TEST(Test_Darknet, read_yolo_voc_stream) { Mat ref; Mat sample = imread(_tf("dog416.png")); Mat inp = blobFromImage(sample, 1.0/255, Size(416, 416), Scalar(), true, false); const std::string cfgFile = findDataFile("dnn/yolo-voc.cfg", false); const std::string weightsFile = findDataFile("dnn/yolo-voc.weights", false); // Import by paths. { Net net = readNetFromDarknet(cfgFile, weightsFile); net.setInput(inp); net.setPreferableBackend(DNN_BACKEND_OPENCV); ref = net.forward(); } // Import from bytes array. { std::string cfg, weights; readFileInMemory(cfgFile, cfg); readFileInMemory(weightsFile, weights); Net net = readNetFromDarknet(&cfg[0], cfg.size(), &weights[0], weights.size()); net.setInput(inp); net.setPreferableBackend(DNN_BACKEND_OPENCV); Mat out = net.forward(); normAssert(ref, out); } }
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_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, 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); }
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); } }
void testInPlaceActivation(LayerParams& lp) { EXPECT_FALSE(lp.name.empty()); LayerParams pool; pool.set("pool", "ave"); pool.set("kernel_w", 2); pool.set("kernel_h", 2); pool.set("stride_w", 2); pool.set("stride_h", 2); pool.type = "Pooling"; Net net; int poolId = net.addLayer(pool.name, pool.type, pool); net.connect(0, 0, poolId, 0); net.addLayerToPrev(lp.name, lp.type, lp); Mat input({1, kNumChannels, 10, 10}, CV_32F); randu(input, -1.0f, 1.0f); net.setInput(input); Mat outputDefault = net.forward(lp.name).clone(); net.setInput(input); net.setPreferableBackend(DNN_BACKEND_HALIDE); Mat outputHalide = net.forward(lp.name).clone(); normAssert(outputDefault, outputHalide); }
OCL_TEST(Torch_Importer, ENet_accuracy) { Net net; { const string model = findDataFile("dnn/Enet-model-best.net", false); Ptr<Importer> importer = createTorchImporter(model, true); ASSERT_TRUE(importer != NULL); importer->populateNet(net); } net.setPreferableBackend(DNN_BACKEND_DEFAULT); net.setPreferableTarget(DNN_TARGET_OPENCL); Mat sample = imread(_tf("street.png", false)); Mat inputBlob = blobFromImage(sample, 1./255); 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 resuults was checked on // Cityscapes dataset and difference in mIOU with Torch is 10E-4% normAssert(ref, out, "", 0.00044, 0.44); const int N = 3; for (int i = 0; i < N; i++) { net.setInput(inputBlob, ""); Mat out = net.forward(); normAssert(ref, out, "", 0.00044, 0.44); } }
static void runTorchNet(String prefix, int targetId = DNN_TARGET_CPU, String outLayerName = "", bool check2ndBlob = false, bool isBinary = false) { String suffix = (isBinary) ? ".dat" : ".txt"; Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary); ASSERT_FALSE(net.empty()); net.setPreferableBackend(DNN_BACKEND_DEFAULT); net.setPreferableTarget(targetId); Mat inp, outRef; ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) ); ASSERT_NO_THROW( outRef = readTorchBlob(_tf(prefix + "_output" + suffix), isBinary) ); if (outLayerName.empty()) outLayerName = net.getLayerNames().back(); net.setInput(inp, "0"); std::vector<Mat> outBlobs; net.forward(outBlobs, outLayerName); normAssert(outRef, outBlobs[0]); if (check2ndBlob) { Mat out2 = outBlobs[1]; Mat ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary); normAssert(out2, ref2); } }
TEST_P(opencv_face_detector, Accuracy) { std::string proto = findDataFile("dnn/opencv_face_detector.prototxt", false); std::string model = findDataFile(get<0>(GetParam()), false); dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam()); Net net = readNetFromCaffe(proto, model); 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(DNN_BACKEND_DEFAULT); net.setPreferableTarget(targetId); 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>(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); normAssertDetections(ref, out, "", 0.5, 1e-5, 2e-4); }
