modules/dnn/src/onnx/onnx_importer.cpp

   1 // This file is part of OpenCV project.
   2 // It is subject to the license terms in the LICENSE file found in the top-level directory
   3 // of this distribution and at http://opencv.org/license.html.
   4
   5 // Copyright (C) 2018, Intel Corporation, all rights reserved.
   6 // Third party copyrights are property of their respective owners.
   7
   8 #include "../precomp.hpp"
   9 #include <opencv2/dnn/shape_utils.hpp>
  10
  11 #ifdef HAVE_PROTOBUF
  12
  13 #include <iostream>
  14 #include <fstream>
  15 #include <string>
  16 #include <limits>
  17 #include <algorithm>
  18
  19
  20 #if defined(__GNUC__) && __GNUC__ >= 5
  21 #pragma GCC diagnostic push
  22 #pragma GCC diagnostic ignored "-Wsuggest-override"
  23 #endif
  24 #include "opencv-onnx.pb.h"
  25 #if defined(__GNUC__) && __GNUC__ >= 5
  26 #pragma GCC diagnostic pop
  27 #endif
  28
  29 namespace cv {
  30 namespace dnn {
  31 CV__DNN_EXPERIMENTAL_NS_BEGIN
  32
  33
  34 class ONNXImporter
  35 {
  36     opencv_onnx::ModelProto model_proto;
  37     struct LayerInfo {
  38         int layerId;
  39         int outputId;
  40         LayerInfo(int _layerId, int _outputId) : layerId(_layerId), outputId(_outputId) {}
  41     };
  42
  43     std::map<std::string, Mat> getGraphTensors(
  44                                     const opencv_onnx::GraphProto& graph_proto);
  45     Mat getBlob(const opencv_onnx::NodeProto& node_proto, const std::map<std::string, Mat>& constBlobs, int index);
  46
  47     LayerParams getLayerParams(const opencv_onnx::NodeProto& node_proto);
  48     bool isCeilMode(const LayerParams& layerParams);
  49
  50 public:
  51
  52     ONNXImporter(const char *onnxFile)
  53     {
  54         std::fstream input(onnxFile, std::ios::in | std::ios::binary);
  55
  56         if (!model_proto.ParseFromIstream(&input))
  57             CV_Error(Error::StsUnsupportedFormat, "Failed to parse onnx model");
  58     }
  59
  60     ONNXImporter(const char* buffer, size_t sizeBuffer)
  61     {
  62         struct _Buf : public std::streambuf
  63         {
  64             _Buf(const char* buffer, size_t sizeBuffer)
  65             {
  66                 char* p = const_cast<char*>(buffer);
  67                 setg(p, p, p + sizeBuffer);
  68             }
  69         };
  70
  71         _Buf buf(buffer, sizeBuffer);
  72         std::istream input(&buf);
  73
  74         if (!model_proto.ParseFromIstream(&input))
  75             CV_Error(Error::StsUnsupportedFormat, "Failed to parse onnx model from in-memory byte array.");
  76     }
  77
  78     void populateNet(Net dstNet);
  79 };
  80
  81 inline void replaceLayerParam(LayerParams& layerParams, const String& oldKey, const String& newKey)
  82 {
  83     if (layerParams.has(oldKey)) {
  84         layerParams.set(newKey, layerParams.get(oldKey));
  85         layerParams.erase(oldKey);
  86     }
  87 }
  88
  89 void releaseONNXTensor(opencv_onnx::TensorProto& tensor_proto)
  90 {
  91     if (!tensor_proto.raw_data().empty()) {
  92         delete tensor_proto.release_raw_data();
  93     }
  94 }
  95
  96 template<typename T1, typename T2>
  97 void convertInt64ToInt32(const T1& src, T2& dst, int size)
  98 {
  99     for (int i = 0; i < size; i++) {
 100         if (src[i] < std::numeric_limits<int32_t>::min() || src[i] > std::numeric_limits<int32_t>::max()) {
 101             CV_Error(Error::StsOutOfRange, "Input is out of OpenCV 32S range");
 102         }
 103         dst[i] = saturate_cast<int32_t>(src[i]);
 104     }
 105 }
 106
 107 Mat getMatFromTensor(opencv_onnx::TensorProto& tensor_proto)
 108 {
 109     CV_Assert(!tensor_proto.raw_data().empty() || !tensor_proto.float_data().empty()
 110                     || !tensor_proto.double_data().empty() || !tensor_proto.int64_data().empty());
 111
 112     opencv_onnx::TensorProto_DataType datatype = tensor_proto.data_type();
 113     Mat blob;
 114     std::vector<int> sizes;
 115     for (int i = 0; i < tensor_proto.dims_size(); i++) {
 116             sizes.push_back(tensor_proto.dims(i));
 117     }
 118     if (sizes.empty())
 119         sizes.assign(1, 1);
 120     if (datatype == opencv_onnx::TensorProto_DataType_FLOAT) {
 121
 122         if (!tensor_proto.float_data().empty()) {
 123             const ::google::protobuf::RepeatedField<float> field = tensor_proto.float_data();
 124             Mat(sizes, CV_32FC1, (void*)field.data()).copyTo(blob);
 125         }
 126         else {
 127             char* val = const_cast<char*>(tensor_proto.raw_data().c_str());
 128             Mat(sizes, CV_32FC1, val).copyTo(blob);
 129         }
 130     }
 131     else if (datatype == opencv_onnx::TensorProto_DataType_DOUBLE)
 132     {
 133         const ::google::protobuf::RepeatedField<double> field = tensor_proto.double_data();
 134         CV_Assert(!field.empty());
 135         Mat(sizes, CV_64FC1, (void*)field.data()).convertTo(blob, CV_32FC1);
 136     }
 137     else if (datatype == opencv_onnx::TensorProto_DataType_INT64)
 138     {
 139         blob.create(sizes, CV_32SC1);
 140         int32_t* dst = reinterpret_cast<int32_t*>(blob.data);
 141
 142         if (!tensor_proto.int64_data().empty()) {
 143             ::google::protobuf::RepeatedField< ::google::protobuf::int64> src = tensor_proto.int64_data();
 144             convertInt64ToInt32(src, dst, blob.total());
 145         }
 146         else
 147         {
 148             char* val = const_cast<char*>(tensor_proto.raw_data().c_str());
 149             int64_t* src = reinterpret_cast<int64_t*>(val);
 150             convertInt64ToInt32(src, dst, blob.total());
 151         }
 152     }
 153     else
 154         CV_Error(Error::StsUnsupportedFormat, "Unsupported data type: " +
 155                         opencv_onnx::TensorProto_DataType_Name(datatype));
 156     if (tensor_proto.dims_size() == 0)
 157         blob.dims = 1;  // To force 1-dimensional cv::Mat for scalars.
