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cifar10_defense_rebAA.py
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cifar10_defense_rebAA.py
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import argparse
import logging
''''If you see bad robustness, it is due to use the wrong normalization in L26-32'''
import sys
import math, time
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import os
from learning.wideresnet import WideResNet, WideResNetBD, WideResNetMed_SSL, WRN34_out_branch
from learning.preactresnet import PreActResNet18Mhead, Res18_out3_model,Res18_out4_model,Res18_out5_model,Res18_out6_model
from data.gaussian_blur import GaussianLayer
from utils import *
mu = torch.tensor(cifar10_mean).view(3,1,1).cuda()
std = torch.tensor(cifar10_std).view(3,1,1).cuda()
# def normalize(X):
# return (X - mu)/std
def normalize(X):
return X
def normal_guassian_normalize(T):
return (T-T.mean()) / T.std()
upper_limit, lower_limit = 1,0
def clamp(X, lower_limit, upper_limit):
return torch.max(torch.min(X, upper_limit), lower_limit)
class Batches():
def __init__(self, dataset, batch_size, shuffle, set_random_choices=False, num_workers=0, drop_last=False):
self.dataset = dataset
self.batch_size = batch_size
self.set_random_choices = set_random_choices
self.dataloader = torch.utils.data.DataLoader(
dataset, batch_size=batch_size, num_workers=num_workers, pin_memory=True, shuffle=shuffle, drop_last=drop_last
)
def __iter__(self):
if self.set_random_choices:
self.dataset.set_random_choices()
return ({'input': x.to(device).float(), 'target': y.to(device).long()} for (x,y) in self.dataloader)
def __len__(self):
return len(self.dataloader)
def attack_constrastive_Mhead(model, model_ssl, scripted_transforms, criterion, X, y, epsilon, alpha, attack_iters, restarts,
norm, early_stop=False,
mixup=False, y_a=None, y_b=None, lam=None, Ltype=None, reverse=False, n_views=2):
delta = torch.zeros_like(X).cuda()
if norm == "l_inf":
delta.uniform_(-epsilon, epsilon)
elif norm == "l_2":
delta.normal_()
d_flat = delta.view(delta.size(0),-1)
n = d_flat.norm(p=2,dim=1).view(delta.size(0),1,1,1)
r = torch.zeros_like(n).uniform_(0, 1)
delta *= r/n*epsilon
elif norm == 'l_1':
pass
else:
raise ValueError
delta = clamp(delta, lower_limit-X, upper_limit-X)
delta.requires_grad = True
for _ in range(attack_iters):
new_x = X + delta
# import pdb; pdb.set_trace()
# TODO: here the neg sample is fixed, we can also try random neg sample to enlarge and diversify
loss = -calculate_contrastive_Mhead_loss(new_x, scripted_transforms, model, criterion,
model_ssl, no_grad=False, n_views=n_views)
loss.backward()
grad = delta.grad.detach()
d = delta
g = grad
x = X
if norm == "l_inf":
d = torch.clamp(d + alpha * torch.sign(g), min=-epsilon, max=epsilon)
elif norm == "l_2":
g_norm = torch.norm(g.view(g.shape[0],-1),dim=1).view(-1,1,1,1)
scaled_g = g/(g_norm + 1e-10)
d = (d + scaled_g*alpha).view(d.size(0),-1).renorm(p=2,dim=0,maxnorm=epsilon).view_as(d)
elif norm == "l_1":
g_norm = torch.sum(torch.abs(g.view(g.shape[0], -1)), dim=1).view(-1, 1, 1, 1)
scaled_g = g / (g_norm + 1e-10)
d = (d + scaled_g * alpha).view(d.size(0), -1).renorm(p=1, dim=0, maxnorm=epsilon).view_as(d)
d = clamp(d, lower_limit - x, upper_limit - x)
delta.data = d
delta.grad.zero_()
max_delta = delta.detach()
return max_delta
def constrastive_loss_func(contrastive_head, criterion, bs, n_views):
features = F.normalize(contrastive_head, dim=1)
labels = torch.cat([torch.arange(bs) for i in range(n_views)], dim=0)
labels = (labels.unsqueeze(0) == labels.unsqueeze(1)).float()
labels = labels.cuda()
similarity_matrix = torch.matmul(features, features.T)
mask = torch.eye(labels.shape[0], dtype=torch.bool).cuda()
labels = labels[~mask].view(labels.shape[0], -1)
