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- # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- from paddlex.ppcls.data.preprocess.ops.autoaugment import ImageNetPolicy as RawImageNetPolicy
- from paddlex.ppcls.data.preprocess.ops.randaugment import RandAugment as RawRandAugment
- from paddlex.ppcls.data.preprocess.ops.timm_autoaugment import RawTimmAutoAugment
- from paddlex.ppcls.data.preprocess.ops.cutout import Cutout
- from paddlex.ppcls.data.preprocess.ops.hide_and_seek import HideAndSeek
- from paddlex.ppcls.data.preprocess.ops.random_erasing import RandomErasing
- from paddlex.ppcls.data.preprocess.ops.grid import GridMask
- from paddlex.ppcls.data.preprocess.ops.operators import DecodeImage
- from paddlex.ppcls.data.preprocess.ops.operators import ResizeImage
- from paddlex.ppcls.data.preprocess.ops.operators import CropImage
- from paddlex.ppcls.data.preprocess.ops.operators import RandCropImage
- from paddlex.ppcls.data.preprocess.ops.operators import RandFlipImage
- from paddlex.ppcls.data.preprocess.ops.operators import NormalizeImage
- from paddlex.ppcls.data.preprocess.ops.operators import ToCHWImage
- from paddlex.ppcls.data.preprocess.ops.operators import AugMix
- from paddlex.ppcls.data.preprocess.batch_ops.batch_operators import MixupOperator, CutmixOperator, OpSampler, FmixOperator
- import numpy as np
- from PIL import Image
- def transform(data, ops=[]):
- """ transform """
- for op in ops:
- data = op(data)
- return data
- class AutoAugment(RawImageNetPolicy):
- """ ImageNetPolicy wrapper to auto fit different img types """
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- def __call__(self, img):
- if not isinstance(img, Image.Image):
- img = np.ascontiguousarray(img)
- img = Image.fromarray(img)
- img = super().__call__(img)
- if isinstance(img, Image.Image):
- img = np.asarray(img)
- return img
- class RandAugment(RawRandAugment):
- """ RandAugment wrapper to auto fit different img types """
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- def __call__(self, img):
- if not isinstance(img, Image.Image):
- img = np.ascontiguousarray(img)
- img = Image.fromarray(img)
- img = super().__call__(img)
- if isinstance(img, Image.Image):
- img = np.asarray(img)
- return img
- class TimmAutoAugment(RawTimmAutoAugment):
- """ TimmAutoAugment wrapper to auto fit different img tyeps. """
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- def __call__(self, img):
- if not isinstance(img, Image.Image):
- img = np.ascontiguousarray(img)
- img = Image.fromarray(img)
- img = super().__call__(img)
- if isinstance(img, Image.Image):
- img = np.asarray(img)
- return img
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