-
Notifications
You must be signed in to change notification settings - Fork 6
/
Copy pathutils.py
31 lines (25 loc) · 1.03 KB
/
utils.py
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
import numpy as np
from scipy.linalg import qr
import torch
def test_filter_sparsity(conv_weights):
for name, W in conv_weights:
zero = sum(w.nonzero().size(0) == 0 for w in W)
print("filter sparsity of layer {} is {}".format(name, zero/W.size(0)))
def qr_null(A, tol=None):
Q, R, _ = qr(A.T, mode='full', pivoting=True)
tol = np.finfo(R.dtype).eps if tol is None else tol
rnk = min(A.shape) - np.abs(np.diag(R))[::-1].searchsorted(tol)
return Q[:, rnk:].conj()
def accuracy(output, target, topk=(1, )):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res