def __init__(self):
self._train_path = fm.join(ICUBWORLD28_PATH, 'train')
self._test_path = fm.join(ICUBWORLD28_PATH, 'test')
with CDataLoaderICubWorld28.__lock:
# Download (if needed) data and extract it
if not fm.folder_exist(self._train_path) \
or not fm.folder_exist(self._test_path):
self._get_data(ICUBWORLD28_URL, ICUBWORLD28_PATH)
def _explore_dir(self,
dir_path,
img_w,
img_h,
img_c,
img_ext,
label_re=None,
load_data=True):
"""Explore input directory and load files if leaf."""
# Folders/files will be loaded in alphabetical order
items_list = sorted(fm.listdir(dir_path))
# A leaf folder is a folder with only files in it
leaf = not any(
fm.folder_exist(fm.join(dir_path, item)) for item in items_list)
if leaf is True: # Leaf directory, time to load files!
return self._load_files(dir_path,
img_w,
img_h,
img_c,
img_ext,
label_re=label_re,
load_data=load_data)
# Placeholder for patterns/labels CArray
patterns = None
labels = None
for subdir in items_list:
subdir_path = fm.join(dir_path, subdir)
# Only consider folders (there could be also files)
if not fm.folder_exist(subdir_path):
continue
# Explore next subfolder
patterns_new, labels_new, img_w, img_h, img_c = self._explore_dir(
subdir_path,
img_w,
img_h,
img_c,
img_ext,
label_re=label_re,
load_data=load_data)
patterns = patterns.append(patterns_new, axis=0) \
if patterns is not None else patterns_new
labels = labels.append(labels_new) \
if labels is not None else labels_new
return patterns, labels, img_w, img_h, img_c
def tearDown(self):
# Remove existing 'models_dict.json' before testing
if fm.file_exist(MODELS_DICT_PATH):
fm.remove_file(MODELS_DICT_PATH)
# Removing folder with test model (force 'cause not empty)
if fm.folder_exist(fm.join(SECML_MODELS_DIR, '_test')):
fm.remove_folder(fm.join(SECML_MODELS_DIR, '_test'), force=True)
def clean_tmp():
"""Cleans temporary files created by the DB loader.
This method deletes the joblib-related files created while loading
the database.
Does not delete the downloaded database archive.
"""
jl_tmp_folder = fm.join(SECML_DS_DIR, 'lfw_home', 'joblib')
if fm.folder_exist(jl_tmp_folder):
fm.remove_folder(jl_tmp_folder, force=True)
def __init__(self):
# Extract the name of the data file from the url
self.data_file = self.data_url.split('/')[-1]
# Path to the downloaded dataset file
data_file_path = fm.join(CIFAR_PATH, self.data_file)
with CDataLoaderCIFAR.__lock:
# Download (if needed) data and extract it
if not fm.file_exist(data_file_path) or \
md5(data_file_path) != self.data_md5:
self._get_data(self.data_url, CIFAR_PATH)
elif not fm.folder_exist(self.data_path):
# Downloaded datafile seems valid, extract only
self._get_data(self.data_url, CIFAR_PATH, extract_only=True)
def _get_data(self, file_url, dl_folder):
"""Download input datafile, unzip and store in output_path.
Parameters
----------
file_url : str
URL of the file to download.
dl_folder : str
Path to the folder where to store the downloaded file.
"""
f_dl = fm.join(dl_folder, 'iCubWorld28_128x128.zip?dl=1')
if not fm.file_exist(f_dl) or md5(f_dl) != ICUBWORLD28_MD5:
# Generate the full path to the downloaded file
f_dl = dl_file(file_url, dl_folder, md5_digest=ICUBWORLD28_MD5)
self.logger.info("Extracting files...")
# Extract the content of downloaded file
zipfile.ZipFile(f_dl, 'r').extractall(dl_folder)
# Remove downloaded file
fm.remove_file(f_dl)
# iCubWorld28 zip file contains a macosx private folder, clean it up
if fm.folder_exist(fm.join(ICUBWORLD28_PATH, '__MACOSX')):
fm.remove_folder(fm.join(ICUBWORLD28_PATH, '__MACOSX'), force=True)
# iCubWorld28 zip file contains a macosx private files, clean it up
for dirpath, dirnames, filenames in os.walk(ICUBWORLD28_PATH):
for file in filenames:
if fnmatch(file, '.DS_Store'):
fm.remove_file(fm.join(dirpath, file))
# Now move all data to an upper folder if needed
if not fm.folder_exist(self._train_path) \
or not fm.folder_exist(self._test_path):
sub_d = fm.join(dl_folder, fm.listdir(dl_folder)[0])
for e in fm.listdir(sub_d):
e_full = fm.join(sub_d, e) # Full path to current element
try: # Call copy_file or copy_folder when applicable
if fm.file_exist(e_full) is True:
fm.copy_file(e_full, dl_folder)
elif fm.folder_exist(e_full) is True:
fm.copy_folder(e_full, fm.join(dl_folder, e))
except:
pass
# Check that the main dataset file is now in the correct folder
if not fm.folder_exist(self._train_path) \
or not fm.folder_exist(self._test_path):
raise RuntimeError("dataset main file not available!")
