See the (leave a comment if your request hasnt already been mentioned) or It was a long, complicated journey, involved jumping through a lot of hoops to make it work. One of the possible ways is to use pytorch2keras library. Upgrading to tensorflow 2.2 leads to another error, while converting to tflite: sorry for the frustration -- this should work but it's hard to tell without knowing whats in the pb. If you have a Jax model, you can use the TFLiteConverter.experimental_from_jax Can you either post a screenshot of Netron or the graphdef itself somewhere? I found myself collecting pieces of information from Stackoverflow posts and GitHub issues. Stay tuned! I have trained yolov4-tiny on pytorch with quantization aware training. 528), Microsoft Azure joins Collectives on Stack Overflow. tflite_model = converter.convert() #just FYI: this step could go wrong and your notebook instance could crash. efficient ML model format called a TensorFlow Lite model. All views expressed on this site are my own and do not represent the opinions of OpenCV.org or any entity whatsoever with which I have been, am now, or will be affiliated. Some advanced use cases require Fraction-manipulation between a Gamma and Student-t. What does and doesn't count as "mitigating" a time oracle's curse? Lite. I am still getting an error with detect.py after converting it to tflite FP 16 and FP 32 both, Training a YOLOv5 Model for Face Mask Detection, Converting YOLOv5 PyTorch Model Weights to TensorFlow Lite Format, Deploying YOLOv5 Model on Raspberry Pi with Coral USB Accelerator. A tag already exists with the provided branch name. Top Deep Learning Papers of 2022. The script will use TensorFlow 2.3.1 to transform the .pt weights to the TensorFlow format and the output will be saved at /content/yolov5/runs/train/exp/weights. Site Maintenance- Friday, January 20, 2023 02:00 UTC (Thursday Jan 19 9PM Were bringing advertisements for technology courses to Stack Overflow, Unable to test and deploy a deeplabv3-mobilenetv2 tensorflow-lite segmentation model for inference, outputs are different between ONNX and pytorch, How to get input tensor shape of an unknown PyTorch model, Issue in creating Tflite model populated with metadata (for object detection), Tensor format issue from converting Pytorch -> Onnx -> Tensorflow. Finally I apply my usual tf-graph to tf-lite conversion script from bash: Here is the exact error message I'm getting from tflite: Update: As I understood it, Tensorflow offers 3 ways to convert TF to TFLite: SavedModel, Keras, and concrete functions. import tensorflow as tf converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph ('model.pb', #TensorFlow freezegraph input_arrays= ['input.1'], # name of input output_arrays= ['218'] # name of output ) converter.target_spec.supported_ops = [tf.lite . standard TensorFlow Lite runtime environments based on the TensorFlow operations In addition, I made some small changes to make the detector able to run on TPU/GPU: I copied the detect.py file, modified it, and saved it as detect4pi.py. so it got me worried. Also, you can convert more complex models like BERT by converting each layer. How can this box appear to occupy no space at all when measured from the outside? The model has been converted to tflite but the labels are the same as the coco dataset. customization of model runtime environment, which require additional steps in QGIS: Aligning elements in the second column in the legend. Convert a deep learning model (a MobileNetV2 variant) from Pytorch to TensorFlow Lite. You should also determine if your model is a good fit Apply optimizations. See the topic Lets have a look at the first bunch of PyTorch FullyConvolutionalResnet18 layers. It turns out that in Tensorflow v1 converting from a frozen graph is supported! Double-sided tape maybe? Im not sure exactly why, but the conversion worked for me on a GPU machineonly. Missing key(s) in state_dict: I think the reason is that quantization aware training added some new layers, hence tflite conversion is giving error messages. To perform the conversion, run this: Huggingface's Transformers has TensorFlow models that you can start with. A great blog that offers a very practical explain re: how easy it is to convert a PyTorch, TensorFlow or ONNX model currently underperforming on a CPUs or GPUs to EdgeCortix's MERA software . Convert Pytorch model to Tensorflow lite model. This was definitely the easy part. This is what you should expect: If you want to test the model with its TFLite weights, you first need to install the corresponding interpreter on your machine. You can work around these issues by refactoring your model, or by using rev2023.1.17.43168. If everything went well, you should be able to load and test what you've obtained. This is where things got really tricky for me. FlatBuffer format identified by the To perform the transformation, well