Loading...
Searching...
No Matches
TensorImagePatch.h
1// This file is part of Eigen, a lightweight C++ template library
2// for linear algebra.
3//
4// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
5//
6// This Source Code Form is subject to the terms of the Mozilla
7// Public License v. 2.0. If a copy of the MPL was not distributed
8// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9
10#ifndef EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
11#define EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
12
13namespace Eigen {
14
29namespace internal {
30template<DenseIndex Rows, DenseIndex Cols, typename XprType>
31struct traits<TensorImagePatchOp<Rows, Cols, XprType> > : public traits<XprType>
32{
33 typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
34 typedef traits<XprType> XprTraits;
35 typedef typename XprTraits::StorageKind StorageKind;
36 typedef typename XprTraits::Index Index;
37 typedef typename XprType::Nested Nested;
38 typedef typename remove_reference<Nested>::type _Nested;
39 static const int NumDimensions = XprTraits::NumDimensions + 1;
40 static const int Layout = XprTraits::Layout;
41};
42
43template<DenseIndex Rows, DenseIndex Cols, typename XprType>
44struct eval<TensorImagePatchOp<Rows, Cols, XprType>, Eigen::Dense>
45{
46 typedef const TensorImagePatchOp<Rows, Cols, XprType>& type;
47};
48
49template<DenseIndex Rows, DenseIndex Cols, typename XprType>
50struct nested<TensorImagePatchOp<Rows, Cols, XprType>, 1, typename eval<TensorImagePatchOp<Rows, Cols, XprType> >::type>
51{
52 typedef TensorImagePatchOp<Rows, Cols, XprType> type;
53};
54
55} // end namespace internal
56
57template<DenseIndex Rows, DenseIndex Cols, typename XprType>
58class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprType>, ReadOnlyAccessors>
59{
60 public:
61 typedef typename Eigen::internal::traits<TensorImagePatchOp>::Scalar Scalar;
62 typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
63 typedef typename XprType::CoeffReturnType CoeffReturnType;
64 typedef typename Eigen::internal::nested<TensorImagePatchOp>::type Nested;
65 typedef typename Eigen::internal::traits<TensorImagePatchOp>::StorageKind StorageKind;
66 typedef typename Eigen::internal::traits<TensorImagePatchOp>::Index Index;
67
68 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols,
69 DenseIndex row_strides, DenseIndex col_strides,
70 DenseIndex in_row_strides, DenseIndex in_col_strides,
71 DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
72 PaddingType padding_type, Scalar padding_value)
73 : m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
74 m_row_strides(row_strides), m_col_strides(col_strides),
75 m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
76 m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
77 m_padding_explicit(false), m_padding_top(0), m_padding_bottom(0), m_padding_left(0), m_padding_right(0),
78 m_padding_type(padding_type), m_padding_value(padding_value) {}
79
80 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols,
81 DenseIndex row_strides, DenseIndex col_strides,
82 DenseIndex in_row_strides, DenseIndex in_col_strides,
83 DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
84 DenseIndex padding_top, DenseIndex padding_bottom,
85 DenseIndex padding_left, DenseIndex padding_right,
86 Scalar padding_value)
87 : m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
88 m_row_strides(row_strides), m_col_strides(col_strides),
89 m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
90 m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
91 m_padding_explicit(true), m_padding_top(padding_top), m_padding_bottom(padding_bottom),
92 m_padding_left(padding_left), m_padding_right(padding_right),
93 m_padding_type(PADDING_VALID), m_padding_value(padding_value) {}
94
95 EIGEN_DEVICE_FUNC
96 DenseIndex patch_rows() const { return m_patch_rows; }
97 EIGEN_DEVICE_FUNC
98 DenseIndex patch_cols() const { return m_patch_cols; }
99 EIGEN_DEVICE_FUNC
100 DenseIndex row_strides() const { return m_row_strides; }
101 EIGEN_DEVICE_FUNC
102 DenseIndex col_strides() const { return m_col_strides; }
