Tensorflow to Pytorch conversion

extract_weights.py

import tensorflow as tf  # tensorflow 1.x
import pickle

'''
<base_folder>
├───checkpoint
├───<model_name>.meta
├───<model_name>.data-00000-of-00001
└───<model_name>.index
'''

# First let's load meta graph and restore weights
sess = tf.Session()
saver = tf.train.import_meta_graph(r'<base_folder>\<model_name>.meta')
saver.restore(sess, tf.train.latest_checkpoint(r'<base_folder>'))

# get all trainable weights and save them in a dictionary
vars = sess.graph.get_collection('trainable_variables')
weights = {}
for v in vars:
    weights[v.name] = sess.run(v)  # retrieve the value from the tf backend

with open('weights.pickle', 'wb') as handle:
    pickle.dump(weights, handle, protocol=pickle.HIGHEST_PROTOCOL)