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766 lines
25 KiB
C++
766 lines
25 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) 2017, 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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#include <opencv2/core/opencl/ocl_defs.hpp>
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#include <opencv2/dnn/layer.details.hpp> // CV_DNN_REGISTER_LAYER_CLASS
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namespace opencv_test { namespace {
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TEST(blobFromImage_4ch, Regression)
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{
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Mat ch[4];
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for(int i = 0; i < 4; i++)
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ch[i] = Mat::ones(10, 10, CV_8U)*i;
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Mat img;
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merge(ch, 4, img);
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Mat blob = dnn::blobFromImage(img, 1., Size(), Scalar(), false, false);
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for(int i = 0; i < 4; i++)
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{
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ch[i] = Mat(img.rows, img.cols, CV_32F, blob.ptr(0, i));
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ASSERT_DOUBLE_EQ(cvtest::norm(ch[i], cv::NORM_INF), i);
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}
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}
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TEST(blobFromImage, allocated)
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{
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int size[] = {1, 3, 4, 5};
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Mat img(size[2], size[3], CV_32FC(size[1]));
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Mat blob(4, size, CV_32F);
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void* blobData = blob.data;
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dnn::blobFromImage(img, blob, 1.0 / 255, Size(), Scalar(), false, false);
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ASSERT_EQ(blobData, blob.data);
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}
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TEST(imagesFromBlob, Regression)
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{
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int nbOfImages = 8;
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std::vector<cv::Mat> inputImgs(nbOfImages);
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for (int i = 0; i < nbOfImages; i++)
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{
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inputImgs[i] = cv::Mat::ones(100, 100, CV_32FC3);
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cv::randu(inputImgs[i], cv::Scalar::all(0), cv::Scalar::all(1));
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}
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cv::Mat blob = cv::dnn::blobFromImages(inputImgs, 1., cv::Size(), cv::Scalar(), false, false);
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std::vector<cv::Mat> outputImgs;
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cv::dnn::imagesFromBlob(blob, outputImgs);
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for (int i = 0; i < nbOfImages; i++)
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{
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EXPECT_EQ(0, cvtest::norm(inputImgs[i], outputImgs[i], NORM_INF))
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<< "i=" << i
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<< " inputImgs[i]=" << inputImgs[i].size
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<< " outputImgs[i]=" << outputImgs[i].size;
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}
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}
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TEST(readNet, Regression)
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{
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Net net = readNet(findDataFile("dnn/squeezenet_v1.1.prototxt"),
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findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
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EXPECT_FALSE(net.empty());
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net = readNet(findDataFile("dnn/opencv_face_detector.caffemodel", false),
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findDataFile("dnn/opencv_face_detector.prototxt"));
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EXPECT_FALSE(net.empty());
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net = readNet(findDataFile("dnn/openface_nn4.small2.v1.t7", false));
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EXPECT_FALSE(net.empty());
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net = readNet(findDataFile("dnn/tiny-yolo-voc.cfg"),
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findDataFile("dnn/tiny-yolo-voc.weights", false));
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EXPECT_FALSE(net.empty());
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net = readNet(findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt"),
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findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false));
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EXPECT_FALSE(net.empty());
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}
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TEST(readNet, do_not_call_setInput) // https://github.com/opencv/opencv/issues/16618
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{
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// 1. load network
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const string proto = findDataFile("dnn/squeezenet_v1.1.prototxt");
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const string model = findDataFile("dnn/squeezenet_v1.1.caffemodel", false);
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Net net = readNetFromCaffe(proto, model);
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// 2. mistake: no inputs are specified through .setInput()
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// 3. try inference
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Mat res;
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EXPECT_THROW(
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{
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res = net.forward(); // no inputs after loading => should fail
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}, cv::Exception);
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EXPECT_TRUE(res.empty()) << res.size;
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}
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#ifdef HAVE_INF_ENGINE
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static
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void test_readNet_IE_do_not_call_setInput(Backend backendId)
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{
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const Target targetId = DNN_TARGET_CPU;
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const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
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const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
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else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
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else
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FAIL() << "Unknown backendId";
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Net net = readNet(model, proto);
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net.setPreferableBackend(backendId);