 158     return blob;
 159 }
 160
 161 void runLayer(LayerParams& params, const std::vector<Mat>& inputs,
 162               std::vector<Mat>& outputs)
 163 {
 164     Ptr<Layer> layer = LayerFactory::createLayerInstance(params.type, params);
 165     CV_Assert((bool)layer);
 166
 167     std::vector<MatShape> inpShapes(inputs.size());
 168     int ddepth = CV_32F;
 169     for (size_t i = 0; i < inputs.size(); ++i)
 170     {
 171         inpShapes[i] = shape(inputs[i]);
 172         if (i > 0 && ddepth != inputs[i].depth())
 173             CV_Error(Error::StsNotImplemented, "Mixed input data types.");
 174         ddepth = inputs[i].depth();
 175     }
 176
 177     std::vector<MatShape> outShapes, internalShapes;
 178     layer->getMemoryShapes(inpShapes, 0, outShapes, internalShapes);
 179
 180     std::vector<Mat> internals(internalShapes.size());
 181     outputs.resize(outShapes.size());
 182     for (size_t i = 0; i < outShapes.size(); ++i)
 183         outputs[i].create(outShapes[i], ddepth);
 184     for (size_t i = 0; i < internalShapes.size(); ++i)
 185         internals[i].create(internalShapes[i], ddepth);
 186
 187     layer->finalize(inputs, outputs);
 188     layer->forward(inputs, outputs, internals);
 189 }
 190
 191 std::map<std::string, Mat> ONNXImporter::getGraphTensors(
 192                                         const opencv_onnx::GraphProto& graph_proto)
 193 {
 194   opencv_onnx::TensorProto tensor_proto;
 195   std::map<std::string, Mat> layers_weights;
 196
 197   for (int i = 0; i < graph_proto.initializer_size(); i++)
 198   {
 199     tensor_proto = graph_proto.initializer(i);
 200     Mat mat = getMatFromTensor(tensor_proto);
 201     releaseONNXTensor(tensor_proto);
 202     layers_weights.insert(std::make_pair(tensor_proto.name(), mat));
 203   }
 204   return layers_weights;
 205 }
 206
 207 static DictValue parse(const ::google::protobuf::RepeatedField< ::google::protobuf::int64>& src) {
 208     std::vector<int32_t> dst(src.size());
 209     convertInt64ToInt32(src, dst, src.size());
 210     return DictValue::arrayInt(&dst[0], src.size());
 211 }
 212
 213 LayerParams ONNXImporter::getLayerParams(const opencv_onnx::NodeProto& node_proto)
 214 {
 215     LayerParams lp;
 216     for(int i = 0; i < node_proto.attribute_size(); i++)
 217     {
 218         opencv_onnx::AttributeProto attribute_proto = node_proto.attribute(i);
 219         std::string attribute_name = attribute_proto.name();
 220
 221         if(attribute_name == "kernel_shape")
 222         {
 223             CV_Assert(attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 3);
 224             lp.set("kernel_size", parse(attribute_proto.ints()));
 225         }
 226         else if(attribute_name == "strides")
 227         {
 228             CV_Assert(attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 3);
 229             lp.set("stride", parse(attribute_proto.ints()));
 230         }
 231         else if(attribute_name == "pads")
 232         {
 233             if (node_proto.op_type() == "Pad")
 234             {
 235                 // Padding layer.
 236                 // Paddings are in order begin0, begin1, .. beginN, end0, end1, ..., endN.
 237                 // We need to shuffle it to begin0, end0, begin1, end1, ...
 238                 CV_Assert(attribute_proto.ints_size() % 2 == 0);
 239                 const int dims = attribute_proto.ints_size() / 2;
 240                 std::vector<int32_t> paddings;
 241                 paddings.reserve(attribute_proto.ints_size());
 242                 for (int i = 0; i < dims; ++i)
 243                 {
 244                     paddings.push_back(attribute_proto.ints(i));
 245                     paddings.push_back(attribute_proto.ints(dims + i));
 246                 }
 247                 lp.set("paddings", DictValue::arrayInt(&paddings[0], paddings.size()));
 248             }
 249             else
 250             {
 251                 // Convolution or pooling.
