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concat_arm.cpp 12 kB

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  1. // Tencent is pleased to support the open source community by making ncnn available.
  2. //
  3. // Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
  4. //
  5. // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
  6. // in compliance with the License. You may obtain a copy of the License at
  7. //
  8. // https://opensource.org/licenses/BSD-3-Clause
  9. //
  10. // Unless required by applicable law or agreed to in writing, software distributed
  11. // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
  12. // CONDITIONS OF ANY KIND, either express or implied. See the License for the
  13. // specific language governing permissions and limitations under the License.
  14. #include "concat_arm.h"
  15. #include <algorithm>
  16. #include "layer_type.h"
  17. namespace ncnn {
  18. DEFINE_LAYER_CREATOR(Concat_arm)
  19. Concat_arm::Concat_arm()
  20. {
  21. #if __ARM_NEON
  22. support_packing = true;
  23. packing_pack4 = 0;
  24. #endif // __ARM_NEON
  25. }
  26. int Concat_arm::create_pipeline(const Option& opt)
  27. {
  28. #if __ARM_NEON
  29. if (opt.use_packing_layout)
  30. {
  31. Option opt_cpu = opt;
  32. opt_cpu.use_vulkan_compute = false;
  33. {
  34. packing_pack4 = ncnn::create_layer(ncnn::LayerType::Packing);
  35. ncnn::ParamDict pd;
  36. pd.set(0, 4);
  37. packing_pack4->load_param(pd);
  38. packing_pack4->create_pipeline(opt_cpu);
  39. }
  40. }
  41. #endif // __ARM_NEON
  42. return 0;
  43. }
  44. int Concat_arm::destroy_pipeline(const Option& opt)
  45. {
  46. #if __ARM_NEON
  47. if (opt.use_packing_layout)
  48. {
  49. Option opt_cpu = opt;
  50. opt_cpu.use_vulkan_compute = false;
  51. if (packing_pack4)
  52. {
  53. packing_pack4->destroy_pipeline(opt_cpu);
  54. delete packing_pack4;
  55. packing_pack4 = 0;
  56. }
  57. }
  58. #endif // __ARM_NEON
  59. return 0;
  60. }
  61. int Concat_arm::forward(const std::vector<Mat>& bottom_blobs, std::vector<Mat>& top_blobs, const Option& opt) const
  62. {
  63. int dims = bottom_blobs[0].dims;
  64. #if __ARM_NEON
  65. if (opt.use_packing_layout)
  66. {
  67. if (dims == 1) // axis == 0
  68. {
  69. // concat vector
  70. // total length
  71. size_t elemsize = bottom_blobs[0].elemsize;
  72. int elempack = bottom_blobs[0].elempack;
  73. int top_w = 0;
  74. for (size_t b=0; b<bottom_blobs.size(); b++)
  75. {
  76. const Mat& bottom_blob = bottom_blobs[b];
  77. top_w += bottom_blob.w * bottom_blob.elempack;
  78. }
  79. int out_elempack = top_w % 4 == 0 ? 4 : 1;
  80. size_t out_elemsize = elemsize / elempack * out_elempack;
  81. Mat& top_blob = top_blobs[0];
  82. top_blob.create(top_w / out_elempack, out_elemsize, out_elempack, opt.blob_allocator);
  83. if (top_blob.empty())
  84. return -100;
  85. float* outptr = top_blob;
  86. for (size_t b=0; b<bottom_blobs.size(); b++)
  87. {
  88. const Mat& bottom_blob = bottom_blobs[b];
  89. const float* ptr = bottom_blob;
  90. memcpy(outptr, ptr, bottom_blob.w * bottom_blob.elemsize);
  91. outptr += bottom_blob.w * bottom_blob.elempack;
  92. }
  93. return 0;
  94. }
  95. if (dims == 2 && axis == 0)
  96. {
  97. // concat image
  98. int w = bottom_blobs[0].w;
  99. // total height
  100. size_t elemsize = bottom_blobs[0].elemsize;
  101. int elempack = bottom_blobs[0].elempack;
  102. int top_h = 0;
  103. for (size_t b=0; b<bottom_blobs.size(); b++)
  104. {
  105. const Mat& bottom_blob = bottom_blobs[b];
  106. elemsize = std::min(elemsize, bottom_blob.elemsize);
  107. elempack = std::min(elempack, bottom_blob.elempack);
  108. top_h += bottom_blob.h * bottom_blob.elempack;
  109. }
  110. int out_elempack = top_h % 4 == 0 ? 4 : 1;
