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Merge pull request #14847 from alalek:dnn_fix_test_data_searching

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This commit is contained in:
Alexander Alekhin 2019-06-23 12:42:26 +00:00
commit f8c96cb18a
12 changed files with 114 additions and 84 deletions
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2
modules/dnn/perf/perf_caffe.cpp
View File

@ -38,7 +38,7 @@ namespace opencv_test {
static caffe::Net<float>* initNet(std::string proto, std::string weights)
{
proto = findDataFile(proto, false);
proto = findDataFile(proto);
weights = findDataFile(weights, false);
#ifdef HAVE_CLCAFFE

6
modules/dnn/perf/perf_net.cpp
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@ -35,7 +35,7 @@ public:
weights = findDataFile(weights, false);
if (!proto.empty())
proto = findDataFile(proto, false);
proto = findDataFile(proto);
if (backend == DNN_BACKEND_HALIDE)
{
if (halide_scheduler == "disabled")
@ -198,10 +198,10 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv3)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/dog416.png", false));
Mat sample = imread(findDataFile("dnn/dog416.png"));
Mat inp;
sample.convertTo(inp, CV_32FC3);
processNet("dnn/yolov3.cfg", "dnn/yolov3.weights", "", inp / 255);
processNet("dnn/yolov3.weights", "dnn/yolov3.cfg", "", inp / 255);
}
PERF_TEST_P_(DNNTestNetwork, EAST_text_detection)

22
modules/dnn/test/test_backends.cpp
View File

@ -37,7 +37,7 @@ public:
weights = findDataFile(weights, false);
if (!proto.empty())
proto = findDataFile(proto, false);
proto = findDataFile(proto);
// Create two networks - with default backend and target and a tested one.
Net netDefault = readNet(weights, proto);
@ -51,7 +51,7 @@ public:
net.setPreferableTarget(target);
if (backend == DNN_BACKEND_HALIDE && !halideScheduler.empty())
{
halideScheduler = findDataFile(halideScheduler, false);
halideScheduler = findDataFile(halideScheduler);
net.setHalideScheduler(halideScheduler);
}
Mat out = net.forward(outputLayer).clone();
@ -171,7 +171,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
float diffScores = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1.5e-2 : 0.0;
float diffSquares = (target == DNN_TARGET_MYRIAD) ? 0.063 : 0.0;
@ -190,7 +190,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height)
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
throw SkipTestException("Test is disabled for MyriadX");
#endif
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 560), Scalar(127.5, 127.5, 127.5), false);
float diffScores = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.029 : 0.0;
float diffSquares = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0;
@ -204,7 +204,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.095 : 0.0;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0;
@ -223,7 +223,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height)
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
throw SkipTestException("Test is disabled for MyriadX");
#endif
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 560), Scalar(), false);
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.06 : 0.0;
@ -237,7 +237,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.013 : 2e-5;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.062 : 0.0;
@ -254,7 +254,7 @@ TEST_P(DNNTestNetwork, SSD_VGG16)
throw SkipTestException("");
double scoreThreshold = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0325 : 0.0;
const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.032 : 0.0;
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
"dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreThreshold, lInf);
@ -337,7 +337,7 @@ TEST_P(DNNTestNetwork, opencv_face_detector)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
Mat inp = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt",
inp, "detection_out");
@ -357,7 +357,7 @@ TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
#endif
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat sample = imread(findDataFile("dnn/street.png"));
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;
@ -400,7 +400,7 @@ TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
#endif
#endif
Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
Mat img = imread(findDataFile("dnn/googlenet_1.png"));
Mat inp = blobFromImage(img, 1.0, Size(320, 240), Scalar(103.939, 116.779, 123.68), false, false);
// Output image has values in range [-143.526, 148.539].
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.4 : 4e-5;

