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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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This commit is contained in:
Alexander Alekhin
2018-09-04 19:35:38 +03:00
committed by Alexander Alekhin
parent e0bfe44cac 351ee2e3a5
commit d74b98c3d9
117 changed files with 6913 additions and 3713 deletions
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modules/dnn/test/test_backends.cpp
+5 -20
View File
@@ -161,7 +161,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : 0.0;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.06 : 0.0;
processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
@@ -173,7 +173,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : 0.0;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.062 : 0.0;
processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt",
@@ -247,8 +247,8 @@ TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.008 : 0.0;
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.015 : 0.0;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0731 : 0.0;
processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt",
inp, "detection_out", "", l1, lInf);
@@ -285,21 +285,6 @@ TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf);
}
const tuple<Backend, Target> testCases[] = {
#ifdef HAVE_HALIDE
tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
#endif
#ifdef HAVE_INF_ENGINE
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
};
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, testing::ValuesIn(testCases));
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets(true, true, false));
}} // namespace
modules/dnn/test/test_caffe_importer.cpp
+7 -8
View File
@@ -417,7 +417,7 @@ TEST_P(Test_Caffe_nets, DenseNet_121)
float l1 = default_l1, lInf = default_lInf;
if (target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.017; lInf = 0.067;
l1 = 0.017; lInf = 0.0795;
}
else if (target == DNN_TARGET_MYRIAD)
{
@@ -490,8 +490,7 @@ INSTANTIATE_TEST_CASE_P(Test_Caffe, opencv_face_detector,
TEST_P(Test_Caffe_nets, FasterRCNN_vgg16)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
static Mat ref = (Mat_<float>(3, 7) << 0, 2, 0.949398, 99.2454, 210.141, 601.205, 462.849,
0, 7, 0.997022, 481.841, 92.3218, 722.685, 175.953,
@@ -502,8 +501,7 @@ TEST_P(Test_Caffe_nets, FasterRCNN_vgg16)
TEST_P(Test_Caffe_nets, FasterRCNN_zf)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
static Mat ref = (Mat_<float>(3, 7) << 0, 2, 0.90121, 120.407, 115.83, 570.586, 528.395,
0, 7, 0.988779, 469.849, 75.1756, 718.64, 186.762,
@@ -514,12 +512,13 @@ TEST_P(Test_Caffe_nets, FasterRCNN_zf)
TEST_P(Test_Caffe_nets, RFCN)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
double scoreDiff = (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ? 4e-3 : default_l1;
double iouDiff = (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ? 8e-2 : default_lInf;
static Mat ref = (Mat_<float>(2, 7) << 0, 7, 0.991359, 491.822, 81.1668, 702.573, 178.234,
0, 12, 0.94786, 132.093, 223.903, 338.077, 566.16);
testFaster("rfcn_pascal_voc_resnet50.prototxt", "resnet50_rfcn_final.caffemodel", ref);
testFaster("rfcn_pascal_voc_resnet50.prototxt", "resnet50_rfcn_final.caffemodel", ref, scoreDiff, iouDiff);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Caffe_nets, dnnBackendsAndTargets());
modules/dnn/test/test_common.hpp
+91
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@@ -42,6 +42,47 @@
#ifndef __OPENCV_TEST_COMMON_HPP__
#define __OPENCV_TEST_COMMON_HPP__
#ifdef HAVE_OPENCL
#include "opencv2/core/ocl.hpp"
#endif
namespace cv { namespace dnn {
CV__DNN_INLINE_NS_BEGIN
static inline void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
{
switch (v) {
case DNN_BACKEND_DEFAULT: *os << "DEFAULT"; return;
case DNN_BACKEND_HALIDE: *os << "HALIDE"; return;
case DNN_BACKEND_INFERENCE_ENGINE: *os << "DLIE"; return;
case DNN_BACKEND_OPENCV: *os << "OCV"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_BACKEND_UNKNOWN(" << v << ")";
}
static inline void PrintTo(const cv::dnn::Target& v, std::ostream* os)
{
switch (v) {
case DNN_TARGET_CPU: *os << "CPU"; return;
case DNN_TARGET_OPENCL: *os << "OCL"; return;
case DNN_TARGET_OPENCL_FP16: *os << "OCL_FP16"; return;
case DNN_TARGET_MYRIAD: *os << "MYRIAD"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_TARGET_UNKNOWN(" << v << ")";
}
using opencv_test::tuple;
using opencv_test::get;
static inline void PrintTo(const tuple<cv::dnn::Backend, cv::dnn::Target> v, std::ostream* os)
{
PrintTo(get<0>(v), os);
