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

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This commit is contained in:
Alexander Alekhin
2020-01-22 16:58:30 +03:00
parent 8b3042da63 3019927a61
commit 3d14dd4e39
32 changed files with 1151 additions and 94 deletions
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modules/dnn/include/opencv2/dnn/dnn.hpp
+2 -2
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@@ -86,7 +86,7 @@ CV__DNN_INLINE_NS_BEGIN
*/
enum Target
{
DNN_TARGET_CPU,
DNN_TARGET_CPU = 0,
DNN_TARGET_OPENCL,
DNN_TARGET_OPENCL_FP16,
DNN_TARGET_MYRIAD,
@@ -97,7 +97,7 @@ CV__DNN_INLINE_NS_BEGIN
};
CV_EXPORTS std::vector< std::pair<Backend, Target> > getAvailableBackends();
CV_EXPORTS std::vector<Target> getAvailableTargets(Backend be);
CV_EXPORTS_W std::vector<Target> getAvailableTargets(dnn::Backend be);
/** @brief This class provides all data needed to initialize layer.
*
modules/dnn/misc/java/gen_dict.json
+8
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@@ -36,6 +36,14 @@
"v_type": "vector_Layer",
"j_import": "org.opencv.dnn.Layer"
},
"vector_Target": {
"j_type": "List<Integer>",
"jn_type": "List<Integer>",
"jni_type": "jobject",
"jni_var": "std::vector< cv::dnn::Target > %(n)s",
"suffix": "Ljava_util_List",
"v_type": "vector_Target"
},
"LayerId": {
"j_type": "DictValue",
"jn_type": "long",
modules/dnn/misc/java/src/cpp/dnn_converters.cpp
+19
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@@ -60,6 +60,25 @@ jobject vector_Ptr_Layer_to_List(JNIEnv* env, std::vector<cv::Ptr<cv::dnn::Layer
return result;
}
jobject vector_Target_to_List(JNIEnv* env, std::vector<cv::dnn::Target>& vs)
{
static jclass juArrayList = ARRAYLIST(env);
static jmethodID m_create = CONSTRUCTOR(env, juArrayList);
jmethodID m_add = LIST_ADD(env, juArrayList);
static jclass jInteger = env->FindClass("java/lang/Integer");
static jmethodID m_create_Integer = env->GetMethodID(jInteger, "<init>", "(I)V");
jobject result = env->NewObject(juArrayList, m_create, vs.size());
for (size_t i = 0; i < vs.size(); ++i)
{
jobject element = env->NewObject(jInteger, m_create_Integer, vs[i]);
env->CallBooleanMethod(result, m_add, element);
env->DeleteLocalRef(element);
}
return result;
}
std::vector<cv::Ptr<cv::dnn::Layer> > List_to_vector_Ptr_Layer(JNIEnv* env, jobject list)
{
static jclass juArrayList = ARRAYLIST(env);
modules/dnn/misc/java/src/cpp/dnn_converters.hpp
+1
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@@ -28,5 +28,6 @@ jobject vector_Ptr_Layer_to_List(JNIEnv* env, std::vector<cv::Ptr<cv::dnn::Layer
std::vector<cv::Ptr<cv::dnn::Layer> > List_to_vector_Ptr_Layer(JNIEnv* env, jobject list);
jobject vector_Target_to_List(JNIEnv* env, std::vector<cv::dnn::Target>& vs);
#endif /* DNN_CONVERTERS_HPP */
modules/dnn/misc/java/test/DnnTensorFlowTest.java
+5
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@@ -141,4 +141,9 @@ public class DnnTensorFlowTest extends OpenCVTestCase {
net = Dnn.readNetFromTensorflow(new MatOfByte(modelBuffer));
checkInceptionNet(net);
}
public void testGetAvailableTargets() {
List<Integer> targets = Dnn.getAvailableTargets(Dnn.DNN_BACKEND_OPENCV);
assertTrue(targets.contains(Dnn.DNN_TARGET_CPU));
}
}
modules/dnn/misc/python/pyopencv_dnn.hpp
+6
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@@ -71,6 +71,12 @@ PyObject* pyopencv_from(const dnn::LayerParams& lp)
return dict;
}
template<>
PyObject* pyopencv_from(const std::vector<dnn::Target> &t)
{
