//------------------------------------------------------------------------------ int main(int argc, char ** argv) { // Run with no args tests default (CPU) backend. // "-backend all" tests all available backends. // "-backend <name>" tests one backend. parseArgs(argc, argv); // Make sure we have an OpenGL context : create dummy GLFW window if (not glfwInit()) { printf("Failed to initialize GLFW\n"); return 1; } int width=10, height=10; #if GLFW_VERSION_MAJOR>=3 static const char windowTitle[] = "OpenSubdiv OSD regression"; if (not (g_window=glfwCreateWindow(width, height, windowTitle, NULL, NULL))) { printf("Failed to open window.\n"); glfwTerminate(); return 1; } glfwMakeContextCurrent(g_window); #else if (glfwOpenWindow(width, height, 8, 8, 8, 8, 24, 8,GLFW_WINDOW) == GL_FALSE) { printf("Failed to open window.\n"); glfwTerminate(); return 1; } #endif #if defined(OSD_USES_GLEW) if (GLenum r = glewInit() != GLEW_OK) { printf("Failed to initialize glew. error = %d\n", r); exit(1); } #endif printf("precision : %f\n",PRECISION); int levels=5, total=0; if (g_Backend == -1) { for (int i = 0; i < kBackendCount; ++i) total += checkBackend (i, levels); } else { total += checkBackend (g_Backend, levels); } glfwTerminate(); if (total==0) printf("All tests passed.\n"); else printf("Total failures : %d\n", total); }
void runTorchNet(const String& prefix, String outLayerName = "", bool check2ndBlob = false, bool isBinary = false, double l1 = 0.0, double lInf = 0.0) { String suffix = (isBinary) ? ".dat" : ".txt"; Mat inp, outRef; ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) ); ASSERT_NO_THROW( outRef = readTorchBlob(_tf(prefix + "_output" + suffix), isBinary) ); checkBackend(backend, target, &inp, &outRef); Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary); ASSERT_FALSE(net.empty()); net.setPreferableBackend(backend); net.setPreferableTarget(target); if (outLayerName.empty()) outLayerName = net.getLayerNames().back(); net.setInput(inp); std::vector<Mat> outBlobs; net.forward(outBlobs, outLayerName); l1 = l1 ? l1 : default_l1; lInf = lInf ? lInf : default_lInf; normAssert(outRef, outBlobs[0], "", l1, lInf); if (check2ndBlob && backend != DNN_BACKEND_INFERENCE_ENGINE) { Mat out2 = outBlobs[1]; Mat ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary); normAssert(out2, ref2, "", l1, lInf); } }
TEST_P(Test_Torch_nets, OpenFace_accuracy) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000 if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) throw SkipTestException("Test is enabled starts from OpenVINO 2018R3"); #endif checkBackend(); if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) throw SkipTestException(""); const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false); Net net = readNetFromTorch(model); net.setPreferableBackend(backend); net.setPreferableTarget(target); Mat sample = imread(findDataFile("cv/shared/lena.png", false)); Mat sampleF32(sample.size(), CV_32FC3); sample.convertTo(sampleF32, sampleF32.type()); sampleF32 /= 255; resize(sampleF32, sampleF32, Size(96, 96), 0, 0, INTER_NEAREST); Mat inputBlob = blobFromImage(sampleF32, 1.0, Size(), Scalar(), /*swapRB*/true); net.setInput(inputBlob); Mat out = net.forward(); Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true); normAssert(out, outRef, "", default_l1, default_lInf); }
TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8) { checkBackend(); std::string proto = findDataFile("dnn/opencv_face_detector.pbtxt", false); std::string model = findDataFile("dnn/opencv_face_detector_uint8.pb", false); Net net = readNetFromTensorflow(model, proto); Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false)); Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false); net.setPreferableBackend(backend); net.setPreferableTarget(target); net.setInput(blob); // Output has shape 1x1xNx7 where N - number of detections. // An every detection is a vector of values [id, classId, confidence, left, top, right, bottom] Mat out = net.forward(); // References are from test for Caffe model. Mat ref = (Mat_<float>(6, 7) << 0, 1, 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631, 0, 1, 0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168, 0, 1, 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290, 0, 1, 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477, 0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494, 0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801); double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 4e-3 : 3.4e-3; double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.024 : 1e-2; normAssertDetections(ref, out, "", 0.9, scoreDiff, iouDiff); }
