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556 lines
22 KiB
C++
556 lines
22 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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#include "npy_blob.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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namespace opencv_test { namespace {
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template<typename TString>
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static std::string _tf(TString filename)
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{
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return (getOpenCVExtraDir() + "/dnn/") + filename;
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}
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class Test_Caffe_nets : public DNNTestLayer
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{
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public:
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void testFaster(const std::string& proto, const std::string& model, const Mat& ref,
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double scoreDiff = 0.0, double iouDiff = 0.0)
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{
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checkBackend();
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Net net = readNetFromCaffe(findDataFile("dnn/" + proto, false),
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findDataFile("dnn/" + model, false));
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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Mat img = imread(findDataFile("dnn/dog416.png", false));
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resize(img, img, Size(800, 600));
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Mat blob = blobFromImage(img, 1.0, Size(), Scalar(102.9801, 115.9465, 122.7717), false, false);
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Mat imInfo = (Mat_<float>(1, 3) << img.rows, img.cols, 1.6f);
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net.setInput(blob, "data");
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net.setInput(imInfo, "im_info");
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// Output has shape 1x1xNx7 where N - number of detections.
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// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
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Mat out = net.forward();
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scoreDiff = scoreDiff ? scoreDiff : default_l1;
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iouDiff = iouDiff ? iouDiff : default_lInf;
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normAssertDetections(ref, out, ("model name: " + model).c_str(), 0.8, scoreDiff, iouDiff);
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}
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};
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TEST(Test_Caffe, memory_read)
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{
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const string proto = findDataFile("dnn/bvlc_googlenet.prototxt", false);
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const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);
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string dataProto;
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ASSERT_TRUE(readFileInMemory(proto, dataProto));
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string dataModel;
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ASSERT_TRUE(readFileInMemory(model, dataModel));
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Net net = readNetFromCaffe(dataProto.c_str(), dataProto.size());
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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ASSERT_FALSE(net.empty());
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Net net2 = readNetFromCaffe(dataProto.c_str(), dataProto.size(),
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dataModel.c_str(), dataModel.size());
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ASSERT_FALSE(net2.empty());
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}
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TEST(Test_Caffe, read_gtsrb)
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{
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Net net = readNetFromCaffe(_tf("gtsrb.prototxt"));
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ASSERT_FALSE(net.empty());
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}
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TEST(Test_Caffe, read_googlenet)
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{
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Net net = readNetFromCaffe(_tf("bvlc_googlenet.prototxt"));
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ASSERT_FALSE(net.empty());
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}
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typedef testing::TestWithParam<tuple<bool, Target> > Reproducibility_AlexNet;
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TEST_P(Reproducibility_AlexNet, Accuracy)
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{
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bool readFromMemory = get<0>(GetParam());
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Net net;
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{
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const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
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const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
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if (readFromMemory)
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{
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string dataProto;
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ASSERT_TRUE(readFileInMemory(proto, dataProto));
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string dataModel;
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ASSERT_TRUE(readFileInMemory(model, dataModel));
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net = readNetFromCaffe(dataProto.c_str(), dataProto.size(),
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dataModel.c_str(), dataModel.size());
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}
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else
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net = readNetFromCaffe(proto, model);
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ASSERT_FALSE(net.empty());
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}
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int targetId = get<1>(GetParam());
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const float l1 = 1e-5;
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const float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 3e-3 : 1e-4;
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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net.setPreferableTarget(targetId);
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Mat sample = imread(_tf("grace_hopper_227.png"));
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ASSERT_TRUE(!sample.empty());
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net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false), "data");
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Mat out = net.forward("prob");
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Mat ref = blobFromNPY(_tf("caffe_alexnet_prob.npy"));
