def testConstructor(self):

with tf.Graph().as_default():

q = tf.FIFOQueue(10, tf.float32, name="Q")

self.assertTrue(isinstance(q.queue_ref, tf.Tensor))

self.assertProtoEquals("""

""", q.queue_ref.op.node_def)

def testMultiQueueConstructor(self):

with tf.Graph().as_default():

q = tf.FIFOQueue(5, (tf.int32, tf.float32), shared_name="foo", name="Q")

self.assertTrue(isinstance(q.queue_ref, tf.Tensor))

self.assertProtoEquals("""

""", q.queue_ref.op.node_def)

def testConstructorWithShapes(self):

with tf.Graph().as_default():

q = tf.FIFOQueue(5, (tf.int32, tf.float32),

shapes=(tf.TensorShape([1, 1, 2, 3]),

tf.TensorShape([5, 8])), name="Q")

self.assertTrue(isinstance(q.queue_ref, tf.Tensor))

self.assertProtoEquals("""

""", q.queue_ref.op.node_def)

def testEnqueue(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32)

enqueue_op = q.enqueue((10.0,))

enqueue_op.run()

def testEnqueueHalf(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float16)

enqueue_op = q.enqueue((10.0,))

enqueue_op.run()

def testEnqueueWithShape(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32, shapes=(3, 2))

enqueue_correct_op = q.enqueue(([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]],))

enqueue_correct_op.run()

with self.assertRaises(ValueError):

q.enqueue(([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],))

self.assertEqual(1, q.size().eval())

def testEnqueueManyWithShape(self):

with self.test_session():

q = tf.FIFOQueue(10, [tf.int32, tf.int32],

shapes=[(), (2,)])

q.enqueue_many([[1, 2, 3, 4], [[1, 1], [2, 2], [3, 3], [4, 4]]]).run()

self.assertEqual(4, q.size().eval())

def testEnqueueDictWithoutNames(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32)

with self.assertRaisesRegexp(ValueError, "must have names"):

q.enqueue({"a": 12.0})

with self.assertRaisesRegexp(ValueError, "must have names"):

q.enqueue_many({"a": [12.0, 13.0]})

def testParallelEnqueue(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, tf.float32)

elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0]

enqueue_ops = [q.enqueue((x,)) for x in elems]

dequeued_t = q.dequeue()

# Run one producer thread for each element in elems.

def enqueue(enqueue_op):

sess.run(enqueue_op)

threads = [self.checkedThread(target=enqueue, args=(e,))

for e in enqueue_ops]

for thread in threads:

thread.start()

for thread in threads:

thread.join()

# Dequeue every element using a single thread.

results = []

for _ in xrange(len(elems)):

results.append(dequeued_t.eval())

self.assertItemsEqual(elems, results)

def testParallelDequeue(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, tf.float32)

elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0]

enqueue_ops = [q.enqueue((x,)) for x in elems]

dequeued_t = q.dequeue()

# Enqueue every element using a single thread.

for enqueue_op in enqueue_ops:

enqueue_op.run()

# Run one consumer thread for each element in elems.

results = []

def dequeue():

results.append(sess.run(dequeued_t))

threads = [self.checkedThread(target=dequeue) for _ in enqueue_ops]

for thread in threads:

thread.start()

for thread in threads:

thread.join()

self.assertItemsEqual(elems, results)

def testDequeue(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32)

elems = [10.0, 20.0, 30.0]

enqueue_ops = [q.enqueue((x,)) for x in elems]

dequeued_t = q.dequeue()

for enqueue_op in enqueue_ops:

enqueue_op.run()

for i in xrange(len(elems)):

vals = dequeued_t.eval()

self.assertEqual([elems[i]], vals)

def testDequeueHalf(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float16)

elems = [10.0, 20.0, 30.0]

enqueue_ops = [q.enqueue((x,)) for x in elems]

dequeued_t = q.dequeue()

for enqueue_op in enqueue_ops:

enqueue_op.run()

for i in xrange(len(elems)):

vals = dequeued_t.eval()

self.assertEqual([elems[i]], vals)

def testEnqueueAndBlockingDequeue(self):

with self.test_session() as sess:

q = tf.FIFOQueue(3, tf.float32)

elems = [10.0, 20.0, 30.0]

enqueue_ops = [q.enqueue((x,)) for x in elems]

dequeued_t = q.dequeue()

def enqueue():

