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Improved and cleaned up the 2d patch extraction code
This commit is contained in:
Benoit Steiner
parent
fa17358c4b
commit
a93af65938
@ -10,6 +10,8 @@
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#ifndef EIGEN_CXX11_TENSOR_TENSOR_BASE_H
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#define EIGEN_CXX11_TENSOR_TENSOR_BASE_H
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// clang-format off
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namespace Eigen {
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/** \class TensorBase
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@ -379,39 +381,28 @@ class TensorBase<Derived, ReadOnlyAccessors>
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return TensorPatchOp<const PatchDims, const Derived>(derived(), patch_dims);
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}
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template <Index Rows, Index Cols> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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const TensorImagePatchOp<Rows, Cols, const Derived>
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extract_image_patches() const {
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return TensorImagePatchOp<Rows, Cols, const Derived>(derived(), Rows, Cols, 1, 1, PADDING_SAME);
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}
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template <Index Rows, Index Cols> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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const TensorImagePatchOp<Rows, Cols, const Derived>
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extract_image_patches(const PaddingType padding_type) const {
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return TensorImagePatchOp<Rows, Cols, const Derived>(derived(), Rows, Cols, 1, 1, padding_type);
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}
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template <Index Rows, Index Cols> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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const TensorImagePatchOp<Rows, Cols, const Derived>
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extract_image_patches(const Index stride, const PaddingType padding_type) const {
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return TensorImagePatchOp<Rows, Cols, const Derived>(derived(), Rows, Cols, stride, stride, padding_type);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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const TensorImagePatchOp<Dynamic, Dynamic, const Derived>
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extract_image_patches(const Index patch_rows, const Index patch_cols,
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const Index row_stride = 1, const Index col_stride = 1) const {
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extract_image_patches(const Index patch_rows = 1, const Index patch_cols = 1,
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const Index row_stride = 1, const Index col_stride = 1,
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const Index in_row_stride = 1, const Index in_col_stride = 1,
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const PaddingType padding_type = PADDING_SAME, const Scalar padding_value = Scalar(0)) const {
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return TensorImagePatchOp<Dynamic, Dynamic, const Derived>(derived(), patch_rows, patch_cols, row_stride, col_stride,
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PADDING_SAME);
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in_row_stride, in_col_stride, 1, 1, padding_type, padding_value);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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const TensorImagePatchOp<Dynamic, Dynamic, const Derived>
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extract_image_patches(const Index patch_rows, const Index patch_cols,
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const Index row_stride, const Index col_stride,
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const PaddingType padding_type) const {
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const Index in_row_stride, const Index in_col_stride,
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const Index row_inflate_stride, const Index col_inflate_stride,
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const Index padding_top, const Index padding_bottom,
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const Index padding_left,const Index padding_right,
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const Scalar padding_value) const {
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return TensorImagePatchOp<Dynamic, Dynamic, const Derived>(derived(), patch_rows, patch_cols, row_stride, col_stride,
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padding_type);
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in_row_stride, in_col_stride, row_inflate_stride, col_inflate_stride,
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padding_top, padding_bottom, padding_left, padding_right, padding_value);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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@ -481,7 +472,7 @@ class TensorBase<Derived, ReadOnlyAccessors>
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return TensorStridingOp<const Strides, const Derived>(derived(), strides);
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}
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// Added support for custom unary and binary operations
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// Support for custom unary and binary operations
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template <typename CustomUnaryFunc>
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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const TensorCustomUnaryOp<const CustomUnaryFunc, const Derived> customOp(const CustomUnaryFunc& op) const {
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@ -30,7 +30,7 @@ namespace internal {
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template<DenseIndex Rows, DenseIndex Cols, typename XprType>
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struct traits<TensorImagePatchOp<Rows, Cols, XprType> > : public traits<XprType>
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{
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typedef typename XprType::Scalar Scalar;
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typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
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typedef traits<XprType> XprTraits;
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typedef typename packet_traits<Scalar>::type Packet;
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typedef typename XprTraits::StorageKind StorageKind;
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@ -70,10 +70,30 @@ class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprT
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols,
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DenseIndex row_strides, DenseIndex col_strides,
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PaddingType padding_type)
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DenseIndex in_row_strides, DenseIndex in_col_strides,
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DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
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PaddingType padding_type, Scalar padding_value)
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: m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
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m_row_strides(row_strides), m_col_strides(col_strides),
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m_padding_type(padding_type) {}
