modules/dnn/src/layers/pooling_layer.cpp

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  42
  43 #include "../precomp.hpp"
  44 #include "layers_common.hpp"
  45 #include "opencv2/core/hal/intrin.hpp"
  46 #include "op_halide.hpp"
  47 #include "opencl_kernels_dnn.hpp"
  48 #include <float.h>
  49 #include <algorithm>
  50 using std::max;
  51 using std::min;
  52 #ifdef HAVE_OPENCL
  53 using namespace cv::dnn::ocl4dnn;
  54 #endif
  55
  56 namespace cv
  57 {
  58 namespace dnn
  59 {
  60
  61 class PoolingLayerImpl : public PoolingLayer
  62 {
  63 public:
  64     PoolingLayerImpl(const LayerParams& params)
  65     {
  66         type = PoolingLayer::MAX;
  67         computeMaxIdx = true;
  68
  69         if (params.has("pool"))
  70         {
  71             String pool = params.get<String>("pool").toLowerCase();
  72             if (pool == "max")
  73                 type = PoolingLayer::MAX;
  74             else if (pool == "ave")
  75                 type = PoolingLayer::AVE;
  76             else if (pool == "stochastic")
  77                 type = PoolingLayer::STOCHASTIC;
  78             else
  79                 CV_Error(Error::StsBadArg, "Unknown pooling type \"" + pool + "\"");
  80         }
  81
  82         getPoolingKernelParams(params, kernel.height, kernel.width, globalPooling,
  83                                pad.height, pad.width, stride.height, stride.width, padMode);
  84         setParamsFrom(params);
  85         ceilMode = params.get<bool>("ceil_mode", true);
  86     }
  87
  88 #ifdef HAVE_OPENCL
  89     Ptr<OCL4DNNPool<float> > poolOp;
  90 #endif
  91
  92     void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
  93     {
  94         CV_Assert(inputs.size() == 1);
  95
  96         cv::Size inp(inputs[0]->size[3], inputs[0]->size[2]),
  97                 out(outputs[0].size[3], outputs[0].size[2]);
  98
  99         if(globalPooling)
 100         {
 101             kernel = inp;
 102         }
 103
 104         getConvPoolPaddings(inp, out, kernel, stride, padMode, Size(1, 1), pad);
 105     }
 106
 107     virtual bool supportBackend(int backendId)
 108     {
 109         return backendId == DNN_BACKEND_DEFAULT ||
 110                backendId == DNN_BACKEND_HALIDE && haveHalide() &&
 111                (type == PoolingLayer::MAX ||
 112                 type == PoolingLayer::AVE && !pad.width && !pad.height);
 113     }
 114
 115 #ifdef HAVE_OPENCL
 116     bool forward_ocl(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
 117     {
 118         if (poolOp.empty())
 119         {
 120             OCL4DNNPoolConfig config;
 121
 122             config.in_shape = shape(*inputs[0]);
 123             config.out_shape = shape(outputs[0]);
 124             config.kernel = kernel;
 125             config.pad = pad;
 126             config.stride = stride;
 127             config.channels = inputs[0]->size[1];
 128             config.pool_method = type == MAX ? LIBDNN_POOLING_METHOD_MAX :
 129                                 (type == AVE ? LIBDNN_POOLING_METHOD_AVE :
 130                                                LIBDNN_POOLING_METHOD_STO);
 131             poolOp = Ptr<OCL4DNNPool<float> >(new OCL4DNNPool<float>(config));
 132         }
 133
 134         for (size_t ii = 0; ii < inputs.size(); ii++)
 135         {
 136             UMat inpMat, outMat, maskMat;
 137
 138             inpMat = inputs[ii]->getUMat(ACCESS_READ);
 139
 140             if (type == MAX)
 141             {
 142                 outMat = outputs[2 * ii].getUMat(ACCESS_WRITE);
 143                 maskMat = outputs[2 * ii + 1].getUMat(ACCESS_WRITE);
 144             } else {
 145                 outMat = outputs[ii].getUMat(ACCESS_WRITE);
 146                 maskMat = UMat();
 147             }
 148
 149             CV_Assert(inpMat.offset == 0 && outMat.offset == 0);
 150
 151             if (!poolOp->Forward(inpMat, outMat, maskMat))
 152                 return false;
 153         }
 154
 155         return true;
 156     }
 157 #endif
 158
 159     void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
 160     {
 161         CV_TRACE_FUNCTION();
 162         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 163
 164         CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
 165                    OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
 166                    forward_ocl(inputs, outputs, internals))
 167
 168         for (size_t ii = 0; ii < inputs.size(); ii++)
 169         {
 170             switch (type)
 171             {
 172                 case MAX:
