def __call__(self, input):
size = round(np.random.uniform(self.min_size, self.max_size))
input = Resize(size)(input)
return input
if tta else eval_image_transform)
to_tensor = ToTensor()
if config.normalize is None:
normalize = T.Compose([])
elif config.normalize == 'experiment':
normalize = NormalizeByExperimentStats(
torch.load('./experiment_stats.pth'))
elif config.normalize == 'plate':
normalize = NormalizeByPlateStats(torch.load('./plate_stats.pth'))
else:
raise AssertionError('invalid normalization {}'.format(config.normalize))
eval_image_transform = T.Compose([
RandomSite(),
Resize(config.resize_size),
center_crop,
to_tensor,
])
test_image_transform = T.Compose([
Resize(config.resize_size),
center_crop,
SplitInSites(),
T.Lambda(lambda xs: torch.stack([to_tensor(x) for x in xs], 0)),
])
train_transform = T.Compose([
ApplyTo(['image'],
T.Compose([
RandomSite(),
Resize(config.resize_size),
random_crop,