potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-29 15:23:05 +04:00
Code Packages Releases Wiki Activity

Darknet cleanup

Browse Source
This commit is contained in:
Prasad Ayush Kumar
2026-04-30 14:43:11 +05:30
parent 0dbe5e7c35
commit 5af1571073
15 changed files with 255 additions and 2612 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/CMakeLists.txt
+1 -1
View File
@@ -504,7 +504,7 @@ set(dnn_plugin_srcs ${dnn_srcs} ${dnn_int_hdrs})
ocv_list_filterout_ex(dnn_plugin_srcs
"/src/dnn.cpp$|/src/dnn_utils.cpp$|/src/dnn_read.cpp$|/src/registry.cpp$|/src/backend.cpp$"
# importers
"/src/(caffe|darknet|onnx|tensorflow)/"
"/src/(caffe|onnx|tensorflow)/"
# executors
"/src/(cuda|cuda4dnn|ocl4dnn|vkcom|webnn)/"
)
modules/dnn/include/opencv2/dnn/dnn.hpp
+2 -30
View File
@@ -1066,31 +1066,6 @@ CV__DNN_INLINE_NS_BEGIN
ENGINE_ORT=4 //!< Try to use ONNX Runtime wrapper (ONNX only, requires build with WITH_ONNXRUNTIME=ON).
};
/** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
* @param cfgFile path to the .cfg file with text description of the network architecture.
* @param darknetModel path to the .weights file with learned network.
* @returns Network object that ready to do forward, throw an exception in failure cases.
*/
CV_EXPORTS_W Net readNetFromDarknet(CV_WRAP_FILE_PATH const String &cfgFile, CV_WRAP_FILE_PATH const String &darknetModel = String());
/** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
* @param bufferCfg A buffer contains a content of .cfg file with text description of the network architecture.
* @param bufferModel A buffer contains a content of .weights file with learned network.
* @returns Net object.
*/
CV_EXPORTS_W Net readNetFromDarknet(const std::vector<uchar>& bufferCfg,
const std::vector<uchar>& bufferModel = std::vector<uchar>());
/** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
* @param bufferCfg A buffer contains a content of .cfg file with text description of the network architecture.
* @param lenCfg Number of bytes to read from bufferCfg
* @param bufferModel A buffer contains a content of .weights file with learned network.
* @param lenModel Number of bytes to read from bufferModel
* @returns Net object.
*/
CV_EXPORTS Net readNetFromDarknet(const char *bufferCfg, size_t lenCfg,
const char *bufferModel = NULL, size_t lenModel = 0);
/** @brief Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.
* @param prototxt path to the .prototxt file with text description of the network architecture.
* @param caffeModel path to the .caffemodel file with learned network.
@@ -1204,14 +1179,12 @@ CV__DNN_INLINE_NS_BEGIN
* extensions are expected for models from different frameworks:
* * `*.caffemodel` (Caffe, http://caffe.berkeleyvision.org/)
* * `*.pb` (TensorFlow, https://www.tensorflow.org/)
* * `*.weights` (Darknet, https://pjreddie.com/darknet/)
* * `*.bin` | `*.onnx` (OpenVINO, https://software.intel.com/openvino-toolkit)
* * `*.onnx` (ONNX, https://onnx.ai/)
* @param[in] config Text file contains network configuration. It could be a
* file with the following extensions:
* * `*.prototxt` (Caffe, http://caffe.berkeleyvision.org/)
* * `*.pbtxt` (TensorFlow, https://www.tensorflow.org/)
* * `*.cfg` (Darknet, https://pjreddie.com/darknet/)
* * `*.xml` (OpenVINO, https://software.intel.com/openvino-toolkit)
* @param[in] framework Explicit framework name tag to determine a format.
* @param[in] engine select DNN engine to be used. With auto selection the new engine is used first and falls back to classic.
@@ -1220,9 +1193,8 @@ CV__DNN_INLINE_NS_BEGIN
* @returns Net object.
*
* This function automatically detects an origin framework of trained model
* and calls an appropriate function such @ref readNetFromCaffe, @ref readNetFromTensorflow
* or @ref readNetFromDarknet. An order of @p model and @p config
* arguments does not matter.
* and calls an appropriate function such @ref readNetFromCaffe, @ref readNetFromTensorflow.
* An order of @p model and @p config arguments does not matter.
