502 lines
19 KiB
Python
502 lines
19 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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import paddle
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import paddle.nn.functional as F
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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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class MixupCutmixHybrid(object):
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""" Mixup/Cutmix that applies different params to each element or whole batch
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Args:
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mixup_alpha (float): mixup alpha value, mixup is active if > 0.
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cutmix_alpha (float): cutmix alpha value, cutmix is active if > 0.
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cutmix_minmax (List[float]): cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None.
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prob (float): probability of applying mixup or cutmix per batch or element
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switch_prob (float): probability of switching to cutmix instead of mixup when both are active
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mode (str): how to apply mixup/cutmix params (per 'batch', 'pair' (pair of elements), 'elem' (element)
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correct_lam (bool): apply lambda correction when cutmix bbox clipped by image borders
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label_smoothing (float): apply label smoothing to the mixed target tensor
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num_classes (int): number of classes for target
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"""
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def __init__(self,
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mixup_alpha=1.,
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cutmix_alpha=0.,
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cutmix_minmax=None,
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prob=1.0,
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switch_prob=0.5,
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mode='batch',
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correct_lam=True,
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label_smoothing=0.1,
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num_classes=4):
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self.mixup_alpha = mixup_alpha
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self.cutmix_alpha = cutmix_alpha
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self.cutmix_minmax = cutmix_minmax
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if self.cutmix_minmax is not None:
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assert len(self.cutmix_minmax) == 2
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# force cutmix alpha == 1.0 when minmax active to keep logic simple & safe
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self.cutmix_alpha = 1.0
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self.mix_prob = prob
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self.switch_prob = switch_prob
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self.label_smoothing = label_smoothing
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self.num_classes = num_classes
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self.mode = mode
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self.correct_lam = correct_lam # correct lambda based on clipped area for cutmix
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self.mixup_enabled = True # set to false to disable mixing (intended tp be set by train loop)
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def _one_hot(self, x, num_classes, on_value=1., off_value=0.):
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x = paddle.cast(x, dtype='int64')
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on_value = paddle.full([x.shape[0], num_classes], on_value)
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off_value = paddle.full([x.shape[0], num_classes], off_value)
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return paddle.where(
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F.one_hot(x, num_classes) == 1, on_value, off_value)
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def _mixup_target(self, target, num_classes, lam=1., smoothing=0.0):
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off_value = smoothing / num_classes
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on_value = 1. - smoothing + off_value
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y1 = self._one_hot(
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target,
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num_classes,
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on_value=on_value,
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off_value=off_value, )
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y2 = self._one_hot(
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target.flip(0),
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num_classes,
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on_value=on_value,
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off_value=off_value)
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return y1 * lam + y2 * (1. - lam)
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def _rand_bbox(self, img_shape, lam, margin=0., count=None):
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""" Standard CutMix bounding-box
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Generates a random square bbox based on lambda value. This impl includes
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support for enforcing a border margin as percent of bbox dimensions.
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Args:
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img_shape (tuple): Image shape as tuple
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lam (float): Cutmix lambda value
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margin (float): Percentage of bbox dimension to enforce as margin (reduce amount of box outside image)
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count (int): Number of bbox to generate
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"""
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ratio = np.sqrt(1 - lam)
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img_h, img_w = img_shape[-2:]
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cut_h, cut_w = int(img_h * ratio), int(img_w * ratio)
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margin_y, margin_x = int(margin * cut_h), int(margin * cut_w)
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cy = np.random.randint(0 + margin_y, img_h - margin_y, size=count)
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cx = np.random.randint(0 + margin_x, img_w - margin_x, size=count)
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yl = np.clip(cy - cut_h // 2, 0, img_h)
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yh = np.clip(cy + cut_h // 2, 0, img_h)
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xl = np.clip(cx - cut_w // 2, 0, img_w)
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xh = np.clip(cx + cut_w // 2, 0, img_w)
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return yl, yh, xl, xh
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def _rand_bbox_minmax(self, img_shape, minmax, count=None):
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""" Min-Max CutMix bounding-box
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Inspired by Darknet cutmix impl, generates a random rectangular bbox
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based on min/max percent values applied to each dimension of the input image.
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Typical defaults for minmax are usually in the .2-.3 for min and .8-.9 range for max.
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Args:
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img_shape (tuple): Image shape as tuple
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minmax (tuple or list): Min and max bbox ratios (as percent of image size)
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count (int): Number of bbox to generate
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"""
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assert len(minmax) == 2
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img_h, img_w = img_shape[-2:]
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cut_h = np.random.randint(
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int(img_h * minmax[0]), int(img_h * minmax[1]), size=count)
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cut_w = np.random.randint(
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int(img_w * minmax[0]), int(img_w * minmax[1]), size=count)
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yl = np.random.randint(0, img_h - cut_h, size=count)
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xl = np.random.randint(0, img_w - cut_w, size=count)
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yu = yl + cut_h
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xu = xl + cut_w
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return yl, yu, xl, xu
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def _cutmix_bbox_and_lam(self,
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img_shape,
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lam,
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ratio_minmax=None,
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correct_lam=True,
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count=None):
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""" Generate bbox and apply lambda correction.
