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Merge pull request #11650 from dkurt:dnn_default_backend

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This commit is contained in:
Vadim Pisarevsky
2018-06-06 09:30:39 +00:00
parent afaf5f940e b781ac7346
commit 3cbd2e2764
45 changed files with 253 additions and 199 deletions
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modules/dnn/src/dnn.cpp
+36 -18
View File
@@ -225,7 +225,7 @@ void imagesFromBlob(const cv::Mat& blob_, OutputArrayOfArrays images_)
class OpenCLBackendWrapper : public BackendWrapper
{
public:
OpenCLBackendWrapper(Mat& m) : BackendWrapper(DNN_BACKEND_DEFAULT, DNN_TARGET_OPENCL)
OpenCLBackendWrapper(Mat& m) : BackendWrapper(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL)
{
m.copyTo(umat);
host = &m;
@@ -233,7 +233,7 @@ public:
}
OpenCLBackendWrapper(const Ptr<BackendWrapper>& baseBuffer, Mat& m)
: BackendWrapper(DNN_BACKEND_DEFAULT, DNN_TARGET_OPENCL)
: BackendWrapper(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL)
{
Ptr<OpenCLBackendWrapper> base = baseBuffer.dynamicCast<OpenCLBackendWrapper>();
CV_Assert(!base.empty());
@@ -654,7 +654,7 @@ private:
static Ptr<BackendWrapper> wrapMat(int backendId, int targetId, cv::Mat& m)
{
if (backendId == DNN_BACKEND_DEFAULT)
if (backendId == DNN_BACKEND_OPENCV)
{
if (targetId == DNN_TARGET_CPU)
return Ptr<BackendWrapper>();
@@ -727,7 +727,7 @@ struct Net::Impl
Ptr<BackendWrapper> wrap(Mat& host)
{
if (preferableBackend == DNN_BACKEND_DEFAULT && preferableTarget == DNN_TARGET_CPU)
if (preferableBackend == DNN_BACKEND_OPENCV && preferableTarget == DNN_TARGET_CPU)
return Ptr<BackendWrapper>();
MatShape shape(host.dims);
@@ -738,7 +738,7 @@ struct Net::Impl
if (backendWrappers.find(data) != backendWrappers.end())
{
Ptr<BackendWrapper> baseBuffer = backendWrappers[data];
if (preferableBackend == DNN_BACKEND_DEFAULT)
if (preferableBackend == DNN_BACKEND_OPENCV)
{
CV_Assert(IS_DNN_OPENCL_TARGET(preferableTarget));
return OpenCLBackendWrapper::create(baseBuffer, host);
@@ -850,9 +850,27 @@ struct Net::Impl
{
CV_TRACE_FUNCTION();
if (preferableBackend == DNN_BACKEND_DEFAULT)
#ifdef HAVE_INF_ENGINE
preferableBackend = DNN_BACKEND_INFERENCE_ENGINE;
#else
preferableBackend = DNN_BACKEND_OPENCV;
#endif
CV_Assert(preferableBackend != DNN_BACKEND_OPENCV ||
preferableTarget == DNN_TARGET_CPU ||
preferableTarget == DNN_TARGET_OPENCL ||
preferableTarget == DNN_TARGET_OPENCL_FP16);
CV_Assert(preferableBackend != DNN_BACKEND_HALIDE ||
preferableTarget == DNN_TARGET_CPU ||
preferableTarget == DNN_TARGET_OPENCL);
CV_Assert(preferableBackend != DNN_BACKEND_INFERENCE_ENGINE ||
preferableTarget == DNN_TARGET_CPU ||
preferableTarget == DNN_TARGET_OPENCL ||
preferableTarget == DNN_TARGET_OPENCL_FP16 ||
preferableTarget == DNN_TARGET_MYRIAD);
if (!netWasAllocated || this->blobsToKeep != blobsToKeep_)
{
if (preferableBackend == DNN_BACKEND_DEFAULT && IS_DNN_OPENCL_TARGET(preferableTarget))
if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
#ifndef HAVE_OPENCL
{
CV_LOG_WARNING(NULL, "DNN: OpenCL target is not available in this OpenCV build, switching to CPU.");
@@ -1036,7 +1054,7 @@ struct Net::Impl
void initBackend()
{
CV_TRACE_FUNCTION();
if (preferableBackend == DNN_BACKEND_DEFAULT)
if (preferableBackend == DNN_BACKEND_OPENCV)
CV_Assert(preferableTarget == DNN_TARGET_CPU || IS_DNN_OPENCL_TARGET(preferableTarget));
else if (preferableBackend == DNN_BACKEND_HALIDE)
initHalideBackend();
@@ -1375,7 +1393,7 @@ struct Net::Impl
std::vector<LayerPin> pinsForInternalBlobs;
blobManager.allocateBlobsForLayer(ld, layerShapesIt->second, pinsForInternalBlobs,
preferableBackend == DNN_BACKEND_INFERENCE_ENGINE,
preferableBackend == DNN_BACKEND_DEFAULT &&
preferableBackend == DNN_BACKEND_OPENCV &&
preferableTarget == DNN_TARGET_OPENCL_FP16);
ld.outputBlobsWrappers.resize(ld.outputBlobs.size());
for (int i = 0; i < ld.outputBlobs.size(); ++i)
@@ -1418,7 +1436,7 @@ struct Net::Impl
void fuseLayers(const std::vector<LayerPin>& blobsToKeep_)
{
if( !fusion || preferableBackend != DNN_BACKEND_DEFAULT &&
if( !fusion || preferableBackend != DNN_BACKEND_OPENCV &&
preferableBackend != DNN_BACKEND_INFERENCE_ENGINE)
return;
@@ -1446,7 +1464,7 @@ struct Net::Impl
// some other layers.