*/
CV_EXPORTS_W Net readNet(CV_WRAP_FILE_PATH const String& model,
CV_WRAP_FILE_PATH const String& config = "",
modules/dnn/misc/js/gen_dict.json
+1 -1
View File
@@ -2,7 +2,7 @@
"whitelist":
{
"dnn_Net": ["setInput", "forward", "setPreferableBackend","getUnconnectedOutLayersNames"],
"": ["readNetFromCaffe", "readNetFromTensorflow", "readNetFromTorch", "readNetFromDarknet",
"": ["readNetFromCaffe", "readNetFromTensorflow", "readNetFromTorch",
"readNetFromONNX", "readNetFromTFLite", "readNet", "blobFromImage"]
},
"namespace_prefix_override":
modules/dnn/misc/objc/gen_dict.json
-2
View File
@@ -3,8 +3,6 @@
"Dnn": {
"(Net*)readNetFromCaffe:(NSString*)prototxt caffeModel:(NSString*)caffeModel engine:(int)engine" : { "readNetFromCaffe" : {"name" : "readNetFromCaffeFile"} },
"(Net*)readNetFromCaffe:(ByteVector*)bufferProto bufferModel:(ByteVector*)bufferModel engine:(int)engine" : { "readNetFromCaffe" : {"name" : "readNetFromCaffeBuffer"} },
"(Net*)readNetFromDarknet:(NSString*)cfgFile darknetModel:(NSString*)darknetModel" : { "readNetFromDarknet" : {"name" : "readNetFromDarknetFile"} },
"(Net*)readNetFromDarknet:(ByteVector*)bufferCfg bufferModel:(ByteVector*)bufferModel" : { "readNetFromDarknet" : {"name" : "readNetFromDarknetBuffer"} },
"(Net*)readNetFromONNX:(NSString*)onnxFile engine:(int)engine" : { "readNetFromONNX" : {"name" : "readNetFromONNXFile"} },
"(Net*)readNetFromONNX:(ByteVector*)buffer engine:(int)engine" : { "readNetFromONNX" : {"name" : "readNetFromONNXBuffer"} },
"(Net*)readNetFromTensorflow:(NSString*)model config:(NSString*)config engine:(int)engine extraOutputs:(NSArray<NSString*>*)extraOutputs" : { "readNetFromTensorflow" : {"name" : "readNetFromTensorflowFile"} },
modules/dnn/perf/perf_net.cpp
+5 -3
View File
@@ -212,8 +212,9 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv3)
#endif
Mat sample = imread(findDataFile("dnn/dog416.png"));
cv::resize(sample, sample, Size(640, 640));
Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(), Scalar(), true);
processNet("dnn/yolov3.weights", "dnn/yolov3.cfg", inp);
processNet("dnn/yolov3.onnx", "", inp);
}
PERF_TEST_P_(DNNTestNetwork, YOLOv4)
@@ -231,8 +232,9 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv4)
throw SkipTestException("Test is disabled in OpenVINO 2020.4");
#endif
Mat sample = imread(findDataFile("dnn/dog416.png"));
cv::resize(sample, sample, Size(608, 608));
Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(), Scalar(), true);
processNet("dnn/yolov4.weights", "dnn/yolov4.cfg", inp);
processNet("dnn/yolov4.onnx", "", inp);
}
PERF_TEST_P_(DNNTestNetwork, YOLOv4_tiny)
@@ -243,7 +245,7 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv4_tiny)
#endif
Mat sample = imread(findDataFile("dnn/dog416.png"));
Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(), Scalar(), true);
processNet("dnn/yolov4-tiny-2020-12.weights", "dnn/yolov4-tiny-2020-12.cfg", inp);
processNet("dnn/yolov4-tiny.onnx", "", inp);
}
PERF_TEST_P_(DNNTestNetwork, YOLOv5) {
modules/dnn/src/darknet/darknet_importer.cpp
-258
View File
@@ -1,258 +0,0 @@
/*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 "../precomp.hpp"
#include <iostream>
#include <fstream>
#include <algorithm>
#include <vector>
#include <map>
#include "darknet_io.hpp"
#include <opencv2/core/utils/fp_control_utils.hpp>
namespace cv {
namespace dnn {
CV__DNN_INLINE_NS_BEGIN
namespace
{
class DarknetImporter
{
FPDenormalsIgnoreHintScope fp_denormals_ignore_scope;
darknet::NetParameter net;
public:
DarknetImporter() {}
DarknetImporter(std::istream &cfgStream, std::istream &darknetModelStream)
