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# Copyright 2015 The TensorFlow Authors. 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 numpy as np
from six.moves import queue
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
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)
# single type
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.eval(), my_func(1.0, 2.0).astype(np.float32))
# 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)
# returns a list
with self.test_session():
def list_func(x):
return [x, x + 1]
x = tf.constant(0.0, tf.float64)
y, z = tf.py_func(list_func, [x], [tf.float64] * 2)
self.assertAllClose(y.eval(), 0.0)
self.assertAllClose(z.eval(), 1.0)
# returns a tuple
with self.test_session():
def tuple_func(x):
return x, x + 1
x = tf.constant(0.0, tf.float64)
y, z = tf.py_func(tuple_func, [x], [tf.float64] * 2)
self.assertAllClose(y.eval(), 0.0)
self.assertAllClose(z.eval(), 1.0)
def testStrings(self):
def read_fixed_length_numpy_strings():
return np.array([b" there"])
def read_and_return_strings(x, y):
return x + y
with self.test_session():
x = tf.constant([b"hello", b"hi"], tf.string)
y, = tf.py_func(read_fixed_length_numpy_strings, [], [tf.string])
z, = tf.py_func(read_and_return_strings, [x, y], [tf.string])
self.assertListEqual(list(z.eval()), [b"hello there", b"hi there"])
def testStringPadding(self):
correct = [b"this", b"is", b"a", b"test"]
with self.test_session():
s, = tf.py_func(lambda: [correct], [], [tf.string])
self.assertAllEqual(s.eval(), correct)
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 testNoInput(self):
with self.test_session():
x, = tf.py_func(lambda: 42.0, [], [tf.float64])
self.assertAllClose(x.eval(), 42.0)
def testCleanup(self):
for _ in xrange(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 testBadNumpyReturnType(self):
with self.test_session():
def bad():
# Structured numpy arrays aren't supported.
return np.array([], dtype=[("foo", np.float32)])
y, = tf.py_func(bad, [], [tf.float32])
with self.assertRaisesRegexp(errors.UnimplementedError,
"Unsupported numpy type"):
y.eval()
def testBadReturnType(self):
with self.test_session():
def bad():
# Non-string python objects aren't supported.
return tf.float32
z, = tf.py_func(bad, [], [tf.float64])
with self.assertRaisesRegexp(errors.UnimplementedError,
"Unsupported object type"):
z.eval()
def testStateful(self):
# Not using self.test_session(), which disables optimization.
with tf.Session() as sess:
producer = iter(range(3))
x, = tf.py_func(lambda: next(producer), [], [tf.int64])
self.assertEqual(sess.run(x), 0)
self.assertEqual(sess.run(x), 1)
self.assertEqual(sess.run(x), 2)
def testStateless(self):
# Not using self.test_session(), which disables optimization.
with tf.Session() as sess:
producer = iter(range(3))
x, = tf.py_func(lambda: next(producer), [], [tf.int64], stateful=False)
self.assertEqual(sess.run(x), 0)
self.assertEqual(sess.run(x), 0)
self.assertEqual(sess.run(x), 0)
def testGradientFunction(self):
# Input to tf.py_func is necessary, otherwise get_gradient_function()
# returns None per default.
a = tf.constant(0)
x, = tf.py_func(lambda a: 0, [a], [tf.int64])
y, = tf.py_func(lambda a: 0, [a], [tf.int64], stateful=False)
self.assertEqual(None, ops.get_gradient_function(x.op))
self.assertEqual(None, ops.get_gradient_function(y.op))
def testCOrder(self):
with self.test_session():
val = [[1, 2], [3, 4]]
x, = tf.py_func(lambda: np.array(val, order="F"), [], [tf.int64])
self.assertAllEqual(val, x.eval())
def testParallel(self):
# Tests that tf.py_func's can run in parallel if they release the GIL.
with self.test_session() as session:
q = queue.Queue(1)
def blocking_put():
q.put(42)
q.join() # Wait for task_done().
return 42
def blocking_get():
v = q.get(block=True) # Wait for put().
q.task_done()
return v
x, = tf.py_func(blocking_put, [], [tf.int64])
y, = tf.py_func(blocking_get, [], [tf.int64])
# This will result in a deadlock if the py_func's don't run in parallel.
session.run([x, y])
if __name__ == "__main__":
tf.test.main()
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