Shortcuts

Source code for torchvision.transforms.autoaugment

import math
from enum import Enum
from typing import Dict, List, Optional, Tuple

import torch
from torch import Tensor

from . import functional as F, InterpolationMode

__all__ = ["AutoAugmentPolicy", "AutoAugment", "RandAugment", "TrivialAugmentWide", "AugMix"]


def _apply_op(
    img: Tensor, op_name: str, magnitude: float, interpolation: InterpolationMode, fill: Optional[List[float]]
):
    if op_name == "ShearX":
        # magnitude should be arctan(magnitude)
        # official autoaug: (1, level, 0, 0, 1, 0)
        # https://github.com/tensorflow/models/blob/dd02069717128186b88afa8d857ce57d17957f03/research/autoaugment/augmentation_transforms.py#L290
        # compared to
        # torchvision:      (1, tan(level), 0, 0, 1, 0)
        # https://github.com/pytorch/vision/blob/0c2373d0bba3499e95776e7936e207d8a1676e65/torchvision/transforms/functional.py#L976
        img = F.affine(
            img,
            angle=0.0,
            translate=[0, 0],
            scale=1.0,
            shear=[math.degrees(math.atan(magnitude)), 0.0],
            interpolation=interpolation,
            fill=fill,
            center=[0, 0],
        )
    elif op_name == "ShearY":
        # magnitude should be arctan(magnitude)
        # See above
        img = F.affine(
            img,
            angle=0.0,
            translate=[0, 0],
            scale=1.0,
            shear=[0.0, math.degrees(math.atan(magnitude))],
            interpolation=interpolation,
            fill=fill,
            center=[0, 0],
        )
    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


