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import os
import collections.abc as container_abcs
import torch
class Logger():
def __init__(self, log_path):
self.log_path = log_path
def log(self, str_to_log):
print(str_to_log)
if not self.log_path is None:
with open(self.log_path, 'a') as f:
f.write(str_to_log + '\n')
f.flush()
def check_imgs(adv, x, norm):
delta = (adv - x).view(adv.shape[0], -1)
if norm == 'Linf':
res = delta.abs().max(dim=1)[0]
elif norm == 'L2':
res = (delta ** 2).sum(dim=1).sqrt()
elif norm == 'L1':
res = delta.abs().sum(dim=1)
str_det = 'max {} pert: {:.5f}, nan in imgs: {}, max in imgs: {:.5f}, min in imgs: {:.5f}'.format(
norm, res.max(), (adv != adv).sum(), adv.max(), adv.min())
print(str_det)
return str_det
def L1_norm(x, keepdim=False):
z = x.abs().view(x.shape[0], -1).sum(-1)
if keepdim:
z = z.view(-1, *[1]*(len(x.shape) - 1))
return z
def L2_norm(x, keepdim=False):
z = (x ** 2).view(x.shape[0], -1).sum(-1).sqrt()
if keepdim:
z = z.view(-1, *[1]*(len(x.shape) - 1))
return z
def L0_norm(x):
return (x != 0.).view(x.shape[0], -1).sum(-1)
def makedir(path):
if not os.path.exists(path):
os.makedirs(path)
def zero_gradients(x):
if isinstance(x, torch.Tensor):
if x.grad is not None:
x.grad.detach_()
x.grad.zero_()
elif isinstance(x, container_abcs.Iterable):
for elem in x:
zero_gradients(elem)
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