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py_func_test.py
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103 lines (86 loc) · 3.35 KB
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# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for py_func op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import errors
from tensorflow.python.ops import script_ops
class PyOpTest(tf.test.TestCase):
def testBasic(self):
def my_func(x, y):
return np.sinh(x) + np.cosh(y)
# scalar
with self.test_session():
x = tf.constant(1.0, tf.float32)
y = tf.constant(2.0, tf.float32)
z = tf.py_func(my_func, [x, y], [tf.float32])
self.assertEqual(z[0].eval(), my_func(1.0, 2.0).astype(np.float32))
# array
with self.test_session():
x = tf.constant([1.0, 2.0], tf.float64)
y = tf.constant([2.0, 3.0], tf.float64)
z = tf.py_func(my_func, [x, y], [tf.float64])
self.assertAllEqual(
z[0].eval(),
my_func([1.0, 2.0], [2.0, 3.0]).astype(np.float64))
# a bit exotic type (complex64)
with self.test_session():
x = tf.constant(1+2j, tf.complex64)
y = tf.constant(3+4j, tf.complex64)
z, = tf.py_func(my_func, [x, y], [tf.complex64])
self.assertAllClose(z.eval(), my_func(1+2j, 3+4j))
# a bit excotic function (rfft)
with self.test_session():
x = tf.constant([1., 2., 3., 4.], tf.float32)
def rfft(x):
return np.fft.rfft(x).astype(np.complex64)
y, = tf.py_func(rfft, [x], [tf.complex64])
self.assertAllClose(y.eval(), np.fft.rfft([1., 2., 3., 4.]))
# returns a python literal.
with self.test_session():
def literal(x):
return 1.0 if x == 0.0 else 0.0
x = tf.constant(0.0, tf.float64)
y, = tf.py_func(literal, [x], [tf.float64])
self.assertAllClose(y.eval(), 1.0)
def testLarge(self):
with self.test_session() as sess:
x = tf.zeros([1000000], dtype=np.float32)
y = tf.py_func(lambda x: x + 1, [x], [tf.float32])
z = tf.py_func(lambda x: x * 2, [x], [tf.float32])
for _ in xrange(100):
sess.run([y[0].op, z[0].op])
def testCleanup(self):
for _ in range(1000):
g = tf.Graph()
with g.as_default():
c = tf.constant([1.], tf.float32)
_ = tf.py_func(lambda x: x + 1, [c], [tf.float32])
self.assertTrue(script_ops._py_funcs.size() < 100)
def testError(self):
with self.test_session():
def bad(_):
return tf.float32 # a python object. We should fail.
x = tf.constant(0.0, tf.float64)
y, = tf.py_func(bad, [x], [tf.float64])
with self.assertRaisesRegexp(errors.UnimplementedError,
"Unsupported numpy type"):
y.eval()
if __name__ == "__main__":
tf.test.main()