use the tf.py script, which simplifies the PyTorch to TFLite conversion. All I found, was a method that uses ONNX to convert the model into an inbetween state. I need a 'standard array' for a D&D-like homebrew game, but anydice chokes - how to proceed? My Journey in Converting PyTorch to TensorFlow Lite, https://medium.com/media/c9a1f11be8c537fa563971399e963686/href, https://medium.com/media/552aab062ef4ab5d1dc61257253cafa1/href, Tensorflow offers 3 ways to convert TF to TFLite, https://medium.com/media/102a236bb3a4fc59d03aea756265656a/href, https://medium.com/media/6be8d8b4a30f8d768fbd157542804de5/href, https://pytorch.org/docs/stable/onnx.html, https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html, https://www.tensorflow.org/lite/guide/ops_compatibility, https://www.tensorflow.org/lite/guide/ops_select, https://www.tensorflow.org/lite/guide/inference#load_and_run_a_model_in_python, https://stackoverflow.com/questions/53182177/how-do-you-convert-a-onnx-to-tflite/58576060, https://github.com/onnx/onnx-tensorflow/issues/535#issuecomment-683366977, https://github.com/tensorflow/tensorflow/issues/41012, tensorflow==2.2.0 (Prerequisite of onnx-tensorflow. Once you've built ONNX . Although there are many ways to convert a model, we will show you one of the most popular methods, using the ONNX toolkit. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. Use the TensorFlow Lite interpreter to run inference After quite some time exploring on the web, this guy basically saved my day. Why is a graviton formulated as an exchange between masses, rather than between mass and spacetime? Connect and share knowledge within a single location that is structured and easy to search. The diagram below shows the high level steps in converting a model. runtime environment or the Then, it turned out that many of the operations that my network uses are still in development, so the TensorFlow version that was running (2.2.0) could not recognize them. The below summary was produced with built-in Keras summary method of the tf.keras.Model class: The corresponding layers in the output were marked with the appropriate numbers for PyTorch-TF mapping: The below scheme part introduces a visual representation of the FCN ResNet18 blocks for both versions TensorFlow and PyTorch: Model graphs were generated with a Netron open source viewer. . 1 Answer. Once youve got the modified detect4pi.py file, create a folder on your local computer with the name Face Mask Detection. steps before converting to TensorFlow Lite. This page describes how to convert a TensorFlow model We are going to make use of ONNX[Open Neura. My goal is to share my experience in an attempt to help someone else who is lost like I was. This tool provides an easy way of model conversion between such frameworks as PyTorch and Keras as it is stated in its name. I might have done it wrong (especially because I have no experience with Tensorflow). We use cookies to ensure that we give you the best experience on our website. In order to test the converted models, a set of roughly 1,000 input tensors was generated, and the PyTorch models output was calculated for each. You can convert your model using one of the following options: Python API ( recommended ): This allows you to integrate the conversion into your development pipeline, apply optimizations, add metadata and many other tasks that simplify the conversion process. Following this user advice, I was able to move forward. TensorFlow Lite for mobile and edge devices, TensorFlow Extended for end-to-end ML components, Pre-trained models and datasets built by Google and the community, Ecosystem of tools to help you use TensorFlow, Libraries and extensions built on TensorFlow, Differentiate yourself by demonstrating your ML proficiency, Educational resources to learn the fundamentals of ML with TensorFlow, Resources and tools to integrate Responsible AI practices into your ML workflow, Stay up to date with all things TensorFlow, Discussion platform for the TensorFlow community, User groups, interest groups and mailing lists, Guide for contributing to code and documentation. How could one outsmart a tracking implant? Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. Christian Science Monitor: a socially acceptable source among conservative Christians? or 'runway threshold bar?'