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a110ede0a2
* G-API: Introduce ONNX backend for Inference
- Basic operations are implemented (Infer, -ROI, -List, -List2);
- Implemented automatic preprocessing for ONNX models;
- Test suite is extended with `OPENCV_GAPI_ONNX_MODEL_PATH` env for test data
(test data is an ONNX Model Zoo repo snapshot);
- Fixed kernel lookup logic in core G-API:
- Lookup NN kernels not in the default package, but in the associated
backend's aux package. Now two NN backends can work in the same graph.
- Added Infer SSD demo and a combined ONNX/IE demo;
* G-API/ONNX: Fix some of CMake issues
Co-authored-by: Pashchenkov, Maxim <maxim.pashchenkov@intel.com>
279 lines
9.7 KiB
C++
279 lines
9.7 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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//
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// Copyright (C) 2020 Intel Corporation
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#include "../test_precomp.hpp"
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#ifdef HAVE_ONNX
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#include <stdexcept>
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#include <onnxruntime_cxx_api.h>
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#include <ade/util/iota_range.hpp>
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#include <opencv2/gapi/infer/onnx.hpp>
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namespace {
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struct ONNXInitPath {
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ONNXInitPath() {
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const char* env_path = getenv("OPENCV_GAPI_ONNX_MODEL_PATH");
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if (env_path)
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cvtest::addDataSearchPath(env_path);
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}
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};
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static ONNXInitPath g_init_path;
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cv::Mat initMatrixRandU(int type, cv::Size sz_in)
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{
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cv::Mat in_mat1 = cv::Mat(sz_in, type);
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if (CV_MAT_DEPTH(type) < CV_32F)
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{
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cv::randu(in_mat1, cv::Scalar::all(0), cv::Scalar::all(255));
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}
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else
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{
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const int fscale = 256; // avoid bits near ULP, generate stable test input
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cv::Mat in_mat32s(in_mat1.size(), CV_MAKE_TYPE(CV_32S, CV_MAT_CN(type)));
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cv::randu(in_mat32s, cv::Scalar::all(0), cv::Scalar::all(255 * fscale));
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in_mat32s.convertTo(in_mat1, type, 1.0f / fscale, 0);
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}
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return in_mat1;
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}
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}
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namespace opencv_test
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{
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namespace {
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// FIXME: taken from the DNN module
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void normAssert(cv::InputArray ref, cv::InputArray test,
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const char *comment /*= ""*/,
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double l1 = 0.00001, double lInf = 0.0001)
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{
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double normL1 = cvtest::norm(ref, test, cv::NORM_L1) / ref.getMat().total();
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EXPECT_LE(normL1, l1) << comment;
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double normInf = cvtest::norm(ref, test, cv::NORM_INF);
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EXPECT_LE(normInf, lInf) << comment;
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}
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std::string findModel(const std::string &model_name)
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{
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return findDataFile("vision/classification/squeezenet/model/" + model_name + ".onnx", false);
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}
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inline void preprocess(const cv::Mat& src,
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cv::Mat& dst,
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const cv::Scalar& mean,
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const cv::Scalar& std) {
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int new_h = 224;
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int new_w = 224;
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cv::Mat tmp, nmat, cvt;
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cv::resize(src, dst, cv::Size(new_w, new_h));
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dst.convertTo(cvt, CV_32F, 1.f / 255);
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nmat = cvt - mean;
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tmp = nmat / std;
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dst.create(cv::Size(new_w, new_h * src.channels()), CV_32F);
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std::vector<cv::Mat> planes;
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for (int i = 0; i < src.channels(); ++i) {
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planes.push_back(dst.rowRange(i * new_h, (i + 1) * new_h));
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}
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cv::split(tmp, planes);
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}
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void InferONNX(const std::string& model_path,
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const cv::Mat& in,
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cv::Mat& out,
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const cv::Scalar& mean,
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const cv::Scalar& std)
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{
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// FIXME: It must be a FIXTURE test!
