vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b

modules/dnn/test/test_onnx_ort_wrapper.cpp

@@ -0,0 +1,90 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#include "test_precomp.hpp"
+#include "npy_blob.hpp"
+
+namespace opencv_test { namespace {
+
+#ifdef HAVE_ONNXRUNTIME
+
+static std::string _tf(const std::string& filename, bool required = true)
+{
+    return findDataFile(std::string("dnn/onnx/") + filename, required);
+}
+
+static cv::dnn::Net readNetFromONNX_ORT(const std::string& onnxModelPath)
+{
+    cv::dnn::Net net = cv::dnn::readNetFromONNX(onnxModelPath, cv::dnn::ENGINE_ORT);
+    EXPECT_FALSE(net.empty());
+    return net;
+}
+
+TEST(Test_ONNX_ORT_Wrapper, SingleInputSingleOutput)
+{
+    const std::string basename = "convolution";
+    const std::string onnxmodel = _tf("models/" + basename + ".onnx", true);
+
+    cv::Mat input = blobFromNPY(_tf("data/input_" + basename + ".npy"));
+    cv::Mat ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
+
+    cv::dnn::Net net = readNetFromONNX_ORT(onnxmodel);
+    net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
+    net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
+
+    net.setInput(input);
+    cv::Mat out = net.forward();
+
+    normAssert(ref, out, "ORT 1in/1out convolution", 1e-5, 1e-4);
+}
+
+TEST(Test_ONNX_ORT_Wrapper, MultipleInputSingleOutput)
+{
+    const std::string basename = "min";
+    const std::string onnxmodel = _tf("models/" + basename + ".onnx", true);
+
+    cv::Mat inp0 = blobFromNPY(_tf("data/input_" + basename + "_0.npy"));
+    cv::Mat inp1 = blobFromNPY(_tf("data/input_" + basename + "_1.npy"));
+    cv::Mat ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
+
+    cv::dnn::Net net = readNetFromONNX_ORT(onnxmodel);
+    net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
+    net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
+
+    net.setInput(inp0, "0");
+    net.setInput(inp1, "1");
+    cv::Mat out = net.forward();
+
+    normAssert(ref, out, "ORT 2in/1out min", 1e-5, 1e-4);
+}
+
+TEST(Test_ONNX_ORT_Wrapper, SingleInputMultipleOutput)
+{
+    const std::string basename = "top_k";
+    const std::string onnxmodel = _tf("models/" + basename + ".onnx", true);
+
+    cv::Mat input = cv::dnn::readTensorFromONNX(_tf("data/input_" + basename + ".pb"));
+    cv::Mat ref_val = cv::dnn::readTensorFromONNX(_tf("data/output_" + basename + "_0.pb"));
+    cv::Mat ref_ind = cv::dnn::readTensorFromONNX(_tf("data/output_" + basename + "_1.pb"));
+
+    cv::dnn::Net net = readNetFromONNX_ORT(onnxmodel);
+    net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
+    net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
+
+    net.setInput(input);
+    std::vector<cv::Mat> outputs;
+    net.forward(outputs, std::vector<std::string>{"values", "indices"});
+    ASSERT_EQ(outputs.size(), 2u);
+
+    normAssert(ref_val, outputs[0], "ORT top_k values", 1e-5, 1e-4);
+    normAssert(ref_ind, outputs[1], "ORT top_k indices", 0.0, 0.0);
+}
+
+#else  // HAVE_ONNXRUNTIME
+
+TEST(Test_ONNX_ORT_Wrapper, DISABLED_NoONNXRuntime) {}
+
+#endif
+
+}} // namespace