mirror of https://github.com/JDAI-CV/DCL.git
233 lines
11 KiB
Python
233 lines
11 KiB
Python
|
from PIL import Image, ImageEnhance, ImageOps
|
||
|
|
import numpy as np
|
||
|
|
import random
|
||
|
|
|
||
|
|
|
||
|
|
class ImageNetPolicy(object):
|
||
|
|
""" Randomly choose one of the best 24 Sub-policies on ImageNet.
|
||
|
|
Example:
|
||
|
|
>>> policy = ImageNetPolicy()
|
||
|
|
>>> transformed = policy(image)
|
||
|
|
Example as a PyTorch Transform:
|
||
|
|
>>> transform=transforms.Compose([
|
||
|
|
>>> transforms.Resize(256),
|
||
|
|
>>> ImageNetPolicy(),
|
||
|
|
>>> transforms.ToTensor()])
|
||
|
|
"""
|
||
|
|
def __init__(self, fillcolor=(128, 128, 128)):
|
||
|
|
self.policies = [
|
||
|
|
SubPolicy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor),
|
||
|
|
SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
|
||
|
|
SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor),
|
||
|
|
SubPolicy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor),
|
||
|
|
SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
|
||
|
|
|
||
|
|
SubPolicy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor),
|
||
|
|
SubPolicy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor),
|
||
|
|
SubPolicy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor),
|
||
|
|
SubPolicy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor),
|
||
|
|
SubPolicy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor),
|
||
|
|
|
||
|
|
SubPolicy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor),
|
||
|
|
SubPolicy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor),
|
||
|
|
SubPolicy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor),
|
||
|
|
SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
|
||
|
|
SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
|
||
|
|
|
||
|
|
SubPolicy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor),
|
||
|
|
SubPolicy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor),
|
||
|
|
SubPolicy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor),
|
||
|
|
SubPolicy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor),
|
||
|
|
SubPolicy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor),
|
||
|
|
|
||
|
|
SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
|
||
|
|
SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
|
||
|
|
SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
|
||
|
|
SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
|
||
|
|
SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor)
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
def __call__(self, img):
|
||
|
|
policy_idx = random.randint(0, len(self.policies) - 1)
|
||
|
|
return self.policies[policy_idx](img)
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
return "AutoAugment ImageNet Policy"
|
||
|
|
|
||
|
|
|
||
|
|
class CIFAR10Policy(object):
|
||
|
|
""" Randomly choose one of the best 25 Sub-policies on CIFAR10.
|
||
|
|
Example:
|
||
|
|
>>> policy = CIFAR10Policy()
|
||
|
|
>>> transformed = policy(image)
|
||
|
|
Example as a PyTorch Transform:
|
||
|
|
>>> transform=transforms.Compose([
|
||
|
|
>>> transforms.Resize(256),
|
||
|
|
>>> CIFAR10Policy(),
|
||
|
|
>>> transforms.ToTensor()])
|
||
|
|
"""
|
||
|
|
def __init__(self, fillcolor=(128, 128, 128)):
|
||
|
|
self.policies = [
|
||
|
|
SubPolicy(0.1, "invert", 7, 0.2, "contrast", 6, fillcolor),
|
||
|
|
SubPolicy(0.7, "rotate", 2, 0.3, "translateX", 9, fillcolor),
|
||
|
|
SubPolicy(0.8, "sharpness", 1, 0.9, "sharpness", 3, fillcolor),
|
||
|
|
SubPolicy(0.5, "shearY", 8, 0.7, "translateY", 9, fillcolor),
|
||
|
|
SubPolicy(0.5, "autocontrast", 8, 0.9, "equalize", 2, fillcolor),
|
||
|
|
|
||
|
|
SubPolicy(0.2, "shearY", 7, 0.3, "posterize", 7, fillcolor),
|
||
|
|
SubPolicy(0.4, "color", 3, 0.6, "brightness", 7, fillcolor),
|
||
|
|
SubPolicy(0.3, "sharpness", 9, 0.7, "brightness", 9, fillcolor),
|
||
|
|
SubPolicy(0.6, "equalize", 5, 0.5, "equalize", 1, fillcolor),
|
||
|
|
SubPolicy(0.6, "contrast", 7, 0.6, "sharpness", 5, fillcolor),
|
||
|
|
|
||
|
|
SubPolicy(0.7, "color", 7, 0.5, "translateX", 8, fillcolor),
|
||
|
|
SubPolicy(0.3, "equalize", 7, 0.4, "autocontrast", 8, fillcolor),
|
||
|
|
SubPolicy(0.4, "translateY", 3, 0.2, "sharpness", 6, fillcolor),
|
||
|
|
SubPolicy(0.9, "brightness", 6, 0.2, "color", 8, fillcolor),
|
||
|
|
SubPolicy(0.5, "solarize", 2, 0.0, "invert", 3, fillcolor),
|
||
|
|
|
||
|
|
SubPolicy(0.2, "equalize", 0, 0.6, "autocontrast", 0, fillcolor),
|
||
|
|
SubPolicy(0.2, "equalize", 8, 0.8, "equalize", 4, fillcolor),
|
||
|
|
SubPolicy(0.9, "color", 9, 0.6, "equalize", 6, fillcolor),
|
||
|
|
SubPolicy(0.8, "autocontrast", 4, 0.2, "solarize", 8, fillcolor),
|
||
|
|
SubPolicy(0.1, "brightness", 3, 0.7, "color", 0, fillcolor),
|
||
|
|
|
||
|
|
SubPolicy(0.4, "solarize", 5, 0.9, "autocontrast", 3, fillcolor),
|
||
|
|
SubPolicy(0.9, "translateY", 9, 0.7, "translateY", 9, fillcolor),
|
||
|
|
SubPolicy(0.9, "autocontrast", 2, 0.8, "solarize", 3, fillcolor),
|
||
|
|
SubPolicy(0.8, "equalize", 8, 0.1, "invert", 3, fillcolor),
|
||
|
|
SubPolicy(0.7, "translateY", 9, 0.9, "autocontrast", 1, fillcolor)
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
def __call__(self, img):
|
||
|
|
policy_idx = random.randint(0, len(self.policies) - 1)
|
||
|
|
return self.policies[policy_idx](img)
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
return "AutoAugment CIFAR10 Policy"
|
||
|
|
|
||
|
|
|
||
|
|
class SVHNPolicy(object):
|
||
|
|
""" Randomly choose one of the best 25 Sub-policies on SVHN.
