modules/dnn/src/layers/eltwise_layer.cpp

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  42
  43 #include "../precomp.hpp"
  44 #include "layers_common.hpp"
  45 #include "../op_halide.hpp"
  46 #include "../op_inf_engine.hpp"
  47 #include "../ie_ngraph.hpp"
  48
  49 #ifdef HAVE_OPENCL
  50 #include "opencl_kernels_dnn.hpp"
  51 #endif
  52
  53 namespace cv
  54 {
  55 namespace dnn
  56 {
  57
  58 class EltwiseLayerImpl CV_FINAL : public EltwiseLayer
  59 {
  60 public:
  61     enum EltwiseOp
  62     {
  63         PROD = 0,
  64         SUM = 1,
  65         MAX = 2,
  66         DIV = 3
  67     } op;
  68     std::vector<float> coeffs;
  69     bool variableChannels;
  70
  71     EltwiseLayerImpl(const LayerParams& params)
  72     {
  73         setParamsFrom(params);
  74         op = SUM;
  75         if (params.has("operation"))
  76         {
  77             String operation = params.get<String>("operation").toLowerCase();
  78             if (operation == "prod")
  79                 op = PROD;
  80             else if (operation == "sum")
  81                 op = SUM;
  82             else if (operation == "max")
  83                 op = MAX;
  84             else if (operation == "div")
  85                 op = DIV;
  86             else
  87                 CV_Error(cv::Error::StsBadArg, "Unknown operation type \"" + operation + "\"");
  88         }
  89
  90         if (params.has("coeff"))
  91         {
  92             DictValue paramCoeff = params.get("coeff");
  93             int i, n = paramCoeff.size();
  94             coeffs.resize(n);
  95             for (i = 0; i < n; i++)
  96             {
  97                 coeffs[i] = paramCoeff.get<float>(i);
  98             }
  99         }
 100     }
 101
 102     virtual bool supportBackend(int backendId) CV_OVERRIDE
 103     {
 104         return backendId == DNN_BACKEND_OPENCV ||
 105                backendId == DNN_BACKEND_HALIDE ||
 106                ((((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (preferableTarget != DNN_TARGET_OPENCL || coeffs.empty()))
 107                 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && !variableChannels));
 108     }
 109
 110     bool getMemoryShapes(const std::vector<MatShape> &inputs,
 111                          const int requiredOutputs,
 112                          std::vector<MatShape> &outputs,
 113                          std::vector<MatShape> &internals) const CV_OVERRIDE
 114     {
 115         CV_Assert(inputs.size() >= 2);
 116         CV_Assert(inputs[0].size() >= 2);
 117         CV_Assert(coeffs.size() == 0 || coeffs.size() == inputs.size());
 118         CV_Assert(op == SUM || coeffs.size() == 0);
 119
 120         int dims = inputs[0].size();
 121         // Number of channels in output shape is determined by the first input tensor.
 122         int numChannels = inputs[0][1];
 123         for (int i = 1; i < inputs.size(); i++)
 124         {
 125             CV_Assert(inputs[0][0] == inputs[i][0]);
 126
 127             // It's allowed for channels axis to be different.
