Currently, I am evaluating Edge Impulse platform & generated .lite model.
Can anyone please guide me how to convert the .lite model to. tflite?
The .lite file is already a TFLite file. Just rename to .tflite.
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I have a Tensorflow model trained in Python, exported to a .pb file and then used with Tensorflow Serving.
I have written a custom op that greatly speeds up the inference of some operators in this Tensorflow model, but only works for inference -- I can't use this custom op during training time.
I am wondering if it's possible for me to use this custom op with the .pb file in Tensorflow serving. I figure I will probably have to edit the .pb file such that it uses my custom op in place of the original op, and Tensorflow serving should then go about looking for the custom op implementation which I can link against its runtime.
So -- how does one go about modifying a Tensorflow .pb file and swap out operators? Are there example codes doing this that I can refer to?
Your best bet, if you for some reason can't train with the original ops, is probably proto surgery. I would look for some tools that let you convert a proto to ascii format, modify it, and convert it back to binary format. I found this gist of someone doing just that for saved model. You could then write tooling on top to try to replace with your custom op.
I followed the tutorial at
https://machinelearningmastery.com/how-to-train-an-object-detection-model-with-keras/
After successful training I got 5 .h5 files:
mask_rcnn_kangaroo_cfg_0001.h5
mask_rcnn_kangaroo_cfg_0002.h5
mask_rcnn_kangaroo_cfg_0003.h5
mask_rcnn_kangaroo_cfg_0004.h5
mask_rcnn_kangaroo_cfg_0005.h5
I am a newbie to this, so my understanding may be wrong:
How can I convert these .h5 files to .pb files or better to .tflite files, so I can use them in an Android Object Detection app?
You don't need to convert these .h5 to .pb, you can directly convert keras .h5 files to tflite. Here is the official documentation on how to.
Make sure to have the model with layers supported by TFLite, as mentioned here.
Once you have the .tflite model you can run an interpreter on Android.
I am working on tensorflow project. I have successfully train, test and make prediction using python flask. But in order to make prediction each time i have to again load full model using checkpoints. If I save the model .h5 format, I don't need to load the dataset to predict the datasets. I am don't know how to save the tensorflow model in .h5 format using checkpoints. If anybody know how to do, please help me or forward me any link if possible.
Thanks.
You can save and restore tensorflow models using tf.train.Saver class.
Although this doesn't store the models in .h5 format.
You can refer to these sections for further clarity:
Save
Restore
Hope this helps!
I want to convert Dlib weights for Face Detection, Face landmarks and Face recognition that is in .dat format into .tflite format. Tensorflow lite requires input format in tensorflow_saved model/ Frozen graph (.pb) or keras model (.h5) format. Conversion of Dlib .dat to any of these will also work. Can anyone help me out that how to do it and are there converted files available?
Tensorflow lite requires input format in tensorflow_saved model/ Frozen graph (.pb) or keras model (.h5) format. Conversion of Dlib .dat to any of these will also work.
I think you're on the right track. You should try to convert Dlib to TensorFlow frozen graph, then convert the TensorFlow frozen graph to TensorFlow Lite format following the guide.
Have you tried this? Did you run into any problem when running tflite_convert? If you have further questions, please update the original question with detailed error messages.
When I quantized the model by lite modules in tensorflow, I cann't check the weights values that had quantized.Is there any way to view these values in the .tflite files? or Is there any way to parse the .tflite files?
There are some neural network visualizers that can also provide an interface to inspect the file. I have been using Netron. You can click on the "weights" tab of the layer you are interested in to view the data. I haven't tried it yet, but there appears to be a floppy-disk save icon when you view weights/biases in the right side-bar.
The data format is a Google FlatBuffer, defined by the schema file here. You may prefer doing this if you want to do something with the data like output it in a different format. I found the output from parsing it myself using the schema.fbs file to match Netron's for the CNN's I passed in. You can check out the FlatBuffers documentation here.
here in first answer is guide, how to create json view of .tflite model. There you can see quantized values