| import math |
| from enum import Enum |
| from typing import List, Tuple, Optional, Dict |
|
|
| import torch |
| from torch import Tensor |
|
|
| from torchvision.transforms import functional as F |
| from torchvision.transforms.functional import InterpolationMode |
|
|
| __all__ = ["AutoAugmentPolicy", "AutoAugment", "RandAugment", "TrivialAugmentWide"] |
|
|
|
|
| def _apply_op( |
| img: Tensor, op_name: str, magnitude: float, interpolation: InterpolationMode, fill: Optional[List[float]] |
| ): |
| if op_name == "ShearX": |
| img = F.affine( |
| img, |
| angle=0.0, |
| translate=[0, 0], |
| scale=1.0, |
| shear=[math.degrees(magnitude), 0.0], |
| interpolation=interpolation, |
| fill=fill, |
| ) |
| elif op_name == "ShearY": |
| img = F.affine( |
| img, |
| angle=0.0, |
| translate=[0, 0], |
| scale=1.0, |
| shear=[0.0, math.degrees(magnitude)], |
| interpolation=interpolation, |
| fill=fill, |
| ) |
| elif op_name == "TranslateX": |
| img = F.affine( |
| img, |
| angle=0.0, |
| translate=[int(magnitude), 0], |
| scale=1.0, |
| interpolation=interpolation, |
| shear=[0.0, 0.0], |
| fill=fill, |
| ) |
| elif op_name == "TranslateY": |
| img = F.affine( |
| img, |
| angle=0.0, |
| translate=[0, int(magnitude)], |
| scale=1.0, |
| interpolation=interpolation, |
| shear=[0.0, 0.0], |
| fill=fill, |
| ) |
| elif op_name == "Rotate": |
| img = F.rotate(img, magnitude, interpolation=interpolation, fill=fill) |
| elif op_name == "Brightness": |
| img = F.adjust_brightness(img, 1.0 + magnitude) |
| elif op_name == "Color": |
| img = F.adjust_saturation(img, 1.0 + magnitude) |
| elif op_name == "Contrast": |
| img = F.adjust_contrast(img, 1.0 + magnitude) |
| elif op_name == "Sharpness": |
| img = F.adjust_sharpness(img, 1.0 + magnitude) |
| elif op_name == "Posterize": |
| img = F.posterize(img, int(magnitude)) |
| elif op_name == "Solarize": |
| img = F.solarize(img, magnitude) |
| elif op_name == "AutoContrast": |
| img = F.autocontrast(img) |
| elif op_name == "Equalize": |
| img = F.equalize(img) |
| elif op_name == "Invert": |
| img = F.invert(img) |
| elif op_name == "Identity": |
| pass |
| else: |
| raise ValueError(f"The provided operator {op_name} is not recognized.") |
| return img |
|
|
|
|
| class AutoAugmentPolicy(Enum): |
| """AutoAugment policies learned on different datasets. |
| Available policies are IMAGENET, CIFAR10 and SVHN. |
| """ |
|
|
| IMAGENET = "imagenet" |
| CIFAR10 = "cifar10" |
| SVHN = "svhn" |
|
|
|
|
| |
| class AutoAugment(torch.nn.Module): |
| r"""AutoAugment data augmentation method based on |
| `"AutoAugment: Learning Augmentation Strategies from Data" <https://arxiv.org/pdf/1805.09501.pdf>`_. |
| If the image is torch Tensor, it should be of type torch.uint8, and it is expected |
| to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. |
| If img is PIL Image, it is expected to be in mode "L" or "RGB". |
| |
| Args: |
| policy (AutoAugmentPolicy): Desired policy enum defined by |
| :class:`torchvision.transforms.autoaugment.AutoAugmentPolicy`. Default is ``AutoAugmentPolicy.IMAGENET``. |
| interpolation (InterpolationMode): Desired interpolation enum defined by |
| :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. |
| If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. |
| fill (sequence or number, optional): Pixel fill value for the area outside the transformed |
| image. If given a number, the value is used for all bands respectively. |
| """ |
|
|
| def __init__( |
| self, |
| policy: AutoAugmentPolicy = AutoAugmentPolicy.IMAGENET, |
