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opencv/modules/dnn/test/test_darknet_importer.cpp

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/*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
// (3-clause BSD License)
//
// Copyright (C) 2017, Intel Corporation, 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:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistributions 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.
//
// * Neither the names of the copyright holders nor the names of the contributors
// may 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 copyright holders 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>
namespace opencv_test { namespace {
template<typename TString>
static std::string _tf(TString filename)
{
return (getOpenCVExtraDir() + "/dnn/") + filename;
}
TEST(Test_Darknet, read_tiny_yolo_voc)
{
Net net = readNetFromDarknet(_tf("tiny-yolo-voc.cfg"));
ASSERT_FALSE(net.empty());
}
TEST(Test_Darknet, read_yolo_voc)
{
Net net = readNetFromDarknet(_tf("yolo-voc.cfg"));
ASSERT_FALSE(net.empty());
}
TEST(Test_Darknet, read_yolo_voc_stream)
{
applyTestTag(CV_TEST_TAG_MEMORY_1GB);
Mat ref;
Mat sample = imread(_tf("dog416.png"));
Mat inp = blobFromImage(sample, 1.0/255, Size(416, 416), Scalar(), true, false);
const std::string cfgFile = findDataFile("dnn/yolo-voc.cfg");
const std::string weightsFile = findDataFile("dnn/yolo-voc.weights", false);
// Import by paths.
{
Net net = readNetFromDarknet(cfgFile, weightsFile);
net.setInput(inp);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
ref = net.forward();
}
// Import from bytes array.
{
std::vector<char> cfg, weights;
readFileContent(cfgFile, cfg);
readFileContent(weightsFile, weights);
Net net = readNetFromDarknet(cfg.data(), cfg.size(), weights.data(), weights.size());
net.setInput(inp);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat out = net.forward();
normAssert(ref, out);
}
}
class Test_Darknet_layers : public DNNTestLayer
{
public:
void testDarknetLayer(const std::string& name, bool hasWeights = false, bool testBatchProcessing = true)
{
SCOPED_TRACE(name);
Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy"));
Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy"));
std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg");
std::string model = "";
if (hasWeights)
model = findDataFile("dnn/darknet/" + name + ".weights");
checkBackend(&inp, &ref);
Net net = readNet(cfg, model);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
Mat out = net.forward();
normAssert(out, ref, "", default_l1, default_lInf);
if (inp.size[0] == 1 && testBatchProcessing) // test handling of batch size
{
SCOPED_TRACE("batch size 2");
#if defined(INF_ENGINE_RELEASE)
if (target == DNN_TARGET_MYRIAD && name == "shortcut")
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
#endif
std::vector<int> sz2 = shape(inp);
sz2[0] = 2;
Net net2 = readNet(cfg, model);
net2.setPreferableBackend(backend);
net2.setPreferableTarget(target);
Range ranges0[4] = { Range(0, 1), Range::all(), Range::all(), Range::all() };
Range ranges1[4] = { Range(1, 2), Range::all(), Range::all(), Range::all() };
Mat inp2(sz2, inp.type(), Scalar::all(0));
inp.copyTo(inp2(ranges0));
inp.copyTo(inp2(ranges1));
net2.setInput(inp2);
Mat out2 = net2.forward();
EXPECT_EQ(0, cv::norm(out2(ranges0), out2(ranges1), NORM_INF)) << "Batch result is not equal: " << name;
Mat ref2 = ref;
if (ref.dims == 2 && out2.dims == 3)
{
int ref_3d_sizes[3] = {1, ref.rows, ref.cols};
ref2 = Mat(3, ref_3d_sizes, ref.type(), (void*)ref.data);
}
/*else if (ref.dims == 3 && out2.dims == 4)
{
int ref_4d_sizes[4] = {1, ref.size[0], ref.size[1], ref.size[2]};
ref2 = Mat(4, ref_4d_sizes, ref.type(), (void*)ref.data);
}*/
ASSERT_EQ(out2.dims, ref2.dims) << ref.dims;
normAssert(out2(ranges0), ref2, "", default_l1, default_lInf);
normAssert(out2(ranges1), ref2, "", default_l1, default_lInf);
}
}
};
class Test_Darknet_nets : public DNNTestLayer
{
public:
// Test object detection network from Darknet framework.
