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mmcls.models.utils.batch_augments.cutmix 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple

import numpy as np
import torch

from mmcls.registry import BATCH_AUGMENTS
from .mixup import Mixup


[文档]@BATCH_AUGMENTS.register_module() class CutMix(Mixup): r"""CutMix batch agumentation. CutMix is a method to improve the network's generalization capability. It's proposed in `CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features <https://arxiv.org/abs/1905.04899>` With this method, patches are cut and pasted among training images where the ground truth labels are also mixed proportionally to the area of the patches. 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:`Mixup`. 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. .. note :: If the ``cutmix_minmax`` is None, how to generate the bounding-box of patches according to the ``alpha``? First, generate a :math:`\lambda`, details can be found in :class:`Mixup`. And then, the area ratio of the bounding-box is calculated by: .. math:: \text{ratio} = \sqrt{1-\lambda} """ def __init__(self, alpha: float, cutmix_minmax: Optional[List[float]] = None, correct_lam: bool = True): super().__init__(alpha=alpha) self.cutmix_minmax = cutmix_minmax self.correct_lam = correct_lam
[文档] def rand_bbox_minmax( self, img_shape: Tuple[int, int], count: Optional[int] = None) -> Tuple[int, int, int, int]: """Min-Max CutMix bounding-box Inspired by Darknet cutmix implementation. It generates a random rectangular bbox based on min/max percent values applied to each dimension of the input image. Typical defaults for minmax are usually in the .2-.3 for min and .8-.9 range for max. Args: img_shape (tuple): Image shape as tuple count (int, optional): Number of bbox to generate. Defaults to None """ assert len(self.cutmix_minmax) == 2 img_h, img_w = img_shape cut_h = np.random.randint( int(img_h * self.cutmix_minmax[0]), int(img_h * self.cutmix_minmax[1]), size=count) cut_w = np.random.randint( int(img_w * self.cutmix_minmax[0]), int(img_w * self.cutmix_minmax[1]), size=count) yl = np.random.randint(0, img_h - cut_h, size=count) xl = np.random.randint(0, img_w - cut_w, size=count) yu = yl + cut_h xu = xl + cut_w return yl, yu, xl, xu
[文档] def rand_bbox(self, img_shape: Tuple[int, int], lam: float, margin: float = 0., count: Optional[int] = None) -> Tuple[int, int, int, int]: """Standard CutMix bounding-box that generates a random square bbox based on lambda value. This implementation includes support for enforcing a border margin as percent of bbox dimensions. Args: img_shape (tuple): Image shape as tuple lam (float): Cutmix lambda value margin (float): Percentage of bbox dimension to enforce as margin (reduce amount of box outside image). Defaults to 0. count (int, optional): Number of bbox to generate. Defaults to None """ ratio = np.sqrt(1 - lam) img_h, img_w = img_shape cut_h, cut_w = int(img_h * ratio), int(img_w * ratio) margin_y, margin_x = int(margin * cut_h), int(margin * cut_w) cy = np.random.randint(0 + margin_y, img_h - margin_y, size=count) cx = np.random.randint(0 + margin_x, img_w - margin_x, size=count) yl = np.clip(cy - cut_h // 2, 0, img_h) yh = np.clip(cy + cut_h // 2, 0, img_h) xl = np.clip(cx - cut_w // 2, 0, img_w) xh = np.clip(cx + cut_w // 2, 0, img_w) return yl, yh, xl, xh
[文档] def cutmix_bbox_and_lam(self, img_shape: Tuple[int, int], lam: float, count: Optional[int] = None) -> tuple: """Generate bbox and apply lambda correction. Args: img_shape (tuple): Image shape as tuple lam (float): Cutmix lambda value count (int, optional): Number of bbox to generate. Defaults to None """ if self.cutmix_minmax is not None: yl, yu, xl, xu = self.rand_bbox_minmax(img_shape, count=count) else: yl, yu, xl, xu = self.rand_bbox(img_shape, lam, count=count) if self.correct_lam or self.cutmix_minmax is not None: bbox_area = (yu - yl) * (xu - xl) lam = 1. - bbox_area / float(img_shape[0] * img_shape[1]) return (yl, yu, xl, xu), lam
[文档] def mix(self, batch_inputs: torch.Tensor, batch_scores: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: """Mix the batch inputs and batch one-hot format ground truth. Args: batch_inputs (Tensor): A batch of images tensor in the shape of ``(N, C, H, W)``. batch_scores (Tensor): A batch of one-hot format labels in the shape of ``(N, num_classes)``. Returns: Tuple[Tensor, Tensor): The mixed inputs and labels. """ lam = np.random.beta(self.alpha, self.alpha) batch_size = batch_inputs.size(0) img_shape = batch_inputs.shape[-2:] index = torch.randperm(batch_size) (y1, y2, x1, x2), lam = self.cutmix_bbox_and_lam(img_shape, lam) batch_inputs[:, :, y1:y2, x1:x2] = batch_inputs[index, :, y1:y2, x1:x2] mixed_scores = lam * batch_scores + (1 - lam) * batch_scores[index, :] return batch_inputs, mixed_scores
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