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m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
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m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
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m_padding_explicit(false), m_padding_top(0), m_padding_bottom(0), m_padding_left(0), m_padding_right(0),
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m_padding_type(padding_type), m_padding_value(padding_value) {}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols,
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DenseIndex row_strides, DenseIndex col_strides,
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DenseIndex in_row_strides, DenseIndex in_col_strides,
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DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
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DenseIndex padding_top, DenseIndex padding_bottom,
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DenseIndex padding_left, DenseIndex padding_right,
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Scalar padding_value)
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: m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
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m_row_strides(row_strides), m_col_strides(col_strides),
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m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
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m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
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m_padding_explicit(true), m_padding_top(padding_top), m_padding_bottom(padding_bottom),
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m_padding_left(padding_left), m_padding_right(padding_right),
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m_padding_type(PADDING_VALID), m_padding_value(padding_value) {}
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EIGEN_DEVICE_FUNC
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DenseIndex patch_rows() const { return m_patch_rows; }
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@ -84,7 +104,27 @@ class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprT
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EIGEN_DEVICE_FUNC
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DenseIndex col_strides() const { return m_col_strides; }
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EIGEN_DEVICE_FUNC
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DenseIndex in_row_strides() const { return m_in_row_strides; }
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EIGEN_DEVICE_FUNC
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DenseIndex in_col_strides() const { return m_in_col_strides; }
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EIGEN_DEVICE_FUNC
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DenseIndex row_inflate_strides() const { return m_row_inflate_strides; }
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EIGEN_DEVICE_FUNC
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DenseIndex col_inflate_strides() const { return m_col_inflate_strides; }
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EIGEN_DEVICE_FUNC
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bool padding_explicit() const { return m_padding_explicit; }
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EIGEN_DEVICE_FUNC
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DenseIndex padding_top() const { return m_padding_top; }
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EIGEN_DEVICE_FUNC
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DenseIndex padding_bottom() const { return m_padding_bottom; }
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EIGEN_DEVICE_FUNC
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DenseIndex padding_left() const { return m_padding_left; }
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EIGEN_DEVICE_FUNC
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DenseIndex padding_right() const { return m_padding_right; }
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EIGEN_DEVICE_FUNC
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PaddingType padding_type() const { return m_padding_type; }
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EIGEN_DEVICE_FUNC
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Scalar padding_value() const { return m_padding_value; }
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EIGEN_DEVICE_FUNC
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const typename internal::remove_all<typename XprType::Nested>::type&
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@ -96,10 +136,19 @@ class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprT
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const DenseIndex m_patch_cols;
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const DenseIndex m_row_strides;
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const DenseIndex m_col_strides;
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const DenseIndex m_in_row_strides;
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const DenseIndex m_in_col_strides;
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const DenseIndex m_row_inflate_strides;
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const DenseIndex m_col_inflate_strides;
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const bool m_padding_explicit;
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const DenseIndex m_padding_top;
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const DenseIndex m_padding_bottom;
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const DenseIndex m_padding_left;
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const DenseIndex m_padding_right;
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const PaddingType m_padding_type;
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const Scalar m_padding_value;
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};
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// Eval as rvalue
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template<DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device>
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struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
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@ -109,7 +158,10 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
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static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
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static const int NumDims = NumInputDims + 1;
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typedef DSizes<Index, NumDims> Dimensions;
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typedef typename XprType::Scalar Scalar;
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typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
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typedef TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>,
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Device> Self;
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typedef TensorEvaluator<ArgType, Device> Impl;
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enum {
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IsAligned = false,
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@ -123,13 +175,17 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
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{
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EIGEN_STATIC_ASSERT(NumDims >= 4, YOU_MADE_A_PROGRAMMING_MISTAKE);
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m_paddingValue = op.padding_value();
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const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
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// Caches a few variables.
