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Open Source Computer Vision Library
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3011 lines
109 KiB
3011 lines
109 KiB
// This file is part of OpenCV project. |
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// It is subject to the license terms in the LICENSE file found in the top-level directory |
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// of this distribution and at http://opencv.org/license.html. |
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// Copyright (C) 2018-2019, Intel Corporation, all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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#include "test_precomp.hpp" |
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#include "npy_blob.hpp" |
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#include <opencv2/dnn/shape_utils.hpp> |
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#include <numeric> |
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namespace opencv_test { namespace { |
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void yoloPostProcessing( |
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std::vector<Mat>& outs, |
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std::vector<int>& keep_classIds, |
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std::vector<float>& keep_confidences, |
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std::vector<Rect2d>& keep_boxes, |
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float conf_threshold, |
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float iou_threshold, |
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const std::string& test_name); |
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template<typename TString> |
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static std::string _tf(TString filename, bool required = true) |
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{ |
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return findDataFile(std::string("dnn/onnx/") + filename, required); |
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} |
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class Test_ONNX_layers : public DNNTestLayer |
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{ |
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public: |
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bool required; |
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Test_ONNX_layers() : required(true) { } |
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enum Extension |
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{ |
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npy, |
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pb |
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}; |
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void testInputShapes(const Net& net, const std::vector<Mat>& inps) |
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{ |
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std::vector<MatShape> inLayerShapes; |
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std::vector<MatShape> outLayerShapes; |
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net.getLayerShapes(MatShape(), 0, inLayerShapes, outLayerShapes); |
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ASSERT_EQ(inLayerShapes.size(), inps.size()); |
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for (int i = 0; i < inps.size(); ++i) { |
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bool hasDynamicShapes = inLayerShapes[i].empty(); |
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if (hasDynamicShapes) |
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continue; |
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if (inLayerShapes[i].size() == 1) { // 1D input |
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ASSERT_EQ(shape(inLayerShapes[i][0], 1), shape(inps[i])); |
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} else { |
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// Compare all axes except batch dimension which is variable. |
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inLayerShapes[i][0] = inps[i].size[0]; |
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ASSERT_EQ(inLayerShapes[i], shape(inps[i])); |
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} |
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} |
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} |
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void testONNXModels(const String& basename, const Extension ext = npy, |
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double l1 = 0, double lInf = 0, const bool useSoftmax = false, |
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bool checkNoFallbacks = true, int numInps = 1, |
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bool testShapes = true, bool useWinograd = true) |
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{ |
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String onnxmodel = _tf("models/" + basename + ".onnx", required); |
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std::vector<Mat> inps(numInps); |
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Mat ref; |
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if (ext == npy) { |
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for (int i = 0; i < numInps; ++i) |
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inps[i] = blobFromNPY(_tf("data/input_" + basename + (numInps > 1 ? format("_%d", i) : "") + ".npy")); |
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ref = blobFromNPY(_tf("data/output_" + basename + ".npy")); |
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} |
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else if (ext == pb) { |
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for (int i = 0; i < numInps; ++i) |
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inps[i] = readTensorFromONNX(_tf("data/input_" + basename + (numInps > 1 ? format("_%d", i) : "") + ".pb")); |
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ref = readTensorFromONNX(_tf("data/output_" + basename + ".pb")); |
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} |
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else |
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CV_Error(Error::StsUnsupportedFormat, "Unsupported extension"); |
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checkBackend(&inps[0], &ref); |
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Net net = readNetFromONNX(onnxmodel); |
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ASSERT_FALSE(net.empty()); |
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if (testShapes) |
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testInputShapes(net, inps); |
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net.setPreferableBackend(backend); |
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net.setPreferableTarget(target); |
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net.enableWinograd(useWinograd); |
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std::vector<String> inputNames; |
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for (int i = 0; i < numInps; ++i) |
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inputNames.push_back(format("%d", i)); |
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net.setInputsNames(inputNames); |
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for (int i = 0; i < numInps; ++i) |
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net.setInput(inps[i], inputNames[i]); |
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Mat out = net.forward(""); |
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if (useSoftmax) |
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{ |
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LayerParams lp; |
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Net netSoftmax; |
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netSoftmax.addLayerToPrev("softmaxLayer", "Softmax", lp); |
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netSoftmax.setPreferableBackend(DNN_BACKEND_OPENCV); |
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netSoftmax.setInput(out); |
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out = netSoftmax.forward(); |
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netSoftmax.setInput(ref); |
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ref = netSoftmax.forward(); |
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} |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) |
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{ |
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l1 = std::max(l1, 1.4e-3); |
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lInf = std::max(lInf, 8e-3); |
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} |
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normAssert(ref, out, basename.c_str(), l1 ? l1 : default_l1, lInf ? lInf : default_lInf); |
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if (checkNoFallbacks) |
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expectNoFallbacksFromIE(net); |
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} |
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}; |
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TEST_P(Test_ONNX_layers, InstanceNorm) |
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{ |
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if (target == DNN_TARGET_MYRIAD) |
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testONNXModels("instancenorm", npy, 0, 0, false, false); |
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else |
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testONNXModels("instancenorm", npy); |
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} |
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TEST_P(Test_ONNX_layers, MaxPooling) |
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{ |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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#endif |
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testONNXModels("maxpooling", npy, 0, 0, false, false); |
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} |
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TEST_P(Test_ONNX_layers, MaxPooling_2) |
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{ |
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testONNXModels("two_maxpooling", npy, 0, 0, false, false); |
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} |
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TEST_P(Test_ONNX_layers, Convolution) |
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{ |
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testONNXModels("convolution"); |
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testONNXModels("conv_asymmetric_pads"); |
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} |
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TEST_P(Test_ONNX_layers, Convolution_variable_weight) |
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{ |
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if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH || |
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backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) && target == DNN_TARGET_MYRIAD) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
