mirror of
https://github.com/opencv/opencv.git
synced 2024-11-26 04:00:30 +08:00
421 lines
12 KiB
C++
421 lines
12 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of the copyright holders may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#include "test_precomp.hpp"
|
|
#include "npy_blob.hpp"
|
|
#include <opencv2/dnn/shape_utils.hpp>
|
|
#include <opencv2/ts/ocl_test.hpp>
|
|
|
|
namespace cvtest
|
|
{
|
|
|
|
using namespace std;
|
|
using namespace testing;
|
|
using namespace cv;
|
|
using namespace cv::dnn;
|
|
|
|
template<typename TStr>
|
|
static std::string _tf(TStr filename, bool inTorchDir = true)
|
|
{
|
|
String path = "dnn/";
|
|
if (inTorchDir)
|
|
path += "torch/";
|
|
path += filename;
|
|
return findDataFile(path, false);
|
|
}
|
|
|
|
TEST(Torch_Importer, simple_read)
|
|
{
|
|
Net net;
|
|
ASSERT_NO_THROW(net = readNetFromTorch(_tf("net_simple_net.txt"), false));
|
|
ASSERT_FALSE(net.empty());
|
|
}
|
|
|
|
static void runTorchNet(String prefix, int targetId = DNN_TARGET_CPU, String outLayerName = "",
|
|
bool check2ndBlob = false, bool isBinary = false)
|
|
{
|
|
String suffix = (isBinary) ? ".dat" : ".txt";
|
|
|
|
Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary);
|
|
ASSERT_FALSE(net.empty());
|
|
|
|
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
|
|
net.setPreferableTarget(targetId);
|
|
|
|
Mat inp, outRef;
|
|
ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) );
|
|
ASSERT_NO_THROW( outRef = readTorchBlob(_tf(prefix + "_output" + suffix), isBinary) );
|
|
|
|
if (outLayerName.empty())
|
|
outLayerName = net.getLayerNames().back();
|
|
|
|
net.setInput(inp, "0");
|
|
std::vector<Mat> outBlobs;
|
|
net.forward(outBlobs, outLayerName);
|
|
normAssert(outRef, outBlobs[0]);
|
|
|
|
if (check2ndBlob)
|
|
{
|
|
Mat out2 = outBlobs[1];
|
|
Mat ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary);
|
|
normAssert(out2, ref2);
|
|
}
|
|
}
|
|
|
|
TEST(Torch_Importer, run_convolution)
|
|
{
|
|
runTorchNet("net_conv");
|
|
}
|
|
|
|
OCL_TEST(Torch_Importer, run_convolution)
|
|
{
|
|
runTorchNet("net_conv", DNN_TARGET_OPENCL);
|
|
}
|
|
|
|
TEST(Torch_Importer, run_pool_max)
|
|
{
|
|
runTorchNet("net_pool_max", DNN_TARGET_CPU, "", true);
|
|
}
|
|
|
|
OCL_TEST(Torch_Importer, run_pool_max)
|
|
{
|
|
runTorchNet("net_pool_max", DNN_TARGET_OPENCL, "", true);
|
|
}
|
|
|
|
TEST(Torch_Importer, run_pool_ave)
|
|
{
|
|
runTorchNet("net_pool_ave");
|
|
}
|
|
|
|
OCL_TEST(Torch_Importer, run_pool_ave)
|
|
{
|
|
runTorchNet("net_pool_ave", DNN_TARGET_OPENCL);
|
|
}
|
|
|
|
TEST(Torch_Importer, run_reshape)
|
|
{
|
|
runTorchNet("net_reshape");
|
|
runTorchNet("net_reshape_batch");
|
|
runTorchNet("net_reshape_single_sample");
|
|
runTorchNet("net_reshape_channels", DNN_TARGET_CPU, "", false, true);
|
|
}
|
|
|
|
TEST(Torch_Importer, run_linear)
|
|
{
|
|
runTorchNet("net_linear_2d");
|
|
}
|
|
|
|
TEST(Torch_Importer, run_paralel)
|
|
{
|
|
runTorchNet("net_parallel", DNN_TARGET_CPU, "l5_torchMerge");
|
|
}
|
|
|
|
TEST(Torch_Importer, run_concat)
|
|
{
|
|
runTorchNet("net_concat", DNN_TARGET_CPU, "l5_torchMerge");
|
|
runTorchNet("net_depth_concat", DNN_TARGET_CPU, "", false, true);
|
|
}
|
|
|
|
OCL_TEST(Torch_Importer, run_concat)
|
|
{
|
|
runTorchNet("net_concat", DNN_TARGET_OPENCL, "l5_torchMerge");
|
|
runTorchNet("net_depth_concat", DNN_TARGET_OPENCL, "", false, true);
|
|
}
|
|
|
|
TEST(Torch_Importer, run_deconv)
|
|
{
|
|
runTorchNet("net_deconv");
|
|
}
|
|
|
|
TEST(Torch_Importer, run_batch_norm)
|
|
{
|
|
runTorchNet("net_batch_norm", DNN_TARGET_CPU, "", false, true);
|
|
}
|
|
|
|
TEST(Torch_Importer, net_prelu)
|
|
{
|
|
runTorchNet("net_prelu");
|
|
}
|
|
|
|
TEST(Torch_Importer, net_cadd_table)
|
|
{
|
|