OCL_TEST(Reproducibility_TinyYoloVoc, Accuracy) { Net net; { const string cfg = findDataFile("dnn/tiny-yolo-voc.cfg", false); const string model = findDataFile("dnn/tiny-yolo-voc.weights", false); net = readNetFromDarknet(cfg, model); ASSERT_FALSE(net.empty()); } net.setPreferableBackend(DNN_BACKEND_DEFAULT); net.setPreferableTarget(DNN_TARGET_OPENCL); // dog416.png is dog.jpg that resized to 416x416 in the lossless PNG format Mat sample = imread(_tf("dog416.png")); ASSERT_TRUE(!sample.empty()); Size inputSize(416, 416); if (sample.size() != inputSize) resize(sample, sample, inputSize); net.setInput(blobFromImage(sample, 1 / 255.F), "data"); Mat out = net.forward("detection_out"); Mat detection; const float confidenceThreshold = 0.24; for (int i = 0; i < out.rows; i++) { const int probability_index = 5; const int probability_size = out.cols - probability_index; float *prob_array_ptr = &out.at<float>(i, probability_index); size_t objectClass = std::max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr; float confidence = out.at<float>(i, (int)objectClass + probability_index); if (confidence > confidenceThreshold) detection.push_back(out.row(i)); } // obtained by: ./darknet detector test ./cfg/voc.data ./cfg/tiny-yolo-voc.cfg ./tiny-yolo-voc.weights -thresh 0.24 ./dog416.png // There are 2 objects (6-car, 11-dog) with 25 values for each: // { relative_center_x, relative_center_y, relative_width, relative_height, unused_t0, probability_for_each_class[20] } float ref_array[] = { 0.736762F, 0.239551F, 0.315440F, 0.160779F, 0.761977F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.761967F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.287486F, 0.653731F, 0.315579F, 0.534527F, 0.782737F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.780595F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F }; const int number_of_objects = 2; Mat ref(number_of_objects, sizeof(ref_array) / (number_of_objects * sizeof(float)), CV_32FC1, &ref_array); normAssert(ref, detection); }
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(Concat, Accuracy) { Vec3i inSize = get<0>(GetParam()); Vec3i numChannels = get<1>(GetParam()); Net net; std::vector<int> convLayerIds; convLayerIds.reserve(numChannels.channels); for (int i = 0, n = numChannels.channels; i < n; ++i) { if (!numChannels[i]) break; Mat weights({numChannels[i], inSize[0], 1, 1}, CV_32F); randu(weights, -1.0f, 1.0f); LayerParams convParam; convParam.set("kernel_w", 1); convParam.set("kernel_h", 1); convParam.set("num_output", numChannels[i]); convParam.set("bias_term", false); convParam.type = "Convolution"; std::ostringstream ss; ss << "convLayer" << i; convParam.name = ss.str(); convParam.blobs.push_back(weights); int layerId = net.addLayer(convParam.name, convParam.type, convParam); convLayerIds.push_back(layerId); net.connect(0, 0, layerId, 0); } LayerParams concatParam; concatParam.type = "Concat"; concatParam.name = "testLayer"; int concatId = net.addLayer(concatParam.name, concatParam.type, concatParam); net.connect(0, 0, concatId, 0); for (int i = 0; i < convLayerIds.size(); ++i) { net.connect(convLayerIds[i], 0, concatId, i + 1); } Mat input({1, inSize[0], inSize[1], inSize[2]}, CV_32F); randu(input, -1.0f, 1.0f); net.setInput(input); Mat outputDefault = net.forward(concatParam.name).clone(); net.setPreferableBackend(DNN_BACKEND_HALIDE); Mat outputHalide = net.forward(concatParam.name).clone(); normAssert(outputDefault, outputHalide); }
TEST(Test_TensorFlow, Mask_RCNN) { std::string proto = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pbtxt", false); std::string model = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pb", false); Net net = readNetFromTensorflow(model, proto); Mat img = imread(findDataFile("dnn/street.png", false)); Mat refDetections = blobFromNPY(path("mask_rcnn_inception_v2_coco_2018_01_28.detection_out.npy")); Mat refMasks = blobFromNPY(path("mask_rcnn_inception_v2_coco_2018_01_28.detection_masks.npy")); Mat blob = blobFromImage(img, 1.0f, Size(800, 800), Scalar(), true, false); net.setPreferableBackend(DNN_BACKEND_OPENCV); net.setInput(blob); // Mask-RCNN predicts bounding boxes and segmentation masks. std::vector<String> outNames(2); outNames[0] = "detection_out_final"; outNames[1] = "detection_masks"; std::vector<Mat> outs; net.forward(outs, outNames); Mat outDetections = outs[0]; Mat outMasks = outs[1]; normAssertDetections(refDetections, outDetections, "", /*threshold for zero confidence*/1e-5); // Output size of masks is NxCxHxW where // N - number of detected boxes // C - number of classes (excluding background) // HxW - segmentation shape const int numDetections = outDetections.size[2]; int masksSize[] = {1, numDetections, outMasks.size[2], outMasks.size[3]}; Mat masks(4, &masksSize[0], CV_32F); std::vector<cv::Range> srcRanges(4, cv::Range::all()); std::vector<cv::Range> dstRanges(4, cv::Range::all()); outDetections = outDetections.reshape(1, outDetections.total() / 7); for (int i = 0; i < numDetections; ++i) { // Get a class id for this bounding box and copy mask only for that class. int classId = static_cast<int>(outDetections.at<float>(i, 1)); srcRanges[0] = dstRanges[1] = cv::Range(i, i + 1); srcRanges[1] = cv::Range(classId, classId + 1); outMasks(srcRanges).copyTo(masks(dstRanges)); } cv::Range topRefMasks[] = {Range::all(), Range(0, numDetections), Range::all(), Range::all()}; normAssert(masks, refMasks(&topRefMasks[0])); }
