m_padding_value(padding_value) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorVolumePatchOp(const XprType& expr, DenseIndex patch_planes, DenseIndex patch_rows, DenseIndex patch_cols,
DenseIndex plane_strides, DenseIndex row_strides, DenseIndex col_strides,
DenseIndex in_plane_strides, DenseIndex in_row_strides, DenseIndex in_col_strides,
DenseIndex plane_inflate_strides, DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
DenseIndex padding_top_z, DenseIndex padding_bottom_z,
DenseIndex padding_top, DenseIndex padding_bottom,
DenseIndex padding_left, DenseIndex padding_right,
Scalar padding_value)
: m_xpr(expr), m_patch_planes(patch_planes), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
m_plane_strides(plane_strides), m_row_strides(row_strides), m_col_strides(col_strides),
m_in_plane_strides(in_plane_strides), m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
m_plane_inflate_strides(plane_inflate_strides), m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
m_padding_explicit(true), m_padding_top_z(padding_top_z), m_padding_bottom_z(padding_bottom_z), m_padding_top(padding_top), m_padding_bottom(padding_bottom),
m_padding_left(padding_left), m_padding_right(padding_right),
m_padding_type(PADDING_VALID), m_padding_value(padding_value) {}
EIGEN_DEVICE_FUNC
DenseIndex patch_planes() const { return m_patch_planes; }
EIGEN_DEVICE_FUNC
DenseIndex patch_rows() const { return m_patch_rows; }
EIGEN_DEVICE_FUNC
DenseIndex patch_cols() const { return m_patch_cols; }
EIGEN_DEVICE_FUNC
DenseIndex plane_strides() const { return m_plane_strides; }
EIGEN_DEVICE_FUNC
DenseIndex row_strides() const { return m_row_strides; }
EIGEN_DEVICE_FUNC
DenseIndex col_strides() const { return m_col_strides; }
EIGEN_DEVICE_FUNC
DenseIndex in_plane_strides() const { return m_in_plane_strides; }
EIGEN_DEVICE_FUNC
DenseIndex in_row_strides() const { return m_in_row_strides; }
EIGEN_DEVICE_FUNC
DenseIndex in_col_strides() const { return m_in_col_strides; }
EIGEN_DEVICE_FUNC
DenseIndex plane_inflate_strides() const { return m_plane_inflate_strides; }
EIGEN_DEVICE_FUNC
DenseIndex row_inflate_strides() const { return m_row_inflate_strides; }
EIGEN_DEVICE_FUNC
DenseIndex col_inflate_strides() const { return m_col_inflate_strides; }
EIGEN_DEVICE_FUNC
bool padding_explicit() const { return m_padding_explicit; }
EIGEN_DEVICE_FUNC
DenseIndex padding_top_z() const { return m_padding_top_z; }
EIGEN_DEVICE_FUNC
DenseIndex padding_bottom_z() const { return m_padding_bottom_z; }
EIGEN_DEVICE_FUNC
DenseIndex padding_top() const { return m_padding_top; }
EIGEN_DEVICE_FUNC
DenseIndex padding_bottom() const { return m_padding_bottom; }
EIGEN_DEVICE_FUNC
DenseIndex padding_left() const { return m_padding_left; }
EIGEN_DEVICE_FUNC
DenseIndex padding_right() const { return m_padding_right; }
EIGEN_DEVICE_FUNC
PaddingType padding_type() const { return m_padding_type; }
EIGEN_DEVICE_FUNC
Scalar padding_value() const { return m_padding_value; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
const DenseIndex m_patch_planes;
const DenseIndex m_patch_rows;
const DenseIndex m_patch_cols;
const DenseIndex m_plane_strides;
const DenseIndex m_row_strides;
const DenseIndex m_col_strides;
const DenseIndex m_in_plane_strides;
const DenseIndex m_in_row_strides;
const DenseIndex m_in_col_strides;
const DenseIndex m_plane_inflate_strides;
const DenseIndex m_row_inflate_strides;
const DenseIndex m_col_inflate_strides;
const bool m_padding_explicit;
const DenseIndex m_padding_top_z;
const DenseIndex m_padding_bottom_z;
const DenseIndex m_padding_top;
const DenseIndex m_padding_bottom;
const DenseIndex m_padding_left;
const DenseIndex m_padding_right;
const PaddingType m_padding_type;
const Scalar m_padding_value;
};
// Eval as rvalue
template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device>
struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, Device>
{
typedef TensorVolumePatchOp<Planes, Rows, Cols, ArgType> XprType;
typedef typename XprType::Index Index;
static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
static const int NumDims = NumInputDims + 1;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
typedef StorageMemory<CoeffReturnType, Device> Storage;
typedef typename Storage::Type EvaluatorPointerType;
enum {
IsAligned = false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
BlockAccess = false,
PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false,
RawAccess = false
};
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockNotImplemented TensorBlock;
//===--------------------------------------------------------------------===//
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) :
m_impl(op.expression(), device)
{
EIGEN_STATIC_ASSERT((NumDims >= 5), YOU_MADE_A_PROGRAMMING_MISTAKE);
m_paddingValue = op.padding_value();
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
// Cache a few variables.