similarity_matrix = similarity_matrix[~mask].view(similarity_matrix.shape[0], -1)
# select and combine multiple positives
positives = similarity_matrix[labels.bool()].view(labels.shape[0], -1)
# select only the negatives the negatives
negatives = similarity_matrix[~labels.bool()].view(similarity_matrix.shape[0], -1)
logits = torch.cat([positives, negatives], dim=1)
labels = torch.zeros(logits.shape[0], dtype=torch.long).cuda()
temperature = 0.2
logits = logits / temperature
xcontrast_loss = criterion(logits, labels)
correct = (logits.max(1)[1] == labels).sum().item()
return xcontrast_loss, correct
def calculate_contrastive_Mhead_loss(X, scripted_transforms, model, criterion, submodel, no_grad=True, n_views=2):
new_x = X
bs = X.size(0)
if n_views == 2:
X_transformed1 = scripted_transforms(new_x)
X_transformed2 = scripted_transforms(new_x)
X_constrastive = torch.cat([X_transformed1, X_transformed2], dim=0)
elif n_views ==4:
X_transformed1 = scripted_transforms(new_x)
X_transformed2 = scripted_transforms(new_x)
X_transformed3 = scripted_transforms(new_x)
X_transformed4 = scripted_transforms(new_x)
X_constrastive = torch.cat([X_transformed1, X_transformed2, X_transformed3, X_transformed4], dim=0)
if no_grad:
with torch.no_grad():
_, out = model(normalize(X_constrastive))
else:
_, out = model(normalize(X_constrastive))
# import pdb; pdb.set_trace()
output = submodel(out)
closs, acc = constrastive_loss_func(output, criterion, bs, n_views)
return closs
def adaptive_attack_pgd(model, X, y, c_head_model, scripted_transforms, criterion, epsilon, alpha, attack_iters, restarts,
norm, early_stop=False,
mixup=False, y_a=None, y_b=None, lam=None, n_views=2, lambda_S=1):
max_loss = torch.zeros(y.shape[0]).cuda()
max_delta = torch.zeros_like(X).cuda()
for _ in range(restarts):
delta = torch.zeros_like(X).cuda()
if norm == "l_inf":
delta.uniform_(-epsilon, epsilon)
elif norm == "l_2":
delta.normal_()
d_flat = delta.view(delta.size(0),-1)
n = d_flat.norm(p=2,dim=1).view(delta.size(0),1,1,1)
r = torch.zeros_like(n).uniform_(0, 1)
delta *= r/n*epsilon
else:
raise ValueError
delta = clamp(delta, lower_limit-X, upper_limit-X)
delta.requires_grad = True
for _ in range(attack_iters):
output, _ = model(normalize(X + delta))
if early_stop:
index = torch.where(output.max(1)[1] == y)[0]
else:
index = slice(None,None,None)
if not isinstance(index, slice) and len(index) == 0:
break
loss_classification = F.cross_entropy(output, y)
loss_ada = -calculate_contrastive_Mhead_loss(X+delta, scripted_transforms, model, criterion,
c_head_model, no_grad=False, n_views=n_views)
loss = loss_classification + loss_ada * lambda_S
loss.backward()
grad = delta.grad.detach()
d = delta[index, :, :, :]
g = grad[index, :, :, :]
x = X[index, :, :, :]
if norm == "l_inf":
d = torch.clamp(d + alpha * torch.sign(g), min=-epsilon, max=epsilon)
elif norm == "l_2":
g_norm = torch.norm(g.view(g.shape[0],-1),dim=1).view(-1,1,1,1)
scaled_g = g/(g_norm + 1e-10)
d = (d + scaled_g*alpha).view(d.size(0),-1).renorm(p=2,dim=0,maxnorm=epsilon).view_as(d)
d = clamp(d, lower_limit - x, upper_limit - x)
delta.data[index, :, :, :] = d
delta.grad.zero_()
if mixup:
criterion = nn.CrossEntropyLoss(reduction='none')
all_loss = mixup_criterion(criterion, model(normalize(X+delta)), y_a, y_b, lam)
else:
all_loss = F.cross_entropy(model(normalize(X+delta))[0], y, reduction='none')
max_delta[all_loss >= max_loss] = delta.detach()[all_loss >= max_loss]
max_loss = torch.max(max_loss, all_loss)
return max_delta
def attack_BIM(model, X, y, epsilon, alpha, attack_iters, restarts,
norm, early_stop=False,
mixup=False, y_a=None, y_b=None, lam=None):
max_loss = torch.zeros(y.shape[0]).cuda()
max_delta = torch.zeros_like(X).cuda()
for _ in range(restarts):
delta = torch.zeros_like(X).cuda()
