maurapintor

import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
plt.rc('axes', titlesize=18)
plt.rc('axes', labelsize=14)
plt.rc('xtick', labelsize=13)
plt.rc('ytick', labelsize=13)
plt.rc('legend', fontsize=13)
plt.rc('font', size=13)
plt.rc('text', usetex=True)

maurapintor

import torch


class PyTorchModelTracker:

    def __init__(self, model):
        self._func_counter = None
        self._tracked_x = None
        self._model = model
        self.reset()

maurapintor

import matplotlib.pyplot as plt
from secml.adv.attacks import CFoolboxPGD
from secml.array import CArray
from secml.core.constants import inf
from secml.data.loader import CDataLoaderMNIST
from secml.ml import CNormalizerMinMax
from secml.ml.peval.metrics import CMetric
from secml.model_zoo import load_model

maurapintor

import h5py as h5py
import numpy as np
import torch.utils.data
from torchvision import datasets, transforms

source_path = 'path-to-the-extracted-imagenet-dataset'
destination_path = 'destination-hdf5-file'

# place here your own transforms
transform = transforms.Compose([