<reponame>BishmoyPaul/lama<gh_stars>1000+
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

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