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)