How to convert from Tensorflow.js (.json) model into Tensorflow (SavedModel) or Tensorflow Lite (.tflite) model?

Question

I have downloaded a pre-trained PoseNet model for Tensorflow.js (tfjs) from Google, so its a json file.

However, I want to use it on Android, so I need the .tflite model. Although someone has 'ported' a similar model from tfjs to tflite here, I have no idea what model (there are many variants of PoseNet) they converted. I want to do the steps myself. Also, I don't want to run some arbitrary code someone uploaded into a file in stackOverflow:

Caution: Be careful with untrusted code—TensorFlow models are code. See Using TensorFlow Securely for details. Tensorflow docs

Does anyone know any convenient ways to do this?

Answer 1

You can find out what tfjs format you have by looking in the json file. It often says "graph-model". The difference between them are here.

From tfjs graph model to SavedModel (more common)

Use tfjs-to-tf by Patrick Levin.

import tfjs_graph_converter.api as tfjs
tfjs.graph_model_to_saved_model(
               "savedmodel/posenet/mobilenet/float/050/model-stride16.json",
               "realsavedmodel"
            )

# Code below taken from https://www.tensorflow.org/lite/convert/python_api
converter = tf.lite.TFLiteConverter.from_saved_model("realsavedmodel")
tflite_model = converter.convert()

# Save the TF Lite model.
with tf.io.gfile.GFile('model.tflite', 'wb') as f:
  f.write(tflite_model)

---

From tfjs layers model to SavedModel

Note: This will only work for layers model format, not graph model format as in the question. I've written the difference between them here.

---

1. Install and use tensorflowjs-convert to convert the .json file into a Keras HDF5 file (from another SO thread).

On mac, you'll face issues running pyenv (fix) and on Z-shell, pyenv won't load correctly (fix). Also, once pyenv is running, use python -m pip install tensorflowjs instead of pip install tensorflowjs, because pyenv did not change python used by pip for me.

Once you've followed the tensorflowjs_converter guide, run tensorflowjs_converter to verify it works with no errors, and should just warn you about Missing input_path argument. Then:

tensorflowjs_converter --input_format=tfjs_layers_model --output_format=keras tfjs_model.json hdf5_keras_model.hdf5

2. Convert the Keras HDF5 file into a SavedModel (standard Tensorflow model file) or directly into .tflite file using the TFLiteConverter. The following runs in a Python file:

# Convert the model.
model = tf.keras.models.load_model('hdf5_keras_model.hdf5')
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert() 
    
# Save the TF Lite model.
with tf.io.gfile.GFile('model.tflite', 'wb') as f:
f.write(tflite_model)

or to save to a SavedModel:

# Convert the model.
model = tf.keras.models.load_model('hdf5_keras_model.hdf5')
tf.keras.models.save_model(
    model, filepath, overwrite=True, include_optimizer=True, save_format=None,
    signatures=None, options=None
)