# if norm == "l_inf":
# delta.uniform_(-epsilon, epsilon)
# elif norm == "l_2":
# delta.normal_()
# d_flat = delta.view(delta.size(0),-1)
# n = d_flat.norm(p=2,dim=1).view(delta.size(0),1,1,1)
# r = torch.zeros_like(n).uniform_(0, 1)
# delta *= r/n*epsilon
# else:
# raise ValueError
delta = clamp(delta, lower_limit-X, upper_limit-X)
delta.requires_grad = True
for _ in range(attack_iters):
output, _ = model(normalize(X + delta))
if early_stop:
index = torch.where(output.max(1)[1] == y)[0]
else:
index = slice(None,None,None)
if not isinstance(index, slice) and len(index) == 0:
break
if mixup:
criterion = nn.CrossEntropyLoss()
# loss = mixup_criterion(criterion, model(normalize(X+delta)), y_a, y_b, lam)
else:
loss = F.cross_entropy(output, y)
loss.backward()
grad = delta.grad.detach()
d = delta[index, :, :, :]
g = grad[index, :, :, :]
x = X[index, :, :, :]
if norm == "l_inf":
d = torch.clamp(d + alpha * torch.sign(g), min=-epsilon, max=epsilon)
elif norm == "l_2":
g_norm = torch.norm(g.view(g.shape[0],-1),dim=1).view(-1,1,1,1)
scaled_g = g/(g_norm + 1e-10)
d = (d + scaled_g*alpha).view(d.size(0),-1).renorm(p=2,dim=0,maxnorm=epsilon).view_as(d)
d = clamp(d, lower_limit - x, upper_limit - x)
delta.data[index, :, :, :] = d
delta.grad.zero_()
if mixup:
criterion = nn.CrossEntropyLoss(reduction='none')
all_loss = mixup_criterion(criterion, model(normalize(X+delta)), y_a, y_b, lam)
else:
all_loss = F.cross_entropy(model(normalize(X+delta))[0], y, reduction='none')
max_delta[all_loss >= max_loss] = delta.detach()[all_loss >= max_loss]
max_loss = torch.max(max_loss, all_loss)
return max_delta
def attack_pgd(model, X, y, epsilon, alpha, attack_iters, restarts,
norm, early_stop=False,
mixup=False, y_a=None, y_b=None, lam=None):
max_loss = torch.zeros(y.shape[0]).cuda()
max_delta = torch.zeros_like(X).cuda()
for _ in range(restarts):
delta = torch.zeros_like(X).cuda()
if norm == "l_inf":
delta.uniform_(-epsilon, epsilon)
elif norm == "l_2":
delta.normal_()
d_flat = delta.view(delta.size(0),-1)
n = d_flat.norm(p=2,dim=1).view(delta.size(0),1,1,1)
r = torch.zeros_like(n).uniform_(0, 1)
delta *= r/n*epsilon
elif norm == "l_1":
pass
else:
raise ValueError
delta = clamp(delta, lower_limit-X, upper_limit-X)
delta.requires_grad = True
for _ in range(attack_iters):
output, _ = model(normalize(X + delta))
if early_stop:
index = torch.where(output.max(1)[1] == y)[0]
else:
index = slice(None,None,None)
if not isinstance(index, slice) and len(index) == 0:
break
if mixup:
criterion = nn.CrossEntropyLoss()
# loss = mixup_criterion(criterion, model(normalize(X+delta)), y_a, y_b, lam)
else:
loss = F.cross_entropy(output, y)
loss.backward()
grad = delta.grad.detach()
d = delta[index, :, :, :]
g = grad[index, :, :, :]
x = X[index, :, :, :]
if norm == "l_inf":
d = torch.clamp(d + alpha * torch.sign(g), min=-epsilon, max=epsilon)
elif norm == "l_2":
g_norm = torch.norm(g.view(g.shape[0],-1),dim=1).view(-1,1,1,1)
scaled_g = g/(g_norm + 1e-10)
d = (d + scaled_g*alpha).view(d.size(0),-1).renorm(p=2,dim=0,maxnorm=epsilon).view_as(d)
elif norm == "l_1":
g_norm = torch.sum(torch.abs(g.view(g.shape[0], -1)), dim=1).view(-1, 1, 1, 1)
scaled_g = g / (g_norm + 1e-10)
d = (d + scaled_g * alpha).view(d.size(0), -1).renorm(p=1, dim=0, maxnorm=epsilon).view_as(d)
d = clamp(d, lower_limit - x, upper_limit - x)
delta.data[index, :, :, :] = d
delta.grad.zero_()
if mixup:
criterion = nn.CrossEntropyLoss(reduction='none')
all_loss = mixup_criterion(criterion, model(normalize(X+delta)), y_a, y_b, lam)
else:
all_loss = F.cross_entropy(model(normalize(X+delta))[0], y, reduction='none')
max_delta[all_loss >= max_loss] = delta.detach()[all_loss >= max_loss]
max_loss = torch.max(max_loss, all_loss)
return max_delta
def one_hot_embedding(labels, num_classes):
"""Embedding labels to one-hot form.
