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dnn(test): skip failed NGRAPH/MYRIAD tests

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pull/17129/head
Alexander Alekhin 5 years ago
parent 775d031366
commit 83c4378d5d
6 changed files with 112 additions and 8 deletions
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  1. 2
      modules/dnn/src/dnn.cpp
  2. 4
      modules/dnn/test/test_darknet_importer.cpp
  3. 5
      modules/dnn/test/test_halide_layers.cpp
  4. 8
      modules/dnn/test/test_ie_models.cpp
  5. 7
      modules/dnn/test/test_onnx_importer.cpp
  6. 94
      modules/dnn/test/test_tf_importer.cpp

2
modules/dnn/src/dnn.cpp
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@ -2102,7 +2102,7 @@ struct Net::Impl
auto ieInpNode = inputNodes[i].dynamicCast<InfEngineNgraphNode>();
CV_Assert(oid < ieInpNode->node->get_output_size());
#if INF_ENGINE_VER_MAJOR_GT(2020020000)
#if INF_ENGINE_VER_MAJOR_GT(2020030000)
inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid)));
#else
inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid, false)));

4
modules/dnn/test/test_darknet_importer.cpp
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@ -544,6 +544,10 @@ TEST_P(Test_Darknet_layers, reorg)
TEST_P(Test_Darknet_layers, maxpool)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testDarknetLayer("maxpool");
}

5
modules/dnn/test/test_halide_layers.cpp
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@ -350,6 +350,11 @@ TEST_P(MaxPooling, Accuracy)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
LayerParams lp;
lp.set("pool", "max");
lp.set("kernel_w", kernel.width);

8
modules/dnn/test/test_ie_models.cpp
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@ -134,6 +134,8 @@ static inline void genData(const InferenceEngine::TensorDesc& desc, Mat& m, Blob
void runIE(Target target, const std::string& xmlPath, const std::string& binPath,
std::map<std::string, cv::Mat>& inputsMap, std::map<std::string, cv::Mat>& outputsMap)
{
SCOPED_TRACE("runIE");
CNNNetReader reader;
reader.ReadNetwork(xmlPath);
reader.ReadWeights(binPath);
@ -247,6 +249,8 @@ void runCV(Backend backendId, Target targetId, const std::string& xmlPath, const
const std::map<std::string, cv::Mat>& inputsMap,
std::map<std::string, cv::Mat>& outputsMap)
{
SCOPED_TRACE("runOCV");
Net net = readNet(xmlPath, binPath);
for (auto& it : inputsMap)
net.setInput(it.second, it.first);
@ -301,8 +305,8 @@ TEST_P(DNNTestOpenVINO, models)
// Single Myriad device cannot be shared across multiple processes.
if (targetId == DNN_TARGET_MYRIAD)
resetMyriadDevice();
runIE(targetId, xmlPath, binPath, inputsMap, ieOutputsMap);
runCV(backendId, targetId, xmlPath, binPath, inputsMap, cvOutputsMap);
EXPECT_NO_THROW(runIE(targetId, xmlPath, binPath, inputsMap, ieOutputsMap)) << "runIE";
EXPECT_NO_THROW(runCV(backendId, targetId, xmlPath, binPath, inputsMap, cvOutputsMap)) << "runCV";
double eps = 0;
#if INF_ENGINE_VER_MAJOR_GE(2020010000)

7
modules/dnn/test/test_onnx_importer.cpp
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@ -95,7 +95,14 @@ TEST_P(Test_ONNX_layers, InstanceNorm)
TEST_P(Test_ONNX_layers, MaxPooling)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testONNXModels("maxpooling", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, MaxPooling_2)
{
testONNXModels("two_maxpooling", npy, 0, 0, false, false);
}

