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,
RandomFlip(),
RandomTranspose(),
to_tensor,
ChannelReweight(config.aug.channel_reweight),
])),
normalize,
Extract(['image', 'exp', 'label', 'id']),
])
eval_transform = T.Compose([
ApplyTo(['image'], infer_image_transform),
normalize,
Extract(['image', 'exp', 'label', 'id']),
])
unsup_transform = T.Compose([
ApplyTo(['image'],
T.Compose([
Resize(config.resize_size),
random_crop,
RandomFlip(),
random_crop = Resetable(RandomCrop)
center_crop = Resetable(CenterCrop)
train_transform = T.Compose([
ApplyTo(
['image'],
T.Compose([
RandomSite(),
Resize(config.resize_size),
random_crop,
RandomFlip(),
RandomTranspose(),
RandomRotation(180), # FIXME:
ToTensor(),
ChannelReweight(config.aug.channel_weight),
])),
# NormalizeByRefStats(),
Extract(['image', 'feat', 'label', 'id']),
])
eval_transform = T.Compose([
ApplyTo(
['image'],
T.Compose([
RandomSite(), # FIXME:
Resize(config.resize_size),
center_crop,
ToTensor(),
])),
# NormalizeByRefStats(),
Extract(['image', 'feat', 'label', 'id']),