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#!/usr/bin/env python
"""Extracts trainable parameters from Tensorflow models and stores them in numpy arrays.
Usage
python tensorflow_data_extractor -m path_to_binary_checkpoint_file -n path_to_metagraph_file
Saves each variable to a {variable_name}.npy binary file.
Note that since Tensorflow version 0.11 the binary checkpoint file which contains the values for each parameter has the format of:
{model_name}.data-{step}-of-{max_step}
instead of:
{model_name}.ckpt
When dealing with binary files with version >= 0.11, only pass {model_name} to -m option;
when dealing with binary files with version < 0.11, pass the whole file name {model_name}.ckpt to -m option.
Also note that this script relies on the parameters to be extracted being in the
'trainable_variables' tensor collection. By default all variables are automatically added to this collection unless
specified otherwise by the user. Thus should a user alter this default behavior and/or want to extract parameters from other
collections, tf.GraphKeys.TRAINABLE_VARIABLES should be replaced accordingly.
Tested with Tensorflow 1.2, 1.3 on Python 2.7.6 and Python 3.4.3.
"""
import argparse
import numpy as np
import os
import tensorflow as tf
if __name__ == "__main__":
# Parse arguments
parser = argparse.ArgumentParser('Extract Tensorflow net parameters')
parser.add_argument('-m', dest='modelFile', type=str, required=True, help='Path to Tensorflow checkpoint binary\
file. For Tensorflow version >= 0.11, only include model name; for Tensorflow version < 0.11, include\
model name with ".ckpt" extension')
parser.add_argument('-n', dest='netFile', type=str, required=True, help='Path to Tensorflow MetaGraph file')
args = parser.parse_args()
# Load Tensorflow Net
saver = tf.train.import_meta_graph(args.netFile)
with tf.Session() as sess:
# Restore session
saver.restore(sess, args.modelFile)
print('Model restored.')
# Save trainable variables to numpy arrays
for t in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):
varname = t.name
if os.path.sep in t.name:
varname = varname.replace(os.path.sep, '_')
print("Renaming variable {0} to {1}".format(t.name, varname))
print("Saving variable {0} with shape {1} ...".format(varname, t.shape))
# Dump as binary
np.save(varname, sess.run(t))
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