void testDarknetModel(const std::string& cfg, const std::string& weights,
const std::vector<std::vector<int> >& refClassIds,
const std::vector<std::vector<float> >& refConfidences,
const std::vector<std::vector<Rect2d> >& refBoxes,
double scoreDiff, double iouDiff, float confThreshold = 0.24, float nmsThreshold = 0.4)
{
checkBackend();
Mat img1 = imread(_tf("dog416.png"));
Mat img2 = imread(_tf("street.png"));
std::vector<Mat> samples(2);
samples[0] = img1; samples[1] = img2;
// determine test type, whether batch or single img
int batch_size = refClassIds.size();
CV_Assert(batch_size == 1 || batch_size == 2);
samples.resize(batch_size);
Mat inp = blobFromImages(samples, 1.0/255, Size(416, 416), Scalar(), true, false);
Net net = readNet(findDataFile("dnn/" + cfg),
findDataFile("dnn/" + weights, false));
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
std::vector<Mat> outs;
net.forward(outs, net.getUnconnectedOutLayersNames());
for (int b = 0; b < batch_size; ++b)
{
std::vector<int> classIds;
std::vector<float> confidences;
std::vector<Rect2d> boxes;
for (int i = 0; i < outs.size(); ++i)
{
Mat out;
if (batch_size > 1){
// get the sample slice from 3D matrix (batch, box, classes+5)
Range ranges[3] = {Range(b, b+1), Range::all(), Range::all()};
out = outs[i](ranges).reshape(1, outs[i].size[1]);
}else{
out = outs[i];
}
for (int j = 0; j < out.rows; ++j)
{
Mat scores = out.row(j).colRange(5, out.cols);
double confidence;
Point maxLoc;
minMaxLoc(scores, 0, &confidence, 0, &maxLoc);
if (confidence > confThreshold) {
float* detection = out.ptr<float>(j);
double centerX = detection[0];
double centerY = detection[1];
double width = detection[2];
double height = detection[3];
boxes.push_back(Rect2d(centerX - 0.5 * width, centerY - 0.5 * height,
width, height));
confidences.push_back(confidence);
classIds.push_back(maxLoc.x);
}
}
}
// here we need NMS of boxes
std::vector<int> indices;
NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);
std::vector<int> nms_classIds;
std::vector<float> nms_confidences;
std::vector<Rect2d> nms_boxes;
for (size_t i = 0; i < indices.size(); ++i)
{
int idx = indices[i];
Rect2d box = boxes[idx];
float conf = confidences[idx];
int class_id = classIds[idx];
nms_boxes.push_back(box);
nms_confidences.push_back(conf);
nms_classIds.push_back(class_id);
#if 0 // use to update test reference data
std::cout << b << ", " << class_id << ", " << conf << "f, "
<< box.x << "f, " << box.y << "f, "
<< box.x + box.width << "f, " << box.y + box.height << "f,"
<< std::endl;
#endif
}
if (cvIsNaN(iouDiff))
{
if (b == 0)
std::cout << "Skip accuracy checks" << std::endl;
continue;
}
normAssertDetections(refClassIds[b], refConfidences[b], refBoxes[b], nms_classIds,
nms_confidences, nms_boxes, format("batch size %d, sample %d\n", batch_size, b).c_str(), confThreshold, scoreDiff, iouDiff);
}
}
void testDarknetModel(const std::string& cfg, const std::string& weights,
const std::vector<int>& refClassIds,
const std::vector<float>& refConfidences,
const std::vector<Rect2d>& refBoxes,
double scoreDiff, double iouDiff, float confThreshold = 0.24, float nmsThreshold = 0.4)
{
testDarknetModel(cfg, weights,
std::vector<std::vector<int> >(1, refClassIds),
std::vector<std::vector<float> >(1, refConfidences),
std::vector<std::vector<Rect2d> >(1, refBoxes),
scoreDiff, iouDiff, confThreshold, nmsThreshold);
}
void testDarknetModel(const std::string& cfg, const std::string& weights,
const cv::Mat& ref, double scoreDiff, double iouDiff,
float confThreshold = 0.24, float nmsThreshold = 0.4)
{
CV_Assert(ref.cols == 7);
std::vector<std::vector<int> > refClassIds;
std::vector<std::vector<float> > refScores;
std::vector<std::vector<Rect2d> > refBoxes;
for (int i = 0; i < ref.rows; ++i)
{
int batchId = static_cast<int>(ref.at<float>(i, 0));
int classId = static_cast<int>(ref.at<float>(i, 1));
float score = ref.at<float>(i, 2);
float left = ref.at<float>(i, 3);
float top = ref.at<float>(i, 4);