 173                     maxPooling(*inputs[ii], outputs[2 * ii], outputs[2 * ii + 1]);
 174                     break;
 175                 case AVE:
 176                     avePooling(*inputs[ii], outputs[ii]);
 177                     break;
 178                 default:
 179                     CV_Error(Error::StsNotImplemented, "Not implemented");
 180                     break;
 181             }
 182         }
 183     }
 184
 185     virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
 186     {
 187         if (type == PoolingLayer::MAX)
 188             return initMaxPoolingHalide(inputs);
 189         else if (type == PoolingLayer::AVE)
 190             return initAvePoolingHalide(inputs);
 191         else
 192             return Ptr<BackendNode>();
 193     }
 194
 195     class PoolingInvoker : public ParallelLoopBody
 196     {
 197     public:
 198         const Mat* src;
 199         Mat *dst, *mask;
 200         Size kernel, stride, pad;
 201         int nstripes;
 202         bool computeMaxIdx;
 203         std::vector<int> ofsbuf;
 204         int poolingType;
 205
 206         PoolingInvoker() : src(0), dst(0), mask(0), nstripes(0), computeMaxIdx(0), poolingType(PoolingLayer::MAX) {}
 207
 208         static void run(const Mat& src, Mat& dst, Mat& mask, Size kernel,
 209                         Size stride, Size pad, int poolingType,
 210                         bool computeMaxIdx, int nstripes)
 211         {
 212             CV_Assert(src.isContinuous() && dst.isContinuous() &&
 213                       src.type() == CV_32F && src.type() == dst.type() &&
 214                       src.dims == 4 && dst.dims == 4 &&
 215                       src.size[0] == dst.size[0] && src.size[1] == dst.size[1] &&
 216                       (mask.empty() || (mask.type() == src.type() && mask.size == dst.size)));
 217
 218             PoolingInvoker p;
 219
 220             p.src = &src;
 221             p.dst = &dst;
 222             p.mask = &mask;
 223             p.kernel = kernel;
 224             p.stride = stride;
 225             p.pad = pad;
 226             p.nstripes = nstripes;
 227             p.computeMaxIdx = computeMaxIdx;
 228             p.poolingType = poolingType;
 229
 230             if( !computeMaxIdx )
 231             {
 232                 p.ofsbuf.resize(kernel.width*kernel.height);
 233                 for( int i = 0; i < kernel.height; i++ )
 234                     for( int j = 0; j < kernel.width; j++ )
 235                         p.ofsbuf[i*kernel.width + j] = src.size[3]*i + j;
 236             }
 237
 238             parallel_for_(Range(0, nstripes), p, nstripes);
 239         }
 240
 241         void operator()(const Range& r) const
 242         {
 243             int channels = dst->size[1], width = dst->size[3], height = dst->size[2];
 244             int inp_width = src->size[3], inp_height = src->size[2];
 245             size_t total = dst->total();
 246             size_t stripeSize = (total + nstripes - 1)/nstripes;
 247             size_t stripeStart = r.start*stripeSize;
 248             size_t stripeEnd = std::min(r.end*stripeSize, total);
 249             int kernel_w = kernel.width, kernel_h = kernel.height;
 250             int pad_w = pad.width, pad_h = pad.height;
 251             int stride_w = stride.width, stride_h = stride.height;
 252             bool compMaxIdx = computeMaxIdx;
 253
 254 #if CV_SIMD128
 255             const int* ofsptr = &ofsbuf[0];
 256             v_float32x4 idx00(0.f, (float)stride_w, (float)(stride_w*2), (float)(stride_w*3));
 257             v_float32x4 ones = v_setall_f32(1.f);
 258             v_float32x4 idx_delta = v_setall_f32((float)(inp_width - kernel_w));
 259 #endif
 260
 261             for( size_t ofs0 = stripeStart; ofs0 < stripeEnd; )
 262             {
 263                 size_t ofs = ofs0;
 264                 int x0 = (int)(ofs % width);
 265                 ofs /= width;
 266                 int y0 = (int)(ofs % height);
 267                 ofs /= height;
 268                 int c = (int)(ofs % channels);
 269                 int n = (int)(ofs / channels);
 270                 int ystart = y0 * stride_h - pad_h;
 271                 int yend = min(ystart + kernel_h, inp_height + pad_h);
 272                 int ydelta = yend - ystart;
 273                 ystart = max(ystart, 0);
 274                 yend = min(yend, inp_height);
 275                 const float *srcData = src->ptr<float>(n, c);
 276                 float *dstData = dst->ptr<float>(n, c, y0);
 277                 float *dstMaskData = mask->data ? mask->ptr<float>(n, c, y0) : 0;
 278
