Do not use size_t for nGraph layers

diff --git a/modules/dnn/src/layers/fully_connected_layer.cpp b/modules/dnn/src/layers/fully_connected_layer.cpp
index 3f3b62b106033df5ef40a3796f21de2251bcc6c2..03349253c0c4b28aff063e8a742c7a2671c7f0f7 100644 (file)
--- a/modules/dnn/src/layers/fully_connected_layer.cpp
+++ b/modules/dnn/src/layers/fully_connected_layer.cpp
@@ -565,7 +565,7 @@ public:
         }
         else
         {
-            std::vector<size_t> data = {(size_t)ieInpNode->get_shape()[0], (size_t)blobs[0].size[1]};
+            std::vector<int64_t> data = {(int64_t)ieInpNode->get_shape()[0], (int64_t)blobs[0].size[1]};
             auto new_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, data.data());
             auto inp = std::make_shared<ngraph::op::v1::Reshape>(ieInpNode, new_shape, true);
 

diff --git a/modules/dnn/src/layers/permute_layer.cpp b/modules/dnn/src/layers/permute_layer.cpp
index d8e5f66678250efad5462d5f7f606f72725d81bc..e08bfa0c5c715a491bc488637ba25b5484a19e7e 100644 (file)
--- a/modules/dnn/src/layers/permute_layer.cpp
+++ b/modules/dnn/src/layers/permute_layer.cpp
@@ -385,8 +385,9 @@ public:
                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
     {
         auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
+        std::vector<int64_t> order(_order.begin(), _order.end());
         auto tr_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
-                       ngraph::Shape({_order.size()}), _order.data());
+                       ngraph::Shape({order.size()}), order.data());
         auto transpose = std::make_shared<ngraph::op::Transpose>(ieInpNode, tr_axes);
         return Ptr<BackendNode>(new InfEngineNgraphNode(transpose));
     }

diff --git a/modules/dnn/test/test_layers.cpp b/modules/dnn/test/test_layers.cpp
index 5713730e54895995acbf2f2cdaa0ea38f8161f38..0c4ce11ca5e164ebf084a91b0906a4ed72fc116a 100644 (file)
--- a/modules/dnn/test/test_layers.cpp
+++ b/modules/dnn/test/test_layers.cpp
@@ -1108,6 +1108,9 @@ TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
     const Backend backendId = get<0>(GetParam());
     const Target targetId = get<1>(GetParam());
 
+    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
+
     if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         throw SkipTestException("No support for async forward");
 
@@ -1118,9 +1121,8 @@ TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
     else
         FAIL() << "Unknown backendId";
 
-    std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
     Net netDefault = readNet(_tf("layer_convolution.caffemodel"), _tf("layer_convolution.prototxt"));
-    Net net = readNet(_tf("layer_convolution" + suffix + ".xml"), _tf("layer_convolution" + suffix + ".bin"));
+    Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
 
     Mat inp = blobFromNPY(_tf("blob.npy"));
 
@@ -1140,7 +1142,10 @@ TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
 
     std::vector<int> outLayers = net.getUnconnectedOutLayers();
     ASSERT_EQ(net.getLayer(outLayers[0])->name, "output");
-    ASSERT_EQ(net.getLayer(outLayers[0])->type, "Convolution");
+    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
+        ASSERT_EQ(net.getLayer(outLayers[0])->type, "Convolution");
+    else
+        ASSERT_EQ(net.getLayer(outLayers[0])->type, "Add");
 }
 
 TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
@@ -1148,6 +1153,9 @@ TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
     const Backend backendId = get<0>(GetParam());
     const Target targetId = get<1>(GetParam());
 
+    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
+
     if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         throw SkipTestException("No support for async forward");
 
@@ -1164,12 +1172,10 @@ TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
     randu(inputs[0], 0, 255);
     inputs[0].convertTo(inputs[1], CV_32F);
 
-    std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
-
     Mat outs[2];
     for (int i = 0; i < 2; ++i)
     {
-        Net net = readNet(_tf("layer_convolution" + suffix + ".xml"), _tf("layer_convolution" + suffix + ".bin"));
+        Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
         net.setPreferableBackend(backendId);
         net.setPreferableTarget(targetId);
         net.setInput(inputs[i]);
@@ -1185,6 +1191,9 @@ TEST_P(Layer_Test_Convolution_DLDT, multithreading)
     const Backend backendId = get<0>(GetParam());
     const Target targetId = get<1>(GetParam());
 
