I have come across googles new BodyPix TensorflowJS model today, and I want to get it running on Android and iOS, but using TensorFlow Lite and CoreML. I was wondering if someone could point me towards the best way to convert this model into TensorFlow Lite. I have done conversions between TensorFlow Lite and CoreML so that's no problem.
I've read a few documents on how to do this, but im a little confused as those documents mention a model.json file or something similar, which the BodyPix src directory contains a multitude of files, which im unsure on what any of them do.
https://github.com/tensorflow/tfjs-models/tree/master/body-pix
Any help or pointers would be appreciated.
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I am currently trying to train a custom model for use in Unity (Barracuda) for object detection and I am struggling near what I believe to be the last part of the pipeline. Following various tutorials and git-repos I have done the following...
Using Darknet, I have trained a custom-model using the Tiny-Yolov2 model. (model tested successfully on a webcam python script)
I have taken the final weights from that training and converted them
to a Keras (h5) file. (model tested successfully on a webcam python
script)
From Keras, I then use tf.save_model to turn it into a
save_model.pd.
From save_model.pd I then convert it using tf2onnx.convert to change
it to an onnx file.
Supposedly from there it can then work in one of a few Unity sample
projects...
...however, this project fails to read in the Unity Sample projects I've tried to use. From various posts it seems that I may need to use a 'frozen' save_model.pd before converting it to ONNX. However all the guides and python functions that seem to be used for freezing save_models require a lot more arguments than I have awareness of or data for after going through so many systems. https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py - for example, after converting into Keras, I only get left with a h5 file, with no knowledge of what an input_graph_def, or output_node_names might refer to.
Additionally, for whatever reason, I cannot find any TF version (1 or 2) that can successfully run this python script using 'from tensorflow.python.checkpoint import checkpoint_management' it genuinely seems like it not longer exists.
I am not sure why I am going through all of these conversions and steps but every attempt to find a cleaner process between training and unity seemed to lead only to dead ends.
Any help or guidance on this topic would be sincerely appreciated, thank you.
I'm kinda new to TensorFlow and Keras, so please excuse any accidental stupidity, but I have an issue. I've been trying to load in models from the TensorFlow Detection Zoo, but haven't had much success.
I can't figure out how to read these saved_model folders (they contain a saved_model.pb file, and an assets and variables folder), so that they're accepted by Keras. Nor can I figure out a way to convert these models so that they may be loaded in. I've tried converting the SavedModel to ONNX, and then convert the ONNX-model to Keras, but that didn't work. Trying to load the original model as a saved_model, and then trying to to save this loaded model in another format gave me no success either.
Since you are new to Tensorflow (and I guess deep learning) I would suggest you stick with the API because the detection zoo models best interface with the object detection API. If you have already downloaded the model, you just need to export it using the exporter_main_v2.py script. This article explains it very well link.
I'm fairly new to TensorFlow so I apologize if I'm saying something absurd.
I've been playing with the PoseNet model in the browser using TensorFlow.js. In this project, I can change the algorithm and parameters so I can get better results on the detection of certain poses. The most important params in my use case are the Multiplier, Quant Bytes and Output Stride.
So far so good, I have the results I want. However, I want to convert these results to TensorFlow Lite so I can use it in an iOS application. I managed to find the PoseNet model in a TensorFlow Lite file (tflite) and I even found an iOS app example provided by TensorFlow to I'm able to load up the model file and have it working on iOS.
The problem is...I'm unable to change the params (Multiplier, Quant Bytes and Output Stride) on the iOS app. I can't find it anywhere how I can do this. I've tried searching for these params in the iOS app source code, I've tried to find ways to convert a TensorFlow.js model to TensorFlow Lite so I can load the model with the params I want in the app but no luck.
I'm writing this post so maybe you guys can point me in the right direction so I'm able to "translate" what I have on TensorFlow.js to TensorFlow Lite.
EDIT:
This is what I've learned in the last couple of days:
TFLite is designed for serving fixed model with lightweight runtime. Thus, modifying model parameters on demand is not a design goal for it.
I looked at the TF.js code for PoseNet, and found similar design. It seems you can modify parameters, because they actually have different models for each params. https://github.com/tensorflow/tfjs-models/blob/b72c10bdbdec6b04a13f780180ed904736fa52a5/posenet/src/checkpoints.ts#L37
TFLite models generally don't support dynamic parameters. Output stride Multiplier and Quant Bytes are fixed params when the neural network is created.
So what I want to do is to extract weights from TF.js model, and put then into existing MobileNet code.
And that's where I need help now. Could anyone point me in the direction to load and change the model so I can then convert it to tflite with my own params?
EDIT2:
I found a repo that is helping me convert TF.js models to TF Lite Griffin98/posenet_tfjs2tflite. I still can't define the Quant Bytes tho.
I need to re-write some code from pyTorch to CNTK.
I know CNTK and deep learning basics quite well.
Is it easy to relate pyTorch and CNTK?
Do I need to be aware of some special things?
I did translate TensorFlow and CNTK codes before, and I found it easy.
But I know Tensorflow reasonably well ... but now I do not want to put effort in learning pyTorch.
One thing you can try is to export your model to ONNX format from PyTorch. Then you can load it in CNTK. See here for saving and here for loading. Note that this is still a very new effort and different toolkits have different degrees of support for ONNX.
I would like to ask whether there is any method at hand to build deep learning models from config file using Tensorflow, just like that in caffe. Thank you very much.
The project caffe-tensorflow allows you to convert Caffe models to TensorFlow.