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https://github.com/opencv/opencv.git
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Abhishek Gola
95c66292b5
Added ONNX Runtime as an optional wrapper #28444 This PR adds ONNXRuntime (ORT) as an _optional_ wrapper, which can be enabled by adding **WITH_ONNXRUNTIME** flag in CMake command. Using ORT wrapper the inference time for _resnet50.onnx model_ has come to _**~7ms**_ from _**~14ms**_. Also, we are able to run models like `ssd_mobilenet_v1.onnx`. ### Pull Request Readiness Checklist 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
91 lines
3.0 KiB
C++
91 lines
3.0 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "test_precomp.hpp"
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#include "npy_blob.hpp"
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namespace opencv_test { namespace {
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#ifdef HAVE_ONNXRUNTIME
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static std::string _tf(const std::string& filename, bool required = true)
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{
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return findDataFile(std::string("dnn/onnx/") + filename, required);
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}
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static cv::dnn::Net readNetFromONNX_ORT(const std::string& onnxModelPath)
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{
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cv::dnn::Net net = cv::dnn::readNetFromONNX(onnxModelPath, cv::dnn::ENGINE_ORT);
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EXPECT_FALSE(net.empty());
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return net;
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}
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TEST(Test_ONNX_ORT_Wrapper, SingleInputSingleOutput)
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{
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const std::string basename = "convolution";
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const std::string onnxmodel = _tf("models/" + basename + ".onnx", true);
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cv::Mat input = blobFromNPY(_tf("data/input_" + basename + ".npy"));
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cv::Mat ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
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cv::dnn::Net net = readNetFromONNX_ORT(onnxmodel);
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net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
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net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
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net.setInput(input);
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cv::Mat out = net.forward();
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normAssert(ref, out, "ORT 1in/1out convolution", 1e-5, 1e-4);
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}
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TEST(Test_ONNX_ORT_Wrapper, MultipleInputSingleOutput)
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{
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const std::string basename = "min";
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const std::string onnxmodel = _tf("models/" + basename + ".onnx", true);
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cv::Mat inp0 = blobFromNPY(_tf("data/input_" + basename + "_0.npy"));
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cv::Mat inp1 = blobFromNPY(_tf("data/input_" + basename + "_1.npy"));
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cv::Mat ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
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cv::dnn::Net net = readNetFromONNX_ORT(onnxmodel);
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net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
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net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
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net.setInput(inp0, "0");
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net.setInput(inp1, "1");
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cv::Mat out = net.forward();
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normAssert(ref, out, "ORT 2in/1out min", 1e-5, 1e-4);
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}
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TEST(Test_ONNX_ORT_Wrapper, SingleInputMultipleOutput)
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{
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const std::string basename = "top_k";
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const std::string onnxmodel = _tf("models/" + basename + ".onnx", true);
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cv::Mat input = cv::dnn::readTensorFromONNX(_tf("data/input_" + basename + ".pb"));
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cv::Mat ref_val = cv::dnn::readTensorFromONNX(_tf("data/output_" + basename + "_0.pb"));
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cv::Mat ref_ind = cv::dnn::readTensorFromONNX(_tf("data/output_" + basename + "_1.pb"));
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cv::dnn::Net net = readNetFromONNX_ORT(onnxmodel);
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net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
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net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
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net.setInput(input);
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std::vector<cv::Mat> outputs;
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net.forward(outputs, std::vector<std::string>{"values", "indices"});
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ASSERT_EQ(outputs.size(), 2u);
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normAssert(ref_val, outputs[0], "ORT top_k values", 1e-5, 1e-4);
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normAssert(ref_ind, outputs[1], "ORT top_k indices", 0.0, 0.0);
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}
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#else // HAVE_ONNXRUNTIME
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TEST(Test_ONNX_ORT_Wrapper, DISABLED_NoONNXRuntime) {}
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#endif
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}} // namespace
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