modules/dnn/src/layers/concat_layer.cpp

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  42
  43 #include "../precomp.hpp"
  44 #include "layers_common.hpp"
  45 #include "../op_cuda.hpp"
  46 #include "../op_halide.hpp"
  47 #include "../op_inf_engine.hpp"
  48 #include "../ie_ngraph.hpp"
  49 #include "../op_vkcom.hpp"
  50 #include "../op_webnn.hpp"
  51 #include "../op_timvx.hpp"
  52 #include "../op_cann.hpp"
  53
  54 #ifdef HAVE_OPENCL
  55 #include "opencl_kernels_dnn.hpp"
  56 #endif
  57
  58 #ifdef HAVE_CUDA
  59 #include "../cuda4dnn/primitives/concat.hpp"
  60 using namespace cv::dnn::cuda4dnn;
  61 #endif
  62
  63 namespace cv
  64 {
  65 namespace dnn
  66 {
  67
  68 class ConcatLayerImpl CV_FINAL : public ConcatLayer
  69 {
  70 public:
  71     ConcatLayerImpl(const LayerParams& params)
  72     {
  73         setParamsFrom(params);
  74         axis = params.get<int>("axis", 1);
  75         padding = params.get<bool>("padding", false);
  76         paddingValue = params.get<int>("padding_value", 0);
  77
  78         zeropoint = params.get<int>("zeropoints", 0);
  79         scale = params.get<float>("scales", 1.0f);
  80     }
  81
  82     virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
  83                                  const int requiredOutputs,
  84                                  std::vector<MatShape> &outputs,
  85                                  std::vector<MatShape> &internals) const CV_OVERRIDE
  86     {
  87         CV_Assert(inputs.size() > 0);
  88         outputs.resize(1, inputs[0]);
  89         int cAxis = normalize_axis(axis, inputs[0]);
  90
  91         int axisSum = 0;
  92         for (size_t i = 0; i < inputs.size(); i++)
  93         {
  94             MatShape curShape = inputs[i];
  95
  96             if (padding)
  97             {
  98                 for (int curAxis = 0; curAxis < outputs[0].size(); curAxis++)
  99                 {
 100                     outputs[0][curAxis] = std::max(outputs[0][curAxis], curShape[curAxis]);
 101                 }
 102             }
 103             else
 104             {
 105                 CV_Assert(curShape.size() == outputs[0].size());
 106                 for (int curAxis = 0; curAxis < outputs[0].size(); curAxis++)
 107                 {
 108                     if (curAxis != cAxis && outputs[0][curAxis] != curShape[curAxis])
 109                         CV_Error(Error::StsBadSize, "Inconsistent shape for ConcatLayer");
 110                 }
 111             }
 112
 113             axisSum += curShape[cAxis];
 114         }
 115         outputs[0][cAxis] = axisSum;
 116         return false;
 117     }
 118
 119     virtual bool supportBackend(int backendId) CV_OVERRIDE
 120     {
 121 #ifdef HAVE_TIMVX
 122         if (backendId == DNN_BACKEND_TIMVX && haveTimVX() && !padding)
 123         {
 124             if (axis == -1)
 125                 return false;
 126             int len = this->type.length();
 127             if (len <= 4)
 128                 return false;
 129             if (this->type.substr(len - 4) == "Int8")
 130                 return true;
 131             else
 132                 return false;
 133         }
 134 #endif
 135
 136 #ifdef HAVE_INF_ENGINE
 137         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
 138             return true;
 139 #endif
 140         return backendId == DNN_BACKEND_OPENCV ||
 141                backendId == DNN_BACKEND_CUDA ||
 142                (backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 && !padding) ||  // By channels
 143                (backendId == DNN_BACKEND_WEBNN && !padding) ||
 144                (backendId == DNN_BACKEND_VKCOM && haveVulkan() && !padding) ||
 145                (backendId == DNN_BACKEND_CANN && !padding);
 146     }
 147
 148     template <class T>
 149     class ChannelConcatInvoker : public ParallelLoopBody
 150     {
 151     public:
 152         std::vector<Mat>* inputs;
 153         Mat* output;
 154         int nstripes;
 155         std::vector<const T*> chptrs;
 156
 157         static void run(std::vector<Mat>& inputs, Mat& output, int nstripes)
 158         {
 159             ChannelConcatInvoker cc;
 160             cc.inputs = &inputs;
 161             cc.output = &output;
