potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-30 07:43:03 +04:00
Code Packages Releases Wiki Activity

Merge remote-tracking branch 'upstream/3.4' into merge-3.4

Browse Source
This commit is contained in:
Alexander Alekhin
2022-02-11 17:32:37 +00:00
parent 76aff84788 1620a1e014
commit 19926e2979
46 changed files with 323 additions and 2889 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/test/test_misc.cpp
+7 -42
View File
@@ -117,12 +117,7 @@ void test_readNet_IE_do_not_call_setInput(Backend backendId)
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);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net net = readNet(model, proto);
net.setPreferableBackend(backendId);
@@ -462,12 +457,7 @@ TEST_P(Async, model_optimizer_pipeline_set_and_forward_single)
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);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net netSync = readNet(model, proto);
netSync.setPreferableBackend(backendId);
@@ -523,12 +513,7 @@ TEST_P(Async, model_optimizer_pipeline_set_and_forward_all)
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);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net netSync = readNet(model, proto);
netSync.setPreferableBackend(backendId);
@@ -586,12 +571,7 @@ TEST_P(Async, create_layer_pipeline_set_and_forward_all)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && dtype == CV_8U)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net netSync;
Net netAsync;
@@ -697,12 +677,7 @@ TEST_P(Test_Model_Optimizer, forward_two_nets)
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);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net net0 = readNet(model, proto);
net0.setPreferableTarget(targetId);
@@ -741,12 +716,7 @@ TEST_P(Test_Model_Optimizer, readFromBuffer)
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);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net net1 = readNetFromModelOptimizer(modelFile, weightsFile);
net1.setPreferableBackend(backendId);
@@ -793,12 +763,7 @@ TEST_P(Test_Model_Optimizer, flexible_inputs)
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);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net net0 = readNet(model, proto);
net0.setPreferableTarget(targetId);