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     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 && (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)
 166         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
 167     if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
 168         applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
 169     processNet("dnn/Enet-model-best.net", "", Size(512, 512), "l367_Deconvolution",
 170                target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_enet.yml" :
 171                                              "dnn/halide_scheduler_enet.yml",
 172                2e-5, 0.15);
 173     expectNoFallbacksFromCUDA(net);
 174 }
 175
 176 TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
 177 {
 178     applyTestTag(CV_TEST_TAG_MEMORY_512MB);
 179     if (backend == DNN_BACKEND_HALIDE)
 180         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 181     Mat sample = imread(findDataFile("dnn/street.png"));
 182     Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
 183     float diffScores = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1.5e-2 : 0.0;
 184     float diffSquares = (target == DNN_TARGET_MYRIAD) ? 0.063  : 0.0;
 185     float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.252  : FLT_MIN;
 186          processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
 187                     inp, "detection_out", "", diffScores, diffSquares, detectionConfThresh);
 188     expectNoFallbacksFromIE(net);
 189 }
 190
 191 TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height)
 192 {
 193     if (backend == DNN_BACKEND_HALIDE)
 194         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 195 #if defined(INF_ENGINE_RELEASE)
 196     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 197             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 198         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
 199 #endif
 200     Mat sample = imread(findDataFile("dnn/street.png"));
 201     Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 560), Scalar(127.5, 127.5, 127.5), false);
 202     float diffScores  = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.029 : 0.0;
 203     float diffSquares = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09  : 0.0;
 204     processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
 205                 inp, "detection_out", "", diffScores, diffSquares);
 206     expectNoFallbacksFromIE(net);
 207 }
 208
 209 TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
 210 {
 211     applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
 212     if (backend == DNN_BACKEND_HALIDE)
 213         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 214
 215     Mat sample = imread(findDataFile("dnn/street.png"));
 216     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 217     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.095 : 0.0;
 218     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0;
 219     float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.216 : 0.2;
 220     processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
 221                inp, "detection_out", "", l1, lInf, detectionConfThresh);
 222     expectNoFallbacksFromIE(net);
 223 }
 224
 225 TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height)
 226 {
 227     if (backend == DNN_BACKEND_HALIDE)
 228         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 229 #if defined(INF_ENGINE_RELEASE)
 230     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 231             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 232         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
 233 #endif
 234 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
 235     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
 236         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
 237 #endif
 238
 239     Mat sample = imread(findDataFile("dnn/street.png"));
 240     Mat inp = blobFromImage(sample, 1.0f, Size(300, 560), Scalar(), false);
 241     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0;
 242     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.06 : 0.0;
 243     processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
 244                inp, "detection_out", "", l1, lInf);
 245     expectNoFallbacksFromIE(net);
 246 }
 247
 248 TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
 249 {
 250     applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
 251     if (backend == DNN_BACKEND_HALIDE)
 252         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 253
 254     Mat sample = imread(findDataFile("dnn/street.png"));
 255     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 256     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.013 : 2e-5;
 257     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.062 : 0.0;
 258     processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt",
 259                inp, "detection_out", "", l1, lInf, 0.25);
 260     expectNoFallbacksFromIE(net);
 261 }
 262
 263 TEST_P(DNNTestNetwork, SSD_VGG16)
 264 {
 265     applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
 266                  CV_TEST_TAG_DEBUG_VERYLONG);
 267     if (backend == DNN_BACKEND_HALIDE && target == DNN_TARGET_CPU)
 268         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);  // TODO HALIDE_CPU
 269     double scoreThreshold = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0325 : 0.0;
 270     const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.032 : 0.0;
 271     Mat sample = imread(findDataFile("dnn/street.png"));
 272     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 273     processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
 274                "dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreThreshold, lInf);
 275     expectNoFallbacksFromIE(net);
 276 }
 277
 278 TEST_P(DNNTestNetwork, OpenPose_pose_coco)
 279 {
 280     applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
 281                  CV_TEST_TAG_DEBUG_LONG);
 282     if (backend == DNN_BACKEND_HALIDE)
 283         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 284 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
 285     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 286             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 287         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
 288 #endif
 289
 290     const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0056 : 0.0;
 291     const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.072 : 0.0;