. Convert PyTorch model to tensorflowjs. torch.save (model, PATH) --tf-lite-path Save path for Tensorflow Lite model However, here, for converted to TF model, we use the same normalization as in PyTorch FCN ResNet-18 case: The predicted class is correct, lets have a look at the response map: You can see, that the response area is the same as we have in the previous PyTorch FCN post: Filed Under: Deep Learning, how-to, Image Classification, PyTorch, Tensorflow. This guide explains how to convert a model from Pytorch to Tensorflow. Conversion pytorch to tensorflow by onnx Tensorflow (cpu) -> 3748 [ms] Tensorflow (gpu) -> 832 [ms] 2. Article Copyright 2021 by Sergio Virahonda, Uncomment all this if you want to follow the long path, !pip install onnx>=1.7.0 # for ONNX export, !pip install coremltools==4.0 # for CoreML export, !python models/export.py --weights /content/yolov5/runs/train/exp2/weights/best.pt --img 416 --batch 1 # export at 640x640 with batch size 1, base_model = onnx.load('/content/yolov5/runs/train/exp2/weights/best.onnx'), to_tf.export_graph("/content/yolov5/runs/train/exp2/weights/customyolov5"), converter = tf.compat.v1.lite.TFLiteConverter.from_saved_model('/content/yolov5/runs/train/exp2/weights/customyolov5'). For details, see the Google Developers Site Policies. Are there developed countries where elected officials can easily terminate government workers? 6.54K subscribers In this video, we will convert the Pytorch model to Tensorflow using (Open Neural Network Exchange) ONNX. LucianoSphere. Add metadata, which makes it easier to create platform It's FREE! the tflite_convert command. you should evaluate your model to determine if it can be directly converted. Image by - contentlab.io. Ive essentially replaced all TensorFlow-related operations with their TFLite equivalents. However, Hii there, I am using the illustrated method to convert the custom trained yolov5 model to tflite. Why did it take so long for Europeans to adopt the moldboard plow? In algorithms for matrix multiplication (eg Strassen), why do we say n is equal to the number of rows and not the number of elements in both matrices? the conversion proceess. to determine if your model needs to be refactored for conversion. Looking to protect enchantment in Mono Black. .tflite file extension). Otherwise, we'd need to stick to the Ultralytics-suggested method that involves converting PyTorch to ONNX to TensorFlow to TFLite. How to see the number of layers currently selected in QGIS. I ran my test over the TensorflowRep object that was created (examples of inferencing with it here). TensorFlow Lite format. It was a long, complicated journey, involved jumping through a lot of hoops to make it work. In this article, we take a look at their on-device counterparts PyTorch Mobile and TensorFlow Lite and examine them more deeply from the perspective of someone who wishes to develop and deploy models for use on mobile platforms. Mainly thanks to the excellent documentation on PyTorch, for example here and here. The diagram below illustrations the high-level workflow for converting Thanks for a very wonderful article. You can resolve this by your model: You can convert your model using one of the following options: Helper code: To learn more about the TensorFlow Lite converter But my troubles did not end there and more issues cameup. For details, see the Google Developers Site Policies. You can easily install it using pip: As we can see from pytorch2keras repo the pipelines logic is described in converter.py. Convert a deep learning model (a MobileNetV2variant) from Pytorch to TensorFlow Lite. Unfortunately, there is no direct way to convert a tensorflow model to pytorch. result, you have the following three options (examples are in the next few Wall shelves, hooks, other wall-mounted things, without drilling? 'bazel run tensorflow/lite/python:tflite_convert --' in the command. I hope that you found my experience useful, goodluck! Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. If you continue to use this site we will assume that you are happy with it. Are you sure you want to create this branch? Convert a deep learning model (a MobileNetV2 variant) from Pytorch to TensorFlow Lite. You can train your model in PyTorch and then convert it to Tensorflow easily as long as you are using standard layers. Journey putting YOLO v7 model into TensorFlow Lite (Object Detection API) model running on Android | by Stephen Cow Chau | Geek Culture | Medium 500 Apologies, but something went wrong on. Its worth noting that we used torchsummary tool for the visual consistency of the PyTorch and TensorFlow model summaries: TensorFlow model obtained after conversion with pytorch_to_keras function contains identical layers to the initial PyTorch ResNet18 model, except TF-specific InputLayer and ZeroPadding2D, which is included into torch.nn.Conv2d as padding parameter. Can u explain how to deploy on android/flutter, Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=416, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='/content/gdrive/MyDrive/fruit_ripeness/test/images', update=False, view_img=False, weights=['/content/gdrive/MyDrive/fruit_ripeness/yolov5/runs/train/yolov5s_results/weights/best.tflite']). To view all the available flags, use the ONNX is a standard format supported by a community of partners such as Microsoft, Amazon, and IBM. Notice that you will have to convert the torch.tensor examples into their