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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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359
unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h
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@@ -16,24 +16,23 @@
namespace Eigen {
/** \class TensorImagePatch
* \ingroup CXX11_Tensor_Module
*
* \brief Patch extraction specialized for image processing.
* This assumes that the input has a least 3 dimensions ordered as follow:
* 1st dimension: channels (of size d)
* 2nd dimension: rows (of size r)
* 3rd dimension: columns (of size c)
* There can be additional dimensions such as time (for video) or batch (for
* bulk processing after the first 3.
* Calling the image patch code with patch_rows and patch_cols is equivalent
* to calling the regular patch extraction code with parameters d, patch_rows,
* patch_cols, and 1 for all the additional dimensions.
*/
* \ingroup CXX11_Tensor_Module
*
* \brief Patch extraction specialized for image processing.
* This assumes that the input has a least 3 dimensions ordered as follow:
* 1st dimension: channels (of size d)
* 2nd dimension: rows (of size r)
* 3rd dimension: columns (of size c)
* There can be additional dimensions such as time (for video) or batch (for
* bulk processing after the first 3.
* Calling the image patch code with patch_rows and patch_cols is equivalent
* to calling the regular patch extraction code with parameters d, patch_rows,
* patch_cols, and 1 for all the additional dimensions.
*/
namespace internal {
template<DenseIndex Rows, DenseIndex Cols, typename XprType>
struct traits<TensorImagePatchOp<Rows, Cols, XprType> > : public traits<XprType>
{
template <DenseIndex Rows, DenseIndex Cols, typename XprType>
struct traits<TensorImagePatchOp<Rows, Cols, XprType> > : public traits<XprType> {
typedef std::remove_const_t<typename XprType::Scalar> Scalar;
typedef traits<XprType> XprTraits;
typedef typename XprTraits::StorageKind StorageKind;
@@ -45,15 +44,14 @@ struct traits<TensorImagePatchOp<Rows, Cols, XprType> > : public traits<XprType>
typedef typename XprTraits::PointerType PointerType;
};
template<DenseIndex Rows, DenseIndex Cols, typename XprType>
struct eval<TensorImagePatchOp<Rows, Cols, XprType>, Eigen::Dense>
{
template <DenseIndex Rows, DenseIndex Cols, typename XprType>
struct eval<TensorImagePatchOp<Rows, Cols, XprType>, Eigen::Dense> {
typedef const TensorImagePatchOp<Rows, Cols, XprType>& type;
};
template<DenseIndex Rows, DenseIndex Cols, typename XprType>
struct nested<TensorImagePatchOp<Rows, Cols, XprType>, 1, typename eval<TensorImagePatchOp<Rows, Cols, XprType> >::type>
{
template <DenseIndex Rows, DenseIndex Cols, typename XprType>
struct nested<TensorImagePatchOp<Rows, Cols, XprType>, 1,
typename eval<TensorImagePatchOp<Rows, Cols, XprType> >::type> {
typedef TensorImagePatchOp<Rows, Cols, XprType> type;
};
@@ -62,9 +60,9 @@ struct ImagePatchCopyOp {
typedef typename Self::Index Index;
typedef typename Self::Scalar Scalar;
typedef typename Self::Impl Impl;
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
const Self& self, const Index num_coeff_to_copy, const Index dst_index,
Scalar* dst_data, const Index src_index) {
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const Self& self, const Index num_coeff_to_copy,
const Index dst_index, Scalar* dst_data,
const Index src_index) {
const Impl& impl = self.impl();
for (Index i = 0; i < num_coeff_to_copy; ++i) {
dst_data[dst_index + i] = impl.coeff(src_index + i);
@@ -78,13 +76,12 @@ struct ImagePatchCopyOp<Self, true> {
typedef typename Self::Scalar Scalar;
typedef typename Self::Impl Impl;
typedef typename packet_traits<Scalar>::type Packet;
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
const Self& self, const Index num_coeff_to_copy, const Index dst_index,
Scalar* dst_data, const Index src_index) {
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const Self& self, const Index num_coeff_to_copy,
const Index dst_index, Scalar* dst_data,
const Index src_index) {
const Impl& impl = self.impl();
const Index packet_size = internal::unpacket_traits<Packet>::size;
const Index vectorized_size =
(num_coeff_to_copy / packet_size) * packet_size;
const Index vectorized_size = (num_coeff_to_copy / packet_size) * packet_size;
for (Index i = 0; i < vectorized_size; i += packet_size) {
Packet p = impl.template packet<Unaligned>(src_index + i);
internal::pstoret<Scalar, Packet, Unaligned>(dst_data + dst_index + i, p);
@@ -100,16 +97,13 @@ struct ImagePatchPaddingOp {
typedef typename Self::Index Index;
typedef typename Self::Scalar Scalar;