{
CV_TRACE_FUNCTION();
ReadNetParamsFromCfgStreamOrDie(cfgStream, &net);
ReadNetParamsFromBinaryStreamOrDie(darknetModelStream, &net);
}
DarknetImporter(std::istream &cfgStream)
{
CV_TRACE_FUNCTION();
ReadNetParamsFromCfgStreamOrDie(cfgStream, &net);
}
struct BlobNote
{
BlobNote(const std::string &_name, int _layerId, int _outNum) :
name(_name), layerId(_layerId), outNum(_outNum) {}
std::string name;
int layerId, outNum;
};
std::vector<BlobNote> addedBlobs;
std::map<String, int> layerCounter;
void populateNet(Net dstNet)
{
CV_TRACE_FUNCTION();
int layersSize = net.layer_size();
layerCounter.clear();
addedBlobs.clear();
addedBlobs.reserve(layersSize + 1);
//setup input layer names
{
std::vector<String> netInputs(net.input_size());
for (int inNum = 0; inNum < net.input_size(); inNum++)
{
addedBlobs.push_back(BlobNote(net.input(inNum), 0, inNum));
netInputs[inNum] = net.input(inNum);
}
dstNet.setInputsNames(netInputs);
}
for (int li = 0; li < layersSize; li++)
{
const darknet::LayerParameter &layer = net.layer(li);
String name = layer.name();
String type = layer.type();
LayerParams layerParams = layer.getLayerParams();
int repetitions = layerCounter[name]++;
if (repetitions)
name += cv::format("_%d", repetitions);
int id = dstNet.addLayer(name, type, layerParams);
// iterate many bottoms layers (for example for: route -1, -4)
for (int inNum = 0; inNum < layer.bottom_size(); inNum++)
addInput(layer.bottom(inNum), id, inNum, dstNet, layer.name());
for (int outNum = 0; outNum < layer.top_size(); outNum++)
addOutput(layer, id, outNum);
}
addedBlobs.clear();
}
void addOutput(const darknet::LayerParameter &layer, int layerId, int outNum)
{
const std::string &name = layer.top(outNum);
bool haveDups = false;
for (int idx = (int)addedBlobs.size() - 1; idx >= 0; idx--)
{
if (addedBlobs[idx].name == name)
{
haveDups = true;
break;
}
}
if (haveDups)
{
bool isInplace = layer.bottom_size() > outNum && layer.bottom(outNum) == name;
if (!isInplace)
CV_Error(Error::StsBadArg, "Duplicate blobs produced by multiple sources");
}
addedBlobs.push_back(BlobNote(name, layerId, outNum));
}
void addInput(const std::string &name, int layerId, int inNum, Net &dstNet, std::string nn)
{
int idx;
for (idx = (int)addedBlobs.size() - 1; idx >= 0; idx--)
{
if (addedBlobs[idx].name == name)
break;
}
if (idx < 0)
{
CV_Error(Error::StsObjectNotFound, "Can't find output blob \"" + name + "\"");
return;
}
dstNet.connect(addedBlobs[idx].layerId, addedBlobs[idx].outNum, layerId, inNum);
}
};
static Net readNetFromDarknet(std::istream &cfgFile, std::istream &darknetModel)
{
Net net;
DarknetImporter darknetImporter(cfgFile, darknetModel);
darknetImporter.populateNet(net);
return net;
}
static Net readNetFromDarknet(std::istream &cfgFile)
{
Net net;
DarknetImporter darknetImporter(cfgFile);
darknetImporter.populateNet(net);
return net;
}
}
Net readNetFromDarknet(const String &cfgFile, const String &darknetModel /*= String()*/)
{
std::ifstream cfgStream(cfgFile.c_str());
if (!cfgStream.is_open())
{
CV_Error(cv::Error::StsParseError, "Failed to open NetParameter file: " + std::string(cfgFile));
}
if (darknetModel != String())
{
std::ifstream darknetModelStream(darknetModel.c_str(), std::ios::binary);
if (!darknetModelStream.is_open())
{
CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter file: " + std::string(darknetModel));
}
return readNetFromDarknet(cfgStream, darknetModelStream);
}
else
return readNetFromDarknet(cfgStream);
}
struct BufferStream : public std::streambuf
{
