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path: root/libavfilter/dnn_interface.h
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* avfilter/dnn: Remove a level of dereferenceZhao Zhili2024-05-30
| | | | | | | | | For code such as 'model->model = ov_model' is confusing. We can just drop the member variable and use cast to get the subclass. Signed-off-by: Zhao Zhili <zhilizhao@tencent.com> Reviewed-by: Wenbin Chen <wenbin.chen@intel.com> Reviewed-by: Guo Yejun <yejun.guo@intel.com>
* avfilter/dnn: Don't show backends which are not supported by a filterZhao Zhili2024-05-30
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* avfilter/dnn: Use dnn_backend_info_list to search for dnn moduleZhao Zhili2024-05-30
| | | | | | Signed-off-by: Zhao Zhili <zhilizhao@tencent.com> Reviewed-by: Wenbin Chen <wenbin.chen@intel.com> Reviewed-by: Guo Yejun <yejun.guo@intel.com>
* avfilter/dnn: Refactor DNN parameter configuration systemZhao Zhili2024-05-18
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This patch trying to resolve mulitiple issues related to parameter configuration: Firstly, each DNN filters duplicate DNN_COMMON_OPTIONS, which should be the common options of backend. Secondly, backend options are hidden behind the scene. It's a AV_OPT_TYPE_STRING backend_configs for user, and parsed by each backend. We don't know each backend support what kind of options from the help message. Third, DNN backends duplicate DNN_BACKEND_COMMON_OPTIONS. Last but not the least, pass backend options via AV_OPT_TYPE_STRING makes it hard to pass AV_OPT_TYPE_BINARY to backend, if not impossible. This patch puts backend common options and each backend options inside DnnContext to reduce code duplication, make options user friendly, and easy to extend for future usecase. For example, ./ffmpeg -h filter=dnn_processing dnn_processing AVOptions: dnn_backend <int> ..FV....... DNN backend (from INT_MIN to INT_MAX) (default tensorflow) tensorflow 1 ..FV....... tensorflow backend flag openvino 2 ..FV....... openvino backend flag torch 3 ..FV....... torch backend flag dnn_base AVOptions: model <string> ..F........ path to model file input <string> ..F........ input name of the model output <string> ..F........ output name of the model backend_configs <string> ..F.......P backend configs (deprecated) options <string> ..F.......P backend configs (deprecated) nireq <int> ..F........ number of request (from 0 to INT_MAX) (default 0) async <boolean> ..F........ use DNN async inference (default true) device <string> ..F........ device to run model dnn_tensorflow AVOptions: sess_config <string> ..F........ config for SessionOptions dnn_openvino AVOptions: batch_size <int> ..F........ batch size per request (from 1 to 1000) (default 1) input_resizable <boolean> ..F........ can input be resizable or not (default false) layout <int> ..F........ input layout of model (from 0 to 2) (default none) none 0 ..F........ none nchw 1 ..F........ nchw nhwc 2 ..F........ nhwc scale <float> ..F........ Add scale preprocess operation. Divide each element of input by specified value. (from INT_MIN to INT_MAX) (default 0) mean <float> ..F........ Add mean preprocess operation. Subtract specified value from each element of input. (from INT_MIN to INT_MAX) (default 0) dnn_th AVOptions: optimize <int> ..F........ turn on graph executor optimization (from 0 to 1) (default 0) Signed-off-by: Zhao Zhili <zhilizhao@tencent.com> Reviewed-by: Wenbin Chen <wenbin.chen@intel.com> Reviewed-by: Guo Yejun <yejun.guo@intel.com>
* libavfi/dnn: add LibTorch as one of DNN backendWenbin Chen2024-03-19
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | PyTorch is an open source machine learning framework that accelerates the path from research prototyping to production deployment. Official website: https://pytorch.org/. We call the C++ library of PyTorch as LibTorch, the same below. To build FFmpeg with LibTorch, please take following steps as reference: 1. download LibTorch C++ library in https://pytorch.org/get-started/locally/, please select C++/Java for language, and other options as your need. Please download cxx11 ABI version: (libtorch-cxx11-abi-shared-with-deps-*.zip). 2. unzip the file to your own dir, with command unzip libtorch-shared-with-deps-latest.zip -d your_dir 3. export libtorch_root/libtorch/include and libtorch_root/libtorch/include/torch/csrc/api/include to $PATH export libtorch_root/libtorch/lib/ to $LD_LIBRARY_PATH 4. config FFmpeg with ../configure --enable-libtorch \ --extra-cflag=-I/libtorch_root/libtorch/include \ --extra-cflag=-I/libtorch_root/libtorch/include/torch/csrc/api/include \ --extra-ldflags=-L/libtorch_root/libtorch/lib/ 5. make To run FFmpeg DNN inference with LibTorch backend: ./ffmpeg -i input.jpg -vf \ dnn_processing=dnn_backend=torch:model=LibTorch_model.pt -y output.jpg The LibTorch_model.pt can be generated by Python with torch.jit.script() api. https://pytorch.org/tutorials/advanced/cpp_export.html. This is pytorch official guide about how to convert and load torchscript model. Please note, torch.jit.trace() is not recommanded, since it does not support ambiguous input size. Signed-off-by: Ting Fu <ting.fu@intel.com> Signed-off-by: Wenbin Chen <wenbin.chen@intel.com> Reviewed-by: Guo Yejun <yejun.guo@intel.com>