| interpolation: InterpolationMode = InterpolationMode.NEAREST, |
| fill: Optional[List[float]] = None, |
| ) -> None: |
| super().__init__() |
| self.policy = policy |
| self.interpolation = interpolation |
| self.fill = fill |
| self.policies = self._get_policies(policy) |
|
|
| def _get_policies( |
| self, policy: AutoAugmentPolicy |
| ) -> List[Tuple[Tuple[str, float, Optional[int]], Tuple[str, float, Optional[int]]]]: |
| if policy == AutoAugmentPolicy.IMAGENET: |
| return [ |
| (("Posterize", 0.4, 8), ("Rotate", 0.6, 9)), |
| (("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)), |
| (("Equalize", 0.8, None), ("Equalize", 0.6, None)), |
| (("Posterize", 0.6, 7), ("Posterize", 0.6, 6)), |
| (("Equalize", 0.4, None), ("Solarize", 0.2, 4)), |
| (("Equalize", 0.4, None), ("Rotate", 0.8, 8)), |
| (("Solarize", 0.6, 3), ("Equalize", 0.6, None)), |
| (("Posterize", 0.8, 5), ("Equalize", 1.0, None)), |
| (("Rotate", 0.2, 3), ("Solarize", 0.6, 8)), |
| (("Equalize", 0.6, None), ("Posterize", 0.4, 6)), |
| (("Rotate", 0.8, 8), ("Color", 0.4, 0)), |
| (("Rotate", 0.4, 9), ("Equalize", 0.6, None)), |
| (("Equalize", 0.0, None), ("Equalize", 0.8, None)), |
| (("Invert", 0.6, None), ("Equalize", 1.0, None)), |
| (("Color", 0.6, 4), ("Contrast", 1.0, 8)), |
| (("Rotate", 0.8, 8), ("Color", 1.0, 2)), |
| (("Color", 0.8, 8), ("Solarize", 0.8, 7)), |
| (("Sharpness", 0.4, 7), ("Invert", 0.6, None)), |
| (("ShearX", 0.6, 5), ("Equalize", 1.0, None)), |
| (("Color", 0.4, 0), ("Equalize", 0.6, None)), |
| (("Equalize", 0.4, None), ("Solarize", 0.2, 4)), |
| (("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)), |
| (("Invert", 0.6, None), ("Equalize", 1.0, None)), |
| (("Color", 0.6, 4), ("Contrast", 1.0, 8)), |
| (("Equalize", 0.8, None), ("Equalize", 0.6, None)), |
| ] |
| elif policy == AutoAugmentPolicy.CIFAR10: |
| return [ |
| (("Invert", 0.1, None), ("Contrast", 0.2, 6)), |
| (("Rotate", 0.7, 2), ("TranslateX", 0.3, 9)), |
| (("Sharpness", 0.8, 1), ("Sharpness", 0.9, 3)), |
| (("ShearY", 0.5, 8), ("TranslateY", 0.7, 9)), |
| (("AutoContrast", 0.5, None), ("Equalize", 0.9, None)), |
| (("ShearY", 0.2, 7), ("Posterize", 0.3, 7)), |
| (("Color", 0.4, 3), ("Brightness", 0.6, 7)), |
| (("Sharpness", 0.3, 9), ("Brightness", 0.7, 9)), |
| (("Equalize", 0.6, None), ("Equalize", 0.5, None)), |
| (("Contrast", 0.6, 7), ("Sharpness", 0.6, 5)), |
| (("Color", 0.7, 7), ("TranslateX", 0.5, 8)), |
| (("Equalize", 0.3, None), ("AutoContrast", 0.4, None)), |
| (("TranslateY", 0.4, 3), ("Sharpness", 0.2, 6)), |
| (("Brightness", 0.9, 6), ("Color", 0.2, 8)), |
| (("Solarize", 0.5, 2), ("Invert", 0.0, None)), |
| (("Equalize", 0.2, None), ("AutoContrast", 0.6, None)), |
| (("Equalize", 0.2, None), ("Equalize", 0.6, None)), |
| (("Color", 0.9, 9), ("Equalize", 0.6, None)), |
| (("AutoContrast", 0.8, None), ("Solarize", 0.2, 8)), |
| (("Brightness", 0.1, 3), ("Color", 0.7, 0)), |
| (("Solarize", 0.4, 5), ("AutoContrast", 0.9, None)), |
| (("TranslateY", 0.9, 9), ("TranslateY", 0.7, 9)), |
| (("AutoContrast", 0.9, None), ("Solarize", 0.8, 3)), |
| (("Equalize", 0.8, None), ("Invert", 0.1, None)), |
| (("TranslateY", 0.7, 9), ("AutoContrast", 0.9, None)), |
| ] |
| elif policy == AutoAugmentPolicy.SVHN: |
| return [ |
| (("ShearX", 0.9, 4), ("Invert", 0.2, None)), |
| (("ShearY", 0.9, 8), ("Invert", 0.7, None)), |
| (("Equalize", 0.6, None), ("Solarize", 0.6, 6)), |
| (("Invert", 0.9, None), ("Equalize", 0.6, None)), |
| (("Equalize", 0.6, None), ("Rotate", 0.9, 3)), |
| (("ShearX", 0.9, 4), ("AutoContrast", 0.8, None)), |
