def test_auto_init(self):
epoch_counter = [0]
seq_flow = DataStream.int_seq(0, 10, batch_size=2)
map_flow = seq_flow.map(
lambda x: (x + epoch_counter[0] * 10,))
def make_iterator():
epoch_counter[0] += 1
return map_flow
it_flow = DataStream.generator(make_iterator)
flow = it_flow.threaded(3)
batches = [b[0] for b in flow]
np.testing.assert_array_equal(
[[10, 11], [12, 13], [14, 15], [16, 17], [18, 19]], batches)
batches = [b[0] for b in flow]
np.testing.assert_array_equal(
[[20, 21], [22, 23], [24, 25], [26, 27], [28, 29]], batches)
flow.close()
batches = [b[0] for b in flow]
np.testing.assert_array_equal(
[[40, 41], [42, 43], [44, 45], [46, 47], [48, 49]], batches)
flow.close()
def test_construct(self):
# test properties
args = {
'batch_size': 12,
'array_count': 2,
'data_shapes': ((), (3, 5)),
'data_length': 25,
'random_state': np.random.RandomState(),
}
s1 = DataStream()
s2 = DataStream(**args)
for attr in args:
self.assertIsNone(getattr(s1, attr))
self.assertEqual(getattr(s2, attr), args[attr])
# test argument validation
s = DataStream(array_count=3)
self.assertEqual(s.array_count, 3)
self.assertIsNone(s.data_shapes)
s = DataStream(data_shapes=args['data_shapes'])
self.assertEqual(s.data_shapes, args['data_shapes'])
self.assertEqual(s.array_count, 2)
with pytest.raises(TypeError,
match='`random_state` is not np.random.RandomState'
': .*'):
_ = DataStream(random_state=object())
def test_source_error(self):
class _MyError(Exception):
pass
def g():
for i in range(6):
if i >= 1:
raise _MyError('err msg')
yield np.arange(i * 3, (i + 1) * 3)
source = DataStream.generator(g)
threaded = source.threaded()
self.assertEqual(threaded.prefetch, 5)
def f():
# get the first batch should be okay
it = iter(threaded)
batch = next(it)
self.assertIsInstance(batch, tuple)
self.assertEqual(len(batch), 1)
np.testing.assert_equal(batch[0], np.arange(3))
# get the second batch should raise an error
with pytest.raises(_MyError, match='err msg'):
_ = next(it)
# enter the first loop
f()
# should be able to recover from the error in previous loop
f()
def test_select(self):
# additional tests when source stream does not report array_count
# and data_shapes
def mapper(x, y, z):
return x + y, y - z
x = np.random.normal(size=[5, 3])
y = np.random.normal(size=[5, 1])
z = np.random.normal(size=[5, 3])
source = DataStream.arrays([x, y, z], batch_size=3).map(mapper)
self.assertIsNone(source.data_shapes)
self.assertIsNone(source.array_count)
stream = source.select([-1, 0, 1])
self.assertEqual(stream.array_count, 3)
self.assertIsNone(stream.data_shapes)
a, b, c = stream.get_arrays()
np.testing.assert_allclose(a, y - z)
np.testing.assert_allclose(b, x + y)
np.testing.assert_allclose(c, y - z)
# index out of range error
stream = source.select([0, 1, 2])
with pytest.raises(IndexError, match='.* index out of range'):
for _ in stream:
pass
def test_copy(self):
source = DataStream.int_seq(5, batch_size=3)
stream = source.threaded(3)
self.assertIs(stream.source, source)
self.assertEqual(stream.prefetch, 3)
stream2 = stream.copy(prefetch=1)
self.assertIsInstance(stream2, ThreadingDataStream)
self.assertIs(stream2.source, source)
self.assertEqual(stream2.prefetch, 1)
def test_override_random_state(self):
rs0 = np.random.RandomState(1234)
x = rs0.normal(size=[5, 1])
y = rs0.normal(size=[5, 2])
z = rs0.normal(size=[5, 3])
rs = np.random.RandomState(1234)
