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(Test_Torch_nets, ENet_accuracy) { Net net; { const string model = findDataFile("dnn/Enet-model-best.net", false); net = readNetFromTorch(model, true); ASSERT_TRUE(!net.empty()); } net.setPreferableTarget(GetParam()); 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 results 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); } }
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(Reproducibility_ResNet50, Accuracy) { Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false), findDataFile("dnn/ResNet-50-model.caffemodel", false)); int targetId = GetParam(); net.setPreferableTarget(targetId); float l1 = (targetId == DNN_TARGET_OPENCL_FP16) ? 3e-5 : 1e-5; float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 6e-3 : 1e-4; Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(224,224), Scalar(), false); ASSERT_TRUE(!input.empty()); net.setInput(input); Mat out = net.forward(); Mat ref = blobFromNPY(_tf("resnet50_prob.npy")); normAssert(ref, out, "", l1, lInf); if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16) { UMat out_umat; net.forward(out_umat); normAssert(ref, out_umat, "out_umat", l1, lInf); std::vector<UMat> out_umats; net.forward(out_umats); normAssert(ref, out_umats[0], "out_umat_vector", l1, lInf); } }
TEST_P(Test_TensorFlow_nets, MobileNet_SSD) { 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> target(outNames.size()); for (int i = 0; i < outNames.size(); ++i) { std::string path = findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco." + outNames[i] + ".npy", false); target[i] = blobFromNPY(path); } Net net = readNetFromTensorflow(netPath, netConfig); net.setPreferableTarget(GetParam()); net.setInput(inp); std::vector<Mat> output; net.forward(output, outNames); normAssert(target[0].reshape(1, 1), output[0].reshape(1, 1), "", 1e-5, 1.5e-4); normAssert(target[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 3e-4); normAssert(target[2].reshape(1, 1), output[2].reshape(1, 1), "", 4e-5, 1e-2); }
// 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); }
TEST_P(Test_TensorFlow_nets, Inception_v2_SSD) { 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 / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), true, false); net.setPreferableTarget(GetParam()); 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(); out = out.reshape(1, out.total() / 7); Mat detections; for (int i = 0; i < out.rows; ++i) { if (out.at<float>(i, 2) > 0.5) detections.push_back(out.row(i).colRange(1, 7)); } Mat ref = (Mat_<float>(5, 6) << 1, 0.90176028, 0.19872092, 0.36311883, 0.26461923, 0.63498729, 3, 0.93569964, 0.64865261, 0.45906419, 0.80675775, 0.65708131, 3, 0.75838411, 0.44668293, 0.45907149, 0.49459291, 0.52197015, 10, 0.95932811, 0.38349164, 0.32528657, 0.40387636, 0.39165527, 10, 0.93973452, 0.66561931, 0.37841269, 0.68074018, 0.42907384); normAssert(detections, ref); }
TEST_P(Reproducibility_SqueezeNet_v1_1, Accuracy) { Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false), findDataFile("dnn/squeezenet_v1.1.caffemodel", false)); int targetId = GetParam(); net.setPreferableTarget(targetId); Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false); ASSERT_TRUE(!input.empty()); Mat out; if (targetId == DNN_TARGET_OPENCL) { // Firstly set a wrong input blob and run the model to receive a wrong output. // Then set a correct input blob to check CPU->GPU synchronization is working well. net.setInput(input * 2.0f); out = net.forward(); } net.setInput(input); out = net.forward(); Mat ref = blobFromNPY(_tf("squeezenet_v1.1_prob.npy")); normAssert(ref, out); }
TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8) { 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.setPreferableTarget(GetParam()); 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, 5) << 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631, 0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168, 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290, 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801); normAssert(out.reshape(1, out.total() / 7).rowRange(0, 6).colRange(2, 7), ref, "", 2.8e-4, 3.4e-3); }
// 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) { 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.setPreferableTarget(GetParam()); 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); normAssert(out, refBlob, "", 0.5, 1.1); } }
TEST(Test_Caffe, multiple_inputs) { const string proto = findDataFile("dnn/layers/net_input.prototxt", false); Net net = readNetFromCaffe(proto); Mat first_image(10, 11, CV_32FC3); Mat second_image(10, 11, CV_32FC3); randu(first_image, -1, 1); randu(second_image, -1, 1); first_image = blobFromImage(first_image); second_image = blobFromImage(second_image); Mat first_image_blue_green = slice(first_image, Range::all(), Range(0, 2), Range::all(), Range::all()); Mat first_image_red = slice(first_image, Range::all(), Range(2, 3), Range::all(), Range::all()); Mat second_image_blue_green = slice(second_image, Range::all(), Range(0, 2), Range::all(), Range::all()); Mat second_image_red = slice(second_image, Range::all(), Range(2, 3), Range::all(), Range::all()); net.setInput(first_image_blue_green, "old_style_input_blue_green"); net.setInput(first_image_red, "different_name_for_red"); net.setInput(second_image_blue_green, "input_layer_blue_green"); net.setInput(second_image_red, "old_style_input_red"); Mat out = net.forward(); normAssert(out, first_image + second_image); }
