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557 lines
17 KiB
557 lines
17 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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namespace opencv_test { namespace { |
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template<typename TString> |
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static std::string _tf(TString filename) |
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{ |
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String rootFolder = "dnn/onnx/"; |
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return findDataFile(rootFolder + filename, false); |
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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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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 testONNXModels(const String& basename, const Extension ext = npy, |
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const double l1 = 0, const float lInf = 0, const bool useSoftmax = false, |
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bool checkNoFallbacks = true) |
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{ |
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String onnxmodel = _tf("models/" + basename + ".onnx"); |
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Mat inp, ref; |
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if (ext == npy) { |
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inp = blobFromNPY(_tf("data/input_" + basename + ".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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inp = readTensorFromONNX(_tf("data/input_" + basename + ".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(&inp, &ref); |
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Net net = readNetFromONNX(onnxmodel); |
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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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net.setInput(inp); |
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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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normAssert(ref, out, "", 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, MaxPooling) |
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{ |
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testONNXModels("maxpooling"); |
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testONNXModels("two_maxpooling"); |
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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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} |
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TEST_P(Test_ONNX_layers, Convolution3D) |
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{ |
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if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU) |
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throw SkipTestException("Only DLIE backend on CPU is supported"); |
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testONNXModels("conv3d"); |
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testONNXModels("conv3d_bias"); |
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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) && INF_ENGINE_VER_MAJOR_GE(2018050000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && 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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throw SkipTestException("Test is disabled for MyriadX"); // 2018R5+ is failed |
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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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testONNXModels("deconv_adjpad_2d", npy, 0, 0, false, false); |
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} |
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TEST_P(Test_ONNX_layers, Dropout) |
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{ |
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testONNXModels("dropout"); |
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} |
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TEST_P(Test_ONNX_layers, Linear) |
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{ |
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) |
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throw SkipTestException(""); |
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testONNXModels("linear"); |
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} |
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TEST_P(Test_ONNX_layers, ReLU) |
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{ |
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testONNXModels("ReLU"); |
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} |
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TEST_P(Test_ONNX_layers, MaxPooling_Sigmoid) |
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{ |
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testONNXModels("maxpooling_sigmoid"); |
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} |
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TEST_P(Test_ONNX_layers, Concatenation) |
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{ |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && |
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(target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL || target == DNN_TARGET_MYRIAD)) |
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throw SkipTestException(""); |
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testONNXModels("concatenation"); |
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} |
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TEST_P(Test_ONNX_layers, AveragePooling) |
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{ |
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testONNXModels("average_pooling"); |
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} |
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TEST_P(Test_ONNX_layers, MaxPooling3D) |
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{ |
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if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU) |
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throw SkipTestException("Only DLIE backend on CPU is supported"); |
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testONNXModels("max_pool3d"); |
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} |
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TEST_P(Test_ONNX_layers, AvePooling3D) |
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{ |
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if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU) |
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throw SkipTestException("Only DLIE backend on CPU is supported"); |
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testONNXModels("ave_pool3d"); |
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} |
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TEST_P(Test_ONNX_layers, BatchNormalization) |
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{ |
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testONNXModels("batch_norm"); |
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} |
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TEST_P(Test_ONNX_layers, BatchNormalization3D) |
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{ |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) |
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throw SkipTestException(""); |
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testONNXModels("batch_norm_3d"); |
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} |
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TEST_P(Test_ONNX_layers, Transpose) |
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{ |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && |
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(target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL || target == DNN_TARGET_MYRIAD)) |
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throw SkipTestException(""); |
