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Merge pull request #24039 from dkurt:tflite_test_backends

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TFLite models on different backends (tests and improvements) #24039

### Pull Request Readiness Checklist

* MaxUnpooling with OpenVINO
* Fully connected with transposed inputs/weights with OpenVINO
* Enable backends tests for TFLite (related to https://github.com/opencv/opencv/issues/23992#issuecomment-1640691722)
* Increase existing tests thresholds

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
2023-08-04 11:28:51 +03:00
committed by GitHub
parent 96f23e3da1
commit 4b8aeb1129
12 changed files with 211 additions and 67 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/src/layers/fully_connected_layer.cpp
+16 -10
View File
@@ -180,15 +180,12 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
bool tranAorB = transA || transB;
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return axis == 1 && !tranAorB;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
(backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 && !tranAorB) ||
(backendId == DNN_BACKEND_WEBNN && axis == 1 && !tranAorB) ||
backendId == DNN_BACKEND_CANN ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
(backendId == DNN_BACKEND_VKCOM && haveVulkan() && !tranAorB);
}
@@ -802,17 +799,26 @@ public:
if (nodes.size() == 2)
{
auto& inp2 = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
matmul = std::make_shared<ngraph::op::MatMul>(ieInpNode, inp2, false, false);
matmul = std::make_shared<ngraph::op::MatMul>(ieInpNode, inp2, transA, transB);
}
else
{
std::vector<int64_t> data = {(int64_t)ieInpNode->get_shape()[0], (int64_t)blobs[0].size[1]};
auto new_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, data.data());
auto inp = std::make_shared<ngraph::op::v1::Reshape>(ieInpNode, new_shape, true);
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::vector<size_t> weight_shape{(size_t)blobs[0].size[0], (size_t)blobs[0].size[1]};
std::vector<size_t> weight_shape;
if (isMatMul) {
weight_shape = getShape<size_t>(oriMat);
} else {
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, false, true);
matmul = std::make_shared<ngraph::op::MatMul>(inp, ieWeights, transA, transB);
}
if (bias) {
modules/dnn/src/layers/max_unpooling_layer.cpp
+46
View File
@@ -13,6 +13,7 @@ Implementation of Batch Normalization layer.
#include "layers_common.hpp"
#include "../op_cuda.hpp"
#include "../op_halide.hpp"
#include "../ie_ngraph.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/core/utils/logger.hpp>
@@ -41,6 +42,7 @@ public:
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
(backendId == DNN_BACKEND_HALIDE && haveHalide() && !poolPad.width && !poolPad.height);
}
@@ -181,6 +183,50 @@ public:
#endif // HAVE_HALIDE
return Ptr<BackendNode>();
}
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto features = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto indices = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
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();
inpShapes[i] = std::vector<int>(shape.begin(), shape.end());
}
getMemoryShapes(inpShapes, 1, outShapes, internals);
Mat zeros = Mat::zeros(1, total(outShapes[0]), CV_32F);
auto zeroInp = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{zeros.total()}, zeros.data);
int newShape = -1;
features = std::make_shared<ngraph::op::v1::Reshape>(
features,
std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{1}, &newShape),
true
);
indices = std::make_shared<ngraph::op::v1::Reshape>(
indices,
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) {
indices = std::make_shared<ngraph::op::Convert>(indices, ngraph::element::i64);
}
int axis = 0;
std::shared_ptr<ngraph::Node> unpool = std::make_shared<ngraph::op::ScatterElementsUpdate>(zeroInp, indices, features,
std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{1}, &axis));
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{outShapes[0].size()}, outShapes[0].data());
unpool = std::make_shared<ngraph::op::v1::Reshape>(unpool, shape, true);
return Ptr<BackendNode>(new InfEngineNgraphNode(unpool));
}
#endif // HAVE_DNN_NGRAPH
};
Ptr<MaxUnpoolLayer> MaxUnpoolLayer::create(const LayerParams& params)
modules/dnn/src/layers/pooling_layer.cpp
+12 -4
View File
@@ -209,7 +209,7 @@ public:
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
return !computeMaxIdx && type != STOCHASTIC && kernel_size.size() > 1 && (kernel_size.size() != 3 || !isArmComputePlugin());
return type != STOCHASTIC && kernel_size.size() > 1 && (kernel_size.size() != 3 || !isArmComputePlugin());
}
#endif
if (backendId == DNN_BACKEND_OPENCV)
@@ -613,9 +613,17 @@ public:
return Ptr<BackendNode>(new InfEngineNgraphNode(reduce_sum));
}
else if (type == MAX) {
auto max_pool = std::make_shared<ngraph::op::v1::MaxPool>(ieInpNode, ngraph::Strides(strides),
ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size),
rounding_type, pad_type);
std::shared_ptr<ngraph::Node> max_pool;
if (computeMaxIdx) {
std::vector<size_t> dilations(kernel_size.size(), 1);
max_pool = std::make_shared<ngraph::op::v8::MaxPool>(ieInpNode, ngraph::Strides(strides), ngraph::Strides(dilations),
ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size),
rounding_type, pad_type);
} else {
max_pool = std::make_shared<ngraph::op::v1::MaxPool>(ieInpNode, ngraph::Strides(strides),
ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size),
rounding_type, pad_type);
}
return Ptr<BackendNode>(new InfEngineNgraphNode(max_pool));
}
else if (type == ROI) {
modules/dnn/src/layers/resize_layer.cpp
+8 -2
View File
@@ -410,7 +410,10 @@ public:
}
attrs.shape_calculation_mode = ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES;
if (alignCorners) {
CV_Assert(!halfPixelCenters || !alignCorners);
if (halfPixelCenters) {
attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::HALF_PIXEL;
} else if (alignCorners) {
attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::ALIGN_CORNERS;
}
@@ -427,7 +430,10 @@ public:
}
attrs.shape_calculation_mode = ngraph::op::v4::Interpolate::ShapeCalcMode::sizes;
if (alignCorners) {
CV_Assert(!halfPixelCenters || !alignCorners);
if (halfPixelCenters) {
attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::half_pixel;
} else if (alignCorners) {
attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::align_corners;
}