# 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