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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
15 changed files with 107 additions and 29 deletions
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modules/dnn/src/layers/batch_norm_layer.cpp
+4
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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));
}
modules/dnn/src/layers/convolution_layer.cpp
+8 -5
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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
modules/dnn/src/layers/elementwise_layers.cpp
+6 -2
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
modules/dnn/src/layers/normalize_bbox_layer.cpp
+11 -12
View File
@@ -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
modules/dnn/src/layers/padding_layer.cpp
+6 -3
View File
@@ -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);
modules/dnn/src/layers/permute_layer.cpp
+4
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@@ -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());
}
modules/dnn/src/layers/pooling_layer.cpp
+3 -1
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)
{
modules/dnn/src/layers/region_layer.cpp
+4 -2
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@@ -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);
}
modules/dnn/src/layers/scale_layer.cpp
+5 -1
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@@ -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)
{