# The enqueue_ops should run after the dequeue op has blocked.

# TODO(mrry): Figure out how to do this without sleeping.

time.sleep(0.1)

for enqueue_op in enqueue_ops:

sess.run(enqueue_op)

results = []

def dequeue():

for _ in xrange(len(elems)):

results.append(sess.run(dequeued_t))

enqueue_thread = self.checkedThread(target=enqueue)

dequeue_thread = self.checkedThread(target=dequeue)

enqueue_thread.start()

dequeue_thread.start()

enqueue_thread.join()

dequeue_thread.join()

for elem, result in zip(elems, results):

self.assertEqual([elem], result)

def testMultiEnqueueAndDequeue(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, (tf.int32, tf.float32))

elems = [(5, 10.0), (10, 20.0), (15, 30.0)]

enqueue_ops = [q.enqueue((x, y)) for x, y in elems]

dequeued_t = q.dequeue()

for enqueue_op in enqueue_ops:

enqueue_op.run()

for i in xrange(len(elems)):

x_val, y_val = sess.run(dequeued_t)

x, y = elems[i]

self.assertEqual([x], x_val)

self.assertEqual([y], y_val)

def testQueueSizeEmpty(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32)

self.assertEqual([0], q.size().eval())

def testQueueSizeAfterEnqueueAndDequeue(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32)

enqueue_op = q.enqueue((10.0,))

dequeued_t = q.dequeue()

size = q.size()

self.assertEqual([], size.get_shape())

enqueue_op.run()

self.assertEqual(1, size.eval())

dequeued_t.op.run()

self.assertEqual(0, size.eval())

def testEnqueueMany(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32)

elems = [10.0, 20.0, 30.0, 40.0]

enqueue_op = q.enqueue_many((elems,))

dequeued_t = q.dequeue()

enqueue_op.run()

enqueue_op.run()

for i in range(8):

vals = dequeued_t.eval()

self.assertEqual([elems[i % 4]], vals)

def testEmptyEnqueueMany(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32)

empty_t = tf.constant([], dtype=tf.float32,

shape=[0, 2, 3])

enqueue_op = q.enqueue_many((empty_t,))

size_t = q.size()

self.assertEqual([0], size_t.eval())

enqueue_op.run()

self.assertEqual([0], size_t.eval())

def testEmptyDequeueMany(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32, shapes=())

enqueue_op = q.enqueue((10.0,))

dequeued_t = q.dequeue_many(0)

self.assertEqual([], dequeued_t.eval().tolist())

enqueue_op.run()

self.assertEqual([], dequeued_t.eval().tolist())

def testEmptyDequeueUpTo(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32, shapes=())

enqueue_op = q.enqueue((10.0,))

dequeued_t = q.dequeue_up_to(0)

self.assertEqual([], dequeued_t.eval().tolist())

enqueue_op.run()

self.assertEqual([], dequeued_t.eval().tolist())

def testEmptyDequeueManyWithNoShape(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32)