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Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "test");
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Ort::SessionOptions session_options;
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Ort::Session session(env, model_path.data(), session_options);
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auto input_node_dims = // 0 - one input
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session.GetInputTypeInfo(0).GetTensorTypeAndShapeInfo().GetShape();
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auto output_node_dims = // 0 - one output
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session.GetOutputTypeInfo(0).GetTensorTypeAndShapeInfo().GetShape();
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Ort::AllocatorWithDefaultOptions allocator;
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char* in_node_name_p = session.GetInputName(0, allocator);
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char* out_node_name_p = session.GetOutputName(0, allocator);
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std::string in_node_name(in_node_name_p);
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std::string out_node_name(out_node_name_p);
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allocator.Free(in_node_name_p);
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allocator.Free(out_node_name_p);
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auto memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
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cv::Mat dst;
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preprocess(in, dst, mean, std);
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out.create(std::vector<int>(output_node_dims.begin(),
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output_node_dims.end()), CV_32F); // empty output Mat
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auto in_tensor = Ort::Value::CreateTensor<float>(memory_info,
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dst.ptr<float>(),
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dst.total(),
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input_node_dims.data(),
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input_node_dims.size());
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auto out_tensor = Ort::Value::CreateTensor<float>(memory_info,
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out.ptr<float>(),
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out.total(),
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output_node_dims.data(),
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output_node_dims.size());
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std::vector<const char *> in_names = {in_node_name.data()};
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std::vector<const char *> out_names = {out_node_name.data()};
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session.Run(Ort::RunOptions{nullptr},
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in_names.data(),
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&in_tensor,
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session.GetInputCount(),
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out_names.data(),
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&out_tensor,
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session.GetOutputCount());
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}
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} // anonymous namespace
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TEST(ONNX, Infer)
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{
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cv::Mat in_mat1, out_gapi, out_onnx;
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std::string model_path = findModel("squeezenet1.0-9");
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// NOTE: All tests chek "random" image
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// Ideally it should be a real image
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in_mat1 = initMatrixRandU(CV_8UC3, cv::Size{640, 480});
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cv::Scalar mean = { 0.485, 0.456, 0.406 };
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cv::Scalar std = { 0.229, 0.224, 0.225 };
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// ONNX_API code
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InferONNX(model_path, in_mat1, out_onnx, mean, std);
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// G_API code
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G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet");
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cv::GMat in;
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cv::GMat out = cv::gapi::infer<SqueezNet>(in);
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cv::GComputation comp(cv::GIn(in), cv::GOut(out));
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// NOTE: We have to normalize U8 tensor
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// so cfgMeanStd() is here
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auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({mean},{std});
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comp.apply(cv::gin(in_mat1),
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cv::gout(out_gapi),
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cv::compile_args(cv::gapi::networks(net)));
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// Validate
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ASSERT_EQ(1000u, out_onnx.total());
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ASSERT_EQ(1000u, out_gapi.total());
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normAssert(out_onnx, out_gapi, "Test classification output");
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}
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TEST(ONNX, InferROI)
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{
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cv::Mat in_mat1, out_gapi, out_onnx;
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std::string model_path = findModel("squeezenet1.0-9");
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in_mat1 = initMatrixRandU(CV_8UC3, cv::Size{640, 480});
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cv::Scalar mean = { 0.485, 0.456, 0.406 }; // squeeznet mean
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cv::Scalar std = { 0.229, 0.224, 0.225 }; // squeeznet std
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cv::Rect ROI(cv::Point{0, 0}, cv::Size{250, 250});
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// ONNX_API code
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InferONNX(model_path, in_mat1(ROI), out_onnx, mean, std);
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// G_API code
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G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet");
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cv::GMat in;
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cv::GOpaque<cv::Rect> rect;
