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import einops
import torch
import torch.nn.functional as F
@torch.no_grad()
def find_flat_region(mask):
device = mask.device
kernel_x = torch.Tensor([[-1, 0, 1], [-1, 0, 1],
[-1, 0, 1]]).unsqueeze(0).unsqueeze(0).to(device)
kernel_y = torch.Tensor([[-1, -1, -1], [0, 0, 0],
[1, 1, 1]]).unsqueeze(0).unsqueeze(0).to(device)
mask_ = F.pad(mask.unsqueeze(0), (1, 1, 1, 1), mode='replicate')
grad_x = torch.nn.functional.conv2d(mask_, kernel_x)
grad_y = torch.nn.functional.conv2d(mask_, kernel_y)
return ((abs(grad_x) + abs(grad_y)) == 0).float()[0]
def numpy2tensor(img):
x0 = torch.from_numpy(img.copy()).float().cuda() / 255.0 * 2.0 - 1.
x0 = torch.stack([x0], dim=0)
return einops.rearrange(x0, 'b h w c -> b c h w').clone()
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