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Merge pull request #28678 from omrope79:caffe-importer-cleanup
Caffe importer cleanup #28678 Merge with: https://github.com/opencv/opencv_extra/pull/1324 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable - [x] The feature is well documented and sample code can be built with the project CMake
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@@ -55,7 +55,7 @@ CV__DNN_INLINE_NS_BEGIN
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Classes listed here, in fact, provides C++ API for creating instances of built-in layers.
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In addition to this way of layers instantiation, there is a more common factory API (see @ref dnnLayerFactory), it allows to create layers dynamically (by name) and register new ones.
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You can use both API, but factory API is less convenient for native C++ programming and basically designed for use inside importers (see @ref readNetFromCaffe(), @ref readNetFromTensorflow()).
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You can use both API, but factory API is less convenient for native C++ programming and basically designed for use inside importers (see @ref readNetFromTensorflow()).
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Built-in layers partially reproduce functionality of corresponding ONNX, TensorFlow and Caffe layers.
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In particular, the following layers and Caffe importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:
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@@ -1090,43 +1090,6 @@ CV__DNN_INLINE_NS_BEGIN
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ENGINE_ORT=4 //!< Try to use ONNX Runtime wrapper (ONNX only, requires build with WITH_ONNXRUNTIME=ON).
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};
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/** @brief Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.
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* @param prototxt path to the .prototxt file with text description of the network architecture.
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* @param caffeModel path to the .caffemodel file with learned network.
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* @param engine select DNN engine to be used. With auto selection the new engine is used.
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* Please pay attention that the new DNN does not support non-CPU back-ends for now.
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* @returns Net object.
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*/
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CV_EXPORTS_W Net readNetFromCaffe(CV_WRAP_FILE_PATH const String &prototxt,
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CV_WRAP_FILE_PATH const String &caffeModel = String(),
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int engine = ENGINE_AUTO);
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/** @brief Reads a network model stored in Caffe model in memory.
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* @param bufferProto buffer containing the content of the .prototxt file
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* @param bufferModel buffer containing the content of the .caffemodel file
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* @param engine select DNN engine to be used. With auto selection the new engine is used.
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* Please pay attention that the new DNN does not support non-CPU back-ends for now.
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* @returns Net object.
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*/
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CV_EXPORTS_W Net readNetFromCaffe(const std::vector<uchar>& bufferProto,
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const std::vector<uchar>& bufferModel = std::vector<uchar>(),
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int engine = ENGINE_AUTO);
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/** @brief Reads a network model stored in Caffe model in memory.
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* @details This is an overloaded member function, provided for convenience.
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* It differs from the above function only in what argument(s) it accepts.
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* @param bufferProto buffer containing the content of the .prototxt file
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* @param lenProto length of bufferProto
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* @param bufferModel buffer containing the content of the .caffemodel file
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* @param lenModel length of bufferModel
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* @param engine select DNN engine to be used. With auto selection the new engine is used.
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* Please pay attention that the new DNN does not support non-CPU back-ends for now.
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* @returns Net object.
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*/
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CV_EXPORTS Net readNetFromCaffe(const char *bufferProto, size_t lenProto,
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const char *bufferModel = NULL, size_t lenModel = 0,
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int engine = ENGINE_AUTO);
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/** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
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* @param model path to the .pb file with binary protobuf description of the network architecture
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* @param config path to the .pbtxt file that contains text graph definition in protobuf format.
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@@ -1201,13 +1164,11 @@ CV__DNN_INLINE_NS_BEGIN
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* @brief Read deep learning network represented in one of the supported formats.
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* @param[in] model Binary file contains trained weights. The following file
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* extensions are expected for models from different frameworks:
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* * `*.caffemodel` (Caffe, http://caffe.berkeleyvision.org/)
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* * `*.pb` (TensorFlow, https://www.tensorflow.org/)
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* * `*.bin` | `*.onnx` (OpenVINO, https://software.intel.com/openvino-toolkit)
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* * `*.onnx` (ONNX, https://onnx.ai/)
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* @param[in] config Text file contains network configuration. It could be a
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* file with the following extensions:
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* * `*.prototxt` (Caffe, http://caffe.berkeleyvision.org/)
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* * `*.pbtxt` (TensorFlow, https://www.tensorflow.org/)
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* * `*.xml` (OpenVINO, https://software.intel.com/openvino-toolkit)
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* @param[in] framework Explicit framework name tag to determine a format.
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@@ -1217,7 +1178,7 @@ CV__DNN_INLINE_NS_BEGIN
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* @returns Net object.
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*
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* This function automatically detects an origin framework of trained model
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* and calls an appropriate function such @ref readNetFromCaffe, @ref readNetFromTensorflow.
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* and calls an appropriate function such @ref readNetFromTensorflow, @ref readNetFromONNX.
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* An order of @p model and @p config arguments does not matter.
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*/
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CV_EXPORTS_W Net readNet(CV_WRAP_FILE_PATH const String& model,
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@@ -1467,22 +1428,6 @@ CV__DNN_INLINE_NS_BEGIN
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*/
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CV_EXPORTS_W void imagesFromBlob(const cv::Mat& blob_, OutputArrayOfArrays images_);
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/** @brief Convert all weights of Caffe network to half precision floating point.
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* @param src Path to origin model from Caffe framework contains single
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* precision floating point weights (usually has `.caffemodel` extension).
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* @param dst Path to destination model with updated weights.
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* @param layersTypes Set of layers types which parameters will be converted.
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* By default, converts only Convolutional and Fully-Connected layers'
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* weights.
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*
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* @note Shrinked model has no origin float32 weights so it can't be used
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* in origin Caffe framework anymore. However the structure of data
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* is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe.
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* So the resulting model may be used there.
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*/
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CV_EXPORTS_W void shrinkCaffeModel(CV_WRAP_FILE_PATH const String& src, CV_WRAP_FILE_PATH const String& dst,
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const std::vector<String>& layersTypes = std::vector<String>());
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/** @brief Create a text representation for a binary network stored in protocol buffer format.
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* @param[in] model A path to binary network.
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* @param[in] output A path to output text file to be created.
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