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Open Source Computer Vision Library
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1523 lines
58 KiB
1523 lines
58 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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// |
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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 "opencv2/core/ocl.hpp" |
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namespace opencv_test { namespace { |
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class DNNTestNetwork : public DNNTestLayer |
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{ |
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public: |
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void processNet(const std::string& weights, const std::string& proto, |
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Size inpSize, const std::string& outputLayer = "", |
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const std::string& halideScheduler = "", |
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double l1 = 0.0, double lInf = 0.0) |
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{ |
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// Create a common input blob. |
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int blobSize[] = {1, 3, inpSize.height, inpSize.width}; |
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Mat inp(4, blobSize, CV_32FC1); |
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randu(inp, 0.0f, 1.0f); |
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processNet(weights, proto, inp, outputLayer, halideScheduler, l1, lInf); |
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} |
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void processNet(std::string weights, std::string proto, |
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Mat inp, const std::string& outputLayer = "", |
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std::string halideScheduler = "", |
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double l1 = 0.0, double lInf = 0.0, double detectionConfThresh = 0.2, bool useWinograd = true) |
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{ |
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checkBackend(); |
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l1 = l1 ? l1 : default_l1; |
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lInf = lInf ? lInf : default_lInf; |
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weights = findDataFile(weights, false); |
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if (!proto.empty()) |
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proto = findDataFile(proto); |
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// Create two networks - with default backend and target and a tested one. |
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Net netDefault = readNet(weights, proto); |
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netDefault.setPreferableBackend(DNN_BACKEND_OPENCV); |
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netDefault.setInput(inp); |
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Mat outDefault = netDefault.forward(outputLayer).clone(); |
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net = readNet(weights, proto); |
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net.setInput(inp); |
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net.setPreferableBackend(backend); |
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net.setPreferableTarget(target); |
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if (target == DNN_TARGET_CPU_FP16) |
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net.enableWinograd(false); |
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if (backend == DNN_BACKEND_HALIDE && !halideScheduler.empty()) |
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{ |
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halideScheduler = findDataFile(halideScheduler); |
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net.setHalideScheduler(halideScheduler); |
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} |
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Mat out = net.forward(outputLayer).clone(); |
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check(outDefault, out, outputLayer, l1, lInf, detectionConfThresh, "First run"); |
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// Test 2: change input. |
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float* inpData = (float*)inp.data; |
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for (int i = 0; i < inp.size[0] * inp.size[1]; ++i) |
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{ |
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Mat slice(inp.size[2], inp.size[3], CV_32F, inpData); |
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cv::flip(slice, slice, 1); |
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inpData += slice.total(); |
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} |
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netDefault.setInput(inp); |
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net.setInput(inp); |
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outDefault = netDefault.forward(outputLayer).clone(); |
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out = net.forward(outputLayer).clone(); |
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check(outDefault, out, outputLayer, l1, lInf, detectionConfThresh, "Second run"); |
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} |
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void check(Mat& ref, Mat& out, const std::string& outputLayer, double l1, double lInf, |
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double detectionConfThresh, const char* msg) |
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{ |
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if (outputLayer == "detection_out") |
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{ |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
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{ |
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// Inference Engine produces detections terminated by a row which starts from -1. |
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out = out.reshape(1, out.total() / 7); |
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int numDetections = 0; |
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while (numDetections < out.rows && out.at<float>(numDetections, 0) != -1) |
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{ |
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numDetections += 1; |
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} |
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out = out.rowRange(0, numDetections); |
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} |
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normAssertDetections(ref, out, msg, detectionConfThresh, l1, lInf); |
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} |
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else |
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normAssert(ref, out, msg, l1, lInf); |
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} |
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Net net; |
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}; |
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TEST_P(DNNTestNetwork, AlexNet) |
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{ |
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applyTestTag(CV_TEST_TAG_MEMORY_1GB); |
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processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt", |
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Size(227, 227), "prob", |
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target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_alexnet.yml" : |
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"dnn/halide_scheduler_alexnet.yml"); |
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expectNoFallbacksFromIE(net); |
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expectNoFallbacksFromCUDA(net); |
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} |
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TEST_P(DNNTestNetwork, ResNet_50) |
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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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processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt", |
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Size(224, 224), "prob", |
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target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_resnet_50.yml" : |
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"dnn/halide_scheduler_resnet_50.yml"); |
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expectNoFallbacksFromIE(net); |
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expectNoFallbacksFromCUDA(net); |
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} |
