endif()
endif()
endif()
-
-# ----------------------------------------------------------------------------
-# Torch7 importer of blobs and models, produced by Torch.nn module
-# ----------------------------------------------------------------------------
-OCV_OPTION(${the_module}_BUILD_TORCH_IMPORTER "Build Torch model importer" ON)
-if(${the_module}_BUILD_TORCH_IMPORTER)
- message(STATUS "Torch importer has been enabled. To run the tests you have to install Torch "
- "('th' executable should be available) "
- "and generate testdata using opencv_extra/testdata/dnn/generate_torch_models.py script.")
- add_definitions(-DENABLE_TORCH_IMPORTER=1)
- ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4702 /wd4127 /wd4267) #supress warnings in original torch files
-endif()
You can use both API, but factory API is less convinient for native C++ programming and basically designed for use inside importers (see @ref readNetFromCaffe(), @ref readNetFromTorch(), @ref readNetFromTensorflow()).
Bult-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers.
- In partuclar, the following layers and Caffe
@ref Importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:
+ In partuclar, the following layers and Caffe
importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:
- Convolution
- Deconvolution
- Pooling
void forward(std::vector<std::vector<Mat> >& outputBlobs,
const std::vector<String>& outBlobNames);
- //TODO:
- /** @brief Optimized forward.
- * @warning Not implemented yet.
- * @details Makes forward only those layers which weren't changed after previous forward().
- */
- void forwardOpt(LayerId toLayer);
- /** @overload */
- void forwardOpt(const std::vector<LayerId> &toLayers);
-
/**
* @brief Compile Halide layers.
* @param[in] scheduler Path to YAML file with scheduling directives.
Ptr<Impl> impl;
};
- /**
- * @deprecated Deprecated as external interface. Will be for internal needs only.
- * @brief Small interface class for loading trained serialized models of different dnn-frameworks. */
- class CV_EXPORTS_W Importer : public Algorithm
- {
- public:
-
- /** @brief Adds loaded layers into the @p net and sets connections between them. */
- CV_DEPRECATED CV_WRAP virtual void populateNet(Net net) = 0;
-
- virtual ~Importer();
- };
-
/** @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.
- * @details This is shortcut consisting from DarknetImporter and Net::populateNet calls.
+ * @returns Net object.
*/
CV_EXPORTS_W Net readNetFromDarknet(const String &cfgFile, const String &darknetModel = String());
- /**
- * @deprecated Use @ref readNetFromCaffe instead.
- * @brief Creates the importer of <a href="http://caffe.berkeleyvision.org">Caffe</a> framework network.
- * @param prototxt path to the .prototxt file with text description of the network architecture.
- * @param caffeModel path to the .caffemodel file with learned network.
- * @returns Pointer to the created importer, NULL in failure cases.
- */
- CV_DEPRECATED CV_EXPORTS_W Ptr<Importer> createCaffeImporter(const String &prototxt, const String &caffeModel = String());
-
- /** @brief Reads a network model stored in Caffe model files.
- * @details This is shortcut consisting from createCaffeImporter and Net::populateNet calls.
+ /** @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.
+ * @returns Net object.
*/
CV_EXPORTS_W Net readNetFromCaffe(const String &prototxt, const String &caffeModel = String());
* @param lenProto length of bufferProto
* @param bufferModel buffer containing the content of the .caffemodel file
* @param lenModel length of bufferModel
+ * @returns Net object.
*/
CV_EXPORTS Net readNetFromCaffe(const char *bufferProto, size_t lenProto,
const char *bufferModel = NULL, size_t lenModel = 0);
- /** @brief Reads a network model stored in Tensorflow model file.
- * @details This is shortcut consisting from createTensorflowImporter and Net::populateNet calls.
+ /** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
+ * @param model path to the .pb file with binary protobuf description of the network architecture
+ * @param config path to the .pbtxt file that contains text graph definition in protobuf format.
+ * Resulting Net object is built by text graph using weights from a binary one that
+ * let us make it more flexible.
+ * @returns Net object.
*/
CV_EXPORTS_W Net readNetFromTensorflow(const String &model, const String &config = String());
- /** @brief Reads a network model stored in Tensorflow model in memory.
+ /** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
* @details This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
* @param bufferModel buffer containing the content of the pb file
CV_EXPORTS Net readNetFromTensorflow(const char *bufferModel, size_t lenModel,
const char *bufferConfig = NULL, size_t lenConfig = 0);
- /** @brief Reads a network model stored in Torch model file.
- * @details This is shortcut consisting from createTorchImporter and Net::populateNet calls.
- */
- CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true);
-
/**
- * @deprecated Use @ref readNetFromTensorflow instead.
- * @brief Creates the importer of <a href="http://www.tensorflow.org">TensorFlow</a> framework network.
- * @param model path to the .pb file with binary protobuf description of the network architecture.
- * @returns Pointer to the created importer, NULL in failure cases.
