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https://github.com/open-mmlab/mmpretrain.git
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3a08db9182
* add mytrain.py for test * test before layers * test attr in layers * test classifier * delete mytrain.py * add rand_bbox_minmax rand_bbox and cutmix_bbox_and_lam to BaseCutMixLayer * add mixup_prob to BatchMixupLayer * add cutmixup * add cutmixup to __init__ * test classifier with cutmixup * delete some comments * set mixup_prob default to 1.0 * add cutmixup to classifier * use cutmixup * use cutmixup * fix bugs * test cutmixup * move mixup and cutmix to augment * inherit from BaseAugment * add BaseAugment * inherit from BaseAugment * rename identity.py * add @ * build augment * register module * rename to augment.py * delete cutmixup.py * do not inherit from BaseAugment * add augments * use augments in classifier * prob default to 1.0 * add comments * use augments * use augments * assert sum of augmentation probabilities should equal to 1 * augmentation probabilities equal to 1 * calculate Identity prob * replace xxx with self.xxx * add comments * sync with augments * for BC-breaking * delete useless comments in mixup.py
30 lines
809 B
Python
30 lines
809 B
Python
import torch.nn.functional as F
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from .builder import AUGMENT
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@AUGMENT.register_module(name='Identity')
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class Identity(object):
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"""Change gt_label to one_hot encoding and keep img as the same.
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Args:
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num_classes (int): The number of classes.
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prob (float): MixUp probability. It should be in range [0, 1].
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Default to 1.0
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"""
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def __init__(self, num_classes, prob=1.0):
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super(Identity, self).__init__()
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assert isinstance(num_classes, int)
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assert isinstance(prob, float) and 0.0 <= prob <= 1.0
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self.num_classes = num_classes
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self.prob = prob
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def one_hot(self, gt_label):
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return F.one_hot(gt_label, num_classes=self.num_classes)
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def __call__(self, img, gt_label):
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return img, self.one_hot(gt_label)
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