 292     processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
 293                Size(46, 46), "", "", l1, lInf);
 294     expectNoFallbacksFromIE(net);
 295     expectNoFallbacksFromCUDA(net);
 296 }
 297
 298 TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
 299 {
 300     applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
 301                  CV_TEST_TAG_DEBUG_VERYLONG);
 302     if (backend == DNN_BACKEND_HALIDE)
 303         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 304 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
 305     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 306             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 307         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
 308 #endif
 309
 310     // output range: [-0.001, 0.97]
 311     const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0;
 312     const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.16 : 0.0;
 313     processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
 314                Size(46, 46), "", "", l1, lInf);
 315     expectNoFallbacksFromIE(net);
 316     expectNoFallbacksFromCUDA(net);
 317 }
 318
 319 TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
 320 {
 321     applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_MEMORY_1GB);
 322     if (backend == DNN_BACKEND_HALIDE)
 323         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 324 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
 325     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 326             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 327         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
 328 #endif
 329
 330     // The same .caffemodel but modified .prototxt
 331     // See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
 332     processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt",
 333                Size(46, 46));
 334     expectNoFallbacksFromIE(net);
 335     expectNoFallbacksFromCUDA(net);
 336 }
 337
 338 TEST_P(DNNTestNetwork, OpenFace)
 339 {
 340 #if defined(INF_ENGINE_RELEASE)
 341 #if INF_ENGINE_VER_MAJOR_EQ(2018050000)
 342     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
 343         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
 344 #endif
 345 #endif
 346     if (backend == DNN_BACKEND_HALIDE)
 347         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 348     const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0024 : 0.0;
 349     const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.0071 : 0.0;
 350     processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "", "", l1, lInf);
 351
 352     expectNoFallbacksFromCUDA(net);
 353 }
 354
 355 TEST_P(DNNTestNetwork, opencv_face_detector)
 356 {
 357     if (backend == DNN_BACKEND_HALIDE)
 358         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 359     Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
 360     Mat inp = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
 361     processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt",
 362                inp, "detection_out");
 363     expectNoFallbacksFromIE(net);
 364 }
 365
 366 TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
 367 {
 368     applyTestTag(
 369         (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
 370         CV_TEST_TAG_DEBUG_LONG
 371     );
 372 #if defined(INF_ENGINE_RELEASE)
 373     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 374             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 375         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
 376 #endif
 377 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
 378     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
 379         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
 380 #endif
 381     if (backend == DNN_BACKEND_HALIDE)
 382         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 383     Mat sample = imread(findDataFile("dnn/street.png"));
 384     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
 385     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.015 : 0.0;
 386     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0731 : 0.0;
 387     processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt",
 388                inp, "detection_out", "", l1, lInf);
 389     expectNoFallbacksFromIE(net);
 390 }
 391
 392 TEST_P(DNNTestNetwork, DenseNet_121)
 393 {
 394     applyTestTag(CV_TEST_TAG_MEMORY_512MB);
 395     if (backend == DNN_BACKEND_HALIDE)
 396         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 397     // Reference output values are in range [-3.807, 4.605]
 398     float l1 = 0.0, lInf = 0.0;
 399     if (target == DNN_TARGET_OPENCL_FP16)
 400     {
 401         l1 = 2e-2; lInf = 9e-2;
 402     }
 403     else if (target == DNN_TARGET_MYRIAD)
 404     {
 405         l1 = 0.1; lInf = 0.6;
 406     }
 407     processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "", l1, lInf);
 408     if (target != DNN_TARGET_MYRIAD || getInferenceEngineVPUType() != CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 409         expectNoFallbacksFromIE(net);
 410     expectNoFallbacksFromCUDA(net);
 411 }
 412
 413 TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
 414 {
 415     applyTestTag(CV_TEST_TAG_MEMORY_512MB, CV_TEST_TAG_DEBUG_VERYLONG);
 416
 417     if (backend == DNN_BACKEND_HALIDE)
 418         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
 419     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
 420         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
 421
 422 #if defined(INF_ENGINE_RELEASE)
 423 #if INF_ENGINE_VER_MAJOR_LE(2018050000)
 424     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
 425         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
 426 #endif
 427 #endif
 428
 429     Mat img = imread(findDataFile("dnn/googlenet_1.png"));
 430     Mat inp = blobFromImage(img, 1.0, Size(320, 240), Scalar(103.939, 116.779, 123.68), false, false);
 431     // Output image has values in range [-143.526, 148.539].
 432     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.4 : 4e-5;
 433     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 7.45 : 2e-3;
 434     processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf);
 435 #if defined(HAVE_INF_ENGINE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
 436     expectNoFallbacksFromIE(net);
 437 #endif
 438     expectNoFallbacksFromCUDA(net);
 439 }
 440
 441 INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets(true, true, false, true, true));
 442
 443 }} // namespace