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cv::GMat out = cv::gapi::infer<SqueezNet>(rect, in);
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cv::GComputation comp(cv::GIn(in, rect), cv::GOut(out));
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auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({mean},{std});
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comp.apply(cv::gin(in_mat1, ROI),
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cv::gout(out_gapi),
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cv::compile_args(cv::gapi::networks(net)));
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// Validate
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ASSERT_EQ(1000u, out_onnx.total());
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ASSERT_EQ(1000u, out_gapi.total());
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normAssert(out_onnx, out_gapi, "Test classification output");
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}
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TEST(ONNX, InferROIList)
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{
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cv::Mat in_mat1;
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std::string model_path = findModel("squeezenet1.0-9");
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in_mat1 = initMatrixRandU(CV_8UC3, cv::Size{640, 480});
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cv::Scalar mean = { 0.485, 0.456, 0.406 }; // squeeznet mean
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cv::Scalar std = { 0.229, 0.224, 0.225 }; // squeeznet std
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std::vector<cv::Rect> rois = {
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cv::Rect(cv::Point{ 0, 0}, cv::Size{80, 120}),
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cv::Rect(cv::Point{50, 100}, cv::Size{250, 360}),
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};
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std::vector<cv::Mat> out_gapi;
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std::vector<cv::Mat> out_onnx(rois.size());
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// ONNX_API code
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for (size_t i = 0; i < rois.size(); ++i) {
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InferONNX(model_path, in_mat1(rois[i]), out_onnx[i], mean, std);
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}
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// G_API code
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G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet");
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cv::GMat in;
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cv::GArray<cv::Rect> rr;
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cv::GArray<cv::GMat> out = cv::gapi::infer<SqueezNet>(rr, in);
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cv::GComputation comp(cv::GIn(in, rr), cv::GOut(out));
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auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({mean},{std});
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comp.apply(cv::gin(in_mat1, rois),
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cv::gout(out_gapi),
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cv::compile_args(cv::gapi::networks(net)));
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// Validate
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for (size_t i = 0; i < rois.size(); ++i) {
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ASSERT_EQ(1000u, out_onnx[i].total());
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ASSERT_EQ(1000u, out_gapi[i].total());
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normAssert(out_onnx[i], out_gapi[i], "Test classification output");
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}
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}
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TEST(ONNX, Infer2ROIList)
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{
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cv::Mat in_mat1;
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std::string model_path = findModel("squeezenet1.0-9");
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in_mat1 = initMatrixRandU(CV_8UC3, cv::Size{640, 480});
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cv::Scalar mean = { 0.485, 0.456, 0.406 }; // squeeznet mean
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cv::Scalar std = { 0.229, 0.224, 0.225 }; // squeeznet std
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std::vector<cv::Rect> rois = {
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cv::Rect(cv::Point{ 0, 0}, cv::Size{80, 120}),
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cv::Rect(cv::Point{50, 100}, cv::Size{250, 360}),
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};
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std::vector<cv::Mat> out_gapi;
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std::vector<cv::Mat> out_onnx(rois.size());
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// ONNX_API code
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for (size_t i = 0; i < rois.size(); ++i) {
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InferONNX(model_path, in_mat1(rois[i]), out_onnx[i], mean, std);
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}
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// G_API code
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G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet");
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cv::GMat in;
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cv::GArray<cv::Rect> rr;
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cv::GArray<cv::GMat> out = cv::gapi::infer2<SqueezNet>(in,rr);
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cv::GComputation comp(cv::GIn(in, rr), cv::GOut(out));
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auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({mean},{std});
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comp.apply(cv::gin(in_mat1, rois),
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cv::gout(out_gapi),
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cv::compile_args(cv::gapi::networks(net)));
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// Validate
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for (size_t i = 0; i < rois.size(); ++i) {
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ASSERT_EQ(1000u, out_onnx[i].total());
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ASSERT_EQ(1000u, out_gapi[i].total());
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normAssert(out_onnx[i], out_gapi[i], "Test classification output");
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}
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}
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} // namespace opencv_test
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#endif // HAVE_ONNX
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