From 30adb023045cb1eb21ff1ac709b1aa4318fca882 Mon Sep 17 00:00:00 2001 From: weishengyu Date: Tue, 8 Jun 2021 15:25:02 +0800 Subject: [PATCH] remove data/imaug --- docs/zh_CN/faq_series/faq_2020_s1.md | 4 +- ppcls/data/imaug/__init__.py | 94 ---------- ppcls/data/imaug/autoaugment.py | 264 --------------------------- ppcls/data/imaug/batch_operators.py | 117 ------------ ppcls/data/imaug/cutout.py | 41 ----- ppcls/data/imaug/fmix.py | 217 ---------------------- ppcls/data/imaug/grid.py | 89 --------- ppcls/data/imaug/hide_and_seek.py | 44 ----- ppcls/data/imaug/operators.py | 244 ------------------------- ppcls/data/imaug/randaugment.py | 106 ----------- ppcls/data/imaug/random_erasing.py | 55 ------ 11 files changed, 2 insertions(+), 1273 deletions(-) delete mode 100644 ppcls/data/imaug/__init__.py delete mode 100644 ppcls/data/imaug/autoaugment.py delete mode 100644 ppcls/data/imaug/batch_operators.py delete mode 100644 ppcls/data/imaug/cutout.py delete mode 100644 ppcls/data/imaug/fmix.py delete mode 100644 ppcls/data/imaug/grid.py delete mode 100644 ppcls/data/imaug/hide_and_seek.py delete mode 100644 ppcls/data/imaug/operators.py delete mode 100644 ppcls/data/imaug/randaugment.py delete mode 100644 ppcls/data/imaug/random_erasing.py diff --git a/docs/zh_CN/faq_series/faq_2020_s1.md b/docs/zh_CN/faq_series/faq_2020_s1.md index 796c1fd2c..e0f3c98c9 100644 --- a/docs/zh_CN/faq_series/faq_2020_s1.md +++ b/docs/zh_CN/faq_series/faq_2020_s1.md @@ -128,8 +128,8 @@ ResNet系列模型中，相比于其他模型，ResNet_vd模型在预测速度 **A**： -* 对于单张图像的增广，可以参考[基于单张图片的数据增广脚本](../../../ppcls/data/imaug/operators.py)，参考`ResizeImage`或者`CropImage`等数据算子的写法，创建一个新的类，然后在`__call__`中，实现对应的增广方法即可。 -* 对于一个batch图像的增广，可以参考[基于batch数据的数据增广脚本](../../../ppcls/data/imaug/batch_operators.py)，参考`MixupOperator`或者`CutmixOperator`等数据算子的写法，创建一个新的类，然后在`__call__`中，实现对应的增广方法即可。 +* 对于单张图像的增广，可以参考[基于单张图片的数据增广脚本](../../../ppcls/data/preprocess/ops)，参考`ResizeImage`或者`CropImage`等数据算子的写法，创建一个新的类，然后在`__call__`中，实现对应的增广方法即可。 +* 对于一个batch图像的增广，可以参考[基于batch数据的数据增广脚本](../../../ppcls/data/preprocess/batch_ops)，参考`MixupOperator`或者`CutmixOperator`等数据算子的写法，创建一个新的类，然后在`__call__`中，实现对应的增广方法即可。 ## Q3.5: 怎么进一步加速模型训练过程呢？ diff --git a/ppcls/data/imaug/__init__.py b/ppcls/data/imaug/__init__.py deleted file mode 100644 index 6860382bc..000000000 --- a/ppcls/data/imaug/__init__.py +++ /dev/null @@ -1,94 +0,0 @@ -# Copyright (c) 2020 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 .autoaugment import ImageNetPolicy as RawImageNetPolicy -from .randaugment import RandAugment as RawRandAugment -from .cutout import Cutout - -from .hide_and_seek import HideAndSeek -from .random_erasing import RandomErasing -from .grid import GridMask - -from .operators import DecodeImage -from .operators import ResizeImage -from .operators import CropImage -from .operators import RandCropImage -from .operators import RandFlipImage -from .operators import NormalizeImage -from .operators import ToCHWImage - -from .batch_operators import MixupOperator -from .batch_operators import CutmixOperator -from .batch_operators import FmixOperator - -import six -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): - if six.PY2: - super(AutoAugment, self).__init__(*args, **kwargs) - else: - super().__init__(*args, **kwargs) - - def __call__(self, img): - if not isinstance(img, Image.Image): - img = np.ascontiguousarray(img) - img = Image.fromarray(img) - - if six.PY2: - img = super(AutoAugment, self).__call__(img) - else: - 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): - if six.PY2: - super(RandAugment, self).__init__(*args, **kwargs) - else: - super().__init__(*args, **kwargs) - - def __call__(self, img): - if not isinstance(img, Image.Image): - img = np.ascontiguousarray(img) - img = Image.fromarray(img) - - if six.PY2: - img = super(RandAugment, self).__call__(img) - else: - img = super().__call__(img) - - if isinstance(img, Image.Image): - img = np.asarray(img) - - return img diff --git a/ppcls/data/imaug/autoaugment.py b/ppcls/data/imaug/autoaugment.py deleted file mode 100644 index 6065697e2..000000000 --- a/ppcls/data/imaug/autoaugment.py +++ /dev/null @@ -1,264 +0,0 @@ -# Copyright (c) 2020 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. - -# This code is based on https://github.com/DeepVoltaire/AutoAugment/blob/master/autoaugment.py - -from PIL import Image, ImageEnhance, ImageOps -import numpy as np -import random - - -class ImageNetPolicy(object): - """ Randomly choose one of the best 24 Sub-policies on ImageNet. - - Example: - >>> policy = ImageNetPolicy() - >>> transformed = policy(image) - - Example as a PyTorch Transform: - >>> transform=transforms.Compose([ - >>> transforms.Resize(256), - >>> ImageNetPolicy(), - >>> transforms.ToTensor()]) - """ - - def __init__(self, fillcolor=(128, 128, 128)): - self.policies = [ - SubPolicy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor), - SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor), - SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor), - SubPolicy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor), - SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor), - SubPolicy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor), - SubPolicy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor), - SubPolicy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor), - SubPolicy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor), - SubPolicy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor), - SubPolicy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor), - SubPolicy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor), - SubPolicy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor), - SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor), - SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor), - SubPolicy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor), - SubPolicy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor), - SubPolicy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor), - SubPolicy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor), - SubPolicy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor), - SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor), - SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor), - SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor), - SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor), - SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor) - ] - - def __call__(self, img, policy_idx=None): - if policy_idx is None or not isinstance(policy_idx, int): - policy_idx = random.randint(0, len(self.policies) - 1) - else: - policy_idx = policy_idx % len(self.policies) - return self.policies[policy_idx](img) - - def __repr__(self): - return "AutoAugment ImageNet Policy" - - -class CIFAR10Policy(object): - """ Randomly choose one of the best 25 Sub-policies on CIFAR10. - - Example: - >>> policy = CIFAR10Policy() - >>> transformed = policy(image) - - Example as a PyTorch Transform: - >>> transform=transforms.Compose([ - >>> transforms.Resize(256), - >>> CIFAR10Policy(), - >>> transforms.ToTensor()]) - """ - - def __init__(self, fillcolor=(128, 128, 128)): - self.policies = [ - SubPolicy(0.1, "invert", 7, 0.2, "contrast", 6, fillcolor), - SubPolicy(0.7, "rotate", 2, 0.3, "translateX", 9, fillcolor), - SubPolicy(0.8, "sharpness", 1, 0.9, "sharpness", 3, fillcolor), - SubPolicy(0.5, "shearY", 8, 0.7, "translateY", 9, fillcolor), - SubPolicy(0.5, "autocontrast", 8, 0.9, "equalize", 2, fillcolor), - SubPolicy(0.2, "shearY", 7, 0.3, "posterize", 7, fillcolor), - SubPolicy(0.4, "color", 3, 0.6, "brightness", 7, fillcolor), - SubPolicy(0.3, "sharpness", 9, 0.7, "brightness", 9, fillcolor), - SubPolicy(0.6, "equalize", 5, 0.5, "equalize", 1, fillcolor), - SubPolicy(0.6, "contrast", 7, 0.6, "sharpness", 5, fillcolor), - SubPolicy(0.7, "color", 7, 0.5, "translateX", 8, fillcolor), - SubPolicy(0.3, "equalize", 7, 0.4, "autocontrast", 8, fillcolor), - SubPolicy(0.4, "translateY", 3, 0.2, "sharpness", 6, fillcolor), - SubPolicy(0.9, "brightness", 6, 0.2, "color", 8, fillcolor), - SubPolicy(0.5, "solarize", 2, 0.0, "invert", 3, fillcolor), - SubPolicy(0.2, "equalize", 0, 0.6, "autocontrast", 0, fillcolor), - SubPolicy(0.2, "equalize", 8, 0.8, "equalize", 4, fillcolor), - SubPolicy(0.9, "color", 9, 0.6, "equalize", 6, fillcolor), - SubPolicy(0.8, "autocontrast", 4, 0.2, "solarize", 8, fillcolor), - SubPolicy(0.1, "brightness", 3, 0.7, "color", 0, fillcolor), - SubPolicy(0.4, "solarize", 5, 0.9, "autocontrast", 3, fillcolor), - SubPolicy(0.9, "translateY", 9, 0.7, "translateY", 9, fillcolor), - SubPolicy(0.9, "autocontrast", 2, 0.8, "solarize", 3, fillcolor), - SubPolicy(0.8, "equalize", 8, 0.1, "invert", 3, fillcolor), - SubPolicy(0.7, "translateY", 9, 0.9, "autocontrast", 1, fillcolor) - ] - - def __call__(self, img, policy_idx=None): - if policy_idx is None or not isinstance(policy_idx, int): - policy_idx = random.randint(0, len(self.policies) - 1) - else: - policy_idx = policy_idx % len(self.policies) - return self.policies[policy_idx](img) - - def __repr__(self): - return "AutoAugment CIFAR10 Policy" - - -class SVHNPolicy(object): - """ Randomly choose one of the best 25 Sub-policies on SVHN. - - Example: - >>> policy = SVHNPolicy() - >>> transformed = policy(image) - - Example as a PyTorch Transform: - >>> transform=transforms.Compose([ - >>> transforms.Resize(256), - >>> SVHNPolicy(), - >>> transforms.ToTensor()]) - """ - - def __init__(self, fillcolor=(128, 128, 128)): - self.policies = [ - SubPolicy(0.9, "shearX", 4, 0.2, "invert", 3, fillcolor), - SubPolicy(0.9, "shearY", 8, 0.7, "invert", 5, fillcolor), - SubPolicy(0.6, "equalize", 5, 