# LICENSE HEADER MANAGED BY add-license-header # # Copyright 2018 Kornia Team # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from typing import Any, Dict, List, Optional, Tuple, Union from kornia.augmentation import random_generator as rg from kornia.augmentation._2d.geometric.base import GeometricAugmentationBase2D from kornia.constants import Resample from kornia.core import Tensor, as_tensor from kornia.geometry.transform import affine from kornia.geometry.transform.affwarp import _compute_rotation_matrix, _compute_tensor_center from kornia.utils.misc import eye_like class RandomRotation(GeometricAugmentationBase2D): r"""Apply a random rotation to a tensor image or a batch of tensor images given an amount of degrees. .. image:: _static/img/RandomRotation.png Args: degrees: range of degrees to select from. If degrees is a number the range of degrees to select from will be (-degrees, +degrees). resample: Default: the interpolation mode. same_on_batch: apply the same transformation across the batch. align_corners: interpolation flag. p: probability of applying the transformation. keepdim: whether to keep the output shape the same as input (True) or broadcast it to the batch form (False). Shape: - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)` - Output: :math:`(B, C, H, W)` .. note:: This function internally uses :func:`kornia.geometry.transform.affine`. Examples: >>> rng = torch.manual_seed(0) >>> input = torch.tensor([[1., 0., 0., 2.], ... [0., 0., 0., 0.], ... [0., 1., 2., 0.], ... [0., 0., 1., 2.]]) >>> aug = RandomRotation(degrees=45.0, p=1.) >>> out = aug(input) >>> out tensor([[[[0.9824, 0.0088, 0.0000, 1.9649], [0.0000, 0.0029, 0.0000, 0.0176], [0.0029, 1.0000, 1.9883, 0.0000], [0.0000, 0.0088, 1.0117, 1.9649]]]]) >>> aug.transform_matrix tensor([[[ 1.0000, -0.0059, 0.0088], [ 0.0059, 1.0000, -0.0088], [ 0.0000, 0.0000, 1.0000]]]) >>> inv = aug.inverse(out) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.randn(1, 3, 32, 32) >>> aug = RandomRotation(degrees=45.0, p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ # Note: Extra params, center=None, fill=0 in TorchVision def __init__( self, degrees: Union[Tensor, float, Tuple[float, float], List[float]], resample: Union[str, int, Resample] = Resample.BILINEAR.name, same_on_batch: bool = False, align_corners: bool = True, p: float = 0.5, keepdim: bool = False, ) -> None: super().__init__(p=p, same_on_batch=same_on_batch, keepdim=keepdim) self._param_generator = rg.PlainUniformGenerator((degrees, "degrees", 0.0, (-360.0, 360.0))) self.flags = {"resample": Resample.get(resample), "align_corners": align_corners} def compute_transformation(self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any]) -> Tensor: # TODO: Update to use `get_rotation_matrix2d` angles: Tensor = params["degrees"].to(input) center: Tensor = _compute_tensor_center(input) rotation_mat: Tensor = _compute_rotation_matrix(angles, center.expand(angles.shape[0], -1)) # rotation_mat is B x 2 x 3 and we need a B x 3 x 3 matrix trans_mat: Tensor = eye_like(3, input, shared_memory=False) trans_mat[:, 0] = rotation_mat[:, 0] trans_mat[:, 1] = rotation_mat[:, 1] return trans_mat def apply_transform( self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None ) -> Tensor: if not isinstance(transform, Tensor): raise TypeError(f"Expected the `transform` be a Tensor. Got {type(transform)}.") return affine(input, transform[..., :2, :3], flags["resample"].name.lower(), "zeros", flags["align_corners"]) def inverse_transform( self, input: Tensor, flags: Dict[str, Any], transform: Optional[Tensor] = None, size: Optional[Tuple[int, int]] = None, ) -> Tensor: if not isinstance(transform, Tensor): raise TypeError(f"Expected the `transform` be a Tensor. Got {type(transform)}.") return self.apply_transform( input, params=self._params, transform=as_tensor(transform, device=input.device, dtype=input.dtype), flags=flags, ) class RandomRotation90(GeometricAugmentationBase2D): r"""Apply a random 90 * n degree rotation to a tensor image or a batch of tensor images. Args: times: the range of n times 90 degree rotation needs to be applied. resample: Default: the interpolation mode. same_on_batch: apply the same transformation across the batch. align_corners: interpolation flag. p: probability of applying the transformation. keepdim: whether to keep the output shape the same as input (True) or broadcast it to the batch form (False). Shape: - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)` - Output: :math:`(B, C, H, W)` .. note:: This function internally uses :func:`kornia.geometry.transform.affine`. This version is relatively slow as it operates based on affine transformations. Examples: >>> rng = torch.manual_seed(1) >>> torch.set_printoptions(sci_mode=False) >>> input = torch.tensor([[1., 0., 0., 2.], ... [0., 0., 0., 0.], ... [0., 1., 2., 0.], ... [0., 0., 1., 2.]]) >>> aug = RandomRotation90(times=(1, 1), p=1.) >>> out = aug(input) >>> out tensor([[[[ 2.0000, 0.0000, 0.0000, 2.0000], [ 0.0000, 0.0000, 2.0000, 1.0000], [ 0.0000, 0.0000, 1.0000, 0.0000], [ 1.0000, 0.0000, 0.0000, 0.0000]]]]) >>> aug.transform_matrix tensor([[[ -0.0000, 1.0000, 0.0000], [ -1.0000, -0.0000, 3.0000], [ 0.0000, 0.0000, 1.0000]]]) >>> inv = aug.inverse(out) >>> torch.set_printoptions(profile='default') To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.randn(1, 3, 32, 32) >>> aug = RandomRotation90(times=(-1, 1), p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ def __init__( self, times: tuple[int, int], resample: Union[str, int, Resample] = Resample.BILINEAR.name, same_on_batch: bool = False, align_corners: bool = True, p: float = 0.5, keepdim: bool = False, ) -> None: super().__init__(p=p, same_on_batch=same_on_batch, keepdim=keepdim) self._param_generator = rg.PlainUniformGenerator((times, "times", 0.0, (-3, 3))) self.flags = {"resample": Resample.get(resample), "align_corners": align_corners} def compute_transformation(self, input: Tensor, params: dict[str, Tensor], flags: dict[str, Any]) -> Tensor: # TODO: Update to use `get_rotation_matrix2d` angles: Tensor = 90.0 * params["times"].round().to(input) center: Tensor = _compute_tensor_center(input) rotation_mat: Tensor = _compute_rotation_matrix(angles, center.expand(angles.shape[0], -1)) # rotation_mat is B x 2 x 3 and we need a B x 3 x 3 matrix trans_mat: Tensor = eye_like(3, input, shared_memory=False) trans_mat[:, 0] = rotation_mat[:, 0] trans_mat[:, 1] = rotation_mat[:, 1] return trans_mat def apply_transform( self, input: Tensor, params: dict[str, Tensor], flags: dict[str, Any], transform: Optional[Tensor] = None ) -> Tensor: if not isinstance(transform, Tensor): raise TypeError(f"Expected the `transform` be a Tensor. Got {type(transform)}.") return affine(input, transform[..., :2, :3], flags["resample"].name.lower(), "zeros", flags["align_corners"]) def inverse_transform( self, input: Tensor, flags: dict[str, Any], transform: Optional[Tensor] = None, size: Optional[tuple[int, int]] = None, ) -> Tensor: if not isinstance(transform, Tensor): raise TypeError(f"Expected the `transform` be a Tensor. Got {type(transform)}.") return self.apply_transform( input, params=self._params, transform=as_tensor(transform, device=input.device, dtype=input.dtype), flags=flags, )