def test_ndarray_batch_source(self):
# Make sure that with enough epochs we sample everything.
stream = RandomFixedSizeCrop(self.batch_stream, (5, 4),
which_sources=('source1', ))
seen_indices = numpy.array([], dtype='uint8')
for i in range(30):
for batch in stream.get_epoch_iterator():
assert batch[0].shape[1:] == (3, 5, 4)
assert batch[0].shape[0] in (1, 2)
seen_indices = numpy.union1d(seen_indices, batch[0].flatten())
if 3 * 7 * 5 == len(seen_indices):
break
else:
assert False
def test_ndarray_batch_source(self):
# Make sure that with enough epochs we sample everything.
stream = RandomFixedSizeCrop(self.batch_stream, (5, 4),
which_sources=('source1',))
seen_indices = numpy.array([], dtype='uint8')
for i in range(30):
for batch in stream.get_epoch_iterator():
assert batch[0].shape[1:] == (3, 5, 4)
assert batch[0].shape[0] in (1, 2)
seen_indices = numpy.union1d(seen_indices, batch[0].flatten())
if 3 * 7 * 5 == len(seen_indices):
break
else:
assert False
def test_list_batch_source(self):
# Make sure that with enough epochs we sample everything.
stream = RandomFixedSizeCrop(self.batch_stream, (5, 4),
which_sources=('source2', ))
seen_indices = numpy.array([], dtype='uint8')
for i in range(30):
for batch in stream.get_epoch_iterator():
for example in batch[1]:
assert example.shape == (2, 5, 4)
seen_indices = numpy.union1d(seen_indices,
example.flatten())
assert len(batch[1]) in (1, 2)
if self.source2_biggest == len(seen_indices):
break
else:
assert False
def test_list_batch_source(self):
# Make sure that with enough epochs we sample everything.
stream = RandomFixedSizeCrop(self.batch_stream, (5, 4),
which_sources=('source2',))
seen_indices = numpy.array([], dtype='uint8')
for i in range(30):
for batch in stream.get_epoch_iterator():
for example in batch[1]:
assert example.shape == (2, 5, 4)
seen_indices = numpy.union1d(seen_indices,
example.flatten())
assert len(batch[1]) in (1, 2)
if self.source2_biggest == len(seen_indices):
break
else:
assert False