runTorchNet("net_cadd_table");
|
|
}
|
|
|
|
TEST(Torch_Importer, net_softmax)
|
|
{
|
|
runTorchNet("net_softmax");
|
|
runTorchNet("net_softmax_spatial");
|
|
}
|
|
|
|
OCL_TEST(Torch_Importer, net_softmax)
|
|
{
|
|
runTorchNet("net_softmax", DNN_TARGET_OPENCL);
|
|
runTorchNet("net_softmax_spatial", DNN_TARGET_OPENCL);
|
|
}
|
|
|
|
TEST(Torch_Importer, net_logsoftmax)
|
|
{
|
|
runTorchNet("net_logsoftmax");
|
|
runTorchNet("net_logsoftmax_spatial");
|
|
}
|
|
|
|
OCL_TEST(Torch_Importer, net_logsoftmax)
|
|
{
|
|
runTorchNet("net_logsoftmax", DNN_TARGET_OPENCL);
|
|
runTorchNet("net_logsoftmax_spatial", DNN_TARGET_OPENCL);
|
|
}
|
|
|
|
TEST(Torch_Importer, net_lp_pooling)
|
|
{
|
|
runTorchNet("net_lp_pooling_square", DNN_TARGET_CPU, "", false, true);
|
|
runTorchNet("net_lp_pooling_power", DNN_TARGET_CPU, "", false, true);
|
|
}
|
|
|
|
TEST(Torch_Importer, net_conv_gemm_lrn)
|
|
{
|
|
runTorchNet("net_conv_gemm_lrn", DNN_TARGET_CPU, "", false, true);
|
|
}
|
|
|
|
TEST(Torch_Importer, net_inception_block)
|
|
{
|
|
runTorchNet("net_inception_block", DNN_TARGET_CPU, "", false, true);
|
|
}
|
|
|
|
TEST(Torch_Importer, net_normalize)
|
|
{
|
|
runTorchNet("net_normalize", DNN_TARGET_CPU, "", false, true);
|
|
}
|
|
|
|
TEST(Torch_Importer, net_padding)
|
|
{
|
|
runTorchNet("net_padding", DNN_TARGET_CPU, "", false, true);
|
|
runTorchNet("net_spatial_zero_padding", DNN_TARGET_CPU, "", false, true);
|
|
runTorchNet("net_spatial_reflection_padding", DNN_TARGET_CPU, "", false, true);
|
|
}
|
|
|
|
TEST(Torch_Importer, net_non_spatial)
|
|
{
|
|
runTorchNet("net_non_spatial", DNN_TARGET_CPU, "", false, true);
|
|
}
|
|
|
|
TEST(Torch_Importer, ENet_accuracy)
|
|
{
|
|
Net net;
|
|
{
|
|
const string model = findDataFile("dnn/Enet-model-best.net", false);
|
|
net = readNetFromTorch(model, true);
|
|
ASSERT_FALSE(net.empty());
|
|
}
|
|
|
|
Mat sample = imread(_tf("street.png", false));
|
|
Mat inputBlob = blobFromImage(sample, 1./255);
|
|
|
|
net.setInput(inputBlob, "");
|
|
Mat out = net.forward();
|
|
Mat ref = blobFromNPY(_tf("torch_enet_prob.npy", false));
|
|
// Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
|
|
// thresholds for ENet must be changed. Accuracy of resuults was checked on
|
|
// Cityscapes dataset and difference in mIOU with Torch is 10E-4%
|
|
normAssert(ref, out, "", 0.00044, 0.44);
|
|
|
|
const int N = 3;
|
|
for (int i = 0; i < N; i++)
|
|
{
|
|
net.setInput(inputBlob, "");
|
|
Mat out = net.forward();
|
|
normAssert(ref, out, "", 0.00044, 0.44);
|
|
}
|
|
}
|
|
|
|
TEST(Torch_Importer, OpenFace_accuracy)
|
|
{
|
|
const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false);
|
|
Net net = readNetFromTorch(model);
|
|
|
|
Mat sample = imread(findDataFile("cv/shared/lena.png", false));
|
|
Mat sampleF32(sample.size(), CV_32FC3);
|
|
sample.convertTo(sampleF32, sampleF32.type());
|
|
sampleF32 /= 255;
|
|
resize(sampleF32, sampleF32, Size(96, 96), 0, 0, INTER_NEAREST);
|
|
|
|
Mat inputBlob = blobFromImage(sampleF32);
|
|
|
|
net.setInput(inputBlob);
|
|
Mat out = net.forward();
|
|
|
|
Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true);
|
|
normAssert(out, outRef);
|
|
}
|
|
|
|
OCL_TEST(Torch_Importer, OpenFace_accuracy)
|
|
{
|
|
const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false);
|
|
Net net = readNetFromTorch(model);
|
|
|
|
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
|
|
net.setPreferableTarget(DNN_TARGET_OPENCL);
|
|
|
|
Mat sample = imread(findDataFile("cv/shared/lena.png", false));
|
|
Mat sampleF32(sample.size(), CV_32FC3);
|
|
sample.convertTo(sampleF32, sampleF32.type());
|
|
sampleF32 /= 255;
|
|
resize(sampleF32, sampleF32, Size(96, 96), 0, 0, INTER_NEAREST);
|
|
|
|
