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_PGD_div_mnist.py
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_PGD_div_mnist.py
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# PGD + Diversity Regularization on MNIST
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import numpy as np
import matplotlib.pyplot as plt
import traceback
import warnings
warnings.filterwarnings('ignore')
import datetime
import glob
import os
import pickle
import pandas as pd
from models import *
from div_attacks import *
from neuron_coverage import *
from inception_score import *
from fid_score import *
from utils import *
# check if CUDA is available
device = torch.device("cpu")
use_cuda = False
if torch.cuda.is_available():
print('CUDA is available!')
device = torch.device("cuda")
use_cuda = True
else:
print('CUDA is not available.')
random_seed = 1
torch.manual_seed(random_seed)
date = datetime.date.today()
data_dir = "data"
if not os.path.exists(data_dir):
os.makedirs(data_dir)
# Generate a custom batch to ensure that each class is equally represented
num_per_class = 10
dataset = torchvision.datasets.MNIST(root=data_dir,
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor()
]))
class_distribution = torch.ones(len(np.unique(dataset.targets))) * num_per_class
inputs, targets = generate_batch(dataset, class_distribution, device)
# # Load Pretrained Models if available
## DenseNet5
fcnet5 = FCNet5().to(device)
fcnet5 = get_pretrained_weights(fcnet5, device)
## DenseNet10
fcnet10 = FCNet10().to(device)
fcnet10 = get_pretrained_weights(fcnet10, device)
## Conv1DNet
conv1dnet = Conv1DNet().to(device)
conv1dnet = get_pretrained_weights(conv1dnet, device)
## Conv2DNet
conv2dnet = Conv2DNet().to(device)
conv2dnet = get_pretrained_weights(conv2dnet, device)
# # Attack Time
def main():
models = [fcnet5, fcnet10, conv1dnet, conv2dnet]
# attack params
epsilon = 100.
num_steps = 10
step_size = 0.01
log_frequency = 100
attack_versions = [pgd_attack]
reg_weights = [0, 1, 10, 100, 1000, 10000, 100000, 1000000]
epsilons = [0.1, 0.2, 0.3]
# neuron coverage params
nc_threshold = 0. # all activations are scaled to (0,1) after relu
# inception score (is) params
is_cuda = use_cuda
is_batch_size = 10
is_resize = True
is_splits = 10
# frechet inception distance score (fid) params
real_path = "temp_imgs/mnist/real_pgd_mnist/"
fake_path = "temp_imgs/mnist/fake_pgd_mnist/"
fid_batch_size = 64
fid_cuda = use_cuda
with open('logs/pgd_mnist_error_log_' + str(date) + '.txt', 'w') as error_log:
for model in models:
results = []
model_name = model.__class__.__name__
save_file_path = 'assets/pgd_results_mnist_' + model_name + '_' + str(date) + '.pkl'
# neuron coverage
covered_neurons, total_neurons, neuron_coverage_000 = eval_nc(model, inputs, 0.00)
print('neuron_coverage_000:', neuron_coverage_000)
covered_neurons, total_neurons, neuron_coverage_020 = eval_nc(model, inputs, 0.20)
print('neuron_coverage_020:', neuron_coverage_020)
covered_neurons, total_neurons, neuron_coverage_050 = eval_nc(model, inputs, 0.50)
print('neuron_coverage_050:', neuron_coverage_050)
covered_neurons, total_neurons, neuron_coverage_075 = eval_nc(model, inputs, 0.75)
print('neuron_coverage_075:', neuron_coverage_075)
init = {'desc': 'Initial inputs, targets, classes',
'inputs': inputs,
'targets': targets,
'classes': 'NA',
'neuron_coverage_000': neuron_coverage_000,
'neuron_coverage_020': neuron_coverage_020,
'neuron_coverage_050': neuron_coverage_050,
'neuron_coverage_075': neuron_coverage_075}
results.append(init)
n=2 # skip relu layers
layer_dict = get_model_modules(model)
target_layers = list(layer_dict)[0::n]
for attack in attack_versions:
for layer_idx in target_layers:
module = layer_dict[layer_idx]
for rw in reg_weights:
for e in epsilons:
try:
timestamp = str(datetime.datetime.now()).replace(':','.')
