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Merge pull request #19632 from l-bat:lb/ie_arm_target

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Added OpenVINO ARM target

* Added IE ARM target

* Added OpenVINO ARM target

* Delete ARM target

* Detect ARM platform

* Changed device name in ArmPlugin

* Change ARM detection
This commit is contained in:
Liubov Batanina 2021-03-20 14:20:02 +03:00 committed by GitHub
parent 1211a8b9cd
commit c0dd82fb53
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
15 changed files with 107 additions and 29 deletions
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7
modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp
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@ -49,6 +49,8 @@ CV_EXPORTS_W void resetMyriadDevice();
#define CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2 "Myriad2"
/// Intel(R) Neural Compute Stick 2, NCS2 (USB 03e7:2485), MyriadX (https://software.intel.com/ru-ru/neural-compute-stick)
#define CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X "MyriadX"
#define CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE "ARM_COMPUTE"
#define CV_DNN_INFERENCE_ENGINE_CPU_TYPE_X86 "X86"
/** @brief Returns Inference Engine VPU type.
@ -57,6 +59,11 @@ CV_EXPORTS_W void resetMyriadDevice();
*/
CV_EXPORTS_W cv::String getInferenceEngineVPUType();
/** @brief Returns Inference Engine CPU type.
*
* Specify OpenVINO plugin: CPU or ARM.
*/
CV_EXPORTS_W cv::String getInferenceEngineCPUType();
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace

8
modules/dnn/src/dnn.cpp
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@ -1286,17 +1286,19 @@ struct Net::Impl : public detail::NetImplBase
CV_Assert(preferableBackend != DNN_BACKEND_HALIDE ||
preferableTarget == DNN_TARGET_CPU ||
preferableTarget == DNN_TARGET_OPENCL);
#ifdef HAVE_INF_ENGINE
if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
CV_Assert(
preferableTarget == DNN_TARGET_CPU ||
(preferableTarget == DNN_TARGET_CPU && (!isArmComputePlugin() || preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)) ||
preferableTarget == DNN_TARGET_OPENCL ||
preferableTarget == DNN_TARGET_OPENCL_FP16 ||
preferableTarget == DNN_TARGET_MYRIAD ||
preferableTarget == DNN_TARGET_FPGA
);
}
#endif
if (!netWasAllocated || this->blobsToKeep != blobsToKeep_)
{
if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
@ -1972,8 +1974,8 @@ struct Net::Impl : public detail::NetImplBase
return;
}
bool supportsCPUFallback = preferableTarget == DNN_TARGET_CPU ||
BackendRegistry::checkIETarget(DNN_TARGET_CPU);
bool supportsCPUFallback = !isArmComputePlugin() && (preferableTarget == DNN_TARGET_CPU ||
BackendRegistry::checkIETarget(DNN_TARGET_CPU));
// Build Inference Engine networks from sets of layers that support this
// backend. Split a whole model on several Inference Engine networks if

4
modules/dnn/src/layers/batch_norm_layer.cpp
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@ -382,7 +382,11 @@ public:
shape[1] = weights_.total();
auto weight = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), weights_.data);
auto bias = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), bias_.data);
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2021_2)
auto scale_node = std::make_shared<ngraph::op::v1::Multiply>(ieInpNode, weight, ngraph::op::AutoBroadcastType::NUMPY);
#else
auto scale_node = std::make_shared<ngraph::op::v0::Multiply>(ieInpNode, weight, ngraph::op::AutoBroadcastType::NUMPY);
#endif
auto scale_shift = std::make_shared<ngraph::op::v1::Add>(scale_node, bias, ngraph::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(scale_shift));
}

13
modules/dnn/src/layers/convolution_layer.cpp
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@ -273,10 +273,13 @@ public:
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (ksize == 1)
bool isArmTarget = preferableTarget == DNN_TARGET_CPU && isArmComputePlugin();
if (isArmTarget && blobs.empty())
return false;
if (ksize == 1)
return isArmTarget;
if (ksize == 3)
return preferableTarget == DNN_TARGET_CPU;
return preferableTarget != DNN_TARGET_MYRIAD && !isArmTarget;
if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || preferableTarget != DNN_TARGET_MYRIAD) && blobs.empty())
return false;
return (preferableTarget != DNN_TARGET_MYRIAD || dilation.width == dilation.height);
@ -578,7 +581,7 @@ public:
CV_Assert_N(inputs.size() >= 1, nodes.size() >= 1);
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> dims = ieInpNode->get_shape();
CV_Assert(dims.size() == 4 || dims.size() == 5);
CV_Check(dims.size(), dims.size() >= 3 && dims.size() <= 5, "");
std::shared_ptr<ngraph::Node> ieWeights = nodes.size() > 1 ? nodes[1].dynamicCast<InfEngineNgraphNode>()->node : nullptr;
if (nodes.size() > 1)
CV_Assert(ieWeights); // dynamic_cast should not fail
@ -616,7 +619,7 @@ public:
else
{
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{kernel_shape.size()}, kernel_shape.data());
ngraph::Shape{kernel_shape.size()}, std::vector<int64_t>(kernel_shape.begin(), kernel_shape.end()));
ieWeights = std::make_shared<ngraph::op::v1::Reshape>(ieWeights, shape, true);
}
@ -651,7 +654,7 @@ public:
if (nodes.size() == 3)
{
auto bias_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{shape.size()}, shape.data());
ngraph::Shape{shape.size()}, std::vector<int64_t>(shape.begin(), shape.end()));
bias = std::make_shared<ngraph::op::v1::Reshape>(nodes[2].dynamicCast<InfEngineNgraphNode>()->node, bias_shape, true);
}
else

