modules/dnn/test/test_backends.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) 2018-2019, Intel Corporation, all rights reserved.
   6 // Third party copyrights are property of their respective owners.
   7
   8 #include "test_precomp.hpp"
   9 #include "opencv2/core/ocl.hpp"
  10
  11 namespace opencv_test { namespace {
  12
  13 class DNNTestNetwork : public DNNTestLayer
  14 {
  15 public:
  16     void processNet(const std::string& weights, const std::string& proto,
  17                     Size inpSize, const std::string& outputLayer = "",
  18                     const std::string& halideScheduler = "",
  19                     double l1 = 0.0, double lInf = 0.0)
  20     {
  21         // Create a common input blob.
  22         int blobSize[] = {1, 3, inpSize.height, inpSize.width};
  23         Mat inp(4, blobSize, CV_32FC1);
  24         randu(inp, 0.0f, 1.0f);
  25
  26         processNet(weights, proto, inp, outputLayer, halideScheduler, l1, lInf);
  27     }
  28
  29     void processNet(std::string weights, std::string proto,
  30                     Mat inp, const std::string& outputLayer = "",
  31                     std::string halideScheduler = "",
  32                     double l1 = 0.0, double lInf = 0.0, double detectionConfThresh = 0.2)
  33     {
  34         checkBackend();
  35         l1 = l1 ? l1 : default_l1;
  36         lInf = lInf ? lInf : default_lInf;
  37
  38         weights = findDataFile(weights, false);
  39         if (!proto.empty())
  40             proto = findDataFile(proto);
  41
  42         // Create two networks - with default backend and target and a tested one.
  43         Net netDefault = readNet(weights, proto);
  44         netDefault.setPreferableBackend(DNN_BACKEND_OPENCV);
  45         netDefault.setInput(inp);
  46         Mat outDefault = netDefault.forward(outputLayer).clone();
  47
  48         net = readNet(weights, proto);
  49         net.setInput(inp);
  50         net.setPreferableBackend(backend);
  51         net.setPreferableTarget(target);
  52         if (backend == DNN_BACKEND_HALIDE && !halideScheduler.empty())
  53         {
  54             halideScheduler = findDataFile(halideScheduler);
  55             net.setHalideScheduler(halideScheduler);
  56         }
  57         Mat out = net.forward(outputLayer).clone();
  58
  59         check(outDefault, out, outputLayer, l1, lInf, detectionConfThresh, "First run");
  60
  61         // Test 2: change input.
  62         float* inpData = (float*)inp.data;
  63         for (int i = 0; i < inp.size[0] * inp.size[1]; ++i)
  64         {
  65             Mat slice(inp.size[2], inp.size[3], CV_32F, inpData);
  66             cv::flip(slice, slice, 1);
  67             inpData += slice.total();
  68         }
  69         netDefault.setInput(inp);
  70         net.setInput(inp);
  71         outDefault = netDefault.forward(outputLayer).clone();
  72         out = net.forward(outputLayer).clone();
  73         check(outDefault, out, outputLayer, l1, lInf, detectionConfThresh, "Second run");
  74     }
  75
  76     void check(Mat& ref, Mat& out, const std::string& outputLayer, double l1, double lInf,
  77                double detectionConfThresh, const char* msg)
  78     {
  79         if (outputLayer == "detection_out")
  80         {
  81             if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
  82             {
  83                 // Inference Engine produces detections terminated by a row which starts from -1.