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normAssert(ref, out, "", l1, lInf);
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}
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INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_AlexNet, Combine(testing::Bool(),
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Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16)));
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#if !defined(_WIN32) || defined(_WIN64)
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TEST(Reproducibility_FCN, Accuracy)
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{
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Net net;
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{
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const string proto = findDataFile("dnn/fcn8s-heavy-pascal.prototxt", false);
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const string model = findDataFile("dnn/fcn8s-heavy-pascal.caffemodel", false);
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net = readNetFromCaffe(proto, model);
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ASSERT_FALSE(net.empty());
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}
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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Mat sample = imread(_tf("street.png"));
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ASSERT_TRUE(!sample.empty());
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std::vector<int> layerIds;
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std::vector<size_t> weights, blobs;
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net.getMemoryConsumption(shape(1,3,227,227), layerIds, weights, blobs);
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net.setInput(blobFromImage(sample, 1.0f, Size(500, 500), Scalar(), false), "data");
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Mat out = net.forward("score");
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Mat refData = imread(_tf("caffe_fcn8s_prob.png"), IMREAD_ANYDEPTH);
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int shape[] = {1, 21, 500, 500};
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Mat ref(4, shape, CV_32FC1, refData.data);
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normAssert(ref, out);
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}
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#endif
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TEST(Reproducibility_SSD, Accuracy)
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{
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Net net;
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{
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const string proto = findDataFile("dnn/ssd_vgg16.prototxt", false);
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const string model = findDataFile("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", false);
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net = readNetFromCaffe(proto, model);
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ASSERT_FALSE(net.empty());
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}
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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Mat sample = imread(_tf("street.png"));
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ASSERT_TRUE(!sample.empty());
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if (sample.channels() == 4)
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cvtColor(sample, sample, COLOR_BGRA2BGR);
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Mat in_blob = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
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net.setInput(in_blob, "data");
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Mat out = net.forward("detection_out");
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Mat ref = blobFromNPY(_tf("ssd_out.npy"));
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normAssertDetections(ref, out);
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}
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typedef testing::TestWithParam<Target> Reproducibility_MobileNet_SSD;
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TEST_P(Reproducibility_MobileNet_SSD, Accuracy)
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{
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const string proto = findDataFile("dnn/MobileNetSSD_deploy.prototxt", false);
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const string model = findDataFile("dnn/MobileNetSSD_deploy.caffemodel", false);
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Net net = readNetFromCaffe(proto, model);
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int targetId = GetParam();
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const float l1 = (targetId == DNN_TARGET_OPENCL_FP16) ? 1.5e-4 : 1e-5;
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const float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 4e-4 : 1e-4;
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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net.setPreferableTarget(targetId);
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Mat sample = imread(_tf("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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net.setInput(inp);
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Mat out = net.forward();
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const float scores_diff = (targetId == DNN_TARGET_OPENCL_FP16) ? 4e-4 : 1e-5;
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const float boxes_iou_diff = (targetId == DNN_TARGET_OPENCL_FP16) ? 5e-3 : 1e-4;
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Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy"));
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normAssertDetections(ref, out, "", 0.0, scores_diff, boxes_iou_diff);
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// Check that detections aren't preserved.
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inp.setTo(0.0f);
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net.setInput(inp);
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out = net.forward();
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out = out.reshape(1, out.total() / 7);
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const int numDetections = out.rows;
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ASSERT_NE(numDetections, 0);
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for (int i = 0; i < numDetections; ++i)
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{
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float confidence = out.ptr<float>(i)[2];
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ASSERT_EQ(confidence, 0);
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}
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// Check batching mode.
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ref = ref.reshape(1, numDetections);
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inp = blobFromImages(std::vector<Mat>(2, sample), 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
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net.setInput(inp);
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Mat outBatch = net.forward();
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// Output blob has a shape 1x1x2Nx7 where N is a number of detection for
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// a single sample in batch. The first numbers of detection vectors are batch id.