BufferStream(const char* s, std::size_t n)
{
char* ptr = const_cast<char*>(s);
setg(ptr, ptr, ptr + n);
}
};
Net readNetFromDarknet(const char *bufferCfg, size_t lenCfg, const char *bufferModel, size_t lenModel)
{
BufferStream cfgBufferStream(bufferCfg, lenCfg);
std::istream cfgStream(&cfgBufferStream);
if (lenModel)
{
BufferStream weightsBufferStream(bufferModel, lenModel);
std::istream weightsStream(&weightsBufferStream);
return readNetFromDarknet(cfgStream, weightsStream);
}
else
return readNetFromDarknet(cfgStream);
}
Net readNetFromDarknet(const std::vector<uchar>& bufferCfg, const std::vector<uchar>& bufferModel)
{
const char* bufferCfgPtr = reinterpret_cast<const char*>(&bufferCfg[0]);
const char* bufferModelPtr = bufferModel.empty() ? NULL :
reinterpret_cast<const char*>(&bufferModel[0]);
return readNetFromDarknet(bufferCfgPtr, bufferCfg.size(),
bufferModelPtr, bufferModel.size());
}
CV__DNN_INLINE_NS_END
}} // namespace
modules/dnn/src/darknet/darknet_io.cpp
-1107
View File
File diff suppressed because it is too large Load Diff
modules/dnn/src/darknet/darknet_io.hpp
-117
View File
@@ -1,117 +0,0 @@
/*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*/
/*M///////////////////////////////////////////////////////////////////////////////////////
//MIT License
//
//Copyright (c) 2017 Joseph Redmon
//
//Permission is hereby granted, free of charge, to any person obtaining a copy
//of this software and associated documentation files (the "Software"), to deal
//in the Software without restriction, including without limitation the rights
//to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
//copies of the Software, and to permit persons to whom the Software is
//furnished to do so, subject to the following conditions:
//
//The above copyright notice and this permission notice shall be included in all
//copies or substantial portions of the Software.
//
//THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
//IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
//FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
//AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
//LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
//OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
//SOFTWARE.
//
//M*/
#ifndef __OPENCV_DNN_DARKNET_IO_HPP__
#define __OPENCV_DNN_DARKNET_IO_HPP__
#include <opencv2/dnn/dnn.hpp>
namespace cv {
namespace dnn {
namespace darknet {
class LayerParameter {
std::string layer_name, layer_type;
std::vector<std::string> bottom_indexes;
cv::dnn::LayerParams layerParams;
public:
friend class setLayersParams;
cv::dnn::LayerParams getLayerParams() const { return layerParams; }
std::string name() const { return layer_name; }
std::string type() const { return layer_type; }
int bottom_size() const { return bottom_indexes.size(); }
std::string bottom(const int index) const { return bottom_indexes.at(index); }
int top_size() const { return 1; }
std::string top(const int index) const { return layer_name; }
};
class NetParameter {
public:
int width, height, channels;
std::vector<LayerParameter> layers;
std::vector<int> out_channels_vec;
std::map<int, std::map<std::string, std::string> > layers_cfg;
std::map<std::string, std::string> net_cfg;
NetParameter() : width(0), height(0), channels(0) {}
int layer_size() const { return layers.size(); }
int input_size() const { return 1; }
std::string input(const int index) const { return "data"; }
LayerParameter layer(const int index) const { return layers.at(index); }
};
}
// Read parameters from a stream into a NetParameter message.
