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| import torch | |
| from kornia import SamplePadding | |
| from kornia.augmentation import RandomAffine, CenterCrop | |
| class FakeFakesGenerator: | |
| def __init__(self, aug_proba=0.5, img_aug_degree=30, img_aug_translate=0.2): | |
| self.grad_aug = RandomAffine(degrees=360, | |
| translate=0.2, | |
| padding_mode=SamplePadding.REFLECTION, | |
| keepdim=False, | |
| p=1) | |
| self.img_aug = RandomAffine(degrees=img_aug_degree, | |
| translate=img_aug_translate, | |
| padding_mode=SamplePadding.REFLECTION, | |
| keepdim=True, | |
| p=1) | |
| self.aug_proba = aug_proba | |
| def __call__(self, input_images, masks): | |
| blend_masks = self._fill_masks_with_gradient(masks) | |
| blend_target = self._make_blend_target(input_images) | |
| result = input_images * (1 - blend_masks) + blend_target * blend_masks | |
| return result, blend_masks | |
| def _make_blend_target(self, input_images): | |
| batch_size = input_images.shape[0] | |
| permuted = input_images[torch.randperm(batch_size)] | |
| augmented = self.img_aug(input_images) | |
| is_aug = (torch.rand(batch_size, device=input_images.device)[:, None, None, None] < self.aug_proba).float() | |
| result = augmented * is_aug + permuted * (1 - is_aug) | |
| return result | |
| def _fill_masks_with_gradient(self, masks): | |
| batch_size, _, height, width = masks.shape | |
| grad = torch.linspace(0, 1, steps=width * 2, device=masks.device, dtype=masks.dtype) \ | |
| .view(1, 1, 1, -1).expand(batch_size, 1, height * 2, width * 2) | |
| grad = self.grad_aug(grad) | |
| grad = CenterCrop((height, width))(grad) | |
| grad *= masks | |
| grad_for_min = grad + (1 - masks) * 10 | |
| grad -= grad_for_min.view(batch_size, -1).min(-1).values[:, None, None, None] | |
| grad /= grad.view(batch_size, -1).max(-1).values[:, None, None, None] + 1e-6 | |
| grad.clamp_(min=0, max=1) | |
| return grad | |