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Merge pull request #24196 from dkurt:ov_backend_cleanups

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Use ngraph::Output in OpenVINO backend wrapper #24196

### Pull Request Readiness Checklist

resolves https://github.com/opencv/opencv/issues/24102

* Use `ngraph::Output<ngraph::Node>>` insead of `std::shared_ptr<ngraph::Node>` as a backend wrapper. It lets access to multi-output nodes: https://github.com/opencv/opencv/blob/588ddf1b181aa7243144b27d65fc7690fb89e344/modules/dnn/src/net_openvino.cpp#L501-L504
* All layers can be customizable with OpenVINO >= 2022.1. nGraph reference code used for default layer implementation does not required CPU plugin also (might be tested by commenting CPU plugin at `/opt/intel/openvino/runtime/lib/intel64/plugins.xml`).
* Correct inference if only intermediate blobs requested.


See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Dmitry Kurtaev
2023-09-05 18:08:28 +03:00
committed by GitHub
parent 2c53e3f53d
commit 178fdbbda8
26 changed files with 136 additions and 95 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/src/layers/batch_norm_layer.cpp
+1 -1
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@@ -457,7 +457,7 @@ public:
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> shape(ieInpNode->get_shape().size(), 1);
std::vector<size_t> shape(ieInpNode.get_shape().size(), 1);
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);
modules/dnn/src/layers/blank_layer.cpp
+1 -1
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@@ -148,7 +148,7 @@ public:
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
ngraph::OutputVector inp{ieInpNode};
auto blank = std::make_shared<ngraph::op::Concat>(inp, 0);
return Ptr<BackendNode>(new InfEngineNgraphNode(blank));
modules/dnn/src/layers/concat_layer.cpp
+2 -2
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@@ -392,7 +392,7 @@ public:
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
const int numDims = nodes[0].dynamicCast<InfEngineNgraphNode>()->node->get_shape().size();
const int numDims = nodes[0].dynamicCast<InfEngineNgraphNode>()->node.get_shape().size();
const int cAxis = normalize_axis(axis, numDims);
std::vector<size_t> maxDims(numDims, 0);
@@ -403,7 +403,7 @@ public:
auto inp = nodes[i].dynamicCast<InfEngineNgraphNode>()->node;
inp_nodes.push_back(inp);
std::vector<size_t> inpShape = inp->get_shape();
std::vector<size_t> inpShape = inp.get_shape();
for (int i = 0; i < numDims; ++i)
maxDims[i] = std::max(maxDims[i], inpShape[i]);
}
modules/dnn/src/layers/convolution_layer.cpp
+4 -4
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@@ -822,13 +822,13 @@ public:
CV_Assert(!blobs.empty());
CV_Assert_N(inputs.size() >= 1, nodes.size() >= 1);
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> dims = ieInpNode->get_shape();
std::vector<size_t> dims = ieInpNode.get_shape();
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;
ngraph::Output<ngraph::Node> ieWeights;
if (nodes.size() > 1)
CV_Assert(ieWeights); // dynamic_cast should not fail
ieWeights = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
const int inpCn = dims[1];
const int inpGroupCn = nodes.size() > 1 ? ieWeights->get_shape()[1] : blobs[0].size[1];
const int inpGroupCn = nodes.size() > 1 ? ieWeights.get_shape()[1] : blobs[0].size[1];
const int group = inpCn / inpGroupCn;
std::vector<size_t> kernel_shape;
modules/dnn/src/layers/crop_and_resize_layer.cpp
+1 -1
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@@ -133,7 +133,7 @@ public:
auto input = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto rois = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
auto rois_shape = rois->get_shape();
auto rois_shape = rois.get_shape();
std::vector<int64_t> dims(rois_shape.begin(), rois_shape.end()), offsets(4, 0);
offsets[3] = 2;
dims[3] = 7;
modules/dnn/src/layers/elementwise_layers.cpp
+14 -14
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@@ -490,7 +490,7 @@ struct ReLUFunctor : public BaseFunctor
