// 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. // Copyright (C) 2018-2019, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. #include "test_precomp.hpp" #include "npy_blob.hpp" #include namespace opencv_test { namespace { template static std::string _tf(TString filename, bool required = true) { return findDataFile(std::string("dnn/onnx/") + filename, required); } class Test_ONNX_layers : public DNNTestLayer { public: bool required; Test_ONNX_layers() : required(true) { } enum Extension { npy, pb }; 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", required); 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_ONNX_layers, InstanceNorm) { if(backend == DNN_BACKEND_CUDA) applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); /* MVN is not supported */ if (target == DNN_TARGET_MYRIAD) testONNXModels("instancenorm", npy, 0, 0, false, false); else testONNXModels("instancenorm", npy); } TEST_P(Test_ONNX_layers, MaxPooling) { testONNXModels("maxpooling", npy, 0, 0, false, false); testONNXModels("two_maxpooling", npy, 0, 0, false, false); } TEST_P(Test_ONNX_layers, Convolution) { testONNXModels("convolution"); } TEST_P(Test_ONNX_layers, Convolution3D) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif if (target != DNN_TARGET_CPU && backend != DNN_BACKEND_CUDA) throw SkipTestException("Only CPU and CUDA is supported"); testONNXModels("conv3d"); testONNXModels("conv3d_bias"); } TEST_P(Test_ONNX_layers, Two_convolution) { #if defined(INF_ENGINE_RELEASE) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X ) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); #endif // Reference output values are in range [-0.855, 0.611] testONNXModels("two_convolution"); } TEST_P(Test_ONNX_layers, Deconvolution) { testONNXModels("deconvolution", npy, 0, 0, false, false); testONNXModels("two_deconvolution", npy, 0, 0, false, false); testONNXModels("deconvolution_group", npy, 0, 0, false, false); testONNXModels("deconvolution_output_shape", npy, 0, 0, false, false); testONNXModels("deconv_adjpad_2d", npy, 0, 0, false, false); } TEST_P(Test_ONNX_layers, Deconvolution3D) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif if (backend == DNN_BACKEND_CUDA) { // ok } else if (backend == DNN_BACKEND_OPENCV || target != DNN_TARGET_CPU) throw SkipTestException("Only DLIE backend on CPU is supported"); testONNXModels("deconv3d"); testONNXModels("deconv3d_bias"); testONNXModels("deconv3d_pad"); testONNXModels("deconv3d_adjpad"); } TEST_P(Test_ONNX_layers, Dropout) { testONNXModels("dropout"); } TEST_P(Test_ONNX_layers, Linear) { if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); testONNXModels("linear"); } TEST_P(Test_ONNX_layers, ReLU) { testONNXModels("ReLU"); } TEST_P(Test_ONNX_layers, Clip) { testONNXModels("clip", npy); } TEST_P(Test_ONNX_layers, ReduceMean) { testONNXModels("reduce_mean"); } TEST_P(Test_ONNX_layers, ReduceMean3D) { if (backend == DNN_BACKEND_CUDA) { // ok } else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported else if (target != DNN_TARGET_CPU) throw SkipTestException("Only CPU is supported"); testONNXModels("reduce_mean3d"); } TEST_P(Test_ONNX_layers, MaxPooling_Sigmoid) { testONNXModels("maxpooling_sigmoid"); } TEST_P(Test_ONNX_layers, Concatenation) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); } testONNXModels("concatenation"); } TEST_P(Test_ONNX_layers, Eltwise3D) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported testONNXModels("eltwise3d"); } TEST_P(Test_ONNX_layers, AveragePooling) { testONNXModels("average_pooling"); } TEST_P(Test_ONNX_layers, MaxPooling3D) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif if (backend == DNN_BACKEND_CUDA) { // ok } else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported else if (target != DNN_TARGET_CPU) throw SkipTestException("Only CPU is supported"); testONNXModels("max_pool3d", npy, 