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441 lines
13 KiB
C++
441 lines
13 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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#include <opencv2/ts/ocl_test.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 = "dnn/";
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if (inTorchDir)
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path += "torch/";
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path += filename;
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return findDataFile(path, false);
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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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ASSERT_NO_THROW(net = readNetFromTorch(_tf("net_simple_net.txt"), false));
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ASSERT_FALSE(net.empty());
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}
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static void runTorchNet(String prefix, int targetId = DNN_TARGET_CPU, 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 = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary);
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ASSERT_FALSE(net.empty());
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net.setPreferableBackend(DNN_BACKEND_DEFAULT);
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net.setPreferableTarget(targetId);
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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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OCL_TEST(Torch_Importer, run_convolution)
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{
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runTorchNet("net_conv", DNN_TARGET_OPENCL);
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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", DNN_TARGET_CPU, "", true);
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}
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OCL_TEST(Torch_Importer, run_pool_max)
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{
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runTorchNet("net_pool_max", DNN_TARGET_OPENCL, "", 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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OCL_TEST(Torch_Importer, run_pool_ave)
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{
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runTorchNet("net_pool_ave", DNN_TARGET_OPENCL);
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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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runTorchNet("net_reshape_channels", DNN_TARGET_CPU, "", false, true);
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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", DNN_TARGET_CPU, "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", DNN_TARGET_CPU, "l5_torchMerge");
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runTorchNet("net_depth_concat", DNN_TARGET_CPU, "", false, true);
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}
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OCL_TEST(Torch_Importer, run_concat)
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{
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runTorchNet("net_concat", DNN_TARGET_OPENCL, "l5_torchMerge");
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runTorchNet("net_depth_concat", DNN_TARGET_OPENCL, "", false, true);
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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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OCL_TEST(Torch_Importer, run_deconv)
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{
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runTorchNet("net_deconv", DNN_TARGET_OPENCL);
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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", DNN_TARGET_CPU, "", false, true);
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}
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OCL_TEST(Torch_Importer, run_batch_norm)
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{
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runTorchNet("net_batch_norm", DNN_TARGET_OPENCL, "", false, true);
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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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OCL_TEST(Torch_Importer, net_softmax)
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{
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runTorchNet("net_softmax", DNN_TARGET_OPENCL);
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runTorchNet("net_softmax_spatial", DNN_TARGET_OPENCL);
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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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OCL_TEST(Torch_Importer, net_logsoftmax)
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{
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runTorchNet("net_logsoftmax", DNN_TARGET_OPENCL);
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runTorchNet("net_logsoftmax_spatial", DNN_TARGET_OPENCL);
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}
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TEST(Torch_Importer, net_lp_pooling)
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{
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runTorchNet("net_lp_pooling_square", DNN_TARGET_CPU, "", false, true);
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runTorchNet("net_lp_pooling_power", DNN_TARGET_CPU, "", false, true);
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}
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TEST(Torch_Importer, net_conv_gemm_lrn)
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{
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runTorchNet("net_conv_gemm_lrn", DNN_TARGET_CPU, "", false, true);
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}
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TEST(Torch_Importer, net_inception_block)
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{
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runTorchNet("net_inception_block", DNN_TARGET_CPU, "", false, true);
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}
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TEST(Torch_Importer, net_normalize)
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{
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runTorchNet("net_normalize", DNN_TARGET_CPU, "", false, true);
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}
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OCL_TEST(Torch_Importer, net_normalize)
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{
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runTorchNet("net_normalize", DNN_TARGET_OPENCL, "", false, true);
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}
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TEST(Torch_Importer, net_padding)
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{
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runTorchNet("net_padding", DNN_TARGET_CPU, "", false, true);
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runTorchNet("net_spatial_zero_padding", DNN_TARGET_CPU, "", false, true);
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runTorchNet("net_spatial_reflection_padding", DNN_TARGET_CPU, "", false, true);
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}
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TEST(Torch_Importer, net_non_spatial)
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{
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runTorchNet("net_non_spatial", DNN_TARGET_CPU, "", false, true);
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}
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OCL_TEST(Torch_Importer, net_non_spatial)
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{
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runTorchNet("net_non_spatial", DNN_TARGET_OPENCL, "", false, true);
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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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net = readNetFromTorch(model, true);
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ASSERT_FALSE(net.empty());
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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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TEST(Torch_Importer, OpenFace_accuracy)
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{
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const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false);
