232 lines
7.4 KiB
Python
232 lines
7.4 KiB
Python
# 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 __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import random
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import numpy as np
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from ppcls.utils import logger
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from ppcls.data.preprocess.ops.fmix import sample_mask
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class BatchOperator(object):
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""" BatchOperator """
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def __init__(self, *args, **kwargs):
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pass
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def _unpack(self, batch):
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""" _unpack """
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assert isinstance(batch, list), \
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'batch should be a list filled with tuples (img, label)'
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bs = len(batch)
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assert bs > 0, 'size of the batch data should > 0'
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#imgs, labels = list(zip(*batch))
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imgs = []
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labels = []
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for item in batch:
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imgs.append(item[0])
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labels.append(item[1])
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return np.array(imgs), np.array(labels), bs
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def _one_hot(self, targets):
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return np.eye(self.class_num, dtype="float32")[targets]
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def _mix_target(self, targets0, targets1, lam):
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one_hots0 = self._one_hot(targets0)
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one_hots1 = self._one_hot(targets1)
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return one_hots0 * lam + one_hots1 * (1 - lam)
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def __call__(self, batch):
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return batch
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class MixupOperator(BatchOperator):
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""" Mixup operator
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reference: https://arxiv.org/abs/1710.09412
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"""
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def __init__(self, class_num, alpha: float=1.):
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"""Build Mixup operator
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Args:
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alpha (float, optional): The parameter alpha of mixup. Defaults to 1..
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Raises:
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Exception: The value of parameter is illegal.
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"""
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if alpha <= 0:
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raise Exception(
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f"Parameter \"alpha\" of Mixup should be greater than 0. \"alpha\": {alpha}."
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)
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if not class_num:
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msg = "Please set \"Arch.class_num\" in config if use \"MixupOperator\"."
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logger.error(Exception(msg))
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raise Exception(msg)
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self._alpha = alpha
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self.class_num = class_num
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def __call__(self, batch):
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imgs, labels, bs = self._unpack(batch)
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idx = np.random.permutation(bs)
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lam = np.random.beta(self._alpha, self._alpha)
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imgs = lam * imgs + (1 - lam) * imgs[idx]
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targets = self._mix_target(labels, labels[idx], lam)
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return list(zip(imgs, targets))
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class CutmixOperator(BatchOperator):
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""" Cutmix operator
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reference: https://arxiv.org/abs/1905.04899
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"""
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def __init__(self, class_num, alpha=0.2):
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"""Build Cutmix operator
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Args:
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alpha (float, optional): The parameter alpha of cutmix. Defaults to 0.2.
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Raises:
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Exception: The value of parameter is illegal.
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"""
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if alpha <= 0:
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raise Exception(
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f"Parameter \"alpha\" of Cutmix should be greater than 0. \"alpha\": {alpha}."
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)
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if not class_num:
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msg = "Please set \"Arch.class_num\" in config if use \"CutmixOperator\"."
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logger.error(Exception(msg))
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raise Exception(msg)
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self._alpha = alpha
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self.class_num = class_num
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def _rand_bbox(self, size, lam):
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""" _rand_bbox """
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w = size[2]
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h = size[3]
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cut_rat = np.sqrt(1. - lam)
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cut_w = int(w * cut_rat)
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cut_h = int(h * cut_rat)
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# uniform
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cx = np.random.randint(w)
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cy = np.random.randint(h)
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bbx1 = np.clip(cx - cut_w // 2, 0, w)
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bby1 = np.clip(cy - cut_h // 2, 0, h)
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bbx2 = np.clip(cx + cut_w // 2, 0, w)
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bby2 = np.clip(cy + cut_h // 2, 0, h)
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return bbx1, bby1, bbx2, bby2
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def __call__(self, batch):
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imgs, labels, bs = self._unpack(batch)
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idx = np.random.permutation(bs)
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lam = np.random.beta(self._alpha, self._alpha)
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bbx1, bby1, bbx2, bby2 = self._rand_bbox(imgs.shape, lam)
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imgs[:, :, bbx1:bbx2, bby1:bby2] = imgs[idx, :, bbx1:bbx2, bby1:bby2]
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lam = 1 - (float(bbx2 - bbx1) * (bby2 - bby1) /
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(imgs.shape[-2] * imgs.shape[-1]))
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targets = self._mix_target(labels, labels[idx], lam)
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return list(zip(imgs, targets))
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class FmixOperator(BatchOperator):
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""" Fmix operator
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reference: https://arxiv.org/abs/2002.12047
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"""
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def __init__(self,
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class_num,
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alpha=1,
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decay_power=3,
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max_soft=0.,
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reformulate=False):
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if not class_num:
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msg = "Please set \"Arch.class_num\" in config if use \"FmixOperator\"."
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logger.error(Exception(msg))
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raise Exception(msg)
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self._alpha = alpha
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self._decay_power = decay_power
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self._max_soft = max_soft
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self._reformulate = reformulate
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self.class_num = class_num
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def __call__(self, batch):
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imgs, labels, bs = self._unpack(batch)
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idx = np.random.permutation(bs)
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size = (imgs.shape[2], imgs.shape[3])
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lam, mask = sample_mask(self._alpha, self._decay_power, \
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size, self._max_soft, self._reformulate)
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imgs = mask * imgs + (1 - mask) * imgs[idx]
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targets = self._mix_target(labels, labels[idx], lam)
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return list(zip(imgs, targets))
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class OpSampler(object):
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""" Sample a operator from """
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def __init__(self, class_num, **op_dict):
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"""Build OpSampler
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Raises:
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Exception: The parameter \"prob\" of operator(s) are be set error.
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"""
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if not class_num:
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msg = "Please set \"Arch.class_num\" in config if use \"OpSampler\"."
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logger.error(Exception(msg))
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raise Exception(msg)
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if len(op_dict) < 1:
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msg = f"ConfigWarning: No operator in \"OpSampler\". \"OpSampler\" has been skipped."
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logger.warning(msg)
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self.ops = {}
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total_prob = 0
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for op_name in op_dict:
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param = op_dict[op_name]
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if "prob" not in param:
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msg = f"ConfigWarning: Parameter \"prob\" should be set when use operator in \"OpSampler\". The operator \"{op_name}\"'s prob has been set \"0\"."
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logger.warning(msg)
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prob = param.pop("prob", 0)
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total_prob += prob
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param.update({"class_num": class_num})
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op = eval(op_name)(**param)
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self.ops.update({op: prob})
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if total_prob > 1:
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msg = f"ConfigError: The total prob of operators in \"OpSampler\" should be less 1."
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logger.error(Exception(msg))
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raise Exception(msg)
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# add "None Op" when total_prob < 1, "None Op" do nothing
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self.ops[None] = 1 - total_prob
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def __call__(self, batch):
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op = random.choices(
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list(self.ops.keys()), weights=list(self.ops.values()), k=1)[0]
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# return batch directly when None Op
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return op(batch) if op else batch
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