0.6, "solarize", 6, fillcolor), - SubPolicy(0.9, "invert", 3, 0.6, "equalize", 3, fillcolor), - SubPolicy(0.6, "equalize", 1, 0.9, "rotate", 3, fillcolor), - SubPolicy(0.9, "shearX", 4, 0.8, "autocontrast", 3, fillcolor), - SubPolicy(0.9, "shearY", 8, 0.4, "invert", 5, fillcolor), - SubPolicy(0.9, "shearY", 5, 0.2, "solarize", 6, fillcolor), - SubPolicy(0.9, "invert", 6, 0.8, "autocontrast", 1, fillcolor), - SubPolicy(0.6, "equalize", 3, 0.9, "rotate", 3, fillcolor), - SubPolicy(0.9, "shearX", 4, 0.3, "solarize", 3, fillcolor), - SubPolicy(0.8, "shearY", 8, 0.7, "invert", 4, fillcolor), - SubPolicy(0.9, "equalize", 5, 0.6, "translateY", 6, fillcolor), - SubPolicy(0.9, "invert", 4, 0.6, "equalize", 7, fillcolor), - SubPolicy(0.3, "contrast", 3, 0.8, "rotate", 4, fillcolor), - SubPolicy(0.8, "invert", 5, 0.0, "translateY", 2, fillcolor), - SubPolicy(0.7, "shearY", 6, 0.4, "solarize", 8, fillcolor), - SubPolicy(0.6, "invert", 4, 0.8, "rotate", 4, fillcolor), - SubPolicy( - 0.3, "shearY", 7, 0.9, "translateX", 3, fillcolor), SubPolicy( - 0.1, "shearX", 6, 0.6, "invert", 5, fillcolor), SubPolicy( - 0.7, "solarize", 2, 0.6, "translateY", 7, - fillcolor), SubPolicy(0.8, "shearY", 4, 0.8, "invert", - 8, fillcolor), SubPolicy( - 0.7, "shearX", 9, 0.8, - "translateY", 3, - fillcolor), SubPolicy( - 0.8, "shearY", 5, 0.7, - "autocontrast", 3, - fillcolor), - SubPolicy(0.7, "shearX", 2, 0.1, "invert", 5, fillcolor) - ] - - def __call__(self, img, policy_idx=None): - if policy_idx is None or not isinstance(policy_idx, int): - policy_idx = random.randint(0, len(self.policies) - 1) - else: - policy_idx = policy_idx % len(self.policies) - return self.policies[policy_idx](img) - - def __repr__(self): - return "AutoAugment SVHN Policy" - - -class SubPolicy(object): - def __init__(self, - p1, - operation1, - magnitude_idx1, - p2, - operation2, - magnitude_idx2, - fillcolor=(128, 128, 128)): - ranges = { - "shearX": np.linspace(0, 0.3, 10), - "shearY": np.linspace(0, 0.3, 10), - "translateX": np.linspace(0, 150 / 331, 10), - "translateY": np.linspace(0, 150 / 331, 10), - "rotate": np.linspace(0, 30, 10), - "color": np.linspace(0.0, 0.9, 10), - "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int), - "solarize": np.linspace(256, 0, 10), - "contrast": np.linspace(0.0, 0.9, 10), - "sharpness": np.linspace(0.0, 0.9, 10), - "brightness": np.linspace(0.0, 0.9, 10), - "autocontrast": [0] * 10, - "equalize": [0] * 10, - "invert": [0] * 10 - } - - # from https://stackoverflow.com/questions/5252170/specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand - def rotate_with_fill(img, magnitude): - rot = img.convert("RGBA").rotate(magnitude) - return Image.composite(rot, - Image.new("RGBA", rot.size, (128, ) * 4), - rot).convert(img.mode) - - func = { - "shearX": lambda img, magnitude: img.transform( - img.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0), - Image.BICUBIC, fillcolor=fillcolor), - "shearY": lambda img, magnitude: img.transform( - img.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0), - Image.BICUBIC, fillcolor=fillcolor), - "translateX": lambda img, magnitude: img.transform( - img.size, Image.AFFINE, (1, 0, magnitude * img.size[0] * random.choice([-1, 1]), 0, 1, 0), - fillcolor=fillcolor), - "translateY": lambda img, magnitude: img.transform( - img.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * img.size[1] * random.choice([-1, 1])), - fillcolor=fillcolor), - "rotate": lambda img, magnitude: rotate_with_fill(img, magnitude), - # "rotate": lambda img, magnitude: img.rotate(magnitude * random.choice([-1, 1])), - "color": lambda img, magnitude: ImageEnhance.Color(img).enhance(1 + magnitude * random.choice([-1, 1])), - "posterize": lambda img, magnitude: ImageOps.posterize(img, magnitude), - "solarize": lambda img, magnitude: ImageOps.solarize(img, magnitude), - "contrast": lambda img, magnitude: ImageEnhance.Contrast(img).enhance( - 1 + magnitude * random.choice([-1, 1])), - "sharpness": lambda img, magnitude: ImageEnhance.Sharpness(img).enhance( - 1 + magnitude * random.choice([-1, 1])), - "brightness": lambda img, magnitude: ImageEnhance.Brightness(img).enhance( - 1 + magnitude * random.choice([-1, 1])), - "autocontrast": lambda img, magnitude: ImageOps.autocontrast(img), - "equalize": lambda img, magnitude: ImageOps.equalize(img), - "invert": lambda img, magnitude: ImageOps.invert(img) - } - - self.p1 = p1 - self.operation1 = func[operation1] - self.magnitude1 = ranges[operation1][magnitude_idx1] - self.p2 = p2 - self.operation2 = func[operation2] - self.magnitude2 = ranges[operation2][magnitude_idx2] - - def __call__(self, img): - if random.random() < self.p1: - img = self.operation1(img, self.magnitude1) - if random.random() < self.p2: - img = self.operation2(img, self.magnitude2) - return img diff --git a/ppcls/data/imaug/batch_operators.py b/ppcls/data/imaug/batch_operators.py