#endif
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
{
if (slope) {
auto param = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &slope);
@@ -674,7 +674,7 @@ struct ReLU6Functor : public BaseFunctor
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
{
return std::make_shared<ngraph::op::Clamp>(node, minValue, maxValue);
}
@@ -796,7 +796,7 @@ struct BaseDefaultFunctor : public BaseFunctor
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
{
CV_Error(Error::StsNotImplemented, "");
}
@@ -929,7 +929,7 @@ struct TanHFunctor : public BaseDefaultFunctor<TanHFunctor>
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
{
return std::make_shared<ngraph::op::Tanh>(node);
}
@@ -998,7 +998,7 @@ struct SwishFunctor : public BaseDefaultFunctor<SwishFunctor>
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
{
auto sigmoid = std::make_shared<ngraph::op::Sigmoid>(node);
return std::make_shared<ngraph::op::v1::Multiply>(node, sigmoid);
@@ -1074,7 +1074,7 @@ struct MishFunctor : public BaseDefaultFunctor<MishFunctor>
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
{
float one = 1.0f;
auto constant = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &one);
@@ -1157,7 +1157,7 @@ struct SigmoidFunctor : public BaseDefaultFunctor<SigmoidFunctor>
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
{
return std::make_shared<ngraph::op::Sigmoid>(node);
}
@@ -1237,7 +1237,7 @@ struct ELUFunctor : public BaseDefaultFunctor<ELUFunctor>
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
{
return std::make_shared<ngraph::op::Elu>(node, alpha);
}
@@ -1307,7 +1307,7 @@ struct AbsValFunctor : public BaseDefaultFunctor<AbsValFunctor>
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
{
float coeff = -0.999999f;
// float coeff = preferableTarget == DNN_TARGET_MYRIAD ? -0.999f : -0.999999f;
@@ -1603,7 +1603,7 @@ struct SqrtFunctor : public BaseDefaultFunctor<SqrtFunctor>
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
{
return std::make_shared<ngraph::op::v0::Sqrt>(node);
}
@@ -2329,7 +2329,7 @@ struct PowerFunctor : public BaseFunctor
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
{
auto scale_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &scale);
@@ -2439,7 +2439,7 @@ struct ExpFunctor : public BaseDefaultFunctor<ExpFunctor>
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
{
auto scale_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &normScale);
@@ -2598,7 +2598,7 @@ struct ChannelsPReLUFunctor : public BaseFunctor
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
{
const size_t numChannels = scale.total();
auto slope = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{numChannels}, scale.data);
@@ -2678,7 +2678,7 @@ struct PReLUFunctor : public ChannelsPReLUFunctor
}
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
{
auto shape = getShape<size_t>(scale);
auto slope = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, shape, scale.ptr<float>());
modules/dnn/src/layers/eltwise_layer.cpp
+9 -6
View File
@@ -896,12 +896,14 @@ public:
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(nodes.size() >= 2);
auto curr_node = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
if (!coeffs.empty()) {
auto coeff = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &coeffs[0]);
curr_node = std::make_shared<ngraph::op::v1::Multiply>(curr_node, coeff, ngraph::op::AutoBroadcastType::NUMPY);
}
std::shared_ptr<ngraph::Node> res;
for (size_t i = 1; i < nodes.size(); i++)
{
auto next_node = nodes[i].dynamicCast<InfEngineNgraphNode>()->node;
@@ -910,15 +912,16 @@ public:
next_node = std::make_shared<ngraph::op::v1::Multiply>(next_node, coeff, ngraph::op::AutoBroadcastType::NUMPY);
}
switch (op) {
case SUM: curr_node = std::make_shared<ngraph::op::v1::Add>(curr_node, next_node); break;
case PROD: curr_node = std::make_shared<ngraph::op::v1::Multiply>(curr_node, next_node); break;
case DIV: curr_node = std::make_shared<ngraph::op::v1::Divide>(curr_node, next_node); break;