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(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(Reproducibility_FCN, Accuracy) { Net net; { const string proto = findDataFile("dnn/fcn8s-heavy-pascal.prototxt", false); const string model = findDataFile("dnn/fcn8s-heavy-pascal.caffemodel", false); net = readNetFromCaffe(proto, model); ASSERT_FALSE(net.empty()); } Mat sample = imread(_tf("street.png")); ASSERT_TRUE(!sample.empty()); std::vector<int> layerIds; std::vector<size_t> weights, blobs; net.getMemoryConsumption(shape(1,3,227,227), layerIds, weights, blobs); net.setInput(blobFromImage(sample, 1.0f, Size(500, 500), Scalar(), false), "data"); Mat out = net.forward("score"); Mat refData = imread(_tf("caffe_fcn8s_prob.png"), IMREAD_ANYDEPTH); int shape[] = {1, 21, 500, 500}; Mat ref(4, shape, CV_32FC1, refData.data); 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); }
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])); }
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); } }
// 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); }
TEST(Reproducibility_DenseNet_121, Accuracy) { const string proto = findDataFile("dnn/DenseNet_121.prototxt", false); const string model = findDataFile("dnn/DenseNet_121.caffemodel", false); Mat inp = imread(_tf("dog416.png")); inp = blobFromImage(inp, 1.0 / 255, Size(224, 224)); Mat ref = blobFromNPY(_tf("densenet_121_output.npy")); Net net = readNetFromCaffe(proto, model); net.setInput(inp); Mat out = net.forward(); normAssert(out, ref); }
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); }
TEST(Reproducibility_AlexNet_fp16, Accuracy) { const float l1 = 1e-5; const float lInf = 3e-3; const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false); const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false); shrinkCaffeModel(model, "bvlc_alexnet.caffemodel_fp16"); Net net = readNetFromCaffe(proto, "bvlc_alexnet.caffemodel_fp16"); Mat sample = imread(findDataFile("dnn/grace_hopper_227.png", false)); net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false)); Mat out = net.forward(); Mat ref = blobFromNPY(findDataFile("dnn/caffe_alexnet_prob.npy", false)); normAssert(ref, out, "", l1, 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(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(Test_TensorFlow, inception_accuracy) { Net net; { const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false); net = readNetFromTensorflow(model); ASSERT_FALSE(net.empty()); } Mat sample = imread(_tf("grace_hopper_227.png")); ASSERT_TRUE(!sample.empty()); resize(sample, sample, Size(224, 224)); Mat inputBlob = blobFromImage(sample); net.setInput(inputBlob, "input"); Mat out = net.forward("softmax2"); Mat ref = blobFromNPY(_tf("tf_inception_prob.npy")); normAssert(ref, out); }
// https://github.com/richzhang/colorization TEST(Reproducibility_Colorization, Accuracy) { const float l1 = 3e-5; const float lInf = 3e-3; Mat inp = blobFromNPY(_tf("colorization_inp.npy")); Mat ref = blobFromNPY(_tf("colorization_out.npy")); Mat kernel = blobFromNPY(_tf("colorization_pts_in_hull.npy")); const string proto = findDataFile("dnn/colorization_deploy_v2.prototxt", false); const string model = findDataFile("dnn/colorization_release_v2.caffemodel", false); Net net = readNetFromCaffe(proto, model); net.getLayer(net.getLayerId("class8_ab"))->blobs.push_back(kernel); net.getLayer(net.getLayerId("conv8_313_rh"))->blobs.push_back(Mat(1, 313, CV_32F, 2.606)); net.setInput(inp); Mat out = net.forward(); normAssert(out, ref, "", l1, lInf); }
TEST_P(Test_Torch_nets, OpenFace_accuracy) { const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false); Net net = readNetFromTorch(model); net.setPreferableTarget(GetParam()); 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); }
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(Reproducibility_MobileNet_SSD, Accuracy) { const string proto = findDataFile("dnn/MobileNetSSD_deploy.prototxt", false); const string model = findDataFile("dnn/MobileNetSSD_deploy.caffemodel", false); Net net = readNetFromCaffe(proto, model); int targetId = GetParam(); const float l1 = (targetId == DNN_TARGET_OPENCL_FP16) ? 1.5e-4 : 1e-5; const float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 4e-4 : 1e-4; net.setPreferableTarget(targetId); Mat sample = imread(_tf("street.png")); Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false); net.setInput(inp); Mat out = net.forward(); const float scores_diff = (targetId == DNN_TARGET_OPENCL_FP16) ? 4e-4 : 1e-5; const float boxes_iou_diff = (targetId == DNN_TARGET_OPENCL_FP16) ? 5e-3 : 1e-4; Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy")); normAssertDetections(ref, out, "", 0.0, scores_diff, boxes_iou_diff); // Check that detections aren't preserved. inp.setTo(0.0f); net.setInput(inp); out = net.forward(); out = out.reshape(1, out.total() / 7); const int numDetections = out.rows; ASSERT_NE(numDetections, 0); for (int i = 0; i < numDetections; ++i) { float confidence = out.ptr<float>(i)[2]; ASSERT_EQ(confidence, 0); } // Check batching mode. ref = ref.reshape(1, numDetections); inp = blobFromImages(std::vector<Mat>(2, sample), 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false); net.setInput(inp); Mat outBatch = net.forward(); // Output blob has a shape 1x1x2Nx7 where N is a number of detection for // a single sample in batch. The first numbers of detection vectors are batch id. outBatch = outBatch.reshape(1, outBatch.total() / 7); EXPECT_EQ(outBatch.rows, 2 * numDetections); normAssert(outBatch.rowRange(0, numDetections), ref, "", l1, lInf); normAssert(outBatch.rowRange(numDetections, 2 * numDetections).colRange(1, 7), ref.colRange(1, 7), "", l1, lInf); }
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); }