 128             for (int j = 2; j < dims; j++)
 129                 CV_Assert(inputs[0][j] == inputs[i][j]);
 130         }
 131
 132         outputs.assign(1, inputs[0]);
 133         outputs[0][1] = numChannels;
 134         return false;
 135     }
 136
 137     void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays) CV_OVERRIDE
 138     {
 139         std::vector<Mat> inputs;
 140         inputs_arr.getMatVector(inputs);
 141         variableChannels = false;
 142         for (int i = 1; i < inputs.size(); ++i)
 143         {
 144             if (inputs[i].size[1] != inputs[0].size[1])
 145             {
 146                 variableChannels = true;
 147                 break;
 148             }
 149         }
 150     }
 151
 152
 153     class EltwiseInvoker : public ParallelLoopBody
 154     {
 155     public:
 156         std::vector<const Mat*> srcs;
 157         int nsrcs;
 158         Mat* dst;
 159         std::vector<float> coeffs;
 160         EltwiseOp op;
 161         int nstripes;
 162         const ActivationLayer* activ;
 163         int channels;
 164         size_t planeSize;
 165
 166         EltwiseInvoker() : nsrcs(0), dst(0), op(PROD), nstripes(0), activ(0), channels(0), planeSize(0)  {}
 167
 168         static void run(const Mat* srcs, int nsrcs, Mat& dst,
 169                         const std::vector<float>& coeffs, EltwiseOp op,
 170                         const ActivationLayer* activ, int nstripes)
 171         {
 172             CV_Check(dst.dims, 1 < dst.dims && dst.dims <= 5, ""); CV_CheckTypeEQ(dst.type(), CV_32FC1, ""); CV_Assert(dst.isContinuous());
 173             CV_Assert(coeffs.empty() || coeffs.size() == (size_t)nsrcs);
 174
 175             EltwiseInvoker p;
 176             p.srcs.resize(nsrcs);
 177             p.coeffs = coeffs;
 178             for( int i = 0; i < nsrcs; i++ )
 179             {
 180                 p.srcs[i] = srcs + i;
 181                 CV_Assert(srcs[i].type() == dst.type() &&
 182                           srcs[i].isContinuous());
 183                 // Sort srcs and coefficients in the order by number of channels
 184                 for( int j = i; j >= 1 && p.srcs[j - 1]->size[1] < p.srcs[j]->size[1]; j-- )
 185                 {
 186                     std::swap(p.srcs[j - 1], p.srcs[j]);
 187                     if (!p.coeffs.empty())
 188                         std::swap(p.coeffs[j - 1], p.coeffs[j]);
 189                 }
 190             }
 191
 192             p.nsrcs = nsrcs;
 193             p.dst = &dst;
 194             p.op = op;
 195             p.nstripes = nstripes;
 196             p.channels = (dst.dims >= 4 ? dst.size[1] : 1);
 197
 198             p.planeSize = dst.total(dst.dims >= 4 ? 2 : 1);
 199             CV_Assert(dst.total() == dst.size[0] * p.channels * p.planeSize);
 200
 201             bool simpleCoeffs = true;
 202             if( op == SUM && !coeffs.empty() )
 203             {
 204                 CV_Assert( coeffs.size() == (size_t)nsrcs );
 205
 206                 for( size_t i = 0; i < coeffs.size(); i++ )
 207                     if( coeffs[i] != 1 )
 208                     {
 209                         simpleCoeffs = false;
 210                         break;
 211                     }
 212             }
 213             if (simpleCoeffs)
 214                 p.coeffs.clear();
 215             p.activ = activ;
 216
 217             parallel_for_(Range(0, nstripes), p, nstripes);
 218         }
 219
 220         void operator()(const Range& r) const CV_OVERRIDE
 221         {
 222             size_t total = dst->size[0]*planeSize;
 223             size_t stripeSize = (total + nstripes - 1)/nstripes;
 224             size_t stripeStart = r.start*stripeSize;
 225             size_t stripeEnd = std::min(r.end*stripeSize, total);
 226             int c, j, k, n;
 227             const float* coeffsptr = !coeffs.empty() ? &coeffs[0] : 0;
 228             float* dstptr0 = dst->ptr<float>();
 229             int blockSize0 = 1 << 12, blockSize;
 230
 231             for( size_t ofs = stripeStart; ofs < stripeEnd; ofs += blockSize )
 232             {
 233                 int sampleIdx = (int)(ofs / planeSize);
 234                 int delta = (int)ofs - sampleIdx * planeSize;
 235                 blockSize = std::min(blockSize0, std::min((int)(stripeEnd - ofs), (int)planeSize - delta));
 236                 if( blockSize <= 0 )
 237                     break;
 238
 239                 for( c = 0; c < channels; c++ )
 240                 {
 241                     size_t globalDelta = delta + (sampleIdx*channels + c)*planeSize;
 242                     const float* srcptr0 = srcs[0]->ptr<float>() + globalDelta;
 243                     float* dstptr = dstptr0 + globalDelta;
 244
 245                     // This code assumes that srcs are sorted in descending order by channels.
