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dnn: move module from opencv_contrib
https://github.com/opencv/opencv_contrib/tree/e6f63c7a38ca40c5dc33e38736e3027e3528d6cb/modules/dnn
This commit is contained in:
Alexander Alekhin
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/*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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#ifdef ENABLE_TORCH_IMPORTER
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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 std;
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using namespace testing;
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using namespace cv;
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using namespace cv::dnn;
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template<typename TStr>
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static std::string _tf(TStr filename, bool inTorchDir = true)
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{
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String path = getOpenCVExtraDir() + "/dnn/";
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if (inTorchDir)
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path += "torch/";
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path += filename;
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return path;
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}
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TEST(Torch_Importer, simple_read)
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{
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Net net;
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Ptr<Importer> importer;
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ASSERT_NO_THROW( importer = createTorchImporter(_tf("net_simple_net.txt"), false) );
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ASSERT_TRUE( importer != NULL );
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importer->populateNet(net);
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}
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static void runTorchNet(String prefix, String outLayerName = "",
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bool check2ndBlob = false, bool isBinary = false)
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{
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String suffix = (isBinary) ? ".dat" : ".txt";
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Net net;
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Ptr<Importer> importer = createTorchImporter(_tf(prefix + "_net" + suffix), isBinary);
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ASSERT_TRUE(importer != NULL);
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importer->populateNet(net);
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Mat inp, outRef;
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ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) );
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ASSERT_NO_THROW( outRef = readTorchBlob(_tf(prefix + "_output" + suffix), isBinary) );
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if (outLayerName.empty())
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outLayerName = net.getLayerNames().back();
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net.setInput(inp, "0");
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std::vector<Mat> outBlobs;
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net.forward(outBlobs, outLayerName);
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normAssert(outRef, outBlobs[0]);
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if (check2ndBlob)
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{
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Mat out2 = outBlobs[1];
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Mat ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary);
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normAssert(out2, ref2);
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}
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}
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TEST(Torch_Importer, run_convolution)
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{
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runTorchNet("net_conv");
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}
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TEST(Torch_Importer, run_pool_max)
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{
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runTorchNet("net_pool_max", "", true);
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}
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TEST(Torch_Importer, run_pool_ave)
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{
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runTorchNet("net_pool_ave");
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}
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TEST(Torch_Importer, run_reshape)
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{
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runTorchNet("net_reshape");
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runTorchNet("net_reshape_batch");
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runTorchNet("net_reshape_single_sample");
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}
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TEST(Torch_Importer, run_linear)
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{
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runTorchNet("net_linear_2d");
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}
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TEST(Torch_Importer, run_paralel)
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{
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runTorchNet("net_parallel", "l5_torchMerge");
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}
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TEST(Torch_Importer, run_concat)
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{
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runTorchNet("net_concat", "l5_torchMerge");
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}
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TEST(Torch_Importer, run_deconv)
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{
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runTorchNet("net_deconv");
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}
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TEST(Torch_Importer, run_batch_norm)
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{
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runTorchNet("net_batch_norm");
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}
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TEST(Torch_Importer, net_prelu)
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{
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runTorchNet("net_prelu");
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}
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TEST(Torch_Importer, net_cadd_table)
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{
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runTorchNet("net_cadd_table");
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}
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TEST(Torch_Importer, net_softmax)
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{
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runTorchNet("net_softmax");
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runTorchNet("net_softmax_spatial");
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}
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TEST(Torch_Importer, net_logsoftmax)
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{
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runTorchNet("net_logsoftmax");
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runTorchNet("net_logsoftmax_spatial");
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}
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TEST(Torch_Importer, ENet_accuracy)
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{
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Net net;
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{
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const string model = findDataFile("dnn/Enet-model-best.net", false);
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Ptr<Importer> importer = createTorchImporter(model, true);
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ASSERT_TRUE(importer != NULL);
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importer->populateNet(net);
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}
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Mat sample = imread(_tf("street.png", false));
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Mat inputBlob = blobFromImage(sample, 1./255);
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net.setInput(inputBlob, "");
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Mat out = net.forward();
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Mat ref = blobFromNPY(_tf("torch_enet_prob.npy", false));
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// Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
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// thresholds for ENet must be changed. Accuracy of resuults was checked on
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// Cityscapes dataset and difference in mIOU with Torch is 10E-4%
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normAssert(ref, out, "", 0.00044, 0.44);
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const int N = 3;
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for (int i = 0; i < N; i++)
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{
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net.setInput(inputBlob, "");
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Mat out = net.forward();
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normAssert(ref, out, "", 0.00044, 0.44);
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}
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}
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}
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#endif
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