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101 lines
3.2 KiB
101 lines
3.2 KiB
3 years ago
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# 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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from ppcls.data.preprocess.ops.autoaugment import ImageNetPolicy as RawImageNetPolicy
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from ppcls.data.preprocess.ops.randaugment import RandAugment as RawRandAugment
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from ppcls.data.preprocess.ops.timm_autoaugment import RawTimmAutoAugment
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from ppcls.data.preprocess.ops.cutout import Cutout
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from ppcls.data.preprocess.ops.hide_and_seek import HideAndSeek
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from ppcls.data.preprocess.ops.random_erasing import RandomErasing
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from ppcls.data.preprocess.ops.grid import GridMask
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from ppcls.data.preprocess.ops.operators import DecodeImage
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from ppcls.data.preprocess.ops.operators import ResizeImage
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from ppcls.data.preprocess.ops.operators import CropImage
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from ppcls.data.preprocess.ops.operators import RandCropImage
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from ppcls.data.preprocess.ops.operators import RandFlipImage
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from ppcls.data.preprocess.ops.operators import NormalizeImage
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from ppcls.data.preprocess.ops.operators import ToCHWImage
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from ppcls.data.preprocess.ops.operators import AugMix
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from ppcls.data.preprocess.batch_ops.batch_operators import MixupOperator, CutmixOperator, OpSampler, FmixOperator
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import numpy as np
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from PIL import Image
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def transform(data, ops=[]):
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""" transform """
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for op in ops:
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data = op(data)
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return data
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class AutoAugment(RawImageNetPolicy):
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""" ImageNetPolicy wrapper to auto fit different img types """
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def __call__(self, img):
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if not isinstance(img, Image.Image):
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img = np.ascontiguousarray(img)
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img = Image.fromarray(img)
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img = super().__call__(img)
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if isinstance(img, Image.Image):
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img = np.asarray(img)
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return img
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class RandAugment(RawRandAugment):
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""" RandAugment wrapper to auto fit different img types """
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def __call__(self, img):
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if not isinstance(img, Image.Image):
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img = np.ascontiguousarray(img)
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img = Image.fromarray(img)
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img = super().__call__(img)
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if isinstance(img, Image.Image):
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img = np.asarray(img)
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return img
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class TimmAutoAugment(RawTimmAutoAugment):
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""" TimmAutoAugment wrapper to auto fit different img tyeps. """
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def __call__(self, img):
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if not isinstance(img, Image.Image):
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img = np.ascontiguousarray(img)
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img = Image.fromarray(img)
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img = super().__call__(img)
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if isinstance(img, Image.Image):
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img = np.asarray(img)
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return img
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