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outBatch = outBatch.reshape(1, outBatch.total() / 7);
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EXPECT_EQ(outBatch.rows, 2 * numDetections);
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normAssert(outBatch.rowRange(0, numDetections), ref, "", l1, lInf);
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normAssert(outBatch.rowRange(numDetections, 2 * numDetections).colRange(1, 7), ref.colRange(1, 7),
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"", l1, lInf);
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}
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INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_MobileNet_SSD,
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Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16));
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typedef testing::TestWithParam<Target> Reproducibility_ResNet50;
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TEST_P(Reproducibility_ResNet50, Accuracy)
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{
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Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
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findDataFile("dnn/ResNet-50-model.caffemodel", false));
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int targetId = GetParam();
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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net.setPreferableTarget(targetId);
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float l1 = (targetId == DNN_TARGET_OPENCL_FP16) ? 3e-5 : 1e-5;
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float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 6e-3 : 1e-4;
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Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(224,224), Scalar(), false);
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ASSERT_TRUE(!input.empty());
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net.setInput(input);
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Mat out = net.forward();
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Mat ref = blobFromNPY(_tf("resnet50_prob.npy"));
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normAssert(ref, out, "", l1, lInf);
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if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
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{
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UMat out_umat;
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net.forward(out_umat);
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normAssert(ref, out_umat, "out_umat", l1, lInf);
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std::vector<UMat> out_umats;
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net.forward(out_umats);
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normAssert(ref, out_umats[0], "out_umat_vector", l1, lInf);
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}
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}
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INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_ResNet50,
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Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16));
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typedef testing::TestWithParam<Target> Reproducibility_SqueezeNet_v1_1;
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TEST_P(Reproducibility_SqueezeNet_v1_1, Accuracy)
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{
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int targetId = GetParam();
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if(targetId == DNN_TARGET_OPENCL_FP16)
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throw SkipTestException("This test does not support FP16");
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Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
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findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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net.setPreferableTarget(targetId);
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Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false, true);
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ASSERT_TRUE(!input.empty());
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Mat out;
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if (targetId == DNN_TARGET_OPENCL)
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{
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// Firstly set a wrong input blob and run the model to receive a wrong output.
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// Then set a correct input blob to check CPU->GPU synchronization is working well.
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net.setInput(input * 2.0f);
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out = net.forward();
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}
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net.setInput(input);
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out = net.forward();
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Mat ref = blobFromNPY(_tf("squeezenet_v1.1_prob.npy"));
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normAssert(ref, out);
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}
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INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_SqueezeNet_v1_1,
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testing::ValuesIn(getAvailableTargets(DNN_BACKEND_OPENCV)));
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TEST(Reproducibility_AlexNet_fp16, Accuracy)
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{
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const float l1 = 1e-5;
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const float lInf = 3e-3;
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const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
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const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
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shrinkCaffeModel(model, "bvlc_alexnet.caffemodel_fp16");
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Net net = readNetFromCaffe(proto, "bvlc_alexnet.caffemodel_fp16");
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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Mat sample = imread(findDataFile("dnn/grace_hopper_227.png", false));
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net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false));
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Mat out = net.forward();
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Mat ref = blobFromNPY(findDataFile("dnn/caffe_alexnet_prob.npy", false));
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normAssert(ref, out, "", l1, lInf);
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}
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TEST(Reproducibility_GoogLeNet_fp16, Accuracy)
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{
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const float l1 = 1e-5;
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const float lInf = 3e-3;
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const string proto = findDataFile("dnn/bvlc_googlenet.prototxt", false);
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const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);
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shrinkCaffeModel(model, "bvlc_googlenet.caffemodel_fp16");
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Net net = readNetFromCaffe(proto, "bvlc_googlenet.caffemodel_fp16");