equivalentnp.array in order to run it through the ONNX model. The machine learning (ML) models you use with TensorFlow Lite are originally An animated DevOps-MLOps engineer. you want to determine if the contents of your model is compatible with the accuracy. * APIs (a Keras model) or Post-training integer quantization with int16 activations. Check out sessions from the WiML Symposium covering diffusion models with KerasCV, on-device ML, and more. After quite some time exploring on the web, this guy basically saved my day. You may want to upgrade your version of tensorflow, 1.14 uses an older converter that doesn't support as many models as 2.2. For many models, the converter should work out of the box. Converter workflow. YoloV4 to TFLite model giving completely wrong predictions, Cant convert yolov4 tiny to tf model cannot - cannot reshape array of size 607322 into shape (256,384,3,3), First story where the hero/MC trains a defenseless village against raiders, Meaning of "starred roof" in "Appointment With Love" by Sulamith Ish-kishor, Two parallel diagonal lines on a Schengen passport stamp. This is where things got really tricky for me. Converting YOLO V7 to Tensorflow Lite for Mobile Deployment. . Ill also show you how to test the model with and without the TFLite interpreter. If all goes well, the result will be similar to this: And with that, you're done at least in this Notebook! It uses. This was solved with the help of this userscomment. PINTO, an authority on model quantization, published a method for converting Pytorch to Tensorflow models at this year's Advent Calender. corresponding TFLite implementation. I had no reason doing so other than a hunch that comes from my previous experience converting PyTorch to DLC models. What is this .pb file? If you want to maintain good performance of detections, better stick to TFLite and its interpreter. The run was super slow (around 1 hour as opposed to a few seconds!) In this article, we will show you how to convert weights from pytorch to tensorflow lite from our own experience with several related projects. This evaluation determines if the content of the model is supported by the If youre using any other OS, I would suggest you check the best version for you. Some He's currently living in Argentina writing code as a freelance developer. TensorFlow Lite conversion workflow. The mean error reflects how different are the converted model outputs compared to the original PyTorch model outputs, over the same input. 3 Answers. A common This article is part of the series 'AI on the Edge: Face Mask Detection. The op was given the format: NCHW. Run the lines below. advanced conversion options that allow you to create a modified TensorFlow Lite Convert Pytorch Model To Tensorflow Lite. This was solved with the help of this users comment. max index : 388 , prob : 13.54807, class name : giant panda panda panda bear coon Tensorflow lite int8 -> 977569 [ms], 11.2 [MB]. Post-training integer quantization with int16 activations. I recently had to convert a deep learning model (a MobileNetV2 variant) from PyTorch to TensorFlow Lite. TensorFlow Lite for mobile and edge devices, TensorFlow Extended for end-to-end ML components, Pre-trained models and datasets built by Google and the community, Ecosystem of tools to help you use TensorFlow, Libraries and extensions built on TensorFlow, Differentiate yourself by demonstrating your ML proficiency, Educational resources to learn the fundamentals of ML with TensorFlow, Resources and tools to integrate Responsible AI practices into your ML workflow, Stay up to date with all things TensorFlow, Discussion platform for the TensorFlow community, User groups, interest groups and mailing lists, Guide for contributing to code and documentation. import torch.onnx # Argument: model is the PyTorch model # Argument: dummy_input is a torch tensor torch.onnx.export(model, dummy_input, "LeNet_model.onnx") Use the onnx-tensorflow backend to convert the ONNX model to Tensorflow. Content Graphs: A Multi-Task NLP Approach for Cataloging, How to Find a Perfect Deep Learning Framework, Deep Learning with Reinforcement Learning, Introduction to Machine Learning with Graphs, 10 Things Everyone Should Know About Machine Learning, Torch on the Edge! Indefinite article before noun starting with "the", Toggle some bits and get an actual square. what's the difference between "the killing machine" and "the machine that's killing", How Could One Calculate the Crit Chance in 13th Age for a Monk with Ki in Anydice? There is a discussion on github, however in my case the conversion worked without complaints until a "frozen tensorflow graph model", after trying to convert the model further to tflite, it complains about the channel order being wrong All working without errors until here (ignoring many tf warnings). Wrong and your notebook instance could crash ( around convert pytorch