139 lines
4.4 KiB
Python
139 lines
4.4 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# encoding: utf-8
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import numpy as np
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from PIL import Image, ImageOps, ImageEnhance
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def int_parameter(level, maxval):
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"""Helper function to scale `val` between 0 and maxval .
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Args:
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level: Level of the operation that will be between [0, `PARAMETER_MAX`].
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maxval: Maximum value that the operation can have. This will be scaled to
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level/PARAMETER_MAX.
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Returns:
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An int that results from scaling `maxval` according to `level`.
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"""
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return int(level * maxval / 10)
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def float_parameter(level, maxval):
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"""Helper function to scale `val` between 0 and maxval.
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Args:
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level: Level of the operation that will be between [0, `PARAMETER_MAX`].
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maxval: Maximum value that the operation can have. This will be scaled to
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level/PARAMETER_MAX.
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Returns:
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A float that results from scaling `maxval` according to `level`.
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"""
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return float(level) * maxval / 10.
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def sample_level(n):
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return np.random.uniform(low=0.1, high=n)
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def autocontrast(pil_img, *args):
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return ImageOps.autocontrast(pil_img)
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def equalize(pil_img, *args):
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return ImageOps.equalize(pil_img)
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def posterize(pil_img, level, *args):
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level = int_parameter(sample_level(level), 4)
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return ImageOps.posterize(pil_img, 4 - level)
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def rotate(pil_img, level, *args):
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degrees = int_parameter(sample_level(level), 30)
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if np.random.uniform() > 0.5:
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degrees = -degrees
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return pil_img.rotate(degrees, resample=Image.BILINEAR)
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def solarize(pil_img, level, *args):
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level = int_parameter(sample_level(level), 256)
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return ImageOps.solarize(pil_img, 256 - level)
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def shear_x(pil_img, level):
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level = float_parameter(sample_level(level), 0.3)
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if np.random.uniform() > 0.5:
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level = -level
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return pil_img.transform(pil_img.size,
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Image.AFFINE, (1, level, 0, 0, 1, 0),
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resample=Image.BILINEAR)
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def shear_y(pil_img, level):
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level = float_parameter(sample_level(level), 0.3)
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if np.random.uniform() > 0.5:
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level = -level
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return pil_img.transform(pil_img.size,
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Image.AFFINE, (1, 0, 0, level, 1, 0),
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resample=Image.BILINEAR)
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def translate_x(pil_img, level):
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level = int_parameter(sample_level(level), pil_img.size[0] / 3)
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if np.random.random() > 0.5:
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level = -level
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return pil_img.transform(pil_img.size,
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Image.AFFINE, (1, 0, level, 0, 1, 0),
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resample=Image.BILINEAR)
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def translate_y(pil_img, level):
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level = int_parameter(sample_level(level), pil_img.size[1] / 3)
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if np.random.random() > 0.5:
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level = -level
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return pil_img.transform(pil_img.size,
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Image.AFFINE, (1, 0, 0, 0, 1, level),
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resample=Image.BILINEAR)
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# operation that overlaps with ImageNet-C's test set
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def color(pil_img, level, *args):
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level = float_parameter(sample_level(level), 1.8) + 0.1
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return ImageEnhance.Color(pil_img).enhance(level)
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# operation that overlaps with ImageNet-C's test set
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def contrast(pil_img, level, *args):
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level = float_parameter(sample_level(level), 1.8) + 0.1
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return ImageEnhance.Contrast(pil_img).enhance(level)
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# operation that overlaps with ImageNet-C's test set
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def brightness(pil_img, level, *args):
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level = float_parameter(sample_level(level), 1.8) + 0.1
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return ImageEnhance.Brightness(pil_img).enhance(level)
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# operation that overlaps with ImageNet-C's test set
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def sharpness(pil_img, level, *args):
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level = float_parameter(sample_level(level), 1.8) + 0.1
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return ImageEnhance.Sharpness(pil_img).enhance(level)
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augmentations = [
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autocontrast, equalize, posterize, rotate, solarize, shear_x, shear_y,
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translate_x, translate_y
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]
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