Delete AutoAugmentDemo folder; Didn't seem to need it

MetaAugment/AutoAugmentDemo/LICENSE

-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.

MetaAugment/AutoAugmentDemo/README.md

-# AutoAugment - Learning Augmentation Policies from Data
-Unofficial implementation of the ImageNet, CIFAR10 and SVHN Augmentation Policies learned by [AutoAugment](https://arxiv.org/abs/1805.09501v1), described in this [Google AI Blogpost](https://ai.googleblog.com/2018/06/improving-deep-learning-performance.html).
-
-__Update July 13th, 2018:__ Wrote a [Blogpost](https://towardsdatascience.com/how-to-improve-your-image-classifier-with-googles-autoaugment-77643f0be0c9) about AutoAugment and Double Transfer Learning.
-
-##### Tested with Python 3.6. Needs pillow>=5.0.0
-
-![Examples of the best ImageNet Policy](figures/Figure2_Paper.png)
-
-
-------------------
-
-
-
-## Example
-
-```python
-from autoaugment import ImageNetPolicy
-image = PIL.Image.open(path)
-policy = ImageNetPolicy()
-transformed = policy(image)
-```
-
-To see examples of all operations and magnitudes applied to images, take a look at  [AutoAugment_Exploration.ipynb](AutoAugment_Exploration.ipynb).
-
-## Example as a PyTorch Transform - ImageNet 
-
-```python
-from autoaugment import ImageNetPolicy
-data = ImageFolder(rootdir, transform=transforms.Compose(
-                        [transforms.RandomResizedCrop(224), 
-                         transforms.RandomHorizontalFlip(), ImageNetPolicy(), 
-                         transforms.ToTensor(), transforms.Normalize(...)]))
-loader = DataLoader(data, ...)
-```
-
-## Example as a PyTorch Transform - CIFAR10
-
-```python
-from autoaugment import CIFAR10Policy
-data = ImageFolder(rootdir, transform=transforms.Compose(
-                        [transforms.RandomCrop(32, padding=4, fill=128), # fill parameter needs torchvision installed from source
-                         transforms.RandomHorizontalFlip(), CIFAR10Policy(), 
-			 transforms.ToTensor(), 
-                         Cutout(n_holes=1, length=16), # (https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py)
-                         transforms.Normalize(...)]))
-loader = DataLoader(data, ...)
-```
-
-## Example as a PyTorch Transform - SVHN
-
-```python
-from autoaugment import SVHNPolicy
-data = ImageFolder(rootdir, transform=transforms.Compose(
-                        [SVHNPolicy(), 
-			 transforms.ToTensor(), 
-                         Cutout(n_holes=1, length=20), # (https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py)
-                         transforms.Normalize(...)]))
-loader = DataLoader(data, ...)
-```
-
-------------------
-
-
-## Results with AutoAugment
-
-### Generalizable Data Augmentations
-
-> Finally, we show that policies found on one task can generalize well across different models and datasets. 
-> For example, the policy found on ImageNet leads to significant improvements on a variety of FGVC datasets. Even on datasets for
-> which fine-tuning weights pre-trained on ImageNet does not help significantly [26], e.g. Stanford
-> Cars [27] and FGVC Aircraft [28], training with the ImageNet policy reduces test set error by 1.16%
-> and 1.76%, respectively. __This result suggests that transferring data augmentation policies offers an
-> alternative method for transfer learning__.
-
-### CIFAR 10
-
-![CIFAR10 Results](figures/CIFAR10_results.png)
-
-### CIFAR 100
-
-![CIFAR10 Results](figures/CIFAR100_results.png)
-
-### ImageNet
-
-![ImageNet Results](figures/ImageNet_results.png)
-
-### SVHN
-
-![SVHN Results](figures/SVHN_results.png)
-
-### Fine Grained Visual Classification Datasets
-
-![SVHN Results](figures/FGVC_results.png)

MetaAugment/AutoAugmentDemo/autoaugment.py

-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(np.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

MetaAugment/AutoAugmentDemo/ops.py

-from PIL import Image, ImageEnhance, ImageOps
-import random
-
-
-class ShearX(object):
-    def __init__(self, fillcolor=(128, 128, 128)):
-        self.fillcolor = fillcolor
-
-    def __call__(self, x, magnitude):
-        return x.transform(
-            x.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
-            Image.BICUBIC, fillcolor=self.fillcolor)
-
-
-class ShearY(object):
-    def __init__(self, fillcolor=(128, 128, 128)):
-        self.fillcolor = fillcolor
-
-    def __call__(self, x, magnitude):
-        return x.transform(
-            x.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
-            Image.BICUBIC, fillcolor=self.fillcolor)
-
-
-class TranslateX(object):
-    def __init__(self, fillcolor=(128, 128, 128)):
-        self.fillcolor = fillcolor
-
-    def __call__(self, x, magnitude):
-        return x.transform(
-            x.size, Image.AFFINE, (1, 0, magnitude * x.size[0] * random.choice([-1, 1]), 0, 1, 0),
-            fillcolor=self.fillcolor)
-
-
-class TranslateY(object):
-    def __init__(self, fillcolor=(128, 128, 128)):
-        self.fillcolor = fillcolor
-
-    def __call__(self, x, magnitude):
-        return x.transform(
-            x.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * x.size[1] * random.choice([-1, 1])),
-            fillcolor=self.fillcolor)
-
-
-class Rotate(object):
-    # from https://stackoverflow.com/questions/
-    # 5252170/specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
-    def __call__(self, x, magnitude):
-        rot = x.convert("RGBA").rotate(magnitude * random.choice([-1, 1]))
-        return Image.composite(rot, Image.new("RGBA", rot.size, (128,) * 4), rot).convert(x.mode)
-
-
-class Color(object):
-    def __call__(self, x, magnitude):
-        return ImageEnhance.Color(x).enhance(1 + magnitude * random.choice([-1, 1]))
-
-
-class Posterize(object):
-    def __call__(self, x, magnitude):
-        return ImageOps.posterize(x, magnitude)
-
-
-class Solarize(object):
-    def __call__(self, x, magnitude):
-        return ImageOps.solarize(x, magnitude)
-
-
-class Contrast(object):
-    def __call__(self, x, magnitude):
-        return ImageEnhance.Contrast(x).enhance(1 + magnitude * random.choice([-1, 1]))
-
-
-class Sharpness(object):
-    def __call__(self, x, magnitude):
-        return ImageEnhance.Sharpness(x).enhance(1 + magnitude * random.choice([-1, 1]))
-
-
-class Brightness(object):
-    def __call__(self, x, magnitude):
-        return ImageEnhance.Brightness(x).enhance(1 + magnitude * random.choice([-1, 1]))
-
-
-class AutoContrast(object):
-    def __call__(self, x, magnitude):
-        return ImageOps.autocontrast(x)
-
-
-class Equalize(object):
-    def __call__(self, x, magnitude):
-        return ImageOps.equalize(x)
-
-
-class Invert(object):
-    def __call__(self, x, magnitude):
-        return ImageOps.invert(x)