deleted file mode 100644 index e12b7b4d2..000000000 --- a/ppcls/data/imaug/batch_operators.py +++ /dev/null @@ -1,117 +0,0 @@ -# Copyright (c) 2020 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 __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import numpy as np - -from .fmix import sample_mask - - -class BatchOperator(object): - """ BatchOperator """ - - def __init__(self, *args, **kwargs): - pass - - def _unpack(self, batch): - """ _unpack """ - assert isinstance(batch, list), \ - 'batch should be a list filled with tuples (img, label)' - bs = len(batch) - assert bs > 0, 'size of the batch data should > 0' - imgs, labels = list(zip(*batch)) - return np.array(imgs), np.array(labels), bs - - def __call__(self, batch): - return batch - - -class MixupOperator(BatchOperator): - """ Mixup operator """ - - def __init__(self, alpha=0.2): - assert alpha > 0., \ - 'parameter alpha[%f] should > 0.0' % (alpha) - self._alpha = alpha - - def __call__(self, batch): - imgs, labels, bs = self._unpack(batch) - idx = np.random.permutation(bs) - lam = np.random.beta(self._alpha, self._alpha) - lams = np.array([lam] * bs, dtype=np.float32) - imgs = lam * imgs + (1 - lam) * imgs[idx] - return list(zip(imgs, labels, labels[idx], lams)) - - -class CutmixOperator(BatchOperator): - """ Cutmix operator """ - - def __init__(self, alpha=0.2): - assert alpha > 0., \ - 'parameter alpha[%f] should > 0.0' % (alpha) - self._alpha = alpha - - def _rand_bbox(self, size, lam): - """ _rand_bbox """ - w = size[2] - h = size[3] - cut_rat = np.sqrt(1. - lam) - cut_w = np.int(w * cut_rat) - cut_h = np.int(h * cut_rat) - - # uniform - cx = np.random.randint(w) - cy = np.random.randint(h) - - bbx1 = np.clip(cx - cut_w // 2, 0, w) - bby1 = np.clip(cy - cut_h // 2, 0, h) - bbx2 = np.clip(cx + cut_w // 2, 0, w) - bby2 = np.clip(cy + cut_h // 2, 0, h) - - return bbx1, bby1, bbx2, bby2 - - def __call__(self, batch): - imgs, labels, bs = self._unpack(batch) - idx = np.random.permutation(bs) - lam = np.random.beta(self._alpha, self._alpha) - - bbx1, bby1, bbx2, bby2 = self._rand_bbox(imgs.shape, lam) - imgs[:, :, bbx1:bbx2, bby1:bby2] = imgs[idx, :, bbx1:bbx2, bby1:bby2] - lam = 1 - (float(bbx2 - bbx1) * (bby2 - bby1) / - (imgs.shape[-2] * imgs.shape[-1])) - lams = np.array([lam] * bs, dtype=np.float32) - return list(zip(imgs, labels, labels[idx], lams)) - - -class FmixOperator(BatchOperator): - """ Fmix operator """ - - def __init__(self, alpha=1, decay_power=3, max_soft=0., reformulate=False): - self._alpha = alpha - self._decay_power = decay_power - self._max_soft = max_soft - self._reformulate = reformulate - - def __call__(self, batch): - imgs, labels, bs = self._unpack(batch) - idx = np.random.permutation(bs) - size = (imgs.shape[2], imgs.shape[3]) - lam, mask = sample_mask(self._alpha, self._decay_power, \ - size, self._max_soft, self._reformulate) - imgs = mask * imgs + (1 - mask) * imgs[idx] - return list(zip(imgs, labels, labels[idx], [lam] * bs)) diff --git a/ppcls/data/imaug/cutout.py b/ppcls/data/imaug/cutout.py deleted file mode 100644 index 43d557f86..000000000 --- a/ppcls/data/imaug/cutout.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright (c) 2020 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. - -# This code is based on https://github.com/uoguelph-mlrg/Cutout - -import numpy as np -import random - - -class Cutout(object): - def __init__(self, n_holes=1, length=112): - self.n_holes = n_holes - self.length = length - - def __call__(self, img): - """ cutout_image """ - h, w = img.shape[:2] - mask = np.ones((h, w), np.float32) - - for n in range(self.n_holes): - y = np.random.randint(h) - x = np.random.randint(w) - - y1 = np.clip(y - self.length // 2, 0, h) - y2 = np.clip(y + self.length // 2, 0, h) - x1 = np.clip(x - self.length // 2, 0, w) - x2 = np.clip(x + self.length // 2, 0, w) - - img[y1:y2, x1:x2] = 0 - return img diff --git a/ppcls/data/imaug/fmix.py b/ppcls/data/imaug/fmix.py deleted file mode 100644 index fb9382115..000000000 --- a/ppcls/data/imaug/fmix.py +++ /dev/null @@ -1,217 +0,0 @@ -# Copyright (c) 2020 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. - -import math -import random - -import numpy as np -from scipy.stats import beta - - -def fftfreqnd(h, w=None, z=None): - """ Get bin values for discrete fourier transform of size (h, w, z) - - :param h: Required, first dimension size - :param w: Optional, second dimension size - :param z: Optional, third dimension size - """ - fz = fx = 0 - fy = np.fft.fftfreq(h) - - if w is not None: - fy = np.expand_dims(fy, -1) - - if w % 2 == 1: - fx = np.fft.fftfreq(w)[:w // 2 + 2] - else: - fx = np.fft.fftfreq(w)[:w // 2 + 1] - - if z is not None: - fy = np.expand_dims(fy, -1) - if z % 2 == 1: - fz = np.fft.fftfreq(z)[:, None] - else: - fz = np.fft.fftfreq(z)[:, None] - - return np.sqrt(fx * fx + fy * fy + fz * fz) - - -def get_spectrum(freqs, decay_power, ch, h, w=0, z=0): - """ Samples a fourier image with given size and frequencies decayed by decay power - - :param freqs: Bin values for the discrete fourier transform - :param decay_power: Decay power for frequency decay prop 1/f**d - :param ch: Number of channels for the resulting mask - :param h: Required, first dimension size - :param w: Optional, second dimension size - :param z: Optional, third dimension size - """ - scale = np.ones(1) / (np.maximum(freqs, np.array([1. / max(w, h, z)])) - **decay_power) - - param_size = [ch] + list(freqs.shape) + [2] - param = np.random.randn(*param_size) - - scale = np.expand_dims(scale, -1)[None, :] - - return scale * param - - -def make_low_freq_image(decay, shape, ch=1): - """ Sample a low frequency image from fourier space - - :param decay_power: Decay power for frequency decay prop 1/f**d - :param shape: Shape of desired mask, list up to 3 dims - :param ch: Number of channels for desired mask - """ - freqs = fftfreqnd(*shape) - spectrum = get_spectrum(freqs, decay, ch, - *shape) #.reshape((1, *shape[:-1], -1)) - spectrum = spectrum[:, 0] + 1j * spectrum[:, 1] - mask = np.real(np.fft.irfftn(spectrum, shape)) - - if len(shape) == 1: - mask = mask[:1, :shape[0]] - if len(shape) == 2: - mask = mask[:1, :shape[0], :shape[1]] - if len(shape) == 3: - mask = mask[:1, :shape[0], :shape[1], :shape[2]] - - mask = mask - mask = (mask - mask.min()) - mask = mask / mask.max() - return mask - - -def sample_lam(alpha, reformulate=False): - """ Sample a lambda from symmetric beta distribution with given alpha - - :param alpha: Alpha value for beta distribution - :param reformulate: If True, uses the reformulation of [1]. - """ - if reformulate: - lam = beta.rvs(alpha + 1, alpha) - else: - lam = beta.rvs(alpha, alpha) - - return lam - - -def binarise_mask(mask, lam, in_shape, max_soft=0.0): - """ Binarises a given low frequency image such that it has mean lambda. - - :param mask: Low frequency image, usually the result of `make_low_freq_image` - :param lam: Mean value of final mask - :param in_shape: Shape of inputs - :param max_soft: Softening value between 0 and 0.5 which smooths hard edges in the mask. - :return: - """ - idx = mask.reshape(-1).argsort()[::-1] - mask = mask.reshape(-1) - num = math.ceil(lam * mask.size) if random.random() > 0.5 else math.floor( - lam * mask.size) - - eff_soft = max_soft - if max_soft > lam or max_soft > (1 - lam): - eff_soft = min(lam, 1 - lam) - - soft = int(mask.size * eff_soft) - num_low = int(num - soft) - num_high = int(num + soft) - - mask[idx[:num_high]] = 1 - mask[idx[num_low:]] = 0 - mask[idx[num_low:num_high]] = np.linspace(1, 0, (num_high - num_low)) - - mask = mask.reshape((1, 1, in_shape[0], in_shape[1])) - return mask - - -def sample_mask(alpha, decay_power, shape, max_soft=0.0, reformulate=False): - """ Samples a mean lambda from beta distribution parametrised by alpha, creates a low frequency image and binarises - it based on this lambda - - :param alpha: Alpha value for beta distribution from which to sample mean of mask - :param decay_power: Decay power for frequency decay prop 1/f**d - :param shape: Shape of desired mask, list up to 3 dims - :param max_soft: Softening value between 0 and 0.5 which smooths hard edges in the mask. - :param reformulate: If True, uses the reformulation of [1]. - """ - if isinstance(shape, int): - shape = (shape, ) - - # Choose lambda - lam = sample_lam(alpha, reformulate) - - # Make mask, get mean / std - mask = make_low_freq_image(decay_power, shape) - mask = binarise_mask(mask, lam, shape, max_soft) - - return float(lam), mask - - -def sample_and_apply(x, - alpha, - decay_power, - shape, - max_soft=0.0, - reformulate=False): - """ - - :param x: Image batch on which to apply fmix of shape [b, c, shape*] - :param alpha: Alpha value for beta distribution from which to sample mean of mask - :param decay_power: Decay power for frequency decay prop 1/f**d - :param shape: Shape of desired mask, list up to 3 dims - :param max_soft: Softening value between 0 and 0.5 which smooths hard edges in the mask. - :param reformulate: If True, uses the reformulation of [1]. - :return: mixed input, permutation indices, lambda value of mix, - """ - lam, mask = sample_mask(alpha, decay_power, shape, max_soft, reformulate) - index = np.random.permutation(x.shape[0]) - - x1, x2 = x * mask, x[index] * (1 - mask) - return x1 + x2, index, lam - - -class FMixBase: - """ FMix augmentation - - Args: - decay_power (float): Decay power for frequency decay prop 1/f**d - alpha (float): Alpha value for beta distribution from which to sample mean of mask - size ([int] | [int, int] | [int, int, int]): Shape of desired mask, list up to 3 dims - max_soft (float): Softening value between 0 and 0.5 which smooths hard edges in the mask. - reformulate (bool): If True, uses the reformulation of [1]. - """ - - def __init__(self, - decay_power=3, - alpha=1, - size=(32, 32), - max_soft=0.0, - reformulate=False): - super().