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net.setPreferableTarget(targetId);
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// 2. mistake: no inputs are specified through .setInput()
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// 3. try inference
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Mat res;
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EXPECT_THROW(
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{
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res = net.forward(); // no inputs after loading => should fail
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}, cv::Exception);
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EXPECT_TRUE(res.empty()) << res.size;
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}
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TEST(readNet, do_not_call_setInput_IE_NN_BUILDER_2019)
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{
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test_readNet_IE_do_not_call_setInput(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019);
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}
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TEST(readNet, do_not_call_setInput_IE_NGRAPH)
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{
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test_readNet_IE_do_not_call_setInput(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
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}
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#endif // HAVE_INF_ENGINE
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typedef testing::TestWithParam<tuple<Backend, Target> > dump;
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TEST_P(dump, Regression)
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{
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const int backend = get<0>(GetParam());
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const int target = get<1>(GetParam());
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Net net = readNet(findDataFile("dnn/squeezenet_v1.1.prototxt"),
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findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
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ASSERT_EQ(net.getLayerInputs(net.getLayerId("fire2/concat")).size(), 2);
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int size[] = {1, 3, 227, 227};
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Mat input = cv::Mat::ones(4, size, CV_32F);
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net.setInput(input);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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EXPECT_FALSE(net.dump().empty());
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net.forward();
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EXPECT_FALSE(net.dump().empty());
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}
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INSTANTIATE_TEST_CASE_P(/**/, dump, dnnBackendsAndTargets());
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class FirstCustomLayer CV_FINAL : public Layer
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{
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public:
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FirstCustomLayer(const LayerParams ¶ms) : Layer(params) {}
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static Ptr<Layer> create(LayerParams& params)
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{
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return Ptr<Layer>(new FirstCustomLayer(params));
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}
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void forward(InputArrayOfArrays, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays) CV_OVERRIDE
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{
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CV_TRACE_FUNCTION();
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CV_TRACE_ARG_VALUE(name, "name", name.c_str());
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std::vector<Mat> outputs;
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outputs_arr.getMatVector(outputs);
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outputs[0].setTo(1);
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}
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};
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class SecondCustomLayer CV_FINAL : public Layer
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{
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public:
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SecondCustomLayer(const LayerParams ¶ms) : Layer(params) {}
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static Ptr<Layer> create(LayerParams& params)
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{
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return Ptr<Layer>(new SecondCustomLayer(params));
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}
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void forward(InputArrayOfArrays, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays) CV_OVERRIDE
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{
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CV_TRACE_FUNCTION();
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CV_TRACE_ARG_VALUE(name, "name", name.c_str());
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std::vector<Mat> outputs;
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outputs_arr.getMatVector(outputs);
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outputs[0].setTo(2);
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}
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};
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TEST(LayerFactory, custom_layers)
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{
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LayerParams lp;
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lp.name = "name";
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lp.type = "CustomType";
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Mat inp(1, 1, CV_32FC1);
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for (int i = 0; i < 3; ++i)
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{
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if (i == 0) { CV_DNN_REGISTER_LAYER_CLASS(CustomType, FirstCustomLayer); }
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else if (i == 1) { CV_DNN_REGISTER_LAYER_CLASS(CustomType, SecondCustomLayer); }
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else if (i == 2) { LayerFactory::unregisterLayer("CustomType"); }
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Net net;
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net.addLayerToPrev(lp.name, lp.type, lp);
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net.setInput(inp);
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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Mat output = net.forward();
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if (i == 0) { EXPECT_EQ(output.at<float>(0), 1); }
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else if (i == 1) { EXPECT_EQ(output.at<float>(0), 2); }
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else if (i == 2) { EXPECT_EQ(output.at<float>(0), 1); }
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}
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LayerFactory::unregisterLayer("CustomType");
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}
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typedef testing::TestWithParam<tuple<float, Vec3f, int, tuple<Backend, Target> > > setInput;
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TEST_P(setInput, normalization)
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{
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const float kScale = get<0>(GetParam());
