Module 'tensorflow' has no attribute 'contrib'

According to TF 1:1 Symbols Map, in TF 2.0 you should use tf.compat.v1.Session() instead of tf.Session()

https://docs.google.com/spreadsheets/d/1FLFJLzg7WNP6JHODX5q8BDgptKafq_slHpnHVbJIteQ/edit#gid=0

To get TF 1.x like behaviour in TF 2.0 one can run

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

but then one cannot benefit of many improvements made in TF 2.0. For more details please refer to the migration guide https://www.tensorflow.org/guide/migrate

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
)