peacock-data-public-datasets-idc-mint
/
docker
/bloom13b
/Megatron-DeepSpeed
/megatron
/data
/autoaugment.py
"""AutoAugment data augmentation policy for ImageNet. | |
-- Begin license text. | |
MIT License | |
Copyright (c) 2018 Philip Popien | |
Permission is hereby granted, free of charge, to any person obtaining a copy | |
of this software and associated documentation files (the "Software"), to deal | |
in the Software without restriction, including without limitation the rights | |
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
copies of the Software, and to permit persons to whom the Software is | |
furnished to do so, subject to the following conditions: | |
The above copyright notice and this permission notice shall be included in all | |
copies or substantial portions of the Software. | |
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
SOFTWARE. | |
-- End license text. | |
Code adapted from https://github.com/DeepVoltaire/AutoAugment. | |
This module implements the fixed AutoAugment data augmentation policy for ImageNet provided in | |
Appendix A, Table 9 of reference [1]. It does not include any of the search code for augmentation | |
policies. | |
Reference: | |
[1] https://arxiv.org/abs/1805.09501 | |
""" | |
import random | |
import numpy as np | |
from PIL import Image | |
from PIL import ImageEnhance | |
from PIL import ImageOps | |
_MAX_LEVEL = 10 # Maximum integer strength of an augmentation, if applicable. | |
class ImageNetPolicy: | |
"""Definition of an ImageNetPolicy. | |
Implements a fixed AutoAugment data augmentation policy targeted at | |
ImageNet training by randomly applying at runtime one of the 25 pre-defined | |
data augmentation sub-policies provided in Reference [1]. | |
Usage example as a Pytorch Transform: | |
>>> transform=transforms.Compose([transforms.Resize(256), | |
>>> ImageNetPolicy(), | |
>>> transforms.ToTensor()]) | |
""" | |
def __init__(self, fillcolor=(128, 128, 128)): | |
"""Initialize an ImageNetPolicy. | |
Args: | |
fillcolor (tuple): RGB color components of the color to be used for | |
filling when needed (default: (128, 128, 128), which | |
corresponds to gray). | |
""" | |
# Instantiate a list of sub-policies. | |
# Each entry of the list is a SubPolicy which consists of | |
# two augmentation operations, | |
# each of those parametrized as operation, probability, magnitude. | |
# Those two operations are applied sequentially on the image upon call. | |
self.policies = [ | |
SubPolicy("posterize", 0.4, 8, "rotate", 0.6, 9, fillcolor), | |
SubPolicy("solarize", 0.6, 5, "autocontrast", 0.6, 5, fillcolor), | |
SubPolicy("equalize", 0.8, 8, "equalize", 0.6, 3, fillcolor), | |
SubPolicy("posterize", 0.6, 7, "posterize", 0.6, 6, fillcolor), | |
SubPolicy("equalize", 0.4, 7, "solarize", 0.2, 4, fillcolor), | |
SubPolicy("equalize", 0.4, 4, "rotate", 0.8, 8, fillcolor), | |
SubPolicy("solarize", 0.6, 3, "equalize", 0.6, 7, fillcolor), | |
SubPolicy("posterize", 0.8, 5, "equalize", 1.0, 2, fillcolor), | |
SubPolicy("rotate", 0.2, 3, "solarize", 0.6, 8, fillcolor), | |
SubPolicy("equalize", 0.6, 8, "posterize", 0.4, 6, fillcolor), | |
SubPolicy("rotate", 0.8, 8, "color", 0.4, 0, fillcolor), | |
SubPolicy("rotate", 0.4, 9, "equalize", 0.6, 2, fillcolor), | |
SubPolicy("equalize", 0.0, 7, "equalize", 0.8, 8, fillcolor), | |
SubPolicy("invert", 0.6, 4, "equalize", 1.0, 8, fillcolor), | |
SubPolicy("color", 0.6, 4, "contrast", 1.0, 8, fillcolor), | |
SubPolicy("rotate", 0.8, 8, "color", 1.0, 2, fillcolor), | |
SubPolicy("color", 0.8, 8, "solarize", 0.8, 7, fillcolor), | |
SubPolicy("sharpness", 0.4, 7, "invert", 0.6, 8, fillcolor), | |
SubPolicy("shearX", 0.6, 5, "equalize", 1.0, 9, fillcolor), | |
SubPolicy("color", 0.4, 0, "equalize", 0.6, 3, fillcolor), | |
SubPolicy("equalize", 0.4, 7, "solarize", 0.2, 4, fillcolor), | |
SubPolicy("solarize", 0.6, 5, "autocontrast", 0.6, 5, fillcolor), | |
SubPolicy("invert", 0.6, 4, "equalize", 1.0, 8, fillcolor), | |
SubPolicy("color", 0.6, 4, "contrast", 1.0, 8, fillcolor), | |
SubPolicy("equalize", 0.8, 8, "equalize", 0.6, 3, fillcolor), | |
] | |
def __call__(self, img): | |
"""Define call method for ImageNetPolicy class.""" | |
policy_idx = random.randint(0, len(self.policies) - 1) | |
return self.policies[policy_idx](img) | |
def __repr__(self): | |
"""Define repr method for ImageNetPolicy class.""" | |
return "ImageNetPolicy" | |
class SubPolicy: | |
"""Definition of a SubPolicy. | |
A SubPolicy consists of two augmentation operations, | |
each of those parametrized as operation, probability, magnitude. | |
The two operations are applied sequentially on the image upon call. | |
""" | |
def __init__( | |
self, | |
operation1, | |
probability1, | |
magnitude_idx1, | |
operation2, | |
probability2, | |
magnitude_idx2, | |
fillcolor, | |
): | |