# Expect the operation to fail due to the shape not being constrained.

with self.assertRaisesOpError("specified shapes"):

q.dequeue_many(0).eval()

def testMultiEnqueueMany(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, (tf.float32, tf.int32))

float_elems = [10.0, 20.0, 30.0, 40.0]

int_elems = [[1, 2], [3, 4], [5, 6], [7, 8]]

enqueue_op = q.enqueue_many((float_elems, int_elems))

dequeued_t = q.dequeue()

enqueue_op.run()

enqueue_op.run()

for i in range(8):

float_val, int_val = sess.run(dequeued_t)

self.assertEqual(float_elems[i % 4], float_val)

self.assertAllEqual(int_elems[i % 4], int_val)

def testDequeueMany(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32, ())

elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0]

enqueue_op = q.enqueue_many((elems,))

dequeued_t = q.dequeue_many(4)

enqueue_op.run()

self.assertAllEqual(elems[0:4], dequeued_t.eval())

self.assertAllEqual(elems[4:8], dequeued_t.eval())

def testDequeueUpToNoBlocking(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32, ())

elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0]

enqueue_op = q.enqueue_many((elems,))

dequeued_t = q.dequeue_up_to(4)

enqueue_op.run()

self.assertAllEqual(elems[0:4], dequeued_t.eval())

self.assertAllEqual(elems[4:8], dequeued_t.eval())

def testMultiDequeueMany(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, (tf.float32, tf.int32),

shapes=((), (2,)))

float_elems = [

10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0]

int_elems = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10],

[11, 12], [13, 14], [15, 16], [17, 18], [19, 20]]

enqueue_op = q.enqueue_many((float_elems, int_elems))

dequeued_t = q.dequeue_many(4)

dequeued_single_t = q.dequeue()

enqueue_op.run()

float_val, int_val = sess.run(dequeued_t)

self.assertAllEqual(float_elems[0:4], float_val)

self.assertAllEqual(int_elems[0:4], int_val)

self.assertEqual(float_val.shape, dequeued_t[0].get_shape())

self.assertEqual(int_val.shape, dequeued_t[1].get_shape())

float_val, int_val = sess.run(dequeued_t)

self.assertAllEqual(float_elems[4:8], float_val)

self.assertAllEqual(int_elems[4:8], int_val)

float_val, int_val = sess.run(dequeued_single_t)

self.assertAllEqual(float_elems[8], float_val)

self.assertAllEqual(int_elems[8], int_val)

self.assertEqual(float_val.shape, dequeued_single_t[0].get_shape())

self.assertEqual(int_val.shape, dequeued_single_t[1].get_shape())

def testMultiDequeueUpToNoBlocking(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, (tf.float32, tf.int32),

shapes=((), (2,)))

float_elems = [

10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0]

int_elems = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10],

[11, 12], [13, 14], [15, 16], [17, 18], [19, 20]]

enqueue_op = q.enqueue_many((float_elems, int_elems))

dequeued_t = q.dequeue_up_to(4)

enqueue_op.run()

float_val, int_val = sess.run(dequeued_t)

self.assertAllEqual(float_elems[0:4], float_val)

self.assertAllEqual(int_elems[0:4], int_val)

self.assertEqual([None], dequeued_t[0].get_shape().as_list())

self.assertEqual([None, 2], dequeued_t[1].get_shape().as_list())

float_val, int_val = sess.run(dequeued_t)

self.assertAllEqual(float_elems[4:8], float_val)

self.assertAllEqual(int_elems[4:8], int_val)

def testHighDimension(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.int32, (4, 4, 4, 4))

elems = np.array([[[[[x] * 4] * 4] * 4] * 4 for x in range(10)], np.int32)

enqueue_op = q.enqueue_many((elems,))

dequeued_t = q.dequeue_many(10)

enqueue_op.run()

self.assertAllEqual(dequeued_t.eval(), elems)

def testEnqueueWrongShape(self):

q = tf.FIFOQueue(10, (tf.int32, tf.int32), ((), (2)))

with self.assertRaises(ValueError):

q.enqueue(([1, 2], [2, 2]))

with self.assertRaises(ValueError):

q.enqueue_many((7, [[1, 2], [3, 4], [5, 6]]))

def testBatchSizeMismatch(self):

q = tf.FIFOQueue(10, (tf.int32, tf.int32, tf.int32), ((), (), ()))

with self.assertRaises(ValueError):

q.enqueue_many(([1, 2, 3], [1, 2], [1, 2, 3]))

