def apply_hflip(input: torch.Tensor,
params: Dict[str, torch.Tensor]) -> torch.Tensor:
r"""Apply Horizontally flip on a tensor image or a batch of tensor images with given random parameters.
Input should be a tensor of shape (H, W), (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
params (dict): A dict that must have {'batch_prob': torch.Tensor}. Can be generated from
kornia.augmentation.random_generator.random_prob_generator.
return_transform (bool): if ``True`` return the matrix describing the transformation applied to each
input tensor.
Returns:
torch.Tensor: The horizontally flipped input
torch.Tensor: The applied transformation matrix :math: `(*, 3, 3)` if return_transform flag
is set to ``True``
"""
input = _transform_input(input)
_validate_input_dtype(
input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
flipped: torch.Tensor = input.clone()
to_flip = params['batch_prob'].to(input.device)
flipped[to_flip] = hflip(input[to_flip])
return flipped
def apply_hflip(input: torch.Tensor) -> torch.Tensor:
r"""Apply Horizontally flip on a tensor image or a batch of tensor images with given random parameters.
Input should be a tensor of shape (H, W), (C, H, W) or a batch of tensors :math:`(B, C, H, W)`.
Args:
input (torch.Tensor): Tensor to be transformed with shape (H, W), (C, H, W), (B, C, H, W).
Returns:
torch.Tensor: The horizontally flipped input
"""
return hflip(input)
def apply_hflip(input: torch.Tensor,
params: Dict[str, torch.Tensor],
return_transform: bool = False) -> UnionType:
r"""Apply Horizontally flip on a tensor image or a batch of tensor images with given random parameters.
Input should be a tensor of shape (H, W), (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
params (dict): A dict that must have {'batch_prob': torch.Tensor}. Can be generated from
kornia.augmentation.random_generator.random_prob_generator.
return_transform (bool): if ``True`` return the matrix describing the transformation applied to each
input tensor.
Returns:
torch.Tensor: The horizontally flipped input
torch.Tensor: The applied transformation matrix :math: `(*, 3, 3)` if return_transform flag
is set to ``True``
"""
input = _transform_input(input)
_validate_input_dtype(
input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
if not isinstance(return_transform, bool):
raise TypeError(
f"The return_transform flag must be a bool. Got {type(return_transform)}"
)
flipped: torch.Tensor = input.clone()
to_flip = params['batch_prob'].to(input.device)
flipped[to_flip] = hflip(input[to_flip])
if return_transform:
trans_mat: torch.Tensor = torch.eye(3,
device=input.device,
dtype=input.dtype).repeat(
input.shape[0], 1, 1)
w: int = input.shape[-1]
flip_mat: torch.Tensor = torch.tensor([[-1, 0, w], [0, 1, 0],
[0, 0, 1]])
trans_mat[to_flip] = flip_mat.type_as(input)
return flipped, trans_mat
return flipped
def apply_hflip(input: torch.Tensor) -> torch.Tensor:
r"""Apply Horizontally flip on a tensor image or a batch of tensor images with given random parameters.
Input should be a tensor of shape (H, W), (C, H, W) or a batch of tensors :math:`(B, C, H, W)`.
Args:
input (torch.Tensor): Tensor to be transformed with shape (H, W), (C, H, W), (B, C, H, W).
Returns:
torch.Tensor: The horizontally flipped input
"""
input = _transform_input(input)
_validate_input_dtype(
input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
return hflip(input)
def apply_hflip(input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
r"""Apply Horizontally flip on a tensor image or a batch of tensor images with given random parameters.
Input should be a tensor of shape (H, W), (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
input (torch.Tensor): Tensor to be transformed with shape (H, W), (C, H, W), (*, C, H, W).
params (Dict[str, torch.Tensor]):
- params['batch_prob']: A boolean tensor thatindicating whether if to transform an image in a batch.
Returns:
torch.Tensor: The horizontally flipped input
"""
input = _transform_input(input)
_validate_input_dtype(input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
flipped: torch.Tensor = input.clone()
to_flip = params['batch_prob'].to(input.device)
flipped[to_flip] = hflip(input[to_flip])
return flipped
