mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
420 lines
12 KiB
420 lines
12 KiB
/*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); |
|
} |
|
} |
|
|
|
}
|
|
|