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if (backend == DNN_BACKEND_CUDA) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported |
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if (backend == DNN_BACKEND_VKCOM) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); // not supported |
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String basename = "conv_variable_w"; |
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Net net = readNetFromONNX(_tf("models/" + basename + ".onnx")); |
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ASSERT_FALSE(net.empty()); |
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net.setPreferableBackend(backend); |
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net.setPreferableTarget(target); |
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for (int i = 0; i < 2; i++) |
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{ |
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Mat input = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_0.npy")); |
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Mat weights = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_1.npy")); |
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Mat ref = blobFromNPY(_tf("data/output_" + basename + format("_%d", i) + ".npy")); |
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net.setInput(input, "0"); |
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net.setInput(weights, "1"); |
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Mat out = net.forward(); |
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normAssert(ref, out, "", default_l1, default_lInf); |
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} |
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} |
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TEST_P(Test_ONNX_layers, Convolution_variable_weight_bias) |
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{ |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
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// openvino/src/plugins/intel_myriad/common/src/ngraph/transformations/extract_dynamic_batch/slice_convolution.cpp:14 Expecting operation v1::GroupConvolution GroupConvolution_6904725 (Reshape_17[0]:f32{1,4,5,5}, Reshape_6904719[0]:f32{4,1,1,2,2}) -> (f32{1,4,4,4}) to have constant kernel, got Reshape_6904719[0]:f32{4,1,1,2,2} |
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// openvino\src\plugins\intel_myriad\common\src\ngraph\transformations\extract_dynamic_batch\slice_convolution.cpp:15 Expecting operation v1::GroupConvolution GroupConvolution_6904692 (Reshape_17[0]:f32{1,4,5,5}, Reshape_6904686[0]:f32{4,1,1,2,2}) -> (f32{1,4,4,4}) to have constant kernel, got Reshape_6904686[0]:f32{4,1,1,2,2} |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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// accuracy (depends on OpenCL version / HW) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) |
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applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, |
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CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
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); |
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#elif defined(INF_ENGINE_RELEASE) |
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if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH || |
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backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) && target == DNN_TARGET_MYRIAD) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU && |
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getInferenceEngineCPUType() == CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
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#endif |
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if (backend == DNN_BACKEND_CUDA) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // supports only <= 2 inputs |
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if (backend == DNN_BACKEND_VKCOM) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); // not supported |
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String basename = "conv_variable_wb"; |
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Net net = readNetFromONNX(_tf("models/" + basename + ".onnx")); |
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ASSERT_FALSE(net.empty()); |
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net.setPreferableBackend(backend); |
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net.setPreferableTarget(target); |
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for (int i = 0; i < 2; i++) |
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{ |
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Mat input = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_0.npy")); |
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Mat weights = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_1.npy")); |
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Mat bias = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_2.npy")); |
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Mat ref = blobFromNPY(_tf("data/output_" + basename + format("_%d", i) + ".npy")); |
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net.setInput(input, "0"); |
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net.setInput(weights, "1"); |
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net.setInput(bias, "bias"); |
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Mat out = net.forward(); |
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normAssert(ref, out, "", default_l1, default_lInf); |
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} |
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} |
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TEST_P(Test_ONNX_layers, Gather) |
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{ |
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testONNXModels("gather", npy, 0, 0, false, false); |
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} |
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TEST_P(Test_ONNX_layers, Gather_Scalar) |
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{ |
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testONNXModels("gather_scalar", npy, 0, 0, false, false); |
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} |
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TEST_P(Test_ONNX_layers, GatherMulti) |
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{ |
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// GPU plugin unsupported slice for constant |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
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testONNXModels("gather_multi", npy, 0, 0, false, false); |
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} |
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TEST_P(Test_ONNX_layers, Gather_shared_indices) { |
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testONNXModels("gather_shared_indices", npy, 0, 0, false, false, 1); |
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} |
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TEST_P(Test_ONNX_layers, Two_resizes_with_shared_subgraphs) { |
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testONNXModels("two_resizes_with_shared_subgraphs", npy, 0, 0, false, false, 3, /*testShapes*/ false); |
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} |
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TEST_P(Test_ONNX_layers, Convolution3D) |
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{ |
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if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16) |
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{ |
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// CUDA_FP16: cuDNN did not return a suitable algorithm for convolution. |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16); |
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} |
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testONNXModels("conv3d"); |
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} |
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TEST_P(Test_ONNX_layers, Convolution3D_bias) |
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{ |
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if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16) |
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{ |
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// CUDA_FP16: cuDNN did not return a suitable algorithm for convolution. |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16); |
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} |
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testONNXModels("conv3d_bias"); |
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testONNXModels("conv3d_depthwise_bias"); // kernel 1x1 |
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} |
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TEST_P(Test_ONNX_layers, Two_convolution) |
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{ |
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#if defined(INF_ENGINE_RELEASE) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD |
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&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X |
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) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
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#endif |
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// Reference output values are in range [-0.855, 0.611] |
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testONNXModels("two_convolution"); |
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} |
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TEST_P(Test_ONNX_layers, Deconvolution) |
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{ |
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testONNXModels("deconvolution", npy, 0, 0, false, false); |
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testONNXModels("two_deconvolution", npy, 0, 0, false, false); |
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testONNXModels("deconvolution_group", npy, 0, 0, false, false); |
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testONNXModels("deconvolution_output_shape", npy, 0, 0, false, false); |
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if (target != DNN_TARGET_CUDA_FP16) // bug |
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testONNXModels("deconv_adjpad_2d", npy, 0, 0, false, false); |
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} |
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TEST_P(Test_ONNX_layers, Deconvolution3D) |
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{ |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
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{ |
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// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "2": |
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// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively) |
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if (target == DNN_TARGET_MYRIAD) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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} |