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TEST_P(DNNTestNetwork, SqueezeNet_v1_1) |
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{ |
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processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt", |
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Size(227, 227), "prob", |
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target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_squeezenet_v1_1.yml" : |
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"dnn/halide_scheduler_squeezenet_v1_1.yml"); |
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expectNoFallbacksFromIE(net); |
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expectNoFallbacksFromCUDA(net); |
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} |
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TEST_P(DNNTestNetwork, GoogLeNet) |
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{ |
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applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB); |
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processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt", |
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Size(224, 224), "prob"); |
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expectNoFallbacksFromIE(net); |
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expectNoFallbacksFromCUDA(net); |
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} |
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TEST_P(DNNTestNetwork, Inception_5h) |
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{ |
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applyTestTag(CV_TEST_TAG_MEMORY_512MB); |
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double l1 = default_l1, lInf = default_lInf; |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_CPU || target == DNN_TARGET_OPENCL)) |
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{ |
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l1 = 1.72e-5; |
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lInf = 8e-4; |
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} |
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processNet("dnn/tensorflow_inception_graph.pb", "", Size(224, 224), "softmax2", |
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target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_inception_5h.yml" : |
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"dnn/halide_scheduler_inception_5h.yml", |
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l1, lInf); |
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expectNoFallbacksFromIE(net); |
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expectNoFallbacksFromCUDA(net); |
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} |
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TEST_P(DNNTestNetwork, ENet) |
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{ |
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applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB); |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2023000000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
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#endif |
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); |
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if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16); |
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_CPU_FP16) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16); |
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processNet("dnn/Enet-model-best.net", "", Size(512, 512), "l367_Deconvolution", |
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target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_enet.yml" : |
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"dnn/halide_scheduler_enet.yml", |
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2e-5, 0.15); |
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expectNoFallbacksFromCUDA(net); |
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} |
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TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe) |
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{ |
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applyTestTag(CV_TEST_TAG_MEMORY_512MB); |
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if (backend == DNN_BACKEND_HALIDE) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
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Mat sample = imread(findDataFile("dnn/street.png")); |
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Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false); |
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float scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) ? 1.5e-2 : 0.0; |
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float iouDiff = (target == DNN_TARGET_MYRIAD) ? 0.063 : 0.0; |
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float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.262 : FLT_MIN; |
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processNet("dnn/MobileNetSSD_deploy_19e3ec3.caffemodel", "dnn/MobileNetSSD_deploy_19e3ec3.prototxt", |
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inp, "detection_out", "", scoreDiff, iouDiff, detectionConfThresh); |
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expectNoFallbacksFromIE(net); |
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} |
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TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height) |
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{ |
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if (backend == DNN_BACKEND_HALIDE) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
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// May hang on some configurations |
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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) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
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// IE exception: Ngraph operation Transpose with name conv15_2_mbox_conf_perm has dynamic output shape on 0 port, but CPU plug-in supports only static shape |
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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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if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && |
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target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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#elif defined(INF_ENGINE_RELEASE) |
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if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && |
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target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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#endif |
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Mat sample = imread(findDataFile("dnn/street.png")); |
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Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 560), Scalar(127.5, 127.5, 127.5), false); |
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float scoreDiff = 0.0, iouDiff = 0.0; |
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if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) |
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{ |
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scoreDiff = 0.029; |
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iouDiff = 0.09; |
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} |
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else if (target == DNN_TARGET_CUDA_FP16) |
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{ |
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scoreDiff = 0.03; |
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iouDiff = 0.08; |
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} |
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processNet("dnn/MobileNetSSD_deploy_19e3ec3.caffemodel", "dnn/MobileNetSSD_deploy_19e3ec3.prototxt", |
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inp, "detection_out", "", scoreDiff, iouDiff); |
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expectNoFallbacksFromIE(net); |
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} |
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TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow) |
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{ |
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applyTestTag((target == DNN_TARGET_CPU || target == DNN_TARGET_CPU_FP16) ? "" : CV_TEST_TAG_MEMORY_512MB); |
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if (backend == DNN_BACKEND_HALIDE) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
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Mat sample = imread(findDataFile("dnn/street.png")); |
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Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false); |
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float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.216 : 0.2; |