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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m_inputDepth = input_dims[0];
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m_inputRows = input_dims[1];
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m_inputCols = input_dims[2];
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} else {
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m_inputDepth = input_dims[NumInputDims-1];
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m_inputRows = input_dims[NumInputDims-2];
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m_inputCols = input_dims[NumInputDims-3];
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}
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@ -137,27 +193,57 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
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m_row_strides = op.row_strides();
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m_col_strides = op.col_strides();
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// We only support same strides for both dimensions and square patches.
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eigen_assert(m_row_strides == m_col_strides);
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// Input strides and effective input/patch size
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m_in_row_strides = op.in_row_strides();
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m_in_col_strides = op.in_col_strides();
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m_row_inflate_strides = op.row_inflate_strides();
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m_col_inflate_strides = op.col_inflate_strides();
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// The "effective" input rows and input cols are the input rows and cols
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// after inflating them with zeros.
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// For examples, a 2x3 matrix with row_inflate_strides and
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// col_inflate_strides of 2 comes from:
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// A B C
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// D E F
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//
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// to a matrix is 3 x 5:
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//
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// A . B . C
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// . . . . .
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// D . E . F
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switch (op.padding_type()) {
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case PADDING_VALID:
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m_outputRows = numext::ceil((m_inputRows - op.patch_rows() + 1.f) / static_cast<float>(m_row_strides));
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m_outputCols = numext::ceil((m_inputCols - op.patch_cols() + 1.f) / static_cast<float>(m_col_strides));
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// Calculate the padding
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m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + op.patch_rows() - m_inputRows) / 2;
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m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + op.patch_cols() - m_inputCols) / 2;
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break;
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case PADDING_SAME:
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m_outputRows = numext::ceil(m_inputRows / static_cast<float>(m_row_strides));
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m_outputCols = numext::ceil(m_inputCols / static_cast<float>(m_col_strides));
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// Calculate the padding
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m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + op.patch_rows() - m_inputRows) / 2;
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m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + op.patch_cols() - m_inputCols) / 2;
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break;
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default:
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eigen_assert(false && "unexpected padding");
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m_input_rows_eff = (m_inputRows - 1) * m_row_inflate_strides + 1;
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m_input_cols_eff = (m_inputCols - 1) * m_col_inflate_strides + 1;
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m_patch_rows_eff = op.patch_rows() + (op.patch_rows() - 1) * (m_in_row_strides - 1);
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m_patch_cols_eff = op.patch_cols() + (op.patch_cols() - 1) * (m_in_col_strides - 1);
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if (op.padding_explicit()) {
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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));
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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));
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m_rowPaddingTop = op.padding_top();
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m_colPaddingLeft = op.padding_left();
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} else {
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// Computing padding from the type
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switch (op.padding_type()) {
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case PADDING_VALID:
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m_outputRows = numext::ceil((m_input_rows_eff - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
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m_outputCols = numext::ceil((m_input_cols_eff - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
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// Calculate the padding
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m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2;
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m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2;
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break;
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case PADDING_SAME:
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m_outputRows = numext::ceil(m_input_rows_eff / static_cast<float>(m_row_strides));
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m_outputCols = numext::ceil(m_input_cols_eff / static_cast<float>(m_col_strides));
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// Calculate the padding
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m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2;
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m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2;
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break;
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default:
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eigen_assert(false && "unexpected padding");
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}
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}
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eigen_assert(m_outputRows > 0);
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eigen_assert(m_outputCols > 0);
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// Dimensions for result of extraction.
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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@ -202,26 +288,24 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
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}
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// Strides for navigating through the input tensor.
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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m_rowInputStride = input_dims[0];
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m_colInputStride = input_dims[0] * input_dims[1];
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m_patchInputStride = input_dims[0] * input_dims[1] * input_dims[2];
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} else {
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m_rowInputStride = input_dims[NumInputDims-1];
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m_colInputStride = input_dims[NumInputDims-1] * input_dims[NumInputDims-2];
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m_patchInputStride = input_dims[NumInputDims-1] * input_dims[NumInputDims-2] * input_dims[NumInputDims-3];
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}
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m_rowInputStride = m_inputDepth;
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m_colInputStride = m_inputDepth * m_inputRows;
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m_patchInputStride = m_inputDepth * m_inputRows * m_inputCols;
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// Fast representations of different variables.