Args:
labels: (LongTensor) class labels, sized [N,].
num_classes: (int) number of classes.
Returns:
(tensor) encoded labels, sized [N, #classes].
"""
y = torch.eye(num_classes)
return y[labels]
def attack_CW(model, X, y, epsilon, alpha, attack_iters, restarts,
norm, early_stop=False,
mixup=False, y_a=None, y_b=None, lam=None, num_class=10):
max_loss = torch.zeros(y.shape[0]).cuda()
max_delta = torch.zeros_like(X).cuda()
batchsize=X.size(0)
for _ in range(restarts):
delta = torch.zeros_like(X).cuda()
if norm == "l_inf":
delta.uniform_(-epsilon, epsilon)
elif norm == "l_2":
delta.normal_()
d_flat = delta.view(delta.size(0),-1)
n = d_flat.norm(p=2,dim=1).view(delta.size(0),1,1,1)
r = torch.zeros_like(n).uniform_(0, 1)
delta *= r/n*epsilon
else:
raise ValueError
delta = clamp(delta, lower_limit-X, upper_limit-X)
delta.requires_grad = True
for _ in range(attack_iters):
output, _ = model(normalize(X + delta))
if early_stop:
index = torch.where(output.max(1)[1] == y)[0]
else:
index = slice(None,None,None)
if not isinstance(index, slice) and len(index) == 0:
break
# loss = F.cross_entropy(output, y) # cross entropy in pytorch combines logsoftmax with nll_loss together.
# label_mask = torch.FloatTensor(batchsize, num_class)
# label_mask.zero_()
#
# ones = torch.ones(batchsize)
# y_t = y.unsqueeze(1)
# label_mask.scatter(1, y_t, ones)
label_mask = one_hot_embedding(y, num_class) # this works
label_mask=label_mask.cuda()
# import pdb; pdb.set_trace()
correct_logit = torch.sum(label_mask*output, dim=1)
wrong_logit, _ = torch.max((1-label_mask)*output - 1e4*label_mask, axis=1) # select the seond best (but of course it is wrong)
# import pdb;
# pdb.set_trace()
loss = - torch.sum(F.relu(correct_logit - wrong_logit + 50))
loss.backward()
grad = delta.grad.detach()
d = delta[index, :, :, :]
g = grad[index, :, :, :]
x = X[index, :, :, :]
if norm == "l_inf":
d = torch.clamp(d + alpha * torch.sign(g), min=-epsilon, max=epsilon)
elif norm == "l_2":
g_norm = torch.norm(g.view(g.shape[0],-1),dim=1).view(-1,1,1,1)
scaled_g = g/(g_norm + 1e-10)
d = (d + scaled_g*alpha).view(d.size(0),-1).renorm(p=2,dim=0,maxnorm=epsilon).view_as(d)
d = clamp(d, lower_limit - x, upper_limit - x)
delta.data[index, :, :, :] = d
delta.grad.zero_()
if mixup:
criterion = nn.CrossEntropyLoss(reduction='none')
all_loss = mixup_criterion(criterion, model(normalize(X+delta)), y_a, y_b, lam)
else:
all_loss = F.cross_entropy(model(normalize(X+delta))[0], y, reduction='none')
max_delta[all_loss >= max_loss] = delta.detach()[all_loss >= max_loss]
max_loss = torch.max(max_loss, all_loss)
