Alec  Nikolaus

Alec Nikolaus

1602954000

My Journey in Converting PyTorch to TensorFlow Lite

Intro

I recently had to convert a deep learning model (a MobileNetV2 variant) from PyTorch to TensorFlow Lite. It was a long, complicated journey, involved jumping through a lot of hoops to make it work. I found myself collecting pieces of information from Stackoverflow posts and GitHub issues. My goal is to share my experience in an attempt to help someone else who is lost like I was.

DISCLAIMER: This is not a guide_ on how to properly do this conversion. I only wish to share my experience. I might have done it wrong (especially because I have no experience with Tensorflow). If you notice something that I could have done better/differently — please comment and I’ll update the post accordingly._

The Mission

Convert a deep learning model (a MobileNetV2 variant) from Pytorch to TensorFlow Lite. The conversion process should be:

Pytorch →ONNX → Tensorflow → TFLite

Tests

In order to test the converted models, a set of roughly 1,000 input tensors was generated, and the PyTorch model’s output was calculated for each. That set was later used to test each of the converted models, by comparing their yielded outputs against the original outputs, via a mean error metric, over the entire set. The mean error reflects how different are the converted model outputs compared to the original PyTorch model outputs, over the same input.

I decided to treat a model with a mean error smaller than 1e-6 as a successfully converted model.

It might also be important to note that I added the batch dimension in the tensor, even though it was 1. I had no reason doing so other than a hunch that comes from my previous experience converting PyTorch to DLC models.

#mlops #tensorflow #onnx #pytorch #tflite

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My Journey in Converting PyTorch to TensorFlow Lite
Alec  Nikolaus

Alec Nikolaus

1602954000

My Journey in Converting PyTorch to TensorFlow Lite

Intro

I recently had to convert a deep learning model (a MobileNetV2 variant) from PyTorch to TensorFlow Lite. It was a long, complicated journey, involved jumping through a lot of hoops to make it work. I found myself collecting pieces of information from Stackoverflow posts and GitHub issues. My goal is to share my experience in an attempt to help someone else who is lost like I was.

DISCLAIMER: This is not a guide_ on how to properly do this conversion. I only wish to share my experience. I might have done it wrong (especially because I have no experience with Tensorflow). If you notice something that I could have done better/differently — please comment and I’ll update the post accordingly._

The Mission

Convert a deep learning model (a MobileNetV2 variant) from Pytorch to TensorFlow Lite. The conversion process should be:

Pytorch →ONNX → Tensorflow → TFLite

Tests

In order to test the converted models, a set of roughly 1,000 input tensors was generated, and the PyTorch model’s output was calculated for each. That set was later used to test each of the converted models, by comparing their yielded outputs against the original outputs, via a mean error metric, over the entire set. The mean error reflects how different are the converted model outputs compared to the original PyTorch model outputs, over the same input.

I decided to treat a model with a mean error smaller than 1e-6 as a successfully converted model.

It might also be important to note that I added the batch dimension in the tensor, even though it was 1. I had no reason doing so other than a hunch that comes from my previous experience converting PyTorch to DLC models.

#mlops #tensorflow #onnx #pytorch #tflite

TensorFlow Lite Object Detection using Raspberry Pi and Pi Camera

I have not created the Object Detection model, I have just merely cloned Google’s Tensor Flow Lite model and followed their Raspberry Pi Tutorial which they talked about in the Readme! You don’t need to use this article if you understand everything from the Readme. I merely talk about what I did!

Prerequisites:

  • I have used a Raspberry Pi 3 Model B and PI Camera Board (3D printed a case for camera board). **I had this connected before starting and did not include this in the 90 minutes **(plenty of YouTube videos showing how to do this depending on what Pi model you have. I used a video like this a while ago!)

  • I have used my Apple Macbook which is Linux at heart and so is the Raspberry Pi. By using Apple you don’t need to install any applications to interact with the Raspberry Pi, but on Windows you do (I will explain where to go in the article if you use windows)

#raspberry-pi #object-detection #raspberry-pi-camera #tensorflow-lite #tensorflow #tensorflow lite object detection using raspberry pi and pi camera

How PyTorch Is Challenging TensorFlow Lately

  • PyTorch gives our researchers unprecedented flexibility in designing their models and running their experiments.

Google’s TensorFlow and Facebook’s PyTorch are the most popular machine learning frameworks. The former has a two-year head start over PyTorch (released in 2016). TensorFlow’s popularity reportedly declined after PyTorch bursted into the scene. However, Google released a more user-friendly TensorFlow 2.0 in January 2019 to recover lost ground.

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Interest over time for TensorFlow (top) and PyTorch (bottom) in India (Credit: Google Trends)

PyTorch–a framework for deep learning that integrates with important Python add-ons like NumPy and data-science tasks that require faster GPU processing–made some recent additions:

  • Enterprise support**: **After taking over the Windows 10 PyTorch library from Facebook to boost GPU-accelerated machine learning training on Windows 10’s Subsystem for Linux(WSL), Microsoft recently added enterprise support for PyTorch AI on Azure to give PyTorch users a more reliable production experience. “This new enterprise-level offering by Microsoft closes an important gap. PyTorch gives our researchers unprecedented flexibility in designing their models and running their experiments,” Jeremy Jancsary, a senior principal research scientist at Nuance, said.
  • PyTorchVideois a deep learning library for video understanding unveiled by Facebook AI recently. The source code is available on GitHub. With this, Facebook aims to support researchers develop cutting-edge machine learning models and tools. These models can enhance video understanding capabilities along with providing a unified repository of reproducible and efficient video understanding components for research and production applications.
  • PyTorch Profiler: In April this year, PyTorch announced its new performance debug profiler, PyTorch Profiler, along with its 1.8.1 version release. The new tool enables accurate and efficient performance analysis in large scale deep learning models.

#opinions #deep learning frameworks #machine learning pytorch #open-source frameworks #pytorch #tensorflow #tensorflow 2.0

Justyn  Ortiz

Justyn Ortiz

1610436416

Guide to Conda for TensorFlow and PyTorch

Learn how to set up anaconda environments for different versions of CUDA, TensorFlow, and PyTorch

It’s a real shame that the first experience that most people have with deep learning is having to spend days trying to figure out why the model they downloaded off of GitHub just… won’t… run….

Dependency issues are incredibly common when trying to run an off-the-shelf model. The most problematic of which is needing to have the correct version of CUDA for TensorFlow. TensorFlow has been prominent for a number of years meaning that even new models that are released could use an old version of TensorFlow. This wouldn’t be an issue except that it feels like every version of TensorFlow needs a specific version of CUDA where anything else is incompatible. Sadly, installing multiple versions of CUDA on the same machine can be a real pain!

#machine-learning #pytorch #tensorflow #pytorch

Dominic  Feeney

Dominic Feeney

1624684910

Painless GPU and tensorflow/PyTorch integration on Ubuntu 18.04

There are numerous posts about setting up your GPU on the internet. I tried to follow so many of them to set up my machine, but was somehow not able to do so. There are so many variables in this process which span from nvidia driver version, cuda tool kit version, CUDnn version, python version, tensorflow version etc. The list goes on. It is also a good idea to see if your GPU supports CUDA integration here.

I assume the following:

  1. There are no previous nvidia drivers on the machine. If there are, I recommend to uninstall them first otherwise following my instructions may or may not work.
  2. Python is already installed on the machine. I recommend using anaconda since it makes the job of tracking library versions and virtual environments very easy.

#cuda #tensorflow #pytorch #machine-learning #deep-learning #painless gpu and tensorflow/pytorch integration on ubuntu 18.04