 162             cc.nstripes = nstripes;
 163
 164             size_t i, ninputs = inputs.size();
 165             int nchannels = 0, batchsz = output.size[0];
 166             for( i = 0; i < ninputs; i++ )
 167             {
 168                 Mat& inp = inputs[i];
 169                 CV_Assert( inp.isContinuous() && (inp.type() == CV_32F || inp.type() == CV_16S || inp.type() == CV_8S) &&
 170                            inp.dims == 4 && inp.size[0] == output.size[0] &&
 171                            inp.size[2] == output.size[2] &&
 172                            inp.size[3] == output.size[3] );
 173                 nchannels += inp.size[1];
 174             }
 175             CV_Assert( nchannels == output.size[1] );
 176             CV_Assert( output.isContinuous() && (output.type() == CV_32F || output.type() == CV_16S || output.type() == CV_8S) );
 177
 178             cc.chptrs.resize(nchannels*batchsz);
 179
 180             int ofs = 0;
 181             for( i = 0; i < ninputs; i++)
 182             {
 183                 Mat& inp = inputs[i];
 184                 for( int j = 0; j < batchsz; j++ )
 185                     for( int k = 0; k < inp.size[1]; k++ )
 186                     {
 187                         const T* ptr = inp.ptr<T>(j, k);
 188                         cc.chptrs[ofs + j*nchannels + k] = ptr;
 189                     }
 190                 ofs += inp.size[1];
 191             }
 192
 193             parallel_for_(Range(0, nstripes), cc, nstripes);
 194         }
 195
 196         ChannelConcatInvoker()  : inputs(0), output(0), nstripes(0) {}
 197
 198         void operator()(const Range& r) const CV_OVERRIDE
 199         {
 200             size_t planeSize = (size_t)output->size[2]*output->size[3];
 201             size_t nch = chptrs.size();
 202             size_t total = nch*planeSize;
 203             size_t stripeSize = (total + nstripes - 1)/nstripes;
 204             size_t stripeStart = r.start*stripeSize;
 205             size_t stripeEnd = std::min(total, r.end*stripeSize);
 206             const T** ptrs = (const T**)&chptrs[0];
 207             T* outptr = output->ptr<T>();
 208             size_t blockSize0 = 1 << 16;
 209
 210             for( size_t ofs0 = stripeStart; ofs0 < stripeEnd; )
 211             {
 212                 size_t ch = ofs0/planeSize;
 213                 size_t ofs = ofs0 - ch*planeSize;
 214                 size_t blockSize = std::min(blockSize0, planeSize - ofs);
 215                 memcpy(outptr + ofs0, ptrs[ch] + ofs, blockSize*sizeof(outptr[0]));
 216                 ofs0 += blockSize;
 217             }
 218         }
 219     };
 220
 221 #ifdef HAVE_OPENCL
 222     bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
 223     {
 224         std::vector<UMat> inputs;
 225         std::vector<UMat> outputs;
 226
 227         bool use_half = (inps.depth() == CV_16S);
 228         inps.getUMatVector(inputs);
 229         outs.getUMatVector(outputs);
 230
 231         int cAxis = normalize_axis(axis, inputs[0].dims);
 232         if (padding)
 233             return false;
 234
 235         int bottom_concat_axis;
 236         int concat_size = total(shape(inputs[0]), cAxis + 1);
 237         int top_concat_axis = outputs[0].size[cAxis];
 238         int num_concats = total(shape(inputs[0]), 0, cAxis);
 239         int offset_concat_axis = 0;
 240         UMat& outMat = outputs[0];
 241         String buildopt = format(" -DDtype=%s", (use_half) ? "half" : "float");
 242         String kname = format("concat_%s", use_half ? "half" : "float");
 243
 244         for (size_t i = 0; i < inputs.size(); i++)
 245         {
 246             ocl::Kernel kernel(kname.c_str(), ocl::dnn::concat_oclsrc, buildopt);
 247             if (kernel.empty())
 248                 return false;
 249
 250             UMat& inpMat = inputs[i];
 251             bottom_concat_axis = inputs[i].size[cAxis];
 252             size_t nthreads = inputs[i].total();
 253
 254             kernel.set(0, (int)nthreads);
 255             kernel.set(1, ocl::KernelArg::PtrReadOnly(inpMat));
 256             kernel.set(2, (int)num_concats);
 257             kernel.set(3, (int)concat_size);
 258             kernel.set(4, (int)top_concat_axis);