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testONNXModels("transpose"); |
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} |
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TEST_P(Test_ONNX_layers, Multiplication) |
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{ |
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if ((backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) || |
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(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)) |
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throw SkipTestException(""); |
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testONNXModels("mul"); |
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} |
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TEST_P(Test_ONNX_layers, Constant) |
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{ |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD |
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&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) |
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throw SkipTestException("Test is disabled for OpenVINO <= 2018R5 + MyriadX target"); |
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#endif |
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testONNXModels("constant"); |
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} |
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TEST_P(Test_ONNX_layers, Padding) |
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{ |
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testONNXModels("padding"); |
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} |
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TEST_P(Test_ONNX_layers, Resize) |
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{ |
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testONNXModels("resize_nearest"); |
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} |
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TEST_P(Test_ONNX_layers, MultyInputs) |
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{ |
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const String model = _tf("models/multy_inputs.onnx"); |
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Net net = readNetFromONNX(model); |
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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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Mat inp1 = blobFromNPY(_tf("data/input_multy_inputs_0.npy")); |
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Mat inp2 = blobFromNPY(_tf("data/input_multy_inputs_1.npy")); |
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Mat ref = blobFromNPY(_tf("data/output_multy_inputs.npy")); |
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checkBackend(&inp1, &ref); |
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net.setInput(inp1, "0"); |
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net.setInput(inp2, "1"); |
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Mat out = net.forward(); |
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normAssert(ref, out, "", default_l1, default_lInf); |
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expectNoFallbacksFromIE(net); |
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} |
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TEST_P(Test_ONNX_layers, DynamicReshape) |
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{ |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) |
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throw SkipTestException(""); |
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testONNXModels("dynamic_reshape"); |
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} |
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TEST_P(Test_ONNX_layers, Reshape) |
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{ |
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testONNXModels("unsqueeze"); |
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} |
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TEST_P(Test_ONNX_layers, Softmax) |
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{ |
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testONNXModels("softmax"); |
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testONNXModels("log_softmax", npy, 0, 0, false, false); |
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} |
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INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets()); |
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class Test_ONNX_nets : public Test_ONNX_layers {}; |
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TEST_P(Test_ONNX_nets, Alexnet) |
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{ |
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applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB); |
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const String model = _tf("models/alexnet.onnx"); |
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Net net = readNetFromONNX(model); |
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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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Mat inp = imread(_tf("../grace_hopper_227.png")); |
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Mat ref = blobFromNPY(_tf("../caffe_alexnet_prob.npy")); |
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checkBackend(&inp, &ref); |
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net.setInput(blobFromImage(inp, 1.0f, Size(227, 227), Scalar(), false)); |
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ASSERT_FALSE(net.empty()); |
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Mat out = net.forward(); |
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normAssert(out, ref, "", default_l1, default_lInf); |
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expectNoFallbacksFromIE(net); |
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} |
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TEST_P(Test_ONNX_nets, Squeezenet) |
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{ |
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testONNXModels("squeezenet", pb); |
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} |
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TEST_P(Test_ONNX_nets, Googlenet) |
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{ |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE) |
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throw SkipTestException(""); |
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const String model = _tf("models/googlenet.onnx"); |
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Net net = readNetFromONNX(model); |
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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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std::vector<Mat> images; |
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images.push_back( imread(_tf("../googlenet_0.png")) ); |
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images.push_back( imread(_tf("../googlenet_1.png")) ); |
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Mat inp = blobFromImages(images, 1.0f, Size(), Scalar(), false); |
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Mat ref = blobFromNPY(_tf("../googlenet_prob.npy")); |
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checkBackend(&inp, &ref); |
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net.setInput(inp); |
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ASSERT_FALSE(net.empty()); |
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Mat out = net.forward(); |
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normAssert(ref, out, "", default_l1, default_lInf); |
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expectNoFallbacksFromIE(net); |
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} |
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TEST_P(Test_ONNX_nets, CaffeNet) |
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{ |
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applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB); |
