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         (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
 162         throw SkipTestException("");
 163     processNet("dnn/Enet-model-best.net", "", Size(512, 512), "l367_Deconvolution",
 164                target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_enet.yml" :
 165                                              "dnn/halide_scheduler_enet.yml",
 166                2e-5, 0.15);
 167 }
 168
 169 TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
 170 {
 171     applyTestTag(CV_TEST_TAG_MEMORY_512MB);
 172     if (backend == DNN_BACKEND_HALIDE)
 173         throw SkipTestException("");
 174     Mat sample = imread(findDataFile("dnn/street.png"));
 175     Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
 176     float diffScores = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1.5e-2 : 0.0;
 177     float diffSquares = (target == DNN_TARGET_MYRIAD) ? 0.063  : 0.0;
 178     float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.252  : FLT_MIN;
 179          processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
 180                     inp, "detection_out", "", diffScores, diffSquares, detectionConfThresh);
 181     expectNoFallbacksFromIE(net);
 182 }
 183
 184 TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height)
 185 {
 186     if (backend == DNN_BACKEND_HALIDE)
 187         throw SkipTestException("");
 188 #if defined(INF_ENGINE_RELEASE)
 189     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 190             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 191         throw SkipTestException("Test is disabled for MyriadX");
 192 #endif
 193     Mat sample = imread(findDataFile("dnn/street.png"));
 194     Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 560), Scalar(127.5, 127.5, 127.5), false);
 195     float diffScores  = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.029 : 0.0;
 196     float diffSquares = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09  : 0.0;
 197     processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
 198                 inp, "detection_out", "", diffScores, diffSquares);
 199     expectNoFallbacksFromIE(net);
 200 }
 201
 202 TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
 203 {
 204     applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
 205     if (backend == DNN_BACKEND_HALIDE)
 206         throw SkipTestException("");
 207     Mat sample = imread(findDataFile("dnn/street.png"));
 208     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 209     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.095 : 0.0;
 210     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0;
 211     float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.216 : 0.2;
 212     processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
 213                inp, "detection_out", "", l1, lInf, detectionConfThresh);
 214     expectNoFallbacksFromIE(net);
 215 }
 216
 217 TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height)
 218 {
 219     if (backend == DNN_BACKEND_HALIDE)
 220         throw SkipTestException("");
 221 #if defined(INF_ENGINE_RELEASE)
 222     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 223             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 224         throw SkipTestException("Test is disabled for MyriadX");
 225 #endif
 226     Mat sample = imread(findDataFile("dnn/street.png"));
 227     Mat inp = blobFromImage(sample, 1.0f, Size(300, 560), Scalar(), false);
 228     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0;
 229     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.06 : 0.0;
 230     processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
 231                inp, "detection_out", "", l1, lInf);
 232     expectNoFallbacksFromIE(net);
 233 }
 234
 235 TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
 236 {
 237     applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
 238     if (backend == DNN_BACKEND_HALIDE)
 239         throw SkipTestException("");
 240     Mat sample = imread(findDataFile("dnn/street.png"));
 241     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 242     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.013 : 2e-5;
 243     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.062 : 0.0;
 244     processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt",
 245                inp, "detection_out", "", l1, lInf, 0.25);
 246     expectNoFallbacksFromIE(net);
 247 }
 248
 249 TEST_P(DNNTestNetwork, SSD_VGG16)
 250 {
 251     applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
 252                  CV_TEST_TAG_DEBUG_VERYLONG);
 253     if (backend == DNN_BACKEND_HALIDE && target == DNN_TARGET_CPU)
 254         throw SkipTestException("");
 255     double scoreThreshold = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0325 : 0.0;
 256     const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.032 : 0.0;
 257     Mat sample = imread(findDataFile("dnn/street.png"));
 258     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 259     processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
 260                "dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreThreshold, lInf);
 261     expectNoFallbacksFromIE(net);
 262 }
 263
 264 TEST_P(DNNTestNetwork, OpenPose_pose_coco)
 265 {
 266     applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
 267                  CV_TEST_TAG_DEBUG_VERYLONG);
 268     if (backend == DNN_BACKEND_HALIDE)
 269         throw SkipTestException("");
 270 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
 271     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 272             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 273         throw SkipTestException("Test is disabled for OpenVINO <= 2018R5 + MyriadX target");
 274 #endif
 275
 276     const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0056 : 0.0;