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m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
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m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
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m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
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m_fastInputRowStride = internal::TensorIntDivisor<Index>(m_row_inflate_strides);
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m_fastInputColStride = internal::TensorIntDivisor<Index>(m_col_inflate_strides);
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m_fastInputColsEff = internal::TensorIntDivisor<Index>(m_input_cols_eff);
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// Number of patches in the width dimension.
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m_fastOutputRows = internal::TensorIntDivisor<Index>(m_outputRows);
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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m_fastDimZero = internal::TensorIntDivisor<Index>(m_dimensions[0]);
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m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[0]);
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} else {
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m_fastDimZero = internal::TensorIntDivisor<Index>(m_dimensions[NumDims-1]);
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m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims-1]);
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}
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}
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@ -244,33 +328,36 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
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// Patch index corresponding to the passed in index.
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const Index patchIndex = index / m_fastPatchStride;
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// Find the offset of the element wrt the location of the first element.
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const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastDimZero;
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const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastOutputDepth;
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// Other ways to index this element.
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const Index otherIndex = (NumDims == 4) ? 0 : index / m_fastOtherStride;
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const Index patch2DIndex = (NumDims == 4) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride;
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// Calculate col index in the input original tensor.
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const Index colIndex = patch2DIndex / m_fastOutputRows;
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const Index colOffset = patchOffset / m_fastColStride;
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// Calculate col index in the input original tensor.
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const Index inputCol = colIndex * m_col_strides + colOffset - m_colPaddingLeft;
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if (inputCol < 0 || inputCol >= m_inputCols) {
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return Scalar(0);
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const Index inputCol = colIndex * m_col_strides + colOffset * m_in_col_strides - m_colPaddingLeft;
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const Index origInputCol = (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInputColStride) : 0);
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if (inputCol < 0 || inputCol >= m_input_cols_eff ||
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((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides))) {
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return Scalar(m_paddingValue);
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}
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const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
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const Index rowOffset = patchOffset - colOffset * m_colStride;
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// Calculate row index in the original input tensor.
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const Index inputRow = rowIndex * m_row_strides + rowOffset - m_rowPaddingTop;
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if (inputRow < 0 || inputRow >= m_inputRows) {
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return Scalar(0);
|
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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_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);
|
||||
}
|
||||
|
||||