typedef typename packet_traits<Scalar>::type Packet;
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
const Index num_coeff_to_pad, const Scalar padding_value,
const Index dst_index, Scalar* dst_data) {
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const Index num_coeff_to_pad, const Scalar padding_value,
const Index dst_index, Scalar* dst_data) {
const Index packet_size = internal::unpacket_traits<Packet>::size;
const Packet padded_packet = internal::pset1<Packet>(padding_value);
const Index vectorized_size =
(num_coeff_to_pad / packet_size) * packet_size;
const Index vectorized_size = (num_coeff_to_pad / packet_size) * packet_size;
for (Index i = 0; i < vectorized_size; i += packet_size) {
internal::pstoret<Scalar, Packet, Unaligned>(dst_data + dst_index + i,
padded_packet);
internal::pstoret<Scalar, Packet, Unaligned>(dst_data + dst_index + i, padded_packet);
}
for (Index i = vectorized_size; i < num_coeff_to_pad; ++i) {
dst_data[dst_index + i] = padding_value;
@@ -119,10 +113,9 @@ struct ImagePatchPaddingOp {
} // end namespace internal
template<DenseIndex Rows, DenseIndex Cols, typename XprType>
class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprType>, ReadOnlyAccessors>
{
public:
template <DenseIndex Rows, DenseIndex Cols, typename XprType>
class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprType>, ReadOnlyAccessors> {
public:
typedef typename Eigen::internal::traits<TensorImagePatchOp>::Scalar Scalar;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
@@ -130,100 +123,101 @@ class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprT
typedef typename Eigen::internal::traits<TensorImagePatchOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorImagePatchOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols,
DenseIndex row_strides, DenseIndex col_strides,
DenseIndex in_row_strides, DenseIndex in_col_strides,
DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
PaddingType padding_type, Scalar padding_value)
: m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
m_row_strides(row_strides), m_col_strides(col_strides),
m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
m_padding_explicit(false), 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 TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols,
DenseIndex row_strides, DenseIndex col_strides,
DenseIndex in_row_strides, DenseIndex in_col_strides,
DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
DenseIndex padding_top, DenseIndex padding_bottom,
DenseIndex padding_left, DenseIndex padding_right,
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows,
DenseIndex patch_cols, DenseIndex row_strides,
DenseIndex col_strides, DenseIndex in_row_strides,
DenseIndex in_col_strides, DenseIndex row_inflate_strides,
DenseIndex col_inflate_strides, PaddingType padding_type,
Scalar padding_value)
: m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
m_row_strides(row_strides), m_col_strides(col_strides),
m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
m_padding_explicit(true), 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) {}
: m_xpr(expr),
m_patch_rows(patch_rows),
m_patch_cols(patch_cols),
m_row_strides(row_strides),
m_col_strides(col_strides),
m_in_row_strides(in_row_strides),
m_in_col_strides(in_col_strides),
m_row_inflate_strides(row_inflate_strides),
m_col_inflate_strides(col_inflate_strides),
m_padding_explicit(false),
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 TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows,
DenseIndex patch_cols, DenseIndex row_strides,
DenseIndex col_strides, DenseIndex in_row_strides,
DenseIndex in_col_strides, DenseIndex row_inflate_strides,
DenseIndex col_inflate_strides, DenseIndex padding_top,
DenseIndex padding_bottom, DenseIndex padding_left,
DenseIndex padding_right, Scalar padding_value)
: m_xpr(expr),
m_patch_rows(patch_rows),
m_patch_cols(patch_cols),
m_row_strides(row_strides),
m_col_strides(col_strides),
m_in_row_strides(in_row_strides),
m_in_col_strides(in_col_strides),
m_row_inflate_strides(row_inflate_strides),
m_col_inflate_strides(col_inflate_strides),
m_padding_explicit(true),
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_rows() const { return m_patch_rows; }
EIGEN_DEVICE_FUNC
DenseIndex patch_cols() const { return m_patch_cols; }
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_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 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() 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_rows() const { return m_patch_rows; }
EIGEN_DEVICE_FUNC DenseIndex patch_cols() const { return m_patch_cols; }