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const Scalar kMean = get<1>(GetParam());
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const int dtype = get<2>(GetParam());
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const int backend = get<0>(get<3>(GetParam()));
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const int target = get<1>(get<3>(GetParam()));
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const bool kSwapRB = true;
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16 && dtype != CV_32F)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
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Mat inp(5, 5, CV_8UC3);
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randu(inp, 0, 255);
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Mat ref = blobFromImage(inp, kScale, Size(), kMean, kSwapRB, /*crop*/false);
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LayerParams lp;
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Net net;
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net.addLayerToPrev("testLayer", "Identity", lp);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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Mat blob = blobFromImage(inp, 1.0, Size(), Scalar(), kSwapRB, /*crop*/false, dtype);
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ASSERT_EQ(blob.type(), dtype);
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net.setInput(blob, "", kScale, kMean);
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Mat out = net.forward();
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ASSERT_EQ(out.type(), CV_32F);
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normAssert(ref, out, "", 4e-4, 1e-3);
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}
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INSTANTIATE_TEST_CASE_P(/**/, setInput, Combine(
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Values(1.0f, 1.0 / 127.5),
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Values(Vec3f(), Vec3f(50, 50, 50), Vec3f(10, 50, 140)),
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Values(CV_32F, CV_8U),
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dnnBackendsAndTargets()
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));
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class CustomLayerWithDeprecatedForward CV_FINAL : public Layer
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{
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public:
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CustomLayerWithDeprecatedForward(const LayerParams ¶ms) : Layer(params) {}
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static Ptr<Layer> create(LayerParams& params)
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{
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return Ptr<Layer>(new CustomLayerWithDeprecatedForward(params));
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}
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virtual void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE
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{
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CV_Assert_N(inputs[0]->depth() == CV_32F, outputs[0].depth() == CV_32F);
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cv::add(*inputs[0], 0.5f, outputs[0]);
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}
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};
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class CustomLayerWithDeprecatedForwardAndFallback CV_FINAL : public Layer
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{
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public:
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CustomLayerWithDeprecatedForwardAndFallback(const LayerParams ¶ms) : Layer(params) {}
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static Ptr<Layer> create(LayerParams& params)
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{
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return Ptr<Layer>(new CustomLayerWithDeprecatedForwardAndFallback(params));
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}
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void forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals) CV_OVERRIDE
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{
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CV_TRACE_FUNCTION();
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CV_TRACE_ARG_VALUE(name, "name", name.c_str());
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CV_OCL_RUN(preferableTarget == DNN_TARGET_OPENCL || preferableTarget == DNN_TARGET_OPENCL_FP16,
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forward_ocl(inputs, outputs, internals));
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Layer::forward_fallback(inputs, outputs, internals);
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}
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virtual void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE
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{
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CV_Assert_N(inputs[0]->depth() == CV_32F, outputs[0].depth() == CV_32F);
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cv::add(*inputs[0], 0.5f, outputs[0]);
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}
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#ifdef HAVE_OPENCL
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bool forward_ocl(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
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{
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if (inputs_arr.depth() != CV_32F)
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return false;
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std::vector<UMat> inputs;
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std::vector<UMat> outputs;
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inputs_arr.getUMatVector(inputs);
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outputs_arr.getUMatVector(outputs);
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cv::add(inputs[0], 0.5f, outputs[0]);
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return true;
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}
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#endif
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};
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typedef testing::TestWithParam<tuple<Backend, Target> > DeprecatedForward;
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TEST_P(DeprecatedForward, CustomLayer)
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{
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const int backend = get<0>(GetParam());
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const int target = get<1>(GetParam());
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Mat inp(5, 5, CV_32FC1);
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randu(inp, -1.0f, 1.0f);
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inp = blobFromImage(inp);
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CV_DNN_REGISTER_LAYER_CLASS(CustomType, CustomLayerWithDeprecatedForward);
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try
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{
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LayerParams lp;
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Net net;
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net.addLayerToPrev("testLayer", "CustomType", lp);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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net.setInput(inp);
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Mat out = net.forward();
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normAssert(out, inp + 0.5f, "", 2e-4, 7e-4);
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}
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catch (...)