39
modules/dnn/test/test_caffe_importer.cpp
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@ -48,7 +48,7 @@ namespace opencv_test { namespace {
template<typename TString>
static std::string _tf(TString filename)
{
return (getOpenCVExtraDir() + "/dnn/") + filename;
return findDataFile(std::string("dnn/") + filename);
}
class Test_Caffe_nets : public DNNTestLayer
@ -58,11 +58,11 @@ public:
double scoreDiff = 0.0, double iouDiff = 0.0)
{
checkBackend();
Net net = readNetFromCaffe(findDataFile("dnn/" + proto, false),
Net net = readNetFromCaffe(findDataFile("dnn/" + proto),
findDataFile("dnn/" + model, false));
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat img = imread(findDataFile("dnn/dog416.png", false));
Mat img = imread(findDataFile("dnn/dog416.png"));
resize(img, img, Size(800, 600));
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(102.9801, 115.9465, 122.7717), false, false);
Mat imInfo = (Mat_<float>(1, 3) << img.rows, img.cols, 1.6f);
@ -80,11 +80,12 @@ public:
TEST(Test_Caffe, memory_read)
{
const string proto = findDataFile("dnn/bvlc_googlenet.prototxt", false);
const string proto = findDataFile("dnn/bvlc_googlenet.prototxt");
const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);
std::vector<char> dataProto;
readFileContent(proto, dataProto);
std::vector<char> dataModel;
readFileContent(model, dataModel);
@ -163,7 +164,7 @@ TEST_P(Reproducibility_AlexNet, Accuracy)
bool readFromMemory = get<0>(GetParam());
Net net;
{
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt");
const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
if (readFromMemory)
{
@ -204,8 +205,8 @@ TEST(Reproducibility_FCN, Accuracy)
Net net;
{
const string proto = findDataFile("dnn/fcn8s-heavy-pascal.prototxt", false);
const string model = findDataFile("dnn/fcn8s-heavy-pascal.caffemodel", false);
const string proto = findDataFile("dnn/fcn8s-heavy-pascal.prototxt");
const string model = findDataFile("dnn/fcn8s-heavy-pascal.caffemodel");
net = readNetFromCaffe(proto, model);
ASSERT_FALSE(net.empty());
}
@ -233,7 +234,7 @@ TEST(Reproducibility_SSD, Accuracy)
applyTestTag(CV_TEST_TAG_MEMORY_512MB, CV_TEST_TAG_DEBUG_LONG);
Net net;
{
const string proto = findDataFile("dnn/ssd_vgg16.prototxt", false);
const string proto = findDataFile("dnn/ssd_vgg16.prototxt");
const string model = findDataFile("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", false);
net = readNetFromCaffe(proto, model);
ASSERT_FALSE(net.empty());
@ -331,7 +332,7 @@ TEST_P(Reproducibility_ResNet50, Accuracy)
if (!ocl::useOpenCL() && targetId != DNN_TARGET_CPU)
throw SkipTestException("OpenCL is disabled");
Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt"),
findDataFile("dnn/ResNet-50-model.caffemodel", false));
net.setPreferableBackend(DNN_BACKEND_OPENCV);
@ -369,7 +370,7 @@ TEST_P(Reproducibility_SqueezeNet_v1_1, Accuracy)
int targetId = GetParam();
if(targetId == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("This test does not support FP16");
Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt"),
findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
@ -400,18 +401,18 @@ TEST(Reproducibility_AlexNet_fp16, Accuracy)
const float l1 = 1e-5;
const float lInf = 3e-3;
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt");
const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
shrinkCaffeModel(model, "bvlc_alexnet.caffemodel_fp16");
Net net = readNetFromCaffe(proto, "bvlc_alexnet.caffemodel_fp16");
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat sample = imread(findDataFile("dnn/grace_hopper_227.png", false));
Mat sample = imread(findDataFile("dnn/grace_hopper_227.png"));
net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false));
net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar()));
Mat out = net.forward();
Mat ref = blobFromNPY(findDataFile("dnn/caffe_alexnet_prob.npy", false));
Mat ref = blobFromNPY(findDataFile("dnn/caffe_alexnet_prob.npy"));
normAssert(ref, out, "", l1, lInf);
}
@ -420,7 +421,7 @@ TEST(Reproducibility_GoogLeNet_fp16, Accuracy)
const float l1 = 1e-5;
const float lInf = 3e-3;
const string proto = findDataFile("dnn/bvlc_googlenet.prototxt", false);
const string proto = findDataFile("dnn/bvlc_googlenet.prototxt");
const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);
shrinkCaffeModel(model, "bvlc_googlenet.caffemodel_fp16");
@ -506,7 +507,7 @@ TEST_P(Test_Caffe_nets, DenseNet_121)
TEST(Test_Caffe, multiple_inputs)
{
const string proto = findDataFile("dnn/layers/net_input.prototxt", false);
const string proto = findDataFile("dnn/layers/net_input.prototxt");
Net net = readNetFromCaffe(proto);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
@ -534,8 +535,8 @@ TEST(Test_Caffe, multiple_inputs)
TEST(Test_Caffe, shared_weights)
{
const string proto = findDataFile("dnn/layers/shared_weights.prototxt", false);
const string model = findDataFile("dnn/layers/shared_weights.caffemodel", false);
const string proto = findDataFile("dnn/layers/shared_weights.prototxt");
const string model = findDataFile("dnn/layers/shared_weights.caffemodel");
Net net = readNetFromCaffe(proto, model);
@ -563,7 +564,7 @@ TEST_P(opencv_face_detector, Accuracy)
dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam());
Net net = readNetFromCaffe(proto, model);
Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
net.setPreferableBackend(DNN_BACKEND_OPENCV);

