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| import torch |
| import torch.nn as nn |
| import numpy as np |
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| def edge_str(i, j): |
| return f'{i}_{j}' |
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| def i_j_ij(ij): |
| return edge_str(*ij), ij |
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| def edge_conf(conf_i, conf_j, edge): |
| return float(conf_i[edge].mean() * conf_j[edge].mean()) |
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| 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} |
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| def NoGradParamDict(x): |
| assert isinstance(x, dict) |
| return nn.ParameterDict(x).requires_grad_(False) |
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| def GradParamDict(x): |
| assert isinstance(x, dict) |
| return nn.ParameterDict(x).requires_grad_(True) |
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| 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 |
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| def conf_trf_log(x): |
| return x.log() |
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| def conf_trf_sqrt(x): |
| return x.sqrt() |
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| def conf_trf_m1(x): |
| return x - 1 |
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| def conf_trf_id(x): |
| return x |
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| def get_conf_trf(mode): |
| if mode == 'log': |
| return conf_trf_log |
| elif mode == 'sqrt': |
| return conf_trf_sqrt |
| elif mode == 'm1': |
| return conf_trf_m1 |
| elif mode in ('id', 'none'): |
| return conf_trf_id |
| else: |
| raise ValueError(f"bad mode {mode=}") |
|
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| def l2_dist(a, b, weight=None): |
| if weight == None: |
| return (a - b).square().sum(dim=-1) |
| return ((a - b).square().sum(dim=-1) * weight) |
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| def l1_dist(a, b, weight=None): |
| if weight == None: |
| return (a - b).norm(dim=-1) |
| return ((a - b).norm(dim=-1) * weight) |
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| ALL_DISTS = dict(l1=l1_dist, l2=l2_dist) |
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| def signed_log1p(x): |
| sign = torch.sign(x) |
| return sign * torch.log1p(torch.abs(x)) |
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| def signed_expm1(x): |
| sign = torch.sign(x) |
| return sign * torch.expm1(torch.abs(x)) |
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| 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 |
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| def linear_schedule(t, lr_start, lr_end): |
| assert 0 <= t <= 1 |
| return lr_start + (lr_end - lr_start) * t |
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