(window_size, sigma)
| 22 | return ((network_output - gt) ** 2).mean() |
| 23 | |
| 24 | def gaussian(window_size, sigma): |
| 25 | gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)]) |
| 26 | return gauss / gauss.sum() |
| 27 | |
| 28 | def create_window(window_size, channel): |
| 29 | _1D_window = gaussian(window_size, 1.5).unsqueeze(1) |