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float scoreDiff = 0.0, iouDiff = 0.0; |
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if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) |
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{ |
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scoreDiff = 0.095; |
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iouDiff = 0.09; |
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} |
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else if (target == DNN_TARGET_CUDA_FP16) |
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{ |
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scoreDiff = 0.007; |
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iouDiff = 0.08; |
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} |
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processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt", |
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inp, "detection_out", "", scoreDiff, iouDiff, detectionConfThresh); |
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expectNoFallbacksFromIE(net); |
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} |
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TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height) |
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{ |
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if (backend == DNN_BACKEND_HALIDE) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
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if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && |
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target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); |
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#endif |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000) |
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if (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_VERSION); |
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#endif |
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Mat sample = imread(findDataFile("dnn/street.png")); |
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Mat inp = blobFromImage(sample, 1.0f, Size(300, 560), Scalar(), false); |
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float scoreDiff = 0.0, iouDiff = 0.0; |
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if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) |
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{ |
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scoreDiff = 0.013; |
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iouDiff = 0.06; |
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} |
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else if (target == DNN_TARGET_CUDA_FP16) |
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{ |
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scoreDiff = 0.007; |
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iouDiff = 0.06; |
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} |
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processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt", |
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inp, "detection_out", "", scoreDiff, iouDiff); |
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expectNoFallbacksFromIE(net); |
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} |
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TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow) |
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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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if (backend == DNN_BACKEND_HALIDE) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
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Mat sample = imread(findDataFile("dnn/street.png")); |
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Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false); |
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float scoreDiff = 2e-5, iouDiff = 0.0; |
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if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) |
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{ |
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scoreDiff = 0.013; |
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iouDiff = 0.062; |
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} |
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else if (target == DNN_TARGET_CUDA_FP16) |
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{ |
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scoreDiff = 0.02; |
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iouDiff = 0.07; |
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} |
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processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt", |
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inp, "detection_out", "", scoreDiff, iouDiff, 0.25); |
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expectNoFallbacksFromIE(net); |
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} |
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TEST_P(DNNTestNetwork, SSD_VGG16) |
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{ |
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applyTestTag( |
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CV_TEST_TAG_MEMORY_2GB, |
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CV_TEST_TAG_LONG, |
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CV_TEST_TAG_DEBUG_VERYLONG |
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); |
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if (backend == DNN_BACKEND_HALIDE && target == DNN_TARGET_CPU) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); // TODO HALIDE_CPU |
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Mat sample = imread(findDataFile("dnn/street.png")); |
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Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false); |
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float scoreDiff = 0.0, iouDiff = 0.0; |
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if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CPU_FP16) |
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{ |
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scoreDiff = 0.04; |
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} |
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else if (target == DNN_TARGET_MYRIAD) |
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{ |
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scoreDiff = 0.0325; |
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iouDiff = 0.032; |
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} |
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else if (target == DNN_TARGET_CUDA_FP16) |
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{ |
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scoreDiff = 0.03; |
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iouDiff = 0.13; |
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} |
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processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", |
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"dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreDiff, |
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iouDiff, 0.2, false); |
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expectNoFallbacksFromIE(net); |
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} |
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TEST_P(DNNTestNetwork, OpenPose_pose_coco) |
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{ |
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applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB), |
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CV_TEST_TAG_DEBUG_LONG); |
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if (backend == DNN_BACKEND_HALIDE) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
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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_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD |
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&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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#endif |
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const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.009 : 0.0; |
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const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0; |
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processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt", |
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Size(46, 46), "", "", l1, lInf); |
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expectNoFallbacksFromIE(net); |
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expectNoFallbacksFromCUDA(net); |
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} |
