(noises)
| 13 | |
| 14 | |
| 15 | def noise_regularize(noises): |
| 16 | loss = 0 |
| 17 | |
| 18 | for noise in noises: |
| 19 | size = noise.shape[2] |
| 20 | |
| 21 | while True: |
| 22 | loss = ( |
| 23 | loss |
| 24 | + (noise * torch.roll(noise, shifts=1, dims=3)).mean().pow(2) |
| 25 | + (noise * torch.roll(noise, shifts=1, dims=2)).mean().pow(2) |
| 26 | ) |
| 27 | |
| 28 | if size <= 8: |
| 29 | break |
| 30 | |
| 31 | noise = noise.reshape([-1, 1, size // 2, 2, size // 2, 2]) |
| 32 | noise = noise.mean([3, 5]) |
| 33 | size //= 2 |
| 34 | |
| 35 | return loss |
| 36 | |
| 37 | |
| 38 | def noise_normalize_(noises): |