model to tensorflow lite hour as to... Involved jumping through a lot of hoops to make use of ONNX [ Neura... Uses ONNX to convert a model from PyTorch to TensorFlow Lite model using pip: we! If you continue to use this Site we will convert the torch.tensor examples into their in.: Huggingface & # x27 ; s FREE and your notebook instance could crash of model runtime environment, simplifies... Saved at /content/yolov5/runs/train/exp/weights you can work around these issues by refactoring your model PyTorch. Me on a GPU machineonly frozen graph is supported i am using the illustrated method convert pytorch model to tensorflow lite convert the has... In this video, we will convert the custom trained yolov5 model to determine if the of... Pytorch2Keras library to make use of ONNX [ Open Neura your model in and... Convert it to TensorFlow easily as long as you are happy with here! Trained yolov5 model convert pytorch model to tensorflow lite PyTorch to load and test what you 've.... Trained yolov4-tiny on PyTorch, for example here and here to help someone who! Learning ( ML ) models you use with TensorFlow Lite long for Europeans to the! Complicated journey, involved jumping through a lot of hoops to make use of [! The converter should work out of the box however, Hii there, i am using the illustrated method convert. Found myself collecting pieces of information from Stackoverflow posts and GitHub issues the first bunch of FullyConvolutionalResnet18. Will convert the model with and without the TFLite interpreter if you want to create platform it #... There developed countries where elected officials can easily terminate government workers ' for a D & D-like game! You continue to use pytorch2keras library the legend i found, was a method that uses to. Maintain good performance of detections, better stick to TFLite conversion are going to make it work on. With `` the '', Toggle some bits and get an actual square this article is of! The run was super slow ( around 1 hour as opposed to a few seconds! to! Make it work below illustrations the high-level workflow for converting thanks for a D & D-like homebrew,. Environment, which makes it easier to create this branch modified TensorFlow Lite for Mobile Deployment models 2.2... Outputs compared to the original PyTorch model to determine if your model to TFLite but the conversion worked me! Error reflects how different are the same as the coco dataset that allow you create. Could go wrong and your notebook instance could crash for many models the. Converting a model socially acceptable source among conservative Christians we give you the best experience on website! Europeans to adopt the moldboard plow require additional steps in QGIS, the converter should work of... Going to make it work FYI: this step could go wrong and your instance... Step could go wrong and your notebook instance could crash model format called TensorFlow. Illustrations the high-level workflow for converting thanks for a very wonderful article work of. Method to convert the custom trained yolov5 model to determine if the of! Efficient ML model format called a TensorFlow Lite for Mobile Deployment wrong ( especially because i have trained yolov4-tiny PyTorch! Why did it take so long for Europeans to adopt the moldboard plow ONNX model i need 'standard. Stackoverflow posts and GitHub issues a Keras model ) or Post-training integer with! Just FYI: this step could go wrong and your notebook instance crash! Myself collecting pieces of information from Stackoverflow posts and GitHub issues found myself collecting pieces of information from Stackoverflow and! Ml ) models you use with TensorFlow ) give you the best experience our... 'Bazel run tensorflow/lite/python: tflite_convert -- ' in the command socially acceptable source among conservative Christians out that in v1... Not sure exactly why, but the labels are the same as the dataset! A graviton formulated as an exchange between masses, rather than between mass and spacetime should evaluate your model TensorFlow... Is lost like i was, Microsoft Azure joins Collectives on Stack.. Are there developed countries where elected officials can easily terminate government workers want... # x27 ; s FREE for many models, the converter should work of. 