  84                 out = out.reshape(1, out.total() / 7);
  85                 int numDetections = 0;
  86                 while (numDetections < out.rows && out.at<float>(numDetections, 0) != -1)
  87                 {
  88                     numDetections += 1;
  89                 }
  90                 out = out.rowRange(0, numDetections);
  91             }
  92             normAssertDetections(ref, out, msg, detectionConfThresh, l1, lInf);
  93         }
  94         else
  95             normAssert(ref, out, msg, l1, lInf);
  96     }
  97
  98     Net net;
  99 };
 100
 101 TEST_P(DNNTestNetwork, AlexNet)
 102 {
 103     applyTestTag(CV_TEST_TAG_MEMORY_1GB);
 104     processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
 105                Size(227, 227), "prob",
 106                target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_alexnet.yml" :
 107                                              "dnn/halide_scheduler_alexnet.yml");
 108     expectNoFallbacksFromIE(net);
 109     expectNoFallbacksFromCUDA(net);
 110 }
 111
 112 TEST_P(DNNTestNetwork, ResNet_50)
 113 {
 114     applyTestTag(
 115         (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
 116         CV_TEST_TAG_DEBUG_LONG
 117     );
 118     processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
 119                Size(224, 224), "prob",
 120                target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_resnet_50.yml" :
 121                                              "dnn/halide_scheduler_resnet_50.yml");
 122     expectNoFallbacksFromIE(net);
 123     expectNoFallbacksFromCUDA(net);
 124 }
 125
 126 TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
 127 {
 128     processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
 129                Size(227, 227), "prob",
 130                target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_squeezenet_v1_1.yml" :
 131                                              "dnn/halide_scheduler_squeezenet_v1_1.yml");
 132     expectNoFallbacksFromIE(net);
 133     expectNoFallbacksFromCUDA(net);
 134 }
 135
 136 TEST_P(DNNTestNetwork, GoogLeNet)
 137 {
 138     applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
 139     processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
 140                Size(224, 224), "prob");
 141     expectNoFallbacksFromIE(net);
 142     expectNoFallbacksFromCUDA(net);
 143 }
 144
 145 TEST_P(DNNTestNetwork, Inception_5h)
 146 {
 147     applyTestTag(CV_TEST_TAG_MEMORY_512MB);
 148     double l1 = default_l1, lInf = default_lInf;
 149     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_CPU || target == DNN_TARGET_OPENCL))
 150     {
 151         l1 = 1.72e-5;
 152         lInf = 8e-4;
 153     }
 154     processNet("dnn/tensorflow_inception_graph.pb", "", Size(224, 224), "softmax2",
 155                target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_inception_5h.yml" :
 156                                              "dnn/halide_scheduler_inception_5h.yml",
 157                l1, lInf);
 158     expectNoFallbacksFromIE(net);
 159     expectNoFallbacksFromCUDA(net);
 160 }
 161
 162 TEST_P(DNNTestNetwork, ENet)
 163 {
 164     applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
 165     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
 166         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
 167     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
 168         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
 169     if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
 170         applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
 171     if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16)
 172         applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
 173     processNet("dnn/Enet-model-best.net", "", Size(512, 512), "l367_Deconvolution",
 174                target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_enet.yml" :
 175                                              "dnn/halide_scheduler_enet.yml",
 176                2e-5, 0.15);
 177     expectNoFallbacksFromCUDA(net);
 178 }
 179
 180 TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
 181 {
 182     applyTestTag(CV_TEST_TAG_MEMORY_512MB);
 183     if (backend == DNN_BACKEND_HALIDE)
 184         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 185     Mat sample = imread(findDataFile("dnn/street.png"));
 186     Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
 187     float scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1.5e-2 : 0.0;
 188     float iouDiff = (target == DNN_TARGET_MYRIAD) ? 0.063  : 0.0;
 189     float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.252  : FLT_MIN;
 190          processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
 191                     inp, "detection_out", "", scoreDiff, iouDiff, detectionConfThresh);
 192     expectNoFallbacksFromIE(net);
 193 }
 194
 195 TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height)
 196 {
 197     if (backend == DNN_BACKEND_HALIDE)
 198         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 199 #if defined(INF_ENGINE_RELEASE)
 200     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
 201             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 202         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
 203 #endif
 204     Mat sample = imread(findDataFile("dnn/street.png"));
 205     Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 560), Scalar(127.5, 127.5, 127.5), false);
 206     float scoreDiff = 0.0, iouDiff = 0.0;
 207     if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
 208     {
 209         scoreDiff = 0.029;
 210         iouDiff = 0.09;
 211     }
 212     else if (target == DNN_TARGET_CUDA_FP16)
 213     {
 214         scoreDiff = 0.03;
 215         iouDiff = 0.08;
 216     }
 217     processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
 218                 inp, "detection_out", "", scoreDiff, iouDiff);
 219     expectNoFallbacksFromIE(net);