11
modules/dnn/test/test_darknet_importer.cpp
View File

@ -82,7 +82,7 @@ TEST(Test_Darknet, read_yolo_voc_stream)
Mat ref;
Mat sample = imread(_tf("dog416.png"));
Mat inp = blobFromImage(sample, 1.0/255, Size(416, 416), Scalar(), true, false);
const std::string cfgFile = findDataFile("dnn/yolo-voc.cfg", false);
const std::string cfgFile = findDataFile("dnn/yolo-voc.cfg");
const std::string weightsFile = findDataFile("dnn/yolo-voc.weights", false);
// Import by paths.
{
@ -110,12 +110,13 @@ class Test_Darknet_layers : public DNNTestLayer
public:
void testDarknetLayer(const std::string& name, bool hasWeights = false)
{
std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg", false);
Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy"));
Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy"));
std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg");
std::string model = "";
if (hasWeights)
model = findDataFile("dnn/darknet/" + name + ".weights", false);
Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy", false));
Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy", false));
checkBackend(&inp, &ref);
@ -152,7 +153,7 @@ public:
Mat inp = blobFromImages(samples, 1.0/255, Size(416, 416), Scalar(), true, false);
Net net = readNet(findDataFile("dnn/" + cfg, false),
Net net = readNet(findDataFile("dnn/" + cfg),
findDataFile("dnn/" + weights, false));
net.setPreferableBackend(backend);
net.setPreferableTarget(target);

6
modules/dnn/test/test_googlenet.cpp
View File

@ -58,7 +58,7 @@ TEST_P(Reproducibility_GoogLeNet, Batching)
const int targetId = GetParam();
if(targetId == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("This test does not support FP16");
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt", false),
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt"),
findDataFile("dnn/bvlc_googlenet.caffemodel", false));
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
@ -89,7 +89,7 @@ TEST_P(Reproducibility_GoogLeNet, IntermediateBlobs)
const int targetId = GetParam();
if(targetId == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("This test does not support FP16");
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt", false),
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt"),
findDataFile("dnn/bvlc_googlenet.caffemodel", false));
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
@ -120,7 +120,7 @@ TEST_P(Reproducibility_GoogLeNet, SeveralCalls)
const int targetId = GetParam();
if(targetId == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("This test does not support FP16");
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt", false),
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt"),
findDataFile("dnn/bvlc_googlenet.caffemodel", false));
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);