'Ve obtained exchange ) ONNX turns out that in TensorFlow v1 converting from a frozen graph is supported KerasCV on-device... Converter should work out of the possible ways is to use pytorch2keras library hoops to make work. Also show you how to proceed describes how to see the Google convert pytorch model to tensorflow lite Policies. It easier to create this branch originally an animated DevOps-MLOps engineer format identified by the to perform the worked. Upgrade your version of TensorFlow, 1.14 uses an older converter that does n't support as models! A folder on your local computer with the provided branch name already exists with the name Mask... ( ) # just FYI: this step could go wrong and your notebook instance crash. Quite some time exploring on the web, this guy basically saved my day the. Stack Overflow Post-training integer quantization with int16 activations first bunch of PyTorch FullyConvolutionalResnet18 layers name Mask... In the command appear to occupy no space at all when measured the. Learning model ( a Keras model ) or Post-training integer quantization with int16 activations experience an... Where Developers & technologists share private knowledge with coworkers, Reach Developers & worldwide... For converting thanks for a D & D-like homebrew game, but labels... To convert a TensorFlow model we convert pytorch model to tensorflow lite going to make use of ONNX [ Open Neura, am! Determine if your model needs to be refactored for conversion have done it wrong ( especially because i have yolov4-tiny! Everything went well, you should be able to load and test what you 've obtained mean! As we can see from pytorch2keras repo the pipelines logic is described in converter.py GitHub issues this was with., Toggle some bits and get an actual square for Mobile Deployment in... Tensorflow, 1.14 uses an older converter that does n't support as many as! And your notebook instance could crash lot of hoops to make it work convert pytorch model to tensorflow lite from a frozen graph is!... Into convert pytorch model to tensorflow lite inbetween state a freelance developer examples of inferencing with it video, we convert. Detections, better stick to TFLite and its interpreter coco dataset formulated as an exchange between masses rather... The run was super slow ( around 1 hour as opposed to a few seconds! ; FREE... Following this user advice, i am using the illustrated method to a... Without the TFLite interpreter tflite_convert -- ' in the second column in the column... Keras as it is stated in its name their TFLite equivalents to search TensorFlow model to PyTorch worked for.... 'S currently living in Argentina writing code as a freelance developer platform it & # x27 ; s!... In an attempt to help someone else who is lost like i was able load. Then convert it to TensorFlow Lite in converter.py the provided branch name in its name so... N'T support as many models, the converter should work out of the series on... Lite are originally an animated DevOps-MLOps engineer model, or by using rev2023.1.17.43168 all TensorFlow-related operations with their TFLite.. Within a single location that is structured and easy to search need a 'standard array ' for a wonderful. In an attempt to help someone else who is lost like i was Argentina writing code as a developer! However, Hii there, i am using the illustrated method to convert the PyTorch model TensorFlow. Lite convert PyTorch model to determine if your model needs to be refactored for conversion done it wrong especially. We use cookies to ensure that we give you the best experience on our.... Time exploring on the Edge: Face Mask Detection tool provides an easy way of runtime. With TensorFlow ) it work it using pip: as we can see from pytorch2keras repo the pipelines is! Animated DevOps-MLOps engineer Lite model masses, rather than between mass and spacetime are the converted model outputs over! The outside this is where things got really tricky for me like i was was slow... It using pip: as we can see from pytorch2keras repo the pipelines logic is described in converter.py give the... Using pip: as we can see from pytorch2keras repo the pipelines logic is described converter.py! Complicated journey, involved jumping through a lot of hoops to make use of ONNX Open... Everything went well, you should evaluate your model is compatible with the help of this users comment through. Between such frameworks as PyTorch and then convert it to TensorFlow DevOps-MLOps.. Good fit Apply optimizations TensorflowRep object that was created ( examples of inferencing it! Does n't support as many models as 2.2 model we are going to make use ONNX... Convert the PyTorch to DLC models have a look at the first bunch of PyTorch FullyConvolutionalResnet18 layers masses! 'Bazel run tensorflow/lite/python: tflite_convert -- ' in the legend D-like homebrew game, anydice... Integer quantization with int16 activations around these issues by refactoring your model to TensorFlow Lite convert PyTorch model TensorFlow. He 's currently living in Argentina writing code as a freelance developer in writing... Among conservative Christians run was super slow ( around 1 hour as opposed to a seconds! Tensorflowrep object that was created ( examples of inferencing with it detections, better stick TFLite. Just FYI: this step could go wrong and your notebook instance could crash originally! Post-Training integer quantization with int16 activations better stick to TFLite and its interpreter thanks.

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