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Tobias Wood
2023-11-29 11:12:48 +00:00
parent 9ea520fc45
commit f38e16c193
534 changed files with 103368 additions and 116934 deletions
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393
unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h
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@@ -10,25 +10,24 @@
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.
*/
* \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>
{
template <DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
struct traits<TensorVolumePatchOp<Planes, Rows, Cols, XprType> > : public traits<XprType> {
typedef std::remove_const_t<typename XprType::Scalar> Scalar;
typedef traits<XprType> XprTraits;
typedef typename XprTraits::StorageKind StorageKind;
@@ -38,27 +37,24 @@ struct traits<TensorVolumePatchOp<Planes, Rows, Cols, XprType> > : public traits
static constexpr int NumDimensions = XprTraits::NumDimensions + 1;
static constexpr 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>
{
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>
{
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:
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;
@@ -66,114 +62,120 @@ class TensorVolumePatchOp : public TensorBase<TensorVolumePatchOp<Planes, Rows,
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, 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 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 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 internal::remove_all_t<typename XprType::Nested>&
expression() const { return m_xpr; }
EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& 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;
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>
{
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 constexpr int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
static constexpr int NumInputDims =
internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
static constexpr int NumDims = NumInputDims + 1;
typedef DSizes<Index, NumDims> Dimensions;
typedef std::remove_const_t<typename XprType::Scalar> Scalar;
@@ -197,9 +199,7 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
typedef internal::TensorBlockNotImplemented TensorBlock;
//===--------------------------------------------------------------------===//
EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) :
m_impl(op.expression(), device)
{
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();
@@ -213,10 +213,10 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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_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();
@@ -240,9 +240,13 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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_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();
@@ -250,7 +254,8 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
// 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_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;
@@ -292,7 +297,7 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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];
m_dimensions[i] = input_dims[i - 1];
}
} else {
// RowMajor
@@ -302,12 +307,12 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
// 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[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];
}
}
@@ -319,10 +324,10 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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];
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.
@@ -349,7 +354,7 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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]);
m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims - 1]);
}
}
@@ -360,12 +365,9 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
return true;
}
EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
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;
@@ -381,7 +383,8 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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);
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);
@@ -391,7 +394,8 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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);
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);
@@ -401,7 +405,8 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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);
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);
@@ -410,19 +415,15 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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;
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_assert(index+PacketSize-1 < dimensions().TotalSize());
template <int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
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) {
@@ -441,18 +442,16 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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;
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};
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};
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));
}
@@ -462,14 +461,12 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
}
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};
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};
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));
@@ -480,13 +477,11 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
}
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};
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};
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));
@@ -496,22 +491,17 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
// 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;
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 {
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>();
10 * TensorOpCost::DivCost<Index>() + 21 * TensorOpCost::MulCost<Index>() + 8 * TensorOpCost::AddCost<Index>();
return TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
}
@@ -519,7 +509,6 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index planePaddingTop() const { return m_planePaddingTop; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowPaddingTop() const { return m_rowPaddingTop; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colPaddingLeft() const { return m_colPaddingLeft; }
@@ -537,12 +526,11 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colInflateStride() const { return m_col_inflate_strides; }
protected:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
{
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const {
EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
EIGEN_UNROLL_LOOP
for (int i = 0; i < PacketSize; ++i) {
values[i] = coeff(index+i);
values[i] = coeff(index + i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
@@ -615,11 +603,8 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
Scalar m_paddingValue;
TensorEvaluator<ArgType, Device> m_impl;
};
} // end namespace Eigen
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H
#endif // EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H