 277     const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.072 : 0.0;
 278     processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
 279                Size(46, 46), "", "", l1, lInf);
 280     expectNoFallbacksFromIE(net);
 281 }
 282
 283 TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
 284 {
 285     applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
 286                  CV_TEST_TAG_DEBUG_VERYLONG);
 287     if (backend == DNN_BACKEND_HALIDE)
 288         throw SkipTestException("");
 289 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
 290     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 291             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 292         throw SkipTestException("Test is disabled for OpenVINO <= 2018R5 + MyriadX target");
 293 #endif
 294
 295     // output range: [-0.001, 0.97]
 296     const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0;
 297     const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.16 : 0.0;
 298     processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
 299                Size(46, 46), "", "", l1, lInf);
 300     expectNoFallbacksFromIE(net);
 301 }
 302
 303 TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
 304 {
 305     applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_MEMORY_1GB);
 306     if (backend == DNN_BACKEND_HALIDE)
 307         throw SkipTestException("");
 308 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
 309     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 310             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 311         throw SkipTestException("Test is disabled for OpenVINO <= 2018R5 + MyriadX target");
 312 #endif
 313
 314     // The same .caffemodel but modified .prototxt
 315     // See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
 316     processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt",
 317                Size(46, 46));
 318     expectNoFallbacksFromIE(net);
 319 }
 320
 321 TEST_P(DNNTestNetwork, OpenFace)
 322 {
 323 #if defined(INF_ENGINE_RELEASE)
 324 #if INF_ENGINE_VER_MAJOR_EQ(2018050000)
 325     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
 326         throw SkipTestException("Test is disabled for Myriad targets");
 327 #endif
 328 #endif
 329     if (backend == DNN_BACKEND_HALIDE)
 330         throw SkipTestException("");
 331     const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0024 : 0.0;
 332     const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.0071 : 0.0;
 333     processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "", "", l1, lInf);
 334 }
 335
 336 TEST_P(DNNTestNetwork, opencv_face_detector)
 337 {
 338     if (backend == DNN_BACKEND_HALIDE)
 339         throw SkipTestException("");
 340     Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
 341     Mat inp = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
 342     processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt",
 343                inp, "detection_out");
 344     expectNoFallbacksFromIE(net);
 345 }
 346
 347 TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
 348 {
 349     applyTestTag(
 350         (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
 351         CV_TEST_TAG_DEBUG_LONG
 352     );
 353 #if defined(INF_ENGINE_RELEASE)
 354     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 355             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 356         throw SkipTestException("Test is disabled for MyriadX");
 357 #endif
 358     if (backend == DNN_BACKEND_HALIDE)
 359         throw SkipTestException("");
 360     Mat sample = imread(findDataFile("dnn/street.png"));
 361     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 362     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.015 : 0.0;
 363     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0731 : 0.0;
 364     processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt",
 365                inp, "detection_out", "", l1, lInf);
 366     expectNoFallbacksFromIE(net);
 367 }
 368
 369 TEST_P(DNNTestNetwork, DenseNet_121)
 370 {
 371     applyTestTag(CV_TEST_TAG_MEMORY_512MB);
 372     if (backend == DNN_BACKEND_HALIDE)
 373         throw SkipTestException("");
 374     // Reference output values are in range [-3.807, 4.605]
 375     float l1 = 0.0, lInf = 0.0;
 376     if (target == DNN_TARGET_OPENCL_FP16)
 377     {
 378         l1 = 9e-3; lInf = 5e-2;
 379     }
 380     else if (target == DNN_TARGET_MYRIAD)
 381     {
 382         l1 = 0.1; lInf = 0.6;
 383     }
 384     processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "", l1, lInf);
 385     expectNoFallbacksFromIE(net);
 386 }
 387
 388 TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
 389 {
 390     applyTestTag(CV_TEST_TAG_MEMORY_512MB, CV_TEST_TAG_DEBUG_VERYLONG);
 391
 392     if (backend == DNN_BACKEND_HALIDE ||
 393         (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
 394         throw SkipTestException("");
 395
 396 #if defined(INF_ENGINE_RELEASE)
 397 #if INF_ENGINE_RELEASE <= 2018050000
 398     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
 399         throw SkipTestException("");
 400 #endif
 401 #endif
 402
 403     Mat img = imread(findDataFile("dnn/googlenet_1.png"));
 404     Mat inp = blobFromImage(img, 1.0, Size(320, 240), Scalar(103.939, 116.779, 123.68), false, false);
 405     // Output image has values in range [-143.526, 148.539].
 406     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.4 : 4e-5;
 407     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 7.45 : 2e-3;
 408     processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf);
 409 #if defined(HAVE_INF_ENGINE) && INF_ENGINE_RELEASE >= 2019010000
 410     expectNoFallbacksFromIE(net);
 411 #endif
 412 }
 413
 414 INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets(true, true, false));
 415
 416 }} // namespace