103 EIGEN_DEVICE_FUNC
104 DenseIndex in_row_strides() const { return m_in_row_strides; }
105 EIGEN_DEVICE_FUNC
106 DenseIndex in_col_strides() const { return m_in_col_strides; }
107 EIGEN_DEVICE_FUNC
108 DenseIndex row_inflate_strides() const { return m_row_inflate_strides; }
109 EIGEN_DEVICE_FUNC
110 DenseIndex col_inflate_strides() const { return m_col_inflate_strides; }
111 EIGEN_DEVICE_FUNC
112 bool padding_explicit() const { return m_padding_explicit; }
113 EIGEN_DEVICE_FUNC
114 DenseIndex padding_top() const { return m_padding_top; }
115 EIGEN_DEVICE_FUNC
116 DenseIndex padding_bottom() const { return m_padding_bottom; }
117 EIGEN_DEVICE_FUNC
118 DenseIndex padding_left() const { return m_padding_left; }
119 EIGEN_DEVICE_FUNC
120 DenseIndex padding_right() const { return m_padding_right; }
121 EIGEN_DEVICE_FUNC
122 PaddingType padding_type() const { return m_padding_type; }
123 EIGEN_DEVICE_FUNC
124 Scalar padding_value() const { return m_padding_value; }
125
126 EIGEN_DEVICE_FUNC
127 const typename internal::remove_all<typename XprType::Nested>::type&
128 expression() const { return m_xpr; }
129
130 protected:
131 typename XprType::Nested m_xpr;
132 const DenseIndex m_patch_rows;
133 const DenseIndex m_patch_cols;
134 const DenseIndex m_row_strides;
135 const DenseIndex m_col_strides;
136 const DenseIndex m_in_row_strides;
137 const DenseIndex m_in_col_strides;
138 const DenseIndex m_row_inflate_strides;
139 const DenseIndex m_col_inflate_strides;
140 const bool m_padding_explicit;
141 const DenseIndex m_padding_top;
142 const DenseIndex m_padding_bottom;
143 const DenseIndex m_padding_left;
144 const DenseIndex m_padding_right;
145 const PaddingType m_padding_type;
146 const Scalar m_padding_value;
147};
148
149// Eval as rvalue
150template<DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device>
151struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
152{
153 typedef TensorImagePatchOp<Rows, Cols, ArgType> XprType;
154 typedef typename XprType::Index Index;
155 static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
156 static const int NumDims = NumInputDims + 1;
157 typedef DSizes<Index, NumDims> Dimensions;
158 typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
159 typedef TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>,
160 Device> Self;
161 typedef TensorEvaluator<ArgType, Device> Impl;
162 typedef typename XprType::CoeffReturnType CoeffReturnType;
163 typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
164 static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
165
166 enum {
167 IsAligned = false,
168 PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
169 Layout = TensorEvaluator<ArgType, Device>::Layout,
170 CoordAccess = false,
171 RawAccess = false
172 };
173
174 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
175 : m_impl(op.expression(), device)
176 {
177 EIGEN_STATIC_ASSERT((NumDims >= 4), YOU_MADE_A_PROGRAMMING_MISTAKE);
178
179 m_paddingValue = op.padding_value();
180
181 const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
182
183 // Caches a few variables.
184 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
185 m_inputDepth = input_dims[0];
186 m_inputRows = input_dims[1];
187 m_inputCols = input_dims[2];
188 } else {
189 m_inputDepth = input_dims[NumInputDims-1];
190 m_inputRows = input_dims[NumInputDims-2];
191 m_inputCols = input_dims[NumInputDims-3];
192 }
193
194 m_row_strides = op.row_strides();
195 m_col_strides = op.col_strides();
196
197 // Input strides and effective input/patch size
198 m_in_row_strides = op.in_row_strides();
199 m_in_col_strides = op.in_col_strides();
200 m_row_inflate_strides = op.row_inflate_strides();
201 m_col_inflate_strides = op.col_inflate_strides();
202 // The "effective" input rows and input cols are the input rows and cols
203 // after inflating them with zeros.
204 // For examples, a 2x3 matrix with row_inflate_strides and
205 // col_inflate_strides of 2 comes from:
206 // A B C
207 // D E F
208 //
209 // to a matrix is 3 x 5:
210 //
211 // A . B . C
212 // . . . . .