[docs]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
[docs]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: Tuple[int, 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[1], num_bins), True), "TranslateY": (torch.linspace(0.0, 150.0 / 331.0 * image_size[0], 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), }
[docs] @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
[docs] 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 channels, height, width = F.get_dimensions(img) if isinstance(img, Tensor): if isinstance(fill, (int, float)): fill = [float(fill)] * channels elif fill is not None: fill = [float(f) for f in fill] transform_id, probs, signs = self.get_params(len(self.policies)) op_meta = self._augmentation_space(10, (height, width)) for i, (op_name, p, magnitude_id) in enumerate(self.policies[transform_id]): if probs[i] <= p: 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 f"{self.__class__.__name__}(policy={self.policy}, fill={self.fill})"
[docs]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: Tuple[int, 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[1], num_bins), True), "TranslateY": (torch.linspace(0.0, 150.0 / 331.0 * image_size[0], 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), }
[docs] 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 channels, height, width = F.get_dimensions(img) if isinstance(img, Tensor): if isinstance(fill, (int, float)): fill = [float(fill)] * channels elif fill is not None: fill = [float(f) for f in fill] op_meta = self._augmentation_space(self.num_magnitude_bins, (height, width)) for _ in range(self.num_ops): 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 = ( f"{self.__class__.__name__}(" f"num_ops={self.num_ops}" f", magnitude={self.magnitude}" f", num_magnitude_bins={self.num_magnitude_bins}" f", interpolation={self.interpolation}" f", fill={self.fill}" f")" ) return s
[docs]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 { # 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), }
[docs] 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 channels, height, width = F.get_dimensions(img) if isinstance(img, Tensor): if isinstance(fill, (int, float)): fill = [float(fill)] * channels 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 = ( f"{self.__class__.__name__}(" f"num_magnitude_bins={self.num_magnitude_bins}" f", interpolation={self.interpolation}" f", fill={self.fill}" f")" ) return s
[docs]class AugMix(torch.nn.Module): r"""AugMix data augmentation method based on `"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty" <https://arxiv.org/abs/1912.02781>`_. 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: severity (int): The severity of base augmentation operators. Default is ``3``. mixture_width (int): The number of augmentation chains. Default is ``3``. chain_depth (int): The depth of augmentation chains. A negative value denotes stochastic depth sampled from the interval [1, 3]. Default is ``-1``. alpha (float): The hyperparameter for the probability distributions. Default is ``1.0``. all_ops (bool): Use all operations (including brightness, contrast, color and sharpness). Default is ``True``. 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, severity: int = 3, mixture_width: int = 3, chain_depth: int = -1, alpha: float = 1.0, all_ops: bool = True, interpolation: InterpolationMode = InterpolationMode.BILINEAR, fill: Optional[List[float]] = None, ) -> None: super().__init__() self._PARAMETER_MAX = 10 if not (1 <= severity <= self._PARAMETER_MAX): raise ValueError(f"The severity must be between [1, {self._PARAMETER_MAX}]. Got {severity} instead.") self.severity = severity self.mixture_width = mixture_width self.chain_depth = chain_depth self.alpha = alpha self.all_ops = all_ops self.interpolation = interpolation self.fill = fill def _augmentation_space(self, num_bins: int, image_size: Tuple[int, int]) -> Dict[str, Tuple[Tensor, bool]]: s = { # 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, image_size[1] / 3.0, num_bins), True), "TranslateY": (torch.linspace(0.0, image_size[0] / 3.0, num_bins), True), "Rotate": (torch.linspace(0.0, 30.0, num_bins), True), "Posterize": (4 - (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), } if self.all_ops: s.update( { "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), } ) return s @torch.jit.unused def _pil_to_tensor(self, img) -> Tensor: return F.pil_to_tensor(img) @torch.jit.unused def _tensor_to_pil(self, img: Tensor): return F.to_pil_image(img) def _sample_dirichlet(self, params: Tensor) -> Tensor: # Must be on a separate method so that we can overwrite it in tests. return torch._sample_dirichlet(params)
[docs] def forward(self, orig_img: Tensor) -> Tensor: """ img (PIL Image or Tensor): Image to be transformed. Returns: PIL Image or Tensor: Transformed image. """ fill = self.fill channels, height, width = F.get_dimensions(orig_img) if isinstance(orig_img, Tensor): img = orig_img if isinstance(fill, (int, float)): fill = [float(fill)] * channels elif fill is not None: fill = [float(f) for f in fill] else: img = self._pil_to_tensor(orig_img) op_meta = self._augmentation_space(self._PARAMETER_MAX, (height, width)) orig_dims = list(img.shape) batch = img.view([1] * max(4 - img.ndim, 0) + orig_dims) batch_dims = [batch.size(0)] + [1] * (batch.ndim - 1) # Sample the beta weights for combining the original and augmented image. To get Beta, we use a Dirichlet # with 2 parameters. The 1st column stores the weights of the original and the 2nd the ones of augmented image. m = self._sample_dirichlet( torch.tensor([self.alpha, self.alpha], device=batch.device).expand(batch_dims[0], -1) ) # Sample the mixing weights and combine them with the ones sampled from Beta for the augmented images. combined_weights = self._sample_dirichlet( torch.tensor([self.alpha] * self.mixture_width, device=batch.device).expand(batch_dims[0], -1) ) * m[:, 1].view([batch_dims[0], -1]) mix = m[:, 0].view(batch_dims) * batch for i in range(self.mixture_width): aug = batch depth = self.chain_depth if self.chain_depth > 0 else int(torch.randint(low=1, high=4, size=(1,)).item()) for _ in range(depth): 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(self.severity, (1,), dtype=torch.long)].item()) if magnitudes.ndim > 0 else 0.0 ) if signed and torch.randint(2, (1,)): magnitude *= -1.0 aug = _apply_op(aug, op_name, magnitude, interpolation=self.interpolation, fill=fill) mix.add_(combined_weights[:, i].view(batch_dims) * aug) mix = mix.view(orig_dims).to(dtype=img.dtype) if not isinstance(orig_img, Tensor): return self._tensor_to_pil(mix) return mix
def __repr__(self) -> str: s = ( f"{self.__class__.__name__}(" f"severity={self.severity}" f", mixture_width={self.mixture_width}" f", chain_depth={self.chain_depth}" f", alpha={self.alpha}" f", all_ops={self.all_ops}" f", interpolation={self.interpolation}" f", fill={self.fill}" f")" ) return s

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources