I have to create a workflow that uses pytorch models or torch script models in c++. However, I can't find the binary format. I tried looking through the serializer but to no avail. Does anybody have documentation or code that gives me a clue as to the model format?
PyTorch has a great tutorial of Tracing/Scripting models and Loading them via C++.
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I want to use tensorflow.js in my ReactNative app for machine learning.
The model creation api of tfjs seems quite intimidating, while on the other hand i found the AutoML and Model Builder of ML.NET quite interesting.
Is there a way to convert an ML.NET model to a tensorflow.js model and how?
I have trained the classification model on Nvidia GPU and saved the model weights(checkpoint.pth). If I want to deploy this model in jetson nano and test it.
Should I convert it to TenorRT? How to convert it to TensorRT?
I am new to this. It would be helpful if someone can even correct me.
The best way to achieve the way is to export the Onnx model from Pytorch.
Next, use the TensorRT tool, trtexec, which is provided by the official Tensorrt package, to convert the TensorRT model from onnx model.
You can refer to this page: https://github.com/NVIDIA/TensorRT/blob/master/samples/opensource/trtexec/README.md
The TRTEXEC is a more native tool that you can take it from NVIDIA NGC images or downloading from the official website directly.
If you use a tool such as torch2trt, it is easy to encounter the operator issue and complicated to resolve it indeed (if you are not familiar to deal with plugin issues).
You can use this tool:
https://github.com/NVIDIA-AI-IOT/torch2trt
Here are more details how to implent a converter to a engine file:
https://github.com/NVIDIA-AI-IOT/torch2trt/issues/254
I know there are unofficial bindings with .predict method in python(fasttext, pyfasttext) but they do not work with recent models trained on on official FastText bash tool or do not have all the options. Official python bindings have only load_model(path)and tokenize(text) methods described , which sounds strange as with this you can not do any predictions. Am I missing something here?
I use the Python package built and installed according to this link https://github.com/facebookresearch/fastText/blob/master/README.md#building-fasttext-for-python. I consider it official.
The model object loaded via load_model has the requested predict method.
I am quiet new to Machine learning, and I am working on iOS app for object detection using tensorflow, I have been using the sample data model that is provided by tensorflow example in the form of .pb (graph.pb) file which works just fine with object detection.
But My backend team has given me model2_BN.ckpt for data model file, I have tried to research on how to use this file and I have no clue.
Is it possible to use the ckpt file on client side as data model? If yes How can I use it in the iOS tensorflow example as data model?
Please help.
Thanks
This one from my backend developer:
The .ckpt is the model given by tensorflow which includes all the
weights/parameters in the model. The .pb file stores the computational
graph. To make tensorflow work we need both the graph and the
parameters. There are two ways to get the graph:
(1) use the python program that builds it in the first place (tensorflowNetworkFunctions.py).
(2) Use a .pb file (which would have to be generated by tensorflowNetworkFunctions.py).
.ckpt file is were all the intelligence is.
I am struggling to create a custom haar classifier. I have found a couple tutorials on the web, but they do not specify which version of opencv they are using. What I need is a very concise and simplified example of the steps that are required, along with a simple dataset of images. I also need to know the opencv version and the OS platform so I can get it running. I have tried a matrix of opencv versions on both windows and linux and I have run into memory error after memory error. I would like to start with a known good set of data and simple commands before expanding it to fit my problem.
Thanks for your help,
Chris
OpenCV provides two utility commands createsamples.exe and haartraining.exe, which can generate xml files used by Haar Classifiers. That is, with the xml file outputted from haartraining.exe, you can directly use the face detection sample with your xml file to detect any customized objects.
About the detailed procedures to use the commands, you may consult Page 513-516 in the book "Learning OpenCV", or this tutorial.
About the internal mechanism of how the classifier works, you may consult the paper "Rapid Object Detection using a Boosted Cascade of Simple
Features", which has been cited 5500+ times.