213 // D . E . F
214
215 m_input_rows_eff = (m_inputRows - 1) * m_row_inflate_strides + 1;
216 m_input_cols_eff = (m_inputCols - 1) * m_col_inflate_strides + 1;
217 m_patch_rows_eff = op.patch_rows() + (op.patch_rows() - 1) * (m_in_row_strides - 1);
218 m_patch_cols_eff = op.patch_cols() + (op.patch_cols() - 1) * (m_in_col_strides - 1);
219
220 if (op.padding_explicit()) {
221 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));
222 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));
223 m_rowPaddingTop = op.padding_top();
224 m_colPaddingLeft = op.padding_left();
225 } else {
226 // Computing padding from the type
227 switch (op.padding_type()) {
228 case PADDING_VALID:
229 m_outputRows = numext::ceil((m_input_rows_eff - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
230 m_outputCols = numext::ceil((m_input_cols_eff - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
231 // Calculate the padding
232 m_rowPaddingTop = numext::maxi<Index>(0, ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2);
233 m_colPaddingLeft = numext::maxi<Index>(0, ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2);
234 break;
235 case PADDING_SAME:
236 m_outputRows = numext::ceil(m_input_rows_eff / static_cast<float>(m_row_strides));
237 m_outputCols = numext::ceil(m_input_cols_eff / static_cast<float>(m_col_strides));
238 // Calculate the padding
239 m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2;
240 m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2;
241 break;
242 default:
243 eigen_assert(false && "unexpected padding");
244 }
245 }
246 eigen_assert(m_outputRows > 0);
247 eigen_assert(m_outputCols > 0);
248
249 // Dimensions for result of extraction.
250 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
251 // ColMajor
252 // 0: depth
253 // 1: patch_rows
254 // 2: patch_cols
255 // 3: number of patches
256 // 4 and beyond: anything else (such as batch).
257 m_dimensions[0] = input_dims[0];
258 m_dimensions[1] = op.patch_rows();
259 m_dimensions[2] = op.patch_cols();
260 m_dimensions[3] = m_outputRows * m_outputCols;
261 for (int i = 4; i < NumDims; ++i) {
262 m_dimensions[i] = input_dims[i-1];
263 }
264 } else {
265 // RowMajor
266 // NumDims-1: depth
267 // NumDims-2: patch_rows
268 // NumDims-3: patch_cols
269 // NumDims-4: number of patches
270 // NumDims-5 and beyond: anything else (such as batch).
271 m_dimensions[NumDims-1] = input_dims[NumInputDims-1];
272 m_dimensions[NumDims-2] = op.patch_rows();
273 m_dimensions[NumDims-3] = op.patch_cols();
274 m_dimensions[NumDims-4] = m_outputRows * m_outputCols;
275 for (int i = NumDims-5; i >= 0; --i) {
276 m_dimensions[i] = input_dims[i];
277 }
278 }
279
280 // Strides for moving the patch in various dimensions.
281 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
282 m_colStride = m_dimensions[1];
283 m_patchStride = m_colStride * m_dimensions[2] * m_dimensions[0];
284 m_otherStride = m_patchStride * m_dimensions[3];
285 } else {
286 m_colStride = m_dimensions[NumDims-2];
287 m_patchStride = m_colStride * m_dimensions[NumDims-3] * m_dimensions[NumDims-1];
288 m_otherStride = m_patchStride * m_dimensions[NumDims-4];
289 }
290
291 // Strides for navigating through the input tensor.
292 m_rowInputStride = m_inputDepth;
293 m_colInputStride = m_inputDepth * m_inputRows;
294 m_patchInputStride = m_inputDepth * m_inputRows * m_inputCols;
295
296 // Fast representations of different variables.
297 m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
298 m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
299 m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
300 m_fastInflateRowStride = internal::TensorIntDivisor<Index>(m_row_inflate_strides);
301 m_fastInflateColStride = internal::TensorIntDivisor<Index>(m_col_inflate_strides);
302 m_fastInputColsEff = internal::TensorIntDivisor<Index>(m_input_cols_eff);
303
304 // Number of patches in the width dimension.
