Modify SSD from TensorFlow graph generation script to enable MyriadX

modules/dnn/perf/perf_net.cpp

@@ -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/test/test_backends.cpp

@@ -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);

modules/dnn/test/test_tf_importer.cpp

@@ -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

@@ -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)