- */
- CV_DEPRECATED CV_EXPORTS_W Ptr<Importer> createTensorflowImporter(const String &model);
-
- /**
- * @deprecated Use @ref readNetFromTorch instead.
- * @brief Creates the importer of <a href="http://torch.ch">Torch7</a> framework network.
- * @param filename path to the file, dumped from Torch by using torch.save() function.
+ * @brief Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.
+ * @param model path to the file, dumped from Torch by using torch.save() function.
* @param isBinary specifies whether the network was serialized in ascii mode or binary.
- * @returns Pointer to the created importer, NULL in failure cases.
- *
- * @warning Torch7 importer is experimental now, you need explicitly set CMake `opencv_dnn_BUILD_TORCH_IMPORTER` flag to compile its.
+ * @returns Net object.
*
* @note Ascii mode of Torch serializer is more preferable, because binary mode extensively use `long` type of C language,
* which has various bit-length on different systems.
*
* Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
*/
- CV_DEPRECATED CV_EXPORTS_W Ptr<Importer> createTorchImporter(const String &filename, bool isBinary = true);
+ CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true);
/** @brief Loads blob which was serialized as torch.Tensor object of Torch7 framework.
- * @warning This function has the same limitations as createTorchImporter().
+ * @warning This function has the same limitations as readNetFromTorch().
*/
CV_EXPORTS_W Mat readTorchBlob(const String &filename, bool isBinary = true);
/** @brief Creates 4-dimensional blob from image. Optionally resizes and crops @p image from center,
return net;
}
-PERF_TEST(GoogLeNet_caffe, CaffePerfTest)
+PERF_TEST(AlexNet_caffe, CaffePerfTest)
{
- caffe::Net<float>* net = initNet("dnn/bvlc_googlenet.prototxt",
- "dnn/bvlc_googlenet.caffemodel");
+ caffe::Net<float>* net = initNet("dnn/bvlc_alexnet.prototxt",
+ "dnn/bvlc_alexnet.caffemodel");
TEST_CYCLE() net->Forward();
SANITY_CHECK_NOTHING();
}
-PERF_TEST(AlexNet_caffe, CaffePerfTest)
+PERF_TEST(GoogLeNet_caffe, CaffePerfTest)
{
- caffe::Net<float>* net = initNet("dnn/bvlc_alexnet.prototxt",
- "dnn/bvlc_alexnet.caffemodel");
+ caffe::Net<float>* net = initNet("dnn/bvlc_googlenet.prototxt",
+ "dnn/bvlc_googlenet.caffemodel");
TEST_CYCLE() net->Forward();
SANITY_CHECK_NOTHING();
}
SANITY_CHECK_NOTHING();
}
+PERF_TEST(MobileNet_SSD, CaffePerfTest)
+{
+ caffe::Net<float>* net = initNet("dnn/MobileNetSSD_deploy.prototxt",
+ "dnn/MobileNetSSD_deploy.caffemodel");
+ TEST_CYCLE() net->Forward();
+ SANITY_CHECK_NOTHING();
+}
+
} // namespace cvtest
#endif // HAVE_CAFFE
}
else if (framework == "tensorflow")
{
- net = cv::dnn::readNetFromTensorflow(weights);
+ net = cv::dnn::readNetFromTensorflow(weights, proto);
}
else
CV_Error(Error::StsNotImplemented, "Unknown framework " + framework);
Mat(cv::Size(300, 300), CV_32FC3), "detection_out", "caffe");
}
+PERF_TEST_P_(DNNTestNetwork, OpenFace)
+{
+ processNet("dnn/openface_nn4.small2.v1.t7", "", "",
+ Mat(cv::Size(96, 96), CV_32FC3), "", "torch");
+}
+
+PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_Caffe)
+{
+ processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt", "",
+ Mat(cv::Size(300, 300), CV_32FC3), "detection_out", "caffe");
+}
+
+PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_TensorFlow)
+{
+ processNet("dnn/ssd_mobilenet_v1_coco.pb", "ssd_mobilenet_v1_coco.pbtxt", "",
+ Mat(cv::Size(300, 300), CV_32FC3), "", "tensorflow");
+}
+
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork,
testing::Combine(
::testing::Values(TEST_DNN_BACKEND),
return ss.str();
}
-class CaffeImporter : public Importer
+class CaffeImporter
{
caffe::NetParameter net;
caffe::NetParameter netBinary;
dstNet.connect(addedBlobs[idx].layerId, addedBlobs[idx].outNum, layerId, inNum);
}
-
- ~CaffeImporter()
- {
-
- }
-
};
}
-Ptr<Importer> createCaffeImporter(const String &prototxt, const String &caffeModel)
-{
- return Ptr<Importer>(new CaffeImporter(prototxt.c_str(), caffeModel.c_str()));
-}
-
Net readNetFromCaffe(const String &prototxt, const String &caffeModel /*= String()*/)
{
CaffeImporter caffeImporter(prototxt.c_str(), caffeModel.c_str());
namespace
{
-class DarknetImporter : public Importer
+class DarknetImporter
{
darknet::NetParameter net;
dstNet.connect(addedBlobs[idx].layerId, addedBlobs[idx].outNum, layerId, inNum);
