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# Copyright 2016 Google Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for flopsometer."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import tensorflow as tf
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import flopsometer
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class FlopsometerTest(tf.test.TestCase):
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def testConv2d(self):
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inputs = tf.zeros([2, 16, 16, 4])
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_, flops = flopsometer.conv2d(
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inputs, 8, [3, 3], stride=1, padding='SAME', output_mask=None)
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expected_flops = 2 * 16 * 16 * 3 * 3 * 8 * 4
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with self.test_session() as sess:
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sess.run(tf.global_variables_initializer())
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flops_out = sess.run(flops)
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self.assertAllEqual(flops_out, [expected_flops, expected_flops])
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def testConv2dUnknownSize(self):
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inputs = np.zeros([2, 16, 16, 4], dtype=np.float32)
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inputs_tf = tf.placeholder(tf.float32, shape=(2, None, None, 4))
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_, flops = flopsometer.conv2d(
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inputs_tf, 8, [3, 3], stride=1, padding='SAME', output_mask=None)
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expected_flops = 2 * 16 * 16 * 3 * 3 * 8 * 4
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with self.test_session() as sess:
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sess.run(tf.global_variables_initializer())
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flops_out = sess.run(flops, feed_dict={inputs_tf: inputs})
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self.assertAllEqual(flops_out, [expected_flops, expected_flops])
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def testConv2dStride(self):
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inputs = tf.zeros([2, 16, 16, 4])
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_, flops = flopsometer.conv2d(
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inputs, 8, [3, 3], stride=2, padding='SAME', output_mask=None)
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output_positions = 8 * 8
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expected_flops = 2 * output_positions * 3 * 3 * 8 * 4
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with self.test_session() as sess:
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sess.run(tf.global_variables_initializer())
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flops_out = sess.run(flops)
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self.assertAllEqual(flops_out, [expected_flops, expected_flops])
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def testConv2dOutputMask(self):
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inputs = tf.zeros([2, 16, 16, 4])
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mask = np.random.random([2, 16, 16]) <= 0.6
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mask_tf = tf.constant(np.float32(mask))
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_, flops = flopsometer.conv2d(
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inputs, 8, [3, 3], stride=1, padding='SAME', output_mask=mask_tf)
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per_position_flops = 2 * 3 * 3 * 8 * 4
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num_positions = np.sum(np.sum(np.int32(mask), 2), 1)
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expected_flops = [
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per_position_flops * num_positions[0],
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per_position_flops * num_positions[1]
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]
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with self.test_session() as sess:
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sess.run(tf.global_variables_initializer())
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flops_out = sess.run(flops)
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self.assertAllEqual(flops_out, expected_flops)
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if __name__ == '__main__':
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tf.test.main()
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# <FILESEP>
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# ------------------------------------------------
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# ONNX Model Editor and Graph Extractor
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# License under The MIT License
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# Written by Saurabh Shandilya
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# -----------------------------------------------
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import onnx
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from onnx import helper, checker
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from onnx import TensorProto
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import re
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import argparse
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def createGraphMemberMap(graph_member_list):
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member_map=dict();
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for n in graph_member_list:
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