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Code Packages Releases Wiki Activity

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

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This commit is contained in:
Alexander Alekhin
2021-07-01 10:52:31 +00:00
parent d70053aba5 b699fe7a9d
commit 8fad85edda
19 changed files with 107 additions and 53 deletions
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modules/core/include/opencv2/core/utils/filesystem.private.hpp
+2 -2
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@@ -16,8 +16,8 @@
# define OPENCV_HAVE_FILESYSTEM_SUPPORT 1
# elif defined(__APPLE__)
# include <TargetConditionals.h>
# if (defined(TARGET_OS_OSX) && TARGET_OS_OSX) || (!defined(TARGET_OS_OSX) && !TARGET_OS_IPHONE)
# define OPENCV_HAVE_FILESYSTEM_SUPPORT 1 // OSX only
# if (defined(TARGET_OS_OSX) && TARGET_OS_OSX) || (defined(TARGET_OS_IOS) && TARGET_OS_IOS)
# define OPENCV_HAVE_FILESYSTEM_SUPPORT 1 // OSX, iOS only
# endif
# else
/* unknown */
modules/dnn/src/ie_ngraph.cpp
+37 -14
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@@ -657,7 +657,11 @@ void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net)
try
{
InferenceEngine::IExtensionPtr extension =
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2021_4)
std::make_shared<InferenceEngine::Extension>(libName);
#else
InferenceEngine::make_so_pointer<InferenceEngine::IExtension>(libName);
#endif
ie.AddExtension(extension, "CPU");
CV_LOG_INFO(NULL, "DNN-IE: Loaded extension plugin: " << libName);
@@ -1005,35 +1009,54 @@ void InfEngineNgraphNet::forward(const std::vector<Ptr<BackendWrapper> >& outBlo
reqWrapper->req.SetInput(inpBlobs);
reqWrapper->req.SetOutput(outBlobs);
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2021_4)
InferenceEngine::InferRequest infRequest = reqWrapper->req;
NgraphReqWrapper* wrapperPtr = reqWrapper.get();
CV_Assert(wrapperPtr && "Internal error");
#else
InferenceEngine::IInferRequest::Ptr infRequestPtr = reqWrapper->req;
infRequestPtr->SetUserData(reqWrapper.get(), 0);
CV_Assert(infRequestPtr);
InferenceEngine::IInferRequest& infRequest = *infRequestPtr.get();
infRequest.SetUserData(reqWrapper.get(), 0);
#endif
infRequestPtr->SetCompletionCallback(
[](InferenceEngine::IInferRequest::Ptr request, InferenceEngine::StatusCode status)
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2021_4)
// do NOT capture 'reqWrapper' (smart ptr) in the lambda callback
infRequest.SetCompletionCallback<std::function<void(InferenceEngine::InferRequest, InferenceEngine::StatusCode)>>(
[wrapperPtr](InferenceEngine::InferRequest /*request*/, InferenceEngine::StatusCode status)
#else
infRequest.SetCompletionCallback(
[](InferenceEngine::IInferRequest::Ptr requestPtr, InferenceEngine::StatusCode status)
#endif
{
CV_LOG_DEBUG(NULL, "DNN(nGraph): completionCallback(" << (int)status << ")");
#if !INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2021_4)
CV_Assert(requestPtr);
InferenceEngine::IInferRequest& request = *requestPtr.get();
NgraphReqWrapper* wrapper;
request->GetUserData((void**)&wrapper, 0);
CV_Assert(wrapper && "Internal error");
NgraphReqWrapper* wrapperPtr;
request.GetUserData((void**)&wrapperPtr, 0);
CV_Assert(wrapperPtr && "Internal error");
#endif
NgraphReqWrapper& wrapper = *wrapperPtr;
size_t processedOutputs = 0;
try
{
for (; processedOutputs < wrapper->outProms.size(); ++processedOutputs)
for (; processedOutputs < wrapper.outProms.size(); ++processedOutputs)
{
const std::string& name = wrapper->outsNames[processedOutputs];
Mat m = ngraphBlobToMat(wrapper->req.GetBlob(name));
const std::string& name = wrapper.outsNames[processedOutputs];
Mat m = ngraphBlobToMat(wrapper.req.GetBlob(name));
try
{
CV_Assert(status == InferenceEngine::StatusCode::OK);
wrapper->outProms[processedOutputs].setValue(m.clone());
wrapper.outProms[processedOutputs].setValue(m.clone());
}
catch (...)
{
try {
wrapper->outProms[processedOutputs].setException(std::current_exception());
wrapper.outProms[processedOutputs].setException(std::current_exception());
} catch(...) {
CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation");
}
@@ -1043,16 +1066,16 @@ void InfEngineNgraphNet::forward(const std::vector<Ptr<BackendWrapper> >& outBlo
catch (...)
