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84a242a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | import torch
import numpy as np
def random_so3(pos, device):
q = torch.randn(4, device=device)
q = q / q.norm()
w, x, y, z = q
R = torch.tensor([
[1-2*(y*y+z*z), 2*(x*y - z*w), 2*(x*z + y*w)],
[2*(x*y + z*w), 1-2*(x*x+z*z), 2*(y*z - x*w)],
[2*(x*z - y*w), 2*(y*z + x*w), 1-2*(x*x+y*y)]
], device=device)
pos = pos @ R.t()
return pos
def grad_norm(model):
total_norm = 0.0
for p in model.parameters():
if p.grad is not None:
param_norm = p.grad.data.norm(2)
total_norm += param_norm.item() ** 2
return total_norm ** 0.5
def linear_CKA(X, Y, eps=1e-8):
# X,Y: [N, D] zero-centered
X = X - X.mean(0, keepdim=True)
Y = Y - Y.mean(0, keepdim=True)
K = X @ X.t()
L = Y @ Y.t()
hsic = (K * L).sum()
nX = torch.linalg.norm(K)
nY = torch.linalg.norm(L)
return (hsic / (nX*nY + eps)).item()
def mean_pairwise_cosine(X):
# X: [N, D] node embeddings for one graph
X = F.normalize(X, dim=-1)
S = X @ X.t()
n = S.size(0)
return (S.sum() - n) / (n*(n-1) + 1e-8)
@torch.no_grad()
def oversmoothing_curve(layer_outputs):
# layer_outputs: list of [N,D] (per layer) for the SAME batch/graph
return [mean_pairwise_cosine(H).item() for H in layer_outputs]
def reliability_curve(scores, labels, n_bins=10):
scores = np.asarray(scores); labels = np.asarray(labels).astype(int)
bins = np.linspace(0, 1, n_bins+1)
ece = 0.0
bin_stats = []
for i in range(n_bins):
lo, hi = bins[i], bins[i+1]
idx = (scores >= lo) & (scores < hi) if i < n_bins-1 else (scores >= lo) & (scores <= hi)
if idx.sum() == 0:
bin_stats.append(( (lo+hi)/2, np.nan, np.nan, 0 ))
continue
conf = scores[idx].mean()
acc = labels[idx].mean()
ece += (idx.mean()) * abs(acc - conf)
bin_stats.append(((lo+hi)/2, conf, acc, idx.sum()))
return ece, bin_stats |