| from torch.nn import functional as F | |
| def d_clip_loss(x, y, use_cosine=False): | |
| x = F.normalize(x, dim=-1) | |
| y = F.normalize(y, dim=-1) | |
| if use_cosine: | |
| distance = 1 - (x @ y.t()).squeeze() | |
| else: | |
| distance = (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2) | |
| return distance | |
| def range_loss(input): | |
| return (input - input.clamp(-1, 1)).pow(2).mean([1, 2, 3]) | |