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randaugment.py
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randaugment.py
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# from https://github.com/LeeDoYup/FixMatch-pytorch/blob/main/datasets/augmentation/randaugment.py
import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from PIL import Image
def AutoContrast(img, _):
return PIL.ImageOps.autocontrast(img)
def Brightness(img, v):
assert v >= 0.0
return PIL.ImageEnhance.Brightness(img).enhance(v)
def Color(img, v):
assert v >= 0.0
return PIL.ImageEnhance.Color(img).enhance(v)
def Contrast(img, v):
assert v >= 0.0
return PIL.ImageEnhance.Contrast(img).enhance(v)
def Equalize(img, _):
return PIL.ImageOps.equalize(img)
def Invert(img, _):
return PIL.ImageOps.invert(img)
def Identity(img, v):
return img
def Posterize(img, v): # [4, 8]
v = int(v)
v = max(1, v)
return PIL.ImageOps.posterize(img, v)
def Rotate(img, v): # [-30, 30]
# assert -30 <= v <= 30
# if random.random() > 0.5:
# v = -v
return img.rotate(v)
def Sharpness(img, v): # [0.1,1.9]
assert v >= 0.0
return PIL.ImageEnhance.Sharpness(img).enhance(v)
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 -0.3 <= v <= 0.3
# 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 v >= 0.0
# 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 -0.3 <= v <= 0.3
# 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 Solarize(img, v): # [0, 256]
assert 0 <= v <= 256
return PIL.ImageOps.solarize(img, v)
def Cutout(img, v): # [0, 60] => percentage: [0, 0.2] => change to [0, 0.5]
assert 0.0 <= v <= 0.5
if v <= 0.0:
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 / 2.0))
y0 = int(max(0, y0 - v / 2.0))
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 augment_list():
l = [
(AutoContrast, 0, 1),
(Brightness, 0.05, 0.95),
(Color, 0.05, 0.95),
(Contrast, 0.05, 0.95),
(Equalize, 0, 1),
(Identity, 0, 1),
(Posterize, 4, 8),
(Rotate, -30, 30),
(Sharpness, 0.05, 0.95),
(ShearX, -0.3, 0.3),
(ShearY, -0.3, 0.3),
(Solarize, 0, 256),
(TranslateX, -0.3, 0.3),
(TranslateY, -0.3, 0.3),
]
return l
class RandAugment:
def __init__(self, n, m):
self.n = n
self.m = m # [0, 30] in fixmatch, deprecated.
self.augment_list = augment_list()
def __call__(self, img):
ops = random.choices(self.augment_list, k=self.n)
for op, min_val, max_val in ops:
val = min_val + float(max_val - min_val) * random.random()
img = op(img, val)
cutout_val = random.random() * 0.5
img = Cutout(img, cutout_val) # for fixmatch
return img