with self.assertRaises(ValueError):

q.enqueue_many(([1, 2, 3], [1, 2], tf.placeholder(tf.int32)))

with self.assertRaises(ValueError):

q.enqueue_many((tf.placeholder(tf.int32), [1, 2], [1, 2, 3]))

def testEnqueueManyEmptyTypeConversion(self):

q = tf.FIFOQueue(10, (tf.int32, tf.float32), ((), ()))

enq = q.enqueue_many(([], []))

self.assertEqual(tf.int32, enq.inputs[1].dtype)

self.assertEqual(tf.float32, enq.inputs[2].dtype)

def testEnqueueWrongType(self):

q = tf.FIFOQueue(10, (tf.int32, tf.float32), ((), ()))

with self.assertRaises(ValueError):

q.enqueue((tf.placeholder(tf.int32), tf.placeholder(tf.int32)))

with self.assertRaises(ValueError):

q.enqueue_many((tf.placeholder(tf.int32), tf.placeholder(tf.int32)))

def testEnqueueWrongShapeAtRuntime(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, (tf.int32, tf.int32), ((2, 2), (3, 3)))

elems_ok = np.array([1] * 4).reshape((2, 2)).astype(np.int32)

elems_bad = tf.placeholder(tf.int32)

enqueue_op = q.enqueue((elems_ok, elems_bad))

with self.assertRaisesRegexp(

tf.errors.InvalidArgumentError, r"Expected \[3,3\], got \[3,4\]"):

sess.run([enqueue_op],

feed_dict={elems_bad: np.array([1] * 12).reshape((3, 4))})

def testEnqueueDequeueManyWrongShape(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, (tf.int32, tf.int32), ((2, 2), (3, 3)))

elems_ok = np.array([1] * 8).reshape((2, 2, 2)).astype(np.int32)

elems_bad = tf.placeholder(tf.int32)

enqueue_op = q.enqueue_many((elems_ok, elems_bad))

dequeued_t = q.dequeue_many(2)

with self.assertRaisesRegexp(

tf.errors.InvalidArgumentError,

"Shape mismatch in tuple component 1. "

r"Expected \[2,3,3\], got \[2,3,4\]"):

sess.run([enqueue_op],

feed_dict={elems_bad: np.array([1] * 24).reshape((2, 3, 4))})

dequeued_t.eval()

def testParallelEnqueueMany(self):

with self.test_session() as sess:

q = tf.FIFOQueue(1000, tf.float32, shapes=())

elems = [10.0 * x for x in range(100)]

enqueue_op = q.enqueue_many((elems,))

dequeued_t = q.dequeue_many(1000)

# Enqueue 100 items in parallel on 10 threads.

def enqueue():

sess.run(enqueue_op)

threads = [self.checkedThread(target=enqueue) for _ in range(10)]

for thread in threads:

thread.start()

for thread in threads:

thread.join()

self.assertItemsEqual(dequeued_t.eval(), elems * 10)

def testParallelDequeueMany(self):

with self.test_session() as sess:

q = tf.FIFOQueue(1000, tf.float32, shapes=())

elems = [10.0 * x for x in range(1000)]

enqueue_op = q.enqueue_many((elems,))

dequeued_t = q.dequeue_many(100)

enqueue_op.run()

# Dequeue 100 items in parallel on 10 threads.

dequeued_elems = []

def dequeue():

dequeued_elems.extend(sess.run(dequeued_t))

threads = [self.checkedThread(target=dequeue) for _ in range(10)]

for thread in threads:

thread.start()

for thread in threads:

thread.join()

self.assertItemsEqual(elems, dequeued_elems)

def testParallelDequeueUpTo(self):

with self.test_session() as sess:

q = tf.FIFOQueue(1000, tf.float32, shapes=())

elems = [10.0 * x for x in range(1000)]

enqueue_op = q.enqueue_many((elems,))

close_op = q.close()

dequeued_t = q.dequeue_up_to(101)

enqueue_op.run()

close_op.run()

