def default_val_transforms(image_size: Tuple[int, int]) -> Dict[str, Callable]:
return {
"post_tensor_transform":
nn.Sequential(
ApplyToKeys(
[DefaultDataKeys.INPUT, DefaultDataKeys.TARGET],
KorniaParallelTransforms(
K.geometry.Resize(image_size, interpolation='nearest')),
),
ApplyToKeys(DefaultDataKeys.TARGET, prepare_target),
),
"per_batch_transform_on_device":
ApplyToKeys(DefaultDataKeys.INPUT, K.enhance.Normalize(0., 255.)),
}
def to_tensor(self) -> Dict[str, Callable]:
return {
"to_tensor_transform":
nn.Sequential(
ApplyToKeys(
DefaultDataKeys.INPUT,
torch.from_numpy,
self.to_float,
),
ApplyToKeys(
DefaultDataKeys.TARGET,
torch.as_tensor,
self.to_float,
self.format_targets,
),
),
}
def default_train_transforms(
image_size: Tuple[int, int]) -> Dict[str, Callable]:
return {
"post_tensor_transform":
nn.Sequential(
ApplyToKeys(
[DefaultDataKeys.INPUT, DefaultDataKeys.TARGET],
KorniaParallelTransforms(
K.geometry.Resize(image_size, interpolation='nearest'),
K.augmentation.RandomHorizontalFlip(p=0.75),
),
),
ApplyToKeys(DefaultDataKeys.TARGET, prepare_target),
),
"per_batch_transform_on_device":
ApplyToKeys(
DefaultDataKeys.INPUT,
K.enhance.Normalize(0., 255.),
K.augmentation.ColorJitter(0.4, p=0.5),
),
}
def default_train_transforms(
image_size: Tuple[int, int]) -> Dict[str, Callable]:
if _KORNIA_AVAILABLE and not os.getenv("FLASH_TESTING", "0") == "1":
# Better approach as all transforms are applied on tensor directly
return {
"to_tensor_transform":
nn.Sequential(
ApplyToKeys(DefaultDataKeys.INPUT,
torchvision.transforms.ToTensor()),
ApplyToKeys(DefaultDataKeys.TARGET, torch.as_tensor),
),
"post_tensor_transform":
ApplyToKeys(
DefaultDataKeys.INPUT,
K.geometry.Resize(image_size),
K.augmentation.RandomHorizontalFlip(),
),
"collate":
kornia_collate,
"per_batch_transform_on_device":
ApplyToKeys(
DefaultDataKeys.INPUT,
K.augmentation.Normalize(torch.tensor([0.485, 0.456, 0.406]),
torch.tensor([0.229, 0.224, 0.225])),
)
}
else:
return {
"pre_tensor_transform":
ApplyToKeys(DefaultDataKeys.INPUT, T.Resize(image_size),
T.RandomHorizontalFlip()),
"to_tensor_transform":
nn.Sequential(
ApplyToKeys(DefaultDataKeys.INPUT,
torchvision.transforms.ToTensor()),
ApplyToKeys(DefaultDataKeys.TARGET, torch.as_tensor),
),
"post_tensor_transform":
ApplyToKeys(
DefaultDataKeys.INPUT,
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
),
"collate":
kornia_collate,
}
def default_transforms() -> Dict[str, Callable]:
return {
"to_tensor_transform": nn.Sequential(
ApplyToKeys('input', torchvision.transforms.ToTensor()),
ApplyToKeys(
'target',
nn.Sequential(
ApplyToKeys('boxes', torch.as_tensor),
ApplyToKeys('labels', torch.as_tensor),
ApplyToKeys('image_id', torch.as_tensor),
ApplyToKeys('area', torch.as_tensor),
ApplyToKeys('iscrowd', torch.as_tensor),
)
),
),
}