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testONNXModels("caffenet", pb); |
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} |
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TEST_P(Test_ONNX_nets, RCNN_ILSVRC13) |
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{ |
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applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB); |
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// Reference output values are in range [-4.992, -1.161] |
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testONNXModels("rcnn_ilsvrc13", pb, 0.0045); |
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} |
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TEST_P(Test_ONNX_nets, VGG16) |
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{ |
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applyTestTag(CV_TEST_TAG_MEMORY_6GB); // > 2.3Gb |
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// output range: [-69; 72], after Softmax [0; 0.96] |
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testONNXModels("vgg16", pb, default_l1, default_lInf, true); |
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} |
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TEST_P(Test_ONNX_nets, VGG16_bn) |
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{ |
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applyTestTag(CV_TEST_TAG_MEMORY_6GB); // > 2.3Gb |
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// output range: [-16; 27], after Softmax [0; 0.67] |
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const double lInf = (target == DNN_TARGET_MYRIAD) ? 0.038 : default_lInf; |
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testONNXModels("vgg16-bn", pb, default_l1, lInf, true); |
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} |
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TEST_P(Test_ONNX_nets, ZFNet) |
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{ |
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applyTestTag(CV_TEST_TAG_MEMORY_2GB); |
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testONNXModels("zfnet512", pb); |
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} |
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TEST_P(Test_ONNX_nets, ResNet18v1) |
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{ |
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applyTestTag(CV_TEST_TAG_MEMORY_512MB); |
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// output range: [-16; 22], after Softmax [0, 0.51] |
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testONNXModels("resnet18v1", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD); |
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} |
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TEST_P(Test_ONNX_nets, ResNet50v1) |
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{ |
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applyTestTag(CV_TEST_TAG_MEMORY_512MB); |
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// output range: [-67; 75], after Softmax [0, 0.98] |
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testONNXModels("resnet50v1", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD); |
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} |
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TEST_P(Test_ONNX_nets, ResNet101_DUC_HDC) |
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{ |
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applyTestTag(CV_TEST_TAG_VERYLONG); |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE) |
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throw SkipTestException("Test is disabled for DLIE targets"); |
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#endif |
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#if defined(INF_ENGINE_RELEASE) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) |
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throw SkipTestException("Test is disabled for Myriad targets"); |
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#endif |
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if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL) |
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throw SkipTestException("Test is disabled for OpenCL targets"); |
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testONNXModels("resnet101_duc_hdc", pb); |
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} |
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TEST_P(Test_ONNX_nets, TinyYolov2) |
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{ |
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applyTestTag(CV_TEST_TAG_MEMORY_512MB); |
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if (cvtest::skipUnstableTests) |
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throw SkipTestException("Skip unstable test"); |
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#if defined(INF_ENGINE_RELEASE) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE |
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&& (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16) |
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) |
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throw SkipTestException("Test is disabled for DLIE OpenCL targets"); |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && 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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throw SkipTestException("Test is disabled for MyriadX"); |
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#endif |
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// output range: [-11; 8] |
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double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.017 : default_l1; |
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double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.14 : default_lInf; |
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testONNXModels("tiny_yolo2", pb, l1, lInf); |
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} |
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TEST_P(Test_ONNX_nets, CNN_MNIST) |
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{ |
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// output range: [-1952; 6574], after Softmax [0; 1] |
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testONNXModels("cnn_mnist", pb, default_l1, default_lInf, true); |
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} |
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TEST_P(Test_ONNX_nets, MobileNet_v2) |
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{ |
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// output range: [-166; 317], after Softmax [0; 1] |
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testONNXModels("mobilenetv2", pb, default_l1, default_lInf, true); |
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} |
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TEST_P(Test_ONNX_nets, LResNet100E_IR) |
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{ |
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applyTestTag( |
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(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB), |
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CV_TEST_TAG_DEBUG_LONG |
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); |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && |
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(target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL || target == DNN_TARGET_MYRIAD)) |
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throw SkipTestException(""); |
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double l1 = default_l1; |
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double lInf = default_lInf; |
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// output range: [-3; 3] |
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) { |
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l1 = 0.009; |
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lInf = 0.035; |
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} |