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#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
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{ |
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// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2": |
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// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively) |
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if (target == DNN_TARGET_MYRIAD) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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} |
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#endif |
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if (backend == DNN_BACKEND_OPENCV) |
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throw SkipTestException("OpenCV backend is not supported"); // FIXIT use tags |
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if (backend == DNN_BACKEND_VKCOM) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); |
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testONNXModels("deconv3d"); |
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} |
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TEST_P(Test_ONNX_layers, Deconvolution3D_bias) |
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{ |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
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{ |
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// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "3": |
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// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 3@weights Const data got different desc and content byte sizes (270 and 810 respectively) |
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if (target == DNN_TARGET_MYRIAD) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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} |
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#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
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{ |
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// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2": |
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// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively) |
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if (target == DNN_TARGET_MYRIAD) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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} |
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#endif |
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if (backend == DNN_BACKEND_OPENCV) |
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throw SkipTestException("OpenCV backend is not supported"); // FIXIT use tags |
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if (backend == DNN_BACKEND_VKCOM) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); |
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testONNXModels("deconv3d_bias"); |
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} |
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TEST_P(Test_ONNX_layers, Deconvolution3D_pad) |
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{ |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
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{ |
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// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "3": |
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// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 3@weights Const data got different desc and content byte sizes (108 and 432 respectively) |
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if (target == DNN_TARGET_MYRIAD) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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} |
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#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
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{ |
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// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2": |
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// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively) |
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if (target == DNN_TARGET_MYRIAD) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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} |
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#endif |
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if (backend == DNN_BACKEND_OPENCV) |
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throw SkipTestException("OpenCV backend is not supported"); // FIXIT use tags |
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if (backend == DNN_BACKEND_VKCOM) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); |
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testONNXModels("deconv3d_pad"); |
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} |
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TEST_P(Test_ONNX_layers, Deconvolution3D_adjpad) |
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{ |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
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{ |
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// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "3": |
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// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 3@weights Const data got different desc and content byte sizes (90 and 180 respectively) |
|
if (target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
} |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
{ |
|
// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2": |
|
// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively) |
|
if (target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
} |
|
#endif |
|
|
|
if (backend == DNN_BACKEND_OPENCV) |
|
throw SkipTestException("OpenCV backend is not supported"); // FIXIT use tags |
|
|
|
if (backend == DNN_BACKEND_VKCOM) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); |
|
|
|
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, PReLU) |
|
{ |
|
testONNXModels("PReLU_slope"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Clip) |
|
{ |
|
testONNXModels("clip", npy); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Clip_init) |
|
{ |
|
testONNXModels("clip_init_min_max"); |
|
testONNXModels("clip_init_min"); |
|
testONNXModels("clip_init_max"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Shape) |
|
{ |
|
testONNXModels("shape_of_constant"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, ReduceMean) |
|
{ |
|
testONNXModels("reduce_mean"); |
|
testONNXModels("reduce_mean_axis1"); |
|
testONNXModels("reduce_mean_axis2"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, ReduceSum) |
|
{ |
|
testONNXModels("reduce_sum"); |
|
testONNXModels("reduce_sum_axis_dynamic_batch"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, ReduceMax) |
|
{ |
|
testONNXModels("reduce_max"); |
|
} |
|
TEST_P(Test_ONNX_layers, ReduceMax_axis_0) |
|
{ |
|
testONNXModels("reduce_max_axis_0"); |
|
} |
|
TEST_P(Test_ONNX_layers, ReduceMax_axis_1) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// [ GENERAL_ERROR ] AssertionFailed: !out.networkInputs.empty() |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#endif |
|
testONNXModels("reduce_max_axis_1"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Min) |
|
{ |
|
testONNXModels("min", npy, 0, 0, false, true, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, ArgLayer) |
|
{ |
|
if (backend != DNN_BACKEND_OPENCV || target != DNN_TARGET_CPU) |
|
throw SkipTestException("Only CPU is supported"); // FIXIT use tags |
|
|
|
testONNXModels("argmax"); |
|
testONNXModels("argmin"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Scale) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
|
// accuracy (inf/nan) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// accuracy |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
// IE exception: mkldnn_node.cpp:238 Ngraph operation Reshape with name ReduceMean_0 has dynamic output shape on 0 port, but CPU plug-in supports only static shape |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// Ngraph operation Reshape with name ReduceMean_0 has dynamic output shape on 0 port, but CPU plug-in supports only static shape |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#endif |
|
testONNXModels("scale"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Scale_broadcast) |
|
{ |
|
if (backend == DNN_BACKEND_CUDA) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // doesn't support broadcasting |
|
testONNXModels("scale_broadcast", npy, 0, 0, false, true, 3); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Scale_broadcast_mid) |
|
{ |
|
if (backend == DNN_BACKEND_CUDA) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // doesn't support broadcasting |
|
testONNXModels("scale_broadcast_mid", npy, 0, 0, false, true, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, ReduceMean3D) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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 |
|
#endif |
|
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU) |
|
throw SkipTestException("Only CPU is supported"); // FIXIT use tags |
|
|
|
if (backend == DNN_BACKEND_VKCOM) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); |
|
|
|
testONNXModels("reduce_mean3d"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, MaxPooling_Sigmoid) |
|
{ |
|
testONNXModels("maxpooling_sigmoid"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Cast) |
|
{ |
|
testONNXModels("cast"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Power) |
|
{ |
|
testONNXModels("pow2", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Exp) |
|
{ |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
testONNXModels("exp"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Elementwise_Ceil) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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); |
|
#endif |
|
testONNXModels("ceil"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Elementwise_Floor) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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); |
|
#endif |
|
testONNXModels("floor"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Elementwise_Log) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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); |
|
#endif |
|
testONNXModels("log"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Elementwise_Round) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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); |
|
#endif |
|
testONNXModels("round"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Elementwise_Sqrt) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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("sqrt"); |
|
#endif |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Elementwise_not) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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); |
|
#endif |
|
testONNXModels("not"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Compare_EQ) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// IE exception: Function contains several inputs and outputs with one friendly name! |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