| (("ShearY", 0.9, 8), ("Invert", 0.4, None)), |
| (("ShearY", 0.9, 5), ("Solarize", 0.2, 6)), |
| (("Invert", 0.9, None), ("AutoContrast", 0.8, None)), |
| (("Equalize", 0.6, None), ("Rotate", 0.9, 3)), |
| (("ShearX", 0.9, 4), ("Solarize", 0.3, 3)), |
| (("ShearY", 0.8, 8), ("Invert", 0.7, None)), |
| (("Equalize", 0.9, None), ("TranslateY", 0.6, 6)), |
| (("Invert", 0.9, None), ("Equalize", 0.6, None)), |
| (("Contrast", 0.3, 3), ("Rotate", 0.8, 4)), |
| (("Invert", 0.8, None), ("TranslateY", 0.0, 2)), |
| (("ShearY", 0.7, 6), ("Solarize", 0.4, 8)), |
| (("Invert", 0.6, None), ("Rotate", 0.8, 4)), |
| (("ShearY", 0.3, 7), ("TranslateX", 0.9, 3)), |
| (("ShearX", 0.1, 6), ("Invert", 0.6, None)), |
| (("Solarize", 0.7, 2), ("TranslateY", 0.6, 7)), |
| (("ShearY", 0.8, 4), ("Invert", 0.8, None)), |
| (("ShearX", 0.7, 9), ("TranslateY", 0.8, 3)), |
| (("ShearY", 0.8, 5), ("AutoContrast", 0.7, None)), |
| (("ShearX", 0.7, 2), ("Invert", 0.1, None)), |
| ] |
| else: |
| raise ValueError(f"The provided policy {policy} is not recognized.") |
|
|
| def _augmentation_space(self, num_bins: int, image_size: List[int]) -> Dict[str, Tuple[Tensor, bool]]: |
| return { |
| |
| "ShearX": (torch.linspace(0.0, 0.3, num_bins), True), |
| "ShearY": (torch.linspace(0.0, 0.3, num_bins), True), |
| "TranslateX": (torch.linspace(0.0, 150.0 / 331.0 * image_size[0], num_bins), True), |
| "TranslateY": (torch.linspace(0.0, 150.0 / 331.0 * image_size[1], num_bins), True), |
| "Rotate": (torch.linspace(0.0, 30.0, num_bins), True), |
| "Brightness": (torch.linspace(0.0, 0.9, num_bins), True), |
| "Color": (torch.linspace(0.0, 0.9, num_bins), True), |
| "Contrast": (torch.linspace(0.0, 0.9, num_bins), True), |
| "Sharpness": (torch.linspace(0.0, 0.9, num_bins), True), |
| "Posterize": (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4)).round().int(), False), |
| "Solarize": (torch.linspace(255.0, 0.0, num_bins), False), |
| "AutoContrast": (torch.tensor(0.0), False), |
| "Equalize": (torch.tensor(0.0), False), |
| "Invert": (torch.tensor(0.0), False), |
| } |
|
|
| @staticmethod |
| def get_params(transform_num: int) -> Tuple[int, Tensor, Tensor]: |
| """Get parameters for autoaugment transformation |
| |
| Returns: |
| params required by the autoaugment transformation |
| """ |
| policy_id = int(torch.randint(transform_num, (1,)).item()) |
| probs = torch.rand((2,)) |
| signs = torch.randint(2, (2,)) |
|
|
| return policy_id, probs, signs |
|
|
| def forward(self, img: Tensor) -> Tensor: |
| """ |
| img (PIL Image or Tensor): Image to be transformed. |
| |
| Returns: |
| PIL Image or Tensor: AutoAugmented image. |
| """ |
| fill = self.fill |
| if isinstance(img, Tensor): |
| if isinstance(fill, (int, float)): |
| fill = [float(fill)] * F.get_image_num_channels(img) |
| elif fill is not None: |
| fill = [float(f) for f in fill] |
|
|
| transform_id, probs, signs = self.get_params(len(self.policies)) |
|
|
| for i, (op_name, p, magnitude_id) in enumerate(self.policies[transform_id]): |
| if probs[i] <= p: |
| op_meta = self._augmentation_space(10, F.get_image_size(img)) |
| magnitudes, signed = op_meta[op_name] |
| magnitude = float(magnitudes[magnitude_id].item()) if magnitude_id is not None else 0.0 |
| if signed and signs[i] == 0: |
| magnitude *= -1.0 |
| img = _apply_op(img, op_name, magnitude, interpolation=self.interpolation, fill=fill) |
|
|
| return img |
|
|
| def __repr__(self) -> str: |
| return self.__class__.__name__ + f"(policy={self.policy}, fill={self.fill})" |
|