  111. size_t out_elemsize = elemsize / elempack * out_elempack;
  112. Mat& top_blob = top_blobs[0];
  113. top_blob.create(w, top_h / out_elempack, out_elemsize, out_elempack, opt.blob_allocator);
  114. if (top_blob.empty())
  115. return -100;
  116. Mat top_blob_unpacked = top_blob;
  117. if (elempack == 1 && out_elempack == 4)
  118. {
  119. top_blob_unpacked.create(w, top_h / elempack, elemsize, elempack, opt.workspace_allocator);
  120. if (top_blob_unpacked.empty())
  121. return -100;
  122. }
  123. float* outptr = top_blob_unpacked;
  124. for (size_t b=0; b<bottom_blobs.size(); b++)
  125. {
  126. const Mat& bottom_blob = bottom_blobs[b];
  127. if (bottom_blob.elempack == 4 && elempack == 1)
  128. {
  129. for (int i=0; i<bottom_blob.h; i++)
  130. {
  131. const float* r0 = bottom_blob.row(i);
  132. float* outptr0 = outptr;
  133. float* outptr1 = outptr + w;
  134. float* outptr2 = outptr + w*2;
  135. float* outptr3 = outptr + w*3;
  136. for (int j=0; j<w; j++)
  137. {
  138. *outptr0++ = r0[0];
  139. *outptr1++ = r0[1];
  140. *outptr2++ = r0[2];
  141. *outptr3++ = r0[3];
  142. r0 += 4;
  143. }
  144. outptr += w * 4;
  145. }
  146. }
  147. else // if (bottom_blob.elempack == 1 && elempack == 1) if (bottom_blob.elempack == 4 && elempack == 4)
  148. {
  149. int size = w * bottom_blob.h;
  150. const float* ptr = bottom_blob;
  151. memcpy(outptr, ptr, size * bottom_blob.elemsize);
  152. outptr += size * bottom_blob.elempack;
  153. }
  154. }
  155. // packing
  156. if (elempack == 1 && out_elempack == 4)
  157. {
  158. packing_pack4->forward(top_blob_unpacked, top_blob, opt);
  159. }
  160. return 0;
  161. }
  162. if (dims == 2 && axis == 1)
  163. {
  164. // interleave image row
  165. int h = bottom_blobs[0].h;
  166. size_t elemsize = bottom_blobs[0].elemsize;
  167. int elempack = bottom_blobs[0].elempack;
  168. // total width
  169. int top_w = 0;
  170. for (size_t b=0; b<bottom_blobs.size(); b++)
  171. {
  172. const Mat& bottom_blob = bottom_blobs[b];
  173. top_w += bottom_blob.w;
  174. }
  175. Mat& top_blob = top_blobs[0];
  176. top_blob.create(top_w, h, elemsize, elempack, opt.blob_allocator);
  177. if (top_blob.empty())
  178. return -100;
  179. #pragma omp parallel for num_threads(opt.num_threads)
  180. for (int i=0; i<h; i++)
  181. {
  182. float* outptr = top_blob.row(i);
  183. for (size_t b=0; b<bottom_blobs.size(); b++)
  184. {
  185. const Mat& bottom_blob = bottom_blobs[b];
  186. const float* ptr = bottom_blob.row(i);
  187. memcpy(outptr, ptr, bottom_blob.w * elemsize);
  188. outptr += bottom_blob.w * elempack;
  189. }
  190. }
  191. return 0;
  192. }
  193. if (dims == 3 && axis == 0)
  194. {
  195. // concat dim
  196. int w = bottom_blobs[0].w;
  197. int h = bottom_blobs[0].h;
  198. // total channels
  199. size_t elemsize = bottom_blobs[0].elemsize;
  200. int elempack = bottom_blobs[0].elempack;
  201. int top_channels = 0;
  202. for (size_t b=0; b<bottom_blobs.size(); b++)
  203. {
  204. const Mat& bottom_blob = bottom_blobs[b];
  205. elemsize = std::min(elemsize, bottom_blob.elemsize);
  206. elempack = std::min(elempack, bottom_blob.elempack);
  207. top_channels += bottom_blob.c * bottom_blob.elempack;
  208. }
  209. int out_elempack = top_channels % 4 == 0 ? 4 : 1;
  210. size_t out_elemsize = elemsize / elempack * out_elempack;
  211. Mat& top_blob = top_blobs[0];
  212. top_blob.create(w, h, top_channels / out_elempack, out_elemsize, out_elempack, opt.blob_allocator);
  213. if (top_blob.empty())
  214. return -100;
  215. Mat top_blob_unpacked = top_blob;
  216. if (elempack == 1 && out_elempack == 4)
  217. {
  218. top_blob_unpacked.create(w, h, top_channels / elempack, elemsize, elempack, opt.workspace_allocator);
