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Open Source Computer Vision Library
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582 lines
24 KiB
582 lines
24 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) |
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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 (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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if (backend == DNN_BACKEND_HALIDE) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
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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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if (backend == DNN_BACKEND_HALIDE) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
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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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if (backend == DNN_BACKEND_HALIDE) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
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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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if (backend == DNN_BACKEND_HALIDE) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
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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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if (backend == DNN_BACKEND_HALIDE) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
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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 (backend == DNN_BACKEND_HALIDE) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); |
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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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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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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) ? 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.caffemodel", "dnn/MobileNetSSD_deploy.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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// IE exception: Cannot get memory! |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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// IE exception: Cannot get memory! |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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// 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) |
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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.caffemodel", "dnn/MobileNetSSD_deploy.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 ? "" : 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) |
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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) |
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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) |
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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(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 && target == DNN_TARGET_CPU) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); // TODO HALIDE_CPU |
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|
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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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|
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float scoreDiff = 0.0, iouDiff = 0.0; |
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if (target == DNN_TARGET_OPENCL_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, iouDiff); |
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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); |
|
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 |
|
|
|
// output range: [-0.001, 0.97] |
|
const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.02 : 0.0; |
|
const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_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) |
|
{ |
|
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) |
|
{ |
|
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 = 4e-5, lInf = 2e-3; |
|
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) |
|
{ |
|
l1 = 0.4; |
|
lInf = 7.45; |
|
} |
|
else if (target == DNN_TARGET_CUDA_FP16) |
|
{ |
|
l1 = 0.3; |
|
lInf = 7.6; |
|
} |
|
|
|
#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)); |
|
|
|
}} // namespace
|
|
|