# Dequeue up to 101 items in parallel on 10 threads, from closed queue.

dequeued_elems = []

def dequeue():

dequeued_elems.extend(sess.run(dequeued_t))

threads = [self.checkedThread(target=dequeue) for _ in range(10)]

for thread in threads:

thread.start()

for thread in threads:

thread.join()

self.assertItemsEqual(elems, dequeued_elems)

def testParallelEnqueueAndDequeue(self):

with self.test_session() as sess:

q = tf.FIFOQueue(50, tf.float32, shapes=())

initial_elements = [10.0] * 49

q.enqueue_many((initial_elements,)).run()

enqueue_op = q.enqueue((20.0,))

dequeued_t = q.dequeue()

def enqueue():

for _ in xrange(100):

sess.run(enqueue_op)

def dequeue():

for _ in xrange(100):

self.assertTrue(sess.run(dequeued_t) in (10.0, 20.0))

enqueue_threads = [self.checkedThread(target=enqueue) for _ in range(10)]

dequeue_threads = [self.checkedThread(target=dequeue) for _ in range(10)]

for enqueue_thread in enqueue_threads:

enqueue_thread.start()

for dequeue_thread in dequeue_threads:

dequeue_thread.start()

for enqueue_thread in enqueue_threads:

enqueue_thread.join()

for dequeue_thread in dequeue_threads:

dequeue_thread.join()

# Dequeue the initial count of elements to clean up.

cleanup_elems = q.dequeue_many(49).eval()

for elem in cleanup_elems:

self.assertTrue(elem in (10.0, 20.0))

def testMixtureOfEnqueueAndEnqueueMany(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, tf.int32, shapes=())

enqueue_placeholder = tf.placeholder(tf.int32, shape=())

enqueue_op = q.enqueue((enqueue_placeholder,))

enqueuemany_placeholder = tf.placeholder(

tf.int32, shape=(None,))

enqueuemany_op = q.enqueue_many((enqueuemany_placeholder,))

dequeued_t = q.dequeue()

close_op = q.close()

def dequeue():

for i in xrange(250):

self.assertEqual(i, sess.run(dequeued_t))

dequeue_thread = self.checkedThread(target=dequeue)

dequeue_thread.start()

elements_enqueued = 0

while elements_enqueued < 250:

# With equal probability, run Enqueue or enqueue_many.

if random.random() > 0.5:

enqueue_op.run({enqueue_placeholder: elements_enqueued})

elements_enqueued += 1

else:

count = random.randint(0, min(20, 250 - elements_enqueued))

range_to_enqueue = np.arange(elements_enqueued,

elements_enqueued + count,

dtype=np.int32)

enqueuemany_op.run({enqueuemany_placeholder: range_to_enqueue})

elements_enqueued += count

close_op.run()

dequeue_thread.join()

self.assertEqual(0, q.size().eval())

def testMixtureOfDequeueAndDequeueMany(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, tf.int32, shapes=())

enqueue_op = q.enqueue_many((np.arange(250, dtype=np.int32),))

dequeued_t = q.dequeue()

count_placeholder = tf.placeholder(tf.int32, shape=())

dequeuemany_t = q.dequeue_many(count_placeholder)

def enqueue():

sess.run(enqueue_op)

enqueue_thread = self.checkedThread(target=enqueue)

enqueue_thread.start()

elements_dequeued = 0

while elements_dequeued < 250:

# With equal probability, run Dequeue or dequeue_many.

if random.random() > 0.5:

self.assertEqual(elements_dequeued, dequeued_t.eval())

elements_dequeued += 1

else:

count = random.randint(0, min(20, 250 - elements_dequeued))

expected_range = np.arange(elements_dequeued,

elements_dequeued + count,

dtype=np.int32)

self.assertAllEqual(

expected_range, dequeuemany_t.eval({count_placeholder: count}))

elements_dequeued += count

q.close().run()

enqueue_thread.join()

self.assertEqual(0, q.size().eval())

def testBlockingDequeueMany(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, tf.float32, ())

elems = [10.0, 20.0, 30.0, 40.0]

enqueue_op = q.enqueue_many((elems,))

dequeued_t = q.dequeue_many(4)

dequeued_elems = []

def enqueue():