 246                     for (n = 1; n < nsrcs && c < srcs[n]->size[1]; ++n) {}
 247
 248                     if (n == 1)
 249                     {
 250                         if( !coeffsptr )
 251                         {
 252                             for( j = 0; j < blockSize; j++ )
 253                             {
 254                                 dstptr[j] = srcptr0[j];
 255                             }
 256                         }
 257                         else
 258                         {
 259                             float c0 = coeffsptr[0];
 260                             for( j = 0; j < blockSize; j++ )
 261                             {
 262                                 dstptr[j] = c0*srcptr0[j];
 263                             }
 264                         }
 265                     }
 266                     else if( op == PROD )
 267                     {
 268                         for( k = 1; k < n; k++ )
 269                         {
 270                             const float* srcptr1 = srcs[k]->ptr<float>() + globalDelta;
 271                             for( j = 0; j < blockSize; j++ )
 272                             {
 273                                 dstptr[j] = srcptr0[j]*srcptr1[j];
 274                             }
 275                             srcptr0 = (const float*)dstptr;
 276                         }
 277                     }
 278                     else if( op == DIV )
 279                     {
 280                         for( k = 1; k < n; k++ )
 281                         {
 282                             const float* srcptr1 = srcs[k]->ptr<float>() + globalDelta;
 283                             for( j = 0; j < blockSize; j++ )
 284                             {
 285                                 dstptr[j] = srcptr0[j]/srcptr1[j];
 286                             }
 287                             srcptr0 = (const float*)dstptr;
 288                         }
 289                     }
 290                     else if( op == MAX )
 291                     {
 292                         for( k = 1; k < n; k++ )
 293                         {
 294                             const float* srcptr1 = srcs[k]->ptr<float>() + globalDelta;
 295                             for( j = 0; j < blockSize; j++ )
 296                             {
 297                                 dstptr[j] = std::max(srcptr0[j], srcptr1[j]);
 298                             }
 299                             srcptr0 = (const float*)dstptr;
 300                         }
 301                     }
 302                     else if( !coeffsptr )
 303                     {
 304                         for( k = 1; k < n; k++ )
 305                         {
 306                             const float* srcptr1 = srcs[k]->ptr<float>() + globalDelta;
 307                             for( j = 0; j < blockSize; j++ )
 308                             {
 309                                 dstptr[j] = srcptr0[j] + srcptr1[j];
 310                             }
 311                             srcptr0 = (const float*)dstptr;
 312                         }
 313                     }
 314                     else
 315                     {
 316                         float c0 = coeffsptr[0];
 317                         for( k = 1; k < n; k++ )
 318                         {
 319                             const float* srcptr1 = srcs[k]->ptr<float>() + globalDelta;
 320                             float c1 = coeffsptr[k];
 321                             for( j = 0; j < blockSize; j++ )
 322                             {
 323                                 dstptr[j] = c0*srcptr0[j] + c1*srcptr1[j];
 324                             }
 325                             srcptr0 = (const float*)dstptr;
 326                             c0 = 1;
 327                         }
 328                     }
 329                 }
 330
 331                 if( activ )
 332                 {
 333                     float* ptr = dstptr0 + delta + sampleIdx*channels*planeSize;
 334                     activ->forwardSlice(ptr, ptr, blockSize, planeSize, 0, channels);
 335                 }
 336             }
 337         }
 338     };
 339
 340 #ifdef HAVE_OPENCL
 341     bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
 342     {
 343         std::vector<UMat> inputs;
 344         std::vector<UMat> outputs;
 345
 346         if ((inputs_.depth() == CV_16S && op != SUM) || variableChannels)
 347             return false;
 348
 349         inputs_.getUMatVector(inputs);
 350         outputs_.getUMatVector(outputs);
 351
 352         switch (op)
 353         {
 354             case SUM:
 355                 {
 356                     int channels = total(shape(outputs[0]), 0, 2);
 357                     int plane_size = total(shape(outputs[0]), 2);
 358                     if (channels % 4 == 0 && plane_size % 4 == 0)
 359                     {
 360                         size_t localsize[] = { 128 };
 361                         size_t globalsize[] = { (size_t)channels / 4 * localsize[0] };
