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Merge pull request #17998 from dkurt:dnn_fix_ngraph

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pull/18037/head
Alexander Alekhin 4 years ago
parent d695208727 cf8f65d806
commit a28533933f
4 changed files with 46 additions and 27 deletions
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  1. 2
      modules/dnn/src/layers/fully_connected_layer.cpp
  2. 3
      modules/dnn/src/layers/permute_layer.cpp
  3. 29
      modules/dnn/test/test_layers.cpp
  4. 39
      modules/dnn/test/test_misc.cpp

2
modules/dnn/src/layers/fully_connected_layer.cpp
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@ -565,7 +565,7 @@ public:
} }
else 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 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); auto inp = std::make_shared<ngraph::op::v1::Reshape>(ieInpNode, new_shape, true);

3
modules/dnn/src/layers/permute_layer.cpp
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@ -385,8 +385,9 @@ public:
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{ {
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node; 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, 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); auto transpose = std::make_shared<ngraph::op::Transpose>(ieInpNode, tr_axes);
return Ptr<BackendNode>(new InfEngineNgraphNode(transpose)); return Ptr<BackendNode>(new InfEngineNgraphNode(transpose));
} }

29
modules/dnn/test/test_layers.cpp
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@ -1108,6 +1108,9 @@ TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
const Backend backendId = get<0>(GetParam()); const Backend backendId = get<0>(GetParam());
const Target targetId = get<1>(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) if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward"); throw SkipTestException("No support for async forward");
@ -1118,9 +1121,8 @@ TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
else else
FAIL() << "Unknown backendId"; 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 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")); Mat inp = blobFromNPY(_tf("blob.npy"));
@ -1140,7 +1142,10 @@ TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
std::vector<int> outLayers = net.getUnconnectedOutLayers(); std::vector<int> outLayers = net.getUnconnectedOutLayers();
ASSERT_EQ(net.getLayer(outLayers[0])->name, "output"); 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) 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 Backend backendId = get<0>(GetParam());
const Target targetId = get<1>(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) if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward"); throw SkipTestException("No support for async forward");
@ -1164,12 +1172,10 @@ TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
randu(inputs[0], 0, 255); randu(inputs[0], 0, 255);
inputs[0].convertTo(inputs[1], CV_32F); inputs[0].convertTo(inputs[1], CV_32F);
std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
Mat outs[2]; Mat outs[2];
for (int i = 0; i < 2; ++i) 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.setPreferableBackend(backendId);
net.setPreferableTarget(targetId); net.setPreferableTarget(targetId);
net.setInput(inputs[i]); net.setInput(inputs[i]);
@ -1185,6 +1191,9 @@ TEST_P(Layer_Test_Convolution_DLDT, multithreading)
const Backend backendId = get<0>(GetParam()); const Backend backendId = get<0>(GetParam());
const Target targetId = get<1>(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) if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward"); throw SkipTestException("No support for async forward");
@ -1195,9 +1204,8 @@ TEST_P(Layer_Test_Convolution_DLDT, multithreading)
else else
FAIL() << "Unknown backendId"; FAIL() << "Unknown backendId";
std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : ""; std::string xmlPath = _tf("layer_convolution.xml");
std::string xmlPath = _tf("layer_convolution" + suffix + ".xml"); std::string binPath = _tf("layer_convolution.bin");
std::string binPath = _tf("layer_convolution" + suffix + ".bin");
Net firstNet = readNet(xmlPath, binPath); Net firstNet = readNet(xmlPath, binPath);
Net secondNet = readNet(xmlPath, binPath); Net secondNet = readNet(xmlPath, binPath);
Mat inp = blobFromNPY(_tf("blob.npy")); Mat inp = blobFromNPY(_tf("blob.npy"));
@ -1256,8 +1264,7 @@ TEST_P(Test_DLDT_two_inputs_3dim, as_IR)
int secondInpType = get<1>(GetParam()); int secondInpType = get<1>(GetParam());
Target targetId = get<2>(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.xml"), _tf("net_two_inputs.bin"));
Net net = readNet(_tf("net_two_inputs" + suffix + ".xml"), _tf("net_two_inputs.bin"));
std::vector<int> inpSize = get<3>(GetParam()); std::vector<int> inpSize = get<3>(GetParam());
Mat firstInp(3, inpSize.data(), firstInpType); Mat firstInp(3, inpSize.data(), firstInpType);
Mat secondInp(3, inpSize.data(), secondInpType); Mat secondInp(3, inpSize.data(), secondInpType);

39
modules/dnn/test/test_misc.cpp
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@ -440,12 +440,14 @@ TEST_P(Async, model_optimizer_pipeline_set_and_forward_single)
const Backend backendId = get<0>(get<1>(GetParam())); const Backend backendId = get<0>(get<1>(GetParam()));
const Target targetId = get<1>(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) if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward"); 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.bin");
const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin"); const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); 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 Backend backendId = get<0>(get<1>(GetParam()));
const Target targetId = get<1>(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) if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward"); 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.bin");
const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin"); const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); 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 Backend backendId = get<0>(GetParam());
const Target targetId = get<1>(GetParam()); const Target targetId = get<1>(GetParam());
const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : ""; if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin"); applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); 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 Backend backendId = get<0>(GetParam());
const Target targetId = get<1>(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) if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward"); 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.bin");
const std::string& weightsFile = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin"); const std::string& modelFile = findDataFile("dnn/layers/layer_convolution.xml");
const std::string& modelFile = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); 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 Backend backendId = get<0>(GetParam());
const Target targetId = get<1>(GetParam()); const Target targetId = get<1>(GetParam());
const std::string& model = findDataFile("dnn/layers/layer_convolution_fp16.bin"); if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
const std::string& proto = findDataFile("dnn/layers/layer_convolution_fp16.xml"); 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) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);

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