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Open Source Computer Vision Library
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63 lines
2.3 KiB
63 lines
2.3 KiB
7 years ago
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# This file is part of OpenCV project.
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# It is subject to the license terms in the LICENSE file found in the top-level directory
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# of this distribution and at http://opencv.org/license.html.
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#
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# Copyright (C) 2017, Intel Corporation, all rights reserved.
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# Third party copyrights are property of their respective owners.
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import tensorflow as tf
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import struct
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import argparse
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import numpy as np
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parser = argparse.ArgumentParser(description='Convert weights of a frozen TensorFlow graph to fp16.')
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parser.add_argument('--input', required=True, help='Path to frozen graph.')
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parser.add_argument('--output', required=True, help='Path to output graph.')
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parser.add_argument('--ops', default=['Conv2D', 'MatMul'], nargs='+',
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help='List of ops which weights are converted.')
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args = parser.parse_args()
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DT_FLOAT = 1
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DT_HALF = 19
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# For the frozen graphs, an every node that uses weights connected to Const nodes
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# through an Identity node. Usually they're called in the same way with '/read' suffix.
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# We'll replace all of them to Cast nodes.
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# Load the model
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with tf.gfile.FastGFile(args.input) as f:
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graph_def = tf.GraphDef()
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graph_def.ParseFromString(f.read())
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# Set of all inputs from desired nodes.
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inputs = []
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for node in graph_def.node:
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if node.op in args.ops:
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inputs += node.input
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weightsNodes = []
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for node in graph_def.node:
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# From the whole inputs we need to keep only an Identity nodes.
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if node.name in inputs and node.op == 'Identity' and node.attr['T'].type == DT_FLOAT:
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weightsNodes.append(node.input[0])
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# Replace Identity to Cast.
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node.op = 'Cast'
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node.attr['DstT'].type = DT_FLOAT
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node.attr['SrcT'].type = DT_HALF
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del node.attr['T']
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del node.attr['_class']
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# Convert weights to halfs.
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for node in graph_def.node:
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if node.name in weightsNodes:
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node.attr['dtype'].type = DT_HALF
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node.attr['value'].tensor.dtype = DT_HALF
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floats = node.attr['value'].tensor.tensor_content
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floats = struct.unpack('f' * (len(floats) / 4), floats)
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halfs = np.array(floats).astype(np.float16).view(np.uint16)
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node.attr['value'].tensor.tensor_content = struct.pack('H' * len(halfs), *halfs)
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tf.train.write_graph(graph_def, "", args.output, as_text=False)
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