stream_x = DataStream.arrays([x], batch_size=3, random_state=rs)
stream_yz = DataStream.arrays([y, z], batch_size=3)
# test overriding random state
rs2 = np.random.RandomState(1234)
stream = GatherDataStream([stream_x, stream_yz], random_state=rs2)
self.assertIs(stream.random_state, rs2)
# test copy with overrided random state
rs3 = np.random.RandomState(1234)
stream2 = stream.copy(random_state=rs3)
self.assertIsInstance(stream2, GatherDataStream)
self.assertIs(stream2.random_state, rs3)
def test_to_arrays_stream(self):
x = np.random.normal(size=[5, 4])
y = np.random.normal(size=[5, 2, 3])
stream = DataStream.arrays([x, y], batch_size=3)
self.assertIsNone(stream.random_state)
stream2 = stream.to_arrays_stream()
self.assertIsNone(stream2.random_state)
rs = np.random.RandomState()
stream3 = stream.to_arrays_stream(random_state=rs)
self.assertIs(stream3.random_state, rs)
def test_stream(self):
np.random.seed(1234)
rs = np.random.RandomState()
x = np.random.normal(size=[5, 1])
y = np.random.normal(size=[5, 2])
source = DataStream.arrays([x, y], batch_size=3, random_state=rs)
# test argument validation
with pytest.raises(TypeError,
match='`source` is not a DataStream: <object.*>'):
_ = ThreadingDataStream(object(), prefetch=2)
with pytest.raises(ValueError,
match='`prefetch` must be at least 1'):
_ = ThreadingDataStream(source, prefetch=0)
# test threaded with context
stream = ThreadingDataStream(source, prefetch=3)
self.assertIs(stream.source, source)
self.assertEqual(stream.prefetch, 3)
self.assertEqual(stream.batch_size, 3)
self.assertEqual(stream.array_count, 2)
self.assertIs(stream.random_state, rs)
self.assertEqual(stream.data_length, 5)
self.assertEqual(stream.data_shapes, ((1,), (2,)))
self.assertFalse(stream._worker_alive)
self.assertFalse(stream._initialized)
with stream:
arrays = stream.get_arrays()
self.assertEqual(len(arrays), 2)
np.testing.assert_equal(arrays[0], x)
np.testing.assert_equal(arrays[1], y)
self.assertFalse(stream._worker_alive)
self.assertFalse(stream._initialized)
# test threaded without context
stream = ThreadingDataStream(source, prefetch=3)
self.assertFalse(stream._worker_alive)
self.assertFalse(stream._initialized)
arrays = stream.get_arrays()
self.assertTrue(stream._worker_alive)
self.assertTrue(stream._initialized)
stream.close()
self.assertFalse(stream._worker_alive)
self.assertFalse(stream._initialized)
stream.close() # double close should not cause an error
self.assertEqual(len(arrays), 2)
np.testing.assert_equal(arrays[0], x)
np.testing.assert_equal(arrays[1], y)
def test_stream(self):
def identity(*args):
return args
# test argument validation
with pytest.raises(TypeError,
match='`source` is not a DataStream: <object.*>'):
_ = MapperDataStream(object(), identity)
# test property inheritance
np.random.seed(1234)
rs = np.random.RandomState()
x = np.random.normal(size=[5, 1])
y = np.random.normal(size=[5, 2])
source = DataStream.arrays([x, y], batch_size=3, random_state=rs)
mapped = MapperDataStream(source, identity, preserve_shapes=False)
self.assertIs(mapped.source, source)
self.assertEqual(mapped.data_length, 5)
self.assertEqual(mapped.batch_size, 3)
self.assertEqual(mapped.batch_count, 2)
self.assertIsNone(mapped.array_count)
self.assertIsNone(mapped.data_shapes)
self.assertIs(mapped.random_state, rs)
mapped = MapperDataStream(source, identity, preserve_shapes=True)
self.assertEqual(mapped.data_length, 5)