+    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
+
     if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         throw SkipTestException("No support for async forward");
 
@@ -1195,9 +1204,8 @@ TEST_P(Layer_Test_Convolution_DLDT, multithreading)
     else
         FAIL() << "Unknown backendId";
 
-    std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
-    std::string xmlPath = _tf("layer_convolution" + suffix + ".xml");
-    std::string binPath = _tf("layer_convolution" + suffix + ".bin");
+    std::string xmlPath = _tf("layer_convolution.xml");
+    std::string binPath = _tf("layer_convolution.bin");
     Net firstNet = readNet(xmlPath, binPath);
     Net secondNet = readNet(xmlPath, binPath);
     Mat inp = blobFromNPY(_tf("blob.npy"));
@@ -1256,8 +1264,7 @@ TEST_P(Test_DLDT_two_inputs_3dim, as_IR)
     int secondInpType = get<1>(GetParam());
     Target targetId = get<2>(GetParam());
 
-    std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
-    Net net = readNet(_tf("net_two_inputs" + suffix + ".xml"), _tf("net_two_inputs.bin"));
+    Net net = readNet(_tf("net_two_inputs.xml"), _tf("net_two_inputs.bin"));
     std::vector<int> inpSize = get<3>(GetParam());
     Mat firstInp(3, inpSize.data(), firstInpType);
     Mat secondInp(3, inpSize.data(), secondInpType);

diff --git a/modules/dnn/test/test_misc.cpp b/modules/dnn/test/test_misc.cpp
index 1ca0d39672d02880ca23d0d6c4193b155a5a39e3..6df089e11d51a1f849b7fd4739818db5a1ba2a81 100644 (file)
--- a/modules/dnn/test/test_misc.cpp
+++ b/modules/dnn/test/test_misc.cpp
@@ -440,12 +440,14 @@ TEST_P(Async, model_optimizer_pipeline_set_and_forward_single)
     const Backend backendId = get<0>(get<1>(GetParam()));
     const Target targetId = get<1>(get<1>(GetParam()));
 
+    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
+
     if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         throw SkipTestException("No support for async forward");
 
-    const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
-    const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
-    const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
+    const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
+    const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
 
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
         setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
@@ -499,12 +501,14 @@ TEST_P(Async, model_optimizer_pipeline_set_and_forward_all)
     const Backend backendId = get<0>(get<1>(GetParam()));
     const Target targetId = get<1>(get<1>(GetParam()));
 
+    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
+
     if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         throw SkipTestException("No support for async forward");
 
-    const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
-    const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
-    const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
+    const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
+    const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
 
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
         setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
@@ -673,9 +677,11 @@ TEST_P(Test_Model_Optimizer, forward_two_nets)
     const Backend backendId = get<0>(GetParam());
     const Target targetId = get<1>(GetParam());
 
-    const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
-    const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
-    const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
+    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
+
+    const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
+    const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
 
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
         setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
@@ -712,12 +718,14 @@ TEST_P(Test_Model_Optimizer, readFromBuffer)
     const Backend backendId = get<0>(GetParam());
     const Target targetId = get<1>(GetParam());
 
+    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
+
     if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         throw SkipTestException("No support for async forward");
 
-    const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
-    const std::string& weightsFile = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
-    const std::string& modelFile = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
+    const std::string& weightsFile = findDataFile("dnn/layers/layer_convolution.bin");
+    const std::string& modelFile = findDataFile("dnn/layers/layer_convolution.xml");
 
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
         setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
@@ -765,8 +773,11 @@ TEST_P(Test_Model_Optimizer, flexible_inputs)
     const Backend backendId = get<0>(GetParam());
     const Target targetId = get<1>(GetParam());
 
-    const std::string& model = findDataFile("dnn/layers/layer_convolution_fp16.bin");
-    const std::string& proto = findDataFile("dnn/layers/layer_convolution_fp16.xml");
+    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
+
+    const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
+    const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
 
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
         setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);