o oi@sddlmZmZmZddlmZmZmZddlm Z ddl m Z m Z m Z ddlmZddlmZddlmZddlmZmZGd d d e ZGd d d eZd S))AnyDictOptional)float16float32float64)_AugmentationBase)_transform_input_transform_input_by_shape_validate_input_dtype)Tensor)Boxes) Keypoints)eye_likeis_autocast_enabledc @sHeZdZdZdeddfddZdddded eed edefd d ZdS) AugmentationBase2Da$AugmentationBase2D base class for customized augmentation implementations. AugmentationBase2D aims at offering a generic base class for a greater level of customization. If the subclass contains routined matrix-based transformations, `RigidAffineAugmentationBase2D` might be a better fit. 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. keepdim: whether to keep the output shape the same as input ``True`` or broadcast it to the batch form ``False``. inputreturnNcCs6t|tttgdt|jdkrtd|jddS)z3Check if the input tensor is formatted as expected.accepted_dtypeszExpect (B, C, H, W). Got .N)r rrrlenshape RuntimeErrorselfrrP/home/ubuntu/.local/lib/python3.10/site-packages/kornia/augmentation/_2d/base.pyvalidate_tensor0sz"AugmentationBase2D.validate_tensorT)r match_channelrr cCs0t|tttgd|durt|St|||dS)zJConvert any incoming (H, W), (C, H, W) and (B, C, H, W) into (B, C, H, W).rN)reference_shaper )r rrrr r )rrrr rrrtransform_tensor6sz#AugmentationBase2D.transform_tensor) __name__ __module__ __qualname____doc__r rrboolr"rrrrrs*rc @seZdZUdZeeed<edeefddZdedefddZ ded e e efd e e e fdefd d Z ded e e efd e e e fdefd dZ dded e e efd e e e fdeedef ddZ dded e e efd e e e fdeedef ddZ dded e e efd e e e fdeedef ddZ dded e e efd e e e fdeedef ddZ dded e e efd e e e fdeedef ddZ dded e e efd ee e e fdefddZdS)RigidAffineAugmentationBase2DaAugmentationBase2D base class for rigid/affine augmentation implementations. RigidAffineAugmentationBase2D enables routined transformation with given transformation matrices for different data types like masks, boxes, and keypoints. 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. keepdim: whether to keep the output shape the same as input ``True`` or broadcast it to the batch form ``False``. _transform_matrixrcCs|jSN)r))rrrrtransform_matrixSsz.RigidAffineAugmentationBase2D.transform_matrixrcCs td|S)zReturn 3x3 identity matrix.)rrrrridentity_matrixWs z-RigidAffineAugmentationBase2D.identity_matrixparamsflagscCtr*NotImplementedError)rrr.r/rrrcompute_transformation[sz4RigidAffineAugmentationBase2D.compute_transformationc Cs|d}|dk}||}|s||}|S|r&|j|||d}|S||}|j||||d} trD||j}| |j} ||f| }|S)zIGenerate transformation matrices with the given input and param settings. batch_probg?)r.r/) r"anyr-allr3rtypedtype index_put) rrr.r/r4to_apply in_tensor trans_matrixtrans_matrix_Atrans_matrix_Brrrgenerate_transformation_matrix^s       zs    "