305 m_fastOutputRows = internal::TensorIntDivisor<Index>(m_outputRows);
306 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
307 m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[0]);
308 } else {
309 m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims-1]);
310 }
311 }
312
313 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
314
315 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
316 m_impl.evalSubExprsIfNeeded(NULL);
317 return true;
318 }
319
320 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
321 m_impl.cleanup();
322 }
323
324 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
325 {
326 // Patch index corresponding to the passed in index.
327 const Index patchIndex = index / m_fastPatchStride;
328 // Find the offset of the element wrt the location of the first element.
329 const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastOutputDepth;
330
331 // Other ways to index this element.
332 const Index otherIndex = (NumDims == 4) ? 0 : index / m_fastOtherStride;
333 const Index patch2DIndex = (NumDims == 4) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride;
334
335 // Calculate col index in the input original tensor.
336 const Index colIndex = patch2DIndex / m_fastOutputRows;
337 const Index colOffset = patchOffset / m_fastColStride;
338 const Index inputCol = colIndex * m_col_strides + colOffset * m_in_col_strides - m_colPaddingLeft;
339 const Index origInputCol = (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInflateColStride) : 0);
340 if (inputCol < 0 || inputCol >= m_input_cols_eff ||
341 ((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides))) {
342 return Scalar(m_paddingValue);
343 }
344
345 // Calculate row index in the original input tensor.
346 const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
347 const Index rowOffset = patchOffset - colOffset * m_colStride;
348 const Index inputRow = rowIndex * m_row_strides + rowOffset * m_in_row_strides - m_rowPaddingTop;
349 const Index origInputRow = (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInflateRowStride) : 0);
350 if (inputRow < 0 || inputRow >= m_input_rows_eff ||
351 ((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) {
352 return Scalar(m_paddingValue);
353 }
354
355 const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
356 const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
357
358 const Index inputIndex = depth + origInputRow * m_rowInputStride + origInputCol * m_colInputStride + otherIndex * m_patchInputStride;
359 return m_impl.coeff(inputIndex);
360 }
361
362 template<int LoadMode>
363 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
364 {
365 EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
366 eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
367
368 if (m_in_row_strides != 1 || m_in_col_strides != 1 || m_row_inflate_strides != 1 || m_col_inflate_strides != 1) {
369 return packetWithPossibleZero(index);
370 }
371
372 const Index indices[2] = {index, index + PacketSize - 1};
373 const Index patchIndex = indices[0] / m_fastPatchStride;
374 if (patchIndex != indices[1] / m_fastPatchStride) {
375 return packetWithPossibleZero(index);
376 }
377 const Index otherIndex = (NumDims == 4) ? 0 : indices[0] / m_fastOtherStride;
378 eigen_assert(otherIndex == indices[1] / m_fastOtherStride);
379
380 // Find the offset of the element wrt the location of the first element.
381 const Index patchOffsets[2] = {(indices[0] - patchIndex * m_patchStride) / m_fastOutputDepth,
382 (indices[1] - patchIndex * m_patchStride) / m_fastOutputDepth};
383
384 const Index patch2DIndex = (NumDims == 4) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride;
385 eigen_assert(patch2DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride);
386
387 const Index colIndex = patch2DIndex / m_fastOutputRows;
388 const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, patchOffsets[1] / m_fastColStride};
389
390 // Calculate col indices in the original input tensor.
391 const Index inputCols[2] = {colIndex * m_col_strides + colOffsets[0] -
392 m_colPaddingLeft, colIndex * m_col_strides + colOffsets[1] - m_colPaddingLeft};
393 if (inputCols[1] < 0 || inputCols[0] >= m_inputCols) {
394 return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
395 }
396
397 if (inputCols[0] == inputCols[1]) {
398 const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
399 const Index rowOffsets[2] = {patchOffsets[0] - colOffsets[0]*m_colStride, patchOffsets[1] - colOffsets[1]*m_colStride};
400 eigen_assert(rowOffsets[0] <= rowOffsets[1]);
401 // Calculate col indices in the original input tensor.
