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" # FIXME: Eliminate copy-pasted code for fill standardization and _augmentation_space() by moving stuff on a base class class AutoAugment(torch.nn.Module): r"""AutoAugment data augmentation method based on `"AutoAugment: Learning Augmentation Strategies from Data" `_. 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 { # op_name: (magnitudes, signed) "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" `_. 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 { # op_name: (magnitudes, signed) "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" `_. 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 { # op_name: (magnitudes, signed) "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__)