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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 tensorflow.ops.session_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
class SessionOpsTest(tf.test.TestCase):
def testHandleBasic(self):
with self.test_session() as sess:
# Return a handle.
a = tf.constant(10)
b = tf.constant(5)
c = tf.mul(a, b)
h = tf.get_session_handle(c)
h = sess.run(h)
# Feed a tensor handle.
f, x = tf.get_session_tensor(tf.int32)
y = tf.mul(x, 10)
self.assertEqual(500, sess.run(y, feed_dict={f: h.handle}))
def testHandleEval(self):
with self.test_session() as sess:
# Return a handle.
a = tf.constant(10)
b = tf.constant(5)
c = tf.mul(a, b)
h = tf.get_session_handle(c)
h = sess.run(h)
# Get the tensor from its handle.
self.assertEqual(50, h.eval())
def testHandleAndValue(self):
with self.test_session() as sess:
# Return a handle and a value.
a = tf.constant(10)
b = tf.constant(5)
c = tf.mul(a, b)
h = tf.get_session_handle(c)
v = tf.mul(a, c)
h, v = sess.run([h, v])
self.assertEqual(50, h.eval())
self.assertEqual(500, v)
def testHandleCond(self):
with self.test_session() as sess:
# Return a handle and a value
a = tf.constant(10)
b = tf.constant(5)
p = tf.less(a, b)
c = tf.mul(a, b)
h = tf.get_session_handle(c)
p, h = sess.run([p, h])
# Run by feeding a tensor handle.
f, x = tf.get_session_tensor(tf.int32)
if p:
y = tf.mul(x, 10)
else:
y = tf.mul(x, 100)
result = sess.run(y, feed_dict={f: h.handle})
self.assertEqual(5000, result)
def testHandleForLoop(self):
with self.test_session() as sess:
# Initialize a handle.
a = tf.constant(0)
h = tf.get_session_handle(a)
h = sess.run(h)
# Do some computation.
f, x = tf.get_session_tensor(tf.int32)
# Must define the loop body outside the loop.
h_x = tf.get_session_handle(tf.add(x, 1))
for _ in range(100):
# This exercises garbage collection.
h = sess.run(h_x, feed_dict={f: h.handle})
self.assertEqual(100, h.eval())
def testHandleWhileLoop(self):
with self.test_session() as sess:
# Initialize a handle.
a = tf.constant(0)
h = tf.get_session_handle(a)
h = sess.run(h)
# Do some computation.
f, x = tf.get_session_tensor(tf.int32)
b = tf.constant(100)
p = tf.less(x, b)
# Must define the loop body outside the loop.
h_x = tf.get_session_handle(tf.add(x, 1))
while True:
rp, h = sess.run([p, h_x], feed_dict={f: h.handle})
if not rp:
break
self.assertEqual(101, h.eval())
def testHandleMover(self):
with self.test_session() as sess:
# Return a handle.
a = tf.constant(10)
b = tf.constant(5)
c = tf.mul(a, b)
h = tf.get_session_handle(c)
h = sess.run(h)
# Feed a tensor handle.
f, x = tf.get_session_tensor(tf.int32)
y = tf.mul(x, 10)
self.assertEqual(500, sess.run(y, feed_dict={f: h.handle}))
# Feed another tensor handle.
with tf.device("/gpu:0"):
a = tf.constant(10)
h = tf.get_session_handle(a)
h = sess.run(h)
self.assertEqual(100, sess.run(y, feed_dict={f: h.handle}))
def testHandleDeleter(self):
with self.test_session() as sess:
# Return a handle.
a = tf.constant(10)
b = tf.constant(5)
c = tf.mul(a, b)
h = tf.get_session_handle(c)
h = sess.run(h)
# Delete using a raw tensor handle.
h = h.get_raw_handle()
f, x = tf.delete_session_tensor()
sess.run(x, feed_dict={f: h})
def testMultiDevices(self):
with self.test_session() as sess:
with tf.device("/gpu:0"):
a = tf.constant(1.0)
a_handle = sess.run(tf.get_session_handle(a))
with tf.device("/cpu:0"):
b = tf.constant(2.0)
b_handle = sess.run(tf.get_session_handle(b))
a_p, a_t = tf.get_session_tensor(tf.float32)
b_p, b_t = tf.get_session_tensor(tf.float32)
c = tf.add(a_t, b_t)
c_handle = sess.run(
tf.get_session_handle(c),
feed_dict={a_p: a_handle.handle,
b_p: b_handle.handle})
self.assertEqual(3.0, c_handle.eval())
if __name__ == "__main__":
tf.test.main()