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_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 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() 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_rows;
const DenseIndex m_patch_cols;
const DenseIndex m_row_strides;
const DenseIndex m_col_strides;
const DenseIndex m_in_row_strides;
const DenseIndex m_in_col_strides;
const DenseIndex m_row_inflate_strides;
const DenseIndex m_col_inflate_strides;
const bool m_padding_explicit;
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_rows;
const DenseIndex m_patch_cols;
const DenseIndex m_row_strides;
const DenseIndex m_col_strides;
const DenseIndex m_in_row_strides;
const DenseIndex m_in_col_strides;
const DenseIndex m_row_inflate_strides;
const DenseIndex m_col_inflate_strides;
const bool m_padding_explicit;
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 Rows, DenseIndex Cols, typename ArgType, typename Device>
struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
{
template <DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device>
struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device> {
typedef TensorImagePatchOp<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;
typedef TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>,
Device> Self;
typedef TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device> Self;
typedef TensorEvaluator<ArgType, Device> Impl;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
@@ -233,21 +227,20 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
enum {
IsAligned = false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
BlockAccess = false,
IsAligned = false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
BlockAccess = false,
PreferBlockAccess = true,
CoordAccess = false,
RawAccess = false
CoordAccess = false,
RawAccess = false
};
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockNotImplemented TensorBlock;
//===--------------------------------------------------------------------===//
EIGEN_STRONG_INLINE TensorEvaluator( const XprType& op, const Device& device)
: m_device(device), m_impl(op.expression(), device)
{
EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_device(device), m_impl(op.expression(), device) {
EIGEN_STATIC_ASSERT((NumDims >= 4), YOU_MADE_A_PROGRAMMING_MISTAKE);
m_paddingValue = op.padding_value();
@@ -260,9 +253,9 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
m_inputRows = input_dims[1];
m_inputCols = input_dims[2];
} else {
m_inputDepth = input_dims[NumInputDims-1];
m_inputRows = input_dims[NumInputDims-2];
m_inputCols = input_dims[NumInputDims-3];
m_inputDepth = input_dims[NumInputDims - 1];
m_inputRows = input_dims[NumInputDims - 2];
m_inputCols = input_dims[NumInputDims - 3];
}
m_row_strides = op.row_strides();
@@ -292,8 +285,10 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
m_patch_cols_eff = op.patch_cols() + (op.patch_cols() - 1) * (m_in_col_strides - 1);
if (op.padding_explicit()) {
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_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_rowPaddingTop = op.padding_top();
m_colPaddingLeft = op.padding_left();
} else {
@@ -303,8 +298,10 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
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));
// Calculate the padding
m_rowPaddingTop = numext::maxi<Index>(0, ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2);
m_colPaddingLeft = numext::maxi<Index>(0, ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2);
m_rowPaddingTop =
numext::maxi<Index>(0, ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2);
m_colPaddingLeft =
numext::maxi<Index>(0, ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2);
break;
case PADDING_SAME:
m_outputRows = numext::ceil(m_input_rows_eff / static_cast<float>(m_row_strides));
@@ -319,8 +316,8 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
break;
default:
eigen_assert(false && "unexpected padding");
m_outputCols=0; // silence the uninitialised warning;
m_outputRows=0; //// silence the uninitialised warning;
m_outputCols = 0; // silence the uninitialised warning;
m_outputRows = 0; //// silence the uninitialised warning;
}
}
eigen_assert(m_outputRows > 0);
@@ -339,7 +336,7 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
m_dimensions[2] = op.patch_cols();
m_dimensions[3] = m_outputRows * m_outputCols;
for (int i = 4; i < NumDims; ++i) {
m_dimensions[i] = input_dims[i-1];
m_dimensions[i] = input_dims[i - 1];
}
} else {
// RowMajor
@@ -348,11 +345,11 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
// NumDims-3: patch_cols
// NumDims-4: number of patches
// NumDims-5 and beyond: anything else (such as batch).
