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#!/usr/bin/env python
""" Extract trainable parameters from a frozen model and stores them in numpy arrays.
Usage:
python tf_frozen_model_extractor -m path_to_frozem_model -d path_to_store_the_parameters
Saves each variable to a {variable_name}.npy binary file.
Note that the script permutes the trainable parameters to NCHW format. This is a pretty manual step thus it's not thoroughly tested.
"""
import argparse
import os
import numpy as np
import tensorflow as tf
from tensorflow.python.platform import gfile
strings_to_remove=["read", "/:0"]
permutations = { 1 : [0], 2 : [1, 0], 3 : [2, 1, 0], 4 : [3, 2, 0, 1]}
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 frozen graph file (.pb)')
parser.add_argument('-d', dest='dumpPath', type=str, required=False, default='./', help='Path to store the resulting files.')
parser.add_argument('--nostore', dest='storeRes', action='store_false', help='Specify if files should not be stored. Used for debugging.')
parser.set_defaults(storeRes=True)
args = parser.parse_args()
# Create directory if not present
if not os.path.exists(args.dumpPath):
os.makedirs(args.dumpPath)
# Extract parameters
with tf.Graph().as_default() as graph:
with tf.Session() as sess:
print("Loading model.")
with gfile.FastGFile(args.modelFile, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sess.graph.as_default()
tf.import_graph_def(graph_def, input_map=None, return_elements=None, name="", op_dict=None, producer_op_list=None)
for op in graph.get_operations():
for op_val in op.values():
varname = op_val.name
# Skip non-const values
if "read" in varname:
t = op_val.eval()
tT = t.transpose(permutations[len(t.shape)])
t = np.ascontiguousarray(tT)
for s in strings_to_remove:
varname = varname.replace(s, "")
if os.path.sep in varname:
varname = varname.replace(os.path.sep, '_')
print("Renaming variable {0} to {1}".format(op_val.name, varname))
# Store files
if args.storeRes:
print("Saving variable {0} with shape {1} ...".format(varname, t.shape))
np.save(os.path.join(args.dumpPath, varname), t)
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