# The enqueue_op should run after the dequeue op has blocked.

# TODO(mrry): Figure out how to do this without sleeping.

time.sleep(0.1)

sess.run(enqueue_op)

def dequeue():

dequeued_elems.extend(sess.run(dequeued_t).tolist())

enqueue_thread = self.checkedThread(target=enqueue)

dequeue_thread = self.checkedThread(target=dequeue)

enqueue_thread.start()

dequeue_thread.start()

enqueue_thread.join()

dequeue_thread.join()

self.assertAllEqual(elems, dequeued_elems)

def testBlockingDequeueUpTo(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, tf.float32, ())

elems = [10.0, 20.0, 30.0, 40.0]

enqueue_op = q.enqueue_many((elems,))

dequeued_t = q.dequeue_up_to(4)

dequeued_elems = []

def enqueue():

# The enqueue_op should run after the dequeue op has blocked.

# TODO(mrry): Figure out how to do this without sleeping.

time.sleep(0.1)

sess.run(enqueue_op)

def dequeue():

dequeued_elems.extend(sess.run(dequeued_t).tolist())

enqueue_thread = self.checkedThread(target=enqueue)

dequeue_thread = self.checkedThread(target=dequeue)

enqueue_thread.start()

dequeue_thread.start()

enqueue_thread.join()

dequeue_thread.join()

self.assertAllEqual(elems, dequeued_elems)

def testDequeueManyWithTensorParameter(self):

with self.test_session():

# Define a first queue that contains integer counts.

dequeue_counts = [random.randint(1, 10) for _ in range(100)]

count_q = tf.FIFOQueue(100, tf.int32, ())

enqueue_counts_op = count_q.enqueue_many((dequeue_counts,))

total_count = sum(dequeue_counts)

# Define a second queue that contains total_count elements.

elems = [random.randint(0, 100) for _ in range(total_count)]

q = tf.FIFOQueue(total_count, tf.int32, ())

enqueue_elems_op = q.enqueue_many((elems,))

# Define a subgraph that first dequeues a count, then DequeuesMany

# that number of elements.

dequeued_t = q.dequeue_many(count_q.dequeue())

enqueue_counts_op.run()

enqueue_elems_op.run()

dequeued_elems = []

for _ in dequeue_counts:

dequeued_elems.extend(dequeued_t.eval())

self.assertEqual(elems, dequeued_elems)

def testDequeueFromClosedQueue(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32)

elems = [10.0, 20.0, 30.0, 40.0]

enqueue_op = q.enqueue_many((elems,))

close_op = q.close()

dequeued_t = q.dequeue()

enqueue_op.run()

close_op.run()

for elem in elems:

self.assertEqual([elem], dequeued_t.eval())

# Expect the operation to fail due to the queue being closed.

with self.assertRaisesRegexp(tf.errors.OutOfRangeError,

"is closed and has insufficient"):

dequeued_t.eval()

def testBlockingDequeueFromClosedQueue(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, tf.float32)

elems = [10.0, 20.0, 30.0, 40.0]

enqueue_op = q.enqueue_many((elems,))

close_op = q.close()

dequeued_t = q.dequeue()

enqueue_op.run()

def dequeue():

for elem in elems:

self.assertEqual([elem], sess.run(dequeued_t))

# Expect the operation to fail due to the queue being closed.

with self.assertRaisesRegexp(tf.errors.OutOfRangeError,

"is closed and has insufficient"):

sess.run(dequeued_t)

dequeue_thread = self.checkedThread(target=dequeue)

dequeue_thread.start()