return max_delta
# TODO: bug here, even random noise decrease rob acc on medium model.
# simple Module to normalize an image
class Normalize(nn.Module):
def __init__(self, mean, std):
super(Normalize, self).__init__()
self.mean = torch.tensor(mean)
self.std = torch.tensor(std)
def forward(self, x):
return (x - self.mean.type_as(x)[None, :, None, None]) / self.std.type_as(x)[None, :, None, None]
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--model', default='WideResNet') #WideResNet
parser.add_argument('--l2', default=0, type=float)
parser.add_argument('--l1', default=0, type=float)
parser.add_argument('--batch-size', default=1024, type=int)
parser.add_argument('--contrastive_bs', default=512, type=int)
parser.add_argument('--data-dir', default='../cifar-data', type=str)
parser.add_argument('--epochs', default=200, type=int)
parser.add_argument('--lr-schedule', default='piecewise', choices=['superconverge', 'piecewise', 'linear', 'piecewisesmoothed', 'piecewisezoom', 'onedrop', 'multipledecay', 'cosine'])
parser.add_argument('--lr-max', default=0.1, type=float)
parser.add_argument('--lr-one-drop', default=0.01, type=float)
parser.add_argument('--lam_res', default=1, type=float)
parser.add_argument('--adda_times', default=1, type=float)
parser.add_argument('--lr-drop-epoch', default=100, type=int)
parser.add_argument('--attack', default='pgd', type=str, choices=['pgd', 'fgsm', 'free', 'none'])
parser.add_argument('--epsilon', default=8, type=int)
parser.add_argument('--attack-iters', default=10, type=int)
parser.add_argument('--bd_attack_iters', default=4, type=int)
parser.add_argument('--restarts', default=1, type=int)
parser.add_argument('--neg_size', default=10, type=int)
parser.add_argument('--pgd-alpha', default=2, type=float)
parser.add_argument('--fgsm-alpha', default=1.25, type=float)
parser.add_argument('--norm', default='l_inf', type=str, choices=['l_inf', 'l_2', 'l_1'])
parser.add_argument('--fgsm-init', default='random', choices=['zero', 'random', 'previous'])
parser.add_argument('--fname', default='train_ssl', type=str)
import socket
# if 'cv10' in socket.gethostname():
# parser.add_argument('--save_root_path', default='/local/vondrick/chengzhi/SSRobust', type=str)
if 'cv' in socket.gethostname():
parser.add_argument('--save_root_path', default='/proj/vondrick/mcz/SSRobust/Ours', type=str)
else:
parser.add_argument('--save_root_path', default='/local/rcs/mcz/2021Spring/SSRobdata/', type=str)
parser.add_argument('--ssl_model_path', default='', type=str)
parser.add_argument('--attack_type', default='', type=str)
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--half', action='store_true')
parser.add_argument('--width-factor', default=10, type=int)
parser.add_argument('--constrastive_head', default=16, type=int)
parser.add_argument('--md_path', default='', type=str)
parser.add_argument('--cutout', action='store_true')
parser.add_argument('--cutout-len', type=int)
parser.add_argument('--mixup', action='store_true')
parser.add_argument('--rand', action='store_true')
parser.add_argument('--debug', action='store_true')
parser.add_argument('--MCtimes', default=1, type=int)
parser.add_argument('--n_views', default=4, type=int)
parser.add_argument('--eval_freq', default=10, type=int)
parser.add_argument('--mixup-alpha', type=float)
parser.add_argument('--eval', action='store_true')
parser.add_argument('--val', action='store_true')
parser.add_argument('--carmon', action='store_true')
parser.add_argument('--TRADES', action='store_true')
parser.add_argument('--Bag', action='store_true')
parser.add_argument('--res18', action='store_true')
parser.add_argument('--new', action='store_true')
parser.add_argument('--eval_only', action='store_true')
parser.add_argument('--random_noise', action='store_true')
parser.add_argument('--foolbox', action='store_true')
return parser.parse_args()
def main():
print("eval only")
adda_times=1
args = get_args()
import uuid
import datetime
unique_str = str(uuid.uuid4())[:8]
timestamp = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d_%H:%M:%S')
args.fname = os.path.join(args.save_root_path, args.fname, timestamp + unique_str)
if not os.path.exists(args.fname):
os.makedirs(args.fname)
logger = logging.getLogger(__name__)
logging.basicConfig(
format='[%(asctime)s] - %(message)s',
datefmt='%Y/%m/%d %H:%M:%S',
level=logging.DEBUG,
handlers=[
logging.FileHandler(os.path.join(args.fname, 'eval.log' if args.eval else 'output.log')),
logging.StreamHandler()
])
logger.info(args)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
transforms = [Crop(32, 32), FlipLR()]
dataset = cifar10(args.data_dir)
train_set = list(zip(transpose(pad(dataset['train']['data'], 4) / 255.),
dataset['train']['labels']))
train_set_x = Transform(train_set, transforms)
train_batches = Batches(train_set_x, args.batch_size, shuffle=True, set_random_choices=True, num_workers=2)
test_set = list(zip(transpose(dataset['test']['data'] / 255.), dataset['test']['labels']))
test_batches = Batches(test_set, args.batch_size, shuffle=False, num_workers=2)
epsilon = (args.epsilon / 255.)
