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‎.gitignore

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# mac
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results/
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.DS_Store
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# contains the data for the project
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# data/
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# swap files
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*.swp
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# matlab/output
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# Created by https://www.toptal.com/developers/gitignore/api/python
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# Edit at https://www.toptal.com/developers/gitignore?templates=python
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### Python ###
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# End of https://www.toptal.com/developers/gitignore/api/python

‎LICENSE

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BSD 3-Clause License
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Copyright (c) 2025, Los Alamos National Laboratory
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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1. Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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2. Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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3. Neither the name of the copyright holder nor the names of its
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contributors may be used to endorse or promote products derived from
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this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

‎LaFA/__init__.py

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from LaFA.utils import file_name_gens, grad_attacks
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from LaFA.experiments import experiments

‎LaFA/experiments/__init__.py

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from .exp_face import *
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from .exp_mnist import *
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from .exp_swim import *
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from .exp_synthetic import *
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from .exp_wtsi import *
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from .experiments import *

‎LaFA/experiments/exp_face.py

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import time
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import torch
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from numpy import linspace as linspace
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import numpy.linalg as la
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import numpy as np
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from scipy.io import loadmat
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import LaFA.utils.file_name_gens as file_name_gens
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import pandas as pd
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import pickle
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import tqdm
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import os
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import glob
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# from grad_attacks_dev import *
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from LaFA.utils.grad_attacks import *
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if torch.cuda.is_available():
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device=torch.device('cuda:0')
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else:
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device=torch.device('cpu')
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def read_pgm(pgmf):
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"""Return a raster of integers from a PGM as a list of lists."""
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header = pgmf.readline()
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header1 = pgmf.readline()
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header2 = pgmf.readline()
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assert header[:2] == b'P5'
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(width, height) = [int(i) for i in header1.split()[:2]]
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depth = int(header2)
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assert depth <= 65535
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raster = []
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for y in range(height):
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row = []
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for y in range(width):
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row.append(ord(pgmf.read(1)))
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raster.append(row)
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return raster
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def experiment(args):
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print(args)
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print("FACE experiment")
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log_file = file_name_gens.log_name_gen(args)
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print("Log is saved to: ", log_file)
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if os.path.isfile(log_file):
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log = pd.read_pickle(log_file)
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else:
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log = pd.DataFrame()
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print("Load data")
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allFace = []
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for curF in glob.glob('data/face/*.pgm'):
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file = open(curF, 'rb')
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im = np.array(read_pgm(file))
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file.close()
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allFace.append(im.flatten())
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allFace = np.array(allFace)
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X = allFace.astype(np.float32)
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X = X/np.max(X)
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XorigT = Tcons(X,device).float()
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print("-------------------------")
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print("* DATA STATS *")
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print("Min: ", torch.min(XorigT).cpu().detach().numpy())
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print("Max: ", torch.max(XorigT).cpu().detach().numpy())
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print("Shape: ", XorigT.shape)
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print("Data type: ", XorigT.dtype)
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print("-------------------------")
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np.random.seed(seed=args.seed)
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Winit = np.random.rand(X.shape[0],args.rank)
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Hinit = np.random.rand(args.rank,X.shape[1])
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Winit = torch.Tensor(Winit)
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Hinit = torch.Tensor(Hinit)
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WinitT = Tcons(Winit,device).float()
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HinitT = Tcons(Hinit,device).float()
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Wstay,Hstay = NMFiter_KL(XorigT,args.base_iter,WinitT,HinitT)
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# Test stationary condition of base:
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Wnext,Hnext = NMFiter_KL(XorigT,100,Wstay,Hstay)
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X0 = Wstay@Hstay
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Xnext = Wnext@Hnext
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print("-------------------------")
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print("* SETUP STATIONARY BASE STATE *")
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print("Reconstruction error Linf: ", torch.max(torch.abs(X0-XorigT)).cpu().detach().numpy())
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print("Reconstruction error L2: ", (torch.norm(X0-XorigT)/torch.norm(XorigT)).cpu().detach().numpy())
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print("Recon base error: ", torch.max(torch.abs(X0-Xnext)).cpu().detach().numpy())
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print("W stationary error: ", torch.max(torch.abs(Wstay-Wnext)).cpu().detach().numpy())
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print("H stationary error: ", torch.max(torch.abs(Hstay-Hnext)).cpu().detach().numpy())
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print("-------------------------")
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print("Generate attacker")
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min_eps = args.eps_min
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no_eps = args.no_eps
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max_eps = args.eps_max
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norm = args.norm
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eps_range = max_eps - min_eps
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nmf_rank = args.rank
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nmf_iter = args.nmf_iter
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attacker = Gradient_based_attack(XorigT,
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nmf_rank = nmf_rank,
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base_nmf_iters = nmf_iter,
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use_cuda = True,
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implicit_func = args.implicit,
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taylor = args.taylor,
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norm = norm,
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rec_loss = args.recloss,
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verbose = True)
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for step in tqdm.tqdm(range(no_eps)):
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eps = step/no_eps * (eps_range) + min_eps
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print("Generate attack for eps = ", eps)
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# Attack
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start_time = time.time()
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torch.cuda.reset_peak_memory_stats()
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start_mem_allocated = torch.cuda.memory_stats()['active_bytes.all.peak']
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Xperturb = attacker.pgd_attack(eps=eps, alpha=args.alpha, iters= args.no_iter, record = False, average_grad = args.average)
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end_mem_allocated = torch.cuda.memory_stats()['active_bytes.all.peak']
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duration = time.time()-start_time
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memory = end_mem_allocated
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memory_Mb_ifunc = memory/1000000
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# Stats
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in_X_dist = (torch.norm(Xperturb - XorigT)/torch.norm(XorigT)).cpu().detach().numpy()
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Wp,Hp = NMFiter_KL(Xperturb,10000,WinitT,HinitT)
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out_X_dist = (torch.norm(Wp@Hp - XorigT)/torch.norm(XorigT)).cpu().detach().numpy()
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out_FE_dist = loss_wh(Wstay,Wp,Hstay,Hp).cpu().detach().numpy()
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Hstay = Hstay + 1e-10
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Hp = Hp + 1e-10
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Wstay = Wstay + 1e-10
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Wp = Wp + 1e-10
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out_W_dist = loss_w(Wstay,Wp).cpu().detach().numpy()
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out_H_dist = loss_w(Hstay,Hp).cpu().detach().numpy()
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# Log
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print("Input distortion: ", in_X_dist)
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print("FE distortion: ", out_FE_dist)
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print("W distortion: ", out_W_dist)
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print("H distortion: ", out_H_dist)
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print("Duration (sec): ", duration)
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print("Memory (Mb): ", memory_Mb_ifunc)
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print("----")
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result = {
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'eps': eps,
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'alpha': args.alpha,
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'pgd iters': args.no_iter,
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'in': in_X_dist,
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'out rec': out_X_dist,
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'out fe': out_FE_dist,
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'out W': out_W_dist,
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'out H': out_H_dist,
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'duration': duration,
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'memory': memory,
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'Implicit gradient': args.implicit,
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'norm': args.norm,
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'NMF iters': args.nmf_iter,
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'Seed': args.seed
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}
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log = pd.concat([log, pd.DataFrame.from_records([result])])
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with open(log_file, 'wb') as logfile:
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pickle.dump(log, logfile)
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Hp_np = Hp.cpu().detach().numpy()
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FEATURE_FILES = file_name_gens.feature_name_gen(args, eps = eps)
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with open(FEATURE_FILES, 'wb') as f:
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np.save(f, Hp_np)
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return
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