File size: 6,885 Bytes
f71ac1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 | """A wrap for timm transforms."""
from __future__ import annotations
from typing import Union
import numpy as np
from PIL import Image
from vis4d.common.imports import TIMM_AVAILABLE
from vis4d.common.typing import NDArrayUI8
from vis4d.data.const import CommonKeys as K
from .base import Transform
if TIMM_AVAILABLE:
from timm.data.auto_augment import (
_RAND_INCREASING_TRANSFORMS,
_RAND_TRANSFORMS,
AugMixAugment,
AutoAugment,
RandAugment,
augmix_ops,
auto_augment_policy,
rand_augment_ops,
)
else:
raise ImportError("timm is not installed.")
AugOp = Union[AutoAugment, RandAugment, AugMixAugment]
def _apply_aug(images: NDArrayUI8, aug_op: AugOp) -> NDArrayUI8:
"""Apply augmentation to a batch of images with shape [N, H, W, C]."""
assert images.shape[-1] == 3, "Images must be in RGB format."
imgs: list[Image.Image] = []
for img in images:
# convert to uint8 if necessary
if img.dtype != np.uint8:
img = img.astype(np.uint8)
imgs.append(aug_op(Image.fromarray(img)))
return np.stack([np.array(img).astype(np.float32) for img in imgs])
@Transform(K.images, K.images)
class _AutoAug:
"""Apply Timm's AutoAugment to a image array."""
def __init__(self) -> None:
self.aug_op: AugOp | None = None
def _create(self, policy: str, hparams: dict[str, float]) -> AugOp:
"""Create augmentation op."""
aa_policy = auto_augment_policy(policy, hparams=hparams)
return AutoAugment(aa_policy)
def __call__(self, images: list[NDArrayUI8]) -> list[NDArrayUI8]:
"""Execute the transform."""
assert self.aug_op is not None, "Augmentation op is not created."
for i, img in enumerate(images):
images[i] = _apply_aug(img, self.aug_op)
return images
class AutoAugV0(_AutoAug):
"""Apply Timm's AutoAugment (policy=v0) to a image array."""
def __init__(self, magnitude_std: float = 0.5):
"""Create an instance of AutoAug.
Args:
magnitude_std (float, optional): Standard deviation of the
magnitude for random autoaugment. Defaults to 0.5.
"""
super().__init__()
self.aug_op = self._create("v0", {"magnitude_std": magnitude_std})
class AutoAugOriginal(_AutoAug):
"""Apply Timm's AutoAugment (policy=original) to a image array."""
def __init__(self, magnitude_std: float = 0.5):
"""Create an instance of AutoAug.
Args:
magnitude_std (float, optional): Standard deviation of the
magnitude for random autoaugment. Defaults to 0.5.
"""
super().__init__()
self.aug_op = self._create(
"original", {"magnitude_std": magnitude_std}
)
@Transform(K.images, K.images)
class RandAug:
"""Apply Timm's RandomAugment to a image tensor."""
def __init__(
self,
magnitude: int = 10,
num_layers: int = 2,
use_increasing: bool = False,
magnitude_std: float = 0.5,
):
"""Create an instance of RandAug.
Args:
magnitude (int): Level of magnitude for augments, ranging from 1 to
9.
num_layers (int, optional): Number of layers for rand augment.
Defaults to 2.
use_increasing (bool, optional): Whether to use increasing setting
for transforms. Defaults to False.
magnitude_std (float, optional): Standard deviation of the
magnitude for random autoaugment. Defaults to 0.5.
Returns:
Callable: A function that takes a tensor of shape [N, C, H, W] and
returns a tensor of the same shape.
Example:
Rand augment with magnitude 9. (`https://arxiv.org/abs/1909.13719`)
>>> rand_augment(magnitude=9)
"""
super().__init__()
assert TIMM_AVAILABLE, "timm is not installed."
self.magnitude = magnitude
self.num_layers = num_layers
self.use_increasing = use_increasing
self.magnitude_std = magnitude_std
hparams = {"magnitude_std": self.magnitude_std}
if self.use_increasing:
transforms = _RAND_INCREASING_TRANSFORMS
else:
transforms = _RAND_TRANSFORMS
ra_ops = rand_augment_ops(
magnitude=self.magnitude, hparams=hparams, transforms=transforms
)
self.aug_op = RandAugment(ra_ops, self.num_layers)
def __call__(self, images: list[NDArrayUI8]) -> list[NDArrayUI8]:
"""Execute the transform."""
for i, img in enumerate(images):
images[i] = _apply_aug(img, self.aug_op)
return images
@Transform(K.images, K.images)
class AugMix:
"""Apply Timm's AugMix to a image tensor."""
def __init__(
self,
magnitude: int = 10,
width: int = 3,
alpha: float = 1.0,
depth: int = -1,
blended: bool = True,
magnitude_std: float = 0.5,
):
"""Create an instance of AugMix.
Args:
magnitude (int): Level of magnitude, ranging from 1 to 9.
width (int, optional): Width of the augmentation chain. Defaults to
3.
alpha (float, optional): Alpha for beta distribution. Defaults to
1.0.
depth (int, optional): Depth of the augmentation chain. Defaults to
-1.
blended (bool, optional): Whether to blend the original image with
the augmented image. Defaults to True.
magnitude_std (float, optional): Standard deviation of the
magnitude for random autoaugment. Defaults to 0.5.
Returns:
Callable: A function that takes a tensor of shape [N, C, H, W] and
returns a tensor of the same shape.
Example:
Augmix with magnitude 9. (`https://arxiv.org/abs/1912.02781`)
>>> augmix(magnitude=9)
"""
super().__init__()
assert TIMM_AVAILABLE, "timm is not installed."
self.magnitude = magnitude
self.width = width
self.alpha = alpha
self.depth = depth
self.blended = blended
self.magnitude_std = magnitude_std
hparams = {"magnitude_std": self.magnitude_std}
am_ops = augmix_ops(magnitude=self.magnitude, hparams=hparams)
self.aug_op = AugMixAugment(
am_ops,
alpha=self.alpha,
width=self.width,
depth=self.depth,
blended=self.blended,
)
def __call__(self, images: list[NDArrayUI8]) -> list[NDArrayUI8]:
"""Execute the transform."""
for i, img in enumerate(images):
images[i] = _apply_aug(img, self.aug_op)
return images
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