c614b35b40
Also fix warning with deprecated assert method. PiperOrigin-RevId: 314782149 Change-Id: I0f02cd9908e584aa0b9404584e80d7679bbc5c1f
103 lines
3.9 KiB
Python
103 lines
3.9 KiB
Python
# Copyright 2016 The TensorFlow Authors. 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 the SWIG-wrapped quantize training rewriting."""
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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 os
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from tensorflow.python.client import session
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import importer
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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from tensorflow.python.training import quantize_training
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from tensorflow.python.training import saver as saver_module
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class PywrapQuantizeTrainingTest(test.TestCase):
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# Mainly to verify the python interface is working.
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# More tests for this function can be found in the related c++ tests.
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def testQuantizeTraining(self):
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with session.Session() as sess:
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a = constant_op.constant(6.0, shape=[1, 1])
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b = constant_op.constant(7.0, shape=[1, 1])
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c = math_ops.matmul(a, b, name='matmul')
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self.assertEqual(c.eval(), 42.0)
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self.assertEqual(len(sess.graph_def.node), 3)
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result = quantize_training.do_quantize_training_on_graphdef(
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sess.graph_def, 8)
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# We just want to guarantee that some rewrite happened.
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self.assertGreater(len(result.node), 3)
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# Test that save/restoring works for EMA variables generated in the
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# quantized training rewrite.
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@test_util.run_v1_only('The API is only expect to work with v1 session mode.')
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def testQuantizedSaveRestore(self):
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save_path = os.path.join(self.get_temp_dir(), 'quantized_save_restore')
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g = ops.Graph()
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with session.Session(graph=g) as sess:
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a = constant_op.constant(6.0, shape=[1, 1], name='a')
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b = variables.VariableV1(
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constant_op.constant(7.0, shape=[1, 1]), name='b')
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c = math_ops.matmul(a, b, name='matmul')
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init_op = variables.global_variables_initializer()
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saver = saver_module.Saver({'b': b})
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result = quantize_training.do_quantize_training_on_graphdef(
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sess.graph_def, 8)
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with ops.Graph().as_default() as g, session.Session(graph=g) as sess:
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_ = importer.import_graph_def(result, name='')
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# Initialize the variable.
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self.evaluate(g.get_operation_by_name(init_op.name))
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# Run the graph for one step to assign values to the quantization min/max
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# variables.
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self.evaluate(g.get_tensor_by_name(c.name))
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saver.save(sess, save_path)
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with ops.Graph().as_default() as g, session.Session(graph=g) as sess:
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_ = importer.import_graph_def(result, name='')
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# When we restore the saved variabled, the quantization variables should
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# be restored as well.
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saver.restore(sess, save_path)
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self.assertEqual(7.0, sess.run(g.get_tensor_by_name('b:0')))
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self.assertEqual(6.0, sess.run(g.get_tensor_by_name('a/Min/Variable:0')))
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self.assertEqual(6.0, sess.run(g.get_tensor_by_name('a/Max/Variable:0')))
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self.assertEqual(7.0,
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sess.run(g.get_tensor_by_name('b/read/Min/Variable:0')))
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self.assertEqual(7.0,
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sess.run(g.get_tensor_by_name('b/read/Max/Variable:0')))
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if __name__ == '__main__':
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test.main()
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