// IE exception: Function contains several inputs and outputs with one friendly name! |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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); |
|
#endif |
|
|
|
testONNXModels("equal"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Compare_GT) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// IE exception: Function contains several inputs and outputs with one friendly name! |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
// IE exception: Function contains several inputs and outputs with one friendly name! |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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); |
|
#endif |
|
|
|
testONNXModels("greater"); |
|
} |
|
TEST_P(Test_ONNX_layers, Greater_input_dtype_int64) { |
|
testONNXModels("greater_input_dtype_int64"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Compare_LT) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// IE exception: Function contains several inputs and outputs with one friendly name! |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
// IE exception: Function contains several inputs and outputs with one friendly name! |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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); |
|
#endif |
|
|
|
testONNXModels("less"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Compare_GTorEQ) |
|
{ |
|
testONNXModels("greater_or_equal"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Compare_LEorEQ) |
|
{ |
|
testONNXModels("less_or_equal"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, CompareSameDims_EQ) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// IE exception: Function contains several inputs and outputs with one friendly name! |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
// IE exception: Function contains several inputs and outputs with one friendly name! |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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); |
|
#endif |
|
|
|
testONNXModels("equal_same_dims", npy, 0, 0, false, true, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, CompareSameDims_GT) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// IE exception: Function contains several inputs and outputs with one friendly name! |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
// IE exception: Function contains several inputs and outputs with one friendly name! |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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); |
|
#endif |
|
|
|
testONNXModels("greater_same_dims", npy, 0, 0, false, true, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, CompareSameDims_LT) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// IE exception: Function contains several inputs and outputs with one friendly name! |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
// IE exception: Function contains several inputs and outputs with one friendly name! |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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); |
|
#endif |
|
|
|
testONNXModels("less_same_dims", npy, 0, 0, false, true, 2); |
|
} |
|
|
|
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"); |
|
testONNXModels("concat_const_blobs"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, CumSumExclusiveInplace) |
|
{ |
|
testONNXModels("cumsum_exclusive_inplace"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Eltwise3D) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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 |
|
#endif |
|
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_EQ(2022010000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
{ |
|
// accuracy |
|
if (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
// IE exception: [ GENERAL_ERROR ] AssertionFailed: !expired() |
|
if (target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
} |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
{ |
|
// accuracy |
|
if (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
// IE exception: [ GENERAL_ERROR ] AssertionFailed: !expired() |
|
if (target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
} |
|
#endif |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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 |
|
#endif |
|
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU) |
|
throw SkipTestException("Only CPU is supported"); // FIXIT use tags |
|
|
|
if (backend == DNN_BACKEND_VKCOM) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); |
|
|
|
testONNXModels("max_pool3d", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, AvePooling3D) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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 |
|
#endif |
|
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU) |
|
throw SkipTestException("Only CPU is supported"); // FIXIT use tags |
|
|
|
if (backend == DNN_BACKEND_VKCOM) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); |
|
|
|
testONNXModels("ave_pool3d"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, PoolConv3D) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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 |
|
#endif |
|
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU) |
|
throw SkipTestException("Only CPU is supported"); // FIXIT use tags |
|
|
|
if (backend == DNN_BACKEND_VKCOM) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); |
|
|
|
if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16) |
|
{ |
|
// CUDA_FP16: cuDNN did not return a suitable algorithm for convolution. |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16); |
|
} |
|
|
|
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, BatchNormalizationUnfused) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception |
|
#endif |
|
testONNXModels("frozenBatchNorm2d"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, BatchNormalizationSubgraph) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception |
|
#endif |
|
testONNXModels("batch_norm_subgraph"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, NormalizeFusionSubgraph) |
|
{ |
|
testONNXModels("normalize_fusion"); |
|
} |
|
|
|
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, MatMul_2d) |
|
{ |
|
testONNXModels("matmul_2d"); |
|
} |
|
TEST_P(Test_ONNX_layers, MatMul_3d) |
|
{ |
|
testONNXModels("matmul_3d"); |
|
} |
|
TEST_P(Test_ONNX_layers, MatMul_4d) |
|
{ |
|
testONNXModels("matmul_4d"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, MatMul_2d_init) |
|
{ |
|
testONNXModels("matmul_2d_init"); |
|
} |
|
TEST_P(Test_ONNX_layers, MatMul_3d_init) |
|
{ |
|
testONNXModels("matmul_3d_init"); |
|
} |
|
TEST_P(Test_ONNX_layers, MatMul_4d_init) |
|
{ |
|
testONNXModels("matmul_4d_init"); |
|
} |
|
TEST_P(Test_ONNX_layers, MatMul_init_2) |
|
{ |
|
testONNXModels("matmul_init_2"); |
|
} |
|
TEST_P(Test_ONNX_layers, MatMul_init_bcast) |
|
{ |
|
testONNXModels("matmul_init_bcast"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, MatMulAdd) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
|
// accuracy |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021010000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
#endif |
|
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); |
|
testONNXModels("matmul_add"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Expand) |
|
{ |
|
testONNXModels("expand"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, ExpandIdentity) { |
|
testONNXModels("expand_identity"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, ExpandBatch) { |
|
testONNXModels("expand_batch"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, ExpandChannels) { |
|
testONNXModels("expand_channels"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, ExpandNegBatch) { |
|
testONNXModels("expand_neg_batch"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, ExpandHW) |
|
{ |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
testONNXModels("expand_hw"); |
|
} |
|
|
|
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"); |
|
testONNXModels("tf_half_pixel_for_nn"); |
|
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, ResizeUnfused) |
|
{ |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
testONNXModels("upsample_unfused_torch1.2"); |
|
testONNXModels("upsample_unfused_opset9_torch1.4"); |
|
testONNXModels("resize_nearest_unfused_opset11_torch1.4"); |
|
testONNXModels("resize_nearest_unfused_opset11_torch1.3"); |
|
testONNXModels("resize_bilinear_unfused_opset11_torch1.4"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, ResizeUnfusedTwoInputs) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2023000000) |
|
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); |
|
#endif |
|
testONNXModels("upsample_unfused_two_inputs_opset9_torch1.4", npy, 0, 0, false, true, 2); |
|
testONNXModels("upsample_unfused_two_inputs_opset11_torch1.4", npy, 0, 0, false, true, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, MultyInputs) |
|
{ |
|
testONNXModels("multy_inputs", npy, 0, 0, false, true, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Broadcast) |
|
{ |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
testONNXModels("channel_broadcast", npy, 0, 0, false, true, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DynamicResize) |
|
{ |
|
testONNXModels("dynamic_resize_9", npy, 0, 0, false, true, 2); |
|
testONNXModels("dynamic_resize_10", npy, 0, 0, false, true, 2); |
|
testONNXModels("dynamic_resize_11", npy, 0, 0, false, true, 2); |
|
testONNXModels("dynamic_resize_13", npy, 0, 0, false, true, 2); |
|
testONNXModels("dynamic_resize_scale_9", npy, 0, 0, false, true, 2); |
|
testONNXModels("dynamic_resize_scale_10", npy, 0, 0, false, true, 2); |
|
testONNXModels("dynamic_resize_scale_11", npy, 0, 0, false, true, 2); |
|
testONNXModels("dynamic_resize_scale_13", npy, 0, 0, false, true, 2); |
|
|
|
testONNXModels("resize_size_opset11"); |
|
testONNXModels("resize_size_opset13"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Resize_HumanSeg) |
|
{ |
|
testONNXModels("resize_humanseg"); |
|
} |
|
|
|
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); |
|
|
|
// NaryEltwise layer suuports only CPU for now |
|
testONNXModels("div_test_1x1", npy, 0, 0, false, false, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DynamicReshape) |
|
{ |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
|
|
testONNXModels("dynamic_reshape"); |
|
testONNXModels("dynamic_reshape_opset_11"); |
|
testONNXModels("flatten_by_prod"); |
|
testONNXModels("flatten_const"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Reshape) |
|
{ |
|
testONNXModels("unsqueeze"); |
|
testONNXModels("unsqueeze_opset_13"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Unsqueeze_Neg_Axes) |
|
{ |
|
testONNXModels("unsqueeze_neg_axes"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Squeeze) |
|
{ |
|
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("squeeze"); |
|
testONNXModels("squeeze_axes_op13"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, ReduceL2) |
|
{ |
|
testONNXModels("reduceL2"); |
|
testONNXModels("reduceL2_subgraph"); |
|
testONNXModels("reduceL2_subgraph_2"); |
|
testONNXModels("reduceL2_subgraph2_2"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Split) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2023000000) |
|
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); |
|
#endif |
|
testONNXModels("split_0"); |
|
testONNXModels("split_1"); |
|
testONNXModels("split_2"); |
|
testONNXModels("split_3"); |
|
testONNXModels("split_4"); |
|
testONNXModels("split_5"); |
|