return pyopencv_from(std::vector<int>(t.begin(), t.end()));
}
class pycvLayer CV_FINAL : public dnn::Layer
{
public:
modules/dnn/misc/python/test/test_dnn.py
+4
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@@ -117,6 +117,10 @@ class dnn_test(NewOpenCVTests):
return False
return True
def test_getAvailableTargets(self):
targets = cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_OPENCV)
self.assertTrue(cv.dnn.DNN_TARGET_CPU in targets)
def test_blobFromImage(self):
np.random.seed(324)
modules/dnn/src/darknet/darknet_io.cpp
+4 -3
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@@ -556,6 +556,7 @@ namespace cv {
{
int kernel_size = getParam<int>(layer_params, "size", -1);
int pad = getParam<int>(layer_params, "pad", 0);
int padding = getParam<int>(layer_params, "padding", 0);
int stride = getParam<int>(layer_params, "stride", 1);
int filters = getParam<int>(layer_params, "filters", -1);
bool batch_normalize = getParam<int>(layer_params, "batch_normalize", 0) == 1;
@@ -563,13 +564,13 @@ namespace cv {
if (flipped == 1)
CV_Error(cv::Error::StsNotImplemented, "Transpose the convolutional weights is not implemented");
// correct the strange value of pad=1 for kernel_size=1 in the Darknet cfg-file
if (kernel_size < 3) pad = 0;
if (pad)
padding = kernel_size / 2;
CV_Assert(kernel_size > 0 && filters > 0);
CV_Assert(current_channels > 0);
setParams.setConvolution(kernel_size, pad, stride, filters, current_channels,
setParams.setConvolution(kernel_size, padding, stride, filters, current_channels,
batch_normalize);
current_channels = filters;
modules/dnn/src/dnn.cpp
+8 -2
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@@ -109,7 +109,7 @@ public:
#ifdef HAVE_INF_ENGINE
static inline bool checkIETarget(Target target)
{
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2019R3)
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R3)
// Lightweight detection
const std::vector<std::string> devices = getCore().GetAvailableDevices();
for (std::vector<std::string>::const_iterator i = devices.begin(); i != devices.end(); ++i)
@@ -3098,7 +3098,9 @@ struct Net::Impl
catch (const cv::Exception& e)
{
CV_LOG_ERROR(NULL, "OPENCV/DNN: [" << l->type << "]:(" << l->name << "): getMemoryShapes() throws exception." <<
" inputs=" << is.size() << " outputs=" << os.size() << "/" << requiredOutputs);
" inputs=" << is.size() <<
" outputs=" << os.size() << "/" << requiredOutputs <<
" blobs=" << l->blobs.size());
for (size_t i = 0; i < is.size(); ++i)
{
CV_LOG_ERROR(NULL, " input[" << i << "] = " << toString(is[i]));
@@ -3107,6 +3109,10 @@ struct Net::Impl
{
CV_LOG_ERROR(NULL, " output[" << i << "] = " << toString(os[i]));
}
for (size_t i = 0; i < l->blobs.size(); ++i)
{
CV_LOG_ERROR(NULL, " blobs[" << i << "] = " << typeToString(l->blobs[i].type()) << " " << toString(shape(l->blobs[i])));
}
CV_LOG_ERROR(NULL, "Exception message: " << e.what());
throw;
}
modules/dnn/src/ie_ngraph.cpp
+8 -1
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@@ -323,7 +323,14 @@ void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net)
}
// Some of networks can work without a library of extra layers.
// OpenCV fallbacks as extensions.
ie.AddExtension(std::make_shared<InfEngineExtension>(), "CPU");
try
{
ie.AddExtension(std::make_shared<InfEngineExtension>(), "CPU");
}
catch(const std::exception& e)
{
CV_LOG_INFO(NULL, "DNN-IE: Can't register OpenCV custom layers extension: " << e.what());
}
#ifndef _WIN32
// Limit the number of CPU threads.