8
modules/dnn/src/layers/elementwise_layers.cpp
View File

@ -1164,11 +1164,15 @@ struct PowerFunctor : public BaseFunctor
ngraph::Shape{1}, &scale);
auto shift_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &shift);
auto power_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &power);
auto mul = std::make_shared<ngraph::op::v1::Multiply>(scale_node, node, ngraph::op::AutoBroadcastType::NUMPY);
auto scale_shift = std::make_shared<ngraph::op::v1::Add>(mul, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
if (power == 1)
return scale_shift;
auto power_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &power);
return std::make_shared<ngraph::op::v1::Power>(scale_shift, power_node, ngraph::op::AutoBroadcastType::NUMPY);
}
#endif // HAVE_DNN_NGRAPH

23
modules/dnn/src/layers/normalize_bbox_layer.cpp
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@ -324,8 +324,8 @@ public:
if (!acrossSpatial) {
axes_data.push_back(1);
} else {
axes_data.resize(ieInpNode->get_shape().size());
std::iota(axes_data.begin(), axes_data.end(), 0);
axes_data.resize(ieInpNode->get_shape().size() - 1);
std::iota(axes_data.begin(), axes_data.end(), 1);
}
auto axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{axes_data.size()}, axes_data);
auto norm = std::make_shared<ngraph::op::NormalizeL2>(ieInpNode, axes, epsilon, ngraph::op::EpsMode::ADD);
@ -334,19 +334,18 @@ public:
std::vector<size_t> shape(ieInpNode->get_shape().size(), 1);
shape[0] = blobs.empty() ? 1 : batch;
shape[1] = numChannels;
std::shared_ptr<ngraph::op::Constant> weight;
if (blobs.empty())
if (!blobs.empty())
{
std::vector<float> ones(numChannels, 1);
weight = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), ones.data());
}
else
{
weight = std::make_shared<ngraph::op::Constant>(
auto weight = std::make_shared<ngraph::op::Constant>(
ngraph::element::f32, ngraph::Shape(shape), blobs[0].data);
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2021_2)
auto mul = std::make_shared<ngraph::op::v1::Multiply>(norm, weight, ngraph::op::AutoBroadcastType::NUMPY);
#else
auto mul = std::make_shared<ngraph::op::v0::Multiply>(norm, weight, ngraph::op::AutoBroadcastType::NUMPY);
#endif
return Ptr<BackendNode>(new InfEngineNgraphNode(mul));
}
auto mul = std::make_shared<ngraph::op::v0::Multiply>(norm, weight, ngraph::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(mul));
return Ptr<BackendNode>(new InfEngineNgraphNode(norm));
}
#endif // HAVE_DNN_NGRAPH

9
modules/dnn/src/layers/padding_layer.cpp
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@ -97,9 +97,12 @@ public:
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) &&
(preferableTarget != DNN_TARGET_MYRIAD ||
(dstRanges.size() == 4 && paddings[0].first == 0 && paddings[0].second == 0));
{
if (INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) && preferableTarget == DNN_TARGET_MYRIAD)
return dstRanges.size() == 4 && paddings[0].first == 0 && paddings[0].second == 0;
return (dstRanges.size() <= 4 || !isArmComputePlugin());
}
#endif
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_HALIDE && haveHalide() && dstRanges.size() == 4);

4
modules/dnn/src/layers/permute_layer.cpp
View File

@ -105,6 +105,10 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && preferableTarget == DNN_TARGET_CPU)
return _order.size() <= 4 || !isArmComputePlugin();
#endif
return backendId == DNN_BACKEND_OPENCV ||
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine());
}

4
modules/dnn/src/layers/pooling_layer.cpp
View File

@ -205,7 +205,9 @@ public:
#endif
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
return !computeMaxIdx && type != STOCHASTIC && kernel_size.size() > 1;
#ifdef HAVE_DNN_NGRAPH
return !computeMaxIdx && type != STOCHASTIC && kernel_size.size() > 1 && (kernel_size.size() != 3 || !isArmComputePlugin());
#endif
}
else if (backendId == DNN_BACKEND_OPENCV)
{