pgd_alpha = (args.pgd_alpha / 255.)
if args.model == 'PreActResNet18' or args.res18:
from learning.preactresnet import PreActResNet18SSL, PreActResNet18
model = PreActResNet18SSL() # TODO: make it single head?
c_head_model = Res18_out6_model()
elif args.model == 'WideResNet':
# model = WideResNetMed_SSL(34, 10, widen_factor=args.width_factor, dropRate=0.0)
if args.carmon:
from learning.unlabel_WRN import WideResNet_2
model = WideResNet_2(depth=28, widen_factor=10)
elif args.TRADES or args.Bag:
from learning.unlabel_WRN import WideResNet_2
model = WideResNet_2(depth=34, widen_factor=10)
else:
model = WideResNetMed_SSL(34, 10, widen_factor=args.width_factor, dropRate=0.0)
# print(model)
c_head_model = WRN34_out_branch()
else:
raise ValueError("Unknown model")
if not args.TRADES and not args.Bag:
model = nn.DataParallel(model).cuda()
c_head_model = nn.DataParallel(c_head_model).cuda()
c_head_model.train()
if args.l2:
decay, no_decay = [], []
for name, param in model.named_parameters():
if 'bn' not in name and 'bias' not in name:
decay.append(param)
else:
no_decay.append(param)
params_bkbone = [{'params': decay, 'weight_decay': args.l2},
{'params': no_decay, 'weight_decay': 0}]
else:
# params_bkbone = model.parameters()
decay, no_decay = [], []
for name, param in c_head_model.named_parameters():
if 'bn' not in name and 'bias' not in name:
decay.append(param)
else:
no_decay.append(param)
params = [{'params': decay, 'weight_decay': args.l2},
{'params': no_decay, 'weight_decay': 0}]
def lr_schedule(t):
if t / args.epochs < 0.5:
return args.lr_max
elif t / args.epochs < 0.75:
return args.lr_max / 10.
else:
return args.lr_max / 100.
