mix-bt / augmentations /augmentations_tiny.py

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	# 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