|
||
|
|
Example:
|
||
|
|
>>> policy = SVHNPolicy()
|
||
|
|
>>> transformed = policy(image)
|
||
|
|
Example as a PyTorch Transform:
|
||
|
|
>>> transform=transforms.Compose([
|
||
|
|
>>> transforms.Resize(256),
|
||
|
|
>>> SVHNPolicy(),
|
||
|
|
>>> transforms.ToTensor()])
|
||
|
|
"""
|
||
|
|
def __init__(self, fillcolor=(128, 128, 128)):
|
||
|
|
self.policies = [
|
||
|
|
SubPolicy(0.9, "shearX", 4, 0.2, "invert", 3, fillcolor),
|
||
|
|
SubPolicy(0.9, "shearY", 8, 0.7, "invert", 5, fillcolor),
|
||
|
|
SubPolicy(0.6, "equalize", 5, 0.6, "solarize", 6, fillcolor),
|
||
|
|
SubPolicy(0.9, "invert", 3, 0.6, "equalize", 3, fillcolor),
|
||
|
|
SubPolicy(0.6, "equalize", 1, 0.9, "rotate", 3, fillcolor),
|
||
|
|
|
||
|
|
SubPolicy(0.9, "shearX", 4, 0.8, "autocontrast", 3, fillcolor),
|
||
|
|
SubPolicy(0.9, "shearY", 8, 0.4, "invert", 5, fillcolor),
|
||
|
|
SubPolicy(0.9, "shearY", 5, 0.2, "solarize", 6, fillcolor),
|
||
|
|
SubPolicy(0.9, "invert", 6, 0.8, "autocontrast", 1, fillcolor),
|
||
|
|
SubPolicy(0.6, "equalize", 3, 0.9, "rotate", 3, fillcolor),
|
||
|
|
|
||
|
|
SubPolicy(0.9, "shearX", 4, 0.3, "solarize", 3, fillcolor),
|
||
|
|
SubPolicy(0.8, "shearY", 8, 0.7, "invert", 4, fillcolor),
|
||
|
|
SubPolicy(0.9, "equalize", 5, 0.6, "translateY", 6, fillcolor),
|
||
|
|
SubPolicy(0.9, "invert", 4, 0.6, "equalize", 7, fillcolor),
|
||
|
|
SubPolicy(0.3, "contrast", 3, 0.8, "rotate", 4, fillcolor),
|
||
|
|
|
||
|
|
SubPolicy(0.8, "invert", 5, 0.0, "translateY", 2, fillcolor),
|
||
|
|
SubPolicy(0.7, "shearY", 6, 0.4, "solarize", 8, fillcolor),
|
||
|
|
SubPolicy(0.6, "invert", 4, 0.8, "rotate", 4, fillcolor),
|
||
|
|
SubPolicy(0.3, "shearY", 7, 0.9, "translateX", 3, fillcolor),
|
||
|
|
SubPolicy(0.1, "shearX", 6, 0.6, "invert", 5, fillcolor),
|
||
|
|
|
||
|
|
SubPolicy(0.7, "solarize", 2, 0.6, "translateY", 7, fillcolor),
|
||
|
|
SubPolicy(0.8, "shearY", 4, 0.8, "invert", 8, fillcolor),
|
||
|
|
SubPolicy(0.7, "shearX", 9, 0.8, "translateY", 3, fillcolor),
|
||
|
|
SubPolicy(0.8, "shearY", 5, 0.7, "autocontrast", 3, fillcolor),
|
||
|
|
SubPolicy(0.7, "shearX", 2, 0.1, "invert", 5, fillcolor)
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
def __call__(self, img):
|
||
|
|
policy_idx = random.randint(0, len(self.policies) - 1)
|
||
|
|
return self.policies[policy_idx](img)
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
return "AutoAugment SVHN Policy"
|
||
|
|
|
||
|
|
|
||
|
|
class SubPolicy(object):
|
||
|
|
def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx2, fillcolor=(128, 128, 128)):
|
||
|
|
ranges = {
|
||
|
|
"shearX": np.linspace(0, 0.3, 10),
|
||
|
|
"shearY": np.linspace(0, 0.3, 10),
|
||
|
|
"translateX": np.linspace(0, 150 / 331, 10),
|
||
|
|
"translateY": np.linspace(0, 150 / 331, 10),
|
||
|
|
"rotate": np.linspace(0, 30, 10),
|
||
|
|
"color": np.linspace(0.0, 0.9, 10),
|
||
|
|
"posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
|
||
|
|
"solarize": np.linspace(256, 0, 10),
|
||
|
|
"contrast": np.linspace(0.0, 0.9, 10),