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TEST_P(DNNTestNetwork, OpenPose_pose_mpi) |
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{ |
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applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB), |
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CV_TEST_TAG_DEBUG_VERYLONG); |
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if (backend == DNN_BACKEND_HALIDE) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
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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_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD |
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&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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#endif |
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// output range: [-0.001, 0.97] |
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const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.02 : 0.0; |
|
const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CPU_FP16) ? 0.2 : 0.0; |
|
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt", |
|
Size(46, 46), "", "", l1, lInf); |
|
expectNoFallbacksFromIE(net); |
|
expectNoFallbacksFromCUDA(net); |
|
} |
|
|
|
TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages) |
|
{ |
|
applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_MEMORY_1GB); |
|
if (backend == DNN_BACKEND_HALIDE) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(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_VERSION); |
|
#endif |
|
|
|
// The same .caffemodel but modified .prototxt |
|
// See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp |
|
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt", |
|
Size(46, 46)); |
|
expectNoFallbacksFromIE(net); |
|
expectNoFallbacksFromCUDA(net); |
|
} |
|
|
|
TEST_P(DNNTestNetwork, OpenFace) |
|
{ |
|
#if defined(INF_ENGINE_RELEASE) |
|
#if INF_ENGINE_VER_MAJOR_EQ(2018050000) |
|
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_VERSION); |
|
#endif |
|
#endif |
|
if (backend == DNN_BACKEND_HALIDE) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
|
const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0024 : 0.0; |
|
const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.0071 : 0.0; |
|
processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "", "", l1, lInf); |
|
|
|
expectNoFallbacksFromCUDA(net); |
|
} |
|
|
|
TEST_P(DNNTestNetwork, opencv_face_detector) |
|
{ |
|
if (backend == DNN_BACKEND_HALIDE) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
|
Mat img = imread(findDataFile("gpu/lbpcascade/er.png")); |
|
Mat inp = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false); |
|
processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt", |
|
inp, "detection_out"); |
|
expectNoFallbacksFromIE(net); |
|
} |
|
|
|
TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow) |
|
{ |
|
applyTestTag( |
|
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB), |
|
CV_TEST_TAG_DEBUG_LONG |
|
); |
|
#if defined(INF_ENGINE_RELEASE) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD |
|
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); |
|
#endif |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000) |
|
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, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#endif |
|
if (backend == DNN_BACKEND_HALIDE) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
|
Mat sample = imread(findDataFile("dnn/street.png")); |
|
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false); |
|
float scoreDiff = 0.0, iouDiff = 0.0; |
|
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) |
|
{ |
|
scoreDiff = 0.02; |
|
iouDiff = 0.1; |
|
} |
|
else if (target == DNN_TARGET_CUDA_FP16) |
|
{ |
|
scoreDiff = 0.015; |
|
iouDiff = 0.08; |
|
} |
|
processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt", |
|
inp, "detection_out", "", scoreDiff, iouDiff); |
|
expectNoFallbacksFromIE(net); |
|
} |
|
|
|
TEST_P(DNNTestNetwork, DenseNet_121) |
|
{ |
|
applyTestTag(CV_TEST_TAG_MEMORY_512MB); |
|
if (backend == DNN_BACKEND_HALIDE) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
|
// Reference output values are in range [-3.807, 4.605] |
|
float l1 = 0.0, lInf = 0.0; |
|
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CPU_FP16) |
|
{ |
|
l1 = 2e-2; |
|
lInf = 9e-2; |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
|
lInf = 0.1f; |
|
} |
|
else if (target == DNN_TARGET_MYRIAD) |
|
{ |
|
l1 = 0.1; |
|
lInf = 0.6; |
|
} |
|
else if (target == DNN_TARGET_CUDA_FP16) |
|
{ |
|
l1 = 0.008; |
|
lInf = 0.06; |
|
} |
|
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "", l1, lInf); |
|
if (target != DNN_TARGET_MYRIAD || getInferenceEngineVPUType() != CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) |
|
expectNoFallbacksFromIE(net); |
|
expectNoFallbacksFromCUDA(net); |
|
} |
|
|
|
TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16) |
|
{ |
|
applyTestTag(CV_TEST_TAG_MEMORY_512MB, CV_TEST_TAG_DEBUG_VERYLONG); |
|
|
|
if (backend == DNN_BACKEND_HALIDE) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
|
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); |
|
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); |
|
|
|
#if defined(INF_ENGINE_RELEASE) |
|
#if INF_ENGINE_VER_MAJOR_LE(2018050000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#endif |
|
#endif |
|
|
|
Mat img = imread(findDataFile("dnn/googlenet_1.png")); |
|
Mat inp = blobFromImage(img, 1.0, Size(320, 240), Scalar(103.939, 116.779, 123.68), false, false); |
|
// Output image has values in range [-143.526, 148.539]. |
|
float l1 = 2e-4, lInf = 2.4e-3; |
|
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) |
|
{ |
|
l1 = 0.4; |
|
lInf = 7.46; |
|
} |
|
else if (target == DNN_TARGET_CUDA_FP16) |
|
{ |
|
l1 = 0.3; |
|
lInf = 7.6; |
|
} |
|
else if (target == DNN_TARGET_CPU_FP16) |
|
{ |
|
l1 = 0.4; |
|
lInf = 22.; |
|
} |
|
else if (target == DNN_TARGET_VULKAN) |
|
{ |
|
l1 = 0.4; |
|
lInf = 7.46; |
|
} |
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) |
|
{ |
|
l1 = 5e-3; |
|
lInf = 5e-3; |
|
} |
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) |
|
{ |
|
lInf = 25; |
|
} |
|
#endif |
|
|
|
|
|
processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf); |
|
#if defined(HAVE_INF_ENGINE) && INF_ENGINE_VER_MAJOR_GE(2019010000) |
|
expectNoFallbacksFromIE(net); |
|
#endif |
|
expectNoFallbacksFromCUDA(net); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets(true, true, false, true, true)); |
|
|
|
/* |
|
Backend tests of layers |
|
*/ |
|
|
|
static void testLayer(Mat& input, Net& net, Backend backendId, Target targetId, bool skipCheck = false, bool randInput = true, double l1 = 0.0, double lInf = 0.0) |
|
{ |
|
DNNTestLayer::checkBackend(backendId, targetId); |
|
if (randInput) |
|
randu(input, -1.0f, 1.0f); |
|
|
|
net.setInput(input); |
|
net.setPreferableBackend(DNN_BACKEND_OPENCV); |
|
Mat outputDefault = net.forward().clone(); |
|
|
|
net.setPreferableBackend(backendId); |
|
net.setPreferableTarget(targetId); |
|
Mat output = net.forward().clone(); |
|
|
|
if (skipCheck) |
|
return; |
|
|
|
double default_l1, default_lInf; |
|
DNNTestLayer::getDefaultThresholds(backendId, targetId, &default_l1, &default_lInf); |
|
if (l1 == 0.0) |
|
l1 = default_l1; |
|
if (lInf == 0.0) |
|
lInf = default_lInf; |
|
normAssert(outputDefault, output, "", l1, lInf); |
|
if (cvtest::debugLevel > 0 || testing::Test::HasFailure()) |
|
{ |
|
std::cout << "l1=" << l1 << " lInf=" << lInf << std::endl; |
|
std::cout << outputDefault.reshape(1, outputDefault.total()).t() << std::endl; |
|
std::cout << output.reshape(1, outputDefault.total()).t() << std::endl; |
|
} |
|
} |
|
|
|
static void testLayer(LayerParams& params, Mat& input, Backend backendId, Target targetId, bool skipCheck = false, double l1 = 0.0, double lInf = 0.0) |
|
{ |
|
Net net; |
|
net.addLayerToPrev(params.name, params.type, params); |
|
testLayer(input, net, backendId, targetId, skipCheck, true, l1, lInf); |
|
} |
|
|
|
class Test_layers_backends : public DNNTestLayer {}; |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// Padding |
|
//////////////////////////////////////////////////////////////////////////////// |
|
TEST_P(Test_layers_backends, Padding) |
|
{ |
|
static const int kNumRuns = 10; |
|
std::vector<int> paddings(8); |
|
cv::RNG& rng = cv::theRNG(); |
|
for (int t = 0; t < kNumRuns; ++t) |
|
{ |
|
for (int i = 0; i < paddings.size(); ++i) |
|
paddings[i] = rng(5); |
|
|
|
LayerParams lp; |
|
lp.set("paddings", DictValue::arrayInt<int*>(&paddings[0], paddings.size())); |
|
lp.type = "Padding"; |
|
lp.name = "testLayer"; |
|
|
|
int sz[] = {1 + (int)rng(10), 1 + (int)rng(10), 1 + (int)rng(10), 1 + (int)rng(10)}; |
|
Mat input(4, &sz[0], CV_32F); |
|
testLayer(lp, input, backend, target); |
|
} |
|
} |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// Convolution |
|
//////////////////////////////////////////////////////////////////////////////// |
|
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Size, bool, tuple<Backend, Target> > > Convolution; |
|
TEST_P(Convolution, Accuracy) |
|
{ |
|
int inChannels = get<0>(GetParam())[0]; |
|
int outChannels = get<0>(GetParam())[1]; |
