1 | /* Copyright 2015 The TensorFlow Authors. All Rights Reserved. |
2 | |
3 | Licensed under the Apache License, Version 2.0 (the "License"); |
4 | you may not use this file except in compliance with the License. |
5 | You may obtain a copy of the License at |
6 | |
7 | http://www.apache.org/licenses/LICENSE-2.0 |
8 | |
9 | Unless required by applicable law or agreed to in writing, software |
10 | distributed under the License is distributed on an "AS IS" BASIS, |
11 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
12 | See the License for the specific language governing permissions and |
13 | limitations under the License. |
14 | ==============================================================================*/ |
15 | |
16 | #ifndef TENSORFLOW_CORE_KERNELS_EIGEN_CUBOID_CONVOLUTION_H_ |
17 | #define TENSORFLOW_CORE_KERNELS_EIGEN_CUBOID_CONVOLUTION_H_ |
18 | |
19 | #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" |
20 | |
21 | #if defined(TENSORFLOW_USE_CUSTOM_CONTRACTION_KERNEL) |
22 | #include "tensorflow/core/kernels/eigen_contraction_kernel.h" |
23 | #endif |
24 | |
25 | #include "tensorflow/core/kernels/eigen_convolution_helpers.h" |
26 | |
27 | namespace Eigen { |
28 | |
29 | namespace internal { |
30 | |
31 | #if !EIGEN_ALTIVEC_USE_CUSTOM_PACK |
32 | // WARNING: Most of the code here implicitly assumes that the matrix is in |
33 | // ColMajor layout. This is guaranteed by the tensor contraction (see |
34 | // TensorContraction.h). |
35 | // |
36 | // Inside Eigen a tensor contraction is represented by a matrix multiplication. |
37 | // We don't want to actually extract volume patches and reshape the result into |
38 | // a matrix (this involves allocating huge extra memory), so the patch |
39 | // extraction and reshape operations are implicit. |
40 | // |
41 | // TensorContractionInputMapper takes a matrix index and returns the coefficient |
42 | // (or the packet) of the "virtual tensor", that would be at that index if we |
43 | // were to actually reshape the result of patch extraction. |
44 | // |
45 | // TensorContractionSubMapper provides a similar view into the "virtual matrix" |
46 | // at the given vertical and horizontal offsets. |
47 | // |
48 | // "Virtual matrix" dimensions: |
49 | // *0: kernelChannels * kernelPlanes * kernelRows * kernelCols |
50 | // 1: out_planes * out_height * out_width * OTHERS (e.g batches, etc...) |
51 | // |
52 | // *) extracted patches are continuous in memory (innermost dimension assuming |
53 | // col major layout) |
54 | // |
55 | // With this dimensions: |
56 | // row - offset within a single patch (in code: patchId) |
57 | // col - index of the extracted patch (in code: patchIndex) |
58 | // patchIndex ∈ [0..num_patches * OTHERS] (batch and other dimensions) |
59 | // |
60 | template <typename NewDimension, Index Planes, Index Rows, Index Cols, |
61 | typename ArgType, typename Device, typename Scalar_, typename Index, |
62 | typename nocontract_t, typename contract_t, int Side, int packet_size, |
63 | bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment> |
64 | class TensorContractionInputMapper< |
65 | Scalar_, Index, Side, |
66 | TensorEvaluator<const TensorReshapingOp<NewDimension, |
67 | const TensorVolumePatchOp< |
68 | Planes, Rows, Cols, ArgType> >, |
69 | Device>, |
70 | nocontract_t, contract_t, packet_size, inner_dim_contiguous, |
71 | inner_dim_reordered, Alignment> { |
72 | public: |
73 | typedef Scalar_ Scalar; |
74 | typedef TensorContractionInputMapper< |
75 | Scalar, Index, Side, |
76 | TensorEvaluator<const TensorReshapingOp< |
77 | NewDimension, const TensorVolumePatchOp< |
78 | Planes, Rows, Cols, ArgType> >, |
79 | Device>, |
80 | nocontract_t, contract_t, packet_size, inner_dim_contiguous, |
81 | inner_dim_reordered, Alignment> |
82 | Self; |
83 | typedef TensorContractionSubMapper< |
84 | Scalar, Index, Side, |
85 | TensorEvaluator<const TensorReshapingOp< |
86 | NewDimension, const TensorVolumePatchOp< |
87 | Planes, Rows, Cols, ArgType> >, |
88 | Device>, |
89 | nocontract_t, contract_t, packet_size, inner_dim_contiguous, |
90 | inner_dim_reordered, Alignment> |
91 | SubMapper; |
92 | typedef SubMapper VectorMapper; |
93 | typedef SubMapper LinearMapper; |
94 | typedef typename packet_traits<Scalar>::type Packet; |
95 | |
96 | EIGEN_DEVICE_FUNC |
97 | TensorContractionInputMapper( |
98 | const TensorEvaluator< |
99 | const TensorReshapingOp< |
100 | NewDimension, |
101 | const TensorVolumePatchOp<Planes, Rows, Cols, ArgType> >, |
102 | Device>& tensor, |
103 | const nocontract_t&, const nocontract_t&, const contract_t&, |
104 | const contract_t&) |
105 | : m_impl(tensor.impl().impl()) { |
106 | if (internal::traits<ArgType>::Layout == ColMajor) { |
107 | m_patch_depth = tensor.impl().dimensions()[0]; |
108 | m_patch_planes = tensor.impl().dimensions()[1]; |
109 | m_patch_rows = tensor.impl().dimensions()[2]; |
110 | m_patch_cols = tensor.impl().dimensions()[3]; |
111 | m_num_patches = tensor.impl().dimensions()[4]; |
112 | } else { |
113 | const int NumDims = tensor.impl().dimensions().size(); |
114 | m_patch_depth = tensor.impl().dimensions()[NumDims - 1]; |
115 | m_patch_planes = tensor.impl().dimensions()[NumDims - 2]; |
116 | m_patch_rows = tensor.impl().dimensions()[NumDims - 3]; |
117 | m_patch_cols = tensor.impl().dimensions()[NumDims - 4]; |
118 | m_num_patches = tensor.impl().dimensions()[NumDims - 5]; |
119 | } |
120 | |
121 | // Strides for navigating through the single patch. |
122 | m_patch_plane_stride = m_patch_depth; |
123 | m_patch_row_stride = m_patch_planes * m_patch_plane_stride; |
124 | m_patch_col_stride = m_patch_rows * m_patch_row_stride; |
125 | |
126 | // Strides for the output tensor. |
127 | // IMPORTANT: These strides are used to locate an element in a patch at a |
128 | // depth zero (channel), which is not quite the same as "traditional" |
129 | // stride. |
130 | m_rowStride = m_patch_planes; |
131 | m_colStride = m_patch_rows * m_rowStride; |
132 | m_patchStride = m_colStride * m_patch_cols * m_patch_depth; |
133 | m_otherStride = m_patchStride * m_num_patches; |
134 | |
135 | m_outputPlanes = tensor.impl().outputPlanes(); |
136 | m_outputRows = tensor.impl().outputRows(); |
137 | m_outputCols = tensor.impl().outputCols(); |
138 | |
139 | m_outputPlanesRows = m_outputPlanes * m_outputRows; |
140 | |
141 | m_plane_strides = tensor.impl().userPlaneStride(); |
142 | m_row_strides = tensor.impl().userRowStride(); |
143 | m_col_strides = tensor.impl().userColStride(); |
144 | |
145 | m_in_plane_strides = tensor.impl().userInPlaneStride(); |
146 | m_in_row_strides = tensor.impl().userInRowStride(); |
147 | m_in_col_strides = tensor.impl().userInColStride(); |
148 | |
149 | m_patch_plane_inflate_strides = tensor.impl().planeInflateStride(); |
150 | m_patch_row_inflate_strides = tensor.impl().rowInflateStride(); |
151 | m_patch_col_inflate_strides = tensor.impl().colInflateStride(); |
152 | |
153 | if (internal::traits<ArgType>::Layout == ColMajor) { |
154 | m_inputDepth = tensor.impl().impl().dimensions()[0]; |
155 | m_inputPlanes = tensor.impl().impl().dimensions()[1]; |
156 | m_inputRows = tensor.impl().impl().dimensions()[2]; |
157 | m_inputCols = tensor.impl().impl().dimensions()[3]; |
158 | } else { |
159 | const int NumDims = tensor.impl().impl().dimensions().size(); |
160 | m_inputDepth = tensor.impl().impl().dimensions()[NumDims - 1]; |
161 | m_inputPlanes = tensor.impl().impl().dimensions()[NumDims - 2]; |
162 | m_inputRows = tensor.impl().impl().dimensions()[NumDims - 3]; |
163 | m_inputCols = tensor.impl().impl().dimensions()[NumDims - 4]; |
164 | } |
165 | |
166 | // Strides for navigating through the input tensor. |
167 | m_planeInputStride = m_inputDepth; |
168 | m_rowInputStride = m_inputDepth * m_inputPlanes; |
169 | m_colInputStride = m_inputDepth * m_inputRows * m_inputPlanes; |
170 | m_patchInputStride = |
171 | m_inputDepth * m_inputRows * m_inputCols * m_inputPlanes; |
172 | |
173 | m_planePaddingTop = tensor.impl().planePaddingTop(); |
174 | m_rowPaddingTop = tensor.impl().rowPaddingTop(); |
175 | m_colPaddingLeft = tensor.impl().colPaddingLeft(); |
176 | |
177 | m_fastNumPatches = internal::TensorIntDivisor<Index>(m_num_patches); |
178 | |
179 | m_fastPatchPlaneStride = |
180 | internal::TensorIntDivisor<Index>(m_patch_plane_stride); |
181 | m_fastPatchRowStride = |
182 | internal::TensorIntDivisor<Index>(m_patch_row_stride); |
183 | m_fastPatchColStride = |
184 | internal::TensorIntDivisor<Index>(m_patch_col_stride); |
185 | |
186 | m_fastInputPlaneStride = |
187 | internal::TensorIntDivisor<Index>(m_patch_plane_inflate_strides); |
188 | m_fastInputRowStride = |
189 | internal::TensorIntDivisor<Index>(m_patch_row_inflate_strides); |
190 | m_fastInputColStride = |
191 | internal::TensorIntDivisor<Index>(m_patch_col_inflate_strides); |
192 | |
193 | m_fastRowStride = internal::TensorIntDivisor<Index>(m_rowStride); |
194 | m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride); |
195 | |
196 | m_fastDimZero = internal::TensorIntDivisor<Index>(m_patch_depth); |
197 | m_fastOutputRows = internal::TensorIntDivisor<Index>(m_outputRows); |
198 | m_fastOutputPlanes = internal::TensorIntDivisor<Index>(m_outputPlanes); |
199 | m_fastOutputRows = internal::TensorIntDivisor<Index>(m_outputRows); |
200 | m_fastOutputCols = internal::TensorIntDivisor<Index>(m_outputCols); |
201 | |
202 | m_fastOutputPlanesRows = |
203 | internal::TensorIntDivisor<Index>(m_outputPlanesRows); |
204 | } |
205 | |
206 | EIGEN_DEVICE_FUNC |
207 | TensorContractionInputMapper(const TensorContractionInputMapper& base_mapper) |
208 | : m_impl(base_mapper.m_impl) { |
209 | m_patch_depth = base_mapper.m_patch_depth; |
210 | m_patch_planes = base_mapper.m_patch_planes; |
211 | m_patch_rows = base_mapper.m_patch_rows; |
212 | m_patch_cols = base_mapper.m_patch_cols; |
213 | m_num_patches = base_mapper.m_num_patches; |
214 | |
215 | m_patch_plane_stride = base_mapper.m_patch_plane_stride; |
216 | m_patch_row_stride = base_mapper.m_patch_row_stride; |
217 | m_patch_col_stride = base_mapper.m_patch_col_stride; |
218 | |
219 | m_rowStride = base_mapper.m_rowStride; |
220 | m_colStride = base_mapper.m_colStride; |
221 | m_patchStride = base_mapper.m_patchStride; |
222 | m_otherStride = base_mapper.m_otherStride; |
223 | |
224 | m_planeInputStride = base_mapper.m_planeInputStride; |
225 | m_rowInputStride = base_mapper.m_rowInputStride; |
226 | m_colInputStride = base_mapper.m_colInputStride; |
227 | m_patchInputStride = base_mapper.m_patchInputStride; |
228 | m_otherInputStride = base_mapper.m_otherInputStride; |
229 | |
230 | m_inputDepth = base_mapper.m_inputDepth; |
231 | m_inputPlanes = base_mapper.m_inputPlanes; |
232 | m_inputRows = base_mapper.m_inputRows; |
233 | m_inputCols = base_mapper.m_inputCols; |
234 | |
235 | m_outputPlanes = base_mapper.m_outputPlanes; |
236 | m_outputRows = base_mapper.m_outputRows; |
237 | m_outputCols = base_mapper.m_outputCols; |
238 | |
