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238 lines
8.0 KiB
C++
238 lines
8.0 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 cvtest
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{
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using namespace cv;
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using namespace cv::dnn;
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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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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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TEST(Reproducibility_AlexNet, Accuracy)
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{
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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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net = readNetFromCaffe(proto, model);
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ASSERT_FALSE(net.empty());
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}
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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);
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}
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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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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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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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normAssert(ref, out);
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}
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TEST(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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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);
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}
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TEST(Reproducibility_SqueezeNet_v1_1, Accuracy)
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{
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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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Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), 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("squeezenet_v1.1_prob.npy"));
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normAssert(ref, out);
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}
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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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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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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(Reproducibility_Colorization, 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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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.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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normAssert(out, ref, "", l1, lInf);
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}
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}
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