testONNXModels("split_6"); |
|
testONNXModels("split_neg_axis"); |
|
} |
|
|
|
// Mul inside with 0-d tensor, output should be A x 1, but is 1 x A. PR #22652 |
|
TEST_P(Test_ONNX_layers, DISABLED_Split_sizes_0d) |
|
{ |
|
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_sizes"); |
|
} |
|
|
|
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"); |
|
testONNXModels("slice_neg_starts"); |
|
testONNXModels("slice_opset_11"); |
|
testONNXModels("slice_neg_steps", pb); |
|
#endif |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Slice_Steps_2DInput) |
|
{ |
|
testONNXModels("slice_opset_11_steps_2d"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Slice_Steps_3DInput) |
|
{ |
|
testONNXModels("slice_opset_11_steps_3d"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Slice_Steps_4DInput) |
|
{ |
|
testONNXModels("slice_opset_11_steps_4d"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Slice_Steps_5DInput) |
|
{ |
|
testONNXModels("slice_opset_11_steps_5d"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Slice_Nonseq_Axes) |
|
{ |
|
testONNXModels("slice_nonseq_axes"); |
|
testONNXModels("slice_nonseq_axes_steps"); |
|
testONNXModels("slice_nonseq_miss_axes_steps"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Slice_Neg_Axes) |
|
{ |
|
testONNXModels("slice_neg_axes"); |
|
testONNXModels("slice_neg_axes_steps"); |
|
testONNXModels("slice_neg_miss_axes_steps"); |
|
} |
|
|
|
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 defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2023000000) |
|
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); |
|
#endif |
|
testONNXModels("split_max"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, LSTM_Activations) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
|
// IE exception: Node Block1326/lstm/reshape_0/permute was not assigned on any pointed device |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// IE Exception: Ngraph operation Reshape with name Block1237_Output_0_before_reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
#endif |
|
|
|
testONNXModels("lstm_cntk_tanh", pb, 0, 0, false, false); |
|
} |
|
|
|
// disabled due to poor handling of 1-d mats |
|
TEST_P(Test_ONNX_layers, DISABLED_LSTM) |
|
{ |
|
testONNXModels("lstm", npy, 0, 0, false, false); |
|
} |
|
|
|
// disabled due to poor handling of 1-d mats |
|
TEST_P(Test_ONNX_layers, DISABLED_LSTM_bidirectional) |
|
{ |
|
testONNXModels("lstm_bidirectional", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, LSTM_hidden) |
|
{ |
|
testONNXModels("hidden_lstm", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, LSTM_hidden_bidirectional) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
|
// IE exception: Node Transpose_45 was not assigned on any pointed device. |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
#endif |
|
|
|
testONNXModels("hidden_lstm_bi", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, GRU) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
|
// IE exception: Node GRU_22 was not assigned on any pointed device |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
#endif |
|
testONNXModels("gru", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, gru_cell_batchsize_50_seqlen_1) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
|
// IE exception: Node GRU_22 was not assigned on any pointed device |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
#endif |
|
if(backend == DNN_BACKEND_CUDA) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); |
|
testONNXModels("gru_cell_batchsize_50_seqlen_1", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, gru_cell_batchsize_5_seqlen_5) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
|
// IE exception: Node GRU_22 was not assigned on any pointed device |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
#endif |
|
if(backend == DNN_BACKEND_CUDA) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); |
|
testONNXModels("gru_cell_batchsize_5_seqlen_5", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, gru_cell_batchsize_1_seqlen_50) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
|
// IE exception: Node GRU_22 was not assigned on any pointed device |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
#endif |
|
if(backend == DNN_BACKEND_CUDA) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); |
|
testONNXModels("gru_cell_batchsize_1_seqlen_50", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, GRU_bidirectional) |
|
{ |
|
testONNXModels("gru_bi", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, LSTM_cell_forward) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
|
// accuracy! |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// Ngraph operation Reshape with name LSTM_16/lstm_y/reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#endif |
|
testONNXModels("lstm_cell_forward", npy, 0, 0, false, false); |
|
} |
|
TEST_P(Test_ONNX_layers, LSTM_cell_bidirectional) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// Ngraph operation Reshape with name LSTM_16/lstm_y/reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#endif |
|
testONNXModels("lstm_cell_bidirectional", npy, 0, 0, false, false); |
|
} |
|
TEST_P(Test_ONNX_layers, LSTM_cell_with_peepholes) |
|
{ |
|
testONNXModels("lstm_cell_with_peepholes", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, LSTM_cell_batchsize_50_seqlen_1) |
|
{ |
|
if(backend == DNN_BACKEND_CUDA) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); |
|
testONNXModels("lstm_cell_batchsize_50_seqlen_1", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, LSTM_cell_batchsize_1_seqlen_50) |
|
{ |
|
if(backend == DNN_BACKEND_CUDA) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); |
|
testONNXModels("lstm_cell_batchsize_1_seqlen_50", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, LSTM_cell_batchsize_5_seqlen_5) |
|
{ |
|
if(backend == DNN_BACKEND_CUDA) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); |
|
testONNXModels("lstm_cell_batchsize_5_seqlen_5", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, LSTM_init_h0_c0) |
|
{ |
|
if(backend == DNN_BACKEND_CUDA) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); |
|
testONNXModels("lstm_init_h0_c0", npy, 0, 0, false, false, 3); |
|
} |
|
// epsilon is larger because onnx does not match with torch/opencv exactly |
|
TEST_P(Test_ONNX_layers, LSTM_layout_seq) |
|
{ |
|
if(backend == DNN_BACKEND_CUDA) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); |
|
testONNXModels("lstm_layout_0", npy, 0.005, 0.005, false, false, 3); |
|
} |
|
// epsilon is larger because onnx does not match with torch/opencv exactly |
|
TEST_P(Test_ONNX_layers, LSTM_layout_batch) |
|
{ |
|
if(backend == DNN_BACKEND_CUDA) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); |
|
testONNXModels("lstm_layout_1", npy, 0.005, 0.005, false, false, 3); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DISABLED_Einsum_1D) |
|
{ |
|
testONNXModels("einsum_1d", npy, 0, 0, false, false, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Einsum_2D) |
|
{ |
|
testONNXModels("einsum_2d", npy, 0, 0, false, false, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Einsum_2D_Ellipses) |
|
{ |
|
testONNXModels("einsum_2d_ellipses", npy, 0, 0, false, false, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Einsum_3D) |
|
{ |
|
testONNXModels("einsum_3d", npy, 0, 0, false, false, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Einsum_4D) |
|
{ |
|
testONNXModels("einsum_4d", npy, 0, 0, false, false, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Einsum_5D) |
|
{ |
|
testONNXModels("einsum_5d", npy, 0, 0, false, false, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DISABLED_Einsum_InnerProduct) |
|
{ |
|
testONNXModels("einsum_inner", npy, 0, 0, false, false, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DISABLED_Einsum_HadamardProduct) |
|
{ |
|
testONNXModels("einsum_hadamard", npy, 0, 0, false, false, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Einsum_Batch_Diagonal) |
|
{ |
|
testONNXModels("einsum_batch_diagonal", npy, 0, 0, false, false, 1); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Einsum_Sum) |
|
{ |
|
testONNXModels("einsum_sum", npy, 0, 0, false, false, 1); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Einsum_transpose) |
|
{ |
|
testONNXModels("einsum_transpose", npy, 0, 0, false, false, 1); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Pad2d_Unfused) |
|
{ |
|
testONNXModels("ReflectionPad2d"); |
|
testONNXModels("ZeroPad2d"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, LinearWithConstant) |
|
{ |
|
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2020040000) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE); |
|
#endif |
|
if (backend == DNN_BACKEND_CUDA) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); |
|
testONNXModels("lin_with_constant"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, MatmulWithTwoInputs) |
|
{ |
|
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2020040000) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE); |
|
#endif |
|
testONNXModels("matmul_with_two_inputs"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, ResizeOpset11_Torch1_6) |
|
{ |
|
testONNXModels("resize_opset11_torch1.6"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Mish) |
|
{ |
|
testONNXModels("mish"); |
|
testONNXModels("mish_no_softplus"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, CalculatePads) |
|
{ |
|
testONNXModels("calc_pads"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Conv1d) |
|
{ |
|
testONNXModels("conv1d"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Conv1d_bias) |
|
{ |
|
testONNXModels("conv1d_bias"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Conv1d_variable_weight) |
|
{ |
|
if (backend == DNN_BACKEND_CUDA) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported |
|
if (backend == DNN_BACKEND_VKCOM) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); // not supported |
|
String basename = "conv1d_variable_w"; |
|
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx")); |
|
ASSERT_FALSE(net.empty()); |
|
|
|
net.setPreferableBackend(backend); |
|
net.setPreferableTarget(target); |
|
|
|
Mat input = blobFromNPY(_tf("data/input_" + basename + "_0.npy")); |
|
Mat weights = blobFromNPY(_tf("data/input_" + basename + "_1.npy")); |
|
Mat ref = blobFromNPY(_tf("data/output_" + basename + ".npy")); |
|
|
|
net.setInput(input, "0"); |
|
net.setInput(weights, "1"); |
|
|
|
Mat out = net.forward(); |
|
normAssert(ref, out, "", default_l1, default_lInf); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Conv1d_variable_weight_bias) |
|
{ |
|
if (backend == DNN_BACKEND_CUDA) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported |
|
if (backend == DNN_BACKEND_VKCOM) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); // not supported |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
if (target == DNN_TARGET_CPU && getInferenceEngineCPUType() == CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
String basename = "conv1d_variable_wb"; |
|
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx")); |
|
ASSERT_FALSE(net.empty()); |
|
|
|
net.setPreferableBackend(backend); |
|
net.setPreferableTarget(target); |
|
|
|
Mat input = blobFromNPY(_tf("data/input_" + basename + "_0.npy")); |
|
Mat weights = blobFromNPY(_tf("data/input_" + basename + "_1.npy")); |
|
Mat bias = blobFromNPY(_tf("data/input_" + basename + "_2.npy")); |
|
Mat ref = blobFromNPY(_tf("data/output_" + basename + ".npy")); |
|
|
|
net.setInput(input, "0"); |
|
net.setInput(weights, "1"); |
|
