Created by: Edwardwaw

class CutmixImage(BaseOperator): def init(self, alpha=1.5, beta=1.5): """ CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features, see https://https://arxiv.org/abs/1905.04899 Cutmix image and gt_bbbox/gt_score Args: alpha (float): alpha parameter of beta distribute beta (float): beta parameter of beta distribute """ super(CutmixImage, self).init() self.alpha = alpha self.beta = beta if self.alpha <= 0.0: raise ValueError("alpha shold be positive in {}".format(self)) if self.beta <= 0.0: raise ValueError("beta shold be positive in {}".format(self))

def _rand_bbox(self, img1, img2, factor):
    """ _rand_bbox """
    h = max(img1.shape[0], img2.shape[0])
    w = max(img1.shape[1], img2.shape[1])
    cut_rat = np.sqrt(1. - factor)

    cut_w = np.int(w * cut_rat)
    cut_h = np.int(h * cut_rat)

    # uniform
    cx = np.random.randint(w)
    cy = np.random.randint(h)

    bbx1 = np.clip(cx - cut_w // 2, 0, w)
    bby1 = np.clip(cy - cut_h // 2, 0, h)
    bbx2 = np.clip(cx + cut_w // 2, 0, w)
    bby2 = np.clip(cy + cut_h // 2, 0, h)

    img_1 = np.zeros((h, w, img1.shape[2]), 'float32')
    img_1[:img1.shape[0], :img1.shape[1], :] = img1.astype('float32')
    img_2 = np.zeros((h, w, img2.shape[2]), 'float32')
    img_2[:img2.shape[0], :img2.shape[1], :] = img2.astype('float32')
    img_1[bby1:bby2, bbx1:bbx2, :] = img_2[bby1:bby2, bbx1:bbx2, :]

    print('debug', bbx1, bby1, bbx2, bby2)
    print('debug', img1.shape, img2.shape)

    return img_1

def __call__(self, sample, context=None):
    if 'cutmix' not in sample:
        return sample
    factor = np.random.beta(self.alpha, self.beta)
    factor = max(0.0, min(1.0, factor))
    if factor >= 1.0:
        sample.pop('cutmix')
        return sample
    if factor <= 0.0:
        return sample['cutmix']
    img1 = sample['image']
    img2 = sample['cutmix']['image']
    img = self._rand_bbox(img1, img2, factor)

    gt_bbox1 = sample['gt_bbox']
    gt_bbox2 = sample['cutmix']['gt_bbox']
    gt_bbox = np.concatenate((gt_bbox1, gt_bbox2), axis=0)
    gt_class1 = sample['gt_class']
    gt_class2 = sample['cutmix']['gt_class']
    gt_class = np.concatenate((gt_class1, gt_class2), axis=0)
    gt_score1 = sample['gt_score']
    gt_score2 = sample['cutmix']['gt_score']
    gt_score = np.concatenate(
        (gt_score1 * factor, gt_score2 * (1. - factor)), axis=0)
    sample['image'] = img
    sample['gt_bbox'] = gt_bbox
    sample['gt_score'] = gt_score
    sample['gt_class'] = gt_class
    sample['h'] = img.shape[0]
    sample['w'] = img.shape[1]
    sample.pop('cutmix')
    return sample

请问当前版本的repo中,cutmix的效果是否经过检验？直觉上来说该部分对gt score gt box的处理似乎有些不妥