 259             kernel.set(5, (int)bottom_concat_axis);
 260             kernel.set(6, (int)offset_concat_axis);
 261             kernel.set(7, ocl::KernelArg::PtrWriteOnly(outMat));
 262
 263             if (!kernel.run(1, &nthreads, NULL, false))
 264                 return false;
 265
 266             offset_concat_axis += bottom_concat_axis;
 267         }
 268
 269         return true;
 270     }
 271 #endif
 272
 273     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
 274     {
 275         CV_TRACE_FUNCTION();
 276         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 277
 278         CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
 279                    inputs_arr.depth() != CV_8S,
 280                    forward_ocl(inputs_arr, outputs_arr, internals_arr))
 281
 282         std::vector<Mat> inputs, outputs;
 283         inputs_arr.getMatVector(inputs);
 284         outputs_arr.getMatVector(outputs);
 285
 286         int cAxis = normalize_axis(axis, inputs[0].dims);
 287         Mat& outMat = outputs[0];
 288
 289         if (padding)
 290             outMat.setTo(paddingValue);
 291
 292         if( cAxis == 1 && outMat.dims == 4 && !padding)
 293         {
 294             int nstripes = getNumThreads();
 295             if (outMat.type() == CV_8S)
 296                 ChannelConcatInvoker<int8_t>::run(inputs, outMat, nstripes);
 297             else
 298                 ChannelConcatInvoker<float>::run(inputs, outMat, nstripes);
 299         }
 300         else
 301         {
 302             std::vector<Range> ranges(outputs[0].dims, Range::all());
 303
 304             ranges[cAxis].start = 0;
 305             for (size_t i = 0; i < inputs.size(); i++)
 306             {
 307                 ranges[cAxis].end = ranges[cAxis].start + inputs[i].size[cAxis];
 308                 for (int j = 0; j < outMat.dims; ++j)
 309                 {
 310                     if (j == cAxis) continue;
 311                     ranges[j].start = (outMat.size[j] - inputs[i].size[j]) / 2;
 312                     ranges[j].end = ranges[j].start + inputs[i].size[j];
 313                 }
 314                 inputs[i].copyTo(outMat(&ranges[0]));
 315                 ranges[cAxis].start = ranges[cAxis].end;
 316             }
 317         }
 318     }
 319
 320 #ifdef HAVE_CUDA
 321     Ptr<BackendNode> initCUDA(
 322         void *context_,
 323         const std::vector<Ptr<BackendWrapper>>& inputs,
 324         const std::vector<Ptr<BackendWrapper>>& outputs
 325     ) override
 326     {
 327         auto context = reinterpret_cast<csl::CSLContext*>(context_);
 328
 329         auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
 330         auto concat_axis = normalize_axis(axis, input_wrapper->getRank());
 331         return make_cuda_node<cuda4dnn::ConcatOp>(preferableTarget, std::move(context->stream), concat_axis, padding);
 332     }
 333 #endif
 334
 335     virtual Ptr<BackendNode> initVkCom(const std::vector<Ptr<BackendWrapper> > &input) CV_OVERRIDE
 336     {
 337 #ifdef HAVE_VULKAN
 338         vkcom::Tensor in = VkComTensor(input[0]);
 339         int cAxis = normalize_axis(axis, in.dimNum());
 340         std::shared_ptr<vkcom::OpBase> op(new vkcom::OpConcat(cAxis));
 341         return Ptr<BackendNode>(new VkComBackendNode(input, op));
 342 #endif // HAVE_VULKAN
 343         return Ptr<BackendNode>();
 344     }
 345
 346     virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &input) CV_OVERRIDE
 347     {
 348 #ifdef HAVE_HALIDE
 349         std::vector<Halide::Buffer<> > inputBuffers = halideBuffers(input);
 350
 351         Halide::Var x("x"), y("y"), c("c"), n("n");
 352         Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
 353         int offset = inputBuffers[0].channels();
 354         Halide::Expr topExpr = select(c < offset,
 355                                       inputBuffers[0](x, y, c, n),
 356                                       inputBuffers[1](x, y, c - offset, n));
 357         for (int i = 2; i < input.size(); ++i)
 358         {
 359             offset += inputBuffers[i - 1].channels();
 360             topExpr = select(c < offset, topExpr,
 361                              inputBuffers[i](x, y, c - offset, n));
 362         }
 363         top(x, y, c, n) = topExpr;