|
|
|
| class RandAugment(torch.nn.Module): |
| r"""RandAugment data augmentation method based on |
| `"RandAugment: Practical automated data augmentation with a reduced search space" |
| <https://arxiv.org/abs/1909.13719>`_. |
| If the image is torch Tensor, it should be of type torch.uint8, and it is expected |
| to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. |
| If img is PIL Image, it is expected to be in mode "L" or "RGB". |
| |
| Args: |
| num_ops (int): Number of augmentation transformations to apply sequentially. |
| magnitude (int): Magnitude for all the transformations. |
| num_magnitude_bins (int): The number of different magnitude values. |
| interpolation (InterpolationMode): Desired interpolation enum defined by |
| :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. |
| If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. |
| fill (sequence or number, optional): Pixel fill value for the area outside the transformed |
| image. If given a number, the value is used for all bands respectively. |
| """ |
|
|
| def __init__( |
| self, |
| num_ops: int = 2, |
| magnitude: int = 9, |
| num_magnitude_bins: int = 31, |
| interpolation: InterpolationMode = InterpolationMode.NEAREST, |
| fill: Optional[List[float]] = None, |
| ) -> None: |
| super().__init__() |
| self.num_ops = num_ops |
| self.magnitude = magnitude |
| self.num_magnitude_bins = num_magnitude_bins |
| self.interpolation = interpolation |
| self.fill = fill |
|
|
| def _augmentation_space(self, num_bins: int, image_size: List[int]) -> Dict[str, Tuple[Tensor, bool]]: |
| return { |
| |
| "Identity": (torch.tensor(0.0), False), |
| "ShearX": (torch.linspace(0.0, 0.3, num_bins), True), |
| "ShearY": (torch.linspace(0.0, 0.3, num_bins), True), |
| "TranslateX": (torch.linspace(0.0, 150.0 / 331.0 * image_size[0], num_bins), True), |
| "TranslateY": (torch.linspace(0.0, 150.0 / 331.0 * image_size[1], num_bins), True), |
| "Rotate": (torch.linspace(0.0, 30.0, num_bins), True), |
| "Brightness": (torch.linspace(0.0, 0.9, num_bins), True), |
| "Color": (torch.linspace(0.0, 0.9, num_bins), True), |
| "Contrast": (torch.linspace(0.0, 0.9, num_bins), True), |
| "Sharpness": (torch.linspace(0.0, 0.9, num_bins), True), |
| "Posterize": (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4)).round().int(), False), |
| "Solarize": (torch.linspace(255.0, 0.0, num_bins), False), |
| "AutoContrast": (torch.tensor(0.0), False), |
| "Equalize": (torch.tensor(0.0), False), |
| } |
|
|
| def forward(self, img: Tensor) -> Tensor: |
| """ |
| img (PIL Image or Tensor): Image to be transformed. |
| |
| Returns: |
| PIL Image or Tensor: Transformed image. |
| """ |
| fill = self.fill |
| if isinstance(img, Tensor): |
| if isinstance(fill, (int, float)): |
| fill = [float(fill)] * F.get_image_num_channels(img) |
| elif fill is not None: |
| fill = [float(f) for f in fill] |
|
|
| for _ in range(self.num_ops): |
| op_meta = self._augmentation_space(self.num_magnitude_bins, F.get_image_size(img)) |
| op_index = int(torch.randint(len(op_meta), (1,)).item()) |
| op_name = list(op_meta.keys())[op_index] |
| magnitudes, signed = op_meta[op_name] |
| magnitude = float(magnitudes[self.magnitude].item()) if magnitudes.ndim > 0 else 0.0 |
| if signed and torch.randint(2, (1,)): |
| magnitude *= -1.0 |
| img = _apply_op(img, op_name, magnitude, interpolation=self.interpolation, fill=fill) |
|
|
| return img |
|
|
| def __repr__(self) -> str: |
| s = self.__class__.__name__ + "(" |
| s += "num_ops={num_ops}" |
| s += ", magnitude={magnitude}" |