__init__() - self.decay_power = decay_power - self.reformulate = reformulate - self.size = size - self.alpha = alpha - self.max_soft = max_soft - self.index = None - self.lam = None - - def __call__(self, x): - raise NotImplementedError - - def loss(self, *args, **kwargs): - raise NotImplementedError diff --git a/ppcls/data/imaug/grid.py b/ppcls/data/imaug/grid.py deleted file mode 100644 index 93e0c58ac..000000000 --- a/ppcls/data/imaug/grid.py +++ /dev/null @@ -1,89 +0,0 @@ -# Copyright (c) 2020 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. - -# This code is based on https://github.com/akuxcw/GridMask - -import numpy as np -from PIL import Image -import pdb - -# curr -CURR_EPOCH = 0 -# epoch for the prob to be the upper limit -NUM_EPOCHS = 240 - - -class GridMask(object): - def __init__(self, d1=96, d2=224, rotate=1, ratio=0.5, mode=0, prob=1.): - self.d1 = d1 - self.d2 = d2 - self.rotate = rotate - self.ratio = ratio - self.mode = mode - self.st_prob = prob - self.prob = prob - self.last_prob = -1 - - def set_prob(self): - global CURR_EPOCH - global NUM_EPOCHS - self.prob = self.st_prob * min(1, 1.0 * CURR_EPOCH / NUM_EPOCHS) - - def __call__(self, img): - self.set_prob() - if abs(self.last_prob - self.prob) > 1e-10: - global CURR_EPOCH - global NUM_EPOCHS - print( - "self.prob is updated, self.prob={}, CURR_EPOCH: {}, NUM_EPOCHS: {}". - format(self.prob, CURR_EPOCH, NUM_EPOCHS)) - self.last_prob = self.prob - # print("CURR_EPOCH: {}, NUM_EPOCHS: {}, self.prob is set as: {}".format(CURR_EPOCH, NUM_EPOCHS, self.prob) ) - if np.random.rand() > self.prob: - return img - _, h, w = img.shape - hh = int(1.5 * h) - ww = int(1.5 * w) - d = np.random.randint(self.d1, self.d2) - #d = self.d - self.l = int(d * self.ratio + 0.5) - mask = np.ones((hh, ww), np.float32) - st_h = np.random.randint(d) - st_w = np.random.randint(d) - for i in range(-1, hh // d + 1): - s = d * i + st_h - t = s + self.l - s = max(min(s, hh), 0) - t = max(min(t, hh), 0) - mask[s:t, :] *= 0 - for i in range(-1, ww // d + 1): - s = d * i + st_w - t = s + self.l - s = max(min(s, ww), 0) - t = max(min(t, ww), 0) - mask[:, s:t] *= 0 - r = np.random.randint(self.rotate) - mask = Image.fromarray(np.uint8(mask)) - mask = mask.rotate(r) - mask = np.asarray(mask) - mask = mask[(hh - h) // 2:(hh - h) // 2 + h, (ww - w) // 2:(ww - w) // - 2 + w] - - if self.mode == 1: - mask = 1 - mask - - mask = np.expand_dims(mask, axis=0) - img = (img * mask).astype(img.dtype) - - return img diff --git a/ppcls/data/imaug/hide_and_seek.py b/ppcls/data/imaug/hide_and_seek.py deleted file mode 100644 index 5d4a8f97d..000000000 --- a/ppcls/data/imaug/hide_and_seek.py +++ /dev/null @@ -1,44 +0,0 @@ -# Copyright (c) 2020 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. - -# This code is based on https://github.com/kkanshul/Hide-and-Seek - -import numpy as np -import random - - -class HideAndSeek(object): - def __init__(self): - # possible grid size, 0 means no hiding - self.grid_sizes = [0, 16, 32, 44, 56] - # hiding probability - self.hide_prob = 0.5 - - def __call__(self, img): - # randomly choose one grid size - grid_size = np.random.choice(self.grid_sizes) - - _, h, w = img.shape - - # hide the patches - if grid_size == 0: - return img - for x in range(0, w, grid_size): - for y in range(0, h, grid_size): - x_end = min(w, x + grid_size) - y_end = min(h, y + grid_size) - if (random.random() <= self.hide_prob): - img[:, x:x_end, y:y_end] = 0 - - return img diff --git a/ppcls/data/imaug/operators.py b/ppcls/data/imaug/operators.py deleted file mode 100644 index f4e2a27a2..000000000 --- a/ppcls/data/imaug/operators.py +++ /dev/null @@ -1,244 +0,0 @@ -""" -# Copyright (c) 2020 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 __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import six -import math -import random -import cv2 -import numpy as np - -from .autoaugment import ImageNetPolicy - - -class OperatorParamError(ValueError): - """ OperatorParamError - """ - pass - - -class DecodeImage(object): - """ decode image """ - - def __init__(self, to_rgb=True, to_np=False, channel_first=False): - self.to_rgb = to_rgb - self.to_np = to_np # to numpy - self.channel_first = channel_first # only enabled when to_np is True - - def __call__(self, img): - if six.PY2: - assert type(img) is str and len( - img) > 0, "invalid input 'img' in DecodeImage" - else: - assert type(img) is bytes and len( - img) > 0, "invalid input 'img' in DecodeImage" - data = np.frombuffer(img, dtype='uint8') - img = cv2.imdecode(data, 1) - if self.to_rgb: - assert img.shape[2] == 3, 'invalid shape of image[%s]' % ( - img.shape) - img = img[:, :, ::-1] - - if