TEST_P(Test_TensorFlow_nets, Faster_RCNN) { static std::string names[] = {"faster_rcnn_inception_v2_coco_2018_01_28", "faster_rcnn_resnet50_coco_2018_01_28"}; checkBackend(); if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) || (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)) throw SkipTestException(""); for (int i = 1; i < 2; ++i) { std::string proto = findDataFile("dnn/" + names[i] + ".pbtxt", false); std::string model = findDataFile("dnn/" + names[i] + ".pb", false); Net net = readNetFromTensorflow(model, proto); net.setPreferableBackend(backend); net.setPreferableTarget(target); Mat img = imread(findDataFile("dnn/dog416.png", false)); Mat blob = blobFromImage(img, 1.0f, Size(800, 600), Scalar(), true, false); net.setInput(blob); Mat out = net.forward(); Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/" + names[i] + ".detection_out.npy")); normAssertDetections(ref, out, names[i].c_str(), 0.3); } }
TEST_P(Test_TensorFlow_nets, Inception_v2_SSD) { checkBackend(); std::string proto = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pbtxt", false); std::string model = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pb", false); Net net = readNetFromTensorflow(model, proto); Mat img = imread(findDataFile("dnn/street.png", false)); Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false); net.setPreferableBackend(backend); net.setPreferableTarget(target); net.setInput(blob); // Output has shape 1x1xNx7 where N - number of detections. // An every detection is a vector of values [id, classId, confidence, left, top, right, bottom] Mat out = net.forward(); Mat ref = (Mat_<float>(5, 7) << 0, 1, 0.90176028, 0.19872092, 0.36311883, 0.26461923, 0.63498729, 0, 3, 0.93569964, 0.64865261, 0.45906419, 0.80675775, 0.65708131, 0, 3, 0.75838411, 0.44668293, 0.45907149, 0.49459291, 0.52197015, 0, 10, 0.95932811, 0.38349164, 0.32528657, 0.40387636, 0.39165527, 0, 10, 0.93973452, 0.66561931, 0.37841269, 0.68074018, 0.42907384); double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0097 : default_l1; double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : default_lInf; normAssertDetections(ref, out, "", 0.5, scoreDiff, iouDiff); }
// inp = cv.imread('opencv_extra/testdata/cv/ximgproc/sources/08.png') // inp = inp[:,:,[2, 1, 0]].astype(np.float32).reshape(1, 512, 512, 3) // outs = sess.run([sess.graph.get_tensor_by_name('feature_fusion/Conv_7/Sigmoid:0'), // sess.graph.get_tensor_by_name('feature_fusion/concat_3:0')], // feed_dict={'input_images:0': inp}) // scores = np.ascontiguousarray(outs[0].transpose(0, 3, 1, 2)) // geometry = np.ascontiguousarray(outs[1].transpose(0, 3, 1, 2)) // np.save('east_text_detection.scores.npy', scores) // np.save('east_text_detection.geometry.npy', geometry) TEST_P(Test_TensorFlow_nets, EAST_text_detection) { checkBackend(); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000 if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) throw SkipTestException("Test is enabled starts from OpenVINO 2018R3"); #endif std::string netPath = findDataFile("dnn/frozen_east_text_detection.pb", false); std::string imgPath = findDataFile("cv/ximgproc/sources/08.png", false); std::string refScoresPath = findDataFile("dnn/east_text_detection.scores.npy", false); std::string refGeometryPath = findDataFile("dnn/east_text_detection.geometry.npy", false); Net net = readNet(findDataFile("dnn/frozen_east_text_detection.pb", false)); net.setPreferableBackend(backend); net.setPreferableTarget(target); Mat img = imread(imgPath); Mat inp = blobFromImage(img, 1.0, Size(), Scalar(123.68, 116.78, 103.94), true, false); net.setInput(inp); std::vector<Mat> outs; std::vector<String> outNames(2); outNames[0] = "feature_fusion/Conv_7/Sigmoid"; outNames[1] = "feature_fusion/concat_3"; net.forward(outs, outNames); Mat scores = outs[0]; Mat geometry = outs[1]; // Scores are in range [0, 1]. Geometry values are in range [-0.23, 290] double l1_scores = default_l1, lInf_scores = default_lInf; double l1_geometry = default_l1, lInf_geometry = default_lInf; if (target == DNN_TARGET_OPENCL_FP16) { lInf_scores = 0.11; l1_geometry = 0.28; lInf_geometry = 5.94; } else if (target == DNN_TARGET_MYRIAD) { lInf_scores = 0.214; l1_geometry = 0.47; lInf_geometry = 15.34; } else { l1_geometry = 1e-4, lInf_geometry = 3e-3; } normAssert(scores, blobFromNPY(refScoresPath), "scores", l1_scores, lInf_scores); normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", l1_geometry, lInf_geometry); }