402 const Index inputRows[2] = {rowIndex * m_row_strides + rowOffsets[0] -
403 m_rowPaddingTop, rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop};
404
405 if (inputRows[1] < 0 || inputRows[0] >= m_inputRows) {
406 return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
407 }
408
409 if (inputRows[0] >= 0 && inputRows[1] < m_inputRows) {
410 // no padding
411 const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
412 const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
413 const Index inputIndex = depth + inputRows[0] * m_rowInputStride + inputCols[0] * m_colInputStride + otherIndex * m_patchInputStride;
414 return m_impl.template packet<Unaligned>(inputIndex);
415 }
416 }
417
418 return packetWithPossibleZero(index);
419 }
420
421 EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
422
423 const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
424
425 Index rowPaddingTop() const { return m_rowPaddingTop; }
426 Index colPaddingLeft() const { return m_colPaddingLeft; }
427 Index outputRows() const { return m_outputRows; }
428 Index outputCols() const { return m_outputCols; }
429 Index userRowStride() const { return m_row_strides; }
430 Index userColStride() const { return m_col_strides; }
431 Index userInRowStride() const { return m_in_row_strides; }
432 Index userInColStride() const { return m_in_col_strides; }
433 Index rowInflateStride() const { return m_row_inflate_strides; }
434 Index colInflateStride() const { return m_col_inflate_strides; }
435
436 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
437 costPerCoeff(bool vectorized) const {
438 // We conservatively estimate the cost for the code path where the computed
439 // index is inside the original image and
440 // TensorEvaluator<ArgType, Device>::CoordAccess is false.
441 const double compute_cost = 3 * TensorOpCost::DivCost<Index>() +
442 6 * TensorOpCost::MulCost<Index>() +
443 8 * TensorOpCost::MulCost<Index>();
444 return m_impl.costPerCoeff(vectorized) +
445 TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
446 }
447
448 protected:
449 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
450 {
451 EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
452 for (int i = 0; i < PacketSize; ++i) {
453 values[i] = coeff(index+i);
454 }
455 PacketReturnType rslt = internal::pload<PacketReturnType>(values);
456 return rslt;
457 }
458
459 Dimensions m_dimensions;
460
461 Index m_otherStride;
462 Index m_patchStride;
463 Index m_colStride;
464 Index m_row_strides;
465 Index m_col_strides;
466
467 Index m_in_row_strides;
468 Index m_in_col_strides;
469 Index m_row_inflate_strides;
470 Index m_col_inflate_strides;
471
472 Index m_input_rows_eff;
473 Index m_input_cols_eff;
474 Index m_patch_rows_eff;
475 Index m_patch_cols_eff;
476
477 internal::TensorIntDivisor<Index> m_fastOtherStride;
478 internal::TensorIntDivisor<Index> m_fastPatchStride;
479 internal::TensorIntDivisor<Index> m_fastColStride;
480 internal::TensorIntDivisor<Index> m_fastInflateRowStride;
481 internal::TensorIntDivisor<Index> m_fastInflateColStride;
482 internal::TensorIntDivisor<Index> m_fastInputColsEff;
483
484 Index m_rowInputStride;
485 Index m_colInputStride;
486 Index m_patchInputStride;
487
488 Index m_inputDepth;
489 Index m_inputRows;
490 Index m_inputCols;
491
492 Index m_outputRows;
493 Index m_outputCols;
494
495 Index m_rowPaddingTop;
496 Index m_colPaddingLeft;
497
498 internal::TensorIntDivisor<Index> m_fastOutputRows;
499 internal::TensorIntDivisor<Index> m_fastOutputDepth;
500
501 Scalar m_paddingValue;
502
503 TensorEvaluator<ArgType, Device> m_impl;
504};
505
506
507} // end namespace Eigen
508
509#endif // EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
Namespace containing all symbols from the Eigen library.
Definition: AdolcForward:45
const Device & device() const
required by sycl in order to construct sycl buffer from raw pointer
Definition: TensorEvaluator.h:114