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"""
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if ratio_minmax is not None:
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yl, yu, xl, xu = self._rand_bbox_minmax(
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img_shape, ratio_minmax, count=count)
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else:
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yl, yu, xl, xu = self._rand_bbox(img_shape, lam, count=count)
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if correct_lam or ratio_minmax is not None:
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bbox_area = (yu - yl) * (xu - xl)
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lam = 1. - bbox_area / float(img_shape[-2] * img_shape[-1])
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return (yl, yu, xl, xu), lam
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def _params_per_elem(self, batch_size):
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lam = np.ones(batch_size, dtype=np.float32)
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use_cutmix = np.zeros(batch_size, dtype=np.bool)
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if self.mixup_enabled:
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if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
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use_cutmix = np.random.rand(batch_size) < self.switch_prob
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lam_mix = np.where(
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use_cutmix,
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np.random.beta(
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self.cutmix_alpha, self.cutmix_alpha, size=batch_size),
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np.random.beta(
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self.mixup_alpha, self.mixup_alpha, size=batch_size))
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elif self.mixup_alpha > 0.:
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lam_mix = np.random.beta(
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self.mixup_alpha, self.mixup_alpha, size=batch_size)
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elif self.cutmix_alpha > 0.:
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use_cutmix = np.ones(batch_size, dtype=np.bool)
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lam_mix = np.random.beta(
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self.cutmix_alpha, self.cutmix_alpha, size=batch_size)
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else:
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assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
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lam = np.where(
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np.random.rand(batch_size) < self.mix_prob,
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lam_mix.astype(np.float32), lam)
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return lam, use_cutmix
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def _params_per_batch(self):
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lam = 1.
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use_cutmix = False
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if self.mixup_enabled and np.random.rand() < self.mix_prob:
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if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
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use_cutmix = np.random.rand() < self.switch_prob
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lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha) if use_cutmix else \
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np.random.beta(self.mixup_alpha, self.mixup_alpha)
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elif self.mixup_alpha > 0.:
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lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha)
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elif self.cutmix_alpha > 0.:
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use_cutmix = True
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lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha)
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else:
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assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
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lam = float(lam_mix)
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return lam, use_cutmix
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def _mix_elem(self, x):
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batch_size = len(x)
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lam_batch, use_cutmix = self._params_per_elem(batch_size)
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x_orig = x.clone(
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) # need to keep an unmodified original for mixing source
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for i in range(batch_size):
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j = batch_size - i - 1
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lam = lam_batch[i]
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if lam != 1.:
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if use_cutmix[i]:
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(yl, yh, xl, xh), lam = self._cutmix_bbox_and_lam(
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x[i].shape,
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lam,
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ratio_minmax=self.cutmix_minmax,
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correct_lam=self.correct_lam)
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if yl < yh and xl < xh:
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x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]
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lam_batch[i] = lam
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else:
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x[i] = x[i] * lam + x_orig[j] * (1 - lam)
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return paddle.to_tensor(lam_batch, dtype=x.dtype).unsqueeze(1)
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def _mix_pair(self, x):
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batch_size = len(x)
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lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)
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x_orig = x.clone(
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) # need to keep an unmodified original for mixing source
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for i in range(batch_size // 2):
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j = batch_size - i - 1
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lam = lam_batch[i]
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if lam != 1.:
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if use_cutmix[i]:
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(yl, yh, xl, xh), lam = self._cutmix_bbox_and_lam(
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x[i].shape,
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lam,
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ratio_minmax=self.cutmix_minmax,
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correct_lam=self.correct_lam)
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if yl < yh and xl < xh:
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x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]
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x[j][:, yl:yh, xl:xh] = x_orig[i][:, yl:yh, xl:xh]
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lam_batch[i] = lam
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else:
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x[i] = x[i] * lam + x_orig[j] * (1 - lam)
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x[j] = x[j] * lam + x_orig[i] * (1 - lam)
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lam_batch = np.concatenate((lam_batch, lam_batch[::-1]))
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return paddle.to_tensor(lam_batch, dtype=x.dtype).unsqueeze(1)
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def _mix_batch(self, x):
|
|
lam, use_cutmix = self._params_per_batch()
|
|
if lam == 1.:
|
|
return 1.
|
|
if use_cutmix:
|
|
(yl, yh, xl, xh), lam = self._cutmix_bbox_and_lam(
|
|
x.shape,
|
|
lam,
|
|
ratio_minmax=self.cutmix_minmax,
|
|
correct_lam=self.correct_lam)
|
|
if yl < yh and xl < xh:
|
|
x[:, :, yl:yh, xl:xh] = x.flip(0)[:, :, yl:yh, xl:xh]
|
|
|
|
else:
|
|
x_flipped = x.flip(0) * (1. - lam)
|
|
x[:] = x * lam + x_flipped
|
|
return lam
|
|
|
|
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 __call__(self, batch):
|
|
x, target, bs = self._unpack(batch)
|
|
x = paddle.to_tensor(x)
|
|
target = paddle.to_tensor(target)
|
|
assert len(x) % 2 == 0, 'Batch size should be even when using this'
|
|
if self.mode == 'elem':
|
|
lam = self._mix_elem(x)
|
|
elif self.mode == 'pair':
|
|
lam = self._mix_pair(x)
|
|
else:
|
|
lam = self._mix_batch(x)
|
|
target = self._mixup_target(target, self.num_classes, lam,
|
|
self.label_smoothing)
|
|
|
|
return list(zip(x.numpy(), target.numpy()))
|