Now, the conversion back to cv::Mat (or whatever other data structure you might use) is quite straightforward, I’ll leave it as an excersise for the reader. (We use torch::empty here since it can be somewhat faster than `zeros` // or `ones` since it is allowed to fill the tensor with garbage.) auto tensor = torch::empty( ) Use cv::Mat::clone to make sure it is continuous: (I believe this in only done when extracting submatrices, but I am not sure.) Copying raw data from such a non-continuous cv::Mat would cause the resulting Tensor to be messed up. Sometimes, instead of applying an actual operation on the data, OpenCV will simply set some internal attributes to change the way the data is read instead of copying to preserve performance. First, we need to make sure that the raw memory inside the cv::Mat is continuous. You have to make sure that the data is in the right format to avoid any memory issues.įor example, let’s look at some code to convert an OpenCV matrix image ( cv::Mat) to Torch. To do this, you can create an empty Tensor, acquire a pointer to the Tensor contents, and then copy over the data. Converting between raw data and Tensor and backĪt some point, you will have to convert between raw data (for example: images) and a proper torch::Tensor and back. Some of this stuff is hardly documented, but you can find some information in the class reference documentation of torch::Module. ) // See parameters (similar ot Python API) std::cout << model.parameters() << std::endl Get a list of methods in the model class for ( const auto& method : model.get_methods()) std::cout << method.name() << " \n " // Run a model class member function with parameters other than `forward`: // (Note that `model.forward` is just a shortcut for `n_method("forward". We modify the model so that the initialization function can be changed by setting the init_func attribute on Sequence: Let’s start of by modifying some of the class attributes of the PyTorch model from the C++ side. Now it’s time for the code! Setting class attributes The whole performance thing is a subject for another post, especially since it is not libtorch-specific. So if you are worried about performance - and you probably are, why else use C++? - you should grab a profiler to measure what operations are taking the most time. Now, internally a lot of things might happen when doing operations on the tensor (especially the more complicated ones like permute, more on that later) including copying around. You cannot accidentally copy the tensor except by calling torch::Tensor::clone. Don’t worry though, the memory of those objects is fully managed as if it were wrapped by a shared_ptr. Lastly, some of you C++ programmer might be worried about the copy semantics of torch::Tensor (I was). There’s a great blog series on how to do all this by Kushashwa Ravi Shrimali here. I would say close to 90% of Python functionality is included. Whilst this blog series focuses mostly on using an existing PyTorch model in C++, the libtorch API also allows for creating models, training and inference all from C++. The same Torch Script based approach is also used for all the other libtorch functionality. But libtorch also provides a bunch of other functions for interacting with the Torch Script model such as attr, set_attr and run_method. In libtorch, the AST is loaded in and used to correctly execute the model when calling model.forward(). ASTs are commonplace in interpreters and source code parsers. This is what you see when you do print(de) in Python. Basically, it parses your Python code, in the same way the interpreter would, and builds an Abstract Syntax Tree representation of your code. The Abstract Syntax Treeīefore I show you the code, I want to quickly go over how PyTorch converts the model to a C++-usable one. This part discusses some more advanced topics. Part 2 covers the basics of getting your model up-and-running in libtorch. Part 1 covers the rationale for PyTorch and using libtorch in production. This is part 3 of a 3-part series on libtorch. Garry's Blog - Advanced libtorch Garry's BlogĪbout machine learning, debugging, Python, C++ and other interesting stuff.Ĭarefully documenting everything I screwed up for future generations.Īdvanced libtorch Part 3 of 3 - Bringing your Deep Learning Model to Production with libtorch
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