|
int group = get<0>(GetParam())[2]; |
|
Size inSize = get<1>(GetParam()); |
|
Size kernel = get<2>(GetParam()); |
|
Size stride = get<3>(GetParam()); |
|
Size pad = get<4>(GetParam()); |
|
Size dilation = get<5>(GetParam()); |
|
bool hasBias = get<6>(GetParam()); |
|
Backend backendId = get<0>(get<7>(GetParam())); |
|
Target targetId = get<1>(get<7>(GetParam())); |
|
|
|
bool skipCheck = false; |
|
|
|
int sz[] = {outChannels, inChannels / group, kernel.height, kernel.width}; |
|
Mat weights(4, &sz[0], CV_32F); |
|
randu(weights, -1.0f, 1.0f); |
|
|
|
LayerParams lp; |
|
lp.set("kernel_w", kernel.width); |
|
lp.set("kernel_h", kernel.height); |
|
lp.set("pad_w", pad.width); |
|
lp.set("pad_h", pad.height); |
|
lp.set("stride_w", stride.width); |
|
lp.set("stride_h", stride.height); |
|
lp.set("dilation_w", dilation.width); |
|
lp.set("dilation_h", dilation.height); |
|
lp.set("num_output", outChannels); |
|
lp.set("group", group); |
|
lp.set("bias_term", hasBias); |
|
lp.type = "Convolution"; |
|
lp.name = "testLayer"; |
|
lp.blobs.push_back(weights); |
|
if (hasBias) |
|
{ |
|
Mat bias(1, outChannels, CV_32F); |
|
randu(bias, -1.0f, 1.0f); |
|
lp.blobs.push_back(bias); |
|
} |
|
int inpSz[] = {1, inChannels, inSize.height, inSize.width}; |
|
Mat input(4, &inpSz[0], CV_32F); |
|
testLayer(lp, input, backendId, targetId, skipCheck); |
|
if (skipCheck) |
|
throw SkipTestException("Skip checks in unstable test"); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Convolution, testing::Combine( |
|
/*in channels, out channels, group*/ |
|
testing::Values(Vec3i(6, 4, 1), Vec3i(6, 9, 1), |
|
Vec3i(6, 4, 2), Vec3i(6, 9, 3)), |
|
/*in size*/ testing::Values(Size(5, 6)), |
|
/*kernel*/ testing::Values(Size(3, 1), Size(1, 3)), |
|
/*stride*/ testing::Values(Size(1, 1), Size(2, 2)), |
|
/*pad*/ testing::Values(Size(1, 0), Size(0, 1)), |
|
/*dilation*/ testing::Values(Size(1, 1), Size(2, 2)), |
|
/*has bias*/ testing::Bool(), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// Deconvolution |
|
//////////////////////////////////////////////////////////////////////////////// |
|
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Vec4i, bool, tuple<Backend, Target> > > Deconvolution; |
|
TEST_P(Deconvolution, Accuracy) |
|
{ |
|
int inChannels = get<0>(GetParam())[0]; |
|
int outChannels = get<0>(GetParam())[1]; |
|
int group = get<0>(GetParam())[2]; |
|
Size inSize = get<1>(GetParam()); |
|
Size kernel = get<2>(GetParam()); |
|
Size pad = get<3>(GetParam()); |
|
Size dilation = get<4>(GetParam()); |
|
Size stride = Size(get<5>(GetParam())[0], get<5>(GetParam())[1]); |
|
Size adjPad = Size(get<5>(GetParam())[2], get<5>(GetParam())[3]); |
|
bool hasBias = get<6>(GetParam()); |
|
Backend backendId = get<0>(get<7>(GetParam())); |
|
Target targetId = get<1>(get<7>(GetParam())); |
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) |
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16) |
|
&& inChannels == 6 && outChannels == 4 && group == 1 |
|
&& kernel == Size(3, 1) && pad == Size(0, 1) |
|
&& stride == Size(1, 1) && dilation == Size(1, 1)) |
|
applyTestTag(targetId == 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 |
|
); |
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16) |
|
&& inChannels == 6 && outChannels == 4 && group == 1 |
|
&& kernel == Size(1, 3) && pad == Size(1, 0) |
|
&& stride == Size(1, 1) && dilation == Size(1, 1)) |
|
applyTestTag(targetId == 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_GE(2019010000) |
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD |
|
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X |
|
&& inChannels == 6 && outChannels == 4 && group == 1 |
|
&& kernel == Size(1, 3) && pad == Size(1, 0) |
|
&& stride == Size(1, 1) && dilation == Size(1, 1)) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); |
|
#endif |
|
|
|
if (targetId == DNN_TARGET_CUDA_FP16) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16); |
|
|
|
int sz[] = {inChannels, outChannels / group, kernel.height, kernel.width}; |
|
Mat weights(4, &sz[0], CV_32F); |
|
randu(weights, -1.0f, 1.0f); |
|
|
|
LayerParams lp; |
|
lp.set("kernel_w", kernel.width); |
|
lp.set("kernel_h", kernel.height); |
|
lp.set("pad_w", pad.width); |
|
lp.set("pad_h", pad.height); |
|
lp.set("stride_w", stride.width); |
|
lp.set("stride_h", stride.height); |
|
lp.set("dilation_w", dilation.width); |
|
lp.set("dilation_h", dilation.height); |
|
lp.set("adj_w", adjPad.width); |
|
lp.set("adj_h", adjPad.height); |
|
lp.set("num_output", outChannels); |
|
lp.set("group", group); |
|
lp.set("bias_term", hasBias); |
|
lp.type = "Deconvolution"; |
|
lp.name = "testLayer"; |
|
lp.blobs.push_back(weights); |
|
if (hasBias) |
|
{ |
|
Mat bias(1, outChannels, CV_32F); |
|
randu(bias, -1.0f, 1.0f); |
|
lp.blobs.push_back(bias); |
|
} |
|
int inpSz[] = {1, inChannels, inSize.height, inSize.width}; |
|
Mat input(4, &inpSz[0], CV_32F); |
|
testLayer(lp, input, backendId, targetId); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Deconvolution, testing::Combine( |
|
/*in channels, out channels, group*/ |
|
testing::Values(Vec3i(6, 4, 1), Vec3i(6, 9, 3)), |
|
/*in size*/ testing::Values(Size(5, 6)), |
|
/*kernel*/ testing::Values(Size(3, 1), Size(1, 3)), |
|
/*pad*/ testing::Values(Size(1, 0), Size(0, 1)), |
|
/*dilation*/ testing::Values(Size(1, 1)), |
|
/*stride, adj. pad*/ testing::Values(Vec4i(1,1, 0,0), Vec4i(2,2, 1,0), Vec4i(1,2, 0,1)), |
|
/*has bias*/ testing::Bool(), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// LRN |
|
//////////////////////////////////////////////////////////////////////////////// |
|
typedef TestWithParam<tuple<Vec3i, int, Vec3f, bool, std::string, tuple<Backend, Target> > > LRN; |
|
TEST_P(LRN, Accuracy) |
|
{ |
|
int inChannels = get<0>(GetParam())[0]; |
|
Size inSize = Size(get<0>(GetParam())[1], get<0>(GetParam())[2]); |
|
int localSize = get<1>(GetParam()); |
|
float alpha = get<2>(GetParam())[0]; |
|
float beta = get<2>(GetParam())[1]; |
|
float bias = get<2>(GetParam())[2]; |
|
bool normBySize = get<3>(GetParam()); |
|
std::string nrmType = get<4>(GetParam()); |
|
Backend backendId = get<0>(get<5>(GetParam())); |
|
Target targetId = get<1>(get<5>(GetParam())); |
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
if ((inSize.width == 5 || inSize.height == 5) && targetId == DNN_TARGET_MYRIAD && |
|
nrmType == "ACROSS_CHANNELS") |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD); |
|
#endif |
|
|
|
LayerParams lp; |
|
lp.set("norm_region", nrmType); |
|
lp.set("local_size", localSize); |
|
lp.set("alpha", alpha); |
|
lp.set("beta", beta); |
|
lp.set("bias", bias); |
|
lp.set("norm_by_size", normBySize); |
|
lp.type = "LRN"; |
|
lp.name = "testLayer"; |
|
|
|
int sz[] = {1, inChannels, inSize.height, inSize.width}; |
|
Mat input(4, &sz[0], CV_32F); |
|
|
|
double l1 = 0.0, lInf = 0.0; |
|
// The OpenCL kernels use the native_ math functions which have |
|
// implementation defined accuracy, so we use relaxed thresholds. See |
|
// https://github.com/opencv/opencv/issues/9821 for more details. |
|
if (targetId == DNN_TARGET_OPENCL) |
|
{ |
|
l1 = 0.01; |
|
lInf = 0.01; |
|
} |
|
testLayer(lp, input, backendId, targetId, false, l1, lInf); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, LRN, testing::Combine( |
|
/*input ch,w,h*/ testing::Values(Vec3i(6, 5, 8), Vec3i(7, 11, 6)), |
|
/*local size*/ testing::Values(3, 5), |
|
testing::Values(Vec3f(0.9f, 1.0f, 1.1f), Vec3f(0.9f, 1.1f, 1.0f), |
|
/*alpha, beta, bias*/ Vec3f(1.0f, 0.9f, 1.1f), Vec3f(1.0f, 1.1f, 0.9f), |
|
Vec3f(1.1f, 0.9f, 1.0f), Vec3f(1.1f, 1.0f, 0.9f)), |
|
/*norm_by_size*/ testing::Bool(), |
|
/*norm_type*/ testing::Values("ACROSS_CHANNELS", "WITHIN_CHANNEL"), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// Average pooling |
|
//////////////////////////////////////////////////////////////////////////////// |
|
typedef TestWithParam<tuple<int, Size, Size, Size, tuple<Backend, Target> > > AvePooling; |
|
TEST_P(AvePooling, Accuracy) |
|
{ |
|
int inChannels = get<0>(GetParam()); |
|
Size outSize = get<1>(GetParam());; // Input size will be computed from parameters. |
|
Size kernel = get<2>(GetParam()); |
|
Size stride = get<3>(GetParam()); |
|
Backend backendId = get<0>(get<4>(GetParam())); |
|
Target targetId = get<1>(get<4>(GetParam())); |
|
|
|
#if defined(INF_ENGINE_RELEASE) |
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD |
|
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X |
|
&& kernel == Size(1, 1) && (stride == Size(1, 1) || stride == Size(2, 2))) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); |
|
#endif |
|
|
|
const int inWidth = (outSize.width - 1) * stride.width + kernel.width; |
|
const int inHeight = (outSize.height - 1) * stride.height + kernel.height; |
|
|
|
LayerParams lp; |
|
lp.set("pool", "ave"); |
|
lp.set("kernel_w", kernel.width); |
|
lp.set("kernel_h", kernel.height); |
|
lp.set("stride_w", stride.width); |
|
lp.set("stride_h", stride.height); |
|
lp.type = "Pooling"; |
|
lp.name = "testLayer"; |
|
|
|
int sz[] = {1, inChannels, inHeight, inWidth}; |
|
Mat input(4, &sz[0], CV_32F); |
|
testLayer(lp, input, backendId, targetId); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, AvePooling, testing::Combine( |
|
/*in channels*/ testing::Values(3, 4), |
