| 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, 0.6, "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 |
| } |
|
|
| |
| 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), |
| "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.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 |
