Network.H

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// ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//
// JeVois Smart Embedded Machine Vision Toolkit - Copyright (C) 2021 by Laurent Itti, the University of Southern
// California (USC), and iLab at USC. See http://iLab.usc.edu and http://jevois.org for information about this project.
//
// This file is part of the JeVois Smart Embedded Machine Vision Toolkit.  This program is free software; you can
// redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software
// Foundation, version 2.  This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
// without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public
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// if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
//
// Contact information: Laurent Itti - 3641 Watt Way, HNB-07A - Los Angeles, CA 90089-2520 - USA.
// Tel: +1 213 740 3527 - itti@pollux.usc.edu - http://iLab.usc.edu - http://jevois.org
// ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/*! \file */
 
#pragma once
 
#include <jevois/Component/Component.H>
#include <jevois/Types/Enum.H>
 
#include <ovxlib/vsi_nn_pub.h> // for data types and quantization types
 
#include <opencv2/core/core.hpp>
#include <vector>
 
namespace jevois
{
  namespace dnn
  {
    namespace network
    {
      // We define all parameters for all derived classes here to avoid duplicate definitions. Different derived classes
      // will use different subsets of all available parameters:
      static jevois::ParameterCategory const ParamCateg("DNN Network Options");
 
      //! Parameter \relates jevois::dnn::Network
      JEVOIS_DECLARE_PARAMETER(comment, std::string, "Optional comment about the network",
                               "", ParamCateg);
 
      //! Parameter \relates jevois::dnn::Network
      JEVOIS_DECLARE_PARAMETER(url, std::string, "Optional URL for the network",
                               "", ParamCateg);
 
      //! Parameter \relates jevois::dnn::Network
      JEVOIS_DECLARE_PARAMETER(dataroot, std::string, "Root directory to use when config or model parameters "
                               "are relative paths.",
                               JEVOIS_SHARE_PATH, ParamCateg);
 
      //! Parameter \relates jevois::dnn::Network
      JEVOIS_DECLARE_PARAMETER(config, std::string, "Path to a text file that contains network configuration. "
                               "Can have extension .prototxt (Caffe), .pbtxt (TensorFlow), or .cfg (Darknet). "
                               "If path is relative, it will be prefixed by dataroot.",
                               "", ParamCateg);
      
      //! Parameter \relates jevois::dnn::Network
      JEVOIS_DECLARE_PARAMETER(model, std::string, "Path to a binary file of model contains trained weights. "
                               "Can have extension .caffemodel (Caffe), .pb (TensorFlow), .t7 or .net (Torch), "
                               ".tflite (TensorFlow Lite), or .weights (Darknet). If path is relative, it will be "
                               "prefixed by dataroot.",
                               "", ParamCateg);
#ifdef JEVOIS_PRO
      //! Parameter \relates jevois::dnn::NetworkNPU
      JEVOIS_DECLARE_PARAMETER(library, std::string, "Optional path to a compiled .so library that can load and "
                               "instantiate a NPU model. If a library is provided, then intensors and outtensors "
                               "can be left empty, otherwise they have to describe exactly the inputs and outputs "
                               "of the NPU model. This library is obtained along with the NPU .nb model when "
                               "converting for NPU using the KSNN convert tool. If path is relative, it will be "
                               "prefixed by dataroot.",
                               "", ParamCateg);
#endif
      
#ifdef JEVOIS_PRO
      //! Enum \relates jevois::dnn::Network
      JEVOIS_DEFINE_ENUM_CLASS(Target, (CPU) (OpenCL) (OpenCL_FP16) (Myriad) (NPU) );
#else
      //! Enum \relates jevois::dnn::Network
      JEVOIS_DEFINE_ENUM_CLASS(Target, (CPU) );
#endif
      
      //! Parameter \relates jevois::dnn::Network
      JEVOIS_DECLARE_PARAMETER(target, Target, "OpenCV compute target to use. Changes will take effect "
                               "next time you load a different model.",
                               Target::CPU, Target_Values, ParamCateg);
#ifdef JEVOIS_PRO
      //! Enum \relates jevois::dnn::Network
      JEVOIS_DEFINE_ENUM_CLASS(Backend, (OpenCV) (InferenceEngine) (TimVX) );
#define JEVOIS_BACKEND_DEFAULT Backend::OpenCV
#else
      //! Enum \relates jevois::dnn::Network
      JEVOIS_DEFINE_ENUM_CLASS(Backend, (Default) );
#define JEVOIS_BACKEND_DEFAULT Backend::Default
#endif
      
