o ϯiB@stddlmZmZmZddlZddlmZddlmZddddddddddd d d d d Z d dd dZ GdddZ dS))AnyDictOptionalN)AugmentTransform) OpSampler)g?)r)r )rr)rg333333?)rg?)AdjustBrightnessAdjustContrastAdjustSaturationAdjustSharpness AutoContrastEqualizeInvertRotate PosterizeSolarizeShearXShearY TranslateX TranslateYint?)sampling_data_type sampling_min sampling_stdc@sreZdZdZ      ddeded ed eeee fd ed eeee fd dfddZ de j d e j fddZ dS) RandAugmentz This implements RandAugment for video. Assume the input video tensor with shape (T, C, H, W). RandAugment: Practical automated data augmentation with a reduced search space (https://arxiv.org/abs/1909.13719) rNgaussian magnitude num_layersprobtransform_hparas sampling_typesampling_hparasreturncs\dvsJp tdkrdvsJfddttD}t||d|_dS)aX This implements RandAugment for video. Args: magnitude (int): Magnitude used for transform function. num_layers (int): How many transform functions to apply for each augmentation. prob (float): The probablity of applying each transform function. transform_hparas (Optional[Dict[Any]]): Transform hyper parameters. Needs to have key fill. By default, it uses transform_default_hparas. sampling_type (str): Sampling method for magnitude of transform. It should be either gaussian or uniform. sampling_hparas (Optional[Dict[Any]]): Hyper parameters for sampling. If gaussian sampling is used, it needs to have key sampling_std. By default, it uses SAMPLING_RANDAUG_DEFAULT_HPARAS. )r uniformr rc s"g|] }t|tdqS))r#transform_max_parasr$r%r&)r_TRANSFORM_RANDAUG_MAX_PARAMS).0transform_namer!r#r&r%r$X/home/ubuntu/.local/lib/python3.10/site-packages/pytorchvideo/transforms/rand_augment.py Ps z(RandAugment.__init__..) num_sample_opN)SAMPLING_RANDAUG_DEFAULT_HPARASlistr*keysr randaug_fn)selfr!r"r#r$r%r&r5r.r-r/__init__2s    zRandAugment.__init__videocCs ||S)z Perform RandAugment to the input video tensor. Args: video (torch.Tensor): Input video tensor with shape (T, C, H, W). )r5)r6r8r.r.r/__call__^s zRandAugment.__call__)rrrNr N)__name__ __module__ __qualname____doc__rfloatrrstrrr7torchTensorr9r.r.r.r/r)s0  ,r) typingrrrr@%pytorchvideo.transforms.augmentationsr"pytorchvideo.transforms.transformsrr*r2rr.r.r.r/s0