 279                 int delta = std::min((int)(stripeEnd - ofs0), width - x0);
 280                 ofs0 += delta;
 281                 int x1 = x0 + delta;
 282
 283                 if( poolingType == PoolingLayer::MAX )
 284                     for( ; x0 < x1; x0++ )
 285                     {
 286                         int xstart = x0 * stride_w - pad_w;
 287                         int xend = min(xstart + kernel_w, inp_width);
 288                         xstart = max(xstart, 0);
 289
 290 #if CV_SIMD128
 291                         if( xstart > 0 && x0 + 7 < x1 && (x0 + 7) * stride_w - pad_w + kernel_w < inp_width )
 292                         {
 293                             if( compMaxIdx )
 294                             {
 295                                 v_float32x4 max_val0 = v_setall_f32(-FLT_MAX);
 296                                 v_float32x4 max_val1 = max_val0;
 297                                 v_float32x4 max_idx0 = v_setall_f32(-1.f);
 298                                 v_float32x4 max_idx1 = max_idx0;
 299                                 int index0 = ystart * inp_width + xstart;
 300                                 v_float32x4 idx0 = idx00 + v_setall_f32((float)index0);
 301                                 v_float32x4 idx1 = idx0 + v_setall_f32((float)(stride_w*4));
 302
 303                                 for (int y = ystart; y < yend; ++y)
 304                                 {
 305                                     for (int x = xstart; x < xend; ++x, idx0 += ones, idx1 += ones)
 306                                     {
 307                                         const int index = y * inp_width + x;
 308                                         v_float32x4 v0(srcData[index], srcData[index + stride_w],
 309                                                        srcData[index + stride_w*2], srcData[index + stride_w*3]);
 310                                         v_float32x4 v1(srcData[index + stride_w*4], srcData[index + stride_w*5],
 311                                                        srcData[index + stride_w*6], srcData[index + stride_w*7]);
 312                                         max_idx0 = v_select(v0 > max_val0, idx0, max_idx0);
 313                                         max_idx1 = v_select(v1 > max_val1, idx1, max_idx1);
 314                                         max_val0 = v_max(max_val0, v0);
 315                                         max_val1 = v_max(max_val1, v1);
 316                                     }
 317                                     idx0 += idx_delta;
 318                                     idx1 += idx_delta;
 319                                 }
 320                                 v_store(dstData + x0, max_val0);
 321                                 v_store(dstData + x0 + 4, max_val1);
 322                                 if (dstMaskData)
 323                                 {
 324                                     v_store(dstMaskData + x0, max_idx0);
 325                                     v_store(dstMaskData + x0 + 4, max_idx1);
 326                                 }
 327                                 x0 += 7;
 328                             }
 329                             else
 330                             {
 331                                 v_float32x4 max_val0 = v_setall_f32(-FLT_MAX);
 332                                 v_float32x4 max_val1 = max_val0;
 333
 334                                 if( yend - ystart == kernel_h )
 335                                 {
 336                                     const float* srcData1 = srcData + ystart*inp_width + xstart;
 337                                     if( stride_w == 1 )
 338                                         for (int k = 0; k < kernel_w*kernel_h; k++)
 339                                         {
 340                                             int index = ofsptr[k];
 341                                             v_float32x4 v0 = v_load(srcData1 + index);
 342                                             v_float32x4 v1 = v_load(srcData1 + index + 4);
 343                                             max_val0 = v_max(max_val0, v0);
 344                                             max_val1 = v_max(max_val1, v1);
 345                                         }
 346 #if CV_SSE2
 347                                     else if( stride_w == 2 )
 348                                         for (int k = 0; k < kernel_w*kernel_h; k++)
 349                                         {
 350                                             int index = ofsptr[k];