 362                         String opts;
 363                         if (inputs_.depth() == CV_16S)
 364                             opts = " -DDtype=half -DDtype4=half4 -DDtype8=half8";
 365                         else
 366                             opts = " -DDtype=float -DDtype4=float4 -DDtype8=float8";
 367
 368                         for (int i = 0; i < (inputs.size() - 1); ++i)
 369                         {
 370                             String buildopt = format("-DLOOP=%d", i) + opts;
 371                             ocl::Kernel kernel("op_sum4", ocl::dnn::eltwise_oclsrc, buildopt);
 372                             int idx = 0;
 373                             UMat inpMat = (i == 0) ? inputs[0] : UMat();
 374                             float coeff1 = (coeffs.empty() || i > 0) ? 1.0f : coeffs[i];
 375                             float coeff2 = coeffs.empty() ? 1.0f : coeffs[i + 1];
 376                             kernel.set(idx++, ocl::KernelArg::PtrReadOnly(inputs[0]));
 377                             kernel.set(idx++, ocl::KernelArg::PtrReadOnly(inputs[1]));
 378                             kernel.set(idx++, (int)plane_size);
 379                             kernel.set(idx++, (float)coeff1);
 380                             kernel.set(idx++, (float)coeff2);
 381                             kernel.set(idx++, ocl::KernelArg::PtrReadWrite(outputs[0]));
 382                             bool ret = kernel.run(1, globalsize, localsize, false);
 383                             if (!ret)
 384                                 return false;
 385                         }
 386                     }
 387                     else
 388                     {
 389                         if (inputs_.depth() == CV_16S)
 390                             return false;
 391
 392                         float coeff1 = coeffs.empty() ? 1.f : coeffs[0];
 393                         float coeff2 = coeffs.empty() ? 1.f : coeffs[1];
 394                         UMat mul0, mul1;
 395                         multiply(coeff1, inputs[0], mul0);
 396                         multiply(coeff2, inputs[1], mul1);
 397                         add(mul0, mul1, outputs[0]);
 398                         for (int i = 2; i < inputs.size(); ++i)
 399                         {
 400                             float coeff = coeffs.empty() ? 1.f : coeffs[i];
 401                             multiply(coeff, inputs[i], mul0);
 402                             add(mul0, outputs[0], outputs[0]);
 403                         }
 404                     }
 405                 }
 406                 break;
 407             case PROD:
 408                 multiply(inputs[0], inputs[1], outputs[0]);
 409                 for (int i = 2; i < inputs.size(); ++i)
 410                     multiply(inputs[i], outputs[0], outputs[0]);
 411                 break;
 412             case DIV:
 413                 divide(inputs[0], inputs[1], outputs[0]);
 414                 for (int i = 2; i < inputs.size(); ++i)
 415                     divide(outputs[0], inputs[i], outputs[0]);
 416                 break;
 417             case MAX:
 418                 max(inputs[0], inputs[1], outputs[0]);
 419                 for (int i = 2; i < inputs.size(); ++i)
 420                     max(inputs[i], outputs[0], outputs[0]);
 421                 break;
 422             default:
 423                 return false;
 424         }
 425         return true;
 426     }
 427 #endif
 428
 429     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
 430     {
 431         CV_TRACE_FUNCTION();
 432         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 433
 434         CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
 435                    forward_ocl(inputs_arr, outputs_arr, internals_arr))
 436
 437         if (inputs_arr.depth() == CV_16S)
 438         {
 439             forward_fallback(inputs_arr, outputs_arr, internals_arr);
 440             return;
 441         }
 442
 443         std::vector<Mat> inputs, outputs;
 444         inputs_arr.getMatVector(inputs);
 445         outputs_arr.getMatVector(outputs);
 446
 447         CV_Assert(outputs.size() == 1);
 448         const int nstripes = getNumThreads();
 449         EltwiseInvoker::run(&inputs[0], (int)inputs.size(), outputs[0],
 450                             coeffs, op, activ.get(), nstripes);
 451     }
 452
 453     virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &input) CV_OVERRIDE
 454     {
 455 #ifdef HAVE_HALIDE
 456         Halide::Var x("x"), y("y"), c("c"), n("n");
 457         Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
 458         Halide::Expr topExpr;
 459         std::vector<Halide::Buffer<> > inputBuffers = halideBuffers(input);
 460         switch (op)
 461         {
 462             case SUM:
 463                 if (coeffs.empty())
 464                 {
 465                     topExpr = inputBuffers[0](x, y, c, n) +
 466                               inputBuffers[1](x, y, c, n);
 467                     for (int i = 2; i < inputBuffers.size(); ++i)