0, 0, false, false); } TEST_P(Test_ONNX_layers, AvePooling3D) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif if (backend == DNN_BACKEND_CUDA) { // ok } else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported else if (target != DNN_TARGET_CPU) throw SkipTestException("Only CPU is supported"); testONNXModels("ave_pool3d"); } TEST_P(Test_ONNX_layers, PoolConv3D) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif if (backend == DNN_BACKEND_CUDA) { // ok } else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported else if (target != DNN_TARGET_CPU) throw SkipTestException("Only CPU is supported"); testONNXModels("pool_conv_3d"); } TEST_P(Test_ONNX_layers, BatchNormalization) { testONNXModels("batch_norm"); } TEST_P(Test_ONNX_layers, BatchNormalization3D) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); } testONNXModels("batch_norm_3d"); } TEST_P(Test_ONNX_layers, Transpose) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); } testONNXModels("transpose"); } TEST_P(Test_ONNX_layers, Multiplication) { if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); testONNXModels("mul"); } TEST_P(Test_ONNX_layers, Constant) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif testONNXModels("constant"); } TEST_P(Test_ONNX_layers, Padding) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000) testONNXModels("padding", npy, 0, 0, false, false); #else testONNXModels("padding"); #endif } TEST_P(Test_ONNX_layers, Resize) { testONNXModels("resize_nearest"); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); testONNXModels("resize_bilinear"); } 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); } TEST_P(Test_ONNX_layers, Div) { const String model = _tf("models/div.onnx"); Net net = readNetFromONNX(model); ASSERT_FALSE(net.empty()); net.setPreferableBackend(backend); net.setPreferableTarget(target); // Reference output values range is -68.80928, 2.991873. So to avoid computational // difference for FP16 we'll perform reversed division (just swap inputs). Mat inp1 = blobFromNPY(_tf("data/input_div_1.npy")); Mat inp2 = blobFromNPY(_tf("data/input_div_0.npy")); Mat ref = blobFromNPY(_tf("data/output_div.npy")); cv::divide(1.0, ref, ref); checkBackend(&inp1, &ref); net.setInput(inp1, "0"); net.setInput(inp2, "1"); Mat out = net.forward(); normAssert(ref, out, "", default_l1, default_lInf); expectNoFallbacksFromIE(net); expectNoFallbacksFromCUDA(net); } TEST_P(Test_ONNX_layers, DynamicReshape) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); } testONNXModels("dynamic_reshape"); } TEST_P(Test_ONNX_layers, Reshape) { testONNXModels("unsqueeze"); } TEST_P(Test_ONNX_layers, Squeeze) { testONNXModels("squeeze"); } TEST_P(Test_ONNX_layers, ReduceL2) { testONNXModels("reduceL2"); } TEST_P(Test_ONNX_layers, Split) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); testONNXModels("split_1"); testONNXModels("split_2"); testONNXModels("split_3"); testONNXModels("split_4"); } TEST_P(Test_ONNX_layers, Slice) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000) testONNXModels("slice", npy, 0, 0, false, false); #else testONNXModels("slice"); #endif } TEST_P(Test_ONNX_layers, Softmax) { testONNXModels("softmax"); testONNXModels("log_softmax", npy, 0, 0, false, false); testONNXModels("softmax_unfused"); } TEST_P(Test_ONNX_layers, Split_EltwiseMax) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); testONNXModels("split_max"); } INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets()); class Test_ONNX_nets : public Test_ONNX_layers { public: Test_ONNX_nets() { required = false; } }; TEST_P(Test_ONNX_nets, Alexnet) { #if defined(OPENCV_32BIT_CONFIGURATION) && defined(HAVE_OPENCL) applyTestTag(CV_TEST_TAG_MEMORY_2GB); #else applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB); #endif const String model = _tf("models/alexnet.onnx", false); 