// 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); } }
TEST(Test_TensorFlow, two_inputs) { Net net = readNet(path("two_inputs_net.pbtxt")); net.setPreferableBackend(DNN_BACKEND_OPENCV); Mat firstInput(2, 3, CV_32FC1), secondInput(2, 3, CV_32FC1); randu(firstInput, -1, 1); randu(secondInput, -1, 1); net.setInput(firstInput, "first_input"); net.setInput(secondInput, "second_input"); Mat out = net.forward(); normAssert(out, firstInput + secondInput); }
static void test(LayerParams& params, Mat& input) { randu(input, -1.0f, 1.0f); Net net; int lid = net.addLayer(params.name, params.type, params); net.connect(0, 0, lid, 0); net.setInput(input); Mat outputDefault = net.forward(params.name).clone(); net.setPreferableBackend(DNN_BACKEND_HALIDE); Mat outputHalide = net.forward(params.name).clone(); normAssert(outputDefault, outputHalide); }
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); }
////////////////////////////////////////////////////////////////////////////// // Max pooling - unpooling ////////////////////////////////////////////////////////////////////////////// TEST(MaxPoolUnpool_Halide, Accuracy) { LayerParams pool; pool.set("pool", "max"); pool.set("kernel_w", 2); pool.set("kernel_h", 2); pool.set("stride_w", 2); pool.set("stride_h", 2); pool.set("pad_w", 0); pool.set("pad_h", 0); pool.type = "Pooling"; pool.name = "testPool"; LayerParams unpool; unpool.set("pool_k_w", 2); unpool.set("pool_k_h", 2); unpool.set("pool_stride_w", 2); unpool.set("pool_stride_h", 2); unpool.set("pool_pad_w", 0); unpool.set("pool_pad_h", 0); unpool.type = "MaxUnpool"; unpool.name = "testUnpool"; Net net; int poolId = net.addLayer(pool.name, pool.type, pool); net.connect(0, 0, poolId, 0); int unpoolId = net.addLayer(unpool.name, unpool.type, unpool); net.connect(poolId, 0, unpoolId, 0); net.connect(poolId, 1, unpoolId, 1); Mat input({1, 1, 4, 4}, CV_32F); randu(input, -1.0f, 1.0f); net.setInput(input); Mat outputDefault = net.forward("testUnpool").clone(); net.setPreferableBackend(DNN_BACKEND_HALIDE); net.setInput(input); Mat outputHalide = net.forward("testUnpool").clone(); normAssert(outputDefault, outputHalide); }
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(Test_TensorFlow, inception_accuracy) { Net net; { const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false); net = readNetFromTensorflow(model); ASSERT_FALSE(net.empty()); } net.setPreferableBackend(DNN_BACKEND_OPENCV); Mat sample = imread(_tf("grace_hopper_227.png")); ASSERT_TRUE(!sample.empty()); Mat inputBlob = blobFromImage(sample, 1.0, Size(224, 224), Scalar(), /*swapRB*/true); net.setInput(inputBlob, "input"); Mat out = net.forward("softmax2"); Mat ref = blobFromNPY(_tf("tf_inception_prob.npy")); normAssert(ref, out); }
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); }
OCL_TEST(Torch_Importer, OpenFace_accuracy) { const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false); Net net = readNetFromTorch(model); net.setPreferableBackend(DNN_BACKEND_DEFAULT); net.setPreferableTarget(DNN_TARGET_OPENCL); 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); net.setInput(inputBlob); Mat out = net.forward(); Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true); normAssert(out, outRef); }
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_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); }
static void runTensorFlowNet(const std::string& prefix, int targetId = DNN_TARGET_CPU, bool hasText = false, double l1 = 1e-5, double lInf = 1e-4, 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"); 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(DNN_BACKEND_DEFAULT); net.setPreferableTarget(targetId); cv::Mat input = blobFromNPY(inpPath); cv::Mat target = blobFromNPY(outPath); net.setInput(input); cv::Mat output = net.forward(); normAssert(target, output, "", l1, 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_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); }