const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
|
||||
const Index depth = index - (index / m_fastDimZero) * m_dimensions[depth_index];
|
||||
const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
|
||||
|
||||
const Index inputIndex = depth + inputRow * m_rowInputStride + inputCol * m_colInputStride + otherIndex * m_patchInputStride;
|
||||
const Index inputIndex = depth + origInputRow * m_rowInputStride + origInputCol * m_colInputStride + otherIndex * m_patchInputStride;
|
||||
return m_impl.coeff(inputIndex);
|
||||
}
|
||||
|
||||
@ -281,6 +368,10 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
|
||||
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) {
|
||||
return packetWithPossibleZero(index);
|
||||
}
|
||||
|
||||
const Index indices[2] = {index, index + packetSize - 1};
|
||||
const Index patchIndex = indices[0] / m_fastPatchStride;
|
||||
if (patchIndex != indices[1] / m_fastPatchStride) {
|
||||
@ -290,8 +381,8 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
|
||||
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_fastDimZero,
|
||||
(indices[1] - patchIndex * m_patchStride) / m_fastDimZero};
|
||||
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;
|
||||
eigen_assert(patch2DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride);
|
||||
@ -303,8 +394,7 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
|
||||
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) {
|
||||
// all zeros
|
||||
return internal::pset1<PacketReturnType>(Scalar(0));
|
||||
return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
|
||||
}
|
||||
|
||||
if (inputCols[0] == inputCols[1]) {
|
||||
@ -316,14 +406,13 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
|
||||
m_rowPaddingTop, rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop};
|
||||
|
||||
if (inputRows[1] < 0 || inputRows[0] >= m_inputRows) {
|
||||
// all zeros
|
||||
return internal::pset1<PacketReturnType>(Scalar(0));
|
||||
return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
|
||||
}
|
||||
|
||||
if (inputRows[0] >= 0 && inputRows[1] < m_inputRows) {
|
||||
// no padding
|
||||
const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
|
||||
const Index depth = index - (index / m_fastDimZero) * m_dimensions[depth_index];
|
||||
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;
|
||||
return m_impl.template packet<Unaligned>(inputIndex);
|
||||
}
|
||||
@ -342,6 +431,10 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
|
||||
Index outputCols() const { return m_outputCols; }
|
||||
Index userRowStride() const { return m_row_strides; }
|
||||
Index userColStride() const { return m_col_strides; }
|
||||
Index userInRowStride() const { return m_in_row_strides; }
|
||||
Index userInColStride() const { return m_in_col_strides; }
|
||||
Index rowInflateStride() const { return m_row_inflate_strides; }
|
||||
Index colInflateStride() const { return m_col_inflate_strides; }
|
||||
|
||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<Index, NumDims>& coords) const
|
||||
{
|
||||
@ -350,24 +443,30 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
|
||||
// 0: d, 1: patch_rows, 2: patch_cols, 3: number of patches, 4: number of batches
|
||||
// RowMajor
|
||||
// 0: number of batches, 1: number of patches, 2: patch_cols , 3: patch_rows, 4: d
|
||||
const Index patchIndex = coords[static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 3 : 1];
|
||||
const Index patch2DIndex = coords[static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 3 : 1];
|
||||
|
||||
array<Index, NumDims-1> inputCoords;
|
||||
Index input_col_idx = patch2DIndex / m_fastInputColsEff;
|
||||
Index inputCol = input_col_idx + coords[1] * m_in_row_strides - m_rowPaddingTop;
|
||||
Index inputRow = patch2DIndex - input_col_idx * m_input_cols_eff + coords[2] * m_in_col_strides - m_colPaddingLeft;
|
||||
const Index origInputCol = (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInputColStride) : 0);
|
||||
const Index origInputRow = (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInputRowStride) : 0);
|
||||
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
|
||||
inputCoords[0] = coords[0]; // depth
|
||||
inputCoords[1] = patchIndex / m_inputCols + coords[1] - m_rowPaddingTop;
|
||||
inputCoords[2] = patchIndex - patchIndex / m_inputCols * m_inputCols + coords[2] - m_colPaddingLeft;
|
||||
inputCoords[1] = origInputCol;
|
||||
inputCoords[2] = origInputRow;
|
||||
inputCoords[3] = coords[4]; // batch
|
||||
} else {
|
||||
inputCoords[3] = coords[4]; // depth
|
||||
inputCoords[2] = patchIndex / m_inputCols + coords[3] - m_rowPaddingTop;
|
||||
inputCoords[1] = patchIndex - patchIndex / m_inputCols * m_inputCols + coords[2] - m_colPaddingLeft;
|
||||
inputCoords[2] = origInputCol;
|
||||
inputCoords[1] = origInputRow;
|
||||
inputCoords[0] = coords[0]; // batch
|
||||
}
|
||||
// If the computed coordinates are outside the original image perimeter, return 0.