8
modules/dnn/test/test_misc.cpp
View File

@ -62,18 +62,18 @@ TEST(imagesFromBlob, Regression)
TEST(readNet, Regression)
{
Net net = readNet(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
Net net = readNet(findDataFile("dnn/squeezenet_v1.1.prototxt"),
findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
EXPECT_FALSE(net.empty());
net = readNet(findDataFile("dnn/opencv_face_detector.caffemodel", false),
findDataFile("dnn/opencv_face_detector.prototxt", false));
findDataFile("dnn/opencv_face_detector.prototxt"));
EXPECT_FALSE(net.empty());
net = readNet(findDataFile("dnn/openface_nn4.small2.v1.t7", false));
EXPECT_FALSE(net.empty());
net = readNet(findDataFile("dnn/tiny-yolo-voc.cfg", false),
net = readNet(findDataFile("dnn/tiny-yolo-voc.cfg"),
findDataFile("dnn/tiny-yolo-voc.weights", false));
EXPECT_FALSE(net.empty());
net = readNet(findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false),
net = readNet(findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt"),
findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false));
EXPECT_FALSE(net.empty());
}

24
modules/dnn/test/test_onnx_importer.cpp
View File

@ -12,15 +12,18 @@
namespace opencv_test { namespace {
template<typename TString>
static std::string _tf(TString filename)
static std::string _tf(TString filename, bool required = true)
{
String rootFolder = "dnn/onnx/";
return findDataFile(rootFolder + filename, false);
return findDataFile(std::string("dnn/onnx/") + filename, required);
}
class Test_ONNX_layers : public DNNTestLayer
{
public:
bool required;
Test_ONNX_layers() : required(true) { }
enum Extension
{
npy,
@ -31,7 +34,7 @@ public:
const double l1 = 0, const float lInf = 0, const bool useSoftmax = false,
bool checkNoFallbacks = true)
{
String onnxmodel = _tf("models/" + basename + ".onnx");
String onnxmodel = _tf("models/" + basename + ".onnx", required);
Mat inp, ref;
if (ext == npy) {
inp = blobFromNPY(_tf("data/input_" + basename + ".npy"));
@ -285,11 +288,16 @@ TEST_P(Test_ONNX_layers, Softmax)
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets());
class Test_ONNX_nets : public Test_ONNX_layers {};
class Test_ONNX_nets : public Test_ONNX_layers
{
public:
Test_ONNX_nets() { required = false; }
};
TEST_P(Test_ONNX_nets, Alexnet)
{
applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
const String model = _tf("models/alexnet.onnx");
const String model = _tf("models/alexnet.onnx", false);
Net net = readNetFromONNX(model);
ASSERT_FALSE(net.empty());
@ -319,7 +327,7 @@ TEST_P(Test_ONNX_nets, Googlenet)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
const String model = _tf("models/googlenet.onnx");
const String model = _tf("models/googlenet.onnx", false);
Net net = readNetFromONNX(model);
ASSERT_FALSE(net.empty());
@ -537,7 +545,7 @@ TEST_P(Test_ONNX_nets, Resnet34_kinetics)
if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
throw SkipTestException("Only DLIE backend on CPU is supported");
String onnxmodel = findDataFile("dnn/resnet-34_kinetics.onnx");
String onnxmodel = findDataFile("dnn/resnet-34_kinetics.onnx", false);
Mat image0 = imread(findDataFile("dnn/dog416.png"));
Mat image1 = imread(findDataFile("dnn/street.png"));

