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Source code for mmcls.models.utils.augment.resizemix

# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.nn.functional as F

from mmcls.models.utils.augment.builder import AUGMENT
from .cutmix import BatchCutMixLayer
from .utils import one_hot_encoding


[docs]@AUGMENT.register_module(name='BatchResizeMix') class BatchResizeMixLayer(BatchCutMixLayer): r"""ResizeMix Random Paste layer for a batch of data. The ResizeMix will resize an image to a small patch and paste it on another image. It's proposed in `ResizeMix: Mixing Data with Preserved Object Information and True Labels <https://arxiv.org/abs/2012.11101>`_ Args: alpha (float): Parameters for Beta distribution to generate the mixing ratio. It should be a positive number. More details can be found in :class:`BatchMixupLayer`. num_classes (int): The number of classes. lam_min(float): The minimum value of lam. Defaults to 0.1. lam_max(float): The maximum value of lam. Defaults to 0.8. interpolation (str): algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area'. Default to 'bilinear'. prob (float): The probability to execute resizemix. It should be in range [0, 1]. Defaults to 1.0. cutmix_minmax (List[float], optional): The min/max area ratio of the patches. If not None, the bounding-box of patches is uniform sampled within this ratio range, and the ``alpha`` will be ignored. Otherwise, the bounding-box is generated according to the ``alpha``. Defaults to None. correct_lam (bool): Whether to apply lambda correction when cutmix bbox clipped by image borders. Defaults to True **kwargs: Any other parameters accpeted by :class:`BatchCutMixLayer`. Note: The :math:`\lambda` (``lam``) is the mixing ratio. It's a random variable which follows :math:`Beta(\alpha, \alpha)` and is mapped to the range [``lam_min``, ``lam_max``]. .. math:: \lambda = \frac{Beta(\alpha, \alpha)} {\lambda_{max} - \lambda_{min}} + \lambda_{min} And the resize ratio of source images is calculated by :math:`\lambda`: .. math:: \text{ratio} = \sqrt{1-\lambda} """ def __init__(self, alpha, num_classes, lam_min: float = 0.1, lam_max: float = 0.8, interpolation='bilinear', prob=1.0, cutmix_minmax=None, correct_lam=True, **kwargs): super(BatchResizeMixLayer, self).__init__( alpha=alpha, num_classes=num_classes, prob=prob, cutmix_minmax=cutmix_minmax, correct_lam=correct_lam, **kwargs) self.lam_min = lam_min self.lam_max = lam_max self.interpolation = interpolation def cutmix(self, img, gt_label): one_hot_gt_label = one_hot_encoding(gt_label, self.num_classes) lam = np.random.beta(self.alpha, self.alpha) lam = lam * (self.lam_max - self.lam_min) + self.lam_min batch_size = img.size(0) index = torch.randperm(batch_size) (bby1, bby2, bbx1, bbx2), lam = self.cutmix_bbox_and_lam(img.shape, lam) img[:, :, bby1:bby2, bbx1:bbx2] = F.interpolate( img[index], size=(bby2 - bby1, bbx2 - bbx1), mode=self.interpolation) mixed_gt_label = lam * one_hot_gt_label + ( 1 - lam) * one_hot_gt_label[index, :] return img, mixed_gt_label
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