(window_size, sigma)
| 43 | return ((network_output - gt) ** 2).mean() |
| 44 | |
| 45 | def gaussian(window_size, sigma): |
| 46 | gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)]) |
| 47 | return gauss / gauss.sum() |
| 48 | |
| 49 | def create_window(window_size, channel): |
| 50 | _1D_window = gaussian(window_size, 1.5).unsqueeze(1) |