mirror of
https://github.com/opencv/opencv.git
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9cd5a0a1e6
Fix CUDA compilation issues and adjust thresholds. * Fix CUDA compilation issues and adjust thresholds. * add conformance tests to denylist
583 lines
24 KiB
C++
583 lines
24 KiB
C++
// 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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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)
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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",
|
|
"dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreDiff, iouDiff);
|
|
expectNoFallbacksFromIE(net);
|
|
}
|
|
|
|
TEST_P(DNNTestNetwork, OpenPose_pose_coco)
|
|
{
|
|
applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
|
|
CV_TEST_TAG_DEBUG_LONG);
|
|
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
|
|
|
|
const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.009 : 0.0;
|
|
const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0;
|
|
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
|
|
Size(46, 46), "", "", l1, lInf);
|
|
expectNoFallbacksFromIE(net);
|
|
expectNoFallbacksFromCUDA(net);
|
|
}
|
|
|
|
TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
|
|
{
|
|
applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
|
|
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
|