*os << "/";
PrintTo(get<1>(v), os);
}
CV__DNN_INLINE_NS_END
}} // namespace
static inline const std::string &getOpenCVExtraDir()
{
return cvtest::TS::ptr()->get_data_path();
@@ -190,4 +231,54 @@ static inline bool readFileInMemory(const std::string& filename, std::string& co
return true;
}
namespace opencv_test {
using namespace cv::dnn;
static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargets(
bool withInferenceEngine = true,
bool withHalide = false,
bool withCpuOCV = true
)
{
std::vector<tuple<Backend, Target> > targets;
#ifdef HAVE_HALIDE
if (withHalide)
{
targets.push_back(make_tuple(DNN_BACKEND_HALIDE, DNN_TARGET_CPU));
#ifdef HAVE_OPENCL
if (cv::ocl::useOpenCL())
targets.push_back(make_tuple(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL));
#endif
}
#endif
#ifdef HAVE_INF_ENGINE
if (withInferenceEngine)
{
targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU));
#ifdef HAVE_OPENCL
if (cv::ocl::useOpenCL())
{
targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL));
targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16));
}
#endif
if (checkMyriadTarget())
targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD));
}
#endif
if (withCpuOCV)
targets.push_back(make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU));
#ifdef HAVE_OPENCL
if (cv::ocl::useOpenCL())
{
targets.push_back(make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL));
targets.push_back(make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16));
}
#endif
return testing::ValuesIn(targets);
}
} // namespace
#endif
modules/dnn/test/test_halide_layers.cpp
+2 -16
View File
@@ -44,23 +44,9 @@ static void test(LayerParams& params, Mat& input, Backend backendId, Target targ
test(input, net, backendId, targetId, skipCheck);
}
static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargetsWithHalide()
static inline testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargetsWithHalide()
{
static const tuple<Backend, Target> testCases[] = {
#ifdef HAVE_HALIDE
tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
#endif
#ifdef HAVE_INF_ENGINE
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
};
return testing::ValuesIn(testCases);
return dnnBackendsAndTargets(true, true, false); // OpenCV/CPU is used as reference
}
class Test_Halide_layers : public DNNTestLayer {};
modules/dnn/test/test_ie_models.cpp
-4
View File
@@ -177,10 +177,6 @@ TEST_P(DNNTestOpenVINO, models)
Target target = (dnn::Target)(int)get<0>(GetParam());
std::string modelName = get<1>(GetParam());
if ((modelName == "semantic-segmentation-adas-0001" && target == DNN_TARGET_OPENCL_FP16) ||
(modelName == "vehicle-license-plate-detection-barrier-0106"))
throw SkipTestException("");
std::string precision = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? "FP16" : "FP32";
std::string prefix = utils::fs::join("intel_models",
utils::fs::join(modelName,
modules/dnn/test/test_precomp.hpp
-45
View File
@@ -49,35 +49,6 @@
#include "opencv2/dnn.hpp"
#include "test_common.hpp"
namespace cv {
namespace dnn {
CV__DNN_INLINE_NS_BEGIN
static inline void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
{
switch (v) {
case DNN_BACKEND_DEFAULT: *os << "DNN_BACKEND_DEFAULT"; return;
case DNN_BACKEND_HALIDE: *os << "DNN_BACKEND_HALIDE"; return;
case DNN_BACKEND_INFERENCE_ENGINE: *os << "DNN_BACKEND_INFERENCE_ENGINE"; return;
case DNN_BACKEND_OPENCV: *os << "DNN_BACKEND_OPENCV"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_BACKEND_UNKNOWN(" << v << ")";
}
static inline void PrintTo(const cv::dnn::Target& v, std::ostream* os)
{
switch (v) {
case DNN_TARGET_CPU: *os << "DNN_TARGET_CPU"; return;
case DNN_TARGET_OPENCL: *os << "DNN_TARGET_OPENCL"; return;
case DNN_TARGET_OPENCL_FP16: *os << "DNN_TARGET_OPENCL_FP16"; return;
case DNN_TARGET_MYRIAD: *os << "DNN_TARGET_MYRIAD"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_TARGET_UNKNOWN(" << v << ")";
}
CV__DNN_INLINE_NS_END
}} // namespace
namespace opencv_test {
using namespace cv::dnn;
@@ -95,22 +66,6 @@ static testing::internal::ParamGenerator<Target> availableDnnTargets()
return testing::ValuesIn(targets);
}
static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargets()
{
static const tuple<Backend, Target> testCases[] = {
#ifdef HAVE_INF_ENGINE
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
};
return testing::ValuesIn(testCases);
}
class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
{
public:
modules/dnn/test/test_tf_importer.cpp
+48 -17
View File
@@ -296,7 +296,7 @@ TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("dnn/street.png", false));