void ReadNetParamsFromCfgStreamOrDie(std::istream &ifile, darknet::NetParameter *net);
void ReadNetParamsFromBinaryStreamOrDie(std::istream &ifile, darknet::NetParameter *net);
}
}
#endif
modules/dnn/src/dnn_read.cpp
+2 -2
View File
@@ -37,7 +37,7 @@ Net readNet(const String& _model, const String& _config, const String& _framewor
{
if (modelExt == "cfg" || configExt == "weights")
std::swap(model, config);
return readNetFromDarknet(config, model);
CV_Error(Error::StsError, "Darknet importer has been removed. Please use readNet(\"darknet\", ...) with ONNX-converted models or use an older OpenCV version.");
}
if (framework == "dldt" || framework == "openvino" ||
modelExt == "bin" || configExt == "bin" ||
@@ -65,7 +65,7 @@ Net readNet(const String& _framework, const std::vector<uchar>& bufferModel,
else if (framework == "tensorflow")
return readNetFromTensorflow(bufferModel, bufferConfig, engine);
else if (framework == "darknet")
return readNetFromDarknet(bufferConfig, bufferModel);
CV_Error(Error::StsError, "Darknet importer has been removed. Please use ONNX-converted models or use an older OpenCV version.");
else if (framework == "dldt" || framework == "openvino")
return readNetFromModelOptimizer(bufferConfig, bufferModel);
else if (framework == "tflite")
modules/dnn/test/test_common.hpp
+18
View File
@@ -6,6 +6,7 @@
#define __OPENCV_TEST_COMMON_HPP__
#include "opencv2/dnn/utils/inference_engine.hpp"
#include <string>
#ifdef HAVE_OPENCL
#include "opencv2/core/ocl.hpp"
@@ -247,6 +248,23 @@ protected:
void runLayer(cv::Ptr<cv::dnn::Layer> layer, std::vector<cv::Mat> &inpBlobs, std::vector<cv::Mat> &outBlobs);
inline std::string getCurrentTestNameNoParams()
{
const ::testing::TestInfo* const test_info =
::testing::UnitTest::GetInstance()->current_test_info();
if (!test_info)
return std::string();
std::string suite = test_info->test_case_name();
std::string name = test_info->name();
const auto suite_slash = suite.find('/');
if (suite_slash != std::string::npos)
suite = suite.substr(0, suite_slash);
const auto name_slash = name.find('/');
if (name_slash != std::string::npos)
name = name.substr(0, name_slash);
return suite + "." + name;
}
} // namespace
#endif
modules/dnn/test/test_darknet_importer.cpp
+211 -867
View File
File diff suppressed because it is too large Load Diff
modules/dnn/test/test_int8_layers.cpp
+14 -97
View File
@@ -648,9 +648,9 @@ public:
normAssert(ref, out, "", l1, lInf);
}
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, bool perChannel = true)
void testYOLOModel(const std::string& model,
const cv::Mat& ref, double scoreDiff, double iouDiff,
float confThreshold = 0.24, float nmsThreshold = 0.4, bool perChannel = true)
{
CV_Assert(ref.cols == 7);
std::vector<std::vector<int> > refClassIds;
@@ -689,7 +689,7 @@ public:
Mat inp = blobFromImages(samples, 1.0/255, Size(416, 416), Scalar(), true, false);
Net baseNet = readNetFromDarknet(findDataFile("dnn/" + cfg), findDataFile("dnn/" + weights, false));
Net baseNet = readNet(findDataFile("dnn/" + model, false));
Net qnet = baseNet.quantize(inp, CV_32F, CV_32F, perChannel);
qnet.setPreferableBackend(backend);
qnet.setPreferableTarget(target);
@@ -1225,86 +1225,6 @@ TEST_P(Test_Int8_nets, RFCN)
testFaster(net, ref, confThreshold, scoreDiff, iouDiff);
}
TEST_P(Test_Int8_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,
CV_TEST_TAG_DEBUG_VERYLONG
);
if (target == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
Mat ref = (Mat_<float>(6, 7) << 0, 6, 0.750469f, 0.577374f, 0.127391f, 0.902949f, 0.300809f,
0, 1, 0.780879f, 0.270762f, 0.264102f, 0.732475f, 0.745412f,
0, 11, 0.901615f, 0.1386f, 0.338509f, 0.421337f, 0.938789f,
1, 14, 0.623813f, 0.183179f, 0.381921f, 0.247726f, 0.625847f,
1, 6, 0.667770f, 0.446555f, 0.453578f, 0.499986f, 0.519167f,
1, 6, 0.844947f, 0.637058f, 0.460398f, 0.828508f, 0.66427f);
std::string config_file = "yolo-voc.cfg";
std::string weights_file = "yolo-voc.weights";