self.channel_first: - img = img.transpose((2, 0, 1)) - - return img - - -class ResizeImage(object): - """ resize image """ - - def __init__(self, size=None, resize_short=None, interpolation=-1): - self.interpolation = interpolation if interpolation >= 0 else None - if resize_short is not None and resize_short > 0: - self.resize_short = resize_short - self.w = None - self.h = None - elif size is not None: - self.resize_short = None - self.w = size if type(size) is int else size[0] - self.h = size if type(size) is int else size[1] - else: - raise OperatorParamError("invalid params for ReisizeImage for '\ - 'both 'size' and 'resize_short' are None") - - def __call__(self, img): - img_h, img_w = img.shape[:2] - if self.resize_short is not None: - percent = float(self.resize_short) / min(img_w, img_h) - w = int(round(img_w * percent)) - h = int(round(img_h * percent)) - else: - w = self.w - h = self.h - if self.interpolation is None: - return cv2.resize(img, (w, h)) - else: - return cv2.resize(img, (w, h), interpolation=self.interpolation) - - -class CropImage(object): - """ crop image """ - - def __init__(self, size): - if type(size) is int: - self.size = (size, size) - else: - self.size = size # (h, w) - - def __call__(self, img): - w, h = self.size - img_h, img_w = img.shape[:2] - w_start = (img_w - w) // 2 - h_start = (img_h - h) // 2 - - w_end = w_start + w - h_end = h_start + h - return img[h_start:h_end, w_start:w_end, :] - - -class RandCropImage(object): - """ random crop image """ - - def __init__(self, size, scale=None, ratio=None, interpolation=-1): - - self.interpolation = interpolation if interpolation >= 0 else None - if type(size) is int: - self.size = (size, size) # (h, w) - else: - self.size = size - - self.scale = [0.08, 1.0] if scale is None else scale - self.ratio = [3. / 4., 4. / 3.] if ratio is None else ratio - - def __call__(self, img): - size = self.size - scale = self.scale - ratio = self.ratio - - aspect_ratio = math.sqrt(random.uniform(*ratio)) - w = 1. * aspect_ratio - h = 1. / aspect_ratio - - img_h, img_w = img.shape[:2] - - bound = min((float(img_w) / img_h) / (w**2), - (float(img_h) / img_w) / (h**2)) - scale_max = min(scale[1], bound) - scale_min = min(scale[0], bound) - - target_area = img_w * img_h * random.uniform(scale_min, scale_max) - target_size = math.sqrt(target_area) - w = int(target_size * w) - h = int(target_size * h) - - i = random.randint(0, img_w - w) - j = random.randint(0, img_h - h) - - img = img[j:j + h, i:i + w, :] - if self.interpolation is None: - return cv2.resize(img, size) - else: - return cv2.resize(img, size, interpolation=self.interpolation) - - -class RandFlipImage(object): - """ random flip image - flip_code: - 1: Flipped Horizontally - 0: Flipped Vertically - -1: Flipped Horizontally & Vertically - """ - - def __init__(self, flip_code=1): - assert flip_code in [-1, 0, 1 - ], "flip_code should be a value in [-1, 0, 1]" - self.flip_code = flip_code - - def __call__(self, img): - if random.randint(0, 1) == 1: - return cv2.flip(img, self.flip_code) - else: - return img - - -class AutoAugment(object): - def __init__(self): - self.policy = ImageNetPolicy() - - def __call__(self, img): - from PIL import Image - img = np.ascontiguousarray(img) - img = Image.fromarray(img) - img = self.policy(img) - img = np.asarray(img) - - -class NormalizeImage(object): - """ normalize image such as substract mean, divide std - """ - - def __init__(self, scale=None, mean=None, std=None, order='chw', output_fp16=False, channel_num=3): - if isinstance(scale, str): - scale = eval(scale) - assert channel_num in [3, 4], "channel number of input image should be set to 3 or 4." - self.channel_num = channel_num - self.output_dtype = 'float16' if output_fp16 else 'float32' - self.scale = np.float32(scale if scale is not None else 1.0 / 255.0) - self.order = order - mean = mean if mean is not None else [0.485, 0.456, 0.406] - std = std if std is not None else [0.229, 0.224, 0.225] - - shape = (3, 1, 1) if self.order == 'chw' else (1, 1, 3) - self.mean = np.array(mean).reshape(shape).astype('float32') - self.std = np.array(std).reshape(shape).astype('float32') - - def __call__(self, img): - from PIL import Image - if isinstance(img, Image.Image): - img = np.array(img) - - assert isinstance(img, - np.ndarray), "invalid input 'img' in NormalizeImage" - - img = (img.astype('float32') * self.scale - self.mean) / self.std - - if self.channel_num == 4: - img_h = img.shape[1] if self.order == 'chw' else img.shape[0] - img_w = img.shape[2] if self.order == 'chw' else img.shape[1] - pad_zeros = np.zeros((1, img_h, img_w)) if self.order == 'chw' else np.zeros((img_h, img_w, 1)) - img = (np.concatenate((img, pad_zeros), axis=0) if self.order == 'chw' - else np.concatenate((img, pad_zeros), axis=2)) - return img.astype(self.output_dtype) - - -class