 252                 CV_Assert(attribute_proto.ints_size() == 4 || attribute_proto.ints_size() == 6);
 253                 lp.set("pad", parse(attribute_proto.ints()));
 254             }
 255         }
 256         else if(attribute_name == "auto_pad")
 257         {
 258             if (attribute_proto.s() == "SAME_UPPER" || attribute_proto.s() == "SAME_LOWER") {
 259                 lp.set("pad_mode",  "SAME");
 260             }
 261             else if (attribute_proto.s() == "VALID") {
 262                 lp.set("pad_mode", "VALID");
 263             }
 264         }
 265         else if(attribute_name == "dilations")
 266         {
 267             CV_Assert(attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 3);
 268             lp.set("dilation", parse(attribute_proto.ints()));
 269         }
 270         else if (attribute_proto.has_i())
 271         {
 272             ::google::protobuf::int64 src = attribute_proto.i();
 273             if (src < std::numeric_limits<int32_t>::min() || src > std::numeric_limits<int32_t>::max())
 274                 CV_Error(Error::StsOutOfRange, "Input is out of OpenCV 32S range");
 275             else
 276                 lp.set(attribute_name, saturate_cast<int32_t>(src));
 277         }
 278         else if (attribute_proto.has_f())
 279         {
 280             lp.set(attribute_name, attribute_proto.f());
 281         }
 282         else if (attribute_proto.has_s())
 283         {
 284             lp.set(attribute_name, attribute_proto.s());
 285         }
 286         else if (attribute_proto.floats_size() > 0)
 287         {
 288             lp.set(attribute_name, DictValue::arrayReal(
 289                 attribute_proto.floats().data(), attribute_proto.floats_size()));
 290         }
 291         else if (attribute_proto.ints_size() > 0)
 292         {
 293             lp.set(attribute_proto.name(), parse(attribute_proto.ints()));
 294         }
 295         else if (attribute_proto.has_t())
 296         {
 297             opencv_onnx::TensorProto tensor = attribute_proto.t();
 298             Mat blob = getMatFromTensor(tensor);
 299             lp.blobs.push_back(blob);
 300         }
 301         else if (attribute_proto.has_g() || attribute_proto.strings_size() > 0 ||
 302                     attribute_proto.tensors_size() > 0 || attribute_proto.graphs_size() > 0)
 303         {
 304                 CV_Error(Error::StsNotImplemented, "Unexpected attribute type");
 305         }
 306         else
 307             CV_Error(Error::StsNotImplemented, "Unsupported attribute type");
 308     }
 309     return lp;
 310 }
 311
 312 Mat ONNXImporter::getBlob(const opencv_onnx::NodeProto& node_proto,
 313                     const std::map<std::string, Mat>& constBlobs, int index)
 314 {
 315     CV_Assert(index < node_proto.input_size());
 316     std::map<std::string, Mat>::const_iterator constBlob;
 317     constBlob = constBlobs.find(node_proto.input(index));
 318     if (constBlob == constBlobs.end()) {
 319         CV_Error(Error::StsObjectNotFound,
 320              "Blob " + node_proto.input(index) + " not found in const blobs");
 321     }
 322     return constBlob->second;
 323 }
 324
 325 void ONNXImporter::populateNet(Net dstNet)
 326 {
 327     CV_Assert(model_proto.has_graph());
 328     opencv_onnx::GraphProto graph_proto = model_proto.graph();
 329     std::map<std::string, Mat> constBlobs = getGraphTensors(graph_proto);
 330     // List of internal blobs shapes.
 331     std::map<std::string, MatShape> outShapes;
 332     // Add all the inputs shapes. It includes as constant blobs as network's inputs shapes.
 333     for (int i = 0; i < graph_proto.input_size(); ++i)
 334     {
 335         opencv_onnx::ValueInfoProto valueInfoProto = graph_proto.input(i);
 336         CV_Assert(valueInfoProto.has_type());
 337         opencv_onnx::TypeProto typeProto = valueInfoProto.type();
 338         CV_Assert(typeProto.has_tensor_type());
 339         opencv_onnx::TypeProto::Tensor tensor = typeProto.tensor_type();
 340         CV_Assert(tensor.has_shape());
 341         opencv_onnx::TensorShapeProto tensorShape = tensor.shape();
 342
 343         MatShape inpShape(tensorShape.dim_size());
 344         for (int j = 0; j < inpShape.size(); ++j)
 345         {
 346             inpShape[j] = tensorShape.dim(j).dim_value();
 347         }
 348         outShapes[valueInfoProto.name()] = inpShape;
 349     }
 350
 351     std::string framework_name;
 352     if (model_proto.has_producer_name()) {
 353         framework_name = model_proto.producer_name();
 354     }
 355
 356     // create map with network inputs (without const blobs)
 357     std::map<std::string, LayerInfo> layer_id;
 358     std::map<std::string, LayerInfo>::iterator layerId;
 359     std::map<std::string, MatShape>::iterator shapeIt;
 360     // fill map: push layer name, layer id and output id
 361     std::vector<String> netInputs;
 362     for (int j = 0; j < graph_proto.input_size(); j++)
 363     {
 364         const std::string& name = graph_proto.input(j).name();
 365         if (constBlobs.find(name) == constBlobs.end()) {
 366             netInputs.push_back(name);
 367             layer_id.insert(std::make_pair(name, LayerInfo(0, netInputs.size() - 1)));
 368         }
 369     }
 370     dstNet.setInputsNames(netInputs);
 371
 372     int layersSize = graph_proto.node_size();
 373     LayerParams layerParams;
 374     opencv_onnx::NodeProto node_proto;
 375
 376     for(int li = 0; li < layersSize; li++)
 377     {
 378         node_proto = graph_proto.node(li);
 379         layerParams = getLayerParams(node_proto);
 380         CV_Assert(node_proto.output_size() >= 1);
 381         layerParams.name = node_proto.output(0);
 382
 383         std::string layer_type = node_proto.op_type();
 384         layerParams.type = layer_type;
 385
 386
 387         if (layer_type == "MaxPool")
 388         {
 389             layerParams.type = "Pooling";
 390             layerParams.set("pool", "MAX");