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Net net = readNetFromTorch(model);
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Mat sample = imread(findDataFile("cv/shared/lena.png", false));
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Mat sampleF32(sample.size(), CV_32FC3);
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sample.convertTo(sampleF32, sampleF32.type());
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sampleF32 /= 255;
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resize(sampleF32, sampleF32, Size(96, 96), 0, 0, INTER_NEAREST);
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Mat inputBlob = blobFromImage(sampleF32);
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net.setInput(inputBlob);
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Mat out = net.forward();
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Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true);
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normAssert(out, outRef);
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}
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OCL_TEST(Torch_Importer, OpenFace_accuracy)
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{
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const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false);
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Net net = readNetFromTorch(model);
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net.setPreferableBackend(DNN_BACKEND_DEFAULT);
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net.setPreferableTarget(DNN_TARGET_OPENCL);
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Mat sample = imread(findDataFile("cv/shared/lena.png", false));
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Mat sampleF32(sample.size(), CV_32FC3);
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sample.convertTo(sampleF32, sampleF32.type());
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sampleF32 /= 255;
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resize(sampleF32, sampleF32, Size(96, 96), 0, 0, INTER_NEAREST);
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Mat inputBlob = blobFromImage(sampleF32);
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net.setInput(inputBlob);
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Mat out = net.forward();
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Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true);
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normAssert(out, outRef);
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}
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OCL_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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net = readNetFromTorch(model, true);
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ASSERT_TRUE(!net.empty());
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}
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net.setPreferableBackend(DNN_BACKEND_DEFAULT);
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net.setPreferableTarget(DNN_TARGET_OPENCL);
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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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// Check accuracy of style transfer models from https://github.com/jcjohnson/fast-neural-style
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// th fast_neural_style.lua \
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// -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \
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// -output_image lena.png \
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// -median_filter 0 \
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// -image_size 0 \
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// -model models/eccv16/starry_night.t7
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// th fast_neural_style.lua \
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// -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \
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// -output_image lena.png \
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// -median_filter 0 \
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// -image_size 0 \
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// -model models/instance_norm/feathers.t7
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TEST(Torch_Importer, FastNeuralStyle_accuracy)
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{
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std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
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"dnn/fast_neural_style_instance_norm_feathers.t7"};
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std::string targets[] = {"dnn/lena_starry_night.png", "dnn/lena_feathers.png"};
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for (int i = 0; i < 2; ++i)
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{
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const string model = findDataFile(models[i], false);
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Net net = readNetFromTorch(model);
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Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
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Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);
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net.setInput(inputBlob);
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Mat out = net.forward();
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// Deprocessing.
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getPlane(out, 0, 0) += 103.939;
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getPlane(out, 0, 1) += 116.779;
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getPlane(out, 0, 2) += 123.68;
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out = cv::min(cv::max(0, out), 255);
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Mat ref = imread(findDataFile(targets[i]));
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Mat refBlob = blobFromImage(ref, 1.0, Size(), Scalar(), false);
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normAssert(out, refBlob, "", 0.5, 1.1);
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}
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}
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OCL_TEST(Torch_Importer, FastNeuralStyle_accuracy)
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{
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std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
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"dnn/fast_neural_style_instance_norm_feathers.t7"};
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std::string targets[] = {"dnn/lena_starry_night.png", "dnn/lena_feathers.png"};
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for (int i = 0; i < 2; ++i)
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{
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const string model = findDataFile(models[i], false);
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Net net = readNetFromTorch(model);
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net.setPreferableBackend(DNN_BACKEND_DEFAULT);
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net.setPreferableTarget(DNN_TARGET_OPENCL);
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Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
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Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);
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net.setInput(inputBlob);
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Mat out = net.forward();
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// Deprocessing.
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getPlane(out, 0, 0) += 103.939;
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getPlane(out, 0, 1) += 116.779;
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getPlane(out, 0, 2) += 123.68;
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out = cv::min(cv::max(0, out), 255);
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Mat ref = imread(findDataFile(targets[i]));
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Mat refBlob = blobFromImage(ref, 1.0, Size(), Scalar(), false);
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normAssert(out, refBlob, "", 0.5, 1.1);
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}
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}
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}
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