net.setInput(bias, "bias"); |
|
|
|
Mat out = net.forward(); |
|
normAssert(ref, out, "", default_l1, default_lInf); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, GatherMultiOutput) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// IE Exception: Ngraph operation Reshape with name 6 has dynamic output shape on 0 port, but CPU plug-in supports only static shape |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
#endif |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception |
|
#endif |
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2021030000) |
|
if (target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE); |
|
#endif |
|
|
|
testONNXModels("gather_multi_output", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DynamicAxes_squeeze_and_conv) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
} |
|
#if INF_ENGINE_VER_MAJOR_LT(2021000000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
#endif |
|
testONNXModels("squeeze_and_conv_dynamic_axes"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DynamicAxes_unsqueeze_and_conv) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
} |
|
#if INF_ENGINE_VER_MAJOR_LT(2021000000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
#endif |
|
testONNXModels("unsqueeze_and_conv_dynamic_axes"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DynamicAxes_gather) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
} |
|
#if INF_ENGINE_VER_MAJOR_LT(2021000000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
#endif |
|
testONNXModels("gather_dynamic_axes", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DynamicAxes_gather_scalar) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
|
// accuracy |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// accuracy |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
#elif defined(INF_ENGINE_RELEASE) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
} |
|
#if INF_ENGINE_VER_MAJOR_LT(2021000000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
#endif |
|
testONNXModels("gather_scalar_dynamic_axes", npy, 0, 0, false, false); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DynamicAxes_slice) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
} |
|
#if INF_ENGINE_VER_MAJOR_LT(2021000000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
#endif |
|
testONNXModels("slice_dynamic_axes"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DynamicAxes_slice_opset_11) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
} |
|
#if INF_ENGINE_VER_MAJOR_LT(2021000000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
#endif |
|
testONNXModels("slice_opset_11_dynamic_axes"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DynamicAxes_resize_opset11_torch16) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
} |
|
#if INF_ENGINE_VER_MAJOR_LT(2021000000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
#endif |
|
testONNXModels("resize_opset11_torch1.6_dynamic_axes"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DynamicAxes_average_pooling) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
} |
|
#if INF_ENGINE_VER_MAJOR_LT(2021000000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
#endif |
|
testONNXModels("average_pooling_dynamic_axes"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DynamicAxes_maxpooling_sigmoid) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
} |
|
#if INF_ENGINE_VER_MAJOR_LT(2021000000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
#endif |
|
testONNXModels("maxpooling_sigmoid_dynamic_axes"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DynamicAxes_dynamic_batch) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
|
} |
|
#if INF_ENGINE_VER_MAJOR_LT(2021000000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
{ |
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
#endif |
|
testONNXModels("dynamic_batch"); |
|
} |
|
|
|
|
|
TEST_P(Test_ONNX_layers, MaxPool1d) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
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_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021040000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) |
|
{ |
|
// 2021.4: [ GENERAL_ERROR ] AssertionFailed: !expired() |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
testONNXModels("maxpooling_1d"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, MaxPoolSigmoid1d) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
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_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
testONNXModels("maxpooling_sigmoid_1d"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, MaxPool1d_Twise) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
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_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
testONNXModels("two_maxpooling_1d"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, AvePool1d) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
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_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
testONNXModels("average_pooling_1d"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, PoolConv1d) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
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_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
#endif |
|
testONNXModels("pool_conv_1d"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, ConvResizePool1d) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// IE Exception: Ngraph operation Reshape with name 15 has dynamic output shape on 0 port, but CPU plug-in supports only static shape |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
#endif |
|
#if defined(INF_ENGINE_RELEASE) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
|
{ |
|
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_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
#if INF_ENGINE_VER_MAJOR_EQ(2021030000) |
|
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception |
|
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception |
|
#endif |
|
} |
|
#endif |
|
|
|
const double lInf = (target == DNN_TARGET_CPU_FP16) ? 0.024 : default_lInf; |
|
testONNXModels("conv_resize_pool_1d", npy, default_l1, lInf); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DepthWiseAdd) |
|
{ |
|
testONNXModels("depthwiseconv_add"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DepthStride2) |
|
{ |
|
testONNXModels("depthwise_stride2"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, SubFromConst) |
|
{ |
|
testONNXModels("sub_from_const1"); |
|
testONNXModels("sub_from_const_eltwise"); |
|
testONNXModels("sub_from_const_broadcast"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, DivConst) |
|
{ |
|
testONNXModels("div_const"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Gemm) |
|
{ |
|
testONNXModels("gemm_no_transB"); |
|
testONNXModels("gemm_transB_0"); |
|
testONNXModels("gemm_first_const"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Gemm_bias) |
|
{ |
|
testONNXModels("gemm_vector_bias"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_Convolution) |
|
{ |
|
// The difference of QOperator and QDQ format: |
|
// https://onnxruntime.ai/docs/performance/quantization.html#onnx-quantization-representation-format. |
|
{ |
|
SCOPED_TRACE("QOperator quantized model."); |
|
testONNXModels("quantized_conv_uint8_weights", npy, 0.004, 0.02); |
|
testONNXModels("quantized_conv_int8_weights", npy, 0.03, 0.5); |
|
testONNXModels("quantized_conv_per_channel_weights", npy, 0.06, 0.4); |
|
testONNXModels("quantized_conv_asymmetric_pads_int8_weights"); |
|
} |
|
|
|
{ |
|
SCOPED_TRACE("QDQ quantized model."); |
|
testONNXModels("quantized_conv_uint8_weights_qdq", npy, 0.004, 0.02); |
|
testONNXModels("quantized_conv_int8_weights_qdq", npy, 0.03, 0.5); |
|
testONNXModels("quantized_conv_per_channel_weights_qdq", npy, 0.06, 0.4); |
|
} |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_MatMul) |
|
{ |
|
testONNXModels("quantized_matmul_uint8_weights", npy, 0.005, 0.007); |
|
testONNXModels("quantized_matmul_int8_weights", npy, 0.06, 0.2); |
|
testONNXModels("quantized_matmul_per_channel_weights", npy, 0.06, 0.22); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_Gemm) |
|
{ |
|
testONNXModels("quantized_gemm", npy); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_MatMul_Variable_Weights) |
|
{ |
|
// Unsupported |
|
EXPECT_THROW( |
|
{ |
|
testONNXModels("quantized_matmul_variable_inputs"); |
|
}, cv::Exception); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_Eltwise) |
|
{ |
|
testONNXModels("quantized_eltwise"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_Eltwise_Scalar) |
|
{ |
|
testONNXModels("quantized_eltwise_scalar"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_Eltwise_Broadcast) |
|
{ |
|
testONNXModels("quantized_eltwise_broadcast"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_LeakyReLU) |
|
{ |
|
testONNXModels("quantized_leaky_relu"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_Sigmoid) |
|
{ |
|
testONNXModels("quantized_sigmoid"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_MaxPool) |
|
{ |
|
testONNXModels("quantized_maxpool"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_AvgPool) |
|
{ |
|
testONNXModels("quantized_avgpool"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_Split) |
|
{ |
|
testONNXModels("quantized_split"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_Pad) |
|
{ |
|
testONNXModels("quantized_padding"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_Reshape) |
|
{ |
|
testONNXModels("quantized_reshape"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_Transpose) |
|
{ |
|
testONNXModels("quantized_transpose"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_Squeeze) |
|
{ |
|
testONNXModels("quantized_squeeze"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_Unsqueeze) |
|
{ |
|
testONNXModels("quantized_unsqueeze"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_Resize) |
|
{ |
|
testONNXModels("quantized_resize_nearest"); |
|
double l1 = backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ? 0.0013 : 2e-4; |
|
testONNXModels("quantized_resize_bilinear", npy, l1, 0.003); |
|
l1 = backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ? 0.0013 : 3e-4; |
|
testONNXModels("quantized_resize_bilinear_align", npy, l1, 0.003); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_Concat) |
|
{ |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
testONNXModels("quantized_concat"); |
|
testONNXModels("quantized_concat_const_blob"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Quantized_Constant) |
|
{ |
|
testONNXModels("quantized_constant", npy, 0.002, 0.008); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, OutputRegistration) |
|
{ |
|
testONNXModels("output_registration", npy, 0, 0, false, true, 2); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, QLinearSoftmax) |
|
{ |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
testONNXModels("qlinearsoftmax_v11", npy, 0.002, 0.002); // 2D coerced |
|
testONNXModels("qlinearsoftmax_v13", npy, 0.002, 0.002); |
|
} |
|
|
|
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) || defined(_WIN32)) |
|
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); |
|
net.enableWinograd(false); |
|
|
|
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, RAFT) |
|
{ |
|
applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_DEBUG_VERYLONG, CV_TEST_TAG_MEMORY_2GB); |
|
|
|
std::string weight_path = _tf("models/optical_flow_estimation_raft_2023aug.onnx", false); |
|
