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utils.py
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utils.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.distributions import laplace
from torch.distributions import uniform
from torch.nn.modules.loss import _Loss
def replicate_input(x):
"""
Clone the input tensor x.
"""
return x.detach().clone()
def replicate_input_withgrad(x):
"""
Clone the input tensor x and set requires_grad=True.
"""
return x.detach().clone().requires_grad_()
def calc_l2distsq(x, y):
"""
Calculate L2 distance between tensors x and y.
"""
d = (x - y)**2
return d.view(d.shape[0], -1).sum(dim=1)
def clamp(input, min=None, max=None):
"""
Clamp a tensor by its minimun and maximun values.
"""
ndim = input.ndimension()
if min is None:
pass
elif isinstance(min, (float, int)):
input = torch.clamp(input, min=min)
elif isinstance(min, torch.Tensor):
if min.ndimension() == ndim - 1 and min.shape == input.shape[1:]:
input = torch.max(input, min.view(1, *min.shape))
else:
assert min.shape == input.shape
input = torch.max(input, min)
else:
raise ValueError("min can only be None | float | torch.Tensor")
if max is None:
pass
elif isinstance(max, (float, int)):
input = torch.clamp(input, max=max)
elif isinstance(max, torch.Tensor):
if max.ndimension() == ndim - 1 and max.shape == input.shape[1:]:
input = torch.min(input, max.view(1, *max.shape))
else:
assert max.shape == input.shape
input = torch.min(input, max)
else:
raise ValueError("max can only be None | float | torch.Tensor")
return input
def _batch_multiply_tensor_by_vector(vector, batch_tensor):
"""Equivalent to the following.
for ii in range(len(vector)):
batch_tensor.data[ii] *= vector[ii]
return batch_tensor
"""
return (
batch_tensor.transpose(0, -1) * vector).transpose(0, -1).contiguous()
def _batch_clamp_tensor_by_vector(vector, batch_tensor):
"""Equivalent to the following.
for ii in range(len(vector)):
batch_tensor[ii] = clamp(
batch_tensor[ii], -vector[ii], vector[ii])
"""
return torch.min(
torch.max(batch_tensor.transpose(0, -1), -vector), vector
).transpose(0, -1).contiguous()
def batch_multiply(float_or_vector, tensor):
"""
Multpliy a batch of tensors with a float or vector.
"""
if isinstance(float_or_vector, torch.Tensor):
assert len(float_or_vector) == len(tensor)
tensor = _batch_multiply_tensor_by_vector(float_or_vector, tensor)
elif isinstance(float_or_vector, float):
tensor *= float_or_vector
else:
raise TypeError("Value has to be float or torch.Tensor")
return tensor
def batch_clamp(float_or_vector, tensor):
"""
Clamp a batch of tensors.
"""
if isinstance(float_or_vector, torch.Tensor):
assert len(float_or_vector) == len(tensor)
tensor = _batch_clamp_tensor_by_vector(float_or_vector, tensor)
return tensor
elif isinstance(float_or_vector, float):
tensor = clamp(tensor, -float_or_vector, float_or_vector)
else:
raise TypeError("Value has to be float or torch.Tensor")
return tensor
def _get_norm_batch(x, p):
"""
Returns the Lp norm of batch x.
"""
batch_size = x.size(0)
return x.abs().pow(p).view(batch_size, -1).sum(dim=1).pow(1. / p)
def _thresh_by_magnitude(theta, x):
"""
Threshold by magnitude.
"""
return torch.relu(torch.abs(x) - theta) * x.sign()
def clamp_by_pnorm(x, p, r):
"""
Clamp tensor by its norm.
"""
assert isinstance(p, float) or isinstance(p, int)
norm = _get_norm_batch(x, p)
if isinstance(r, torch.Tensor):
assert norm.size() == r.size()
else:
assert isinstance(r, float)
factor = torch.min(r / norm, torch.ones_like(norm))
return batch_multiply(factor, x)
def is_float_or_torch_tensor(x):
"""
Return whether input x is a float or a torch.Tensor.
"""
return isinstance(x, torch.Tensor) or isinstance(x, float)
def normalize_by_pnorm(x, p=2, small_constant=1e-6):
"""
Normalize gradients for gradient (not gradient sign) attacks.
Arguments:
x (torch.Tensor): tensor containing the gradients on the input.
p (int): (optional) order of the norm for the normalization (1 or 2).
small_constant (float): (optional) to avoid dividing by zero.
Returns:
normalized gradients.
"""
assert isinstance(p, float) or isinstance(p, int)
norm = _get_norm_batch(x, p)
norm = torch.max(norm, torch.ones_like(norm) * small_constant)
return batch_multiply(1. / norm, x)
def rand_init_delta(delta, x, ord, eps, clip_min, clip_max):
"""
Randomly initialize the perturbation.
"""
if isinstance(eps, torch.Tensor):
assert len(eps) == len(delta)
if ord == np.inf:
delta.data.uniform_(-1, 1)
delta.data = batch_multiply(eps, delta.data)
elif ord == 2:
delta.data.uniform_(clip_min, clip_max)
delta.data = delta.data - x
delta.data = clamp_by_pnorm(delta.data, ord, eps)
elif ord == 1:
ini = laplace.Laplace(
loc=delta.new_tensor(0), scale=delta.new_tensor(1))
delta.data = ini.sample(delta.data.shape)
delta.data = normalize_by_pnorm(delta.data, p=1)
ray = uniform.Uniform(0, eps).sample()
delta.data *= ray
delta.data = clamp(x.data + delta.data, clip_min, clip_max) - x.data
else:
error = "Only ord = inf, ord = 1 and ord = 2 have been implemented"
raise NotImplementedError(error)
delta.data = clamp(
x + delta.data, min=clip_min, max=clip_max) - x
return delta.data
def CWLoss(output, target, confidence=0):
"""
CW loss (Marging loss).
"""
num_classes = output.shape[-1]
target = target.data
target_onehot = torch.zeros(target.size() + (num_classes,))
target_onehot = target_onehot.cuda()
target_onehot.scatter_(1, target.unsqueeze(1), 1.)
target_var = Variable(target_onehot, requires_grad=False)
real = (target_var * output).sum(1)
other = ((1. - target_var) * output - target_var * 10000.).max(1)[0]
loss = - torch.clamp(real - other + confidence, min=0.)
loss = torch.sum(loss)
return loss