Add package

ImageNet-C/imagenet_c/MANIFEST.in

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+include imagenet_c/frost/*
+

ImageNet-C/imagenet_c/README.md

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+# ImageNet-C Corruption Functions
+
+With this package, it is possible to corrupt an image with ImageNet-C corruptions.
+These functions are exposed with the function ```corrupt```.
+
+Try
+```
+from imagenet_c import corrupt
+
+corrupt(<image>, corruption_number=0)
+```
+
+This function ```corrupt``` is
+```
+corruption_tuple = (gaussian_noise, shot_noise, impulse_noise, defocus_blur,
+ glass_blur, motion_blur, zoom_blur, snow, frost, fog,
+ brightness, contrast, elastic_transform, pixelate, jpeg_compression,
+ speckle_noise, gaussian_blur, spatter, saturate)
+
+corruption_dict = {corr_func.__name__: corr_func for corr_func in corruption_tuple}
+
+
+def corrupt(x, severity=1, corruption_name=None, corruption_number=-1):
+ """
+ :param x: image to corrupt; a 224x224x3 numpy array in [0, 255]
+ :param severity: strength with which to corrupt x; an integer in [0, 5]
+ :param corruption_name: specifies which corruption function to call;
+ must be one of 'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur',
+ 'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog',
+ 'brightness', 'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression',
+ 'speckle_noise', 'gaussian_blur', 'spatter', 'saturate';
+ the last four are validation functions
+ :param corruption_number: the position of the corruption_name in the above list;
+ an integer in [0, 18]; useful for easy looping; 15, 16, 17, 18 are validation corruption numbers
+ :return: the image x corrupted by a corruption function at the given severity; same shape as input
+ """
+
+ if corruption_name:
+ x_corrupted = corruption_dict[corruption_name](Image.fromarray(x), severity)
+ elif corruption_number == -1:
+ x_corrupted = corruption_tuple[corruption_number](Image.fromarray(x), severity)
+ else:
+ raise ValueError("Either corruption_name or corruption_number must be passed")
+
+ return np.uint8(x_corrupted)
+```
+
+## Citation
+
+If you find this useful in your research, please consider citing:
+
+ @article{hendrycks2018robustness,
+  title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
+  author={Dan Hendrycks and Thomas Dietterich},
+  journal={arXiv preprint arXiv:1807.01697},
+  year={2018}
+ }
+

ImageNet-C/imagenet_c/imagenet_c/__init__.py

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+import numpy as np
+from PIL import Image
+from .corruptions import *
+
+corruption_tuple = (gaussian_noise, shot_noise, impulse_noise, defocus_blur,
+ glass_blur, motion_blur, zoom_blur, snow, frost, fog,
+ brightness, contrast, elastic_transform, pixelate, jpeg_compression,
+ speckle_noise, gaussian_blur, spatter, saturate)
+
+corruption_dict = {corr_func.__name__: corr_func for corr_func in corruption_tuple}
+
+
+def corrupt(x, severity=1, corruption_name=None, corruption_number=-1):
+ """
+ :param x: image to corrupt; a 224x224x3 numpy array in [0, 255]
+ :param severity: strength with which to corrupt x; an integer in [0, 5]
+ :param corruption_name: specifies which corruption function to call;
+ must be one of 'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur',
+ 'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog',
+ 'brightness', 'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression',
+ 'speckle_noise', 'gaussian_blur', 'spatter', 'saturate';
+ the last four are validation functions
+ :param corruption_number: the position of the corruption_name in the above list;
+ an integer in [0, 18]; useful for easy looping; 15, 16, 17, 18 are validation corruption numbers
+ :return: the image x corrupted by a corruption function at the given severity; same shape as input
+ """
+
+ if corruption_name:
+ x_corrupted = corruption_dict[corruption_name](Image.fromarray(x), severity)
+ elif corruption_number != -1:
+ x_corrupted = corruption_tuple[corruption_number](Image.fromarray(x), severity)
+ else:
+ raise ValueError("Either corruption_name or corruption_number must be passed")
+
+ return np.uint8(x_corrupted)