}
-
- ~DarknetImporter()
- {
-
- }
-
};
}
//////////////////////////////////////////////////////////////////////////
-Importer::~Importer() {}
-
Layer::Layer() { preferableTarget = DNN_TARGET_CPU; }
Layer::Layer(const LayerParams ¶ms)
net.mutable_node()->DeleteSubrange(layer_index, 1);
}
-class TFImporter : public Importer {
+class TFImporter {
public:
TFImporter(const char *model, const char *config = NULL);
TFImporter(const char *dataModel, size_t lenModel,
const char *dataConfig = NULL, size_t lenConfig = 0);
void populateNet(Net dstNet);
- ~TFImporter() {}
private:
void kernelFromTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob);
} // namespace
-Ptr<Importer> createTensorflowImporter(const String &model)
-{
- return Ptr<Importer>(new TFImporter(model.c_str()));
-}
-
-#else //HAVE_PROTOBUF
-
-Ptr<Importer> createTensorflowImporter(const String&)
-{
- CV_Error(cv::Error::StsNotImplemented, "libprotobuf required to import data from TensorFlow models");
- return Ptr<Importer>();
-}
-
#endif //HAVE_PROTOBUF
Net readNetFromTensorflow(const String &model, const String &config)
#include "../precomp.hpp"
-#if defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
#include "THGeneral.h"
#include "THDiskFile.h"
#include "THFilePrivate.h"
}
}
-#endif
#include "../precomp.hpp"
-#if defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
#include "THFile.h"
#include "THFilePrivate.h"
IMPLEMENT_THFILE_SCALAR(Double, double)
} // namespace
-#endif
#define TH_FILE_INC
//#include "THStorage.h"
-#if defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
#include "opencv2/core/hal/interface.h"
#include "THGeneral.h"
TH_API void THFile_close(THFile *self);
TH_API void THFile_free(THFile *self);
} // namespace
-#endif //defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
#endif //TH_FILE_INC
#include "../precomp.hpp"
-#if defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
#if defined(TH_DISABLE_HEAP_TRACKING)
#elif (defined(__unix) || defined(_WIN32))
#endif
#include "THGeneral.h"
-
-#endif
#include <iostream>
#include <fstream>
-#if defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
#include "THDiskFile.h"
-#endif
namespace cv {
namespace dnn {
CV__DNN_EXPERIMENTAL_NS_BEGIN
-#if defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
using namespace TH;
//#ifdef NDEBUG
return str.rfind(substr) == str.length() - strlen(substr);
}
-struct TorchImporter : public ::cv::dnn::Importer
+struct TorchImporter
{
typedef std::map<String, std::pair<int, Mat> > TensorsMap;
Net net;
}
};
-Ptr<Importer> createTorchImporter(const String &filename, bool isBinary)
-{
- return Ptr<Importer>(new TorchImporter(filename, isBinary));
-}
-
-
Mat readTorchBlob(const String &filename, bool isBinary)
{
- Ptr<TorchImporter> importer(new TorchImporter(filename, isBinary));
- importer->readObject();
- CV_Assert(importer->tensors.size() == 1);
+ TorchImporter importer(filename, isBinary);
+ importer.readObject();
+ CV_Assert(importer.tensors.size() == 1);
- return importer->tensors.begin()->second;
+ return importer.tensors.begin()->second;
}
Net readNetFromTorch(const String &model, bool isBinary)
return net;
}
-#else
-
-Ptr<Importer> createTorchImporter(const String&, bool)
-{
- CV_Error(Error::StsNotImplemented, "Torch importer is disabled in current build");
- return Ptr<Importer>();
-}
-
-Mat readTorchBlob(const String&, bool)
-{
- CV_Error(Error::StsNotImplemented, "Torch importer is disabled in current build");
- return Mat();
-}
-
-Net readNetFromTorch(const String &model, bool isBinary)
-{
- CV_Error(Error::StsNotImplemented, "Torch importer is disabled in current build");
- return Net();
-}
-
-#endif //defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
-
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace
//
//M*/
-#ifdef ENABLE_TORCH_IMPORTER
-
#include "test_precomp.hpp"
#include "npy_blob.hpp"
#include <opencv2/dnn/shape_utils.hpp>
Net net;
{
const string model = findDataFile("dnn/Enet-model-best.net", false);
- Ptr<Importer> importer = createTorchImporter(model, true);
- ASSERT_TRUE(importer != NULL);
- importer->populateNet(net);
+ net = readNetFromTorch(model, true);
+ ASSERT_TRUE(!net.empty());
}
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
}
}
-
-#endif
projects / platform / upstream / opencv.git / commitdiff