Mat blob = blobFromImage(img, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), true, false);
Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
@@ -310,32 +310,61 @@ TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
0, 3, 0.75838411, 0.44668293, 0.45907149, 0.49459291, 0.52197015,
0, 10, 0.95932811, 0.38349164, 0.32528657, 0.40387636, 0.39165527,
0, 10, 0.93973452, 0.66561931, 0.37841269, 0.68074018, 0.42907384);
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 5e-3 : default_l1;
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0097 : default_l1;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : default_lInf;
normAssertDetections(ref, out, "", 0.5, scoreDiff, iouDiff);
}
TEST_P(Test_TensorFlow_nets, Inception_v2_Faster_RCNN)
TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD)
{
checkBackend();
std::string model = findDataFile("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", false);
std::string proto = findDataFile("dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt", false);
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("dnn/dog416.png", false));
Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(blob);
Mat out = net.forward();
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco_2017_11_17.detection_out.npy"));
float scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 7e-3 : 1e-5;
float iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0098 : 1e-3;
normAssertDetections(ref, out, "", 0.3, scoreDiff, iouDiff);
}
TEST_P(Test_TensorFlow_nets, Faster_RCNN)
{
static std::string names[] = {"faster_rcnn_inception_v2_coco_2018_01_28",
"faster_rcnn_resnet50_coco_2018_01_28"};
checkBackend();
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
std::string proto = findDataFile("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pbtxt", false);
std::string model = findDataFile("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pb", false);
for (int i = 1; i < 2; ++i)
{
std::string proto = findDataFile("dnn/" + names[i] + ".pbtxt", false);
std::string model = findDataFile("dnn/" + names[i] + ".pb", false);
Net net = readNetFromTensorflow(model, proto);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat img = imread(findDataFile("dnn/dog416.png", false));
Mat blob = blobFromImage(img, 1.0f / 127.5, Size(800, 600), Scalar(127.5, 127.5, 127.5), true, false);
Net net = readNetFromTensorflow(model, proto);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat img = imread(findDataFile("dnn/dog416.png", false));
Mat blob = blobFromImage(img, 1.0f, Size(800, 600), Scalar(), true, false);
net.setInput(blob);
Mat out = net.forward();
net.setInput(blob);
Mat out = net.forward();
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/faster_rcnn_inception_v2_coco_2018_01_28.detection_out.npy"));
normAssertDetections(ref, out, "", 0.3);
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/" + names[i] + ".detection_out.npy"));
normAssertDetections(ref, out, names[i].c_str(), 0.3);
}
}
TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
@@ -347,15 +376,17 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("dnn/dog416.png", false));
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_ppn_coco.detection_out.npy", false));
Mat blob = blobFromImage(img, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), true, false);
Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(blob);
Mat out = net.forward();
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.006 : default_l1;
normAssertDetections(ref, out, "", 0.4, scoreDiff, default_lInf);
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : default_l1;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.021 : default_lInf;
normAssertDetections(ref, out, "", 0.4, scoreDiff, iouDiff);
}
TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
modules/dnn/test/test_torch_importer.cpp
+2 -2
View File
@@ -301,14 +301,14 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
// Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
// thresholds for ENet must be changed. Accuracy of results was checked on
// Cityscapes dataset and difference in mIOU with Torch is 10E-4%
normAssert(ref, out, "", 0.00044, 0.44);
normAssert(ref, out, "", 0.00044, target == DNN_TARGET_CPU ? 0.453 : 0.44);
const int N = 3;
for (int i = 0; i < N; i++)
{
net.setInput(inputBlob, "");
Mat out = net.forward();
normAssert(ref, out, "", 0.00044, 0.44);
normAssert(ref, out, "", 0.00044, target == DNN_TARGET_CPU ? 0.453 : 0.44);
}
}