case MAX: curr_node = std::make_shared<ngraph::op::v1::Maximum>(curr_node, next_node); break;
case MIN: curr_node = std::make_shared<ngraph::op::v1::Minimum>(curr_node, next_node); break;
case SUM: res = std::make_shared<ngraph::op::v1::Add>(curr_node, next_node); break;
case PROD: res = std::make_shared<ngraph::op::v1::Multiply>(curr_node, next_node); break;
case DIV: res = std::make_shared<ngraph::op::v1::Divide>(curr_node, next_node); break;
case MAX: res = std::make_shared<ngraph::op::v1::Maximum>(curr_node, next_node); break;
case MIN: res = std::make_shared<ngraph::op::v1::Minimum>(curr_node, next_node); break;
default: CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
}
curr_node = res;
}
return Ptr<BackendNode>(new InfEngineNgraphNode(curr_node));
return Ptr<BackendNode>(new InfEngineNgraphNode(res));
}
#endif // HAVE_DNN_NGRAPH
modules/dnn/src/layers/flatten_layer.cpp
+1 -1
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@@ -209,7 +209,7 @@ public:
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> dims = ieInpNode->get_shape();
std::vector<size_t> dims = ieInpNode.get_shape();
int numAxes = dims.size();
int startAxis = normalize_axis(_startAxis, numAxes);
modules/dnn/src/layers/fully_connected_layer.cpp
+1 -1
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@@ -803,7 +803,7 @@ public:
}
else
{
std::vector<int> shape(1 + normalize_axis(axis, ieInpNode->get_shape().size()), 0);
std::vector<int> shape(1 + normalize_axis(axis, ieInpNode.get_shape().size()), 0);
shape[shape.size() - 1] = -1;
auto inp = std::make_shared<ngraph::op::v1::Reshape>(
ieInpNode,
modules/dnn/src/layers/lrn_layer.cpp
+1 -1
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@@ -480,7 +480,7 @@ public:
if (type != SPATIAL_NRM) {
axes = {1};
} else {
axes.resize(ieInpNode->get_shape().size() - 2);
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());
modules/dnn/src/layers/max_unpooling_layer.cpp
+2 -2
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@@ -194,7 +194,7 @@ public:
std::vector<MatShape> inpShapes(nodes.size());
std::vector<MatShape> outShapes, internals;
for (int i = 0; i < nodes.size(); ++i) {
std::vector<size_t> shape = nodes[i].dynamicCast<InfEngineNgraphNode>()->node->get_shape();
std::vector<size_t> shape = nodes[i].dynamicCast<InfEngineNgraphNode>()->node.get_shape();
inpShapes[i] = std::vector<int>(shape.begin(), shape.end());
}
getMemoryShapes(inpShapes, 1, outShapes, internals);
@@ -213,7 +213,7 @@ public:
std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{1}, &newShape),
true
);
if (indices->get_element_type() != ngraph::element::i32 && indices->get_element_type() != ngraph::element::i64) {
if (indices.get_element_type() != ngraph::element::i32 && indices.get_element_type() != ngraph::element::i64) {
indices = std::make_shared<ngraph::op::Convert>(indices, ngraph::element::i64);
}
modules/dnn/src/layers/mvn_layer.cpp
+1 -1
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@@ -390,7 +390,7 @@ public:
auto mvn = std::make_shared<ngraph::op::MVN>(ieInpNode, acrossChannels, normVariance, eps);
#else
int64_t start_axis = acrossChannels ? 1 : 2;
std::vector<int64_t> axes_v(ieInpNode->get_shape().size() - start_axis);
std::vector<int64_t> axes_v(ieInpNode.get_shape().size() - start_axis);
std::iota(axes_v.begin(), axes_v.end(), start_axis);
auto axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{axes_v.size()}, axes_v.data());
auto mvn = std::make_shared<ngraph::op::v6::MVN>(ieInpNode, axes, normVariance, eps, ngraph::op::MVNEpsMode::INSIDE_SQRT);
modules/dnn/src/layers/nary_eltwise_layers.cpp
+3 -3
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@@ -900,12 +900,12 @@ public:
auto& inp0 = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto& inp1 = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
if (inp0->get_element_type() != inp1->get_element_type()) {
if (inp0.get_element_type() != inp1.get_element_type()) {
auto dtype = preferableTarget == DNN_TARGET_OPENCL_FP16 || preferableTarget == DNN_TARGET_MYRIAD ?