|
||||
if (inputCoords[1] < 0 || inputCoords[1] >= m_inputRows ||
|
||||
inputCoords[2] < 0 || inputCoords[2] >= m_inputCols) {
|
||||
return Scalar(0);
|
||||
if (inputCol < 0 || inputCol >= m_input_cols_eff || inputRow < 0 || inputRow >= m_input_rows_eff ||
|
||||
((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides)) ||
|
||||
((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) {
|
||||
return Scalar(m_paddingValue);
|
||||
}
|
||||
if (TensorEvaluator<ArgType, Device>::CoordAccess) {
|
||||
return m_impl.coeff(inputCoords);
|
||||
@ -409,14 +508,29 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
|
||||
Index m_colStride;
|
||||
Index m_row_strides;
|
||||
Index m_col_strides;
|
||||
|
||||
Index m_in_row_strides;
|
||||
Index m_in_col_strides;
|
||||
Index m_row_inflate_strides;
|
||||
Index m_col_inflate_strides;
|
||||
|
||||
Index m_input_rows_eff;
|
||||
Index m_input_cols_eff;
|
||||
Index m_patch_rows_eff;
|
||||
Index m_patch_cols_eff;
|
||||
|
||||
internal::TensorIntDivisor<Index> m_fastOtherStride;
|
||||
internal::TensorIntDivisor<Index> m_fastPatchStride;
|
||||
internal::TensorIntDivisor<Index> m_fastColStride;
|
||||
internal::TensorIntDivisor<Index> m_fastInputRowStride;
|
||||
internal::TensorIntDivisor<Index> m_fastInputColStride;
|
||||
internal::TensorIntDivisor<Index> m_fastInputColsEff;
|
||||
|
||||
Index m_rowInputStride;
|
||||
Index m_colInputStride;
|
||||
Index m_patchInputStride;
|
||||
|
||||
Index m_inputDepth;
|
||||
Index m_inputRows;
|
||||
Index m_inputCols;
|
||||
|
||||
@ -427,7 +541,9 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
|
||||
Index m_colPaddingLeft;
|
||||
|
||||
internal::TensorIntDivisor<Index> m_fastOutputRows;
|
||||
internal::TensorIntDivisor<Index> m_fastDimZero;
|
||||
internal::TensorIntDivisor<Index> m_fastOutputDepth;
|
||||
|
||||
Scalar m_paddingValue;
|
||||
|
||||
TensorEvaluator<ArgType, Device> m_impl;
|
||||
};
|
||||
|
||||
@ -75,7 +75,7 @@ struct TensorIntDivisor {
|
||||
eigen_assert(numerator >= 0);
|
||||
eigen_assert(static_cast<unsigned long long>(numerator) <= (1ull<<N) - 1);
|
||||
|
||||
uint32_t t1 = (multiplier * numerator) >> 32;
|
||||
uint32_t t1 = (multiplier * numerator) >> N;
|
||||
uint32_t t = (static_cast<uint32_t>(numerator) - t1) >> shift1;
|
||||
return (t1 + t) >> shift2;
|
||||
}
|
||||
|
||||
@ -25,7 +25,7 @@ static void test_simple_patch()
|
||||
|
||||
// Single pixel patch: ColMajor
|
||||
Tensor<float, 5> single_pixel_patch;
|
||||
single_pixel_patch = tensor.extract_image_patches<1, 1>();
|
||||
single_pixel_patch = tensor.extract_image_patches(1, 1);
|
||||
VERIFY_IS_EQUAL(single_pixel_patch.dimension(0), 2);
|
||||
VERIFY_IS_EQUAL(single_pixel_patch.dimension(1), 1);
|
||||
VERIFY_IS_EQUAL(single_pixel_patch.dimension(2), 1);
|
||||
@ -34,7 +34,7 @@ static void test_simple_patch()
|
||||
|
||||
// Single pixel patch: RowMajor
|
||||
Tensor<float, 5, RowMajor> single_pixel_patch_row_major;
|
||||
single_pixel_patch_row_major = tensor_row_major.extract_image_patches<1, 1>();
|
||||
single_pixel_patch_row_major = tensor_row_major.extract_image_patches(1, 1);
|
||||
VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(0), 7);
|
||||
VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(1), 3*5);
|
||||
VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(2), 1);
|
||||
@ -64,7 +64,7 @@ static void test_simple_patch()
|
||||
|
||||
// Entire image patch: ColMajor
|
||||
Tensor<float, 5> entire_image_patch;
|
||||
entire_image_patch = tensor.extract_image_patches<3, 5>();
|
||||