239 | m_plane_strides = base_mapper.m_plane_strides; |
240 | m_row_strides = base_mapper.m_row_strides; |
241 | m_col_strides = base_mapper.m_col_strides; |
242 | |
243 | m_in_plane_strides = base_mapper.m_in_plane_strides; |
244 | m_in_row_strides = base_mapper.m_in_row_strides; |
245 | m_in_col_strides = base_mapper.m_in_col_strides; |
246 | |
247 | m_patch_plane_inflate_strides = base_mapper.m_patch_plane_inflate_strides; |
248 | m_patch_row_inflate_strides = base_mapper.m_patch_row_inflate_strides; |
249 | m_patch_col_inflate_strides = base_mapper.m_patch_col_inflate_strides; |
250 | |
251 | m_planePaddingTop = base_mapper.m_planePaddingTop; |
252 | m_rowPaddingTop = base_mapper.m_rowPaddingTop; |
253 | m_colPaddingLeft = base_mapper.m_colPaddingLeft; |
254 | |
255 | m_outputPlanesRows = base_mapper.m_outputPlanesRows; |
256 | |
257 | m_fastNumPatches = base_mapper.m_fastNumPatches; |
258 | m_fastPatchPlaneStride = base_mapper.m_fastPatchPlaneStride; |
259 | m_fastPatchRowStride = base_mapper.m_fastPatchRowStride; |
260 | m_fastPatchColStride = base_mapper.m_fastPatchColStride; |
261 | m_fastInputPlaneStride = base_mapper.m_fastInputPlaneStride; |
262 | m_fastInputRowStride = base_mapper.m_fastInputRowStride; |
263 | m_fastInputColStride = base_mapper.m_fastInputColStride; |
264 | m_fastRowStride = base_mapper.m_fastRowStride; |
265 | m_fastColStride = base_mapper.m_fastColStride; |
266 | m_fastOutputPlanes = base_mapper.m_fastOutputPlanes; |
267 | m_fastOutputRows = base_mapper.m_fastOutputRows; |
268 | m_fastOutputCols = base_mapper.m_fastOutputCols; |
269 | m_fastDimZero = base_mapper.m_fastDimZero; |
270 | m_fastOutputPlanesRows = base_mapper.m_fastOutputPlanesRows; |
271 | } |
272 | |
273 | // If true, turns off some optimizations for loading packets since the image |
274 | // patches are "non-standard" such as there are non-trivial strides or |
275 | // inflations in the input. |
276 | EIGEN_DEVICE_FUNC |
277 | EIGEN_ALWAYS_INLINE bool nonStandardPatches() const { |
278 | return m_in_plane_strides != 1 || m_in_row_strides != 1 || |
279 | m_in_col_strides != 1 || m_patch_plane_inflate_strides != 1 || |
280 | m_patch_row_inflate_strides != 1 || m_patch_col_inflate_strides != 1; |
281 | } |
282 | |
283 | EIGEN_DEVICE_FUNC |
284 | EIGEN_STRONG_INLINE SubMapper getSubMapper(Index i, Index j) const { |
285 | return SubMapper(*this, i, j); |
286 | } |
287 | |
288 | EIGEN_DEVICE_FUNC |
289 | EIGEN_STRONG_INLINE LinearMapper getLinearMapper(Index i, Index j) const { |
290 | return LinearMapper(*this, i, j); |
291 | } |
292 | |
293 | EIGEN_DEVICE_FUNC |
294 | EIGEN_ALWAYS_INLINE Scalar operator()(Index row) const { |
295 | Index planeIndex, rowIndex, colIndex, otherIndex; |
296 | computeBaseIndices(0, planeIndex, rowIndex, colIndex, otherIndex); |
297 | return loadCoeff(row, planeIndex, rowIndex, colIndex, otherIndex); |
298 | } |
299 | |
300 | // Load the coefficient at the patchIndex location instead of the usual |
301 | // m_rowIndex, m_colIndex, m_otherIndex. This is currently only used by the |
302 | // gpu code. |
303 | EIGEN_DEVICE_FUNC |
304 | EIGEN_STRONG_INLINE Scalar operator()(Index row, Index patchIndex) const { |
305 | Index planeIndex, rowIndex, colIndex, otherIndex; |
306 | computeBaseIndices(patchIndex, planeIndex, rowIndex, colIndex, otherIndex); |
307 | return loadCoeff(row, planeIndex, rowIndex, colIndex, otherIndex); |
308 | } |
309 | |
310 | EIGEN_DEVICE_FUNC |
311 | EIGEN_ALWAYS_INLINE Packet loadPacket(Index row) const { |
312 | Index planeIndex, rowIndex, colIndex, otherIndex; |
313 | computeBaseIndices(0, planeIndex, rowIndex, colIndex, otherIndex); |
314 | return loadPacket(row, planeIndex, rowIndex, colIndex, otherIndex); |
315 | } |
316 | |
317 | // Load the packet at the patchIndex location instead of the usual m_rowIndex, |
318 | // m_colIndex, m_otherIndex. This is currently only used by the gpu code. |
319 | EIGEN_DEVICE_FUNC |
320 | EIGEN_ALWAYS_INLINE Packet loadPacket(Index row, Index patchIndex) const { |
321 | Index planeIndex, rowIndex, colIndex, otherIndex; |
322 | computeBaseIndices(patchIndex, planeIndex, rowIndex, colIndex, otherIndex); |
323 | return loadPacket(row, planeIndex, rowIndex, colIndex, otherIndex); |
324 | } |
325 | |
326 | EIGEN_DEVICE_FUNC |
327 | EIGEN_ALWAYS_INLINE const TensorEvaluator<ArgType, Device>& impl() const { |
328 | return m_impl; |
329 | } |
330 | |
331 | EIGEN_DEVICE_FUNC |
332 | EIGEN_ALWAYS_INLINE Index patchDepth() const { return m_planeInputStride; } |
333 | EIGEN_DEVICE_FUNC |
334 | EIGEN_ALWAYS_INLINE Index patchPlanes() const { return m_rowStride; } |
335 | EIGEN_DEVICE_FUNC |
336 | EIGEN_ALWAYS_INLINE Index patchRows() const { return m_patch_rows; } |
337 | EIGEN_DEVICE_FUNC |
338 | EIGEN_ALWAYS_INLINE Index patchCols() const { return m_patch_cols; } |
339 | |
340 | private: |
341 | friend class TensorContractionSubMapper< |
342 | Scalar, Index, Side, |
343 | TensorEvaluator<const TensorReshapingOp< |
344 | NewDimension, const TensorVolumePatchOp< |
345 | Planes, Rows, Cols, ArgType> >, |
346 | Device>, |
347 | nocontract_t, contract_t, packet_size, inner_dim_contiguous, |
348 | inner_dim_reordered, Alignment>; |
349 | |
350 | // Load coefficient from a patch specified by the "within patch offset" |
351 | // (patchId) and the precomputed indices of the first element of the patch. |
352 | EIGEN_DEVICE_FUNC |
353 | EIGEN_STRONG_INLINE Scalar loadCoeff(Index patchId, Index planeIndex, |
354 | Index rowIndex, Index colIndex, |
355 | Index otherIndex) const { |
356 | // Find the offset of the element wrt the location of the first element. |
357 | const Index patchOffset = patchId / m_fastDimZero; |
358 | |
359 | const Index colOffset = patchOffset / m_fastColStride; |
360 | const Index inputCol = colIndex + colOffset * m_in_col_strides; |
361 | const Index origInputCol = |
362 | (m_patch_col_inflate_strides == 1) |
363 | ? inputCol |
364 | : ((inputCol >= 0) ? (inputCol / m_fastInputColStride) : 0); |
365 | |
366 | const Index rowOffset = |
367 | (patchOffset - colOffset * m_colStride) / m_fastRowStride; |
368 | const Index inputRow = rowIndex + rowOffset * m_in_row_strides; |
369 | const Index origInputRow = |
370 | (m_patch_row_inflate_strides == 1) |
371 | ? inputRow |
372 | : ((inputRow >= 0) ? (inputRow / m_fastInputRowStride) : 0); |
373 | |
374 | const Index planeOffset = |
375 | patchOffset - colOffset * m_colStride - rowOffset * m_rowStride; |
376 | const Index inputPlane = planeIndex + planeOffset * m_in_plane_strides; |
377 | const Index origInputPlane = |
378 | (m_patch_plane_inflate_strides == 1) |
379 | ? inputPlane |
380 | : ((inputPlane >= 0) ? (inputPlane / m_fastInputPlaneStride) : 0); |
381 | |
382 | if (origInputCol < 0 || origInputRow < 0 || origInputPlane < 0 || |
383 | origInputCol >= m_inputCols || origInputRow >= m_inputRows || |
384 | origInputPlane >= m_inputPlanes || |
385 | (inputCol != origInputCol * m_patch_col_inflate_strides) || |
386 | (inputRow != origInputRow * m_patch_row_inflate_strides) || |
387 | (inputPlane != origInputPlane * m_patch_plane_inflate_strides)) { |
388 | return Scalar(0); |
389 | } |
390 | |
391 | const Index depth = patchId - patchOffset * patchDepth(); |
392 | const Index inputIndex = depth + origInputPlane * m_planeInputStride + |
393 | origInputRow * m_rowInputStride + |
394 | origInputCol * m_colInputStride + otherIndex; |
395 | |
396 | return m_impl.coeff(inputIndex); |
397 | } |
398 | |
399 | // This is the same as loadCoeff(...), but optimized for all `inflate_strides` |
400 | // and `in_strides` equal to 1 (template specialization without templates). |
401 | EIGEN_DEVICE_FUNC |
402 | EIGEN_STRONG_INLINE Scalar loadCoeffStandard(Index patchId, Index planeIndex, |
403 | Index rowIndex, Index colIndex, |
404 | Index otherIndex) const { |
405 | eigen_assert(!nonStandardPatches()); |
406 | |
407 | // Find the offset of the element wrt the location of the first element. |
408 | const Index patchOffset = patchId / m_fastDimZero; |
409 | |
410 | const Index colOffset = patchOffset / m_fastColStride; |
411 | const Index rowOffset = |
412 | (patchOffset - colOffset * m_colStride) / m_fastRowStride; |
413 | const Index planeOffset = |
414 | patchOffset - colOffset * m_colStride - rowOffset * m_rowStride; |
415 | |
416 | const Index inputCol = colIndex + colOffset; |
417 | const Index inputRow = rowIndex + rowOffset; |
418 | const Index inputPlane = planeIndex + planeOffset; |
419 | |
420 | if (inputCol < 0 || inputCol >= m_inputCols || inputRow < 0 || |
421 | inputRow >= m_inputRows || inputPlane < 0 || |
422 | inputPlane >= m_inputPlanes) { |
423 | return Scalar(0); |
424 | } |
425 | |
426 | const Index depth = patchId - patchOffset * patchDepth(); |
427 | const Index inputIndex = depth + inputPlane * m_planeInputStride + |
428 | inputRow * m_rowInputStride + |
429 | inputCol * m_colInputStride + otherIndex; |
430 | |
431 | return m_impl.coeff(inputIndex); |
432 | } |
433 | |
434 | // Load packet from a patch specified by the "within patch offset" |
435 | // (patchId) and the precomputed indices of the first element of the patch. |
436 | EIGEN_DEVICE_FUNC |
437 | EIGEN_ALWAYS_INLINE Packet loadPacket(Index patchId, Index planeIndex, |
438 | Index rowIndex, Index colIndex, |
439 | Index otherIndex) const { |
440 | const Index packetSize = internal::unpacket_traits<Packet>::size; |
441 | |
442 | EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE) |
443 | eigen_assert(patchId < |
444 | patchDepth() * patchPlanes() * patchRows() * patchCols()); |
445 | |
446 | if (nonStandardPatches()) { |
447 | return packetWithPossibleZero(patchId, planeIndex, rowIndex, colIndex, |
448 | otherIndex); |
449 | } |
450 | typedef decltype(m_impl) TensorEvaluatorT; |
451 | return loadPacketStandard<Packet, TensorEvaluatorT>( |
452 | patchId, planeIndex, rowIndex, colIndex, otherIndex); |
453 | } |
454 | |
455 | // Helper function to load a 'partial' packet - this is the single row part of |
456 | // a packet that is split across two rows (but single column). In the |
457 | // 'partial' packet, the elements corresponding to the row (specified through |
458 | // rowOffset) are loaded and the rest of the elements are zero-filled into the |
459 | // 'partial' packet. This function is called from |
460 | // loadPacketStandardFromSingleColumnTwoRows(). This code path is exercised |
461 | // only when the packet type supports masked load and when the partial packet |
462 | // load is available in the TensorEvaluator. |
463 | EIGEN_DEVICE_FUNC |
464 | EIGEN_ALWAYS_INLINE Packet loadPartialPacketStandard( |
465 | Index planeIndex, Index rowIndex, Index colIndex, Index otherIndex, |
466 | Index patchId, const Index span[], const Index patchOffsets[], |
467 | Index colOffset, Index rowOffset) const { |
468 | const Index inputCol = colIndex + colOffset; |
469 | const Index inputRow = rowIndex + rowOffset; |
470 | const Index planeOffsets[2] = { |
471 | patchOffsets[0] - colOffset * m_colStride - rowOffset * m_rowStride, |