 391             layerParams.set("ceil_mode", layerParams.has("pad_mode"));
 392         }
 393         else if (layer_type == "AveragePool")
 394         {
 395             layerParams.type = "Pooling";
 396             layerParams.set("pool", "AVE");
 397             layerParams.set("ceil_mode", layerParams.has("pad_mode"));
 398             layerParams.set("ave_pool_padded_area", framework_name == "pytorch");
 399         }
 400         else if (layer_type == "GlobalAveragePool" || layer_type == "GlobalMaxPool" || layer_type == "ReduceMean")
 401         {
 402             CV_Assert(node_proto.input_size() == 1);
 403             layerParams.type = "Pooling";
 404             layerParams.set("pool", layer_type == "GlobalMaxPool"? "MAX" : "AVE");
 405             layerParams.set("global_pooling", layer_type == "GlobalAveragePool" || layer_type == "GlobalMaxPool");
 406
 407             if (layer_type == "ReduceMean")
 408             {
 409                 if (layerParams.get<int>("keepdims") == 0 || !layerParams.has("axes"))
 410                     CV_Error(Error::StsNotImplemented, "Unsupported mode of ReduceMean operation.");
 411
 412                 MatShape inpShape = outShapes[node_proto.input(0)];
 413                 if (inpShape.size() != 4 && inpShape.size() != 5)
 414                     CV_Error(Error::StsNotImplemented, "Unsupported input shape of reduce_mean operation.");
 415
 416                 DictValue axes = layerParams.get("axes");
 417                 CV_Assert(axes.size() <= inpShape.size() - 2);
 418                 std::vector<int> kernel_size(inpShape.size() - 2, 1);
 419                 for (int i = 0; i < axes.size(); i++) {
 420                     int axis = axes.get<int>(i);
 421                     CV_Assert_N(axis >= 2 + i, axis < inpShape.size());
 422                     kernel_size[axis - 2] = inpShape[axis];
 423                 }
 424
 425                 layerParams.set("kernel_size", DictValue::arrayInt(&kernel_size[0], kernel_size.size()));
 426             }
 427         }
 428         else if (layer_type == "Slice")
 429         {
 430             if (layerParams.has("steps")) {
 431                 DictValue steps = layerParams.get("steps");
 432                 for (int i = 0; i < steps.size(); ++i) {
 433                     if (steps.get<int>(i) != 1)
 434                         CV_Error(Error::StsNotImplemented,
 435                                  "Slice layer only supports steps = 1");
 436                 }
 437             }
 438
 439             int axis = 0;
 440             if (layerParams.has("axes")) {
 441                 DictValue axes = layerParams.get("axes");
 442                 for (int i = 1; i < axes.size(); ++i) {
 443                     CV_Assert(axes.get<int>(i - 1) == axes.get<int>(i) - 1);
 444                 }
 445                 axis = axes.get<int>(0);
 446             }
 447             layerParams.set("axis", axis);
 448
 449             DictValue starts = layerParams.get("starts");
 450             DictValue ends = layerParams.get("ends");
 451             CV_Assert(starts.size() == ends.size());
 452
 453             std::vector<int> begin;
 454             std::vector<int> end;
 455             if (axis > 0) {
 456                 begin.resize(axis, 0);
 457                 end.resize(axis, -1);
 458             }
 459
 460             for (int i = 0; i < starts.size(); ++i)
 461             {
 462                 begin.push_back(starts.get<int>(i));
 463                 int finish = ends.get<int>(i);
 464                 end.push_back((finish < 0) ? --finish : finish); // numpy doesn't include last dim
 465             }
 466             layerParams.set("begin", DictValue::arrayInt(&begin[0], begin.size()));
 467             layerParams.set("end", DictValue::arrayInt(&end[0], end.size()));
 468          }
 469         else if (layer_type == "Split")
 470         {
 471             DictValue splits = layerParams.get("split");
 472             const int numSplits = splits.size();
 473             CV_Assert(numSplits > 1);
 474
 475             std::vector<int> slicePoints(numSplits - 1, splits.get<int>(0));
 476             for (int i = 1; i < splits.size() - 1; ++i)
 477             {
 478                 slicePoints[i] = slicePoints[i - 1] + splits.get<int>(i - 1);
 479             }
 480             layerParams.set("slice_point", DictValue::arrayInt(&slicePoints[0], slicePoints.size()));
 481             layerParams.type = "Slice";
 482         }
 483         else if (layer_type == "Add" || layer_type == "Sum")
 484         {
 485             if (layer_id.find(node_proto.input(1)) == layer_id.end())
 486             {
 487                 Mat blob = getBlob(node_proto, constBlobs, 1);
 488                 blob = blob.reshape(1, 1);
 489                 if (blob.total() == 1) {
 490                     layerParams.type = "Power";
 491                     layerParams.set("shift", blob.at<float>(0));
 492                 }
 493                 else {
 494                     layerParams.type = "Scale";
 495                     layerParams.set("bias_term", true);
 496                     layerParams.blobs.push_back(blob);
 497                 }
 498             }
 499             else {
 500                 layerParams.type = "Eltwise";
 501             }
 502         }
 503         else if (layer_type == "Max")
 504         {
 505             layerParams.type = "Eltwise";
 506             layerParams.set("operation", "max");
 507         }
 508         else if (layer_type == "Sub")
 509         {
 510             Mat blob = getBlob(node_proto, constBlobs, 1);
 511             if (blob.total() == 1) {
 512                 layerParams.type = "Power";
 513                 layerParams.set("shift", -blob.at<float>(0));
 514             }
 515             else {
 516                 layerParams.type = "Scale";
 517                 layerParams.set("has_bias", true);
 518                 layerParams.blobs.push_back(-1.0f * blob.reshape(1, 1));