entire_image_patch = tensor.extract_image_patches(3, 5);
|
||||
VERIFY_IS_EQUAL(entire_image_patch.dimension(0), 2);
|
||||
VERIFY_IS_EQUAL(entire_image_patch.dimension(1), 3);
|
||||
VERIFY_IS_EQUAL(entire_image_patch.dimension(2), 5);
|
||||
@ -73,7 +73,7 @@ static void test_simple_patch()
|
||||
|
||||
// Entire image patch: RowMajor
|
||||
Tensor<float, 5, RowMajor> entire_image_patch_row_major;
|
||||
entire_image_patch_row_major = tensor_row_major.extract_image_patches<3, 5>();
|
||||
entire_image_patch_row_major = tensor_row_major.extract_image_patches(3, 5);
|
||||
VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(0), 7);
|
||||
VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(1), 3*5);
|
||||
VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(2), 5);
|
||||
@ -118,7 +118,7 @@ static void test_simple_patch()
|
||||
|
||||
// 2D patch: ColMajor
|
||||
Tensor<float, 5> twod_patch;
|
||||
twod_patch = tensor.extract_image_patches<2, 2>();
|
||||
twod_patch = tensor.extract_image_patches(2, 2);
|
||||
VERIFY_IS_EQUAL(twod_patch.dimension(0), 2);
|
||||
VERIFY_IS_EQUAL(twod_patch.dimension(1), 2);
|
||||
VERIFY_IS_EQUAL(twod_patch.dimension(2), 2);
|
||||
@ -127,7 +127,7 @@ static void test_simple_patch()
|
||||
|
||||
// 2D patch: RowMajor
|
||||
Tensor<float, 5, RowMajor> twod_patch_row_major;
|
||||
twod_patch_row_major = tensor_row_major.extract_image_patches<2, 2>();
|
||||
twod_patch_row_major = tensor_row_major.extract_image_patches(2, 2);
|
||||
VERIFY_IS_EQUAL(twod_patch_row_major.dimension(0), 7);
|
||||
VERIFY_IS_EQUAL(twod_patch_row_major.dimension(1), 3*5);
|
||||
VERIFY_IS_EQUAL(twod_patch_row_major.dimension(2), 2);
|
||||
@ -194,7 +194,7 @@ static void test_patch_padding_valid()
|
||||
tensor.data()[i] = i + 1;
|
||||
}
|
||||
// ColMajor
|
||||
Tensor<float, 5> result = tensor.extract_image_patches(ksize, ksize, stride, stride, PADDING_VALID);
|
||||
Tensor<float, 5> result = tensor.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID);
|
||||
|
||||
VERIFY_IS_EQUAL(result.dimension(0), input_depth); // depth
|
||||
VERIFY_IS_EQUAL(result.dimension(1), ksize); // kernel rows
|
||||
@ -209,7 +209,7 @@ static void test_patch_padding_valid()
|
||||
VERIFY_IS_EQUAL(tensor.dimension(2), tensor_row_major.dimension(1));
|
||||
VERIFY_IS_EQUAL(tensor.dimension(3), tensor_row_major.dimension(0));
|
||||
|
||||
Tensor<float, 5, RowMajor> result_row_major = tensor_row_major.extract_image_patches(ksize, ksize, stride, stride, PADDING_VALID);
|
||||
Tensor<float, 5, RowMajor> result_row_major = tensor_row_major.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID);
|
||||
VERIFY_IS_EQUAL(result.dimension(0), result_row_major.dimension(4));
|
||||
VERIFY_IS_EQUAL(result.dimension(1), result_row_major.dimension(3));
|
||||
VERIFY_IS_EQUAL(result.dimension(2), result_row_major.dimension(2));
|
||||
@ -267,7 +267,7 @@ static void test_patch_padding_valid_same_value()
|
||||
// ColMajor
|
||||
Tensor<float, 4> tensor(input_depth, input_rows, input_cols, input_batches);
|
||||
tensor = tensor.constant(11.0f);
|
||||
Tensor<float, 5> result = tensor.extract_image_patches(ksize, ksize, stride, stride, PADDING_VALID);
|
||||
Tensor<float, 5> result = tensor.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID);
|
||||
|
||||
VERIFY_IS_EQUAL(result.dimension(0), input_depth); // depth
|
||||
VERIFY_IS_EQUAL(result.dimension(1), ksize); // kernel rows
|
||||
@ -282,7 +282,7 @@ static void test_patch_padding_valid_same_value()
|
||||
VERIFY_IS_EQUAL(tensor.dimension(2), tensor_row_major.dimension(1));
