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b637e3a66e
Change opencv_face_detector related tests and samples from caffe to onnx #25463 Part of https://github.com/opencv/opencv/issues/25314 This PR aims to change the tests related to opencv_face_detector from caffe framework to onnx. Tests in `test_int8_layer.cpp` and `test_caffe_importer.cpp` will be removed in https://github.com/opencv/opencv/pull/25323 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
1531 lines
57 KiB
C++
1531 lines
57 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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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, 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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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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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, DISABLED_YOLOv8n) {
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processNet("dnn/onnx/models/yolov8n.onnx", "", Size(640, 640), "output0");
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expectNoFallbacksFromIE(net);
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expectNoFallbacksFromCUDA(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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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_VERYLONG
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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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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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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", 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, MobileNet_SSD_Caffe)
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{
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applyTestTag(CV_TEST_TAG_MEMORY_512MB);
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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 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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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 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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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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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 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 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;
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const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CPU_FP16) ? 0.2 : 0.0;
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processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.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_faster_4_stages)
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{
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applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_MEMORY_1GB);
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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
|
|
&& 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, YuNet)
|
|
{
|
|
Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
|
|
resize(img, img, Size(320, 320));
|
|
Mat inp = blobFromImage(img);
|
|
processNet("dnn/onnx/models/yunet-202303.onnx", "", inp);
|
|
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_VERYLONG
|
|
);
|
|
#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
|
|
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);
|
|
// 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_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(224, 224), Scalar(0.0, 0.0, 0.0), true, false);
|
|
// Output image has values in range [0.0, 255.0].
|
|
float l1 = 5e-4, lInf = 1e-2;
|
|
if (target == DNN_TARGET_MYRIAD)
|
|
{
|
|
l1 = 0.4;
|
|
lInf = 7.46;
|
|
}
|
|
else if (target == DNN_TARGET_CUDA)
|
|
{
|
|
l1 = 8e-4;
|
|
lInf = 2e-2;
|
|
}
|
|
else if (target == DNN_TARGET_CUDA_FP16)
|
|
{
|
|
l1 = 0.9;
|
|
lInf = 16;
|
|
}
|
|
else if (target == DNN_TARGET_CPU_FP16)
|
|
{
|
|
l1 = 0.4;
|
|
lInf = 26.;
|
|
}
|
|
else if (target == DNN_TARGET_VULKAN)
|
|
{
|
|
l1 = 0.4;
|
|
lInf = 7.46;
|
|
}
|
|
else if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL)
|
|
{
|
|
l1 = 5.5e-4;
|
|
}
|
|
else if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
|
|
{
|
|
l1 = 0.86;
|
|
lInf = 16;
|
|
}
|
|
|
|
#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/mosaic-9.onnx", "", 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(/* withInferenceEngine = */ true,
|
|
/* obsolete_withHalide = */ false,
|
|
/* withCpuOCV = */ false,
|
|
/* withVkCom = */ true,
|
|
/* withCUDA = */ 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()
|
|
));
|
|
|
|
TEST_P(Test_layers_backends, ChannelsPReLU)
|
|
{
|
|
LayerParams lp;
|
|
lp.type = "ChannelsPReLU";
|
|
lp.name = "testLayer";
|
|
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
|
|
randu(lp.blobs[0], -1.0f, 1.0f);
|
|
|
|
testInPlaceActivation(lp, backend, target);
|
|
}
|
|
|
|
typedef TestWithParam<tuple<bool, tuple<Backend, Target> > > Scale;
|
|
TEST_P(Scale, Accuracy)
|
|
{
|
|
bool hasBias = get<0>(GetParam());
|
|
Backend backendId = get<0>(get<1>(GetParam()));
|
|
Target targetId = get<1>(get<1>(GetParam()));
|
|
|
|
LayerParams lp;
|
|
lp.set("bias_term", hasBias);
|
|
lp.type = "Scale";
|
|
lp.name = "testLayer";
|
|
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
|
|
randu(lp.blobs[0], -1.0f, 1.0f);
|
|
if (hasBias)
|
|
{
|
|
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
|
|
randu(lp.blobs[1], -1.0f, 1.0f);
|
|
}
|
|
testInPlaceActivation(lp, backendId, targetId);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Scale, testing::Combine(
|
|
testing::Bool(),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// Concat layer