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{
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LayerFactory::unregisterLayer("CustomType");
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throw;
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}
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LayerFactory::unregisterLayer("CustomType");
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}
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TEST_P(DeprecatedForward, CustomLayerWithFallback)
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{
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const int backend = get<0>(GetParam());
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const int target = get<1>(GetParam());
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Mat inp(5, 5, CV_32FC1);
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randu(inp, -1.0f, 1.0f);
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inp = blobFromImage(inp);
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CV_DNN_REGISTER_LAYER_CLASS(CustomType, CustomLayerWithDeprecatedForwardAndFallback);
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try
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{
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LayerParams lp;
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Net net;
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net.addLayerToPrev("testLayer", "CustomType", lp);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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net.setInput(inp);
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Mat out = net.forward();
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normAssert(out, inp + 0.5f, "", 2e-4, 7e-4);
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}
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catch (...)
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{
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LayerFactory::unregisterLayer("CustomType");
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throw;
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}
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LayerFactory::unregisterLayer("CustomType");
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}
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INSTANTIATE_TEST_CASE_P(/**/, DeprecatedForward, dnnBackendsAndTargets());
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TEST(Net, forwardAndRetrieve)
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{
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std::string prototxt =
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"input: \"data\"\n"
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"layer {\n"
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" name: \"testLayer\"\n"
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" type: \"Slice\"\n"
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" bottom: \"data\"\n"
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" top: \"firstCopy\"\n"
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" top: \"secondCopy\"\n"
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" slice_param {\n"
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" axis: 0\n"
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" slice_point: 2\n"
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" }\n"
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"}";
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Net net = readNetFromCaffe(&prototxt[0], prototxt.size());
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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Mat inp(4, 5, CV_32F);
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randu(inp, -1, 1);
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net.setInput(inp);
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std::vector<String> outNames;
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outNames.push_back("testLayer");
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std::vector<std::vector<Mat> > outBlobs;
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net.forward(outBlobs, outNames);
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EXPECT_EQ(outBlobs.size(), 1);
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EXPECT_EQ(outBlobs[0].size(), 2);
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normAssert(outBlobs[0][0], inp.rowRange(0, 2), "first part");
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normAssert(outBlobs[0][1], inp.rowRange(2, 4), "second part");
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}
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#ifdef HAVE_INF_ENGINE
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static const std::chrono::milliseconds async_timeout(10000);
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// This test runs network in synchronous mode for different inputs and then
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// runs the same model asynchronously for the same inputs.
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typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Async;
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TEST_P(Async, model_optimizer_pipeline_set_and_forward_single)
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{
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const int dtype = get<0>(GetParam());
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const Backend backendId = get<0>(get<1>(GetParam()));
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const Target targetId = get<1>(get<1>(GetParam()));
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if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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throw SkipTestException("No support for async forward");
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const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
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const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
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const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
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else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
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else
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FAIL() << "Unknown backendId";
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Net netSync = readNet(model, proto);
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netSync.setPreferableBackend(backendId);
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netSync.setPreferableTarget(targetId);
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Net netAsync = readNet(model, proto);
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netAsync.setPreferableBackend(backendId);
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netAsync.setPreferableTarget(targetId);
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// Generate inputs.
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const int numInputs = 10;
|
|
std::vector<Mat> inputs(numInputs);
|
|
int blobSize[] = {2, 6, 75, 113};
|
|
for (int i = 0; i < numInputs; ++i)
|
|
{
|
|
inputs[i].create(4, &blobSize[0], dtype);
|
|
randu(inputs[i], 0, 255);
|
|
}
|
|
|
|
// Run synchronously.
|
|
std::vector<Mat> refs(numInputs);
|
|
for (int i = 0; i < numInputs; ++i)
|
|
{
|
|
netSync.setInput(inputs[i]);
|
|
refs[i] = netSync.forward().clone();
|
|
}
|
|
|
|
// Run asynchronously. To make test more robust, process inputs in the reversed order.
