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Commit b584c230 authored by Alexander Alekhin's avatar Alexander Alekhin
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Merge pull request #15158 from dkurt:fix_tf_ssd_configs

parents ba934ff1 77d4e3e8
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Showing with 20 additions and 29 deletions
modules/dnn/perf/perf_net.cpp
View file @ b584c230
......@@ -142,8 +142,6 @@ PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "ssd_mobilenet_v1_coco_2017_11_17.pbtxt", "",
Mat(cv::Size(300, 300), CV_32FC3));
}
......@@ -152,8 +150,6 @@ PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "ssd_mobilenet_v2_coco_2018_03_29.pbtxt", "",
Mat(cv::Size(300, 300), CV_32FC3));
}
......
modules/dnn/src/layers/elementwise_layers.cpp
View file @ b584c230
......@@ -744,7 +744,7 @@ struct AbsValFunctor
#ifdef HAVE_INF_ENGINE
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(-1);
return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(-0.999999f);
}
#endif // HAVE_INF_ENGINE
......
modules/dnn/test/test_backends.cpp
View file @ b584c230
......@@ -205,10 +205,6 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
if (backend == DNN_BACKEND_HALIDE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
#endif
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
......@@ -248,10 +244,6 @@ 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)
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
#endif
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
......@@ -395,7 +387,7 @@ TEST_P(DNNTestNetwork, DenseNet_121)
float l1 = 0.0, lInf = 0.0;
if (target == DNN_TARGET_OPENCL_FP16)
{
l1 = 9e-3; lInf = 5e-2;
l1 = 2e-2; lInf = 9e-2;
}
else if (target == DNN_TARGET_MYRIAD)
{
......
modules/dnn/test/test_caffe_importer.cpp
View file @ b584c230
......@@ -496,7 +496,11 @@ TEST_P(Test_Caffe_nets, DenseNet_121)
float l1 = default_l1, lInf = default_lInf;
if (target == DNN_TARGET_OPENCL_FP16)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
l1 = 0.04; lInf = 0.21;
#else
l1 = 0.017; lInf = 0.0795;
#endif
}
else if (target == DNN_TARGET_MYRIAD)
{
......
modules/dnn/test/test_darknet_importer.cpp
View file @ b584c230
......@@ -360,7 +360,7 @@ TEST_P(Test_Darknet_nets, YOLOv3)
1, 2, 0.989633f, 0.450719f, 0.463353f, 0.496305f, 0.522258f, // a car
1, 2, 0.997412f, 0.647584f, 0.459939f, 0.821038f, 0.663947f); // a car
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0047 : 8e-5;
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.006 : 8e-5;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.018 : 3e-4;
std::string config_file = "yolov3.cfg";
......
modules/dnn/test/test_layers.cpp
View file @ b584c230
......@@ -233,9 +233,14 @@ TEST_P(Test_Caffe_layers, Dropout)
TEST_P(Test_Caffe_layers, Concat)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
#if defined(INF_ENGINE_RELEASE)
#if INF_ENGINE_VER_MAJOR_GE(2019010000) && INF_ENGINE_VER_MAJOR_LT(2019020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_2019R1, CV_TEST_TAG_DNN_SKIP_IE_2019R1_1);
#elif INF_ENGINE_VER_MAJOR_EQ(2019020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
#endif
#endif
testLayerUsingCaffeModels("layer_concat");
testLayerUsingCaffeModels("layer_concat_optim", true, false);
......
modules/dnn/test/test_tf_importer.cpp
View file @ b584c230
......@@ -436,11 +436,6 @@ TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
#endif
checkBackend();
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);
......@@ -456,7 +451,7 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD)
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 : 1.5e-5;
float scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : 1.5e-5;
float iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.012 : 1e-3;
float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.35 : 0.3;
......@@ -515,11 +510,6 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
#endif
checkBackend();
std::string proto = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pbtxt");
std::string model = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pb", false);
......
samples/dnn/tf_text_graph_ssd.py
View file @ b584c230
......@@ -312,12 +312,16 @@ def createSSDGraph(modelPath, configPath, outputPath):
addConcatNode('PriorBox/concat', priorBoxes, 'concat/axis_flatten')
# Sigmoid for classes predictions and DetectionOutput layer
addReshape('ClassPredictor/concat', 'ClassPredictor/concat3d', [0, -1, num_classes + 1], graph_def)
sigmoid = NodeDef()
sigmoid.name = 'ClassPredictor/concat/sigmoid'
sigmoid.op = 'Sigmoid'
sigmoid.input.append('ClassPredictor/concat')
sigmoid.input.append('ClassPredictor/concat3d')
graph_def.node.extend([sigmoid])
addFlatten(sigmoid.name, sigmoid.name + '/Flatten', graph_def)
detectionOut = NodeDef()
detectionOut.name = 'detection_out'
detectionOut.op = 'DetectionOutput'
......@@ -326,7 +330,7 @@ def createSSDGraph(modelPath, configPath, outputPath):
detectionOut.input.append('BoxEncodingPredictor/concat')
else:
detectionOut.input.append('BoxPredictor/concat')
detectionOut.input.append(sigmoid.name)
detectionOut.input.append(sigmoid.name + '/Flatten')
detectionOut.input.append('PriorBox/concat')
detectionOut.addAttr('num_classes', num_classes + 1)
......
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