 519             }
 520         }
 521         else if (layer_type == "Div")
 522         {
 523             Mat blob = getBlob(node_proto, constBlobs, 1);
 524             CV_Assert_N(blob.type() == CV_32F, blob.total());
 525             if (blob.total() == 1)
 526             {
 527                 layerParams.set("scale", 1.0f / blob.at<float>(0));
 528                 layerParams.type = "Power";
 529             }
 530             else
 531             {
 532                 layerParams.type = "Scale";
 533                 divide(1.0, blob, blob);
 534                 layerParams.blobs.push_back(blob);
 535                 layerParams.set("bias_term", false);
 536             }
 537         }
 538         else if (layer_type == "Neg")
 539         {
 540             layerParams.type = "Power";
 541             layerParams.set("scale", -1);
 542         }
 543         else if (layer_type == "Constant")
 544         {
 545             CV_Assert(node_proto.input_size() == 0);
 546             CV_Assert(layerParams.blobs.size() == 1);
 547             constBlobs.insert(std::make_pair(layerParams.name, layerParams.blobs[0]));
 548             continue;
 549         }
 550         else if (layer_type == "ImageScaler")
 551         {
 552             const float scale = layerParams.has("scale") ? layerParams.get<float>("scale") : 1.0f;
 553             layerParams.erase("scale");
 554
 555             if (layerParams.has("bias"))
 556             {
 557                 layerParams.type = "Scale";
 558                 layerParams.blobs.push_back(
 559                     Mat(Size(1,  layerParams.get("bias").size()), CV_32FC1, scale));
 560
 561                 layerParams.set("bias_term", true);
 562                 Mat bias(1, layerParams.get("bias").size(), CV_32FC1);
 563                 for (int j = 0; j < bias.total(); j++) {
 564                     bias.at<float>(0, j) = layerParams.get("bias").getRealValue(j);
 565                 }
 566                 layerParams.blobs.push_back(bias);
 567                 layerParams.erase("bias");
 568             }
 569             else {
 570                 layerParams.set("scale", scale);
 571                 layerParams.type = "Power";
 572             }
 573         }
 574         else if (layer_type == "Clip")
 575         {
 576             layerParams.type = "ReLU6";
 577             replaceLayerParam(layerParams, "min", "min_value");
 578             replaceLayerParam(layerParams, "max", "max_value");
 579
 580         }
 581         else if (layer_type == "LeakyRelu")
 582         {
 583             layerParams.type = "ReLU";
 584             replaceLayerParam(layerParams, "alpha", "negative_slope");
 585         }
 586         else if (layer_type == "LRN")
 587         {
 588             replaceLayerParam(layerParams, "size", "local_size");
 589         }
 590         else if (layer_type == "InstanceNormalization")
 591         {
 592             if (node_proto.input_size() != 3)
 593                 CV_Error(Error::StsNotImplemented,
 594                          "Expected input, scale, bias");
 595
 596             layerParams.blobs.resize(4);
 597             layerParams.blobs[2] = getBlob(node_proto, constBlobs, 1);  // weightData
 598             layerParams.blobs[3] = getBlob(node_proto, constBlobs, 2);  // biasData
 599             layerParams.set("has_bias", true);
 600             layerParams.set("has_weight", true);
 601
 602             // Get number of channels in input
 603             int size = layerParams.blobs[2].total();
 604             layerParams.blobs[0] = Mat::zeros(size, 1, CV_32F); // mean
 605             layerParams.blobs[1] = Mat::ones(size, 1, CV_32F); // std
 606
 607             LayerParams mvnParams;
 608             mvnParams.name = layerParams.name + "/MVN";
 609             mvnParams.type = "MVN";
 610             mvnParams.set("eps", layerParams.get<float>("epsilon"));
 611             layerParams.erase("epsilon");
 612
 613             //Create MVN layer
 614             int id = dstNet.addLayer(mvnParams.name, mvnParams.type, mvnParams);
 615             //Connect to input
 616             layerId = layer_id.find(node_proto.input(0));
 617             CV_Assert(layerId != layer_id.end());
 618             dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
 619             //Add shape
 620             layer_id.insert(std::make_pair(mvnParams.name, LayerInfo(id, 0)));
 621             outShapes[mvnParams.name] = outShapes[node_proto.input(0)];
 622
 623             //Replace Batch Norm's input to MVN
 624             node_proto.set_input(0, mvnParams.name);
 625             layerParams.type = "BatchNorm";
 626         }
 627         else if (layer_type == "BatchNormalization")
 628         {
 629             if (node_proto.input_size() != 5)
 630                 CV_Error(Error::StsNotImplemented,
 631                          "Expected input, scale, bias, mean and var");
 632
 633             layerParams.type = "BatchNorm";
 634             replaceLayerParam(layerParams, "epsilon", "eps");
 635             replaceLayerParam(layerParams, "spatial", "use_global_stats");
 636
 637             Mat meanData = getBlob(node_proto, constBlobs, 3);
 638             Mat stdData =  getBlob(node_proto, constBlobs, 4);
 639
 640             layerParams.blobs.push_back(meanData);
 641             layerParams.blobs.push_back(stdData);
 642
 643             if (!node_proto.input(1).empty()) {
 644                 layerParams.set("has_weight", true);
 645                 layerParams.blobs.push_back(getBlob(node_proto, constBlobs, 1));  // weightData
 646             } else {
 647                 layerParams.set("has_weight", false);
 648             }
 649
 650             if (!node_proto.input(2).empty()) {
 651                 layerParams.set("has_bias", true);
 652                 layerParams.blobs.push_back(getBlob(node_proto, constBlobs, 2)); // biasData
 653             } else {
 654                 layerParams.set("has_bias", false);