* libavfilter/dnn_interface: use dims to represent shapesWenbin Chen2024-01-28
| | | | | | | | | For detect and classify output, width and height make no sence, so change width, height to dims to represent the shape of tensor. Use layout and dims to get width, height and channel. Signed-off-by: Wenbin Chen <wenbin.chen@intel.com> Reviewed-by: Guo Yejun <yejun.guo@intel.com>
* libavfilter/dnn: Add scale and mean preprocess to openvino backendWenbin Chen2023-09-27
| | | | | | | Dnn models has different data preprocess requirements. Scale and mean parameters are added to preprocess input data. Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
* libavfilter/dnn: add layout option to openvino backendWenbin Chen2023-09-27
| | | | | | | | | | Dnn models have different input layout (NCHW or NHWC), so a "layout" option is added Use openvino's API to do layout conversion for input data. Use swscale to do layout conversion for output data as openvino doesn't have similiar C API for output. Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
* avfilter/dnn: add log context to ff_get_dnn_moduleZhao Zhili2023-06-08
| | | | | | Print backend type when failed. Signed-off-by: Zhao Zhili <zhilizhao@tencent.com>
* avfilter/dnn: refactor ff_get_dnn_module to remove allocationZhao Zhili2023-06-08
| | | | Signed-off-by: Zhao Zhili <zhilizhao@tencent.com>
* lavfi/dnn: Remove DNN native backendTing Fu2023-04-28
| | | | | | | | | | According to discussion in https://etherpad.mit.edu/p/FF_dev_meeting_20221202 and the proposal in http://ffmpeg.org/pipermail/ffmpeg-devel/2022-December/304534.html, the DNN native backend should be removed at first step. All the DNN native backend related codes are deleted. Signed-off-by: Ting Fu <ting.fu@intel.com>
* libavfilter: Remove DNNReturnType from DNN ModuleShubhanshu Saxena2022-03-12
| | | | | | | | | | This patch removes all occurences of DNNReturnType from the DNN module. This commit replaces DNN_SUCCESS by 0 (essentially the same), so the functions with DNNReturnType now return 0 in case of success, the negative values otherwise. Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com> Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
* lavfi/dnn_backend_openvino: Return Specific Error CodesShubhanshu Saxena2022-03-12
| | | | | | | | Switch to returning specific error codes or DNN_GENERIC_ERROR when an error is encountered. For OpenVINO API errors, currently DNN_GENERIC_ERROR is returned. Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
* lavfi/dnn: Error Specificity in Native Backend LayersShubhanshu Saxena2022-03-12
| | | | | | | This commit returns specific error codes from the execution functions in the Native Backend layers instead of DNN_ERROR. Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
* libavfilter: Unify Execution Modes in DNN FiltersShubhanshu Saxena2021-08-28
| | | | | | | | | | | | | | | | | | | | | | | This commit unifies the async and sync mode from the DNN filters' perspective. As of this commit, the Native backend only supports synchronous execution mode. Now the user can switch between async and sync mode by using the 'async' option in the backend_configs. The values can be 1 for async and 0 for sync mode of execution. This commit affects the following filters: 1. vf_dnn_classify 2. vf_dnn_detect 3. vf_dnn_processing 4. vf_sr 5. vf_derain This commit also updates the filters vf_dnn_detect and vf_dnn_classify to send only the input frame and send NULL as output frame instead of input frame to the DNN backends. Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
* dnn: add DCO_RGB color order to enum DNNColorOrderTing Fu2021-05-11
| | | | | | | Adding DCO_RGB color order to DNNColorOrder, since tensorflow model needs this kind of color oder as input. Signed-off-by: Ting Fu <ting.fu@intel.com>
* lavfi/dnn: add classify support with openvino backendGuo, Yejun2021-05-06
| | | | Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* lavfi/dnn: refine dnn interface to add DNNExecBaseParamsGuo, Yejun2021-05-06
| | | | | | | | | | | | | Different function type of model requires different parameters, for example, object detection detects lots of objects (cat/dog/...) in the frame, and classifcation needs to know which object (cat or dog) it is going to classify. The current interface needs to add a new function with more parameters to support new requirement, with this change, we can just add a new struct (for example DNNExecClassifyParams) based on DNNExecBaseParams, and so we can continue to use the current interface execute_model just with params changed.
