import numpy as np from .ops import * 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(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 } func = { "shearX": ShearX(fillcolor=fillcolor), "shearY": ShearY(fillcolor=fillcolor), "translateX": TranslateX(fillcolor=fillcolor), "translateY": TranslateY(fillcolor=fillcolor), "rotate": Rotate(), "color": Color(), "posterize": Posterize(), "solarize": Solarize(), "contrast": Contrast(), "sharpness": Sharpness(), "brightness": Brightness(), "autocontrast": AutoContrast(), "equalize": Equalize(), "invert": Invert() } 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