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Avoid ImageNet-C evaluation every epoch
# Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Reference implementation of AugMix's data augmentation method in numpy.""" import augmentations import numpy as np from PIL import Image # CIFAR-10 constants MEAN = [0.4914, 0.4822, 0.4465] STD = [0.2023, 0.1994, 0.2010] def normalize(image): """Normalize input image channel-wise to zero mean and unit variance.""" image = image.transpose(2, 0, 1) # Switch to channel-first mean, std = np.array(MEAN), np.array(STD) image = (image - mean[:, None, None]) / std[:, None, None] return image.transpose(1, 2, 0) def apply_op(image, op, severity): image = np.clip(image * 255., 0, 255).astype(np.uint8) pil_img = Image.fromarray(image) # Convert to PIL.Image pil_img = op(pil_img, severity) return np.asarray(pil_img) / 255. def augment_and_mix(image, severity=3, width=3, depth=-1, alpha=1.): """Perform AugMix augmentations and compute mixture. Args: image: Raw input image as float32 np.ndarray of shape (h, w, c) severity: Severity of underlying augmentation operators (between 1 to 10). width: Width of augmentation chain depth: Depth of augmentation chain. -1 enables stochastic depth uniformly from [1, 3] alpha: Probability coefficient for Beta and Dirichlet distributions. Returns: mixed: Augmented and mixed image. """ ws = np.float32( np.random.dirichlet([alpha] * width)) m = np.float32(np.random.beta(alpha, alpha)) mix = np.zeros_like(image) for i in range(width): image_aug = image.copy() d = depth if depth > 0 else np.random.randint(1, 4) for _ in range(d): op = np.random.choice(augmentations.augmentations) image_aug = apply_op(image_aug, op, severity) # Preprocessing commutes since all coefficients are convex mix += ws[i] * normalize(image_aug) mixed = (1 - m) * normalize(image) + m * mix return mixed