learning_rate=1e-4
opt = torch.optim.Adam(params, lr=learning_rate)
# opt4 = torch.optim.Adam(params4, lr=learning_rate)
# opt5 = torch.optim.Adam(params5, lr=learning_rate)
# opt6 = torch.optim.Adam(params6, lr=learning_rate)
if args.md_path != '':
# try:
if args.TRADES or args.Bag:
tmp=torch.load(args.md_path)
else:
tmp=torch.load(args.md_path)['state_dict']
# tmp=torch.load(args.md_path)
# print(tmp.keys())
# import pdb; pdb.set_trace()
model.load_state_dict(tmp)
if args.TRADES or args.Bag:
model = nn.DataParallel(model).cuda()
s = 1
size = 32
from torchvision.transforms import transforms
# color_jitter = transforms.ColorJitter(0.8 * s, 0.8 * s, 0.8 * s, 0.2 * s)
transforms = torch.nn.Sequential(
transforms.RandomResizedCrop(size=size),
transforms.RandomHorizontalFlip(),
# transforms.RandomApply([color_jitter], p=0.8),
transforms.ColorJitter(0.8 * s, 0.8 * s, 0.8 * s, 0.2 * s),
transforms.RandomGrayscale(p=0.2),
# GaussianBlur(kernel_size=int(0.1 * size)),
)
scripted_transforms = torch.jit.script(transforms)
criterion = torch.nn.CrossEntropyLoss().cuda()
model.eval()
test_loss = 0
test_acc = 0
test_robust_loss = 0
test_robust_acc = 0
contrastive_attack_loss = 0
contrastive_clean_loss = 0
if args.res18:
# import pdb; pdb.set_trace()
if args.new:
tmp = torch.load(args.ssl_model_path)['ssl_model']
else:
tmp = torch.load(args.ssl_model_path)
else:
tmp = torch.load(args.ssl_model_path)['ssl_model']
c_head_model.load_state_dict(tmp)
c_head_model.eval()
for i, batch in enumerate(train_batches):
X, y = batch['input'], batch['target']
contrastive_Loss = \
calculate_contrastive_Mhead_loss(X, scripted_transforms, model, criterion, c_head_model)
break
from AAattack.autoattack import AutoAttack
adversary = AutoAttack(model, normalize, norm='Linf', eps=epsilon, log_path='./log/tmp.txt')
adversary.attacks_to_run = ['apgd-ce', 'fab']
adversary.apgd.n_restarts = 2
adversary.fab.n_restarts = 2
test_loss = 0
test_acc = 0
test_robust_loss = 0
test_robust_acc = 0
db_rob_acc_all=0
TestX = []
TestY = []
Testdelta = []
test_n = 0
db_test_acc_clean_all=0
print('epsilon', epsilon)
contrastive_attack_loss=0
contrastive_clean_loss=0
for i, batch in enumerate(test_batches):
if args.debug and i > 0:
break
X, y = batch['input'], batch['target']
TestX.append(X)
TestY.append(y)
X = X.cuda()
y = y.cuda()
# Generate attack with AutoAttack
adv_complete = adversary.run_standard_evaluation(X, y, bs=X.size(0))
print('low', adv_complete.min(), adv_complete.max())
delta = adv_complete - X
print('delta', delta.min(), delta.max())
with torch.no_grad():
db_predict = model(normalize(adv_complete))[0]
db_test_acc = (db_predict.max(1)[1] == y).sum().item()
db_rob_acc_all += db_test_acc
n = y.size(0)
db_predict_clean = model(normalize(X))[0]
db_test_acc_clean_all += (db_predict_clean.max(1)[1] == y).sum().item()
print("db rob acc ", db_test_acc/n, '\n\n\n\n')
# TODO: the saved adv is weaker than reported in AA paper, maybe of because rounding error?
delta = delta.detach()
Testdelta.append(delta)
# import pdb; pdb.set_trace()
with torch.no_grad():
robust_output, _ = model(
normalize(torch.clamp(X + delta[:X.size(0)], min=lower_limit, max=upper_limit)))
output, _ = model(normalize(X))
robust_loss = criterion(robust_output, y)
loss = criterion(output, y)
Adv_image = torch.clamp(X + delta[:X.size(0)], min=lower_limit, max=upper_limit)
contrastive_attack = \
calculate_contrastive_Mhead_loss(Adv_image, scripted_transforms, model, criterion, c_head_model, n_views=4)
contrastive_clean = \
calculate_contrastive_Mhead_loss(X, scripted_transforms, model, criterion, c_head_model, n_views=4)
contrastive_attack_loss += contrastive_attack.item() * y.size(0)
contrastive_clean_loss += contrastive_clean.item() * y.size(0)
test_robust_loss += robust_loss.item() * y.size(0)
test_robust_acc += (robust_output.max(1)[1] == y).sum().item()
test_loss += loss.item() * y.size(0)