float right = ref.at<float>(i, 5);
float bottom = ref.at<float>(i, 6);
Rect2d box(left, top, right - left, bottom - top);
if (batchId >= refClassIds.size())
{
refClassIds.resize(batchId + 1);
refScores.resize(batchId + 1);
refBoxes.resize(batchId + 1);
}
refClassIds[batchId].push_back(classId);
refScores[batchId].push_back(score);
refBoxes[batchId].push_back(box);
}
testDarknetModel(cfg, weights, refClassIds, refScores, refBoxes,
scoreDiff, iouDiff, confThreshold, nmsThreshold);
}
};
TEST_P(Test_Darknet_nets, YoloVoc)
{
applyTestTag(
#if defined(OPENCV_32BIT_CONFIGURATION) && defined(HAVE_OPENCL)
CV_TEST_TAG_MEMORY_2GB,
#else
CV_TEST_TAG_MEMORY_1GB,
#endif
CV_TEST_TAG_LONG
);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
#endif
#if defined(INF_ENGINE_RELEASE)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) &&
target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); // need to update check function
#endif
// batchId, classId, confidence, left, top, right, bottom
Mat ref = (Mat_<float>(6, 7) << 0, 6, 0.750469f, 0.577374f, 0.127391f, 0.902949f, 0.300809f, // a car
0, 1, 0.780879f, 0.270762f, 0.264102f, 0.732475f, 0.745412f, // a bicycle
0, 11, 0.901615f, 0.1386f, 0.338509f, 0.421337f, 0.938789f, // a dog
1, 14, 0.623813f, 0.183179f, 0.381921f, 0.247726f, 0.625847f, // a person
1, 6, 0.667770f, 0.446555f, 0.453578f, 0.499986f, 0.519167f, // a car
1, 6, 0.844947f, 0.637058f, 0.460398f, 0.828508f, 0.66427f); // a car
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-2 : 8e-5;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.018 : 3e-4;
double nmsThreshold = (target == DNN_TARGET_MYRIAD) ? 0.397 : 0.4;
std::string config_file = "yolo-voc.cfg";
std::string weights_file = "yolo-voc.weights";
{
SCOPED_TRACE("batch size 1");
testDarknetModel(config_file, weights_file, ref.rowRange(0, 3), scoreDiff, iouDiff);
}
{
SCOPED_TRACE("batch size 2");
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff, 0.24, nmsThreshold);
}
}
TEST_P(Test_Darknet_nets, TinyYoloVoc)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) &&
target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); // need to update check function
#endif
// batchId, classId, confidence, left, top, right, bottom
Mat ref = (Mat_<float>(4, 7) << 0, 6, 0.761967f, 0.579042f, 0.159161f, 0.894482f, 0.31994f, // a car
0, 11, 0.780595f, 0.129696f, 0.386467f, 0.445275f, 0.920994f, // a dog
1, 6, 0.651450f, 0.460526f, 0.458019f, 0.522527f, 0.5341f, // a car
1, 6, 0.928758f, 0.651024f, 0.463539f, 0.823784f, 0.654998f); // a car
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 8e-3 : 8e-5;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.018 : 3e-4;
std::string config_file = "tiny-yolo-voc.cfg";
std::string weights_file = "tiny-yolo-voc.weights";
{
SCOPED_TRACE("batch size 1");
testDarknetModel(config_file, weights_file, ref.rowRange(0, 2), scoreDiff, iouDiff);
}
{
SCOPED_TRACE("batch size 2");
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff);
}
}
#ifdef HAVE_INF_ENGINE
static const std::chrono::milliseconds async_timeout(10000);
typedef testing::TestWithParam<tuple<std::string, tuple<Backend, Target> > > Test_Darknet_nets_async;
TEST_P(Test_Darknet_nets_async, Accuracy)
{
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
if (INF_ENGINE_VER_MAJOR_LT(2019020000) && backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
std::string prefix = get<0>(GetParam());
if (targetId == DNN_TARGET_MYRIAD && prefix == "yolov4") // NC_OUT_OF_MEMORY
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
const int numInputs = 2;
std::vector<Mat> inputs(numInputs);
int blobSize[] = {1, 3, 416, 416};
for (int i = 0; i < numInputs; ++i)
{
inputs[i].create(4, &blobSize[0], CV_32F);
randu(inputs[i], 0, 1);
}
Net netSync = readNet(findDataFile("dnn/" + prefix + ".cfg"),
findDataFile("dnn/" + prefix + ".weights", false));
netSync.setPreferableBackend(backendId);
netSync.setPreferableTarget(targetId);
// Run synchronously.