472 | patchOffsets[1] - colOffset * m_colStride - rowOffset * m_rowStride}; |
473 | const Index inputPlanes[2] = {planeIndex + planeOffsets[0], |
474 | planeIndex + planeOffsets[1]}; |
475 | |
476 | if (inputRow >= m_inputRows || inputRow < 0 || inputCol >= m_inputCols || |
477 | inputCol < 0 || inputPlanes[0] >= m_inputPlanes || inputPlanes[1] < 0) { |
478 | // Partial packet is all zeros |
479 | return internal::pset1<Packet>(Scalar(0)); |
480 | } else if (inputPlanes[0] >= 0 && inputPlanes[1] < m_inputPlanes) { |
481 | // From inputIndex-span[0], we need to load elements starting from index |
482 | // span[0] all the way upto (and including) span[1]. |
483 | const Index depth = patchId - patchOffsets[0] * patchDepth(); |
484 | const Index inputIndex = depth + inputPlanes[0] * m_planeInputStride + |
485 | inputRow * m_rowInputStride + |
486 | inputCol * m_colInputStride + otherIndex; |
487 | return m_impl.template partialPacket<Packet>( |
488 | inputIndex - span[0], mask<Packet>(span[0], span[1] + 1)); |
489 | } else { |
490 | // Using slow path for this partial packet. |
491 | // We need to load elements starting from index span[0] all the way upto |
492 | // (and including) span[1]. We split this load into 3 parts: |
493 | // 0 : span[0]-1 - Zeros will be loaded for these indices |
494 | // span[0] : span[1] - Elements will be loaded here for these indices |
495 | // span[1]+1 : packetSize-1 - Zeross will be loaded for these indices |
496 | const Index packetSize = internal::unpacket_traits<Packet>::size; |
497 | EIGEN_ALIGN_MAX |
498 | std::remove_const_t<Scalar> values[packetSize]; |
499 | for (int i = 0; i < span[0]; ++i) values[i] = Scalar(0); |
500 | for (int i = span[0]; i < span[1] + 1; ++i) |
501 | values[i] = loadCoeff(patchId - span[0] + i, planeIndex, rowIndex, |
502 | colIndex, otherIndex); |
503 | for (int i = span[1] + 1; i < packetSize; ++i) values[i] = Scalar(0); |
504 | return internal::pload<Packet>(values); |
505 | } |
506 | } |
507 | |
508 | // Helper function to load a packet that is split across two rows (but single |
509 | // column). If required, this function is called from loadPacketStandard() |
510 | // when the packet type supports masked load and when the partial packet load |
511 | // is available in the TensorEvaluator. |
512 | EIGEN_DEVICE_FUNC |
513 | EIGEN_ALWAYS_INLINE Packet loadPacketStandardFromSingleColumnTwoRows( |
514 | Index patchId, Index planeIndex, Index rowIndex, Index colIndex, |
515 | Index otherIndex, const Index patchOffsets[], const Index colOffsets[], |
516 | const Index rowOffsets[]) const { |
517 | eigen_assert(colOffsets[1] == colOffsets[0] && |
518 | rowOffsets[1] == rowOffsets[0] + 1); |
519 | const Index packetSize = internal::unpacket_traits<Packet>::size; |
520 | |
521 | // Packet to load will be split into 2 parts where each part spans a single |
522 | // row and both the parts span the same column. |
523 | // First determine where to split. |
524 | const Index patchIdSplit = |
525 | (((rowOffsets[1] * m_rowStride) + (colOffsets[0] * m_colStride)) * |
526 | m_patch_depth) - |
527 | 1; |
528 | const Index patchOffsetSplit = patchIdSplit / m_fastDimZero; |
529 | |
530 | // patchIds[i]: patchId corresponding to partial packet i |
531 | // spans[i]: Start and end indices corresponding to the elements |
532 | // to be loaded for partial packet i |
533 | // patchOffsets2Cols[i]: patchOffsets corresponding to partial packet i |
534 | const Index patchIds[2] = {patchId, patchIdSplit + 1}; |
535 | const Index spans[2][2] = {{0, patchIdSplit - patchId}, |
536 | {patchIdSplit - patchId + 1, packetSize - 1}}; |
537 | const Index patchOffsets2Cols[2][2] = { |
538 | {patchOffsets[0], patchOffsetSplit}, |
539 | {patchOffsetSplit + 1, patchOffsets[1]}}; |
540 | |
541 | // Load partial packets and do bit-wise OR to generate required packet |
542 | return internal::por<Packet>( |
543 | loadPartialPacketStandard(planeIndex, rowIndex, colIndex, otherIndex, |
544 | patchIds[0], spans[0], patchOffsets2Cols[0], |
545 | colOffsets[0], rowOffsets[0]), |
546 | loadPartialPacketStandard(planeIndex, rowIndex, colIndex, otherIndex, |
547 | patchIds[1], spans[1], patchOffsets2Cols[1], |
548 | colOffsets[1], rowOffsets[1])); |
549 | } |
550 | |
551 | // Helper function to load a packet that is present in a single column and |
552 | // row. If required, this function is called from loadPacketStandard(). |
553 | EIGEN_DEVICE_FUNC |
554 | EIGEN_ALWAYS_INLINE Packet loadPacketStandardFromSingleColumnSingleRow( |
555 | Index patchId, Index planeIndex, Index rowIndex, Index colIndex, |
556 | Index otherIndex, const Index patchOffsets[], const Index colOffsets[], |
557 | const Index rowOffsets[], const Index inputCols[], |
558 | const Index inputRows[]) const { |
559 | eigen_assert(colOffsets[1] == colOffsets[0] && |
560 | rowOffsets[1] == rowOffsets[0]); |
561 | const Index planeOffsets[2] = { |
562 | patchOffsets[0] - colOffsets[0] * m_colStride - |
563 | rowOffsets[0] * m_rowStride, |
564 | patchOffsets[1] - colOffsets[1] * m_colStride - |
565 | rowOffsets[1] * m_rowStride}; |
566 | eigen_assert(planeOffsets[0] <= planeOffsets[1]); |
567 | const Index inputPlanes[2] = {planeIndex + planeOffsets[0], |
568 | planeIndex + planeOffsets[1]}; |
569 | |
570 | if (inputPlanes[0] >= m_inputPlanes || inputPlanes[1] < 0) { |
571 | return internal::pset1<Packet>(Scalar(0)); |
572 | } |
573 | if (inputPlanes[0] >= 0 && inputPlanes[1] < m_inputPlanes) { |
574 | const Index depth = patchId - patchOffsets[0] * patchDepth(); |
575 | const Index inputIndex = depth + inputPlanes[0] * m_planeInputStride + |
576 | inputRows[0] * m_rowInputStride + |
577 | inputCols[0] * m_colInputStride + otherIndex; |
578 | return m_impl.template packet<Unaligned>(inputIndex); |
579 | } |
580 | return packetWithPossibleZero(patchId, planeIndex, rowIndex, colIndex, |
581 | otherIndex); |
582 | } |
583 | |
584 | // Load standard packet from a patch specified by the "within patch offset" |
585 | // (patchId) and the precomputed indices of the first element of the patch. |
586 | // This function will be called if partial packet loading is not available |
587 | // for the TensorEvaluator or if the packet type does not support masked |
588 | // load. |
589 | template <typename PacketT, typename TensorEvaluatorT> |
590 | EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE typename std::enable_if< |
591 | !TensorEvaluatorHasPartialPacket<TensorEvaluatorT, PacketT, Index>::value, |
592 | PacketT>::type |
593 | loadPacketStandard(Index patchId, Index planeIndex, Index rowIndex, |
594 | Index colIndex, Index otherIndex) const { |
595 | const Index packetSize = internal::unpacket_traits<Packet>::size; |
596 | EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE) |
597 | eigen_assert(patchId < |
598 | patchDepth() * patchPlanes() * patchRows() * patchCols()); |
599 | eigen_assert(!nonStandardPatches()); |
600 | |
601 | if ((patchDepth() % packetSize) == 0) { |
602 | return loadPacketFast(patchId, planeIndex, rowIndex, colIndex, |
603 | otherIndex); |
604 | } else { |
605 | // Offsets and input calculation here are identical to |
606 | // loadCoeffStandard(...), but repeated twice. |
607 | |
608 | const Index patchOffsets[2] = { |
609 | patchId / m_fastDimZero, (patchId + packetSize - 1) / m_fastDimZero}; |
610 | |
611 | const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, |
612 | patchOffsets[1] / m_fastColStride}; |
613 | eigen_assert(colOffsets[0] <= colOffsets[1]); |
614 | |
615 | const Index inputCols[2] = {colIndex + colOffsets[0], |
616 | colIndex + colOffsets[1]}; |
617 | if (inputCols[0] >= m_inputCols || inputCols[1] < 0) { |
618 | return internal::pset1<Packet>(Scalar(0)); |
619 | } |
620 | |
621 | if (inputCols[0] == inputCols[1]) { |
622 | const Index rowOffsets[2] = { |
623 | (patchOffsets[0] - colOffsets[0] * m_colStride) / m_fastRowStride, |
624 | (patchOffsets[1] - colOffsets[1] * m_colStride) / m_fastRowStride}; |
625 | eigen_assert(rowOffsets[0] <= rowOffsets[1]); |
626 | const Index inputRows[2] = {rowIndex + rowOffsets[0], |
627 | rowIndex + rowOffsets[1]}; |
628 | |
629 | if (inputRows[0] >= m_inputRows || inputRows[1] < 0) { |
630 | return internal::pset1<Packet>(Scalar(0)); |
631 | } |
632 | |
633 | if (inputRows[0] == inputRows[1]) { |
634 | return loadPacketStandardFromSingleColumnSingleRow( |
635 | patchId, planeIndex, rowIndex, colIndex, otherIndex, patchOffsets, |
636 | colOffsets, rowOffsets, inputCols, inputRows); |
637 | } |
638 | } |
639 | } |
640 | |
641 | return packetWithPossibleZero(patchId, planeIndex, rowIndex, colIndex, |
642 | otherIndex); |
643 | } |
644 | |
645 | // Load standard packet from a patch specified by the "within patch offset" |
646 | // (patchId) and the precomputed indices of the first element of the patch. |
647 | // This function will be called if partial packet loading is available for |
648 | // the TensorEvaluator and if the packet type supports masked load. |
649 | // The only difference between this and the other case is that if the packet |
650 | // to load is split across two rows (but in same column), then in this case |
651 | // instead of going to the slow (element-by-element) load, we load two packets |
652 | // - each containing elements from one of the rows (rest of the elements of |
653 | // the packets are zeroes), and then combine these two packets to generate the |
654 | // required packet. The idea is to enable fast load (if possible) of these |
655 | // 'partial' packets. |
656 | template <typename PacketT, typename TensorEvaluatorT> |
657 | EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE typename std::enable_if< |
658 | TensorEvaluatorHasPartialPacket<TensorEvaluatorT, PacketT, Index>::value, |
659 | PacketT>::type |
660 | loadPacketStandard(Index patchId, Index planeIndex, Index rowIndex, |
661 | Index colIndex, Index otherIndex) const { |
662 | const Index packetSize = internal::unpacket_traits<Packet>::size; |
663 | EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE) |
664 | eigen_assert(patchId < |
665 | patchDepth() * patchPlanes() * patchRows() * patchCols()); |
666 | eigen_assert(!nonStandardPatches()); |
667 | |
668 | if ((patchDepth() % packetSize) == 0) { |
669 | return loadPacketFast(patchId, planeIndex, rowIndex, colIndex, |
670 | otherIndex); |
671 | } else { |
672 | // Offsets and input calculation here are identical to |
673 | // loadCoeffStandard(...), but repeated twice. |
674 | |
675 | const Index patchOffsets[2] = { |
676 | patchId / m_fastDimZero, (patchId + packetSize - 1) / m_fastDimZero}; |
677 | |
678 | const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, |
679 | patchOffsets[1] / m_fastColStride}; |
680 | eigen_assert(colOffsets[0] <= colOffsets[1]); |
681 | |
682 | const Index inputCols[2] = {colIndex + colOffsets[0], |
683 | colIndex + colOffsets[1]}; |
684 | if (inputCols[0] >= m_inputCols || inputCols[1] < 0) { |
685 | return internal::pset1<Packet>(Scalar(0)); |
686 | } |