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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std::vector<Mat> inpMats;
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inpMats.push_back( imread(_tf("googlenet_0.png")) );
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inpMats.push_back( imread(_tf("googlenet_1.png")) );
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ASSERT_TRUE(!inpMats[0].empty() && !inpMats[1].empty());
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net.setInput(blobFromImages(inpMats, 1.0f, Size(), Scalar(), false), "data");
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Mat out = net.forward("prob");
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Mat ref = blobFromNPY(_tf("googlenet_prob.npy"));
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normAssert(out, ref, "", l1, lInf);
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}
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// https://github.com/richzhang/colorization
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TEST_P(Test_Caffe_nets, Colorization)
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{
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checkBackend();
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Mat inp = blobFromNPY(_tf("colorization_inp.npy"));
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Mat ref = blobFromNPY(_tf("colorization_out.npy"));
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Mat kernel = blobFromNPY(_tf("colorization_pts_in_hull.npy"));
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const string proto = findDataFile("dnn/colorization_deploy_v2.prototxt", false);
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const string model = findDataFile("dnn/colorization_release_v2.caffemodel", false);
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Net net = readNetFromCaffe(proto, model);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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net.getLayer(net.getLayerId("class8_ab"))->blobs.push_back(kernel);
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net.getLayer(net.getLayerId("conv8_313_rh"))->blobs.push_back(Mat(1, 313, CV_32F, 2.606));
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net.setInput(inp);
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Mat out = net.forward();
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// Reference output values are in range [-29.1, 69.5]
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const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.25 : 4e-4;
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const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 5.3 : 3e-3;
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normAssert(out, ref, "", l1, lInf);
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}
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TEST_P(Test_Caffe_nets, DenseNet_121)
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{
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checkBackend();
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const string proto = findDataFile("dnn/DenseNet_121.prototxt", false);
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const string model = findDataFile("dnn/DenseNet_121.caffemodel", false);
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Mat inp = imread(_tf("dog416.png"));
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inp = blobFromImage(inp, 1.0 / 255, Size(224, 224), Scalar(), true, true);
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Mat ref = blobFromNPY(_tf("densenet_121_output.npy"));
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Net net = readNetFromCaffe(proto, model);
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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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// Reference is an array of 1000 values from a range [-6.16, 7.9]
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float l1 = default_l1, lInf = default_lInf;
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if (target == DNN_TARGET_OPENCL_FP16)
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{
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l1 = 0.017; lInf = 0.0795;
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}
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else if (target == DNN_TARGET_MYRIAD)
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{
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l1 = 0.097; lInf = 0.52;
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}
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normAssert(out, ref, "", l1, lInf);
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}
|
|
|
|
TEST(Test_Caffe, multiple_inputs)
|
|
{
|
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const string proto = findDataFile("dnn/layers/net_input.prototxt", false);
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Net net = readNetFromCaffe(proto);
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
|
|
Mat first_image(10, 11, CV_32FC3);
|
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Mat second_image(10, 11, CV_32FC3);
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randu(first_image, -1, 1);
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randu(second_image, -1, 1);
|
|
|
|
first_image = blobFromImage(first_image);
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second_image = blobFromImage(second_image);
|
|
|
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Mat first_image_blue_green = slice(first_image, Range::all(), Range(0, 2), Range::all(), Range::all());
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|
Mat first_image_red = slice(first_image, Range::all(), Range(2, 3), Range::all(), Range::all());
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|
Mat second_image_blue_green = slice(second_image, Range::all(), Range(0, 2), Range::all(), Range::all());
|
|
Mat second_image_red = slice(second_image, Range::all(), Range(2, 3), Range::all(), Range::all());
|
|
|
|
net.setInput(first_image_blue_green, "old_style_input_blue_green");
|
|
net.setInput(first_image_red, "different_name_for_red");
|
|
net.setInput(second_image_blue_green, "input_layer_blue_green");
|
|
net.setInput(second_image_red, "old_style_input_red");
|
|
Mat out = net.forward();
|
|
|
|
normAssert(out, first_image + second_image);
|
|
}
|
|
|
|
TEST(Test_Caffe, shared_weights)
|
|
{
|
|
const string proto = findDataFile("dnn/layers/shared_weights.prototxt", false);
|
|
const string model = findDataFile("dnn/layers/shared_weights.caffemodel", false);
|
|
|
|
Net net = readNetFromCaffe(proto, model);
|
|
|
|
Mat input_1 = (Mat_<float>(2, 2) << 0., 2., 4., 6.);
|
|
Mat input_2 = (Mat_<float>(2, 2) << 1., 3., 5., 7.);
|
|
|
|
Mat blob_1 = blobFromImage(input_1);
|
|
Mat blob_2 = blobFromImage(input_2);
|
|
|
|
net.setInput(blob_1, "input_1");
|
|
net.setInput(blob_2, "input_2");
|
|
net.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
|
|
Mat sum = net.forward();
|
|
|
|
EXPECT_EQ(sum.at<float>(0,0), 12.);
|
|
EXPECT_EQ(sum.at<float>(0,1), 16.);
|
|
}
|
|
|
|
typedef testing::TestWithParam<tuple<std::string, Target> > opencv_face_detector;
|
|
TEST_P(opencv_face_detector, Accuracy)
|
|
{
|
|
std::string proto = findDataFile("dnn/opencv_face_detector.prototxt", false);
|
|
std::string model = findDataFile(get<0>(GetParam()), false);
|
|
dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam());
|
|
|
|
Net net = readNetFromCaffe(proto, model);
|
|
Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
|
|
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
|
|
|
|
net.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
net.setPreferableTarget(targetId);
|
|
|
|
net.setInput(blob);
|
|
// Output has shape 1x1xNx7 where N - number of detections.