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_inputDepth = input_dims[0];
m_inputPlanes = input_dims[1];
m_inputRows = input_dims[2];
m_inputCols = input_dims[3];
} else {
m_inputDepth = input_dims[NumInputDims-1];
m_inputPlanes = input_dims[NumInputDims-2];
m_inputRows = input_dims[NumInputDims-3];
m_inputCols = input_dims[NumInputDims-4];
}
m_plane_strides = op.plane_strides();
m_row_strides = op.row_strides();
m_col_strides = op.col_strides();
// Input strides and effective input/patch size
m_in_plane_strides = op.in_plane_strides();
m_in_row_strides = op.in_row_strides();
m_in_col_strides = op.in_col_strides();
m_plane_inflate_strides = op.plane_inflate_strides();
m_row_inflate_strides = op.row_inflate_strides();
m_col_inflate_strides = op.col_inflate_strides();
// The "effective" spatial size after inflating data with zeros.
m_input_planes_eff = (m_inputPlanes - 1) * m_plane_inflate_strides + 1;
m_input_rows_eff = (m_inputRows - 1) * m_row_inflate_strides + 1;
m_input_cols_eff = (m_inputCols - 1) * m_col_inflate_strides + 1;
m_patch_planes_eff = op.patch_planes() + (op.patch_planes() - 1) * (m_in_plane_strides - 1);
m_patch_rows_eff = op.patch_rows() + (op.patch_rows() - 1) * (m_in_row_strides - 1);
m_patch_cols_eff = op.patch_cols() + (op.patch_cols() - 1) * (m_in_col_strides - 1);
if (op.padding_explicit()) {
m_outputPlanes = numext::ceil((m_input_planes_eff + op.padding_top_z() + op.padding_bottom_z() - m_patch_planes_eff + 1.f) / static_cast<float>(m_plane_strides));
m_outputRows = numext::ceil((m_input_rows_eff + op.padding_top() + op.padding_bottom() - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
m_outputCols = numext::ceil((m_input_cols_eff + op.padding_left() + op.padding_right() - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
m_planePaddingTop = op.padding_top_z();
m_rowPaddingTop = op.padding_top();
m_colPaddingLeft = op.padding_left();
} else {
// Computing padding from the type
switch (op.padding_type()) {
case PADDING_VALID:
m_outputPlanes = numext::ceil((m_input_planes_eff - m_patch_planes_eff + 1.f) / static_cast<float>(m_plane_strides));
m_outputRows = numext::ceil((m_input_rows_eff - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
m_outputCols = numext::ceil((m_input_cols_eff - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
m_planePaddingTop = 0;
m_rowPaddingTop = 0;
m_colPaddingLeft = 0;
break;
case PADDING_SAME: {
m_outputPlanes = numext::ceil(m_input_planes_eff / static_cast<float>(m_plane_strides));
m_outputRows = numext::ceil(m_input_rows_eff / static_cast<float>(m_row_strides));
m_outputCols = numext::ceil(m_input_cols_eff / static_cast<float>(m_col_strides));
const Index dz = (m_outputPlanes - 1) * m_plane_strides + m_patch_planes_eff - m_input_planes_eff;
const Index dy = (m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff;
const Index dx = (m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff;
m_planePaddingTop = dz / 2;
m_rowPaddingTop = dy / 2;
m_colPaddingLeft = dx / 2;
break;
}
default:
eigen_assert(false && "unexpected padding");
}
}
eigen_assert(m_outputRows > 0);
eigen_assert(m_outputCols > 0);
eigen_assert(m_outputPlanes > 0);
// Dimensions for result of extraction.