687 | |
688 | if (inputCols[0] == inputCols[1]) { |
689 | const Index rowOffsets[2] = { |
690 | (patchOffsets[0] - colOffsets[0] * m_colStride) / m_fastRowStride, |
691 | (patchOffsets[1] - colOffsets[1] * m_colStride) / m_fastRowStride}; |
692 | eigen_assert(rowOffsets[0] <= rowOffsets[1]); |
693 | const Index inputRows[2] = {rowIndex + rowOffsets[0], |
694 | rowIndex + rowOffsets[1]}; |
695 | |
696 | if (inputRows[0] >= m_inputRows || inputRows[1] < 0) { |
697 | return internal::pset1<Packet>(Scalar(0)); |
698 | } |
699 | |
700 | if (inputRows[0] == inputRows[1]) { |
701 | return loadPacketStandardFromSingleColumnSingleRow( |
702 | patchId, planeIndex, rowIndex, colIndex, otherIndex, patchOffsets, |
703 | colOffsets, rowOffsets, inputCols, inputRows); |
704 | } |
705 | if (inputRows[0] + 1 == inputRows[1]) { |
706 | return loadPacketStandardFromSingleColumnTwoRows( |
707 | patchId, planeIndex, rowIndex, colIndex, otherIndex, patchOffsets, |
708 | colOffsets, rowOffsets); |
709 | } |
710 | } |
711 | } |
712 | |
713 | return packetWithPossibleZero(patchId, planeIndex, rowIndex, colIndex, |
714 | otherIndex); |
715 | } |
716 | |
717 | EIGEN_DEVICE_FUNC |
718 | EIGEN_ALWAYS_INLINE Packet loadPacketFast(Index patchId, Index planeIndex, |
719 | Index rowIndex, Index colIndex, |
720 | Index otherIndex) const { |
721 | const Index packetSize = internal::unpacket_traits<Packet>::size; |
722 | EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE) |
723 | eigen_assert(patchId < |
724 | patchDepth() * patchPlanes() * patchRows() * patchCols()); |
725 | |
726 | eigen_assert(!nonStandardPatches()); |
727 | eigen_assert((patchDepth() % packetSize) == 0); |
728 | |
729 | // Find the offset of the element wrt the location of the first element. |
730 | const Index patchOffset = patchId / m_fastDimZero; |
731 | eigen_assert((patchId + packetSize - 1) / m_fastDimZero == patchOffset); |
732 | |
733 | const Index colOffset = patchOffset / m_fastColStride; |
734 | const Index rowOffset = |
735 | (patchOffset - colOffset * m_colStride) / m_fastRowStride; |
736 | const Index planeOffset = |
737 | patchOffset - colOffset * m_colStride - rowOffset * m_rowStride; |
738 | |
739 | const Index inputCol = colIndex + colOffset; |
740 | const Index inputRow = rowIndex + rowOffset; |
741 | const Index inputPlane = planeIndex + planeOffset; |
742 | |
743 | if (inputCol < 0 || inputRow < 0 || inputPlane < 0 || |
744 | inputCol >= m_inputCols || inputRow >= m_inputRows || |
745 | inputPlane >= m_inputPlanes) { |
746 | return internal::pset1<Packet>(Scalar(0)); |
747 | } |
748 | |
749 | const Index depth = patchId - patchOffset * patchDepth(); |
750 | const Index inputIndex = depth + inputPlane * m_planeInputStride + |
751 | inputRow * m_rowInputStride + |
752 | inputCol * m_colInputStride + otherIndex; |
753 | return m_impl.template packet<Unaligned>(inputIndex); |
754 | } |
755 | |
756 | EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet |
757 | packetWithPossibleZero(Index patchId, Index planeIndex, Index rowIndex, |
758 | Index colIndex, Index otherIndex) const { |
759 | const int packetSize = internal::unpacket_traits<Packet>::size; |
760 | EIGEN_ALIGN_MAX |
761 | std::remove_const_t<Scalar> values[packetSize]; |
762 | for (int i = 0; i < packetSize; ++i) { |
763 | values[i] = |
764 | loadCoeff(patchId + i, planeIndex, rowIndex, colIndex, otherIndex); |
765 | } |
766 | Packet rslt = internal::pload<Packet>(values); |
767 | return rslt; |
768 | } |
769 | |
770 | // Precompute the indices (plane, row, col, other) of the first element of |
771 | // the given patch index, within the output tensor of the TensorVolumePatchOp. |
772 | EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void computeBaseIndices( |
773 | Index patchIndex, Index& planeIndex, Index& rowIndex, Index& colIndex, |
774 | Index& otherIndex) const { |
775 | const size_t NumInputDims = array_size< |
776 | typename TensorEvaluator<ArgType, Device>::Dimensions>::value; |
777 | |
778 | // Check if patchIndex might contain batch and other dimensions. |
779 | otherIndex = (NumInputDims == 4) ? 0 : patchIndex / m_fastNumPatches; |
780 | |
781 | // Compute index of the patch within the batch (and other dimensions). |
782 | const Index patch3DIndex = (NumInputDims == 4) |
783 | ? patchIndex |
784 | : (patchIndex - otherIndex * m_num_patches); |
785 | |
786 | otherIndex *= m_patchInputStride; |
787 | |
788 | colIndex = patch3DIndex / m_fastOutputPlanesRows; |
789 | rowIndex = |
790 | (patch3DIndex - colIndex * m_outputPlanesRows) / m_fastOutputPlanes; |
791 | planeIndex = |
792 | patch3DIndex - (colIndex * m_outputRows + rowIndex) * m_outputPlanes; |
793 | |
794 | colIndex = colIndex * m_col_strides - m_colPaddingLeft; |
795 | rowIndex = rowIndex * m_row_strides - m_rowPaddingTop; |
796 | planeIndex = planeIndex * m_plane_strides - m_planePaddingTop; |
797 | } |
798 | |
799 | Index m_patch_depth; // number of channels in the patch |
800 | Index m_patch_planes; // number of planes in the patch |
801 | Index m_patch_rows; // number of rows in the patch |
802 | Index m_patch_cols; // number of columns in the patch |
803 | Index m_num_patches; // number of patches to extract |
804 | |
805 | // Strides for navigating through the single patch. |
806 | Index m_patch_plane_stride; |
807 | Index m_patch_row_stride; |
808 | Index m_patch_col_stride; |
809 | |
810 | // Strides for the output tensor (depth is not the part of the stride). |
811 | Index m_rowStride; |
812 | Index m_colStride; |
813 | Index m_patchStride; |
814 | Index m_otherStride; |
815 | |
816 | Index m_planeInputStride; // Plane stride in the input tensor |
817 | Index m_rowInputStride; // Row stride in the input tensor |
818 | Index m_colInputStride; // Col stride in the input tensor |
819 | Index m_patchInputStride; // Patch stride in the input tensor |
820 | Index m_otherInputStride; |
821 | |
822 | Index m_inputDepth; // Depth of the input tensor |
823 | Index m_inputPlanes; // Number of planes in the input tensor |
824 | Index m_inputRows; // Number of rows in the input tensor |
825 | Index m_inputCols; // Number of cols in the input tensor |
826 | |
827 | Index m_outputPlanes; // Number of output planes |
828 | Index m_outputRows; // Number of output rows |
829 | Index m_outputCols; // Number of output cols |
830 | Index m_outputPlanesRows; // Cached outputPlanes * outputRows. |
831 | |
832 | Index m_plane_strides; // User specified plane stride |
833 | Index m_row_strides; // User specified row stride |
834 | Index m_col_strides; // User specified col stride |
835 | |
836 | // User specified plane/row/col atrous convolution strides. |
837 | Index m_in_plane_strides; |
838 | Index m_in_row_strides; |
839 | Index m_in_col_strides; |
840 | |
841 | // User specified plane/row/col inflation strides in the image patch. |
842 | Index m_patch_plane_inflate_strides; |
843 | Index m_patch_row_inflate_strides; |
844 | Index m_patch_col_inflate_strides; |
845 | |
846 | Index m_planePaddingTop; // Plane padding |
847 | Index m_rowPaddingTop; // Row padding |
848 | Index m_colPaddingLeft; // Column padding |
849 | |
850 | // Fast representation of various divisors. |
851 | internal::TensorIntDivisor<Index> m_fastNumPatches; |
852 | |
853 | internal::TensorIntDivisor<Index> m_fastPatchPlaneStride; |
854 | internal::TensorIntDivisor<Index> m_fastPatchRowStride; |
855 | internal::TensorIntDivisor<Index> m_fastPatchColStride; |
856 | |
857 | internal::TensorIntDivisor<Index> m_fastInputPlaneStride; |
858 | internal::TensorIntDivisor<Index> m_fastInputRowStride; |
859 | internal::TensorIntDivisor<Index> m_fastInputColStride; |
860 | |
861 | internal::TensorIntDivisor<Index> m_fastRowStride; |
862 | internal::TensorIntDivisor<Index> m_fastColStride; |
863 | |
864 | internal::TensorIntDivisor<Index> m_fastDimZero; // aka output depth |
865 | internal::TensorIntDivisor<Index> m_fastOutputPlanes; |
866 | internal::TensorIntDivisor<Index> m_fastOutputRows; |
867 | internal::TensorIntDivisor<Index> m_fastOutputCols; |
868 | internal::TensorIntDivisor<Index> m_fastOutputPlanesRows; |
869 | |
870 | const TensorEvaluator<ArgType, Device> m_impl; |
871 | }; |
872 | |
873 | template <typename NewDimension, Index Planes, Index Rows, Index Cols, |
874 | typename ArgType, typename Device, typename Scalar, typename Index, |
875 | typename nocontract_t, typename contract_t, int Side, int packet_size, |
876 | bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment> |
877 | class TensorContractionSubMapper< |
878 | Scalar, Index, Side, |
879 | TensorEvaluator<const TensorReshapingOp<NewDimension, |
880 | const TensorVolumePatchOp< |
881 | Planes, Rows, Cols, ArgType> >, |
882 | Device>, |
883 | nocontract_t, contract_t, packet_size, inner_dim_contiguous, |
884 | inner_dim_reordered, Alignment> { |
885 | public: |
886 | typedef typename packet_traits<Scalar>::type Packet; |
887 | typedef typename packet_traits<Scalar>::half HalfPacket; |
888 | |
889 | typedef TensorContractionInputMapper< |
890 | Scalar, Index, Side, |
891 | TensorEvaluator<const TensorReshapingOp< |
892 | NewDimension, const TensorVolumePatchOp< |
893 | Planes, Rows, Cols, ArgType> >, |
894 | Device>, |
895 | nocontract_t, contract_t, packet_size, inner_dim_contiguous, |
896 | inner_dim_reordered, Alignment> |
897 | ParentMapper; |
898 | typedef TensorContractionSubMapper< |
899 | Scalar, Index, Side, |
900 | TensorEvaluator<const TensorReshapingOp< |
901 | NewDimension, const TensorVolumePatchOp< |
902 | Planes, Rows, Cols, ArgType> >, |
903 | Device>, |
904 | nocontract_t, contract_t, packet_size, inner_dim_contiguous, |
905 | inner_dim_reordered, Alignment> |
906 | Self; |
907 | typedef Self LinearMapper; |
908 | |
909 | EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionSubMapper( |
910 | const ParentMapper& base_mapper, Index vert_offset, Index horiz_offset) |
911 | : m_base_mapper(base_mapper), |
912 | m_depth_offset(vert_offset), |
913 | m_col_offset(horiz_offset) { |
914 | m_base_mapper.computeBaseIndices(m_col_offset, m_planeIndex, m_rowIndex, |
915 | m_colIndex, m_otherIndex); |
916 | } |
917 | EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionSubMapper( |
918 | const Self& base_mapper, Index vert_offset, Index horiz_offset) |
919 | : m_base_mapper(base_mapper.m_base_mapper), |
920 | m_depth_offset(vert_offset + base_mapper.m_depth_offset), |
921 | m_col_offset(horiz_offset + base_mapper.m_col_offset) { |
922 | m_base_mapper.computeBaseIndices(m_col_offset, m_planeIndex, m_rowIndex, |
923 | m_colIndex, m_otherIndex); |
924 | } |
925 | EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i) const { |
926 | return m_base_mapper.loadCoeff(i + m_depth_offset, m_planeIndex, m_rowIndex, |
927 | m_colIndex, m_otherIndex); |
928 | } |
929 | EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i, |
930 | Index j) const { |
931 | return m_base_mapper(i + m_depth_offset, j + m_col_offset); |
932 | } |
933 | |