double scoreDiff = 0.12, iouDiff = 0.3;
{
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);
}
}
TEST_P(Test_Int8_nets, TinyYoloVoc)
{
applyTestTag(
CV_TEST_TAG_MEMORY_512MB,
CV_TEST_TAG_DEBUG_VERYLONG
);
if (target == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
Mat ref = (Mat_<float>(4, 7) << 0, 6, 0.761967f, 0.579042f, 0.159161f, 0.894482f, 0.31994f,
0, 11, 0.780595f, 0.129696f, 0.386467f, 0.445275f, 0.920994f,
1, 6, 0.651450f, 0.460526f, 0.458019f, 0.522527f, 0.5341f,
1, 6, 0.928758f, 0.651024f, 0.463539f, 0.823784f, 0.654998f);
std::string config_file = "tiny-yolo-voc.cfg";
std::string weights_file = "tiny-yolo-voc.weights";
double scoreDiff = 0.043, iouDiff = 0.12;
{
SCOPED_TRACE("batch size 1");
testDarknetModel(config_file, weights_file, ref.rowRange(0, 2), scoreDiff, iouDiff);
{
SCOPED_TRACE("Per-tensor quantize");
testDarknetModel(config_file, weights_file, ref.rowRange(0, 2), 0.1, 0.2, 0.24, 0.6, false);
}
}
{
SCOPED_TRACE("batch size 2");
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff);
{
SCOPED_TRACE("Per-tensor quantize");
testDarknetModel(config_file, weights_file, ref, 0.1, 0.2, 0.24, 0.6, false);
}
}
}
TEST_P(Test_Int8_nets, YOLOv3)
{
applyTestTag(
@@ -1334,18 +1254,17 @@ TEST_P(Test_Int8_nets, YOLOv3)
};
Mat ref(N0 + N1, 7, CV_32FC1, (void*)ref_);
std::string config_file = "yolov3.cfg";
std::string weights_file = "yolov3.weights";
std::string model_file = "yolov3.onnx";
double scoreDiff = 0.08, iouDiff = 0.21, confThreshold = 0.25;
{
SCOPED_TRACE("batch size 1");
testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff, confThreshold);
testYOLOModel(model_file, ref.rowRange(0, N0), scoreDiff, iouDiff, confThreshold);
}
{
SCOPED_TRACE("batch size 2");
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff, confThreshold);
testYOLOModel(model_file, ref, scoreDiff, iouDiff, confThreshold);
}
}
@@ -1379,18 +1298,17 @@ TEST_P(Test_Int8_nets, YOLOv4)
};
Mat ref(N0 + N1, 7, CV_32FC1, (void*)ref_);
std::string config_file = "yolov4.cfg";
std::string weights_file = "yolov4.weights";
std::string model_file = "yolov4.onnx";
double scoreDiff = 0.15, iouDiff = 0.2;
{
SCOPED_TRACE("batch size 1");
testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff);
testYOLOModel(model_file, ref.rowRange(0, N0), scoreDiff, iouDiff);
}
{
SCOPED_TRACE("batch size 2");
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff);
testYOLOModel(model_file, ref, scoreDiff, iouDiff);
}
}
@@ -1421,18 +1339,17 @@ TEST_P(Test_Int8_nets, YOLOv4_tiny)
};
Mat ref(N0 + N1, 7, CV_32FC1, (void*)ref_);
std::string config_file = "yolov4-tiny-2020-12.cfg";
std::string weights_file = "yolov4-tiny-2020-12.weights";
std::string model_file = "yolov4-tiny.onnx";
double scoreDiff = 0.12;
double iouDiff = target == DNN_TARGET_OPENCL_FP16 ? 0.2 : 0.118;
{
SCOPED_TRACE("batch size 1");
testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff, confThreshold);
testYOLOModel(model_file, ref.rowRange(0, N0), scoreDiff, iouDiff, confThreshold);
{
SCOPED_TRACE("Per-tensor quantize");
testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, 0.224, 0.7, 0.4, false);
testYOLOModel(model_file, ref.rowRange(0, N0), scoreDiff, 0.224, 0.7, 0.4, false);
}
}
@@ -1440,7 +1357,7 @@ TEST_P(Test_Int8_nets, YOLOv4_tiny)
/* bad accuracy on second image
{
SCOPED_TRACE("batch size 2");
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff, confThreshold);
testYOLOModel(model_file, ref, scoreDiff, iouDiff, confThreshold);
}
*/
}
modules/dnn/test/test_misc.cpp
-3
View File
@@ -247,9 +247,6 @@ TEST(readNet, Regression)
Net net = readNet(findDataFile("dnn/squeezenet_v1.1.prototxt"),
findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
EXPECT_FALSE(net.empty());
net = readNet(findDataFile("dnn/tiny-yolo-voc.cfg"),
findDataFile("dnn/tiny-yolo-voc.weights", false));
EXPECT_FALSE(net.empty());
net = readNet(findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt"),
findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false));
EXPECT_FALSE(net.empty());
modules/dnn/test/test_model.cpp
-123
View File
@@ -289,129 +289,6 @@ TEST_P(Test_Model, Classify)
}
TEST_P(Test_Model, DetectRegion)
{
applyTestTag(
CV_TEST_TAG_MEMORY_2GB,
CV_TEST_TAG_LONG,
CV_TEST_TAG_DEBUG_VERYLONG
);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// accuracy
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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// accuracy
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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif 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);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
// FIXIT DNN_BACKEND_INFERENCE_ENGINE is misused
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
#endif
#if defined(INF_ENGINE_RELEASE)
if (target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
#endif
std::vector<int> refClassIds = {6, 1, 11};
std::vector<float> refConfidences = {0.750469f, 0.780879f, 0.901615f};
std::vector<Rect2d> refBoxes = {Rect2d(240, 53, 135, 72),
Rect2d(112, 109, 192, 200),
Rect2d(58, 141, 117, 249)};
std::string img_path = _tf("dog416.png");
std::string weights_file = _tf("yolo-voc.weights", false);
std::string config_file = _tf("yolo-voc.cfg");
double scale = 1.0 / 255.0;
Size size{416, 416};
bool swapRB = true;
double confThreshold = 0.24;
double nmsThreshold = (target == DNN_TARGET_MYRIAD) ? 0.397 : 0.4;
double scoreDiff = 8e-5, iouDiff = 1e-5;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_CPU_FP16)
{
scoreDiff = 1e-2;
iouDiff = 1.6e-2;
}
testDetectModel(weights_file, config_file, img_path, refClassIds, refConfidences,
refBoxes, scoreDiff, iouDiff, confThreshold, nmsThreshold, size,
Scalar(), scale, swapRB);
}
TEST_P(Test_Model, DetectRegionWithNmsAcrossClasses)
{
applyTestTag(
CV_TEST_TAG_MEMORY_2GB,
CV_TEST_TAG_LONG,
CV_TEST_TAG_DEBUG_VERYLONG
);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// accuracy
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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// accuracy
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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif 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);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
#endif
#if defined(INF_ENGINE_RELEASE)
if (target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
#endif
std::vector<int> refClassIds = { 6, 11 };
std::vector<float> refConfidences = { 0.750469f, 0.901615f };
std::vector<Rect2d> refBoxes = { Rect2d(240, 53, 135, 72),
Rect2d(58, 141, 117, 249) };
std::string img_path = _tf("dog416.png");
std::string weights_file = _tf("yolo-voc.weights", false);
std::string config_file = _tf("yolo-voc.cfg");
double scale = 1.0 / 255.0;
Size size{ 416, 416 };
bool swapRB = true;
bool crop = false;
bool nmsAcrossClasses = true;
double confThreshold = 0.24;
double nmsThreshold = (target == DNN_TARGET_MYRIAD) ? 0.15: 0.15;
double scoreDiff = 8e-5, iouDiff = 1e-5;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_CPU_FP16)
{
scoreDiff = 1e-2;
iouDiff = 1.6e-2;
}
testDetectModel(weights_file, config_file, img_path, refClassIds, refConfidences,
refBoxes, scoreDiff, iouDiff, confThreshold, nmsThreshold, size,
Scalar(), scale, swapRB, crop,
nmsAcrossClasses);
}
TEST_P(Test_Model, DetectionOutput)
{
applyTestTag(CV_TEST_TAG_DEBUG_VERYLONG);
platforms/js/opencv_js.config.py
+1 -1
View File
@@ -172,7 +172,7 @@ video = {
}
dnn = {'dnn_Net': ['setInput', 'forward', 'setPreferableBackend','getUnconnectedOutLayersNames'],
'': ['readNetFromCaffe', 'readNetFromTensorflow', 'readNetFromDarknet',
'': ['readNetFromCaffe', 'readNetFromTensorflow',
'readNetFromONNX', 'readNetFromTFLite', 'readNet', 'blobFromImage']}
features = {'Feature2D': ['detect', 'compute', 'detectAndCompute', 'descriptorSize', 'descriptorType', 'defaultNorm', 'empty', 'getDefaultName'],