self.assertEqual(mapped.batch_size, 3)
self.assertEqual(mapped.batch_count, 2)
self.assertEqual(mapped.array_count, 2)
self.assertEqual(mapped.data_shapes, ((1, ), (2, )))
self.assertIs(mapped.random_state, rs)
# test override
rs2 = np.random.RandomState()
mapped = MapperDataStream(
source,
identity,
# in fact, these overrides are incorrect
batch_size=7,
array_count=1,
data_shapes=((3, ), ),
data_length=11,
random_state=rs2,
)
self.assertEqual(mapped.data_length, 11)
self.assertEqual(mapped.batch_size, 7)
self.assertEqual(mapped.batch_count, 2)
self.assertEqual(mapped.array_count, 1)
self.assertEqual(mapped.data_shapes, ((3, ), ))
self.assertIs(mapped.random_state, rs2)
def test_iter_reentrant_warn(self):
stream = DataStream.int_seq(5, batch_size=3)
# test open and close, no warning
iterator = iter(stream)
np.testing.assert_equal(next(iterator)[0], [0, 1, 2])
iterator.close()
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
batches = list(stream)
self.assertEqual(len(batches), 2)
np.testing.assert_equal(batches[0][0], [0, 1, 2])
np.testing.assert_equal(batches[1][0], [3, 4])
self.assertEqual(len(w), 0)
# test open without close, cause warning
iterator = iter(stream)
np.testing.assert_equal(next(iterator)[0], [0, 1, 2])
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
batches = list(stream)
self.assertEqual(len(batches), 2)
np.testing.assert_equal(batches[0][0], [0, 1, 2])
np.testing.assert_equal(batches[1][0], [3, 4])
self.assertEqual(len(w), 1)
self.assertTrue(issubclass(w[-1].category, UserWarning))
self.assertRegex(
str(w[-1].message),
r'Another iterator of the DataStream .* is still active, '
r'will close it automatically.')
with pytest.raises(StopIteration):
_ = next(iterator) # this iterator should have been closed
# test no warning the second time
iterator = iter(stream)
np.testing.assert_equal(next(iterator)[0], [0, 1, 2])
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
batches = list(stream)
self.assertEqual(len(batches), 2)
np.testing.assert_equal(batches[0][0], [0, 1, 2])
np.testing.assert_equal(batches[1][0], [3, 4])
self.assertEqual(len(w), 0)
def test_copy(self):
rs = np.random.RandomState()
source = DataStream.int_seq(5, batch_size=3)
mapped = MapperDataStream(source, lambda *args: args)
mapped2 = mapped.copy(batch_size=7,
array_count=1,
data_shapes=((3, ), ),
data_length=11,
random_state=rs)
self.assertEqual(mapped2.data_length, 11)
self.assertEqual(mapped2.batch_size, 7)
self.assertEqual(mapped2.array_count, 1)
self.assertEqual(mapped2.data_shapes, ((3, ), ))
self.assertIs(mapped2.random_state, rs)
def test_iterator(self):
class _MyError(Exception):
pass
epoch_counter = [0]
external_counter = [1]
seq_flow = DataStream.int_seq(0, 10, batch_size=2)
map_flow = seq_flow.map(
lambda x: (x + epoch_counter[0] * 10 + external_counter[0] * 100,))
def make_iterator():
epoch_counter[0] += 1
return map_flow
it_flow = DataStream.generator(make_iterator)
with it_flow.threaded(prefetch=2) as flow:
# the first epoch, expect 0 .. 10
np.testing.assert_array_equal(
[[110, 111], [112, 113], [114, 115], [116, 117], [118, 119]],
[a[0] for a in flow]
)
time.sleep(.1)
external_counter[0] += 1
# the second epoch, the epoch counter should affect more than
# the external counter
np.testing.assert_array_equal(
# having `prefetch = 2` should affect 3 items, because
# while the queue size is 2, there are 1 additional prefetched
# item waiting to be enqueued
[[120, 121], [122, 123], [124, 125], [226, 227], [228, 229]],
