1602954000
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._
Convert a deep learning model (a MobileNetV2 variant) from Pytorch to TensorFlow Lite. The conversion process should be:
Pytorch →ONNX → Tensorflow → TFLite
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
1602954000
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._
Convert a deep learning model (a MobileNetV2 variant) from Pytorch to TensorFlow Lite. The conversion process should be:
Pytorch →ONNX → Tensorflow → TFLite
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
1647415674
https://www.youtube.com/playlist?list=PLxqBkZuBynVRnkwNgULYmJJs_JQZOAqpU
#ComputerVision #OpenCV #MachineLearning #imageprocessing #DataScience #TensorFlow #DeepLearning #Python #DataScientist #Statistics #ArtificialIntelligence #100DaysOfMLCode #Pytorch
***********************************
Playlist of 12 Videos - Deep Learning / Computer Vision Algorithm Implementations
👉 https://www.youtube.com/playlist?list=PLxqBkZuBynVRyOJs4RWmB_fKlOVe5S8CR
#ComputerVision #Pytorch #MachineLearning #imageprocessing #DataScience #TensorFlow #DeepLearning #Python #DataScientist #Statistics #ArtificialIntelligence #100DaysOfMLCode
👉 Github Repo (Numbered) - https://github.com/rohan-paul/MachineLearning-DeepLearning-Code-for-my-YouTube-Channel
👉 Blog - https://rohan-paul-ai.netlify.app/blog
You can find me here:
**********************************************
🐦 TWITTER: https://twitter.com/paulr_rohan
👨🔧 Kaggle: https://www.kaggle.com/paulrohan2020
👨🏻💼 LINKEDIN: https://www.linkedin.com/in/rohan-paul-b27285129/
👨💻 GITHUB: https://github.com/rohan-paul
🦾🤖: My Website and Blog: https://rohan-paul-ai.netlify.app/
🧑🦰 Facebook Page: https://www.facebook.com/Computer-Vision-with-Rohan-Paul-109348958325690
📸 Instagram: https://www.instagram.com/rohan_paul_2020/
**********************************************
1623737882
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!
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
1623745500
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.
Register for Hands-on Workshop (17th Jun) - oneAPI AI Analytics Toolkit
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:
#opinions #deep learning frameworks #machine learning pytorch #open-source frameworks #pytorch #tensorflow #tensorflow 2.0
1610436416
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