|
||
|
|
"sharpness": np.linspace(0.0, 0.9, 10),
|
||
|
|
"brightness": np.linspace(0.0, 0.9, 10),
|
||
|
|
"autocontrast": [0] * 10,
|
||
|
|
"equalize": [0] * 10,
|
||
|
|
"invert": [0] * 10
|
||
|
|
}
|
||
|
|
|
||
|
|
# from https://stackoverflow.com/questions/5252170/specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
|
||
|
|
def rotate_with_fill(img, magnitude):
|
||
|
|
rot = img.convert("RGBA").rotate(magnitude)
|
||
|
|
return Image.composite(rot, Image.new("RGBA", rot.size, (128,) * 4), rot).convert(img.mode)
|
||
|
|
|
||
|
|
func = {
|
||
|
|
"shearX": lambda img, magnitude: img.transform(
|
||
|
|
img.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
|
||
|
|
Image.BICUBIC, fillcolor=fillcolor),
|
||
|
|
"shearY": lambda img, magnitude: img.transform(
|
||
|
|
img.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
|
||
|
|
Image.BICUBIC, fillcolor=fillcolor),
|
||
|
|
"translateX": lambda img, magnitude: img.transform(
|
||
|
|
img.size, Image.AFFINE, (1, 0, magnitude * img.size[0] * random.choice([-1, 1]), 0, 1, 0),
|
||
|
|
fillcolor=fillcolor),
|
||
|
|
"translateY": lambda img, magnitude: img.transform(
|
||
|
|
img.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * img.size[1] * random.choice([-1, 1])),
|
||
|
|
fillcolor=fillcolor),
|
||
|
|
"rotate": lambda img, magnitude: rotate_with_fill(img, magnitude),
|
||
|
|
# "rotate": lambda img, magnitude: img.rotate(magnitude * random.choice([-1, 1])),
|
||
|
|
"color": lambda img, magnitude: ImageEnhance.Color(img).enhance(1 + magnitude * random.choice([-1, 1])),
|
||
|
|
"posterize": lambda img, magnitude: ImageOps.posterize(img, magnitude),
|
||
|
|
"solarize": lambda img, magnitude: ImageOps.solarize(img, magnitude),
|
||
|
|
"contrast": lambda img, magnitude: ImageEnhance.Contrast(img).enhance(
|
||
|
|
1 + magnitude * random.choice([-1, 1])),
|
||
|
|
"sharpness": lambda img, magnitude: ImageEnhance.Sharpness(img).enhance(
|
||
|
|
1 + magnitude * random.choice([-1, 1])),
|
||
|
|
"brightness": lambda img, magnitude: ImageEnhance.Brightness(img).enhance(
|
||
|
|
1 + magnitude * random.choice([-1, 1])),
|
||
|
|
"autocontrast": lambda img, magnitude: ImageOps.autocontrast(img),
|
||
|
|
"equalize": lambda img, magnitude: ImageOps.equalize(img),
|
||
|
|
"invert": lambda img, magnitude: ImageOps.invert(img)
|
||
|
|
}
|
||
|
|
|
||
|
|
# self.name = "{}_{:.2f}_and_{}_{:.2f}".format(
|
||
|
|
# operation1, ranges[operation1][magnitude_idx1],
|
||
|
|
# operation2, ranges[operation2][magnitude_idx2])
|
||
|
|
self.p1 = p1
|
||
|
|
self.operation1 = func[operation1]
|
||
|
|
self.magnitude1 = ranges[operation1][magnitude_idx1]
|
||
|
|
self.p2 = p2
|
||
|
|
self.operation2 = func[operation2]
|
||
|
|
self.magnitude2 = ranges[operation2][magnitude_idx2]
|
||
|
|
|
||
|
|
|
||
|
|
def __call__(self, img):
|
||
|
|
if random.random() < self.p1: img = self.operation1(img, self.magnitude1)
|
||
|
|
if random.random() < self.p2: img = self.operation2(img, self.magnitude2)
|
||
|
|
return img
|