ToCHWImage(object): - """ convert hwc image to chw image - """ - - def __init__(self): - pass - - def __call__(self, img): - from PIL import Image - if isinstance(img, Image.Image): - img = np.array(img) - - return img.transpose((2, 0, 1)) diff --git a/ppcls/data/imaug/randaugment.py b/ppcls/data/imaug/randaugment.py deleted file mode 100644 index cb3e9695c..000000000 --- a/ppcls/data/imaug/randaugment.py +++ /dev/null @@ -1,106 +0,0 @@ -# Copyright (c) 2020 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. - -# This code is based on https://github.com/heartInsert/randaugment - -from PIL import Image, ImageEnhance, ImageOps -import numpy as np -import random - - -class RandAugment(object): - def __init__(self, num_layers=2, magnitude=5, fillcolor=(128, 128, 128)): - self.num_layers = num_layers - self.magnitude = magnitude - self.max_level = 10 - - abso_level = self.magnitude / self.max_level - self.level_map = { - "shearX": 0.3 * abso_level, - "shearY": 0.3 * abso_level, - "translateX": 150.0 / 331 * abso_level, - "translateY": 150.0 / 331 * abso_level, - "rotate": 30 * abso_level, - "color": 0.9 * abso_level, - "posterize": int(4.0 * abso_level), - "solarize": 256.0 * abso_level, - "contrast": 0.9 * abso_level, - "sharpness": 0.9 * abso_level, - "brightness": 0.9 * abso_level, - "autocontrast": 0, - "equalize": 0, - "invert": 0 - } - - # from https://stackoverflow.com/questions/5252170/ - # specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand - def rotate_with_fill(img, magnitude): - rot = img.convert("RGBA").rotate(magnitude) - return Image.composite(rot, - Image.new("RGBA", rot.size, (128, ) * 4), - rot).convert(img.mode) - - rnd_ch_op = random.choice - - self.func = { - "shearX": lambda img, magnitude: img.transform( - img.size, - Image.AFFINE, - (1, magnitude * rnd_ch_op([-1, 1]), 0, 0, 1, 0), - Image.BICUBIC, - fillcolor=fillcolor), - "shearY": lambda img, magnitude: img.transform( - img.size, - Image.AFFINE, - (1, 0, 0, magnitude * rnd_ch_op([-1, 1]), 1, 0), - Image.BICUBIC, - fillcolor=fillcolor), - "translateX": lambda img, magnitude: img.transform( - img.size, - Image.AFFINE, - (1, 0, magnitude * img.size[0] * rnd_ch_op([-1, 1]), 0, 1, 0), - fillcolor=fillcolor), - "translateY": lambda img, magnitude: img.transform( - img.size, - Image.AFFINE, - (1, 0, 0, 0, 1, magnitude * img.size[1] * rnd_ch_op([-1, 1])), - fillcolor=fillcolor), - "rotate": lambda img, magnitude: rotate_with_fill(img, magnitude), - "color": lambda img, magnitude: ImageEnhance.Color(img).enhance( - 1 + magnitude * rnd_ch_op([-1, 1])), - "posterize": lambda img, magnitude: - ImageOps.posterize(img, magnitude), - "solarize": lambda img, magnitude: - ImageOps.solarize(img, magnitude), - "contrast": lambda img, magnitude: - ImageEnhance.Contrast(img).enhance( - 1 + magnitude * rnd_ch_op([-1, 1])), - "sharpness": lambda img, magnitude: - ImageEnhance.Sharpness(img).enhance( - 1 + magnitude * rnd_ch_op([-1, 1])), - "brightness": lambda img, magnitude: - ImageEnhance.Brightness(img).enhance( - 1 + magnitude * rnd_ch_op([-1, 1])), - "autocontrast": lambda img, magnitude: - ImageOps.autocontrast(img), - "equalize": lambda img, magnitude: ImageOps.equalize(img), - "invert": lambda img, magnitude: ImageOps.invert(img) - } - - def __call__(self, img): - avaiable_op_names = list(self.level_map.keys()) - for layer_num in range(self.num_layers): - op_name = np.random.choice(avaiable_op_names) - img = self.func[op_name](img, self.level_map[op_name]) - return img diff --git a/ppcls/data/imaug/random_erasing.py b/ppcls/data/imaug/random_erasing.py deleted file mode 100644 index 76e4abf39..000000000 --- a/ppcls/data/imaug/random_erasing.py +++ /dev/null @@ -1,55 +0,0 @@ -# Copyright (c) 2020 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. - -#This code is based on https://github.com/zhunzhong07/Random-Erasing - -import math -import random - -import numpy as np - - -class RandomErasing(object): - def __init__(self, EPSILON=0.5, sl=0.02, sh=0.4, r1=0.3, - mean=[0., 0., 0.]): - self.EPSILON = EPSILON - self.mean = mean - self.sl = sl - self.sh = sh - self.r1 = r1 - - def __call__(self, img): - if random.uniform(0, 1) > self.EPSILON: - return img - - for attempt in range(100): - area = img.shape[1] * img.shape[2] - - target_area = random.uniform(self.sl, self.sh) * area - aspect_ratio = random.uniform(self.r1, 1 / self.r1) - - h = int(round(math.sqrt(target_area * aspect_ratio))) - w = int(round(math.sqrt(target_area / aspect_ratio))) - - if w < img.shape[2] and h < img.shape[1]: - x1 = random.randint(0, img.shape[1] - h) - y1 = random.randint(0, img.shape[2] - w) - if img.shape[0] == 3: - img[0, x1:x1 + h, y1:y1 + w] = self.mean[0] - img[1, x1:x1 + h, y1:y1 + w] = self.mean[1] - img[2, x1:x1 + h, y1:y1 + w] = self.mean[2] - else: - img[0, x1:x1 + h, y1:y1 + w] = self.mean[1] - return img - return img