std::vector<Mat> refs(numInputs);
for (int i = 0; i < numInputs; ++i)
{
netSync.setInput(inputs[i]);
refs[i] = netSync.forward().clone();
}
Net netAsync = readNet(findDataFile("dnn/" + prefix + ".cfg"),
findDataFile("dnn/" + prefix + ".weights", false));
netAsync.setPreferableBackend(backendId);
netAsync.setPreferableTarget(targetId);
// Run asynchronously. To make test more robust, process inputs in the reversed order.
for (int i = numInputs - 1; i >= 0; --i)
{
netAsync.setInput(inputs[i]);
AsyncArray out = netAsync.forwardAsync();
ASSERT_TRUE(out.valid());
Mat result;
EXPECT_TRUE(out.get(result, async_timeout));
normAssert(refs[i], result, format("Index: %d", i).c_str(), 0, 0);
}
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets_async, Combine(
Values("yolo-voc", "tiny-yolo-voc", "yolov3", "yolov4", "yolov4-tiny"),
dnnBackendsAndTargets()
));
#endif
TEST_P(Test_Darknet_nets, YOLOv3)
{
applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
// batchId, classId, confidence, left, top, right, bottom
const int N0 = 3;
const int N1 = 6;
static const float ref_[/* (N0 + N1) * 7 */] = {
0, 16, 0.998836f, 0.160024f, 0.389964f, 0.417885f, 0.943716f,
0, 1, 0.987908f, 0.150913f, 0.221933f, 0.742255f, 0.746261f,
0, 7, 0.952983f, 0.614621f, 0.150257f, 0.901368f, 0.289251f,
1, 2, 0.997412f, 0.647584f, 0.459939f, 0.821037f, 0.663947f,
1, 2, 0.989633f, 0.450719f, 0.463353f, 0.496306f, 0.522258f,
1, 0, 0.980053f, 0.195856f, 0.378454f, 0.258626f, 0.629257f,
1, 9, 0.785341f, 0.665503f, 0.373543f, 0.688893f, 0.439244f,
1, 9, 0.733275f, 0.376029f, 0.315694f, 0.401776f, 0.395165f,
1, 9, 0.384815f, 0.659824f, 0.372389f, 0.673927f, 0.429412f,
};
Mat ref(N0 + N1, 7, CV_32FC1, (void*)ref_);
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.006 : 8e-5;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.042 : 3e-4;
std::string config_file = "yolov3.cfg";
std::string weights_file = "yolov3.weights";
#if defined(INF_ENGINE_RELEASE)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
{
scoreDiff = 0.04;
iouDiff = 0.2;
}
#endif
{
SCOPED_TRACE("batch size 1");
testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff);
}
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
else if (target == DNN_TARGET_OPENCL_FP16 && INF_ENGINE_VER_MAJOR_LE(202010000))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
else if (target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
}
#endif
{
SCOPED_TRACE("batch size 2");
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff);
}
}
TEST_P(Test_Darknet_nets, YOLOv4)
{
applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE)
if (target == DNN_TARGET_MYRIAD) // NC_OUT_OF_MEMORY
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
// batchId, classId, confidence, left, top, right, bottom
const int N0 = 3;
const int N1 = 7;
static const float ref_[/* (N0 + N1) * 7 */] = {
0, 16, 0.992194f, 0.172375f, 0.402458f, 0.403918f, 0.932801f,
0, 1, 0.988326f, 0.166708f, 0.228236f, 0.737208f, 0.735803f,
0, 7, 0.94639f, 0.602523f, 0.130399f, 0.901623f, 0.298452f,
1, 2, 0.99761f, 0.646556f, 0.45985f, 0.816041f, 0.659067f,
1, 0, 0.988913f, 0.201726f, 0.360282f, 0.266181f, 0.631728f,
1, 2, 0.98233f, 0.452007f, 0.462217f, 0.495612f, 0.521687f,
1, 9, 0.919195f, 0.374642f, 0.316524f, 0.398126f, 0.393714f,
1, 9, 0.856303f, 0.666842f, 0.372215f, 0.685539f, 0.44141f,
1, 9, 0.313516f, 0.656791f, 0.374734f, 0.671959f, 0.438371f,
1, 9, 0.256625f, 0.940232f, 0.326931f, 0.967586f, 0.374002f,
};
Mat ref(N0 + N1, 7, CV_32FC1, (void*)ref_);
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.006 : 8e-5;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.042 : 3e-4;
std::string config_file = "yolov4.cfg";
std::string weights_file = "yolov4.weights";
#if defined(INF_ENGINE_RELEASE)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