attack_detail = ['timestamp', timestamp,
'attack', attack.__name__,
'layer: ', layer_idx,
'regularization_weight: ', rw,
'epsilon: ', e]
print(*attack_detail, sep=' ')
# adversarial attack
orig_err, pgd_err, adversaries = attack(model,
module,
rw,
inputs,
targets,
device,
epsilon=e,
num_steps=num_steps,
step_size=step_size,
log_frequency=log_frequency)
# evaluate adversary effectiveness
pert_acc, orig_acc = eval_performance(model, inputs, adversaries, targets)
# sample_1D_images(model, inputs, adversaries, targets)
pert_acc = pert_acc.item() / 100.
orig_acc = orig_acc.item() / 100.
attack_success_rate = 1 - pert_acc
# neuron coverage
covered_neurons, total_neurons, neuron_coverage_000 = eval_nc(model, adversaries, 0.00)
print('neuron_coverage_000:', neuron_coverage_000)
covered_neurons, total_neurons, neuron_coverage_020 = eval_nc(model, adversaries, 0.20)
print('neuron_coverage_020:', neuron_coverage_020)
covered_neurons, total_neurons, neuron_coverage_050 = eval_nc(model, adversaries, 0.50)
print('neuron_coverage_050:', neuron_coverage_050)
covered_neurons, total_neurons, neuron_coverage_075 = eval_nc(model, adversaries, 0.75)
print('neuron_coverage_075:', neuron_coverage_075)
# inception score
preprocessed_advs = preprocess_1D_imgs(adversaries)
mean_is, std_is = inception_score(preprocessed_advs, is_cuda, is_batch_size, is_resize, is_splits)
print('inception_score:', mean_is)
# fid score
paths = [real_path, fake_path]
# dimensionality = 64
target_num = 64
generate_imgs(inputs, real_path, target_num)
generate_imgs(adversaries, fake_path, target_num)
fid_score_64 = calculate_fid_given_paths(paths, fid_batch_size, fid_cuda, dims=64)
print('fid_score_64:', fid_score_64)
# dimensionality = 2048
target_num = 2048
generate_imgs(inputs, real_path, target_num)
generate_imgs(adversaries, fake_path, target_num)
fid_score_2048 = calculate_fid_given_paths(paths, fid_batch_size, fid_cuda, dims=2048)
print('fid_score_2048:', fid_score_2048)
# output impoartiality
pert_output = model(adversaries)
y_pred = torch.argmax(pert_output, dim=1)
output_impartiality, y_pred_entropy, max_entropy = calculate_output_impartiality(targets, y_pred)
print('output_impartiality:', output_impartiality)
out = {'timestamp': timestamp,
'attack': attack.__name__,
'model': model_name,
'layer': layer_idx,
'regularization_weight': rw,
'epsilon': e,
'adversaries': adversaries,
'pert_acc':pert_acc,
'orig_acc': orig_acc,
'attack_success_rate': attack_success_rate,
'neuron_coverage_000': neuron_coverage_000,
'neuron_coverage_020': neuron_coverage_020,
'neuron_coverage_050': neuron_coverage_050,
'neuron_coverage_075': neuron_coverage_075,
'inception_score': mean_is,
'fid_score_64': fid_score_64,
'fid_score_2048': fid_score_2048,
'output_impartiality': output_impartiality}
results.append(out)
# save incremental outputs
with open(save_file_path, 'wb') as handle:
pickle.dump(results, handle, protocol=pickle.HIGHEST_PROTOCOL)
except Exception as e:
print(str(traceback.format_exc()))
error_log.write("Failed on attack_detail {0}: {1}\n".format(str(attack_detail), str(traceback.format_exc())))
finally:
pass
if __name__ == '__main__':
try:
main()
except Exception as e:
print(traceback.format_exc())