m_dimensions[NumDims-1] = input_dims[NumInputDims-1];
m_dimensions[NumDims-2] = op.patch_rows();
m_dimensions[NumDims-3] = op.patch_cols();
m_dimensions[NumDims-4] = m_outputRows * m_outputCols;
for (int i = NumDims-5; i >= 0; --i) {
m_dimensions[NumDims - 1] = input_dims[NumInputDims - 1];
m_dimensions[NumDims - 2] = op.patch_rows();
m_dimensions[NumDims - 3] = op.patch_cols();
m_dimensions[NumDims - 4] = m_outputRows * m_outputCols;
for (int i = NumDims - 5; i >= 0; --i) {
m_dimensions[i] = input_dims[i];
}
}
@@ -363,9 +360,9 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
m_patchStride = m_colStride * m_dimensions[2] * m_dimensions[0];
m_otherStride = m_patchStride * m_dimensions[3];
} else {
m_colStride = m_dimensions[NumDims-2];
m_patchStride = m_colStride * m_dimensions[NumDims-3] * m_dimensions[NumDims-1];
m_otherStride = m_patchStride * m_dimensions[NumDims-4];
m_colStride = m_dimensions[NumDims - 2];
m_patchStride = m_colStride * m_dimensions[NumDims - 3] * m_dimensions[NumDims - 1];
m_otherStride = m_patchStride * m_dimensions[NumDims - 4];
}
// Strides for navigating through the input tensor.
@@ -386,7 +383,7 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
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]);
}
}
@@ -399,18 +396,14 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
#ifdef EIGEN_USE_THREADS
template <typename EvalSubExprsCallback>
EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
EvaluatorPointerType, EvalSubExprsCallback done) {
EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
}
#endif // EIGEN_USE_THREADS
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;
// Find the offset of the element wrt the location of the first element.
@@ -424,7 +417,8 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
const Index colIndex = patch2DIndex / m_fastOutputRows;
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_fastInflateColStride) : 0);
const Index origInputCol =
(m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInflateColStride) : 0);
if (inputCol < 0 || inputCol >= m_input_cols_eff ||
((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides))) {
return Scalar(m_paddingValue);
@@ -434,7 +428,8 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
const Index rowOffset = patchOffset - colOffset * m_colStride;
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_fastInflateRowStride) : 0);
const Index origInputRow =
(m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInflateRowStride) : 0);
if (inputRow < 0 || inputRow >= m_input_rows_eff ||
((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) {
return Scalar(m_paddingValue);
@@ -443,14 +438,14 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
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 + otherIndex * m_patchInputStride;
const Index inputIndex =
depth + origInputRow * m_rowInputStride + origInputCol * m_colInputStride + otherIndex * m_patchInputStride;
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) {
return packetWithPossibleZero(index);
@@ -468,26 +463,28 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
const Index patchOffsets[2] = {(indices[0] - patchIndex * m_patchStride) / m_fastOutputDepth,
(indices[1] - patchIndex * m_patchStride) / m_fastOutputDepth};
const Index patch2DIndex = (NumDims == 4) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride;
const Index patch2DIndex =
(NumDims == 4) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride;
eigen_assert(patch2DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride);
const Index colIndex = patch2DIndex / m_fastOutputRows;
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));
}
if (inputCols[0] == inputCols[1]) {
const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
const Index rowOffsets[2] = {patchOffsets[0] - colOffsets[0]*m_colStride, patchOffsets[1] - colOffsets[1]*m_colStride};
const Index rowOffsets[2] = {patchOffsets[0] - colOffsets[0] * m_colStride,
patchOffsets[1] - colOffsets[1] * m_colStride};
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));
@@ -497,7 +494,8 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
// 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 + otherIndex * m_patchInputStride;
const Index inputIndex =
depth + inputRows[0] * m_rowInputStride + inputCols[0] * m_colInputStride + otherIndex * m_patchInputStride;
return m_impl.template packet<Unaligned>(inputIndex);
}
}
@@ -519,25 +517,21 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowInflateStride() const { return m_row_inflate_strides; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colInflateStride() const { return m_col_inflate_strides; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
costPerCoeff(bool vectorized) const {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
// We conservatively estimate the cost for the code path where the computed
// index is inside the original image and
// TensorEvaluator<ArgType, Device>::CoordAccess is false.
const double compute_cost = 3 * TensorOpCost::DivCost<Index>() +
6 * TensorOpCost::MulCost<Index>() +
8 * TensorOpCost::MulCost<Index>();
return m_impl.costPerCoeff(vectorized) +
TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
const double compute_cost =
3 * TensorOpCost::DivCost<Index>() + 6 * TensorOpCost::MulCost<Index>() + 8 * TensorOpCost::MulCost<Index>();
return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
}
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;
@@ -591,7 +585,6 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
TensorEvaluator<ArgType, Device> m_impl;
};
} // end namespace Eigen
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
#endif // EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H