|
|
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
|
|
Mat out = net.forward();
|
|
Mat ref = (Mat_<float>(6, 7) << 0, 1, 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631,
|
|
0, 1, 0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168,
|
|
0, 1, 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290,
|
|
0, 1, 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
|
|
0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
|
|
0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
|
|
normAssertDetections(ref, out, "", 0.5, 1e-5, 2e-4);
|
|
}
|
|
INSTANTIATE_TEST_CASE_P(Test_Caffe, opencv_face_detector,
|
|
Combine(
|
|
Values("dnn/opencv_face_detector.caffemodel",
|
|
"dnn/opencv_face_detector_fp16.caffemodel"),
|
|
Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL)
|
|
)
|
|
);
|
|
|
|
TEST_P(Test_Caffe_nets, FasterRCNN_vgg16)
|
|
{
|
|
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE > 2018030000
|
|
|| (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
|
|
#endif
|
|
)
|
|
throw SkipTestException("");
|
|
static Mat ref = (Mat_<float>(3, 7) << 0, 2, 0.949398, 99.2454, 210.141, 601.205, 462.849,
|
|
0, 7, 0.997022, 481.841, 92.3218, 722.685, 175.953,
|
|
0, 12, 0.993028, 133.221, 189.377, 350.994, 563.166);
|
|
testFaster("faster_rcnn_vgg16.prototxt", "VGG16_faster_rcnn_final.caffemodel", ref);
|
|
}
|
|
|
|
TEST_P(Test_Caffe_nets, FasterRCNN_zf)
|
|
{
|
|
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) ||
|
|
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
|
|
throw SkipTestException("");
|
|
static Mat ref = (Mat_<float>(3, 7) << 0, 2, 0.90121, 120.407, 115.83, 570.586, 528.395,
|
|
0, 7, 0.988779, 469.849, 75.1756, 718.64, 186.762,
|
|
0, 12, 0.967198, 138.588, 206.843, 329.766, 553.176);
|
|
testFaster("faster_rcnn_zf.prototxt", "ZF_faster_rcnn_final.caffemodel", ref);
|
|
}
|
|
|
|
TEST_P(Test_Caffe_nets, RFCN)
|
|
{
|
|
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) ||
|
|
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
|
|
throw SkipTestException("");
|
|
double scoreDiff = (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ? 4e-3 : default_l1;
|
|
double iouDiff = (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ? 8e-2 : default_lInf;
|
|
static Mat ref = (Mat_<float>(2, 7) << 0, 7, 0.991359, 491.822, 81.1668, 702.573, 178.234,
|
|
0, 12, 0.94786, 132.093, 223.903, 338.077, 566.16);
|
|
testFaster("rfcn_pascal_voc_resnet50.prototxt", "resnet50_rfcn_final.caffemodel", ref, scoreDiff, iouDiff);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Caffe_nets, dnnBackendsAndTargets());
|
|
|
|
}} // namespace
|