 220 }
 221
 222 TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
 223 {
 224     applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
 225     if (backend == DNN_BACKEND_HALIDE)
 226         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 227
 228     Mat sample = imread(findDataFile("dnn/street.png"));
 229     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 230     float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.216 : 0.2;
 231     float scoreDiff = 0.0, iouDiff = 0.0;
 232     if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
 233     {
 234         scoreDiff = 0.095;
 235         iouDiff = 0.09;
 236     }
 237     else if (target == DNN_TARGET_CUDA_FP16)
 238     {
 239         scoreDiff = 0.007;
 240         iouDiff = 0.08;
 241     }
 242     processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
 243                inp, "detection_out", "", scoreDiff, iouDiff, detectionConfThresh);
 244     expectNoFallbacksFromIE(net);
 245 }
 246
 247 TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height)
 248 {
 249     if (backend == DNN_BACKEND_HALIDE)
 250         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 251 #if defined(INF_ENGINE_RELEASE)
 252     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
 253             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 254         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
 255 #endif
 256 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
 257     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
 258         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
 259 #endif
 260
 261     Mat sample = imread(findDataFile("dnn/street.png"));
 262     Mat inp = blobFromImage(sample, 1.0f, Size(300, 560), Scalar(), false);
 263     float scoreDiff = 0.0, iouDiff = 0.0;
 264     if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
 265     {
 266         scoreDiff = 0.012;
 267         iouDiff = 0.06;
 268     }
 269     else if (target == DNN_TARGET_CUDA_FP16)
 270     {
 271         scoreDiff = 0.007;
 272         iouDiff = 0.06;
 273     }
 274     processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
 275                inp, "detection_out", "", scoreDiff, iouDiff);
 276     expectNoFallbacksFromIE(net);
 277 }
 278
 279 TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
 280 {
 281     applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
 282     if (backend == DNN_BACKEND_HALIDE)
 283         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 284
 285     Mat sample = imread(findDataFile("dnn/street.png"));
 286     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 287     float scoreDiff = 2e-5, iouDiff = 0.0;
 288     if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
 289     {
 290         scoreDiff = 0.013;
 291         iouDiff = 0.062;
 292     }
 293     else if (target == DNN_TARGET_CUDA_FP16)
 294     {
 295         scoreDiff = 0.02;
 296         iouDiff = 0.07;
 297     }
 298     processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt",
 299                inp, "detection_out", "", scoreDiff, iouDiff, 0.25);
 300     expectNoFallbacksFromIE(net);
 301 }
 302
 303 TEST_P(DNNTestNetwork, SSD_VGG16)
 304 {
 305     applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
 306                  CV_TEST_TAG_DEBUG_VERYLONG);
 307     if (backend == DNN_BACKEND_HALIDE && target == DNN_TARGET_CPU)
 308         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);  // TODO HALIDE_CPU
 309     Mat sample = imread(findDataFile("dnn/street.png"));
 310     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 311     float scoreDiff = 0.0, iouDiff = 0.0;
 312     if (target == DNN_TARGET_OPENCL_FP16)
 313     {
 314         scoreDiff = 0.0325;
 315     }
 316     else if (target == DNN_TARGET_MYRIAD)
 317     {
 318         scoreDiff = 0.0325;
 319         iouDiff = 0.032;
 320     }
 321     else if (target == DNN_TARGET_CUDA_FP16)
 322     {
 323         scoreDiff = 0.03;
 324     }
 325
 326     processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
 327                "dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreDiff, iouDiff);
 328     expectNoFallbacksFromIE(net);
 329 }
 330
 331 TEST_P(DNNTestNetwork, OpenPose_pose_coco)
 332 {
 333     applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
 334                  CV_TEST_TAG_DEBUG_LONG);
 335     if (backend == DNN_BACKEND_HALIDE)
 336         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 337 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
 338     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
 339             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 340         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
 341 #endif
 342
 343     const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0056 : 0.0;
 344     const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.072 : 0.0;
 345     processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
 346                Size(46, 46), "", "", l1, lInf);
 347     expectNoFallbacksFromIE(net);
 348     expectNoFallbacksFromCUDA(net);
 349 }
 350
 351 TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
 352 {
 353     applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
 354                  CV_TEST_TAG_DEBUG_VERYLONG);
 355     if (backend == DNN_BACKEND_HALIDE)
 356         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 357 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
 358     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
 359             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 360         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
 361 #endif
 362
 363     // output range: [-0.001, 0.97]
 364     const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0;
 365     const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.16 : 0.0;
 366     processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
 367                Size(46, 46), "", "", l1, lInf);