94
modules/dnn/test/test_tf_importer.cpp
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@ -128,13 +128,32 @@ TEST_P(Test_TensorFlow_layers, reduce_mean)
runTensorFlowNet("global_pool_by_axis");
}
TEST_P(Test_TensorFlow_layers, conv)
TEST_P(Test_TensorFlow_layers, conv_single_conv)
{
runTensorFlowNet("single_conv");
}
TEST_P(Test_TensorFlow_layers, conv_atrous_conv2d_valid)
{
runTensorFlowNet("atrous_conv2d_valid");
}
TEST_P(Test_TensorFlow_layers, conv_atrous_conv2d_same)
{
runTensorFlowNet("atrous_conv2d_same");
}
TEST_P(Test_TensorFlow_layers, conv_depthwise_conv2d)
{
runTensorFlowNet("depthwise_conv2d");
}
TEST_P(Test_TensorFlow_layers, conv_keras_atrous_conv2d_same)
{
runTensorFlowNet("keras_atrous_conv2d_same");
}
TEST_P(Test_TensorFlow_layers, conv_pool_nchw)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
runTensorFlowNet("conv_pool_nchw");
}
@ -277,11 +296,32 @@ TEST_P(Test_TensorFlow_layers, slim_batch_norm)
runTensorFlowNet("slim_batch_norm", false, l1, lInf);
}
TEST_P(Test_TensorFlow_layers, pooling)
TEST_P(Test_TensorFlow_layers, pooling_max_pool_even)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
runTensorFlowNet("max_pool_even");
}
TEST_P(Test_TensorFlow_layers, pooling_max_pool_odd_valid)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
runTensorFlowNet("max_pool_odd_valid");
}
TEST_P(Test_TensorFlow_layers, pooling_max_pool_odd_same)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
runTensorFlowNet("max_pool_odd_same");
}
TEST_P(Test_TensorFlow_layers, pooling_reduce_mean)
{
runTensorFlowNet("reduce_mean"); // an average pooling over all spatial dimensions.
}
@ -738,23 +778,67 @@ TEST_P(Test_TensorFlow_nets, EAST_text_detection)
INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_nets, dnnBackendsAndTargets());
TEST_P(Test_TensorFlow_layers, fp16_weights)
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_single_conv)
{
float l1 = 0.00078;
float lInf = 0.012;
float l1 = 0.00078, lInf = 0.012;
runTensorFlowNet("fp16_single_conv", false, l1, lInf);
}
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_max_pool_odd_same)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
float l1 = 0.00078, lInf = 0.012;
runTensorFlowNet("fp16_max_pool_odd_same", false, l1, lInf);
}
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_eltwise_add_mul)
{
float l1 = 0.00078, lInf = 0.012;
runTensorFlowNet("fp16_eltwise_add_mul", false, l1, lInf);
}
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_pad_and_concat)
{
float l1 = 0.00078, lInf = 0.012;
runTensorFlowNet("fp16_pad_and_concat", false, l1, lInf);
}
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_padding_valid)
{
float l1 = 0.00078, lInf = 0.012;
runTensorFlowNet("fp16_padding_valid", false, l1, lInf);
}
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_max_pool_even)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
float l1 = 0.00078, lInf = 0.012;
// Reference output values are in range [0.0889, 1.651]
runTensorFlowNet("fp16_max_pool_even", false, (target == DNN_TARGET_MYRIAD) ? 0.003 : l1, lInf);
}
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_deconvolution)
{
float l1 = 0.00078, lInf = 0.012;
if (target == DNN_TARGET_MYRIAD) {
l1 = 0.0041;
lInf = 0.024;
}
// Reference output values are in range [0, 10.75]
runTensorFlowNet("fp16_deconvolution", false, l1, lInf);
}
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_max_pool_odd_valid)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
float l1 = 0.00078, lInf = 0.012;
if (target == DNN_TARGET_MYRIAD) {
l1 = 0.0041;
lInf = 0.024;
}
// Reference output values are in range [0.418, 2.297]
runTensorFlowNet("fp16_max_pool_odd_valid", false, l1, lInf);
}

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