// TODO: OpenCL target support more fusion styles.
if ( preferableBackend == DNN_BACKEND_DEFAULT && IS_DNN_OPENCL_TARGET(preferableTarget) &&
if ( preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget) &&
(!cv::ocl::useOpenCL() || (ld.layerInstance->type != "Convolution" &&
ld.layerInstance->type != "MVN")) )
continue;
@@ -1481,7 +1499,7 @@ struct Net::Impl
break;
}
if (preferableBackend != DNN_BACKEND_DEFAULT)
if (preferableBackend != DNN_BACKEND_OPENCV)
continue; // Go to the next layer.
// For now, OpenCL target support fusion with activation of ReLU/ChannelsPReLU/Power/Tanh
@@ -1624,7 +1642,7 @@ struct Net::Impl
}
}
if (preferableBackend != DNN_BACKEND_DEFAULT)
if (preferableBackend != DNN_BACKEND_OPENCV)
continue; // Go to the next layer.
// the optimization #2. if there is no layer that takes max pooling layer's computed
@@ -1735,7 +1753,7 @@ struct Net::Impl
{
CV_Assert(layers[0].outputBlobs[i].total());
if (layers[0].outputBlobs[i].depth() == CV_32F &&
preferableBackend == DNN_BACKEND_DEFAULT &&
preferableBackend == DNN_BACKEND_OPENCV &&
preferableTarget == DNN_TARGET_OPENCL_FP16)
{
Mat mat = layers[0].outputBlobs[i].clone();
@@ -1781,12 +1799,12 @@ struct Net::Impl
TickMeter tm;
tm.start();
if (preferableBackend == DNN_BACKEND_DEFAULT ||
if (preferableBackend == DNN_BACKEND_OPENCV ||
!layer->supportBackend(preferableBackend))
{
if( !ld.skip )
{
if (preferableBackend == DNN_BACKEND_DEFAULT && IS_DNN_OPENCL_TARGET(preferableTarget))
if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
{
std::vector<UMat> umat_outputBlobs = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers);
layer->forward(OpenCLBackendWrapper::getUMatVector(ld.inputBlobsWrappers),
@@ -2132,7 +2150,7 @@ void Net::forward(OutputArrayOfArrays outputBlobs, const String& outputName)
{
std::vector<UMat> & outputvec = *(std::vector<UMat> *)outputBlobs.getObj();
if (impl->preferableBackend == DNN_BACKEND_DEFAULT &&
if (impl->preferableBackend == DNN_BACKEND_OPENCV &&
IS_DNN_OPENCL_TARGET(impl->preferableTarget))
{
if (impl->preferableTarget == DNN_TARGET_OPENCL)
@@ -2270,7 +2288,7 @@ void Net::setInput(InputArray blob, const String& name)
ld.outputBlobsWrappers.resize(ld.outputBlobs.size());
MatShape prevShape = shape(ld.outputBlobs[pin.oid]);
Mat blob_;
if (impl->preferableBackend == DNN_BACKEND_DEFAULT &&
if (impl->preferableBackend == DNN_BACKEND_OPENCV &&
impl->preferableTarget == DNN_TARGET_OPENCL_FP16)
{
Mat blob_mat = blob.getMat();
@@ -2664,7 +2682,7 @@ int Layer::outputNameToIndex(const String&)
bool Layer::supportBackend(int backendId)
{
return backendId == DNN_BACKEND_DEFAULT;
return backendId == DNN_BACKEND_OPENCV;
}
Ptr<BackendNode> Layer::initHalide(const std::vector<Ptr<BackendWrapper> > &)
modules/dnn/src/layers/batch_norm_layer.cpp
+1 -1
View File
@@ -109,7 +109,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
modules/dnn/src/layers/blank_layer.cpp
+1 -1
View File
@@ -56,7 +56,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
modules/dnn/src/layers/concat_layer.cpp
+1 -1
View File
@@ -103,7 +103,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 && !padding || // By channels
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && !padding;
}
modules/dnn/src/layers/convolution_layer.cpp
+34 -1
View File
@@ -81,7 +81,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
@@ -1568,6 +1568,39 @@ public:
return Ptr<BackendNode>();
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn;
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "Deconvolution";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::DeconvolutionLayer> ieLayer(new InferenceEngine::DeconvolutionLayer(lp));