* lavfi/dnn: add post process for detectionGuo, Yejun2021-04-08
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* lavfi/dnn: refine code for frame pre/proc processingGuo, Yejun2021-04-08
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* dnn_interface.h: add enum DNNColorOrderGuo, Yejun2021-02-18
| | | | | | | | the data type and order together decide the color format, we could not use AVPixelFormat directly because not all the possible formats are covered by it. Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn: add function type for modelGuo, Yejun2021-02-18
| | | | | | | | So the backend knows the usage of model is for frame processing, detect, classify, etc. Each function type has different behavior in backend when handling the input/output data of the model. Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* libavfilter/dnn: add batch mode for async executionGuo, Yejun2021-01-15
| | | | | | | | the default number of batch_size is 1 Signed-off-by: Xie, Lin <lin.xie@intel.com> Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_interface.h: fix redefining typedefsGuo, Yejun2020-12-31
| | | | Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_interface: change from 'void *userdata' to 'AVFilterContext *filter_ctx'Guo, Yejun2020-12-29
| | | | | | | | | | 'void *' is too flexible, since we can derive info from AVFilterContext*, so we just unify the interface with this data structure. Signed-off-by: Xie, Lin <lin.xie@intel.com> Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn: add async execution support for openvino backendGuo, Yejun2020-12-29
| | | | | | Signed-off-by: Xie, Lin <lin.xie@intel.com> Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_interface: add interface to support async executionGuo, Yejun2020-12-29
| | | | | | Signed-off-by: Xie, Lin <lin.xie@intel.com> Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn: add a new interface DNNModel.get_outputGuo, Yejun2020-09-21
| | | | | | | | | | for some cases (for example, super resolution), the DNN model changes the frame size which impacts the filter behavior, so the filter needs to know the out frame size at very beginning. Currently, the filter reuses DNNModule.execute_model to query the out frame size, it is not clear from interface perspective, so add a new explict interface DNNModel.get_output for such query.
* dnn: put DNNModel.set_input and DNNModule.execute_model togetherGuo, Yejun2020-09-21
| | | | | | | | | | | | | | suppose we have a detect and classify filter in the future, the detect filter generates some bounding boxes (BBox) as AVFrame sidedata, and the classify filter executes DNN model for each BBox. For each BBox, we need to crop the AVFrame, copy data to DNN model input and do the model execution. So we have to save the in_frame at DNNModel.set_input and use it at DNNModule.execute_model, such saving is not feasible when we support async execute_model. This patch sets the in_frame as execution_model parameter, and so all the information are put together within the same function for each inference. It also makes easy to support BBox async inference.
* dnn: change dnn interface to replace DNNData* with AVFrame*Guo, Yejun2020-09-21
| | | | | | | | | | | | Currently, every filter needs to provide code to transfer data from AVFrame* to model input (DNNData*), and also from model output (DNNData*) to AVFrame*. Actually, such transfer can be implemented within DNN module, and so filter can focus on its own business logic. DNN module also exports the function pointer pre_proc and post_proc in struct DNNModel, just in case that a filter has its special logic to transfer data between AVFrame* and DNNData*. The default implementation within DNN module is used if the filter does not set pre/post_proc.