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else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU) { |
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l1 = 4.6e-5; |
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lInf = 1.9e-4; |
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} |
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testONNXModels("LResNet100E_IR", pb, l1, lInf); |
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} |
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TEST_P(Test_ONNX_nets, Emotion_ferplus) |
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{ |
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#if defined(INF_ENGINE_RELEASE) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && 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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throw SkipTestException("Test is disabled for MyriadX"); |
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#endif |
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double l1 = default_l1; |
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double lInf = default_lInf; |
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// Output values are in range [-2.011, 2.111] |
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) |
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l1 = 0.007; |
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else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) |
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{ |
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l1 = 0.021; |
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lInf = 0.034; |
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} |
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else if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_CPU || target == DNN_TARGET_OPENCL)) { |
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l1 = 2.4e-4; |
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lInf = 6e-4; |
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} |
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testONNXModels("emotion_ferplus", pb, l1, lInf); |
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} |
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TEST_P(Test_ONNX_nets, Inception_v2) |
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{ |
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testONNXModels("inception_v2", pb, default_l1, default_lInf, true); |
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} |
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TEST_P(Test_ONNX_nets, DenseNet121) |
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{ |
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applyTestTag(CV_TEST_TAG_MEMORY_512MB); |
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// output range: [-87; 138], after Softmax [0; 1] |
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testONNXModels("densenet121", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD); |
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} |
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TEST_P(Test_ONNX_nets, Inception_v1) |
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{ |
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#if defined(INF_ENGINE_RELEASE) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) |
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throw SkipTestException("Test is disabled for Myriad targets"); |
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#endif |
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testONNXModels("inception_v1", pb); |
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} |
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TEST_P(Test_ONNX_nets, Shufflenet) |
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{ |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && |
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(target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL || target == DNN_TARGET_MYRIAD)) |
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throw SkipTestException(""); |
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testONNXModels("shufflenet", pb); |
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} |
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TEST_P(Test_ONNX_nets, Resnet34_kinetics) |
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{ |
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if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU) |
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throw SkipTestException("Only DLIE backend on CPU is supported"); |
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String onnxmodel = findDataFile("dnn/resnet-34_kinetics.onnx"); |
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Mat image0 = imread(findDataFile("dnn/dog416.png")); |
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Mat image1 = imread(findDataFile("dnn/street.png")); |
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Mat ref0 = blobFromNPY(_tf("data/output_kinetics0.npy")); |
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Mat ref1 = blobFromNPY(_tf("data/output_kinetics1.npy")); |
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std::vector<Mat> images_0(16, image0); |
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std::vector<Mat> images_1(16, image1); |
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Mat blob0 = blobFromImages(images_0, 1.0, Size(112, 112), Scalar(114.7748, 107.7354, 99.4750), true, true); |
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Mat blob1 = blobFromImages(images_1, 1.0, Size(112, 112), Scalar(114.7748, 107.7354, 99.4750), true, true); |
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Net permute; |
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LayerParams lp; |
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int order[] = {1, 0, 2, 3}; |
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lp.set("order", DictValue::arrayInt<int*>(&order[0], 4)); |
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permute.addLayerToPrev("perm", "Permute", lp); |
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permute.setInput(blob0); |
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Mat input0 = permute.forward().clone(); |
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permute.setInput(blob1); |
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Mat input1 = permute.forward().clone(); |
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int dims[] = {1, 3, 16, 112, 112}; |
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input0 = input0.reshape(0, 5, &dims[0]); |
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input1 = input1.reshape(0, 5, &dims[0]); |
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Net net = readNetFromONNX(onnxmodel); |
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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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// output range [-5, 11] |
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float l1 = 0.0013; |
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float lInf = 0.009; |
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checkBackend(&input0, &ref0); |
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net.setInput(input0); |
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Mat out = net.forward().clone(); |
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normAssert(ref0, out, "", l1, lInf); |
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checkBackend(&input1, &ref1); |
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net.setInput(input1); |
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out = net.forward().clone(); |
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normAssert(ref1, out, "", l1, lInf); |
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expectNoFallbacksFromIE(net); |
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} |
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INSTANTIATE_TEST_CASE_P(/**/, Test_ONNX_nets, dnnBackendsAndTargets()); |
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}} // namespace
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