ngraph::element::f16 : ngraph::element::f32;
if (inp0->get_element_type() != dtype)
if (inp0.get_element_type() != dtype)
inp0 = std::make_shared<ngraph::op::v0::Convert>(inp0, dtype);
if (inp1->get_element_type() != dtype)
if (inp1.get_element_type() != dtype)
inp1 = std::make_shared<ngraph::op::v0::Convert>(inp1, dtype);
}
modules/dnn/src/layers/normalize_bbox_layer.cpp
+4 -4
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@@ -273,21 +273,21 @@ public:
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
const size_t batch = ieInpNode->get_shape()[0];
const size_t numChannels = ieInpNode->get_shape()[1];
const size_t batch = ieInpNode.get_shape()[0];
const size_t numChannels = ieInpNode.get_shape()[1];
std::vector<int64_t> axes_data;
if (!acrossSpatial) {
axes_data.push_back(1);
} else {
axes_data.resize(ieInpNode->get_shape().size() - 1);
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::v0::NormalizeL2>(ieInpNode, axes, epsilon, ngraph::op::EpsMode::ADD);
CV_Assert(blobs.empty() || numChannels == blobs[0].total());
std::vector<size_t> shape(ieInpNode->get_shape().size(), 1);
std::vector<size_t> shape(ieInpNode.get_shape().size(), 1);
shape[0] = blobs.empty() ? 1 : batch;
shape[1] = numChannels;
if (!blobs.empty())
modules/dnn/src/layers/pooling_layer.cpp
+1 -1
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@@ -601,7 +601,7 @@ public:
return Ptr<BackendNode>(new InfEngineNgraphNode(ave_pool));
}
else if (type == SUM) {
ngraph::Shape inpShape = ieInpNode->get_shape();
ngraph::Shape inpShape = ieInpNode.get_shape();
CV_Assert(inpShape.size() == 2 + kernel_size.size());
std::vector<int64_t> axes;
for (size_t i = 0; i < kernel_size.size(); i++)
modules/dnn/src/layers/proposal_layer.cpp
+2 -2
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@@ -366,10 +366,10 @@ public:
auto& class_logits = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
auto& image_shape = nodes[2].dynamicCast<InfEngineNgraphNode>()->node;
CV_Assert_N(image_shape->get_shape().size() == 2, image_shape->get_shape().front() == 1);
CV_Assert_N(image_shape.get_shape().size() == 2, image_shape.get_shape().front() == 1);
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{1},
std::vector<int64_t>{(int64_t)image_shape->get_shape().back()});
std::vector<int64_t>{(int64_t)image_shape.get_shape().back()});
auto reshape = std::make_shared<ngraph::op::v1::Reshape>(image_shape, shape, true);
auto proposal = std::make_shared<ngraph::op::Proposal>(class_probs, class_logits, reshape, attr);
modules/dnn/src/layers/region_layer.cpp
+2 -2
View File
@@ -466,7 +466,7 @@ public:
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& input = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto parent_shape = input->get_shape();
auto parent_shape = input.get_shape();
int64_t b = parent_shape[0];
int64_t h = parent_shape[1];
int64_t w = parent_shape[2];
@@ -567,7 +567,7 @@ public:
int hNorm, wNorm;
if (nodes.size() > 1)
{
auto node_1_shape = nodes[1].dynamicCast<InfEngineNgraphNode>()->node->get_shape();
auto node_1_shape = nodes[1].dynamicCast<InfEngineNgraphNode>()->node.get_shape();
hNorm = node_1_shape[2];