| s += ", num_magnitude_bins={num_magnitude_bins}" |
| s += ", interpolation={interpolation}" |
| s += ", fill={fill}" |
| s += ")" |
| return s.format(**self.__dict__) |
|
|
|
|
| class TrivialAugmentWide(torch.nn.Module): |
| r"""Dataset-independent data-augmentation with TrivialAugment Wide, as described in |
| `"TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation" <https://arxiv.org/abs/2103.10158>`_. |
| If the image is torch Tensor, it should be of type torch.uint8, and it is expected |
| to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. |
| If img is PIL Image, it is expected to be in mode "L" or "RGB". |
| |
| Args: |
| num_magnitude_bins (int): The number of different magnitude values. |
| interpolation (InterpolationMode): Desired interpolation enum defined by |
| :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. |
| If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. |
| fill (sequence or number, optional): Pixel fill value for the area outside the transformed |
| image. If given a number, the value is used for all bands respectively. |
| """ |
|
|
| def __init__( |
| self, |
| num_magnitude_bins: int = 31, |
| interpolation: InterpolationMode = InterpolationMode.NEAREST, |
| fill: Optional[List[float]] = None, |
| ) -> None: |
| super().__init__() |
| self.num_magnitude_bins = num_magnitude_bins |
| self.interpolation = interpolation |
| self.fill = fill |
|
|
| def _augmentation_space(self, num_bins: int) -> Dict[str, Tuple[Tensor, bool]]: |
| return { |
| |
| "Identity": (torch.tensor(0.0), False), |
| "ShearX": (torch.linspace(0.0, 0.99, num_bins), True), |
| "ShearY": (torch.linspace(0.0, 0.99, num_bins), True), |
| "TranslateX": (torch.linspace(0.0, 32.0, num_bins), True), |
| "TranslateY": (torch.linspace(0.0, 32.0, num_bins), True), |
| "Rotate": (torch.linspace(0.0, 135.0, num_bins), True), |
| "Brightness": (torch.linspace(0.0, 0.99, num_bins), True), |
| "Color": (torch.linspace(0.0, 0.99, num_bins), True), |
| "Contrast": (torch.linspace(0.0, 0.99, num_bins), True), |
| "Sharpness": (torch.linspace(0.0, 0.99, num_bins), True), |
| "Posterize": (8 - (torch.arange(num_bins) / ((num_bins - 1) / 6)).round().int(), False), |
| "Solarize": (torch.linspace(255.0, 0.0, num_bins), False), |
| "AutoContrast": (torch.tensor(0.0), False), |
| "Equalize": (torch.tensor(0.0), False), |
| } |
|
|
| def forward(self, img: Tensor) -> Tensor: |
| """ |
| img (PIL Image or Tensor): Image to be transformed. |
| |
| Returns: |
| PIL Image or Tensor: Transformed image. |
| """ |
| fill = self.fill |
| if isinstance(img, Tensor): |
| if isinstance(fill, (int, float)): |
| fill = [float(fill)] * F.get_image_num_channels(img) |
| elif fill is not None: |
| fill = [float(f) for f in fill] |
|
|
| op_meta = self._augmentation_space(self.num_magnitude_bins) |
| op_index = int(torch.randint(len(op_meta), (1,)).item()) |
| op_name = list(op_meta.keys())[op_index] |
| magnitudes, signed = op_meta[op_name] |
| magnitude = ( |
| float(magnitudes[torch.randint(len(magnitudes), (1,), dtype=torch.long)].item()) |
| if magnitudes.ndim > 0 |
| else 0.0 |
| ) |
| if signed and torch.randint(2, (1,)): |
| magnitude *= -1.0 |
|
|
| return _apply_op(img, op_name, magnitude, interpolation=self.interpolation, fill=fill) |
|
|
| def __repr__(self) -> str: |
| s = self.__class__.__name__ + "(" |
| s += "num_magnitude_bins={num_magnitude_bins}" |
| s += ", interpolation={interpolation}" |
| s += ", fill={fill}" |
| s += ")" |
| return s.format(**self.__dict__) |
|
|