|
|
for (int i = numInputs - 1; i >= 0; --i)
|
|
{
|
|
netAsync.setInput(inputs[i]);
|
|
|
|
AsyncArray out = netAsync.forwardAsync();
|
|
ASSERT_TRUE(out.valid());
|
|
Mat result;
|
|
EXPECT_TRUE(out.get(result, async_timeout));
|
|
normAssert(refs[i], result, format("Index: %d", i).c_str(), 0, 0);
|
|
}
|
|
}
|
|
|
|
TEST_P(Async, model_optimizer_pipeline_set_and_forward_all)
|
|
{
|
|
const int dtype = get<0>(GetParam());
|
|
const Backend backendId = get<0>(get<1>(GetParam()));
|
|
const Target targetId = get<1>(get<1>(GetParam()));
|
|
|
|
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
throw SkipTestException("No support for async forward");
|
|
|
|
const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
|
|
const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
|
|
const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
|
|
|
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
|
|
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
|
|
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
|
|
else
|
|
FAIL() << "Unknown backendId";
|
|
|
|
Net netSync = readNet(model, proto);
|
|
netSync.setPreferableBackend(backendId);
|
|
netSync.setPreferableTarget(targetId);
|
|
|
|
Net netAsync = readNet(model, proto);
|
|
netAsync.setPreferableBackend(backendId);
|
|
netAsync.setPreferableTarget(targetId);
|
|
|
|
// Generate inputs.
|
|
const int numInputs = 10;
|
|
std::vector<Mat> inputs(numInputs);
|
|
int blobSize[] = {2, 6, 75, 113};
|
|
for (int i = 0; i < numInputs; ++i)
|
|
{
|
|
inputs[i].create(4, &blobSize[0], dtype);
|
|
randu(inputs[i], 0, 255);
|
|
}
|
|
|
|
// Run synchronously.
|
|
std::vector<Mat> refs(numInputs);
|
|
for (int i = 0; i < numInputs; ++i)
|
|
{
|
|
netSync.setInput(inputs[i]);
|
|
refs[i] = netSync.forward().clone();
|
|
}
|
|
|
|
// Run asynchronously. To make test more robust, process inputs in the reversed order.
|
|
std::vector<AsyncArray> outs(numInputs);
|
|
for (int i = numInputs - 1; i >= 0; --i)
|
|
{
|
|
netAsync.setInput(inputs[i]);
|
|
outs[i] = netAsync.forwardAsync();
|
|
}
|
|
|
|
for (int i = numInputs - 1; i >= 0; --i)
|
|
{
|
|
ASSERT_TRUE(outs[i].valid());
|
|
Mat result;
|
|
EXPECT_TRUE(outs[i].get(result, async_timeout));
|
|
normAssert(refs[i], result, format("Index: %d", i).c_str(), 0, 0);
|
|
}
|
|
}
|
|
|
|
TEST_P(Async, create_layer_pipeline_set_and_forward_all)
|
|
{
|
|
const int dtype = get<0>(GetParam());
|
|
const Backend backendId = get<0>(get<1>(GetParam()));
|
|
const Target targetId = get<1>(get<1>(GetParam()));
|
|
|
|
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
throw SkipTestException("No support for async forward");
|
|
|
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
|
|
|
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
|
|
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
|
|
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
|
|
else
|
|
FAIL() << "Unknown backendId";
|
|
|
|
Net netSync;
|
|
Net netAsync;
|
|
{
|
|
int inChannels = 4;
|
|
int outChannels = 12;
|
|
int group = 3;
|
|
Size inSize(113, 75);
|
|
Size kernel(4, 5);
|
|
Size stride(2, 3);
|
|
Size pad(0, 1);
|
|
Size dilation(1, 1);
|
|
bool hasBias = true;
|
|
|
|
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);
|
|
|
|
netSync.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
netAsync.addLayerToPrev(lp.name, lp.type, lp);
|
|
}
|
|
|
|
netSync.setPreferableBackend(backendId);
|
|
netSync.setPreferableTarget(targetId);
|
|
|
|
netAsync.setPreferableBackend(backendId);
|
|
netAsync.setPreferableTarget(targetId);
|
|
|
|
// Generate inputs.
|
|
const int numInputs = 10;
|
|
std::vector<Mat> inputs(numInputs);
|
|
int blobSize[] = {1, 4, 75, 113};
|
|
for (int i = 0; i < numInputs; ++i)
|
|
{
|
|
inputs[i].create(4, &blobSize[0], dtype);
|
|
randu(inputs[i], 0, 255);
|
|
}
|
|
|
|
// Run synchronously.