|
/*out size*/ testing::Values(Size(1, 1), Size(2, 2), Size(3, 2), Size(4, 7)), |
|
/*kernel*/ testing::Values(Size(1, 1), Size(2, 2), Size(3, 3), Size(3, 2)), |
|
/*stride*/ testing::Values(Size(1, 1), Size(2, 2), Size(3, 2)), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// Maximum pooling |
|
//////////////////////////////////////////////////////////////////////////////// |
|
typedef TestWithParam<tuple<int, Size, Size, Size, Size, tuple<Backend, Target> > > MaxPooling; |
|
TEST_P(MaxPooling, Accuracy) |
|
{ |
|
int inChannels = get<0>(GetParam()); |
|
Size inSize = get<1>(GetParam()); |
|
Size kernel = get<2>(GetParam()); |
|
Size stride = get<3>(GetParam()); |
|
Size pad = get<4>(GetParam()); |
|
Backend backendId = get<0>(get<5>(GetParam())); |
|
Target targetId = get<1>(get<5>(GetParam())); |
|
|
|
// https://github.com/openvinotoolkit/openvino/issues/18731 |
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && stride != Size(1, 1)) { |
|
int ow = ceil(static_cast<float>(inSize.width + 2 * pad.width - kernel.width) / stride.width); |
|
int oh = ceil(static_cast<float>(inSize.height + 2 * pad.height - kernel.height) / stride.height); |
|
if (ow * stride.width >= inSize.width + pad.width || oh * stride.height >= inSize.height + pad.height) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
|
} |
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) |
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD |
|
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X |
|
&& (stride == Size(1, 1) || stride == Size(2, 2)) |
|
&& (pad == Size(0, 1) || pad == Size(1, 1)) |
|
) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#endif |
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) |
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == 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 |
|
|
|
LayerParams lp; |
|
lp.set("pool", "max"); |
|
lp.set("kernel_w", kernel.width); |
|
lp.set("kernel_h", kernel.height); |
|
lp.set("stride_w", stride.width); |
|
lp.set("stride_h", stride.height); |
|
lp.set("pad_w", pad.width); |
|
lp.set("pad_h", pad.height); |
|
lp.type = "Pooling"; |
|
lp.name = "testLayer"; |
|
|
|
int sz[] = {1, inChannels, inSize.height, inSize.width}; |
|
Mat input(4, &sz[0], CV_32F); |
|
testLayer(lp, input, backendId, targetId); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, MaxPooling, testing::Combine( |
|
/*in channels*/ testing::Values(3, 4), |
|
/*in size*/ testing::Values(Size(5, 5), Size(7, 6)), |
|
/*kernel*/ testing::Values(Size(2, 2), Size(3, 3), Size(3, 2)), |
|
/*stride*/ testing::Values(Size(1, 1), Size(2, 2), Size(3, 2)), |
|
/*pad*/ testing::Values(Size(0, 0), Size(1, 1), Size(0, 1)), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// Fully-connected |
|
//////////////////////////////////////////////////////////////////////////////// |
|
typedef TestWithParam<tuple<int, int, Size, int, bool, tuple<Backend, Target> > > FullyConnected; |
|
TEST_P(FullyConnected, Accuracy) |
|
{ |
|
int batch = get<0>(GetParam()); |
|
int inChannels = get<1>(GetParam()); |
|
Size inSize = get<2>(GetParam()); |
|
int outChannels = get<3>(GetParam()); |
|
bool hasBias = get<4>(GetParam()); |
|
Backend backendId = get<0>(get<5>(GetParam())); |
|
Target targetId = get<1>(get<5>(GetParam())); |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || |
|
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && (targetId == DNN_TARGET_OPENCL_FP16 || |
|
(targetId == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X))) { |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16); |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); |
|
} |
|
#endif |
|
// https://github.com/openvinotoolkit/openvino/issues/19436 |
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL_FP16 && batch == 16) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16); |
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2023000000) |
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL && batch == 16) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL); |
|
#endif |
|
|
|
Mat weights(outChannels, inChannels * inSize.height * inSize.width, CV_32F); |
|
randu(weights, -1.0f, 1.0f); |
|
|
|
Mat bias(1, outChannels, CV_32F); |
|
randu(bias, -1.0f, 1.0f); |
|
|
|
LayerParams lp; |
|
lp.set("num_output", outChannels); |
|
lp.set("bias_term", hasBias); |
|
lp.blobs.push_back(weights); |
|
lp.blobs.push_back(bias); |
|
lp.type = "InnerProduct"; |
|
lp.name = "testLayer"; |
|
|
|
int sz[] = {batch, inChannels, inSize.height, inSize.width}; |
|
Mat input(4, &sz[0], CV_32F); |
|
|
|
double l1 = 0.0; |
|
double lInf = 0.0; |
|
#if defined(INF_ENGINE_RELEASE) |
|
if (targetId == DNN_TARGET_MYRIAD) |
|
{ |
|
l1 = 0.015; |
|
lInf = 0.025; |
|
} |
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL_FP16) |
|
{ |
|
l1 = 0.01; |
|
if (INF_ENGINE_VER_MAJOR_GE(2023000000)) |
|
lInf = 0.016; |
|
} |
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL) |
|
{ |
|
l1 = 5e-3; |
|
lInf = INF_ENGINE_VER_MAJOR_GE(2023000000) ? 0.016 : 7e-3; |
|
} |
|
#endif |
|
if (targetId == DNN_TARGET_CUDA_FP16) |
|
l1 = 0.015; |
|
|
|
testLayer(lp, input, backendId, targetId, false, l1, lInf); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, FullyConnected, testing::Combine( |
|
/*batch*/ testing::Values(1, 2, 4, 8, 16), |
|
/*in channels*/ testing::Values(3, 4), |
|
/*in size*/ testing::Values(Size(5, 4), Size(4, 5), Size(1, 1)), |
|
/*out channels*/ testing::Values(3, 4), |
|
/*has bias*/ testing::Bool(), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// SoftMax |
|
//////////////////////////////////////////////////////////////////////////////// |
|
typedef TestWithParam<tuple<int, tuple<Backend, Target> > > SoftMax; |
|
TEST_P(SoftMax, Accuracy) |
|
{ |
|
int inChannels = get<0>(GetParam()); |
|
Backend backendId = get<0>(get<1>(GetParam())); |
|
Target targetId = get<1>(get<1>(GetParam())); |
|
LayerParams lp; |
|
lp.type = "Softmax"; |
|
lp.name = "testLayer"; |
|
|
|
int sz[] = {1, inChannels, 1, 1}; |
|
Mat input(4, &sz[0], CV_32F); |
|
testLayer(lp, input, backendId, targetId); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, SoftMax, testing::Combine( |
|
testing::Values(3, 4, 5, 1024), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
////////////////////////////////////////////////////////////////////////////// |
|
// Max pooling - unpooling |
|
////////////////////////////////////////////////////////////////////////////// |
|
TEST_P(Test_layers_backends, MaxPoolUnpool) |
|
{ |
|
#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 |
|
|
|
LayerParams pool; |
|
pool.set("pool", "max"); |
|
pool.set("kernel_w", 2); |
|
pool.set("kernel_h", 2); |
|
pool.set("stride_w", 2); |
|
pool.set("stride_h", 2); |
|
pool.set("pad_w", 0); |
|
pool.set("pad_h", 0); |
|
pool.type = "Pooling"; |
|
pool.name = "testPool"; |
|
|
|
LayerParams unpool; |
|
unpool.set("pool_k_w", 2); |
|
unpool.set("pool_k_h", 2); |
|
unpool.set("pool_stride_w", 2); |
|
unpool.set("pool_stride_h", 2); |
|
unpool.set("pool_pad_w", 0); |
|
unpool.set("pool_pad_h", 0); |
|
unpool.type = "MaxUnpool"; |
|
unpool.name = "testUnpool"; |
|
|
|
Net net; |
|
int poolId = net.addLayer(pool.name, pool.type, pool); |
|
net.connect(0, 0, poolId, 0); |
|
|
|
int unpoolId = net.addLayer(unpool.name, unpool.type, unpool); |
|
net.connect(poolId, 0, unpoolId, 0); |
|
net.connect(poolId, 1, unpoolId, 1); |
|
|
|
int sz[] = {1, 1, 4, 4}; |
|
Mat input(4, &sz[0], CV_32F); |
|
testLayer(input, net, backend, target); |
|
} |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// AvePooling + in-place layers |
|
//////////////////////////////////////////////////////////////////////////////// |
|
static const int kNumChannels = 3; |
|
|
|
void testInPlaceActivation(LayerParams& lp, Backend backendId, Target targetId, double l1 = 0.0, double lInf = 0.0) |
|
{ |
|
EXPECT_FALSE(lp.name.empty()); |
|
|
|
LayerParams pool; |
|
pool.set("pool", "ave"); |
|
pool.set("kernel_w", 2); |
|
pool.set("kernel_h", 2); |
|
pool.set("stride_w", 2); |
|
pool.set("stride_h", 2); |
|
pool.type = "Pooling"; |
|
pool.name = "ave_pool"; |
|
|
|
Net net; |
|
int poolId = net.addLayer(pool.name, pool.type, pool); |
|
net.connect(0, 0, poolId, 0); |
|
net.addLayerToPrev(lp.name, lp.type, lp); |
|
|
|
int sz[] = {1, kNumChannels, 10, 10}; |
|
Mat input(4, &sz[0], CV_32F); |
|
testLayer(input, net, backendId, targetId, false, true, l1, lInf); |
|
} |
|
|
|
typedef TestWithParam<tuple<bool, bool, float, tuple<Backend, Target> > > BatchNorm; |
|
TEST_P(BatchNorm, Accuracy) |
|
{ |
|
bool hasWeights = get<0>(GetParam()); |
|
bool hasBias = get<1>(GetParam()); |
|
float epsilon = get<2>(GetParam()); |
|
Backend backendId = get<0>(get<3>(GetParam())); |
|
Target targetId = get<1>(get<3>(GetParam())); |
|
|
|
LayerParams lp; |
|
lp.set("has_weight", hasWeights); |
|
lp.set("has_bias", hasBias); |
|
lp.set("eps", epsilon); |
|
lp.type = "BatchNorm"; |
|
lp.name = "testLayer"; |
|
|
|
lp.blobs.reserve(4); |
|
for (int i = 0; i < 3; ++i) |
|
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F)); |
|
if (hasBias || hasWeights) |
|
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F)); |
|
|
|
for (int i = 0; i < lp.blobs.size(); ++i) |
|
randu(lp.blobs[i], 0.0f, 1.0f); |
