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| # Copyright (C) 2024-present Naver Corporation. All rights reserved. | |
| # Licensed under CC BY-NC-SA 4.0 (non-commercial use only). | |
| # | |
| # -------------------------------------------------------- | |
| # utility functions for global alignment | |
| # -------------------------------------------------------- | |
| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| def edge_str(i, j): | |
| return f'{i}_{j}' | |
| def i_j_ij(ij): | |
| return edge_str(*ij), ij | |
| def edge_conf(conf_i, conf_j, edge): | |
| return float(conf_i[edge].mean() * conf_j[edge].mean()) | |
| def edge_conf2(conf_i, conf_j, edge): | |
| return float(conf_j[edge].mean()) | |
| def compute_edge_scores(edges, conf_i, conf_j): | |
| return {(i, j): edge_conf(conf_i, conf_j, e) for e, (i, j) in edges} | |
| def compute_edge_scores2(edges, conf_i, conf_j): | |
| return {(i, j): edge_conf2(conf_i, conf_j, e) for e, (i, j) in edges} | |
| def NoGradParamDict(x): | |
| assert isinstance(x, dict) | |
| return nn.ParameterDict(x).requires_grad_(False) | |
| def get_imshapes(edges, pred_i, pred_j): | |
| n_imgs = max(max(e) for e in edges) + 1 | |
| imshapes = [None] * n_imgs | |
| for e, (i, j) in enumerate(edges): | |
| shape_i = tuple(pred_i[e].shape[0:2]) | |
| shape_j = tuple(pred_j[e].shape[0:2]) | |
| if imshapes[i]: | |
| assert imshapes[i] == shape_i, f'incorrect shape for image {i}' | |
| if imshapes[j]: | |
| assert imshapes[j] == shape_j, f'incorrect shape for image {j}' | |
| imshapes[i] = shape_i | |
| imshapes[j] = shape_j | |
| return imshapes | |
| def get_conf_trf(mode): | |
| if mode == 'log': | |
| def conf_trf(x): return x.log() | |
| elif mode == 'sqrt': | |
| def conf_trf(x): return x.sqrt() | |
| elif mode == 'm1': | |
| def conf_trf(x): return x-1 | |
| elif mode in ('id', 'none'): | |
| def conf_trf(x): return x | |
| else: | |
| raise ValueError(f'bad mode for {mode=}') | |
| return conf_trf | |
| def l2_dist(a, b, weight): | |
| return ((a - b).square().sum(dim=-1) * weight) | |
| def l1_dist(a, b, weight): | |
| return ((a - b).norm(dim=-1) * weight) | |
| ALL_DISTS = dict(l1=l1_dist, l2=l2_dist) | |
| def signed_log1p(x): | |
| sign = torch.sign(x) | |
| return sign * torch.log1p(torch.abs(x)) | |
| def signed_expm1(x): | |
| sign = torch.sign(x) | |
| return sign * torch.expm1(torch.abs(x)) | |
| def cosine_schedule(t, lr_start, lr_end): | |
| assert 0 <= t <= 1 | |
| return lr_end + (lr_start - lr_end) * (1+np.cos(t * np.pi))/2 | |
| def linear_schedule(t, lr_start, lr_end): | |
| assert 0 <= t <= 1 | |
| return lr_start + (lr_end - lr_start) * t | |