a d{@sPddlmZmZmZmZddlZddlmZddlm Z m Z GdddeZ dS))Tensorfloat16float32float64N)_AugmentationBase)_transform_input3d_validate_input_dtypec@s<eZdZdZedddZeedddZedd d Zd S) AugmentationBase3DaAugmentationBase3D base class for customized augmentation implementations. For any augmentation, the implementation of "generate_parameters" and "apply_transform" are required while the "compute_transformation" is only required when passing "return_transform" as True. Args: p: probability for applying an augmentation. This param controls the augmentation probabilities element-wise for a batch. p_batch: probability for applying an augmentation to a batch. This param controls the augmentation probabilities batch-wise. same_on_batch: apply the same transformation across the batch. )inputcCst|tr|\}}t|jdkrlt|jdkr6td|jd|jdkrtd|jdd|jdn:t|jdvrt|jdkrtd ntd t|jdS) Nz>Input tensor is in batch mode but transformation matrix is notrzIn batch dimension, input has z but transformation matrix has )r zBInput tensor is in non-batch mode but transformation matrix is notz3Unrecognized output shape. Expected 3, 4 or 5, got ) isinstancetuplelenshapeAssertionError ValueError)selfr inpmatre/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/kornia/augmentation/_3d/base.py__check_batching__s  z%AugmentationBase3D.__check_batching__)r returncCst|tttgdt|S)zVConvert any incoming (D, H, W), (C, D, H, W) and (B, C, D, H, W) into (B, C, D, H, W).)Zaccepted_dtypes)rrrrrrr rrrtransform_tensor&sz#AugmentationBase3D.transform_tensor)rcCs td|S)zReturn 4x4 identity matrix.r )korniaZeye_likerrrridentity_matrix+sz"AugmentationBase3D.identity_matrixN)__name__ __module__ __qualname____doc__rrrrrrrrr s r ) ZtorchrrrrrZkornia.augmentation.baserZkornia.augmentation.utilsrrr rrrrs