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_nets, Squeezenet) { testONNXModels("squeezenet", pb); } TEST_P(Test_ONNX_nets, Googlenet) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); const String model = _tf("models/googlenet.onnx", false); Net net = readNetFromONNX(model); ASSERT_FALSE(net.empty()); net.setPreferableBackend(backend); net.setPreferableTarget(target); std::vector 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); } TEST_P(Test_ONNX_nets, CaffeNet) { #if defined(OPENCV_32BIT_CONFIGURATION) && defined(HAVE_OPENCL) applyTestTag(CV_TEST_TAG_MEMORY_2GB); #else applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB); #endif #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019030000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif testONNXModels("caffenet", pb); } TEST_P(Test_ONNX_nets, RCNN_ILSVRC13) { #if defined(OPENCV_32BIT_CONFIGURATION) && defined(HAVE_OPENCL) applyTestTag(CV_TEST_TAG_MEMORY_2GB); #else applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB); #endif #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019030000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif // Reference output values are in range [-4.992, -1.161] testONNXModels("rcnn_ilsvrc13", pb, 0.0046); } TEST_P(Test_ONNX_nets, VGG16_bn) { applyTestTag(CV_TEST_TAG_MEMORY_6GB); // > 2.3Gb // output range: [-16; 27], after Softmax [0; 0.67] const double lInf = (target == DNN_TARGET_MYRIAD) ? 0.038 : default_lInf; testONNXModels("vgg16-bn", pb, default_l1, lInf, true); } TEST_P(Test_ONNX_nets, ZFNet) { applyTestTag(CV_TEST_TAG_MEMORY_2GB); testONNXModels("zfnet512", pb); } TEST_P(Test_ONNX_nets, ResNet18v1) { applyTestTag(CV_TEST_TAG_MEMORY_512MB); // output range: [-16; 22], after Softmax [0, 0.51] testONNXModels("resnet18v1", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD); } TEST_P(Test_ONNX_nets, ResNet50v1) { applyTestTag(CV_TEST_TAG_MEMORY_512MB); // output range: [-67; 75], after Softmax [0, 0.98] testONNXModels("resnet50v1", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD); } TEST_P(Test_ONNX_nets, ResNet101_DUC_HDC) { applyTestTag(CV_TEST_TAG_VERYLONG); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif #if defined(INF_ENGINE_RELEASE) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); #endif if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL) { if (backend == DNN_BACKEND_OPENCV) applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_OPENCL : CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); throw SkipTestException("Test is disabled for OpenCL targets"); } testONNXModels("resnet101_duc_hdc", pb); } TEST_P(Test_ONNX_nets, TinyYolov2) { applyTestTag(CV_TEST_TAG_MEMORY_512MB); if (cvtest::skipUnstableTests) throw SkipTestException("Skip unstable test"); #if defined(INF_ENGINE_RELEASE) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16) ) applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X ) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ? CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER : CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); #endif // output range: [-11; 8] double l1 = default_l1, lInf = default_lInf; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) { l1 = 0.017; lInf = 0.14; } else if (target == DNN_TARGET_CUDA_FP16) { l1 = 0.018; lInf = 0.16; } testONNXModels("tiny_yolo2", pb, l1, lInf); } TEST_P(Test_ONNX_nets, CNN_MNIST) { // output range: [-1952; 6574], after Softmax [0; 1] testONNXModels("cnn_mnist", pb, default_l1, default_lInf, true); } TEST_P(Test_ONNX_nets, MobileNet_v2) { // output range: [-166; 317], after Softmax [0; 1] testONNXModels("mobilenetv2", pb, default_l1, default_lInf, true); } TEST_P(Test_ONNX_nets, LResNet100E_IR) { applyTestTag( #if defined(OPENCV_32BIT_CONFIGURATION) && defined(HAVE_OPENCL) CV_TEST_TAG_MEMORY_2GB, #else (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB), #endif CV_TEST_TAG_DEBUG_LONG ); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); } if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); } double l1 = default_l1, lInf = default_lInf; // output range: [-3; 3] if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) { l1 = 0.009; lInf = 0.035; } else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_CPU) { l1 = 4.6e-5; lInf = 1.9e-4; } else if (target == DNN_TARGET_CUDA_FP16) { l1 = 0.008; lInf = 0.04; } testONNXModels("LResNet100E_IR", pb, l1, lInf); } TEST_P(Test_ONNX_nets, Emotion_ferplus) { #if defined(INF_ENGINE_RELEASE) if (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ? CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER : CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); #endif double l1 = default_l1; double lInf = default_lInf; // Output values are in range [-2.011, 2.111] if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) l1 = 0.007; else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL_FP16) { l1 = 0.021; lInf = 0.034; } else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_CPU || target == DNN_TARGET_OPENCL)) { l1 = 2.4e-4; lInf = 6e-4; } testONNXModels("emotion_ferplus", pb, l1, lInf); } TEST_P(Test_ONNX_nets, Inception_v2) { testONNXModels("inception_v2", pb, default_l1, default_lInf, true); } TEST_P(Test_ONNX_nets, DenseNet121) { applyTestTag(CV_TEST_TAG_MEMORY_512MB); // output range: [-87; 138], after Softmax [0; 1] testONNXModels("densenet121", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD); } TEST_P(Test_ONNX_nets, Inception_v1) { #if defined(INF_ENGINE_RELEASE) if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD); #endif testONNXModels("inception_v1", pb); } TEST_P(Test_ONNX_nets, Shufflenet) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); } testONNXModels("shufflenet", pb); } TEST_P(Test_ONNX_nets, Resnet34_kinetics) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif if (backend == DNN_BACKEND_CUDA) { // ok } else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported else if (target != DNN_TARGET_CPU) throw SkipTestException("Only CPU is supported"); String onnxmodel = findDataFile("dnn/resnet-34_kinetics.onnx", false); Mat image0 = imread(findDataFile("dnn/dog416.png")); Mat image1 = imread(findDataFile("dnn/street.png")); Mat ref0 = blobFromNPY(_tf("data/output_kinetics0.npy")); Mat ref1 = blobFromNPY(_tf("data/output_kinetics1.npy")); std::vector images_0(16, image0); std::vector images_1(16, image1); Mat blob0 = blobFromImages(images_0, 1.0, Size(112, 112), Scalar(114.7748, 107.7354, 99.4750), true, true); Mat blob1 = blobFromImages(images_1, 1.0, Size(112, 112), Scalar(114.7748, 107.7354, 99.4750), true, true); Net permute; LayerParams lp; int order[] = {1, 0, 2, 3}; lp.set("order", DictValue::arrayInt(&order[0], 4)); permute.addLayerToPrev("perm", "Permute", lp); permute.setPreferableBackend(backend); permute.setPreferableTarget(target); permute.setInput(blob0); Mat input0 = permute.forward().clone(); permute.setInput(blob1); Mat input1 = permute.forward().clone(); int dims[] = {1, 3, 16, 112, 112}; input0 = input0.reshape(0, 5, &dims[0]); input1 = input1.reshape(0, 5, &dims[0]); Net net = readNetFromONNX(onnxmodel); ASSERT_FALSE(net.empty()); net.setPreferableBackend(backend); net.setPreferableTarget(target); // output range [-5, 11] float l1 = 0.0013, lInf = 0.009; if (target == DNN_TARGET_CUDA_FP16) { l1 = 0.008; lInf = 0.04; } checkBackend(&input0, &ref0); net.setInput(input0); Mat out = net.forward().clone(); normAssert(ref0, out, "", l1, lInf); checkBackend(&input1, &ref1); net.setInput(input1); out = net.forward().clone(); normAssert(ref1, out, "", l1, lInf); expectNoFallbacksFromIE(net); } INSTANTIATE_TEST_CASE_P(/**/, Test_ONNX_nets, dnnBackendsAndTargets()); }} // namespace