 351                                             v_float32x4 v00 = v_load(srcData1 + index), v01 = v_load(srcData1 + index + 4);
 352                                             v_float32x4 v0(_mm_shuffle_ps(v00.val, v01.val, _MM_SHUFFLE(2, 0, 2, 0)));
 353                                             v_float32x4 v10 = v_load(srcData1 + index + 8), v11 = v_load(srcData1 + index + 12);
 354                                             v_float32x4 v1(_mm_shuffle_ps(v10.val, v11.val, _MM_SHUFFLE(2, 0, 2, 0)));
 355                                             max_val0 = v_max(max_val0, v0);
 356                                             max_val1 = v_max(max_val1, v1);
 357                                         }
 358 #endif
 359                                     else
 360                                         for (int k = 0; k < kernel_w*kernel_h; k++)
 361                                         {
 362                                             int index = ofsptr[k];
 363                                             v_float32x4 v0(srcData1[index], srcData1[index + stride_w],
 364                                                            srcData1[index + stride_w*2], srcData1[index + stride_w*3]);
 365                                             v_float32x4 v1(srcData1[index + stride_w*4], srcData1[index + stride_w*5],
 366                                                            srcData1[index + stride_w*6], srcData1[index + stride_w*7]);
 367                                             max_val0 = v_max(max_val0, v0);
 368                                             max_val1 = v_max(max_val1, v1);
 369                                         }
 370                                 }
 371                                 else
 372                                 {
 373                                     for (int y = ystart; y < yend; ++y)
 374                                     {
 375                                         for (int x = xstart; x < xend; ++x)
 376                                         {
 377                                             const int index = y * inp_width + x;
 378                                             v_float32x4 v0(srcData[index], srcData[index + stride_w],
 379                                                            srcData[index + stride_w*2], srcData[index + stride_w*3]);
 380                                             v_float32x4 v1(srcData[index + stride_w*4], srcData[index + stride_w*5],
 381                                                            srcData[index + stride_w*6], srcData[index + stride_w*7]);
 382                                             max_val0 = v_max(max_val0, v0);
 383                                             max_val1 = v_max(max_val1, v1);
 384                                         }
 385                                     }
 386                                 }
 387                                 v_store(dstData + x0, max_val0);
 388                                 v_store(dstData + x0 + 4, max_val1);
 389                                 x0 += 7;
 390                             }
 391                         }
 392                         else
 393 #endif
 394                         {
 395                             float max_val = -FLT_MAX;
 396                             if( compMaxIdx )
 397                             {
 398                                 int max_index = -1;
 399                                 for (int y = ystart; y < yend; ++y)
 400                                     for (int x = xstart; x < xend; ++x)
 401                                     {
 402                                         const int index = y * inp_width + x;
 403                                         float val = srcData[index];
 404                                         if (val > max_val)
 405                                         {
 406                                             max_val = val;
 407                                             max_index = index;
 408                                         }
 409                                     }
 410
 411                                 dstData[x0] = max_val;
 412                                 if (dstMaskData)
 413                                     dstMaskData[x0] = max_index;
 414                             }
 415                             else
 416                             {
 417                                 for (int y = ystart; y < yend; ++y)
 418                                     for (int x = xstart; x < xend; ++x)
 419                                     {
 420                                         const int index = y * inp_width + x;
 421                                         float val = srcData[index];
 422                                         max_val = std::max(max_val, val);
 423                                     }
 424
 425                                 dstData[x0] = max_val;
 426                             }
 427                         }
 428                     }
 429                 else
 430                 {
 431                     for( ; x0 < x1; x0++ )
 432                     {
 433                         int xstart = x0 * stride_w - pad_w;
 434                         int xend = min(xstart + kernel_w, inp_width + pad_w);
 435                         int xdelta = xend - xstart;
 436                         xstart = max(xstart, 0);
 437                         xend = min(xend, inp_width);
 438                         float inv_kernel_area = 1.f/(ydelta*xdelta);
 439
 440 #if CV_SIMD128
 441                         if( xstart > 0 && x0 + 7 < x1 && (x0 + 7) * stride_w - pad_w + kernel_w < inp_width )
 442                         {
 443                             v_float32x4 sum_val0 = v_setzero_f32(), sum_val1 = v_setzero_f32();
 444                             v_float32x4 ikarea = v_setall_f32(inv_kernel_area);
 445
 446                             for (int y = ystart; y < yend; ++y)
 447                             {
 448                                 for (int x = xstart; x < xend; ++x)
 449                                 {
 450                                     const int index = y * inp_width + x;
 451                                     v_float32x4 v0(srcData[index], srcData[index + stride_w],
 452                                                    srcData[index + stride_w*2], srcData[index + stride_w*3]);
 453                                     v_float32x4 v1(srcData[index + stride_w*4], srcData[index + stride_w*5],
 454                                                    srcData[index + stride_w*6], srcData[index + stride_w*7]);
 455                                     sum_val0 += v0;
 456                                     sum_val1 += v1;
 457                                 }
 458                             }
 459                             v_store(dstData + x0, sum_val0*ikarea);
 460                             v_store(dstData + x0 + 4, sum_val1*ikarea);
 461                             x0 += 7;
 462                         }
 463                         else
 464 #endif
 465                         {
 466                             float sum_val = 0.f;
 467                             for (int y = ystart; y < yend; ++y)
 468                                 for (int x = xstart; x < xend; ++x)
 469                                 {
 470                                     const int index = y * inp_width + x;
 471                                     float val = srcData[index];
 472                                     sum_val += val;
 473                                 }
 474
 475                             dstData[x0] = sum_val*inv_kernel_area;
 476                         }
 477                     }
 478                 }
 479             }
 480         }
 481     };
 482
 483     void maxPooling(Mat &src, Mat &dst, Mat &mask)
 484     {
 485         const int nstripes = getNumThreads();
 486         PoolingInvoker::run(src, dst, mask, kernel, stride, pad, type, computeMaxIdx, nstripes);
 487     }
 488
 489     void avePooling(Mat &src, Mat &dst)
 490     {
 491         const int nstripes = getNumThreads();
 492         Mat mask;
 493         PoolingInvoker::run(src, dst, mask, kernel, stride, pad, type, computeMaxIdx, nstripes);
 494     }
 495
 496     virtual Ptr<BackendNode> initMaxPoolingHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
 497     {
 498 #ifdef HAVE_HALIDE
 499         Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
 500         const int inWidth = inputBuffer.width();
 501         const int inHeight = inputBuffer.height();
 502
 503         Halide::Var x("x"), y("y"), c("c"), n("n");
 504         Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
 505         Halide::RDom r(0, kernel.width, 0, kernel.height);
 506         Halide::Expr kx, ky;
 507         if (pad.width || pad.height)
 508         {
 509             kx = clamp(x * stride.width + r.x - pad.width, 0, inWidth - 1);
 510             ky = clamp(y * stride.height + r.y - pad.height, 0, inHeight - 1);
 511         }
 512         else
 513         {
 514             kx = min(x * stride.width + r.x, inWidth - 1);
 515             ky = min(y * stride.height + r.y, inHeight - 1);
 516         }
 517
 518         // Halide::argmax returns tuple (r.x, r.y, max).