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// input --- conv --- concat --- output
|
|
// `--- conv ----^ ^ ^
|
|
// `---- ... ------' '
|
|
// `-----------------'
|
|
typedef TestWithParam<tuple<Vec3i, Vec3i, tuple<Backend, Target> > > Concat;
|
|
TEST_P(Concat, Accuracy)
|
|
{
|
|
Vec3i inSize = get<0>(GetParam());
|
|
Vec3i numChannels = get<1>(GetParam());
|
|
Backend backendId = get<0>(get<2>(GetParam()));
|
|
Target targetId = get<1>(get<2>(GetParam()));
|
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
|
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
|
|
&& inSize == Vec3i(1, 4, 5) && numChannels == Vec3i(1, 6, 2)
|
|
)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); // crash
|
|
#endif
|
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
|
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_CPU
|
|
&& inSize == Vec3i(1, 4, 5) && numChannels == Vec3i(1, 6, 2)
|
|
)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); // TODO: IE_CPU
|
|
#endif
|
|
|
|
Net net;
|
|
|
|
std::vector<int> convLayerIds;
|
|
convLayerIds.reserve(numChannels.channels);
|
|
for (int i = 0, n = numChannels.channels; i < n; ++i)
|
|
{
|
|
if (!numChannels[i])
|
|
break;
|
|
|
|
int sz[] = {numChannels[i], 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", numChannels[i]);
|
|
convParam.set("bias_term", false);
|
|
convParam.type = "Convolution";
|
|
std::ostringstream ss;
|
|
ss << "convLayer" << i;
|
|
convParam.name = ss.str();
|
|
convParam.blobs.push_back(weights);
|
|
|
|
int layerId = net.addLayer(convParam.name, convParam.type, convParam);
|
|
convLayerIds.push_back(layerId);
|
|
net.connect(0, 0, layerId, 0);
|
|
}
|
|
|
|
LayerParams concatParam;
|
|
concatParam.type = "Concat";
|
|
concatParam.name = "testLayer";
|
|
int concatId = net.addLayer(concatParam.name, concatParam.type, concatParam);
|
|
net.connect(0, 0, concatId, 0);
|
|
for (int i = 0; i < convLayerIds.size(); ++i)
|
|
{
|
|
net.connect(convLayerIds[i], 0, concatId, i + 1);
|
|
}
|
|
|
|
int sz[] = {1, inSize[0], inSize[1], inSize[2]};
|
|
Mat input(4, &sz[0], CV_32F);
|
|
testLayer(input, net, backendId, targetId);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Concat, testing::Combine(
|
|
/*input size*/ testing::Values(Vec3i(1, 4, 5), Vec3i(2, 8, 6)),
|
|
/*channels*/ testing::Values(Vec3i(2, 0, 0), Vec3i(3, 4, 0), Vec3i(1, 6, 2)),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// Element-wise layers
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// input --- conv --- eltwise --- output
|
|
// `--- conv ----^ ^ ^
|
|
// `---- ... ------' '
|
|
// `-----------------'
|
|
typedef TestWithParam<tuple<Vec3i, std::string, int, bool, tuple<Backend, Target> > > Eltwise;
|
|
TEST_P(Eltwise, Accuracy)
|
|
{
|
|
Vec3i inSize = get<0>(GetParam());
|
|
std::string op = get<1>(GetParam());
|
|
int numConv = get<2>(GetParam());
|
|
bool weighted = get<3>(GetParam());
|
|
Backend backendId = get<0>(get<4>(GetParam()));
|
|
Target targetId = get<1>(get<4>(GetParam()));
|
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
|
|
// accuracy
|
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL &&
|
|
inSize == Vec3i(1, 4, 5) && op == "sum" && numConv == 1 && !weighted)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, 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 &&
|
|
inSize == Vec3i(2, 8, 6) && op == "sum" && numConv == 1 && !weighted)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
#endif
|
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
|
|
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);
|
|
#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)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
#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);
|
|
#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()
|
|
));
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// Element-wise layers
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
using NaryEltwiseConcat = TestWithParam<tuple<std::vector<int>, tuple<Backend, Target>>>;
|
|
TEST_P(NaryEltwiseConcat, Accuracy) {
|
|
auto param = GetParam();
|
|
std::vector<int> input_shape = get<0>(param);
|
|
auto backend_id = get<0>(get<1>(param));
|
|
auto target_id = get<1>(get<1>(param));
|
|
|
|
/* Build the following net:
|
|
|
|
<1x4x84>
|
|
/
|
|
[Input] -+-> Mul(B<1x84>) -> Concat(axis=1) -> [Output]
|
|
| |
|
|
+-> Sigmoid ----------+
|
|
|
|
*/
|
|
Net net;
|
|
|
|
std::vector<int> mul_B_shape(input_shape.size() - 1, 1);
|
|
mul_B_shape.back() = input_shape.back();
|
|
Mat mul_B(mul_B_shape, CV_32FC1);
|
|
randn(mul_B, 0.f, 1.f);
|
|
LayerParams mul_B_lp;
|
|
mul_B_lp.name = "mul_B";
|
|
mul_B_lp.type = "Const";
|
|
mul_B_lp.blobs.push_back(mul_B);
|
|
int id_mul_B = net.addLayer(mul_B_lp.name, mul_B_lp.type, mul_B_lp);
|
|
|
|
LayerParams mul_lp;
|
|
mul_lp.name = "mul";
|
|
mul_lp.type = "NaryEltwise";
|
|
mul_lp.set("operation", "mul");
|
|
int id_mul = net.addLayer(mul_lp.name, mul_lp.type, mul_lp);
|
|
net.connect(0, 0, id_mul, 0);
|
|
net.connect(id_mul_B, 0, id_mul, 1);
|
|
|
|
LayerParams sigmoid_lp;
|
|
sigmoid_lp.name = "sigmoid";
|
|
sigmoid_lp.type = "Sigmoid";
|
|
int id_sigmoid = net.addLayer(sigmoid_lp.name, sigmoid_lp.type, sigmoid_lp);
|
|
net.connect(0, 0, id_sigmoid, 0);
|
|
|
|
LayerParams concat_lp;
|
|
concat_lp.name = "concat";
|
|
concat_lp.type = "Concat";
|
|
concat_lp.set("axis", 1);
|
|
int id_concat = net.addLayer(concat_lp.name, concat_lp.type, concat_lp);
|
|
net.connect(id_mul, 0, id_concat, 0);
|
|
net.connect(id_sigmoid, 0, id_concat, 1);
|
|
|
|
// Run test
|
|
Mat input(input_shape, CV_32FC1);
|
|
testLayer(input, net, backend_id, target_id, false);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, NaryEltwiseConcat, testing::Combine(
|
|
testing::Values(std::vector<int>{1, 4, 84}),
|
|
dnnBackendsAndTargets())
|
|
);
|
|
|
|
|
|
|
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_layers_backends, dnnBackendsAndTargets());
|
|
|
|
}} // namespace
|