|
|
|
testInPlaceActivation(lp, backendId, targetId); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, BatchNorm, testing::Combine( |
|
/*has weights*/ testing::Bool(), |
|
/*has bias*/ testing::Bool(), |
|
/*epsilon*/ testing::Values(1e-3f, 1e-5f), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
typedef TestWithParam<tuple<float, tuple<Backend, Target> > > ReLU; |
|
TEST_P(ReLU, Accuracy) |
|
{ |
|
float negativeSlope = get<0>(GetParam()); |
|
Backend backendId = get<0>(get<1>(GetParam())); |
|
Target targetId = get<1>(get<1>(GetParam())); |
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019020000) |
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD && negativeSlope < 0) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#endif |
|
|
|
LayerParams lp; |
|
lp.set("negative_slope", negativeSlope); |
|
lp.type = "ReLU"; |
|
lp.name = "testLayer"; |
|
testInPlaceActivation(lp, backendId, targetId); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, ReLU, testing::Combine( |
|
/*negative slope*/ testing::Values(2.0f, 0.3f, -0.1f, 0.0f), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
typedef TestWithParam<tuple<std::string, tuple<Backend, Target> > > NoParamActivation; |
|
TEST_P(NoParamActivation, Accuracy) |
|
{ |
|
Backend backendId = get<0>(get<1>(GetParam())); |
|
Target targetId = get<1>(get<1>(GetParam())); |
|
std::string layer_type = get<0>(GetParam()); |
|
|
|
LayerParams lp; |
|
lp.type = layer_type; |
|
lp.name = "testLayer"; |
|
testInPlaceActivation(lp, backendId, targetId); |
|
} |
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, NoParamActivation, testing::Combine( |
|
/*type*/ testing::Values("TanH", "Sigmoid", "AbsVal", "BNLL", "Swish", "Mish"), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
typedef TestWithParam<tuple<Vec3f, tuple<Backend, Target> > > Power; |
|
TEST_P(Power, Accuracy) |
|
{ |
|
float power = get<0>(GetParam())[0]; |
|
float scale = get<0>(GetParam())[1]; |
|
float shift = get<0>(GetParam())[2]; |
|
Backend backendId = get<0>(get<1>(GetParam())); |
|
Target targetId = get<1>(get<1>(GetParam())); |
|
|
|
LayerParams lp; |
|
lp.set("power", power); |
|
lp.set("scale", scale); |
|
lp.set("shift", shift); |
|
lp.type = "Power"; |
|
lp.name = "testLayer"; |
|
testInPlaceActivation(lp, backendId, targetId); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Power, testing::Combine( |
|
/*power, scale, shift*/ testing::Values(Vec3f(0.9f, 1.0f, 1.1f), Vec3f(0.9f, 1.1f, 1.0f), |
|
Vec3f(1.0f, 0.9f, 1.1f), Vec3f(1.0f, 1.1f, 0.9f), |
|
Vec3f(1.1f, 0.9f, 1.0f), Vec3f(1.1f, 1.0f, 0.9f)), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
typedef TestWithParam<tuple<Vec3f, tuple<Backend, Target> > > Exp; |
|
TEST_P(Exp, Accuracy) |
|
{ |
|
float base = get<0>(GetParam())[0]; |
|
float scale = get<0>(GetParam())[1]; |
|
float shift = get<0>(GetParam())[2]; |
|
Backend backendId = get<0>(get<1>(GetParam())); |
|
Target targetId = get<1>(get<1>(GetParam())); |
|
|
|
LayerParams lp; |
|
lp.set("base", base); |
|
lp.set("scale", scale); |
|
lp.set("shift", shift); |
|
lp.type = "Exp"; |
|
lp.name = "testLayer"; |
|
testInPlaceActivation(lp, backendId, targetId); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Exp, testing::Combine( |
|
/*base, scale, shift*/ testing::Values(Vec3f(0.9f, -1.0f, 1.1f), Vec3f(0.9f, 1.1f, -1.0f), |
|
Vec3f(-1.0f, 0.9f, 1.1f), Vec3f(-1.0f, 1.1f, 0.9f), |
|
Vec3f(1.1f, 0.9f, -1.0f), Vec3f(1.1f, -1.0f, 0.9f)), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
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TEST_P(Test_layers_backends, ChannelsPReLU) |
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{ |
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LayerParams lp; |
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lp.type = "ChannelsPReLU"; |
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lp.name = "testLayer"; |
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lp.blobs.push_back(Mat(1, kNumChannels, CV_32F)); |
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randu(lp.blobs[0], -1.0f, 1.0f); |
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|
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testInPlaceActivation(lp, backend, target); |
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} |
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|
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typedef TestWithParam<tuple<bool, tuple<Backend, Target> > > Scale; |
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TEST_P(Scale, Accuracy) |
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{ |
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bool hasBias = get<0>(GetParam()); |
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Backend backendId = get<0>(get<1>(GetParam())); |
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Target targetId = get<1>(get<1>(GetParam())); |
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|
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LayerParams lp; |
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lp.set("bias_term", hasBias); |
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lp.type = "Scale"; |
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lp.name = "testLayer"; |
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lp.blobs.push_back(Mat(1, kNumChannels, CV_32F)); |
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randu(lp.blobs[0], -1.0f, 1.0f); |
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if (hasBias) |
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{ |
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lp.blobs.push_back(Mat(1, kNumChannels, CV_32F)); |
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randu(lp.blobs[1], -1.0f, 1.0f); |
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} |
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testInPlaceActivation(lp, backendId, targetId); |
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} |
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|
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INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Scale, testing::Combine( |
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testing::Bool(), |
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dnnBackendsAndTargets() |
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)); |
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|
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//////////////////////////////////////////////////////////////////////////////// |
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// Concat layer |
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//////////////////////////////////////////////////////////////////////////////// |
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// |
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// input --- conv --- concat --- output |
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// `--- conv ----^ ^ ^ |
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// `---- ... ------' ' |
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// `-----------------' |
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typedef TestWithParam<tuple<Vec3i, Vec3i, tuple<Backend, Target> > > Concat; |
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TEST_P(Concat, Accuracy) |
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{ |
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Vec3i inSize = get<0>(GetParam()); |
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Vec3i numChannels = get<1>(GetParam()); |
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Backend backendId = get<0>(get<2>(GetParam())); |
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Target targetId = get<1>(get<2>(GetParam())); |
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|
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000) |
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD |
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&& inSize == Vec3i(1, 4, 5) && numChannels == Vec3i(1, 6, 2) |
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) |
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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_VERSION); // crash |
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#endif |
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|
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) |
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_CPU |
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&& inSize == Vec3i(1, 4, 5) && numChannels == Vec3i(1, 6, 2) |
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) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); // TODO: IE_CPU |
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#endif |
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|
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Net net; |
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|
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std::vector<int> convLayerIds; |
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convLayerIds.reserve(numChannels.channels); |
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for (int i = 0, n = numChannels.channels; i < n; ++i) |