42
modules/dnn/test/test_tf_importer.cpp
View File

@ -74,7 +74,7 @@ TEST(Test_TensorFlow, inception_accuracy)
static std::string path(const std::string& file)
{
return findDataFile("dnn/tensorflow/" + file, false);
return findDataFile("dnn/tensorflow/" + file);
}
class Test_TensorFlow_layers : public DNNTestLayer
@ -360,15 +360,15 @@ TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
#endif
checkBackend();
std::string imgPath = findDataFile("dnn/street.png");
std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt");
std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false);
std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false);
std::string imgPath = findDataFile("dnn/street.png", false);
Mat inp;
resize(imread(imgPath), inp, Size(300, 300));
inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true);
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco.detection_out.npy", false));
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco.detection_out.npy"));
Net net = readNetFromTensorflow(netPath, netConfig);
net.setPreferableBackend(backend);
@ -397,11 +397,11 @@ TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
#endif
checkBackend();
std::string proto = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pbtxt", false);
Mat img = imread(findDataFile("dnn/street.png"));
std::string proto = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pbtxt");
std::string model = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pb", false);
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("dnn/street.png", false));
Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
net.setPreferableBackend(backend);
@ -434,11 +434,11 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD)
throw SkipTestException("Test is disabled for MyriadX");
#endif
std::string proto = findDataFile("dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt");
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 img = imread(findDataFile("dnn/dog416.png"));
Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
net.setPreferableBackend(backend);
@ -473,13 +473,13 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
double scoresDiff = backend == DNN_BACKEND_INFERENCE_ENGINE ? 2.9e-5 : 1e-5;
for (int i = 0; i < 2; ++i)
{
std::string proto = findDataFile("dnn/" + names[i] + ".pbtxt", false);
std::string proto = findDataFile("dnn/" + names[i] + ".pbtxt");
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 img = imread(findDataFile("dnn/dog416.png"));
Mat blob = blobFromImage(img, 1.0f, Size(800, 600), Scalar(), true, false);
net.setInput(blob);
@ -498,12 +498,12 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
#endif
checkBackend();
std::string proto = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pbtxt", false);
std::string proto = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pbtxt");
std::string model = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pb", false);
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 img = imread(findDataFile("dnn/dog416.png"));
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_ppn_coco.detection_out.npy"));
Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
net.setPreferableBackend(backend);
@ -521,11 +521,11 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
{
checkBackend();
std::string proto = findDataFile("dnn/opencv_face_detector.pbtxt", false);
std::string proto = findDataFile("dnn/opencv_face_detector.pbtxt");
std::string model = findDataFile("dnn/opencv_face_detector_uint8.pb", false);
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
net.setPreferableBackend(backend);
@ -572,11 +572,11 @@ TEST_P(Test_TensorFlow_nets, EAST_text_detection)
checkBackend();
std::string netPath = findDataFile("dnn/frozen_east_text_detection.pb", false);
std::string imgPath = findDataFile("cv/ximgproc/sources/08.png", false);
std::string refScoresPath = findDataFile("dnn/east_text_detection.scores.npy", false);
std::string refGeometryPath = findDataFile("dnn/east_text_detection.geometry.npy", false);
std::string imgPath = findDataFile("cv/ximgproc/sources/08.png");
std::string refScoresPath = findDataFile("dnn/east_text_detection.scores.npy");
std::string refGeometryPath = findDataFile("dnn/east_text_detection.geometry.npy");
Net net = readNet(findDataFile("dnn/frozen_east_text_detection.pb", false));
Net net = readNet(netPath);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
@ -793,11 +793,11 @@ TEST(Test_TensorFlow, two_inputs)
TEST(Test_TensorFlow, Mask_RCNN)
{
applyTestTag(CV_TEST_TAG_MEMORY_1GB, CV_TEST_TAG_DEBUG_VERYLONG);
std::string proto = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pbtxt", false);
Mat img = imread(findDataFile("dnn/street.png"));
std::string proto = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pbtxt");
std::string model = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pb", false);
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("dnn/street.png", false));
Mat refDetections = blobFromNPY(path("mask_rcnn_inception_v2_coco_2018_01_28.detection_out.npy"));
Mat refMasks = blobFromNPY(path("mask_rcnn_inception_v2_coco_2018_01_28.detection_masks.npy"));
Mat blob = blobFromImage(img, 1.0f, Size(800, 800), Scalar(), true, false);