Mat inputBlob = blobFromImage(sampleF32);
|
|
|
|
net.setInput(inputBlob);
|
|
Mat out = net.forward();
|
|
|
|
Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true);
|
|
normAssert(out, outRef);
|
|
}
|
|
|
|
OCL_TEST(Torch_Importer, ENet_accuracy)
|
|
{
|
|
Net net;
|
|
{
|
|
const string model = findDataFile("dnn/Enet-model-best.net", false);
|
|
net = readNetFromTorch(model, true);
|
|
ASSERT_TRUE(!net.empty());
|
|
}
|
|
|
|
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
|
|
net.setPreferableTarget(DNN_TARGET_OPENCL);
|
|
|
|
Mat sample = imread(_tf("street.png", false));
|
|
Mat inputBlob = blobFromImage(sample, 1./255);
|
|
|
|
net.setInput(inputBlob, "");
|
|
Mat out = net.forward();
|
|
Mat ref = blobFromNPY(_tf("torch_enet_prob.npy", false));
|
|
// Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
|
|
// thresholds for ENet must be changed. Accuracy of resuults was checked on
|
|
// Cityscapes dataset and difference in mIOU with Torch is 10E-4%
|
|
normAssert(ref, out, "", 0.00044, 0.44);
|
|
|
|
const int N = 3;
|
|
for (int i = 0; i < N; i++)
|
|
{
|
|
net.setInput(inputBlob, "");
|
|
Mat out = net.forward();
|
|
normAssert(ref, out, "", 0.00044, 0.44);
|
|
}
|
|
}
|
|
|
|
// Check accuracy of style transfer models from https://github.com/jcjohnson/fast-neural-style
|
|
// th fast_neural_style.lua \
|
|
// -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \
|
|
// -output_image lena.png \
|
|
// -median_filter 0 \
|
|
// -image_size 0 \
|
|
// -model models/eccv16/starry_night.t7
|
|
// th fast_neural_style.lua \
|
|
// -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \
|
|
// -output_image lena.png \
|
|
// -median_filter 0 \
|
|
// -image_size 0 \
|
|
// -model models/instance_norm/feathers.t7
|
|
TEST(Torch_Importer, FastNeuralStyle_accuracy)
|
|
{
|
|
std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
|
|
"dnn/fast_neural_style_instance_norm_feathers.t7"};
|
|
std::string targets[] = {"dnn/lena_starry_night.png", "dnn/lena_feathers.png"};
|
|
|
|
for (int i = 0; i < 2; ++i)
|
|
{
|
|
const string model = findDataFile(models[i], false);
|
|
Net net = readNetFromTorch(model);
|
|
|
|
Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
|
|
Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);
|
|
|
|
net.setInput(inputBlob);
|
|
Mat out = net.forward();
|
|
|
|
// Deprocessing.
|
|
getPlane(out, 0, 0) += 103.939;
|
|
getPlane(out, 0, 1) += 116.779;
|
|
getPlane(out, 0, 2) += 123.68;
|
|
out = cv::min(cv::max(0, out), 255);
|
|
|
|
Mat ref = imread(findDataFile(targets[i]));
|
|
Mat refBlob = blobFromImage(ref, 1.0, Size(), Scalar(), false);
|
|
|
|
normAssert(out, refBlob, "", 0.5, 1.1);
|
|
}
|
|
}
|
|
|
|
OCL_TEST(Torch_Importer, FastNeuralStyle_accuracy)
|
|
{
|
|
std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
|
|
"dnn/fast_neural_style_instance_norm_feathers.t7"};
|
|
std::string targets[] = {"dnn/lena_starry_night.png", "dnn/lena_feathers.png"};
|
|
|
|
for (int i = 0; i < 2; ++i)
|
|
{
|
|
const string model = findDataFile(models[i], false);
|
|
Net net = readNetFromTorch(model);
|
|
|
|
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
|
|
net.setPreferableTarget(DNN_TARGET_OPENCL);
|
|
|
|
Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
|
|
Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);
|
|
|
|
net.setInput(inputBlob);
|
|
Mat out = net.forward();
|
|
|
|
// Deprocessing.
|
|
getPlane(out, 0, 0) += 103.939;
|
|
getPlane(out, 0, 1) += 116.779;
|
|
getPlane(out, 0, 2) += 123.68;
|
|
out = cv::min(cv::max(0, out), 255);
|
|
|
|
Mat ref = imread(findDataFile(targets[i]));
|
|
Mat refBlob = blobFromImage(ref, 1.0, Size(), Scalar(), false);
|
|
|
|
normAssert(out, refBlob, "", 0.5, 1.1);
|
|
}
|
|
}
|
|
|
|
}
|