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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 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 get_conf_trf(mode): |
| if mode == "log": |
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| def conf_trf(x): |
| return x.log() |
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|
| elif mode == "sqrt": |
|
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| def conf_trf(x): |
| return x.sqrt() |
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|
| elif mode == "m1": |
|
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| def conf_trf(x): |
| return x - 1 |
|
|
| elif mode in ("id", "none"): |
|
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| def conf_trf(x): |
| return x |
|
|
| else: |
| raise ValueError(f"bad mode for {mode=}") |
| return conf_trf |
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|
|
| def l2_dist(a, b, weight): |
| return (a - b).square().sum(dim=-1) * weight |
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| def l1_dist(a, b, weight): |
| 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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|
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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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|