[a[0] for a in flow]
)
time.sleep(.1)
external_counter[0] += 1
# the third epoch, we shall carry out an incomplete epoch by break
for a in flow:
np.testing.assert_array_equal([230, 231], a[0])
break
time.sleep(.1)
external_counter[0] += 1
# verify that the epoch counter increases after break
for i, (a,) in enumerate(flow):
# because the interruption is not well-predictable under
# multi-threading context, we shall have a weaker verification
# than the above
self.assertTrue((340 + i * 2 == a[0]) or (440 + i * 2 == a[0]))
self.assertTrue((341 + i * 2 == a[1]) or (441 + i * 2 == a[1]))
time.sleep(.1)
external_counter[0] += 1
# carry out the fourth, incomplete epoch by error
try:
for a in flow:
np.testing.assert_array_equal([450, 451], a[0])
raise _MyError()
except _MyError:
pass
time.sleep(.1)
external_counter[0] += 1
# verify that the epoch counter increases after error
for i, (a,) in enumerate(flow):
self.assertTrue((560 + i * 2 == a[0]) or (660 + i * 2 == a[0]))
self.assertTrue((561 + i * 2 == a[1]) or (661 + i * 2 == a[1]))
def test_stream(self):
# argument validation
with pytest.raises(ValueError,
match='At least one data stream should be '
'specified'):
_ = GatherDataStream([])
with pytest.raises(TypeError,
match='The 1-th element of `streams` is not '
'an instance of DataStream: <object.*>'):
_ = GatherDataStream([DataStream.int_seq(5, batch_size=3),
object()])
def my_generator():
if False:
yield
with pytest.raises(ValueError,
match='Inconsistent batch size among the specified '
'streams: encountered 4 at the 3-th stream, '
'but has already encountered 3 before.'):
_ = GatherDataStream([
DataStream.generator(my_generator),
DataStream.int_seq(5, batch_size=3),
DataStream.generator(my_generator),
DataStream.int_seq(5, batch_size=4),
])
with pytest.raises(ValueError,
match='Inconsistent data length among the specified '
'streams: encountered 6 at the 3-th stream, '
'but has already encountered 5 before.'):
_ = GatherDataStream([
DataStream.generator(my_generator),
DataStream.int_seq(5, batch_size=3),
DataStream.generator(my_generator),
DataStream.int_seq(6, batch_size=3),
])
# test property inheritance
rs0 = np.random.RandomState(1234)
x = rs0.normal(size=[5, 1])
y = rs0.normal(size=[5, 2])
z = rs0.normal(size=[5, 3])
rs = np.random.RandomState(1234)
stream_x = DataStream.arrays([x], batch_size=3, random_state=rs)
stream_yz = DataStream.arrays([y, z], batch_size=3)
stream = GatherDataStream([stream_x, stream_yz])
self.assertTupleEqual(stream.streams, (stream_x, stream_yz))
self.assertEqual(stream.batch_size, 3)
self.assertEqual(stream.data_shapes, ((1,), (2,), (3,)))
self.assertEqual(stream.data_length, 5)
self.assertEqual(stream.array_count, 3)
self.assertIs(stream.random_state, rs)
arrays = stream.get_arrays()
self.assertEqual(len(arrays), 3)
np.testing.assert_equal(arrays[0], x)
np.testing.assert_equal(arrays[1], y)
np.testing.assert_equal(arrays[2], z)
# test no property to inherit
stream_1 = DataStream.generator(my_generator)
stream_2 = DataStream.generator(my_generator)
stream = GatherDataStream([stream_1, stream_2])
for attr in ('batch_size', 'array_count', 'data_length', 'data_shapes',
'random_state'):
self.assertIsNone(getattr(stream, attr))
stream = GatherDataStream([stream_1, stream_2], random_state=rs)
self.assertIs(stream.random_state, rs)