std::string img0_path = findDataFile(std::string("gpu/opticalflow/frame0.png")); |
|
std::string img1_path = findDataFile(std::string("gpu/opticalflow/frame1.png")); |
|
|
|
Size target_size{480, 360}; |
|
auto img0 = imread(img0_path); |
|
auto img1 = imread(img1_path); |
|
auto blob0 = blobFromImage(img0, 1.0, target_size, 0, true); |
|
auto blob1 = blobFromImage(img1, 1.0, target_size, 0, true); |
|
|
|
auto net = readNet(weight_path); |
|
net.setInput(blob0, "0"); |
|
net.setInput(blob1, "1"); |
|
std::vector<std::string> outnames{"12007", "12006"}; |
|
std::vector<Mat> outs; |
|
net.forward(outs, outnames); |
|
|
|
// output 12006 is not checked to save space in opencv_extra since its ref is > 1MB, |
|
// and output 12006 is calculated from 12007 so checking 12007 is sufficient. |
|
std::string ref_12700_path = _tf("data/output_optical_flow_estimation_raft_2023aug.npy"); |
|
auto ref0 = blobFromNPY(ref_12700_path); |
|
normAssert(ref0, outs[0], "", 1e-5, 1.8e-4); |
|
} |
|
|
|
TEST_P(Test_ONNX_nets, Squeezenet) |
|
{ |
|
testONNXModels("squeezenet", pb); |
|
} |
|
|
|
TEST_P(Test_ONNX_nets, Googlenet) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
|
// accuracy |
|
if (target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
// accuracy |
|
if (target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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); |
|
#endif |
|
|
|
const String model = _tf("models/googlenet.onnx", false); |
|
|
|
Net net = readNetFromONNX(model); |
|
ASSERT_FALSE(net.empty()); |
|
|
|
net.setPreferableBackend(backend); |
|
net.setPreferableTarget(target); |
|
|
|
if (target == DNN_TARGET_CPU_FP16) |
|
net.enableWinograd(false); |
|
|
|
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); |
|
} |
|
|
|
TEST_P(Test_ONNX_nets, CaffeNet) |
|
{ |
|
#if defined(OPENCV_32BIT_CONFIGURATION) && (defined(HAVE_OPENCL) || defined(_WIN32)) |
|
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) || defined(_WIN32)) |
|
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, ResNet50_Int8) |
|
{ |
|
testONNXModels("resnet50_int8", pb, default_l1, default_lInf, true); |
|
} |
|
|
|
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 || target == DNN_TARGET_CPU_FP16) |
|
{ |
|
l1 = 0.02; |
|
lInf = 0.2; |
|
} |
|
else if (target == DNN_TARGET_CUDA_FP16) |
|
{ |
|
l1 = 0.018; |
|
lInf = 0.16; |
|
} |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) |
|
{ |
|
l1 = 0.018f; lInf = 0.16f; |
|
} |
|
#endif |
|
|
|
testONNXModels("tiny_yolo2", pb, l1, lInf, false, true, 1, true, false); |
|
} |
|
|
|
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, MobileNet_v2_FP16) |
|
{ |
|
testONNXModels("mobilenetv2_fp16", npy, 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] |
|
bool useWinograd = true; |
|
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 = 0.009; |
|
lInf = 0.04; |
|
} |
|
else if (target == DNN_TARGET_CPU_FP16) |
|
{ |
|
useWinograd = false; |
|
l1 = 0.009; |
|
lInf = 0.035; |
|
} |
|
|
|
testONNXModels("LResNet100E_IR", pb, l1, lInf, false, true, 1, true, useWinograd); |
|
} |
|
|
|
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; |
|
bool useWinograd = true; |
|
// Output values are in range [-2.011, 2.111] |
|
if ((backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) || (target == DNN_TARGET_CUDA_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; |
|
} |
|
else if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_CPU_FP16) |
|
{ |
|
useWinograd = false; |
|
l1 = 0.007; |
|
} |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020040000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) |
|
{ |
|
l1 = 0.013f; lInf = 0.035f; |
|
} |
|
#endif |
|
|
|
testONNXModels("emotion_ferplus", pb, l1, lInf, false, true, 1, true, useWinograd); |
|
} |
|
|
|
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) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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 defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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); |
|
} |
|
#endif |
|
testONNXModels("shufflenet", pb); |
|
} |
|
|
|
TEST_P(Test_ONNX_nets, Resnet34_kinetics) |
|
{ |
|
applyTestTag(CV_TEST_TAG_DEBUG_VERYLONG); |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
|
// IE exception: Failed to allocate graph: MYRIAD device is not opened |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
// accuracy |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION |
|
); |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
{ |
|
// IE exception: Function contains several inputs and outputs with one friendly name! |
|
if (target == DNN_TARGET_MYRIAD) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
} |
|
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
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 |
|
#endif |
|
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU) |
|
throw SkipTestException("Only CPU is supported"); // FIXIT use tags |
|
|
|
if (backend == DNN_BACKEND_VKCOM) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); |
|
|
|
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<Mat> images_0(16, image0); |
|
std::vector<Mat> 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<int*>(&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; |
|
float lInf = 0.009; |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) |
|
{ |
|
l1 = 0.02; |
|
lInf = 0.07; |
|
} |
|
if (target == DNN_TARGET_CUDA_FP16) |
|
{ |
|
l1 = 0.01; |
|
lInf = 0.06; |
|
} |
|
|
|
testInputShapes(net, {input0}); |
|
|
|
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); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, CumSum) |
|
{ |
|
testONNXModels("cumsum_1d_exclusive_1"); |
|
testONNXModels("cumsum_1d_reverse"); |
|
testONNXModels("cumsum_1d_exclusive_1_reverse"); |
|
testONNXModels("cumsum_2d_dim_1"); |
|
testONNXModels("cumsum_3d_dim_2"); |
|
} |
|
|
|
static void testYOLO(const std::string& weightPath, const std::vector<int>& refClassIds, |
|
const std::vector<float>& refScores, const std::vector<Rect2d>& refBoxes, |
|
Image2BlobParams imgParams, float conf_threshold = 0.3, float iou_threshold = 0.5, |
|
double scores_diff = 1e-5, double boxes_iou_diff = 1e-4, const std::string test_name = "") |
|
{ |
|
std::string imgPath = _tf("../dog_orig_size.png"); |
|
|
|
Mat img = imread(imgPath); |
|
|
|
Mat inp = blobFromImageWithParams(img, imgParams); |
|
|
|
Net net = readNet(weightPath); |
|
|
|
net.setInput(inp); |
|
std::vector<Mat> outs; |
|
net.forward(outs, net.getUnconnectedOutLayersNames()); |
|
|
|
// Retrieve |
|
std::vector<int> keep_classIds; |
|
std::vector<float> keep_confidences; |
|
std::vector<Rect2d> keep_boxes; |
|
yoloPostProcessing(outs, keep_classIds, keep_confidences, keep_boxes, conf_threshold, iou_threshold, test_name); |
|
|
|
normAssertDetections( |
|
refClassIds, refScores, refBoxes, |
|
keep_classIds, keep_confidences, keep_boxes, |
|
"", 0.0, scores_diff, boxes_iou_diff); |
|
} |
|
|
|
void yoloPostProcessing( |
|
std::vector<Mat>& outs, |
|
std::vector<int>& keep_classIds, |
|
std::vector<float>& keep_confidences, |
|
std::vector<Rect2d>& keep_boxes, |
|
float conf_threshold, |
|
float iou_threshold, |
|
const std::string& test_name |
|
){ |
|
|
|
// Retrieve |
|
std::vector<int> classIds; |
|
std::vector<float> confidences; |
|
std::vector<Rect2d> boxes; |
|
|
|
if (test_name == "yolov8"){ |
|
cv::transposeND(outs[0], {0, 2, 1}, outs[0]); |
|
} |
|
|
|
// each row is [cx, cy, w, h, conf_obj, conf_class1, ..., conf_class80] |
|
for (auto preds : outs){ |
|
|
|
preds = preds.reshape(1, preds.size[1]); // [1, 8400, 85] -> [8400, 85] |
|
|
|
for (int i = 0; i < preds.rows; ++i) |
|
{ |
|
// filter out non objects |
|
float obj_conf = (test_name != "yolov8") ? preds.at<float>(i, 4) : 1.0f; |
|
if (obj_conf < conf_threshold) |
|
continue; |
|
|
|
Mat scores = preds.row(i).colRange((test_name != "yolov8") ? 5 : 4, preds.cols); |
|
double conf; |
|
Point maxLoc; |
|
minMaxLoc(scores, 0, &conf, 0, &maxLoc); |
|
|
|
conf = (test_name != "yolov8") ? conf * obj_conf : conf; |
|
if (conf < conf_threshold) |
|
continue; |
|
|
|
// get bbox coords |
|
float* det = preds.ptr<float>(i); |
|
double cx = det[0]; |
|
double cy = det[1]; |
|
double w = det[2]; |
|
double h = det[3]; |
|
|
|
// [x1, y1, x2, y2] |
|
boxes.push_back(Rect2d(cx - 0.5 * w, cy - 0.5 * h, |
|
cx + 0.5 * w, cy + 0.5 * h)); |
|
classIds.push_back(maxLoc.x); |
|
confidences.push_back(conf); |
|
} |
|
} |
|
|
|
// NMS |
|
std::vector<int> keep_idx; |
|
NMSBoxes(boxes, confidences, conf_threshold, iou_threshold, keep_idx); |
|
|
|
for (auto i : keep_idx) |
|
{ |
|
keep_classIds.push_back(classIds[i]); |
|
keep_confidences.push_back(confidences[i]); |
|
keep_boxes.push_back(boxes[i]); |
|
} |
|
} |
|
|
|
|
|
TEST_P(Test_ONNX_nets, YOLOX) |
|
{ |
|
std::string weightPath = _tf("models/yolox_s_inf_decoder.onnx", false); |
|
|
|
Size targetSize{640, 640}; |
|
float conf_threshold = 0.50; |
|
float iou_threshold = 0.50; |
|
|
|
std::vector<int> refClassIds{1, 16, 7}; |
|
std::vector<float> refScores{0.9649f, 0.9163f, 0.6879f}; |
|
|
|
std::vector<Rect2d> refBoxes{ |
|
Rect2d(105.5384, 179.4100, 470.6339, 428.5553), |
|
Rect2d(111.4482, 263.4098, 258.7438, 526.1140), |
|
Rect2d(389.1421, 143.9286, 577.9495, 222.0294) |
|
}; |
|
|
|
Image2BlobParams imgParams( |
|
Scalar::all(1), |
|
targetSize, |
|
Scalar::all(0), |
|
true, |
|
CV_32F, |
|
DNN_LAYOUT_NCHW, |
|
DNN_PMODE_LETTERBOX, |
|
Scalar::all(114) |
|
); |
|
|
|
testYOLO( |
|
weightPath, refClassIds, refScores, refBoxes, |
|
imgParams, conf_threshold, iou_threshold, |
|
1.0e-4, 1.0e-4); |
|
} |
|
|
|
TEST_P(Test_ONNX_nets, YOLOv8) |
|
{ |
|
std::string weightPath = _tf("models/yolov8n.onnx", false); |
|
|
|
Size targetSize{640, 640}; |
|
float conf_threshold = 0.25; |
|
float iou_threshold = 0.50; |
|
|
|
std::vector<int> refClassIds{16, 1, 2}; |
|
std::vector<float> refScores{0.9332f, 0.8959f, 0.6157f}; |
|
// [x1, y1, x2, y2] |
|
std::vector<Rect2d> refBoxes{ |
|
Rect2d(108.8965, 261.9094, 257.1633, 530.3049), |
|
Rect2d(110.4020, 192.9843, 473.4418, 429.5965), |
|
Rect2d(389.1603, 143.2506, 577.3542, 223.0615), |
|
}; |
|
|
|
Image2BlobParams imgParams( |
|
Scalar::all(1/255.0), |
|
targetSize, |
|
Scalar::all(0), |
|
true, |
|
CV_32F, |
|
DNN_LAYOUT_NCHW, |
|
DNN_PMODE_LETTERBOX, |
|
Scalar::all(114) |
|
); |
|
|
|
testYOLO( |
|
weightPath, refClassIds, refScores, refBoxes, |
|
imgParams, conf_threshold, iou_threshold, |
|
1.0e-4, 1.0e-4, "yolov8"); |
|
} |
|
|
|
// This test is mainly to test: |
|
// 1. identity node with constant input |
|
// 2. limited support to range operator (all inputs are constant) |
|
// 3. parseExpand with multiple broadcast axes |
|
// 4. 1D mat dimension issue with the output of range operator |
|
TEST_P(Test_ONNX_nets, YOLOv7) |
|
{ |
|
std::string weightPath = _tf("models/yolov7_not_simplified.onnx", false); |
|
// Reference, which is collected with input size of 640x640 |
|
std::vector<int> refClassIds{1, 16, 7}; |
|
std::vector<float> refScores{0.9614331f, 0.9589417f, 0.8679074f}; |
|
// [x1, y1, x2, y2] x 3 |
|
std::vector<Rect2d> refBoxes{Rect2d(105.973236f, 150.16716f, 472.59012f, 466.48834f), |
|
Rect2d(109.97953f, 246.17862f, 259.83676f, 600.76624f), |
|
Rect2d(385.96185f, 83.02809f, 576.07355f, 189.82793f)}; |
|
|
|
Size targetSize{640, 640}; |
|
|
|
Image2BlobParams imgParams( |
|
Scalar::all(1/255.0), |
|
targetSize, |
|
Scalar::all(0), |
|
true, |
|
CV_32F, |
|
DNN_LAYOUT_NCHW, |
|
DNN_PMODE_NULL, |
|
Scalar::all(0) |
|
); |
|
|
|