"""Initialize a SubPolicy. | |
Args: | |
operation1 (str): Key specifying the first augmentation operation. | |
There are fourteen key values altogether (see supported_ops below | |
listing supported operations). probability1 (float): Probability | |
within [0., 1.] of applying the first augmentation operation. | |
magnitude_idx1 (int): Integer specifiying the strength of the first | |
operation as an index further used to derive the magnitude from a | |
range of possible values. | |
operation2 (str): Key specifying the second augmentation operation. | |
probability2 (float): Probability within [0., 1.] of applying the | |
second augmentation operation. | |
magnitude_idx2 (int): Integer specifiying the strength of the | |
second operation as an index further used to derive the magnitude | |
from a range of possible values. | |
fillcolor (tuple): RGB color components of the color to be used for | |
filling. | |
Returns: | |
""" | |
# List of supported operations for operation1 and operation2. | |
supported_ops = [ | |
"shearX", | |
"shearY", | |
"translateX", | |
"translateY", | |
"rotate", | |
"color", | |
"posterize", | |
"solarize", | |
"contrast", | |
"sharpness", | |
"brightness", | |
"autocontrast", | |
"equalize", | |
"invert", | |
] | |
assert (operation1 in supported_ops) and ( | |
operation2 in supported_ops | |
), "SubPolicy:one of oper1 or oper2 refers to an unsupported operation." | |
assert ( | |
0.0 <= probability1 <= 1.0 and 0.0 <= probability2 <= 1.0 | |
), "SubPolicy: prob1 and prob2 should be within [0., 1.]." | |
assert ( | |
isinstance(magnitude_idx1, int) and 0 <= magnitude_idx1 <= 10 | |
), "SubPolicy: idx1 should be specified as an integer within [0, 10]." | |
assert ( | |
isinstance(magnitude_idx2, int) and 0 <= magnitude_idx2 <= 10 | |
), "SubPolicy: idx2 should be specified as an integer within [0, 10]." | |
# Define a dictionary where each key refers to a specific type of | |
# augmentation and the corresponding value is a range of ten possible | |
# magnitude values for that augmentation. | |
num_levels = _MAX_LEVEL + 1 | |
ranges = { | |
"shearX": np.linspace(0, 0.3, num_levels), | |
"shearY": np.linspace(0, 0.3, num_levels), | |
"translateX": np.linspace(0, 150 / 331, num_levels), | |
"translateY": np.linspace(0, 150 / 331, num_levels), | |
"rotate": np.linspace(0, 30, num_levels), | |
"color": np.linspace(0.0, 0.9, num_levels), | |
"posterize": np.round(np.linspace(8, 4, num_levels), 0).astype( | |
np.int | |
), | |
"solarize": np.linspace(256, 0, num_levels), # range [0, 256] | |
"contrast": np.linspace(0.0, 0.9, num_levels), | |
"sharpness": np.linspace(0.0, 0.9, num_levels), | |
"brightness": np.linspace(0.0, 0.9, num_levels), | |
"autocontrast": [0] | |
* num_levels, # This augmentation doesn't use magnitude parameter. | |
"equalize": [0] | |
* num_levels, # This augmentation doesn't use magnitude parameter. | |
"invert": [0] | |
* num_levels, # This augmentation doesn't use magnitude parameter. | |
} | |
def rotate_with_fill(img, magnitude): | |
"""Define rotation transformation with fill. | |
The input image is first rotated, then it is blended together with | |
a gray mask of the same size. Note that fillcolor as defined | |
elsewhere in this module doesn't apply here. | |
Args: | |
magnitude (float): rotation angle in degrees. | |
Returns: | |
rotated_filled (PIL Image): rotated image with gray filling for | |
disoccluded areas unveiled by the rotation. | |
""" | |
rotated = img.convert("RGBA").rotate(magnitude) | |
rotated_filled = Image.composite( | |
rotated, Image.new("RGBA", rotated.size, (128,) * 4), rotated | |
) | |
return rotated_filled.convert(img.mode) | |
# Define a dictionary of augmentation functions where each key refers | |
# to a specific type of augmentation and the corresponding value defines | |
# the augmentation itself using a lambda function. | |
# pylint: disable=unnecessary-lambda | |
func_dict = { | |
"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), | |
} | |
# Store probability, function and magnitude of the first augmentation | |
# for the sub-policy. | |
self.probability1 = probability1 | |
self.operation1 = func_dict[operation1] | |
self.magnitude1 = ranges[operation1][magnitude_idx1] | |
# Store probability, function and magnitude of the second augmentation | |
# for the sub-policy. | |
self.probability2 = probability2 | |
self.operation2 = func_dict[operation2] | |
self.magnitude2 = ranges[operation2][magnitude_idx2] | |
def __call__(self, img): | |
"""Define call method for SubPolicy class.""" | |
# Randomly apply operation 1. | |
if random.random() < self.probability1: | |
img = self.operation1(img, self.magnitude1) | |
# Randomly apply operation 2. | |
if random.random() < self.probability2: | |
img = self.operation2(img, self.magnitude2) | |
return img | |