|
||||
VERIFY_IS_EQUAL(tensor.dimension(3), tensor_row_major.dimension(0));
|
||||
|
||||
Tensor<float, 5, RowMajor> result_row_major = tensor_row_major.extract_image_patches(ksize, ksize, stride, stride, PADDING_VALID);
|
||||
Tensor<float, 5, RowMajor> result_row_major = tensor_row_major.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID);
|
||||
VERIFY_IS_EQUAL(result.dimension(0), result_row_major.dimension(4));
|
||||
VERIFY_IS_EQUAL(result.dimension(1), result_row_major.dimension(3));
|
||||
VERIFY_IS_EQUAL(result.dimension(2), result_row_major.dimension(2));
|
||||
@ -416,7 +416,7 @@ static void test_patch_no_extra_dim()
|
||||
|
||||
// Single pixel patch: ColMajor
|
||||
Tensor<float, 4> single_pixel_patch;
|
||||
single_pixel_patch = tensor.extract_image_patches<1, 1>();
|
||||
single_pixel_patch = tensor.extract_image_patches(1, 1);
|
||||
VERIFY_IS_EQUAL(single_pixel_patch.dimension(0), 2);
|
||||
VERIFY_IS_EQUAL(single_pixel_patch.dimension(1), 1);
|
||||
VERIFY_IS_EQUAL(single_pixel_patch.dimension(2), 1);
|
||||
@ -424,7 +424,7 @@ static void test_patch_no_extra_dim()
|
||||
|
||||
// Single pixel patch: RowMajor
|
||||
Tensor<float, 4, RowMajor> single_pixel_patch_row_major;
|
||||
single_pixel_patch_row_major = tensor_row_major.extract_image_patches<1, 1>();
|
||||
single_pixel_patch_row_major = tensor_row_major.extract_image_patches(1, 1);
|
||||
VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(0), 3*5);
|
||||
VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(1), 1);
|
||||
VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(2), 1);
|
||||
@ -451,7 +451,7 @@ static void test_patch_no_extra_dim()
|
||||
|
||||
// Entire image patch: ColMajor
|
||||
Tensor<float, 4> entire_image_patch;
|
||||
entire_image_patch = tensor.extract_image_patches<3, 5>();
|
||||
entire_image_patch = tensor.extract_image_patches(3, 5);
|
||||
VERIFY_IS_EQUAL(entire_image_patch.dimension(0), 2);
|
||||
VERIFY_IS_EQUAL(entire_image_patch.dimension(1), 3);
|
||||
VERIFY_IS_EQUAL(entire_image_patch.dimension(2), 5);
|
||||
@ -459,7 +459,7 @@ static void test_patch_no_extra_dim()
|
||||
|
||||
// Entire image patch: RowMajor
|
||||
Tensor<float, 4, RowMajor> entire_image_patch_row_major;
|
||||
entire_image_patch_row_major = tensor_row_major.extract_image_patches<3, 5>();
|
||||
entire_image_patch_row_major = tensor_row_major.extract_image_patches(3, 5);
|
||||
VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(0), 3*5);
|
||||
VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(1), 5);
|
||||
VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(2), 3);
|
||||
@ -499,7 +499,7 @@ static void test_patch_no_extra_dim()
|
||||
|
||||
// 2D patch: ColMajor
|
||||
Tensor<float, 4> twod_patch;
|
||||
twod_patch = tensor.extract_image_patches<2, 2>();
|
||||
twod_patch = tensor.extract_image_patches(2, 2);
|
||||
VERIFY_IS_EQUAL(twod_patch.dimension(0), 2);
|
||||
VERIFY_IS_EQUAL(twod_patch.dimension(1), 2);
|
||||
VERIFY_IS_EQUAL(twod_patch.dimension(2), 2);
|
||||
@ -507,7 +507,7 @@ static void test_patch_no_extra_dim()
|
||||
|
||||
// 2D patch: RowMajor
|
||||
Tensor<float, 4, RowMajor> twod_patch_row_major;
|
||||
twod_patch_row_major = tensor_row_major.extract_image_patches<2, 2>();
|
||||
twod_patch_row_major = tensor_row_major.extract_image_patches(2, 2);
|
||||
VERIFY_IS_EQUAL(twod_patch_row_major.dimension(0), 3*5);
|
||||
VERIFY_IS_EQUAL(twod_patch_row_major.dimension(1), 2);
|
||||
VERIFY_IS_EQUAL(twod_patch_row_major.dimension(2), 2);
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user