      //! Parameter \relates jevois::dnn::Network
      JEVOIS_DECLARE_PARAMETER(backend, Backend, "OpenCV compute backend to use. Default will use the inference "
                               "engine if available, otherwise OpenCV (note that inference engine only works on Intel "
                               "processors or MyriadX hardware, thus you should normally select OpenCV when running "
                               "on JeVois-Pro Platform, unless you want to use an optional MyriadX accelerator). "
                               "Changes will take effect next time you load a model.",
                               JEVOIS_BACKEND_DEFAULT, Backend_Values, ParamCateg);
 
      //! Parameter \relates jevois::dnn::Network
      JEVOIS_DECLARE_PARAMETER(intensors, std::string, "Specification of input tensors",
                               "", ParamCateg);
 
      //! Parameter \relates jevois::dnn::Network
      JEVOIS_DECLARE_PARAMETER(extraintensors, std::string, "Specification of extra fixed input tensors that will be "
                               "added after the regular intensors. Format is: "
                               "<type>:<shape>:val1 val2 ... valN, <type>:<shape>:val1 ... valN. For example, for "
                               "URetinex-Net: 32F:1x1x1:3.0",
                               "", ParamCateg);
 
      //! Parameter \relates jevois::dnn::Network
      JEVOIS_DECLARE_PARAMETER(outtensors, std::string, "Specification of output tensors",
                               "", ParamCateg);
 
      //! Parameter \relates jevois::dnn::Network
      JEVOIS_DECLARE_PARAMETER_WITH_CALLBACK(outtransform, std::string, "Specification of a sequence of operations "
                                             "(reshape, transpose, split, merge, etc) to be applied to the output "
                                             "tensors. Useful to match a particular network's outputs to the "
                                             "tensor shapes expected by a particular post-processor. The sequence of "
                                             "operations is separated by semicolons, and applied from left to right. "
                                             "Operations specified here are applied after any de-quantization.",
                                             "", ParamCateg);
      //"Specification of reshaped output tensors; "
      //"sometimes useful to re-interpret tensors to what a post-processor expects; for "
      //"example, TPU YoloV4-Int-VOC outputs 5D tensors 32F:1x52x52x3x85, 32F:1x26x26x3x85, "
      //"32F:1x13x13x3x85 but the YOLO post-processor expects 4D, which would be specified here "
      //"as 32F:1x52x52x255, 32F:1x26x26x255, 32F:1x13x13x255. Note that this only changes "
      //"the description of dimensions, but does not move any pixel data around (e.g., cannot "
      //"convert from NCHW to NHWC, convert data types, etc). Use sparingly and with caution.",
      
      //! Parameter \relates jevois::dnn::Network
      JEVOIS_DECLARE_PARAMETER(dequant, bool, "Dequantize output tensors to float32 from their native quantized type",
                               true, ParamCateg);
 
      //! Parameter \relates jevois::dnn::NetworkPython
      JEVOIS_DECLARE_PARAMETER_WITH_CALLBACK(pynet, std::string, "Full path of the python network processor file. "
                                             "Name of class defined in the file must match the file name without "
                                             "the trailing '.py'",
                                             "", ParamCateg);
#ifdef JEVOIS_PRO
      //! Parameter \relates jevois::dnn::Network
      JEVOIS_DECLARE_PARAMETER(tpunum, size_t, "Coral EdgeTPU number to use to run this model, typically 0, or can be "
                               "1 when using a dual-TPU add-on board, or more when using additional TPUs connected "
                               "to USB ports",
                               0, ParamCateg);
 
      //! Parameter \relates jevois::dnn::Network
      JEVOIS_DECLARE_PARAMETER(spunum, size_t, "Hailo8 device number to use to run this model, typically 0 unless "
                               "several Hailo8 accelerators are connected to the system",
                               0, ParamCateg);
 
      //! Parameter \relates jevois::dnn::NetworkNPU
      JEVOIS_DECLARE_PARAMETER(verifygraph, bool, "Verify NPU graph after loading it",
                               true, ParamCateg);
 
      //! Parameter \relates jevois::dnn::NetworkNPU
      JEVOIS_DECLARE_PARAMETER(ovxver, std::string, "ovxlib version to use with NPU network, or leave blank "
                               "to use latest version",
                               "", boost::regex("^$|^[0-9]+\\.[0-9]+\\.[0-9]+$"), ParamCateg);
 
      //! Parameter \relates jevois::dnn::NetworkHailo
      JEVOIS_DECLARE_PARAMETER_WITH_CALLBACK(turbo, bool, "Turbo mode. Has no significant effect on small or "
                                             "fast networks. Use with caution as it may lead to overheating, not "
                                             "recommended for production",
                                             false, ParamCateg);
#endif
    }
    
    //! Abstract class to represent a neural network
    /*! Derived classes provide implementation via OpenCV (on CPU, OpenCL, or OpenVino/Myriad-X), Amlogic/Vivante NPU,
        Hailo-8, Python, or Google Coral TPU.
 
