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Flexible inputs for OpenVINO IR models

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This commit is contained in:
Dmitry Kurtaev 2020-04-18 19:42:48 +03:00
parent e59e978fcd
commit 908bf935f7
6 changed files with 62 additions and 0 deletions
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2
modules/dnn/src/dnn.cpp
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@ -1629,6 +1629,7 @@ struct Net::Impl
Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
CV_Assert(!ieNode.empty());
ieNode->net->reset();
for (it = layers.begin(); it != layers.end(); ++it)
{
@ -1930,6 +1931,7 @@ struct Net::Impl
Ptr<InfEngineNgraphNode> ieNode = node.dynamicCast<InfEngineNgraphNode>();
CV_Assert(!ieNode.empty());
ieNode->net->reset();
for (it = layers.begin(); it != layers.end(); ++it)
{

7
modules/dnn/src/ie_ngraph.cpp
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@ -780,6 +780,13 @@ void forwardNgraph(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
ieNode->net->forward(outBlobsWrappers, isAsync);
}
void InfEngineNgraphNet::reset()
{
allBlobs.clear();
infRequests.clear();
isInit = false;
}
void InfEngineNgraphNet::addBlobs(const std::vector<cv::Ptr<BackendWrapper> >& ptrs)
{
auto wrappers = ngraphWrappers(ptrs);

2
modules/dnn/src/ie_ngraph.hpp
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@ -52,6 +52,8 @@ public:
void createNet(Target targetId);
void setNodePtr(std::shared_ptr<ngraph::Node>* ptr);
void reset();
private:
void release();
int getNumComponents();

7
modules/dnn/src/op_inf_engine.cpp
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@ -891,6 +891,13 @@ bool InfEngineBackendNet::isInitialized()
#endif
}
void InfEngineBackendNet::reset()
{
allBlobs.clear();
infRequests.clear();
isInit = false;
}
void InfEngineBackendNet::addBlobs(const std::vector<cv::Ptr<BackendWrapper> >& ptrs)
{
auto wrappers = infEngineWrappers(ptrs);

2
modules/dnn/src/op_inf_engine.hpp
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@ -112,6 +112,8 @@ public:
void addBlobs(const std::vector<cv::Ptr<BackendWrapper> >& ptrs);
void reset();
private:
InferenceEngine::Builder::Network netBuilder;

42
modules/dnn/test/test_misc.cpp
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@ -760,6 +760,48 @@ TEST_P(Test_Model_Optimizer, readFromBuffer)
normAssert(ref, actual, "", 0, 0);
}
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)
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";
Net net0 = readNet(model, proto);
net0.setPreferableTarget(targetId);
Net net1 = readNet(model, proto);
net1.setPreferableTarget(targetId);
// Generate inputs.
int blobSize0[] = {2, 6, 75, 113};
Mat input0(4, &blobSize0[0], CV_32F);
randu(input0, 0, 255);
net0.setInput(input0);
Mat ref = net0.forward().clone();
int blobSize1[] = {1, 6, 10, 9};
Mat input1(4, &blobSize1[0], CV_32F);
randu(input1, 0, 255);
net1.setInput(input1);
Mat out = net1.forward();
EXPECT_NE(out.size, ref.size);
net1.setInput(input0);
out = net1.forward();
normAssert(ref, out, 0, 0);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Model_Optimizer,
dnnBackendsAndTargetsIE()
);