def testVariableDevicePlacement(self):
classes = np.random.randint(5, size=(20000,)) # Uniformly sampled
target_dist = [0.9, 0.05, 0.05, 0.0, 0.0]
with ops.device(
device_setter.replica_device_setter(ps_tasks=1, ps_device="/cpu:0")):
dataset = (dataset_ops.Dataset.from_tensor_slices(classes)
.shuffle(200, seed=21)
.map(lambda c: (c, string_ops.as_string(c))))
dataset = dataset_ops.rejection_resample(
dataset, target_dist=target_dist, initial_dist=None,
class_func=lambda c, _: c, seed=27)
self.assertEqual(1, len(variables.local_variables()))
self.assertEqual(b"",
compat.as_bytes(variables.local_variables()[0].device))
def testVariableDevicePlacement(self):
classes = np.random.randint(5, size=(20000, )) # Uniformly sampled
target_dist = [0.9, 0.05, 0.05, 0.0, 0.0]
with ops.device(
device_setter.replica_device_setter(ps_tasks=1,
ps_device="/cpu:0")):
dataset = (dataset_ops.Dataset.from_tensor_slices(classes).shuffle(
200, seed=21).map(lambda c: (c, string_ops.as_string(c))))
dataset = dataset_ops.rejection_resample(dataset,
target_dist=target_dist,
initial_dist=None,
class_func=lambda c, _: c,
seed=27)
self.assertEqual(1, len(variables.local_variables()))
self.assertEqual(
b"", compat.as_bytes(variables.local_variables()[0].device))
def _testDistribution(self, initial_known):
classes = np.random.randint(5, size=(20000,)) # Uniformly sampled
target_dist = [0.9, 0.05, 0.05, 0.0, 0.0]
initial_dist = [0.2] * 5 if initial_known else None
iterator = dataset_ops.Iterator.from_dataset(
dataset_ops.rejection_resample(
(dataset_ops.Dataset.from_tensor_slices(classes)
.shuffle(200, seed=21)
.map(lambda c: (c, string_ops.as_string(c)))),
target_dist=target_dist,
initial_dist=initial_dist,
class_func=lambda c, _: c,
seed=27))
init_op = iterator.initializer
get_next = iterator.get_next()
variable_init_op = variables.global_variables_initializer()
with self.test_session() as sess:
sess.run(variable_init_op)
sess.run(init_op)
returned = []
with self.assertRaises(errors.OutOfRangeError):
while True:
returned.append(sess.run(get_next))
returned_classes, returned_classes_and_data = zip(*returned)
_, returned_data = zip(*returned_classes_and_data)
self.assertAllEqual([compat.as_bytes(str(c))
for c in returned_classes], returned_data)
total_returned = len(returned_classes)
# Subsampling rejects a large precentage of the initial data in
# this case.
self.assertGreater(total_returned, 20000 * 0.2)
class_counts = np.array([
len([True for v in returned_classes if v == c])
for c in range(5)])
returned_dist = class_counts / total_returned
self.assertAllClose(target_dist, returned_dist, atol=1e-2)
def _testDistribution(self, initial_known):
classes = np.random.randint(5, size=(20000, )) # Uniformly sampled
target_dist = [0.9, 0.05, 0.05, 0.0, 0.0]
initial_dist = [0.2] * 5 if initial_known else None
iterator = dataset_ops.Iterator.from_dataset(
dataset_ops.rejection_resample(
(dataset_ops.Dataset.from_tensor_slices(classes).shuffle(
200, seed=21).map(lambda c: (c, string_ops.as_string(c)))),
target_dist=target_dist,
initial_dist=initial_dist,
class_func=lambda c, _: c,
seed=27))
init_op = iterator.initializer
get_next = iterator.get_next()
variable_init_op = variables.global_variables_initializer()
with self.test_session() as sess:
sess.run(variable_init_op)
sess.run(init_op)
returned = []
with self.assertRaises(errors.OutOfRangeError):
while True:
returned.append(sess.run(get_next))
returned_classes, returned_classes_and_data = zip(*returned)
_, returned_data = zip(*returned_classes_and_data)
self.assertAllEqual(
[compat.as_bytes(str(c)) for c in returned_classes], returned_data)
total_returned = len(returned_classes)
# Subsampling rejects a large percentage of the initial data in
# this case.
self.assertGreater(total_returned, 20000 * 0.2)
class_counts = np.array([
len([True for v in returned_classes if v == c]) for c in range(5)
])
returned_dist = class_counts / total_returned
self.assertAllClose(target_dist, returned_dist, atol=1e-2)