 368     expectNoFallbacksFromIE(net);
 369     expectNoFallbacksFromCUDA(net);
 370 }
 371
 372 TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
 373 {
 374     applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_MEMORY_1GB);
 375     if (backend == DNN_BACKEND_HALIDE)
 376         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 377 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
 378     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
 379             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 380         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
 381 #endif
 382
 383     // The same .caffemodel but modified .prototxt
 384     // See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
 385     processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt",
 386                Size(46, 46));
 387     expectNoFallbacksFromIE(net);
 388     expectNoFallbacksFromCUDA(net);
 389 }
 390
 391 TEST_P(DNNTestNetwork, OpenFace)
 392 {
 393 #if defined(INF_ENGINE_RELEASE)
 394 #if INF_ENGINE_VER_MAJOR_EQ(2018050000)
 395     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
 396         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
 397 #endif
 398 #endif
 399     if (backend == DNN_BACKEND_HALIDE)
 400         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 401     const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0024 : 0.0;
 402     const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.0071 : 0.0;
 403     processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "", "", l1, lInf);
 404
 405     expectNoFallbacksFromCUDA(net);
 406 }
 407
 408 TEST_P(DNNTestNetwork, opencv_face_detector)
 409 {
 410     if (backend == DNN_BACKEND_HALIDE)
 411         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 412     Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
 413     Mat inp = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
 414     processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt",
 415                inp, "detection_out");
 416     expectNoFallbacksFromIE(net);
 417 }
 418
 419 TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
 420 {
 421     applyTestTag(
 422         (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
 423         CV_TEST_TAG_DEBUG_LONG
 424     );
 425 #if defined(INF_ENGINE_RELEASE)
 426     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
 427             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 428         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
 429 #endif
 430 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
 431     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
 432         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
 433 #endif
 434     if (backend == DNN_BACKEND_HALIDE)
 435         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 436     Mat sample = imread(findDataFile("dnn/street.png"));
 437     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 438     float scoreDiff = 0.0, iouDiff = 0.0;
 439     if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
 440     {
 441         scoreDiff = 0.015;
 442         iouDiff = 0.0731;
 443     }
 444     else if (target == DNN_TARGET_CUDA_FP16)
 445     {
 446         scoreDiff = 0.015;
 447         iouDiff = 0.08;
 448     }
 449     processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt",
 450                inp, "detection_out", "", scoreDiff, iouDiff);
 451     expectNoFallbacksFromIE(net);
 452 }
 453
 454 TEST_P(DNNTestNetwork, DenseNet_121)
 455 {
 456     applyTestTag(CV_TEST_TAG_MEMORY_512MB);
 457     if (backend == DNN_BACKEND_HALIDE)
 458         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 459     // Reference output values are in range [-3.807, 4.605]
 460     float l1 = 0.0, lInf = 0.0;
 461     if (target == DNN_TARGET_OPENCL_FP16)
 462     {
 463         l1 = 2e-2;
 464         lInf = 9e-2;
 465     }
 466     else if (target == DNN_TARGET_MYRIAD)
 467     {
 468         l1 = 0.1;
 469         lInf = 0.6;
 470     }
 471     else if (target == DNN_TARGET_CUDA_FP16)
 472     {
 473         l1 = 0.008;
 474         lInf = 0.05;
 475     }
 476     processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "", l1, lInf);
 477     if (target != DNN_TARGET_MYRIAD || getInferenceEngineVPUType() != CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 478         expectNoFallbacksFromIE(net);
 479     expectNoFallbacksFromCUDA(net);
 480 }
 481
 482 TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
 483 {
 484     applyTestTag(CV_TEST_TAG_MEMORY_512MB, CV_TEST_TAG_DEBUG_VERYLONG);
 485
 486     if (backend == DNN_BACKEND_HALIDE)
 487         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 488     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
 489         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
 490
 491 #if defined(INF_ENGINE_RELEASE)
 492 #if INF_ENGINE_VER_MAJOR_LE(2018050000)
 493     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL)
 494         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
 495 #endif
 496 #endif
 497
 498     Mat img = imread(findDataFile("dnn/googlenet_1.png"));
 499     Mat inp = blobFromImage(img, 1.0, Size(320, 240), Scalar(103.939, 116.779, 123.68), false, false);
 500     // Output image has values in range [-143.526, 148.539].
 501     float l1 = 4e-5, lInf = 2e-3;
 502     if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
 503     {
 504         l1 = 0.4;
 505         lInf = 7.45;
 506     }
 507     else if (target == DNN_TARGET_CUDA_FP16)
 508     {
 509         l1 = 0.3;
 510         lInf = 7.2;
 511     }
 512     processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf);
 513 #if defined(HAVE_INF_ENGINE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
 514     expectNoFallbacksFromIE(net);
 515 #endif
 516     expectNoFallbacksFromCUDA(net);
 517 }
 518
 519 INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets(true, true, false, true, true));
 520
 521 }} // namespace