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)
  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 }
 110
 111 TEST_P(DNNTestNetwork, ResNet_50)
 112 {
 113     applyTestTag(
 114         (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
 115         CV_TEST_TAG_DEBUG_LONG
 116     );
 117     processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
 118                Size(224, 224), "prob",
 119                target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_resnet_50.yml" :
 120                                              "dnn/halide_scheduler_resnet_50.yml");
 121     expectNoFallbacksFromIE(net);
 122 }
 123
 124 TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
 125 {
 126     processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
 127                Size(227, 227), "prob",
 128                target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_squeezenet_v1_1.yml" :
 129                                              "dnn/halide_scheduler_squeezenet_v1_1.yml");
 130     expectNoFallbacksFromIE(net);
 131 }
 132
 133 TEST_P(DNNTestNetwork, GoogLeNet)
 134 {
 135     applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
 136     processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
 137                Size(224, 224), "prob");
 138     expectNoFallbacksFromIE(net);
 139 }
 140
 141 TEST_P(DNNTestNetwork, Inception_5h)
 142 {
 143     applyTestTag(CV_TEST_TAG_MEMORY_512MB);
 144     double l1 = default_l1, lInf = default_lInf;
 145     if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_CPU || target == DNN_TARGET_OPENCL))
 146     {
 147         l1 = 1.72e-5;
 148         lInf = 8e-4;
 149     }
 150     processNet("dnn/tensorflow_inception_graph.pb", "", Size(224, 224), "softmax2",
 151                target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_inception_5h.yml" :
 152                                              "dnn/halide_scheduler_inception_5h.yml",
 153                l1, lInf);
 154     expectNoFallbacksFromIE(net);
 155 }
 156
 157 TEST_P(DNNTestNetwork, ENet)
 158 {
 159     applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
 160     if (backend == DNN_BACKEND_INFERENCE_ENGINE)
 161         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
 162     if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
 163         applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
 164     processNet("dnn/Enet-model-best.net", "", Size(512, 512), "l367_Deconvolution",
 165                target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_enet.yml" :
 166                                              "dnn/halide_scheduler_enet.yml",
 167                2e-5, 0.15);
 168 }
 169
 170 TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
 171 {
 172     applyTestTag(CV_TEST_TAG_MEMORY_512MB);
 173     if (backend == DNN_BACKEND_HALIDE)
 174         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 175     Mat sample = imread(findDataFile("dnn/street.png"));
 176     Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
 177     float diffScores = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1.5e-2 : 0.0;
 178     float diffSquares = (target == DNN_TARGET_MYRIAD) ? 0.063  : 0.0;
 179     float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.252  : FLT_MIN;
 180          processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
 181                     inp, "detection_out", "", diffScores, diffSquares, detectionConfThresh);
 182     expectNoFallbacksFromIE(net);
 183 }
 184
 185 TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height)
 186 {
 187     if (backend == DNN_BACKEND_HALIDE)
 188         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 189 #if defined(INF_ENGINE_RELEASE)
 190     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 191             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 192         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
 193 #endif
 194     Mat sample = imread(findDataFile("dnn/street.png"));
 195     Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 560), Scalar(127.5, 127.5, 127.5), false);
 196     float diffScores  = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.029 : 0.0;
 197     float diffSquares = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09  : 0.0;
 198     processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
 199                 inp, "detection_out", "", diffScores, diffSquares);
 200     expectNoFallbacksFromIE(net);
 201 }
 202
 203 TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
 204 {
 205     applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
 206     if (backend == DNN_BACKEND_HALIDE)
 207         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 208
 209     Mat sample = imread(findDataFile("dnn/street.png"));
 210     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 211     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.095 : 0.0;
 212     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0;
 213     float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.216 : 0.2;
 214     processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
 215                inp, "detection_out", "", l1, lInf, detectionConfThresh);
 216     expectNoFallbacksFromIE(net);
 217 }
 218
 219 TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height)
 220 {
 221     if (backend == DNN_BACKEND_HALIDE)
 222         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 223 #if defined(INF_ENGINE_RELEASE)
 224     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 225             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 226         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
 227 #endif
 228 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
 229     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
 230         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
 231 #endif
 232
 233     Mat sample = imread(findDataFile("dnn/street.png"));
 234     Mat inp = blobFromImage(sample, 1.0f, Size(300, 560), Scalar(), false);
 235     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0;
 236     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.06 : 0.0;
 237     processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
 238                inp, "detection_out", "", l1, lInf);
 239     expectNoFallbacksFromIE(net);
 240 }
 241
 242 TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
 243 {
 244     applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
 245     if (backend == DNN_BACKEND_HALIDE)
 246         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 247
 248     Mat sample = imread(findDataFile("dnn/street.png"));
 249     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 250     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.013 : 2e-5;
 251     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.062 : 0.0;
 252     processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt",
 253                inp, "detection_out", "", l1, lInf, 0.25);
 254     expectNoFallbacksFromIE(net);
 255 }
 256
 257 TEST_P(DNNTestNetwork, SSD_VGG16)
 258 {
 259     applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
 260                  CV_TEST_TAG_DEBUG_VERYLONG);
 261     if (backend == DNN_BACKEND_HALIDE && target == DNN_TARGET_CPU)
 262         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);  // TODO HALIDE_CPU
 263     double scoreThreshold = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0325 : 0.0;