{
std::exception_ptr e = std::current_exception();
for (; processedOutputs < wrapper->outProms.size(); ++processedOutputs)
for (; processedOutputs < wrapper.outProms.size(); ++processedOutputs)
{
try {
wrapper->outProms[processedOutputs].setException(e);
wrapper.outProms[processedOutputs].setException(e);
} catch(...) {
CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation");
}
}
}
wrapper->isReady = true;
wrapper.isReady = true;
}
);
}
modules/dnn/src/layers/batch_norm_layer.cpp
+7 -9
View File
@@ -35,6 +35,7 @@ namespace dnn
class BatchNormLayerImpl CV_FINAL : public BatchNormLayer
{
public:
Mat origin_weights, origin_bias;
Mat weights_, bias_;
UMat umat_weight, umat_bias;
mutable int dims;
@@ -88,11 +89,11 @@ public:
const float* weightsData = hasWeights ? blobs[weightsBlobIndex].ptr<float>() : 0;
const float* biasData = hasBias ? blobs[biasBlobIndex].ptr<float>() : 0;
weights_.create(1, (int)n, CV_32F);
bias_.create(1, (int)n, CV_32F);
origin_weights.create(1, (int)n, CV_32F);
origin_bias.create(1, (int)n, CV_32F);
float* dstWeightsData = weights_.ptr<float>();
float* dstBiasData = bias_.ptr<float>();
float* dstWeightsData = origin_weights.ptr<float>();
float* dstBiasData = origin_bias.ptr<float>();
for (size_t i = 0; i < n; ++i)
{
@@ -100,15 +101,12 @@ public:
dstWeightsData[i] = w;
dstBiasData[i] = (hasBias ? biasData[i] : 0.0f) - w * meanData[i] * varMeanScale;
}
// We will use blobs to store origin weights and bias to restore them in case of reinitialization.
weights_.copyTo(blobs[0].reshape(1, 1));
bias_.copyTo(blobs[1].reshape(1, 1));
}
virtual void finalize(InputArrayOfArrays, OutputArrayOfArrays) CV_OVERRIDE
{
blobs[0].reshape(1, 1).copyTo(weights_);
blobs[1].reshape(1, 1).copyTo(bias_);
origin_weights.reshape(1, 1).copyTo(weights_);
origin_bias.reshape(1, 1).copyTo(bias_);
}
void getScaleShift(Mat& scale, Mat& shift) const CV_OVERRIDE
modules/dnn/src/onnx/onnx_importer.cpp
+17
View File
@@ -1954,6 +1954,23 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
addConstant(layerParams.name, concatenated[0]);
return;
}
else
{
for (int i = 0; i < node_proto.input_size(); ++i)
{
if (constBlobs.find(node_proto.input(i)) != constBlobs.end())
{
LayerParams constParams;
constParams.name = node_proto.input(i);
constParams.type = "Const";
constParams.blobs.push_back(getBlob(node_proto, i));
opencv_onnx::NodeProto proto;
proto.add_output(constParams.name);
addLayer(constParams, proto);
}
}
}
}
else if (layer_type == "Resize")
{
modules/dnn/src/op_inf_engine.hpp
+3 -2
View File
@@ -30,10 +30,11 @@
#define INF_ENGINE_RELEASE_2021_1 2021010000
#define INF_ENGINE_RELEASE_2021_2 2021020000
#define INF_ENGINE_RELEASE_2021_3 2021030000
#define INF_ENGINE_RELEASE_2021_4 2021040000
#ifndef INF_ENGINE_RELEASE
#warning("IE version have not been provided via command-line. Using 2021.3 by default")
#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2021_3
#warning("IE version have not been provided via command-line. Using 2021.4 by default")
#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2021_4
#endif
#define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000))
modules/dnn/test/test_backends.cpp
+5 -5
View File
@@ -204,7 +204,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
float scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1.5e-2 : 0.0;
float iouDiff = (target == DNN_TARGET_MYRIAD) ? 0.063 : 0.0;
float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.252 : FLT_MIN;
float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.262 : FLT_MIN;
processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
inp, "detection_out", "", scoreDiff, iouDiff, detectionConfThresh);
expectNoFallbacksFromIE(net);
@@ -359,8 +359,8 @@ TEST_P(DNNTestNetwork, OpenPose_pose_coco)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0056 : 0.0;
const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.072 : 0.0;
const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.009 : 0.0;
const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0;
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
Size(46, 46), "", "", l1, lInf);
expectNoFallbacksFromIE(net);
@@ -380,8 +380,8 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
#endif
// output range: [-0.001, 0.97]
const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0;
const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.16 : 0.0;
const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.02 : 0.0;
const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.2 : 0.0;
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
Size(46, 46), "", "", l1, lInf);
expectNoFallbacksFromIE(net);
modules/dnn/test/test_ie_models.cpp
+9
View File
@@ -307,6 +307,15 @@ TEST_P(DNNTestOpenVINO, models)
ASSERT_FALSE(backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) <<
"Inference Engine backend is required";
#if INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (targetId == DNN_TARGET_MYRIAD && (
modelName == "person-detection-retail-0013" || // ncDeviceOpen:1013 Failed to find booted device after boot
modelName == "age-gender-recognition-retail-0013" // ncDeviceOpen:1013 Failed to find booted device after boot
)
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if INF_ENGINE_VER_MAJOR_GE(2020020000)
if (targetId == DNN_TARGET_MYRIAD && backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
modules/dnn/test/test_onnx_importer.cpp
+1
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@@ -349,6 +349,7 @@ TEST_P(Test_ONNX_layers, Concatenation)
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
testONNXModels("concatenation");
testONNXModels("concat_const_blobs");
}
TEST_P(Test_ONNX_layers, Eltwise3D)
modules/dnn/test/test_torch_importer.cpp
+7 -2
View File
@@ -290,9 +290,14 @@ TEST_P(Test_Torch_layers, net_padding)
TEST_P(Test_Torch_layers, net_non_spatial)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000)
#if defined(INF_ENGINE_RELEASE) && ( \
INF_ENGINE_VER_MAJOR_EQ(2021030000) || \
INF_ENGINE_VER_MAJOR_EQ(2021040000) \
)
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); // crash
// 2021.3: crash
// 2021.4: [ GENERAL_ERROR ] AssertionFailed: !out.networkInputs.empty()
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)