934 | EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPacket(Index i) const { |
935 | return m_base_mapper.loadPacket(i + m_depth_offset, m_planeIndex, |
936 | m_rowIndex, m_colIndex, m_otherIndex); |
937 | } |
938 | EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPacket(Index i, |
939 | Index j) const { |
940 | return m_base_mapper.template loadPacket<Alignment>(i + m_depth_offset, |
941 | j + m_col_offset); |
942 | } |
943 | |
944 | EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar |
945 | loadCoeffStandard(Index i) const { |
946 | return m_base_mapper.loadCoeffStandard( |
947 | i + m_depth_offset, m_planeIndex, m_rowIndex, m_colIndex, m_otherIndex); |
948 | } |
949 | |
950 | EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPacketFast(Index i) const { |
951 | return m_base_mapper.loadPacketFast(i + m_depth_offset, m_planeIndex, |
952 | m_rowIndex, m_colIndex, m_otherIndex); |
953 | } |
954 | EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet |
955 | loadPacketStandard(Index i) const { |
956 | typedef decltype(m_base_mapper.m_impl) TensorEvaluatorT; |
957 | return m_base_mapper.template loadPacketStandard<Packet, TensorEvaluatorT>( |
958 | i + m_depth_offset, m_planeIndex, m_rowIndex, m_colIndex, m_otherIndex); |
959 | } |
960 | template <typename Packet> |
961 | EIGEN_DEVICE_FUNC bool aligned(Index) const { |
962 | return false; |
963 | } |
964 | |
965 | EIGEN_DEVICE_FUNC |
966 | EIGEN_ALWAYS_INLINE bool nonStandardPatches() const { |
967 | return m_base_mapper.nonStandardPatches(); |
968 | } |
969 | |
970 | // Max(Col|Row|Plane|Depth): compute the upper limit for the column, row, |
971 | // plane and depth index respectively that fits into the peeled_k elements |
972 | // starting at m_depth_offset. |
973 | |
974 | EIGEN_DEVICE_FUNC |
975 | EIGEN_ALWAYS_INLINE Index maxCol(const Index peeled_k) const { |
976 | const Index max_col = |
977 | fastPatchColStride().divide(m_depth_offset + peeled_k); |
978 | return std::min<Index>(1 + max_col, patchCols()); |
979 | } |
980 | |
981 | EIGEN_DEVICE_FUNC |
982 | EIGEN_ALWAYS_INLINE Index maxRow(const Index peeled_k, |
983 | const Index col) const { |
984 | const Index max_row = fastPatchRowStride().divide( |
985 | m_depth_offset + peeled_k - col * patchColStride()); |
986 | return std::min<Index>(1 + max_row, patchRows()); |
987 | } |
988 | |
989 | EIGEN_DEVICE_FUNC |
990 | EIGEN_ALWAYS_INLINE Index maxPlane(const Index peeled_k, const Index col, |
991 | const Index row) const { |
992 | const Index max_plane = fastPatchPlaneStride().divide( |
993 | m_depth_offset + peeled_k - col * patchColStride() - |
994 | row * patchRowStride()); |
995 | return std::min<Index>(1 + max_plane, patchPlanes()); |
996 | } |
997 | |
998 | // MaxDepth uses only the remaining number of elements in the peeled_k. |
999 | EIGEN_DEVICE_FUNC |
1000 | EIGEN_ALWAYS_INLINE Index maxDepth(const Index num_elements, |
1001 | const Index start_depth) const { |
1002 | return std::min<Index>(start_depth + num_elements, patchDepth()); |
1003 | } |
1004 | |
1005 | // Every register matters in this code, so sometimes to prevent register |
1006 | // spilling, instead of the variable that you would expect to see, we use |
1007 | // another one, that is guaranteed to have the same value. E.g. patch depth is |
1008 | // always the same as input depth, and it's also the same as input plane |
1009 | // stride. Bunch of other parameters have similar relations. |
1010 | |
1011 | typedef internal::TensorIntDivisor<Index> IndexDivisor; |
1012 | |
1013 | EIGEN_DEVICE_FUNC |
1014 | EIGEN_ALWAYS_INLINE Index patchDepth() const { |
1015 | eigen_assert(m_base_mapper.m_patch_depth == |
1016 | m_base_mapper.m_planeInputStride && |
1017 | "Patch depth must be equal to plane input stride." ); |
1018 | return m_base_mapper.m_planeInputStride; |
1019 | } |
1020 | |
1021 | EIGEN_DEVICE_FUNC |
1022 | EIGEN_ALWAYS_INLINE Index patchPlanes() const { |
1023 | eigen_assert(m_base_mapper.m_patch_planes == m_base_mapper.m_rowStride && |
1024 | "Patch planes must be equal to row stride." ); |
1025 | return m_base_mapper.m_rowStride; |
1026 | } |
1027 | EIGEN_DEVICE_FUNC |
1028 | EIGEN_ALWAYS_INLINE Index patchRows() const { |
1029 | return m_base_mapper.m_patch_rows; |
1030 | } |
1031 | EIGEN_DEVICE_FUNC |
1032 | EIGEN_ALWAYS_INLINE Index patchCols() const { |
1033 | return m_base_mapper.m_patch_cols; |
1034 | } |
1035 | |
1036 | EIGEN_DEVICE_FUNC |
1037 | EIGEN_ALWAYS_INLINE Index patchPlaneStride() const { |
1038 | eigen_assert(patchDepth() == m_base_mapper.m_patch_plane_stride && |
1039 | "Patch depth must be equal to patch plane stride." ); |
1040 | return patchDepth(); |
1041 | } |
1042 | EIGEN_DEVICE_FUNC |
1043 | EIGEN_ALWAYS_INLINE Index patchRowStride() const { |
1044 | return m_base_mapper.m_patch_row_stride; |
1045 | } |
1046 | EIGEN_DEVICE_FUNC |
1047 | EIGEN_ALWAYS_INLINE Index patchColStride() const { |
1048 | return m_base_mapper.m_patch_col_stride; |
1049 | } |
1050 | |
1051 | EIGEN_DEVICE_FUNC |
1052 | EIGEN_ALWAYS_INLINE IndexDivisor fastPatchPlaneStride() const { |
1053 | eigen_assert(patchDepth() == m_base_mapper.m_patch_plane_stride && |
1054 | "Patch depth must be equal to patch plane stride." ); |
1055 | return m_base_mapper.m_fastDimZero; // patch_depth |
1056 | } |
1057 | EIGEN_DEVICE_FUNC |
1058 | EIGEN_ALWAYS_INLINE IndexDivisor fastPatchRowStride() const { |
1059 | return m_base_mapper.m_fastPatchRowStride; |
1060 | } |
1061 | EIGEN_DEVICE_FUNC |
1062 | EIGEN_ALWAYS_INLINE IndexDivisor fastPatchColStride() const { |
1063 | return m_base_mapper.m_fastPatchColStride; |
1064 | } |
1065 | |
1066 | EIGEN_DEVICE_FUNC |
1067 | EIGEN_ALWAYS_INLINE Packet packetNoPadding(const Index depth, |
1068 | const Index baseIndex) const { |
1069 | const Index inputIndex = depth + baseIndex; |
1070 | return m_base_mapper.m_impl.template packet<Unaligned>(inputIndex); |
1071 | } |
1072 | EIGEN_DEVICE_FUNC |
1073 | EIGEN_ALWAYS_INLINE Scalar coeffNoPadding(const Index depth, |
1074 | const Index baseIndex) const { |
1075 | const Index inputIndex = depth + baseIndex; |
1076 | return m_base_mapper.m_impl.coeff(inputIndex); |
1077 | } |
1078 | |
1079 | EIGEN_DEVICE_FUNC |
1080 | EIGEN_ALWAYS_INLINE bool padPlane(const Index plane) const { |
1081 | const Index p = m_planeIndex + plane; |
1082 | return p < 0 || p >= m_base_mapper.m_inputPlanes; |
1083 | } |
1084 | EIGEN_DEVICE_FUNC |
1085 | EIGEN_ALWAYS_INLINE bool padRow(const Index row) const { |
1086 | const Index r = m_rowIndex + row; |
1087 | return r < 0 || r >= m_base_mapper.m_inputRows; |
1088 | } |
1089 | EIGEN_DEVICE_FUNC |
1090 | EIGEN_ALWAYS_INLINE bool padCol(const Index col) const { |
1091 | const Index c = m_colIndex + col; |
1092 | return c < 0 || c >= m_base_mapper.m_inputCols; |
1093 | } |
1094 | EIGEN_DEVICE_FUNC |
1095 | EIGEN_ALWAYS_INLINE Index baseIndex(const Index plane, const Index row, |
1096 | const Index col) const { |
1097 | const Index p = m_planeIndex + plane; |
1098 | const Index r = m_rowIndex + row; |
1099 | const Index c = m_colIndex + col; |
1100 | return p * m_base_mapper.m_planeInputStride + |
1101 | r * m_base_mapper.m_rowInputStride + |
1102 | c * m_base_mapper.m_colInputStride + m_otherIndex; |
1103 | } |
1104 | |
1105 | EIGEN_DEVICE_FUNC |
1106 | EIGEN_ALWAYS_INLINE Index planeOffset() const { |
1107 | const Index patchOffset = m_depth_offset / m_base_mapper.m_fastDimZero; |
1108 | const Index colOffset = patchOffset / m_base_mapper.m_fastColStride; |
1109 | const Index rowOffset = |
1110 | (patchOffset - colOffset * m_base_mapper.m_colStride) / |
1111 | m_base_mapper.m_fastRowStride; |
1112 | const Index planeOffset = patchOffset - |
1113 | colOffset * m_base_mapper.m_colStride - |
1114 | rowOffset * m_base_mapper.m_rowStride; |
1115 | return planeOffset; |
1116 | } |
1117 | |
1118 | EIGEN_DEVICE_FUNC |
1119 | EIGEN_ALWAYS_INLINE Index rowOffset() const { |
1120 | const Index patchOffset = m_depth_offset / m_base_mapper.m_fastDimZero; |
1121 | const Index colOffset = patchOffset / m_base_mapper.m_fastColStride; |
1122 | const Index rowOffset = |
1123 | (patchOffset - colOffset * m_base_mapper.m_colStride) / |
1124 | m_base_mapper.m_fastRowStride; |
1125 | return rowOffset; |
1126 | } |
1127 | |
1128 | EIGEN_DEVICE_FUNC |
1129 | EIGEN_ALWAYS_INLINE Index colOffset() const { |
1130 | const Index patchOffset = m_depth_offset / m_base_mapper.m_fastDimZero; |
1131 | const Index colOffset = patchOffset / m_base_mapper.m_fastColStride; |
1132 | return colOffset; |
1133 | } |
1134 | |
1135 | EIGEN_DEVICE_FUNC |
1136 | EIGEN_ALWAYS_INLINE Index depthOffset() const { |
1137 | return m_depth_offset % patchDepth(); |
1138 | } |
1139 | |
1140 | EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE LinearMapper |
1141 | getLinearMapper(Index i, Index j) const { |
1142 | return LinearMapper(m_base_mapper, i + m_depth_offset, j + m_col_offset); |
1143 | } |
1144 | |
1145 | private: |
1146 | const ParentMapper m_base_mapper; // Keeping a copy instead of a reference |
1147 | // performs better in benchmarks. |
1148 | |
1149 | Index m_depth_offset; // First row in the input matrix |
1150 | Index m_col_offset; // First col in the input matrix |
1151 | |
1152 | // Knowing that: col_offset == patchIndex * OTHERS, we keep precomputed base |
1153 | // indices for the first element in a patch specified by col_offset |
1154 | // (see computeBaseIndices(...) for details). |
1155 | Index m_planeIndex; |
1156 | Index m_rowIndex; |
1157 | Index m_colIndex; |
1158 | Index m_otherIndex; |
1159 | }; |
1160 | |
1161 | // Arrange a block of the right input matrix (in our case it's always a "virtual |
1162 | // matrix" constructed from extracted volume patches) in contiguous memory. |
1163 | // |
1164 | // Given column major input (A0 beside A1 in memory): |
1165 | // A0 B0 C0 D0 E0 F0 G0 H0 ... Z0 |
1166 | // A1 B1 C1 D1 E1 F1 G1 H1 ... Z1 |
1167 | // A2 B2 C2 D2 E2 F2 G2 H2 ... Z2 |
1168 | // A3 B3 C3 D3 E3 F3 G3 H3 ... Z3 |
1169 | // A4 B4 C4 D4 E4 F4 G4 H4 ... Z4 |
1170 | // A5 B5 C5 D5 E5 F5 G5 H5 ... Z5 |
1171 | // A6 B6 C6 D6 E6 F6 G6 H6 ... Z6 |
1172 | // A7 B7 C7 D7 E7 F7 G7 H7 ... Z7 |
1173 | // A8 ... |
1174 | // ... |
1175 | // |
1176 | // *) A, B, C, ... - patches extracted from the original input. |
1177 | // *) A0, A1, A2 ... - values from the same patch at different offsets. |
1178 | // |
1179 | // The traversal (packed rhs memory) order (B0 besides A0 in memory): |
1180 | // A0 B0 C0 D0 A1 B1 C1 D1 ... |
1181 | // E0 F0 G0 H0 E1 F1 G1 H1 ... |
1182 | // ... |
1183 | // Z0 Z1 Z2 Z3 Z4 Z5 Z6 Z7 ... <- doesn't belong to any block (nr = 4) |
1184 | // |
1185 | // This traversal order must be the same as in default gemm_pack_rhs defined in |
1186 | // GeneralBlockPanelKernel.h. |
1187 | // |
1188 | // *) nr - number of registers along the 'n' dimension. |
1189 | // See GeneralBlockPanelKernel.h and "Anatomy of High-Performance Matrix |
1190 | // Multiplication" paper. |
1191 | // |
1192 | // TODO(ezhulenev): Add support for squeezing reads along two innermost |