# The subdirectory can now be removed
fm.remove_folder(sub_d, force=True)
def load(self,
ds_type,
day='day4',
icub7=False,
resize_shape=(128, 128),
crop_shape=None,
normalize=True):
"""Load the dataset.
The pre-cropped version of the images is loaded, with size 128 x 128.
An additional resize/crop shape could be passed as input if needed.
Extra dataset attributes:
- 'img_w', 'img_h': size of the images in pixels.
- 'y_orig': CArray with the original labels of the objects.
Parameters
----------
ds_type : str
Identifier of the dataset to download, either 'train' or 'test'.
day : str, optional
Acquisition day from which to load the images. Default 'day4'.
The available options are: 'day1', 'day2', 'day3', 'day4'.
icub7 : bool or int, optional
If True, load a reduced dataset with 7 objects by
taking the 3rd object for each category. Default False.
If int, the Nth object for each category will be loaded.
resize_shape : tuple, optional
Images will be resized to (height, width) shape. Default (128, 128).
crop_shape : tuple or None, optional
If a tuple, a crop of (height, width) shape will be extracted
from the center of each image. Default None.
normalize : bool, optional
If True, images are normalized between 0-1. Default True.
Returns
-------
CDataset
Output dataset.
"""
if ds_type == 'train':
data_path = self._train_path
elif ds_type == 'test':
data_path = self._test_path
else:
raise ValueError("use ds_type = {'train', 'test'}.")
day_path = fm.join(data_path, day)
if not fm.folder_exist(day_path):
raise ValueError("{:} not available.".format(day))
self.logger.info(
"Loading iCubWorld{:} {:} {:} dataset from {:}".format(
'7' if icub7 else '28', day, ds_type, day_path))
icub7 = 3 if icub7 is True else icub7 # Use the 3rd sub-obj by default
x = None
y_orig = []
for obj in sorted(fm.listdir(day_path)): # Objects (cup, sponge, ..)
obj_path = fm.join(day_path, obj)
# Sub-objects (cup1, cup2, ...)
for sub_obj in sorted(fm.listdir(obj_path)):
if icub7 and sub_obj[-1] != str(icub7):
continue # Load only the `icub7`th object
self.logger.debug("Loading images for {:}".format(sub_obj))
sub_obj_path = fm.join(obj_path, sub_obj)
for f in sorted(fm.listdir(sub_obj_path)):
img = Image.open(fm.join(sub_obj_path, f))
if resize_shape is not None:
img = resize_img(img, resize_shape)
if crop_shape is not None:
img = crop_img(img, crop_shape)
img = CArray(img.getdata(), dtype='uint8').ravel()
x = x.append(img, axis=0) if x is not None else img
y_orig.append(sub_obj) # Label is given by sub-obj name
# Create the int-based array of labels. Keep original labels in y_orig
y_orig = CArray(y_orig)
y = CArray(y_orig).unique(return_inverse=True)[1]
if normalize is True:
x /= 255.0
# Size of images is the crop shape (if any) otherwise, the resize shape
img_h, img_w = crop_shape if crop_shape is not None else resize_shape
header = CDatasetHeader(img_w=img_w, img_h=img_h, y_orig=y_orig)
return CDataset(x, y, header=header)
import os
import numpy as np
from secml.array import CArray
from secml.data.loader import CDLRandomBlobs
from secml.optim.constraints import \
CConstraintBox, CConstraintL1, CConstraintL2
from secml.ml.features.normalization import CNormalizerMinMax
from secml.ml.classifiers import CClassifierSVM, CClassifierDecisionTree
from secml.core.type_utils import is_list, is_float
from secml.figure import CFigure
from secml.utils import fm
IMAGES_FOLDER = fm.join(fm.abspath(__file__), 'test_images')
if not fm.folder_exist(IMAGES_FOLDER):
fm.make_folder(IMAGES_FOLDER)
class CAttackEvasionTestCases(CUnitTest):
"""Unittests interface for CAttackEvasion."""
images_folder = IMAGES_FOLDER
make_figures = os.getenv('MAKE_FIGURES', False) # True to produce figures
def _load_blobs(self, n_feats, n_clusters, sparse=False, seed=None):
"""Load Random Blobs dataset.
- n_samples = 50
- center_box = (-0.5, 0.5)
- cluster_std = 0.5
def _plot_optimization(self,
solver,
x_0,
g_min,
grid_limits,
method=None,
vmin=None,
vmax=None,
label=None):
"""Plots the optimization problem.