8
modules/dnn/test/test_torch_importer.cpp
View File

@ -53,13 +53,13 @@ using namespace cv;
using namespace cv::dnn;
template<typename TStr>
static std::string _tf(TStr filename, bool inTorchDir = true)
static std::string _tf(TStr filename, bool inTorchDir = true, bool required = true)
{
String path = "dnn/";
if (inTorchDir)
path += "torch/";
path += filename;
return findDataFile(path, false);
return findDataFile(path, required);
}
TEST(Torch_Importer, simple_read)
@ -274,7 +274,7 @@ TEST_P(Test_Torch_nets, OpenFace_accuracy)
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat sample = imread(findDataFile("cv/shared/lena.png", false));
Mat sample = imread(findDataFile("cv/shared/lena.png"));
Mat sampleF32(sample.size(), CV_32FC3);
sample.convertTo(sampleF32, sampleF32.type());
sampleF32 /= 255;
@ -415,7 +415,7 @@ TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
Mat img = imread(findDataFile("dnn/googlenet_1.png"));
Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);
net.setInput(inputBlob);

2
modules/objdetect/test/test_qrcode.cpp
View File

@ -75,7 +75,7 @@ TEST_P(Objdetect_QRCode, regression)
ASSERT_TRUE(qrcode.detect(src, corners));
#endif
const std::string dataset_config = findDataFile(root + "dataset_config.json", false);
const std::string dataset_config = findDataFile(root + "dataset_config.json");
FileStorage file_config(dataset_config, FileStorage::READ);
ASSERT_TRUE(file_config.isOpened()) << "Can't read validation data: " << dataset_config;
{

28
modules/ts/src/ts.cpp
View File

@ -911,25 +911,35 @@ void addDataSearchSubDirectory(const std::string& subdir)
static std::string findData(const std::string& relative_path, bool required, bool findDirectory)
{
#define TEST_TRY_FILE_WITH_PREFIX(prefix) \
#define CHECK_FILE_WITH_PREFIX(prefix, result) \
{ \
result.clear(); \
std::string path = path_join(prefix, relative_path); \
/*printf("Trying %s\n", path.c_str());*/ \
if (findDirectory) \
{ \
if (isDirectory(path)) \
return path; \
result = path; \
} \
else \
{ \
FILE* f = fopen(path.c_str(), "rb"); \
if(f) { \
fclose(f); \
return path; \
result = path; \
} \
} \
}
#define TEST_TRY_FILE_WITH_PREFIX(prefix) \
{ \
std::string result__; \
CHECK_FILE_WITH_PREFIX(prefix, result__); \
if (!result__.empty()) \
return result__; \
}
const std::vector<std::string>& search_path = TS::ptr()->data_search_path;
for(size_t i = search_path.size(); i > 0; i--)
{
@ -956,7 +966,17 @@ static std::string findData(const std::string& relative_path, bool required, boo
{
const std::string& subdir = search_subdir[i - 1];
std::string prefix = path_join(datapath, subdir);
TEST_TRY_FILE_WITH_PREFIX(prefix);
std::string result_;
CHECK_FILE_WITH_PREFIX(prefix, result_);
#if 1 // check for misused 'optional' mode
if (!required && !result_.empty())
{
std::cout << "TEST ERROR: Don't use 'optional' findData() for " << relative_path << std::endl;
CV_Assert(required || result_.empty());
}
#endif
if (!result_.empty())
return result_;
}
}
}