 519         Halide::Tuple res = argmax(inputBuffer(kx, ky, c, n));
 520
 521         // Compute offset from argmax in range [0, kernel_size).
 522         Halide::Expr max_index;
 523         if (pad.width || pad.height)
 524         {
 525             max_index = clamp(y * stride.height + res[1] - pad.height,
 526                               0, inHeight - 1) * inWidth +
 527                         clamp(x * stride.width + res[0] - pad.width,
 528                               0, inWidth - 1);
 529         }
 530         else
 531         {
 532             max_index = min(y * stride.height + res[1], inHeight - 1) * inWidth +
 533                         min(x * stride.width + res[0], inWidth - 1);
 534         }
 535         top(x, y, c, n) = { res[2], Halide::cast<float>(max_index) };
 536         return Ptr<BackendNode>(new HalideBackendNode(top));
 537 #endif  // HAVE_HALIDE
 538         return Ptr<BackendNode>();
 539     }
 540
 541     virtual Ptr<BackendNode> initAvePoolingHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
 542     {
 543 #ifdef HAVE_HALIDE
 544         Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
 545
 546         const int inW = inputBuffer.width(), inH = inputBuffer.height();
 547         if ((inW - kernel.width) % stride.width || (inH - kernel.height) % stride.height)
 548         {
 549             CV_Error(cv::Error::StsNotImplemented,
 550                      "Halide backend for average pooling with partial "
 551                      "kernels is not implemented");
 552         }
 553
 554         const float norm = 1.0f / (kernel.width * kernel.height);
 555
 556         Halide::Var x("x"), y("y"), c("c"), n("n");
 557         Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
 558         Halide::RDom r(0, kernel.width, 0, kernel.height);
 559         top(x, y, c, n) = sum(
 560             inputBuffer(x * stride.width + r.x,
 561                         y * stride.height + r.y, c, n)) * norm;
 562         return Ptr<BackendNode>(new HalideBackendNode(top));
 563 #endif  // HAVE_HALIDE
 564         return Ptr<BackendNode>();
 565     }
 566
 567     virtual void applyHalideScheduler(Ptr<BackendNode>& node,
 568                                       const std::vector<Mat*> &inputs,
 569                                       const std::vector<Mat> &outputs,
 570                                       int targetId) const
 571     {
 572 #ifdef  HAVE_HALIDE
 573         if (targetId != DNN_TARGET_CPU)
 574         {
 575             Layer::applyHalideScheduler(node, inputs, outputs, targetId);
 576             return;
 577         }
 578         Halide::Var x("x"), y("y"), c("c"), n("n"), tile("tile"),
 579                     xi("xi"), yi("yi"), ci("ci"), xo("xo"), yo("yo"), co("co");
 580         Halide::Func& top = node.dynamicCast<HalideBackendNode>()->funcs.back();
 581
 582         int outW, outH, outC, outN;
 583         getCanonicalSize(outputs[0].size, &outW, &outH, &outC, &outN);
 584
 585         if (outW < 8 || outH < 8)
 586         {
 587             if (outC > 8)
 588                 top.split(c, co, ci, 8)
 589                    .fuse(x, y, tile).fuse(co, tile, tile).fuse(n, tile, tile)
 590                    .parallel(tile)
 591                    .vectorize(ci);
 592             else
 593             {
 594                 top.fuse(y, c, tile).fuse(n, tile, tile)
 595                    .parallel(tile);
 596                 if (outW > 1)
 597                     top.vectorize(x);
 598             }
 599         }
 600         else
 601         {
 602             if (outC > 8)
