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