{
scoreDiff = 0.04;
iouDiff = 0.2;
}
#endif
{
SCOPED_TRACE("batch size 1");
testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff);
}
{
SCOPED_TRACE("batch size 2");
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
else if (target == DNN_TARGET_OPENCL_FP16 && INF_ENGINE_VER_MAJOR_LE(202010000))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
else if (target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
}
#endif
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff);
}
}
TEST_P(Test_Darknet_nets, YOLOv4_tiny)
{
applyTestTag(
target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB
);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021010000) // nGraph compilation failure
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
const double confThreshold = 0.5;
// batchId, classId, confidence, left, top, right, bottom
const int N0 = 2;
const int N1 = 3;
static const float ref_[/* (N0 + N1) * 7 */] = {
0, 7, 0.85935f, 0.593484f, 0.141211f, 0.920356f, 0.291593f,
0, 16, 0.795188f, 0.169207f, 0.386886f, 0.423753f, 0.933004f,
1, 2, 0.996832f, 0.653802f, 0.464573f, 0.815193f, 0.653292f,
1, 2, 0.963325f, 0.451151f, 0.458915f, 0.496255f, 0.52241f,
1, 0, 0.926244f, 0.194851f, 0.361743f, 0.260277f, 0.632364f,
};
Mat ref(N0 + N1, 7, CV_32FC1, (void*)ref_);
double scoreDiff = 0.01f;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.15 : 0.01f;
std::string config_file = "yolov4-tiny.cfg";
std::string weights_file = "yolov4-tiny.weights";
#if defined(INF_ENGINE_RELEASE)
if (target == DNN_TARGET_MYRIAD) // bad accuracy
iouDiff = std::numeric_limits<double>::quiet_NaN();
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL)
iouDiff = std::numeric_limits<double>::quiet_NaN();
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_OPENCL_FP16)
iouDiff = std::numeric_limits<double>::quiet_NaN();
#endif
{
SCOPED_TRACE("batch size 1");
testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff, confThreshold);
}
{
SCOPED_TRACE("batch size 2");
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff, confThreshold);
}
#if defined(INF_ENGINE_RELEASE)
if (target == DNN_TARGET_MYRIAD) // bad accuracy
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets, dnnBackendsAndTargets());
TEST_P(Test_Darknet_layers, shortcut)
{
testDarknetLayer("shortcut");
testDarknetLayer("shortcut_leaky");
testDarknetLayer("shortcut_unequal");
testDarknetLayer("shortcut_unequal_2");
}
TEST_P(Test_Darknet_layers, upsample)
{
testDarknetLayer("upsample");
}
TEST_P(Test_Darknet_layers, mish)
{
testDarknetLayer("mish", true);
}
TEST_P(Test_Darknet_layers, avgpool_softmax)
{
testDarknetLayer("avgpool_softmax");
}
TEST_P(Test_Darknet_layers, region)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && INF_ENGINE_VER_MAJOR_GE(2020020000))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testDarknetLayer("region");
}
TEST_P(Test_Darknet_layers, reorg)
{
testDarknetLayer("reorg");
}
TEST_P(Test_Darknet_layers, route)
{
testDarknetLayer("route");
testDarknetLayer("route_multi");
}
TEST_P(Test_Darknet_layers, maxpool)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testDarknetLayer("maxpool");
}
TEST_P(Test_Darknet_layers, convolutional)
{
if (target == DNN_TARGET_MYRIAD)
{
default_l1 = 0.01f;
}
testDarknetLayer("convolutional", true);
}
TEST_P(Test_Darknet_layers, scale_channels)
{
// TODO: test fails for batches due to a bug/missing feature in ScaleLayer
testDarknetLayer("scale_channels", false, false);
}
TEST_P(Test_Darknet_layers, connected)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
testDarknetLayer("connected", true);
}
TEST_P(Test_Darknet_layers, relu)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
testDarknetLayer("relu");
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_layers, dnnBackendsAndTargets());
}} // namespace