 364         return Ptr<BackendNode>(new HalideBackendNode(top));
 365 #endif  // HAVE_HALIDE
 366         return Ptr<BackendNode>();
 367     }
 368
 369 #ifdef HAVE_CANN
 370     virtual Ptr<BackendNode> initCann(const std::vector<Ptr<BackendWrapper> > &inputsWrapper, const int index, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
 371     {
 372         CV_Assert(inputsWrapper.size() == nodes.size());
 373
 374         // create operator
 375         std::string op_name = cv::format("concat_%d", index);
 376         auto op = std::make_shared<ge::op::ConcatD>(op_name);
 377
 378         // set attributes
 379         int N = inputsWrapper.size();
 380         op->set_attr_concat_dim(axis);
 381         op->set_attr_N(N);
 382
 383         // set inputs : x (dynamic)
 384         op->create_dynamic_input_x(N);
 385         for (int i = 0; i < N; i++)
 386         {
 387             auto x_i = inputsWrapper[i].dynamicCast<CannBackendWrapper>();
 388             auto x_i_desc = x_i->getTensorDesc();
 389             auto op_x_i = nodes[i].dynamicCast<CannBackendNode>()->getOp();
 390             op->set_dynamic_input_x(i, *op_x_i, "y");
 391             op->update_dynamic_input_desc_x(i, *x_i_desc);
 392         }
 393
 394         // set outputs
 395         auto output_y_desc = std::make_shared<ge::TensorDesc>(ge::Shape(), ge::FORMAT_NCHW, ge::DT_FLOAT);
 396         op->update_output_desc_y(*output_y_desc);
 397
 398         return Ptr<BackendNode>(new CannBackendNode(op));
 399     }
 400 #endif
 401
 402 #ifdef HAVE_DNN_NGRAPH
 403     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
 404                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
 405     {
 406         InferenceEngine::DataPtr data = ngraphDataNode(inputs[0]);
 407         const int numDims = data->getDims().size();
 408         const int cAxis = normalize_axis(axis, numDims);
 409         std::vector<size_t> maxDims(numDims, 0);
 410
 411         CV_Assert(inputs.size() == nodes.size());
 412         ngraph::OutputVector inp_nodes;
 413         for (int i = 0; i < nodes.size(); ++i)
 414         {
 415             inp_nodes.push_back(nodes[i].dynamicCast<InfEngineNgraphNode>()->node);
 416
 417             std::vector<size_t> inpShape = ngraphDataNode(inputs[i])->getDims();
 418             for (int i = 0; i < numDims; ++i)
 419                 maxDims[i] = std::max(maxDims[i], inpShape[i]);
 420         }
 421         for (int i = 0; i < inp_nodes.size(); ++i)
 422         {
 423             bool needPadding = false;
 424             std::vector<size_t> inpShape = ngraphDataNode(inputs[i])->getDims();
 425             std::vector<int64_t> begins(inpShape.size(), 0), ends(inpShape.size(), 0);
 426             for (int j = 0; j < inpShape.size(); ++j)
 427             {
 428                 if (j != cAxis && inpShape[j] != maxDims[j])
 429                 {
 430                     needPadding = true;
 431                     begins[j] = static_cast<int64_t>((maxDims[j] - inpShape[j]) / 2);
 432                     ends[j] = static_cast<int64_t>(maxDims[j] - inpShape[j] - begins[j]);
 433                 }
 434             }
 435             if (needPadding)
 436             {
 437                 inp_nodes[i] = std::make_shared<ngraph::op::v1::Pad>(
 438                     inp_nodes[i],
 439                     std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{begins.size()}, begins.data()),
 440                     std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{ends.size()}, ends.data()),
 441                     ngraph::op::PadMode::CONSTANT);
 442             }
 443         }
 444         auto concat = std::make_shared<ngraph::op::Concat>(inp_nodes, cAxis);
 445         return Ptr<BackendNode>(new InfEngineNgraphNode(concat));
 446     }
 447 #endif  // HAVE_DNN_NGRAPH
 448
 449 #ifdef HAVE_TIMVX
 450     virtual Ptr<BackendNode> initTimVX(void* timVXInfo_,
 451                                        const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
 452                                        const std::vector<Ptr<BackendWrapper> > &outputsWrapper,
 453                                        bool isLast) CV_OVERRIDE
 454     {
 455         // tvGraph Initialization.