        The Network class can apply optional transforms to the output tensors:
 
        - `dequant`: dequantize the output tensors
        
        - `outtransform`: apply a semicolon-separated sequence (from left to right) of transforms to the
          outputs. Outputs are numbered starting at 0. Available transforms are:
          
          + `shape(outnum, AxBxC...)` to reshape an output to a given new shape. Does not change or move any tensor
            data. Total number of data elements must remain the same. Useful to squeeze/unsqueeze tensors.
 
          + `transpose(outnum, oldaxisA, oldaxisB, ...)` where transposed new axis 0 (the outermost dimension, typically
            batch size) will be from oldaxisA, new axis 1 from oldaxisB, etc. If outnum is *, transpose all outputs.
 
          + `order(oldidx0, oldidx1, ...)` where the new ordering of the output tensors will be: new tensor 0: old
            tensor oldidx0 (which is zero-based); new tensor 1: old tensor oldidx1, etc. It is ok to have duplicated or
            missing entries.
 
          + `split(outnum, axis, newsize1, ..., newsizeN)` where axis 0 is the outtermost dimension (typically, batch
            size), and newsize1 + ... + newsizeN must be equal to the original size of that axis. If outnum is *, split
            all outputs.
 
          + `merge(axis, outnum1, ..., outnumN)` where axis 0 is the outermost dimension (typically, batch size) and
            outnum1, ..., outnumN are the outputs to merge along that axis. All the outputs to be merged must have
            matching number of dimensions, and matching sizes on all other axes.  The merged tensor will replace the
            first output listed in the merge, and the other listed will be removed. Outputs to merge must be listed in
            ascending order (use an order() transform first if needed)
 
        See the model zoo files in /jevoispro/share/dnn/ for examples. For instance:
        \code{.py}
        outtransform: "split(*,1,80,64); order(1,0,3,2,5,4); transpose(*,0,2,3,1)"
        \endcode
        
        \ingroup dnn */
    class Network : public Component,
                    public Parameter<network::comment, network::url, network::outtransform, network::extraintensors>
    {
      public:
        //! Inherited constructor ok
        using jevois::Component::Component;
 
        //! Destructor
        /*! CAUTION: derived classes must call waitBeforeDestroy() in their destructor */
        virtual ~Network();
 
        //! If network is currently loading, wait until that is done before destroying
        /*! CAUTION: derived classes must call waitBeforeDestroy() in their destructor */
        void waitBeforeDestroy();
        
        //! Returns true when network is ready to run (loaded and initialized)
        bool ready();
 
        //! Get shapes of all input tensors
        virtual std::vector<vsi_nn_tensor_attr_t> inputShapes() = 0;
 
        //! Get shapes of all output tensors
        virtual std::vector<vsi_nn_tensor_attr_t> outputShapes() = 0;
        
        //! Process input blobs and obtain output blobs
        /*! Network implementations may push information data into the info string, which will be displayed to
            user. Convention is: if an info line starts with '* ', it is a header, and if it starts with '- ' it is a
            bullet. Info should always be organized into headers at the top level. */
        std::vector<cv::Mat> process(std::vector<cv::Mat> const & blobs, std::vector<std::string> & info);
 
        //! Freeze/unfreeze parameters that users should not change while running
        /*! Note: derived classes can freeze their own params by overriding this function, and should remember to still
            call the base class jevois::dnn::Network::freeze(doit) */
        virtual void freeze(bool doit);
        
      protected:
        //! Load from disk
        virtual void load() = 0;
 
        //! Process input blobs and obtain output blobs
        virtual std::vector<cv::Mat> doprocess(std::vector<cv::Mat> const & blobs,
                                               std::vector<std::string> & info) = 0;
 
        void onParamChange(network::outtransform const & param, std::string const & val) override;
        
      private:
        std::atomic<bool> itsLoading = false;
        std::atomic<bool> itsLoaded = false;
        std::future<void> itsLoadFut;
 
        // Output transformations:
        enum class Operator { Shape, Transpose, Order, Split, Merge };
        struct Oper
        {
            Operator op;
            std::vector<size_t> tnum; // output tensor numbers (indices within the output array)
            std::vector<int> newvals; // New values (operator-dependent: could be new output orders, new tensor dims)
        };
        std::vector<Oper> itsOps;
    };
    
  } // namespace dnn
} // namespace jevois