augmentations/augmentations_tiny.py

# 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.
# ==============================================================================
"""Base augmentations operators."""

import numpy as np
from PIL import Image, ImageOps, ImageEnhance

# ImageNet code should change this value
IMAGE_SIZE = 64
import torch
from torchvision import transforms


def int_parameter(level, maxval):
  """Helper function to scale `val` between 0 and maxval .

  Args:
    level: Level of the operation that will be between [0, `PARAMETER_MAX`].
    maxval: Maximum value that the operation can have. This will be scaled to
      level/PARAMETER_MAX.

  Returns:
    An int that results from scaling `maxval` according to `level`.
  """
  return int(level * maxval / 10)


def float_parameter(level, maxval):
  """Helper function to scale `val` between 0 and maxval.

  Args:
    level: Level of the operation that will be between [0, `PARAMETER_MAX`].
    maxval: Maximum value that the operation can have. This will be scaled to
      level/PARAMETER_MAX.

  Returns:
    A float that results from scaling `maxval` according to `level`.
  """
  return float(level) * maxval / 10.


def sample_level(n):
  return np.random.uniform(low=0.1, high=n)


def autocontrast(pil_img, _):
  return ImageOps.autocontrast(pil_img)


def equalize(pil_img, _):
  return ImageOps.equalize(pil_img)


def posterize(pil_img, level):
  level = int_parameter(sample_level(level), 4)
  return ImageOps.posterize(pil_img, 4 - level)


def rotate(pil_img, level):
  degrees = int_parameter(sample_level(level), 30)
  if np.random.uniform() > 0.5:
    degrees = -degrees
  return pil_img.rotate(degrees, resample=Image.BILINEAR)


def solarize(pil_img, level):
  level = int_parameter(sample_level(level), 256)
  return ImageOps.solarize(pil_img, 256 - level)


def shear_x(pil_img, level):
  level = float_parameter(sample_level(level), 0.3)
  if np.random.uniform() > 0.5:
    level = -level
  return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE),
                           Image.AFFINE, (1, level, 0, 0, 1, 0),
                           resample=Image.BILINEAR)


def shear_y(pil_img, level):
  level = float_parameter(sample_level(level), 0.3)
  if np.random.uniform() > 0.5:
    level = -level
  return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE),
                           Image.AFFINE, (1, 0, 0, level, 1, 0),
                           resample=Image.BILINEAR)


def translate_x(pil_img, level):
  level = int_parameter(sample_level(level), IMAGE_SIZE / 3)
  if np.random.random() > 0.5:
    level = -level
  return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE),
                           Image.AFFINE, (1, 0, level, 0, 1, 0),
                           resample=Image.BILINEAR)


def translate_y(pil_img, level):
  level = int_parameter(sample_level(level), IMAGE_SIZE / 3)
  if np.random.random() > 0.5:
    level = -level
  return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE),
                           Image.AFFINE, (1, 0, 0, 0, 1, level),
                           resample=Image.BILINEAR)


# operation that overlaps with ImageNet-C's test set
def color(pil_img, level):
    level = float_parameter(sample_level(level), 1.8) + 0.1
    return ImageEnhance.Color(pil_img).enhance(level)


# operation that overlaps with ImageNet-C's test set
def contrast(pil_img, level):
    level = float_parameter(sample_level(level), 1.8) + 0.1
    return ImageEnhance.Contrast(pil_img).enhance(level)


# operation that overlaps with ImageNet-C's test set
def brightness(pil_img, level):
    level = float_parameter(sample_level(level), 1.8) + 0.1
    return ImageEnhance.Brightness(pil_img).enhance(level)


# operation that overlaps with ImageNet-C's test set
def sharpness(pil_img, level):
    level = float_parameter(sample_level(level), 1.8) + 0.1
    return ImageEnhance.Sharpness(pil_img).enhance(level)

def random_resized_crop(pil_img, level):
  return transforms.RandomResizedCrop(32)(pil_img)

def random_flip(pil_img, level):
  return transforms.RandomHorizontalFlip(p=0.5)(pil_img)

def grayscale(pil_img, level):
  return transforms.Grayscale(num_output_channels=3)(pil_img)

augmentations = [
    autocontrast, equalize, posterize, rotate, solarize, shear_x, shear_y,
    translate_x, translate_y, grayscale #random_resized_crop, random_flip
]

augmentations_all = [
    autocontrast, equalize, posterize, rotate, solarize, shear_x, shear_y,
    translate_x, translate_y, color, contrast, brightness, sharpness, grayscale #, random_resized_crop, random_flip
]

def aug_tiny(image, preprocess, mixture_width=3, mixture_depth=-1, aug_severity=3):
  """Perform AugMix augmentations and compute mixture.

  Args:
    image: PIL.Image input image
    preprocess: Preprocessing function which should return a torch tensor.

  Returns:
    mixed: Augmented and mixed image.
  """
  aug_list = augmentations
  # if args.all_ops:
  #   aug_list = augmentations.augmentations_all

  ws = np.float32(np.random.dirichlet([1] * mixture_width))
  m = np.float32(np.random.beta(1, 1))

  mix = torch.zeros_like(preprocess(image))
  for i in range(mixture_width):
    image_aug = image.copy()
    depth = mixture_depth if mixture_depth > 0 else np.random.randint(
        1, 4)
    for _ in range(depth):
      op = np.random.choice(aug_list)
      image_aug = op(image_aug, aug_severity)
    # Preprocessing commutes since all coefficients are convex
    mix += ws[i] * preprocess(image_aug)

  mixed = (1 - m) * preprocess(image) + m * mix
  return mixed