# The close_op should run after the dequeue_thread has blocked.

# TODO(mrry): Figure out how to do this without sleeping.

time.sleep(0.1)

close_op.run()

dequeue_thread.join()

def testBlockingDequeueFromClosedEmptyQueue(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, tf.float32)

close_op = q.close()

dequeued_t = q.dequeue()

def dequeue():

# Expect the operation to fail due to the queue being closed.

with self.assertRaisesRegexp(tf.errors.OutOfRangeError,

"is closed and has insufficient"):

sess.run(dequeued_t)

dequeue_thread = self.checkedThread(target=dequeue)

dequeue_thread.start()

# The close_op should run after the dequeue_thread has blocked.

# TODO(mrry): Figure out how to do this without sleeping.

time.sleep(0.1)

close_op.run()

dequeue_thread.join()

def testBlockingDequeueManyFromClosedQueue(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, tf.float32, ())

elems = [10.0, 20.0, 30.0, 40.0]

enqueue_op = q.enqueue_many((elems,))

close_op = q.close()

dequeued_t = q.dequeue_many(4)

enqueue_op.run()

def dequeue():

self.assertAllEqual(elems, sess.run(dequeued_t))

# Expect the operation to fail due to the queue being closed.

with self.assertRaisesRegexp(tf.errors.OutOfRangeError,

"is closed and has insufficient"):

sess.run(dequeued_t)

dequeue_thread = self.checkedThread(target=dequeue)

dequeue_thread.start()

# The close_op should run after the dequeue_thread has blocked.

# TODO(mrry): Figure out how to do this without sleeping.

time.sleep(0.1)

close_op.run()

dequeue_thread.join()

def testBlockingDequeueManyButNotAllFromClosedQueue(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, tf.float32, ())

elems = [10.0, 20.0, 30.0, 40.0]

enqueue_op = q.enqueue_many((elems,))

close_op = q.close()

dequeued_t = q.dequeue_many(3)

enqueue_op.run()

def dequeue():

self.assertAllEqual(elems[:3], sess.run(dequeued_t))

# Expect the operation to fail due to the queue being closed.

with self.assertRaisesRegexp(tf.errors.OutOfRangeError,

"is closed and has insufficient"):

sess.run(dequeued_t)

dequeue_thread = self.checkedThread(target=dequeue)

dequeue_thread.start()

# The close_op should run after the dequeue_thread has blocked.

# TODO(mrry): Figure out how to do this without sleeping.

time.sleep(0.1)

close_op.run()

dequeue_thread.join()

def testDequeueUpToFromClosedQueueReturnsRemainder(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, tf.float32, ())

elems = [10.0, 20.0, 30.0, 40.0]

enqueue_op = q.enqueue_many((elems,))

close_op = q.close()

dequeued_t = q.dequeue_up_to(3)

enqueue_op.run()

def dequeue():

self.assertAllEqual(elems[:3], sess.run(dequeued_t))

self.assertAllEqual(elems[3:], sess.run(dequeued_t))

dequeue_thread = self.checkedThread(target=dequeue)

dequeue_thread.start()

# The close_op should run after the dequeue_thread has blocked.

# TODO(mrry): Figure out how to do this without sleeping.

time.sleep(0.1)

close_op.run()

dequeue_thread.join()

def testEnqueueManyLargerThanCapacityWithConcurrentDequeueMany(self):

with self.test_session() as sess:

q = tf.FIFOQueue(4, tf.float32, ())

elems = [10.0, 20.0, 30.0, 40.0]

enqueue_op = q.enqueue_many((elems,))

close_op = q.close()

dequeued_t = q.dequeue_many(3)

cleanup_dequeue_t = q.dequeue()

def enqueue():

sess.run(enqueue_op)

def dequeue():

self.assertAllEqual(elems[0:3], sess.run(dequeued_t))

with self.assertRaises(tf.errors.OutOfRangeError):

sess.run(dequeued_t)

self.assertEqual(elems[3], sess.run(cleanup_dequeue_t))

def close():

sess.run(close_op)

enqueue_thread = self.checkedThread(target=enqueue)

enqueue_thread.start()

dequeue_thread = self.checkedThread(target=dequeue)

dequeue_thread.start()

