Automatic Augmentation Methods#
Augmentation Policy#
This module contains common data augmentation policies that can improve the accuracy of image classification models.
- class kornia.augmentation.auto.AutoAugment(policy='imagenet', transformation_matrix_mode='silent')#
Apply AutoAugment [CZM+18] searched strategies.
- Parameters:
policy (
Union
[str
,List
[List
[Tuple
[str
,Union
[float
,int
],Union
[float
,int
,None
]]]]], optional) – a customized policy config or presets of “imagenet”, “cifar10”, and “svhn”. Default:"imagenet"
transformation_matrix_mode (
str
, optional) – computation mode for the chained transformation matrix, via .transform_matrix attribute. If silent, transformation matrix will be computed silently and the non-rigid modules will be ignored as identity transformations. If rigid, transformation matrix will be computed silently and the non-rigid modules will trigger errors. If skip, transformation matrix will be totally ignored. Default:"silent"
Examples
>>> import torch >>> import kornia.augmentation as K >>> in_tensor = torch.rand(5, 3, 30, 30) >>> aug = K.AugmentationSequential(AutoAugment()) >>> aug(in_tensor).shape torch.Size([5, 3, 30, 30])
- get_transformation_matrix(input, params=None, recompute=False, extra_args={})#
Compute the transformation matrix according to the provided parameters.
- forward(input, params=None, extra_args={})#
Define the computation performed at every call.
Should be overridden by all subclasses. :rtype:
Tensor
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class kornia.augmentation.auto.RandAugment(n, m, policy=None, transformation_matrix_mode='silent')#
Apply RandAugment [CZSL20] augmentation strategies.
- Parameters:
n (
int
) – the number of augmentations to apply sequentially.m (
int
) – magnitude for all the augmentations, ranged from [0, 30].policy (
Optional
[List
[List
[Tuple
[str
,Union
[float
,int
],Union
[float
,int
,None
]]]]], optional) – candidate transformations. If None, a default candidate list will be used. Default:None
transformation_matrix_mode (
str
, optional) – computation mode for the chained transformation matrix, via .transform_matrix attribute. If silent, transformation matrix will be computed silently and the non-rigid modules will be ignored as identity transformations. If rigid, transformation matrix will be computed silently and the non-rigid modules will trigger errors. If skip, transformation matrix will be totally ignored. Default:"silent"
Examples
>>> import kornia.augmentation as K >>> in_tensor = torch.rand(5, 3, 30, 30) >>> aug = K.AugmentationSequential(RandAugment(n=2, m=10)) >>> aug(in_tensor).shape torch.Size([5, 3, 30, 30])
- get_transformation_matrix(input, params=None, recompute=False, extra_args={})#
Compute the transformation matrix according to the provided parameters.
- forward(input, params=None, extra_args={})#
Define the computation performed at every call.
Should be overridden by all subclasses. :rtype:
Tensor
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class kornia.augmentation.auto.TrivialAugment(policy=None, transformation_matrix_mode='silent')#
Apply TrivialAugment [MullerH21] augmentation strategies.
- Parameters:
policy (
Optional
[List
[List
[Tuple
[str
,Union
[float
,int
],Union
[float
,int
,None
]]]]], optional) – candidate transformations. If None, a default candidate list will be used. Default:None
transformation_matrix_mode (
str
, optional) – computation mode for the chained transformation matrix, via .transform_matrix attribute. If silent, transformation matrix will be computed silently and the non-rigid modules will be ignored as identity transformations. If rigid, transformation matrix will be computed silently and the non-rigid modules will trigger errors. If skip, transformation matrix will be totally ignored. Default:"silent"
Examples
>>> import kornia.augmentation as K >>> in_tensor = torch.rand(5, 3, 30, 30) >>> aug = K.AugmentationSequential(TrivialAugment()) >>> aug(in_tensor).shape torch.Size([5, 3, 30, 30])
- get_transformation_matrix(input, params=None, recompute=False, extra_args={})#
Compute the transformation matrix according to the provided parameters.
- forward(input, params=None, extra_args={})#
Define the computation performed at every call.
Should be overridden by all subclasses. :rtype:
Tensor
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
Augmentation Search Methods#
WIP. This module contains common data augmentation search methods.