|
|
std::vector<Mat> refs(numInputs);
|
|
for (int i = 0; i < numInputs; ++i)
|
|
{
|
|
netSync.setInput(inputs[i]);
|
|
refs[i] = netSync.forward().clone();
|
|
}
|
|
|
|
// Run asynchronously. To make test more robust, process inputs in the reversed order.
|
|
std::vector<AsyncArray> outs(numInputs);
|
|
for (int i = numInputs - 1; i >= 0; --i)
|
|
{
|
|
netAsync.setInput(inputs[i]);
|
|
outs[i] = netAsync.forwardAsync();
|
|
}
|
|
|
|
for (int i = numInputs - 1; i >= 0; --i)
|
|
{
|
|
ASSERT_TRUE(outs[i].valid());
|
|
Mat result;
|
|
EXPECT_TRUE(outs[i].get(result, async_timeout));
|
|
normAssert(refs[i], result, format("Index: %d", i).c_str(), 0, 0);
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Async, Combine(
|
|
Values(CV_32F, CV_8U),
|
|
dnnBackendsAndTargetsIE()
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<Backend, Target> > Test_Model_Optimizer;
|
|
TEST_P(Test_Model_Optimizer, forward_two_nets)
|
|
{
|
|
const Backend backendId = get<0>(GetParam());
|
|
const Target targetId = get<1>(GetParam());
|
|
|
|
const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
|
|
const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
|
|
const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
|
|
|
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
|
|
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
|
|
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
|
|
else
|
|
FAIL() << "Unknown backendId";
|
|
|
|
Net net0 = readNet(model, proto);
|
|
net0.setPreferableTarget(targetId);
|
|
|
|
Net net1 = readNet(model, proto);
|
|
net1.setPreferableTarget(targetId);
|
|
|
|
// Generate inputs.
|
|
int blobSize[] = {2, 6, 75, 113};
|
|
Mat input(4, &blobSize[0], CV_32F);
|
|
randu(input, 0, 255);
|
|
|
|
net0.setInput(input);
|
|
Mat ref0 = net0.forward().clone();
|
|
|
|
net1.setInput(input);
|
|
Mat ref1 = net1.forward();
|
|
|
|
net0.setInput(input);
|
|
Mat ref2 = net0.forward();
|
|
|
|
normAssert(ref0, ref2, 0, 0);
|
|
}
|
|
|
|
TEST_P(Test_Model_Optimizer, readFromBuffer)
|
|
{
|
|
const Backend backendId = get<0>(GetParam());
|
|
const Target targetId = get<1>(GetParam());
|
|
|
|
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
throw SkipTestException("No support for async forward");
|
|
|
|
const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
|
|
const std::string& weightsFile = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
|
|
const std::string& modelFile = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
|
|
|
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
|
|
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
|
|
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
|
|
else
|
|
FAIL() << "Unknown backendId";
|
|
|
|
Net net1 = readNetFromModelOptimizer(modelFile, weightsFile);
|
|
net1.setPreferableBackend(backendId);
|
|
net1.setPreferableTarget(targetId);
|
|
|
|
|
|
std::vector<char> modelConfig;
|
|
readFileContent(modelFile, modelConfig);
|
|
std::vector<char> weights;
|
|
readFileContent(weightsFile, weights);
|
|
|
|
Net net2 = readNetFromModelOptimizer(
|
|
(const uchar*)modelConfig.data(), modelConfig.size(),
|
|
(const uchar*)weights.data(), weights.size()
|
|
);
|
|
net2.setPreferableBackend(backendId);
|
|
net2.setPreferableTarget(targetId);
|
|
|
|
int blobSize[] = {2, 6, 75, 113};
|
|
Mat input(4, &blobSize[0], CV_32F);
|
|
randu(input, 0, 255);
|
|
|
|
Mat ref, actual;
|
|
{
|
|
net1.setInput(input);
|
|
ref = net1.forward();
|
|
}
|
|
{
|
|
net2.setInput(input);
|
|
actual = net2.forward();
|
|
}
|
|
|
|
normAssert(ref, actual, "", 0, 0);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Model_Optimizer,
|
|
dnnBackendsAndTargetsIE()
|
|
);
|
|
|
|
#endif // HAVE_INF_ENGINE
|
|
|
|
}} // namespace
|