6
modules/dnn/src/layers/region_layer.cpp
View File

@ -393,8 +393,10 @@ public:
std::vector<int64_t> mask(anchors, 1);
region = std::make_shared<ngraph::op::RegionYolo>(tr_input, coords, classes, anchors, useSoftmax, mask, 1, 3, anchors_vec);
auto tr_shape = tr_input->get_shape();
auto shape_as_inp = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{tr_input->get_shape().size()}, tr_input->get_shape().data());
ngraph::Shape{tr_shape.size()},
std::vector<int64_t>(tr_shape.begin(), tr_shape.end()));
region = std::make_shared<ngraph::op::v1::Reshape>(region, shape_as_inp, true);
new_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{4}, std::vector<int64_t>{0, 2, 3, 1});
@ -540,7 +542,7 @@ public:
result = std::make_shared<ngraph::op::Transpose>(result, tr_axes);
if (b > 1)
{
std::vector<size_t> sizes = {(size_t)b, result->get_shape()[0] / b, result->get_shape()[1]};
std::vector<int64_t> sizes{b, static_cast<int64_t>(result->get_shape()[0]) / b, static_cast<int64_t>(result->get_shape()[1])};
auto shape_node = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{sizes.size()}, sizes.data());
result = std::make_shared<ngraph::op::v1::Reshape>(result, shape_node, true);
}

6
modules/dnn/src/layers/scale_layer.cpp
View File

@ -249,7 +249,11 @@ public:
auto weight = blobs.empty() ? ieInpNode1 :
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), blobs[0].data);
node = std::make_shared<ngraph::op::v0::Multiply>(node, weight, ngraph::op::AutoBroadcastType::NUMPY);
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2021_2)
node = std::make_shared<ngraph::op::v1::Multiply>(node, weight, ngraph::op::AutoBroadcastType::NUMPY);
#else
node = std::make_shared<ngraph::op::v0::Multiply>(node, weight, ngraph::op::AutoBroadcastType::NUMPY);
#endif
}
if (hasBias || !hasWeights)
{

35
modules/dnn/src/op_inf_engine.cpp
View File

@ -651,6 +651,22 @@ InferenceEngine::Core& getCore(const std::string& id)
}
#endif
static bool detectArmPlugin_()
{
InferenceEngine::Core& ie = getCore("CPU");
const std::vector<std::string> devices = ie.GetAvailableDevices();
for (std::vector<std::string>::const_iterator i = devices.begin(); i != devices.end(); ++i)
{
if (i->find("CPU") != std::string::npos)
{
const std::string name = ie.GetMetric(*i, METRIC_KEY(FULL_DEVICE_NAME)).as<std::string>();
CV_LOG_INFO(NULL, "CPU plugin: " << name);
return name.find("arm_compute::NEON") != std::string::npos;
}
}
return false;
}
#if !defined(OPENCV_DNN_IE_VPU_TYPE_DEFAULT)
static bool detectMyriadX_()
{
@ -1162,6 +1178,12 @@ bool isMyriadX()
return myriadX;
}
bool isArmComputePlugin()
{
static bool armPlugin = getInferenceEngineCPUType() == CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE;
return armPlugin;
}
static std::string getInferenceEngineVPUType_()
{
static std::string param_vpu_type = utils::getConfigurationParameterString("OPENCV_DNN_IE_VPU_TYPE", "");
@ -1199,6 +1221,14 @@ cv::String getInferenceEngineVPUType()
return vpu_type;
}
cv::String getInferenceEngineCPUType()
{
static cv::String cpu_type = detectArmPlugin_() ?
CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE :
CV_DNN_INFERENCE_ENGINE_CPU_TYPE_X86;
return cpu_type;
}
#else // HAVE_INF_ENGINE
cv::String getInferenceEngineBackendType()
@ -1214,6 +1244,11 @@ cv::String getInferenceEngineVPUType()
{
CV_Error(Error::StsNotImplemented, "This OpenCV build doesn't include InferenceEngine support");
}
cv::String getInferenceEngineCPUType()
{
CV_Error(Error::StsNotImplemented, "This OpenCV build doesn't include InferenceEngine support");
}
#endif // HAVE_INF_ENGINE

2
modules/dnn/src/op_inf_engine.hpp
View File

@ -254,6 +254,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
bool isMyriadX();
bool isArmComputePlugin();
CV__DNN_EXPERIMENTAL_NS_END
InferenceEngine::Core& getCore(const std::string& id);

1
modules/dnn/test/test_common.hpp
View File

@ -35,6 +35,7 @@
#define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2 "dnn_skip_ie_myriad2"
#define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X "dnn_skip_ie_myriadx"
#define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2, CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X
#define CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU "dnn_skip_ie_arm_cpu"
#ifdef HAVE_INF_ENGINE

6
modules/dnn/test/test_onnx_importer.cpp
View File

@ -144,6 +144,10 @@ TEST_P(Test_ONNX_layers, Convolution_variable_weight_bias)
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU &&
getInferenceEngineCPUType() == CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
String basename = "conv_variable_wb";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
@ -717,6 +721,8 @@ TEST_P(Test_ONNX_layers, Conv1d_variable_weight_bias)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (target == DNN_TARGET_CPU && getInferenceEngineCPUType() == CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
String basename = "conv1d_variable_wb";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));