118 lines
3.9 KiB
Python
118 lines
3.9 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 numpy as np
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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 __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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def __init__(self, alpha=0.2):
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assert alpha > 0., \
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'parameter alpha[%f] should > 0.0' % (alpha)
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self._alpha = alpha
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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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lams = np.array([lam] * bs, dtype=np.float32)
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imgs = lam * imgs + (1 - lam) * imgs[idx]
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return list(zip(imgs, labels, labels[idx], lams))
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class CutmixOperator(BatchOperator):
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""" Cutmix operator """
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def __init__(self, alpha=0.2):
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assert alpha > 0., \
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'parameter alpha[%f] should > 0.0' % (alpha)
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self._alpha = alpha
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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 = np.int(w * cut_rat)
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cut_h = np.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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lams = np.array([lam] * bs, dtype=np.float32)
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return list(zip(imgs, labels, labels[idx], lams))
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class FmixOperator(BatchOperator):
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""" Fmix operator """
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def __init__(self, alpha=1, decay_power=3, max_soft=0., reformulate=False):
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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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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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return list(zip(imgs, labels, labels[idx], [lam] * bs))
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