* dnn: add userdata for load model parameterGuo, Yejun2020-09-21
| | | | the userdata will be used for the interaction between AVFrame and DNNData
* dnn: move output name from DNNModel.set_input_output to DNNModule.execute_modelGuo, Yejun2020-08-25
| | | | | | | | | | | currently, output is set both at DNNModel.set_input_output and DNNModule.execute_model, it makes sense that the output name is provided at model inference time so all the output info is set at a single place. and so DNNModel.set_input_output is renamed to DNNModel.set_input Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn: add backend options when load the modelGuo, Yejun2020-08-12
| | | | | | | different backend might need different options for a better performance, so, add the parameter into dnn interface, as a preparation. Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn: add openvino as one of dnn backendGuo, Yejun2020-07-02
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | OpenVINO is a Deep Learning Deployment Toolkit at https://github.com/openvinotoolkit/openvino, it supports CPU, GPU and heterogeneous plugins to accelerate deep learning inferencing. Please refer to https://github.com/openvinotoolkit/openvino/blob/master/build-instruction.md to build openvino (c library is built at the same time). Please add option -DENABLE_MKL_DNN=ON for cmake to enable CPU path. The header files and libraries are installed to /usr/local/deployment_tools/inference_engine/ with default options on my system. To build FFmpeg with openvion, take my system as an example, run with: $ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/deployment_tools/inference_engine/lib/intel64/:/usr/local/deployment_tools/inference_engine/external/tbb/lib/ $ ../ffmpeg/configure --enable-libopenvino --extra-cflags=-I/usr/local/deployment_tools/inference_engine/include/ --extra-ldflags=-L/usr/local/deployment_tools/inference_engine/lib/intel64 $ make Here are the features provided by OpenVINO inference engine: - support more DNN model formats It supports TensorFlow, Caffe, ONNX, MXNet and Kaldi by converting them into OpenVINO format with a python script. And torth model can be first converted into ONNX and then to OpenVINO format. see the script at https://github.com/openvinotoolkit/openvino/tree/master/model-optimizer/mo.py which also does some optimization at model level. - optimize at inference stage It optimizes for X86 CPUs with SSE, AVX etc. It also optimizes based on OpenCL for Intel GPUs. (only Intel GPU supported becuase Intel OpenCL extension is used for optimization) Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* avfilter/dnn: add a new interface to query dnn model's input infoGuo, Yejun2019-10-30
| | | | | | | | | | | | to support dnn networks more general, we need to know the input info of the dnn model. background: The data type of dnn model's input could be float32, uint8 or fp16, etc. And the w/h of input image could be fixed or variable. Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* avfilter/dnn: get the data type of network output from dnn execution resultGuo, Yejun2019-10-30
| | | | | | | | | | | so, we can make a filter more general to accept different network models, by adding a data type convertion after getting data from network. After we add dt field into struct DNNData, it becomes the same as DNNInputData, so merge them with one struct: DNNData. Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* dnn: export operand info in python script and load in c codeGuo, Yejun2019-08-30
| | | | | Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* libavfilter/dnn: add more data type support for dnn model inputGuo, Yejun2019-05-08
| | | | | | | | currently, only float is supported as model input, actually, there are other data types, this patch adds uint8. Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* libavfilter/dnn: support multiple outputs for tensorflow modelGuo, Yejun2019-05-08
| | | | | | | | | | | | some models such as ssd, yolo have more than one output. the clean up code in this patch is a little complex, it is because that set_input_output_tf could be called for many times together with ff_dnn_execute_model_tf, we have to clean resources for the case that the two interfaces are called interleaved. Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* libavfilter/dnn: determine dnn output during execute_model instead of ↵Guo, Yejun2019-05-08
| | | | | | | | | | | | | | | | | | | set_input_output Currently, within interface set_input_output, the dims/memory of the tensorflow dnn model output is determined by executing the model with zero input, actually, the output dims might vary with different input data for networks such as object detection models faster-rcnn, ssd and yolo. This patch moves the logic from set_input_output to execute_model which is suitable for all the cases. Since interface changed, and so dnn_backend_native also changes. In vf_sr.c, it knows it's srcnn or espcn by executing the model with zero input, so execute_model has to be called in function config_props Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* libavfilter/dnn: remove limit for the name of DNN model input/outputGuo, Yejun2019-05-08
| | | | | | | | remove the requirment that the name of DNN model input/output should be "x"/"y", Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* libavfilter: Removes stored DNN models. Adds support for native backend ↵Sergey Lavrushkin2018-09-17
| | | | | | model file format in tf backend. Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* libavfilter: Code style fixes for pointers in DNN module and sr filter.Sergey Lavrushkin2018-08-07
| | | | Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* Adds ESPCN super resolution filter merged with SRCNN filter.Sergey Lavrushkin2018-07-02
| | | | Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* Adds TensorFlow backend for dnn inference module.Sergey Lavrushkin2018-06-05
| | | | Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* Adds dnn inference module for simple convolutional networks. Reimplements ↵Sergey Lavrushkin2018-05-29
srcnn filter based on it. Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
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