| |
|
|
| import numpy as np |
| from scipy.optimize import least_squares |
| import torch |
|
|
| def align_scale_shift(pred, target, clip_max): |
| mask = (target > 0) & (target < clip_max) |
| if mask.sum() > 10: |
| target_mask = target[mask] |
| pred_mask = pred[mask] |
| scale, shift = np.polyfit(pred_mask, target_mask, deg=1) |
| return scale, shift |
| else: |
| return 1, 0 |
|
|
| def align_scale(pred: torch.tensor, target: torch.tensor): |
| mask = target > 0 |
| if torch.sum(mask) > 10: |
| scale = torch.median(target[mask]) / (torch.median(pred[mask]) + 1e-8) |
| else: |
| scale = 1 |
| pred_scale = pred * scale |
| return pred_scale, scale |
|
|
| def align_shift(pred: torch.tensor, target: torch.tensor): |
| mask = target > 0 |
| if torch.sum(mask) > 10: |
| shift = torch.median(target[mask]) - (torch.median(pred[mask]) + 1e-8) |
| else: |
| shift = 0 |
| pred_shift = pred + shift |
| return pred_shift, shift |