 603                 top.split(x, xo, xi, 8).split(y, yo, yi, 8).split(c, co, ci, 8)
 604                    .fuse(xo, yo, tile).fuse(co, tile, tile).fuse(n, tile, tile)
 605                    .parallel(tile)
 606                    .vectorize(xi);
 607             else
 608                 top.split(x, xo, xi, 8).split(y, yo, yi, 8)
 609                    .fuse(xo, yo, tile).fuse(c, tile, tile).fuse(n, tile, tile)
 610                    .parallel(tile)
 611                    .vectorize(xi);
 612         }
 613 #endif  // HAVE_HALIDE
 614     }
 615
 616     bool getMemoryShapes(const std::vector<MatShape> &inputs,
 617                          const int requiredOutputs,
 618                          std::vector<MatShape> &outputs,
 619                          std::vector<MatShape> &internals) const
 620     {
 621         CV_Assert(inputs.size() != 0);
 622         Size in(inputs[0][3], inputs[0][2]), out;
 623
 624         if (globalPooling)
 625         {
 626             out.height = 1;
 627             out.width = 1;
 628         }
 629         else if (padMode.empty())
 630         {
 631             float height = (float)(in.height + 2 * pad.height - kernel.height) / stride.height;
 632             float width = (float)(in.width + 2 * pad.width - kernel.width) / stride.width;
 633             out.height = 1 + (ceilMode ? ceil(height) : floor(height));
 634             out.width = 1 + (ceilMode ? ceil(width) : floor(width));
 635
 636             if (pad.height || pad.width)
 637             {
 638                 // If we have padding, ensure that the last pooling starts strictly
 639                 // inside the image (instead of at the padding); otherwise clip the last.
 640                 if ((out.height - 1) * stride.height >= in.height + pad.height)
 641                     --out.height;
 642                 if ((out.width - 1) * stride.width >= in.width + pad.width)
 643                     --out.width;
 644                 CV_Assert((out.height - 1) * stride.height < in.height + pad.height);
 645                 CV_Assert((out.width - 1) * stride.width < in.width + pad.width);
 646             }
 647         }
 648         else
 649         {
 650             getConvPoolOutParams(in, kernel, stride, padMode, Size(1, 1), out);
 651         }
 652
 653         outputs.resize(type == MAX ? 2 * inputs.size() : inputs.size());
 654         for (size_t i = 0; i < inputs.size(); i++)
 655         {
 656             size_t index = type == MAX ? 2*i : i;
 657             int dims[] = {inputs[i][0], inputs[i][1], out.height, out.width};
 658             outputs[index] = shape(dims);
 659
 660             if (type == MAX)
 661                 outputs[index + 1] = shape(dims);
 662         }
 663
 664         return false;
 665     }
 666
 667     virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
 668                            const std::vector<MatShape> &outputs) const
 669     {
 670         (void)inputs; // suppress unused variable warning
 671         long flops = 0;
 672
 673         for(int i = 0; i < outputs.size(); i++)
 674         {
 675             if (type == MAX)
 676             {
 677                 if (i%2 == 0)
 678                     flops += total(outputs[i])*kernel.area();
 679             }
 680             else
 681             {
 682                 flops += total(outputs[i])*(kernel.area() + 1);
 683             }
 684         }
 685         return flops;
 686     }
 687 };
 688
 689 Ptr<PoolingLayer> PoolingLayer::create(const LayerParams& params)
 690 {
 691     return Ptr<PoolingLayer>(new PoolingLayerImpl(params));
 692 }
 693
 694 }
 695 }