 655             }
 656         }
 657         else if (layer_type == "Gemm")
 658         {
 659             CV_Assert(node_proto.input_size() >= 2);
 660             layerParams.type = "InnerProduct";
 661             Mat weights = getBlob(node_proto, constBlobs, 1);
 662             int ind_num_out = 0;
 663             if (layerParams.has("transB") && !layerParams.get<int>("transB")) {
 664                 transpose(weights, weights);
 665                 ind_num_out = 1;
 666             }
 667             layerParams.blobs.push_back(weights);
 668
 669             if (node_proto.input_size() == 3) {
 670                 Mat bias = getBlob(node_proto, constBlobs, 2);
 671                 layerParams.blobs.push_back(bias);
 672             }
 673
 674             layerParams.set("num_output", layerParams.blobs[0].size[ind_num_out]);
 675             layerParams.set("bias_term", node_proto.input_size() == 3);
 676         }
 677         else if (layer_type == "MatMul")
 678         {
 679             CV_Assert(node_proto.input_size() == 2);
 680             layerParams.type = "InnerProduct";
 681             Mat blob = getBlob(node_proto, constBlobs, 1);
 682             layerParams.blobs.push_back(blob.t());
 683             layerParams.set("bias_term", false);
 684             layerParams.set("num_output", layerParams.blobs[0].size[0]);
 685         }
 686         else if (layer_type == "Mul")
 687         {
 688             CV_Assert(node_proto.input_size() == 2);
 689             if (layer_id.find(node_proto.input(1)) == layer_id.end()) {
 690                 Mat blob = getBlob(node_proto, constBlobs, 1);
 691                 blob = blob.reshape(1, 1);
 692                 if (blob.total() == 1) {
 693                     layerParams.set("scale", blob.at<float>(0));
 694                     layerParams.type = "Power";
 695                 }
 696                 else {
 697                     layerParams.blobs.push_back(blob);
 698                     layerParams.type = "Scale";
 699                 }
 700             }
 701             else {
 702                 layerParams.type = "Eltwise";
 703                 layerParams.set("operation", "prod");
 704             }
 705         }
 706         else if (layer_type == "Conv")
 707         {
 708             CV_Assert(node_proto.input_size() >= 2);
 709             layerParams.type = "Convolution";
 710             for (int j = 1; j < node_proto.input_size(); j++) {
 711                 layerParams.blobs.push_back(getBlob(node_proto, constBlobs, j));
 712             }
 713             layerParams.set("num_output", layerParams.blobs[0].size[0]);
 714             layerParams.set("bias_term", node_proto.input_size() == 3);
 715         }
 716         else if (layer_type == "ConvTranspose")
 717         {
 718             CV_Assert(node_proto.input_size() >= 2);
 719             layerParams.type = "Deconvolution";
 720             for (int j = 1; j < node_proto.input_size(); j++) {
 721                 layerParams.blobs.push_back(getBlob(node_proto, constBlobs, j));
 722             }
 723             layerParams.set("num_output", layerParams.blobs[0].size[1] * layerParams.get<int>("group", 1));
 724             layerParams.set("bias_term", node_proto.input_size() == 3);
 725
 726             if (!layerParams.has("kernel_size"))
 727                 CV_Error(Error::StsNotImplemented,
 728                          "Required attribute 'kernel_size' is not present.");
 729
 730             if (layerParams.has("output_shape"))
 731             {
 732                 const DictValue& outShape = layerParams.get("output_shape");
 733                 DictValue strides = layerParams.get("stride");
 734                 DictValue kernel = layerParams.get("kernel_size");
 735
 736                 String padMode;
 737                 std::vector<int> adjust_pads;
 738                 if (layerParams.has("pad_mode"))
 739                 {
 740                     padMode = toUpperCase(layerParams.get<String>("pad_mode"));
 741                     if (padMode != "SAME" && padMode != "VALID")
 742                         CV_Error(Error::StsError, "Unsupported padding mode " + padMode);
 743
 744                     for (int i = 0; i < strides.size(); i++)
 745                     {
 746                         int sz = outShape.get<int>(2 + i);
 747                         int stride = strides.get<int>(i);
 748                         adjust_pads.push_back(padMode == "SAME"? (sz - 1) % stride :
 749                                                                  (sz - kernel.get<int>(i)) % stride);
 750                     }
 751                     layerParams.set("adj", DictValue::arrayInt(&adjust_pads[0], adjust_pads.size()));
 752                 }
 753             }
 754             else if (layerParams.has("output_padding"))
 755             {
 756                 replaceLayerParam(layerParams, "output_padding", "adj");
 757             }
 758         }
 759         else if (layer_type == "Transpose")
 760         {
 761             layerParams.type = "Permute";
 762             replaceLayerParam(layerParams, "perm", "order");
 763
 764             CV_Assert(node_proto.input_size() == 1);
 765             if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
 766             {
 767                 std::vector<Mat> inputs(1, getBlob(node_proto, constBlobs, 0)), transposed;
 768                 runLayer(layerParams, inputs, transposed);
 769                 CV_Assert(transposed.size() == 1);
 770                 constBlobs.insert(std::make_pair(layerParams.name, transposed[0]));
 771                 continue;
 772             }
 773         }
 774         else if (layer_type == "Unsqueeze")
 775         {
 776             CV_Assert(node_proto.input_size() == 1);
 777             DictValue axes = layerParams.get("axes");
 778             if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
 779             {
 780                 // Constant input.