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{ |
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if (!numChannels[i]) |
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break; |
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|
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int sz[] = {numChannels[i], inSize[0], 1, 1}; |
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Mat weights(4, &sz[0], CV_32F); |
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randu(weights, -1.0f, 1.0f); |
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|
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LayerParams convParam; |
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convParam.set("kernel_w", 1); |
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convParam.set("kernel_h", 1); |
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convParam.set("num_output", numChannels[i]); |
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convParam.set("bias_term", false); |
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convParam.type = "Convolution"; |
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std::ostringstream ss; |
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ss << "convLayer" << i; |
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convParam.name = ss.str(); |
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convParam.blobs.push_back(weights); |
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|
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int layerId = net.addLayer(convParam.name, convParam.type, convParam); |
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convLayerIds.push_back(layerId); |
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net.connect(0, 0, layerId, 0); |
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} |
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|
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LayerParams concatParam; |
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concatParam.type = "Concat"; |
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concatParam.name = "testLayer"; |
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int concatId = net.addLayer(concatParam.name, concatParam.type, concatParam); |
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net.connect(0, 0, concatId, 0); |
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for (int i = 0; i < convLayerIds.size(); ++i) |
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{ |
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net.connect(convLayerIds[i], 0, concatId, i + 1); |
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} |
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|
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int sz[] = {1, inSize[0], inSize[1], inSize[2]}; |
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Mat input(4, &sz[0], CV_32F); |
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testLayer(input, net, backendId, targetId); |
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} |
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|
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INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Concat, testing::Combine( |
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/*input size*/ testing::Values(Vec3i(1, 4, 5), Vec3i(2, 8, 6)), |
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/*channels*/ testing::Values(Vec3i(2, 0, 0), Vec3i(3, 4, 0), Vec3i(1, 6, 2)), |
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dnnBackendsAndTargets() |
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)); |
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|
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//////////////////////////////////////////////////////////////////////////////// |
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// Element-wise layers |
|
//////////////////////////////////////////////////////////////////////////////// |
|
// |
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// input --- conv --- eltwise --- output |
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// `--- conv ----^ ^ ^ |
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// `---- ... ------' ' |
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// `-----------------' |
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typedef TestWithParam<tuple<Vec3i, std::string, int, bool, tuple<Backend, Target> > > Eltwise; |
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TEST_P(Eltwise, Accuracy) |
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{ |
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Vec3i inSize = get<0>(GetParam()); |
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std::string op = get<1>(GetParam()); |
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int numConv = get<2>(GetParam()); |
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bool weighted = get<3>(GetParam()); |
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Backend backendId = get<0>(get<4>(GetParam())); |
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Target targetId = get<1>(get<4>(GetParam())); |
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|
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) |
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// accuracy |
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL && |
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inSize == Vec3i(1, 4, 5) && op == "sum" && numConv == 1 && !weighted) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL && |
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inSize == Vec3i(2, 8, 6) && op == "sum" && numConv == 1 && !weighted) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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#endif |
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000) |
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD && |
|
inSize == Vec3i(1, 4, 5)) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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#endif |
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && numConv > 1) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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#endif |
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_OPENCL && |
|
op == "sum" && numConv == 1 && !weighted) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); |
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#endif |
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) |
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && numConv > 1) |
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
|
#endif |
|
|
|
bool convInputShift = 1; |
|
int numEltwiseInputs = numConv; |
|
if (op == "div") |
|
{ |
|
numConv = 1; |
|
convInputShift = 0; // first input is convolution |
|
} |
|
|
|
Net net; |
|
|
|
std::vector<int> convLayerIds(numConv); |
|
for (int i = 0; i < numConv; ++i) |
|
{ |
|
int sz[] = {inSize[0], inSize[0], 1, 1}; |
|
Mat weights(4, &sz[0], CV_32F); |
|
randu(weights, -1.0f, 1.0f); |
|
|
|
LayerParams convParam; |
|
convParam.set("kernel_w", 1); |
|
convParam.set("kernel_h", 1); |
|
convParam.set("num_output", inSize[0]); |
|
convParam.set("bias_term", false); |
|
convParam.type = "Convolution"; |
|
std::ostringstream ss; |
|
ss << "convLayer" << i; |
|
convParam.name = ss.str(); |
|
convParam.blobs.push_back(weights); |
|
|
|
convLayerIds[i] = net.addLayer(convParam.name, convParam.type, convParam); |
|
net.connect(0, 0, convLayerIds[i], 0); |
|
} |
|
|
|
LayerParams eltwiseParam; |
|
eltwiseParam.set("operation", op); |
|
if (op == "sum" && weighted) |
|
{ |
|
RNG& rng = cv::theRNG(); |
|
std::vector<float> coeff(1 + numConv); |
|
for (int i = 0; i < coeff.size(); ++i) |
|
{ |
|
coeff[i] = rng.uniform(-2.0f, 2.0f); |
|
} |
|
eltwiseParam.set("coeff", DictValue::arrayReal<float*>(&coeff[0], coeff.size())); |
|
} |
|
eltwiseParam.type = "Eltwise"; |
|
eltwiseParam.name = "testLayer"; |
|
int eltwiseId = net.addLayer(eltwiseParam.name, eltwiseParam.type, eltwiseParam); |
|
if (convInputShift == 1) |
|
net.connect(0, 0, eltwiseId, 0); |
|
for (int i = 0; i < numConv; ++i) |
|
{ |
|
net.connect(convLayerIds[i], 0, eltwiseId, i + convInputShift); |
|
} |
|
if (convInputShift == 0) |
|
net.connect(0, 0, eltwiseId, numConv); |
|
for (int i = numConv; i < numEltwiseInputs; ++i) |
|
{ |
|
net.connect(0, 0, eltwiseId, i + 1); |
|
} |
|
|
|
int sz[] = {1, inSize[0], inSize[1], inSize[2]}; |
|
Mat input(4, &sz[0], CV_32F); |
|
if (op == "div") |
|
randu(input, 1.0f, 1.0f); // ensure no divisor value has absouluate value of less than 0.5 |
|
testLayer(input, net, backendId, targetId, /*skipCheck*/false, (op == "div") ? false : true); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Eltwise, testing::Combine( |
|
/*input size*/ testing::Values(Vec3i(1, 4, 5), Vec3i(2, 8, 6)), |
|
/*operation*/ testing::Values("prod", "sum", "div", "max", "min"), |
|
/*num convs*/ testing::Values(1, 2, 3), |
|
/*weighted(for sum only)*/ testing::Bool(), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_layers_backends, dnnBackendsAndTargets()); |
|
|
|
}} // namespace
|
|
|