1193 | // dimensions (see eigen_spatial_convolutions). |
1194 | template <typename NewDimension, Index Planes, Index Rows, Index Cols, |
1195 | typename ArgType, typename Device, typename Scalar, typename Index, |
1196 | typename nocontract_t, typename contract_t, int packet_size, |
1197 | bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment, |
1198 | int nr> |
1199 | struct gemm_pack_rhs< |
1200 | Scalar, Index, |
1201 | TensorContractionSubMapper< |
1202 | Scalar, Index, Rhs, |
1203 | TensorEvaluator<const TensorReshapingOp< |
1204 | NewDimension, const TensorVolumePatchOp< |
1205 | Planes, Rows, Cols, ArgType> >, |
1206 | Device>, |
1207 | nocontract_t, contract_t, packet_size, inner_dim_contiguous, |
1208 | inner_dim_reordered, Alignment>, |
1209 | nr, ColMajor, false, false> { |
1210 | typedef TensorContractionSubMapper< |
1211 | Scalar, Index, Rhs, |
1212 | TensorEvaluator<const TensorReshapingOp< |
1213 | NewDimension, const TensorVolumePatchOp< |
1214 | Planes, Rows, Cols, ArgType> >, |
1215 | Device>, |
1216 | nocontract_t, contract_t, packet_size, inner_dim_contiguous, |
1217 | inner_dim_reordered, Alignment> |
1218 | SubMapper; |
1219 | |
1220 | typedef SubMapper DataMapper; |
1221 | typedef typename packet_traits<Scalar>::type Packet; |
1222 | |
1223 | EIGEN_STATIC_ASSERT((nr == 4), YOU_MADE_A_PROGRAMMING_MISTAKE); |
1224 | |
1225 | EIGEN_DEVICE_FUNC |
1226 | EIGEN_DONT_INLINE void operator()(Scalar* block, const DataMapper& rhs, |
1227 | Index depth, Index cols, Index stride = 0, |
1228 | Index offset = 0) const { |
1229 | eigen_assert(stride == 0); |
1230 | eigen_assert(offset == 0); |
1231 | |
1232 | const Index packet_cols4 = (cols / 4) * 4; |
1233 | const Index peeled_k = (depth / packet_size) * packet_size; |
1234 | const bool non_standard_patches = rhs.nonStandardPatches(); |
1235 | |
1236 | for (Index j2 = 0; j2 < packet_cols4; j2 += 4) { |
1237 | const SubMapper dm0 = rhs.getLinearMapper(0, j2 + 0); |
1238 | const SubMapper dm1 = rhs.getLinearMapper(0, j2 + 1); |
1239 | const SubMapper dm2 = rhs.getLinearMapper(0, j2 + 2); |
1240 | const SubMapper dm3 = rhs.getLinearMapper(0, j2 + 3); |
1241 | |
1242 | Index k = 0; |
1243 | if ((packet_size % 4) == 0 && !non_standard_patches) { |
1244 | // FAST PATH: |
1245 | // Iterate over patch columns, rows and planes if we know that a single |
1246 | // packet do not span across multiple planes, rows or columns. |
1247 | if ((rhs.patchDepth() % packet_size) == 0) { |
1248 | const Index start_col = rhs.colOffset(); |
1249 | const Index max_col = rhs.maxCol(peeled_k); |
1250 | |
1251 | for (Index c = start_col; c < max_col; ++c) { |
1252 | eigen_assert(k <= peeled_k); |
1253 | |
1254 | const Index start_row = (c == start_col) ? rhs.rowOffset() : 0; |
1255 | const Index max_row = rhs.maxRow(peeled_k, c); |
1256 | |
1257 | const bool pad_col0 = dm0.padCol(c); |
1258 | const bool pad_col1 = dm1.padCol(c); |
1259 | const bool pad_col2 = dm2.padCol(c); |
1260 | const bool pad_col3 = dm3.padCol(c); |
1261 | |
1262 | for (Index r = start_row; r < max_row; ++r) { |
1263 | eigen_assert(k <= peeled_k); |
1264 | |
1265 | const Index start_plane = ((c == start_col) && (r == start_row)) |
1266 | ? rhs.planeOffset() |
1267 | : 0; |
1268 | const Index max_plane = rhs.maxPlane(peeled_k, c, r); |
1269 | |
1270 | const bool pad_row0 = pad_col0 || dm0.padRow(r); |
1271 | const bool pad_row1 = pad_col1 || dm1.padRow(r); |
1272 | const bool pad_row2 = pad_col2 || dm2.padRow(r); |
1273 | const bool pad_row3 = pad_col3 || dm3.padRow(r); |
1274 | |
1275 | for (Index p = start_plane; p < max_plane; ++p) { |
1276 | eigen_assert(k <= peeled_k); |
1277 | |
1278 | const bool pad0 = pad_row0 || dm0.padPlane(p); |
1279 | const bool pad1 = pad_row1 || dm1.padPlane(p); |
1280 | const bool pad2 = pad_row2 || dm2.padPlane(p); |
1281 | const bool pad3 = pad_row3 || dm3.padPlane(p); |
1282 | |
1283 | const Index idx0 = dm0.baseIndex(p, r, c); |
1284 | const Index idx1 = dm1.baseIndex(p, r, c); |
1285 | const Index idx2 = dm2.baseIndex(p, r, c); |
1286 | const Index idx3 = dm3.baseIndex(p, r, c); |
1287 | |
1288 | const Index start_depth = |
1289 | ((c == start_col) && (r == start_row) && (p == start_plane)) |
1290 | ? rhs.depthOffset() |
1291 | : 0; |
1292 | const Index max_depth = rhs.maxDepth(peeled_k - k, start_depth); |
1293 | eigen_assert((max_depth - start_depth) % packet_size == 0); |
1294 | |
1295 | for (Index d = start_depth; d < max_depth; d += packet_size) { |
1296 | eigen_assert(k < peeled_k); |
1297 | PacketBlock<Packet, 4> kernel; |
1298 | kernel.packet[0] = pad0 ? pset1<Packet>(Scalar(0)) |
1299 | : rhs.packetNoPadding(d, idx0); |
1300 | kernel.packet[1] = pad1 ? pset1<Packet>(Scalar(0)) |
1301 | : rhs.packetNoPadding(d, idx1); |
1302 | kernel.packet[2] = pad2 ? pset1<Packet>(Scalar(0)) |
1303 | : rhs.packetNoPadding(d, idx2); |
1304 | kernel.packet[3] = pad3 ? pset1<Packet>(Scalar(0)) |
1305 | : rhs.packetNoPadding(d, idx3); |
1306 | ptranspose(kernel); |
1307 | pstoreu(block + 0 * packet_size, kernel.packet[0]); |
1308 | pstoreu(block + 1 * packet_size, kernel.packet[1]); |
1309 | pstoreu(block + 2 * packet_size, kernel.packet[2]); |
1310 | pstoreu(block + 3 * packet_size, kernel.packet[3]); |
1311 | block += 4 * packet_size; |
1312 | k += packet_size; |
1313 | } |
1314 | } |
1315 | } |
1316 | } |
1317 | |
1318 | // The loop above should fill peeled_k elements. |
1319 | eigen_assert(peeled_k == k); |
1320 | |
1321 | } else { |
1322 | // Packet can span multiple planes, rows or columns, so we have to go |
1323 | // though the slower "standard" path. |
1324 | for (; k < peeled_k; k += packet_size) { |
1325 | PacketBlock<Packet, 4> kernel; |
1326 | kernel.packet[0] = dm0.loadPacketStandard(k); |
1327 | kernel.packet[1] = dm1.loadPacketStandard(k); |
1328 | kernel.packet[2] = dm2.loadPacketStandard(k); |
1329 | kernel.packet[3] = dm3.loadPacketStandard(k); |
1330 | ptranspose(kernel); |
1331 | pstoreu(block + 0 * packet_size, kernel.packet[0]); |
1332 | pstoreu(block + 1 * packet_size, kernel.packet[1]); |
1333 | pstoreu(block + 2 * packet_size, kernel.packet[2]); |
1334 | pstoreu(block + 3 * packet_size, kernel.packet[3]); |
1335 | block += 4 * packet_size; |
1336 | } |
1337 | } |
1338 | } |
1339 | |
1340 | // Copy the remaining coefficients of the column block after the peeled_k. |
1341 | if (!non_standard_patches) { |
1342 | for (; k < depth; k++) { |
1343 | block[0] = dm0.loadCoeffStandard(k); |
1344 | block[1] = dm1.loadCoeffStandard(k); |
1345 | block[2] = dm2.loadCoeffStandard(k); |
1346 | block[3] = dm3.loadCoeffStandard(k); |
1347 | block += 4; |
1348 | } |
1349 | } else { |
1350 | for (; k < depth; k++) { |
1351 | block[0] = dm0(k); |
1352 | block[1] = dm1(k); |
1353 | block[2] = dm2(k); |
1354 | block[3] = dm3(k); |
1355 | block += 4; |
1356 | } |
1357 | } |
1358 | } |
1359 | |
1360 | // Copy the remaining columns one at a time (nr==1). |
1361 | for (Index j2 = packet_cols4; j2 < cols; ++j2) { |
1362 | const SubMapper dm0 = rhs.getLinearMapper(0, j2); |
1363 | for (Index k = 0; k < depth; k++) { |
1364 | *block = dm0(k); |
1365 | block += 1; |
1366 | } |
1367 | } |
1368 | } |
1369 | }; |
1370 | |
1371 | // Template specialization for packet_size = 2. We must special-case packet |
1372 | // blocks with nr > packet_size, e.g. PacketBlock<Packet2d, 4>. |
1373 | // |
1374 | // TODO(ezhulenev): Add support for squeezing reads along two innermost |
1375 | // dimensions (see eigen_spatial_convolutions). |
1376 | template <typename NewDimension, Index Planes, Index Rows, Index Cols, |
1377 | typename ArgType, typename Device, typename Scalar, typename Index, |
1378 | typename nocontract_t, typename contract_t, bool inner_dim_contiguous, |
1379 | bool inner_dim_reordered, int Alignment, int nr> |
1380 | struct gemm_pack_rhs< |
1381 | Scalar, Index, |
1382 | TensorContractionSubMapper< |
1383 | Scalar, Index, Rhs, |
1384 | TensorEvaluator<const TensorReshapingOp< |
1385 | NewDimension, const TensorVolumePatchOp< |
1386 | Planes, Rows, Cols, ArgType> >, |
1387 | Device>, |
1388 | nocontract_t, contract_t, /*packet_size*/ 2, inner_dim_contiguous, |
1389 | inner_dim_reordered, Alignment>, |
1390 | nr, ColMajor, false, false> { |
1391 | typedef TensorContractionSubMapper< |
1392 | Scalar, Index, Rhs, |
1393 | TensorEvaluator<const TensorReshapingOp< |
1394 | NewDimension, const TensorVolumePatchOp< |
1395 | Planes, Rows, Cols, ArgType> >, |
1396 | Device>, |
1397 | nocontract_t, contract_t, /*packet_size*/ 2, inner_dim_contiguous, |
1398 | inner_dim_reordered, Alignment> |
1399 | SubMapper; |
1400 | typedef SubMapper DataMapper; |
1401 | typedef typename packet_traits<Scalar>::type Packet; |
1402 | |
1403 | EIGEN_STATIC_ASSERT((nr == 4), YOU_MADE_A_PROGRAMMING_MISTAKE); |
1404 | |
1405 | EIGEN_DEVICE_FUNC |
1406 | EIGEN_DONT_INLINE void operator()(Scalar* block, const DataMapper& rhs, |
1407 | Index depth, Index cols, Index stride = 0, |
1408 | Index offset = 0) const { |
1409 | eigen_assert(stride == 0); |
1410 | eigen_assert(offset == 0); |
1411 | |
1412 | const int packet_size = 2; |
1413 | |
1414 | const Index packet_cols4 = (cols / 4) * 4; |
1415 | const Index peeled_k = (depth / packet_size) * packet_size; |
1416 | const bool non_standard_patches = rhs.nonStandardPatches(); |
1417 | |
1418 | for (Index j2 = 0; j2 < packet_cols4; j2 += 4) { |
1419 | const SubMapper dm0 = rhs.getLinearMapper(0, j2 + 0); |
1420 | const SubMapper dm1 = rhs.getLinearMapper(0, j2 + 1); |
1421 | const SubMapper dm2 = rhs.getLinearMapper(0, j2 + 2); |
1422 | const SubMapper dm3 = rhs.getLinearMapper(0, j2 + 3); |
1423 | |
1424 | Index k = 0; |
1425 | if (!non_standard_patches) { |
1426 | // FAST PATH: |
1427 | // Iterate over patch columns, rows and planes if we know that a single |
1428 | // packet do not span across multiple planes, rows or columns. |
1429 | if ((rhs.patchDepth() % packet_size) == 0) { |
1430 | const Index start_col = rhs.colOffset(); |
1431 | const Index max_col = rhs.maxCol(peeled_k); |
1432 | |
1433 | for (Index c = start_col; c < max_col; ++c) { |
1434 | eigen_assert(k <= peeled_k); |
1435 | |
1436 | const Index start_row = (c == start_col) ? rhs.rowOffset() : 0; |
1437 | const Index max_row = rhs.maxRow(peeled_k, c); |
1438 | |
1439 | const bool pad_col0 = dm0.padCol(c); |
1440 | const bool pad_col1 = dm1.padCol(c); |
1441 | const bool pad_col2 = dm2.padCol(c); |
1442 | const bool pad_col3 = dm3.padCol(c); |
1443 | |
1444 | for (Index r = start_row; r < max_row; ++r) { |
1445 | eigen_assert(k <= peeled_k); |
1446 | |
1447 | const Index start_plane = ((c == start_col) && (r == start_row)) |
1448 | ? rhs.planeOffset() |
1449 | : 0; |
1450 | const Index max_plane = rhs.maxPlane(peeled_k, c, r); |
1451 | |
1452 | const bool pad_row0 = dm0.padRow(r); |
1453 | const bool pad_row1 = dm1.padRow(r); |
1454 | const bool pad_row2 = dm2.padRow(r); |
1455 | const bool pad_row3 = dm3.padRow(r); |
1456 | |
1457 | for (Index p = start_plane; p < max_plane; ++p) { |