test_acc += (output.max(1)[1] == y).sum().item()
test_n += y.size(0)
torch.cuda.empty_cache()
print("test_robust_acc", test_robust_acc/test_n, db_rob_acc_all/test_n, 'clean', db_test_acc_clean_all/test_n)
print('clean contrastive=%.6f \t adv contrastive=%.6f' %
((contrastive_clean_loss / test_n), (contrastive_attack_loss / test_n)))
TestX = torch.cat(TestX, dim=0)
TestY = torch.cat(TestY, dim=0)
Testdelta = torch.cat(Testdelta, dim=0)
total_len = TestX.shape[0]
ind = [i for i in range(total_len)]
from random import shuffle
shuffle(ind)
TestX = TestX[ind]
TestY = TestY[ind]
Testdelta = Testdelta[ind]
bs = args.contrastive_bs
num_bs = TestX.size(0) // bs
if num_bs * bs < TestX.size(0):
num_bs += 1
count_test = 0
Base_atack_steps = [20]
for base_attack_step in Base_atack_steps:
for adda_times in [2]:
test_robust_ada_acc = 0
test_clean_ada_acc = 0
test_robust_ada_loss = 0
test_clean_ada_loss = 0
# test_clean_loss = 0
# test_clean_acc = 0
test_n = 0
adaadv_contrastive_loss=0
print('contrastive bs', bs)
for bs_ind in range(num_bs):
if args.debug and bs_ind > 0:
break
X = TestX[bs_ind * bs:(bs_ind + 1) * bs]
y = TestY[bs_ind * bs:(bs_ind + 1) * bs]
delta = Testdelta[bs_ind * bs:(bs_ind + 1) * bs]
X = X.cuda()
y = y.cuda()
delta = delta.cuda()
# Random initialization
if args.random_noise:
delta2 = torch.zeros_like(X)
delta2.uniform_(-epsilon* adda_times, epsilon* adda_times)
else:
delta2 = attack_constrastive_Mhead(model, c_head_model, scripted_transforms, criterion,
torch.clamp(X + delta[:X.size(0)], min=lower_limit, max=upper_limit),
torch.zeros_like(y), epsilon * adda_times, pgd_alpha, # 1, 0.2,
int(base_attack_step * adda_times) if not args.rand else 0,
args.restarts, args.norm, n_views=args.n_views
)
delta2 = delta2.detach()
robust_output_ada, hidden = model(
normalize(torch.clamp(X + delta[:X.size(0)], min=lower_limit, max=upper_limit) + delta2))
test_robust_ada_acc += (robust_output_ada.max(1)[1] == y).sum().item()
robust_ada_loss = criterion(robust_output_ada, y)
test_robust_ada_loss += robust_ada_loss.item() * y.size(0)
torch.cuda.empty_cache()
contrastive_ada_attack = \
calculate_contrastive_Mhead_loss(
torch.clamp(
torch.clamp(X + delta[:X.size(0)], min=lower_limit, max=upper_limit) + delta2,
min=lower_limit, max=upper_limit),
scripted_transforms, model, criterion, c_head_model)
adaadv_contrastive_loss += contrastive_ada_attack.item() * y.size(0)
torch.cuda.empty_cache()
if args.random_noise:
delta3 = delta2
else:
delta3 = attack_constrastive_Mhead(model, c_head_model, scripted_transforms,
criterion,
X,
torch.zeros_like(y), epsilon * adda_times, pgd_alpha, # 1, 0.2,
int(base_attack_step * adda_times) if not args.rand else 0,
args.restarts, args.norm,
early_stop=args.eval, n_views=args.n_views)
# #epsilon * args.adda_times, pgd_alpha
delta3 = delta3.detach()
clean_output_ada, hidden = model(
normalize(X + delta3))
test_clean_ada_acc += (clean_output_ada.max(1)[1] == y).sum().item()
clean_ada_loss = criterion(clean_output_ada, y)
test_clean_ada_loss += clean_ada_loss.item() * y.size(0)
test_n += y.size(0)
# print(test_robust_ada_acc, test_robust_ada_loss)
torch.cuda.empty_cache()
# print(bs_ind)
print(
'e=%d scale=%.4f step=%d epsilon=%d \t TestLoss=%.4f TestAcc=%.4f TestCleanAdaAcc=%.4f \t TestRobLoss=%.4f TestRobAcc %.4f \t AdaTestLoss=%.4f AdaTestAcc %.4f' %
(0, adda_times, (int(base_attack_step * adda_times)), (epsilon*255),
(test_loss / test_n), (test_acc / test_n * 100), (test_clean_ada_acc / test_n * 100),
(test_robust_loss / test_n), (test_robust_acc / test_n * 100),
(test_robust_loss / test_n), (test_robust_ada_acc / test_n * 100)))
print('clean contrastive=%.6f \t adv contrastive=%.6f \t adaadv contrastive=%.6f' %
((contrastive_clean_loss / test_n), (contrastive_attack_loss / test_n),
(adaadv_contrastive_loss / test_n)))
print("\n\n\nmogu")
if __name__ == "__main__":
main()