  219. if (top_blob_unpacked.empty())
  220. return -100;
  221. }
  222. int p = 0;
  223. for (size_t b=0; b<bottom_blobs.size(); b++)
  224. {
  225. const Mat& bottom_blob = bottom_blobs[b];
  226. if (bottom_blob.elempack == 4 && elempack == 1)
  227. {
  228. int size = bottom_blob.w * bottom_blob.h;
  229. for (int q=0; q<bottom_blob.c; q++)
  230. {
  231. const float* r0 = bottom_blob.channel(q);
  232. float* outptr0 = top_blob_unpacked.channel(p);
  233. float* outptr1 = top_blob_unpacked.channel(p+1);
  234. float* outptr2 = top_blob_unpacked.channel(p+2);
  235. float* outptr3 = top_blob_unpacked.channel(p+3);
  236. for (int i=0; i<size; i++)
  237. {
  238. *outptr0++ = r0[0];
  239. *outptr1++ = r0[1];
  240. *outptr2++ = r0[2];
  241. *outptr3++ = r0[3];
  242. r0 += 4;
  243. }
  244. p += 4;
  245. }
  246. }
  247. else // if (bottom_blob.elempack == 1 && elempack == 1) if (bottom_blob.elempack == 4 && elempack == 4)
  248. {
  249. int size = bottom_blob.total();
  250. const float* ptr = bottom_blob;
  251. float* outptr = top_blob_unpacked.channel(p);
  252. memcpy(outptr, ptr, size * bottom_blob.elemsize);
  253. p += bottom_blob.c;
  254. }
  255. }
  256. // packing
  257. if (elempack == 1 && out_elempack == 4)
  258. {
  259. packing_pack4->forward(top_blob_unpacked, top_blob, opt);
  260. }
  261. return 0;
  262. }
  263. if (dims == 3 && axis == 1)
  264. {
  265. // interleave dim height
  266. int w = bottom_blobs[0].w;
  267. int channels = bottom_blobs[0].c;
  268. size_t elemsize = bottom_blobs[0].elemsize;
  269. int elempack = bottom_blobs[0].elempack;
  270. // total height
  271. int top_h = 0;
  272. for (size_t b=0; b<bottom_blobs.size(); b++)
  273. {
  274. const Mat& bottom_blob = bottom_blobs[b];
  275. top_h += bottom_blob.h;
  276. }
  277. Mat& top_blob = top_blobs[0];
  278. top_blob.create(w, top_h, channels, elemsize, elempack, opt.blob_allocator);
  279. if (top_blob.empty())
  280. return -100;
  281. #pragma omp parallel for num_threads(opt.num_threads)
  282. for (int q=0; q<channels; q++)
  283. {
  284. float* outptr = top_blob.channel(q);
  285. for (size_t b=0; b<bottom_blobs.size(); b++)
  286. {
  287. const Mat& bottom_blob = bottom_blobs[b];
  288. int size = bottom_blob.w * bottom_blob.h;
  289. const float* ptr = bottom_blob.channel(q);
  290. memcpy(outptr, ptr, size * elemsize);
  291. outptr += size * elempack;
  292. }
  293. }
  294. return 0;
  295. }
  296. if (dims == 3 && axis == 2)
  297. {
  298. // interleave dim width
  299. int h = bottom_blobs[0].h;
  300. int channels = bottom_blobs[0].c;
  301. size_t elemsize = bottom_blobs[0].elemsize;
  302. int elempack = bottom_blobs[0].elempack;
  303. // total height
  304. int top_w = 0;
  305. for (size_t b=0; b<bottom_blobs.size(); b++)
  306. {
  307. const Mat& bottom_blob = bottom_blobs[b];
  308. top_w += bottom_blob.w;
  309. }
  310. Mat& top_blob = top_blobs[0];
  311. top_blob.create(top_w, h, channels, elemsize, elempack, opt.blob_allocator);
  312. if (top_blob.empty())
  313. return -100;
  314. #pragma omp parallel for num_threads(opt.num_threads)
  315. for (int q=0; q<channels; q++)
  316. {
  317. float* outptr = top_blob.channel(q);
  318. for (int i=0; i<h; i++)
  319. {
  320. for (size_t b=0; b<bottom_blobs.size(); b++)
  321. {
  322. const Mat& bottom_blob = bottom_blobs[b];
  323. const float* ptr = bottom_blob.channel(q).row(i);
  324. memcpy(outptr, ptr, bottom_blob.w * elemsize);
  325. outptr += bottom_blob.w * elempack;
  326. }
  327. }
  328. }
  329. return 0;
  330. }
  331. } // opt.use_packing_layout
  332. #endif // __ARM_NEON
  333. return Concat::forward(bottom_blobs, top_blobs, opt);
  334. }
  335. } // namespace ncnn