1458 | eigen_assert(k <= peeled_k); |
1459 | |
1460 | const bool pad0 = pad_col0 || pad_row0 || dm0.padPlane(p); |
1461 | const bool pad1 = pad_col1 || pad_row1 || dm1.padPlane(p); |
1462 | const bool pad2 = pad_col2 || pad_row2 || dm2.padPlane(p); |
1463 | const bool pad3 = pad_col3 || pad_row3 || dm3.padPlane(p); |
1464 | |
1465 | const Index idx0 = dm0.baseIndex(p, r, c); |
1466 | const Index idx1 = dm1.baseIndex(p, r, c); |
1467 | const Index idx2 = dm2.baseIndex(p, r, c); |
1468 | const Index idx3 = dm3.baseIndex(p, r, c); |
1469 | |
1470 | const Index start_depth = |
1471 | ((c == start_col) && (r == start_row) && (p == start_plane)) |
1472 | ? rhs.depthOffset() |
1473 | : 0; |
1474 | const Index max_depth = rhs.maxDepth(peeled_k - k, start_depth); |
1475 | eigen_assert((max_depth - start_depth) % packet_size == 0); |
1476 | |
1477 | for (Index d = start_depth; d < max_depth; d += packet_size) { |
1478 | eigen_assert(k < peeled_k); |
1479 | PacketBlock<Packet, 2> kernel0; |
1480 | PacketBlock<Packet, 2> kernel1; |
1481 | kernel0.packet[0] = pad0 ? pset1<Packet>(Scalar(0)) |
1482 | : rhs.packetNoPadding(d, idx0); |
1483 | kernel0.packet[1] = pad1 ? pset1<Packet>(Scalar(0)) |
1484 | : rhs.packetNoPadding(d, idx1); |
1485 | kernel1.packet[0] = pad2 ? pset1<Packet>(Scalar(0)) |
1486 | : rhs.packetNoPadding(d, idx2); |
1487 | kernel1.packet[1] = pad3 ? pset1<Packet>(Scalar(0)) |
1488 | : rhs.packetNoPadding(d, idx3); |
1489 | ptranspose(kernel0); |
1490 | ptranspose(kernel1); |
1491 | pstoreu(block + 0 * packet_size, kernel0.packet[0]); |
1492 | pstoreu(block + 1 * packet_size, kernel1.packet[0]); |
1493 | pstoreu(block + 2 * packet_size, kernel0.packet[1]); |
1494 | pstoreu(block + 3 * packet_size, kernel1.packet[1]); |
1495 | block += 4 * packet_size; |
1496 | k += packet_size; |
1497 | } |
1498 | } |
1499 | } |
1500 | } |
1501 | |
1502 | // The loop above should fill peeled_k elements. |
1503 | eigen_assert(peeled_k == k); |
1504 | |
1505 | } else { |
1506 | for (; k < peeled_k; k += packet_size) { |
1507 | PacketBlock<Packet, 2> kernel0; |
1508 | PacketBlock<Packet, 2> kernel1; |
1509 | kernel0.packet[0] = dm0.loadPacketStandard(k); |
1510 | kernel0.packet[1] = dm1.loadPacketStandard(k); |
1511 | kernel1.packet[0] = dm2.loadPacketStandard(k); |
1512 | kernel1.packet[1] = dm3.loadPacketStandard(k); |
1513 | ptranspose(kernel0); |
1514 | ptranspose(kernel1); |
1515 | pstoreu(block + 0 * packet_size, kernel0.packet[0]); |
1516 | pstoreu(block + 1 * packet_size, kernel1.packet[0]); |
1517 | pstoreu(block + 2 * packet_size, kernel0.packet[1]); |
1518 | pstoreu(block + 3 * packet_size, kernel1.packet[1]); |
1519 | block += 4 * packet_size; |
1520 | } |
1521 | } |
1522 | } |
1523 | |
1524 | // Copy the remaining coefficients of the column block after the peeled_k. |
1525 | if (!rhs.nonStandardPatches()) { |
1526 | for (; k < depth; k++) { |
1527 | block[0] = dm0.loadCoeffStandard(k); |
1528 | block[1] = dm1.loadCoeffStandard(k); |
1529 | block[2] = dm2.loadCoeffStandard(k); |
1530 | block[3] = dm3.loadCoeffStandard(k); |
1531 | block += 4; |
1532 | } |
1533 | } else { |
1534 | for (; k < depth; k++) { |
1535 | block[0] = dm0(k); |
1536 | block[1] = dm1(k); |
1537 | block[2] = dm2(k); |
1538 | block[3] = dm3(k); |
1539 | block += 4; |
1540 | } |
1541 | } |
1542 | } |
1543 | |
1544 | // Copy the remaining columns one at a time (nr==1). |
1545 | for (Index j2 = packet_cols4; j2 < cols; ++j2) { |
1546 | const SubMapper dm0 = rhs.getLinearMapper(0, j2); |
1547 | for (Index k = 0; k < depth; k++) { |
1548 | *block = dm0(k); |
1549 | block += 1; |
1550 | } |
1551 | } |
1552 | } |
1553 | }; |
1554 | |
1555 | // Special case for non-vectorized types such as float16 (packet_size = 1). |
1556 | template <typename NewDimension, Index Planes, Index Rows, Index Cols, |
1557 | typename ArgType, typename Device, typename Scalar, typename Index, |
1558 | typename nocontract_t, typename contract_t, bool inner_dim_contiguous, |
1559 | bool inner_dim_reordered, int Alignment, int nr> |
1560 | struct gemm_pack_rhs< |
1561 | Scalar, Index, |
1562 | TensorContractionSubMapper< |
1563 | Scalar, Index, Rhs, |
1564 | TensorEvaluator<const TensorReshapingOp< |
1565 | NewDimension, const TensorVolumePatchOp< |
1566 | Planes, Rows, Cols, ArgType> >, |
1567 | Device>, |
1568 | nocontract_t, contract_t, /*packet_size*/ 1, inner_dim_contiguous, |
1569 | inner_dim_reordered, Alignment>, |
1570 | nr, ColMajor, false, false> { |
1571 | typedef TensorContractionSubMapper< |
1572 | Scalar, Index, Rhs, |
1573 | TensorEvaluator<const TensorReshapingOp< |
1574 | NewDimension, const TensorVolumePatchOp< |
1575 | Planes, Rows, Cols, ArgType> >, |
1576 | Device>, |
1577 | nocontract_t, contract_t, 1, inner_dim_contiguous, inner_dim_reordered, |
1578 | Alignment> |
1579 | SubMapper; |
1580 | typedef SubMapper DataMapper; |
1581 | |
1582 | EIGEN_STATIC_ASSERT((nr == 4), YOU_MADE_A_PROGRAMMING_MISTAKE); |
1583 | |
1584 | EIGEN_DEVICE_FUNC |
1585 | EIGEN_DONT_INLINE void operator()(Scalar* block, const DataMapper& rhs, |
1586 | Index depth, Index cols, Index stride = 0, |
1587 | Index offset = 0) const { |
1588 | eigen_assert(stride == 0); |
1589 | eigen_assert(offset == 0); |
1590 | |
1591 | const Index packet_cols4 = (cols / 4) * 4; |
1592 | |
1593 | for (Index j2 = 0; j2 < packet_cols4; j2 += 4) { |
1594 | const SubMapper dm0 = rhs.getLinearMapper(0, j2 + 0); |
1595 | const SubMapper dm1 = rhs.getLinearMapper(0, j2 + 1); |
1596 | const SubMapper dm2 = rhs.getLinearMapper(0, j2 + 2); |
1597 | const SubMapper dm3 = rhs.getLinearMapper(0, j2 + 3); |
1598 | |
1599 | if (!rhs.nonStandardPatches()) { |
1600 | for (Index k = 0; k < depth; k++) { |
1601 | block[0] = dm0.loadCoeffStandard(k); |
1602 | block[1] = dm1.loadCoeffStandard(k); |
1603 | block[2] = dm2.loadCoeffStandard(k); |
1604 | block[3] = dm3.loadCoeffStandard(k); |
1605 | block += 4; |
1606 | } |
1607 | } else { |
1608 | for (Index k = 0; k < depth; k++) { |
1609 | block[0] = dm0(k); |
1610 | block[1] = dm1(k); |
1611 | block[2] = dm2(k); |
1612 | block[3] = dm3(k); |
1613 | block += 4; |
1614 | } |
1615 | } |
1616 | } |
1617 | |
1618 | // Copy the remaining columns one at a time (nr==1). |
1619 | for (Index j2 = packet_cols4; j2 < cols; ++j2) { |
1620 | const SubMapper dm0 = rhs.getLinearMapper(0, j2); |
1621 | for (Index k = 0; k < depth; k++) { |
1622 | *block = dm0(k); |
1623 | block += 1; |
1624 | } |
1625 | } |
1626 | } |
1627 | }; |
1628 | #endif |
1629 | |
1630 | #if defined(TENSORFLOW_USE_CUSTOM_CONTRACTION_KERNEL) |
1631 | // Pack a block of the right input matrix (in our case it's always a "virtual |
1632 | // matrix" constructed from extracted image patches) in contiguous block in |
1633 | // column-major storage order. Knowing the properties of the original patch op |
1634 | // we can do it more efficient than the default gemm_pack_colmajor_block. |
1635 | // |
1636 | // TODO(ezhulenev): gemm_pack_colmajor_block for spatial convolutions supports |
1637 | // squeezing reads along the 2 innermost dimensions, add it here if needed. |
1638 | template <typename NewDimension, Index Planes, Index Rows, Index Cols, |
1639 | typename ArgType, typename Device, typename Scalar, |
1640 | typename StorageIndex, typename nocontract_t, typename contract_t, |
1641 | int packet_size, bool inner_dim_contiguous, bool inner_dim_reordered, |
1642 | int Alignment> |
1643 | struct gemm_pack_colmajor_block< |
1644 | Scalar, StorageIndex, |
1645 | TensorContractionSubMapper< |
1646 | Scalar, StorageIndex, Rhs, |
1647 | TensorEvaluator<const TensorReshapingOp< |
1648 | NewDimension, const TensorVolumePatchOp< |
1649 | Planes, Rows, Cols, ArgType> >, |
1650 | Device>, |
1651 | nocontract_t, contract_t, packet_size, inner_dim_contiguous, |
1652 | inner_dim_reordered, Alignment>, |
1653 | ColMajor> { |
1654 | typedef TensorContractionSubMapper< |
1655 | Scalar, StorageIndex, Rhs, |
1656 | TensorEvaluator<const TensorReshapingOp< |
1657 | NewDimension, const TensorVolumePatchOp< |
1658 | Planes, Rows, Cols, ArgType> >, |
1659 | Device>, |
1660 | nocontract_t, contract_t, packet_size, inner_dim_contiguous, |
1661 | inner_dim_reordered, Alignment> |
1662 | SubMapper; |
1663 | |
1664 | typedef SubMapper DataMapper; |
1665 | typedef typename packet_traits<Scalar>::type Packet; |
1666 | |
1667 | EIGEN_DONT_INLINE |
1668 | void operator()(Scalar* block, const DataMapper& rhs, StorageIndex rows, |
1669 | StorageIndex cols) { |
1670 | const bool standard_patches = !rhs.nonStandardPatches(); |
1671 | |
1672 | if (standard_patches && rhs.patchDepth() % packet_size == 0) { |
1673 | packStandardPatches<true>(block, rhs, rows, cols); |
1674 | |
1675 | } else if (standard_patches) { |
1676 | packStandardPatches<false>(block, rhs, rows, cols); |
1677 | |
1678 | } else { |
1679 | // With non-standard patches we don't do any vectorized loads. |
1680 | // TODO(ezhulenev): It doesn't look like that we should completely give up |
1681 | // on packets. Make this code path faster! |
1682 | for (StorageIndex col = 0; col < cols; ++col) { |
1683 | SubMapper lm = rhs.getLinearMapper(0, col); |
1684 | for (StorageIndex i = 0; i < rows; ++i) { |
1685 | *block = lm(i); |
1686 | ++block; |
1687 | } |
1688 | } |
1689 | } |
1690 | } |
1691 | |
1692 | private: |
1693 | // Pack standard volume patches: |
1694 | // |
1695 | // - patch_depth_is_multiple_of_packet_size=true: We are guaranteed to have |
1696 | // depth dimension size to be a multiple of packet size, so we can skip all |
1697 | // non vectorized loads and checks. |
1698 | // |
1699 | template <bool patch_depth_is_multiple_of_packet_size> |
1700 | EIGEN_ALWAYS_INLINE void packStandardPatches(Scalar* block, |
1701 | const DataMapper& rhs, |
1702 | StorageIndex rows, |
1703 | StorageIndex cols) { |
1704 | eigen_assert(!rhs.nonStandardPatches()); |
1705 | |
1706 | // Give vectorized_rows the name used in all other gemm_pack_rhs above. |
1707 | const Index peeled_k = (rows / packet_size) * packet_size; |
1708 | |
1709 | const Index start_col = rhs.colOffset(); |
1710 | const Index max_col = rhs.maxCol(peeled_k); |
1711 | |
1712 | for (StorageIndex col = 0; col < cols; ++col) { |
1713 | SubMapper lm = rhs.getLinearMapper(0, col); |
1714 | |
1715 | Index k = 0; |
1716 | for (Index c = start_col; c < max_col; ++c) { |
1717 | eigen_assert(k <= peeled_k); |
1718 | |
1719 | const Index start_row = (c == start_col) ? rhs.rowOffset() : 0; |
1720 | const Index max_row = rhs.maxRow(peeled_k, c); |
1721 | const bool pad_col = lm.padCol(c); |
1722 | |
1723 | for (Index r = start_row; r < max_row; ++r) { |
1724 | eigen_assert(k <= peeled_k); |
1725 | |
1726 | const Index start_plane = |
1727 | ((c == start_col) && (r == start_row)) ? rhs.planeOffset() : 0; |
1728 | const Index max_plane = rhs.maxPlane(peeled_k, c, r); |
1729 | const bool pad_row = pad_col || lm.padRow(r); |
1730 | |
1731 | for (Index p = start_plane; p < max_plane; ++p) { |
1732 | eigen_assert(k <= peeled_k); |
1733 | |