// Check accuracy of style transfer models from https://github.com/jcjohnson/fast-neural-style // th fast_neural_style.lua \ // -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \ // -output_image lena.png \ // -median_filter 0 \ // -image_size 0 \ // -model models/eccv16/starry_night.t7 // th fast_neural_style.lua \ // -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \ // -output_image lena.png \ // -median_filter 0 \ // -image_size 0 \ // -model models/instance_norm/feathers.t7 TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy) { checkBackend(); #if defined(INF_ENGINE_RELEASE) #if INF_ENGINE_RELEASE <= 2018050000 if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL) throw SkipTestException(""); #endif #endif std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7", "dnn/fast_neural_style_instance_norm_feathers.t7"}; std::string targets[] = {"dnn/lena_starry_night.png", "dnn/lena_feathers.png"}; for (int i = 0; i < 2; ++i) { const string model = findDataFile(models[i], false); Net net = readNetFromTorch(model); net.setPreferableBackend(backend); net.setPreferableTarget(target); Mat img = imread(findDataFile("dnn/googlenet_1.png", false)); Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false); net.setInput(inputBlob); Mat out = net.forward(); // Deprocessing. getPlane(out, 0, 0) += 103.939; getPlane(out, 0, 1) += 116.779; getPlane(out, 0, 2) += 123.68; out = cv::min(cv::max(0, out), 255); Mat ref = imread(findDataFile(targets[i])); Mat refBlob = blobFromImage(ref, 1.0, Size(), Scalar(), false); if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) { double normL1 = cvtest::norm(refBlob, out, cv::NORM_L1) / refBlob.total(); if (target == DNN_TARGET_MYRIAD) EXPECT_LE(normL1, 4.0f); else EXPECT_LE(normL1, 0.6f); } else normAssert(out, refBlob, "", 0.5, 1.1); } }
void testDarknetLayer(const std::string& name, bool hasWeights = false) { std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg", false); std::string model = ""; if (hasWeights) model = findDataFile("dnn/darknet/" + name + ".weights", false); Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy", false)); Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy", false)); checkBackend(&inp, &ref); Net net = readNet(cfg, model); net.setPreferableBackend(backend); net.setPreferableTarget(target); net.setInput(inp); Mat out = net.forward(); normAssert(out, ref, "", default_l1, default_lInf); }
void testONNXModels(const String& basename, const Extension ext = npy, const double l1 = 0, const float lInf = 0, const bool useSoftmax = false, bool checkNoFallbacks = true) { String onnxmodel = _tf("models/" + basename + ".onnx"); Mat inp, ref; if (ext == npy) { inp = blobFromNPY(_tf("data/input_" + basename + ".npy")); ref = blobFromNPY(_tf("data/output_" + basename + ".npy")); } else if (ext == pb) { inp = readTensorFromONNX(_tf("data/input_" + basename + ".pb")); ref = readTensorFromONNX(_tf("data/output_" + basename + ".pb")); } else CV_Error(Error::StsUnsupportedFormat, "Unsupported extension"); checkBackend(&inp, &ref); Net net = readNetFromONNX(onnxmodel); ASSERT_FALSE(net.empty()); net.setPreferableBackend(backend); net.setPreferableTarget(target); net.setInput(inp); Mat out = net.forward(""); if (useSoftmax) { LayerParams lp; Net netSoftmax; netSoftmax.addLayerToPrev("softmaxLayer", "SoftMax", lp); netSoftmax.setPreferableBackend(DNN_BACKEND_OPENCV); netSoftmax.setInput(out); out = netSoftmax.forward(); netSoftmax.setInput(ref); ref = netSoftmax.forward(); } normAssert(ref, out, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf); if (checkNoFallbacks) expectNoFallbacksFromIE(net); }
TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN) { checkBackend(); std::string proto = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pbtxt", false); std::string model = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pb", false); Net net = readNetFromTensorflow(model, proto); Mat img = imread(findDataFile("dnn/dog416.png", false)); Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_ppn_coco.detection_out.npy", false)); Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false); net.setPreferableBackend(backend); net.setPreferableTarget(target); net.setInput(blob); Mat out = net.forward(); double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : default_l1; double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.021 : default_lInf; normAssertDetections(ref, out, "", 0.4, scoreDiff, iouDiff); }
TEST_P(Test_TensorFlow_nets, MobileNet_SSD) { checkBackend(); if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) || (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)) throw SkipTestException(""); std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false); std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false); std::string imgPath = findDataFile("dnn/street.png", false); Mat inp; resize(imread(imgPath), inp, Size(300, 300)); inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true); std::vector<String> outNames(3); outNames[0] = "concat"; outNames[1] = "concat_1"; outNames[2] = "detection_out"; std::vector<Mat> refs(outNames.size()); for (int i = 0; i < outNames.size(); ++i) { std::string path = findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco." + outNames[i] + ".npy", false); refs[i] = blobFromNPY(path); } Net net = readNetFromTensorflow(netPath, netConfig); net.setPreferableBackend(backend); net.setPreferableTarget(target); net.setInput(inp); std::vector<Mat> output; net.forward(output, outNames); normAssert(refs[0].reshape(1, 1), output[0].reshape(1, 1), "", 1e-5, 1.5e-4); normAssert(refs[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 3e-4); normAssertDetections(refs[2], output[2], "", 0.2); }
TEST_P(Test_ONNX_nets, Alexnet) { applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB); const String model = _tf("models/alexnet.onnx"); Net net = readNetFromONNX(model); ASSERT_FALSE(net.empty()); net.setPreferableBackend(backend); net.setPreferableTarget(target); Mat inp = imread(_tf("../grace_hopper_227.png")); Mat ref = blobFromNPY(_tf("../caffe_alexnet_prob.npy")); checkBackend(&inp, &ref); net.setInput(blobFromImage(inp, 1.0f, Size(227, 227), Scalar(), false)); ASSERT_FALSE(net.empty()); Mat out = net.forward(); normAssert(out, ref, "", default_l1, default_lInf); expectNoFallbacksFromIE(net); }
TEST_P(Test_ONNX_layers, MultyInputs) { const String model = _tf("models/multy_inputs.onnx"); Net net = readNetFromONNX(model); ASSERT_FALSE(net.empty()); net.setPreferableBackend(backend); net.setPreferableTarget(target); Mat inp1 = blobFromNPY(_tf("data/input_multy_inputs_0.npy")); Mat inp2 = blobFromNPY(_tf("data/input_multy_inputs_1.npy")); Mat ref = blobFromNPY(_tf("data/output_multy_inputs.npy")); checkBackend(&inp1, &ref); net.setInput(inp1, "0"); net.setInput(inp2, "1"); Mat out = net.forward(); normAssert(ref, out, "", default_l1, default_lInf); expectNoFallbacksFromIE(net); }
void runTensorFlowNet(const std::string& prefix, bool hasText = false, double l1 = 0.0, double lInf = 0.0, bool memoryLoad = false) { std::string netPath = path(prefix + "_net.pb"); std::string netConfig = (hasText ? path(prefix + "_net.pbtxt") : ""); std::string inpPath = path(prefix + "_in.npy"); std::string outPath = path(prefix + "_out.npy"); cv::Mat input = blobFromNPY(inpPath); cv::Mat ref = blobFromNPY(outPath); checkBackend(&input, &ref); Net net; if (memoryLoad) { // Load files into a memory buffers string dataModel; ASSERT_TRUE(readFileInMemory(netPath, dataModel)); string dataConfig; if (hasText) { ASSERT_TRUE(readFileInMemory(netConfig, dataConfig)); } net = readNetFromTensorflow(dataModel.c_str(), dataModel.size(), dataConfig.c_str(), dataConfig.size()); } else net = readNetFromTensorflow(netPath, netConfig); ASSERT_FALSE(net.empty()); net.setPreferableBackend(backend); net.setPreferableTarget(target); net.setInput(input); cv::Mat output = net.forward(); normAssert(ref, output, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf); }
