def test_image_pipeline_and_pin_memory(self):
'''
This just should not crash
:return:
'''
try:
import torch
except ImportError:
'''dont test if torch is not installed'''
return
from batchgenerators.transforms import MirrorTransform, NumpyToTensor, TransposeAxesTransform, Compose
tr_transforms = []
tr_transforms.append(MirrorTransform())
tr_transforms.append(
TransposeAxesTransform(transpose_any_of_these=(0, 1),
p_per_sample=0.5))
tr_transforms.append(NumpyToTensor(keys='data', cast_to='float'))
composed = Compose(tr_transforms)
dl = self.dl_images
mt = MultiThreadedAugmenter(dl, composed, 4, 1, None, True)
for _ in range(50):
res = mt.next()
assert isinstance(res['data'], torch.Tensor)
assert res['data'].is_pinned()
# let mt finish caching, otherwise it's going to print an error (which is not a problem and will not prevent
# the success of the test but it does not look pretty)
sleep(2)
def test_no_crash(self):
"""
This one should just not crash, that's all
:return:
"""
dl = self.dl_images
mt_dl = MultiThreadedAugmenter(dl, None, self.num_threads, 1, None, False)
for _ in range(20):
_ = mt_dl.next()
def test_image_pipeline(self):
'''
This just should not crash
:return:
'''
from batchgenerators.transforms import MirrorTransform, TransposeAxesTransform, Compose
tr_transforms = []
tr_transforms.append(MirrorTransform())
tr_transforms.append(TransposeAxesTransform(transpose_any_of_these=(0, 1), p_per_sample=0.5))
composed = Compose(tr_transforms)
dl = self.dl_images
mt = MultiThreadedAugmenter(dl, composed, 4, 1, None, False)
for _ in range(50):
res = mt.next()
# let mt finish caching, otherwise it's going to print an error (which is not a problem and will not prevent
# the success of the test but it does not look pretty)
sleep(2)