wNorm = node_1_shape[3];
}
modules/dnn/src/layers/resize_layer.cpp
+1 -1
View File
@@ -443,7 +443,7 @@ public:
std::vector<int64_t> shape = {outHeight, outWidth};
auto out_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, shape.data());
auto& input_shape = ieInpNode->get_shape();
auto& input_shape = ieInpNode.get_shape();
CV_Assert_N(input_shape[2] != 0, input_shape[3] != 0);
std::vector<float> scales = {static_cast<float>(outHeight) / input_shape[2], static_cast<float>(outWidth) / input_shape[3]};
auto scales_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{2}, scales.data());
modules/dnn/src/layers/scale_layer.cpp
+10 -8
View File
@@ -331,34 +331,36 @@ public:
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto ieInpNode0 = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto ieInpNode1 = nodes.size() > 1 ? nodes[1].dynamicCast<InfEngineNgraphNode>()->node : nullptr;
ngraph::Output<ngraph::Node> ieInpNode1;
if (nodes.size() > 1)
ieInpNode1 = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
size_t numChannels = 1;
if (blobs.empty())
for (const size_t& dim : ieInpNode1->get_shape())
for (const size_t& dim : ieInpNode1.get_shape())
numChannels *= dim;
else
numChannels = blobs[0].total();
std::vector<size_t> shape(ieInpNode0->get_shape().size(), 1);
std::vector<size_t> shape(ieInpNode0.get_shape().size(), 1);
int cAxis = normalize_axis(axis, shape.size());
shape[cAxis] = numChannels;
auto node = ieInpNode0;
std::shared_ptr<ngraph::Node> node;
if (hasWeights)
{
auto weight = blobs.empty() ? ieInpNode1 :
ngraph::Output<ngraph::Node> weight = blobs.empty() ? ieInpNode1 :
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)
node = std::make_shared<ngraph::op::v1::Multiply>(node, weight, ngraph::op::AutoBroadcastType::NUMPY);
node = std::make_shared<ngraph::op::v1::Multiply>(ieInpNode0, weight, ngraph::op::AutoBroadcastType::NUMPY);
#else
node = std::make_shared<ngraph::op::v0::Multiply>(node, weight, ngraph::op::AutoBroadcastType::NUMPY);
node = std::make_shared<ngraph::op::v0::Multiply>(ieInpNode0, weight, ngraph::op::AutoBroadcastType::NUMPY);
#endif
}
if (hasBias || !hasWeights)
{
std::shared_ptr<ngraph::Node> bias;
ngraph::Output<ngraph::Node> bias;
if (hasBias)
{
bias = blobs.empty() ? ieInpNode1 :
modules/dnn/src/layers/slice_layer.cpp
+1 -1
View File
@@ -759,7 +759,7 @@ public:
{
CV_Assert_N(nodes.size() <= 2);
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
CV_Assert(finalSliceRanges[0].size() == ieInpNode->get_shape().size());
CV_Assert(finalSliceRanges[0].size() == ieInpNode.get_shape().size());
std::vector<int64_t> offsets, dims;
for (int i = 0; i < finalSliceRanges[0].size(); ++i)
modules/dnn/src/layers/softmax_layer.cpp
+1 -1
View File
@@ -385,7 +385,7 @@ public:
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
int axis = normalize_axis(axisRaw, ieInpNode->get_shape().size());
int axis = normalize_axis(axisRaw, ieInpNode.get_shape().size());
auto softmax = std::make_shared<ngraph::op::v1::Softmax>(ieInpNode, axis);
if (logSoftMax)
return Ptr<BackendNode>(new InfEngineNgraphNode(std::make_shared<ngraph::op::v0::Log>(softmax)));