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Fix custom IE layers in case of no MKLDNN plugin

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This commit is contained in:
Dmitry Kurtaev 2019-10-21 19:09:44 +03:00
parent a8d14e88fe
commit dfe0368835
2 changed files with 58 additions and 46 deletions
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66
modules/dnn/src/dnn.cpp
View File

@ -97,6 +97,37 @@ public:
static BackendRegistry impl;
return impl;
}
static inline bool checkIETarget(int target)
{
#ifndef HAVE_INF_ENGINE
return false;
#else
cv::dnn::Net net;
cv::dnn::LayerParams lp;
lp.set("kernel_size", 1);
lp.set("num_output", 1);
lp.set("bias_term", false);
lp.type = "Convolution";
lp.name = "testLayer";
lp.blobs.push_back(Mat({1, 2, 1, 1}, CV_32F, Scalar(1)));
net.addLayerToPrev(lp.name, lp.type, lp);
net.setPreferableBackend(cv::dnn::DNN_BACKEND_INFERENCE_ENGINE);
net.setPreferableTarget(target);
static int inpDims[] = {1, 2, 3, 4};
net.setInput(cv::Mat(4, &inpDims[0], CV_32FC1, cv::Scalar(0)));
try
{
net.forward();
}
catch(...)
{
return false;
}
return true;
#endif
}
private:
BackendRegistry()
{
@ -136,35 +167,6 @@ private:
backends.push_back(std::make_pair(DNN_BACKEND_OPENCV, DNN_TARGET_CPU));
}
static inline bool checkIETarget(int target)
{
#ifndef HAVE_INF_ENGINE
return false;
#else
cv::dnn::Net net;
cv::dnn::LayerParams lp;
lp.set("kernel_size", 1);
lp.set("num_output", 1);
lp.set("bias_term", false);
lp.type = "Convolution";
lp.name = "testLayer";
lp.blobs.push_back(Mat({1, 2, 1, 1}, CV_32F, Scalar(1)));
net.addLayerToPrev(lp.name, lp.type, lp);
net.setPreferableBackend(cv::dnn::DNN_BACKEND_INFERENCE_ENGINE);
net.setPreferableTarget(target);
static int inpDims[] = {1, 2, 3, 4};
net.setInput(cv::Mat(4, &inpDims[0], CV_32FC1, cv::Scalar(0)));
try
{
net.forward();
}
catch(...)
{
return false;
}
return true;
#endif
}
BackendsList backends;
};
@ -1544,6 +1546,9 @@ struct Net::Impl
// backend. Split a whole model on several Inference Engine networks if
// some of layers are not implemented.
bool supportsCPUFallback = preferableTarget == DNN_TARGET_CPU ||
BackendRegistry::checkIETarget(DNN_TARGET_CPU);
// Set of all input and output blobs wrappers for current network.
std::map<LayerPin, Ptr<BackendWrapper> > netBlobsWrappers;
for (it = layers.begin(); it != layers.end(); ++it)
@ -1557,7 +1562,8 @@ struct Net::Impl
if (!fused && !layer->supportBackend(preferableBackend))
{
bool customizable = ld.id != 0 && ld.outputBlobs.size() == 1 &&
INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R2);
INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R2) &&
supportsCPUFallback;
// TODO: there is a bug in Myriad plugin with custom layers shape infer.
if (preferableTarget == DNN_TARGET_MYRIAD)
{

38
modules/dnn/test/test_darknet_importer.cpp
View File

@ -390,12 +390,6 @@ TEST_P(Test_Darknet_nets, YOLOv3)
{
applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB));
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
#endif
// batchId, classId, confidence, left, top, right, bottom
Mat ref = (Mat_<float>(9, 7) << 0, 7, 0.952983f, 0.614622f, 0.150257f, 0.901369f, 0.289251f, // a truck
0, 1, 0.987908f, 0.150913f, 0.221933f, 0.742255f, 0.74626f, // a bicycle
@ -413,23 +407,35 @@ TEST_P(Test_Darknet_nets, YOLOv3)
std::string config_file = "yolov3.cfg";
std::string weights_file = "yolov3.weights";
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
{
scoreDiff = 0.04;
iouDiff = 0.2;
}
#endif
{
SCOPED_TRACE("batch size 1");
testDarknetModel(config_file, weights_file, ref.rowRange(0, 3), scoreDiff, iouDiff);
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL) // Test with 'batch size 2' is disabled for DLIE/OpenCL target
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
{
if (target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
if (target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
if (INF_ENGINE_VER_MAJOR_LE(2018050000) && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
else if (INF_ENGINE_VER_MAJOR_EQ(2019020000))
{
if (target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
if (target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
}
else if (target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
}
#endif