1734 | const Index start_depth = |
1735 | ((c == start_col) && (r == start_row) && (p == start_plane)) |
1736 | ? rhs.depthOffset() |
1737 | : 0; |
1738 | const Index max_depth = rhs.maxDepth(peeled_k - k, start_depth); |
1739 | |
1740 | const bool pad = pad_col || pad_row || lm.padPlane(p); |
1741 | const Index base_idx = lm.baseIndex(p, r, c); |
1742 | |
1743 | if (patch_depth_is_multiple_of_packet_size) |
1744 | eigen_assert((max_depth - start_depth) % packet_size == 0); |
1745 | |
1746 | // If patch depth is a multiple of packet size, it's guaranteed that |
1747 | // we can process all values in depth dimension with packets. |
1748 | const Index max_vectorized_depth = |
1749 | patch_depth_is_multiple_of_packet_size |
1750 | ? max_depth |
1751 | : max_depth - packet_size; |
1752 | |
1753 | Index d = start_depth; |
1754 | |
1755 | // 1. Process depth dimension with vectorized instructions. |
1756 | for (; d < max_vectorized_depth; d += packet_size) { |
1757 | eigen_assert(k < peeled_k); |
1758 | const Packet packet = pad ? pset1<Packet>(Scalar(0)) |
1759 | : rhs.packetNoPadding(d, base_idx); |
1760 | internal::pstoreu(block, packet); |
1761 | block += packet_size; |
1762 | k += packet_size; |
1763 | } |
1764 | |
1765 | // 2. Finish with coefficients. |
1766 | if (!patch_depth_is_multiple_of_packet_size) { |
1767 | for (; d < max_depth; d++) { |
1768 | eigen_assert(k < peeled_k); |
1769 | *block = pad ? Scalar(0) : rhs.coeffNoPadding(d, base_idx); |
1770 | ++block; |
1771 | ++k; |
1772 | } |
1773 | } |
1774 | } |
1775 | } |
1776 | } |
1777 | |
1778 | // The loop above should fill peeled_k elements. |
1779 | eigen_assert(peeled_k == k); |
1780 | |
1781 | // Fill remaining elements using loadCoeffStandard. |
1782 | for (; k < rows; ++k) { |
1783 | *block = lm.loadCoeffStandard(k); |
1784 | ++block; |
1785 | } |
1786 | } |
1787 | } |
1788 | }; |
1789 | #endif // defined(TENSORFLOW_USE_CUSTOM_CONTRACTION_KERNEL) |
1790 | |
1791 | } // namespace internal |
1792 | |
1793 | /** CuboidConvolution |
1794 | * \ingroup CXX11_NeuralNetworks_Module |
1795 | * |
1796 | * \brief Applies a 3D convolution over a multichannel input voxel block. |
1797 | * |
1798 | * The input parameter is expected to be a tensor with a rank of 4 or more |
1799 | * (channels, depth, height, width, and optionally others). |
1800 | * The kernel parameter is expected to be a 5D tensor (filters, channels, |
1801 | * kernel_depth, kernel_height, kernel_width). |
1802 | * The result can be assigned to a tensor of rank equal to the rank of the |
1803 | * input. The dimensions of the result will be filters, depth, height, width |
1804 | * (and others if applicable). |
1805 | * |
1806 | * The input and kernel have to be in the same layout, and both row-major and |
1807 | * col-major are supported. The shapes given above are for col-major layout. |
1808 | * For row-major, all dimensions should be reversed. |
1809 | * |
1810 | * It is possible to swap the order of the depth, width, and height dimensions |
1811 | * provided that the same order is used in the input, the kernel, and the |
1812 | * output. |
1813 | */ |
1814 | template <typename Input, typename Kernel> |
1815 | EIGEN_ALWAYS_INLINE static const std::conditional_t< |
1816 | internal::traits<Input>::Layout == ColMajor, |
1817 | TensorReshapingOp< |
1818 | const DSizes<typename internal::traits<Input>::Index, |
1819 | internal::traits<Input>::NumDimensions>, |
1820 | const TensorContractionOp< |
1821 | const array<IndexPair<typename internal::traits<Input>::Index>, 1>, |
1822 | const TensorReshapingOp< |
1823 | const DSizes<typename internal::traits<Input>::Index, 2>, |
1824 | const Kernel>, |
1825 | const TensorReshapingOp< |
1826 | const DSizes<typename internal::traits<Input>::Index, 2>, |
1827 | const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, |
1828 | const Input> > > >, |
1829 | TensorReshapingOp< |
1830 | const DSizes<typename internal::traits<Input>::Index, |
1831 | internal::traits<Input>::NumDimensions>, |
1832 | const TensorContractionOp< |
1833 | const array<IndexPair<typename internal::traits<Input>::Index>, 1>, |
1834 | const TensorReshapingOp< |
1835 | const DSizes<typename internal::traits<Input>::Index, 2>, |
1836 | const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, |
1837 | const Input> >, |
1838 | const TensorReshapingOp< |
1839 | const DSizes<typename internal::traits<Input>::Index, 2>, |
1840 | const Kernel> > > > |
1841 | CuboidConvolution(const Input& input, const Kernel& kernel, |
1842 | const Index stridePlanes = 1, const Index strideRows = 1, |
1843 | const Index strideCols = 1, |
1844 | const PaddingType padding_type = PADDING_SAME) { |
1845 | typedef typename internal::traits<Input>::Index TensorIndex; |
1846 | TensorRef<Tensor<typename internal::traits<Input>::Scalar, |
1847 | internal::traits<Input>::NumDimensions, |
1848 | internal::traits<Input>::Layout, TensorIndex> > |
1849 | in(input); |
1850 | TensorRef<Tensor<typename internal::traits<Kernel>::Scalar, |
1851 | internal::traits<Kernel>::NumDimensions, |
1852 | internal::traits<Kernel>::Layout, TensorIndex> > |
1853 | kern(kernel); |
1854 | |
1855 | EIGEN_STATIC_ASSERT( |
1856 | internal::traits<Input>::Layout == internal::traits<Kernel>::Layout, |
1857 | YOU_MADE_A_PROGRAMMING_MISTAKE); |
1858 | static const bool isColMajor = (internal::traits<Input>::Layout == ColMajor); |
1859 | static const int NumDims = internal::traits<Input>::NumDimensions; |
1860 | |
1861 | // Number of filters to apply. This is the same as the output depth of the |
1862 | // result. |
1863 | const TensorIndex kernelFilters = |
1864 | isColMajor ? kern.dimensions()[0] : kern.dimensions()[4]; |
1865 | const TensorIndex kernelChannels = |
1866 | isColMajor ? kern.dimensions()[1] : kern.dimensions()[3]; |
1867 | |
1868 | // Spatial size of the kernel. |
1869 | const TensorIndex kernelPlanes = |
1870 | isColMajor ? kern.dimensions()[2] : kern.dimensions()[2]; |
1871 | const TensorIndex kernelRows = |
1872 | isColMajor ? kern.dimensions()[3] : kern.dimensions()[1]; |
1873 | const TensorIndex kernelCols = |
1874 | isColMajor ? kern.dimensions()[4] : kern.dimensions()[0]; |
1875 | |
1876 | if (isColMajor) { |
1877 | eigen_assert(kernelChannels == in.dimension(0)); |
1878 | } else { |
1879 | eigen_assert(kernelChannels == in.dimension(NumDims - 1)); |
1880 | } |
1881 | |
1882 | const TensorIndex inputPlanes = |
1883 | isColMajor ? in.dimension(1) : in.dimension(NumDims - 2); |
1884 | const TensorIndex inputRows = |
1885 | isColMajor ? in.dimension(2) : in.dimension(NumDims - 3); |
1886 | const TensorIndex inputCols = |
1887 | isColMajor ? in.dimension(3) : in.dimension(NumDims - 4); |
1888 | |
1889 | TensorIndex out_planes; |
1890 | TensorIndex out_height; |
1891 | TensorIndex out_width; |
1892 | switch (padding_type) { |
1893 | case PADDING_VALID: |
1894 | out_planes = Eigen::divup(inputPlanes - kernelPlanes + 1, |
1895 | static_cast<TensorIndex>(stridePlanes)); |
1896 | out_height = Eigen::divup(inputRows - kernelRows + 1, |
1897 | static_cast<TensorIndex>(strideRows)); |
1898 | out_width = Eigen::divup(inputCols - kernelCols + 1, |
1899 | static_cast<TensorIndex>(strideCols)); |
1900 | break; |
1901 | case PADDING_SAME: |
1902 | out_planes = |
1903 | Eigen::divup(inputPlanes, static_cast<TensorIndex>(stridePlanes)); |
1904 | out_height = |
1905 | Eigen::divup(inputRows, static_cast<TensorIndex>(strideRows)); |
1906 | out_width = Eigen::divup(inputCols, static_cast<TensorIndex>(strideCols)); |
1907 | break; |
1908 | default: |
1909 | out_planes = 0; |
1910 | out_height = 0; |
1911 | out_width = 0; |
1912 | eigen_assert(false && "unexpected padding" ); |
1913 | } |
1914 | |
1915 | DSizes<TensorIndex, 2> kernel_dims; |
1916 | if (isColMajor) { |
1917 | kernel_dims[0] = kernelFilters; |
1918 | kernel_dims[1] = kernelChannels * kernelPlanes * kernelRows * kernelCols; |
1919 | } else { |
1920 | kernel_dims[0] = kernelChannels * kernelPlanes * kernelRows * kernelCols; |
1921 | kernel_dims[1] = kernelFilters; |
1922 | } |
1923 | |
1924 | // Molds the output of the patch extraction result into a 2D tensor: |
1925 | // - the first dimension (dims[0]): the patch values to be multiplied with the |
1926 | // kernels |
1927 | // - the second dimension (dims[1]): everything else |
1928 | DSizes<TensorIndex, 2> pre_contract_dims; |
1929 | if (isColMajor) { |
1930 | pre_contract_dims[0] = |
1931 | kernelChannels * kernelPlanes * kernelRows * kernelCols; |
1932 | pre_contract_dims[1] = out_planes * out_height * out_width; |
1933 | for (int i = 4; i < NumDims; ++i) { |
1934 | pre_contract_dims[1] *= in.dimension(i); |
1935 | } |
1936 | } else { |
1937 | pre_contract_dims[1] = |
1938 | kernelChannels * kernelPlanes * kernelRows * kernelCols; |
1939 | pre_contract_dims[0] = out_planes * out_height * out_width; |
1940 | for (int i = 0; i < NumDims - 4; ++i) { |
1941 | pre_contract_dims[0] *= in.dimension(i); |
1942 | } |
1943 | } |
1944 | |
1945 | array<IndexPair<TensorIndex>, 1> contract_dims; |
1946 | contract_dims[0] = IndexPair<TensorIndex>(1, 0); |
1947 | |
1948 | // Molds the output of the contraction into the shape expected by the user |
1949 | // (assuming ColMajor): |
1950 | // - 1st dim: kernel filters |
1951 | // - 2nd dim: output depth |
1952 | // - 3nd dim: output height |
1953 | // - 4rd dim: output width |
1954 | // - 5th dim and beyond: everything else including batch size |
1955 | DSizes<TensorIndex, NumDims> post_contract_dims; |
1956 | if (isColMajor) { |
1957 | post_contract_dims[0] = kernelFilters; |
1958 | post_contract_dims[1] = out_planes; |
1959 | post_contract_dims[2] = out_height; |
1960 | post_contract_dims[3] = out_width; |
1961 | for (int i = 4; i < NumDims; ++i) { |
1962 | post_contract_dims[i] = in.dimension(i); |
1963 | } |
1964 | } else { |
1965 | post_contract_dims[NumDims - 1] = kernelFilters; |
1966 | post_contract_dims[NumDims - 2] = out_planes; |
1967 | post_contract_dims[NumDims - 3] = out_height; |
1968 | post_contract_dims[NumDims - 4] = out_width; |
1969 | for (int i = 0; i < NumDims - 4; ++i) { |
1970 | post_contract_dims[i] = in.dimension(i); |
1971 | } |
1972 | } |
1973 | |
1974 | return choose( |
1975 | Cond<internal::traits<Input>::Layout == ColMajor>(), |
1976 | kernel.reshape(kernel_dims) |
1977 | .contract(input |
1978 | .extract_volume_patches( |
1979 | kernelPlanes, kernelRows, kernelCols, stridePlanes, |
1980 | strideRows, strideCols, padding_type) |
1981 | .reshape(pre_contract_dims), |
1982 | contract_dims) |
1983 | .reshape(post_contract_dims), |
1984 | input |
1985 | .extract_volume_patches(kernelPlanes, kernelRows, kernelCols, |
1986 | stridePlanes, strideRows, strideCols, |
1987 | padding_type) |
1988 | .reshape(pre_contract_dims) |
1989 | .contract(kernel.reshape(kernel_dims), contract_dims) |
1990 | .reshape(post_contract_dims)); |
1991 | } |
1992 | |
1993 | } // end namespace Eigen |
1994 | |
1995 | #endif // TENSORFLOW_CORE_KERNELS_EIGEN_CUBOID_CONVOLUTION_H_ |
1996 | |