 468                         topExpr += inputBuffers[i](x, y, c, n);
 469                 }
 470                 else
 471                 {
 472                   topExpr = coeffs[0] * inputBuffers[0](x, y, c, n) +
 473                             coeffs[1] * inputBuffers[1](x, y, c, n);
 474                   for (int i = 2; i < inputBuffers.size(); ++i)
 475                       topExpr += coeffs[i] * inputBuffers[i](x, y, c, n);
 476                 }
 477                 break;
 478             case PROD:
 479                 topExpr = inputBuffers[0](x, y, c, n) *
 480                           inputBuffers[1](x, y, c, n);
 481                 for (int i = 2; i < inputBuffers.size(); ++i)
 482                     topExpr *= inputBuffers[i](x, y, c, n);
 483                 break;
 484             case MAX:
 485                 topExpr = max(inputBuffers[0](x, y, c, n),
 486                               inputBuffers[1](x, y, c, n));
 487                 for (int i = 2; i < inputBuffers.size(); ++i)
 488                     topExpr = max(topExpr, inputBuffers[i](x, y, c, n));
 489                 break;
 490             default:
 491                 return Ptr<BackendNode>();
 492         }
 493         top(x, y, c, n) = topExpr;
 494         return Ptr<BackendNode>(new HalideBackendNode(top));
 495 #endif  // HAVE_HALIDE
 496         return Ptr<BackendNode>();
 497     }
 498
 499 #ifdef HAVE_INF_ENGINE
 500     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
 501     {
 502         InferenceEngine::Builder::EltwiseLayer ieLayer(name);
 503
 504         ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
 505
 506         if (op == SUM)
 507             ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::SUM);
 508         else if (op == PROD)
 509             ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MUL);
 510         else if (op == DIV)
 511             ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::DIV);
 512         else if (op == MAX)
 513             ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MAX);
 514         else
 515             CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
 516
 517         InferenceEngine::Builder::Layer l = ieLayer;
 518         if (!coeffs.empty())
 519             l.getParameters()["coeff"] = coeffs;
 520
 521         return Ptr<BackendNode>(new InfEngineBackendNode(l));
 522     }
 523 #endif  // HAVE_INF_ENGINE
 524
 525
 526 #ifdef HAVE_DNN_NGRAPH
 527     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
 528                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
 529     {
 530         auto curr_node = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
 531         if (!coeffs.empty()) {
 532             auto coeff = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &coeffs[0]);
 533             curr_node = std::make_shared<ngraph::op::v1::Multiply>(curr_node, coeff, ngraph::op::AutoBroadcastType::NUMPY);
 534         }
 535
 536         for (size_t i = 1; i < nodes.size(); i++)
 537         {
 538             auto next_node = nodes[i].dynamicCast<InfEngineNgraphNode>()->node;
 539             if (!coeffs.empty()) {
 540                 auto coeff = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &coeffs[i]);
 541                 next_node = std::make_shared<ngraph::op::v1::Multiply>(next_node, coeff, ngraph::op::AutoBroadcastType::NUMPY);
 542             }
 543             switch (op) {
 544                 case SUM:  curr_node = std::make_shared<ngraph::op::v1::Add>(curr_node, next_node); break;
 545                 case PROD: curr_node = std::make_shared<ngraph::op::v1::Multiply>(curr_node, next_node); break;
 546                 case DIV:  curr_node = std::make_shared<ngraph::op::v1::Divide>(curr_node, next_node); break;
 547                 case MAX:  curr_node = std::make_shared<ngraph::op::v1::Maximum>(curr_node, next_node); break;
 548                 default: CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
 549             }
 550         }
 551         return Ptr<BackendNode>(new InfEngineNgraphNode(curr_node));
 552     }
 553 #endif  // HAVE_DNN_NGRAPH
 554
 555     virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
 556                            const std::vector<MatShape> &outputs) const CV_OVERRIDE
 557     {
 558         CV_UNUSED(outputs); // suppress unused variable warning
 559         CV_Assert(inputs.size());
 560
 561         long flops = inputs.size() * total(inputs[0]);
 562
 563         return flops;
 564     }
 565
 566     bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
 567     {
 568         if (activ.empty() || layer.empty())
 569         {
 570             activ = layer;
 571             return !activ.empty();
 572         }
 573         else
 574             return false;
 575     }
 576
 577     Ptr<ActivationLayer> activ;
 578 };
 579
 580 Ptr<EltwiseLayer> EltwiseLayer::create(const LayerParams& params)
 581 {
 582     return Ptr<EltwiseLayer>(new EltwiseLayerImpl(params));
 583 }
 584
 585 }
 586 }