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randomaug.py
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randomaug.py
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# code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from PIL import Image
def ShearX(img, v): # [-0.3, 0.3]
assert -0.3 <= v <= 0.3
if random.random() > 0.5:
v = -v
return img.transform(img.size, PIL.Image.AFFINE, (1, v, 0, 0, 1, 0))
def ShearY(img, v): # [-0.3, 0.3]
assert -0.3 <= v <= 0.3
if random.random() > 0.5:
v = -v
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, v, 1, 0))
def TranslateX(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert -0.45 <= v <= 0.45
if random.random() > 0.5:
v = -v
v = v * img.size[0]
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0))
def TranslateXabs(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert 0 <= v
if random.random() > 0.5:
v = -v
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0))
def TranslateY(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert -0.45 <= v <= 0.45
if random.random() > 0.5:
v = -v
v = v * img.size[1]
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v))
def TranslateYabs(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert 0 <= v
if random.random() > 0.5:
v = -v
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v))
def Rotate(img, v): # [-30, 30]
assert -30 <= v <= 30
if random.random() > 0.5:
v = -v
return img.rotate(v)
def AutoContrast(img, _):
return PIL.ImageOps.autocontrast(img)
def Invert(img, _):
return PIL.ImageOps.invert(img)
def Equalize(img, _):
return PIL.ImageOps.equalize(img)
def Flip(img, _): # not from the paper
return PIL.ImageOps.mirror(img)
def Solarize(img, v): # [0, 256]
assert 0 <= v <= 256
return PIL.ImageOps.solarize(img, v)
def SolarizeAdd(img, addition=0, threshold=128):
img_np = np.array(img).astype(int)
img_np = img_np + addition
img_np = np.clip(img_np, 0, 255)
img_np = img_np.astype(np.uint8)
img = Image.fromarray(img_np)
return PIL.ImageOps.solarize(img, threshold)
def Posterize(img, v): # [4, 8]
v = int(v)
v = max(1, v)
return PIL.ImageOps.posterize(img, v)
def Contrast(img, v): # [0.1,1.9]
assert 0.1 <= v <= 1.9
return PIL.ImageEnhance.Contrast(img).enhance(v)
def Color(img, v): # [0.1,1.9]
assert 0.1 <= v <= 1.9
return PIL.ImageEnhance.Color(img).enhance(v)
def Brightness(img, v): # [0.1,1.9]
assert 0.1 <= v <= 1.9
return PIL.ImageEnhance.Brightness(img).enhance(v)
def Sharpness(img, v): # [0.1,1.9]
assert 0.1 <= v <= 1.9
return PIL.ImageEnhance.Sharpness(img).enhance(v)
def Cutout(img, v): # [0, 60] => percentage: [0, 0.2]
assert 0.0 <= v <= 0.2
if v <= 0.:
return img
v = v * img.size[0]
return CutoutAbs(img, v)
def CutoutAbs(img, v): # [0, 60] => percentage: [0, 0.2]
# assert 0 <= v <= 20
if v < 0:
return img
w, h = img.size
x0 = np.random.uniform(w)
y0 = np.random.uniform(h)
x0 = int(max(0, x0 - v / 2.))
y0 = int(max(0, y0 - v / 2.))
x1 = min(w, x0 + v)
y1 = min(h, y0 + v)
xy = (x0, y0, x1, y1)
color = (125, 123, 114)
# color = (0, 0, 0)
img = img.copy()
PIL.ImageDraw.Draw(img).rectangle(xy, color)
return img
def SamplePairing(imgs): # [0, 0.4]
def f(img1, v):
i = np.random.choice(len(imgs))
img2 = PIL.Image.fromarray(imgs[i])
return PIL.Image.blend(img1, img2, v)
return f
def Identity(img, v):
return img
def augment_list(): # 16 oeprations and their ranges
# https://github.com/google-research/uda/blob/master/image/randaugment/policies.py#L57
# l = [
# (Identity, 0., 1.0),
# (ShearX, 0., 0.3), # 0
# (ShearY, 0., 0.3), # 1
# (TranslateX, 0., 0.33), # 2
# (TranslateY, 0., 0.33), # 3
# (Rotate, 0, 30), # 4
# (AutoContrast, 0, 1), # 5
# (Invert, 0, 1), # 6
# (Equalize, 0, 1), # 7
# (Solarize, 0, 110), # 8
# (Posterize, 4, 8), # 9
# # (Contrast, 0.1, 1.9), # 10
# (Color, 0.1, 1.9), # 11
# (Brightness, 0.1, 1.9), # 12
# (Sharpness, 0.1, 1.9), # 13
# # (Cutout, 0, 0.2), # 14
# # (SamplePairing(imgs), 0, 0.4), # 15
# ]
# https://github.com/tensorflow/tpu/blob/8462d083dd89489a79e3200bcc8d4063bf362186/models/official/efficientnet/autoaugment.py#L505
l = [
(AutoContrast, 0, 1),
(Equalize, 0, 1),
(Invert, 0, 1),
(Rotate, 0, 30),
(Posterize, 0, 4),
(Solarize, 0, 256),
(SolarizeAdd, 0, 110),
(Color, 0.1, 1.9),
(Contrast, 0.1, 1.9),
(Brightness, 0.1, 1.9),
(Sharpness, 0.1, 1.9),
(ShearX, 0., 0.3),
(ShearY, 0., 0.3),
(CutoutAbs, 0, 40),
(TranslateXabs, 0., 100),
(TranslateYabs, 0., 100),
]
return l
class Lighting(object):
"""Lighting noise(AlexNet - style PCA - based noise)"""
def __init__(self, alphastd, eigval, eigvec):
self.alphastd = alphastd
self.eigval = torch.Tensor(eigval)
self.eigvec = torch.Tensor(eigvec)
def __call__(self, img):
if self.alphastd == 0:
return img
alpha = img.new().resize_(3).normal_(0, self.alphastd)
rgb = self.eigvec.type_as(img).clone() \
.mul(alpha.view(1, 3).expand(3, 3)) \
.mul(self.eigval.view(1, 3).expand(3, 3)) \
.sum(1).squeeze()
return img.add(rgb.view(3, 1, 1).expand_as(img))
class CutoutDefault(object):
"""
Reference : https://github.com/quark0/darts/blob/master/cnn/utils.py
"""
def __init__(self, length):
self.length = length
def __call__(self, img):
h, w = img.size(1), img.size(2)
mask = np.ones((h, w), np.float32)
y = np.random.randint(h)
x = np.random.randint(w)
y1 = np.clip(y - self.length // 2, 0, h)
y2 = np.clip(y + self.length // 2, 0, h)
x1 = np.clip(x - self.length // 2, 0, w)
x2 = np.clip(x + self.length // 2, 0, w)
mask[y1: y2, x1: x2] = 0.
mask = torch.from_numpy(mask)
mask = mask.expand_as(img)
img *= mask
return img
class RandAugment:
def __init__(self, n, m):
self.n = n
self.m = m # [0, 30]
self.augment_list = augment_list()
def __call__(self, img):
ops = random.choices(self.augment_list, k=self.n)
for op, minval, maxval in ops:
val = (float(self.m) / 30) * float(maxval - minval) + minval
img = op(img, val)
return img