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
// ColMajor
// 0: depth
// 1: patch_planes
// 2: patch_rows
// 3: patch_cols
// 4: number of patches
// 5 and beyond: anything else (such as batch).
m_dimensions[0] = input_dims[0];
m_dimensions[1] = op.patch_planes();
m_dimensions[2] = op.patch_rows();
m_dimensions[3] = op.patch_cols();
m_dimensions[4] = m_outputPlanes * m_outputRows * m_outputCols;
for (int i = 5; i < NumDims; ++i) {
m_dimensions[i] = input_dims[i-1];
}
} else {
// RowMajor
// NumDims-1: depth
// NumDims-2: patch_planes
// NumDims-3: patch_rows
// NumDims-4: patch_cols
// NumDims-5: number of patches
// NumDims-6 and beyond: anything else (such as batch).
m_dimensions[NumDims-1] = input_dims[NumInputDims-1];
m_dimensions[NumDims-2] = op.patch_planes();
m_dimensions[NumDims-3] = op.patch_rows();
m_dimensions[NumDims-4] = op.patch_cols();
m_dimensions[NumDims-5] = m_outputPlanes * m_outputRows * m_outputCols;
for (int i = NumDims-6; i >= 0; --i) {
m_dimensions[i] = input_dims[i];
}
}
// Strides for the output tensor.
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_rowStride = m_dimensions[1];
m_colStride = m_dimensions[2] * m_rowStride;
m_patchStride = m_colStride * m_dimensions[3] * m_dimensions[0];
m_otherStride = m_patchStride * m_dimensions[4];
} else {
m_rowStride = m_dimensions[NumDims-2];
m_colStride = m_dimensions[NumDims-3] * m_rowStride;
m_patchStride = m_colStride * m_dimensions[NumDims-4] * m_dimensions[NumDims-1];
m_otherStride = m_patchStride * m_dimensions[NumDims-5];
}
// Strides for navigating through the input tensor.
m_planeInputStride = m_inputDepth;
m_rowInputStride = m_inputDepth * m_inputPlanes;
m_colInputStride = m_inputDepth * m_inputRows * m_inputPlanes;
m_otherInputStride = m_inputDepth * m_inputRows * m_inputCols * m_inputPlanes;
m_outputPlanesRows = m_outputPlanes * m_outputRows;
// Fast representations of different variables.
m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
m_fastRowStride = internal::TensorIntDivisor<Index>(m_rowStride);
m_fastInputRowStride = internal::TensorIntDivisor<Index>(m_row_inflate_strides);
m_fastInputColStride = internal::TensorIntDivisor<Index>(m_col_inflate_strides);
m_fastInputPlaneStride = internal::TensorIntDivisor<Index>(m_plane_inflate_strides);
m_fastInputColsEff = internal::TensorIntDivisor<Index>(m_input_cols_eff);
m_fastOutputPlanes = internal::TensorIntDivisor<Index>(m_outputPlanes);
m_fastOutputPlanesRows = internal::TensorIntDivisor<Index>(m_outputPlanesRows);
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[0]);
} else {
m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims-1]);
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
// Patch index corresponding to the passed in index.
const Index patchIndex = index / m_fastPatchStride;
// Spatial offset within the patch. This has to be translated into 3D
// coordinates within the patch.
const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastOutputDepth;
// Batch, etc.
const Index otherIndex = (NumDims == 5) ? 0 : index / m_fastOtherStride;
const Index patch3DIndex = (NumDims == 5) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride;
// Calculate column index in the input original tensor.
const Index colIndex = patch3DIndex / m_fastOutputPlanesRows;
const Index colOffset = patchOffset / m_fastColStride;
const Index inputCol = colIndex * m_col_strides + colOffset * m_in_col_strides - m_colPaddingLeft;
const Index origInputCol = (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInputColStride) : 0);
if (inputCol < 0 || inputCol >= m_input_cols_eff ||
((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides))) {
return Scalar(m_paddingValue);
}
// Calculate row index in the original input tensor.
const Index rowIndex = (patch3DIndex - colIndex * m_outputPlanesRows) / m_fastOutputPlanes;
const Index rowOffset = (patchOffset - colOffset * m_colStride) / m_fastRowStride;
const Index inputRow = rowIndex * m_row_strides + rowOffset * m_in_row_strides - m_rowPaddingTop;
const Index origInputRow = (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInputRowStride) : 0);
if (inputRow < 0 || inputRow >= m_input_rows_eff ||
((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) {
return Scalar(m_paddingValue);
}
// Calculate plane index in the original input tensor.
const Index planeIndex = (patch3DIndex - m_outputPlanes * (colIndex * m_outputRows + rowIndex));
const Index planeOffset = patchOffset - colOffset * m_colStride - rowOffset * m_rowStride;
const Index inputPlane = planeIndex * m_plane_strides + planeOffset * m_in_plane_strides - m_planePaddingTop;
const Index origInputPlane = (m_plane_inflate_strides == 1) ? inputPlane : ((inputPlane >= 0) ? (inputPlane / m_fastInputPlaneStride) : 0);
if (inputPlane < 0 || inputPlane >= m_input_planes_eff ||
((m_plane_inflate_strides != 1) && (inputPlane != origInputPlane * m_plane_inflate_strides))) {
return Scalar(m_paddingValue);
}
const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
const Index inputIndex = depth +
origInputRow * m_rowInputStride +
origInputCol * m_colInputStride +
origInputPlane * m_planeInputStride +
otherIndex * m_otherInputStride;
return m_impl.coeff(inputIndex);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
if (m_in_row_strides != 1 || m_in_col_strides != 1 || m_row_inflate_strides != 1 || m_col_inflate_strides != 1 ||
m_in_plane_strides != 1 || m_plane_inflate_strides != 1) {
return packetWithPossibleZero(index);
}
const Index indices[2] = {index, index + PacketSize - 1};
const Index patchIndex = indices[0] / m_fastPatchStride;
if (patchIndex != indices[1] / m_fastPatchStride) {
return packetWithPossibleZero(index);
}
const Index otherIndex = (NumDims == 5) ? 0 : indices[0] / m_fastOtherStride;
eigen_assert(otherIndex == indices[1] / m_fastOtherStride);
// Find the offset of the element wrt the location of the first element.
const Index patchOffsets[2] = {(indices[0] - patchIndex * m_patchStride) / m_fastOutputDepth,
(indices[1] - patchIndex * m_patchStride) / m_fastOutputDepth};
const Index patch3DIndex = (NumDims == 5) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride;
eigen_assert(patch3DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride);
const Index colIndex = patch3DIndex / m_fastOutputPlanesRows;
const Index colOffsets[2] = {
patchOffsets[0] / m_fastColStride,
patchOffsets[1] / m_fastColStride};
// Calculate col indices in the original input tensor.
const Index inputCols[2] = {
colIndex * m_col_strides + colOffsets[0] - m_colPaddingLeft,
colIndex * m_col_strides + colOffsets[1] - m_colPaddingLeft};
if (inputCols[1] < 0 || inputCols[0] >= m_inputCols) {
return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
}
if (inputCols[0] != inputCols[1]) {
return packetWithPossibleZero(index);
}
const Index rowIndex = (patch3DIndex - colIndex * m_outputPlanesRows) / m_fastOutputPlanes;
const Index rowOffsets[2] = {
(patchOffsets[0] - colOffsets[0] * m_colStride) / m_fastRowStride,
(patchOffsets[1] - colOffsets[1] * m_colStride) / m_fastRowStride};
eigen_assert(rowOffsets[0] <= rowOffsets[1]);
// Calculate col indices in the original input tensor.
