o oi@stddlmZmZmZmZmZddlmZddl m Z ddl m Z ddl mZddlmZmZmZGddde Zd S) )AnyDictOptionalTupleUnion)random_generator)GeometricAugmentationBase3D)Resample)Tensor)deg2radget_affine_matrix3d warp_affine3dcseZdZdZdddejjddddfdeee e e e fe e e e fe e e e fe e e fe e e fffde eee e e e ffde eee e e fe e e e fe e e fe e e fffdedee e e e fe e e e e e e fe e e e fe e e fe e e fe e e fe e e fe e e fffd ee e efd ed ed e d eddffdd Zdedee efdee efdefddZ ddedee efdee efde edef ddZZS)RandomAffine3DagApply affine transformation 3D volumes (5D tensor). The transformation is computed so that the center is kept invariant. Args: degrees: Range of yaw (x-axis), pitch (y-axis), roll (z-axis) to select from. If degrees is a number, then yaw, pitch, roll will be generated from the range of (-degrees, +degrees). If degrees is a tuple of (min, max), then yaw, pitch, roll will be generated from the range of (min, max). If degrees is a list of floats [a, b, c], then yaw, pitch, roll will be generated from (-a, a), (-b, b) and (-c, c). If degrees is a list of tuple ((a, b), (m, n), (x, y)), then yaw, pitch, roll will be generated from (a, b), (m, n) and (x, y). Set to 0 to deactivate rotations. translate: tuple of maximum absolute fraction for horizontal, vertical and depthical translations (dx,dy,dz). For example translate=(a, b, c), then horizontal shift will be randomly sampled in the range -img_width * a < dx < img_width * a vertical shift will be randomly sampled in the range -img_height * b < dy < img_height * b. depthical shift will be randomly sampled in the range -img_depth * c < dz < img_depth * c. Will not translate by default. scale: scaling factor interval. If (a, b) represents isotropic scaling, the scale is randomly sampled from the range a <= scale <= b. If ((a, b), (c, d), (e, f)), the scale is randomly sampled from the range a <= scale_x <= b, c <= scale_y <= d, e <= scale_z <= f. Will keep original scale by default. shears: Range of degrees to select from. If shear is a number, a shear to the 6 facets in the range (-shear, +shear) will be applied. If shear is a tuple of 2 values, a shear to the 6 facets in the range (shear[0], shear[1]) will be applied. If shear is a tuple of 6 values, a shear to the i-th facet in the range (-shear[i], shear[i]) will be applied. If shear is a tuple of 6 tuples, a shear to the i-th facet in the range (-shear[i, 0], shear[i, 1]) will be applied. resample: resample mode from "nearest" (0) or "bilinear" (1). same_on_batch: apply the same transformation across the batch. align_corners: interpolation flag. keepdim: whether to keep the output shape the same as input (True) or broadcast it to the batch form (False). Default: False. Shape: - Input: :math:`(C, D, H, W)` or :math:`(B, C, D, H, W)`, Optional: :math:`(B, 4, 4)` - Output: :math:`(B, C, D, H, W)` Note: Input tensor must be float and normalized into [0, 1] for the best differentiability support. Additionally, this function accepts another transformation tensor (:math:`(B, 4, 4)`), then the applied transformation will be merged int to the input transformation tensor and returned. Examples: >>> import torch >>> rng = torch.manual_seed(0) >>> input = torch.rand(1, 1, 3, 3, 3) >>> aug = RandomAffine3D((15., 20., 20.), p=1.) >>> aug(input), aug.transform_matrix (tensor([[[[[0.4503, 0.4763, 0.1680], [0.2029, 0.4267, 0.3515], [0.3195, 0.5436, 0.3706]], [[0.5255, 0.3508, 0.4858], [0.0795, 0.1689, 0.4220], [0.5306, 0.7234, 0.6879]], [[0.2971, 0.2746, 0.3471], [0.4924, 0.4960, 0.6460], [0.3187, 0.4556, 0.7596]]]]]), tensor([[[ 0.9722, -0.0603, 0.2262, -0.1381], [ 0.1131, 0.9669, -0.2286, 0.1486], [-0.2049, 0.2478, 0.9469, 0.0102], [ 0.0000, 0.0000, 0.0000, 1.0000]]])) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.rand(1, 3, 32, 32, 32) >>> aug = RandomAffine3D((15., 20., 20.), p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) NFg?degrees translatescaleshearsresample same_on_batch align_cornerspkeepdimreturnc sRtj||| d||_||_||_||_t||d|_t |||||_ dS)N)rrr)rr) super__init__rrrrr getflagsrgAffineGenerator3D_param_generator) selfrrrrrrrrr __class__\/home/ubuntu/.local/lib/python3.10/site-packages/kornia/augmentation/_3d/geometric/affine.pyrfs"zRandomAffine3D.__init__inputparamsrc Csdt|d|d|d|dt|dt|dt|dt|dt|d t|d  |}|S) N translationscenterranglessxysxzsyxsyzszxszy)r r tor r%r&r transformr#r#r$compute_transformations        z%RandomAffine3D.compute_transformationr2cCsjt|tstdt|t||ddddddf|jd|jd|jdf|dj|ddS) Nz.Expected the transform to be a Tensor. Gotcha rr)r) isinstancer TypeErrortyper shapenamelowerr1r#r#r$apply_transforms  zRandomAffine3D.apply_transform)N)__name__ __module__ __qualname____doc__r BILINEARr<rr floatrrstrintboolrrrr3r> __classcell__r#r#r!r$rsS  $  6          !*+  rN)typingrrrrrkornia.augmentationrr&kornia.augmentation._3d.geometric.baserkornia.constantsr kornia.corer kornia.geometryr r r rr#r#r#r$s