 264     const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.032 : 0.0;
 265     Mat sample = imread(findDataFile("dnn/street.png"));
 266     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 267     processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
 268                "dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreThreshold, lInf);
 269     expectNoFallbacksFromIE(net);
 270 }
 271
 272 TEST_P(DNNTestNetwork, OpenPose_pose_coco)
 273 {
 274     applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
 275                  CV_TEST_TAG_DEBUG_LONG);
 276     if (backend == DNN_BACKEND_HALIDE)
 277         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 278 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
 279     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 280             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 281         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
 282 #endif
 283
 284     const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0056 : 0.0;
 285     const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.072 : 0.0;
 286     processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
 287                Size(46, 46), "", "", l1, lInf);
 288     expectNoFallbacksFromIE(net);
 289 }
 290
 291 TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
 292 {
 293     applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
 294                  CV_TEST_TAG_DEBUG_VERYLONG);
 295     if (backend == DNN_BACKEND_HALIDE)
 296         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 297 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
 298     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 299             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 300         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
 301 #endif
 302
 303     // output range: [-0.001, 0.97]
 304     const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0;
 305     const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.16 : 0.0;
 306     processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
 307                Size(46, 46), "", "", l1, lInf);
 308     expectNoFallbacksFromIE(net);
 309 }
 310
 311 TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
 312 {
 313     applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_MEMORY_1GB);
 314     if (backend == DNN_BACKEND_HALIDE)
 315         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 316 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
 317     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 318             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 319         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
 320 #endif
 321
 322     // The same .caffemodel but modified .prototxt
 323     // See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
 324     processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt",
 325                Size(46, 46));
 326     expectNoFallbacksFromIE(net);
 327 }
 328
 329 TEST_P(DNNTestNetwork, OpenFace)
 330 {
 331 #if defined(INF_ENGINE_RELEASE)
 332 #if INF_ENGINE_VER_MAJOR_EQ(2018050000)
 333     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
 334         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
 335 #endif
 336 #endif
 337     if (backend == DNN_BACKEND_HALIDE)
 338         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 339     const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0024 : 0.0;
 340     const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.0071 : 0.0;
 341     processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "", "", l1, lInf);
 342 }
 343
 344 TEST_P(DNNTestNetwork, opencv_face_detector)
 345 {
 346     if (backend == DNN_BACKEND_HALIDE)
 347         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 348     Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
 349     Mat inp = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
 350     processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt",
 351                inp, "detection_out");
 352     expectNoFallbacksFromIE(net);
 353 }
 354
 355 TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
 356 {
 357     applyTestTag(
 358         (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
 359         CV_TEST_TAG_DEBUG_LONG
 360     );
 361 #if defined(INF_ENGINE_RELEASE)
 362     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 363             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 364         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
 365 #endif
 366 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
 367     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
 368         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
 369 #endif
 370     if (backend == DNN_BACKEND_HALIDE)
 371         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 372     Mat sample = imread(findDataFile("dnn/street.png"));
 373     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 374     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.015 : 0.0;
 375     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0731 : 0.0;
 376     processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt",
 377                inp, "detection_out", "", l1, lInf);
 378     expectNoFallbacksFromIE(net);
 379 }
 380
 381 TEST_P(DNNTestNetwork, DenseNet_121)
 382 {
 383     applyTestTag(CV_TEST_TAG_MEMORY_512MB);
 384     if (backend == DNN_BACKEND_HALIDE)
 385         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 386     // Reference output values are in range [-3.807, 4.605]
 387     float l1 = 0.0, lInf = 0.0;
 388     if (target == DNN_TARGET_OPENCL_FP16)
 389     {
 390         l1 = 2e-2; lInf = 9e-2;
 391     }
 392     else if (target == DNN_TARGET_MYRIAD)
 393     {
 394         l1 = 0.1; lInf = 0.6;
 395     }
 396     processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "", l1, lInf);
 397     if (target != DNN_TARGET_MYRIAD || getInferenceEngineVPUType() != CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 398         expectNoFallbacksFromIE(net);
 399 }
 400
 401 TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
 402 {
 403     applyTestTag(CV_TEST_TAG_MEMORY_512MB, CV_TEST_TAG_DEBUG_VERYLONG);
 404
 405     if (backend == DNN_BACKEND_HALIDE)
 406         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 407     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
 408         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
 409
 410 #if defined(INF_ENGINE_RELEASE)
 411 #if INF_ENGINE_VER_MAJOR_LE(2018050000)
 412     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
 413         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
 414 #endif
 415 #endif
 416
 417     Mat img = imread(findDataFile("dnn/googlenet_1.png"));
 418     Mat inp = blobFromImage(img, 1.0, Size(320, 240), Scalar(103.939, 116.779, 123.68), false, false);
 419     // Output image has values in range [-143.526, 148.539].
 420     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.4 : 4e-5;
 421     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 7.45 : 2e-3;
 422     processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf);
 423 #if defined(HAVE_INF_ENGINE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
 424     expectNoFallbacksFromIE(net);
 425 #endif
 426 }
 427
 428 INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets(true, true, false, true));
 429
 430 }} // namespace