 456         auto timVxInfo = reinterpret_cast<TimVXInfo *>(timVXInfo_);
 457         CV_Assert(timVxInfo);
 458         Ptr<TimVXGraph> tvGraph = timVxInfo->getGraph();
 459         CV_Assert(tvGraph);
 460         Ptr<tim::vx::Graph> graph = tvGraph->graph;
 461
 462         Ptr<TimVXBackendWrapper> inputWrapper = inputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
 463         // convert axis from OpenCV NCHW toTimVX WHCN.
 464         Mat blob0 = inputWrapper->getMat();
 465
 466         // TODO! support TimVX 5 dim in future.
 467         if(blob0.dims >4)
 468             return Ptr<TimVXBackendNode>();
 469
 470         int cAxis = normalize_axis(axis, blob0.dims);
 471         int tvAxis = blob0.dims - 1 - cAxis;
 472         CV_Assert(tvAxis>= 0);
 473         std::vector<int> inputsIndex, outputsIndex;
 474         int input_index = -1, output_index = -1;
 475
 476         // Input
 477         Ptr<tim::vx::Quantization> tvQuant = Ptr<tim::vx::Quantization>(
 478                 new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, scale, zeropoint));
 479
 480         for (int i = 0; i<inputsWrapper.size(); i++)
 481         {
 482             inputWrapper = inputsWrapper[i].dynamicCast<TimVXBackendWrapper>();
 483             if (inputWrapper->isTensor())
 484             {
 485                 input_index = tvGraph->getTensorIndex(inputWrapper->getTensor());
 486                 if (input_index == -1)
 487                 {
 488                     // Copy To New inputWrapper
 489                     Mat tmp = inputWrapper->getMat();
 490                     inputWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tmp));
 491                 }
 492             }
 493
 494             if (!inputWrapper->isTensor())
 495             {
 496                 inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT, tvQuant);
 497                 input_index = tvGraph->addWrapper(inputWrapper);
 498             }
 499             inputsIndex.push_back(input_index);
 500         }
 501
 502         //Output
 503         CV_Assert(outputsWrapper.size() == 1);
 504         Ptr<TimVXBackendWrapper> outputWrapper = outputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
 505
 506         if (isLast)
 507         {
 508             auto shapeType = getShapeTypeFromMat(outputWrapper->getMat());
 509
 510             // For Graph Output tensor, we need to set tensor shape before createTensor().
 511             outputWrapper->setTensorShape(shapeType);
 512             outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, tvQuant);
 513         }
 514         else
 515         {
 516             outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, tvQuant);
 517         }
 518         output_index = tvGraph->addWrapper(outputWrapper);
 519         outputsIndex.push_back(output_index);
 520
 521         std::shared_ptr<tim::vx::Operation> tvConcate = graph->CreateOperation<tim::vx::ops::Concat>(tvAxis, inputsWrapper.size());
 522
 523         Ptr<TimVXBackendNode> tvBackendNode = new TimVXBackendNode(tvGraph, tvConcate, inputsIndex, outputsIndex);
 524
 525         return tvBackendNode;
 526     }
 527 #endif // HAVE_TIMVX
 528
 529     virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
 530                              const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
 531     {
 532         if (padding)
 533             params.set("padding_value", zeropoints[1][0]);
 534         return true;
 535     }
 536
 537 #ifdef HAVE_WEBNN
 538     virtual Ptr<BackendNode> initWebnn(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
 539     {
 540         Ptr<WebnnBackendNode> node = nodes[0].dynamicCast<WebnnBackendNode>();
 541         auto& webnnGraphBuilder = node->net->builder;
 542         std::vector<ml::Operand> inputsOperand;
 543         for (int i = 0; i < nodes.size(); i++)
 544         {
 545             inputsOperand.push_back(nodes[i].dynamicCast<WebnnBackendNode>()->operand);
 546         }
 547         auto operand = webnnGraphBuilder.Concat(inputsOperand.size(), inputsOperand.data(), axis);
 548         return Ptr<BackendNode>(new WebnnBackendNode(operand));
 549     }
 550 #endif
 551
 552     int zeropoint;
 553     float scale;
 554 };
 555
 556 Ptr<ConcatLayer> ConcatLayer::create(const LayerParams& params)
 557 {
 558     return Ptr<ConcatLayer>(new ConcatLayerImpl(params));
 559 }
 560
 561 }
 562 }