 781                 Mat input = getBlob(node_proto, constBlobs, 0);
 782
 783                 std::vector<int> dims;
 784                 for (int j = 0; j < input.dims; j++) {
 785                     dims.push_back(input.size[j]);
 786                 }
 787                 CV_Assert(axes.getIntValue(axes.size()-1) <= dims.size());
 788                 for (int j = 0; j < axes.size(); j++) {
 789                     dims.insert(dims.begin() + axes.getIntValue(j), 1);
 790                 }
 791
 792                 Mat out = input.reshape(0, dims);
 793                 constBlobs.insert(std::make_pair(layerParams.name, out));
 794                 continue;
 795             }
 796
 797             // Variable input.
 798             if (axes.size() != 1)
 799                 CV_Error(Error::StsNotImplemented, "Multidimensional unsqueeze");
 800
 801             MatShape inpShape = outShapes[node_proto.input(0)];
 802             int axis = axes.getIntValue(0);
 803             CV_Assert(0 <= axis && axis <= inpShape.size());
 804             std::vector<int> outShape = inpShape;
 805             outShape.insert(outShape.begin() + axis, 1);
 806             layerParams.type = "Reshape";
 807             layerParams.set("dim", DictValue::arrayInt(&outShape[0], outShape.size()));
 808         }
 809         else if (layer_type == "Reshape")
 810         {
 811             CV_Assert(node_proto.input_size() == 2 || layerParams.has("shape"));
 812
 813             if (node_proto.input_size() == 2) {
 814                 Mat blob = getBlob(node_proto, constBlobs, 1);
 815                 CV_Assert(blob.type() == CV_32SC1);
 816
 817                 layerParams.set("dim", DictValue::arrayInt<int*>(
 818                             blob.ptr<int>(), blob.total() ));
 819
 820                 if (layer_id.find(node_proto.input(0)) == layer_id.end()) {
 821                     std::vector<Mat> inputs(1, getBlob(node_proto, constBlobs, 0)), outputs;
 822                     runLayer(layerParams, inputs, outputs);
 823                     constBlobs.insert(std::make_pair(layerParams.name, outputs[0]));
 824                     continue;
 825                 }
 826             }
 827             else {
 828                 DictValue shape = layerParams.get("shape");
 829                 std::vector<int> dim;
 830                 for (int j = 0; j < shape.size(); j++) {
 831                     dim.push_back(shape.getIntValue(j));
 832                 }
 833
 834                 if (layer_id.find(node_proto.input(0)) == layer_id.end()) {
 835                     Mat input = getBlob(node_proto, constBlobs, 0);
 836                     Mat out = input.reshape(0, dim);
 837                     constBlobs.insert(std::make_pair(layerParams.name, out));
 838                     continue;
 839                 }
 840                 replaceLayerParam(layerParams, "shape", "dim");
 841             }
 842         }
 843         else if (layer_type == "Pad")
 844         {
 845             layerParams.type = "Padding";
 846         }
 847         else if (layer_type == "Shape")
 848         {
 849             CV_Assert(node_proto.input_size() == 1);
 850             shapeIt = outShapes.find(node_proto.input(0));
 851             CV_Assert(shapeIt != outShapes.end());
 852             MatShape inpShape = shapeIt->second;
 853
 854             Mat shapeMat(inpShape.size(), 1, CV_32S);
 855             for (int j = 0; j < inpShape.size(); ++j)
 856                 shapeMat.at<int>(j) = inpShape[j];
 857             shapeMat.dims = 1;
 858
 859             constBlobs.insert(std::make_pair(layerParams.name, shapeMat));
 860             continue;
 861         }
 862         else if (layer_type == "Gather")
 863         {
 864             CV_Assert(node_proto.input_size() == 2);
 865             CV_Assert(layerParams.has("axis"));
 866             Mat input = getBlob(node_proto, constBlobs, 0);
 867             Mat indexMat = getBlob(node_proto, constBlobs, 1);
 868             CV_Assert_N(indexMat.type() == CV_32S, indexMat.total() == 1);
 869             int index = indexMat.at<int>(0);
 870             int axis = layerParams.get<int>("axis");
 871
 872             std::vector<cv::Range> ranges(input.dims, Range::all());
 873             ranges[axis] = Range(index, index + 1);
 874
 875             Mat out = input(ranges);
 876             constBlobs.insert(std::make_pair(layerParams.name, out));
 877             continue;
 878         }
 879         else if (layer_type == "Concat")
 880         {
 881             bool hasVariableInps = false;
 882             for (int i = 0; i < node_proto.input_size(); ++i)