if (device_name == "CPU")
modules/dnn/src/layers/lrn_layer.cpp
+10 -2
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@@ -103,7 +103,7 @@ public:
return bias == (int)bias;
}
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
return type == CHANNEL_NRM && bias == (int)bias;
return bias == (int)bias;
}
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
@@ -471,7 +471,15 @@ public:
alphaSize *= (type == SPATIAL_NRM ? size*size : size);
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto lrn = std::make_shared<ngraph::op::LRN>(ieInpNode, (double)alphaSize, (double)beta, (double)bias, (size_t)size);
std::vector<int64_t> axes;
if (type != SPATIAL_NRM) {
axes = {1};
} else {
axes.resize(ieInpNode->get_shape().size() - 2);
std::iota(axes.begin(), axes.end(), 2);
}
auto ngraph_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{axes.size()}, axes.data());
auto lrn = std::make_shared<ngraph::op::LRN>(ieInpNode, ngraph_axes, alphaSize, beta, bias, size);
return Ptr<BackendNode>(new InfEngineNgraphNode(lrn));
}
#endif // HAVE_DNN_NGRAPH
modules/dnn/src/layers/mvn_layer.cpp
+3 -1
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@@ -119,8 +119,10 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
return !zeroDev && (preferableTarget != DNN_TARGET_MYRIAD || eps <= 1e-7f);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
else
#endif // HAVE_INF_ENGINE
return backendId == DNN_BACKEND_OPENCV;
modules/dnn/src/onnx/onnx_graph_simplifier.cpp
+1 -1
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@@ -98,7 +98,7 @@ private:
class SoftMaxSubgraph : public Subgraph
{
public:
SoftMaxSubgraph()
SoftMaxSubgraph() : axis(1)
{
int input = addNodeToMatch("");
int inpExp = addNodeToMatch("Exp", input);
modules/dnn/src/onnx/onnx_importer.cpp
+12 -2
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@@ -147,8 +147,18 @@ Mat getMatFromTensor(opencv_onnx::TensorProto& tensor_proto)
}
else
{
char* val = const_cast<char*>(tensor_proto.raw_data().c_str());
int64_t* src = reinterpret_cast<int64_t*>(val);
const char* val = tensor_proto.raw_data().c_str();
// Aligned pointer is required: https://github.com/opencv/opencv/issues/16373
// this doesn't work: typedef int64_t CV_DECL_ALIGNED(1) unaligned_int64_t;
AutoBuffer<int64_t, 16> aligned_val;
if (!isAligned<sizeof(int64_t)>(val))
{
size_t sz = tensor_proto.raw_data().size();
aligned_val.allocate(divUp(sz, sizeof(int64_t)));
memcpy(aligned_val.data(), val, sz);
val = (const char*)aligned_val.data();
}
const int64_t* src = reinterpret_cast<const int64_t*>(val);
convertInt64ToInt32(src, dst, blob.total());
}
}
modules/dnn/src/op_inf_engine.cpp
+17 -4
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@@ -574,7 +574,7 @@ InferenceEngine::Core& getCore()
#if !defined(OPENCV_DNN_IE_VPU_TYPE_DEFAULT)
static bool detectMyriadX_()
{
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2019R3)
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R3)
// Lightweight detection
InferenceEngine::Core& ie = getCore();
const std::vector<std::string> devices = ie.GetAvailableDevices();
@@ -739,7 +739,14 @@ void InfEngineBackendNet::initPlugin(InferenceEngine::CNNNetwork& net)
// Some of networks can work without a library of extra layers.
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2019R1)
// OpenCV fallbacks as extensions.
ie.AddExtension(std::make_shared<InfEngineExtension>(), "CPU");
try
{
ie.AddExtension(std::make_shared<InfEngineExtension>(), "CPU");
}
catch(const std::exception& e)
{
CV_LOG_INFO(NULL, "DNN-IE: Can't register OpenCV custom layers extension: " << e.what());
}
#endif
#ifndef _WIN32
// Limit the number of CPU threads.
@@ -1068,8 +1075,14 @@ void resetMyriadDevice()
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
getSharedPlugins().erase("MYRIAD");
#else
// To unregister both "MYRIAD" and "HETERO:MYRIAD,CPU" plugins
getCore() = InferenceEngine::Core();
// Unregister both "MYRIAD" and "HETERO:MYRIAD,CPU" plugins
InferenceEngine::Core& ie = getCore();
try
{
ie.UnregisterPlugin("MYRIAD");
ie.UnregisterPlugin("HETERO");
}
catch (...) {}
#endif
#endif // HAVE_INF_ENGINE
}
modules/dnn/test/test_darknet_importer.cpp
+10 -1
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@@ -106,7 +106,7 @@ public:
std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg");
std::string model = "";
if (hasWeights)
model = findDataFile("dnn/darknet/" + name + ".weights", false);
model = findDataFile("dnn/darknet/" + name + ".weights");
checkBackend(&inp, &ref);
@@ -554,6 +554,15 @@ TEST_P(Test_Darknet_layers, reorg)
testDarknetLayer("reorg");
}
TEST_P(Test_Darknet_layers, convolutional)
{
if (target == DNN_TARGET_MYRIAD)
{
default_l1 = 0.01f;
}
testDarknetLayer("convolutional", true);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_layers, dnnBackendsAndTargets());
}} // namespace