const Index inputRows[2] = {
rowIndex * m_row_strides + rowOffsets[0] - m_rowPaddingTop,
rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop};
if (inputRows[1] < 0 || inputRows[0] >= m_inputRows) {
return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
}
if (inputRows[0] != inputRows[1]) {
return packetWithPossibleZero(index);
}
const Index planeIndex = (patch3DIndex - m_outputPlanes * (colIndex * m_outputRows + rowIndex));
const Index planeOffsets[2] = {
patchOffsets[0] - colOffsets[0] * m_colStride - rowOffsets[0] * m_rowStride,
patchOffsets[1] - colOffsets[1] * m_colStride - rowOffsets[1] * m_rowStride};
eigen_assert(planeOffsets[0] <= planeOffsets[1]);
const Index inputPlanes[2] = {
planeIndex * m_plane_strides + planeOffsets[0] - m_planePaddingTop,
planeIndex * m_plane_strides + planeOffsets[1] - m_planePaddingTop};
if (inputPlanes[1] < 0 || inputPlanes[0] >= m_inputPlanes) {
return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
}
if (inputPlanes[0] >= 0 && inputPlanes[1] < m_inputPlanes) {
// no padding
const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
const Index inputIndex = depth +
inputRows[0] * m_rowInputStride +
inputCols[0] * m_colInputStride +
m_planeInputStride * inputPlanes[0] +
otherIndex * m_otherInputStride;
return m_impl.template packet<Unaligned>(inputIndex);
}
return packetWithPossibleZero(index);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
costPerCoeff(bool vectorized) const {
const double compute_cost =
10 * TensorOpCost::DivCost<Index>() + 21 * TensorOpCost::MulCost<Index>() +
8 * TensorOpCost::AddCost<Index>();
return TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
Index planePaddingTop() const { return m_planePaddingTop; }
Index rowPaddingTop() const { return m_rowPaddingTop; }
Index colPaddingLeft() const { return m_colPaddingLeft; }
Index outputPlanes() const { return m_outputPlanes; }
Index outputRows() const { return m_outputRows; }
Index outputCols() const { return m_outputCols; }
Index userPlaneStride() const { return m_plane_strides; }
Index userRowStride() const { return m_row_strides; }
Index userColStride() const { return m_col_strides; }
Index userInPlaneStride() const { return m_in_plane_strides; }
Index userInRowStride() const { return m_in_row_strides; }
Index userInColStride() const { return m_in_col_strides; }
Index planeInflateStride() const { return m_plane_inflate_strides; }
Index rowInflateStride() const { return m_row_inflate_strides; }
Index colInflateStride() const { return m_col_inflate_strides; }
#ifdef EIGEN_USE_SYCL
// binding placeholder accessors to a command group handler for SYCL
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
m_impl.bind(cgh);
}
#endif
protected:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
{
EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
EIGEN_UNROLL_LOOP
for (int i = 0; i < PacketSize; ++i) {
values[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
Dimensions m_dimensions;
// Parameters passed to the constructor.
Index m_plane_strides;
Index m_row_strides;
Index m_col_strides;
Index m_outputPlanes;
Index m_outputRows;
Index m_outputCols;
Index m_planePaddingTop;
Index m_rowPaddingTop;
Index m_colPaddingLeft;
Index m_in_plane_strides;
Index m_in_row_strides;
Index m_in_col_strides;
Index m_plane_inflate_strides;
Index m_row_inflate_strides;
Index m_col_inflate_strides;
// Cached input size.
Index m_inputDepth;
Index m_inputPlanes;
Index m_inputRows;
Index m_inputCols;
// Other cached variables.
Index m_outputPlanesRows;
// Effective input/patch post-inflation size.
Index m_input_planes_eff;
Index m_input_rows_eff;
Index m_input_cols_eff;
Index m_patch_planes_eff;
Index m_patch_rows_eff;
Index m_patch_cols_eff;
// Strides for the output tensor.
Index m_otherStride;
Index m_patchStride;
Index m_rowStride;
Index m_colStride;
// Strides for the input tensor.
Index m_planeInputStride;
Index m_rowInputStride;
Index m_colInputStride;
Index m_otherInputStride;
internal::TensorIntDivisor<Index> m_fastOtherStride;
internal::TensorIntDivisor<Index> m_fastPatchStride;
internal::TensorIntDivisor<Index> m_fastColStride;
internal::TensorIntDivisor<Index> m_fastRowStride;
internal::TensorIntDivisor<Index> m_fastInputPlaneStride;
internal::TensorIntDivisor<Index> m_fastInputRowStride;
internal::TensorIntDivisor<Index> m_fastInputColStride;
internal::TensorIntDivisor<Index> m_fastInputColsEff;
internal::TensorIntDivisor<Index> m_fastOutputPlanesRows;
internal::TensorIntDivisor<Index> m_fastOutputPlanes;
internal::TensorIntDivisor<Index> m_fastOutputDepth;
Scalar m_paddingValue;
TensorEvaluator<ArgType, Device> m_impl;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H
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