# The close_op should run after the dequeue_thread has blocked.

# TODO(mrry): Figure out how to do this without sleeping.

time.sleep(0.1)

close_thread = self.checkedThread(target=close)

close_thread.start()

enqueue_thread.join()

dequeue_thread.join()

close_thread.join()

def testClosedBlockingDequeueManyRestoresPartialBatch(self):

with self.test_session() as sess:

q = tf.FIFOQueue(4, (tf.float32, tf.float32), ((), ()))

elems_a = [1.0, 2.0, 3.0]

elems_b = [10.0, 20.0, 30.0]

enqueue_op = q.enqueue_many((elems_a, elems_b))

dequeued_a_t, dequeued_b_t = q.dequeue_many(4)

cleanup_dequeue_a_t, cleanup_dequeue_b_t = q.dequeue()

close_op = q.close()

enqueue_op.run()

def dequeue():

with self.assertRaises(tf.errors.OutOfRangeError):

sess.run([dequeued_a_t, dequeued_b_t])

dequeue_thread = self.checkedThread(target=dequeue)

dequeue_thread.start()

# The close_op should run after the dequeue_thread has blocked.

# TODO(mrry): Figure out how to do this without sleeping.

time.sleep(0.1)

close_op.run()

dequeue_thread.join()

# Test that the elements in the partially-dequeued batch are

# restored in the correct order.

for elem_a, elem_b in zip(elems_a, elems_b):

val_a, val_b = sess.run([cleanup_dequeue_a_t, cleanup_dequeue_b_t])

self.assertEqual(elem_a, val_a)

self.assertEqual(elem_b, val_b)

self.assertEqual(0, q.size().eval())

def testBlockingDequeueManyFromClosedEmptyQueue(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, tf.float32, ())

close_op = q.close()

dequeued_t = q.dequeue_many(4)

def dequeue():

# Expect the operation to fail due to the queue being closed.

with self.assertRaisesRegexp(tf.errors.OutOfRangeError,

"is closed and has insufficient"):

sess.run(dequeued_t)

dequeue_thread = self.checkedThread(target=dequeue)

dequeue_thread.start()

# The close_op should run after the dequeue_thread has blocked.

# TODO(mrry): Figure out how to do this without sleeping.

time.sleep(0.1)

close_op.run()

dequeue_thread.join()

def testBlockingDequeueUpToFromClosedEmptyQueue(self):

with self.test_session() as sess:

q = tf.FIFOQueue(10, tf.float32, ())

close_op = q.close()

dequeued_t = q.dequeue_up_to(4)

def dequeue():

# Expect the operation to fail due to the queue being closed.

with self.assertRaisesRegexp(tf.errors.OutOfRangeError,

"is closed and has insufficient"):

sess.run(dequeued_t)

dequeue_thread = self.checkedThread(target=dequeue)

dequeue_thread.start()

# The close_op should run after the dequeue_thread has blocked.

# TODO(mrry): Figure out how to do this without sleeping.

time.sleep(0.1)

close_op.run()

dequeue_thread.join()

def testEnqueueToClosedQueue(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32)

enqueue_op = q.enqueue((10.0,))

close_op = q.close()

enqueue_op.run()

close_op.run()

# Expect the operation to fail due to the queue being closed.

with self.assertRaisesRegexp(tf.errors.CancelledError, "is closed"):

enqueue_op.run()

def testEnqueueManyToClosedQueue(self):

with self.test_session():

q = tf.FIFOQueue(10, tf.float32)

elems = [10.0, 20.0, 30.0, 40.0]

enqueue_op = q.enqueue_many((elems,))

close_op = q.close()

enqueue_op.run()

close_op.run()

# Expect the operation to fail due to the queue being closed.

with self.assertRaisesRegexp(tf.errors.CancelledError, "is closed"):