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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 __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
- from __future__ import unicode_literals
- import random
- import numpy as np
- from paddlex.ppcls.utils import logger
- from paddlex.ppcls.data.preprocess.ops.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))
- imgs = []
- labels = []
- for item in batch:
- imgs.append(item[0])
- labels.append(item[1])
- return np.array(imgs), np.array(labels), bs
- def _one_hot(self, targets):
- return np.eye(self.class_num, dtype="float32")[targets]
- def _mix_target(self, targets0, targets1, lam):
- one_hots0 = self._one_hot(targets0)
- one_hots1 = self._one_hot(targets1)
- return one_hots0 * lam + one_hots1 * (1 - lam)
- def __call__(self, batch):
- return batch
- class MixupOperator(BatchOperator):
- """ Mixup operator
- reference: https://arxiv.org/abs/1710.09412
- """
- def __init__(self, class_num, alpha: float=1.):
- """Build Mixup operator
- Args:
- alpha (float, optional): The parameter alpha of mixup. Defaults to 1..
- Raises:
- Exception: The value of parameter is illegal.
- """
- if alpha <= 0:
- raise Exception(
- f"Parameter \"alpha\" of Mixup should be greater than 0. \"alpha\": {alpha}."
- )
- if not class_num:
- msg = "Please set \"Arch.class_num\" in config if use \"MixupOperator\"."
- logger.error(Exception(msg))
- raise Exception(msg)
- self._alpha = alpha
- self.class_num = class_num
- def __call__(self, batch):
- imgs, labels, bs = self._unpack(batch)
- idx = np.random.permutation(bs)
- lam = np.random.beta(self._alpha, self._alpha)
- imgs = lam * imgs + (1 - lam) * imgs[idx]
- targets = self._mix_target(labels, labels[idx], lam)
- return list(zip(imgs, targets))
- class CutmixOperator(BatchOperator):
- """ Cutmix operator
- reference: https://arxiv.org/abs/1905.04899
- """
- def __init__(self, class_num, alpha=0.2):
- """Build Cutmix operator
- Args:
- alpha (float, optional): The parameter alpha of cutmix. Defaults to 0.2.
- Raises:
- Exception: The value of parameter is illegal.
- """
- if alpha <= 0:
- raise Exception(
- f"Parameter \"alpha\" of Cutmix should be greater than 0. \"alpha\": {alpha}."
- )
- if not class_num:
- msg = "Please set \"Arch.class_num\" in config if use \"CutmixOperator\"."
- logger.error(Exception(msg))
- raise Exception(msg)
- self._alpha = alpha
- self.class_num = class_num
- def _rand_bbox(self, size, lam):
- """ _rand_bbox """
- w = size[2]
- h = size[3]
- cut_rat = np.sqrt(1. - lam)
- cut_w = int(w * cut_rat)
- cut_h = 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]))
- targets = self._mix_target(labels, labels[idx], lam)
- return list(zip(imgs, targets))
- class FmixOperator(BatchOperator):
- """ Fmix operator
- reference: https://arxiv.org/abs/2002.12047
- """
- def __init__(self,
- class_num,
- alpha=1,
- decay_power=3,
- max_soft=0.,
- reformulate=False):
- if not class_num:
- msg = "Please set \"Arch.class_num\" in config if use \"FmixOperator\"."
- logger.error(Exception(msg))
- raise Exception(msg)
- self._alpha = alpha
- self._decay_power = decay_power
- self._max_soft = max_soft
- self._reformulate = reformulate
- self.class_num = class_num
- 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]
- targets = self._mix_target(labels, labels[idx], lam)
- return list(zip(imgs, targets))
- class OpSampler(object):
- """ Sample a operator from """
- def __init__(self, class_num, **op_dict):
- """Build OpSampler
- Raises:
- Exception: The parameter \"prob\" of operator(s) are be set error.
- """
- if not class_num:
- msg = "Please set \"Arch.class_num\" in config if use \"OpSampler\"."
- logger.error(Exception(msg))
- raise Exception(msg)
- if len(op_dict) < 1:
- msg = f"ConfigWarning: No operator in \"OpSampler\". \"OpSampler\" has been skipped."
- logger.warning(msg)
- self.ops = {}
- total_prob = 0
- for op_name in op_dict:
- param = op_dict[op_name]
- if "prob" not in param:
- msg = f"ConfigWarning: Parameter \"prob\" should be set when use operator in \"OpSampler\". The operator \"{op_name}\"'s prob has been set \"0\"."
- logger.warning(msg)
- prob = param.pop("prob", 0)
- total_prob += prob
- param.update({"class_num": class_num})
- op = eval(op_name)(**param)
- self.ops.update({op: prob})
- if total_prob > 1:
- msg = f"ConfigError: The total prob of operators in \"OpSampler\" should be less 1."
- logger.error(Exception(msg))
- raise Exception(msg)
- # add "None Op" when total_prob < 1, "None Op" do nothing
- self.ops[None] = 1 - total_prob
- def __call__(self, batch):
- op = random.choices(
- list(self.ops.keys()), weights=list(self.ops.values()), k=1)[0]
- # return batch directly when None Op
- return op(batch) if op else batch
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