TEST_P(Test_Torch_nets, ENet_accuracy) { checkBackend(); if (backend == DNN_BACKEND_INFERENCE_ENGINE || (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)) throw SkipTestException(""); Net net; { const string model = findDataFile("dnn/Enet-model-best.net", false); net = readNetFromTorch(model, true); ASSERT_TRUE(!net.empty()); } net.setPreferableBackend(backend); net.setPreferableTarget(target); Mat sample = imread(_tf("street.png", false)); Mat inputBlob = blobFromImage(sample, 1./255, Size(), Scalar(), /*swapRB*/true); net.setInput(inputBlob, ""); Mat out = net.forward(); Mat ref = blobFromNPY(_tf("torch_enet_prob.npy", false)); // Due to numerical instability in Pooling-Unpooling layers (indexes jittering) // thresholds for ENet must be changed. Accuracy of results was checked on // Cityscapes dataset and difference in mIOU with Torch is 10E-4% normAssert(ref, out, "", 0.00044, /*target == DNN_TARGET_CPU ? 0.453 : */0.552); normAssertSegmentation(ref, out); const int N = 3; for (int i = 0; i < N; i++) { net.setInput(inputBlob, ""); Mat out = net.forward(); normAssert(ref, out, "", 0.00044, /*target == DNN_TARGET_CPU ? 0.453 : */0.552); normAssertSegmentation(ref, out); } }
// Test object detection network from Darknet framework. void testDarknetModel(const std::string& cfg, const std::string& weights, const std::vector<std::vector<int> >& refClassIds, const std::vector<std::vector<float> >& refConfidences, const std::vector<std::vector<Rect2d> >& refBoxes, double scoreDiff, double iouDiff, float confThreshold = 0.24, float nmsThreshold = 0.4) { checkBackend(); Mat img1 = imread(_tf("dog416.png")); Mat img2 = imread(_tf("street.png")); std::vector<Mat> samples(2); samples[0] = img1; samples[1] = img2; // determine test type, whether batch or single img int batch_size = refClassIds.size(); CV_Assert(batch_size == 1 || batch_size == 2); samples.resize(batch_size); Mat inp = blobFromImages(samples, 1.0/255, Size(416, 416), Scalar(), true, false); Net net = readNet(findDataFile("dnn/" + cfg, false), findDataFile("dnn/" + weights, false)); net.setPreferableBackend(backend); net.setPreferableTarget(target); net.setInput(inp); std::vector<Mat> outs; net.forward(outs, getOutputsNames(net)); for (int b = 0; b < batch_size; ++b) { std::vector<int> classIds; std::vector<float> confidences; std::vector<Rect2d> boxes; for (int i = 0; i < outs.size(); ++i) { Mat out; if (batch_size > 1){ // get the sample slice from 3D matrix (batch, box, classes+5) Range ranges[3] = {Range(b, b+1), Range::all(), Range::all()}; out = outs[i](ranges).reshape(1, outs[i].size[1]); }else{ out = outs[i]; } for (int j = 0; j < out.rows; ++j) { Mat scores = out.row(j).colRange(5, out.cols); double confidence; Point maxLoc; minMaxLoc(scores, 0, &confidence, 0, &maxLoc); if (confidence > confThreshold) { float* detection = out.ptr<float>(j); double centerX = detection[0]; double centerY = detection[1]; double width = detection[2]; double height = detection[3]; boxes.push_back(Rect2d(centerX - 0.5 * width, centerY - 0.5 * height, width, height)); confidences.push_back(confidence); classIds.push_back(maxLoc.x); } } } // here we need NMS of boxes std::vector<int> indices; NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices); std::vector<int> nms_classIds; std::vector<float> nms_confidences; std::vector<Rect2d> nms_boxes; for (size_t i = 0; i < indices.size(); ++i) { int idx = indices[i]; Rect2d box = boxes[idx]; float conf = confidences[idx]; int class_id = classIds[idx]; nms_boxes.push_back(box); nms_confidences.push_back(conf); nms_classIds.push_back(class_id); } normAssertDetections(refClassIds[b], refConfidences[b], refBoxes[b], nms_classIds, nms_confidences, nms_boxes, format("batch size %d, sample %d\n", batch_size, b).c_str(), confThreshold, scoreDiff, iouDiff); } }
void checkBackend(CheckedFrom c, ArrayRef<Tensor> tensors, at::Backend backend) { for (auto &t : tensors) { checkBackend(c, t, backend); } }
TEST_P(Test_ONNX_nets, Googlenet) { if (backend == DNN_BACKEND_INFERENCE_ENGINE) throw SkipTestException(""); const String model = _tf("models/googlenet.onnx"); Net net = readNetFromONNX(model); ASSERT_FALSE(net.empty()); net.setPreferableBackend(backend); net.setPreferableTarget(target); std::vector<Mat> images; images.push_back( imread(_tf("../googlenet_0.png")) ); images.push_back( imread(_tf("../googlenet_1.png")) ); Mat inp = blobFromImages(images, 1.0f, Size(), Scalar(), false); Mat ref = blobFromNPY(_tf("../googlenet_prob.npy")); checkBackend(&inp, &ref); net.setInput(inp); ASSERT_FALSE(net.empty()); Mat out = net.forward(); normAssert(ref, out, "", default_l1, default_lInf); expectNoFallbacksFromIE(net); }