Parameters
----------
solver : COptimizer
x_0 : CArray
Starting point.
g_min : CArray
Final point (after optimization).
grid_limits : list of tuple
vmin, vmax : int or None, optional
label : str or None, optional
"""
fig = CFigure(markersize=12)
# Plot objective function
fig.sp.plot_fun(func=CArray.apply_along_axis,
plot_background=True,
n_grid_points=30,
n_colors=25,
grid_limits=grid_limits,
levels=[0.5],
levels_color='gray',
levels_style='--',
colorbar=True,
func_args=(
solver.f.fun,
1,
),
vmin=vmin,
vmax=vmax)
if solver.bounds is not None: # Plot box constraint
fig.sp.plot_fun(func=lambda x: solver.bounds.constraint(x),
plot_background=False,
n_grid_points=20,
grid_limits=grid_limits,
levels=[0],
colorbar=False)
if solver.constr is not None: # Plot distance constraint
fig.sp.plot_fun(func=lambda x: solver.constr.constraint(x),
plot_background=False,
n_grid_points=20,
grid_limits=grid_limits,
levels=[0],
colorbar=False)
# Plot optimization trace
if solver.x_seq is not None:
fig.sp.plot_path(solver.x_seq)
else:
fig.sp.plot_path(x_0.append(g_min, axis=0))
fig.sp.title("{:}(fun={:}) - Glob Min @ {:}".format(
solver.class_type, solver.f.class_type,
solver.f.global_min_x().round(2).tolist()))
test_img_fold_name = 'test_images'
test_img_fold_path = fm.join(fm.abspath(__file__), test_img_fold_name)
if not fm.folder_exist(test_img_fold_path):
fm.make_folder(test_img_fold_path)
if method is None:
filename = fm.join(test_img_fold_path,
solver.class_type + '-' + solver.f.class_type)
else:
filename = fm.join(
test_img_fold_path,
solver.class_type + '-' + method + '-' + solver.f.class_type)
filename += '-' + label if label is not None else ''
test_img_fold_name = 'test_images'
if not fm.folder_exist(test_img_fold_name):
fm.make_folder(test_img_fold_name)
fig.savefig('{:}.pdf'.format(filename))
def dl_file(url,
output_dir,
user=None,
headers=None,
chunk_size=1024,
md5_digest=None):
"""Download file from input url and store in output_dir.
Parameters
----------
url : str
Url of the file to download.
output_dir : str
Path to the directory where the file should be stored.
If folder does not exists, will be created.
user : str or None, optional
String with the user[:password] if required for accessing url.
headers : dict or None, optional
Dictionary with any additional header for the download request.
chunk_size : int, optional
Size of the data chunk to read from url in bytes. Default 1024.
md5_digest : str or None, optional
Expected MD5 digest of the downloaded file.
If a different digest is computed, the downloaded file will be
removed and ValueError is raised.
"""
# Parsing user string
auth = tuple(user.split(':')) if user is not None else None
# If no password is specified, use an empty string
auth = (auth[0], '') if auth is not None and len(auth) == 1 else auth
r = requests.get(url, auth=auth, headers=headers, stream=True)
if r.status_code != 200:
raise RuntimeError("File is not available (error code {:})".format(
r.status_code))
# Get file size (bytes)
if "content-length" in r.headers:
total_size = r.headers.get('content-length').strip()
total_size = int(total_size)
else: # Total size unknown
total_size = None
dl = 0
if chunk_size < 1:
raise ValueError("chunk_size must be at least 1 byte")
sys.stdout.write("Downloading from `{:}`".format(url))
if total_size is not None:
sys.stdout.write(" ({:} bytes)".format(total_size))
sys.stdout.write("\n")
sys.stdout.flush()
# Create output directory if not exists
if not fm.folder_exist(output_dir):
fm.make_folder(output_dir)
try: # Get the filename from the response headers
fname = re.findall(r"filename=\"(.+)\"",
r.headers["Content-Disposition"])[0]
except (KeyError, IndexError):
# Or use the last part of download url (removing parameters)
fname = url.split('/')[-1].split('?', 1)[0]
# Build full path of output file
out_path = fm.join(output_dir, fname)
# Read data and store each chunk
with open(out_path, 'wb') as f:
for chunk in r.iter_content(chunk_size=chunk_size):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
# Report progress (if total_size is known)
if total_size is not None:
dl += len(chunk)
done = int((50 * dl) / total_size)
if sys.stdout.isatty() is True:
# Provide real-time updates (if stdout is a tty)
sys.stdout.write("\r[{:}{:}] {:}/{:}".format(
'=' * done, ' ' * (50 - done), dl, total_size))
sys.stdout.flush()
sys.stdout.write("\nFile stored in `{:}`\n".format(out_path))
sys.stdout.flush()
if md5_digest is not None and md5_digest != md5(out_path, chunk_size):
fm.remove_file(out_path) # Remove the probably-corrupted file
raise ValueError("Unexpected MD5 hash for the downloaded file.")
return out_path