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Merge pull request #24309 from dkurt:gemm_ov_hotfix

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Update OpenVINO init of new GEMM layer #24309

### Pull Request Readiness Checklist

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

CI validation:

- [x] 2022.1.0: https://pullrequest.opencv.org/buildbot/builders/precommit_custom_linux/builds/100368
- [ ] 2021.4.2: https://pullrequest.opencv.org/buildbot/builders/precommit_custom_linux/builds/100373

Checklist:
- [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 2023-09-27 10:25:45 +03:00 committed by GitHub
parent bb171a0c05
commit 2b6d0f36f0
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 31 additions and 21 deletions
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10
modules/dnn/perf/perf_gemm.cpp
View File

@ -160,8 +160,7 @@ PERF_TEST_P_(Gemm, gemm)
}
Net net;
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 0, id, 0);
net.addLayerToPrev(lp.name, lp.type, lp);
net.setPreferableBackend(backend_id);
net.setPreferableTarget(target_id);
@ -221,14 +220,13 @@ PERF_TEST_P_(Gemm, innerproduct)
}
Net net;
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 0, id, 0);
net.addLayerToPrev(lp.name, lp.type, lp);
net.setPreferableBackend(backend_id);
net.setPreferableTarget(target_id);
// warmup
{
std::vector<std::string> input_names(2);
std::vector<std::string> input_names(1);
input_names[0] = "A";
net.setInputsNames(input_names);
net.setInput(A, input_names[0]);
@ -248,4 +246,4 @@ INSTANTIATE_TEST_CASE_P(/**/, Gemm, Combine(
dnnBackendsAndTargets(false, false) // defined in ../test/test_common.hpp
));
} // namespace
} // namespace

42
modules/dnn/src/layers/gemm_layer.cpp
View File

@ -287,33 +287,45 @@ 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;
std::shared_ptr<ngraph::Node> matmul;
int axis = -2;
if (nodes.size() == 2)
{
auto& inp2 = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
matmul = std::make_shared<ngraph::op::MatMul>(ieInpNode, inp2, transA, transB);
matmul = std::make_shared<ngraph::op::MatMul>(ieInpNode, inp2, trans_a, trans_b);
}
else
{
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,
std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{shape.size()}, shape.data()),
true
);
std::shared_ptr<ngraph::Node> ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, getShape(blobs[0]), blobs[0].data);
std::vector<size_t> weight_shape{(size_t)blobs[0].size[0], (size_t)blobs[0].size[1]};
auto ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, weight_shape, blobs[0].data);
matmul = std::make_shared<ngraph::op::MatMul>(inp, ieWeights, transA, transB);
int flatten_axis = ieInpNode.get_shape().size() - ieWeights->get_shape().size();
if (flatten_axis > 0) {
std::vector<int> shape(1 + flatten_axis, 0);
shape[shape.size() - 1] = -1;
ieInpNode = std::make_shared<ngraph::op::v1::Reshape>(
ieInpNode,
std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{shape.size()}, shape.data()),
true
);
}
matmul = std::make_shared<ngraph::op::MatMul>(ieInpNode, ieWeights, trans_a, trans_b);
}
if (alpha != 1.0f) {
matmul = std::make_shared<ngraph::op::v1::Multiply>(matmul,
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &alpha)
);
}
if (have_bias && const_C) {
auto bias_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{(size_t)blobs.back().size[1]}, blobs.back().data);
Mat bias = blobs.back();
auto shape = bias.total() == bias.size[0] ? ngraph::Shape{bias.total()} : getShape(bias);
std::shared_ptr<ngraph::Node> bias_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, shape, bias.data);
if (beta != 1.0f) {
bias_node = std::make_shared<ngraph::op::v1::Multiply>(bias_node,
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &beta)
);
}
matmul = std::make_shared<ngraph::op::v1::Add>(matmul, bias_node, ngraph::op::AutoBroadcastType::NUMPY);
}
return Ptr<BackendNode>(new InfEngineNgraphNode(matmul));