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Enable some tests for Inference Engine 2019R1

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This commit is contained in:
Dmitry Kurtaev
2019-04-08 11:29:10 +03:00
parent dad2247b56
commit a2bbfa1db5
9 changed files with 70 additions and 98 deletions
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modules/dnn/test/test_backends.cpp
+1 -6
View File
@@ -289,12 +289,7 @@ TEST_P(DNNTestNetwork, OpenFace)
#if INF_ENGINE_VER_MAJOR_EQ(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is disabled for Myriad targets");
#elif INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
throw SkipTestException("Test is disabled for MyriadX target");
#else
#elif INF_ENGINE_VER_MAJOR_EQ(2018030000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("Test has been fixed in OpenVINO 2018R4");
#endif
modules/dnn/test/test_halide_layers.cpp
-13
View File
@@ -561,12 +561,6 @@ TEST_P(ReLU, Accuracy)
float negativeSlope = get<0>(GetParam());
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE
&& negativeSlope < 0
)
throw SkipTestException("Test is disabled");
#endif
LayerParams lp;
lp.set("negative_slope", negativeSlope);
@@ -589,13 +583,6 @@ TEST_P(NoParamActivation, Accuracy)
LayerParams lp;
lp.type = get<0>(GetParam());
lp.name = "testLayer";
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE
&& lp.type == "AbsVal"
)
throw SkipTestException("Test is disabled");
#endif
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, NoParamActivation, Combine(
modules/dnn/test/test_onnx_importer.cpp
+1 -1
View File
@@ -379,7 +379,7 @@ TEST_P(Test_ONNX_nets, LResNet100E_IR)
lInf = 0.035;
}
else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU) {
l1 = 4.5e-5;
l1 = 4.6e-5;
lInf = 1.9e-4;
}
testONNXModels("LResNet100E_IR", pb, l1, lInf);
modules/dnn/test/test_tf_importer.cpp
+12 -38
View File
@@ -140,10 +140,6 @@ TEST_P(Test_TensorFlow_layers, padding)
TEST_P(Test_TensorFlow_layers, padding_same)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("Test is disabled for DLIE");
#endif
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
@@ -251,10 +247,6 @@ TEST_P(Test_TensorFlow_layers, reshape)
TEST_P(Test_TensorFlow_layers, flatten)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("Test is disabled for DLIE");
#endif
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
@@ -267,11 +259,6 @@ TEST_P(Test_TensorFlow_layers, flatten)
TEST_P(Test_TensorFlow_layers, unfused_flatten)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("Test is disabled for DLIE");
#endif
runTensorFlowNet("unfused_flatten");
runTensorFlowNet("unfused_flatten_unknown_batch");
}
@@ -320,11 +307,14 @@ class Test_TensorFlow_nets : public DNNTestLayer {};
TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
{
checkBackend();
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
throw SkipTestException("Test is disabled for MyriadX");
#endif
checkBackend();
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);
@@ -333,30 +323,18 @@ TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
resize(imread(imgPath), inp, Size(300, 300));
inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true);
std::vector<String> outNames(3);
outNames[0] = "concat";
outNames[1] = "concat_1";
outNames[2] = "detection_out";
std::vector<Mat> refs(outNames.size());
for (int i = 0; i < outNames.size(); ++i)
{
std::string path = findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco." + outNames[i] + ".npy", false);
refs[i] = blobFromNPY(path);
}
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco.detection_out.npy", false));
Net net = readNetFromTensorflow(netPath, netConfig);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
Mat out = net.forward();
std::vector<Mat> output;
net.forward(output, outNames);
normAssert(refs[0].reshape(1, 1), output[0].reshape(1, 1), "", 1e-5, 1.5e-4);
normAssert(refs[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 3e-4);
normAssertDetections(refs[2], output[2], "", 0.2);
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0043 : default_l1;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.037 : default_lInf;
normAssertDetections(ref, out, "", 0.2, scoreDiff, iouDiff);
}
TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
@@ -597,10 +575,6 @@ TEST_P(Test_TensorFlow_layers, fp16_weights)
TEST_P(Test_TensorFlow_layers, fp16_padding_same)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("Test is disabled for DLIE");
#endif
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X