ieLayer->_kernel_x = kernel.width;
ieLayer->_kernel_y = kernel.height;
ieLayer->_stride_x = stride.width;
ieLayer->_stride_y = stride.height;
ieLayer->_out_depth = numOutput;
ieLayer->_padding_x = pad.width;
ieLayer->_padding_y = pad.height;
ieLayer->_dilation_x = dilation.width;
ieLayer->_dilation_y = dilation.height;
ieLayer->_group = group;
ieLayer->_weights = wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW);
if (hasBias())
{
ieLayer->_biases = wrapToInfEngineBlob(blobs[1], {(size_t)numOutput}, InferenceEngine::Layout::C);
}
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
modules/dnn/src/layers/detection_output_layer.cpp
+1 -1
View File
@@ -195,7 +195,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && !_locPredTransposed;
}
modules/dnn/src/layers/elementwise_layers.cpp
+4 -3
View File
@@ -115,7 +115,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
@@ -496,8 +496,9 @@ struct TanHFunctor
#ifdef HAVE_INF_ENGINE
InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp)
{
CV_Error(Error::StsNotImplemented, "TanH");
return InferenceEngine::CNNLayerPtr();
lp.type = "TanH";
std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
return ieLayer;
}
#endif // HAVE_INF_ENGINE
modules/dnn/src/layers/eltwise_layer.cpp
+1 -1
View File
@@ -96,7 +96,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
modules/dnn/src/layers/flatten_layer.cpp
+1 -1
View File
@@ -64,7 +64,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
modules/dnn/src/layers/fully_connected_layer.cpp
+1 -1
View File
@@ -128,7 +128,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && axis == 1;
}
modules/dnn/src/layers/lrn_layer.cpp
+1 -1
View File
@@ -90,7 +90,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
modules/dnn/src/layers/max_unpooling_layer.cpp
+1 -1
View File
@@ -34,7 +34,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() &&
!poolPad.width && !poolPad.height;
}
modules/dnn/src/layers/normalize_bbox_layer.cpp
+1 -1
View File
@@ -63,7 +63,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() &&
pnorm == 2 && !blobs.empty();
}
modules/dnn/src/layers/padding_layer.cpp
+1 -1
View File
@@ -87,7 +87,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() && dstRanges.size() == 4;
}
modules/dnn/src/layers/permute_layer.cpp
+1 -1
View File
@@ -118,7 +118,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
modules/dnn/src/layers/pooling_layer.cpp
+1 -1
View File
@@ -135,7 +135,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() &&
(type == MAX || type == AVE && !pad.width && !pad.height) ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && (type == MAX || type == AVE);
modules/dnn/src/layers/prior_box_layer.cpp
+1 -1
View File
@@ -270,7 +270,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
modules/dnn/src/layers/reorg_layer.cpp
-5
View File
@@ -85,11 +85,6 @@ public:
return false;
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT;
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
modules/dnn/src/layers/reshape_layer.cpp
+1 -1
View File
@@ -168,7 +168,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
modules/dnn/src/layers/resize_nearest_neighbor_layer.cpp
+1 -1
View File
@@ -42,7 +42,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
modules/dnn/src/layers/scale_layer.cpp
+1 -1
View File
@@ -48,7 +48,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
modules/dnn/src/layers/softmax_layer.cpp
+1 -1
View File
@@ -88,7 +88,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() && axisRaw == 1 ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && !logSoftMax;
}