 883             {
 884                 if (layer_id.find(node_proto.input(i)) != layer_id.end())
 885                 {
 886                     hasVariableInps = true;
 887                     break;
 888                 }
 889             }
 890
 891             if (!hasVariableInps)
 892             {
 893                 std::vector<Mat> inputs(node_proto.input_size()), concatenated;
 894                 for (size_t i = 0; i < inputs.size(); ++i)
 895                 {
 896                     inputs[i] = getBlob(node_proto, constBlobs, i);
 897                 }
 898                 runLayer(layerParams, inputs, concatenated);
 899
 900                 CV_Assert(concatenated.size() == 1);
 901                 constBlobs.insert(std::make_pair(layerParams.name, concatenated[0]));
 902                 continue;
 903             }
 904         }
 905         else if (layer_type == "Upsample")
 906         {
 907             layerParams.type = "Resize";
 908             if (layerParams.has("scales"))
 909             {
 910                 // Pytorch layer
 911                 DictValue scales = layerParams.get("scales");
 912                 CV_Assert(scales.size() == 4);
 913                 layerParams.set("zoom_factor_y", scales.getIntValue(2));
 914                 layerParams.set("zoom_factor_x", scales.getIntValue(3));
 915             }
 916             else
 917             {
 918                 // Caffe2 layer
 919                 replaceLayerParam(layerParams, "height_scale", "zoom_factor_y");
 920                 replaceLayerParam(layerParams, "width_scale", "zoom_factor_x");
 921             }
 922             replaceLayerParam(layerParams, "mode", "interpolation");
 923         }
 924         else if (layer_type == "LogSoftmax")
 925         {
 926             layerParams.type = "Softmax";
 927             layerParams.set("log_softmax", true);
 928         }
 929         else
 930         {
 931             for (int j = 0; j < node_proto.input_size(); j++) {
 932                 if (layer_id.find(node_proto.input(j)) == layer_id.end())
 933                     layerParams.blobs.push_back(getBlob(node_proto, constBlobs, j));
 934             }
 935         }
 936
 937         int id = dstNet.addLayer(layerParams.name, layerParams.type, layerParams);
 938         for (int i = 0; i < node_proto.output_size(); ++i)
 939         {
 940             layer_id.insert(std::make_pair(node_proto.output(i), LayerInfo(id, i)));
 941         }
 942
 943         std::vector<MatShape> layerInpShapes, layerOutShapes, layerInternalShapes;
 944         for (int j = 0; j < node_proto.input_size(); j++) {
 945             layerId = layer_id.find(node_proto.input(j));
 946             if (layerId != layer_id.end()) {
 947                 dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, j);
 948                 // Collect input shapes.
 949                 shapeIt = outShapes.find(node_proto.input(j));
 950                 CV_Assert(shapeIt != outShapes.end());
 951                 layerInpShapes.push_back(shapeIt->second);
 952             }
 953         }
 954
 955         // Compute shape of output blob for this layer.
 956         Ptr<Layer> layer = dstNet.getLayer(id);
 957         layer->getMemoryShapes(layerInpShapes, 0, layerOutShapes, layerInternalShapes);
 958         for (int i = 0; i < node_proto.output_size() && i < (int)layerOutShapes.size(); ++i)
 959         {
 960             outShapes[node_proto.output(i)] = layerOutShapes[i];
 961         }
 962     }
 963 }
 964
 965 Net readNetFromONNX(const String& onnxFile)
 966 {
 967     ONNXImporter onnxImporter(onnxFile.c_str());
 968     Net net;
 969     onnxImporter.populateNet(net);
 970     return net;
 971 }
 972
 973 Net readNetFromONNX(const char* buffer, size_t sizeBuffer)
 974 {
 975     ONNXImporter onnxImporter(buffer, sizeBuffer);
 976     Net net;
 977     onnxImporter.populateNet(net);
 978     return net;
 979 }
 980
 981 Net readNetFromONNX(const std::vector<uchar>& buffer)
 982 {
 983     return readNetFromONNX(reinterpret_cast<const char*>(buffer.data()), buffer.size());
 984 }
 985
 986 Mat readTensorFromONNX(const String& path)
 987 {
 988     opencv_onnx::TensorProto tensor_proto = opencv_onnx::TensorProto();
 989     std::fstream input(path.c_str(), std::ios::in | std::ios::binary);
 990     if (!tensor_proto.ParseFromIstream(&input)) {
 991         CV_Error(Error::StsUnsupportedFormat, "Failed to parse data");
 992     }
 993     Mat mat = getMatFromTensor(tensor_proto);
 994     releaseONNXTensor(tensor_proto);
 995     return mat;
 996 }
 997
 998 CV__DNN_EXPERIMENTAL_NS_END
 999 }} // namespace
1000
1001 #endif