testYOLO(weightPath, refClassIds, refScores, refBoxes, imgParams); |
|
} |
|
|
|
TEST_P(Test_ONNX_nets, YOLOv6) |
|
{ |
|
std::string weightPath = _tf("models/yolov6n.onnx", false); |
|
|
|
Size targetSize{640, 640}; |
|
float conf_threshold = 0.30; |
|
float iou_threshold = 0.50; |
|
|
|
std::vector<int> refClassIds{1, 16, 7, 1}; |
|
std::vector<float> refScores{0.95031f, 0.87123f, 0.65453f, 0.34142f}; |
|
// [x1, y1, x2, y2] x 3 |
|
std::vector<Rect2d> refBoxes{Rect2d(98.84, 177.91, 473.29, 431.19), |
|
Rect2d(109.80, 265.50, 258.86, 531.97), |
|
Rect2d(387.79, 141.61, 576.98, 223.52), |
|
Rect2d(105.62, 199.24, 218.37, 389.84), |
|
}; |
|
|
|
Image2BlobParams imgParams( |
|
Scalar::all(1/255.0), |
|
targetSize, |
|
Scalar::all(0), |
|
true, |
|
CV_32F, |
|
DNN_LAYOUT_NCHW, |
|
DNN_PMODE_LETTERBOX, |
|
Scalar::all(114) |
|
); |
|
|
|
testYOLO( |
|
weightPath, refClassIds, refScores, refBoxes, |
|
imgParams, conf_threshold, iou_threshold, |
|
1.0e-4, 1.0e-3); |
|
} |
|
|
|
TEST_P(Test_ONNX_nets, YOLOv5n) |
|
{ |
|
std::string weightPath = findDataFile("dnn/yolov5n.onnx", false); |
|
// Reference, which is collected with input size of 640x640 |
|
std::vector<int> refClassIds{16, 2, 1}; |
|
std::vector<float> refScores{0.749053f, 0.616853f, 0.32506f}; |
|
// [x1, y1, x2, y2] x 4 |
|
|
|
std::vector<Rect2d> refBoxes{Rect2d(108.088f, 239.293f, 266.196f, 607.658f), |
|
Rect2d(392.028f, 89.9233f, 579.152f, 190.447f), |
|
Rect2d(120.278f, 159.76, 214.481f, 241.473f)}; |
|
|
|
Size targetSize{640, 640}; |
|
|
|
Image2BlobParams imgParams( |
|
Scalar::all(1/255.0), |
|
targetSize, |
|
Scalar::all(0), |
|
true, |
|
CV_32F, |
|
DNN_LAYOUT_NCHW, |
|
DNN_PMODE_NULL, |
|
Scalar::all(0) |
|
); |
|
|
|
testYOLO(weightPath, refClassIds, refScores, refBoxes, imgParams); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Tile) |
|
{ |
|
testONNXModels("tile", pb); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Gelu) |
|
{ |
|
testONNXModels("gelu"); |
|
testONNXModels("gelu_approximation"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, OpenAI_CLIP_head) |
|
{ |
|
testONNXModels("clip-vit-base-head"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, where_node) |
|
{ |
|
testONNXModels("where_layer"); |
|
} |
|
|
|
TEST_P(Test_ONNX_layers, Gemm_all_attributes) { |
|
testONNXModels("test_gemm_all_attributes", pb, 0, 0, false, true, 2); |
|
} |
|
TEST_P(Test_ONNX_layers, Gemm_alpha) { |
|
testONNXModels("test_gemm_alpha", pb, 0, 0, false, true, 2); |
|
} |
|
TEST_P(Test_ONNX_layers, Gemm_beta) { |
|
testONNXModels("test_gemm_beta", pb, 0, 0, false, true, 2); |
|
} |
|
TEST_P(Test_ONNX_layers, Gemm_default_matrix_bias) { |
|
testONNXModels("test_gemm_default_matrix_bias", pb, 0, 0, false, true, 2); |
|
} |
|
TEST_P(Test_ONNX_layers, Gemm_default_no_bias) { |
|
testONNXModels("test_gemm_default_no_bias", pb, 0, 0, false, true, 2); |
|
} |
|
TEST_P(Test_ONNX_layers, Gemm_default_scalar_bias) { |
|
testONNXModels("test_gemm_default_scalar_bias", pb, 0, 0, false, true, 2); |
|
} |
|
TEST_P(Test_ONNX_layers, Gemm_default_single_elem_vector_bias) { |
|
testONNXModels("test_gemm_default_single_elem_vector_bias", pb, 0, 0, false, true, 2); |
|
} |
|
TEST_P(Test_ONNX_layers, Gemm_default_vector_bias) { |
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testONNXModels("test_gemm_default_vector_bias", pb, 0, 0, false, true, 2); |
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} |
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TEST_P(Test_ONNX_layers, Gemm_default_zero_bias) { |
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testONNXModels("test_gemm_default_zero_bias", pb, 0, 0, false, true, 2); |
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} |
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TEST_P(Test_ONNX_layers, Gemm_transposeA) { |
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testONNXModels("test_gemm_transposeA", pb, 0, 0, false, true, 2); |
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} |
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TEST_P(Test_ONNX_layers, Gemm_transposeB) { |
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testONNXModels("test_gemm_transposeB", pb, 0, 0, false, true, 2); |
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} |
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|
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// Note: These tests are converted from onnx/onnx so that they have constant shape as input. |
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// TODO: They can be moved into conformance tests once dynamic input is properly supported. |
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TEST_P(Test_ONNX_layers, Expand_dim_changed) { |
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testONNXModels("test_expand_dim_changed", pb, 0, 0, false, true, 1); |
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} |
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TEST_P(Test_ONNX_layers, Expand_dim_unchanged) { |
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testONNXModels("test_expand_dim_unchanged", pb, 0, 0, false, true, 1); |
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} |
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TEST_P(Test_ONNX_layers, Expand_shape_model1) { |
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testONNXModels("test_expand_shape_model1", pb, 0, 0, false, true, 1); |
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} |
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TEST_P(Test_ONNX_layers, Expand_shape_model2) { |
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testONNXModels("test_expand_shape_model2", pb, 0, 0, false, true, 1); |
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} |
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TEST_P(Test_ONNX_layers, Expand_shape_model3) { |
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testONNXModels("test_expand_shape_model3", pb, 0, 0, false, true, 1); |
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} |
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TEST_P(Test_ONNX_layers, Expand_shape_model4) { |
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testONNXModels("test_expand_shape_model4", pb, 0, 0, false, true, 1); |
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} |
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|
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TEST_P(Test_ONNX_layers, Attention) { |
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testONNXModels("attention"); |
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} |
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TEST_P(Test_ONNX_layers, AttentionSingleHead) { |
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testONNXModels("attention_single_head"); |
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} |
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|
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TEST_P(Test_ONNX_nets, ViT_B_32) { |
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applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_DEBUG_LONG); |
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|
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if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16) |
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{ |
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// does not pass test for now |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16); |
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} |
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|
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const std::string model_path = _tf("models/vit_b_32.onnx", false); |
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|
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auto net = readNet(model_path); |
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ASSERT_FALSE(net.empty()); |
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|
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net.setPreferableBackend(backend); |
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net.setPreferableTarget(target); |
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|
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auto image = imread(_tf("../googlenet_0.png")); |
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auto blob = blobFromImage(image, 1.f, Size(224, 224)); |
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auto ref = blobFromNPY(_tf("data/output_vit_b_32.npy")); |
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checkBackend(&blob, &ref); |
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|
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net.setInput(blob); |
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auto out = net.forward(); |
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|
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normAssert(ref, out, "ViTB_32", default_l1, default_lInf); |
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} |
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|
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TEST_P(Test_ONNX_nets, VitTrack) { |
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auto image = imread(_tf("../dog_orig_size.png")); |
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auto input0 = blobFromImage(image, 1.f, Size(128, 128)); |
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auto input1 = blobFromImage(image, 1.f, Size(256, 256)); |
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|
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auto net = readNet(_tf("models/object_tracking_vittrack_2023sep.onnx", false)); |
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net.setInput(input0, "template"); |
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net.setInput(input1, "search"); |
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|
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std::vector<std::string> output_names{"output1", "output2", "output3"}; |
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std::vector<Mat> outputs; |
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net.forward(outputs, output_names); |
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|
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auto ref_output1 = blobFromNPY(_tf("data/output_object_tracking_vittrack_2023sep_0.npy")); |
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auto ref_output2 = blobFromNPY(_tf("data/output_object_tracking_vittrack_2023sep_1.npy")); |
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auto ref_output3 = blobFromNPY(_tf("data/output_object_tracking_vittrack_2023sep_2.npy")); |
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|
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normAssert(ref_output1, outputs[0], "VitTrack output1"); |
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normAssert(ref_output2, outputs[1], "VitTrack output2"); |
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normAssert(ref_output3, outputs[2], "VitTrack output3"); |
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} |
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|
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INSTANTIATE_TEST_CASE_P(/**/, Test_ONNX_nets, dnnBackendsAndTargets()); |
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|
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}} // namespace
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